"keyword","repo_name","file_path","file_extension","file_size","line_count","content","language"
"Assay","FDA/precisionFDA","LOCAL_INSTALL.md",".md","3325","99","# Localhost setup on OSX 10.11.6

## Overview

To develop on pFDA locally you need to manually add a new user and organization.
Because when you first get started in a new system,
there is no existing user. So you can't log in to provision new accounts.
This requires manually ""bootstrapping"" the situation in steps described in
[pFDA localhost user](#pFDA-localhost-user).

## Install

### Setup for localhost development on MacOS

* Install XCode
    * Search and install XCode from the App Store.
* Install Apple Command Line Tools
    * Open XCode (this just needs to run once to initialize it) and close it.
    * Install XCode command line tools by running `xcode-select --install` in
    the terminal.
* Install [Homebrew](http://brew.sh/)
    * `/usr/bin/ruby -e ""$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)""`
    * Run `brew update` to make sure all your formulas are current
* Install RVM
    * `gpg --keyserver hkp://keys.gnupg.net --recv-keys 409B6B1796C275462A1703113804BB82D39DC0E3`
    * `\curl -sSL https://get.rvm.io | bash -s stable`
* Install ruby
    * `rvm install 2.2.3`
* Install bundler
    * `gem install bundler`
    * to keep the current bundler version from Gemfile.lock:
        * `gem install bundler -v 1.16.6`
* Update the `libv8` gem before `bundle i`:
    * `gem install libv8 -v '3.16.14.13' -- --with-system-v8`
* Install git
    * `brew install git`
* Set up git ssh
    * `ssh-keygen -t rsa -b 4096 -C ""your_email@dnanexus.com""`
    * Use `pbcopy < ~/.ssh/id_rsa.pub` to add SSH key to github [here](https://github.com/settings/keys)
* Set up git config
    * `git config --global core.editor ""vim""`
    * `git config --global user.email “your_email@dnanexus.com”`
    * `git config --global user.name “FirstName LastName”`
    * `git config --global push.default simple`

### Docker setup

* Install Docker and Docker-Compose. See [instructions](https://docs.docker.com/compose/install/)
* Install gems
  * `docker-compose run web bundle install`
* Prepare db
  * `docker-compose run web bundle exec rake db:create`
  * `docker-compose run web bundle exec rake db:schema:load`
* start rails server
  * `docker-compose up`
* start rails console
  * `docker compose exec web bundle exec rails c`  

## pFDA setup

* Clone Repo
    * `git clone git@github.com:dnanexus/precision-fda.git`
* `bundle install`
* `bundle exec rake db:schema:load`

### Issues

On your first `bundle`, you may have issues installing the libv8 and
therubyracer gems. See [here](https://github.com/cowboyd/libv8/issues/205) for
potential solutions. Try `bundle update libv8`.

Create a User and Org, and other required records by running


The _dxuser_ of the user record must match your DNAnexus username, and the _handle_ of the org record must
match the DNAnexus org handle without the pfda.. prefix, i.e. floranteorg.

## pFDA running

* start rails server
    * `bundle exec thin --ssl start`
    * **You must use https, ex: [https://localhost:3000](https://localhost:3000)**
* start rails console
    * `bundle exec rails c`

## Dev team Backend Testing

* Run all tests
    * `bundle exec rspec`

* Run tests with Rspec Guard
    * `bundle exec guard`
    
* To exit from Guard mode:
    * `exit`

* Check current code coverage:
    * `open coverage/index.html`
","Markdown"
"Assay","FDA/precisionFDA","packages/cli/pfda_test.go",".go","25574","803","package main

import (
	""flag""
	""os""
	""testing""

	""dnanexus.com/precision-fda-cli/precisionfda""
	""dnanexus.com/precision-fda-cli/precisionfda/test""
)

func TestMain(m *testing.M) {
	// Not the most elegant way of avoiding usage spew in unit test output
	// but works since main uses the default flagset
	flag.Usage = func() {}

	// Run tests
	exitVal := m.Run()

	// Teardown

	// Exit with exit value from tests
	os.Exit(exitVal)
}

type InputFlags struct {
	InputFilePath    *string
	FilePaths        *[]string
	FolderID         *string
	SpaceID          *string
	AssetFolderPath  *string
	AssetName        *string
	ReadmeFilePath   *string
	FileID           *string
	Args             *[]string
	Arg              *string
	OutputFilePath   *string
	EntityID         *string
	EntityType       *string
	Overwrite        *string
	FlagWithProgress bool

	// optional flags for ls, list-spaces, download, mkdir, head
	FlagRecursive     bool
	FlagHelp          bool
	FlagBrief         bool
	FlagJson          bool
	FlagFoldersOnly   bool
	FlagFilesOnly     bool
	FlagLocked        bool
	FlagUnactivated   bool
	FlagPhiProtected  bool
	FlagGroups        bool
	FlagReview        bool
	FlagPrivate       bool
	FlagAdministrator bool
	FlagGovernment    bool
	FlagParents       bool
	FlagPublic		  bool
	FlagLines         int
}

func (f *InputFlags) Reset() {
	f.InputFilePath = nil
	f.FilePaths = nil
	f.FolderID = nil
	f.SpaceID = nil
	f.AssetFolderPath = nil
	f.AssetName = nil
	f.ReadmeFilePath = nil
	f.FileID = nil
	f.Args = nil
	f.Arg = nil
	f.OutputFilePath = nil
	f.EntityID = nil
	f.EntityType = nil
	f.FlagRecursive = false
	f.FlagWithProgress = true
	// optional flags - default to false
	f.FlagHelp = false
	f.FlagJson = false
	f.FlagFoldersOnly = false
	f.FlagFilesOnly = false
	f.FlagBrief = false
	f.FlagLocked = false
	f.FlagUnactivated = false
	f.FlagReview = false
	f.FlagGroups = false
	f.FlagPrivate = false
	f.FlagAdministrator = false
	f.FlagGovernment = false
	f.FlagParents = false
	f.FlagPublic = false
	f.FlagLines = 10
}

func runMainInternal(surpressStdout bool) int {
	var originalStdout = os.Stdout
	if surpressStdout {
		os.Stdout, _ = os.Open(os.DevNull)
	}
	returnCode := mainInternal()
	if surpressStdout {
		os.Stdout = originalStdout
	}
	return returnCode
}

func TestMainNoArgs(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	os.Args = []string{""pfda""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
}

func TestWrongCmdError(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	os.Args = []string{""pfda"", ""--cmd"", ""foobar""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
}

func TestPositionalCmdInWrongPlace(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	invokeDownload = func(client precisionfda.IPFDAClient, args *[]string, folderID *string, spaceID *string, public bool, recursive bool, outputFilePath *string, overwriteFile *string) error {
		funcWasCalled = true
		return nil
	}

	os.Args = []string{""pfda"", ""--file-id"", ""file-12345"", ""--key"", ""HELLO"", ""download""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)

	invokeUploadFile = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		return nil
	}

	os.Args = []string{""pfda"", ""--file"", ""./README.md"", ""--key"", ""HELLO"", ""upload-file""}
	returnCode = runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
}

func TestPositionalCmdAndCommandBothSpecified(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	invokeUploadFile = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		return nil
	}
	os.Args = []string{""pfda"", ""upload-file"", ""--cmd"", ""upload-file"", ""--file"", ""./README.md"", ""--key"", ""HELLO""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
}

func TestInvokeUploadFile(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeUploadFile = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		input.InputFilePath = inputFilePath
		input.FolderID = folderID
		input.SpaceID = spaceID
		return nil
	}

	// Case: --file is missing
	os.Args = []string{""pfda"", ""--cmd"", ""upload-file""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	reset()

	// Case: --file does not exist
	os.Args = []string{""pfda"", ""upload-file"", ""--file"", ""./IDoNot.Exist""}
	returnCode = runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, (*string)(nil), input.InputFilePath)
	reset()

	// Case: --file does not exist with --cmd
	os.Args = []string{""pfda"", ""--cmd"", ""upload-file"", ""--file"", ""./IDoNot.Exist""}
	returnCode = runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, (*string)(nil), input.InputFilePath)
	reset()

	// Case: upload-file upload success
	os.Args = []string{""pfda"", ""--cmd"", ""upload-file"", ""--file"", ""./README.md"", ""--key"", ""HELLO""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""README.md"", *input.InputFilePath)
	reset()

	// Case: upload-file success with folder and space
	os.Args = []string{""pfda"", ""upload-file"", ""--file"", ""./Dockerfile"", ""--key"", ""HELLO"", ""--folder-id"", ""test-folder"", ""--space-id"", ""test-space""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""Dockerfile"", *input.InputFilePath)
	test.Equals(t, ""test-folder"", *input.FolderID)
	test.Equals(t, ""test-space"", *input.SpaceID)
	reset()

	invokeUploadMultipleFiles = func(client precisionfda.IPFDAClient, filePaths *[]string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		input.FilePaths = filePaths
		input.FolderID = folderID
		input.SpaceID = spaceID
		return nil
	}

	// Case: upload-file success with multiple files/folders separated by commas
	os.Args = []string{""pfda"", ""upload-file"", ""file1.csv"", ""file2.xlsx"", ""./src/file123.txt"", ""./src/folder123"", ""--key"", ""HELLO""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file1.csv"", ""file2.xlsx"", ""./src/file123.txt"", ""./src/folder123""}, *input.FilePaths)
	reset()

	// Case: upload-file success with folder and space with --cmd
	os.Args = []string{""pfda"", ""--cmd"", ""upload-file"", ""--file"", ""./Dockerfile"", ""--key"", ""HELLO"", ""--folder-id"", ""test-folder"", ""--space-id"", ""test-space""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""Dockerfile"", *input.InputFilePath)
	test.Equals(t, ""test-folder"", *input.FolderID)
	test.Equals(t, ""test-space"", *input.SpaceID)
	reset()

	invokeUploadFolder = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		input.InputFilePath = inputFilePath
		input.FolderID = folderID
		input.SpaceID = spaceID
		return nil
	}

	// Case: upload-file with single folder
	os.Args = []string{""pfda"", ""upload-file"", ""helpers/"", ""--key"", ""HELLO""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""helpers"", *input.InputFilePath)
	reset()

}

func TestInvokeUploadAsset(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeUploadAsset = func(client precisionfda.IPFDAClient, assetFolderPath *string, assetName *string, readmeFilePath *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		input.AssetFolderPath = assetFolderPath
		input.AssetName = assetName
		input.ReadmeFilePath = readmeFilePath
		return nil
	}

	// Case: upload-asset with folder and space
	os.Args = []string{""pfda"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""./precisionfda"", ""--readme"", ""./README.md""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""./precisionfda"", *input.AssetFolderPath)
	test.Equals(t, ""test-asset.tar.gz"", *input.AssetName)
	test.Equals(t, ""./README.md"", *input.ReadmeFilePath)
	reset()

	// Case: upload-asset with folder and space with --cmd
	os.Args = []string{""pfda"", ""--cmd"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""./precisionfda"", ""--readme"", ""./README.md""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""./precisionfda"", *input.AssetFolderPath)
	test.Equals(t, ""test-asset.tar.gz"", *input.AssetName)
	test.Equals(t, ""./README.md"", *input.ReadmeFilePath)
	reset()

	// Case: upload-asset fails if --root doesn't exist
	os.Args = []string{""pfda"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""root-folder"", ""--readme"", ""./README.md""}
	returnCode = runMainInternal(false)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, (*string)(nil), input.AssetFolderPath)
	test.Equals(t, (*string)(nil), input.AssetName)
	test.Equals(t, (*string)(nil), input.ReadmeFilePath)
	reset()

	// Case: upload-asset fails if --file doesn't exist
	os.Args = []string{""pfda"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""./i-do-not-exist"", ""--readme"", ""./README.md""}
	returnCode = runMainInternal(false)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, (*string)(nil), input.AssetFolderPath)
	test.Equals(t, (*string)(nil), input.AssetName)
	test.Equals(t, (*string)(nil), input.ReadmeFilePath)
	reset()

	// Case: upload-asset with all parameters
	os.Args = []string{""pfda"", ""--cmd"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""./"", ""--readme"", ""./README.md""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""./"", *input.AssetFolderPath)
	test.Equals(t, ""test-asset.tar.gz"", *input.AssetName)
	test.Equals(t, ""./README.md"", *input.ReadmeFilePath)
	reset()
}

func TestInvokeDownloadFile(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeDownload = func(client precisionfda.IPFDAClient, args *[]string, folderID *string, spaceID *string, public bool, recursive bool, outputFilePath *string, overwriteFile *string) error {
		funcWasCalled = true
		input.Args = args
		input.FolderID = folderID
		input.SpaceID = spaceID
		input.FlagRecursive = recursive
		input.OutputFilePath = outputFilePath
		input.Overwrite = overwriteFile
		return nil
	}

	// Case: download with --cmd
	os.Args = []string{""pfda"", ""--cmd"", ""download"", ""--file-id"", ""file-12345"", ""--key"", ""HELLO""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file-12345""}, *input.Args)
	reset()

	// Case: download without --cmd, with outputFilePath
	os.Args = []string{""pfda"", ""download"", ""--file-id"", ""file-23456"", ""--key"", ""HELLO"", ""--output"", ""/tmp/canyoupleaseworksoicanbedonefortheday.argh""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file-23456""}, *input.Args)
	reset()

	// Case: download without --cmd, with -overwrite flag
	os.Args = []string{""pfda"", ""download"", ""--file-id"", ""file-23456"", ""--key"", ""HELLO"", ""--overwrite"", ""true""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file-23456""}, *input.Args)
	test.Equals(t, ""true"", *input.Overwrite)
	reset()

	// Case: download whole folder with -overwrite and recursive flag
	os.Args = []string{""pfda"", ""download"", ""-folder-id"", ""12223"", ""-key"", ""HELLO"", ""-overwrite"", ""true"", ""-recursive""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""12223"", *input.FolderID)
	test.Equals(t, ""true"", *input.Overwrite)
	test.Equals(t, true, input.FlagRecursive)
	reset()

	// Case: download without --cmd, with -overwrite flag
	os.Args = []string{""pfda"", ""download"", ""file-23456"", ""file-2221"", ""file-11111"", ""-key"", ""HELLO"", ""-overwrite"", ""true""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file-23456"", ""file-2221"", ""file-11111""}, *input.Args)
	test.Equals(t, ""true"", *input.Overwrite)
	reset()

}

func TestInvokeListing(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeListing = func(client precisionfda.IPFDAClient, folderID *string, spaceID *string, flags map[string]bool) error {
		funcWasCalled = true
		input.FolderID = folderID
		input.SpaceID = spaceID
		input.FlagBrief = flags[""brief""]
		input.FlagFilesOnly = flags[""files_only""]
		input.FlagFoldersOnly = flags[""folders_only""]
		input.FlagJson = flags[""json""]
		return nil
	}

	// Case: ls without folderID and spaceID and no flags
	os.Args = []string{""pfda"", ""ls"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, false, input.FlagBrief)
	test.Equals(t, false, input.FlagFilesOnly)
	test.Equals(t, false, input.FlagFoldersOnly)
	test.Equals(t, false, input.FlagJson)
	reset()

	// Case: ls with help - only show help and do not call the func
	os.Args = []string{""pfda"", ""ls"", ""--help""}
	returnCode = runMainInternal(false)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, false, input.FlagBrief)
	test.Equals(t, false, input.FlagFilesOnly)
	test.Equals(t, false, input.FlagFoldersOnly)
	test.Equals(t, false, input.FlagJson)
	reset()

	// Case: ls return only files in json format
	os.Args = []string{""pfda"", ""ls"", ""--key"", ""AUTH_KEY"", ""--json"", ""--files""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, false, input.FlagBrief)
	test.Equals(t, true, input.FlagFilesOnly)
	test.Equals(t, false, input.FlagFoldersOnly)
	test.Equals(t, true, input.FlagJson)
	reset()

	// Case: ls return only folders in brief format
	os.Args = []string{""pfda"", ""ls"", ""--key"", ""AUTH_KEY"", ""--brief"", ""--folders""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, true, input.FlagBrief)
	test.Equals(t, false, input.FlagFilesOnly)
	test.Equals(t, true, input.FlagFoldersOnly)
	test.Equals(t, false, input.FlagJson)
	reset()
}

func TestInvokeListSpaces(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeListSpaces = func(client precisionfda.IPFDAClient, flags map[string]bool) error {
		funcWasCalled = true
		input.FlagJson = flags[""json""]
		input.FlagLocked = flags[""locked""]
		input.FlagUnactivated = flags[""unactivated""]
		input.FlagReview = flags[""review""]
		input.FlagGroups = flags[""groups""]
		input.FlagPrivate = flags[""private_type""]
		input.FlagAdministrator = flags[""administrator""]
		input.FlagGovernment = flags[""government""]
		return nil
	}

	// Case: list-spaces with no flags
	os.Args = []string{""pfda"", ""list-spaces"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, false, input.FlagLocked)
	test.Equals(t, false, input.FlagUnactivated)
	test.Equals(t, false, input.FlagReview)
	test.Equals(t, false, input.FlagGroups)
	test.Equals(t, false, input.FlagPrivate)
	test.Equals(t, false, input.FlagAdministrator)
	test.Equals(t, false, input.FlagGovernment)
	test.Equals(t, false, input.FlagJson)
	reset()

	// Case: list-spaces with help - only show help and do not call the func
	os.Args = []string{""pfda"", ""list-spaces"", ""--help""}
	returnCode = runMainInternal(false)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, false, input.FlagLocked)
	test.Equals(t, false, input.FlagUnactivated)
	test.Equals(t, false, input.FlagReview)
	test.Equals(t, false, input.FlagGroups)
	test.Equals(t, false, input.FlagPrivate)
	test.Equals(t, false, input.FlagAdministrator)
	test.Equals(t, false, input.FlagGovernment)
	test.Equals(t, false, input.FlagJson)
	reset()

	// Case: list-spaces only locked and in json format
	os.Args = []string{""pfda"", ""list-spaces"", ""--key"", ""AUTH_KEY"", ""--locked"", ""--json""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, true, input.FlagLocked)
	test.Equals(t, false, input.FlagUnactivated)
	test.Equals(t, false, input.FlagReview)
	test.Equals(t, false, input.FlagGroups)
	test.Equals(t, false, input.FlagPrivate)
	test.Equals(t, false, input.FlagAdministrator)
	test.Equals(t, false, input.FlagGovernment)
	test.Equals(t, true, input.FlagJson)
	reset()

	// Case: list spaces - multi-type filter
	os.Args = []string{""pfda"", ""list-spaces"", ""--key"", ""AUTH_KEY"", ""--groups"", ""--private"", ""--administrator"", ""--review"", ""--government""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, false, input.FlagLocked)
	test.Equals(t, false, input.FlagUnactivated)
	test.Equals(t, true, input.FlagReview)
	test.Equals(t, true, input.FlagGroups)
	test.Equals(t, true, input.FlagPrivate)
	test.Equals(t, true, input.FlagAdministrator)
	test.Equals(t, true, input.FlagGovernment)
	test.Equals(t, false, input.FlagJson)
	reset()
}

func TestInvokeDescribeEntity(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeDescribe = func(client precisionfda.IPFDAClient, entityID *string, entityType *string) error {
		funcWasCalled = true
		input.EntityID = entityID
		input.EntityType = entityType
		return nil
	}

	// Case: describe-app entity - old syntax
	os.Args = []string{""pfda"", ""describe-app"", ""--app-id"", ""APP_ID"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""APP_ID"", *input.EntityID)
	test.Equals(t, ""app"", *input.EntityType)
	reset()

	// Case: describe-app entity - new syntax
	os.Args = []string{""pfda"", ""describe-app"", ""APP_ID"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""APP_ID"", *input.EntityID)
	test.Equals(t, ""app"", *input.EntityType)
	reset()

	// Case: describe-workflow entity - old syntax
	os.Args = []string{""pfda"", ""describe-workflow"", ""--workflow-id"", ""WORKFLOW_ID"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""WORKFLOW_ID"", *input.EntityID)
	test.Equals(t, ""workflow"", *input.EntityType)
	reset()

	// Case: describe-workflow entity - new syntax
	os.Args = []string{""pfda"", ""describe-workflow"", ""WORKFLOW_ID"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""WORKFLOW_ID"", *input.EntityID)
	test.Equals(t, ""workflow"", *input.EntityType)
	reset()
}

func TestInvokeMkdir(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeMkdir = func(client precisionfda.IPFDAClient, names *[]string, folderID *string, spaceID *string, parents bool) error {
		funcWasCalled = true
		input.Args = names
		input.FolderID = folderID
		input.SpaceID = spaceID
		input.FlagParents = parents
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""mkdir"", ""dir1"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""dir1""}, *input.Args)
	reset()

	// Case: mkdir with multiple args
	os.Args = []string{""pfda"", ""mkdir"", ""dir1"",""dir2"", ""dir3"", ""-folder-id"",""123"", ""-space-id"",""333"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""dir1"", ""dir2"",""dir3""}, *input.Args)
	test.Equals(t, ""123"", *input.FolderID)
	test.Equals(t, ""333"", *input.SpaceID)
	test.Equals(t, false, input.FlagParents)
	reset()

	// Case: mkdir nested folders with parents flag
	os.Args = []string{""pfda"", ""mkdir"", ""dir1/dir2/dir3"", ""-p"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""dir1/dir2/dir3""}, *input.Args)
	test.Equals(t, true, input.FlagParents)
	reset()
}

func TestInvokeRmdir(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeRmdir = func(client precisionfda.IPFDAClient, args *[]string) error {
		funcWasCalled = true
		input.Args = args
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""rmdir"", ""123"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""123""}, *input.Args)
	reset()

	// Case: mkdir with multiple args
	os.Args = []string{""pfda"", ""rmdir"", ""123"", ""333"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""123"", ""333""}, *input.Args)

	test.Equals(t, false, input.FlagParents)
	reset()
}

func TestInvokeRm(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeRm = func(client precisionfda.IPFDAClient, args *[]string,folderID *string, spaceID *string) error {
		funcWasCalled = true
		input.Args = args
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""rm"", ""123"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""123""}, *input.Args)
	reset()

	// Case: mkdir with multiple args
	os.Args = []string{""pfda"", ""rm"", ""123*"", ""333"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""123*"", ""333""}, *input.Args)

	test.Equals(t, false, input.FlagParents)
	reset()
}

func TestInvokeHead(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeHead = func(client precisionfda.IPFDAClient, arg *string, lines int) error {
		funcWasCalled = true
		input.Arg = arg
		input.FlagLines = lines
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""head"", ""testfile.txt"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""testfile.txt"", *input.Arg)
	test.Equals(t, 10, input.FlagLines)
	reset()

	// Case: mkdir with multiple args
	os.Args = []string{""pfda"", ""head"", ""testfile.txt"", ""-lines"", ""222"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""testfile.txt"", *input.Arg)
	test.Equals(t, 222, input.FlagLines)
	reset()
}

func TestInvokeCat(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeCat = func(client precisionfda.IPFDAClient, arg *string) error {
		funcWasCalled = true
		input.Arg = arg
		input.FlagLines = -1
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""cat"", ""testfile.txt"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""testfile.txt"", *input.Arg)
	test.Equals(t, -1, input.FlagLines)
	reset()

}
","Go"
"Assay","FDA/precisionFDA","packages/cli/build-dist.sh",".sh","1132","45","#!/bin/sh
#
# N.B. run from precision-fda/go

VERSION=2.8.0
COMMITID=`git rev-parse HEAD`

BuildAndPackage() {
    PLATFORM=$1
    ARCH=$2
    BUILDTIME=$(date +%Y-%m-%d-%H%M%S)
    echo ""Building pfda CLI (v$VERSION) for $PLATFORM $ARCH""
    docker run --rm --mount type=bind,source=""$(pwd)"",target=/go/src/dnanexus.com/precision-fda-cli \
           -e GOOS=""$PLATFORM"" -e GOARCH=""$ARCH"" -e COMMITID=""$COMMITID"" \
           -e VERSION=""$VERSION"" -e BUILDTIME=""$BUILDTIME"" precisionfda-cli
    cd ./dist

    OUT_FILE=pfda_${PLATFORM}_${ARCH}
    if [ ! -f ""$OUT_FILE"" ]; then
        echo
        echo ""$OUT_FILE does not exist. Exiting""
        exit 1
    fi

    EXEC_NAME=""pfda""
    if [ $PLATFORM = ""windows"" ]; then
        EXEC_NAME=""pfda.exe""
    fi

    mv ${OUT_FILE} ${EXEC_NAME}

    if [ $PLATFORM = ""windows"" ]; then
        zip -rj pfda-${PLATFORM}-${VERSION}.zip ${EXEC_NAME}
    else
        tar -czvf pfda-${PLATFORM}-${VERSION}.tar.gz ${EXEC_NAME}
    fi
    rm ${EXEC_NAME}

    cd ..
}

BuildAndPackage 'linux' 'amd64'
BuildAndPackage 'darwin' 'amd64' # N.B. darwin is macOS
BuildAndPackage 'windows' 'amd64'
","Shell"
"Assay","FDA/precisionFDA","packages/cli/pfda.go",".go","31100","967","// PrecisionFDA CLI
// Version 2.8.0
package main

import (
	""crypto/tls""
	""encoding/json""
	""flag""
	""fmt""
	""net/http""
	""os""
	""path/filepath""
	""runtime""
	""strconv""
	""strings""

	_ ""crypto/tls/fipsonly""
	""dnanexus.com/precision-fda-cli/helpers""
	""dnanexus.com/precision-fda-cli/precisionfda""
	""rsc.io/goversion/version""
)

// CONSTANTS
const defaultNumRoutines = 10
const defaultChunkSize = 1 << 26 // default 64MB (min. 16MB)
const defaultSkipVerify = ""false""
const usageString = `
****************************
PFDA COMMAND LINE TOOL v2.8.0
****************************

All available commands:
   pfda cat
   pfda describe
   pfda download
   pfda get-scope
   pfda head
   pfda ls
   pfda ls-apps
   pfda ls-assets
   pfda ls-discussions
   pfda ls-executions
   pfda ls-members
   pfda ls-spaces
   pfda ls-workflows
   pfda mkdir
   pfda rm
   pfda rmdir
   pfda upload-asset
   pfda upload-file
   pfda view-link

Command specific help section with description, examples and available flags:
   pfda <COMMAND> -help

To print version info and exit:
   pfda -version

Full documentation can be found in the Docs section of the precisionFDA website - https://precision.fda.gov/docs/cli
`

//
// N.B. the -cmd flag exists and is now deprecated, but the following should all work
//
// # Testing upload to localhost
// $ ./pfda upload-file -server localhost:3000 -skipverify true -key $KEY -file fileYouWantToUpload.pdf
// $ ./pfda -cmd upload-file -server localhost:3000 -skipverify true -key $KEY -file fileYouWantToUpload.pdf
//
// # Testing download from localhost
// $ ./pfda download -server localhost:3000 -skipverify true -key $KEY -file file-yourfileuuid-1
// $ ./pfda -cmd download -server localhost:3000 -skipverify true -key $KEY -file file-yourfileuuid-1
//
// # Testing the API for file download
// $ ./pfda api -server localhost:3000 -skipverify true -key $KEY -route ""files/file-G70fbKj0qp9YGkg24kGxQvF4-1/download"" -json '{ ""format"": ""json"" }'
// $ ./pfda -cmd api -server localhost:3000 -skipverify true -key $KEY -route ""files/file-G70fbKj0qp9YGkg24kGxQvF4-1/download"" -json '{ ""format"": ""json"" }'

// GLOBAL VARIABLES
var defaultURL = ""precision.fda.gov""

// these varaibles are populated by -ldflags -X command line options
var (
	commitID  string
	Version   string
	BuildTime string
	OsArch    string
)

// ACTION FUNCTIONS
// Wrapping calls to pfdaclient allow us to replace these functions in pfda_test.go with mocks
var invokeUploadFile = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadFile(*inputFilePath, *folderID, *spaceID, true)
}

var invokeUploadStdinFile = func(client precisionfda.IPFDAClient, fileName *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadStdin(*fileName, *folderID, *spaceID, true)

}

var invokeUploadMultipleFiles = func(client precisionfda.IPFDAClient, filePaths *[]string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadMultipleFiles(*filePaths, *folderID, *spaceID)
}

var invokeUploadFolder = func(client precisionfda.IPFDAClient, inputPath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadFolder(*inputPath, *folderID, *spaceID)
}

var invokeUploadAsset = func(client precisionfda.IPFDAClient, assetFolderPath *string, assetName *string, readmeFilePath *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadAsset(*assetFolderPath, *assetName, *readmeFilePath)
}

var invokeDownload = func(client precisionfda.IPFDAClient, args *[]string, folderID *string, spaceID *string, public bool, recursive bool, outputFilePath *string, overwriteFile *string) error {
	return client.Download(*args, *folderID, *spaceID, public, recursive, *outputFilePath, *overwriteFile)
}

var invokeFileViewLink = func(client precisionfda.IPFDAClient, fileID *string, preauthenticated bool, duration int64) error {
	return client.FileViewLink(*fileID, preauthenticated, duration)
}

var invokeUploadResources = func(client precisionfda.IPFDAClient, args *[]string, portalID *string) error {
	return client.UploadResources(*args, *portalID)
}

var invokeDescribe = func(client precisionfda.IPFDAClient, entityID *string) error {
	return client.DescribeEntity(*entityID)
}

var invokeLsSpaces = func(client precisionfda.IPFDAClient, flags map[string]bool) error {
	return client.LsSpaces(flags)
}

var invokeListing = func(client precisionfda.IPFDAClient, folderID *string, spaceID *string, flags map[string]bool) error {
	return client.Ls(*folderID, *spaceID, flags)
}

var invokeLsApps = func(client precisionfda.IPFDAClient, spaceID *string, flags map[string]bool) error {
	return client.LsApps(*spaceID, flags)
}

var invokeLsAssets = func(client precisionfda.IPFDAClient, spaceID *string, flags map[string]bool) error {
	return client.LsAssets(*spaceID, flags)
}

var invokeLsWorkflows = func(client precisionfda.IPFDAClient, spaceID *string, flags map[string]bool) error {
	return client.LsWorkflows(*spaceID, flags)
}

var invokeLsExecutions = func(client precisionfda.IPFDAClient, spaceID *string, flags map[string]bool) error {
	return client.LsExecutions(*spaceID, flags)
}

var invokeLsMembers = func(client precisionfda.IPFDAClient, spaceID *string) error {
	return client.LsMembers(*spaceID)
}

var invokeLsDiscussions = func(client precisionfda.IPFDAClient, spaceID *string) error {
	return client.LsDiscussions(*spaceID, map[string]bool{})
}

var invokeMkdir = func(client precisionfda.IPFDAClient, names *[]string, folderID *string, spaceID *string, parents bool) error {
	return client.Mkdir(*names, *folderID, *spaceID, parents)
}

var invokeRmdir = func(client precisionfda.IPFDAClient, args *[]string) error {
	return client.Rmdir(*args)
}

var invokeRm = func(client precisionfda.IPFDAClient, args *[]string, folderID *string, spaceID *string) error {
	return client.Rm(*args, *folderID, *spaceID)
}

var invokeHead = func(client precisionfda.IPFDAClient, arg *string, lines int) error {
	return client.Head(*arg, lines)
}

var invokeCat = func(client precisionfda.IPFDAClient, arg *string) error {
	// reusing head command with unreachable limit
	return client.Head(*arg, -1)
}

var invokeGetScope = func(client precisionfda.IPFDAClient) error {
	return client.GetScope()
}

var invokeCreateDiscussion = func(client precisionfda.IPFDAClient, spaceID *string, jsonBody *string) error {
	return client.CreateDiscussion(*spaceID, *jsonBody)
}

var invokeCreateReply = func(client precisionfda.IPFDAClient, jsonBody *string) error {
	return client.CreateReply(*jsonBody)
}

var invokeEditDiscussion = func(client precisionfda.IPFDAClient, jsonBody *string) error {
	return client.EditDiscussion(*jsonBody)
}

var invokeEditReply = func(client precisionfda.IPFDAClient, jsonBody *string) error {
	return client.EditReply(*jsonBody)
}

var invokeRefreshToken = func(client precisionfda.IPFDAClient, autoRefresh bool) (string, error) {
	return client.RefreshToken(autoRefresh)
}

var invokeGetLatestVersion = func(client precisionfda.IPFDAClient) (string, error) {
	return client.GetLatestVersion()
}

func main() {
	returnCode := mainInternal()
	os.Exit(returnCode)
}

func mainInternal() int {
	// This isn't necessary for the CLI to run correctly, but we define command line flags
	// inside mainInternal so that they can be unit tested
	flag.CommandLine = flag.NewFlagSet(os.Args[0], flag.ExitOnError)

	command := flag.String(""cmd"", """", ""[Deprecated - please use the format ./pfda <cmd>] Command to execute."")
	authKey := flag.String(""key"", """", ""Authorization key. Required if a previous config doesn't exist."")
	apiRoute := flag.String(""route"", """", ""Name of precisionFDA API route to call."")
	jsonInput := flag.String(""json-payload"", """", ""JSON payload for specified API call (if any)."")
	inputFilePath := flag.String(""file"", """", ""Path to file for 'upload-file'"")
	assetFolderPath := flag.String(""root"", """", ""Path to root folder for 'upload-asset'"")
	fileName := flag.String(""name"", """", ""Name of uploaded file."")
	readmeFilePath := flag.String(""readme"", """", ""Readme file for uploaded asset. Must end with '.txt' or '.md'."")
	fileID := flag.String(""file-id"", """", ""File ID of the file to be downloaded"")
	appID := flag.String(""app-id"", """", ""App ID of the app to be described"")
	workflowID := flag.String(""workflow-id"", """", ""Workflow ID of the workflow to be described"")
	folderID := flag.String(""folder-id"", """", ""Folder ID of the target folder"")
	spaceID := flag.String(""space-id"", """", ""Space ID of the target space"")
	portalID := flag.String(""portal-id"", """", ""Slug or ID of the target portal"")
	outputFilePath := flag.String(""output"", """", ""[optional] File path to write api call response data. Defaults to stdout."")
	inputChunkSize := flag.Int(""chunksize"", defaultChunkSize, ""[optional] Size of each upload chunk in bytes (Min 5MB,/Max 2GB)."")
	inputNumRoutines := flag.Int(""threads"", defaultNumRoutines, ""[optional] Maximum number of upload threads to spawn (Max 100)."")
	server := flag.String(""server"", defaultURL, ""[optional] Server to connect and make requests to."")
	skipVerify := flag.String(""skipverify"", defaultSkipVerify, ""[optional] Boolean string to skip certificate verification."")
	pfda_version := flag.Bool(""version"", false, ""[optional] Print version"")

	// help flag - does not run any command just prints help info
	flagHelp := flag.Bool(""help"", false, ""[optional] Print help info for the particular command"")
	flagHelpShort := flag.Bool(""h"", false, ""[optional] Print help info for the particular command"") // is there a better way to have aliases ??

	// optional flags for adjusting the desired output values, format and behavior
	flagBrief := flag.Bool(""brief"", false, ""[optional] Only present brief info"")
	flagJson := flag.Bool(""json"", false, ""[optional] Present result as JSON"")
	flagFilesOnly := flag.Bool(""files"", false, ""[optional] Only present files"")
	flagFoldersOnly := flag.Bool(""folders"", false, ""[optional] Only present folders"")
	flagPublic := flag.Bool(""public"", false, ""[optional] Only present public scope"")
	flagMe := flag.Bool(""me"", false, ""[optional] Only present private scope"")
	flagLocked := flag.Bool(""locked"", false, ""[optional] Only present locked spaces"")
	flagUnactivated := flag.Bool(""unactivated"", false, ""[optional] Only present unactivated spaces"")
	flagProtected := flag.Bool(""protected"", false, ""[optional] Only present protected spaces"")
	flagGroups := flag.Bool(""groups"", false, ""[optional] Only present groups spaces"")
	flagReview := flag.Bool(""review"", false, ""[optional] Only present review spaces"")
	flagPrivate := flag.Bool(""private"", false, ""[optional] Only present private spaces"")
	flagAdministrator := flag.Bool(""administrator"", false, ""[optional] Only present administrator spaces"")
	flagGovernment := flag.Bool(""government"", false, ""[optional] Only present government spaces"")
	flagOverwriteFile := flag.String(""overwrite"", """", ""[optional] Preselect overwrite option what to do with already existing file."")
	flagRecursive := flag.Bool(""recursive"", false, ""[optional] Recurse into a directory."")
	flagRecursiveShort := flag.Bool(""r"", false, ""[optional] Recurse into a directory."")
	flagParents := flag.Bool(""parents"", false, ""[optional] No error if existing, make parent directories as needed"")
	flagParentsShort := flag.Bool(""p"", false, ""[optional] No error if existing, make parent directories as needed"")
	flagLines := flag.Int(""lines"", 10, ""[optional] Number of lines to print, default 10"")
	flagPreauthenticated := flag.Bool(""auth"", false, ""[optional] Use preauthenticated URL for viewing file"")
	flagDuration := flag.Int64(""duration"", 86_400, ""[optional] Time to live for preauthenticated URL in seconds"")

	// Support for ./pfda upload-file option of specifying a command, making -cmd optional
	var positionalCmd string
	var args []string
	var index int
	if len(os.Args) > 1 && !strings.HasPrefix(os.Args[1], ""-"") {
		// Storing first positional arg after ./pfda
		positionalCmd = os.Args[1]
		// check possible non-flag args
		args, index = helpers.ParseArgsUntilFlag(os.Args)
		flag.CommandLine.Parse(os.Args[index:])

	} else {
		flag.Parse()
	}

	//	TODO: REMOVE IN V3.0.0
	if *command != """" {
		fmt.Println(""\nWARNING! THIS SYNTAX IS BEING DEPRECATED. PLEASE USE THE FOLLOWING SYNTAX INSTEAD: ./pfda <command> <args> \n"")
	}

	if positionalCmd != """" {
		if *command != """" {
			return helpers.ErrorFromString(""Error input >>> both positional command and -cmd option specified. Please remove one or the other"", *flagJson)
		}
		*command = positionalCmd
	}

	// always check if config != nil
	config, configErr := helpers.GetConfig()
	if configErr != nil {
		if !os.IsNotExist(configErr) {
			return helpers.ErrorFromError(configErr, *flagJson)
		}
	}

	serverURL := defaultURL
	if *server != """" && *server != defaultURL {
		serverURL = *server
	} else if config != nil && config.Server != """" {
		serverURL = config.Server
	}

	pfdaclient := precisionfda.NewPFDAClient(serverURL)
	pfdaclient.Platform = OsArch
	pfdaclient.JsonResponse = *flagJson

	skipVerifyBool, _ := strconv.ParseBool(*skipVerify)
	transport := pfdaclient.Client.HTTPClient.Transport.(*http.Transport)
	if skipVerifyBool {
		// Setting '-skipverify' true will allow devs to connect to local instances with self-signed certs
		transport.TLSClientConfig = &tls.Config{InsecureSkipVerify: true, ServerName: *server}
	} else {
		transport.TLSClientConfig = &tls.Config{MinVersion: tls.VersionTLS12}
	}

	if *pfda_version {
		printInfo(pfdaclient)
		checkLatestVersion(pfdaclient)
		return 0
	}

	recursive := *flagRecursive || *flagRecursiveShort
	parents := *flagParents || *flagParentsShort
	help := *flagHelp || *flagHelpShort

	if !help && *command != """" {
		// Set AuthKey based on provided authKey or from config.
		pfdaclient.AuthKey = *authKey
		if *authKey == """" {
			if config == nil {
				return helpers.ErrorFromString(""Missing authorization key - could not find config file and no key was provided with the command."", *flagJson)
			}
			pfdaclient.AuthKey = config.Key
		}

		// Set spaceID from config if it's not provided and neither flagMe nor flagPublic is set.
		if *spaceID == """" && !*flagMe && !*flagPublic {
			if config != nil {
				*spaceID = config.GetSpaceId()
			}
		}
	}

	switch *command {
	case ""upload-asset"":
		if help {
			return helpers.PrintUploadAssetHelp()
		}

		if *assetFolderPath == """" {
			return helpers.ErrorFromString(""Root directory for the asset is required - provide it as [-root <ASSET_ROOT>]"", *flagJson)
		}

		f, err := os.Stat(*assetFolderPath)
		if os.IsNotExist(err) || !f.IsDir() {
			return helpers.ErrorFromString(fmt.Sprintf(""Input asset folder path '%s' does not exist or is not a directory"", *assetFolderPath), *flagJson)
		}

		if *fileName == """" {
			return helpers.ErrorFromString(""Asset name (ending with .tar or .tar.gz) is required - provide it as [-name <ASSET_NAME>]"", *flagJson)
		}

		if !strings.HasSuffix(*fileName, "".tar"") && !strings.HasSuffix(*fileName, "".tar.gz"") {
			return helpers.ErrorFromString(fmt.Sprintf(""input asset name '%s' does not end with '.tar' or '.tar.gz'."", *fileName), *flagJson)
		}

		if *readmeFilePath == """" {
			return helpers.ErrorFromString(""Readme file for the asset (ending with .txt or .md) is required - provide it as [-readme <ASSET_README>]"", *flagJson)
		}

		f, err = os.Stat(*readmeFilePath)
		if os.IsNotExist(err) || f.IsDir() {
			return helpers.ErrorFromString(fmt.Sprintf(""Input readme file path '%s' does not exist or is a directory"", *readmeFilePath), *flagJson)
		}

		err = invokeUploadAsset(pfdaclient, assetFolderPath, fileName, readmeFilePath, inputChunkSize, inputNumRoutines)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""upload-file"":
		if help {
			return helpers.PrintUploadFileHelp()
		}

		stdinData := false
		stdinFile := os.Stdin

		fi, _ := os.Stdin.Stat()
		if (fi.Mode() & os.ModeCharDevice) == 0 {
			stdinData = true
		}

		// if -file flag used, override args.
		if *inputFilePath != """" {
			args = []string{*inputFilePath}
		}

		// STDIN upload logic
		if len(args) == 0 && stdinData {
			if *fileName == """" {
				return helpers.ErrorFromString(""Filename for stdin input is required - provide it as [-name <FILE_NAME>]"", *flagJson)
			}
			err := invokeUploadStdinFile(pfdaclient, fileName, folderID, spaceID, inputChunkSize, inputNumRoutines)
			if err != nil {
				return helpers.ErrorFromError(err, *flagJson)

			}
			defer stdinFile.Close()
			break
		}

		if len(args) != 0 && stdinData {
			return helpers.ErrorFromString(""Cannot combine multiple file sources - use either args or stdin"", *flagJson)
		}

		if len(args) == 0 {
			return helpers.ErrorFromString(""Path for upload is required - provide it as an argument or stdin"", *flagJson)
		}

		if *fileName != """" && !*flagJson {
			fmt.Println("">> Filename flag ignored for upload"")
		}

		// uploading more than one file/folder
		if len(args) > 1 {
			err := invokeUploadMultipleFiles(pfdaclient, &args, folderID, spaceID, inputChunkSize, inputNumRoutines)
			if err != nil {
				return helpers.ErrorFromError(err, *flagJson)
			}
			// just one file/folder to upload
		} else {
			path := filepath.Clean(args[0])
			f, err := os.Stat(path)
			if os.IsNotExist(err) {
				return helpers.ErrorFromString(fmt.Sprintf(""Input path '%s' does not exist"", path), *flagJson)
			}

			if f.IsDir() {
				err = invokeUploadFolder(pfdaclient, &path, folderID, spaceID, inputChunkSize, inputNumRoutines)
				if err != nil {
					return helpers.ErrorFromError(err, *flagJson)

				}
			} else {
				if *inputChunkSize == defaultChunkSize {
					// if chunkSize was not set by user, calculate it based on file size
					*inputChunkSize = helpers.CalculateChunkSize(f.Size(), pfdaclient.MinChunkSize)
				}
				err = invokeUploadFile(pfdaclient, &path, folderID, spaceID, inputChunkSize, inputNumRoutines)
				if err != nil {
					return helpers.ErrorFromError(err, *flagJson)
				}
			}
		}

	case ""download"":
		if help {
			return helpers.PrintDownloadHelp()
		}

		if *fileID == """" && *folderID == """" && *spaceID == """" && len(args) == 0 {
			return helpers.ErrorFromString(""File ID or name of the file to be downloaded is required: [-file-id <FILE_ID>]\n  Or provide either of Space and/or Folder ID you want to bulk-download: [-folder-id <FOLDER_ID> -space-id <SPACE_ID>]"", *flagJson)
		}

		if *fileID != """" && *folderID != """" {
			return helpers.ErrorFromString(""Cannot combine file-id and folder-id flags"", *flagJson)
		}

		// we need tri-state as there is different behavior for -overwrite=false and completely omitted -overwrite flag
		validOverwrite := map[string]bool{"""": true, ""true"": true, ""false"": true}
		if !validOverwrite[*flagOverwriteFile] {
			return helpers.ErrorFromString(""Invalid value for -overwrite flag - acceptable values are true, false, or omit it completely"", *flagJson)
		}

		// Override args with -file-id if provided
		if *fileID != """" {
			args = []string{*fileID}
		}

		err := invokeDownload(pfdaclient, &args, folderID, spaceID, *flagPublic, recursive, outputFilePath, flagOverwriteFile)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""view-link"":
		if help {
			return helpers.PrintViewLinkHelp()
		}

		if len(args) == 0 {
			return helpers.ErrorFromString(""File ID is required"", *flagJson)
		}

		if !helpers.IsFileId(args[0]) {
			return helpers.ErrorFromString(fmt.Sprintf(""File ID '%s' is invalid"", args[0]), *flagJson)
		}

		err := invokeFileViewLink(pfdaclient, &args[0], *flagPreauthenticated, *flagDuration)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	//	TODO: REMOVE IN V3.0.0
	case ""describe-app"":

		fmt.Println(""\nWARNING! THIS COMMAND IS BEING DEPRECATED. PLEASE USE THE FOLLOWING SYNTAX INSTEAD: ./pfda describe <APP_ID>"")

		if help {
			return helpers.PrintDescribeAppHelp()
		}

		if *appID != """" {
			args = []string{*appID}
		}

		if len(args) == 0 {
			return helpers.ErrorFromString(""App ID is required"", *flagJson)
		}

		err := invokeDescribe(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""describe"":

		if help {
			return helpers.PrintDescribeHelp()
		}

		if len(args) == 0 {
			return helpers.ErrorFromString(""Entity ID is required"", *flagJson)
		}

		entityType := helpers.ParseEntityType(args[0])
		if entityType == """" {
			return helpers.ErrorFromString(fmt.Sprintf(""Invalid entity type '%s' - must be one of: app, job, file, worklfow, discussion."", args[0]), *flagJson)
		}

		err := invokeDescribe(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""upload-resource"":
		if help {
			return helpers.PrintUploadResourceHelp()
		}

		if len(args) == 0 {
			return helpers.ErrorFromString(""Path to the resource(s) is required"", *flagJson)
		}

		if *portalID == """" {
			return helpers.ErrorFromString(""Portal ID is required"", *flagJson)
		}

		err := invokeUploadResources(pfdaclient, &args, portalID)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	//	TODO: REMOVE IN V3.0.0
	case ""describe-workflow"":

		fmt.Println(""\nWARNING! THIS COMMAND IS BEING DEPRECATED. PLEASE USE THE FOLLOWING SYNTAX INSTEAD: ./pfda describe <WORKFLOW_ID>"")

		if help {
			return helpers.PrintDescribeWorkflowHelp()
		}

		if *workflowID != """" {
			args = []string{*workflowID}
		}

		if len(args) == 0 {
			return helpers.ErrorFromString(""Workflow ID is required"", *flagJson)
		}

		err := invokeDescribe(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""ls-apps"":
		if help {
			return helpers.PrintLsAppsHelp()
		}

		flags := map[string]bool{}

		if *flagPublic {
			flags[""public_scope""] = true
		}

		err := invokeLsApps(pfdaclient, spaceID, flags)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""ls-assets"":
		if help {
			return helpers.PrintLsAssetsHelp()
		}

		flags := map[string]bool{}
		if *flagPublic {
			flags[""public_scope""] = true
		}

		err := invokeLsAssets(pfdaclient, spaceID, flags)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}
	case ""ls-workflows"":
		if help {
			return helpers.PrintLsWorkflowsHelp()
		}

		flags := map[string]bool{}
		if *flagPublic {
			flags[""public_scope""] = true
		}

		err := invokeLsWorkflows(pfdaclient, spaceID, flags)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""ls-executions"":
		if help {
			return helpers.PrintLsExecutionsHelp()
		}

		flags := map[string]bool{}
		if *flagPublic {
			flags[""public_scope""] = true
		}

		err := invokeLsExecutions(pfdaclient, spaceID, flags)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)

		}

	case ""ls-spaces"", ""list-spaces"":
		if help {
			return helpers.PrintLsSpacesHelp()
		}

		flags := map[string]bool{
			// state of space, must be exclusive
			""locked"":      *flagLocked,
			""unactivated"": *flagUnactivated,
			// types of space flag, multiple allowed
			""protected"":     *flagProtected,
			""review"":        *flagReview,
			""groups"":        *flagGroups,
			""private_type"":  *flagPrivate,
			""administrator"": *flagAdministrator,
			""government"":    *flagGovernment,
			// present as JSON / pretty print
			""json"": *flagJson,
		}
		err := invokeLsSpaces(pfdaclient, flags)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""ls-members"":
		if help {
			return helpers.PrintLsMembersHelp()
		}

		if *spaceID != """" {
			args = []string{*spaceID}
		}
		if len(args) == 0 {
			return helpers.ErrorFromString(""Space ID is required"", *flagJson)
		}

		err := invokeLsMembers(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""ls-discussions"":
		if help {
			return helpers.PrintLsDiscussionsHelp()
		}

		if *spaceID != """" {
			args = []string{*spaceID}
		}

		if len(args) == 0 {
			return helpers.ErrorFromString(""Space ID is required"", *flagJson)
		}

		if len(args) != 1 {
			return helpers.ErrorFromString(""Only one space ID is allowed"", *flagJson)
		}

		err := invokeLsDiscussions(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)

		}

	case ""ls"":
		if help {
			return helpers.PrintLsHelp()
		}

		if *flagFilesOnly && *flagFoldersOnly {
			return helpers.ErrorFromString(""The flags '-folders' and '-files' cannot be used together - use only one of these flags."", *flagJson)
		}

		flags := map[string]bool{
			""brief"":        *flagBrief,
			""files_only"":   *flagFilesOnly,
			""folders_only"": *flagFoldersOnly,
			// present as JSON / pretty print
			""json"": *flagJson,
		}

		if *flagPublic {
			flags[""public_scope""] = true
		}

		err := invokeListing(pfdaclient, folderID, spaceID, flags)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""mkdir"":
		if help {
			return helpers.PrintMkdirHelp()
		}
		err := invokeMkdir(pfdaclient, &args, folderID, spaceID, parents)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""rmdir"":
		if help {
			return helpers.PrintRmdirHelp()
		}
		err := invokeRmdir(pfdaclient, &args)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""rm"":
		if help {
			return helpers.PrintRmHelp()
		}
		err := invokeRm(pfdaclient, &args, folderID, spaceID)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""head"":
		if help {
			return helpers.PrintHeadHelp()
		}
		err := invokeHead(pfdaclient, &args[0], *flagLines)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""cat"":
		if help {
			return helpers.PrintCatHelp()
		}
		err := invokeCat(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""get-scope"":
		if help {
			return helpers.PrintGetScopeHelp()
		}

		err := invokeGetScope(pfdaclient)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""create-discussion"":
		if help {
			return helpers.PrintCreateDiscussionHelp()
		}

		if *spaceID == """" {
			return helpers.ErrorFromString(""Space ID is required"", *flagJson)
		}

		err := invokeCreateDiscussion(pfdaclient, spaceID, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""create-reply"":
		if help {
			return helpers.PrintCreateReplyHelp()
		}

		err := invokeCreateReply(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""edit-discussion"":
		if help {
			return helpers.PrintEditDiscussionHelp()
		}

		err := invokeEditDiscussion(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""edit-reply"":
		if help {
			return helpers.PrintEditReplyHelp()
		}

		err := invokeEditReply(pfdaclient, &args[0])
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case ""refresh-key"":
		// add option for auto-refresh later
		newToken, err := invokeRefreshToken(pfdaclient, false)
		if err != nil {
			return helpers.ErrorFromString(""There was an error during key refresh action, please provide new Key from precisionFDA website"", *flagJson)
		}

		if configErr != nil {
			return helpers.ErrorFromString(fmt.Sprintf(""Could not save authorization key in config file '%s': %s"", helpers.ConfigPath, err.Error()), *flagJson)
		}

		config.Key = newToken
		helpers.SaveConfig(config, *flagJson)
		return 0

	case ""api"":
		if *apiRoute == """" {
			return helpers.ErrorFromString(""API route is required - provide it as '-route <API_ROUTE_NAME>'."", *flagJson)
		}

		if *jsonInput != """" && !isValidJSON(*jsonInput) {
			return helpers.ErrorFromString(fmt.Sprintf(""Provided JSON '%s' is not valid - provide the input in valid JSON format."", *jsonInput), *flagJson)
		}

		err := pfdaclient.CallAPI(*apiRoute, *jsonInput, *outputFilePath)
		if err != nil {
			return helpers.ErrorFromError(err, *flagJson)
		}

	case """":
		// Empty command
		fmt.Println(usageString)
		checkLatestVersion(pfdaclient)
		return 0

	default:
		// Invalid, non-empty command
		// both 'upload-resource' and 'refresh-key' are intentionally omitted.
		return helpers.ErrorFromString(fmt.Sprintf(""Command '%s' not found. Must be one of: \n'cat' \n'describe' \n'download' \n'get-scope' \n'head' \n'ls' \n'ls-apps' \n'ls-assets' \n'ls-executions' \n'ls-members' \n'ls-discussions' \n'ls-spaces' \n'ls-workflows' \n'mkdir' \n'rm' \n'rmdir' \n'upload-asset' \n'upload-file' \n'view-link'\n"", *command), *flagJson)
	}

	// Write configuration and save key
	if *authKey != """" {
		if configErr != nil && os.IsNotExist(configErr) {
			config = helpers.CreateConfig()
		}
		config.Key = *authKey
		helpers.SaveConfig(config, *flagJson)
	}

	return 0
}

// PRINT FUNCTIONS
func printInfo(pfdaclient *precisionfda.PFDAClient) {
	fmt.Printf(""\npFDA CLI Info\n"")
	fmt.Printf(""  Commit ID   :    %s\n"", commitID)
	fmt.Printf(""  CLI Version :    %s\n"", Version)
	fmt.Printf(""  Os/Arch     :    %s\n"", OsArch)
	fmt.Printf(""  Build Time  :    %s\n"", BuildTime)
	fmt.Printf(""  Go Version  :    %s\n"", runtime.Version())

	transport := pfdaclient.Client.HTTPClient.Transport.(*http.Transport)
	fmt.Printf(""  TLS Version :    %s\n"", GetTLSVersion(transport))

	// printCryptoInfo()
}

func checkLatestVersion(pfdaclient *precisionfda.PFDAClient) {
	res, err := invokeGetLatestVersion(pfdaclient)
	if err != nil {
		fmt.Println(""Error while checking for latest available version."")
	}
	if res != Version {
		fmt.Printf(""\nThere is a newer version available for you to download - v%s \nVisit https://precision.fda.gov/docs/cli to get the latest version!\n"", res)
	}
}

func printCryptoInfo() {
	executable, err := os.Executable()
	if err != nil {
		fmt.Println(""Unable to retrieve executable path"")
	}

	v, err := version.ReadExe(executable)
	if err != nil {
		fmt.Println(""Unable to retrieve goversion info"")
	}

	// TODO: find a new way to verify the build is FIPS 140-2 compliant.
	if v.FIPSOnly {
		fmt.Println(""  FIPS        :    +crypto/tls/fipsonly verified"")
	} else {
		fmt.Println(""  FIPS        :    warning FIPS mode not verified"")
	}
}

func GetTLSVersion(tr *http.Transport) string {
	switch tr.TLSClientConfig.MinVersion {
	case tls.VersionTLS10:
		return ""TLS 1.0""
	case tls.VersionTLS11:
		return ""TLS 1.1""
	case tls.VersionTLS12:
		return ""TLS 1.2""
	case tls.VersionTLS13:
		return ""TLS 1.3""
	}
	return ""Unknown""
}

func isValidJSON(s string) bool {
	var js interface{}
	return json.Unmarshal([]byte(s), &js) == nil
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/helpers/config.go",".go","2750","86","package helpers

import (
	""encoding/json""
	""fmt""
	""log""
	""os""
	""path/filepath""
	""regexp""
)

var ConfigPath = filepath.Join(getUserHomeDir(), "".pfda_config"")

type jsonConfig struct {
	Key    string `json:""key""`
	Server string `json:""server""`
	Scope  string `json:""scope""`
}

func CreateConfig() *jsonConfig {
	config := jsonConfig{}
	return &config
}

// GetConfig loads the pFDA config file and returns the struct.
// A successful call returns (config, nil).
// In case the config was not found in FS or an error occurred, (nil, err) is returned.
func GetConfig() (*jsonConfig, error) {
	f, err := os.ReadFile(ConfigPath)
	if err != nil {
		// Internal error reading config
		return nil, err
	}
	var config jsonConfig
	err = json.Unmarshal(f, &config)
	if err != nil {
		return nil, err
	}
	return &config, nil
}

func SaveConfig(config *jsonConfig, jsonFlag bool) {
	// If key was given by -key option in the command line
	// marshal it to json and write into .pfda__config
	// if marshaling fails, issue warning and exit
	jsonData, err := json.Marshal(config)
	if err != nil && !jsonFlag {
		fmt.Printf(""While the file has been uploaded succesfully\n, the authorization key can't be marshaled to json and saved in '%s': %s\n"", ConfigPath, err.Error())
		fmt.Printf(""You will need to submit authorization key in the command line in the next operation.\n"")
		// exit gracefully, without panic
	}

	// below is a more compact and cleaner implementation which is recommended when writing small files
	// It doesn't use separate Create / Write from os package, as before but takes advantage of
	// os.WriteFile which opens, writes and closes a file in one swoop
	// denote, that it also works on Windows ( checked on AWS EC2 windows instance )
	// despite Linux style file permissions are given
	// if .pfda_config exists it is truncated before writing
	// denote also there is no need in defer f.Close(), since ioutil.WriteFile closes the file immediately after writing it
	err = os.WriteFile(ConfigPath, jsonData, 0644) // 0644 is '-rw -r- -r-'
	if err != nil && !jsonFlag {
		fmt.Printf(""Could not save authorization key in config file '%s': %s\n"", ConfigPath, err.Error())
	} else if !jsonFlag {
		fmt.Printf(""Saved authorization key in config file '%s'. \nA new key does not need to be provided for 24 hours from the generation time of the provided key.\n"", ConfigPath)
	}
}

func (c *jsonConfig) GetSpaceId() string {
	re := regexp.MustCompile(`^space-(\d+)$`)
	matches := re.FindStringSubmatch(c.Scope)
	if len(matches) != 2 {
		// scope is private/public
		return """"
	}
	// only can be space-{id} now
	return matches[1]
}

func getUserHomeDir() string {
	homeDir, err := os.UserHomeDir()
	if err != nil {
		log.Fatal(err)
	}
	return homeDir
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/helpers/printer.go",".go","26878","518","package helpers

import (
	""fmt""
	""os""
	""strings""
	""text/tabwriter""
)

// Function type for writing a line to a tabwriter
type lineWriterFunc func(left, right string)

// Create a line writer for a given writer
func newLineWriter(writer *tabwriter.Writer) lineWriterFunc {
	return func(left, right string) {
		fmt.Fprintln(writer, strings.Join([]string{left, right}, ""\t"")+""\t"")
	}
}

func PrintLsSpacesHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Listing active spaces you have access to.\n"")
	writeLine(""  Usage:"", ""ls-spaces [FLAG...] | list-spaces [FLAG...]\n"")
	writeLine(""  Examples:"", ""ls-spaces -groups -private [Shows all active spaces of type groups or private]"")
	writeLine(""  "", ""ls-spaces -unactivated -json [Shows only unactivated spaces and present result as JSON]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exit"")
	writeLine(""   -locked"", ""Shows only locked spaces"")
	writeLine(""   -unactivated"", ""Shows only unactivated spaces"")
	// writeLine(""   -protected"", ""Show PHI protected spaces only"") // UNCOMMENT THIS ONCE PHI FEATURE IN PROD
	writeLine(""   -review"", ""Shows only review spaces"")
	writeLine(""   -groups"", ""Shows only groups spaces"")
	writeLine(""   -private"", ""Shows only private spaces"")
	writeLine(""   -administrator"", ""Shows only administrator spaces"")
	writeLine(""   -government"", ""Shows only government spaces"")
	writeLine(""   -json"", ""Displays response as JSON array"")

	writer.Flush()
	return 0
}

func PrintLsMembersHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Listing members of a specified space.\n"")
	writeLine(""  Usage:"", ""ls-members -space-id <SPACE_ID> \n"")
	writeLine(""  Example:"", ""ls-members -space-id 27 [Lists all members of the specified space]\n"")
	writeLine(""  Flags:"", ""The following flags are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exits"")
	writeLine(""   -space-id <ID>"", ""Lists executions in the specified space"")

	writer.Flush()
	return 0
}

func PrintLsDiscussionsHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)
	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Listing discussions in a given space. Always responds in JSON format.\n"")
	writeLine(""  Usage:"", ""ls-discussions [FLAG...]\n"")
	writeLine(""  Example:"", ""ls-discussions -space-id 27 [Lists all discussions in the specified space]\n"")
	writeLine(""  Flags: "", ""The following flags are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exits"")
	writeLine(""   -space-id <ID>"", ""Lists discussions in the specified space"")

	writer.Flush()
	return 0
}

func PrintLsHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Listing files in a given location. If no location provided, the root of My Home is used."")
	writeLine(""      "", ""Public files are not listed by default.\n"")
	writeLine(""  Usage:"", ""ls [FLAG...]\n"")
	writeLine(""  Examples:"", ""ls -files -folder-id 55 [Show only files from private folder with id 55]"")
	writeLine(""  "", ""ls -folders -brief [Show only folders from the My Home root in a brief version]"")
	writeLine(""  "", ""ls -space-id 24 -folder-id 42 -json [Show the content of the folder with id 42 from the space with id 24 and present the result as JSON]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")
	writeLine(""   -brief"", ""Displays a brief version of the response; Only shows ID and name"")
	writeLine(""   -files"", ""Displays only files"")
	writeLine(""   -folders"", ""Displays only folders"")
	writeLine(""   -public"", ""Displays only public files & folders"")
	writeLine(""   -space-id <ID>"", ""Executes in the specified space"")
	writeLine(""   -folder-id <ID>"", ""Executes in the specified folder"")
	writeLine(""   -json"", ""Displays response as JSON array"")

	writer.Flush()
	return 0
}

func PrintLsAssetsHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Listing assets in a given location. If no location provided, My Home is used. Always responds in JSON format."")
	writeLine(""      "", ""Public assets are not listed by default.\n"")
	writeLine(""  Usage:"", ""ls-assets [FLAG...]\n"")
	writeLine(""  Examples:"", ""ls-assets -space-id 24 [Lists all assets from the space with id 24]"")
	writeLine(""  Examples:"", ""ls-assets [Lists all assets in your My Home]"")
	writeLine(""  "", ""ls-assets -public [Lists all public assets accessible to the user]\n"")
	writeLine(""  Flags:"", ""The following flags are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exits"")
	writeLine(""   -public"", ""Lists public assets"")
	writeLine(""   -space-id <ID>"", ""Lists assets in the specified space"")

	writer.Flush()
	return 0
}

func PrintLsAppsHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Listing applications in a given location. If no location provided, My Home is used. Always responds in JSON format."")
	writeLine(""      "", ""Public applications are not listed by default.\n"")
	writeLine(""  Usage:"", ""ls-apps [FLAG...]\n"")
	writeLine(""  Examples:"", ""ls-apps -space-id 24 [Lists all applications from the space with id 24]"")
	writeLine(""  Examples:"", ""ls-apps [Lists all applications in your My Home]"")
	writeLine(""  "", ""ls-apps -public [Lists all public applications accessible to the user]\n"")
	writeLine(""  Flags:"", ""The following flags are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exits"")
	writeLine(""   -public"", ""Lists public applications"")
	writeLine(""   -space-id <ID>"", ""Lists applications in the specified space"")

	writer.Flush()
	return 0
}

func PrintLsWorkflowsHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Listing workflows in a given location. If no location provided, My Home is used. Always responds in JSON format."")
	writeLine(""      "", ""Public workflows are not listed by default.\n"")
	writeLine(""  Usage:"", ""ls-workflows [FLAG...]\n"")
	writeLine(""  Examples:"", ""ls-workflows -space-id 24 [Lists all workflows from the space with id 24]"")
	writeLine(""  "", ""ls-workflows -public [Lists all public workflows accessible to the user]\n"")
	writeLine(""  Flags:"", ""The following flags are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exits"")
	writeLine(""   -public"", ""Lists public workflows"")
	writeLine(""   -space-id <ID>"", ""Lists workflows in the specified space"")

	writer.Flush()
	return 0
}

func PrintLsExecutionsHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Listing executions in a given location. If no location provided, My Home is used. Always responds in JSON format."")
	writeLine(""      "", ""Public executions are not listed by default.\n"")
	writeLine(""  Usage:"", ""ls-executions [FLAG...]\n"")
	writeLine(""  Examples:"", ""ls-executions -space-id 24 [Lists all executions from the space with id 24]"")
	writeLine(""  "", ""ls-executions -public [Lists all public executions accessible to the user]\n"")
	writeLine(""  Flags:"", ""The following flags are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exits"")
	writeLine(""   -public"", ""Lists public executions"")
	writeLine(""   -space-id <ID>"", ""Lists executions in the specified space"")

	writer.Flush()
	return 0
}

func PrintDownloadHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Downloading a file or folder from pFDA. If you want download the root of My Home or a space (which has no folder-id), use the flag \""-folder-id root\""."")
	writeLine(""  "", ""Supports downloading multiple files - simply pass them as positional args before any flags (check examples)."")
	writeLine(""  "", ""For files you can use either filename or file-id - name might not be unique, file-id always is."")
	writeLine(""  "", ""Filename wildcards are supported, use \""?\"" for exactly 1 char, \""*\"" for 0 or more characters. \n"")
	writeLine(""  "", ""By default, folder download only applies to top level files. Use flag \""-recursive\"" to download whole folder content."")
	writeLine(""  "", ""If you want to download content of a public folder, use flag -public together with the command."")
	writeLine(""  Usage:"", ""download <FILE-ID> [FLAG...] OR download <FILENAME> [FLAG...] OR download -folder-id <ID> [FLAG]\n"")
	writeLine(""  Examples:"", ""download file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Downloads the file to current working directory]"")
	writeLine(""  "", ""download file-GJk1kpQ05xgQd8bP54kJFjzkz-1 -output \""data/results_final.csv\"" [Downloads the file to existing folder named \""data\"" with new name \""results_final.csv\""]"")
	writeLine(""  "", ""download outputs_334_1.csv -output \""data/results_final.csv\"" [Downloads the file to existing folder named \""data\"" with new name \""results_final.csv\""]"")
	writeLine(""  "", ""download 'data*.csv' -folder-id 1222 -overwrite true [Downloads only CSV files with 'data' in their name from folder (id:1222) and overwrites already existing files if exist]"")
	writeLine(""  "", ""download file-GJk1kpQ05xgQd8bP54kJFjzkz-1 file-YZm9QpQ0b69Qd8bP454kmcf76-2 [Downloads multiple files to the current working directory]"")
	writeLine(""  "", ""download results.csv results_final.csv -folder-id 2221 [Downloads multiple files by name from folder (id:2221) to the current working directory]"")
	writeLine(""  "", ""download -space-id 27 -folder-id root -recursive [Downloads recursively from the root folder of the Space]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exit"")
	writeLine(""   -space-id <ID>"", ""Downloads from the specified space"")
	writeLine(""   -folder-id <ID>"", ""Downloads from the specified folder"")
	writeLine(""   -output <PATH/TO/FILE>"", ""Downloads to given path"")
	writeLine(""   -overwrite true|false"", ""Preselects overwrite option for dialog if path already exists in the target location."")
	writeLine(""   -recursive"", ""Recursively downloads content of selected folder."")
	writeLine(""   -json"", ""Displays response in JSON format"")

	writer.Flush()
	return 0
}

func PrintUploadFileHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Uploading a file or folder into a given location. If no location provided, the root of My Home is used."")
	writeLine(""  "", ""Supports uploading multiple files - simply pass them as positional args before any flags (check examples)."")
	writeLine(""  "", ""Supports uploading via stdin (piped input) - requires setting -name flag (check examples).\n"")
	writeLine(""  Usage:"", ""upload-file <PATH/TO/FILE> [FLAG...]\n"")
	writeLine(""  Examples:"", ""upload-file script01.py [Uploads the file to the root folder of My Home]"")
	writeLine(""  "", ""upload-file script01.py -space-id 12 [Uploads the file to the root folder of the space]"")
	writeLine(""  "", ""upload-file script01.py -folder-id 10 [Uploads the file to the specified folder]"")
	writeLine(""  "", ""upload-file data_folder/ -folder-id 10 [Uploads the folder and its content to the specified folder]"")
	writeLine(""  "", ""upload-file script01.py info/readme.txt data_folder/ -folder-id 111 [Uploads multiple files to the specified folder]"")
	writeLine(""  "", ""upload-file script01.py parser.py validator.py -space-id 21 [Uploads multiple files to the specified space]"")
	writeLine(""  "", ""upload-file script01.py parser.py validator.py -space-id 21 -folder-id 222 [Uploads multiple files to the specified space's folder]"")
	writeLine(""  "", ""upload-file -name piped_file.csv [Uploads file with given name provided via stdin]"")
	writeLine(""  "", ""upload-file large_file.sql -threads 20 -chunksize 134217728 [Uploads large file with custom number of threads and chunksize]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays this help message and exit"")
	writeLine(""   -space-id <ID>"", ""Uploads into the specified space"")
	writeLine(""   -folder-id <ID>"", ""Uploads into the specified folder"")
	writeLine(""   -name <NAME>"", ""Uploads a stdin file with given name [required for stdin input]"")
	writeLine(""   -json"", ""Displays response in JSON format"")
	writeLine(""   -threads"", ""Changes number of upload threads to spawn (Max 100). Consider memory usage, not controlled."")
	writeLine(""   -chunksize"", ""Changes size of each upload chunk in bytes (Min 16MB, Max 4GB). Consider memory usage, not controlled."")

	writer.Flush()
	return 0
}

func PrintDescribeAppHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Getting details about a given app. Always responds in JSON format.\n"")
	writeLine(""  Usage:"", ""describe-app <APP_ID>\n"")
	writeLine(""  Examples:"", ""describe-app app-GJk3k5v85a4ZfgQ8bP5911Xg0-1 [Describes the app]"")

	writer.Flush()
	return 0
}

func PrintDescribeWorkflowHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Getting details about a given workflow. Always responds in JSON format.\n"")
	writeLine(""  Usage:"", ""describe-workflow <WORKFLOW_ID>\n"")
	writeLine(""  Examples:"", ""describe-workflow workflow-GJkk5v005xggB4JcB4Zf9326V-1 [Describes the workflow]"")

	writer.Flush()
	return 0
}

func PrintUploadResourceHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Uploading a new portal resource. Responds with sharable non-expiring link to the created resource.\n"")
	writeLine(""  "", ""Supports uploading multiple resources - simply pass them as positional args before any flags (check examples)."")
	writeLine(""  Usage:"", ""upload-resource <PATH/TO/FILE> -portal-id <PORTAL_ID | PORTAL_SLUG> [FLAG...]\n"")
	writeLine(""  Examples:"", ""upload-resource script01.py -portal-id 12 [Creates a new resource in the specified portal]"")
	writeLine(""  "", ""upload-resource script01.py processed_data.py results.pdf -portal-id dna-sequences-101  [Creates new resources in the specified portal defined by slug]"")

	writer.Flush()
	return 0
}

func PrintDescribeHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Getting details about a given entity. Entity can be one of: app, job, file, workflow, discussion. Always responds in JSON format.\n"")
	writeLine(""  Usage:"", ""describe <ENTITY_ID>\n"")
	writeLine(""  Examples:"", ""describe file-GJk3k5v85a4ZfgQ8bP5911Xg0-1 [Describes the file]"")
	writeLine(""  "", ""describe file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Describes the asset]"")
	writeLine(""  "", ""describe app-YZm95v85a4ZfgQB4JcAB4g0-3 [Describes the app]"")
	writeLine(""  "", ""describe job-YZm9QpQ0b69Qd8bP454kmcf76-2 [Describes the execution]"")
	writeLine(""  "", ""describe workflow-GJkk5v005xggB4JcB4Zf9326V-1 [Describes the workflow]"")
	writeLine(""  "", ""describe discussion-15 [Describes the discussion]"")

	writer.Flush()
	return 0
}

func PrintUploadAssetHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Uploading an asset. Assets can be uploaded to the root of My Home only.\n"")
	writeLine(""  Usage:"", ""upload-asset -name <NAME{.tar,.tar.gz}> -root </PATH/TO/ROOT/FOLDER> -readme <README{.txt,.md}\n"")
	writeLine(""  Examples:"", ""upload-asset -name asset01.tar.gz -root ./asset01 -readme ./readme.md [Uploads asset with root folder asset01 and readme file readme.md from current directory]"")

	writer.Flush()
	return 0
}

func PrintMkdirHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Creating a folder.\n"")
	writeLine(""  Usage:"", ""mkdir <NAME> [FLAG...]\n"")
	writeLine(""  Examples:"", ""mkdir DATA [Creates a new folder named DATA in your My Home section]"")
	writeLine(""  "", ""mkdir DATA -space-id 12 [Creates a new folder named DATA in root of the space]"")
	writeLine(""  "", ""mkdir DATA scripts results -folder-id 2221 [Creates 3 new folders in folder (id:2221)]"")
	writeLine(""  "", ""mkdir scripts/python/v1 scripts/python/v2 -p [Creates the specified folder structure]"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")
	writeLine(""   -space-id <ID>"", ""Creates the folder in the specified space"")
	writeLine(""   -folder-id <ID>"", ""Creates the folder in the specified folder"")
	writeLine(""   -p, -parents"", ""Creates parent directories as needed, no error if existing"")
	writeLine(""   -json"", ""Displays response in JSON format"")

	writer.Flush()
	return 0
}

func PrintRmHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Removing a file.\n"")
	writeLine(""  Usage:"", ""rm <FILE_ID | FILE_NAME> [FLAG...] OR rm <NAME> [FLAG...]\n"")
	writeLine(""  Examples:"", ""rm file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Removes file with given id]"")
	writeLine(""  "", ""rm file-GJk1kpQ05xgQd8bP54kJFjzkz-1 file-YZm9QpQ0b69Qd8bP454kmcf76-2 [Removes multiple files given by ids]"")
	writeLine(""  "", ""rm inputs_01.csv inputs_02.csv -folder-id 2221 [Removes multiple files given by names in the folder (id:2221)]"")
	writeLine(""  "", ""rm 'inputs*.csv' -space-id 12 [Removes all CSV files with 'inputs' in their name from the root of given space]"")
	writeLine(""  "", ""rm '*.txt' -space-id 12 -folder-id 1212 [Removes all txt files with from the folder of given space]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")
	writeLine(""   -space-id <ID>"", ""Executes in the specified space"")
	writeLine(""   -folder-id <ID>"", ""Executes in the specified folder"")
	writeLine(""   -json"", ""Displays response in JSON format"")

	writer.Flush()
	return 0
}

func PrintRmdirHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Deleting a folder. Only empty folders are allowed to be deleted.\n"")
	writeLine(""  Usage:"", ""rmdir <FOLDER_ID> [FLAG...]\n"")
	writeLine(""  Examples:"", ""rmdir 2221 [Removes folder (id:2221)]"")
	writeLine(""  "", ""rmdir 2221 2332 2333 [Removes folders with given ids if empty]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")
	writeLine(""   -json"", ""Displays response in JSON format"")

	writer.Flush()
	return 0
}

func PrintCatHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Displaying the content of a file.\n"")
	writeLine(""  Usage:"", ""cat <FILE_ID>\n"")
	writeLine(""  Examples:"", ""cat file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Prints content of the given file]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")

	writer.Flush()
	return 0
}

func PrintHeadHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Displays the first lines of a file. By default, the first 10 lines are displayed.\n"")
	writeLine(""  Usage:"", ""head <FILE_ID> [FLAGS...]\n"")
	writeLine(""  Examples:"", ""head file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Prints the first 10 lines of the given file]"")
	writeLine(""  "", ""head file-GJk1kpQ05xgQd8bP54kJFjzkz-1 -lines 50 [Prints the first 50 lines of the given file]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -lines"", ""Alter number of lines to display"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")

	writer.Flush()
	return 0
}

func PrintGetScopeHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Displays the scope of the current context. If you are running it in a private scope, 'private' is printed to the console.\n In case you are in a space, only the integer Space ID is printed to the console.\n"")
	writeLine(""  Usage:"", ""get-scope [FLAG...]\n"")
	writeLine(""  Examples:"", ""get-scope [Prints current scope]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")
	writeLine(""   -json"", ""Displays response in JSON format"")

	writer.Flush()
	return 0
}

func PrintViewLinkHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Get view link of the file. You can choose between protected and pre-authenticated links.\n\tPre-authenticated links are valid for 24 hours by default, unless -duration flag is specified.\n"")
	writeLine(""  Usage:"", ""view-link <FILE_ID> [FLAG...]\n"")
	writeLine(""  Examples:"", ""view-link file-GbKF3qQ0Z0gqk80j1QF47K8j-1 [Prints view link for specified file.]\n"")
	writeLine("" "", ""view-link file-GbKF3qQ0Z0gqk80j1QF47K8j-1 -auth [Prints pre-authenticated view link for specified file.]\n"")
	writeLine("" "", ""view-link file-GbKF3qQ0Z0gqk80j1QF47K8j-1 -auth -duration 172800 [Prints pre-authenticated view link for specified file - valid for 48 hours.]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -auth"", ""Generate pre-authenticated link instead of protected link"")
	writeLine(""   -duration"", ""Set duration of pre-authenticated link in seconds (default 86400)"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")
	writeLine(""   -json"", ""Displays response in JSON format"")

	writer.Flush()
	return 0
}

func PrintNoHelpAvailable() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)
	writeLine(""  "", ""  "")
	writeLine(""  Error:"", ""No help available for the given command. For assistance, please contact the support team."")
	writer.Flush()
	return 0
}

func PrintCreateDiscussionHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Creates a new discussion in the selected space. Only accepts JSON body with the following attributes:\n\t""+
		""title, content - required string, attachments - optional object. All attributes in the attachments object are also optional:\n\t""+
		""files, assets, apps, jobs, workflows - string array of UIDs; folders, comparisons - integer array of IDs.\n"")
	writeLine(""  Usage:"", ""create-discussion '<JSON_BODY>' -space-id <SPACE_ID> [FLAG...]\n"")
	//writeLine(""  Examples:"", ""create-discussion '{\""title\"":\""..title..\"", \""content\"":\""..content..\""}' -space-id 12 [Creates discussion without attachments in given space]"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")

	writer.Flush()
	return 0
}

func PrintEditDiscussionHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Only allows appending content and/or attachments to the selected discussion. Only accepts JSON body with the following attributes:\n\t""+
		""discussionId - required integer, content - optional string, attachments - optional object. All attributes in the attachments object are also optional:\n\t""+
		""files, assets, apps, jobs, workflows - string array of UIDs; folders, comparisons - integer array of IDs.\n"")
	writeLine(""  Usage:"", ""edit-discussion '<JSON_BODY>' [FLAG...]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")

	writer.Flush()
	return 0
}

func PrintCreateReplyHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Creates a new reply in the selected discussion or answer. Only accepts JSON body with the following attributes:\n""+
		""\tdiscussionId or answerId - integer ID (exclusive), replyType - required string ('answer' or 'comment'), content - required string,\n""+
		""\tattachments - optional object, only allowed if replyType is 'answer'. All attributes in the attachments object are also optional:\n""+
		""\tfiles, assets, apps, jobs, workflows - string array of UIDs; folders, comparisons - integer array of IDs.\n"")
	writeLine(""  Usage:"", ""create-reply '<JSON_BODY>' [FLAG...]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")

	writer.Flush()
	return 0
}

func PrintEditReplyHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	writeLine := newLineWriter(writer)

	writeLine(""  "", ""  "")
	writeLine(""  For:"", ""Only allows appending content and/or attachments to the selected reply. Only accepts JSON body with the following attributes:\n\t""+
		""answerId or commentId - required integer, content - optional string, attachments - optional object, only allowed if appending to an answer.\n""+
		""\tAll attributes in the attachments object are also optional:\n""+
		""\tfiles, assets, apps, jobs, workflows - string array of UIDs; folders, comparisons - integer array of IDs.\n"")
	writeLine(""  Usage:"", ""edit-discussion '<JSON_BODY>' [FLAG...]\n"")
	writeLine(""  Flags:"", ""All flags listed below are OPTIONAL"")
	writeLine(""   -h, -help"", ""Displays the help message and exit"")

	writer.Flush()
	return 0
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/helpers/parser.go",".go","724","39","package helpers

import (
	""strings""
)

func ParseArgsUntilFlag(args []string) ([]string, int) {
	const offset = 2 // skip first two which is ""pfda <command>""

	validArgs := make([]string, 0)
	for index, arg := range args[offset:] {
		if strings.HasPrefix(arg, ""-"") {
			return validArgs, index + offset
		}
		validArgs = append(validArgs, arg)
	}

	return validArgs, len(args)
}

func ParseEntityType(entityType string) string {

	validTypes := map[string]bool{
		""app"":        true,
		""job"":        true,
		""file"":       true,
		""folder"":     true,
		""workflow"":   true,
		""discussion"": true,
	}
	parts := strings.SplitN(entityType, ""-"", 2)

	if len(parts) > 0 && validTypes[parts[0]] {
		return parts[0]
	}

	return """"
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/helpers/utils.go",".go","3190","144","package helpers

import (
	""encoding/json""
	""fmt""
	""math""
	""net/url""
	""strconv""
	""strings""
)

type ErrorResponse struct {
	Error string `json:""error""`
}

func Min(a, b int) int {
	if a < b {
		return a
	}
	return b
}

func Max(a, b int) int {
	if a > b {
		return a
	}
	return b
}

func IsFileId(arg string) bool {
	return strings.HasPrefix(arg, ""file-"") && len(arg) >= 31
}

func IsFolderId(arg string) bool {
	num, err := strconv.Atoi(arg)
	return err == nil && num > 0
}

func ContainsWildcard(arg string) bool {
	return strings.Contains(arg, ""?"") || strings.Contains(arg, ""*"")
}

func TransformToSQLWildcards(nameWithWildcards string) string {
	mysqlWildcard := strings.ReplaceAll(nameWithWildcards, ""*"", ""%"")
	mysqlWildcard = strings.ReplaceAll(mysqlWildcard, ""_"", ""\\_"")
	mysqlWildcard = strings.ReplaceAll(mysqlWildcard, ""?"", ""_"")
	return mysqlWildcard
}

func GetChunkSize(filesCount int) int {
	switch size := filesCount; {
	case size < 5:
		return 1
	case size < 10:
		return 2
	case size < 20:
		return 4
	case size < 35:
		return 7
	case size < 50:
		return 10
	case size < 100:
		return 20
	default:
		return 25
	}
}

// Calculate chunk size using math.Ceil to round up,
// ensuring the number of chunks does not exceed 10 000.
func CalculateChunkSize(fileSize int64, minChunkSize int) int {
	const maxChunks = 10_000
	idealChunkSize := int(math.Ceil(float64(fileSize) / float64(maxChunks-1)))
	return Max(idealChunkSize, minChunkSize)
}

func ValidateID(id string, paramName string, params url.Values) error {
	if id != """" {
		if _, err := strconv.Atoi(id); err != nil {
			return fmt.Errorf(""Invalid %s - expected an integer"", paramName)
		}
		params.Add(paramName, id)
	}
	return nil
}

func CheckErr(e error) {
	if e != nil {
		panic(e)
	}
}

// PrintError prints the error to standard output. If asJSON is true, it prints in JSON format.
func PrintError(err error, asJSON bool) {
	if asJSON {
		jsonErr, _ := json.Marshal(ErrorResponse{Error: err.Error()})
		fmt.Println(string(jsonErr))
	} else {
		// Default to plain text
		fmt.Println(""Error:"", err.Error())
	}
}

func ErrorFromError(err error, asJSON bool) int {
	PrintError(err, asJSON)
	return 1
}

func ErrorFromString(msg string, asJSON bool) int {
	PrintError(fmt.Errorf(msg), asJSON)
	return 1
}

func PrintResult(result string, asJSON bool) {
	if asJSON {
		jsonData, _ := json.MarshalIndent(struct {
			Result string `json:""result""`
		}{Result: result}, """", """")
		fmt.Println(string(jsonData))
	} else {
		// Default to plain text
		fmt.Println(result)
	}
}

// PrettyPrint takes an empty interface as input, which can be either a byte slice of raw JSON
// or any Go data structure. It then pretty-prints the JSON representation of the input.
func PrettyPrint(data interface{}) {
	var jsonData []byte

	switch v := data.(type) {
	case []byte:
		// Attempt to unmarshal and marshal to pretty-print, ignore errors.
		var tmp interface{}
		if err := json.Unmarshal(v, &tmp); err == nil {
			jsonData, _ = json.MarshalIndent(tmp, """", ""  "")
		}
	default:
		// Attempt to marshal directly to pretty-printed JSON, ignore errors.
		jsonData, _ = json.MarshalIndent(data, """", ""  "")
	}
	fmt.Println(string(jsonData))
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/helpers/formatter.go",".go","1057","51","package helpers

import (
	""strconv""
	""strings""
)

func FormatValue(print bool, value string) string {
	if print {
		return value + "" ""
	}
	return "" ""
}

func HumanReadableSize(size int64) string {
	const (
		KB int64 = 1 << 10
		MB int64 = 1 << 20
		GB int64 = 1 << 30
		TB int64 = 1 << 40
	)

	formatSize := func(size float64, suffix string) string {
		// Format with two decimal points
		str := strconv.FormatFloat(size, 'f', 2, 64)
		// Trim unnecessary trailing zeros and the decimal point if not needed
		str = strings.TrimRight(str, ""0"")
		str = strings.TrimRight(str, ""."")
		return str + "" "" + suffix
	}

	var sizeInFloat float64
	var suffix string

	switch {
	case size < KB:
		return formatSize(float64(size), ""B"")
	case size < MB:
		sizeInFloat, suffix = float64(size)/float64(KB), ""KB""
	case size < GB:
		sizeInFloat, suffix = float64(size)/float64(MB), ""MB""
	case size < TB:
		sizeInFloat, suffix = float64(size)/float64(GB), ""GB""
	default:
		sizeInFloat, suffix = float64(size)/float64(TB), ""TB""
	}

	return formatSize(sizeInFloat, suffix)
}

","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/pfdaclient_test.go",".go","1871","70","package precisionfda

import (
	""fmt""
	""net/http""
	""net/http/httptest""
	""testing""

	""dnanexus.com/precision-fda-cli/precisionfda/test""
)

func TestNewPFDAClient(t *testing.T) {
	server := ""test.precisionfda.com""
	pfdaclient := NewPFDAClient(server)

	test.Equals(t, pfdaclient.BaseURL, ""https://test.precisionfda.com"")
}

func TestChunkSize(t *testing.T) {
	server := ""test.precisionfda.com""
	pfdaclient := NewPFDAClient(server)
	test.Equals(t, pfdaclient.ChunkSize, 1<<26)

	pfdaclient.SetChunkSize(10000000)
	test.Equals(t, pfdaclient.ChunkSize, 10000000)
}

func TestMaxRoutines(t *testing.T) {
	server := ""test.precisionfda.com""
	pfdaclient := NewPFDAClient(server)
	test.Equals(t, pfdaclient.NumRoutines, 10)

	pfdaclient.SetNumRoutines(5)
	test.Equals(t, pfdaclient.NumRoutines, 5)
}

func TestUploadFile(t *testing.T) {
	t.Skip(""Skipping httptests for now as I haven't been able to make them work"")

	// Inspired by https://medium.com/zus-health/mocking-outbound-http-requests-in-go-youre-probably-doing-it-wrong-60373a38d2aa
	server := httptest.NewServer(http.HandlerFunc(func(rw http.ResponseWriter, req *http.Request) {
		fmt.Println(req.URL.String())
		test.Equals(t, req.URL.Path, ""/api/create_file"")

		if req.Header.Get(""Accept"") != ""application/json"" {
			t.Errorf(""Expected Accept: application/json header, got: %s"", req.Header.Get(""Accept""))
		}

		// Send response to be tested
		rw.WriteHeader(http.StatusOK)
		rw.Write([]byte(`{""value"":""fixed""}`))
	}))
	defer server.Close()	// Close the server when test finishes

	pfdaclient := NewPFDAClient(server.URL)
	pfdaclient.UploadFile(""./README.md"", """", """", true)
}

func TestUploadFileToSpace(t *testing.T) {
	t.Skip(""Skipping httptests for now"")
}

func TestUploadAsset(t *testing.T) {
	t.Skip(""Skipping httptests for now"")
}

func TestDownloadFile(t *testing.T) {
	t.Skip(""Skipping httptests for now"")
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/resources.go",".go","3230","140","package precisionfda

import (
	""dnanexus.com/precision-fda-cli/helpers""
	""encoding/json""
	""fmt""
	""os""
	""path/filepath""
	""sync""
	""time""
)

func (c *PFDAClient) UploadResources(args []string, portalID string) error {

	var wg sync.WaitGroup
	semaphore := make(chan struct{}, 3) // Limit to 3 concurrent goroutines

	results := make(chan error, len(args))
	for _, path := range args {
		wg.Add(1)
		go func(path string) {
			defer wg.Done()
			semaphore <- struct{}{} // Acquire
			err := c.UploadResource(path, portalID, !c.JsonResponse)
			<-semaphore // Release
			results <- err
		}(path)
	}

	wg.Wait()
	close(results)

	if !c.JsonResponse {
		fmt.Println("">> Waiting for all resources links to be generated..."")
	}

	maxRetries := 5
	retryInterval := 2 * time.Second
	getResourceApiURL := fmt.Sprintf(""%s/api/data_portals/%s/resources"", c.BaseURL, portalID)

	for attempt := 0; attempt < maxRetries; attempt++ {
		if attempt != 0 {
			time.Sleep(retryInterval)
			retryInterval *= 2 // exponential backoff
		}

		_, body, err := c.makeRequestFail(""GET"", getResourceApiURL, nil)
		if err != nil {
			return err
		}

		var response []JsonResource
		if err := json.Unmarshal(body, &response); err != nil {
			return err
		}

		allURLsPresent := true
		for _, resource := range response {
			if resource.URL == """" {
				allURLsPresent = false
				break
			}
		}

		if allURLsPresent {
			if c.JsonResponse {
				helpers.PrettyPrint(response)
			} else {
				for _, resource := range response {
					fmt.Printf("">> Resource URL: %s\n"", resource.URL)
				}
			}
			return nil // Exit function successfully when all resources have URLs
		}

	}

	return fmt.Errorf(""Failed to generate URLs for all resources"")
}

func (c *PFDAClient) UploadResource(path string, portalID string, withProgressBar bool) error {
	createURL := fmt.Sprintf(""%s/api/data_portals/%s/resources"", c.BaseURL, portalID)
	closeURL := c.BaseURL + ""/api/close_file""

	file, err := os.Open(path)
	if err != nil {
		return err
	}
	defer file.Close()

	info, err := file.Stat()
	if err != nil {
		return err
	}
	size := info.Size()
	if size > maxFileSize {
		return fmt.Errorf(""size of file '%s' (%d) exceeds maximum allowed file size(%d)"", path, size, maxFileSize)
	}
	if size == 0 {
		return fmt.Errorf(""size of file '%s' is 0 - uploading an empty resource is not allowed"", path)
	}

	jsonData, err := json.Marshal(map[string]interface{}{
		""name"":        filepath.Base(path),
		""description"": ""Resource created by precisionFDA CLI"",
	})
	if err != nil {
		return err
	}

	_, body, err := c.makeRequestFail(""POST"", createURL, jsonData)
	if err != nil {
		return err
	}

	var result map[string]interface{}
	err = json.Unmarshal(body, &result)
	if err != nil {
		return err
	}

	fileUid := result[""fileUid""].(string)
	chunkPool := make(chan uploadChunk, c.NumRoutines)
	wg := c.initWaitGroup(fileUid, chunkPool, &size, withProgressBar)
	c.readAndChunk(file, chunkPool, &size)
	close(chunkPool)
	wg.Wait()

	jsonData, err = json.Marshal(map[string]interface{}{
		""uid"": fileUid,
	})
	if err != nil {
		return err
	}

	// This is async; we cannot wait for close, but some delay is called in the main function
	c.makeRequestFail(""POST"", closeURL, jsonData)
	return nil
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/remove.go",".go","1658","79","package precisionfda

import (
	""dnanexus.com/precision-fda-cli/helpers""
	""encoding/json""
	""fmt""
	""strconv""
)

// RemoveFile Remove files by uid.
func (c *PFDAClient) RemoveFile(uids []string) error {
	deleteFileURL := c.BaseURL + ""/api/files/cli_remove""
	data := map[string]interface{}{""uids"": uids}
	jsonData, err := json.Marshal(data)
	if err != nil {
		return err
	}
	_, body, err := c.makeRequestFail(""POST"", deleteFileURL, jsonData)
	if err != nil {
		return err
	}
	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)

	if err != nil {
		return err
	}

	if resultJSON[""count""].(float64) == 0 {
		return fmt.Errorf(""File(s) %s not found or inaccessible"", uids)
	}

	for _, uid := range uids {
		if c.JsonResponse {
			helpers.PrettyPrint(struct {
				Uid string `json:""uid""`
			}{Uid: uid})
		} else {
			fmt.Printf(""Removed %s \n"", uid)
		}
	}
	return nil
}

func (c *PFDAClient) RemoveDir(uid string) error {
	deleteFileURL := c.BaseURL + ""/api/files/cli_remove""

	data := map[string]interface{}{""ids"": []string{uid}}
	jsonData, err := json.Marshal(data)
	if err != nil {
		return err
	}

	_, body, err := c.makeRequestFail(""POST"", deleteFileURL, jsonData)
	if err != nil {
		return err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	if resultJSON[""count""].(float64) == 0 {
		return fmt.Errorf(""Folder with id: %s not found or inaccessible"", uid)
	}

	if c.JsonResponse {
		folderId, _ := strconv.Atoi(uid)
		helpers.PrettyPrint(struct {
			ID int `json:""id""`
		}{ID: folderId})
	} else {
		fmt.Printf(""Removed dir (id: %s) \n"", uid)
	}
	return nil
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/pfdaclient.go",".go","41288","1505","package precisionfda

import (
	""bufio""
	""bytes""
	""crypto/md5""
	""dnanexus.com/precision-fda-cli/helpers""
	""encoding/hex""
	""encoding/json""
	""fmt""
	""io""
	""log""
	""math""
	""net/url""
	""os""
	""os/exec""
	""path""
	""path/filepath""
	""regexp""
	""runtime""
	""strconv""
	""strings""
	""sync""
	""sync/atomic""
	""text/tabwriter""
	""time""

	""github.com/docker/go-units""
	""github.com/gosuri/uilive""
	""github.com/hashicorp/go-cleanhttp""     // required by go-retryablehttp
	""github.com/hashicorp/go-retryablehttp"" // use http libraries from hashicorp for implement retry logic
	""github.com/manifoldco/promptui""
)

const userAgent = ""precisionFDA CLI/2.8.0 ""
const defaultNumRoutines = 10
const defaultChunkSize = 1 << 26 // default 64MB (min. 16MB)
const minRoutines = 1
const maxRoutines = 100
const minChunkSize = 1 << 24    // min. 16MB
const maxChunkSize = 1 << 32    // max. 4GB
const maxFileSize = 5 * 1 << 40 // max. 5TB

// retryablehttp defaults
const maxRetryCount = 5
const minRetryTime = 1  // seconds
const maxRetryTime = 30 // seconds

const https = ""https://""

type IPFDAClient interface {
	CallAPI(route string, data string, outputFile string) error
	UploadAsset(rootFolderPath string, name string, readmeFilePath string) error
	Upload(file io.ReadCloser, path string, folderID string, spaceID string, size int64, withProgressBar bool) error
	UploadFile(path string, folderID string, spaceID string, withProgressBar bool) error
	UploadStdin(fileName string, folderID string, spaceID string, withProgressBar bool) error
	UploadFolder(folderPath string, folderID string, spaceID string) error
	UploadMultipleFiles(paths []string, folderID string, spaceID string) error
	DownloadFile(arg string, outputFilePath string, overwrite string) error
	Download(args []string, folderID string, spaceID string, public bool, recursive bool, outputFilePath string, overwrite string) error
	FileViewLink(arg string, preauthenticated bool, ttl int64) error
	UploadResources(args []string, portalID string) error
	DescribeEntity(entityID string) error
	LsSpaces(flags map[string]bool) error
	LsApps(spaceID string, flags map[string]bool) error
	LsAssets(spaceID string, flags map[string]bool) error
	LsWorkflows(spaceID string, flags map[string]bool) error
	LsExecutions(spaceID string, flags map[string]bool) error
	Ls(folderID string, spaceID string, flags map[string]bool) error
	LsMembers(spaceID string) error
	LsDiscussions(spaceID string, flags map[string]bool) error // might refactor to be able present public discussions as well.
	Mkdir(names []string, folderID string, spaceID string, parents bool) error
	Rmdir(args []string) error
	Rm(args []string, folderID string, spaceID string) error
	Head(arg string, lines int) error
	GetScope() error
	CreateDiscussion(spaceID string, jsonBody string) error
	CreateReply(jsonBody string) error
	EditDiscussion(jsonBody string) error
	EditReply(jsonBody string) error
	RefreshToken(autoRefresh bool) (string, error)
	GetLatestVersion() (string, error)
	SetChunkSize(chunkSize int)
	SetNumRoutines(numRoutines int)
}

type PFDAClient struct {
	BaseURL         string
	Platform        string
	NumRoutines     int
	ChunkSize       int
	MinRoutines     int
	MaxRoutines     int
	MinChunkSize    int
	MaxChunkSize    int
	MaxFileSize     int
	ContinueOnError bool
	JsonResponse    bool

	Client  *retryablehttp.Client
	AuthKey string
}

func NewPFDAClient(serverURL string) *PFDAClient {
	c := PFDAClient{}
	c.BaseURL = https + serverURL
	c.NumRoutines = defaultNumRoutines
	c.ChunkSize = defaultChunkSize
	c.MinRoutines = minRoutines
	c.MaxRoutines = maxRoutines
	c.MinChunkSize = minChunkSize
	c.MaxChunkSize = maxChunkSize
	c.MaxFileSize = maxFileSize
	c.Client = &retryablehttp.Client{
		HTTPClient:   cleanhttp.DefaultClient(),
		RetryWaitMin: minRetryTime * time.Second,
		RetryWaitMax: maxRetryTime * time.Second,
		RetryMax:     maxRetryCount,
		CheckRetry:   retryablehttp.DefaultRetryPolicy,
		Backoff:      retryablehttp.DefaultBackoff,
	}
	c.ContinueOnError = false
	return &c
}

// Wire objects
type jsonID struct {
	ID string `json:""id""`
}

type bulkIDsPayload struct {
	IDs []string `json:""ids""`
}

type jsonChunkInfo struct {
	ID    string `json:""id""`
	Size  int    `json:""size""`
	Index int    `json:""index""`
	Md5   string `json:""md5""`
}

type jsonCreateFilePayload struct {
	Name       string `json:""name""`
	Desc       string `json:""description""`
	FolderID   string `json:""folder_id""`
	Scope      string `json:""scope""`
	ParentType string `json:""parent_type""`
	ParentID   string `json:""parent_id""`
}

type jsonCreateAssetPayload struct {
	Name  string   `json:""name""`
	Desc  string   `json:""description""`
	Paths []string `json:""paths""`
}

type jsonCreateFolderPayload struct {
	Name           string `json:""name""`
	ParentFolderID string `json:""parent_folder_id,omitempty""`
	SpaceID        string `json:""space_id,omitempty""`
}

type jsonRmPayload struct {
	Name           string `json:""name""`
	ParentFolderID string `json:""parent_folder_id,omitempty""`
	SpaceID        string `json:""space_id,omitempty""`
	Type           string `json:""type,omitempty""`
}

type jsonFileDownloadPayload struct {
	FileURL  string `json:""file_url""`
	FileSize int64  `json:""file_size""`
}

type uploadChunk struct {
	index int
	data  []byte // slice/ptr
}

// better name needed
type jsonSpaceResponse struct {
	Id        int    `json:""id""`
	Title     string `json:""title""`
	Role      string `json:""role""`
	Side      string `json:""side""`
	Type      string `json:""type""`
	State     string `json:""state""`
	Protected bool   `json:""protected""`
}

type jsonMembersResponse struct {
	Id        int    `json:""id""`
	Active    bool   `json:""active""`
	CreatedAt string `json:""createdAt""`
	Role      string `json:""role""`
	Side      string `json:""side""`
	Name      string `json:""name""`
	Username  string `json:""username""`
}

type jsonCreateFolderResponse struct {
	Id      int    `json:""id""`
	Path    string `json:""path""`
	Message struct {
		Type string `json:""type""`
	} `json:""message""`
}

type JsonResource struct {
	ID   int    `json:""id""`
	Name string `json:""name""`
	URL  string `json:""url""`
}

// Below were migrated from pfda.go:
//
// COMMAND FUNCTIONS
func (c *PFDAClient) CallAPI(route string, data string, outputFile string) error {
	// sanitize input
	route = strings.ToLower(route)
	url := c.BaseURL + ""/api/"" + route
	_, body, err := c.makeRequestFail(""POST"", url, []byte(data))
	if err != nil {
		return err
	}

	if outputFile == """" {
		fmt.Printf(""Return response data for API call '%s:\n%s\n"", route, string(body))
	} else {
		f, err := os.Create(outputFile)
		if err != nil {
			return err
		}

		defer f.Close()
		bytesWritten, err := f.Write(body)
		if err != nil {
			return err
		}

		fmt.Printf(""Downloaded response data for API call: %s (%d bytes) to file '%s'\n"", route, bytesWritten, outputFile)
	}
	return nil
}

func (c *PFDAClient) UploadAsset(rootFolderPath string, name string, readmeFilePath string) error {
	createURL := c.BaseURL + ""/api/create_asset""
	closeURL := c.BaseURL + ""/api/close_asset""

	// Get list of all asset files
	var fileList []string
	assetSize := int64(0)
	err := filepath.Walk(rootFolderPath, func(path string, f os.FileInfo, err error) error {
		if !f.IsDir() {
			relPath, err := filepath.Rel(rootFolderPath, path)
			if err != nil {
				return err
			}
			fileList = append(fileList, relPath)
			assetSize += f.Size()
		}
		return nil
	})
	if err != nil {
		return err
	}

	if assetSize > maxFileSize {
		return fmt.Errorf(""Size of asset folder '%s' (%d) exceeds maximum allowed file size(%d)"", rootFolderPath, assetSize, maxFileSize)
	}

	if assetSize == 0 {
		return fmt.Errorf(""Size of asset folder '%s' is 0 - uploading an empty asset is not allowed"", rootFolderPath)
	}

	// Read in the readme all at once
	readmeBuf, err := os.ReadFile(readmeFilePath)
	if err != nil {
		return err
	}

	jsonData, err := json.Marshal(jsonCreateAssetPayload{
		Name:  name,
		Desc:  string(readmeBuf),
		Paths: fileList[:],
	})
	if err != nil {
		return err
	}

	fileID, err := c.createFileID(createURL, jsonData)
	if err != nil {
		return err
	}

	chunkPool := make(chan uploadChunk, c.NumRoutines)
	wg := c.initWaitGroup(fileID, chunkPool, &assetSize, true)

	if !c.JsonResponse {
		fmt.Println("">> Archiving asset..."")
	}
	// different approach for WinOS tar command
	if runtime.GOOS == ""windows"" {
		cmd := exec.Command(""tar"", ""-cf"", name, ""-C"", rootFolderPath, ""."")
		if strings.HasSuffix(name, "".tar.gz"") {
			cmd = exec.Command(""tar"", ""-czf"", name, ""-C"", rootFolderPath, ""."")
		}
		err = cmd.Start()
		if err != nil {
			return err
		}
		err = cmd.Wait()
		if err != nil {
			return err
		}
		tarArchive, err := os.Open(name)
		if err != nil {
			return err
		}
		defer os.Remove(name)
		defer tarArchive.Close()
		c.readAndChunk(tarArchive, chunkPool, &assetSize)
	} else {
		cmd := exec.Command(""tar"", ""-c"", ""-C"", rootFolderPath, ""."")
		if strings.HasSuffix(name, "".tar.gz"") {
			cmd = exec.Command(""tar"", ""-cz"", ""-C"", rootFolderPath, ""."")
		}
		stdout, err := cmd.StdoutPipe()
		err = cmd.Start()
		if err != nil {
			return err
		}
		c.readAndChunk(stdout, chunkPool, &assetSize)
	}
	if !c.JsonResponse {
		fmt.Print("">> Uploading asset |"")
	}

	close(chunkPool)
	wg.Wait()

	if !c.JsonResponse {
		fmt.Println("">| Uploaded 100%\n>> Finalizing asset..."")
	}
	jsonData, err = json.Marshal(jsonID{
		ID: fileID,
	})
	if err != nil {
		return err
	}

	c.makeRequestFail(""POST"", closeURL, jsonData)
	assetURL := c.BaseURL + ""/home/assets/"" + fileID
	if c.JsonResponse {
		helpers.PrettyPrint(struct {
			Url string `json:""url""`
		}{Url: assetURL})
	} else {
		fmt.Println("">> Done! Access your asset at "" + assetURL)
	}
	return nil
}

// If folderID is not empty, the file will be uploaded to the specified folder
// If spaceID is empty, the file will be uploaded to the user's home
func (c *PFDAClient) UploadFile(path string, folderID string, spaceID string, withProgressBar bool) error {

	file, err := os.Open(path)
	if err != nil {
		return err
	}
	defer file.Close()

	info, err := file.Stat()
	if err != nil {
		return err
	}

	size := info.Size()
	if size > maxFileSize {
		return fmt.Errorf(""Size of file '%s' (%d) exceeds maximum allowed file size(%d)"", path, size, maxFileSize)
	}

	if size == 0 {
		return fmt.Errorf(""Size of file '%s' is 0 - uploading an empty file is not allowed"", path)
	}

	err = c.Upload(&*file, path, folderID, spaceID, size, withProgressBar)
	c.HandleError(err)

	return nil
}

func (c *PFDAClient) UploadStdin(fileName string, folderID string, spaceID string, withProgressBar bool) error {
	// we do not know the size, stdin is buffered stream of data
	size := int64(1)
	// stdin has to be wrapped in those - otherwise not working as expected with linux piping
	stdin := io.NopCloser(bufio.NewReader(os.Stdin))
	err := c.Upload(stdin, fileName, folderID, spaceID, size, withProgressBar)
	c.HandleError(err)
	return nil
}

func (c *PFDAClient) Upload(file io.ReadCloser, path string, folderID string, spaceID string, size int64, withProgressBar bool) error {
	createURL := c.BaseURL + ""/api/create_file""
	closeURL := c.BaseURL + ""/api/close_file""

	scope, parentType, parentId := """", """", """"
	if spaceID != """" {
		if _, err := strconv.Atoi(spaceID); err != nil {
			return err
		}
		scope = ""space-"" + spaceID
	}

	dxJobId, isPresent := os.LookupEnv(""DX_JOB_ID"")
	if isPresent {
		parentType = ""Job""
		parentId = dxJobId
	}

	jsonData, err := json.Marshal(jsonCreateFilePayload{
		Name:       filepath.Base(path),
		Desc:       """",
		FolderID:   folderID,
		Scope:      scope,
		ParentType: parentType,
		ParentID:   parentId,
	})
	if err != nil {
		return err
	}

	fileID, err := c.createFileID(createURL, jsonData)
	if err != nil {
		return err
	}

	chunkPool := make(chan uploadChunk, c.NumRoutines)

	if withProgressBar && !c.JsonResponse {
		fmt.Printf("">> Uploading file %s\n"", path)
	}

	wg := c.initWaitGroup(fileID, chunkPool, &size, withProgressBar)
	c.readAndChunk(file, chunkPool, &size)

	close(chunkPool)
	c.TagCliFile(fileID)
	wg.Wait()

	if withProgressBar && !c.JsonResponse {
		fmt.Println("">> Finalizing file..."")
	}

	jsonData, err = json.Marshal(jsonID{
		ID: fileID,
	})
	if err != nil {
		return err
	}

	c.makeRequestFail(""POST"", closeURL, jsonData)
	var finalUrl string
	if spaceID != """" {
		finalUrl = c.BaseURL + ""/spaces/"" + spaceID + ""/files/"" + fileID
	} else {
		finalUrl = c.BaseURL + ""/home/files/"" + fileID
	}

	if c.JsonResponse {
		helpers.PrettyPrint(struct {
			Url string `json:""url""`
		}{Url: finalUrl})
	} else {
		fmt.Println("">> Uploaded: "", path)
	}

	if withProgressBar && !c.JsonResponse {
		fmt.Println("">> Done! Access your file at "" + finalUrl)
	}

	return nil
}

func (c *PFDAClient) UploadFolder(folderPath string, folderID string, spaceID string) error {
	folders := make(map[string]string, 20)

	p, _ := filepath.Split(folderPath)
	folders[filepath.Clean(p)] = folderID

	if !c.JsonResponse {
		fmt.Println("">> Uploading content of:"", folderPath)
	}

	var fileList []string
	err := filepath.Walk(folderPath, func(currentPath string, f os.FileInfo, err error) error {
		if f.IsDir() {
			parent, _ := filepath.Split(currentPath)
			id, err := c.createNewFolder(filepath.Base(currentPath), folders[filepath.Dir(parent)], spaceID)
			if err != nil {
				return err
			}
			folders[currentPath] = id
		} else {
			fileList = append(fileList, currentPath)
		}
		return nil
	})

	if err != nil {
		return err
	}

	var wg = sync.WaitGroup{}
	maxGoroutines := 4
	guard := make(chan struct{}, maxGoroutines)

	for _, file := range fileList {
		guard <- struct{}{}
		wg.Add(1)
		go func(file string) {
			parent, _ := filepath.Split(file)
			err := c.UploadFile(file, folders[filepath.Dir(parent)], spaceID, false)
			if err != nil {
				helpers.PrintError(err, c.JsonResponse)
			}
			<-guard
			wg.Done()

		}(file)
	}
	wg.Wait()

	var finalUrl string
	if spaceID != """" {
		finalUrl = c.BaseURL + ""/spaces/"" + spaceID + ""/files?folder_id="" + folders[folderPath]
	} else {
		finalUrl = c.BaseURL + ""/home/files?folder_id="" + folders[folderPath]
	}

	if c.JsonResponse {
		helpers.PrettyPrint(struct {
			Url string `json:""url""`
		}{Url: finalUrl})
	} else {
		fmt.Println("">> Done! Access your files at "" + finalUrl)
	}
	return nil
}

func (c *PFDAClient) UploadMultipleFiles(paths []string, folderID string, spaceID string) error {

	if !c.JsonResponse {
		fmt.Printf("">> Uploading multiple files...\n"")
	}

	// this could be done in parallel - be careful to used memory otherwise will get terminated by kernel OOM-killer.
	for _, path := range paths {
		f, err := os.Stat(path)
		if os.IsNotExist(err) {
			helpers.PrintError(fmt.Errorf(""Input path '%s' does not exist - skipping"", path), c.JsonResponse)
			continue
		}
		path = filepath.Clean(path)
		if err := func() error {
			if f.IsDir() {
				return c.UploadFolder(path, folderID, spaceID)
			} else {
				return c.UploadFile(path, folderID, spaceID, false)
			}
		}(); err != nil {
			helpers.PrintError(err, c.JsonResponse)
		}
	}

	return nil
}

func (c *PFDAClient) DownloadFile(arg string, outputFilePath string, overwrite string) error {

	apiURL := fmt.Sprintf(""%s/api/files/%s/download?format=json"", c.BaseURL, arg)
	if !c.JsonResponse {
		fmt.Println("">> Preparing to download"")
	}
	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf(""%s not found. Please check that this file exists and you have access to it"", arg)
		} else {
			return err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	if resultJSON[""file_url""] == nil {
		return fmt.Errorf(""No file_url in response!\n\nResponse: %s"", string(body))
	}
	fileURL := resultJSON[""file_url""].(string)

	originalName := path.Base(fileURL)
	fileName, err := url.PathUnescape(originalName)
	if err != nil {
		fileName = originalName
	}
	fileName = sanitizeFileName(fileName)

	fileSize := resultJSON[""file_size""].(float64)
	if !c.JsonResponse {
		fmt.Printf(""   Downloading :  %s\n"", fileName)
		fmt.Printf(""     File Size :  %s\n"", units.BytesSize(fileSize))
	}
	if outputFilePath == """" {
		// If output is not specified, use the original filename and current working directory
		dir, err := os.Getwd()
		if err != nil {
			return err
		}

		outputFilePath = filepath.Join(dir, fileName)
	} else {
		if fileInfo, err := os.Stat(outputFilePath); err == nil && fileInfo.IsDir() {
			// If outputFilePath exists and it is a directory then the file should be downloaded
			// to that directory while retaining its original name
			// fmt.Printf("">> Specified outputFilePath %s is an existing directory\n"", outputFilePath)
			outputFilePath = filepath.Join(outputFilePath, fileName)
		} else if strings.HasSuffix(outputFilePath, ""/"") {
			// A trailing / means the user has specified a directory, but it doesn't exist.
			if err := os.MkdirAll(outputFilePath, os.ModePerm); err != nil {
				return err
			}
			outputFilePath = filepath.Join(outputFilePath, fileName)

		} else if _, err := os.Stat(filepath.Dir(outputFilePath)); err != nil {
			// This is now assumed to be a file path and not a dir path, and the parent directory does not exist
			return fmt.Errorf(""The parent directory %s of the specified output doesn't exist"", filepath.Dir(outputFilePath))
		}
	}
	// After the above block, outputFilePath should contain the target file path and cannot be a directory
	if _, err := os.Stat(outputFilePath); err == nil && overwrite == """" {
		fmt.Printf("">> File %s already exists\n"", outputFilePath)
		dialogOverwrite := yesNo(""  Overwrite already existing path? "")
		if !dialogOverwrite {
			return fmt.Errorf(""Download cancelled"")
		}
	} else if err == nil && overwrite == ""false"" {
		return fmt.Errorf(""Path %s already exists but -overwrite flag not set to true - skipping download"", outputFilePath)
	}

	if !c.JsonResponse {
		fmt.Printf("">> Output File :  %s\n"", outputFilePath)
	}
	withProgressBar := !c.JsonResponse
	err = c.DownloadFromUrl(fileURL, outputFilePath, int64(fileSize), withProgressBar)
	if err != nil {
		return err
	}
	if c.JsonResponse {
		helpers.PrintResult(outputFilePath, true)
	} else {
		fmt.Printf("">> Done!\n\n"")
	}
	return nil
}

func (c *PFDAClient) Download(args []string, folderID string, spaceID string, public bool, recursive bool, outputFilePath string, overwrite string) error {

	c.ContinueOnError = len(args) > 1

	fileIDs := make([]string, 0)
	fileNames := make([]string, 0)

	if len(args) == 0 {
		args = append(args, """")
	}

	// prepare lists of files to downloads defined by name or id.
	for _, arg := range args {
		arg = strings.TrimSpace(arg)
		if helpers.IsFileId(arg) {
			fileIDs = append(fileIDs, arg)
		} else {
			fileNames = append(fileNames, arg)
		}
	}

	// iterate over filenames with possible wildcards and download them.
	for _, fileName := range fileNames {
		apiURL := fmt.Sprintf(""%s/api/files/cli?"", c.BaseURL)
		params := url.Values{}

		if spaceID != """" {
			params.Add(""space_id"", spaceID)
		}
		if folderID != """" && folderID != ""root"" {
			params.Add(""folder_id"", folderID)
		}
		if fileName != """" {
			//respecting ruby backend specific filters
			params.Add(""filters[filter]"", helpers.TransformToSQLWildcards(fileName))
		}

		if public {
			params.Add(""public_scope"", ""true"")
		}

		_, body, err := c.makeRequestFail(""GET"", apiURL+params.Encode(), nil)
		if err != nil {
			return err
		}

		var children jsonListingResponse
		if err := json.Unmarshal(body, &children); err != nil {
			return err
		}

		var uids []string
		for _, child := range children.Files {
			if child.Type == ""UserFile"" {
				uids = append(uids, child.Uid)
			} else if recursive {
				// create the child folder first.
				if err := os.MkdirAll(filepath.Join(outputFilePath, child.Name), os.ModePerm); err != nil {
					log.Fatal(err)
				}
				c.Download([]string{fileName}, strconv.Itoa(child.Id), spaceID, public, recursive, filepath.Join(outputFilePath, child.Name), overwrite)
			}
		}

		if (len(uids) > 1 && (helpers.ContainsWildcard(fileName))) || len(uids) == 1 || args[0] == """" {
			fileIDs = append(fileIDs, uids...)
		} else if len(uids) > 1 {
			selected := pickFile(children.Files, ""Multiple files found matching the given name, select which to download"")
			fileIDs = append(fileIDs, selected)
		} else if !recursive {
			c.HandleError(fmt.Errorf(""Unable to find any files matching '%s' - verify it exists and you have access to it"", fileName))
		}
	}

	if len(fileIDs) > 1 || len(args) > 1 || recursive {
		c.parallelDownload(fileIDs, outputFilePath, overwrite)
	} else if len(fileIDs) == 1 {
		err := c.DownloadFile(fileIDs[0], outputFilePath, overwrite)
		c.HandleError(err)
	}
	return nil
}

func (c *PFDAClient) FileViewLink(arg string, preauthenticated bool, duration int64) error {

	baseURL, _ := url.Parse(fmt.Sprintf(""%s/api/files/%s/download"", c.BaseURL, arg))

	// Prepare query parameters
	params := url.Values{}
	params.Add(""format"", ""json"")
	params.Add(""preauthenticated"", fmt.Sprintf(""%t"", preauthenticated))
	params.Add(""duration"", fmt.Sprintf(""%d"", duration))
	params.Add(""inline"", ""true"")

	// Encode query parameters and append to the base URL
	baseURL.RawQuery = params.Encode()

	apiURL := baseURL.String()

	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf(""%s not found. Please check that this file exists and you have access to it"", arg)
		} else {
			return err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	if resultJSON[""file_url""] == nil {
		return fmt.Errorf(""Error while getting the url"")
	}

	resultUrl := resultJSON[""file_url""].(string)

	if c.JsonResponse {
		helpers.PrettyPrint(struct {
			Url string `json:""url""`
		}{Url: resultUrl})
	} else {
		fmt.Println(""Url to view file:"", resultUrl)
	}
	return nil
}

func (c *PFDAClient) DescribeEntity(entityID string) error {
	apiURL := fmt.Sprintf(""%s/api/v2/cli/%s/describe"", c.BaseURL, entityID)

	_, body, err := c.makeRequest(""GET"", apiURL, nil)
	if err != nil {
		return err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	prettyJSON, _ := json.MarshalIndent(resultJSON, """", ""    "")
	fmt.Printf(""%s\n"", string(prettyJSON))

	return nil
}

func (c *PFDAClient) GetScope() error {

	dxJobId, isPresent := os.LookupEnv(""DX_JOB_ID"")
	if !isPresent {
		return fmt.Errorf(""No space detected"")
	}

	apiURL := fmt.Sprintf(""%s/api/jobs/%s/scope"", c.BaseURL, dxJobId)

	_, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		return err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	scope, ok := resultJSON[""scope""].(string)
	if !ok {
		return fmt.Errorf(""scope is not a string"")
	}

	// Check if the scope is 'private'.
	if scope == ""private"" {
		helpers.PrintResult(""private"", c.JsonResponse)
		return nil
	}

	// Use regex to find the number after 'space-'.
	re := regexp.MustCompile(`^space-(\d+)$`)
	matches := re.FindStringSubmatch(scope)
	if len(matches) != 2 {
		return fmt.Errorf(""scope format is incorrect"")
	}

	// Print only the number part if the scope is in the format 'space-{number}'.
	helpers.PrintResult(matches[1], c.JsonResponse)
	return nil
}

func (c *PFDAClient) Ls(folderID string, spaceID string, flags map[string]bool) error {
	apiURL := fmt.Sprintf(""%s/api/files/cli"", c.BaseURL)

	params := url.Values{}

	if err := helpers.ValidateID(spaceID, ""space_id"", params); err != nil {
		return err
	}

	if err := helpers.ValidateID(folderID, ""folder_id"", params); err != nil {
		return err
	}

	for flag, value := range flags {
		if value {
			params.Add(flag, strconv.FormatBool(value))
		}
	}

	fullURL := fmt.Sprintf(""%s?%s"", apiURL, params.Encode())
	status, body, err := c.makeRequestFail(""GET"", fullURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf(""Target location not found or inaccessible"")
		}
		return err
	}

	var response jsonListingResponse
	if err := json.Unmarshal(body, &response); err != nil {
		return err
	}

	printListingResponse(response, flags[""json""], flags[""brief""])
	return nil
}

func (c *PFDAClient) Mkdir(dirs []string, folderID string, spaceID string, parents bool) error {

	c.ContinueOnError = len(dirs) > 1

	if parents {
		for _, dir := range dirs {
			parts := strings.Split(strings.TrimSuffix(dir, string(os.PathSeparator)), string(os.PathSeparator))
			parentId := folderID
			// created nested folders.
			for _, folder := range parts {
				id, err := c.createNewFolder(folder, parentId, spaceID)
				if err == nil {
					if c.JsonResponse {
						folderId, _ := strconv.Atoi(id)
						helpers.PrettyPrint(struct {
							Name string `json:""name""`
							ID   int    `json:""id""`
						}{Name: folder, ID: folderId})
					} else {
						fmt.Printf(""Created folder %s (id: %s) \n"", folder, id)
					}
				}
				parentId = id
			}
		}

		return nil
	}

	for _, dir := range dirs {
		id, err := c.createNewFolder(dir, folderID, spaceID)
		c.HandleError(err)
		if err == nil {
			if c.JsonResponse {
				folderId, _ := strconv.Atoi(id)
				helpers.PrettyPrint(struct {
					Name string `json:""name""`
					ID   int    `json:""id""`
				}{Name: dir, ID: folderId})
			} else {
				fmt.Printf(""Created folder %s (id: %s) \n"", dir, id)
			}
		}
	}
	return nil
}

func (c *PFDAClient) Rmdir(args []string) error {

	c.ContinueOnError = len(args) > 1

	for _, arg := range args {
		if !helpers.IsFolderId(arg) {
			c.HandleError(fmt.Errorf(""Invalid folder id: %s - expected an integer"", arg))
			continue
		}

		jsonData, err := json.Marshal(jsonRmPayload{Name: arg, Type: ""Folder""})
		if err != nil {
			return err
		}
		_, body, err := c.makeRequestFail(""POST"", c.BaseURL+""/api/files/cli_node_search"", jsonData)
		c.HandleError(err)

		var response []jsonFileResponse
		if err := json.Unmarshal(body, &response); err != nil {
			return err
		}
		if len(response) == 0 {
			c.HandleError(fmt.Errorf(""Target folder not found or inaccessible""))
			continue
		}

		if response[0].Children == 0 {
			err := c.RemoveDir(arg)
			c.HandleError(err)
		} else {
			c.HandleError(fmt.Errorf(""Unable to remove non-empty folder""))
		}
	}

	return nil
}

func (c *PFDAClient) Rm(args []string, folderID string, spaceID string) error {

	c.ContinueOnError = len(args) > 1
	for _, arg := range args {

		if helpers.IsFileId(arg) {
			err := c.RemoveFile([]string{arg})
			c.HandleError(err)
			continue
		}

		jsonData, err := json.Marshal(jsonRmPayload{Name: helpers.TransformToSQLWildcards(arg), Type: ""UserFile"", ParentFolderID: folderID, SpaceID: spaceID})
		if err != nil {
			return err
		}
		// first check for matching files to be deleted - filename (with wildcard) logic
		_, body, err := c.makeRequestFail(""POST"", c.BaseURL+""/api/files/cli_node_search"", jsonData)
		if err != nil {
			return err
		}
		var response []jsonFileResponse
		err = json.Unmarshal(body, &response)
		if err != nil {
			return err
		}

		toBeDeletedCount := len(response)
		if toBeDeletedCount > 1 {
			// given arg matches more files, let user select one and delete it
			if !helpers.ContainsWildcard(arg) {
				result := pickFile(response, ""Multiple files found matching the given name, select which to delete"")
				err := c.RemoveFile([]string{result})
				c.HandleError(err)
				// arg processed, continue to next
				continue
			}
			// wildcard used, user wants to match more files, just inform about the count.
			agree := yesNo(fmt.Sprintf(""%d files match the given arg \""%s\"", are you sure you want to continue?"", toBeDeletedCount, arg))
			if agree {
				uids := make([]string, 0)
				for _, file := range response {
					uids = append(uids, file.Uid)
				}
				err := c.RemoveFile(uids)
				c.HandleError(err)
				// arg processed, continue to next
				continue
			}
			if c.JsonResponse {
				helpers.PrintResult(""delete aborted"", true)
			} else {
				fmt.Println("">> Delete aborted"")
			}
			// arg processed, continue to next
			continue
		}
		if toBeDeletedCount == 0 {
			c.HandleError(fmt.Errorf(""Target file not found or inaccessible""))
			// arg processed, continue to next
			continue
		}
		// single file matches the name, delete it
		err = c.RemoveFile([]string{response[0].Uid})
		c.HandleError(err)
		// arg processed, continue to next
		continue

	}
	return nil
}

// RefreshToken Just gets the new token, the logic for token replacement is in go/pfda.go
func (c *PFDAClient) RefreshToken(autoRefresh bool) (string, error) {
	apiURL := fmt.Sprintf(""%s/api/auth_key"", c.BaseURL)

	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return """", fmt.Errorf(""Something went wrong"")
		} else {
			return """", err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON) // do this just to ensure the json is valid
	if err != nil {
		return """", err
	}

	return resultJSON[""Key""].(string), nil
}

func (c *PFDAClient) GetLatestVersion() (string, error) {
	apiURL := fmt.Sprintf(""%s/api/v2/cli/version/latest"", c.BaseURL)

	_, body, err := c.makeRequest(""GET"", apiURL, nil)
	if err != nil {
		return """", err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON) // do this just to ensure the json is valid
	if err != nil {
		return """", err
	}

	return resultJSON[""version""].(string), nil
}

func (c *PFDAClient) SetChunkSize(chunkSize int) {
	if chunkSize > maxChunkSize || chunkSize < minChunkSize {
		inputError(""Chunk size must be between 5MB and 5GB"")
	} else {
		c.ChunkSize = chunkSize
	}
}

func (c *PFDAClient) SetNumRoutines(numRoutines int) {
	if numRoutines > maxRoutines || numRoutines < minRoutines {
		inputError(""Maximum number of threads must an integer within the range of [1-100]"")
	} else {
		c.NumRoutines = numRoutines
	}
}

func (c *PFDAClient) createFileID(url string, data []byte) (string, error) {
	status, body, err := c.makeRequestFail(""POST"", url, data)
	if err != nil {
		if status == ""404 Not Found"" {
			// 404 should only be returned if the specified spaceId is invalid
			// For invalid folder-id, an error json is returned
			return """", fmt.Errorf(""uploading file - Please check that the space-id is correct and that you have access to that Space"")
		} else {
			return """", err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return """", err
	}

	if resultJSON[""id""] == nil {
		return """", fmt.Errorf(""No id in response!\n\nResponse: %s"", string(body))
	}
	fileID := resultJSON[""id""].(string)
	return fileID, nil
}

func (c *PFDAClient) createNewFolder(name string, parentFolderID string, spaceID string) (string, error) {

	createFolderURL := c.BaseURL + ""/api/files/create_folder""
	jsonData, err := json.Marshal(jsonCreateFolderPayload{
		Name:           name,
		ParentFolderID: parentFolderID, // might be """" -> not used
		SpaceID:        spaceID,        // might be """" -> not used
	})

	if err != nil {
		return """", err
	}

	_, body, err := c.makeRequestFail(""POST"", createFolderURL, jsonData)
	if err != nil {
		return """", err
	}

	var resultJSON jsonCreateFolderResponse
	if err := json.Unmarshal(body, &resultJSON); err != nil {
		return """", err
	}

	newFolderID := strconv.Itoa(resultJSON.Id)
	if resultJSON.Message.Type == ""error"" {
		return newFolderID, fmt.Errorf(""unable to create folder: %s - already exists in target location"", name)
	}

	c.TagCliFolder(newFolderID)
	return newFolderID, nil
}

func (c *PFDAClient) Head(arg string, lines int) error {

	if !helpers.IsFileId(arg) {
		return fmt.Errorf(""invalid file-id provided: %s"", arg)
	}

	apiURL := fmt.Sprintf(""%s/api/files/%s/download?format=json"", c.BaseURL, arg)
	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf(""%s not found. Please check that this file exists and you have access to it"", arg)
		}
		return err
	}

	var resultJSON map[string]interface{}
	if err := json.Unmarshal(body, &resultJSON); err != nil {
		return err
	}

	if resultJSON[""file_url""] == nil {
		return fmt.Errorf(""no file_url in response!\n\nResponse: %s"", string(body))
	}
	fileURL := resultJSON[""file_url""].(string)

	fileSize := resultJSON[""file_size""].(float64)
	// user wants print whole file, check size first.
	if lines == -1 && fileSize > 10_000_000 {
		agree := yesNo(""The size of the file is over 10Mb - are you sure you want to display the whole content?"")
		if !agree {
			return fmt.Errorf(""cat cancelled"")
		}
	}

	err = c.HeadFile(fileURL, lines)
	if err != nil {
		return err
	}

	return nil
}

func (c *PFDAClient) downloadByChunks(uidsChunk []string, outputFilePath string, overwrite string) {
	apiURL := fmt.Sprintf(""%s/api/files/bulk_download"", c.BaseURL)
	jsonData, _ := json.Marshal(bulkIDsPayload{
		IDs: uidsChunk,
	})
	_, body, err := c.makeRequestFail(""POST"", apiURL, jsonData)
	if err != nil {
		helpers.PrintError(fmt.Errorf(""unable to download: %s"", strings.Join(uidsChunk, "","")), c.JsonResponse)
		return
	}
	var resultJSON []map[string]interface{}
	_ = json.Unmarshal(body, &resultJSON)

	downloaded := make(map[string]string)
	for _, file := range resultJSON {
		c.DownloadDirectly(file[""url""].(string), outputFilePath, overwrite)
		downloaded[file[""uid""].(string)] = """"
	}

	// check if all requested files were downloaded.
	for _, uid := range uidsChunk {
		_, found := downloaded[uid]
		if !found {
			if c.JsonResponse {
				helpers.PrintError(fmt.Errorf(""unable to download: %s"", uid), c.JsonResponse)
			} else {
				fmt.Printf("">> Unable to download: %s\n"", uid)
			}
		}
	}

}

func (c *PFDAClient) parallelDownload(uids []string, outputFilePath string, overwrite string) {

	if len(uids) == 0 {
		return
	}
	if !c.JsonResponse {
		fmt.Printf("">> Preparing to download: %d files \n"", len(uids))
	}
	var wg = sync.WaitGroup{}
	maxGoroutines := 5 // do not exceed 10 - magical TCP issues with lost packages appears.
	guard := make(chan struct{}, maxGoroutines)

	// create outputFilePath in case it was specified - we assume user specified a dir name
	if outputFilePath != """" {
		if err := os.MkdirAll(outputFilePath, os.ModePerm); err != nil {
			log.Fatal(err)
		}
	}

	var divided [][]string
	var chunkSize = helpers.GetChunkSize(len(uids))

	for i := 0; i < len(uids); i += chunkSize {
		end := i + chunkSize
		if end > len(uids) {
			end = len(uids)
		}
		divided = append(divided, uids[i:end])
	}

	for _, uidsChunk := range divided {
		guard <- struct{}{}
		wg.Add(1)
		go func(uidsChunk []string) {
			c.downloadByChunks(uidsChunk, outputFilePath, overwrite)
			<-guard
			wg.Done()

		}(uidsChunk)
	}
	wg.Wait()
}

func (c *PFDAClient) initWaitGroup(fileID string, chunkPool <-chan uploadChunk, size *int64, withProgressBar bool) (wg *sync.WaitGroup) {
	numRoutines := helpers.Min(c.NumRoutines, int(math.Ceil(float64(*size)/float64(c.ChunkSize))))

	var totalSent uint64 = 0
	writer := uilive.New()
	writer.Start()
	defer writer.Stop()

	var g sync.WaitGroup
	for i := 0; i < numRoutines; i++ {
		g.Add(1)
		go func() {
			for chunk := range chunkPool {
				err := c.sendToStore(fileID, chunk)
				c.HandleError(err)
				atomic.AddUint64(&totalSent, uint64(len(chunk.data)))
				currentSize := atomic.LoadUint64(&totalSent)
				totalSize := atomic.LoadInt64(size)
				if withProgressBar && !c.JsonResponse {
					fmt.Fprintf(writer, ""     %.1f%% (%s / %s)\n"",
						100*float64(currentSize)/float64(totalSize), units.BytesSize(float64(currentSize)), units.BytesSize(float64(totalSize)))
					writer.Flush()
				}
			}
			g.Done()
		}()
	}
	wg = &g
	return
}

// Deprecated: This method is not handling errors correctly, use makeRequest instead.
func (c *PFDAClient) makeRequestFail(requestType string, url string, data []byte) (status string, body []byte, err error) {
	req, err := retryablehttp.NewRequest(requestType, url, bytes.NewReader(data))
	helpers.CheckErr(err)
	c.setPostHeaders(req)

	resp, err := c.Client.Do(req)
	helpers.CheckErr(err)
	defer resp.Body.Close()
	status = resp.Status
	body, _ = io.ReadAll(resp.Body)

	if !strings.HasPrefix(status, ""2"") {
		err = fmt.Errorf(""%s Request to '%s' failed with status %s. For 4xx status, check that the provided id and auth-key are still valid.\n"", requestType, url, status)
	}
	return status, body, err
}

func (c *PFDAClient) makeRequestWithHeadersFail(requestType string, url string, headers map[string]interface{}, data []byte) (status string, body []byte, err error) {
	req, err := retryablehttp.NewRequest(requestType, url, bytes.NewReader(data))
	helpers.CheckErr(err)
	for header, value := range headers {
		req.Header.Set(header, value.(string))
	}

	resp, err := c.Client.Do(req)
	helpers.CheckErr(err)
	defer resp.Body.Close()

	status = resp.Status
	body, _ = io.ReadAll(resp.Body)

	if !strings.HasPrefix(status, ""2"") {
		err = fmt.Errorf(""%s Request to '%s' failed with status %s. For 4xx status, check that the provided id and auth-key are still valid.\n"", requestType, url, status)
	}
	return status, body, err
}

func (c *PFDAClient) readAndChunk(f io.ReadCloser, ch chan<- uploadChunk, size *int64) {
	// dynamically adjust chunkSize
	chunkIndex := 1
	var totalDataLength = 0
	for {
		byteBuf := make([]byte, c.ChunkSize)
		i := 0
		for i < c.ChunkSize {
			tempBuf := byteBuf[i:]
			m, err := f.Read(tempBuf)
			if err != nil && err != io.EOF {
				panic(err)
			}
			i += m
			if err == io.EOF {
				break
			}
		}
		totalDataLength += i
		// Only upload an empty chunk if empty file
		if i == 0 && chunkIndex > 1 {
			break
		}
		ch <- uploadChunk{
			index: chunkIndex,
			data:  byteBuf[:i], // use slice
		}
		chunkIndex++
	}
	atomic.StoreInt64(size, int64(totalDataLength))
}

func (c *PFDAClient) sendToStore(id string, chunk uploadChunk) error {
	uploadURL := c.BaseURL + ""/api/get_upload_url""
	md5Sum := md5.Sum(chunk.data)
	jsonData, err := json.Marshal(jsonChunkInfo{
		ID:    id,
		Size:  len(chunk.data),
		Index: chunk.index,
		Md5:   hex.EncodeToString(md5Sum[:]),
	})

	_, body, err := c.makeRequestFail(""POST"", uploadURL, jsonData)
	if err != nil {
		return err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	helpers.CheckErr(err)
	if resultJSON[""url""] == """" {
		panic(""No url in response!"")
	}
	_, _, err = c.makeRequestWithHeadersFail(""PUT"", resultJSON[""url""].(string), resultJSON[""headers""].(map[string]interface{}), chunk.data)
	return err
}

func (c *PFDAClient) setPostHeaders(req *retryablehttp.Request) {
	req.Header.Set(""User-Agent"", userAgent+""(""+c.Platform+"")"")
	if c.AuthKey != """" {
		req.Header.Set(""Authorization"", ""Key ""+c.AuthKey)
	}
	req.Header.Set(""Content-Type"", ""application/json"")
}

func yesNo(label string) bool {
	prompt := promptui.Select{
		Label: label + "" [Yes/No]"",
		Items: []string{""Yes"", ""No""},
	}
	_, result, err := prompt.Run()
	if err != nil {
		log.Fatalf(""Prompt failed %v\n"", err)
	}
	return result == ""Yes""
}

// Filters out only files to pick from.
func pickFile(files []jsonFileResponse, label string) string {

	options := make([]string, 0)
	ids := make([]string, 0)
	for _, file := range files {
		if file.Type == ""UserFile"" {
			options = append(options, ""created ""+file.CreatedAt+"" - ""+helpers.HumanReadableSize(file.Size)+"" - ""+file.Name+"" (""+file.Uid+"")"")
			ids = append(ids, file.Uid)
		}
	}

	prompt := promptui.Select{
		Label: label,
		Items: options,
	}
	index, _, err := prompt.Run()
	if err != nil {
		log.Fatalf(""Prompt failed %v\n"", err)
	}
	return ids[index]
}

func printListSpacesResponse(spaces []jsonSpaceResponse, asJSON bool) {
	if asJSON {
		prettyJSON, _ := json.MarshalIndent(spaces, """", ""    "")
		fmt.Println(string(prettyJSON))
	} else if len(spaces) > 0 {
		writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)

		// Function to join and print a line
		printLine := func(columns []string) {
			fmt.Fprintln(writer, strings.Join(columns, ""\t"")+""\t"")
		}

		headers := []string{""ID"", ""Type"", ""Status"", ""Role"", ""Side"", ""Name""}
		printLine(headers)

		for _, space := range spaces {
			columns := []string{strconv.Itoa(space.Id), space.Type, helpers.FormatValue(space.Protected, ""Protected""), space.Role, space.Side, space.Title}
			printLine(columns)
		}
		writer.Flush()
	}
}

func printSpaceMembersResponse(members []jsonMembersResponse, asJSON bool) {
	if asJSON {
		prettyJSON, _ := json.MarshalIndent(members, """", ""    "")
		fmt.Println(string(prettyJSON))
	} else if len(members) > 0 {
		writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)

		// Function to join and print a line
		printLine := func(columns []string) {
			fmt.Fprintln(writer, strings.Join(columns, ""\t"")+""\t"")
		}

		headers := []string{""ID"", ""Name"", ""Role"", ""Side"", ""Active"", ""Added at""}
		printLine(headers)

		for _, member := range members {
			columns := []string{strconv.Itoa(member.Id), member.Name, member.Role, member.Side, strconv.FormatBool(member.Active), member.CreatedAt}
			printLine(columns)
		}
		writer.Flush()
	}
}

func sanitizeFileName(name string) string {
	re := regexp.MustCompile(`[<>:""/\\|?*]+`)
	return re.ReplaceAllString(name, ""_"")
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/download.go",".go","5180","201","package precisionfda

import (
	""dnanexus.com/precision-fda-cli/helpers""
	""fmt""
	""github.com/docker/go-units""
	""github.com/gosuri/uilive""
	""github.com/hashicorp/go-retryablehttp""
	""io""
	""net/http""
	""net/url""
	""os""
	""path""
	""strings""
)

// ProgressWriter counts the number of bytes written to it.
type ProgressWriter struct {
	Total      int64
	Downloaded int64
	Writer     *uilive.Writer
}

// Write implements the io.Writer interface.
func (wc *ProgressWriter) Write(p []byte) (int, error) {
	n := len(p)
	wc.Downloaded += int64(n)
	percent := 100 * float64(wc.Downloaded) / float64(wc.Total)
	fmt.Fprintf(wc.Writer, ""                  %.1f%% (%s of %s)\n"", percent, units.BytesSize(float64(wc.Downloaded)), units.BytesSize(float64(wc.Total)))
	wc.Writer.Flush()
	return n, nil
}

func NewProgressWriter(totalBytes int64, writer *uilive.Writer) *ProgressWriter {
	return &ProgressWriter{totalBytes, 0, writer}
}

type Writer struct {
	Total  int64
	Writer *uilive.Writer
}

type Printer struct {
	LinesToPrint int // max lines to print.
	Lines        int
	Writer       *uilive.Writer
}

func NewWriter(totalBytes int64, writer *uilive.Writer) *Writer {
	return &Writer{totalBytes, writer}
}

func NewPrinter(lines int, writer *uilive.Writer) *Printer {
	return &Printer{lines, 0, writer}
}

// Write implements the io.Writer interface.
func (wc *Printer) Write(p []byte) (int, error) {
	content := string(p[:])
	lines := strings.Split(strings.ReplaceAll(content, ""\r\n"", ""\n""), ""\n"")
	for _, line := range lines {
		if wc.Lines == wc.LinesToPrint {
			return -1, fmt.Errorf(""Line limit reached"")
		}
		fmt.Println(line)
		wc.Lines = wc.Lines + 1
	}
	return len(p), nil
}

// Write implements the io.Writer interface.
func (wc *Writer) Write(p []byte) (int, error) {
	wc.Writer.Flush()
	return len(p), nil
}

func (c *PFDAClient) DownloadFromUrl(fileURL string, outputFilePath string, fileSize int64, withProgressBar bool) error {
	out, err := os.Create(outputFilePath) // Create the file
	if err != nil {
		return err
	}
	defer out.Close()

	req, err := retryablehttp.NewRequest(""GET"", fileURL, nil)
	c.setPostHeaders(req)
	resp, err := c.Client.Do(req)
	if err != nil {
		return err
	}
	defer resp.Body.Close()

	writer := uilive.New()
	writer.Start()
	defer writer.Stop()

	// Writer the body to file
	if withProgressBar {
		body := io.TeeReader(resp.Body, NewProgressWriter(fileSize, writer))
		_, err = io.Copy(out, body)
		return err
	} else {
		body := io.TeeReader(resp.Body, NewWriter(0, writer))
		_, err = io.Copy(out, body)
		return err
	}
}

func (c *PFDAClient) DownloadDirectly(downloadUrl string, outputFilePath string, overwrite string) error {
	fileURL := downloadUrl
	originalName := path.Base(fileURL)
	fileName, err := url.PathUnescape(originalName)
	if err != nil {
		fileName = originalName
	}
	// fileSize := resultJSON[""file_size""].(float64)
	if outputFilePath == """" {
		// If output is not specified, use the original filename and current working directory
		dir, err := os.Getwd()
		if err != nil {
			return err
		}

		outputFilePath = path.Join(dir, fileName)
	} else if fileInfo, err := os.Stat(outputFilePath); err == nil && fileInfo.IsDir() {
		// If outputFilePath exists, and it is a directory then the file should be downloaded
		// to that directory while retaining its original name
		outputFilePath = path.Join(outputFilePath, fileName)
	}

	if _, err := os.Stat(outputFilePath); err == nil && (overwrite == ""false"" || overwrite == """") {
		helpers.PrintError(fmt.Errorf(""Path %s already exists but -overwrite flag not set to true - skipping download"", outputFilePath), c.JsonResponse)
		return nil
	}

	err = c.DownloadFromUrl(fileURL, outputFilePath, 0, false)
	if err != nil {
		helpers.PrintError(fmt.Errorf(""Download of %s failed - %s.\n"", fileName, err), c.JsonResponse)
		return nil
	}

	if c.JsonResponse {
		helpers.PrettyPrint(struct {
			FileName string `json:""file_name""`
			Path     string `json:""path""`
		}{FileName: fileName, Path: outputFilePath})
	} else {
		fmt.Printf(""Downloaded %s to %s\n"", fileName, outputFilePath)
	}
	return nil
}

func (c *PFDAClient) HeadFile(fileURL string, lines int) error {
	tmp, err := os.CreateTemp("""", ""head"") // Create temp fake data destination
	if err != nil {
		return err
	}

	req, err := retryablehttp.NewRequest(""GET"", fileURL, nil)
	c.setPostHeaders(req)
	resp, err := c.Client.Do(req)
	if err != nil {
		return err
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return fmt.Errorf(""Error while getting file: %s"", resp.Status)
	}

	writer := uilive.New()
	writer.Start()
	defer writer.Stop()

	body := io.TeeReader(resp.Body, NewPrinter(lines, writer))
	_, err = io.Copy(tmp, body)

	defer os.Remove(tmp.Name())
	return nil
}

func (c *PFDAClient) processDownloadArgs(args []string) ([]string, []string) {
	c.ContinueOnError = len(args) > 1

	fileIDs := make([]string, 0)
	fileNames := make([]string, 0)

	if len(args) == 0 {
		args = append(args, """")
	}

	for _, arg := range args {
		arg = strings.TrimSpace(arg)
		if helpers.IsFileId(arg) {
			fileIDs = append(fileIDs, arg)
		} else {
			fileNames = append(fileNames, arg)
		}
	}
	return fileIDs, fileNames
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/tagging.go",".go","751","35","package precisionfda

import (
	""encoding/json""
)

const tagCLI = ""CLI_created""

// TagCliFile tags a file with a predefined tag.
func (c *PFDAClient) TagCliFile(fileID string) {
	c.tagResource(""taggable_uid"", fileID)
}

// TagCliFolder tags a folder with a predefined tag.
func (c *PFDAClient) TagCliFolder(folderID string) {
	c.tagResource(""taggable_folder_id"", folderID)
}

func (c *PFDAClient) tagResource(resourceKey, resourceID string) {
	jsonData, err := json.Marshal(map[string]interface{}{
		resourceKey: resourceID,
		""tags"":      tagCLI,
	})
	if err != nil {
		return
	}

	c.addCLITag(jsonData)
}

func (c *PFDAClient) addCLITag(jsonData []byte) {
	setTagURL := c.BaseURL + ""/api/set_tags""
	c.makeRequestFail(""POST"", setTagURL, jsonData)
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/errors.go",".go","382","23","package precisionfda

import (
	""dnanexus.com/precision-fda-cli/helpers""
	""fmt""
	""os""
)

// HandleError Check for error, if found behave accordingly
func (c *PFDAClient) HandleError(err error) {
	if err != nil {
		helpers.ErrorFromError(err, c.JsonResponse)
		if !c.ContinueOnError {
			os.Exit(1)
		}
	}
}

func inputError(msg string) {
	fmt.Println(fmt.Errorf(msg))
	os.Exit(1)
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/listing.go",".go","6244","237","package precisionfda

import (
	""dnanexus.com/precision-fda-cli/helpers""
	""encoding/json""
	""fmt""
	""net/url""
	""os""
	""strconv""
	""strings""
	""text/tabwriter""
)

// apiConfig holds configuration for making API calls.
type apiConfig struct {
	Path     string
	Endpoint string
	SpaceID  string
	Flags    map[string]bool
}

// better name needed
type jsonFileResponse struct {
	Id        int    `json:""id""`
	Uid       string `json:""uid""`
	Name      string `json:""name""`
	Type      string `json:""type""`
	Locked    bool   `json:""locked""`
	State     string `json:""state""`
	AddedBy   string `json:""added_by""`
	CreatedAt string `json:""created_at""`
	Size      int64  `json:""file_size""`
	// populated for Folders only
	Children int `json:""children,omitempty""`
}

type jsonMetaResponse struct {
	Scope string `json:""scope""`
	Path  string `json:""path""`
}

type jsonListingResponse struct {
	Meta  jsonMetaResponse   `json:""meta""`
	Files []jsonFileResponse `json:""files""`
}

// lsResource generic function to list various resources.
func (c *PFDAClient) lsResource(config apiConfig) error {
	apiURL := fmt.Sprintf(""%s/api/%s/cli_%s"", c.BaseURL, config.Path, config.Endpoint)

	params := url.Values{}
	if err := helpers.ValidateID(config.SpaceID, ""space_id"", params); err != nil {
		return err
	}

	for flag, value := range config.Flags {
		if value {
			params.Add(flag, strconv.FormatBool(value))
		}
	}

	fullURL := fmt.Sprintf(""%s?%s"", apiURL, params.Encode())
	status, body, err := c.makeRequestFail(""GET"", fullURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf(""Target location not found or inaccessible"")
		}
		return err
	}

	var result []map[string]interface{}
	if err := json.Unmarshal(body, &result); err != nil {
		return err
	}

	prettyJSON, _ := json.MarshalIndent(result, """", ""    "")
	fmt.Printf(""%s\n"", string(prettyJSON))
	return nil
}

// Simplified specific listing functions
func (c *PFDAClient) LsApps(spaceID string, flags map[string]bool) error {
	return c.lsResource(apiConfig{Path: ""apps"", Endpoint: ""apps"", SpaceID: spaceID, Flags: flags})
}

func (c *PFDAClient) LsAssets(spaceID string, flags map[string]bool) error {
	return c.lsResource(apiConfig{Path: ""assets"", Endpoint: ""assets"", SpaceID: spaceID, Flags: flags})
}

func (c *PFDAClient) LsWorkflows(spaceID string, flags map[string]bool) error {
	return c.lsResource(apiConfig{Path: ""workflows"", Endpoint: ""workflows"", SpaceID: spaceID, Flags: flags})
}

func (c *PFDAClient) LsExecutions(spaceID string, flags map[string]bool) error {
	return c.lsResource(apiConfig{Path: ""jobs"", Endpoint: ""jobs"", SpaceID: spaceID, Flags: flags})
}

func (c *PFDAClient) LsDiscussions(spaceID string, flags map[string]bool) error {
	return c.lsResource(apiConfig{Path: ""spaces/"" + spaceID, Endpoint: ""discussions"", SpaceID: """", Flags: flags})
}

func (c *PFDAClient) LsSpaces(flags map[string]bool) error {
	apiURL := fmt.Sprintf(""%s/api/spaces/cli"", c.BaseURL)

	params := url.Values{}
	for flag, value := range flags {
		if value {
			params.Add(flag, strconv.FormatBool(value))
		}
	}
	fullURL := fmt.Sprintf(""%s?%s"", apiURL, params.Encode())
	status, body, err := c.makeRequestFail(""GET"", fullURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf(""Something went wrong"")
		} else {
			return err
		}
	}

	var spaces []jsonSpaceResponse
	if err := json.Unmarshal(body, &spaces); err != nil {
		return err
	}

	printListSpacesResponse(spaces, flags[""json""])
	return nil
}

func (c *PFDAClient) LsMembers(spaceID string) error {
	apiURL := fmt.Sprintf(""%s/api/spaces/%s/cli_members"", c.BaseURL, spaceID)

	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf(""Space not found or inaccessible"")
		}
		return err
	}

	var members []jsonMembersResponse
	if err := json.Unmarshal(body, &members); err != nil {
		return err
	}

	printSpaceMembersResponse(members, c.JsonResponse)
	return nil
}

func printListingVerbose(files []jsonFileResponse, meta jsonMetaResponse) {
	if len(files) == 0 {
		return
	}
	fmt.Printf(""Scope: %s\nPath: %s\n\n"", meta.Scope, meta.Path)

	isSpaceOrPublicContext := strings.Contains(meta.Scope, ""space"") || strings.Contains(meta.Scope, ""Public"")
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 2, '\t', tabwriter.AlignRight)

	// Function to join and print a line
	printLine := func(columns ...string) {
		fmt.Fprintln(writer, strings.Join(columns, ""\t"")+""\t"")
	}

	// Determine the headers based on the context
	headers := []string{""File/Folder ID"", ""State"", ""Type"", ""Status"", ""Size"", ""Created""}
	if isSpaceOrPublicContext {
		headers = append(headers, ""Added By"")
	}
	headers = append(headers, ""Name"")
	printLine(headers...)

	for _, file := range files {
		columns := []string{
			getFileID(file),
			file.State,
			file.Type,
			helpers.FormatValue(file.Locked, ""Locked""),
			getFileSize(file),
			file.CreatedAt,
		}
		if isSpaceOrPublicContext {
			columns = append(columns, file.AddedBy)
		}
		columns = append(columns, file.Name)
		printLine(columns...)
	}
	writer.Flush()
}

// pass all flags, so we can optimize the table header - if in 'private' do not show added-by
func printListingResponse(response jsonListingResponse, asJSON bool, brief bool) {
	if asJSON {
		prettyJSON, _ := json.MarshalIndent(response, """", ""  "")
		fmt.Println(string(prettyJSON))
	} else if brief {
		printListingSimple(response.Files)
	} else {
		printListingVerbose(response.Files, response.Meta)
	}
}

func printListingSimple(files []jsonFileResponse) {
	if len(files) == 0 {
		return
	}

	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)

	// Function to join and print a line
	printLine := func(columns []string) {
		fmt.Fprintln(writer, strings.Join(columns, ""\t"")+""\t"")
	}

	// Print header
	printLine([]string{""File/Folder ID"", ""Name""})

	for _, file := range files {
		printLine([]string{getFileID(file), file.Name})
	}

	writer.Flush()
}

func getFileID(file jsonFileResponse) string {
	if file.Type == ""UserFile"" {
		return file.Uid
	}
	return strconv.Itoa(file.Id)
}

func getFileSize(file jsonFileResponse) string {
	if file.Type == ""UserFile"" {
		return helpers.HumanReadableSize(file.Size)
	}
	return """"
}
","Go"
"Assay","FDA/precisionFDA","packages/cli/precisionfda/test/testutils.go",".go","1018","38","package test

// From https://github.com/benbjohnson/testing
import (
	""fmt""
	""path/filepath""
	""runtime""
	""reflect""
	""testing""
)

// assert fails the test if the condition is false.
func Assert(tb testing.TB, condition bool, msg string, v ...interface{}) {
	if !condition {
		_, file, line, _ := runtime.Caller(1)
		fmt.Printf(""\033[31m%s:%d: ""+msg+""\033[39m\n\n"", append([]interface{}{filepath.Base(file), line}, v...)...)
		tb.FailNow()
	}
}

// ok fails the test if an err is not nil.
func Ok(tb testing.TB, err error) {
	if err != nil {
		_, file, line, _ := runtime.Caller(1)
		fmt.Printf(""\033[31m%s:%d: unexpected error: %s\033[39m\n\n"", filepath.Base(file), line, err.Error())
		tb.FailNow()
	}
}

// equals fails the test if exp is not equal to act.
func Equals(tb testing.TB, exp, act interface{}) {
	if !reflect.DeepEqual(exp, act) {
		_, file, line, _ := runtime.Caller(1)
		fmt.Printf(""\033[31m%s:%d:\n\n\texpected: %#v\n\n\tgot: %#v\033[39m\n\n"", filepath.Base(file), line, exp, act)
		tb.FailNow()
	}
}
","Go"
"Assay","FDA/precisionFDA","packages/server/docker/entrypoint/dev.entrypoint.sh",".sh","763","26","#!/bin/bash
# Check if args were provided
if [[ ""$#"" -eq 0 ]]; then
    echo ""No Command provided""
    exit 1
fi

if [[ ! $SKIP_NODEJS_DEPS_SETUP || $SKIP_NODEJS_DEPS_SETUP = 0 ]]; then
    pnpm i --frozen-lockfile
fi

# Reuse service-level env variables for db polling
source .env

if [[ ! $SKIP_NODEJS_DB_WAITING || $SKIP_NODEJS_DB_WAITING = 0 ]]; then
  while ! mysql --user=${NODE_DATABASE_USER} --password=${NODE_DATABASE_PASSWORD} --host=db --database=${NODE_DATABASE_NAME} --silent --execute 'SELECT 1;'; do
    echo ""Database not ready - waiting ${NODEJS_DB_POLLING_INTERVAL} second(s)""
    sleep ${NODEJS_DB_POLLING_INTERVAL}
  done
fi

echo ""Database connection established""

# TODO(samuel) wrap the original docker entrypoint
docker-entrypoint.sh ""$@""
","Shell"
"Assay","FDA/precisionFDA","packages/server/docker/entrypoint/qa.entrypoint.sh",".sh","580","21","#!/bin/bash
# Check if args were provided
if [[ ""$#"" -eq 0 ]]; then
    echo ""No Command provided""
    exit 1
fi

# Reuse service-level env variables for db polling
source .env

while ! mysql --user=${NODE_DATABASE_USER} --password=${NODE_DATABASE_PASSWORD} --host=db --database=${NODE_DATABASE_NAME} --silent --execute 'SELECT 1;'; do
    echo ""Database not ready - waiting ${NODEJS_DB_POLLING_INTERVAL} second(s)""
    sleep ${NODEJS_DB_POLLING_INTERVAL}
done

echo ""Database connection established""

# NOTE(samuel) wrap the original docker entrypoint
docker-entrypoint.sh ""$@""

","Shell"
"Assay","FDA/precisionFDA","packages/tools/appkit/ReleaseNotes.md",".md","196","8","version 1.0 - unknown date
- original version 

version 1.1 - 14. 7. 2023
- added support for input/output arrays 

version 1.2 - 27. 7. 2023
- added support for Ubuntu 20.04 which uses Python 3.8","Markdown"
"Assay","FDA/precisionFDA","packages/rails/docker/entrypoint/sidekiq.entrypoint.sh",".sh","221","10","#!/usr/bin/env bash

sed -i '/^#/!s/CipherString = DEFAULT@SECLEVEL=2/#CipherString = DEFAULT@SECLEVEL=2/g' /etc/ssl/openssl.cnf

# Install gems if needed
bundle check || bundle install

# Run sidekiq
bundle exec sidekiq
","Shell"
"Assay","FDA/precisionFDA","packages/rails/docker/entrypoint/web.entrypoint.sh",".sh","370","13","#!/usr/bin/env bash

sed -i '/^#/!s/CipherString = DEFAULT@SECLEVEL=2/#CipherString = DEFAULT@SECLEVEL=2/g' /etc/ssl/openssl.cnf

# Install gems if needed
bundle check || bundle install

if [[ -f ./key.pem && -f ./cert.pem ]]; then
  bundle exec thin --debug start --ssl --ssl-key-file ./key.pem --ssl-cert-file ./cert.pem
else
  bundle exec thin --ssl --debug start
fi
","Shell"
"Assay","FDA/precisionFDA","packages/rails/docker/entrypoint/isolation.docker-entrypoint.sh",".sh","1089","34","#!/usr/bin/env bash

sed -i '/^#/!s/CipherString = DEFAULT@SECLEVEL=2/#CipherString = DEFAULT@SECLEVEL=2/g' /etc/ssl/openssl.cnf

# Need to check bundler version as well, because ruby image come with ""incorrect"" pre-installed bundler
if ! which bundler || [[ $(bundler -v) != $BUNDLER_VERSION ]]; then
  gem install bundler -v $BUNDLER_VERSION
fi

wait-for-it db:3306 -- echo ""DB connection established""

cp config/database.sample.yml config/database.yml

if [[ ! $SKIP_RUBY_DEPS_SETUP || $SKIP_RUBY_DEPS_SETUP = 0 ]]; then
  bundle check || bundle install
fi

if [[ ! $SKIP_BOWER_DEPS_SETUP || $SKIP_BOWER_DEPS_SETUP = 0 ]]; then
  bower install --allow-root
fi

if [[ ! $SKIP_DB_SETUP || $SKIP_DB_SETUP = 0 ]]; then
  # Runs setup if database does not exist, or runs migrations if it does
  bundle exec rake db:prepare
  bundle exec rake db:generate_mock_data
  bundle exec rake user:generate_test_users
fi

if [[ -f /key.pem && -f /cert.pem ]]; then
  bundle exec thin --debug start --ssl --ssl-key-file /key.pem --ssl-cert-file /cert.pem
else
  bundle exec thin --ssl --debug start
fi
","Shell"
"Assay","FDA/precisionFDA","packages/rails/docker/entrypoint/ui_test.entrypoint.sh",".sh","800","36","#!/bin/bash

sed -i '/^#/!s/CipherString = DEFAULT@SECLEVEL=2/#CipherString = DEFAULT@SECLEVEL=2/g' /etc/ssl/openssl.cnf

cp config/database.sample.yml config/database.yml

service mysql start

bundle exec rake db:setup
bundle exec rake db:generate_mock_data
bundle exec rake user:generate_test_users
bundle exec thin --ssl -d start

#warm up
result=7
while [[ ""$result"" == 7 ]]
do
  curl -k -o /dev/null https://localhost:3000
  result=$?
  sleep 1
done

echo ""Server up""

suite=${TEST_SUITE:-fullregression}

echo ""Running suite $suite""

cd test/functional && mvn -B test -Dsuite=$suite.xml -Dheadless=true -Denv=loc

#save logs and screenshots
rm -rf /log_storage/docker
mkdir /log_storage/docker
mv /precision-fda/test/functional/target/debug-log/* /log_storage/
chmod -R 777 /log_storage/docker
","Shell"
"Assay","FDA/precisionFDA","packages/rails/docker/entrypoint/docker-test-entrypoint.sh",".sh","126","9","#!/bin/sh

cp config/database.sample.yml config/database.yml

service mysql start

bundle exec rake db:setup
bundle exec rake
","Shell"
"Assay","FDA/precisionFDA","packages/rails/docker/misc/gsrs/switch-frontend.sh",".sh","966","27","echo ""Enter [1] to use frontend from your local source code (instant rebuilt on code change)""
echo ""Enter [2] to use pre-built frontend""

read choice

if [[ $choice -eq 1 ]]
then
  oldPwd=$(pwd)
  cd /usr/local/GSRSFrontend
  bash /usr/local/GSRSFrontend/build.sh
  npm install webpack
  sed -i 's$http://localhost:8080/frontend/ginas/app/beta$http://localhost:4200/ginas/app/beta$g' /usr/local/tomcat/webapps/ROOT/WEB-INF/classes/application.yml
  /usr/local/tomcat/bin/catalina.sh stop && rm -rf /usr/local/tomcat/work
  /usr/local/tomcat/bin/catalina.sh start
  npm run start:fda:local &
  cd $oldPwd
elif [[ $choice -eq 2 ]]
then
  sed -i 's$http://localhost:4200/ginas/app/beta$http://localhost:8080/frontend/ginas/app/beta$g' /usr/local/tomcat/webapps/ROOT/WEB-INF/classes/application.yml
  /usr/local/tomcat/bin/catalina.sh stop && rm -rf /usr/local/tomcat/work
  /usr/local/tomcat/bin/catalina.sh start
else
  echo ""Invalid option. Not doing anything.""
fi


","Shell"
"Assay","FDA/precisionFDA","packages/rails/spec/support/files/app_script.sh",".sh","1863","78","pip install --upgrade setuptools==12.4
PYTHONUSERBASE=$DNANEXUS_HOME pip install --upgrade --user requests==2.21.0
pip install cwltool

cat <<EOF > description.cwl
class: CommandLineTool
id: app-1
label: default_title
cwlVersion: v1.0
baseCommand: [""/opt/wtsi-cgp/bin/sums2json.sh""]
requirements:
- class: DockerRequirement
  dockerPull: app_a
  dockerOutputDirectory: ""/data/out""
- class: InlineJavascriptRequirement
  expressionLib:
  - function file_string() { return self[0].contents.replace(/(\r\n|\n|\r)/gm,"""")
    }
inputs:
  anything:
    doc: anything
    inputBinding:
      position: 1
      prefix: ""--anything""
    type: string
outputs:
  my_file:
    doc: my_file
    type: File
    outputBinding:
      glob: my_file/*
  my_string:
    doc: my_string
    type: string
    outputBinding:
      glob: my_string
      loadContents: true
      outputEval: $(file_string())
EOF

cat <<EOF > input_settings.json
{""anything"":""${anything}""}
EOF

cwltool --user-space-docker-cmd=dx-docker description.cwl input_settings.json > cwl_job_outputs.json

python <<EOF
import os
import json
import subprocess

def sh(cmd, ignore_error=False):
  try:
    print cmd
    subprocess.check_call(cmd, shell=True)
  except subprocess.CalledProcessError as e:
    sys.exit(e.returncode)

with open(""cwl_job_outputs.json"") as f:
  cwloutputs = json.loads(f.read())

def is_output_file(ovalue):
  return 'class' in ovalue and ovalue['class'] == 'File'

def compile_output_generic(oname, ovalue):
  if isinstance(ovalue, list):
    return [ compile_output_generic(oname, x) for x in ovalue ]
  elif isinstance(ovalue, dict):
    if is_output_file(ovalue):
      sh(""emit {} {}"".format(oname, ovalue['path']))
  else:
    if ovalue is not None:
      sh(""emit {} {}"".format(oname, ovalue))

for oname, ovalue in cwloutputs.items():
  compile_output_generic(oname, ovalue)
EOF
","Shell"
"Assay","FDA/precisionFDA","packages/rails/public/tuts/workstations-databases.md",".md","54633","1377","# Tutorial: Collaborative Data Science with Interactive Workstations and Databases

# Introduction

This hands-on tutorial presents design patterns for collaborative data science using the featured precisionFDA pfda-ttyd, pfda-jupyterLab, and guacmole interactive workstation apps, and precisionFDA Databases. Through the development of the tutorial assets, precisionFDA’s powerful capabilities for secure sharing and analysis of FISMA-Moderate authorized data are clearly demonstrated, and users will be empowered to develop their own collaborative regulatory data science use cases.

As always, keep in mind that all of these workstations, notebooks, and databases are strictly within the sole provenance of the user that launched them, and that in compliance with  precisionFDA’s FISMA authorization, the ability to deliver multi-user web services or databases is specifically not supported on precisionFDA. 

Users can however use the power of the cloud to efficiently achieve their collaborative data science and bioinformatics objectives, and use the regulatory-grade platform to share the tools and results with full chain of provenance tracking. For cross-cutting analysis across FDA datasets, users will need to bring their data into the FDA's Intelligent Data Lifecycle Ecosystem (FiDLE).

# Learning Objectives

Through this hands-on tutorial you will:
- Use the precisionFDA command line utility (pfda) to programmatically transfer files to and from precisionFDA and workstations and notebooks.
- Configure ttyd workstations to present multiple web services on ports 8080 using a reverse proxy for secure browser-based access with a rich UI.
- Launch a data analysis ttyd workstation with a local PostgreSQL database server, psql command line database client, pgadmin GUI database client, and RStudio configured with PostgreSQL access.
- Launch a SAS Studio workstation using a pfda-ttyd snapshot.
- Launch a KNIME Analytics Platform guacamole workstation with a local PostgreSQL database server, psql command line database client, and pgadmin GUI database client.
- Launch a precisionFDA Database cluster and access it from the data analysis workstation using psql and pgadmin to configure and install a database on the cluster from DDL and delimited data files.
- Use pgadmin to backup the cluster database to a precisionFDA file.
- Use pgadmin to restore the database backup to the data analysis workstation local database.
- Access the cluster and the workstation local databases from RStudio.
- Launch a jupyterLab workstation with a local PostgreSQL database server, and psql command line database client, and an example Python database analysis notebook.
- Launch a series of epidemiology-related Jupyter notebooks and use papermill to execute a long-running notebook non-interactively,
- Use pgadmin to restore the database backup to the jupyterLab workstation local database.
- Access the cluster and the workstation local databases from a Python notebook.

![alt text](./assets/image-121.png)

# Build Data Analysis Workstation

## Run the pfda-ttyd Featured App

Using the smallest instance type, run the Data Analysis Workstation job using the pfda-ttyd featured app.

![alt text](./assets/image-122.png)

![alt text](./assets/image-123.png)

![alt text](./assets/image-124.png)

Refresh the execution status using the refresh button until the job is running and open the workstation.

![alt text](./assets/image-125.png)

![alt text](./assets/image-126.png)

Use dx-get-timeout and dx-set-timeout to view and set the workstation application time-to-live after which it will self-terminate.
```
dx-set-timeout 2d
dx-get-timeout
0 days 23 hours 59 minutes 56 seconds
```

## Present a simple web server on port 8080

The ttyd and guacamole workstations enable presentation of web services that can be accessed via the job URL port 8080. Let's startup a simple Python-based web server and browse to it.

```bash
python3 -m http.server 8080 &
```

Now copy the URL from your ttyd window and append port 8080 to it(e.g. https://job-gxjjzj80kj2qgp2z6p9x1yvq.dnanexus.cloud:8080) to browse to your web service on the wo rkstation.

![alt text](./assets/image-127.png)

Kill the http.server job to free up port 8080.

## Deploy PostgreSQL, psql, pgadmin, and RStudio

A docker-compose.yml file is configured to launch a local PostgreSQL database, and RStudio, and pgadmin web services that connect to the database. RStudio and pgadmin are accessed using the workstation job URL extended to target the specific service on port 8080 (e.g. https://job-gb1y8jj0kj2xvbggbp3qgv55.dnanexus.cloud:8080/pgadmin/). The postgres-local database container is accessed from the pgadmin and rstudio containers and from the psql command line client on the workstation shell.

The pfda-ttyd and guacamole workstation apps provide port 8080 for access to user deployed web services on the workstation. In order to access multiple different web services (e.g. pgadmin and RStudio) on the single port 8080, a Docker-based reverse proxy is configured using the traefik open source cloud-native application proxy application (https://github.com/traefik/traefik).

Install docker-compose-plugin.

```bash
install-docker-compose.sh
```

Download the traefik-postgres-pgadmin-rstudio-docker-compose.yml file to the workstation.

```bash
pfda download --file-id file-Gb21QGj0Kj2pBzZ9Q3Yqv1Kp-2
```

Start the traefik, postgres-local, rstudio, and pgadmin docker services.

```bash
docker compose -f traefik-postgres-pgadmin-rstudio-docker-compose.yml create
docker compose -f traefik-postgres-pgadmin-rstudio-docker-compose.yml start
```

Install the psql postgres command line client and test the connection to the database.

```bash
apt update
apt install postgresql-client -y < ""/dev/null""

PGPASSWORD=""password"" psql -h localhost -U postgres
```

Ctrl-D to exit psql.

Configure the pgadmin container mounted directory permissions.
```bash
sudo chown -R 5050:5050 db_backups/
sudo chmod ugo+w db_backups/
```

## Access the postgres-local DB from pgadmin

Access the pgadmin web service from your web browser (e.g. https://job-gk0qpfj0kj2ybz63p36by5kj.dnanexus.cl oud:8080/pgadmin).

Add the workstation local database as a new server (e.g. Data Analysis Workstation DB Server) using hostname postgres-local and port 5432, maintenance database postgres, user postgres, and password password.

![alt text](./assets/image-128.png)

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-1.png"" alt=""1""/>
  <img src=""./assets/image-2.png"" alt=""1""/>
</div>

![alt text](./assets/image-3.png)

## Create a new database and tables

Connect to the cluster database from psql in the data analysis workstation shell.

```bash
PGPASSWORD=""password"" psql -h localhost -U postgres
```

Using psql, create a new database.

```sql
-- Database: workstations_and_databases_tutorial_db
CREATE DATABASE workstations_and_databases_tutorial_db
    WITH
    OWNER = postgres
    ENCODING = 'UTF8'
    CONNECTION LIMIT = -1
    IS_TEMPLATE = False;

```

Connect to the new database and create two tables.

```
\c workstations_and_databases_tutorial_db;

psql (9.5.25, server 11.16)
WARNING: psql major version 9.5, server major version 11.
         Some psql features might not work.
SSL connection (protocol: TLSv1.2, cipher: ECDHE-RSA-AES128-GCM-SHA256, bits: 128, compression: off)
You are now connected to database ""workstations_and_databases_tutorial_db"" as user ""root"".
workstations_and_databases_tutorial_db=>

CREATE TABLE public.""PATIENT"" (
    patient_id bigint NOT NULL,
    name character varying,
    gender character varying,
    zip character varying,
    country character varying,
    created_date date
);

CREATE TABLE public.""OBSERVATION"" (
    observation_id bigint NOT NULL,
    patient_id bigint,
    observation_name character varying,
    loinc character varying,
    created_date date
);

\dt
          List of relations
 Schema |    Name     | Type  | Owner 
--------+-------------+-------+-------
 public | OBSERVATION | table | root
 public | PATIENT     | table | root
(2 rows)
```

## Load the cluster database from delimited text files
In the data analysis workstation shell, create a datafiles directory
```bash
mkdir datafiles
cd datafiles
```

Create file `patients.txt` with the following content:
```bash
cat > patients.txt
12345|Fred Foobar|M|94040|USA|2022-10-25
12346|Mary Merry|F|94040|USA|2022-09-24
12347|Barney Rubble|M|94040|USA|2022-08-23
ctrl-D
```

Create file `observations.txt` with the following content:
```bash
cat > observations.txt
9870|12345|Annual check up|66678-4|2022-11-01
9871|12345|Emergency|LG32756-5|2022-11-02
9872|12346|Clinic visit|66678-4|2022-11-03
9873|12347|Lab results|74418-5|2022-11-04
9874|12347|Post-op checkup|65375-8|2022-11-05
ctrl-D
```

## Copy the data into the cluster DB tables

```bash
PGPASSWORD=""password"" psql -h localhost -U postgres
workstations_and_databases_tutorial_db=>
```
In psql:
```sql
\copy public.""PATIENT"" from '/home/dnanexus/datafiles/patients.txt' delimiter '|' NULL ''

\copy public.""OBSERVATION"" from '/home/dnanexus/datafiles/observations.txt' delimiter '|' NULL ''

select * from public.""PATIENT"";
patient_id |     name      | gender |  zip  | country | created_date 
------------+---------------+--------+-------+---------+--------------
      12345 | Fred Foobar   | M      | 94040 | USA     | 2022-10-25
      12346 | Mary Merry    | F      | 94040 | USA     | 2022-09-24
      12347 | Barney Rubble | M      | 94040 | USA     | 2022-08-23

select * from public.""OBSERVATION"";
observation_id | patient_id | observation_name |   loinc   |created_date 
----------------+------------+------------------+-----------+-----------
           9870 |      12345 | Annual check up  | 66678-4   | 2022-11-01
           9871 |      12345 | Emergency        | LG32756-5 | 2022-11-02
           9872 |      12346 | Clinic visit     | 66678-4   | 2022-11-03
           9873 |      12347 | Lab results      | 74418-5   | 2022-11-04
           9874 |      12347 | Post-op checkup  | 65375-8   | 2022-11-05
```
Observe the new tables and data in the pgadmin Workstations and Databases Tutorial server connection.

## Share Files Between Workstation FS and PostgreSQL Docker FS

Since pgadmin is running in a Docker container on the workstation, we are going to have to connect to the pgadmin container shell and copy files we want to share between the workstation and pgadmin to and from the mount point shared by the container and the workstation (i.e. /home/dnanexus/db_backups).

When performing a database backup in pgadmin, save the file in /home/dnanexus/db_backups.

![pgadmin backup](./assets/image-4.png)

The backup file is accessible in workstation /home/dnanexus/db_backups. Files can be placed in this directory for access by pgadmin.
```bash
root@job-Gb21Kfj0Kj2jxbjv0pzxQx8Y:~# ls /home/dnanexus/db_backups/
backup.sql  foo.txt  moo.txt
```

## Access the postgres-local DB from rstudio

Access the RStudio web service from your web browser (e.g. https://job-gb1y8jj0kj2xvbggbp3qgv55.dnanexus.cloud:8080/rstudio/).

![rstudio console](./assets/image-5.png)

Install the RPostgres packages and test access to the workstation local database. In the R Studio console:
```r
# Install RPostgres
install.packages(""RPostgres"")

# Connect to local database
library(DBI)
con <- DBI::dbConnect(
    RPostgres::Postgres(),
    host = ""172.17.0.1"", 
    port = 5432, dbname = ""postgres"",
    user = ""postgres"", password = ""password""
)
# List the tables in db postgres	
dbListTables(con)
```
Since there are no tables in the postgres database, the response is character(0). Let’s add two tables using psql and run the same R query.
Using the psql command line client in the data analysis workstation shell:
```sql
PGPASSWORD=""password"" psql -h localhost -U postgres

CREATE TABLE public.""PATIENT"" (
    patient_id bigint NOT NULL,
    name character varying,
    gender character varying,
    zip character varying,
    country character varying,
    created_date date
);

CREATE TABLE public.""OBSERVATION"" (
    observation_id bigint NOT NULL,
    patient_id bigint,
    observation_name character varying,
    loinc character varying,
    created_date date
);
```
The same query in the RStudio console now shows the two new tables.
```sql
dbListTables(con)
[1] ""PATIENT""     ""OBSERVATION""
```
Let’s drop the tables since we will be populating this database from a backup a later stage in this tutorial. Using the psql command line client in the data analysis workstation shell:
```sql
DROP TABLE public.""PATIENT""
DROP TABLE public.""OBSERVATION”
```
Control-D to exit psql.


## Share Files Between Workstation FS and RStudio Docker FS

The rstudio container shares the /home/dnanexus mount point with the workstation filesystem so it straightforward to access the workstation filesystem from within RStudio. Simply set the path in the RStudio file browser to /home/rstudio/dnanexus.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-6.png"" alt=""rstudio environments panel"" />
  <img src=""./assets/image-7.png"" alt=""rstudio files list"" />
</div>

## PostgreSQL Tips
### Force drop connections
If you need to drop a database, you’ll need to close all the sessions connected to it using the following postgreSQL code:

```sql
SELECT 
    pg_terminate_backend(pid) 
FROM 
    pg_stat_activity 
WHERE 
    -- don't kill my own connection!
    pid <> pg_backend_pid()
    -- don't kill the connections to other databases
    AND datname = 'ehr_data'
    ;
```
### Quick queries for estimated row counts
To present the estimated row count for each table:
```sql
SELECT relname, TO_CHAR(n_live_tup, 'fmG999G999G999')
  FROM pg_stat_user_tables 
ORDER BY relname ASC;
Alternatively:
SELECT
  pgClass.relname   AS tableName,
  TO_CHAR(pgClass.reltuples, 'fmG999G999G999') AS rowCount
FROM
  pg_class pgClass
INNER JOIN
  pg_namespace pgNamespace ON (pgNamespace.oid = pgClass.relnamespace)
WHERE
  pgNamespace.nspname NOT IN ('pg_catalog', 'information_schema') AND
  pgClass.relkind='r'
ORDER BY pgClass.reltuples ASC
```
To present to total row count for all tables:
```sql
SELECT TO_CHAR(SUM(n_live_tup), 'fmG999G999G999')
  FROM pg_stat_user_tables
```

# Build SAS Studio Workstation

## SAS Studio Workstation Space
You will need to be a member of the SAS Studio Workstation Space (https://precision.fda.gov/spaces/537) in order to use the FDA's enterprise SAS license that is incorporated into a SAS Studio Snapshot file located in the Space (e.g. SASStudio_FDA.snapshot). FDA users that wish to use SAS Studio on precisionFDA should contact precisionFDA Support to request membership in the SAS Studio Workstation Space.
## Run the pfda-ttyd Featured App with SAS Studio Snapshot
Follow the procedure in the Run the pfda-ttyd Featured App section of this tutorial, selecting the execution Context as SAS Studio Workstation Space and specifying SASStudio_FDA.snapshot in the Snapshot inputs section.

<div style=""display: grid; grid-template-columns: 1.5fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-10.png"" alt=""pfda app run"">
  <img src=""./assets/image-9.png"" alt=""slect file modal"">
</div>
 
Open the workstation once it is running, download the sample SAS code, and start SAS Studio.
```bash
pfda download -space-id 537 -folder-id 8331897 
mv *.sas /home/sasuser/
mv *.xpt /home/sasuser/
mkdir /home/sasuser/SAS_Datasets
chown sasuser /home/sasuser/SAS_Datasets/
./sasstudio.sh start
```
Copy the ttyd job URL and append :8080/SASStudio to it (e.g. https://job-gb95v8j0x42xgk0k36v02qgy.dnanexus.cloud:8080/SASStudio) to open SAS Studio and login with user ""sasuser"" password ""sas"".

![sas studio](./assets/image-11.png)

Note that the home folder in SAS Studio is mapped to /home/sasuser on the workstation local filesystem.
```bash
ls /home/sasuser/
SAS_histograms.sas  SAS_prog_structure.sas  SAS_sort_data_sets.sas  SAS_write_data.sas  sasuser.v94
```
Open up one of the SAS example files and run it.

![alt text](./assets/image-12.png)

![alt text](./assets/image-13.png)

## Open an SDTM File in SAS Studio

Open  the Open_SDTM.sas app and run it to read a SDTM file in xport format into SAS Studio then select View Column Labels to explore the sample clinical data.

# Build KNIME Workstation
This tutorial demonstrates installation of the KNIME Analysis Platform as a desktop client application on a Guacamole workstation. The KNIME tutorial workflow:
- Creates a database on the local PostgreSQL server
- Downloads data files from a designated precisionFDA folder to the local filesystem
- ETLs the data from the local filesystem into the database
- Performs pivot analysis and geomap presentation of the data
- Uploads analysis results to precisionFDA My Home area
This provides KNIME examples for connecting to precisionFDA, running shell scripts, and executing DB operations using SQL.

## Run the guacamole Featured App
Using the a baseline-4 instance type, run the KNIME Workstation job using the guacamole featured app. Specify a maximum session length of 5y.

![alt text](./assets/image-14.png)

![alt text](./assets/image-15.png)

![alt text](./assets/image-16.png)

Refresh the execution status using the button until the job is running and open the workstation. Note that it takes a few minutes for the guacamole workstation to come up after going into running status.

![alt text](./assets/image-17.png)

Allow the desktop to see text and images copied to the clipboard and login with user “guacuser” password “test”.

![alt text](./assets/image-18.png)

Use the default panel configuration when first entering the Linux desktop environment.

![alt text](./assets/image-19.png)

Open a terminal emulator window, check the OS version. Note that I needed to use ctrl-shift-v to paste from my laptop to the workstation.

```bash
lsb_release -a
```

![alt text](./assets/image-20.png)

Adjust environment variables to enable interaction with file on precisionFDA.
```bash
unset DX_WORKSPACE_ID
dx cd $DX_PROJECT_CONTEXT_ID
```
Use dx-get-timeout and dx-set-timeout to view and set the workstation application time-to-live after which it will self-terminate.
```bash
dx-set-timeout 5y
dx-get-timeout
```
## Install Additional Utilities and Dependencies
```bash
# Browser, tree, dos2unix
sudo apt update
sudo apt-get install -y chromium-browser < ""/dev/null""
sudo apt install -y tree < ""/dev/null""
sudo apt install -y dos2unix < ""/dev/null""

# KNIME Dependencies
sudo apt install -y libwebkit2gtk-4.0-37 < ""/dev/null""
sudo apt install -y libgtk-3-dev < ""/dev/null""
```
## Install and Start KNIME
Install start KNIME and accept the default Workspace directory. Accept the offer to help improve KNIME since that will enable some of KNIME’s wizard capabilities.
```bash
# KNIME
cd ~
mkdir -p knime
cd knime
wget https://download.knime.org/analytics-platform/linux/knime-latest-linux.gtk.x86_64.tar.gz
tar xvf knime-latest-linux.gtk.x86_64.tar.gz
cd
./knime/knime_4.7.3/knime &
```

![alt text](./assets/image-21.png)

![alt text](./assets/image-22.png)

## Install US City Geo Data Using the Chromium Browser
Start the Chromium Browser and download simplemaps_uscities_basicv1.75.zip from https://simplemaps.com/data/us-cities.

![alt text](./assets/image-23.png)

![alt text](./assets/image-24.png)

Leaving the previous terminal for KNIME to run in the background, start a new terminal window and set the key variable to the cli authentication token.
```bash
key=""<copied key>""

cd
mv Downloads/simplemaps_uscities_basicv1.76.zip .
unzip simplemaps_uscities_basicv1.76.zip 
rm license.txt uscities.xlsx 
mv uscities.csv knime-workspace/
rm simplemaps_uscities_basicv1.76.zip 
```
## Deploy Local PostgreSQL DB Server and CLI
Deploy a local PostgreSQL DB server on the Data Analysis workstation. Map the postgres port from the container to the workstation (host) OS. Note that there is already a dockerized PostgreSQL DB used by Guacamole so this will be a second instance.
```bash
# Install and start a second postgreSQL server (and psql CLI)
# Note there is already a postgres docker container that is used by guacamole
sudo docker run --name postgres2 -e POSTGRES_PASSWORD=password -p 5432:5432 -d postgres:13.4-buster

# Install postgres client
sudo apt update
sudo apt install -y postgresql-client < ""/dev/null""

# Connect to local postgres db
PGPASSWORD=""password"" psql -h localhost -U postgres -c '\l'
```
## Deploy pgadmin and Connect to the Local DB
pgadmin4 is deployed in a Docker container mapping the pgadmin web service port 80 to workstation port 8080. A directory is created on the workstation with the appropriate ownership to enable database backup files created in pgadmin to be copied from the container to the workstation.
```bash
# Create and configure host directory for backup files from pgadmin
cd
mkdir /home/dnanexus/db_backups
sudo chown -R 5050:5050 db_backups/
sudo chmod ugo+w db_backups/

# Run pgadmin
sudo docker run --name pgadmin -it -v /home/dnanexus/db_backups:/home/dnanexus/db_backups -p 8080:80 -e 'PGADMIN_DEFAULT_EMAIL=user@domain.com' -e 'PGADMIN_DEFAULT_PASSWORD=password' -d dpage/pgadmin4
```
Access the pgadmin web service from your web browser (e.g. https://job-gk0qpfj0kj2ybz63p36by5kj.dnanexus.cloud:8080) with the specified credentials (user@domain.com, password).

![alt text](./assets/image-25.png)

To connect pgadmin in the container to the postgres database server port on the host, first obtain the docker0 interface IP address. This will be used in place of localhost in pgadmin (since localhost in pgadmin refers to the container local host). Add the workstation local database as a new server (data analysis workstation db) using the docker0 address (user postgres, password password).
```
ip addr show docker0

2: docker0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc noqueue state UP group default 
    link/ether 02:42:cd:c8:f1:0e brd ff:ff:ff:ff:ff:ff
    inet 172.17.0.1/16 brd 172.17.255.255 scope global docker0
```

<div style=""display: grid; grid-template-columns: 1.2fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-26.png"" alt=""pgadmin connect"">
  <img src=""./assets/image-27.png"" alt=""pgadmin database list"">
</div>

## Share Files Between Workstation FS and PostgreSQL Docker FS
Since pgadmin is running in a Docker container on the workstation, we are going to have to connect to the pgadmin container shell and copy files we want to share with pgadmin to the mount point shared by the container and the workstation (i.e. /home/dnanexus/db_backups). On a KNIME workstation terminal:

Connect to the shell in the pgadmin container.
```bash
sudo docker exec -it pgadmin sh
/pgadmin4 $
```
Copy files between the pgadmin backup directory to the container-host shared volume.
```bash
ls /var/lib/pgadmin/storage/user_domain.com
ls /home/dnanexus/db_backups
```
Control-D to exit the pgadmin container.
## Add Shell and SQL Scripts for Use With KNIME

```bash
# Shell scripts for pfda cli upload-file, download, and ls
#
cd
pfda download -key $key --file-id file-GPf54j00Fk5xb2zgbKxV0JQ4-1
chmod ugo+x pfda-download-runner
sudo mv pfda-download-runner /usr/bin

pfda download -key $key --file-id file-GPgQZF00Fk5zxYX1QqY1v6XP-1
chmod ugo+x pfda-upload-runner
sudo mv pfda-upload-runner /usr/bin
pfda download -key $key --file-id file-GPf54j80Fk5x0BY71qvBB3Jf-1
chmod ugo+x pfda-ls-runner
sudo mv pfda-ls-runner /usr/bin

# Shell script for executing SQL from files using psql client
#
pfda download -key $key --file-id file-GPf54j00Fk5bVVK3BXK22p41-1
chmod ugo+x sql-runner
sudo mv sql-runner /usr/bin

# Shell script for ETL of data from csv.gz files into DB
#
pfda download -key $key --file-id file-GPgPGJ00Fk5q805K278f7V3G-1
chmod ugo+x EHR_Data_ETL.bash
sudo mv EHR_Data_ETL.bash /usr/bin

# DDL for tutorial DB
#
pfda download -key $key --file-id file-GPgKJ4j0Kj2k48B1yFGB117b-1
mv KNIME_Tutorial_EHR_Data_TableDDL_No2ndIndex.sql knime-workspace/
```

## Download the KNIME Workflow
```bash
pfda download -key $key --file-id file-GPgy0bj0Fk5f7PGJf9vVJQPB-1
mv KNIME-Tutorial-20230217.knwf ~/knime-workspace/
```
## Run the KNIME Data Transformation Workflow
Restart KNIME to pickup the newly added files.

<div style=""display: grid; grid-template-columns: 1.2fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-28.png"" alt=""pgadmin database list"">
</div>

## Import and Open the Workflow and Update Dependencies

![alt text](./assets/image-29.png)

![alt text](./assets/image-30.png)

![alt text](./assets/image-31.png)

![alt text](./assets/image-32.png)

![alt text](./assets/image-33.png)

Ignore the warnings and errors.

![alt text](./assets/image-34.png)

![alt text](./assets/image-35.png)

## Set the pFDA CLI Auth Token and Data Folder Variables

Configure the pfdacli-access-key String Widget to set the pfda CLI authentication token.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-36.png"" alt=""1"">
  <img src=""./assets/image-37.png"" alt=""1"">
</div>

(Temporary workaround until the precisionFDA CLI is updated to properly perform ls on folders in the Everyone scope). 

In precisionFDA, navigate to the KNIME Workstation Tutorial / Datafiles folder in the My Home Everyone context and select and download all six files to your local machine.  Then, My Home / Files / Add Folder calling it “ KNIME sample data”, (or whatever you’d like since we’ll be referencing it by folder ID not name). Click into the new folder, and Add Files to re-upload the six files just downloaded. Copy the folder_id from the URL.

In KNIME, Configure the datasource-folderid String Widget to set the folder ID.

(Steps once the pFDA CLI is updated; ignore for now) Navigate to the KNIME Workstation Tutorial folder in the My Home Everyone context and copy the folder ID for the Datafiles folder from the browser URL. Configure the datasource-folderid String Widget to set the folder ID.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-38.png"" alt=""1"">
  <img src=""./assets/image-39.png"" alt=""1"">
</div>

### Create the DB
Execute the Create ehr_data DB tables with primary keys node.

<div style=""display: grid; grid-template-columns: 1.5fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-40.png"" alt=""1"">
  <img src=""./assets/image-41.png"" alt=""1"">
</div>

Once the node shows green, refresh the KNIME Tutorial DB Server in pgadmin to see the newly created knime_tutorial_ehr_data DB.

![alt text](./assets/image-42.png)

### Download the Data from precisionFDA Folder
Execute the Ingest compressed EHR Data files from precisionFDA to local FS node.

<div style=""display: grid; grid-template-columns: 1.5fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-43.png"" alt=""1"">
  <img src=""./assets/image-44.png"" alt=""1"">
</div>

Once the node shows green, check the downloaded files in the newly created EHR_Data directory.

![alt text](./assets/image-45.png)

### ETL the Data into the DB
Execute the ETL EHR Data from files into a PostgreSQL DB node to ETL the data from the compressed csv files into the DB.

<div style=""display: grid; grid-template-columns: 3fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-46.png"" alt=""1"">
  <img src=""./assets/image-47.png"" alt=""1"">
</div>

Once the shows green, check the DB for content in pgadmin.

<div style=""display: grid; grid-template-columns: 1fr 2fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-48.png"" alt=""1"">
  <img src=""./assets/image-49.png"" alt=""1"">
</div>

### Analyze the Data and Create Reports
Execute the Dashboard node to and when it shows green, inspect the data table and geomap in the interactive node view .

<div style=""display: grid; grid-template-columns: 1fr 1.4fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-50.png"" alt=""1"">
  <img src=""./assets/image-51.png"" alt=""1"">
</div>

### Publish the Reports to precisionFDA My Home
Execute the Publish dashboards and reports to precisionFDA node to upload the reports to your My Home files.

![alt text](./assets/image-52.png)

![alt text](./assets/image-53.png)

# Deploy a precisionFDA Database Cluster

PrecisionFDA provides AWS Aurora RDS database clusters that are accessible from Apps and Workstations. You will need to request DB Cluster access for your precisionFDA username in order to use this capability.
## Create the Database
Select the Databases tab in My Home and click the Create Database button.
![alt text](./assets/image-54.png)

Create a “Workstations and Databases Tutorial” database, “password”, PostgreSQL 11.16 on the smallest available database instance type, and click the Submit button.

![alt text](./assets/image-55.png)

Refresh the database status using the refresh button until the database is available.

![alt text](./assets/image-56.png)

Click on the Workstations and Databases Tutorial database to open the detail page and copy the host endpoint URL.

![alt text](./assets/image-57.png)

## Connect to the cluster DB from pgadmin

In the pgadmin web service, add a new server for the Workstations and Databases Tutorial DB cluster using the host endpoint,  user root, and the password specified when the database was created.

![alt text](./assets/image-58.png)

Note that we now have connections to both the local database on the data analysis workstation, and the cluster database.

![alt text](./assets/image-59.png)

## Create a new database and tables
Connect to the cluster database from psql in the data analysis workstation shell.

```bash
PGPASSWORD=""password"" psql --host=dbcluster-gbfqzqq0kj2jxgj109354vjj.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com --username=root -d postgres
```

Using psql, create a new database.

```sql
-- Database: workstations_and_databases_tutorial_db
CREATE DATABASE workstations_and_databases_tutorial_db
    WITH
    OWNER = root
    ENCODING = 'UTF8'
    CONNECTION LIMIT = -1
    IS_TEMPLATE = False;
```

Connect to the new database and create two tables.

```sql
\c workstations_and_databases_tutorial_db;

psql (9.5.25, server 11.16)
WARNING: psql major version 9.5, server major version 11.
         Some psql features might not work.
SSL connection (protocol: TLSv1.2, cipher: ECDHE-RSA-AES128-GCM-SHA256, bits: 128, compression: off)
You are now connected to database ""workstations_and_databases_tutorial_db"" as user ""root"".
workstations_and_databases_tutorial_db=>

CREATE TABLE public.""PATIENT"" (
    patient_id bigint NOT NULL,
    name character varying,
    gender character varying,
    zip character varying,
    country character varying,
    created_date date
);

CREATE TABLE public.""OBSERVATION"" (
    observation_id bigint NOT NULL,
    patient_id bigint,
    observation_name character varying,
    loinc character varying,
    created_date date
);

\dt
          List of relations
 Schema |    Name     | Type  | Owner 
--------+-------------+-------+-------
 public | OBSERVATION | table | root
 public | PATIENT     | table | root
(2 rows)

```
### Load the cluster database from delimited text files
Although the workflow illustrated here may seem over-engineered for loading two data files, the techniques presented here were used to reliably and efficiently transfer tens of thousands of files and 15+ TB of data to precisionFDA.

In the data analysis workstation shell, create a datafiles directory
```bash
mkdir datafiles
```
#### Create and upload delimited data files
On your local client (i.e. laptop), create file patients.txt with the following content:
```
12345|Fred Foobar|M|94040|USA|2022-10-25
12346|Mary Merry|F|94040|USA|2022-09-24
12347|Barney Rubble|M|94040|USA|2022-08-23
```
Create file observations.txt with the following content:
```
9870|12345|Annual check up|66678-4|2022-11-01
9871|12345|Emergency|LG32756-5|2022-11-02
9872|12346|Clinic visit|66678-4|2022-11-03
9873|12347|Lab results|74418-5|2022-11-04
9874|12347|Post-op checkup|65375-8|2022-11-05
```
In My Home / Files use the Add Files button to upload the two files to your private area.

![alt text](./assets/image-60.png)

![alt text](./assets/image-61.png)

#### Create and upload a manifest of data file IDs
Click into patients.txt and observations.txt details pages and copy their file IDs into a file named manifest.txt file on your local client.

![alt text](./assets/image-62.png)

![alt text](./assets/image-63.png)

```
-- manifest.txt
file-GK0fqQ80Kj2zkg3kKgF8Bg9G-1
file-GK0fqGQ0Kj2gBZjxF24493PY-1
```
Use the Add Files button to upload the manifest.txt file to your private area. Click into the details for the uploaded file and copy the file ID.

![alt text](./assets/image-64.png)

#### Download the files in the manifest to the Data Analysis Workstation
Using pfda CLI in the data analysis workstation shell, download the manifest.txt file to the workstation filesystem.
```bash
pfda download -file-id file-GK0fzGQ0Kj2pj77v7xbZbXYG-1

ls -l
-rw-r--r-- 1 root root 66 Nov 26 01:58 manifest.txt
```
### Iterate through manifest and download data files
In the data analysis workstation shell install and run dos2unix on the manifest.txt file to ensure there are no cross-OS end-of-line issues.

```bash
cd/datafiles
apt install dos2unix
dos2unix manifest.txt

for FILE in $(cat manifest.txt); do pfda download -key $key -file-id $FILE; done

ls
manifest.txt  observations.txt  patients.txt
```

### Copy the data into the cluster DB tables
#### Connect to the workstations_and_databases_tutorial_db cluster database
Using the database host endpoint, connect to the workstations_and_databases_tutorial_db cluster database using psql on the data analysis workstation:

![alt text](./assets/image-65.png)

```
PGPASSWORD=""password"" psql --host=dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com --username=root -d workstations_and_databases_tutorial_db

workstations_and_databases_tutorial_db=>
```

#### Copy the patients and observations data into the cluster DB
In psql:
```sql
\copy public.""PATIENT"" from '/home/dnanexus/datafiles/patients.txt' delimiter '|' NULL ''

\copy public.""OBSERVATION"" from '/home/dnanexus/datafiles/observations.txt' delimiter '|' NULL ''

select * from public.""PATIENT"";
patient_id |     name      | gender |  zip  | country | created_date 
------------+---------------+--------+-------+---------+--------------
      12345 | Fred Foobar   | M      | 94040 | USA     | 2022-10-25
      12346 | Mary Merry    | F      | 94040 | USA     | 2022-09-24
      12347 | Barney Rubble | M      | 94040 | USA     | 2022-08-23

select * from public.""OBSERVATION"";
observation_id | patient_id | observation_name |   loinc   |created_date 
----------------+------------+------------------+-----------+-----------
           9870 |      12345 | Annual check up  | 66678-4   | 2022-11-01
           9871 |      12345 | Emergency        | LG32756-5 | 2022-11-02
           9872 |      12346 | Clinic visit     | 66678-4   | 2022-11-03
           9873 |      12347 | Lab results      | 74418-5   | 2022-11-04
           9874 |      12347 | Post-op checkup  | 65375-8   | 2022-11-05
```
Observe the new tables and data in the pgadmin Workstations and Databases Tutorial server connection.

![alt text](./assets/image-66.png)

### Connect RStudio to the cluster DB
In the RStudio console:
```r
library(DBI)
con <- DBI::dbConnect(
    RPostgres::Postgres(),
    host = ""dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com"", 
	port = 5432, dbname = ""workstations_and_databases_tutorial_db"",
    user = ""root"", password = ""password""
)
dbListTables(con)
[1] ""OBSERVATION"" ""PATIENT""   
```

# Backup the cluster DB and restore it to local DBs
## Add a postgres role to the cluster DB

Right-click Login/Group Roles  in the Workstations and Databases Tutorial server connection in pgadmin and add a postgres role. 

![alt text](./assets/image-67.png)

![alt text](./assets/image-68.png)

Right-click the postgres role and select properties, and add the to the root group with admin privileges in the Membership tab.

![alt text](./assets/image-69.png)

## Backup the cluster DB using pgadmin

Select the workstations_and_databases_tutorial_db database in the Workstations and Databases Tutorial server connection in pgadmin and right-click to backup the database. 

![alt text](./assets/image-70.png)

Specify a backup filename (e.g. workstations_and_databases_tutorial_db-2022-11-25.tar), format as Tar, assign role name postgres and set all the Data/Objects Do not save options..

![alt text](./assets/image-71.png)

![alt text](./assets/image-72.png)

![alt text](./assets/image-73.png)

## Copy the backup file from the pgadmin container to the workstation filesystem

Since pgadmin is running in a Docker container on the data analysis workstation, we are going to have to connect to the pgadmin container shell and copy the backup file to the mount point shared by the container and the workstation (i.e. /home/dnanexus/db_backups). On the data analysis workstation:

Connect to the shell in the pgadmin container.
```bash
docker exec -it pgadmin sh
/pgadmin4 $
```
Copy the backup file from the pgadmin backup directory to the container-host shared volume.
```bash
cd /var/lib/pgadmin/storage/user_domain.com
workstations_and_databases_tutorial_db-2022-11-25

cp /var/lib/pgadmin/storage/user_domain.com/workstations_and_databases_tutorial_db-2022-11-25.tar /home/dnanexus/db_backups
```
Control-D to exit the container shell and verify the presence of the backup file on the workstation in the container-host shared mount point.
```
ls db_backups/
workstations_and_databases_tutorial_db-2022-11-25
```
## Upload the backup file to precisionFDA
Under My Home Assets, click on the How to create assets button to find the button to generate the temporary authorization key that you’ll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-74.png"" alt=""1"">
  <div>
    <img src=""./assets/image-75.png"" alt=""1"">
    <img src=""./assets/image-76.png"" alt=""1"">
  </div>
</div>

On the data analysis workstation shell:

```bash
key=""...""

pfda upload-file -key $key -file ~/db_backups/workstations_and_databases_tutorial_db-2022-11-25.tar
```

![alt text](./assets/image-77.png)

## Restore the backup to the data analysis workstation local DB

Using the pgadmin connection to the data analysis workstation db, create a new database workstations_and_databases_tutorial_db, owner postgres.

![alt text](./assets/image-78.png)

![alt text](./assets/image-79.png)

Right-click on the new database on the data analysis and workstation db server connection and restore the backup to the local server (from the file in the pgadmin container), using custom or tar format, and the postgres role name.

![alt text](./assets/image-80.png)

![alt text](./assets/image-81.png)

![alt text](./assets/image-82.png)

Select the contents of the restored PATIENT and OBSERVATION tables.

![alt text](./assets/image-83.png)

## Restore the backup to the data analysis notebook local DB

Under My Home Assets, click on the How to create assets button to find the button to generate the temporary authorization key that you’ll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-84.png"" alt=""1"">
  <div>
    <img src=""./assets/image-85.png"" alt=""1"">
    <img src=""./assets/image-86.png"" alt=""1"">
  </div>
</div>

Click into the detail page for the backup file and copy the file ID.

![alt text](./assets/image-87.png)

In a terminal window in the data analysis jupyterLab notebook, download the backup file using its file ID as copied in the step above:

```bash
mkdir ~/db_backups
cd db_backups
key=""...""
pfda download -key $key -file-id file-GK172180Kj2x743JPy4KGbf9-1
```

![alt text](./assets/image-88.png)

In psql connected to the local host, create a new database workstations_and_databases_tutorial_db, and a new user root.

```sql
psql -U postgres -h 127.0.0.1
psql (15.1 (Ubuntu 15.1-1.pgdg18.04+1))
postgres=#

CREATE USER root;

CREATE DATABASE workstations_and_databases_tutorial_db
    WITH
    OWNER = postgres
    ENCODING = 'UTF8'
    CONNECTION LIMIT = -1
    IS_TEMPLATE = False;
```

Ctrl-D to exit psql and use restore the database from the backup file.

```bash
pg_restore --dbname=workstations_and_databases_tutorial_db --verbose ~/db_backups/workstations_and_databases_tutorial_db-2022-11-25.tar -U postgres
```

You can ignore the errors associated with the root role not existing and use the Python notebook to select the contents from the restored database. We can observe the same results from newly restored database as from the cluster database that was the backup source. In a notebook Python code block:

```python
import psycopg2
conn = psycopg2.connect(""dbname='workstations_and_databases_tutorial_db' user='postgres' host='127.0.0.1'"")

cur = conn.cursor()
cur.execute('SELECT * FROM public.""PATIENT"" limit 10')
# fetch results
rows = cur.fetchall()
# iterate through results
for row in rows:
    print (""PATIENT"", row[0], row[1], row[2])

cur.execute('SELECT * FROM public.""OBSERVATION"" limit 10')
# fetch results
rows = cur.fetchall()
# iterate through results
for row in rows:
    print (""OBSERVATION"", row[0], row[1], row[2])
```

![alt text](./assets/image-89.png)

## Stop or Terminate the Database Cluster

In My Home / Databases, select the database for action and either Stop or Terminate the database using the Action dropdown menu. If your data is already stored on precisionFDA and can be readily reconstituted into a new database, then select Terminate. If your database is a work in progress and you’d like to keep it intact while not using it overnight, or the weekend, then select Stop. 

![alt text](./assets/image-90.png)




























# Build Data Analysis Notebook
## Run the pfda-jupyterLab Featured App
Using the smallest instance type, run the Data Analysis Notebook job specifying PYTHON_R.

![alt text](./assets/image-91.png)

![alt text](./assets/image-92.png)

![alt text](./assets/image-93.png)

Refresh the execution status using the button until the job is running and open the workstation. It may take a few minutes after the job is running for the notebook to open.

![alt text](./assets/image-94.png)

![alt text](./assets/image-95.png)

Adjust the remaining time-to-live for the notebook using the Update duration button.

![alt text](./assets/image-96.png)

## Download and Install the pfda CLI
Copy the link for the current version of the Linux pFDA CLI from the CLI Docs page https://precision.fda.gov/docs/cli. Open a Terminal in the Data Analysis notebook and download unpack the CLI.
```bash
-- Install pfda CLI
wget https://pfda-production-static-files.s3.amazonaws.com/cli/pfda-linux-2.1.2.tar.gz
tar xf pfda-linux-2.1.2.tar.gz 
mv pfda /usr/bin/
pfda –-version
```
Retrieve a CLI authorization key. Under My Home Assets, click on the How to create assets button to find links to the precisionFDA CLI, and the button to generate the temporary authorization key that you’ll use with the CLI.
 
<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-97.png"" alt=""1"">
  <div>
    <img src=""./assets/image-98.png"" alt=""1"">
    <img src=""./assets/image-99.png"" alt=""1"">
  </div>
</div>

![alt text](./assets/image-100.png)
```
pfda download -key Mk5VTENlTS83R2I1U3dXQkRnWEhzamJvVVFrTVZrOHA4STI4OTM0MitRWnNqZWVBSVRndlBicG1IUU9PeStjbTBLRXUzNW5rMmMrMjV6bGVhSnVTUlhDd2dEOVhRdUZvdmE1a29pcHdWWS92RGNyN1ljTlZtdnNjbE15RXVyVnl1Zkd3UUVxODZpYzNsWi9JWVVBcEw3VE5uaXdMSTdYNHNWVFJpZGJYdXlVa2hsRFFnR2dDc1JISzhuYWxla2JXLS1zVjRhSVBCWFdaRXBFWnBsMXNtSXB3PT0=--e19f53de7644d63dd3898717896a88bd0a383db6 -file-id file-GJv1zKj0Kj2vzFP4Gg475ZyX-1
```
Upload a file from the workstation local filesystem to precisionFDA (note the key is cached).
```
mv foo2.txt moo2.txt
pfda upload-file -file moo2.txt
```
![alt text](./assets/image-101.png)

## Deploy Local PostgreSQL DB Server
Deploy a local PostgreSQL DB server on the Data Analysis workstation. Map the postgres port from the container to the workstation (host) OS. In the notebook terminal:
```bash
sh -c 'echo ""deb http://apt.postgresql.org/pub/repos/apt $(lsb_release -cs)-pgdg main"" > /etc/apt/sources.list.d/pgdg.list'
wget --quiet -O - https://www.postgresql.org/media/keys/ACCC4CF8.asc | apt-key add -
apt-get update
apt-get -y install postgresql < ""/dev/null""
```
Configure postgres to enable password-free local login.  Find pg_hba.conf in /etc/postgresql/ and configure with permissive permissions. 
```
find /etc/postgresql -name pg_hba.conf | xargs sed -i 's/peer/trust/'
find /etc/postgresql -name pg_hba.conf | xargs sed -i 's/md5/trust/'
```
Start the local PostgreSQL DB server on the Data Analysis notebook.
```
/etc/init.d/postgresql start
 * Starting PostgreSQL 15 database server                                                                            [ OK ] 
/etc/init.d/postgresql status
15/main (port 5432): online

psql -U postgres -h 127.0.0.1
psql (15.1 (Ubuntu 15.1-1.pgdg18.04+1))
SSL connection (protocol: TLSv1.3, cipher: TLS_AES_256_GCM_SHA384, compression: off)
Type ""help"" for help.

postgres=#
```
## Create a Table with some data in the Local DB
In psql in the notebook terminal create a table, then copy two records from stdin into the table display them.
```sql
CREATE TABLE public.""PATIENT"" (
    patient_id int NOT NULL,
    name character varying,
    gender character varying,
    zip character varying,
    country character varying,
    created_date date
);

COPY public.""PATIENT"" (patient_id, name, gender, zip, country, created_date) from stdin;
```
Add these two records when prompted from the above COPY, and terminate with the \. record.
```
12345	foo	m	94040	usa	2022-11-25
54321	bar	m	94040	usa	2022-11-25
\.

select * from public.""PATIENT"";
 patient_id | name | gender |  zip  | country | created_date 
------------+------+--------+-------+---------+--------------
      12345 | foo  | m      | 94040 | usa     | 2022-11-25
      54321 | bar  | m      | 94040 | usa     | 2022-11-25
(2 rows)
```
Create a Notebook and Connect to the Local DB
In the notebook terminal, install the psycopg2 binary.
```
pip install psycopg2-binary
```
Open a Python 3 notebook.
![alt text](./assets/image-103.png)


And enter the following code:

```py
import psycopg2
conn = psycopg2.connect(""dbname='postgres' user='postgres' host='127.0.0.1'"")
cur = conn.cursor()
cur.execute('SELECT * FROM public.""PATIENT"" limit 10')
# fetch results
rows = cur.fetchall()
# iterate through results
for row in rows:
    print (""   "", row)
```

## Connect to the Cluster DB
Open a Python 3 notebook.

![alt text](./assets/image-103.png)

And enter the following code:
```py
import psycopg2

conn = psycopg2.connect(""dbname='workstations_and_databases_tutorial_db' user='root' host='dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com' password='password'"")

cur = conn.cursor()
cur.execute('SELECT * FROM public.""PATIENT"" limit 10')
# fetch results
rows = cur.fetchall()
# iterate through results
for row in rows:
    print (""PATIENT"", row[0], row[1], row[2])

cur.execute('SELECT * FROM public.""OBSERVATION"" limit 10')
# fetch results
rows = cur.fetchall()
# iterate through results
for row in rows:
    print (""OBSERVATION"", row[0], row[1], row[2])

PATIENT 12345 Fred Foobar M
PATIENT 12346 Mary Merry F
PATIENT 12347 Barney Rubble M
OBSERVATION 9870 12345 Annual check up
OBSERVATION 9871 12345 Emergency
OBSERVATION 9872 12346 Clinic visit
OBSERVATION 9873 12347 Lab results
OBSERVATION 9874 12347 Post-op checkup
```
## Load a Complete Notebook from a Snapshot
Using My Home / Applications, run the featured pfda-jupyterLab app on the smallest instance type, providing the Jupyter-DESeq2-notebook-snapshot.tar file as input. This snapshot contains a complete RNA-seq DESeq2 quantification JupyterLab workbook with R package, notebook, input file and sample sheet all included.

![alt text](./assets/image-104.png)

![alt text](./assets/image-105.png)

Once the app is running, click the open workstation button to access  a rich visual and interactive analysis environment.

![alt text](./assets/image-106.png)
![alt text](./assets/image-107.png)

Open the rnaseq_diffex_r notebook to explore the data.

![alt text](./assets/image-108.png)




















# Build Epidemiology Data Analysis Notebooks
The Epidemiologist's R Handbook (https://epirhandbook.com/en) provides a wealth of R code examples for applied epidemiology and public health. The following pipelines have been created for deployment on Jupyter Notebooks using snapshots: 
- Data cleaning and de-duplication
- Time series and outbreak detection
- Epidemic modeling and contract tracking

## Copy the Notebook Resources to a New Private Space
In order to access these resources from the Notebooks that you launch, you will need to copy them to a private space. Create a new private space for this purpose.

![alt text](./assets/image-109.png)

The resources to launch and test these pipelines are available in the Epidemiology_tutorial folder that can be accessed under My Home / Files / Featured (https://precision.fda.gov/home/files?folder_id=8314805&scope=featured). Select all the items and copy to your new private space using the Actions pulldown menu.
You only have to perform this operation once.

![alt text](./assets/image-110.png)

## Running in non-interactive mode with papermill
The epidemic_modeling.ipynb notebook can take a considerable amount of time to run (1.5 hours on a Baseline 16 instance) and thus has issues when attempting to run it interactively. It is recommended that you first run the notebook non-interactively using ""papermill"".  This is a two-step process, first running the notebook non-interactively and then opening the rendered result in a new notebook.

You will need the file ID for the epidemic_modeling.ipynb notebook. Remove the -x suffix for use in the runtime command invocation (e.g. file-GYXjgGj0J85qJ8x618yybg8v).

<div style=""display: grid; grid-template-columns: 1.5fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-111.png"" alt=""1"">
  <img src=""./assets/image-112.png"" alt=""1"">
</div>

Follow the procedure in the Run the pfda-jupyterLab Featured App section of this tutorial, selecting the execution Context as Epidemiology_Notebooks, specifying snapshot-epidemic-modeling.tar.gz in the Snapshot inputs section, and specifying the following in the Command Line input:
```
dx download file-GYXjgGj0J85qJ8x618yybg8v && papermill epidemic_modeling.ipynb epidemic_modeling_eval.ipynb
```
You'll notice the following error in the job log but be patient, it will complete.
```
Executing: 18%|█▊ | 7/39 [00:01<00:17, 1.79cell/s]Failed to create bus connection: 
No such file or directory
```
## Build Epidemic Modeling Notebook
Follow the procedure in the Run the pfda-jupyterLab Featured App section of this tutorial, selecting the execution Context as Epidemiology_Notebooks and specifying snapshot-epidemic-modeling.tar.gz in the Snapshot inputs section, to launch the epidemic modeling notebook.

<div style=""display: grid; grid-template-columns: 1.5fr 1fr; gap: 16px;"" markdown=""1"">
  <img src=""./assets/image-113.png"" alt=""1"">
  <img src=""./assets/image-114.png"" alt=""1"">
</div>
 
You'll find this execution running in your new space. It will take some time for the large snapshot to be unpacked. Use the Action View Logs pull down menu to observe when the snapshot restore is complete and the notebook is started. Then click the Open Workstation button to open the notebook. Click on the DNAnexus sidebar to view the resources in your space and open the epidemic_modeling.ipynb_eval notebook that was produced in the previous step. All the cells have been rendered with results presented.

![alt text](./assets/image-115.png)

## Build Time Series and Outbreak Detection Notebook
Follow the procedure in the Build Epidemic Modeling Notebook section above, selecting the execution Context as Epidemiology_Notebooks and specifying time_series_snapshot.tar.gz in the Snapshot inputs section, to launch the time series and outbreak detection notebook. Once the notebook is running and the snapshot restored, click on the DNAnexus sidebar to view the resources in your space and open the time_series.ipynbnotebook. Select Run All Cells from the Run menu and view the calculation results appear in the notebook cells.

![alt text](./assets/image-116.png)

## Build Contact Tracing Notebook
Follow the procedure in the Build Epidemic Modeling Notebook section above, selecting the execution Context as Epidemiology_Notebooks and specifying contact_tracing_snapshot.tar.gz in the Snapshot inputs section, to launch the contract tracing notebook. Once the notebook is running and the snapshot restored, click on the DNAnexus sidebar to view the resources in your space and open the contact_tracing.ipynb notebook. Select Run All Cells from the Run menu and view the calculation results appear in the notebook cells.

![alt text](./assets/image-117.png)

# Snapshot and Terminate Workstations
In keeping with good cloud usage practice, we will snapshot and terminate the workstations, preserving their entire state as built out through this tutorial. Additionally since we’ve backed up the database to a precisionFDA file, we can safely terminate the cluster database as well.

## Stop the Docker Containers and Snapshot Data Analysis Workstation
Using the data analysis workstation shell, create a snapshot of the workstation in you My Home files area.
```bash
Docker stop 
dx-create-snapshot

dx ls -al *snapshot
```
### Terminate the Workstation
In My Home / Executions, select (one at a time unfortunately) the Data Analysis Workstation and Data Analysis Notebook executions and select Terminate under the Action dropdown menu.

![alt text](./assets/image-118.png)

## Snapshot and Terminate the Data Analysis Notebook
Select Create Snapshot in the precisionFDA menu in the jupyterLabs interface.

![alt text](./assets/image-119.png)

### Terminate the Workstation and Notebook and Database Cluster
In My Home / Executions, select (one at a time unfortunately) the Data Analysis Workstation and Data Analysis Notebook executions and select Terminate under the Action dropdown menu.

![alt text](./assets/image-120.png)
","Markdown"
"Assay","FDA/precisionFDA","packages/rails/public/tuts/apps.md",".md","51257","1350","# Tutorial: Design Patterns for Apps and Workflows

# Introduction

This hands-on tutorial presents design patterns for collaborative bioinformatics using precisionFDA Apps and Workflows. There is a considerable range of design patterns for deploying code, marshalling data, and producing results, within precisionFDA's file and transient worker-driven (i.e. batch, non-interactive) application framework. This course will present this framework, including:

- Apps, their input/out specifications for files and other data types, virtual environment specification, and scripting
- Assets (i.e. tar archives) that can be bundled with assets and overlaid onto the worker's root volume
- Incorporation of Docker images into apps from file inputs and from assets
- Workflow creation using the web interface and WDL

Through the development of the tutorial artifacts, precisionFDA's powerful capabilities for secure collaborative development of bioinformatics tools, and sharing of -omics and RWD, are clearly demonstrated, and users will be empowered to develop their own bioinformatics use cases.

# Learning Objectives

Through this hands-on tutorial you will:

- Install the precisionFDA command line utility and use it upload and download files.
- Explore the multiple types of app inputs and outputs, assets, and their presentation to the app script as shell variables and files.
- Build a bioinformatics app from a biocontainers Docker image.
- Run the latest Ubuntu OS as a Docker image in an app.
- Use the precisionFDA Command Line Interface asset to process an arbitrary number of inputs or produce an arbitrary number of outputs to make up for the lack of an array data type.
- Create a workflow through the web interface.
- Create a workflow through by importing WDL.
- Access a database cluster from an app.

# Download and Install the precisionFDA CLI

The precisionFDA CLI is useful for programmatic uploading and downloading of files from Windows, MacOS, and Linux clients (e.g. your laptop) to and from precisionFDA. Installation and testing for Windows and Linux OS are described below.

Under My Home Assets, click on the How to create assets button to find links to the precisionFDA CLI, and the button to generate the temporary authorization key that you'll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image1.png"" alt=""1""></img>
  <div>
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image2.png"" alt=""2"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image3.png"" alt=""3"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image4.png"" alt=""4"">
  </div>
</div>

## Windows

Click the Windows download button to place the zip file and double click it in the browser footer to open it. 

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image5.png)

Click on Extract all to decompress the pfda CLI and browse to select the Desktop as the destination for the pfda.exe file. NOTE: On FDA laptops, you will need to install the pfda.exe file in C:\temp\ or you will get a permissions error when trying to run it.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image6.png"" alt=""1"" />
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image7.png"" alt=""1"" />
</div>


Using the Windows start menu, bring up a Command Prompt window with the pfda.exe file visible in a file explorer side-by-side. Drag *pfda.exe* onto the Command Prompt window to expand the full path the executable and add *--version* and hit return.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image8.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image9.png)

First, copy a file ID to test downloading, and retrieve an authorization key that is required for all file transfers.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image10.png)

```
# Provide the auth key
set key=<paste key>

# Download the selected file
C:\Users\oserang\Desktop\pfda.exe download -key <key> -file-id file-GJv1zKj0Kj2vzFP4Gg475ZyX-1

# Copy it to a new file and upload it
copy foo2.txt moo2.txt
C:\Users\oserang\Desktop\pfda.exe upload-file -file moo2.txt

dir *.txt
11/26/2022  04:26 PM                15 foo2.txt
11/26/2022  04:26 PM                15 moo2.txt 
```
![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image11.png)

## Linux

Copy the download URL and install and test uploading and downloading files. First, copy a file ID to test downloading, and retrieve an authorization key that is required for all file transfers.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image10.png)

```
-- Install pfda CLI
wget <paste URL from CLI download>
tar xf pfda-linux-<version>.tar.gz 
mv pfda /usr/bin/
pfda --version

-- Provide the auth key
key=""<paste key>""

-- Download the selected file
pfda download -key $key -file-id file-GJv1zKj0Kj2vzFP4Gg475ZyX-1

-- Copy it to a new file and upload it
cp foo2.txt moo2.txt
pfda upload-file -key $key -file moo2.txt

ls *.txt
foo2.txt  moo2.txt
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image11.png)

# First App: *docker_pull_and_save*

The first app demonstrates the use of string input variables, allowing the app to access the internet, how to use the emit shell function to output a named file. This app pulls a docker image and saves it to a .tar file suitable for use in precisionFDA apps. We'll create three image files in our Files:

<table>
  <thead>
    <tr><th></th><th>docker_source</th><th>docker_image_filename</th></tr>
  </thead>
  <tbody>
    <tr><td>1</td><td>ubuntu:latest</td><td>ubuntu_latest.tar</td></tr>
    <tr><td>2</td><td>biocontainers/samtools:v1.9-4-deb_cv1</td><td>samtools_biocontainers.tar</td></tr>
    <tr><td>3</td><td>postgres:13.4-buster</td><td>postgres_13.4-buster.tar</td></tr>
  </tbody>
</table>

## Create the docker_pull_and_save App

From My Home / Apps, click on Create App to create the *docker_pull_and_save* app. In the I/O Spec tab, add the input and output fields.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image12.png)

Select the VM Environment tab, enable internet access, and select Baseline 2 as the default instance type.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image13.png)

Select the Script tab and enter the following shell script:
```bash
set -euxo pipefail
sudo docker pull ""$docker_source""
sudo docker save ""$docker_source"" > ""$docker_image_filename""
emit ""docker_image_file"" ""$docker_image_filename""
```

The set -euxo pipefail set is advisable at the start of all your app scripts. The docker source string is resolved and used to pull the image and to save it to the docker image filename. Lastly the resulting image is saved as a precisionFDA file with the specified image filename.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image14.png)

Select the Readme tab and describe your app, then hit the Create button to create your app.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image15.png)

## Run the docker_pull_and_save App

Run this app three times with the inputs listed above. You don't have to wait for one to finish to start the others as they will run in parallel.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image16.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image17.png)

Select one of the completed executions My Home / Executions for the app and View Logs using the Action dropdown menu and we can see the desired actions took place and the image .tar files appear in My Home / Files.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image18.png)

```
Downloading files using 2 threads++ set -euxo pipefail

++ sudo docker pull postgres:13.4-buster

13.4-buster: Pulling from library/postgres
.
.
.
Status: Downloaded newer image for postgres:13.4-buster

++ sudo docker save postgres:13.4-buster

++ emit docker_image_file postgres_13.4-buster.tar
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image19.png)

# Second App: *worker_layout_inspection*

Since assets are such a great app developer convenience, our second app, worker_layout_inspection, will illustrate the use of assets and inputs for presenting code and data to the app. First let's create some assets that will be incorporated into the app. Additionally, since Docker is also such a great app developer tool, the incorporation of Docker images into the app by packaging them in an asset or providing them as an input file, are both illustrated.

## Create an asset with code and data

### Layout your asset directories and files

The file system structure that you layout in your asset will be overlaid on the worker / (root) mount when the app is run. If you've include code in the asset's /usr/bin, it will be available to your app running on the worker. The app framework uses /work as the directory to present input files to apps so any files placed in the asset's /work directory will be available with other input files, though you could place files in whatever asset directory structure you want.

We going to construct our asset in a Linux shell, downloading from precisionFDA the following for inclusion in the asset:

- ubuntu_latest.tar (file-GK1FP9j05gK8z93Y1xGQpf5B-1)

- countries.txt (file-GK1F6j80Kj2XbJzx29f25y42-1)

Create your asset directory structure.
```bash
apt install tree
mkdir -p ~/fakeroot/work
mkdir -p ~/fakeroot/usr/bin
tree ~/fakeroot/
/home/dnanexus/fakeroot/
├── usr
│   └── bin
└── work
```
Create a shell script to install tree and run it, then move the script into the asset directory structure.
```bash
cat > tree_script.sh
  sudo apt install tree
  tree $1
  CTRL-D
chmod ugo+x tree_script.sh
./tree_script.sh ~
/home/dnanexus
├── EHR_Sample_backup_nodbcreate_postgres.sql
├── datafiles
│   ├── manifest.txt
│   ├── observations.txt
│   └── patients.txt
├── db_backups
mv tree_script.sh ~/fakeroot/usr/bin
```

Create a Readme file as required for the asset. This doesn't need to reside in the asset /fakeroot directory structure.

```
echo ""Assets for the worker layout inspection tutorial app"" > ~/readme.txt
```

Download the Docker and data files and move them into the asset directory structure. Under My Home Assets, click on the How to create assets button to find links to the precisionFDA CLI, and the button to generate the temporary authorization key that you'll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image1.png"" alt=""1""></img>
  <div>
      <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image3.png"" alt=""1""></img>
      <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image4.png"" alt=""1""></img>
  </div>
</div>
<br/>

```bash
key=""...""

pfda download -key $key -file-id file-GK1FP9j05gK8z93Y1xGQpf5B-1
pfda download -key $key -file-id file-GK1F6j80Kj2XbJzx29f25y42-1
ls *.tar *txt
countries.txt  foo2.txt  moo2.txt  ubuntu_latest.tar

mv countries.txt postgres_13.4-buster.tar ubuntu_latest.tar ~/fakeroot/work tree ~/fakeroot

/home/dnanexus/fakeroot
├── usr
│   └── bin
│       └── tree_script.sh
└── work
    ├── countries.txt
    └── ubuntu_latest.tar
```

### Create the asset using the CLI

Now that the asset contents have been laid out, creating the asset on precisionFDA is a straightforward process using the precision FDA CLI.

```bash
key=""...""

pfda upload-asset --key $key --name worker_layout_inspection.tar --root ~/fakeroot --readme ~/readme.txt

>> Archiving asset...

>> Finalizing asset...

>> Done! Access your asset at https://precision.fda.gov/home/assets/file-GK1JJB00Kj2fkz464yxB5zY2-1
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image20.png)

### Manually deploying an asset tar file

While this workflow should not be required, it is worth knowing to understand how assets work. Upload the asset tarball (e.g. *worker_layout_inspection.tar*) as a file, then in your app, add a file to the I/O Spec (e.g. ""asset_tarball"") and select this tarball as the default. Add the following line to the Script, right after the `set -euxo pipefail` command:

```
tar zxvf ${asset_tarball_path} -C / --strip-components=1 --no-same-owner
```
Everything that was installed in the fake_root/ in the tarball will be placed into the root directory of the worker, including any executable you may have. For example:
```
fake_root/usr/local/bin/sambamba
```
from the asset_tarball input, will be available in:
```
/usr/local/bin/sambamba
```
after the tar command above is run in the app script.

## Create the App and Specify the I/O Spec

In My Home / Apps, click the Create App button to create the *worker_layout_inspection* app. In the I/O Spec tab, add the input and output fields.

![](/tuts/apps-media/image21.png)

<table>
  <thead>
    <tr>
      <th>Class</th>
      <th>Array?</th>
      <th>Input Name</th>
      <th>Label</th>
      <th>Default Value</th>
    </tr>
  </thead>
  <tr>
    <td>string</td>
    <td></td>
    <td>string_input</td>
    <td>example string input</td>
    <td>this is a string</td>
  </tr>
  <tr>
    <td>int</td>
    <td></td>
    <td>integer_input</td>
    <td>example integer input</td>
    <td>54321</td>
  </tr>
  <tr>
    <td>float</td>
    <td></td>
    <td>float_input</td>
    <td>example float input</td>
    <td>1.234</td>
  </tr>
  <tr>
    <td>boolean</td>
    <td></td>
    <td>boolean_input</td>
    <td>example boolean input</td>
    <td>FALSE</td>
  </tr>
  <tr>
    <td>file</td>
    <td></td>
    <td>observation_data</td>
    <td>Delimited observation data</td>
    <td>observations.txt</td>
  </tr>
  <tr>
    <td>file</td>
    <td></td>
    <td>patient data -</td>
    <td>Delimited patient data</td>
    <td>patients.txt</td>
  </tr>
  <tr>
    <td>file</td>
    <td></td>
    <td>samtools docker - image</td>
    <td>Docker image for Samtools</td>
    <td>samtools biocontaine rs.tar</td>
  </tr>
  <tr>
    <td>file</td>
    <td></td>
    <td>postgres_docker_ image</td>
    <td>Docker image for postgres server</td>
    <td>postgres_13.4- buster.tar</td>
  </tr>
  <tr>
    <td>string</td>
    <td></td>
    <td>inspection resul ts filename -</td>
    <td>Inspection results filename</td>
    <td>inspection_results.t xt</td>
  </tr>
  <tr>
    <td>string</td>
    <td></td>
    <td>url_to_fetch</td>
    <td>URL to pass to next app in workflow</td>
    <td>https://pfda- production-static- files.s3.amazonaws.c om/cli/pfda-linux- 2.2.tar.gz</td>
  </tr>

  <tr>
    <td>string</td>
    <td>Yes</td>
    <td>string_array_input image</td>
    <td>example string array input</td>
    <td>string1,string2,string3</td>
  </tr>
  <tr>
    <td>int</td>
    <td>Yes</td>
    <td>integer_array_input</td>
    <td>example integer array input</td>
    <td>5,4,3,2,1</td>
  </tr>
  <tr>
    <td>file</td>
    <td>Yes</td>
    <td>file_array_input</td>
    <td>example file array input</td>
    <td>(select three files)</td>
  </tr>
  <thead>
    <tr>
      <th>Class</th>
      <th>Array?</th>
      <th>Output Name</th>
      <th>Label</th>
      <th></th>
    </tr>
  </thead>
  <tr>
    <td>file</td>
    <td></td>
    <td>Inspection resul - ts</td>
    <td>Worker layout and variable inspection results</td>
    <td></td>
  </tr>
  <tr>
    <td>string</td>
    <td></td>
    <td>url_to_fetch</td>
    <td>URL to pass to next app in workflow</td>
    <td></td>
  </tr>
  <tr>
    <td>file</td>
    <td>Yes</td>
    <td>file_array_output</td>
    <td>example file array output</td>
    <td></td>
  </tr>
  <tr>
    <td>string</td>
    <td>Yes</td>
    <td>string_array_output</td>
    <td>example string array output</td>
    <td></td>
  </tr>
</table>

### Specify the VM Environment

In the VM Environment tab, enable internet access, select default instance Baseline 2, and add the following assets: worker_layout_inspection, pfda_cli_2.2, and ubuntu_asset.

![](/tuts/apps-media/image22.png)

### Specify the Script

Add the following code to the script tab:
```bash
set -euxo pipefail

# Append the worker OS information to the specified inspection results file.
cat /etc/os-release  | tee -a ""$inspection_results_filename""

# Install tree
sudo apt update
sudo apt install tree

# Inspect the scalar input variables.
echo ""string_input"" ""$string_input"" 
echo ""integer_input"" ""$integer_input""
echo ""float_input"" ""$float_input""
echo ""url_to_fetch"" ""$url_to_fetch""
echo """"

# Inspect the file input variables and the different
# operators for accessing them on the worker FS.
echo ""patient_data"" ""$patient_data""
echo ""patient_data_name"" ""$patient_data_name""
echo ""patient_data_path"" ""$patient_data_path""
echo ""patient_data_prefix"" ""$patient_data_prefix""
echo """"
echo ""observation_data"" ""$observation_data""
echo ""observation_data_name"" ""$observation_data_name""
echo ""observation_data_path"" ""$observation_data_path""
echo ""observation_data_prefix"" ""$observation_data_prefix""
echo """"
echo ""samtools_docker_image"" ""$samtools_docker_image""
echo ""samtools_docker_image_name"" ""$samtools_docker_image_name""
echo ""samtools_docker_image_path"" ""$samtools_docker_image_path""
echo ""samtools_docker_image_prefix"" ""$samtools_docker_image_prefix""

# Use the pfda CLI that was loaded with the pfda_cli_2.2 asset.
ls -al /usr/bin/pfda*
pfda --version

# Use the simple script that was loaded with the worker_layout_inspection asset.
ls -al /usr/bin/tree_script.sh
tree_script.sh /work

# Using the Docker image that was loaded with the ubuntu_asset asset,
# we can run an up-to-date Ubuntu OS in a docker container on the worker.
# Append the container OS information to the specified inspection results file.
docker load -i /ubuntu_latest.tar
docker run --rm -v /work:/work -w /work ubuntu:latest cat /etc/os-release | tee -a ""$inspection_results_filename""

# Using the Docker image that was provided as an input file,
# we can run samtools. Add inputs and outputs to this script and
# pass them to the samtools invocation to create your own
# samtools app.
docker load -i ""$samtools_docker_image_path""
docker run --rm biocontainers/samtools:v1.9-4-deb_cv1 samtools --version

docker images
docker ps

# Pass the URL to fetch input to the same output variable to pass
# to the next app in the tutorial workflow (i.e. url fetcher).
emit ""url_to_fetch"" ""$url_to_fetch""

# Save the inspection results file with specified name.
emit ""inspection_results"" ""$inspection_results_filename""
```

### Specify the Readme

Add the following in the Readme tab:
```
Produce an inspection report of the worker filesystem and file inputs from the context of the app.

Also provide the multiple representations available for file inputs.

Echo the non-file input variables.
```
## Run the app

In My Home / Apps, select the *worker_layout_inspection* app and click Run App to launch the latest version of the app with all default inputs.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image23.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image24.png)

Refresh the execution status using the ![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image25.png) button until the job is first idle, runnable, running, and done, (or failed).

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image26.png)

## Inspect the execution logs and the inspection results file

Note that the execution of the app took just over a minute and produced inspection_results.txt file and URL string outputs.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image27.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image28.png)

Select View Logs from the the Actions dropdown menu. Let's look at a condensed and annotated version of the log output to understand what our app just did.
```bash
# Worker initialization
Logging initialized (priority)
CPU: 20% (2 cores) * Memory: 779/3720MB * Storage: 32GB free * Net: 0↓/0↑MBps
umount: /proc/diskstats: not mounted
dxpy/0.331.0 (Linux-5.4.0-1088-aws-x86_64-with-Ubuntu-16.04-xenial)
/usr/sbin/rsyslogd already running.
bash running (job ID job-GK2ZQj00Kj2z9q76KbZbkG3G)

# Fetch the app's assets.
Fetching asset ubuntu_asset.tar (file-GJqz9J00Kj2zZQPGKVZBg436)
Fetching asset pfda_cli_2.2.tar (file-GJv0kFQ0Kj2YzFb86g4B8px5)
Fetching asset worker_layout_inspection2.tar (file-GK1xxbQ0Kj2gBZjxF244F21k)

# Download the input files.
downloading file: file-GK1F6jj0Kj2gyF8jG8jPjGpV to filesystem: /work/in/patient_data/patients.txt
downloading file: file-GK1F6jQ0Kj2v90jxPV0ZBQ5G to filesystem: /work/in/observation_data/observations.txt
downloading file: file-GK1FXK80pyBj68X3K6jVX55b to filesystem: /work/in/samtools_docker_image/samtools_biocontainers.tar
downloading file: file-GK1Fg68072kf3ZXYGJvxPGPj to filesystem: /work/in/postgres_docker_image/postgres_13.4-buster.tar

# The worker's OS release information.
cat /etc/os-release 
NAME=""Ubuntu""
VERSION=""16.04.7 LTS (Xenial Xerus)""
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME=""Ubuntu 16.04.7 LTS""
VERSION_ID=""16.04""
HOME_URL=""http://www.ubuntu.com/""
SUPPORT_URL=""http://help.ubuntu.com/""
BUG_REPORT_URL=""http://bugs.launchpad.net/ubuntu/""
VERSION_CODENAME=xenial
UBUNTU_CODENAME=xenial

# Install tree
sudo apt update
apt install tree
Unpacking tree (1.7.0-3) ...
Processing triggers for man-db (2.7.5-1) ...
Setting up tree (1.7.0-3) ...

# Resolution of each scalar input variable
string_input this is a string
integer_input 54321
float_input 1.234
url_to_fetch https://pfda-production-static-files.s3.amazonaws.com/tuts/cli/pfda-linux-2.2.tar.gz

# Resolution of each file input variable into the four
# types of references available
patient_data file-GK1F6jj0Kj2gyF8jG8jPjGpV
patient_data_name patients.txt
patient_data_path /work/in/patient_data/patients.txt
patient_data_prefix patients

observation_data file-GK1F6jQ0Kj2v90jxPV0ZBQ5G
observation_data_name observations.txt
observation_data_path /work/in/observation_data/observations.txt
observation_data_prefix observations

samtools_docker_image file-GK1FXK80pyBj68X3K6jVX55b
samtools_docker_image_name samtools_biocontainers.tar
samtools_docker_image_path /work/in/samtools_docker_image/samtools_biocontainers.tar
samtools_docker_image_prefix samtools_biocontainers

# View the pfda CLI that was installed with the pfda CLI asset and run it.
ls -al /usr/bin/pfda
-rwxr-xr-x 1 root root 11810776 Aug  3 08:07 /usr/bin/pfda

pfda --version
pFDA CLI Info
  Commit ID   :    e2325fdba10e06ee32a8035fb6b7161f1e82ffe6
  CLI Version :    2.2
  Os/Arch     :    linux/amd64
  Build Time  :    2022-08-03-100606
  Go Version  :    go1.16.7b7
  TLS Version :    TLS 1.2
  FIPS        :    +crypto/tls/fipsonly verified

# View the tree script that was installed with the workstation asset and run it.
# Note the countries.txt file in /work as it was packaged in the worker_layout_inspection asset.
# Note the directory layout of the file input types.

ls -al /usr/bin/tree_script.sh
-rwxr-xr-x 1 root root 30 Nov 27 21:32 /usr/bin/tree_script.sh

tree_script.sh /work
/work
├── countries.txt
├── in
│   ├── observation_data
│   │   └── observations.txt
│   ├── patient_data
│   │   └── patients.txt
│   ├── postgres_docker_image
│   │   └── postgres_13.4-buster.tar
│   └── samtools_docker_image
│       └── samtools_biocontainers.tar
└── inspection_results.txt

# Load the docker image that was installed with ubuntu asset and run it,
# presenting the container OS release information.
docker load -i /ubuntu_latest.tar

5 directories, 6 files
Loaded image: ubuntu:latest
docker run --rm -v /work:/work -w /work ubuntu:latest cat /etc/os-release | tee -a inspection_results.txt

PRETTY_NAME=""Ubuntu 22.04.1 LTS""
NAME=""Ubuntu""
VERSION_ID=""22.04""
VERSION=""22.04.1 LTS (Jammy Jellyfish)""
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
HOME_URL=""https://www.ubuntu.com/""
SUPPORT_URL=""https://help.ubuntu.com/""
BUG_REPORT_URL=""https://bugs.launchpad.net/ubuntu/""
PRIVACY_POLICY_URL=""https://www.ubuntu.com/legal/terms-and-policies/privacy-policy""
UBUNTU_CODENAME=jammy

# Load the samtools docker image that was provided as an input file and run it.
docker load -i /work/in/samtools_docker_image/samtools_biocontainers.tar
Loaded image: biocontainers/samtools:v1.9-4-deb_cv1

docker run --rm biocontainers/samtools:v1.9-4-deb_cv1 samtools --version
samtools 1.9
Using htslib 1.9
Copyright (C) 2018 Genome Research Ltd.

# Docker images and running containers on the worker
docker images
REPOSITORY               TAG                 IMAGE ID            CREATED             SIZE
ubuntu                   latest              a8780b506fa4        3 weeks ago         77.8MB
biocontainers/samtools   v1.9-4-deb_cv1      f210eb625ba6        3 years ago         666MB

docker ps
CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES

# Emit the URL string output variable.
emit url_to_fetch https://pfda-production-static-files.s3.amazonaws.com/tuts/cli/pfda-linux-2.2.tar.gz

# Emit the inspection results file.
emit inspection_results inspection_results.txt
```

# Utility Apps: *untar_files*, *tar_files_from_manifest*

We are going to use the pfda_cli_2.2.tar asset to create two very useful apps that demonstrate a design pattern that is readily extensible. The precisionFDA app I/O specification supports scalar and file and output types but does not support arrays for input or output variables. This means that using the app input variable and output emit framework, you cannot create apps with an arbitrary number of inputs or outputs. For instance, you really can't even create an untar app due to this limitation. These two apps demonstrate a design pattern to overcome this limitation.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <div>
    Note that these apps require a temporary authorization key that you'll use with the CLI and this key will appear in the execution log files. Thus you should only run this app in your My Home or Private Space contexts so as not to expose the key to other users.
  </div>
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image4.png"" />
</div>


## tar_files_from_manifest: Tar a manifest of fileIDs

From My Home / Files, select the detail page and copy the file ID for a
number of files. Create and upload *manifest.txt* with the list to be
incorporated into an archive file (e.g.):

```bash
cat manifest.txt
file-GJv1zKj0Kj2vzFP4Gg475ZyX-1
file-GJv1zKj0Kj2XY800GgJY2f4G-1
file-GJv1zKQ0Kj2vfZkVFxp436B9-1

key=""...""
pfda upload-file -key $key -file manifest.txt
```

Create a *tar_files_from_manifest* app titled ""Tar a manifest of
fileIDs"", with the following I/O Spec.

<table>
<thead>
  <tr>
    <th>Class</th>
    <th>Input Name</th>
    <th>Label</th>
    <th>Default Value</th>
  </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>manifest</td>
    <td>Text manifest of fileIDs</td>
    <td>manifext.file</td>
  </tr>
  <tr>
    <td>string</td>
    <td>tar_filename</td>
    <td>Filename for tar archive</td>
    <td>archive.tar</td>
  </tr>
  <tr>
    <td>string</td>
    <td>pfda_key</td>
    <td>Token for pfda CLI</td>
    <td></td>
  </tr>
  <thead>
    <tr>
      <th>Class</th>
      <th>Output Name</th>
      <th>Label</th>
      <th></th>
    </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>tarfile</td>
    <td>Tar archive file</td>
    <td></td>
  </tr>
</table>

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image29.png)

Setup the VM environment with internet access enabled, Baseline 2
default instance type, and select the pfda_cli_2.2 asset. Note that it
can take minutes for the list of assets to loaded before they can be
searched.

Enter the following Script:

```bash
set -euxo pipefail
echo ""$pfda_key""
echo ""$tar_filename""
echo ""$manifest""
sudo apt-get update
sudo apt-get install -y dos2unix
pfda -version
mkdir temp
cd temp
dos2unix ""$manifest_path""
for file in $(cat ""$manifest_path""); do pfda download -key ""$pfda_key"" -file-id $file; done
cd ..
tar cvf ""$tar_filename"" temp/*
emit ""tarfile"" ""$tar_filename""
```

Enter a Readme and Create the app.
```
Input a manifest file containing a list of fileIDs and the name of tar archive file. You\'ll need to provide a temporary authorization key for use with the pfda CLI and thus you should only run this app in your My Home or Private Spaces.
```
Run the app with the default inputs and a fresh authorization token for
the pfda CLI and observe the new archive.tar file.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image30.png)

When the execution is done, download the archive.tar file to verify its
contents.
```
tar tvf archive.tar
-rw-r--r-- root/root        15 2022-11-29 02:22 temp/foo.txt
-rw-r--r-- root/root        15 2022-11-29 02:22 temp/foo2.txt
-rw-r--r-- root/root        15 2022-11-29 02:22 temp/foo3.txt
```

## untar_files: Untar archive to files

Create a *untar_files* app titled ""Untar archive to files"", with the
following I/O Spec.

<table>
  <thead>
    <tr>
        <th>Class</th>
        <th>Input Name</th>
        <th>Label</th>
        <th>Default Value</th>
    </tr>
    </thead>
    <tr>
        <td>string</td>
        <td>pfda_key</td>
        <td>CLI access authorization token</td>
        <td></td>
    </tr>
    <tr>
        <td>file</td>
        <td>input_tarfile</td>
        <td>Tar file to extract</td>
        <td>archive.tar</td>
    </tr>
    <tr>
        <td>string</td>
        <td>extracted_list_f ilename</td>
        <td>List of extracted files</td>
        <td>extracted_list.txt</td>
    </tr>
    <thead>
      <tr>
        <th>Class</th>
        <th>Output Name</th>
        <th>Label</th>
        <th></th>
      </tr>
    </thead>
    <tr>
      <td>file</td>
      <td>tarfile_contents</td>
      <td>Listing of the tarfile contents</td>
      <td></td>
    </tr>
</table>

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image31.png)

Setup the VM environment with internet access enabled, Baseline 2
default instance type, and select the pfda_cli_2.2 asset. Note that it
can take minutes for the list of assets to loaded before they can be
searched.

Enter the following Script:
```bash
set -euxo pipefail
echo ""$pfda_key""
echo ""$input_tarfile""
echo ""$extracted_list_filename""
pfda -version
tar tvf ""$input_tarfile_path"" > ""$extracted_list_filename""
mkdir temp
tar xvf ""$input_tarfile_path"" --directory temp
ls temp
for FILE in $(find ./temp -type f -print); do echo $FILE; done
emit ""tarfile_contents"" ""$extracted_list_filename""
```

Enter a Readme and Create the app.
```
Input a tar archive file to extract into individual files. You'll need to provide a temporary authorization key for use with the pfda CLI and thus you should only run this app in your My Home or Private Spaces.
```
Run the app with the default inputs and a fresh authorization token for the pfda CLI.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image32.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image33.png)

When the execution is done, observe the new extracted files, and open the extracted_list.txt file to see the list of extracted files.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image32.png)

# First Workflow: *simple_workflow*

Workflows enable you to string together multiple stages, each running on its own instance type and its own app(s). Outputs from a given stage can be routed to inputs in the next stage. We will create this workflow using the web interface. In My Home / Workflows, click Create Workflow and name it *simple_workflow* with a title ""Workflow example with two single-app stages"".

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image34.png)

## Add the workflow stages

Click the Add Stage button and add the private workflow_layout_inspection app to the stage.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image35.png)

Add a second stage with the public url-fetcher app.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image36.png)

## Configure the workflow stages

Now we are ready to configure the stages.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image37.png"" width=""400"">

Click on Stage 1 to display the worker_layout_inspection app's configuration; we will accept all the default values provided in the app so there is nothing more that needs to be configured for Stage 1.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image38.png"" width=""200"">

Click on Stage 2 to display the url-fetcher app's configuration. Note the red color indicating that a required input has not been specified either from the output of a previous stage, or explicitly in the workflow stage's input spec.

Click the <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image39.png"" width=""30""> button to enter an explicit URL for this Stage.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image40.png"" width=""300"">

However, we want to specify this input field using the output field from the previous stage by clicking on the <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image41.png"" width=""30""> button.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image42.png"" width=""300"">

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image43.png"" width=""300"">

Select the Baseline 2 instance type to override the app default value and close the stage. Note that everything is green now and ready to Create.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image44.png"" width=""500"">

You can view the two stages of your new workflow.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image45.png"" width=""500"">
<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image46.png"" width=""500"">

## Run the workflow

Click on the Run Workflow button, accept all the default values for the workflow_layout_inspection stage, but enter *pfdacl.tar.gz* in the *Rename into* field in the second stage, then run the workflow.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image47.png"" width=""500"">
<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image48.png"" width=""500"">

In the Executions tab, expand the simple_workflow listing to see the two executions associated with the workflow.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image49.png)

Once the stages have completed, the workflow is done and we can open the executions associated with the stages and inspect the logs. Also note the inspection results file from stage 1 and the renamed fetched URL file from stage 2.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image50.png)

# Second Workflow: *workflow_from_wdl*

Importing a workflow from a WDL specification based on Docker images provides convenient way to express precisionFDA workflows in a serialized textual format. From My Home / Workflows, click the Create Workflow button, and click the Import from \*.wdl file button. Enter the following WDL in the text input and click Import.

```
version 1.0

workflow bwa {
  Int threads
  Int min_seed_length
  Int min_std_max_min
  File reference
  File reads

  call bwa_mem_tool {
    threads = threads,
    min_seed_length = min_seed_length,
    min_std_max_min = min_std_max_min,
    reference = reference,
    reads = reads
  }
}

task bwa_mem_tool {
  Int threads
  Int min_seed_length
  Int min_std_max_min
  File reference
  File reads
  
  command {
    bwa mem -t ${threads} \
    -k ${min_seed_length} \
    -I ${sep=',' min_std_max_min+} \
    ${reference} \
    ${sep=' ' reads+} > output.sam
  }

  output {
    File sam = ""output.sam""
  }

  runtime {
    docker: ""broadinstitute/baseimg""
  }
}
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image51.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image52.png)

Like workflows created through the web inteface, WDL-based workflows can be updated and run.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image53.png"" width=""300"">

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image54.png"" width=""400"">

# Database App: *worker_database*

This app demonstrates the use of a precisionFDA Database cluster (PostgreSQL), a convenient and very power resource for RDBMS-based analytics. You will need to be authorized for DB Clusters in order to create the database for this app.

## Create the Database

Select the Databases tab in My Home and click the Create Database button.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image55.png)

Create a ""Workstations and Databases Tutorial"" database, ""password"", PostgreSQL 11.16 on the smallest available database instance type, and click the Submit button.


<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image56.png"" width=""500"">

Refresh the database status using the <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image25.png"" width=""30""> button until the database is available.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image57.png)

## Fork *workstation_layout_inspection* to *workstation_database* app 

Using My Home / Apps, select the worker_layout_inspection app and select Fork. Name the new application *worker_database* titled ""Database cluster app example"".

### Specify the I/O Spec

Update the I/O spec to the following:

<table>
  <thead>
    <tr>
      <th>Class</th>
      <th>Input Name</th>
      <th>Label</th>
      <th>Default Value</th>
    </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>observation data</td>
    <td>Delimited data file for ETL into OBSERVATION table.</td>
    <td>observations.txt</td>
  </tr>
  <tr>
    <td>file</td>
    <td>patient_data</td>
    <td>Delimited data file for ETL into PATIENT table.</td>
    <td>patients.txt</td>
  </tr>
  <tr>
    <td>file</td>
    <td>db_and_table_ddl</td>
    <td>Database and table creation DDL</td>
    <td></td>
  </tr>
  <tr>
    <td>file</td>
    <td>query_sql</td>
    <td>Query to run</td>
    <td>(optional)</td>
  </tr>
  <tr>
    <td>string</td>
    <td>db endpoint url</td>
    <td>Database host endpoint</td>
    <td></td>
  </tr>
  <tr>
    <td>string</td>
    <td>query_results_: fi lename</td>
    <td>Query results file name</td>
    <td>query_results.txt</td>
  </tr>
  <thead>
    <tr>
      <th>Class</th>
      <th>Output Name</th>
      <th>Label</th>
      <th></th>
    </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>query_results</td>
    <td>SQL query results</td>
    <td></td>
  </tr>
</table>

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image58.png)

### Specify the VM Environment

Update the VM environment to keep internet access enabled, Baseline 2 default instance type, and remove the pfda_cli_2.2 and ubuntu_asset assets so that only the worker_layout_inspection asset remains. Note that it can take minutes for the list of assets to loaded before they can be searched.

### Specify the Script

Enter the following Script:
```bash
set -euxo pipefail

# Install postgres client
sudo apt install -y postgresql-client
psql --version

# Inspect the database and table DDL
# and the three data files for ETL.
# Two data files specified as inputs.
#cat ""$db_and_table_ddl_path""
#cat ""$observation_data_path""
#cat ""$patient_data_path""

# Third data file installed with the worker_layout_inspection asset
#cat /work/countries.txt

# Connect to the DB cluster and list the databases
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d postgres -c '\l'

# Create the apps_and_workflows_tutorial_db and three tables
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d postgres -f ""$db_and_table_ddl_path""
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c '\d'

# ETL the OBSERVATION table data
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""\copy public.\""OBSERVATION\"" from '/work/in/observation_data/observations.txt' delimiter '|' NULL ''""

PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""select * from public.\""OBSERVATION\""""

# ETL the PATIENT table data
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""\copy public.\""PATIENT\"" from '/work/in/patient_data/patients.txt' delimiter '|' NULL ''""

PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""select * from public.\""PATIENT\""""

# ETL the COUNTRY table data
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""\copy public.\""COUNTRY\"" from '/work/countries.txt' delimiter '|' NULL ''""

PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""select * from public.\""COUNTRY\""""

# Run the specified query and put the results into the specified filename.
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -f ""$query_sql_path"" | tee -a ""$query_results_filename""

emit ""query_results"" ""$query_results_filename""
```

### Specify the Readme

Enter a Readme and Create the app.
```
Connect to a database cluster and create a database and three tables. Load two tables with data provided as input files, and the third table with data from an asset. Present a specified query file and retrieve the query results file.
```
## Run the app

Click on the Workstations and Databases Tutorial database to open the detail page and copy the host endpoint URL.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image59.png)

Run the app providing the database host endpoint URL copied above, and leave the remaining inputs at their default values.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image60.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image61.png)

## Inspect the execution logs

View the logs for the *Database cluster app example* execution using the Actions dropdown menu. Let's look at a condensed and annotated version of the log output to understand what our app just did.
```bash
# Install the assets and file inputs on the worker filesystem
Fetching asset worker_layout_inspection2.tar (file-GK1xxbQ0Kj2gBZjxF244F21k)
downloading file: file-GK2xgy80Kj2x48j54fXGqF1V to filesystem: /work/in/query_sql/query.sql
downloading file: file-GK1F6jj0Kj2gyF8jG8jPjGpV to filesystem: /work/in/patient_data/patients.txt
downloading file: file-GK2v2Zj0Kj2gk9GB1920BYP3 to filesystem: /work/in/db_and_table_ddl/apps_and_workflows_tutorial_DDL.sql
downloading file: file-GK1F6jQ0Kj2v90jxPV0ZBQ5G to filesystem: /work/in/observation_data/observations.txt

# Install postgres CLI client and display version
++ sudo apt install -y postgresql-client
++ psql --version
psql (PostgreSQL) 9.5.25

# Providing the password, connect to the specified host,
# user root, database postgres, and list the databases on the cluster.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d postgres -c '\l'
                                                List of databases
                  Name                  |  Owner   | Encoding |   Collate   |    Ctype    |   Access privileges   
----------------------------------------+----------+----------+-------------+-------------+-----------------------
 apps_and_workflows_tutorial_db         | root     | UTF8     | en_US.UTF-8 | en_US.UTF-8 | 
 postgres                               | root     | UTF8     | en_US.UTF-8 | en_US.UTF-8 | 
 rdsadmin                               | rdsadmin | UTF8     | en_US.UTF-8 | en_US.UTF-8 | rdsadmin=CTc/rdsadmin
 template0                              | rdsadmin | UTF8     | en_US.UTF-8 | en_US.UTF-8 | =c/rdsadmin          +
                                        |          |          |             |             | rdsadmin=CTc/rdsadmin

# Create a new database and tables
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d postgres -f /work/in/db_and_table_ddl/apps_and_workflows_tutorial_DDL.sql
 template1                              | root     | UTF8     | en_US.UTF-8 | en_US.UTF-8 | =c/root              +
                                        |          |          |             |             | root=CTc/root
 workstations_and_databases_tutorial_db | root     | UTF8     | en_US.UTF-8 | en_US.UTF-8 | 
(6 rows)

 pg_terminate_backend 
----------------------
(0 rows)

DROP DATABASE
CREATE DATABASE
You are now connected to database ""apps_and_workflows_tutorial_db"" as user ""root"".
CREATE TABLE
CREATE TABLE
CREATE TABLE

# Connect to the new database and list the new tables
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c '\d'
          List of relations
 Schema |    Name     | Type  | Owner 
--------+-------------+-------+-------
 public | COUNTRY     | table | root
 public | OBSERVATION | table | root
 public | PATIENT     | table | root
(3 rows)

# ETL the OBSERVATION table from the specified file.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c '\copy public.""OBSERVATION"" from '\''/work/in/observation_data/observations.txt'\'' delimiter '\''|'\'' NULL '\'''\'''
COPY 5
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c 'select * from public.""OBSERVATION""'
 observation_id | patient_id | observation_name |   loinc   | created_date 
----------------+------------+------------------+-----------+--------------
           9870 |      12345 | Annual check up  | 66678-4   | 2022-11-01
           9871 |      12345 | Emergency        | LG32756-5 | 2022-11-02
           9872 |      12346 | Clinic visit     | 66678-4   | 2022-11-03
           9873 |      12347 | Lab results      | 74418-5   | 2022-11-04
           9874 |      12347 | Post-op checkup  | 65375-8   | 2022-11-05
(5 rows)

# ETL the PATIENT table from the specified file.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c '\copy public.""PATIENT"" from '\''/work/in/patient_data/patients.txt'\'' delimiter '\''|'\'' NULL '\'''\'''
COPY 3
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c 'select * from public.""PATIENT""'
 patient_id |     name      | gender |  zip  | country_id | created_date 
------------+---------------+--------+-------+------------+--------------
      12345 | Fred Foobar   | M      | 94040 |       1001 | 2022-10-25
      12346 | Mary Merry    | F      | 94040 |       1002 | 2022-09-24
      12347 | Barney Rubble | M      | 94040 |       1003 | 2022-08-23
(3 rows)

# ETL the COUNTRY table as installed from the asset.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c '\copy public.""COUNTRY"" from '\''/work/countries.txt'\'' delimiter '\''|'\'' NULL '\'''\'''
COPY 3
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c 'select * from public.""COUNTRY""'
 country_id | country_name 
------------+--------------
       1001 | USA
       1002 | CAN
       1003 | EU
(3 rows)

# Run the specified query.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -f /work/in/query_sql/query.sql
++ tee -a query_results.txt
Expanded display is on.
-[ RECORD 1 ]----+----------------
patient_id       | 12345
name             | Fred Foobar
gender           | M
zip              | 94040
country_id       | 1001
created_date     | 2022-10-25
observation_id   | 9870
observation_name | Annual check up
loinc            | 66678-4
created_date     | 2022-11-01
country_id       | 1001
country_name     | USA
-[ RECORD 2 ]----+----------------
patient_id       | 12345
name             | Fred Foobar
gender           | M
zip              | 94040
country_id       | 1001
created_date     | 2022-10-25
observation_id   | 9871
observation_name | Emergency
loinc            | LG32756-5
created_date     | 2022-11-02
country_id       | 1001
country_name     | USA
-[ RECORD 3 ]----+----------------
patient_id       | 12346
name             | Mary Merry
gender           | F
zip              | 94040
country_id       | 1002
created_date     | 2022-09-24
observation_id   | 9872
observation_name | Clinic visit
loinc            | 66678-4
created_date     | 2022-11-03
country_id       | 1002
country_name     | CAN
-[ RECORD 4 ]----+----------------
patient_id       | 12347
name             | Barney Rubble
gender           | M
zip              | 94040
country_id       | 1003
created_date     | 2022-08-23
observation_id   | 9873
observation_name | Lab results
loinc            | 74418-5
created_date     | 2022-11-04
country_id       | 1003
country_name     | EU
-[ RECORD 5 ]----+----------------
patient_id       | 12347
++ emit query_results query_results.txt
name             | Barney Rubble
gender           | M
zip              | 94040
country_id       | 1003
created_date     | 2022-08-23
observation_id   | 9874
observation_name | Post-op checkup
loinc            | 65375-8
created_date     | 2022-11-05
country_id       | 1003
country_name     | EU
```","Markdown"
"Assay","FDA/precisionFDA","packages/client/docker/entrypoint/dev.entrypoint.sh",".sh","250","14","#!/bin/bash

# Check if args were provided
if [[ ""$#"" -eq 0 ]]; then
    echo ""No Command provided""
    exit 1
fi

if [[ ! $SKIP_FRONTEND_DEPS_SETUP || $SKIP_FRONTEND_DEPS_SETUP = 0 ]]; then
    pnpm i --frozen-lockfile
fi

docker-entrypoint.sh ""$@""
","Shell"
"Assay","FDA/precisionFDA","packages/client/src/features/lexi/images/image/LICENSE.md",".md","138","6","yellow-flower.jpg by Andrew Haimerl
https://unsplash.com/photos/oxQHb8Yqt14

Licensed under Unsplash License
https://unsplash.com/license
","Markdown"
"Assay","FDA/precisionFDA","packages/client/src/features/lexi/images/emoji/LICENSE.md",".md","135","6","OpenMoji
https://openmoji.org

Licensed under Attribution-ShareAlike 4.0 International
https://creativecommons.org/licenses/by-sa/4.0/
","Markdown"
"Assay","FDA/precisionFDA","packages/client/src/features/lexi/images/icons/LICENSE.md",".md","126","6","Bootstrap Icons
https://icons.getbootstrap.com

Licensed under MIT license
https://github.com/twbs/icons/blob/main/LICENSE.md
","Markdown"
"Assay","FDA/precisionFDA","packages/scripts/jenkins_entrypoint.sh",".sh","396","11","#!/bin/bash
docker build -t pfda_ui -f docker/build/ui_test.Dockerfile . && \
docker run \
-e PFDA_AT_USER_1_PASSWORD_LOC=$PFDA_PASSWORD \
-e PFDA_AT_USER_2_PASSWORD_LOC=$PFDA_PASSWORD \
-e PFDA_AT_USER_ADMIN_PASSWORD_LOC=$PFDA_PASSWORD \
-e PFDA_BASIC_AUTH_DNX_PASSWORD_LOC=$DNX_STAGING_PASSWORD \
-e TEST_SUITE=$TEST_SUITE \
--mount type=bind,source=""$(pwd)""/tmp/,target=/log_storage \
pfda_ui
","Shell"
"Assay","FDA/precisionFDA","packages/scripts/gsrs-index-generation.sh",".sh","1007","38","GSRS_PORT=8080

job_name=""Reindex all core entities from backup tables""

job=$(
  curl -s localhost:$GSRS_PORT/api/v1/scheduledjobs -H ""AUTHENTICATION_USERNAME: admin"" |
  jq "".content[] | select(.description==\""$job_name\"")""
)

if [ -z ""$job"" ]; then
  echo ""ERROR: can't find the job with description: $job_name""
  exit 1
fi

job_id=`echo $job | jq '.[""@execute""]' | sed 's/.*\/scheduledjobs(\([^)]*\)).*/\1/'`
job_url=""localhost:$GSRS_PORT/api/v1/scheduledjobs($job_id)?view=full""
job_execute_url=""localhost:$GSRS_PORT/api/v1/scheduledjobs($job_id)/@execute""

echo ""Run the job via $job_execute_url""
run_result=$(curl -s ""$job_execute_url"" -H ""AUTHENTICATION_USERNAME: admin"")

echo ""Waiting for the job to be finished...""

sleep 3

while true ; do
  job=$(curl -s -H ""AUTHENTICATION_USERNAME: admin"" $job_url)
  is_running=`echo $job | jq '.running'`

  if [[ $is_running == ""false"" ]] ; then
    break
  fi

  task_message=`echo $job | jq '.taskDetails.message'`
  echo ""$task_message""

  sleep 10
done","Shell"
"Assay","FDA/precisionFDA","packages/scripts/gsrs-db-import.sh",".sh","3029","107","#!/usr/bin/env bash
#
# GSRS Database Update Script
# v0.6
#
# Currently, one must edit and insert the appropriate variables below before running
# as well as making sure the AWS CLI is configured:
#    export AWS_ACCESS_KEY_ID=ABCDEF123456
#    export AWS_SECRET_ACCESS_KEY=ABCDEF123456

# Flow:
# Download the latest dump from S3
# Restore dump to a new database
# Create a trigger for user roles assignment

set -o errexit -o pipefail

DUMP_BUCKET=""gsrs-database-dumps""

# NEW_DB_HOST=""pfda-dev-gsrs-db-mysql.cyy6pahwar0b.us-west-2.rds.amazonaws.com""
# NEW_DB_PORT=3306
# NEW_DB_NAME=""ixginas""
# NEW_DB_USER=""admin""
# NEW_DB_PASS=""INSERT_PASSWORD""

NEW_DB_HOST=""127.0.0.1""
NEW_DB_PORT=32900
NEW_DB_NAME=""ixginas20231218""
NEW_DB_USER=""root""
NEW_DB_PASS=""password""

ROLES_TRIGGER=$(cat <<-EOF
delimiter //
CREATE TRIGGER ix_core_userprof_update_roles BEFORE UPDATE ON ix_core_userprof
FOR EACH ROW
BEGIN
  IF NEW.roles_json IS NULL THEN
    SET NEW.roles_json = '[""Query"",""Updater"",""SuperUpdate"",""DataEntry"",""SuperDataEntry""]';
  END IF;
END; //
delimiter ;
EOF
)

# Search the last created dump on S3.
# Replace '&Key' by '&LastModified' if you need to sort by modified time.
search_dump() {
    echo $(aws s3api list-objects-v2 --bucket ""$DUMP_BUCKET"" \
        --query 'sort_by(Contents, &Key)[-1].Key' --output=text)
}

# Download the last dump from S3.
download_dump() {
    aws s3 cp s3://$DUMP_BUCKET/""$1"" ""$2""
}

restore_dump() {
    mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER -p$NEW_DB_PASS --ssl-mode=DISABLED --execute=""CREATE DATABASE $NEW_DB_NAME;""
    # If getting an error here while running on mac, just change ""zcat $1"" to ""zcat < $1""
    zcat ""$1"" | mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER -p$NEW_DB_PASS --ssl-mode=DISABLED $NEW_DB_NAME
}

create_roles_trigger() {
    mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER -p$NEW_DB_PASS --ssl-mode=DISABLED $NEW_DB_NAME \
        -e ""$ROLES_TRIGGER""
}

cleanup() {
    rm -f ""$1"" # remove dump archive
    rm -f ix_core_group.sql ix_core_group_principal.sql ix_core_principal.sql ix_core_userprof.sql
}

main() {
    local dump_name=$(search_dump)
    if [ -z ""$dump_name"" ]; then
        echo ""Error obtaining latest dump file name from S3""
        exit 1
    fi

    # local dump_name=""gsrsDump2023-12-18_3.1_cdk_src2023-12-14.sql.gz""
    local dump_path=""./$dump_name""

    echo
    echo ""Latest dump name: $dump_name""
    download_dump ""$dump_name"" ""$dump_path""
    if [ ! -f ""$dump_path"" ]; then
        echo ""Error downloading database dump file""
        exit 1
    fi

    echo
    echo ""Restore dump to the new database...""
    restore_dump ""$dump_path""

    echo
    echo ""Create roles trigger... (may already exist)""
    create_roles_trigger

    # Better to explicitly uncomment this when needing to clean up, because if something went wrong during
    # the upload to s3 step, the following will wipe the ginas.ix folder preventing us from trying again
    # echo
    # echo ""Cleanup...""
    # cleanup $dump_path
}

main
","Shell"
"Assay","FDA/precisionFDA","packages/scripts/compare-db-tables.sh",".sh","2474","51","error_found=0

# Database 1 connection details
DB1_USER=""admin""
DB1_PASSWORD=""!""
DB1_HOST=""0.0.0.0""
DB1_PORT=""3306""
DB1_NAME=""precision_fda""

# Database 2 connection details
DB2_USER=""admin""
DB2_PASSWORD=""!""
DB2_HOST=""0.0.0.0""
DB2_PORT=""3306""
DB2_NAME=""precision_fda""

# Get list of tables from both databases, excluding views and skipping the header row
TABLES_DB1=$(mysql -u""$DB1_USER"" -p""$DB1_PASSWORD"" -h""$DB1_HOST"" -P""$DB1_PORT"" -D""$DB1_NAME"" -e ""SELECT table_name FROM information_schema.tables WHERE table_schema = '$DB1_NAME' AND table_type = 'BASE TABLE';"" 2>&1 | grep -v ""Using a password on the command line interface can be insecure."" | tail -n +2)
TABLES_DB2=$(mysql -u""$DB2_USER"" -p""$DB2_PASSWORD"" -h""$DB2_HOST"" -P""$DB2_PORT"" -D""$DB2_NAME"" -e ""SELECT table_name FROM information_schema.tables WHERE table_schema = '$DB2_NAME' AND table_type = 'BASE TABLE';"" 2>&1 | grep -v ""Using a password on the command line interface can be insecure."" | tail -n +2)
# Compare tables and row counts
for TABLE in $TABLES_DB1; do
    if echo ""$TABLES_DB2"" | grep -wq ""$TABLE""; then
        COUNT_DB1=$(mysql -u""$DB1_USER"" -p""$DB1_PASSWORD"" -h""$DB1_HOST"" -P""$DB1_PORT"" -D""$DB1_NAME"" -e ""SELECT COUNT(*) FROM \`$TABLE\`;"" 2>&1 | grep -v ""Using a password on the command line interface can be insecure."" | tail -n +2 | awk '{print $1}')
        COUNT_DB2=$(mysql -u""$DB2_USER"" -p""$DB2_PASSWORD"" -h""$DB2_HOST"" -P""$DB2_PORT"" -D""$DB2_NAME"" -e ""SELECT COUNT(*) FROM \`$TABLE\`;"" 2>&1 | grep -v ""Using a password on the command line interface can be insecure."" | tail -n +2 | awk '{print $1}')
        if [ ""$COUNT_DB1"" == ""$COUNT_DB2"" ]; then
            echo ""Table $TABLE matches: $COUNT_DB1 rows.""
        else
            printf ""\033[0;31mTable %s does not match: %s has %s rows, %s has %s rows.\033[0m\n"" ""$TABLE"" ""$DB1_HOST"" ""$COUNT_DB1"" ""$DB2_HOST"" ""$COUNT_DB2""
            error_found=1
        fi
    else
        printf ""\033[0;31mTable %s exists in %s but not in %s.\033[0m\n"" ""$TABLE"" ""$DB1_HOST"" ""$DB2_HOST""
        error_found=1
    fi
done

# Check for tables in DB2 that are not in DB1
for TABLE in $TABLES_DB2; do
    if ! echo ""$TABLES_DB1"" | grep -wq ""$TABLE""; then
        printf ""\033[0;31mTable %s exists in %s but not in %s.\033[0m\n"" ""$TABLE"" ""$DB2_HOST"" ""$DB1_HOST""
        error_found=1
    fi
done

# Result message
if [ $error_found -eq 0 ]; then
    echo ""Success - everything matches.""
else
    echo -e ""\033[0;31mError - see details above.\033[0m""
fi
","Shell"
"Assay","FDA/precisionFDA","utils/scripts/check-unpublished-env-variables.sh",".sh","1672","51","#!/bin/bash

# Note(samuel) - didn't manage to implement it in makefile
# My efforts ended on redirecting pipeline output '<(...)'
# comm -13 <(grep -v '^#\|^\s*$' ""$dotenv_file"" | awk -F = '{print $1}' | sort)
# Reason for this bash script

# Formatting util
function print_missing_env_list() {
    max_len=$(awk 'BEGIN {max=0} {if (length>(max + 0)) max=length fi} END {print max}' <(echo ""$1""))
    printf '%0.s-' $(seq 1 $max_len) >&2
    echo >&2
    echo ""$1"" >&2
    printf '%0.s-' $(seq 1 $max_len) >&2
    echo >&2
}

# Expected to be .env
dotenv_file=""$1""
# Expected to be .env.spec
spec_file=""$2""

if [[ ! -f ""$dotenv_file"" ]]; then
    echo ""File \""$dotenv_file\"" not found"" >&2
    exit 1
fi
if [[ ! -f ""$spec_file"" ]]; then
    echo ""File \""$spec_file\"" not found"" >&2
    exit 1
fi

# Pipeline Explanation
# * `grep` - filter out comments from .env file and skip empty line
# * `awk` - extract just variable names - discard values
# * `sort` - sorts
# This is done for both files
comparison_result=$(comm -23 <(grep -v '^#\|^\s*$' ""$dotenv_file"" | awk -F = '{print $1}' | sort) <(grep -v '^#\|^\s*$' ""$spec_file"" | awk -F = '{print $1}' | sort))
if [[ $comparison_result ]]; then
    echo ""WARNING: Following env variables from \""$dotenv_file\"" aren't reflected in \""$spec_file\"""" >&2
    print_missing_env_list ""$comparison_result""
    exit 1
fi
echo ""OK: \""$spec_file\"" contains all used variables from \""$dotenv_file\"", no unpublished config""

# ----------

# NOTE(samuel) - forked from ""check-missing-env-variables"" script
# Summary of differences
# * `comm` flag -23 used instead of -13, this displays missing parts in .env.example
# * stdout messages
","Shell"
"Assay","FDA/precisionFDA","utils/scripts/check-missing-env-variables.sh",".sh","1431","44","#!/bin/bash

# Note(samuel) - didn't manage to implement it in makefile
# My efforts ended on redirecting pipeline output '<(...)'
# comm -13 <(grep -v '^#\|^\s*$' ""$dotenv_file"" | awk -F = '{print $1}' | sort)
# Reason for this bash script

# Formatting util
function print_missing_env_list() {
    max_len=$(awk 'BEGIN {max=0} {if (length>(max + 0)) max=length fi} END {print max}' <(echo ""$1""))
    printf '%0.s-' $(seq 1 $max_len) >&2
    echo >&2
    echo ""$1"" >&2
    printf '%0.s-' $(seq 1 $max_len) >&2
    echo >&2
}

# Expected to be .env
dotenv_file=""$1""
# Expected to be .env.spec
spec_file=""$2""

if [[ ! -f ""$dotenv_file"" ]]; then
    echo ""File \""$dotenv_file\"" not found"" >&2
    exit 1
fi
if [[ ! -f ""$spec_file"" ]]; then
    echo ""File \""$spec_file\"" not found"" >&2
    exit 1
fi

# Pipeline Explanation
# * `grep` - filter out comments from .env file and skip empty line
# * `awk` - extract just variable names - discard values
# * `sort` - sorts
# This is done for both files
comparison_result=$(comm -13 <(grep -v '^#\|^\s*$' ""$dotenv_file"" | awk -F = '{print $1}' | sort) <(grep -v '^#\|^\s*$' ""$spec_file"" | awk -F = '{print $1}' | sort))
if [[ $comparison_result ]]; then
    echo ""WARNING: Following env variables missing in \""$dotenv_file\"" according to \""$spec_file\"""" >&2
    print_missing_env_list ""$comparison_result""
    exit 1
fi
echo ""OK: \""$dotenv_file\"" matches \""$spec_file\"", no missing config""
","Shell"
"Assay","FDA/precisionFDA","go/pfda_test.go",".go","25574","803","package main

import (
	""flag""
	""os""
	""testing""

	""dnanexus.com/precision-fda-cli/precisionfda""
	""dnanexus.com/precision-fda-cli/precisionfda/test""
)

func TestMain(m *testing.M) {
	// Not the most elegant way of avoiding usage spew in unit test output
	// but works since main uses the default flagset
	flag.Usage = func() {}

	// Run tests
	exitVal := m.Run()

	// Teardown

	// Exit with exit value from tests
	os.Exit(exitVal)
}

type InputFlags struct {
	InputFilePath    *string
	FilePaths        *[]string
	FolderID         *string
	SpaceID          *string
	AssetFolderPath  *string
	AssetName        *string
	ReadmeFilePath   *string
	FileID           *string
	Args             *[]string
	Arg              *string
	OutputFilePath   *string
	EntityID         *string
	EntityType       *string
	Overwrite        *string
	FlagWithProgress bool

	// optional flags for ls, list-spaces, download, mkdir, head
	FlagRecursive     bool
	FlagHelp          bool
	FlagBrief         bool
	FlagJson          bool
	FlagFoldersOnly   bool
	FlagFilesOnly     bool
	FlagLocked        bool
	FlagUnactivated   bool
	FlagPhiProtected  bool
	FlagGroups        bool
	FlagReview        bool
	FlagPrivate       bool
	FlagAdministrator bool
	FlagGovernment    bool
	FlagParents       bool
	FlagPublic		  bool
	FlagLines         int
}

func (f *InputFlags) Reset() {
	f.InputFilePath = nil
	f.FilePaths = nil
	f.FolderID = nil
	f.SpaceID = nil
	f.AssetFolderPath = nil
	f.AssetName = nil
	f.ReadmeFilePath = nil
	f.FileID = nil
	f.Args = nil
	f.Arg = nil
	f.OutputFilePath = nil
	f.EntityID = nil
	f.EntityType = nil
	f.FlagRecursive = false
	f.FlagWithProgress = true
	// optional flags - default to false
	f.FlagHelp = false
	f.FlagJson = false
	f.FlagFoldersOnly = false
	f.FlagFilesOnly = false
	f.FlagBrief = false
	f.FlagLocked = false
	f.FlagUnactivated = false
	f.FlagReview = false
	f.FlagGroups = false
	f.FlagPrivate = false
	f.FlagAdministrator = false
	f.FlagGovernment = false
	f.FlagParents = false
	f.FlagPublic = false
	f.FlagLines = 10
}

func runMainInternal(surpressStdout bool) int {
	var originalStdout = os.Stdout
	if surpressStdout {
		os.Stdout, _ = os.Open(os.DevNull)
	}
	returnCode := mainInternal()
	if surpressStdout {
		os.Stdout = originalStdout
	}
	return returnCode
}

func TestMainNoArgs(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	os.Args = []string{""pfda""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
}

func TestWrongCmdError(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	os.Args = []string{""pfda"", ""--cmd"", ""foobar""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
}

func TestPositionalCmdInWrongPlace(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	invokeDownload = func(client precisionfda.IPFDAClient, args *[]string, folderID *string, spaceID *string, public bool, recursive bool, outputFilePath *string, overwriteFile *string) error {
		funcWasCalled = true
		return nil
	}

	os.Args = []string{""pfda"", ""--file-id"", ""file-12345"", ""--key"", ""HELLO"", ""download""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)

	invokeUploadFile = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		return nil
	}

	os.Args = []string{""pfda"", ""--file"", ""./README.md"", ""--key"", ""HELLO"", ""upload-file""}
	returnCode = runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
}

func TestPositionalCmdAndCommandBothSpecified(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	invokeUploadFile = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		return nil
	}
	os.Args = []string{""pfda"", ""upload-file"", ""--cmd"", ""upload-file"", ""--file"", ""./README.md"", ""--key"", ""HELLO""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
}

func TestInvokeUploadFile(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeUploadFile = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		input.InputFilePath = inputFilePath
		input.FolderID = folderID
		input.SpaceID = spaceID
		return nil
	}

	// Case: --file is missing
	os.Args = []string{""pfda"", ""--cmd"", ""upload-file""}
	returnCode := runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	reset()

	// Case: --file does not exist
	os.Args = []string{""pfda"", ""upload-file"", ""--file"", ""./IDoNot.Exist""}
	returnCode = runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, (*string)(nil), input.InputFilePath)
	reset()

	// Case: --file does not exist with --cmd
	os.Args = []string{""pfda"", ""--cmd"", ""upload-file"", ""--file"", ""./IDoNot.Exist""}
	returnCode = runMainInternal(true)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, (*string)(nil), input.InputFilePath)
	reset()

	// Case: upload-file upload success
	os.Args = []string{""pfda"", ""--cmd"", ""upload-file"", ""--file"", ""./README.md"", ""--key"", ""HELLO""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""README.md"", *input.InputFilePath)
	reset()

	// Case: upload-file success with folder and space
	os.Args = []string{""pfda"", ""upload-file"", ""--file"", ""./Dockerfile"", ""--key"", ""HELLO"", ""--folder-id"", ""test-folder"", ""--space-id"", ""test-space""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""Dockerfile"", *input.InputFilePath)
	test.Equals(t, ""test-folder"", *input.FolderID)
	test.Equals(t, ""test-space"", *input.SpaceID)
	reset()

	invokeUploadMultipleFiles = func(client precisionfda.IPFDAClient, filePaths *[]string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		input.FilePaths = filePaths
		input.FolderID = folderID
		input.SpaceID = spaceID
		return nil
	}

	// Case: upload-file success with multiple files/folders separated by commas
	os.Args = []string{""pfda"", ""upload-file"", ""file1.csv"", ""file2.xlsx"", ""./src/file123.txt"", ""./src/folder123"", ""--key"", ""HELLO""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file1.csv"", ""file2.xlsx"", ""./src/file123.txt"", ""./src/folder123""}, *input.FilePaths)
	reset()

	// Case: upload-file success with folder and space with --cmd
	os.Args = []string{""pfda"", ""--cmd"", ""upload-file"", ""--file"", ""./Dockerfile"", ""--key"", ""HELLO"", ""--folder-id"", ""test-folder"", ""--space-id"", ""test-space""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""Dockerfile"", *input.InputFilePath)
	test.Equals(t, ""test-folder"", *input.FolderID)
	test.Equals(t, ""test-space"", *input.SpaceID)
	reset()

	invokeUploadFolder = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		input.InputFilePath = inputFilePath
		input.FolderID = folderID
		input.SpaceID = spaceID
		return nil
	}

	// Case: upload-file with single folder
	os.Args = []string{""pfda"", ""upload-file"", ""helpers/"", ""--key"", ""HELLO""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""helpers"", *input.InputFilePath)
	reset()

}

func TestInvokeUploadAsset(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeUploadAsset = func(client precisionfda.IPFDAClient, assetFolderPath *string, assetName *string, readmeFilePath *string, inputChunkSize *int, inputNumRoutines *int) error {
		funcWasCalled = true
		input.AssetFolderPath = assetFolderPath
		input.AssetName = assetName
		input.ReadmeFilePath = readmeFilePath
		return nil
	}

	// Case: upload-asset with folder and space
	os.Args = []string{""pfda"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""./precisionfda"", ""--readme"", ""./README.md""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""./precisionfda"", *input.AssetFolderPath)
	test.Equals(t, ""test-asset.tar.gz"", *input.AssetName)
	test.Equals(t, ""./README.md"", *input.ReadmeFilePath)
	reset()

	// Case: upload-asset with folder and space with --cmd
	os.Args = []string{""pfda"", ""--cmd"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""./precisionfda"", ""--readme"", ""./README.md""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""./precisionfda"", *input.AssetFolderPath)
	test.Equals(t, ""test-asset.tar.gz"", *input.AssetName)
	test.Equals(t, ""./README.md"", *input.ReadmeFilePath)
	reset()

	// Case: upload-asset fails if --root doesn't exist
	os.Args = []string{""pfda"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""root-folder"", ""--readme"", ""./README.md""}
	returnCode = runMainInternal(false)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, (*string)(nil), input.AssetFolderPath)
	test.Equals(t, (*string)(nil), input.AssetName)
	test.Equals(t, (*string)(nil), input.ReadmeFilePath)
	reset()

	// Case: upload-asset fails if --file doesn't exist
	os.Args = []string{""pfda"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""./i-do-not-exist"", ""--readme"", ""./README.md""}
	returnCode = runMainInternal(false)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, (*string)(nil), input.AssetFolderPath)
	test.Equals(t, (*string)(nil), input.AssetName)
	test.Equals(t, (*string)(nil), input.ReadmeFilePath)
	reset()

	// Case: upload-asset with all parameters
	os.Args = []string{""pfda"", ""--cmd"", ""upload-asset"", ""--name"", ""test-asset.tar.gz"", ""--key"", ""HELLO"", ""--root"", ""./"", ""--readme"", ""./README.md""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""./"", *input.AssetFolderPath)
	test.Equals(t, ""test-asset.tar.gz"", *input.AssetName)
	test.Equals(t, ""./README.md"", *input.ReadmeFilePath)
	reset()
}

func TestInvokeDownloadFile(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeDownload = func(client precisionfda.IPFDAClient, args *[]string, folderID *string, spaceID *string, public bool, recursive bool, outputFilePath *string, overwriteFile *string) error {
		funcWasCalled = true
		input.Args = args
		input.FolderID = folderID
		input.SpaceID = spaceID
		input.FlagRecursive = recursive
		input.OutputFilePath = outputFilePath
		input.Overwrite = overwriteFile
		return nil
	}

	// Case: download with --cmd
	os.Args = []string{""pfda"", ""--cmd"", ""download"", ""--file-id"", ""file-12345"", ""--key"", ""HELLO""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file-12345""}, *input.Args)
	reset()

	// Case: download without --cmd, with outputFilePath
	os.Args = []string{""pfda"", ""download"", ""--file-id"", ""file-23456"", ""--key"", ""HELLO"", ""--output"", ""/tmp/canyoupleaseworksoicanbedonefortheday.argh""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file-23456""}, *input.Args)
	reset()

	// Case: download without --cmd, with -overwrite flag
	os.Args = []string{""pfda"", ""download"", ""--file-id"", ""file-23456"", ""--key"", ""HELLO"", ""--overwrite"", ""true""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file-23456""}, *input.Args)
	test.Equals(t, ""true"", *input.Overwrite)
	reset()

	// Case: download whole folder with -overwrite and recursive flag
	os.Args = []string{""pfda"", ""download"", ""-folder-id"", ""12223"", ""-key"", ""HELLO"", ""-overwrite"", ""true"", ""-recursive""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""12223"", *input.FolderID)
	test.Equals(t, ""true"", *input.Overwrite)
	test.Equals(t, true, input.FlagRecursive)
	reset()

	// Case: download without --cmd, with -overwrite flag
	os.Args = []string{""pfda"", ""download"", ""file-23456"", ""file-2221"", ""file-11111"", ""-key"", ""HELLO"", ""-overwrite"", ""true""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""file-23456"", ""file-2221"", ""file-11111""}, *input.Args)
	test.Equals(t, ""true"", *input.Overwrite)
	reset()

}

func TestInvokeListing(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeListing = func(client precisionfda.IPFDAClient, folderID *string, spaceID *string, flags map[string]bool) error {
		funcWasCalled = true
		input.FolderID = folderID
		input.SpaceID = spaceID
		input.FlagBrief = flags[""brief""]
		input.FlagFilesOnly = flags[""files_only""]
		input.FlagFoldersOnly = flags[""folders_only""]
		input.FlagJson = flags[""json""]
		return nil
	}

	// Case: ls without folderID and spaceID and no flags
	os.Args = []string{""pfda"", ""ls"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, false, input.FlagBrief)
	test.Equals(t, false, input.FlagFilesOnly)
	test.Equals(t, false, input.FlagFoldersOnly)
	test.Equals(t, false, input.FlagJson)
	reset()

	// Case: ls with help - only show help and do not call the func
	os.Args = []string{""pfda"", ""ls"", ""--help""}
	returnCode = runMainInternal(false)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, false, input.FlagBrief)
	test.Equals(t, false, input.FlagFilesOnly)
	test.Equals(t, false, input.FlagFoldersOnly)
	test.Equals(t, false, input.FlagJson)
	reset()

	// Case: ls return only files in json format
	os.Args = []string{""pfda"", ""ls"", ""--key"", ""AUTH_KEY"", ""--json"", ""--files""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, false, input.FlagBrief)
	test.Equals(t, true, input.FlagFilesOnly)
	test.Equals(t, false, input.FlagFoldersOnly)
	test.Equals(t, true, input.FlagJson)
	reset()

	// Case: ls return only folders in brief format
	os.Args = []string{""pfda"", ""ls"", ""--key"", ""AUTH_KEY"", ""--brief"", ""--folders""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, true, input.FlagBrief)
	test.Equals(t, false, input.FlagFilesOnly)
	test.Equals(t, true, input.FlagFoldersOnly)
	test.Equals(t, false, input.FlagJson)
	reset()
}

func TestInvokeListSpaces(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeListSpaces = func(client precisionfda.IPFDAClient, flags map[string]bool) error {
		funcWasCalled = true
		input.FlagJson = flags[""json""]
		input.FlagLocked = flags[""locked""]
		input.FlagUnactivated = flags[""unactivated""]
		input.FlagReview = flags[""review""]
		input.FlagGroups = flags[""groups""]
		input.FlagPrivate = flags[""private_type""]
		input.FlagAdministrator = flags[""administrator""]
		input.FlagGovernment = flags[""government""]
		return nil
	}

	// Case: list-spaces with no flags
	os.Args = []string{""pfda"", ""list-spaces"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, false, input.FlagLocked)
	test.Equals(t, false, input.FlagUnactivated)
	test.Equals(t, false, input.FlagReview)
	test.Equals(t, false, input.FlagGroups)
	test.Equals(t, false, input.FlagPrivate)
	test.Equals(t, false, input.FlagAdministrator)
	test.Equals(t, false, input.FlagGovernment)
	test.Equals(t, false, input.FlagJson)
	reset()

	// Case: list-spaces with help - only show help and do not call the func
	os.Args = []string{""pfda"", ""list-spaces"", ""--help""}
	returnCode = runMainInternal(false)
	test.Equals(t, 1, returnCode)
	test.Equals(t, false, funcWasCalled)
	test.Equals(t, false, input.FlagLocked)
	test.Equals(t, false, input.FlagUnactivated)
	test.Equals(t, false, input.FlagReview)
	test.Equals(t, false, input.FlagGroups)
	test.Equals(t, false, input.FlagPrivate)
	test.Equals(t, false, input.FlagAdministrator)
	test.Equals(t, false, input.FlagGovernment)
	test.Equals(t, false, input.FlagJson)
	reset()

	// Case: list-spaces only locked and in json format
	os.Args = []string{""pfda"", ""list-spaces"", ""--key"", ""AUTH_KEY"", ""--locked"", ""--json""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, true, input.FlagLocked)
	test.Equals(t, false, input.FlagUnactivated)
	test.Equals(t, false, input.FlagReview)
	test.Equals(t, false, input.FlagGroups)
	test.Equals(t, false, input.FlagPrivate)
	test.Equals(t, false, input.FlagAdministrator)
	test.Equals(t, false, input.FlagGovernment)
	test.Equals(t, true, input.FlagJson)
	reset()

	// Case: list spaces - multi-type filter
	os.Args = []string{""pfda"", ""list-spaces"", ""--key"", ""AUTH_KEY"", ""--groups"", ""--private"", ""--administrator"", ""--review"", ""--government""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, false, input.FlagLocked)
	test.Equals(t, false, input.FlagUnactivated)
	test.Equals(t, true, input.FlagReview)
	test.Equals(t, true, input.FlagGroups)
	test.Equals(t, true, input.FlagPrivate)
	test.Equals(t, true, input.FlagAdministrator)
	test.Equals(t, true, input.FlagGovernment)
	test.Equals(t, false, input.FlagJson)
	reset()
}

func TestInvokeDescribeEntity(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeDescribe = func(client precisionfda.IPFDAClient, entityID *string, entityType *string) error {
		funcWasCalled = true
		input.EntityID = entityID
		input.EntityType = entityType
		return nil
	}

	// Case: describe-app entity - old syntax
	os.Args = []string{""pfda"", ""describe-app"", ""--app-id"", ""APP_ID"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""APP_ID"", *input.EntityID)
	test.Equals(t, ""app"", *input.EntityType)
	reset()

	// Case: describe-app entity - new syntax
	os.Args = []string{""pfda"", ""describe-app"", ""APP_ID"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""APP_ID"", *input.EntityID)
	test.Equals(t, ""app"", *input.EntityType)
	reset()

	// Case: describe-workflow entity - old syntax
	os.Args = []string{""pfda"", ""describe-workflow"", ""--workflow-id"", ""WORKFLOW_ID"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""WORKFLOW_ID"", *input.EntityID)
	test.Equals(t, ""workflow"", *input.EntityType)
	reset()

	// Case: describe-workflow entity - new syntax
	os.Args = []string{""pfda"", ""describe-workflow"", ""WORKFLOW_ID"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""WORKFLOW_ID"", *input.EntityID)
	test.Equals(t, ""workflow"", *input.EntityType)
	reset()
}

func TestInvokeMkdir(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeMkdir = func(client precisionfda.IPFDAClient, names *[]string, folderID *string, spaceID *string, parents bool) error {
		funcWasCalled = true
		input.Args = names
		input.FolderID = folderID
		input.SpaceID = spaceID
		input.FlagParents = parents
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""mkdir"", ""dir1"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""dir1""}, *input.Args)
	reset()

	// Case: mkdir with multiple args
	os.Args = []string{""pfda"", ""mkdir"", ""dir1"",""dir2"", ""dir3"", ""-folder-id"",""123"", ""-space-id"",""333"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""dir1"", ""dir2"",""dir3""}, *input.Args)
	test.Equals(t, ""123"", *input.FolderID)
	test.Equals(t, ""333"", *input.SpaceID)
	test.Equals(t, false, input.FlagParents)
	reset()

	// Case: mkdir nested folders with parents flag
	os.Args = []string{""pfda"", ""mkdir"", ""dir1/dir2/dir3"", ""-p"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""dir1/dir2/dir3""}, *input.Args)
	test.Equals(t, true, input.FlagParents)
	reset()
}

func TestInvokeRmdir(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeRmdir = func(client precisionfda.IPFDAClient, args *[]string) error {
		funcWasCalled = true
		input.Args = args
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""rmdir"", ""123"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""123""}, *input.Args)
	reset()

	// Case: mkdir with multiple args
	os.Args = []string{""pfda"", ""rmdir"", ""123"", ""333"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""123"", ""333""}, *input.Args)

	test.Equals(t, false, input.FlagParents)
	reset()
}

func TestInvokeRm(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeRm = func(client precisionfda.IPFDAClient, args *[]string,folderID *string, spaceID *string) error {
		funcWasCalled = true
		input.Args = args
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""rm"", ""123"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""123""}, *input.Args)
	reset()

	// Case: mkdir with multiple args
	os.Args = []string{""pfda"", ""rm"", ""123*"", ""333"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, []string{""123*"", ""333""}, *input.Args)

	test.Equals(t, false, input.FlagParents)
	reset()
}

func TestInvokeHead(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeHead = func(client precisionfda.IPFDAClient, arg *string, lines int) error {
		funcWasCalled = true
		input.Arg = arg
		input.FlagLines = lines
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""head"", ""testfile.txt"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""testfile.txt"", *input.Arg)
	test.Equals(t, 10, input.FlagLines)
	reset()

	// Case: mkdir with multiple args
	os.Args = []string{""pfda"", ""head"", ""testfile.txt"", ""-lines"", ""222"", ""--key"", ""AUTH_KEY""}
	returnCode = runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""testfile.txt"", *input.Arg)
	test.Equals(t, 222, input.FlagLines)
	reset()
}

func TestInvokeCat(t *testing.T) {
	oldArgs := os.Args
	defer func() { os.Args = oldArgs }()

	var funcWasCalled = false
	input := &InputFlags{}
	var reset = func() {
		funcWasCalled = false
		input.Reset()
	}

	invokeCat = func(client precisionfda.IPFDAClient, arg *string) error {
		funcWasCalled = true
		input.Arg = arg
		input.FlagLines = -1
		return nil
	}

	// Case: simple mkdir
	os.Args = []string{""pfda"", ""cat"", ""testfile.txt"", ""--key"", ""AUTH_KEY""}
	returnCode := runMainInternal(false)
	test.Equals(t, 0, returnCode)
	test.Equals(t, true, funcWasCalled)
	test.Equals(t, ""testfile.txt"", *input.Arg)
	test.Equals(t, -1, input.FlagLines)
	reset()

}
","Go"
"Assay","FDA/precisionFDA","go/build-dist.sh",".sh","1130","45","#!/bin/sh
#
# N.B. run from precision-fda/go

VERSION=2.4
COMMITID=`git rev-parse HEAD`

BuildAndPackage() {
    PLATFORM=$1
    ARCH=$2
    BUILDTIME=$(date +%Y-%m-%d-%H%M%S)
    echo ""Building pfda CLI (v$VERSION) for $PLATFORM $ARCH""
    docker run --rm --mount type=bind,source=""$(pwd)"",target=/go/src/dnanexus.com/precision-fda-cli \
           -e GOOS=""$PLATFORM"" -e GOARCH=""$ARCH"" -e COMMITID=""$COMMITID"" \
           -e VERSION=""$VERSION"" -e BUILDTIME=""$BUILDTIME"" precisionfda-cli
    cd ./dist

    OUT_FILE=pfda_${PLATFORM}_${ARCH}
    if [ ! -f ""$OUT_FILE"" ]; then
        echo
        echo ""$OUT_FILE does not exist. Exiting""
        exit 1
    fi

    EXEC_NAME=""pfda""
    if [ $PLATFORM = ""windows"" ]; then
        EXEC_NAME=""pfda.exe""
    fi

    mv ${OUT_FILE} ${EXEC_NAME}

    if [ $PLATFORM = ""windows"" ]; then
        zip -rj pfda-${PLATFORM}-${VERSION}.zip ${EXEC_NAME}
    else
        tar -czvf pfda-${PLATFORM}-${VERSION}.tar.gz ${EXEC_NAME}
    fi
    rm ${EXEC_NAME}

    cd ..
}

BuildAndPackage 'linux' 'amd64'
BuildAndPackage 'darwin' 'amd64' # N.B. darwin is macOS
BuildAndPackage 'windows' 'amd64'
","Shell"
"Assay","FDA/precisionFDA","go/pfda.go",".go","24362","765","// PrecisionFDA CLI
// Version 2.4
package main

import (
	""crypto/tls""
	""encoding/json""
	""flag""
	""fmt""
	""net/http""
	""os""
	""path/filepath""
	""runtime""
	""strconv""
	""strings""

	_ ""crypto/tls/fipsonly""
	""dnanexus.com/precision-fda-cli/helpers""
	""dnanexus.com/precision-fda-cli/precisionfda""
	""rsc.io/goversion/version""
)

// CONSTANTS
const defaultNumRoutines = 10
const defaultChunkSize = 1 << 24 // default 16.8MB (min. 5MB)
const defaultSkipVerify = ""false""
const usageString = `
****************************
PFDA COMMAND LINE TOOL v2.4
****************************

To upload a file:
   pfda upload-file </PATH/TO/FILE> [-key <KEY>] 

To upload a file to a space:
   pfda upload-file </PATH/TO/FILE> [-key <KEY>]

To upload an asset:
   pfda upload-asset -name <NAME{.tar,.tar.gz}> -root </PATH/TO/ROOT/FOLDER> -readme <README{.txt,.md}> [-key <KEY>]

To download a file:
   pfda download <FILE_ID> [-key <KEY>]

To list files:
   pfda ls [-key <KEY>]

To list available spaces:
   pfda list-spaces [-key <KEY>]

To describe an app:
   pfda describe-app <APP_ID> [-key <KEY>]

To describe a workflow:
   pfda describe-workflow <WORKFLOW_ID> [-key <KEY>] 

To create a new folder:
   pfda mkdir <NAME> [-key <KEY>]

To delete a folder:
   pfda rmdir <FOLDER_ID> [-key <KEY>]

To delete a file:
   pfda rm <FILE_ID> [-key <KEY>]

To print content of a file:
   pfda cat <FILE_ID> [-key <KEY>]

To print first 10 lines of a file:
   pfda head <FILE_ID> [-key <KEY>]

To get current space id (on workstation):
   pfda get-space-id [-key <KEY>]

To print version info and exit :
   pfda -version

All available commands:
   pfda describe-app
   pfda describe-workflow
   pfda download
   pfda ls
   pfda list-spaces
   pfda upload-asset
   pfda upload-file
   pfda mkdir
   pfda rmdir
   pfda rm
   pfda head
   pfda cat

Command specific help section with description, examples and available flags:
   pfda <COMMAND> -help

Full documentation can be found in the Docs section of the precisionFDA website - https://precision.fda.gov/docs/cli`

//
// N.B. the -cmd flag exists and is now deprecated, but the following should all work
//
// # Testing upload to localhost
// $ ./pfda upload-file -server localhost:3000 -skipverify true -key $KEY -file fileYouWantToUpload.pdf
// $ ./pfda -cmd upload-file -server localhost:3000 -skipverify true -key $KEY -file fileYouWantToUpload.pdf
//
// # Testing download from localhost
// $ ./pfda download -server localhost:3000 -skipverify true -key $KEY -file file-yourfileuuid-1
// $ ./pfda -cmd download -server localhost:3000 -skipverify true -key $KEY -file file-yourfileuuid-1
//
// # Testing the API for file download
// $ ./pfda api -server localhost:3000 -skipverify true -key $KEY -route ""files/file-G70fbKj0qp9YGkg24kGxQvF4-1/download"" -json '{ ""format"": ""json"" }'
// $ ./pfda -cmd api -server localhost:3000 -skipverify true -key $KEY -route ""files/file-G70fbKj0qp9YGkg24kGxQvF4-1/download"" -json '{ ""format"": ""json"" }'

// GLOBAL VARIABLES
var defaultURL = ""precision.fda.gov""

// these varaibles are populated by -ldflags -X command line options
var (
	commitID  string
	Version   string
	BuildTime string
	OsArch    string
)

var pfdaclient *precisionfda.PFDAClient

// ACTION FUNCTIONS
// Wrapping calls to pfdaclient allow us to replace these functions in pfda_test.go with mocks
var invokeUploadFile = func(client precisionfda.IPFDAClient, inputFilePath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadFile(*inputFilePath, *folderID, *spaceID, true)
}

var invokeUploadStdinFile = func(client precisionfda.IPFDAClient, fileName *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadStdin(*fileName, *folderID, *spaceID, true)

}

var invokeUploadMultipleFiles = func(client precisionfda.IPFDAClient, filePaths *[]string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadMultipleFiles(*filePaths, *folderID, *spaceID)
}

var invokeUploadFolder = func(client precisionfda.IPFDAClient, inputPath *string, folderID *string, spaceID *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadFolder(*inputPath, *folderID, *spaceID)
}

var invokeUploadAsset = func(client precisionfda.IPFDAClient, assetFolderPath *string, assetName *string, readmeFilePath *string, inputChunkSize *int, inputNumRoutines *int) error {
	client.SetChunkSize(*inputChunkSize)
	client.SetNumRoutines(*inputNumRoutines)
	return client.UploadAsset(*assetFolderPath, *assetName, *readmeFilePath)
}

var invokeDownload = func(client precisionfda.IPFDAClient, args *[]string, folderID *string, spaceID *string, public bool, recursive bool, outputFilePath *string, overwriteFile *string) error {
	return client.Download(*args, *folderID, *spaceID, public, recursive, *outputFilePath, *overwriteFile)
}

var invokeDescribe = func(client precisionfda.IPFDAClient, entityID *string, entityType *string) error {
	return client.DescribeEntity(*entityID, *entityType)
}

var invokeListSpaces = func(client precisionfda.IPFDAClient, flags map[string]bool) error {
	return client.ListSpaces(flags)
}

var invokeListing = func(client precisionfda.IPFDAClient, folderID *string, spaceID *string, flags map[string]bool) error {
	return client.Ls(*folderID, *spaceID, flags)
}

var invokeMkdir = func(client precisionfda.IPFDAClient, names *[]string, folderID *string, spaceID *string, parents bool) error {
	return client.Mkdir(*names, *folderID, *spaceID, parents)
}

var invokeRmdir = func(client precisionfda.IPFDAClient, args *[]string) error {
	return client.Rmdir(*args)
}

var invokeRm = func(client precisionfda.IPFDAClient, args *[]string, folderID *string, spaceID *string) error {
	return client.Rm(*args, *folderID, *spaceID)
}

var invokeHead = func(client precisionfda.IPFDAClient, arg *string, lines int) error {
	return client.Head(*arg, lines)
}

var invokeCat = func(client precisionfda.IPFDAClient, arg *string) error {
	// reusing head command with unreachable limit
	return client.Head(*arg, -1)
}

var invokeRefreshToken = func(client precisionfda.IPFDAClient, autoRefresh bool) (string, error) {
	return client.RefreshToken(autoRefresh)
}

var invokeGetLatestVersion = func(client precisionfda.IPFDAClient) (string, error) {
	return client.GetLatestVersion()
}

func main() {
	returnCode := mainInternal()
	os.Exit(returnCode)
}

func mainInternal() int {
	// This isn't necessary for the CLI to run correctly, but we define command line flags
	// inside mainInternal so that they can be unit tested
	flag.CommandLine = flag.NewFlagSet(os.Args[0], flag.ExitOnError)

	command := flag.String(""cmd"", """", ""[Deprecated - please use the format ./pfda <cmd>] Command to execute. Must be one of ['upload-file','upload-asset','api']."")
	authKey := flag.String(""key"", """", ""Authorization key. Required if a previous config doesn't exist."")
	apiRoute := flag.String(""route"", """", ""Name of precisionFDA API route to call."")
	jsonInput := flag.String(""json-payload"", """", ""JSON payload for specified API call (if any)."")
	inputFilePath := flag.String(""file"", """", ""Path to file for 'upload-file'"")
	assetFolderPath := flag.String(""root"", """", ""Path to root folder for 'upload-asset'"")
	fileName := flag.String(""name"", """", ""Name of uploaded file."")
	readmeFilePath := flag.String(""readme"", """", ""Readme file for uploaded asset. Must end with '.txt' or '.md'."")
	fileID := flag.String(""file-id"", """", ""File ID of the file to be downloaded"")
	appID := flag.String(""app-id"", """", ""App ID of the app to be described"")
	workflowID := flag.String(""workflow-id"", """", ""Workflow ID of the workflow to be described"")
	folderID := flag.String(""folder-id"", """", ""Folder ID of the target folder"")
	spaceID := flag.String(""space-id"", """", ""Space ID of the target space"")
	outputFilePath := flag.String(""output"", """", ""[optional] File path to write api call response data. Defaults to stdout."")
	inputChunkSize := flag.Int(""chunksize"", defaultChunkSize, ""[optional] Size of each upload chunk in bytes (Min 5MB,/Max 2GB)."")
	inputNumRoutines := flag.Int(""threads"", defaultNumRoutines, ""[optional] Maximum number of upload threads to spawn (Max 100)."")
	server := flag.String(""server"", defaultURL, ""[optional] Server to connect and make requests to."")
	skipVerify := flag.String(""skipverify"", defaultSkipVerify, ""[optional] Boolean string to skip certificate verification."")
	pfda_version := flag.Bool(""version"", false, ""[optional] Print version"")

	// help flag - does not run any command just prints help info
	flagHelp := flag.Bool(""help"", false, ""[optional] Print help info for the particular command"")
	flagHelpShort := flag.Bool(""h"", false, ""[optional] Print help info for the particular command"") // is there a better way to have aliases ??

	// optional flags for adjusting the desired output values, format and behavior
	flagBrief := flag.Bool(""brief"", false, ""[optional] Only present brief info"")
	flagJson := flag.Bool(""json"", false, ""[optional] Present result as JSON"")
	flagFilesOnly := flag.Bool(""files"", false, ""[optional] Only present files"")
	flagFoldersOnly := flag.Bool(""folders"", false, ""[optional] Only present folders"")
	flagPublic := flag.Bool(""public"", false, ""[optional] Only present public scope"")
	flagMe := flag.Bool(""me"", false, ""[optional] Only present private scope"")
	flagLocked := flag.Bool(""locked"", false, ""[optional] Only present locked spaces"")
	flagUnactivated := flag.Bool(""unactivated"", false, ""[optional] Only present unactivated spaces"")
	flagProtected := flag.Bool(""protected"", false, ""[optional] Only present protected spaces"")
	flagGroups := flag.Bool(""groups"", false, ""[optional] Only present groups spaces"")
	flagReview := flag.Bool(""review"", false, ""[optional] Only present review spaces"")
	flagPrivate := flag.Bool(""private"", false, ""[optional] Only present private spaces"")
	flagAdministrator := flag.Bool(""administrator"", false, ""[optional] Only present administrator spaces"")
	flagGovernment := flag.Bool(""government"", false, ""[optional] Only present government spaces"")
	flagOverwriteFile := flag.String(""overwrite"", """", ""[optional] Preselect overwrite option what to do with already existing file."")
	flagRecursive := flag.Bool(""recursive"", false, ""[optional] Recurse into a directory."")
	flagRecursiveShort := flag.Bool(""r"", false, ""[optional] Recurse into a directory."")
	flagParents := flag.Bool(""parents"", false, ""[optional] No error if existing, make parent directories as needed"")
	flagParentsShort := flag.Bool(""p"", false, ""[optional] No error if existing, make parent directories as needed"")
	flagLines := flag.Int(""lines"", 10, ""[optional] Number of lines to print, default 10"")

	// Support for ./pfda upload-file option of specifying a command, making -cmd optional
	var positionalCmd string
	var args []string
	var index int
	if len(os.Args) > 1 && !strings.HasPrefix(os.Args[1], ""-"") {
		// Storing first positional arg after ./pfda
		positionalCmd = os.Args[1]
		// check possible non-flag args
		args, index = helpers.ParseArgsUntilFlag(os.Args)
		flag.CommandLine.Parse(os.Args[index:])

	} else {
		flag.Parse()
	}

	if positionalCmd != """" {
		if *command == """" {
			*command = positionalCmd
		} else {
			return helpers.InputError(""Error input >>> both positional command and -cmd option specified. Please remove one or the other"")
		}
	}

	// always check if config != nil
	config, configErr := helpers.GetConfig()
	if configErr != nil && !os.IsNotExist(configErr) {
		fmt.Println(""Error >>> "", configErr)
	}

	if *server != """" && *server != defaultURL {
		pfdaclient = precisionfda.NewPFDAClient(*server)
	} else {
		if config != nil && config.Server != """" {
			pfdaclient = precisionfda.NewPFDAClient(config.Server)
		} else {
			pfdaclient = precisionfda.NewPFDAClient(defaultURL)
		}
	}
	pfdaclient.Platform = OsArch

	b, err := strconv.ParseBool(*skipVerify)
	if err != nil {
		helpers.PrintError(err)
		return 1
	}

	if b {
		// Setting '-skipverify' true will allow devs to connect to local instances with self-signed certs
		pfdaclient.Client.HTTPClient.Transport.(*http.Transport).TLSClientConfig = &tls.Config{
			InsecureSkipVerify: true,
			ServerName:         *server,
		}
	} else {
		pfdaclient.Client.HTTPClient.Transport.(*http.Transport).TLSClientConfig = &tls.Config{
			MinVersion: tls.VersionTLS12,
		}
	}

	// if -version flag was given, print pfda info
	if *pfda_version {
		printInfo(pfdaclient)
		// if only -version without any command , exit
		checkLatestVersion(pfdaclient)
		return 0
	}

	recursive := *flagRecursive || *flagRecursiveShort
	parents := *flagParents || *flagParentsShort
	help := *flagHelp || *flagHelpShort

	if !help && *command != """" {

		if config == nil {
			if *authKey == """" {
				return helpers.InputError(""Missing authorization key >>> Could not find config file and no key was provided with the command."")
			}
			pfdaclient.AuthKey = *authKey
		} else {
			if *authKey == """" {
				pfdaclient.AuthKey = config.Key
			} else {
				pfdaclient.AuthKey = *authKey
			}
			if *spaceID == """" {
				if !*flagMe && !*flagPublic {
					*spaceID = config.GetSpaceId()
				}
			}
		}
	}

	switch *command {
	case ""upload-asset"":
		if help {
			return helpers.PrintUploadAssetHelp()
		}

		if *assetFolderPath == """" {
			return helpers.InputError(""Root directory for the asset is required. Provide it as [-root <ASSET_ROOT>]."")
		}

		f, err := os.Stat(*assetFolderPath)
		if os.IsNotExist(err) || !f.IsDir() {
			return helpers.InputError(fmt.Sprintf(""Input asset folder path '%s' does not exist or is not a directory."", *assetFolderPath))
		}

		if *fileName == """" {
			return helpers.InputError(""Asset name (ending with .tar or .tar.gz) is required. Provide it as [-name <ASSET_NAME>]."")
		}

		if !strings.HasSuffix(*fileName, "".tar"") && !strings.HasSuffix(*fileName, "".tar.gz"") {
			return helpers.InputError(fmt.Sprintf(""Input asset name '%s' does not end with '.tar' or '.tar.gz'."", *fileName))
		}

		if *readmeFilePath == """" {
			return helpers.InputError(""Readme file for the asset (ending with .txt or .md) is required. Provide it as [-readme <ASSET_README>]."")
		}

		f, err = os.Stat(*readmeFilePath)
		if os.IsNotExist(err) || f.IsDir() {
			return helpers.InputError(fmt.Sprintf(""Input readme file path '%s' does not exist or is a directory."", *readmeFilePath))
		}

		err = invokeUploadAsset(pfdaclient, assetFolderPath, fileName, readmeFilePath, inputChunkSize, inputNumRoutines)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""upload-file"":
		if help {
			return helpers.PrintUploadFileHelp()
		}

		stdinData := false
		stdinFile := os.Stdin

		fi, _ := os.Stdin.Stat()
		if (fi.Mode() & os.ModeCharDevice) == 0 {
			stdinData = true
		}

		// if -file flag used, override args.
		if *inputFilePath != """" {
			args = []string{*inputFilePath}
		}

		if len(args) == 0 && stdinData {
			if *fileName == """" {
				return helpers.InputError(""Filename for stdin input is required. Provide it as [-name <FILE_NAME>]"")
			}
			err := invokeUploadStdinFile(pfdaclient, fileName, folderID, spaceID, inputChunkSize, inputNumRoutines)
			if err != nil {
				helpers.PrintError(err)
				return 1
			}
			defer stdinFile.Close()
			break
		}

		if len(args) != 0 && stdinData {
			return helpers.InputError(""Cannot combine multiple file sources. Use either args or stdin."")
		}

		if len(args) == 0 {
			return helpers.InputError(""Path for upload is required. Please provide it as an argument or stdin."")
		}

		if *fileName != """" {
			fmt.Println("">> Filename flag ignored for upload"")
		}

		// uploading more than one file/folder
		if len(args) > 1 {
			err = invokeUploadMultipleFiles(pfdaclient, &args, folderID, spaceID, inputChunkSize, inputNumRoutines)
			if err != nil {
				helpers.PrintError(err)
				return 1
			}
			// just one file/folder to upload
		} else {
			path := filepath.Clean(args[0])
			f, err := os.Stat(path)
			if os.IsNotExist(err) {
				return helpers.InputError(fmt.Sprintf(""Input path '%s' does not exist."", path))
			}

			if f.IsDir() {
				err = invokeUploadFolder(pfdaclient, &path, folderID, spaceID, inputChunkSize, inputNumRoutines)
				if err != nil {
					helpers.PrintError(err)
					return 1
				}
			} else {
				err = invokeUploadFile(pfdaclient, &path, folderID, spaceID, inputChunkSize, inputNumRoutines)
				if err != nil {
					helpers.PrintError(err)
					return 1
				}
			}
		}

	case ""download"":
		if help {
			return helpers.PrintDownloadHelp()
		}

		if *fileID == """" && *folderID == """" && *spaceID == """" && len(args) == 0 {
			return helpers.InputError(""File ID or name of the file to be downloaded is required: [-file-id <FILE_ID>]\n  Or provide either of Space and/or Folder ID you want to bulk-download: [-folder-id <FOLDER_ID> -space-id <SPACE_ID>]"")
		}

		if *fileID != """" && *folderID != """" {
			return helpers.InputError(""Cannot combine FileID and folderID flags"")
		}

		// we need tri-state as there is different behavior for -overwrite=false and completely ommited -overwrite flag
		if *flagOverwriteFile != ""true"" && *flagOverwriteFile != ""false"" && *flagOverwriteFile != """" {
			return helpers.InputError(""-overwrite flag only accepts true/false or omit it completely."")
		}

		// if -file-id flag used, override args.
		if *fileID != """" {
			args = []string{*fileID}
		}

		err := invokeDownload(pfdaclient, &args, folderID, spaceID, *flagPublic, recursive, outputFilePath, flagOverwriteFile)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""describe-app"":
		if help {
			return helpers.PrintDescribeAppHelp()
		}

		if *appID != """" {
			args = []string{*appID}
		}

		if len(args) == 0 {
			return helpers.InputError("">> App ID is required."")
		}

		var entityType = ""app""
		err := invokeDescribe(pfdaclient, &args[0], &entityType)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""describe-workflow"":
		if help {
			return helpers.PrintDescribeWorkflowHelp()
		}

		if *workflowID != """" {
			args = []string{*workflowID}
		}

		if len(args) == 0 {
			return helpers.InputError("">> Workflow ID is required."")
		}

		var entityType = ""workflow""
		err := invokeDescribe(pfdaclient, &args[0], &entityType)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""list-spaces"":
		if help {
			return helpers.PrintListSpacesHelp()
		}

		flags := map[string]bool{
			// state of space, must be exclusive
			""locked"":      *flagLocked,
			""unactivated"": *flagUnactivated,
			// types of space flag, multiple allowed
			""protected"":     *flagProtected,
			""review"":        *flagReview,
			""groups"":        *flagGroups,
			""private_type"":  *flagPrivate,
			""administrator"": *flagAdministrator,
			""government"":    *flagGovernment,
			// present as JSON / pretty print
			""json"": *flagJson,
		}
		err := invokeListSpaces(pfdaclient, flags)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""ls"":
		if help {
			return helpers.PrintLsHelp()
		}

		if *flagFilesOnly && *flagFoldersOnly {
			return helpers.InputError(""Cannot combine -folders and -files flags together. Please choose one of the flags only."")
		}

		flags := map[string]bool{
			""brief"":        *flagBrief,
			""files_only"":   *flagFilesOnly,
			""folders_only"": *flagFoldersOnly,
			// present as JSON / pretty print
			""json"": *flagJson,
		}

		if *flagPublic {
			flags[""public_scope""] = *flagPublic
		}

		err := invokeListing(pfdaclient, folderID, spaceID, flags)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""mkdir"":
		if help {
			return helpers.PrintMkdirHelp()
		}

		err := invokeMkdir(pfdaclient, &args, folderID, spaceID, parents)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""rmdir"":
		if help {
			return helpers.PrintRmdirHelp()
		}

		err := invokeRmdir(pfdaclient, &args)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""rm"":
		if help {
			return helpers.PrintRmHelp()
		}

		err := invokeRm(pfdaclient, &args, folderID, spaceID)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""head"":
		if help {
			return helpers.PrintHeadHelp()
		}
		err := invokeHead(pfdaclient, &args[0], *flagLines)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""cat"":
		if help {
			return helpers.PrintCatHelp()
		}
		err := invokeCat(pfdaclient, &args[0])
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case ""get-space-id"":
		if help {
			return helpers.PrintGetSpaceIdHelp()
		}

		if config == nil || config.GetSpaceId() == """" {
			// error while getting config or spaceID not set
			fmt.Println(""No space detected."")
			return 1
		}
		fmt.Println(config.GetSpaceId())

	case ""refresh-key"":
		// add option for auto-refresh later
		newToken, err := invokeRefreshToken(pfdaclient, false)
		if err != nil {
			fmt.Printf(""There was an error during key refresh action. Please provide new Key from pFDA website.\n"")
			return 1
		}

		if configErr != nil {
			fmt.Printf(""Could not save authorization key in config file '%s': %s\n"", helpers.ConfigPath, err.Error())
			return 1
		}

		config.Key = newToken
		helpers.SaveConfig(config)
		return 0

	case ""api"":
		if *apiRoute == """" {
			return helpers.InputError(""API route is required. Please provide it as '-route <API_ROUTE_NAME>'."")
		}

		if *jsonInput != """" && !isValidJSON(*jsonInput) {
			return helpers.InputError(fmt.Sprintf(""Provided JSON '%s' is not valid. Please provide the input in valid JSON format."", *jsonInput))
		}

		err := pfdaclient.CallAPI(*apiRoute, *jsonInput, *outputFilePath)
		if err != nil {
			helpers.PrintError(err)
			return 1
		}

	case """":
		// Empty command
		fmt.Println(usageString)
		checkLatestVersion(pfdaclient)
		return 1

	default:
		// Invalid, non-empty command
		fmt.Printf(""Command '%s' not found. Must be one of \n'cat' \n'describe-app' \n'describe-workflow' \n'download' \n'get-space-id' \n'head \n'list-spaces' \n'ls' \n'rm' \n'rmdir' \n'upload-asset' \n'upload-file' \n"", *command)
		return 1
	}

	// Write configuration and save key
	if *authKey != """" {
		if configErr != nil && os.IsNotExist(configErr) {
			config = helpers.CreateConfig()
		}
		config.Key = *authKey
		helpers.SaveConfig(config)
	}

	return 0
}

// PRINT FUNCTIONS
func printInfo(pfdaclient *precisionfda.PFDAClient) {
	fmt.Printf(""\npFDA CLI Info\n"")
	fmt.Printf(""  Commit ID   :    %s\n"", commitID)
	fmt.Printf(""  CLI Version :    %s\n"", Version)
	fmt.Printf(""  Os/Arch     :    %s\n"", OsArch)
	fmt.Printf(""  Build Time  :    %s\n"", BuildTime)
	fmt.Printf(""  Go Version  :    %s\n"", runtime.Version())

	transport := pfdaclient.Client.HTTPClient.Transport.(*http.Transport)
	fmt.Printf(""  TLS Version :    %s\n"", GetTLSVersion(transport))

	// printCryptoInfo()
}

func checkLatestVersion(pfdaclient *precisionfda.PFDAClient) {
	res, err := invokeGetLatestVersion(pfdaclient)
	if err != nil {
		fmt.Println(""Error while checking for latest available version."")
	}
	if res != Version {
		fmt.Printf(""\nThere is a newer version available for you to download - v%s \nVisit https://precision.fda.gov/docs/cli to get the latest version!\n"", res)
	}
}

func printCryptoInfo() {
	executable, err := os.Executable()
	if err != nil {
		fmt.Println(""Unable to retrieve executable path"")
	}

	v, err := version.ReadExe(executable)
	if err != nil {
		fmt.Println(""Unable to retrieve goversion info"")
	}

	// TODO: find a new way to verify the build is FIPS 140-2 compliant.
	if v.FIPSOnly {
		fmt.Println(""  FIPS        :    +crypto/tls/fipsonly verified"")
	} else {
		fmt.Println(""  FIPS        :    warning FIPS mode not verified"")
	}
}

func GetTLSVersion(tr *http.Transport) string {
	switch tr.TLSClientConfig.MinVersion {
	case tls.VersionTLS10:
		return ""TLS 1.0""
	case tls.VersionTLS11:
		return ""TLS 1.1""
	case tls.VersionTLS12:
		return ""TLS 1.2""
	case tls.VersionTLS13:
		return ""TLS 1.3""
	}

	return ""Unknown""
}

func isValidJSON(s string) bool {
	var js interface{}
	return json.Unmarshal([]byte(s), &js) == nil
}
","Go"
"Assay","FDA/precisionFDA","go/helpers/config.go",".go","2766","91","package helpers

import (
	""encoding/json""
	""fmt""
	""log""
	""os""
	""path/filepath""
	""strconv""
	""strings""
)

var ConfigPath = filepath.Join(getUserHomeDir(), "".pfda_config"")

type jsonConfig struct {
	Key string `json:key`
	Server string `json:server`
	Scope  string `json:scope`
}

func CreateConfig() *jsonConfig {
	config := jsonConfig{}
	return &config
}

// GetConfig loads the pFDA config file and returns the struct.
// A successful call returns (config, nil).
// In case the config was not found in FS or an error occurred, (nil, err) is returned.
func GetConfig() (*jsonConfig, error) {
	f, err := os.ReadFile(ConfigPath)
	if err != nil {
		// Internal error reading config
		return nil, err
	}
	var config jsonConfig
	err = json.Unmarshal(f, &config)
	if err != nil {
		return nil, err
	}
	return &config, nil
}

func SaveConfig(config *jsonConfig) {
	// If key was given by -key option in the command line
	// marshal it to json and write into .pfda__config
	// if marshaling fails, issue warning and exit
	jsonData, err := json.Marshal(config)
	if err != nil {
		fmt.Printf(""While the file has been uploaded succesfully\n, the authorization key can't be marshaled to json and saved in '%s': %s\n"", ConfigPath, err.Error())
		fmt.Printf(""You will need to submit authorization key in the command line in the next operation.\n"")
		// exit gracefully, without panic
	}

	// below is a more compact and cleaner implementation which is recommended when writing small files
	// It doesn't use separate Create / Write from os package, as before but takes advantage of
	// os.WriteFile which opens, writes and closes a file in one swoop
	// denote, that it also works on Windows ( checked on AWS EC2 windows instance )
	// despite Linux style file permissions are given
	// if .pfda_config exists it is truncaters before writing
	// denote also there is no need in defer f.Close(), since ioutil.WriteFile closes the file immediately after writing it
	err = os.WriteFile(ConfigPath, jsonData, 0644) // 0644 is '-rw -r- -r-'
	if err != nil {
		fmt.Printf(""Could not save authorization key in config file '%s': %s\n"", ConfigPath, err.Error())
	} else {
		fmt.Printf(""Saved authorization key in config file '%s'. \nA new key does not need to be provided for 24 hours from the generation time of the provided key.\n"", ConfigPath)
	}
}

func (c *jsonConfig) GetSpaceId() string {
	parts := strings.Split(c.Scope, ""-"")
	// scope is private/public
	if len(parts) != 2 {
		return """"
	}
	// only can be space-{id} now
	_, err := strconv.Atoi(parts[1])
	if (err) != nil {
		fmt.Println(""Error while parsing space-id"", err)
		return """"
	}
	return parts[1]
}

func getUserHomeDir() string {
	homeDir, err := os.UserHomeDir()
	if err != nil {
		log.Fatal(err)
	}
	return homeDir
}
","Go"
"Assay","FDA/precisionFDA","go/helpers/printer.go",".go","21498","264","package helpers

import (
	""fmt""
	""os""
	""strings""
	""text/tabwriter""
)

func PrintListSpacesHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Listing active spaces you have access to.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""list-spaces [FLAG...]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""list-spaces -groups -private [Show all active spaces of type group or private]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""list-spaces -unactivated -json [Show only unactivated spaces and present result as JSON]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -json"", ""Display response as JSON array""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -locked"", ""Show only locked spaces""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -unactivated"", ""Show only unactivated spaces""}, ""\t"")+""\t"")
	// fmt.Fprintln(writer, strings.Join([]string{""   -protected"",""Show PHI protected spaces only""}, ""\t"") + ""\t"") // UNCOMMENT THIS ONCE PHI FEATURE IN PROD
	fmt.Fprintln(writer, strings.Join([]string{""   -review"", ""Show review spaces""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -groups"", ""Show groups spaces""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -private"", ""Show private spaces""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -administrator"", ""Show administrator spaces""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -government"", ""Show government spaces""}, ""\t"")+""\t"")
	writer.Flush()
	return 1
}

func PrintLsHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Listing files in given location. If no location provided, root of My Home is used.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""      "", ""Public files are not listed by default.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""ls [FLAG...]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""ls -files -folder-id 55 [Show only files from private folder with id 55]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""ls -folders -brief [Show only folders from My Home root in brief version]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""ls -space-id 24 -folder-id 42 -json [Show content of folder with id 42 from space with id 24 and present result as JSON]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -json"", ""Display response as JSON array""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -brief"", ""Display a brief version of the response; Only shows ID and name""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -files"", ""Display only files""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -folders"", ""Display only folders""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -space-id <ID>"", ""Execute in given space""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -folder-id <ID>"", ""Execute in given folder""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -public"", ""Display only public files""}, ""\t"")+""\t"")
	writer.Flush()
	return 1
}

func PrintDownloadHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Downloading a file or folder from pFDA. If you want download root of My Home or a space (has no folder-id), use flag \""-folder-id root\"".""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""Supports downloading multiple files - simply pass them as positional args before any flags (check examples).""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""For files you can use either filename or file-id - name might not be unique, file-id always is.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""Filename wildcards are supported, use \""?\"" for exactly 1 char, \""*\"" for 0 or more characters. \n""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""By default, folder download only applies to top level files. Use flag \""-recursive\"" to download whole folder content.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""If you want to download content of a public folder, use flag -public together with the command.""}, ""\t"")+""\t"")

	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""download <FILE-ID> [FLAG...] OR download <FILENAME> [FLAG...] OR download -folder-id <ID> [FLAG]\n""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""download file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Downloads the file to current working directory]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download file-GJk1kpQ05xgQd8bP54kJFjzkz-1 -output \""data/results_final.csv\"" [Downloads the file to existing folder named \""data\"" with new name \""results_final.csv\""]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download outputs_334_1.csv -output \""data/results_final.csv\"" [Downloads the file to existing folder named \""data\"" with new name \""results_final.csv\""]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download file-GJk1kpQ05xgQd8bP54kJFjzkz-1 -output \""data/\"" [Downloads the file to existing folder named \""data/\"" and keep original name]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download file-GJk1kpQ05xgQd8bP54kJFjzkz-1 -overwrite true [Downloads the file to current working directory and overwrites already existing file]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download -folder-id 1222 -output \""my_outputs/\"" -overwrite true [Downloads folder content to existing folder named \""my_outputs/ and overwrites already existing files]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download -folder-id 2221 -space-id 123 -overwrite false [Downloads space's folder content to current working directory and skip files that already exists there]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download -folder-id 3333 -public [Downloads all files from given public folder]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download -folder-id root -space-id 123 -recursive [Recursively downloads space's folder content to current working directory]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download -folder-id root -overwrite true [Downloads top level content of 'My Home' to current working directory and overwrites already existing files if exist]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download 'data*.csv' -folder-id 1222 -overwrite true [Downloads only CSV files with 'data' in their name from folder (id:1222) and overwrites already existing files if exist]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download file-GJk1kpQ05xgQd8bP54kJFjzkz-1 file-YZm9QpQ0b69Qd8bP454kmcf76-2 [Downloads multiple files to the current working directory]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""download results.csv results_final.csv -folder-id 2221 [Downloads multiple files by name from folder (id:2221) to the current working directory]""}, ""\t"")+""\t"")

	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -output <PATH/TO/FILE>"", ""Downloads file to given path""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -overwrite true|false"", ""Preselect overwrite option for dialog if path already exists in the target location.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -recursive"", ""Recursively download content of selected folder.""}, ""\t"")+""\t"")

	writer.Flush()
	return 1
}

func PrintUploadFileHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Uploading a file or folder into given location. If no location provided, root of My Home is used.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""Supports uploading multiple files - simply pass them as positional args before any flags (check examples).\n""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""Supports uploading via stdin (piped input) - requires setting -name flag (check examples).\n""}, ""\t"")+""\t"")

	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""upload-file <PATH/TO/FILE> [FLAG...]\n""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""upload-file script01.py [Uploads the file to the root folder of My Home]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""upload-file script01.py -space-id 12 [Uploads the file to the root folder of the space]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""upload-file script01.py -folder-id 10 [Uploads the file to the specified folder]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""upload-file data_folder/ -folder-id 10 [Uploads the folder and its content to the specified folder]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""upload-file script01.py info/readme.txt data_folder/ -folder-id 111 [Uploads multiple files to the specified folder]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""upload-file script01.py parser.py validator.py -space-id 21 [Uploads multiple files to the specified space]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""upload-file script01.py parser.py validator.py -space-id 21 -folder-id 222 [Uploads multiple files to the specified space's folder]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""upload-file -name piped_file.csv [Uploads file with given name provided via stdin]""}, ""\t"")+""\t"")

	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -space-id <ID>"", ""Uploads into the given space""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -folder-id <ID>"", ""Uploads into the given folder""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -name <NAME>"", ""Uploads stdin file with given name [required for stdin input]""}, ""\t"")+""\t"")
	writer.Flush()
	return 1
}

func PrintDescribeAppHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Getting details about given app.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""describe-app -app-id <ID>""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""describe-app -app-id app-GJk3k5v85a4ZfgQ8bP5911Xg0-1 [Describes the app]""}, ""\t"")+""\t"")
	writer.Flush()
	return 1
}

func PrintDescribeWorkflowHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Getting details about given workflow.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""describe-workflow -workflow-id <ID>""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""describe-workflow -workflow-id workflow-GJkk5v005xggB4JcB4Zf9326V-1 [Describes the workflow]""}, ""\t"")+""\t"")
	writer.Flush()
	return 1
}

func PrintUploadAssetHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Uploading an asset.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""upload-asset -name <NAME{.tar,.tar.gz}> -root </PATH/TO/ROOT/FOLDER> -readme <README{.txt,.md} ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""upload-asset -name asset01.tar.gz -root ./asset01 -readme ./readme.md [Uploads asset with root folder asset01 and readme file readme.md from current directory]""}, ""\t"")+""\t"")
	writer.Flush()
	return 1
}

func PrintMkdirHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Creating a folder.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""mkdir <NAME> [FLAG...] ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""mkdir DATA [Created new folder named DATA in your My Home section]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""mkdir DATA -space-id 12 [Creates a new folder named DATA in root of the space]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""mkdir DATA scripts results -folder-id 2221 [Creates 3 new folders in folder (id:2221)]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""mkdir scripts/python/v1 scripts/python/v2 -p [Creates the given folder structure]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -space-id <ID>"", ""Create the folder in the given space""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -folder-id <ID>"", ""Create the folder in the given folder""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -p, -parents"", ""Create parent directories as needed, no error if existing""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")

	writer.Flush()
	return 1
}

func PrintRmHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Removing a file.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""rm <FILE-ID> [FLAG...] OR rm <NAME> [FLAG...] ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""rm file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Removes file with given id]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""rm file-GJk1kpQ05xgQd8bP54kJFjzkz-1 file-YZm9QpQ0b69Qd8bP454kmcf76-2 [Removes multiple files given by ids]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""rm inputs_01.csv inputs_02.csv -folder-id 2221 [Removes multiple files given by names in folder (id:2221)]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""rm 'inputs*.csv' -space-id 12 [Removes all CSV files with 'inputs' in their name from root of given space]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""rm '*.txt' -space-id 12 -folder-id 1212 [Removes all txt files with from the folder of given space]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -space-id <ID>"", ""Execute in given space""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -folder-id <ID>"", ""Execute in given folder""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")

	writer.Flush()
	return 1
}

func PrintRmdirHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Deleting a folder. Only empty folders are allowed to be deleted.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""rmdir <FOLDER-ID> [FLAG...] ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""rmdir 2221 [Removes folder (id:2221)] ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""rmdir 2221 2332 2333 [Removes folders with given ids if empty] ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")

	writer.Flush()
	return 1
}

func PrintCatHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Display content of a file.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""cat <FILE-ID>""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""cat file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Prints content of the given file] ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")

	writer.Flush()
	return 1
}

func PrintHeadHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Display first lines of a file. By default, fist 10 lines are displayed.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""head <FILE-ID> [FLAGS...]""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""head file-GJk1kpQ05xgQd8bP54kJFjzkz-1 [Prints first 10 lines of the given file] ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{"" "", ""head file-GJk1kpQ05xgQd8bP54kJFjzkz-1 -lines 50 [Prints first 50 lines of the given file] ""}, ""\t"")+""\t"")

	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -lines"", ""Alter number of lines to display""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")

	writer.Flush()
	return 1
}

func PrintGetSpaceIdHelp() int {
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  For:"", ""Display space ID of current workstation's context.""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Usage:"", ""get-space-id""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Examples:"", ""get-space-id [Prints integer representing id of current space] ""}, ""\t"")+""\t"")

	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  Flags:"", ""All flags listed below are OPTIONAL""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""  "", ""  ""}, ""\t"")+""\t"")
	fmt.Fprintln(writer, strings.Join([]string{""   -h, -help"", ""Show this help message and exit""}, ""\t"")+""\t"")

	writer.Flush()
	return 1
}

func PrintError(err error) {
	fmt.Println()
	fmt.Println(err)
}
","Go"
"Assay","FDA/precisionFDA","go/helpers/parser.go",".go","387","23","package helpers

import (
	""strings""
)

func ParseArgsUntilFlag(args []string) ([]string, int) {

	validArgs := make([]string, 0)
	// skip first two which is ""pfda <command>""
	OFFSET := 2

	for index, arg := range args[OFFSET:] {
		if !strings.HasPrefix(arg, ""-"") {
			validArgs = append(validArgs, arg)
		} else {
			return validArgs, index + OFFSET
		}
	}

	return validArgs, OFFSET
}
","Go"
"Assay","FDA/precisionFDA","go/helpers/utils.go",".go","1103","65","package helpers

import (
	""fmt""
	""strconv""
	""strings""
)

func Min(a, b int) int {
	if a < b {
		return a
	}
	return b
}

func IsFileId(arg string) bool {
	return strings.HasPrefix(arg, ""file-"") && len(arg) >= 31
}

func IsFolderId(arg string) bool {
	num, err := strconv.Atoi(arg)
	return err == nil && num > 0
}

func ContainsWildcard(arg string) bool {
	return strings.Contains(arg, ""?"") || strings.Contains(arg, ""*"")
}

func TransformToSQLWildcards(nameWithWildcards string) string {
	mysqlWildcard := strings.ReplaceAll(nameWithWildcards, ""*"", ""%"")
	mysqlWildcard = strings.ReplaceAll(mysqlWildcard, ""_"", ""\\_"")
	mysqlWildcard = strings.ReplaceAll(mysqlWildcard, ""?"", ""_"")
	return mysqlWildcard
}

func GetChunkSize(filesCount int) int {
	switch size := filesCount; {
	case size < 5:
		return 1
	case size < 10:
		return 2
	case size < 20:
		return 4
	case size < 35:
		return 7
	case size < 50:
		return 10
	case size < 100:
		return 20
	default:
		return 25
	}
}

func CheckErr(e error) {
	if e != nil {
		panic(e)
	}
}

func InputError(msg string) int {
	PrintError(fmt.Errorf(msg))
	return 1
}
","Go"
"Assay","FDA/precisionFDA","go/helpers/formatter.go",".go","119","9","package helpers

func FormatValue(print bool, value string) string {
	if print {
		return value + "" ""
	}
	return "" ""
}
","Go"
"Assay","FDA/precisionFDA","go/precisionfda/pfdaclient_test.go",".go","1871","70","package precisionfda

import (
	""fmt""
	""net/http""
	""net/http/httptest""
	""testing""

	""dnanexus.com/precision-fda-cli/precisionfda/test""
)

func TestNewPFDAClient(t *testing.T) {
	server := ""test.precisionfda.com""
	pfdaclient := NewPFDAClient(server)

	test.Equals(t, pfdaclient.BaseURL, ""https://test.precisionfda.com"")
}

func TestChunkSize(t *testing.T) {
	server := ""test.precisionfda.com""
	pfdaclient := NewPFDAClient(server)
	test.Equals(t, pfdaclient.ChunkSize, 1<<26)

	pfdaclient.SetChunkSize(10000000)
	test.Equals(t, pfdaclient.ChunkSize, 10000000)
}

func TestMaxRoutines(t *testing.T) {
	server := ""test.precisionfda.com""
	pfdaclient := NewPFDAClient(server)
	test.Equals(t, pfdaclient.NumRoutines, 10)

	pfdaclient.SetNumRoutines(5)
	test.Equals(t, pfdaclient.NumRoutines, 5)
}

func TestUploadFile(t *testing.T) {
	t.Skip(""Skipping httptests for now as I haven't been able to make them work"")

	// Inspired by https://medium.com/zus-health/mocking-outbound-http-requests-in-go-youre-probably-doing-it-wrong-60373a38d2aa
	server := httptest.NewServer(http.HandlerFunc(func(rw http.ResponseWriter, req *http.Request) {
		fmt.Println(req.URL.String())
		test.Equals(t, req.URL.Path, ""/api/create_file"")

		if req.Header.Get(""Accept"") != ""application/json"" {
			t.Errorf(""Expected Accept: application/json header, got: %s"", req.Header.Get(""Accept""))
		}

		// Send response to be tested
		rw.WriteHeader(http.StatusOK)
		rw.Write([]byte(`{""value"":""fixed""}`))
	}))
	defer server.Close()	// Close the server when test finishes

	pfdaclient := NewPFDAClient(server.URL)
	pfdaclient.UploadFile(""./README.md"", """", """", true)
}

func TestUploadFileToSpace(t *testing.T) {
	t.Skip(""Skipping httptests for now"")
}

func TestUploadAsset(t *testing.T) {
	t.Skip(""Skipping httptests for now"")
}

func TestDownloadFile(t *testing.T) {
	t.Skip(""Skipping httptests for now"")
}
","Go"
"Assay","FDA/precisionFDA","go/precisionfda/remove.go",".go","1356","65","package precisionfda

import (
	""encoding/json""
	""fmt""
)

// Remove single file by uid.
func (c *PFDAClient) RemoveFile(uids []string) error {
	deleteFileURL := c.BaseURL + ""/api/files/cli_remove""

	data := map[string]interface{}{""uids"": uids}
	jsonData, err := json.Marshal(data)
	if err != nil {
		return err
	}
	_, body, err := c.makeRequestFail(""POST"", deleteFileURL, jsonData)
	if err != nil {
		return err
	}
	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)

	if err != nil {
		return err
	}

	if resultJSON[""count""].(float64) == 0 {
		return fmt.Errorf("">> File(s) %s not found or inaccessible"", uids)
	}

	for _,uid := range uids {
		fmt.Printf("">> Deleted %s \n"", uid)
	}
	return nil
}

func (c *PFDAClient) RemoveDir(uid string) error {
	deleteFileURL := c.BaseURL + ""/api/files/cli_remove""

	data := map[string]interface{}{""ids"": []string{uid}}
	jsonData, err := json.Marshal(data)
	if err != nil {
		return err
	}

	_, body, err := c.makeRequestFail(""POST"", deleteFileURL, jsonData)
	if err != nil {
		return err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	if resultJSON[""count""].(float64) == 0 {
		return fmt.Errorf("">> Folder with id: %s not found or inaccessible"", uid)
	}

	fmt.Printf("">> Deleted dir (id: %s) \n"", uid)
	return nil
}
","Go"
"Assay","FDA/precisionFDA","go/precisionfda/pfdaclient.go",".go","39685","1430","package precisionfda

import (
	""bufio""
	""bytes""
	""crypto/md5""
	""dnanexus.com/precision-fda-cli/helpers""
	""encoding/hex""
	""encoding/json""
	""fmt""
	""io""
	""log""
	""math""
	""net/url""
	""os""
	""os/exec""
	""path""
	""path/filepath""
	""runtime""
	""strconv""
	""strings""
	""sync""
	""sync/atomic""
	""text/tabwriter""
	""time""

	""github.com/docker/go-units""
	""github.com/gosuri/uilive""
	""github.com/hashicorp/go-cleanhttp""     // required by go-retryablehttp
	""github.com/hashicorp/go-retryablehttp"" // use http libraries from hashicorp for implement retry logic
	""github.com/manifoldco/promptui""
)

const userAgent = ""precisionFDA CLI/2.4 ""
const defaultNumRoutines = 10
const defaultChunkSize = 1 << 26 // default 67MB (min. 5MB)
const minRoutines = 1
const maxRoutines = 100
const minChunkSize = 5 * 1 << 11 // min. 5MB
const maxChunkSize = 1 << 32     // max. 2GB
const maxFileSize = 5 * 1 << 41  // max. 5TB

// retryablehttp defaults
const maxRetryCount = 5
const minRetryTime = 1  // seconds
const maxRetryTime = 30 // seconds

const https = ""https://""

type IPFDAClient interface {
	CallAPI(route string, data string, outputFile string) error
	UploadAsset(rootFolderPath string, name string, readmeFilePath string) error
	Upload(file io.ReadCloser, path string, folderID string, spaceID string, size int64, withProgressBar bool) error
	UploadFile(path string, folderID string, spaceID string, withProgressBar bool) error
	UploadStdin(fileName string, folderID string, spaceID string, withProgressBar bool) error
	UploadFolder(folderPath string, folderID string, spaceID string) error
	UploadMultipleFiles(paths []string, folderID string, spaceID string) error
	DownloadFile(arg string, outputFilePath string, overwrite string) error
	Download(args []string, folderID string, spaceID string, public bool, recursive bool, outputFilePath string, overwrite string) error
	DescribeEntity(entityID string, entityType string) error
	ListSpaces(flags map[string]bool) error
	Ls(folderID string, spaceID string, flags map[string]bool) error
	Mkdir(names []string, folderID string, spaceID string, parents bool) error
	Rmdir(args []string) error
	Rm(args []string, folderID string, spaceID string) error
	Head(arg string, lines int) error

	RefreshToken(autoRefresh bool) (string, error)
	GetLatestVersion() (string, error)
	SetChunkSize(chunkSize int)
	SetNumRoutines(numRoutines int)
}

type PFDAClient struct {
	BaseURL         string
	Platform        string
	NumRoutines     int
	ChunkSize       int
	MinRoutines     int
	MaxRoutines     int
	MinChunkSize    int
	MaxChunkSize    int
	MaxFileSize     int
	ContinueOnError bool

	Client  *retryablehttp.Client
	AuthKey string
}

func NewPFDAClient(serverURL string) *PFDAClient {
	c := PFDAClient{}
	c.BaseURL = https + serverURL
	c.NumRoutines = defaultNumRoutines
	c.ChunkSize = defaultChunkSize
	c.MinRoutines = minRoutines
	c.MaxRoutines = maxRoutines
	c.MinChunkSize = minChunkSize
	c.MaxChunkSize = maxChunkSize
	c.MaxFileSize = maxFileSize
	c.Client = &retryablehttp.Client{
		HTTPClient:   cleanhttp.DefaultClient(),
		RetryWaitMin: minRetryTime * time.Second,
		RetryWaitMax: maxRetryTime * time.Second,
		RetryMax:     maxRetryCount,
		CheckRetry:   retryablehttp.DefaultRetryPolicy,
		Backoff:      retryablehttp.DefaultBackoff,
	}
	c.ContinueOnError = false
	return &c
}

// Wire objects
type jsonID struct {
	ID string `json:""id""`
}

type bulkIDs struct {
	IDs []string `json:""ids""`
}

type jsonChunkInfo struct {
	ID    string `json:""id""`
	Size  int    `json:""size""`
	Index int    `json:""index""`
	Md5   string `json:""md5""`
}

type jsonCreateFilePayload struct {
	Name       string `json:""name""`
	Desc       string `json:""description""`
	FolderID   string `json:""folder_id""`
	Scope      string `json:""scope""`
	ParentType string `json:""parent_type""`
	ParentID   string `json:""parent_id""`
}

type jsonCreateAssetPayload struct {
	Name  string   `json:""name""`
	Desc  string   `json:""description""`
	Paths []string `json:""paths""`
}

type jsonCreateFolderPayload struct {
	Name           string `json:""name""`
	ParentFolderID string `json:""parent_folder_id,omitempty""`
	SpaceID        string `json:""space_id,omitempty""`
}

type rmPayload struct {
	Name           string `json:""name""`
	ParentFolderID string `json:""parent_folder_id,omitempty""`
	SpaceID        string `json:""space_id,omitempty""`
	Type           string `json:""type,omitempty""`
}

type jsonFileDownloadPayload struct {
	FileURL  string `json:""file_url""`
	FileSize int64  `json:""file_size""`
}

type uploadChunk struct {
	index int
	data  []byte // slice/ptr
}

// better name needed
type jsonSpace struct {
	Id        int    `json:""id""`
	Title     string `json:""title""`
	Role      string `json:""role""`
	Side      string `json:""side""`
	Type      string `json:""type""`
	State     string `json:""state""`
	Protected bool   `json:""protected""`
}

// better name needed
type jsonFile struct {
	Id        int    `json:""id""`
	Uid       string `json:""uid""`
	Name      string `json:""name""`
	Type      string `json:""type""`
	Locked    bool   `json:""locked""`
	State     string `json:""state""`
	AddedBy   string `json:""added_by""`
	CreatedAt string `json:""created_at""`
	Size      string `json:""file_size""`
	// populated for Folders only
	Children int `json:""children,omitempty""`
}

type jsonMeta struct {
	Scope string `json:""scope""`
	Path  string `json:""path""`
}

type jsonListingPayload struct {
	Meta  jsonMeta   `json:""meta""`
	Files []jsonFile `json:""files""`
}

// Below were migrated from pfda.go:
//
// COMMAND FUNCTIONS
func (c *PFDAClient) CallAPI(route string, data string, outputFile string) error {
	// sanitize input
	route = strings.ToLower(route)
	url := c.BaseURL + ""/api/"" + route
	_, body, err := c.makeRequestFail(""POST"", url, []byte(data))
	if err != nil {
		return err
	}

	if outputFile == """" {
		fmt.Printf(""Return response data for API call '%s:\n%s\n"", route, string(body))
	} else {
		f, err := os.Create(outputFile)
		if err != nil {
			return err
		}

		defer f.Close()
		bytesWritten, err := f.Write(body)
		if err != nil {
			return err
		}

		fmt.Printf(""Downloaded response data for API call: %s (%d bytes) to file '%s'\n"", route, bytesWritten, outputFile)
	}
	return nil
}

func (c *PFDAClient) UploadAsset(rootFolderPath string, name string, readmeFilePath string) error {
	createURL := c.BaseURL + ""/api/create_asset""
	closeURL := c.BaseURL + ""/api/close_asset""

	// Get list of all asset files
	fileList := []string{}
	assetSize := int64(0)
	err := filepath.Walk(rootFolderPath, func(path string, f os.FileInfo, err error) error {
		if !f.IsDir() {
			relPath, err := filepath.Rel(rootFolderPath, path)
			if err != nil {
				return err
			}
			fileList = append(fileList, relPath)
			assetSize += f.Size()
		}
		return nil
	})
	if err != nil {
		return err
	}

	if assetSize > maxFileSize {
		inputError(fmt.Sprintf(""Size of asset folder '%s' (%d) exceeds maximum allowed file size(%d)."", rootFolderPath, assetSize, maxFileSize))
	}

	if assetSize == 0 {
		inputError(fmt.Sprintf(""Size of asset folder '%s' is 0. Uploading an empty asset is not allowed."", rootFolderPath))
	}

	// Read in the readme all at once
	readmeBuf, err := os.ReadFile(readmeFilePath)
	if err != nil {
		return err
	}

	jsonData, err := json.Marshal(jsonCreateAssetPayload{
		Name:  name,
		Desc:  string(readmeBuf),
		Paths: fileList[:],
	})
	if err != nil {
		return err
	}

	fileID, err := c.createFileID(createURL, jsonData)
	if err != nil {
		return err
	}

	chunkPool := make(chan uploadChunk, c.NumRoutines)
	wg := c.initWaitGroup(fileID, chunkPool, &assetSize, true)

	fmt.Println("">> Archiving asset..."")

	// different approach for WinOS tar command
	if runtime.GOOS == ""windows"" {
		cmd := exec.Command(""tar"", ""-cf"", name, ""-C"", rootFolderPath, ""."")
		if strings.HasSuffix(name, "".tar.gz"") {
			cmd = exec.Command(""tar"", ""-czf"", name, ""-C"", rootFolderPath, ""."")
		}
		err = cmd.Start()
		if err != nil {
			return err
		}
		err = cmd.Wait()
		if err != nil {
			return err
		}
		tarArchive, err := os.Open(name)
		if err != nil {
			return err
		}
		defer os.Remove(name)
		defer tarArchive.Close()
		c.readAndChunk(tarArchive, chunkPool, &assetSize)
	} else {
		cmd := exec.Command(""tar"", ""-c"", ""-C"", rootFolderPath, ""."")
		if strings.HasSuffix(name, "".tar.gz"") {
			cmd = exec.Command(""tar"", ""-cz"", ""-C"", rootFolderPath, ""."")
		}
		stdout, err := cmd.StdoutPipe()
		err = cmd.Start()
		if err != nil {
			return err
		}
		c.readAndChunk(stdout, chunkPool, &assetSize)
	}

	fmt.Print("">> Uploading asset |"")
	// no need to open the rootFolderPath, asset is read from stdout
	// f, err := os.Open(rootFolderPath)
	// if err != nil {
	// 	return err
	// }
	// defer f.Close()
	close(chunkPool)
	wg.Wait()

	fmt.Println("">| Uploaded 100%\n>> Finalizing asset..."")
	jsonData, err = json.Marshal(jsonID{
		ID: fileID,
	})
	if err != nil {
		return err
	}

	c.makeRequestFail(""POST"", closeURL, jsonData)
	fmt.Println("">> Done! Access your asset at "" + c.BaseURL + ""/home/assets/"" + fileID)
	return nil
}

// If folderID is not empty, the file will be uploaded to the specified folder
// If spaceID is empty, the file will be uploaded to the user's home
func (c *PFDAClient) UploadFile(path string, folderID string, spaceID string, withProgressBar bool) error {

	file, err := os.Open(path)
	if err != nil {
		return err
	}
	defer file.Close()

	info, err := file.Stat()
	if err != nil {
		return err
	}

	size := info.Size()
	if size > maxFileSize {
		return fmt.Errorf("">> Size of file '%s' (%d) exceeds maximum allowed file size(%d)."", path, size, maxFileSize)
	}

	if size == 0 {
		return fmt.Errorf("">> Size of file '%s' is 0. Uploading an empty file is not allowed."", path)
	}

	err = c.Upload(&*file, path, folderID, spaceID, size, withProgressBar)
	c.HandleError(err)

	return nil
}

func (c *PFDAClient) UploadStdin(fileName string, folderID string, spaceID string, withProgressBar bool) error {
	// we do not know the size, stdin is buffered stream of data
	size := int64(1)
	// stdin has to be wrapped in those - otherwise not working as expected with linux piping
	stdin := io.NopCloser(bufio.NewReader(os.Stdin))
	err := c.Upload(stdin, fileName, folderID, spaceID, size, withProgressBar)
	c.HandleError(err)
	return nil
}

func (c *PFDAClient) Upload(file io.ReadCloser, path string, folderID string, spaceID string, size int64, withProgressBar bool) error {
	createURL := c.BaseURL + ""/api/create_file""
	closeURL := c.BaseURL + ""/api/close_file""

	scope := """"
	parentType := """"
	parentId := """"
	if spaceID != """" {
		_, err := strconv.Atoi(spaceID)
		if err != nil {
			return err
		}
		scope = ""space-"" + spaceID
	}

	dxJobId, isPresent := os.LookupEnv(""DX_JOB_ID"")
	if isPresent {
		parentType = ""Job""
		parentId = dxJobId
	}

	jsonData, err := json.Marshal(jsonCreateFilePayload{
		Name:       filepath.Base(path),
		Desc:       """",
		FolderID:   folderID,
		Scope:      scope,
		ParentType: parentType,
		ParentID:   parentId,
	})
	if err != nil {
		return err
	}

	fileID, err := c.createFileID(createURL, jsonData)
	if err != nil {
		return err
	}

	chunkPool := make(chan uploadChunk, c.NumRoutines)

	if withProgressBar {
		fmt.Printf("">> Uploading file %s\n"", path)
	}

	wg := c.initWaitGroup(fileID, chunkPool, &size, withProgressBar)
	c.readAndChunk(file, chunkPool, &size)

	close(chunkPool)
	wg.Wait()

	if withProgressBar {
		fmt.Println("">> Finalizing file..."")
	}

	jsonData, err = json.Marshal(jsonID{
		ID: fileID,
	})
	if err != nil {
		return err
	}

	c.makeRequestFail(""POST"", closeURL, jsonData)
	fmt.Println("">> Uploaded: "", path)
	if withProgressBar {
		if spaceID != """" {
			fmt.Println("">> Done! Access your file at "" + c.BaseURL + ""/spaces/"" + spaceID + ""/files/"" + fileID)
		} else {
			fmt.Println("">> Done! Access your file at "" + c.BaseURL + ""/home/files/"" + fileID)
		}
	}

	return nil
}

func (c *PFDAClient) UploadFolder(folderPath string, folderID string, spaceID string) error {
	folders := make(map[string]string, 20)

	p, _ := path.Split(folderPath)
	folders[path.Clean(p)] = folderID

	fmt.Println("">> Uploading content of:"", folderPath)

	var fileList []string
	err := filepath.Walk(folderPath, func(currentPath string, f os.FileInfo, err error) error {
		if f.IsDir() {
			parent, _ := path.Split(currentPath)
			id, err := c.createNewFolder(filepath.Base(currentPath), folders[filepath.Dir(parent)], spaceID)
			if err != nil {
				return err
			}
			folders[currentPath] = id
		} else {
			fileList = append(fileList, currentPath)
		}
		return nil
	})

	if err != nil {
		return err
	}

	var wg = sync.WaitGroup{}
	maxGoroutines := 4
	guard := make(chan struct{}, maxGoroutines)

	for _, file := range fileList {
		guard <- struct{}{}
		wg.Add(1)
		go func(file string) {
			parent, _ := path.Split(file)
			err := c.UploadFile(file, folders[filepath.Dir(parent)], spaceID, false)
			if err != nil {
				fmt.Println(err)
			}
			<-guard
			wg.Done()

		}(file)
	}
	wg.Wait()

	if spaceID != """" {
		fmt.Println("">> Done! Access your files at "" + c.BaseURL + ""/spaces/"" + spaceID + ""/files?folder_id="" + folders[folderPath])

	} else {
		fmt.Println("">> Done! Access your files at "" + c.BaseURL + ""/home/files?folder_id="" + folders[folderPath])
	}
	return nil
}

func (c *PFDAClient) UploadMultipleFiles(paths []string, folderID string, spaceID string) error {

	fmt.Printf("">> Uploading multiple files...\n"")

	// this could be done in parallel - be careful to used memory otherwise will get terminated by kernel OOM-killer.
	for _, path := range paths {
		f, err := os.Stat(path)
		if os.IsNotExist(err) {
			fmt.Println(fmt.Sprintf(""Input path '%s' does not exist - skipping."", path))
			continue
		}
		path = filepath.Clean(path)
		if f.IsDir() {
			err = c.UploadFolder(path, folderID, spaceID)
			if err != nil {
				fmt.Println(err)
			}
		} else {
			err = c.UploadFile(path, folderID, spaceID, false)
			if err != nil {
				fmt.Println(err)
			}
		}
	}

	return nil
}

func (c *PFDAClient) DownloadFile(arg string, outputFilePath string, overwrite string) error {

	apiURL := fmt.Sprintf(""%s/api/files/%s/download?format=json"", c.BaseURL, arg)
	fmt.Println("">> Preparing to download"")
	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf("">> %s not found. Please check that this file exists and you have access to it"", arg)
		} else {
			return err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	if resultJSON[""file_url""] == nil {
		return fmt.Errorf(""No file_url in response!\n\nResponse: %s"", string(body))
	}
	fileURL := resultJSON[""file_url""].(string)

	originalName := path.Base(fileURL)
	fileName, err := url.PathUnescape(originalName)
	if err != nil {
		fmt.Printf(""Error while unescaping file name, using the original name."")
		fileName = originalName
	}
	fmt.Printf(""   Downloading :  %s\n"", fileName)

	fileSize := resultJSON[""file_size""].(float64)
	fmt.Printf(""     File Size :  %s\n"", units.BytesSize(fileSize))

	if outputFilePath == """" {
		// If output is not specified, use the original filename and current working directory
		dir, err := os.Getwd()
		if err != nil {
			return err
		}

		outputFilePath = path.Join(dir, fileName)
	} else {
		if fileInfo, err := os.Stat(outputFilePath); err == nil && fileInfo.IsDir() {
			// If outputFilePath exists and it is a directory then the file should be downloaded
			// to that directory while retaining its original name
			// fmt.Printf("">> Specified outputFilePath %s is an existing directory\n"", outputFilePath)
			outputFilePath = path.Join(outputFilePath, fileName)
		} else if strings.HasSuffix(outputFilePath, ""/"") {
			// A trailing / means the user has specified a directory, but it doesn't exist.
			if err := os.MkdirAll(outputFilePath, os.ModePerm); err != nil {
				return err
			}
			outputFilePath = path.Join(outputFilePath, fileName)

		} else if _, err := os.Stat(filepath.Dir(outputFilePath)); err != nil {
			// This is now assumed to be a file path and not a dir path, and the parent directory does not exist
			return fmt.Errorf(""Error: The parent directory %s of the specified output doesn't exist"", filepath.Dir(outputFilePath))
		}
	}
	// After the above block, outputFilePath should contain the target file path and cannot be a directory

	if _, err := os.Stat(outputFilePath); err == nil && overwrite == """" {
		fmt.Printf("">> File %s already exists\n"", outputFilePath)
		dialogOverwrite := yesNo(""  Overwrite already existing path? "")
		if !dialogOverwrite {
			return fmt.Errorf("">> Download cancelled"")
		}
	} else if err == nil && overwrite == ""false"" {
		return fmt.Errorf("">> Error: path %s already exists but -overwrite flag not set to true. Skipping download.\n"", outputFilePath)
	}

	fmt.Printf("">> Output File :  %s\n"", outputFilePath)

	withProgressBar := true
	err = Download(fileURL, outputFilePath, int64(fileSize), withProgressBar)
	if err != nil {
		return err
	}

	fmt.Printf("">> Done!\n\n"")
	return nil
}

func (c *PFDAClient) Download(args []string, folderID string, spaceID string, public bool, recursive bool, outputFilePath string, overwrite string) error {

	fileIDs := make([]string, 0)
	fileNames := make([]string, 0)

	if len(args) == 0 {
		args = append(args, """")
	}

	// prepare lists of files to downloads defined by name or id.
	for _, arg := range args {
		arg = strings.TrimSpace(arg)
		if helpers.IsFileId(arg) {
			fileIDs = append(fileIDs, arg)
		} else {
			fileNames = append(fileNames, arg)
		}
	}

	// iterate over filenames with possible wildcards and download them.
	for _, fileName := range fileNames {
		apiURL := fmt.Sprintf(""%s/api/files/cli?"", c.BaseURL)
		params := url.Values{}

		if spaceID != """" {
			params.Add(""space_id"", spaceID)
		}
		if folderID != """" && folderID != ""root"" {
			params.Add(""folder_id"", folderID)
		}
		if fileName != """" {
			//respecting ruby backend specific filters
			params.Add(""filters[filter]"", helpers.TransformToSQLWildcards(fileName))
		}

		if public {
			params.Add(""public_scope"", ""true"")
		}

		_, body, err := c.makeRequestFail(""GET"", apiURL+params.Encode(), nil)
		if err != nil {
			return err
		}

		var children jsonListingPayload
		err = json.Unmarshal(body, &children)
		if err != nil {
			return err
		}

		var uids []string
		for _, child := range children.Files {
			if child.Type == ""UserFile"" {
				uids = append(uids, child.Uid)
			} else if recursive {
				// create the child folder first.
				if err := os.MkdirAll(filepath.Join(outputFilePath, child.Name), os.ModePerm); err != nil {
					log.Fatal(err)
				}
				c.Download([]string{fileName}, strconv.Itoa(child.Id), spaceID, public, recursive, filepath.Join(outputFilePath, child.Name), overwrite)
			}
		}

		if (len(uids) > 1 && (helpers.ContainsWildcard(fileName))) || len(uids) == 1 || args[0] == """" {
			fileIDs = append(fileIDs, uids...)
		} else if len(uids) > 1 {
			selected := pickFile(children.Files, ""Multiple files found matching the given name, select which to download"")
			fileIDs = append(fileIDs, selected)
		} else if !recursive {
			fmt.Println(fmt.Errorf("">> No files found matching: %s - please check it does exist and you have access to it"", fileName))
		}
	}

	if len(fileIDs) > 1 || len(args) > 1 || recursive {
		c.parallelDownload(fileIDs, outputFilePath, overwrite)
	} else if len(fileIDs) == 1 {
		err := c.DownloadFile(fileIDs[0], outputFilePath, overwrite)
		c.HandleError(err)
	}
	return nil
}

func (c *PFDAClient) DescribeEntity(entityID string, entityType string) error {
	apiURL := fmt.Sprintf(""%s/api/%ss/%s/describe"", c.BaseURL, entityType, entityID)

	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf("">> %s not found - please check that it does exist and you have access to it"", entityID)
		} else {
			return err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	if resultJSON == nil {
		return fmt.Errorf(""No response!\n\nResponse: %s"", string(body))
	}

	prettyJSON, _ := json.MarshalIndent(resultJSON, """", ""    "")
	fmt.Printf(""%s\n"", string(prettyJSON))

	return nil
}

func (c *PFDAClient) ListSpaces(flags map[string]bool) error {
	apiURL := fmt.Sprintf(""%s/api/spaces/cli?"", c.BaseURL)

	params := url.Values{}
	for flag, value := range flags {
		if value {
			params.Add(flag, strconv.FormatBool(value))
		}
	}

	status, body, err := c.makeRequestFail(""GET"", apiURL+params.Encode(), nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf("">> Something went wrong"")
		} else {
			return err
		}
	}

	var spaces []jsonSpace
	err = json.Unmarshal(body, &spaces)
	if err != nil {
		return err
	}

	printListSpacesResponse(spaces, flags)

	return nil
}

func (c *PFDAClient) Ls(folderID string, spaceID string, flags map[string]bool) error {
	apiURL := fmt.Sprintf(""%s/api/files/cli?"", c.BaseURL)

	params := url.Values{}

	if spaceID != """" {
		_, err := strconv.Atoi(spaceID)
		if err != nil {
			return fmt.Errorf("">> invalid space ID - expected an integer"")
		}
		params.Add(""space_id"", spaceID)
	}

	if folderID != """" {
		_, err := strconv.Atoi(folderID)
		if err != nil {
			return fmt.Errorf("">> invalid folder ID - expected an integer"")
		}
		params.Add(""folder_id"", folderID)
	}

	for flag, value := range flags {
		if value {
			params.Add(flag, strconv.FormatBool(value))
		}
	}

	status, body, err := c.makeRequestFail(""GET"", apiURL+params.Encode(), nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf("">> Target location not found or inaccessible"")
		} else {
			return err
		}
	}

	var response jsonListingPayload
	err = json.Unmarshal(body, &response)
	if err != nil {
		return err
	}

	printListingResponse(response, flags)
	return nil
}

func (c *PFDAClient) Mkdir(dirs []string, folderID string, spaceID string, parents bool) error {

	c.ContinueOnError = len(dirs) > 1

	if parents {
		for _, dir := range dirs {
			parts := strings.Split(dir, string(os.PathSeparator))
			parentId := folderID
			// created nested folders.
			for _, folder := range parts {
				id, err := c.createNewFolder(folder, parentId, spaceID)
				if err == nil {
					fmt.Printf("">> Created folder %s (id: %s) \n"", folder, id)
				}
				parentId = id
			}
		}
		return nil
	}

	for _, dir := range dirs {
		id, err := c.createNewFolder(dir, folderID, spaceID)
		c.HandleError(err)
		if err == nil {
			fmt.Printf("">> Created folder %s (id: %s) \n"", dir, id)
		}
	}
	return nil
}

func (c *PFDAClient) Rmdir(args []string) error {

	c.ContinueOnError = len(args) > 1

	for _, arg := range args {
		if !helpers.IsFolderId(arg) {
			c.HandleError(fmt.Errorf("">> Invalid folder id: %s - expected an integer"", arg))
			continue
		}

		jsonData, err := json.Marshal(rmPayload{Name: arg, Type: ""Folder""})
		if err != nil {
			return err
		}
		_, body, err := c.makeRequestFail(""POST"", c.BaseURL+""/api/files/cli_node_search"", jsonData)
		c.HandleError(err)
		var response []jsonFile

		err = json.Unmarshal(body, &response)
		if err != nil {
			return err
		}
		if len(response) == 0 {
			fmt.Println("">> Target folder not found or inaccessible"")
			continue
		}

		if response[0].Children == 0 {
			err := c.RemoveDir(arg)
			c.HandleError(err)
		} else {
			fmt.Println("">> Unable to remove non-empty folder."")
		}
	}

	return nil
}

func (c *PFDAClient) Rm(args []string, folderID string, spaceID string) error {

	c.ContinueOnError = len(args) > 1

	for _, arg := range args {

		if helpers.IsFileId(arg) {
			err := c.RemoveFile([]string{arg})
			c.HandleError(err)
			continue
		}

		jsonData, err := json.Marshal(rmPayload{Name: helpers.TransformToSQLWildcards(arg), Type: ""UserFile"", ParentFolderID: folderID, SpaceID: spaceID})
		if err != nil {
			return err
		}
		// first check for matching files to be deleted - filename (with wildcard) logic
		_, body, err := c.makeRequestFail(""POST"", c.BaseURL+""/api/files/cli_node_search"", jsonData)
		if err != nil {
			return err
		}
		var response []jsonFile
		err = json.Unmarshal(body, &response)
		if err != nil {
			return err
		}

		toBeDeletedCount := len(response)
		if toBeDeletedCount > 1 {
			// given arg matches more files, let user select one and delete it
			if !helpers.ContainsWildcard(arg) {
				result := pickFile(response, ""Multiple files found matching the given name, select which to delete"")
				err := c.RemoveFile([]string{result})
				c.HandleError(err)
				// arg processed, continue to next
				continue
			}
			// wildcard used, user wants to match more files, just inform about the count.
			agree := yesNo(fmt.Sprintf(""%d files match the given arg \""%s\"", are you sure you want to continue?"", toBeDeletedCount, arg))
			if agree {
				uids := make([]string, 0)
				for _, file := range response {
					uids = append(uids, file.Uid)
				}
				err := c.RemoveFile(uids)
				c.HandleError(err)
				// arg processed, continue to next
				continue
			}

			fmt.Println("">> Delete aborted"")
			// arg processed, continue to next
			continue
		}
		if toBeDeletedCount == 0 {
			fmt.Println("">> Target file not found or inaccessible"")
			// arg processed, continue to next
			continue
		}
		// single file matches the name, delete it
		err = c.RemoveFile([]string{response[0].Uid})
		c.HandleError(err)
		// arg processed, continue to next
		continue

	}
	return nil
}

// RefreshToken Just gets the new token, the logic for token replacement is in go/pfda.go
func (c *PFDAClient) RefreshToken(autoRefresh bool) (string, error) {
	apiURL := fmt.Sprintf(""%s/api/auth_key"", c.BaseURL)

	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return """", fmt.Errorf(""Something went wrong."")
		} else {
			return """", err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON) // do this just to ensure the json is valid
	if err != nil {
		return """", err
	}

	return resultJSON[""Key""].(string), nil
}

func (c *PFDAClient) GetLatestVersion() (string, error) {
	apiURL := fmt.Sprintf(""%s/api/cli_latest_version"", c.BaseURL)

	_, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		return """", err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON) // do this just to ensure the json is valid
	if err != nil {
		return """", err
	}

	return resultJSON[""version""].(string), nil
}

func (c *PFDAClient) SetChunkSize(chunkSize int) {
	if chunkSize > maxChunkSize || chunkSize < minChunkSize {
		inputError(""Chunk size must be between 5MB and 5GB."")
	} else {
		c.ChunkSize = chunkSize
	}
}

func (c *PFDAClient) SetNumRoutines(numRoutines int) {
	if numRoutines > maxRoutines || numRoutines < minRoutines {
		inputError(""Maximum number of threads must an integer within the range of [1-100]."")
	} else {
		c.NumRoutines = numRoutines
	}
}

func (c *PFDAClient) createFileID(url string, data []byte) (string, error) {
	status, body, err := c.makeRequestFail(""POST"", url, data)
	if err != nil {
		if status == ""404 Not Found"" {
			// 404 should only be returned if the specified spaceId is invalid
			// For invalid folder-id, an error json is returned
			return """", fmt.Errorf(""Error uploading the file. Please check that the space-id is correct and that you have access to that Space."")
		} else {
			return """", err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return """", err
	}

	if resultJSON[""id""] == nil {
		return """", fmt.Errorf(""No id in response!\n\nResponse: %s"", string(body))
	}
	fileID := resultJSON[""id""].(string)
	return fileID, nil
}

func (c *PFDAClient) createNewFolder(name string, parentFolderID string, spaceID string) (string, error) {

	createFolderURL := c.BaseURL + ""/api/files/create_folder""
	jsonData, err := json.Marshal(jsonCreateFolderPayload{
		Name:           name,
		ParentFolderID: parentFolderID, // might be """" -> not used
		SpaceID:        spaceID,        // might be """" -> not used
	})

	if err != nil {
		return """", err
	}

	_, body, err := c.makeRequestFail(""POST"", createFolderURL, jsonData)
	if err != nil {
		return """", err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return """", err
	}

	newFolderID := fmt.Sprintf(""%g"", resultJSON[""id""])
	if resultJSON[""message""].(map[string]interface{})[""type""] == ""error"" {
		return newFolderID, fmt.Errorf("">> Unable to create %s - already exists in target location"", name)
	}

	return newFolderID, nil
}

func (c *PFDAClient) Head(arg string, lines int) error {

	if !helpers.IsFileId(arg) {
		return fmt.Errorf("">> Invalid file-id provided: %s"", arg)
	}

	apiURL := fmt.Sprintf(""%s/api/files/%s/download?format=json"", c.BaseURL, arg)
	status, body, err := c.makeRequestFail(""GET"", apiURL, nil)
	if err != nil {
		if status == ""404 Not Found"" {
			return fmt.Errorf("">> %s not found. Please check that this file exists and you have access to it"", arg)
		} else {
			return err
		}
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	if err != nil {
		return err
	}

	if resultJSON[""file_url""] == nil {
		return fmt.Errorf(""No file_url in response!\n\nResponse: %s"", string(body))
	}
	fileURL := resultJSON[""file_url""].(string)

	fileSize := resultJSON[""file_size""].(float64)
	// user wants print whole file, check size first.
	if lines == -1 && fileSize > 10_000_000 {
		agree := yesNo(""The size of the file is over 10Mb - are you sure you want to display the whole content?"")
		if !agree {
			return fmt.Errorf("">> Cat cancelled"")
		}
	}

	err = Head(fileURL, lines)
	if err != nil {
		return err
	}

	return nil
}

func (c *PFDAClient) downloadByChunks(uidsChunk []string, outputFilePath string, overwrite string) {
	apiURL := fmt.Sprintf(""%s/api/files/bulk_download"", c.BaseURL)
	jsonData, _ := json.Marshal(bulkIDs{
		IDs: uidsChunk,
	})
	_, body, _ := c.makeRequestFail(""POST"", apiURL, jsonData)

	var resultJSON []map[string]interface{}
	_ = json.Unmarshal(body, &resultJSON)

	downloaded := make(map[string]string)
	for _, file := range resultJSON {
		DownloadDirectly(file[""url""].(string), outputFilePath, overwrite)
		downloaded[file[""uid""].(string)] = """"
	}

	// check if all requested files were downloaded.
	for _, uid := range uidsChunk {
		_, found := downloaded[uid]
		if !found {
			fmt.Println(fmt.Errorf("">> Unable to download: %s"", uid))
		}
	}

}

func (c *PFDAClient) parallelDownload(uids []string, outputFilePath string, overwrite string) {

	if len(uids) == 0 {
		return
	}
	fmt.Printf("">> Preparing to download: %d files \n"", len(uids))
	var wg = sync.WaitGroup{}
	maxGoroutines := 5 // do not exceed 10 - magical TCP issues with lost packages appears.
	guard := make(chan struct{}, maxGoroutines)

	// create outputFilePath in case it was specified & doesn't exist yet - we assume user specified a dir name
	if outputFilePath != """" {
		if err := os.MkdirAll(outputFilePath, os.ModePerm); err != nil {
			log.Fatal(err)
		}
	}

	var divided [][]string
	var chunkSize = helpers.GetChunkSize(len(uids))

	for i := 0; i < len(uids); i += chunkSize {
		end := i + chunkSize
		if end > len(uids) {
			end = len(uids)
		}
		divided = append(divided, uids[i:end])
	}

	for _, uidsChunk := range divided {
		guard <- struct{}{}
		wg.Add(1)
		go func(uidsChunk []string) {
			c.downloadByChunks(uidsChunk, outputFilePath, overwrite)
			<-guard
			wg.Done()

		}(uidsChunk)
	}
	wg.Wait()
}

func (c *PFDAClient) initWaitGroup(fileID string, chunkPool <-chan uploadChunk, size *int64, withProgressBar bool) (wg *sync.WaitGroup) {
	numRoutines := helpers.Min(c.NumRoutines, int(math.Ceil(float64(*size)/float64(c.ChunkSize))))

	var totalSent uint64 = 0
	writer := uilive.New()
	writer.Start()
	defer writer.Stop()

	var g sync.WaitGroup
	for i := 0; i < numRoutines; i++ {
		g.Add(1)
		go func() {
			for chunk := range chunkPool {
				c.sendToStore(fileID, chunk)
				atomic.AddUint64(&totalSent, uint64(len(chunk.data)))
				currentSize := atomic.LoadUint64(&totalSent)
				totalSize := atomic.LoadInt64(size)
				if withProgressBar {
					fmt.Fprintf(writer, ""     %.1f%% (%s / %s)\n"",
						100*float64(currentSize)/float64(totalSize), units.BytesSize(float64(currentSize)), units.BytesSize(float64(totalSize)))
					writer.Flush()
				}
			}
			g.Done()
		}()
	}
	wg = &g
	return
}

func (c *PFDAClient) makeRequestFail(requestType string, url string, data []byte) (status string, body []byte, err error) {
	req, err := retryablehttp.NewRequest(requestType, url, bytes.NewReader(data))
	helpers.CheckErr(err)
	c.setPostHeaders(req)

	resp, err := c.Client.Do(req)
	helpers.CheckErr(err)
	defer resp.Body.Close()
	status = resp.Status
	body, _ = io.ReadAll(resp.Body)

	if !strings.HasPrefix(status, ""2"") {
		err = fmt.Errorf(""%s Request to '%s' failed with status %s. For 4xx status, check that the provided id and auth-key are still valid.\n\nResponse: %s"", requestType, url, status, string(body))
	}
	return status, body, err
}

func (c *PFDAClient) makeRequestWithHeadersFail(requestType string, url string, headers map[string]interface{}, data []byte) (status string, body []byte, err error) {
	req, err := retryablehttp.NewRequest(requestType, url, bytes.NewReader(data))
	helpers.CheckErr(err)
	for header, value := range headers {
		req.Header.Set(header, value.(string))
	}

	resp, err := c.Client.Do(req)
	helpers.CheckErr(err)
	defer resp.Body.Close()

	status = resp.Status
	body, _ = io.ReadAll(resp.Body)

	if !strings.HasPrefix(status, ""2"") {
		err = fmt.Errorf(""%s Request to '%s' failed with status %s. For 4xx status, check that the provided id and auth-key are still valid.\n\nResponse: %s"", requestType, url, status, string(body))
	}
	return status, body, err
}

func (c *PFDAClient) readAndChunk(f io.ReadCloser, ch chan<- uploadChunk, size *int64) {
	// dynamically adjust chunkSize
	chunkIndex := 1
	var totalDataLength = 0
	for {
		byteBuf := make([]byte, c.ChunkSize)
		i := 0
		for i < c.ChunkSize {
			tempBuf := byteBuf[i:]
			m, err := f.Read(tempBuf)
			if err != nil && err != io.EOF {
				panic(err)
			}
			i += m
			if err == io.EOF {
				break
			}
		}
		totalDataLength += i
		// Only upload an empty chunk if empty file
		if i == 0 && chunkIndex > 1 {
			break
		}
		ch <- uploadChunk{
			index: chunkIndex,
			data:  byteBuf[:i], // use slice
		}
		chunkIndex++
	}
	atomic.StoreInt64(size, int64(totalDataLength))
}

func (c *PFDAClient) sendToStore(id string, chunk uploadChunk) error {
	uploadURL := c.BaseURL + ""/api/get_upload_url""
	md5Sum := md5.Sum(chunk.data)
	jsonData, err := json.Marshal(jsonChunkInfo{
		ID:    id,
		Size:  len(chunk.data),
		Index: chunk.index,
		Md5:   hex.EncodeToString(md5Sum[:]),
	})

	_, body, err := c.makeRequestFail(""POST"", uploadURL, jsonData)
	if err != nil {
		return err
	}

	var resultJSON map[string]interface{}
	err = json.Unmarshal(body, &resultJSON)
	helpers.CheckErr(err)
	if resultJSON[""url""] == """" {
		panic(""No url in response!"")
	}
	_, _, err = c.makeRequestWithHeadersFail(""PUT"", resultJSON[""url""].(string), resultJSON[""headers""].(map[string]interface{}), chunk.data)
	return err
}

func (c *PFDAClient) setPostHeaders(req *retryablehttp.Request) {
	req.Header.Set(""User-Agent"", userAgent+""(""+c.Platform+"")"")
	req.Header.Set(""Authorization"", ""Key ""+c.AuthKey)
	req.Header.Set(""Content-Type"", ""application/json"")
}

func inputError(msg string) {
	fmt.Println(fmt.Errorf(msg))
	os.Exit(1)
}

func yesNo(label string) bool {
	prompt := promptui.Select{
		Label: label + "" [Yes/No]"",
		Items: []string{""Yes"", ""No""},
	}
	_, result, err := prompt.Run()
	if err != nil {
		log.Fatalf(""Prompt failed %v\n"", err)
	}
	return result == ""Yes""
}

// Filters out only files to pick from.
func pickFile(files []jsonFile, label string) string {

	options := make([]string, 0)
	ids := make([]string, 0)
	for _, file := range files {
		if file.Type == ""UserFile"" {
			options = append(options, ""created ""+file.CreatedAt+"" - ""+file.Size+"" - ""+file.Name+"" (""+file.Uid+"")"")
			ids = append(ids, file.Uid)
		}
	}

	prompt := promptui.Select{
		Label: label,
		Items: options,
	}
	index, _, err := prompt.Run()
	if err != nil {
		log.Fatalf(""Prompt failed %v\n"", err)
	}
	return ids[index]
}

// pass all flags, so we can optimize the table header - if in 'private' do not show added-by
func printListingResponse(response jsonListingPayload, flags map[string]bool) {
	if flags[""json""] {
		prettyJSON, _ := json.MarshalIndent(response.Files, """", ""    "")
		fmt.Printf(""%s\n"", string(prettyJSON))
	} else if flags[""brief""] {
		printListingSimple(response.Files)
	} else {
		printListingVerbose(response.Files, response.Meta)
	}
}

func printListingSimple(files []jsonFile) {
	if len(files) == 0 {
		return
	}
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)

	fmt.Fprintln(writer, strings.Join([]string{""File/Folder ID"", ""Name""}, ""\t"")+""\t"")

	for _, file := range files {
		if file.Type == ""UserFile"" {
			fmt.Fprintln(writer, strings.Join([]string{file.Uid, file.Name}, ""\t"")+""\t"")
		} else {
			fmt.Fprintln(writer, strings.Join([]string{strconv.Itoa(file.Id), file.Name}, ""\t"")+""\t"")
		}
	}
	writer.Flush()
}

func printListSpacesResponse(spaces []jsonSpace, flags map[string]bool) {
	if flags[""json""] {
		prettyJSON, _ := json.MarshalIndent(spaces, """", ""    "")
		fmt.Printf(""%s\n"", string(prettyJSON))
	} else if len(spaces) > 0 {
		writer := tabwriter.NewWriter(os.Stdout, 0, 8, 1, '\t', tabwriter.AlignRight)

		fmt.Fprintln(writer, strings.Join([]string{""ID"", ""Type"", ""Status"", ""Role"", ""Side"", ""Name""}, ""\t"")+""\t"")
		for _, space := range spaces {
			fmt.Fprintln(writer, strings.Join([]string{strconv.Itoa(space.Id), space.Type, helpers.FormatValue(space.Protected, ""Protected""), space.Role, space.Side, space.Title}, ""\t"")+""\t"")
		}
		writer.Flush()

	}
}

func printListingVerbose(files []jsonFile, meta jsonMeta) {
	if len(files) == 0 {
		return
	}
	fmt.Printf(""Scope: %s\nPath: %s\n\n"", meta.Scope, meta.Path)

	isSpaceOrPublicContext := strings.Contains(meta.Scope, ""space"") || strings.Contains(meta.Scope, ""Public"")
	writer := tabwriter.NewWriter(os.Stdout, 0, 8, 2, '\t', tabwriter.AlignRight)

	if isSpaceOrPublicContext {
		fmt.Fprintln(writer, strings.Join([]string{""File/Folder ID"", ""State "" + ""\t"", ""Type"", ""Status"", ""Size"", ""Created"", ""Added By"", ""Name""}, ""\t"")+""\t"")
		for _, file := range files {
			if file.Type == ""UserFile"" {
				fmt.Fprintln(writer, strings.Join([]string{file.Uid, file.State + ""\t"", file.Type, helpers.FormatValue(file.Locked, ""Locked""), file.Size, file.CreatedAt, file.AddedBy, file.Name}, ""\t"")+""\t"")
			} else {
				fmt.Fprintln(writer, strings.Join([]string{strconv.Itoa(file.Id), ""\t"", file.Type, helpers.FormatValue(file.Locked, ""Locked""), """", file.CreatedAt, file.AddedBy, file.Name}, ""\t"")+""\t"")
			}
		}
	} else {
		fmt.Fprintln(writer, strings.Join([]string{""File/Folder ID"", ""State"" + ""\t"", ""Type"", ""Status"", ""Size"", ""Created"", ""Name""}, ""\t"")+""\t"")
		for _, file := range files {
			if file.Type == ""UserFile"" {
				fmt.Fprintln(writer, strings.Join([]string{file.Uid, file.State + ""\t"", file.Type, helpers.FormatValue(file.Locked, ""Locked""), file.Size, file.CreatedAt, file.Name}, ""\t"")+""\t"")
			} else {
				fmt.Fprintln(writer, strings.Join([]string{strconv.Itoa(file.Id), ""\t"", file.Type, helpers.FormatValue(file.Locked, ""Locked""), """", file.CreatedAt, file.Name}, ""\t"")+""\t"")
			}
		}
	}
	writer.Flush()
}

// HandleError Check for error, if found behave accordingly
func (c *PFDAClient) HandleError(err error) {
	if err != nil {
		fmt.Println(err)
		if !c.ContinueOnError {
			os.Exit(1)
		}
	}
}
","Go"
"Assay","FDA/precisionFDA","go/precisionfda/download.go",".go","4320","172","package precisionfda

import (
	""fmt""
	""io""
	""net/http""
	""net/url""
	""os""
	""path""
	""strings""

	""github.com/docker/go-units""
	""github.com/gosuri/uilive""
)

// ProgressWriter counts the number of bytes written to it.
type ProgressWriter struct {
	Total      int64
	Downloaded int64
	Writer     *uilive.Writer
}

// Write implements the io.Writer interface.
func (wc *ProgressWriter) Write(p []byte) (int, error) {
	n := len(p)
	wc.Downloaded += int64(n)
	percent := 100 * float64(wc.Downloaded) / float64(wc.Total)
	fmt.Fprintf(wc.Writer, ""                  %.1f%% (%s of %s)\n"", percent, units.BytesSize(float64(wc.Downloaded)), units.BytesSize(float64(wc.Total)))
	wc.Writer.Flush()
	return n, nil
}

func NewProgressWriter(totalBytes int64, writer *uilive.Writer) *ProgressWriter {
	return &ProgressWriter{totalBytes, 0, writer}
}

type Writer struct {
	Total  int64
	Writer *uilive.Writer
}

type Printer struct {
	LinesToPrint int // max lines to print.
	Lines int
	Writer *uilive.Writer
}

func NewWriter(totalBytes int64, writer *uilive.Writer) *Writer {
	return &Writer{totalBytes, writer}
}

func NewPrinter(lines int, writer *uilive.Writer) *Printer {
	return &Printer{lines, 0, writer}
}

// Write implements the io.Writer interface.
func (wc *Printer) Write(p []byte) (int, error) {
	content := string(p[:])
	lines := strings.Split(strings.ReplaceAll(content, ""\r\n"", ""\n""), ""\n"")
	for _, line := range lines {
		if wc.Lines == wc.LinesToPrint {
			return -1, fmt.Errorf("">> Line limit reached"")
		}
		fmt.Println(line)
		wc.Lines = wc.Lines + 1
	}
	return len(p), nil
}

// Write implements the io.Writer interface.
func (wc *Writer) Write(p []byte) (int, error) {
	wc.Writer.Flush()
	return len(p), nil
}

func Download(fileURL string, outputFilePath string, fileSize int64, withProgressBar bool) error {
	out, err := os.Create(outputFilePath) // Create the file
	if err != nil {
		return err
	}
	defer out.Close()

	resp, err := http.Get(fileURL)
	if err != nil {
		return err
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return fmt.Errorf("">> Error downloading file: %s"", resp.Status)
	}

	writer := uilive.New()
	writer.Start()
	defer writer.Stop()

	// Writer the body to file
	if withProgressBar {
		body := io.TeeReader(resp.Body, NewProgressWriter(fileSize, writer))
		_, err = io.Copy(out, body)
		return err
	} else {
		body := io.TeeReader(resp.Body, NewWriter(0, writer))
		_, err = io.Copy(out, body)
		return err
	}
}

func DownloadDirectly(downloadUrl string, outputFilePath string, overwrite string) error {
	fileURL := downloadUrl
	originalName := path.Base(fileURL)
	fileName, err := url.PathUnescape(originalName)
	if err != nil {
		fileName = originalName
	}
	// fileSize := resultJSON[""file_size""].(float64)
	if outputFilePath == """" {
		// If output is not specified, use the original filename and current working directory
		dir, err := os.Getwd()
		if err != nil {
			return err
		}

		outputFilePath = path.Join(dir, fileName)
	} else if fileInfo, err := os.Stat(outputFilePath); err == nil && fileInfo.IsDir() {
		// If outputFilePath exists and it is a directory then the file should be downloaded
		// to that directory while retaining its original name
		outputFilePath = path.Join(outputFilePath, fileName)
	}

	if _, err := os.Stat(outputFilePath); err == nil && (overwrite == ""false"" || overwrite == """") {
		fmt.Printf("">> Error: path %s already exists but -overwrite flag not set to true. Skipping download.\n"", outputFilePath)
		return nil
	}

	withProgressBar := false
	err = Download(fileURL, outputFilePath, 0, withProgressBar)
	if err != nil {
		fmt.Printf("">> Error during download of %s - %s.\n"", fileName, err)
		return nil
	}
	fmt.Printf("">> Downloaded:  %s\n"", outputFilePath)
	return nil
}


func Head(fileURL string, lines int) error {
	tmp, err := os.CreateTemp("""",""head"") // Create temp fake data destination
	if err != nil {
		return err
	}

	resp, err := http.Get(fileURL)
	if err != nil {
		return err
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return fmt.Errorf("">> Error while getting file: %s"", resp.Status)
	}

	writer := uilive.New()
	writer.Start()
	defer writer.Stop()

	body := io.TeeReader(resp.Body, NewPrinter(lines, writer))
	_, err = io.Copy(tmp, body)

	defer os.Remove(tmp.Name())
	return nil
}","Go"
"Assay","FDA/precisionFDA","go/precisionfda/test/testutils.go",".go","1018","38","package test

// From https://github.com/benbjohnson/testing
import (
	""fmt""
	""path/filepath""
	""runtime""
	""reflect""
	""testing""
)

// assert fails the test if the condition is false.
func Assert(tb testing.TB, condition bool, msg string, v ...interface{}) {
	if !condition {
		_, file, line, _ := runtime.Caller(1)
		fmt.Printf(""\033[31m%s:%d: ""+msg+""\033[39m\n\n"", append([]interface{}{filepath.Base(file), line}, v...)...)
		tb.FailNow()
	}
}

// ok fails the test if an err is not nil.
func Ok(tb testing.TB, err error) {
	if err != nil {
		_, file, line, _ := runtime.Caller(1)
		fmt.Printf(""\033[31m%s:%d: unexpected error: %s\033[39m\n\n"", filepath.Base(file), line, err.Error())
		tb.FailNow()
	}
}

// equals fails the test if exp is not equal to act.
func Equals(tb testing.TB, exp, act interface{}) {
	if !reflect.DeepEqual(exp, act) {
		_, file, line, _ := runtime.Caller(1)
		fmt.Printf(""\033[31m%s:%d:\n\n\texpected: %#v\n\n\tgot: %#v\033[39m\n\n"", filepath.Base(file), line, exp, act)
		tb.FailNow()
	}
}
","Go"
"Assay","FDA/precisionFDA","tools/appkit/ReleaseNotes.md",".md","196","8","version 1.0 - unknown date
- original version 

version 1.1 - 14. 7. 2023
- added support for input/output arrays 

version 1.2 - 27. 7. 2023
- added support for Ubuntu 20.04 which uses Python 3.8","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/moment/CHANGELOG.md",".md","20086","460","Changelog
=========

### 2.10.6

[#2515](https://github.com/moment/moment/pull/2515) Fix regression introduced
in `2.10.5` related to `moment.ISO_8601` parsing.

### 2.10.5 [See full changelog](https://gist.github.com/ichernev/6ec13ac7efc396da44b2)

Important changes:
* [#2357](https://github.com/moment/moment/pull/2357) Improve unit bubbling for ISO dates
  this fixes day to year conversions to work around end-of-year (~365 days). As
  a side effect 365 days is 11 months and 30 days, and 366 days is one year.
* [#2438](https://github.com/moment/moment/pull/2438) Fix inconsistent moment.min and moment.max results
  Return invalid result if any of the inputs is invalid
* [#2494](https://github.com/moment/moment/pull/2494) Fix two digit year parsing with YYYY format
  This brings the benefits of YY to YYYY
* [#2368](https://github.com/moment/moment/pull/2368) perf: use faster form of copying dates, across the board improvement


### 2.10.3 [See full changelog](https://gist.github.com/ichernev/f264b9bed5b00f8b1b7f)

* add `moment.fn.to` and `moment.fn.toNow` (similar to `from` and `fromNow`)
* new locales (Sinhalese (si), Montenegrin (me), Javanese (ja))
* performance improvements

### 2.10.2

* fixed moment-with-locales in browser env caused by esperanto change

### 2.10.1

* regression: Add moment.duration.fn back

### 2.10.0

Ported code to es6 modules.

### 2.9.0 [See full changelog](https://gist.github.com/ichernev/0c9a9b49951111a27ce7)

languages:
* [2104](https://github.com/moment/moment/issues/2104) Frisian (fy) language file with unit test
* [2097](https://github.com/moment/moment/issues/2097) add ar-tn locale

deprecations:
* [2074](https://github.com/moment/moment/issues/2074) Implement `moment.fn.utcOffset`, deprecate `momen.fn.zone`

features:
* [2088](https://github.com/moment/moment/issues/2088) add moment.fn.isBetween
* [2054](https://github.com/moment/moment/issues/2054) Call updateOffset when creating moment (needed for default timezone in
  moment-timezone)
* [1893](https://github.com/moment/moment/issues/1893) Add moment.isDate method
* [1825](https://github.com/moment/moment/issues/1825) Implement toJSON function on Duration
* [1809](https://github.com/moment/moment/issues/1809) Allowing moment.set() to accept a hash of units
* [2128](https://github.com/moment/moment/issues/2128) Add firstDayOfWeek, firstDayOfYear locale getters
* [2131](https://github.com/moment/moment/issues/2131) Add quarter diff support

Some bugfixes and language improvements -- [full changelog](https://gist.github.com/ichernev/0c9a9b49951111a27ce7)

### 2.8.4 [See full changelog](https://gist.github.com/ichernev/a4fcb0a46d74e4b9b996)

Features:

* [#2000](https://github.com/moment/moment/issues/2000) Add LTS localised format that includes seconds
* [#1960](https://github.com/moment/moment/issues/1960) added formatToken 'x' for unix offset in milliseconds #1938
* [#1965](https://github.com/moment/moment/issues/1965) Support 24:00:00.000 to mean next day, at midnight.
* [#2002](https://github.com/moment/moment/issues/2002) Accept 'date' key when creating moment with object
* [#2009](https://github.com/moment/moment/issues/2009) Use native toISOString when we can

Some bugfixes and language improvements -- [full changelog](https://gist.github.com/ichernev/a4fcb0a46d74e4b9b996)

### 2.8.3

Bugfixes:

* [#1801](https://github.com/moment/moment/issues/1801) proper pluralization for Arabic
* [#1833](https://github.com/moment/moment/issues/1833) improve spm integration
* [#1871](https://github.com/moment/moment/issues/1871) fix zone bug caused by Firefox 24
* [#1882](https://github.com/moment/moment/issues/1882) Use hh:mm in Czech
* [#1883](https://github.com/moment/moment/issues/1883) Fix 2.8.0 regression in duration as conversions
* [#1890](https://github.com/moment/moment/issues/1890) Faster travis builds
* [#1892](https://github.com/moment/moment/issues/1892) Faster isBefore/After/Same
* [#1848](https://github.com/moment/moment/issues/1848) Fix flaky month diffs
* [#1895](https://github.com/moment/moment/issues/1895) Fix 2.8.0 regression in moment.utc with format array
* [#1896](https://github.com/moment/moment/issues/1896) Support setting invalid instance locale (noop)
* [#1897](https://github.com/moment/moment/issues/1897) Support moment([str]) in addition to moment([int])

### 2.8.2

Minor bugfixes:

* [#1874](https://github.com/moment/moment/issues/1874) use `Object.prototype.hasOwnProperty`
  instead of `obj.hasOwnProperty` (ie8 bug)
* [#1873](https://github.com/moment/moment/issues/1873) add `duration#toString()`
* [#1859](https://github.com/moment/moment/issues/1859) better month/weekday names in norwegian
* [#1812](https://github.com/moment/moment/issues/1812) meridiem parsing for greek
* [#1804](https://github.com/moment/moment/issues/1804) spanish del -> de
* [#1800](https://github.com/moment/moment/issues/1800) korean LT improvement

### 2.8.1

* bugfix [#1813](https://github.com/moment/moment/issues/1813): fix moment().lang([key]) incompatibility

### 2.8.0 [See changelog](https://gist.github.com/ichernev/ac3899324a5fa6c8c9b4)

* incompatible changes
    * [#1761](https://github.com/moment/moment/issues/1761): moments created without a language are no longer following the global language, in case it changes. Only newly created moments take the global language by default. In case you're affected by this, wait, comment on [#1797](https://github.com/moment/moment/issues/1797) and wait for a proper reimplementation
    * [#1642](https://github.com/moment/moment/issues/1642): 45 days is no longer ""a month"" according to humanize, cutoffs for month, and year have changed. Hopefully your code does not depend on a particular answer from humanize (which it shouldn't anyway)
    * [#1784](https://github.com/moment/moment/issues/1784): if you use the human readable English datetime format in a weird way (like storing them in a database) that would break when the format changes you're at risk.

* deprecations (old behavior will be dropped in 3.0)
    * [#1761](https://github.com/moment/moment/issues/1761) `lang` is renamed to `locale`, `langData` -> `localeData`. Also there is now `defineLocale` that should be used when creating new locales
    * [#1763](https://github.com/moment/moment/issues/1763) `add(unit, value)` and `subtract(unit, value)` are now deprecated. Use `add(value, unit)` and `subtract(value, unit)` instead.
    * [#1759](https://github.com/moment/moment/issues/1759) rename `duration.toIsoString` to `duration.toISOString`. The js standard library and moment's `toISOString` follow that convention.

* new locales
    * [#1789](https://github.com/moment/moment/issues/1789) Tibetan (bo)
    * [#1786](https://github.com/moment/moment/issues/1786) Africaans (af)
    * [#1778](https://github.com/moment/moment/issues/1778) Burmese (my)
    * [#1727](https://github.com/moment/moment/issues/1727) Belarusian (be)

* bugfixes, locale bugfixes, performance improvements, features

### 2.7.0 [See changelog](https://gist.github.com/ichernev/b0a3d456d5a84c9901d7)

* new languages

  * [#1678](https://github.com/moment/moment/issues/1678) Bengali (bn)
  * [#1628](https://github.com/moment/moment/issues/1628) Azerbaijani (az)
  * [#1633](https://github.com/moment/moment/issues/1633) Arabic, Saudi Arabia (ar-sa)
  * [#1648](https://github.com/moment/moment/issues/1648) Austrian German (de-at)

* features

  * [#1663](https://github.com/moment/moment/issues/1663) configurable relative time thresholds
  * [#1554](https://github.com/moment/moment/issues/1554) support anchor time in moment.calendar
  * [#1693](https://github.com/moment/moment/issues/1693) support moment.ISO_8601 as parsing format
  * [#1637](https://github.com/moment/moment/issues/1637) add moment.min and moment.max and deprecate min/max instance methods
  * [#1704](https://github.com/moment/moment/issues/1704) support string value in add/subtract
  * [#1647](https://github.com/moment/moment/issues/1647) add spm support (package manager)

* bugfixes

### 2.6.0 [See changelog](https://gist.github.com/ichernev/10544682)

* languages
  * [#1529](https://github.com/moment/moment/issues/1529) Serbian-Cyrillic (sr-cyr)
  * [#1544](https://github.com/moment/moment/issues/1544), [#1546](https://github.com/moment/moment/issues/1546) Khmer Cambodia (km)

* features
    * [#1419](https://github.com/moment/moment/issues/1419), [#1468](https://github.com/moment/moment/issues/1468), [#1467](https://github.com/moment/moment/issues/1467), [#1546](https://github.com/moment/moment/issues/1546) better handling of timezone-d moments around DST
    * [#1462](https://github.com/moment/moment/issues/1462) add weeksInYear and isoWeeksInYear
    * [#1475](https://github.com/moment/moment/issues/1475) support ordinal parsing
    * [#1499](https://github.com/moment/moment/issues/1499) composer support
    * [#1577](https://github.com/moment/moment/issues/1577), [#1604](https://github.com/moment/moment/issues/1604) put Date parsing in moment.createFromInputFallback so it can be properly deprecated and controlled in the future
    * [#1545](https://github.com/moment/moment/issues/1545) extract two-digit year parsing in moment.parseTwoDigitYear, so it can be overwritten
    * [#1590](https://github.com/moment/moment/issues/1590) (see [#1574](https://github.com/moment/moment/issues/1574)) set AMD global before module definition to better support non AMD module dependencies used in AMD environment
    * [#1589](https://github.com/moment/moment/issues/1589) remove global in Node.JS environment (was not working before, nobody complained, was scheduled for removal anyway)
    * [#1586](https://github.com/moment/moment/issues/1586) support quarter setting and parsing

* 18 bugs fixed

### 2.5.1

* languages
  * [#1392](https://github.com/moment/moment/issues/1392) Armenian (hy-am)

* bugfixes
  * [#1429](https://github.com/moment/moment/issues/1429) fixes [#1423](https://github.com/moment/moment/issues/1423) weird chrome-32 bug with js object creation
  * [#1421](https://github.com/moment/moment/issues/1421) remove html entities from Welsh
  * [#1418](https://github.com/moment/moment/issues/1418) fixes [#1401](https://github.com/moment/moment/issues/1401) improved non-padded tokens in strict matching
  * [#1417](https://github.com/moment/moment/issues/1417) fixes [#1404](https://github.com/moment/moment/issues/1404) handle buggy moment object created by property cloning
  * [#1398](https://github.com/moment/moment/issues/1398) fixes [#1397](https://github.com/moment/moment/issues/1397) fix Arabic-like week number parsing
  * [#1396](https://github.com/moment/moment/issues/1396) add leftZeroFill(4) to GGGG and gggg formats
  * [#1373](https://github.com/moment/moment/issues/1373) use lowercase for months and days in Catalan

* testing
  * [#1374](https://github.com/moment/moment/issues/1374) run tests on multiple browser/os combos via SauceLabs and Travis

### 2.5.0 [See changelog](https://gist.github.com/ichernev/8104451)

* New languages
  * Luxemburish (lb) [1247](https://github.com/moment/moment/issues/1247)
  * Serbian (rs) [1319](https://github.com/moment/moment/issues/1319)
  * Tamil (ta) [1324](https://github.com/moment/moment/issues/1324)
  * Macedonian (mk) [1337](https://github.com/moment/moment/issues/1337)

* Features
  * [1311](https://github.com/moment/moment/issues/1311) Add quarter getter and format token `Q`
  * [1303](https://github.com/moment/moment/issues/1303) strict parsing now respects number of digits per token (fix [1196](https://github.com/moment/moment/issues/1196))
  * 0d30bb7 add jspm support
  * [1347](https://github.com/moment/moment/issues/1347) improve zone parsing
  * [1362](https://github.com/moment/moment/issues/1362) support merideam parsing in Korean

* 22 bugfixes

### 2.4.0

* **Deprecate** globally exported moment, will be removed in next major
* New languages
  * Farose (fo) [#1206](https://github.com/moment/moment/issues/1206)
  * Tagalog/Filipino (tl-ph) [#1197](https://github.com/moment/moment/issues/1197)
  * Welsh (cy) [#1215](https://github.com/moment/moment/issues/1215)
* Bugfixes
  * properly handle Z at the end of iso RegExp [#1187](https://github.com/moment/moment/issues/1187)
  * chinese meridian time improvements [#1076](https://github.com/moment/moment/issues/1076)
  * fix language tests [#1177](https://github.com/moment/moment/issues/1177)
  * remove some failing tests (that should have never existed :))
    [#1185](https://github.com/moment/moment/issues/1185)
    [#1183](https://github.com/moment/moment/issues/1183)
  * handle russian noun cases in weird cases [#1195](https://github.com/moment/moment/issues/1195)

### 2.3.1

Removed a trailing comma [1169] and fixed a bug with `months`, `weekdays` getters [#1171](https://github.com/moment/moment/issues/1171).

### 2.3.0 [See changelog](https://gist.github.com/ichernev/6864354)

Changed isValid, added strict parsing.
Week tokens parsing.

### 2.2.1

Fixed bug in string prototype test.
Updated authors and contributors.

### 2.2.0 [See changelog](https://gist.github.com/ichernev/00f837a9baf46a3565e4)

Added bower support.

Language files now use UMD.

Creating moment defaults to current date/month/year.

Added a bundle of moment and all language files.

### 2.1.0 [See changelog](https://gist.github.com/timrwood/b8c2d90d528eddb53ab5)

Added better week support.

Added ability to set offset with `moment#zone`.

Added ability to set month or weekday from a string.

Added `moment#min` and `moment#max`

### 2.0.0 [See changelog](https://gist.github.com/timrwood/e72f2eef320ed9e37c51)

Added short form localized tokens.

Added ability to define language a string should be parsed in.

Added support for reversed add/subtract arguments.

Added support for `endOf('week')` and `startOf('week')`.

Fixed the logic for `moment#diff(Moment, 'months')` and `moment#diff(Moment, 'years')`

`moment#diff` now floors instead of rounds.

Normalized `moment#toString`.

Added `isSame`, `isAfter`, and `isBefore` methods.

Added better week support.

Added `moment#toJSON`

Bugfix: Fixed parsing of first century dates

Bugfix: Parsing 10Sep2001 should work as expected

Bugfix: Fixed wierdness with `moment.utc()` parsing.

Changed language ordinal method to return the number + ordinal instead of just the ordinal.

Changed two digit year parsing cutoff to match strptime.

Removed `moment#sod` and `moment#eod` in favor of `moment#startOf` and `moment#endOf`.

Removed `moment.humanizeDuration()` in favor of `moment.duration().humanize()`.

Removed the lang data objects from the top level namespace.

Duplicate `Date` passed to `moment()` instead of referencing it.

### 1.7.2 [See discussion](https://github.com/timrwood/moment/issues/456)

Bugfixes

### 1.7.1 [See discussion](https://github.com/timrwood/moment/issues/384)

Bugfixes

### 1.7.0 [See discussion](https://github.com/timrwood/moment/issues/288)

Added `moment.fn.endOf()` and `moment.fn.startOf()`.

Added validation via `moment.fn.isValid()`.

Made formatting method 3x faster. http://jsperf.com/momentjs-cached-format-functions

Add support for month/weekday callbacks in `moment.fn.format()`

Added instance specific languages.

Added two letter weekday abbreviations with the formatting token `dd`.

Various language updates.

Various bugfixes.

### 1.6.0 [See discussion](https://github.com/timrwood/moment/pull/268)

Added Durations.

Revamped parser to support parsing non-separated strings (YYYYMMDD vs YYYY-MM-DD).

Added support for millisecond parsing and formatting tokens (S SS SSS)

Added a getter for `moment.lang()`

Various bugfixes.

There are a few things deprecated in the 1.6.0 release.

1. The format tokens `z` and `zz` (timezone abbreviations like EST CST MST etc) will no longer be supported. Due to inconsistent browser support, we are unable to consistently produce this value. See [this issue](https://github.com/timrwood/moment/issues/162) for more background.

2. The method `moment.fn.native` is deprecated in favor of `moment.fn.toDate`. There continue to be issues with Google Closure Compiler throwing errors when using `native`, even in valid instances.

3. The way to customize am/pm strings is being changed. This would only affect you if you created a custom language file. For more information, see [this issue](https://github.com/timrwood/moment/pull/222).

### 1.5.0 [See milestone](https://github.com/timrwood/moment/issues?milestone=10&page=1&state=closed)

Added UTC mode.

Added automatic ISO8601 parsing.

Various bugfixes.

### 1.4.0 [See milestone](https://github.com/timrwood/moment/issues?milestone=8&state=closed)

Added `moment.fn.toDate` as a replacement for `moment.fn.native`.

Added `moment.fn.sod` and `moment.fn.eod` to get the start and end of day.

Various bugfixes.

### 1.3.0 [See milestone](https://github.com/timrwood/moment/issues?milestone=7&state=closed)

Added support for parsing month names in the current language.

Added escape blocks for parsing tokens.

Added `moment.fn.calendar` to format strings like 'Today 2:30 PM', 'Tomorrow 1:25 AM', and 'Last Sunday 4:30 AM'.

Added `moment.fn.day` as a setter.

Various bugfixes

### 1.2.0 [See milestone](https://github.com/timrwood/moment/issues?milestone=4&state=closed)

Added timezones to parser and formatter.

Added `moment.fn.isDST`.

Added `moment.fn.zone` to get the timezone offset in minutes.

### 1.1.2 [See milestone](https://github.com/timrwood/moment/issues?milestone=6&state=closed)

Various bugfixes

### 1.1.1 [See milestone](https://github.com/timrwood/moment/issues?milestone=5&state=closed)

Added time specific diffs (months, days, hours, etc)

### 1.1.0

Added `moment.fn.format` localized masks. 'L LL LLL LLLL' [issue 29](https://github.com/timrwood/moment/pull/29)

Fixed [issue 31](https://github.com/timrwood/moment/pull/31).

### 1.0.1

Added `moment.version` to get the current version.

Removed `window !== undefined` when checking if module exists to support browserify. [issue 25](https://github.com/timrwood/moment/pull/25)

### 1.0.0

Added convenience methods for getting and setting date parts.

Added better support for `moment.add()`.

Added better lang support in NodeJS.

Renamed library from underscore.date to Moment.js

### 0.6.1

Added Portuguese, Italian, and French language support

### 0.6.0

Added _date.lang() support.
Added support for passing multiple formats to try to parse a date. _date(""07-10-1986"", [""MM-DD-YYYY"", ""YYYY-MM-DD""]);
Made parse from string and single format 25% faster.

### 0.5.2

Bugfix for [issue 8](https://github.com/timrwood/underscore.date/pull/8) and [issue 9](https://github.com/timrwood/underscore.date/pull/9).

### 0.5.1

Bugfix for [issue 5](https://github.com/timrwood/underscore.date/pull/5).

### 0.5.0

Dropped the redundant `_date.date()` in favor of `_date()`.
Removed `_date.now()`, as it is a duplicate of `_date()` with no parameters.
Removed `_date.isLeapYear(yearNumber)`. Use `_date([yearNumber]).isLeapYear()` instead.
Exposed customization options through the `_date.relativeTime`, `_date.weekdays`, `_date.weekdaysShort`, `_date.months`, `_date.monthsShort`, and `_date.ordinal` variables instead of the `_date.customize()` function.

### 0.4.1

Added date input formats for input strings.

### 0.4.0

Added underscore.date to npm. Removed dependencies on underscore.

### 0.3.2

Added `'z'` and `'zz'` to `_.date().format()`. Cleaned up some redundant code to trim off some bytes.

### 0.3.1

Cleaned up the namespace. Moved all date manipulation and display functions to the _.date() object.

### 0.3.0

Switched to the Underscore methodology of not mucking with the native objects' prototypes.
Made chaining possible.

### 0.2.1

Changed date names to be a more pseudo standardized 'dddd, MMMM Do YYYY, h:mm:ss a'.
Added `Date.prototype` functions `add`, `subtract`, `isdst`, and `isleapyear`.

### 0.2.0

Changed function names to be more concise.
Changed date format from php date format to custom format.

### 0.1.0

Initial release

","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/sortable/docs/intro.md",".md","3241","83","## Sortable

Sortable is an open-source JavaScript and CSS library which adds sorting functionality to tables. It is tiny (<code>&lt;2kb</code> min+gzip) and has no dependencies.

#### Demo

Here is a demo of the “Bootstrap” theme.

<p style=""display: none""></p>
<table class=""sortable-theme-bootstrap"" data-sortable> <thead> <tr> <th data-sortable=""false"">Browser</th> <th data-sorted=""true"" data-sorted-direction=""descending"">Usage</th> <th>Initial release</th> <th>Stable version</th> </tr> </thead> <tbody> <tr> <td>Chrome</td> <td>42.68%</td> <td data-value=""2008"">September 2, 2008</td> <td>31.0.1650.57</td> </tr> <tr> <td>Internet Explorer</td> <td>25.44%</td> <td data-value=""1995"">August 16, 1995</td> <td>11.0.1</td> </tr> <tr> <td>Firefox</td> <td>20.01%</td> <td data-value=""2002"">September 23, 2002</td> <td>25.0.1</td> </tr> <tr> <td>Safari</td> <td>8.39%</td> <td data-value=""2003"">January 7, 2003</td> <td>7.0</td> </tr> <tr> <td>Opera</td> <td>1.03%</td> <td data-value=""1994"">Late 1994</td> <td>18.0.1284.49</td> </tr> <tr> <td>Other</td> <td data-value=""0"">2.44%</td> <td data-value=""-1""></td> <td></td> </tr> </tbody> </table>
<p style=""display: none""></p>

#### Install with Eager

The easiest way to add Sortable to your site is with [Eager](http://eager.io). Click Install to see a live preview of Sortable on your website.

<iframe style=""height: 48px; width: 180px"" src=""//install.eager.io?appId=sortable"" allowtransparency=""true"" scroll=""no"" frameBorder=""0""></iframe>

#### Features

- Drop-in script and styles
- 6 beautiful CSS themes
- Tiny footprint (<code>&lt;2kb</code> min+gzip) and no dependencies
- Looks and behaves great on mobile devices

#### Requirements

- None

#### Browser Support

- IE8+
- Firefox 4+
- Current WebKit (Chrome, Safari)
- Opera

#### Manually Including

For the most common usage of sortable, you’ll want to include following:

```html
<script src=""sortable.min.js""></script>
<link rel=""stylesheet"" href=""sortable-theme-bootstrap.css"" />
```

Now any table with the attribute `sortable` will be made sortable. To get the styling, you’ll also need to add a class name to the table to match the theme you chose:

```html
<table class=""sortable-theme-bootstrap"" data-sortable>
    <!-- ... -->
</table>
```

This example uses the “Bootstrap” theme. If the tables on your site already have styling, you may want to try the “Minimal” theme instead. For a full list of supported themes, check out [Themes](/sortable/api/themes).

#### Learn More

To learn more about how to use sortable, visit our API pages.

- [Options](http://github.hubspot.com/sortable/api/options)
- [Themes](http://github.hubspot.com/sortable/api/themes)

#### Credits

Sortable is free and open-source, released on the [MIT License](https://github.com/HubSpot/sortable/blob/master/LICENSE).

#### Credits

Sortable was built by [Adam Schwartz](http://twitter.com/adamfschwartz)

<!-- Resources for the demos -->
<p style=""-webkit-transform: translateZ(0)""></p>
<script src=""/sortable/js/sortable.js""></script>
<style>
.hs-doc-content table {
    border: 0;
}
.hs-doc-content table th {
    background: initial;
}
</style>
<link rel=""stylesheet"" href=""/sortable/css/sortable-theme-bootstrap.css"">
","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/sortable/docs/api/2-Themes.md",".md","2402","61","## Themes

To use a builtin theme, you must include the theme style sheet, and add the class name to the table to match:

```html
<link rel=""stylesheet"" href=""sortable-theme-light.css"" />
<table class=""sortable-theme-light"" data-sortable>
    <!-- ... -->
</table>
```

At the moment, there are 6 themes:

<table id=""exampleTable"" class=""sortable-theme-light"" data-sortable>
<thead>
<tr>
<th data-sortable=""false""></th>
<th>Theme</th>
<th>Class name</th>
<th></th>
</tr>
</thead>
<tbody>
<tr><td>1</td><td data-value=""0"">Minimal</td><td>(No class name necessary)</td><td><a href data-theme=""sortable-theme-default"">Preview</td></tr>
<tr><td>2</td><td data-value=""1"">Light</td><td>sortable-theme-light</td><td><a href data-theme=""sortable-theme-light"">Preview</td></tr>
<tr><td>3</td><td data-value=""2"">Dark</td><td>sortable-theme-dark</td><td><a href data-theme=""sortable-theme-dark"">Preview</td></tr>
<tr><td>4</td><td data-value=""3"">Bootstrap</td><td>sortable-bootstrap</td><td><a href data-theme=""sortable-theme-bootstrap"">Preview</td></tr>
<tr><td>5</td><td data-value=""4"">Slick</td><td>sortable-theme-slick</td><td><a href data-theme=""sortable-theme-flat-slick"">Preview</td></tr>
<tr><td>6</td><td data-value=""5"">Finder</td><td>sortable-theme-finder</td><td><a href data-theme=""sortable-theme-finder"">Preview</td></tr>
</tbody>
</table>

You can download a release (containing all 6 themes) [here](/sortable). Or you can download them individually [here](https://github.com/HubSpot/sortable/tree/master/css).

<!-- Resources for the demos -->
<p style=""-webkit-transform: translateZ(0)""></p>
<script src=""/sortable/js/sortable.js""></script>
<style>
.hs-doc-content table {
    border: 0;
}
.hs-doc-content table th {
    background: initial;
}
</style>
<link rel=""stylesheet"" href=""/sortable/css/sortable-theme-minimal.css"" />
<link rel=""stylesheet"" href=""/sortable/css/sortable-theme-light.css"">
<link rel=""stylesheet"" href=""/sortable/css/sortable-theme-dark.css"">
<link rel=""stylesheet"" href=""/sortable/css/sortable-theme-bootstrap.css"">
<link rel=""stylesheet"" href=""/sortable/css/sortable-theme-slick.css"">
<link rel=""stylesheet"" href=""/sortable/css/sortable-theme-finder.css"">
<script>
$('[data-theme]').each(function(){
    $(this).click(function(e){
        e.preventDefault();
        $('#exampleTable').get(0).className = $(this).data('theme');
        return false;
    });
});
</script>
","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/sortable/docs/api/1-Options.md",".md","4303","148","## API Options

#### Initializing sortable

To be properly initialized and pick up the default styling, your table must add the attribute `data-sortable`.

Example:

```html
<table data-sortable>
    <!-- ... -->
</table>
```

##### `init`

All tables on the page will be automatically initted when the page is loaded.

If you add tables with JavaScript, call `init` after they are added to the page:

```coffeescript
Sortable.init()
```

##### `initTable`

To initialize an individual table, call `initTable`.

```coffeescript
exampleTable = document.querySelector('#exampleTable')
Sortable.initTable(exampleTable)
```

#### Events

An `CustomEvent` called `Sortable.sorted` is fired whenever a sort is completed.

Here’s an example of how you might listen to this event:

```coffeescript
exampleTable = document.querySelector('#exampleTable')
exampleTable.addEventListener 'Sortable.sorted', -> console.log '#exampleTable was sorted!'
```

#### Sorting options

##### `data-value`

By default, sortable will automatically detect whether a column contains alpha or numeric data. If you'd rather have a particular column sort on custom data, set the attribute `data-value` on a `<td>`.

```html
<table data-sortable>
    <thead><!-- ... --></thead>
    <tbody>
        <tr>
            <td>Adam</td>
            <td data-value=""1384904153699"">3 hours ago</td>
            <td><a href=""#"">New</a></td>
        </tr>
        <!-- ... -->
    </tbody>
</table>
```

##### `th` `data-sortable=""false""`

To disable sorting on a particular column, add `data-sortable=""false""` to the `<th>` for that column.

```html
<table data-sortable>
    <thead>
        <tr>
            <th>Name</th>
            <th>Date</th>
            <th data-sortable=""false"">Actions</th>
        </tr>
    </thead>
    <tbody><!-- ... --></tbody>
</table>
```

##### `th` `data-sortable-type=""TYPE_NAME""`

By default, the `type` of data in each column is determined by reading the first cell of a column and trying to `match` it to the list of types. To specify a type directly, use `data-sortable-type`.

The default types supplied by Sortable are `alpha`, `numeric`, and `date`.

```html
<table data-sortable>
    <thead>
        <tr>
            <th data-sortable-type=""alpha"">Numbers sorted alphabetically</th>
            <!-- ... -->
        </tr>
    </thead>
    <tbody>
        <tr>
            <td>10</td>
            <td>2</td>
            <td>312</td>
            <td>4</td>
        </tr>
        <!-- ... -->
    </tbody>
</table>
```

#### Custom Types

The default types supplied by Sortable are `alpha`, `numeric`, and `date`. To supply you own, call `Sortable.setupTypes(customTypesArray)` and pass in your custom types array.

Here’s how Sortable internally sets up the defaults.

```coffeescript
sortable.setupTypes [{
  name: 'numeric'
  defaultSortDirection: 'descending'
  match: (a) -> a.match numberRegExp
  comparator: (a) -> parseFloat(a.replace(/[^0-9.-]/g, ''), 10) or 0
}, {
  name: 'date'
  defaultSortDirection: 'ascending'
  reverse: true
  match: (a) -> not isNaN Date.parse a
  comparator: (a) -> Date.parse(a) or 0
}, {
  name: 'alpha'
  defaultSortDirection: 'ascending'
  match: -> true
  compare: (a, b) -> a.localeCompare b
}]
```

Each type must specify the following:

- A `name` which is used to identify the type, in particular the `data-sortable-type=""NAME""` attibute which can be specified on a `th`.
- A `defaultSortDirection` which can be either `ascending` or `descending`.
- A `match` function which is used to decide which columns are which types (unless `data-sortable-type` is specified).
- Either a `comparator` or a `compare` function for the custom sorting handled by this type:
    - `comparator` is used when you want to simply write a function to process the values before the each sort comparison is made by Sortable.
    - `compare` is used when you want to actually do handle the entire sort comparison yourself.
- Optionally specify `reverse` to change what `ascending` and `descending` mean with respect to the sort direction of the data for this column type.

<!-- Resources for the demos -->
<p style=""-webkit-transform: translateZ(0)""></p>
<script src=""/sortable/js/sortable.js""></script>
<link rel=""stylesheet"" href=""/sortable/css/sortable-theme-light.css"" />
","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/bootstrap-sass/CHANGELOG.md",".md","7492","205","# Changelog

## 3.3.6

Bumps Sass dependency to 3.3.4+ to avoid compatibility issues with @at-root.
Bumps node-sass dependency to ~3.4.2 for Node.js v5 compatibility. [#986](https://github.com/twbs/bootstrap-sass/issues/986)
Fixes breadcrumb content issues on libsass. [#919](https://github.com/twbs/bootstrap-sass/issues/919)
Fixes a Rails 5 compatibility issue. [#965](https://github.com/twbs/bootstrap-sass/pull/965)

Framework version: Bootstrap **v3.3.6**

## 3.3.5

Fix for standalone Compass extension compatibility. [#914](https://github.com/twbs/bootstrap-sass/issues/914)

Framework version: Bootstrap **v3.3.5**

## 3.3.4

No Sass-specific changes.

Framework version: Bootstrap **v3.3.4**

## 3.3.3

This is a re-packaged release of 3.3.2.1 (v3.3.2+1).

Versions are now strictly semver.
The PATCH version may be ahead of the upstream.

Framework version: Bootstrap **v3.3.2**.

## 3.3.2.1

* Fix glyphicons regression (revert 443d5b49eac84aec1cb2f8ea173554327bfc8c14)

## 3.3.2.0

* Autoprefixer is now required, and `autoprefixer-rails` is now a dependency for the ruby gem. [#824](https://github.com/twbs/bootstrap-sass/issues/824)
* Minimum precision reduced from 10 to 8 [#821](https://github.com/twbs/bootstrap-sass/issues/821)
* Requiring bootstrap JS from npm now works [#812](https://github.com/twbs/bootstrap-sass/issues/812)
* Fix Sass 3.4.x + IE10 compatibility issue [#803](https://github.com/twbs/bootstrap-sass/issues/803)
* Provide minified JS bundle [#777](https://github.com/twbs/bootstrap-sass/issues/777)
* Bower package is now at bootstrap-sass [#813](https://github.com/twbs/bootstrap-sass/issues/813)


## 3.3.1.0

* Variables override template at templates/project/_bootstrap-variables.sass
* Readme: Bower + Rails configuration

## 3.3.0.1

* Fix loading issue with the ruby gem version

## 3.3.0

* Improve libsass compatibility
* Support using Bower package with Rails

## 3.2.0.2

Main bootstrap file is now a partial (_bootstrap.scss), for compatibility with Compass 1+.

Fixed a number of bugs. [Issues closed in v3.2.0.2](https://github.com/twbs/bootstrap-sass/issues?q=is%3Aissue+is%3Aclosed+milestone%3Av3.2.0.2).

## 3.2.0.1

Fixed a number of bugs: [Issues closed in v3.2.0.1](https://github.com/twbs/bootstrap-sass/issues?q=is%3Aissue+is%3Aclosed+milestone%3Av3.2.0.1).

## 3.2.0.0

- Assets (Sass, JS, fonts) moved from `vendor/assets` to `assets`. `bootstrap.js` now contains concatenated JS.
- Compass generator now copies JS and fonts, and provides a better default `styles.sass`.
- Compass, Sprockets, and Mincer asset path helpers are now provided in pure Sass: `bootstrap-compass`, `bootstrap-sprockets`, and `bootstrap-mincer`.
Asset path helpers must be imported before `bootstrap`, more in Readme.
- Sprockets / Mincer JS manifest has been moved to `bootstrap-sprockets.js`.
It can be required without adding Bootstrap JS directory to load path, as it now uses relative paths.
- Sprockets: `depend_on_asset` (`glyphicons.scss`) has been changed to `depend_on` to work around an issue with `depend_on_asset`.
[More information](https://github.com/twbs/bootstrap-sass/issues/592#issuecomment-46570286).

## 3.1.1.0

- Updated Bower docs

## 3.1.0.2

- #523: Rails 3.2 compatibility
- Bugfixes from upstream up to 7eb532262fbd1112215b5a547b9285794b5360ab.

## 3.1.0.1

- #518: `scale` mixin Sass compatibility issue

## 3.1.0.0

* compiles with libsass master

## 3.0.2.1

* fix vendor paths for compass

## 3.0.0.0

* Fully automated (lots of string juggling) LESS -> Sass conversion. - *Gleb Mazovetskiy*
* Ported rake task from vwall/compass-twitter-bootstrap to convert Bootstrap upstream - *Peter Gumeson*
* Moved javascripts to us `bootstrap-component.js` to `bootstrap/component.js` - *Peter Gumeson*

## 2.3.2.2

* Allow sass-rails `>= 3.2` - *Thomas McDonald*

## 2.3.2.1

## 2.3.2.0

* Update to Bootstrap 2.3.2 - *Dan Allen*

## 2.3.1.3

* Find the correct Sprockets context for the `image_path` function - *Tristan Harward, Gleb Mazovetskiy*

## 2.3.1.2

* Fix changes to image url - *Gleb Mazovetskiy*
* Copy _variables into project on Compass install - *Phil Thompson*
* Add `bootstrap-affix` to the Compass template file - *brief*

## 2.3.1.1 (yanked)

* Change how image_url is handled internally - *Tristan Harward*
* Fix some font variables not having `!default` - *Thomas McDonald*

## 2.3.0.0
* [#290] Update to Bootstrap 2.3.0 - *Tristan Harward*
* Fix `rake:debug` with new file locations - *Thomas McDonald*
* Add draft contributing document - *Thomas McDonald*
* [#260] Add our load path to the global Sass load path - *Tristan Harward*
* [#275] Use GitHub notation in Sass head testing gemfile - *Timo Schilling*
* [#279, #283] Readme improvements - *theverything, Philip Arndt*

## 2.2.2.0
* [#270] Update to Bootstrap 2.2.2 - *Tristan Harward*
* [#266] Add license to gemspec - *Peter Marsh*

## 2.2.1.1
* [#258] Use `bootstrap` prefix for `@import`ing files in `bootstrap/bootstrap.scss` - *Umair Siddique*

## 2.2.1.0
* [#246] Update to Bootstrap 2.2.1 - *Tristan Harward*
* [#246] Pull Bootstrap updates from jlong/sass-twitter-bootstrap - *Tristan Harward*

## 2.1.1.0
* Update to Bootstrap 2.1.1
* [#222] Remove 100% multiplier in vertical-three-colours
* [#227] Fix IE component animation collapse
* [#228] Fix variables documentation link
* [#231] Made .input-block-level a class as well as mixin

## 2.1.0.1
* [#219] Fix expected a color. Got: transparent.
* [#207] Add missing warning style for table row highlighting
* [#208] Use grid-input-span for input spans

## 2.1.0.0
* Updated to Bootstrap 2.1
* Changed some mixin names to be more consistent. Nested mixins in Less are separated by a `-` when they are flattened in Sass.

## 2.0.4.1
* Fix `.row-fluid > spanX` nesting
* Small Javascript fixes for those staying on the 2.0.4 release
* Add `!default` to z-index variables.

## 2.0.4.0
* Updated to Bootstrap 2.0.4
* Switched to Bootstrap 2.0.3+'s method of separating responsive files
* [#149, #150] Fix off by one error introduced with manual revert of media query breakpoints
* `rake debug` and `rake test` both compile bootstrap & bootstrap-responsive

## 2.0.3.1
* [#145, #146] Fix button alignment in collapsing navbar as a result of an incorrect variable

## 2.0.3
* Updated to Bootstrap 2.0.3
* [#106] Support for Rails < 3.1 through Compass
* [#132] Add CI testing
* [#106] Support Rails w/Compass
* [#134] Fix support for Rails w/Compass

## 2.0.2
* [#86] Updated to Bootstrap 2.0.2
Things of note: static navbars now have full width. (to be fixed in 2.0.3) `.navbar-inner > .container { width:940px; }` seems to work in the meanwhile
* [#62] Fixed asset compilation taking a *very* long time.
* [#69, #79, #80] \(Hopefully) clarified README. Now with less cat humour.
* [#91] Removed doubled up Sass extensions for Rails.
* [#63, #73] Allow for overriding of image-path
* [[SO](http://stackoverflow.com/a/9909626/241212)] Added makeFluidColumn mixin for defining fluid columns. Fluid rows must use `@extend .row-fluid`, and any column inside it can use `@include makeFluidColumn(num)`, where `num` is the number of columns. Unfortunately, there is a rather major limitation to this: margins on first-child elements must be overriden. See the attached Stack Overflow answer for more information.

## 2.0.1
* Updated to Bootstrap 2.0.1
* Modified `@mixin opacity()` to take an argument `0...1` rather than `0...100` to be consistent with Compass.

## 2.0.0
* Updated to Bootstrap 2.0.0
","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/bootstrap-sass/CONTRIBUTING.md",".md","3470","87","# Contributing to bootstrap-sass

## Asset Changes

Any changes to `bootstrap-sass` assets (scss, javascripts, fonts) should be checked against the `convert` rake task.
For usage instructions, see the [README](/README.md).

If something is broken in the converter, it's preferable to update the converter along with the asset itself.


## Bugs

A bug is a _demonstrable problem_ that is caused by the code in the
repository. Good bug reports are extremely helpful - thank you!

Guidelines for bug reports:

1. **Does it belong here?** &mdash; is this a problem with bootstrap-sass, or
   it an issue with [twbs/bootstrap](https://github.com/twbs/bootstrap)?
   We only distribute a direct port and will not modify files if they're not
   changed upstream.

2. **Use the GitHub issue search** &mdash; check if the issue has already been
   reported.

3. **Isolate the problem** &mdash; ideally create a [reduced test
   case](http://css-tricks.com/6263-reduced-test-cases/) and a live example.

A good bug report shouldn't leave others needing to chase you up for more
information. Please try to be as detailed as possible in your report. What is
your environment? What steps will reproduce the issue? What browser(s) and OS
experience the problem? What would you expect to be the outcome? All these
details will help people to fix any potential bugs.

Example:

> Short and descriptive example bug report title
>
> A summary of the issue and the browser/OS environment in which it occurs. If
> suitable, include the steps required to reproduce the bug.
>
> 1. This is the first step
> 2. This is the second step
> 3. Further steps, etc.
>
> `<url>` (a link to the reduced test case)
>
> Any other information you want to share that is relevant to the issue being
> reported. This might include the lines of code that you have identified as
> causing the bug, and potential solutions (and your opinions on their
> merits).

**[File a bug report](https://github.com/twbs/bootstrap-sass/issues/)**


## Pull requests

**We will not accept pull requests that modify the SCSS beyond fixing bugs caused by *our* code!**

We use a [converter script][converter-readme] to automatically convert upstream bootstrap, written in LESS, to Sass.

Issues related to styles or javascript but unrelated to the conversion process should go to [twbs/bootstrap][upstream].

Pull requests that fix bugs caused by our code should not modify the SCSS directly, but should patch the converter instead.

Good pull requests - patches, improvements, new features - are a fantastic
help. They should remain focused in scope and avoid containing unrelated
commits. If your contribution involves a significant amount of work or substantial
changes to any part of the project, please open an issue to discuss it first.

Make sure to adhere to the coding conventions used throughout a project
(indentation, accurate comments, etc.). Please update any documentation that is
relevant to the change you're making.

## Do not…

Please **do not** use the issue tracker for personal support requests (use
[Stack Overflow](http://stackoverflow.com/)).

Please **do not** derail or troll issues. Keep the
discussion on topic and respect the opinions of others.

*props [html5-boilerplate](https://github.com/h5bp/html5-boilerplate/blob/master/CONTRIBUTING.md)*

[upstream]: https://github.com/twbs/bootstrap
[converter-readme]: https://github.com/twbs/bootstrap-sass/blob/master/README.md#upstream-converter
","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/eonasdan-bootstrap-datetimepicker/CONTRIBUTING.md",".md","1769","38","Submitting Issues
=================

If you are submitting a bug, please test and/or fork [this jsfiddle](http://jsfiddle.net/Eonasdan/0Ltv25o8/) demonstrating the issue. Code issues and fringe case bugs that do not include a jsfiddle (or similar) will be closed.

Issues that are submitted without a description (title only) will be closed with no further explanation.

Contributing code
=================

To contribute, fork the library and install grunt and dependencies. You need [node](http://nodejs.org/); use [nvm](https://github.com/creationix/nvm) or [nenv](https://github.com/ryuone/nenv) to install it.

```bash
git clone https://github.com/Eonasdan/bootstrap-datetimepicker.git
cd bootstrap-datetimepicker
npm install -g grunt-cli
npm install
git checkout development  # all patches against development branch, please!
grunt                 # this runs tests and jshint
```

Very important notes
====================

 * **Pull requests to the `master` branch will be closed.** Please submit all pull requests to the `development` branch.
 * **Do not include the minified files in your pull request.** Don't worry, we'll build them when we cut a release.
 * Pull requests that do not include a description (title only) and the following will be closed:
  * What the change does
  * A use case (for new features or enhancements)

Grunt tasks
===========

We use Grunt for managing the build. Here are some useful Grunt tasks:

  * `grunt` The default task lints the code and runs the tests. You should make sure you do this before submitting a PR.
  * `grunt build` Compiles the less stylesheet and minifies the javascript source in build directory.
  * `grunt build:travis` Compliles and runs the jasmine/travis tests. **All PR's MUST pass tests in place**","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/objectivec.m",".m","2672","105","@protocol Printing: someParent
-(void) print;
@end

@interface Fraction: NSObject <Printing, NSCopying> {
    int numerator;
    int denominator;
}
@end

@""blah\8"" @""a\222sd\d"" @""\faw\""\? \' \4 n\\"" @""\56""
@""\xSF42""

-(NSDecimalNumber*)addCount:(id)addObject{

return [count decimalNumberByAdding:addObject.count];

}

  NS_DURING  NS_HANDLER NS_ENDHANDLER

@try {
   if (argc > 1)    {
    @throw [NSException exceptionWithName:@""Throwing a test exception"" reason:@""Testing the @throw directive."" userInfo:nil];
   }
} 
@catch (id theException) {
    NSLog(@""%@"", theException);
    result = 1  ;
} 
@finally {
    NSLog(@""This always happens."");
    result += 2 ;
}

    @synchronized(lock) {
        NSLog(@""Hello World"");
    }

struct { @defs( NSObject) }

char *enc1 = @encode(int);

         IBOutlet|IBAction|BOOL|SEL|id|unichar|IMP|Class 


 @class @protocol

@public
  // instance variables
@package
  // instance variables
@protected
  // instance variables
@private
  // instance variables

  YES NO Nil nil
NSApp()
NSRectToCGRect (Protocol ProtocolFromString:""NSTableViewDelegate""))

[SPPoint pointFromCGPoint:self.position]

NSRoundDownToMultipleOfPageSize

#import <stdio.h>

int main( int argc, const char *argv[] ) {
    printf( ""hello world\n"" );
    return 0;
}

NSChangeSpelling

@""0 != SUBQUERY(image, $x, 0 != SUBQUERY($x.bookmarkItems, $y, $y.@count == 0).@count).@count""

@selector(lowercaseString) @selector(uppercaseString:)

NSFetchRequest *localRequest = [[NSFetchRequest alloc] init];  
localRequest.entity = [NSEntityDescription entityForName:@""VNSource"" inManagedObjectContext:context];  
localRequest.sortDescriptors = [NSArray arrayWithObject:[NSSortDescriptor sortDescriptorWithKey:@""resolution"" ascending:YES]];  
NSPredicate *predicate = [NSPredicate predicateWithFormat:@""0 != SUBQUERY(image, $x, 0 != SUBQUERY($x.bookmarkItems, $y, $y.@count == 0).@count).@count""];
[NSPredicate predicateWithFormat:]
NSString *predicateString = [NSString stringWithFormat:@""SELF beginsWith[cd] %@"", searchString];
NSPredicate *pred = [NSPredicate predicateWithFormat:predicateString];
NSArray *filteredKeys = [[myMutableDictionary allKeys] filteredArrayUsingPredicate:pred]; 

localRequest.predicate = [NSPredicate predicateWithFormat:@""whichChart = %@"" argumentArray: listChartToDownload];
localRequest.fetchBatchSize = 100;
arrayRequest    = [context  executeFetchRequest:localRequest error:&error1];

[localRequest   release];

#ifndef Nil
#define Nil __DARWIN_NULL   /* id of Nil class */
#endif

@implementation MyObject
- (unsigned int)areaOfWidth:(unsigned int)width
                height:(unsigned int)height
{
  return width*height;
}
@end
","MATLAB"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/python.py",".py","478","19","#!/usr/local/bin/python

import string, sys

# If no arguments were given, print a helpful message
if len(sys.argv)==1:
    print '''Usage:
celsius temp1 temp2 ...'''
    sys.exit(0)

# Loop over the arguments
for i in sys.argv[1:]:
    try:
        fahrenheit=float(string.atoi(i))
    except string.atoi_error:
        print repr(i), ""not a numeric value""
    else:
        celsius=(fahrenheit-32)*5.0/9.0
        print '%i\260F = %i\260C' % (int(fahrenheit), int(celsius+.5))","Python"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/julia.jl",".jl","210","16","for op = (:+, :*, :&, :|, :$)
  @eval ($op)(a,b,c) = ($op)(($op)(a,b),c)
end

v = α';
function g(x,y)
  return x * y
  x + y
end

cd(""data"") do
    open(""outfile"", ""w"") do f
        write(f, data)
    end
end
","Julia"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/r.r",".r","668","20","Call:
lm(formula = y ~ x)
 
Residuals:
1       2       3       4       5       6
3.3333 -0.6667 -2.6667 -2.6667 -0.6667  3.3333
 
Coefficients:
            Estimate Std. Error t value Pr(>|t|)
(Intercept)  -9.3333     2.8441  -3.282 0.030453 *
x             7.0000     0.7303   9.585 0.000662 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
 
Residual standard error: 3.055 on 4 degrees of freedom
Multiple R-squared: 0.9583,     Adjusted R-squared: 0.9478
F-statistic: 91.88 on 1 and 4 DF,  p-value: 0.000662
 
> par(mfrow=c(2, 2))     # Request 2x2 plot layout
> plot(lm_1)             # Diagnostic plot of regression model","R"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/java.java",".java","396","15","public class InfiniteLoop {

    /*
     * This will cause the program to hang...
     *
     * Taken from:
     * http://www.exploringbinary.com/java-hangs-when-converting-2-2250738585072012e-308/
     */
    public static void main(String[] args) {
        double d = Double.parseDouble(""2.2250738585072012e-308"");

        // unreachable code
        System.out.println(""Value: "" + d);
    }
}","Java"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/markdown.md",".md","7743","186","Ace (Ajax.org Cloud9 Editor)
============================

Ace is a standalone code editor written in JavaScript. Our goal is to create a browser based editor that matches and extends the features, usability and performance of existing native editors such as TextMate, Vim or Eclipse. It can be easily embedded in any web page or JavaScript application. Ace is developed as the primary editor for [Cloud9 IDE](http://www.cloud9ide.com/) and the successor of the Mozilla Skywriter (Bespin) Project.

Features
--------

* Syntax highlighting
* Automatic indent and outdent
* An optional command line
* Handles huge documents (100,000 lines and more are no problem)
* Fully customizable key bindings including VI and Emacs modes
* Themes (TextMate themes can be imported)
* Search and replace with regular expressions
* Highlight matching parentheses
* Toggle between soft tabs and real tabs
* Displays hidden characters
* Drag and drop text using the mouse
* Line wrapping
* Unstructured / user code folding
* Live syntax checker (currently JavaScript/CoffeeScript)

Take Ace for a spin!
--------------------

Check out the Ace live [demo](http://ajaxorg.github.com/ace/) or get a [Cloud9 IDE account](http://run.cloud9ide.com) to experience Ace while editing one of your own GitHub projects.

If you want, you can use Ace as a textarea replacement thanks to the [Ace Bookmarklet](http://ajaxorg.github.com/ace/build/textarea/editor.html).

History
-------

Previously known as “Bespin” and “Skywriter” it’s now known as Ace (Ajax.org Cloud9 Editor)! Bespin and Ace started as two independent projects, both aiming to build a no-compromise code editor component for the web. Bespin started as part of Mozilla Labs and was based on the canvas tag, while Ace is the Editor component of the Cloud9 IDE and is using the DOM for rendering. After the release of Ace at JSConf.eu 2010 in Berlin the Skywriter team decided to merge Ace with a simplified version of Skywriter's plugin system and some of Skywriter's extensibility points. All these changes have been merged back to Ace. Both Ajax.org and Mozilla are actively developing and maintaining Ace.

Getting the code
----------------

Ace is a community project. We actively encourage and support contributions. The Ace source code is hosted on GitHub. It is released under the BSD License. This license is very simple, and is friendly to all kinds of projects, whether open source or not. Take charge of your editor and add your favorite language highlighting and keybindings!

```bash
    git clone git://github.com/ajaxorg/ace.git
    cd ace
    git submodule update --init --recursive
```

Embedding Ace
-------------

Ace can be easily embedded into any existing web page. The Ace git repository ships with a pre-packaged version of Ace inside of the `build` directory. The same packaged files are also available as a separate [download](https://github.com/ajaxorg/ace/downloads). Simply copy the contents of the `src` subdirectory somewhere into your project and take a look at the included demos of how to use Ace.

The easiest version is simply:

```html
    <div id=""editor"">some text</div>
    <script src=""src/ace.js"" type=""text/javascript"" charset=""utf-8""></script>
    <script>
    window.onload = function() {
        var editor = ace.edit(""editor"");
    };
    </script>
```

With ""editor"" being the id of the DOM element, which should be converted to an editor. Note that this element must be explicitly sized and positioned `absolute` or `relative` for Ace to work. e.g.

```css
    #editor {
        position: absolute;
        width: 500px;
        height: 400px;
    }
```

To change the theme simply include the Theme's JavaScript file

```html
    <script src=""src/theme-twilight.js"" type=""text/javascript"" charset=""utf-8""></script>
```

and configure the editor to use the theme:

```javascript
    editor.setTheme(""ace/theme/twilight"");
```

By default the editor only supports plain text mode; many other languages are available as separate modules. After including the mode's JavaScript file:

```html
    <script src=""src/mode-javascript.js"" type=""text/javascript"" charset=""utf-8""></script>
```

Then the mode can be used like this:

```javascript
    var JavaScriptMode = require(""ace/mode/javascript"").Mode;
    editor.getSession().setMode(new JavaScriptMode());
```

Documentation
-------------

You find a lot more sample code in the [demo app](https://github.com/ajaxorg/ace/blob/master/demo/demo.js).

There is also some documentation on the [wiki page](https://github.com/ajaxorg/ace/wiki).

If you still need help, feel free to drop a mail on the [ace mailing list](http://groups.google.com/group/ace-discuss).

Running Ace
-----------

After the checkout Ace works out of the box. No build step is required. Open 'editor.html' in any browser except Google Chrome. Google Chrome doesn't allow XMLHTTPRequests from files loaded from disc (i.e. with a file:/// URL). To open Ace in Chrome simply start the bundled mini HTTP server:

```bash
    ./static.py
```

Or using Node.JS

```bash
    ./static.js
```

The editor can then be opened at http://localhost:8888/index.html.

Package Ace
-----------

To package Ace we use the dryice build tool developed by the Mozilla Skywriter team. Before you can build you need to make sure that the submodules are up to date.

```bash
    git submodule update --init --recursive
```

Afterwards Ace can be built by calling

```bash
    ./Makefile.dryice.js normal
```

The packaged Ace will be put in the 'build' folder.

To build the bookmarklet version execute

```bash
    ./Makefile.dryice.js bm
```

Running the Unit Tests
----------------------

The Ace unit tests run on node.js. Before the first run a couple of node modules have to be installed. The easiest way to do this is by using the node package manager (npm). In the Ace base directory simply call

```bash
    npm link .
```

To run the tests call:

```bash
    node lib/ace/test/all.js
```

You can also run the tests in your browser by serving:

    http://localhost:8888/lib/ace/test/tests.html

This makes debugging failing tests way more easier.

Contributing
------------

Ace wouldn't be what it is without contributions! Feel free to fork and improve/enhance Ace any way you want. If you feel that the editor or the Ace community will benefit from your changes, please open a pull request. To protect the interests of the Ace contributors and users we require contributors to sign a Contributors License Agreement (CLA) before we pull the changes into the main repository. Our CLA is the simplest of agreements, requiring that the contributions you make to an ajax.org project are only those you're allowed to make. This helps us significantly reduce future legal risk for everyone involved. It is easy, helps everyone, takes ten minutes, and only needs to be completed once.  There are two versions of the agreement:

1. [The Individual CLA](https://github.com/ajaxorg/ace/raw/master/doc/Contributor_License_Agreement-v2.pdf): use this version if you're working on an ajax.org in your spare time, or can clearly claim ownership of copyright in what you'll be submitting.
2. [The Corporate CLA](https://github.com/ajaxorg/ace/raw/master/doc/Corporate_Contributor_License_Agreement-v2.pdf): have your corporate lawyer review and submit this if your company is going to be contributing to ajax.org  projects

If you want to contribute to an ajax.org project please print the CLA and fill it out and sign it. Then either send it by snail mail or fax to us or send it back scanned (or as a photo) by email.

Email: fabian.jakobs@web.de

Fax: +31 (0) 206388953

Address: Ajax.org B.V.
  Keizersgracht 241
  1016 EA, Amsterdam
  the Netherlands","Markdown"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/c_cpp.cpp",".cpp","808","46","// compound assignment operators

#include <iostream>

#include \
   <iostream>

#include \
   \
   <iostream>

#include \
   \
   ""iostream""

#include <boost/asio/io_service.hpp>
#include ""boost/asio/io_service.hpp""

#include \
   \
   ""iostream"" \
   ""string"" \
   <vector>
   
using namespace std;

//
int main ()
{
    int a, b=3; /* foobar */
    a = b; // single line comment\
        continued
    a+=2; // equivalent to a=a+2
    cout << a;
    #if VERBOSE >= 2
        prints(""trace message\n"");
    #endif
    return 0;
}

/* Print an error message and get out */
#define ABORT                             \
    do {                                  \
        print( ""Abort\n"" );                \
        exit(8);                          \
} while (0)                      /* Note: No semicolon */","C++"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/golang.go",".go","641","35","// Concurrent computation of pi.
// See http://goo.gl/ZuTZM.
//
// This demonstrates Go's ability to handle
// large numbers of concurrent processes.
// It is an unreasonable way to calculate pi.
package main

import (
    ""fmt""
    ""math""
)

func main() {
    fmt.Println(pi(5000))
}

// pi launches n goroutines to compute an
// approximation of pi.
func pi(n int) float64 {
    ch := make(chan float64)
    for k := 0; k <= n; k++ {
        go term(ch, float64(k))
    }
    f := 0.0
    for k := 0; k <= n; k++ {
        f += <-ch
    }
    return f
}

func term(ch chan float64, k float64) {
    ch <- 4 * math.Pow(-1, k) / (2*k + 1)
}
","Go"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/rust.rs",".rs","495","21","use core::rand::RngUtil;

fn main() {
    for [""Alice"", ""Bob"", ""Carol""].each |&name| {
        do spawn {
            let v = rand::Rng().shuffle([1, 2, 3]);
            for v.each |&num| {
                print(fmt!(""%s says: '%d'\n"", name, num + 1))
            }
        }
    }
}

fn map<T, U>(vector: &[T], function: &fn(v: &T) -> U) -> ~[U] {
    let mut accumulator = ~[];
    for vec::each(vector) |element| {
        accumulator.push(function(element));
    }
    return accumulator;
}
","Rust"
"Assay","FDA/precisionFDA","vendor/assets/bower_components/ace-builds/demo/kitchen-sink/docs/sh.sh",".sh","1154","41","#!/bin/sh

# Script to open a browser to current branch
# Repo formats:
# ssh   git@github.com:richo/gh_pr.git
# http  https://richoH@github.com/richo/gh_pr.git
# git   git://github.com/richo/gh_pr.git

username=`git config --get github.user`

get_repo() {
    git remote -v | grep ${@:-$username} | while read remote; do
      if repo=`echo $remote | grep -E -o ""git@github.com:[^ ]*""`; then
          echo $repo | sed -e ""s/^git@github\.com://"" -e ""s/\.git$//""
          exit 1
      fi
      if repo=`echo $remote | grep -E -o ""https?://([^@]*@)?github.com/[^ ]*\.git""`; then
          echo $repo | sed -e ""s|^https?://||"" -e ""s/^.*github\.com\///"" -e ""s/\.git$//""
          exit 1
      fi
      if repo=`echo $remote | grep -E -o ""git://github.com/[^ ]*\.git""`; then
          echo $repo | sed -e ""s|^git://github.com/||"" -e ""s/\.git$//""
          exit 1
      fi
    done

    if [ $? -eq 0 ]; then
        echo ""Couldn't find a valid remote"" >&2
        exit 1
    fi
}

echo ${#x[@]}

if repo=`get_repo $@`; then
    branch=`git symbolic-ref HEAD 2>/dev/null`
    echo ""http://github.com/$repo/pull/new/${branch##refs/heads/}""
else
    exit 1
fi
","Shell"
"Assay","FDA/precisionFDA","gsrs/docker/entrypoint/gsrs.entrypoint.sh",".sh","891","33","#!/usr/bin/env bash

MYSQL_USER=root

ROLES_TRIGGER=$(cat <<-EOF
delimiter //
CREATE TRIGGER ix_core_userprof_update_roles BEFORE INSERT ON ix_core_userprof
FOR EACH ROW
BEGIN
  IF NEW.roles_json IS NULL THEN
    SET NEW.roles_json = '[""Query"", ""Updater"", ""DataEntry""]';
  END IF;
END; //
delimiter ;
EOF
)

# wait for database to start
while ! mysqladmin ping -u $MYSQL_USER -h $MYSQL_HOST --silent; do
  echo ""wait for db..""
  sleep 1
done

# init database
if [ ! $(mysql -u $MYSQL_USER -h $MYSQL_HOST -p$MYSQL_ROOT_PASSWORD $MYSQL_DATABASE -sse ""SHOW tables like 'ix_core_userprof';"") ]; then
  chmod +x bin/evolutions.sh
  bin/evolutions.sh ""$GSRS_CONFIG""
  mysql -h $MYSQL_HOST -u $MYSQL_USER -p$MYSQL_ROOT_PASSWORD $MYSQL_DATABASE -e ""$ROLES_TRIGGER""
fi

# run GSRS
bin/ginas -J-Xmx4G -Dconfig.file=$GSRS_CONFIG -Dhttp.port=$GSRS_PORT -Djava.awt.headless=true -Dpidfile.path=/dev/null
","Shell"
"Assay","FDA/precisionFDA","spec/support/files/app_script.sh",".sh","1863","78","pip install --upgrade setuptools==12.4
PYTHONUSERBASE=$DNANEXUS_HOME pip install --upgrade --user requests==2.21.0
pip install cwltool

cat <<EOF > description.cwl
class: CommandLineTool
id: app-1
label: default_title
cwlVersion: v1.0
baseCommand: [""/opt/wtsi-cgp/bin/sums2json.sh""]
requirements:
- class: DockerRequirement
  dockerPull: app_a
  dockerOutputDirectory: ""/data/out""
- class: InlineJavascriptRequirement
  expressionLib:
  - function file_string() { return self[0].contents.replace(/(\r\n|\n|\r)/gm,"""")
    }
inputs:
  anything:
    doc: anything
    inputBinding:
      position: 1
      prefix: ""--anything""
    type: string
outputs:
  my_file:
    doc: my_file
    type: File
    outputBinding:
      glob: my_file/*
  my_string:
    doc: my_string
    type: string
    outputBinding:
      glob: my_string
      loadContents: true
      outputEval: $(file_string())
EOF

cat <<EOF > input_settings.json
{""anything"":""${anything}""}
EOF

cwltool --user-space-docker-cmd=dx-docker description.cwl input_settings.json > cwl_job_outputs.json

python <<EOF
import os
import json
import subprocess

def sh(cmd, ignore_error=False):
  try:
    print cmd
    subprocess.check_call(cmd, shell=True)
  except subprocess.CalledProcessError as e:
    sys.exit(e.returncode)

with open(""cwl_job_outputs.json"") as f:
  cwloutputs = json.loads(f.read())

def is_output_file(ovalue):
  return 'class' in ovalue and ovalue['class'] == 'File'

def compile_output_generic(oname, ovalue):
  if isinstance(ovalue, list):
    return [ compile_output_generic(oname, x) for x in ovalue ]
  elif isinstance(ovalue, dict):
    if is_output_file(ovalue):
      sh(""emit {} {}"".format(oname, ovalue['path']))
  else:
    if ovalue is not None:
      sh(""emit {} {}"".format(oname, ovalue))

for oname, ovalue in cwloutputs.items():
  compile_output_generic(oname, ovalue)
EOF
","Shell"
"Assay","FDA/precisionFDA","client/docker/entrypoint/dev.entrypoint.sh",".sh","525","21","#!/bin/bash

# Check if args were provided
if [[ ""$#"" -eq 0 ]]; then
    echo ""No Command provided""
    exit 1
fi

if [[ ! $SKIP_FRONTEND_DEPS_SETUP || $SKIP_FRONTEND_DEPS_SETUP = 0 ]]; then
  is_node_modules_empty=$(find ./node_modules -maxdepth 0 -empty)
  # In case of empty node modules, `yarn check` is a waste of time
  if [[ $is_node_modules_empty ]]; then
    yarn --frozen-lockfile
  else
    yarn check || yarn --frozen-lockfile
  fi
fi

# NOTE(samuel) wrap the original docker entrypoint
docker-entrypoint.sh ""$@""
","Shell"
"Assay","FDA/precisionFDA","docs/DEVELOPMENT_SETUP.md",".md","4670","109","# Development Setup
```
You can skip any section if you have already installed the dependencies.
```

## MacOS specific prerequisites
* Install XCode
    * Search and install Xcode from the App Store.
        
        Need different Xcode version? See [list of xip archive links](https://stackoverflow.com/a/10335943)

* Install Apple Command Line Tools
    * Open XCode (this just needs to run once to initialize it) and close it.
    * Install XCode command line tools by running `xcode-select --install` in
    the terminal.

* Install [Homebrew](http://brew.sh/)
    * `/bin/bash -c ""$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)""`
    * Run `brew update` to make sure all your formulas are current

* Install git
    * On Mavericks (10.9) or above you can do this simply by trying to run git from the Terminal the very first time:

        `git --version`

        If you don’t have it installed already, it will prompt you to install it.
    * Installing via Homebrew:

        `brew install git`

## Ubuntu/Linux specific prerequisites
* Update software packages definitions
    * `apt-get update`

* Install git
    * `apt-get install git`


## Common steps (MacOS and Ubuntu Linux)

* [Set up git ssh](https://docs.github.com/articles/generating-an-ssh-key/)
    * `ssh-keygen -t ed25519-sk -C ""your_email@dnanexus.com""`

* Add generated SSH key to [Github](https://github.com/settings/keys)

* Set up git config
    * `git config --global core.editor ""vim""`
    * `git config --global user.email “your_email@dnanexus.com”`
    * `git config --global user.name “FirstName LastName”`
    * `git config --global push.default simple`

* Clone repo
    * `git clone git@github.com:dnanexus/precision-fda.git`

* Create database config file
    * `cp config/database.sample.yml config/database.yml`

* Create environment config file
    * `cp .env.example .env`

* Ask Dev team for additional parameters to add into `.env` file


# Certificate and dev account

To develop on pFDA locally you need to manually add a new user and organization.
Because when you first get started in a new system,
there is no existing user. So you can't log in to provision new accounts.
This requires manually ""bootstrapping"" the situation in steps described below.

## Certificate and Key
- `brew install mkcert` - Install MKCert on your computer: https://github.com/FiloSottile/mkcert
- `mkcert -install`
- From project root run: `mkcert -key-file key.pem -cert-file cert.pem localhost 0.0.0.0 pfda.dev`
- This will create two files `key.pem` and `cert.pem`. They need to be generated on your machine to
  get rid of the annoying unprotected site warning in the browser.

## New account registration
Register for a new user account at [https://staging.dnanexus.com/register](https://staging.dnanexus.com/register).
Note: the first time you visit this, you will be prompted to enter the credentials 
(ask around for what it is).
You must choose an unused email address, but you can use the google ""+"" trick
to signup with a variable email address, e.g. `your_username+pfdalocal@dnanexus.com`.
Activate your account with the link sent to your email.

## Create a new organization:
- Download & install the DX Toolkit [https://documentation.dnanexus.com/downloads#dnanexus-platform-sdk](https://documentation.dnanexus.com/downloads#dnanexus-platform-sdk).
- Choose an unused handle, e.g. `{{yourname}}org`. Prepend `pfda..` (two dots),
 so that the final handle will be e.g. `pfda..farnsworthorg`.
- Type `dx login --staging` and log in with your new account.
- Type `dx new org --handle pfda..{{yourname}}org ""{{Yourname}}'s org""`
- Log into the web UI [https://staging.dnanexus.com](https://staging.dnanexus.com)
with your account, access your profile on the upper right.
- Click __Billing Accounts__
- Click __Add Billing Info__ in the pfda..floranteorg entry.
  - Note: You may need to contact Laura (sales) for the billing info.
- Enter info (it doesn't have to be real, you can type ""."" in most entries). Enter your real DNAnexus email, however.
- Click __Update Billing Information__
- Check your email for a new email message asking you to confirm by clicking the link
- Click the link to confirm.


# Continue setup either using Docker or OS

[Docker-based setup (preferred)](DOCKER_BASED_SETUP.md) will require to install docker into your system. Dependencies MySQL and required gems will be installed into docker containers to minimize conflicts in the host system with other projects.

[OS-based setup](OS_BASED_SETUP.md) will require to install some additional software 
like MySQL, RVM and required gems directly into your OS.
","Markdown"
"Assay","FDA/precisionFDA","docs/COLIMA_GUIDE.md",".md","787","27","# Colima Guide

To avoid using Docker Desktop and the licensing issues that come with using it. Colima is an alternative that works in the same way, however there is no user interface software for the container runtime. Colima may be slower than Docker Desktop 

This guide contains steps to get started with colima on PFDA

https://github.com/abiosoft/colima

_Last updated: 19.06.2024_

## Install
- `brew install docker`
- `brew install colima`

## Start Colima
- `colima start -f --cpu 4 --memory 12 --disk 20`

## Run PFDA
- From inside the project root `make run`

## Docker Viewers
- VSCode Extension for Docker - https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-docker
  - restart/remove containers
  - inspect files
  - view logs
  - ssh into containers
","Markdown"
"Assay","FDA/precisionFDA","docs/DOCKER_COMPOSE_GUIDE.md",".md","4612","85","# Docker compose guide

This guide contains summary and best practices for dockerization in this repo.

If you decide to update anything in `docker-compose.yml`, make sure that [QA troubleshooting Confluence guide](https://confluence.internal.dnanexus.com/display/XVGEN/Docker+troubleshooting+guide) stays up to date as well

_Last updated: 25.11.2022_

## Prerequisites

Make sure that you understand local configurations, described in [""docker-based setup"" document](./DOCKER_BASED_SETUP.md)

## `extends` keyword

Although according to [docker documentation](https://docs.docker.com/compose/extends/#extending-services), `extend`ing is no longer supported in the most recent compose specification-v3, [this update](https://github.com/docker/compose/pull/7588) however makes it possible, to use blend of features from two different compose specifiactions (read through PR comments for more information) - in other words, we can use `extends` keyword

We use `extends` as primary tool to structure docker configuration with DRY principles

## Shared vs. specific

As mentioned in [""docker-based setup""](./DOCKER_BASED_SETUP.md), there are 4 different configurations

* `dev`
* `qa`
* `arm64v8-dev`
* `arm64v8-qa`

When creating/updating options, you should take into consideration, whether the option impacts colleagues - is it applicable/desired/required for their situation? Or is it only my specific use case?

In order to retain DRY principles, majority of settings is shared in following files

* [`base.services.yml`](../docker/base.services.yml)
* [`external.services.yml`](../docker/external.services.yml)
    * Details described in section [External services](#external-services) 

Due to maintenance of multiple configurations, that have their own specifics, each configuration has its own `docker-compose`

If you take a look at [one of docker-compose files](../docker/dev.docker-compose.yml) implementation mostly consists of `extend`ing services defined in `base.services.yml` and applying additional settings.

### Volumes

`extends` keyword in `docker compose` works only for services and currently there's no elegant way of reusing `docker` volumes (external volumes don't count)

_if you happen to find anything, feel free to apply it here_ :)

## Summary of best practices

### Secrets

First, **never** add value that is considered secret into `docker-compose` (fun fact k8s already solved this problem - [here](https://github.com/bitnami-labs/sealed-secrets))

Environment variables that are considered secrets, shouldn't be versioned and consider defining them in corresponding `.env` file - preferably on service level. If secret value is required during build, consider adding it into [`./docker/.env`](../docker/.env.example), more information in [docker documentation](https://docs.docker.com/compose/environment-variables/#the-env-file)

### Configuration settings

Keep as many settings shared as possible in `*.services.yml` files.

If you encounter
* setting specific only for `M1-silicon` CPU, such as emulated image
  * For instance `node-sass` requires emulation on arm64v8 architectures, because of its dependencies on native libs
* qa specific setting
* Workaround, that could have drastic impact

You should add these type of settings to specific `docker-compose.yml`

### Versions

Keep all the versions at single place - preferably [`base.services.yml`](../docker/base.services.yml) (at least for now)

Updating at multiple places is tedious and is reason why emulated services, such as `db_emulated` remain in [`base.services.yml`](../docker/base.services.yml), even if they could be defined in specific `docker-compose.yml`

## External services

Integrated services that aren't considered part of 'standard' stack are defined in `external.services.yml`.
In order to minimize image build and setup parts of container processes, they are excluded by default.
You can run them by merging multiple `docker-compose.yml` files, as mentioned in [docker documentation](https://docs.docker.com/compose/extends/#multiple-compose-files).

Similar as `base.services.yml`, platform-related specifics are defined in
* [`external.docker-compose.yml`](../docker/external.docker-compose.yml)
* [`external.arm64v8.docker-compose.yml`](../docker/external.arm64v8.docker-compose.yml)

## (Hopefully) Future simplification

As mentioned, differences in M1-silicon make the setup difficult. In event, when [here mentioned](https://docs.docker.com/desktop/mac/apple-silicon/) issues get resolved, deprecation of `arm64v8-dev` and `arm64v8-qa` configurations should be taken into consideration.
","Markdown"
"Assay","FDA/precisionFDA","docs/MACOS_ARCHITECTURE_DIFFERENCES.md",".md","1418","29","# Summary of Platform Differences

_Last updated 14.6.2022_

## Docker issues

As stated in [this article](https://docs.docker.com/desktop/mac/apple-silicon/), there are a handful of things that are not fully functional on M1-Silicon CPUs

* `mysql` docker images aren't available for `arm64v8` docker architecture
* Some libraries are dependant on native libraries writted in C, that don't compile for `arm64v8` architectures
  * `therubyracer` for `web`
  * `node-sass` for `frontend`

We solve both of these cases by emulating images, or by building different images

```yml
# Instead of
# image: mysql
image: amd64/mysql
```

## Ruby watch mode

As stated also in [this article](https://docs.docker.com/desktop/mac/apple-silicon/), `inotify` file system notification API doesn't work on emulated images. This API is used internally by `listen` package and `EventedFileSystemWatcher`. Rails (according to [Rails config](https://guides.rubyonrails.org/configuring.html#config-file-watcher)) ships by default with `FileSystemWatcher`, therefore configuration option for `EventedFileSystemWatcher` is disabled for `arm64v8` with `ARM64V8_DEVELOPMENT_PATCH` env variable.

[See here](../config/environments/development.rb)

The env variable `ARM64V8_DEVELOPMENT_PATCH` is defined in arm64v8 docker compose files, such as [this one for arm64v8 dev](../docker/arm64v8.dev.docker-compose.yml).
","Markdown"
"Assay","FDA/precisionFDA","docs/USEFUL_COMMANDS.md",".md","1375","62","# Useful commands

If you use [OS-based setup](OS_BASED_SETUP.md), remove from each command the
`docker compose exec <container_name>` portion of the command when applicable.

## Ruby client

Get to rails console
`docker compose exec web bundle exec rails c`

Run database migration
`docker compose exec web bundle exec rake db:migrate`

Run all Ruby tests
`docker compose exec web bundle exec rspec`

Run specific test
`docker compose exec web bundle exec rspec spec/<path_to_spec>`

Run rubocop
`docker compose exec web bundle exec rubocop`

Run brakeman
`docker compose exec web brakeman -A --parser-timeout 30 -w2`

#### A couple commands that don't work in a docker container:

Run tests with Rspec Guard (doesn't work in docker container)
`bundle exec guard`

* To exit from Guard mode (doesn't work in docker container)
    * `exit`

* Check current code coverage
    * `open coverage/index.html`


## React Frontend

Run lint
`docker compose exec frontend yarn lint .`

Run all unit tests
`docker compose exec frontend yarn test`

Run specific unit tests
`docker compose exec frontend yarn test --testNamePattern=Challenge`

## Database

```bash
# Hook into db container
docker compose exec -it db bash
# Log in
mysql -uroot -p
# You should be able to find the password depending on your configuration
```

## TODO - missing

mention remaining rake tasks in this doc
","Markdown"
"Assay","FDA/precisionFDA","docs/DOCKER_BASED_SETUP.md",".md","6926","190","# Docker-based setup


This guide covers all the steps required to get docker based
development environment. There are also a few _optional_ sections, that are recommended to use for full-stack developer roles

_Last updated: 25.11.2022_

## Installing docker and docker-compose

> If you already have docker installed in your system, you can skip this step.

The first step you have to do is to install [docker](https://docs.docker.com/install/) on your workstation. Instructions are **platform-specific**

## Platform differences

Because of [platform differences](./MACOS_ARCHITECTURE_DIFFERENCES.md) between M1-silicon and Intel MacOS CPUs and technologies used in our stack, the docker environment setup is **platform-specific**.

For this reason localhost docker stack is maintained for two different architectures

* MacOS intel
* MacOS M1-sillicon (`arm64v8`)

Docker stack contains performance differences for different roles - for instance QA engineers are in higher need to drop volumes because of frequent branch changes. These changes shouldn't have impact on business logic of application code, instead they modify image build and container runtime.

That produces in total 4 different configurations

* `dev` (MacOS intel)
* `qa` (MacOS intel)
* `arm64v8.dev` (M1-sillicon)
* `arm64v8.qa` (M1-sillicon)

(Optional) to learn more about the `docker-compose.yml` files, feel free to look at [Docker compose guide](./DOCKER_COMPOSE_GUIDE.md)

## Makefile

Make sure that you understand your role (dev, qa)

You can also find out architecture of your workstation by running `uname -m` (unless it's windows)



Most of the developemnt/testing use cases are documented in this [Makefile]
(../Makefile)

If you'd like to understand more about [Makefile](../Makefile), feel free to look at these resources

* [Makefile tutorial](https://makefiletutorial.com/)
* [Makefile built-in functions](https://www.gnu.org/software/make/manual/html_node/Functions.html)

In order not to duplicate every [Makefile](../Makefile) target, user role (`dev`, `qa`) is defined as environment variable

```bash
# Add following into ~/.bashrc or ~/.zshrc as dev
export PFDA_ROLE=dev
# Add following into ~/.bashrc or ~/.zshrc as qa
export PFDA_ROLE=qa
```

## Setup before running

Run

```bash
make repo-init
```

to do most of the required setup at once

This does multiple things

* Sets up .env files in `.`, `./docker`, `./https-apps-api` directories
* Creates default database config in `config/database.sample.yml` 
* Creates custom pre-push githooks that validate `.env.example` files

What remains to be done, is ask a colleague for secrets to fill missing `.env` variables. Migration to [AWS Secrets Manager](https://docs.aws.amazon.com/secretsmanager/latest/userguide/getting-started.html) is in progress


To get summary of all [Makefile](../Makefile) commands, take a look at [this doc](./SUMMARY_OF_MAKEFILE_COMMANDS.md)

## Configuration differences for arm64v8.dev


If you're running `make run` with `arm64v8.dev` configuration (i.e. `arm64v8` architecture and `PFDA_ROLE=dev`), it uses different key paths for `nodejs-api`. Edit `https-apps-api/.env` with following values

```
NODE_PATH_CERT=/keys/cert.pem
NODE_PATH_KEY_CERT=/keys/key.pem
```

### Githooks - use cases and troubleshooting



If you happen to encounter blocking issues with githooks you can roll-back to initial state (with no adjustments) with following command

```bash
find ./utils/githooks -type f -exec sh -c 'rm "".git/hooks/$(basename {})""' \;
```

## (Optional) Account setup

To use your own account to log-in to the system, run the command to
create database with the environment variables set with your account's data (if you don't have
an account yet, please refer to
[New account registration](DEVELOPMENT_SETUP.md#new-account-registration)):

```bash
# TODO refactor this into Makefile as well
docker compose exec \
    -e PFDA_USER_FIRST_NAME=Florante \
    -e PFDA_USER_LAST_NAME=DelaCruz \
    -e PFDA_USER_EMAIL=fdelacruz+pfdalocal@dnanexus.com \
    -e PFDA_USER_ORG_HANDLE=floranteorg \
    -e PFDA_USER_DXUSER=fdelacruz \
    web bundle exec rake {db:setup,db:migrate,db:generate_mock_data,user:generate_test_users}
# ! Don't forget to add respective flags before running this command
# For instance, dev with ""arm64v8""
# docker compose 
#     -f docker/arm64v8.dev.docker-compose.yml \
#     exec \
#     -e PFDA_USER_FIRST_NAME=Florante \
#     -e PFDA_USER_LAST_NAME=DelaCruz \
#     -e PFDA_USER_EMAIL=fdelacruz+pfdalocal@dnanexus.com \
#     -e PFDA_USER_ORG_HANDLE=floranteorg \
#     -e PFDA_USER_DXUSER=fdelacruz \
#     web bundle exec rake {db:setup,db:migrate,db:generate_mock_data,user:generate_test_users}
```

## Running application

```bash
make run
```

Once the application is correctly installed & configured, you should be able to access the portal at `https://localhost:3000/`.
In order to log in to the system, ask for shared DEV credentials (ask some1 from the team)

### Running application with external services

To run PFDA with external integration part of local stack, set up the following env variable in your `.rc` file

```bash
# Add following into ~/.bashrc or ~/.zshrc to run GSRS
export PFDA_SHOULD_RUN_GSRS=1
```

Run in the same way with

```bash
make run
```

## (Optional) Setup for impatient personalities

There are a few [docker-related](../docker/arm64v8.dev..docker-compose.yml) [env variables](../docker/.env.example), that are used in `docker-compose.yml` files, such as `SKIP_RUBY_SETUP`
After you run your docker setup successfully, and want to save some time by skipping dependency checks and reinstallations (assuming you've got them correct)

For obvious reasons these settings aren't versioned, and therefore are kept in separate [docker/.env](../docker/.env.example) file. Feel free to setup according your own need


## (Alternative) Symlink docker-compose.yml for less typing

If you want to use `docker compose` command with minimum of typing, you can symlink your preconfigured `docker-compose.yml` (for instance `docker/arm64v8.dev.docker-compose.yml`) into `docker/docker-compose.yml`. It is `.gitignore`d for this purposed

For instance

```bash
# Run in 'precision-fda' root directory
# intel dev
ln -s docker/dev.docker-compose.yml docker/docker-compose.yml
# intel qa
ln -s docker/qa.docker-compose.yml docker/docker-compose.yml
# arm64v8 (Apple M1 Silicon) dev
ln -s docker/arm64v8.dev.docker-compose.yml docker/docker-compose.yml
# arm64v8 (Apple M1 Silicon) qa
ln -s docker/arm64v8.qa.docker-compose.yml docker/docker-compose.yml
```

This should make use of docker compose pretty trivial as you can start it simply by running - in most cases you should be fine without GSRS

```bash
# PWD=./docker
docker compose up --build
```

## Further reading

* [Summary of Makefile commands](./SUMMARY_OF_MAKEFILE_COMMANDS.md)
* [Docker compose guide](./DOCKER_COMPOSE_GUIDE.md)","Markdown"
"Assay","FDA/precisionFDA","docs/OS_BASED_SETUP.md",".md","3447","101","# OS-based setup

## MySQL database

### MySQL 5.6 on MacOS
At the moment latest MySQL 5.6 (mysql@5.6 or mysql@5.6.46) has a bug that
doesn't allow to build mysql gem, so it's __REQUIRED__ to use version 5.6.43

`brew install mysql@5.6.43`

### MySQL 5.6 on Ubuntu Linux
Since Ubuntu 18.04 doesn't have MySQL 5.6 support out-of-box, you have to add
apt repository in your system and install MySQL from that repo. For more information
look [here](https://dev.mysql.com/doc/mysql-apt-repo-quick-guide/en/).

## Installation

* Install RVM
    * `gpg --keyserver hkp://keys.gnupg.net --recv-keys 409B6B1796C275462A1703113804BB82D39DC0E3`
    * `\curl -sSL https://get.rvm.io | bash -s stable`

* Install ruby
    * `rvm install 3.2.2`

* Move to project's root directory
    * `cd <wherever you've clonned project>`

* Install bundler
    * `gem install bundler`

      __OR__ to keep the current bundler version from Gemfile.lock, run

      `gem install bundler -v 2.2.33`

* (Mac) To overcome `bundle install` errors, you may need to install the following dependencies manually:
    * `cmake` (required to build `rugged` gem)
        * `brew install cmake`
    * V8, libv8 (required to install `therubyracer` gem)
        * `brew install v8@3.15`
        * `gem install libv8 -v '3.16.14.19' -- --with-system-v8`
            * Note: you may need to update the version to match the one bundler's trying to install.
    * `therubyracer`
        * First, find your V8 directory - look for `/usr/local/opt/v8@*`
        * `gem install therubyracer -- --with-v8-dir=/usr/local/opt/v8@3.15`
        * Source: https://gist.github.com/fernandoaleman/868b64cd60ab2d51ab24e7bf384da1ca

* Install the other required gems
    * `bundle install`

## Installation of bower dependencies

* Install [nvm](https://github.com/nvm-sh/nvm) according to [steps](https://github.com/nvm-sh/nvm)
* Install latest `node` version

    ```
    nvm install node
    ```
* `npm i -g bower`
* `bower install` - in the project directory

## Configure

See [the Configure section in `DOCKER_BASED_SETUP.md`](./DOCKER_BASED_SETUP.md#1.-configure) for the details.

### DB setup

#### Configure local database

After pointing the application to your local DB (`config/database.yml`), use the same commands to setup the DB as when setting-up a Docker-based DB (see [`DOCKER_BASED_SETUP.md`](./DOCKER_BASED_SETUP.md#3.-prepare-the-database).

#### Use Databse running in Docker

Alternatively, you can use a database running inside Docker (see [`DOCKER_BASED_SETUP.md`](./DOCKER_BASED_SETUP.md) for further details).

To point your local Rails application to the Docker-based DB, set `default.host` to `127.0.0.1` in `config/database.yml`.

## Run

To start rails server, run:
    * `bundle exec thin --ssl --debug start`

* Point your browser to [https://localhost:3000](https://localhost:3000), if you're seeing
index page, setup is done.

## Test

To run unit & integration tests, use the following commands
* Rails (RSpec):  `rspec` or `bundle exec rspec`
  * Note: some of these tests require a DB to be configured
* CoffeeScript (Jest): `yarn test` or `yarn test:watch` for watch mode

### Issues

On your first `bundle`, you may have issues installing the libv8 and
therubyracer gems. See [here](https://github.com/cowboyd/libv8/issues/205) for
potential solutions. Try `bundle update libv8`.

## Useful commands

You can find some useful commands [here](USEFUL_COMMANDS.md).
","Markdown"
"Assay","FDA/precisionFDA","docs/REACT_FRONTEND_DEVELOPMENT.md",".md","1801","79","# React-based frontend development

This guide will cover all required steps to start working on
React-based frontend part.

## Setting up NodeJS
If you already have NodeJS installed in your system, you may use
it, but be careful since it's version can be outdated.

### Installing NodeJS via NVM
NodeJS can be installed by several ways, but the most preferred
is via [NVM](https://github.com/nvm-sh/nvm). Follow NVM's
guide to get it installed.

Run this command to install NodeJS:

``nvm install v12``

In order to use installed version run:

``nvm use``

If you want NVM to change node version automatically, please refer
to [this part of NVM manual](https://github.com/nvm-sh/nvm#automatically-call-nvm-use)

## Installing yarn

Run this command to install yarn globally:

`` npm i -g yarn ``

## Almost there...

Now change directory to ``client`` of project's root.

#### ALL THE FOLLOWING COMMANDS TILL THE END OF THIS MANUAL ARE SUPPOSED TO BE RUN FROM ``client`` DIRECTORY
 
Install required packages:

``yarn``

After all packages are installed, everything is ready to
start development.

### Defined yarn commands

Build production bundle:

``yarn run build:production``

Build development bundle:

``yarn run build``

Run instant rebuilding based on changes:

``yarn run watch``

Run webpack dev server:

`` yarn run server``

Run linter (ESLint):

`` yarn run lint <path_to_file_or_directory>``

Run tests:

``yarn run test <path_to_test_or_directory>``

## Note

Please run linter and tests before making a pull request, that
will economy other developers' time :)

##### For those who uses IDEs instead of text editors:
Most of IDEs have built-in support for ESLint, so please configure
them if not already.
","Markdown"
"Assay","FDA/precisionFDA","docs/SUMMARY_OF_MAKEFILE_COMMANDS.md",".md","2252","95","# Summary of Makefile commands

_Last updated: 25.11.2022_

## Basic docker commands

```bash
# setup that runs db migrations (keeps db and web container running)
make prepare-db
# setup the test db for unit tests
make prepare-db-test
# run the stack
make run
# cleanup stack
make cleanup
```

## Basic deugging commands

```bash
# Restart for each service
make restart-web
make restart-frontend
make restart-nodejs-api
make restart-nodejs-worker
make restart-db
make restart-redis
make restart-sidekiq

# Extra restarts when running with GSRS
make restart-gsrs
make restart-gsrsdb

# Hook (exec bash) for each service
make hook-into-web
make hook-into-frontend
make hook-into-nodejs-api
make hook-into-nodejs-worker
make hook-into-db
make hook-into-redis
make hook-into-sidekiq

# Extra hooks (exec bash) when running with GSRS
make hook-into-gsrs
make hook-into-gsrsdb
```

## Image cleanup

```bash
# Image cleanup for each service
make image-cleanup-web
make image-cleanup-frontend
make image-cleanup-nodejs-api
make image-cleanup-nodejs-worker
make image-cleanup-db
make image-cleanup-redis
make image-cleanup-sidekiq

# Image cleanup for GSRS
make image-cleanup-gsrs
make image-cleanup-gsrsdb
```

## Db Wipe

```bash
# Database wipe
make db-wipe
```

## Cache cleanup

```bash
# Cache cleanups - volume removal
# cache-cleanup-ruby-sidekiq removes ruby dependencies, and triggers their reinstallation (roughly takes 15 minutes)
make cache-cleanup-ruby-sidekiq
# cache-cleanup-frontend removes webpack build cache
# for arm64v8.dev it also removes cached yarn dependencies in docker volume
make cache-cleanup-frontend
# cache-cleanup-nodejs-api removes yarn dependencies - trigger reinstallation from network cache
# only available for arm64v8.dev
make cache-cleanup-nodejs-api
# cache-cleanup-nodejs-worker removes yarn dependencies - trigger reinstallation from network cache
# only available for arm64v8.dev
make cache-cleanup-nodejs-worker

# Cache cleanups with database wipes (preferable for QAs)
# Each one works same way as described above + performs DB wipe
make cache-cleanup-ruby-sidekiq-with-db-wipe
make cache-cleanup-frontend-with-db-wipe
make cache-cleanup-nodejs-api-with-db-wipe
make cache-cleanup-nodejs-worker-with-db-wipe
```
","Markdown"
"Assay","FDA/precisionFDA","https-apps-api/docker/entrypoint/dev.entrypoint.sh",".sh","1169","34","#!/bin/bash
# Check if args were provided
if [[ ""$#"" -eq 0 ]]; then
    echo ""No Command provided""
    exit 1
fi

if [[ ! $SKIP_NODEJS_DEPS_SETUP || $SKIP_NODEJS_DEPS_SETUP = 0 ]]; then
  is_node_modules_empty=$(find ./node_modules -maxdepth 0 -empty)
  # In case of empty node modules, `yarn check` is a waste of time
  if [[ $is_node_modules_empty ]]; then
    yarn --frozen-lockfile
  else
    # Verifies only direct dependencies - i.e. skips errors from transitive dependencies
    # https://classic.yarnpkg.com/en/docs/cli/check#toc-yarn-check-verify-tree
    yarn check --verify-tree || yarn --frozen-lockfile
  fi
fi

# Reuse service-level env variables for db polling
source .env

if [[ ! $SKIP_NODEJS_DB_WAITING || $SKIP_NODEJS_DB_WAITING = 0 ]]; then
  while ! mysql --user=${NODE_DATABASE_USER} --password=${NODE_DATABASE_PASSWORD} --host=db --database=${NODE_DATABASE_NAME} --silent --execute 'SELECT 1;'; do
    echo ""Database not ready - waiting ${NODEJS_DB_POLLING_INTERVAL} second(s)""
    sleep ${NODEJS_DB_POLLING_INTERVAL}
  done
fi

echo ""Database connection established""

# TODO(samuel) wrap the original docker entrypoint
docker-entrypoint.sh ""$@""
","Shell"
"Assay","FDA/precisionFDA","https-apps-api/docker/entrypoint/qa.entrypoint.sh",".sh","580","21","#!/bin/bash
# Check if args were provided
if [[ ""$#"" -eq 0 ]]; then
    echo ""No Command provided""
    exit 1
fi

# Reuse service-level env variables for db polling
source .env

while ! mysql --user=${NODE_DATABASE_USER} --password=${NODE_DATABASE_PASSWORD} --host=db --database=${NODE_DATABASE_NAME} --silent --execute 'SELECT 1;'; do
    echo ""Database not ready - waiting ${NODEJS_DB_POLLING_INTERVAL} second(s)""
    sleep ${NODEJS_DB_POLLING_INTERVAL}
done

echo ""Database connection established""

# NOTE(samuel) wrap the original docker entrypoint
docker-entrypoint.sh ""$@""

","Shell"
"Assay","FDA/precisionFDA","scripts/jenkins_entrypoint.sh",".sh","396","11","#!/bin/bash
docker build -t pfda_ui -f docker/build/ui_test.Dockerfile . && \
docker run \
-e PFDA_AT_USER_1_PASSWORD_LOC=$PFDA_PASSWORD \
-e PFDA_AT_USER_2_PASSWORD_LOC=$PFDA_PASSWORD \
-e PFDA_AT_USER_ADMIN_PASSWORD_LOC=$PFDA_PASSWORD \
-e PFDA_BASIC_AUTH_DNX_PASSWORD_LOC=$DNX_STAGING_PASSWORD \
-e TEST_SUITE=$TEST_SUITE \
--mount type=bind,source=""$(pwd)""/tmp/,target=/log_storage \
pfda_ui
","Shell"
"Assay","FDA/precisionFDA","scripts/gsrs.sh",".sh","16313","437","#!/usr/bin/env bash
#
# GSRS Database Update Script 
# v0.5
#
# This should be run on an instance such as m5.4xlarge to have sufficient memory
# Currently, one must edit and insert the appropriate variables below before running
# as well as making sure the AWS CLI is configured:
#    export AWS_ACCESS_KEY_ID=ABCDEF123456
#    export AWS_SECRET_ACCESS_KEY=ABCDEF123456
#    export AWS_DEFAULT_REGION=us-west-2


# Flow:
# Download the latest dump from S3
# Restore dump to a new database
# Migrate users, roles and groups
# Create a trigger for user roles assignment
# Run GSRS locally
# Run GSRS Jobs:
#  - Regenerate structure properties
#  - Resave all backups
#  - Create indexes for substances
# Upload indexes to S3
# Re-deploy GSRS with the new database connection (manual step)

set -o errexit -o pipefail

DUMP_BUCKET=""gsrs-database-dumps""

# gsrs-indexes-dev
# gsrs-indexes-staging
# gsrs-indexes-production
GSRS_INDEXES_BUCKET=""gsrs-indexes-dev""

# If OLD_DB_HOST is not set, the new database will still receive the database
# dump but user roles will not be transferred
# OLD_DB_HOST=""pfda-dev-gsrs-db.cyy6pahwar0b.us-west-2.rds.amazonaws.com""
# OLD_DB_PORT=3306
# OLD_DB_NAME=""ixginas""
# OLD_DB_USER=""admin""
# OLD_DB_PASS=""INSERT_PASSWORD""

# NEW_DB_HOST=""pfda-dev-gsrs-db-mysql.cyy6pahwar0b.us-west-2.rds.amazonaws.com""
# NEW_DB_PORT=3306
# NEW_DB_NAME=""ixginas""
# NEW_DB_USER=""admin""
# NEW_DB_PASS=""INSERT_PASSWORD""
NEW_DB_HOST=""pfda-staging-gsrs-db-mysql.cyy6pahwar0b.us-west-2.rds.amazonaws.com""
NEW_DB_PORT=3306
NEW_DB_NAME=""ixginas""
NEW_DB_USER=""admin""
NEW_DB_PASS=""INSERT_PASSWORD""

ROLES_TRIGGER=$(cat <<-EOF
delimiter //
CREATE TRIGGER ix_core_userprof_update_roles BEFORE UPDATE ON ix_core_userprof
FOR EACH ROW
BEGIN
  IF NEW.roles_json IS NULL THEN
    SET NEW.roles_json = '[""Query"",""Updater"",""SuperUpdate"",""DataEntry"",""SuperDataEntry""]';
  END IF;
END; //
delimiter ;
EOF
)

GINAS_CONF=$(cat <<-EOF
include ""substances-core.conf""

application.host=""http://localhost:8080""
ix.home=""ginas.ix""

spring.application.name=""substances""

gsrs.sessions.sessionSecure=false
management.health.rabbit.enabled: false
server.port=8080
server.tomcat.max-threads=2000
ix.ginas.approvalIdGenerator.generatorClass=""ix.ginas.utils.UNIIGenerator""
spring.main.allow-bean-definition-overriding=true
eureka.client.enabled=false
gsrs.renderers.selected=""USP""


spring.jpa.hibernate.ddl-auto=none  #### THIS IS VERY IMPORTANT, OTHERWISE Hibernate will WIPE OUT our database
spring.jpa.show-sql=false
spring.jpa.properties.hibernate.format_sql=false
eureka.client.enabled=false

## START AUTHENTICATION
# SSO HTTP proxy authentication settings - right now this is only used by FDA
ix.authentication.trustheader=true
ix.authentication.usernameheader=""AUTHENTICATION_HEADER_NAME""
ix.authentication.useremailheader=""AUTHENTICATION_HEADER_NAME_EMAIL""

# set this ""false"" to only allow authenticated users to see the application
ix.authentication.allownonauthenticated=true

# set this ""true"" to allow any user that authenticates to be registered
# as a user automatically
ix.authentication.autoregister=true

#Set this to ""true"" to allow autoregistered users to be active as well
ix.authentication.autoregisteractive=true
## END AUTHENTICATION

gsrs.entityProcessors +={
               ""entityClassName"" = ""ix.ginas.models.v1.Substance"",
               ""processor"" = ""gsrs.module.substance.processors.UniqueCodeGenerator"",
               ""with""=  {
                        ""codesystem""=""BDNUM"",
                       ""suffix""=""AB"",
                       ""length""=9,
                       ""padding""=true
               }
        }
gsrs.entityProcessors +=
        {
        ""entityClassName"" = ""ix.ginas.models.v1.Substance"",
        ""processor"" = ""gsrs.module.substance.processors.ApprovalIdProcessor"",
        ""parameters"" = {
            ""codeSystem"" = ""FDA UNII""
        }
        }
gsrs.entityProcessors+=
      {
           ""entityClassName"":""ix.ginas.models.v1.Substance"",
           ""processor"":""gsrs.module.substance.processors.CodeProcessor"",
           ""with"":{
               ""class"":""gsrs.module.substance.datasource.DefaultCodeSystemUrlGenerator"",
               ""json"":{
                  ""filename"": ""codeSystem.json""
               }
           }
      }

ix.ginas.approvalIdGenerator.generatorClass=ix.ginas.utils.UNIIGenerator
gsrs.validators.substances +=
    {
        ""validatorClass"" = ""fda.gsrs.substance.validators.BdNumModificationValidator"",
        ""newObjClass"" = ""ix.ginas.models.v1.Substance""
    }
gsrs.validators.substances +=
    {
        ""validatorClass"" = ""ix.ginas.utils.validation.validators.CodeUniquenessValidator"",
                           ""newObjClass"" = ""ix.ginas.models.v1.Substance"",
        ""configClass"" = ""SubstanceValidatorConfig"",
        ""parameters""= {""singletonCodeSystems"" =[""BDNUM"", ""CAS"", ""FDA UNII"", ""PUBCHEM"", ""DRUG BANK"", ""EPA CompTox"", ""RS_ITEM_NUM"", ""STARI"", ""INN"", ""NCI_THESAURUS"", ""WIKIPEDIA"", ""EVMPD"", ""RXCUI"", ""ECHA (EC/EINECS)"", ""FDA ORPHAN DRUG"", ""EU-Orphan Drug"", ""NSC"", ""NCBI TAXONOMY"", ""ITIS"", ""ALANWOOD"", ""EPA PESTICIDE CODE"", ""CAYMAN"", ""USDA PLANTS"", ""PFAF"", ""MPNS"", ""GRIN"", ""DARS"", ""BIOLOGIC SUBSTANCE CLASSIFICATION CODE"", ""CERES""]}
    }

# Standardize substance name entries
# Inherited from the long-used Name Standardizer bookmarklet
# Implemented by Mitch Miller
gsrs.validators.substances += {
          ""validatorClass"" = ""ix.ginas.utils.validation.validators.StandardNameValidator"",
          ""newObjClass"" = ""ix.ginas.models.v1.Substance"",
          ""parameters"" = {
             ""warningOnMismatch"" = true
          }
        }
gsrs.scheduled-tasks.list+=
    {
        ""scheduledTaskClass"" : ""gsrs.module.substance.tasks.ScheduledExportTaskInitializer"",
        ""parameters"" :
        {
            ""username"":""admin"",
            ""cron"":""0 9 2 * * ?"", #2:09 AM every day
           #""cron"":""0 0/6 * * * ?"" #every 6 mins
            ""autorun"":false,
            ""name"":""Full GSRS export""
        }
    }
## START MySQL
#spring.jpa.properties.hibernate.dialect=org.hibernate.dialect.MySQL5InnoDBDialect
spring.jpa.hibernate.use-new-id-generator-mappings=false
spring.datasource.driverClassName=""org.mariadb.jdbc.Driver""
spring.datasource.url=""jdbc:mariadb://$NEW_DB_HOST:$NEW_DB_PORT/$NEW_DB_NAME""
spring.datasource.username=""$NEW_DB_USER""
spring.datasource.password=""$NEW_DB_PASS""
spring.datasource.hikari.maximum-pool-size= 500 #maximum pool size
spring.datasource.maximumPoolSize=500
EOF
)

CODE_SYSTEM=$(cat <<-EOF
[
    {""codeSystem"":""JMPR-PESTICIDE RESIDUE"",""url"":""http://www.codexalimentarius.net/pestres/data/pesticides/details.html?id=$CODE$""},
    {""codeSystem"":""CODEX ALIMENTARIUS (GSFA)"",""url"":""http://www.fao.org/gsfaonline/additives/details.html?id=$CODE$""},
    {""codeSystem"":""Food Contact Substance Notif, (FCN No.)"",""url"":""http://www.accessdata.fda.gov/scripts/fcn/fcnDetailNavigation.cfm?rpt=fcslisting&id=$CODE$""},
    {""codeSystem"":""JECFA EVALUATION"",""url"":""http://apps.who.int/food-additives-contaminants-jecfa-database/chemical.aspx?chemINS=$CODE$""},
    {""codeSystem"":""IUPHAR"",""url"":""http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?ligandId=$CODE$""},
    {""codeSystem"":""ALANWOOD"",""url"":""http://www.alanwood.net/pesticides/$CODE$""},
    {""codeSystem"":""MERCK INDEX"",""url"":""https://www-rsc-org.fda.idm.oclc.org/Merck-Index/monograph/$CODE$""},
    {""codeSystem"":""INN"",""url"":""https://extranet.who.int/soinn/mod/page/view.php?id=137&inn_n=$CODE$""},
    {""codeSystem"":""GRIN"",""url"":""https://npgsweb.ars-grin.gov/gringlobal/taxonomydetail.aspx?id=$CODE$""},
    {""codeSystem"":""DEA NO."",""url"":""http://forendex.southernforensic.org/index.php/detail/index/$CODE$""},
    {""codeSystem"":""DRUG BANK"",""url"":""http://www.drugbank.ca/drugs/$CODE$""},
    {""codeSystem"":""PHAROS"",""url"":""https://pharos.nih.gov/idg/targets/$CODE$""},
    {""codeSystem"":""PFAF"",""url"":""http://www.pfaf.org/user/Plant.aspx?LatinName=$CODE$""},
    {""codeSystem"":""CAS"",""url"":""https://chem.nlm.nih.gov/chemidplus/rn/$CODE$""},
    {""codeSystem"":""ChEMBL"",""url"":""https://www.ebi.ac.uk/chembl/compound/inspect/$CODE$""},
    {""codeSystem"":""NDF-RT"",""url"":""https://nciterms.nci.nih.gov/ncitbrowser/ConceptReport.jsp?dictionary=VA_NDFRT&code=$CODE$""},
    {""codeSystem"":""RXCUI"",""url"":""https://rxnav.nlm.nih.gov/REST/rxcui/$CODE$/allProperties.xml?prop=all""},
    {""codeSystem"":""WHO-ATC"",""url"":""http://www.whocc.no/atc_ddd_index/?code=$CODE$&showdescription=yes""},
    {""codeSystem"":""CLINICAL_TRIALS.GOV"",""url"":""https://clinicaltrials.gov/ct2/show/$CODE$""},
    {""codeSystem"":""ITIS"",""url"":""https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=$CODE$""},
    {""codeSystem"":""NCBI TAXONOMY"",""url"":""https://www.ncbi.nlm.nih.gov/Taxonomy/Browser/wwwtax.cgi?mode=Info&id=$CODE$""},
    {""codeSystem"":""USDA PLANTS"",""url"":""https://plants.sc.egov.usda.gov/home/plantProfile?symbol=$CODE$""},
    {""codeSystem"":""PUBCHEM"",""url"":""https://pubchem.ncbi.nlm.nih.gov/compound/$CODE$""},
    {""codeSystem"":""CFR"",""url"":""https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfCFR/CFRSearch.cfm?fr=$CODE$""},
    {""codeSystem"":""NCI_THESAURUS"",""url"":""https://ncit.nci.nih.gov/ncitbrowser/ConceptReport.jsp?dictionary=NCI%20Thesaurus&code=$CODE$""},
    {""codeSystem"":""MESH"",""url"":""https://www.ncbi.nlm.nih.gov/mesh/$CODE$""},
    {""codeSystem"":""UNIPROT"",""url"":""http://www.uniprot.org/uniprot/$CODE$""},
    {""codeSystem"":""USP-RS ITEM"",""url"":""https://store.usp.org/product/$CODE$""}
]
EOF
)

GSRS_BRANCH=""main""
GSRS_URL=""https://github.com/ncats/gsrs3-main-deployment.git""
GSRS_PORT=8080
GSRS_PATH=./gsrs3-main-deployment

install_deps() {
  sudo snap install jq
}

# Search the last created dump on S3.
# Replace '&Key' by '&LastModified' if you need to sort by modified time.
search_dump() {
  echo `aws s3api list-objects-v2 --bucket ""$DUMP_BUCKET"" \
        --query 'sort_by(Contents, &Key)[-1].Key' --output=text`
}

# Download the last dump from S3.
download_dump() {
  aws s3 cp s3://$DUMP_BUCKET/$1 $2
}

restore_dump() {
  mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER -p$NEW_DB_PASS --ssl-mode=DISABLED --execute=""CREATE DATABASE $NEW_DB_NAME;""
  zcat $1 | mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER -p$NEW_DB_PASS --ssl-mode=DISABLED $NEW_DB_NAME
}

dump_users_and_roles() {
  mysqldump -h $OLD_DB_HOST -u $OLD_DB_USER -p$OLD_DB_PASS -P $OLD_DB_PORT --skip-opt \
            --ssl-mode=DISABLED  --column-statistics=0 \
            --skip-tz-utc --no-create-info --extended-insert=FALSE --set-gtid-purged=OFF $OLD_DB_NAME ix_core_principal \
            --where=""ID > 10000"" > ix_core_principal.sql
  mysqldump -h $OLD_DB_HOST -u $OLD_DB_USER -p$OLD_DB_PASS -P $OLD_DB_PORT --skip-opt \
            --ssl-mode=DISABLED  --column-statistics=0 \
            --skip-triggers --skip-tz-utc --no-create-info --extended-insert=FALSE --set-gtid-purged=OFF $OLD_DB_NAME ix_core_userprof \
            --where=""ID > 10000"" > ix_core_userprof.sql
  mysqldump -h $OLD_DB_HOST -u $OLD_DB_USER -p$OLD_DB_PASS -P $OLD_DB_PORT --skip-opt \
            --ssl-mode=DISABLED  --column-statistics=0 \
            --add-drop-table --skip-tz-utc --set-gtid-purged=OFF $OLD_DB_NAME \
            ix_core_group_principal > ix_core_group_principal.sql
  mysqldump -h $OLD_DB_HOST -u $OLD_DB_USER -p$OLD_DB_PASS -P $OLD_DB_PORT --skip-opt \
            --ssl-mode=DISABLED  --column-statistics=0 \
            --add-drop-table --skip-tz-utc --set-gtid-purged=OFF $OLD_DB_NAME ix_core_group > ix_core_group.sql
}

# this will fail if the dump has anyone else rather than admin.
restore_users_and_roles() {
  mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER \
        -p$NEW_DB_PASS --ssl-mode=DISABLED $NEW_DB_NAME < ix_core_principal.sql
  mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER \
        -p$NEW_DB_PASS --ssl-mode=DISABLED $NEW_DB_NAME < ix_core_userprof.sql
  mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER \
        -p$NEW_DB_PASS --ssl-mode=DISABLED $NEW_DB_NAME < ix_core_group.sql
  mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER \
        -p$NEW_DB_PASS --ssl-mode=DISABLED $NEW_DB_NAME < ix_core_group_principal.sql
}

create_roles_trigger() {
  mysql -h $NEW_DB_HOST -P $NEW_DB_PORT -u $NEW_DB_USER -p$NEW_DB_PASS --ssl-mode=DISABLED $NEW_DB_NAME \
        -e ""$ROLES_TRIGGER""
}

run_gsrs() {
  if [ ! -d ""$GSRS_PATH"" ] ; then
    git clone --depth 1 -b $GSRS_BRANCH $GSRS_URL $GSRS_PATH
  fi

  cd $GSRS_PATH/substances
  echo ""$GINAS_CONF"" > src/main/resources/application.conf
  echo ""$CODE_SYSTEM"" > src/main/resources/codeSystem.json
  sed -i '' ""/\<\/configuration/ i \\
  \                    <jvmArguments>-Xmx25400m-Xms12000m</jvmArguments>\\
  "" pom.xml

  chmod +x mvnw
  # N.B. need 32GB heap for GSRS jobs can be VERY memory intensive (16GB might be ok but 8GB fails)
  ./mvnw clean spring-boot:run -DskipTests > gsrs.out 2>&1 &
  cd -

  echo ""Wait until GSRS runs..""
  while true ; do
    if curl localhost:$GSRS_PORT/api/v1/whoami > /dev/null 2>&1  ; then
      break
    fi
    sleep 2
  done
}

shutdown_gsrs() {
  kill `ps -eaf | \
        grep 'substances' | \
        grep -v grep | \
        awk '{print $2}'` \
  > /dev/null 2>&1 \
  || true
}

# Reindex all core entities from backup tables
run_job() {
  echo $1

  local job=$(
    curl -s localhost:$GSRS_PORT/api/v1/scheduledjobs -H ""AUTHENTICATION_HEADER_NAME: admin"" |
    jq "".content[] | select(.description==\""$1\"")""
  )

  if [ -z ""$job"" ]; then
    echo ""ERROR: can't find the job with description: $1""
    exit 1
  fi

  local job_url=`echo $job | jq '.url' | sed 's/\(http:\/\/\|""\)//g'`
  local job_execute_url=`echo $job | jq '.[""@execute""]' | sed 's/\(http:\/\/\|""\)//g'`

  echo ""Run the job via $job_execute_url""
  local run_result=$(curl -s ""$job_execute_url"" -H ""AUTHENTICATION_HEADER_NAME: admin"")

  echo ""Waiting for the job to be finished...""
  local is_running
  local task_message

  sleep 3

  while true ; do
    job=$(curl -s -H ""AUTHENTICATION_HEADER_NAME: admin"" $job_url)
    is_running=`echo $job | jq '.running'`

    if [[ $is_running == ""false"" ]] ; then
      break
    fi

    task_message=`echo $job | jq '.taskDetails.message'`
    echo ""$task_message""

    sleep 5
  done
}

upload_indexes() {
  if [[ -d ""$GSRS_PATH/substances/ginas.ix"" ]]; then
    aws s3 rm s3://$GSRS_INDEXES_BUCKET/ginas.ix --recursive
    aws s3 cp $GSRS_PATH/substances/ginas.ix s3://$GSRS_INDEXES_BUCKET/ginas.ix --recursive
  fi
}

cleanup() {
  rm -rf $GSRS_PATH/substances/ginas.ix # remove gsrs
  rm -f $1 # remove dump archive
  rm -f ix_core_group.sql ix_core_group_principal.sql ix_core_principal.sql ix_core_userprof.sql
}

main() {
  install_deps

  local dump_name=$(search_dump)
  if [ -z $dump_name ]; then
    echo ""Error obtaining latest dump file name from S3""
    exit 1
  fi

  local dump_path=""./$dump_name""
  echo
  echo ""Latest dump name: $dump_name""
  download_dump $dump_name $dump_path
  if [ ! -f $dump_path ]; then
    echo ""Error downloading database dump file""
    exit 1
  fi

  echo
  echo ""Restore dump to the new database...""
  restore_dump $dump_path
  if [ ! -z $OLD_DB_HOST ]
  then
    echo ""Dump users, roles and groups...""
    dump_users_and_roles
    echo ""Restore users, roles and groups to the new database...""
    restore_users_and_roles
  else
    echo ""\$OLD_DB_HOST not set, skipping user roles transfer""
  fi

  echo
  echo ""Create roles trigger...""
  create_roles_trigger

  echo
  echo ""Run GSRS...""
  run_gsrs

  echo
  run_job ""Regenerate standardized names, force inconsistent standardized names to be regenerated""
  sleep 10
  echo
  run_job ""Regenerate structure properties collection for all chemicals in the database""
  sleep 10
  echo
  run_job ""Re-backup all Substance entities""
  sleep 10
  echo
  run_job ""Reindex all core entities from backup tables""
  sleep 10 # we should wait some time until the indexes creation process will be completely finished
  echo
  upload_indexes
  sleep 5 # wait before starting shutdown and cleanup
  echo
  echo ""Shutdown GSRS...""
  shutdown_gsrs
  sleep 5

  # Better to explicitly uncomment this when needing to clean up, because if something went wrong during
  # the upload to s3 step, the following will wipe the ginas.ix folder preventing us from trying again
  # echo
  # echo ""Cleanup...""
  # cleanup $dump_path
}

main
","Shell"
"Assay","FDA/precisionFDA","public/tuts/workstations.md",".md","37539","902","# Tutorial: Collaborative Data Science with Interactive Workstations and Databases

# Introduction

This hands-on tutorial presents design patterns for collaborative data science using the featured precisionFDA pfda-ttyd and pfda-jupyterLab interactive workstation apps, and precisionFDA Databases. Through the development of the tutorial assets, precisionFDA’s powerful capabilities for secure sharing and analysis of FISMA-Moderate authorized data are clearly demonstrated, and users will be empowered to develop their own collaborative regulatory data science use cases.

As always, keep in mind that all of these workstations, notebooks, and databases are strictly within the sole provenance of the user that launched them, and that in compliance with  precisionFDA’s FISMA authorization, the ability to deliver multi-user web services or databases is specifically not supported on precisionFDA. 

Users can however use the power of the cloud to efficiently achieve their collaborative data science and bioinformatics objectives, and use the regulatory-grade platform to share the tools and results with full chain of provenance tracking. For cross-cutting analysis across FDA datasets, users will need to bring their data into the FDA's Intelligent Data Lifecycle Ecosystem (FiDLE).

# Learning Objectives

Through this hands-on tutorial you will:

-   Use the precisionFDA command line utility (pfda) to programmatically transfer files to and from precisionFDA and workstations and notebooks.

-   Configure ttyd workstations to present web services on ports 8080 and 8081 for secure browser-based access with a rich UI.

-   Launch a data analysis ttyd workstation with a local PostgreSQL database server, psql command line database client, pgadmin GUI database client, and RStudio configured with PostgreSQL access.

-   Launch a precisionFDA Database cluster and access it from the data analysis workstation using psql and pgadmin to configure and install a database on the cluster from DDL and delimited data files.

-   Use pgadmin to backup the cluster database to a precisionFDA file.

-   Use pgadmin to restore the database backup to the data analysis workstation local database.

-   Access the cluster and the workstation local databases from RStudio.

-   Launch a jupyterLab workstation with a local PostgreSQL database server, and psql command line database client, and an example Python database analysis notebook.

-   Use pgadmin to restore the database backup to the jupyterLab workstation local database.

-   Access the cluster and the workstation local databases from a Python notebook.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image1.png)

# Build Data Analysis Workstation

## Run the pfda-ttyd Featured App

Using the smallest instance type, run the Data Analysis Workstation job.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image2.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image3.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image4.png)

Refresh the execution status using the
![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image5.png) button until the job is running and open the workstation.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image6.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image7.png)

Use dx-get-timeout and dx-set-timeout to view and set the workstation application time-to-live after which it will self-terminate.
```
dx-set-timeout 1d
dx-get-timeout
0 days 23 hours 59 minutes 56 seconds
```

## Download and Install the precisionFDA CLI

Under My Home Assets, click on the How to create assets button to find links to the precisionFDA CLI, and the button to generate the temporary authorization key that you'll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image8.png"" alt=""1""></img>
  <div>
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image9.png"" alt=""2"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image10.png"" alt=""3"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image11.png"" alt=""4"">
  </div>
</div>

```bash
# Install pfda CLI
wget https://pfda-production-static-files.s3.amazonaws.com/tuts/cli/pfda-linux-2.2.tar.gz
tar xf pfda-linux-2.2.tar.gz
mv pfda /usr/bin/
pfda --version
```

Copy a file ID and retrieve an authorization key to and download a file from precisionFDA to the workstation local FS.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image12.png)

```bash
key="".....""

pfda download -key $key -file-id file-GJv1zKj0Kj2vzFP4Gg475ZyX-1
```
Upload a file from the workstation local filesystem to precisionFDA (note the key is cached).
```bash
mv foo2.txt moo2.txt
pfda upload-file -file moo2.txt
```

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image13.png"" alt=""1"" style=""max-width: 600px;"" />

## Present Test Web Servers on Ports 8080 and 8081

To see how a ttyd workstation can present web services to an external browser, start up test web servers on ports 8080 and 8081 and using the workstation job's URL, access the web services from your web browser (e.g. https://job-gk0qpfj0kj2ybz63p36by5kj.dnanexus.cloud:8080).
```
python3 -m http.server 8080 &
python3 -m http.server 8081 &
```
![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image14.png)

<div style=""max-width: 400px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image15.png"" alt=""1"" />
</div>

Kill the http.server jobs to free up ports 8080 and 8081 for pgadmin and RStudio (e.g. kill 1937).

## Deploy Local PostgreSQL DB Server and CLI

Deploy a local PostgreSQL DB server on the Data Analysis workstation. Map the postgres port from the container to the workstation (host) OS.
```bash
# Install and start a postgreSQL server (and psql CLI)

docker run --name postgres -e POSTGRES_PASSWORD=password -p 5432:5432 -d postgres:13.4-buster

# Install postgres client

apt update

apt install postgresql-client -y

# Connect to local postgres db

PGPASSWORD=""password"" psql -h localhost -U postgres
```

## Deploy pgadmin and Connect to Local DB

pgadmin4 is deployed in a Docker container mapping the pgadmin web service port 80 to workstation port 8080. A directory is created on the workstation with the appropriate ownership to enable database backup files created in pgadmin to be copied from the container to the workstation.
```bash
# Create and configure host directory for backup files from pgadmin

mkdir /home/dnanexus/db_backups

sudo chown -R 5050:5050 db_backups/

sudo chmod ugo+w db_backups/

# Run pgadmin

docker run --name pgadmin -it -v /home/dnanexus/db_backups:/home/dnanexus/db_backups -p 8080:80 -e 'PGADMIN_DEFAULT_EMAIL=user@domain.com' -e 'PGADMIN_DEFAULT_PASSWORD=password' -d dpage/pgadmin4
```
Access the pgadmin web service from your web browser (e.g. https://job-gk0qpfj0kj2ybz63p36by5kj.dnanexus.cloud:8080) with the specified credentials (user@domain.com, password).

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image14.png)

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image16.png"" alt=""1"" style=""max-width: 600px;"" />

To connect pgadmin in the container to the postgres database server port on the host, first obtain the docker0 interface IP address. This will be used in place of localhost in pgadmin (since localhost in pgadmin refers to the container local host). Add the workstation local database as a new server (data analysis workstation db) using the docker0 address (user *postgres*, password *password*).
```bash
ip addr show docker0

2: docker0: <BROADCAST,MULTICAST,UP,LOWER_UP> mtu 1500 qdisc noqueue state UP group default
    link/ether 02:42:cd:c8:f1:0e brd ff:ff:ff:ff:ff:ff
    inet 172.17.0.1/16 brd 172.17.255.255 scope global docker0
```
<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image17.png"" alt=""1"" style=""max-width: 600px;"" />

## Deploy RStudio

RStudio is deployed in a Docker container mapping the RStudio web service port 8787 to host port 8081. A directory is created on the workstation with the appropriate ownership to enable database backup files created in pgadmin to be copied from the container to the workstation.
```bash
# Deploy RStudio

docker pull rocker/rstudio:4.1.0

docker run --name rstudio -p 8081:8787 -v /home/dnanexus:/home/rstudio/dnanexus -e DISABLE_AUTH=true -d rocker/rstudio:4.1.0
```

Access the RStudio web service from your web browser (e.g. https://job-gk0qpfj0kj2ybz63p36by5kj.dnanexus.cloud:8081).

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image14.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image18.png)

Install the RPostgres packages and test access to the workstation local database. In the R Studio console:
```r
# Install RPostgres
install.packages(""RPostgres"")

# Connect to local database
library(DBI)

con <- DBI::dbConnect(
    RPostgres::Postgres(),
    host = ""172.17.0.1"",
    port = 5432, dbname = ""postgres"",
    user = ""postgres"", password = ""password""
)

# List the tables in db postgres
dbListTables(con)
```

Since there are no tables in the postgres database, the response is **character(0)**. Let's add two tables using psql and run the same R query. In psql on the data analysis workstation:
```sql
CREATE TABLE public.""PATIENT"" (
    patient_id bigint NOT NULL,
    name character varying,
    gender character varying,
    zip character varying,
    country character varying,
    created_date date
);

CREATE TABLE public.""OBSERVATION"" (
    observation_id bigint NOT NULL,
    patient_id bigint,
    observation_name character varying,
    loinc character varying,
    created_date date
);
```
The query in the RStudio console now shows the two new tables.
```
dbListTables(con)
[1] ""PATIENT""      ""OBSERVATION""
```
Let's drop the tables since we will be populating this database from a backup a later stage in this tutorial. In psql on the data analysis workstation:
```sql
DROP TABLE public.""PATIENT""
DROP TABLE public.""OBSERVATION""
```
# Deploy a precisionFDA Database Cluster

## Create the Database

Select the Databases tab in My Home and click the Create Database button.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image19.png)

Create a ""Workstations and Databases Tutorial"" database, ""password"", PostgreSQL 11.16 on the smallest available database instance type, and click the Submit button.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image20.png)

Refresh the database status using the
![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image5.png) button until the database is available.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image21.png)

Click on the Workstations and Databases Tutorial database to open the detail page and copy the host endpoint URL.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image22.png)

## Connect to the cluster DB from pgadmin

In the pgadmin web service, add a new server for the Workstations and Databases Tutorial DB cluster using the host endpoint, user root, and the password specified when the database was created.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image23.png"" alt=""1"" style=""max-width: 700px;"" />

Note that we now have connections to both the local database on the data analysis workstation, and the cluster database.


<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image24.png"" alt=""1"" style=""max-width: 400px;"" />

## Create a new database and tables

Connect to the cluster database from psql in the data analysis workstation shell.
```bash
psql --host=dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com --username=root -d postgres
```
Using psql, create a new database.
```sql
-- Database: workstations_and_databases_tutorial_db
CREATE DATABASE workstations_and_databases_tutorial_db
    WITH
    OWNER = root
    ENCODING = 'UTF8'
    CONNECTION LIMIT = -1
    IS_TEMPLATE = False;
```
Connect to the new database and create two tables.
```bash
\c workstations_and_databases_tutorial_db;

psql (9.5.25, server 11.16)
WARNING: psql major version 9.5, server major version 11. Some psql features might not work.

SSL connection (protocol: TLSv1.2, cipher: ECDHE-RSA-AES128-GCM-SHA256, bits: 128, compression: off)

You are now connected to database
""workstations_and_databases_tutorial_db"" as user ""root"".

workstations_and_databases_tutorial_db=>
```
```sql
CREATE TABLE public.""PATIENT"" (
    patient_id bigint NOT NULL,
    name character varying,
    gender character varying,
    zip character varying,
    country character varying,
    created_date date
);

CREATE TABLE public.""OBSERVATION"" (
    observation_id bigint NOT NULL,
    patient_id bigint,
    observation_name character varying,
    loinc character varying,
    created_date date
);
```
```
\dt
          List of relations

Schema | Name        | Type  | Owner
-------+-------------+-------+-------
public | OBSERVATION | table | root
public | PATIENT     | table | root
(2 rows)
```
## Load the cluster database from delimited text files

Although the workflow illustrated here may seem over-engineered for loading two data files, the techniques presented here were used to reliably and efficiently transfer tens of thousands of files and 15+ TB of data to precisionFDA.

In the data analysis workstation shell, create a datafiles directory
```bash
mkdir datafiles
```

### Create and upload delimited data files

On your local client (i.e. laptop), create file `patients.txt` with the following content:
```
12345|Fred Foobar|M|94040|USA|2022-10-25
12346|Mary Merry|F|94040|USA|2022-09-24
12347|Barney Rubble|M|94040|USA|2022-08-23
```
Create file **observations.txt** with the following content:
```
9870|12345|Annual check up|66678-4|2022-11-01
9871|12345|Emergency|LG32756-5|2022-11-02
9872|12346|Clinic visit|66678-4|2022-11-03
9873|12347|Lab results|74418-5|2022-11-04
9874|12347|Post-op checkup|65375-8|2022-11-05
```

In My Home / Files use the Add Files button to upload the two files to your private area.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image25.png)

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image26.png"" alt=""1"" style=""max-width: 500px;"" />

### Create and upload a manifest of data file IDs

Click into patients.txt and observations.txt details pages and copy their file IDs into a file named manifest.txt file on your local client.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image27.png"" alt=""1"" style=""max-width: 600px;"" />

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image28.png"" alt=""1"" style=""max-width: 400px;"" />

```
-- manifest.txt
file-GK0fqQ80Kj2zkg3kKgF8Bg9G-1
file-GK0fqGQ0Kj2gBZjxF24493PY-1
```

Use the Add Files button to upload the manifest.txt file to your private area. Click into the details for the uploaded file and copy the file ID.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image29.png"" alt=""1"" style=""max-width: 600px;"" />

### Download the files in the manifest to the Data Analysis Workstation

Under My Home Assets, click on the How to create assets button to find the button to generate the temporary authorization key that you'll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image8.png"" alt=""1""></img>
  <div>
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image10.png"" alt=""3"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image11.png"" alt=""4"">
  </div>
</div>

Using pfda CLI in the data analysis workstation shell, download the `manifest.txt` file to the workstation filesystem.
```bash
key=""...""

pfda download -key $key -file-id file-GK0fzGQ0Kj2pj77v7xbZbXYG-1

ls -l
-rw-r--r-- 1 root root 66 Nov 26 01:58 manifest.txt
```

## Iterate through manifest and download data files

In the data analysis workstation shell install and run dos2unix on the `manifest.txt` file to ensure there are no cross-OS end-of-line issues.
```bash
cd ~/datafiles
apt install dos2unix
dos2unix manifest.txt

for FILE in $(cat manifest.txt); do pfda download -key $key -file-id $FILE; done

ls
manifest.txt observations.txt patients.txt
```

## Copy the data into the cluster DB tables

### Connect to the workstations_and_databases_tutorial_db cluster database

Using the database host endpoint, connect to the workstations_and_databases_tutorial_db cluster database using psql on the data analysis workstation:

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image22.png)
```
psql --host=dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com --username=root -d workstations_and_databases_tutorial_db

workstations_and_databases_tutorial_db=>
```

### Copy the patients and observations data into the cluster DB

In psql:
```sql
\copy public.""PATIENT"" from '/home/dnanexus/datafiles/patients.txt'
delimiter '|' NULL ''

copy public.""OBSERVATION"" from
'/home/dnanexus/datafiles/observations.txt' delimiter '|' NULL ''

select * from public.""PATIENT"";

patient_id  | name          | gender | zip   | country | created_date
------------+---------------+--------+-------+---------+--------------
12345       | Fred Foobar   | M      | 94040 | USA     | 2022-10-25
12346       | Mary Merry    | F      | 94040 | USA     | 2022-09-24
12347       | Barney Rubble | M      | 94040 | USA     | 2022-08-23

select * from public.""OBSERVATION"";

observation_id | patient_id | observation_name | loinc    |created_date
---------------+------------+------------------+----------+------------
          9870 | 12345      | Annual check up | 66678-4   | 2022-11-01
          9871 | 12345      | Emergency       | LG32756-5 | 2022-11-02
          9872 | 12346      | Clinic visit    | 66678-4   | 2022-11-03
          9873 | 12347      | Lab results     | 74418-5   | 2022-11-04
          9874 | 12347      | Post-op checkup | 65375-8   | 2022-11-05
```

Observe the new tables and data in the pgadmin Workstations and Databases Tutorial server connection.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image30.png)

## Connect RStudio to the cluster DB

In the RStudio console:
```r
library(DBI)

con <- DBI::dbConnect(
    RPostgres::Postgres(),
    host = ""dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com"",
    port = 5432, dbname = ""workstations_and_databases_tutorial_db"",
    user = ""root"", password = ""password""
)

dbListTables(con)
[1] ""OBSERVATION"" ""PATIENT""
```

# Build Data Analysis Notebook

## Run the pfda-jupyterLab Featured App

Using the smallest instance type, run the Data Analysis Notebook job specifying PYTHON_R.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image31.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image32.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image33.png)

Refresh the execution status using the ![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image5.png) button until the job is running and open the workstation. It may take a few minutes after the job is running for the notebook to open.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image34.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image35.png)

Adjust the remaining time-to-live for the notebook using the Update duration button.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image36.png)

## Download and Install the precisionFDA CLI

Under My Home Assets, click on the How to create assets button to find links to the precisionFDA CLI, and the button to generate the temporary authorization key that you'll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image8.png"" alt=""1""></img>
  <div>
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image9.png"" alt=""2"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image10.png"" alt=""3"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image11.png"" alt=""4"">
  </div>
</div>

Open a Terminal in the Data Analysis notebook.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image37.png)

```bash
-- Install pfda CLI
wget https://pfda-production-static-files.s3.amazonaws.com/tuts/cli/pfda-linux-2.1.2.tar.gz
tar xf pfda-linux-2.1.2.tar.gz
mv pfda /usr/bin/
pfda ---version
```

Copy a file ID and retrieve an authorization key to and download a file from precisionFDA to the workstation local FS.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image12.png)
```
pfda download -key Mk5VTENlTS83R2I1U3dXQkRnWEhzamJvVVFrTVZrOHA4STI4OTM0MitRWnNqZWVBSVRndlBicG1IUU9PeStjbTBLRXUzNW5rMmMrMjV6bGVhSnVTUlhDd2dEOVhRdUZvdmE1a29pcHdWWS92RGNyN1ljTlZtdnNjbE15RXVyVnl1Zkd3UUVxODZpYzNsWi9JWVVBcEw3VE5uaXdMSTdYNHNWVFJpZGJYdXlVa2hsRFFnR2dDc1JISzhuYWxla2JXLS1zVjRhSVBCWFdaRXBFWnBsMXNtSXB3PT0=--e19f53de7644d63dd3898717896a88bd0a383db6 -file-id file-GJv1zKj0Kj2vzFP4Gg475ZyX-1
```

Upload a file from the workstation local filesystem to precisionFDA (note the key is cached).
```
mv foo2.txt moo2.txt
pfda upload-file -file moo2.txt
```

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image13.png"" alt=""1"" style=""max-width: 600px;"" />


## Deploy Local PostgreSQL DB Server

Deploy a local PostgreSQL DB server on the Data Analysis workstation. Map the postgres port from the container to the workstation (host) OS. In the notebook terminal:
```bash
sh -c 'echo ""deb http://apt.postgresql.org/pub/repos/apt $(lsb_release -cs)-pgdg main"" > /etc/apt/sources.list.d/pgdg.list'

wget --quiet -O - https://www.postgresql.org/media/keys/ACCC4CF8.asc | apt-key add -

apt-get update

apt-get -y install postgresql
```
Configure postgres to enable password-free local login. Find pg_hba.conf
in /etc/postgresql/ and configure with permissive permissions.
```bash
find /etc/postgresql -name pg_hba.conf | xargs sed -i 's/peer/trust/'
find /etc/postgresql -name pg_hba.conf | xargs sed -i 's/md5/trust/'
```
Start the local PostgreSQL DB server on the Data Analysis notebook.
```
/etc/init.d/postgresql start
* Starting PostgreSQL 15 database server [ OK ]
/etc/init.d/postgresql status
15/main (port 5432): online
```
```bash
psql -U postgres -h 127.0.0.1
    psql (15.1 (Ubuntu 15.1-1.pgdg18.04+1))
    SSL connection (protocol: TLSv1.3, cipher: TLS_AES_256_GCM_SHA384, compression: off)
    Type ""help"" for help.

    postgres=#
```

## Create a Table with some data in the Local DB

In psql in the notebook terminal create a table, then copy two records from stdin into the table display them.
```sql
CREATE TABLE public.""PATIENT"" (
    patient_id int NOT NULL,
    name character varying,
    gender character varying,
    zip character varying,
    country character varying,
    created_date date
);

COPY public.""PATIENT"" (patient_id, name, gender, zip, country, created_date) from stdin;
```

Add these two records when prompted from the above COPY, and terminate with the \\. record.
```sql
12345 foo m 94040 usa 2022-11-25
54321 bar m 94040 usa 2022-11-25
\.

select * from public.""PATIENT"";

patient_id | name | gender | zip   | country | created_date
-----------+------+--------+-------+---------+--------------
12345      | foo  | m      | 94040 | usa     | 2022-11-25
54321      | bar  | m      | 94040 | usa     | 2022-11-25
(2 rows)
```

## Create a Notebook and Connect to the Local DB

In the notebook terminal, install the psycopg2 binary.
```bash
pip install psycopg2-binary
```
Open a Python 3 notebook.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image38.png)

And enter the following code:
```py
import psycopg2

conn = psycopg2.connect(""dbname='postgres' user='postgres'
host='127.0.0.1'"")

cur = conn.cursor()
cur.execute('SELECT * FROM public.""PATIENT"" limit 10')

# fetch results
rows = cur.fetchall()

# iterate through results
for row in rows:
    print (""    "", row)
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image39.png)

## Connect to the Cluster DB

Open a Python 3 notebook.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image38.png)

And enter the following code:
```py
import psycopg2

conn = psycopg2.connect(""dbname='workstations_and_databases_tutorial_db'
user='root'
host='dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com' password='password'"")

cur = conn.cursor()
cur.execute('SELECT * FROM public.""PATIENT"" limit 10')

# fetch results
rows = cur.fetchall()

# iterate through results
for row in rows:
    print (""PATIENT"", row[0], row[1], row[2])

cur.execute('SELECT * FROM public.""OBSERVATION"" limit 10')

# fetch results
rows = cur.fetchall()

# iterate through results
for row in rows:
    print (""OBSERVATION"", row[0], row[1], row[2])
```
```
PATIENT 12345 Fred Foobar M
PATIENT 12346 Mary Merry F
PATIENT 12347 Barney Rubble M
OBSERVATION 9870 12345 Annual check up
OBSERVATION 9871 12345 Emergency
OBSERVATION 9872 12346 Clinic visit
OBSERVATION 9873 12347 Lab results
OBSERVATION 9874 12347 Post-op checkup
```
## Load a Complete Notebook from a Snapshot

Using My Home / Applications, run the featured pfda-jupyterLab app on the smallest instance type, providing the *Jupyter-DESeq2-notebook-snapshot.tar* file as input. This snapshot contains a complete RNA-seq DESeq2 quantification JupyterLab workbook with R package, notebook, input file and sample sheet all included.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image40.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image41.png)

Once the app is running, click the open workstation button to access a rich visual and interactive analysis environment.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image42.png)

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image43.png"" alt=""1"" style=""max-width: 600px;"" />

Open the *rnaseq_diffex_r* notebook to explore the data.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image44.png"" alt=""1"" style=""max-width: 650px;"" />

# Backup the cluster DB and restore it to local DBs

## Add a postgres role to the cluster DB

Right-click Login/Group Roles in the Workstations and Databases Tutorial server connection in pgadmin and add a postgres role.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image45.png"" alt=""1"" style=""max-width: 600px;"" />

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image46.png"" alt=""1"" style=""max-width: 600px;"" />

Right-click the postgres role and select properties, and add the to the root group with admin privileges in the Membership tab.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image47.png)

## Backup the cluster DB using pgadmin

Select the workstations_and_databases_tutorial_db database in the Workstations and Databases Tutorial server connection in pgadmin and right-click to backup the database.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image48.png"" alt=""1"" style=""max-width: 600px;"" />

Specify a backup filename (e.g. workstations_and_databases_tutorial_db-2022-11-25.tar), format as Tar, assign role name *postgres* and set all the Data/Objects Do not save options..

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image49.png"" alt=""1"" style=""max-width: 600px;"" />

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image50.png"" alt=""1"" style=""max-width: 600px;"" />

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image51.png)

## Copy the backup file from the pgadmin container to the workstation filesystem

Since pgadmin is running in a Docker container on the data analysis workstation, we are going to have to connect to the pgadmin container shell and copy the backup file to the mount point shared by the container and the workstation (i.e. /home/dnanexus/db_backups). On the data analysis workstation:

Connect to the shell in the pgadmin container.
```bash
docker exec -it pgadmin sh

/pgadmin4 $
```
Copy the backup file from the pgadmin backup directory to the
container-host shared volume.
```bash
ls /var/lib/pgadmin/storage/user_domain.com

workstations_and_databases_tutorial_db-2022-11-25

cp /var/lib/pgadmin/storage/user_domain.com workstations_and_databases_tutorial_db-2022-11-25.tar /home/dnanexus/db_backups
```

**Control-D** to exit the container shell and verify the presence of the backup file on the workstation in the container-host shared mount point.
```bash
ls db_backups/

workstations_and_databases_tutorial_db-2022-11-25
```

## Upload the backup file to precisionFDA

Under My Home Assets, click on the How to create assets button to find the button to generate the temporary authorization key that you'll use with the CLI.


<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image8.png"" alt=""1""></img>
  <div>
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image10.png"" alt=""3"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image11.png"" alt=""4"">
  </div>
</div>

On the data analysis workstation shell:
```bash
key=""...""

pfda upload-file -key $key -file ~/db_backups/workstations_and_databases_tutorial_db-2022-11-25.tar
```

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image52.png"" alt=""1"" style=""max-width: 600px;"" />


## Restore the backup to the data analysis workstation local DB

Using the pgadmin connection to the data analysis workstation db, create a new database *workstations_and_databases_tutorial_db*, owner *postgres*.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image53.png"" alt=""1"" style=""max-width: 600px;"" />

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image54.png"" alt=""1"" style=""max-width: 600px;"" />

Right-click on the new database on the data analysis and workstation db server connection and restore the backup to the local server (from the file in the pgadmin container), using custom or tar format, and the postgres role name.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image55.png"" alt=""1"" style=""max-width: 500px;"" />

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image56.png"" alt=""1"" style=""max-width: 600px;"" />

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image57.png"" alt=""1"" style=""max-width: 600px;"" />

Select the contents of the restored PATIENT and OBSERVATION tables.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image58.png)

## Restore the backup to the data analysis notebook local DB

Under My Home Assets, click on the How to create assets button to find the button to generate the temporary authorization key that you'll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image8.png"" alt=""1""></img>
  <div>
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image10.png"" alt=""3"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image11.png"" alt=""4"">
  </div>
</div>

Click into the detail page for the backup file and copy the file ID.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image59.png"" alt=""1"" style=""max-width: 600px;"" />

In a terminal window in the data analysis jupyterLab notebook, download the backup file using its file ID as copied in the step above:
```bash
mkdir ~/db_backups
cd db_backups

key=""...""
pfda download -key $key -file-id file-GK172180Kj2x743JPy4KGbf9-1
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image60.png)

In psql connected to the local host, create a new database *workstations_and_databases_tutorial_db*, and a new user *root*.
```bash
psql -U postgres -h 127.0.0.1
psql (15.1 (Ubuntu 15.1-1.pgdg18.04+1))
postgres=#

CREATE USER root;

CREATE DATABASE workstations_and_databases_tutorial_db
    WITH
    OWNER = postgres
    ENCODING = 'UTF8'
    CONNECTION LIMIT = -1
    IS_TEMPLATE = False;
```

Ctrl-D to exit psql and use restore the database from the backup file.
```bash
pg_restore --dbname=workstations_and_databases_tutorial_db --verbose ~/db_backups/workstations_and_databases_tutorial_db-2022-11-25.tar -U postgres
```
You can ignore the errors associated with the *root* role not existing and use the Python notebook to select the contents from the restored database. We can observe the same results from newly restored database as from the cluster database that was the backup source. In a notebook Python code block:
```py
import psycopg2

conn = psycopg2.connect(""dbname='workstations_and_databases_tutorial_db' user='postgres' host='127.0.0.1'"")

cur = conn.cursor()
cur.execute('SELECT * FROM public.""PATIENT"" limit 10')

# fetch results
rows = cur.fetchall()

# iterate through results
for row in rows:
    print (""PATIENT"", row[0], row[1], row[2])

cur.execute('SELECT * FROM public.""OBSERVATION"" limit 10')

# fetch results
rows = cur.fetchall()

# iterate through results
for row in rows:
    print (""OBSERVATION"", row[0], row[1], row[2])
```
![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image61.png)

# Snapshot, Terminate, and Restore Workstations

In keeping with good cloud usage practice, we will snapshot and terminate the workstations, preserving their entire state as built out through this tutorial. Additionally since we've backed up the database to a precisionFDA file, we can safely terminate the cluster database as well.

## Stop the Docker Containers and Snapshot Data Analysis Workstation

Using the data analysis workstation shell, create a snapshot of the workstation in you My Home files area.
```bash
Docker stop

dx-create-snapshot

dx ls -al *snapshot
```

### Terminate the Workstation

In My Home / Executions, select (one at a time unfortunately) the Data Analysis Workstation and Data Analysis Notebook executions and select Terminate under the Action dropdown menu.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image62.png)

## Snapshot and Terminate the Data Analysis Notebook

Select Create Snapshot in the precisionFDA menu in the jupyterLabs interface.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image63.png"" alt=""1"" style=""max-width: 600px;"" />

### Terminate the Workstation and Notebook and Database Cluster

In My Home / Executions, select (one at a time unfortunately) the Data Analysis Workstation and Data Analysis Notebook executions and select Terminate under the Action dropdown menu.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image62.png)

# Stop or Terminate the Database Cluster

In My Home / Databases, select the database for action and either Stop or Terminate the database using the Action dropdown menu. If your data is already stored on precisionFDA and can be readily reconstituted into a new database, then select Terminate. If your database is a work in progress and you'd like to keep it intact while not using it overnight, or the weekend, then select Stop.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/workstations-media/image64.png)

# Restore Workstation and Notebook from Snapshots
","Markdown"
"Assay","FDA/precisionFDA","public/tuts/apps.md",".md","50023","1301","# Tutorial: Design Patterns for Apps and Workflows

# Introduction

This hands-on tutorial presents design patterns for collaborative bioinformatics using precisionFDA Apps and Workflows. There is a considerable range of design patterns for deploying code, marshalling data, and producing results, within precisionFDA's file and transient worker-driven (i.e. batch, non-interactive) application framework. This course will present this framework, including:

-   Apps, their input/out specifications for files and other data types, virtual environment specification, and scripting

-   Assets (i.e. tar archives) that can be bundled with assets and overlaid onto the worker's root volume

-   Incorporation of Docker images into apps from file inputs and from assets

-   Workflow creation using the web interface and WDL

Through the development of the tutorial artifacts, precisionFDA's powerful capabilities for secure collaborative development of bioinformatics tools, and sharing of -omics and RWD, are clearly demonstrated, and users will be empowered to develop their own bioinformatics use cases.

# Learning Objectives

Through this hands-on tutorial you will:

-   Install the precisionFDA command line utility and use it upload and
    download files.

-   Explore the multiple types of app inputs and outputs, assets, and
    their presentation to the app script as shell variables and files.

-   Build a bioinformatics app from a biocontainers Docker image.

-   Run the latest Ubuntu OS as a Docker image in an app.

-   Use the precisionFDA Command Line Interface asset to process an
    arbitrary number of inputs or produce an arbitrary number of outputs
    to make up for the lack of an array data type.

-   Create a workflow through the web interface.

-   Create a workflow through by importing WDL.

-   Access a database cluster from an app.

# Download and Install the precisionFDA CLI

The precisionFDA CLI is useful for programmatic uploading and downloading of files from Windows, MacOS, and Linux clients (e.g. your laptop) to and from precisionFDA. Installation and testing for Windows and Linux OS are described below.

Under My Home Assets, click on the How to create assets button to find links to the precisionFDA CLI, and the button to generate the temporary authorization key that you'll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image1.png"" alt=""1""></img>
  <div>
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image2.png"" alt=""2"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image3.png"" alt=""3"">
    <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image4.png"" alt=""4"">
  </div>
</div>

## Windows

Click the Windows download button to place the zip file and double click it in the browser footer to open it. 

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image5.png)

Click on Extract all to decompress the pfda CLI and browse to select the Desktop as the destination for the pfda.exe file.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image6.png"" alt=""1"" />
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image7.png"" alt=""1"" />
</div>


Using the Windows start menu, bring up a Command Prompt window with the pfda.exe file visible in a file explorer side-by-side. Drag *pfda.exe* onto the Command Prompt window to expand the full path the executable and add *--version* and hit return.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image8.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image9.png)

First, copy a file ID to test downloading, and retrieve an authorization key that is required for all file transfers.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image10.png)

```
:: Provide the auth key
:: Download the selected file
C:\Users\oserang\Desktop\pfda.exe download -key <key> -file-id file-GJv1zKj0Kj2vzFP4Gg475ZyX-1

:: Copy it to a new file and upload it
copy foo2.txt moo2.txt
C:\Users\oserang\Desktop\pfda.exe upload-file -file moo2.txt

dir *.txt
11/26/2022  04:26 PM                15 foo2.txt
11/26/2022  04:26 PM                15 moo2.txt 
```
![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image11.png)

## Linux

Copy the download URL and install and test uploading and downloading files. First, copy a file ID to test downloading, and retrieve an authorization key that is required for all file transfers.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image10.png)

```bash
-- Install pfda CLI
wget https://pfda-production-static-files.s3.amazonaws.com/tuts/cli/pfda-linux-2.2.tar.gz
tar xf pfda-linux-2.2.tar.gz 
mv pfda /usr/bin/
pfda --version

-- Provide the auth key
key=""…..""

-- Download the selected file
pfda download -key $key -file-id file-GJv1zKj0Kj2vzFP4Gg475ZyX-1

-- Copy it to a new file and upload it
cp foo2.txt moo2.txt
pfda upload-file -key $key -file moo2.txt

ls *.txt
foo2.txt  moo2.txt
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image11.png)

# First App: *docker_pull_and_save*

The first app demonstrates the use of string input variables, allowing the app to access the internet, how to use the emit shell function to output a named file. This app pulls a docker image and saves it to a .tar file suitable for use in precisionFDA apps. We'll create three image files in our Files:

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/table.png)

## Create the docker_pull_and_save App

From My Home / Apps, click on Create App to create the *docker_pull_and_save* app. In the I/O Spec tab, add the input and output fields.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image12.png)

Select the VM Environment tab, enable internet access, and select Baseline 2 as the default instance type.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image13.png)

Select the Script tab and enter the following shell script:
```bash
set -euxo pipefail
sudo docker pull ""$docker_source""
sudo docker save ""$docker_source"" > ""$docker_image_filename""
emit ""docker_image_file"" ""$docker_image_filename""
```

The set -euxo pipefail set is advisable at the start of all your app scripts. The docker source string is resolved and used to pull the image and to save it to the docker image filename. Lastly the resulting image is saved as a precisionFDA file with the specified image filename.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image14.png)

Select the Readme tab and describe your app, then hit the Create button to create your app.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image15.png)

## Run the docker_pull_and_save App

Run this app three times with the inputs listed above. You don't have to wait for one to finish to start the others as they will run in parallel.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image16.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image17.png)

Select one of the completed executions My Home / Executions for the app and View Logs using the Action dropdown menu and we can see the desired actions took place and the image .tar files appear in My Home / Files.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image18.png)

```
Downloading files using 2 threads++ set -euxo pipefail

++ sudo docker pull postgres:13.4-buster

13.4-buster: Pulling from library/postgres
.
.
.
Status: Downloaded newer image for postgres:13.4-buster

++ sudo docker save postgres:13.4-buster

++ emit docker_image_file postgres_13.4-buster.tar
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image19.png)

# Second App: *worker_layout_inspection*

Since assets are such a great app developer convenience, our second app, worker_layout_inspection, will illustrate the use of assets and inputs for presenting code and data to the app. First let's create some assets that will be incorporated into the app. Additionally, since Docker is also such a great app developer tool, the incorporation of Docker images into the app by packaging them in an asset or providing them as an input file, are both illustrated.

## Create an asset with code and data

### Layout your asset directories and files

The file system structure that you layout in your asset will be overlaid on the worker / (root) mount when the app is run. If you've include code in the asset's /usr/bin, it will be available to your app running on the worker. The app framework uses /work as the directory to present input files to apps so any files placed in the asset's /work directory will be available with other input files, though you could place files in whatever asset directory structure you want.

We going to construct our asset in a Linux shell, downloading from precisionFDA the following for inclusion in the asset:

-   ubuntu_latest.tar (file-GK1FP9j05gK8z93Y1xGQpf5B-1)

-   countries.txt (file-GK1F6j80Kj2XbJzx29f25y42-1)

Create your asset directory structure.
```bash
apt install tree
mkdir -p ~/fakeroot/work
mkdir -p ~/fakeroot/usr/bin
tree ~/fakeroot/
/home/dnanexus/fakeroot/
├── usr
│   └── bin
└── work
```
Create a shell script to install tree and run it, then move the script into the asset directory structure.
```bash
cat > tree_script.sh
  sudo apt install tree
  tree $1
  CTRL-D
chmod ugo+x tree_script.sh
./tree_script.sh ~
/home/dnanexus
├── EHR_Sample_backup_nodbcreate_postgres.sql
├── datafiles
│   ├── manifest.txt
│   ├── observations.txt
│   └── patients.txt
├── db_backups
mv tree_script.sh ~/fakeroot/usr/bin
```

Create a Readme file as required for the asset. This doesn't need to reside in the asset /fakeroot directory structure.

```
echo ""Assets for the worker layout inspection tutorial app"" > ~/readme.txt
```

Download the Docker and data files and move them into the asset directory structure. Under My Home Assets, click on the How to create assets button to find links to the precisionFDA CLI, and the button to generate the temporary authorization key that you'll use with the CLI.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image1.png"" alt=""1""></img>
  <div>
      <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image3.png"" alt=""1""></img>
      <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image4.png"" alt=""1""></img>
  </div>
</div>
<br/>

```bash
key=""...""

pfda download -key $key -file-id file-GK1FP9j05gK8z93Y1xGQpf5B-1
pfda download -key $key -file-id file-GK1F6j80Kj2XbJzx29f25y42-1
ls *.tar *txt
countries.txt  foo2.txt  moo2.txt  ubuntu_latest.tar

mv countries.txt postgres_13.4-buster.tar ubuntu_latest.tar ~/fakeroot/work tree ~/fakeroot

/home/dnanexus/fakeroot
├── usr
│   └── bin
│       └── tree_script.sh
└── work
    ├── countries.txt
    └── ubuntu_latest.tar
```

### Create the asset using the CLI

Now that the asset contents have been laid out, creating the asset on precisionFDA is a straightforward process using the precision FDA CLI.

```bash
key=""...""

pfda upload-asset --key $key --name worker_layout_inspection.tar --root ~/fakeroot --readme ~/readme.txt

>> Archiving asset...

>> Finalizing asset...

>> Done! Access your asset at https://precision.fda.gov/home/assets/file-GK1JJB00Kj2fkz464yxB5zY2-1
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image20.png)

### Manually deploying an asset tar file

While this workflow should not be required, it is worth knowing to understand how assets work. Upload the asset tarball (e.g. *worker_layout_inspection.tar*) as a file, then in your app, add a file to the I/O Spec (e.g. ""asset_tarball"") and select this tarball as the default. Add the following line to the Script, right after the `set -euxo pipefail` command:

```
tar zxvf ${asset_tarball_path} -C / --strip-components=1 --no-same-owner
```
Everything that was installed in the fake_root/ in the tarball will be placed into the root directory of the worker, including any executable you may have. For example:
```
fake_root/usr/local/bin/sambamba
```
from the asset_tarball input, will be available in:
```
/usr/local/bin/sambamba
```
after the tar command above is run in the app script.

## Create the App and Specify the I/O Spec

In My Home / Apps, click the Create App button to create the *worker_layout_inspection* app. In the I/O Spec tab, add the input and output fields.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image21.png)

<table>
  <thead>
    <tr>
      <th>Class</th>
      <th>Input Name</th>
      <th>Label</th>
      <th>Default Value</th>
    </tr>
  </thead>
  <tr>
    <td>string</td>
    <td>string_input</td>
    <td>example string input</td>
    <td>this is a string</td>
  </tr>
  <tr>
    <td>int</td>
    <td>integer_input</td>
    <td>example integer input</td>
    <td>54321</td>
  </tr>
  <tr>
    <td>float</td>
    <td>float_input</td>
    <td>example float input</td>
    <td>1.234</td>
  </tr>
  <tr>
    <td>boolean</td>
    <td>boolean_input</td>
    <td>example boolean input</td>
    <td>FALSE</td>
  </tr>
  <tr>
    <td>file</td>
    <td>observation_data</td>
    <td>Delimited observation data</td>
    <td>observations.txt</td>
  </tr>
  <tr>
    <td>file</td>
    <td>patient data -</td>
    <td>Delimited patient data</td>
    <td>patients.txt</td>
  </tr>
  <tr>
    <td>file</td>
    <td>samtools docker - image</td>
    <td>Docker image for Samtools</td>
    <td>samtools biocontaine rs.tar</td>
  </tr>
  <tr>
    <td>file</td>
    <td>postgres_docker_ image</td>
    <td>Docker image for postgres server</td>
    <td>postgres_13.4- buster.tar</td>
  </tr>
  <tr>
    <td>string</td>
    <td>inspection resul ts filename -</td>
    <td>Inspection results filename</td>
    <td>inspection_results.t xt</td>
  </tr>
  <tr>
    <td>string</td>
    <td>url_to_fetch</td>
    <td>URL to pass to next app in workflow</td>
    <td>https://pfda- production-static- files.s3.amazonaws.c om/cli/pfda-linux- 2.2.tar.gz</td>
  </tr>
  <thead>
    <tr>
      <th>Class</th>
      <th>Output Name</th>
      <th>Label</th>
      <th></th>
    </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>Inspection resul - ts</td>
    <td>Worker layout and variable inspection results</td>
    <td></td>
  </tr>
  <tr>
    <td>string</td>
    <td>url_to_fetch</td>
    <td>URL to pass to next app in workflow</td>
    <td></td>
  </tr>
</table>

### Specify the VM Environment

In the VM Environment tab, enable internet access, select default instance Baseline 2, and add the following assets: worker_layout_inspection, pfda_cli_2.2, and ubuntu_asset.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image22.png)

### Specify the Script

Add the following code to the script tab:
```bash
set -euxo pipefail

# Append the worker OS information to the specified inspection results file.
cat /etc/os-release  | tee -a ""$inspection_results_filename""

# Install tree
sudo apt update
sudo apt install tree

# Inspect the scalar input variables.
echo ""string_input"" ""$string_input"" 
echo ""integer_input"" ""$integer_input""
echo ""float_input"" ""$float_input""
echo ""url_to_fetch"" ""$url_to_fetch""
echo """"

# Inspect the file input variables and the different
# operators for accessing them on the worker FS.
echo ""patient_data"" ""$patient_data""
echo ""patient_data_name"" ""$patient_data_name""
echo ""patient_data_path"" ""$patient_data_path""
echo ""patient_data_prefix"" ""$patient_data_prefix""
echo """"
echo ""observation_data"" ""$observation_data""
echo ""observation_data_name"" ""$observation_data_name""
echo ""observation_data_path"" ""$observation_data_path""
echo ""observation_data_prefix"" ""$observation_data_prefix""
echo """"
echo ""samtools_docker_image"" ""$samtools_docker_image""
echo ""samtools_docker_image_name"" ""$samtools_docker_image_name""
echo ""samtools_docker_image_path"" ""$samtools_docker_image_path""
echo ""samtools_docker_image_prefix"" ""$samtools_docker_image_prefix""

# Use the pfda CLI that was loaded with the pfda_cli_2.2 asset.
ls -al /usr/bin/pfda*
pfda --version

# Use the simple script that was loaded with the worker_layout_inspection asset.
ls -al /usr/bin/tree_script.sh
tree_script.sh /work

# Using the Docker image that was loaded with the ubuntu_asset asset,
# we can run an up-to-date Ubuntu OS in a docker container on the worker.
# Append the container OS information to the specified inspection results file.
docker load -i /ubuntu_latest.tar
docker run --rm -v /work:/work -w /work ubuntu:latest cat /etc/os-release | tee -a ""$inspection_results_filename""

# Using the Docker image that was provided as an input file,
# we can run samtools. Add inputs and outputs to this script and
# pass them to the samtools invocation to create your own
# samtools app.
docker load -i ""$samtools_docker_image_path""
docker run --rm biocontainers/samtools:v1.9-4-deb_cv1 samtools --version

docker images
docker ps

# Pass the URL to fetch input to the same output variable to pass
# to the next app in the tutorial workflow (i.e. url fetcher).
emit ""url_to_fetch"" ""$url_to_fetch""

# Save the inspection results file with specified name.
emit ""inspection_results"" ""$inspection_results_filename""
```

### Specify the Readme

Add the following in the Readme tab:
```
Produce an inspection report of the worker filesystem and file inputs from the context of the app.

Also provide the multiple representations available for file inputs.

Echo the non-file input variables.
```
## Run the app

In My Home / Apps, select the *worker_layout_inspection* app and click Run App to launch the latest version of the app with all default inputs.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image23.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image24.png)

Refresh the execution status using the ![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image25.png) button until the job is first idle, runnable, running, and done, (or failed).

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image26.png)

## Inspect the execution logs and the inspection results file

Note that the execution of the app took just over a minute and produced inspection_results.txt file and URL string outputs.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image27.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image28.png)

Select View Logs from the the Actions dropdown menu. Let's look at a condensed and annotated version of the log output to understand what our app just did.
```bash
# Worker initialization
Logging initialized (priority)
CPU: 20% (2 cores) * Memory: 779/3720MB * Storage: 32GB free * Net: 0↓/0↑MBps
umount: /proc/diskstats: not mounted
dxpy/0.331.0 (Linux-5.4.0-1088-aws-x86_64-with-Ubuntu-16.04-xenial)
/usr/sbin/rsyslogd already running.
bash running (job ID job-GK2ZQj00Kj2z9q76KbZbkG3G)

# Fetch the app's assets.
Fetching asset ubuntu_asset.tar (file-GJqz9J00Kj2zZQPGKVZBg436)
Fetching asset pfda_cli_2.2.tar (file-GJv0kFQ0Kj2YzFb86g4B8px5)
Fetching asset worker_layout_inspection2.tar (file-GK1xxbQ0Kj2gBZjxF244F21k)

# Download the input files.
downloading file: file-GK1F6jj0Kj2gyF8jG8jPjGpV to filesystem: /work/in/patient_data/patients.txt
downloading file: file-GK1F6jQ0Kj2v90jxPV0ZBQ5G to filesystem: /work/in/observation_data/observations.txt
downloading file: file-GK1FXK80pyBj68X3K6jVX55b to filesystem: /work/in/samtools_docker_image/samtools_biocontainers.tar
downloading file: file-GK1Fg68072kf3ZXYGJvxPGPj to filesystem: /work/in/postgres_docker_image/postgres_13.4-buster.tar

# The worker's OS release information.
cat /etc/os-release 
NAME=""Ubuntu""
VERSION=""16.04.7 LTS (Xenial Xerus)""
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME=""Ubuntu 16.04.7 LTS""
VERSION_ID=""16.04""
HOME_URL=""http://www.ubuntu.com/""
SUPPORT_URL=""http://help.ubuntu.com/""
BUG_REPORT_URL=""http://bugs.launchpad.net/ubuntu/""
VERSION_CODENAME=xenial
UBUNTU_CODENAME=xenial

# Install tree
sudo apt update
apt install tree
Unpacking tree (1.7.0-3) ...
Processing triggers for man-db (2.7.5-1) ...
Setting up tree (1.7.0-3) ...

# Resolution of each scalar input variable
string_input this is a string
integer_input 54321
float_input 1.234
url_to_fetch https://pfda-production-static-files.s3.amazonaws.com/tuts/cli/pfda-linux-2.2.tar.gz

# Resolution of each file input variable into the four
# types of references available
patient_data file-GK1F6jj0Kj2gyF8jG8jPjGpV
patient_data_name patients.txt
patient_data_path /work/in/patient_data/patients.txt
patient_data_prefix patients

observation_data file-GK1F6jQ0Kj2v90jxPV0ZBQ5G
observation_data_name observations.txt
observation_data_path /work/in/observation_data/observations.txt
observation_data_prefix observations

samtools_docker_image file-GK1FXK80pyBj68X3K6jVX55b
samtools_docker_image_name samtools_biocontainers.tar
samtools_docker_image_path /work/in/samtools_docker_image/samtools_biocontainers.tar
samtools_docker_image_prefix samtools_biocontainers

# View the pfda CLI that was installed with the pfda CLI asset and run it.
ls -al /usr/bin/pfda
-rwxr-xr-x 1 root root 11810776 Aug  3 08:07 /usr/bin/pfda

pfda --version
pFDA CLI Info
  Commit ID   :    e2325fdba10e06ee32a8035fb6b7161f1e82ffe6
  CLI Version :    2.2
  Os/Arch     :    linux/amd64
  Build Time  :    2022-08-03-100606
  Go Version  :    go1.16.7b7
  TLS Version :    TLS 1.2
  FIPS        :    +crypto/tls/fipsonly verified

# View the tree script that was installed with the workstation asset and run it.
# Note the countries.txt file in /work as it was packaged in the worker_layout_inspection asset.
# Note the directory layout of the file input types.

ls -al /usr/bin/tree_script.sh
-rwxr-xr-x 1 root root 30 Nov 27 21:32 /usr/bin/tree_script.sh

tree_script.sh /work
/work
├── countries.txt
├── in
│   ├── observation_data
│   │   └── observations.txt
│   ├── patient_data
│   │   └── patients.txt
│   ├── postgres_docker_image
│   │   └── postgres_13.4-buster.tar
│   └── samtools_docker_image
│       └── samtools_biocontainers.tar
└── inspection_results.txt

# Load the docker image that was installed with ubuntu asset and run it,
# presenting the container OS release information.
docker load -i /ubuntu_latest.tar

5 directories, 6 files
Loaded image: ubuntu:latest
docker run --rm -v /work:/work -w /work ubuntu:latest cat /etc/os-release | tee -a inspection_results.txt

PRETTY_NAME=""Ubuntu 22.04.1 LTS""
NAME=""Ubuntu""
VERSION_ID=""22.04""
VERSION=""22.04.1 LTS (Jammy Jellyfish)""
VERSION_CODENAME=jammy
ID=ubuntu
ID_LIKE=debian
HOME_URL=""https://www.ubuntu.com/""
SUPPORT_URL=""https://help.ubuntu.com/""
BUG_REPORT_URL=""https://bugs.launchpad.net/ubuntu/""
PRIVACY_POLICY_URL=""https://www.ubuntu.com/legal/terms-and-policies/privacy-policy""
UBUNTU_CODENAME=jammy

# Load the samtools docker image that was provided as an input file and run it.
docker load -i /work/in/samtools_docker_image/samtools_biocontainers.tar
Loaded image: biocontainers/samtools:v1.9-4-deb_cv1

docker run --rm biocontainers/samtools:v1.9-4-deb_cv1 samtools --version
samtools 1.9
Using htslib 1.9
Copyright (C) 2018 Genome Research Ltd.

# Docker images and running containers on the worker
docker images
REPOSITORY               TAG                 IMAGE ID            CREATED             SIZE
ubuntu                   latest              a8780b506fa4        3 weeks ago         77.8MB
biocontainers/samtools   v1.9-4-deb_cv1      f210eb625ba6        3 years ago         666MB

docker ps
CONTAINER ID        IMAGE               COMMAND             CREATED             STATUS              PORTS               NAMES

# Emit the URL string output variable.
emit url_to_fetch https://pfda-production-static-files.s3.amazonaws.com/tuts/cli/pfda-linux-2.2.tar.gz

# Emit the inspection results file.
emit inspection_results inspection_results.txt
```

# Utility Apps: *untar_files*, *tar_files_from_manifest*

We are going to use the pfda_cli_2.2.tar asset to create two very useful apps that demonstrate a design pattern that is readily extensible. The precisionFDA app I/O specification supports scalar and file and output types but does not support arrays for input or output variables. This means that using the app input variable and output emit framework, you cannot create apps with an arbitrary number of inputs or outputs. For instance, you really can't even create an untar app due to this limitation. These two apps demonstrate a design pattern to overcome this limitation.

<div style=""display: grid; grid-template-columns: 1fr 1fr; gap: 16px;"">
  <div>
    Note that these apps require a temporary authorization key that you'll use with the CLI and this key will appear in the execution log files. Thus you should only run this app in your My Home or Private Space contexts so as not to expose the key to other users.
  </div>
  <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image4.png"" />
</div>


## tar_files_from_manifest: Tar a manifest of fileIDs

From My Home / Files, select the detail page and copy the file ID for a
number of files. Create and upload *manifest.txt* with the list to be
incorporated into an archive file (e.g.):

```bash
cat manifest.txt
file-GJv1zKj0Kj2vzFP4Gg475ZyX-1
file-GJv1zKj0Kj2XY800GgJY2f4G-1
file-GJv1zKQ0Kj2vfZkVFxp436B9-1

key=""...""
pfda upload-file -key $key -file manifest.txt
```

Create a *tar_files_from_manifest* app titled ""Tar a manifest of
fileIDs"", with the following I/O Spec.

<table>
<thead>
  <tr>
    <th>Class</th>
    <th>Input Name</th>
    <th>Label</th>
    <th>Default Value</th>
  </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>manifest</td>
    <td>Text manifest of fileIDs</td>
    <td>manifext.file</td>
  </tr>
  <tr>
    <td>string</td>
    <td>tar_filename</td>
    <td>Filename for tar archive</td>
    <td>archive.tar</td>
  </tr>
  <tr>
    <td>string</td>
    <td>pfda_key</td>
    <td>Token for pfda CLI</td>
    <td></td>
  </tr>
  <thead>
    <tr>
      <th>Class</th>
      <th>Output Name</th>
      <th>Label</th>
      <th></th>
    </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>tarfile</td>
    <td>Tar archive file</td>
    <td></td>
  </tr>
</table>

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image29.png)

Setup the VM environment with internet access enabled, Baseline 2
default instance type, and select the pfda_cli_2.2 asset. Note that it
can take minutes for the list of assets to loaded before they can be
searched.

Enter the following Script:

```bash
set -euxo pipefail
echo ""$pfda_key""
echo ""$tar_filename""
echo ""$manifest""
sudo apt-get update
sudo apt-get install -y dos2unix
pfda -version
mkdir temp
cd temp
dos2unix ""$manifest_path""
for file in $(cat ""$manifest_path""); do pfda download -key ""$pfda_key"" -file-id $file; done
cd ..
tar cvf ""$tar_filename"" temp/*
emit ""tarfile"" ""$tar_filename""
```

Enter a Readme and Create the app.
```
Input a manifest file containing a list of fileIDs and the name of tar archive file. You\'ll need to provide a temporary authorization key for use with the pfda CLI and thus you should only run this app in your My Home or Private Spaces.
```
Run the app with the default inputs and a fresh authorization token for
the pfda CLI and observe the new archive.tar file.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image30.png)

When the execution is done, download the archive.tar file to verify its
contents.
```
tar tvf archive.tar
-rw-r--r-- root/root        15 2022-11-29 02:22 temp/foo.txt
-rw-r--r-- root/root        15 2022-11-29 02:22 temp/foo2.txt
-rw-r--r-- root/root        15 2022-11-29 02:22 temp/foo3.txt
```

## untar_files: Untar archive to files

Create a *untar_files* app titled ""Untar archive to files"", with the
following I/O Spec.

<table>
    <tr>
        <th>Class</th>
        <th>Input Name</th>
        <th>Label</th>
        <th>Default Value</th>
    </tr>
    <tr>
        <td>string</td>
        <td>pfda_key</td>
        <td>CLI access authorization token</td>
        <td></td>
    </tr>
    <tr>
        <td>file</td>
        <td>input_tarfile</td>
        <td>Tar file to extract</td>
        <td>archive.tar</td>
    </tr>
    <tr>
        <td>string</td>
        <td>extracted_list_f ilename</td>
        <td>List of extracted files</td>
        <td>extracted_list.txt</td>
    </tr>
    <thead>
      <tr>
        <th>Class</th>
        <th>Output Name</th>
        <th>Label</th>
        <th></th>
      </tr>
    </thead>
    <tr>
      <td>file</td>
      <td>tarfile_contents</td>
      <td>Listing of the tarfile contents</td>
      <td></td>
    </tr>
</table>

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image31.png)

Setup the VM environment with internet access enabled, Baseline 2
default instance type, and select the pfda_cli_2.2 asset. Note that it
can take minutes for the list of assets to loaded before they can be
searched.

Enter the following Script:
```bash
set -euxo pipefail
echo ""$pfda_key""
echo ""$input_tarfile""
echo ""$extracted_list_filename""
pfda -version
tar tvf ""$input_tarfile_path"" > ""$extracted_list_filename""
mkdir temp
tar xvf ""$input_tarfile_path"" --directory temp
ls temp
for FILE in $(find ./temp -type f -print); do echo $FILE; done
emit ""tarfile_contents"" ""$extracted_list_filename""
```

Enter a Readme and Create the app.
```
Input a tar archive file to extract into individual files. You'll need to provide a temporary authorization key for use with the pfda CLI and thus you should only run this app in your My Home or Private Spaces.
```
Run the app with the default inputs and a fresh authorization token for the pfda CLI.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image32.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image33.png)

When the execution is done, observe the new extracted files, and open the extracted_list.txt file to see the list of extracted files.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image32.png)

# First Workflow: *simple_workflow*

Workflows enable you to string together multiple stages, each running on its own instance type and its own app(s). Outputs from a given stage can be routed to inputs in the next stage. We will create this workflow using the web interface. In My Home / Workflows, click Create Workflow and name it *simple_workflow* with a title ""Workflow example with two single-app stages"".

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image34.png)

## Add the workflow stages

Click the Add Stage button and add the private workflow_layout_inspection app to the stage.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image35.png)

Add a second stage with the public url-fetcher app.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image36.png)

## Configure the workflow stages

Now we are ready to configure the stages.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image37.png"" width=""400"">

Click on Stage 1 to display the worker_layout_inspection app's configuration; we will accept all the default values provided in the app so there is nothing more that needs to be configured for Stage 1.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image38.png"" width=""200"">

Click on Stage 2 to display the url-fetcher app's configuration. Note the red color indicating that a required input has not been specified either from the output of a previous stage, or explicitly in the workflow stage's input spec.

Click the <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image39.png"" width=""30""> button to enter an explicit URL for this Stage.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image40.png"" width=""300"">

However, we want to specify this input field using the output field from the previous stage by clicking on the <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image41.png"" width=""30""> button.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image42.png"" width=""300"">

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image43.png"" width=""300"">

Select the Baseline 2 instance type to override the app default value and close the stage. Note that everything is green now and ready to Create.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image44.png"" width=""500"">

You can view the two stages of your new workflow.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image45.png"" width=""500"">
<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image46.png"" width=""500"">

## Run the workflow

Click on the Run Workflow button, accept all the default values for the workflow_layout_inspection stage, but enter *pfdacl.tar.gz* in the *Rename into* field in the second stage, then run the workflow.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image47.png"" width=""500"">
<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image48.png"" width=""500"">

In the Executions tab, expand the simple_workflow listing to see the two executions associated with the workflow.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image49.png)

Once the stages have completed, the workflow is done and we can open the executions associated with the stages and inspect the logs. Also note the inspection results file from stage 1 and the renamed fetched URL file from stage 2.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image50.png)

# Second Workflow: *workflow_from_wdl*

Importing a workflow from a WDL specification based on Docker images provides convenient way to express precisionFDA workflows in a serialized textual format. From My Home / Workflows, click the Create Workflow button, and click the Import from \*.wdl file button. Enter the following WDL in the text input and click Import.

```
version 1.0

workflow bwa {
  Int threads
  Int min_seed_length
  Int min_std_max_min
  File reference
  File reads

  call bwa_mem_tool {
    threads = threads,
    min_seed_length = min_seed_length,
    min_std_max_min = min_std_max_min,
    reference = reference,
    reads = reads
  }
}

task bwa_mem_tool {
  Int threads
  Int min_seed_length
  Int min_std_max_min
  File reference
  File reads
  
  command {
    bwa mem -t ${threads} \
    -k ${min_seed_length} \
    -I ${sep=',' min_std_max_min+} \
    ${reference} \
    ${sep=' ' reads+} > output.sam
  }

  output {
    File sam = ""output.sam""
  }

  runtime {
    docker: ""broadinstitute/baseimg""
  }
}
```

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image51.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image52.png)

Like workflows created through the web inteface, WDL-based workflows can be updated and run.

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image53.png"" width=""300"">

<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image54.png"" width=""400"">

# Database App: *worker_database*

This app demonstrates the use of a precisionFDA Database cluster (PostgreSQL), a convenient and very power resource for RDBMS-based analytics. You will need to be authorized for DB Clusters in order to create the database for this app.

## Create the Database

Select the Databases tab in My Home and click the Create Database button.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image55.png)

Create a ""Workstations and Databases Tutorial"" database, ""password"", PostgreSQL 11.16 on the smallest available database instance type, and click the Submit button.


<img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image56.png"" width=""500"">

Refresh the database status using the <img src=""https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image25.png"" width=""30""> button until the database is available.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image57.png)

## Fork *workstation_layout_inspection* to *workstation_database* app 

Using My Home / Apps, select the worker_layout_inspection app and select Fork. Name the new application *worker_database* titled ""Database cluster app example"".

### Specify the I/O Spec

Update the I/O spec to the following:

<table>
  <thead>
    <tr>
      <th>Class</th>
      <th>Input Name</th>
      <th>Label</th>
      <th>Default Value</th>
    </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>observation data</td>
    <td>Delimited data file for ETL into OBSERVATION table.</td>
    <td>observations.txt</td>
  </tr>
  <tr>
    <td>file</td>
    <td>patient_data</td>
    <td>Delimited data file for ETL into PATIENT table.</td>
    <td>patients.txt</td>
  </tr>
  <tr>
    <td>file</td>
    <td>db_and_table_ddl</td>
    <td>Database and table creation DDL</td>
    <td></td>
  </tr>
  <tr>
    <td>file</td>
    <td>query_sql</td>
    <td>Query to run</td>
    <td>(optional)</td>
  </tr>
  <tr>
    <td>string</td>
    <td>db endpoint url</td>
    <td>Database host endpoint</td>
    <td></td>
  </tr>
  <tr>
    <td>string</td>
    <td>query_results_: fi lename</td>
    <td>Query results file name</td>
    <td>query_results.txt</td>
  </tr>
  <thead>
    <tr>
      <th>Class</th>
      <th>Output Name</th>
      <th>Label</th>
      <th></th>
    </tr>
  </thead>
  <tr>
    <td>file</td>
    <td>query_results</td>
    <td>SQL query results</td>
    <td></td>
  </tr>
</table>

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image58.png)

### Specify the VM Environment

Update the VM environment to keep internet access enabled, Baseline 2 default instance type, and remove the pfda_cli_2.2 and ubuntu_asset assets so that only the worker_layout_inspection asset remains. Note that it can take minutes for the list of assets to loaded before they can be searched.

### Specify the Script

Enter the following Script:
```bash
set -euxo pipefail

# Install postgres client
sudo apt install -y postgresql-client
psql --version

# Inspect the database and table DDL
# and the three data files for ETL.
# Two data files specified as inputs.
#cat ""$db_and_table_ddl_path""
#cat ""$observation_data_path""
#cat ""$patient_data_path""

# Third data file installed with the worker_layout_inspection asset
#cat /work/countries.txt

# Connect to the DB cluster and list the databases
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d postgres -c '\l'

# Create the apps_and_workflows_tutorial_db and three tables
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d postgres -f ""$db_and_table_ddl_path""
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c '\d'

# ETL the OBSERVATION table data
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""\copy public.\""OBSERVATION\"" from '/work/in/observation_data/observations.txt' delimiter '|' NULL ''""

PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""select * from public.\""OBSERVATION\""""

# ETL the PATIENT table data
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""\copy public.\""PATIENT\"" from '/work/in/patient_data/patients.txt' delimiter '|' NULL ''""

PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""select * from public.\""PATIENT\""""

# ETL the COUNTRY table data
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""\copy public.\""COUNTRY\"" from '/work/countries.txt' delimiter '|' NULL ''""

PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -c ""select * from public.\""COUNTRY\""""

# Run the specified query and put the results into the specified filename.
PGPASSWORD=""password"" psql -h ""$db_host_endpoint"" -U root -d apps_and_workflows_tutorial_db -f ""$query_sql_path"" | tee -a ""$query_results_filename""

emit ""query_results"" ""$query_results_filename""
```

### Specify the Readme

Enter a Readme and Create the app.
```
Connect to a database cluster and create a database and three tables. Load two tables with data provided as input files, and the third table with data from an asset. Present a specified query file and retrieve the query results file.
```
## Run the app

Click on the Workstations and Databases Tutorial database to open the detail page and copy the host endpoint URL.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image59.png)

Run the app providing the database host endpoint URL copied above, and leave the remaining inputs at their default values.

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image60.png)

![](https://pfda-production-static-files.s3.amazonaws.com/tuts/apps-media/image61.png)

## Inspect the execution logs

View the logs for the *Database cluster app example* execution using the Actions dropdown menu. Let's look at a condensed and annotated version of the log output to understand what our app just did.
```bash
# Install the assets and file inputs on the worker filesystem
Fetching asset worker_layout_inspection2.tar (file-GK1xxbQ0Kj2gBZjxF244F21k)
downloading file: file-GK2xgy80Kj2x48j54fXGqF1V to filesystem: /work/in/query_sql/query.sql
downloading file: file-GK1F6jj0Kj2gyF8jG8jPjGpV to filesystem: /work/in/patient_data/patients.txt
downloading file: file-GK2v2Zj0Kj2gk9GB1920BYP3 to filesystem: /work/in/db_and_table_ddl/apps_and_workflows_tutorial_DDL.sql
downloading file: file-GK1F6jQ0Kj2v90jxPV0ZBQ5G to filesystem: /work/in/observation_data/observations.txt

# Install postgres CLI client and display version
++ sudo apt install -y postgresql-client
++ psql --version
psql (PostgreSQL) 9.5.25

# Providing the password, connect to the specified host,
# user root, database postgres, and list the databases on the cluster.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d postgres -c '\l'
                                                List of databases
                  Name                  |  Owner   | Encoding |   Collate   |    Ctype    |   Access privileges   
----------------------------------------+----------+----------+-------------+-------------+-----------------------
 apps_and_workflows_tutorial_db         | root     | UTF8     | en_US.UTF-8 | en_US.UTF-8 | 
 postgres                               | root     | UTF8     | en_US.UTF-8 | en_US.UTF-8 | 
 rdsadmin                               | rdsadmin | UTF8     | en_US.UTF-8 | en_US.UTF-8 | rdsadmin=CTc/rdsadmin
 template0                              | rdsadmin | UTF8     | en_US.UTF-8 | en_US.UTF-8 | =c/rdsadmin          +
                                        |          |          |             |             | rdsadmin=CTc/rdsadmin

# Create a new database and tables
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d postgres -f /work/in/db_and_table_ddl/apps_and_workflows_tutorial_DDL.sql
 template1                              | root     | UTF8     | en_US.UTF-8 | en_US.UTF-8 | =c/root              +
                                        |          |          |             |             | root=CTc/root
 workstations_and_databases_tutorial_db | root     | UTF8     | en_US.UTF-8 | en_US.UTF-8 | 
(6 rows)

 pg_terminate_backend 
----------------------
(0 rows)

DROP DATABASE
CREATE DATABASE
You are now connected to database ""apps_and_workflows_tutorial_db"" as user ""root"".
CREATE TABLE
CREATE TABLE
CREATE TABLE

# Connect to the new database and list the new tables
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c '\d'
          List of relations
 Schema |    Name     | Type  | Owner 
--------+-------------+-------+-------
 public | COUNTRY     | table | root
 public | OBSERVATION | table | root
 public | PATIENT     | table | root
(3 rows)

# ETL the OBSERVATION table from the specified file.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c '\copy public.""OBSERVATION"" from '\''/work/in/observation_data/observations.txt'\'' delimiter '\''|'\'' NULL '\'''\'''
COPY 5
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c 'select * from public.""OBSERVATION""'
 observation_id | patient_id | observation_name |   loinc   | created_date 
----------------+------------+------------------+-----------+--------------
           9870 |      12345 | Annual check up  | 66678-4   | 2022-11-01
           9871 |      12345 | Emergency        | LG32756-5 | 2022-11-02
           9872 |      12346 | Clinic visit     | 66678-4   | 2022-11-03
           9873 |      12347 | Lab results      | 74418-5   | 2022-11-04
           9874 |      12347 | Post-op checkup  | 65375-8   | 2022-11-05
(5 rows)

# ETL the PATIENT table from the specified file.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c '\copy public.""PATIENT"" from '\''/work/in/patient_data/patients.txt'\'' delimiter '\''|'\'' NULL '\'''\'''
COPY 3
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c 'select * from public.""PATIENT""'
 patient_id |     name      | gender |  zip  | country_id | created_date 
------------+---------------+--------+-------+------------+--------------
      12345 | Fred Foobar   | M      | 94040 |       1001 | 2022-10-25
      12346 | Mary Merry    | F      | 94040 |       1002 | 2022-09-24
      12347 | Barney Rubble | M      | 94040 |       1003 | 2022-08-23
(3 rows)

# ETL the COUNTRY table as installed from the asset.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c '\copy public.""COUNTRY"" from '\''/work/countries.txt'\'' delimiter '\''|'\'' NULL '\'''\'''
COPY 3
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -c 'select * from public.""COUNTRY""'
 country_id | country_name 
------------+--------------
       1001 | USA
       1002 | CAN
       1003 | EU
(3 rows)

# Run the specified query.
++ PGPASSWORD=password
++ psql -h dbcluster-gk0b58j0kj2y4v1k899bq0x6.cluster-cqy4cenhebvb.us-east-1.rds.amazonaws.com -U root -d apps_and_workflows_tutorial_db -f /work/in/query_sql/query.sql
++ tee -a query_results.txt
Expanded display is on.
-[ RECORD 1 ]----+----------------
patient_id       | 12345
name             | Fred Foobar
gender           | M
zip              | 94040
country_id       | 1001
created_date     | 2022-10-25
observation_id   | 9870
observation_name | Annual check up
loinc            | 66678-4
created_date     | 2022-11-01
country_id       | 1001
country_name     | USA
-[ RECORD 2 ]----+----------------
patient_id       | 12345
name             | Fred Foobar
gender           | M
zip              | 94040
country_id       | 1001
created_date     | 2022-10-25
observation_id   | 9871
observation_name | Emergency
loinc            | LG32756-5
created_date     | 2022-11-02
country_id       | 1001
country_name     | USA
-[ RECORD 3 ]----+----------------
patient_id       | 12346
name             | Mary Merry
gender           | F
zip              | 94040
country_id       | 1002
created_date     | 2022-09-24
observation_id   | 9872
observation_name | Clinic visit
loinc            | 66678-4
created_date     | 2022-11-03
country_id       | 1002
country_name     | CAN
-[ RECORD 4 ]----+----------------
patient_id       | 12347
name             | Barney Rubble
gender           | M
zip              | 94040
country_id       | 1003
created_date     | 2022-08-23
observation_id   | 9873
observation_name | Lab results
loinc            | 74418-5
created_date     | 2022-11-04
country_id       | 1003
country_name     | EU
-[ RECORD 5 ]----+----------------
patient_id       | 12347
++ emit query_results query_results.txt
name             | Barney Rubble
gender           | M
zip              | 94040
country_id       | 1003
created_date     | 2022-08-23
observation_id   | 9874
observation_name | Post-op checkup
loinc            | 65375-8
created_date     | 2022-11-05
country_id       | 1003
country_name     | EU
```","Markdown"
"Assay","PMBio/MuDataSeurat","R/ReadH5MU.R",".R","10510","347","#' Read an .h5mu file and create a \code{\link{Seurat}} object.
#'
#' @param file Path to the .h5mu file.
#'
#' @return A \code{\link{Seurat}} object
#'
#' @export
ReadH5AD <- function(file) {
  # Connect to the the file
  h5 <- open_anndata(file)

  # Get metadata
  obs <- read_table(h5[[""obs""]])
  var <- read_table(h5[[""var""]])

  # X
  assay <- read_layers_to_assay(h5)

  # obsm
  obsm <- read_attr_m(h5, 'obs')

  # varm
  varm <- read_attr_m(h5, 'var')

  # obsp
  obsp <- read_attr_p(h5, 'obs')

  # If there are var pairs, there's no place to store it
  # in the Seurat object
  # var_pairs <- read_attr_p(h5, 'var')
  var_pairs_names <- c()
  if (""varp"" %in% names(h5))
    var_pairs_names <- names(h5[[""varp""]])
  if (!is.null(var_pairs_names) && !isFALSE(var_pairs_names) && length(var_pairs_names) > 0)
    missing_on_read(""/varp"", ""pairwise annotation of variables"")

  # Create a Seurat object
  # If read from .h5mu modality, give an assay name
  path_fragments <- strsplit(file, ""\\.h5mu"")[[1]]
  if (length(path_fragments) == 2) {
    mod_path_fragments <- strsplit(path_fragments[2], ""\\/"")[[1]]
    assay_name <- mod_path_fragments[length(mod_path_fragments)]
    srt <- Seurat::CreateSeuratObject(assay, assay = assay_name)
  } else {
    srt <- Seurat::CreateSeuratObject(assay)
  }

  # Specify highly variable features
  if (""highly_variable"" %in% colnames(var)) {
    if (is.logical(var$highly_variable)) {
      srt@assays[[1]]@var.features <- rownames(srt)[var$highly_variable]
    }
  }

  # Add metadata
  meta_data_names <- rownames(srt@meta.data)
  srt@meta.data <- cbind.data.frame(obs, srt@meta.data)
  rownames(srt@meta.data) <- meta_data_names

  # Add feature metadata
  meta_features_names <- rownames(srt@assays[[1]]@meta.features)
  srt@assays[[1]]@meta.features <- cbind.data.frame(var, srt@assays[[1]]@meta.features)
  rownames(srt@assays[[1]]@meta.features) <- meta_features_names

  # Add embeddings
  for (emb in names(obsm)) {
    emb_name <- gsub('X_', '', emb)

    maybe_loadings <- matrix()
    if (emb %in% names(OBSM2VARM)) {
      varm_key = OBSM2VARM[[emb]]
      if (varm_key %in% names(varm)) {
        maybe_loadings <- varm[[varm_key]]
      }
    }

    emb_stdev <- numeric()
    if (""uns"" %in% names(h5)) {
      if (emb_name %in% names(h5[[""uns""]])) {
        if (""variance"" %in% names(h5[[""uns""]][[emb_name]])) {
          emb_stdev <- sqrt(h5[[""uns""]][[emb_name]][[""variance""]]$read())
        }
      }
    }

    srt[[emb_name]] <- Seurat::CreateDimReducObject(
      embeddings = obsm[[emb]][rownames(obs),,drop=FALSE],
      loadings = maybe_loadings,
      key = paste0(emb_name, ""_""),
      assay = Seurat::DefaultAssay(srt),
      stdev = emb_stdev
    )
  }

  # Add graphs
  srt@graphs <- lapply(obsp, Seurat::as.Graph)

  # Close the connection
  h5$close()

  srt
}

#' Create a \code{Seurat} object from .h5mu file contents
#'
#' @param file Path to the .h5mu file
#'
#' @import hdf5r Matrix Seurat
#' @importFrom utils hasName
#'
#' @return A \code{Seurat} object
#' '
#' @export ReadH5MU
ReadH5MU <- function(file) {
  # Connect to the the file
  h5 <- open_and_check_mudata(file)

  # Get assays (modalities)
  assays <- h5[[""mod""]]$names
  if (""mod-order"" %in% names(h5attributes(h5[[""mod""]]))) {
    modorder <- h5attributes(h5[[""mod""]])$`mod-order`
    if (all(assays %in% modorder)) {
      modorder <- modorder[modorder %in% assays]
      if (!any(duplicated(modorder))) {
        assays <- modorder
      }
    }
  }

  # Get global metadata
  metadata <- read_table(h5[[""obs""]])

  # NOTE: there's no global feature metadata in the Seurat object
  ft_metadata <- tryCatch({
      read_table(h5[[""var""]])
    },
    error = function(err) {
      warning(err)
      read_table(h5[[""var""]], set_index = FALSE)
    }
  )

  if (ncol(ft_metadata) > 0)
    missing_on_read(""/var"", paste0(""global variables metadata ("", paste(colnames(ft_metadata), collapse = "", ""), "")""))

  # Get (multimodal) embeddings
  embeddings <- read_attr_m(h5, 'obs', rownames(metadata))
  # If obs->mod mappings are in the file, dismiss them
  embeddings <- embeddings[!names(embeddings) %in% assays]

  # Get (multimodal) loadings
  # NOTE: features can be set as row names only if they are unique
  loadings <- read_attr_m(h5, 'var', rownames(ft_metadata))

  # Get obs pairs
  obs_pairs <- read_attr_p(h5, 'obs')

  # If there are var pairs, there's no place to store it
  # in the Seurat object
  var_pairs_names <- c()
  if (""varp"" %in% names(h5))
    var_pairs_names <- names(h5[[""varp""]])
  if (!is.null(var_pairs_names) && !isFALSE(var_pairs_names) && length(var_pairs_names) > 0)
    missing_on_read(""/varp"", ""pairwise annotation of variables"")

  # mod/.../X, raw, and layers
  modalities <- lapply(assays, function(mod) {
   read_layers_to_assay(h5[['mod']][[mod]], mod)
  })
  names(modalities) <- assays

  # mod/.../obs
  mod_obs <- lapply(assays, function(mod) {
    read_table(h5[['mod']][[mod]][['obs']])
  })
  names(mod_obs) <- assays

  # mod/.../obsm
  mod_obsm <- lapply(assays, function(mod) {
    read_attr_m(h5[['mod']][[mod]], 'obs')
  })
  names(mod_obsm) <- assays

  # mod/.../varm
  mod_varm <- lapply(assays, function(mod) {
    read_attr_m(h5[['mod']][[mod]], 'var')
  })
  names(mod_varm) <- assays

  # mod/.../obsp
  mod_obsp <- lapply(assays, function(mod) {
    read_attr_p(h5[['mod']][[mod]], 'obs')
  })
  names(mod_obsp) <- assays

  # If there are var pairs in individual modalities,
  # there's no place to store it in the Seurat object.
  for (mod in assays) {
    if (""varp"" %in% names(h5[['mod']][[mod]])) {
      if (length(h5[['mod']][[mod]][['varp']]) > 0) {
        missing_on_read(paste0(""/mod"", mod, ""/varp""), ""pairwise annotation of variables"")
      }
    }
  }

  var_pairs_names <- c()
  if (""varp"" %in% names(h5))
    var_pairs_names <- names(h5[[""varp""]])
  if (!is.null(var_pairs_names) && !isFALSE(var_pairs_names) && length(var_pairs_names) > 0)
    missing_on_read(""/varp"", ""pairwise annotation of variables"")

  # Only common observations can be read
  obs_names <- Reduce(intersect, lapply(modalities, colnames))
  mods_n_obs <- unique(vapply(modalities, ncol, 1))
  if (length(mods_n_obs) > 1 || mods_n_obs[1] != length(obs_names)) {
    warning(""Only the intersection of observations (samples) is loaded. Observations that are not present in all the modalities (assays) are discarded."")
  }

  # Create a Seurat object
  srt <- Seurat::CreateSeuratObject(subset(modalities[[1]], cells = obs_names), assay = names(modalities)[1])
  for (modality in names(modalities)[2:length(modalities)]) {
    srt[[modality]] <- subset(modalities[[modality]], cells = obs_names)
  }

  # Metadata, features metadata, and variable features
  for (modality in names(modalities)) {
    # Append modality metadata
    srt@meta.data <- cbind.data.frame(srt@meta.data, mod_obs[[modality]][obs_names,])

    metafeatures <- srt[[modality]]@meta.features

    # Specify highly variable features
    if (""highly_variable"" %in% colnames(metafeatures)) {
      if (is.logical(metafeatures$highly_variable)) {
        srt[[modality]]@var.features <- rownames(metafeatures)[metafeatures$highly_variable]
      }
    }
  }

  # Add joint embeddings
  for (emb in names(embeddings)) {
    emb_name <- toupper(gsub('X_', '', emb))

    maybe_loadings <- matrix()
    varm_key <- emb_name
    if (emb %in% names(OBSM2VARM)) {
      varm_key <- OBSM2VARM[[emb]]
    }
    if (hasName(loadings, varm_key)) {
      maybe_loadings <- loadings[[varm_key]]
    }

    emb_stdev <- numeric()
    if (""uns"" %in% names(h5)) {
      if (emb_name %in% names(h5[[""uns""]])) {
        if (""variance"" %in% names(h5[[""uns""]][[emb_name]])) {
          emb_stdev <- sqrt(h5[[""uns""]][[emb_name]][[""variance""]]$read())
        }
      }
    }

    srt[[emb_name]] <- Seurat::CreateDimReducObject(
      embeddings = embeddings[[emb]][obs_names,,drop=FALSE],
      loadings = maybe_loadings,
      key = paste0(emb_name, ""_""),
      stdev = emb_stdev,
      assay = Seurat::DefaultAssay(srt),  # this is not true but an existing assay must be provided
    )
  }

  # Do embeddings across modalities have unique names?
  # If each modality has e.g. X_pca, we will need to harmonise the names
  # by prepending modality name: e.g. RNAPCA.
  unique_emb <- FALSE
  all_embeddings <- c(names(embeddings), unlist(lapply(mod_obsm, function(obsm) names(obsm))))
  if (!any(duplicated(all_embeddings))) {
    unique_emb <- TRUE
  }

  # Add modality-specific embeddings
  for (mod in names(mod_obsm)) {
    mod_embeddings <- mod_obsm[[mod]]
    for (emb in names(mod_embeddings)) {
      emb_name <- gsub('X_', '', emb)
      if (!startsWith(emb_name, mod))
        emb_name <- toupper(emb_name)
      modemb_name <- emb_name
      if (!unique_emb)
        modemb_name <- paste(mod, emb_name, sep = """")
      # Embeddings keys will have to follow the format alphanumericcharacters_, e.g. RNAPCA_.

      maybe_loadings <- matrix()
      varm_key <- emb_name
      if (emb %in% names(OBSM2VARM))
        varm_key = OBSM2VARM[[emb]]
      if (hasName(mod_varm[[mod]], varm_key))
        maybe_loadings <- mod_varm[[mod]][[varm_key]]

      emb_stdev <- numeric()
      h5_mod <- h5[[""mod""]][[mod]]
      if (""uns"" %in% names(h5_mod)) {
        if (emb_name %in% names(h5_mod[[""uns""]])) {
          if (""variance"" %in% names(h5_mod[[""uns""]][[emb_name]])) {
            emb_stdev <- sqrt(h5_mod[[""uns""]][[emb_name]][[""variance""]]$read())
          }
        }
      }

      srt[[modemb_name]] <- Seurat::CreateDimReducObject(
        embeddings = mod_embeddings[[emb]][obs_names,,drop=FALSE],
        loadings = maybe_loadings,
        key = paste0(modemb_name, ""_""),
        stdev = emb_stdev,
        assay = mod,
      )
    }
  }

  # Add graphs

  # Only take into account common observations
  if (length(obs_pairs) > 0) {
    srt@graphs <- lapply(obs_pairs, function(graph) {
      graph[obs_names,obs_names,drop=FALSE]
    })
    names(srt@graphs) <- names(obs_pairs)
  }

  for (mod in names(mod_obsp)) {
    for (graph in names(mod_obsp[[mod]])) {
      graph_name <- graph
      # /mod/RNA/obsp/distances -> @graphs$RNA_distances
      if (graph %in% names(srt@graphs)) {
        graph_name <- paste(mod, graph, sep = ""_"")
      }
      srt@graphs[[graph_name]] <- Seurat::as.Graph(mod_obsp[[mod]][[graph]][obs_names, obs_names])
      srt@graphs[[graph_name]]@assay.used <- mod
    }
  }


  # Close the connection
  h5$close_all()

  srt
}
","R"
"Assay","PMBio/MuDataSeurat","R/WriteH5MU.R",".R","15107","433","#' @rdname WriteH5MU
setGeneric(""WriteH5MU"", function(object, file, overwrite = TRUE) standardGeneric(""WriteH5MU""))

#' @rdname WriteH5AD
setGeneric(""WriteH5AD"", function(object, file, assay = NULL, overwrite = TRUE) standardGeneric(""WriteH5AD""))

#' A helper function to write a modality (an assay) to an .h5mu file
#'
#' @keywords internal
#'
#' @import hdf5r methods
#' @importFrom Matrix t
WriteH5ADHelper <- function(object, assay, root, global = FALSE) {
  mod_object <- Seurat::GetAssay(object, assay)

  # .obs
  obs_names <- colnames(object)
  # There is no local metadata in Seurat objects
  if (global) {
    obs <- object@meta.data
  } else {
    obs <- data.frame(row.names = obs_names)
  }
  write_data_frame(root, ""obs"", obs)

  # .var
  var <- mod_object@meta.features
  var_names <- rownames(mod_object@meta.features)

  # Define highly variable features, if any
  if ('var.features' %in% slotNames(mod_object)) {
    if (length(mod_object@var.features) > 0) {
      var$highly_variable <- rownames(var) %in% mod_object@var.features
      message(""Added .var['highly_variable'] with highly variable features"")
    }
  }

  write_data_frame(root, ""var"", var)

  # .X, .layers['counts']. .raw.X
  # Assumptions:
  #   1. counts/data/scale.data -> X
  #   3. counts & data -> layers['counts'], X
  #   2. data & scale.data -> layers['data'], X
  #   4. counts & scale.data -> layers['counts'], X
  #   5. counts & data & scale.data -> layers['counts'], layers['data'], X

  x_names <- list(""counts"", ""data"", ""scale.data"")

  x <- lapply(x_names, function(x_name) {
    x <- NULL
    if (x_name %in% slotNames(mod_object)) {
      x <- Seurat::GetAssayData(mod_object, x_name)
      if (nrow(x) == 0 || ncol(x) == 0)
        x <- NULL
    }
    x
  })
  names(x) <- x_names

  if (!any(vapply(x, is.null, TRUE))) {
    # 5
    layers_group <- root$create_group(""layers"")
    write_matrix(layers_group, ""counts"", x[[""counts""]])
    write_matrix(layers_group, ""data"", x[[""data""]])
    write_matrix(root, ""X"", x[[""scale.data""]])
  } else if (!is.null(x[[""counts""]]) && !is.null(x[[""scale.data""]])) {
    # 4
    layers_group <- root$create_group(""layers"")
    write_matrix(layers_group, ""counts"", x[[""counts""]])
    write_matrix(root, ""X"", x[[""scale.data""]])
  } else if (!is.null(x[[""data""]]) && !is.null(x[[""scale.data""]])) {
    # 3
    layers_group <- root$create_group(""layers"")
    write_matrix(layers_group, ""data"", x[[""data""]])
    write_matrix(root, ""X"", x[[""scale.data""]])
  } else if (!is.null(x[[""counts""]]) && !is.null(x[[""data""]])) {
    # 2
    layers_group <- root$create_group(""layers"")
    write_matrix(layers_group, ""counts"", x[[""counts""]])
    write_matrix(root, ""X"", x[[""data""]])
  } else {
    which_x <- which(!is.null(x))
    write_matrix(root, ""X"", x[[which_x]])
  }

  uns_group <- root$create_group(""uns"")

  # reductions -> .obsm
  if ('reductions' %in% slotNames(object)) {
    obsm_group <- root$create_group(""obsm"")
    varm_group <- root$create_group(""varm"")
    for (red_name in names(object@reductions)) {
      red <- object@reductions[[red_name]]
      emb <- t(red@cell.embeddings)
      emb_assay <- red@assay.used
      loadings <- red@feature.loadings

      modality_specific <- FALSE
      # Modality-specific reductions can be identified with all their feature names
      # coming from the @assay.used.
      if (!modality_specific) {
        if (!is.null(loadings) && ncol(loadings) == ncol(red)) {
          if (all(rownames(loadings) %in% var_names)) {
            modality_specific <- TRUE
          }
        }
      }

      # Modality-specific reductions in Seurat objects
      # can also start with modality name by the convention used in this package.
      # Multimodal reductions also have the @assay.used set because this is enforced
      # by the current Seurat package.
      if (!is.null(emb_assay) && emb_assay != """" && emb_assay == assay) {
        # Only count reduction as modality-specific
        # if its name can be found in the reduction name or reduction key.
        # This is required since Seurat does require having an existing modality
        # in assay.used, which complicates loading multimodal embeddings.
        # The latter are currently loaded with the default assay set as assay.used.
        if (grepl(tolower(emb_assay), tolower(red_name)) || grepl(tolower(emb_assay), tolower(red@key))) {
          modality_specific <- TRUE
        }

        # Strip away modality name if the embedding starts with it
        if (emb_assay == substr(red_name, 1, length(emb_assay))) {
          red_name <- substr(red_name, length(emb_assay) + 1, length(red_name))
        }
      }

      if (!modality_specific) {
        next
      }

      write_matrix(obsm_group, paste0(""X_"", red_name), emb)

      # loadings -> .varm
      if (!is.null(loadings) && ncol(loadings) == ncol(red)) {
        varm_key <- red_name
        if (paste0(""X_"", red_name) %in% names(OBSM2VARM)) {
          varm_key = OBSM2VARM[[paste0(""X_"", red_name)]]
        }

        # If only a subset of features was used,
        # this has to be accounted for
        if (nrow(loadings) < nrow(var)) {
          warning(paste0(""Loadings for "", red_name, "" are computed only for a some features."",
            "" For it, an array with full var dimension will be recorded as it has to be match the var dimension of the data.""))
          all_loadings <- matrix(
            ncol = ncol(loadings),
            nrow = nrow(var)
          )
          rownames(all_loadings) <- rownames(var)
          all_loadings[rownames(loadings),] <- loadings
        } else {
          all_loadings <- loadings
        }

        write_matrix(varm_group, varm_key, t(all_loadings))
      }

      # stdev -> .uns[...]['variance']
      if (length(red@stdev) > 0) {
        if (!red_name %in% names(uns_group)) {
          uns_red <- uns_group$create_group(red_name)
          write_attribute(uns_red, ""encoding-type"", ""dict"")
          write_attribute(uns_red, ""encoding-version"", ""0.1.0"")
          write_matrix(uns_red, ""variance"", red@stdev^2)
        }
      }
    }
  }

  # graphs -> .obsp
  if ('graphs' %in% slotNames(object)) {
    obsp_group <- root$create_group(""obsp"")
    for (graph_name in names(object@graphs)) {
      graph <- object@graphs[[graph_name]]
      # Only write the graphs with the correct assay.used
      if ('assay.used' %in% slotNames(graph)) {
        if (length(graph@assay.used) > 0 && graph@assay.used == assay) {
          # Strip away modality name if the graph name starts with it:
          # RNA_distances -> distances
          if (assay == substr(graph_name, 1, nchar(graph@assay.used))) {
            graph_name <- substr(graph_name, nchar(graph@assay.used) + 1, nchar(graph_name))
            # Account for _, which is added by ReadH5AD / ReadH5MU
            if (substr(graph_name, 1, 1) == ""_"") {
              graph_name <- substr(graph_name, 2, nchar(graph_name))
            }
          }
          write_matrix(obsp_group, graph_name, graph)
        }
      }
    }
  }

  finalize_anndata_internal(root)

  TRUE
}

#' Write one assay to .h5ad
#'
#' This function writes the data of one of the assays (modalities) of a \code{Seurat} object into an .h5ad file.
#' The behavior of this function if NAs are present is undefined.
#'
#' The following slots are saved: count matrices (`@counts`, `@scale.data` and `@data`), `@metadata`, `@reductions`, `@feature.loadings`, `@graphs`.
#'
#' @param object \code{Seurat} object.
#' @param file Path to the .h5ad file.
#' @param assay Assay to write; can be omitted if there is a single assay in the object.
#' @param overwrite Boolean value to indicate if to overwrite the \code{file} if it exists (\code{TRUE} by default).
#'
#' @rdname WriteH5AD
#'
#' @import hdf5r
#'
#' @exportMethod WriteH5AD
setMethod(""WriteH5AD"", ""Seurat"", function(object, file, assay = NULL, overwrite = TRUE) {
  if (isFALSE(overwrite) && file.exists(file)) {
    stop(paste0(""File "", file, "" already exists. Use `overwrite = TRUE` to overwrite it or choose a different file name.""))
  }

  h5 <- open_h5(file)

  # When multiple modalities are present,
  # an assay has to be specified.
  # Do not default to Seurat::DefaultAssay(object)
  # as it is not explicit, is hard to reason about,
  # and does not mean anything for MuData.
  if (length(object@assays) > 1 && is.null(assay)) {
    h5$close()
    stop(paste0(
      ""An assay to be written has to be provided, one of: "",
      paste(names(object@assays), collapse = "", ""),
      "".\nUse WriteH5MU() to write all the modalities.""
    ))
  } else {
    assay <- names(object@assays)[1]
  }

  # ""Global"" attributes such as metadata have to be written
  WriteH5ADHelper(object, assay, h5, global = TRUE)

  finalize_anndata(h5)

  invisible(TRUE)
})

#' Create an .h5mu file with data from a \code{\link{Seurat}} object
#'
#' Save \code{\link{Seurat}} object to .h5mu file.
#' The behavior of this function if NAs are present is undefined.
#'
#' The following slots are saved: count matrices (`@counts`, `@scale.data` and `@data`), `@metadata`, `@reductions`, `@feature.loadings`, `@graphs`.
#'
#' @param object \code{Seurat} object.
#' @param file Path to the .h5mu file.
#' @param overwrite Boolean value to indicate if to overwrite the \code{file} if it exists (\code{TRUE} by default).
#'
#' @rdname WriteH5MU
#'
#' @import hdf5r methods
#'
#' @exportMethod WriteH5MU
setMethod(""WriteH5MU"", ""Seurat"", function(object, file, overwrite) {
  h5 <- open_h5(file)

  # .obs
  obs <- object@meta.data

  write_data_frame(h5, ""obs"", obs)

  modalities <- Seurat::Assays(object)

  h5mod <- h5$create_group(""mod"")
  h5mod$create_attr(""mod-order"", modalities)
  var_names <- lapply(modalities, function(mod) {
    mod_group <- h5$create_group(paste0(""mod/"", mod))

    WriteH5ADHelper(object, mod, mod_group)

    mod_object <- object[[mod]]
    rownames(mod_object)
  })
  names(var_names) <- modalities
  write_data_frame(h5, ""var"", do.call(c, var_names))

  uns_group <- h5$create_group(""uns"")

  # reductions -> .obsm
  # Reductions starting with modality name
  # that corresponds to the assay.used value
  # will be stored in .obsm slots of individual modalities:
  # RNAUMAP -> /mod/RNA/obsm/UMAP
  if ('reductions' %in% slotNames(object)) {
    for (red_name in names(object@reductions)) {
      red <- object@reductions[[red_name]]
      emb <- t(red@cell.embeddings)
      assay_emb <- red@assay.used
      loadings <- red@feature.loadings

      modality_specific <- FALSE
      # Modality-specific reductions can be identified with all their feature names
      # coming from the @assay.used.
      if (!modality_specific) {
        if (!is.null(loadings) && ncol(loadings) == ncol(red)) {
          if (all(rownames(loadings) %in% var_names[[assay_emb]])) {
            modality_specific <- TRUE
          }
        }
      }

      # Modality-specific reductions in Seurat objects
      # can also start with modality name by the convention used in this package.
      # Multimodal reductions also have the @assay.used set because this is enforced
      # by the current Seurat package.
      if (!is.null(assay_emb) && assay_emb != """" && assay_emb %in% modalities) {
        # Only count reduction as modality-specific
        # if its name can be found in the reduction name or reduction key.
        # This is required since Seurat does require having an existing modality
        # in assay.used, which complicates loading multimodal embeddings.
        # The latter are currently loaded with the default assay set as assay.used.
        if (grepl(tolower(assay_emb), tolower(red_name)) || grepl(tolower(assay_emb), tolower(red@key))) {
          modality_specific <- TRUE
        }

        # Strip away modality name if the embedding starts with it
        if (assay_emb == substr(red_name, 1, length(assay_emb))) {
          red_name <- substr(red_name, length(assay_emb) + 1, length(red_name))
        }
      }

      if (modality_specific) {
        next
      }

      if (!""obsm"" %in% names(h5)) {
        obsm <- h5$create_group(""obsm"")
      } else {
        obsm <- h5[[""obsm""]]
      }

      write_matrix(obsm, paste0(""X_"", red_name), emb)

      # loadings -> .varm
      if (!is.null(loadings) && ncol(loadings) == ncol(red)) {
        varm_key <- red_name
        if (paste0(""X_"", red_name) %in% names(OBSM2VARM)) {
          varm_key <- OBSM2VARM[[paste0(""X_"", red_name)]]
        }

        if (modality_specific) {
          # this should have been written with WriteH5ADHelper
          next
        }

        if (!""varm"" %in% names(h5)) {
          varm <- h5$create_group(""varm"")
        } else {
          varm <- h5[[""varm""]]
        }

        # If only a subset of features was used,
        # this has to be accounted for
        var_names_for_loadings <- do.call(c, var_names)

        if (nrow(loadings) < length(var_names_for_loadings)) {
          warning(paste0(""Loadings for "", red_name, "" are computed only for a some features."",
            "" For it, an array with full var dimension will be recorded as it has to be match the var dimension of the data.""))
          all_loadings <- matrix(
            ncol = ncol(loadings),
            nrow = length(var_names_for_loadings)
          )
          rownames(all_loadings) <- var_names_for_loadings
          all_loadings[rownames(loadings),] <- loadings
        } else {
          all_loadings <- loadings
        }

        write_matrix(varm, varm_key, t(all_loadings))
      }

      # stdev -> .uns[...]['variance']
      if (length(red@stdev) > 0) {
        if (modality_specific) {
          # REMOVE: this should have been written with WriteH5ADHelper
          if (!red_name %in% names(h5[[paste0(""mod/"", assay_emb, ""/uns"")]])) {
            uns <- h5$create_group(paste0(""mod/"", assay_emb, ""/uns/"", red_name))
            write_attribute(uns, ""encoding-type"", ""dict"")
            write_attribute(uns, ""encoding-version"", ""0.1.0"")
          } else {
            uns <- uns_group[[paste0(""mod/"", assay_emb, ""/uns/"", red_name)]]
          }
        } else {
          if (!red_name %in% names(uns_group)) {
            uns <- uns_group$create_group(red_name)
            write_attribute(uns, ""encoding-type"", ""dict"")
            write_attribute(uns, ""encoding-version"", ""0.1.0"")
          } else {
            uns <- uns_group[[red_name]]
          }
        }
        write_matrix(uns, ""variance"", red@stdev^2)
      }
    }
  }

  # graphs -> .obsp
  if ('graphs' %in% slotNames(object)) {
    obsp_group <- h5$create_group(""obsp"")
    for (graph_name in names(object@graphs)) {
      graph <- object@graphs[[graph_name]]

      # Only write the graphs with no (correct) assay.used
      graph_no_assay <- FALSE
      if (!'assay.used' %in% slotNames(graph)) {
        graph_no_assay <- TRUE
      } else {
        if (!graph@assay.used %in% modalities)
          graph_no_assay <- TRUE
      }

      if (graph_no_assay) {
        write_matrix(obsp_group, graph_name, graph)
      }
    }
  }

  finalize_mudata(h5)

  invisible(TRUE)
})
","R"
"Assay","PMBio/MuDataSeurat","R/ReadUtils.R",".R","10246","341","#' @importFrom hdf5r is_hdf5 H5File
open_and_check_mudata <- function(filename) {
    if (readChar(filename, 6) != ""MuData"") {
        if (is_hdf5(filename)) {
            warning(""The HDF5 file was not created by MuData tooling, we can't guarantee that everything will work correctly"", call.=FALSE)
        } else (
            stop(""The file is not an HDF5 file"", call.=FALSE)
        )
    }
    H5File$new(filename, mode=""r"")
}

#' @import hdf5r
open_anndata <- function(filename) {
  # PATH/filename.h5mu/mod/rna => read a single modality from the .h5mu file
  path_fragments <- strsplit(filename, ""\\.h5mu"")[[1]]
  if (length(path_fragments) == 1) {
    h5 <- H5File$new(filename, mode=""r"")
  } else {
    h5 <- H5File$new(paste0(path_fragments[1], "".h5mu""), mode=""r"")
    mod_path <- path_fragments[2]
    if (substr(mod_path, 1, 4) != ""/mod"") {
      mod_path <- paste0(""/mod"", mod_path)
    }
    h5 <- h5[[mod_path]]
  }
  h5
}

missing_on_read <- function(loc, desc = """") {
  details <- """"
  if (!is.null(desc) && desc != """") {
    details <- paste0(""Seurat does not support "", desc, ""."")
  }
  warning(paste0(""Missing on read: "", loc, "". "", details), call.=FALSE)
}

read_table_encv1 <- function(dataset, set_index = TRUE) {
  columns <- names(dataset)
  columns <- columns[columns != ""__categories""]

  col_list <- lapply(columns, function(name) {
    values <- dataset[[name]]$read()
    values_attr <- tryCatch({
      h5attributes(dataset[[name]])
    }, error = function(e) {
      list()
    })
    if (length(values_attr) > 0) {
      if (""categories"" %in% names(values_attr)) {
        # Make factors out of categorical data
        ref <- values_attr$categories
        values_labels <- ref$dereference(obj = NULL)[[1]]
        # NOTE: number of labels have to be strictly matching the number of unique integer values.
        values_notna <- unique(values)
        values_notna <- values_notna[!is.na(values_notna)]
        values <- factor(as.integer(values), labels = values_labels$read()[1:length(values_notna)])
      }
    }
    values
  })
  table <- data.frame(Reduce(cbind.data.frame, col_list))
  colnames(table) <- columns
  table
}

read_column <- function(column, etype, eversion) {
  values <- NULL
  if (etype == ""categorical"") {
    if (eversion == ""0.2.0"") {
      codes <- column[[""codes""]]$read()
      categories <- column[[""categories""]]$read()

      # NOTE: number of labels have to be strictly matching the number of unique integer values.
      codes_notna <- unique(codes)
      codes_notna <- codes_notna[!is.na(codes_notna)]

      values <- factor(as.integer(codes), labels = categories[1:length(codes_notna)])
    } else {
      warning(paste0(""Cannot recognise encoving-version "", eversion))
    }
  } else {
    values <- column$read()
  }
  # } else {
  #   stop(paste0(""Cannot recognise encoving-type "", etype))
  # }
  values
}

read_table_encv2 <- function(dataset, set_index = TRUE) {
  columns <- names(dataset)

  col_list <- lapply(columns, function(name) {

    col_attr <- tryCatch({
      h5attributes(dataset[[name]])
    }, error = function(e) {
      list(""encoding-type"" = NULL)
    })

    values <- read_column(dataset[[name]], col_attr$`encoding-type`, col_attr$`encoding-version`)

    values
  })
  table <- data.frame(Reduce(cbind.data.frame, col_list))
  colnames(table) <- columns
  table
}

read_table <- function(dataset, set_index = TRUE) {
  if (""H5Group"" %in% class(dataset)) {
    # Table is saved as a group rather than a dataset
    dataset_attr <- tryCatch({
      h5attributes(dataset)
    }, error = function(e) {
      list(""_index"" = ""_index"")
    })
    indexcol <- ""_index""
    if (""_index"" %in% names(dataset_attr)) {
      indexcol <- dataset_attr$`_index`
    }

    encv <- ""0.1.0""  # some encoding version by default
    if (""encoding-version"" %in% names(dataset_attr)) {
      encv <- dataset_attr$`encoding-version`
    }

    if (encv == ""0.1.0"") {
      table <- read_table_encv1(dataset, set_index)
    } else if (encv == ""0.2.0"") {
      table <- read_table_encv2(dataset, set_index)
    } else {
      stop(paste0(""Encoding version "", encv, "" is not recognised.""))
    }

    columns <- colnames(table)

    if ((indexcol %in% colnames(table)) && set_index) {
      rownames(table) <- table[,indexcol,drop=TRUE]
      table <- table[,!colnames(table) %in% c(indexcol),drop=FALSE]
    }

    # Fix column order
    if (""column-order"" %in% names(dataset_attr)) {
      ordered_columns <- dataset_attr[[""column-order""]]
      # Do not consider index as a column
      ordered_columns <- ordered_columns[ordered_columns != indexcol]
      table <- table[,ordered_columns[ordered_columns %in% columns],drop=FALSE]
    }
  } else {
    table <- dataset$read()
    dataset_attr <- h5attributes(dataset)

    indexcol <- ""_index""
    if (""_index"" %in% names(dataset_attr)) {
      indexcol <- dataset_attr$`_index`
    }

    if ((indexcol %in% colnames(table)) && set_index) {
      rownames(table) <- table[,indexcol,drop=TRUE]
      table <- table[,!colnames(table) %in% c(indexcol),drop=FALSE]
    }
  }
  table
}

#' @import Matrix
read_matrix <- function(dataset) {
  if (""data"" %in% names(dataset) && ""indices"" %in% names(dataset) && ""indptr"" %in% names(dataset)) {
      i <- dataset[[""indices""]]$read()
      p <- dataset[[""indptr""]]$read()
      x <- dataset[[""data""]]$read()

      rowwise <- FALSE
      if (""encoding-type"" %in% h5attr_names(dataset)) {
        rowwise <- h5attr(dataset, ""encoding-type"") == ""csr_matrix""
      }

      if (""shape"" %in% h5attr_names(dataset)) {
        X_dims <- h5attr(dataset, ""shape"")
      } else {
        X_dims <- c(length(p) - 1, max(i) + 1)
        if (rowwise) {
          X_dims <- rev(X_dims)
        }
      }

      if (rowwise)
          X <- Matrix::sparseMatrix(j=i, p=p, x=x, dims=X_dims, index1=FALSE)
      else
          X <- Matrix::sparseMatrix(i=i, p=p, x=x, dims=X_dims, index1=FALSE)

      Matrix::t(X)

    } else {
      dataset$read()
    }
}

#' @import Matrix
read_layers_to_assay <- function(root, modalityname="""") {
  X <- read_matrix(root[['X']])

  var <- read_table(root[['var']])
  if (any(grepl(""_"", rownames(var)))) {
    example_which <- grep(""_"", rownames(var))[1]
    example_before <- rownames(var)[example_which]
    rownames(var) <- gsub(""_"", ""-"", rownames(var))
    example_after <- rownames(var)[example_which]
    warning(paste0(""The var_names from modality "", modalityname, "" have been renamed as feature names cannot contain '_'."",
      "" E.g. "", example_before, "" -> "", example_after, "".""))
  }

  obs <- read_table(root[['obs']])
  if (is(""obs"", ""data.frame""))
    rownames(obs) <- paste(modalityname, rownames(obs), sep=""-"")

  colnames(X) <- rownames(obs)
  rownames(X) <- rownames(var)

  raw <- NULL
  if (""raw"" %in% names(root)) {
    raw <- root[['raw']]
    raw.X <- read_matrix(raw[['X']])
    raw.var <- read_table(raw[['var']])
    rownames(raw.X) <- rownames(raw.var)
    colnames(raw.X) <- colnames(X)
    if (nrow(raw.X) != nrow(X)) {
      warning(paste0(""Only a subset of mod/"", modalityname, ""/raw/X is loaded, variables (features) that are not present in mod/"", modalityname, ""/X are discarded.""))
      raw.X <- raw.X[rownames(X),]
    }
  }

  layers <- NULL
  custom_layers <- NULL
  if (""layers"" %in% names(root)) {
    layers <- lapply(root[['layers']]$names, function(layer_name) {
      layer <- read_matrix(root[['layers']][[layer_name]])
      rownames(layer) <- rownames(X)
      colnames(layer) <- colnames(X)
      layer
    })
    names(layers) <- root[['layers']]$names
    custom_layers <- names(layers)[!names(layers) %in% c(""counts"")]
    if (length(custom_layers) > 0) {
      missing_on_read(paste0(""some of mod/"", modalityname, ""/layers""), ""custom layers, unless labeled 'counts'"")
    }
  }

  # Assumptions:
  #   1. X -> counts
  #   2. raw & X -> data & scale.data
  #   3. layers['counts'] & X -> counts & data
  #   4. layers['counts'], raw, X -> counts, data, scale.data
  counts_as_layer <- !is.null(layers) && ""counts"" %in% names(layers)
  if (!counts_as_layer && is.null(raw)) {
    # 1
    assay <- Seurat::CreateAssayObject(counts = X)
  } else {
    if (!is.null(raw)) {
      if (counts_as_layer) {
        # 4
        assay <- Seurat::CreateAssayObject(counts = layers[['counts']])
        assay@data <- raw.X
        assay@scale.data <- X
      } else {
        # 2
        assay <- Seurat::CreateAssayObject(data = raw.X)
        assay@scale.data <- X
      }
    } else {
      # 3
      assay <- Seurat::CreateAssayObject(counts = layers[['counts']])
      assay@data <- X
    }
  }

  assay@meta.features <- var

  assay
}

read_attr_m <- function(root, attr_name, dim_names = NULL) {
  if (is.null(dim_names)) {
    attr_df <- read_table(root[[attr_name]])
    dim_names <- rownames(attr_df)
  }
  attrm_name <- paste0(attr_name, ""m"")

  attrm <- list()
  if (attrm_name %in% names(root)) {
    attrm <- lapply(names(root[[attrm_name]]), function(space) {
      dset <- root[[attrm_name]][[space]]
      if (dset$attr_exists(""encoding-type"") && h5attr(dset, ""encoding-type"") == ""dataframe"") {
        missing_on_read(paste0(root$get_obj_name(), attrm_name, ""/"", space), ""additional metadata dataframes"")
        mx <- NULL
      } else {
        mx <- t(read_matrix(dset))
        if (dim(mx)[1] == 1) {
          mx <- t(mx)
        }
        rownames(mx) <- dim_names
      }
      mx
    })

    names(attrm) <- names(root[[attrm_name]])
    attrm <- attrm[!sapply(attrm, is.null)]
  }

  attrm
}

#' @import Seurat methods
read_attr_p <- function(root, attr_name, dim_names = NULL) {
  if (is.null(dim_names)) {
    attr_df <- read_table(root[[attr_name]])
    dim_names <- rownames(attr_df)
  }
  attrp_name <- paste0(attr_name, ""p"")

  attrp <- list()
  if (attrp_name %in% names(root)) {
    attrp <- lapply(names(root[[attrp_name]]), function(graph) {
      mx <- read_matrix(root[[attrp_name]][[graph]])
      rownames(mx) <- dim_names
      colnames(mx) <- dim_names
      mx
    })

    names(attrp) <- names(root[[attrp_name]])
  }

  attrp
}

# For some common reductions,
# there are conventional names for the loadings slots
OBSM2VARM <- list(""X_pca"" = ""PCs"", ""X_mofa"" = ""LFs"")
","R"
"Assay","PMBio/MuDataSeurat","R/WriteUtils.R",".R","5254","146",".mudataversion <- ""0.1.0""
.anndataversion <- ""0.1.0""
.name <- paste0(getPackageName(), "".r"")
.version <- as.character(packageVersion(getPackageName()))

#' @import hdf5r
open_h5 <- function(filename) {
    h5p_create <- H5P_FILE_CREATE$new()
    h5p_create$set_userblock(512)
    H5File$new(filename, mode=""w"", file_create_pl=h5p_create)
}

#' @import hdf5r
finalize_mudata <- function(h5) {
    h5$create_attr(""encoding-type"", ""MuData"", space=H5S$new(""scalar""))
    h5$create_attr(""encoding-version"", .mudataversion, space=H5S$new(""scalar""))
    h5$create_attr(""encoder"", .name, space=H5S$new(""scalar""))
    h5$create_attr(""encoder-version"", .version, space=H5S$new(""scalar""))

    filename <- h5$get_filename()
    h5$close_all()
    h5 <- file(filename, ""r+b"")
    writeChar(paste0(""MuData (format-version="", .mudataversion, "";creator="", .name, "";creator-version="", .version, "")""), h5)
    close(h5)
}

#' @import hdf5r
finalize_anndata_internal <- function(h5) {
    h5$create_attr(""encoding-type"", ""anndata"", space=H5S$new(""scalar""))
    h5$create_attr(""encoding-version"", .anndataversion, space=H5S$new(""scalar""))
    h5$create_attr(""encoder"", .name, space=H5S$new(""scalar""))
    h5$create_attr(""encoder-version"", .version, space=H5S$new(""scalar""))
}

#' @import hdf5r
finalize_anndata <- function(h5, internal = FALSE) {
    if (internal) {
      finalize_anndata_internal(h5)
    }
    filename <- h5$get_filename()
    h5$close_all()
    h5 <- file(filename, ""r+b"")
    writeChar(paste0(""AnnData (format-version="", .mudataversion, "";creator="", .name, "";creator-version="", .version, "")""), h5)
    close(h5)
}

write_dataset <- function(parent, key, obj, scalar=FALSE) {
    dtype <- NULL
    space <- NULL
    if (is.character(obj)) {
      dtype <- H5T_STRING$new(type=""c"", size=Inf)
      dtype$set_cset(""UTF-8"")
    }
    if (scalar) {
        space <- H5S$new(""scalar"")
    }
    parent$create_dataset(key, obj, dtype=dtype, space=space)
}

write_attribute <- function(obj, name, value, scalar=TRUE) {
    dtype <- NULL
    space <- NULL
    if (is.character(value)) {
      dtype <- H5T_STRING$new(type=""c"", size=Inf)
      dtype$set_cset(""UTF-8"")
    }
    if (length(value) == 1 && scalar) {
        space <- H5S$new(""scalar"")
    }
    obj$create_attr(name, value, dtype=dtype,space=space)
}

write_matrix <- function(parent, key, mat) {
    if (is.matrix(mat) || is.vector(mat) || is.array(mat)) {
        hasna <- anyNA(mat)
        if (hasna && is.double(mat)) {
            # FIXME: extend anndata spec to handle double NAs?
            mat[is.na(mat)] <- NaN
            hasna <- FALSE
        }

        if (!hasna) {
            dset <- write_dataset(parent, key, mat)
            write_attribute(dset, ""encoding-type"", ifelse(is.character(mat), ""string-array"", ""array""))
            write_attribute(dset, ""encoding-version"", ""0.2.0"")
        } else {
            grp <- parent$create_group(key)
            write_matrix(grp, ""values"", mat)
            write_matrix(grp, ""mask"", is.na(mat))
            write_attribute(grp, ""encoding-type"", ifelse(is.logical(mat), ""nullable-boolean"", ""nullable-integer""))
            write_attribute(grp, ""encoding-version"", ""0.1.0"")
        }
    } else if (is.factor(mat)) {
        grp <- parent$create_group(key)
        codes <- as.integer(mat)
        codes[is.na(mat)] <- 0L
        write_matrix(grp, ""codes"", codes - 1L)
        write_matrix(grp, ""categories"", levels(mat))
        write_attribute(grp, ""ordered"", is.ordered(mat))
        write_attribute(grp, ""encoding-type"", ""categorical"")
        write_attribute(grp, ""encoding-version"", ""0.2.0"")
    } else if (is(mat, ""dgCMatrix"") || is(mat, ""dgRMatrix"")) {
        grp <- parent$create_group(key)
        write_dataset(grp, ""indptr"", mat@p)
        write_dataset(grp, ""data"", mat@x)
        write_attribute(grp, ""shape"", rev(dim(mat)))
        write_attribute(grp, ""encoding-version"", ""0.1.0"")
        if (is(mat, ""dgCMatrix"")) {
            write_dataset(grp, ""indices"", mat@i)
            write_attribute(grp, ""encoding-type"", ""csr_matrix"")
        } else {
            write_dataset(grp, ""indices"", mat@j)
            write_attribute(grp, ""encoding-type"", ""csc_matrix"")
        }
    } else {
        stop(""Writing matrices of type "", class(mat), "" is not implemented."")
    }
}

write_data_frame <- function(parent, key, attr_df) {
  grp <- parent$create_group(key)
  if (!is.data.frame(attr_df)) { # row names only. Creating a data.frame with duplicated row.names is not possible
      attr_df <- data.frame(""_index""=attr_df, check.names=FALSE)
      attr_columns <- character()
  } else {
      attr_columns <- colnames(attr_df)
      attr_df[""_index""] <- rownames(attr_df)
  }

  for (col in colnames(attr_df)) {
      write_matrix(grp, col, attr_df[[col]])
  }

  # Write attributes
  write_attribute(grp, ""_index"", ""_index"")
  write_attribute(grp, ""encoding-type"", ""dataframe"")
  write_attribute(grp, ""encoding-version"", ""0.2.0"")
  if (length(attr_columns) > 0) {
    write_attribute(grp, ""column-order"", attr_columns, scalar=FALSE)
  } else {
    # When there are no columns, null buffer can't be written to a file.
    grp$create_attr(""column-order"", dtype=h5types$H5T_NATIVE_DOUBLE, space=H5S$new(""simple"", 0, 0))
  }

}
","R"
"Assay","PMBio/MuDataSeurat","tests/testthat/test_sparse.R",".R","3220","111","context(""Creating .h5ad and .h5mu files with sparse matrices"")
library(Seurat)
library(MuDataSeurat)
library(Matrix)
library(hdf5r)
library(fs)  # for file_temp()

# csc
fileh5mu_r <- paste0(file_temp(), "".h5mu"")
fileh5ad_r <- paste0(file_temp(), "".h5ad"")
# csc
fileh5mu_c <- paste0(file_temp(), "".h5mu"")
fileh5ad_c <- paste0(file_temp(), "".h5ad"")


nobs <- 10
nvar <- 20
nvar2 <- 31

obs_names <- paste(""obs"", 1:nobs, sep=""-"")
var_names <- paste(""var"", 1:nvar, sep=""-"")

# Hard-coded value to inject
true_val <- 0.1234569
true_val_i <- 3
true_val_j <- 7

test_that(""dgCMatrix can be written to .h5ad"", {
    x <- rnbinom(n = nobs * nvar, prob = .95, size = 10)
    x <- Matrix(matrix(x, ncol = nobs), sparse = TRUE)  # => dgCMatrix
    x[true_val_i,true_val_j] <- true_val

    colnames(x) <- obs_names
    rownames(x) <- var_names

    srt <- CreateSeuratObject(counts = x)

    expect_true(WriteH5AD(srt, fileh5ad_c))

    h5 <- H5File$new(fileh5ad_c, mode=""a"")
    expect_equal(h5attr(h5[[""X""]], ""encoding-type""), ""csc_matrix"")
    h5$close()
})

test_that(""dgRMatrix can be written to .h5ad"", {
    # Seurat objects would not accept dgRMatrix,
    # and there is no native dgRMatrix -> dgCMatrix conversion.
    # We will write a transposed matrix as CSC and then change it to CSR manually
    # to emulate CSR matrices written by other tools.

    x <- rnbinom(n = nobs * nvar, prob = .95, size = 10)
    x <- x[x != 0]
    i <- sample(1:nvar, length(x), replace = T)
    j <- sample(1:nobs, length(x), replace = T)
    
    x_c <- sparseMatrix(i = i, j = j, x = x, dims = c(nvar, nobs), repr = ""C"")    # => dgCMatrix
    x_c[true_val_i,true_val_j] <- true_val

    x_r <- sparseMatrix(i = i, j = j, x = x, dims = c(nvar, nobs), repr = ""R"")  # => dgRMatrix
    # Value assignment will automatically convert it to dgTMatrix,
    # and there is no dgTMatrix -> dgRMatrix coercion,
    # so in this case we won't inject the value.
    # x_r[true_val_i,true_val_j] <- true_val

    colnames(x_c) <- obs_names
    rownames(x_c) <- var_names

    srt <- CreateSeuratObject(counts = x_c)

    expect_true(WriteH5AD(srt, fileh5ad_r))

    h5 <- H5File$new(fileh5ad_r, mode=""a"")
    h5x <- h5[[""X""]]
    expect_equal(h5attr(h5x, ""encoding-type""), ""csc_matrix"")

    h5x$link_delete(""indices"")
    h5x[[""indices""]] <- x_r@j
    h5x$link_delete(""indptr"")
    h5x[[""indptr""]] <- x_r@p
    h5x$link_delete(""data"")
    h5x[[""data""]] <- x_r@x
    h5attr(h5x, ""encoding-type"") <- ""csr_matrix""

    h5$close()
})

test_that(""dgCMatrix can be read from .h5ad"", {
    srt <- ReadH5AD(fileh5ad_c)

    counts <- srt@assays[[1]]@counts
    expect_true(""dgCMatrix"" %in% class(counts))
    expect_equal(dim(counts), c(nvar, nobs))
    expect_equal(counts[true_val_i, true_val_j], true_val)
    expect_equal(rownames(srt), var_names)
    expect_equal(colnames(srt), obs_names)
})

test_that(""dgRMatrix can be read from .h5ad"", {
    srt <- ReadH5AD(fileh5ad_r)
    counts <- srt@assays[[1]]@counts
    
    # Seurat only support dgCMatrix as counts
    expect_true(""dgCMatrix"" %in% class(counts))
    expect_equal(dim(counts), c(nvar, nobs))
    
    expect_equal(rownames(srt), var_names)
    expect_equal(colnames(srt), obs_names)
})


","R"
"Assay","HICAI-ZJU/iMoLD","exputils.py",".py","5518","177","import os
import re
import sys
import time
import json
import torch
import pickle
import random
import getpass
import logging
import argparse
import subprocess
import numpy as np
from datetime import timedelta, date, datetime


class LogFormatter:
    def __init__(self):
        self.start_time = time.time()

    def format(self, record):
        elapsed_seconds = round(record.created - self.start_time)

        prefix = ""%s - %s - %s"" % (
            record.levelname,
            time.strftime('%x %X'),
            timedelta(seconds=elapsed_seconds)
        )
        message = record.getMessage()
        message = message.replace('\n', '\n' + ' ' * (len(prefix) + 3))
        return ""%s - %s"" % (prefix, message) if message else ''


def create_logger(filepath, rank):
    """"""
    Create a logger.
    Use a different log file for each process.
    """"""
    # create log formatter
    log_formatter = LogFormatter()

    # create file handler and set level to debug
    if filepath is not None:
        if rank > 0:
            filepath = '%s-%i' % (filepath, rank)
        file_handler = logging.FileHandler(filepath, ""a"", encoding='utf-8')
        file_handler.setLevel(logging.DEBUG)
        file_handler.setFormatter(log_formatter)

    # create console handler and set level to info
    console_handler = logging.StreamHandler()
    console_handler.setLevel(logging.INFO)
    console_handler.setFormatter(log_formatter)

    # create logger and set level to debug
    logger = logging.getLogger()
    logger.handlers = []
    logger.setLevel(logging.DEBUG)
    logger.propagate = False
    if filepath is not None:
        logger.addHandler(file_handler)
    logger.addHandler(console_handler)

    # reset logger elapsed time
    def reset_time():
        log_formatter.start_time = time.time()

    logger.reset_time = reset_time

    return logger


def initialize_exp(params):
    """"""
    Initialize the experiment:
    - dump parameters
    - create a logger
    """"""
    # dump parameters
    exp_folder = get_dump_path(params)
    json.dump(vars(params), open(os.path.join(exp_folder, 'params.pkl'), 'w'), indent=4)

    # get running command
    command = [""python"", sys.argv[0]]
    for x in sys.argv[1:]:
        if x.startswith('--'):
            assert '""' not in x and ""'"" not in x
            command.append(x)
        else:
            assert ""'"" not in x
            if re.match('^[a-zA-Z0-9_]+$', x):
                command.append(""%s"" % x)
            else:
                command.append(""'%s'"" % x)
    command = ' '.join(command)
    params.command = command + ' --exp_id ""%s""' % params.exp_id

    # check experiment name
    assert len(params.exp_name.strip()) > 0

    # create a logger
    logger = create_logger(os.path.join(exp_folder, 'train.log'), rank=getattr(params, 'global_rank', 0))
    logger.info(""============ Initialized logger ============"")
    logger.info(""\n"".join(""%s: %s"" % (k, str(v))
                          for k, v in sorted(dict(vars(params)).items())))

    logger.info(""The experiment will be stored in %s\n"" % exp_folder)
    logger.info(""Running command: %s"" % command)
    return logger


def get_dump_path(params):
    """"""
    Create a directory to store the experiment.
    """"""
    assert len(params.exp_name) > 0
    assert not params.dump_path in ('', None), \
        'Please choose your favorite destination for dump.'
    dump_path = params.dump_path

    # create the sweep path if it does not exist
    when = date.today().strftime('%m%d-')
    sweep_path = os.path.join(dump_path, when + params.exp_name)
    if not os.path.exists(sweep_path):
        subprocess.Popen(""mkdir -p %s"" % sweep_path, shell=True).wait()

    # create an random ID for the job if it is not given in the parameters.
    if params.exp_id == '':
        # exp_id = time.strftime('%H-%M-%S')
        exp_id = datetime.now().strftime('%H-%M-%S.%f')[:-3]
        exp_id += ''.join(random.sample('abcdefghijklmnopqrstuvwxyz', 3))
        # chars = 'abcdefghijklmnopqrstuvwxyz0123456789'
        # while True:
        #     exp_id = ''.join(random.choice(chars) for _ in range(10))
        #     if not os.path.isdir(os.path.join(sweep_path, exp_id)):
        #         break
        params.exp_id = exp_id

    # create the dump folder / update parameters
    exp_folder = os.path.join(sweep_path, params.exp_id)
    if not os.path.isdir(exp_folder):
        subprocess.Popen(""mkdir -p %s"" % exp_folder, shell=True).wait()
    return exp_folder


def describe_model(model, path, name='model'):
    file_path = os.path.join(path, f'{name}.describe')
    with open(file_path, 'w') as fout:
        print(model, file=fout)


def set_seed(seed):
    """"""
    Freeze every seed for reproducibility.
    torch.cuda.manual_seed_all is useful when using random generation on GPUs.
    e.g. torch.cuda.FloatTensor(100).uniform_()
    """"""
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


def save_model(model, save_dir, epoch=None, model_name='model'):
    model_to_save = model.module if hasattr(model, ""module"") else model
    if epoch is None:
        save_path = os.path.join(save_dir, f'{model_name}.pkl')
    else:
        save_path = os.path.join(save_dir, f'{model_name}-{epoch}.pkl')
    os.makedirs(save_dir, exist_ok=True)
    torch.save(model_to_save.state_dict(), save_path)


def load_model(path, map_location):
    return torch.load(path, map_location=map_location)
","Python"
"Assay","HICAI-ZJU/iMoLD","eval.py",".py","4605","122","import os
import logging
from tqdm import tqdm
from munch import Munch, munchify

import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from torch_geometric.loader import DataLoader
import numpy as np

from GOOD import register
from GOOD.utils.config_reader import load_config
from GOOD.utils.metric import Metric
from GOOD.data.dataset_manager import read_meta_info
from GOOD.utils.evaluation import eval_data_preprocess, eval_score
from GOOD.utils.train import nan2zero_get_mask

from args_parse import args_parser
from exputils import initialize_exp, set_seed, get_dump_path, describe_model, save_model, load_model
from models import MyModel
from dataset import DrugOODDataset

logger = logging.getLogger()


class Runner:
    def __init__(self, args, logger_path):
        self.args = args
        self.device = torch.device(f'cuda')

        if args.dataset.startswith('GOOD'):
            # for GOOD, load Config
            cfg_path = os.path.join(args.config_path, args.dataset, args.domain, args.shift, 'base.yaml')
            cfg, _, _ = load_config(path=cfg_path)
            cfg = munchify(cfg)
            cfg.device = self.device
            dataset, meta_info = register.datasets[cfg.dataset.dataset_name].load(dataset_root=args.data_root,
                                                                                  domain=cfg.dataset.domain,
                                                                                  shift=cfg.dataset.shift_type,
                                                                                  generate=cfg.dataset.generate)
            read_meta_info(meta_info, cfg)
            # cfg.dropout
            # cfg.bs
            # update dropout & bs
            cfg.model.dropout_rate = args.dropout
            cfg.train.train_bs = args.bs
            cfg.random_seed = args.random_seed

            loader = register.dataloader[cfg.dataset.dataloader_name].setup(dataset, cfg)
            self.train_loader = loader['train']
            self.valid_loader = loader['val']
            self.test_loader = loader['test']

            self.metric = Metric()
            self.metric.set_score_func(dataset['metric'] if type(dataset) is dict else getattr(dataset, 'metric'))
            self.metric.set_loss_func(dataset['task'] if type(dataset) is dict else getattr(dataset, 'task'))
            cfg.metric = self.metric
        else:
            # DrugOOD
            dataset = DrugOODDataset(name=args.dataset, root=args.data_root)
            self.train_set = dataset[dataset.train_index]
            self.valid_set = dataset[dataset.valid_index]
            self.test_set = dataset[dataset.test_index]

            self.train_loader = DataLoader(self.train_set, batch_size=args.bs, shuffle=True, drop_last=True)
            self.valid_loader = DataLoader(self.valid_set, batch_size=args.bs, shuffle=False)
            self.test_loader = DataLoader(self.test_set, batch_size=args.bs, shuffle=False)
            self.metric = Metric()
            self.metric.set_loss_func(task_name='Binary classification')
            self.metric.set_score_func(metric_name='ROC-AUC')
            cfg = Munch()
            cfg.metric = self.metric
            cfg.model = Munch()
            cfg.model.model_level = 'graph'

        self.model = MyModel(args=args, config=cfg).to(self.device)
        self.model.load_state_dict(load_model(args.load_path, map_location=self.device))
        self.logger_path = logger_path

        self.cfg = cfg

    
    def run(self):
        train_score = self.test_step(self.train_loader)
        val_score = self.test_step(self.valid_loader)
        test_score = self.test_step(self.test_loader)
        logger.info(f""TRAIN={train_score:.5f}, VAL={val_score:.5f}, TEST={test_score:.5f}"")


    @torch.no_grad()
    def test_step(self, loader):

        self.model.eval()
        y_pred, y_gt = [], []
        for data in loader:
            data = data.to(self.device)
            logit, _, _, _, _ = self.model(data)
            mask, _ = nan2zero_get_mask(data, 'None', self.cfg)
            pred, target = eval_data_preprocess(data.y, logit, mask, self.cfg)
            y_pred.append(pred)
            y_gt.append(target)

        score = eval_score(y_pred, y_gt, self.cfg)

        return score

def main():
    args = args_parser()
    torch.cuda.set_device(int(args.gpu))

    logger = initialize_exp(args)
    set_seed(args.random_seed)
    logger_path = get_dump_path(args)

    runner = Runner(args, logger_path)
    runner.run()


if __name__ == '__main__':
    main()
","Python"
"Assay","HICAI-ZJU/iMoLD","args_parse.py",".py","1869","48","import argparse


def args_parser():
    parser = argparse.ArgumentParser()
    # exp
    parser.add_argument(""--exp_name"", default=""run"", type=str,
                        help=""Experiment name"")
    parser.add_argument(""--dump_path"", default=""dump/"", type=str,
                        help=""Experiment dump path"")
    parser.add_argument(""--exp_id"", default="""", type=str,
                        help=""Experiment ID"")
    parser.add_argument(""--gpu"", default='0', type=str)
    parser.add_argument(""--random_seed"", default=0, type=int)
    parser.add_argument(""--load_path"", default=None, type=str)

    # dataset
    parser.add_argument(""--data_root"", default='data', type=str)
    parser.add_argument(""--config_path"", default='configs', type=str)
    parser.add_argument(""--dataset"", default='GOODHIV', type=str)
    parser.add_argument(""--domain"", default='scaffold', type=str)
    parser.add_argument(""--shift"", default='covariate', type=str)

    # VQ
    parser.add_argument(""--num_e"", default=4000, type=int)
    parser.add_argument(""--commitment_weight"", default=0.1, type=float)

    # Encoder
    parser.add_argument(""--emb_dim"", default=128, type=int)
    parser.add_argument(""--layer"", default=4, type=int)
    parser.add_argument(""--dropout"", default=0.5, type=float)
    parser.add_argument(""--gnn_type"", default='gin', type=str, choices=['gcn', 'gin'])
    parser.add_argument(""--pooling_type"", default='mean', type=str)

    # Model
    parser.add_argument(""--inv_w"", default=0.01, type=float)
    parser.add_argument(""--reg_w"", default=0.5, type=float)
    parser.add_argument(""--gamma"", default=0.9, type=float)

    # Training
    parser.add_argument(""--lr"", default=0.001, type=float)
    parser.add_argument(""--bs"", default=128, type=int)
    parser.add_argument(""--epoch"", default=200, type=int)

    args = parser.parse_args()

    return args
","Python"
"Assay","HICAI-ZJU/iMoLD","run.py",".py","7998","204","import os
import logging
from tqdm import tqdm
from munch import Munch, munchify

import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from torch_geometric.loader import DataLoader
import numpy as np

from GOOD import register
from GOOD.utils.config_reader import load_config
from GOOD.utils.metric import Metric
from GOOD.data.dataset_manager import read_meta_info
from GOOD.utils.evaluation import eval_data_preprocess, eval_score
from GOOD.utils.train import nan2zero_get_mask

from args_parse import args_parser
from exputils import initialize_exp, set_seed, get_dump_path, describe_model, save_model, load_model
from models import MyModel
from dataset import DrugOODDataset

logger = logging.getLogger()


def set_requires_grad(nets, requires_grad=False):
    """"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
    Parameters:
        nets (network list)   -- a list of networks
        requires_grad (bool)  -- whether the networks require gradients or not
    """"""
    if not isinstance(nets, list):
        nets = [nets]
    for net in nets:
        if net is not None:
            for param in net.parameters():
                param.requires_grad = requires_grad


class Runner:
    def __init__(self, args, writer, logger_path):
        self.args = args
        self.device = torch.device(f'cuda')

        if args.dataset.startswith('GOOD'):
            # for GOOD, load Config
            cfg_path = os.path.join(args.config_path, args.dataset, args.domain, args.shift, 'base.yaml')
            cfg, _, _ = load_config(path=cfg_path)
            cfg = munchify(cfg)
            cfg.device = self.device
            dataset, meta_info = register.datasets[cfg.dataset.dataset_name].load(dataset_root=args.data_root,
                                                                                  domain=cfg.dataset.domain,
                                                                                  shift=cfg.dataset.shift_type,
                                                                                  generate=cfg.dataset.generate)
            read_meta_info(meta_info, cfg)
            # cfg.dropout
            # cfg.bs
            # update dropout & bs
            cfg.model.dropout_rate = args.dropout
            cfg.train.train_bs = args.bs
            cfg.random_seed = args.random_seed

            loader = register.dataloader[cfg.dataset.dataloader_name].setup(dataset, cfg)
            self.train_loader = loader['train']
            self.valid_loader = loader['val']
            self.test_loader = loader['test']

            self.metric = Metric()
            self.metric.set_score_func(dataset['metric'] if type(dataset) is dict else getattr(dataset, 'metric'))
            self.metric.set_loss_func(dataset['task'] if type(dataset) is dict else getattr(dataset, 'task'))
            cfg.metric = self.metric
        else:
            # DrugOOD
            dataset = DrugOODDataset(name=args.dataset, root=args.data_root)
            self.train_set = dataset[dataset.train_index]
            self.valid_set = dataset[dataset.valid_index]
            self.test_set = dataset[dataset.test_index]

            self.train_loader = DataLoader(self.train_set, batch_size=args.bs, shuffle=True, drop_last=True)
            self.valid_loader = DataLoader(self.valid_set, batch_size=args.bs, shuffle=False)
            self.test_loader = DataLoader(self.test_set, batch_size=args.bs, shuffle=False)
            self.metric = Metric()
            self.metric.set_loss_func(task_name='Binary classification')
            self.metric.set_score_func(metric_name='ROC-AUC')
            cfg = Munch()
            cfg.metric = self.metric
            cfg.model = Munch()
            cfg.model.model_level = 'graph'

        self.model = MyModel(args=args, config=cfg).to(self.device)
        self.opt = torch.optim.Adam(self.model.parameters(), lr=args.lr)

        self.total_step = 0
        self.writer = writer
        describe_model(self.model, path=logger_path)
        self.logger_path = logger_path

        self.cfg = cfg

    def run(self):
        if self.metric.lower_better == 1:
            best_valid_score, best_test_score = float('inf'), float('inf')
        else:
            best_valid_score, best_test_score = -1, -1
        
        for e in range(self.args.epoch):
            self.train_step(e)
            valid_score = self.test_step(self.valid_loader)
            
            logger.info(f""E={e}, valid={valid_score:.5f}, test-score={best_test_score:.5f}"")
            # if valid_score > best_valid_score:
            if (valid_score > best_valid_score and self.metric.lower_better == -1) or \
                    (valid_score < best_valid_score and self.metric.lower_better == 1):
                test_score = self.test_step(self.test_loader)
                best_valid_score = valid_score
                best_test_score = test_score
                logger.info(f""UPDATE test-score={best_test_score:.5f}"")
             

        logger.info(f""test-score={best_test_score:.5f}"")

    def train_step(self, epoch):
        self.model.train()
        if epoch % 4 in range(1):
            # train separator
            set_requires_grad([self.model.separator], requires_grad=True)
            set_requires_grad([self.model.encoder], requires_grad=False)
        else:
            # train classifier
            set_requires_grad([self.model.separator], requires_grad=False)
            set_requires_grad([self.model.encoder], requires_grad=True)

        pbar = tqdm(self.train_loader, desc=f""E [{epoch}]"")

        for data in pbar:
            data = data.to(self.device)
            c_logit, c_f, s_f, cmt_loss, reg_loss = self.model(data)
            # classification loss on c
            mask, target = nan2zero_get_mask(data, 'None', self.cfg)
            cls_loss = self.metric.loss_func(c_logit, target.float(), reduction='none') * mask
            cls_loss = cls_loss.sum() / mask.sum()

            mix_f = self.model.mix_cs_proj(c_f, s_f)
            inv_loss = self.simsiam_loss(c_f, mix_f)
            # inv_w: lambda_1
            # reg_w: lambda_2
            loss = cls_loss + cmt_loss + self.args.inv_w * inv_loss + self.args.reg_w * reg_loss

            self.opt.zero_grad()
            loss.backward()
            torch.nn.utils.clip_grad_norm_(self.model.parameters(), 5)
            self.opt.step()

            pbar.set_postfix_str(f""loss={loss.item():.4f}"")
            self.writer.add_scalar('loss', loss.item(), self.total_step)
            self.writer.add_scalar('cls-loss', cls_loss.item(), self.total_step)
            self.writer.add_scalar('cmt-loss', cmt_loss.item(), self.total_step)
            self.writer.add_scalar('reg-loss', reg_loss.item(), self.total_step)

            self.total_step += 1


    @torch.no_grad()
    def test_step(self, loader):

        self.model.eval()
        y_pred, y_gt = [], []
        for data in loader:
            data = data.to(self.device)
            logit, _, _, _, _ = self.model(data)
            mask, _ = nan2zero_get_mask(data, 'None', self.cfg)
            pred, target = eval_data_preprocess(data.y, logit, mask, self.cfg)
            y_pred.append(pred)
            y_gt.append(target)

        score = eval_score(y_pred, y_gt, self.cfg)

        return score

    def simsiam_loss(self, causal_rep, mix_rep):
        causal_rep = causal_rep.detach()
        causal_rep = F.normalize(causal_rep, dim=1)
        mix_rep = F.normalize(mix_rep, dim=1)
        return -(causal_rep * mix_rep).sum(dim=1).mean()


def main():
    args = args_parser()
    torch.cuda.set_device(int(args.gpu))

    logger = initialize_exp(args)
    set_seed(args.random_seed)
    logger_path = get_dump_path(args)
    writer = SummaryWriter(log_dir=os.path.join(logger_path, 'tensorboard'))

    runner = Runner(args, writer, logger_path)
    runner.run()
    writer.close()


if __name__ == '__main__':
    main()
","Python"
"Assay","HICAI-ZJU/iMoLD","dataset/__init__.py",".py","40","2","from .drugdataset import DrugOODDataset
","Python"
"Assay","HICAI-ZJU/iMoLD","dataset/smiles2graph.py",".py","5183","173","# import dgl
import numpy as np
import rdkit
import torch
from rdkit import Chem


def get_atom_features(atom):
    # The usage of features is along with the Attentive FP.
    feature = np.zeros(39)

    # Symbol
    symbol = atom.GetSymbol()
    symbol_list = ['B', 'C', 'N', 'O', 'F', 'Si', 'P', 'S', 'Cl', 'As', 'Se', 'Br', 'Te', 'I', 'At']
    if symbol in symbol_list:
        loc = symbol_list.index(symbol)
        feature[loc] = 1
    else:
        feature[15] = 1

    # Degree
    degree = atom.GetDegree()
    if degree > 5:
        print(""atom degree larger than 5. Please check before featurizing."")
        raise RuntimeError

    feature[16 + degree] = 1

    # Formal Charge
    charge = atom.GetFormalCharge()
    feature[22] = charge

    # radical electrons
    radelc = atom.GetNumRadicalElectrons()
    feature[23] = radelc

    # Hybridization
    hyb = atom.GetHybridization()
    hybridization_list = [rdkit.Chem.rdchem.HybridizationType.SP,
                          rdkit.Chem.rdchem.HybridizationType.SP2,
                          rdkit.Chem.rdchem.HybridizationType.SP3,
                          rdkit.Chem.rdchem.HybridizationType.SP3D,
                          rdkit.Chem.rdchem.HybridizationType.SP3D2]
    if hyb in hybridization_list:
        loc = hybridization_list.index(hyb)
        feature[loc + 24] = 1
    else:
        feature[29] = 1

    # aromaticity
    if atom.GetIsAromatic():
        feature[30] = 1

    # hydrogens
    hs = atom.GetNumImplicitHs()
    feature[31 + hs] = 1

    # chirality, chirality type
    if atom.HasProp('_ChiralityPossible'):
        # TODO what kind of error
        feature[36] = 1

        try:
            chi = atom.GetProp('_CIPCode')
            chi_list = ['R', 'S']
            loc = chi_list.index(chi)
            feature[37 + loc] = 1
        except KeyError:
            feature[37] = 0
            feature[38] = 0

    return feature


def get_bond_features(bond):
    feature = np.zeros(10)

    # bond type
    type = bond.GetBondType()
    bond_type_list = [rdkit.Chem.rdchem.BondType.SINGLE,
                      rdkit.Chem.rdchem.BondType.DOUBLE,
                      rdkit.Chem.rdchem.BondType.TRIPLE,
                      rdkit.Chem.rdchem.BondType.AROMATIC]
    if type in bond_type_list:
        loc = bond_type_list.index(type)
        feature[0 + loc] = 1
    else:
        print(""Wrong type of bond. Please check before feturization."")
        raise RuntimeError

    # conjugation
    conj = bond.GetIsConjugated()
    feature[4] = conj

    # ring
    ring = bond.IsInRing()
    feature[5] = ring

    # stereo
    stereo = bond.GetStereo()
    stereo_list = [rdkit.Chem.rdchem.BondStereo.STEREONONE,
                   rdkit.Chem.rdchem.BondStereo.STEREOANY,
                   rdkit.Chem.rdchem.BondStereo.STEREOZ,
                   rdkit.Chem.rdchem.BondStereo.STEREOE]
    if stereo in stereo_list:
        loc = stereo_list.index(stereo)
        feature[6 + loc] = 1
    else:
        print(""Wrong stereo type of bond. Please check before featurization."")
        raise RuntimeError

    return feature


def smile2graph4drugood(smile):
    mol = Chem.MolFromSmiles(smile)
    # if (mol is None):
    #     return None
    src = []
    dst = []
    atom_feature = []
    bond_feature = []

    for atom in mol.GetAtoms():
        one_atom_feature = get_atom_features(atom)
        atom_feature.append(one_atom_feature)
    atom_feature = np.array(atom_feature)
    atom_feature = torch.tensor(atom_feature).float()

    if len(mol.GetBonds()) > 0:  # mol has bonds
        for bond in mol.GetBonds():
            i = bond.GetBeginAtomIdx()
            j = bond.GetEndAtomIdx()
            one_bond_feature = get_bond_features(bond)
            src.append(i)
            dst.append(j)
            bond_feature.append(one_bond_feature)
            src.append(j)
            dst.append(i)
            bond_feature.append(one_bond_feature)

        src = torch.tensor(src).long()
        dst = torch.tensor(dst).long()
        bond_feature = np.array(bond_feature)
        bond_feature = torch.tensor(bond_feature).float()
        edge_index = torch.vstack([src, dst])
        # graph_cur_smile = dgl.graph((src, dst), num_nodes=len(mol.GetAtoms()))
        # graph_cur_smile.ndata['x'] = atom_feature
        # graph_cur_smile.edata['x'] = bond_feature
    else:
        edge_index = torch.empty((2, 0)).long()
        bond_feature = torch.empty((0, 10)).float()

    return atom_feature, edge_index, bond_feature


def featurize_atoms(mol):
    feats = []
    for atom in mol.GetAtoms():
        feats.append(atom.GetAtomicNum())
    return {'atomic': torch.tensor(feats).reshape(-1).to(torch.int64)}


def featurize_bonds(mol):
    feats = []
    bond_types = [Chem.rdchem.BondType.SINGLE, Chem.rdchem.BondType.DOUBLE,
                  Chem.rdchem.BondType.TRIPLE, Chem.rdchem.BondType.AROMATIC]
    for bond in mol.GetBonds():
        btype = bond_types.index(bond.GetBondType())
        # One bond between atom u and v corresponds to two edges (u, v) and (v, u)
        feats.extend([btype, btype])
    return {'type': torch.tensor(feats).reshape(-1).to(torch.int64)}
","Python"
"Assay","HICAI-ZJU/iMoLD","dataset/drugdataset.py",".py","4981","116","import os
import os.path as osp
import json
import pickle


import torch
from torch_geometric.data import InMemoryDataset, Data, DataLoader
from rdkit import Chem
from tqdm import tqdm

from .smiles2graph import smile2graph4drugood



class DrugOODDataset(InMemoryDataset):
    def __init__(self, name, version='chembl30', type='lbap', root='data', drugood_root='drugood-data',
                 transform=None, pre_transform=None, pre_filter=None):
        self.name = name
        self.root = root
        # self.dir_name = '_'.join(name.split('-'))
        self.drugood_root = drugood_root
        self.version = version
        self.type = type
        super(DrugOODDataset, self).__init__(root, transform, pre_transform, pre_filter)
        self.data, self.slices = torch.load(self.processed_paths[0])
        self.data_cfg = pickle.load(open(self.processed_paths[1], 'rb'))
        self.data_statistics = pickle.load(open(self.processed_paths[2], 'rb'))
        self.train_index, self.valid_index, self.test_index = pickle.load(open(self.processed_paths[3], 'rb'))
        self.num_tasks = 1

    @property
    def raw_dir(self):
        # return osp.join(self.ogb_root, self.dir_name, 'mapping')
        # return self.drugood_root
        return self.drugood_root + '-' + self.version

    @property
    def raw_file_names(self):
        # return 'lbap_core_' + self.name + '.json'
        return f'{self.type}_core_{self.name}.json'
        # return 'mol.csv.gz'
        # return f'{self.names[self.name][2]}.csv'

    @property
    def processed_dir(self):
        # return osp.join(self.root, self.name, f'{self.decomp}-processed')
        # return osp.join(self.root, self.dir_name, f'{self.decomp}-processed')
        # return osp.join(self.root, f'{self.name}-{self.version}')
        return osp.join(self.root, f'{self.type}-{self.name}-{self.version}')

    @property
    def processed_file_names(self):
        return 'data.pt', 'cfg.pt', 'statistics.pt', 'split.pt'

    def __subprocess(self, datalist):
        processed_data = []
        for datapoint in tqdm(datalist):
            # ['smiles', 'reg_label', 'assay_id', 'cls_label', 'domain_id']
            smiles = datapoint['smiles']
            x, edge_index, edge_attr = smile2graph4drugood(smiles)
            y = torch.tensor([datapoint['cls_label']]).unsqueeze(0)
            if self.type == 'lbap':
                data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, smiles=smiles,
                            reg_label=datapoint['reg_label'], assay_id=datapoint['assay_id'],
                            domain_id=datapoint['domain_id'])
            else:
                protein = datapoint['protein']
                data = Data(x=x, edge_index=edge_index, edge_attr=edge_attr, y=y, smiles=smiles, protein=protein,
                            reg_label=datapoint['reg_label'], assay_id=datapoint['assay_id'],
                            domain_id=datapoint['domain_id'])

            data.batch_num_nodes = data.num_nodes
            # if self.pre_filter is not None and not self.pre_filter(data):
            #     continue

            if self.pre_transform is not None:
                data = self.pre_transform(data)
            processed_data.append(data)
        return processed_data, len(processed_data)

    def process(self):
        # data_list = []
        json_data = json.load(open(self.raw_paths[0], 'r', encoding='utf-8'))
        data_cfg, data_statistics = json_data['cfg'], json_data['statistics']
        train_data = json_data['split']['train']
        valid_data = json_data['split']['ood_val']
        test_data = json_data['split']['ood_test']
        train_data_list, train_num = self.__subprocess(train_data)
        valid_data_list, valid_num = self.__subprocess(valid_data)
        test_data_list, test_num = self.__subprocess(test_data)
        data_list = train_data_list + valid_data_list + test_data_list
        train_index = list(range(train_num))
        valid_index = list(range(train_num, train_num + valid_num))
        test_index = list(range(train_num + valid_num, train_num + valid_num + test_num))
        torch.save(self.collate(data_list), self.processed_paths[0])
        pickle.dump(data_cfg, open(self.processed_paths[1], 'wb'))
        pickle.dump(data_statistics, open(self.processed_paths[2], 'wb'))
        pickle.dump([train_index, valid_index, test_index], open(self.processed_paths[3], 'wb'))


    def __repr__(self):
        return '{}({})'.format(self.name, len(self))


# if __name__ == '__main__':
#     dataset = DrugOODDataset(name='ic50_assay', root='../data', drugood_root='../drugood-data')
#     # data = json.load(open('../drugood-data/lbap_core_ec50_size.json', 'r', encoding='utf-8'))
#     train_set = dataset[dataset.train_index]
#     test_set = dataset[dataset.test_index]
#     loader = DataLoader(train_set, batch_size=4, shuffle=True)
#     for data in loader:
#         import pdb;
#
#         pdb.set_trace()
","Python"
"Assay","HICAI-ZJU/iMoLD","models/model.py",".py","5745","148","import torch
from torch import nn
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool
from torch_scatter import scatter_add, scatter_mean
import numpy as np

from GOOD.networks.models.GINs import GINFeatExtractor
from GOOD.networks.models.GINvirtualnode import vGINFeatExtractor

from vector_quantize_pytorch import VectorQuantize
# from .vq_update import VectorQuantize

from .gnnconv import GNN_node


class Separator(nn.Module):
    def __init__(self, args, config):
        super(Separator, self).__init__()
        if args.dataset.startswith('GOOD'):
            # GOOD
            if config.model.model_name == 'GIN':
                self.r_gnn = GINFeatExtractor(config, without_readout=True)
            else:
                self.r_gnn = vGINFeatExtractor(config, without_readout=True)
            emb_d = config.model.dim_hidden
        else:
            self.r_gnn = GNN_node(num_layer=args.layer, emb_dim=args.emb_dim,
                                  drop_ratio=args.dropout, gnn_type=args.gnn_type)
            emb_d = args.emb_dim

        self.separator = nn.Sequential(nn.Linear(emb_d, emb_d * 2),
                                       nn.BatchNorm1d(emb_d * 2),
                                       nn.ReLU(),
                                       nn.Linear(emb_d * 2, emb_d),
                                       nn.Sigmoid())
        self.args = args

    def forward(self, data):
        if self.args.dataset.startswith('GOOD'):
            # DrugOOD
            node_feat = self.r_gnn(data=data)
        else:
            # GOOD
            node_feat = self.r_gnn(data)
        score = self.separator(node_feat)  # [n, d]

        # reg on score

        pos_score_on_node = score.mean(1)  # [n]
        pos_score_on_batch = scatter_add(pos_score_on_node, data.batch, dim=0)  # [B]
        neg_score_on_batch = scatter_add((1 - pos_score_on_node), data.batch, dim=0)  # [B]
        return score, pos_score_on_batch + 1e-8, neg_score_on_batch + 1e-8


class DiscreteEncoder(nn.Module):
    def __init__(self, args, config):
        super(DiscreteEncoder, self).__init__()
        self.args = args
        self.config = config
        if args.dataset.startswith('GOOD'):
            emb_dim = config.model.dim_hidden
            if config.model.model_name == 'GIN':
                self.gnn = GINFeatExtractor(config, without_readout=True)
            else:
                self.gnn = vGINFeatExtractor(config, without_readout=True)
            self.classifier = nn.Sequential(*(
                [nn.Linear(emb_dim, config.dataset.num_classes)]
            ))
        else:
            emb_dim = args.emb_dim
            self.gnn = GNN_node(num_layer=args.layer, emb_dim=args.emb_dim,
                                drop_ratio=args.dropout, gnn_type=args.gnn_type)
            self.classifier = nn.Sequential(nn.Linear(emb_dim, emb_dim * 2),
                                            nn.BatchNorm1d(emb_dim * 2),
                                            nn.ReLU(),
                                            nn.Dropout(),
                                            nn.Linear(emb_dim * 2, 1))

        self.pool = global_mean_pool

        self.vq = VectorQuantize(dim=emb_dim,
                                 codebook_size=args.num_e,
                                 commitment_weight=args.commitment_weight,
                                 decay=0.9)

        self.mix_proj = nn.Sequential(nn.Linear(emb_dim * 2, emb_dim),
                                      nn.BatchNorm1d(emb_dim),
                                      nn.ReLU(),
                                      nn.Dropout(),
                                      nn.Linear(emb_dim, emb_dim))

        self.simsiam_proj = nn.Sequential(nn.Linear(emb_dim, emb_dim * 2),
                                          nn.BatchNorm1d(emb_dim * 2),
                                          nn.ReLU(),
                                          nn.Linear(emb_dim * 2, emb_dim))

    def vector_quantize(self, f, vq_model):
        v_f, indices, v_loss = vq_model(f)

        return v_f, v_loss

    def forward(self, data, score):
        if self.args.dataset.startswith('GOOD'):
            # DrugOOD
            node_feat = self.gnn(data=data)
        else:
            # GOOD
            node_feat = self.gnn(data)

        node_v_feat, cmt_loss = self.vector_quantize(node_feat.unsqueeze(0), self.vq)
        node_v_feat = node_v_feat.squeeze(0)
        node_res_feat = node_feat + node_v_feat
        c_node_feat = node_res_feat * score
        s_node_feat = node_res_feat * (1 - score)

        c_graph_feat = self.pool(c_node_feat, data.batch)
        s_graph_feat = self.pool(s_node_feat, data.batch)

        c_logit = self.classifier(c_graph_feat)

        return c_logit, c_graph_feat, s_graph_feat, cmt_loss


class MyModel(nn.Module):
    def __init__(self, args, config):
        super(MyModel, self).__init__()
        self.args = args
        self.config = config

        self.separator = Separator(args, config)
        self.encoder = DiscreteEncoder(args, config)

    def forward(self, data):
        score, pos_score, neg_score = self.separator(data)
        c_logit, c_graph_feat, s_graph_feat, cmt_loss = self.encoder(data, score)
        # reg on score
        loss_reg = torch.abs(pos_score / (pos_score + neg_score) - self.args.gamma * torch.ones_like(pos_score)).mean()
        return c_logit, c_graph_feat, s_graph_feat, cmt_loss, loss_reg

    def mix_cs_proj(self, c_f: torch.Tensor, s_f: torch.Tensor):
        n = c_f.size(0)
        perm = np.random.permutation(n)
        mix_f = torch.cat([c_f, s_f[perm]], dim=-1)
        proj_mix_f = self.encoder.mix_proj(mix_f)
        return proj_mix_f

","Python"
"Assay","HICAI-ZJU/iMoLD","models/__init__.py",".py","27","2","from .model import MyModel
","Python"
"Assay","HICAI-ZJU/iMoLD","models/gnnconv.py",".py","11994","357","import torch
from torch import nn
from torch_geometric.nn import MessagePassing
import torch.nn.functional as F
from torch_geometric.nn import global_mean_pool, global_add_pool
from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
from torch_geometric.utils import degree

import math


class MLP(nn.Module):
    """"""MLP with linear output""""""

    def __init__(self, num_layers, input_dim, hidden_dim, output_dim):
        """"""MLP layers construction

        Paramters
        ---------
        num_layers: int
            The number of linear layers
        input_dim: int
            The dimensionality of input features
        hidden_dim: int
            The dimensionality of hidden units at ALL layers
        output_dim: int
            The number of classes for prediction

        """"""
        super(MLP, self).__init__()
        self.linear_or_not = True  # default is linear model
        self.num_layers = num_layers
        self.output_dim = output_dim

        if num_layers < 1:
            raise ValueError(""number of layers should be positive!"")
        elif num_layers == 1:
            # Linear model
            self.linear = nn.Linear(input_dim, output_dim)
        else:
            # Multi-layer model
            self.linear_or_not = False
            self.linears = torch.nn.ModuleList()
            self.batch_norms = torch.nn.ModuleList()

            self.linears.append(nn.Linear(input_dim, hidden_dim))
            for _ in range(num_layers - 2):
                self.linears.append(nn.Linear(hidden_dim, hidden_dim))
            self.linears.append(nn.Linear(hidden_dim, output_dim))

            for _ in range(num_layers - 1):
                self.batch_norms.append(nn.BatchNorm1d(hidden_dim))

    def forward(self, x):
        if self.linear_or_not:
            # If linear model
            return self.linear(x)
        else:
            # If MLP
            h = x
            for i in range(self.num_layers - 1):
                h = F.relu(self.batch_norms[i](self.linears[i](h)))
            return self.linears[-1](h)


# GIN convolution along the graph structure
class GINConv(MessagePassing):
    def __init__(self, emb_dim):
        '''
            emb_dim (int): node embedding dimensionality
        '''

        super(GINConv, self).__init__(aggr=""add"")

        self.mlp = torch.nn.Sequential(
            torch.nn.Linear(emb_dim, 2 * emb_dim),
            torch.nn.BatchNorm1d(2 * emb_dim),
            torch.nn.ReLU(),
            torch.nn.Linear(2 * emb_dim, emb_dim)
        )
        self.eps = torch.nn.Parameter(torch.Tensor([0]))
        # if datatype == 'ogb':
        #     self.bond_encoder = BondEncoder(emb_dim=emb_dim)
        # else:
        self.bond_encoder = MLP(num_layers=1, input_dim=10, output_dim=emb_dim, hidden_dim=emb_dim)

    def forward(self, x, edge_index, edge_attr):
        edge_embedding = self.bond_encoder(edge_attr)
        out = self.mlp(
            (1 + self.eps) * x +
            self.propagate(edge_index, x=x, edge_attr=edge_embedding)
        )

        return out

    def message(self, x_j, edge_attr):
        return F.relu(x_j + edge_attr)

    def update(self, aggr_out):
        return aggr_out


# GCN convolution along the graph structure


class GCNConv(MessagePassing):
    def __init__(self, emb_dim):
        super(GCNConv, self).__init__(aggr='add')

        self.linear = torch.nn.Linear(emb_dim, emb_dim)
        self.root_emb = torch.nn.Embedding(1, emb_dim)
        # if datatype == 'ogb':
        #     self.bond_encoder = BondEncoder(emb_dim=emb_dim)
        # else:
        self.bond_encoder = MLP(num_layers=1, input_dim=10, output_dim=emb_dim, hidden_dim=emb_dim)

    def forward(self, x, edge_index, edge_attr):
        x = self.linear(x)
        edge_embedding = self.bond_encoder(edge_attr)

        row, col = edge_index

        # edge_weight = torch.ones((edge_index.size(1), ), device=edge_index.device)
        deg = degree(row, x.size(0), dtype=x.dtype) + 1
        deg_inv_sqrt = deg.pow(-0.5)
        deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0

        norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]

        return self.propagate(
            edge_index, x=x, edge_attr=edge_embedding, norm=norm
        ) + F.relu(x + self.root_emb.weight) * 1. / deg.view(-1, 1)

    def message(self, x_j, edge_attr, norm):
        return norm.view(-1, 1) * F.relu(x_j + edge_attr)

    def update(self, aggr_out):
        return aggr_out


# GNN to generate node embedding
class GNN_node(torch.nn.Module):
    """"""
    Output:
        node representations
    """"""

    def __init__(
            self, num_layer, emb_dim, drop_ratio=0.5,
            JK=""last"", residual=False, gnn_type='gin'
    ):
        '''
            emb_dim (int): node embedding dimensionality
            num_layer (int): number of GNN message passing layers

        '''

        super(GNN_node, self).__init__()
        self.num_layer = num_layer
        self.drop_ratio = drop_ratio
        self.JK = JK
        # add residual connection or not
        self.residual = residual

        # if self.num_layer < 2:
        #     raise ValueError(""Number of GNN layers must be greater than 1."")
        # if datatype == 'ogb':
        #     self.atom_encoder = AtomEncoder(emb_dim)
        # else:
        self.atom_encoder = MLP(input_dim=39, hidden_dim=emb_dim, output_dim=emb_dim, num_layers=2)

        # List of GNNs
        self.convs = torch.nn.ModuleList()
        self.batch_norms = torch.nn.ModuleList()

        for layer in range(num_layer):
            if gnn_type == 'gin':
                self.convs.append(GINConv(emb_dim))
            elif gnn_type == 'gcn':
                self.convs.append(GCNConv(emb_dim))
            else:
                raise ValueError(
                    'Undefined GNN type called {}'.format(gnn_type))

            self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))

    def forward(self, *argv):

        # computing input node embedding
        if len(argv) == 4:
            x, edge_index, edge_attr, batch = argv[0], argv[1], argv[2], argv[3]
        elif len(argv) == 1:
            batched_data = argv[0]
            x, edge_index = batched_data.x, batched_data.edge_index
            edge_attr, batch = batched_data.edge_attr, batched_data.batch
        else:
            raise ValueError(""unmatched number of arguments."")

        h_list = [self.atom_encoder(x)]
        for layer in range(self.num_layer):

            h = self.convs[layer](h_list[layer], edge_index, edge_attr)
            h = self.batch_norms[layer](h)

            if layer == self.num_layer - 1:
                # remove relu for the last layer
                h = F.dropout(h, self.drop_ratio, training=self.training)
                # h = h
            else:
                h = F.dropout(
                    F.relu(h), self.drop_ratio, training=self.training
                )

            if self.residual:
                h += h_list[layer]

            h_list.append(h)

        # Different implementations of Jk-concat
        if self.JK == ""last"":
            node_representation = h_list[-1]
        elif self.JK == ""sum"":
            node_representation = 0
            for layer in range(self.num_layer + 1):
                node_representation += h_list[layer]

        return node_representation


# Virtual GNN to generate node embedding
class GNN_node_Virtualnode(torch.nn.Module):
    """"""
    Output:
        node representations
    """"""

    def __init__(
            self, num_layer, emb_dim, drop_ratio=0.5,
            JK=""last"", residual=False, gnn_type='gin'
    ):
        '''
            emb_dim (int): node embedding dimensionality
        '''

        super(GNN_node_Virtualnode, self).__init__()
        self.num_layer = num_layer
        self.drop_ratio = drop_ratio
        self.JK = JK
        # add residual connection or not
        self.residual = residual

        if self.num_layer < 2:
            raise ValueError(""Number of GNN layers must be greater than 1."")

        # self.atom_encoder = AtomEncoder(emb_dim)
        # if datatype == 'ogb':
        #     self.atom_encoder = AtomEncoder(emb_dim)
        # else:
        self.atom_encoder = MLP(input_dim=39, hidden_dim=emb_dim, output_dim=emb_dim, num_layers=2)


        # set the initial virtual node embedding to 0.
        self.virtualnode_embedding = torch.nn.Embedding(1, emb_dim)
        torch.nn.init.constant_(self.virtualnode_embedding.weight.data, 0)

        # List of GNNs
        self.convs = torch.nn.ModuleList()
        # batch norms applied to node embeddings
        self.batch_norms = torch.nn.ModuleList()

        # List of MLPs to transform virtual node at every layer
        self.mlp_virtualnode_list = torch.nn.ModuleList()

        for layer in range(num_layer):
            if gnn_type == 'gin':
                self.convs.append(GINConv(emb_dim))
            elif gnn_type == 'gcn':
                self.convs.append(GCNConv(emb_dim))
            else:
                raise ValueError(f'Undefined GNN type called {gnn_type}')

            self.batch_norms.append(torch.nn.BatchNorm1d(emb_dim))

        for layer in range(num_layer - 1):
            self.mlp_virtualnode_list.append(torch.nn.Sequential(
                torch.nn.Linear(emb_dim, 2 * emb_dim),
                torch.nn.BatchNorm1d(2 * emb_dim),
                torch.nn.ReLU(),
                torch.nn.Linear(2 * emb_dim, emb_dim),
                torch.nn.BatchNorm1d(emb_dim),
                torch.nn.ReLU()
            ))

    def forward(self, batched_data):

        x, edge_index = batched_data.x, batched_data.edge_index
        edge_attr, batch = batched_data.edge_attr, batched_data.batch
        # virtual node embeddings for graphs
        virtualnode_embedding = self.virtualnode_embedding(torch.zeros(
            batch[-1].item() + 1).to(edge_index.dtype).to(edge_index.device))

        h_list = [self.atom_encoder(x)]
        for layer in range(self.num_layer):
            # add message from virtual nodes to graph nodes
            h_list[layer] = h_list[layer] + virtualnode_embedding[batch]

            # Message passing among graph nodes
            h = self.convs[layer](h_list[layer], edge_index, edge_attr)

            h = self.batch_norms[layer](h)
            if layer == self.num_layer - 1:
                # remove relu for the last layer
                h = F.dropout(h, self.drop_ratio, training=self.training)
            else:
                h = F.dropout(
                    F.relu(h), self.drop_ratio, training=self.training
                )

            if self.residual:
                h = h + h_list[layer]

            h_list.append(h)

            # update the virtual nodes
            if layer < self.num_layer - 1:
                # add message from graph nodes to virtual nodes
                virtualnode_embedding_temp = global_add_pool(
                    h_list[layer], batch) + virtualnode_embedding
                # transform virtual nodes using MLP

                if self.residual:
                    virtualnode_embedding = virtualnode_embedding + F.dropout(
                        self.mlp_virtualnode_list[layer](
                            virtualnode_embedding_temp
                        ), self.drop_ratio, training=self.training
                    )
                else:
                    virtualnode_embedding = F.dropout(
                        self.mlp_virtualnode_list[layer](
                            virtualnode_embedding_temp
                        ), self.drop_ratio, training=self.training
                    )

        # Different implementations of Jk-concat
        if self.JK == ""last"":
            node_representation = h_list[-1]
        elif self.JK == ""sum"":
            node_representation = 0
            for layer in range(self.num_layer + 1):
                node_representation += h_list[layer]

        return node_representation


if __name__ == ""__main__"":
    pass
","Python"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_findOverlaps-methods.R",".R","3147","93","###

M1 <- matrix(1, 5, 3)
M2 <- matrix(1, 3, 3)
assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
rowRangesList <-
    list(gr=GRanges(""chr1"", IRanges(1:5, 10)),
         grl=split(GRanges(""chr1"", IRanges(1:5, 10)), c(1,1,2,2,3)))
names(rowRangesList[[""grl""]]) <- NULL
colData <- DataFrame(x=letters[1:3])

## a list of one SE with GRanges and one with GRangesList
rseList <-
    list(SummarizedExperiment(
           assays=assaysList[[""gr""]],
           rowRanges=rowRangesList[[""gr""]],
           colData=colData),
         SummarizedExperiment(
           assays=assaysList[[""grl""]],
           rowRanges=rowRangesList[[""grl""]],
           colData=colData))


test_interfaces <- function()
{
    fun <- ""findOverlaps""
    signatures <- list(
        c(""RangedSummarizedExperiment"", ""Vector""),
        c(""Vector"", ""RangedSummarizedExperiment""),
        c(""RangedSummarizedExperiment"", ""RangedSummarizedExperiment"")
    )
    generic <- getGeneric(fun)
    for (sig in signatures) {
        method <- getMethod(fun, sig)
        checkIdentical(c(""query"", ""subject""), generic@signature)
        checkIdentical(formals(generic@.Data), formals(method@.Data))
    }
}

test_findOverlaps_methods <- function()
{
    identical_SummarizedExperiment <- function(x, y) {
        x@assays <- as(assays(x), ""SimpleAssays"")
        y@assays <- as(assays(y), ""SimpleAssays"")
        identical(x, y)
    }
    for (i in 1:2) {
        x <- rseList[[i]]
        for (j in 1:2) {
            y <- rseList[[j]]

            ## findOverlaps
            target <- findOverlaps(rowRanges(x), rowRanges(y))
            current <- findOverlaps(x, rowRanges(y))
            checkIdentical(target, current)
            current <- findOverlaps(rowRanges(x), y)
            checkIdentical(target, current)
            current <- findOverlaps(x, y)
            checkIdentical(target, current)

            ## countOverlaps
            target <- countOverlaps(rowRanges(x), rowRanges(y))
            current <- countOverlaps(x, rowRanges(y))
            checkIdentical(target, current)
            current <- countOverlaps(rowRanges(x), y)
            checkIdentical(target, current)
            current <- countOverlaps(x, y)
            checkIdentical(target, current)

            ## overlapsAny
            target <- overlapsAny(rowRanges(x), rowRanges(y))
            current <- overlapsAny(x, rowRanges(y))
            checkIdentical(target, current)
            current <- overlapsAny(rowRanges(x), y)
            checkIdentical(target, current)
            current <- overlapsAny(x, y)
            checkIdentical(target, current)

            ## subsetByOverlaps
            target <- subsetByOverlaps(x, rowRanges(y))
            current <- subsetByOverlaps(x, rowRanges(y))
            checkTrue(identical_SummarizedExperiment(target, current))
            current <- subsetByOverlaps(x, y)
            checkTrue(identical_SummarizedExperiment(target, current))

            target <- subsetByOverlaps(rowRanges(x), rowRanges(y))
            current <- subsetByOverlaps(rowRanges(x), y)
            checkIdentical(target, current)
        }
    }
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_makeSummarizedExperimentFromDataFrame.R",".R","2186","57","##

rowNames <- paste0(""GENE"", letters[5:1])

range_info <- list(chr=""chr2"", start = 11:15, end = 12:16,
                      strand = c(""+"", ""-"", ""+"", ""*"", "".""))
expr_info <- list(expr0 = 3:7, expr1 = 8:12, expr2 = 12:16)

df <- as.data.frame(c(range_info, expr_info), row.names = rowNames)
DF <- DataFrame(c(range_info, expr_info), row.names = rowNames)

test_makeSummarizedExperimentFromDataFrame <- function()
{
    validObject(makeSummarizedExperimentFromDataFrame(df))
    validObject(makeSummarizedExperimentFromDataFrame(DF))

    rangesA <- GRanges(as.data.frame(range_info, row.names = rowNames))
    rangesB <- rowRanges(makeSummarizedExperimentFromDataFrame(df))
    # Check rowRanges to be identical
    checkIdentical(rangesA, rangesB)
    # Check assay matrix and expr_info matrix are identical
    checkIdentical(assay(makeSummarizedExperimentFromDataFrame(df)),
                   as.matrix(as.data.frame(expr_info, row.names = rowNames)))
    checkIdentical(assay(makeSummarizedExperimentFromDataFrame(DF)),
                   as.matrix(as.data.frame(expr_info, row.names = rowNames)))

    checkEquals(makeSummarizedExperimentFromDataFrame(df),
                makeSummarizedExperimentFromDataFrame(DF))

    checkException(
        makeSummarizedExperimentFromDataFrame(
            cbind(df, expr3 = letters[seq_len(nrow(df))])))

    checkException(
        makeSummarizedExperimentFromDataFrame(
            cbind(DF, DataFrame(expr3 = letters[seq_len(nrow(df))]))))

    checkIdentical(nrow(df),
                   length(rowRanges(
                       makeSummarizedExperimentFromDataFrame(df))))

    checkIdentical(nrow(DF),
                   length(rowRanges(
                       makeSummarizedExperimentFromDataFrame(DF))))

    checkIdentical(colnames(makeSummarizedExperimentFromDataFrame(df)),
                   names(expr_info))
    checkIdentical(rownames(makeSummarizedExperimentFromDataFrame(df)),
                   rowNames)

    checkIdentical(colnames(makeSummarizedExperimentFromDataFrame(DF)),
                   names(expr_info))
    checkIdentical(rownames(makeSummarizedExperimentFromDataFrame(DF)),
                   rowNames)
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_combine-methods.R",".R","15709","396","test_combineRows_unnamed <- function() {
    se <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    colData(se)$A <- 1
    rowData(se)$A <- 1

    se2 <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10), 
        normalized=matrix(rnorm(1000), ncol=10)))
    colData(se2)$B <- 2
    rowData(se2)$B <- ""B""

    stuff <- combineRows(se, se2, use.names=FALSE)

    # Column data is correctly combined.
    checkIdentical(stuff$A, rep(1, ncol(stuff)))
    checkIdentical(stuff$B, rep(2, ncol(stuff)))

    # Row data is correctly combined.
    checkIdentical(rowData(stuff)$A, rep(c(1, NA), c(nrow(se), nrow(se2))))
    checkIdentical(rowData(stuff)$B, rep(c(NA, ""B""), c(nrow(se), nrow(se2))))

    # Assay data is correctly combined.
    checkIdentical(as.matrix(assay(stuff)), rbind(assay(se), assay(se2)))
    checkIdentical(as.matrix(assay(stuff, 2)), rbind(matrix(NA, nrow(se), ncol(se)), assay(se2, 2)))

    # Unary methods work as expected.
    checkIdentical(se, combineRows(se, delayed=FALSE, use.names=FALSE))
}

test_combineRows_named <- function() {
    se <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    colData(se)$A <- 1
    rowData(se)$A <- 1

    se2 <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=20), 
        normalized=matrix(rnorm(1000), ncol=20)))
    colData(se2)$B <- 2
    rowData(se2)$B <- ""B""

    # This fails, because we expect matching numbers of columns when use.names=TRUE.
    checkException(combineRows(se, se2), silent=TRUE)

    colnames(se) <- letters[1:10]
    colnames(se2) <- letters[3:22]
    stuff <- combineRows(se, se2)

    # Column data is correctly combined
    checkIdentical(colnames(stuff), letters[1:22])
    checkIdentical(stuff$A, rep(c(1, NA), c(ncol(se), 12)))
    checkIdentical(stuff$B, rep(c(NA, 2), c(2, ncol(se2))))

    # Row data is correctly combined.
    checkIdentical(rowData(stuff)$A, rep(c(1, NA), c(nrow(se), nrow(se2))))
    checkIdentical(rowData(stuff)$B, rep(c(NA, ""B""), c(nrow(se), nrow(se2))))

    # Assay data is correctly combined.
    mat <- as.matrix(assay(stuff))
    ref <- rbind(
        cbind(assay(se), matrix(NA, nrow(se), ncol=12)),
        cbind(NA, NA, assay(se2))
    )
    colnames(ref) <- letters[1:22]
    checkIdentical(mat, ref)

    mat <- as.matrix(assay(stuff, 2))
    ref <- rbind(
        matrix(NA, nrow(se), ncol(stuff)),
        cbind(NA, NA, assay(se2, 2))
    )
    colnames(ref) <- letters[1:22]
    checkIdentical(mat, ref)

    # Unary methods work as expected.
    checkIdentical(se, combineRows(se, delayed=FALSE))
}

test_combineRows_assays <- function() {
    # Deep dive into correct assay name behavior.
    se <- SummarizedExperiment(list(matrix(rpois(1000, 10), ncol=10)))
    se2 <- SummarizedExperiment(list(matrix(rpois(1000, 10), ncol=10), 
        matrix(rnorm(1000), ncol=10)))
    colnames(se) <- letters[1:10]
    colnames(se2) <- letters[15:24]
    rownames(se) <- paste0(""GENE_"", 1:100)
    rownames(se2) <- paste0(""SPIKE_"", 1:100)

    # This should fail due to differences in the number of _unnamed_ assays.
    checkException(combineRows(se, se2), silent=TRUE)

    # Either all assays are named, or all are unnamed.
    assays(se) <- assays(se)[c(1, 1)]
    assayNames(se2) <- c(""WHEE"", ""BLAH"")
    checkException(combineRows(se, se2), silent=TRUE)

    assays(se2) <- unname(assays(se2))
    out <- combineRows(se, se2)
    checkIdentical(colnames(out), letters[c(1:10, 15:24)])
    checkIdentical(rownames(out), c(rownames(se), rownames(se2)))
}

test_combineRows_ranges_named <- function() {
    se <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    se2 <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    rownames(se) <- paste0(""GENE_"", 1:100)
    rownames(se2) <- paste0(""SPIKE_"", 1:100)

    # Returns a vanilla SE.
    out <- combineRows(se, se2, use.names=FALSE)
    checkIdentical(as.character(class(out)), ""SummarizedExperiment"")
    checkIdentical(rownames(out), c(rownames(se), rownames(se2)))

    # Returns a GRanges.
    replace <- GRanges(""chrA"", IRanges(1, 1:100))
    names(replace) <- rownames(se)
    rowRanges(se) <- replace

    replace2 <- GRanges(""chrB"", IRanges(1, 1:100))
    names(replace2) <- rownames(se2)
    rowRanges(se2) <- replace2

    suppressWarnings(out <- combineRows(se, se2, use.names=FALSE))
    checkIdentical(rowRanges(out), suppressWarnings(c(replace, replace2)))

    # Testing different objects.
    se3 <- se2
    rowRanges(se3) <- NULL
    rownames(se3) <- rownames(se2)
    suppressWarnings(out <- combineRows(se, se3, use.names=FALSE))
    checkTrue(is(rowRanges(out), ""GRangesList""))
    checkIdentical(unname(lengths(rowRanges(out))), rep(c(1L, 0L), c(nrow(se), nrow(se3))))

    se4 <- se2
    rowRanges(se4) <- as(rowRanges(se4), ""GRangesList"")
    suppressWarnings(out <- combineRows(se, se4, use.names=FALSE))
    expected <- suppressWarnings(as(c(replace, replace2), ""GRangesList""))
    checkIdentical(rowRanges(out), expected)

    # Order doesn't affect conversion to GRL.
    suppressWarnings(out <- combineRows(se4, se, use.names=FALSE))
    expected <- suppressWarnings(as(c(replace2, replace), ""GRangesList""))
    checkIdentical(rowRanges(out), expected)

    suppressWarnings(combined <- rowRanges(combineRows(se, se3, se4, use.names=FALSE)))
    checkIdentical(unname(lengths(combined)), rep(c(1L, 0L, 1L), c(nrow(se), nrow(se3), nrow(se4))))
    suppressWarnings(combined <- rowRanges(combineRows(se3, se, se4, use.names=FALSE)))
    checkIdentical(unname(lengths(combined)), rep(c(0L, 1L, 1L), c(nrow(se3), nrow(se), nrow(se4))))
}

test_combineRows_ranges_unnamed <- function() {
    # Repeating the same suite of tests for SEs without rownames.
    # This checks the correctness of some edge-case behaviors.

    se <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    se2 <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))

    # Returns a vanilla SE.
    out <- combineRows(se, se2, use.names=FALSE)
    checkIdentical(as.character(class(out)), ""SummarizedExperiment"")
    checkIdentical(nrow(out), nrow(se) + nrow(se2))

    # Returns a GRanges.
    rowRanges(se) <- GRanges(""chrA"", IRanges(1, 1:100))
    rowRanges(se2) <- GRanges(""chrB"", IRanges(1, 1:100))
    suppressWarnings(out <- combineRows(se, se2, use.names=FALSE))
    checkIdentical(rowRanges(out), suppressWarnings(c(rowRanges(se), rowRanges(se2))))

    # Testing different objects.
    se3 <- se2
    rowRanges(se3) <- NULL
    suppressWarnings(out <- combineRows(se, se3, use.names=FALSE))
    checkTrue(is(rowRanges(out), ""GRangesList""))
    checkIdentical(unname(lengths(rowRanges(out))), rep(c(1L, 0L), c(nrow(se), nrow(se3))))

    se4 <- se2
    rowRanges(se4) <- as(rowRanges(se4), ""GRangesList"")
    suppressWarnings(out <- combineRows(se, se4, use.names=FALSE))
    expected <- suppressWarnings(as(c(rowRanges(se), rowRanges(se2)), ""GRangesList""))
    checkIdentical(rowRanges(out), expected)

    suppressWarnings(combined <- rowRanges(combineRows(se, se3, se4, use.names=FALSE)))
    checkIdentical(unname(lengths(combined)), rep(c(1L, 0L, 1L), c(nrow(se), nrow(se3), nrow(se4))))

    # Handles partial row names.
    rownames(se) <- paste0(""GENE_"", 1:100)
    suppressWarnings(out <- combineRows(se, se2, use.names=FALSE))
    checkIdentical(rownames(out), c(rownames(se), character(nrow(se2))))
}

test_combineCols_unnamed <- function() {
    se <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    colData(se)$A <- 1L
    rowData(se)$A <- 1

    se2 <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10), 
        normalized=matrix(rnorm(1000), ncol=10)))
    colData(se2)$A <- 2L
    colData(se2)$B <- 3
    rowData(se2)$B <- ""B""

    stuff <- combineCols(se, se2, use.names=FALSE)

    # Column data is correctly combined.
    checkIdentical(stuff$A, rep(1:2, each=10))
    checkIdentical(stuff$B, rep(c(NA, 3), each=10))

    # Row data is correctly combined.
    checkIdentical(rowData(stuff)$A, rep(1, nrow(se)))
    checkIdentical(rowData(stuff)$B, rep(""B"", nrow(se)))

    # Assay data is correctly combined.
    checkIdentical(as.matrix(assay(stuff)), cbind(assay(se), assay(se2)))
    checkIdentical(as.matrix(assay(stuff, 2)), cbind(matrix(NA, nrow(se), ncol(se)), assay(se2, 2)))

    # Unary methods work as expected.
    checkIdentical(se, combineCols(se, delayed=FALSE, use.names=FALSE))
}

test_combineCols_named <- function() {
    se <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=100)))
    colData(se)$A <- 1L
    rowData(se)$A <- 1

    se2 <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=50), 
        normalized=matrix(rnorm(1000), ncol=50)))
    colData(se2)$A <- 2L
    colData(se2)$B <- 3
    rowData(se2)$B <- ""B""

    # This fails, because we expect matching numbers of columns when use.names=TRUE.
    checkException(combineCols(se, se2), silent=TRUE)

    rownames(se) <- letters[1:10]
    rownames(se2) <- letters[3:22]
    stuff <- combineCols(se, se2)

    # Column data is correctly combined
    checkIdentical(rownames(stuff), letters[1:22])
    checkIdentical(stuff$A, rep(1:2, c(ncol(se), ncol(se2))))
    checkIdentical(stuff$B, rep(c(NA, 3), c(ncol(se), ncol(se2))))

    # Row data is correctly combined.
    checkIdentical(rowData(stuff)$A, rep(c(1, NA), c(nrow(se), 12)))
    checkIdentical(rowData(stuff)$B, rep(c(NA, ""B""), c(2, nrow(se2))))

    # Assay data is correctly combined.
    mat <- as.matrix(assay(stuff))
    ref <- cbind(
        rbind(assay(se), matrix(NA, 12, ncol(se))),
        rbind(NA, NA, assay(se2))
    )
    rownames(ref) <- letters[1:22]
    checkIdentical(mat, ref)

    mat <- as.matrix(assay(stuff, 2))
    ref <- cbind(
        matrix(NA, nrow(stuff), ncol(se)),
        rbind(NA, NA, assay(se2, 2))
    )
    rownames(ref) <- letters[1:22]
    checkIdentical(mat, ref)

    # Unary methods work as expected.
    checkIdentical(se, combineCols(se, delayed=FALSE))
}

test_combineCols_assays <- function() {
    # Deep dive into correct assay name behavior.
    se <- SummarizedExperiment(list(matrix(rpois(1000, 10), ncol=10)))
    se2 <- SummarizedExperiment(list(matrix(rpois(1000, 10), ncol=10), 
        matrix(rnorm(1000), ncol=10)))
    colnames(se) <- letters[1:10]
    colnames(se2) <- letters[15:24]
    rownames(se) <- paste0(""GENE_"", 1:100)
    rownames(se2) <- paste0(""SPIKE_"", 1:100)

    # This should fail due to differences in the number of _unnamed_ assays.
    checkException(combineCols(se, se2), silent=TRUE)

    # Either all assays are named, or all are unnamed.
    assays(se) <- assays(se)[c(1, 1)]
    assayNames(se2) <- c(""WHEE"", ""BLAH"")
    checkException(combineCols(se, se2), silent=TRUE)

    assays(se2) <- unname(assays(se2))
    out <- combineCols(se, se2)
    checkIdentical(colnames(out), letters[c(1:10, 15:24)])
    checkIdentical(rownames(out), c(rownames(se), rownames(se2)))
}

test_combineCols_ranges_named <- function() {
    se <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    se2 <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    rownames(se) <- paste0(""GENE_"", 1:100)
    rownames(se2) <- paste0(""GENE_"", 21:120)

    # Checking that an SE is returned.
    out <- combineCols(se, se2, use.names=FALSE)
    checkIdentical(as.character(class(out)), ""SummarizedExperiment"")
    checkIdentical(rownames(out), rownames(se)) # ignoring other row names when use.names=FALSE.

    out <- combineCols(se, se2)
    checkIdentical(as.character(class(out)), ""SummarizedExperiment"")
    checkIdentical(rownames(out), union(rownames(se), rownames(se2))) 

    # Checking that an RSE is returned.
    ref <- GRanges(""chrA"", IRanges(1, 1:120), seqinfo=Seqinfo(seqlengths=c(chrA=1000)))
    names(ref) <- paste0(""GENE_"", 1:120)
    rowRanges(se) <- ref[1:100]
    rowRanges(se2) <- ref[21:120]

    suppressWarnings(out <- combineCols(se, se2, use.names=FALSE)) # should have a warning here due to differences in values.
    checkIdentical(as.character(class(out)), ""RangedSummarizedExperiment"")
    checkIdentical(rowRanges(out), rowRanges(se)) 

    out <- combineCols(se, se2)
    checkIdentical(rowRanges(out), ref)

    # Checking that it works with mixtures of object classes in rowRanges.
    se3 <- se2
    rowRanges(se3) <- NULL
    rownames(se3) <- rownames(se2)

    out <- combineCols(se, se3)
    checkTrue(is(rowRanges(out), ""GRangesList""))
    checkIdentical(rownames(out), paste0(""GENE_"", 1:120)) 
    checkIdentical(unname(lengths(rowRanges(out))), rep(1:0, c(100, 20))) 

    out2 <- combineCols(se3, se) # flipping the order.
    checkIdentical(rownames(out2), paste0(""GENE_"", c(21:120, 1:20)))
    checkIdentical(unname(lengths(rowRanges(out2))), rep(c(1L,0L,1L), c(80, 20, 20)))

    out3 <- combineCols(se, se2, se3) 
    checkIdentical(rowRanges(out3), ref) # avoid unnecessary conversion to a GRL.

    se4 <- se2
    rowRanges(se4) <- as(rowRanges(se4), ""GRangesList"")
    out <- combineCols(se, se4)
    checkIdentical(rowRanges(out), as(ref, ""GRangesList""))

    # Checking that we get the same object class, regardless of ordering of inputs.
    checkIdentical(rowRanges(out), rowRanges(combineCols(se, se3, se4)))
    checkIdentical(rowRanges(out), rowRanges(combineCols(se3, se, se4))[rownames(out)]) 
    checkIdentical(rowRanges(out), rowRanges(combineCols(se3, se4, se))[rownames(out)])

    # Handles conflicting features correctly.
    se5 <- se2
    strand(rowRanges(se5)[1]) <- ""+""
    suppressWarnings(out <- combineCols(se, se5)) # this should emit a warning.
    checkTrue(is(rowRanges(out), ""GRangesList""))
    checkIdentical(unname(lengths(rowRanges(out))), rep(1:0, c(100, 20))) 
}

test_combineCols_ranges_unnamed <- function() {
    # Repeating the same suite of tests for SEs without rownames.
    # This checks the correctness of some edge-case behaviors.

    se <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))
    se2 <- SummarizedExperiment(list(counts=matrix(rpois(1000, 10), ncol=10)))

    # Checking that an SE is returned.
    out <- combineCols(se, se2, use.names=FALSE)
    checkIdentical(as.character(class(out)), ""SummarizedExperiment"")
    checkIdentical(nrow(out), nrow(se)) 
    checkException(combineCols(se, se2), silent=TRUE)

    # Checking that an RSE is returned.
    ref <- GRanges(""chrA"", IRanges(1, 1:120), seqinfo=Seqinfo(seqlengths=c(chrA=1000)))
    rowRanges(se) <- ref[1:100]
    rowRanges(se2) <- ref[21:120]

    suppressWarnings(out <- combineCols(se, se2, use.names=FALSE)) # should have a warning here due to differences in values.
    checkIdentical(as.character(class(out)), ""RangedSummarizedExperiment"")
    checkIdentical(rowRanges(out), rowRanges(se)) 

    # Checking that mixtures of objects work.
    se3 <- se2
    rowRanges(se3) <- NULL

    out <- combineCols(se, se3, use.names=FALSE) # no warning.
    checkIdentical(rowRanges(out), rowRanges(se)) 
    out2 <- combineCols(se3, se, use.names=FALSE)
    checkIdentical(rowRanges(out2), rowRanges(se)) 

    se4 <- se2
    rowRanges(se4) <- as(rowRanges(se4), ""GRangesList"")
    suppressWarnings(out <- combineCols(se, se4, use.names=FALSE)) # has warning.
    checkIdentical(rowRanges(out), as(rowRanges(se), ""GRangesList""))

    se5 <- se
    rowRanges(se5) <- as(rowRanges(se5), ""GRangesList"")
    out <- combineCols(se, se5, use.names=FALSE) # no warning.
    checkIdentical(rowRanges(out), as(rowRanges(se), ""GRangesList""))

    multi.com <- suppressWarnings(combineCols(se, se3, se4, use.names=FALSE))
    checkIdentical(rowRanges(out), rowRanges(multi.com))
}
","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_inter-range-methods.R",".R","1452","53","###

M1 <- matrix(1, 5, 3)
M2 <- matrix(1, 3, 3)
assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
rowRangesList <-
    list(gr=GRanges(""chr1"", IRanges(1:5, 10), Rle(c(""+"", ""-""), 3:2)),
         grl=split(GRanges(""chr1"", IRanges(1:5, 10)), c(1,1,2,2,3)))
names(rowRangesList[[""grl""]]) <- NULL
colData <- DataFrame(x=letters[1:3])

## a list of one SE with GRanges and one with GRangesList
rseList <-
    list(SummarizedExperiment(
           assays=assaysList[[""gr""]],
           rowRanges=rowRangesList[[""gr""]],
           colData=colData),
         SummarizedExperiment(
           assays=assaysList[[""grl""]],
           rowRanges=rowRangesList[[""grl""]],
           colData=colData))


test_interfaces <- function()
{
    generic_functions <- c(""isDisjoint"", ""disjointBins"")
    for (fun in generic_functions) {
        generic <- getGeneric(fun)
        method <- getMethod(fun, ""RangedSummarizedExperiment"")
        checkIdentical(""x"", generic@signature)
        checkIdentical(formals(generic@.Data), formals(method@.Data))
    }
}

test_inter_range_methods <- function()
{
    #for (i in 1:2) {
    for (i in 1L) {
        x <- rseList[[i]]

        ## isDisjoint
        target <- isDisjoint(rowRanges(x))
        current <- isDisjoint(x)
        checkIdentical(target, current)

        ## disjointBins
        target <- disjointBins(rowRanges(x))
        current <- disjointBins(x)
        checkIdentical(target, current)
    }
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_coverage-methods.R",".R","1843","57","###

M1 <- matrix(1, 5, 3)
M2 <- matrix(1, 3, 3)
assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
rowRangesList <-
    list(gr=GRanges(""chr1"", IRanges(1:5, 10)),
         grl=split(GRanges(""chr1"", IRanges(1:5, 10)), c(1,1,2,2,3)))
names(rowRangesList[[""grl""]]) <- NULL
colData <- DataFrame(x=letters[1:3])

## a list of one SE with GRanges and one with GRangesList
rseList <-
    list(SummarizedExperiment(
           assays=assaysList[[""gr""]],
           rowRanges=rowRangesList[[""gr""]],
           colData=colData),
         SummarizedExperiment(
           assays=assaysList[[""grl""]],
           rowRanges=rowRangesList[[""grl""]],
           colData=colData))


test_interfaces <- function()
{
    generic_functions <- ""coverage""
    for (fun in generic_functions) {
        generic <- getGeneric(fun)
        method <- getMethod(fun, ""RangedSummarizedExperiment"")
        checkIdentical(""x"", generic@signature)
        checkIdentical(formals(generic@.Data), formals(method@.Data))
    }
}

test_coverage_RangedSummarizedExperiment <- function()
{
    for (i in 1:2) {
        x <- rseList[[i]]

        target <- coverage(rowRanges(x))
        current <- coverage(x)
        checkIdentical(target, current)

        weight <- runif(length(x))
        ## Issues a warning (in BioC 3.3) when rowRanges(x) is a GRangesList
        ## object, which reveals a problem with how the ""coverage"" method for
        ## GRangesList objects handles the 'weight' argument. The warning is
        ## expected and healthy, don't try to suppress it here. It will go
        ## away when we fix the ""coverage"" method for GRangesList objects
        ## (defined in the GenomicRanges package). 
        target <- coverage(rowRanges(x), weight=weight)
        current <- coverage(x, weight=weight)
        checkIdentical(target, current)
    }
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_makeSummarizedExperimentFromExpressionSet.R",".R","6755","200","M1 <- matrix(1, 5, 3)
M2 <- matrix(1, 3, 3)
assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
rowRangesList <- 
    list(gr=GRanges(""chr1"", IRanges(1:5, 10)), 
         grl=split(GRanges(""chr1"", IRanges(1:5, 10)), c(1,1,2,2,3)))
names(rowRangesList[[""grl""]]) <- NULL
colData <- DataFrame(x=letters[1:3])

## a list of one SE with GRanges and one with GRangesList
rseList <- 
    list(SummarizedExperiment(
           assays=assaysList[[""gr""]], 
           rowRanges=rowRangesList[[""gr""]], 
           colData=colData),
         SummarizedExperiment(
           assays=assaysList[[""grl""]], 
           rowRanges=rowRangesList[[""grl""]], 
           colData=colData))


test_SummarizedExperiment_GenomicRanges_coercion <- function()
{
        eset1 <- ExpressionSet()

        checkTrue(validObject(eset1))

        se1 <- as(eset1, ""RangedSummarizedExperiment"")

        checkTrue(validObject(se1))

        data(""sample.ExpressionSet"", package = ""Biobase"")

        eset2 <- sample.ExpressionSet
        checkTrue(validObject(eset2))

        se2 <- as(eset2, ""RangedSummarizedExperiment"")

        checkTrue(validObject(se2))

        checkIdentical(experimentData(eset2),
                       metadata(se2)$experimentData)

        checkIdentical(annotation(eset2),
                       metadata(se2)$annotation)

        checkIdentical(protocolData(eset2),
                       metadata(se2)$protocolData)

        eset2Assays <- SimpleList(as.list(assayData(eset2)))
        se2Assays <- assays(se2)
        checkIdentical(eset2Assays$exprs, se2Assays$exprs)
        checkIdentical(eset2Assays$se.exprs, se2Assays$se.exprs)

        checkIdentical(featureNames(eset2), rownames(se2))

        checkIdentical(sampleNames(eset2), colnames(se2))
}

test_GenomicRanges_SummarizedExperiment_coercion <- function()
{
        ## empty SE
        simpleSE <- SummarizedExperiment()

        eset1 <- as(simpleSE, ""ExpressionSet"")

        checkTrue(validObject(eset1))

        ## Back and forth empty ES
        simpleES <- ExpressionSet()

        simpleES2 <- as(as(simpleES, ""RangedSummarizedExperiment""),
                        ""ExpressionSet"")

        checkTrue(validObject(simpleES2))

        checkEquals(as.list(assayData(simpleES)),
                    as.list(assayData(simpleES2)))

        ## Simple SE
        simpleSE <- rseList[[1]]
        assayNames(simpleSE) <- ""exprs"" # No warning 'No assay named exprs...""
        eset2 <- as(simpleSE, ""ExpressionSet"")
        checkTrue(validObject(eset2))

        ## The ExpressionSet features should have the data from the
        ## SummarizedExperiment rows if they are from GRanges.
        checkIdentical(pData(featureData(eset2)),
                       as.data.frame(rowRanges(rseList[[1]])))

        # the rowRanges are retained if the object has them to begin with.
        se2_2 <- as(eset2, ""RangedSummarizedExperiment"")
        rr_se2_2 <- unname(rowRanges(se2_2))
        rr_eset2 <- rowRanges(rseList[[1]])
        checkEquals(rr_se2_2, rr_eset2)

        simpleSE <- rseList[[2]]
        assayNames(simpleSE) <- ""exprs"" # No warning 'No assay named exprs...""
        eset3 <- as(simpleSE, ""ExpressionSet"")
        checkTrue(validObject(eset3))

        ## The ExpressionSet features should not have the data from the
        ## SummarizedExperiment rows if they are from GRangesList, but they
        ## should be empty and the same length as the number of ranges.
        checkEquals(unname(NROW(featureData(eset3))),
                    unname(length(rowRanges(rseList[[2]]))))

        data(""sample.ExpressionSet"", package = ""Biobase"")
        eset4 <- sample.ExpressionSet

        eset5 <- as(as(eset4, ""RangedSummarizedExperiment""), ""ExpressionSet"")

        checkTrue(validObject(eset5))

        ## this is necessary because the order in environments is undefined.
        compareLists <- function(x, y) {
            nmsX <- names(x)
            nmsY <- names(y)

            reorderY <- match(nmsY, nmsX)

            checkIdentical(x, y[reorderY])
        }

        compareLists(as.list(assayData(eset4)),
                       as.list(assayData(eset5)))

        checkIdentical(experimentData(eset4),
                       experimentData(eset5))

        checkIdentical(annotation(eset4),
                       annotation(eset5))

        checkIdentical(protocolData(eset4),
                       protocolData(eset5))

        checkIdentical(featureNames(eset4),
                       featureNames(eset5))

        checkIdentical(sampleNames(eset4),
                       sampleNames(eset5))
}

test_GenomicRanges_SummarizedExperiment_coercion_lockedEnvironment <- function()
{
    ## https://github.com/Bioconductor/SummarizedExperiment/issues/43
    se = SummarizedExperiment(list(exprs = matrix(1:10, 5)))
    es1 = es2 = as(se, ""ExpressionSet"")
    original <- exprs(es2)
    checkIdentical(original, exprs(es2))
    exprs(es1)[1, 1] = 2
    checkTrue(!identical(original, exprs(es1)))
    checkIdentical(original, exprs(es2))
}
    
test_GenomicRanges_SummarizedExperiment_coercion_mappingFunctions <- function()
{
    ## naiveRangeMapper
    ## valid object from empty object
    checkTrue(validObject(makeSummarizedExperimentFromExpressionSet(ExpressionSet())))

    ## valid object from sample ExpressionSet
    data(""sample.ExpressionSet"", package = ""Biobase"")
    eset1 <- sample.ExpressionSet
    checkTrue(validObject(makeSummarizedExperimentFromExpressionSet(eset1)))

    ## makeSummarizedExperimentFromExpressionSet should be the same as `as`
    ## with default args
    checkEquals(makeSummarizedExperimentFromExpressionSet(eset1),
                as(eset1, ""RangedSummarizedExperiment""))

    ## probeRangeMapper
    ## valid object from empty object
    checkTrue(validObject(
            makeSummarizedExperimentFromExpressionSet(ExpressionSet(),
                probeRangeMapper)))

    ## valid object from sample ExpressionSet
    se1 <- makeSummarizedExperimentFromExpressionSet(eset1, probeRangeMapper)
    checkTrue(validObject(se1))

    ## Granges returned have rownames that were from the featureNames
    checkTrue(all(rownames(rowRanges(se1)) %in% featureNames(eset1)))

    ## geneRangeMapper
    ## valid object from empty object
    checkTrue(validObject(
            makeSummarizedExperimentFromExpressionSet(ExpressionSet(),
                geneRangeMapper(NULL))))

    ## valid object from sample ExpressionSet
    se2 <- makeSummarizedExperimentFromExpressionSet(eset1,
        geneRangeMapper(""TxDb.Hsapiens.UCSC.hg19.knownGene""))
    checkTrue(validObject(se2))

    ## Granges returned have rownames that were from the featureNames
    checkTrue(all(rownames(rowRanges(se2)) %in% featureNames(eset1)))
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_SummarizedExperiment-class.R",".R","16639","448","M1 <- matrix(1, 5, 3)
M2 <- matrix(1, 3, 3)
mList <- list(M1, M2)
assaysList <- list(M1=SimpleList(m=M1), M2=SimpleList(m=M2))
rowData1 <- DataFrame(id1=LETTERS[1:5])
rowData2 <- S4Vectors:::make_zero_col_DataFrame(3L)
rowDataList <- list(rowData1, rowData2)
colData0 <- DataFrame(x=letters[1:3])

se0List <-
    list(SummarizedExperiment(
           assays=assaysList[[""M1""]],
           rowData=rowData1,
           colData=colData0),
         SummarizedExperiment(
           assays=assaysList[[""M2""]],
           colData=colData0))


test_SummarizedExperiment_construction <- function()
{
    ## empty-ish
    m1 <- matrix(0, 0, 0)
    checkTrue(validObject(new(""SummarizedExperiment"")))
    checkTrue(validObject(SummarizedExperiment()), ""empty constructor"")
    checkTrue(validObject(SummarizedExperiment(SimpleList())))
    checkTrue(validObject(SummarizedExperiment(assays=SimpleList(m1))),
              ""0x0 constructor"")
    checkException(SummarizedExperiment(assays=SimpleList(m1, matrix())),
                   ""assays dim mismatch"", TRUE)

    ## substance
    for (i in seq_along(se0List)) {
        se0 <- se0List[[i]]
        checkTrue(validObject(se0))
        checkIdentical(SimpleList(m=mList[[i]]), assays(se0))
        checkIdentical(rowDataList[[i]], rowData(se0))
        checkIdentical(colData0, colData(se0))
    }

    ## array in assays slot
    ss <- se0List[[1]]
    assays(ss, withDimnames=FALSE) <- SimpleList(array(1:5, c(5,3,2)))
    checkTrue(validObject(ss))
    checkTrue(all(dim(assays(ss[1:3,1:2])[[1]]) == c(3, 2, 2)))

    ## matrix-of-list in assay slot
    m <- matrix(list(), 2, 3, dimnames=list(LETTERS[1:2], letters[1:3]))
    checkTrue(validObject(se <- SummarizedExperiment(m)))
    checkIdentical(m, assay(se))
    checkIdentical(m[,1:2], assay(se[,1:2]))

    ## DataFrame in assay slot
    df <- DataFrame(a=1:3, b=1:3, row.names=LETTERS[1:3])
    checkTrue(validObject(SummarizedExperiment(list(df))))
}

test_SummarizedExperiment_construction_dimnames <- function()
{
    do_tests <- function(m, rowData, colData) {
        ## Some quick tests to make sure that the rowData(), colData(), and
        ## assay() getters handle the rownames and colnames as expected.
        ## Getters are tested more thoroughly in dedicated unit
        ## test_SummarizedExperiment_getters().
        test_rowData_colData_assay <- function(se) {
            checkIdentical(rownames(rowData(se)), rownames(se))
            checkIdentical(rownames(colData(se)), colnames(se))
            a1 <- assay(se, withDimnames=FALSE)
            checkIdentical(dimnames(m), dimnames(a1))
            a1 <- assay(se)
            checkIdentical(rownames(se), rownames(a1))
            checkIdentical(colnames(se), colnames(a1))
        }
        target_rownames <- function() {
            rownames <- rownames(rowData)
            if (is.null(rownames)) rownames(m) else rownames
        }
        target_colnames <- function() {
            colnames <- rownames(colData)
            if (is.null(colnames)) colnames(m) else colnames
        }

        se <- SummarizedExperiment(m)
        checkTrue(validObject(se))
        checkIdentical(rownames(m), rownames(se))
        checkIdentical(colnames(m), colnames(se))
        test_rowData_colData_assay(se)

        se <- SummarizedExperiment(m, rowData=rowData)
        checkTrue(validObject(se))
        checkIdentical(target_rownames(), rownames(se))
        checkIdentical(colnames(m), colnames(se))
        test_rowData_colData_assay(se)

        se <- SummarizedExperiment(m, colData=colData)
        checkTrue(validObject(se))
        checkIdentical(rownames(m), rownames(se))
        checkIdentical(target_colnames(), colnames(se))
        test_rowData_colData_assay(se)

        se <- SummarizedExperiment(m, rowData=rowData, colData=colData)
        checkTrue(validObject(se))
        checkIdentical(target_rownames(), rownames(se))
        checkIdentical(target_colnames(), colnames(se))
        test_rowData_colData_assay(se)
    }

    m <- matrix(0, nrow=4, ncol=3)
    rowData <- DataFrame(stuff=11:14)  # no rownames
    colData <- DataFrame(row.names=letters[1:3])
    do_tests(m, rowData, colData)

    rownames(m) <- paste0(""ROW"", 1:4)
    do_tests(m, rowData, colData)

    colnames(m) <- letters[1:3]
    do_tests(m, rowData, colData)

    dimnames(m) <- NULL
    rownames(rowData) <- LETTERS[1:4]
    do_tests(m, rowData, colData)

    rownames(m) <- LETTERS[1:4]
    do_tests(m, rowData, colData)

    ## Empty strings in the dimnames are supported.
    dimnames(m) <- list(c(""A"", """", """", ""D""), c(""a"", ""b"", """"))
    rowData <- DataFrame(row.names=rownames(m))
    colData <- DataFrame(row.names=colnames(m))
    do_tests(m, rowData, colData)

    ## NAs in the dimnames.
    ## WARNINGS:
    ## - NAs in the **rownames** are tolerated but will cause problems
    ##   downstream e.g. they break the rowData() getter unless
    ##   'use.names=FALSE' is used.
    ## - NAs in the **colnames** are not and cannot be supported at the moment!
    ##   Right now they break the SummarizedExperiment() constructor in an ugly
    ##   way (error message not super helpful):
    ##       > SummarizedExperiment(m)
    ##       Error in DataFrame(x = seq_len(ncol(a1)), row.names = nms) :
    ##         missing values in 'row.names'
    ##   This should be improved.
    ## - At the root of these problems is the fact that at the moment
    ##   DataFrame objects do not support NAs in their rownames (this is
    ##   a of BioC 3.16).
    ## Bottom line: NAs in the dimnames of a SummarizedExperiment object
    ## should be avoided at all cost. One way to deal with them is to
    ## replace them with empty strings (""""). It's not clear whether the
    ## SummarizedExperiment() constructor should automatically take care
    ## of that (with a warning), or if it should just fail with an informative
    ## error message asking the user to ""fix"" the dimnames on the assay
    ## **before** calling the constructor. One possible downside of the latter
    ## is that there is no guarantee that the dimnames of an assay can be
    ## modified in general (think on-disk assay), and, even if they are, doing
    ## so could be costly (e.g. trigger a copy of a huge object). But if the
    ## SummarizedExperiment() constructor were to take care of the fix, it
    ## wouldn't need to modify the dimnames of the supplied assay.
    rownames(m)[c(1L, 3L)] <- NA
    se <- SummarizedExperiment(m)
    checkTrue(validObject(se))
}

test_SummarizedExperiment_getters <- function()
{
    for (i in seq_along(se0List)) {
        se0 <- se0List[[i]]

        ## dim, dimnames
        checkIdentical(c(nrow(mList[[i]]), nrow(colData0)), dim(se0))
        checkIdentical(NULL, dimnames(se0))

        ## col / metadata
        checkIdentical(rowDataList[[i]], rowData(se0))
        checkIdentical(colData0, colData(se0))
        checkIdentical(list(), metadata(se0))
    }

    ## assays
    m0 <- matrix(0L, 0, 0)
    m1 <- matrix(0, 0, 0)
    a <- SimpleList(a=m0, b=m1)
    checkIdentical(a, assays(SummarizedExperiment(assays=a)))
    ## assay
    checkException(
        assay(SummarizedExperiment()), ""0-length assay"", TRUE)
    checkIdentical(m0,
        assay(SummarizedExperiment(assays=a)), ""default assay"")
    checkIdentical(m1,
        assay(SummarizedExperiment(assays=a), 2),
        ""assay, numeric index"")
    checkException(
        assay(SummarizedExperiment(assays=a), 3),
        ""invalid assay index"", TRUE)
    checkIdentical(m1,
        assay(SummarizedExperiment(assays=a), ""b""),
        ""assay, character index"")
    checkException(
        assay(SummarizedExperiment(assays=a), ""c""),
        ""invalid assay name"", TRUE)
}

test_SummarizedExperiment_setters <- function()
{
    for (i in seq_along(se0List)) {
        se0 <- se0List[[i]]

        ## row / col / metadata<-
        se1 <- se0
        rowData <- rowDataList[[i]]

        rowData <- rowData[rev(seq_len(nrow(rowData))),,drop=FALSE]
        rowData(se1) <- rowData
        checkIdentical(rowData, rowData(se1))

        colData <- colData0[rev(seq_len(nrow(colData0))),,drop=FALSE]
        colData(se1) <- colData
        checkIdentical(colData, colData(se1))

        ## The rowData (alias for mcols) setter recycles the supplied
        ## DataFrame. This is consistent with what the mcols/elementMetadata
        ## setter does on Vector objects in general.
        rowData(se1) <- rowData(se0)[1:2,,drop=FALSE]
        idx <- rep(1:2, length.out=length(se1))
        target_se1_rowData <- rowData(se0)[idx,,drop=FALSE]
        checkIdentical(target_se1_rowData, rowData(se1))

        ## The colData setter does NOT recycle the supplied DataFrame.
        checkException(colData(se1) <- colData(se0)[1:2,,drop=FALSE],
                       ""incorrect col dimensions"", TRUE)

        lst <- list(""foo"", ""bar"")
        metadata(se1) <- lst
        checkIdentical(lst, metadata(se1))

        ## assay / assays
        se1 <- se0
        assay(se1) <- assay(se1)+1
        checkIdentical(assay(se0)+1, assay(se1))
        se1 <- se0
        assay(se1, 1) <- assay(se1, 1) + 1
        checkIdentical(assay(se0, ""m"") + 1, assay(se1, ""m""))
        se1 <- se0
        assay(se1, ""m"") <- assay(se1, ""m"") + 1
        checkIdentical(assay(se0, ""m"")+1, assay(se1, ""m""))

        ## dimnames<-
        se1 <- se0
        dimnames <- list(letters[seq_len(nrow(se1))],
                         LETTERS[seq_len(ncol(se1))])
        rownames(se1) <- dimnames[[1]]
        colnames(se1) <- dimnames[[2]]
        checkIdentical(dimnames, dimnames(se1))
        colData1 <- colData0
        row.names(colData1) <- dimnames[[2]]
        checkIdentical(colData1, colData(se1))
        se1 <- se0
        dimnames(se1) <- dimnames
        checkIdentical(dimnames, dimnames(se1))
        dimnames(se1) <- NULL
        checkIdentical(NULL, dimnames(se1))
    }

    ## With empty strings in the dimnames.
    m <- matrix(1:12, nrow=4, dimnames=list(c(a=""A"", b=""B"", c="""", d=""D""),
                                            c(X=""x"", Y=""y"", Z="""")))
    se <- se0 <- SummarizedExperiment(m)

    assay(se) <- m  # should be a no-op
    checkIdentical(se0, se)

    dimnames(se) <- dimnames(m)  # should be a no-op
    checkIdentical(se0, se)
}

test_SummarizedExperiment_subset <- function()
{
    for (i in seq_along(se0List)) {
        se0 <- se0List[[i]]

        ## numeric
        se1 <- se0[2:3,]
        checkIdentical(c(2L, ncol(se0)), dim(se1))
        checkIdentical(rowData(se1), rowData(se0)[2:3,,drop=FALSE])
        checkIdentical(colData(se1), colData(se0))
        se1 <- se0[,2:3]
        checkIdentical(c(nrow(se0), 2L), dim(se1))
        checkIdentical(rowData(se1), rowData(se0))
        checkIdentical(colData(se1), colData(se0)[2:3,,drop=FALSE])
        se1 <- se0[2:3, 2:3]
        checkIdentical(c(2L, 2L), dim(se1))
        checkIdentical(colData(se1), colData(se0)[2:3,,drop=FALSE])

        ## character
        se1 <- se0
        dimnames(se1) <- list(LETTERS[seq_len(nrow(se1))],
                               letters[seq_len(ncol(se1))])
        ridx <- c(""B"", ""C"")
        checkException(se1[LETTERS,], ""i-index out of bounds"", TRUE)
        cidx <- c(""b"", ""c"")
        checkIdentical(colData(se1[,cidx]), colData(se1)[cidx,,drop=FALSE])
        checkIdentical(colData(se1[,""a""]), colData(se1)[""a"",,drop=FALSE])
        checkException(se1[,letters], ""j-index out of bounds"", TRUE)

        ## logical
        se1 <- se0
        dimnames(se1) <- list(LETTERS[seq_len(nrow(se1))],
                               letters[seq_len(ncol(se1))])
        checkEquals(se1, se1[TRUE,])
        checkIdentical(c(0L, ncol(se1)), dim(se1[FALSE,]))
        checkEquals(se1, se1[,TRUE])
        checkIdentical(c(nrow(se1), 0L), dim(se1[,FALSE]))
        idx <- c(TRUE, FALSE)               # recycling
        se2 <- se1[idx,]
        se2 <- se1[,idx]
        checkIdentical(colData(se1)[idx,,drop=FALSE], colData(se2))

        ## Rle
        se1 <- se0
        rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(se1))
        checkIdentical(assays(se1[rle]), assays(se1[Rle(rle)]))
    }

    ## 0 columns
    se <- SummarizedExperiment(matrix(integer(0), nrow=5))
    checkIdentical(dim(se[1:5, ]), c(5L, 0L))
    ## 0 rows
    se <- SummarizedExperiment(colData=DataFrame(samples=1:10))
    checkIdentical(dim(se[ ,1:5]), c(0L, 5L))
}

test_SummarizedExperiment_subsetassign <- function()
{
    for (i in seq_along(se0List)) {
        se0 <- se0List[[i]]
        dimnames(se0) <- list(LETTERS[seq_len(nrow(se0))],
                               letters[seq_len(ncol(se0))])
        ## rows
        se1 <- se0
        se1[1:2,] <- se1[2:1,]
        checkIdentical(colData(se0), colData(se1))
        checkIdentical(c(metadata(se0), metadata(se0)), metadata(se1))
        ## Rle
        se1rle <- se1Rle <- se0
        rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(se1))
        se1rle[rle,] <- se1rle[rle,]
        se1Rle[Rle(rle),] <- se1Rle[Rle(rle),]
        checkIdentical(assays(se1rle), assays(se1Rle))
        ## cols
        se1 <- se0
        se1[,1:2] <- se1[,2:1,drop=FALSE]
        checkIdentical(colData(se0)[2:1,,drop=FALSE],
                       colData(se1)[1:2,,drop=FALSE])
        checkIdentical(colData(se0)[-(1:2),,drop=FALSE],
                       colData(se1)[-(1:2),,drop=FALSE])
        checkIdentical(c(metadata(se0), metadata(se0)), metadata(se1))
    }

    ## full replacement
    se1 <- se2 <- se0List[[1]]
    se1[,] <- se2
    checkIdentical(se1, se2)
}

test_SummarizedExperiment_assays_4d <- function()
{
    ## construction/validation
    A <- array(0, c(3, 2, 5, 4), list(c(""x1"", ""x2"", ""x3""),
                                      c(""y1"", ""y2""),
                                      NULL,
                                      c(""t1"", ""t2"", ""t3"", ""t4"")))
    B <- array(0, c(3, 2, 6),    list(c(""x1"", ""x2"", ""x3""),
                                      c(""y1"", ""y2""),
                                      NULL))
    assays0 <- SimpleList(A=A, B=B)
    checkTrue(validObject(SummarizedExperiment(assays0)))

    dimnames(B)[[2]] <- c(""y1"", ""oops"")
    assays0 <- SimpleList(A=A, B=B)
    checkException(SummarizedExperiment(assays0))

    dimnames(B)[1:2] <- dimnames(A)[1:2]
    C <- array(0, c(3, 2, 4),    list(NULL,
                                      c(""y1"", ""y2""),
                                      c(""z1"", ""z2"", ""z3"", ""z4"")))
    D <- array(0, c(3, 2, 7, 2), list(NULL,
                                      NULL,
                                      NULL,
                                      c(""t1"", ""t2"")))
    E <- array(0, c(3, 2, 0))

    assays0 <- SimpleList(A=A, B=B, C=C, D=D, E=E)
    se0 <- se1 <- SummarizedExperiment(assays0)
    dimnames(se0) <- NULL
    checkTrue(validObject(se0, complete=TRUE))
    checkTrue(validObject(se1, complete=TRUE))

    ## dimnames
    checkIdentical(NULL, dimnames(se0))
    checkIdentical(dimnames(A)[1:2], dimnames(se1))

    ## assays
    for (i in seq_along(assays0)) {
        target <- assays0[[i]]
        checkIdentical(target, assay(se0, i, withDimnames=FALSE))
        checkIdentical(target, assay(se1, i, withDimnames=FALSE))
    }

    target0 <- target1 <- dimnames(assays0[[1]])
    target0[1:2] <- list(NULL)
    checkIdentical(target0, dimnames(assay(se0, 1)))
    checkIdentical(target1, dimnames(assay(se1, 1)))

    checkIdentical(NULL, dimnames(assay(se0, 2)))
    checkIdentical(dimnames(assays0[[2]]), dimnames(assay(se1, 2)))

    target0 <- target1 <- dimnames(assays0[[3]])
    target0[1:2] <- list(NULL)
    checkIdentical(target0, dimnames(assay(se0, 3)))
    target1[1:2] <- dimnames(se1)
    checkIdentical(target1, dimnames(assay(se1, 3)))

    target0 <- target1 <- dimnames(assays0[[4]])
    checkIdentical(target0, dimnames(assay(se0, 4)))
    target1[1:2] <- dimnames(se1)
    checkIdentical(target1, dimnames(assay(se1, 4)))

    checkIdentical(NULL, dimnames(assay(se0, 5)))
    target1 <- c(dimnames(se1), list(NULL))
    checkIdentical(target1, dimnames(assay(se1, 5)))

    ## [
    se2 <- se1[3:2, ]
    checkIdentical(A[3:2, , , , drop=FALSE],
                   assay(se2, 1, withDimnames=FALSE))
    checkIdentical(B[3:2, , , drop=FALSE],
                   assay(se2, 2, withDimnames=FALSE))
    checkIdentical(C[3:2, , , drop=FALSE],
                   assay(se2, 3, withDimnames=FALSE))

    ## [<-
    A1 <- A; A1[1, , , ] <- A[1, , , , drop=FALSE] + 1
    assays(se1[1, ], withDimnames=FALSE)[[1]] <-
        1 + assays(se1[1, ], withDimnames=FALSE)[[1]]
    checkIdentical(A1, assays(se1, withDimnames=FALSE)[[1]])
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_Assays-class.R",".R","3026","89","test_bind_Assays <- function() {
    ## unnamed -- map by position
    l1 <- list(matrix(1, 3, 4), matrix(2, 3, 4))
    l2 <- list(matrix(3, 3, 4), matrix(4, 3, 4))
    a1 <- Assays(l1)
    a2 <- Assays(l2)

    target <- Map(rbind, l1, l2)
    current <- rbind(a1, a2)
    checkTrue(is(current, ""SimpleAssays""))
    checkIdentical(as(target, ""SimpleList""), as(current, ""SimpleList""))

    target <- Map(cbind, l1, l2)
    current <- cbind(a1, a2)
    checkTrue(is(current, ""SimpleAssays""))
    checkIdentical(as(target, ""SimpleList""), as(current, ""SimpleList""))

    ## named -- map by name
    l1 <- list(x=matrix(1, 3, 4), y=matrix(2, 3, 4))
    l2 <- list(y=matrix(4, 3, 4), x=matrix(3, 3, 4))
    a1 <- Assays(l1)
    a2 <- Assays(l2)

    target <- Map(rbind, l1, l2[match(names(l2), names(l1))])
    current <- rbind(a1, a2)
    checkTrue(is(current, ""SimpleAssays""))
    checkIdentical(as(target, ""SimpleList""), as(current, ""SimpleList""))

    target <- Map(cbind, l1, l2[match(names(l2), names(l1))])
    current <- cbind(a1, a2)
    checkTrue(is(current, ""SimpleAssays""))
    checkIdentical(as(target, ""SimpleList""), as(current, ""SimpleList""))
}

test_bind_higher_order_Assays <- function() {
    ## unnamed -- map by position
    l1 <- list(array(1, dim = c(3, 4, 5, 6)),
               array(2, dim = c(3, 4, 5, 6)))
    l2 <- list(array(4, dim = c(3, 4, 5, 6)),
               array(3, dim = c(3, 4, 5, 6)))
    a1 <- Assays(l1)
    a2 <- Assays(l2)

    target <- Map(arbind, l1, l2)
    current <- rbind(a1, a2)
    checkTrue(is(current, ""SimpleAssays""))
    checkIdentical(as(target, ""SimpleList""), as(current, ""SimpleList""))

    target <- Map(acbind, l1, l2)
    current <- cbind(a1, a2)
    checkTrue(is(current, ""SimpleAssays""))
    checkIdentical(as(target, ""SimpleList""), as(current, ""SimpleList""))

    ## named -- map by name
    l1 <- list(x = array(1, dim = c(3, 4, 5, 6)),
               y = array(2, dim = c(3, 4, 5, 6)))
    l2 <- list(y = array(4, dim = c(3, 4, 5, 6)),
               x = array(3, dim = c(3, 4, 5, 6)))
    a1 <- Assays(l1)
    a2 <- Assays(l2)

    target <- Map(arbind, l1, l2[match(names(l2), names(l1))])
    current <- rbind(a1, a2)
    checkTrue(is(current, ""SimpleAssays""))
    checkIdentical(as(target, ""SimpleList""), as(current, ""SimpleList""))

    target <- Map(acbind, l1, l2[match(names(l2), names(l1))])
    current <- cbind(a1, a2)
    checkTrue(is(current, ""SimpleAssays""))
    checkIdentical(as(target, ""SimpleList""), as(current, ""SimpleList""))
}

test_bind_error_on_incompatible_dimension_Assays <- function() {
    l1 <- list(x = array(1, dim = c(3, 4, 5, 6)),
               y = array(2, dim = c(3, 4, 5, 6)))
    l2 <- list(y = matrix(4, 3, 4), x = matrix(3, 3, 4))
    a1 <- Assays(l1)
    a2 <- Assays(l2)

    ## arbind
    checkException(rbind(a1, a2), silent = TRUE)
    checkException(arbind(l1[[1]], l2[[1]]), silent = TRUE)

    ## acbind
    checkException(cbind(a1, a2), silent = TRUE)
    checkException(acbind(l1[[1]], l2[[1]]), silent = TRUE)
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_nearest-methods.R",".R","2068","71","###

M1 <- matrix(1, 5, 3)
M2 <- matrix(1, 3, 3)
assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
rowRangesList <-
    list(gr=GRanges(""chr1"", IRanges(1:5, 10)),
         grl=split(GRanges(""chr1"", IRanges(1:5, 10)), c(1,1,2,2,3)))
names(rowRangesList[[""grl""]]) <- NULL
colData <- DataFrame(x=letters[1:3])

## a list of one SE with GRanges and one with GRangesList
rseList <-
    list(SummarizedExperiment(
           assays=assaysList[[""gr""]],
           rowRanges=rowRangesList[[""gr""]],
           colData=colData),
         SummarizedExperiment(
           assays=assaysList[[""grl""]],
           rowRanges=rowRangesList[[""grl""]],
           colData=colData))


.GENERIC_SIGNATURES <- list(
    precede=c(""x"", ""subject""),
    follow=c(""x"", ""subject""),
    nearest=c(""x"", ""subject""),
    distance=c(""x"", ""y""),
    distanceToNearest=c(""x"", ""subject"")
)

test_interfaces <- function()
{
    method_signatures <- list(
        c(""RangedSummarizedExperiment"", ""ANY""),
        c(""ANY"", ""RangedSummarizedExperiment""),
        c(""RangedSummarizedExperiment"", ""RangedSummarizedExperiment"")
    )
    for (fun in names(.GENERIC_SIGNATURES)) {
        generic <- getGeneric(fun)
        checkIdentical(.GENERIC_SIGNATURES[[fun]], generic@signature)
        for (sig in method_signatures) {
            method <- getMethod(fun, sig)
            checkIdentical(formals(generic@.Data), formals(method@.Data))
        }
    }
}

test_nearest_methods <- function()
{
    #for (i in 1:2) {
    for (i in 1L) {
        x <- rseList[[i]]
        #for (j in 1:2) {
        for (j in 1L) {
            y <- rseList[[j]]
            for (fun in names(.GENERIC_SIGNATURES)) {
                fun <- get(fun)
                target <- fun(rowRanges(x), rowRanges(y))
                current <- fun(x, rowRanges(y))
                checkIdentical(target, current)
                current <- fun(rowRanges(x), y)
                checkIdentical(target, current)
                current <- fun(x, y)
                checkIdentical(target, current)
            }
        }
    }
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_RangedSummarizedExperiment-class.R",".R","13617","392","library(digest)

.singleDispatch <-
    c(""duplicated"", ""end"", ""end<-"", ""granges"", ""ranges"", 
      ""seqinfo"", ""seqinfo<-"", ""seqnames"", ""start"", ""start<-"",
      ""strand"", ""width"", ""width<-"")

.twoDispatch <- c(""pcompare"", ""Compare"")

.otherFuns <- c(""is.unsorted"", ""order"", ""rank"", ""sort"")

M1 <- matrix(1, 5, 3)
M2 <- matrix(1, 3, 3)
mList <- list(M1, M2)
assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
rowRangesList <- 
    list(gr=GRanges(""chr1"", IRanges(1:5, 10)), 
         grl=split(GRanges(""chr1"", IRanges(1:5, 10)), c(1,1,2,2,3)))
names(rowRangesList[[""grl""]]) <- NULL
colData <- DataFrame(x=letters[1:3])

## a list of one SE with GRanges and one with GRangesList
rseList <- 
    list(SummarizedExperiment(
           assays=assaysList[[""gr""]], 
           rowRanges=rowRangesList[[""gr""]], 
           colData=colData),
         SummarizedExperiment(
           assays=assaysList[[""grl""]], 
           rowRanges=rowRangesList[[""grl""]], 
           colData=colData))


test_RangedSummarizedExperiment_construction <- function()
{
    ## empty-ish
    m1 <- matrix(0, 0, 0)
    checkTrue(validObject(new(""RangedSummarizedExperiment"")))

    ## substance
    for (i in seq_along(rseList)) {
        rse <- rseList[[i]]
        checkTrue(validObject(rse))
        checkIdentical(SimpleList(m=mList[[i]]), assays(rse))
        checkIdentical(rowRangesList[[i]], rowRanges(rse))
        checkIdentical(DataFrame(x=letters[1:3]), colData(rse))
    }

    ## array in assays slot
    ss <- rseList[[1]]
    assays(ss) <- SimpleList(array(1:5, c(5,3,2)))
    checkTrue(validObject(ss))
    checkTrue(all(dim(assays(ss[1:3,1:2])[[1]]) == c(3, 2, 2)))
}

test_RangedSummarizedExperiment_getters <- function()
{
    for (i in seq_along(rseList)) {
        rse <- rseList[[i]]
        rowRanges <- rowRangesList[[i]]

        ## dim, dimnames
        checkIdentical(c(length(rowRanges), nrow(colData)), dim(rse))
        checkIdentical(NULL, dimnames(rse))

        ## row / col / metadata
        checkIdentical(rowRanges, rowRanges(rse))
        checkIdentical(colData, colData(rse))
        checkIdentical(list(), metadata(rse))
    }
}

test_RangedSummarizedExperiment_setters <- function()
{
    for (i in seq_along(rseList)) {
        rse <- rseList[[i]]
        rowRanges <- rowRangesList[[i]]

        ## row / col / metadata<-
        ss1 <- rse
        revData <- rev(rowRanges)
        rowRanges(ss1) <- revData
        checkIdentical(revData, rowRanges(ss1))
        checkException(rowRanges(ss1) <- rowRanges(rse)[1:2,,drop=FALSE],
                       ""incorrect row dimensions"", TRUE)
        revData <- colData[rev(seq_len(nrow(colData))),,drop=FALSE]
        colData(ss1) <- revData
        checkIdentical(revData, colData(ss1))
        checkException(colData(ss1) <- colData(rse)[1:2,,drop=FALSE],
                       ""incorrect col dimensions"", TRUE)
        lst <- list(""foo"", ""bar"")
        metadata(ss1) <- lst
        checkIdentical(lst, metadata(ss1))

        ## assay / assays
        ss1 <- rse
        assay(ss1) <- assay(ss1)+1
        checkIdentical(assay(rse)+1, assay(ss1))
        ss1 <- rse
        assay(ss1, 1) <- assay(ss1, 1) + 1
        checkIdentical(assay(rse, ""m"") + 1, assay(ss1, ""m""))
        ss1 <- rse
        assay(ss1, ""m"") <- assay(ss1, ""m"") + 1
        checkIdentical(assay(rse, ""m"")+1, assay(ss1, ""m""))

        ## dimnames<-
        ss1 <- rse
        dimnames <- list(letters[seq_len(nrow(ss1))],
                         LETTERS[seq_len(ncol(ss1))])
        rownames(ss1) <- dimnames[[1]]
        colnames(ss1) <- dimnames[[2]]
        checkIdentical(dimnames, dimnames(ss1))
        rowRanges1 <- rowRanges
        names(rowRanges1) <- dimnames[[1]]
        checkIdentical(rowRanges1, rowRanges(ss1))
        colData1 <- colData
        row.names(colData1) <- dimnames[[2]]
        checkIdentical(colData1, colData(ss1))
        ss1 <- rse
        dimnames(ss1) <- dimnames
        checkIdentical(dimnames, dimnames(ss1))
        dimnames(ss1) <- NULL
        checkIdentical(NULL, dimnames(ss1))
    }
}

test_RangedSummarizedExperiment_subset <- function()
{
    for (i in seq_along(rseList)) {
        rse <- rseList[[i]]
        rowRanges <- rowRangesList[[i]]

        ## numeric
        ss1 <- rse[2:3,]
        checkIdentical(c(2L, ncol(rse)), dim(ss1))
        checkIdentical(rowRanges(ss1), rowRanges(rse)[2:3,])
        checkIdentical(colData(ss1), colData(rse))
        ss1 <- rse[,2:3]
        checkIdentical(c(nrow(rse), 2L), dim(ss1))
        checkIdentical(rowRanges(ss1), rowRanges(rse))
        checkIdentical(colData(ss1), colData(rse)[2:3,,drop=FALSE])
        ss1 <- rse[2:3, 2:3]
        checkIdentical(c(2L, 2L), dim(ss1))
        checkIdentical(rowRanges(ss1), rowRanges(rse)[2:3,,drop=FALSE])
        checkIdentical(colData(ss1), colData(rse)[2:3,,drop=FALSE])

        ## character
        ss1 <- rse
        dimnames(ss1) <- list(LETTERS[seq_len(nrow(ss1))],
                              letters[seq_len(ncol(ss1))])
        ridx <- c(""B"", ""C"")
        checkIdentical(rowRanges(ss1[ridx,]), rowRanges(ss1)[ridx,])
        checkIdentical(rowRanges(ss1[""C"",]), rowRanges(ss1)[""C"",,drop=FALSE])
        checkException(ss1[LETTERS,], ""i-index out of bounds"", TRUE)
        cidx <- c(""b"", ""c"")
        checkIdentical(colData(ss1[,cidx]), colData(ss1)[cidx,,drop=FALSE])
        checkIdentical(colData(ss1[,""a""]), colData(ss1)[""a"",,drop=FALSE])
        checkException(ss1[,letters], ""j-index out of bounds"", TRUE)

        ## logical
        ss1 <- rse
        dimnames(ss1) <- list(LETTERS[seq_len(nrow(ss1))],
                              letters[seq_len(ncol(ss1))])
        checkEquals(ss1, ss1[TRUE,])
        checkIdentical(c(0L, ncol(ss1)), dim(ss1[FALSE,]))
        checkEquals(ss1, ss1[,TRUE])
        checkIdentical(c(nrow(ss1), 0L), dim(ss1[,FALSE]))
        idx <- c(TRUE, FALSE)               # recycling
        ss2 <- ss1[idx,]
        checkIdentical(rowRanges(ss1)[idx,,drop=FALSE], rowRanges(ss2))
        ss2 <- ss1[,idx]
        checkIdentical(colData(ss1)[idx,,drop=FALSE], colData(ss2))

        ## Rle
        ss1 <- rse
        rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(ss1))
        checkIdentical(rowRanges(ss1[rle]), rowRanges(ss1[Rle(rle)]))
        checkIdentical(assays(ss1[rle]), assays(ss1[Rle(rle)]))
    }

    ## 0 columns
    se <- SummarizedExperiment(rowRanges=GRanges(""chr1"", IRanges(1:10, width=1)))
    checkIdentical(dim(se[1:5, ]), c(5L, 0L))
    ## 0 rows 
    se <- SummarizedExperiment(colData=DataFrame(samples=1:10))
    checkIdentical(dim(se[ ,1:5]), c(0L, 5L))
}

test_RangedSummarizedExperiment_subsetassign <- function()
{
    for (i in seq_along(rseList)) {
        rse <- rseList[[i]]
        dimnames(rse) <- list(LETTERS[seq_len(nrow(rse))],
                              letters[seq_len(ncol(rse))])
        ## rows
        ss1 <- rse
        ss1[1:2,] <- ss1[2:1,]
        checkIdentical(rowRanges(rse)[2:1,], rowRanges(ss1)[1:2,])
        checkIdentical(rowRanges(rse[-(1:2),]), rowRanges(ss1)[-(1:2),])
        checkIdentical(colData(rse), colData(ss1))
        checkIdentical(c(metadata(rse), metadata(rse)), metadata(ss1))
        ## Rle
        ss1rle <- ss1Rle <- rse
        rle <- rep(c(TRUE, FALSE), each=3, length.out=nrow(ss1))
        ss1rle[rle,] <- ss1rle[rle,]
        ss1Rle[Rle(rle),] <- ss1Rle[Rle(rle),]
        checkIdentical(rowRanges(ss1rle), rowRanges(ss1Rle))
        checkIdentical(assays(ss1rle), assays(ss1Rle))
        ## cols
        ss1 <- rse
        ss1[,1:2] <- ss1[,2:1,drop=FALSE]
        checkIdentical(colData(rse)[2:1,,drop=FALSE],
                       colData(ss1)[1:2,,drop=FALSE])
        checkIdentical(colData(rse)[-(1:2),,drop=FALSE],
                       colData(ss1)[-(1:2),,drop=FALSE])
        checkIdentical(rowRanges(rse), rowRanges(ss1))
        checkIdentical(c(metadata(rse), metadata(rse)), metadata(ss1))
    }

    ## full replacement
    ss1 <- ss2 <- rseList[[1]]
    rowRanges(ss2) <- rev(rowRanges(ss2))
    ss1[,] <- ss2
    checkIdentical(ss1, ss2)
}

quiet <- suppressWarnings
test_RangedSummarizedExperiment_cbind <- function()
## requires matching ranges
{
    ## empty
    se <- SummarizedExperiment()
    empty <- cbind(se, se)
    checkTrue(all.equal(se, empty))

    ## different ranges 
    se1 <- rseList[[1]]
    se2 <- se1[2:4]
    rownames(se2) <- month.name[seq_len(nrow(se2))]
    checkException(quiet(cbind(se1, se2)), silent=TRUE)

    ## same ranges 
    se1 <- rseList[[1]]
    se2 <- se1[,1:2]
    colnames(se2) <- month.name[seq_len(ncol(se2))]
    res <- cbind(se1, se2)
    checkTrue(nrow(res) == 5)
    checkTrue(ncol(res) == 5)
    ## rowRanges
    rowData(se1) <- DataFrame(""one""=1:5)
    rowData(se2) <- DataFrame(""two""=6:10)
    res <- quiet(cbind(se1, se2))
    checkIdentical(names(mcols(rowRanges(res))), c(""one"", ""two""))
    rowData(se2) <- DataFrame(""one""=6:10, ""two""=6:10)
    checkException(cbind(se1, se2), silent=TRUE)
    ## colData
    checkTrue(nrow(colData(res)) == 5)
    ## assays 
    se1 <- rseList[[1]]
    se2 <- se1[,1:2]
    assays(se1) <- SimpleList(""m""=matrix(rep(""m"", 15), nrow=5),
                              ""a""=array(rep(""a"", 30), c(5,3,2)))
    assays(se2) <- SimpleList(""m""=matrix(LETTERS[1:10], nrow=5),
                              ""a""=array(LETTERS[1:20], c(5,2,2)))
    res <- cbind(se1, se2) ## same variables
    checkTrue(nrow(res) == 5)
    checkTrue(ncol(res) == 5)
    checkTrue(all.equal(dim(assays(res)$m), c(5L, 5L)))
    checkTrue(all.equal(dim(assays(res)$a), c(5L, 5L, 2L)))
    names(assays(se1)) <- c(""mm"", ""aa"")
    checkException(cbind(se1, se2), silent=TRUE) ## different variables
}

test_RangedSummarizedExperiment_rbind <- function()
## requires matching samples 
{
    ## empty
    se <- SummarizedExperiment()
    empty <- rbind(se, se)
    checkTrue(all.equal(se, empty))

    ## different samples 
    se1 <- rseList[[1]]
    se2 <- se1[,1]
    checkException(quiet(rbind(se1, se2)), silent=TRUE)

    ## same samples 
    se1 <- rseList[[1]]
    se2 <- se1
    rownames(se2) <- LETTERS[seq_len(nrow(se2))]
    res <- rbind(se1, se2)
    checkTrue(nrow(res) == 10)
    checkTrue(ncol(res) == 3)
    ## rowRanges
    rowData(se1) <- DataFrame(""one""=1:5)
    rowData(se2) <- DataFrame(""two""=6:10)
    checkIdentical(
        rbind(
            cbind(rowData(se1), two = NA_integer_),
            cbind(one = NA_integer_, rowData(se2))
        ),
        rowData(rbind(se1, se2), use.names = FALSE)
    )
    ## colDat
    se1 <- rseList[[1]]
    se2 <- se1
    colData(se2) <- DataFrame(""one""=1:3, ""two""=4:6)
    res <- quiet(rbind(se1, se2))
    checkTrue(ncol(colData(res)) == 3)
    ## assays 
    se1 <- rseList[[1]]
    se2 <- se1
    assays(se1) <- SimpleList(""m""=matrix(rep(""m"", 15), nrow=5),
                              ""a""=array(rep(""a"", 30), c(5,3,2)))
    assays(se2) <- SimpleList(""m""=matrix(LETTERS[1:15], nrow=5),
                              ""a""=array(LETTERS[1:30], c(5,3,2)))
    res <- rbind(se1, se2) ## same variables
    checkTrue(nrow(res) == 10)
    checkTrue(ncol(res) == 3)
    checkTrue(all.equal(dim(assays(res)$m), c(10L, 3L)))
    checkTrue(all.equal(dim(assays(res)$a), c(10L, 3L, 2L)))
    names(assays(se1)) <- c(""mm"", ""aa"")
    checkException(rbind(se1, se2), silent=TRUE) ## different variables
}

test_RangedSummarizedExperiment_GRanges_API <- function()
{
    ## are we targetting the correct API? signature for
    ## RangedSummarizedExperiment method should match signature for
    ## GenomicRanges or similar, as in each test below

    for (.fun in .singleDispatch) {
        generic <- getGeneric(.fun)
        method <- getMethod(.fun, ""RangedSummarizedExperiment"")
        checkIdentical(""x"", generic@signature)
        checkIdentical(formals(generic@.Data), formals(method@.Data))
    }

    ## FIXME: pcompare, Compare

    .sig <- ""RangedSummarizedExperiment""
    for (.fun in .otherFuns) {
        generic <- getGeneric(.fun)
        method <- getMethod(.fun, ""RangedSummarizedExperiment"")
        checkIdentical(formals(generic@.Data), formals(method@.Data))
    }        
}

test_RangedSummarizedExperiment_GRanges_values <- function()
{
    x <- rseList[[1]]
    isAssign <- grep(""<-$"", .singleDispatch, value=TRUE)
    .funs <- setdiff(.singleDispatch, isAssign)
    ## 'exp' created after manual inspection of results
    exp <- setNames(c(""02dde"", ""80339"", ""093c1"", ""410ea"", ""a18ce"",
                      ""ec53a"", ""35e2c"", ""625d9"", ""3c90a""), .funs)
    obs <- sapply(.funs, function(.fun) {
        substr(digest(getGeneric(.fun)(x)), 1, 5)
    })
    checkIdentical(exp, obs)

    .funs <- isAssign
    .gets <- sub(""<-$"", """", isAssign)
    for (i in seq_along(isAssign)) {
        ## self-assignment isomorphism
        value <- getGeneric(.gets[[i]])(x)
        x1 <- do.call(isAssign[[i]], list(x, value=value))
        checkIdentical(x, x1)
    }
}

test_RangedSummarizedExperiment_split <- function()
{
    gr <- GRanges(Rle(c(""A"", ""B""), c(2, 3)), IRanges(1:5, 10))
    se <- SummarizedExperiment(M1, rowRanges=gr, colData=colData)
    ## FIXME: unname should not be necessary
    obs <- split(se, seqnames(se))
    exp <- SimpleList(A=se[1:2], B=se[3:5])
    checkEquals(obs, exp)
}

test_RangedSummarizedExperiment_NULL_rowRanges <- function()
{
    se <- SummarizedExperiment(M1, colData=colData)
    rse <- rseList[[1L]]
    rowRanges(rse) <- NULL
    checkTrue(identical(rowRanges(rse), NULL))
    checkTrue(is(rse, ""SummarizedExperiment"") &&
        !is(rse, ""RangedSummarizedExperiment""))
    checkTrue(identical(rowRanges(se), NULL))
}
","R"
"Assay","Bioconductor/SummarizedExperiment","inst/unitTests/test_intra-range-methods.R",".R","3294","95","###

M1 <- matrix(1, 5, 3)
M2 <- matrix(1, 3, 3)
assaysList <- list(gr=SimpleList(m=M1), grl=SimpleList(m=M2))
rowRangesList <-
    list(gr=GRanges(""chr1"", IRanges(1:5, 10), Rle(c(""+"", ""-""), 3:2)),
         grl=split(GRanges(""chr1"", IRanges(1:5, 10)), c(1,1,2,2,3)))
names(rowRangesList[[""grl""]]) <- NULL
colData <- DataFrame(x=letters[1:3])

## a list of one SE with GRanges and one with GRangesList
rseList <-
    list(SummarizedExperiment(
           assays=assaysList[[""gr""]],
           rowRanges=rowRangesList[[""gr""]],
           colData=colData),
         SummarizedExperiment(
           assays=assaysList[[""grl""]],
           rowRanges=rowRangesList[[""grl""]],
           colData=colData))


test_interfaces <- function()
{
    generic_functions <- c(""shift"", ""narrow"", ""resize"",
                           ""flank"", ""promoters"",
                           ""restrict"", ""trim"")
    for (fun in generic_functions) {
        generic <- getGeneric(fun)
        method <- getMethod(fun, ""RangedSummarizedExperiment"")
        checkIdentical(""x"", generic@signature)
        checkIdentical(formals(generic@.Data), formals(method@.Data))
    }
}

test_intra_range_methods <- function()
{
    identical_SummarizedExperiment <- function(x, y) {
        x@assays <- as(assays(x), ""SimpleAssays"")
        y@assays <- as(assays(y), ""SimpleAssays"")
        identical(x, y)
    }
    #for (i in 1:2) {
    for (i in 1L) {
        ## shift
        target <- rseList[[i]]
        rowRanges(target) <- shift(rowRanges(target), 50)
        current <- shift(rseList[[i]], 50)
        checkTrue(identical_SummarizedExperiment(target, current))

        ## narrow
        target <- rseList[[i]]
        rowRanges(target) <- narrow(rowRanges(target), 2, -2)
        current <- narrow(rseList[[i]], 2, -2)
        checkTrue(identical_SummarizedExperiment(target, current))

        ## resize
        target <- rseList[[i]]
        rowRanges(target) <- resize(rowRanges(target), 8)
        current <- resize(rseList[[i]], 8)
        checkTrue(identical_SummarizedExperiment(target, current))

        ## flank
        target <- rseList[[i]]
        rowRanges(target) <- flank(rowRanges(target), 5, both=TRUE)
        current <- flank(rseList[[i]], 5, both=TRUE)
        checkTrue(identical_SummarizedExperiment(target, current))

        ## promoters
        target <- rseList[[i]]
        rowRanges(target) <- promoters(rowRanges(target),
                                       upstream=20, downstream=5)
        current <- promoters(rseList[[i]], upstream=20, downstream=5)
        checkTrue(identical_SummarizedExperiment(target, current))

        ## restrict
        target <- rseList[[i]]
        rowRanges(target) <- restrict(rowRanges(target), start=2, end=3,
                                      keep.all.ranges=TRUE)
        current <- restrict(rseList[[i]], start=2, end=3,
                            keep.all.ranges=TRUE)
        checkTrue(identical_SummarizedExperiment(target, current))

        ## trim
        suppressWarnings(seqlengths(rseList[[i]]) <- 8)
        target <- rseList[[i]]
        rowRanges(target) <- trim(rowRanges(target))
        current <- trim(rseList[[i]])
        checkTrue(identical_SummarizedExperiment(target, current))
        seqlengths(rseList[[i]]) <- NA
    }
}

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/scripts/Find_and_update_objects/collect_rda_objects_to_update.R",".R","5070","134","### =========================================================================
### collect_rda_objects_to_update.R
### -------------------------------------------------------------------------
###
### This script performs STEP 3 of the ""Find and update objects"" procedure
### described in the README file located in the same folder.
###
### Before you run this script, make sure you performed STEPS 1 & 2.
### See README file for more information.
###
### Then to run STEP 3 in ""batch"" mode:
###
###   cd <dir/you/want/to/search>  # RDA_OBJECTS file should be here
###   R CMD BATCH collect_rda_objects_to_update.R \
###              >collect_rda_objects_to_update.log 2>&1 &
###
### The output of STEP 3 is a file created in the current directory and named
### RDA_OBJECTS_TO_UPDATE. It is a subset of input file RDA_OBJECTS.
###

INFILE <- ""RDA_OBJECTS""
OUTFILE <- ""RDA_OBJECTS_TO_UPDATE""

library(BiocManager)
library(SummarizedExperiment)

if (FALSE) {
### Unfortunately, loading all the required packages in the main process will
### sometimes hit the maximal number of DLLs that can be loaded (""maximal
### number of DLLs reached..."" infamous error).
.check_classes <- function(classes, package)
{
    suppressWarnings(suppressPackageStartupMessages(
        library(package, character.only=TRUE, quietly=TRUE)
    ))
    sapply(classes, function(class) {
           extends(class, ""SummarizedExperiment"") ||
           extends(class, ""RangedSummarizedExperiment"")
    })
}
}

### We check the classes in a subprocess to work around the ""maximal number
### of DLLs reached..."" infamous error.
.check_classes <- function(classes, package)
{
    classes_in1string <- paste0(""\"""", classes, ""\"""")
    classes_in1string <- paste0(""c("",
                                paste(classes_in1string, collapse="", ""),
                                "")"")
    outfile <- file.path(tempdir(), paste0(package, ""_class_summary""))
    input <- c(""suppressWarnings(suppressPackageStartupMessages("",
       sprintf(""    library(%s)"", ""SummarizedExperiment""),
               ""))"",
               ""suppressWarnings(suppressPackageStartupMessages("",
       sprintf(""    library(%s)"", package),
               ""))"",
       sprintf(""classes <- %s"", classes_in1string),
               ""ok <- sapply(classes, function(class) {"",
               ""    extends(class, \""SummarizedExperiment\"") ||"",
               ""    extends(class, \""RangedSummarizedExperiment\"")"",
               ""})"",
               ""class_summary <- data.frame(class=classes, ok=unname(ok))"",
       sprintf(""write.table(class_summary, file=\""%s\"", sep=\""\t\"")"", outfile)
    )
    command <- file.path(R.home(""bin""), ""R"")
    args <- c(""--vanilla"", ""--slave"")
    system2(command, args=args, input=input)
    class_summary <- read.table(outfile, stringsAsFactors=FALSE)
    file.remove(outfile)
    stopifnot(identical(class_summary[ , ""class""], classes))  # sanity check
    setNames(class_summary[ , ""ok""], classes)
}

collectRdaObjectsToUpdate <- function(rda_objects, outfile="""")
{
    rda_objects2 <- unique(rda_objects[ , c(""objclass"", ""objclass_pkg"")])
    objclass2 <- rda_objects2[ , ""objclass""]
    objclass_pkg2 <- rda_objects2[ , ""objclass_pkg""]
    idx <- which(duplicated(objclass2))
    if (length(idx) != 0L) {
        msg <- c(""the following classes are defined in more than 1 package: "",
                 paste0(unique(objclass2[idx]), collapse="", ""))
        warning(msg)
    }
    pkg2class <- split(objclass2, objclass_pkg2)
    pkg2class[c(""."", "".GlobalEnv"")] <- NULL
    pkgs <- names(pkg2class)

    ## Install missing packages.
    installed_pkgs <- rownames(installed.packages())
    missing_pkgs <- setdiff(pkgs, installed_pkgs)
    if (length(missing_pkgs) != 0L) {
        install(missing_pkgs)
        installed_pkgs <- rownames(installed.packages())
        missing_pkgs <- setdiff(pkgs, installed_pkgs)
        if (length(missing_pkgs) != 0L) {
            ## Some packages could not be installed.
            pkgs <- intersect(pkgs, installed_pkgs)
            pkg2class <- pkg2class[pkgs]
        }
    }

    ## Check classes, one package at a time.
    class2ok <- unlist(
        lapply(seq_along(pkgs),
            function(i) {
                pkg <- pkgs[[i]]
                cat(""["", i , ""/"", length(pkgs), ""] Check classes defined "",
                    ""in package "", pkg, "" ... "", sep="""")
                ans <- .check_classes(pkg2class[[pkg]], pkg)
                cat(""OK\n"")
                ans
            }
        )
    )

    ## Write output to file.
    objclass <- rda_objects[ , ""objclass""]
    ok <- class2ok[objclass]
    ok[is.na(ok)] <- FALSE
    rda_objects_to_update <- rda_objects[ok, , drop=FALSE]
    rda_objects_to_update <- do.call(
        paste,
        c(as.list(rda_objects_to_update), list(sep=""\t""))
    )
    writeLines(rda_objects_to_update, con=outfile)
}

rda_objects <- read.table(INFILE, stringsAsFactors=FALSE)
colnames(rda_objects) <- c(""rda_file"", ""objname"", ""objclass"", ""objclass_pkg"")
collectRdaObjectsToUpdate(rda_objects, outfile=OUTFILE)

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/scripts/Find_and_update_objects/collect_rda_objects.R",".R","2500","69","### =========================================================================
### collect_rda_objects.R
### -------------------------------------------------------------------------
###
### This script performs STEP 2 of the ""Find and update objects"" procedure
### described in the README file located in the same folder.
###
### Before you run this script, make sure you performed STEP 1, that is, you
### need to generate input file RDA_FILES. This can be achieved with
### something like:
###
###   cd <dir/you/want/to/search>
###   find . -type d -name '.git' -prune -o -type f -print | \
###       grep -Ei '\.(rda|RData)$' >RDA_FILES
###
### See README file for more information.
###
### Then to run STEP 2 in ""batch"" mode:
###
###   cd <dir/you/want/to/search>  # RDA_FILES file should be here
###   R CMD BATCH collect_rda_objects.R >collect_rda_objects.log 2>&1 &
###
### This can take a couple of hours to complete...
###
### The output of STEP 2 is a file created in the current directory and named
### RDA_OBJECTS. It has 1 line per serialized object and the 4 following
### fields (separated by tabs):
###   1. Path to rda file (as found in input file RDA_FILES).
###   2. Name of object in rda file.
###   3. Class of object in rda file.
###   4. Package where class of object is defined.
###

INFILE <- ""RDA_FILES""
OUTFILE <- ""RDA_OBJECTS""

collect_rda_objects <- function(rda_files, outfile="""")
{
    cat("""", file=outfile)  # create (or overwrite) empty output file
    for (i in seq_along(rda_files)) {
        rda_file <- rda_files[[i]]

        cat(""["", i , ""/"", length(rda_files), ""] Loading "", rda_file, "" ... "",
            sep="""")
        envir <- new.env(parent=emptyenv())
        load(rda_file, envir=envir)
        cat(""OK\n"")

        for (objname in names(envir)) {
            obj <- get(objname, envir=envir)
            objclass <- class(obj)
            objclass_pkg <- attr(objclass, ""package"")

            ## Fix 'objclass' and 'objclass_pkg' (they both need to be
            ## character vectors of length 1 before we pass them to paste()).
            objclass <- objclass[[1L]]
            if (is.null(objclass_pkg))
                objclass_pkg <- "".""
            outline <- paste(rda_file, objname, objclass, objclass_pkg,
                             sep=""\t"")
            cat(outline, ""\n"", sep="""", file=OUTFILE, append=TRUE)
        }
    }
}

rda_files <- read.table(INFILE, stringsAsFactors=FALSE)[[1L]]
collect_rda_objects(rda_files, outfile=OUTFILE)

","R"
"Assay","Bioconductor/SummarizedExperiment","inst/scripts/Find_and_update_objects/update_rda_objects.R",".R","2331","71","### =========================================================================
### update_rda_objects.R
### -------------------------------------------------------------------------
###
### This script performs STEP 4 of the ""Find and update objects"" procedure
### described in the README file located in the same folder.
###
### Before you run this script, make sure you performed STEPS 1 & 2 & 3.
### See README file for more information.
###
### Then to run STEP 4 in ""batch"" mode:
###
###   cd <dir/you/want/to/search>  # RDA_OBJECTS_TO_UPDATE file should be here
###   R CMD BATCH update_rda_objects.R >update_rda_objects.log 2>&1 &
###

INFILE <- ""RDA_OBJECTS_TO_UPDATE""

library(SummarizedExperiment)

.update_objects <- function(envir)
{
    updated <- FALSE
    for (objname in names(envir)) {
        obj <- get(objname, envir=envir, inherits=FALSE)
        objclass <- class(obj)
        objclass_pkg <- attr(objclass, ""package"")
        if (!is.null(objclass_pkg)) {
            suppressWarnings(suppressPackageStartupMessages(
                library(objclass_pkg, character.only=TRUE, quietly=TRUE)
            ))
        }
        if (!SummarizedExperiment:::.has_SummarizedExperiment_internal_structure(obj))
            next()

        cat(""  Updating "", objname, "" ... "", sep="""")
        obj <- updateObject(obj)
        validObject(obj, complete=TRUE)
        assign(objname, obj, envir=envir, inherits=FALSE)
        cat(""OK\n"")
        updated <- TRUE
    }
    updated
}

updateRdaObjects <- function(rda_objects)
{
    rda_files <- unique(rda_objects[ , ""rda_file""])
    for (i in seq_along(rda_files)) {
        rda_file <- rda_files[[i]]

        cat(""["", i , ""/"", length(rda_files), ""] Loading "", rda_file, "" ... "",
            sep="""")
        envir <- new.env(parent=emptyenv())
        load(rda_file, envir=envir)
        cat(""OK\n"")

        if (.update_objects(envir)) {
            cat(""  Saving updated objects to "", rda_file, "" ... "", sep="""")
            save(list=names(envir), file=rda_file, envir=envir, compress=""xz"")
            cat(""OK\n"")
        }
    }
}

rda_objects_to_update <- read.table(INFILE, stringsAsFactors=FALSE)
colnames(rda_objects_to_update) <- c(""rda_file"", ""objname"",
                                     ""objclass"", ""objclass_pkg"")
updateRdaObjects(rda_objects_to_update)

","R"
"Assay","Bioconductor/SummarizedExperiment","R/nearest-methods.R",".R","2981","118","### =========================================================================
### nearest (and related) methods
### -------------------------------------------------------------------------
###


### precede & follow

for (f in c(""precede"", ""follow"")) {
    setMethod(f, c(""RangedSummarizedExperiment"", ""ANY""),
        function(x, subject, select=c(""arbitrary"", ""all""), ignore.strand=FALSE)
        {
            x <- rowRanges(x)
            callGeneric()
        }
    )
    setMethod(f, c(""ANY"", ""RangedSummarizedExperiment""),
        function(x, subject, select=c(""arbitrary"", ""all""), ignore.strand=FALSE)
        {
            subject <- rowRanges(subject)
            callGeneric()
        }
    )
    setMethod(f, c(""RangedSummarizedExperiment"", ""RangedSummarizedExperiment""),
        function(x, subject, select=c(""arbitrary"", ""all""), ignore.strand=FALSE)
        {
            x <- rowRanges(x)
            subject <- rowRanges(subject)
            callGeneric()
        }
    )
}

### nearest

setMethod(""nearest"", c(""RangedSummarizedExperiment"", ""ANY""),
    function(x, subject, select=c(""arbitrary"", ""all""), ignore.strand=FALSE)
    {
        x <- rowRanges(x)
        callGeneric()
    }
)

setMethod(""nearest"", c(""ANY"", ""RangedSummarizedExperiment""),
    function(x, subject, select=c(""arbitrary"", ""all""), ignore.strand=FALSE)
    {
        subject <- rowRanges(subject)
        callGeneric()
    }
)

setMethod(""nearest"", c(""RangedSummarizedExperiment"",
                       ""RangedSummarizedExperiment""),
    function(x, subject, select=c(""arbitrary"", ""all""), ignore.strand=FALSE)
    {
        x <- rowRanges(x)
        subject <- rowRanges(subject)
        callGeneric()
    }
)

### distance

setMethod(""distance"", c(""RangedSummarizedExperiment"", ""ANY""),
    function(x, y, ignore.strand=FALSE, ...)
    {
        x <- rowRanges(x)
        callGeneric()
    }
)

setMethod(""distance"", c(""ANY"", ""RangedSummarizedExperiment""),
    function(x, y, ignore.strand=FALSE, ...)
    {
        y <- rowRanges(y)
        callGeneric()
    }
)

setMethod(""distance"", c(""RangedSummarizedExperiment"",
                        ""RangedSummarizedExperiment""),
    function(x, y, ignore.strand=FALSE, ...)
    {
        x <- rowRanges(x)
        y <- rowRanges(y)
        callGeneric()
    }
)

### distanceToNearest

setMethod(""distanceToNearest"", c(""RangedSummarizedExperiment"", ""ANY""),
    function(x, subject, ignore.strand=FALSE, ...)
    {
        x <- rowRanges(x)
        callGeneric()
    }
)

setMethod(""distanceToNearest"", c(""ANY"", ""RangedSummarizedExperiment""),
    function(x, subject, ignore.strand=FALSE, ...)
    {
        subject <- rowRanges(subject)
        callGeneric()
    }
)

setMethod(""distanceToNearest"", c(""RangedSummarizedExperiment"",
                                 ""RangedSummarizedExperiment""),
    function(x, subject, ignore.strand=FALSE, ...)
    {
        x <- rowRanges(x)
        subject <- rowRanges(subject)
        callGeneric()
    }
)

","R"
"Assay","Bioconductor/SummarizedExperiment","R/intra-range-methods.R",".R","2017","90","### =========================================================================
### Intra-range methods
### -------------------------------------------------------------------------
###


setMethod(""shift"", ""RangedSummarizedExperiment"",
    function(x, shift=0L, use.names=TRUE)
    {
        x0 <- x
        x <- rowRanges(x)
        rowRanges(x0) <- callGeneric()
        x0
    }
)

setMethod(""narrow"", ""RangedSummarizedExperiment"",
    function(x, start=NA, end=NA, width=NA, use.names=TRUE)
    {
        x0 <- x
        x <- rowRanges(x)
        rowRanges(x0) <- callGeneric()
        x0
    }
)

setMethod(""resize"", ""RangedSummarizedExperiment"",
    function(x, width, fix=""start"", use.names=TRUE, ignore.strand=FALSE)
    {
        x0 <- x
        x <- rowRanges(x)
        rowRanges(x0) <- callGeneric()
        x0
    }
)

setMethod(""flank"", ""RangedSummarizedExperiment"", 
    function(x, width, start=TRUE, both=FALSE, use.names=TRUE,
             ignore.strand=FALSE)
    {
        x0 <- x
        x <- rowRanges(x)
        rowRanges(x0) <- callGeneric()
        x0
    }
)

setMethod(""promoters"", ""RangedSummarizedExperiment"",
    function(x, upstream=2000, downstream=200)
    {
        x0 <- x
        x <- rowRanges(x)
        rowRanges(x0) <- callGeneric()
        x0
    }
)

setMethod(""terminators"", ""RangedSummarizedExperiment"",
    function(x, upstream=2000, downstream=200)
    {
        x0 <- x
        x <- rowRanges(x)
        rowRanges(x0) <- callGeneric()
        x0
    }
)

### Because 'keep.all.ranges' is FALSE by default, it will break if some
### ranges are dropped.
setMethod(""restrict"", ""RangedSummarizedExperiment"",
    function(x, start=NA, end=NA, keep.all.ranges=FALSE, use.names=TRUE)
    {
        x0 <- x
        x <- rowRanges(x)
        rowRanges(x0) <- callGeneric()
        x0
    }
)

setMethod(""trim"", ""RangedSummarizedExperiment"",
    function(x, use.names=TRUE)
    {
        x0 <- x
        x <- rowRanges(x)
        rowRanges(x0) <- callGeneric()
        x0
    }
)

","R"
"Assay","Bioconductor/SummarizedExperiment","R/makeSummarizedExperimentFromExpressionSet.R",".R","9100","272","##
## makeSummarizedExperimentFromExpressionSet

## coercion

.from_rowRanges_to_FeatureData <- function(from)
{
    if (is(from, ""GRanges"")) {
        fd <- .from_GRanges_to_FeatureData(from)
    } else if (is(from, ""GRangesList"")) {
        fd <- .from_GRangesList_to_FeatureData(from)
    } else {
        stop(""class "", sQuote(class(from)),
             "" is not a supported type for rowRanges coercion"")
    }
    featureNames(fd) <- names(from)
    fd
}

.from_GRanges_to_FeatureData <- function(from)
{
    data <- as.data.frame(from)

    ## the first mcols are automatically included in the data.frame from
    ## as.data.frame, the secondary mcols holds the metadata for the first
    ## metadata columns.
    metaData <- mcols(mcols(from, use.names=FALSE), use.names=FALSE)
    if (is.null(metaData)) {
        metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data)))
    } else {
        metaData <- as.data.frame(metaData)
    }
    AnnotatedDataFrame(data, metaData)
}
.from_GRangesList_to_FeatureData <- function(from)
{
    data <- as.data.frame(mcols(from, use.names=FALSE))

    ## the first mcols are automatically included in the data.frame from
    ## as.data.frame, the secondary mcols holds the metadata for the first
    ## metadata columns.
    metaData <- mcols(mcols(from, use.names=FALSE), use.names=FALSE)
    if (is.null(metaData)) {
        metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data)))
    } else {
        metaData <- as.data.frame(metaData)
    }
    AnnotatedDataFrame(data, metaData)
}

.from_AnnotatedDataFrame_to_DataFrame <- function(from)
{
    df <- DataFrame(pData(from), row.names=rownames(from))
    mcols(df) <- DataFrame(varMetadata(from))
    df
}

.from_DataFrame_to_AnnotatedDataFrame <- function(df)
{
    data <- as(df, ""data.frame"")
    metaData <- mcols(df, use.names=FALSE)
    if (is.null(metaData)) {
        metaData <- as.data.frame(matrix(ncol=0, nrow=NCOL(data)))
    } else {
        metaData <- as(metaData, ""data.frame"")
    }
    AnnotatedDataFrame(data, metaData)
}

## If the ExpressionSet has featureData with range information make
## GRanges out of that, otherwise make an empty GRangesList with names
## from the featureNames
naiveRangeMapper <- function(from)
{
    nms <- featureNames(from)
    res <- tryCatch({
        makeGRangesFromDataFrame(pData(featureData(from)),
                                 keep.extra.columns = TRUE)
    }, error = function(e) {
        res <- relist(GRanges(), vector(""list"", length=length(nms)))
        mcols(res) <- .from_AnnotatedDataFrame_to_DataFrame(featureData(from))
        res
    })
    names(res) <- nms
    res
}

# Simple ProbeId to Range mapper
# Probes with multiple ranges are dropped
# The sign of the chromosome location is assumed to contain the strand
# information
probeRangeMapper <- function(from)
{
    annotation <- annotation(from)
    if (identical(annotation, character(0))) {
        return(naiveRangeMapper(from))
    }
    if (requireNamespace(""annotate"", quietly = TRUE)) {
        annotationPackage <- annotate::annPkgName(annotation)
        test <- require(annotationPackage, character.only = TRUE,
                        quietly = TRUE)
        if (test) {
            db <- get(annotationPackage, envir = asNamespace(annotationPackage))
            pid <- featureNames(from)
            locs <- AnnotationDbi::select(
                db, pid, columns = c(""CHR"", ""CHRLOC"", ""CHRLOCEND""))
            locs <- na.omit(locs)
            dups <- duplicated(locs$PROBEID)
            if (any(dups)) {
                locs <- locs[!dups, , drop = FALSE]
            }
            strand <- ifelse(locs$CHRLOC > 0, ""+"", ""-"")
            res <- GRanges(seqnames = locs$CHR,
                           ranges = IRanges(abs(locs$CHRLOC),
                                            abs(locs$CHRLOCEND)),
                           strand = strand)
            names(res) <- locs$PROBEID

            if (NROW(res) < length(pid)) {
                warning(length(pid) - NROW(res),
                        "" probes could not be mapped."", call. = FALSE)
            }
            res
        } else {
            stop(""Failed to load "", sQuote(annotationPackage), "" package"",
                 call. = FALSE)
        }
    } else {
        stop(""Failed to load annotate package"", call. = FALSE)
    }
}

# Simple ProbeId to Gene mapper
# Is there a way to get the txDb given the annotation package?
geneRangeMapper <- function(txDbPackage, key = ""ENTREZID"")
{
    function(from) {
        annotation <- annotation(from)
        if (identical(annotation, character(0))) {
            return(naiveRangeMapper(from))
        }
        if (requireNamespace(""annotate"", quietly = TRUE)) {
            annotationPackage <- annotate::annPkgName(annotation)
            test <- require(annotationPackage, character.only = TRUE,
                            quietly = TRUE)
            if (test) {
                db <- get(annotationPackage,
                          envir = asNamespace(annotationPackage))
                pid <- featureNames(from)
                probeIdToGeneId <-
                    AnnotationDbi::mapIds(db, pid, key, ""PROBEID"")
                geneIdToProbeId <-
                    setNames(names(probeIdToGeneId), probeIdToGeneId)

                if (requireNamespace(txDbPackage, quietly = TRUE)) {
                    txDb <- get(txDbPackage, envir = asNamespace(txDbPackage))
                    genes <- GenomicFeatures::genes(txDb)
                    probesWithAMatch <-
                        probeIdToGeneId[probeIdToGeneId %in% names(genes)]
                    res <- genes[probesWithAMatch]
                    names(res) <- geneIdToProbeId[names(res)]
                    if (NROW(res) < length(pid)) {
                        warning(length(pid) - NROW(res),
                                "" probes could not be mapped."", call. = FALSE)
                    }
                    res
                } else {
                    stop(""Failed to load "", sQuote(txDbPackage), "" package"",
                         call. = FALSE)
                }

            } else {
                stop(""Failed to load "", sQuote(annotationPackage), "" package"",
                     call. = FALSE)
            }
        } else {
            stop(""Failed to load annotate package"", call. = FALSE)
        }
    }
}

makeSummarizedExperimentFromExpressionSet <-
    function(from, mapFun = naiveRangeMapper, ...)
{
    mapFun <- match.fun(mapFun)
    rowRanges <- mapFun(from, ...)
    matches <- match(names(rowRanges),
                     featureNames(from),
                     nomatch = 0)
    from <- from[matches, drop = FALSE]
    assays <- as.list(assayData(from))
    colData <- .from_AnnotatedDataFrame_to_DataFrame(phenoData(from))
    metadata <- SimpleList(
        experimentData = experimentData(from),
        annotation = annotation(from),
        protocolData = protocolData(from)
    )

    SummarizedExperiment(
        assays = assays,
        rowRanges = rowRanges,
        colData = colData,
        metadata = metadata
    )
}

setAs(""ExpressionSet"", ""RangedSummarizedExperiment"", function(from)
{
    makeSummarizedExperimentFromExpressionSet(from)
})

setAs(""ExpressionSet"", ""SummarizedExperiment"", function(from)
{
    as(makeSummarizedExperimentFromExpressionSet(from), ""SummarizedExperiment"")
})

setAs(""RangedSummarizedExperiment"", ""ExpressionSet"",
      function(from)
{
    assayData <- list2env(as.list(assays(from)))

    numAssays <- length(assayData)

    if (numAssays == 0) {
        assayData$exprs <- new(""matrix"")
    } else if (!""exprs"" %in% ls(assayData)) {
        ## if there isn't an exprs assay we need to pick one as exprs,
        ## so rename the first element exprs and issue a warning.
        exprs <- ls(assayData)[[1]]
        warning(""No assay named "", sQuote(""exprs""), "" found, renaming "",
                exprs, "" to "", sQuote(""exprs""), ""."")
        assayData[[""exprs""]] <- assayData[[exprs]]
        rm(list=exprs, envir=assayData)
    }
    lockEnvironment(assayData, bindings = TRUE)

    featureData <- .from_rowRanges_to_FeatureData(rowRanges(from))
    phenoData <- .from_DataFrame_to_AnnotatedDataFrame(colData(from))

    metadata <- metadata(from)

    experimentData <- if (!is.null(metadata$experimentData)) {
        metadata$experimentData
    } else {
        MIAME()
    }

    annotation <- if (!is.null(metadata$annotation)) {
        metadata$annotation
    } else {
        character()
    }

    protocolData <- if (!is.null(metadata$protocolData)) {
        metadata$protocolData
    } else {
        annotatedDataFrameFrom(assayData, byrow=FALSE)
    }

    ExpressionSet(assayData,
                  phenoData = phenoData,
                  featureData = featureData,
                  experimentData = experimentData,
                  annotation = annotation,
                  protocolData = protocolData
                  )
})

setAs(""SummarizedExperiment"", ""ExpressionSet"", function(from)
    as(as(from, ""RangedSummarizedExperiment""), ""ExpressionSet"")
)
","R"
"Assay","Bioconductor/SummarizedExperiment","R/combine-methods.R",".R","10494","317","# Contains methods for combineRows and combineCols. These serve as more
# fault-tolerant relaxed counterparts to rbind and cbind, respectively.

setMethod(""combineRows"", ""SummarizedExperiment"", function(x, ..., delayed=TRUE, fill=NA, use.names=TRUE) {
    all.se <- list(x, ...)

    # Combining the rowData.
    all.rd <- lapply(all.se, rowData)
    tryCatch({
        com.rd <- do.call(combineRows, all.rd)
    }, error=function(e) {
        stop(paste0(""failed to combine rowData of SummarizedExperiment objects:\n  "", conditionMessage(e)))
    })

    # Combining the colData. This constructs mappings of the columns for each
    # SE to the columns of the final object.
    all.cd <- lapply(all.se, colData)
    tryCatch({
        com.cd <- do.call(combineUniqueCols, c(all.cd, list(use.names=use.names)))
    }, error=function(e) {
        stop(paste0(""failed to combine colData of SummarizedExperiment objects:\n  "", conditionMessage(e)))
    })

    if (use.names) {
        # If combineUniqueCols succeeded, all SE's should have valid column names.
        all.names <- rownames(com.cd)
        mappings <- vector(""list"", length(all.se))
        for (i in seq_along(mappings)) {
            mappings[[i]] <- match(all.names, rownames(all.cd[[i]]))
        }
    } else {
        mappings <- NULL
    }

    args <- list(
        assays=combine_assays_by(all.se, mappings, delayed=delayed, fill=fill, by.row=TRUE),
        colData=com.cd,
        metadata=unlist(lapply(all.se, metadata), recursive=FALSE, use.names=FALSE),
        checkDimnames=FALSE
    )

    # Finally, filling in the rowRanges. Rows for SummarizedExperiment
    # inputs are filled in with empty GRangesList objects.
    extracted <- extract_granges_from_se(all.se)

    if (!is.null(extracted)) {
        filled.ranges <- FALSE
        for (i in seq_along(extracted)) {
            if (is.null(extracted[[i]])) {
                filled.ranges <- TRUE
                cur.se <- all.se[[i]]
                levels <- rownames(cur.se)
                if (is.null(levels)) {
                    levels <- seq_len(nrow(cur.se))
                }
                rr <- splitAsList(GRanges(), factor(character(0), levels))
                if (is.null(rownames(cur.se))) {
                    names(rr) <- NULL
                }
                extracted[[i]] <- rr
            }
        }

        if (filled.ranges) {
            for (s in seq_along(extracted)) {
                extracted[[s]] <- as(extracted[[s]], ""GRangesList"")
            }
        }

        com.rr <- do.call(c, extracted)
        mcols(com.rr) <- com.rd
        args$rowRanges <- com.rr
    } else {
        args$rowData <- com.rd
    }

    # Assembling the SE.
    do.call(SummarizedExperiment, args)
})

combine_assays_by <- function(all.se, mappings, delayed, fill, by.row) {
    all.assays <- lapply(all.se, assays, withDimnames=FALSE)
    each.assay.names <- lapply(all.assays, names)
    no.assay.names <- vapply(each.assay.names, is.null, TRUE)

    if (by.row) {
        INFLATE <- inflate_matrix_by_column
    } else {
        INFLATE <- inflate_matrix_by_row
    }

    if (any(no.assay.names)) {
        if (!all(no.assay.names)) {
            stop(""named and unnamed assays cannot be mixed"")
        }
        n.assays <- unique(lengths(all.assays))
        if (length(n.assays)!=1L) {
            stop(""all SummarizedExperiments should have the same number of unnamed assays"")
        }

        for (s in seq_along(all.se)) {
            all.assays[[s]] <- lapply(all.assays[[s]], FUN=INFLATE,
                                      idx=mappings[[s]], delayed=delayed, fill=fill)
        }
    } else {
        all.assay.names <- Reduce(union, each.assay.names)
        for (s in seq_along(all.se)) {
            cur.se <- all.se[[s]]
            cur.assays <- all.assays[[s]]
            idx <- mappings[[s]]

            # Filling in all missing assay names and columns.
            for (a in all.assay.names) {
                if (a %in% names(cur.assays)) {
                    mat <- INFLATE(cur.assays[[a]], idx, delayed=delayed, fill=fill)
                } else {
                    nr <- nrow(cur.se)
                    nc <- ncol(cur.se)
                    if (!is.null(idx)) {
                        if (by.row) {
                            nc <- length(idx)
                        } else {
                            nr <- length(idx)
                        }
                    }
                    mat <- create_dummy_matrix(nr, nc, delayed=delayed, fill=fill)
                }
                cur.assays[[a]] <- mat
            }
            all.assays[[s]] <- cur.assays
        }
    }

    # Re-use assay r/cbind'ing machinery.
    all.assays <- lapply(all.assays, Assays)
    if (by.row) {
        combined <- do.call(rbind, all.assays)
    } else {
        combined <- do.call(cbind, all.assays)
    }
    as(combined, ""SimpleList"")
}

create_dummy_matrix <- function(nr, nc, delayed, fill) {
    if (!delayed) {
        array(c(nr, nc), data=fill)
    } else {
        ConstantArray(c(nr, nc), value=fill)
    }
}

inflate_matrix_by_column <- function(mat, idx, delayed, fill) {
    if (delayed) {
        mat <- DelayedArray(mat)
    }
    if (!is.null(idx)) {
        absent <- is.na(idx)
        if (any(absent)) {
            idx[absent] <- ncol(mat)+1L
            mat <- cbind(mat, create_dummy_matrix(nrow(mat), 1L, delayed, fill))
        }
        mat <- mat[,idx,drop=FALSE]
    }
    mat
}

inflate_matrix_by_row <- function(mat, idx, delayed, fill) {
    if (delayed) {
        mat <- DelayedArray(mat)
    }
    if (!is.null(idx)) {
        absent <- is.na(idx)
        if (any(absent)) {
            idx[absent] <- nrow(mat)+1L
            mat <- rbind(mat, create_dummy_matrix(1L, ncol(mat), delayed, fill))
        }
        mat <- mat[idx,,drop=FALSE]
    }
    mat
}

extract_granges_from_se <- function(all.se) {
    has.ranges <- vapply(all.se, is, class2=""RangedSummarizedExperiment"", FUN.VALUE=TRUE)
    if (!any(has.ranges)) {
        return(NULL)
    }

    final.rr <- vector(""list"", length(all.se))
    for (s in which(has.ranges)) {
        cur.se <- all.se[[s]]
        rr <- rowRanges(cur.se)
        mcols(rr) <- NULL
        names(rr) <- rownames(cur.se)
        final.rr[[s]] <- rr
    }

    # Coercing everyone to a GRL if anyone is a GRL. Note that we don't fill in
    # NULLs with GRLs yet, to give a chance for the caller to decide how to
    # handle them (e.g., fill in combineRows or merge in combineCols).
    is.grl <- vapply(final.rr[has.ranges], function(x) is(x, ""GRangesList""), TRUE)
    if (any(is.grl)) {
        for (s in which(has.ranges)) {
            final.rr[[s]] <- as(final.rr[[s]], ""GRangesList"")
        }
    }

    final.rr
}

setMethod(""combineCols"", ""SummarizedExperiment"", function(x, ..., delayed=TRUE, fill=NA, use.names=TRUE) {
    all.se <- list(x, ...)

    # Combining the rowData. This constructs mappings of the rows for each
    # SE to the columns of the final object.
    all.rd <- lapply(all.se, rowData)
    tryCatch({
        com.rd <- do.call(combineUniqueCols, c(all.rd, list(use.names=use.names)))
    }, error=function(e) {
        stop(paste0(""failed to combine rowData of SummarizedExperiment objects:\n  "", conditionMessage(e)))
    })

    # Combining the colData.
    all.cd <- lapply(all.se, colData)
    tryCatch({
        com.cd <- do.call(combineRows, all.cd)
    }, error=function(e) {
        stop(paste0(""failed to combine colData of SummarizedExperiment objects:\n  "", conditionMessage(e)))
    })

    if (use.names) {
        # If combineUniqueCols succeeded for the rowData, all SE's should have valid row names.
        all.names <- rownames(com.rd)
        mappings <- vector(""list"", length(all.se))
        for (i in seq_along(mappings)) {
            mappings[[i]] <- match(all.names, rownames(all.rd[[i]]))
        }
    } else {
        mappings <- NULL
    }

    args <- list(
        assays=combine_assays_by(all.se, mappings, delayed=delayed, fill=fill, by.row=FALSE),
        colData=com.cd,
        metadata=unlist(lapply(all.se, metadata), recursive=FALSE, use.names=FALSE),
        checkDimnames=FALSE
    )

    com.rr <- merge_granges_from_se(all.se, mappings)
    if (!is.null(com.rr)) {
        mcols(com.rr) <- com.rd
        names(com.rr) <- rownames(com.rd)
        args$rowRanges <- com.rr
    } else {
        args$rowData <- com.rd
    }

    # Assembling the SE.
    do.call(SummarizedExperiment, args)
})

merge_granges_from_se <- function(all.se, mappings) {
    extracted <- extract_granges_from_se(all.se)
    if (is.null(extracted)) {
        return(NULL)
    }

    has.ranges <- which(!vapply(extracted, is.null, FALSE))
    extracted.ranges <- extracted[has.ranges]

    if (!is.null(mappings)) {
        # We concatenate everything to automatically merge the seqinfo. We
        # then create a container for the filling process.
        temp.rr <- do.call(c, extracted.ranges)
        names(temp.rr) <- NULL
        nentries <- lengths(extracted.ranges)
        starts <- cumsum(c(0L, nentries))

        com.rr <- temp.rr
        if (length(temp.rr) > 0) {
            com.rr <- temp.rr[rep(1L, length(mappings[[1]]))]
        }
        filled <- logical(length(com.rr))

        for (i in seq_along(extracted.ranges)) {
            idx <- mappings[[has.ranges[i]]]
            candidates <- temp.rr[starts[i] + seq_len(nentries[i])]

            available <- which(!is.na(idx))
            new.rr <- candidates[idx[available]]
            old.rr <- com.rr[available]

            existing <- filled[available]
            if (!identical(new.rr[existing], old.rr[existing])) {
                warning(wmsg(""different 'rowRanges' for shared rows across SummarizedExperiment objects, "",
                             ""ignoring 'rowRanges' for "", class(all.se[[i]])[1], "" "", i))
                next
            }

            com.rr[available[!existing]] <- new.rr[!existing]
            filled[available[!existing]] <- TRUE
        }

        if (!all(filled)) {
            com.rr <- as(com.rr, ""GRangesList"")
            com.rr[!filled] <- GRangesList(GRanges())
        }
    } else {
        if (length(unique(extracted.ranges)) > 1) {
            warning(wmsg(""'rowRanges' are not identical across input objects, "",
                          ""using 'rowRanges' from the first object only""))
        }
        com.rr <- extracted.ranges[[1]]
    }

    com.rr
}
","R"
"Assay","Bioconductor/SummarizedExperiment","R/makeSummarizedExperimentFromLoom.R",".R","1375","39",".loom_make_rownames <- function(df, colname) {
    rownames(df) <- df[[colname]]
    df[, -match(colname, colnames(df)), drop = FALSE]
}

makeSummarizedExperimentFromLoom <-
    function(file, rownames_attr = NULL, colnames_attr = NULL)
{
    stopifnot(file.exists(file))

    ls <- rhdf5::h5ls(file)
    rowColnames <- ls[ls$group == ""/row_attrs"", ""name"", drop=TRUE]
    colColnames <- ls[ls$group == ""/col_attrs"", ""name"", drop=TRUE]
    stopifnot(
        is.null(rownames_attr) || rownames_attr %in% rowColnames,
        is.null(colnames_attr) || colnames_attr %in% colColnames
    )

    assay <- t(HDF5Array::HDF5Array(file, ""/matrix""))
    layerNames <- ls[ls$group == ""/layers"", ""name"", drop = TRUE]
    layers <- lapply(setNames(layerNames, layerNames), function(layer) {
        layer <- paste0(""/layers/"", layer)
        t(HDF5Array::HDF5Array(file, layer))
    })
    assays <- c(list(matrix = assay), layers)

    rowData <- DataFrame(rhdf5::h5read(file, ""row_attrs""))
    if (!is.null(rownames_attr))
        rowData <- .loom_make_rownames(rowData, rownames_attr)

    colData <- DataFrame(rhdf5::h5read(file, ""col_attrs""))
    if (!is.null(colnames_attr))
        colData <- .loom_make_rownames(colData, colnames_attr)

    se <- SummarizedExperiment(assays, rowData = rowData, colData = colData)
    metadata(se) <- rhdf5::h5readAttributes(file, ""/"")
    se
}
","R"
"Assay","Bioconductor/SummarizedExperiment","R/zzz.R",".R","72","3",".test <- function() BiocGenerics:::testPackage(""SummarizedExperiment"")

","R"
"Assay","Bioconductor/SummarizedExperiment","R/makeSummarizedExperimentFromDataFrame.R",".R","1126","33","### =========================================================================
### makeSummarizedExperimentFromDataFrame()
### -------------------------------------------------------------------------

### 'df' must be a data.frame or DataFrame object.
makeSummarizedExperimentFromDataFrame <-
    function(df,
             ...,
             seqinfo = NULL,
             starts.in.df.are.0based = FALSE)
    {
        rowRanges <- makeGRangesFromDataFrame(
            df,
            ...,
            keep.extra.columns = FALSE,
            seqinfo = seqinfo,
            starts.in.df.are.0based = starts.in.df.are.0based)

        # Find column names for rowRanges
        granges_cols <-
            GenomicRanges:::find_core_GRanges_cols(names(df), ...)

        rangedNames <- names(df)[na.omit(granges_cols)]
        idx <- match(rangedNames, names(df))
        counts <- as.matrix(df[, -idx, drop = FALSE])

        if (!is(as.vector(counts), ""numeric""))
            stop(""failed to coerce non-range columns to 'numeric'"")

        SummarizedExperiment(
            assays=SimpleList(counts), rowRanges=rowRanges)
    }
","R"
"Assay","Bioconductor/SummarizedExperiment","R/SummarizedExperiment-class.R",".R","36751","1080","### =========================================================================
### SummarizedExperiment objects
### -------------------------------------------------------------------------
###


setClassUnion(""Assays_OR_NULL"", c(""Assays"", ""NULL""))

setClass(""SummarizedExperiment"",
    contains=c(""RectangularData"", ""Vector""),
    representation(
        colData=""DataFrame"",            # columns and their annotations
        assays=""Assays_OR_NULL"",        # Data -- e.g., list of matrices
        NAMES=""character_OR_NULL"",
        elementMetadata=""DataFrame""
    ),
    prototype(
        colData=new(""DFrame""),
        elementMetadata=new(""DFrame"")
    )
)

### Combine the new ""parallel slots"" with those of the parent class. Make
### sure to put the new parallel slots **first**. See R/Vector-class.R file
### in the S4Vectors package for what slots should or should not be considered
### ""parallel"".
setMethod(""parallel_slot_names"", ""SummarizedExperiment"",
    function(x) c(""assays"", ""NAMES"", callNextMethod())
)

setMethod(""vertical_slot_names"", ""SummarizedExperiment"",
    function(x) parallel_slot_names(x)
)

### Like parallel_slot_names() methods, horizontal_slot_names() methods for
### SummarizedExperiment derivatives should be defined in an incremental
### fashion, that is, they should only explicitly list the new ""horizontal
### slots"" (i.e. the horizontal slots that they add to their parent class).
### See R/RectangularData-class.R file in the S4Vectors package for what
### slots should or should not be considered ""horizontal"".
setMethod(""horizontal_slot_names"", ""SummarizedExperiment"",
    function(x) ""colData""
)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Validity
###

.valid.SummarizedExperiment.assays_nrow <- function(x)
{
    if (length(x@assays) == 0L)
        return(NULL)
    assays_nrow <- nrow(x@assays)
    rowData_nrow <- length(x)
    if (assays_nrow != rowData_nrow) {
        txt <- sprintf(
            ""\n  nb of rows in 'assay' (%d) must equal nb of rows in 'rowData' (%d)"",
            assays_nrow, rowData_nrow)
        return(txt)
    }
    NULL
}

.valid.SummarizedExperiment.assays_ncol <- function(x)
{
    if (length(x@assays) == 0L)
        return(NULL)
    assays_ncol <- ncol(x@assays)
    colData_nrow <- nrow(colData(x))
    if (assays_ncol != colData_nrow) {
        txt <- sprintf(
            ""\n  nb of cols in 'assay' (%d) must equal nb of rows in 'colData' (%d)"",
            assays_ncol, colData_nrow)
        return(txt)
    }
    NULL
}

.valid.SummarizedExperiment.assays_dim <- function(x)
{
    c(.valid.SummarizedExperiment.assays_nrow(x),
      .valid.SummarizedExperiment.assays_ncol(x))
}

.valid.SummarizedExperiment <- function(x)
{
    .valid.SummarizedExperiment.assays_dim(x)
}

setValidity2(""SummarizedExperiment"", .valid.SummarizedExperiment)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Accessors
###

setMethod(""length"", ""SummarizedExperiment"",
    function(x) nrow(x@elementMetadata)
)

setMethod(""names"", ""SummarizedExperiment"", function(x) x@NAMES)

setReplaceMethod(""names"", ""SummarizedExperiment"",
    function(x, value)
    {
        NAMES <- S4Vectors:::normarg_names(value, class(x), length(x))
        BiocGenerics:::replaceSlots(x, NAMES=NAMES, check=FALSE)
    }
)

## rowData, colData seem too vague, but from eSet derived classes wanted to
## call the rows / cols something different from 'features' or 'samples', so
## might as well avoid the issue

setGeneric(""rowData"", signature=""x"",
    function(x, use.names=TRUE, ...) standardGeneric(""rowData"")
)

### Fix old DataFrame instances on-the-fly (mcols() does it).
setMethod(""rowData"", ""SummarizedExperiment"",
    function(x, use.names=TRUE, ...) mcols(x, use.names=use.names, ...)
)

setGeneric(""rowData<-"",
    function(x, ..., value) standardGeneric(""rowData<-""))

setReplaceMethod(""rowData"", ""SummarizedExperiment"",
    function(x, ..., value) `mcols<-`(x, ..., value=value)
)

setGeneric(""colData"", function(x, ...) standardGeneric(""colData""))

### Fix old DataFrame instances on-the-fly.
setMethod(""colData"", ""SummarizedExperiment"",
    function(x, ...) updateObject(x@colData, check=FALSE)
)

setGeneric(""colData<-"",
    function(x, ..., value) standardGeneric(""colData<-""))

setReplaceMethod(""colData"", c(""SummarizedExperiment"", ""DataFrame""),
    function(x, ..., value)
{
    if (nrow(value) != ncol(x))
        stop(""nrow of supplied 'colData' must equal ncol of object"")
    x <- updateObject(x, check=FALSE)
    BiocGenerics:::replaceSlots(x, colData=value, check=FALSE)
})

setReplaceMethod(""colData"", c(""SummarizedExperiment"", ""NULL""),
    function(x, ..., value)
{
    value <- new2(""DFrame"", nrows=ncol(x), rownames=colnames(x), check=FALSE)
    x <- updateObject(x, check=FALSE)
    BiocGenerics:::replaceSlots(x, colData=value, check=FALSE)
})

setGeneric(""assays"", signature=""x"",
    function(x, withDimnames=TRUE, ...) standardGeneric(""assays"")
)

setMethod(""assays"", ""SummarizedExperiment"",
    function(x, withDimnames=TRUE, ...)
{
    if (!isTRUEorFALSE(withDimnames))
        stop(wmsg(""'withDimnames' must be TRUE or FALSE""))
    assays <- as(x@assays, ""SimpleList"")
    if (withDimnames) {
        x_dimnames <- dimnames(x)
        if (is.null(x_dimnames))
            x_dimnames <- list(NULL, NULL)
        assays <- endoapply(assays,
            function(a) {
                a_dimnames <- dimnames(a)
                a_dimnames[1:2] <- x_dimnames
                a_dimnames <- S4Arrays:::simplify_NULL_dimnames(a_dimnames)
                S4Arrays:::set_dimnames(a, a_dimnames)
            }
        )
    }
    assays
})

setGeneric(""assays<-"", signature=c(""x"", ""value""),
    function(x, withDimnames=TRUE, ..., value) standardGeneric(""assays<-""),
)

### 'expected_dimnames' must be a NULL or a list of length 2 where each
### list element is a character vector or a NULL.
### For assays with more than 2 dimensions, **only** the first 2
### dimnames components (i.e. rownames and colnames) are compared
### with 'expected_dimnames'.
### If 'strict' is FALSE, then a NULL on either side of the comparison
### is enough for the comparison to be considered succesful.
.assays_have_expected_dimnames <- function(assays, expected_dimnames,
                                           strict=TRUE)
{
    if (length(assays) == 0L)
        return(TRUE)
    ## Returns the rownames and colnames in a list of length 2. Also list
    ## elements in the returned list that are equal to character(0) are
    ## replaced with NULLs. This is to accommodate the fact that, unlike
    ## an ordinary vector or data.frame, an ordinary matrix or array object
    ## will never hold a character(0) along a dimension of zero extend.
    get_normalized_rownames_and_colnames <- function(dn) {
        if (is.null(dn))
            return(vector(""list"", length=2L))
        rownames <- dn[[1L]]
        colnames <- dn[[2L]]
        ## We use as.character() to drop the names on the rownames/colnames
        ## below. Also in the unlikely (but possible in theory) situation
        ## where an assay uses unconventional representation of the
        ## rownames/colnames (e.g. factor?), this will make sure that
        ## the rownames/colnames are returned in a character vector.
        if (length(rownames) == 0L) {
            rownames <- NULL
        } else {
            rownames <- as.character(rownames)
        }
        if (length(colnames) == 0L) {
            colnames <- NULL
        } else {
            colnames <- as.character(colnames)
        }
        list(rownames, colnames)
    }
    expected_dimnames <- get_normalized_rownames_and_colnames(expected_dimnames)
    expected_rownames <- expected_dimnames[[1L]]
    expected_colnames <- expected_dimnames[[2L]]
    if (!strict) {
        expected_rownames_is_NULL <- is.null(expected_rownames)
        expected_colnames_is_NULL <- is.null(expected_colnames)
        if (expected_rownames_is_NULL && expected_colnames_is_NULL)
            return(TRUE)
    }
    ok <- vapply(seq_along(assays),
        function(i) {
            a <- getListElement(assays, i)
            a_dimnames <- get_normalized_rownames_and_colnames(dimnames(a))
            a_rownames <- a_dimnames[[1L]]
            a_colnames <- a_dimnames[[2L]]
            ok1 <- identical(a_rownames, expected_rownames)
            ok2 <- identical(a_colnames, expected_colnames)
            if (!strict) {
                ok1 <- ok1 || is.null(a_rownames) || expected_rownames_is_NULL
                ok2 <- ok2 || is.null(a_colnames) || expected_colnames_is_NULL
            }
            ok1 && ok2
        },
        logical(1)
    )
    all(ok)
}

.SummarizedExperiment.assays.replace <-
    function(x, withDimnames=TRUE, ..., value)
{
    if (!isTRUEorFALSE(withDimnames))
        stop(wmsg(""'withDimnames' must be TRUE or FALSE""))
    value <- normarg_assays(value, as.null.if.no.assay=TRUE)
    ## By default the dimnames on the supplied assays must be identical to
    ## the dimnames on 'x'. The user must use 'withDimnames=FALSE' if it's
    ## not the case. This is for symetry with the behavior of the getter.
    ## See https://github.com/Bioconductor/SummarizedExperiment/issues/35
    if (withDimnames && !.assays_have_expected_dimnames(value, dimnames(x)))
        stop(wmsg(""please use 'assay(x, withDimnames=FALSE) <- value' "",
                  ""or 'assays(x, withDimnames=FALSE) <- value' when "",
                  ""the rownames or colnames of the supplied assay(s) "",
                  ""are not identical to those of the receiving "",
                  class(x), "" object 'x'""))
    new_assays <- Assays(value, as.null.if.no.assay=TRUE)
    x <- BiocGenerics:::replaceSlots(x, assays=new_assays, check=FALSE)
    ## validObject(x) should NOT be called below because it would then
    ## fully re-validate objects that derive from SummarizedExperiment
    ## (e.g. DESeqDataSet objects) after the user sets the assays slot with
    ## assays(x) <- value. For example the assays slot of a DESeqDataSet
    ## object must contain a matrix named 'counts' and calling validObject(x)
    ## would check that but .valid.SummarizedExperiment(x) doesn't.
    ## The FourC() constructor function defined in the FourCSeq package
    ## actually takes advantage of the incomplete validation below to
    ## purposedly return invalid FourC objects!
    msg <- .valid.SummarizedExperiment(x)
    if (!is.null(msg))
        stop(msg)
    x
}

setReplaceMethod(""assays"", c(""SummarizedExperiment"", ""SimpleList""),
    .SummarizedExperiment.assays.replace)

setReplaceMethod(""assays"", c(""SummarizedExperiment"", ""list""),
    .SummarizedExperiment.assays.replace)

setGeneric(""assay"", signature=c(""x"", ""i""),
    function(x, i, withDimnames=TRUE, ...) standardGeneric(""assay"")
)

## convenience for common use case
setMethod(""assay"", c(""SummarizedExperiment"", ""missing""),
    function(x, i, withDimnames=TRUE, ...)
{
    assays <- assays(x, withDimnames=withDimnames, ...)
    if (0L == length(assays))
        stop(""'assay(<"", class(x), "">, i=\""missing\"", ...) "",
             ""length(assays(<"", class(x), "">)) is 0'"")
    assays[[1]]
})

setMethod(""assay"", c(""SummarizedExperiment"", ""numeric""),
    function(x, i, withDimnames=TRUE, ...)
{
    tryCatch({
        assays(x, withDimnames=withDimnames, ...)[[i]]
    }, error=function(err) {
        stop(""'assay(<"", class(x), "">, i=\""numeric\"", ...)' "",
             ""invalid subscript 'i'\n"", conditionMessage(err))
    })
})

setMethod(""assay"", c(""SummarizedExperiment"", ""character""),
    function(x, i, withDimnames=TRUE, ...)
{
    msg <- paste0(""'assay(<"", class(x), "">, i=\""character\"", ...)' "",
                  ""invalid subscript 'i'"")
    res <- tryCatch({
        assays(x, withDimnames=withDimnames, ...)[[i]]
    }, error=function(err) {
        stop(msg, ""\n"", conditionMessage(err))
    })
    if (is.null(res))
        stop(msg, ""\n'"", i, ""' not in names(assays(<"", class(x), "">))"")
    res
})

setGeneric(""assay<-"", signature=c(""x"", ""i""),
    function(x, i, withDimnames=TRUE, ..., value) standardGeneric(""assay<-""))

setReplaceMethod(""assay"", c(""SummarizedExperiment"", ""missing""),
    function(x, i, withDimnames=TRUE, ..., value)
{
    if (0L == length(assays(x, withDimnames=FALSE)))
        stop(""'assay(<"", class(x), "">) <- value' "", ""length(assays(<"",
             class(x), "">)) is 0"")
    assays(x, withDimnames=withDimnames, ...)[[1]] <- value
    x
})

setReplaceMethod(""assay"", c(""SummarizedExperiment"", ""numeric""),
    function(x, i, withDimnames=TRUE, ..., value)
{
    assays(x, withDimnames=withDimnames, ...)[[i]] <- value
    x
})

setReplaceMethod(""assay"", c(""SummarizedExperiment"", ""character""),
    function(x, i, withDimnames=TRUE, ..., value)
{
    assays(x, withDimnames=withDimnames, ...)[[i]] <- value
    x
})

setGeneric(""assayNames"", function(x, ...) standardGeneric(""assayNames""))

setMethod(""assayNames"", ""SummarizedExperiment"",
    function(x, ...)
{
    names(assays(x, withDimnames=FALSE))
})

setGeneric(""assayNames<-"",
    function(x, ..., value) standardGeneric(""assayNames<-""))

setReplaceMethod(""assayNames"", c(""SummarizedExperiment"", ""character""),
    function(x, ..., value)
{
    names(assays(x, withDimnames=FALSE)) <- value
    x
})

setMethod(""nrow"", ""SummarizedExperiment"", function(x) length(x))
setMethod(""ncol"", ""SummarizedExperiment"", function(x) nrow(colData(x)))

setMethod(""rownames"", ""SummarizedExperiment"", function(x) names(x))
setMethod(""colnames"", ""SummarizedExperiment"", function(x) rownames(colData(x)))

setReplaceMethod(""dimnames"", c(""SummarizedExperiment"", ""list""),
    function(x, value)
{
    NAMES <- S4Vectors:::normarg_names(value[[1L]], class(x), length(x))
    colData <- colData(x)
    rownames(colData) <- value[[2L]]
    BiocGenerics:::replaceSlots(x, NAMES=NAMES, colData=colData, check=FALSE)
})

setReplaceMethod(""dimnames"", c(""SummarizedExperiment"", ""NULL""),
    function(x, value)
{
    dimnames(x) <- list(NULL, NULL)
    x
})


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Low-level constructor (not exported)
###

new_SummarizedExperiment <- function(assays, names, rowData, colData,
                                     metadata)
{
    assays <- Assays(assays, as.null.if.no.assay=TRUE)
    if (is.null(rowData)) {
        if (!is.null(names)) {
            nrow <- length(names)
        } else if (!is.null(assays)) {
            nrow <- nrow(assays)
        } else {
            nrow <- 0L
        }
        rowData <- S4Vectors:::make_zero_col_DataFrame(nrow)
    } else {
        rownames(rowData) <- NULL
    }
    new(""SummarizedExperiment"", NAMES=names,
                                elementMetadata=rowData,
                                colData=colData,
                                assays=assays,
                                metadata=as.list(metadata))
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### High-level SummarizedExperiment/RangedSummarizedExperiment constructor
###

### We use as.character() in .get_rownames_from_first_assay() and
### .get_colnames_from_first_assay() below to drop the names on the
### rownames/colnames. Also in the unlikely (but possible in theory)
### situation where an assay uses unconventional representation of the
### rownames/colnames (e.g. factor?), this will make sure that the
### rownames/colnames are returned in a character vector.
.get_rownames_from_first_assay <- function(assays)
{
    if (length(assays) == 0L)
        return(NULL)
    rownames <- rownames(assays[[1L]])
    if (!is.null(rownames))
        rownames <- as.character(rownames)
    rownames
}

.get_colnames_from_first_assay <- function(assays)
{
    if (length(assays) == 0L)
        return(NULL)
    colnames <- colnames(assays[[1L]])
    if (!is.null(colnames))
        colnames <- as.character(colnames)
    colnames
}

SummarizedExperiment <- function(assays=SimpleList(),
                                 rowData=NULL, rowRanges=NULL,
                                 colData=DataFrame(),
                                 metadata=list(),
                                 checkDimnames=TRUE)
{
    if (!isTRUEorFALSE(checkDimnames))
        stop(wmsg(""'checkDimnames' must be TRUE or FALSE""))

    assays <- normarg_assays(assays, as.null.if.no.assay=TRUE)

    if (!missing(colData)) {
        if (!is(colData, ""DataFrame""))
            colData <- as(colData, ""DataFrame"")
        if (is.null(rownames(colData)))
            rownames(colData) <- .get_colnames_from_first_assay(assays)
    } else if (length(assays) != 0L) {
        a1 <- assays[[1L]]
        nms <- colnames(a1)
        colData <- DataFrame(x=seq_len(ncol(a1)), row.names=nms)[, FALSE]
    }

    if (is.null(rowData)) {
        if (is.null(rowRanges)) {
            ans_rownames <- .get_rownames_from_first_assay(assays)
        } else {
            if (!is(rowRanges, ""GenomicRanges_OR_GRangesList""))
                stop(""'rowRanges' must be a GRanges or GRangesList object"")
            if (is.null(names(rowRanges)))
                names(rowRanges) <- .get_rownames_from_first_assay(assays)
            ans_rownames <- names(rowRanges)
        }
    } else {
        if (!is.null(rowRanges))
            stop(""only one of 'rowData' and 'rowRanges' can be specified"")
        if (is(rowData, ""GenomicRanges_OR_GRangesList"")) {
            rowRanges <- rowData
            if (is.null(names(rowRanges)))
                names(rowRanges) <- .get_rownames_from_first_assay(assays)
            ans_rownames <- names(rowRanges)
        } else {
            if (!is(rowData, ""DataFrame""))
                rowData <- as(rowData, ""DataFrame"")
            ans_rownames <- rownames(rowData)
            if (is.null(ans_rownames))
                ans_rownames <- .get_rownames_from_first_assay(assays)
        }
    }

    if (is.null(rowRanges)) {
        ans <- new_SummarizedExperiment(assays, ans_rownames, rowData, colData,
                                        metadata)
    } else {
        ans <- new_RangedSummarizedExperiment(assays, rowRanges, colData,
                                              metadata)
    }
    if (!checkDimnames)
        return(ans)
    ans_dimnames <- dimnames(ans)
    ok <- .assays_have_expected_dimnames(ans@assays, ans_dimnames, strict=FALSE)
    if (!ok) {
        if (is.null(ans_dimnames[[1L]])) {
            what <- ""colnames""
        } else if (is.null(ans_dimnames[[2L]])) {
            what <- ""rownames""
        } else {
            what <- ""rownames and colnames""
        }
        stop(wmsg(""the "", what, "" of the supplied assay(s) must be NULL "",
                  ""or identical to those of the "", class(ans), "" object "",
                  ""(or derivative) to construct""))
    }
    ans
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Subsetting
###

.SummarizedExperiment.charbound <-
    function(idx, txt, fmt)
{
    orig <- idx
    idx <- match(idx, txt)
    if (any(bad <- is.na(idx))) {
        msg <- paste(S4Vectors:::selectSome(orig[bad]), collapse="" "")
        stop(sprintf(fmt, msg))
    }
    idx
}

### TODO: Refactor this to use extractROWS().
setMethod(""["", c(""SummarizedExperiment"", ""ANY"", ""ANY""),
    function(x, i, j, ..., drop=TRUE)
{
    if (1L != length(drop) || (!missing(drop) && drop))
        warning(""'drop' ignored '[,"", class(x), "",ANY,ANY-method'"")

    if (missing(i) && missing(j))
        return(x)

    if (!missing(i)) {
        if (is.character(i)) {
            fmt <- paste0(""<"", class(x), "">[i,] index out of bounds: %s"")
            i <- .SummarizedExperiment.charbound(i, rownames(x), fmt)
        }
        ii <- as.vector(i)
        ans_elementMetadata <- x@elementMetadata[i, , drop=FALSE]
        if (is(x, ""RangedSummarizedExperiment"")) {
            ans_rowRanges <- x@rowRanges[i]
        } else {
            ans_NAMES <- x@NAMES[ii]
        }
    }
    if (!missing(j)) {
        if (is.character(j)) {
            fmt <- paste0(""<"", class(x), "">[,j] index out of bounds: %s"")
            j <- .SummarizedExperiment.charbound(j, colnames(x), fmt)
        }
        ans_colData <- x@colData[j, , drop=FALSE]
        jj <- as.vector(j)
    }

    if (missing(i)) {
        ans_assays <- x@assays[ , jj]
        ans <- BiocGenerics:::replaceSlots(x, ...,
                       colData=ans_colData,
                       assays=ans_assays,
                       check=FALSE)
    } else if (missing(j)) {
        ans_assays <- x@assays[ii, ]
        if (is(x, ""RangedSummarizedExperiment"")) {
            ans <- BiocGenerics:::replaceSlots(x, ...,
                       elementMetadata=ans_elementMetadata,
                       rowRanges=ans_rowRanges,
                       assays=ans_assays,
                       check=FALSE)
        } else {
            ans <- BiocGenerics:::replaceSlots(x, ...,
                       elementMetadata=ans_elementMetadata,
                       NAMES=ans_NAMES,
                       assays=ans_assays,
                       check=FALSE)
        }
    } else {
        ans_assays <- x@assays[ii, jj]
        if (is(x, ""RangedSummarizedExperiment"")) {
            ans <- BiocGenerics:::replaceSlots(x, ...,
                       elementMetadata=ans_elementMetadata,
                       rowRanges=ans_rowRanges,
                       colData=ans_colData,
                       assays=ans_assays,
                       check=FALSE)
        } else {
            ans <- BiocGenerics:::replaceSlots(x, ...,
                       elementMetadata=ans_elementMetadata,
                       NAMES=ans_NAMES,
                       colData=ans_colData,
                       assays=ans_assays,
                       check=FALSE)
        }
    }
    ans
})

### TODO: Refactor this to use replaceROWS().
setReplaceMethod(""["",
    c(""SummarizedExperiment"", ""ANY"", ""ANY"", ""SummarizedExperiment""),
    function(x, i, j, ..., value)
{
    if (missing(i) && missing(j))
        return(value)

    ans_metadata <- metadata(x)

    if (!missing(i)) {
        if (is.character(i)) {
            fmt <- paste0(""<"", class(x), "">[i,] index out of bounds: %s"")
            i <- .SummarizedExperiment.charbound(i, rownames(x), fmt)
        }
        ii <- as.vector(i)
        ans_elementMetadata <- local({
            emd <- x@elementMetadata
            emd[i,] <- value@elementMetadata
            emd
        })
        if (is(x, ""RangedSummarizedExperiment"")) {
            ans_rowRanges <- local({
                r <- x@rowRanges
                r[i] <- value@rowRanges
                names(r)[ii] <- names(value@rowRanges)
                r
            })
        } else {
            ans_NAMES <- local({
                nms <- x@NAMES
                nms[ii] <- value@NAMES
                nms
            })
        }
    }

    if (!missing(j)) {
        if (is.character(j)) {
            fmt <- paste0(""<"", class(x), "">[,j] index out of bounds: %s"")
            j <- .SummarizedExperiment.charbound(j, colnames(x), fmt)
        }
        jj <- as.vector(j)
        ans_colData <- local({
            c <- x@colData
            c[j,] <- value@colData
            rownames(c)[jj] <- rownames(value@colData)
            c
        })
    }

    if (missing(i)) {
        ans_assays <- local({
            a <- x@assays
            a[ , jj] <- value@assays
            a
        })
        ans <- BiocGenerics:::replaceSlots(x, ...,
                   metadata=ans_metadata,
                   colData=ans_colData,
                   assays=ans_assays,
                   check=FALSE)
        msg <- .valid.SummarizedExperiment.assays_ncol(ans)
    } else if (missing(j)) {
        ans_assays <- local({
            a <- x@assays
            a[ii, ] <- value@assays
            a
        })
        if (is(x, ""RangedSummarizedExperiment"")) {
            ans <- BiocGenerics:::replaceSlots(x, ...,
                       metadata=ans_metadata,
                       elementMetadata=ans_elementMetadata,
                       rowRanges=ans_rowRanges,
                       assays=ans_assays,
                       check=FALSE)
        } else {
            ans <- BiocGenerics:::replaceSlots(x, ...,
                       metadata=ans_metadata,
                       elementMetadata=ans_elementMetadata,
                       NAMES=ans_NAMES,
                       assays=ans_assays,
                       check=FALSE)
        }
        msg <- .valid.SummarizedExperiment.assays_nrow(ans)
    } else {
        ans_assays <- local({
            a <- x@assays
            a[ii, jj] <- value@assays
            a
        })
        if (is(x, ""RangedSummarizedExperiment"")) {
            ans <- BiocGenerics:::replaceSlots(x, ...,
                       metadata=ans_metadata,
                       elementMetadata=ans_elementMetadata,
                       rowRanges=ans_rowRanges,
                       colData=ans_colData,
                       assays=ans_assays,
                       check=FALSE)
        } else {
            ans <- BiocGenerics:::replaceSlots(x, ...,
                       metadata=ans_metadata,
                       elementMetadata=ans_elementMetadata,
                       NAMES=ans_NAMES,
                       colData=ans_colData,
                       assays=ans_assays,
                       check=FALSE)
        }
        msg <- .valid.SummarizedExperiment.assays_dim(ans)
    }
    if (!is.null(msg))
        stop(msg)
    ans
})

setMethod(""subset"", ""SummarizedExperiment"",
    function(x, subset, select, ...)
{
    i <- S4Vectors:::evalqForSubset(subset, rowData(x, use.names=FALSE), ...)
    j <- S4Vectors:::evalqForSubset(select, colData(x), ...)
    x[i, j]
})


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Quick colData access
###

setMethod(""[["", c(""SummarizedExperiment"", ""ANY"", ""missing""),
    function(x, i, j, ...)
{
    colData(x)[[i, ...]]
})

setReplaceMethod(""[["", c(""SummarizedExperiment"", ""ANY"", ""missing""),
    function(x, i, j, ..., value)
{
    colData(x)[[i, ...]] <- value
    x
})

.DollarNames.SummarizedExperiment <- function(x, pattern = """")
    grep(pattern, names(colData(x)), value=TRUE)

setMethod(""$"", ""SummarizedExperiment"",
    function(x, name)
{
    colData(x)[[name]]
})

setReplaceMethod(""$"", ""SummarizedExperiment"",
    function(x, name, value)
{
    colData(x)[[name]] <- value
    x
})


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Show
###

setMethod(""show"", ""SummarizedExperiment"",
    function(object)
{
    cat(""class:"", class(object), ""\n"")
    cat(""dim:"", dim(object), ""\n"")

    ## metadata()
    expt <- names(metadata(object))
    if (is.null(expt))
        expt <- character(length(metadata(object)))
    coolcat(""metadata(%d): %s\n"", expt)

    ## assays()
    nms <- assayNames(object)
    if (is.null(nms))
        nms <- character(length(assays(object, withDimnames=FALSE)))
    coolcat(""assays(%d): %s\n"", nms)

    ## rownames()
    rownames <- rownames(object)
    if (!is.null(rownames)) coolcat(""rownames(%d): %s\n"", rownames)
    else cat(""rownames: NULL\n"")

    ## rowData`()
    coolcat(""rowData names(%d): %s\n"", names(rowData(object, use.names=FALSE)))

    ## colnames()
    colnames <- colnames(object)
    if (!is.null(colnames)) coolcat(""colnames(%d): %s\n"", colnames)
    else cat(""colnames: NULL\n"")

    ## colData()
    coolcat(""colData names(%d): %s\n"", names(colData(object)))
})

setMethod(""showAsCell"", ""SummarizedExperiment"",
    function(object) rep.int(""####"", NROW(object))
)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Combine
###

### Appropriate for objects with different ranges and same samples.
setMethod(""rbind"", ""SummarizedExperiment"",
    function(..., deparse.level=1)
{
    args <- unname(list(...))
    .rbind.SummarizedExperiment(args)
})

.rbind.SummarizedExperiment <- function(args)
{
    if (!.compare(lapply(args, colnames)))
            stop(""'...' objects must have the same colnames"")
    if (!.compare(lapply(args, ncol)))
            stop(""'...' objects must have the same number of samples"")

    if (is(args[[1L]], ""RangedSummarizedExperiment"")) {
        rowRanges <- do.call(c, lapply(args, rowRanges))
    } else {
        ## Code below taken from combine_GAlignments_objects() from the
        ## GenomicAlignments package.

        ## Combine ""NAMES"" slots.
        NAMES_slots <- lapply(args, function(x) x@NAMES)
        ## TODO: Use elementIsNull() here when it becomes available.
        has_no_names <- sapply(NAMES_slots, is.null, USE.NAMES=FALSE)
        if (all(has_no_names)) {
            NAMES <- NULL
        } else {
            noname_idx <- which(has_no_names)
            if (length(noname_idx) != 0L)
                NAMES_slots[noname_idx] <-
                    lapply(elementNROWS(args[noname_idx]), character)
            NAMES <- unlist(NAMES_slots, use.names=FALSE)
        }
    }
    colData <- .cbind.DataFrame(args, colData, ""colData"")
    assays <- do.call(rbind, lapply(args, slot, ""assays""))
    elementMetadata <- do.call(rbind, lapply(args, slot, ""elementMetadata""))
    metadata <- do.call(c, lapply(args, metadata))

    if (is(args[[1L]], ""RangedSummarizedExperiment"")) {
        BiocGenerics:::replaceSlots(args[[1L]],
            rowRanges=rowRanges, colData=colData, assays=assays,
            elementMetadata=elementMetadata, metadata=metadata)
    } else {
        BiocGenerics:::replaceSlots(args[[1L]],
            NAMES=NAMES, colData=colData, assays=assays,
            elementMetadata=elementMetadata, metadata=metadata)
    }
}

### Appropriate for objects with same ranges and different samples.
setMethod(""cbind"", ""SummarizedExperiment"",
    function(..., deparse.level=1)
{
    args <- unname(list(...))
    .cbind.SummarizedExperiment(args)
})

.cbind.SummarizedExperiment <- function(args)
{
    if (is(args[[1L]], ""RangedSummarizedExperiment"")) {
        if (!.compare(lapply(args, rowRanges), TRUE))
            stop(""'...' object ranges (rows) are not compatible"")
        rowRanges <- rowRanges(args[[1L]])
        mcols(rowRanges) <- .cbind.DataFrame(args, mcols, ""mcols"")
    } else {
        elementMetadata <- .cbind.DataFrame(args, mcols, ""mcols"")
    }
    colData <- do.call(rbind, lapply(args, colData))
    assays <- do.call(cbind, lapply(args, slot, ""assays""))
    metadata <- do.call(c, lapply(args, metadata))

    if (is(args[[1L]], ""RangedSummarizedExperiment"")) {
        BiocGenerics:::replaceSlots(args[[1L]],
            rowRanges=rowRanges,
            colData=colData, assays=assays, metadata=metadata)
    } else {
        BiocGenerics:::replaceSlots(args[[1L]],
            elementMetadata=elementMetadata,
            colData=colData, assays=assays, metadata=metadata)
    }
}

.compare <- function(x, GenomicRanges=FALSE)
{
    x1 <- x[[1]]
    if (GenomicRanges) {
        if (is(x1, ""GRangesList"")) {
            x <- lapply(x, unlist)
            x1 <- x[[1]]
        }
        for (i in seq_along(x)[-1]) {
            if (!identicalVals(x1, x[[i]]))
                return(FALSE)
        }
        return(TRUE)
    } else {
        all(sapply(x[-1],
            function(xelt) all(identical(xelt, x[[1]]))))
    }
}

.cbind.DataFrame <- function(args, accessor, accessorName)
{
    lst <- lapply(args, accessor, use.names=FALSE)
    if (!.compare(lst)) {
        nms <- lapply(lst, names)
        nmsv <- unlist(nms, use.names=FALSE)
        names(nmsv) <- rep(seq_along(nms), elementNROWS(nms))
        dups <- duplicated(nmsv)
        ## no duplicates
        if (!any(dups))
            return(do.call(cbind, lst))
        ## confirm duplicates are the same
        lapply(nmsv[duplicated(nmsv)], function(d) {
            if (!.compare(lapply(lst, ""["", d)))
                stop(""column(s) '"", unname(d),
                     ""' in "", sQuote(accessorName),
                     "" are duplicated and the data do not match"")})
        ## remove duplicates
        do.call(cbind, lst)[,!dups]
    } else {
        lst[[1]]
    }
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### identicalVals()
###
### Internal generic and methods (i.e. not exported).
### Provides a fast implementation of 'length(x) == length(y) && all(x == y)'
### for various kinds of vector-like objects.
### TODO: Move this to S4Vectors (for the generic and methods for factor and
### Rle objects) and IRanges (for the method for IntegerRanges objects).
###

setGeneric(""identicalVals"", function(x, y) standardGeneric(""identicalVals""))

### Semantically equivalent to identical(as.character(x), as.character(y))
### but avoids turning the 2 factor objects into character vectors so is more
### efficient.
setMethod(""identicalVals"", c(""factor"", ""factor""),
    function(x, y)
    {
        m <- match(levels(y), levels(x), nomatch=0L)
        identical(as.integer(x), m[y])
    }
)

### Only support factor-Rle objects at the moment!
### Semantically equivalent to identical(as.character(x), as.character(y))
### but avoids turning the 2 factor-Rle objects into character vectors so is
### more efficient.
setMethod(""identicalVals"", c(""Rle"", ""Rle""),
    function(x, y) identical(runLength(x), runLength(y)) &&
                   identicalVals(runValue(x), runValue(y))
)

setMethod(""identicalVals"", c(""IntegerRanges"", ""IntegerRanges""),
    function(x, y) identical(start(x), start(y)) &&
                   identical(width(x), width(y))
)

### Like 'x == y' this method ignores circularity of the underlying sequences
### e.g. ranges [1, 10] and [101, 110] represent the same position on a
### circular sequence of length 100 so should be considered equal. However
### for 'x == y' and the method below, they are not.
### TODO: Take circularity of the underlying sequences into account.
setMethod(""identicalVals"", c(""GenomicRanges"", ""GenomicRanges""),
    function(x, y)
    {
        ## Trying to merge 'seqinfo(x)' and 'seqinfo(y)' will raise an error
        ## if 'x' and 'y' are not based on the same reference genome. This is
        ## the standard way to check that 'x' and 'y' are based on the same
        ## reference genome.
        merge(seqinfo(x), seqinfo(y))  # we ignore the returned value

        identicalVals(seqnames(x), seqnames(y)) &&
            identicalVals(ranges(x), ranges(y)) &&
            identicalVals(strand(x), strand(y))
    }
)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### On-disk realization.
###

setMethod(""realize"", ""SummarizedExperiment"",
    function(x, BACKEND=getAutoRealizationBackend())
    {
        for (i in seq_along(assays(x))) {
            ## The dimnames of the individual assays are kind of irrelevant so
            ## we drop them. The dimnames that really matter are 'dimnames(x)'
            ## and they are stored somewhere else in 'x'. So we don't loose
            ## them by not realizing the assay dimnames on disk. As a small
            ## extra bonus, this saves a little bit of time and disk space.
            a <- assay(x, i, withDimnames=FALSE)
            a <- S4Arrays:::set_dimnames(a, NULL)
            assay(x, i, withDimnames=FALSE) <- realize(a, BACKEND=BACKEND)
        }
        x
    }
)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### saveRDS() method
###

setMethod(""saveRDS"", ""SummarizedExperiment"",
    function(object, file="""", ascii=FALSE, version=NULL,
             compress=TRUE, refhook=NULL)
    {
        if (containsOutOfMemoryData(object))
            stop(wmsg(""This "", class(object), "" object contains out-of-memory "",
                      ""data so cannot be serialized reliably. Please use "",
                      ""saveHDF5SummarizedExperiment() from the HDF5Array "",
                      ""package instead. Alternatively you can call "",
                      ""base::saveRDS() on it but only if you know what "",
                      ""you are doing.""),
                ""\n  "",
                 wmsg(""See '?containsOutOfMemoryData' in the BiocGenerics "",
                      ""package for more information.""))
        invisible(callNextMethod())
    }
)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### updateObject() method
###

.updateObject_SummarizedExperiment <- function(object, ..., verbose=FALSE)
{
    object@assays <- updateObject(object@assays, ..., verbose=verbose)
    object@colData <- updateObject(object@colData, ..., verbose=verbose)
    callNextMethod()  # call method for Vector objects
}

setMethod(""updateObject"", ""SummarizedExperiment"",
    .updateObject_SummarizedExperiment
)

","R"
"Assay","Bioconductor/SummarizedExperiment","R/Assays-class.R",".R","15653","467","### =========================================================================
### Assays objects
### -------------------------------------------------------------------------
###
### The Assays API consists of:
###   (a) The Assays() constructor function.
###   (b) Lossless back and forth coercion from/to SimpleList. The coercion
###       method from SimpleList doesn't need (and should not) validate the
###       returned object.
###   (c) The following methods, split in 2 groups:
###       - List-like methods:   length, names, names<-, getListElement, and
###                              setListElement
###       - Matrix-like methods: dim, [, [<-, rbind, cbind
###
### An Assays concrete subclass needs to implement (b) (required) plus
### optionally any of the methods in (c). The reason they are optionals
### is that default methods are provided and they work on any Assays
### derivative as long as lossless back and forth coercion from/to
### SimpleList works.
###
### IMPORTANT
### ---------
###
### 1. Nobody in the Assays hierarchy is allowed to inherit from SimpleList
###    because of the conflicting semantic of [.
###
### 2. Methods that return a modified Assays object (a.k.a. endomorphisms),
###    that is, [ as well as replacement methods names<-, setListElement,
###    and [<-, must respect the copy-on-change contract. With objects that
###    don't make use of references internally, the developer doesn't need
###    to take any special action for that because it's automatically taken
###    care of by R itself. However, for objects that do make use of
###    references internally (e.g. environments, external pointers, pointer
###    to a file on disk, etc...), the developer needs to be careful to
###    implement endomorphisms with copy-on-change semantics. This can
###    easily be achieved (and is what the default methods for Assays objects
###    do) by performaing a full (deep) copy of the object before modifying it
###    instead of trying to modify it in-place. However note that this full
###    (deep) copy can be very expensive and is actually not necessary in
###    order to achieve copy-on-change semantics: it's enough (and often
###    preferrable for performance reasons) to copy only the parts of the
###    object that need to be modified.


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Assays class
###

setClass(""Assays"", contains=""RectangularData"", representation(""VIRTUAL""))
### Should Assays contain Vector? The length would be the 3rd dimension
### i.e. the nb of assays (1st and 2nd dimensions being the nb of rows
### and cols). That would seem like the right thing to do.
#setClass(""Assays"",
#    contains=c(""RectangularData"", ""Vector""),
#    representation(""VIRTUAL"")
#)

### Validity

.valid.Assays <- function(x)
{
    assays <- try(as(x, ""SimpleList""), silent=TRUE)
    if (inherits(assays, ""try-error""))
        return(""'as(x, \""SimpleList\"")' must work"")
    if (!is(assays, ""SimpleList""))
        return(""'as(x, \""SimpleList\"")' must return a SimpleList object"")
    if (length(assays) == 0L)
        return(NULL)

    ## Check dims.
    all_dims <- sapply(assays, function(assay) dim(assay)[1:2])
    if (any(is.na(all_dims)))
        return(wmsg(""all assays must be matrix-like objects "",
                    ""with 2 (or more?) dimensions""))
    if (!all(all_dims == all_dims[ , 1L]))
        stop(""all assays must have the same nrow and ncol"")

    NULL
}

setValidity2(""Assays"", .valid.Assays)

### Constructor

### Always return a SimpleList object by default. Will return a NULL only
### if 'as.null.if.no.assay' is set to TRUE and no assays are supplied.
normarg_assays <- function(assays, as.null.if.no.assay=FALSE)
{
    if (!isTRUEorFALSE(as.null.if.no.assay))
        stop(wmsg(""'as.null.if.no.assay' must be TRUE or FALSE""))

    if (is.null(assays)) {
        if (as.null.if.no.assay)
            return(NULL)
        return(SimpleList())
    }

    ## The truth is that the assays can be any array-like objects
    ## with at least 2 dimensions, not just matrix-like objects.
    error_msg <- c(""'assays' must be a list or SimpleList of "",
                   ""matrix-like elements, or a matrix-like object, "",
                   ""or a NULL (see '?SummarizedExperiment')"")

    if (is(assays, ""Assays""))
        stop(wmsg(error_msg))

    assays_dim <- dim(assays)
    ## Some objects like SplitDataFrameList have a dim() method that
    ## returns a non-NULL object (a matrix!) even though they don't have
    ## an array-like semantic.
    if (!is.matrix(assays_dim) && length(assays_dim) >= 2L)
        #return(SimpleList(assays))  # broken on a data frame
        return(new2(""SimpleList"", listData=list(assays), check=FALSE))

    if (!is(assays, ""SimpleList"")) {
        if (is.list(assays)) {
            #assays <- do.call(SimpleList, assays) # broken on a list of
                                                   # data frames
            assays <- new2(""SimpleList"", listData=assays, check=FALSE)
        } else if (is(assays, ""List"")) {
            assays <- as(assays, ""SimpleList"")  # could fail
        } else {
            stop(wmsg(error_msg))
        }
    }
    if (length(assays) == 0L && as.null.if.no.assay)
        return(NULL)
    assays
}

### Always return a SimpleAssays object by default. Will return a NULL only
### if 'as.null.if.no.assay' is set to TRUE and no assays are supplied.
Assays <- function(assays=SimpleList(), as.null.if.no.assay=FALSE)
{
    if (!isTRUEorFALSE(as.null.if.no.assay))
        stop(wmsg(""'as.null.if.no.assay' must be TRUE or FALSE""))
    ## Starting with SummarizedExperiment 1.15.4, we wrap the user-supplied
    ## assays in a SimpleAssays object instead of a ShallowSimpleListAssays
    ## object. Note that there are probably hundreds (if not thousands) of
    ## serialized SummarizedExperiment objects around that use
    ## ShallowSimpleListAssays. These objects should keep working as before!
    if (!is(assays, ""SimpleAssays"")) {
        if (is(assays, ""Assays"")) {
            ## Will turn any Assays derivative (e.g. ShallowSimpleListAssays)
            ## into a SimpleAssays object.
            assays <- as(as(assays, ""SimpleList""), ""SimpleAssays"")
        } else {
            assays <- normarg_assays(assays, as.null.if.no.assay)
            if (is.null(assays))
                return(NULL)
            assays <- as(assays, ""SimpleAssays"")
            validObject(assays)
        }
    }
    if (length(assays) == 0L && as.null.if.no.assay)
        return(NULL)
    assays
}

### updateObject

.updateObject_Assays <- function(object, ..., verbose=FALSE)
{
    assays <- as(object, ""SimpleList"")
    assays <- endoapply(assays,
        function(assay)
            updateObject(assay, ..., verbose=verbose)
    )
    if (length(assays) == 0L)
        return(NULL)
    as(assays, ""SimpleAssays"")
}

setMethod(""updateObject"", ""Assays"", .updateObject_Assays)

### Accessors

setMethod(""length"", ""Assays"",
    function(x)
    {
        x <- as(x, ""SimpleList"")
        callGeneric()
    }
)

setMethod(""names"", ""Assays"",
    function(x)
    {
        x <- as(x, ""SimpleList"")
        callGeneric()
    }
)

setReplaceMethod(""names"", ""Assays"",
    function(x, value)
    {
        ans_class <- class(x)
        x <- as(x, ""SimpleList"")
        as(callGeneric(), ans_class)
    }
)

setMethod(""getListElement"", ""Assays"",
    function(x, i, exact=TRUE)
    {
        x <- as(x, ""SimpleList"")
        callGeneric()
    }
)

setMethod(""setListElement"", ""Assays"",
    function(x, i, value)
    {
        ans_class <- class(x)
        x <- as(x, ""SimpleList"")
        ans <- as(callGeneric(), ans_class)
        validObject(ans)
        ans
    }
)

setMethod(""dim"", ""Assays"",
    function(x)
    {
        if (length(x) == 0L)
            return(c(0L, 0L))
        dim(getListElement(x, 1L))[1:2]
    }
)

### 2D-Subsetting

### Subset each assay in Assays object 'x' along its first 2 dimensions.
### If not missing, 'i' and/or 'j' are assumed to be valid subscripts that
### can be used to subset an ordinary matrix or array.
.extract_Assays_subset <- function(x, i, j)
{
    subscripts12 <- list(if (missing(i)) quote(expr=) else i,
                         if (missing(j)) quote(expr=) else j)
    extract_assay_subset <- function(a) {
        ndim <- length(dim(a))
        stopifnot(ndim >= 2L)  # should never happen
        more_subscripts <- rep.int(list(quote(expr=)), ndim - 2L)
        args <- c(list(a), subscripts12, more_subscripts, list(drop=FALSE))
        do.call(`[`, args)
    }
    assays <- as(x, ""SimpleList"")
    as(endoapply(assays, extract_assay_subset), class(x))
}

setMethod(""["", ""Assays"",
    function(x, i, j, ..., drop=TRUE) .extract_Assays_subset(x, i, j)
)

### Subassign each assay in Assays object 'x' along its first 2 dimensions.
### If not missing, 'i' and/or 'j' are assumed to be valid subscripts that
### can be used to subassign an ordinary matrix or array.
.replace_Assays_subset <- function(x, i, j, value)
{
    subscripts12 <- list(if (missing(i)) quote(expr=) else i,
                         if (missing(j)) quote(expr=) else j)
    replace_assay_subset <- function(a, v) {
        ndim <- length(dim(a))
        stopifnot(ndim >= 2L)  # should never happen
        more_subscripts <- rep.int(list(quote(expr=)), ndim - 2L)
        args <- c(list(a), subscripts12, more_subscripts, list(value=v))
        do.call(`[<-`, args)
    }
    assays <- as(x, ""SimpleList"")
    values <- as(value, ""SimpleList"")
    as(mendoapply(replace_assay_subset, assays, values), class(x))
}

setReplaceMethod(""["", ""Assays"",
    function(x, i, j, ..., value) .replace_Assays_subset(x, i, j, value)
)

### rbind/cbind

### 'assays' is assumed to be an unnamed list of length >= 1
.bind_assays <- function(assays, along.cols=FALSE)
{
    if (length(dim(getListElement(assays, 1L))) == 2L) {
        BINDING_FUN <- if (along.cols) ""cbind"" else ""rbind""
    } else {
        BINDING_FUN <- if (along.cols) ""acbind"" else ""arbind""
    }
    do.call(BINDING_FUN, assays)
}

.bind_Assays_objects <- function(objects, along.cols=FALSE)
{
    if (length(objects) == 0L)
        return(Assays())
    lens <- sapply(objects, length)
    if (length(unique(lens)) != 1)
        stop(""the objects to bind must have the same number of assays"")
    len1 <- lens[1L]
    if (len1 == 0L)
        return(Assays())
    var <- lapply(objects, names)
    uvar <- unique(unlist(var))
    if (is.null(uvar)) {
        ## no names, match by position
        res <- lapply(seq_len(len1), function(index) {
            assays <- lapply(objects, getListElement, index)
            .bind_assays(assays, along.cols=along.cols)
        })
    } else {
        ## match by name
        ok <- all(vapply(var, function(x, y) identical(sort(x), y),
                         logical(1), sort(uvar)))
        if (!ok)
            stop(""assays must have the same names()"")
        res <- lapply(uvar, function(index) {
            assays <- lapply(objects, getListElement, index)
            .bind_assays(assays, along.cols=along.cols)
        })
        names(res) <- uvar
    }
    as(SimpleList(res), class(getListElement(objects, 1L)))
}

setMethod(""rbind"", ""Assays"",
    function(..., deparse.level=1)
    {
        objects <- unname(list(...))
        .bind_Assays_objects(objects, along.cols=FALSE)
    }
)

setMethod(""cbind"", ""Assays"",
    function(..., deparse.level=1)
    {
        objects <- unname(list(...))
        .bind_Assays_objects(objects, along.cols=TRUE)
    }
)

### Having ""arbind"" and ""acbind"" methods for Matrix objects will make rbind()
### and cbind() work on Assays objects with Matrix list elements.
### Maybe these methods should be defined next to the arbind() and acbind()
### generics (which are defined in the IRanges package) but that would require
### to make IRanges depend on the Matrix package.
setMethod(""arbind"", ""Matrix"", function(...) rbind(...))
setMethod(""acbind"", ""Matrix"", function(...) cbind(...))


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### SimpleAssays class
###
### Store the Assays in a SimpleList object.
###

### SimpleAssays cannot contain SimpleList because of the conflicting
### semantic of [.
setClass(""SimpleAssays"",
    contains=""Assays"",
    representation(data=""SimpleList"")
)

### We only need to implement the REQUIRED coercions.

setAs(""SimpleList"", ""SimpleAssays"",
    function(from) new2(""SimpleAssays"", data=from, check=FALSE)
)

setAs(""SimpleAssays"", ""SimpleList"", function(from) from@data)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### ShallowSimpleListAssays class
###
### WARNING: Looks like reference classes as implemented in the methods
### package are a bit problematic e.g. all.equal() can return false negatives
### after a serialization/deserialization cycle on a ref object as reported
### here https://stat.ethz.ch/pipermail/bioc-devel/2019-May/015112.html
### Anyway their use in the assays slot of a SummarizedExperiment object is
### probably not needed anymore now that R is ""shallow copy by default""
### according to this comment by Michael:
###     https://github.com/Bioconductor/SummarizedExperiment/issues/16#issuecomment-455541415
###

.ShallowData <- setRefClass(""ShallowData"",
    fields = list( data = ""ANY"" ))

.ShallowSimpleListAssays0 <- setRefClass(""ShallowSimpleListAssays"",
    fields = list( data = ""SimpleList"" ),
    contains = c(""ShallowData"", ""Assays""))

### We only need to implement the REQUIRED coercions.

setAs(""SimpleList"", ""ShallowSimpleListAssays"",
    function(from) .ShallowSimpleListAssays0(data=from)
)

setAs(""ShallowSimpleListAssays"", ""SimpleList"", function(from) from$data)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### AssaysInEnv class
###
### A *broken* alternative to ShallowSimpleListAssays that does NOT respect
### the copy-on-change contract (only provided for illustration purposes).
###
### We implement the REQUIRED coercions plus OPTIONAL methods: length, names,
### names<-, getListElement, and setListElement.
###

setClass(""AssaysInEnv"",
    contains=""Assays"",
    representation(envir=""environment"")
)

.NAMES_SYMBOL <- "".names""  # must begin with a . so is ommitted by ls() 

setMethod(""length"", ""AssaysInEnv"", function(x) length(x@envir) - 1L)

setMethod(""names"", ""AssaysInEnv"", function(x) x@envir[[.NAMES_SYMBOL]])

### Does NOT respect the copy-on-change contract!
setReplaceMethod(""names"", ""AssaysInEnv"",
    function(x, value)
    {
        value <- S4Vectors:::normarg_names(value, class(x), length(x))
        x@envir[[.NAMES_SYMBOL]] <- value
        x
    }
)

setMethod(""getListElement"", ""AssaysInEnv"",
    function(x, i, exact=TRUE)
    {
        key <- setNames(ls(x@envir, sorted=TRUE), names(x))[[i]]
        get(key, envir=x@envir)
    }
)

### Does NOT respect the copy-on-change contract!
setMethod(""setListElement"", ""AssaysInEnv"",
    function(x, i, value)
    {
        key <- setNames(ls(x@envir, sorted=TRUE), names(x))[[i]]
        assign(key, value, envir=x@envir)
        x
    }
)

setAs(""SimpleList"", ""AssaysInEnv"",
    function(from)
    {
        from <- as.list(from)
        from_names <- names(from)
        keys <- paste(sprintf(""%09d"", seq_along(from)), from_names, sep="":"")
        names(from) <- keys
        envir <- list2env(from, parent=emptyenv())
        envir[[.NAMES_SYMBOL]] <- from_names
        new(""AssaysInEnv"", envir=envir)
    }
)

setAs(""AssaysInEnv"", ""SimpleList"",
    function(from)
        SimpleList(setNames(as.list(from@envir, sorted=TRUE), names(from)))
)

","R"
"Assay","Bioconductor/SummarizedExperiment","R/findOverlaps-methods.R",".R","1350","43","### =========================================================================
### findOverlaps methods
### -------------------------------------------------------------------------


### findOverlaps

setMethod(""findOverlaps"", c(""RangedSummarizedExperiment"", ""Vector""),
    function(query, subject, maxgap=-1L, minoverlap=0L,
             type=c(""any"", ""start"", ""end"", ""within"", ""equal""),
             select=c(""all"", ""first"", ""last"", ""arbitrary""),
             ignore.strand=FALSE)
    {
        query <- rowRanges(query)
        callGeneric()
    }
)

setMethod(""findOverlaps"", c(""Vector"", ""RangedSummarizedExperiment""),
    function(query, subject, maxgap=-1L, minoverlap=0L,
             type=c(""any"", ""start"", ""end"", ""within"", ""equal""),
             select=c(""all"", ""first"", ""last"", ""arbitrary""),
             ignore.strand=FALSE)
    {
        subject <- rowRanges(subject)
        callGeneric()
    }
)

setMethod(""findOverlaps"", c(""RangedSummarizedExperiment"",
                            ""RangedSummarizedExperiment""),
    function(query, subject, maxgap=-1L, minoverlap=0L,
             type=c(""any"", ""start"", ""end"", ""within"", ""equal""),
             select=c(""all"", ""first"", ""last"", ""arbitrary""),
             ignore.strand=FALSE)
    {
        query <- rowRanges(query)
        subject <- rowRanges(subject)
        callGeneric()
    }
)

","R"
"Assay","Bioconductor/SummarizedExperiment","R/inter-range-methods.R",".R","498","23","### =========================================================================
### Inter-range methods
### -------------------------------------------------------------------------
###


setMethod(""isDisjoint"", ""RangedSummarizedExperiment"",
    function(x, ignore.strand=FALSE)
    {
        x <- rowRanges(x)
        callGeneric()
    }
)

setMethod(""disjointBins"", ""RangedSummarizedExperiment"",
    function(x, ignore.strand = FALSE)
    {
        x <- rowRanges(x)
        callGeneric()
    }
)

","R"
"Assay","Bioconductor/SummarizedExperiment","R/RangedSummarizedExperiment-class.R",".R","14406","455","### =========================================================================
### RangedSummarizedExperiment objects
### -------------------------------------------------------------------------
###


### The 'elementMetadata' slot must contain a zero-column DataFrame at all time
### (this is checked by the validity method). The top-level mcols are stored on
### the rowRanges component.
setClass(""RangedSummarizedExperiment"",
    contains=""SummarizedExperiment"",
    representation(
        rowRanges=""GenomicRanges_OR_GRangesList""
    ),
    prototype(
        rowRanges=GRanges()
    )
)

### Combine the new ""parallel slots"" with those of the parent class. Make
### sure to put the new parallel slots **first**. See R/Vector-class.R file
### in the S4Vectors package for what slots should or should not be considered
### ""parallel"".
setMethod(""parallel_slot_names"", ""RangedSummarizedExperiment"",
    function(x) c(""rowRanges"", callNextMethod())
)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Validity
###

### The names and mcols of a RangedSummarizedExperiment must be set on its
### rowRanges slot, not in its NAMES and elementMetadata slots!
.valid.RangedSummarizedExperiment <- function(x)
{
    if (!is.null(x@NAMES))
        return(""'NAMES' slot must be set to NULL at all time"")
    if (ncol(x@elementMetadata) != 0L)
        return(wmsg(""'elementMetadata' slot must contain a zero-column "",
                    ""DataFrame at all time""))
    rowRanges_len <- length(x@rowRanges)
    x_nrow <- length(x)
    if (rowRanges_len != x_nrow) {
        txt <- sprintf(
            ""\n  length of 'rowRanges' (%d) must equal nb of rows in 'x' (%d)"",
            rowRanges_len, x_nrow)
        return(txt)
    }
    NULL
}

setValidity2(""RangedSummarizedExperiment"", .valid.RangedSummarizedExperiment)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Constructor
###

new_RangedSummarizedExperiment <- function(assays, rowRanges, colData,
                                            metadata)
{
    assays <- Assays(assays, as.null.if.no.assay=TRUE)
    elementMetadata <- S4Vectors:::make_zero_col_DataFrame(length(rowRanges))
    new(""RangedSummarizedExperiment"", rowRanges=rowRanges,
                                      colData=colData,
                                      assays=assays,
                                      elementMetadata=elementMetadata,
                                      metadata=as.list(metadata))
}


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Coercion
###
### See makeSummarizedExperimentFromExpressionSet.R for coercion back and
### forth between SummarizedExperiment and ExpressionSet.
###

.from_RangedSummarizedExperiment_to_SummarizedExperiment <- function(from)
{
    new_SummarizedExperiment(from@assays,
                             names(from@rowRanges),
                             mcols(from@rowRanges, use.names=FALSE),
                             from@colData,
                             from@metadata)
}

setAs(""RangedSummarizedExperiment"", ""SummarizedExperiment"",
    .from_RangedSummarizedExperiment_to_SummarizedExperiment
)

.from_SummarizedExperiment_to_RangedSummarizedExperiment <- function(from)
{
    partitioning <- PartitioningByEnd(integer(length(from)), names=names(from))
    rowRanges <- relist(GRanges(), partitioning)
    mcols(rowRanges) <- mcols(from, use.names=FALSE)
    new_RangedSummarizedExperiment(from@assays,
                                   rowRanges,
                                   from@colData,
                                   from@metadata)
}

setAs(""SummarizedExperiment"", ""RangedSummarizedExperiment"",
    .from_SummarizedExperiment_to_RangedSummarizedExperiment
)


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Accessors
###

### The rowRanges() generic is defined in the MatrixGenerics package.
setMethod(""rowRanges"", ""SummarizedExperiment"",
    function(x, ...) NULL
)

### Fix old GRanges instances on-the-fly.
setMethod(""rowRanges"", ""RangedSummarizedExperiment"",
    function(x, ...) updateObject(x@rowRanges, check=FALSE)
)

setGeneric(""rowRanges<-"",
    function(x, ..., value) standardGeneric(""rowRanges<-""))

### No-op.
setReplaceMethod(""rowRanges"", c(""SummarizedExperiment"", ""NULL""),
    function(x, ..., value) x
)

### Degrade 'x' to SummarizedExperiment instance.
setReplaceMethod(""rowRanges"", c(""RangedSummarizedExperiment"", ""NULL""),
    function(x, ..., value) as(x, ""SummarizedExperiment"", strict=TRUE)
)

.SummarizedExperiment.rowRanges.replace <-
    function(x, ..., value)
{
    if (is(x, ""RangedSummarizedExperiment"")) {
        x <- updateObject(x, check=FALSE)
    } else {
        x <- as(x, ""RangedSummarizedExperiment"")
    }
    x <- BiocGenerics:::replaceSlots(x, ...,
             rowRanges=value,
             elementMetadata=S4Vectors:::make_zero_col_DataFrame(length(value)),
             check=FALSE)
    msg <- .valid.SummarizedExperiment.assays_nrow(x)
    if (!is.null(msg))
        stop(msg)
    x
}

setReplaceMethod(""rowRanges"", c(""SummarizedExperiment"", ""GenomicRanges""),
    .SummarizedExperiment.rowRanges.replace)

setReplaceMethod(""rowRanges"", c(""SummarizedExperiment"", ""GRangesList""),
    .SummarizedExperiment.rowRanges.replace)

setMethod(""names"", ""RangedSummarizedExperiment"",
    function(x) names(rowRanges(x))
)

setReplaceMethod(""names"", ""RangedSummarizedExperiment"",
    function(x, value)
{
    rowRanges <- rowRanges(x)
    names(rowRanges) <- value
    BiocGenerics:::replaceSlots(x, rowRanges=rowRanges, check=FALSE)
})

setReplaceMethod(""dimnames"", c(""RangedSummarizedExperiment"", ""list""),
    function(x, value)
{
    rowRanges <- rowRanges(x)
    names(rowRanges) <- value[[1]]
    colData <- colData(x)
    rownames(colData) <- value[[2]]
    BiocGenerics:::replaceSlots(x,
        rowRanges=rowRanges,
        colData=colData,
        check=FALSE)
})


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Subsetting
###

.DollarNames.RangedSummarizedExperiment <- .DollarNames.SummarizedExperiment

setMethod(""subset"", ""RangedSummarizedExperiment"",
    function(x, subset, select, ...)
{
    i <- S4Vectors:::evalqForSubset(subset, rowRanges(x), ...)
    j <- S4Vectors:::evalqForSubset(select, colData(x), ...)
    x[i, j]
})


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
## colData-as-GRanges compatibility: allow direct access to GRanges /
## GRangesList colData for select functions

## Not supported:
## 
## Not consistent SummarizedExperiment structure: length, names,
##   as.data.frame, c.
## Length-changing endomorphisms: disjoin, gaps, reduce, unique.
## 'legacy' data types / functions: as ""RangedData"", as ""IntegerRangesList"",
##   renameSeqlevels, keepSeqlevels.
## Possile to implement, but not yet: Ops, map, window, window<-

## mcols
setMethod(""mcols"", ""RangedSummarizedExperiment"",
    function(x, use.names=TRUE, ...)
{
    mcols(rowRanges(x), use.names=use.names, ...)
})

setReplaceMethod(""mcols"", ""RangedSummarizedExperiment"",
    function(x, ..., value)
{
    BiocGenerics:::replaceSlots(x,
        rowRanges=local({
            r <- rowRanges(x)
            mcols(r) <- value
            r
        }),
        check=FALSE)
})

### mcols() is the recommended way for accessing the metadata columns.
### Use of values() or elementMetadata() is discouraged.

setMethod(""elementMetadata"", ""RangedSummarizedExperiment"",
    function(x, use.names=FALSE, ...)
{
    elementMetadata(rowRanges(x), use.names=use.names, ...)
})

setReplaceMethod(""elementMetadata"", ""RangedSummarizedExperiment"",
    function(x, ..., value)
{
    elementMetadata(rowRanges(x), ...) <- value
    x
})

## Single dispatch, generic signature fun(x, ...)
local({
    .funs <-
        c(""duplicated"", ""end"", ""end<-"", ""ranges"", ""seqinfo"", ""seqnames"",
          ""start"", ""start<-"", ""strand"", ""width"", ""width<-"")

    endomorphisms <- .funs[grepl(""<-$"", .funs)]

    tmpl <- function() {}
    environment(tmpl) <- parent.frame(2)
    for (.fun in .funs) {
        generic <- getGeneric(.fun)
        formals(tmpl) <- formals(generic)
        fmls <- as.list(formals(tmpl))
        fmls[] <- sapply(names(fmls), as.symbol)
        fmls[[generic@signature]] <- quote(rowRanges(x))
        if (.fun %in% endomorphisms)
            body(tmpl) <- substitute({
                rowRanges(x) <- do.call(FUN, ARGS)
                x
            }, list(FUN=.fun, ARGS=fmls))
        else
            body(tmpl) <-
                substitute(do.call(FUN, ARGS),
                           list(FUN=as.symbol(.fun), ARGS=fmls))
        setMethod(.fun, ""RangedSummarizedExperiment"", tmpl)
    }
})

setMethod(""granges"", ""RangedSummarizedExperiment"",
    function(x, use.mcols=FALSE, ...)
{
    if (!identical(use.mcols, FALSE))
        stop(""\""granges\"" method for RangedSummarizedExperiment objects "",
             ""does not support the 'use.mcols' argument"")
    rowRanges(x)
})

## 2-argument dispatch:
## pcompare / Compare 
## 
.RangedSummarizedExperiment.pcompare <-
    function(x, y)
{
    if (is(x, ""RangedSummarizedExperiment""))
        x <- rowRanges(x)
    if (is(y, ""RangedSummarizedExperiment""))
        y <- rowRanges(y)
    pcompare(x, y)
}

.RangedSummarizedExperiment.Compare <-
    function(e1, e2)
{
    if (is(e1, ""RangedSummarizedExperiment""))
        e1 <- rowRanges(e1)
    if (is(e2, ""RangedSummarizedExperiment""))
        e2 <- rowRanges(e2)
    callGeneric(e1=e1, e2=e2)
}

local({
    .signatures <- list(
        c(""RangedSummarizedExperiment"", ""ANY""),
        c(""ANY"", ""RangedSummarizedExperiment""),
        c(""RangedSummarizedExperiment"", ""RangedSummarizedExperiment""))

    for (.sig in .signatures) {
        setMethod(""pcompare"", .sig, .RangedSummarizedExperiment.pcompare)
        setMethod(""Compare"", .sig, .RangedSummarizedExperiment.Compare)
    }
})

## additional getters / setters

setReplaceMethod(""strand"", ""RangedSummarizedExperiment"",
    function(x, ..., value)
{
    strand(rowRanges(x)) <- value
    x
})

setReplaceMethod(""ranges"", ""RangedSummarizedExperiment"",
    function(x, ..., value)
{
    ranges(rowRanges(x)) <- value
    x
})

## order, rank, sort

setMethod(""is.unsorted"", ""RangedSummarizedExperiment"",
    function(x, na.rm = FALSE, strictly = FALSE, ignore.strand = FALSE)
{
    x <- rowRanges(x)
    if (!is(x, ""GenomicRanges""))
        stop(""is.unsorted() is not yet supported when 'rowRanges(x)' is a "",
             class(x), "" object"")
    callGeneric()
})

setMethod(""order"", ""RangedSummarizedExperiment"",
    function(..., na.last=TRUE, decreasing=FALSE,
             method=c(""auto"", ""shell"", ""radix""))
{
    args <- lapply(list(...), rowRanges)
    do.call(""order"", c(args, list(na.last=na.last,
                                  decreasing=decreasing,
                                  method=method)))
})

setMethod(""rank"", ""RangedSummarizedExperiment"",
    function(x, na.last = TRUE,
        ties.method = c(""average"", ""first"", ""last"", ""random"", ""max"", ""min""))
{
    ties.method <- match.arg(ties.method)
    rank(rowRanges(x), na.last=na.last, ties.method=ties.method)
})

setMethod(""sort"", ""RangedSummarizedExperiment"",
    function(x, decreasing = FALSE, ignore.strand = FALSE)
{
    x_rowRanges <- rowRanges(x)
    if (!is(x_rowRanges, ""GenomicRanges""))
        stop(""sort() is not yet supported when 'rowRanges(x)' is a "",
             class(x_rowRanges), "" object"")
    oo <- GenomicRanges:::order_GenomicRanges(x_rowRanges,
                                              decreasing = decreasing,
                                              ignore.strand = ignore.strand)
    x[oo]
})

## seqinfo (also seqlevels, genome, seqlevels<-, genome<-), seqinfo<-

setMethod(""seqinfo"", ""RangedSummarizedExperiment"",
    function(x)
{
    seqinfo(x@rowRanges)
})

.set_RangedSummarizedExperiment_seqinfo <-
    function(x, new2old=NULL,
             pruning.mode=c(""error"", ""coarse"", ""fine"", ""tidy""),
             value)
{
    if (!is(value, ""Seqinfo""))
        stop(""the supplied 'seqinfo' must be a Seqinfo object"")
    pruning.mode <- match.arg(pruning.mode)
    if (pruning.mode == ""fine"") {
        if (is(x@rowRanges, ""GenomicRanges""))
            stop(wmsg(""\""fine\"" pruning mode is not supported on "",
                      class(x), "" objects with a rowRanges component that "",
                      ""is a GRanges object or a GenomicRanges derivative""))
    } else {
        dangling_seqlevels <- Seqinfo:::getDanglingSeqlevels(x@rowRanges,
                                                   new2old=new2old,
                                                   pruning.mode=pruning.mode,
                                                   seqlevels(value))
        if (length(dangling_seqlevels) != 0L) {
            idx <- !(seqnames(x@rowRanges) %in% dangling_seqlevels)
            ## 'idx' should be either a logical vector or a list-like
            ## object where all the list elements are logical vectors (e.g.
            ## a LogicalList or RleList object). If the latter, we transform
            ## it into a logical vector.
            if (is(idx, ""List"")) {
                if (pruning.mode == ""coarse"") {
                    idx <- all(idx)  # ""coarse"" pruning
                } else {
                    idx <- any(idx) | elementNROWS(idx) == 0L  # ""tidy"" pruning
                }
            }
            ## 'idx' now guaranteed to be a logical vector.
            x <- x[idx]
        }
    }
    seqinfo(x@rowRanges, new2old=new2old, pruning.mode=pruning.mode) <- value
    if (is.character(msg <- .valid.RangedSummarizedExperiment(x)))
        stop(msg)
    x
}
setReplaceMethod(""seqinfo"", ""RangedSummarizedExperiment"",
    .set_RangedSummarizedExperiment_seqinfo
)

setMethod(""split"", ""RangedSummarizedExperiment"",
    function(x, f, drop=FALSE, ...)
{
    splitAsList(x, f, drop=drop)
})


### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### updateObject()
###

.updateObject_RangedSummarizedExperiment <- function(object, ..., verbose=FALSE)
{
    object <- callNextMethod()  # call method for SummarizedExperiment objects
    object@rowRanges <- updateObject(object@rowRanges, ..., verbose=verbose)
    object
}

setMethod(""updateObject"", ""RangedSummarizedExperiment"",
    .updateObject_RangedSummarizedExperiment
)

","R"
"Assay","Bioconductor/SummarizedExperiment","R/coverage-methods.R",".R","398","16","### =========================================================================
### ""coverage"" method
### -------------------------------------------------------------------------
###


setMethod(""coverage"", ""RangedSummarizedExperiment"",
    function(x, shift=0L, width=NULL, weight=1L,
                method=c(""auto"", ""sort"", ""hash""))
    {
        x <- rowRanges(x)
        callGeneric()
    }
)

","R"
"Assay","Bioconductor/SummarizedExperiment","tests/run_unitTests.R",".R","118","3","require(""SummarizedExperiment"") || stop(""unable to load SummarizedExperiment package"")
SummarizedExperiment:::.test()
","R"
"Assay","choderalab/assaytools","setup.py",".py","3414","99","""""""AssayTools: Assay modeling and Bayesian analysis made easy.

AssayTools is a python library that allows users to model automated assays and analyze assay data using powerful Bayesian techniques that allow complete characterization of the uncertainty in fit models.

""""""

from __future__ import print_function, absolute_import
DOCLINES = __doc__.split(""\n"")

import sys
from setuptools import setup, Extension
sys.path.insert(0, '.')
from basesetup import (find_packages, write_version_py, build_ext,
                       StaticLibrary, CompilerDetection)

try:
    import numpy
except ImportError:
    print('Building and running assaytools requires numpy', file=sys.stderr)
    sys.exit(1)

try:
    # add an optional command line flag --no-install-deps to setup.py
    # to turn off setuptools automatic downloading of dependencies
    sys.argv.remove('--no-install-deps')
    no_install_deps = True
except ValueError:
    no_install_deps = False


setup_kwargs = {}
if 'setuptools' in sys.modules:
    setup_kwargs['zip_safe'] = False
    setup_kwargs['entry_points'] = {'console_scripts':
              ['xml2png = assaytools.scripts.xml2png:entry_point',
               'quickmodel = assaytools.scripts.quickmodel:entry_point',
               'ipnbdoctest = assaytools.scripts.ipnbdoctest:entry_point',
               'calculate_Lstated_array = assaytools.scripts.calculate_Lstated_array:entry_point',
               'plot_trace = assaytools.scripts.plot_trace:entry_point']}

    if sys.version_info[0] == 2:
        # required to fix cythoninze() for old versions of setuptools on
        # python 2
        m = sys.modules['setuptools.extension']
        m.Extension.__dict__ = m._Extension.__dict__
else:
    setup_kwargs['scripts'] = ['scripts/xml2png.py','scripts/quickmodel.py','scripts/calculate_Lstated_array.py', 'scripts/plot_trace.py']


##########################
VERSION = ""0.3.1""
ISRELEASED = False
__version__ = VERSION
##########################


CLASSIFIERS = """"""\
Development Status :: 5 - Production/Stable
Intended Audience :: Science/Research
Intended Audience :: Developers
License :: OSI Approved :: GNU Lesser General Public License v2 or later (LGPLv2+)
Programming Language :: C
Programming Language :: Python
Programming Language :: Python :: 3
Topic :: Scientific/Engineering :: Bio-Informatics
Topic :: Scientific/Engineering :: Chemistry
Operating System :: Microsoft :: Windows
Operating System :: POSIX
Operating System :: Unix
Operating System :: MacOS
""""""

write_version_py(VERSION, ISRELEASED)
setup(name='assaytools',
      author='John D. Chodera',
      author_email='john.chodera@choderalab.org',
      description=DOCLINES[0],
      long_description=""\n"".join(DOCLINES[2:]),
      version=__version__,
      license='LGPLv2.1+',
      url='http://assaytools.choderalab.org',
      download_url = ""https://github.com/choderalab/assaytools/releases/latest"",
      platforms=['Linux', 'Mac OS-X', 'Unix', 'Windows'],
      classifiers=CLASSIFIERS.splitlines(),
      packages=find_packages(),
      package_dir={'assaytools': 'AssayTools', 'assaytools.scripts': 'scripts'},
      cmdclass={'build_ext': build_ext},
      package_data={'assaytools.html': ['static/*']},
      install_requires=[
        #'numpy',
        #'pandas',
        #'pymc',
        #'matplotlib',
        #'seaborn',
        #'lxml',
        #'autoprotocol'
        ],
      **setup_kwargs)
","Python"
"Assay","choderalab/assaytools","basesetup.py",".py","11692","330","from __future__ import print_function, absolute_import
import os
import sys
import json
import string
import shutil
import subprocess
import tempfile
from distutils.dep_util import newer_group
from distutils.core import Extension
from distutils.errors import DistutilsExecError
from distutils.ccompiler import new_compiler
from distutils.sysconfig import customize_compiler, get_config_vars
from distutils.command.build_ext import build_ext as _build_ext


def find_packages():
    """"""Find all of assaytools's python packages.
    Adapted from IPython's setupbase.py. Copyright IPython
    contributors, licensed under the BSD license.
    """"""
    packages = ['assaytools.scripts']
    for dir,subdirs,files in os.walk('AssayTools'):
        package = dir.replace(os.path.sep, '.')
        if '__init__.py' not in files:
            # not a package
            continue
        packages.append(package.replace('AssayTools', 'assaytools'))
    return packages



################################################################################
# Detection of compiler capabilities
################################################################################

class CompilerDetection(object):
    # Necessary for OSX. See https://github.com/assaytools/assaytools/issues/576
    # The problem is that distutils.sysconfig.customize_compiler()
    # is necessary to properly invoke the correct compiler for this class
    # (otherwise the CC env variable isn't respected). Unfortunately,
    # distutils.sysconfig.customize_compiler() DIES on OSX unless some
    # appropriate initialization routines have been called. This line
    # has a side effect of calling those initialzation routes, and is therefor
    # necessary for OSX, even though we don't use the result.
    _DONT_REMOVE_ME = get_config_vars()

    def __init__(self, disable_openmp):
        cc = new_compiler()
        customize_compiler(cc)

        self.msvc = cc.compiler_type == 'msvc'
        self._print_compiler_version(cc)

        if disable_openmp:
            self.openmp_enabled = False
        else:
            self.openmp_enabled, openmp_needs_gomp = self._detect_openmp()
        self.sse3_enabled = self._detect_sse3() if not self.msvc else True
        self.sse41_enabled = self._detect_sse41() if not self.msvc else True

        self.compiler_args_sse2  = ['-msse2'] if not self.msvc else ['/arch:SSE2']
        self.compiler_args_sse3  = ['-mssse3'] if (self.sse3_enabled and not self.msvc) else []

        self.compiler_args_sse41, self.define_macros_sse41 = [], []
        if self.sse41_enabled:
            self.define_macros_sse41 = [('__SSE4__', 1), ('__SSE4_1__', 1)]
            if not self.msvc:
                self.compiler_args_sse41 = ['-msse4']

        if self.openmp_enabled:
            self.compiler_libraries_openmp = []

            if self.msvc:
                self.compiler_args_openmp = ['/openmp']
            else:
                self.compiler_args_openmp = ['-fopenmp']
                if openmp_needs_gomp:
                    self.compiler_libraries_openmp = ['gomp']
        else:
            self.compiler_libraries_openmp = []
            self.compiler_args_openmp = []

        if self.msvc:
            self.compiler_args_opt = ['/O2']
        else:
            self.compiler_args_opt = ['-O3', '-funroll-loops']
        print()

    def _print_compiler_version(self, cc):
        print(""C compiler:"")
        try:
            if self.msvc:
                if not cc.initialized:
                    cc.initialize()
                cc.spawn([cc.cc])
            else:
                cc.spawn([cc.compiler[0]] + ['-v'])
        except DistutilsExecError:
            pass

    def hasfunction(self, funcname, include=None, libraries=None, extra_postargs=None):
        # running in a separate subshell lets us prevent unwanted stdout/stderr
        part1 = '''
from __future__ import print_function
import os
import json
from distutils.ccompiler import new_compiler
from distutils.sysconfig import customize_compiler, get_config_vars

FUNCNAME = json.loads('%(funcname)s')
INCLUDE = json.loads('%(include)s')
LIBRARIES = json.loads('%(libraries)s')
EXTRA_POSTARGS = json.loads('%(extra_postargs)s')
        ''' % {
            'funcname': json.dumps(funcname),
            'include': json.dumps(include),
            'libraries': json.dumps(libraries or []),
            'extra_postargs': json.dumps(extra_postargs)}

        part2 = '''
get_config_vars()  # DON'T REMOVE ME
cc = new_compiler()
customize_compiler(cc)
for library in LIBRARIES:
    cc.add_library(library)

status = 0
try:
    with open('func.c', 'w') as f:
        if INCLUDE is not None:
            f.write('#include %s\\n' % INCLUDE)
        f.write('int main(void) {\\n')
        f.write('    %s;\\n' % FUNCNAME)
        f.write('}\\n')
    objects = cc.compile(['func.c'], output_dir='.',
                         extra_postargs=EXTRA_POSTARGS)
    cc.link_executable(objects, 'a.out')
except Exception as e:
    status = 1
exit(status)
        '''
        tmpdir = tempfile.mkdtemp(prefix='hasfunction-')
        try:
            curdir = os.path.abspath(os.curdir)
            os.chdir(tmpdir)
            with open('script.py', 'w') as f:
                f.write(part1 + part2)
            proc = subprocess.Popen(
                [sys.executable, 'script.py'],
                stderr=subprocess.PIPE, stdout=subprocess.PIPE)
            proc.communicate()
            status = proc.wait()
        finally:
            os.chdir(curdir)
            shutil.rmtree(tmpdir)

        return status == 0

    def _print_support_start(self, feature):
        print('Attempting to autodetect {0:6} support...'.format(feature), end=' ')

    def _print_support_end(self, feature, status):
        if status is True:
            print('Compiler supports {0}'.format(feature))
        else:
            print('Did not detect {0} support'.format(feature))

    def _detect_openmp(self):
        self._print_support_start('OpenMP')
        hasopenmp = self.hasfunction('omp_get_num_threads()', extra_postargs=['-fopenmp', '/openmp'])
        needs_gomp = hasopenmp
        if not hasopenmp:
            hasopenmp = self.hasfunction('omp_get_num_threads()', libraries=['gomp'])
            needs_gomp = hasopenmp
        self._print_support_end('OpenMP', hasopenmp)
        return hasopenmp, needs_gomp

    def _detect_sse3(self):
        ""Does this compiler support SSE3 intrinsics?""
        self._print_support_start('SSE3')
        result = self.hasfunction('__m128 v; _mm_hadd_ps(v,v)',
                           include='<pmmintrin.h>',
                           extra_postargs=['-msse3'])
        self._print_support_end('SSE3', result)
        return result

    def _detect_sse41(self):
        ""Does this compiler support SSE4.1 intrinsics?""
        self._print_support_start('SSE4.1')
        result = self.hasfunction( '__m128 v; _mm_round_ps(v,0x00)',
                           include='<smmintrin.h>',
                           extra_postargs=['-msse4'])
        self._print_support_end('SSE4.1', result)
        return result

################################################################################
# Writing version control information to the module
################################################################################

def git_version():
    # Return the git revision as a string
    # copied from numpy setup.py
    def _minimal_ext_cmd(cmd):
        # construct minimal environment
        env = {}
        for k in ['SYSTEMROOT', 'PATH']:
            v = os.environ.get(k)
            if v is not None:
                env[k] = v
        # LANGUAGE is used on win32
        env['LANGUAGE'] = 'C'
        env['LANG'] = 'C'
        env['LC_ALL'] = 'C'
        out = subprocess.Popen(
            cmd, stdout=subprocess.PIPE, env=env).communicate()[0]
        return out

    try:
        out = _minimal_ext_cmd(['git', 'rev-parse', 'HEAD'])
        GIT_REVISION = out.strip().decode('ascii')
    except OSError:
        GIT_REVISION = 'Unknown'

    return GIT_REVISION


def write_version_py(VERSION, ISRELEASED, filename='AssayTools/version.py'):
    cnt = """"""
# THIS FILE IS GENERATED FROM ASSAYTOOLS SETUP.PY
short_version = '%(version)s'
version = '%(version)s'
full_version = '%(full_version)s'
git_revision = '%(git_revision)s'
release = %(isrelease)s

if not release:
    version = full_version
""""""
    # Adding the git rev number needs to be done inside write_version_py(),
    # otherwise the import of numpy.version messes up the build under Python 3.
    FULLVERSION = VERSION
    if os.path.exists('.git'):
        GIT_REVISION = git_version()
    else:
        GIT_REVISION = 'Unknown'

    if not ISRELEASED:
        FULLVERSION += '.dev-' + GIT_REVISION[:7]

    a = open(filename, 'w')
    try:
        a.write(cnt % {'version': VERSION,
                       'full_version': FULLVERSION,
                       'git_revision': GIT_REVISION,
                       'isrelease': str(ISRELEASED)})
    finally:
        a.close()


class StaticLibrary(Extension):
    def __init__(self, *args, **kwargs):
        self.export_include = kwargs.pop('export_include', [])
        Extension.__init__(self, *args, **kwargs)


class build_ext(_build_ext):

    def build_extension(self, ext):
        if isinstance(ext, StaticLibrary):
            self.build_static_extension(ext)
        else:
            _build_ext.build_extension(self, ext)

    def build_static_extension(self, ext):
        from distutils import log

        sources = ext.sources
        if sources is None or not isinstance(sources, (list, tuple)):
            raise DistutilsSetupError(
                  (""in 'ext_modules' option (extension '%s'), "" +
                   ""'sources' must be present and must be "" +
                   ""a list of source filenames"") % ext.name)
        sources = list(sources)

        ext_path = self.get_ext_fullpath(ext.name)
        depends = sources + ext.depends
        if not (self.force or newer_group(depends, ext_path, 'newer')):
            log.debug(""skipping '%s' extension (up-to-date)"", ext.name)
            return
        else:
            log.info(""building '%s' extension"", ext.name)

        extra_args = ext.extra_compile_args or []
        macros = ext.define_macros[:]
        for undef in ext.undef_macros:
            macros.append((undef,))
        objects = self.compiler.compile(sources,
                                         output_dir=self.build_temp,
                                         macros=macros,
                                         include_dirs=ext.include_dirs,
                                         debug=self.debug,
                                         extra_postargs=extra_args,
                                         depends=ext.depends)
        self._built_objects = objects[:]
        if ext.extra_objects:
            objects.extend(ext.extra_objects)
        extra_args = ext.extra_link_args or []

        language = ext.language or self.compiler.detect_language(sources)

        libname = os.path.basename(ext_path).split(os.extsep)[0]
        output_dir = os.path.dirname(ext_path)

        if (self.compiler.static_lib_format.startswith('lib') and
            libname.startswith('lib')):
            libname = libname[3:]

        if not os.path.exists(output_dir):
            # necessary for windows
            os.makedirs(output_dir)

        self.compiler.create_static_lib(objects,
            output_libname=libname,
            output_dir=output_dir,
            target_lang=language)

        for item in ext.export_include:
            shutil.copy(item, output_dir)
","Python"
"Assay","choderalab/assaytools","devtools/conda-recipe/build.sh",".sh","27","4","#!/bin/bash

pip install .
","Shell"
"Assay","choderalab/assaytools","devtools/travis-ci/push-docs-to-s3.py",".py","925","33","import os
import pip
import tempfile
import subprocess
import mdtraj.version


BUCKET_NAME = 'assaytools'
if not mdtraj.version.release:
    PREFIX = 'latest'
else:
    PREFIX = mdtraj.version.short_version

if not any(d.project_name == 's3cmd' for d in pip.get_installed_distributions()):
    raise ImportError('The s3cmd pacakge is required. try $ pip install s3cmd')

# The secret key is available as a secure environment variable
# on travis-ci to push the build documentation to Amazon S3.
with tempfile.NamedTemporaryFile('w') as f:
    f.write('''[default]
access_key = {AWS_ACCESS_KEY_ID}
secret_key = {AWS_SECRET_ACCESS_KEY}
'''.format(**os.environ))
    f.flush()

    template = ('s3cmd --config {config} '
                'sync docs/_build/ s3://{bucket}/{prefix}/')
    cmd = template.format(
            config=f.name,
            bucket=BUCKET_NAME,
            prefix=PREFIX)
    subprocess.call(cmd.split())
","Python"
"Assay","choderalab/assaytools","devtools/travis-ci/after_success.sh",".sh","1044","37","#!/bin/bash
# Must be invoked with $PACKAGENAME

echo $TRAVIS_PULL_REQUEST $TRAVIS_BRANCH
PUSH_DOCS_TO_S3=false

if [ ""$TRAVIS_PULL_REQUEST"" = true ]; then
    echo ""This is a pull request. No deployment will be done.""; exit 0
fi


if [ ""$TRAVIS_BRANCH"" != ""master"" ]; then
    echo ""No deployment on BRANCH='$TRAVIS_BRANCH'""; exit 0
fi


# Deploy to binstar
conda install --yes anaconda-client jinja2
binstar -t $BINSTAR_TOKEN upload --force -u omnia -p ${PACKAGENAME}-dev $HOME/miniconda/conda-bld/*/${PACKAGENAME}-dev-*.tar.bz2

if [ $PUSH_DOCS_TO_S3 = true ]; then
   # Create the docs and push them to S3
   # -----------------------------------
    conda install --yes pip
    conda config --add channels http://conda.binstar.org/omnia
    conda install --yes `conda build devtools/conda-recipe --output`
    pip install numpydoc s3cmd msmb_theme
    conda install --yes `cat docs/requirements.txt | xargs`

    conda list -e

    (cd docs && make html && cd -)
    ls -lt docs/_build
    pwd
    python devtools/ci/push-docs-to-s3.py
fi
","Shell"
"Assay","choderalab/assaytools","devtools/travis-ci/update-versions.py",".py","1078","35","from __future__ import print_function
import os
import boto
from boto.s3.key import Key
import assaytools.version

if assaytools.version.release:
    # The secret key is available as a secure environment variable
    # on travis-ci to push the build documentation to Amazon S3.
    AWS_ACCESS_KEY_ID = os.environ['AWS_ACCESS_KEY_ID']
    AWS_SECRET_ACCESS_KEY = os.environ['AWS_SECRET_ACCESS_KEY']
    BUCKET_NAME = 'assaytools'

    bucket_name = AWS_ACCESS_KEY_ID.lower() + '-' + BUCKET_NAME
    conn = boto.connect_s3(AWS_ACCESS_KEY_ID,
                AWS_SECRET_ACCESS_KEY)
    bucket = conn.get_bucket(BUCKET_NAME)

    root = 'doc/_build'

    versions = json.load(urllib2.urlopen('http://mdtraj.org/versions.json'))

    # new release so all the others are now old
    for i in xrange(len(versions)):
        versions[i]['latest'] = False

    versions.append({'version' : msmbuilder.version.short_version, 'latest' : True})

    k = Key(bucket)
    k.key = 'versions.json'
    k.set_contents_from_string(json.dumps(versions))

else:
    print(""This is not a release."")
","Python"
"Assay","choderalab/assaytools","devtools/travis-ci/ipythontests.sh",".sh","264","17","#!/bin/sh
# Run ipython notebook tests

cd examples/fluorescence-binding-assay
testfail=0
shopt -s nullglob
for fn in *.ipynb; do
    echo ""Testing IPython notebook $fn""
    ipynbtest.py ""$fn"" || testfail=1
done
cd ../..
if [ $testfail -eq 1 ]
then
    exit 1
fi

","Shell"
"Assay","choderalab/assaytools","devtools/travis-ci/install.sh",".sh","646","20","# Temporarily change directory to $HOME to install software
pushd .
cd $HOME

if [[ ""$TRAVIS_OS_NAME"" == ""osx"" ]];   then MINICONDA=Miniconda3-latest-MacOSX-x86_64.sh; fi
if [[ ""$TRAVIS_OS_NAME"" == ""linux"" ]]; then MINICONDA=Miniconda3-latest-Linux-x86_64.sh;  fi

MINICONDA_MD5=$(curl -s https://repo.continuum.io/miniconda/ | grep -A3 $MINICONDA | sed -n '4p' | sed -n 's/ *<td>\(.*\)<\/td> */\1/p')
wget https://repo.continuum.io/miniconda/$MINICONDA
bash $MINICONDA -b
rm -f $MINICONDA

export PATH=$HOME/miniconda3/bin:$PATH

conda update -yq conda
conda install -yq conda-build jinja2 anaconda-client pip

# Restore original directory
popd
","Shell"
"Assay","choderalab/assaytools","AssayTools/experiments.py",".py","20447","492","from autoprotocol.container import Well, WellGroup, Container
from autoprotocol.container_type import ContainerType
from autoprotocol.unit import Unit
import numpy as np
import random, string

#
# Define simple solutions
# Solutions are the fundamental liquids that have concentrations (and concentration uncertainties) for a single species.
# In future, we will associate densities, allow mixing of solutions, propagate uncertainty.
# Solutions have unknown *true* concentrations, except for buffer solutions which have exactly zero concentration.
#

class Buffer(object):
    def __init__(self, shortname, description):
        """"""
        Parameters
        ----------
        shortname : str
            A short name for the solution. (e.g. 'buffer')
        description:
            A descriptive name for the solution (e.g. '20 mM Tris 50 mM NaCl')
        """"""
        self.shortname = shortname
        self.description = description
        self.species = None
        self.concentration = Unit(0.0, 'moles/liter')
        self.uncertainty = Unit(0.0, 'moles/liter')

DMSO = Buffer('DMSO', 'DMSO')

class Solution(object):
    """"""A solution of a single component with a defined concentration and uncertainty.
    """"""
    def __init__(self, shortname, description, species, buffer, concentration, uncertainty):
        """"""
        Parameters
        ----------
        shortname : str
            A short name for the solution. (e.g. 'bosutinib-stock')
        description:
            A descriptive name for the solution (e.g. '10 mM bosutinib in DMSO')
        species : str
            The name of the species dissolved in buffer (e.g. 'bosutinib')
        buffer : Buffer
            The buffer the solution is prepared in.
        concentration : Unit
            The concentration of the species in the solution.
        uncertainty : Unit
            The concentration uncertainty of the species in the solution.
        """"""
        self.shortname = shortname
        self.species = species
        self.buffer = buffer
        self.concentration = concentration
        self.uncertainty = uncertainty

class ProteinSolution(Solution):
    """"""A protein solution in buffer prepared spectrophotometrically.
    """"""
    def __init__(self, shortname, description, species, buffer, absorbance, extinction_coefficient, molecular_weight, protein_stock_volume, buffer_volume, spectrophotometer_CV=0.10):
        """"""
        Parameters
        ----------
        shortname : str
            A short name for the solution (e.g. 'protein')
        description:
            A descriptive name for the solution (e.g. '1 uM Abl')
        species : str
            The protein name
        buffer : BufferSolution
            The corresponding buffer solution.
        absorbance : float
            Absorbance for 1 cm equivalent path length
        extinction_coefficient : Unit
            Extinction coefficient (1/M/cm)
        molecular_weight : Unit
            Molecular weight (g/mol or Daltons)
        protein_stock_volume : Unit
            Volume of protein stock added to make solution
        buffer_volume : Unit
            Volume of buffer added to make solution
        spectrophotometer_CV : float, optional, default=0.10
            CV for spectrophotometer readings

        """"""
        self.shortname = shortname
        self.description = description
        self.species = species
        self.buffer = buffer
        path_length = Unit(1.0, 'centimeter')
        self.concentration = (absorbance / (extinction_coefficient * path_length)) * (protein_stock_volume / buffer_volume) # M
        self.uncertainty = spectrophotometer_CV * self.concentration

class DMSOStockSolution(Solution):
    """"""A stock solution representing a compound dissolved in DMSO.
    """"""
    def __init__(self, dmso_stock):
        """"""
        Parameters
        ----------
        dmso_stock : dict
            The dictionary containing 'id', 'compound_name', 'compound mass (mg)', 'molecular weight', 'purity', 'solvent_mass'
        """"""
        self.shortname = dmso_stock['id']
        self.species = dmso_stock['compound name']
        self.description = '10 mM ' + dmso_stock['compound name'] + ' DMSO stock'
        dmso_density = Unit(1.1004, 'grams/milliliter')
        mass_uncertainty = 0.01 # TODO: Calculate from balance precision
        concentration = Unit(dmso_stock['compound mass (mg)'], 'milligrams') * dmso_stock['purity'] / Unit(dmso_stock['molecular weight'], 'grams/mole') / (Unit(dmso_stock['solvent mass (g)'], 'grams') / dmso_density) # mol/liter
        self.concentration = concentration.to('moles/liter')
        self.uncertainty = mass_uncertainty * self.concentration
        self.buffer = DMSO

def DMSOStockSolutions(dmso_stocks_csv_filename):
    """"""
    Create DMSO stock solutions.
    """"""

    #
    # Load information about DMSO stocks
    # In future, this might come from a database query that returns JSON.
    #

    import csv
    dmso_stocks = dict()
    with open(dmso_stocks_csv_filename, 'r') as csvfile:
         csvreader = csv.DictReader(csvfile, delimiter=',', quotechar='|')
         for row in csvreader:
             if row['id'] != '':
                for key in ['compound mass (mg)', 'purity', 'solvent mass (g)', 'molecular weight']:
                    row[key] = float(row[key])
                dmso_stocks[row['id']] = row

    solutions = dict()
    solutions['DMSO'] = DMSO # Add DMSO
    for id in dmso_stocks:
        solutions[id] = DMSOStockSolution(dmso_stocks[id])

    return solutions

#
# Define container types
#

def define_container_types():
    #
    # Define assay plate container
    #

    container_types = dict()

    # Define the container type for 4titude 4ti-0223.
    # info: http://4ti.co.uk/microplates/black-clear-bottom/96-well/
    # drawing: http://4ti.co.uk/files/1614/0542/7662/4ti-0223_Marketing_Drawing.pdf
    # All arguments to ContainerType are required!
    capabilities = ['pipette', 'spin', 'absorbance', 'fluorescence', 'luminescence', 'incubate', 'gel_separate', 'cover', 'seal', 'stamp', 'dispense']
    container_type = ContainerType(
        name='4titude 4ti-0223',
        is_tube=False,
        well_count=96,
        well_depth_mm=Unit(11.15, 'millimeter'),
        well_volume_ul=Unit(300, 'microliter'),
        well_coating='polystyrene',
        sterile=False,
        cover_types=[],
        seal_types=[],
        capabilities=capabilities,
        shortname='4ti-0223',
        col_count=12,
        dead_volume_ul=Unit(20,'microliter'),
        safe_min_volume_ul=Unit(50, 'microliter')
        )
    # Attach well area.
    well_diameter = Unit(6.30, ""millimeters"")
    well_area = np.pi * (well_diameter/2)**2
    setattr(container_type, 'well_area', well_area)

    container_types[container_type.name] = container_type

    return container_types

container_types = define_container_types()

#
# Helper functions for our common assays
#

def generate_uuid(size=6, chars=(string.ascii_uppercase + string.digits)):
    """"""
    Generate convenient universally unique id (UUID)

    Parameters
    ----------
    size : int, optional, default=6
       Number of alphanumeric characters to generate.
    chars : list of chars, optional, default is all uppercase characters and digits
       Characters to use for generating UUIDs

    NOTE
    ----
    This is not really universally unique, but it is convenient.

    """"""
    return ''.join(random.choice(chars) for _ in range(size))

def provision_assay_plate(name, plate_type='4titude 4ti-0223', id=None):
    """"""
    Provision a new assay plate.

    Parameters
    ----------
    name : str
       The name of the container
    plate_type : str, optional, default='4titude 4ti-0223'
       The name of the plate type used to retrieve the `container_type` from library
    id : str, optional, default=None
       Unless `id` is specified, a unique container ID will be autogenerated.
    """"""
    if id == None:
        id = generate_uuid

    # Define the container
    container_type = container_types[plate_type]
    container = Container(name=""assay-plate"", id=id, container_type=container_type)

    # Initialize well properties for this container
    for well in container.all_wells():
        well.set_volume(Unit(0.0, 'microliters')) # well starts empty

    return container

def dispense_evo(container, solution, volume, rows):
    """"""
    Dispense a given volume into the specified rows with a Tecan EVO pipetting robot.

    Parameters
    ----------
    container : autoprotocol.containers.Container
       The container to dispense the specified solution into
    solution : Solution
       The solution to dispense
    volume : Unit compatible with 'microliters'
       The volume to dispense
    rows : list of char
       The rows to disepnse into (e.g. ['A', 'C', 'E', 'G'])

    TODO
    ----
    * Are `contents` stored by solution name?
    * Generalize beyond only specifying 'rows'
    * Handle case where the same solution is dispensed multiple times.
    """"""
    CV = 0.004 # for 100 uL volume; TODO: compute this automatically based on dispense volume
    for well in container.all_wells():
        if 'contents' not in well.properties:
            well.properties['contents'] = dict()
        contents = well.properties['contents']

        well_name = well.humanize()
        if well_name[0] in rows:
            contents[solution.shortname] = (volume, CV * volume) # volume, error
            well.set_volume(well.volume + volume)

def dispense_hpd300(container, solutions, xml_filename, plate_index=0):
    """"""
    Dispense one or more solutions into a container using an HP D300.

    Parameters
    ----------
    container :

    solutions : list of Solutions
       List of solutils corresponding to the <Fluids/> block in the HP D300 XML filename
    xml_filename : str
       HP D300 simulation DATA XML filename
    plate_index : int, optional, default=0
       If the XML file contains multiple <Plate> tags, use the specified index.
    """"""
    CV = 0.08 # CV for HP D300

    # Read HP D300 XML file
    import xml.etree.ElementTree as ET
    tree = ET.parse(xml_filename)
    root = tree.getroot()

    # Read fluids
    fluids = root.findall('./Fluids/Fluid')

    # Rewrite fluid names to match stock names.
    for (index, solution) in enumerate(solutions):
        fluids[index].attrib['Name'] = solution.shortname

    def humanize_d300_well(row, column):
        """"""
        Return the humanized version of a D300 well index.
        """"""
        return chr(row + 97) + str(column+1)

    # Read dispensed volumes from the specified Plate entry
    dispensed = root.findall('./Dispensed')[0]
    volume_unit = dispensed.attrib['VolumeUnit'] # dispensed volume unit
    hpd300_wells = dispensed.findall(""Plate[@Index='%d']/Well"" % plate_index)
    for hpd300_well in hpd300_wells:
        # Get corresponding container well.
        row_index = int(hpd300_well.attrib['R'])
        column_index = int(hpd300_well.attrib['C'])
        well_name = humanize_d300_well(row_index, column_index)

        # Retrieve well from container.
        well = container.well(well_name)
        if 'contents' not in well.properties:
            well.properties['contents'] = dict()
        contents = well.properties['contents']

        # Handle dispensed fluids.
        # TODO: Deal with case where we might dispense the same fluid twice.
        dispensed_fluids = hpd300_well.findall('Fluid')
        for dispensed_fluid in dispensed_fluids:
            dispensed_fluid_index = int(dispensed_fluid.attrib['Index'])
            fluid_name = fluids[dispensed_fluid_index].attrib['Name']
            total_volume = Unit(float(dispensed_fluid.attrib['TotalVolume']), volume_unit)
            detail = dispensed_fluid.findall('Detail')

            # Gather details
            time = detail[0].attrib['Time']
            cassette = int(detail[0].attrib['Cassette'])
            head = int(detail[0].attrib['Head'])
            dispensed_volume = Unit(float(detail[0].attrib['Volume']), volume_unit)

            # Fill well
            contents[fluid_name] = (dispensed_volume, CV * dispensed_volume) # (volume, error)
            well.set_volume(well.volume + dispensed_volume)

def read_infinite(container, xml_filename, wavelengths_to_analyze=None, measurements_to_analyze=None):
    """"""
    Read measurements from a Tecan Infinite reader XML file.

    Parameters
    ----------
    wavelengths_to_analyze : list, optional, default=None
        If not None, only read measurements involving these wavelengths
    measurements_to_analyze : list, optional, default=None
        If not None, only read these kinds of measurements (e.g. 'absorbance', 'fluorescence bottom', 'fluorescence top')

    Measurements are stored in `well.properties['measurements']` under
    * `absorbance` : e.g. { '280:nanometers' : 0.425 }
    * `fluorescence` : e.g. { ('280:nanometers', '350:nanometers', 'top') : 15363 }

    TODO
    ----
    * Eventually, we can read all components of the Infinite file directly here.
    """"""
    # TODO: Replace read_icontrol_xml with direct processing of XML file
    from assaytools import platereader
    data = platereader.read_icontrol_xml(xml_filename)

    for well in container.all_wells():
        well_name = well.humanize()

        # Attach plate reader data
        # TODO: Only process wells for which measurements are available
        if 'measurements' not in well.properties:
            well.properties['measurements'] = dict()
        measurements = well.properties['measurements']

        for key in data:
            if key.startswith('Abs_'):
                # absorbance
                [prefix, wavelength] = key.split('_')
                wavelength = wavelength + ':nanometers'
                # Skip if requested
                if wavelengths_to_analyze and not (wavelength in wavelengths_to_analyze):
                    continue
                if measurements_to_analyze and not ('absorbance' in measurements_to_analyze):
                    continue
                # Store
                if 'absorbance' not in measurements:
                    measurements['absorbance'] = dict()
                measurements['absorbance'][wavelength] = float(data[key][well_name])
            elif (key.endswith('_TopRead') or key.endswith('_BottomRead')):
                # top fluorescence read
                [wavelength, suffix] = key.split('_')
                excitation_wavelength = wavelength + ':nanometers'
                emission_wavelength = '480:nanometers' # This is hard-coded in for now because this information is not available in the platereader.read_icontrol_xml results
                if key.endswith('_TopRead'):
                    geometry = 'top'
                else:
                    geometry = 'bottom'
                # Skip if requested
                if wavelengths_to_analyze and not ((excitation_wavelength in wavelengths_to_analyze) and (emission_wavelength in wavelengths_to_analyze)):
                    continue
                if measurements_to_analyze and not (('fluorescence %s' % geometry) in measurements_to_analyze):
                    continue
                # Store
                if 'fluorescence' not in measurements:
                    measurements['fluorescence'] = dict()
                measurements['fluorescence'][(excitation_wavelength, emission_wavelength, geometry)] = float(data[key] [well_name])



class Assay(object):
    """"""
    Assay base class
    """"""
    pass

class SingleWavelengthAssay(Assay):
    def __init__(self,
        d300_xml_filename,
        infinite_xml_filename,
        dmso_stocks_csv_filename,
        hpd300_fluids,
        hpd300_plate_index,
        receptor_species,
        protein_absorbance,
        protein_extinction_coefficient,
        protein_molecular_weight,
        protein_stock_volume,
        buffer_volume,
        rows_to_analyze,
        assay_volume,
        wavelengths_to_analyze=None,
        measurements_to_analyze=None
        ):
        """"""
        Set up a single-point assay.

        Parameters
        ----------
        d300_xml_filename : str
            HP D300 dispense simulated DATA file
        infinite_xml_filename : str
            Tecan Infinite plate reader output data, e.g. 'Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate_1.xml'
        dmso_stocks_csv_filename : str
            CSV file of DMSO stock inventory, e.g. 'dmso'stocks-Sheet1.csv'
        hpd300_fluids : list of str
            uuids of DMSO stocks from dmso_stocks_csv_filename (or 'DMSO' for pure DMSO) used to define HP D300 XML <Fluids> block, e.g. ['GEF001', 'IMA001', 'DMSO']
        hpd300_plate_index : int
            Plate index for HP D300 dispense
        receptor_species
            Name of receptor species, e.g. 'Abl(D382N)'
        protein_absorbance
            Absorbance reading of concentrated protein stock before dilution
        protein_extinction_coefficient : Unit compatible with 1/(moles/liter)/centimeters
            Extinction coefficient for protein, e.g. Unit(49850, '1/(moles/liter)/centimeter')
        protein_molecular_weight : Unit compatible with daltons
            Protein molecular weight
        protein_stock_volume : Unit compatible with microliters
            Volume of high-concentration protein stock solution used to make 1 uM protein stock
        buffer_volume : Unit compatible with milliliters
            Volume of buffer used to make ~1 uM protein stock used to fill wells
        rows_to_analyze : list
            Rows to analyze, e.g. ['A', 'B']
        assay_volume : Unit compatible with microliters
            Quantity of protein or buffer dispensed into plate
        wavelengths_to_analyze : list, optional, default=None
            If not None, only these wavelengths will be analyzed. e.g. ['280:nanometers', '480:nanometers']
        measurements_to_analyze : list, optional, default=None
            if not None, only these measurements will be analyzed. e.g. ['fluorescence top', 'absorbance'] or ['fluorescence bottom']

        """"""
        # Read DMSO stock solutions from inventory CSV file
        from assaytools.experiments import DMSOStockSolutions, DMSO, Buffer, ProteinSolution
        solutions = DMSOStockSolutions(dmso_stocks_csv_filename) # all solutions from DMSO stocks inventory

        # Enumerate all ligand species from DMSO stocks.
        ligand_species = set( [ solution.species for solution in solutions.values() if (solution.species != None)] )

        # Add buffer and protein stock solutions
        solutions['buffer'] = Buffer(shortname='buffer', description='20 mM Tris buffer')
        solutions['protein'] = ProteinSolution(shortname='protein', description='1 uM %s' % receptor_species, species=receptor_species, buffer=solutions['buffer'],
        absorbance=protein_absorbance, extinction_coefficient=protein_extinction_coefficient, molecular_weight=protein_molecular_weight, protein_stock_volume=protein_stock_volume, buffer_volume=buffer_volume)

        # Populate the Container data structure with well contents and measurements
        from assaytools.experiments import provision_assay_plate, dispense_evo, dispense_hpd300, read_infinite
        plate = provision_assay_plate(name='assay-plate', plate_type='4titude 4ti-0223')
        dispense_evo(plate, solution=solutions['protein'], volume=assay_volume, rows=['A', 'C', 'E', 'G'])
        dispense_evo(plate, solution=solutions['buffer'], volume=assay_volume, rows=['B', 'D', 'F', 'H'])
        dispense_hpd300(plate, solutions=[solutions[id] for id in hpd300_fluids], xml_filename=d300_xml_filename, plate_index=hpd300_plate_index)
        read_infinite(plate, xml_filename=infinite_xml_filename, wavelengths_to_analyze=wavelengths_to_analyze, measurements_to_analyze=measurements_to_analyze)
        self.plate = plate

        # Select specified rows for analysis.
        from autoprotocol import WellGroup
        well_group = WellGroup([well for well in plate.all_wells() if (well.humanize()[0] in rows_to_analyze)])

        # Create a model
        from assaytools.analysis import CompetitiveBindingAnalysis
        #from assaytools.analysis3 import CompetitiveBindingAnalysis
        self.experiment = CompetitiveBindingAnalysis(solutions=solutions, wells=well_group, receptor_name=receptor_species, DeltaG_prior='chembl')
","Python"
"Assay","choderalab/assaytools","AssayTools/platereader.py",".py","6626","201","#!/usr/bin/env python

""""""
Tools for assisting in reading and manipulating data from plate readers.

""""""

#=============================================================================================
# Imports
#=============================================================================================

import numpy as np
import re
import string
from lxml import etree

#=============================================================================================
# Tecan Infinite plate reader helper functions
#=============================================================================================

def read_icontrol_xml(filename):
    """"""
    Read a Tecan iControl XML-formatted file and return all section data.

    Parameters
    ----------
    filename : str
       The name of the XML file to be read.
    section_name : str
       The 'Name' attribute of the section to read.

    Returns
    -------
    data : dict
       data[well_label] is the well reading for well 'well_label' (e.g. data['A4'] = 324)

    Examples
    --------

    """"""
    # Parse XML file into nodes.
    root_node = etree.parse(filename)

    # Build a dict of section nodes.
    section_nodes = { section_node.get('Name') : section_node for section_node in root_node.xpath(""/*/Section"") }

    def extract_well_data(section_node):
        """"""Return a dict of all wells in a section.

        Parameters
        ----------
        section_node : xml node
           The XML node for a given section of the iControl file.

        Returns
        -------
        well_data : dict
           Returns all values for each section as a dictionary of dictionaries
           (e.g. well_data['280_TopRead'] is {'A1': 62213.0,'A10': 10505.0,...})
           Above is an example return for singlet data xml file, if input is a spectra data
           xml file well_data will also include a dictionary for each well where the keys are the wavelength
           (e.g. well_data['em280'] is {'A1': {'280': '3216344', '285': '2587710'...}...})

        """"""

        # Get a list of all well nodes.
        well_nodes = section_node.xpath(""*/Well"")

        # Process all wells into data.
        well_data = dict()
        for well_node in well_nodes:
            if well_node.get('Type') == 'Single':
                well_name = well_node.get('Pos')
                for r in well_node:
                    read = r.text
                well_data[well_name] = read
            else:
                for well_node in well_nodes:
                    well_name = well_node.get('Pos')
                    wavelength_data = dict()
                    for wave in well_node:
                        wavelength = wave.attrib['WL']
                        wavelength_data[wavelength] = wave.text
                    well_data[well_name] = dict()
                    well_data[well_name] = wavelength_data

        return well_data

    # Process all sections.
    sections = dict()
    for (section_name, section_node) in section_nodes.items():
        well_data = extract_well_data(section_node)
        sections[section_name] = dict()
        sections[section_name] = well_data

    return sections

def select_data(file_data,section_name,selection,*args,**kwargs):
    """"""
    Read a Tecan iControl XML-formatted file and extract a particular part (row, column,
    well, or well selection) for a particular section.
    Parameters
    ----------
    input : str or dict
       EITHER the name of the XML file to be read OR the dict of an XML file made by read_icontrol_xml
    section_name : str
       The 'Name' attribute of the section to read.
    selection : str
       The selection to extract, for example 'A', '1', or ['A1'].
    wavelength (optional arg): str
       The wavelength to extract, for example '480'.
    Returns
    -------
    data : dictionary
       (e.g. {'A1': 471.0, 'A2': 418.0})
       or dictionary of dictionaries if spectra with no wavelength selected
       (e.g. {'A1': {'280': '3216344','285': '2587710'...}}
    Examples
    --------
    gefitinib_abl_singlet_A1 = select_data(singlet_file, '350_TopRead', ['A1'])
    gefitinib_abl_singlet_A = select_data(singlet_file, '350_TopRead', 'A')
    gefitinib_abl_singlet_1 = select_data(singlet_file, '350_TopRead', '1')
    bosutinib_abl_spectra_A1 = select_data(spectra_file, 'em280', ['A1'])
    bosutinib_abl_spectra_A_480 = select_data(spectra_file, 'em280', 'A', wavelength='480')
    """"""
    wavelength = kwargs.get('wavelength', None)

    #construct all possible wells and columns for 96 or 384 well plate
    rows =[]
    cols =[]
    for i in string.ascii_uppercase:
        rows.append('%s' % i)
    for i in range(1,25):
        cols.append('%s' % i)

    # read data from xml or dict

    if type(file_data) == dict:
        well_data = file_data
    else:
        well_data = read_icontrol_xml(file_data)

    section_data = well_data[section_name]

    # extract selection
    data = dict()
    for select in selection:
        if select in section_data:                          # if individual wells
            data[select] = section_data[select]
        elif any([selection == r for r in rows]):           # if row
            for col in cols:
                try:
                    data[selection + col] = section_data[selection + col]
                except KeyError:
                    continue
        elif any([selection == c for c in cols]):            # if column
            for row in rows:
                try:
                    data[row + selection] = section_data[row + selection]
                except KeyError:
                    continue
        else:
            print('bad selection')

    # extract wavelength (only relevant for spectra)
    # if you don't include wavelength for spectra, data will be a dict of all wavelengths
    datatypes = [ type(entry) for entry in data.values() ]
    if datatypes[0] == dict and wavelength != None:
        new_data = dict()
        for key in data.keys():
            new_data[key] = data[key][wavelength]
        data = new_data

    return data

def read_emission_spectra_text(filename):
    """"""
    Read text-formatted emission spectra.

    Parameters
    ----------
    filename : str
       The Tecan Infinite output filen to be read.

    Returns
    -------
    SRC_280 : numpy.array
    SRC_280_x : numpy.array
    SRC_280_x_num : numpy.array

    Examples
    --------

    """"""

    SRC_280 = np.genfromtxt(filename, dtype='str')
    SRC_280_x = SRC_280[0,:]
    SRC_280_x_num = re.findall(r'\d+', str(SRC_280_x )[1:-1])

    return [SRC_280, SRC_280_x, SRC_280_x_num]
","Python"
"Assay","choderalab/assaytools","AssayTools/analysis.py",".py","47987","911","""""""
Classes for the analysis of fluorescence assay data.

""""""

#=============================================================================================
# IMPORTS
#=============================================================================================

import numpy as np
import pymc
from scipy.misc import logsumexp
from autoprotocol.unit import Unit
import time

import matplotlib as mpl
mpl.use('Agg')
import seaborn as sns
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.pyplot as plt

#=============================================================================================
# Physical constants
#=============================================================================================

Na = 6.02214179e23 # Avogadro's number (number/mol)
kB = Na * 1.3806504e-23 / 4184.0 # Boltzmann constant (kcal/mol/K)
C0 = 1.0 # standard concentration (M)

#=============================================================================================
# Parameters for MCMC sampling
#=============================================================================================

DG_min = np.log(1e-15) # kT, most favorable (negative) binding free energy possible; 1 fM
DG_max = +0 # kT, least favorable binding free energy possible
niter = 500000 # number of iterations
nburn = 50000 # number of burn-in iterations to discard
nthin = 500 # thinning interval

volume_unit = Unit(1.0, 'liter')
concentration_unit = Unit(1.0, 'moles/liter')

#=============================================================================================
# SUBROUTINES
#=============================================================================================

def LogNormalWrapper(name, mean, stddev):
    """"""
    Create a PyMC Normal stochastic, automatically converting parameters to be appropriate for a `LogNormal` distribution.
    Note that the resulting distribution is Normal, not LogNormal.
    This is appropriate if we want to describe the log of a volume or a fluorescence reading.

    Parameters
    ----------
    mean : float
       Mean of exp(X), where X is lognormal variate.
    stddev : float
       Standard deviation of exp(X), where X is lognormal variate.

    Returns
    -------
    stochastic : pymc.Normal
       Normal stochastic for random variable X

    """"""
    # Compute parameters of lognormal distribution
    mu = np.log(mean**2 / np.sqrt(stddev**2 + mean**2))
    tau = np.sqrt(np.log(1.0 + (stddev/mean)**2))**(-2)
    stochastic = pymc.Normal(name, mu=mu, tau=tau)
    return stochastic

def wellname(well):
    """"""
    Concatenate container and well names.

    Parameters
    ----------
    well : autoprotocol.container.Well
       The well

    Returns
    -------
    name : str
       [container name] [humanized well name]
    """"""
    return well.container.name + ' ' + well.humanize()

#=============================================================================================
# PyMC2 models
#=============================================================================================

class CompetitiveBindingAnalysis(object):
    def __init__(self, solutions, wells, receptor_name, DeltaG_prior='uniform'):
        """"""
        Parameters
        ----------
        solutions : dict of str : Solution
            `solutions[name]` is the Solution object corresponding to component `name`
        wells : autoprotocol.container.WellGroup
            Group of wells with `contents` and `measurements` properties defined
        receptor_name : str
            Name of receptor
        DeltaG_prior : str, optional, default='uniform'
            Prior to use on DeltaG values for reaction ['uniform', 'chembl']

        """"""
        # Store data
        self.solutions = solutions
        self.wells = wells
        self.receptor_name = receptor_name

        # Set up internal data structures.
        self.model = dict() # the PyMC model; self.model[paramname] is the PyMC variable correspoding to 'paramname'
        self.parameter_names = dict() # dict to keep track of groups of related parameter names; self.parameter_names[groupname] is the list of PyMC variable names under 'groupname'

        # DEBUG
        print('There are %d wells to analyze in the provided WellGroup' % len(wells))

        # Construct components of the pymc model.
        self._create_solutions_model()
        self._identify_ligand_names()
        self._create_dispensing_model()
        self._create_competitive_binding_model(receptor_name, DeltaG_prior)
        self._create_equilibrium_concentrations_model()
        self._create_extinction_coefficients_model()
        self._create_absorbance_model()
        self._create_fluorescence_model()

        # Create the PyMC Model object from the dictionary of pymc stochastics and deterministics.
        self.model = pymc.Model(self.model)
        for group in self.parameter_names:
            print('%s:' % group)
            for parameter_name in self.parameter_names[group]:
                print('  %s' % parameter_name)
        print('')
        print('Model has %d stochastics and %d deterministics...' % (len(self.model.stochastics), len(self.model.deterministics)))

    def _create_solutions_model(self):
        """"""
        Create pymc model components for true concentrations of source receptor and ligand solutions.

        Populates the following fields:
        * parameter_names['concentrations'] : parameters associated with true concentrations of receptor and ligand solutions
        """"""
        # Determine solutions in use in plate
        solutions_in_use = set()
        for well in self.wells:
            for shortname in well.properties['contents']:
                solutions_in_use.add(shortname)
        print('Solutions in use: %s' % str(solutions_in_use))

        # Retain only solutions that appear in the plate
        self.solutions = { shortname : self.solutions[shortname] for shortname in solutions_in_use }

        self.parameter_names['solution concentrations'] = list()
        for solution in self.solutions.values():
            if solution.species is None:
                continue # skip buffers or pure solvents
            name = 'log concentration of %s' % solution.shortname
            self.model[name] = LogNormalWrapper(name, mean=solution.concentration.to_base_units().m, stddev=solution.uncertainty.to_base_units().m)
            self.parameter_names['solution concentrations'].append(name)

    def _identify_ligand_names(self):
        """"""
        Ligands are species that appear in wells that are not the receptor.

        """"""
        self.ligand_names = set()
        for solution in self.solutions.values():
            if solution.species is None:
                continue # skip buffers and pure solvents
            if solution.species == self.receptor_name:
                continue # sip receptor
            self.ligand_names.add(solution.species)
        self.ligand_names = list(self.ligand_names)

    def _create_dispensing_model(self):
        """"""
        Create nuisance parameters for dispensed volumes and actual concentrations of all species in each well.

        Populates the following fields:
        * parameter_names['dispensed_volumes'] : actual volumes dispensed into each well
        * parameter_names['well_volumes'] : actual well total volumes
        *
        """"""
        self.parameter_names['dispensed volumes'] = list() # pymc variable names associated with dispensed volumes
        self.parameter_names['well volumes'] = list() # pymc variable names associated with well volumes
        self.parameter_names['well concentrations'] = list()

        for well in self.wells:
            # Volumes dispensed into each well
            log_volumes = list() # log volumes are in Liters
            for component in well.properties['contents']:
                name = 'volume of %s dispensed into well %s' % (component, wellname(well))
                logname = 'log %s' % name
                (volume, error) = well.properties['contents'][component]
                log_volume_dispensed = LogNormalWrapper(logname, mean=volume.to_base_units().m, stddev=error.to_base_units().m)
                self.model[logname] = log_volume_dispensed
                #self.parameter_names['dispensed volumes'].append(logname)
                log_volumes.append(log_volume_dispensed)
                # Store real (non-log) value
                self.model[name] = pymc.Lambda(name, lambda log_quantity=self.model[logname]: np.exp(log_quantity) / volume_unit.to_base_units().m, trace=True)
                self.parameter_names['dispensed volumes'].append(name)

            # Total volume in well
            name = 'volume of well %s' % wellname(well)
            logname = 'log %s' % name
            @pymc.deterministic(name=logname)
            def log_total_volume(log_volumes=log_volumes):
                return logsumexp(log_volumes)
            self.model[logname] = log_total_volume
            #self.parameter_names['well volumes'].append(logname)
            # Store real (non-log) value
            self.model[name] = pymc.Lambda(name, lambda log_quantity=self.model[logname]: np.exp(log_quantity) / volume_unit.to_base_units().m, trace=True)
            self.parameter_names['well volumes'].append(name)

            # Total concentration of all species involving each component in well
            # TODO: In future, can we simply calculate initial concentrations of each species and use that in GeneralBindingModel instead?
            for component in well.properties['contents']:
                solution = self.solutions[component]
                if solution.species is None:
                    continue # skip buffers or pure solvents
                species = solution.species
                log_solution_concentration = self.model['log concentration of %s' % solution.shortname] # log concentrations are in molar
                log_solution_volume = self.model['log volume of %s dispensed into well %s' % (component, wellname(well))]
                log_total_volume = self.model['log volume of well %s' % wellname(well)]

                name = 'total concentration of %s in well %s' % (species, wellname(well))
                logname = 'log %s' % name
                @pymc.deterministic(name=logname)
                def log_total_concentration(log_solution_concentration=log_solution_concentration, log_solution_volume=log_solution_volume, log_total_volume=log_total_volume):
                    return log_solution_concentration + log_solution_volume - log_total_volume
                self.model[logname] = log_total_concentration
                #self.parameter_names['well concentrations'].append(logname)
                # Store real (non-log) value
                self.model[name] = pymc.Lambda(name, lambda log_quantity=self.model[logname]: np.exp(log_quantity) / concentration_unit.to_base_units().m, trace=True)
                self.parameter_names['well concentrations'].append(name)


    def _all_initial_species_in_well(self, well):
        """"""
        List all species initially added to the well, before association reactions occur.

        Parameters
        ----------
        well : autoprotocol.container.Well
           The well for which all species are to be listed.

        Returns
        -------
        all_species : list of str
           The names of all initial species added to the well

        """"""
        all_species = set()
        for component in well.properties['contents']:
            solution = self.solutions[component]
            if solution.species is None:
                continue # skip buffers and pure solvents
            species = solution.species
            all_species.add(species)
        return all_species

    def _binding_reactions_for_well(self, well):
        """"""
        Determine subset of binding reactions relevant to a well, as determined by
        which species were initially added to the well.

        Parameters
        ----------
        well : autoprotocol.container.Well
           The well for which all binding reactions are to be compiled.

        Returns
        -------
        reactions : list
           The subset of binding reactions from self.reactions that can occur in this well

        """"""
        # Determine all relevant species
        all_species = self._all_equilibrium_species_in_well(well)
        reactions = list()
        for reaction in self.reactions:
            # Decompose reaction into product and reactant sets
            (DeltaG, stoichiometry) = reaction
            # Reactants and products should all be present in equilibrium concentration
            if set(stoichiometry.keys()).issubset(all_species):
                reactions.append(reaction)

        return reactions

    def _log_total_concentrations_for_well(self, well):
        """"""
        Return list of pymc variables of log total concentrations of each species in the well.

        """"""
        log_total_concentations = dict()
        for component in well.properties['contents']:
            solution = self.solutions[component]
            if solution.species is None:
                continue # skip buffers and pure solvents
            species = solution.species
            name = 'log total concentration of %s in well %s' % (species, wellname(well))
            log_total_concentration = self.model[name]
            log_total_concentrations[species] = log_total_concentration

        return log_total_concentrations

    def _create_competitive_binding_model(self, receptor_name, DeltaG_prior):
        """"""
        Create the binding free energy priors, binding reaction models, and list of all species whose concentrations will be tracked.

        Populates the following fields:
        * parameter_names['DeltaGs'] : parameters associated with DeltaG priors
        * reactions : reactions for GeneralBindingModel with DeltaG parameters substituted for free energies
          formatted like [ ('DeltaG1', {'RL': -1, 'R' : +1, 'L' : +1}), ('DeltaG2', {'RP' : -1, 'R' : +1, 'P' : +1}) ]
        * conservation_equations : conservation relationships for GeneralBindingModel with species names substituted with log concentrations
          formatted like [ ('receptor', {'receptor' : +1, 'receptor:ligand' : +1}), ('ligand' : {'receptor:ligand' : +1, 'ligand' : +1}) ]
        * complex_names : names of all complexes
        * all_species : names of all species (ligands, receptor, complexes)
        """"""
        self.parameter_names['binding affinities'] = list()
        self.complex_names = list()
        self.reactions = list() # reactions for GeneralBindingModel with pymc parameters substituted for free energies
        self.conservation_equations = list() # list of conservation relationships for GeneralBindingModel with pymc parameters substituted for log concentrations
        receptor_conservation_equation = { receptor_name : +1 } # Begin to populate receptor conservation equation
        for ligand_name in self.ligand_names:
            # Create complex name
            complex_name = receptor_name + ':' + ligand_name
            self.complex_names.append(complex_name)
            # Create the DeltaG prior
            name = 'DeltaG (%s + %s -> %s)' % (receptor_name, ligand_name, complex_name) # form the name of the pymc variable
            if DeltaG_prior == 'uniform':
                DeltaG = pymc.Uniform(name, lower=DG_min, upper=DG_max) # binding free energy (kT), uniform over huge range
            elif DeltaG_prior == 'chembl':
                DeltaG = pymc.Normal(name, mu=0, tau=1./(12.5**2)) # binding free energy (kT), using a Gaussian prior inspured by ChEMBL
            else:
                raise Exception(""DeltaG_prior = '%s' unknown. Must be one of 'uniform' or 'chembl'."" % DeltaG_prior)
            self.model[name] = DeltaG
            self.parameter_names['binding affinities'].append(name)
            # Create the reaction for GeneralBindingModel
            self.reactions.append( (DeltaG, {complex_name : -1, receptor_name : +1, ligand_name : +1}) )
            # Create the conservation equation for GeneralBindingModel
            self.conservation_equations.append( (ligand_name, {ligand_name : +1, complex_name : +1}) )
            # Keep track of receptor conservation.
            receptor_conservation_equation[complex_name] = +1
        # Create the receptor conservation equation for GeneralBindingModel
        self.conservation_equations.append( [receptor_name, receptor_conservation_equation] )

        # Create a list of all species that may be present in assay plate
        self.all_species = [self.receptor_name] + self.ligand_names + self.complex_names

    def _conservation_equations_for_well(self, well):
        """"""
        Return list of conservation equations for each well.

        """"""
        # Get pymc variables corresponding to total concentrations of each initial species.
        conservation_equations = list()
        initial_species_in_well =  self._all_initial_species_in_well(well)
        for conservation_equation in self.conservation_equations:
            (species, stoichiometry) = conservation_equation
            if species in initial_species_in_well:
                log_total_concentration = self.model['log total concentration of %s in well %s' % (species, wellname(well))]
                conservation_equations.append( [log_total_concentration, stoichiometry] )

        return conservation_equations

    def _all_equilibrium_species_in_well(self, well):
        """"""
        List all species present in the well after equilibrium has been reached.

        Parameters
        ----------
        well : autoprotocol.container.Well
           The well for which all species are to be listed.

        Returns
        -------
        all_species : list of str
           The names of all possible species that could be formed after equilibrium has been reached

        """"""
        initial_species = self._all_initial_species_in_well(well)
        all_species = set(initial_species)

        previous_nspecies = 0
        current_nspecies = len(all_species)
        while (current_nspecies != previous_nspecies):
            for reaction in self.reactions:
                # Decompose reaction into product and reactant sets
                (DeltaG, stoichiometry) = reaction
                reactants = set([ species for species in stoichiometry.keys() if stoichiometry[species] < 0 ])
                products = set([ species for species in stoichiometry.keys() if stoichiometry[species] > 0 ])
                # If reactants are present, add products; and vice-versa
                if reactants.issubset(all_species): all_species.update(products)
                if products.issubset(all_species): all_species.update(reactants)
                # Update count of number of species
                previous_nspecies = current_nspecies
                current_nspecies = len(all_species)

        return all_species

    def _create_equilibrium_concentrations_model(self):
        """"""
        Create model for equilibrium concentration of each species in each well.
        This function is agnostic to the exact binding model in use.

        """"""
        from assaytools.bindingmodels import GeneralBindingModel

        self.parameter_names['well concentrations'] = list()
        for well in self.wells:
            # Determine list of all species that can be in this well
            all_initial_species     = self._all_initial_species_in_well(well)
            all_equilibrium_species = self._all_equilibrium_species_in_well(well)

            # Empty wells don't need models.
            if len(all_equilibrium_species) == 0:
                continue

            # Determine relevant list of binding reactions for this well.
            reactions = self._binding_reactions_for_well(well)
            if len(reactions)==0:
                # Initial concentrations will not change.
                for species in all_initial_species:
                    name = 'concentration of %s in well %s' % (species, wellname(well))
                    logname = 'log %s' % name
                    log_total_concentration = self.model['log total concentration of %s in well %s' % (species, wellname(well))]
                    print('%s slaved to parent' % logname)
                    self.model[logname] = pymc.Lambda(logname, lambda log_total_concentration=log_total_concentration: log_total_concentration, trace=True)
                    #self.parameter_names['well concentrations'].append(logname)
                    # Store real (non-log) value
                    self.model[name] = pymc.Lambda(name, lambda log_quantity=self.model[logname]: np.exp(log_quantity), trace=True)
                    self.parameter_names['well concentrations'].append(name)
                continue

            # Build list of conservation equations for this well
            conservation_equations = self._conservation_equations_for_well(well)
            if len(conservation_equations)==0:
                msg = 'No conservation equations for well %s\n' % wellname(well)
                msg += str(all_initial_species) + '\n'
                msg += str(all_equilibrium_species) + '\n'
                raise Exception(msg)

            # Compute equilibrium concentration of each component in well
            name = 'log equilibrium concentration of all species in well %s' % wellname(well)
            @pymc.deterministic(name=name, trace=False) # Can't store dict() in some storage layers, so trace=False
            def log_equilibrium_concentrations(reactions=reactions, conservation_equations=conservation_equations):
                if len(reactions)==0 or len(conservation_equations)==0:
                    raise Exception(reactions, conservation_equations)
                initial_time = time.time()
                solution = GeneralBindingModel.equilibrium_concentrations(reactions, conservation_equations)
                final_time = time.time()
                elapsed_time = final_time - initial_time
                #print('  GeneralBindingModel elapased time: %.6f s' % elapsed_time)
                return solution
            self.model[name] = log_equilibrium_concentrations

            # Separate out individual concentration components
            for species in all_equilibrium_species:
                name = 'concentration of %s in well %s' % (species, wellname(well))
                logname = 'log %s' % name
                @pymc.deterministic(name=logname, trace=True)
                def log_equilibrium_concentration(log_equilibrium_concentrations=log_equilibrium_concentrations):
                    return log_equilibrium_concentrations[species]
                self.model[logname] = log_equilibrium_concentration
                #self.parameter_names['well concentrations'].append(logname)
                # Store real (non-log) value
                self.model[name] = pymc.Lambda(name, lambda log_quantity=self.model[logname]: np.exp(log_quantity), trace=True)
                self.parameter_names['well concentrations'].append(name)

    def _create_extinction_coefficients_model(self):
        """"""
        Determine all spectroscopic wavelengths in use and create model of extinction coefficients

        Populates the following fields:
        * parameter_names['extinction coefficients'] : all extinction coefficients
        * all_wavelengths : list of all wavelengths in use
        * absorbance : True if absorbance in use
        * fluorescence : True if fluorescence in use

        """"""
        #
        # Spectroscopic measurements
        #

        # TODO: Switch to log extinction coefficients and uniform prior in log extinction coefficients
        MIN_EXTINCTION_COEFFICIENT = Unit(0.1, '1/(moles/liter)/centimeter') # maximum physically reasonable extinction coefficient
        MAX_EXTINCTION_COEFFICIENT = Unit(100e3, '1/(moles/liter)/centimeter') # maximum physically reasonable extinction coefficient
        EXTINCTION_COEFFICIENT_GUESS = Unit(1.0, '1/(moles/liter)/centimeter') # maximum physically reasonable extinction coefficient

        # Determine all wavelengths and detection technologies in use
        self.all_wavelengths = set()
        for well in self.wells:
            measurements = well.properties['measurements']
            if 'absorbance' in measurements:
                for wavelength in measurements['absorbance'].keys():
                    self.all_wavelengths.add(wavelength)
            if 'fluorescence' in measurements:
                for (excitation_wavelength, emission_wavelength, geometry) in measurements['fluorescence'].keys():
                    self.all_wavelengths.add(excitation_wavelength)
                    self.all_wavelengths.add(emission_wavelength)
        print(""all wavelengths in use:"")
        print(self.all_wavelengths)

        if (len(self.all_wavelengths) > 0):
            # Priors for spectroscopic parameters of each component
            self.parameter_names['extinction coefficients'] = list()
            for species in self.all_species:
                for wavelength in self.all_wavelengths:
                    name = 'log extinction coefficient of %s at wavelength %s' % (species, wavelength)
                    log_extinction_coefficient = pymc.Uniform(name, lower=np.log(MIN_EXTINCTION_COEFFICIENT.to_base_units().m), upper=np.log(MAX_EXTINCTION_COEFFICIENT.to_base_units().m), value=np.log(EXTINCTION_COEFFICIENT_GUESS.to_base_units().m)) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm
                    self.model[name] = log_extinction_coefficient
                    self.parameter_names['extinction coefficients'].append(name)

    def _create_absorbance_model(self):
        """"""
        Absorbance measurements.

        Populates the following fields

        """"""
        # Determine if absorbance is in use
        absorbance = False
        Amax = 0.0 # max absorbance
        for well in self.wells:
            measurements = well.properties['measurements']
            if 'absorbance' in measurements:
                absorbance = True
                for wavelength in measurements['absorbance'].keys():
                    measured_absorbance = measurements['absorbance'][wavelength]
                    Amax = max(measured_absorbance, Amax)

        # Prior on absorbance measurement error
        if absorbance:
            print('Absorbance measurements are available')
            self.model['log absorbance error'] = pymc.Uniform('log absorbance error', lower=-10, upper=0, value=np.log(0.01))
            self.model['absorbance error'] = pymc.Lambda('absorbance error', lambda log_sigma=self.model['log absorbance error'] : np.exp(log_sigma), trace=True)
            self.model['absorbance precision'] = pymc.Lambda('absorbance precision', lambda log_sigma=self.model['log absorbance error'] : np.exp(-2*log_sigma), trace=True)
            self.parameter_names['absorbance'] = ['log absorbance error', 'absorbance error', 'absorbance precision']
            # Prior on plate absorbance at each wavelength
            for wavelength in self.all_wavelengths:
                name = 'plate absorbance at wavelength %s' % wavelength
                self.model[name] = pymc.Uniform(name, lower=0.0, upper=Amax, value=0.0)
                self.parameter_names['absorbance'].append(name)

        # Absorbance measurements (for wells that have them)
        for well in self.wells:
            # Compute path length
            log_well_volume = self.model['log volume of well %s' % wellname(well)]
            all_equilibrium_species_in_well = self._all_equilibrium_species_in_well(well)
            measurements = well.properties['measurements']
            if 'absorbance' in measurements:
                for wavelength in measurements['absorbance'].keys():
                    # Compile all parameters needed for absorbance model
                    extinction_coefficients = [ self.model['log extinction coefficient of %s at wavelength %s' % (species, wavelength)] for species in all_equilibrium_species_in_well ]
                    log_concentrations = [ self.model['log extinction coefficient of %s at wavelength %s' % (species, wavelength)] for species in all_equilibrium_species_in_well ]
                    plate_absorbance = self.model['plate absorbance at wavelength %s' % wavelength]
                    log_well_area = np.log(well.container.container_type.well_area.to_base_units().m)

                    # Add computed absorbance model
                    name = 'computed absorbance of well %s at wavelength %s' % (wellname(well), wavelength)
                    @pymc.deterministic(name=name)
                    def absorbance_model(log_concentrations=log_concentrations, log_extinction_coefficients=log_extinction_coefficients, plate_absorbance=plate_absorbance, log_well_volume=log_well_volume):
                        log_path_length = log_well_volume - log_well_area
                        absorbance = 0.0
                        for (log_extinction_coefficient, log_concentration) in zip(log_extinction_coefficients, log_concentrations):
                            absorbance += np.exp(log_extinction_coefficient + log_path_length + log_concentration)
                        absorbance += plate_absorbance
                        return absorbance
                    self.model[name] = absorbance_model
                    self.parameter_names['absorbance'].append(name)

                    # Add measured absorbance model
                    measured_absorbance = measurements['absorbance'][wavelength]
                    name = 'measured absorbance of well %s at wavelength %s' % (wellname(well), wavelength) # TODO: Include plate name
                    self.model[name] = pymc.Normal(name, mu=absorbance_model, tau=self.model['absorbance precision'], observed=True, value=measured_absorbance)
                    self.parameter_names['absorbance'].append(name)

    def _create_fluorescence_model(self):
        """"""
        Create model for fluorescence measurements.

        """"""

        # Determine if fluorescence is in use
        fluorescence = False
        fluorescence_top = False
        fluorescence_bottom = False
        fluorescence_wavelength_pairs = set()
        for well in self.wells:
            measurements = well.properties['measurements']
            if 'fluorescence' in measurements:
                fluorescence = True
                for (excitation_wavelength, emission_wavelength, geometry) in measurements['fluorescence'].keys():
                    if geometry == 'top': fluorescence_top = True
                    if geometry == 'bottom': fluorescence_bottom = True
                    fluorescence_wavelength_pairs.add( (excitation_wavelength, emission_wavelength) )

        if fluorescence:
            self.parameter_names['fluorescence'] = list()
            for (excitation_wavelength, emission_wavelength) in fluorescence_wavelength_pairs:
                logname = 'log plate background fluorescence for fluorescence excitation at %s and emission at %s' % (excitation_wavelength, emission_wavelength)
                log_background_fluorescence = pymc.Uniform(logname, lower=-10, upper=+10, value=-7)
                self.model[logname] = log_background_fluorescence
                #self.parameter_names['fluorescence'].append(logname)

                name = 'plate background fluorescence for fluorescence excitation at %s and emission at %s' % (excitation_wavelength, emission_wavelength)
                self.model[name] = pymc.Lambda(name, lambda log_background_fluorescence=self.model[logname] : np.exp(log_background_fluorescence))
                self.parameter_names['fluorescence'].append(name)

                # TODO: If we had an estimate of quantum yield of some reference species, we could restraint these much better.
                for species in self.all_species:
                    logname = 'log quantum yield of %s for fluorescence excitation at %s and emission at %s' % (species, excitation_wavelength, emission_wavelength)
                    log_quantum_yield = pymc.Uniform(logname, lower=-10, upper=+10, value=-2.0)
                    self.model[logname] = log_quantum_yield
                    #self.parameter_names['fluorescence'].append(logname)

                    name = 'quantum yield of %s for fluorescence excitation at %s and emission at %s' % (species, excitation_wavelength, emission_wavelength)
                    self.model[name] = pymc.Lambda(name, lambda log_quantum_yield=self.model[logname] : np.exp(log_quantum_yield))
                    self.parameter_names['fluorescence'].append(name)


            MIN_LOG_FLUORESCENCE_INTENSITY = -10 # TODO: Determine minimum possible fluorescence intensity
            MAX_LOG_FLUORESCENCE_INTENSITY = +20 # TODO: Determine maximum possible fluorescence intensity
            MIN_LOG_FLUORESCENCE_UNCERTAINTY = -10
            MAX_LOG_FLUORESCENCE_UNCERTAINTY = +10

            # Fluorescence intensities * gains
            # TODO: If multiple gains are in use, slave them together through this intensity times a fixed gain factor.
            if fluorescence_top:
                name = 'top fluorescence log illumination intensity'
                self.model[name] = pymc.Uniform(name, lower=MIN_LOG_FLUORESCENCE_INTENSITY, upper=MAX_LOG_FLUORESCENCE_INTENSITY, value=0)
                self.parameter_names['fluorescence'].append(name)

                logname = 'top fluorescence log uncertainty'
                self.model[logname] = pymc.Uniform(logname, lower=MIN_LOG_FLUORESCENCE_UNCERTAINTY, upper=MAX_LOG_FLUORESCENCE_UNCERTAINTY, value=0)
                #self.parameter_names['fluorescence'].append(name)

                name = 'top fluorescence precision'
                self.model[name] = pymc.Lambda(name, lambda log_sigma=self.model[logname] : np.exp(-2*log_sigma))
                self.parameter_names['fluorescence'].append(name)

            if fluorescence_bottom:
                name = 'bottom fluorescence log illumination intensity'
                self.model[name] = pymc.Uniform(name, lower=MIN_LOG_FLUORESCENCE_INTENSITY, upper=MAX_LOG_FLUORESCENCE_INTENSITY, value=0)
                self.parameter_names['fluorescence'].append(name)

                logname = 'bottom fluorescence log uncertainty'
                self.model[logname] = pymc.Uniform(logname, lower=MIN_LOG_FLUORESCENCE_UNCERTAINTY, upper=MAX_LOG_FLUORESCENCE_UNCERTAINTY, value=0)
                #self.parameter_names['fluorescence'].append(name)

                name = 'bottom fluorescence precision'
                self.model[name] = pymc.Lambda(name, lambda log_sigma=self.model[logname] : np.exp(-2*log_sigma))
                self.parameter_names['fluorescence'].append(name)

        # Fluorescence measurements (for wells that have them)
        for well in self.wells:
            log_well_volume = self.model['log volume of well %s' % wellname(well)]
            measurements = well.properties['measurements']
            if 'fluorescence' in measurements:
                for (excitation_wavelength, emission_wavelength, geometry) in measurements['fluorescence'].keys():
                    # Extract extinction coefficients and concentrations for all species in well and pack them into lists
                    all_species = self._all_equilibrium_species_in_well(well)
                    quantum_yields = [ self.model['quantum yield of %s for fluorescence excitation at %s and emission at %s' % (species, excitation_wavelength, emission_wavelength)] for species in all_species ]
                    log_excitation_extinction_coefficients = [ self.model['log extinction coefficient of %s at wavelength %s' % (species, excitation_wavelength)] for species in all_species ]
                    log_emission_extinction_coefficients = [ self.model['log extinction coefficient of %s at wavelength %s' % (species, emission_wavelength)] for species in all_species ]
                    log_concentrations = [ self.model['log concentration of %s in well %s' % (species, wellname(well))] for species in all_species ]
                    plate_fluorescence = self.model['plate background fluorescence for fluorescence excitation at %s and emission at %s' % (excitation_wavelength, emission_wavelength)]
                    top_log_illumination_intensity = self.model['top fluorescence log illumination intensity'] if fluorescence_top else 0
                    bottom_log_illumination_intensity = self.model['bottom fluorescence log illumination intensity'] if fluorescence_bottom else 0
                    name = 'computed %s fluorescence of well %s at excitation wavelength %s and emission wavelength %s' % (geometry, wellname(well), excitation_wavelength, emission_wavelength)
                    log_well_area = np.log(well.container.container_type.well_area.to_base_units().m)

                    @pymc.deterministic(name=name, trace=True)
                    def fluorescence_model(log_well_volume=log_well_volume,
                        log_concentrations=log_concentrations, quantum_yields=quantum_yields,
                        log_excitation_extinction_coefficients=log_excitation_extinction_coefficients, log_emission_extinction_coefficients=log_emission_extinction_coefficients,
                        plate_fluorescence=plate_fluorescence,
                        top_log_illumination_intensity=top_log_illumination_intensity, bottom_log_illumination_intensity=bottom_log_illumination_intensity, geometry=geometry):

                        # TODO: Work entirely in log space?

                        # Compute path length
                        log_path_length = log_well_volume - log_well_area

                        # Calculate attenuation due to inner filter effects
                        # TODO: We may need to fix some scaling factors in the extinction coefficient to go between log10 and ln-based absorbance/transmission.
                        inner_filter_effect_scaling = 1.0 # scaling factor applied from inner filter effect
                        ELC_excitation = 0.0 # sum of (extinction_coefficient * path_length * concentration) for all species at excitation wavelength
                        ELC_emission   = 0.0 # sum of (extinction_coefficient * path_length * concentration) for all species at emission wavelength
                        for (log_concentration, log_excitation_extinction_coefficient, log_emission_extinction_coefficient) in zip(log_concentrations, log_excitation_extinction_coefficients, log_emission_extinction_coefficients):
                            # TODO: Accumulate using logsumexp?
                            ELC_excitation += np.exp(log_excitation_extinction_coefficient + log_path_length + log_concentration)
                            ELC_emission   += np.exp(log_emission_extinction_coefficient   + log_path_length + log_concentration)
                        if geometry == 'top':
                            alpha = (ELC_excitation + ELC_emission) * np.log(10)
                        elif geometry == 'bottom':
                            alpha = (ELC_excitation - ELC_emission) * np.log(10)
                        try:
                            inner_filter_effect_attenuation = (1 - np.exp(-alpha)) / alpha
                        except RuntimeWarning as e:
                            print('alpha = %s' % str(alpha))
                            print(e)
                        # Handle alpha -> 0 case explicitly.
                        if np.abs(alpha) < 0.01:
                            inner_filter_effect_attenuation = 1. - alpha/2. + (alpha**2)/6. - (alpha**3)/24. + (alpha**4)/120.
                        if geometry == 'bottom':
                            # Include additional term for bottom detection geometry.
                            inner_filter_effect_attenuation *= np.exp(-ELC_emission)

                        # TODO: Fix this; ignoring for now
                        inner_filter_effect_attenuation = 1.0 # NO ATTENUATION

                        # Compute incident intensity
                        intensity = (geometry == 'top') * np.exp(top_log_illumination_intensity) + (geometry == 'bottom') * np.exp(bottom_log_illumination_intensity) # select appropriate illumination intensity
                        intensity *= inner_filter_effect_attenuation # include inner filter effects

                        # Compute fluorescence intensity
                        fluorescence = intensity * plate_fluorescence # start by including plate background fluorescence
                        for (quantum_yield, log_concentration) in zip(quantum_yields, log_concentrations):
                            fluorescence += intensity * quantum_yield * np.exp(log_concentration)

                        return fluorescence
                    self.model[name] = fluorescence_model
                    self.parameter_names['fluorescence'].append(name)

                    measured_fluorescence = measurements['fluorescence'][(excitation_wavelength, emission_wavelength, geometry)]
                    name = 'measured %s fluorescence of well %s at excitation wavelength %s and emission wavelength %s' % (geometry, wellname(well), excitation_wavelength, emission_wavelength)
                    self.model[name] = pymc.Normal(name, mu=fluorescence_model, tau=self.model['%s fluorescence precision' % geometry], observed=True, value=measured_fluorescence)
                    self.parameter_names['fluorescence'].append(name)

    def map_fit(self):
        """"""
        Find the maximum a posteriori (MAP) fit.

        Parameters
        ----------

        Returns
        -------
        map : pymc.MAP
           The MAP fit.

        """"""
        map = pymc.MAP(self.model)
        ncycles = 50
        nshow = 5
        maxits = 1 # number of iterations per cycle

        # Perform MAP fit
        print('Performing MAP fit...')
        for cycle in range(ncycles):
            if (cycle+1)%nshow==0: print('MAP fitting cycle %d/%d' % (cycle+1, ncycles))
            initial_time = time.time()
            map.fit(iterlim=maxits)
            final_time = time.time()
            elapsed_time = final_time - initial_time
            print('  elapsed time %.6f s' % elapsed_time)

        return map

    def run_mcmc(self, dbfilename='output'):
        """"""
        Sample the model with pymc using sensible defaults.

        Parameters
        ----------
        dbfilename : str, optional, default='output'
           Name of storage filename for database.

        Returns
        -------
        mcmc : pymc.MCMC
           The MCMC samples.

        """"""

        # DEBUG: Write model
        print('Writing graph...')
        pymc.graph.moral_graph(self.model, format='ps')

        # Sample the model with pymc
        # TODO: Allow
        mcmc = pymc.MCMC(self.model, db='sqlite', name='output', verbose=True)

        nthin = 10
        nburn = nthin*100
        niter = nthin*100

        # Specify initial parameter standard deviations to apply to specific classes of parameters
        keywords = {
            'concentration' : 0.1,
            'affinity' : 0.1,
            'volume' : 0.01,
        }

        print('Assigning initial guesses for Metropolis step method proposal standard deviations:')
        for stochastic in self.model.stochastics:
            if hasattr(stochastic, '__name__'):
                sigma = 1.0 # default proposal standard deviation
                parameter_name = stochastic.__name__
                # See if we have specified a special standard deviation for this parameter class
                for keyword in keywords:
                    if keyword in parameter_name:
                        sigma = keywords[keyword]

                print('%-64ss : %8.5f' % (parameter_name, sigma))
                mcmc.use_step_method(pymc.Metropolis, stochastic, proposal_sd=sigma, proposal_distribution=None)

        print('Running MCMC...')
        mcmc.sample(iter=(nburn+niter), burn=nburn, thin=nthin, progress_bar=True, tune_throughout=True)

        # Close the database.
        #mcmc.db.close()

        return mcmc

    def show_summary(self, mcmc, map_fit=None):
        """"""
        Show summary statistics of MCMC and (optionally) MAP estimates.

        Parameters
        ----------
        mcmc : pymc.MCMC
           MCMC samples
        map_fit : pymc.MAP, optional, default=None
           The MAP fit.

        TODO
        ----
        * Automatically determine appropriate number of decimal places from statistical uncertainty.
        * Automatically adjust concentration units (e.g. pM, nM, uM) depending on estimated affinity.

        """"""

        # Compute summary statistics
        alpha = 0.95 # confidence interval width
        from scipy.stats import bayes_mvs
        for group in self.parameter_names:
            print(group)
            for name in self.parameter_names[group]:
                try:
                    if map_fit:
                        mle = getattr(map_fit, name).value
                    else:
                        mle = getattr(mcmc, name).trace().mean()
                    trace = getattr(mcmc, name).trace()
                    mean_cntr, var_cntr, std_cntr = bayes_mvs(trace, alpha=alpha)
                    (center, (lower, upper)) = mean_cntr
                    if trace.std() == 0.0:
                        lower = upper = trace[0]

                    if ('concentration' in name) or ('volume' in name):
                        print(""%-64s : initial %7.1e final %7.1e : %7.1e [%7.1e, %7.1e]"" % (name, trace[0], trace[-1], mle, lower, upper))
                    else:
                        print(""%-64s : initial %7.1f final %7.1f : %7.1f [%7.1f, %7.1f]"" % (name, trace[0], trace[-1], mle, lower, upper))

                except AttributeError as e:
                    # Skip observed stochastics
                    pass
            print('')

    def generate_plots(self, mcmc, map_fit=None, pdf_filename=None):
        """"""
        Generate interactive or PDF plots from MCMC trace.

        Parameters
        ----------
        mcmc : pymc.MCMC
           MCMC samples to plot
        map_fit : pymc.MAP, optional, default=None
           Plot the maximum a posteriori (MAP) estimate if provided.
        pdf_filename : str, optional, default=None
           If specified, generate a PDF containing plots.

        """"""
        alpha = 0.95 # confidence interval width
        print('')
        print('Generating plots...')
        from scipy.stats import bayes_mvs
        with PdfPages(pdf_filename) as pdf:
            for group in self.parameter_names:
                print(group)
                for name in self.parameter_names[group]:
                    try:
                        if map_fit:
                            mle = getattr(map_fit, name).value
                        else:
                            mle = getattr(mcmc, name).trace().mean()
                        trace = getattr(mcmc, name).trace()
                        mean_cntr, var_cntr, std_cntr = bayes_mvs(trace, alpha=alpha)
                        (center, (lower, upper)) = mean_cntr
                        if trace.std() == 0.0:
                            lower = upper = trace[0]

                        plt.figure(figsize=(12, 8))
                        plt.hold(True)
                        niterations = len(trace)
                        plt.plot([0, niterations], [mle, mle], 'r-')
                        plt.plot(trace, 'k.')
                        plt.xlabel('iteration')
                        plt.ylabel(name)
                        plt.title(name)
                        pdf.savefig()
                        plt.close()

                    except AttributeError as e:
                        pass
","Python"
"Assay","choderalab/assaytools","AssayTools/pymcmodels.py",".py","41713","843","""""""
pymc models for the analysis of fluorescence assay data.

""""""

#=============================================================================================
# IMPORTS
#=============================================================================================

import numpy as np
import pymc

#=============================================================================================
# Physical constants
#=============================================================================================

Na = 6.02214179e23 # Avogadro's number (number/mol)
kB = Na * 1.3806504e-23 / 4184.0 # Boltzmann constant (kcal/mol/K)
C0 = 1.0 # standard concentration (M)

#=============================================================================================
# Parameters for MCMC sampling
#=============================================================================================

DG_min = np.log(1e-15) # kT, most favorable (negative) binding free energy possible; 1 fM
DG_max = +0 # kT, least favorable binding free energy possible
niter = 500000 # number of iterations
nburn = 50000 # number of burn-in iterations to discard
nthin = 500 # thinning interval


#=============================================================================================
# SUBROUTINES
#=============================================================================================

def LogNormalWrapper(name, mean, stddev, log_prefix='log_', size=1, observed=False, value=None):
    """"""
    Create a PyMC Normal stochastic, automatically converting parameters to be appropriate for a `LogNormal` distribution.
    Note that the resulting distribution is Normal, not LogNormal.
    This is appropriate if we want to describe the log of a volume or a fluorescence reading.

    Notes
    -----
    Everything is coerced into a 1D array.

    Parameters
    ----------
    mean : float or array-like (but not pymc variable)
       Mean of exp(X), where X is lognormal variate.
    stddev : float or array-like (but not pymc variable)
       Standard deviation of exp(X), where X is lognormal variate.
    log_prefix : str, optional, default='log_'
       Prefix appended to stochastic
    size : list of int, optional, default=1
       Size vector
    observed : bool, optional, default=False
       If True, the stochastic is fixed to this observed value.
    value : float, optional, default=None
       If observed=True, the observed value of the real quantity

    Returns
    -------
    stochastic : pymc.Normal
       Normal stochastic for random variable X.
       Name is prefixed with log_prefix
    deterministic : pymc.deterministic
       Deterministic encoding the exponentiated lognormal (real quantity)

    """"""
    def stable_tau(mean=mean, stddev=stddev):
        # Ensure tau has same dimensionality and type as 'mean'
        tau = 0*mean
        SMALL = 1.0e-6
        # Handle scalars
        if np.isscalar(mean):
            x = (stddev/mean)**2
            if (x < SMALL):
                # Use Taylor series expansion
                tau = 1.0 / (x - x**2/2 + x**3/3 - x**4/4)
            else:
                tau = 1.0 / np.log(1.0 + x)
        else:
            x = (stddev/mean)**2
            small_indices = np.where(x < SMALL)[0]
            other_indices = np.where(x >= SMALL)[0]
            tau[small_indices] = (x[small_indices] - x[small_indices]**2/2 + x[small_indices]**3/3 - x[small_indices]**4/4 + x[small_indices]**5/5)**(-1)
            tau[other_indices] = np.log(1.0 + x[other_indices])**(-1)

        return tau

    if observed and (value is not None):
        # Compute parameters of lognormal distribution
        @pymc.deterministic(name=name + '_mu')
        def mu(mean=mean, stddev=stddev):
            mu = np.log(mean**2 / np.sqrt(stddev**2 + mean**2))
            return mu
        @pymc.deterministic(name=name + '_tau')
        def tau(mean=mean, stddev=stddev):
            if np.any(mean == 0.0):
                raise Exception(""'mean' for variable '%s' is zero: mean = %s, stddev = %s"" % (name, mean, stddev))
            tau = stable_tau(mean=mean, stddev=stddev)
            return tau
        stochastic = pymc.Normal(log_prefix + name, mu=mu, tau=tau, size=size, observed=observed, value=np.log(value))

        # Create deterministic from stochastic
        @pymc.deterministic(name=name)
        def deterministic(log_value=stochastic):
            return value

        return stochastic, deterministic

    #
    # Handle non-observed case
    #

    # Promote to array
    if size == 1:
        mean, stddev = np.array([mean]), np.array([stddev])
        size = [1]

    # Handle only strictly positive elements---all others are set to zero as constants
    try:
        nonzero_indices = np.where(mean > 0)[0]
        zero_indices = np.where(mean <= 0)[0]
    except:
        nonzero_indices = range(size[0])
        zero_indices = []
    nnonzero = len(nonzero_indices)
    nzeros = len(zero_indices)

    # Compute parameters of lognormal distribution
    @pymc.deterministic(name=name + '_mu')
    def mu(mean=mean, stddev=stddev):
        if not np.isscalar(mean):
            mean = mean[nonzero_indices]
        if not np.isscalar(stddev):
            stddev = stddev[nonzero_indices]
        mu = np.log(mean**2 / np.sqrt(stddev**2 + mean**2))
        return mu
    @pymc.deterministic(name=name + '_tau')
    def tau(mean=mean, stddev=stddev):
        if not np.isscalar(mean):
            mean = mean[nonzero_indices]
        if not np.isscalar(stddev):
            stddev = stddev[nonzero_indices]
        tau = stable_tau(mean=mean, stddev=stddev)
        return tau
    stochastic = pymc.Normal(log_prefix + name, mu=mu, tau=tau, size=nnonzero)

    # Create deterministic from stochastic
    @pymc.deterministic(name=name)
    def deterministic(log_value=stochastic):
        value = np.zeros(size, np.float64)
        value[nonzero_indices] = np.exp(log_value)
        return value

    return stochastic, deterministic

def NormalWrapper(name, mean, stddev, size=1, observed=False, value=None):
    """"""
    Create a PyMC Normal stochastic, automatically converting parameters to be appropriate for a `Normal` distribution.

    Notes
    -----
    Everything is coerced into a 1D array.

    Parameters
    ----------
    mean : float or array-like (but not pymc variable)
       Mean of exp(X), where X is lognormal variate.
    stddev : float or array-like (but not pymc variable)
       Standard deviation of exp(X), where X is lognormal variate.
    size : list of int, optional, default=1
       Size vector
    observed : bool, optional, default=False
       If True, the stochastic is fixed to this observed value.
    value : float, optional, default=None
       If observed=True, the observed value of the real quantity

    Returns
    -------
    stochastic : pymc.Normal
       Normal stochastic for random variable X.
       Name is prefixed with log_prefix
       Deterministic encoding the exponentiated lognormal (real quantity)

    """"""
    @pymc.deterministic(name=name + '_mu')
    def mu(mean=mean, stddev=stddev):
        mu = mean
        return mu
    @pymc.deterministic(name=name + '_tau')
    def tau(mean=mean, stddev=stddev):
        tau = stddev**(-2)
        return tau
    stochastic = pymc.Normal(name, mu=mu, tau=tau, size=size, observed=observed, value=value)

    return stochastic

#=============================================================================================
# PyMC models
#=============================================================================================

def inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, concentration, geometry='top'):
    """"""
    Compute primary and secondar inner filter effect attenuation for top and bottom observation geometries.

    Parameters
    ----------
    epsilon_ex : float
       Exctinction coefficient at excitation wavelength. Units of 1/M/cm
    epsilon_em : float
       Extinction coefficient at emission wavelength. Units of 1/M/cm
    path_length : float
       Path length. Units of cm.
    concentration : float
       Concentration of species whose extinction coefficient is provided. Units of M.
    geometry : str, optional, default='top'
       Observation geometry, one of ['top', 'bottom'].

    Returns
    -------
    scaling : factor by which expected fluorescence is attenuated by primary and secondary inner filter effects

    """"""

    # Ensure concentration is a vector.
    if not hasattr(concentration, '__getitem__'):
        concentration = np.array([concentration])

    assert np.all(epsilon_ex >= 0)
    assert np.all(epsilon_em >= 0)
    assert np.all(concentration >= 0), 'concentration should not be negative, but are: {}'.format(concentration)
    assert (path_length > 0)

    ELC_ex = epsilon_ex*path_length*concentration
    ELC_em = epsilon_em*path_length*concentration

    scaling = 1.0 # no attenuation

    def compute_scaling(alpha):
        assert np.all(alpha >= 0.0)
        scaling = np.zeros(alpha.shape, np.float64)
        small_indices = np.where(np.abs(alpha) < 0.01)
        large_indices = np.where(np.abs(alpha) >= 0.01)
        scaling[large_indices] = (1 - np.exp(-alpha[large_indices])) / alpha[large_indices]
        scaling[small_indices] = 1. - alpha[small_indices]/2. + (alpha[small_indices]**2)/6. - (alpha[small_indices]**3)/24. + (alpha[small_indices]**4)/120.
        return scaling

    if geometry == 'top':
        alpha = (ELC_ex + ELC_em)
        scaling = compute_scaling(alpha)
    elif geometry == 'bottom':
        # Check order to make sure to avoid exp overflow
        print(ELC_ex - ELC_em)
        if ELC_ex > ELC_em:
            alpha = (ELC_ex - ELC_em)
            scaling = compute_scaling(alpha)
            # Include additional term.
            scaling *= np.exp(-ELC_em)
        else:
            alpha = (ELC_em - ELC_ex)
            scaling = compute_scaling(alpha)
            # Include additional term.
            scaling *= np.exp(-ELC_ex)
    else:
        raise Exception(""geometry '%s' unknown, must be one of ['top', 'bottom']"" % geometry)

    return scaling

# Create a pymc model
def make_model(Pstated, dPstated, Lstated, dLstated,
               top_complex_fluorescence=None, top_ligand_fluorescence=None,
               bottom_complex_fluorescence=None, bottom_ligand_fluorescence=None,
               DG_prior='uniform',
               concentration_priors='lognormal',
               quantum_yield_priors='lognormal',
               use_primary_inner_filter_correction=True,
               use_secondary_inner_filter_correction=True,
               assay_volume=100e-6, well_area=0.1586,
               epsilon_ex=None, depsilon_ex=None,
               epsilon_em=None, depsilon_em=None,
               ligand_ex_absorbance=None, ligand_em_absorbance=None,
               link_top_and_bottom_sigma=True,
               F_PL=None, dF_PL=None):
    """"""
    Build a PyMC model for an assay that consists of N wells of protein:ligand at various concentrations and an additional N wells of ligand in buffer, with the ligand at the same concentrations as the corresponding protein:ligand wells.

    Parameters
    ----------
    Pstated : numpy.array of N values
       Stated protein concentrations for all protein:ligand wells of assay. Units of molarity.
    dPstated : numpy.array of N values
       Absolute uncertainty in stated protein concentrations for all wells of assay. Units of molarity.
       Uncertainties currently cannot be zero.
    Lstated : numpy.array of N values
       Stated ligand concentrations for all protein:ligand and ligand wells of assay, which must be the same with and without protein. Units of molarity.
       Zero concentrations will automatically be handled as special cases.
    dLstated : numpy.array of N values
       Absolute uncertainty in stated protein concentrations for all wells of assay. Units of molarity.
       Uncertainties cannot be zero if the corresponding concentration is nonzero.
    top_complex_fluorecence : numpy.array of N values, optional, default=None
       Fluorescence intensity (top) for protein:ligand mixture.
    top_ligand_fluorescence : numpy.array of N values, optional, default=None
       Fluorescence intensity (top) for ligand control.
    bottom_complex_fluorescence: numpy.array of N values, optional, default=None
       Fluorescence intensity (bottom) for protein:ligand mixture.
    bottom_ligand_fluorescence : numpy.array of N values, optional, default=None
       Fluorescence intensity (bottom) for ligand control.
    DG_prior : str, optional, default='uniform'
       Prior to use for reduced free energy of binding (DG): 'uniform' (uniform over reasonable range), or 'chembl' (ChEMBL-inspired distribution); default: 'uniform'
    concentration_priors : str, optional, default='lognormal'
       Prior to use for protein and ligand concentrations. Available options are ['lognormal'].
    quantum_yield_priors : str, optional, default='lognormal'
       Prior to use for quantum yields. Available options are ['lognormal', 'uniform'].
    use_primary_inner_filter_correction : bool, optional, default=True
       If true, will infer ligand extinction coefficient epsilon and apply primary inner filter correction to attenuate excitation light.
    use_secondary_inner_filter_correction : bool, optional, default=True
       If true, will infer ligand extinction coefficient epsilon and apply secondary inner filter correction to attenuate excitation light.
    assay_volume : float, optional, default=100e-6
       Assay volume. Units of L. Default 100 uL.
    well_area : float, optional, default=0.1586
       Well area. Units of cm^2. Default 0.1586 cm^2, for half-area plate.
    epsilon_ex, depsilon_ex : float, optional, default=None
       Orthogonal measurement of ligand extinction coefficient at excitation wavelength (and uncertainty). If None, will use a uniform prior.
    epsilon_em, depsilon_em : float, optional, default=None
       Orthogonal measurement of ligand extinction coefficient at excitation wavelength (and uncertainty). If None, will use a uniform prior.
    ligand_ex_absorbance : np.array of N values, optional, default=None
       Ligand absorbance measurement for excitation wavelength.
    ligand_em_absorbance : np.array of N values, optional, default=None
       Ligand absorbance measurement for emission wavelength.
    link_top_and_bottom_sigma : bool, optional, default=True
       If True, will link top and bottom fluorescence uncertainty sigma.
    F_PL, dF_PL : float, optional, default=None
       Prior on fluorescence quantum yield of protein-ligand complex, in units of RFU/M.
       Both F_PL and dF_PL must be specified for a lognormal prior to be used;
       otherwise a uniform prior is used.

    Returns
    -------
    pymc_model : dict
       A dict mapping variable names to onbjects that can be used as a PyMC model object.

    Examples
    --------
    Create a simple model

    >>> N = 12 # 12 wells per series of protein:ligand or ligand alone
    >>> Pstated = np.ones([N], np.float64) * 1e-6
    >>> Lstated = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(N)])
    >>> dPstated = 0.10 * Pstated
    >>> dLstated = 0.08 * Lstated
    >>> top_complex_fluorescence = np.array([ 689., 683., 664., 588., 207., 80., 28., 17., 10., 11., 10., 10.], np.float32)
    >>> top_ligand_fluorescence = np.array([ 174., 115., 57., 20., 7., 6., 6., 6., 6., 7., 6., 7.], np.float32)
    >>> from pymcmodels import make_model
    >>> pymc_model = make_model(Pstated, dPstated, Lstated, dLstated, top_complex_fluorescence=top_complex_fluorescence, top_ligand_fluorescence=top_ligand_fluorescence)

    """"""

    # Compute path length.
    path_length = assay_volume * 1000 / well_area # cm, needed for inner filter effect corrections

    # Compute number of samples.
    N = len(Lstated)

    # Check input.
    # TODO: Check fluorescence and absorbance measurements for correct dimensions.
    if (len(Pstated) != N):
        raise Exception('len(Pstated) [%d] must equal len(Lstated) [%d].' % (len(Pstated), len(Lstated)))
    if (len(dPstated) != N):
        raise Exception('len(dPstated) [%d] must equal len(Lstated) [%d].' % (len(dPstated), len(Lstated)))
    if (len(dLstated) != N):
        raise Exception('len(dLstated) [%d] must equal len(Lstated) [%d].' % (len(dLstated), len(Lstated)))

    # Note whether we have top or bottom fluorescence measurements.
    top_fluorescence = (top_complex_fluorescence is not None) or (top_ligand_fluorescence is not None) # True if any top fluorescence measurements provided
    bottom_fluorescence = (bottom_complex_fluorescence is not None) or (bottom_ligand_fluorescence is not None) # True if any bottom fluorescence measurements provided

    # Create an empty dict to hold the model.
    model = dict()

    # Prior on binding free energies.
    if DG_prior == 'uniform':
        DeltaG = pymc.Uniform('DeltaG', lower=DG_min, upper=DG_max) # binding free energy (kT), uniform over huge range
    elif DG_prior == 'chembl':
        DeltaG = pymc.Normal('DeltaG', mu=0, tau=1./(12.5**2)) # binding free energy (kT), using a Gaussian prior inspured by ChEMBL
    else:
        raise Exception(""DG_prior = '%s' unknown. Must be one of 'uniform' or 'chembl'."" % DG_prior)
    # Add to model.
    model['DeltaG'] = DeltaG

    # Create priors on true concentrations of protein and ligand.
    if concentration_priors != 'lognormal':
        raise Exception(""concentration_priors = '%s' unknown. Must be one of ['lognormal']."" % concentration_priors)
    model['log_Ptrue'], model['Ptrue'] = LogNormalWrapper('Ptrue', mean=Pstated, stddev=dPstated, size=Pstated.shape) # protein concentration (M)
    model['log_Ltrue'], model['Ltrue'] = LogNormalWrapper('Ltrue', mean=Lstated, stddev=dLstated, size=Lstated.shape) # ligand concentration (M)
    model['log_Ltrue_control'], model['Ltrue_control'] = LogNormalWrapper('Ltrue_control', mean=Lstated, stddev=dLstated, size=Lstated.shape) # ligand concentration in ligand-only wells (M)

    # extinction coefficient
    model['epsilon_ex'] = 0.0
    if use_primary_inner_filter_correction:
        if epsilon_ex:
            model['log_epsilon_ex'], model['epsilon_ex'] = LogNormalWrapper('epsilon_ex', mean=epsilon_ex, stddev=depsilon_ex) # prior is centered on measured extinction coefficient
        else:
            model['epsilon_ex'] = pymc.Uniform('epsilon_ex', lower=0.0, upper=1000e3, value=70000.0) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm

    model['epsilon_em'] = 0.0
    if use_secondary_inner_filter_correction:
        if epsilon_em:
            model['log_epsilon_em'], model['epsilon_em'] = LogNormalWrapper('epsilon_em', mean=epsilon_em, stddev=depsilon_em) # prior is centered on measured extinction coefficient
        else:
            model['epsilon_em'] = pymc.Uniform('epsilon_em', lower=0.0, upper=1000e3, value=0.0) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm

    # Min and max observed fluorescence.
    Fmax = 0.0; Fmin = 1e6;
    if top_complex_fluorescence is not None:
        Fmax = max(Fmax, top_complex_fluorescence.max()); Fmin = min(Fmin, top_complex_fluorescence.min())
    if top_ligand_fluorescence is not None:
        Fmax = max(Fmax, top_ligand_fluorescence.max()); Fmin = min(Fmin, top_ligand_fluorescence.min())
    if bottom_complex_fluorescence is not None:
        Fmax = max(Fmax, bottom_complex_fluorescence.max()); Fmin = min(Fmin, bottom_complex_fluorescence.min())
    if bottom_ligand_fluorescence is not None:
        Fmax = max(Fmax, bottom_ligand_fluorescence.max()); Fmin = min(Fmin, bottom_ligand_fluorescence.min())

    # Compute initial guesses for fluorescence quantum yield quantities.
    F_plate_guess = Fmin
    F_buffer_guess = Fmin / path_length
    F_L_guess = (Fmax - Fmin) / Lstated.max()
    F_P_guess = Fmin
    F_P_guess = Fmin / Pstated.min()
    F_PL_guess = (Fmax - Fmin) / min(Pstated.max(), Lstated.max())

    # Priors on fluorescence intensities of complexes (later divided by a factor of Pstated for scale).
    if quantum_yield_priors == 'uniform':
        model['F_plate'] = pymc.Uniform('F_plate', lower=0.0, upper=Fmax, value=F_plate_guess) # plate fluorescence
        model['F_buffer'] = pymc.Uniform('F_buffer', lower=0.0, upper=Fmax/path_length, value=F_buffer_guess) # buffer fluorescence
        model['F_buffer_control'] = pymc.Uniform('F_buffer_control', lower=0.0, upper=Fmax/path_length, value=F_buffer_guess) # buffer fluorescence
        if (F_PL is not None) and (dF_PL is not None):
            model['log_F_PL'], model['F_PL'] = LogNormalWrapper('F_PL', mean=F_PL, stddev=dF_PL)
        else:
            model['F_PL'] = pymc.Uniform('F_PL', lower=0.0, upper=2*Fmax/min(Pstated.max(),Lstated.max()), value=F_PL_guess) # complex fluorescence
        model['F_P'] = pymc.Uniform('F_P', lower=0.0, upper=2*(Fmax/Pstated).max(), value=F_P_guess) # protein fluorescence
        model['F_L'] = pymc.Uniform('F_L', lower=0.0, upper=2*(Fmax/Lstated).max(), value=F_L_guess) # ligand fluorescence
    elif quantum_yield_priors == 'lognormal':
        stddev = 1.0 # relative factor for stddev guess
        model['log_F_plate'], model['F_plate'] = LogNormalWrapper('F_plate', mean=F_plate_guess, stddev=stddev*F_plate_guess) # plate fluorescence
        model['log_F_buffer'], model['F_buffer'] = LogNormalWrapper('F_buffer', mean=F_buffer_guess, stddev=stddev*F_buffer_guess) # buffer fluorescence
        model['log_F_buffer_control'], model['F_buffer_control'] = LogNormalWrapper('F_buffer_control', mean=F_buffer_guess, stddev=stddev*F_buffer_guess) # buffer fluorescence
        if (F_PL is not None) and (dF_PL is not None):
            model['log_F_PL'], model['F_PL'] = LogNormalWrapper('F_PL', mean=F_PL, stddev=dF_PL)
        else:
            model['log_F_PL'], model['F_PL'] = LogNormalWrapper('F_PL', mean=F_PL_guess, stddev=stddev*F_PL_guess) # complex fluorescence
        model['log_F_P'], model['F_P'] = LogNormalWrapper('F_P', mean=F_P_guess, stddev=stddev*F_P_guess) # protein fluorescence
        model['log_F_L'], model['F_L'] = LogNormalWrapper('F_L', mean=F_L_guess, stddev=stddev*F_L_guess) # ligand fluorescence
    else:
        raise Exception(""quantum_yield_priors = '%s' unknown. Must be one of ['lognormal', 'uniform']."" % quantum_yield_priors)

    # Unknown experimental measurement error.
    if top_fluorescence:
        model['log_sigma_top'] = pymc.Uniform('log_sigma_top', lower=-10, upper=np.log(Fmax), value=np.log(5))
        model['sigma_top'] = pymc.Lambda('sigma_top', lambda log_sigma=model['log_sigma_top'] : np.exp(log_sigma) )
        model['precision_top'] = pymc.Lambda('precision_top', lambda log_sigma=model['log_sigma_top'] : np.exp(-2*log_sigma) )

    if bottom_fluorescence:
        if top_fluorescence and bottom_fluorescence and link_top_and_bottom_sigma:
            # Use the same log_sigma for top and bottom fluorescence
            model['log_sigma_bottom'] = pymc.Lambda('log_sigma_bottom', lambda log_sigma_top=model['log_sigma_top'] : log_sigma_top )
        else:
            model['log_sigma_bottom'] = pymc.Uniform('log_sigma_bottom', lower=-10, upper=np.log(Fmax), value=np.log(5))
        model['sigma_bottom'] = pymc.Lambda('sigma_bottom', lambda log_sigma=model['log_sigma_bottom'] : np.exp(log_sigma) )
        model['precision_bottom'] = pymc.Lambda('precision_bottom', lambda log_sigma=model['log_sigma_bottom'] : np.exp(-2*log_sigma) )

    if top_fluorescence and bottom_fluorescence:
        # Gain that attenuates bottom fluorescence relative to top.
        # TODO: Replace this with plate absorbance?
        log_gain_guess = - np.log((top_complex_fluorescence.max() - top_complex_fluorescence.min()) / (bottom_complex_fluorescence.max() - bottom_complex_fluorescence.min()))
        model['log_gain_bottom'] = pymc.Uniform('log_gain_bottom', lower=-6.0, upper=6.0, value=log_gain_guess) # plate material absorbance at emission wavelength
        model['gain_bottom'] = pymc.Lambda('gain_bottom', lambda log_gain_bottom=model['log_gain_bottom'] : np.exp(log_gain_bottom) )
    elif (not top_fluorescence) and bottom_fluorescence:
        model['log_gain_bottom'] = 0.0 # no gain
        model['gain_bottom'] = pymc.Lambda('gain_bottom', lambda log_gain_bottom=model['log_gain_bottom'] : np.exp(log_gain_bottom) )

    if top_fluorescence:
        model['log_sigma_abs'] = pymc.Uniform('log_sigma_abs', lower=-10, upper=0, value=np.log(0.01))
        model['sigma_abs'] = pymc.Lambda('sigma_abs', lambda log_sigma=model['log_sigma_abs'] : np.exp(log_sigma) )
        model['precision_abs'] = pymc.Lambda('precision_abs', lambda log_sigma=model['log_sigma_abs'] : np.exp(-2*log_sigma) )

    # Fluorescence model.
    from assaytools.bindingmodels import TwoComponentBindingModel

    if top_complex_fluorescence is not None:
        @pymc.deterministic
        def top_complex_fluorescence_model(F_plate=model['F_plate'], F_buffer=model['F_buffer'],
                                           F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],
                                           Ptrue=model['Ptrue'], Ltrue=model['Ltrue'], DeltaG=model['DeltaG'],
                                           epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):
            [P_i, L_i, PL_i] = TwoComponentBindingModel.equilibrium_concentrations(DeltaG, Ptrue[:], Ltrue[:])
            IF_i = inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, L_i, geometry='top')
            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*L_i) # inner filter effect applied only to plate
            Fmodel_i = IF_i[:]*(F_PL*PL_i + F_L*L_i + F_P*P_i + F_buffer*path_length) + IF_i_plate*F_plate
            return Fmodel_i
        # Add to model.
        model['top_complex_fluorescence_model'] = top_complex_fluorescence_model
        model['log_top_complex_fluorescence'], model['top_complex_fluorescence'] = LogNormalWrapper('top_complex_fluorescence',
            mean=model['top_complex_fluorescence_model'], stddev=model['sigma_top'],
            size=[N], observed=True, value=top_complex_fluorescence) # observed data

    if top_ligand_fluorescence is not None:
        @pymc.deterministic
        def top_ligand_fluorescence_model(F_plate=model['F_plate'], F_buffer_control=model['F_buffer_control'],
                                          F_L=model['F_L'],
                                          Ltrue_control=model['Ltrue_control'],
                                          epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):
            IF_i = inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue_control, geometry='top')
            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*Ltrue_control) # inner filter effect applied only to plate
            Fmodel_i = IF_i[:]*(F_L*Ltrue_control + F_buffer_control*path_length) + IF_i_plate*F_plate
            return Fmodel_i
        # Add to model.
        model['top_ligand_fluorescence_model'] = top_ligand_fluorescence_model
        model['log_top_ligand_fluorescence'], model['top_ligand_fluorescence'] = LogNormalWrapper('top_ligand_fluorescence',
                                                       mean=model['top_ligand_fluorescence_model'], stddev=model['sigma_top'],
                                                       size=[N], observed=True, value=top_ligand_fluorescence) # observed data

    if bottom_complex_fluorescence is not None:
        @pymc.deterministic
        def bottom_complex_fluorescence_model(F_plate=model['F_plate'], F_buffer=model['F_buffer'],
                                              F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],
                                              Ptrue=model['Ptrue'], Ltrue=model['Ltrue'], DeltaG=model['DeltaG'],
                                              epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em'],
                                              log_gain_bottom=model['log_gain_bottom']):
            [P_i, L_i, PL_i] = TwoComponentBindingModel.equilibrium_concentrations(DeltaG, Ptrue[:], Ltrue[:])
            IF_i = inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, L_i, geometry='bottom')
            IF_i_plate = np.exp(-epsilon_ex*path_length*L_i) # inner filter effect applied only to plate
            Fmodel_i = IF_i[:]*(F_PL*PL_i + F_L*L_i + F_P*P_i + F_buffer*path_length)*np.exp(log_gain_bottom) + IF_i_plate*F_plate
            return Fmodel_i
        # Add to model.
        model['bottom_complex_fluorescence_model'] = bottom_complex_fluorescence_model
        model['log_bottom_complex_fluorescence'], model['bottom_complex_fluorescence'] = LogNormalWrapper('bottom_complex_fluorescence',
                                                           mean=model['bottom_complex_fluorescence_model'], stddev=model['sigma_bottom'],
                                                           size=[N], observed=True, value=bottom_complex_fluorescence) # observed data

    if bottom_ligand_fluorescence is not None:
        @pymc.deterministic
        def bottom_ligand_fluorescence_model(F_plate=model['F_plate'], F_buffer_control=model['F_buffer_control'],
                                             F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],
                                             Ltrue_control=model['Ltrue_control'],
                                             epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em'],
                                             log_gain_bottom=model['log_gain_bottom']):
            IF_i = inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue_control, geometry='bottom')
            IF_i_plate = np.exp(-epsilon_ex*path_length*Ltrue_control) # inner filter effect applied only to plate
            Fmodel_i = IF_i[:]*(F_L*Ltrue_control + F_buffer_control*path_length)*np.exp(log_gain_bottom) + IF_i_plate*F_plate
            return Fmodel_i
        # Add to model.
        model['bottom_ligand_fluorescence_model'] = bottom_ligand_fluorescence_model
        model['log_bottom_ligand_fluorescence'], model['bottom_ligand_fluorescence'] = LogNormalWrapper('bottom_ligand_fluorescence',
                                                          mean=model['bottom_ligand_fluorescence_model'], stddev=model['sigma_bottom'],
                                                          size=[N], observed=True, value=bottom_ligand_fluorescence) # observed data

    # Absorbance measurements

    if ligand_ex_absorbance is not None:
        model['plate_abs_ex'] = pymc.Uniform('plate_abs_ex', lower=0.0, upper=1.0, value=ligand_ex_absorbance.min())
        @pymc.deterministic
        def ligand_ex_absorbance_model(Ltrue_control=model['Ltrue_control'],
                                       epsilon_ex=model['epsilon_ex'],
                                       plate_abs_ex=model['epsilon_em']):
            Fmodel_i = (1.0 - np.exp(-epsilon_ex*path_length*Ltrue_control)) + plate_abs_ex
            return Fmodel_i
        # Add to model.
        model['ligand_ex_absorbance_model'] = ligand_ex_absorbance_model
        model['log_ligand_ex_absorbance'], model['ligand_ex_absorbance'] = LogNormalWrapper('ligand_ex_absorbance',
                                                    mean=model['ligand_ex_absorbance_model'], stddev=model['sigma_abs'],
                                                    size=[N], observed=True, value=ligand_ex_absorbance) # observed data

    if ligand_em_absorbance is not None:
        model['plate_abs_em'] = pymc.Uniform('plate_abs_em', lower=0.0, upper=1.0, value=ligand_em_absorbance.min())
        @pymc.deterministic
        def ligand_em_absorbance_model(Ltrue_control=model['Ltrue_control'],
                                       epsilon_em=model['epsilon_em'],
                                       plate_abs_em=model['plate_abs_em']):
            Fmodel_i = (1.0 - np.exp(-epsilon_em*path_length*Ltrue_control)) + plate_abs_em
            return Fmodel_i
        # Add to model.
        model['ligand_em_absorbance_model'] = ligand_em_absorbance_model
        model['log_ligand_em_absorbance'], model['ligand_em_absorbance'] = LogNormalWrapper('ligand_em_absorbance',
                                                    mean=model['ligand_em_absorbance_model'], stddev=model['sigma_abs'],
                                                    size=[N], observed=True, value=ligand_em_absorbance) # observed data

    # Promote this to a full-fledged PyMC model.
    pymc_model = pymc.Model(model)

    # Return the pymc model
    return pymc_model

def map_fit(pymc_model):
    """"""
    Find the maximum a posteriori (MAP) fit.

    Parameters
    ----------
    pymc_model : pymc model
       The pymc model to sample.

    Returns
    -------
    map : pymc.MAP
       The MAP fit.

    """"""
    map = pymc.MAP(pymc_model)
    ncycles = 50

    # DEBUG
    ncycles = 5

    for cycle in range(ncycles):
        if (cycle+1)%5==0: print('MAP fitting cycle %d/%d' % (cycle+1, ncycles))
        map.fit()

    return map

def run_mcmc_emcee(pymc_model, nwalkers=100, nburn=100, niter=1000, nthin=None):
    """"""
    Sample the model with pymc using sensible defaults and the emcee sampler.

    Parameters
    ----------
    pymc_model : pymc model
       The pymc model to sample.
    nwalkers: int
        The number ensemble walkers.
    nburn: int
        The number of initial steps that will be discarded.
    niter: int
        The number of MCMC iterations that are performed after the burn-in period.
    nthin: int
        The frequency with which to discard samples. If None, then nthin=nwalkers.

    Returns
    -------
    pymc_model : pymc.Model.Model
        The PyMC object used to initialize the class.
    mcmc_model: pymc.MCMC.MCMC
        The PyMC object that contains the MCMC traces.

    """"""
    if nthin is None:
        nthin = nwalkers

    import emcee

    def unpack_parameters(model):
        """"""
        Takes the parameters from the a pymc, which could be floats or arrays and unpacks them into a single array.

        Parameter
        ---------
        model: pymc.Model.Model or pymc.MCMC.MCMC
            The PyMC object from which the parameters will be extracted.

        Returns
        -------
        parameters: numpy.ndarray
            Vectorized form of the pymc model parameters
        """"""
        parameters = []
        for stoch in model.stochastics:
            if stoch.value.shape == ():
                parameters.append(float(stoch.value))
            else:
                for val in stoch.value:
                    parameters.append(val)
        return np.array(parameters)

    def log_post(parameters, model):
        """"""
        Packs a vectorized form of the parameters back into the pymc model to allow the evaluation of the log posterior.

        Parameters
        ----------
        parameters: numpy.ndarray
            Vectorized form of the pymc model parameters
        model: pymc.Model.Model
            The pymc that contains all the variables that need to be inferred.

        Returns
        -------
        logp: float
            The log of the posterior density.
        """"""
        # Basic error handling in case emcee proposes moves outside the domain of the variables.
        try:
            p_ind = 0
            for stoch in model.stochastics:
                if stoch.value.shape == ():
                    stoch.value = np.array(parameters[p_ind])
                    p_ind += 1
                else:
                    stoch.value = parameters[p_ind:(p_ind + len(stoch.value))]
                    p_ind += len(stoch.value)
            logp = model.logp
        except pymc.ZeroProbability:
            logp = -np.inf

        return logp

    # Find MAP. This will be used to initialize the emcee sampler.
    pymc.MAP(pymc_model).fit()

    # Perform a dummy run with pymc to initial trace data structures
    mcmc_model = pymc.MCMC(pymc_model)
    mcmc_model.sample(1)

    # Defining the emcee parameters by extracting the stochastic variables from pymc
    parameters = unpack_parameters(mcmc_model)
    ndim = len(parameters)

    # sample starting points for walkers around the MAP
    p0 = np.zeros((nwalkers, ndim))
    for walker in range(nwalkers):
        # Initializing walkers to be within about 20% of the (local) MAP estimate
        p0[walker, :] = parameters + np.random.normal(0, 0.2 * np.absolute(parameters))

    # Initiate emcee sampler by passing the likelihood function
    sampler = emcee.EnsembleSampler(nwalkers, ndim, log_post, args=[mcmc_model])

    # Burn-in
    pos, prob, state = sampler.run_mcmc(p0, nburn)
    sampler.reset()

    # Production
    sampler.run_mcmc(pos, niter)

    # Packing the trace of the parameters into the PyMC MCMC object
    p_ind = 0
    for stoch in mcmc_model.stochastics:
        if stoch.value.shape == ():
            trace = sampler.flatchain[:, p_ind]
            stoch.trace._trace[0] = trace[::nthin].copy()
            p_ind += 1
        else:
            trace = sampler.flatchain[:, p_ind:(p_ind + len(stoch.value))]
            stoch.trace._trace[0] = trace[::nthin].copy()
            p_ind += len(stoch.value)

    return mcmc_model, pymc_model

def run_mcmc(pymc_model, nthin=20, nburn=0, niter=20000, map=True, db='ram', dbname=None):
    """"""
    Sample the model with pymc. Initial values of the parameters can be chosen with a maximum a posteriori estimate.

    Parameters
    ----------
    pymc_model : pymc model
       The pymc model to sample.
    nthin: int
        The number of MCMC steps that constitute 1 iteration.
    nburn: int
        The number of MCMC iterations during the burn-in.
    niter: int
        The number of production iterations.
    map: bool
        Whether to initialize the parameters before MCMC with the maximum a posteriori estimate.
    db : str
        How to store model, default = 'ram' means not storing it. To store model use storage = 'pickle'. If not,
        supply the name of the database backend that will store the values of the stochastics and deterministics sampled
        during the MCMC loop.
   dbname : str
        name for storage object, default = None. To store model use e.g. dbname = 'my_mcmc.pickle'

    Returns
    -------
    mcmc : pymc.MCMC
       The MCMC samples.

    """"""
    # Find MAP:
    if map == True:
        pymc.MAP(pymc_model).fit()

    # Sample the model with pymc
    mcmc = pymc.MCMC(pymc_model, db=db, dbname=dbname, name='Sampler', verbose=True)

    step_methods = 'AdaptiveMetropolis'
    mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'DeltaG'), proposal_sd=0.1, proposal_distribution='Normal')
    mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'log_F_PL'), proposal_sd=0.1, proposal_distribution='Normal')
    mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'log_F_P'), proposal_sd=0.1, proposal_distribution='Normal')
    mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'log_F_L'), proposal_sd=0.1, proposal_distribution='Normal')
    mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'log_F_plate'), proposal_sd=0.1, proposal_distribution='Normal')
    mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'log_F_buffer'), proposal_sd=0.1, proposal_distribution='Normal')
    mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'log_F_buffer_control'), proposal_sd=0.1, proposal_distribution='Normal')
    if hasattr(pymc_model, 'epsilon_ex'):
        mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'epsilon_ex'), proposal_sd=10000.0, proposal_distribution='Normal')
    if hasattr(pymc_model, 'epsilon_em'):
        mcmc.use_step_method(pymc.Metropolis, getattr(pymc_model, 'epsilon_em'), proposal_sd=10000.0, proposal_distribution='Normal')
    # Uncomment below to use log_F_PL highly correlated with DeltaG to improve sampling somewhat.
    #mcmc.use_step_method(pymc.AdaptiveMetropolis, [pymc_model.log_F_PL, pymc_model.DeltaG], scales={ pymc_model.log_F_PL : 0.1, pymc_model.DeltaG : 0.1  })

    mcmc.sample(iter=(nburn+niter), burn=nburn, thin=nthin, progress_bar=False, tune_throughout=True)

    return mcmc

def show_summary(pymc_model, mcmc, map):
    """"""
    Show summary statistics of MCMC and MAP estimates.

    Parameters
    ----------
    pymc_model : pymc model
       The pymc model to sample.
    map : pymc.MAP
       The MAP fit.
    mcmc : pymc.MCMC
       MCMC samples

    TODO
    ----
    * Automatically determine appropriate number of decimal places from statistical uncertainty.
    * Automatically adjust concentration units (e.g. pM, nM, uM) depending on estimated affinity.

    """"""

    # Compute summary statistics.
    DeltaG = map.DeltaG.value
    dDeltaG = mcmc.DeltaG.trace().std()
    Kd = np.exp(map.DeltaG.value)
    dKd = np.exp(mcmc.DeltaG.trace()).std()
    text = ""DeltaG = %.1f +- %.1f kT\n"" % (DeltaG, dDeltaG)
    if (Kd < 1e-12):
        text += ""Kd = %.1f nM +- %.1f fM"" % (Kd/1e-15, dKd/1e-15)
    elif (Kd < 1e-9):
        text += ""Kd = %.1f pM +- %.1f pM"" % (Kd/1e-12, dKd/1e-12)
    elif (Kd < 1e-6):
        text += ""Kd = %.1f nM +- %.1f nM"" % (Kd/1e-9, dKd/1e-9)
    elif (Kd < 1e-3):
        text += ""Kd = %.1f uM +- %.1f uM"" % (Kd/1e-6, dKd/1e-6)
    elif (Kd < 1):
        text += ""Kd = %.1f mM +- %.1f mM"" % (Kd/1e-3, dKd/1e-3)
    else:
        text += ""Kd = %.3e M +- %.3e M"" % (Kd, dKd)
    text += '\n'
    print(text)
","Python"
"Assay","choderalab/assaytools","AssayTools/bindingmodels.py",".py","15781","376","#!/usr/bin/env python

""""""
Various ligand binding models for use in assays.

""""""

#=============================================================================================
# IMPORTS
#=============================================================================================

import numpy as np
import copy

import scipy.optimize
import scipy.integrate

from math import sqrt, exp, log

#=============================================================================================
# Physical constants
#=============================================================================================

Na = 6.02214179e23 # Avogadro's number (number/mol)
kB = Na * 1.3806504e-23 / 4184.0 # Boltzmann constant (kcal/mol/K)
C0 = 1.0 # standard concentration (M)

#=============================================================================================
# Binding models
#=============================================================================================

class BindingModel(object):
    """"""
    Abstract base class for reaction models.

    """"""

    def __init__(self):
        pass

#=============================================================================================
# Two-component binding model
#=============================================================================================

class TwoComponentBindingModel(BindingModel):
   """"""
   Simple two-component association.

   """"""

   @classmethod
   def equilibrium_concentrations(cls, DeltaG, Ptot, Ltot):
      """"""
      Compute equilibrium concentrations for simple two-component association.

      Parameters
      ----------
      DeltaG : float
         Reduced free energy of binding (in units of kT)
      Ptot : float or numpy array
         Total protein concentration summed over bound and unbound species, molarity.
      Ltot : float or numpy array
         Total ligand concentration summed over bound and unbound speciesl, molarity.

      Returns
      -------
      P : float or numpy array with same dimensions as Ptot
         Free protein concentration, molarity.
      L : float or numpy array with same dimensions as Ptot
         Free ligand concentration, molarity.
      PL : float or numpy array with same dimensions as Ptot
         Bound complex concentration, molarity.

      """"""
      # Handle only strictly positive elements---all others are set to zero as constants
      try:
          nonzero_indices = np.where(Ltot > 0)[0]
          zero_indices = np.where(Ltot <= 0)[0]
      except:
          nonzero_indices = range(size[0])
          zero_indices = []
      nnonzero = len(nonzero_indices)
      nzeros = len(zero_indices)

      # Original form (suffers from numerical instabilities)
      #Kd = np.exp(DeltaG)
      #sqrt_arg = (Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot
      #sqrt_arg[sqrt_arg < 0.0] = 0.0
      #PL = 0.5 * ((Ptot + Ltot + Kd) - np.sqrt(sqrt_arg));  # complex concentration (M)

      # Numerically stable variant
      dtype = np.float128
      Ptot = np.array(Ptot, dtype) # promote to dtype
      Ltot = np.array(Ltot, dtype) # promote to dtype
      PL = np.zeros(Ptot.shape, dtype)
      logP = np.log(Ptot[nonzero_indices])
      logL = np.log(Ltot[nonzero_indices])
      logPLK = np.logaddexp(np.logaddexp(logP, logL), DeltaG)
      PLK = np.exp(logPLK);
      sqrt_arg = 1.0 - np.exp(np.log(4.0) + logP + logL - 2.0*logPLK);
      sqrt_arg[sqrt_arg < 0.0] = 0.0 # ensure always positive
      PL[nonzero_indices] = 0.5 * PLK * (1.0 - np.sqrt(sqrt_arg));  # complex concentration (M)

      # Another variant
      #PL = 2*Ptot*Ltot / ((Ptot+Ltot+Kd) + np.sqrt((Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot));  # complex concentration (M)
      # Yet another numerically stable variant?
      #logPLK = np.logaddexp(np.log(Ptot + Ltot),  DeltaG);
      #PLK = np.exp(logPLK);
      #xy = np.exp(np.log(Ptot) + np.log(Ltot) - 2.0*logPLK);
      #chi = 1.0 - 4.0 * xy;
      #chi[chi < 0.0] = 0.0 # prevent square roots of negative numbers
      #PL = 0.5 * PLK * (1 - np.sqrt(chi))

      # Compute remaining concentrations.
      P = Ptot - PL; # free protein concentration in sample cell after n injections (M)
      L = Ltot - PL; # free ligand concentration in sample cell after n injections (M)

      # Ensure all concentrations are within limits, correcting cases where numerical issues cause problems.
      PL[PL < 0.0] = 0.0 # complex cannot have negative concentration
      P[P < 0.0] = 0.0
      L[L < 0.0] = 0.0

      # Check all concentrations are nonnegative
      assert np.all(P >= 0)
      assert np.all(L >= 0)
      assert np.all(PL >= 0)

      return [P, L, PL]

#=============================================================================================
# Analytical competitive binding model
#=============================================================================================

class CompetitionBindingModel(BindingModel):
     """"""
     Analytic solution for competitive binding model problem.
     As described in Wang, Z. X. An exact mathematical expression
     for describing competitive binding of two different ligands to
     a protein molecule. FEBS Lett. 1995, 360, 111−114.
     """"""

     @classmethod
     def equilibrium_concentrations(cls, Ptot, Ltot, DeltaG_L, Btot, DeltaG_B):
        """"""

        Compute equilibrium concentrations for analytical competition assay association.
        Parameters
        ----------
        Ptot : float or numpy array
          Total protein concentration summed over bound and unbound species, molarity.
        Ltot : float or numpy array
           Total fluorescent ligand concentration summed over bound and unbound speciesl, molarity.
        DeltaG_L : float
           Reduced free energy of binding of fluorescent L to P (in units of kT)
        Btot : float or numpy array
           Total competitive non-fluorescent ligand concentration summed over bound and unbound speciesl, molarity.
        DeltaG_B : float
           Reduced free energy of binding of non-fluorescent B to P (in units of kT)

        Returns
        -------
        P : float or numpy array with same dimensions as Ptot
           Free protein concentration, molarity.
        L : float or numpy array with same dimensions as Ptot
           Free fluorescent ligand concentration, molarity.
        PL : float or numpy array with same dimensions as Ptot
           Bound fluorescent ligand complex concentration, molarity.
        B : float or numpy array with same dimensions as Ptot
           Free non-fluorescent competitive ligand concentration, molarity.
        PB : float or numpy array with same dimensions as Ptot
           Bound non-fluorescent competitive ligand complex concentration, molarity.
        """"""

        # Handle only strictly positive elements---all others are set to zero as constants
        try:
            nonzero_indices = np.where(Ltot > 0)[0]
            zero_indices = np.where(Ltot <= 0)[0]
        except:
            nonzero_indices = range(size[0])
            zero_indices = []
        nnonzero = len(nonzero_indices)
        nzeros = len(zero_indices)

        # Original form:
        Kd_L = np.exp(DeltaG_L)
        Kd_B = np.exp(DeltaG_B)

        # P^3 + aP^2 + bP + c = 0
        a = Kd_L + Kd_B + Ltot + Btot - Ptot
        b = Kd_L*Kd_B + Kd_B*(Ltot-Ptot) + Kd_B*(Btot - Ptot)
        c = -Kd_L*Kd_B*Ptot

        # Subsitute P=u-a/3
        # u^3 - qu - r = 0 where
        q = (a**2)/3.0 - b
        r = (-2.0/27.0)*a**3 +(1.0/3.0)*a*b - c

        # Discriminant
        delta = (r**2)/4.0 -(q**3)/27.0

        # 3 roots. Physically meaningful root is u.
        #theta = np.arccos((-2*(a**3)+9*a*b-27*c)/(2*np.sqrt((a**2-3*b)**3)))

        theta_intermediate = (-2*(a**3)+9*a*b-27*c)/(2*np.sqrt((a**2-3*b)**3))

        # this function prevents nans that occur when taking arccos directly
        def better_theta(theta_intermediate):
            global value
            if -1.0 <= theta_intermediate <= 1.0:
                value = np.arccos( theta_intermediate )
            elif theta_intermediate < -1.0:
                value = np.pi
            elif theta_intermediate > 1.0:
                value = 0.0
            return value

        theta = np.asarray(list(map(better_theta,theta_intermediate)))

        u = (2.0/3.0)*np.sqrt(a**2-3*b)*np.cos(theta/3.0)

        # Compute remaining concentrations.
        P = u - a/3.0           # free protein concentration in sample cell after n injections (M)
        PL = P*Ltot/(Kd_L + P)  # fluorescent ligand complex concentration (M)
        PB = P*Btot/(Kd_B + P)  # non-fluorescent ligand complex concentration (M)
        L = Ltot - PL           # free fluorescent ligand concentration in sample cell after n injections (M)
        B = Btot - PB           # free non-fluorescent ligand concentration in sample cell after n injections (M)

        # Check all concentrations are nonnegative
        assert np.all(P >= 0)
        assert np.all(L >= 0)
        assert np.all(PL >= 0)
        assert np.all(B >= 0)
        assert np.all(PB >= 0)

        return [P, L, PL, B, PB]



#=============================================================================================
# General robust competitive binding model
#=============================================================================================

class GeneralBindingModel(BindingModel):
   """"""
   General robust binding model for one protein and arbitrary number of competitive ligands.

   """"""

   @classmethod
   def equilibrium_concentrations(cls, reactions, conservation_equations, tol=1.0e-8, initial_guess='zero'):
      """"""
      Compute the equilibrium concentrations of each complex species for a general set of binding reactions.

      Parameters
      ----------
      reactions : list
          List of binding reactions.
          Each binding reaction is encoded as a tuple of (log equilibrium constant, dict of stoichiometry)
          Example: K_d = [RL] / ([R] [L]) becomes [ (-10, {'RL': -1, 'R' : +1, 'L' : +1}) ]
      conservation_equations : list
          List of mass conservation laws.
          Each mass conservation law is encoded as a tuple of (log total concentration, dict of stoichiometry of all species)
          Example: [R]tot = 10^-6 M = [RL] + [R] and [L]tot = 10^-6 M = [RL] + [L] becomes [ (-6, {'RL' : +1, 'R' : +1}), (-6, {'RL' : +1, 'L' : +1}) ]
      tol : float, optional, default=1.0e-8
          Solution tolerance for log concentrations.
      initial_guess : str, optional, default='zero'
          If 'zero', will assume all initial log concentrations are zero.
          If 'spread', will spread out concentration among all species.

      Returns
      -------
      log_concentrations : dict of str : float
          log_concentrations[species] is the log concentration of specified species

      Examples
      --------

      Simple 1:1 association

      >>> reactions = [ (-10, {'RL': -1, 'R' : +1, 'L' : +1}) ]
      >>> conservation_equations = [ (-6, {'RL' : +1, 'R' : +1}), (-6, {'RL' : +1, 'L' : +1}) ]
      >>> log_concentrations = GeneralBindingModel.equilibrium_concentrations(reactions, conservation_equations)

     Competitive 1:1 association

      >>> reactions = [ (-10, {'RL': -1, 'R' : +1, 'L' : +1}), (-5, {'RP' : -1, 'R' : +1, 'P' : +1}) ]
      >>> conservation_equations = [ (-6, {'RL' : +1, 'RP' : +1, 'R' : +1}), (-6, {'RL' : +1, 'L' : +1}), (-5, {'RP' : +1, 'P' : +1}) ]
      >>> log_concentrations = GeneralBindingModel.equilibrium_concentrations(reactions, conservation_equations)

      TODO
      ----
      * Can we allow the caller to specify initial conditions instead of conservation laws?

      """"""

      nreactions = len(reactions)
      nconservation = len(conservation_equations)
      nequations = nreactions + nconservation

      # Determine names of all species.
      all_species = dict() # ordered
      for (log_equilibrium_constant, reaction) in reactions:
          for species in reaction.keys():
              all_species[species] = 1
      all_species = tuple(all_species.keys()) # order is now fixed
      nspecies = len(all_species)

      # Construct function with appropriate roots.
      def ftarget(X):
          target = np.zeros([nequations], np.float64)
          jacobian = np.zeros([nequations, nspecies], np.float64)
          equation_index = 0
          # Reactions
          for (log_equilibrium_constant, reaction) in reactions:
              target[equation_index] = - log_equilibrium_constant
              for (species_index, species) in enumerate(all_species):
                  if species in reaction:
                      stoichiometry = reaction[species]
                      target[equation_index] += stoichiometry * X[species_index]
                      jacobian[equation_index][species_index] = stoichiometry
              equation_index += 1
          # Conservation of mass
          from scipy.misc import logsumexp
          for (log_total_concentration, conservation_equation) in conservation_equations:
              target[equation_index] = - log_total_concentration
              log_concentrations = list()
              for (species_index, species) in enumerate(all_species):
                  if species in conservation_equation:
                      stoichiometry = conservation_equation[species]
                      log_concentrations.append(X[species_index] + np.log(stoichiometry))
              log_concentrations = np.array(log_concentrations)
              logsum = logsumexp(log_concentrations)
              target[equation_index] += logsum
              for (species_index, species) in enumerate(all_species):
                  log_concentrations = list()
                  if species in conservation_equation:
                      stoichiometry = conservation_equation[species]
                      jacobian[equation_index, species_index] = stoichiometry * np.exp(X[species_index] - logsum)
              equation_index += 1

          return (target, jacobian)

      # Construct initial guess
      if initial_guess == 'spread':
          # We assume that all matter is equally spread out among all species via the conservation equations
          from scipy.misc import logsumexp
          LOG_ZERO = -100
          X = LOG_ZERO * np.ones([nspecies], np.float64)
          for (species_index, species) in enumerate(all_species):
              for (log_total_concentration, conservation_equation) in conservation_equations:
                  log_total_stoichiometry = np.log(np.sum([stoichiometry for stoichiometry in conservation_equation.values()]))
                  if species in conservation_equation:
                      stoichiometry = conservation_equation[species]
                      #X[species_index] = logsumexp([X[species_index], log_total_concentration + np.log(stoichiometry) - log_total_stoichiometry])
                      X[species_index] = np.logaddexp(X[species_index], log_total_concentration + np.log(stoichiometry) - log_total_stoichiometry)
      elif initial_guess == 'zero':
          # Simple guess: All log concentrations are zero
          X = np.zeros([nspecies], np.float64)
      else:
          raise ValueError('Unknown initial_guess {}'.format(initial_guess))

      # Solve
      from scipy.optimize import root
      options = {'xtol' : tol}
      sol = root(ftarget, X, method='lm', jac=True, tol=tol, options=options)
      if (sol.success == False):
          msg  = ""root-finder failed to converge:\n""
          msg += str(sol)
          raise Exception(msg)

      # TODO: Ensure all constraints and conservation equations are satisfied?

      log_concentrations = { all_species[index] : sol.x[index] for index in range(nspecies) }

      return log_concentrations
","Python"
"Assay","choderalab/assaytools","AssayTools/__init__.py",".py","1191","29","""""""AssayTools is a python library that allows users to model automated assays and analyze assay data using powerful Bayesian techniques that allow complete characterization of the uncertainty in fit models.
""""""

from assaytools import platereader
from assaytools import pymcmodels
from assaytools import bindingmodels
from assaytools import plots

def test(label='full', verbose=2):
    """"""Run tests for mdtraj using nose.

    Parameters
    ----------
    label : {'fast', 'full'}
        Identifies the tests to run. The fast tests take about 10 seconds,
        and the full test suite takes about two minutes (as of this writing).
    verbose : int, optional
        Verbosity value for test outputs, in the range 1-10. Default is 2.
    """"""
    raise Exception(""Eventually, this will test the package, but this has not currently been implemented."")

    import assaytools
    from assaytools.testing.nosetester import AssaytoolsTester
    tester = AssaytoolsTester(assaytools)
    return tester.test(label=label, verbose=verbose, extra_argv=('--exe',))
# prevent nose from discovering this function, or otherwise when its run
# the test suite in an infinite loop
test.__test__ = False
","Python"
"Assay","choderalab/assaytools","AssayTools/parser.py",".py","8790","201","#!/usr/bin/env python

""""""
Tools for assisting in parsing data to input into analysis procedure.
""""""

#=============================================================================================
# Imports
#=============================================================================================

from assaytools import platereader
import string
import numpy as np

#=============================================================================================
# Functions and things to help with parsing
#=============================================================================================

def reorder2list(data,well):

    sorted_keys = sorted(well.keys(), key=lambda k:well[k])

    reorder_data = []

    for key in sorted_keys:
        try:
            reorder_data.append(data[key])
        except:
            pass

    reorder_data = [r.replace('OVER','70000') for r in reorder_data]

    reorder_data = np.asarray(reorder_data,np.float64)

    return reorder_data

ALPHABET = string.ascii_uppercase

well = dict()
for j in string.ascii_uppercase:
    for i in range(1,25):
        well['%s' %j + '%s' %i] = i

#=============================================================================================
# Parsing functions
#=============================================================================================


def get_data_using_inputs(inputs):

    """"""
    Parses your data according to inputs dictionary. Requires you have a 'file_set' in your inputs.
    Parameters
    ----------
    inputs : dict
        Dictionary of input information
    """"""
    complex_fluorescence = {}
    ligand_fluorescence = {}
        
    for protein in inputs['file_set'].keys():

        #concatenate xmls into one dictionary with all ligand data

        my_file = []
        sw_data = []

        # Data just helps define our keys, which will be our data sections.
        # We are importing the first two files, because sometimes we have two files per
        # data collection, e.g. if we wanted a lot of types of reads 
        # (there is a limited number of type of read on the infinite per script).
        data = platereader.read_icontrol_xml(inputs['file_set']['%s'%protein][0])
        try:
            data.update(platereader.read_icontrol_xml(inputs['file_set']['%s'%protein][1]))
        except:
            pass
        
        if 'single_well' in inputs:
            
            for file in inputs['file_set']['%s'%protein]:
                my_file.append(file)
                data_to_append = {key:None for key in data}
                new_dict = platereader.read_icontrol_xml(file)

                data_to_append.update(new_dict)

                sw_data.append(data_to_append) 
                #sw_data is a list of dataframes in the order of the list of files
            
            if inputs['protein_wells'][protein][0] not in range(1,25): #make sure not a two digit number
                if len(str(inputs['protein_wells'][protein][0]))==2 or 3: # is it e.g. 'A1' or 'A11'
                
                    if len(inputs['protein_wells'][protein]) == len(inputs['ligand_order']):  
                        pass

                    else:
                        print('***To define individual wells, you need to define the same number of wells as ligands.***')
                        break
            
            for i in range(0,len(inputs['ligand_order'])): # i defines ligand
                
                for j,protein_well_ID in enumerate(inputs['protein_wells'][protein]): # j defines protein
                
                    if str(protein_well_ID) in ALPHABET:
                        #print('Your ligands are in a row (1,2,3,4, etc. are different ligands)!')
                        protein_well = '%s%s'%(protein_well_ID,i)
                        buffer_well = '%s%s'%(inputs['buffer_wells'][protein][j],i)
                        
                        name = ""%s-%s-%s%s""%(protein,inputs['ligand_order'][i],protein_well,buffer_well)
                        
                    elif len(str(protein_well_ID)) == 2 or 3:
                        if inputs['protein_wells'][protein][0] in range(1,25): # make sure not a two digit number
                            pass
                        else:
                            protein_well = protein_well_ID                         
                            buffer_well = inputs['buffer_wells'][protein][j]
                        
                            name = ""%s-%s-%s%s""%(protein,inputs['ligand_order'][j],protein_well,buffer_well)
                        
                    else:
                        #print('Your ligands are in a column (A,B,C,D, etc. are different ligands)!')
                        protein_well = '%s%s'%(ALPHABET[i],protein_well_ID)
                        buffer_well = '%s%s'%(ALPHABET[i],inputs['buffer_wells'][protein][j])
                           
                        name = ""%s-%s-%s%s""%(protein,inputs['ligand_order'][i],protein_well,buffer_well)
                        #print(name)

                    protein_data = []
                    buffer_data = []
                
                    for k in range(len(sw_data)): # k should define ligand concentration if your list of files was ordered correctly
                
                        # for spectra assays
                        if 'wavelength' in inputs:
                    
                            protein_data.append(float(platereader.select_data(sw_data[k], inputs['section'], [protein_well], 
                                                                    wavelength = '%s' %inputs['wavelength'])[protein_well]))
                            buffer_data.append(float(platereader.select_data(sw_data[k], inputs['section'], [buffer_well], 
                                                                    wavelength = '%s' %inputs['wavelength'])[buffer_well]))
        
                        # for single wavelength assays
                        else:
                
                            protein_data.append(float(platereader.select_data(sw_data[k], inputs['section'], [protein_well])[protein_well]))
                            buffer_data.append(float(platereader.select_data(sw_data[k], inputs['section'], [buffer_well])[buffer_well]))
    
                    complex_fluorescence[name] = np.asarray(protein_data)
                    ligand_fluorescence[name] = np.asarray(buffer_data)                 

        else:
            for file in inputs['file_set']['%s'%protein]:
                my_file.append(file)
                new_dict = platereader.read_icontrol_xml(file)
                for key in data:
                    try:
                        data[key].update(new_dict[key])
                    except:
                        pass
               
            # Are there any experiments the user wants to skip analyzing?
            skipped_experiments=[]
            for i, ligand in enumerate(inputs['ligand_order']):
                if ligand == None:
                    skipped_experiments.append(i*2)
                else:
                    continue

            skipped_rows=[]
            for i in skipped_experiments:
                skipped_rows.append(ALPHABET[i])
                skipped_rows.append(ALPHABET[i+1])
            if len(skipped_rows) != 0:
                print(""Skipping analysis of rows: "", skipped_rows)

            for i in range(0,len(inputs['ligand_order']*2),2):

                if i in skipped_experiments:
                    continue
                else:
                    protein_row = ALPHABET[i]
                    buffer_row = ALPHABET[i+1]

                    name = ""%s-%s-%s%s""%(protein,inputs['ligand_order'][int(i/2)],protein_row,buffer_row)
 
                    # for spectra assays
                    if 'wavelength' in inputs:
        
                        complex_fluorescence_data = platereader.select_data(data, inputs['section'], protein_row, wavelength = '%s' %inputs['wavelength'])
                        ligand_fluorescence_data = platereader.select_data(data, inputs['section'], buffer_row, wavelength = '%s' %inputs['wavelength'])

                    # for single wavelength assays
                    else:
                
                        complex_fluorescence_data = platereader.select_data(data, inputs['section'], protein_row)
                        ligand_fluorescence_data = platereader.select_data(data, inputs['section'], buffer_row)
                
                    complex_fluorescence[name] = reorder2list(complex_fluorescence_data,well)
                    ligand_fluorescence[name] = reorder2list(ligand_fluorescence_data,well)
            
    return [complex_fluorescence, ligand_fluorescence]
","Python"
"Assay","choderalab/assaytools","AssayTools/plots.py",".py","18954","447","#!/usr/bin/env python

""""""
Tools for assisting in plotting data.

""""""

#=============================================================================================
# Imports
#=============================================================================================

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns

#=============================================================================================
# Plot fluorescence.
#=============================================================================================

def plot_scans(SRC_280, SRC_280_x, SRC_280_x_num, figsize=(15,3)):
    """"""
    Plot fluorescence scans.

    Parameters
    ----------
    SRC_280 : numpy.array
    SRC_280_x : numpy.array
    SRC_280_x_num : numpy.array
    figsize : (x,y) tuple, optional, default=(10,5)
       Figure size (in inches).

    TODO
    ----
    * Refactor this to be more generally useful.

    """"""

    legend1=(SRC_280[1:,0])
    num_wells=len(legend1)

    plt.rcParams['figure.figsize'] = figsize

    for n in np.arange(num_wells)+1:
        plt.plot(SRC_280_x_num, SRC_280[n,1:])

    plt.axis([300, 800, 0, 5000]);
    plt.xlabel('emission wavelength (nm)');
    plt.ylabel('fluorescence units');
    return

def plot_measurements(Lstated, Pstated, pymc_model, map=None, mcmc=None, figsize=(15,3), subsample=10):
    """"""
    Plot pbserved fluorescence and absorbance.

    Parameters
    ----------
    Lstated : np.array
       Stated ligand concentrations (in M) corresponding to fluorescence measurements.
    Pstated : np.array
       Stated protein concentrations (in M) corresponding to fluorescence measurements.
    pymc_model : pymc model
       pymc model (created with pymcmodels.make_model) containing data
    map : pymc.MAP, optional, default=None
       If specified, will plot the MAP fit.
    mcmc : pymc.MCMC, optional, default=None
       If specified, will plot MCMC samples.
    figsize : (x,y) tuple, optional, default=(10,5)
       Figure size (in inches).
    subsample : int, optional, default=10
       For mcmc, degree to subsample trace data for plotting.

    Returns
    -------
    figure : matplotlib Figure
       The matplotlib-generated figure

    """"""

    # Determine how many plots we will generate
    nplots = 0
    if hasattr(pymc_model, 'top_complex_fluorescence') or hasattr(pymc_model, 'top_ligand_fluorescence'):
        nplots += 1
    if hasattr(pymc_model, 'bottom_complex_fluorescence') or hasattr(pymc_model, 'bottom_ligand_fluorescence'):
        nplots += 1
    if hasattr(pymc_model, 'ligand_ex_absorbance') or hasattr(pymc_model, 'ligand_em_absorbance'):
        nplots += 1

    figsize = (figsize[0], nplots*figsize[1])

    figure = plt.figure(figsize=figsize)
    plt.clf();
    plt.hold(True)

    # Plot sections.
    plot_index = 0
    if hasattr(pymc_model, 'top_complex_fluorescence') or hasattr(pymc_model, 'top_ligand_fluorescence'):
        subplot = plt.subplot(nplots, 1, plot_index+1)
        legend = list()

        property_name = 'top_complex_fluorescence'
        if hasattr(pymc_model, property_name):
            property = getattr(pymc_model, property_name)
            plt.semilogx(Lstated, property.value, 'ko');
            legend.append('complex');

        property_name = 'top_ligand_fluorescence'
        if hasattr(pymc_model, property_name):
            property = getattr(pymc_model, property_name)
            plt.semilogx(Lstated, property.value, 'ro');
            legend.append('ligand');

        if map and hasattr(pymc_model, 'top_complex_fluorescence'):
            plt.semilogx(Lstated, map.top_complex_fluorescence_model.value, 'k-')

        if map and hasattr(pymc_model, 'top_ligand_fluorescence'):
            plt.semilogx(Lstated, map.top_ligand_fluorescence_model.value, 'r-')

        if mcmc and hasattr(pymc_model, 'top_complex_fluorescence'):
            for top_complex_fluorescence_model in mcmc.top_complex_fluorescence_model.trace()[::subsample]:
                plt.semilogx(Lstated, top_complex_fluorescence_model, 'k:')

        if mcmc and hasattr(pymc_model, 'top_ligand_fluorescence'):
            for top_ligand_fluorescence_model in mcmc.top_ligand_fluorescence_model.trace()[::subsample]:
                plt.semilogx(Lstated, top_ligand_fluorescence_model, 'r:')

        plt.xlabel('$[L]_T$ (M)');
        plt.ylabel('fluorescence units');
        plt.title('top fluorescence');
        plt.legend(legend, loc='best');

        plot_index += 1

    if hasattr(pymc_model, 'bottom_complex_fluorescence') or hasattr(pymc_model, 'bottom_ligand_fluorescence'):
        subplot = plt.subplot(nplots, 1, plot_index+1)
        legend = list()

        property_name = 'bottom_complex_fluorescence'
        if hasattr(pymc_model, property_name):
            property = getattr(pymc_model, property_name)
            plt.semilogx(Lstated, property.value, 'ko');
            legend.append('complex');

        property_name = 'bottom_ligand_fluorescence'
        if hasattr(pymc_model, property_name):
            property = getattr(pymc_model, property_name)
            plt.semilogx(Lstated, property.value, 'ro');
            legend.append('ligand');

        if map and hasattr(map, 'bottom_complex_fluorescence_model'):
            plt.semilogx(Lstated, map.bottom_complex_fluorescence_model.value, 'k-')

        if map and hasattr(map, 'bottom_ligand_fluorescence_model'):
            plt.semilogx(Lstated, map.bottom_ligand_fluorescence_model.value, 'r-')

        if mcmc and hasattr(mcmc, 'bottom_complex_fluorescence_model'):
            for bottom_complex_fluorescence_model in mcmc.bottom_complex_fluorescence_model.trace()[::subsample]:
                plt.semilogx(Lstated, bottom_complex_fluorescence_model, 'k:')

        if mcmc and hasattr(mcmc, 'bottom_ligand_fluorescence_model'):
            for bottom_ligand_fluorescence_model in mcmc.bottom_ligand_fluorescence_model.trace()[::subsample]:
                plt.semilogx(Lstated, bottom_ligand_fluorescence_model, 'r:')

        plt.xlabel('$[L]_T$ (M)');
        plt.ylabel('fluorescence units');
        plt.title('bottom fluorescence');
        plt.legend(legend, loc='best');

        plot_index += 1

    if hasattr(pymc_model, 'ligand_ex_absorbance') or hasattr(pymc_model, 'ligand_em_absorbance'):
        subplot = plt.subplot(nplots, 1, plot_index+1)
        legend = list()

        property_name = 'ligand_ex_absorbance'
        if hasattr(pymc_model, property_name):
            property = getattr(pymc_model, property_name)
            plt.semilogx(Lstated, property.value, 'ko');
            legend.append('excitation');

        property_name = 'ligand_em_absorbance'
        if hasattr(pymc_model, property_name):
            property = getattr(pymc_model, property_name)
            plt.semilogx(Lstated, property.value, 'ro');
            legend.append('emission');

        if map and hasattr(pymc_model, 'ligand_ex_absorbance'):
            plt.semilogx(Lstated, map.ligand_ex_absorbance_model.value, 'k-')

        if map and hasattr(pymc_model, 'ligand_em_absorbance'):
            plt.semilogx(Lstated, map.ligand_em_absorbance_model.value, 'r-')

        if mcmc and hasattr(pymc_model, 'ligand_ex_absorbance'):
            for ligand_ex_absorbance_model in mcmc.ligand_ex_absorbance_model.trace()[::subsample]:
                plt.semilogx(Lstated, ligand_ex_absorbance_model, 'k:')

        if mcmc and hasattr(pymc_model, 'ligand_em_absorbance'):
            for ligand_em_absorbance_model in mcmc.ligand_em_absorbance_model.trace()[::subsample]:
                plt.semilogx(Lstated, ligand_em_absorbance_model, 'r:')

        plt.xlabel('$[L]_T$ (M)');
        plt.ylabel('absorbance');
        plt.title('ligand absorbance');
        plt.legend(legend, loc='best');
        oldaxis = plt.axis();
        plt.axis([oldaxis[0], oldaxis[1], 0, oldaxis[3]]);

        plot_index += 1

    return figure

def plot_mcmc_results(Lstated, Pstated, path_length, mcmc, subsample=10, figsize=(15,3)):
    """"""
    Plot the results of MCMC sampling.

    Parameters
    ----------
    Lstated : np.array
       Stated ligand concentrations (in M) corresponding to fluorescence measurements.
    Pstated : np.array
       Stated protein concentrations (in M) corresponding to fluorescence measurements.
    path_length : float
       The liquid height in the plate (in cm).
    mcmc : pymc.MCMC object
       The results of an MCMC simulation.
    figsize : (x,y) in inches, optional, default=(15,3)
       The figure size for each subplot.

    """"""

    from assaytools import pymcmodels

    # Determine how many samples we ended up with
    nsamples = mcmc.DeltaG.trace().size

    # Set figure size.
    plt.rcParams['figure.figsize'] = figsize

    # Determine which geometries we are observing.
    top_fluorescence = hasattr(mcmc, 'top_complex_fluorescence') or hasattr(mcmc, 'top_ligand_fluorescence')
    bottom_fluorescence = hasattr(mcmc, 'bottom_complex_fluorescence') or hasattr(mcmc, 'bottom_ligand_fluorescence')

    # Plot trace of DeltaG.
    figure = plt.figure(figsize=figsize);
    from assaytools.pymcmodels import DG_min, DG_max
    plt.plot(mcmc.DeltaG.trace(), 'o');
    plt.xlabel('MCMC sample');
    plt.ylabel('$\Delta G$ ($k_B T$)');
    plt.axis([0, nsamples, DG_min, DG_max]);

    # Plot histogram of DeltaG.
    figure = plt.figure(figsize=figsize);
    plt.hold(True)
    nbins = 40
    [N,x,patches] = plt.hist(mcmc.DeltaG.trace(), nbins);
    plt.hist(mcmc.DeltaG.trace(), nbins);
    plt.xlabel('$\Delta G$ ($k_B T$)');
    plt.ylabel('$P(\Delta G)$');
    plt.axis([DG_min, DG_max, 0, N.max()]);
    plt.title('histogram of estimates for binding free energy');
    DGmean = mcmc.DeltaG.trace().mean()
    plt.plot([DGmean, DGmean], [0, N.max()], 'r-');

    # Plot trace of true protein concentration.
    figure = plt.figure(figsize=figsize)
    plt.plot(mcmc.Ptrue.trace()*1e6, 'o');
    plt.xlabel('MCMC sample');
    plt.ylabel('$[P]$ ($\mu$M)');
    plt.title('Trace of true protein concentrations');

    # Plot trace of true protein concentration.
    figure = plt.figure(figsize=figsize)
    X = mcmc.Ptrue.trace() * 1e6;
    plt.hist(X.reshape(X.size,1), 40);
    plt.xlabel('$[P]$ ($\mu$M)');
    plt.ylabel('frequency');
    plt.title('Histogram of true protein concentrations');

    # Plot trace of true ligand concentration.
    figure = plt.figure(figsize=figsize)
    plt.semilogy(mcmc.Ltrue.trace()*1e6, 'o');
    plt.xlabel('MCMC sample');
    plt.ylabel('$[L]$ ($\mu$M)');
    plt.title('Trace of true ligand concentrations in complex experiment');

    # Plot histograms of true ligand concentration.
    figure = plt.figure(figsize=figsize)
    X = np.log10(mcmc.Ltrue.trace())
    plt.clf();
    plt.hold(True);
    for i in range(X.shape[1]):
        [N,x,patches] = plt.hist(X[:,i], histtype='stepfilled');
        plt.hist(X[:,i], histtype='stepfilled');
        plt.plot(np.log10(Lstated[i])*np.array([1,1]), [0, N.max()], 'k-', linewidth=3);
    plt.hold(False);
    plt.xlabel('$\log_{10} ([L]/M)$');
    plt.ylabel('frequency');
    plt.title('Ligand true concentrations for complex experiment');

    # Plot histograms of true ligand concentration.
    figure = plt.figure(figsize=figsize)
    X = np.log10(mcmc.Ltrue_control.trace())
    plt.clf()
    plt.hold(True)
    for i in range(X.shape[1]):
        [N,x,patches] = plt.hist(X[:,i], histtype='stepfilled');
        plt.hist(X[:,i], histtype='stepfilled');
        plt.plot(np.log10(Lstated[i])*np.array([1,1]), [0, N.max()], 'k-', linewidth=3);
    plt.hold(False)
    plt.xlabel('$\log_{10} ([L]/M)$');
    plt.ylabel('frequency');
    plt.title('Ligand true concentrations for ligand-only experiment');

    # Plot trace of intrinsic fluorescence parameters.
    figure = plt.figure(figsize=figsize)
    plt.semilogy(mcmc.F_PL.trace(), 'o', mcmc.F_L.trace(), 'o', mcmc.F_P.trace(), 'o', mcmc.F_buffer.trace(), 'o', mcmc.F_plate.trace(), 'o');
    plt.legend(['complex fluorescence', 'ligand fluorescence', 'protein fluorescence', 'buffer fluorescence', 'plate fluorescence']);
    plt.xlabel('MCMC sample');
    plt.ylabel('relative fluorescence intensity');
    plt.title('trace of relative fluorescence intensities of various species');

    # Plot trace of intrinsic fluorescence parameters.
    figure = plt.figure(figsize=figsize)
    plt.hold(True);
    nbins = 40;
    print(mcmc.F_PL.trace().shape)
    plt.hist(np.log10(mcmc.F_PL.trace()), nbins, histtype='stepfilled');
    plt.hist(np.log10(mcmc.F_L.trace()), nbins, histtype='stepfilled');
    plt.hist(np.log10(mcmc.F_P.trace()), nbins, histtype='stepfilled');
    plt.hist(np.log10(mcmc.F_buffer.trace()), nbins, histtype='stepfilled');
    plt.hist(np.log10(mcmc.F_plate.trace()), nbins, histtype='stepfilled');
    plt.hold(False);
    plt.legend(['complex fluorescence', 'ligand fluorescence', 'protein fluorescence', 'buffer fluorescence', 'plate fluorescence']);
    plt.xlabel('relative fluorescence intensity $log_{10} F$ ');
    plt.ylabel('frequency');
    plt.title('histogram of relative fluorescence intensities of various species');

    # Plot trace of measurement error.
    figure = plt.figure(figsize=figsize)
    plt.hold(True)
    legend = list()
    if hasattr(mcmc, 'sigma_top'):
        plt.semilogy(mcmc.sigma_top.trace(), 'ro');
        legend.append('top')
    if hasattr(mcmc, 'sigma_bottom'):
        plt.semilogy(mcmc.sigma_bottom.trace(), 'kv');
        legend.append('bottom')
    plt.xlabel('MCMC sample');
    plt.ylabel('fluorescence measurement error');
    plt.legend(legend);

    if hasattr(mcmc, 'sigma_abs'):
        # Plot trace of absorbance measurement error.
        figure = plt.figure(figsize=figsize)
        plt.hold(True)
        plt.plot(mcmc.sigma_abs.trace(), 'ro');
        plt.xlabel('MCMC sample');
        plt.ylabel('absorbance measurement error');

    if hasattr(mcmc, 'epsilon_ex') or hasattr(mcmc, 'epsilon_em'):
        # Plot extinction coefficient trace
        figure = plt.figure(figsize=figsize)
        plt.hold(True);
        legend = list()
        if hasattr(mcmc, 'epsilon_ex'):
            plt.plot(mcmc.epsilon_ex.trace() / 1e3, 'ko');
            legend.append('$\epsilon_{ex}$')
        if hasattr(mcmc, 'epsilon_em'):
            plt.plot(mcmc.epsilon_em.trace() / 1e3, 'ro');
            legend.append('$\epsilon_{em}$')
        plt.legend(legend);
        plt.xlabel('MCMC sample');
        plt.ylabel('ligand extinction coefficient $\epsilon / 10^3$ (1/M/cm)');
        plt.title('trace of ligand extinction coefficient $\epsilon$');

        # Plot extinction coefficient histogram
        figure = plt.figure(figsize=figsize)
        plt.hold(True);
        legend = list()
        if hasattr(mcmc, 'epsilon_ex'):
            plt.hist(mcmc.epsilon_ex.trace() / 1e3, nbins, histtype='stepfilled');
            legend.append('$\epsilon_{ex}$')
        if hasattr(mcmc, 'epsilon_em'):
            plt.hist(mcmc.epsilon_em.trace() / 1e3, nbins, histtype='stepfilled');
            legend.append('$\epsilon_{em}$')
        plt.xlabel('ligand extinction coefficient $\epsilon / 10^3$ (1/M/cm)');
        plt.ylabel('frequency');
        plt.title('histogram of ligand extinction coefficient $\epsilon$');
        plt.legend(legend);

        # Plot top inner filter effect
        # TODO: Fix broken legend.
        if top_fluorescence:
            figure = plt.figure(figsize=figsize)
            plt.clf();
            plt.hold(True)
            legend = list()
            if hasattr(mcmc, 'epsilon_ex'):
                legend.append('primary')
                for (Ltrue, epsilon_ex) in zip(mcmc.Ltrue.trace()[::subsample], mcmc.epsilon_ex.trace()[::subsample]):
                    primary_scaling = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, 0.0, path_length, Ltrue, geometry='top')
                    plt.semilogx(Lstated, primary_scaling, 'r-');
            if hasattr(mcmc, 'epsilon_em'):
                legend.append('secondary')
                for (Ltrue, epsilon_em) in zip(mcmc.Ltrue.trace()[::subsample], mcmc.epsilon_em.trace()[::subsample]):
                    secondary_scaling = pymcmodels.inner_filter_effect_attenuation(0.0, epsilon_em, path_length, Ltrue, geometry='top')
                    plt.semilogx(Lstated, secondary_scaling, 'b-');
            if hasattr(mcmc, 'epsilon_ex') and hasattr(mcmc, 'epsilon_em'):
                legend.append('total')
                for (Ltrue, epsilon_ex, epsilon_em) in zip(mcmc.Ltrue.trace()[::subsample], mcmc.epsilon_ex.trace()[::subsample], mcmc.epsilon_em.trace()[::subsample]):
                    scaling = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue, geometry='top')
                    plt.semilogx(Lstated, scaling, 'k-');
            plt.hold(False)
            plt.xlabel('stated $[L]_s$ (M)');
            plt.ylabel('attenuation');
            plt.title('top inner filter effect attenuation');
            plt.axis([Lstated.min(), Lstated.max(), 0, 1]);
            plt.legend(legend, loc='best');

        # Plot bottom inner filter effect
        if bottom_fluorescence:
            figure = plt.figure(figsize=figsize)
            plt.clf();
            plt.hold(True)
            legend = list()
            if hasattr(mcmc, 'epsilon_ex'):
                legend.append('primary')
                for (Ltrue, epsilon_ex) in zip(mcmc.Ltrue.trace()[::subsample], mcmc.epsilon_ex.trace()[::subsample]):
                    primary_scaling = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, 0.0, path_length, Ltrue, geometry='bottom')
                    plt.semilogx(Lstated, primary_scaling, 'r-');
            if hasattr(mcmc, 'epsilon_em'):
                legend.append('secondary')
                for (Ltrue, epsilon_em) in zip(mcmc.Ltrue.trace()[::subsample], mcmc.epsilon_em.trace()[::subsample]):
                    secondary_scaling = pymcmodels.inner_filter_effect_attenuation(0.0, epsilon_em, path_length, Ltrue, geometry='bottom')
                    plt.semilogx(Lstated, secondary_scaling, 'b-');
            if hasattr(mcmc, 'epsilon_ex') and hasattr(mcmc, 'epsilon_em'):
                legend.append('total')
                for (Ltrue, epsilon_ex, epsilon_em) in zip(mcmc.Ltrue.trace()[::subsample], mcmc.epsilon_ex.trace()[::subsample], mcmc.epsilon_em.trace()[::subsample]):
                    scaling = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue, geometry='bottom')
                    plt.semilogx(Lstated, scaling, 'k-');
            plt.hold(False)
            plt.xlabel('stated $[L]_s$ (M)');
            plt.ylabel('attenuation');
            plt.title('bottom inner filter effect attenuation');
            plt.axis([Lstated.min(), Lstated.max(), 0, 1]);
            plt.legend(legend, loc='best');
","Python"
"Assay","choderalab/assaytools","AssayTools/tests/__init__.py",".py","0","0","","Python"
"Assay","choderalab/assaytools","AssayTools/tests/test_bindingmodels.py",".py","3633","67","""""""
Test binding models
""""""


import numpy as np
from scipy.misc import logsumexp
import copy

def test_general_binding_model():
    """"""
    Test GeneralBindingModel with multiple parameter combinations.
    """"""
    from assaytools.bindingmodels import GeneralBindingModel

    decimal = 7 # number of decimal places to expect agreement
    nsamples = 100

    #
    # Simple 1:1 binding
    #

    for sample in range(nsamples):
        log_affinity_true = np.random.uniform(-15, 0)
        log_Rtot_true = np.random.uniform(-15, 0)
        log_Ltot_true = np.random.uniform(-15, 0)

        reactions = [ (log_affinity_true, {'RL': -1, 'R' : +1, 'L' : +1}) ]
        conservation_equations = [ (log_Rtot_true, {'RL' : +1, 'R' : +1}), (log_Ltot_true, {'RL' : +1, 'L' : +1}) ]
        log_concentrations = GeneralBindingModel.equilibrium_concentrations(reactions, conservation_equations)

        log_affinity = (log_concentrations['R'] + log_concentrations['L'] - log_concentrations['RL'])
        np.testing.assert_almost_equal(log_affinity, log_affinity_true, decimal=decimal, err_msg=""log_affinity was %f, expected %f."" % (log_affinity, log_affinity_true))
        log_Rtot = logsumexp([log_concentrations['R'], log_concentrations['RL']])
        np.testing.assert_almost_equal(log_Rtot, log_Rtot_true, decimal=decimal, err_msg=""log [R]tot was %f, expected %f"" % (log_Rtot, log_Rtot_true))
        log_Ltot = logsumexp([log_concentrations['L'], log_concentrations['RL']])
        np.testing.assert_almost_equal(log_Ltot, log_Ltot_true, decimal=decimal, err_msg=""log [L]tot was %f, expected %f"" % (log_Ltot, log_Ltot_true))

    #
    # Competitive binding
    #
    for sample in range(nsamples):
        log_K_RL_true = np.random.uniform(-15, 0)
        log_K_RP_true = np.random.uniform(-15, 0)
        log_Rtot_true = np.random.uniform(-15, 0)
        log_Ltot_true = np.random.uniform(-15, 0)
        log_Ptot_true = np.random.uniform(-15, 0)

        reactions = [ (log_K_RL_true, {'RL': -1, 'R' : +1, 'L' : +1}), (log_K_RP_true, {'RP':-1, 'R' : +1, 'P' : +1}) ]
        conservation_equations = [ (log_Rtot_true, {'RL' : +1, 'RP' : +1, 'R' : +1}), (log_Ltot_true, {'RL' : +1, 'L' : +1}), (log_Ptot_true, {'RP' : +1, 'P' : +1}) ]
        log_concentrations = GeneralBindingModel.equilibrium_concentrations(reactions, conservation_equations)

        log_K_RL = (log_concentrations['R'] + log_concentrations['L'] - log_concentrations['RL'])
        np.testing.assert_almost_equal(log_K_RL, log_K_RL_true, decimal=decimal, err_msg=""log_K_RL was %f, expected %f."" % (log_K_RL, log_K_RL_true))
        log_K_RP = (log_concentrations['R'] + log_concentrations['P'] - log_concentrations['RP'])
        np.testing.assert_almost_equal(log_K_RP, log_K_RP_true, decimal=decimal, err_msg=""log_K_RP was %f, expected %f."" % (log_K_RP, log_K_RP_true))
        log_Rtot = logsumexp([log_concentrations['R'], log_concentrations['RL'], log_concentrations['RP']])
        np.testing.assert_almost_equal(log_Rtot, log_Rtot_true, decimal=decimal, err_msg=""log [R]tot was %f, expected %f"" % (log_Rtot, log_Rtot_true))
        log_Ltot = logsumexp([log_concentrations['L'], log_concentrations['RL']])
        np.testing.assert_almost_equal(log_Ltot, log_Ltot_true, decimal=decimal, err_msg=""log [L]tot was %f, expected %f"" % (log_Ltot, log_Ltot_true))
        log_Ptot = logsumexp([log_concentrations['P'], log_concentrations['RP']])
        np.testing.assert_almost_equal(log_Ptot, log_Ptot_true, decimal=decimal, err_msg=""log [P]tot was %f, expected %f"" % (log_Ptot, log_Ptot_true))


if __name__ == ""__main__"":
   test_general_binding_model()
","Python"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/1a-modelling-CompetitiveBinding-ThreeComponentBinding.ipynb",".ipynb","147056","1151","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""##Modeling Competitive Binding\n"",
    ""\n"",
    ""We will model binding of two ligands, one is fluorescent (L), the other competing ligand (A) is not. Kd of both of their binding to protein (P) are known. \n"",
    ""\n"",
    ""Complex concentration and fluorescence of the complex are assumed to be directly related.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    }
   ],
   ""source"": [
    ""import matplotlib.pyplot as plt\n"",
    ""import numpy as np\n"",
    ""import seaborn as sns\n"",
    ""from IPython.display import display, Math, Latex #Do we even need this anymore?\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Strictly Competitive Binding Model""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$L + P \\underset{k_dL}{\\stackrel{k_L}{\\rightleftharpoons}} PL$$\n"",
    ""\n"",
    ""$$A + P \\underset{k_dA}{\\stackrel{k_A}{\\rightleftharpoons}} PA$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": true
   },
   ""source"": [
    ""### Modelling the competition experiment - Expected Binding Curve\n"",
    ""P: protein (HSA)\n"",
    ""\n"",
    ""L: tracer ligand (dansyl amide), \n"",
    ""\n"",
    ""A: non-fluorescent ligand (phenylbutazone)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Dissociation constant for fluorescent ligand L: K_dL (uM)\n"",
    ""Kd_L = 15""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Ideally protein concentration should be 10 fold lower then Kd. It must be at least hafl of Kd if fluorescence detection requires higher protein concentration.\n"",
    ""\n"",
    ""For our assay it will be 0.5 uM.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Total protein (uM)\n"",
    ""Ptot = 0.5""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Ideally ligand concentation should span 100-fold Kd to 0.01-fold Kd, log dilution.\n"",
    ""\n"",
    ""The ligand concentration will be in half log dilution from 20 uM ligand.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([  2.00000000e+03,   7.02238347e+02,   2.46569348e+02,\n"",
       ""         8.65752256e+01,   3.03982217e+01,   1.06733985e+01,\n"",
       ""         3.74763485e+00,   1.31586645e+00,   4.62025940e-01,\n"",
       ""         1.62226166e-01,   5.69607174e-02,   2.00000000e-02])""
      ]
     },
     ""execution_count"": 4,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""num_wells = 12.0\n"",
    ""# (uM)\n"",
    ""Lmax = 2000\n"",
    ""Lmin = 0.02\n"",
    ""# Factor for logarithmic dilution (n)\n"",
    ""# Lmax*((1/n)^(11)) = Lmin for 12 wells\n"",
    ""n = (Lmax/Lmin)**(1/(num_wells-1))\n"",
    ""\n"",
    ""# Ligand titration series (uM)\n"",
    ""Ltot = Lmax / np.array([n**(float(i)) for i in range(12)])\n"",
    ""Ltot""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Dissociation constant for non-fluorescent ligand A: K_dA (uM)\n"",
    ""Kd_A = 3.85""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Constant concentration of A will be added to all wells (uM)\n"",
    ""Atot= 50""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""For the assumption that free $[A] = A_{tot}$, it is required that $[A] >> [P]$.\n"",
    ""Ligand depletion can cause additional shift of $ K_{dL,app}$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""---""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""If competitive ligand is not present ($[A] = A_{tot} = 0$). Wel can calculate [PL] as a function of Ptot, Ltot, and Kd as follows:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$[PL] = \\frac{[L][P]}{K_{d} }$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Then we need to put L and P in terms of Ltot and Ptot, using""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$[L] = [Ltot]-[PL]$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$[P] = [Ptot]-[PL]$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""This gives us:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$[PL] = \\frac{([Ltot]-[PL])([Ptot]-[PL])}{K_{d} }$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Solving this for 0 you get:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$0 = [PL]^2 - [PL]([Ptot]+[Ltot]+K_{d}) + [Ptot][Ltot]$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Using the quadratic equation:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a}$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""where x is $[PL]$, a is 1, $-([Ptot]+[Ltot]+Kd)$ is b, and $[Ptot][Ltot]$ is c. We get as the only reasonable solution:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$[PL] = \\frac{([Ptot] + [Ltot] + K_{d}) - \\sqrt{([Ptot] + [Ltot] + K_{d})^2 - 4[Ptot][Ltot]}}{2}$$ ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""In the presence of non-zero concentration of A -a strictly competitive ligand for the same binding site- titration curve will be shifted. Half maximum saturation point gives us apparent dissociation constant for L ( $K_{dL,app}$). This shift is dependent of $[A]$ and $K_{dA}$.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$K_{dL,app} = K_{dL}(1+\\frac{[A]}{K_{dA}})$$ ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$[PL] = \\frac{([Ptot] + [Ltot] + K_{dL,app}) - \\sqrt{([Ptot] + [Ltot] + K_{dL,app})^2 - 4[Ptot][Ltot]}}{2}$$ ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Based on Hulme EC, Trevethick MA. Ligand binding assays at equilibrium: validation\n"",
    ""and interpretation. Br J Pharmacol. 2010;161(6):1219–37. [doi: 10.1111/j.1476-5381.2009.00604.x.](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3000649/pdf/bph0161-1219.pdf)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Competitive binding function\n"",
    ""def three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total tracer(fluorescent) ligand concentration\n"",
    ""    Kd_L : float\n"",
    ""        Dissociation constant\n"",
    ""    Atot : float\n"",
    ""        Total competitive ligand concentration\n"",
    ""    Kd_A : float\n"",
    ""        Dissociation constant\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    A : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    Kd_L_app : float\n"",
    ""        Apparent dissociation constant of L in the presence of A\n"",
    ""        \n"",
    ""    Usage\n"",
    ""    -----\n"",
    ""    [P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)\n"",
    ""    \""\""\""\n"",
    ""    Kd_L_app = Kd_L*(1+Atot/Kd_A)                                \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd_L_app) - np.sqrt((Ptot + Ltot + Kd_L_app)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free tracer ligand concentration in sample cell after n injections (uM)\n"",
    ""    A = Atot - PL; # free competitive ligand concentration in sample cell after n injections (uM)\n"",
    ""    return [P, L, A, PL, Kd_L_app]""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Without competitive ligand""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# If there is no competitive ligand \n"",
    ""Atot=0 #(uM)\n"",
    ""[P_A0, L_A0, A_A0, PL_A0, Kd_L_app_A0] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x1091c6bd0>""
      ]
     },
     ""execution_count"": 9,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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qfwQS6mY8cSqe5ikgN3cxH4mkEISIinnwbQTjnMoC5QB5QAVxgZps92s0HdpnZL/yqRURE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\niHj6P42pApyh0/JkAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10905f810>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,PL_A0, 'o')\n"",
    ""plt.xlabel('Ltot')\n"",
    ""plt.ylabel('[PL]')\n"",
    ""plt.ylim(1e-3,6e-1)\n"",
    ""plt.axhline(Ptot,color='0.75',linestyle='--',label='[Ptot]')\n"",
    ""plt.axvline(Kd_L,color='k',linestyle='--',label='Kd_L')\n"",
    ""plt.axvline(Kd_L_app_A0,color='r',linestyle='--',label='Kd_L_app without A')\n"",
    ""plt.legend()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### In the presence of 10 uM competitive ligand""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# If we change competitor concentration \n"",
    ""Atot=10 #(uM)\n"",
    ""[P_A10, L_A10, A_A10, PL_A10, Kd_L_app_A10] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x1097e6750>""
      ]
     },
     ""execution_count"": 11,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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3rFBAikpMmjelHYbijeg5p0GWuIpKTevUIt8s7m4NEASGSIUEai0kEdIhJRESaoIAQERFP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\nKSBERMSTAkJERDz9f1wmdNw/ogQOAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x108fce810>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""#plt.subplot(1,2,1)\n"",
    ""plt.semilogx(Ltot,PL_A10, 'o')\n"",
    ""plt.xlabel('Ltot')\n"",
    ""plt.ylabel('[PL]')\n"",
    ""plt.ylim(1e-3,6e-1)\n"",
    ""plt.axhline(Ptot,color='0.75',linestyle='--',label='[Ptot]')\n"",
    ""plt.axvline(Kd_L,color='k',linestyle='--',label='Kd_L')\n"",
    ""plt.axvline(Kd_L_app_A10,color='r',linestyle='--',label='Kd_L_app with 10 uM A')\n"",
    ""plt.legend()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### In the presence of 50 uM competitive ligand""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Competitor concentration \n"",
    ""Atot=50 #(uM)\n"",
    ""[P_A50, L_A50, A_A50, PL_A50, Kd_L_app_A50] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x109da5bd0>""
      ]
     },
     ""execution_count"": 13,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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xgHXABMJfI75lAaONn89zoY192zhWZ2dMt7S+tRhDOue8Bi4gM8SCmIyxQTKQj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\nk6vnEKySAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10906a350>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plotting complex concentration [PL] as a function of fluorescent ligand concentration Ltot¶\n"",
    ""plt.semilogx(Ltot,PL_A50, 'o')\n"",
    ""plt.xlabel('Ltot')\n"",
    ""plt.ylabel('[PL]')\n"",
    ""plt.ylim(1e-3,6e-1)\n"",
    ""plt.axhline(Ptot,color='0.75',linestyle='--',label='[Ptot]')\n"",
    ""plt.axvline(Kd_L,color='k',linestyle='--',label='Kd_L')\n"",
    ""plt.axvline(Kd_L_app_A50,color='r',linestyle='--',label='Kd_L_app with 50 uM A')\n"",
    ""plt.legend()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Predicting experimental fluorescence signal of saturation binding experiment""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Molar fluorescence values based on dansyl amide.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Background fluorescence\n"",
    ""BKG = 86.2\n"",
    ""\n"",
    ""# Molar fluorescence of free ligand\n"",
    ""MF = 2.5\n"",
    ""\n"",
    ""# Molar fluorescence of ligand in complex\n"",
    ""FR = 306.1\n"",
    ""MFC = FR * MF""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Fluorescent ligand (L) titration into buffer""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([  2.00000000e+03,   7.02238347e+02,   2.46569348e+02,\n"",
       ""         8.65752256e+01,   3.03982217e+01,   1.06733985e+01,\n"",
       ""         3.74763485e+00,   1.31586645e+00,   4.62025940e-01,\n"",
       ""         1.62226166e-01,   5.69607174e-02,   2.00000000e-02])""
      ]
     },
     ""execution_count"": 15,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Ltot""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([ 5086.2       ,  1841.79586711,   702.62336972,   302.63806405,\n"",
       ""         162.19555415,   112.88349616,    95.56908711,    89.48966612,\n"",
       ""          87.35506485,    86.60556542,    86.34240179,    86.25      ])""
      ]
     },
     ""execution_count"": 16,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# Fluorescence measurement of buffer + ligand L titrations\n"",
    ""L=Ltot\n"",
    ""Flu_buffer = MF*L + BKG\n"",
    ""Flu_buffer""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(50, 8000)""
      ]
     },
     ""execution_count"": 17,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": ""iVBORw0KGgoAAAANSUhEUgAAAZEAAAEWCAYAAACnlKo3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x109493d50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,Flu_buffer,'o')\n"",
    ""plt.xlabel('[L]')\n"",
    ""plt.ylabel('Fluorescence')\n"",
    ""plt.ylim(50,8000)\n"",
    ""#plt.legend()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Fluorescent ligand titration into HSA (without competitive ligand)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Atot=0 #(uM)\n"",
    ""[P_A0, L_A0, A_A0, PL_A0, Kd_L_app_A0] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([ 5464.73528075,  2215.18951449,  1062.08849907,   627.45685606,\n"",
       ""         416.62792621,   269.63738774,   170.20461403,   119.40258614,\n"",
       ""          98.40411325,    90.55707746,    87.73894389,    86.74148303])""
      ]
     },
     ""execution_count"": 19,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# Free ligand concentration in each well (uM)\n"",
    ""L=L_A0\n"",
    ""#complex concentration\n"",
    ""PL=PL_A0\n"",
    ""# Fluorescence measurement of the HSA + ligand measurements\n"",
    ""Flu_HSA = MF*L + BKG + FR*MF*PL\n"",
    ""Flu_HSA""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x10a46af90>""
      ]
     },
     ""execution_count"": 20,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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WRSZJUE0P2bCPit4APZeYnKHq4V0fEr1UemSRJNdHOaeRPA9cDZOZy4OKyTZIktaGdYvuy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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x109493d10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,Flu_buffer,'o',label='buffer')\n"",
    ""plt.semilogx(Ltot, Flu_HSA ,'o', label='protein')\n"",
    ""plt.xlabel('[L_tot]')\n"",
    ""plt.ylabel('Fluorescence')\n"",
    ""plt.ylim(50,8000)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x10a2acf90>""
      ]
     },
     ""execution_count"": 21,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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Yw5a37VeXDY19RR1GSJEmd0soM2e/joXC0A7AvxUzWazJzXV2FSZIkdcKU4SgzD6kuR8QT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\nklTxXzjTPQ51FGYQAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10a07cf10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,(Flu_HSA-Flu_buffer),'.', label=\""difference in fluorescence signal\"")\n"",
    ""plt.xlabel('[L_tot]')\n"",
    ""plt.ylabel('Flu_HSA-Flu_buffer')\n"",
    ""plt.ylim(0,500)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### Checking ligand depletion""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x10b830a50>""
      ]
     },
     ""execution_count"": 22,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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aelStX11jlYLVao/Y3xOxveJXcGxjkTY/sFlJNU54cEeP77o4Dzo+I2cCtwOKGa5IkFa7p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\nQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10905f850>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""L_percent_depletion=((Ltot-L_A0)/Ltot)*100\n"",
    ""plt.semilogx(Ltot,L_percent_depletion,'.',label='L')\n"",
    ""plt.xlabel('[L_tot]')\n"",
    ""plt.ylabel('% ligand depletion')\n"",
    ""plt.ylim(0,100)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""### Fluorescent ligand titration into HSA (with 10 uM competitive ligand)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""Atot=10 #(uM)\n"",
    ""[P_A10, L_A10, A_A10, PL_A10, Kd_L_app_A10] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Free ligand concentration in each well (uM)\n"",
    ""L=L_A10\n"",
    ""#complex concentration\n"",
    ""PL=PL_A10\n"",
    ""# Fluorescence measurement of the HSA + ligand measurements\n"",
    ""Flu_HSA = MF*L + BKG + FR*MF*PL""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x10b980810>""
      ]
     },
     ""execution_count"": 25,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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3KI5MkqSZGHNlGxO8B78/MT1CMcC+JiN+qPDJJkmqindPInwIuB8jMe4APlm2SJKkN7RTb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\nrkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10a480e90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,Flu_buffer,'o',label='buffer')\n"",
    ""plt.semilogx(Ltot, Flu_HSA ,'o', label='protein')\n"",
    ""plt.xlabel('[L_tot]')\n"",
    ""plt.ylabel('Fluorescence')\n"",
    ""plt.ylim(50,8000)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x10beca890>""
      ]
     },
     ""execution_count"": 26,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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VKJzNkv5uN4Wg7YD+qmayXZeaKpgqTJEnqhSnDUWYeWi5HxGOBDzRVkCRJUi9Ne8xRZv4I\neGIDtUiSJPVcJ2OOPlUsjgD7At+fZHdJkqSB1smYo+uoxhyN1K+fB77eZFGSJEm9Mmk4iog96rfX\nbrKpBewK/EdTRUmSJPVKu56jZUxwC3/hsTNciyRJUs9NGo4yc68u1iFJktQXJr1bLSKuLN4f0p1y\nJEmSeqvdrfy7Fu+d10iSJG0VfLaaJElSwXAkSZJUaHe32p4R8Umq+Y32KN4DtDLzNY1XJ0mS1GXt\nwtH/ZOOt/NcV70dof4u/JEnSwGp3K//Fm66LiKMy87JGK5IkSeqh6Y45Or2RKiRJkvqEA7IlTWrJ\n0uUsWbq812VIUldNNxx5SU3aSixZupwVK9ewYuUaA5Kkrcp0w9GZEfHqiLixkWokSZJ6rN3dag+I\niH2B1wGvAn6NM2ZLQ2/R/NkP9Bgtmj+7x9VIUvdMGo4iYnvgJVShaH/gcuA+4AmZ6a380lbAUCRp\na9TustrPgJcCHwR2ycyjgd8bjCRJ0jBrd1ntM8DLgL8AHhURl3anJEmSpN6ZtOcoM98CPA54P/Bc\n4CfALhHx0ojYtkv1SZIkdVXbAdmZuR74CvCViBgDXgksphqQvXvz5UmSJHVXR3erAWTmKqpepPdH\nxIHNlSRJktQ77e5WezDwHuBzmXlLRLwfOA64FXhFl+qTJEnqqnZ3q30Q2An4SUQcCRxNdUv/+cCH\nulCbJElS17ULR88AFmTmL4F5wOcz887M/BLwxK5UJ0mS1GXtxhytL+Y0mgucUmzbfqoDR8R2wCeB\nPYEdgDOAO4CLgQ3AbVThqxURxwHHA+uBMzLzimm2Q5IkaUa06zm6OyKeFhGHAbsBVwNExFzgpx0c\n+2hgVWYeDPwN8GHgPGBhvW4EmBcRuwJvBOZQTRlwVj07tyRJUte16zl6M/A5YBeqHp7fRsRiqiDz\ngg6O/QVgfOLIbYB1wAGZuaxedyVwBHA/cENmrgPWRcSdwH6AjwGXJEldN2k4yszvAftusvoS4ILM\nvHeqA2fmbwEiYpQqKL0TOLfYZS2wMzALuHeC9ZIkSV3X7lb+VwPlc9RG6tdWRJCZn5nq4BHxGOCL\nwIcz85KIeF+xeRZwD7AGGC3WjwKrpzr22NjoVLsMNNs32Gzf4BrmtknqTLvLanOpwtEIcBTVTNml\ntuEoInYBrgJOzMxr69XfjYhDMvM64HnANcAtwJKI2AHYkaq36rapCl+1au1UuwyssbFR2zfAbN/g\nGua2gcFP6lS7y2rHjL+PiO9m5rHTPPZCqstjp0bEqfW6k4AL6gHXtwOX1nerXQBcTzU2aWFm3jfN\nc0mSJM2Ijh8fMl2ZeRJVGNrUoRPsexFwUVO1SJIkdardrfySJElbnXYDsg9h45ijh0bE+NxELYDi\nlnxJkqSh0e6y2rvZeLfaynq5NLeRiiRJknqo3YDsQ6f6cEQcn5kfn9GKJEmSemhLxxy9fkaqkCRJ\n6hMOyJYkSSoYjiRJkgqGI0mSpILhSJIkqbCl4WjKB8RKkiQNkikfHxIRp22yanwSyNMz87BGqpIk\nSeqRTnqORor32wPzgF2aKUeSJKm3puw5ysx3lcsRcTpwdVMFSZIk9dLmjDkaBR4z04VIkiT1g07G\nHP2oWBwBHgac01hFkiRJPTRlOKJ6wGyLKhhtAO7JzDWNViVJktQjk4ajiHg19Z1pVMGoVWwjMz/T\ncG2SJEld167naD7w0zbbDUeSJGnotAtHj8jMwwEi4i2ZeW6XapIkSeqZTu9WO7rRKiRJkvqEz1aT\nJEkqGI4kSZIK7cYcPbmY42i3TeY7amXm4xqsS5IkqSfahaN9ulaFpGlbsnQ5AIvmz+5xJZI0XCYN\nR5n54y7WIWkalixdzoqVax54b0CSpJnjmCNJkqSC4UgaQIvmz2bv3Wex9+6z7DWSpBnWybPVJPUh\nQ5EkNcOeI0mSpILhSJIkqWA4kiRJKhiOJEmSCoYjSZKkguFIkiSpYDiSJEkqGI4kSZIKhiNJkqRC\n4zNkR8TTgLMzc25EPB64GNgA3AYsyMxWRBwHHA+sB87IzCuarkuSJGkijfYcRcQpwIXADvWq84GF\nmXkwMALMi4hdgTcCc4DnAmdFxPZN1iVJkjSZpi+r3Qm8iCoIARyQmcvq91cChwMHATdk5rrMXFN/\nZr+G65IkSZpQo+EoM79Idals3Ejxfi2wMzALuHeC9ZIkSV3X+JijTWwo3s8C7gHWAKPF+lFg9VQH\nGhsbnWqXgWb7BpvtG1zD3DZJnel2OPpuRBySmdcBzwOuAW4BlkTEDsCOwL5Ug7XbWrVqbaOF9tLY\n2KjtG2C2b3ANc9vA4Cd1qlvhqFW/ngxcWA+4vh24tL5b7QLgeqrLfAsz874u1SVJkvQnRlqt1tR7\n9Z/WsP/vzvYNLts3uIa5bQBjY6MjU+8lyUkgJUmSCoYjSZKkguFIkiSpYDiSJEkqGI4kSZIKhiNJ\nkqSC4UiSJKlgOJIkSSoYjiRJkgqGI0mSpILhSJIkqWA4kmbYWy9YxpKly3tdhiRpMxmOpBm0ZOly\n/u0nq1mxco0BSZIGlOFIkiSpYDiSZtCi+bN54p4PY+/dZ7Fo/uxelyNJ2gwP6nUB0rA5500Hs2rV\n2l6XIUnaTPYcSZIkFQxHkiRJBcORJElSwXAkSZJUMBxJkiQVDEeSJEkFw5EkSVLBcCRJklQwHEmS\nJBUMR5IkSQXDkSRJUsFwJEmSVDAcaauyZOlylixd3usyJEl9zHCkrcaSpctZsXINK1auMSBJkiZl\nOJIkSSoYjrTVWDR/NnvvPou9d5/Fovmze12OJKlPPajXBUjdZCiSJE3FniNJkqSC4UiSJKlgOFLf\n8DZ7SVI/6JsxRxGxDfAPwH7AH4HXZuaK3lalbhm/zX78vWODJEm90k89Ry8Ets/MOcDbgfN6XI9q\n9uhIkrYm/RSOngn8C0BmfgsY+K6DboSKps/RrYkTvc1ektQv+ikczQLWFMv315faZly3QkvToWLY\nZnxeNH+2wUiS1HP9FI7WAKPF8jaZuWGmTzJsgaJp9uhIkrY2I61Wq9c1ABARLwKOysxjI+LpwOLM\nfP5Mn+eok798I/CMevGmy86bN2emz7HJuRj0c0iStDXpp3A0wsa71QCOzcx/72FJkiRpK9Q34UiS\nJKkf9NOYI0mSpJ4zHEmSJBUMR5IkSQXDkSRJUqFvnq22pSLi2cDLgQcD78vM7/W4pBkXEYcBr8jM\n43pdy0yKiDnA8fXiSZl5by/racIQf++G+ucuIg4E3gCMAKdk5q96XNKMi4hdgMsz86Be1yL1i2Hq\nOdopM48HzgWO6HUxMy0i9gb2B3bsdS0NOI4qHH2C6hftUBny791Q/9wBOwD/A7iCjfOjDY16CpW3\nAj/ucSlSXxmacJSZl0fEQ4A3ARf3uJwZl5krMvP8XtfRkG0z8z7gF8Cje13MTBvm791W8HN3I/Ak\n4C3Av/a4nCacAHwW+EOvC5H6SV9fVouIpwFnZ+bc+jlr45NE/hF4bWauiIj3AI8HTgLOBk7NzLt6\nVvQ0TLN9r8/Me3pY7mbppI3A7yJie2A34D97V+30ddi+gdTh389HAu9jgH7uxnXYvoOA5cDzgNOo\n/p0ZCB3+3Ty8XvfXEfHizPxfvatY6h9923MUEacAF1J1awO8ENg+M+cAbwfOA8jMxZn5CuAcYBfg\nrIh4cQ9Knpbptm9Ag1FHbQQ+DnyM6vLa0m7Xubmm0b6BM422nccA/dyNm0b7Hgp8kurfl3/sdp2b\naxr/vrw4M18PfMtgJG3Uzz1HdwIvYuMvy2cB/wKQmd+KiD95Cmpmvrq75W2xabVvXGbO7055M6Kj\nNmbmrcCxPalwy0z37+gwfu8G7eduXKftuxa4ticVbpnp/t18VXfLk/pb3/YcZeYXgfXFqlFgTbF8\nf91VPJCGvX0w/G0c5vYNc9vA9jHg7ZOaNkg/HGuofsDHbZOZG3pVTAOGvX0w/G0c5vYNc9vA9kkq\nDFI4ugE4EiAing4M1XwqDH/7YPjbOMztG+a2ge2TVOjnMUfjWvXr/waeExE31MuDOEZlIsPePhj+\nNg5z+4a5bWD7JE1gpNVqTb2XJEnSVmKQLqtJkiQ1znAkSZJUMBxJkiQVDEeSJEkFw5EkSVLBcCRJ\nklQwHEmSJBUMR5IkSYVBmCFb6rqIOBS4HLgJeHxmPnYan/0UcGpm/rTNPsdTPe/qFuCLwH6Z6X9W\nJKkP+I+xNLlbgNduxucOZeqfrTnADpn5w8zcfzPOIUlqiD1H0uRGpvuBiHg7sBtwRUQcDOwDfADY\nEbgLeB3wWOAoYG5E/Dwzr565kiVJW8qeI2kGZebZwM+pnoD+G+ASYEHdO/RR4JLM/DrwFWCxwUiS\n+o/hSGrOPsDqzPwOQGZeCjw+ImbV26fdMyVJap7hSGrORD9fI8C29ftWF2uRJHXIcCTNvPXAdkAC\nj4iI2QAR8TLgx5m5uthHktRnHJAtTW2PiFhbLC/LzOe32f9y4KvAEcDLgQ9FxEOAu+tlgK8DZ0bE\n6sz8YhNFS5I2z0irZc++tKl6nqPTMnNul863wXmOJKk/2HMkTawFzI6IqzLziHJDROwE3DjJ5xZn\n5uWdniQiHkc1CaT/S5GkPmHPkSRJUsFufEmSpILhSJIkqWA4kiRJKhiOJEmSCoYjSZKkguFIkiSp\n8P8Bhf03EjbkbGQAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10b814250>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,(Flu_HSA-Flu_buffer),'.',label='difference in fluorescence signal')\n"",
    ""plt.xlabel('[L_tot]')\n"",
    ""plt.ylabel('Flu_HSA-Flu_buffer')\n"",
    ""plt.ylim(0,500)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Fluorescent ligand titration into HSA (with 50 uM competitive ligand)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Atot=50 #(uM)\n"",
    ""[P_A50, L_A50, A_A50, PL_A50, Kd_L_app_A50] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Free ligand concentration in each well (uM)\n"",
    ""L=L_A50\n"",
    ""#complex concentration\n"",
    ""PL=PL_A50\n"",
    ""# Fluorescence measurement of the HSA + ligand measurements\n"",
    ""Flu_HSA = MF*L + BKG + FR*MF*PL""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 29,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x10c04d8d0>""
      ]
     },
     ""execution_count"": 29,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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5Wfjys/0/N2J7vvXbS6fmpWl2NxtYu2EmSpOHgZWRJkipmsZUkqWIWW0mSKmax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\nsZUkqWIWW0mSKmaxlSSpYhZbSZIq9v8BxzGQg5XINNMAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x109c62190>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,Flu_buffer,'o',label='buffer')\n"",
    ""plt.semilogx(Ltot, Flu_HSA ,'o', label='protein')\n"",
    ""plt.xlabel('[L_tot]')\n"",
    ""plt.ylabel('Fluorescence')\n"",
    ""plt.ylim(50,8000)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 30,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x10c3c9910>""
      ]
     },
     ""execution_count"": 30,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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jaCtiTaibrlZm5uqnCJEmSemHGcJSZB5SvI+KxwDlNFSRJktRLsx5zlJk/BJ7YQC2SJEk9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\nJElSwXAkSZJUMBxJkiQVDEeSJEmF/w8ZMU4yb0g+KwAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10905f790>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,(Flu_HSA-Flu_buffer),'.',label='difference in fluorescence signal')\n"",
    ""plt.xlabel('[L_tot]')\n"",
    ""plt.ylabel('Flu_HSA-Flu_buffer')\n"",
    ""plt.ylim(0,500)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": true
   },
   ""source"": [
    ""#### Checking ligand depletion""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 31,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x10c99fe50>""
      ]
     },
     ""execution_count"": 31,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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fNmgdqrHKwxsZG7d8Qs3/Dq+S+gUHe9MhuCdU05UkRMb7v7hjg3IiYC9wKLG+4JklS4Zre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\nYSdJKp5hJ0kqnmEnSSqeYSdJKt7/A+b2n2Qv2GiFAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10c02e610>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""L_percent_depletion=((Ltot-L_A50)/Ltot)*100\n"",
    ""plt.semilogx(Ltot,L_percent_depletion,'.',label='L')\n"",
    ""plt.xlabel('[L_tot]')\n"",
    ""plt.ylabel('% ligand depletion')\n"",
    ""plt.ylim(0,100)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.10""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/inputs_Gef_bot.py",".py","826","20","import json
import numpy as np

inputs = {
    'xml_file_path' :  ""./data/Abl_Gef_copy/"",
    'section'       :  '280_BottomRead',
    'ligand_order'  :  ['Gefitinib','Gefitinib','Gefitinib','Gefitinib'],
    'Lstated'       :  np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0001e-6], np.float64), # ligand concentration
    'protein'       :  'Abl',
    'Pstated'       :  0.5e-6 * np.ones([12],np.float64), # protein concentration, M
    'assay_volume'  :  100e-6, # assay volume, L
    'well_area'     :  0.3969, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
    }

inputs['Lstated'] = inputs['Lstated'].tolist()
inputs['Pstated'] = inputs['Pstated'].tolist()

with open('inputs.json', 'w') as fp:
    json.dump(inputs, fp)
","Python"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/inputs_Gef_App_bot.py",".py","823","21","import json
import numpy as np

inputs = {
    'xml_file_path' :  ""./data/Abl_Gef_Ima_copy/"",
    'section'       :  '280_BottomRead',
    'ligand_order'  :  ['Gef-Ima','Gef-Ima','Gef-Ima','Gef-Ima'],
    'Lstated'       :  np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0001e-6], np.float64), # ligand concentration
    'protein'       :  'Abl',
    'Pstated'       :  0.5e-6 * np.ones([12],np.float64), # protein concentration, M
    'assay_volume'  :  100e-6, # assay volume, L
    'well_area'     :  0.3969, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
    }

inputs['Lstated'] = inputs['Lstated'].tolist()
inputs['Pstated'] = inputs['Pstated'].tolist()

with open('inputs.json', 'w') as fp:
    json.dump(inputs, fp)

","Python"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/4b-Analytic-Bayesian-Model-w-simulated-data.ipynb",".ipynb","114731","850","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Analytical Bayesian Model with Simulated Data""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""In this notebook we are using our Bayesian model to fit competitive binding experiment to fit simulated data.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    }
   ],
   ""source"": [
    ""#First we simulate some competitive binding data.\n"",
    ""import numpy as np\n"",
    ""from glob import glob\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""deltaG_L = -13.5 #kT\n"",
    ""deltaG_B = -15.5 #kT""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""1.3709590863840845e-06""
      ]
     },
     ""execution_count"": 3,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#This corresponds to Kd's of\n"",
    ""Kd_L = np.exp(deltaG_L)\n"",
    ""Kd_L""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""1.8553913626159784e-07""
      ]
     },
     ""execution_count"": 4,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Kd_B = np.exp(deltaG_B)\n"",
    ""Kd_B""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#Competitive binding function\n"",
    ""def three_component_competitive_binding_exact(Ptot, Ltot, K_L, Btot, K_B):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration summed over bound and unbound species, molarity.\n"",
    ""    Ltot : float\n"",
    ""        Total fluorescent ligand concentration summed over bound and unbound speciesl, molarity.\n"",
    ""    K_L : float\n"",
    ""        Dissociation constant of fluoerscent L to P\n"",
    ""    Btot : float\n"",
    ""        Total competitive ligand concentration\n"",
    ""    K_B : float\n"",
    ""        Dissociation constant of B to B\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    B : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Concentration of PA complex\n"",
    ""    PB : float\n"",
    ""        Concentration of PB complex\n"",
    ""        \n"",
    ""    Usage\n"",
    ""    -----\n"",
    ""    [P, L, B, PL, PB] = three_component_competitive_binding(Ptot, Ltot, K_L, Btot, K_B)\n"",
    ""    \""\""\""\n"",
    ""    \n"",
    ""    # P^3 + aP^2 + bP + c = 0\n"",
    ""    a = K_L + K_B + Ltot + Btot - Ptot\n"",
    ""    b = K_L*K_B + K_B*(Ltot-Ptot) + K_B*(Btot - Ptot)\n"",
    ""    c = -K_L*K_B*Ptot\n"",
    ""    \n"",
    ""    # Subsitute P=u-a/3\n"",
    ""    # u^3 - qu - r = 0 where \n"",
    ""    q = (a**2)/3.0 - b\n"",
    ""    r = (-2.0/27.0)*a**3 +(1.0/3.0)*a*b - c\n"",
    ""    \n"",
    ""    # Discriminant\n"",
    ""    delta = (r**2)/4.0 -(q**3)/27.0\n"",
    ""    \n"",
    ""    # 3 roots. Physically meaningful root is u.\n"",
    ""    #theta = np.arccos((-2*(a**3)+9*a*b-27*c)/(2*np.sqrt((a**2-3*b)**3)))\n"",
    ""\n"",
    ""    theta_intermediate = (-2*(a**3)+9*a*b-27*c)/(2*np.sqrt((a**2-3*b)**3))\n"",
    ""        \n"",
    ""    # this function prevents nans that occur when taking arccos directly\n"",
    ""    def better_theta(theta_intermediate):\n"",
    ""        global value\n"",
    ""        if -1.0 <= theta_intermediate <= 1.0:\n"",
    ""            value = np.arccos( theta_intermediate )\n"",
    ""        elif theta_intermediate < -1.0:\n"",
    ""            value = np.pi\n"",
    ""        elif theta_intermediate > 1.0:\n"",
    ""            value = 0.0\n"",
    ""        return value\n"",
    ""        \n"",
    ""    theta = np.asarray(list(map(better_theta,theta_intermediate)))\n"",
    ""\n"",
    ""    u = (2.0/3.0)*np.sqrt(a**2-3*b)*np.cos(theta/3.0)\n"",
    ""    \n"",
    ""    # Free protein concentration [P]\n"",
    ""    P = u - a/3.0\n"",
    ""    \n"",
    ""    # [PA]\n"",
    ""    PL = P*Ltot/(K_L + P)\n"",
    ""    \n"",
    ""    # [PB]\n"",
    ""    PB = P*Btot/(K_B + P)\n"",
    ""    \n"",
    ""    # Free A concentration [A]\n"",
    ""    L = Ltot - PL\n"",
    ""    \n"",
    ""    # Free B concentration [B]\n"",
    ""    B = Btot - PB\n"",
    ""    \n"",
    ""    # Apparent Kd of L (shift caused by competitive ligand)\n"",
    ""    # K_L_app = K_L*(1+B/K_B)\n"",
    ""    \n"",
    ""    return [P, L, B, PL, PB]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""inputs = {\n"",
    ""    'xml_file_path' :  \""../../../assaytools/assaytools/examples/competition-fluorescence-assay/data/\"",\n"",
    ""    'file_set'      :  {'Abl-IMA': glob(\""../../../assaytools/assaytools/examples/competition-fluorescence-assay/data/Abl*16-22-45_plate*.xml\"")},\n"",
    ""    'ligand_order'  :  ['Gefitinib','Gefitinib','Gefitinib','Gefitinib'],\n"",
    ""    'competitive_ligand'  :  'Imatinib',\n"",
    ""    'section'       :  '280_BottomRead',\n"",
    ""    'Lstated'       :  np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0], np.float64), # ligand concentration, M\n"",
    ""    'Bstated'       :  10.0e-6 * np.ones([12],np.float64), # competitive ligand concentration, M\n"",
    ""    'Pstated'       :  0.5e-6 * np.ones([12],np.float64), # protein concentration, M\n"",
    ""    'assay_volume'  :  100e-6, # assay volume, L\n"",
    ""    'well_area'     :  0.3969, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf],\n"",
    ""    'DG_L_mean'     :  -12.5, #DeltaG of Gefitinib mean estimate (kT)\n"",
    ""    'DG_L_std'      :  1      #DeltaG of Gefitinib standard deviation estimate (kT)\n"",
    ""    }""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[P_bos_ima, L_bos_ima, B_bos_ima, PL_bos_ima, PB_bos_ima] = three_component_competitive_binding_exact(inputs['Pstated'][0], inputs['Lstated'], Kd_L, inputs['Bstated'][0], Kd_B)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""sigma = 60.\n"",
    ""F_background = 20000\n"",
    ""F_PL = (40/1e-9)\n"",
    ""F_L = (2/1e-8)\n"",
    ""\n"",
    ""F_PL_i = F_background + F_PL * PL_bos_ima + F_L * L_bos_ima + sigma * np.random.randn()\n"",
    ""F_L_i = F_background + F_L * L_bos_ima + sigma * np.random.randn()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x110d74f28>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, ax = plt.subplots(figsize=(8,4))\n"",
    ""\n"",
    ""plt.semilogx(inputs['Lstated'], F_PL_i,'o',color='orange',label='%s (M)'%inputs['Bstated'][0])\n"",
    ""\n"",
    ""plt.semilogx(inputs['Lstated'],F_L_i,'o',color='gray',label='ligand')\n"",
    ""    \n"",
    ""ax.spines['top'].set_visible(False)\n"",
    ""ax.spines['right'].set_visible(False)\n"",
    ""\n"",
    ""plt.xlabel('$[L]_T$ (M)',fontsize=16);\n"",
    ""plt.yticks([])\n"",
    ""plt.xticks(fontsize=16)\n"",
    ""plt.ylabel('Fluorescence',fontsize=20);\n"",
    ""\n"",
    ""plt.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## That's our simulated data, now let's try to fit it with our Bayesian model""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# We know errors from our pipetting instruments.\n"",
    ""P_error = 0.15\n"",
    ""L_error = 0.08\n"",
    ""B_error = 0.08\n"",
    ""\n"",
    ""assay_volume = 100e-6 # assay volume, L\n"",
    ""\n"",
    ""#Because we are interested in the orange value\n"",
    ""\n"",
    ""dPstated = P_error * inputs['Pstated']\n"",
    ""dLstated = L_error * inputs['Lstated']\n"",
    ""dBstated = B_error * inputs['Bstated']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""import pymc""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""DG_min = np.log(1e-15) # kT, most favorable (negative) binding free energy possible; 1 fM\n"",
    ""DG_max = +0 # kT, least favorable binding free energy possible""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#this is just for the lognormal wrapper and inner filter effect and run_mcmc\n"",
    ""from assaytools import pymcmodels\n"",
    ""from assaytools import bindingmodels""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#This is our new competition assay model, which has competition as an option (note just for top fluroescence)\n"",
    ""#if the values on the second line are included\n"",
    ""def make_comp_model(Pstated, dPstated, Lstated, dLstated,\n"",
    ""               Bstated = None, dBstated = None, DG_L_mean = None, DG_L_std = None, # specific for competition assay\n"",
    ""               top_complex_fluorescence=None, top_ligand_fluorescence=None,\n"",
    ""               DG_prior='uniform',\n"",
    ""               concentration_priors='lognormal',\n"",
    ""               quantum_yield_priors='lognormal',\n"",
    ""               use_primary_inner_filter_correction=True,\n"",
    ""               use_secondary_inner_filter_correction=True,\n"",
    ""               assay_volume=100e-6, well_area=0.1586,\n"",
    ""               epsilon_ex=None, depsilon_ex=None,\n"",
    ""               epsilon_em=None, depsilon_em=None,\n"",
    ""               ligand_ex_absorbance=None, ligand_em_absorbance=None,\n"",
    ""               F_PL=None, dF_PL=None):\n"",
    ""\n"",
    ""    # Compute path length.\n"",
    ""    path_length = assay_volume * 1000 / well_area # cm, needed for inner filter effect corrections\n"",
    ""\n"",
    ""    # Compute number of samples.\n"",
    ""    N = len(Lstated)\n"",
    ""    \n"",
    ""    # Check input.\n"",
    ""    # TODO: Check fluorescence and absorbance measurements for correct dimensions.\n"",
    ""    if (len(Pstated) != N):\n"",
    ""        raise Exception('len(Pstated) [%d] must equal len(Lstated) [%d].' % (len(Pstated), len(Lstated)))\n"",
    ""    if (len(dPstated) != N):\n"",
    ""        raise Exception('len(dPstated) [%d] must equal len(Lstated) [%d].' % (len(dPstated), len(Lstated)))\n"",
    ""    if (len(dLstated) != N):\n"",
    ""        raise Exception('len(dLstated) [%d] must equal len(Lstated) [%d].' % (len(dLstated), len(Lstated)))\n"",
    ""\n"",
    ""    # Note whether we have top or bottom fluorescence measurements.\n"",
    ""    top_fluorescence = (top_complex_fluorescence is not None) or (top_ligand_fluorescence is not None) # True if any top fluorescence measurements provided\n"",
    ""\n"",
    ""    # Create an empty dict to hold the model.\n"",
    ""    model = dict()\n"",
    ""    \n"",
    ""    # Prior on binding free energies.\n"",
    ""    if DG_prior == 'uniform':\n"",
    ""        DeltaG_B = pymc.Uniform('DeltaG_B', lower=DG_min, upper=DG_max) # binding free energy (kT), uniform over huge range\n"",
    ""    elif DG_prior == 'chembl':\n"",
    ""        DeltaG_B = pymc.Normal('DeltaG_B', mu=0, tau=1./(12.5**2)) # binding free energy (kT), using a Gaussian prior inspured by ChEMBL\n"",
    ""    else:\n"",
    ""        raise Exception(\""DG_prior = '%s' unknown. Must be one of 'uniform' or 'chembl'.\"" % DG_prior)\n"",
    ""    model['DeltaG_B'] = DeltaG_B\n"",
    ""    \n"",
    ""    # Prior on known binding free energy.\n"",
    ""    if DG_L_mean == None:\n"",
    ""        DeltaG_L = pymc.Uniform('DeltaG_L', lower=DG_min, upper=DG_max) # binding free energy (kT), uniform over huge range\n"",
    ""    else:\n"",
    ""        DeltaG_L = pymc.Normal('DeltaG_L', mu=DG_L_mean, tau=DG_L_std) \n"",
    ""    model['DeltaG_L'] = DeltaG_L\n"",
    ""        \n"",
    ""    if concentration_priors != 'lognormal':\n"",
    ""        raise Exception(\""concentration_priors = '%s' unknown. Must be one of ['lognormal'].\"" % concentration_priors)\n"",
    ""    model['log_Ptrue'], model['Ptrue'] = pymcmodels.LogNormalWrapper('Ptrue', mean=Pstated, stddev=dPstated, size=Pstated.shape) # protein concentration (M)\n"",
    ""    model['log_Ltrue'], model['Ltrue'] = pymcmodels.LogNormalWrapper('Ltrue', mean=Lstated, stddev=dLstated, size=Lstated.shape) # ligand concentration (M)\n"",
    ""    model['log_Btrue'], model['Btrue'] = pymcmodels.LogNormalWrapper('Btrue', mean=Bstated, stddev=dBstated, size=Bstated.shape) # ligand concentration (M)\n"",
    ""    model['log_Ltrue_control'], model['Ltrue_control'] = pymcmodels.LogNormalWrapper('Ltrue_control', mean=Lstated, stddev=dLstated, size=Lstated.shape) # ligand concentration in ligand-only wells (M)\n"",
    ""\n"",
    ""    # extinction coefficient\n"",
    ""    model['epsilon_ex'] = 0.0\n"",
    ""    if use_primary_inner_filter_correction:\n"",
    ""        if epsilon_ex:\n"",
    ""            model['log_epsilon_ex'], model['epsilon_ex'] =  pymcmodels.LogNormalWrapper('epsilon_ex', mean=epsilon_ex, stddev=depsilon_ex) # prior is centered on measured extinction coefficient\n"",
    ""        else:\n"",
    ""            model['epsilon_ex'] = pymc.Uniform('epsilon_ex', lower=0.0, upper=1000e3, value=70000.0) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm\n"",
    ""\n"",
    ""    model['epsilon_em'] = 0.0\n"",
    ""    if use_secondary_inner_filter_correction:\n"",
    ""        if epsilon_em:\n"",
    ""            model['log_epsilon_em'], model['epsilon_em'] =  pymcmodels.LogNormalWrapper('epsilon_em', mean=epsilon_em, stddev=depsilon_em) # prior is centered on measured extinction coefficient\n"",
    ""        else:\n"",
    ""            model['epsilon_em'] = pymc.Uniform('epsilon_em', lower=0.0, upper=1000e3, value=0.0) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm\n"",
    ""\n"",
    ""    # Min and max observed fluorescence.\n"",
    ""    Fmax = 0.0; Fmin = 1e6;\n"",
    ""    if top_complex_fluorescence is not None:\n"",
    ""        Fmax = max(Fmax, top_complex_fluorescence.max()); Fmin = min(Fmin, top_complex_fluorescence.min())\n"",
    ""    if top_ligand_fluorescence is not None:\n"",
    ""        Fmax = max(Fmax, top_ligand_fluorescence.max()); Fmin = min(Fmin, top_ligand_fluorescence.min())\n"",
    ""\n"",
    ""    # Compute initial guesses for fluorescence quantum yield quantities.\n"",
    ""    F_plate_guess = Fmin\n"",
    ""    F_buffer_guess = Fmin / path_length\n"",
    ""    F_L_guess = (Fmax - Fmin) / Lstated.max()\n"",
    ""    F_P_guess = Fmin\n"",
    ""    F_P_guess = Fmin / Pstated.min()\n"",
    ""    F_PL_guess = (Fmax - Fmin) / min(Pstated.max(), Lstated.max())\n"",
    ""\n"",
    ""    # Priors on fluorescence intensities of complexes (later divided by a factor of Pstated for scale).\n"",
    ""\n"",
    ""    if quantum_yield_priors == 'lognormal':\n"",
    ""        stddev = 1.0 # relative factor for stddev guess\n"",
    ""        model['log_F_plate'], model['F_plate'] =  pymcmodels.LogNormalWrapper('F_plate', mean=F_plate_guess, stddev=stddev*F_plate_guess) # plate fluorescence\n"",
    ""        model['log_F_buffer'], model['F_buffer'] =  pymcmodels.LogNormalWrapper('F_buffer', mean=F_buffer_guess, stddev=stddev*F_buffer_guess) # buffer fluorescence\n"",
    ""        model['log_F_buffer_control'], model['F_buffer_control'] =  pymcmodels.LogNormalWrapper('F_buffer_control', mean=F_buffer_guess, stddev=stddev*F_buffer_guess) # buffer fluorescence\n"",
    ""        if (F_PL is not None) and (dF_PL is not None):\n"",
    ""            model['log_F_PL'], model['F_PL'] =  pymcmodels.LogNormalWrapper('F_PL', mean=F_PL, stddev=dF_PL)\n"",
    ""        else:\n"",
    ""            model['log_F_PL'], model['F_PL'] =  pymcmodels.LogNormalWrapper('F_PL', mean=F_PL_guess, stddev=stddev*F_PL_guess) # complex fluorescence\n"",
    ""        model['log_F_P'], model['F_P'] =  pymcmodels.LogNormalWrapper('F_P', mean=F_P_guess, stddev=stddev*F_P_guess) # protein fluorescence\n"",
    ""        model['log_F_L'], model['F_L'] =  pymcmodels.LogNormalWrapper('F_L', mean=F_L_guess, stddev=stddev*F_L_guess) # ligand fluorescence\n"",
    ""    else:\n"",
    ""        raise Exception(\""quantum_yield_priors = '%s' unknown. Must be one of ['lognormal', 'uniform'].\"" % quantum_yield_priors)\n"",
    ""\n"",
    ""    # Unknown experimental measurement error.\n"",
    ""    if top_fluorescence:\n"",
    ""        model['log_sigma_top'] = pymc.Uniform('log_sigma_top', lower=-10, upper=np.log(Fmax), value=np.log(5))\n"",
    ""        model['sigma_top'] = pymc.Lambda('sigma_top', lambda log_sigma=model['log_sigma_top'] : np.exp(log_sigma) )\n"",
    ""        model['precision_top'] = pymc.Lambda('precision_top', lambda log_sigma=model['log_sigma_top'] : np.exp(-2*log_sigma) )\n"",
    ""\n"",
    ""    if top_fluorescence:\n"",
    ""        model['log_sigma_abs'] = pymc.Uniform('log_sigma_abs', lower=-10, upper=0, value=np.log(0.01))\n"",
    ""        model['sigma_abs'] = pymc.Lambda('sigma_abs', lambda log_sigma=model['log_sigma_abs'] : np.exp(log_sigma) )\n"",
    ""        model['precision_abs'] = pymc.Lambda('precision_abs', lambda log_sigma=model['log_sigma_abs'] : np.exp(-2*log_sigma) )\n"",
    ""\n"",
    ""    if top_complex_fluorescence is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_complex_fluorescence_model(F_plate=model['F_plate'], F_buffer=model['F_buffer'],\n"",
    ""                                           F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],\n"",
    ""                                           Ptrue=model['Ptrue'], Ltrue=model['Ltrue'], Btrue=model['Btrue'], DeltaG_L=model['DeltaG_L'], DeltaG_B=model['DeltaG_B'],\n"",
    ""                                           epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            [P_i, L_i, PL_i, B_i, PB_i] = bindingmodels.CompetitionBindingModel.equilibrium_concentrations(Ptrue[:], Ltrue[:], DeltaG_L, Btrue[:], DeltaG_B)\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, L_i, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*L_i) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_PL*PL_i + F_L*L_i + F_P*P_i + F_buffer*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_complex_fluorescence_model'] = top_complex_fluorescence_model\n"",
    ""        model['log_top_complex_fluorescence'], model['top_complex_fluorescence'] = pymcmodels.LogNormalWrapper('top_complex_fluorescence',\n"",
    ""            mean=model['top_complex_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""            size=[N], observed=True, value=top_complex_fluorescence) # observed data\n"",
    ""        \n"",
    ""    if top_ligand_fluorescence is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_ligand_fluorescence_model(F_plate=model['F_plate'], F_buffer_control=model['F_buffer_control'],\n"",
    ""                                          F_L=model['F_L'],\n"",
    ""                                          Ltrue_control=model['Ltrue_control'],\n"",
    ""                                          epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue_control, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*Ltrue_control) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_L*Ltrue_control + F_buffer_control*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_ligand_fluorescence_model'] = top_ligand_fluorescence_model\n"",
    ""        model['log_top_ligand_fluorescence'], model['top_ligand_fluorescence'] = pymcmodels.LogNormalWrapper('top_ligand_fluorescence',\n"",
    ""                                                       mean=model['top_ligand_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""                                                       size=[N], observed=True, value=top_ligand_fluorescence) # observed data\n"",
    ""        \n"",
    ""    #Below this is competition model stuff!    \n"",
    ""    \n"",
    ""    # Compute number of samples.\n"",
    ""    if Bstated is not None:\n"",
    ""        N_B = len(Bstated)\n"",
    ""\n"",
    ""        # Check input.\n"",
    ""        # TODO: Check fluorescence and absorbance measurements for correct dimensions.\n"",
    ""        if (len(Lstated) != N_B):\n"",
    ""            raise Exception('len(Lstated) [%d] must equal len(Bstated) [%d].' % (len(Lstated), len(Bstated)))\n"",
    ""        if (len(Pstated) != N_B):\n"",
    ""            raise Exception('len(Pstated) [%d] must equal len(Bstated) [%d].' % (len(Pstated), len(Bstated)))\n"",
    ""        if (len(dPstated) != N_B):\n"",
    ""            raise Exception('len(dPstated) [%d] must equal len(Bstated) [%d].' % (len(dPstated), len(Bstated)))\n"",
    ""        if (len(dBstated) != N_B):\n"",
    ""            raise Exception('len(dBstated) [%d] must equal len(Bstated) [%d].' % (len(dBstated), len(Bstated)))\n"",
    ""\n"",
    ""            \n"",
    ""    if Bstated is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_complex_fluorescence_model(F_plate=model['F_plate'], F_buffer=model['F_buffer'],\n"",
    ""                                           F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],\n"",
    ""                                           Ptrue=model['Ptrue'], Ltrue=model['Ltrue'], Btrue=model['Btrue'],\n"",
    ""                                           DeltaG_L=model['DeltaG_L'], DeltaG_B=model['DeltaG_B'],\n"",
    ""                                           epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            [P_i, L_i, PL_i, B_i, PB_i] = bindingmodels.CompetitionBindingModel.equilibrium_concentrations(Ptrue[:], Ltrue[:], DeltaG_L, Btrue[:], DeltaG_B)\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, L_i, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*L_i) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_PL*PL_i + F_L*L_i + F_P*P_i + F_buffer*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_complex_fluorescence_model'] = top_complex_fluorescence_model\n"",
    ""        model['log_top_complex_fluorescence'], model['top_complex_fluorescence'] = pymcmodels.LogNormalWrapper('top_complex_fluorescence',\n"",
    ""            mean=model['top_complex_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""            size=[N], observed=True, value=top_complex_fluorescence) # observed data\n"",
    ""\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_ligand_fluorescence_model(F_plate=model['F_plate'], F_buffer_control=model['F_buffer_control'],\n"",
    ""                                          F_L=model['F_L'],\n"",
    ""                                          Ltrue_control=model['Ltrue_control'],\n"",
    ""                                          epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue_control, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*Ltrue_control) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_L*Ltrue_control + F_buffer_control*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_ligand_fluorescence_model'] = top_ligand_fluorescence_model\n"",
    ""        model['log_top_ligand_fluorescence'], model['top_ligand_fluorescence'] = pymcmodels.LogNormalWrapper('top_ligand_fluorescence',\n"",
    ""                                                       mean=model['top_ligand_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""                                                       size=[N], observed=True, value=top_ligand_fluorescence) # observed data\n"",
    ""    # Promote this to a full-fledged PyMC model.\n"",
    ""    pymc_model = pymc.Model(model)\n"",
    ""\n"",
    ""    # Return the pymc model\n"",
    ""    return pymc_model""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""pymc_comp_model = make_comp_model(inputs['Pstated'], dPstated, inputs['Lstated'], dLstated,\n"",
    ""    inputs['Bstated'], dBstated, inputs['DG_L_mean'], inputs['DG_L_std'],# specific for competition assay\n"",
    ""    top_complex_fluorescence=F_PL_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""nburn = 500\n"",
    ""niter = 50000\n"",
    ""nthin = 20""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""comp_mcmc = pymc.MCMC(pymc_comp_model)\n"",
    ""comp_mcmc.sample(iter=(nburn+niter), burn=nburn, thin=nthin, progress_bar=False, tune_throughout=True)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Abl_gefitinib = 2200e-9 # 2200 nM from DiscoverRx screen data \n"",
    ""\n"",
    ""AblGef_dG = np.log(Abl_gefitinib)\n"",
    ""\n"",
    ""Abl_imatinib = 1.1e-9 # 1.1 nM from DiscoverRx screen data \n"",
    ""\n"",
    ""AblIma_dG = np.log(Abl_imatinib)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x117861748>""
      ]
     },
     ""execution_count"": 23,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1175e3c50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, ax = plt.subplots(figsize=(8,4))\n"",
    ""plt.plot(comp_mcmc.DeltaG_B.trace(),'o',label='B')\n"",
    ""plt.plot(comp_mcmc.DeltaG_L.trace(),'o',label='L')\n"",
    ""plt.ylabel('$\\Delta G$ ($k_B T$)',fontsize=20);\n"",
    ""plt.xlabel('MCMC sample',fontsize=20);\n"",
    ""plt.legend()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x117886cc0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.figure(figsize=(12,10))\n"",
    ""plt.hist(comp_mcmc.DeltaG_L.trace(),normed=True,label='DelG_L(Gef) = %s' %comp_mcmc.DeltaG_L.trace().mean());\n"",
    ""plt.axvline(x=AblGef_dG,color='b',linestyle='--',label='IUPHARM Gef')\n"",
    ""plt.hist(comp_mcmc.DeltaG_B.trace(),normed=True,label='DelG_B(Ima) = %s' %comp_mcmc.DeltaG_B.trace().mean());\n"",
    ""plt.axvline(x=AblIma_dG,color='orange',linestyle='--',label='IUPHARM Ima')\n"",
    ""plt.xlabel('$\\Delta G$ ($k_B T$)');\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/envs/python3/lib/python3.5/site-packages/matplotlib/pyplot.py:3397: MatplotlibDeprecationWarning: The 'hold' keyword argument is deprecated since 2.0.\n"",
      ""  mplDeprecation)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1178c54e0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""fig, ax = plt.subplots(figsize=(8,4))\n"",
    ""\n"",
    ""for top_complex_fluorescence_model in comp_mcmc.top_complex_fluorescence_model.trace()[::10]:\n"",
    ""    plt.semilogx(inputs['Lstated'], top_complex_fluorescence_model, color='orange',marker='.', alpha=0.2, hold=True)\n"",
    ""\n"",
    ""for top_ligand_fluorescence_model in comp_mcmc.top_ligand_fluorescence_model.trace()[::10]:\n"",
    ""    plt.semilogx(inputs['Lstated'], top_ligand_fluorescence_model, color='gray',marker='.', alpha=0.2, hold=True)\n"",
    ""    \n"",
    ""plt.semilogx(inputs['Lstated'], F_PL_i,'o',color='orange',label='%s (M)'%inputs['Bstated'][0])\n"",
    ""\n"",
    ""plt.semilogx(inputs['Lstated'],F_L_i,'o',color='gray',label='ligand')\n"",
    ""    \n"",
    ""ax.spines['top'].set_visible(False)\n"",
    ""ax.spines['right'].set_visible(False)\n"",
    ""\n"",
    ""plt.xlabel('$[L]_T$ (M)',fontsize=16);\n"",
    ""plt.yticks([])\n"",
    ""plt.xticks(fontsize=16)\n"",
    ""plt.ylabel('Fluorescence',fontsize=20);\n"",
    ""\n"",
    ""plt.tight_layout()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
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   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
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   },
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  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 3"",
   ""language"": ""python"",
   ""name"": ""python3""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 3
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython3"",
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","Unknown"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/1c Simulating Experimental Fluorescent Competition Assay Data.ipynb",".ipynb","137860","416","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## In this notebook we will explore experimental design of competition assays.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      "":0: FutureWarning: IPython widgets are experimental and may change in the future.\n""
     ]
    }
   ],
   ""source"": [
    ""import matplotlib.pyplot as plt\n"",
    ""import seaborn as sns\n"",
    ""import numpy as np\n"",
    ""\n"",
    ""sns.set_style(\""whitegrid\"")\n"",
    ""from matplotlib.colors import LogNorm\n"",
    ""\n"",
    ""%matplotlib inline""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#Three component competitive binding function\n"",
    ""#This function and its assumptions are defined in greater detail in this notebook: \n"",
    ""## 1a-modelling-CompetitiveBinding-ThreeComponentBinding.ipynb\n"",
    ""\n"",
    ""def three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total tracer(fluorescent) ligand concentration\n"",
    ""    Kd_L : float\n"",
    ""        Dissociation constant of the fluorescent ligand\n"",
    ""    Atot : float\n"",
    ""        Total competitive ligand concentration\n"",
    ""    Kd_A : float\n"",
    ""        Dissociation constant of the competitive ligand\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    A : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    Kd_L_app : float\n"",
    ""        Apparent dissociation constant of L in the presence of A\n"",
    ""        \n"",
    ""    Usage\n"",
    ""    -----\n"",
    ""    [P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)\n"",
    ""    \""\""\""\n"",
    ""    Kd_L_app = Kd_L*(1+Atot/Kd_A)                                \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd_L_app) - np.sqrt((Ptot + Ltot + Kd_L_app)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free tracer ligand concentration in sample cell after n injections (uM)\n"",
    ""    A = Atot - PL; # free competitive ligand concentration in sample cell after n injections (uM)\n"",
    ""    return [P, L, A, PL, Kd_L_app]""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""We can use this function to help us decide the appropriate concentrations to use to determine the Kd_A of a competitive ligand. For example, if we see no shift in the Kd_L_app upon adding the competitive ligand, we will not be able to calculate the Kd_A.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Let's take the example of these three ligands affinity for Src Kinase\n"",
    ""Kd_Bos = 1.0e-9 # M  # Fluorescent\n"",
    ""Kd_Gef = 3800e-9 # M # Fluorescent\n"",
    ""Kd_Ima = 3000e-9 # M # Non-Fluorescent (Competitive)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Ptot = 0.5e-6 # M\n"",
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Create grid of fluorescence as a function of imatinib concentration\n"",
    ""\n"",
    ""#we want to make a grid of 8 Imatinib concentrations\n"",
    ""#what if we just go be factors of ten from 10e-5 to 10e-12 M\n"",
    ""concentration_range = [10e-5,10e-6,10e-7,10e-8,10e-9,10e-10,10e-11,10e-12] # M\n"",
    ""\n"",
    ""competition_grid_bosutinib = []\n"",
    ""\n"",
    ""for i,conc in enumerate(concentration_range):\n"",
    ""    [P_bos_ima, L_bos_ima, A_bos_ima, PL_bos_ima, Kd_bos_ima] = three_component_competitive_binding(Ptot, Ltot, Kd_Bos, conc, Kd_Ima)\n"",
    ""    if i == 0: \n"",
    ""          competition_grid_bosutinib = PL_bos_ima\n"",
    ""    else:\n"",
    ""          competition_grid_bosutinib = np.vstack((competition_grid_bosutinib,PL_bos_ima))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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6z1r6TzsmttbUAxphi/GTsqi7EKiIiHXDUdVMOPmXfkdS7KRbNffMi4BVgPTAt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\nMBEREZEQ/H/FDF5LCmXtQQAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11a541950>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""from matplotlib.pyplot import cm \n"",
    ""color=cm.PuBu_r(np.linspace(0,0.5,len(competition_grid_bosutinib)))\n"",
    ""\n"",
    ""plt.figure(figsize=(10,4))\n"",
    ""for i,PL in enumerate(competition_grid_bosutinib):\n"",
    ""    plt.semilogx(Ltot,PL,color = color[i])\n"",
    ""plt.xlabel('fluorescent ligand concentration (M)')\n"",
    ""plt.ylabel('complex concentration (M)');""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### Here we can see that even though we've used a large range of competitor concentration (from 100 uM to 1 pM), we only see a shift of the apparent Kd at the highest concentration for this Bosutinib-Imatinib experiment!""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""for i,conc in enumerate(concentration_range):\n"",
    ""    [P_gef_ima, L_gef_ima, A_gef_ima, PL_gef_ima, Kd_gef_ima] = three_component_competitive_binding(Ptot, Ltot, Kd_Gef, conc, Kd_Ima)\n"",
    ""    if i == 0: \n"",
    ""          competition_grid_gefitinib = PL_gef_ima\n"",
    ""    else:\n"",
    ""          competition_grid_gefitinib= np.vstack((competition_grid_gefitinib,PL_gef_ima))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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DzmZzLGGOMMa078d4JfXcrKz4ikUjw5oyFCeD36Qrbeq7Hlc76Z6y6YkL2ojSb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\nDvXel7JHugEAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11cac9a10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""from matplotlib.pyplot import cm \n"",
    ""color=cm.PuBu_r(np.linspace(0,0.5,len(competition_grid_gefitinib)))\n"",
    ""\n"",
    ""plt.figure(figsize=(10,4))\n"",
    ""for i,PL in enumerate(competition_grid_gefitinib):\n"",
    ""    plt.semilogx(Ltot,PL,color = color[i],label='%s M'%concentration_range[i])\n"",
    ""plt.text(Ltot[0],competition_grid_gefitinib[0][0],'%s uM'%(concentration_range[0]*1e6))\n"",
    ""plt.text(Ltot[0],competition_grid_gefitinib[1][0],'%s uM'%(concentration_range[1]*1e6))\n"",
    ""plt.text(Ltot[0],competition_grid_gefitinib[2][0],'%s uM'%(concentration_range[2]*1e6))\n"",
    ""plt.xlabel('fluorescent ligand concentration (M)')\n"",
    ""plt.ylabel('complex concentration (M)');\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### Here we can see that for the Gefitinib-Imatinib experiment we can get a shift of the K_L_app with much lower concentration of the competitive ligand!""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Let's take look at how this would look like for Gefitinib and Imatinib with Abl\n"",
    ""Kd_Gef = 2200e-9 # M # Fluorescent\n"",
    ""Kd_Ima = 1.1e-9 # M # Non-Fluorescent (Competitive)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""for i,conc in enumerate(concentration_range):\n"",
    ""    [P_gef_ima, L_gef_ima, A_gef_ima, PL_gef_ima, Kd_gef_ima] = three_component_competitive_binding(Ptot, Ltot, Kd_Gef, conc, Kd_Ima)\n"",
    ""    if i == 0: \n"",
    ""          competition_grid_gefitinib = PL_gef_ima\n"",
    ""    else:\n"",
    ""          competition_grid_gefitinib= np.vstack((competition_grid_gefitinib,PL_gef_ima))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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Wi146aqm6Uzw1UNS0id+J2RR6YcWockLnTpAG3y9EYY4wZsmLx5ErAVUDbJ8ta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\nTQAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c9fc5d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""from matplotlib.pyplot import cm \n"",
    ""color=cm.PuBu_r(np.linspace(0,0.7,len(competition_grid_gefitinib)))\n"",
    ""\n"",
    ""plt.figure(figsize=(10,4))\n"",
    ""for i,PL in enumerate(competition_grid_gefitinib):\n"",
    ""    plt.semilogx(Ltot,PL,color = color[i],label='%s M'%concentration_range[i])\n"",
    ""plt.text(Ltot[0],competition_grid_gefitinib[3][0],'%s uM'%(concentration_range[3]*1e6))\n"",
    ""plt.text(Ltot[0],competition_grid_gefitinib[4][0],'%s uM'%(concentration_range[4]*1e6))\n"",
    ""plt.text(Ltot[0],competition_grid_gefitinib[5][0],'%s uM'%(concentration_range[5]*1e6))\n"",
    ""plt.xlabel('fluorescent ligand concentration (M)')\n"",
    ""plt.ylabel('complex concentration (M)');\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Even Better!""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### What does this look like if we make a plate out of it?""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""['2e-05',\n"",
       "" '6.3e-06',\n"",
       "" '2e-06',\n"",
       "" '6.3e-07',\n"",
       "" '2e-07',\n"",
       "" '6.3e-08',\n"",
       "" '2e-08',\n"",
       "" '6.3e-09',\n"",
       "" '2e-09',\n"",
       "" '6.3e-10',\n"",
       "" '2e-10',\n"",
       "" '6.3e-11']""
      ]
     },
     ""execution_count"": 12,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#Changing sig figs for Ltot\n"",
    ""Ltot_visual = ['%.2g' % a for a in Ltot]\n"",
    ""Ltot_visual""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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JSrtwgHwn0UobaDiwvWO4ys1RRm4+W/bMMgYr06Lr7s8CM+Pl9wAEJ2x3bx/pNhy46ERER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\nAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c9ff390>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.figure(figsize=(12, 6))\n"",
    ""plt.pcolor(competition_grid_gefitinib, \n"",
    ""           norm=LogNorm(vmin=competition_grid_gefitinib.min(), \n"",
    ""                        vmax=competition_grid_gefitinib.max()), \n"",
    ""           edgecolors='w',\n"",
    ""           linewidths=2,\n"",
    ""           cmap='PuBu_r')\n"",
    ""plt.ticklabel_format(style='plain')\n"",
    ""plt.xlabel('fluorescent ligand concentration (M)')\n"",
    ""plt.ylabel('non-fluorescent ligand concentration (M)')\n"",
    ""plt.xticks(np.arange(0.5, 12.5),Ltot_visual,rotation='vertical');\n"",
    ""plt.yticks(np.arange(0.5, 12.5),concentration_range);\n"",
    ""plt.ylim((0, len(concentration_range)))\n"",
    ""plt.title('Competition Grid for Gefitinib X Imatinib in 0.5 uM Abl')\n"",
    ""plt.colorbar(label='complex concentration (M)');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.11""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/4-Analytic-v-General-Bayesian-Model.ipynb",".ipynb","121335","1001","{
 ""cells"": [
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""import pymc\n"",
    ""from glob import glob\n"",
    ""import numpy as np""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""DG_min = np.log(1e-15) # kT, most favorable (negative) binding free energy possible; 1 fM\n"",
    ""DG_max = +0 # kT, least favorable binding free energy possible""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#this is just for the lognormal wrapper and inner filter effect and run_mcmc\n"",
    ""from assaytools import pymcmodels\n"",
    ""from assaytools import bindingmodels""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    }
   ],
   ""source"": [
    ""from assaytools import parser\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#This is our new competition assay model, which has competition as an option (note just for top fluroescence)\n"",
    ""#if the values on the second line are included\n"",
    ""def make_comp_model(Pstated, dPstated, Lstated, dLstated,\n"",
    ""               Bstated = None, dBstated = None, DG_L_mean = None, DG_L_std = None, # specific for competition assay\n"",
    ""               top_complex_fluorescence=None, top_ligand_fluorescence=None,\n"",
    ""               DG_prior='uniform',\n"",
    ""               concentration_priors='lognormal',\n"",
    ""               quantum_yield_priors='lognormal',\n"",
    ""               use_primary_inner_filter_correction=True,\n"",
    ""               use_secondary_inner_filter_correction=True,\n"",
    ""               assay_volume=100e-6, well_area=0.1586,\n"",
    ""               epsilon_ex=None, depsilon_ex=None,\n"",
    ""               epsilon_em=None, depsilon_em=None,\n"",
    ""               ligand_ex_absorbance=None, ligand_em_absorbance=None,\n"",
    ""               F_PL=None, dF_PL=None):\n"",
    ""\n"",
    ""    # Compute path length.\n"",
    ""    path_length = assay_volume * 1000 / well_area # cm, needed for inner filter effect corrections\n"",
    ""\n"",
    ""    # Compute number of samples.\n"",
    ""    N = len(Lstated)\n"",
    ""    \n"",
    ""    # Check input.\n"",
    ""    # TODO: Check fluorescence and absorbance measurements for correct dimensions.\n"",
    ""    if (len(Pstated) != N):\n"",
    ""        raise Exception('len(Pstated) [%d] must equal len(Lstated) [%d].' % (len(Pstated), len(Lstated)))\n"",
    ""    if (len(dPstated) != N):\n"",
    ""        raise Exception('len(dPstated) [%d] must equal len(Lstated) [%d].' % (len(dPstated), len(Lstated)))\n"",
    ""    if (len(dLstated) != N):\n"",
    ""        raise Exception('len(dLstated) [%d] must equal len(Lstated) [%d].' % (len(dLstated), len(Lstated)))\n"",
    ""\n"",
    ""    # Note whether we have top or bottom fluorescence measurements.\n"",
    ""    top_fluorescence = (top_complex_fluorescence is not None) or (top_ligand_fluorescence is not None) # True if any top fluorescence measurements provided\n"",
    ""\n"",
    ""    # Create an empty dict to hold the model.\n"",
    ""    model = dict()\n"",
    ""    \n"",
    ""    # Prior on binding free energies.\n"",
    ""    if DG_prior == 'uniform':\n"",
    ""        DeltaG_B = pymc.Uniform('DeltaG_B', lower=DG_min, upper=DG_max) # binding free energy (kT), uniform over huge range\n"",
    ""    elif DG_prior == 'chembl':\n"",
    ""        DeltaG_B = pymc.Normal('DeltaG_B', mu=0, tau=1./(12.5**2)) # binding free energy (kT), using a Gaussian prior inspured by ChEMBL\n"",
    ""    else:\n"",
    ""        raise Exception(\""DG_prior = '%s' unknown. Must be one of 'uniform' or 'chembl'.\"" % DG_prior)\n"",
    ""    model['DeltaG_B'] = DeltaG_B\n"",
    ""    \n"",
    ""    # Prior on known binding free energy.\n"",
    ""    if DG_L_mean == None:\n"",
    ""        DeltaG_L = pymc.Uniform('DeltaG_L', lower=DG_min, upper=DG_max) # binding free energy (kT), uniform over huge range\n"",
    ""    else:\n"",
    ""        DeltaG_L = pymc.Normal('DeltaG_L', mu=DG_L_mean, tau=DG_L_std) \n"",
    ""    model['DeltaG_L'] = DeltaG_L\n"",
    ""        \n"",
    ""    if concentration_priors != 'lognormal':\n"",
    ""        raise Exception(\""concentration_priors = '%s' unknown. Must be one of ['lognormal'].\"" % concentration_priors)\n"",
    ""    model['log_Ptrue'], model['Ptrue'] = pymcmodels.LogNormalWrapper('Ptrue', mean=Pstated, stddev=dPstated, size=Pstated.shape) # protein concentration (M)\n"",
    ""    model['log_Ltrue'], model['Ltrue'] = pymcmodels.LogNormalWrapper('Ltrue', mean=Lstated, stddev=dLstated, size=Lstated.shape) # ligand concentration (M)\n"",
    ""    model['log_Btrue'], model['Btrue'] = pymcmodels.LogNormalWrapper('Btrue', mean=Bstated, stddev=dBstated, size=Bstated.shape) # ligand concentration (M)\n"",
    ""    model['log_Ltrue_control'], model['Ltrue_control'] = pymcmodels.LogNormalWrapper('Ltrue_control', mean=Lstated, stddev=dLstated, size=Lstated.shape) # ligand concentration in ligand-only wells (M)\n"",
    ""\n"",
    ""    # extinction coefficient\n"",
    ""    model['epsilon_ex'] = 0.0\n"",
    ""    if use_primary_inner_filter_correction:\n"",
    ""        if epsilon_ex:\n"",
    ""            model['log_epsilon_ex'], model['epsilon_ex'] =  pymcmodels.LogNormalWrapper('epsilon_ex', mean=epsilon_ex, stddev=depsilon_ex) # prior is centered on measured extinction coefficient\n"",
    ""        else:\n"",
    ""            model['epsilon_ex'] = pymc.Uniform('epsilon_ex', lower=0.0, upper=1000e3, value=70000.0) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm\n"",
    ""\n"",
    ""    model['epsilon_em'] = 0.0\n"",
    ""    if use_secondary_inner_filter_correction:\n"",
    ""        if epsilon_em:\n"",
    ""            model['log_epsilon_em'], model['epsilon_em'] =  pymcmodels.LogNormalWrapper('epsilon_em', mean=epsilon_em, stddev=depsilon_em) # prior is centered on measured extinction coefficient\n"",
    ""        else:\n"",
    ""            model['epsilon_em'] = pymc.Uniform('epsilon_em', lower=0.0, upper=1000e3, value=0.0) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm\n"",
    ""\n"",
    ""    # Min and max observed fluorescence.\n"",
    ""    Fmax = 0.0; Fmin = 1e6;\n"",
    ""    if top_complex_fluorescence is not None:\n"",
    ""        Fmax = max(Fmax, top_complex_fluorescence.max()); Fmin = min(Fmin, top_complex_fluorescence.min())\n"",
    ""    if top_ligand_fluorescence is not None:\n"",
    ""        Fmax = max(Fmax, top_ligand_fluorescence.max()); Fmin = min(Fmin, top_ligand_fluorescence.min())\n"",
    ""\n"",
    ""    # Compute initial guesses for fluorescence quantum yield quantities.\n"",
    ""    F_plate_guess = Fmin\n"",
    ""    F_buffer_guess = Fmin / path_length\n"",
    ""    F_L_guess = (Fmax - Fmin) / Lstated.max()\n"",
    ""    F_P_guess = Fmin\n"",
    ""    F_P_guess = Fmin / Pstated.min()\n"",
    ""    F_PL_guess = (Fmax - Fmin) / min(Pstated.max(), Lstated.max())\n"",
    ""\n"",
    ""    # Priors on fluorescence intensities of complexes (later divided by a factor of Pstated for scale).\n"",
    ""\n"",
    ""    if quantum_yield_priors == 'lognormal':\n"",
    ""        stddev = 1.0 # relative factor for stddev guess\n"",
    ""        model['log_F_plate'], model['F_plate'] =  pymcmodels.LogNormalWrapper('F_plate', mean=F_plate_guess, stddev=stddev*F_plate_guess) # plate fluorescence\n"",
    ""        model['log_F_buffer'], model['F_buffer'] =  pymcmodels.LogNormalWrapper('F_buffer', mean=F_buffer_guess, stddev=stddev*F_buffer_guess) # buffer fluorescence\n"",
    ""        model['log_F_buffer_control'], model['F_buffer_control'] =  pymcmodels.LogNormalWrapper('F_buffer_control', mean=F_buffer_guess, stddev=stddev*F_buffer_guess) # buffer fluorescence\n"",
    ""        if (F_PL is not None) and (dF_PL is not None):\n"",
    ""            model['log_F_PL'], model['F_PL'] =  pymcmodels.LogNormalWrapper('F_PL', mean=F_PL, stddev=dF_PL)\n"",
    ""        else:\n"",
    ""            model['log_F_PL'], model['F_PL'] =  pymcmodels.LogNormalWrapper('F_PL', mean=F_PL_guess, stddev=stddev*F_PL_guess) # complex fluorescence\n"",
    ""        model['log_F_P'], model['F_P'] =  pymcmodels.LogNormalWrapper('F_P', mean=F_P_guess, stddev=stddev*F_P_guess) # protein fluorescence\n"",
    ""        model['log_F_L'], model['F_L'] =  pymcmodels.LogNormalWrapper('F_L', mean=F_L_guess, stddev=stddev*F_L_guess) # ligand fluorescence\n"",
    ""    else:\n"",
    ""        raise Exception(\""quantum_yield_priors = '%s' unknown. Must be one of ['lognormal', 'uniform'].\"" % quantum_yield_priors)\n"",
    ""\n"",
    ""    # Unknown experimental measurement error.\n"",
    ""    if top_fluorescence:\n"",
    ""        model['log_sigma_top'] = pymc.Uniform('log_sigma_top', lower=-10, upper=np.log(Fmax), value=np.log(5))\n"",
    ""        model['sigma_top'] = pymc.Lambda('sigma_top', lambda log_sigma=model['log_sigma_top'] : np.exp(log_sigma) )\n"",
    ""        model['precision_top'] = pymc.Lambda('precision_top', lambda log_sigma=model['log_sigma_top'] : np.exp(-2*log_sigma) )\n"",
    ""\n"",
    ""    if top_fluorescence:\n"",
    ""        model['log_sigma_abs'] = pymc.Uniform('log_sigma_abs', lower=-10, upper=0, value=np.log(0.01))\n"",
    ""        model['sigma_abs'] = pymc.Lambda('sigma_abs', lambda log_sigma=model['log_sigma_abs'] : np.exp(log_sigma) )\n"",
    ""        model['precision_abs'] = pymc.Lambda('precision_abs', lambda log_sigma=model['log_sigma_abs'] : np.exp(-2*log_sigma) )\n"",
    ""\n"",
    ""    if top_complex_fluorescence is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_complex_fluorescence_model(F_plate=model['F_plate'], F_buffer=model['F_buffer'],\n"",
    ""                                           F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],\n"",
    ""                                           Ptrue=model['Ptrue'], Ltrue=model['Ltrue'], Btrue=model['Btrue'], DeltaG_L=model['DeltaG_L'], DeltaG_B=model['DeltaG_B'],\n"",
    ""                                           epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            [P_i, L_i, PL_i, B_i, PB_i] = bindingmodels.CompetitionBindingModel.equilibrium_concentrations(Ptrue[:], Ltrue[:], DeltaG_L, Btrue[:], DeltaG_B)\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, L_i, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*L_i) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_PL*PL_i + F_L*L_i + F_P*P_i + F_buffer*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_complex_fluorescence_model'] = top_complex_fluorescence_model\n"",
    ""        model['log_top_complex_fluorescence'], model['top_complex_fluorescence'] = pymcmodels.LogNormalWrapper('top_complex_fluorescence',\n"",
    ""            mean=model['top_complex_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""            size=[N], observed=True, value=top_complex_fluorescence) # observed data\n"",
    ""        \n"",
    ""    if top_ligand_fluorescence is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_ligand_fluorescence_model(F_plate=model['F_plate'], F_buffer_control=model['F_buffer_control'],\n"",
    ""                                          F_L=model['F_L'],\n"",
    ""                                          Ltrue_control=model['Ltrue_control'],\n"",
    ""                                          epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue_control, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*Ltrue_control) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_L*Ltrue_control + F_buffer_control*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_ligand_fluorescence_model'] = top_ligand_fluorescence_model\n"",
    ""        model['log_top_ligand_fluorescence'], model['top_ligand_fluorescence'] = pymcmodels.LogNormalWrapper('top_ligand_fluorescence',\n"",
    ""                                                       mean=model['top_ligand_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""                                                       size=[N], observed=True, value=top_ligand_fluorescence) # observed data\n"",
    ""        \n"",
    ""    #Below this is competition model stuff!    \n"",
    ""    \n"",
    ""    # Compute number of samples.\n"",
    ""    if Bstated is not None:\n"",
    ""        N_B = len(Bstated)\n"",
    ""\n"",
    ""        # Check input.\n"",
    ""        # TODO: Check fluorescence and absorbance measurements for correct dimensions.\n"",
    ""        if (len(Lstated) != N_B):\n"",
    ""            raise Exception('len(Lstated) [%d] must equal len(Bstated) [%d].' % (len(Lstated), len(Bstated)))\n"",
    ""        if (len(Pstated) != N_B):\n"",
    ""            raise Exception('len(Pstated) [%d] must equal len(Bstated) [%d].' % (len(Pstated), len(Bstated)))\n"",
    ""        if (len(dPstated) != N_B):\n"",
    ""            raise Exception('len(dPstated) [%d] must equal len(Bstated) [%d].' % (len(dPstated), len(Bstated)))\n"",
    ""        if (len(dBstated) != N_B):\n"",
    ""            raise Exception('len(dBstated) [%d] must equal len(Bstated) [%d].' % (len(dBstated), len(Bstated)))\n"",
    ""\n"",
    ""            \n"",
    ""    if Bstated is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_complex_fluorescence_model(F_plate=model['F_plate'], F_buffer=model['F_buffer'],\n"",
    ""                                           F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],\n"",
    ""                                           Ptrue=model['Ptrue'], Ltrue=model['Ltrue'], Btrue=model['Btrue'],\n"",
    ""                                           DeltaG_L=model['DeltaG_L'], DeltaG_B=model['DeltaG_B'],\n"",
    ""                                           epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            [P_i, L_i, PL_i, B_i, PB_i] = bindingmodels.CompetitionBindingModel.equilibrium_concentrations(Ptrue[:], Ltrue[:], DeltaG_L, Btrue[:], DeltaG_B)\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, L_i, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*L_i) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_PL*PL_i + F_L*L_i + F_P*P_i + F_buffer*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_complex_fluorescence_model'] = top_complex_fluorescence_model\n"",
    ""        model['log_top_complex_fluorescence'], model['top_complex_fluorescence'] = pymcmodels.LogNormalWrapper('top_complex_fluorescence',\n"",
    ""            mean=model['top_complex_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""            size=[N], observed=True, value=top_complex_fluorescence) # observed data\n"",
    ""\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_ligand_fluorescence_model(F_plate=model['F_plate'], F_buffer_control=model['F_buffer_control'],\n"",
    ""                                          F_L=model['F_L'],\n"",
    ""                                          Ltrue_control=model['Ltrue_control'],\n"",
    ""                                          epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue_control, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*Ltrue_control) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_L*Ltrue_control + F_buffer_control*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_ligand_fluorescence_model'] = top_ligand_fluorescence_model\n"",
    ""        model['log_top_ligand_fluorescence'], model['top_ligand_fluorescence'] = pymcmodels.LogNormalWrapper('top_ligand_fluorescence',\n"",
    ""                                                       mean=model['top_ligand_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""                                                       size=[N], observed=True, value=top_ligand_fluorescence) # observed data\n"",
    ""    # Promote this to a full-fledged PyMC model.\n"",
    ""    pymc_model = pymc.Model(model)\n"",
    ""\n"",
    ""    # Return the pymc model\n"",
    ""    return pymc_model""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""inputs = {\n"",
    ""    'xml_file_path' :  \""./data/\"",\n"",
    ""    'file_set'      :  {'Abl-IMA': glob(\""./data/Abl*16-22-45_plate*.xml\"")},\n"",
    ""    'ligand_order'  :  ['Gefitinib','Gefitinib','Gefitinib','Gefitinib'],\n"",
    ""    'competitive_ligand'  :  'Imatinib',\n"",
    ""    'section'       :  '280_BottomRead',\n"",
    ""    'Lstated'       :  np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0], np.float64), # ligand concentration, M\n"",
    ""    'Bstated'       :  10.0e-6 * np.ones([12],np.float64), # competitive ligand concentration, M\n"",
    ""    'Pstated'       :  0.5e-6 * np.ones([12],np.float64), # protein concentration, M\n"",
    ""    'assay_volume'  :  100e-6, # assay volume, L\n"",
    ""    'well_area'     :  0.3969, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf],\n"",
    ""    'DG_L_mean'     :  -12.5, #DeltaG of Gefitinib mean estimate (kT)\n"",
    ""    'DG_L_std'      :  1      #DeltaG of Gefitinib standard deviation estimate (kT)\n"",
    ""    }""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""dPstated = 0.35 * inputs['Pstated'] # protein concentration uncertainty\n"",
    ""dLstated = 0.08 * inputs['Lstated'] # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)\n"",
    ""dBstated = 0.08 * inputs['Bstated'] # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Skipping analysis of rows:  []\n""
     ]
    }
   ],
   ""source"": [
    ""[complex_fluorescence_comp, ligand_fluorescence_comp] = parser.get_data_using_inputs(inputs)  ""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""name = 'Abl-IMA-Gefitinib-AB'""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""pymc_comp_model = make_comp_model(inputs['Pstated'], dPstated, inputs['Lstated'], dLstated,\n"",
    ""    inputs['Bstated'], dBstated, inputs['DG_L_mean'], inputs['DG_L_std'],# specific for competition assay\n"",
    ""    top_complex_fluorescence=complex_fluorescence_comp[name],\n"",
    ""    top_ligand_fluorescence=ligand_fluorescence_comp[name],\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=inputs['assay_volume'], DG_prior='uniform')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/choderaj/miniconda/lib/python3.6/site-packages/pymc/MCMC.py:81: UserWarning: Instantiating a Model object directly is deprecated. We recommend passing variables directly to the Model subclass.\n"",
      ""  warnings.warn(message)\n""
     ]
    }
   ],
   ""source"": [
    ""comp_mcmc = pymc.MCMC(pymc_comp_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""nburn = 500\n"",
    ""niter = 50000\n"",
    ""nthin = 20""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""from datetime import datetime""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'2019-03-25 22:39:53.001431'""
      ]
     },
     ""execution_count"": 14,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""str(datetime.now())""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""scrolled"": false
   },
   ""outputs"": [
    {
     ""ename"": ""AssertionError"",
     ""evalue"": """",
     ""output_type"": ""error"",
     ""traceback"": [
      ""\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"",
      ""\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)"",
      ""\u001b[0;32m<ipython-input-15-df5fd6e9e97d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcomp_mcmc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnburn\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mniter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mburn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnburn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthin\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnthin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtune_throughout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/MCMC.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, iter, burn, thin, tune_interval, tune_throughout, save_interval, burn_till_tuned, stop_tuning_after, verbose, progress_bar)\u001b[0m\n\u001b[1;32m    284\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    285\u001b[0m         \u001b[0;31m# Run sampler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 286\u001b[0;31m         \u001b[0mSampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlength\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    287\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    288\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/Model.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, iter, length, verbose)\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[0;31m# Loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_current_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    246\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_finalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/MCMC.py\u001b[0m in \u001b[0;36m_loop\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    320\u001b[0m                         \u001b[0mprint_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Step method %s stepping'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mstep_method\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m                     \u001b[0;31m# Step the step method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m                     \u001b[0mstep_method\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m                 \u001b[0;31m# Record sample to trace, if appropriate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/StepMethods.py\u001b[0m in \u001b[0;36mstep\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    499\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstochastic\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 501\u001b[0;31m                 \u001b[0mlogp_p\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogp_plus_loglike\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    503\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mZeroProbability\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/StepMethods.py\u001b[0m in \u001b[0;36mlogp_plus_loglike\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    306\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstochastics\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstochastics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    307\u001b[0m         '''\n\u001b[0;32m--> 308\u001b[0;31m         \u001b[0msum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlogp_of_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmarkov_blanket\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    309\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m             print_('\\t' + self._id +\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/Node.py\u001b[0m in \u001b[0;36mlogp_of_set\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlogp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/six.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(tp, value, tb)\u001b[0m\n\u001b[1;32m    617\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    618\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 619\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    620\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    621\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/Node.py\u001b[0m in \u001b[0;36mlogp_of_set\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m             \u001b[0mlogp\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mZeroProbability\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/PyMCObjects.py\u001b[0m in \u001b[0;36mget_logp\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    901\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    902\u001b[0m             \u001b[0mprint_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\t'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m': logp accessed.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 903\u001b[0;31m         \u001b[0mlogp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_logp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32mLazyFunction.pyx\u001b[0m in \u001b[0;36mpymc.LazyFunction.LazyFunction.get (pymc/LazyFunction.c:2554)\u001b[0;34m()\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/Container.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    539\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;31m# DCValue(self)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDCValue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32mContainer_values.pyx\u001b[0m in \u001b[0;36mpymc.Container_values.DCValue.run (pymc/Container_values.c:1226)\u001b[0;34m()\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/PyMCObjects.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    466\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m             \u001b[0mprint_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\t'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m': value accessed.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 468\u001b[0;31m         \u001b[0m_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    469\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m             \u001b[0m_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'W'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32mLazyFunction.pyx\u001b[0m in \u001b[0;36mpymc.LazyFunction.LazyFunction.get (pymc/LazyFunction.c:2554)\u001b[0;34m()\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/Container.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    539\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;31m# DCValue(self)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDCValue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32mContainer_values.pyx\u001b[0m in \u001b[0;36mpymc.Container_values.DCValue.run (pymc/Container_values.c:1226)\u001b[0;34m()\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/pymc/PyMCObjects.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    466\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    467\u001b[0m             \u001b[0mprint_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\t'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m': value accessed.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 468\u001b[0;31m         \u001b[0m_value\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    469\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    470\u001b[0m             \u001b[0m_value\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'W'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32mLazyFunction.pyx\u001b[0m in \u001b[0;36mpymc.LazyFunction.LazyFunction.get (pymc/LazyFunction.c:2568)\u001b[0;34m()\u001b[0m\n"",
      ""\u001b[0;32m<ipython-input-5-c922b9d32c47>\u001b[0m in \u001b[0;36mtop_complex_fluorescence_model\u001b[0;34m(F_plate, F_buffer, F_PL, F_P, F_L, Ptrue, Ltrue, Btrue, DeltaG_L, DeltaG_B, epsilon_ex, epsilon_em)\u001b[0m\n\u001b[1;32m    174\u001b[0m                                            \u001b[0mDeltaG_L\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'DeltaG_L'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDeltaG_B\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'DeltaG_B'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m                                            epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n\u001b[0;32m--> 176\u001b[0;31m             \u001b[0;34m[\u001b[0m\u001b[0mP_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mL_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPL_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mB_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mPB_i\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbindingmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCompetitionBindingModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mequilibrium_concentrations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPtrue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mLtrue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDeltaG_L\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mBtrue\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDeltaG_B\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    177\u001b[0m             \u001b[0mIF_i\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpymcmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner_filter_effect_attenuation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepsilon_ex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepsilon_em\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpath_length\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mL_i\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgeometry\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'top'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m             \u001b[0mIF_i_plate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexp\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepsilon_ex\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mepsilon_em\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpath_length\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mL_i\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# inner filter effect applied only to plate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m~/miniconda/lib/python3.6/site-packages/assaytools-0.3.1-py3.6.egg/assaytools/bindingmodels.py\u001b[0m in \u001b[0;36mequilibrium_concentrations\u001b[0;34m(cls, Ptot, Ltot, DeltaG_L, Btot, DeltaG_B)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    228\u001b[0m         \u001b[0;31m# Check all concentrations are nonnegative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 229\u001b[0;31m         \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mP\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    230\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mL\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    231\u001b[0m         \u001b[0;32massert\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPL\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;31mAssertionError\u001b[0m: ""
     ]
    }
   ],
   ""source"": [
    ""comp_mcmc.sample(iter=(nburn+niter), burn=nburn, thin=nthin, progress_bar=False, tune_throughout=True)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""str(datetime.now())""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""plt.plot(comp_mcmc.DeltaG_L.trace())\n"",
    ""plt.plot(comp_mcmc.DeltaG_B.trace())""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""Abl_gefitinib = 2200e-9 # 2200 nM from DiscoverRx screen data \n"",
    ""\n"",
    ""AblGef_dG = np.log(Abl_gefitinib)\n"",
    ""\n"",
    ""Abl_imatinib = 1.1e-9 # 1.1 nM from DiscoverRx screen data \n"",
    ""\n"",
    ""AblIma_dG = np.log(Abl_imatinib)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""plt.figure(figsize=(12,10))\n"",
    ""plt.hist(comp_mcmc.DeltaG_L.trace(),normed=True,label='DelG_L(Gef) = %s' %comp_mcmc.DeltaG_L.trace().mean());\n"",
    ""plt.axvline(x=AblGef_dG,color='b',linestyle='--',label='IUPHARM Gef')\n"",
    ""plt.hist(comp_mcmc.DeltaG_B.trace(),normed=True,label='DelG_B(Ima) = %s' %comp_mcmc.DeltaG_B.trace().mean());\n"",
    ""plt.axvline(x=AblIma_dG,color='orange',linestyle='--',label='IUPHARM Ima')\n"",
    ""plt.xlabel('$\\Delta G$ ($k_B T$)');\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Below we do the same thing but for the general binding model""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""def make_gen_model(Pstated, dPstated, Lstated, dLstated,\n"",
    ""               Bstated = None, dBstated = None, DG_L_mean = None, DG_L_std = None, # specific for competition assay\n"",
    ""               top_complex_fluorescence=None, top_ligand_fluorescence=None,\n"",
    ""               DG_prior='uniform',\n"",
    ""               concentration_priors='lognormal',\n"",
    ""               quantum_yield_priors='lognormal',\n"",
    ""               use_primary_inner_filter_correction=True,\n"",
    ""               use_secondary_inner_filter_correction=True,\n"",
    ""               assay_volume=100e-6, well_area=0.1586,\n"",
    ""               epsilon_ex=None, depsilon_ex=None,\n"",
    ""               epsilon_em=None, depsilon_em=None,\n"",
    ""               ligand_ex_absorbance=None, ligand_em_absorbance=None,\n"",
    ""               F_PL=None, dF_PL=None):\n"",
    ""\n"",
    ""    assert (DG_L_std > 0)\n"",
    ""    \n"",
    ""    # Compute path length.\n"",
    ""    assert (well_area > 0)\n"",
    ""    path_length = assay_volume * 1000 / well_area # cm, needed for inner filter effect corrections\n"",
    ""\n"",
    ""    # Compute number of samples.\n"",
    ""    N = len(Lstated)\n"",
    ""    \n"",
    ""    # Check input.\n"",
    ""    # TODO: Check fluorescence and absorbance measurements for correct dimensions.\n"",
    ""    if (len(Pstated) != N):\n"",
    ""        raise Exception('len(Pstated) [%d] must equal len(Lstated) [%d].' % (len(Pstated), len(Lstated)))\n"",
    ""    if (len(dPstated) != N):\n"",
    ""        raise Exception('len(dPstated) [%d] must equal len(Lstated) [%d].' % (len(dPstated), len(Lstated)))\n"",
    ""    if (len(dLstated) != N):\n"",
    ""        raise Exception('len(dLstated) [%d] must equal len(Lstated) [%d].' % (len(dLstated), len(Lstated)))\n"",
    ""\n"",
    ""    # Note whether we have top or bottom fluorescence measurements.\n"",
    ""    top_fluorescence = (top_complex_fluorescence is not None) or (top_ligand_fluorescence is not None) # True if any top fluorescence measurements provided\n"",
    ""\n"",
    ""    # Create an empty dict to hold the model.\n"",
    ""    model = dict()\n"",
    ""    \n"",
    ""    # Prior on binding free energies.\n"",
    ""    if DG_prior == 'uniform':\n"",
    ""        DeltaG_B = pymc.Uniform('DeltaG_B', lower=DG_min, upper=DG_max) # binding free energy (kT), uniform over huge range\n"",
    ""    elif DG_prior == 'chembl':\n"",
    ""        DeltaG_B = pymc.Normal('DeltaG_B', mu=0, tau=1./(12.5**2)) # binding free energy (kT), using a Gaussian prior inspured by ChEMBL\n"",
    ""    else:\n"",
    ""        raise Exception(\""DG_prior = '%s' unknown. Must be one of 'uniform' or 'chembl'.\"" % DG_prior)\n"",
    ""    model['DeltaG_B'] = DeltaG_B\n"",
    ""    \n"",
    ""    # Prior on known binding free energy.\n"",
    ""    if DG_L_mean == None:\n"",
    ""        DeltaG_L = pymc.Uniform('DeltaG_L', lower=DG_min, upper=DG_max) # binding free energy (kT), uniform over huge range\n"",
    ""    else:\n"",
    ""        DeltaG_L = pymc.Normal('DeltaG_L', mu=DG_L_mean, tau=DG_L_std**(-2)) \n"",
    ""    model['DeltaG_L'] = DeltaG_L\n"",
    ""        \n"",
    ""    if concentration_priors != 'lognormal':\n"",
    ""        raise Exception(\""concentration_priors = '%s' unknown. Must be one of ['lognormal'].\"" % concentration_priors)\n"",
    ""    model['log_Ptrue'], model['Ptrue'] = pymcmodels.LogNormalWrapper('Ptrue', mean=Pstated, stddev=dPstated, size=Pstated.shape) # protein concentration (M)\n"",
    ""    model['log_Ltrue'], model['Ltrue'] = pymcmodels.LogNormalWrapper('Ltrue', mean=Lstated, stddev=dLstated, size=Lstated.shape) # ligand concentration (M)\n"",
    ""    model['log_Btrue'], model['Btrue'] = pymcmodels.LogNormalWrapper('Btrue', mean=Bstated, stddev=dBstated, size=Bstated.shape) # ligand concentration (M)\n"",
    ""    model['log_Ltrue_control'], model['Ltrue_control'] = pymcmodels.LogNormalWrapper('Ltrue_control', mean=Lstated, stddev=dLstated, size=Lstated.shape) # ligand concentration in ligand-only wells (M)\n"",
    ""\n"",
    ""    # extinction coefficient\n"",
    ""    model['epsilon_ex'] = 0.0\n"",
    ""    if use_primary_inner_filter_correction:\n"",
    ""        if epsilon_ex:\n"",
    ""            model['log_epsilon_ex'], model['epsilon_ex'] =  pymcmodels.LogNormalWrapper('epsilon_ex', mean=epsilon_ex, stddev=depsilon_ex) # prior is centered on measured extinction coefficient\n"",
    ""        else:\n"",
    ""            model['epsilon_ex'] = pymc.Uniform('epsilon_ex', lower=0.0, upper=1000e3, value=70000.0) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm\n"",
    ""\n"",
    ""    model['epsilon_em'] = 0.0\n"",
    ""    if use_secondary_inner_filter_correction:\n"",
    ""        if epsilon_em:\n"",
    ""            model['log_epsilon_em'], model['epsilon_em'] =  pymcmodels.LogNormalWrapper('epsilon_em', mean=epsilon_em, stddev=depsilon_em) # prior is centered on measured extinction coefficient\n"",
    ""        else:\n"",
    ""            model['epsilon_em'] = pymc.Uniform('epsilon_em', lower=0.0, upper=1000e3, value=0.0) # extinction coefficient or molar absorptivity for ligand, units of 1/M/cm\n"",
    ""\n"",
    ""    # Min and max observed fluorescence.\n"",
    ""    Fmax = 0.0; Fmin = 1e6;\n"",
    ""    if top_complex_fluorescence is not None:\n"",
    ""        Fmax = max(Fmax, top_complex_fluorescence.max()); Fmin = min(Fmin, top_complex_fluorescence.min())\n"",
    ""    if top_ligand_fluorescence is not None:\n"",
    ""        Fmax = max(Fmax, top_ligand_fluorescence.max()); Fmin = min(Fmin, top_ligand_fluorescence.min())\n"",
    ""\n"",
    ""    # Compute initial guesses for fluorescence quantum yield quantities.\n"",
    ""    F_plate_guess = Fmin\n"",
    ""    assert (path_length > 0)\n"",
    ""    F_buffer_guess = Fmin / path_length\n"",
    ""    F_L_guess = (Fmax - Fmin) / Lstated.max()\n"",
    ""    F_P_guess = Fmin\n"",
    ""    assert (Pstated.min() > 0)\n"",
    ""    F_P_guess = Fmin / Pstated.min()\n"",
    ""    assert (min(Pstated.max(), Lstated.max()) > 0)\n"",
    ""    F_PL_guess = (Fmax - Fmin) / min(Pstated.max(), Lstated.max())\n"",
    ""\n"",
    ""    # Priors on fluorescence intensities of complexes (later divided by a factor of Pstated for scale).\n"",
    ""\n"",
    ""    if quantum_yield_priors == 'lognormal':\n"",
    ""        stddev = 1.0 # relative factor for stddev guess\n"",
    ""        model['log_F_plate'], model['F_plate'] =  pymcmodels.LogNormalWrapper('F_plate', mean=F_plate_guess, stddev=stddev*F_plate_guess) # plate fluorescence\n"",
    ""        model['log_F_buffer'], model['F_buffer'] =  pymcmodels.LogNormalWrapper('F_buffer', mean=F_buffer_guess, stddev=stddev*F_buffer_guess) # buffer fluorescence\n"",
    ""        model['log_F_buffer_control'], model['F_buffer_control'] =  pymcmodels.LogNormalWrapper('F_buffer_control', mean=F_buffer_guess, stddev=stddev*F_buffer_guess) # buffer fluorescence\n"",
    ""        if (F_PL is not None) and (dF_PL is not None):\n"",
    ""            model['log_F_PL'], model['F_PL'] =  pymcmodels.LogNormalWrapper('F_PL', mean=F_PL, stddev=dF_PL)\n"",
    ""        else:\n"",
    ""            model['log_F_PL'], model['F_PL'] =  pymcmodels.LogNormalWrapper('F_PL', mean=F_PL_guess, stddev=stddev*F_PL_guess) # complex fluorescence\n"",
    ""        model['log_F_P'], model['F_P'] =  pymcmodels.LogNormalWrapper('F_P', mean=F_P_guess, stddev=stddev*F_P_guess) # protein fluorescence\n"",
    ""        model['log_F_L'], model['F_L'] =  pymcmodels.LogNormalWrapper('F_L', mean=F_L_guess, stddev=stddev*F_L_guess) # ligand fluorescence\n"",
    ""    else:\n"",
    ""        raise Exception(\""quantum_yield_priors = '%s' unknown. Must be one of ['lognormal', 'uniform'].\"" % quantum_yield_priors)\n"",
    ""\n"",
    ""    # Unknown experimental measurement error.\n"",
    ""    if top_fluorescence:\n"",
    ""        model['log_sigma_top'] = pymc.Uniform('log_sigma_top', lower=-10, upper=np.log(Fmax), value=np.log(5))\n"",
    ""        model['sigma_top'] = pymc.Lambda('sigma_top', lambda log_sigma=model['log_sigma_top'] : np.exp(log_sigma) )\n"",
    ""        model['precision_top'] = pymc.Lambda('precision_top', lambda log_sigma=model['log_sigma_top'] : np.exp(-2*log_sigma) )\n"",
    ""\n"",
    ""    if top_fluorescence:\n"",
    ""        model['log_sigma_abs'] = pymc.Uniform('log_sigma_abs', lower=-10, upper=0, value=np.log(0.01))\n"",
    ""        model['sigma_abs'] = pymc.Lambda('sigma_abs', lambda log_sigma=model['log_sigma_abs'] : np.exp(log_sigma) )\n"",
    ""        model['precision_abs'] = pymc.Lambda('precision_abs', lambda log_sigma=model['log_sigma_abs'] : np.exp(-2*log_sigma) )\n"",
    ""\n"",
    ""    if top_complex_fluorescence is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_complex_fluorescence_model(F_plate=model['F_plate'], F_buffer=model['F_buffer'],\n"",
    ""                                           F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],\n"",
    ""                                           Ptrue=model['Ptrue'], Ltrue=model['Ltrue'], Btrue=model['Btrue'], DeltaG_L=model['DeltaG_L'], DeltaG_B=model['DeltaG_B'],\n"",
    ""                                           epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            P_i = np.ones([len(Ptrue)]); L_i = np.ones([len(Ptrue)]); PL_i = np.ones([len(Ptrue)]); PB_i = np.ones([len(Ptrue)])\n"",
    ""            for index in range(len(Ptrue)):\n"",
    ""                # Replace small Ltrue values with near-zero because we can't easily deal with log(0) yet.\n"",
    ""                Ltrue = np.array(Ltrue)\n"",
    ""                Ltrue[Ltrue == 0] = Ltrue[Ltrue > 0].min() * 1.0e-3    \n"",
    ""    \n"",
    ""                # Check all concentrations are strictly positive            \n"",
    ""                assert (Ptrue[index] > 0), 'Ptrue must be strictly positive, was: {}'.format(Ptrue)\n"",
    ""                assert (Ltrue[index] > 0), 'Ltrue must be strictly positive, was: {}'.format(Ltrue)\n"",
    ""                assert (Btrue[index] > 0), 'Btrue must be strictly positive, was: {}'.format(Btrue)\n"",
    ""                \n"",
    ""                reactions = [ (DeltaG_L, {'PL': -1, 'P' : +1, 'L' : +1}), (DeltaG_B, {'PB' : -1, 'P' : +1, 'B' : +1}) ]\n"",
    ""                conservation_equations = [ (np.log(Ptrue[index]), {'PL' : +1, 'PB' : +1, 'P' : +1}), # total protein in well\n"",
    ""                    (np.log(Ltrue[index]), {'PL' : +1, 'L' : +1}),  # total L in well\n"",
    ""                    (np.log(Btrue[index]), {'PB' : +1, 'B' : +1}) ] # total B in well \n"",
    ""                log_concentrations = bindingmodels.GeneralBindingModel.equilibrium_concentrations(reactions, conservation_equations)\n"",
    ""                # extract concentrations from dict\n"",
    ""                P_i[index] = np.exp(log_concentrations['P'])\n"",
    ""                L_i[index] = np.exp(log_concentrations['L'])\n"",
    ""                PL_i[index] = np.exp(log_concentrations['PL'])\n"",
    ""                PB_i[index] = np.exp(log_concentrations['PB'])\n"",
    ""                # assert all concentrations are positive\n"",
    ""                assert (P_i[index] >= 0)\n"",
    ""                assert (L_i[index] >= 0)\n"",
    ""                assert (PL_i[index] >= 0)\n"",
    ""                assert (PB_i[index] >= 0)\n"",
    ""                                        \n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, L_i, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*L_i) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_PL*PL_i + F_L*L_i + F_P*P_i + F_buffer*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_complex_fluorescence_model'] = top_complex_fluorescence_model\n"",
    ""        model['log_top_complex_fluorescence'], model['top_complex_fluorescence'] = pymcmodels.LogNormalWrapper('top_complex_fluorescence',\n"",
    ""            mean=model['top_complex_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""            size=[N], observed=True, value=top_complex_fluorescence) # observed data\n"",
    ""        \n"",
    ""    if top_ligand_fluorescence is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_ligand_fluorescence_model(F_plate=model['F_plate'], F_buffer_control=model['F_buffer_control'],\n"",
    ""                                          F_L=model['F_L'],\n"",
    ""                                          Ltrue_control=model['Ltrue_control'],\n"",
    ""                                          epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            \n"",
    ""            # assert all concentrations are positive\n"",
    ""            assert np.all(Ltrue_control >= 0)\n"",
    ""            \n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue_control, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*Ltrue_control) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_L*Ltrue_control + F_buffer_control*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_ligand_fluorescence_model'] = top_ligand_fluorescence_model\n"",
    ""        model['log_top_ligand_fluorescence'], model['top_ligand_fluorescence'] = pymcmodels.LogNormalWrapper('top_ligand_fluorescence',\n"",
    ""                                                       mean=model['top_ligand_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""                                                       size=[N], observed=True, value=top_ligand_fluorescence) # observed data\n"",
    ""        \n"",
    ""    #Below this is competition model stuff!    \n"",
    ""    \n"",
    ""    # Compute number of samples.\n"",
    ""    if Bstated is not None:\n"",
    ""        N_B = len(Bstated)\n"",
    ""\n"",
    ""        # Check input.\n"",
    ""        # TODO: Check fluorescence and absorbance measurements for correct dimensions.\n"",
    ""        if (len(Lstated) != N_B):\n"",
    ""            raise Exception('len(Lstated) [%d] must equal len(Bstated) [%d].' % (len(Lstated), len(Bstated)))\n"",
    ""        if (len(Pstated) != N_B):\n"",
    ""            raise Exception('len(Pstated) [%d] must equal len(Bstated) [%d].' % (len(Pstated), len(Bstated)))\n"",
    ""        if (len(dPstated) != N_B):\n"",
    ""            raise Exception('len(dPstated) [%d] must equal len(Bstated) [%d].' % (len(dPstated), len(Bstated)))\n"",
    ""        if (len(dBstated) != N_B):\n"",
    ""            raise Exception('len(dBstated) [%d] must equal len(Bstated) [%d].' % (len(dBstated), len(Bstated)))\n"",
    ""\n"",
    ""            \n"",
    ""    if Bstated is not None:\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_complex_fluorescence_model(F_plate=model['F_plate'], F_buffer=model['F_buffer'],\n"",
    ""                                           F_PL=model['F_PL'], F_P=model['F_P'], F_L=model['F_L'],\n"",
    ""                                           Ptrue=model['Ptrue'], Ltrue=model['Ltrue'], Btrue=model['Btrue'],\n"",
    ""                                           DeltaG_L=model['DeltaG_L'], DeltaG_B=model['DeltaG_B'],\n"",
    ""                                           epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            P_i = np.ones([len(Ptrue)]); L_i = np.ones([len(Ptrue)]); PL_i = np.ones([len(Ptrue)]); PB_i = np.ones([len(Ptrue)])\n"",
    ""            for index in range(len(Ptrue)):\n"",
    ""                # Replace small Ltrue values with near-zero because we can't easily deal with log(0) yet.\n"",
    ""                Ltrue = np.array(Ltrue)\n"",
    ""                Ltrue[Ltrue == 0] = Ltrue[Ltrue > 0].min() * 1.0e-3    \n"",
    ""    \n"",
    ""                # Check all concentrations are strictly positive            \n"",
    ""                assert (Ptrue[index] > 0), 'Ptrue must be strictly positive, was: {}'.format(Ptrue)\n"",
    ""                assert (Ltrue[index] > 0), 'Ltrue must be strictly positive, was: {}'.format(Ltrue)\n"",
    ""                assert (Btrue[index] > 0), 'Btrue must be strictly positive, was: {}'.format(Btrue)\n"",
    ""                \n"",
    ""                reactions = [ (DeltaG_L, {'PL': -1, 'P' : +1, 'L' : +1}), (DeltaG_B, {'PB' : -1, 'P' : +1, 'B' : +1}) ]\n"",
    ""                conservation_equations = [ (np.log(Ptrue[index]), {'PL' : +1, 'PB' : +1, 'P' : +1}), # total protein in well\n"",
    ""                    (np.log(Ltrue[index]), {'PL' : +1, 'L' : +1}),  # total L in well\n"",
    ""                    (np.log(Btrue[index]), {'PB' : +1, 'B' : +1}) ] # total B in well \n"",
    ""                log_concentrations = bindingmodels.GeneralBindingModel.equilibrium_concentrations(reactions, conservation_equations)\n"",
    ""                # extract concentrations from dict\n"",
    ""                P_i[index] = np.exp(log_concentrations['P'])\n"",
    ""                L_i[index] = np.exp(log_concentrations['L'])\n"",
    ""                PL_i[index] = np.exp(log_concentrations['PL'])\n"",
    ""                PB_i[index] = np.exp(log_concentrations['PB'])\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, L_i, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*L_i) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_PL*PL_i + F_L*L_i + F_P*P_i + F_buffer*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_complex_fluorescence_model'] = top_complex_fluorescence_model\n"",
    ""        model['log_top_complex_fluorescence'], model['top_complex_fluorescence'] = pymcmodels.LogNormalWrapper('top_complex_fluorescence',\n"",
    ""            mean=model['top_complex_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""            size=[N], observed=True, value=top_complex_fluorescence) # observed data\n"",
    ""\n"",
    ""        @pymc.deterministic\n"",
    ""        def top_ligand_fluorescence_model(F_plate=model['F_plate'], F_buffer_control=model['F_buffer_control'],\n"",
    ""                                          F_L=model['F_L'],\n"",
    ""                                          Ltrue_control=model['Ltrue_control'],\n"",
    ""                                          epsilon_ex=model['epsilon_ex'], epsilon_em=model['epsilon_em']):\n"",
    ""            IF_i = pymcmodels.inner_filter_effect_attenuation(epsilon_ex, epsilon_em, path_length, Ltrue_control, geometry='top')\n"",
    ""            IF_i_plate = np.exp(-(epsilon_ex+epsilon_em)*path_length*Ltrue_control) # inner filter effect applied only to plate\n"",
    ""            Fmodel_i = IF_i[:]*(F_L*Ltrue_control + F_buffer_control*path_length) + IF_i_plate*F_plate\n"",
    ""            return Fmodel_i\n"",
    ""        # Add to model.\n"",
    ""        model['top_ligand_fluorescence_model'] = top_ligand_fluorescence_model\n"",
    ""        model['log_top_ligand_fluorescence'], model['top_ligand_fluorescence'] = pymcmodels.LogNormalWrapper('top_ligand_fluorescence',\n"",
    ""                                                       mean=model['top_ligand_fluorescence_model'], stddev=model['sigma_top'],\n"",
    ""                                                       size=[N], observed=True, value=top_ligand_fluorescence) # observed data\n"",
    ""    # Promote this to a full-fledged PyMC model.\n"",
    ""    pymc_model = pymc.Model(model)\n"",
    ""\n"",
    ""    # Return the pymc model\n"",
    ""    return pymc_model""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""pymc_gen_model = make_gen_model(inputs['Pstated'], dPstated, inputs['Lstated'], dLstated,\n"",
    ""    inputs['Bstated'], dBstated, inputs['DG_L_mean'], inputs['DG_L_std'],# specific for competition assay\n"",
    ""    top_complex_fluorescence=complex_fluorescence_comp[name],\n"",
    ""    top_ligand_fluorescence=ligand_fluorescence_comp[name],\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=inputs['assay_volume'], DG_prior='uniform')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/choderaj/miniconda/lib/python3.6/site-packages/pymc/MCMC.py:81: UserWarning: Instantiating a Model object directly is deprecated. We recommend passing variables directly to the Model subclass.\n"",
      ""  warnings.warn(message)\n""
     ]
    }
   ],
   ""source"": [
    ""gen_mcmc = pymc.MCMC(pymc_gen_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'2019-03-25 22:39:55.304800'""
      ]
     },
     ""execution_count"": 19,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""str(datetime.now())""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      "" [-----------------100%-----------------] 5501 of 5500 complete in 31152.6 sec""
     ]
    }
   ],
   ""source"": [
    ""nburn = 500\n"",
    ""nthin = 1\n"",
    ""niter = 5000\n"",
    ""gen_mcmc.sample(iter=(nburn+niter), burn=nburn, thin=nthin, progress_bar=True, tune_throughout=True)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'2019-03-26 07:38:33.916083'""
      ]
     },
     ""execution_count"": 27,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# Had to stop after 45 minutes\n"",
    ""str(datetime.now())""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""[<matplotlib.lines.Line2D at 0x1a29201ef0>]""
      ]
     },
     ""execution_count"": 28,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 432x288 with 1 Axes>""
      ]
     },
     ""metadata"": {
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.plot(gen_mcmc.trace('DeltaG_L')[:])\n"",
    ""plt.plot(gen_mcmc.trace('DeltaG_B')[:])""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 29,
   ""metadata"": {},
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/choderaj/miniconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n"",
      ""The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n"",
      ""  alternative=\""'density'\"", removal=\""3.1\"")\n"",
      ""/Users/choderaj/miniconda/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n"",
      ""The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n"",
      ""  alternative=\""'density'\"", removal=\""3.1\"")\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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\n"",
      ""text/plain"": [
       ""<Figure size 864x720 with 1 Axes>""
      ]
     },
     ""metadata"": {
      ""needs_background"": ""light""
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.figure(figsize=(12,10))\n"",
    ""plt.hist(gen_mcmc.trace('DeltaG_L')[:],normed=True,label='DelG_L(Gef) = %s' %gen_mcmc.trace('DeltaG_L')[:].mean());\n"",
    ""plt.axvline(x=AblGef_dG,color='b',linestyle='--',label='IUPHARM Gef')\n"",
    ""plt.hist(gen_mcmc.trace('DeltaG_B')[:],normed=True,label='DelG_B(Ima) = %s' %gen_mcmc.trace('DeltaG_B')[:].mean());\n"",
    ""plt.axvline(x=AblIma_dG,color='orange',linestyle='--',label='IUPHARM Ima')\n"",
    ""plt.xlabel('$\\Delta G$ ($k_B T$)');\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""nburn = 10\n"",
    ""nthin = 1\n"",
    ""niter = 10\n"",
    ""%prun gen_mcmc.sample(iter=(nburn+niter), burn=nburn, thin=nthin, progress_bar=True, tune_throughout=True)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""%load_ext line_profiler\n"",
    ""nburn = 10\n"",
    ""nthin = 1\n"",
    ""niter = 10\n"",
    ""%lprun -f bindingmodels.GeneralBindingModel.equilibrium_concentrations gen_mcmc.sample(iter=(nburn+niter), burn=nburn, thin=nthin, progress_bar=True, tune_throughout=True)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 3"",
   ""language"": ""python"",
   ""name"": ""python3""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 3
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython3"",
   ""version"": ""3.6.7""
  }
 },
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}
","Unknown"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/2 MLE fit for three component binding - simulated data.ipynb",".ipynb","144992","778","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# MLE fit for three component binding - simulated data""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""In this notebook we will see how well we can reproduce Kd of a non-fluorescent ligand from simulated experimental data with a maximum likelihood function.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      "":0: FutureWarning: IPython widgets are experimental and may change in the future.\n""
     ]
    }
   ],
   ""source"": [
    ""import numpy as np\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""\n"",
    ""from scipy import optimize\n"",
    ""import seaborn as sns\n"",
    ""\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Competitive binding function\n"",
    ""#This function and its assumptions are defined in greater detail in this notebook: \n"",
    ""## modelling-CompetitiveBinding-ThreeComponentBinding.ipynb\n"",
    ""def three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total tracer(fluorescent) ligand concentration\n"",
    ""    Kd_L : float\n"",
    ""        Dissociation constant of the fluorescent ligand\n"",
    ""    Atot : float\n"",
    ""        Total competitive ligand concentration\n"",
    ""    Kd_A : float\n"",
    ""        Dissociation constant of the competitive ligand\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    A : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    Kd_L_app : float\n"",
    ""        Apparent dissociation constant of L in the presence of A\n"",
    ""        \n"",
    ""    Usage\n"",
    ""    -----\n"",
    ""    [P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)\n"",
    ""    \""\""\""\n"",
    ""    Kd_L_app = Kd_L*(1+Atot/Kd_A)                                \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd_L_app) - np.sqrt((Ptot + Ltot + Kd_L_app)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free tracer ligand concentration in sample cell after n injections (uM)\n"",
    ""    A = Atot - PL; # free competitive ligand concentration in sample cell after n injections (uM)\n"",
    ""    return [P, L, A, PL, Kd_L_app]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#Let's define our parameters\n"",
    ""Kd = 3800e-9 # M\n"",
    ""Kd_Competitor = 3000e-9 # M\n"",
    ""Ptot = 1e-9 * np.ones([12],np.float64) # M\n"",
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M\n"",
    ""L_Competitor =  10e-6 # M\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot, Ltot, Kd, L_Competitor, Kd_Competitor)\n"",
    ""#usin\n"",
    ""[P_base, L_base, A_base, PL_base, Kd_L_app_base] = three_component_competitive_binding(Ptot, Ltot, Kd, 0, Kd_Competitor)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
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FbHfMMMOb0nU9fJVbi7Dxjuy6Ns3jXwTA+plljO+3cd8c/LIWOgzCzCcCfgLXc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\nYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1196bf950>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.title('Competition assay')\n"",
    ""plt.semilogx(Ltot,PL_base,'green', marker='o', label = 'Fluorescent Ligand')\n"",
    ""plt.semilogx(Ltot,PL,'cyan', marker='o', label = 'Fluorescent Ligand + Competitor')\n"",
    ""plt.xlabel('$[L]_{tot}$')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#What if we change our Kd's a little\n"",
    ""#Kd_Gef_Abl\n"",
    ""Kd = 480e-9 # M\n"",
    ""#Kd_Ima_Abl \n"",
    ""Kd_Competitor = 21.0e-9 # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot, Ltot, Kd, L_Competitor, Kd_Competitor)\n"",
    ""#using _base as a subscript to define when we have no competitive ligand\n"",
    ""[P_base, L_base, A_base, PL_base, Kd_L_app_base] = three_component_competitive_binding(Ptot, Ltot, Kd, 0, Kd_Competitor)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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VYJdtPlRn+6O7jVTxaIykmCwNXEj48nZIW0na1QLtaT7RAspZC/6Pxhr6K6qPxgkbjrd+Q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\nItIQFRAREWnI/wPhnGBvV4kjYwAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c3a0f10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.title('Competition assay')\n"",
    ""plt.semilogx(Ltot,PL_base,'green', marker='o', label = 'Fluorescent Ligand')\n"",
    ""plt.semilogx(Ltot,PL,'cyan', marker='o', label = 'Fluorescent Ligand + Competitor')\n"",
    ""plt.xlabel('$[L]_{tot}$')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Now make this a fluorescence experiment.\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Making max 400 relative fluorescence units, and scaling all of PL to that\n"",
    ""npoints = len(Ltot)\n"",
    ""sigma = 10.0 # size of noise\n"",
    ""F_i = (400/1e-9)*PL + sigma * np.random.randn(npoints)\n"",
    ""F_i_base = (400/1e-9)*PL_base + sigma * np.random.randn(npoints)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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IXjXoXuDfmOCahyi0gW+I/kZQ1nbsxJnWOA5YBLosy0cK4w5hkKncqk39S2XTu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\nYowxpiJWQIwxxlTk/wG8kJ2FKurllQAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1197c4d10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.title('Competition assay')\n"",
    ""plt.semilogx(Ltot,F_i_base,'green', marker='o', label = 'Fluorescent Ligand')\n"",
    ""plt.semilogx(Ltot,F_i,'cyan', marker='o', label = 'Fluorescent Ligand + Competitor')\n"",
    ""plt.xlabel('$[L]_{tot}$')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#And makeup an F_L\n"",
    ""F_L = 0.3""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### First let's see if we can find Kd of our fluorescent ligand from the three component binding model, if we know there's no competitive ligand""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# This function fits Kd_L when L_Competitor is 0\n"",
    ""def find_Kd_from_fluorescence_base(params):\n"",
    ""    \n"",
    ""    [F_background, F_PL, Kd_L] = params\n"",
    ""    \n"",
    ""    N = len(Ltot)\n"",
    ""    Fmodel_i = np.zeros([N])\n"",
    ""    \n"",
    ""    for i in range(N):\n"",
    ""        [P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot[0], Ltot[i], Kd_L, 0, Kd_Competitor)\n"",
    ""        Fmodel_i[i] = (F_PL*PL + F_L*L) + F_background\n"",
    ""    \n"",
    ""    return Fmodel_i""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n"",
      ""  warnings.warn(\""No labelled objects found. \""\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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jtauCaGOMfsoQsrxbnfhB1z982FZD8SuXvwaGkI+r8VymEMJk0uW/Fpam/bbj7\n18A1119//bG77777uBjjSzkytqlnz32T6p6/beoD6YIutxAGG0paywltI1UIYTywRvGxZsPnNYFt\ngO149TLzE6SlRq4Bro0xPlocP7amxUOGgZ4rIGYWgK8BWwEvAJ9w9/vypmpePy2ELYCL9zlqDh0q\nIrUcSlqV3Jf3ihFQq/NqIVhjkK8nDfJSLwP/QyoYVwO3aoa49JqeKyCkTsBx7r6TmW1P2gFt38yZ\nyuh2C6EWQ0lzKFu8QwjhxRdfZNy4cROBccDKxce4AT43fj2B/ovD3wwRMwKPAw+ShtT+ufjc9+v/\njTE+W/IUiFSqFwvIzqR3XLj7zWY2NXOesvptCcS4fPMQwoy+x7/5zW8yc+bMGTR5LXXzvz/olxtu\nu8/rCsh9t85dGMK+hzQc6vt6rdwOfb5+3e3TTjuNY4455phmH2/v/NgW627+rl3GrfrGN7/43NNP\nPPiba2+6Z8EldwOjSZdrVnwe1c+xQe/b7eNf323VN67Z99Sw5KmHzw8hnEQ/BWHcuHEAz7zuSeU9\nRSoAdzBwUfgz8HiMcVkHvp9Idr1YQCby2n/Qy8xslLvXpfnebwvh2Sf+dzTwzb7HZ86cSX/HB/Lb\nX3ybvzzivHW7D/GGyeuy5KmH+P2vfsSffP5BwEFt5G5Jqh2c2sxj17ad2Xj7/V65vfIbJq++yY77\nf3DJUw/zJ5/fdpbxE9/c7/FVJq25Muly0Qukv60Xio8X99hjj7+77rrrftp4rM/ngY49x6tF4bEY\n44tt/wAiNdNzo7DM7AxggbtfVtx+0N3XG+QpPfUD3Hjbw5x24aLXHd9xgxdYf/LgbzzTSM3m9X18\nN26vOLbi63Zuf/+mv/Lks6//da0+aTQz9nwTo0ePZtSoUa98bvy6mftO+OYiHnrsude9/gZrTeQr\nR+/WzxkUGdHaHkHWiy2Qm4D3A5eZ2Q6k/ZiH0jND6XbZZh1Ou3DR/qQ+jxUduScff9hHB+r/qPtQ\nwKbzn3X1nGWkS06v8dgzLy/bYYcdxrQb5KHHnut3iPMDjyyezsD9TyPm/PegOmeH+udvWy+2QFaM\nwtqyOHSgu98zyFPq/kscMfn3OWrOHfQ/AOCOuWdM26oTYYqO9NcU7yFGYY2Y89+D6pwd6p+/bT1X\nQFpQ91/iiMnfg5MgYQSd/x5U5+xQ//xt01pYUpleWktKRNqnFkh+PZW/hYl4PZW/BcqfT52zQ/3z\nt00FJL9r6evNAAAHDklEQVSeyd/iJaaeyd8i5c+nztmh/vnbpktY0qj2W7aKSHVUQKSR1tkSkaap\ngEijgdbTGvHrbInI66mASKPZAxzPvmWriPQeFRB5hYbZikgZGoWVn/Lnpfz51Dk71D9/29QCERGR\nlqiAiIhIS1RARESkJSogIiLSEhUQERFpiQqIiIi0RAVERERaknVLWzN7GFix2+ACdz+h2Mb2S8BS\n4Dp3/1y2gCIiMqBsBcTM3gIscvdpfe46B/iguz9gZlea2VbufnuGiCIiMoicLZApwDpmdj3wPHAk\n8Cgw1t0fKB5zDfBuQAVERKTHVFJAzOzjpAKxYup/BA4FZrv7j8zsncD3gQ8Cixue+iywYRUZRUSk\nnEoKiLufB5zXeMzMxpMW7MPdbzKztUjFY2LDwyYAT1eRUUREysk5CutE4NMAZrYV8JC7Pwu8aGYb\nmlkA3gPMG+J16r6YmfLnpfz51Dk71D9/23L2gZwCXGhme5NaIv9SHP8kcBGpuF3r7gvzxBMRkcEM\nh+XcRUQkA00kFBGRlqiAiIhIS1RARESkJSogIiLSEhUQERFpSdbFFLvBzHYD/tHdZ/R3u5c1ZjWz\nHYGDSbP2j3D3xYM/uzeY2X6k+TsvAie4e60mgprZ+4B/AMYCp9dtHTYzOwLYGtgYuNDdv545Uilm\n9jbgCGAccJq735U5UtPMbEvgK8B9wAXufkPmSKWZ2RrAT939Hc08fli1QIoFGrch/fG97nYv6yfr\nzOLj28D+uXK1YBqv5p6ZOUsrHgf+tvh4KHOW0tz9y6TzfmfdikfhE8DDwAvAA3mjlLY98AhpXttv\nM2dp1TGUOO893wIxs+2BU9x9t2J2+teArUh/YJ9w9/tWPNbd/wCcaWbf7e921drJDox295fM7FFg\n96qzNyrzcwBnA98i/RGuWnXW/pTMPxP4CLAD8H4gy99Oo5L5AaYDP6445oBK5n8r8M+kxVb/mbQ6\ndzYls88DLgHWIP1HfGzVefsqk9/MDgEuBI5q9vV7ugViZscA5/Lqu/J9gXHuvhNwHHBm8bjPmdlF\nZvbG4nF9lxiofMmBNrKv8JyZjQXWIq1SnEXZnwNYk/Qu8gZ64B18yfwXA28GngOeACZXn/i1Wvg7\nWg3Yxd2vzRK4jxbO/+Ok1bmfIvNSIS387W8NjCat3ze6+sSv1cK5/zDpsvl2ZvahZr5Hr7dAfk9a\nofd7xe2dgasB3P1mM5tafH1in+f1nV6fY7p9q9lXOBf4Bul3dHB3ow6q1M9hZrsC55P6EA6pPO3r\nlc2/PenyWyS9i8yt9N9RsVBpryh7/qeQ/vYDqS8kp7LZdyT1gbwE9MJGeC39H2Rm33X3HzXzDXq6\ngLj75Wa2fsOhicAzDbeXmdkod1/e53n/Z7DbVWg3u7vfChzY/aSDK/tzFB2HPdN52EL+m4Gbq8w4\nmFb+jtz9Y5UFHEIL538R6dJVdi1kXwAsqDLjYDr1/+dgevoSVj8Wk5Z4X+F1P3wPq3P2RnX/OZQ/\nrzrnr3N26EL+uhWQm4C9AYq903+TN04pdc7eqO4/h/LnVef8dc4OXcjf05ew+nE5sIeZ3VTczn6J\np4Q6Z29U959D+fOqc/46Z4cu5Ndy7iIi0pK6XcISEZEeoQIiIiItUQEREZGWqICIiEhLVEBERKQl\nKiAiItISFRAREWmJCoiIiLREBURERFqiAiLSwMyONbPpQzzmH80s+2ZBIrmpgIi81jh3v3jFDTN7\nn5k9YGZnmdleAO5+ER3aJrlhXaIVt9c3s+Vmdk6f41sXxyvfmkBkICogIoP7b2Bl4Bh3v7qTL2xm\nbwXu7eeuJ4G9ii1IV/go8Fgnv79Iu1RARAb3duABd3+xC6/9XuCqfo4vAW4Ddmk4tgfwsy5kEGmZ\nCojI4HYG5g/1IDP7TJnjhT2BgfYu/yGwX/EaU4HbSVulivQMFRCRwe1M2ohnKBPKHDezlYHx7v6X\nfu6OwFxSCwXS5asfkPYJF+kZddtQSqRq7wQ+veKGme1L6hcJDcd2B9Y1s02A3Uh9FU8Bo4vjG7t7\n376Ovwd+MdA3dffnzOzXZvZ3xWseCww6OkykamqBiAzAzNYDXnL3x4vbqwCbunvfS0l/Ie329lHS\nJanrgPeTisjl/RQPGLj/o9GlwCnALTXbe1tGCBUQkX6Y2RTgJGCJmX3czI4CFgC39vPw7YGFwAbu\nfj/wd8AvgR2AX5nZ2v08Z4q7LxoixlxgK+CS4ra2D5Weoi1tRRqY2Ynu/rkyjzOz/Ukjp1YDHgI2\ndvdzG47/3N2f62ZukRzUByLyWs12VL/yOHe/pM99vxjguMiwoktYIq/112aWMgH+WlEekZ6lS1gi\nItIStUBERKQlKiAiItISFRAREWmJCoiIiLREBURERFqiAiIiIi1RARERkZaogIiISEv+P2BXwVSZ\nDPjJAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11cfe1610>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""initial_guess = [1,400/1e-9,3800e-9]\n"",
    ""prediction = find_Kd_from_fluorescence_base(initial_guess)\n"",
    ""\n"",
    ""plt.semilogx(Ltot,prediction,color='k')\n"",
    ""plt.semilogx(Ltot,F_i_base, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""def sumofsquares(params):\n"",
    ""    prediction = find_Kd_from_fluorescence_base(params)\n"",
    ""    return np.sum((prediction - F_i_base)**2)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""The predicted parameters are [ -2.74841327e+00   4.03350513e+11   4.45151221e-07]\n""
     ]
    }
   ],
   ""source"": [
    ""initial_guess = [0,3E11,2000E-9]\n"",
    ""fit = optimize.minimize(sumofsquares,initial_guess,method='Nelder-Mead')\n"",
    ""print \""The predicted parameters are\"", fit.x""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""fit_prediction = find_Kd_from_fluorescence_base(fit.x)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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bAr3h9YOBhwMdijM+TpizpTFfbRbJSAREpSAjhYGABr48u3y3GeEmHQYJndPHU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\nInVRARERkbqogIiISF3+Dw3iJ7Xy4oAnAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11cfe1790>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,fit_prediction,color='k')\n"",
    ""plt.semilogx(Ltot,F_i_base, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Kd_L_MLE = fit.x[2]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""def Kd_format(Kd):\n"",
    ""    if (Kd < 1e-12):\n"",
    ""        Kd_summary = \""Kd = %.1f nM \"" % (Kd/1e-15)\n"",
    ""    elif (Kd < 1e-9):\n"",
    ""        Kd_summary = \""Kd = %.1f pM \"" % (Kd/1e-12)\n"",
    ""    elif (Kd < 1e-6):\n"",
    ""        Kd_summary = \""Kd = %.1f nM \"" % (Kd/1e-9)\n"",
    ""    elif (Kd < 1e-3):\n"",
    ""        Kd_summary = \""Kd = %.1f uM \"" % (Kd/1e-6)\n"",
    ""    elif (Kd < 1):\n"",
    ""        Kd_summary = \""Kd = %.1f mM \"" % (Kd/1e-3)\n"",
    ""    else:\n"",
    ""        Kd_summary = \""Kd = %.3e M \"" % (Kd)\n"",
    ""    \n"",
    ""    return Kd_summary""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'Kd = 445.2 nM '""
      ]
     },
     ""execution_count"": 20,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Kd_format(Kd_L_MLE)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'delG = -14.6248517897 kT'""
      ]
     },
     ""execution_count"": 21,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""delG_summary = \""delG = %s kT\"" %np.log(Kd_L_MLE)\n"",
    ""delG_summary""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Okay, cool now let's try to fit our competitive ligand""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# This function fits Kd_A when Kd_L already has an estimate\n"",
    ""\n"",
    ""def find_Kd_from_fluorescence_competitor(params):\n"",
    ""    \n"",
    ""    [F_background, F_PL, Kd_Competitor] = params\n"",
    ""    \n"",
    ""    N = len(Ltot)\n"",
    ""    Fmodel_i = np.zeros([N])\n"",
    ""    \n"",
    ""    for i in range(N):\n"",
    ""        [P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot[0], Ltot[i], Kd_L_MLE, L_Competitor, Kd_Competitor)\n"",
    ""        Fmodel_i[i] = (F_PL*PL + F_L*L) + F_background\n"",
    ""    \n"",
    ""    return Fmodel_i""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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uF0HELPjWbCkj5lLqZ3Ymtp+JMqUksXTDW6XG3R4CreLNY3NXbmEbJj0+z34Cb\nXaA6bsGnZmu7AmJmAfgBsDHwT2B/d9dFUm8qbTO7rK2nEEIXMBZYZYuPHr3XyqtP2H+5ESPX+eeC\n51568JYLFwBrsfRFuc8AV/BmsbgtxvjcQM9T1uNTodlvwE0tUDNPmnrBrodfCunDWHcBP74kxz6L\ntisgwG7A8u6+tZltSTqNcbfMmWrS5E+RTf1P1MmDlCGEkaQV+Vbp/n7OOecwbdq06cW2VSp+X/nz\nW4CuNWwbxq876V/7GzX2bStvvNMXVu7qGvbAX++9qrJg/LXOsYtSty5b8Abc9BZCkbXtj3W7aLvT\neM3sJOBWd/9lcfsJd1+rn4e01al0zT4VsI79V62/fV9+8m4XknIG0qftLqBr4cKFr4wePXpM5bbi\nK/Tc9uHpFz8WQhjWc+cxxjeuOGX3KcAIYHjxvdafhwMj3jl56gbrbvLhjUaNGbfSKy899crDt/36\nucfv+/0wUiEYVcPhWAQ8X3y9ADz/gf1/uO0KY8ev3Mt975150tSNa9h3r3Y9/NLFwDLHB1g886Sp\nw/t4WFu9/mtUc/biNdouLYQyH/uGaMcWyFjgpYrbi82sy92X5ApUo14/RS549m8/DyF8jx4vuPHj\nx/P0008/1XN7hWW2r/nuKQvW2/yjK4xeec1hC1948o1HbvvNq0/OueEH4WR+wJtv8lT5/V8/b7fv\nqcuPHdez6x7mP/PY+fReWBg9ejTAgj6yL2XBs39l7NvW7WX73GHA7Gr20Z81bBsmTpn2r9srrrz6\nihvv9IUVY1zy9BP3X3s/RSGgoiicffbZJ37mM5/ZvXIb8Hwf4xOL+3jqRnUfahB3AGohtJd2LCDz\ngTEVt6spHm3TjOrqCixZsmyc0ausuZyZrQqQzuh80yqrrDK+t+3dlt3+DM/efibPpe3DVh4WRq/8\n3vf+634hhKV+Huh7989j3rp2r88/5q1vZ8qUKYQQ6OrqWuqrlm2Ll3+Kl1l3mf1PGL+IbY46iuHD\nhzNixAhGjBhR1c89b3/v4r8x7/lFy+x/t0//9/jvH/GH8b3+44Bp06Zd3NfvKq27+ljmzpvf2/bl\naMBr8Mj/nMx3z72jt+0bDbD/tnn916HM2aHc+QfdemrHAvJH4CPAr8zsfcCfq3hM2zQjlyyJ99LL\np8jQNezeBx54oLdujrZpBu96+KV9Zr/++uv76qKppwtuqS6IM0/8akM+Uc778UO9dgHNnTd/MamL\nqzdV559gxGkCAAAHqElEQVQ7b36vXXxz583fmwZ8Kt5u07X47rl3LHN8ttt0rf723TavnzqUOTuU\nP/+gtWMBuRjY0cz+WNye1t+d21CZTwUs+yBl6c/SUReNlEnbDaLXoe0+BdQ40NdW+esYpGyb/HWe\nYNA2+etUTwuwXa4zGVLHvhOpgOSn/A1U5gJYp6rzN/MMvjoNmWPfqVRA8lP+vIZM/r7GuGjQach1\nGDLHvlNpSVuRoaO0sxhIe1IBERk6+jqZQNeZSF1UQESGjm/1sb0MZwhKG1IBERkiioHyvUnTyC8u\nvucaQJcOoEH0/JQ/L+XPp8zZofz5B00tEBERqYsKiIiI1EUFRERE6qICIiIidVEBERGRuqiAiIhI\nXVRARESkLiogIiJSFxUQERGpS9YVCc3sCeDB4ubN7v71Yhnb7wGvA9e4+7HZAoqISJ+yFRAzWw+4\nw92n9vjVDGB3d59rZleY2cbufk+GiCIi0o+cLZDJwFpmdi3wCjAd+Acwwt3nFve5CtgBUAEREWkz\nLSkgZvYZUoHonnwsAocA33L3X5vZvwG/AHYH5lc8dAHwjlZkFBGR2rSkgLj72cDZldvMbBRpSmnc\n/Y9mtjqpeIytuNsY4MVWZBQRkdrkPAvraOBLAGa2MfC4uy8AFpnZO8wsAB8EZg2wn7JPp6z8eSl/\nPmXODuXPP2g5x0BOAM41sw+RWiKfLrZ/DjiPVNyudvfb8sQTEZH+dMKCUiIikoEuJBQRkbqogIiI\nSF1UQEREpC4qICIiUhcVEBERqUvWyRSbwcy2Bz7p7gf0drudVWY1s62Ag0hX7R/m7vP7f3R7MLM9\nSNfvLAK+7u6luhDUzD4MfBQYAZxYtnnYzOwwYBNgfeBcdz8jc6SamNm7gcOA5YHvuvtfMkeqmplt\nBHwfeBT4ibvfkDlSzcxsVeByd9+8mvt3VAukmKBxU9KLb5nb7ayXrAcWXz8G9sqVqw5TeTP3gZmz\n1OMZYM3i6/HMWWrm7qeSjvt9ZSsehf2BJ4B/AnPzRqnZlsA80nVt92fOUq8jqeG4t30LxMy2BE5w\n9+2Lq9N/AGxMeoHt7+6Pdt/X3R8BTjazn/V2u9UGkx0Y5u6vmdk/gA+0OnulWv4dwOnAj0gvwhVb\nnbU3NeY/ENgTeB/wESDLa6dSjfkB9gZ+0+KYfaox/7uA/UiTre5Hmp07mxqzzwIuAFYlvRF/tdV5\ne6olv5kdDJwLHF7t/tu6BWJmRwJn8ean8t2A5d19a+BrwMnF/Y41s/PM7C3F/XpOMdDyKQcGkb3b\ny2Y2AlidNEtxFrX+O4DVSJ8ib6ANPsHXmP984G3Ay8CzwCqtT7y0Ol5HKwPbufvVWQL3UMfxf4Y0\nO/fzZJ4qpI7X/ibAMNL8fcNan3hpdRz7j5O6zbcws49V8xzt3gJ5mDRD78+L29sAvwNw91vNbLPi\n56N7PK7n5fU5LrevN3u3s4AzSX+jg5obtV81/TvMbApwDmkM4eCWp11Wrfm3JHW/RdKnyNxqfh0V\nE5W2i1qP/2TSaz+QxkJyqjX7VqQxkNeAdlgIr673IDP7mbv/uponaOsC4u4Xm9k6FZvGAi9V3F5s\nZl3uvqTH4/bt73YrDDa7u98JTGt+0v7V+u8oBg7bZvCwjvy3Are2MmN/6nkdufunWhZwAHUc/ztI\nXVfZ1ZH9ZuDmVmbsT6PeP/vT1l1YvZhPmuK92zL/+DZW5uyVyv7vUP68ypy/zNmhCfnLVkD+CHwI\noFg7/c9549SkzNkrlf3fofx5lTl/mbNDE/K3dRdWLy4GdjSzPxa3s3fx1KDM2SuV/d+h/HmVOX+Z\ns0MT8ms6dxERqUvZurBERKRNqICIiEhdVEBERKQuKiAiIlIXFRAREamLCoiIiNRFBUREROqiAiIi\nInVRARERkbqogIhUMLOvmtneA9znk2aWfbEgkdxUQESWtry7n999w8w+bGZzzew0M9sZwN3Po0HL\nJFfMS9R9ex0zW2JmM3ps36TY3vKlCUT6ogIi0r/fAiOBI939d43csZm9C3iol189B+xcLEHa7RPA\n0418fpHBUgER6d97gbnuvqgJ+94FuLKX7QuBu4DtKrbtCPy+CRlE6qYCItK/bYDZA93JzL5Ry/bC\nTkBfa5f/Etij2MdmwD2kpVJF2oYKiEj/tiEtxDOQMbVsN7ORwCh3f6GXX0dgJqmFAqn76kLSOuEi\nbaNsC0qJtNq/AV/qvmFmu5HGRULFtg8Aa5vZBsD2pLGK54Fhxfb13b3nWMf7gev7elJ3f9nM7jaz\nbYt9fhXo9+wwkVZTC0SkD2b2duA1d3+muL0CMMHde3YlvUBa7e0TpC6pa4CPkIrIxb0UD+h7/KPS\nRcAJwO0lW3tbhggVEJFemNlk4JvAQjP7jJkdDtwM3NnL3bcEbgPWdffHgG2BW4D3AX8yszV6ecxk\nd79jgBgzgY2BC4rbWj5U2oqWtBWpYGZHu/uxtdzPzPYinTm1MvA4sL67n1Wx/Tp3f7mZuUVy0BiI\nyNKqHaj+1/3c/YIev7u+j+0iHUVdWCJLe7WaqUyAV1uUR6RtqQtLRETqohaIiIjURQVERETqogIi\nIiJ1UQEREZG6qICIiEhdVEBERKQuKiAiIlIXFRAREanL/weqkdPT/YeSJwAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11d00eed0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""initial_guess = [0,400/1e-9,3800e-9]\n"",
    ""prediction = find_Kd_from_fluorescence_competitor(initial_guess)\n"",
    ""\n"",
    ""plt.semilogx(Ltot,prediction,color='k')\n"",
    ""plt.semilogx(Ltot,F_i, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def sumofsquares(params):\n"",
    ""    prediction = find_Kd_from_fluorescence_competitor(params)\n"",
    ""    return np.sum((prediction - F_i)**2)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""The predicted parameters are [ -6.93274088e-04   2.12842582e+11   6.40589610e-08]\n""
     ]
    }
   ],
   ""source"": [
    ""initial_guess = [0,3E11,2000E-9]\n"",
    ""fit_comp = optimize.minimize(sumofsquares,initial_guess,method='Nelder-Mead')\n"",
    ""print \""The predicted parameters are\"", fit_comp.x""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""fit_prediction_comp = find_Kd_from_fluorescence_competitor(fit_comp.x)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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UH2sg+6CEz2wLYBfhDj2I/TdGvA9iZ7K/Im6jAX/AF818F7Ag8BPwZ2L73iTdX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\n5f8DnG9qTwMuxHAAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x119729f10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,fit_prediction_comp,color='k')\n"",
    ""plt.semilogx(Ltot,F_i, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Kd_A_MLE = fit_comp.x[2]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 29,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'Kd = 64.1 nM '""
      ]
     },
     ""execution_count"": 29,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Kd_format(Kd_A_MLE)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 30,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'delG = -16.5634619124 kT'""
      ]
     },
     ""execution_count"": 30,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""delG_summary = \""delG = %s kT\"" %np.log(Kd_A_MLE)\n"",
    ""delG_summary""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""## Let's plot all these results with each other and see how we did""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 31,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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GmPB0GbAzc4r23ahOmVdl2ok8AzlLViQWKxxgTv0TVXft/btqU7SWsT1V1Va5P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\nDaIAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x119760ed0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,fit_prediction_comp,color='k')\n"",
    ""plt.semilogx(Ltot,F_i, 'o')\n"",
    ""plt.axvline(Kd_A_MLE,color='k',label='%s (MLE)'%Kd_format(Kd_A_MLE))\n"",
    ""plt.axvline(Kd_Competitor,color='b',label='%s'%Kd_format(Kd_Competitor))\n"",
    ""\n"",
    ""plt.semilogx(Ltot,fit_prediction,color='k')\n"",
    ""plt.semilogx(Ltot,F_i_base, 'o')\n"",
    ""plt.axvline(Kd_L_MLE,color='k',label='%s (MLE)'%Kd_format(Kd_L_MLE))\n"",
    ""plt.axvline(Kd,color='g',label='%s'%Kd_format(Kd))\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 32,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Awesome""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.11""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/3-Competition-Assay-Data-Plotting.ipynb",".ipynb","485047","1561","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#Competition assay analysis and thoughts""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Here we will analyze two competition assay conducted as a rough beginning to understand how to best design competition assays to the fluorescent kinase inhibitors (bosutinib, bosutinib isomer, erlotinib, and gefitinib) used in other assays in this repository. ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""The **first** (1st) part of this will be looking at data collected trying to compete off bosutinib from Src kinase with imatinib (conducted on March 11, 2015). The **second** (2nd) part of this will be looking at data collected trying to compete off gefitinib from Src kinase with imatinib (conducted on October 30, 2015). The **third** (3rd) part will be some simple modeling to see if these experiments follow our expectations and how we can better design the experiments to get better results from the competition assay. Then in a **fourth** (4th) section we'll work a little on a PYMC model to get affinities from the competition assay.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Couldn't import dot_parser, loading of dot files will not be possible.\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      "":0: FutureWarning: IPython widgets are experimental and may change in the future.\n""
     ]
    }
   ],
   ""source"": [
    ""#import needed libraries\n"",
    ""import re\n"",
    ""import os\n"",
    ""from lxml import etree\n"",
    ""import pandas as pd\n"",
    ""import pymc\n"",
    ""import numpy as np\n"",
    ""import matplotlib as mpl\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""import seaborn as sns\n"",
    ""%matplotlib inline""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Bosutinib Assay""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""The first attempt at a Bosutinib-Imatinib competition assay was on March 11, 2015. The full description of the assay can be found [here](https://docs.google.com/document/d/1IGw5RBzevF5hWLiH82LYZjXSnqvF4t-PD7mP0SP46Eo/edit). \n"",
    ""In short (and very similar to as described in the [lab-protocols repository](https://github.com/choderalab/lab-protocols/blob/master/Fluo_Kinase_Inhibitor_Assay/Fluo_Kinase_Inhibitor_Assay.md)) 100 uL of 0.5 $\\mu$M Src with a titration of Bosutinib up to 20 $\\mu$M in every other row was prepared. In one of two plates 10 $\\mu$M Imatinib was added to the whole plate to try to compete off the Bosutinib.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### importing and plotting data the clunky way for transparency, will change to use platereader.py once it is slightly nicer.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def get_wells_from_section(path):\n"",
    ""    reads = path.xpath(\""*/Well\"")\n"",
    ""    wellIDs = [read.attrib['Pos'] for read in reads]\n"",
    ""\n"",
    ""    data = [(float(s.text), r.attrib['Pos'])\n"",
    ""         for r in reads\n"",
    ""         for s in r]\n"",
    ""\n"",
    ""    datalist = {\n"",
    ""      well : value\n"",
    ""      for (value, well) in data\n"",
    ""    }\n"",
    ""    \n"",
    ""    welllist = [\n"",
    ""                [\n"",
    ""                 datalist[chr(64 + row) + str(col)]          \n"",
    ""                 if chr(64 + row) + str(col) in datalist else None\n"",
    ""                 for row in range(1,9)\n"",
    ""                ]\n"",
    ""                for col in range(1,13)\n"",
    ""                ]\n"",
    ""                \n"",
    ""    return welllist""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""file_BOS= \""data/2015-03-11 18-35-16_plate_1.xml\""\n"",
    ""file_name = os.path.splitext(file_BOS)[0]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""root = etree.parse(file_BOS)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""****The xml file data/2015-03-11 18-35-16_plate_1.xml has 7 data sections:****\n"",
      ""280_TopRead\n"",
      ""280_BottomRead\n"",
      ""Abs_280\n"",
      ""350_TopRead\n"",
      ""350_BottomRead\n"",
      ""Abs_350\n"",
      ""Abs_480\n""
     ]
    }
   ],
   ""source"": [
    ""Sections = root.xpath(\""/*/Section\"")\n"",
    ""much = len(Sections)\n"",
    ""print \""****The xml file \"" + file_BOS + \"" has %s data sections:****\"" % much\n"",
    ""for sect in Sections:\n"",
    ""    print sect.attrib['Name']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Just going to work with topread for now\n"",
    ""TopRead = root.xpath(\""/*/Section\"")[0]\n"",
    ""welllist = get_wells_from_section(TopRead)\n"",
    ""Bos_dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(-0.5, 11.5)""
      ]
     },
     ""execution_count"": 7,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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t4iwTGxidV9MteN0/dC7t6yVuTr1/Zub6mN/Cx21d1zK72XVrhewz4s/32L2fO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\n/4rO/02VCuMAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1031e0690>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""sns.set_palette(\""Paired\"", 10)\n"",
    ""sns.set_context(\""notebook\"", rc={\""lines.linewidth\"": 2.5})\n"",
    ""\n"",
    ""Bos_dataframe.plot(figsize=(6, 4), title=file_name)\n"",
    ""plt.xlim(-0.5,11.5)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>A - Src</th>\n"",
       ""      <th>B - Buffer</th>\n"",
       ""      <th>C - Src</th>\n"",
       ""      <th>D - Buffer</th>\n"",
       ""      <th>E - Src</th>\n"",
       ""      <th>F - Buffer</th>\n"",
       ""      <th>G - Src</th>\n"",
       ""      <th>H - Buffer</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td>451.0</td>\n"",
       ""      <td>382.0</td>\n"",
       ""      <td>449.0</td>\n"",
       ""      <td>358.0</td>\n"",
       ""      <td>473.0</td>\n"",
       ""      <td>400.0</td>\n"",
       ""      <td>466.0</td>\n"",
       ""      <td>346.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td>366.0</td>\n"",
       ""      <td>179.0</td>\n"",
       ""      <td>385.0</td>\n"",
       ""      <td>170.0</td>\n"",
       ""      <td>378.0</td>\n"",
       ""      <td>185.0</td>\n"",
       ""      <td>410.0</td>\n"",
       ""      <td>157.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td>325.0</td>\n"",
       ""      <td>93.0</td>\n"",
       ""      <td>341.0</td>\n"",
       ""      <td>96.0</td>\n"",
       ""      <td>329.0</td>\n"",
       ""      <td>104.0</td>\n"",
       ""      <td>361.0</td>\n"",
       ""      <td>97.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td>295.0</td>\n"",
       ""      <td>79.0</td>\n"",
       ""      <td>311.0</td>\n"",
       ""      <td>77.0</td>\n"",
       ""      <td>315.0</td>\n"",
       ""      <td>85.0</td>\n"",
       ""      <td>316.0</td>\n"",
       ""      <td>78.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td>289.0</td>\n"",
       ""      <td>65.0</td>\n"",
       ""      <td>292.0</td>\n"",
       ""      <td>68.0</td>\n"",
       ""      <td>301.0</td>\n"",
       ""      <td>70.0</td>\n"",
       ""      <td>323.0</td>\n"",
       ""      <td>65.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>5</th>\n"",
       ""      <td>277.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>285.0</td>\n"",
       ""      <td>58.0</td>\n"",
       ""      <td>289.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>304.0</td>\n"",
       ""      <td>58.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>6</th>\n"",
       ""      <td>270.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""      <td>266.0</td>\n"",
       ""      <td>54.0</td>\n"",
       ""      <td>265.0</td>\n"",
       ""      <td>63.0</td>\n"",
       ""      <td>275.0</td>\n"",
       ""      <td>54.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>7</th>\n"",
       ""      <td>272.0</td>\n"",
       ""      <td>57.0</td>\n"",
       ""      <td>200.0</td>\n"",
       ""      <td>54.0</td>\n"",
       ""      <td>299.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>211.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>8</th>\n"",
       ""      <td>272.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>123.0</td>\n"",
       ""      <td>54.0</td>\n"",
       ""      <td>127.0</td>\n"",
       ""      <td>61.0</td>\n"",
       ""      <td>127.0</td>\n"",
       ""      <td>51.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>9</th>\n"",
       ""      <td>261.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""      <td>85.0</td>\n"",
       ""      <td>54.0</td>\n"",
       ""      <td>88.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>89.0</td>\n"",
       ""      <td>53.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>10</th>\n"",
       ""      <td>255.0</td>\n"",
       ""      <td>56.0</td>\n"",
       ""      <td>69.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""      <td>70.0</td>\n"",
       ""      <td>56.0</td>\n"",
       ""      <td>68.0</td>\n"",
       ""      <td>54.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>11</th>\n"",
       ""      <td>254.0</td>\n"",
       ""      <td>58.0</td>\n"",
       ""      <td>57.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""      <td>56.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""      <td>51.0</td>\n"",
       ""      <td>54.0</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""    A - Src  B - Buffer  C - Src  D - Buffer  E - Src  F - Buffer  G - Src  \\\n"",
       ""0     451.0       382.0    449.0       358.0    473.0       400.0    466.0   \n"",
       ""1     366.0       179.0    385.0       170.0    378.0       185.0    410.0   \n"",
       ""2     325.0        93.0    341.0        96.0    329.0       104.0    361.0   \n"",
       ""3     295.0        79.0    311.0        77.0    315.0        85.0    316.0   \n"",
       ""4     289.0        65.0    292.0        68.0    301.0        70.0    323.0   \n"",
       ""5     277.0        59.0    285.0        58.0    289.0        60.0    304.0   \n"",
       ""6     270.0        55.0    266.0        54.0    265.0        63.0    275.0   \n"",
       ""7     272.0        57.0    200.0        54.0    299.0        60.0    211.0   \n"",
       ""8     272.0        59.0    123.0        54.0    127.0        61.0    127.0   \n"",
       ""9     261.0        55.0     85.0        54.0     88.0        60.0     89.0   \n"",
       ""10    255.0        56.0     69.0        55.0     70.0        56.0     68.0   \n"",
       ""11    254.0        58.0     57.0        55.0     56.0        55.0     51.0   \n"",
       ""\n"",
       ""    H - Buffer  \n"",
       ""0        346.0  \n"",
       ""1        157.0  \n"",
       ""2         97.0  \n"",
       ""3         78.0  \n"",
       ""4         65.0  \n"",
       ""5         58.0  \n"",
       ""6         54.0  \n"",
       ""7         55.0  \n"",
       ""8         51.0  \n"",
       ""9         53.0  \n"",
       ""10        54.0  \n"",
       ""11        54.0  ""
      ]
     },
     ""execution_count"": 8,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Bos_dataframe""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""****The xml file data/Ima_WIP_SMH_SrcBos_Extend_013015_mdfx_20150311_18.xml has 7 data sections:****\n"",
      ""280_TopRead\n"",
      ""280_BottomRead\n"",
      ""Abs_280\n"",
      ""350_TopRead\n"",
      ""350_BottomRead\n"",
      ""Abs_350\n"",
      ""Abs_480\n""
     ]
    }
   ],
   ""source"": [
    ""file_BOS_IMA= \""data/Ima_WIP_SMH_SrcBos_Extend_013015_mdfx_20150311_18.xml\""\n"",
    ""file_name = os.path.splitext(file_BOS_IMA)[0]\n"",
    ""root = etree.parse(file_BOS_IMA)\n"",
    ""Sections = root.xpath(\""/*/Section\"")\n"",
    ""much = len(Sections)\n"",
    ""print \""****The xml file \"" + file_BOS_IMA + \"" has %s data sections:****\"" % much\n"",
    ""for sect in Sections:\n"",
    ""    print sect.attrib['Name']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(-0.5, 11.5)""
      ]
     },
     ""execution_count"": 10,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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Zeig6LaoG7RGRq47m1jgOf3d+KD/YM1FqrqakcUF222Ju88O7xUbVgBcWlLwGn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\nAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11dc242d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""#Just going to work with topread for now\n"",
    ""TopRead = root.xpath(\""/*/Section\"")[0]\n"",
    ""welllist = get_wells_from_section(TopRead)\n"",
    ""BosIma_dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])\n"",
    ""sns.set_palette(\""Paired\"", 10)\n"",
    ""sns.set_context(\""notebook\"", rc={\""lines.linewidth\"": 2.5})\n"",
    ""\n"",
    ""BosIma_dataframe.plot(figsize=(6, 4), title=file_name)\n"",
    ""plt.xlim(-0.5,11.5)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.text.Text at 0x120f284d0>""
      ]
     },
     ""execution_count"": 11,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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Fdga2pnbcovf7BwHfAlsAg4HqcG7B8t5olzjXDufN9X573+7qicTFmonEv9vN9GopzDue2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\nKPBzhmv/7I9tmafvY7n+EX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/E9v8YO38VhRnu+A8Ky5T4fh0wox9fg54C7B47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\nhHFC7XA4HA5HhHFC7XA4HA5HhPl/Ngmh19PNN1oAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1031e0110>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.plot(BosIma_dataframe[:].values, 'r');\n"",
    ""plt.plot(Bos_dataframe[:].values, 'k');\n"",
    ""plt.text(8,450,'Bosutinib',fontsize=15)\n"",
    ""plt.text(8,420,'Imatinib + Bosutinib',fontsize=15,color='red')""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Gefitinib Assay""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""The first attempt at a Gefitinib-Imatinib competition assay was on October 30, 2015. The full description of the assay can be found [here](https://docs.google.com/document/d/1IGw5RBzevF5hWLiH82LYZjXSnqvF4t-PD7mP0SP46Eo/edit). \n"",
    ""In short (and very similar to as described in the [lab-protocols repository](https://github.com/choderalab/lab-protocols/blob/master/Fluo_Kinase_Inhibitor_Assay/Fluo_Kinase_Inhibitor_Assay.md)) 100 uL of 0.5 $\\mu$M Src with a titration of Gefitinib up to 20 $\\mu$M in every other row was prepared. In one of two plates 10 $\\mu$M Imatinib was added to the whole plate to try to compete off the Gefitinib. Note the documentation here could be better, which could be why this data doesn't look particularly great. ""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""file_GEF = \""data/Gef_2015-10-30 17-55-48_plate_1.xml\""\n"",
    ""file_name = os.path.splitext(file_GEF)[0]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""root = etree.parse(file_GEF)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""****The xml file data/Gef_2015-10-30 17-55-48_plate_1.xml has 7 data sections:****\n"",
      ""280_TopRead\n"",
      ""280_BottomRead\n"",
      ""Abs_280\n"",
      ""350_TopRead\n"",
      ""350_BottomRead\n"",
      ""Abs_350\n"",
      ""Abs_480\n""
     ]
    }
   ],
   ""source"": [
    ""Sections = root.xpath(\""/*/Section\"")\n"",
    ""much = len(Sections)\n"",
    ""print \""****The xml file \"" + file_GEF + \"" has %s data sections:****\"" % much\n"",
    ""for sect in Sections:\n"",
    ""    print sect.attrib['Name']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(-0.5, 11.5)""
      ]
     },
     ""execution_count"": 15,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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p/HaOxnAB+Hgz4/9e43MzyafnAB6MW+7bY5ZNe7uE3gAN8oVBTes4d1r78krJh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\nuKLu4uLikke4ou7i4uKSR/wPGYRC2Z3zOVgAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1031d5610>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""#Just going to work with topread for now\n"",
    ""TopRead = root.xpath(\""/*/Section\"")[0]\n"",
    ""welllist = get_wells_from_section(TopRead)\n"",
    ""Gef_dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])\n"",
    ""Gef_dataframe.plot(figsize=(6, 4), title=file_name)\n"",
    ""plt.xlim(-0.5,11.5)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""****The xml file data/GefIma_2015-10-30 17-51-13_plate_1.xml has 7 data sections:****\n"",
      ""280_TopRead\n"",
      ""280_BottomRead\n"",
      ""Abs_280\n"",
      ""350_TopRead\n"",
      ""350_BottomRead\n"",
      ""Abs_350\n"",
      ""Abs_480\n""
     ]
    }
   ],
   ""source"": [
    ""file_GEF_IMA= \""data/GefIma_2015-10-30 17-51-13_plate_1.xml\""\n"",
    ""file_name = os.path.splitext(file_GEF_IMA)[0]\n"",
    ""root = etree.parse(file_GEF_IMA)\n"",
    ""Sections = root.xpath(\""/*/Section\"")\n"",
    ""much = len(Sections)\n"",
    ""print \""****The xml file \"" + file_GEF_IMA + \"" has %s data sections:****\"" % much\n"",
    ""for sect in Sections:\n"",
    ""    print sect.attrib['Name']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(-0.5, 11.5)""
      ]
     },
     ""execution_count"": 17,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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rHTTVHg8GQUIxRb0WchSVh9BZa3wGoDZvosWXLgHcPqmpjYa21u00P+ny7Jk1I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\nAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x12109c210>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""#Just going to work with topread for now\n"",
    ""TopRead = root.xpath(\""/*/Section\"")[0]\n"",
    ""welllist = get_wells_from_section(TopRead)\n"",
    ""GefIma_dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])\n"",
    ""sns.set_palette(\""Paired\"", 10)\n"",
    ""sns.set_context(\""notebook\"", rc={\""lines.linewidth\"": 2.5})\n"",
    ""\n"",
    ""GefIma_dataframe.plot(figsize=(6, 4), title=file_name)\n"",
    ""plt.xlim(-0.5,11.5)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.text.Text at 0x12121e210>""
      ]
     },
     ""execution_count"": 18,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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D6qvojIl2Ad3FbABEhsMWv0G1PeOPliu97vKrmzsQj0p5RH4WLxgawnqrOjN0T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\n9UuC6pwlvW8AAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11de3bcd0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.plot(GefIma_dataframe[:].values, 'r');\n"",
    ""plt.plot(Gef_dataframe[:].values, 'k');\n"",
    ""plt.text(8,230,'Gefitinib',fontsize=15)\n"",
    ""plt.text(8,210,'Imatinib + Gefitinib',fontsize=15,color='red')""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Modeled Data""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""So now let's look at what our expected data might look like. Here we are looking at inhibitor affinities for Src. \n"",
    ""Some initial placeholder data from here again:\n"",
    ""http://www.guidetopharmacology.org/GRAC/LigandScreenDisplayForward?ligandId=5710&screenId=2""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Kd_Bos = 1.0e-9 # M\n"",
    ""Kd_Gef = 3800e-9 # M\n"",
    ""Kd_Ima = 3000e-9 # M""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""From our assay setup we know the Src concentration is 0.5 $\\mu$M.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Ptot = 0.5e-6 # M\n"",
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""First let's just plot our two component binding for Bosutinib and Gefitinib.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Now we can use this to define a function that gives us PL from Kd, Ptot, and Ltot.\n"",
    ""def two_component_binding(Kd, Ptot, Ltot):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Kd : float\n"",
    ""        Dissociation constant\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total ligand concentration\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    \""\""\""\n"",
    ""                                    \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd) - np.sqrt((Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free ligand concentration in sample cell after n injections (uM)                                                                                                                                                                                                                           \n"",
    ""    return [P, L, PL]\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[Lb, Pb, PLb] = two_component_binding(Kd_Bos, Ptot, Ltot)\n"",
    ""[Lg, Pg, PLg] = two_component_binding(Kd_Gef, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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Z3SZJqxuQuSVLNmNwlSaoZk7skSTUzKRW3bhNCOBB4XozxZdP162xw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\nXZKkmjG5S5JUMyZ3SZJqxuQuSVLN/H9qy/9/yYQuLwAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1212ad3d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""Bos, = plt.semilogx(Ltot,PLb,'green', label='Bosutinib')\n"",
    ""Gef, = plt.semilogx(Ltot,PLg,'violet', label = 'Gefitinib')\n"",
    ""plt.xlabel('$[L]_{tot}$')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0,6e-7)\n"",
    ""plt.legend(loc=3);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Now let's see how we would expect Imatinib to effect this.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""From our assay setup we know the Imatinib concentration is""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Lima =  10e-6 # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#Competitive binding function\n"",
    ""def three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total tracer(fluorescent) ligand concentration\n"",
    ""    Kd_L : float\n"",
    ""        Dissociation constant\n"",
    ""    Atot : float\n"",
    ""        Total competitive ligand concentration\n"",
    ""    Kd_A : float\n"",
    ""        Dissociation constant\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    A : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    Kd_L_app : float\n"",
    ""        Apparent dissociation constant of L in the presence of A\n"",
    ""        \n"",
    ""    Usage\n"",
    ""    -----\n"",
    ""    [P, L, A, PL, Kd_L_app] = three_component_competitive_binding(Ptot, Ltot, Kd_L, Atot, Kd_A)\n"",
    ""    \""\""\""\n"",
    ""    Kd_L_app = Kd_L*(1+Atot/Kd_A)                                \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd_L_app) - np.sqrt((Ptot + Ltot + Kd_L_app)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free tracer ligand concentration in sample cell after n injections (uM)\n"",
    ""    A = Atot - PL; # free competitive ligand concentration in sample cell after n injections (uM)\n"",
    ""    return [P, L, A, PL, Kd_L_app]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[Pbi, Lbi, Abi, PLbi, Kd_bima] = three_component_competitive_binding(Ptot, Ltot, Kd_Bos, Lima, Kd_Ima)\n"",
    ""[Pgi, Lgi, Agi, PLgi, Kd_gima] = three_component_competitive_binding(Ptot, Ltot, Kd_Gef, Lima, Kd_Ima)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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1e8rTfUArG6fH3VcCPfQbSjsy59nXA1T4DSRIldxEpLeNr96RmomtXDNBKcJ0I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\nAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x12170cd90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.title('Src competition assay')\n"",
    ""Bos, = plt.semilogx(Ltot,PLb,'green', label='Bosutinib')\n"",
    ""Bos_Ima, = plt.semilogx(Ltot,PLbi,'cyan', label='Bosutinib + Ima')\n"",
    ""Gef, = plt.semilogx(Ltot,PLg,'violet', label = 'Gefitinib')\n"",
    ""Gef_Ima, = plt.semilogx(Ltot,PLgi,'pink', label = 'Gefitinib + Ima')\n"",
    ""plt.xlabel('$[L]_{tot}$')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0,6e-7)\n"",
    ""plt.legend(loc=3);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""HMMM.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""So I was right to think that Gefitinib should work despite the fact that Bosutinib didn't, but...""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""###Now let's try modeling new experiments with Abl before we actually do them!""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#Using expected Kd's from same website as above\n"",
    ""Kd_Bos_Abl = 0.1e-9 # M\n"",
    ""Kd_Gef_Abl = 480e-9 # M\n"",
    ""Kd_Ima_Abl = 21.0e-9 # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 29,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[Lb_Abl, Pb_Abl, PLb_Abl] = two_component_binding(Kd_Bos_Abl, Ptot, Ltot)\n"",
    ""[Lg_Abl, Pg_Abl, PLg_Abl] = two_component_binding(Kd_Gef_Abl, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 30,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[Pbi_Abl, Lbi_Abl, Abi_Abl, PLbi_Abl, Kd_bima_Abl] = three_component_competitive_binding(Ptot, Ltot, Kd_Bos_Abl, Lima, Kd_Ima_Abl)\n"",
    ""[Pgi_Abl, Lgi_Abl, Agi_Abl, PLgi_Abl, Kd_gima_Abl] = three_component_competitive_binding(Ptot, Ltot, Kd_Gef_Abl, Lima, Kd_Ima_Abl)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 31,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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vtnlXLnYhIDZi8accvGAMlA+aMX4Vtj5C8KanBGKC4EE+mOND4V1Euerm/80V6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\nqqNeLuYb01sAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11dd79990>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""Bos, = plt.semilogx(Ltot,PLb_Abl,'green', label='Bosutinib')\n"",
    ""Bos_Ima, = plt.semilogx(Ltot,PLbi_Abl,'cyan', label='Bosutinib + Ima')\n"",
    ""Gef, = plt.semilogx(Ltot,PLg_Abl,'violet', label = 'Gefitinib')\n"",
    ""Gef_Ima, = plt.semilogx(Ltot,PLgi_Abl,'pink', label = 'Gefitinib + Ima')\n"",
    ""plt.title('Abl competition assay')\n"",
    ""plt.xlabel('$[L]_{tot}$')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0,6e-7)\n"",
    ""plt.legend(loc=3);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Looks promising!""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""###Let's check out our new data set based on this.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 32,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def get_wells_from_section(path):\n"",
    ""    reads = path.xpath(\""*/Well\"")\n"",
    ""    wellIDs = [read.attrib['Pos'] for read in reads]\n"",
    ""\n"",
    ""    data = [(s.text, r.attrib['Pos'])\n"",
    ""         for r in reads\n"",
    ""         for s in r]\n"",
    ""\n"",
    ""    datalist = {\n"",
    ""      well : value\n"",
    ""      for (value, well) in data\n"",
    ""    }\n"",
    ""    \n"",
    ""    welllist = [\n"",
    ""                [\n"",
    ""                 datalist[chr(64 + row) + str(col)]          \n"",
    ""                 if chr(64 + row) + str(col) in datalist else None\n"",
    ""                 for row in range(1,9)\n"",
    ""                ]\n"",
    ""                for col in range(1,13)\n"",
    ""                ]\n"",
    ""                \n"",
    ""    return welllist""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 33,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""****The xml file data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate_1.xml has 7 data sections:****\n"",
      ""280_TopRead\n"",
      ""280_BottomRead\n"",
      ""Abs_280\n"",
      ""350_TopRead\n"",
      ""350_BottomRead\n"",
      ""Abs_350\n"",
      ""Abs_480\n""
     ]
    }
   ],
   ""source"": [
    ""file_ABL_GEF= \""data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate_1.xml\""\n"",
    ""file_name = os.path.splitext(file_ABL_GEF)[0]\n"",
    ""root = etree.parse(file_ABL_GEF)\n"",
    ""Sections = root.xpath(\""/*/Section\"")\n"",
    ""much = len(Sections)\n"",
    ""print \""****The xml file \"" + file_ABL_GEF + \"" has %s data sections:****\"" % much\n"",
    ""for sect in Sections:\n"",
    ""    print sect.attrib['Name']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 34,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>A - Abl</th>\n"",
       ""      <th>B - Buffer</th>\n"",
       ""      <th>C - Abl</th>\n"",
       ""      <th>D - Buffer</th>\n"",
       ""      <th>E - Abl</th>\n"",
       ""      <th>F - Buffer</th>\n"",
       ""      <th>G - Abl</th>\n"",
       ""      <th>H - Buffer</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td>62213</td>\n"",
       ""      <td>19784</td>\n"",
       ""      <td>62890</td>\n"",
       ""      <td>19797</td>\n"",
       ""      <td>62821</td>\n"",
       ""      <td>20594</td>\n"",
       ""      <td>63317</td>\n"",
       ""      <td>17635</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td>62497</td>\n"",
       ""      <td>17332</td>\n"",
       ""      <td>62936</td>\n"",
       ""      <td>18205</td>\n"",
       ""      <td>62927</td>\n"",
       ""      <td>18102</td>\n"",
       ""      <td>63343</td>\n"",
       ""      <td>14681</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td>63194</td>\n"",
       ""      <td>12950</td>\n"",
       ""      <td>63056</td>\n"",
       ""      <td>13223</td>\n"",
       ""      <td>62794</td>\n"",
       ""      <td>13049</td>\n"",
       ""      <td>63251</td>\n"",
       ""      <td>10586</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td>63017</td>\n"",
       ""      <td>10782</td>\n"",
       ""      <td>62743</td>\n"",
       ""      <td>11030</td>\n"",
       ""      <td>62841</td>\n"",
       ""      <td>10622</td>\n"",
       ""      <td>63045</td>\n"",
       ""      <td>9235</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td>56558</td>\n"",
       ""      <td>10186</td>\n"",
       ""      <td>62107</td>\n"",
       ""      <td>10257</td>\n"",
       ""      <td>61824</td>\n"",
       ""      <td>9884</td>\n"",
       ""      <td>55471</td>\n"",
       ""      <td>8785</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>5</th>\n"",
       ""      <td>34259</td>\n"",
       ""      <td>9837</td>\n"",
       ""      <td>38003</td>\n"",
       ""      <td>9884</td>\n"",
       ""      <td>38786</td>\n"",
       ""      <td>9620</td>\n"",
       ""      <td>35771</td>\n"",
       ""      <td>8796</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>6</th>\n"",
       ""      <td>22106</td>\n"",
       ""      <td>9861</td>\n"",
       ""      <td>24868</td>\n"",
       ""      <td>9716</td>\n"",
       ""      <td>24637</td>\n"",
       ""      <td>9757</td>\n"",
       ""      <td>23488</td>\n"",
       ""      <td>8789</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>7</th>\n"",
       ""      <td>14589</td>\n"",
       ""      <td>9659</td>\n"",
       ""      <td>16880</td>\n"",
       ""      <td>10010</td>\n"",
       ""      <td>17193</td>\n"",
       ""      <td>9518</td>\n"",
       ""      <td>17755</td>\n"",
       ""      <td>8612</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>8</th>\n"",
       ""      <td>11475</td>\n"",
       ""      <td>9601</td>\n"",
       ""      <td>12311</td>\n"",
       ""      <td>10035</td>\n"",
       ""      <td>13272</td>\n"",
       ""      <td>9758</td>\n"",
       ""      <td>12312</td>\n"",
       ""      <td>8625</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>9</th>\n"",
       ""      <td>10505</td>\n"",
       ""      <td>9669</td>\n"",
       ""      <td>11388</td>\n"",
       ""      <td>9891</td>\n"",
       ""      <td>11702</td>\n"",
       ""      <td>9649</td>\n"",
       ""      <td>10925</td>\n"",
       ""      <td>8513</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>10</th>\n"",
       ""      <td>9771</td>\n"",
       ""      <td>9722</td>\n"",
       ""      <td>10457</td>\n"",
       ""      <td>10630</td>\n"",
       ""      <td>10585</td>\n"",
       ""      <td>9889</td>\n"",
       ""      <td>10062</td>\n"",
       ""      <td>8840</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>11</th>\n"",
       ""      <td>8652</td>\n"",
       ""      <td>9757</td>\n"",
       ""      <td>9712</td>\n"",
       ""      <td>9396</td>\n"",
       ""      <td>9653</td>\n"",
       ""      <td>9578</td>\n"",
       ""      <td>9429</td>\n"",
       ""      <td>8671</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""   A - Abl B - Buffer C - Abl D - Buffer E - Abl F - Buffer G - Abl H - Buffer\n"",
       ""0    62213      19784   62890      19797   62821      20594   63317      17635\n"",
       ""1    62497      17332   62936      18205   62927      18102   63343      14681\n"",
       ""2    63194      12950   63056      13223   62794      13049   63251      10586\n"",
       ""3    63017      10782   62743      11030   62841      10622   63045       9235\n"",
       ""4    56558      10186   62107      10257   61824       9884   55471       8785\n"",
       ""5    34259       9837   38003       9884   38786       9620   35771       8796\n"",
       ""6    22106       9861   24868       9716   24637       9757   23488       8789\n"",
       ""7    14589       9659   16880      10010   17193       9518   17755       8612\n"",
       ""8    11475       9601   12311      10035   13272       9758   12312       8625\n"",
       ""9    10505       9669   11388       9891   11702       9649   10925       8513\n"",
       ""10    9771       9722   10457      10630   10585       9889   10062       8840\n"",
       ""11    8652       9757    9712       9396    9653       9578    9429       8671""
      ]
     },
     ""execution_count"": 34,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#Just going to work with topread for now\n"",
    ""TopRead = root.xpath(\""/*/Section\"")[0]\n"",
    ""welllist = get_wells_from_section(TopRead)\n"",
    ""AblGef_dataframe = pd.DataFrame(welllist, columns = ['A - Abl','B - Buffer','C - Abl','D - Buffer', 'E - Abl','F - Buffer','G - Abl','H - Buffer'])\n"",
    ""#AN ERROR FOR 'OVERS' COMES UP UNLESS THE NEXT LINE IS HERE\n"",
    ""#THE MAX VALUE IS TAKEN FROM THE MAX VALUE FOR THE ABL GEF IMA DATA\n"",
    ""dataframe_rep = AblGef_dataframe.replace({'OVER':'64060.0'})\n"",
    ""AblGef_dataframe""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 35,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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xb+lVJzpl7+l6kHfDopwtX8c3ydV5BXm4QKSwMyv/v3leyK0O/aFcZCvdYFrmU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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11dd480d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""#dataframe_rep[['fluorescence']] = dataframe_rep[['fluorescence']].astype('float')\n"",
    ""dataframe_rep = dataframe_rep.astype('float')\n"",
    ""sns.set_palette(\""Paired\"", 10)\n"",
    ""sns.set_context(\""notebook\"", rc={\""lines.linewidth\"": 2.5})\n"",
    ""\n"",
    ""dataframe_rep.plot(figsize=(6, 4), title=file_name)\n"",
    ""plt.xlim(-0.5,11.5);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 36,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""****The xml file data/Abl Gef Ima gain 120 bw1020 2016-01-19 16-22-45_plate_1.xml has 7 data sections:****\n"",
      ""280_TopRead\n"",
      ""280_BottomRead\n"",
      ""Abs_280\n"",
      ""350_TopRead\n"",
      ""350_BottomRead\n"",
      ""Abs_350\n"",
      ""Abs_480\n""
     ]
    }
   ],
   ""source"": [
    ""file_ABL_GEF_IMA= \""data/Abl Gef Ima gain 120 bw1020 2016-01-19 16-22-45_plate_1.xml\""\n"",
    ""file_name = os.path.splitext(file_ABL_GEF_IMA)[0]\n"",
    ""root = etree.parse(file_ABL_GEF_IMA)\n"",
    ""Sections = root.xpath(\""/*/Section\"")\n"",
    ""much = len(Sections)\n"",
    ""print \""****The xml file \"" + file_ABL_GEF_IMA + \"" has %s data sections:****\"" % much\n"",
    ""for sect in Sections:\n"",
    ""    print sect.attrib['Name']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 37,
   ""metadata"": {
    ""collapsed"": false,
    ""scrolled"": true
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(-0.5, 11.5)""
      ]
     },
     ""execution_count"": 37,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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Zg2iNKaiL6A6HYhIaipFrejI7iRWRnYIaI9I3LE2+PiL/eHThARPax9zJEZBt7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\nPJ4OXhQ8Ho/H08GLgsfj8Xg6/H9Y4PdVzF917gAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11de8e590>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""#Just going to work with topread for now\n"",
    ""TopRead = root.xpath(\""/*/Section\"")[0]\n"",
    ""welllist = get_wells_from_section(TopRead)\n"",
    ""AblGefIma_dataframe = pd.DataFrame(welllist, columns = ['A - Abl','B - Buffer','C - Abl','D - Buffer', 'E - Abl','F - Buffer','G - Abl','H - Buffer'])\n"",
    ""sns.set_palette(\""Paired\"", 10)\n"",
    ""sns.set_context(\""notebook\"", rc={\""lines.linewidth\"": 2.5})\n"",
    ""AblGefIma_dataframe = AblGefIma_dataframe.astype('float')\n"",
    ""\n"",
    ""AblGefIma_dataframe.plot(figsize=(6, 4), title=file_name)\n"",
    ""plt.xlim(-0.5,11.5)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 38,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""63432.0""
      ]
     },
     ""execution_count"": 38,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""AblGefIma_dataframe.values.max()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 39,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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4hScQJWGIZMCUgu2Ocg0iph6dTZ6jqlP33n2c1YL8m+s0kX0JaWETakqbUcZye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\n5v8B1uvjBFsSDC0AAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x121816490>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.plot(AblGefIma_dataframe[:].values, 'r');\n"",
    ""plt.plot(AblGef_dataframe[:].values, 'k');\n"",
    ""plt.text(8,60000,'Gefitinib (ABL)',fontsize=15)\n"",
    ""plt.text(8,55000,'Imatinib + Gefitinib (ABL)',fontsize=15,color='red')\n"",
    ""plt.savefig('Abl_Gef_Ima_Jan2016_repeat.png')""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.11""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/competition-fluorescence-assay/1b-modelling-CompetitiveBinding-AnalyticalSolution.ipynb",".ipynb","61838","709","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""##Calculation of Equilibrium Concentrations in Competitive Binding Experiment\n"",
    ""\n"",
    ""This notebook uses analytical solution of equilibrium expressions for 2 ligands competing for 1 population of protein binding sites.\n"",
    ""\n"",
    ""Calculations are based on \n"",
    ""Wang, Z. X. An exact mathematical expression for describing\n"",
    ""competitive binding of two different ligands to a protein molecule. FEBS\n"",
    ""Lett. 1995, 360, 111−114. [doi:10.1016/0014-5793(95)00062-E](http://www.sciencedirect.com/science/article/pii/001457939500062E) \n"",
    ""\n"",
    ""\n"",
    ""We will model binding of two ligands, fluorescent ligand (A) and non-fluorescent ligand (B), to protein (P). ""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    }
   ],
   ""source"": [
    ""import matplotlib.pyplot as plt\n"",
    ""import numpy as np\n"",
    ""import seaborn as sns\n"",
    ""from IPython.display import display, Math, Latex #Do we even need this anymore?\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Strictly Competitive Binding Model""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": true
   },
   ""source"": [
    ""$ {K_A} $ and $ {K_A} $ are dissociation constants of A and B, binding to P.  ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$PA {\\stackrel{K_A}{\\rightleftharpoons}}  A + B $$\n"",
    ""\n"",
    ""$$PB {\\stackrel{K_B}{\\rightleftharpoons}} B + P$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$ K_A = \\frac{[P][A]}{[PA]} $$ $$ K_B = \\frac{[P][B]}{[PB]} $$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$[A]_0$, $[B]_0$ and $[P]_0$ are total concentrations of A, B and P. $[A]$, $[B]$ and $[P]$ are the free concentrations. Conservation of mass:\n"",
    ""$$[A]_0 = [A]+[PA]$$\n"",
    ""$$[B]_0 = [A]+[PB]$$\n"",
    ""$$[P]_0 = [P]+[PA]+[PB]$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Combining with equilibrium expressions:\n"",
    ""$$ K_A = \\frac{[P][A]_0 - [PA]}{[PA]} $$ $$ K_B = \\frac{[P]([B]_0-[PB])}{[PB]} $$\n"",
    ""Reorganize to get [PA] and [PB].\n"",
    ""$$ [PA] = \\frac{[P][A]_0}{K_A+[P]} $$ $$ [PB] = \\frac{[P][B]_0}{K_B+[P]} $$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$ [P]_0 = [P] + \\frac{[P][A]_0}{K_A+[P]} + \\frac{[P][B]_0}{K_B+[P]} $$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Expanding results:\n"",
    ""$ 0 = [P]^3 + K_A[P]^2+ K_B[P]^2 + [A]_0[P]^2 + [B]_0[P]^2 - [P]_0[P]^2 +K_AK_B[P] +[A]_0K_B[P] +[B]_0K_A[P] - K_A[P]_0[P]-K_B[P]_0[P]-K_AK_B[P]_0$\n"",
    ""\n"",
    ""$ 0 = [P]^3 + a[P]^2 + b[P] +c  $\n"",
    ""\n"",
    ""$ a = K_A+ K_B + [A]_0 + [B]_0 - [P]_0 $\n"",
    ""\n"",
    ""$ b = K_AK_B +K_B([A]_0-[P]_0) +K_B([B]_0 - [P]_0) $\n"",
    ""\n"",
    ""$ c = - K_AK_B[P]_0$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Substituting $ [P] = u - \\frac{a}{3} $, gives us:\n"",
    ""\n"",
    ""$ u^3 -qu - r =0 $\n"",
    ""\n"",
    ""$ q=\\frac{a^2}{3}-b $\n"",
    ""\n"",
    ""$ r = \\frac{-2}{27}a^3+\\frac{1}{3}ab-c$\n"",
    ""\n"",
    ""Discriminant:\n"",
    ""$ \\Delta = \\frac{r^2}{4} - \\frac{q^3}{27}$ \n"",
    ""\n"",
    ""Since $ \\Delta < 0$, there are 3 real roots. \n"",
    ""Only physically meaningful root is $ u = \\frac{2}{3}\\sqrt{a^2-3b}\\cos(\\frac{\\theta}{3})$, where $\\theta=\\arccos{\\frac{-2a^3+9ab-27c}{2\\sqrt{(a^2-3b)^3}}}$\n""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Then we can calculate equilibrium concentrations of species.\n"",
    ""\n"",
    ""$$ [P]= u -\\frac{a}{3} = -\\frac{a}{3} + \\frac{2}{3}\\sqrt{a^2-3b}\\cos(\\frac{\\theta}{3}) $$\n"",
    ""\n"",
    ""$$ [PA] = \\frac{[P][A]_0}{K_A+[P]} = \\frac{[A]_0(2\\sqrt{a^2-3b}\\cos(\\frac{\\theta}{3})-a)}{3K_A+(2\\sqrt{a^2-3b}\\cos(\\frac{\\theta}{3})-a)} $$ \n"",
    ""\n"",
    ""$$ [PB] = \\frac{[P][B]_0}{K_B+[P]} = \\frac{[B]_0(2\\sqrt{a^2-3b}\\cos(\\frac{\\theta}{3})-a)}{3K_B+(2\\sqrt{a^2-3b}\\cos(\\frac{\\theta}{3})-a)} $$\n"",
    ""\n"",
    ""$$[A] = [A]_0-[PA] $$\n"",
    ""$$[B] = [B]_0-[PB]$$""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Competitive binding function\n"",
    ""def three_component_competitive_binding_exact(Ptot, Atot, K_A, Btot, K_B):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Atot : float\n"",
    ""        Total tracer(fluorescent) ligand concentration\n"",
    ""    K_A : float\n"",
    ""        Dissociation constant of A\n"",
    ""    Btot : float\n"",
    ""        Total competitive ligand concentration\n"",
    ""    K_B : float\n"",
    ""        Dissociation constant of B\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    A : float\n"",
    ""        Free ligand concentration\n"",
    ""    B : float\n"",
    ""        Free ligand concentration\n"",
    ""    PA : float\n"",
    ""        Concentration of PA complex\n"",
    ""    PB : float\n"",
    ""        Concentration of PB complex\n"",
    ""        \n"",
    ""    Usage\n"",
    ""    -----\n"",
    ""    [P, A, B, PA, PB] = three_component_competitive_binding(Ptot, Atot, K_A, Btot, K_B)\n"",
    ""    \""\""\""\n"",
    ""    \n"",
    ""    # P^3 + aP^2 + bP + c = 0\n"",
    ""    a = K_A + K_B + Atot + Btot - Ptot\n"",
    ""    b = K_A*K_B + K_B*(Atot-Ptot) + K_B*(Btot - Ptot)\n"",
    ""    c = -K_A*K_B*Ptot\n"",
    ""    \n"",
    ""    # Subsitute P=u-a/3\n"",
    ""    # u^3 - qu - r = 0 where \n"",
    ""    q = (a**2)/3.0 - b\n"",
    ""    r = (-2.0/27.0)*a**3 +(1.0/3.0)*a*b - c\n"",
    ""    \n"",
    ""    # Discriminant\n"",
    ""    delta = (r**2)/4.0 -(q**3)/27.0\n"",
    ""    \n"",
    ""    # 3 roots. Physically meaningful root is u.\n"",
    ""    theta = np.arccos((-2*(a**3)+9*a*b-27*c)/(2*sqrt((a**2-3*b)**3)))\n"",
    ""    u = (2.0/3.0)*sqrt(a**2-3*b)*cos(theta/3.0)\n"",
    ""    \n"",
    ""    # Free protein concentration [P]\n"",
    ""    P = u - a/3.0\n"",
    ""    \n"",
    ""    # [PA]\n"",
    ""    PA = P*Atot/(K_A + P)\n"",
    ""    \n"",
    ""    # [PB]\n"",
    ""    PB = P*Btot/(K_B + P)\n"",
    ""    \n"",
    ""    # Free A concentration [A]\n"",
    ""    A = Atot - PA\n"",
    ""    \n"",
    ""    # Free B concentration [B]\n"",
    ""    B = Btot - PB\n"",
    ""    \n"",
    ""    # Apparent Kd of A (shift caused by competitive ligand)\n"",
    ""    # K_A_app = K_A*(1+B/K_B)\n"",
    ""    \n"",
    ""    return [P, A, B, PA, PB]""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### For one set of conditions""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""[0.028199122591431092,\n"",
       "" 199.62471720847722,\n"",
       "" 49.636440501118621,\n"",
       "" 0.37528279152277422,\n"",
       "" 0.36355949888138162]""
      ]
     },
     ""execution_count"": 3,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# Initial total concentrations of P, A and B (uM)\n"",
    ""Ptot=0.5\n"",
    ""Atot=200\n"",
    ""Btot=50\n"",
    ""\n"",
    ""# Dissociation constant for fluorescent ligand A: K_A (uM)\n"",
    ""K_A=15\n"",
    ""\n"",
    ""# Dissociation constant for non-fluorescent ligand B: K_B (uM)\n"",
    ""K_B=3.85\n"",
    ""\n"",
    ""[P, A, B, PA, PB] = three_component_competitive_binding_exact(Ptot, Atot, K_A, Btot, K_B)\n"",
    ""[P, A, B, PA, PB]""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": true
   },
   ""source"": [
    ""### Modelling the competition experiment - Expected Binding Curve\n""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Ideally protein concentration should be 10 fold lower then Kd. It must be at least hafl of Kd if fluorescence detection requires higher protein concentration.\n"",
    ""\n"",
    ""For our assay it will be 0.5 uM.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Ideally ligand concentation should span 100-fold Kd to 0.01-fold Kd, log dilution.\n"",
    ""\n"",
    ""The ligand concentration will be in half log dilution from 20 uM ligand.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([  2.00000000e+03,   7.02238347e+02,   2.46569348e+02,\n"",
       ""         8.65752256e+01,   3.03982217e+01,   1.06733985e+01,\n"",
       ""         3.74763485e+00,   1.31586645e+00,   4.62025940e-01,\n"",
       ""         1.62226166e-01,   5.69607174e-02,   2.00000000e-02])""
      ]
     },
     ""execution_count"": 4,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# Dilution series for fluorescent ligand A\n"",
    ""\n"",
    ""# Number of wells in a dilution series\n"",
    ""num_wells = 12.0\n"",
    ""\n"",
    ""# (uM)\n"",
    ""Amax = 2000\n"",
    ""Amin = 0.02\n"",
    ""\n"",
    ""# Factor for logarithmic dilution (n)\n"",
    ""# Amax*((1/n)^(11)) = Amin for 12 wells\n"",
    ""n = (Amax/Amin)**(1/(num_wells-1))\n"",
    ""\n"",
    ""# Fluorescent-ligand titration series (uM)\n"",
    ""Atot = Amax / np.array([n**(float(i)) for i in range(12)])\n"",
    ""Atot""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Dissociation constant for non-fluorescent ligand A: K_dA (uM)\n"",
    ""K_A = 15\n"",
    ""\n"",
    ""# Dissociation constant for non-fluorescent ligand B: K_B (uM)\n"",
    ""K_B=3.85""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Without competitive ligand""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""\n"",
    ""# Constant concentration of B that will be added to all wells (uM)\n"",
    ""# If there is no competitive ligand \n"",
    ""Btot= 0\n"",
    ""[P_B0, A_B0, B_B0, PA_B0, PB_B0] = three_component_competitive_binding_exact(Ptot, Atot, K_A, Btot, K_B)\n"",
    ""\n"",
    ""# If B concentration is 10 uM\n"",
    ""Btot= 10\n"",
    ""[P_B10, A_B10, B_B10, PA_B10, PB_B10] = three_component_competitive_binding_exact(Ptot, Atot, K_A, Btot, K_B)\n"",
    ""\n"",
    ""# If B concentration is 50 uM\n"",
    ""Btot= 50\n"",
    ""[P_B50, A_B50, B_B50, PA_B50, PB_B50] = three_component_competitive_binding_exact(Ptot, Atot, K_A, Btot, K_B)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x1037a2090>""
      ]
     },
     ""execution_count"": 7,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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xlju/TFteNbEm/y7kSdJmriFTM4pnzWdGxkqe79gAvlMCIQ+EuZxk+jSBEpKIqUQJDoqER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\niEgoJQgREQmlBCEiIqH+P5KePG9GEIb8AAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1044269d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Atot,PA_B0, 'o', label='[Btot]=0 uM')\n"",
    ""plt.semilogx(Atot,PA_B10, 'o', label='[Btot]=10 uM')\n"",
    ""plt.semilogx(Atot,PA_B50, 'o', label='[Btot]=50 uM')\n"",
    ""plt.xlabel('Atot')\n"",
    ""plt.ylabel('[PA]')\n"",
    ""plt.ylim(1e-3,6e-1)\n"",
    ""plt.xlim(1e-2,1e+4)\n"",
    ""plt.axhline(Ptot,color='0.75',linestyle='--',label='[Ptot]')\n"",
    ""plt.axvline(K_A,color='k',linestyle='--',label='K_A')\n"",
    ""plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,\n"",
    ""           ncol=2, mode=\""expand\"", borderaxespad=0.)\n""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""##Predicting experimental fluorescence signal of saturation binding experiment""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Molar fluorescence values based on dansyl amide.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Background fluorescence\n"",
    ""BKG = 86.2\n"",
    ""\n"",
    ""# Molar fluorescence of free ligand\n"",
    ""MF = 2.5\n"",
    ""\n"",
    ""# Molar fluorescence of ligand in complex\n"",
    ""FR = 306.1\n"",
    ""MFC = FR * MF""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Fluorescent ligand (L) titration into buffer""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([  2.00000000e+03,   7.02238347e+02,   2.46569348e+02,\n"",
       ""         8.65752256e+01,   3.03982217e+01,   1.06733985e+01,\n"",
       ""         3.74763485e+00,   1.31586645e+00,   4.62025940e-01,\n"",
       ""         1.62226166e-01,   5.69607174e-02,   2.00000000e-02])""
      ]
     },
     ""execution_count"": 9,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Atot""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([ 5086.2       ,  1841.79586711,   702.62336972,   302.63806405,\n"",
       ""         162.19555415,   112.88349616,    95.56908711,    89.48966612,\n"",
       ""          87.35506485,    86.60556542,    86.34240179,    86.25      ])""
      ]
     },
     ""execution_count"": 10,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# Fluorescence measurement of buffer + ligand L titrations\n"",
    ""A=Atot\n"",
    ""Flu_buffer = MF*A + BKG\n"",
    ""Flu_buffer""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(50, 6000)""
      ]
     },
     ""execution_count"": 11,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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jmq5Ferzd9rxoi4pPALwM5pfliYCvFY3Z3AmvK1VnnU6zOOgK4NDO/UK7OugE4FtgLnJOZ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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x108fc2810>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Atot,Flu_buffer,'o')\n"",
    ""plt.xlabel('[A]')\n"",
    ""plt.ylabel('Fluorescence')\n"",
    ""plt.ylim(50,6000)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Fluorescent ligand titration into protein (HSA) ""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Fluorescence measurement of the HSA + A serial dilution + 0 uM B\n"",
    ""Flu_HSA_B0 = MF*A_B0 + BKG + FR*MF*PA_B0\n"",
    ""\n"",
    ""# Fluorescence measurement of the HSA + A serial dilution + 10 uM B\n"",
    ""Flu_HSA_B10 = MF*A_B10 + BKG + FR*MF*PA_B10\n"",
    ""\n"",
    ""# Fluorescence measurement of the HSA + A serial dilution + 50 uM B\n"",
    ""Flu_HSA_B50 = MF*A_B50 + BKG + FR*MF*PA_B50""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x1098069d0>""
      ]
     },
     ""execution_count"": 13,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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wHJm3Gb19w1ER3R1Ifq/weoTYrWl5PBu6nCDuDLdsHO2xvbhvR0NDgyI16mOPr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\nSUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1090538d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Atot,Flu_buffer,'.',label='buffer + titration of A  ')\n"",
    ""plt.semilogx(Atot, Flu_HSA_B0 ,'.', label='HSA + titration of A')\n"",
    ""plt.semilogx(Atot, Flu_HSA_B10 ,'.', label='HSA + titration of A + 10 uM B ')\n"",
    ""plt.semilogx(Atot, Flu_HSA_B50 ,'.', label='HSA + titration of A + 50 uM B')\n"",
    ""plt.xlabel('[A_tot]')\n"",
    ""plt.ylabel('Fluorescence')\n"",
    ""plt.ylim(50,6000)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### Checking ligand depletion""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.legend.Legend at 0x109ee4a50>""
      ]
     },
     ""execution_count"": 14,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x109cd8e90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""A_percent_depletion_B0=((Atot-A_B0)/Atot)*100\n"",
    ""plt.semilogx(Atot,A_percent_depletion_B0,'.',label='A_B0')\n"",
    ""             \n"",
    ""A_percent_depletion_B50=((Atot-A_B50)/Atot)*100\n"",
    ""plt.semilogx(Atot,A_percent_depletion_B50,'.',label='A_B50') \n"",
    ""\n"",
    ""Btot=50\n"",
    ""B_percent_depletion_B50=((Btot-B_B50)/Btot)*100\n"",
    ""plt.semilogx(Atot,B_percent_depletion_B50,'.',label='B_B50') \n"",
    ""\n"",
    ""plt.xlabel('[A_tot]')\n"",
    ""plt.ylabel('% ligand depletion')\n"",
    ""plt.ylim(-0,50)\n"",
    ""plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.10""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/inputs_single_well.py",".py","2113","43","import json
import numpy as np
from glob import glob

xml_files = ['data/single_well/2017-11-20 15-52-45_plate_1.xml',
             'data/single_well/2017-11-20 16-26-29_plate_1.xml',
             'data/single_well/2017-11-20 16-46-13_plate_1.xml',
             'data/single_well/2017-11-20 17-04-42_plate_1.xml',
             'data/single_well/2017-11-20 17-24-20_plate_1.xml',
             'data/single_well/2017-11-20 17-42-23_plate_1.xml',
             'data/single_well/2017-11-20 18-02-55_plate_1.xml',
             'data/single_well/2017-11-20 18-20-31_plate_1.xml',
             'data/single_well/2017-11-20 18-40-27_plate_1.xml',
             'data/single_well/2017-11-20 18-59-10_plate_1.xml',
             'data/single_well/2017-11-20 19-17-19_plate_1.xml',
             'data/single_well/2017-11-20 19-36-19_plate_1.xml']

ligand_conc = [0.00000000e+00,   8.00000000e-09,   1.74937932e-08,
         3.82541000e-08,   8.36511642e-08,   1.82922021e-07,
         4.00000000e-07,   8.74689659e-07,   1.91270500e-06,
         4.18255821e-06,   9.14610104e-06,   2.00000000e-05]

inputs = {
    'single_well'   :  True,
    'xml_files'     :  xml_files,
    'file_set'      :  {'p38_1': xml_files,'p38_2': xml_files},
    'protein_wells'  :  {'p38_1': [5] , 'p38_2': ['A5','B5','C5','D5','E5','F5','G5','H5']},
    'ligand_order'  :  ['Bosutinib','Bosutinib Isomer','Gefitinib','Erlotinib','Ponatinib','Lapatinib','Pazopanib','Axitinib'],
    'buffer_wells'   :  {'p38_1': [6] , 'p38_2': ['A6','B6','C6','D6','E6','F6','G6','H6']}, 
    'section'       :  'ex280_scan_top_gain100',
    'wavelength'    :  '480',
    'Lstated'       :  np.array(ligand_conc, np.float64), # ligand concentration
    'Pstated'       :  1.0e-6 * np.ones([12],np.float64), # protein concentration, M
    'assay_volume'  :  100e-6, # assay volume, L
    'well_area'     :  0.3969, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
    }

inputs['Lstated'] = inputs['Lstated'].tolist()
inputs['Pstated'] = inputs['Pstated'].tolist()

with open('inputs.json', 'w') as fp:
    json.dump(inputs, fp)
","Python"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/inputs_p38_singlet.py",".py","1067","21","import json
import numpy as np
from glob import glob

inputs = {
    'xml_file_path' :  ""./data/single_wavelength_copy"",
    'file_set'      :  {'p38' : glob( ""./data/single_wavelength_copy/*.xml"")},
    'section'       :  '280_480_TOP_120',
    'ligand_order'  :  ['Bosutinib','Bosutinib Isomer','Erlotinib','Gefitinib','Ponatinib','Lapatinib','Saracatinib','Vandetanib'],
    'Lstated'       :  np.array([20.0e-6,14.0e-6,9.82e-6,6.88e-6,4.82e-6,3.38e-6,2.37e-6,1.66e-6,1.16e-6,0.815e-6,0.571e-6,0.4e-6,0.28e-6,0.196e-6,0.138e-6,0.0964e-6,0.0676e-6,0.0474e-6,0.0320e-6,0.0240e-6,0.0160e-6,0.0120e-6,0.008e-6,0.0], np.float64), # ligand concentration, M
    'Pstated'       :  0.5e-6 * np.ones([24],np.float64), # protein concentration, M
    'assay_volume'  :  50e-6, # assay volume, L
    'well_area'     :  0.1369, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
    }

inputs['Lstated'] = inputs['Lstated'].tolist()
inputs['Pstated'] = inputs['Pstated'].tolist()

with open('inputs.json', 'w') as fp:
    json.dump(inputs, fp)
","Python"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/inputs_Gef_WIP.py",".py","883","22","import json
import numpy as np
from glob import glob

inputs = {
    'xml_file_path' :  ""./data/"",
    'file_set'      :  {'Src': glob(""./data/Gef*.xml"")},
    'ligand_order'  :  ['Gefitinib','Gefitinib','Gefitinib','Gefitinib'],
    'section'       :  '280_TopRead',
    'wavelength'    :  '480',
    'Lstated'       :  np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0], np.float64), # ligand concentration
    'Pstated'       :  0.5e-6 * np.ones([12],np.float64), # protein concentration, M
    'assay_volume'  :  100e-6, # assay volume, L
    'well_area'     :  0.3969, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
    }

inputs['Lstated'] = inputs['Lstated'].tolist()
inputs['Pstated'] = inputs['Pstated'].tolist()

with open('inputs.json', 'w') as fp:
    json.dump(inputs, fp)
","Python"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/2c Bayesian fit for two component binding - simulated data- WITH EMCEE.ipynb",".ipynb","706502","858","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Bayesian fit for two component binding - simulated data - WITH EMCEE\n"",
    ""\n"",
    ""In this notebook we see how well we can reproduce Kd from simulated experimental data with our Bayesian methods, these are the same as those used in the `quickmodel.py` script. We will be testing to see if sampling using the `emcee` library improves our equilibration time and ultimately, the results.\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    }
   ],
   ""source"": [
    ""import numpy as np\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""\n"",
    ""from scipy import optimize\n"",
    ""import seaborn as sns\n"",
    ""\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": []
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""We use the same setup here as we do in the 'Simulating Experimental Fluorescence Binding Data' notebook.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# We define a Kd,\n"",
    ""Kd = 2e-9 # M\n"",
    ""\n"",
    ""# a protein concentration,\n"",
    ""Ptot = 1e-9 * np.ones([12],np.float64) # M\n"",
    ""\n"",
    ""# and a gradient of ligand concentrations for our experiment.\n"",
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def two_component_binding(Kd, Ptot, Ltot):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Kd : float\n"",
    ""        Dissociation constant\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total ligand concentration\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    \""\""\""\n"",
    ""                                    \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd) - np.sqrt((Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free ligand concentration in sample cell after n injections (uM)  \n"",
    ""    \n"",
    ""    return [P, L, PL]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[L, P, PL] = two_component_binding(Kd, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1105de690>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,PL, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$ / M')\n"",
    ""plt.ylim(0,1.3e-9)\n"",
    ""plt.axhline(Ptot[0],color='0.75',linestyle='--',label='$[P]_{tot}$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Now make this a fluorescence experiment""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Making max 1400 relative fluorescence units, and scaling all of PL (complex concentration) \n"",
    ""# to that, adding some random noise\n"",
    ""npoints = len(Ltot)\n"",
    ""sigma = 10.0 # size of noise\n"",
    ""F_PL_i = (1400/1e-9)*PL + sigma * np.random.randn(npoints)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n"",
      ""  warnings.warn(\""No labelled objects found. \""\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": ""iVBORw0KGgoAAAANSUhEUgAAAYoAAAEOCAYAAACXX1DeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1105de290>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,F_PL_i, 'ro')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescendce$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Let's add an F_background just so we don't ever go below zero\n"",
    ""F_background = 40\n"",
    ""#We also need to model fluorescence for our ligand\n"",
    ""F_L_i = F_background + (.4/1e-8)*Ltot + sigma * np.random.randn(npoints)\n"",
    ""\n"",
    ""#Let's also add these to our complex fluorescence readout\n"",
    ""F_PL_i = F_background + ((1400/1e-9)*PL + sigma * np.random.randn(npoints)) + ((.4/1e-8)*L + sigma * np.random.randn(npoints))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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JGVqzSRLZIHYszmb0KaHP3M4DTCO6nuL2cTs1sKXAecD7QCbwDWA+scfclQANwpZktAG4O\n97sEWBeusCc1bLSrm6GtO/nVy/sZ2rpTRUKkzsUpFL929/0A7n4AuB4o9whwCfAc0A88BDwMnEMw\nqgB4hGAFvXOBHe4+Gvb5PHBmmX2KiEgZ4sxR/MzM/p27fzPcHo/cO9pJwL8EPgT8LvAdYI67T4Tv\nDwPzgRaOPoSwsD1Sa+s8crnGGYRXuny+OZF+0pDl3CDb+Sm3+lWL+cUpFDcDD5nZx4FngDMI1s4u\nxyvAXnd/A3AzO0Rw+mlSM/AqwR3gzUXaIw0NjZQZVjz5fDODg8OJ9JW0LOcG2c5PudWvtPObrkiV\nfOrJ3X/l7h3Al4CfA/dR/sJFTwKXmlmDmb0NeAvwg3DuAuAyYDuwC1hiZnPNbD5wOsFEt4iIJCTO\nVU/XAT8GnnD3x2bSqbs/bGYXEhSCOcBNwP8DNpnZ8cAA0Ofu42Z2D0HRmAPc6u6HZtK3iIjEE+fU\n0/kEp5/eZWZOcPrpGXfvLadjd7+lSHNnkf02AZvK6UNERGYuzqmnP3L3swgmoj8NvB24vFqBiYhI\nbYhzeSwA7v6Guz8JfBj4h8qHJCIitaTkQmFm3YVPjnX3XwMLqhKViIjUjDhzFFcDd5rZW4GfAvuB\nt1UlKhERqRlxnh77UQAz+y3gLCAPzOjqJxERqX3HLBRm9hLh2hMcXYPiiWoHJiIitaGUEcVZwPvC\nf58AzjGzMY4WjTurGJ+IiKSslELxe+7+NwQP6gPAzBZytHiIiEiGlVIo1gJ/A2BmV7r7X7v7PxI8\n9fWhagYnIiLpK+Xy2IaC12UvVCQiIvWplEIxUfC6Ydq9REQkk0o59fRuM/sz4CngBDPLuftYleMS\nEZEaUUqhuBw4G/gA8AZwwMxeAP4OeNbdv1zF+EREJGXHLBThc52enNwO16x+D0HxOLt6oYmISC2I\n8wgPANx9lGAdiV2VD0dERGpN7KfHiojI7KJCISIikVQoREQkkgqFiIhEUqEQEZFIsa96qiQzOxn4\nMXARMAZsIbgTfDdwk7sfNrMbgJ7w/bXu/nBK4YqIzEqpjSjM7DigF/jnsGk9sMbdlxA8KuRKM1sA\n3AycD1wCrAvv4xARkYSkeerpT4H/Brwcbp8DbAtfPwIsA84Fdrj7qLsfAJ4Hzkw6UBGR2SyVU09m\n9ofAoLt/18w+GzY3uPvkAwiHgflAC3Cg4KOT7ZFaW+eRyzVWMOLp5fPNifSThiznBtnOT7nVr1rM\nL605iuXAhJktA94LfAM4ueD9ZuBV4GD4emp7pKGhkcpFGiGfb2ZwcDiRvpKW5dwg2/kpt/qVdn7T\nFalUCoW7Xzj52sy2Ah8HvmxmS919K3AZ8ATBY0LuMLO5QBNwOsFEt4iIJCTVq56mWA1sMrPjgQGg\nz93HzeweYDvBfMqt7n4ozSBFRGab1AuFuy8t2Ows8v4mYFNiAYmIyJvohjsREYmkQiEiIpFUKERE\nJJIKhYiIRFKhEBGRSCoUIiISSYVCREQiqVCIiFRBf38fnZ0dLFzYSmdnB/39fWmHVDYVilmgqb+P\n1s4OTlrYSmtnB011/AMrUg/6+/vo6VnOwMAexsfHGRjYQ0/P8rotFioUGdfU30dLz3JyA3toGB8n\nN7CHlp7lKhYiVbRhw91F2zduXJ9wJJWhQpFx86b5gZ1Xpz+wIvVg3769sdprnQpFxjVO84M5XbuI\nzFxb26JY7bVOhSLjxqf5wZyuXURmbuXK1UXbV6xYlXAklaFCkXEj0/zAjtTpD6xIPejq6qa3dzPt\n7YvJ5XK0ty+mt3czXV3daYdWltQfMy7VNdrVzUGCOYnGfXsZb1vEyIpVjNbpD6xIvejq6q7bwjCV\nCsUsMNrVrcIgImXTqScREYmkQiEiIpFUKEREJJIKhYiIREplMtvMjgM2A6cATcBa4GfAFmAC2A3c\n5O6HzewGoAcYA9a6+8NpxCwiMlulNaL4A+AVd18CXAp8FVgPrAnbGoArzWwBcDNwPnAJsM7MmlKK\nWURkVkrr8tj/CUw+la6BYLRwDrAtbHsEuBgYB3a4+ygwambPA2cCTyUbrojI7JVKoXD31wDMrJmg\nYKwB/tTdJ8JdhoH5QAtwoOCjk+2RWlvnkcs1VjTm6eTzzYn0k4Ys5wbZzk+51a9azC+1G+7M7B1A\nP/A1d/8fZvafC95uBl4FDoavp7ZHGhoaqWSo08rnmxkcHE6kr6RlOTfIdn7KrX6lnd90RSqVOQoz\n+23ge8CfuPvmsPknZrY0fH0ZsB3YBSwxs7lmNh84nWCiW0REEpLWZPbngFbg82a21cy2Epx+ut3M\nfgQcD/S5+y+AewiKxuPAre5+KKWYK0qrzolIvWiYmJg49l51ZnBwOJGkyh0mTq46N9XB3s0180ym\ntIfA1Zbl/JRb/Uo7v3y+uaFYu264S4FWnROReqJCkQKtOici9USFIgVadU5E6okKRQq06pyI1BMV\nihSMdnVzsHczY+2LmcjlGGtfXFMT2SJZ1N/fR2dnBwsXttLZ2UG/rjQsmVa4S4lWnRNJTn9/Hz0F\nVxoODOw5sp2V5UqrSSMKEcm8DdNcabhRVxqWRIUipBvgRLJr3zRXFE7XLm+mQsHRG+ByA3toGB8n\nN7CHlp7lKhYiGdE2zRWF07XLm6lQoBvgRLJu5TRXGq7QlYYlUaFAN8CJZF1XVze9vZtpb19MLpej\nvX0xvb2bNZFdIl31RHCjW25gT9F2EcmGrq5uFYYyaUSBboCT0mX9Wvwk88v6/2WWqFCQ/Rvgkv6F\nzOoBYPJa/IGBPYyPjx+5Fl/51XZfMnN6zPgMzOSRwP39fWzYcDf79u2lrW0RK1eursqweOqNRpOO\ndX623NzK7S9p5eTX2dnBQJFTlO3ti9m6dWelQpuxcr93SeZXbl9pP4a72tLOT48ZP4akh9xJ/TWV\n9I1GWb6xKevX4ieZX9b/L7NGhYLkh8FJHkyT/oVM4wCQVJFP41r8JP+ASTI/3ddQX1QoSP6v4CQP\npkn/QibdX5JFPulr8ZP+AybJ/HRfQ31RoSD5v4KTPJgm/QuZdH9JFvmkr8VP+g+YJPPTfQ31RZPZ\nJD+xlvSEb39/Hxs3rj8ycb5ixapj9jPTifq4/ZVr4cJWxsfHf6M9l8vx8sv7p/1c2pOGpchybuXK\ncm6Qfn7TTWbX/A13ZjYH+BrwHmAU+CN3f76SfaxcubrogbtafwVPHjSTOpgmfaNRkv21tS0qWuSz\ncK47y7lJfamHU08fBua6ewfwGaD4eHwG0hgGd3V1s3XrTl5+eT9bt+7UkLtMWT7XneXcpL7U/IgC\nuAB4FMDd/9bM3leNTnR7f31KenSWpCznJvWl5ucozOw+4Nvu/ki4/ffAqe4+Nt1nxsbGJ3K5xqRC\nFBHJivqcowAOAs0F23OiigTA0NBIdSMKpT3xVE1Zzg2ynZ9yq19p55fPNxdtr4c5ih3A5QBm9gHg\nuXTDERGZXephRNEPXGRmOwmGRdelHI+IyKxS84XC3Q8DH087DhGR2aoeTj2JiEiKav6qJxERSZdG\nFCIiEkmFQkREIqlQiIhIJBUKERGJpEIhIiKRVChERCSSCoWIiERSoRARkUg1/wiPemJmpwK3AvPd\nvTtsewvBCn1vAFvd/S9SDLEizKwd+ALwCvADd+9LN6LKMbN3AvcA+4F97n5nyiFVlJktAf49we9+\nu7ufl3JIFROuhvkloAV42t2/nnJIFWNmSwly2wM84O5bk+xfhSJkZpuBDwH/5O6LC9ovBTYCjcB9\nUQcOd/+/wPVmVnjgvAroc/eHzOwvgVQLRSXyBC4DvuLu283sO0BNFIoK5favCL5f94ffr5pRoZ/R\n7cB2M/sw8FSVQy5Zhb53VwJvJ/gD5qUqhhtLhXKbAF4D5pJCbioUR20Bvgp8Y7LBzBqB/wpcRPDN\neSo8MDYC66Z8frm7/1ORr/t2jj4afbzCMZdjCzPME/jvwG1m9vvAiQnEXKotzDy3vwX6zGwyz1qy\nhcr9jF4LXF/tgGPYwsy/dwbsdPfe8I+1HyQQdym2MPPctrv7NjP7bWA9wagwMSoUIXf/oZmdMqX5\nXOD5cKSAmT0AXOnu6wj+QijFSwTF4qfUwJxQBfO8Kfxhf7BqwcZUidzM7I+B28Kv1Qf8eZXDLlml\nvnfh6bUD7l4zKwBV6Hv3EsEpXoDDVQw3lgofW4aApqoEGkGFItrvAD8v2H4JeP90O5vZicAdwFlm\n9tnwm/4g8FUzuwJ4qJrBzkDcPE8BPge8BfhyVSObuVi5EazP/gUzuxZ4sYpxVUrc/CAYSdRMAYwQ\nN7cHga+E8zDbqhlYBcT9nbsKuAT4LYLRSaJUKCrI3V9hytoZ7v46GVtsyd1fBG5MO45qcPfdQHfa\ncVSTu9+WdgzV4O4j1NbptIpx9wdJcfSe+qmQGvcPwDsKtt8etmVNlvPMcm6Q7fyUW43QiCLaU8Bp\nZva7BN/EawgmAbMmy3lmOTfIdn7KrUZoRBEys28CPwpe2ktmdr27jwGfBL4LDADfcvc9acY5U1nO\nM8u5QbbzU261nZtWuBMRkUgaUYiISCQVChERiaRCISIikVQoREQkkgqFiIhEUqEQEZFIKhQiIhJJ\nhUJERCKpUIhMw8wmzOxZM1sWbv9rMxsost9jZrbfzD5ZgT53mlnDlLZTwlh6p7S/N2z/w5n2KxJF\nhUIk2nnu/lj4+izgmak7uPsy4Dsz7cjMTiNYo6DY4xJeAS4N1wCZ9FFgcKb9ihyLCoVI6c4GfnKs\nnczsSjM7oYz3LwMemeZjr4V9X1jQdjHwWPHdRSpHhUKkdEVHFEUsBw6V8f7FwPciPvctwrUyzOz3\ngGc5uqKbSNXoMeMiJTCzeQRrMkeOKMzsauA84Akzu4ng8dHLgAPAVcAVhe9PPjE0HGHMCxe/ms5D\nwFozm0Nw2ukvw68vUlUaUYiU5kzg5+4+dIz9/hfwqLsvBd4GNLl7B8EcxhWF7095rPRSYGvUFw7X\nuP474ALgg+i0kyREhUKkNCXNTwBnAJMF4ApgU/j6MDAy5f1CUfMThb4F3Ak8Ha5pIFJ1KhQipTkL\nWGZmLxb8u7rIfqcD/yd8nQNyZnY88BFg+5T3C70PeLqEOB4C3ktw2kkkEVq4SGQaZjYBNLv7ayXs\nu4XgQL8LuA+4F9gJTN77cJe795vZuZPvu/u9VQlcpMJUKESmYWa/AH4JrC64l6LYfo8B7wK+6O5/\nnlR8IklRoRARkUiaoxARkUgqFCIiEkmFQkREIqlQiIhIJBUKERGJpEIhIiKRVChERCSSCoWIiET6\n/6LhxVeNhrKaAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x112fd61d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,F_PL_i, 'ro')\n"",
    ""plt.semilogx(Ltot,F_L_i, 'ko')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# We know errors from our pipetting instruments.\n"",
    ""P_error = 0.35\n"",
    ""L_error = 0.08\n"",
    ""\n"",
    ""assay_volume = 100e-6 # assay volume, L\n"",
    ""\n"",
    ""dPstated = P_error * Ptot\n"",
    ""dLstated = L_error * Ltot""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.py:70: RuntimeWarning: invalid value encountered in divide\n"",
      ""  scaling = (1 - np.exp(-alpha)) / alpha\n""
     ]
    }
   ],
   ""source"": [
    ""# Now we'll use our Bayesian modeling scheme from assaytools.\n"",
    ""from assaytools import pymcmodels\n"",
    ""pymc_model = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""    top_complex_fluorescence=F_PL_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""mcmc = pymcmodels.run_mcmc(pymc_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""import matplotlib.patches as mpatches #this is for plotting with color patches""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def mcmc_three_plots(pymc_model,mcmc,Lstated):\n"",
    ""\n"",
    ""    sns.set(style='white')\n"",
    ""    sns.set_context('talk')\n"",
    ""    \n"",
    ""    import pymbar\n"",
    ""    [t,g,Neff_max] = pymbar.timeseries.detectEquilibration(mcmc.DeltaG.trace())\n"",
    ""    \n"",
    ""    interval= np.percentile(a=mcmc.DeltaG.trace()[t:], q=[2.5, 50.0, 97.5])\n"",
    ""    [hist,bin_edges] = np.histogram(mcmc.DeltaG.trace()[t:],bins=40,normed=True)\n"",
    ""    binwidth = np.abs(bin_edges[0]-bin_edges[1])\n"",
    ""\n"",
    ""    #set colors for 95% interval\n"",
    ""    clrs = [(0.7372549019607844, 0.5098039215686274, 0.7411764705882353) for xx in bin_edges]\n"",
    ""    idxs = bin_edges.argsort()\n"",
    ""    idxs = idxs[::-1]\n"",
    ""    gray_before = idxs[bin_edges[idxs] < interval[0]]\n"",
    ""    gray_after = idxs[bin_edges[idxs] > interval[2]]\n"",
    ""    for idx in gray_before:\n"",
    ""        clrs[idx] = (.5,.5,.5)\n"",
    ""    for idx in gray_after:\n"",
    ""        clrs[idx] = (.5,.5,.5)\n"",
    ""    \n"",
    ""    plt.clf();\n"",
    ""    plt.figure(figsize=(12,3));\n"",
    ""\n"",
    ""    plt.subplot(131)\n"",
    ""    property_name = 'top_complex_fluorescence'\n"",
    ""    complex = getattr(pymc_model, property_name)\n"",
    ""    property_name = 'top_ligand_fluorescence'\n"",
    ""    ligand = getattr(pymc_model, property_name)\n"",
    ""    for top_complex_fluorescence_model in mcmc.top_complex_fluorescence_model.trace()[::10]:\n"",
    ""        plt.semilogx(Lstated, top_complex_fluorescence_model, marker='.',color='silver')\n"",
    ""    for top_ligand_fluorescence_model in mcmc.top_ligand_fluorescence_model.trace()[::10]:\n"",
    ""        plt.semilogx(Lstated, top_ligand_fluorescence_model, marker='.',color='lightcoral', alpha=0.2)\n"",
    ""    plt.semilogx(Lstated, complex.value, 'ko',label='complex')\n"",
    ""    plt.semilogx(Lstated, ligand.value, marker='o',color='firebrick',linestyle='None',label='ligand')\n"",
    ""    #plt.xlim(.5e-8,5e-5)\n"",
    ""    plt.xlabel('$[L]_T$ (M)');\n"",
    ""    plt.yticks([])\n"",
    ""    plt.ylabel('fluorescence');\n"",
    ""    plt.legend(loc=0);\n"",
    ""\n"",
    ""    plt.subplot(132)\n"",
    ""    plt.bar(bin_edges[:-1]+binwidth/2,hist,binwidth,color=clrs, edgecolor = \""white\"");\n"",
    ""    sns.kdeplot(mcmc.DeltaG.trace()[t:],bw=.4,color=(0.39215686274509803, 0.7098039215686275, 0.803921568627451),shade=False)\n"",
    ""    plt.axvline(x=interval[0],color=(0.5,0.5,0.5),linestyle='--')\n"",
    ""    plt.axvline(x=interval[1],color=(0.5,0.5,0.5),linestyle='--')\n"",
    ""    plt.axvline(x=interval[2],color=(0.5,0.5,0.5),linestyle='--')\n"",
    ""    plt.axvline(x=np.log(Kd),color='k')\n"",
    ""    plt.xlabel('$\\Delta G$ ($k_B T$)',fontsize=16);\n"",
    ""    plt.ylabel('$P(\\Delta G)$',fontsize=16);\n"",
    ""    #plt.xlim(-15,-8)\n"",
    ""    hist_legend = mpatches.Patch(color=(0.7372549019607844, 0.5098039215686274, 0.7411764705882353), \n"",
    ""        label = '$\\Delta G$ =  %.3g [%.3g,%.3g] $k_B T$' \n"",
    ""        %(interval[1],interval[0],interval[2]) )\n"",
    ""    plt.legend(handles=[hist_legend],fontsize=10,loc=0,frameon=True);\n"",
    ""\n"",
    ""    plt.subplot(133)\n"",
    ""    plt.plot(range(0,t),mcmc.DeltaG.trace()[:t], 'g.',label='equil. at %s'%t)\n"",
    ""    plt.plot(range(t,len(mcmc.DeltaG.trace())),mcmc.DeltaG.trace()[t:], '.')\n"",
    ""    plt.xlabel('MCMC sample');\n"",
    ""    plt.ylabel('$\\Delta G$ ($k_B T$)');\n"",
    ""    plt.legend(loc=2);\n"",
    ""\n"",
    ""    plt.tight_layout();\n"",
    ""    \n"",
    ""    return [t,interval,hist,bin_edges,binwidth]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""2e-09""
      ]
     },
     ""execution_count"": 15,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Kd""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Real Kd is 2nm or -20.0301186564 k_B T.\n""
     ]
    }
   ],
   ""source"": [
    ""print 'Real Kd is 2nm or %s k_B T.' %np.log(Kd)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1131688d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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FwJCrmKkunTp3AZDIl6jWpS3SvOm0ZGhrC0dERjo6OoNPpEgGckZERevfurbQNBoMBS0tL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\nc+ZMJCQkaKxfbVnntw4GDAMkvkkEm8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YyPJ09xcvDVK2TZG5ojnl950m1AwBx0+LEaaCp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\npIDFEhERERERkYLfIxu6BYnTLQQAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1164b1a10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""[t,interval,hist,bin_edges,binwidth] = mcmc_three_plots(pymc_model,mcmc,Ltot)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""That works, but the equilibration seems to happen quite late in our sampling! Let's look at some of the other parameters.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""well_area = 0.1586 # well area, cm^2 # half-area wells were used here\n"",
    ""path_length = assay_volume / well_area""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:253: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n"",
      ""  warnings.warn(self.msg_depr_set % key)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n"",
      ""  warnings.warn(\""axes.hold is deprecated, will be removed in 3.0\"")\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:290: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:295: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(False);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:304: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:309: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(False)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:324: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:332: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(False);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:340: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:355: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:363: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True);\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""(10000,)\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:378: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:396: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:413: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(False)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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kEzWtUysFTatQgWPVC/F3PdgsKsLhahA+nZ6tjq4hKz+/CVuWLW5xLgmnjsDqy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\nNEmdO3dWv379tHDhQtdrnE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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x116488210>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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35+PgoPj5e27Zt0wcffCAbGxuFhoZq8eLFCgkJKelTAIDbHu88BAAARSnxkcQf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\nABNJIgAAAADA9P8B84PIk1WIR0oAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11b17d3d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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rZzDIvS5RndMW4rYLvVh9h+6bjzL5/ckgZae02J6u+2MfOrXnoJ0NBy0d3C11k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BFAAAg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\nrkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c01e050>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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w5U/v379eyZcvk6+ursmXLaseOHfriiy9s2r+gz2lpaRbnwO1+J5LUt29f9e3bV6dPn9aW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\nRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c04c310>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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UCUGB3uPDWQHKXUK4eGEttnYPIEF+58wD1g624qEAycsoJMviwYPUeqmtt8nx/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\nRncnWihEurcXLRjk5H+9SPPvtuOJdCFpGpIEdk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Y8aXg5kuXcXP5urTPh8qY6ZO7+w1XU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\nvfXWWzz//PMn/NlCCCHOHJJICiGE+MzoTvj279/POeec0+cyP/7xj9m3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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c010d90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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UvryBZBPKIvPzmDgDArRoxYoQ1FkT4qp0KX7XT2SE5BT228haSRSCPGDh0kI4e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\nAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11cc44250>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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6cOfD29sZ///0HZWVl6OjoiPPp6ekhNTUVAJCamoq2bdtK1KWlpUEQBKk6DQ0N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\nBAAAoHgAgn1FIHvgCGcAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11cf7a590>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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skAoFAIBAIhDrjYU4ZSssrERmob4wQSWQ4dTkLLX2FkFTJIHRyAJ/HhZuzA32P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\nAAAAAHgiNAIAAAAAPH0ArLUOFHOhm3EAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1198595d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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REREREUpgsEhERERERkRQmi0RERERERCSFySIRERERERFJYbJIREREREREUpgs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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x113aa4fd0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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kp8rPv2lX7tykp5u7baruye/E+Up9tNYZVBjvupWvX5otZKSmKC3Ck4y+5JLph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\nERERkVqwwCQiIiIiIiK1+D+5X4s0UkDm1AAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x113aa4110>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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/f7ydMUNeKaW/6rgNK8pK0piYasYFiseQ8O0OemVzuhioPB4CbWx+6Zxq3pLmk\njCF1XcmEf1z7Qv0OrvWdI1gEwMWLF8OGDRsAoFoAvOGGG2Dx4sWojhw6dAjmzp0L9913Hxw5cgQe\nfPBBmD17Nrz22mtV1+3atQu6urqgv78fjh07BuvXr4cFCxag3lFYAVAFlmGyXRfC4kQVAHUiGsLa\n6QpMynSOQrwU/3WpEdcZA52Au7wbg5CWV9/1GDoG0GfNYbSlcvw46iilfROHBdDVlYcb3MoCrAsR\ndc/a4luSQBGyfda2KZuynE89fgkzvljPCJdnlctuLqiukIqC7m5zP/VizlwW5qTv97Gec1jfAezr\nzmQZxdIzm4JAz1hsUgJQYxa5zroQZ6akDxwWQJ1GUPY41pMMU6rJRO+wZ29/v1vokiwLxZm0h4O/\nLkAyGBYB8Kc//Sl0dnbCsmXLoL29Ha699lq49NJLoaOjA5555hlUR5LcRRctWgSbN2+u+u173/se\nzJw5E1588UV444034Ktf/SpcdNFFqHfUhADICR+LU9IG942foGZ7ClnawCTMNjXZ6w8CuJXdwAoZ\nWWkfpZCbReylK1OfRRZQn7GkMoM+dZRM3+S7HqhuP6EQSllAiU2SyNvN2uf9LvPJ+b0h4lhDac2x\nigGsW5kPLeGywPvQagxN09crtpawT0ujfdSspS7rTFcIhzgzKWfJlCm4sAp9n3GXb7JZg7FKfxu/\nZxPysXuXKwELR6mXnJPBsGUB3bt3L9xxxx3Q09MDy5cvh1tvvRX27NmD7sjGjRthyZIlVb/19PTA\nnXfeWfXba6+9BpdddhlMnjwZpkyZAjNnzoSdO3ei3nHSCYA2ItfZiVvEcoP71lByJSShNkmSuxR3\nId6keCIuwXZw0E3wSWMyqPV1qKAyTQ0N+LHxHVcO61lvL/5QkDWbKMJOXZ35m3wFYd903pzgzuJJ\neS6Xm7Wvu5Pv+13mk1MAc3mW7z50ycJIVTi0I4pHp1m3TDXwsvB8oQBjGV22zK2W8FlnuVu49HUT\noji4KpiYBHJuBRHmjFTPNmz5F/XbKfty/3768ynfk0QD0p5lsxhj9y7XPvLNaTBWLIAcWLduHaxY\nsaLqt2uvvRZuueWWqt/2798P1113Hfz85z+HQ4cOwZ133gkLFiyAYYSQcNIJgCYiN2ECbbFiXXrS\nNloeadQpGB7GEyuVyfBhmCiMo4R+IOkxPrbEHaY14ZKEIEkrmvYN1FplmFpRSe9xSfjiuzapVkA1\nLb5rMfbHu00DAAAgAElEQVS0frgIwhwFfeVzOODqLm37TmqmVBcFj4lhpCpZXBlM1/nkFEKoz3Ld\nhxxWs5BWK7WlCYAHD4a3frrszRDjUldXcedztaImrXmXfWKjlZj1yz1ntu9Iyr5KpSm275JzI8fF\nZT585iUJWEEU07i8CFxCokL0wwMsAuCePXvg+uuvhyuuuAIWL148qmGwYcMGWLp0adVvPT09sG7d\nuqrf/u7v/q6qtMTx48dh7ty58MMf/tD6jpNOAARIJnI2rV7aJsW6yaQxYnmn67fB1r+kmATfwH0J\nDBNjc1uVbjA+MUUYC2iSENrVNfJ+KdwkFaWnaH3T1gOnm5SE79oMXf/LZX9QBWEKs6f2JcR8cLtL\nuzwXgO62bnr2zp24sXXV1uvwmU8uLwXqs1wEbg76S92/PtYIVdhW9w0laQ5l/F33JlcegbQx8Imj\n1Pe8ryDmWlaFU2HiqgBx+fa0fWniZ6hjm0dMMaZx8pnlslsNzrGUBfSjH/0ovPe974VbbrkF7rzz\nzlENgy1btoxK5rJo0SJ45JFHqn5btmwZ3HzzzVW/vec974EtW7ZY33FSCoAq5Eajul5gYwDV2Jkk\n5BXfYYNPCm+pMfMN3MccIlSXgyShzcZklEruWlFMP6iMVpK2P4SblG/8WxYWhND7w2VuQrqtpTEp\ng4O4+9Os0xShhPJ9mMQZtjHVNfzlsntSFN+9BuAfR6a7B6p7yNdF1uV6U19963thW0uL2X2fOkcS\n+nhy7M1QdE0y4XIvuiorOL+V+u36N3AoTFwtmb7ZYyWoNMPX5R4jjIXYm9yeZrbvVD1+OHNZeIJF\nAJw+fTrs2rXLqyOHDx+GOXPmwN13312VBfT111+vuu4b3/gGdHV1wXPPPQdHjx6FDRs2wNy5c+HV\nV1+1vuOkFwAB6G54rtk5kw73IsU4JGlGXYN6VSbDhbBgmRiX/rloWuU3uGhFbd9A6UuWSSKw/fJx\nXfNppv3B5XbpMjdZKXWGhvC18JKs02lJnbDaZwyDh2UCMGO7cmWlyXfqZREwTITvXvOBzdU8bT6o\nZwSnV4mcZ98adqFaGvOfti849uY114T7HmodU1u/OQUxV+sVJju7Ca5zRv32tHVDMQ5IV14TMNnW\nbcDwHKecUolHxbitqvsoq5CFNWtyT/iSBBYB8C/+4i9QFjgbdu3aBYsXL4aOjg645JJLTtQBXLt2\nLaxduxYAKi6f69evh/nz50NnZydcccUV8Pzzz6OeHwVAwFuR0mLJTIQG68YYMrsnBtwaJV/mCcPE\nUAV39V6K0Gb7FleNcJKLmUt6aS6GL01BEapfrq2xsXJI2gQdqttl2sEnx8BWz0we3Fm4dWOFAsq+\ndlU4meJ2Q8y/3qjlOHzWtA98LAnYMyLPLIyyP0n95Erfb5sj277gSOpVLvMk38C8G7NmsPuWg9m2\n0bakIutJoNBqDiU5Jk6ci/+xeX1xCIC2eRg/vloQtblk9vbmG7JQILAIgL/61a9g4cKFcPPNN8PX\nv/51uP/++6taURAFQKBlKbSBmlVLZzrz0oiEiNdy/RYsE+MTbEwRTkzz7svk+tb88mX4sAoK2x5x\nZV5cGSl9LFwPGwojgq2TGXJvYMfWtUwNhvmggMsC6LNH0+DjShpiLGzfJc8K29rxUUBI138VrkII\nJW6TgwZwvQezN7HF7X3XsImu1dcnK8KwcLH0YOLlfazwaWdcaCU55/r0VRjb7seeMbLWH4B5zNva\n0nmpUCELBRX+AJgEwM997nPQ2toK55xzDsyfP7+qYYu0Z4GTXgCkMPBUzT0mhko+N4taYiZQk724\nCATc/XEl0BRGjLNUAHaMkoimSszV6zAxjKb32A7icrliYcF8DzU2cfduvtphrkH/IVzrsrAAcvVF\nb2rJBw5wxAD67CUbKAyKj2uUq6KoVKJr5ql7oVyuKDdUmtrYWPmtXMbRYk7X27SGiRny9TrArKMQ\nlm2bkkpfo6tWue1TXy+JctktM7YKbCKZtH6GUJJze6v4ekX4KnnUdSUzmMp5UxPSrVljV2wmZXp3\nQQHdPZPAIgB2dHTAt7/9bdaOhcCYEgCzqHdEWcRcxaxDg0KU5PeHjnOyPd9HAMQE2Nvqy1H66jpG\n5XJF8Es6BCkMVdp7MHOI/bbGxvRv0OtKqiU6THUmbU1l1kIX2MbukaEhHhcfE7B9KZfdxtXmwkSB\nTch2ybCX1nwZjKT7fRlmFZxukDaBAavYKJfNnhSTJ/ONvU4LqM0WM8QhmGHPLRu9mTgxvS5nc3N1\nvCfFIuITT8flkufrRotxI01bk6aakRgkjQ+3QO/7/UllLXRQeA5bFnOXTO8FtuD5gkUAnDdvHrz0\n0kusHQuBmhcAOQ5orDBB0TK7EpW8sn7mlWo8DabnYy1SacSQKxmA2tc0V7K0Zhsj2/hi41bT3nPw\nIG7OscJZQ4N9nIaG0g/2t74VYNw4+nwOD7trValrHjMW7e0AV19tvkar7eoEG80aP96d0Q6R/MSW\nRVf9u4tbKHefZb846/9xx8GZaBTWsolhJDm9PVQ3Vm5PDADcM9MEM84SGXKsV61KnwMuiwiWB+I4\n83wtWBzCFlWBhhkfjFCKVV74WkBNc6h+E4fizKVcA3Wv1BhYBMCHH34Y/vIv/xJ27doFr7/+Ohw+\nfLiqFQU1LQByHdBYZpoqFKQdNLaNnwc46+ZwEQb9+WoSHherUXPzSGA0x9rRDxZsHzBj5GsBTbJg\nuvSX0mwMjW2fLVtGs+yqmlKqMOfCyGC1rphv8NWk+ljAOebSFbbnDg+7WdRDKM44yyl0dfHPUUsL\nzuvFNOYYOuqbLCeNAcecu9QzBZuhU3d3pcbSUc8P7H5KisHk6geXa7rvc3zdLdO8TXzGh7LXh4b8\n4s0piqC050m3bd9YVIqSl7rvaxQsAuB5550H7e3t0NbWltiKgpoWAF0OaJW4qjWwbAlFqBoPn9im\nPHylfQWiUH32YR70NjQ0+tmqhtYmnKlrx1UDlxTLlwSOmARsQiKOwwHDPNgYSVlXENu/7u6RZ7vQ\nAk6rt2szWWj1/5d7IXR9RYw1NySo9eC4tdG+sbX6s0KXRnBVJuzfj3+Ha7IcW/KJ1tbkv0nlqUuM\nmovi1WUNcSlAbTGYJmDpHmdyKl/FSKhEPT41NE3rtL5+9FxQ596HDlAy3bq05cvd7svLWBEYLALg\nU089ZWxFQU0LgJSipKoQodePWr16pHB5WqAslahzJwbJAqEtei79cclcZRvXpPUgD1+d4eAUQGWz\nMRscbjL6OnK1qmDvsx36lBg67OGmphynKjAOHnRjZFxLj2DeY6JTPrGS1EbRsHNB/3Y9VrSlpWKl\n6e0NS5+ozJWNYQ6R/TKtYQtcq+OMcbeVbvOUs0Ey41gXU7nOOdwzXWmzj0XDVQFqUz7bYt4oSiwu\nC6CvspgjvEOOt829k/LN5XKFvmDWoCpscgjNlHnhzljqw8vUSGIXClgEQInjx4/DwMAAHD16tFCu\nnxI1KwByM5Tq5jYV/ebsW1Irilk9xMamJumxEbreXrpfPpbBsyWocNEyU+bZNwuq+vxyme4yKBkP\nzHhhBFrsnpAH0ooV9AMIE2umCxm2APkQc5P0zVlYiigty4PdxlDqlnuAcP2jMFfjxyczhCqyEtpl\ns8UFuq4xTBbGJGacshd8s0v6fmceylfMekv7bqpVjzvu3UdZbLrftmakh0J/v5l+Y5V1FDf/7m63\nfBO+dEDtI+VZmD1Vi8aKgGARAN944w245ZZbYOrUqTBlyhQYGBiAlStXwqpVq2C4QFJzzQqAADjt\nDuVA5xS+XAL+x2JgLTZAPek+m3+7SoCwfvlUF0Mfou1DPDHCL7YQuEvSHFXzLA9rinU8ad7Hj6ft\nBZ9sc5SSFNSsfD5zk9Zcg/Fd1l0WZSooCJVR2CUrNJUZUpUIqjdJFq661LnjsByYYpKycHXFIil+\nHPOOrHkzTL/SvhsjLOkWrhCJ27DumNj7MVmUy2Ue4cYl0Rdl7AYH8XGpmD5SFKlr1piV2BQhOW18\nxyBYBMDbb78dLrzwQvjxj38MHR0dMDAwAD/5yU9g4cKFcP3117N22Ac1LQBiGAfKhuZkfGxB/6ef\nXhxXy1BwPXDKZbf04xitJGU9cBWpxvTdZeww38tVZ08CYx3nYgY7O/kOIAytwDJ/LnNjozscwkJL\ni11pgXHrzfpg5xRIXRVOAHzp4CdM4HmObGoadkxLsphixhnbktZHVq6uLgKavKdoig/KeqMotJLm\nSk9Agg1xoSak4SiZYnKLlYpJSiZN7Frm8tzSx5p7v1HLSNj4BG6ht8bBIgAuXLjwRKyfFAABALZt\n2wbnnnsuU1f9UdMCoI0Rc9FucGkAm5rM7ymVKgSnQNZgdrgymlhmgmIBAghTwNe1YTL4mQi3fm/a\nOvJh+lwZIi5m0FQWgloPipv5o7hA2ax7HNY/eehjhdOQJVwo4ExOgf0u077Lw2pna5JOYksnpMW8\nYt514IDbXsli3HwFtKIpPgDcLYCYuC3X/e4iyHHTFBt9xQpX1Iydvu6QrspPmyurCp91nERHqef1\nxIljVvgDYBIAp0+fDi+//DIAVAuAL774InR0dDB11R81LQAC8Go3uDSAFEHDNx28rR95wpXpxs6Z\ny4FdFAugnoTINv/Dw/SDmUPgdVFQYGLkfBlGSi0o7DhgM7TqsI2RLValXPYfD10DbRNOfWN4OMEl\nnHPE72SZtAXTdCYVqyxwzXrrIpBnpVjzFdCKpPiQcIkBLJftMegNDfiwB512uIxRSOFa9/LBulSq\nQhmW1vlmb3e5v61tJAmhKQnWNdeEUeC5CK1pXgZjACwC4JIlS+Cmm24CgBEB8MiRI9DX1wdLlizh\n660nal4AVOGr3eDUAFIZOq4DiMsNwxeumn0KM+FChIoSA0idf4wQkYSsLYCUec+yb9h3hSgpYIo9\nlHUpfRgPU58xAnzeXghczKOPK5NaaqMIyXhUJlWn6ZgEVEn7AzvOISyAWDfWNMsO5/lIVXyEVKT2\n95u9hZI8HbACkIvrK3UvyrXJUTLFBure1N07Aey0znX/y4zULjRIn2OT1VKWpNAFRl8FXrlcUapi\nkzZ1d/vtx7yNEwawCIAvvfQSzJ8/HxYtWgRnnXUWfOxjH4N58+bBvHnz4MUXX2TtsA/GlACYBOyG\nprqU2ZBHMeOsNZy2TRzSAqiWAKAAux5sWUApTVr7XAsqYwPek8AdA4gZXyyD6lscmyK45KUI4qhD\nZVpXWVjtQh/WHHSLUtMubX+qSVxU5oqSvIijrVqFGxvq/sCOs4tAjr1naMhc9uDMM8OV+sC6zcux\nCq1INc1HXR3AsmXV75P9x5yPMlMmVRFLLZtAWZuuHhYSFBoulWvYOdRr/OrC1emnm9/X3e1nCe/r\nG3k/xb0XgEeB50JnqDxlUYwTFrCVgTh8+DBs2rQJbrzxRvj85z8P999/PxwsmOQ7pgVAmX5ebmgT\nE80tALpsqFqIcaBs4pAxgD7fohN4vS6kXjrAVBsIO68Yi1fa/GMD3pO+c+XK7AojY/uqtrSEGbY+\nu8TsYfcjZzIIKkO1Zg1eCxsyaUXWh7WLZUbvI5fLts5cZWkV1PedqwInbW3091eYVTlWdXWVf0tL\ntBxXqkBOucfG4Koxj74oQjxbGmxzKwUmtf9YC5MUAMtlfKZQ7rIS3OOH/fbmZoDt23Gx0La1Ib/V\npoCVLre+Xi2U8zpLRaWtD5h8BkVzv04BiwD453/+5/D888+zdiwExpwAKJleleg1NlZ+wx48NmCF\neJ2poRBXF4TOckZ1Q3Td9OWyX3FcG9IIf5pLKUdcULnsPv/YgHc9VsK1FpaPxt3lANRjs9as4dur\nKsplfAwVB/Pp6gZtW//c/dSR92Htm/CFoyW5uukxOpyJT9L2nes7kvbH4CB+Xl0Fcsw9WWXjLGI8\nmwrM3PooHrE1kNXv4Swszzl+WFq6atXI+W66DltGifLus87yK6pObepc+BqWXOdSde0OYQzIASwC\n4DnnnAO7d+9m7VgIjCkB0MY4+VgVfDXiPhYgDDiz6SUB44aY5BfuwkjI+3Sf9MbGkRpArnBhCnwZ\nPTmvLvNPqfujwkVo1dNHU+HqAiP7jhVgZRxEKCE1LwughPRaoNT48oHOPPgc1ll5uIRO1oLJMJxE\n2yiFz9XGmbVYrwOqnlsu8+paeiFpLYQ+p1S4ruMs6EPoxDmSP7FdpytTsWPm039T+IZPhl6KcEoN\nx8DyAC41YH1aX5+/lwb3WnTJrhrSm4UIFgHwjjvugA984AOwceNGePTRR+GJJ56oakXBmBIAOZiC\npIPHVyMuD2HXGDAsuDZZEhHGjq1vQoq0+7gsHVSmgIM4ymeGYkiS7nUVWn3H2fe9eiyGyf3WxRp1\n8GC22khKDKCuYLLFSPq6QacptKh0JI/YDlsfpYtjS4t7MifKXpACj8t7THSZklwlyX2dwoxyJeqw\nrYWsmMGQCjfM2vCNkfdp2BrIugCI4XUosYhpTY0HTFszruUPOM5sV4VqS0uyYihEQjnOc5F7LbrU\nV8w7Gdl/g0UAbG1tTW1tbW2sHfbBmBIAuSw1OnwYRuwhzOFa5dtP08FNKWpaIHP+KGRd40rXxodw\nSWpudnNZwa5/LDDCVVIrldLXHoewpq9tk/telllAMXW60mIkffppet+UKbg5U61gpmdxC4HlMj4L\nooxrw3gvUPYCdx1B0xq2rf/GxnT3dZe96MOEYelbFgoYH6bTR0DljJF3bdQayDZ355YWgOXLK14X\nKg21JUbB9NOWaK25eWT8bLyUei2HQKN7pFBpIwCOToZo1D3EvRb1PXKyWQBrBTUtAKqpu7FMgcum\nwS5eF8sZZyY/n5g7032Uw6Rgm7kKrkyBC3FMm1eKSywmNb1aSkAFNyOaBopwldZMwpiNcbetNdvY\nqXWWQmTUVJNQpc05tX6dbz9t76O4nmIFlND0zbaeqQJgWrp7G2NPpRU2Qd5HgUjdh/peorrzcmW8\nVRl5H4RKumXK1kwtuM4pFDQ3uxVLTxsHGTNN3TuU1tlJ2yeSlpr61N5uj8Vzca/3dcsvlysxipSc\nEGkNE85EyVXhmizO1FQ+6mSLARwcHDS2oqDmBEAOhtNEYHRQYrDkf9XDK2vNh0vMHWZzUse4IOb8\nUXCZD24LgkSau3ESo6mnptcPex3cjGgSsMKVaY9yMBemtYZZ25xrVQp8acKCj8WBq5+299nKHsh1\nh6n/5bO+dFDXNDW7oWz19XS3OMy1rgoHSdMpIQQuXgCyP65xRS4Zb03732ZBtjG4PoLcWWfR17HL\n+/Tz2qfpz6XQVr1Eg49wyi1IJI2fb5IXm4DY2+s/x6oCVz8PZs50HwfKvNr2r2mex42rCOiyTqWk\n9c3NdEHYN4wqQ7C5gLa1taU2LHbs2AGXXnopTJ8+HS6++GLYvn174nVPP/00/Nmf/Rl0dHTAokWL\nYOvWrajn15QAyKUxU7UwmIPYtcAw1nLGyYC6xONgDm4K81VUCyCAO1PgIqhg51UtV4Ihkr5ZEl0Z\nUZdkIaaEGTYGUD10XNeaj/UeC33PUWIzso6P8I2P8amPGTrG2TRuPgoszDq31RFT95g+l9i1Z/sG\nPYkT5Zvb2gCuvto9riiUd4VuQaacby4WOfXZpvJALvNjo1XLl7vvy+5u2rjaxiR0kiWXRknykhaL\nJ+ewXHbLNG5bU3rtQZPyM829H9OoyabS9i+WH5L7Vv7Xlk0bo+wI5XXjCRYBcPfu3VXthRdegB/8\n4AfwoQ99CLZs2YLqyKFDh2Du3Llw3333wZEjR+DBBx+E2bNnw2uvvVZ13Z49e2DmzJnwve99D44f\nPw4PP/wwdHZ2wjCCaagpAZCDKKkZqLBMlU+NlFAWwCSmwUXLgj24OWMA86yFGXKM9Hk1fad0u1AJ\nOcbyovYJ860URjTtGVzJQiTUhC+++9mWkRI7V/q3YeHjliiRtZeAq8eEzPLrQ4e7upJdlm1w3YMS\nPgosSsKZtDpiOqiKOspaxsbPyut7e3FlR2x0PWR8tU3xYKpPiGE6Tfu4qal6zYbKbupaQmDChNF7\nyrdEQ8gENT6NkmxJPWeSxt211FDamnJRjHV3u2UOdrknya2dYsnzUXTqKKqHGASOAdy+fTtceOGF\nqGu3bNkC559/ftVvixYtgs2bN1f9tn79eli5cmXVb8899xwcPnzY+o6aEgC5iBKV0XO1PGIsZxSt\nuI1pCJllEttMBzFHymIOuGiiqGMkCWvSd9q0j2nNlCwFwCwUuhBc07p3CYh3GddSyd11pFx2c0ei\nuKS4uiWqcNGm6qAoVVwFOGlh8qUXSQwr5tuo71XHzTUphovgiVmXLi6GlO/HCEwycQwlw2HSvEiE\nzrBsy6Ro2yc+ruJNTRULnen88lXk2O5/y1sqYyAVDnV1lX/LvSTnw0exJhWXrveHbBQLoO2s5Jgv\nfU250FVpqUyjB6ZGvUcX5ihnuIkXkGdsQS16VAQVAHft2gXTpk1DXbtx40ZYsmRJ1W89PT1w5513\nVv22cuVK+PznPw/Lly+Hrq4u+MhHPgLPPvss6h01IwCGIEoURk8XGrCHsakYK/X9tueECnpPa5jk\nFLYA47z9v0NbgZO+k9u9RnUz4RSsOZOFuL5DrikX1xGfccYqZlzdEinaVD1m3BRbgpl7H1f6nh6e\nNau7rCX1kVoWI22/SWC8GDiSqaStH/lNruWAqOvZtndcBOuhofQ15+Jdwal89HHndu2HS3ZTHwui\nGhOuril9PnxiqzE1izkaVTlHiQG0ZXkO4XrvUwJJ36eY0AdMHG3a91CVPrbrV62i7bcCg0UAvP/+\n+0e1f/mXf4GLLroIrrzySlRH1q1bBytWrKj67dprr4Vbbrml6rePf/zj0NHRAU888QQcOXIE7r33\nXuju7oZXXnnF+o6aEQABwhAlLKOnEm1sXIncPBy+z7YNaLMimAiaj4VTHQ/X5xYoA1QqOOJP5XeG\nzKomG0WwTmOYuIvpUsc16RuyOJDVtW0CNTuufC5Gm1oqVYQkNZFOVxcusQ62NqlOk0KvSbXV1ZnH\nlVoWA6OFxmRbdbkPs34o9CNt7VFpkP6cJMUDZY9grPHUsy5E+nn5X6wyxDeDOCa7aVtbdRkF2T9V\nucNp4XU9Y+SacXVHxc7NmjW0mEddEWYaa9u3Y91cKa73nKWXhodp1nQXCz52rYUaq6T/LwhYBMD5\n8+dXtQULFsD73/9++PSnP43OArphwwZYunRp1W89PT2wbt26qt+uuuqqUS6g8+fPh8cee8z6jpoR\nAF1qjLW1jWQwclm4IVwupQ87FZgN6Fu/yMXC6eNWI1tDg7vFKksCQtXSJY2/i9Dg2nzqPmIPNJMb\nCjadu6uCxDT3HONsWtuuCoFVq+z7YsYMNxdh7Nzr8Cli7tMOHKgeT3U9mtr48cnJOWzKAducpa1X\n17lW+4NV0NnWntwrLvTZV4lFdcH0TVbF0VzLH1Ga7lqn0zJbjKVcd1ShyzYfPpZrW19czj59TWBd\n9Lu7R4Q63eLsklgMa9WiKqZd4vLSso1SFKNUYY5ytnNZS3X6ridYyiscKAGFqQO4ZcsWWLBgQdVv\nixYtgkceeaTqtxtuuAE+8YlPVP12/vnnww9/+EPrOwotAOqLhlryYfJk94WL2YCUTerqrgWA34Ac\nsUQAI+NhI2iNjebnUOZKEhsMfMaSC7t304m9JHwu97k0F0uCum4pmdZsdZkoc2v7u23uORg7m0bT\nRUMuywtk4V7lkjjGpizDtLa2ClNju061ALrOl0uhedf16uqihRVqk+bOptyiKvx8rG0cNTltcxKK\nNnK51JpaEg8hFSvY97S10RQ/tjVYKtGEUQrt15/t6g3iWq9P36PUxGK2uLZSiZ6silMABMApRl96\nCfcenb/CePf4XK9/h2st0xzAJgD+8pe/POGGuWXLFvjMZz4D9913H7ojhw8fhjlz5sDdd99dlQX0\n9ddfr7pux44dcPbZZ8N//Md/wLFjx+Duu+9OzBaahMIKgKZFo6axt2061zglSlFb2yZ1iY3QgWXG\nOWutYMY2Da51qJKeo4L7G10QWmstxGgLh6tLTxJjgl3bnG4oPtZIzLhzx1ja+uuSXKa3N1tLG8Vd\n1sXDQm+rVo3MgS1eT40BzCJWU4fPesW4aJnqkNmaHludptyifoOL4qG+vvItWZQ0KpfdkmHYWtoZ\nz5lUTv8OlZ5RrGW9vW51d23zoVveTDwL1eJjE6ZcrbA2Guua1E6dr/5+P1d6CU4X0CTFjz5/lDhP\nKZypz6XSDgyNdi01gZnTDMEWAzhlyhR46qmnYMeOHdDe3g5Lly6F8847D/7pn/4J3Zldu3bB4sWL\noaOjAy655JITdQDXrl0La9euPXHdE088AZdccgl0dHTAhz70IfjZz36Gen5hBUBsjbFQcUou7pRp\nhx+HqwGnQIoBh9mfeoipMZNpAgG324YLQtdIamoa7dLGEYeEnRd1HrjcUHytkZgxoKQwN2nbTZls\nV68GmDiRPhcU7TpHw1hjKB4WNmZMf19/f3q8np4FNESsJrf1LGnsTPFenZ1u30NJlkXZOy4lUVSB\nnmPMMEg6vzgsg0m1F7n2mnrm+CoHXeK0TGvJdu6nnd8uc43hPdLKV+n32dxbTestK8UldcxMa5ND\nCZrWTj99dJI4akkV7Htdyhxh5jRDsAiAF1xwAfzbv/0bAAB87nOfg0svvRQAAJ566imYM2cOU1f9\nUVgBEEOAfOOU1IxQKrgzRHEcnNjDntM90rffLkLLzp3m7+RwQ/KNGwzNwKu1KiVcCH+aRZWytvv7\nzanHXZ6pgnIAc9KEoSGcJtx1/NXacDoTFCLBAmbusetJ9bCQ/afUzJI0aNKk0WPiunYw62pwEEf7\nuOi7S7wX95xSmG4sPQ+pyKRA9sN3v6S51HLQcaqSirLuQnsymOA710kWKwxPIu/DWrdcw3ckOJUa\nLuNavOoAACAASURBVOu0uZlXCUpZt/39NGNBuUz3qHOh7znXCGQRAKdOnQpD/11nZ+HChfDFL37x\nxMM7OjqYuuqPQgqAVG2l7Zq0gzopK5eM7eNIcU/5FmzAvGnDmlxoXNwjfQ8BF6GFGlyOHUsuwZhK\n0Hy0gknfoM6/aybIIlkAsfdR9pGP9T5tnVAP91mzRuqs6XMYSkCQDRMbh/WwUPuNoS0urmA+jLis\n90WN6eO2ZnEy7a59sjHdNrdcH3ru6n5vU8Zx7Jc0l1qfuaqrq/AQ1HVFmWOOUAOfuEzqXCcp0l2f\n4/ttGMUIt1uzq6KC4qXGqXxW9wUmYRuVl5V7jMLTjRUL4EUXXQT3338/PPbYY9Da2go7d+4EAIDb\nb78dPvzhD/P11hOFFAAB+Bb6mjWjSzjY3HcwB44peJf6LS6LXj/s+/p40vKrwBBvzAGuEmLfhkm+\n4PIdFGDWplpo2dUV1gSVyFJdfvOIAUxKsY89gCmuk42NANOnu+0Dn3gU7NriFhD0eGhMZjWKgksX\nJPR36GvNRWnkMybUDHg+/cQgtHeAb2bNNLdcLB3kCDGgKON894vpe01ucLame2kcPMhjzdbXHYdL\nrKtFBTPXSW7karmaUCE4lD2a5P6LFa4pvFmo8k7NzSP9DvF8fV+Y9jKH8phjTgOCRQB85JFHoL29\nHdra2mD58uUAAPD5z38eOjo6YOvWrXy99URhBUAORimtQDbHs9va8IdeKGajXDYXWfchZuo7KNZT\nE4aGwidPcWEwMclnKM/ThR3qenMtqoo96LGB7xRij1mHWEuR2lRXSkohcJd9G8J645qIQ2bTVBkr\ntS6gyoxRLP/lcsVCiOmDab+mWRldY4dMCrnW1uS/URiupEQdIaxZ3OvHNnZJwNAn3xhxADfBgjru\nFIG6oYEWRya/e80atwy4Q0OjBVmfeEXbunMp18JlUeGsH4zpH6bQe5JrdxqkklxVZGH6h+XNQu99\nKViHfAfmm7mUxy7rPyOwZQHdv3//CcsfAMDu3bth3759/j1kRGEFQB/iYnON49LUYIlDCGbDdXwo\nh7au3WturjD6aRZSjOvZzp1+Yz5uXPrf0ph7iiDDmY3SZa4wgfs2yIMwLeDeFPsl47MosXSUdehz\nQAiRrtHHNpPlPoQGV3VhxY6pnnxD37P6v0MUDZauldjnUtYMxr1ZCiaDg+l/o86X/l4Oa5aOkBZA\ntY6XTx9ssX4hQVlT+/e7jY/8LqoygiIElkr+wo9UmFHXHWWNhbSocCnM0uhBmlJLPadsZzZVSa42\nKm+GmZcsy0C5NptrLUcJKSHMniQ5gk0APHz4MLz88svQ398P/f39sHv3bti1axds2rSJrbO+KKwA\nCDD6gMZm36MW3Q2xUdK+RTIt0rKB9b3WwZkVkioo2Da16XtCxsgkMfdYpnTnTlo8HZV5lNePH2/u\nR2dn8v02yIPQtr64k65Q51MmavFhlNWkNFx7gKNwvOmd8r9cscUqKIwudtybm+0CVl3d6L3O4e7e\n35/O1FHrfWHfyyUMhaJvbW04rwvuBGbcwNSXW7mSzii7Zj11zbSMyVKJaVRvD0q5FpsAU4RkaEmW\n+TSX8+bm0TkPTMKIj4uvEBWegALbvIRyEQ3RbEXducqI5JzwJQksAuB3v/tdmDVrFrS1tY1q559/\nPmd/vVBoAVAFJrmDXIy26zgSjchGjX3gKhzv4rKSlKkv7fm+jEyIhA+YuU+CjZmYONFOnG11wbCw\nzVtSbIkNGGFdzgeFQccIi1mUNNCb7ztdspEK4aZFpjZ1nalzn7YOKIdtKDclSg1GU/wlNZkLtyXE\nlyGW32FyZ+3tdRvjNDfYLBLccCHE+mtsTFe8UceB4h7tcv66zkVSnJ3p22SR9rT43zySoWH2pWnv\nqN5Fcp9iXJ19+tbXRxsT2zlcKtW+BVAHZxmRgoBFAHz/+98Pa9euhV//+tfQ1dUFzz77LGzZsgXm\nz58PDz30EGuHfVAzAiCW4GSRYl02NT4JQ0QptfxMgqKLtYKaqY+DsXd1EfNpSQTJRnRtVjkhRgKx\nfdYvxa2SckhjhXWsZVxNMGNaJy+9FH4+TfPhcl8S04elGbJIMyXOiNImTADYvt2cdCVpHYSwAApB\nU5RhaVcaU4rV1Lu4syZZh9R3c5XNUZ+Z5s7qQgOpiTR8Y845BOE0cCqMbCEHLuNQLtvHm0v4k81m\nbTG57Ot9MXkWcYei+M4ltcSBHrtqmycORT+VHuieXrXYONyGi1Cv2REsAmB7ezv8+te/BgCAK6+8\nEn7wgx8AAMCWLVvggx/8IFNX/VEzAiCAneDI+CmMm4lvFlAMQaP2XzJqmM2DJW5JGlLb85cu5SEk\nSVqerC2AnEIn1V0hibm0HVqmtelrVcVYx5MEJL30hJqMhNOaTplnVwu1rnGm7HW1rANGSHZp2Ayz\nlP3sIjS5zEnamrG5R1P6pL/HpnFXE+Xoe9EUz8yVkCBJmYMNZZANQzNUuArh3IJwEjjWHzZmiDoO\nWMtSb6/dGwBLF22WEEwyFNPfOazz3HOZNn9FFpqo9CBkqEuRvjMN/f3p8foTJlTXgi0YWATArq4u\neOmllwAA4O/+7u9g3bp1AAAwODgY6wC6AkOYMdep2b+SmBQfLU4aEeWsY4Zl+MeNqzA5VIsBZ6Mm\nrPBpPimlba2ujrZWXQUDTOY6l3Ulm80KaMqImkUWVyHszJXcp1RFjU9RXZ3J1udapSVZ7C+s61SS\nazl2DqmJLpIUJBilCXW89BI4vb3VQtL48SP/tgl61L1AAYdyAOuSuHv36HdjC8SbEm5QGEIud3WX\ndWZ6n612LkUxUCoBXH21vY9YzwDbGuP0xOF2y6POpbondeVCiBhsbuVkUimjkPMWsk2aVB1LX1dX\n+TeHYFYu+9cczREsAuCnPvUpuOqqq2BwcBAefvhh+NM//VPYs2cP3HXXXTB//nzWDvugpgRALKND\n1fyZDpShoeoDBFuHLukwxBBgLENPcUNRvzkLN0x9LNTx8GEATNZZE6NiY/SxGnlpAcIwOq5pkG1K\nBx+raksLbQ9RC0lzrBcb46Sm/05i/hsbAZYtqy6hkKZx5owhk2sCm4GQY6xUUKxu8lqMa6G81qX+\npm2s5P/7fncopURSkhsKXGhAUmZISuKepP6aCsQ3NNhd4E1rn2I5xMR5hpyTpCywaWvnzDPT+2lT\nUNXX4xKQYJK0cKzj5mae5EBpyeJWrRqZf1OWbtP3h1AMc7rnY+eMc95CtjSagsnkbkJ/P443Hesx\ngL///e/hyiuvhHvuuQeOHj0KH/vYx6C1tRXa29tjFlAfYBkdqhtSEnSCVy7jNpdrYhWKpo5KMCnZ\nHW2tra2SrdJ2XW9vMnPQ308rDp9knaXMq0nDPWUKPgYsra5kEmxjrNeskjFCmH7oh/Q11+Dukxnn\nMFrxLCx96vxiBac0S5yaUUzdt2kMDeWQPuusdMtJGvObhQtQ2rdhLSQUy4+v+5hprHwE8SzG2dUt\nyoUGlMv+XhMcsZb6/kyb0xCW55Bz4jOu1L2pexKp8XlYXoRLkYTxLEqa57R9m5Spt69v9NmOjV/l\nVpjZsoDW19NdsfU+mxDanbWlpcJfpSnETcpyW98aGipzOziI20fqeThpEm1NFhBsZSBUHD9+HJ57\n7jnYs2ePV+e4UXgB0GRtwS4gn9p3OqPvQqjkYYU9MDHMFtVlQiXuWIEhrQ0N2cdh3DictQ4znmmC\nlm1ek7TdMqFGQ8PI4eSTnSstlgRLAHXtvEuZgHIZp5XGjmHW8QtqfTPsuNnmGhPDhK3blBRDZqs1\n2t/vHkuMaVwaVIpCzTWBhO1erAJG92TAziFHM9WPTAJnKnQX97gkJtVnX7vQidCxp0nu8BSEWjtJ\ne1OOH5Xp5Rg3Gb5AVeKY9m1afJe6R7FCp6/FbPz4ZFfGcnl0SZHGRoAVK/xcTpM8m/RxowiAqiJI\nxtibrldLh2BCmfS/UfqW5lXg6yE01i2AAAD79u2DL3/5y7B69WrYt28fbN68GZ5//nm2jnKgkAKg\nCzPH9V5bXRlXNzg1S56N4cIyW66FkDECQ0iCLceD8hyqxtdk3Uj7dtcxSWK2XJKtYDTkadpHGxMt\ntXpZap0xY0tN7Z8mALuWV7G1hga3GDKTdvaUU5J/p+znEDEUGIWKi1eFjfFcvty89qXFRLc6ZBln\nk6ZAMYEz5opK65Ms5VyZc7F9Uu+jvhsTvyVd2l14hZBuepx7s1zGx4GamrRImixG+pj5JtrizHtA\nbZhwEZ939vW5e3rpbdmyCl+prvk0RbCJDzLRb9f6qUnv5bDmj/UYwB07dkBnZycsXrwY2tvbYWBg\nAP76r/8apk2bBlu3bmXtsA8KJwCaFpevf7JNQ+ibccvUTFrBtHGwMVs+SSxcy2VwuWzI8eCu5SUZ\ngizr7STNLVXbiplLE/HHEuWs4k701tSEi8tzcTXkKK/C3TClR9TyDlLA4ZjDLECxZGCEFz27rBwT\nGXebtVtyUqMW7ebMuujCjKtZa332dVI/Ke7qlHdT3eFNrv22PRJCgRBib2I8Q0xNnv1UAZBDYYBV\ngvh6JbmsaVcB16bMpIzbGWekKwTr60dop0sIkw5frwlJB3yt0npIRcHAIgBeccUV8IUvfAEAADo6\nOmBgYAAAAG655Rb48Ic/zNRVfxROALQtrsZGmn8yRUMYWqOMYZqShNS0+yhMkZo8w3Yvxr2DwzVl\neNg9BbzveISeW6rLHCZeyEY0pdKAWjdMh8s+aGuzB3/L97rWvXItgyHXTRFTc0tGVdIpPV5IrwPo\nywSkgeo+h72e6mrV3l4tuEiEmru2tooGHnOtKcFXEmz0KM29KmmsXWibvs99whdc5oMqCOh0AUMT\nTX9P4hWoHgDYVl9fUXCFYmx91j+WcVfXi68isK8PFwst39nfzzsfmLUZgl/o6aFdb4tD7O72i5VL\n4n9nznQfMwC/+Mbx4wst/AEwCYAzZsw4UQdQFQAHBgZg+vTpTF31R+EEQMohVSqZD1AKM+lL8Hyy\n5Pm4vJbLlYMHE9Cc5OOfZGXUA7mpbqoYV0o5HlQibCKGeTH4aYlJsC5zHBnaVFBdz3SG1jaOf/zH\nyTEXLhlMdRw8WGHYsK6GlLGz9S+PmoZCmF0gJZ0KETBPpTsudMplT7q4VNvapEkVQS9pTVGVUKZv\nT0ocZou7SRKyksZ65Uqa4KLvN8p3NjSk0yrsfFBiAF0s+xSrWHOzezkQn3VLVawkQcayuYQoUOLt\n9fXCkSzO5NWg9s3VI8mnqYl61H1mct/HzAHnOUItP6WvG24Bt1z2u58aS50DWATA97znPSdcPVUB\n8Lvf/W4sA5EGVyGsVEquX0I9dEJaANOsLi4WD3moUDXDMn4vCS4ZBZOIp63OnGxJgcy+qebzqr2j\nW1ep44jpO8b6uXo1fgySsrhJhtbl4MDGqaQlcEkK1l+5Mtka5DJ2Wafm5nRBDhEvQaU7LnQKMzeY\ntY6dO7mW0+hImjDtQzdkbLhNMKacRZyMm55kCvtc1SVQF0ax47Vzp9/6Md1DKTfA1bDnE2cOA9sY\nmEpVqBZJFyUjh0JV9sGWddpFuHWN2VfnSoW+V1x5G+524AB93XDNX9KYuQq4kycX3voHwCQArl+/\nHt773vfC9773Pejo6IBHH30U7r33Xuju7oavfOUrrB32QaEEQAD3wziJCacy1T4bxlQ0ub093WWV\nEr+U5iLmMlbcSXVU4mkb97SECj4xM3nX3qEUiU2CTQNq0pxxMoxqxlpqHSXXDKYmrXxSbIoO27rp\n66tcl6WFb/lyvmeFyJjGHaOaZv1w/WYZOyaBpeU2Jk13c+OgG6ZssC7WFy7GLW2/YUvwSCbdlbYk\nJWByKeGj3uPD9Ps0rNvb0BC+tIq+V5JgWwu9vaPHdNWqZMstVlEmMThIDxPRmwxdMAnE2Pg/fd34\nWA0pSjUKb8PdfCyAGD6M0pfGRvxc6cpcXRlRYLBlAb333nvhPe95D7S2tkJrayucd955sHHjRjh+\n/DhbZ31ROAGQw9cdwE3jZWNGbe/WD6ukxAa64IUhyqFi29Ispz4btFy2Z0pNE2ZcrQzYsQzdfBIA\n+AiA3Jo+Wb+RKjC5xB5i+o6pMWdzR85ybZRK/HuW2wWU2x0sKautyxqS60hnFm37A5ucSoYNqM8P\nmTCKku0YWwuT8m7fNeBLW9L64LKeOZUqLg0Tk0ihgRhLIWXfJcWh62WQTM9SSyTJ/09LztTfj7eI\ntbYm/05JmiK/k2LRxmaepoC6H3yVjt3dbv3E0pxVq+gxfTbruwyJUUvccFrFA4O9DuDrr78O//Vf\n/+XUmR07dsCll14K06dPh4svvhi2b99uvH7r1q3Q2toKr732Gur5hRMAy2X3IFM9FsuFaXERAJMI\nytCQvaQEVpsS0vVAWk45Nmh/v33Mk2p56XPgkmoeoBhJPqjuetiYGpMViFu48dGyU7XflMPfhKws\nBZIxwqwBGcvIIWBwHphUgYR6fQiFFSate95eAKa1i93fXN9gy7SHfQ9HtmeuOLi8rH/yOzjOF0zs\nu/QY8q2LyrkHk5IzcaxVLG+zYkX6urDF2Mp1w5FEy2VcbcqrtHU9YUKygh4LF+XB0JD/maUrq30V\n+xmDTQB89tln4aGHHoL7779/VMPg0KFDMHfuXLjvvvvgyJEj8OCDD8Ls2bNThbtXXnkF3vOe98Dk\nyZNrVwAE8MsypBKpEKn49Q0kU1br4Cop0dIS3nrR2+u/QTGCe0MDwPbteEGTqiV2PfQ4XQOxwopL\nTI1PEpQs3B+ltdokwKvMIIWBkFnlMAilMJF7waTcaWurTtYhk3e86128fXCFjBu2MdMuCSFWrqQn\nVaG0JHc3naHL2wsgqWGyHatnke83qBmDTcKX7T2cY+mrwMi6TEDS/HAJVdjs1xjrdNp5YxM6VGsf\nZQxUlMv+5wqWv7ElD7GNZ1IJF+kCnvS7CRQ3apsHV3t7hSfq7k5OrOYDn5Aan72v1xzmLIeTAVgE\nwBtvvBFaW1vh3HPPhfnz51e1BQsWoDqyZcsWOP/886t+W7RoEWzevDnx+k996lNw880317YA6KtV\nUouQUjUPlEW/YoVZkOE6PLOoZ+dbNgAAz/Rh4mV8oBPnt7wF1y+1Rp3voeZa5sDUZJ9c3IR8FCqU\npq8TjPsHZZ8kWY+TDmtuISAtCy4le1xarSeOccbsCYormHyHOrbY/d3ejsu22ttbaRT6ZnJ3o/Yz\nq4a1+FASxtiadOG2KdkwjBn3XnKl83kK9qo1NWnfU7ORcn6Pyu+o+92m4KmrqyjKKGcDZ84EtWFc\ne1ULucv6UPdhUsKxq68erbhT90zae23KRrkX9dwNzc3J1kjXhC8q9HwR1H3I5YWAdfENEePuARYB\ncNasWfDAAw94dWTjxo2wZMmSqt96enrgzjvvHHXtv//7v8OSJUtOdL5mBUAAf+2Drn3HFKCmLPoz\nzzS7I1FrX4VqU6bYa7NhGmaDcggZXJogneG1CXSqcNXX5xdcbhsrrkOTUpNR1S6Gai6Z/Nrb6WPd\n3Z18mMrDmtsNcNw4O8MqhZGsUplTDkwXhUNd3ei45f5+Xhc813g3m1dAuZzuhpxH0xWSGPd2H0sT\nxlUWuzdDWXNd3OTzmj+1bpluMaIWuZeNS/hL8jZYvTpsrCR3QpSWFnx/0zJWY63DO3fS+aFSyZzD\nwbSHKHuRCzbakSZ46uBao1hvnBBljhzBIgDOmzcPXnrpJa+OrFu3DlZovs/XXnst3HLLLVW/DQ4O\nwoIFC2DPnj1jQwDkPnSwtbSwi/6008x/D6E5xTS1YDS2ECu2mcaO64DmiBfhcNPxSTEeosi67V2m\nb8Zma6M0XRBoaBhx/9NhW3+dnRWFCke/KEkFOOdUzkGWMUrYA5Nr/2PLfGCaa7ybSfBNsnKq9NAW\nJxSiNTWlWxX0+dPpHlaJKL8JmxkRWwtWtXil0RZXpRJV459n/N+yZeklari9fUxt/Hi8t0Gops4b\n15nvwyuZBKykxuVNhc1uS92LHOByt+TMRHwyWgA3bdoES5YsgZdeegkOHToEhw8frmoYbNiwAZYu\nXVr1W09PD6xbt+7Ev48dOwZXXHEFPPzww1Wdr2kBEJPVz4XQqEgSNLgWPSYbGHeTiWX6+kZbQG1a\nL9c4AxVFiRfJ0wXMFrTNVc9Mn5c0t5bOzmy/f8oUN7fqtjaAiRN5+rBmTbj6RyZkGaNEOTA59yUX\nTXONd3Oppaquyaxpgy0LYZJr9DXX4AULNdsedjxNaydJqTA4OJq2NDT4e5ZQNP550fQzzgB461vT\n/y5L1GTVPzWrYh5jwl03uUjeUr5jkbSHOAQfqkKc+s6053MmEaKU5ikAWATAH/3oRzBz5kxoa2tL\nbBhs2bJlVLzgokWL4JFHHjnx78HBQZg6dSp0dnZCZ2cnzJgxAyZPngydnZ3w9NNPW99RSAEQYHR2\nJ5+6d3Lh2zJd9vcD/NEfhSceITTRTU3pm//MM9PfiXHD44wBpDQXNwkbAVQDrUPMrW2sbOt34kRa\nQXdTQhJMHAi3oqKxMZw7Jqa1tJjrV7k2Lu+BLNYYAF+NO7WNH+//DF1JQIktTKMFlFqqIcrpuDRT\n4q36+gpNptJln6yRKjAxRD6NqvHP2ptGWtgwQq60lmZhXc6zHl3S/qOe+WnZOIuYuAmzRkzwzeDq\nkpEd+86hIdzzMVndMeN0MmYBXbhwIfT09MCWLVvgqaeeGtUwOHz4MMyZMwfuvvvuqiygr7/+urXz\nNW0BlOBYgGqzpagPHcPT2DhSQydrRsSUQY9jg4ZirijaIQoBDDXOdXVmYm0TuCiCKUdqcoyPfqlE\nF5hDuWNim16/yqdxHfYczbQfdcahVOIR2rib6kLHEbNCKXCtKxbzahjFi0sMka/VIQshOQRN52zY\nLJ3qePqsJ0w2ycbGcGNSKlXzBqoHk6l0gstaScrGWbTETZR1YgKmEDsl/pejFnKplF0yKiGqlW+u\n5b0yBosA2NHRAQMDA96d2bVrFyxevBg6OjrgkksuOVEHcO3atbB27dpR148ZARCjVXNJZWxaqKHd\nNtXDV98QoZN02DLocWzQEMwVt7ZYPi+0YJJErLkPbmoWxaSxsB04Q0Pu7o0h3DFd5sHXXQ3DsHK7\nWsp5tWWNU/eeC+Ouxsll7bYuXejSaI9edywJlD2larpd1mUecWidnbgkZhIYa6jJrSz0fg3h1ZHW\ndMGGUlaBcoaVy+7jId939dXm6/QyCKFiydUQklLJbnnS9y72eyVs2dqL2jB8CWYv6WdLTw/teh02\nI4YtLISzHE3aXi9QwpcksAiA1113HWzYsIG1YyFQWAEQs3lKJb6Mb1lZKpIWP0XjyP3uJMjsZj7g\nrMXGGS8iCVwW451ErDkzwE2e7PcMU4pqte6ea59tAmYttDRBnrruMM1Ez2yMs8v79eRYecUV6Ywn\nNQaYuj4xpSqSmu5FoWYIzGINYuigab/ZMhq6jKWprVrlrlB0KUGStLYksGtc1vXFvsPXAtjdPTJG\naQoGqShRwZ1N2uQ2XyrZa9JRFDFJLoh6tvYs6tb6NK7EYC0tlbHt6sJ9s03wtAmAWAWIj7K6vr4y\nl2r23BoCiwB4ww03wNSpU+GSSy6Bnp4e6Ovrq2pFQWEFwLxddEK0JJO/RLmc7u7z5jfj69m5Eg7Z\nB71AuWsylv5+ngyULhnjMC4OWQgmusV39Wp/K0tDQ4W4+rorm9zL1FgN3+QmQ0OVvhbRFVEd0507\n0y3gUkttipsol/0tjZjswmmgMu4TJya7H02alO3YT5xoT5ZiQxaCq94XtcZlFt4jWLfJpHITJkFV\nr63J1d+6utFjZUPaHjOFTKSdMa5laajhGT4KxbS+p8XKYb8jqSV5FMhzBGO9a262K8KwNMi2FoeG\nKgJRyP2kN0qdVixdwu4nKp9k2k8cChzXEj1qw9YiLSDYLICmVhQUUgDMw+8/K/ceUya7kEWkbQyE\nTXOM0QCq4GLKXDJEYd1Z9etCrIGhIfxhjX0/1YKh18bEpKjm2BumeIMitaQC9jozihFSQiut9DTs\n6v+7PG/58tF7gisba6i5SdvvLrUOsdd1d9vpX2hLoKlEjkk5gfEu4XT7UhvFcwMjmCXR9LTfKW6L\n6j2Uc6tUqk4gh72vsbGypkzXJMXK2b6DYjWjllCQ68S01jjO/OnT7f2qrw9DpzAJ5KhhMbZnUhWj\nJoU4B9+sPt9nPn2VejmCRQCsFRRSAASozcxQPhs4pBYbs+ls70/SAJoOA4750/uNcSWQ18j/Utxe\nKZpzyjfYhK3GxorGbMaMcGtATSEOkM3+sjE5RWhJe4MqUMi4qiz6m+Yq6TqfoSxAXA3rAUCNReJY\nJyrNyXI8dMuzzeOBkhiG6xxqbsbNG/a9Se6c8vtNpTRs0M8Hyj5qahq5D1vMXLpzctZGK5f5QmFM\nzZZApL8/fG3CWbPClI6Q3k7YNYgFN1/nW2fY5qmgPj9ENm3XccwQUQAsArKMR8k6uD8pxi4EQ67H\ncpmAeb+6cU2Mh2vR6KTMYyYhU83slxbL5up2IBmD/v6KIOMTk4Ap/UBdg9RsoSqyYFhd46yybkmJ\nDqi0J6vEQiatqo9LsHTXKarSTe5FbCzJ8LBfNry0xumC69ukcGdbq9hYbNWllYPpozJ5GMaVoqiR\npTRstN/Xki4VhxhLjnS75irXIRE6ezllzstl/+RkphaS1mJdpU1rSIdpjb7rXfT+2TwRbPTAVH5G\nCvF6JunTTw8zhwVFFACLgFpPHpHWdHe81avDF0O1CUKUAuUSNkLjegBMnlwZD9sakMwwxkrn6nYw\nVtZgyGQ0SWuEmkihCE1dIy5jk1Uip7RmOthtLaQCzNd9XQrolFiSgwdx2nyqC10a49Lfz88kYefc\n5grX0oJbz7I0x+Cgn2uhXE8Y4UudL8xz9WQomP2W5EXC5blSV1e5h6LwLJVwCT8olu+sFNiUiAkz\n3AAAIABJREFUPu/fH64fMokM5tpx4yqZLymxzU1N1fyLWrZGHXcsXUpyOe7tdbOW2ngZjEdAmgt0\nf3+6JZkjn4PaCpwJNAqARYFcqEXPCOXbsrKWpBGPgwfx78cGCPtqAFtaeAPBi+C+wdmwFgx9zl2T\n0diu7+31c6cqQnN15ZSMT0iFASabXGg3SGprbgb4yU/8npFGl9LWNcZ9O0lAKZdx/dHXOEeyK9cE\nSdhzkZqRWVUY7t7t/l3t7SPKPBuwtKKxcSQxDJaG6QkpOC3pWNdPasOeV75Jurib6qIeqrW1AXz8\n4+b1XyoBrFhRneSmqcmvNqwqQLnW6uPIuIzJ54CNi1Xj3W37Sc1Ya6OvpudEC2BxUGgBEKCYcSkh\nWlbxUpJ4DA5WH4qYw5QjRXBeLckN0oYiuzAmWTAaGqrruulE30dAsbmOqDFJ6rqqpeaa0l13jc5L\naaW6SoZ6B9XasGZNOEWAWr/MZV3r69aFcZk50/4ek4A4YYK9BpxP8ynF4pIoJK0PNqstlSH+oz/C\nX0tZs729tOtDWN9s7obq2ZWVkg2TeCWPOpl6a2oC2LXLLqS5ZmzG1LYNXTe2rg4f2mKztFFog6R/\nqru47u7b2EirNVhAsAmAL7zwAnz2s5+FK664Avbs2QP33HMPPPHEE2wd5UDhBUCA2rMkuBKuLN7T\n0OAu2MiNe/Bgbc5JUv2hNEJaZBdG1YKRlOwmjej7aB1lNlOTZnEsuMxSXTnTkiNxWq+bm+lJI0Lu\nT7W+G8YtLJQlXX4zhzYdY/lRBU5fJYeaXTTkvlH7nLc3jckDhepKGarlfa6p9dNUerJy5WhG28f6\naHOJrGWvK4yQ5towLtU2CxeXgs4lSZ4O6jipvEV/f/pYUMuzFAgsAuDWrVth2rRp0NfXB2effTYM\nDAzArbfeCmeddRZs3ryZtcM+qAkB0GczS4sIteWRDh0rBI4bl33f2tqq3SlC173ibrYMZjpRKrL7\np2xqvag0QZZDW5xUMy5JyKyFMTM11ZUTqyQplarHORQzT8neRpkLqtZer22JuWdoKJyAg0n4Yvse\njPVP1nDlTpKirhtducLhFTI0VE0LQsyBy3cnxVAVJZlJnmebXv6hXDZbYW2C2rhx1QXWSyX/pGZF\nbxghLfT3q/suCVyKBt96e5R+6LTfdkaqLqOY8iwFAYsAeNlll8G9994LAAAdHR0wMDAAAAAbN26E\niy66iKmr/qgJAdDn0C2VaAxIqQTQ2poP0bIxbU1NFa1fFsKpGpcycWJ2FspQDeuWIBmTWjwg1RiF\npNToHM82geNQmzjRPvah5kYVovr78UKgOs4Yl0D9Xl8BKckF19b35mZ6PUh1jKTggtWGl8v0WDRb\nq6/nsdT39OCu40z0oyYDk4lB5Fg2N1f2K7Uoud6kckJF3m7t0mJe654CoZpuPfJdb729I8/iUsS4\nZK0eKw2bVMl2XnLREZ96e1SFkIy/xWQflmMFUOiEL0lgEQCnT59+QuhTBcCBgQGYOnUqU1f9URMC\nIEDlMHQVQlT3NUygax6ERWpH0gh0U1MlQ2YefeNoKrOTh3UVm0Sj1hkTU6ye76Ft8t3ncpnduRN3\n3YEDvO5aafUAQ1sDdBc9CoMuhThM1lz1ekkPMYKi2nQvAEyiJj0+kptx7O31twBixkDWtcvSRbC+\nvhIf2NvrJrgl7dciWOm5FQG2May1d3DVa9VpWp5zL5V2WcwHVkhzaRT3UtN5aaLVXLG3oWIRKTGU\nNSb8ATAJgBdccAE8+uijAFAtAD7wwANwwQUXMHXVHzUjAHJlTSqX01Pd5lW7TNfgq65Arpr6IjVV\nE1Qu+wmAIQ8QqvWmiC2kwJIW24AVnG3ZDqW2GlPDbfVqHkWCzTUlJNOkp7WnKiDUTGwUYcxF0bFi\nRTpTgo33CDGWdXV+a37FCvy1ecWHuWSn1NeWusYoKfFDzFcW4yj3dRbnZm9v5V0cXjLS1RjAz2U3\nqW4i17i7eGBIxZMeyxiiNTbyxmDLJukZNvOvTNaSlgnXFFPPEbOb5AGQ5fmWtAZrACwC4KZNm2D2\n7Nlw1113wbRp0+Dee++Fm266CaZPnw4PPPAAa4d9UDMCoK+WV0XaxuNO/JFGJNTYLVWDr4NLE5h3\nUxlP30D/3t7aE4a5a+j4NF8BOlTsnyos2J7HsRfa2+1xGgCVvRnKKrxsGf84mppr0hSMezom3qNo\nNKy5uXZoCZZpbmxMTiQiUS4DnHlmmD4WxS1wxYrq7w3t1SHLZnCVhFAV1j57RqXVecZ/ygLjrvMg\n6UperqeNjdX0zIVO22oxJ52rmHqmtmY740LvD45ENRmDLQvoli1b4PLLL4fu7m6YOXMmLF68+IRV\nsCioCQGQg3ilmaJD1S6bOBFgyZLRmbvk4UzJSFmE4H3XpmZJ5GBway02r6GhcvipCoe843DULI6U\nlqZR9N0z3d3VWm/ToeQ6drrSxVZMV92bkyaFKfTd2BiG9piaS9IUSimHNEYnaxqGoROmWla12Do7\n7Rr3UEqGtjbc3sSMeankXog+rcZjHmEHrk1VWLvOV5K3Bsdab2qin8GUwu16k1akPPIy6GPJkR8A\nU8hdPXve8pZ0zxmMMJzmBqoKYkleZ5zj55uoJmOw1gF87bXXTvz/7t27/XoWADUhAPrGjlAKT4bW\nwtu0YWmxSEXRrlIJkXTXK5fzZ7byEh7V+myciSRcmip0UGNxurtH7xcOxl6uET0joJq9TgpuPsKz\nnmEvCabU1qHXRuh3tbTQ3yOLeVPflUTHshzXWk9a5dpsDCa3GyDVe2bVKjv96+vD06g0mi7HQdbN\ny3teqE2tteYSD6aOoUQe547kvVzXnRQe8p4PzkzGaUIZxRpXXw9w5ZX48ZeKVZsgJtcdJ632SVST\nA1gEwJdffhkWLVoEN99884nfZs+eDR/60IdgCON6lBFqQgD0JQCUwpOuBJfSbIlmXFO659m6u82x\nJUWo8SREhbBlKQhOmjSa6OaZZryhobKmbJa2tLFL2i+hvkceEMPDPIxcUv/1b8naOpulm7e0fNpi\nMYUYYez7+9213mvWVGuas6ZjmAyleSulQjTJ/OvuVpyC0Jo1IzHd2LguTCkNvbSNz36UJaB86jXm\n0aTVq78/PZbNVM83rTxQHrVtJQ1wuVfGsea9R7lrmaYZJKjvmDEDv57UdaG3PMtgFbAoPIsAuGTJ\nEujp6YF9+/ad+O2VV16Bnp4e+MQnPsHTUwbUhACIIQCnnIJf3ABmX+RyuRKfk5fFKMuizlytVLIf\n1rVWOxDT8lojaswVtamJQ6SlDbvGVM30ypXhLdPqd5ZK/uNtykqWtYAycWI1PQr5fmoczurV/mUI\nVCuR7Xnc6wjDtPkWjE5LJlaEJsdeuuRSBXkTrVbLzVCUpWpJAt3trMgeLnm0M89M52lOOaUyn9gx\nVHmgLBMAlUqVfgLYeYOJE5NjiItgvaW4wGNb0jlEfUddHZ/Cctas6kSEK1dmU2+a4p2XEVgEwI6O\nDnj55ZdH/b57925497vf7ddDRhReAMQSgJ070zMqSaTVR0sSEPO2ukmrRx5au1CtKPF7Pv2QjJFq\nIcmDEfR1rVGbFA4wpTLkPqrFchl1dWbFTx6xmQ0NI5aKSZPCMMIy7phK07jHo1Qacc1UBZQQZQG6\nu839dxViZGtsrNBmX7fkrBo1EZWsT2k6UynryeTulWVZiLHSOjureRmbclVaWkJkx7TNO6amqswV\noAtGBw/mqwB3dYE3tSShx1XQ5UxiJcvOhPaA01vBSkWwCIDnn38+PPbYY6N+/9GPfgTnnXeeXw8Z\nUXgBEABfbFgiaUHZ3E70wPE8iY5aJHisaUZrgVkytZaW0etL18Rm1cplvmdh6l9KJgLL+JnclPKe\nQz32oQiaZluTgiI1eYmctyJ6EpRKFeVdVn2TZXV0xWBvL841VjZVEZQ1U51VU+mcj8Wiu3vEEpSE\nItKIsdYkj5THWGNrK6vZwinCbaimZtPlHLek+EyXd9TV1a4yVraxagH84he/COeeey584xvfgF/8\n4hfwi1/8Ah544AGYN28e3Hbbbawd9kFNCIAYVx7fZwhRHTie98YYq61Uyl/jWyr5EU2TxiorK6eq\nJOBotn6r9akozHqR0+zrVokiCkhqU5UPlMLxLslfsm5ZZWk0nRVFn/8sm1wzaXBJJpRkARxLHi56\nK1rIA6fCkNKwZ6K0AhZBoKmrG1mjnLHhafGZAHRF0tveNtK/vHkq1zZWYwCPHz8Od9xxB5xzzjnQ\n2toKra2tcO6558L69evhjTfeQHdmx44dcOmll8L06dPh4osvhu3btyde981vfhPe9773wYwZM+DD\nH/4wPP3006jn14QAaCIKtkxC8hDDHu5F1pantaam2uqvSyp6W5s1q+KyhDl0pQXA5YA2FTfdv5/2\nrIkTays9uXRLzrsfnE09gPJ2+6bMgUrfsPfVEo0I1dI0zqHXdUMDwNKl4WrwheivEOb6ZS7lRHTU\nwp6Tbdw4vCAwYULFnS7vPsvmEzJQV1dxOc0ifpByJjc0hKuxq2a8tq1RjICLic+kZi5uavLbj1nM\nkampJcIKBNYyEAAA+/fvh1dffZXckUOHDsHcuXPhvvvugyNHjsCDDz4Is2fPriotAQDwk5/8BLq7\nu2Hnzp1w7NgxeOihh6CzsxPKiMGtCQEQIL14e9I36i4ElI3hWiw5r9bfX2Fesk5f79pCji/GIiaJ\njo/m2aR0oDxnxYpiaDuFCGcBLHpTaxuWy8XJVpvW1MyGf/VXFQurbe5k9tNaoWmhW5oFP9S6VrPP\nSnfTolmHbE1N6CFBXU+hatNl1aglCXp7i0Pfqe77Se1//2+AN7+Zfl8Ir5jQnjaU8lU2Je6sWfa5\nceVH+vp46yxjx50jNCmtrnDOYBMAX3jhBfjWt74F3/zmN+H++++vahhs2bIFzj///KrfFi1aBJs3\nb6767V//9V/hn//5n6t+6+rqgm3btlnfUTMCoISspZYGDheC4eHKhiwK8U5r48ePECdfhuKtb82m\nz6qff9bjW18PIC3oIUqLUKwIMu407zUkW4gYwFppalmekGUt8mwy4yu1oHItMejYcUhDqHXd2Jic\nbGx4uLZct3SNvQvTqp7dteRNIEsSUM6tosRAT5lSneEx67wC2BjAIjasQjAtEVx7u30N+IakSIV0\nHkYA37VUsAQwAEwC4Pr166G1tRW6urpg/vz5VW3BggWojmzcuBGWLFlS9VtPTw/ceeedxvueeeYZ\naG9vh/3791vfURMCoK2AJWetKTW2qlSqEK+ToajwmWeGz/4kD1F1XtesyTY7qExF7kso0zJ5Yd1k\nli0rjqDR3FwRjLHfXJQ4Da5Wi3U3fRrW7bi1tcJUUNbpuHHFTlzV2zs6tm1w0L/mHKapycZkH2pB\n0ag2vbYjZcxq2QKorpuQMVehzgTpypt1zGVbW8UVtsg0wXdOWlrSPdSyGu/Ozuyzd8q2apWb8FnA\nBDAATALgOeecM8oqR8W6detgxYoVVb9de+21cMstt6Te8+KLL56INcSg8AKgidlUs+FxFI5Na+3t\no8tMjMXW2xs2rbkuAEpkyXDX1/MRZVkImZqxrK0NYPLk/OdbiIrw3d9fsVBgv1nuy1pJgW9resIL\n19IAY61Jppd6HyWbZtZN9q1UqiRdyEO5J+trScVXU5Nd0djWVpmPvM8f2Wc5fhSBpZZjAPW6iiEK\nlK9aFV4gzkoQa2kBWL58bNBRbIkN9fzQ+QJTK0p5LNf+uVq6C5gABoCxDuDAwIBXRzZs2ABLly6t\n+q2npwfWrVuXeP0TTzwBM2fOTP17EgovAIY4IFwKXKqaz1rRWlJbqRTeupO06bO2KK1Y4S+4SK0f\npd/19RUmLqTrp8t3URKE6EkhaoWBw86pLJo9FhgXjvEAyLZ49MnckhSNSbHuw8Pu9CsvZjMtbrpW\nvQlKJd4kLyHqzeXV+vpwgk9RPGAwzZbERRf4atnimUWzJW/MESwC4OrVq+GLX/yiV0e2bNkyyl10\n0aJF8Mgjj4y6dtOmTTBjxgx4+OGHSe8ovADILWzV1QFceaXbfUKMXeFPts7OsM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ISAAAW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\niIiI6on/A6yDY3FgH/c9AAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1130c9590>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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Rc5qVurv9DoLJsfGer4PNDaKiWUObnqzIbFzLzuEVo5wxH4zndooW6OjLS6vZ9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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x112a20250>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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/+/v5asWKFXF1dVatWLaWmpur8+fPm/e4eW15eXhZjLyYmRrVr1y7w\n2HF/27Ztm7788ku1aNFCLVq00Pnz5zVp0iR99913jC/km6enp0wmk8VqeyaTSQ0bNmR8Id8uXLiQ\n6a6ei4uLGjVqxPiCTeXnPVfGbVeuXFFCQkKmr5QVNBLCAtS6dWulpKRow4YNSk1N1ZYtWxQXF2ex\njC2Qk7i4OI0cOVLDhw/XtGnT5Ox8+1e4dOnS6tixoxYtWqSkpCQdO3ZMkZGRCgwMlCT17NlTa9eu\n1cWLFxUXF6fVq1erV69ehdkU3Ie2b9+un3/+WVFRUYqKilK1atX0zjvvaNy4cYwv5Fvbtm3l6uqq\nZcuWKS0tTYcPH9Y333yjrl27Mr6Qb23atNHJkyf1ySefyDAMHTx4UN988426d+/O+IJN5ec9V48e\nPfT1118rKipKt27d0jvvvKP27durXLlyhdkkORl8a7ZAnTp1SmFhYYqOjlatWrUUFhYmX1/fwg4L\nRcSqVau0ePHiTFMLnn32WQ0fPlyzZs3STz/9JHd3dz3//PMKCgqSdPtT+KVLl+qTTz5RamqqAgMD\nNW3aNBUrVqwwmoEiIiAgQDNmzNCTTz6pq1evMr6Qb2fPntWcOXP066+/qnTp0ho3bpz69u3L+IJN\n7Nq1S0uWLNG5c+dUrVo1TZgwQU899RTjC/l24MABjR8/XgcOHJCkfI2pbdu2acmSJbp8+bJatGih\nN954Qw8//HChtU0iIQQAAAAAh8WUUQAAAABwUCSEAAAAAOCgSAgBAAAAwEGREAIAAACAgyIhBAAA\nAAAHRUIIAAAAAA6KhBAAAAAAHBQJIQAAAAA4KJfCDgAAADieW7duadCgQUpJSZHJZFKXLl00fvz4\nwg4LAByOk2EYRmEHAQAAHIthGLp586ZKlSql1NRUBQcHa/r06fL19S3s0ADAoTBlFABsIDY2VvXq\n1dPp06clSfXq1dP3339fKLF8+umnatu2rV3OferUKR08eND82lbttNd5C9rBgwcVEBAgHx8ffffd\nd5leW9suW7Y/Y9/eL5ycnFSqVClJUlpamtLS0uTk5JTt/teuXVNgYKASExMtym/duqXu3btn2V+n\nTp1Sz549Jd3u03r16un333/PtN+xY8dUr149DRkyJNvjbcUwDA0cOFAxMTE2PS8A3CsSQgCwgx9/\n/FF+fn6FHYbNjR071pz0SrZrp73OW9BWr16tOnXq6KuvvpKfn1+m19a2y5btz9i39+Ltt9/W5MmT\nJUmHDh1SaGio/P39Va9ePX355ZdZHvPtt98qMDBQ3t7e6ty5s7Zs2ZJpH5PJpF69eqlNmzZq06aN\nmjRpkm0M77zzjvr06aPSpUuby5KSkjRhwgT997//zfKYffv2qU2bNubXxYsX186dOzPt9/XXX2eZ\njGY83hacnJw0duxYzZo1y6bnBYB7RUIIAHZQsWJFlShRorDDsDt7tbOo9l9iYqK8vb31yCOPyNXV\nNdNra9t1v7X/m2++UadOnSRJN2/eVL169XJMaI4ePaoXXnhBnTt3VkREhJ599lnNnDkzUzJWrFgx\nRUREaM+ePTp27FiWd+8k6dKlS4qIiFD//v3NZcePH1dQUJAuXryYbRx79+61SOhatWqVZUL4zTff\nZDlVNePxttK+fXtdunRJUVFRNj83AOQVCSEA2MHdU/7+/PNPPffcc/L19VXnzp21efNm1atXz7zv\n0aNHNWTIEPn6+srHx0f/+Mc/FB0dbXGurVu36plnnlHjxo3Vq1cvHTt2zLw9JiZGzz77rJo0aaK+\nffvq3Llz2cZ16dIljR8/Xk2bNlW7du0UFhamGzduSJK2bt2qhg0b6tSpU5KkK1euyM/PTytWrJAk\nDRkyRH/++afCwsI0derUTO3MLc7z589r7Nixatq0qdq2bauFCxcqPT091/P+9ddfeumll+Tn56cW\nLVpoypQpSkhIsLp/MsouDmvqyqn/AgICdOTIES1fvlwBAQGZXmdsV05x3L1fTnXm1v6s+jYrkZGR\n6tu3r3x9fdW0aVP16tVLly9fliT997//1YULF9SuXTtJUocOHfTiiy/qqaeeyvZ8H3zwgZo3b64X\nXnhBXl5eGjx4sLp3765//vOfWe7/0EMP6fHHH9cPP/yQ5fZNmzapVatW5immkvTTTz+pQ4cO2rRp\nU5bHpKSk6NixY2rZsqW5rFOnTvrtt98sksjo6GglJiaqWbNmOR6f2/Xatm2bAgMD5ePjoxEjRujC\nhQuaPHmy+fd+//79FucPCAjQhg0bsutCACgwJIQAYEdpaWkKCQmRs7Oz/vOf/2jq1KlasmSJeXti\nYqJGjRolX19fffHFF/r3v/+t9PR0vfnmmxbneffddzVx4kRFRESoVKlSCgsLk3T7TeuoUaNUrlw5\nffrppxo+fLg++OCDLGMxDEPPP/+8ihcvrvDwcC1btkynTp3Sq6++Kknq3bu32rZtq9mzZ8swDM2e\nPVvVq1dXSEiIJOm9995TlSpV9PLLL2v69OlZ1pFTnMOHD1dqaqo+/vhjLV68WBEREVq3bl2O501N\nTdWwYcP0999/a926dXr//ff1xx9/mKcv5lZvRjnFkVtdufXfli1b1KhRI40YMUJbtmzJ9NraOPJy\nzXJrvzXX7Pvvv9drr72m4OBgffnll/rkk080ZswYlS9fXtLtu2dt2rSRu7t7lsdn5ZdffpG/v79F\nWbt27XT8+HGlpqZKuv2Bw7Vr1yRJycnJ2rdvn2rXrp3l+fbs2ZPpe7GjRo3SlClT5OrqmuUxhw8f\nVv369eXm5mYuq169uurVq2dxl/DO3U9nZ+dsj7fmei1atEgzZ87URx99pBMnTqhnz55q0KCBPvnk\nE9WpU0ezZ8/O1B979+41J5UAUFh47AQA2NH+/ft19uxZffjhhypfvrzq1q2rF154wfyGPSkpSaNH\nj9aIESPk7OysGjVqKCgoSEuXLrU4z5AhQ9ShQwdJ0nPPPaexY8fKZDJp3759unz5subNm6cyZcrI\ny8tLv/32myIiIrKMJSYmRv/+979VvHhxSdIbb7yhrl276uLFi6pSpYrmzJmj7t27a8qUKdq9e7c+\n++wzFStWTJJUtmxZFStWTKVLl1aZMmWybG9OcV64cEGbNm1SuXLlJElhYWG6fv16juf94YcfdO7c\nOX300UfmBOXtt99Wt27ddOrUKdWvXz/Heu/EfkdOceRWV3x8fK795+LiInd3d/PxGV9bE0der1lO\n7bfmmkVHR+vhhx/WE088oYcffliSLBKznTt36h//+EeWx2YnLi7OfK47KlasqNTUVMXHx6tSpUr6\n66+/NHXqVJlMJhmGoa5du+rJJ5/MdC6TyaRTp07psccey1MM2X3/76mnntLOnTs1ePBgSbe/Pzh1\n6lTt27cv2+OtuV5Dhgwx30308/PTuXPnNGLECEnSgAEDFBoaajEmvby8dP36dcXExMjLyytPbQMA\nWyIhBAA7io6OVs2aNS0SgqZNm5p/rlixooKCgrRhwwadOnVKMTExOnHihB566CGL8zz66KPmn+8s\nqmEymfTf//5X1atXt3iz37hx4ywTwtOnTysxMVGtWrXKtC0mJkZVqlRR1apV9eKLL2revHmaOHFi\nnt+o5hRnjRo1zG+mJZm/k5aT06dPq3r16hb95+XlJQ8PD50+fdqcEGZXb8aEMKc43n///Rzrio+P\nz7X/rGVtf1hzzSTr25+VgQMH6ttvvzXfBVy5cqV5QZsLFy7o1KlT5imvtlS/fn1t3bo11/2uXr0q\nk8lk0VfW2Lt3r2bOnJmpvFOnTlq5cqWuXbum+Ph4Xbp0Sa1atcqUEN59vDXXq2bNmuaf3dzcVKNG\nDfNrV1dXpaenKy0tzXxN7pzrypUrJIQAChUJIQDYkYuLS45Twi5duqS+ffuqbt26ateunXr27Kkz\nZ86Yv7d3x527Q3czDENOTk7K+DjZrPaVbk9frVmzpt5///1M2ypWrGj++bffflOxYsV04MABjRkz\nJsf2ZZRdnNnFlJuSJUtmWW4ymWQymXKt15r4rK3L2v6zhrX9YW2d1rY/I5PJpMmTJ6tWrVqaNm2a\nypYtq6pVq5q379y5U02bNs10hzM3FSpU0N9//21RFhcXJxcXlzwndndW/8zL1MqrV68qNjZW3t7e\nmbbVr19f1apV0+7du3X58mUFBATIxcUlx+OtuV4Zz5FxCmpGd8ZvTo/aAICCwHcIAcCO6tatq9jY\nWF29etVc9uuvv5p//vLLL+Xq6qp169Zp+PDhat26tf7880+r3szfOf+5c+d05coVc9mJEyey3NfL\ny0sXL15UmTJlVKtWLdWqVUtpaWlasGCB+dlue/fu1RdffKE1a9bo119/VXh4+L00O5NHH31UsbGx\n5u+MSdLGjRvNU+qyU7t2bcXGxlokF3/88YcSExPl6elp0zhyq8ua/rNFHHezZZ1Z2b17t44cOaIF\nCxaoSZMmqlWrlsXqpjt37rTqTm5GTZs21d69ey3KfvjhBzVu3DjPHw6UK1dOLi4uio+Pt/qY/fv3\nq2XLltneIe3UqZN2796tnTt3qkuXLrkef6/jNyd32pPXDxMAwNZICAHAjvz8/OTp6ampU6fqjz/+\n0A8//GCxqEzlypV1+fJlff/994qNjdXHH3+sjz76SCkpKVadv3Xr1nr00UfN5//666+1cePGLPdt\n27atvLy8NGnSJJ04cULHjx/X5MmTzd/punnzpmbMmKEhQ4bI399f48eP11tvvaW//vrLfI5SpUrp\nzJkzFgmuNfz9/VW9enVNnz5df/zxh3766SetWrXKvHJldudt06aN6tSpo5dfflknT57UkSNHNGXK\nFDVt2jTLuz/5iSO3unLrP1v2xx22qDOna5aSkqJr164pPDxc586d0+nTp/X555/r0qVLunr1qqKi\nojIlhDdu3NDJkyd18uRJSbdX0T158qTOnj1r3mfYsGGKiorSsmXLdObMGW3cuFGRkZEaOXJknvpJ\nun0H7e7Vb62xb98+tW7dOtvtTz31lPbs2aPTp09n+T3DjMdbe73y4tSpUypbtqzF1FIAKAwkhABg\nR05OTlq+fLmSkpLUp08fzZs3T/369TPfJXn66acVFBSkyZMnq3fv3oqMjNScOXN0/fp1/d///V+u\n53dxcdH7778vwzDMi9EMHTo0y32dnZ21YsUKlS5dWoMHD9aIESP06KOPatmyZZJuP/g7PT1dL7zw\ngiRp8ODBeuSRRyxW7Bw0aJC2bNmSaZXL3BQrVkwrV65UUlKSgoKCNGXKFPXr10/Dhg3L8bx3YnZz\nc1NwcLBGjx6tBg0aaPXq1fc01S6nOHKrK7f+s2V/ZGx/furM6Zp16dJFzz33nFauXKmnn35awcHB\n+uyzz1SmTBnt3r1bderUUfXq1S2OOX78uHr37q3evXtLur26Zu/evfXaa6+Z92nSpImWLl2q7du3\nq2fPnlq/fr1mz559T3cbpduPujh06JDV++f2/EBfX1+VKlVK7du3z/J5jxmPt/Z65UVUVJT8/f1z\nnVoKAPbmZFg7LwkAkGd///23jh8/bl4BUro9TfSdd97Rt99+W4iRATkbN26c6tevb/6AoDBdvHhR\nTz/9tHbt2pXn7yDej9LT0xUQEKC3335bLVq0KOxwADg4PpYCADtycnLS888/r/Xr1ys2NlY///yz\nVqxYoW7duhV2aECOfH19zXcBC1uVKlUUGBhos++0Frbdu3erevXqJIMA7gvcIQQAO9u1a5eWLFmi\nmJgYeXh4qFevXpowYcI9r7wJOKKrV68qODhYmzdvzvaZikWBYRjq37+/FixYwOMmANwXSAgBAAAA\nwEExZRQAAAAAHBQJIQAAAAA4KBJCAAAAAHBQJIQAAAAA4KBICAEAAADAQZEQAgAAAICDIiEEAAAA\nAAdFQggAAAAADoqEEAAAAAAcFAkhAAAAADio/weUaCqEW663mgAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1134e4690>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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+//z6DBw8mLi6OOnXqMG3aNBwcHJg9ezYff/wxDRs2xNHRkS5dutC9e/eH1hEc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\nAhAAAKASX+vkeiDQZMU8AAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x119ce4d10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""from assaytools import plots\n"",
    ""plots.plot_mcmc_results(Ltot, Ptot, path_length, mcmc)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": true
   },
   ""source"": [
    ""### Now let's see if we can get better results using the newly implemented emcee option.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Following instructions as described here: http://twiecki.github.io/blog/2013/09/23/emcee-pymc/""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/bindingmodels.py:84: RuntimeWarning: invalid value encountered in log\n"",
      ""  logL = np.log(Ltot)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/bindingmodels.py:88: RuntimeWarning: invalid value encountered in less\n"",
      ""  sqrt_arg[sqrt_arg < 0.0] = 0.0 # ensure always positive\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/bindingmodels.py:102: RuntimeWarning: invalid value encountered in less\n"",
      ""  PL[PL < 0.0] = 0.0 # complex cannot have negative concentration\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.py:72: RuntimeWarning: invalid value encountered in less\n"",
      ""  indices = np.where(np.abs(alpha) < 0.01)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/bindingmodels.py:83: RuntimeWarning: invalid value encountered in log\n"",
      ""  logP = np.log(Ptot)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.py:70: RuntimeWarning: invalid value encountered in double_scalars\n"",
      ""  scaling = (1 - np.exp(-alpha)) / alpha\n"",
      ""Traceback (most recent call last):\n"",
      ""  File \""/Users/hansons/anaconda2/lib/python2.7/site-packages/emcee-2.2.1-py2.7.egg/emcee/ensemble.py\"", line 519, in __call__\n"",
      ""    return self.f(x, *self.args, **self.kwargs)\n"",
      ""  File \""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.py\"", line 458, in lnprob\n"",
      ""    return model.logp\n"",
      ""  File \""/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/Node.py\"", line 418, in _get_logp\n"",
      ""    self.stochastics | self.potentials | self.observed_stochastics)\n"",
      ""  File \""/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/Node.py\"", line 39, in logp_of_set\n"",
      ""    six.reraise(*exc)\n"",
      ""  File \""/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/Node.py\"", line 30, in logp_of_set\n"",
      ""    logp += obj.logp\n"",
      ""  File \""/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/PyMCObjects.py\"", line 910, in get_logp\n"",
      ""    logp = self._logp.get()\n"",
      ""  File \""LazyFunction.pyx\"", line 280, in pymc.LazyFunction.LazyFunction.get (pymc/LazyFunction.c:2568)\n"",
      ""  File \""/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/distributions.py\"", line 2979, in wrapper\n"",
      ""    return f(value, **kwds)\n"",
      ""  File \""/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/distributions.py\"", line 1769, in lognormal_like\n"",
      ""    return flib.lognormal(x, mu, tau)\n"",
      ""error: (ntau==1||ntau==n) failed for hidden ntau: lognormal:ntau=12\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""emcee: Exception while calling your likelihood function:\n"",
      ""  params: [  1.49495038e+01   1.13590015e+00  -1.32827179e+00   1.19135817e+12\n"",
      ""  -2.03637830e+01   2.71093253e+10  -1.67421620e-01   3.89760615e+07\n"",
      ""   2.01226150e+00   7.64676702e-01   7.00493313e+04   5.09504975e+01\n"",
      ""   3.62823245e+01]\n"",
      ""  args: []\n"",
      ""  kwargs: {}\n"",
      ""  exception:\n""
     ]
    },
    {
     ""ename"": ""error"",
     ""evalue"": ""(ntau==1||ntau==n) failed for hidden ntau: lognormal:ntau=12"",
     ""output_type"": ""error"",
     ""traceback"": [
      ""\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"",
      ""\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)"",
      ""\u001b[0;32m<ipython-input-12-a96facce6205>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmcmc_emcee\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpymcmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_mcmc_emcee\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpymc_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.pyc\u001b[0m in \u001b[0;36mrun_mcmc_emcee\u001b[0;34m(pymc_model)\u001b[0m\n\u001b[1;32m    478\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m     \u001b[0;31m# burn-in\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m     \u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_mcmc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    481\u001b[0m     \u001b[0msampler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/emcee-2.2.1-py2.7.egg/emcee/sampler.py\u001b[0m in \u001b[0;36mrun_mcmc\u001b[0;34m(self, pos0, N, rstate0, lnprob0, **kwargs)\u001b[0m\n\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m         for results in self.sample(pos0, lnprob0, rstate0, iterations=N,\n\u001b[0;32m--> 172\u001b[0;31m                                    **kwargs):\n\u001b[0m\u001b[1;32m    173\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    174\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/emcee-2.2.1-py2.7.egg/emcee/ensemble.py\u001b[0m in \u001b[0;36msample\u001b[0;34m(self, p0, lnprob0, rstate0, blobs0, iterations, thin, storechain, mh_proposal)\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0mblobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mblobs0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    197\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlnprob\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 198\u001b[0;31m             \u001b[0mlnprob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mblobs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lnprob\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    200\u001b[0m         \u001b[0;31m# Check to make sure that the probability function didn't return\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/emcee-2.2.1-py2.7.egg/emcee/ensemble.py\u001b[0m in \u001b[0;36m_get_lnprob\u001b[0;34m(self, pos)\u001b[0m\n\u001b[1;32m    380\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m         \u001b[0;31m# Run the log-probability calculations (optionally in parallel).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mM\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlnprobfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/emcee-2.2.1-py2.7.egg/emcee/ensemble.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    517\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 519\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    520\u001b[0m         \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    521\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mtraceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.pyc\u001b[0m in \u001b[0;36mlnprob\u001b[0;34m(vals)\u001b[0m\n\u001b[1;32m    456\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    457\u001b[0m         \u001b[0;31m# Calculate logp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 458\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m     \u001b[0;31m# emcee parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/Node.pyc\u001b[0m in \u001b[0;36m_get_logp\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    416\u001b[0m         \u001b[0;31m# Return total log-probabilities from all elements\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m         return logp_of_set(\n\u001b[0;32m--> 418\u001b[0;31m             self.stochastics | self.potentials | self.observed_stochastics)\n\u001b[0m\u001b[1;32m    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     \u001b[0;31m# Define log-probability property\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/Node.pyc\u001b[0m in \u001b[0;36mlogp_of_set\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m     37\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mlogp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m         \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mexc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/Node.pyc\u001b[0m in \u001b[0;36mlogp_of_set\u001b[0;34m(s)\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 30\u001b[0;31m             \u001b[0mlogp\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     31\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mZeroProbability\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     32\u001b[0m             \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/PyMCObjects.pyc\u001b[0m in \u001b[0;36mget_logp\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    908\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m             \u001b[0mprint_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\t'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m': logp accessed.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m         \u001b[0mlogp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_logp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32mLazyFunction.pyx\u001b[0m in \u001b[0;36mpymc.LazyFunction.LazyFunction.get (pymc/LazyFunction.c:2568)\u001b[0;34m()\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/distributions.pyc\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(**kwds)\u001b[0m\n\u001b[1;32m   2977\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2978\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'value'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2979\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2980\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2981\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0marguments\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;32m/Users/hansons/anaconda2/lib/python2.7/site-packages/pymc/distributions.pyc\u001b[0m in \u001b[0;36mlognormal_like\u001b[0;34m(x, mu, tau)\u001b[0m\n\u001b[1;32m   1767\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1768\u001b[0m     \""\""\""\n\u001b[0;32m-> 1769\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mflib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlognormal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmu\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtau\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1770\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1771\u001b[0m lognormal_grad_like = {'value': flib.lognormal_gradx,\n"",
      ""\u001b[0;31merror\u001b[0m: (ntau==1||ntau==n) failed for hidden ntau: lognormal:ntau=12""
     ]
    }
   ],
   ""source"": [
    ""mcmc_emcee = pymcmodels.run_mcmc_emcee(pymc_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
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"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/1 Simulating Experimental Fluorescence Binding Data.ipynb",".ipynb","126855","746","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""###In this notebook we will explore plotting complex concentration as a function of Kd. \n"",
    ""\n"",
    ""We will simulate expected fluorescence results for a ligand protein with known Kd.\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      "":0: FutureWarning: IPython widgets are experimental and may change in the future.\n""
     ]
    }
   ],
   ""source"": [
    ""import matplotlib.pyplot as plt\n"",
    ""import numpy as np\n"",
    ""import seaborn as sns\n"",
    ""from IPython.display import display, Math, Latex #Do we even need this anymore?\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""###The Simple Model""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$L + P \\underset{k_-1}{\\stackrel{k_1}{\\rightleftharpoons}} PL$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""This is a simple model of our system.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""We are assuming complex concentration [PL] is proportional to complex fluorescence (in this particular assay).""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""We estimate/know the total Ligand $[L]_{tot} = [L] + [PL]$ and Protein $[P]_{tot} = [P] + [PL]$ concentration from the experimental setup, and presume we can measure the complex concentration in some way $[PL]$.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$K_{d} = \\frac{[L][P]}{[PL]}$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""From this relation can calculate $K_d$ from these three values.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": true
   },
   ""source"": [
    ""###Let's take a hypothetical case where $K_d$ = 2 nM. (2e-9 M)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""What binding curve would we expect?""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""Kd = 2e-9 # M""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""The protein concentration for our assay will be 1 nM (half of the Kd).""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Ptot = 1e-9 # M""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""The ligand concentration will be a 12-point half-log dilution from 20 uM ligand (down to ~60 pM).""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""To calculate $[PL]$ as a function of $[P]_{tot}$, $[L]_{tot}$, and $K_d$, we start with""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$[PL] = \\frac{[L][P]}{K_{d} }$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Then we need to put L and P in terms of $[L]_{tot}$ and $[P]_{tot}$, using""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""$$[L] = [L]_{tot}-[PL]$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$[P] = [P]_{tot}-[PL]$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""This gives us:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$[PL] = \\frac{([L]_{tot}-[PL])([P]_{tot}-[PL])}{K_{d} }$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Rearranging to form a quadratic equations, we get:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$0 = [PL]^2 - [PL]([P]_{tot}+[L]_{tot}+K_{d}) + [P]_{tot} [L]_{tot}$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Using the solution for the quadratic equation:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a}$$""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""where $x = [PL]$, $a = 1$, $b = -([P]_{tot}+[L]_{tot}+K_d)$, and $c = [P]_{tot} [L]_{tot}$. We get as the only reasonable solution:""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": false
   },
   ""source"": [
    ""$$[PL] = \\frac{([P]_{tot} + [L]_{tot} + K_{d}) - \\sqrt{([P]_{tot} + [L]_{tot} + K_{d})^2 - 4[P]_{tot}[L]_{tot}}}{2}$$""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Now we can use this to define a function that gives us PL from Kd, Ptot, and Ltot.\n"",
    ""def two_component_binding(Kd, Ptot, Ltot):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Kd : float\n"",
    ""        Dissociation constant\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total ligand concentration\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    \""\""\""\n"",
    ""                                    \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd) - np.sqrt((Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free ligand concentration in sample cell after n injections (uM)                                                                                                                                                                                                                           \n"",
    ""    return [P, L, PL]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[L, P, PL] = two_component_binding(Kd, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""[  2.00000000e-05   6.32455532e-06   2.00000000e-06   6.32455532e-07\n"",
      ""   2.00000000e-07   6.32455532e-08   2.00000000e-08   6.32455532e-09\n"",
      ""   2.00000000e-09   6.32455532e-10   2.00000000e-10   6.32455532e-11]\n""
     ]
    }
   ],
   ""source"": [
    ""print Ltot""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""[  9.99900005e-10   9.99683822e-10   9.99000500e-10   9.96842730e-10\n"",
      ""   9.90050244e-10   9.68884511e-10   9.05189950e-10   7.36430279e-10\n"",
      ""   4.38447187e-10   1.83368995e-10   6.37708504e-11   2.07876510e-11]\n""
     ]
    }
   ],
   ""source"": [
    ""print PL""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""###Now we can plot our complex concentration as a function of our ligand concentration!""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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KkIhaIJKmIc2HV13J2MH9t5q+r9fxT8QhEklTEApEkFbFAJElFLBBJUhELRJJUxAJRHc6F\nJTXPApEkFbFAJElFLBBJUhELRJJUxAKRJBVxLqz6Ws4O5q/N/HW1nn8qW+b9BSOiA64HtgGPAJdm\n5n0j23cBVwCPA/dk5k/OO6MkaW01TmFdBByZmWcBVwHXLW2IiGcCbwNempnnAM+OiB0VMkqS1lCj\nQM4GbgXIzL3AaSPbHgXOysxHh8tbGBylSJIWTI0CORZ4cGT5YEQcBpCZfWY+ABARbwSOzsw/qZBR\nkrSGuY+BAAeAY0aWD8vMJ5YWhmMkvwicAPzwBPtt+d0ALWcH89dm/rpazT/14H+NArkT2AG8PyLO\nAO5Ztv1dwMOZedGE+231nRAL9y6Orut2AlcDJwH3Atf0fb9nlYeX5V+aB6vvjy/JOEML9/pPyPx1\ntZ5/KnN/G+/Iu7BOGa66BDgVOBq4G/g48JHhth741cz8wBq7bfk/caGyD8tj9wqbdq1SIhZIXeav\nq/X8U/E6kPoWKnvXdfuArSts2tf3/bYV1lsgdZm/rtbzT8Ur0bXcSROul7RJWSBa7t4J10vapCwQ\nLXfNKuuvnWsKSQvPAtFTDAfKdwH7gIPDz6sNoE/zhY5fgPEPSVNwEL2+lrOD+Wszf12t55+KRyCS\npCIWiCSpiAUiSSpigUiSilggqqPr9j95NbqkJlkgkqQiFogkqYgFIkkqYoFIkopYIJKkIk5lUl/L\n2cH8tZm/rtbzT8UjEElSEQtEklTEApEkFbFAJElFLBBJUhELRHU4F5bUPAukQV3X7ey6bl/XdQeH\nn3fWziRp89lSO4AmMyyL3SOrtgK7u65j5vctl6RD8AikPVevsv6quaaQtOlZIO05acL1krQuLJD2\n3DvheklaFxZIe65ZZf21c00xrb4/nr4/vnYMSeUskMYMB8p3AfuAg8PPuxxAlzRvzsZbX8vZwfy1\nmb+u1vNPxSMQSVIRC2QdeKGfpM3AU1gztsKFfktWG6dYmOyFzF+X+etqPf9ULJAZ67puH4Orw5fb\n1/f9thXWL0z2QmX5l+bBqv9OrM35+i8O8zfMU1iz54V+kjaFuc+FFREdcD2wDXgEuDQz7xvZ/krg\n54DHgfdk5g3zzjile1n5CMQL/SRtKDWOQC4CjszMsxjM33Td0oaI2DJcvgA4D7g8Ip5bIeM0NsaF\nfpK0hhoFcjZwK0Bm7gVOG9l2IvCZzDyQmY8DdwDnzj9iOS/0k7RZ1CiQY4EHR5YPRsRhq2z7e+BZ\n8wo2K33f7+n7flvf90cMP1sekjacGvcDOQAcM7J8WGY+MbLt2JFtxwB/N8Y+W34XRMvZoTR//Xdf\nLdmcr//iMH/DahyB3Am8AiAizgDuGdn2f4AXRsSzI+IZDE5f3TX/iJKktcz9OpCRd2GdMlx1CXAq\ncHRm3hARPwD8PINm/63M/M25BpQkjWWjXEgoSZozLySUJBWxQCRJRSwQSVIRC0SSVMQCkSQVqXEh\n4bqKiPOBizPzspWWF91o3og4E3g9gymjr8jMA3XTjSciXgN8P/Ao8LOZOc7FoAth+DbyHwaeAfxy\nZn6ycqSJRMQVwIuBE4Dfbu1t8BFxInAFcCTwS5nZ1CSkEXEK8A7gPuC/ZOafVo40sYj4duCWzDx9\nrcduqCOQiPgnwEsY/PA9bXnRrZD38uHHbwEt3dXwh/hm7ssrZ5nUA8B3DT++WDnLxDLzVxm85n/Z\nWnkMXQr8DYOZuvfXjVJkO/C3DObB+6vKWUq9mTFf+4U/AomI7cDbM/P8taaCz8zPAddFxHtXWq5h\nmvzA4Zn5WETcD7xs3tlHTfJ9AL8O3MDgh/DoeWddbsLslwM/CpwB7ACq/ewsmTA/DCbz/G9zjrmq\nCfO/EHgdg4uLXwf8xrzzLjdh/o8Ae4BvZ/CL+Mp5511ukvwR8Qbgt4E3jbPvhT4CiYg3A+/mm3+R\nrzgVfES8LSLeFxHPHj5u+fw0VearmSL/koeGU7o8D7h/TrGfZtLvA/gOBn9J/imV/4qfMPtu4LnA\nQ8CXgX8w/8RPVfAz9Bzg3Mz8UJXAyxS8/g8A3wC+ygLMM1Xws/9i4HAGc/gdPv/ET1Xw+r+awWnz\n74uIH1lr/4t+BPJZ4FXAjcPlp0wFHxGnDf/9lmXPW355fa3L7UvzL3k38E4G/0+vX9+ohzTR9xER\nLwXew2Ac4Q1zT/tUk2bfzuDUW8/gL8jaJv4Zioij5h3yECZ9/U9l8HPfMRgLqW3S/GcyGAN5DHjb\n3NM+XdHvoIh4b2b+3lo7X+gCycybI+K4kVUrTgU/Mpvv0vNee6jleZk2f2Z+gsFcYVVN+n0MBw4X\nYvCwIPteYO88Mx5Kyc9QZv7LuQVcQ8HrfzeDU1cLoSD/XSzQBLCz+h26moU+hbWCQ00F34LW8y9p\n+ftoOTuYvzbzjz55+jxzdaip4FvQev4lLX8fLWcH89dm/hELfQprBTcDF0bEncPl6qd3JtR6/iUt\nfx8tZwfz12b+EU7nLkkq0topLEnSgrBAJElFLBBJUhELRJJUxAKRJBWxQCRJRSwQSVIRC0SSVMQC\nkUZExJURsWuNx1wcEdXv8yDVZoFIT3VkZu5eWoiIH4iI/RHxaxHxcoDMfB8zusvlyJQSS8vHRcQT\nEfEby9a/eLi+yszS0kosEOnQ/hB4JvDmzLx1ljuOiBcCn1lh01eAlw/vHrfkx4AvzfLrS9OyQKRD\nOxnYn5mPrsO+/wXwRyus/zrw58C5I+suBP5kHTJIxSwQ6dDOBu5Y60ER8XOTrB/658Bqt579r8Br\nhvs4Dfgkg7vcSQvDApEO7WwG91BYyzGTrI+IZwJHZebXVtjcAx9kcIQCg9NXv8sC3CNcGtXa/UCk\neftnwL9dWoiIixiMi3Qj614GfHdEfC9wPoOxiq8Chw/Xn5CZy8c6zgNuX+2LZuZDEfEXEXHOcJ9X\nAod8d5g0bx6BSKuIiBcAj2XmA8PlbwFelJnLTyV9jcGNen6MwSmp/wHsYFAiN69QHrD6+Meom4C3\nA3/W2G1TtUlYINIKIuJU4K3A1yPixyPiTcBdwCdWePh24OPA8Zn5eeAc4H8DZwAfi4jvXOE5p2bm\n3WvE+CCwDdgzXPbub1oo3pFQGhERb8nMt03yuIjYyeCdU88BvgickJnvHll/W2Y+tJ65pRocA5Ge\natyB6icfl5l7lm27fZX10obiKSzpqR4eZyoT4OE55ZEWlqewJElFPAKRJBWxQCRJRSwQSVIRC0SS\nVMQCkSQVsUAkSUUsEElSEQtEklTk/wPfnfNn4IP3nAAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x119ca1450>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot, PL, 'ko')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0, 1.05*np.max(PL))\n"",
    ""plt.axvline(Kd,color='r',linestyle='--',label='K_d')\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Okay, so now lets do something a little more fun.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Let's overlap the curves we get for different amounts of protein in the assay.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[L2, P2, PL2] = two_component_binding(Kd, Ptot/2, Ltot)\n"",
    ""[L3, P3, PL3] = two_component_binding(Kd, Ptot*2, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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c+GgJPV3mptr3/GaH4M0zzQKSRdiU2L91U5+QxALgOWAk4uobkISI1UBOWNNJp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\nFliFAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c6ca5d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,PL,'b',Ltot,PL2,'g',Ltot,PL3,'k')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$ / M')\n"",
    ""plt.ylim(0,2.05e-9)\n"",
    ""plt.axhline(Ptot,color='b',linestyle='--',label='$[P]_{tot}$')\n"",
    ""plt.axhline(Ptot/2,color='g',linestyle='--',label='$[P]_{tot}$/2')\n"",
    ""plt.axhline(Ptot*2,color='k',linestyle='--', label='$[P]_{tot}$*2')\n"",
    ""plt.axvline(Kd,color='r',linestyle='--',label='$K_d$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""###Let's do even more fun things!""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Say we have one molecule that has a different Kd for a bunch of proteins. We'll keep the protein concentration the same, but look at how our complex concentration changes as a function of Kd.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[L4, P4, PL4] = two_component_binding(Kd/10, Ptot, Ltot)\n"",
    ""[L5, P5, PL5] = two_component_binding(Kd*10, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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IHFW9zzv0C323xW/UOpRJF2z9K01l3VdsrtxF9uois0w7inHsbuLa+kVRSRD1R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\niFJKqapoAVFKKVWV/w+VfmCYJ5MBpgAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11cd17e90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,PL,'o',label='$K_d$');\n"",
    ""plt.semilogx(Ltot,PL4,'violet',label='0.1 $K_d$');\n"",
    ""plt.semilogx(Ltot,PL5,'.75',label='10 $K_d$')\n"",
    ""plt.xlabel('$[L]_{tot} / M$')\n"",
    ""plt.ylabel('$[PL]$ / M')\n"",
    ""plt.ylim(0,1.05e-9)\n"",
    ""plt.axhline(Ptot,color='0.75',linestyle='--',label='$[P]_{tot}$')\n"",
    ""#plt.axvline(Kd/10,color='violet',label='Kd/10')\n"",
    ""#plt.axvline(Kd*10,color='.75',label='Kd*10')\n"",
    ""plt.axvline(Kd,color='r',linestyle='--',label='$K_d$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""###Now let's make this new plot for 'simulated model of dilution series experiment' figure""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Let's plot Kd's ranging from 1mM to 10pM\n"",
    ""Kd_max = 1e-3 # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[La, Pa, PLa] = two_component_binding(Kd_max, Ptot, Ltot)\n"",
    ""[Lb, Pb, PLb] = two_component_binding(Kd_max/10, Ptot, Ltot)\n"",
    ""[Lc, Pc, PLc] = two_component_binding(Kd_max/100, Ptot, Ltot)\n"",
    ""[Ld, Pd, PLd] = two_component_binding(Kd_max/1e3, Ptot, Ltot)\n"",
    ""[Le, Pe, PLe] = two_component_binding(Kd_max/1e4, Ptot, Ltot)\n"",
    ""[Lf, Pf, PLf] = two_component_binding(Kd_max/1e5, Ptot, Ltot)\n"",
    ""[Lg, Pg, PLg] = two_component_binding(Kd_max/1e6, Ptot, Ltot)\n"",
    ""[Lh, Ph, PLh] = two_component_binding(Kd_max/1e7, Ptot, Ltot)\n"",
    ""[Li, Pi, PLi] = two_component_binding(Kd_max/1e8, Ptot, Ltot)\n"",
    ""[Lj, Pj, PLj] = two_component_binding(Kd_max/1e9, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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OEBFnVEAuyC1FB9jGwWYKCrHoQWQKd1t8UnbrcNUFBFigzIVLWdQh3J/mpe79I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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11ce5d050>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.figure(figsize=(10,3))\n"",
    ""plt.semilogx(Ltot,PLa,'-bo',label='1 mM');\n"",
    ""plt.semilogx(Ltot,PLb,'-ko',label='100 $\\mu$M');\n"",
    ""plt.semilogx(Ltot,PLc,'-go',label='10 $\\mu$M');\n"",
    ""plt.semilogx(Ltot,PLd,'-ro',label='1 $\\mu$M');\n"",
    ""plt.semilogx(Ltot,PLe,'-co',label='100 nM');\n"",
    ""plt.semilogx(Ltot,PLf,'-mo',label='10 nM');\n"",
    ""plt.semilogx(Ltot,PLg,'-yo',label='1 nM');\n"",
    ""plt.semilogx(Ltot,PLh,'-bo',label='100 pM');\n"",
    ""plt.semilogx(Ltot,PLi,'-ko',label='10 pM');\n"",
    ""plt.semilogx(Ltot,PLj,'-go',label='1 pM');\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$ / M')\n"",
    ""plt.xlim(1.5e-12,1.5e-4)\n"",
    ""plt.axhline(0.1e-9,color='0.75',linestyle='--',label='detection limit');\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""###Okay! Now let's do some stuff with kinases!""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""We're going to pick 10 kinases and look at what binding curves we would expect to the fluorescent inhibitor bosutinib.\n"",
    ""Info from: http://www.guidetopharmacology.org/GRAC/LigandDisplayForward?tab=screens&ligandId=5710\n"",
    ""Specifically: http://www.guidetopharmacology.org/GRAC/LigandScreenDisplayForward?ligandId=5710&screenId=2""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""####Again units in nM. Abl1 value is for nonphosphorylated form. Others don't seem to specify?""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Kd_Src = 1.0e-9 # M\n"",
    ""Kd_Abl = 0.12e-9 # M\n"",
    ""Kd_Abl_T315I = 21.0e-9 # M\n"",
    ""Kd_p38 = 3000.0e-9 # M \n"",
    ""Kd_Aur = 3000.0e-9 # M\n"",
    ""Kd_CK2 = 3000.0e-9 # M\n"",
    ""Kd_SYK = 290.0e-9 # M\n"",
    ""Kd_DDR = 120.0e-9 # M\n"",
    ""Kd_MEK = 19.0e-9 # M\n"",
    ""\n"",
    ""#This CK2, Aur, and p38 value is actually 'greater than'.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""####We'll use the same Ltot and Ptot as before.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[L6, P6, PL6] = two_component_binding(Kd_Src, Ptot, Ltot)\n"",
    ""[L7, P7, PL7] = two_component_binding(Kd_Abl, Ptot, Ltot)\n"",
    ""[L8, P8, PL8] = two_component_binding(Kd_Abl_T315I, Ptot, Ltot)\n"",
    ""[L9, P9, PL9] = two_component_binding(Kd_p38, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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m6qWGt3RAYBzxujHk0dDxiBVI4sYYkgYhSFFafVzv+wc2TD7sBGA/MxG8fOhMY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\nQoiKSAIRQghRkf8PVeVUlrrtylIAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11ce91350>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""Src, = plt.semilogx(Ltot,PL6,'o', label='Src')\n"",
    ""Abl, = plt.semilogx(Ltot,PL7,'violet', label = 'Abl')\n"",
    ""AblGK, = plt.semilogx(Ltot,PL8,'.75', label = 'AblGK')\n"",
    ""p38, = plt.semilogx(Ltot,PL9,'k', label = 'p38')\n"",
    ""plt.axhline(0.1e-9,color='0.75',linestyle='--', label='detection limit');\n"",
    ""plt.xlabel('$[L]_{tot}$')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""#plt.legend(handles=[Src, Abl, AblGK, p38], loc =0);\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### Okay, this is all great! In theory we can use this with whatever protein-ligand combination we want!""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### But in practice there are limitations!""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    "" - Experimental error\n"",
    "" - We want to limit the amount of protein used\n"",
    "" - The ligand also fluoresces.\n"",
    "" - The inner filter effect\n"",
    "" - The Fluorescence detection has a detection limit.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### How do these limit the Kd, kinase, inhibitor, and the concentrations of kinase and inhibitor we can effectively access in our experimental design?""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.11""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/3b Bayesian fit txt file - SrcBosutinib.ipynb",".ipynb","675661","531","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Let's consider some data for a fluorescence assay prepared with a Tecan HP D300 digital dispenser.""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""A summary of the experimental procedure:\n"",
    ""\n"",
    "">>\n"",
    ""Two protein assay plates were prepared with p38 and Src plus  Bosutinib, using rows A-E and columns 1-12, varying protein concentration from row to row (high to low where E is just Buffer)  and Bosutinib concentration from column to column (high to low where 12 has no Bosutinib). \n"",
    ""Bosutinib concentrations started at 20 uM and went down by ½ log as calcuated by the D300 dispenser. Src concentrations were ~1, 0.5, 0.25, and 0.125 uM.  p38 concentrations were 10, 5, 2.5, 1.25 uM.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""application/javascript"": [
       ""IPython.OutputArea.auto_scroll_threshold = 9999;""
      ],
      ""text/plain"": [
       ""<IPython.core.display.Javascript object>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""%%javascript\n"",
    ""IPython.OutputArea.auto_scroll_threshold = 9999;""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""%matplotlib inline""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""import re\n"",
    ""import numpy as np""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Load fluorescence emission scans""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Couldn't import dot_parser, loading of dot files will not be possible.\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      "":0: FutureWarning: IPython widgets are experimental and may change in the future.\n""
     ]
    }
   ],
   ""source"": [
    ""# Load data\n"",
    ""filename = 'data/12022014-019-src-280scan-reboot-bottom.txt' # data filename\n"",
    ""from assaytools import platereader\n"",
    ""[SRC_280, SRC_280_x, SRC_280_x_num] = platereader.read_emission_spectra_text(filename)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""## Plot the emission spectrum for 280 nm excitation""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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kURWGy28GdY3PIt5s50hXglerWvr/rDE6caXNADTFnTjtqXCWnsYidYz/sIsq9iEIIIYQQ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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10c97d8d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""from assaytools import plots\n"",
    ""plots.plot_scans(SRC_280, SRC_280_x, SRC_280_x_num)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Find desired emission wavelength to fit.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Find desired emission wavelength to fit.\n"",
    ""wavelength_to_fit = '480'\n"",
    ""index = SRC_280_x_num.index(wavelength_to_fit)\n"",
    ""\n"",
    ""# Specify row to process\n"",
    ""row_index = 0 # row 0, 1, 2, or 3\n"",
    ""Pstated_rows = np.array([1, 0.5, 0.25, 0.125]) * 1e-6 # protein concentrations, M\n"",
    ""\n"",
    ""# Extract fluorescence intensities at desired wavelength.\n"",
    ""bottom_complex_fluorescence = np.array([ float(SRC_280[i,index]) for i in range(row_index*12+1,row_index*12+13) ])\n"",
    ""bottom_ligand_fluorescence = np.array([ float(SRC_280[i,index]) for i in range(49,61) ])\n"",
    ""\n"",
    ""# Stated concentrations of protein and ligand.\n"",
    ""Pstated = Pstated_rows[row_index] * np.ones([12],np.float64) # protein concentration, M\n"",
    ""Lstated = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)])""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Assay configuration details\n"",
    ""assay_volume = 75e-6 # well volume, L\n"",
    ""well_area = 0.1586 # well area, cm^2 # half-area wells were used here\n"",
    ""path_length = assay_volume / well_area\n"",
    ""\n"",
    ""# Uncertainties in protein and ligand concentrations.\n"",
    ""dPstated = 0.10 * Pstated # protein concentration uncertainty\n"",
    ""dLstated = 0.08 * Lstated # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Create pymc model""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Create the pymc model\n"",
    ""from assaytools import pymcmodels\n"",
    ""pymc_model = pymcmodels.make_model(Pstated, dPstated, Lstated, dLstated,\n"",
    ""               bottom_complex_fluorescence=bottom_complex_fluorescence,\n"",
    ""               bottom_ligand_fluorescence=bottom_ligand_fluorescence,\n"",
    ""               use_primary_inner_filter_correction=True, \n"",
    ""               assay_volume=assay_volume, well_area=well_area)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Plot fluorescence measurements of complex and ligand as a function of ligand concentration""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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OnTio+PtEt92NjegAAAABAlTFlFAAAAACqQd++/T0dQqUxQggAAAAAXsrlI4TLli3T2rVr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\nnK6EAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11d4402d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""figure = plots.plot_measurements(Lstated, Pstated, pymc_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""MAP fitting cycle 5/5\n""
     ]
    }
   ],
   ""source"": [
    ""# Find the maximum a posteriori fit (will only be local optimum, and several cycles are needed for reasonable fit)\n"",
    ""map = pymcmodels.map_fit(pymc_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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CgfjRvHgVA48ZNGDlyDGPGPEFAgD/z5y9m5sxp/Pjj/wBwOBwsXPgSJ04cZ+jQeP7ylzac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\nH4JjijdssMQyAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11a692d50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""figure = plots.plot_measurements(Lstated, Pstated, pymc_model, map=map)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Sample the model posterior with MCMC\n"",
    ""mcmc = pymcmodels.run_mcmc(pymc_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""(10000,)\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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G+4o9NX/0c+YDxtddew4YNG5CVlYWkpCS0atUKer0excXFuHTpEqZMmYLXXntN0UQpzX3A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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11dde9bd0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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ktW7bU4sWL1a5dO9WqVavUigMAAAAAlA6vwuDx48fPe37//ferevXq6tatmw4d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\nQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11de35f10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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dKIpEo2k4NoqLMAkV4n3Ccn0Afj66EvDmeA3ihc35SLxYSJOUiFxtmNsVsUq2M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H5988gnf+ta3eOWVVxgbG1v/g58hlgmXskybpbLYqcUdUsZ5W3/E8m/2m8R6O4Nkr7h2X5X26i5R\n7ElXSYEzHSyfnsLbnWGPe5IRMVn1uQm935b+kMBXOpDtDMp7p07otBI3MizrRMpWqM5obgZ+0lX/\ntqsLq3zd1rhpKNBSzoSYxsG9Uuc87hyOHVrRUN7Y4X5ZDjX8u1+UN2WrOtRm15lpID9IOrIB0dTc\ns6pJtXvmtd+fxN+0X99yLclyKMz/lF8kWrB3vhr17zN7pZow6932iqusUO8oblHmWCWAJxNgaY89\nVU0gq7Jy1fez0edkRPYV0ULLlpbqw1Ut0o/1pftYS27sMde1fnaoxnc2Ema4FziSGqOh66zKFsu9\neiMZAKvWNrUUd0XYZSgfTEYx73cQ3Rkici2OZiOv3kiy30muJHmvFgUrIsSbXE+Gg7dyHwJIGg7e\n0E+gS6Pey2qoflW1PzJabjHjDFs/Q/NMaFjXtOHHX6zVLWYll0dbDd66BVYI8X2tLL5krx6rF2/k\n/s7FyiBeQNxf+qVJ3/XmMusKtKyHi3IXQ8GbPNo21nQNWjM0ZbW4a25GtXejuCh30a0vbT57CWiK\nE21TycT15k3jmtQ8zqpzy+fNMrJ7grbWeEk7YeJGH+cc9qVTJr20UnwrfDtGpsvNRVdz9rgidLxk\nLZ+VIbK4OeblcrGO+x8CmnYUpZR8/PHHvP7665w8eZI9e/bwjW98g6effvpB3t8mUVn3cC9LoBqj\n4uo6KqqbpOIKuCKHbWpq7ZQU7x9Sio/UqOH0mcaWiUYOQOWG/qAMfxWdL+ff5lXn5x6wY2GiPF8m\n9P5i76t6VGYENpsNqXRS7kdtzsXMsNGOYBNYT4zhEXGea2wDjLYotY5is46paSB/FjVlzcDKiG1W\nnGIjULsLTLavr2h4SBm3zYLYYVbvYFS9Wue8XGIXQztv8lDHOb7vedH28xLFNrv6IJ6TlXN+b/Nf\n8KY8wbzWXqzvtJ6LfhJ4yG+amhqcWiMx5Oemd6vtfVhhK3eZpQsNBYHERZ439Uc5q+8t1TtbUdzt\n8YDOA0U0IZRif+1KyfvKeq9m0UyLiY3gLf04qtA32AvzwWL5kosWMPp+Fj5HXGmx1VZouCYeoE1Q\nKxBkjwdbevSufsS2BGizcCYL9HQvEPIm7ktgfnZLF35HxvZZqRSQxYBJj1jgIutfc08xWH//LNfG\nGNNH8Nu0TvqHDCtW0ODA3dLfTRX6jwvWAlHLxXOiVnxL6xf8Te7Llp+xgoaDhM3edW92xD+M0j5o\n0lH8z//5P/Puu+8yMjLC888/z7/9t/8Wn+/+NgN9cLh/G6opT7+RVgzNQRg1CHb4DJxFMHqf1TqK\nzVDP3taPbTpqsh50FAQPLktQiwR+/qzwNduecyYqMwL6Jpqfg3Wjc2tYf7+nRh567B6MbRXN0lBT\nKfC08iFDYpprbKOne6FqMy6hiSSHWdcBlfWdI01li5uDIdiwEUfAigKoPQBDfKmtuVYQhpO4sb01\npoTBpkfdZN82okSaWp92ir+l2g6JsRfdY216JePDvN79yCStZ/il8PENx8uGzP46z7iyT16EOAfF\nOI5IAYej0CSl3ZiLtT0ZJaJUnrB5EZcHexYkev048/kqpcJ/SLDbq+ogxH0P+NwLBJJz/Y9y6/Bv\nVfUSttNWaDYT2wgqheIYWMyZDey5Zt3gCiFbtdT7jY0KEDYDdyyLY4d239hbKcXH5dx2VhzW+5eG\nyqi4xmPqWb5XeH7d79vKXUOYD+4v9VSXxeRv/TNfIYRXz/xD8ks2hPWU6u2TGAp/UfgNHlc+qfq8\nqkqSzo37N7Vnhsk+/Ie0B20WTTmKL7/8MuFwmPHxccbHx/n2t78NlBvjvvnmmw/0JtdDgMRnQiUx\nI9UbacVwz/gMHEQTSVG/OJrJFNYe2qJIF91MdqQWnnQGtz/LXX3jsvSbgyHDvV50bU62M1wUbnhf\nHt7UlaqdlI3TyYbFVOm/dZ2GDXHXg11/uKeVDxlWptG0Yk2lhZBNsyjg4q8KL5LEiw+jVjaB977N\ncVOp7E+b7GsG9RTACb0f1SaLfC/QGwgoVWIza8Wgq9qt0ebaQ0C9YVpHp7IxNDYOYWEcP/h9Thbp\n3s3saSl8/HPHy9UvDhT/v8nSNonglrTLPpbRvIjLZwQFutSlTQtIWUMvritzHWz+eTsobDj7WJnZ\neWBiR+tAIqpUE2tRa/DeD2aDXlR6bv57rM+hLG5+x/GjB1b/2ywEGq2sFm0xWXRam3+Wrr48Vxi+\nr/d0Qd3d4MwQJfG0ZrKjsYp94L6ytBqUwTjWNFKBB18P+aBQ1UvVIjt/SFziTZtWTmZWsrZNz2bG\nPllxZkgJawk/mkf5VWrQ3Dc0tdv+qh3B9ZD+DIp+oaxU2S2WPjtH8TPGnxd+q6qNwGaoZ35SVdLC\n90Jde9Q3BtgrO/6qYHL7YfORT9NJeU87vKko7S25lcc4y4TezyeFfZvekAQ6j6n/P3tvHhzHfR/4\nfn499wFgBgAB8AQJgDcoXpbEQxIti1rZji2f8hEneX6pOHlbeVv16pX3bWV3qzZbqS1v1Xt/ZF3J\nPnuT2uwmfkl8xIkdx1ZiiookkiIp8RLBGyAIgCRuDI65Z7r7/dHTc2C65wAGF9WfKpXAObp7un/H\n9/5epk2ZKIjVz+8hNPhQqxJX5zcuZKOFeJUXRnRFaCnCXHTF2zwxX814I7TxrWDLenLK5gStYg5K\nNzmtHF10Eaz53tVahEOXY6F9SxdzvkrWtFLFZiqrblq5gj5VRRGX5aK2SiJo7d1rQ269rdy4JmOj\nMRP+N6kuuGa/AbXLF51vqKlNZINao2gfwRn50BLtw5XfQxWpQLb4TvrLFX8XtIJLtQ7pnBH1Zdfe\nSts6hfFzRj7Ec7bLy5ae8bTrGhfEwTXr+SrVS1X//xty6erG89v0LOzeqwUKa6f8AEfYuO3cWqMi\nRXHjRuN+Zx988AF79qx8j4/lGuD6grscAlSt0aq4ltswcqE6ehsB0HIze9QdVLogh/EXFGqprgR5\nIRfVA7ypHjXNFVxJrih7FiGcq9lxdEtdmIUzjDdXmWsRsoCXaMEC9zHp3QLhPZWWuHlbu0ZFEdhs\nxVvtLnq5UUH+xWLwESZSInJAV7xLPZMXpQucUo5n53Kte7iZs3RmxRG1uaharzGlBTK96rI+DhYX\n+la+DDzkhOPFVN2rhkkCnFKOlv2cUbEZ0LyslbbAqBx1kUJhjYvbLGMUy+Ko5ndrgmA16Qsu4mWN\ngNWGupdifs/XWlBtr8hS81CTB5Yrc84Y2zwpoNrojyAzWaWgVjhI4iNes2PqRuguaZARtTnbG7qm\nc1xV8STiHPdepss7yLn0wqKiFoeSKYZd+4JI+QbIeNxFmWK0BZxWjrBH9FZ9ThVbgcI65T/AlvBD\nBikfVbLaqXo1+tf/+l/zx3/8x7z33nts2LCBH//4x0txXasSkVkkV5t3qxIcampB37uhdmU2iOom\n8pVMWwmtN9/CF4EwPlSkVWntmiRQdXXRHDkL1mKEAnPPduUbegQ/UwSylrlTynG+k/4K30u/Sq+y\nBYdd4WMvXGB92xhCrIygYCdpqiRKyJyUzmY3BlHit5sVojqv7C/5LMWqM1PkKFexthLsJOlRdxaM\ng8UpbpWNaYGyYI+6jps43eJOhZ8WFV3beeVAUV/RnMe5RDn3BZD77SsrhK89lkahdabivHDqbysK\nFTTqbbpQ9J6vOaVg+RV2J8kyn1iKa6r8mHqhl15lCz9If6LqvfOgdJMNYrSq75QjhYND0o0KP62Q\nSfYu+alTyjH+Iv1qZjzYqfV9d4TTHHFf4bKyl++mv7xC0ZGC37Z/v+R+vVCmaGB2zsfla7u4dXcb\nrrLjOoeS5yRZLJP+AMeS7+MjXJPjrRRV787/4T/8B7q7u3nrrbf43d/9Xe7cqXSDXvuo2LI5LmuN\npCi16ZkvXAsV4HRPwTvK4QV93wwbaVaPQFWrzXzlLPjmAWFae4lTynF6lS14PAkO7b9NIlEcQtOr\nbFlyb6J5L1TNLnlZ2ct30l/mT9JfKqHgmD8vLce5VGjK6s30r9SI4i/RTD1d4+qClTK/5Ue1bOIx\nX7f/LW1iAh8RKhHCKiF/7OssncdZcEo5jmtecapqvm9RO5IONxeP/gvUCoqU3Va3UYv77yLOIxbX\nBH6xCJSaVxmtHWq20ItusJmqypOrZo2Jj9Xa1jtQkOiSBnlJnMVeViHRcyrLh64vZRXSYCLEmxzL\nGgZXcn9bikgSVRV8X3ySPlVLLH9eer/m56iECH7OOQ8v8lmqGbl35ai6HrXf7+fEiROcOHECgFOn\nTtX8olYzlea4rC0EtVa+vBmhtNLY/EpZjZ7F1Ulli28lIYvnlf3Z/m4N9jk+ovQUhKaufCh2LpRo\n9fr9lg6znDnNCCCy+aYjavMTl1sdw7OkuaXvKIezY7+8QGMpbMvL0t3vuK8ywa46A4v59TqQSSzb\n+DEO2fURxUl6VeXK5hA8ZENVhcoKv61m96xaR4Tp4bBd0iBX5L1MrVplW8NFnJnmtRcVVxVCILvt\nTHY3sjN5P/vsryh7VqCY1eLPtdJyb9WK4je/+U22b9/Os88+y65du5icnFyK61q1hGjIDrrzyv4V\na9xbPQsdrAv7XhQv301/eYHnLIUljNWSSkrN54/xaaE1gT2vHCCCpwa5bJUhZYrPrBylwg1Xdkya\n5cx9TDqfXat6lS1PnJII2nq8lIaK/N6AS412npUfT6sV3fAhUGrec3GlqbYlztJcgx+RKdi3elnY\n3LCRzhbqqzXryYUfl27zszrmtma8X/nr0Fnq/PTbzk6e4l62Zka1BZBWnpW/1qpX23//7/89Fy5c\n4Cc/+Qn/9t/+W2ZnZ4lGo5w4cYKOjo6luMayLFd7DMhVw+uSBjmvHFiWc9aO5VuoKiuuYbEY/EQW\nPe7N2mKUQ69eulzW55VXFM1Y+UXc7BrOKwcyhVtWwzUuDQpiEXnCq5En91ktHi2naWkMkCvNUj/3\n/L1/bYbYL4alDK3Xq+4LUX1xnQ87P0h/YsmLmOntMzQl3lpfF0JFimIikcDl0kIIA4EAr7zyCq+8\n8goA4+PjnD9/nh/+8If8m3/zb5buSkuwnNY4vRper7JlUf0BLSwWy1bxaAFKnpaRkN/+ok2Z4A3l\nSNUV8paTUjmKFsYsbH1aHVbvylmJa11r9+jJIMgMvcqWVVQBu7LKvqsDa7wuFfmGqtIeyw/rMzBb\nL2tbfdYcaZWGU68dKlrlvvnNb/KDH/yAcLi4co/H42F2dpaBgQGDby4Xy7VY5+LcVz4vayGUWqg+\nrIuYzmopkFMZfsILTspXEQVWvC5pkL0LKAdtYfHh5MO+Vq4MHmKZKtGrxaBljYO1z+L3fRXNM7Z6\nPN3lftNyj9ulO5+LOE2EVknIdC2uYXXKoRV5FP/Lf/kv/NVf/RVf/OIXqa+vp62tDZvNxqNHj5ie\nnuY3fuM3+Pa3v73U17qqWI68LAsLM8L4FlhWWvvW/Ma0ta4EZ2FhYVEr7CR5yAaTd1fKw1vJOZ8M\n77OfMC5ShGggyAxTNDwhYaq1eDaWx6p2VDdftosBnrNdBsgUOlrJubaK5rlaW4WzIkVRkiS+9rWv\n8bWvfY3bt2/z4MEDJEliy5Yt7Nq1q6YXtJrRWwlofb9W0aCwqAFr73kGmV30BvWm8ixQLgnfwsLC\nYuUoHXq+etfupS7UsVxoYezRTPMZwUZGSijuFivLWh5v1V37sNqS/dtFYtkKjxmziu67qO21VF3M\nZteuXR8q5TAfBTtn5ENPZPVAi7WGIFKDRVHGzinlOALFtJOm8QL4ZFjKnwz0J2c9D4snlbU5tp8E\nJVFDFBQwszxoFquBKRroVbZwWdlb81ZsK4GL5Kr8HU9C7EAVqHn/LYwedUfNrsZiLbA6Y8aBmlrP\nSjepr+b18pRvSmxRHZU0cLawsLCwsHiyOKUczxgu1voeqLKO1dlu8EOmKAoWL1St9cFoUR3W864t\nKvVEsZFe6QuxsLCwWGKs/cPCYqlYvR77hdWPWK3h3E9W11oLC4slZrEhp8tVEh26lnAAACAASURB\nVNtiYazWjdfCwmLpWa6UAit1weJJ5ska22UVxf/23/4byWSS3t5eXC4XmzdvNv2sy+XiG9/4Rk0v\n0MLCYjmodGF7shZAC4u1iyVsW5ix2sfGar42i9qx1OPQGkfFqAusiG9OWUVx/fr1DA8PMzQ0xOjo\nKCdPnuT3f//3DT/793//9zW9OAsLCwsLCwsLi+XAErwtaok1nlaC37F/H/hIzY5XNkfx05/+NIqi\n8Dd/8zecOXOGZ599ljfeeMP0sxYWFhYWFhZLjSWEWZhhjQ0LC4vaUFExm2AwmP37E5/4BKFQaMku\nyMLCwsLCwsKi9igrfQEWFhYWS4jgjHyopkesSFHs7+8nFotl/+31ehd94mvXrvHrv/7rRa+fPn2a\nL37xi3zlK1/hhz/84aLPY2FhYWFhYWHxoSv0XjNWb5sojcW1PavN+S0sVge17vVeUdXT06dP8/Of\n/5xAIMBTTz2FEIIdO3bQ1dXFu+++y9GjR6s66Z/+6Z/yk5/8BJ/PV/B6Op3mP//n/8yPf/xjXC4X\nX/3qV3nppZdobGys6vgWFhYWFhYWFh8elrJwyGoPZV3p61vp81tYLB0Vmdf+3b/7d7z99tt8+9vf\n5uDBg8iyzO/+7u9y/Phx/uAP/qDqk7a3t/PHf/zHRa/39fXR3t6O3+/H4XBw+PBh3nvvvaqPb2Fh\nsRZYy1bYtXztFhYWTx6WsmJhYVF7KvIonjhxAoCtW7eydetWvvCFLwAwOTnJH/7hH1Z90pdffplH\njx4VvR4Oh6mrq8v+2+fzMTc3V/XxLSws1gJrWbBZy9duYWFhYWFhYVGeRQXsNzU18au/+qu1uhb8\nfj/hcDj770gkQn19fc2Ob2FhYWFhYWFhYWFhYVGesoriz372s5Lv7969u+LPzkdVC8O3Ojs7GRgY\nYHZ2lmQyyXvvvceBAweqOqaFhYWFhYWFhYWFhYXF4igbevr48WP+6I/+qKKDuVyuqk4uhBa+9bOf\n/YxYLMZrr73G7/3e7/Gbv/mbqKrKa6+9RktLS1XHtLCwsLCwsLCwsLCwsFgcQp3v1luDfOPnl1f6\nEiwsLCwsLCwsLCwsLFaUP/lk7XopWk2FLD7ErHkbyROA9QwsLCwsLCwsLFYjlqJo8aHFRWKFzmwp\nRzms6qEWFhYWFhYWFqsRS1G0+NCSwM3KKG2WcmRhYWFhYWFhYbG6sRTFZcPyIq1OLKVtdaCyuDli\nzS8LCwuL5cdaey0snmQsRXHZsBQSCwtzBMISOCwsLCzWGJZsY2HxJGMpihYWFqsCdVHLkSWsWOSj\n4iKO5e2wsLCwsLBYOJaiaGGxbFhCq4XFcuFAXulLsPjQsNjQeQsLC4vViaUoWlgsK5YwYWGx9AjC\n+LA8zbXEWrvMscaZhYXFaqG2a7WlKFqsQdam9dZmeTgsLCwsnlAsZdHCwmK5KCUH13YtshRFizWI\nYO1uymv1ui0sLCws1gbLbUhVaSKEn8gyn3e1s/YM2hZrBUEjoWU5k6UoWlgsEzL2lb4ECwsLC4sn\nnuU1SEqovGZ/nSPS1WU972rHquRtsZRMEVyW81iK4jJiI52pxGdhMR9rQ7GwsLCwqJTVk4IRZGal\nL2HV0cjUIit5W1iUY3kMQtYoXkZelC7wvHRpiY6+ejaN0qhF//kJZ0JW1sL1Lw1PtuXxSf5tFhYf\nFqyw+dWEnyh+oit9GQAclG4CcFnZu8JXsnpYLm+Pg9SynMdiKVBW+gIqwlIUlwk/YbqkQbqkQU5K\nZ2kihIRCLYVo35JtGrUU9EXRf0eka/ya/adPuLJUmr3iHpZCZWFRCmt+LAUS6ZW+BIsFEMa7SkI9\nVbqkQQBC1C/pefyEl/D4tWZ5DCsuEjU/pkDJ3Ou14oBYewgUusW9lb6MirAUxWUiijf7d5c0yGv2\n1/mY9G5Nz3F0yTaNahY8lWqtJFeUPQAEma3qe08OCs/ZLq/0RSwh1kZjsXiWQiCyAAXbSl+CxQLp\nkgZXfF7kF7DxElvSczlJ0y3urJDCuDqVpiheusWdmh7TR5QwftZ24cBasHTPW0WiR925JMcWNa6w\nb1XXWCaCzNCrbOGyspcQ9XiJEcFXs+PbkLNWvVPKMVZqcrtIkMBd1XcmCfDd9JeXfJNZvQi+l351\nQfdubfBh3mgsaoOKHdlSFWuKSre4S4+6Y6UvZBWgIqGirCHB2EWCXmULCVwreh1HpGvZv9NLanQQ\nTBFgSg3QLe5wT9264r99NeAlynO2y7QpE5xX9mcUvMVh3VedtbEWzEe12mOsTdaLMU4px5kigIpU\nUyURYD1jgGZhbFzBxHJzRaeUZUYU3BM/ESSUD1G5ba05+JOpJMJaXWwtVgBVwUGSYuu9qPmaaSGW\nzKK9mpBII8pEuQhUftv+/RXdO6slgZvzyoGVvgxG1Obs38ulYPSoOys8Vy29gIsxIuTXZagtYfyc\nkQ9xWdlLBC8+IviJZMb8wnLgUjhqe5FrntXnSS6Frca5j5aiuAxs4jGP1dYlPcdD1vOD9CfoVbZw\nSLqxpOdaGJUvsC6S/Lb9+7xmf50IniW8ptWHn/ATVBl3bS2utUfFRyTzPPXCTRG6xR2aCGHdHwOE\nRAongtpvdhbGSFWFKa29Matgz+SAm6MriAvfOxdyXxavOITzUlpWiltq50pfQglWi4dYu45ucXcB\n31VxE8/UtDCmR91Z4IQI4+Ml6V18H9oorVqzGsZQ5Sg1Vu2s0NNlYJiWih+cnzAx3AvouaeFZZxS\njnNSOou2Aa2twa0ToiH7d5BZpggs4CgKa9EOEsGLtKaEsbU7zpaenBfspHQ2Gxqu84P0J0qM7dV0\nX1XspEjjXMYzCuQlzp2zk0TGhvohz9GrLkdxtYzJ6hhWW2hkiikaDd/Xq3bqc/QN5UhV42JhaQNr\n817OJ3+e+okSrsrzr+91a+leLFy2WJhSLYjjplqjwnnlAJFVYEh4cql8j97EYx6yYWkvJ4+gMl3T\n4609SXoNImOrOP/uiHSNF6ULizrfeWX/or6/0uT3ZFq4hXdlh7YtE+7UWGX4rIq05AJybTFfKBdT\nur2U9XRhrKzyfUo5lvX465Qa25sYXo7LqhB1AYar5UbzzlQzbtwk+R37D7JVqFd6jFgsHVM0GCqJ\nNtJFRhzt78r3DxfxJWx7VZrVkJphy/NIV1+FdbV4/CpnMYbcxe3t1d2npfA250fHrL71svpCikuL\nShMhTkpn+ZT9LU5KZwsqyWrhwUtzD5+6eb6mx7MUxWXAhmw6xSXkbD6evmHpLTQWWrkoV61qbaJb\nd4GCdiLVLkx+wtk2JPr9PSmdxbYM5eBflC7wW+IHfDr15iopYb50uIhnxqq2ALqJc1I6u6jfvYGR\nml2fxkrPh5zHX1cW9bE9f/PoFncyVZJr2z5nodhQFpUcv4nHeeNj6TgpnauqknQYLz9If4I3lKM1\nT/5fS6yWXnxLidnzlbEX5NjpVGNwEGhzeSXaOx2Rrq24srhb9GX/1te06sKZ1xaLCeurvQG0FLVX\nwjWveX57MyMWOw8W9v2T0jn+N/v3l20elqs2LKHymv11uqRBzsiHeFM5QhgfNhS6xV1+zf7TAqfI\n4skpprsO1DbXerWbiZ8Idos+bqjbTd4V/Lb9+0WvdkmDmeqlRqg0Mc0kAVZeAF4IKjZkdos+2sQE\nV5Q9hGggyAwHpZtFIXq68vyd9FeqOksUL79mcG9H1OYlLeLQLe5kf4PHk6jCyLWawg0rJ4WT3zG4\nz73KFnxEsuEvdjVFWlQWvjhMC93iziootlH70Kgryp7s+OiSBtmcfozTkUYI7Z6dUo7X7Fylqey3\nlRaMyo/ZaRp4SdIsnEv32wSnlONVlomXsqG/CwtvX1p0BaC6UD6d0s9Fi3aYyRrljJ5Lt7jDsNqS\nXZsTOBd4LasB83vRo+6kTZnIzsleZQtyFcpAXNWKqgSZWfQ4EkkZe0Ih5bNjS2jKluyyEQyN0RqY\nZdzWbLhXLt+akUFVsQltD89v7aQo2pr2hnJ0ea9nGXAT5znpUgm5rDxL04qm9nLDJh4Tw7OMcz8n\nEwLcULfnGXeMfpv2eQUpu47pc8FXdfjzwigXaq4rgWfkQwVyjIxN+7esRRXVcu5OEsgWuDpcs6M+\noYqijTQKEmpVlp+lE9LbxASP1VbDTWShFoXX7K8vWKhsZIopNZj5uSujmHzD/sPs3/MVw1qhIPiT\n9JfYLXqzm1mvsmXJlY82MVHw70vpPRX57m3IhiF+NtLsFn2Lvm5dOKz1om80ho3GZqVKImiL6VIX\ngKqM2s+P/BxcAFm2EZNteD0JLit7a34+cyr7bY3MEMa74IqGYXycUo7XzvOhKiCMLdo3lO1r0dZi\niIskB6WbC1rjNzFsmBOjC7tGa245g131+83aMXzpxpsF7amZn1gLoU+SVdoujhW8tq3pPvbtCldt\n+wlRT5DZoueTb5BbjjZLjnCag4M9dHUMofgE4YiXgXvrGBzfwvq2Mbx7F9/+S8spDea9Yj6WJBQU\nBAKl4txSgVxVHmoSB13SIO8ohxd1fyXkmiqMS/G8Y3h4zf569t/fTX+58i+XmfZawb5U0VqjqnBW\nOVSRnHNSOme4hvUqW5ZISVSwoSBjw4acadVUej/UjXC31C7D92+pnUWyYjW4iZPAmafnaDdd32+r\neGJleSIVRRWJ37F/n15lS8Hmp1WFMh7B1SdhV84VZY/pJpIfZlnpNemhQvpEyf+NpZQAG2lelC7Q\nJQ0Sjbn4c/vnVmQfX75QGVFgvXnOdnlZBPF8jxHAtNRQ4tM5zDw3nhp1j3tJejcbBlFLZdloDC/2\nPtuQCVG/qGMsClVFEuqCwoz0jdDM4z9fsfZ6cs93RX+zCaW8Tg4lRUqqzABQq5wZTyJB3O0yXMuf\npBDSEA1Fa7xAKZsv6iDFQ9YbvmcnzWVlL28oRwkyyyHpRjZiI9+jlvvMDIcyglyXNMh55cAa9iqa\noxtv3l1AuwmfWrvQXcVdqECsbxvDuU/ilPJ89jU9hF1n/rxM4M56g5cq6ijts+FOZULmhUrKGSNl\ny3hWA7NVH0+vDK8gIaEQEDOmhYcK0bQSfZ2uRvETCPaKO9xSuzIe5MoiKxyL7Oda64qU28VAzY3f\n842ZVRUVFKXvY8Qk0ised3HLYaxU6X1OdcWyUwyiKCDNu5WLl+/MtFwpG2WgqYml1mDtCZ9SjvGm\ncsQ0OkHGtqjrTWFfRLHH6ngiFUVdENM3N1mWEELlB+lPMi0VC2J+whyRri1Z+Mb8SWeEkjGS58+x\nI9JVw2t6VuRyv/I3eCht9c1PpH7kbAV1ZYSqreLRgr7nU6NEhImQklbwq1HCDh9GE/2W2slzXF4W\nQXz+825Q55gW5ceAHkIxv2luGF9NNgJdgX2gblrUcdzESeI09TzA4hUeOzIOEksrlKqZ0EujjU2I\nTPPt6jkiXSvpnShlHFrswi+y+YS1mdubeFzwfOd7naIJF+dcH6nJuSplw/gIoc3BVRkuWkvm72NQ\nmVevVA+0/HUlX+EwO/4UwYLPVNOuqOR6veRU583U77V5lUjV1Et71Kbtx7UwQgaUafyJKcY2ryfc\n7uNh3XokxTjn6oqyh7hJJeJ+dSO/bv/7MpWVF44qBD0H9uGUblAvBmlyKTTtv0NsxwNcriT/pBiP\nE4FSEBroJ8pW8VDb3zKPS8FWoZKoexIX5p3zEl3QvrrYll1mkUMLZVht4aR0tmBtLmUgqMSjOd+Y\nWcsQyfxepb3KFt6Xu5mmHo8cR3aYK9HzU7SMtu3Fyh1mc7w6cpmopYsXiYp0AzNkbMtmWH4iFcX5\ngtjkVAPvXd6HrTUN3cWf1wU709w1VQFhPIAdySRP8wG3nZ1MqQHD0RtkhvflbsN5qwvv4Yi2cNbX\n5bxt863J9coctpsyrZ2TUGf820tbfUV2sl9RS29qS1nOd1htKfsZ3aqth9ockm5w1GasOAO0TE/w\n+bY3TPMY9QlbkSCuqCAtXNCev8g+Jd/hbfszZb+nK11L5fUM0VCT0AwfMb5u/9uSn6lK4dEFobx7\nnsBVg+bNhQ3bjXCE06TqatNc2E+EI9LVRYVSL3ZDVhHZEBmzTxjdC2cyQVq1oTi170lCYU9eyDYU\nG6UAFA/c+aADeY8tY4QrVQU3UqCoVIpNTqMgCExPID228WzXdcakJsP7ZBdp0otoFq2te+tZDSGT\nRgaF/D1higZUWYBtcdd6WdlDB0MIoZquPfo+Vc283p+6xZ27HUzuDZb1MtSe6s63cWoYuan0d37F\n9hZ96hbTEN1aCG0bbGM0OXu5tW9f9jWzsjAhGkyNWXrYZ7n1xEU8s85W+XyEMDQ0eDLREUHFeJw0\nMlMQ0gham6CFUl160XwWNiYXa8xbeBqJ8dqtRx7kr81/kv6S6R5QiUdzvSgMf15IZIMZ+rqWNUpl\nflLMba6A20xnQSGLfTYxPEVKd6lIxMUiLcJoYEOmgfCT6VFUVZXf//3f586dOzidTv7Tf/pPbN68\nOfv+L3/5S77zne8gSRKf//zn+epXv1rV8f2Ei4SZlnUh1reNMTzSAkwx216HXGcrWujNcqKCU+O0\nPh7g9r5iYb/+bpih0fX4iCJaVSa7iy1hB6WbvCEbJ0DrFoXe+9o9OLT/dsH7+QvA5Wu7GB5toVds\nLvocQP/ABpoaZ3jGe43TqnnC9RVlDyG13iRHWKXpRojp7lKWDhWhqqiqApLREFJLWvrKWVGKrdq5\n/pCasFms6CQbNaXCTEjWF5qKBPGya4JaMhdiZ7IPRdLyNnrvb+bAvts4SRUsPuvFGMNqC1NqAHs4\nzdOua3R5aydwGBFkZkGhVfOZqsAKVpXCI0CkFFTnIvI21DylMCuUln6QtrhM/cCc4ZytFhdxfs3+\n04LXygndRmTnemo3IRrwROeI1hVvBNr4MwtpMRcEusVdQyHlyNs/p27yLle8z2Dz7uCT/+LtimT7\ncMTHBu91jjke06eW9nYdka4BOSVHQslca2nBRRYSwdA4+66cYzzZhX9/hDGaDD+7ECVRz91tY4wb\nVCvALU0eXn5BrPnoe0I05uIv7J9dtBATUhvQjSpma8+U2oCiCNYzaiiY5BSOnJfotrOLqT0NWjGv\nGqVkaflYCzcg+YmwVTwsKNJzULpJi2eSaz078e8tn+5h9lxqEQY2IG/k8cHKjpHzHBkh6FW2FBkW\nQC3YtyrLbVPy9tXisWa0nm0Qo5rhfB7rxRhn5EPZcE9bdg2oEFUFFRyRNPUDc0S7PCUVDJOD8JI4\nx2l1YQV3DoobvKEuzJjnIk6bmKhpqKhRjYDdorfEOfKeoaoaGnGMjPmdYpAOaRBVFdxn8wLugVqQ\nW3i+Clkkv7JuKUrLHeXXaiOluzZeeeNzLyYMeT1j7JL6l6WQ1bIriqdOnSKZTPLXf/3XXLt2jW99\n61v81//6X7Pvf+tb3+InP/kJbrebX/mVX+FTn/oUdXUm7jMDdGFkPl3bhhgZbib4cBzfSJgXT7yf\ntX7pmG2S04FmPnf1r9gQiXHV9xQhtQF7Ko2iSEzuCTLbXkf9wBze0RgwRWKnk6jDW6CIXlb2Gg62\nemVOUwBHtImZ3nsXu724TGYqLWU/MzzSwmVg+7YB/L4oqRlN0XwwsRVnOkrC4eeFExd52/40Zhao\noDDe1GRlitHwY+yqsQcUoIlpApP/TP0dO1eOf7rofS230jwfqVwBn1ICtlnYx6ykjRGzBVJfaHKL\n1P6MQGAcdlgKXWgwEihcxNkqP+bq9Z3Z59XVMURXXbGAEY25ePPtZwFIt9kg0/5yqeLOD0o3OSUf\nW7RcqyLx3fSX2SvuFXic8qnWAqmWCDkx/1LeJrcAj0WgbzY7Z2fb60j57YbHsZFGRSppXUwZhICZ\nrSflDCVd0iBb/vk9lN4IP/zV/93wM+aKovl9cKXjPJW6Q4t3ksvyXmZEPYGpMfZdOUfd5F2+99F2\nNvZspwlQwjK2uvLS/eRUA8d3PNb+YVKVfL6n1ahIij5OPERJRyXiHg9CfxaSRKiplbdPfo79Z84S\njvi47Kmd191HjNfsry/Ms1G2cEOEKJ6M56fcGNWqWet7hlFURf6983oSBNOLXysaxUw218ds7Qko\nM/RKmw0V6W5xp2AdKDD01bgBl3mjqVIU3lcjFJ9geKSF1voxwpu3Fb1/RLpKPO4qkhnyqUV4Xljy\nQbCyXF4tTcE8Z1RX4PIF3+qE3kLB/rvpLxuufUbrmZnR/a68laSUU/Sr7SvYMBCivq+wJ3Wsu/pQ\n0G2xAYJuY5msHK3RSXyeyIKK9SRwV6Ug5WNWtMYo8uA522WQtZSb3D2ufO4YPdO5sI93zh3Gn5ji\n2aGf8kLnGNcPHmM60Iw3OkfEwKCZT1CdKVjXzGVENROea8tWQjWTM4BMapmCqkp0SoOcUo+ZyAPl\nf79HjRe9VuvKpPno6UbzoxT6BzYwGNhkKpMAPGQDu+hflurwy64oXrp0ieef1xKz9+/fT09PT8H7\nDoeDmZmZrJAgKhEAM94EvzBPKvf7o3ys788BGPFv49bd3UVeOS/Glbq8yQjOV1rZwWN28FjbCEVu\n4KTqHBmvxBTe0RivdJ6h3lNYsMVssNluygyP5qw3NptxLwX7vNeHR1oYHm7mpcxv6qCfDt7mwuZX\nSQC7nH30KDtMK62aVdOLDIbYuNHPXIn7bpu9x2XXBJ+KF/efgvLl3NsYK/l+KQHbVJDJKJ/zF0ij\nhUbfOD+Ibuecs/r8Kk3wMyaJk/q6CIf23+Yy2nMan3RQb2DrGB1tzCo7uvLf1THEAe8NTi/QYllI\nZl4Q5ZnI+2w5fwU+Vr60tzsdx26XSz5HFamgSJAR+QLK3XQ7pzE593KGpakqtricpySCdzRGpxig\nfl/UcE7oBaDAXNAKMoOqQmTWjfP8AAANL84ZFjIqZyhRommUXm39iPiN58JC8nIE8JeuT+OY1azx\nW4YHefH+9wD4xbF6tvQHCGQ20/S5SWyvlA4Rj8ZcNDXmfouZgSeMlzeVI5ySj+FIxXA4ICbcRcVU\nANJhleT3Bvjz3/49w2P17t9L1+WfEzpendfdNzdNxN9gONZ0gWUhnnxfeJZIXfEzzi8cBpUJ6N3i\nbkF1ZqOoCoAOBkkkNKWlFkJMvqBpdrzDjpumz3e+98E0dF6RUYWEQPPmC7dKWlTnAU7j4KR0ljeV\nZysO2Wpimtfsr6OqKqoqDJebcEQbA8m7Tppmpoh2eYi7XATFDLuSfcze8zLLNsNIHp1cS6tS61l5\nr4ZQzYxAWiRPo6isNYaRsF/tGM9XrM32XqP1zOw8ySoqXzco04QlP7JqQ6gqrpFp6vonIU/B0A19\nc1u8pHyOihQWGzL0zXBoX3VzR++bd79vI892XzPfz8pQSkGSUPESNQzR72KANjHBVbV0dWKd52yX\neQ5tPTFT8s2GomEl80zUW3voOqDd+7YLY9Q5k/iS0zjrbzC4u4tQcF1xlRmgW75Lr62yqsL5VfHL\nca1nR9Yo70qFCbwyy/QCc/+SKTvzg1L0iqz5973acFSz3MeDeYXC8mlpniR5zsnjY63IHvN17v10\nN1+y/wJ3Yo4ryh7STg/pSIrIwCx8suLLK8uyK4rhcLjAQ2i321EUBSkzsH7zN3+TL3zhC3i9Xl5+\n+WX8/gpyWkRhWVgotlpHwu7s828L9zNzex2X2UVXxxB+X5RwxEvK5sAoN1w4Cwe9WfjedIcf70iU\nOn9xVc8uaRBFFbwz+zQpnz0bOqELqwtBcRRXF4s4tUUykXBxyGVeadWoYuoBcZP/7/4WxIslNhNF\nZSxhBzdcPfzcgq77BjtZn9e3aj6lNiQzBfeAyAk7+QtkKVK9AnbEwF6dRTLIDNPUG0bN5zfU7egY\n4sxIMx9pMTZgNDXNFgiuwyMt2QWvc0c/oc0BphfRx+ikdI5OBkj8v/2AFgHme8ZYsM1HjtnYOD1s\naFmfj14kqByORylcrjiJ5qUt214WIQwX3q6OIeolbd6WahNQqnrxXNjH1N+FaQtrx2l39TD9XHXF\nbACEu1al0wsFj7hdu/e6YSsYech4wM77e7z0b3By+MJGwhlj/+ygg+aYjPCYX8voWBPtmx9n/20u\nhGrVhxGQcnpJZV7VlZ/byjaGadVC0Vwym5+7iaqqhkbCiL+Bup5+Ak9NEqpbV/GdiPjKC8geJU5U\nqsybI6kyu0UvrdIIpzlR9H6+kgiVWaXzFa5SURWzN7wc2KcpLPnreFUVLlWVRjGdrWiqowtFV9Q9\nhNQGguoMh+w3S/bGm1IDXL6W20vN0hpUIWjr+SM+cW6WEf82Xv9adWkl+dcIlfcO1OdbKaOzLgSD\nJgD7h+cY2H6dKWCo72D2Pd2Y5/dFiSecBRWLa4Uqmcw5Ff7X09/G+UrOW1eqJoGRsL+YaJVqKrf7\niC06F94uqQUKw+XpnTxWiue8dzSGbyTCx/r+nJ988bfK1lTfRR/SRo+hDLRejJl6Z/Ylezj3fpBD\nl15naN9HF99X3oBAxnhmVC/jBjtpZaIox7MSzJ671lalWMbOf6bxqIObdzt4NOlEqb9IW28/I/5t\n3GjLrXthVyMkGnn29TdoC/dzZe9HufuRQwXGlj2ePn4ol4/aEJU3nkZRyMpMAEm7l8NSz4JDg9MO\nB9GYC49bm9exuIsHN9axa+s9tjdXE46qDQ4bMrvoY/YNP02tmcgln51gaAzpsY3OA8YysN+nyYyy\nq7QsME09f/TL40wBMFnpz6yaZVcU/X4/kUhuKucricPDw3zve9/j9OnTeL1evvnNb/KP//iPvPLK\nK1WdwyhmPn4lWmAoGPdtITHiLxxkLxpbN6O2QuHBrDKa7HHwrOMXCGGs3LZFJ2i7OMb6tjFts3kq\nQjjio/f+ZoZHWhCKTDzuxONJFn03Fi/Oy3jU3s8/evfgn9pO3NaAW55BscURihcV4/YZ+YLvfEuG\nqkLD3oPMSSWEeUnAukM0jMNcs3GeUCWUytMy25AOCGMFd7+4SVvUvMCPhJa8WQAAIABJREFUTjTm\nwu1KZnMHh0ebUXe7qg5mWi/GDPMvoDA/rM4XZQpYZ/A8AUODgk7yrpMvtr+eNcxV0sfIqBqpmi7c\nzQ5fOM3bJz9X8jgpv53R6cqE8EpDh7a1P4aBC5wdOkx4o0crDqWghaYte6ELmG2vKzDS+H2RolA/\nI2ttqTk1flmlLdyf/ezBG/+MAtw88DSy10FdOsyz7g/KFruJzHmyC7MvPFPWQm6G7kkxC6m885GN\nXNqj5WdKipo1MgEMBPcxeWeOD7qPmYY+NjXOEI74sgW4FiqE5ltaZWHnwZ6nCnNO5yH96iYO1t/l\ntFq5oggCW1w2NBLo7XrSabuhodAIRdi4wU6En5ICo6qCokjYHqZpCk2VLOyS7wEyzxUM4B1p0Qwb\nmfuur+NmFVF3pPvoj20h5bPjnQ3jeZjCOxrj+WOXCoqn6bRGJ/Gdi+JTI1quaqbIlNnztYdTzA3Y\neWf4EAiB/RmZVF2xR0FORjh+NQxoxtrFpHd2ikEUIbiq7s4a03Thtlw/SABFBkQujzxfDgDYFH6X\n9AaJF90qvo63iYRd3Lu/ldkBBxeHdpJwaHu8vpfX+SMIofW+NMyhVFQ6Hz0oW63XF541jSIAsB8u\n/q5ZdXQjBW6hHmhVVemYusdHH85wtv0kKZ8DTzLOQekGHW6tsrwkKcyFffTe30Riu7HRvRrme0Q7\nOgYIDTmJ24NFn/UlpwGYDpqtCbmG7lsfPEBs056RUVsYVCUzNrXB6Y5Geebdf2Jr3y0mnA1cbtzF\nUHrPgvNuzYt65QoEuUwacFyWu9lhK18sTYmlswZHIYTpcz8iriFE8ZzpFAOMpRXOx1PcSoWh/RK0\nZ47trsc3bNzOvbfpMG3hfhr8Cv+L/+9yb2SW3Uo82tV46vQikDq+5DRd0gBCXVjhnUYxzWNXK5fl\njBzgmKXLc4P0pekCA025eaTvvQCzcz7e4TDe0Rje0RhCVfhY359zYfOrzHX5GPM1FaUYROMuRp5p\nKb8+pueYaRyGqUw7JAmq0LMrZtkVxUOHDvHmm2/y8Y9/nKtXr7Jjx47se4lEApvNhtPpRAhBY2Mj\ns7PV9+SZVAN8N/1lgszSnb7Nugv3qevpL/hMwl5s7XJEjCsgBuRpeqWcEFkK99NNYDLJe+9vZn3b\nWEH4Sn6YYvDqLWxvj8ErxfH970e7SfrtOCJJ4u4wExu0nDv77DPEM08xbg8iMoPE7dKuoVTy/Xyi\nsoutd98n1rKf2TK9/2zePSTSs9gdCxNiS+VpZQt6pHcTUhsITE+w78o5Ok/GAFH0m2RZcO3+Dg7t\nv1N0LFUls3kVCwOgYo8mkX3Vebl61J0liipI2SICcwlH5izGEpFapj1JtUK4YTXSvEqiM3Yf18NN\nTPdM4Guvx+F3mLaGCG+urDplpdXIAOTNduSoDZAgNUfT3bRmYVtg1VH7ZJx088JKlad8hUvf9UQn\n7zpyxaqMKvrp5I8/XZ+RByLU9YwWnafr3nnO7L8JEZgBdkpOVLsdbAJVFUYROtzrb2c3mpB3+MKb\nhoq9KxYj4Snt/dIFRbM1S5JyAleTw8bW5gf0T3YA0N+1t6DIj9H98PsiTL6jwgltDC1lLoeOX0Rx\nBp3sYBBJyRTpyBSEqh+YM1fEhLl19oh0DUWBpL36cXhT3W74um4IUxB8b/jTNN8O4SVWcrwH8jxA\n5kpZGtD2EqPCZ/q584W+udsebIO3aQ9dZyC4T7P+mxxDfx0gIQnGIj5a6zTrttnzPXrhH+gYusmF\nza8SdjWaFoj6F+4PaP70etKXprOh1dWjTbi5sI9759rpantgeh9KIYTKwF+oDAS3EXYGkNQ0ipDw\nJ6dpD11n6mMBPu3NPQ9/fYKDB+6QHB3llPqF7Ov6nqJfw/PS+4b3qOnGZMlqvTobbg7weE+7YdSH\nLzyLaCzWvoxC40wV5CqEyAJlRYHoDx/hbByla3Azde3pefddO7A2dlTSDhPRsgrjwHyPaL0vTufk\nB9xoLfbg6+GQtsQUaU9xSkwj03xB/BODD9u41ttNfUuhkaTAyJKXTvzCqb+loy+ncLckp2mZmuYD\n6fOV/QgD8ot6mUUBmBVsmhEVhg5HZBL/fRDnb2xG1DmK1oaAMk336bfoeiWGEKJorKgq/NmccbTb\n3a1u9o75sleddXz4IoQjXqRzPjbtnDL8biVyjKSqKIpmxGEozMjGjVptkEzqUb7BMj8SADLjIK7Q\n5amupZDOejFWFPJ/cedx3ENjrHsrhuuQH78vSktkEqctSdJpbA3JN9LMv0bdqNEeus6FiaP0eXKR\nW9l91klFhpYEg9g33EfWFcUlUBJhBRTFl19+mbNnz/KVr2htDL71rW/xs5/9jFgsxmuvvcZnP/tZ\nvvKVr+B2u9myZQuf+1xp74chQqBmrDNv24/wQmy4wNk04t9mKEy4phOGm7g7NMOp5vIDzRaX8fqN\nlURFgUcTrZx45n3D9zd3DCImItj3B1AVNduiQZ1Kkr40zchL65h6Nkgy1UcsfgaAruvGoZ9JR5TZ\nqJuAvzgxtxSj52JcS21j+6Mp2FwmPNHjIjrwkPptC1MUy+VpdUmDbHnzvQKBQrxiHAopSSrDI61c\nRhSEEuvKoSQnUWzFs04gCPRHmeyuPhwyWWLq5Lyl2hiTTHZGIVQ+8fLbBV7lfPIFuUqEcCPlW51K\nMuYM8m6wm1t1mfs3GqNLDBLfu3PR4UGVViPLLtb6rXbWM9kNvqG50oqiohAMjWt5D5k5a5PTzD6O\n4bg3w/pWXzacAxWQtJYXikMgu82fkSOSymh5CrK3nyuKca5qKc835JYRW7sP9blG0mcKN0hhSxX8\n+41YkrafD9M8LXNh86vUtaeLxuzcoHbd9zv3cPHIc6AoSIqMKtkIZKp/AoYKpEDBl4pyxHUte91m\nm7OihLJ/H3E76OoYyiqKs+3G7vmC+zGVoK7nEcm4D/vhAJ2NAyAtIAyyCo5IxT1k4xEbb1zU5sbk\n3mJvA5BpUlv4kps4z0mXNIVOMTcUlsKsPYFeGThEA7E2L8mZUdIPHfgHZgl1G0di7OJG9m+z+V4/\nMAdQkNOsj53JqQa62osNg8nRUZQhbR292Zpr3G50DF2oef7YJfz+KLPx3P0oqKCpBrCHU5qSmBGk\n20PXudF2oqBAVNpvp1HkFZJpduF8pZUko7ji5Y0d82nK7Bv6dWZ/w7ZB/P4o4bCXBw/X420ZoT4w\nyzqbZBhyKiTBhrZR2nq1SsVpSSBUmHQEuNixhRfWT2HkMrIfDuA7N51VtsncP+N71EBgqrBab354\nu175V8kUyhI9KsnZBvZcfZP3nv9s0bnH7e8jC2G483RJg2wX5RXkS5GdVLrkPy9dyv6thpLYFdgw\nkUbyX6fxRWND4q4d/Xg9CXrTHYZrjiOSxjWdILzBl/V+uEIJw5SE+R7RaUWhSRli78hbDAT3EXEG\n8GUU+7ZwP7NeiVmu4+XFomOJHpXXR3Py0nwjiVmo9/WDxwoURZ26mSnmAhVGVGWsib7wLEfqrtMl\naeOlSxo0zx00QYrL/M/EZzMhnbMcFDfYbuBhFEFN3hH+3GgpNHCqJPr6Qdlm6BkVQrDbYedWKl3y\neoodH1F4pRWHapxuc0CUr7+gIPEPb+wn7e5jg+rnyo6cYUBXpJS5NMGrjwiFHagOBdU2y76H12gL\n96OmNhekTMxXkm1zWrVzkjOMbN+I02+nQZ3jsK3HtG3c9YPH+MyP/pSRB9u4EtxH2Bkg+ZLZXqGw\nTQwwkbAxeHtHkVynGzXawv2c8S+8NQyA3bYe4T6f+7ffTjpc+pkt6Dw1P2IZhBD8x//4Hwte27Yt\npwB8/etf5+tf/3pNz6lP9oSw4ZIUBoL7DD+XCBhbcUYDlTUoD/TNEu70GYbzjEW8xJvcvO55gVC6\nOJSryRdFeilv8c14glIZ6+uBIx9w2ncCp6NTu9bkVVwx48XakXbzNtO8Wqb0tZpWQAhS0zJ/P7SH\nkWkHuODeXTtbPWMkm5pQhISRwGeLpojed6FENO+U3W/HHk3imUqTCLhKVmuC8nlaADdaX2A24chu\nBMgq2A1KDCuZ0OW8HD/dynVg323CES+JyxFuzuwj7Cz0OOhCzfQ2L2mvq7LiSZTu36QrbHWuJF63\n+aQVQvtvfvEbnXxBrsM3xLHE+1yydZOwGz9XI+X7x4+eomdLYTiOAJ7vGOLvWGirDK39yfw+e6Uw\n24ijzRJNPVNM7g4a9oMLhsb5zI/+tOC1MWeQv9z6Ueaog9EI60ejuIE4kLan2Nk8jX27zDnMCxXl\nC7gA/+PIC4afq6Yhrm1vfZGi6IsXe1wftjiZbdrG0OFNhBsCXIl0U/9BLl95b+gt7nfuKVAElYzr\nsevmu9nrth9t5FrW0przIszGfEyONWRDhcyUjkTiGg2Sixc8sMfpQG1Q2TvyFjdaXyjyuBrdj/Sl\naWa9EomJBI0/eMRUgx1ejvG55kH61S0LzhPRkInG3sLlPIAkBXGGZV6ov2iotLu8uXsspZRsL8hy\npLBnjxeO+GrWKgVAZDxfZ6anmVb/lFDARzraiTy6Hjcq/s4AtrzQMIBHipOnMt8vyD1UAzjCxTnt\n+esdaMqdEfbDAZIZg5svWajk6MdQpDSK5GDzulEOPZUT/AKZsHk9bL8lMknn/Qd4R1qwp+N0PMjN\nobZwP4xoYctiJEDL4COOPncdZ2PxvLYfDiBMGsnnYnmLv7cz2cflG7uK1smiaJGhjQx1XmFD2ziv\n+o3Xy/z7IhT4v7t+HQDXrjN83ma8vougM6sQ6/h9hfu9LpCrskriR1okk9cfIxzxG4aZdUmDWnja\n6GFkycZw8jB1dx4xt60O1eFHUUIkkldJpe9jq7IPqb7HqyHN4Dz7osn4zhjkpoPNNErFYffpS9OM\nOQP87YbnOT55nXafsUdYz+0qZejwjsbYM3sv54Xy+RgVTWU9omdiSY6oKm2R/oIQf526qMKXfnqB\nd44mmNr4LHbqs71XvWOFBvzhkRZiO+5n03xKVbw34uD7b5dN4dAJTo1l9zDXv9xG/riuNlxf9tiJ\nZcT2KQK8oR5HKMVedDVknO4CeojtHkLfyOQgK8be5yNuR4Gi6BGQUKHJJmF3JZAT7gIjScH5ZRVh\nIKttnRng6fdnuH3gGeaCTYYyoj02y9XAHNDC46c6DSN8r8R38ZmeM8wem+TuVjdKtI5oaBNHk9Ns\n9hvUIMjMydk5H29e/gj2tAo4OT54o0DRPZ02zsOeDmrjoC2sjb2rOzw8lv8VskEUiqoq/D/T2vxo\n8D2i2ePGHavDk5qjc/JKwdiNNCys6I6OJAVR47k1wbNZYu7Wog5pyLIriktBOhXCZm8AhKGgr0/2\nuM3NrReOEh40tjqbCUeyrfRtssXS2SqKZj0Or8S2lwzlklWBZJDsom9mW89f4eTLds0q4tjGekcj\nLv80yXCxpyzuDtOXStN2o4NdW0bx+yKGIW6pN8ZReiPICAY7n2HWqW2o7lYP8WbjBVIn8UALCY6P\nxoiPRvA880/svfhxREaBmjjeaNjfyCanOSGfo8v7qOTxAXbufMCbo89qm/IItJvoZkIU+tsNrVwn\nBEf/8Q1OqV8osuB5R2N4RiMMfNSLzVaZldBWolGqrrAlZYn/68R5w88Y0dUxVKToauEcvmy7jRYm\nCe1oMAwNna98pxXoGSnO2VCBdb6oaVPkUswvhV8pZhux4vYQVa8QvLWLUHfxmNO9Z/m827yDhCcN\nEZgCplCxNQ7T5J3lsOTl0G6t6qhXSeQakyuAEAQz7SDmW4mdkSTJumJDUTnPdwEGiu5UQ+EY2fEg\nTr28rUDI0IvLuFKDPHXzDG3hfn7y8d8yPMX1g8fZe1PzPG2ve8QO6XHRZ/y+KL33N2t5oRQrHamw\nVhVNiezi1aOunJU5lKQt/IgbrS+Yete06q4qqX8aQ+mNcPZYPXe35gviCsyEaZiKEowMEd7cSsrn\nyHhEJbyRWdPqo/kkU7dJpe+TSt/HGa3jmYkdbN9fQUihXHmFifz82gsRGTE7SmOPwkx7PWmfQ1P2\ndGtOlShI/DQc5w5p7RDeMPt33+BZaYh17iTjES/v3NxMz+g62j6meccGpGf4pdzKQekmQWYIiFkC\nYpbOmw/o2lac0z6f+QqLTn644nwlJ3+dmYp48biMY5fSaTu/yLTyyf7GNpnwWB2+6Fx2RW0L97Oh\nbYwHRw5y1fcUf8Zeguni/FbR6CQum3gTVVXLYZ5HdGyEgeutSBX23Gh+3MmtpmE+papIRl7FYO6+\nTObl5wpPhAnZTYu9WETV5wgjWj5Wwu4jHPFqe8z8z0ZyQnZruJ8Phl/mSodxePvsfe1eiEzcY+Ah\nBB7OMdT5NpvbxjnqddMk+U2HYizuMiysk3pjnF61nesHX2L6xXWmFVXzDXJSlxYhoAadWQVT6Y3w\nbuvzhOxBftb6AptT52g1GStQ7MUJZDym3tGYYfpNPRG2Uzy/VVVlXNZz5dKcjJqfUwDN0zKf+8Ul\nfnF8kI+fHUdANiR6PtfvbuOZTLqKaTX16QnDc3X03WSsdaNhb+355Cub6lQS0ZzbZ6oK1zfpeWgU\n9ZK+NI3U5SuSh+e3rpkiaJpi0TTPWJJU4f9sqGM84uFddxxnwm2+5phN0ffG6J3bxL0rMdytkwQM\n9vypsfFsKKUkGRs2Qo0t/M9v/B5SLMm2/jDSRJjHdTb+e902/o/ExayBaz4XvPsZPrGBph6tQ8F8\nRdd0HKgzyELbz9/f4+XuVjcNNjODpETXXXjm3hRNs2NMuvs4V3+QhFsUGTgCoXFCTcatZCpBUUKk\nhzuw+x342uuR3VeBzWW/Vy1PhKIY6rmHs+safu/nDQV9fbJH3C1MDpp7Bx2RFKm6YsXLJqcNLQc6\nUlrNWnqHR1roD8xmhTSducbi0riQm+Q2yVjAEY1OZAHjEwmmBno4sbmfZslOOOLluq2LpEEg88SG\nPhom1zP+cBPjD7XfmxMIoqSnFXhvLBvWOekMQPdm6nuGmMGLr90kDl5VsYVTJAZmmcizbAuPVqAg\n4QnjjmnfPSjdMPTonIifZXvdIyrqaePObXz3Gw8QnL1BQ6DYQzc/odnMymU/HMB5PpotRJBPUigk\nklfxel4qe12ghVyaVUfTFbarUjfrlFDFOaJ+XxQVlQ1t46Z5rMMjLQTvzsBMglB7HXafgyapuHoh\naJE97lYP/q312LwOlEiK6MAMkdE44xEvh3ylNyn7bJK0z5ENEfI/muOZe79AfWkdwl5dgzSzBVhO\nzxD1z+B7NEVjj2CmvY60z6GVeB57xPVwE35nkKbkNJPOAO8Gu7m9LoAayRl7bI3DrAuOsbnvILvz\nvCr5oTYjc16+e/YAX+/7nvH19U0yeqB4jtY9mmBmk0K9ZGyEKvwxxXP4/T2FwvDTNyOcPWlcVj3S\n7mHdxQeMOYOEgsatKRLe3MY5X+jQmY146OowzqscmfPynYuHtBcFBULC+d42Njk1gdfMu3ZQuoka\nTjM6nuTiR1ro3Wp4mYSbo3z+7e9R/06xYPe3X/5tZgIGRSdUFZucZvutK4S4kFVAw4872LTLeE5n\nvpalXJW4fGzIJENJXnco3HKFYd8ZunplXvmRTHNyhglnA6df+xJxf4miOSY5V6pKgTV+t8Oe8Wxp\nAkxrXZQv7r+DdMfGWN7G3qe20ydrrmDf3DSdYrDAw5e/FgxM+pHtadwxPwIpk/NssI/kee50r9/7\nWw/TEYgU5HU3Gyg7OnX+SCZHPk3KZ8+EJgc52/4ZbCmJuoQWArihbYzBl5/mtElrD30+3oh2gdts\nPhm/7vA0Isq0VsrHnbG0T8iKqdKn826wO/u3pDRwPh7mVX/xd9KXtPwi3bMAIAmfYV0Bqd6B1OVD\n6Y3w9kuf5VGHcT7r+cR+giPGBqkDqUaO+OfMfmKW0bGmIpkD4H7rDt7uztXJN4uEyTfIKb2RrKdV\nZ8bmy6UuAO/c38QXdxfva5GUDb9T8/Dnr7+6xxTM92cj/j6SKJhHigRSBXlYx69MEvZ6qIvGiowj\nOudH1jEJHNs2xAHfTU6rxeuykaFSZ2hrZb3r8pXN+UVRjPKKqw3bn1LqUSYSiHmKvfPLG4s+W6qa\n8nz5YVIuvNEy8Ae/zM3rZodgNuIlYGQkmUqSujStRQ5krit6NcY/zB3idlsAIUeIjylM30ng29SI\n0+fKhmFv29rEjb45QJAOp3AYyOQIgSoEss/NVLebph6JztEYH514j/p0FCPVJqY66VO1tXWq3a9V\nN56n6G4Qo4aFCjfYxvmjrxbuybISMtQ37LNRfuX93Dq1LhbmM7F3+Enr87z10mfZc+nnNM4kmWqw\no0SuQNPHi39fhcjKHM37P5KJOniX2O3Ke85XwxOhKMpT60n2gtx1E1vd80Xv65O9v7F0qJ0mHBU/\n+O23rpS0HM33ROb3FtMxr2LXwI+u7eT5jqFs0YB8xhWFP8sboMqoHV/PcTrnLfiqgLh7lokNfcw0\nDRflL+rhOa5UmOcGflTw3rV9R/G0eZHCdcwMyNh9xkqxQOXRxeKCHZ96cJVT3SrjG/rYnCkl3u2+\nh5dEcTUt/yNTAbcUEWc97guPwaC32/xkYVMrV7BEdrBqI3K7Ds8B47L8qCqoWo7FlumHPNWsNS4/\nLx8gKjTPqUdEOSquERCz/FI+pi1KqlblrE0qX7p4QknzsPMqL7Qa74T5HseG0Ri9o9p4+fzzF2n1\nFlvQbqtbCix2tjondd3rkJngnfub+eL+O9xWthn29wFI1+fdLxuEt9TTe7ed3VJlLV3Sj+JILoEI\nOjkQucZpX/FmzR2ZLaOZeTkaxzeq5dX2148idl3i3u5t3Hr46cLvzHu89g19rOvTjmH27Nf5YqhC\nYsbupSFdPM+cE7KmGOd50exzKdJ33HynPgoqdA4d5rmOIVp8UUMPvXxjFhVQ5lke82mckU0r880G\nGrPhb00R4w0yEDIWOvIrttocc7zg6KHewEK/zpd7dvo8v5jey9VbHnpm1sGWvXwEzcu+u/s9btFV\nFA4247XxP3Z+VLtvaIUZGibXs+5xJ66Yn4QnjN1xm3oT6//+S2d4+6XisK0X3vi7rKd3PGDndss6\n0o87kKfWs85nngsrRE4RqibPcPv1y6TefcitzPq640GcT1zMFU9rSU6ztb+f2/uKn5eixIjOXsHt\nega7QSVVVQ0X/PuEx3jtOdnZz1+yH4BOMcAh6QZBZglRT2wgyoZNYTAobNHVMcR7/nqmG+8B2v2X\nTIyN+QWtAJoig/TEn+f5DvOegEW/ZzLBjvA5Tj/zK9h9DlyDc7T2zaGLEGFXIzfaTrD+mbcqEkYv\nYZz+UQqzfcmMuFt7BufjKUOlL3lppjh/G0j0t3Or6xqE4xxxO1gnpAIBfD5KbwTlWArJYNyJZ5p5\nq/1l+rv2mpb9j9i9ppELezaNG74uK5rvUc8r3b2jOBQT4IPdRwxfV1UZkJDjMdLSfS4eeY53PvaZ\nbA70/IiLf24+VPDvnqFNODff5YjbQZNNYjLj9QN41Vkc6pu/R5ut0QAJ1YGDNCHqOR+bLcqRq0RJ\nBC0M9aetH+Ez0XcKQqIjzgBCjnHP7mEKuDCyjguZqBt36wQbN0hEG3IF9PT7EHXYcKVlJFVT4e53\n7inbZkqn9fFA9m+lN0K6bRrb3notAkVW6Uz10+WpoPWCgmE+YSA0QfJHuQitqzs8bAzY2WRQ+KhU\nj+r56M8zy7zlZSIlcyojR8wneWmG8UEno3OdjNZpee8JVG7VqQX7d/yhQvzhBP/y2OUC+Xfcf5Cx\nsI/IwKyh13E+ehXzUF0nwnfd8DNOcr9H9WtzNb9gIMBj1di795C2otfMHAv7rpw1PMbRUA9/Gf8S\nVz8ZJHczh3DE3sBrexrsdTgiabwjEaLrPKQCLlRVJZ2JALJvuoOnbheSFERRQkSnHhC950GNDSHc\nUbCtKzCg15InQlEETVmcuggbtg4hmoMofk/RZI856kvaadyTCdaNDJFwe5mrCxR8v2X0EWc/+ilD\nz6J/Zhp3coaYo56YMF4IzTwqyXA6Gx5oNOHyJ+tuh51ngw7WvXyGqcwGMT7SQhwYbXET23YTRdFy\npMzyF5P2Qg/H+w07uSavxzcSxbutlVcv/DUXwp+H+uKw0UYxw7GnbnPm/mbGIx7W+WIcX9fPjt4H\nnI82MdM0DEDr4E7CES9ddcWFFZSJZJFVzYxkKjc848DooJ22fxwtsFLdGNzJ8Hih8jh/8uuooWTR\n79dxoI0hOS4bCn62uMyGc5qSnMTJW3e1jdPDFNOoTAFNz7Ryuu4Y8wuBukVlvbbOx1PMNA2b9g7V\ne+uAdj903o0n+azBzzov78bIgOxrr0fXIaKmzX+NuX7wGLumflGRop9VpgR0pfpReiJcW3+Y6cZ1\nNIoZmmaHmBxtxMh62pby0Aeo0fJV3oQnkh3vZs9eEir/8thl+hp2c+hScS7X2MFAoWKMpiiPHtAW\nXiXppmdkXXaufrP9DXxdjuxmL9+YJX1miomAnb/8pHme21SDzTTcJBCaYDjzt9kGufVOLpS5V23n\neug5Qv6mgliftL2B0+pxJIPclfFIbl7rkQMzNDCaF6KcEjJO1c4eWx/dorfoGuolid1OOz0j60j2\naoEu66Zy1mt3rB5izzDijxnmEnWGenFKZ7WwYKWeQGiiSDhtnJZJ9OSs1+MRr6EhTUcvcFH/wMXk\nvuL7tn6ol7H17cg2e9ZreeTcLxkP5Ob60zeLx83ohnbD8wnhQAm1EI5MGz6neOJiwb/rJeOdp84W\nQ00qdNkGedn2bvb1JmbgAKiq8Tzz+6Mk7HshrimKM03DjMseY8/ZVKER6Vp9F6CFn1dK+tI09ROj\nTF7UokT25peGzMMREIRkc2F0IuLmn3vbie02z583i+CRY2mSGKnNxuiVwW8m0yjTMkd9Es02wXjE\ny5n7m+nhOdhS/L3dTjsbpXpuyWH+bC7Gv/rLsbLBrsIkbUUK2OlfQixuAAAgAElEQVSv0xRn0/7A\nwjy83e83eUZC4S+ubqFxfAsbWyfwmPRzDNmMUwuEsKGqCknexet6Cb37VqipNRsWv/X+TaJ2D7Iq\n+PToGY6GrvNucB+36rYh3GFupdLGxU4yCnaTzUbEoOK42RoN4BKavNPEDNvsKXrm/aypgI3m6coq\nbevK/9FQD+vCD5CS00VGgXziozFOvPdTWjJVKfOJ+AR/8skWvvbzSZqnZa4fNI4KMSJ/DZG6fNj3\n5z0TuyiSws3CUf2PI4YpJ/O9nsPNDsaOBllffGjTMeiKJ5hVfdmiVhcishZlkYeRGUrfD5/rGGKd\nL8Z4xJOdWx1bBE0VekZ1w/X14Wbeub+ZsbAml8RHYzw9/g/0PvURpoPrUE1SAXRnTcQZMHVEFCjD\nmSiL+UWNzBTpGerxuD9GInkVRQkhCBC5XUfSkanRoUdBDczy1B3jXPGm1DTx/7+9M49uq7oT/+dp\n8yLJu+M4trPZcRInIU0CzQIJCWsgrKU5tAfazq/M/ICWDnA6DG3psLRQCs3Q0+HHnLZ0ykxpT1lK\nhnYmbKVQIAtJSLM5e2LHcTbFq2LJsrW93x/yk5+k96QnWV6S3M85PTSS/N59933vvd/tfq/LR065\nHVP+YN8Ggk24g02YAiU4nbfR7SiMGIeN7fSpMvfMASuhurUABDvGEzgyGPhS71McDs4bQ9FccgrL\nhCN05nmRfXbqttpZ2HqcUn931GtokkiqFpuCMnPGt7KTmdz22xdweAdTPhQlRmsD8/ETIQ7bnChD\naZmGUqM3+M0tZ5GQowNu+dRWSuy9dIRgU39fdCKeYbFykyMHJXWpzNlL2dwD/AHYc7ocXD7sZdPB\nHlE21GmganICPYQkEx3WwsFJ0xPAvacD+yQz5ZbjFHV/hLsgMRw+z7SXusp25lQORjX877oIA5ft\n8PDOpYW4S0/hLj2FWbJoFtMJbD9LR6ed081VTCjr0o3OxHMKmU3Fc7j58CcxaTFOhw3Gx672emXf\nA9vPkhPy0GdJ7BfF8PIc0Vb8io7EHtMiAZ/FTZ16Hu9CPJqfQ2S+ag+FonswIPJvLYXP4x2U3lOq\ne0u2PtDoa7+krSRY7FaWDqT/GDnXSE13STnhJj8mI4Zi5WCbpLIcZpS5qW55D4dSOr0E1pGYAQCQ\n63NQu+1qckMWfESeV7vgNsg+e1Te9d69JEXS/SoW9bKRBVTvPUBpnyca+esv0Z5oA8V28ECwNTbN\naL18Mct/8ceE38enmsbzt9nlzNm+UXMembR7J/uYCQwskPWfsdc6FS9FhMJd2IK7GF8eMSXji91o\noZVOtL65JrqfIW98pK1Bb6znuFU2UQt0hwoosSQqsZI06NQ6ebqSch01uqV4jqah6G0YF01L63/l\nOHJHYjRcvWcMBhUJLWRPkPGe1si9WmFnz2yOTl9Ed1FZ1Nk3+chezVaq31eJO1EB1Yv+SpKFgil1\ndDe2R4+bsdithELd+IN/IxBs0vw7LVx/PcEXluzUPAc2hAmLRiSqk8KYwmbhcBd7/EHNecP/t0ik\nOyiZ2VlQx/vlkb2GqYxvIGY/6tYlBSiZn3qH0ng8+RTn6ezzwc3xbieNp8sZNzGASSulDP0Mnsn2\nNvyzcmBPory0j2vG3lNKbp8jenxUl6OL4OG5hDor2QHsSPirRGaPb4vK2WLy2esPGDJOjCin06Rm\nNsvzEn4zw3+EVio1r9vlN1GqsRewIxTm9OR9nJ68j4XOPPQO9SsKuekya0cZwuEucmzaWVa75y2h\n+tQBHL2Diuo4fzc3uz4B4HBt7Lwg9dUi5XcRDndxIFTAsc31TPFoO8z05uh4GmyR9fTTvgDtwTAh\nn5NNpRXc2K0dMVJzduD4s33OKbqGoRZlfm2jvcQdItzrZMvMfq7f5E5yXmMi6j2KliXafRI+GwB/\nGEpsjPO2U9vezLGiagL2SLq3UgQox+2nbZIDyW6loLuDeX/7JCH6u9QjUaZTwEkvtdLcFuKTg4Nn\nI/ryzsKc9YaeT+1AhcipDlNJNBKT5QM0ni6n25fDcXeiPnLE7eDmP/yKzTU3cXjpDM2MEevAfmC7\nv1s3ELE93DD4jwFjM77yc2HYQ7cpsQ1h2R+dZ02mYnKsc+ntzCGEL8aYA+gosFJ+NnGO6hxod/Bk\nLba6nQnfq8dih0bmnpI5aZnQRPBkbcL3w8l5YSiaS07FdLyU76FpoYf9pRcPni8ClCBTm8LDMc3c\nQr3UQp+UuIBOPbIXnzmHjRdfFeNBiBcULaWmznSMHS0TcCcM/n5MtbsiPzpZy7sHKjlemyhE43QO\nVblsamt0kOba65Fz8+j376BtQhM1RxIXgT02J+unfyXhIHaA0229A9GQVszdG8kxXxzznDukPBxT\n8ym3+5A6+2JScaa39ANu3llSAJIUMXqinsVIaspOaSlHVkS8a92N7fQdjPTbY9es19ykn2MLUjre\nxZbT5ZGiJc4p1FT3cvG0NqzFZgJdIVxNE4i3ILTKvm/3z2DfinryTvdStqcr4V6K4dXn8tHNoOJn\n8yZWG9QjqJMuGJYlzJJ2Wlibz8Z/9sc+gF6qlJK+IwO9FXkw0KbFOdqLQoHk5iyJSkLQG6C8MCLf\n6VZdK5HcWKZnngcfHGdHfc6o0+Glx5NopEmYyAtFVPt8GBi3YU1jMXiyNpr2rH73yiHY8Uyd1c/P\n3V8YcC41IeV6iJTCSkQG/APKpppD4+uRrryFOds3UtTVRmehja0NuRyaPJ7gaSeW8dr7Uo9Ou5zq\nriMse/+/2T1vSYwxs9tTGmMsNB8y8ZW57w5+kAPMLMRbXUxj3nLN66vpkAs5EwxFx9/WQDHtDfOJ\nd4N4W2KdIJE+DlN4sorLJupHOy6b2sonGkVVote1JcqV22znb/IcriMyP1oWFBF4L3HfmXrPGEQU\nieqisyyadCrht8GNsVIxp7GRubsbYz5rK7Kwpa6ES3YFovtdN9dUc3hcL1LYg9znoCO3l3JfrFMn\n39uTNMXMWVtE28ZT0TUgshYlGolnw1Cos33y3iV/o1wncmTSSVdUlB6btRabtZZL5A3Mt7Ro/rZ7\n8SR+Ls9n9vg2lk5tZYl9PW3efJo7C1MaiuEOP672fj5bUsCBKmfUUNQ7Du9QczXzZ2s7RZsaQ+x1\nRRwuZ3Ui5jN2b2HRxj8DJBiLx5nAVRM24A+E2HNwarTS8Slk3EEbltqdSLmRdxk8OZXQkblJn02L\npXH75xpsVlrmF8EHiVsHzuabsPvCdBZacHv9zNYwFDe01BAo9GOxW3H5C7gqf0PCdoyzhwadFf2m\nILawib48D2dKT9J/vIwv1CaOD3WmUZlOhVaAkpPddNVoG4oXb17P9sU3an7XXVSGtVfbOF7s3crR\n0ogyLcsQck2kYPxlWO2Da19Zr36xuvj1udeXi8Ouvb422Kw02Kz8v/Xzaffms0eCcEURi7saKfV3\n4zHnUxhKjE7+tXS+xtVS024r1IwodliL6G+8lF1AoKKZnF4vfQ5j66CyR1Gqc2imJ0PkGItet0Se\nJCNLYUrPduM/GKtLhJE55fLS6Yo879cOv6xZALGkQbtdcjDMiZ4STYdUfMX/3CFEqKYhka+aHXor\n8qJHWJXq6MyAppEI0FFZwc4pV+NpK9bdO68cGzSpazdhjxc/A5lnJTY6pGK2hxui+xNh4HisAdRV\nk00VAYhdegDo61sfzdYLhzvx9X+IuSRRLwDYOjuP6zcmGopbZ+VB26DBl1t3jDDdEcPT9rmo4y+s\nqpSuzNnl9t5IAbSmGhobh1JNPDPOC0PRMkF7/0rMQZQMKkCVSNFFxmXvwjK+hbKTteT2OXB7B84f\n9GpPkjMP7uB/wsn3V+iG409bKOH04P3NQVprG6Mpm8p/tdgX9rFYIx46TqVgWOxWJFNEccAO7bm9\nFLT0YPUG8cnyYFRGw0gECPsGJzFfswVPZ6xXo5FBz9HXj/2Jcf7YCXp6Sz/vLYLwgEIUm5pipsA+\nkaDXHzNRzB7flrSo4OSprbwzcM/Z49tYMrcLRWxtpRYWlp7ibzsLE6oAxpdM95sguChIb0Ue7UBB\ncw/W3kDqaJVmwoUS141FL11Q0rkGQFluAP+euVGDRe5zsPPkVPw2C1dPPY4z7kxIANkERbPL6CaS\nmlCmM4ovlvZqnlnkbTlLW20kmqAX6Q4Hw5g0Ctb4W7uRJ2dUCBKIHBnyh53To2OjbIKLnoPGFqVK\nJDo1+jLUWUkbEK7eT0XHBE66yujoyeOqS7drXkfZpxfqrCTUWUmuJQjap2NEfxePt+UszbNn0VwX\nux8r3NhOwOUj7CnGUnNgINoLsj+XYOt08usmsuWyOqYc3sPSD/5EUXc73UVlbJ/xefZJxo5msDvD\ndAVTG/ehcBcv9aoXZB9mb6IDSGvR7gTe31dCe/c0bp5zSPN9l9t9upElGDxYWM1nZfNBfeZpLZiv\nkghv7yLc6acnz8mHzs9pRgHe2V/L8e6CqOyEuv2wtTNh35jfIpEbiJWTzxry2Rucy+6Jce9SZU9u\nLGjmZt8ncXdNXkXVlBtr/am9vmqj5c82i2ZEVIl069FJEdtDDdFKqF1yIRuO1XCssBKLXY6+w7cC\nVuZdrG28ldt9MZEyGIiuO3v5tKWSySVuxjl647cyArAuV2afkkrdP/isesP/1KkKWuwHuLjyM5ps\ntXTJhZj6++g43BsjZzEOOYcVW18fF+3cyKxdW2jLL+HQxAbN638YXkSo2kSgKE52B8byUNFKx500\ns5CAycypjwOUBro09x+biOiX3b4cnDl+1ZpfCETWURc2POPzWDbtA8ry/JppmcfDpsha5HPA8XrO\nAGFPcYwu8WmglwPSYMqnXrGevl4r/oM2St2dUUVd2QNV2XeQubsbOTp9kXYavE61T4BSbx8MZKLI\n/lwCxxqw1MYaQLkp9hLGrM9SmJVXbaDNm8fRzkJNZ1C7KmU+Pko4s6c5ajgqBc/SiSKqOZZXoWko\nHssbfEf7nFMoM+cZVp4n7fkbf6xYyvIlXVSgPdYlk4S9GECi0NGneVxWc5ye4rEVUeBPdHpLxTqx\nO5NEt127qnt8nQ1lf68aOWSsUNgpVUCmtyIvxrCzOm0xuosRAh477R4nSMSc0arIs7PlLHkuL9Xd\nB6IZLEpBprfqr+TMisR9ujkt2gWipLhAQbg/hF/appkhEm9fKBwYV4q0RObivb2UuIPR+eLAuDIY\n2HIc6qzE1D0DZ41Gdlt7REa05mzl31rV7IeT88JQlPJ0ipdoCLtSTj+Ktwhzl4+uAW9k0G/nFogU\nANCoYhjSrfsbS3w4XvP+ITPmrnFY8jxRpULK69FUyjrC2jNvKDz44/iIlm98Pr7x+QR6/Jqh7Hik\nXC/hXme0gAREqmbG52D3uXyRNFBXvFIF9pPF9NQkTrTB01W4jh1P+DzegxuPuviG3m/VRV70sIZl\n2jfGLkB2EuqikFuRF2PsBZ22gYmuMyaqeFxDgVTSBZtstTEe44jHtC/h9xDZMxbSUHIagdzT4wbK\npceinAZin1RAn8tHm0c7haygt53uI4k59H0uH83FkWhCfNU1W6CX0wf66HP5NN/9aZeFM2WpU9b0\naPPmJYyNkjjnTR5oPneyU0FDnZWc6azknqvXp0xlVu/TA+gLWsjt8JFbluiI6e/QXsziI8/xRpfW\nO4VBZ0JzXayR2d3YDsTeK9nYMBGOOd5Bi35/YrKdlgMoGTtOVbB4ygnN991z1owP/XT+9nCAM3EV\na03OqeST6MjpLZbZU5z6aAu17MzsaeZmV2IfNebXM9F/IppavKWuhL1Bbe+vGvWeJqXNXnv651zp\njWeIOA8rHL2GHS2KJ1yphBro8dNxwIVifAxSSZuvVbOoVZs3T1eWJpe4+fnGSPRl9vi2iEHi8EaL\nk6j3oEnWwUwAvYiiDOw6XM+uwwCegf9pEzlaaVDmW6lnXW09ABVOu+b1Q2ETFVvPcHaSE2uaCqcR\n9NJxOyaM49cT55Mze33M/iKFQiz8Yef0lAqcIr+z4qIuClrOsPj58qK6g/yfSSHKzCbaQ2GOBbW3\nKuw7HCkiku/yxWbEyGEWHYlEbfXS4Ods34iMpOnk9Oap9kMPpOTH6x4BhwWbwYO/e2Upppqm2hmk\nGNzJqoCmm16ajGle7XEy0Rcb1TXrVVcOy9HKwFZvkPwTHXw43U1/42xW21MfCaZmau1RTrrK6Mv1\n0FZ5hM6m2Ah5S/Ec5rg+Tvg731kb+UWBhM/dHjP5Ie0MCXN/bGCkXSv4EjJWTEodkHFP0nYCK7qL\nESaYTKgTKxLkGQAzx4sakMJnqe08MOgwCFeR25i4TvtdPoo1NiOcQk6Yl3Iv2a05X2vZFxDJcDpY\n50koZBccGI8KtiJtjSbyuVt3zlZnEY4U54WhKPvsSBqTt9ENnuqFfQcQHN/GqlCT5tRklo1tpDZK\nvFKRM3u95rOU6mi/6pRGvYhWfGqZHrLPGVNAIt5oUnuD9pGoVG0qns2ZU1OwWvdiHnc8YtHIJkJn\nqgkc0/YQpyqooFbq9X7rSKMoA0AJkQU5DxIiinpHg3RPcpDr6o2mOulFIJsPmVg9952Yzzr9TtAx\nFNc36Z95o6eEBwYOlFX2ROrt31rfVJMw6SlMUVXmjTlGwpfPz10RxVHvb/XuF5bhjCdfNzqhtCme\neOdNRIlKRLsHYzGy70qrDd07OyiaCzmlg/LW3+Gje6d+tVq9/klGKgNTTbKxEdJRnNRV0gIBJ5YJ\nzqgTytx7MUV1E1PeFwbHSBOy7vvO33wCk+8M5CVGJFyEOZZXzu6JsaltiQfmREjmBNBDy7DTjCYk\n+q2SXlP993rVZxWkQJi7b5rFr9ftIxBKHkZRFP7HrtHe+yPL4PJEUvs7+u3stF0Uky4FyefyTw5N\n0Z0HvjBHe3+n2hGntC9iDCXKhXo9Te9gi/TRS+O3eoPYPEHK9nTRDgTSUDiNkGwuhcH9RfVH+7hk\nr5cSd4jOQjObShtolIwrb3qR+FTjYPb4toFU1IihMs5iZpzFzBZXAfPKerCaB+fRnh675jUcqoiZ\nsr8tPg1+6pG9SWPp8Q7leN3j7CSn5hYPLZRtH/Fpdv+9u35EFeKZPc2aFbEBSuOijHryGW9wmYOW\nqEFhZG1S47T72HNJRJcI9ybmix51TkEikm5p93fjtRXRUjyHYIudRUWJMvyxqRefRXuEhomktvYB\n7VN3clYju03tKEqFsqZX2K2ac0I6FYxTRafVHJwwm7UlsSnrWut0RJeIdVDr6XXp2hd6WSXxzkO9\nPlA+11v/y3XStIeT88JQ1NscGjw5VePXxjCV2jSLLLTbhqf8rILes8zEARr7Vc6oCpyko4QaQc9o\nUrxBep68wLEGXcMwnlSTp1qp1/utusiLHsqbLIGYo0Xi97/pDd6g3cq2FGlooJN2fGwC9mOwrP4o\n+QNnQ/b05fDewclJF0J1CoeayBlmg0VI9FOd9a89lEko1f3iS10rdPtshhZ+vec+ZaD/46unjXN6\nWV57jJK8Ps4o1Q512pDMKMwmRg3MZGOjRGdvadATUGUPVMZkBhToOH3i26IeI5VE3vfy9m2UzLUm\nnNV1ie1T3px4o6EFF/SdH0acAFpkM5qgRary7GcPdbPw72YA8Is/7Rny/ZToHijZHH7Dc3mycal3\n/FJPvzWyR1K1B2afgfVU7z1mS4XR6/cCVcpYQUsPvZckzyRJl1RzW6izkqmbT3PdkcGCKmXdIW7s\n3k24osiwLGY6DvSiDBPzg/yxsT7GyK2b2sr2XTMTfjupK7YYzMSmAwkFUZJh75VjHMqQqHu09QVx\nm2Sqw5CjMZfLyDFO2rGQZre4S79ITnxxLT35DOVZokXPA04rnbPLsHRHdKFkBbk076lyPGnpsqeQ\nyXdOjR4/oXDkdJjjoJmuXOBwaBpu4RxzVL/JyT+rWfgrk8qaegZ1fAG1pNfIN+vul40nt994FfeE\n7EK9+2dgX+hlFMX8fYq+0Vv/4zOiRoLzwlA0asEbZenUViz5xoosZBu9Zzlhs0AST6dCJlEOPVJ5\nPLKB3uTZ7cvh/ThDSu+3G5qq6UUmFwigvTApqaKVOj5vJeUnGxObXtrx5jQXPCWFY4LFTG5Ijh50\n7RuoVqmOLujdU49sTEKRAvlyQo/qvaf3DxpTorT2EiczPtTEV08747HTeCq7yuRIkUyx0Ntbqhdx\nSuX0UaMeI0rluvz9bfj3Jy6qpf5uwwsuDM0JMBooCrCjNh9zbi4RZ52JUF8fniO95PsiytzChkhU\n9T/f3kd/ILkL3N1noygv0Qnp7ouddzKZy/XmAT1ZirQj0hZFOf/DzunsOzw36Xo63O9RbXhY7daY\nyo8KVk8wrXnZKKnm0oWt2sba4q5Gw4Zipv2XzMEXb+SaHF78hd0E3YXkAnLIz0VtnyZUIt5RUMfF\n7kTZOKtz5my80aSgJa96660P2KN61rGQZqdX8RQS9b54w1j2BrBaTYRyE1Xq3JzP4aGDxtPlfOGi\nA7rZNgn39AUSIrdqkq2TnXEynHvJuxF3uM4h8epxlM3Ay1Cz3ABOWSWtU2w06cvphb70jvxKRbbt\nC4VUfZMqu2EkOS8MRTBmwRul3N6LaVrEexLc1o3c5UcqtmGaV8S+o4MLgUSqUgeZkWqPi9Go0VDR\nreLpM7b3wAjpRMOS/3bwTZQg6xoZqVJ+9AZvcUsPU5BSFr/JNp1AZzAUu1+wx59WpFhrr+FQJqFU\n3t9MIpzxpGN8xKNXPe1cQ6sfj3YWMrnEzRR7Kxf7P2N7uIGgLS9lxCkdp0/8GClFwmsrwqlROEFP\nadQjEyeAJEXSMkeLZAZbvjO2WmAqIxEiDpOhOFIyQUuWci1BTYP1sqmtNG6cn3Q9HYozxyhKvy9A\nwqQVlTKlp3BmCz2DIj49MRmZ9l8qB5+2kTsweMxWfPZqFvu7Y1K1m+sa2FezkrAzn8Kudibv3kFL\nd2RF1KpDkI6z3GiK7VhIs9OreOo22zUdAPHzQsWKau3tSjmDvaBXT0A5HkPJ2Pj08Hi2u2cl/C4e\no+ukkj6pd0i8ehxl0zDKRpaby+2HQhsVfhlbXwh/rpkeaz9lZxPNl9NmP8kPwcuMbNoXClp9c/ZA\nJwF35pliw8V5YSg+dOcC/uOPjXT2GM+hToYyGZunOTBPGwy3n+7Jh6PgyLOysKGCv2xLLM4ynKQb\nNVIwSxKhDDQtPaPJ2p6dflZI57mM/DbZ5Jkq5UfLU1je4iHf1QcDe+eSHdUwXGQaKdbbZ3q4Ef6w\nM7NJyIj3N1NZFcSSuh+NpcumEynXGiMtxXOYrVE4IZMMi3ScABJQVWbH2dTI4q7dlPndtNsKowd/\njzZu76Ch9fqHhw39zWgpAPGy9OjV2nsljSrnQ3HmpIPufsgwWcmeKXHmpKU76B6hkIHTJN3+G2qU\nIT5VW1kflMS+7tJx7Fh+TeT4KuX4pSFUFDWaYjsW0uz0CvT9tczYURtG5li999e5tZ+8/W1Drtqq\n27aBKGEg2ESvL3Jmn8lUHM2MiB9H2TSMspHl5nL7B8t3+cLgM9Fj72R8yEZufz59Ob2cNvtp96Y3\nBkebVH0zVvSo88JQXDavmt+/uz9rhmKyydiea+Hf7l/Ko/+xOSv3GgnCGbrjtTwed86dyPMffJbl\nFo4cRlJ+1IM3nep0atJVPoaLZCmHjVsym4TGgvf3XCdS0n3kDJ90UoC0xojLORUXMnOyVIbeKDJw\nS3E3DpUCpz74e7SNxULV2XHpjPexoACMBeXcCPr7IbNjpK5eUce6TUc53qZdPT0ePYNiuLelQPad\nDEbqEOwvmEKxI7P1zGiK7VhIszNcHEsHI3Os7vsLlkNdFh5CB3WU0J/bTP/ZtqykT44m7d4iooe4\n9OUzHJFEQYTzwlAEONmeWbl+LZJNxnffND2j+w1XmqoRLGaJgMZRH0ZQG01Ws4m5V8xiQlm+4UV1\nrJFuyk+m1elWr6jjl3/aM2rvXGE49pmOJQXzygXVAHywTevAkrFJ5FiH7Bo+esfYKKSTAqQ7RpxT\n2DoKhlnJzk80zy1NZ09YJjjyrHh8KfbAZavM5ygwFpRzs0kiFE4+codzP2SJMye6v9RoMaKhGhRD\npfF0OXLedPY0Dz2nxcj6UOzIodujNQJTY3S9HStpdkMpjmV0jh0tJ9FwpE8Kxh5mk0RNRWKV3KFw\n3hiK2TZetAazWZKii0q69ysepQhTSUEOXVm677LPTQBg1eLJWanwN1qkk/KTSXU6RfnQ81JbzSaC\noXBaak6JM4f8XCunOrxUltpZtXiSIUM0G8V54kmmYJYM7NkaKVn/y7bjXLmg+pwxEkG/ul6mhk+y\nY2zijUWjKUAjlVZoBP+pk5qfp7MnLBMCwdSV9twqBTrdLAKTFDlSZrSoqJpHl6Wa/q5No6ach8Iy\ns6aUJDV6hnM/5OoVkTCOsq6v29TC8bbBUvh62zaGu9puMq5cUM0dV9dz148/GPoIlXVOxFQ/swR5\nOWa8ffq1CZLtIzY6l4yFKPtQyWYxwfONHKuZ/kB2j5fLJlcuqObAse6Y8X8u8uI/r6C8PLuGorHT\n488BVi2ePOz3UKv26d7P7fVz902zqC53YDZJWM0j0/Wrl9dRVaZ9nlI6KIsTDC6qepTEFXgwitFD\nqEcSPa91Mm+2onzoycjXV82kqjy9d7J6RR0/uOvzvPjPK/jBXZ9nYUOFoWvoFXsYShGIxtPl/GHn\ndE735BMKS5zuyY8eNr16RR1rvnnpiL7Lj3doGxIQ8a5VJ+kne56FkoIczCaJEmdO9P9Xlzu4+6ZZ\n0YhlNslGMQw1ydLHjDAGh10Ue56FYIl21dp094Sli5HCNOrUU2XcJ8MkEZWtXz18xZDaBxHlK1Pu\nuLqeuXMu5ecb5/PDP0f+OxqKutvjx5GXPMOhk0iVzG3I7ElhJKYzbtVr2cKGClYtjj23MpO9/RCR\n2+Fi+8E2IOJ81kJv/dWstqlXglP1udvjp8+fXMGvKnNkvP/hypUAABoMSURBVO4Lzh1mTSmJ6q5W\ns4lZU0qoLnekXO8lCf7uuhkj0EJjqMdnSUEOd980izuurucHd32eu29KXUToQuO8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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11daf8a10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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qFhITo/vvvV69ever8wkVFRbr11lv12muvKSAgQBMmTJDdbld0dLSSk5MlSQsX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\nTAAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11daf8b90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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pe8ZiQfMJvTrmrumjmy1D3ZYD/HrdfzySMusT/uoHG5P+OvgFmbJDqDnssHPtG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\nWLTJ2O60CZOa23ayFFAUoLHRul2CCKUgH/7Sk5H1M4xzeO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C4cpv7t2K3ud0tJCUN7vQR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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11eab28d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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4cSXp6Ok6nk2nTphEREeGvcEVERERERAKS34rCDz/8kIiICKZPn87x48fp0aMH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\nrkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11ece5cd0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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i7BeHtFkSLu9uaHUdERERE/EhFoYiIiIiISA2molBERERERKQG028KRUTEZ+zd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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11f64cf90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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OC2jgkxqe6nM9l4o/soj83jMZqJ/44KQOd//nc9puSpiinpq6LphoP7PsSA/lR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\n10Od9kAiy/xj7eeATT/L/2g1loIzdA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9wSkmnC8x7AnTqSvIs9+3CHZ7aCuC1Xu3oWrbi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\n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dxANFo8ePYqxY8eib9++uHTpEi5dMp0vPXWqY5s+RkdHIzc3F0BL4po1a9Y4UVzrnhm9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\nRERERGSBwSIRERERERFZ+P9OcKh5iGQAHAAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11f7684d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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AfH//QL42QMWNiu3btaN++PY6Ojvzyyy8P/eKb6WkKOzc3NzZv3kxycjIAgYGB1KlTBzMz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\n5MVYFBIREREREXkxFoVERERERERe7P8DPAJsh5EkI5UAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11f75cfd0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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XUajXrd56eXuQzTpz41eKyZBpZRHnU1dWZlTHVYJ85k73u2YTNCGaTkSPHSOoL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\ntaesrFT4+ecTotJycXGBHTt2QHh4HG8emG1+//5voVevVK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NGjFBWVpbi4uIUFxen06dP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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11fa9a710>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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U1dH9tLa0dNcp83U2rWaoP7tixg3bu3CmkYQGFDg7LmqW6AagGq+kA6OJEopCM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\nRK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11fb32cd0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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nkk4ZhGMbq1auNoUOHcn2iXP397383unXrZjzwwAOGYRiXdD3Onj3b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\nPoyiEAAAAAB8GEUhAAAAAPiw/wcSBqmGMz4MPwAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11fa9a6d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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vk5eVlWVEAAAAAgJJxKdxt2rSpyHJAQIAmTZpkSUEAAAAAgJJzKdzFxMRYXQcA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\nAAAAANwA4Q4AAAAA3MD/A/wlOHPiFgKeAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11fcd74d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plots.plot_mcmc_results(Lstated, Pstated, path_length, mcmc)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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s9ePCABw/CsbS0xMOjFIcOHeCnn7YSHx/H2rUbmTLlS06ePEFExAOGDfNi+PDBBAcHsWsX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\nIRQREREREYlRaghFRERERERi1P8DSj5Bl2tpQysAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11f284f90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""figure = plots.plot_measurements(Lstated, Pstated, pymc_model, mcmc=mcmc)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""MAP fitting cycle 5/5\n""
     ]
    }
   ],
   ""source"": [
    ""# Fit maximum a posteriori estimate one more time\n"",
    ""map = pymcmodels.map_fit(pymc_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""DeltaG = -34.5 +- 4.5 kT\n"",
      ""Kd = 1.0 nM +- 2010371.8 fM\n"",
      ""\n""
     ]
    }
   ],
   ""source"": [
    ""pymcmodels.show_summary(pymc_model, map, mcmc)""
   ]
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.11""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/Emcee example with two compenent binding.ipynb",".ipynb","241265","643","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""# Bayesian fit for two component binding - simulated data\n"",
    ""## Comparing sampling with emcee and PyMC\n"",
    ""\n"",
    ""In this notebook we'll be comparing the sampling performance of `emcee` and `pymc` on a toy `assaytools` tools example, where we know the true binding free energy. Of primary concern is the consistency of the sampling methods as well as the compute time.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [],
   ""source"": [
    ""import numpy as np\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""%matplotlib inline\n"",
    ""\n"",
    ""from time import time\n"",
    ""\n"",
    ""from assaytools.bindingmodels import TwoComponentBindingModel\n"",
    ""from assaytools import pymcmodels""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""## Creating a mock experiment to feed into `assaytools`""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""Defining all the parameters of the assay and complex. The sampling methods will be trying to predict the binding free energy, for which we already know the answer.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""The target binding free energy is -15.000000 (thermal units)\n""
     ]
    }
   ],
   ""source"": [
    ""# The complex affinity in thermal units (Kd = exp(DeltaG))\n"",
    ""DeltaG = -15.0\n"",
    ""print('The target binding free energy is {0:1f} (thermal units)'.format(DeltaG))\n"",
    ""\n"",
    ""# The protein concentration in M:\n"",
    ""Ptot = 1e-9 * np.ones([12],np.float64)\n"",
    ""\n"",
    ""# The set of ligand concentrations in M:\n"",
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) \n"",
    ""\n"",
    ""# The concentrations of the complex, free protein and free ligand given the above:\n"",
    ""[P, L, PL] = TwoComponentBindingModel.equilibrium_concentrations(DeltaG,  Ptot, Ltot)\n"",
    ""\n"",
    ""# Detector noise:\n"",
    ""sigma = 10.0 \n"",
    ""\n"",
    ""# The background fluorescence:\n"",
    ""F_background = 100.0\n"",
    ""\n"",
    ""# Ligand fluorescence in the absence of the protein\n"",
    ""F_L_i = F_background + (.4/1e-8)*Ltot + sigma * np.random.randn(len(Ltot))\n"",
    ""\n"",
    ""# The total fluorescence of the complex and free ligand\n"",
    ""F_PL_i = F_background + ((1400/1e-9)*PL + sigma * np.random.randn(len(Ltot))) + ((.4/1e-8)*L + sigma * np.random.randn(len(Ltot)))\n"",
    ""\n"",
    ""# Seting the errors from our pipetting instruments:\n"",
    ""P_error = 0.35\n"",
    ""L_error = 0.08\n"",
    ""dPstated = P_error * Ptot\n"",
    ""dLstated = L_error * Ltot\n"",
    ""\n"",
    ""# Volume of each well in L:\n"",
    ""assay_volume = 100e-6 ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""Plotting the fluorescence of the experiment that's parametrized above:""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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BX2vKKD9TK68V98GWxFUtISICbmxs6d+4Ma2trDB06FBs3biz3el+krq5eYltfnP79998x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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11d9c13d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,F_PL_i, 'ro', label='Complex and free ligand')\n"",
    ""plt.semilogx(Ltot,F_L_i, 'ko', label='Ligand')\n"",
    ""plt.title('Fluorescence as a function of total ligand concentration', fontsize=14)\n"",
    ""plt.xlabel('$[L]_{tot}$ / M', fontsize=16)\n"",
    ""plt.ylabel('Fluorescence', fontsize=16)\n"",
    ""plt.legend(fontsize=16)\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""The sampling examples will attempt to infer the value of the binding free energy, which is set above, using the data that is plotted above. ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""Before moving on to the sampling part, defining a function that will help viewing the traces of the MCMC simulations.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def get_var_trace(mcmc_model, var_name):\n"",
    ""    \""\""\""\n"",
    ""    Exract parameter trace from PyMC MCMC object.\n"",
    ""    \n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    mcmc_model: pymc.MCMC.MCMC\n"",
    ""        PyMC MCMC object\n"",
    ""    var_name: str\n"",
    ""        The name of the parameter you wish to extract\n"",
    ""    \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    If the variable has been found:\n"",
    ""    trace: numpy.ndarray\n"",
    ""        the trace of the parameter of interest.\n"",
    ""    \""\""\""\n"",
    ""    found = False\n"",
    ""    for stoch in mcmc_model.stochastics:\n"",
    ""        if stoch.__name__ == 'DeltaG':\n"",
    ""            found = True\n"",
    ""            trace = stoch.trace._trace[0]\n"",
    ""    if found:\n"",
    ""        return trace\n"",
    ""    else:\n"",
    ""        print('Variable {0} not present in MCMC object'.format(var_name))\n"",
    ""        ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""## Sampling with emcee""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/rossg/miniconda3/envs/py2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.py:70: RuntimeWarning: invalid value encountered in divide\n"",
      ""  scaling = (1 - np.exp(-alpha)) / alpha\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/rossg/miniconda3/envs/py2/lib/python2.7/site-packages/emcee-2.2.1-py2.7.egg/emcee/ensemble.py:335: RuntimeWarning: invalid value encountered in subtract\n"",
      ""/Users/rossg/miniconda3/envs/py2/lib/python2.7/site-packages/emcee-2.2.1-py2.7.egg/emcee/ensemble.py:336: RuntimeWarning: invalid value encountered in greater\n""
     ]
    }
   ],
   ""source"": [
    ""pymc_model = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""    top_complex_fluorescence=F_PL_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')\n"",
    ""\n"",
    ""mcmc_model, pymc_model = pymcmodels.run_mcmc_emcee(pymc_model, nwalkers=200, nburn=10, niter=500)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""### Viewing the trace of the Delta G""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""Defining a quick function to make it easy to view the traces:""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Mean DeltaG = -10.642987 +/- 1.98148995691\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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BOADB17NNoABwSAHL8JrpBi+UGR0Q3wQUAB3FcAKBf876a07hh8HQDJA27uaqb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\nRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x121325050>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""var_name = 'DeltaG'\n"",
    ""trace_emcee = get_var_trace(mcmc_model, var_name)\n"",
    ""\n"",
    ""print('Mean {0} = {1:2f} +/- {2}'.format(var_name, trace_emcee.mean(), trace_emcee.std()))\n"",
    ""plt.plot(trace_emcee)\n"",
    ""plt.title('Emcee trace of binding free energy', fontsize=14)\n"",
    ""plt.xlabel('Iteration', fontsize=16)\n"",
    ""plt.ylabel(var_name, fontsize=16)\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""### Veiwing the sampling consistency""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/rossg/miniconda3/envs/py2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/bindingmodels.py:84: RuntimeWarning: invalid value encountered in log\n"",
      ""  logL = np.log(Ltot)\n"",
      ""/Users/rossg/miniconda3/envs/py2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/bindingmodels.py:88: RuntimeWarning: invalid value encountered in less\n"",
      ""  sqrt_arg[sqrt_arg < 0.0] = 0.0 # ensure always positive\n"",
      ""/Users/rossg/miniconda3/envs/py2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/bindingmodels.py:102: RuntimeWarning: invalid value encountered in less\n"",
      ""  PL[PL < 0.0] = 0.0 # complex cannot have negative concentration\n"",
      ""/Users/rossg/miniconda3/envs/py2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.py:72: RuntimeWarning: invalid value encountered in less\n"",
      ""  indices = np.where(np.abs(alpha) < 0.01)\n"",
      ""/Users/rossg/miniconda3/envs/py2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/bindingmodels.py:83: RuntimeWarning: invalid value encountered in log\n"",
      ""  logP = np.log(Ptot)\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 0 = 59.037190 seconds\n"",
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 1 = 53.799713 seconds\n"",
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 2 = 54.988186 seconds\n"",
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 3 = 65.081131 seconds\n"",
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 4 = 58.544180 seconds\n"",
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 5 = 70.219427 seconds\n"",
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 6 = 57.075174 seconds\n"",
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 7 = 57.293987 seconds\n"",
      "" [-----------------100%-----------------] 1 of 1 complete in 0.0 sec\n"",
      "" Time for MCMC run 8 = 55.943118 seconds\n""
     ]
    }
   ],
   ""source"": [
    ""nrepeats = 9\n"",
    ""var_name = 'DeltaG'\n"",
    ""traces_emcee = []\n"",
    ""\n"",
    ""for r in range(nrepeats):\n"",
    ""    pymc_model = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""        top_complex_fluorescence=F_PL_i,\n"",
    ""        top_ligand_fluorescence=F_L_i,\n"",
    ""        use_primary_inner_filter_correction=True,\n"",
    ""        use_secondary_inner_filter_correction=True,\n"",
    ""        assay_volume=assay_volume, DG_prior='uniform')\n"",
    ""    t0 = time()\n"",
    ""    mcmc_model, pymc_model = pymcmodels.run_mcmc_emcee(pymc_model, nwalkers=200, nburn=100, niter=1000)\n"",
    ""    print('\\n Time for MCMC run {0} = {1:2f} seconds'.format(r, time() - t0))\n"",
    ""    traces_emcee.append(get_var_trace(mcmc_model, var_name))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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W9+uqrKi4u1rp166wqBQAAADgjyxrirVu3KiEhQZLUvn17FRQU+LZFRkbq1Vdf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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x120ee0250>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""coords = [(0,0), (0,1), (0,2), (1,0), (1,1), (1,2), (2,0), (2,1), (2,2)]\n"",
    ""bins = np.arange(-20, 0)\n"",
    ""\n"",
    ""f, axarr = plt.subplots(3, 3, figsize=(10, 10))\n"",
    ""\n"",
    ""for t, c in zip(traces_emcee, coords):\n"",
    ""    hist, edges = np.histogram(t, bins=bins, normed=True)\n"",
    ""    centers = edges[0:-1] + np.diff(edges) / 2.0\n"",
    ""    axarr[c].bar(centers, hist)\n"",
    ""    axarr[c].set_title('Histogram of {0}'.format(var_name))\n"",
    ""    axarr[c].set_xlabel('Free energy (thermal units)')\n"",
    ""    axarr[c].set_ylabel('Frequency')\n"",
    ""    axarr[c].axvline(DeltaG, color='red', ls='--')\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": true,
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""## Sampling with PyMC""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""### Viewing a single trace""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [],
   ""source"": [
    ""pymc_model = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""    top_complex_fluorescence=F_PL_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')\n"",
    ""\n"",
    ""mcmc_model = pymcmodels.run_mcmc(pymc_model, nthin=20, nburn=100, niter=100000, map=True)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Mean DeltaG = -16.022155 +/- 0.0362034615929\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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kglVfWGPTTxxPuoFqCkxu1xctVJaiuUWPVrgt4+/M40WURwuWbDyWNn3OZG5Wj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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x130bd2c90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""var_name = 'DeltaG'\n"",
    ""trace_pymc = get_var_trace(mcmc_model, var_name)\n"",
    ""\n"",
    ""print('Mean {0} = {1:2f} +/- {2}'.format(var_name, trace_pymc.mean(), trace_pymc.std()))\n"",
    ""plt.plot(trace_pymc )\n"",
    ""plt.title('Emcee trace of binding free energy', fontsize=14)\n"",
    ""plt.xlabel('Iteration', fontsize=16)\n"",
    ""plt.ylabel(var_name, fontsize=16)\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""deletable"": true,
    ""editable"": true
   },
   ""source"": [
    ""### Veiwing the sampling consistency""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Time for MCMC run 0 = 102.224875 seconds\n"",
      ""Time for MCMC run 1 = 101.651933 seconds\n"",
      ""Time for MCMC run 2 = 99.374776 seconds\n"",
      ""Time for MCMC run 3 = 104.353155 seconds\n"",
      ""Time for MCMC run 4 = 96.334083 seconds\n"",
      ""Time for MCMC run 5 = 103.196129 seconds\n"",
      ""Time for MCMC run 6 = 98.731760 seconds\n"",
      ""Time for MCMC run 7 = 109.117536 seconds\n"",
      ""Time for MCMC run 8 = 102.003356 seconds\n""
     ]
    }
   ],
   ""source"": [
    ""nrepeats = 9\n"",
    ""var_name = 'DeltaG'\n"",
    ""traces_pymc = []\n"",
    ""\n"",
    ""for r in range(nrepeats):\n"",
    ""    pymc_model = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""        top_complex_fluorescence=F_PL_i,\n"",
    ""        top_ligand_fluorescence=F_L_i,\n"",
    ""        use_primary_inner_filter_correction=True,\n"",
    ""        use_secondary_inner_filter_correction=True,\n"",
    ""        assay_volume=assay_volume, DG_prior='uniform')\n"",
    ""    t0 = time()\n"",
    ""    mcmc_model = pymcmodels.run_mcmc(pymc_model, nthin=20, nburn=100, niter=100000, map=True)\n"",
    ""    print('Time for MCMC run {0} = {1:2f} seconds'.format(r, time() - t0))\n"",
    ""    traces_pymc.append(get_var_trace(mcmc_model, var_name))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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PMtZXFjjVXh6rOqlTz8ur8Ma92qvkcO9Ys2bduqwUir3Z7Lshr9bJjzRdiWUPs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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x130ed5650>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""coords = [(0,0), (0,1), (0,2), (1,0), (1,1), (1,2), (2,0), (2,1), (2,2)]\n"",
    ""bins = np.arange(-20, 0)\n"",
    ""\n"",
    ""f, axarr = plt.subplots(3, 3, figsize=(10, 10))\n"",
    ""\n"",
    ""for t, c in zip(traces_pymc, coords):\n"",
    ""    hist, edges = np.histogram(t, bins=bins, normed=True)\n"",
    ""    centers = edges[0:-1] + np.diff(edges) / 2.0\n"",
    ""    axarr[c].bar(centers, hist)\n"",
    ""    axarr[c].set_title('Histogram of {0}'.format(var_name))\n"",
    ""    axarr[c].set_xlabel('Free energy (thermal units)')\n"",
    ""    axarr[c].set_ylabel('Frequency')\n"",
    ""    axarr[c].axvline(DeltaG, color='red', ls='--')\n"",
    ""plt.tight_layout()\n"",
    ""plt.show()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true,
    ""deletable"": true,
    ""editable"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""anaconda-cloud"": {},
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.13""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/1b Simulating fluorescence binding data - protein concentration design a la Nick Levinson.ipynb",".ipynb","147000","553","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### In this notebook we will explore how varying protein concentration can affect our fluorescence assay results\n"",
    ""\n"",
    ""We will simulate expected fluorescence results for a ligand protein with known Kd and protein concentrations.\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    }
   ],
   ""source"": [
    ""import matplotlib.pyplot as plt\n"",
    ""import numpy as np\n"",
    ""import seaborn as sns\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Here we are using the same setup as 'Simulating Experimental Fluorescence Binding Data'.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Now we can use this to define a function that gives us PL from Kd, Ptot, and Ltot.\n"",
    ""def two_component_binding(Kd, Ptot, Ltot):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Kd : float\n"",
    ""        Dissociation constant\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total ligand concentration\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    \""\""\""\n"",
    ""                                    \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd) - np.sqrt((Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free ligand concentration in sample cell after n injections (uM)                                                                                                                                                                                                                           \n"",
    ""    return [P, L, PL]""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Let's say we have four ligands with 0.1, 1, 10, and 100 nM binding affinities.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Kd_high = 0.1e-9 # M\n"",
    ""Kd_mid_high = 1e-9 # M\n"",
    ""Kd_mid_low = 10e-9 # M\n"",
    ""Kd_low = 100e-9 # M""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""In this case we will define our protein concentration as higher than the Kd of our lowest affinity ligand.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Ptot = 500e-9 # M""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""We will define the ligand concentration as previously done. (Half log dilution from 20 uM.)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[L, P, PL] = two_component_binding(Kd_low, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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hpdW6bc7lUlQUhrc7VOyy/ZqUppBlZENKKTAeiTwJngI3p7W4D1FJVjkEMz/x1tZu/69ZZ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\nkoosCElSkQUhSSqyICRJRX8HsArlu4d7xW4AAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c4865d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot, PL, 'ko')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0, 1.05*np.max(PL))\n"",
    ""plt.axvline(Kd_low,color='r',linestyle='--',label='K_d')\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[L_high, P_high, PL_high] = two_component_binding(Kd_high, Ptot, Ltot)\n"",
    ""[L_mid_high, P_mid_high, PL_mid_high] = two_component_binding(Kd_mid_high, Ptot, Ltot)\n"",
    ""[L_mid_low, P_mid_low, PL_mid_low] = two_component_binding(Kd_mid_low, Ptot, Ltot)\n"",
    ""[L_low, P_low, PL_low] = two_component_binding(Kd_low, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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9aTX0QZnb0xDZ3/x7weeCzrQ4lIiLx1dpJ/SYzm9QP4e7PAl9rbSQREekEtY7FdBzwFjP7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\nAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c52b510>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot, PL_high, 'bo',label = 'high')\n"",
    ""plt.semilogx(Ltot, PL_mid_high, 'ro',label = 'mid_high')\n"",
    ""plt.semilogx(Ltot, PL_mid_low, 'go',label = 'mid_low')\n"",
    ""plt.semilogx(Ltot, PL_low, 'ko',label = 'low')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0, 1.05*np.max(PL))\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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9mdbV2Cs1gNOK0RV+HWNrE84EzI+JR63+QmZ8D/hL4FPAXdFeTG5RzMfXP/HVNnX+bs632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\nSJKK/g90UJmRGhm3JQAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11cbd3210>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot, PL_high, 'bo',label = 'high')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0, 1.05*np.max(PL))\n"",
    ""plt.axvline(Kd_high,color='r',linestyle='--',label='K_d')\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Hmm. Let's see if varying our protein concentration for our high affinity ligand so that it's closer to our Kd can give us better fluorescence data.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#Our high affinity ligand has an affinity of 0.1e-9 # M\n"",
    ""Ptot_dilute = 0.5e-9 # M\n"",
    ""Ptot_mid_dilute = 5e-9 # M\n"",
    ""Ptot_mid_conc = 50e-9 # M\n"",
    ""Ptot_conc = 500e-9 # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[L_dilute, P_dilute, PL_dilute] = two_component_binding(Kd_high, Ptot_dilute, Ltot)\n"",
    ""[L_mid_dilute, P_mid_dilute, PL_mid_dilute] = two_component_binding(Kd_high, Ptot_mid_dilute, Ltot)\n"",
    ""[L_mid_conc, P_mid_conc, PL_mid_conc] = two_component_binding(Kd_high, Ptot_mid_conc, Ltot)\n"",
    ""[L_conc, P_conc, PL_conc] = two_component_binding(Kd_high, Ptot_conc, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11d985710>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot, PL_dilute, 'bo',label = 'dilute')\n"",
    ""plt.semilogx(Ltot, PL_mid_dilute, 'ro',label = 'mid_dilute')\n"",
    ""plt.semilogx(Ltot, PL_mid_conc, 'go',label = 'mid_conc')\n"",
    ""plt.semilogx(Ltot, PL_conc, 'ko',label = 'conc')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0, 1.05*np.max(PL))\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 29,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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wHLjezR4DzOxVORESqq+iCUj3AN9x9bvsi5RLsrwDNhVUJyl+ekLND+PkLyXomekPu/qiZ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\nASYvLwAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11cd80190>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""#This will be easier to see if we do fraction bound\n"",
    ""plt.semilogx(Ltot, PL_dilute/Ptot_dilute, 'bo',label = 'dilute')\n"",
    ""plt.semilogx(Ltot, PL_mid_dilute/Ptot_mid_dilute, 'ro',label = 'mid_dilute')\n"",
    ""plt.semilogx(Ltot, PL_mid_conc/Ptot_mid_conc, 'go',label = 'mid_conc')\n"",
    ""plt.semilogx(Ltot, PL_conc/Ptot_conc, 'ko',label = 'conc')\n"",
    ""plt.axvline(Kd_high,color='0.3',linestyle='--',label='K_d')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]/[P_tot]$')\n"",
    ""plt.ylim(0, 1.05)\n"",
    ""plt.legend(loc=0);\n""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Let's see how well this helps us discern between our 'high' and 'mid_high' affinity ligands.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 30,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[L_dilute_mid_high, P_dilute_mid_high, PL_dilute_mid_high] = two_component_binding(Kd_mid_high, Ptot_dilute, Ltot)\n"",
    ""[L_mid_dilute_mid_high, P_mid_dilute_mid_high, PL_mid_dilute_mid_high] = two_component_binding(Kd_mid_high, Ptot_mid_dilute, Ltot)\n"",
    ""[L_mid_con_mid_highc, P_mid_conc_mid_high, PL_mid_conc_mid_high] = two_component_binding(Kd_mid_high, Ptot_mid_conc, Ltot)\n"",
    ""[L_conc_mid_high, P_conc_mid_high, PL_conc_mid_high] = two_component_binding(Kd_mid_high, Ptot_conc, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Ptot_dilute = 0.5e-9 # M\n"",
    ""Ptot_mid_dilute = 5e-9 # M\n"",
    ""Ptot_mid_conc = 50e-9 # M\n"",
    ""Ptot_conc = 500e-9 # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 46,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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XWn+Pll00fOyPDtt/hzh2dbuNGnS4iwvjxIyKEdq3W+nPk5Zk+fl6ebb+DTucZE/54yjks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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11dbec610>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.figure(figsize=(12,8));\n"",
    ""sns.set(style='white')\n"",
    ""sns.set_context('talk')\n"",
    ""\n"",
    ""plt.subplot(221)\n"",
    ""plt.semilogx(Ltot, PL_dilute/Ptot_dilute, 'bo',label = 'dilute')\n"",
    ""plt.semilogx(Ltot, PL_dilute_mid_high/Ptot_dilute, 'bo',alpha=0.3,label = 'dilute mid_high')\n"",
    ""\n"",
    ""plt.axvline(Kd_high,color='0.3',linestyle='--',label='K_d = 0.1 nM')\n"",
    ""plt.axvline(Kd_mid_high,color='0.7',linestyle='--',label='K_d = 1.0 nM')\n"",
    ""\n"",
    ""plt.title('$[Ptot] = 0.5 nM$')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]/[P_tot]$')\n"",
    ""plt.ylim(0, 1.05)\n"",
    ""plt.legend(loc=0,frameon=True);\n"",
    ""\n"",
    ""plt.subplot(222)\n"",
    ""plt.semilogx(Ltot, PL_mid_dilute/Ptot_mid_dilute, 'ro')\n"",
    ""plt.semilogx(Ltot, PL_mid_dilute_mid_high/Ptot_mid_dilute, 'ro',alpha=0.3)\n"",
    ""\n"",
    ""plt.axvline(Kd_high,color='0.3',linestyle='--',label='K_d = 0.1 nM')\n"",
    ""plt.axvline(Kd_mid_high,color='0.7',linestyle='--',label='K_d = 1.0 nM')\n"",
    ""\n"",
    ""plt.title('$[Ptot] = 5 nM$')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]/[P_tot]$')\n"",
    ""plt.ylim(0, 1.05)\n"",
    ""plt.legend(loc=0,frameon=True);\n"",
    ""\n"",
    ""plt.subplot(223)\n"",
    ""plt.semilogx(Ltot, PL_mid_conc/Ptot_mid_conc, 'go')\n"",
    ""plt.semilogx(Ltot, PL_mid_conc_mid_high/Ptot_mid_conc, 'go',alpha=0.3)\n"",
    ""\n"",
    ""plt.axvline(Kd_high,color='0.3',linestyle='--',label='K_d = 0.1 nM')\n"",
    ""plt.axvline(Kd_mid_high,color='0.7',linestyle='--',label='K_d = 1.0 nM')\n"",
    ""\n"",
    ""plt.title('$[Ptot] = 50 nM$')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]/[P_tot]$')\n"",
    ""plt.ylim(0, 1.05)\n"",
    ""plt.legend(loc=0,frameon=True);\n"",
    ""\n"",
    ""plt.subplot(224)\n"",
    ""plt.semilogx(Ltot, PL_conc/Ptot_conc, 'ko')\n"",
    ""plt.semilogx(Ltot, PL_conc_mid_high/Ptot_conc, 'ko',alpha=0.3)\n"",
    ""\n"",
    ""plt.axvline(Kd_high,color='0.3',linestyle='--',label='K_d = 0.1 nM')\n"",
    ""plt.axvline(Kd_mid_high,color='0.7',linestyle='--',label='K_d = 1.0 nM')\n"",
    ""\n"",
    ""plt.title('$[Ptot] = 500 nM$')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]/[P_tot]$')\n"",
    ""plt.ylim(0, 1.05)\n"",
    ""plt.legend(loc=0,frameon=True);\n"",
    ""\n"",
    ""plt.tight_layout();\n""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""\""As long as the affinity is much tighter than the concentration of macromolecule your binding curve will look similar to this hypothetical extreme case, and you will only be able to place a lower bound on the affinity.\"" - Nick Levinson""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""There you go!""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 48,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#Our other question here, is whether our molar fluorescence for our complex \n"",
    ""# will allow is to go to this low of a concentration.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 54,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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Orq6vu2N7eHo6Ojo22oVAoUF1dDRcXF9253r17tz74hxQVFeGDDz6An58f/Pz8EBQUBCsr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\ngg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11e983d10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.figure(figsize=(12,4));\n"",
    ""\n"",
    ""plt.subplot(121)\n"",
    ""plt.semilogx(Ltot, PL_dilute, 'bo',label = 'dilute')\n"",
    ""plt.semilogx(Ltot, PL_mid_dilute, 'ro',label = 'mid_dilute')\n"",
    ""plt.semilogx(Ltot, PL_mid_conc, 'go',label = 'mid_conc')\n"",
    ""plt.semilogx(Ltot, PL_conc, 'ko',label = 'conc \\n max=%s'%np.max(PL_conc))\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0, 1.05*np.max(PL_conc))\n"",
    ""plt.legend(loc=0);\n"",
    ""\n"",
    ""plt.subplot(122)\n"",
    ""plt.semilogx(Ltot, PL_dilute, 'bo',label = 'dilute \\n max=%s' %np.max(PL_dilute))\n"",
    ""plt.semilogx(Ltot, PL_mid_dilute, 'ro',label = 'mid_dilute')\n"",
    ""plt.semilogx(Ltot, PL_mid_conc, 'go',label = 'mid_conc')\n"",
    ""plt.semilogx(Ltot, PL_conc, 'ko',label = 'conc')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$')\n"",
    ""plt.ylim(0, 1.05*np.max(PL_dilute))\n"",
    ""plt.legend(loc=0);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.11""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/3a Bayesian fit xml file - SrcGefitinib.ipynb",".ipynb","379241","952","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### Analysing data Gefitinib Src experiment on February 20 2015. Full experiment description in google docs 'Fluorescence assay protocol notes and lab notebook'. ""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    }
   ],
   ""source"": [
    ""import numpy as np\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""from lxml import etree\n"",
    ""import pandas as pd\n"",
    ""import os\n"",
    ""import matplotlib.cm as cm \n"",
    ""import seaborn as sns\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""def get_wells_from_section(path):\n"",
    ""    reads = path.xpath(\""*/Well\"")\n"",
    ""    wellIDs = [read.attrib['Pos'] for read in reads]\n"",
    ""\n"",
    ""    data = [(float(s.text), r.attrib['Pos'])\n"",
    ""         for r in reads\n"",
    ""         for s in r]\n"",
    ""\n"",
    ""    datalist = {\n"",
    ""      well : value\n"",
    ""      for (value, well) in data\n"",
    ""    }\n"",
    ""    \n"",
    ""    welllist = [\n"",
    ""                [\n"",
    ""                 datalist[chr(64 + row) + str(col)]          \n"",
    ""                 if chr(64 + row) + str(col) in datalist else None\n"",
    ""                 for row in range(1,9)\n"",
    ""                ]\n"",
    ""                for col in range(1,13)\n"",
    ""                ]\n"",
    ""                \n"",
    ""    return welllist""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""file_GEF = \""data/Gef_WIP_SMH_SrcBos_Extend_013015_mdfx_20150220_18.xml\""\n"",
    ""file_name = os.path.splitext(file_GEF)[0]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""root = etree.parse(file_GEF)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""ename"": ""SyntaxError"",
     ""evalue"": ""Missing parentheses in call to 'print' (<ipython-input-5-44fc32e760af>, line 3)"",
     ""output_type"": ""error"",
     ""traceback"": [
      ""\u001b[0;36m  File \u001b[0;32m\""<ipython-input-5-44fc32e760af>\""\u001b[0;36m, line \u001b[0;32m3\u001b[0m\n\u001b[0;31m    print \""****The xml file \"" + file_GEF + \"" has %s data sections:****\"" % much\u001b[0m\n\u001b[0m                            ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n""
     ]
    }
   ],
   ""source"": [
    ""Sections = root.xpath(\""/*/Section\"")\n"",
    ""much = len(Sections)\n"",
    ""print \""****The xml file \"" + file_GEF + \"" has %s data sections:****\"" % much\n"",
    ""for sect in Sections:\n"",
    ""    print sect.attrib['Name']""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Just going to work with topread for now\n"",
    ""TopRead = root.xpath(\""/*/Section\"")[0]\n"",
    ""welllist = get_wells_from_section(TopRead)\n"",
    ""dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""(-0.5, 11.5)""
      ]
     },
     ""execution_count"": 7,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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9leX4REesjL1v6WlIBFfLxLl2IY8fWdpNdo9FomkMb/yawGQZDcnxkuNQ8wLZQ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\nl4Ndxdxkym6fycyOje3mpIEBIF87Pv38eenvL06i+Lnnl7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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10f0a7d68>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""sns.set_palette(\""Paired\"", 10)\n"",
    ""sns.set_context(\""notebook\"", rc={\""lines.linewidth\"": 2.5})\n"",
    ""\n"",
    ""dataframe.plot(figsize=(6, 4), title=file_name)\n"",
    ""plt.xlim(-0.5,11.5)\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>A - Src</th>\n"",
       ""      <th>B - Buffer</th>\n"",
       ""      <th>C - Src</th>\n"",
       ""      <th>D - Buffer</th>\n"",
       ""      <th>E - Src</th>\n"",
       ""      <th>F - Buffer</th>\n"",
       ""      <th>G - Src</th>\n"",
       ""      <th>H - Buffer</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td>399.0</td>\n"",
       ""      <td>140.0</td>\n"",
       ""      <td>408.0</td>\n"",
       ""      <td>137.0</td>\n"",
       ""      <td>408.0</td>\n"",
       ""      <td>136.0</td>\n"",
       ""      <td>410.0</td>\n"",
       ""      <td>56.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td>365.0</td>\n"",
       ""      <td>114.0</td>\n"",
       ""      <td>377.0</td>\n"",
       ""      <td>112.0</td>\n"",
       ""      <td>388.0</td>\n"",
       ""      <td>112.0</td>\n"",
       ""      <td>383.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td>308.0</td>\n"",
       ""      <td>79.0</td>\n"",
       ""      <td>327.0</td>\n"",
       ""      <td>78.0</td>\n"",
       ""      <td>328.0</td>\n"",
       ""      <td>78.0</td>\n"",
       ""      <td>318.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td>251.0</td>\n"",
       ""      <td>67.0</td>\n"",
       ""      <td>261.0</td>\n"",
       ""      <td>65.0</td>\n"",
       ""      <td>268.0</td>\n"",
       ""      <td>64.0</td>\n"",
       ""      <td>177.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td>191.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>189.0</td>\n"",
       ""      <td>62.0</td>\n"",
       ""      <td>191.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>127.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>5</th>\n"",
       ""      <td>130.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>125.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>131.0</td>\n"",
       ""      <td>61.0</td>\n"",
       ""      <td>97.0</td>\n"",
       ""      <td>57.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>6</th>\n"",
       ""      <td>93.0</td>\n"",
       ""      <td>63.0</td>\n"",
       ""      <td>88.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>91.0</td>\n"",
       ""      <td>62.0</td>\n"",
       ""      <td>71.0</td>\n"",
       ""      <td>57.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>7</th>\n"",
       ""      <td>78.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>75.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>73.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>63.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>8</th>\n"",
       ""      <td>69.0</td>\n"",
       ""      <td>61.0</td>\n"",
       ""      <td>63.0</td>\n"",
       ""      <td>61.0</td>\n"",
       ""      <td>64.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>58.0</td>\n"",
       ""      <td>56.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>9</th>\n"",
       ""      <td>61.0</td>\n"",
       ""      <td>63.0</td>\n"",
       ""      <td>61.0</td>\n"",
       ""      <td>61.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>57.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>10</th>\n"",
       ""      <td>62.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>64.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>57.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>58.0</td>\n"",
       ""      <td>57.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>11</th>\n"",
       ""      <td>61.0</td>\n"",
       ""      <td>62.0</td>\n"",
       ""      <td>59.0</td>\n"",
       ""      <td>58.0</td>\n"",
       ""      <td>56.0</td>\n"",
       ""      <td>60.0</td>\n"",
       ""      <td>55.0</td>\n"",
       ""      <td>57.0</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""    A - Src  B - Buffer  C - Src  D - Buffer  E - Src  F - Buffer  G - Src  \\\n"",
       ""0     399.0       140.0    408.0       137.0    408.0       136.0    410.0   \n"",
       ""1     365.0       114.0    377.0       112.0    388.0       112.0    383.0   \n"",
       ""2     308.0        79.0    327.0        78.0    328.0        78.0    318.0   \n"",
       ""3     251.0        67.0    261.0        65.0    268.0        64.0    177.0   \n"",
       ""4     191.0        60.0    189.0        62.0    191.0        59.0    127.0   \n"",
       ""5     130.0        59.0    125.0        60.0    131.0        61.0     97.0   \n"",
       ""6      93.0        63.0     88.0        60.0     91.0        62.0     71.0   \n"",
       ""7      78.0        59.0     75.0        60.0     73.0        60.0     63.0   \n"",
       ""8      69.0        61.0     63.0        61.0     64.0        59.0     58.0   \n"",
       ""9      61.0        63.0     61.0        61.0     59.0        60.0     57.0   \n"",
       ""10     62.0        60.0     64.0        59.0     57.0        59.0     58.0   \n"",
       ""11     61.0        62.0     59.0        58.0     56.0        60.0     55.0   \n"",
       ""\n"",
       ""    H - Buffer  \n"",
       ""0         56.0  \n"",
       ""1         55.0  \n"",
       ""2         55.0  \n"",
       ""3         60.0  \n"",
       ""4         55.0  \n"",
       ""5         57.0  \n"",
       ""6         57.0  \n"",
       ""7         55.0  \n"",
       ""8         56.0  \n"",
       ""9         59.0  \n"",
       ""10        57.0  \n"",
       ""11        57.0  ""
      ]
     },
     ""execution_count"": 8,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""dataframe""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.axes._subplots.AxesSubplot at 0x10f0a7780>""
      ]
     },
     ""execution_count"": 9,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    },
    {
     ""data"": {
      ""image/png"": 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4Dix2vWQbGNtKAfD638dbKBakPu7cTfZgaSfFPJ2bxgGPzFd5td5FJbOiuUd/Q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\nhIACWUREJAT+P4VQBChjGV+MAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x112606da0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""dataframe['A - Src'].plot()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""ename"": ""TypeError"",
     ""evalue"": ""cannot perform __sub__ with this index type: <class 'pandas.indexes.base.Index'>"",
     ""output_type"": ""error"",
     ""traceback"": [
      ""\u001b[0;31m---------------------------------------------------------------------------\u001b[0m"",
      ""\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)"",
      ""\u001b[0;32m<ipython-input-10-5199ce29ac16>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mto_excl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'D - Buffer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'E - Src'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'F - Buffer'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'G - Src'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'H - Buffer'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mix\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mto_excl\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m"",
      ""\u001b[0;32m/Users/choderaj/miniconda3/lib/python3.5/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m   1794\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__sub__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1795\u001b[0m         raise TypeError(\""cannot perform __sub__ with this index type: \""\n\u001b[0;32m-> 1796\u001b[0;31m                         \""{typ}\"".format(typ=type(self)))\n\u001b[0m\u001b[1;32m   1797\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1798\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__and__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"",
      ""\u001b[0;31mTypeError\u001b[0m: cannot perform __sub__ with this index type: <class 'pandas.indexes.base.Index'>""
     ]
    }
   ],
   ""source"": [
    ""to_excl = ['D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer']\n"",
    ""dataframe.ix[:, dataframe.columns - to_excl].plot()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Section II""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Can I just pick the best single Buffer and Src Data sets from each, and see how they compare?""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""root = etree.parse(file_GEF)\n"",
    ""#Just going to keep working with topread for now\n"",
    ""TopRead = root.xpath(\""/*/Section\"")[0]\n"",
    ""welllist = get_wells_from_section(TopRead)\n"",
    ""dataframe = pd.DataFrame(welllist, columns = ['A - Src','B - Buffer','C - Src','D - Buffer', 'E - Src','F - Buffer','G - Src','H - Buffer'])""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""Complex_Fluorescence = dataframe['A - Src']\n"",
    ""Ligand_Fluorescence = dataframe['B - Buffer']\n"",
    ""F_i = np.array(Complex_Fluorescence, np.float64)\n"",
    ""Fligand_i = np.array(Ligand_Fluorescence, np.float64)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Gefitinib concentration (M):\n"",
    ""Lstated = np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0], np.float64)\n"",
    ""#Protein concentration (M):\n"",
    ""Pstated = 0.5e-6 * np.ones([12],np.float64)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/choderaj/miniconda3/lib/python3.5/site-packages/ipykernel/__main__.py:6: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""/Users/choderaj/miniconda3/lib/python3.5/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n"",
      ""  warnings.warn(self.msg_depr_set % key)\n"",
      ""/Users/choderaj/miniconda3/lib/python3.5/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n"",
      ""  warnings.warn(\""axes.hold is deprecated, will be removed in 3.0\"")\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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8F1yZJktTRGlm+YpTaJt8/BjwV+HtcWV+SJGnZGpkj9mOZ+ZJZx39cn7wvSZKkZWh0Zf0n\nzRxExJOBg8WVJEmS1B0aGRF7E/CZiPgs0EPtCcrXFlqVJElSF2jkqcmbIuIM4CxqI2ivy8xvFV6Z\nJElSh2vkqck3H9N0RkSQmW8pqCZJkqSu0MgcsZ5Zf04Efhl4fJFFSZIkdYNGbk3+wezjiHgr8LHC\nKpIkSeoSjYyIHeuxwJObXYgkSVK3aWSO2Neo7S0JteD2Q8DbiyxKkiSpGzSyfMXmWa+rwIOZub+Y\nciRJkrrHvEEsIn5jgXNk5vsWOH8CsIvalkirgSuALwM3UAtz9wIXZOaRiDgfGAEOAVdk5k1L/zIk\nSZJWnoVGxJ67wLkqMG8QA14BfDszz4uIxwFfqP+5LDN3R8S7gXMj4jPARcAzgZOAT0XELZk5vaSv\nQpIkaQVaKIg9JTOfFxGXZeYVS/zcvwLG6q97qI12nQncVm+7GXgBcBi4sx68piPifuB0apuMS5Ik\ndbSFgthTI+IKYEtEPOrpyoUWdM3M7wJERC+1QHYZ8PbMnJn0PwWcDKwFHpr11pn2BfX1raFSWbXY\nZaXq7+8tuwR1GPuUms0+pSLYr5ZmoSD2q8Av8f3FXJekvlH4OPCnmfmXEfHHs073Ag8C++uvj21f\n0L59B5ZaTkv19/cyOTlVdhnqIPYpNZt9SkWwX81toXA6bxDLzM8Dn4+Iz2XmzUv5CyPi8dQWfb0w\nMz9eb/58RGzOzN3Ai4BPAncBV0bESdQm9Z9GbSK/JElSx2tkZf0lhbC6NwJ9wJsi4k31tq3A1RFx\nIjABjGXm4Yi4GriD2hpll2bmw8fx90mSJK04PdVqdfGr2szk5FRbF+3QrJrNPqVGjY+PsWPHVezd\nex/r129g27ZLGBoaftR19ikVwX41t/7+3nmneDWyoKskaQUYHx9jZGTL0eOJiT1Hj+cKY5LKdzx7\nTRIRr212IZKk5dmx46o523fu3N7iSiQ16riCGPCEplYhSVq2vXvvW1K7pPIdVxDLzMubXYgkaXnW\nr9+wpHZJ5Vtor8n3UNvKaE6ZuWW+c5Kk1tu27ZJHzBGbsXXrxSVUI6kRC03W392qIiRJyzczIX/n\nzu1Hn5rcuvViJ+pLbayh5Ssi4qnAfwM+CjwpM79WcF0LcvkKdRv7lJrNPqUi2K/mttDyFYvOEYuI\nlwIfAXYCjwM+ExGvaF55kiRJ3amRyfpvAJ4NTGXmt4AzgN8rtCpJkqQu0EgQO5yZR8cZM/PfgCPF\nlSRJktQdGllZf09EXAicEBHPAH4L+EKxZUmSJHW+RkbELqC2gOt/AbuA/dTCmCRJkpahkRGxazPz\n1TgvTJIkqakaGRHbGBGPLbwSSZKkLtPIiNgR4F8iIqndngQgM59XWFWSJEldoJEg9ruFVyFJktSF\nFg1imXlbKwqRJEnqNo3MEZMkSVIBDGKSJEklMYhJkiSVxCAmSZJUEoOYJElSSQxikiRJJTGISZIk\nlcQgJkmSVBKDmCRJUkkMYpIkSSUxiEmSJJWkkU2/j1tEPAv4o8zcHBFnADcBX6mffldmfiAizgdG\ngEPAFZl5U5E1SZIktYvCglhE/C5wHvCf9aYzge2ZedWsa04FLgKeCZwEfCoibsnM6aLqkiRJahdF\njoh9FXgJcGP9+EwgIuJcaqNi24CzgDvrwWs6Iu4HTgfuLrAuSZKktlDYHLHM/Gvg4Kymu4Dfycyf\nBf4JuBxYCzw065op4OSiapIkSWonhc4RO8Z4Zj448xq4Brgd6J11TS/w4LFvPFZf3xoqlVXNr7CJ\n+vt7F79IWgL7lJrNPqUi2K+WppVB7KMR8frMvAt4PnAPtVGyKyPiJGA1cBpw72IftG/fgUILXa7+\n/l4mJ6fKLkMdxD6lZrNPqQj2q7ktFE5bGcR+E7gmIg4C3wRem5n7I+Jq4A5qt0kvzcyHW1iTJElS\naXqq1WrZNSzZ5ORUWxftbwRqNvuUms0+pSLYr+bW39/bM985F3SVJEldZ/X4GH2DmzhlXR99g5tY\nPT5WSh2tvDUpSZJUutXjY6wd2XL0uDKxh7UjW9gPTA8Nt7QWR8QkSVJXWbPjqrnbd25vcSUGMUmS\n1GVW7b1vSe1FMohJkqSucnj9hiW1F8kgJkmSusqBbZfM3b714hZXYhCTJEldZnpomP2juzg0sJFq\npcKhgY3sH93V8on6YBCTJMbHxxgc3MS6dX0MDm5ivKTH2CW1zvTQMPt2f5r/+MZ32Lf706WEMHD5\nCkldbnx8jJFZj7FPTOw5ejxU0j/MkrqHI2KSutqOeR5j31nCY+ySuo9BTFJX2zvP4+rztUtSMxnE\nJHW19fM8rj5fuyQ1k0FMUlfbNs9j7FtLeIxdUvcxiEnqakNDw4yO7mJgYCOVSoWBgY2Mju5yor6k\nlvCpSUldb2ho2OAlqRSOiEmSJJXEICZJklQSg5gkSVJJDGKSJEklMYhJkqSGrB4fo29wE6es66Nv\ncBOr3Zd12XxqUpIkLWr1+BhrZ+3LWpnYw9qRLeyH0jbM7gSOiEmSpEWtmWdf1jXuy7osBjFJkrSo\nVfPsvzpfuxpjEJMkSYs6PM/+q/O1qzEGMUmStKgD8+zLesB9WZfFICZJkhY1PTTM/tFdHBrYSLVS\n4dDARvaP7nKi/jL51KQkSWrI9NCwwavJHBGTJEkqSaEjYhHxLOCPMnNzRPwEcANQBe4FLsjMIxFx\nPjACHAKuyMybiqxJkiSpXRQ2IhYRvwtcD5xUb9oOXJaZzwF6gHMj4lTgIuBs4IXA2yJidVE1SZIk\ntZMib01+FXjJrOMzgdvqr28GzgHOAu7MzOnMfAi4Hzi9wJokSZLaRmFBLDP/Gjg4q6knM6v111PA\nycBa4KFZ18y0S5IkdbxWPjV5ZNbrXuBBYH/99bHtC+rrW0Olsqq51TVZf3/v4hdJS2CfUrPZp1QE\n+9XStDKIfT4iNmfmbuBFwCeBu4ArI+IkYDVwGrWJ/Avat+9AkXUuW39/L5OTU2WXoQ5in1Kz2adU\nBPvV3BYKp61cvuIS4A8i4jPAicBYZn4TuBq4A/gEcGlmPtzCmiQdp/HxMQYHN7FuXR+Dg5sYHx8r\nuyRJWnF6qtXq4le1mcnJqbYu2t8I1Gzt1qfGx8cYGdnyqPbR0V0MudjjitBufUqdwX41t/7+3p75\nzrmgq6Ql27Hjqjnbd+7c3uJKJGllM4hJWrK9e+9bUrskaW4GMUlLtn79hiW1S5LmZhCTtGTbtl0y\nZ/vWrRe3uBJJWtkMYpKWbGhomNHRXQwMbKRSqTAwsNGJ+pJ0HFq5jpikDjI0NGzwkqRlckRMkiSp\nJAYxSZKkkhjEJEkq0OrxMfoGN3HKuj76Bjex2l0oNItBTGoxtwaSusfq8THWjmyhMrGHnsOHqUzs\nYe3IFsOYjjKISS00szXQxMQeDh8+zMTEHkZGthjGpA61Zp5dKNa4C4XqDGJSC7k1kNRdVs2z28R8\n7eo+BjGphdwaSOouh+fZbWK+dnUfg5jUQm4NJHWXA/PsQnHAXShUZxCTWsitgaTuMj00zP7RXRwa\n2Ei1UuHQwEb2j+5i2sWQVWcQ04rQKU8aujWQ1H2mh4bZt/vT/Mc3vsO+3Z82hOkReqrVatk1LNnk\n5FRbF93f38vk5FTZZXSMmScNj9VNAcY+pWazT6kI9qu59ff39sx3zhExtT2fNJS6j4ugqlu46bfa\nnk8aSt1lZhHUGTOLoO4Hb+up4zgiprbnk4ZSd3ERVHUTg9gxOmVSOHTO0L5PGranTulfaj8ugqpu\n4q3JWY6dFD6z/Qyw4iaFd9LQ/tDQMD9+92d54o038OPT03x19WoeOO9VnL7Cvo5O0kn9S+3n8PoN\nVCb2zNkudRpHxGbZseMqXgp8EThY/+9LWZmTwg++5c1zt7917vZ2tnp8jOdfP0pMT1MBYnqa518/\n6ghMibx11L46YaTSRVDVTQxiszzjvi/zfuB0akOFpwPvr7evNCf/6wNztq99YO72dtZpP/Q74Qdl\np9066oTvCXx/pLIysYeew4ePjlSutK/HRVDVTbw1OcvlJ54I09OPan/zCSeWUM3yfJlakDzWBLCu\nxbUsVyf90O+UW3qddOuoU74nsPAvLSvta5keGl5xNUvHwxGxWZ528OCS2tvZe3/kiXO2v+8Jc7e3\ns07aNLdTRvc66dZRp3xPoLN+aZG6hUFsliNx2tztG+Zub2dx+Vt4GY+c7/YyYP2b31JqXcejk37o\nd8oPyk66ddQp3xPorF9apG5hEJulk37gDw0N87zRXbx8YCNrKhVePrCR563QLYE66Yd+J/2g7JT9\n8zrpe9JJ/4ZJ3cK9Jo+xenyMNTu3s2rvfRxev4EDWy9e8g8Y99rSfI6djzRjsWBpnyrO8X5P2lWj\n/4bZp1QE+9XcFtprsuVBLCL+AdhfP/wacCVwA1AF7gUuyMwjC32Gm35rJTuesG+fKlYzfgFbaexT\nKoL9am4LBbGWPjUZEScBPZm5eVbbh4HLMnN3RLwbOBcYb2VdUiv5NFj78XsiqSytXr7iJ4E1EfGx\n+t/9RuBM4Lb6+ZuBF2AQkyRJXaDVQewA8HbgeuBp1IJXT2bO3GqcAk5e7EP6+tZQqawqrMhm6O/v\nLbsEdRj7lJrNPqUi2K+WptVBbC9wfz147Y2Ib1MbEZvRCzy42Ifs23egoPKaw3vkajb7lJrNPqUi\n2K/mtlA4bfXyFVuAqwAi4keAtcDHImJz/fyLgDtaXJMkSVIpWj0i9ufADRHxKWpPSW4B/gO4LiJO\npLYDz8raFE2SJOk4tTSIZeb3gJfPcWqwlXVIkiS1A1fWlyRJKsmKXFlfkiSpEzgiJkmSVBKDmCRJ\nUkkMYpIkSSUxiEmSJJXEICZJklQSg5gkSVJJDGKSJEklMYhJkiSVpNV7TXa9iHgycDXwHWBvZv5h\nySVphYuI5wD/ndr/zwOZ+eySS9IKFxGPAd4KrAU+l5nvLbkkdYCI2EytX+0B3p+Zu0stqE0YxJYg\nInYBvwR8KzM3zmr/eWAnsAq4fpFw9XRgLDP/IiI+UGjBanvN6FOZeQdwR0T8CnB3wSWrzTXp36lz\ngScC3wYeKLBcrRBN6ldV4LvASdivjjKILc0NwLXA+2YaImIV8E7g56h1rLsj4sPUOuXbjnn/FuDv\ngbGI2ALc2IKa1d5uYJl9KjO/VX/9cuA1RRestncDy/93KoBPZ+ZoRIwBH29B3WpvN7D8fnVHZt4W\nEY8HtlMbye96BrElyMzbI+KpxzSfBdyfmf8EEBHvB87NzLdR++3hESLit4HL6581Bryn4LLVxprR\np+rXPBl4KDOniqxX7a9J/049AHyvfnikwHK1QjTr36q6fcDqQgpdgQxiy/cE4Ouzjh8AnrXA9X8H\n/H5EvBz45wLr0sq11D4FtZEwQ73ms9Q+9SHgmvr8w9uKLEwr2pL6VUS8BHgh8EPURteEQazlMvNe\nYLjsOtRZMvPysmtQ58jMA3ibW02WmR+iFvI1i8tXLN+/Ak+adfzEept0vOxTajb7lIpgv2oCR8SW\n727gaRHxo9Q64MuoTZqWjpd9Ss1mn1IR7FdN4IjYEkTE/wY+U3sZD0TEazLzEHAh8FFgAvhgZu4p\ns06tHPYpNZt9SkWwXxWnp1qtll2DJElSV3JETJIkqSQGMUmSpJIYxCRJkkpiEJMkSSqJQUySJKkk\nBjFJkqSSGMQkSZJKYhCTJEkqiVscSVrxIqIKfAm4ODNvjYjnAn+amacdc92twE8Bb87Maxv43FXA\nXwGvAH4Y+BrwZ5k5MuuaZwCfB14N3ApcmZmvbM5XJqnTOSImqVM8OzNvrb8+A/iHYy/IzHOADy/h\nM38T+GhmHqgffxv4+XpAm/FSYLL++Q8A/x4Rv7DU4iV1J0fEJHWin6I2SjWviPgd4BeBjcB9wCFg\nS2b+U/18D/B64KxZb/su8AXgZ4FP1tteQG0kbMb7gHcC/3fZX4WkjueImKRONOeI2GyZ+SeZuRnY\nA/xsZm6eCWF1Pwk8lJkPHfPWDwLDABHx08A/At+b9bn3AgMR0bfsr0JSxzOISeooEbEGCBYZEZvl\nMZl5ZI72pwEPzNH+EeBFEfEYarclPzDHNQ8AP97g3y+pixnEJHWa04GvZ+a+xS6MiHXAv81z+gi1\n25WPkJkPE7T3AAABEUlEQVRTwBeBnwGexyNvS844WH+/JC3IICap0yw6P2yWDUDOc+6rwFPmOfdB\n4A+Bz2Xmo8Ia8CRqT1hK0oIMYpI6zRnAORHxz7P+/No8126gNlF/Ll8ETomIk+c49xHgGcxxWzIi\nNgL3NTIiJ0k+NSmpo2Tm+cD5DV4ewI3zfE41InYC5wHXZuY/A0+tn/susGbWta+a9dbXAG9fat2S\nupMjYpI6wb8Dd0bEOQtdVF/QdRD4T4DM3JaZ9yzwlncBP1d/AGBREfEk4PGZ+beNlS2p2/VUq9Wy\na5AkSepKjohJkiSVxCAmSZJUEoOYJElSSQxikiRJJTGISZIklcQgJkmSVBKDmCRJUkkMYpIkSSX5\n/4X/UUr2m+aCAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1126e6cc0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot fluorescence intensity vs ligand concentration.\n"",
    ""fig = figure(figsize=(10,5))\n"",
    ""rcParams['figure.figsize'] = [10, 5];\n"",
    ""clf();\n"",
    ""semilogx(Lstated, F_i, 'ko');\n"",
    ""hold(True)\n"",
    ""semilogx(Lstated, Fligand_i, 'ro');\n"",
    ""xlabel('$[L]_T$ (M)');\n"",
    ""ylabel('rel. fluorescence');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x112a045f8>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot fluorescence intensity vs ligand concentration on a log plot to see if we are running into inner filter effects.\n"",
    ""fig2 = pyplot.figure(figsize=(10,5))\n"",
    ""rcParams['figure.figsize'] = [10, 5];\n"",
    ""clf();\n"",
    ""loglog(Lstated, Fligand_i, 'ro');\n"",
    ""xlabel('$[L]_T$ (M)');\n"",
    ""ylabel('rel. fluorescence');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""[  5.00000000e-08   5.00000000e-08   5.00000000e-08   5.00000000e-08\n"",
      ""   5.00000000e-08   5.00000000e-08   5.00000000e-08   5.00000000e-08\n"",
      ""   5.00000000e-08   5.00000000e-08   5.00000000e-08   5.00000000e-08]\n"",
      ""[  1.60000000e-06   7.32000000e-07   3.34400000e-07   1.52800000e-07\n"",
      ""   7.00000000e-08   3.20000000e-08   1.46400000e-08   6.69600000e-09\n"",
      ""   3.06400000e-09   1.40000000e-09   6.40000000e-10   6.40000000e-10]\n""
     ]
    }
   ],
   ""source"": [
    ""# Remove highest concentrations due to aggregation issues.\n"",
    ""nskip = 0\n"",
    ""F_i = F_i[nskip:]\n"",
    ""Fligand_i = Fligand_i[nskip:]\n"",
    ""Pstated = Pstated[nskip:]\n"",
    ""Lstated = Lstated[nskip:]\n"",
    ""\n"",
    ""# Uncertainties in protein and ligand concentrations.\n"",
    ""dPstated = 0.10 * Pstated # protein concentration uncertainty\n"",
    ""dLstated = 0.08 * Lstated # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)\n"",
    ""\n"",
    ""dLstated[-1] = dLstated[-2]\n"",
    ""\n"",
    ""print(dPstated)\n"",
    ""print(dLstated)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/choderaj/miniconda3/lib/python3.5/site-packages/ipykernel/__main__.py:6: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""/Users/choderaj/miniconda3/lib/python3.5/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n"",
      ""  warnings.warn(self.msg_depr_set % key)\n"",
      ""/Users/choderaj/miniconda3/lib/python3.5/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n"",
      ""  warnings.warn(\""axes.hold is deprecated, will be removed in 3.0\"")\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x115542160>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot fluorescence intensity vs ligand concentration.\n"",
    ""fig2 = pyplot.figure(figsize=(10,5))\n"",
    ""rcParams['figure.figsize'] = [10, 5];\n"",
    ""clf();\n"",
    ""semilogx(Lstated, F_i, 'ko');\n"",
    ""hold(True)\n"",
    ""semilogx(Lstated, Fligand_i, 'ro');\n"",
    ""xlabel('$[L]_T$ (M)');\n"",
    ""ylabel('rel. fluorescence');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/choderaj/miniconda3/lib/python3.5/site-packages/ipykernel/__main__.py:6: RuntimeWarning: invalid value encountered in sqrt\n""
     ]
    }
   ],
   ""source"": [
    ""import pymc\n"",
    ""\n"",
    ""# Two-component binding.\n"",
    ""def two_component_binding(DeltaG, P, L):\n"",
    ""    Kd = np.exp(DeltaG)\n"",
    ""    PL = 0.5 * ((P + L + Kd) - np.sqrt((P + L + Kd)**2 - 4*P*L));  # complex concentration (M)                                                                                                                                                                                                         \n"",
    ""    P = P - PL; # free protein concentration in sample cell after n injections (M)                                                                                                                                                                                                                          \n"",
    ""    L = L - PL; # free ligand concentration in sample cell after n injections (M)                                                                                                                                                                                                                           \n"",
    ""    return [P, L, PL]\n"",
    ""\n"",
    ""# Create a pymc model\n"",
    ""def make_model(Pstated, dPstated, Lstated, dLstated, Fobs_i, Fligand_i):\n"",
    ""    N = len(Lstated)\n"",
    ""    \n"",
    ""    # Prior on binding free energies.\n"",
    ""    DeltaG = pymc.Uniform('DeltaG', lower=-40, upper=+40, value=0.0) # binding free energy (kT), uniform over huge range\n"",
    ""    #DeltaG = pymc.Normal('DeltaG', mu=0, tau=1./(12.5**2)) # binding free energy (kT), using a Gaussian prior inspured by ChEMBL\n"",
    ""        \n"",
    ""    # Priors on true concentrations of protein and ligand.\n"",
    ""    #Ptrue = pymc.Lognormal('Ptrue', mu=np.log(Pstated**2 / np.sqrt(dPstated**2 + Pstated**2)), tau=np.sqrt(np.log(1.0 + dPstated**2/Pstated**2))**(-2)) # protein concentration (M)\n"",
    ""    #Ltrue = pymc.Lognormal('Ltrue', mu=np.log(Lstated**2 / np.sqrt(dLstated**2 + Lstated**2)), tau=np.sqrt(np.log(1.0 + dLstated**2/Lstated**2))**(-2)) # ligand concentration (M)\n"",
    ""    Ptrue = pymc.Normal('Ptrue', mu=Pstated, tau=dPstated**(-2)) # protein concentration (M)\n"",
    ""    Ltrue = pymc.Normal('Ltrue', mu=Lstated, tau=dLstated**(-2)) # ligand concentration (M)\n"",
    ""    Ltrue_control = pymc.Normal('Ltrue_control', mu=Lstated, tau=dLstated**(-2)) # ligand concentration (M)\n"",
    ""\n"",
    ""    # Priors on fluorescence intensities of complexes (later divided by a factor of Pstated for scale).\n"",
    ""    Fmax = max(Fobs_i.max(), Fligand_i.max())\n"",
    ""    F_background = pymc.Uniform('F_background', lower=0.0, upper=Fmax) # background fluorescence\n"",
    ""    F_PL = pymc.Uniform('F_PL', lower=0.0, upper=Fmax/min(Pstated.max(),Lstated.max())) # complex fluorescence\n"",
    ""    F_L = pymc.Uniform('F_L', lower=0.0, upper=Fmax/Lstated.max()) # ligand fluorescence\n"",
    ""\n"",
    ""    # Unknown experimental measurement error.\n"",
    ""    log_sigma = pymc.Uniform('log_sigma', lower=-10, upper=+2, value=0.0) \n"",
    ""    @pymc.deterministic\n"",
    ""    def precision(log_sigma=log_sigma): # measurement precision\n"",
    ""        return 1.0 / np.exp(log_sigma)**2\n"",
    ""\n"",
    ""    # Fluorescence model.\n"",
    ""    @pymc.deterministic\n"",
    ""    def Fmodel(F_background=F_background, F_PL=F_PL, F_L=F_L, Ptrue=Ptrue, Ltrue=Ltrue, DeltaG=DeltaG):\n"",
    ""        Fmodel_i = np.zeros([N])\n"",
    ""        for i in range(N):\n"",
    ""            [P, L, PL] = two_component_binding(DeltaG, Ptrue[i], Ltrue[i])\n"",
    ""            Fmodel_i[i] = (F_PL*PL + F_L*L) + F_background\n"",
    ""        return Fmodel_i\n"",
    ""\n"",
    ""    # Fluorescence model, ligand only.\n"",
    ""    @pymc.deterministic\n"",
    ""    def Fligand(F_background=F_background, F_L=F_L, Ltrue_control=Ltrue_control):\n"",
    ""        Fmodel_i = np.zeros([N])\n"",
    ""        for i in range(N):\n"",
    ""            Fmodel_i[i] = (F_L*Ltrue_control[i]) + F_background\n"",
    ""        return Fmodel_i\n"",
    ""    \n"",
    ""    # Experimental error on fluorescence observations.\n"",
    ""    Fobs_model = pymc.Normal('Fobs_i', mu=Fmodel, tau=precision, size=[N], observed=True, value=Fobs_i) # observed data\n"",
    ""    Fligand_model = pymc.Normal('Fligand_i', mu=Fligand, tau=precision, size=[N], observed=True, value=Fligand_i) # ligand only data\n"",
    ""    \n"",
    ""    # Construct dictionary of model variables.\n"",
    ""    pymc_model = { 'Ptrue' : Ptrue, \n"",
    ""                   'Ltrue' : Ltrue, \n"",
    ""                   'Ltrue_control' : Ltrue_control, \n"",
    ""                   'log_sigma' : log_sigma, \n"",
    ""                   'precision' : precision, \n"",
    ""                   'F_PL' : F_PL, \n"",
    ""                   'F_L' : F_L, \n"",
    ""                   'F_background' : F_background,\n"",
    ""                   'Fmodel_i' : Fmodel, \n"",
    ""                   'Fligand_i' : Fligand, \n"",
    ""                   'Fobs_model' : Fobs_model, \n"",
    ""                   'Fligand_model' : Fligand_model, \n"",
    ""                   'DeltaG' : DeltaG # binding free energy\n"",
    ""                   }\n"",
    ""    return pymc_model\n"",
    ""\n"",
    ""\n"",
    ""# Build model.\n"",
    ""pymc_model = pymc.Model(make_model(Pstated, dPstated, Lstated, dLstated, F_i, Fligand_i))\n"",
    ""\n"",
    ""# Sample with MCMC\n"",
    ""mcmc = pymc.MCMC(pymc_model, db='ram', name='Sampler', verbose=True)\n"",
    ""mcmc.sample(iter=100000, burn=10000, thin=50, progress_bar=False)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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975ZEV4Ang2itc9/pJkmZtl+t6KkiQFEL8z0KIreq/bxFCjEgp/yavwvQR1Ieb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\nMh/Aw2XZAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11d92cb50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot trace of DeltaG.\n"",
    ""rcParams['figure.figsize'] = [15, 3]\n"",
    ""plot(mcmc.DeltaG.trace(), 'o');\n"",
    ""xlabel('MCMC sample');\n"",
    ""ylabel('$\\Delta G$ ($k_B T$)');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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TiPuIVxK0AzoFTWkrGKqkLZFYzAKDF5twIltWQPtemxpIymY2VpT9ImL7GEI/iWWkwqJX8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\nP3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L13nY3gZa5CLEYstmMfaAvtkw6aKrMvUZKwmk9z/7xaURhGZJsvEhUKap1kYNM/aKz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\nebNSDKZsfygAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11d92cb10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot trace of true protein concentration.\n"",
    ""rcParams['figure.figsize'] = [15, 3]\n"",
    ""plot(mcmc.Ptrue.trace()*1e6, 'o');\n"",
    ""xlabel('MCMC sample');\n"",
    ""ylabel('$[P]$ ($\\mu$M)');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""-6.05720932158e-10\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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Z1m3oVJrnvFvwA9W4xi2RDlxCbdVShP0qhq9teBy4Ddwi/APMgy6PQLsGbxer4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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11e363d10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot trace of true protein concentration.\n"",
    ""rcParams['figure.figsize'] = [15, 3]\n"",
    ""plot(mcmc.Ltrue.trace()*1e6, 'o');\n"",
    ""xlabel('MCMC sample');\n"",
    ""ylabel('$[L]$ ($\\mu$M)');\n"",
    ""print mcmc.Ltrue.trace().min()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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Kler2ncacyc3NEfKpUMu2pPV5KK69lwNXARcB2YG1mnlzquSRJkiRJu9br5aldZeaPSiQS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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x10cb58f50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot histogram of DeltaG.\n"",
    ""rcParams['figure.figsize'] = [15, 3]\n"",
    ""hist(mcmc.DeltaG.trace(), 40);\n"",
    ""xlabel('$\\Delta G$ ($k_B T$)');\n"",
    ""ylabel('$P(\\Delta G)$');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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h4DaOop9F11lkH4Dzgd+7+a8p3OlN432XAcHcfCjwInBan93b3Ie5+PTAB2C++5p/AoYRG\nq6nAbsCZwGpmNhxY090HAkOBbc1sT2Ajd98J2AU41cxWi8uY6u67A/cCI81sq5jNdnH9PmdmmwIH\nADsCOwH7mdkm5WNuTnkqhS+Y2e2E2vi9ZvZ74InGFktEREREpFuZVuX0zlgbmO3uM+Ljh+P/s4AB\nZnYt8HNg+cx7no7/vwGsAGwMTHP3hbGzyL90ohwtAGa2VizPvzLlKVSCPb5mbULr5e/jOOq7A+sD\nVxBGNLgbOBZYRLh/8lEAd/8g9pGyBbBNfO9kwpWSG5ZZNwMej+9f6O6jgc2ADYD74r81gG5ZKTwM\n+Cmwvbt/BFwNfKuhpRIRERER6V7Glpl+bh2X8S6wipmtGR9vH/8/BJjl7gcR+vxYKfOe4jEH/w5s\nZmYrmFkLoUUtj3mEyh3ANgCxctrHzD4Vpw8BCh3SFJY7g1Bp2zeOo34+oWVvOPCwu+8G3AycRKhA\nDwAws9XM7E7gReD++N7dgN8TLkEttW7/B2wd39/LzCbHac+7+y6xpfJa4G8517lp5KkUrkKoYR9r\nZqcTPsQfNbRUIiIiIiLdSOxldCShwrEw/j+ylt5Hi8W+PY4H7jSzPwGfIdyTdy+wV5x2CvCkma1D\niUHoY0XuHMKIAnfH93ekMI8bgWGxxW6rzPNHApPM7GFgV8L9hR+/L5b5hFjmKfH104AngR+b2X2E\newzHu/ttwKw4r7uAX8Rx1z80s4cIrYBt7v6fMuv2LHC3mf0FeAi43t2fA+43s0fM7EnC1ZNvVVjn\nptPS1rZEHu2Y2T2EptnnyYTn7mc1tmi5aHDM9JR5eso8PWWenjJPT5mnp8zTU+ZFzOyHwM/cfUG8\nXPRud7+ujotQ5k0oT++jn443YIqIiIiISHObA0w1s/8CrxJa8KSby9NSeA1wobsvjdfO6peI9JR5\neso8PWWenjJPT5mnp8zTU+bpKfMmlKelcHPgaTN7h3CDaAvhWtx+DS2ZiIiIiIiINFyeSuF+DS+F\niIiIiIiIdImKvY+6++uEwRqPJHQJOyROExERERERkSZXsVJoZucBewP7A72AQ8zsZ40umIiIiIiI\niDRenstH9yAM4viUu79vZl8mjJtyYj0LYmYnEMYs2QS4zt1/Xc/5i4iIiIgszW595cIRwBhgU8JY\nfGP37feDTo9TaGYHA2u6+7hOvn8IcLS7j+xsGerFzJ5z9y2Kph0HHANcDezn7jt0SeGWAXkGr2+N\n/xe6Ke2dmVY37n4R4RLV51UhFBEREZHuJFYIJwJbAD3j/xPj9K7U8VAF6ZQqx37A/xKG1VhaytmU\n8rQU/p4Q9Bpm9l3gIMIGm5uZDQTOc/ehZtYC/ArYktCb6eHu/kp86Ujg/1UzbxERERGRZcCYMtNP\nATrdWgjsaWbDgJWBs9z9LjP7GnAsoS7QRmhle8/MxgMDCLeMnQHMBjCzFYE/ANe6+0QzuwTYBngH\n2AjYBzgLWHPrrbfmqaeeWg04DRgc5/87dx9vZlcBE939T2a2BzDC3Q81s78DDwOfB/4FfA1YAbgO\nWAt4hVBR/piZHUG4mvE3wIjM9FcBc/ePzOxc4EV3v8bMLixTnjWBNYBhwMnxNT2Bce7+BzP7NjAK\nWAQ84e7fNbONgSuA5YEP4/JXBC6P5Z5LaOxajlBvmg5sDDzu7t82s7UIrZurx2KPAv4d12WNOO0E\nd3++0odbL3k6mjmfUMCbgPWBM9z9nLwLMLPRwARCCyPAcKC3uw8ibOTZ5uyd3P1PeectIiIiIrKM\n2LTK6Xm96+67Al8FLonTPgfs7e47AS8Ce5jZcMKlpgOBocC28bV9gNuAX8UK4VeBNdx9e+BbQN/M\nsu6bOHEihIrVhvE1XwIONLPNS5St0Lq3EXBqrB+sDWwHHA1Mc/chwHmECtjH3H0C8DShwSrbSrhE\ni2GsFJcrz33uPhjYIb5mJ2AX4FQzWw04GDjW3XcEXjSznsCFwDmxvBcRKqcXAhe5+y7Az4Dz4/w3\nAQ4jVLb3MrNPAqcCt8Z5nhifGwPcGz+ro4BLS+TVMBVbCs1svLsfD9ydmXa1ux+ccxkvE5p2r42P\nBwOTAdx9qpltm3ntijnnmaWm4vSUeXrKPD1lnp4yT0+Zp6fM02uKzFddfi1mfzSjxPS1C615VTvv\nvPNYsGABwDfcneHDhzNr1qy2s88+mylTpow95ZRT6N+/PyNGjDjq3XffZcUVVwQY6e4APP7446cf\nffTRmBmHHHLIrgAnnngiK6ywAoRWPkaMGMG4ceNeHz9+PHvttRcAJ5100u3LLbcchLHNOeecc+jf\nv/9zDz/8MMOGDTsEYMKECdx1110Ah6699to88sgjbwHss88+jBgxYuptt93GkCFDIFQWGTFiBMU5\nDBgwgB//+McvLb/88px44okAbeuttx6TJ0+eD3DEEUew8cYbM3PmTMqVJ5b5F6NHj+amm25i1KhR\nIwcMGMD777/PBRdc8H7Pnj258sornzzooIM46qij+M53vvPrr3zlK9x00037AhSyGjZsGJ/4xCcO\nGDVqFAMGDKBXr1785Cc/+cb3v/99brzxxg+BQlbvnH766YwePRrgssL7jzzySGbNmsWoUaN+PWDA\nAGbNmrXE+nagJefrys+gra30sszsCqAf4VeCJzNPLQes7u5fzLsQM9uA0FQ8yMwmADe7+93xudeA\nfu7emfsU26hDCFIVZZ6eMk9PmaenzNNT5ukp8/SaJvPMPYXFRna2s5nY0cwQdz/MzNYD7ie0wk0D\nPkPI5h7gGmAW8HV3Pyi2kE0ktHYdBZwAPALsTGgV+6a7jzSzTwB/j9POInzfv9vM9gEOcfevm1kv\nYArhcsqjgMfc/WozGwNsHMv2truvE8s8kdBKthXQ191PMrPPApPdfZOi9XsgznM+i+sa04C9CJds\n3gX8DpgJHOru/1NUnhNYfDnrV4Bh7n50bA0cQ7ii8RzgpHg56uT4+LuEltP7zGwE4RLUocCF7v5Y\nbIUcANwH3FDoAMfMHgUOiMud5u6/MbMvEUZ66AU86e43mNm6wIHufmHVH3onddRSeDawIaFJ9KzM\n9IWEZubOmk1ohi7o0ckKoUjT2GjMHUv0Jvbq2GG13B8gy7juvs109/XvCp3NXJ+VSH3s2+8HN9z6\nyoUQbq8q7E/n1tL7aLSGmd0HrEToy2O2mT0CPEq4J9CBdWNFbTcze5hwT93H3//d/d9mdgZwpbvv\nZWZ7xXm8A/wXWECmVcvd7zCznc3sL4TKzo3u/kxsdLrSzL4BvJQpY6nLPy+Lr30YeB14r8S6lWrd\nuoBQGXy18J4OypMt823xNQ8R7r+c5O4fmtlzwCNmNgd4E5gKnARcZmY/iuv/TeBO4FIzW4FwX+EJ\nHazbuXHdvknowPNbhDrSb8zsKEJd6cwS69YwZVsKs8xsVWA1Mr+0uPv0vAspaincH9gn/iqwPXCa\nuw+rvuhA/PVnozF3XETojrZXnD4TOC7+PZ5wgyqZ564n1OY7PIF1dKIr9Vx8W6lppcpwXPEyM/Ms\nXOPckn19Zv6bAR+x+D7NFkKnPZcTdvDi5UG4OfaSV8cOOyEuq2Rmr44ddkO59d5ozB0XtbTwnTKb\nTCHXA0ssezpwco6M3yIcsLLvn0HY2dYrKku5rKD9r4LTgVtKlGsm8AThF68VWLyTPl9iGdnPcwfC\nL0srEDN/deywEzrIbAThV7b1S6bWQTbZ9dlozB0ji+ZT8n0lltfh/Eu8vpB3X8I206voLcW/uBZv\nm5sSfmlcLb53AeEg9wnCNrs88ALwAO33wQcI9xu3Kzch7+x2WjAnLufj7aKoDKU+r4KPP7dMBqX2\nmQUs/uGssM6tLL4Xu7BtFZf7FnIcXzLLLt4+psf37VnqPbFclxL29ex7F7H4Jvxy2z0s3tbfIHwu\nhR/pFgCXFuVSdlsqU/Y5hKx7sfiYBKEzg0LZ2h2LisrVEud9EfCdEus+h3CMeCs+Lnz+JbefShWT\nzH67Ge239+xxJ7uswt99Y3nbdXpQWDfCZ9PpSlK1+3HOeRQUH/vmEb4QbkHpfgZGljgeFu/jxftn\n9n2dOdeU2ifbnTdLnDuy23LJ9+RVqZIb1yl7HvgzYfso+3kX5tmzR8sWi1rbZhA6kijk3UrIdHXa\nH7tK5paj7B1uO1Wer7LHvOzxv93xooN5F++bxfODcN59ADiUJT/DksupsL7lzvnF56q8545SLY1z\nCd/BSu0zhfMXZcpWfO5rd74oVZ6O9pcymWRf9xawK4u3pexnUMoCQuvWjpTep65v+eCfe7bMfmeT\n1s/0X8hHH/bqdc8FLNjr1On0WO5kYIei74olj/lFx99S3w+y098j7OPZY27xd72Tyxxzip+vx/E1\newwofM/5eP8t83kt8VmXOd5lz/FLTC+3L9RDxUqhmZ1C+MViZmZym7v3y7uQokphoffRwuWnh7r7\nS+XfXd5GY+6o5zXic+ncPY0i9dI0l7iI1IG2d1mWzSV8mS3+4UCkEVoJx9M0x9SFH7Hc49fB/DnQ\n1kbrxoNpXX/byu+TYp05D7bFf8UV+0Wvjh2WZ1SJsvJUCv8BbO/u/65lQY1Q50qhiIiIiIhI03l1\n7LCafhTIM3j9dEpfwysiIiIiIiJNLk8z498JN1c+QLh2HgB3/3HDSiUiIiIiIiJJ5KkUvsXim+t1\n/4eIiIiIiMgypGKl0N3PqvQaERERERERaU5lK4Vm9pS7b21mrbQfX6OF0PuoetSqXlf0tlf47Oq9\n3ELX8C2Uvjd1ae1ZcGko19JQhmbV6OzKzV+fmXRkDmFMqzz36XeVPNvwIsIYXOvneK3UrlHn52ZX\n6EkTlr5sivejRYSB37dlyeF/lhVz4v/Fw1N0Fe03pdU85nuucQqXVpucemfbwta2vwHnZsb6mFjm\n5aXG2Co1NlkroWOdwngjbYThM0ptfIUxZ3YmM8hopiynVJj+T5Yclw/CGFnHdzRmSon5QBgnqTBm\n1AuFZRa9p3hMut4s3tFLjvlStOglvliUW9cSz2/WUfk60sGYW3Ubt6XEMhYQvuS9QBiHqtSYb8VG\nVtgWs2N3FY+fV1D8+Zf8MldmLJ7C+GrrUt12V+ji+HkqbzcdjZFYvE/NAJ5k8Zhoi+L0nhSNF9gZ\n5coW/263TcZpeccmKvsFutT2zpJjinW4D3e0z+R4rlTGx3d03Kmw/MLxo7DN/JkSx7Qyy/94/6u0\nn3dwzAWgRwu0Lj4dFfIvNaZkh+uX83hUcjso89lC6bEkOxzXqswxawFh2y8eA7QwNmV2LLEZwPGU\nHquz6nG1yqi0nZcqf7vjbd5trsS8s+/5M4u3ueLt8Z+0z6VQjsKxudQ2UDyO4OVF2+jmLO6+v3Ds\ne5P2x8W83xkARsb/y27fhXlefED/9b+y5bplv8yWOK7PJfyAXzz2Z/E2kR2nNHse+Cflx8kteb7o\noPzZclb9uXd2Ph3sl+W2oQ6P+fHv4s+qFZhP+6HJKn4Xy7tOJcpSPFZgYX8vXq9S65qdlt2Wof1Y\nqcX774z4f8ltvBYdjLlbfD5+n/ZjdBbKDOW/t9a8nXVQ7nJjGk6j/Viuf45lKHk87Oh4SfgsirfV\nUt8nZwC/o/J2nNVKie9tndXUlULKV1BqGpRSOqQWk6iKynCtBzNlnp4yT0+Zp6fMq1SH43ryzBv9\nxboJaDtPT5k3oWWuUigNp8zTU+bpKfP0lHl6yjw9ZZ6eMk9PmTehpfn+BxEREREREWmwir2Pmtny\nwGjACNc7nwCc5+4fNbhsIiIiIiIi0mB5WgovIfSqtjWwENgY+E0jCyUiIiIiIiJp5KkUbuPuY4AF\n7v4hcDDQv94FMbMvmNmvzewqM9u03vMXERERERGRJeWpFLbFS0gLPdKslfm7ng4ndAk9D3itAfMX\nERERERGRIhXvKQR+AdwLfNrMfgHsD5xZzULMbCDhPsShZtYC/ArYklABPNzdXyFclnowsE38/9Jq\nliEiIiIiIiLVq9hS6O7XAkcD5wCvAMPc/cq8CzCz0cAEwiDpAMOB3u4+iDBuzrg4/R3CIKrvoW5s\nRUREREREksjT++gWwI/cfYSZfQG4zMyOcHfPuYyXgf2Aa+PjwcBkAHefambbxOmXESqPLYQeTvNq\n6oEWm5T4bnPrAAAcA0lEQVQyT0+Zp6fM01Pm6Snz9JR5eso8PWWeVs0NankuH51AvFzU3V80s58Q\neh8dnGcB7j7JzDbITFoV+CDzeJGZ9XD3vxIuG62WWhXT0oCk6Snz9JR5eso8PWWenjJPT5mnp8yb\nUJ6OZlZ298mFB+5+D2GIis6aDfTJlsHdW2uYn4iIiIiIiHRSnpbCd83saOC6+HgE4f6/zpoC7APc\nbGbbA8/VMC8RERERERGpQZ6WwkMJlbi3genAMMLwEZ01CZhvZlOAnwHfq2FeIiIiIiIiUoOWtram\nvg9U1yynp8zTU+bpKfP0lHl6yjw9ZZ6eMk9PmTehPL2P7gGcDaxB5gN2934NLJeIiIiIiIgkkOee\nwvHA94HnUfeyIiIiIiIiy5Q8lcIZ7n57w0siIiIiIiIiyVW8p9DMzgd6EQacn1eY7u4PNbZoueia\n5fSUeXrKPD1lnp4yT0+Zp6fM01Pm6SnzJpSnpXBA/L9/ZlobsEv9iyMiIiIiIiIpqfdRqZYyT0+Z\np6fM01Pm6Snz9JR5eso8PWXehPL0ProBcAWwIbATcD1wmLu/1tCSiYiIiIiISMPlGbz+MuAC4D/A\nv4DfAdc0slAiIiIiIiKSRp5K4Vru/icAd29z9yuAVRtbLBEREREREUkhT6Vwrpn1JY5RaGaDgfkN\nLZWIiIiIiIgkkaf30e8DtwOfNbNngDWAr9e7IGb2RWA88ArwW3d/sN7LEBERERERkfYqVgrd/Qkz\n2w74HNATeNXd5zSgLAOBt4GFwAsNmL+IiIiIiIgUqXj5qJn9L/CUu78AfAhMM7N9q1mImQ00swfi\n3y1mdqmZ/cXM7jezfvFljwBHAOcDo6taCxEREREREemUPPcUngrsBuDu/wC2Ac7KuwAzGw1MAHrH\nScOB3u4+CDgFGBenb0VoiXw//i8iIiIiIiINlqdSuLy7v1N44O7vUt2AlC8D+2UeDwYmx3lNJVQy\nAV4j3FN4fvxfREREREREGixPRzOPmNlEwqD1AAcAj+ZdgLtPMrMNMpNWBT7IPF5kZj3c/dFq5pvR\n1on3SG2UeXrKPD1lnp4yT0+Zp6fM01Pm6SnztKppsCspT6XwWOB44ChgAfAgcGkNy5wN9Mk87uHu\nrTXMr+YQpCptKPPUlHl6yjw9ZZ6eMk9PmaenzNNT5k2o4uWj7j4fuN7dvwL8gtA7aK8aljkF2BvA\nzLYHnqthXiIiIiIiIlKDii2FZnYp0GpmlwDXAfcAuwBf6+QyJwG7m9mU+PjQTs5HREREREREatTS\n1tbxJb9m9ldgW+AMAHc/08yecPftEpSvEjVPp6fM01Pm6Snz9JR5eso8PWWenjJPT5k3oTy9j/aM\nr9sXuMvMVgJWbmipREREREREJIk8lcJrgLeB1+IQEk8ClzW0VCIiIiIiIpJExctHAcysp7svin+v\n6e4zG16yfNQ8nZ4yT0+Zp6fM01Pm6Snz9JR5eso8PWXehPLcU7gBcAWwITCE0NnMYe7+WqMLl4M2\nuvSUeXrKPD1lnp4yT0+Zp6fM01Pm6SnzJpTn8tHLgAuA/xAuI/0d4ZJSERERERERaXJ5KoVrufuf\nANy9zd2vAFZtbLFEREREREQkhTyVwrlm1pfQFIyZDQbmN7RUIiIiIiIikkTFweuB7wG3A581s2eA\nNYCvN7RUIiIiIiIikkSeSuGngO2AzxHGLPw/d/+ooaUSERERERGRJPL0PvqCu2+WqDzVUu9G6Snz\n9JR5eso8PWWenjJPT5mnp8zTU+ZNKE+l8I/ADGAqMLcw3d2Xhh5ItdGlp8zTU+bpKfP0lHl6yjw9\nZZ6eMk9PmTehPJePziR8sNtnprXRgGEpzOxTwO3uvl295y0iIiIiIiJLqlgpdPdDzawXYPH1z7v7\nwgaVZzTwWoPmLSIiIiIiIkUqVgrNbBvgD4QWwx7Ap8xsP3efmnchZjYQOM/dh5pZC/ArYEtgHnC4\nu79iZkcD1wEndmI9REREREREpBPyjFN4MXCAu2/j7v2B/YHxeRdgZqOBCUDvOGk40NvdBwGnAOPi\n9N2Bo4ABZva1vPMXERERERGRzstTKVwl2yro7o8BK1SxjJeB/TKPBwOT47ymAtvGv7/m7scAU939\nD1XMX0RERERERDopT0cz75nZvu5+K4CZDSdcSpqLu08ysw0yk1YFPsg8XmhmPdy9Nb5+VN55Rx13\nnyqNoMzTU+bpKfP0lHl6yjw9ZZ6eMk9PmadVc2+veSqFRwHXmtmVcYEvAwfVsMzZQJ/M448rhJ2k\nLm/TUjfD6Snz9JR5eso8PWWenjJPT5mnp8ybUJ7eR18CBprZyoQK3JwalzkF2Ae42cy2B56rcX4i\nIiIiIiLSSXl6H32ATBOwmQHg7rt0cpmTgN3NbEp8fGgn5yMiIiIiIiI1amlr6/iSXzMbknnYC9gX\nmOXupzeyYDmpeTo9ZZ6eMk9PmaenzNNT5ukp8/SUeXrKvAlVrBSWYmZT3X1gA8pTLW106Snz9JR5\neso8PWWenjJPT5mnp8zTU+ZNKM/lo+tnHrYAmwFrNqxEIiIiIiIikkye3kcfzPzdBvwbOL4xxRER\nEREREZGUOnX56FJEzdPpKfP0lHl6yjw9ZZ6eMk9PmaenzNNT5k2obEuhmV1FBwNPuvthDSmRiIiI\niIiIJNPR5aN/AT5KVRARERERERFJr6NK4THuvrWZ3eLuw5OVSERERERERJIpe0+hmT0BzAe+CDxZ\n/HwNg9fXk65ZTk+Zp6fM01Pm6Snz9JR5eso8PWWenjJvQh21FO4C9Ad+A5yVpjgiIiIiIiKSUsXe\nR81sbXf/d6LyVEu/RKSnzNNT5ukp8/SUeXrKPD1lnp4yT0+ZNyENSSHVUubpKfP0lHl6yjw9ZZ6e\nMk9PmaenzJtQnsHrkzCzrYHj48OTluLWSRERERERkWVGrkqhma0MfBZ4DljJ3T9sQFl6AycAewA7\nAH9swDJEREREREQko0elF5jZrsCzwK3AusDrZvblahZiZgPN7IH4d4uZXWpmfzGz+82sH4C7Pwps\nCpwIPFPleoiIiIiIiEgnVKwUAmOBwcD77v4WsBNwQd4FmNloYAKhJRBgONDb3QcBpwDj4uu2A/4K\n7E2oGIqIiIiIiEiD5akU9nD3fxUeuPu0KpfxMrBf5vFgYHKc11Rgmzh9FeBK4KfA9VUuQ0RERERE\nRDohzz2Fb5rZPkCbma0OHAtMz7sAd59kZhtkJq0KfJB5vMjMerj7A8ADeeeb0dTdpzYpZZ6eMk9P\nmaenzNNT5ukp8/SUeXrKPK2ae3vNUyk8CrgI+AzwD+B+4Mgaljkb6JN53MPdW2uYn7q8TUvdDKen\nzNNT5ukp8/SUeXrKPD1lnp4yb0J5KoWDgFHuvqBOy5wC7APcbGbbE3o0FRERERERkS6Q557CbwCv\nmtmvzWxwHZY5CZhvZlOAnwHfq8M8RUREREREpBNa2toqX/JrZn0IvYYeAGwM3OTupzW4bHmoeTo9\nZZ6eMk9PmaenzNNT5ukp8/SUeXrKvAnlaSnE3ecQLvv8CzCfMLi8iIiIiIiINLmK9xSa2YnACMI4\ng9cBw9z9zUYXTERERERERBovT0cz6wJHuPszjS6MiIiIiIiIpFX2nkIz28fdbzezgykx1oi7X9Po\nwuWga5bTU+bpKfP0lHl6yjw9ZZ6eMk9PmaenzJtQRy2F2wG3AzuXeK4NWBoqhSIiIiIiIlKDspVC\ndz8j/vk7d78n+5yZ7d/QUomIiIiIiEgSHV0+egChc5kfA6dnnloOGOPuGze+eBWpeTo9ZZ6eMk9P\nmaenzNNT5ukp8/SUeXrKvAl1dPnoqsAgoA8wNDN9IfCjRhZKRERERERE0qg4eL2Z7eru9yUqT7X0\nS0R6yjw9ZZ6eMk9PmaenzNNT5ukp8/SUeRPKMyTFfDO7FViF8AH3BDZw9w0bWTARERERERFpvB45\nXnMFcAuhAnkJ8HdgXCMLJSIiIiIiImnkaSmc6+5XmdmGwCzgCOBB4OJ6FsTMdgFGACsCP3X35+o5\nfxEREREREVlSnpbCeWa2BuDA9u7eBnyyAWVZ0d2PBH4GfLkB8xcREREREZEieVoKxwE3AvsDT5jZ\nN4CnqlmImQ0EznP3oWbWAvwK2BKYBxzu7q+4+x1mthJwPHByNfMXERERERGRzqnYUujuNwFfdvc5\nwDbAN4Fv5F2AmY0GJhDGPAQYDvR290HAKcT7E81sLWA8cLq7z6hmJURERERERKRzyrYUmtlVhC5l\nC4+LX3JYzmW8DOwHXBsfDwYmA7j7VDPbJk7/GbAWcK6Z3eLu/y/n/EVERERERKSTOrp89M/1WIC7\nTzKzDTKTVgU+yDxeZGY93P3gTi6i44EWpRGUeXrKPD1lnp4yT0+Zp6fM01Pm6SnztGoeF7JspdDd\nry78HXse3Qz4E9DX3V+tYZmzgT6Zxz3cvbUzM7r1lQsBFhHGToTFG+BCoFf8ewFhPbPTsoNqzgWW\nz8wjazqhx9UvUjrs7LILr7+FcIns+iVeU7gsdq2i+cwlXF6bvZy3DXgeeAAYCmwKTMs83izOu7BO\npZZdmFbq9ZXMif8XPqvCZ5Qt43QW3/95fma5M4D/An3jepTKNmseoSMjA1YgfGbzaL+dFC9vDO0z\nya53YfnrAW/FaX2Bjwg5zyd85i/QPt+3gE+UWG4e2WWWKlPBAkIe7wGrUfrzmAlcz5Kf5cn79vvB\nDbe+cuGIuP6bZdaplfY5LyIMIfNo5rXZz794YNk5hFzWyry/0uc2Iy47m1e5AWvLza94/yhsN+W2\n1VL7c0f7WPE+ULw9ziHs45+Jj1tKrEN2/uW2+exrCp/xm8BKLLm/ZxWWn90+s+tWWKdyuZTa70tl\nVFBYtzwDCxdvc9n17Uhh+y0+bpXaHzpSqozzgMuBzwF7FD1far2zn9d4Sn8Wxdtx4TzyPDA2/l28\n7guA+wjrl51eKddyz1c70PNcwroWf77Fx+3CurxB+2NbqeWVe2055c5n9TITOA7YATiW0sePVsL+\n8wnK7z/Fyn3eb7Dk/pr3XFbF59cCtM0EVifsG4VtrHBOm8Xic8M8wg/067F4Xxob98mLgGNYcl1L\nnasLZYQl17P4vFo4ryxPOIbBkufS4uNQ8fnvLWBnFp/PZxM+o+LvLCdXWJfi8mczLl7m2H37/eAG\ngCXPkS0rQNv0+L7ssb5YqfNUqfNxtjyF9St8noXvFJtnlpMte/F6H0nIKfuaVjq+vavU+Tt7Hin+\n7Mp9T4Il999K54hyx9ni81BhGYX9p9z+OYewPWZzXwBcyuLvL4VtP7tdlTpPt5RZ71LfoSt9p85m\nUfydNJtRQZ7jeqXPtdy5O8/3MYDX9+33gw0qv6y8lra2jivyZnYAcCphqIjBwNPAaHe/Lu9CYkvh\nRHcfZGb7A/u4+2Fmtj1wmrsP60zhb33lQv0KISIiIiIi3dq+/X5QU2thniEpTgYGAXPc/V9Af0IH\nMZ01CZhvZlMI9xF+r4Z5iYiIiIiISA3yDEmxyN3nFDqacfd/mVlVl3u6++uEiiVxnMNjqi2oiIiI\niIiI1F+eSuELZnYc0MvMtgK+DTzT2GKJiIiIiIhICnkuHz2WcMPmXOBKwg21325koURERERERCSN\nPC2Fv3T3Q6ntPkIRERERERFZCuVpKdzczFZpeEmaRz17PO3q3lMXELrIXZR4uXO6YJnVmgGMBC4m\n5JRVyK14ei0WEFrja/UR+ber1jots9y8pbylKZ+2ov/zKHXsqOb9Mwj71vQSz3X1cbFW5cpfanrx\ntDks7iq+4VpCb+ltJcrRCNllLOpgmeWmLwImA38jbHtdtZ3MYfHQH+VMB0bGngBHEsqcOmMRWToV\nvl+OpPQ5sJIFhGNh8Xs7M6928gxJMRXYhDCO3MdfIN19l1oXXgfVju0ktVPm6Snz9JR5eso8PWWe\nnjJPT5mnp8ybUJ7LR09qeClERERERESkS1RsKVzK6ZeI9JR5eso8PWWenjJPT5mnp8zTU+bpKfMm\nlOeeQhEREREREVlGqVIoIiIiIiLSjalSKCIiIiIi0o2pUigiIiIiItKNLXWVQjMbamYTurocIiIi\nIiIi3cFSVSk0s88C/YHeXV0WERERERGR7iDZkBRmNhA4z92HmlkL8CtgS2AecLi7v5J57TXuPirH\nbNXlbXrKPD1lnp4yT0+Zp6fM01Pm6Snz9JR5E0rSUmhmo4EJLG4BHA70dvdBwCnAuKK3aEMSERER\nERFJINXloy8D+2UeDwYmA7j7VGDbotenab4UERERERHp5pJUCt19ErAwM2lV4IPM44Vm1iPz+jyX\njoJaFLuCMk9PmaenzNNT5ukp8/SUeXrKPD1l3oS6qqOZ2UCfbDncvbWLyiIiIiIiItJtdVWlcAqw\nN4CZbQ8810XlEBERERER6daW66LlTgJ2N7Mp8fGhXVQOERERERGRbi3ZkBQiIiIiIiKy9FmqBq8X\nERERERGRtFQpFBERERER6ca66p7CTjOzFuBXwJbAPOBwd3+la0u1bDCz5YArgQ2B5YFzgGnAb4FW\n4Hl3Pza+9gjgSGABcI6739EFRV5mmNkngSeB3YBFKPOGMrMfAl8lHAN/Sej86rco84aIx+0rACNs\n30eg7bxhzGwgcJ67DzWzz5IzZzNbAbgO+CShl/CD3X1mV6xDsynKfCvgYsJQXPOBUe7+b2VeX9nM\nM9MOBI5z90HxsTKvo6LtfG1gArA6YQiKUe7+ujKvr6LMjXAubQNecvfD42vqknkzthQOB3rHHf4U\nYFwXl2dZ8k1ghrvvBOxJ+LI8Dhjj7kOAHma2r5l9Cjge2CG+7lwz69VVhW52sTL+a+C/cZIybyAz\nGwLsEI8hQ4HPoswb7cvAyu4+GPgJMBZl3hBmNprwRa13nFRNzscAf4vngGuB05KvQBMqkfkvgGPd\nfRdCx3onK/P6KpE5ZtYfOCzzWJnXUYnMfwpc5+47A2cAmyvz+iqR+ZnA2THHFcxsWD0zb8ZK4WBg\nMoC7TwW27driLFN+z+KNpifhV86t3f3hOO0uYHdgAPCIuy9099nA34Evpi7sMuRC4FLgn4Rf25R5\nY+0BPG9mtwB/jP+UeWPNA1aLLYarEX7NVOaN8TKwX+bxNjlz3pLM+TW+drc0RW56xZkf4O6FobaW\nI2z/yry+2mVuZmsCZwMnZF6jzOureDvfEehrZvcABwL3o8zrrTjzucCa8Vzah3AurVvmzVgpXBX4\nIPN4oZk143osddz9v+7+oZn1AW4CfkSopBTMIeTfh/afwX8IX/SkSmZ2CPCuu9/D4qyz27Myr7+1\ngG2A/yH8knY9yrzRHgFWBP4PuIxwaZ2OLQ3g7pMIP+gVVJNzdnrhtVJBcebu/g6AmQ0CjgV+zpLf\nXZR5DbKZx++AVwDfBz7MvEyZ11GJY8uGwHvuvjvwBvBDlHldlch8POH8+QLhstA/U8fMm7EyNZuw\nogU93L21qwqzrDGzzxB+7bna3W8g3IdS0Ad4n/AZrFpiulTvUMKYnQ8Qftm5Blg787wyr7+ZwN3x\nV7WXiK1YmeeVef2dBExxd2Pxdr585nll3jh5j+GzaH9+VfY1MLMDCP0f7B3v41HmjbM1sDHhipuJ\nwKZmNg5l3mgzgdvi37cRrtz7AGXeSNcBg919U8IloeOoY+bNWCmcAuwNYGbbA891/HLJK16XfDdw\nkrtfHSc/bWY7xb/3Ah4GngAGm9nyZrYa8Hng+eQFXga4+xB3HxpvlH8GOAi4S5k31COE6+4xs3WB\nlYH74r2GoMwbYRUW/2L5PuGSuqeVeRJPVXE8+Qvx/Br/f7h4ZlKZmX2T0EK4s7u/Hic/jjJvhBZ3\nf9Ldt4j3cI4Aprn791HmjfYwi3PciZCtji2NtRKh1Q/CLUerU8fMm673UcJN27ub2ZT4+NCuLMwy\n5hTCBnaamZ1O6N3oBGB8vGn1ReBmd28zs4sJX65bCJ0YfNRVhV4G/QCYoMwbI/bK9SUze5yQ5THA\na8AVyrxhLgCuMrOHCeedHwJ/RZmnkPt4YmaXAlfHz2k+4T4hqUK8lPEi4HVgkpm1AQ+6+1nKvCHa\nyj3h7u8o84b6AeEYfgzhR78D3f0DZd5QRwB/MLO5wEfAEfXczlva2sruTyIiIiIiIrKMa8bLR0VE\nRERERKROVCkUERERERHpxlQpFBERERER6cZUKRQREREREenGVCkUERERERHpxlQpFBERERER6caa\ncZxCERHppsxsA+BV4DJ3PyYzfSvgKeAQd78mTvs+cBBhLLNW4AJ3vzE+9xow192/kJlHT+BfwG3u\nflicNowwhuvKQE/gFuAMd++y8ZzM7FVgiLtP76oyiIjIskUthSIi0mxmAnuaWUtm2gHAu4UHZjYW\n2BX4krtvDewLnGNmu8SXtAErmdlmmXnsCizKzGNP4GLgYHfvD2wHbAmcWfc1qo4GGBYRkbpSS6GI\niDSb/wBPAzsBD8ZpuwP3ApjZysAJwOfd/T8A7v6WmR0A/Dcznz8AXwdeiI8PAG4GVoqPxwBnuvs/\n4jzmm9kxwOeLCxRbJUcRKpWPu/sxZtYH+A2wHrAu8JC7H2xmQ4AfAS1Av1iOD4DhcXZ7u/u/zewN\n4H5gK2A28I3YOtgSl9kDuAAYQmjF/K27X1RNkCIiIqCWQhERaU6/J1ToMLNtgWeBj+Jznwdmu/sb\n2Te4+1/d/cX4sA24Cdg/zqMXofL1eOYt/Yse4+7/dPf7s9PiZac/BLYBtgVazWwdYBjwtLvvCHwO\nGGRm/ePbBgAHA5sDxwDvuPt2wHPAiPia9YA73X1L4EZCq2XWEUCbu28LDASGm9mOZRMTEREpQ5VC\nERFpNm3AbcBe8fEBhEpT4XLS1szfHXkLeN/MPgd8Gbi76H255uPui4ApwJPAGcAl7v62u98A3Gtm\nJwDjgTWAVeLbno8VzLnADEKLIMDrwCfi3x8U7oEErgYKl74W7AZ81cyeBqYSKpFb5FhvERGRdlQp\nFBGRpuPuHwLPmNmXgKHES0ejFwn3C/bNvsfMDjCz4+PDQmXvJuB/478bae8JQstfdh6bmNnVJcqz\nH3B0fHi3mX0pLuunwDuEVr4XM8v9qGgWC0us5qLM3z1LvKYncJK794/3PO4IXFViPiIiIh1SpVBE\nRJrVTcB5wJPu3lqY6O7zgEuAS+N9fZjZhsBYYFrRPG4mVAg/7+7PFj13AXCGmW0c57EK8HPgteyL\nzGxNM5sGPOfuZwJ/Ar5I6Ljmsthi2EK4PLVnFeu3hpl9Of59KHBn0fP3A0ea2XKxbA8RLiMVERGp\niiqFIiLSrG4j9AZ6Q3yc7ZXzR8Bfgcfi5ZU3E1rV7su+1t3fBmYBdxXP3N3vjvO5Mc7jMUInMmcU\nvW4mcBnwpJk9CawO/Ba4CDjTzB4FTovl3ajEepTrTXQBcJCZPUvoSOd7Ra//NfASodOdx4HfuPtD\nZeYlIiJSVktbm3q2FhERWdqY2Vx3X7GryyEiIss+tRSKiIgsnfSrrYiIJKGWQhERERERkW5MLYUi\nIiIiIiLdmCqFIiIiIiIi3ZgqhSIiIiIiIt2YKoUiIiIiIiLdmCqFIiIiIiIi3ZgqhSIiIiIiIt3Y\n/wdc+s0Msp2BsgAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1224f57d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot trace of intrinsic fluorescence parameters.\n"",
    ""rcParams['figure.figsize'] = [15, 3]\n"",
    ""semilogy(mcmc.F_PL.trace(), 'o', mcmc.F_L.trace(), 'o', mcmc.F_background.trace(), 'o');\n"",
    ""legend(['complex fluorescence', 'ligand fluorescence', 'background fluorescence']);\n"",
    ""xlabel('MCMC sample');\n"",
    ""ylabel('relative fluorescence intensity');""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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DMnPP9LS5bkjTGhkwIU0pPdSuNMU4Cdgde2c5KSZKk4QkhTKLMFnoS8Ow2HOI3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\nlAmhJEmSJPUoE0JJkiRJ6lH/Hxl/NLcl318kAAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x122adda50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# Plot model fit.\n"",
    ""rcParams['figure.figsize'] = [15, 3]\n"",
    ""figure = pyplot.gcf() # get current figure\n"",
    ""Fmodels = mcmc.Fmodel_i.trace()\n"",
    ""Fligands = mcmc.Fligand_i.trace()\n"",
    ""clf();\n"",
    ""hold(True)\n"",
    ""for Fmodel in Fmodels:\n"",
    ""    semilogx(Lstated, Fmodel, 'k-')\n"",
    ""semilogx(Lstated, F_i, 'ro')\n"",
    ""for Fligand in Fligands:\n"",
    ""    semilogx(Lstated, Fligand, 'k-')\n"",
    ""semilogx(Lstated, Fligand_i, 'ro')\n"",
    ""hold(False)\n"",
    ""xlabel('$[L]_s$ (M)');\n"",
    ""ylabel('fluorescence units');\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 3"",
   ""language"": ""python"",
   ""name"": ""python3""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 3
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython3"",
   ""version"": ""3.5.2""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/2 MLE fit for two component binding - simulated and real data.ipynb",".ipynb","113972","935","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## MLE fit for two component binding - simulated and real data\n"",
    ""\n"",
    ""In part one of this notebook we see how well we can reproduce Kd from simulated experimental data with a maximum likelihood function.\n"",
    ""\n"",
    ""In part two of this notebook we see how well it can estimate the Kd from real experimental binding data.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      "":0: FutureWarning: IPython widgets are experimental and may change in the future.\n""
     ]
    }
   ],
   ""source"": [
    ""import numpy as np\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""\n"",
    ""from scipy import optimize\n"",
    ""import seaborn as sns\n"",
    ""\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Part I""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""We use the same setup here as we do in the 'Simulating Experimental Fluorescence Binding Data' notebook.\n""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Experimentally we won't know the Kd, but we know the Ptot and Ltot concentrations.\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Kd = 2e-9 # M\n"",
    ""Ptot = 1e-9 * np.ones([12],np.float64) # M\n"",
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def two_component_binding(Kd, Ptot, Ltot):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Kd : float\n"",
    ""        Dissociation constant\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total ligand concentration\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    \""\""\""\n"",
    ""                                    \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd) - np.sqrt((Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free ligand concentration in sample cell after n injections (uM)                                                                                                                                                                                                                           \n"",
    ""    return [P, L, PL]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""[L, P, PL] = two_component_binding(Kd, Ptot, Ltot)\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x119d3a1d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,PL, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$ / M')\n"",
    ""plt.ylim(0,1.3e-9)\n"",
    ""plt.axhline(Ptot[0],color='0.75',linestyle='--',label='$[P]_{tot}$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Now make this a fluorescence experiment.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Making max 400 relative fluorescence units, and scaling all of PL to that\n"",
    ""npoints = len(Ltot)\n"",
    ""sigma = 10.0 # size of noise\n"",
    ""F_i = (400/1e-9)*PL + sigma * np.random.randn(npoints)\n"",
    ""#Pstated = np.ones([npoints],np.float64)*Ptot\n"",
    ""#Lstated = Ltot""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda/lib/python2.7/site-packages/matplotlib/axes/_axes.py:519: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n"",
      ""  warnings.warn(\""No labelled objects found. \""\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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F/0RsQ/zzwoczc17sUeAvND7U2M3cNmKvmy+Vqv9zP/N2pOTuYv2ql8zwC+ACwGfgS8MuZ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\nAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1046b3650>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,F_i, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#And makeup an F_L\n"",
    ""F_L = 0.3""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([ 402.16126549,  407.00935545,  409.4424629 ,  410.63713133,\n"",
       ""        385.29730155,  393.71934784,  366.14748583,  287.22825923,\n"",
       ""        176.2715694 ,   80.58852261,   13.47768747,   10.0711431 ])""
      ]
     },
     ""execution_count"": 9,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""F_i""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""def find_Kd_from_fluorescence(params):\n"",
    ""    \n"",
    ""    [F_background, F_PL, Kd] = params\n"",
    ""    \n"",
    ""    N = len(Ltot)\n"",
    ""    Fmodel_i = np.zeros([N])\n"",
    ""    \n"",
    ""    for i in range(N):\n"",
    ""        [P, L, PL] = two_component_binding(Kd, Ptot[0], Ltot[i])\n"",
    ""        Fmodel_i[i] = (F_PL*PL + F_L*L) + F_background\n"",
    ""    \n"",
    ""    return Fmodel_i""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""400000000000.0""
      ]
     },
     ""execution_count"": 11,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""400/1E-9""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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tF4D9LjiYiHUSXrcrXMfkrheOygduf+J8rr/Ef/fcH1/G2jsnfoJzz55wdlD9r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\nSUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c98bc10>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""\n"",
    ""initial_guess = [0,400/1e-9,2e-9]\n"",
    ""prediction = find_Kd_from_fluorescence(initial_guess)\n"",
    ""\n"",
    ""plt.semilogx(Ltot,prediction,color='k')\n"",
    ""plt.semilogx(Ltot,F_i, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def sumofsquares(params):\n"",
    ""    prediction = find_Kd_from_fluorescence(params)\n"",
    ""    return np.sum((prediction - F_i)**2)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""The predicted parameters are [ -8.91696274e-01   4.05263953e+11   2.08557840e-09]\n""
     ]
    }
   ],
   ""source"": [
    ""initial_guess = [0,3E11,1E-9]\n"",
    ""fit = optimize.minimize(sumofsquares,initial_guess,method='Nelder-Mead')\n"",
    ""print \""The predicted parameters are\"", fit.x""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""fit_prediction = find_Kd_from_fluorescence(fit.x)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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2DH0mkuBDCtg/95pJahQPS2X0iMoiKFo/3kgaIXqrcXzK4cUQaNvUZn82T9988A/g9sLLy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\nAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c746610>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,fit_prediction,color='k')\n"",
    ""plt.semilogx(Ltot,F_i, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""Kd_MLE = fit.x[2]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""if (Kd_MLE < 1e-12):\n"",
    ""    Kd_summary = \""Kd = %.1f nM \"" % (Kd_MLE/1e-15)\n"",
    ""elif (Kd_MLE < 1e-9):\n"",
    ""    Kd_summary = \""Kd = %.1f pM \"" % (Kd_MLE/1e-12)\n"",
    ""elif (Kd_MLE < 1e-6):\n"",
    ""    Kd_summary = \""Kd = %.1f nM \"" % (Kd_MLE/1e-9)\n"",
    ""elif (Kd_MLE < 1e-3):\n"",
    ""    Kd_summary = \""Kd = %.1f uM \"" % (Kd_MLE/1e-66)\n"",
    ""elif (Kd_MLE < 1):\n"",
    ""    Kd_summary = \""Kd = %.1f mM \"" % (Kd_MLE/1e-3)\n"",
    ""else:\n"",
    ""    Kd_summary = \""Kd = %.3e M \"" % (Kd_MLE)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""delG_summary = \""delG = %s kT\"" %np.log(Kd_MLE)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'Kd = 2.1 nM '""
      ]
     },
     ""execution_count"": 20,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Kd_summary""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'delG = -19.9882196118 kT'""
      ]
     },
     ""execution_count"": 21,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""delG_summary ""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Part II""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""Now we will see how well this does for real data.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Couldn't import dot_parser, loading of dot files will not be possible.\n""
     ]
    }
   ],
   ""source"": [
    ""# This requires that we import a few new libraries\n"",
    ""from assaytools import platereader\n"",
    ""import string\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""Ptot = 0.5e-6 * np.ones([24],np.float64) # protein concentration, M\n"",
    ""Ltot = np.array([20.0e-6,14.0e-6,9.82e-6,6.88e-6,4.82e-6,3.38e-6,2.37e-6,1.66e-6,1.16e-6,0.815e-6,0.571e-6,0.4e-6,0.28e-6,0.196e-6,0.138e-6,0.0964e-6,0.0676e-6,0.0474e-6,0.0320e-6,0.0240e-6,0.0160e-6,0.0120e-6,0.008e-6,0.00001e-6], np.float64) # ligand concentration, M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""singlet_file = './data/p38_singlet1_20160420_153238.xml'""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""data = platereader.read_icontrol_xml(singlet_file)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#I want the Bosutinib-p38 data from rows I (protein) and J (buffer).\n"",
    ""data_protein = platereader.select_data(data, '280_480_TOP_120', 'I')\n"",
    ""data_buffer = platereader.select_data(data, '280_480_TOP_120', 'J')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""{'I1': '16546',\n"",
       "" 'I10': '8280',\n"",
       "" 'I11': '7005',\n"",
       "" 'I12': '5827',\n"",
       "" 'I13': '4967',\n"",
       "" 'I14': '4378',\n"",
       "" 'I15': '3859',\n"",
       "" 'I16': '3513',\n"",
       "" 'I17': '3176',\n"",
       "" 'I18': '3001',\n"",
       "" 'I19': '2855',\n"",
       "" 'I2': '16009',\n"",
       "" 'I20': '2736',\n"",
       "" 'I21': '2647',\n"",
       "" 'I22': '2552',\n"",
       "" 'I23': '2515',\n"",
       "" 'I24': '2487',\n"",
       "" 'I3': '15707',\n"",
       "" 'I4': '14688',\n"",
       "" 'I5': '14146',\n"",
       "" 'I6': '12881',\n"",
       "" 'I7': '12583',\n"",
       "" 'I8': '11251',\n"",
       "" 'I9': '10015'}""
      ]
     },
     ""execution_count"": 27,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""data_protein""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""#Sadly we also need to reorder our data and put it into an array to make the analysis easier\n"",
    ""#This whole thing should be moved to assaytools.platereader hopefully before too many other people see this.\n"",
    ""\n"",
    ""well = dict()\n"",
    ""for j in string.ascii_uppercase:\n"",
    ""    for i in range(1,25):\n"",
    ""        well['%s' %j + '%s' %i] = i\n"",
    ""\n"",
    ""def reorder2list(data,well):\n"",
    ""    \n"",
    ""    sorted_keys = sorted(well.keys(), key=lambda k:well[k])\n"",
    ""    \n"",
    ""    reorder_data = []\n"",
    ""    \n"",
    ""    for key in sorted_keys:\n"",
    ""        try:\n"",
    ""            reorder_data.append(data[key])\n"",
    ""        except:\n"",
    ""            pass\n"",
    ""\n"",
    ""    reorder_data = [r.replace('OVER','70000') for r in reorder_data]\n"",
    ""        \n"",
    ""    reorder_data = np.asarray(reorder_data,np.float64)\n"",
    ""    \n"",
    ""    return reorder_data\n"",
    ""\n"",
    ""reorder_protein = reorder2list(data_protein,well)\n"",
    ""reorder_buffer = reorder2list(data_buffer,well)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 29,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([ 16546.,  16009.,  15707.,  14688.,  14146.,  12881.,  12583.,\n"",
       ""        11251.,  10015.,   8280.,   7005.,   5827.,   4967.,   4378.,\n"",
       ""         3859.,   3513.,   3176.,   3001.,   2855.,   2736.,   2647.,\n"",
       ""         2552.,   2515.,   2487.])""
      ]
     },
     ""execution_count"": 29,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""reorder_protein""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 30,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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W+DPJ8QRXcb7lfTDA0AukgpLOznyPTJYSQRLamsC/7OV0CzH32VFb92DhG1E/z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\nREQKUQIREZFClEBERKQQJRARESnk/wPawIYD5WkAuQAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11da00910>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,reorder_protein, 'ro', label='PL')\n"",
    ""plt.semilogx(Ltot,reorder_buffer, 'ko', label='L')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('fluorescence')\n"",
    ""plt.xlim(5e-9,1.3e-4)\n"",
    ""plt.legend(loc=2);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 31,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# for this to work we need to provide some initial values\n"",
    ""# some of these we already have\n"",
    ""F_i = reorder_protein\n"",
    ""\n"",
    ""#And makeup an F_L\n"",
    ""F_L = 0.3\n"",
    ""\n"",
    ""# initial guess for [F_background, F_PL, Kd]\n"",
    ""initial_guess = [0,400/1e-9,2e-9]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 32,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""array([ 16546.,  16009.,  15707.,  14688.,  14146.,  12881.,  12583.,\n"",
       ""        11251.,  10015.,   8280.,   7005.,   5827.,   4967.,   4378.,\n"",
       ""         3859.,   3513.,   3176.,   3001.,   2855.,   2736.,   2647.,\n"",
       ""         2552.,   2515.,   2487.])""
      ]
     },
     ""execution_count"": 32,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""F_i""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 33,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""The predicted parameters [F_background, F_PL, Kd] are  [  2.46679644e+03   2.86639020e+10   9.07519322e-07]\n""
     ]
    }
   ],
   ""source"": [
    ""fit = optimize.minimize(sumofsquares,initial_guess,method='Nelder-Mead')\n"",
    ""print \""The predicted parameters [F_background, F_PL, Kd] are \"", fit.x""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 34,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""2466.7964398297158""
      ]
     },
     ""execution_count"": 34,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""fit.x[0]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 35,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""fit_prediction = find_Kd_from_fluorescence(fit.x)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 36,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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cQX/d2TFTZXy469jmnYpMvyp8t5W9DCGEL4kU9g4C9oyh6scJvoWOfc5qzEZFK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\nERGR1KnYiIhI6v4XfjWcSiPcawkAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11da00750>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,fit_prediction,color='k')\n"",
    ""plt.semilogx(Ltot,reorder_protein, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 37,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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0k0KFkY2YRcC1gwGpgXHI7DWgG5rr7aUndccB4YCUwyd1nmllP4CZgU2AJMNbd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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1211b4290>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.semilogx(Ltot,fit_prediction,color='k', label='prediction')\n"",
    ""plt.semilogx(Ltot,reorder_protein, 'o', label='data')\n"",
    ""plt.axhline(fit.x[0],color='k',linestyle='--', label='$[F]_{background}$')\n"",
    ""plt.axvline(fit.x[2],color='r',linestyle='--', label='$K_d$')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend(loc=2);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 38,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'Kd = 2.1 nM '""
      ]
     },
     ""execution_count"": 38,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Kd_summary""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 39,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""'delG = -19.9882196118 kT'""
      ]
     },
     ""execution_count"": 39,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""delG_summary ""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python 2"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.11""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/direct-fluorescence-assay/2b Bayesian fit for two component binding - simulated data.ipynb",".ipynb","788362","980","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""## Bayesian fit for two component binding - simulated data\n"",
    ""\n"",
    ""In *part one* of this notebook we see how well we can reproduce Kd from simulated experimental data with our Bayesian methods, these are the same as those used in the `quickmodel.py` script.\n"",
    ""\n"",
    ""In *part two* of this notebook we will see if we can reproduce some of the problems we've been having with the real experimental data.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Populating the interactive namespace from numpy and matplotlib\n""
     ]
    }
   ],
   ""source"": [
    ""import numpy as np\n"",
    ""import matplotlib.pyplot as plt\n"",
    ""\n"",
    ""from scipy import optimize\n"",
    ""import seaborn as sns\n"",
    ""\n"",
    ""%pylab inline""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Part I""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""We use the same setup here as we do in the 'Simulating Experimental Fluorescence Binding Data' notebook.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# We define a Kd,\n"",
    ""Kd = 2e-9 # M\n"",
    ""\n"",
    ""# a protein concentration,\n"",
    ""Ptot = 1e-9 * np.ones([12],np.float64) # M\n"",
    ""\n"",
    ""# and a gradient of ligand concentrations for our experiment.\n"",
    ""Ltot = 20.0e-6 / np.array([10**(float(i)/2.0) for i in range(12)]) # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def two_component_binding(Kd, Ptot, Ltot):\n"",
    ""    \""\""\""\n"",
    ""    Parameters\n"",
    ""    ----------\n"",
    ""    Kd : float\n"",
    ""        Dissociation constant\n"",
    ""    Ptot : float\n"",
    ""        Total protein concentration\n"",
    ""    Ltot : float\n"",
    ""        Total ligand concentration\n"",
    ""        \n"",
    ""    Returns\n"",
    ""    -------\n"",
    ""    P : float\n"",
    ""        Free protein concentration\n"",
    ""    L : float\n"",
    ""        Free ligand concentration\n"",
    ""    PL : float\n"",
    ""        Complex concentration\n"",
    ""    \""\""\""\n"",
    ""                                    \n"",
    ""    PL = 0.5 * ((Ptot + Ltot + Kd) - np.sqrt((Ptot + Ltot + Kd)**2 - 4*Ptot*Ltot))  # complex concentration (uM)\n"",
    ""    P = Ptot - PL; # free protein concentration in sample cell after n injections (uM)                                                                                                                                                                                                                          \n"",
    ""    L = Ltot - PL; # free ligand concentration in sample cell after n injections (uM)  \n"",
    ""    \n"",
    ""    return [P, L, PL]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""[L, P, PL] = two_component_binding(Kd, Ptot, Ltot)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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GTqHzieCAkJEkdoJ4Ppp1U/ZmAzNwJ/KT4qrmOJKn91HPK6NMR0cvQl6m4iMqH1yRJbaqe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\nTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1105d93d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,PL, 'o')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$[PL]$ / M')\n"",
    ""plt.ylim(0,1.3e-9)\n"",
    ""plt.axhline(Ptot[0],color='0.75',linestyle='--',label='$[P]_{tot}$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Now make this a fluorescence experiment""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""# Making max 1400 relative fluorescence units, and scaling all of PL (complex concentration) \n"",
    ""# to that, adding some random noise\n"",
    ""npoints = len(Ltot)\n"",
    ""sigma = 10.0 # size of noise\n"",
    ""F_PL_i = (1400/1e-9)*PL + sigma * np.random.randn(npoints)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n"",
      ""  warnings.warn(\""No labelled objects found. \""\n""
     ]
    },
    {
     ""data"": {
      ""image/png"": 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H0L5L+SdL7aulU0lLgdOAMYAnwEmA9sDYiFgMzgOWS5gGXpeedC1ybPr3WmtTs668bv/2G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\nmf8AAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1105d9250>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,F_PL_i, 'ro')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescendce$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""#Let's add an F_background just so we don't ever go below zero\n"",
    ""F_background = 40\n"",
    ""#We also need to model fluorescence for our ligand\n"",
    ""F_L_i = F_background + (.4/1e-8)*Ltot + sigma * np.random.randn(npoints)\n"",
    ""\n"",
    ""#Let's also add these to our complex fluorescence readout\n"",
    ""F_PL_i = F_background + ((1400/1e-9)*PL + sigma * np.random.randn(npoints)) + ((.4/1e-8)*L + sigma * np.random.randn(npoints))""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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ckY7izS4VBJEPinMxeCbS6+3uAswjup/hKKZ2a2SLgAuBCoAN4O7AOWO3uC4E64CozmwPc\nGO53GbA2XGFPREQSEqdQvOHurwC4+yHgeqDUj42XAc8CvcBDwMPAeQSjCoBHCFbQOx/Y5e7DYZ/P\nAwtK7FNEREoQ5xzFc2b2CXffFm6PRu4d7TTgN4GPAL8FfBuY4e5j4fuDwGygieMPIcxvj9TcPItc\nrn4K4RWvpaUxkX7SkOXcINv5KbfaVY35xSkUNwIPmdlngKeB9xCsnV2Kl4H97v464GZ2hGD6aVwj\n8CrBHeCNBdojDQwMlRhWPC0tjfT3DybSV9KynBtkOz/lVrvSzm+yIlX01JO7/9Ld24E/Bn4G3EPp\nCxc9AVxuZnVm9jbgLcD3w3MXAEuAncAeYKGZzTSz2cDZBCe6RUQkIXGuevoU8GPgcXd/bCqduvvD\nZnYxQSGYASwD/h+w2cxOBvqAHncfNbONBEVjBnCLux+ZSt8iIhJPnKmnCwmmn95lZk4w/fS0u28q\npWN3v6lAc0eB/TYDm0vpQ0REpi7O1NPvu/s5BCeivwCcAVxRqcBERKQ6xLk8FgB3f93dnwA+Cvxz\n+UMSEZFqUnShMLOu/CfHuvsbwJyKRCUiIlUjzjmKa4A7zOytwE+BV4C3VSQqERGpGnGeHvtxADP7\nNeAcoAWY0tVPIiJS/U5YKMzsIOHaExxfg+LxSgcmIiLVoZgRxTnA+8M/nwXOM7MRjheNOyoYn4iI\npKyYQvHb7v73BA/qA8DM5nK8eIiISIYVUyjWAH8PYGZXufvfufu/EDz19aFKBiciIukr5vLYurzX\nJS9UJCIitamYQjGW97pu0r1ERCSTipl6ereZ/TnwJHCKmeXcfaTCcYmISJUoplBcAZwLfBB4HThk\nZi8A/wg84+5frWB8IiKSshMWivC5Tk+Mb4drVr+XoHicW7nQRESkGsR5hAcA7j5MsI7EnvKHIyIi\n1Sb202NFRGR6UaEQEZFIKhQiIhJJhUJERCKpUIiISKTYVz2Vk5mdDvwYuAQYAbYS3Am+F1jm7kfN\n7AagO3x/jbs/nFK4IiLTUmojCjM7CdgE/FvYtA5Y7e4LCR4VcpWZzQFuBC4ELgPWhvdxiIhIQtKc\nevoz4H8AL4Xb5wE7wtePAIuB84Fd7j7s7oeA54EFSQcqIjKdpTL1ZGa/B/S7+3fM7Oawuc7dxx9A\nOAjMBpqAQ3lfOt4eqbl5FrlcfRkjnlxLS2Mi/aQhy7lBtvNTbrWrGvNL6xzFUmDMzBYD7wPuA07P\ne78ReBU4HL6e2B5pYGCofJFGaGlppL9/MJG+kpbl3CDb+Sm32pV2fpMVqVQKhbtfPP7azLYDnwG+\namaL3H07sAR4nOAxIbeb2UygATib4ES3iIgkJNWrniZYBWw2s5OBPqDH3UfNbCOwk+B8yi3ufiTN\nIEVEppvUC4W7L8rb7Cjw/mZgc2IBiYjIm+iGOxERiaRCISIikVQoREQkkgqFiIhEUqEQEZFIKhQi\nIhJJhUJERCKpUEwDDb09NHe0c9rcZpo72mno7Uk7JBGpISoUGdfQ20NT91JyffuoGx0l17ePpu6l\nKhYiFdbb20NHRztz5zbT0dFObw3/zqlQZNys9XcVbt+wLuFIRKaP3t4euruX0te3j9HRUfr69tHd\nvbRmi4UKRcbVH9gfq11Epm79JB/QNtToBzQViowbbZ0Xq11Epu7AJB/EJmuvdioUGTe0YlXh9uUr\nE45EZPponeSD2GTt1U6FIuOGO7s4vGkLI23zGcvlGGmbz+FNWxju7Eo7NJHMWjHJB7TlNfoBLfXH\njEvlDXd2qTCIJKgz/H3bsGEdBw7sp7V1HsuXrzzWXmtUKEREKqCzs6tmC8NEmnoSEZFIKhQiIhJJ\nhUJERCKlco7CzE4CtgBnAg3AGuA5YCswBuwFlrn7UTO7AegGRoA17v5wGjGLiExXaY0ofhd42d0X\nApcDXwfWAavDtjrgKjObA9wIXAhcBqw1s4aUYhYRmZbSuurpb4Dxh57UEYwWzgN2hG2PAJcCo8Au\ndx8Ghs3seWAB8GSy4YqITF+pFAp3fw3AzBoJCsZq4M/cfSzcZRCYDTQBh/K+dLw9UnPzLHK5+rLG\nPJmWlsZE+klDlnODbOen3GpXNeaX2n0UZvZ2oBf4hrv/TzP707y3G4FXgcPh64ntkQYGhsoZ6qRa\nWhrp7x9MpK+kZTk3yHZ+yq12pZ3fZEUqlXMUZvbrwHeBP3T3LWHzT8xsUfh6CbAT2AMsNLOZZjYb\nOJvgRLeIiCQkrRHFl4Bm4FYzuzVsWw5sNLOTgT6gx91HzWwjQdGYAdzi7kdSiVhEZJpK6xzFcoLC\nMFFHgX03A5srHpSIiBSkG+5ERCSSCoWIiERSoRCRaaG3t4eOjnbmzm2mo6O9ZtevToMeMy4imdfb\n20N399Jj2319+45tZ+VR4JWkEYWIZN769XcVbN+wYV3CkdQmFYqUNPT20NzRzmlzm2nuaKdBw2CR\nijlwYH+sdnkzFYoUNPT20NS9lFzfPupGR8n17aOpe6mKhUiFtLbOi9Uub6ZCkYJZkwyDZ2kYLFIR\nK1asKti+fPnKhCOpTSoUKaifZLg7WbuITE1nZxebNm2hrW0+uVyOtrb5bNq0RSeyi6RCkYLRSYa7\nk7WLZFHSl6t2dnaxfftuXnrpFbZv360iEYMKRQqGJhkGD2kYLNPE+OWqfX37GB0dPXa5qu5tqE4q\nFKEkr0Ia7uzi8KYtjLTNZyyXY6RtPoc3bWFYn3BkmtDlqrVFhYJ0rkIa7uxiYPtufvnSKwxs360i\nIVUhqekgXa5aW1QoCK5CeoBgjdVc+PcD6CokmV6SnA7S5aq1RYUC+Jv9z/EJ4FmCRbqfBT4RtotM\nF0lOB+ly1dqiQgH8ycknF2xfe1LhdqkuWX7YW5K5JTkdpMtVa4seCgj0vfFGrPZy6O3tYf36uzhw\nYD+trfNYsWKVfklKkOWHvSWdW2vrPPr69hVsr4TOzq6a/xlNFxpRAK12duH2eYXbpyrpSwOT/sSd\nZH9JXz2T5dw0HSSTUaEg+V+QJA8AaRSlJPtLcroky7mBpoNkcioUJP8LkuQBIOlPpUn3l+TVM1nO\nbZzuXpZCqv4chZnNAL4BvBcYBn7f3Z8vdz9JzpcmORec9KfSpPtbsWLVm+bxx1ViNJjl3ESi1MKI\n4qPATHdvB74IFP5YV0OSnOpK+lNp0v0lORrMcm4iUWqhUFwEPArg7j8C3p9uOFOX5AEg6fMvaZwQ\nTWq6JMu5iUQaGxur6j+tra33tLa2Lsnb/qfW1tZc1Ne88cbImBy3bdu2sQULFozlcrmxBQsWjG3b\nti1T/SUpy7mJjE1yTK0bGxtLu1ZFMrN1wI/c/Zvh9kF3PyPqa/r7BxNJqqWlkf7+wSS6SlyWc4Ns\n56fcalfa+bW0NNYVaq+FqaddwBUAZvZBgidsiIhIQqr+qiegF7jEzHYDdcCnUo5HRGRaqfpC4e5H\ngc+kHYeIyHRVC1NPIiKSIhUKERGJVPVXPYmISLo0ohARkUgqFCIiEkmFQkREIqlQiIhIJBUKERGJ\npEIhIiKRVChERCSSCoWIiESq+mc91RIzeydwCzDb3bvCtrcQLOX6OrDd3f8qxRDLwszagC8DLwPf\nd/eedCMqHzN7B7AReAU44O53pBxSWZnZQuC/EPzut7n7BSmHVDbhssl/DDQBT7n7vSmHVDZmtogg\nt33AA+6+Pcn+VShCZrYF+Ajwr+4+P6/9cmADUA/cE3XgcPf/C1xvZvkHzquBHnd/yMz+Gki1UJQj\nT2AJ8DV332lm3waqolCUKbf/SPDzuj/8eVWNMv0f3QnsNLOPAk9WOOSilelndxVwBsEHmIMVDDeW\nMuU2BrwGzCSF3FQojtsKfB24b7zBzOqB/w5cQvDDeTI8MNYDayd8/VJ3/9cC3/cMjq+hMVrmmEux\nlSnmCfwlcJuZ/Q5wagIxF2srU8/tR0CPmY3nWU22Ur7/o9cB11c64Bi2MvWfnQG73X1T+GHt+wnE\nXYytTD23ne6+w8x+HVhHMCpMjApFyN1/aGZnTmg+H3g+HClgZg8AV7n7WoJPCMU4SFAsfkoVnBMq\nY57Lwv/sD1Ys2JjKkZuZ/QFwW/i9eoC/qHDYRSvXzy6cXjvk7lWzVFyZfnYHCaZ4AY5WMNxYynxs\nGQAaKhJoBBWKaL8B/Cxv+yDwgcl2NrNTgduBc8zs5vCH/iDwdTO7EnioksFOQdw8zwS+BLwF+GpF\nI5u6WLkBjwJfNrPrgBcrGFe5xM0PgpFE1RTACHFzexD4WngeZkclAyuDuL9zVwOXAb9GMDpJlApF\nGbn7y0xYZMndf0XGVuVz9xeBT6cdRyW4+16gK+04Ksndb0s7hkpw9yGqazqtbNz9QVIcvac+FVLl\n/hl4e972GWFb1mQ5zyznBtnOT7lVCY0ooj0JnGVmv0XwQ7yW4CRg1mQ5zyznBtnOT7lVCY0oQma2\nDfiH4KUdNLPr3X0E+BzwHaAP+Ka770szzqnKcp5Zzg2ynZ9yq+7ctMKdiIhE0ohCREQiqVCIiEgk\nFQoREYmkQiEiIpFUKEREJJIKhYiIRFKhEBGRSCoUIiISSYVCZBJmNmZmz5jZ4nD7P5lZX4H9HjOz\nV8zsc2Xoc7eZ1U1oOzOMZdOE9veF7b831X5FoqhQiES7wN0fC1+fAzw9cQd3Xwx8e6odmdlZBGsU\nFHpcwsvA5eEaIOM+DvRPtV+RE1GhECneucBPTrSTmV1lZqeU8P4S4JFJvuy1sO+L89ouBR4rvLtI\n+ahQiBSv4IiigKXAkRLevxT4bsTXfZNwrQwz+23gGY6v6CZSMXrMuEgRzGwWwZrMkSMKM7sGuAB4\n3MyWETw+ejFwCLgauDL//fEnhoYjjFnh4leTeQhYY2YzCKad/jr8/iIVpRGFSHEWAD9z94ET7Pe/\ngEfdfRHwNqDB3dsJzmFcmf/+hMdKLwK2R33jcI3rfwQuAj6Epp0kISoUIsUp6vwE8B5gvABcCWwO\nXx8Fhia8ny/q/ES+bwJ3AE+FaxqIVJwKhUhxzgEWm9mLeX+uKbDf2cD/CV/ngJyZnQx8DNg54f18\n7weeKiKOh4D3EUw7iSRCCxeJTMLMxoBGd3+tiH23Ehzo9wD3AHcDu4Hxex/udPdeMzt//H13v7si\ngYuUmQqFyCTM7OfAL4BVefdSFNrvMeBdwB+5+18kFZ9IUlQoREQkks5RiIhIJBUKERGJpEIhIiKR\nVChERCSSCoWIiERSoRARkUgqFCIiEkmFQkREIv1/CsIMhEvRVPoAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x113b9ef90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""# y will be complex concentration\n"",
    ""# x will be total ligand concentration\n"",
    ""plt.semilogx(Ltot,F_PL_i, 'ro')\n"",
    ""plt.semilogx(Ltot,F_L_i, 'ko')\n"",
    ""plt.xlabel('$[L]_{tot}$ / M')\n"",
    ""plt.ylabel('$Fluorescence$')\n"",
    ""plt.legend();""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# We know errors from our pipetting instruments.\n"",
    ""P_error = 0.35\n"",
    ""L_error = 0.08\n"",
    ""\n"",
    ""assay_volume = 100e-6 # assay volume, L\n"",
    ""\n"",
    ""dPstated = P_error * Ptot\n"",
    ""dLstated = L_error * Ltot""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/pymcmodels.py:70: RuntimeWarning: invalid value encountered in divide\n"",
      ""  scaling = (1 - np.exp(-alpha)) / alpha\n""
     ]
    }
   ],
   ""source"": [
    ""# Now we'll use our Bayesian modeling scheme from assaytools.\n"",
    ""from assaytools import pymcmodels\n"",
    ""pymc_model = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""    top_complex_fluorescence=F_PL_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""mcmc = pymcmodels.run_mcmc(pymc_model)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""import matplotlib.patches as mpatches #this is for plotting with color patches""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""def mcmc_three_plots(pymc_model,mcmc,Lstated):\n"",
    ""\n"",
    ""    sns.set(style='white')\n"",
    ""    sns.set_context('talk')\n"",
    ""    \n"",
    ""    import pymbar\n"",
    ""    [t,g,Neff_max] = pymbar.timeseries.detectEquilibration(mcmc.DeltaG.trace())\n"",
    ""    \n"",
    ""    interval= np.percentile(a=mcmc.DeltaG.trace()[t:], q=[2.5, 50.0, 97.5])\n"",
    ""    [hist,bin_edges] = np.histogram(mcmc.DeltaG.trace()[t:],bins=40,normed=True)\n"",
    ""    binwidth = np.abs(bin_edges[0]-bin_edges[1])\n"",
    ""\n"",
    ""    #set colors for 95% interval\n"",
    ""    clrs = [(0.7372549019607844, 0.5098039215686274, 0.7411764705882353) for xx in bin_edges]\n"",
    ""    idxs = bin_edges.argsort()\n"",
    ""    idxs = idxs[::-1]\n"",
    ""    gray_before = idxs[bin_edges[idxs] < interval[0]]\n"",
    ""    gray_after = idxs[bin_edges[idxs] > interval[2]]\n"",
    ""    for idx in gray_before:\n"",
    ""        clrs[idx] = (.5,.5,.5)\n"",
    ""    for idx in gray_after:\n"",
    ""        clrs[idx] = (.5,.5,.5)\n"",
    ""    \n"",
    ""    plt.clf();\n"",
    ""    plt.figure(figsize=(12,3));\n"",
    ""\n"",
    ""    plt.subplot(131)\n"",
    ""    property_name = 'top_complex_fluorescence'\n"",
    ""    complex = getattr(pymc_model, property_name)\n"",
    ""    property_name = 'top_ligand_fluorescence'\n"",
    ""    ligand = getattr(pymc_model, property_name)\n"",
    ""    for top_complex_fluorescence_model in mcmc.top_complex_fluorescence_model.trace()[::10]:\n"",
    ""        plt.semilogx(Lstated, top_complex_fluorescence_model, marker='.',color='silver')\n"",
    ""    for top_ligand_fluorescence_model in mcmc.top_ligand_fluorescence_model.trace()[::10]:\n"",
    ""        plt.semilogx(Lstated, top_ligand_fluorescence_model, marker='.',color='lightcoral', alpha=0.2)\n"",
    ""    plt.semilogx(Lstated, complex.value, 'ko',label='complex')\n"",
    ""    plt.semilogx(Lstated, ligand.value, marker='o',color='firebrick',linestyle='None',label='ligand')\n"",
    ""    #plt.xlim(.5e-8,5e-5)\n"",
    ""    plt.xlabel('$[L]_T$ (M)');\n"",
    ""    plt.yticks([])\n"",
    ""    plt.ylabel('fluorescence');\n"",
    ""    plt.legend(loc=0);\n"",
    ""\n"",
    ""    plt.subplot(132)\n"",
    ""    plt.bar(bin_edges[:-1]+binwidth/2,hist,binwidth,color=clrs, edgecolor = \""white\"");\n"",
    ""    sns.kdeplot(mcmc.DeltaG.trace()[t:],bw=.4,color=(0.39215686274509803, 0.7098039215686275, 0.803921568627451),shade=False)\n"",
    ""    plt.axvline(x=interval[0],color=(0.5,0.5,0.5),linestyle='--')\n"",
    ""    plt.axvline(x=interval[1],color=(0.5,0.5,0.5),linestyle='--')\n"",
    ""    plt.axvline(x=interval[2],color=(0.5,0.5,0.5),linestyle='--')\n"",
    ""    plt.axvline(x=np.log(Kd),color='k')\n"",
    ""    plt.xlabel('$\\Delta G$ ($k_B T$)',fontsize=16);\n"",
    ""    plt.ylabel('$P(\\Delta G)$',fontsize=16);\n"",
    ""    #plt.xlim(-15,-8)\n"",
    ""    hist_legend = mpatches.Patch(color=(0.7372549019607844, 0.5098039215686274, 0.7411764705882353), \n"",
    ""        label = '$\\Delta G$ =  %.3g [%.3g,%.3g] $k_B T$' \n"",
    ""        %(interval[1],interval[0],interval[2]) )\n"",
    ""    plt.legend(handles=[hist_legend],fontsize=10,loc=0,frameon=True);\n"",
    ""\n"",
    ""    plt.subplot(133)\n"",
    ""    plt.plot(range(0,t),mcmc.DeltaG.trace()[:t], 'g.',label='equil. at %s'%t)\n"",
    ""    plt.plot(range(t,len(mcmc.DeltaG.trace())),mcmc.DeltaG.trace()[t:], '.')\n"",
    ""    plt.xlabel('MCMC sample');\n"",
    ""    plt.ylabel('$\\Delta G$ ($k_B T$)');\n"",
    ""    plt.legend(loc=2);\n"",
    ""\n"",
    ""    plt.tight_layout();\n"",
    ""    \n"",
    ""    return [t,interval,hist,bin_edges,binwidth]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""2e-09""
      ]
     },
     ""execution_count"": 20,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""Kd""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 21,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""Real Kd is 2nm or -20.0301186564 k_B T.\n""
     ]
    }
   ],
   ""source"": [
    ""print 'Real Kd is 2nm or %s k_B T.' %np.log(Kd)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x113988310>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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/88IPKecaOHYv8/HyZNldXV6SkpMDPzw/37t3Dq1evIBAIpGp+5ubmKCgowPjx48FisbBr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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x116488c50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""[t,interval,hist,bin_edges,binwidth] = mcmc_three_plots(pymc_model,mcmc,Ltot)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""That works, but the equilibration seems to happen quite late in our sampling! Let's look at some of the other parameters.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 22,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""well_area = 0.1586 # well area, cm^2 # half-area wells were used here\n"",
    ""path_length = assay_volume / well_area""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 25,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:253: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/matplotlib/__init__.py:917: UserWarning: axes.hold is deprecated. Please remove it from your matplotlibrc and/or style files.\n"",
      ""  warnings.warn(self.msg_depr_set % key)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/matplotlib/rcsetup.py:152: UserWarning: axes.hold is deprecated, will be removed in 3.0\n"",
      ""  warnings.warn(\""axes.hold is deprecated, will be removed in 3.0\"")\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:290: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:295: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(False);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:304: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:309: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(False)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:324: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:332: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(False);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:340: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:355: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:363: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True);\n""
     ]
    },
    {
     ""name"": ""stdout"",
     ""output_type"": ""stream"",
     ""text"": [
      ""(10000,)\n""
     ]
    },
    {
     ""name"": ""stderr"",
     ""output_type"": ""stream"",
     ""text"": [
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:378: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True);\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:396: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(True)\n"",
      ""/Users/hansons/anaconda2/lib/python2.7/site-packages/assaytools-0.2.0-py2.7.egg/assaytools/plots.py:413: MatplotlibDeprecationWarning: pyplot.hold is deprecated.\n"",
      ""    Future behavior will be consistent with the long-time default:\n"",
      ""    plot commands add elements without first clearing the\n"",
      ""    Axes and/or Figure.\n"",
      ""  plt.hold(False)\n""
     ]
    },
    {
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f9SelauPpX5V/maBrdIFRT7khQRK3LP8G7JGkZVp0+e/F7sGTbtXrrcW3enVLicj3a1NWU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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11acabcd0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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r1k5eXlxYvXiwnJyclJSUpOTlZoaGhql+/vnx9fRUfH6/Nmzfrww8/lI2NjcLC\nwrRw4UKFhoaW9ikAAAAAQLlV6iOJP/30k9LT0/XSSy/ptttuU/369bV8+XK5urpq06ZNGjZsmCpW\nrCgfHx917dpVa9askSStXbtWzz77rKpVqyYXFxeFhYVp9erVpR0+AAAAAJRrpZ4k7t27V/Xr19e0\nadMUGBioTp066aefflJmZqbs7Ozk5uZmbuvu7q7U1FRJUmpqqurVq2dRl5aWJsMwSvsUAAAAAKDc\nKvUkMTMzU999952qVKmiL7/8Um+++aYmTpyoc+fOycHBwWJbBwcHZWdnS5KysrIs6h0dHVVQUKDc\n3NxSjR8AAAAAyrNS/06ivb297rzzToWFhUmSmjZtqk6dOik6Olo5OTkW22ZnZ8vJyUnShYTx3/VZ\nWVmys7NTxYoVSy94AAAAACjnSn0k0d3dXfn5+RYrk+bn56tRo0bKy8vT4cOHzfK0tDRzimndunWV\nlpZmUVenTp3SCxwAAAAAbgGlniQGBgbKwcFBs2fP1vnz57Vz505t3LhRnTt3VocOHTRjxgxlZWVp\n9+7dWrdunYKDgyVJ3bp1U1xcnI4ePaqMjAy988476t69e2mHDwAoQq+EQWUdAgAAuE5Kfbqpg4OD\nli5dqjfeeEOtW7dWpUqVNHbsWPn6+mrixIkaP3682rRpIycnJ40cOVJNmjSRJPXt21cZGRnq2bOn\n8vLyFBwcrP79+5d2+AAAAABQrpV6kihJtWvXVlxcXKFyZ2dnzZo1q8h9bG1tFRkZqcjIyJIODwAA\nAABuWaU+3RQAAAAAcOMiSQQAlBm+ywgAwI2HJBEAAAAAYCJJBAAAAACYSBIBAAAAACaSRAAAAACA\niSQRAAAAAGAiSQQAAAAAmOyudocDBw7or7/+UnZ2tqpWrSpPT085OTmVRGwAAAAAgFJmVZJ46NAh\nxcfHa926dcrIyJBhGP/XgJ2dmjZtqt69e+vhhx+WjY1NiQULAAAAAChZV0wSJ0+erMTERAUFBenF\nF1+Uj4+PqlWrJgcHB2VmZiolJUU7duzQ22+/rblz52ry5Mny9vYujdgBAAAAANfZFZPE8+fPa8OG\nDXJxcSlUd9dddykgIEABAQEaOnSoNm7cqD/++IMkEQAAAABuUldMEl977TWrG3vwwQf/UzAAAAAA\ngLJl1eqmOTk52r9/f0nHAgAAAAAoY1YtXPP+++/ro48+0qpVqyRJTzzxhGrXrq2GDRua/1WpUqVE\nAwUAAAAvxM4qAAAZ30lEQVQAlDyrksQNGzbo+eefNz//9ttvsrGxUVJSkk6cOCE7OzutX79e9957\nb4kFCgAAAAAoeVZNNz148KC8vLwsymbMmKGkpCR9/fXXCgwM1PLly0skQAAAAABA6bEqSczMzJS9\nvb352c3NzXwfoouLi/r06aOkpKSSiRAAAAAAUGqsmm5arVo1paWl6Z577pEkffjhhxb1derU0cGD\nB69/dAAAAACAUmXVSGJgYKDee++9Yutzc3OvW0AAAAAAgLJjVZIYGhqqrVu36q233iqyftu2bapd\nu/Z1DQwAAAAAUPqsmm7q5uammJgYRURE6Ouvv1afPn3k7e0tOzs7bd++XbNmzdLQoUNLOlYAAAAA\nQAmzKkmUpKCgIH3wwQeaPHmyxo8fL8MwJEmGYahz587q169fiQUJAAAAACgdVieJklS3bl3FxcUp\nPT1d+/fv17lz51S/fn25urpq+fLlJIoAAAAAcJO7qiTxomrVqqlatWr64YcftGDBAn322WfKzs4m\nSQQAAACAm9xVJ4knT57UmjVrlJiYqLS0NPn7++uZZ57R/PnzSyI+AMANrFfCoLIOAQAAXGdWJ4nb\ntm3TihUrtGnTJtWsWVPdu3dXt27dVKNGDf36668kiQAAAABQDliVJD700EM6c+aMunTpovj4eHl7\ne5d0XAAAAACAMmDVexL//PNP+fj4qFWrVmrYsOF1OXBGRoYCAgL05ZdfSpIyMzMVEREhf39/tW3b\nVomJiea2hmFoxowZatWqlZo3b65JkyYpPz//usQBAAAAAPg/ViWJmzZtkqenpyZMmKCgoCBNmDBB\nP/3003868KuvvqqTJ0+an8eNGycnJyclJSUpOjpa06dP165duyRJ8fHx2rx5sz788EOtX79eO3fu\n1MKFC//T8QEAAAAAhVmVJNasWVORkZHavHmzJk2apMOHD6tPnz7q1KmTYmNj9eeff17VQd9//305\nOjrqnnvukSSdPXtWmzZt0rBhw1SxYkX5+Pioa9euWrNmjSRp7dq1evbZZ1WtWjW5uLgoLCxMq1ev\nvspTBQAAAABcyVWtbmpra6uOHTuqY8eOOnLkiBITE7VixQodPXpUNjY2VrWRlpamRYsWacWKFerR\no4ck6Y8//pCdnZ3c3NzM7dzd3bVhwwZJUmpqqurVq2dRl5aWJsMwrD4uAAAAAODKrBpJLMo999yj\nYcOG6YsvvtDcuXPVrl27K+5z/vx5jRo1Sq+++qqcnZ3N8nPnzsnBwcFiWwcHB2VnZ0uSsrKyLOod\nHR1VUFCg3Nzcaw0fAAAAAFCEq35P4qUqVKigdu3aWZUkxsbGqmHDhmrTpo1FuaOjo3JycizKsrOz\n5eTkJOlCwvjv+qysLNnZ2alixYr/NXwAAAAAwL9cMUlMSUmRh4eHVY3l5eXpr7/+0n333Vdk/fr1\n63X8+HGtX79eknTmzBmNGDFCISEhysvL0+HDh1WjRg1JF6alXpxiWrduXaWlpalJkyZmXZ06dayK\nCQBw/fVKGFTWIQAAgBJyxemmISEhioyM1NatW2UYRpHbHD9+XHFxcerUqZO+++67Ytv69NNP9cMP\nP2jHjh3asWOHatSooZkzZyoiIkIdOnTQjBkzlJWVpd27d2vdunUKDg6WJHXr1k1xcXE6evSoMjIy\n9M4776h79+7XeMoAAAAAgOJccSTxk08+0fz58zV8+HCdP39ejRs3lqurqypWrKjMzEz9+uuvOnjw\noPz9/TVlyhS1aNHimgKZOHGixo8frzZt2sjJyUkjR440Rw779u2rjIwM9ezZU3l5eQoODlb//v2v\n6TgAAAAAgOLZGMUND14iNzdXX331lbZv365Dhw4pOztbVatWVePGjXX//ferbt26JR3rNTt06JA6\ndOigzz//XLVq1SrrcADgplfcdFPXo5mKGf6+JGno2310rPqdVre54sm51yU2AABQ2NXkRFYvXLNl\nyxbt2bNHNWvW1MCBA3XXXXf950ABAAAAADcWq5LEOXPmKCYmxnwtxZw5c7Rs2bIbevQQAAAAAHD1\nrHpP4sqVKzV8+HDt2rVLSUlJatq0qaZOnVrSsQEAAAAASplVSeKxY8f06KOPSpKqVq2q119//bKr\nnQIAAAAAbk5WJYkFBQWyt7c3P7u6usrW1lbp6eklFhgAAAAAoPRZlSRK0v/7f/9PGzdu1F9//SVJ\nsrGxUW5ubokFBgAAAAAofVYtXNO2bVt99NFHWrhwoWxsbFSpUiXl5ORo8eLFCggIkLe3t1xdXUs6\nVgAAAABACbMqSZw3b54k6fjx49qzZ4/27t2rPXv26LPPPlN8fLxsbGxUrVo1bdmypUSDBQCUreLe\njwgAAMoPq9+TKEkuLi5q166d2rVrZ5YdO3ZMP//8s/bt23fdgwMAAAAAlK6rShKL4urqKldXV3Xs\n2PF6xAMAAAAAKENWL1wDAAAAACj/SBIBAAAAACaSRAAAAACAiSQRAAAAAGAiSQQAAAAAmP7z6qYA\ngPKP9yMCAHDrYCQRAAAAAGAiSQQAAAAAmEgSAQAAAAAmkkQAAAAAgIkkEQAAAABgIkkEAAAAAJhI\nEgEAAAAAJpJEAAAAAICJJBEAcFm9EgaVdQgAAKAUkSQCAAAAAEwkiQAAAAAAE0kiAAAAAMBUJkni\njh079MQTT8jf318dO3bU8uXLJUmZmZmKiIiQv7+/2rZtq8TERHMfwzA0Y8YMtWrVSs2bN9ekSZOU\nn59fFuEDAAAAQLllV9oHzMzM1ODBgzVu3Dg98sgj+uWXX9S/f3/de++9Wr58uZycnJSUlKTk5GSF\nhoaqfv368vX1VXx8vDZv3qwPP/xQNjY2CgsL08KFCxUaGlrapwAAtwQWrAEA4NZU6iOJhw8fVps2\nbRQcHKwKFSqocePGatmypXbu3KlNmzZp2LBhqlixonx8fNS1a1etWbNGkrR27Vo9++yzqlatmlxc\nXBQWFqbVq1eXdvgAAAAAUK6VepLYsGFDTZs2zfycmZmpHTt2SJLs7Ozk5uZm1rm7uys1NVWSlJqa\nqnr16lnUpaWlyTCMUoocAAAAAMq/Ml245vTp0woPDzdHEx0cHCzqHRwclJ2dLUnKysqyqHd0dFRB\nQYFyc3NLNWYAQMnolTCIKa4AANwASv07iRf9+eefCg8Pl5ubm95++239/vvvysnJsdgmOztbTk5O\nki4kjP+uz8rKkp2dnSpWrFiqcQNAeUeiBgDAra1MRhL37t2rXr16KSgoSLGxsXJwcFDt2rWVl5en\nw4cPm9ulpaWZU0zr1q2rtLQ0i7o6deqUeuwAUF4xkgcAAKQySBIzMjIUEhKi/v37a8yYMapQ4UII\nlSpVUocOHTRjxgxlZWVp9+7dWrdunYKDgyVJ3bp1U1xcnI4ePaqMjAy988476t69e2mHDwAAAADl\nWqlPN125cqVOnDihuXPnau7cuWb5M888o4kTJ2r8+PFq06aNnJycNHLkSDVp0kSS1LdvX2VkZKhn\nz57Ky8tTcHCw+vfvX9rhAwAAAEC5VupJYnh4uMLDw4utnzVrVpHltra2ioyMVGRkZEmFBgAAAAC3\nvDJd3RQAAAAAcGMhSQQAAAAAmEgSAQCsagoAAEwkiQAAAAAAE0kiAAAAAMBEkggAAAAAMJEkAgAA\nAABMpf6eRADAjYMFawAAwKUYSQQA3FB6JQwieQUAoAwxkggAtyCSMAAAUBxGEgEAAAAAJpJEAAAA\nAICJJBEAAAAAYCJJBAAAAACYSBIBAAAAACaSRAAAAACAiSQRAAAAAGDiPYkAcAu5md6PeDHWFU/O\nLeNIAAC4tTCSCAAAAAAwkSQCAAAAAEwkiQAAAAAAE0kiAAAAAMBEkggAt4ibadGaf7tZ4wYA4GZF\nkggAAAAAMJEkAgAAAABMJIkAAAAAABNJIgAAAADAZFfWAQAAShYLvwAAgKtxUyWJ+/bt02uvvabf\nfvtNtWvX1oQJE+Tr61vWYQEAStilie6KJ+eWUSQAAJR/N81005ycHIWHh6tHjx7avn27nn76aQ0a\nNEhnz54t69AA4IbUK2FQuR1FLM/nBgBAWbtpRhK3bdumChUqqG/fvpKknj17asmSJdqyZYu6dOlS\nxtEBAMpCUYkio4wAAPw3N02SmJaWprp161qUubu7KzU19Yr75ufnS5KOHj1aIrEBQFkZsm6sJGl2\n10nmz2Up61SODtnZmT/nVswq9RgOHTpU6scEAOBGdzEXupgbXc5NkySeO3dOjo6OFmUODg7Kzs6+\n4r7Hjx+XJPXr169EYgOAstbhrQ5lHYKpQ506F354b0/ZHP8GuhYAANxojh8/rtq1a192m5smSXR0\ndCyUEGZnZ8vJyemK+3p5eSk+Pl4uLi6ytbUtqRABAAAA4IaUn5+v48ePy8vL64rb3jRJYp06dfTe\ne+9ZlKWlpalr165X3NfBwUHNmjUrqdAAAAAA4IZ3pRHEi26a1U0DAgKUm5urpUuXKi8vTytXrlRG\nRoaCgoLKOjQAAAAAKDdsDMMwyjoIa+3fv1+vv/66kpOTVbt2bb3++uu8JxEAAAAArqObKkkEAAAA\nAJSsm2a6KQAAAACg5JEkAgAAAABMJIkAAAAAABNJIm44ubm5ev3119WqVSv5+/tr0KBBOnbsmFmf\nlJSkrl27ytfXV3379lVaWloZRotrFRsbq7Zt26pZs2Z6+umnlZKSYta98cYb8vLykp+fn/nf4cOH\nyzBaXIvL9THPcfkyadIkTZ061aKM57h8KaqPeY7Lp65du6pJkybmc/vII4+UdUi4Dvbt26eePXvK\n19dX3bt3165duy67PUkibjhz5szR77//rk8//VRbt26Vs7OzJk6cKEnKyMjQkCFDNGLECH3//fdq\n3bq1hgwZItZfurl88MEHWrt2rZYuXapt27YpICBAYWFhKigokHThH7Lp06frxx9/NP+rUaNGGUeN\nq3G5PuY5Lj/++ecfjR49WkuXLi1Ux3NcPhTXxzzH5VN2drZSU1P15Zdfms/txx9/XNZh4T/KyclR\neHi4evTooe3bt+vpp5/WoEGDdPbs2WL3IUnEDWfYsGFasGCBnJ2ddfbsWZ09e1ZVqlSRJG3YsEEN\nGzZU+/btZW9vr0GDBik9PV0///xzGUeNq/HPP/8oPDxcbm5usrOz0zPPPKPDhw/r6NGjKigoUHJy\nsho2bFjWYeI/uFwf8xyXH3379pWtra06depkUc5zXH4U18c8x+VTSkqK7r77blWtWrWsQ8F1tG3b\nNlWoUEF9+/bVbbfdpp49e+ruu+/Wli1bit3HrhTjA0znz5/XuXPnCpVXqFBBlSpVkq2trWbPnq3Z\ns2erWrVqio+PlySlpqaqbt265va2trZyc3NTamqqfHx8Si1+XNnl+njAgAEWZV988YWcnZ1VvXp1\nHThwQNnZ2Zo6dap27typ6tWr64UXXlC7du1KK3RY6Vr7mOf45nGlf6sXL14sV1dXjR492qKe5/jm\nca19zHN887pcn+/bt092dnZ68skn9ccff6hRo0Z69dVXLfoaN5+0tLRCfeju7q7U1NRi9yFJRJn4\n/vvv1b9//0LlNWvW1BdffCFJCg0NVUhIiKZPn64BAwbo448/VlZWlipVqmSxj6Ojo7KyskolbljP\nmj6+uN348eP1xhtvqEKFCjp16pRatGihkJAQeXt7a8uWLRo+fLhWrFghT0/P0jwFXMG19jHP8c3j\nSn3s6upa5H48xzePa+1jnuOb1+X6fODAgfL29tbIkSN19913KzY2VqGhoVq/fr0cHBzKIFpcD+fO\nnZOjo6NFmYODg7Kzs4vdhyQRZaJ169ZKTk6+7DYVK1aUJI0aNUrLly9XSkqKHB0dC93QWVlZcnJy\nKrFYcW2s6eM1a9ZowoQJGjdunIKDgyVJvr6+WrJkiblNx44dFRAQoM2bN/PL5Q3mWvuY5/jmYU0f\nF4Xn+OZxrX3Mc3zzulKf9+7d2/w5MjJS8fHx+uWXX+Tn51ca4aEEFPW8ZmdnX/Z55TuJuOGMGTNG\ny5YtMz/n5+eroKBAlStXVp06dSxWT8vPz9fBgwdVr169sggV/8GcOXP05ptvKjY2Vj169DDLt27d\nquXLl1tsm5OTY/7RADeP4vqY57j84zku/3iOy6eEhAQlJSWZn/Pz83X+/Hme3Zvcpc+rdGEK6uWe\nV5JE3HB8fHy0cOFCHTp0SFlZWYqKipK/v7/c3Nz04IMPas+ePdqwYYNyc3M1d+5cVa9eXY0aNSrr\nsHEVVq1apSVLlmjZsmUKCAiwqLOxsdHUqVO1Y8cO5efn66OPPtJPP/2khx9+uIyixbW4XB/zHJd/\nPMflH89x+ZSenq6oqCgdOXJE2dnZmjJliurUqaMGDRqUdWj4DwICApSbm6ulS5cqLy9PK1euVEZG\nhoKCgordx8ZgrWLcYAzD0Jw5c5SQkKC8vDwFBgbq1VdfNVfa2rZtmyZPnqw///xTDRs2VFRUlNzd\n3cs4alyNTp066dChQ7K3t7coX7lyperWravExEQtWLBA6enpcnd315gxY9SiRYsyihbX4kp9zHNc\nvowePVpVqlTRyy+/bJbxHJcvRfUxz3H5k5eXp+nTp+vjjz/W2bNn1bx5c73++uu8vqYc2L9/v15/\n/XUlJyerdu3aev311+Xr61vs9iSJAAAAAAAT000BAAAAACaSRAAAAACAiSQRAAAAAGAiSQQAAAAA\nmEgSAQAAAAAmkkQAAAAAgIkkEQAAAABgIkkEAAAAAJhIEgEAKEGnTp1ScHCwzpw5Y5bNmTNHjz/+\n+HU/lmEY6t27t9LS0q572wCAW4ddWQcAAEBZO3TokB588EHVrFlTGzdulI2NTaFt/vzzTy1YsEDf\nfPON0tPTdeedd+ree+/VQw89pN69e8vR0bHItmfOnKkePXqoUqVKZtkvv/yixo0bX1WMW7du1XPP\nPXfZbcLCwjR48GCNHz9e77777lW1DwDARSSJAIBbXmJioh544AH99NNP2rZtmwICAizqv/nmGw0Z\nMkStWrXSG2+8IXd3d+Xm5mrPnj1atGiRWrZsqUaNGhVq99ixY1q7dq2++eYbi/J9+/YpNDT0qmL0\n8/OzaKd///7y9PTU6NGjzbI77rhDDg4OioqK0o4dO9SsWbOrOgYAABJJIgDgFnf+/HmtWrVKr732\nmmrVqqUVK1ZYJIm///67Bg8erGeeeUYvvfSSxb7u7u4KDg4utu3ly5erRYsWuv32282yzMxM/fXX\nX2ZSmZeXp6lTp+qLL75QdHS0vLy8imzLwcFBDg4OkqTc3FwdOHBAffr0kYuLS6Ft27dvr6VLl5Ik\nAgCuCd9JBADc0jZv3qy8vDy1bdtW3bt318aNG3XixAmzfvLkyapbt65GjBhx1W1v2bJFgYGBFmW/\n/PKLbG1t5enpqSNHjqhfv35KS0vTqlWrik0QL5WSkqK8vLxip6zef//9+vbbb1VQUHDVMQMAQJII\nALilrVixQp07d5a9vb18fHxUq1YtrVmzRtKF6aJJSUnq3bu3KlT4v/9lBgcHy8/PT35+fsUuQJOf\nn6/9+/erfv36FuW//PKL3N3d9f333+uxxx5TQECAFixYoCpVqki6MPoYFBSk7t2768EHH9TgwYOV\nm5tr0cbevXtla2urBg0aFHnsunXr6vTp0yxgAwC4JiSJAIBb1pEjR/T111+re/fuZln37t2VmJgo\nSUpOTlZBQYF8fHws9ouNjdWaNWtUr1493XfffUW2ffLkSeXn55vJ30X79u1Tenq6hg4dqqioKEVG\nRlokoCkpKYqMjNTatWv12Wef6ddff1VycrJFG3v27FHdunXN6aeXunjMf4+IAgBgLZJEAMAtKzEx\nUW5ubmratKlZ1q1bN6WlpWnHjh06e/asJMnW1tZiPzc3N9WuXVuHDh0qdjTv4gqpl075/OWXX9Sx\nY0dVqFBBGRkZhfZLTk42v6/4xx9/yDAMubu7W2yzd+/ey05Nzc/Pt4gBAICrQZIIALgl5efna9Wq\nVerWrZtFec2aNdW8eXOtWLFCHh4ekqTt27cX2v/48eM6ceKEGjZsWGT7VapUkZ2dnf755x+zLDs7\nW6mpqXr00Uc1c+ZMTZo0Sd9++63Ffr/99ptGjRqlhx9+WI8//rjefPNNi9dn5ObmKiUl5bJJ4sVj\nFrWoDQAAV8LqpgCAW9JXX32lo0ePqkGDBkpJSbGoa9q0qRYvXqyxY8eqc+fOmjlzpgoKCtSqVSvZ\n29srNTVV77//vmxtbYt89YV0YRSvUaNG2r9/v7l4TUpKivLz89WwYUNVrlxZL774ol544QUtX75c\n9erV05EjR1S1alV99NFHkqQ1a9YoNjZWixYtMtu90qI1krR//345OzvLzc3tv14mAMAtiCQRAHBL\nWrFihSQpIiKi2G3Wrl2r6dOnKz4+XitXrtTMmTMlXZhuGhgYqFdeeUVVq1Ytdv82bdpo+/btGjBg\ngKQL30esWbOmKleuLEl67rnn9Mcff2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      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11ae816d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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RTCCEE0dHRGI1G3njjDcaNG0dKSgorV65EURTn+rG7776bTz/9lOnTp/P4449TVlbGm2++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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11aff7710>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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HYfO0IAAAAAKD4buszkjcSHR2tqVOnqmvXrvLw8NDLL78sf39/SVJERIQsFovC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\nGlWqQQIAAAAAyo4bJpKzZ8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PJ5ufSpUuGYRjGwoULja5duxr+/v7GU089ZSQnJ9+Ot6RM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\nTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c150750>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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0kPc8sTxsbqWfSyES8VEratwrySOvTmaUsXHOAtGwj7uKsVEJltSIa6OfNixP6\n0i4mwCMNOrcNISpEX6tRm5ZTht3hkhvu7mncMeYUmikosaDzUQNQWGqjS7tQQvy18oHIfWBxl43t\nhzOxVTg5k2nku13n5Y6CWqXghn5tuHVQW3Q6jVxWC8ts7D2Www3923Iqo4Qfdqez/XA2ZWY7Bl81\n1/VuTWSwjsWfHW4w3zrE+PPcQ30IulDeDp7MpW1MUK3Gxu5jObz28R5c1SoFlVLBM+N70btLpLz9\nOUUWPt90kh92p3ukcetIP8bf3ImUTpHAxYN69ZMT2YVm9Bf2OZPVTk6BmZc+3CmnBYCXCqbdk8yn\n358gu8Ai52mbKD+eHd+nVmMyLaeM4+eKiAjRyZ28CoeLnUey6NIulH2puXWmUXigllenDKi1vOrO\nZJYya9EWKpy1x906MJZW4f4Y9Br6J12s590Ng9Szhbz730OUmu3yuEA/b2bcl0xS+zC542K22nn3\n80NsO5hNQwdTby8Fz0/oQ8c2IRw+lefRMDl0Kp8lXxwhI89cbR9QUFkpodXA8N5xuColtl0oP14K\nGDmkvXzSsLDMxsnzRYQE6Ggd6U+Fw+VxQqm66icsAL7bca7RMmjwVdOxdSBJ7UI4lVnG9iM5OJxV\nea5UgEIBrhqHFPc+q/XxIthfi8lqx3whbd2N822Hsnjrk70e5dBdZjvGBeOtVrHvlxzUXkpCA33R\n6zRy/Q51l3mAwd2j+OlgdoPbFBWm5eUJVeXn4MlcWoX788k3v7D9SA7Wcqfcme6XGMGSL49yMr20\n3mWp1Uoc1faB9jH+jL0hAUmSCA30RaFQsHlvBhv2pFflnwpUyqq6yF+vYUBiBLcP7UAlEOyvJa/I\nwoI1Bzw6Y+1j/Jn1QC9yi8xsP5LLTwcysdiq4hzcPRpLuYMf92d6pGW7aH9uGRRHt/gwMvPKPOqN\nDbvTGN67Ta1t2ZOaTWigr5zG1etNd/1a89hb4XBxOqOYYH8dk9/YUKssuL02pT9RoXr+7+ezfLsj\nDUu5Ey8FpHQJ5293J+OerbjM5tGBq1lmAX7cn8G81ftr7XNKBVRK4KOGYT1jufv6jgRdOJkKkHOh\n7VJZKfHKsl2YrA55Xl9vBXExQRw9U1Qrdh+NinK7C4OvmpRO4exJzcVk9axYYiP8mHV/CgEGH7Yf\nzCQpPoyoEL18LK3esXF3OM7nGvlk/S/sTs31aM/FRRl4ZnxvAg0+OBwuPtt0im93pmGxOfH1UXJD\nv7bcNawOxgx7AAAgAElEQVQD67edZdW3J2rFq1DA9X1aM/7mzuh1GoxWu5yu7uNjfGwQZqudZV8d\nZXdqnnxBIjk+hEm3d/M48XUyvRhruR1ruZNyu5NPvz9FbpG1znyeOqob1/dtU2u4Ow/g4rH62x3n\neO+zw3XWnRoV9O0aSVK7EOJiAmgd6c/WgxmcyzGzcXe6fNKrU5sgdh3Lpb7eTJDBm2JjhccwHy+I\niwngfI4Ja4Wr1jwKqDOme6/rQESIjhVfH6+1zKbQa9U8eldX2sUEym0Yd1u35omLlqKxPpHoSNah\npXUkdx/LYc7y3c0dBqOubc9nG0832Fj6veseH8LIQXE4XJWcOF/Gup9P19kIFS5SqxQ4XJdeKtQq\ncNSuv4VLoPWCu0d0pLDUxrpt5+udzlt9aSePfm3XpkSxcW/DDf2memZ8CpIkYS13EhLgg9qr6gzw\nv74/idHsaHwBfwLXdI9iSx0dKxUgdkEhLlLPuRxzvePra1Q3B3dHUWj5FMDIwXFUOCW+35lW7wkG\nwZNKCc+M703vLpHNHQogOpKXpSV1JFtKJ1IQBEEQBEEQhF/f43clMaJfXHOH0WifSNkMMQmXYO5H\nohMpCIIgCIIgCH8Wiz87TG6RpbnDaJToSLZw4hYOQRAEQRAEQfhzmbN8V3OH0CjRkWzB0nLKmjsE\nQRAEQRAEQRB+Y+nVXnDYUomOZAvWlFfFC4IgCIIgCILwx+N+23VL9YfuSI4fP56+ffvy3nvvNXco\nl+WP+s0ZQRAEQRAEQRAaZm/hr7f3au4Afk1vvPEG27dvJzc3t7lDuWy+Pl5YysX3JwRBEARBEATh\nzySohV9U+kNfkYyIiGjuEK7Y3CkDmjsEQRAEQRAEQRB+QwZ9y3/ErUV3JL/++mvGjBlDcnIynTt3\nrjXe5XLx5ptv0rdvX3r06MHUqVMpLi5uhkh/Pe1iAnjniWvw92v5hUkQBEEQBEEQhCv3ysR+zR1C\no1r0ra0Gg4ExY8ZQXl7OCy+8UGv8kiVL2LRpE//9738JCAjgmWee4amnnuLDDz9shmh/Pe1iAvjk\npRsB+HrrGT744ig1vwrSu3M4R88UYa1wolZCz87h7Dya1+ByW4X7MnFkV0L8tXy94wxb9uVgtjnQ\nasB2Gc/2atQK1F5eWGwOlEBctB9nsup/41Tn1oFkF5spNTkufWVXybCUaDLzTJzMMDZpem+1kgpH\n5RWtMzpEx6jh8Xy0LpUy8+U9RD2iTwy7U/MpMdWef3CPaIb3iiEkQIvV5mTRfw9x/grf/DXt7u64\nXC4WfXakSdN7KWBwz2i8VEq2HsrBegm3Z8dG6Rk5oC17f8llx9H8yw25UT5qBeP/ksi+E3nsSW18\nPVcj7xsTE6anc1wQu47lXnbZ+C15qcDZyOMbfRPD2Pkr5mNTqZTgwg6VTTsp56VS4HRJeKlAgQKH\nq/FvMcVG6fnrXd0BCX+9D0H+Gn7ck8niz47UqrNbJC8zuLRMvq07PTuFARARrOf7HWc5cLKQrYdz\nPCbXqJUoFQrK7Y0/w9NQWVECDe1Zs+/vxYnzhXzx07lG1zP1riQOnCrg4KlCzFYH/noNg7tH0SM+\nlN2/5LNl3/nLOr65xYTpeHFCf3IKTRh8vfn39yc4crYIi82JWgUSVeVGo4KwYF8y85v3O3AKaNay\np1Y1bd9pTkpF3Z9ai43Qc+eQ9khIvPffQ9RbzBUukFSXvf7OsYHcObw97WMCsdgcvLBkB4Vl5Ze9\nvF/TjDHJtI0xEOhXdbulXqcht8iMXqvhlWU7+CWt9LKXrVSCWtXwcVbtpcDhrL88+WqVPPdgb5Z/\nlcqpzBrtOoWLm/q2oWenCPz1GkIDfFn+f0fZd6IAs9WBwVdNYrtgAnw1rN+RXi2w2seNQIM3Y6/v\nSLf4EApLreh8vLCWO0lsF8bZrGLaRgcBsGlPGhl5Jj7bfLZWrCol3HZNWzbvy6TY6Fkp+es1vDyx\nH+1iAurd1pZCIUlSy97DgV27dvHggw+SmprqMXzo0KE8+uijjBo1CoD09HSuu+46Nm3aRHR0NABr\n164lNzeXRx99tMnry8zM5Nprr2Xjxo3ExMRcvQ25CuxOOxovDXanncw8s1xY5eEOFxp1VYV25HQB\nLy7bgsOh9KjkokN0vDSpPwqNhe9O/8SWtJ2Y7BYM3noGt+7LHV1uQK/xxVxh5lRWHqZSNX27RrM3\nNYfgAB/iogIxW+3odRo0ahXFZTb5Hu5cUz4apTdBvv5yTIC8Y+WWlhDkawCFSx4HVW+lMtvsBBm0\n2B0u9DoNZzJLeXnZTkpMVjl+f72a5x7sS9tof+wOF59tOsU3O9Kwljvx03kxJDmGMSM6yemg0YDG\nS0NGXhmFpTZaR/gT5K/1SCc5bR0uMvKMrFifyoEThR7jvDUqpt3fke7tItB76+VtLi6zofdVgaTi\nZHoRbSID0Ouq1tcq3F9+SLrYaEOv1aDXaTBXmLG7HOg1vlXbbnER5K/lxPlC4qINHulyNqsYL00l\nRmMl1nInvbtE1SoLHmlXaUOj0sjLMFeY0Xvr5TTW66rKyI97z5NdZOO7/Scwl6nw12sY3iuWfl3D\ncTir1hUbYSAiWF+r/B08UcSc5btrlU0FMHdKfxJaB8lpW7Nc5hZZmLnwJ0rr6CTdPawdo0fEA3ik\nAcD2Q5kY9Boczkp6JETIyzZX2CgpkVj834OcySyTG0yxUXqeH9+XiOCqNM7IK8PiMOOr1uPro6nz\nmQO7wyWnUV6xmVbh/vLw6mXFnffmCnNVrEotecVm1F4q9NqquDVqlbwM9zJzi8xEBFeVnfOFufRo\nFycvr3pZNVeYMdutBPkEY6+0gUvDivWp/HwgC8uFcj6oexQj+rYmJqyqjKXllBISoKOw1Epiu6oO\nwInzVWXYWu7E4awkMsSXMnMFie3CPMqne9tyi8wEGbRk5JmY/+l+0vNMuI8OcTE6nnmgPxHBvtgd\nLnkfldPEZMaOFYfVm/AgPZn5ZbSNDsLutMv5aXfaOZdllOsPu9OFw+mqiuNCOcktMlNmLic61AAq\nO3pvPSfOF1JUWk5ShzDMrlIi/MI4cb6Q73els+NILiarA18fJVGhfmQXWrHYHOi1Kkb0jSM23Jf4\n9lre2/sxZ4rPIyGhQEGsIYYZgyYSrg+tKkcWz+2Ry2+19HH/bXe45Lx1p6M7jwHMdgv/++V7Np75\nGYvDhq+XlsGt++PMiWPTvkzMpqpGwtDkGAZ2j8Rf70NEsP5i+fNVyXWCu/44l11CaIAvdqeralqn\nnZPnSzGa7Xh5KYkM8ZXLq9lqJ7/EjCQpefHDHZRVO9mkUMCwnq245/oEFvx7H0fPloBvET4Je0El\noVBc3B/6x/ZkQs970SjVHvuj3eEit7QEjU8lEX5htdLJvf4AvZaCUgsJrUPk4Z9tOsWGPemUme1y\nOb7/xi5V+0dpCd9uzfaoz6/v04a7hnWQ09Zdns5lGQkN8CW70CSX5/Bgba16I9dYSJDuYp2aa8on\nwi9MjvdsVjEBeq2c1hl5pqpjjrFCXkagwZuZY1OIizJczONq9ar7t0ap9Sgr1Y9x7n0m9VyhRx3u\nrrvd9eyZzNLaeeZdTEpCGx6/ra/HMff9tYfZebT2+x+ef6g3ndv7YS9XVW2Xs2pf0/tU1XnuMpue\nX8qbH+8jPc8sz9s2xsDTD/QmyOBDRp6RTXsz2XIgkzKzHYOvmmt6RBMd5suyr1JrN+ardaa8lDCi\nXxtGX9cOvbbqeFRcZkOv05CZX0ZuoZX+3WJqlQl/vYYBSRHcPLgVNitEhxooMdloFe4vz1/9WOre\nlmJrCRqVGlwasgqM+AdVlc2MvDJ8fTSUmm1yI9/t6Jl8+bc7P90dIgA7Fuwuh1zG3YrLbGQXmjCW\nWzht38+WtB1y+6lnWE9GtB1G24gwuby622np+aXMX13tOKVw0T4miFn390Kvl+TjdrG1BA2+Humi\nAganRDNpZBIatYodR7KZ9+l+Kqv1t1RKBQ/eFcXIPilVZb3ITHquke7x4ew8kkViu1C5nREdapCP\nSe5pNGoVuaZ8CvIUvL16n8c+4KdT8fRDyXSNq93+KLaWEKQLrEobawl2m4a3Vu3lVMbFDmVYkBdP\n3NMbtZeChNYhFJfZ5Po/PEhPsdFGRLCeM0VptAtuc7GuvVA3u4+37n3M7rRTbLTx1dbTbNqVi6Xc\nKbdhenbXkBDRWl63paKCD3d+wQnTEYwVZgzeeobE9ee2TtdfbIPV2H8B8swFLNixnNPFafKwNgEx\nPJw0joTI+vsF7rLk0faq9vfRM/mEBOgI8r/QVrhwbHS35ewOV4t7JrKxPtHvtiNpNBrp1asX//vf\n/+jUqZM8vGfPnrz11ltce+21PP300xw+fBi73U67du345z//2aT1tbSOpLthsvnsNkz2i2c39Wod\nob7B5FuKsDisGLz19AjvQo+oRD5LXU+m8eLZY6VCSffwrvyl01A+3v9f0suyLikGjVLN09c8Rpew\nBKDqgAxwNPc4vhpfFu36GKdU+6qTt0LDwLjenC05z7mSDI9xKpTc3HE4/Vol0y6otdzBsrsclJYb\n2ZGxj63n92CyW/DT6OkW1omBbXoRHxKHRqXBbLeQVZZD18jOVY0bSy6xAdGUWEv5vxMb+DFtJ2a7\nBb1axzVx/bil43D0ah12V9VB2u5ykFWWQ0Joe4ptpQRpAy4Mt6NRaUBScSjzNP88+CEmu9kj9kh9\nGF3C4tmddRBjhRmdyoeh7QYwosM1FJgKSAhtT0ZZNu2C22CuMGNx2Hj9p3fJNtW+SqxEQZA2kHJX\nBWa7Ba3Sm3bBcZSUl5JtykO60D1qGxjLg8mj+e7UjxzIOYbFYcMbNf3bpHC88Aw55vqv+kTpI5jQ\nczRatZZsYx6rDq2ltOLi2Tq9l47xPUYxuG1fediZojQCtf5ovDR8sPsTjhWcwmy3oMGLfrE9Gddz\nFCUlTiwOMzGh/vKBsMRayn+OrWNnxgEsDitqVFwfP4ShbftTZi2la2TVbepmq51TGcW0aeXDyoNr\n2ZN1EHtl1dVpjUpNn6juDGjdiyhDOEHagKqK1m5h+d41bM/YR2W1axgRvmHMHPQIgeoQ9DoN6aVZ\naFRqiqwlLNix3GNb/b39mDnwEUosZYTog2jlH8XZ4vPoNDpiA6JJL63aN4K0/hTbyojQh8odvG1p\ne/n+7M9kGLM9y4NvOH/r/yABPoaqBg2g99az9dxuBsb1xlxh5nTRed7dvYKyiotXh7UKb7pFd+ZI\n7nEsTlu9+RfrH8347ncSHxyP2WFEr/G9WE6pKrN218W0O1ecTlxQrMf46g1fqDroZ5XlEO0fWXXi\n6ELd4j64Hss7wY6MQ+zJOYDFXlW/DGrdhzu73EieqQCFQsFnR79mb07tq9QpUUkczv1Fzk83Xy8t\nKVFJDI7rQ5hvMP93YiM/n9+NzVmOF0quaduf00XnSC/Lkk8KROnDKbQWeyxLo1QzY+AjdA7tQK6p\niNjAyKrtrNFozizN4cnvXqk3XbVePtic5fhpfBnadgC3dbqePFMBAK38o9B4adh6bjc6tQ+JER0B\nOFFwmoTQ9pwtPk/HsA5V67yQ/v/3ywa+Pr2p3vW5hfmE8OSgCcQYIrG77ORbilh/cjMHc49hrDDj\np/Glb0wyg9r0JsDHUJUv3r5VHdSzW7HYrR4xa5RqjuYeJ8o/giBtAHaXvepkxIX9xt0Id3dujuef\nomNYB47ln+Dlze80Gq9eraNbeGeOF52myOZ5xcF9bAjWBl44QaYjSBfocSLRXdbigmLJLMuhbWBb\nULjIMubwz72r5WODAgXtglrzaMp4KiptqFVqMkuz2Zaxl4O5qTgqq44xaqUXQ+P6cUvH6/jv0a/Z\nl3MEi92KVulNUkQnjhacxOKw1rs9KpQ8mDyalOgk9Bpfcs0F8n6bXZaLTqMlzDvG44TR/qwjfH16\nM2klGXLZVCu9cFY65d9h2hDah7TmSN5xTHYLOpUPHUM7cFvn6+kY2h67006uuQCHyyEfG4ptZQRp\n/dmZvp9wfQhxQbEczE1l4c6PasWtU2uZOWASXcI7kmvKp9RWRowhhpIyOxpfO+9sX8bZkvN1XoFU\noqBfTDIGrV+142pVGbqu9VAC9VXHxnPF6YTqQzw6UB4nVJx2zhafJ0wfQn6JjR0529mWuUsut/1a\npVApOfkpbbe8z6qVXgxu3Zux3e+QT0y4t73QXIS/1oC3UsuiPcvk9HWXhXHd76TAXEykIYxW/lHy\nvpZjymfetg8xOcx1bO3FsjkpZSyJ4fFyZ+dITirR/pE4Kp21OgpRfhEUW4opr6x9ovO+pNsJ14UQ\n6R+OyW6ud78J1gbQJTyBvVmHsTpsqJVeSEg4K134emmr0u1Cm62hq8UGjZ5nh0wlWh9ddfLAWoLZ\nbkWv0QHI2/PloR9ZfXxNrfkf6H4H17e7hmJb1cm3XFP+xfrgQsdb763nWP4JXt/ybq26euaAyRhU\nwaw4upozxWlynL5eOlyVzjrTqGbsod4R8glSN3OFmWO5J+nTOpliawnb0/ez6tBntdLBx8ubcmcF\nShRUNnJN3X2svhQBXn7otDqPNlmkbziP9rkPY7mZt7d/UP/2KX2ZOWQykiSh0+jILMtmzdF1DbbB\noKqdkhDSlu0Ze+W6zM1P48vgNn25s8uN8jG4pfjDdiRzcnIYMmQIGzZsoFWrVvLwoUOHMm3aNEaO\nHHnZ62tJHUmz3cL0b16hpLxpt14Kwu9FmC6EfGth4xMKV02EPpQ8c8Hv4zZL4aqJ1IdRZC7GjngD\nuHBpbu94A31adWfxzhVkmnIan0EQLlCgQELCR6HBLtkbvH1dqKJAwQ0drmFU4l9aTIeysT5Ri35G\nsiG+vhcuSZs9z0YZjUb0en1ds/wuLd7xsehECn9IohP528s1FzR3CEIzaOxMuSDU54vj3/LF8W+b\nOwzhd8h9N1W51PKf928pJCS+OfUjP53bzaJbXmkxncmGtOi3tjbEYDAQFRXFsWPH5GHp6emYzWYS\nEhKaMbKra3/u0eYOQRAEQRAEQRCE34DFaWX2d683dxhN0qI7ki6Xi4qKChyOqnu3KyoqqKiowH03\n7ujRo1m6dCkZGRmYTCbefvttBg4c2Oy3o14t7hcLCIIgCIIgCILw55BvLWJv1qHmDqNRLboj+eWX\nX5KUlMTDDz+My+UiKSmJpKQksrKqXoYxadIkhg0bxl133cXgwYORJIm33367maO+ejLKshufSBAE\nQRAEQRCEP5R/bFvS3CE0qkU/I3nHHXdwxx131DtepVIxa9YsZs2a9RtG9dtp5R/V+ESCIAiCIAiC\nIPyhuKSW/4qiFn1F8s+u5jexBEEQBEEQBEH4c8gqa9lvSxYdyRYuOapLc4cgCIIgCIIgCMJvLNo/\nsrlDaJDoSLZwj/d5EJ1K29xhCIIgCIIgCILwG1EpWn43reVH+Cen1/iy+NY5JEckNncogiAIgiAI\nf2oqp9TcIQi/gZaQz9MHTGruEBolOpK/A3qNL7OveYz/3P0+T/abiALFVVlu9Z3EYHShtbjk31qL\nq0XsRDUFFV79T6Joyivr/PtKl+lOv+rp2hCVU/KYr/pwN73RWWu83uj0+N2U8qHGS56uvm3WWlz4\nlzjk35ryStTOOif93am+XU11tfeHnkSgVnq+72xI6/58eNNr/Ofu93kkeSw+Cu9fNa6aeV/fsuor\nl00t27+VS9l/LzXdVE6pwXlqjtMo1QyITZE/KK1VetMrshs6df13mFxuXrrna2j7L6duq16XuOun\nrmEJ+Gn0DcZxtfYV93IeaX8Lt7a+plZdV9e0NcukvkLFkNh+PJryAG0CGv80WH2xe6uq3lmgQoGq\niU2nYC99k9OisfIFVXno3herT1vXfI2Nb2w9dQkqtNc5zp3mDZWx6jE3JZ66lqUpr6yVv1qLy2Pa\nho6f9XEfDxpKR297JQMOmJn4eQGP/6eAif/NZ8ABM972S9+vaq6nsWN2XXHVlT41j2uNbXtTl1vf\nfCqnhFblw6DgbvQP79bofJejvmN1fceeSz0m1UwDnV3hmc+f5TNojxFveyWd7IENLstXpWPWgCk8\n3OOeJtU1AF4KlUcscllUKHlq4GRSon+ddL2aFJL7o4yCLDMzk2uvvZaNGze2uG9SOkwmAJQaDelp\nx4lu3xmNSo3DZMLsVYlapUY6n4Ul2JdAyRvjyZP4hIRwUltOmLeBYI2B9E8+xbhrD44yY6Prk4BT\nkUq29A3EqlWhwYshbfsx0NCJmLgEtJKKX/ZuJb7vYJwmM7qAQFwVFRQ5TITpQwCwuxxknU4lun1n\nynNyKSstIrxT1bOfpgOHqGwdia9aR4W3EpVTQsrIRhMYiA0XmuAgMjdupOifH3vEJAHf9zOQFeOL\ny27H5evNiLgB9K+MJLJjEuU2C4oAPxyZOXxrOcbOw1soUdlRq72J9YvAmpHJgB2FhBc7a1XhElAQ\nomFdfz3lem9uiezLtZ2G4B8UjuVoKvq2banwVkGpiQpvJQE+Birtds6dOkLZR2uwp2d6LEtx4X+r\nj4LCe4dy06C7CAgOp7QoHykzh+zNP1L6489QY1dUR4TjLClFqqjAPcYjVqUSKqtV+l4qurz8IgGJ\nXTBmZmCIaYXd5aDcasHgF0BRWQG2U+cIT+iENSuLtGUfYz55ymObt3X25safTHjXXFc1CpUKw6B+\ncOswKryVRPiF4K/1p8xWhk6jo8JmIcg/FIvdCifTCOzWjXKrBanMiA0niw//m4xzJygLVAMXKnK1\nirHdbufGNoOwFxfjCvZHVVSGNjISo6mEf+9by77swxilCkIkb1Ja9+Q6QyI+YWEUqu0s2/dvzpVm\nAFUHQruPkuHtBjKqy83orC7KvCVcB37h7PyF4KqWZioV7Z96Er+OCdiLi6m0O/CPi6OwMJtKu4Mg\nQzBn1n5G6eatSBYraH3QdelEp4kTcAb58dXh9Rzcs5k8bydegQYGxvbilpAUvO2VqKOjcBUU4gry\nx15cTGBENOe2/Uje4mXgcnnEEHnvaJRWG3k/bMRpMqHU+xI+5BoiRlxHrredv296F0eZSU4zb3sl\nt6briTmaR6XVBj4+hPbtTdhtfyEgrh1mu5WCkhy+OrOFE6cPYpQqcHkp0JucaFUa/rLbim++6WIM\nvr6oFApcZjPotAQOG0RU3wGc/mApFRkXyzMKUIWF4Mor9CwTej2dZz+FrnM85Tm5OM0WAHSxrfDR\nVXWkjKZSig8cJKRvb/QaHa6KCnLLiwlT6rEXF6MKDSE/+zxBsW0oP3EGe6WDLdYTbM7cQ2mljUCV\njmva9OXWpJuw2G2sP7mJ3Se2Y7daGPmjkSDjxX1ZExdL6Og7CEnpSXlO7oV9wc66Xf+j9NuNtDtl\nRFch4fL1JqRvX/xvv5FicxlRUa0J9g+V69mC3HSsG7aRv2kzkr3qJJbSW4P3wN4k3jeOYmMRls3b\nKNj0Iy6jCbW/Ae+kTnSZ/Cheej22nByUGg2KAAMqZyUq76oTA66KCgoLswmPjuPMrp9xHDlB6Y8/\n4zKZUer16Ab1JnL4dRiiojEX5GGIacXpgjTCtQF4a32x2C3MWz+fVnvSSThnQ+e4WNcAKKIiKRo7\nhG8PrGfoAQsRRU65HsoNVLL5mgjad+jGiPaDsTkraJXvwNAxAanMiL2kBCk8hIDgMGy5uWR9/j/y\nNmz0qGvU0dGcG9qBH9TpVJiM9D9WTuc0O162aif6tD5kdwljT2A5GYESAUof+nUcyC0dh3N46/e0\n7z0IlUtCrVLz3akf+eH8DhTFJhw+SnodsdDpjA1dff1GjZq2z8zEnJWFdCqNgj17wGL1nEal8tjP\ndG3jaH3fGLRRkbiC/fFySnLZLDh9kqKvv6N07z5cRhM2LzjRSsOBniFc274/t/S8Fb3GF7vLgVRq\npODcabad2cUW6RyFinIM3np6hyRyW3gfJKeTku83U7J1O5UXygxKJUHXD0Nz/SAig6Op1HljNpcS\nojFwbvW/KNhwsXxVAscT9PzU1QeXxouRQSm0WbsXZ3Zu7XRQKEClBKcLdD4E9OmF1tePgi0/4zSZ\nwMsLhUKB5HCg8vMjaNhgYu68nfxKM7t2/8C3lmOU2ywE2lT08WtHwvbzuM5nyccin7Zt8O/Xh7zV\na2qtusJPS6W9Ap+KSrlsucufb4d2BN59B74uMB45Ru73G+Ttk0P31RF+3bVE33gDGPywuxzY8wtI\nf28JtjPn5BjUraJROlxU5Nax/TUpFVApodT74hMcjK2wsKre1mlRD0yh7ejRBBqCMWVlknbqKJZ/\nfuJ5DAXQaAi+dggalZecjgpfX6h0IdnKa69TpaTd1McJ7d+Xs8ZsNHYnMSGxmLIyCWjbjtKifPz0\n/lgKCzj/9f9h/mknlSYzKoMf/t2TiBs7Bp+ICMqtFspzc9H6B+Cl15OVeYZja1bhvf8U3g4Jm48S\ne2JbQvKsODKzQapK8yI9BFpAKVU7ZisUKLU+VFpteBn8kLomcLZnFFsLDuIoNZGc5qBLugOVpQK0\n3oT07o0tMwvL2QvprlBgjQxgXS8fcnxd6FQ+DI/qRe/j5ZRs/gmn0VQ7HahqG/j3SSF69J1YgnQA\neKnUfLr3cw4Vn6TcZsFPo+Oa+EH09GmDxuEiOjIOl0qJt1ZHeW4uUmQotj0HOfn3+Z55o1TS+sFx\nZH2+FmdpGVwoc1Y1bE7RM+ygFa2t8mIaeKvZfEs7DmuK0RudKFAQ3qYdk7vfTWCZi4ItP1Pw4xac\nRiNqfwOG/n2IvflmfnntTcqzG3+5Tdh9o2l7621YCvJxBRvQO5U4zWa0kZG4KipQeXtjOX8eV5A/\nLi8F3xz7nq2nt1OoKMfXS8v1Ub24peetKHKLOPvRCoz7D9YqVx2fnkVwr5RGY/m1NdYnEh3JOrS0\njmR5Xh6/vPYm1rTzzR0KeHmBs/HLUgqdFn3btphPnkSyX/rVH+H3IfIvNxFz5+247HbSV39K6cHD\nOISi7KAAACAASURBVI2Nn6CopWan+FIpFFUH49wc5F53jYak8AehVOCl88VpNjd3JJfuSss5yI3l\nK+brCxbLlS9HEIQ/LIVOR3BKMsbUX7AXFl3azF5eKLy8kMrr6IALTdLxuaebvTPZWJ+oRX9HUqjq\nRO6b9Ghzh3FREzqRAJLVhunosV85GKG55axbT8669Ve+oCttXEsS5Tk1ziKKTuQfU6X0++xEwpWX\nc7g6nUgQnUhBEBolWa0U/rT18mZ2OpGa2GYU6nb89bcYsPY/zR1Gg8Qzki1c6pzXmjsEQRAEQRAE\nQRB+S7+DE+KiI9nC2ao/nyQIgiAIgiAIwp+CNbNl9wNER7IFc7/wQRAEQRAEQRCEPxddC3hXS0NE\nR7IFq7Rf/U9dCIIgCIIgCILQwqlUjU/TzERHUhAEQRAEQRAEoQVp/eADzR1Co/7QHckvv/ySu+++\nm7vvvpudO3c2dziXzDs4uLlDEARBEARBEAThNxY14vrmDqFRf9jPfxiNRpYvX85//vMfLBYLDz74\nIF988QVK5e+r76zy88MlnpUUBEEQBEEQhD8NpUbT3CE06vfVq7oEhw4dolevXnh7exMUFERYWBhZ\nWVnNHdYlS3zlxeYOQRAEQRAEQRCE30hA75TmDqFJWnRH8uuvv2bMmDEkJyfTuXPnWuNdLhdvvvkm\nffv2pUePHkydOpXi4mIASktL8ff3l6c1GP6fvfcOr6s6E71/u55+jnq3ZMlVtnHvhWZMIIGEEMIN\nMAnpkzDJvWQyF0Lmftz7ZJ6bxp3vy3wzXyaTQiaVlkIZwgDGienuXe6Wrd6lU/c5u35/HOtYsiRL\nNgYJ2L/n8SOfvdde613tXe+71tp7henv73/HZL9cBOtqmf+/vzXZYri8U1zEirklSOhidrZq8K9L\nFkuY+i+ov1/RJN9ki+AyxRnWRrzeyRPExWUc3ktjzXspL0PRJN+7Lm9Kfj5z/ttXJ1uMCTGlt7aG\nw2HuvPNO0uk0Dz744Ij7P/7xj9myZQtPPPEEeXl5fPOb3+S+++7jpz/9KXl5eUSj0VzYeDxOfn7+\nOyn+ZSNvwXxW/eYXtPz+j3S++BJmPI4UCuIpKCDT3YOVSqFJPnyWdlHxJuQgQTMBgOjxUP/g3yPl\nFRAoKSRtgtbaypEHvoFlOgBIjoUlSEjO2QNSJRE5EMSMx8FxRk9EFJF8PqxkEgQBHCfXoQfjGXSI\ncvILAr6aauq++Dnant9CfNcOzEQSIeBHKi6Hnk7MRAK8HnxFxaS7unDG+cKtVJCPaNkYQ9qEEApR\nd+/XaPv5z9BaJr5arSteVCONFApimyaOlh4ZRvay+DvfwhcJ0vGfL9Dx/IvZMhiF/DWridz0USoX\nzATgzKluplVF0Pv7SbR3cfzb36HP8eGxMsiOxYnCJbTn1Q8pcgfIlm1Y72XjQoXK9cuRZZGj3/9H\n9O6eEWkOq8fB34pI8do11H3hc5jJJKKi0PbMs3Ru3oIZi6FEwhRtWE/s+AmSR48Ne9YSJFQ7Wwe6\nqCKJDqpHxUqlzrUNSQTLBlXBU1FFpq0Vy7ByTrDP0pAiEWZ+5R7EYJBTz7yIdWBXtn15PZRftxFT\n0+jfsRMzNny7tyVIxJUwPaE62kIzMWQfsqlRGT9BTf8B4kqQgkx2kkmORMAyMRNJxGCA4rVrqPjI\nzXhLStD7+9n15a+OeQiw7g1SvH4tyTdfx0wksFRvtlnrBhIWOKCFipi+cT3VH/kQakEBtq4jqipN\nr+2krL6O7rY+xESMU088SeL0mVwfHIogyzimOSKPQ8vZEiQQBIquvJLZX7gbx+PD6ushdqoRSZHp\n2foKvdt34qTTw+KQHAu8Hio/eCNlN1yPt7QUgN6dOxnYtYfe117HiMZwgiHK1q6i6Oor6d+2k+6/\n/AUjGqPDX82i9bOZfvcnAWh69HG6/7w12yd9Xiqu30Tx1Vdx5KF/JNPWDkBMzWNfxfXoki+nB1RL\n48qiThb+1c109qSJ/vLfsufmjqVLODdhEirJx4wnsJJJLEXFlLwIeppAyItvRh2xffuzbe0sMSVM\n2IidV8gCFiKSLIDt5Op8sIzkulnMuvsT5M2bR6qllYZv/W+MC01GCkL2C3tD6k0XVVRbz9Wd5FhI\nTrbNS45FweqVaMePo/f0jOiTE0H3BFj0wN9SsGQxel8foqrS/Pjv6frzXzBjMQiFKV2zkrIP3kCw\ntpbenTvpe3M73a+9jpOa+HgxOL7IeXnM+e9/y8l/+SHp9o4R4TzlZSz89j/ktmNFjxyh6Re/IdXU\nBGTLVpN8w9u8IJCQAkSVfE6WrsEYbCNAMOThjs+vJF/JcPg73yfVeDr3mCVIeMIBzOi5elXy8nAs\nEzOeAEWmeN1a6r7wOSxBYt8fNmM89RswjNEzKUl4y0oxBgawkinkcIjC1auJHz1K6kzTWR0TwmNl\nqL56NbLfT/dftpJK6qgKBCoryXT3YMbjiMEA4fp6kq3tGG0XHlsG24LkWOStXE7NHZ8Y1ncGEUNB\n6r9xHwO7dg/TyaH584gePIQViw9rQ/lrVlP3mU+x+b7vkT9w5oJpwyjj+0Uw6+v3kmo8TecLmzET\nCYSAn+I1q5F8Prr/8jJmPI4cDlF63UaKrlxPsLYW/eykvx6N0nfoCC2/+OW44zgMH7tydo8ogm0j\nhYIUrVnDwMFDZNrahj9YkA99/bnnz8+ruuYqCouCdP1lK1Z8pE4eRBdVBOBM/hXnxhorTWXsODX9\nB1Dsc3kYTKPPU4DP1HI2zqBeOD8/AIgigijimCZCwE/FB66n7Ibrc2Ulygonf/IzEg2HgSG6TRCo\nu+ev6fjTfzLQ1IZkZ9uUEAjgaBrY5/ThUAxRHZYXTI2as+OmYuvoooouqqOOUwDT//rztD72O4yB\ngWH5QlUI1NSQPHkql3Yur7JM+Q3Xk7diGQWLFxPr6kNyLAKlxeh9fWfzKXP4298j1tE7rKw0yUfQ\nSTHzK39D489/gRWLDbMp5aIizL4+YnKYvRXXD9MnOA5l8ePMM44z7SM3ore20r99B0Y0NsIuHcpo\n/SI1bS4V04uJ79mNlRjdtkOSxrQlxkL0qJRev4nqT9yOHAxe1LOTheA4Fxi1pwjbtm3jM5/5DA0N\nDcOuX3PNNdxzzz18/OMfB6CpqYlNmzaxZcsWQqEQd999N4899hipVIq77757wu9ItrS0sHHjRl56\n6SWqpuD5Lc0nOymrLqKnM84jP91OIp7J3fN4BT715fWEgjIZE1Id3XT8/KfoJ4+AAy3+6Ryv2IDN\nudkZWRGZVV/KqWNdZNIXaPSOA4KAhM3yNdO4YuV08oqCKIqEkNFIWhKKIiHG+7EQSUs+ZCWbjpFK\n88arZziwqxXTGF2hAQQCCh/6Lws53tDN0QMdpJI6Xr+MKAikkmcNAAEqKsN87FPLyS8MAJBJZhW0\n5Fgc+s0fib/2MkK0DzNSROU165n+8Y/mOmXXyTae/o+TtDUN5NINSSb1XS8jx7oJ+iXKNl0Hy64k\nGFSJVJZw4kgnr754jPa2eNZvA0orQtx6+3ySW/6TIy9tx0xnOF66jrianxWSrP5asqqajR+qJ53U\nyC+OkErpOPEop1qS/MfvGjD0ix+4J4KswtKlVdTGDtGx9RWceIym0mW0BuowUMDUCDsaaW8euiWi\nekWWr6ll3bUziUU1SsuzK/qGYTHQHcUWJEIRH1pKxzRtnvvdPppPD4xp9w/mff68AsKl+ZimzRtb\njnH8SA9acgxjbgxu/8xy5i4oz8lj6zrJtM2WZw7RcKDzwg+fbbeDfOi2BSxbU4ut68Q1i57OGNW1\nRQAoikS8pZ1j//cPSJ88AUBKDnCg7FoS3gIG63Wi1M0upLMtTjJxYQNJlARuvbmG8rnVBPOyfSox\nkEBSVV56toE925ov5F/l8Ppk5i8q54pllRSVRfD7VZLRJK9tPc3+Hc2kUgb+gMrildNYd+1MfP6s\nwW8Y2TbY3NjL4QMdNOxtQ0sZ+IMq8xeVg+Cw49WmEemt3ziDaz+Y3S2SSWp4AtmVpNamPiqrCwBo\nOd7Owz/aOeEy+/Bt9cxfXku8q4+2Z/5Exxs72B9cTsJTMKweVY+InhmpS4rLgtz2yWV0dyb4/a92\nDSs3UYSP3LGYjuYB9u5szeVxzvwSZEnkwN420qnhDnxFdR4bPzibqulFKIrEib2N5FcVkx9WsQQJ\nM5FAVFU8AR+plE68N86Tjx+ks+08x3WQ89qjIICAg+0ISKZGid7OhqV5VF13JZ6qahRFyk1G2LpO\nR1eKZx/dTXtHMjeHVFYR5uN3L8d2HARBYOdrp9m/s5lUMpu/xStG1rfkWIiqSs/pdiKVJQgZDVFV\nc07giQNNPPm7w6SGtN1AUOXOL6wiLyDQ+fTTdL20hW5NxOdTqdu4lqqPZfVsb08Cn1/F71dpbe7n\n5ReOcbyha8Jt4Hy+8LUNRAr8CMAzj+6m8WQ/mbSJ1y8zZ14Ja9dWoeaF8ftVFEUik9RIpm2eemQ3\nzY0DI+K75poqlq+rw0ymMD1+APKLI7l+MFg2Wkrnj7/dzYnD3RcW8GwdbLp5LiUlQV7bepp9O1vQ\nkjoyNtNSxynuPkS+zyF//QY866/j+RdO09EyvI0sXV3NldfPxgEGehNUlviwdR21INuXolGNSCTb\nx9rPdGfrShDY/kojh/e1k9YMPF6J0sowTSeHTno44MDMjtfJUzOcqLiSgczo6wiKKjBvYTkrrojg\nC/rwRoLET56EokpannuBrje244n3YPhCFG3YQP0nPkzKkigsCg5rV6mUjv9sextsv4Pyp1I6ac3I\nttNdLaQSOh6fxLLV01l+RQQ5EqG3T2NaVYRkVy/9hsKRA53s3d5MWhs5dnh9End9fgWV04vp7UlQ\nWJQd62NdfaRTBt78CFtfOMqRfW1omnmuD9oZBFHFGaLXZUXkE59eSt3ccmxdp6etn7bnX2TrIZOo\nPKiDzna8Ec3AYHnTs3SFZnAmNBNk34j+PhRRFLBtB39AZXZ9EWuvrsMfCaAoUq4cAXp7EvR0xamb\nVYKiSBw91M4T/74L2x4+KOQV+BnoSw2Lf/HKaaxcP52iQh+JgQS+/AiSYxGNahhahof/6XV0JrBa\n5zjgmCxpfYGgESVUU8WMe+8ljpcXnmoYZk+VV4a47e4V5BcG0FI6m589nCt7n19hwZIK5l5RzgvP\nNNDZeq4PiKLA3IVlLF5exUt/OnpOhw4q8fPKcfrMQpob+7AsJ1d3S1dVM31WEY//fOwxR1FEvvTf\nr8YBtr98it3bmnJ2qSQLLFxYytwlVRw51MXRA+05PVpU7KOpMToivplzi7jho1eQF1aJHT9OsKYm\nZ28ahoWZSCAHgyQGEpjRGMUzp9HfHUVyLDIm2IJEwCfh96tT8p3I8Xyid60jGYvFWLFiBU8++ST1\n9fW568uWLeP73/8+Gzdu5Mknn+SRRx4B4N5772XNmjUTSm8qOpIHdjfzx9/snWwxphzTZxTQfLo/\np0guxMx5pcyYVcDzTx1+ByRzeS9QOzOPxhMjDdH3ApIC8xdVsX9ny1uKp6gkQCKeIa2Z4wd2uWhk\nFWpnFJGI6bS3juGcTgCvT2Lm3BJOHu1BS40+kVM3p5iutiiJ+Pv7DGNRgsUrpnFobwuZ9OSbSIoq\nYuhjT8BOJc5uOMiRX+RHS+qufnB5X5BXqLJ8dS2tTQMca+iakG06lIKiAHd9cVVukWQqMJ5PNKW3\ntl6I5NltgsHzln7D4TCJRHYJ/pZbbuGWW255x2W73Ox6o5Fnf3dwssWYkpw+2TfhsCcaOjnRMM7q\nlYvLEN6rTiSAZfCWnUiAnq4xtvW4XBZMHY4fHrk9/WJJaxYH97RfMMypo+OsvL1PsC3Y/WbzZIuR\n493iRMLInen9PanRA7q4vAcZ6NXZ/OzRS36+ryfJP397Cx+9azFXLJ12GSV7+5jSH9u5EIFA1lsf\ndBoHicViI5zLdzNaSnedSBcXFxcXFxcXF5f3AX/8zV4O7J46k1kX4l3rSIbDYSoqKjh06FDuWlNT\nE4lEgjlz5kyiZJeXpx9zt7O6uLi4uLi4uLi4vF/442/20t879Xf8TGlH0rIsMpkMxtmvrGUyGTKZ\nDIOvdd5+++385Cc/obm5mXg8zkMPPcT69eunzHuNl4OjB92tmC4uLi4uLi4uLi7vJx59eMdkizAu\nU/odyaeeeooHHngg93vhwoUAuRc+v/jFLxKLxbjtttvQdZ1169bx0EMPTZa4l53BL8i5uLi4uLi4\nuLi4uLx/6O6Ijx9okpnSjuStt97KrbfeOuZ9SZK4//77uf/++99Bqd45+nrGPsvIxcXFxcXFxcXF\nxeW9Szql4/VPvWNBBpnSW1vf74TOnhfl4uLi4uLi4uLi4vL+wpziuxNdR3IK45/CMxAuLi4uLi4u\nLi4uLm8fwSm+qOQ6klOc/EL/ZIvg4uLi4uLi4uLi4vIO4g9O/QUl15Gc4vzVX6+ecFhRnNrL3y4u\nLi4uLmMhK7D6yjqu/3D9ZIvyrkWW9ckWwcXF5TJx1xdWTbYI4zKlP7bjAvmFAb76zWt5/N930NmW\n/XqT15sinfYjihaiaDOzrplpVR2oiomuQ3NrFU1tNfj8YXq7xj6DxuNzyGgCAH5/glQqiD+o8MFb\nF/LGX07S2jQwLLyswMJl1Zw63kOsP45tS6PGK8s6RaWFLF0zna3PHyMZzyCKVi68JAkIooBp2Hh8\naa5YPAfDtDh2qBMtZSApsGR5DamUTsO+9nHLaGjcl4LqSZNfWExnexyciaUzVpqD1wURVFXGMlMA\nmOaFZ5XKq8LcdPsiMmmD6TOKaTnTw+F9nezb2UIqqeMPqixeUc26a2dgZNLEYgZ5eX6CER+93QM8\n+8SrnD55TnhRFLDtc78VVUQURTJpE19AYcasEDd+bAXR/l4ymszzT++hozUzqmyDbaOwJEDltCAn\njvTnZJo2PZ8TR7qwzNELbry6EQSYv7SSaz4wm0zaIBj0ktYMdrx2mkP72kkl9XHjCQQtFq2Yw7pr\nZ+Dzq3S1NxKKVLLlT4c5vL+DtKZNqH14fTJ1c4oJhz28+fLpC4RTSGtG7reiSlimjm1LBIMxEonw\nmM/Kso6iBjD0JB6fl/mLaujrSXDqeA+2dWllCFBQ7OeuL6xmy3NHOLSnbcLPDQ13ofDn2rWQPX7J\nyeZlvHZ9OZFlHY8vSDqtYxnjhwfIL/Kz4bpZbH6mgVTy3EOD+ckvCqAl9WH16Q8qXLlpNi+/cDzX\n/gB8AZkP376EcJ6XR362nURsZH9RVAnHcTANe9h1UbTweCXmL6rhWEMnsWh6hCwX+v+l8Faff7uQ\nFRHTsFE8AstWFbPm6oWEIj5Mw0JWJNKJHrzBIlZfNZN0ooeBqEQyoRMIqsiySFqzePwXO0nEzo0r\nXr/ErXct59Duwxza1/OOtsvxEET44McW4POpPPXIXozz2sZoBEMeTNMirZkXDCcrIrZloKo6Sxcd\nJi+SQBDAcWAgGmT3vnrS6bG3xY3WRgafHyus1y9z9QfmUDerkLRm8djPd5BMjO68Do4d46U5EfKL\nfFx3Ux2vb2kdZpsMjnWibDFrbgUDfRqdbbFcOhXTImy8eR7TqvMxDYu0ppNIZAgEvfzul7tob4mO\nmldgQnKOpwdlxcY0hq/Z1C8qp6czcclf5BytDM+/pqppdN17wXgEATyes/akZLF8bQHrrl6ILxAi\nncrWqdev0nKmj0d+tgNDv/BixXhl4Q8KzF1QxbK1NXhUgS3PnaBhfzs4Y7eLj961mGk1+UQHNI4f\n7mHv9ibSmobHJzFnXhVNp/vp677AWYsCLF1dzaoNFby+pYX9u1pwhoxfWTvaGlPuwYmZofdFEa5Y\n6OHw4TR6RrhgmUBWBzjndfvxyioQ8nDn51dSXpU3bvyTjeA4o6mM9zctLS1s3Lgxd8zIVCDWd5IT\nux/GsSc+21iz4E4cqYr/eHw3zWd0cCAYijF/XhclRQPYZ52c4QjULf40+SXzSCd6sB0f/lDg7Mu+\nGToa/0xPy3YsM4WkBAgVLKK8djUD/Z1E218mFWvOxeQLVeIPVxHtOohpJBFEL0VVq8grqadx/68x\n9SFfpRW81C35KwLBmpwx4Y+Uo2tRbFvEtkV03UFVdNIZAX9ApqNxM/2de7BNDV2XaW0voae3mJkL\n5rNqw1wU2UT1ekmndFSvF12Lonq99PX2Y6Tj9Jx5HFM/p8hFOUDtwk8RjNSQiGmE8zzYtkFH45/p\nbd2RzYMggwCObSIrAQorV+BIs3D0Bvo79mIaSUQpO3DblpaL23EgkQiyfXc9shpk4bJyVq2vRVH9\niEI2nOqLnH3OQJQUbMvAtoysgaVKw+QQJR+S6sfQekfUYN3iz5BfMg9di5JKmeQVFpJO9IAocGrf\nb9DizSOeGUT1V1A8fT2y6OXMwV8DI40eUfIRKZlP9dybkZVzW68HehMEwz5sK0nL0WeI9R3DMlJI\nsp9gwULOtPg4vE8jkbDx+r3MW1jHhk2z8J19Fzid6Mnl2xsswjRStJ3cQn/7zmzZSz68oVnUzb+R\ndCpN+8k/DGtvoyHKXmwzDciUTl9PqHANHq+Cacr090XJzxcI5pViGhZmpj+brp5CVrP5On2ym4K8\nOIkEFJVWoHrPDczJWJTuphfpbdsFDDf4HAf2HJhLNOrHtiWWLT5CJBxHOH/MEUQKK1ZQMWMTpm4h\nq376+1L86Xc7CXgOU1XRicdjkMlItHeWESxciSOEadjXhJ5OIqthFq8QWbZ6PvlFFWixDno7dtHd\nvA3b0shkZNo6SmhpKyKUN51b/2opeibDs0/sJuzfT2V5F6pq4ThZo0IQveSXLSVcsgjRyRDtOc5A\n5y4sM4WsBIiULCA5cJp08tz5tr7QNKYvuB3VE6avR2P/7mPs3dFLyNdMd295zkmtqvHzgVtWcmhv\nO0f2NtDXr+L1yyxdNZ0lq8rZs62d/TsbScQt/EGVhUurWLIySM+Zp4bVsz88jep5n8AXyKevJ0VB\nkZ++rsMUlNSTGGhGNwsJhnyIgoas+hElBQDTSNF48GkS/Q3YpoYk+ykoW0zFrA8gK356OpspKp0G\nkNUVvgi2ZZCIafgDMqKk5NrnoH5KRDWwu0EsJhjxkehvwh8uxzQsLH2AlpMvEe89hGMPOqoivryZ\ndHQtZKBrB2XFbXg8Bo4jIIoSjmNiWQIgIEk2puXhTFMxJ05NO6sHs2VZN6ecTTfPpbg0ktXNToZU\n0kSRUvR27MrpaFBJZGayY0cY/azfa1oqi1dGmD1vOrPrywGy5WaA11sAZJDVfLx+lZ6OGK++dJiG\n/S25tE1TJRiWuONza/H6bHa9cYCGvf0M9Ev4gwILl9aybE0IxxEAD3n5wbM6zI8o2mjJLk7s/tkw\n3QseBNHCscdynGRmLP00eUVzSCd76Wl9M6cLQQGGzyw4DiRTXvYdnIVuluYm4RTZxDRlulsbKZ1W\nQ09nGxU1ddk6T6dpa4nzyE+3D3P2VDWNaSrYtsTCJXms27SQ/IJgbpyyLYPODoOTx5Ps3d50dpJN\nYPa8Qq7aNJNQJA/bMpBVP6aewtRT9PX2UlEzJ6drutqOUVRaSyqeRM+0Ec6fng1/1rke/Bvrj4GT\nwbFNYr076W3dmZusHIv567+BQ4iH/99X0c722+nT2qmo6MGjGhiGQtn01YSKF+BRZIL51aSTcVSv\nl0yyl87m7US7dmfLWvBQWrOGstprsvpazPYJb7CIRFRDTzfS3dpKoudFBmdlHSf7r6OzkKKiGKpi\nACqdPcU0HCkllQwiihblVYXc8OFC8gvLSKcThPOKSMQ1RMGg9djjw3SA4iulYtZHyCuspP3US/S2\n7cQyUgiiF48/DyMdxTI1EDyUTFtJYcUK/OHyMcsoFU+iegXaTrxAT+v2If0VCipWMW3OB0nEbPIK\ng2gpnTe27EHUnyMYSOYcb8VXxpzlnwZCJONR4j1v5MpNVgKEixdRWLGCcME5mzLR33TWxiogFW9H\nlArp743xu1/twtDjmGZ2rUcQYNbcMnzqAQrCZ1BVk0wGBuKlWKaXooLWs9dEJAkkyc7Jlcko7Ngz\nj1gsQkGRn09+aQWpaAPtJ54els/zUf1FzFr6eUQhq/tk1U+s9xTRXoOjx2yOH9hNPCEDwiiTGH72\nHqhH1xUKCyyuvk4mFd1zdiyWEETxbNoS/nAFGa0Xy8i24+KqtSiBxRSXlyJKCroWxRssIp3spe3k\niwx07sNxBvWEQFHVKgIFs/AHZmDbIrIi5WwoXeug9fiTw9qOIKpj2tGiWkTNvA+T1jL0tWwmM2Sc\ny9pwAsGgM2IcDxTMpm7B7ZhnHW1/uCBrUxhW7mur2d8DNO7/zQg7uW7RJ9HTNl5/iLSmE84fezJ6\nMhjPJ7okR7KxsZHe3l5EUaS4uJhp06ZdFmGnClPNkYz1neT4zh9Nqgz+0DRSF3BA3msIonwBo8Zl\nEEGUqV7wCcJ5tZza9xuS0VMXF4EYBDvJBZeCpxiB8HSSsdOTLcZ7BkFUzjNoZLyBQtLaANijr5K7\nKIQKZ5BJdaOPMpnkcokIHvJK5lJQtoTGg4/jWBd20kYiAZfzFRMPdYvvouPUFlKuznnLCJKPmiv+\nC6FQFaKsDHNCx0MNlJNfcgWdjS9MICUPMLV01+Bk4XsfgalgT4iyj+kL7yIUnoapp+g4/TK9bW9M\n6FlBlCmqWkXFjOuHTdZPFpfNkdy1axe//vWvee2114jFYuciEAQikQgbNmzgjjvuYOnSpZdP+kli\nqjmSu178Bjju+48uLi4uLi4uLi4u73VUXyH1q//rpDuT4/lE474jeebMGR588EE6OjrYuHEj//RP\n/8SMGTPIy8vDtm36+/s5cuQIO3fu5Otf/zpVVVV861vfora29m3J0PuN/q4G14l0cXFxcXFxcXFx\neZ+ga720Hn+Omnkfm2xRLsi4juT999/P3/zN37Bhw4ZR75eXl1NeXs4111zD3/3d37Flyxa+XAtN\nFAAAIABJREFU8Y1v8Nhjj112Yd+PnNr788kWwcXFxcXFxcXFxcXlHaSndfu735F89NFHJxyZIAhs\n3LiRjRs3viWhXFxcXFxcXFxcXFxc3rc4du7jQVMV9xzJKUwqOv7RFy4uLi4uLi4uLi4u7z3siZ53\nNUmMuyL5qU99asKR/fKXv3xLwrgMZ/AIAhcXFxcXFxcXFxcXl6nEuI7k9u3bEUWRxYsXs3TpUoR3\n2feDP/3pT3PkyBE+9alPcc8990y2OBfF4LmCLi4uLi4uLi4uLi7vL6b6otK4juQTTzzBCy+8wAsv\nvMCTTz7Jpk2buP7661m1ahWiOPV3xn73u9/l9ddfp6OjY7JFuSRE2YdtauMHdHFxcXFxcXFxcXF5\nT+ANVUy2COMyrid4xRVX8PWvf53nn3+ehx9+mMLCQr773e+ydu1a/v7v/56tW7dimlP34PaysrLJ\nFuEtMXv5X0+2CC4uLi4uLi4uLi4u7yAzFk389cLJ4qKWFGfPns1XvvIVnnrqKR577DHq6ur413/9\nV9atW8d99913SQI8++yz3HnnnSxdupR58+aNuG9ZFt/73vdYvXo1S5Ys4atf/Sp9fX2XlNa7kUC4\nkrmr70VSQpMqh+lIw/66uFwuhraptK1OoiQu70ZOm0WTLYKLy3uGTjM8oXH+/WILTIUx6f1S1u82\n3tZ6EUTmrr4Xr7/w7UvjMjHu1taxKCwspKSkhPLyco4dO8Ybb7xxSfGEw2HuvPNO0uk0Dz744Ij7\nP/7xj9myZQtPPPEEeXl5fPOb3+S+++7jpz/9KQC33377iGcWL17MN7/5zUuSZyoSCFey+Jps2eha\nFASBpqPP0NtxAFmwSNsqXlGfUFxpW8UnWTiOhawEiJRcQUH5YpqPPE1PtA+vaCALFgAZR2GPPY/D\nTh0ZvIADCHhJM1c4RT3HiEgT3XYrnH1+6CUJX7CMY/065VJfLt2LQvRTNfcmfL4CWo//iVSsCch2\n8InENxhOlL3YZhqAAdNPnpwaEWasZ88KgiCKOPbQ1XmFqvqP0HL4D4A95vMTlXUi+RhkwPTjFc0x\n24UnUEYgXElf+67ctbStYjoiQSk9Irwthais3UD7if8clpeLaXsAouTFttK5tnXEqSONFwEbBwEQ\nwHYooZfrxNcIiynazDz86BOqk7fCeHGajoIsGBcsJ1EOYJspRrT1CXB+WV6obIe2HdNfg1c7A4wt\nu6wE8OfPIL9sEV2nXkJLtA2VmtHa5+XictXV0PLYZc5iB8vO3hHAzJb3CnaxTD5+wXhChXOQFB/R\nroPD+qukBrH0xFuWczRMR0IN12InW0ib9gX6jIggKThW5iJil/GHK8ikurFMDVAQBBvHGa3MR9HD\ngCdQQtWcD9N85Cn0VPdFpH1xuhYYN6wgqkyb9zF83gKO7vzhqPICIIVQC2dh9BzAsUf5qqEgwahl\nAN5gJZaRwMhEx5V7PARRGZ6+IIMzpF0pIRzHODu+CGfDj17/lXNuob99d24cG+T8MhYlD7aVyY3h\n4YpVFORXkU720t3yOqdO7QTsUXXUaOw3a3mdlTCogy0AhxpauFbahkfI5i9pezngzMnpbYU0i7y9\nLLT3oNrxCaSkkF++iHSyAy3WMmao/IrV9Le9Oeo9yVuIbSRxrJF5M8UAspPO1fv55Tb4O2F5Rykb\n6ewYbhCz/Wy219FFIeePSbZNbiw6bRYxXe6ZQL4vnoyj8Ia1iNNMy5X1HE6zQjqYq4/zEdV8BCeD\nZaSGXR/b1lCAiX4RVMAfnoau9WIaSWQlQLh4PnE9jdFzgNH6qaQWMHv5Z4lHW9ETrfS17cI0koCM\nXrQGrwRC704sU0OUfBRWLMM0U/R3HgI7qwMHHXmvqCN5wliZ2Ih0EpYXWbiQXh3JxYxLg/3NkYKE\nSpewy5zJto4EKVvO2cNLxIYx62UispiOlJO/qGo1lbNuRFam9ruRgwiO40zY4uns7GTz5s289NJL\nbN++ncrKSjZu3MimTZtYvHjxW/oQz7Zt2/jMZz5DQ0PDsOvXXHMN99xzDx//+McBaGpqYtOmTWzZ\nsoXKysoJxf2HP/yBjo6OCX9sp6WlhY0bN/LSSy9RVVV1cRl5mzneG2dPZ5TXW3pImsONv4qAyg3T\nCygMBCkLKHjRMZHozgj8+sBpTsezHVMAasI+vri0lpaoxq8ONhE3znUoAZhf6ONMf5y4Pd5cg0Ox\nEOeTi2rIDxRRFsmunJp6Cln1Dzv/JqGb9A90U5xXwNamXn53bOR7q7MjKvesnEtAkUlpCWQsREmh\nK5GmKJLHse5eFpQV09jVQW1JGbplc7o/QcSrYtgOb7b18VpzDwnDIqTKrK0qZE5YoszjEPKp7N/z\nBOl4K7vs+ZyimjRegopIbSTAkb4Ehn2uO4iARxbRTJugIrFuWhGrSny80hZne/sAybNprCyL8IGZ\n5eR7swqvs6+b0oJiAFqiKYKySaL1VU437UG0kuwTFnHYqkNzpJxZF5BF1pQHuWnudHyigygpmHqK\nNCqZVC+dhx/lZH+CCnmAHjNIfriEsulr2BbL45WmbjRbQiKNhXJW8nN9scSn8DfLZ5An26iSmHtx\ne3dLO13Hn+YlbSYD5A15xsGDzo21ZfSmTfb0pEgYFgFZZFlZAVfVFPLLfac4kzinNKdH/Nw9Jw/d\ntBHUMLUFwVw7ONozQGEgREEwQL+mE/TI/M+tB4nqF+u8OICDKoDuiPgl2FBTyo0zSpEcC6/qybW7\nwbQT8XZs/zQA8tRs/kwkbMtAlUQ0W+BPx9t4paUPzbTxYFEvnqDQbqVW6sESVI4ErmVvIoJmjVSV\nAVnk3uV1BCQbny9IUM32l7SeoS+RJBwMk9RNIkKS9oyH2oIgtmVg6ilUX4TuVJof7znN6ei5gV8R\nhVw7FIBpYR931FcSVERebYvyalMXSXOkLKoo8LmFVcwrCuNVPaT1DCYSfsnB5NzMaUI3USURLzqy\n6ieVTtB+cgutLbsJCMlsuqJCpGI1NbOuQ1b8pLQE3ck0NUVFmHqKTLqfE7sfxtRjNJsFlEtRvJ4A\nTu1/oSRURHvaYX93nH1dUZKGhVeAK0JxFumvghEHOUhpWT0lMz6ArPhJp3ppPfQIfQOdpG2ZPDmF\nJ1CJXnkzz7VkOB3TGHSCfMTQCDO0jQ9tI/Uc5yp5F4KY1TuObZB0wtTWLMZbsYHicB4AfZpOniqg\nWzZe1YNu2bRGU3jQKYrkoVs2finbF3UtStyS8TkJ/KFSuqMDRIJhktE2mlr2Q+/2nHGVX7aEipmb\nONZv8JfGVvb1pkd106sCCnctqKE6L0hjbz9+WSbg82EZKbTWrfS0bss5KINOdI+ZR375FeSVLqSi\nqConX0s0RVXknL41jRQdjX+hs2UnPWmHIq9AadVyymqvRlay4fR0DFnx0ZywKQ56AQiqMs3d7cRP\nPI4Wb6HHDGIhUhYppHLOzQTC5eiWjW7ovHiylT29GeK6iV+ymOmcZpmwhwxB9MLFbFhwJel0iuea\nYrzaGhv09VFEgbVlAT46v44j3QPMCUsEg/mkk714A4XoVlZPAaRTvTTu/y19Ax05Iyvpm8kL5ho6\ntXPOWplX4pNXVONXPVQEFQZ0B1USc+UzWC4nurqwRS+zi8K09fdhd79JZ8tOTCOFV/ViFqwkVLqE\noOqhyCeRwIcqiVl59DioIVJaIhdvGpWgKpPQzWG6J6hmxy+/L0hnIk1p0EtazyBKSi5vuhbN6oDe\ndnyh4txzg/mPJuM8dbKPXR0DpEwLnwirq4q5cWYp+V6VjliCPzf3s6O9/2wdCGQs57zpJAcvOjcJ\nL1HqMSiqWoWTv4SQL0DMkvh/th0nYV5YDwckWFaWx66uOEljdMNbAL62vJYZhRFEK42s+onFugmH\ni0nrGbyqJ1cHCd0kqZsYlk2BatOpQW1BkFS8E9uTrf8Cn8q+jj7Kgn4CVi+yr4i0niGDSunZtgrQ\nF+2lXXPY3RllV1eCpGHhAUqCXvozBgnDIqiIFPq8dMZTpM9Tm0FZ5LMLSqktOlf+nfEk/+OVo2OU\nhsNIvZONdBYN1JOdAAgLafLzyqie9zH8wRIOt3fgi+1F69zOQNrCKwlEypej2zb9XUeQrTiy4qOw\ndAGJvJX824EORlHxOcoCKv91xUwKPRJnenvxyGpOZ2npBP0tr3Oy+Qin7BIOOzPI4MnJ7iHDvKDB\ntXPmkHEUFpUVoGtROuMpSgtLEa107riJVk3KjVmDdpxu2XTFk/xkfzPtiXSuREqVDNcIrxKwekEO\n4i1eyvz6q3POkG7Z7Gzt5ecHm0fkRwK+sqKO6ZEgR3sGKA8F+NGuk7SnhjuGZT6ZLy+fiajHOHDo\naZ5OzCONj6G2Sz4D3Ci+TFj1M3/15/H6C0kneznZuI1E1x52Zmo44swgjYegIrKiJMD1tSWo6ITD\n2XbQmUij6D0UFFSRNEx+e7CJhp44iTHaP0BIEfmHqxfgcXR6dRGf0Ykse/H6C9ASXZxp+B1a/Nzk\niRisZa/vOt5sTwwbH6rDPr60tJZiv3dkIpPEeD7RuI7kiRMn2Lx5M5s3b6ahoYE5c+bknMc5c+Zc\nNkFHcyRjsRgrVqzgySefpL6+Pnd92bJlfP/732fjxo3jxvvAAw+wf/9+dF1nxowZ/OhHPxr3manm\nSO7r7Of/29V4CWsck8cts0q5qqaUxv4Evzp4hv7Mxa1IeCWRtPX2rZJMVVQBPl5fwXOnuuhLT913\nj6ciIVVmTkGQm2aWcaw3xu+OtKFP0U5z25xyepIZ/tIy9bbpq6KAKAgX7H9LS4Ls7np7Vu8uFzMi\nPro0g7j+1vrRRNdrBeDKqjy2tgy8pfQGUYHCgEp7cuKz7AAfn1vG8b4Ue7tGztwvLglzy+wKzgwk\nRzXoJpOgIuIgkDQsZGB5RT6NA0k6UxeX/xHxipCYQkOJBMwpCtGbyrzlvF0Mo69FX35kARxn+P4I\ngeza1zuXW5fLzbzCIA29l6bz5XObRt4xPruwkqDq5d/3nyH2FseAS0UEvrqijgXFeTT2ZctOtNMo\nngD/8MphLiTV7XMr2FQ3Nb7x8pYdyblz56IoCitXrmTTpk0XdKzWr19/yYKO5ki2t7dz9dVXs3nz\nZqZNm5a7fs0113DvvffykY985JLTuxBTyZHc19nPv+xqnFQZXFxcXFxcXFxcXFzeGTbVFHH7/OrJ\nFmNcn2hC70gahsFrr73Ga6+9NmYYQRA4fPjwpUs6CoFAAIBEYvgsSCwWIxgMXta0pio/dJ1IFxcX\nFxcXFxcXl/cNL57pYU5RiEWl+ZMtygUZ15E8cuTIOyHHqITDYSoqKjh06FBua2tTUxOJROKybqud\nykyhHTkuLi4uLi4uLi4uLu8A/7q7kR/dOLUdyYs6/uO5556juXnkexVv5RxJy7LIZDIYRvbl3kwm\nQyaTYXDH7e23385PfvITmpubicfjPPTQQ6xfv37St5y+E7REU+MHcnFxcXFxcXFxcXF5TzHKN/6m\nHBd1/MeDDz5IIpEgEAhQX19PfX098+bNw3EcHn74YZ555pmLFuCpp57igQceyP1euHAhQG4v7he/\n+EVisRi33XYbuq6zbt06HnrooYtO592IX73k01lcXFxcXFxcXFxcXN7FtMdSlIen7lEgF+Wp7Nix\ng6amJg4dOkRDQwMHDhzgkUcewTRNiouLL0mAW2+9lVtvvXXM+5Ikcf/993P//fdfUvzvZgp8k38Q\nrouLi4uLi4uLi4vLO89UdiLhIh1JgOrqaqqrq7nxxhsB6O7u5itf+Qqf/exnL7twLuATQHsXLG27\nuLi4uLi4uLi4uFwepNGOS55iXNQ7kqNRXFzM17/+dX7wgx9cDnlczuPv1r4/Pirk4uLi4uLi4uLi\n4pLly0trJ1uEcbkoR7Kvb/TDs0tKSmhtbb0sArkMpzoS4JZZU+NQUhcXFxeX9y6O5X4n3MXFxWWy\nEYCvLKud8kd/wEVubV27di2lpaXMmzcv96+iooJf/epXbNy48e2S8X3Ph2ZVoEoijx9pm2xRXC4T\njmUjSG95Q8AwLM1E8l3eDzQpgHFZY3RxeW/ydvTpdyIN27BJnomhtSWxDRskCFSFCNSEEZW3Nz8u\nLi4uLufIkwXuW19Psd872aJMmIuyOp988kkaGhpoaGjgzTff5Be/+AWJRAKAFStW8J3vfIf6+nrm\nzp3L3Llz3xaB369sqitjcVkeP9lzmsZoCseyyfRnKCjxgSiRMi1CqsyiwjD9iRSH4uncs6MZH+df\nqw17KQp42NEeveAzqiJh2Ode2hzqvFyKkXMxz5T7FdpTl+bWXKqTNVS+0WS1dQtRlSYU16DBlmpL\n4hg2oiLiqwiMMNiGpvN3q+oQbJFHj7RwpjeJqErD7usxnYF93dj6uZUEURUpWVLC6jklHO9N0Js2\nRo17LO6or2RDdTGKJJIxLDpTaf7xzaOkrNHLZVNlIXHHYnfbAPqQchEkAUESxyx7KWnwzesXUB0J\nENV0UobFE0daONATHxG20KsMy8ewcr2IOhjkYtrDRMJ6gKqwn+M98RGyfLCuhD+d6iLTl0YJKrn7\n59eFLMDnF1czMy9MZzJFvt/LD3edomVIXx7K5XYqhsZ3fpk6lo2t20g+edTyyPSl8RScG/gEwDkv\nngvJ++Ul1cgClIeD/HjPaU4POfrIiOko4XMfHjs/Hr8isba8gOWVESKKSurscVR+WeZAdz+nohr7\nOgbQ3uJim5kwkIMKjmUT8iokDCvXp9PtSSx99D49Ef0LUBVQuWl2Gb891ExMd3J5FyQBrT05rt6Y\niOy2nu3Eg3ViGzZdb7aBPuRFfAuSZ+Ikm+IULC+luMBD0KvSmshMKK3BvC0sDnFldT66ZrG1uY/D\nvYlx++ngs4PyjoZfEriypoQbZpQSUGSimo5fzf71eWT+fV8je7tG6pCJElZl7r6iioPdCd5s7UW7\niO/vz87zczqmodsjn3EsG8dyLlpXAXhlkQfXz6VpIMmvDjaTNEdvzIP9TbRsvB6Z1BjhfJIAgoBm\n2rnyXFNZwP958yhx48IdRQa+sKSa2kiIH+w8SVtidP10PkPzLwDWRertRcUhFElgR+vAsHIMKRKf\nX1xNic+HzyPz1NE2trf3kzSyNtGCohAr80OUFQYpCmZ1VCylM6Dr9GcM/mVX44RlGMrQMW4wf8sq\n8vnUwhpShsl3nttPv+UMa8flAQ8x3SRpWHiF7EdUupJpEhlzdN2oW8geGdO5uA9lDOrfQapDXj67\nsIZ8n4eUbtKW0Pjn3ZeW7wtxvl47f8wbGs7SrDH7+FAEoCro4fb5VWxr7ef1lr5h56tPC3j49OLp\n7Ggf4OWmHlKmNWo8lpYdFy40ll/MmBpUJFaXF7CiOEw44MGwbTTT4vXWPl5v7iUzwfY92C9W1xRy\n54JqZAQ8ysXriMlEcJwLt9BkMkkgEBjzflNTEw0NDRw+fJjDhw/T0NBAX18fDQ0Nl13Yd4qWlhY2\nbtyYO4JkqpBI6fzDw2/S0Ng/4t6MyjAnW2PDrqmKiCKLJDUTv1fCVxZAM23SnalhM893rqtlYUUB\nRREfv3n+CC/vaSWW1An6JNbML8eRRLYf7CCW1PF7BFbOK2fXsR7iSX2EHADXrqjkCx9eiG7Z6IZF\nS1eMkrIQIVVhS1MPW0910nG4j0yPhq3bhPwK16+q4SNXzUCUs504ZZiUh/0k0gaiKJDSDBIpnQw2\nv9zTSDdgaiaiKlIT8fPZRTUojoBXlfF5ZBRJ5NDpXr798HZiQ+SMBFUe+MxKppWEEEWBREpHViUc\n2yHsU9F0E8u0+bc/7mfvsR60jIksgiCKGKaN3yOyYUkVh0/309RxzlhRwyo3XD+TD9RXsKNjgFea\ne4j2pwkFFVZVFLKiJMKDP3yNpDbyzNWiPA/TVlZw9HA3WnsSx3QQFYEV9aVctbCCxzYf50zHcMPI\n5xFZMa+Ml/eMvUr9g69dRVlpkN/vb2bzG2cYaInjGDYer8y8mYXI00M0JzSQRGzdYkZxiC8smY5o\nODzzyimef/MMCc0g4BW5anEVK+aX8eqBdt480EFSMxBFsIdo9OqyEI7j0NyZGFWegEfgw1fN4pEX\njo2497mb53HtyhrC/qzD0NGXpDOeojjgQ1UkQn4VjyKR0k0M2+bnW4/z6rYmMtFzdTutPMTKK2vY\n3RdDsx1Cqsy8cIA7ltQQUGS2He3gn3+7h2ji3DN5QZW/vWsJpQVB8sJeJEGgLZrkJ2+e5OCrLThD\njKpQUOXrdyxmVnUhYb/KkTO9lJeE0FIGD/16J8ebB3Cc7MBXURrgf31uDW8ebONnT4+tC0VFpKQm\nzN/fvpTysJ+MYeXKYCgp3cSvyuxv7uHVHa28uqeVeMrA7xG4flUtN22ow3QcPB6ZWCxNXUUeibRB\n0JsdqKOajleWiBkGP9rdyOmeBIIkYBs2icYYereGNYoBGfLLxFOjnxPslSE9yq3Fy8vpaYrT2p1g\ncHQRBLL/H7LaJYhQrsp8afUsktGskzK7uoCMYbFl5xl+9IcDw9oXgEeVyOgWIb/C1UursB2HP+9q\nIZU2CfokPrC6lhtW11BWFKQnqlEU8ZExLM60R9F0g0UzS+mOaRSHs9fjGYO+vhSlxUEiPpVE2sCj\nSChnjYk3Drbxf369C/28svEqAtcur2bH4S66B7QRZVAc8bJyQRkvn62nSFBl9YIyZEnMXQv5Za5f\nNZ0PbqhDBIoiPrbubkKWJL73y52MZzpGAgrf/vI6VI9MYcTHjuMdvNQTpSmWlSfVniB2pB/G8AvO\n779j8T8/v5Ll9eX0RDX8PoWBWNZx8HpkVEUi6FU43jbAa7tbeWHbmVzeBEEglhw5+SP6JQoWlSD7\nZVQR5kQC7NzVTn9jdERYjyLy3+5YyoZFlcRSOqIo5Nr0IBnDyhlex5r6qCmP4FEkDMumJ5lGM01k\nUaQ04KO9J0HQr6Lbdu6ANlWRiAQ9dA2kCPlUwn6VjGGRMSz6ohqVJSGiiQztPXFOtkepq4jQoGXY\n1tZHPG0QUCTWVRfzoVllaCkD3bCoKApyrDvGowebObC9lXTX8DaihlW81SHyirxcO6ucxZEAM8rz\neP1wG0e0NAf7EsR1k6AisX5aEYsjAaqKQsP0w8HGbryKjCCKPPTLHbR2J4elEfTJfGD1dFYsKKW8\nMEhPTKOqOJQ7VixjWIiikJsw9CgSScPkP0928kpTN/GUgZkyUUIKjuVQ7pO5Y141W7e38Pr+NqIJ\nnXBApXpGAakSlZST1bkry/OxHYcdHQNEY2mSxwfQujXGsjRrK8J87a5ldKY0Hj3aSmKI85vnV7ml\nspCgKPPqvjZe3N40rM2WFnr42zuWM6Pq3Pa/wbawdXcz//zEPjL6OcfifAdrdnUeN90wi8dOdYw4\nr+9LS2rIQ+LV1n72DcQZiKaRTQvteJyB/uH1KcsipmnjVWC0+U5ZEvjWF9dwxczs6QYdfUmCXoVH\nNx/jzzubiSV1IkGVa5ZPY8nMIn76zCFaus7pzxmVEb506xXMnV7IoVPdIAo4jsO0sgiRs1/3b+tP\nguUQ9KscPNHFwtmlHDvTx+6j3WzedoZk2iQcUNi0sobbrp1FZzTFzt44b7T3EU/qBH0yKyoK2VRd\nhEeWSGkGqiJhOw5eVeZoRy9zygoB6I1p1JZFaI+lCMkyP/+PQ7y+v52EZiABo7lyokekrDhAW8t5\nkzwi+GrD5Bf5mV8Z4XMrZyIjEE/ppHUT07SZXh4hY1i5+u2Pp3n4mYPsPtKds0vXXlHBh9bVEQx6\nKCsI0BPV6I1q/F8/eg0tM1zReTwSxctKMVQBOWMRjtu0nOonnjIIB1SuWlzBTVfPRFUlmtsHyA/6\nKC8KYlg22A7/9uR+/rJr+Ot8Qa/I1+5czm+fP8qptmiu7jwRleCcArySQHG+l66MiZE2STRmd4EM\nKwpRwLYdIkGV61ZUc9u1swiOYg+804znE43rSN58883cdttt3H777fh8vgsmFo1Gefzxx3nsscfY\nvHnzW5N8EplqjmQipfPD3+/jlb3v7a2tXkXkf3xuJY4DW3a2sO1gG6nMu+udna/ctpB//f3+d8Uh\nsu9lzjcYIkGFaOLds0m3tiLM5z48j0jQS3d/ilf2tvH6vhYyo/t0Y6KIUFESpKs/jZYxzzl0LmPi\nkSEv5KO7XxvLB3O5DBRHFObVFbP1ApNhY7G0vpiy/ACv7msbNlE4lPnTC/j0TfU89tJxdh7ueqvi\nvuOUF/jpjWn/P3v3HSdXXS/+/3XKnOkz23tJdtN72TSyCQkhgFSlBEXKFRU7crEgXH/yk6tXBLvY\nAAsooqLSBBVTgEBCCimkZzdle2/T58ycme8fszvZye5mN5BkF/g8Hw8ebOac8znv8zmfeubMOejR\nM1thTaqEqsgEwwaaSSYeh0g0htOmMrMsk4M1XXR6B8/ToRRk2fn6rQuxWkxYTArfeWwLb1UP/kyN\nsegTV05jwfR8Grq8xHS4/7GtDPHF1juimeQBF6UEYSg56VZ+fOeKUZ9MvuOJpNfr5Qc/+AHPPfcc\nCxYsoLKykgkTJpCenk48Hqerq4uDBw+ybds2tmzZwuWXX86dd96J2+0+awd1to2liaQvoPO5B9bT\n6R3ZbUWCIAiCIAiCILy7LZ9TyFduqhjVGIabEw37IyGn08m9997Lpz/9af785z/z9NNPc/DgQQwj\ncblGVVWmTZvG+eefz7333ktubu6ZP4r3sUee3SMmkYIgCIIgCILwPvLqroZRn0gOZ8RPH8nNzeX2\n22/n9ttvJxaL0d3djSRJpKeP/UfTvput314/2iEIgiAIgiAIgnCO6REDbQw/gOdtPfJPlmUyMjLE\nJPIs6/txsSAIgiAIgiAI7y+dnoEPdBtLxEuixjBv4PR+8C4IgiAIgiAIwntDXqZjtEM4JTGRFARB\nEARBEARBGEM0dexP007/De3vIrW1tdx9993E43Hi8Tj33HMPM2fOHO2wRsw5Bt4fIwgipeqmAAAg\nAElEQVSCIAiCIAjCufX1jy0c7RCG9Z6eSDqdTh566CHS09Oprq7mG9/4Bn/84x9HO6wRM4/hH9cK\ngiAIgiAIgnB2zJ0y9t+EMexE8sUXXzztRFeuXInVan1bAZ1J/R8GpGkaivLum5hdUFEkntwqCIIg\nCIIgCO8TS2bkjXYIIzLsRPLOO+88rQQlSeKll16iuLh4ROu/8MILPPHEExw8eJBQKMT+/ftTlhuG\nwfe+9z2efvppwuEwlZWVfPOb3yQjI2PEMRmGwbe+9S0++clPntaxjAWfvGom+4920twZSHwgGRB/\n902IhXcm02mm4932PtHRKquqD6KD/Dhd1hPxvJfrj2gf3tveD2V4OLIOMfGzD0EQBvEe6QNdDhO3\nXz93tMMYkRHd2vr666+TmZk5ogTnzj29A3e5XNxwww2EQiG+8Y1vDFj+8MMPs379ep566inS0tK4\n5557+OpXv8qjjz4KwJo1awZsM2fOHO655x6A5G8jV6xYwfLly08rtrHAYdP41ucW8MP1T3E0uBdU\nnXhEhc4SQvXjwTAlVpR6XxUy0gp0cmXr+7dkIMsGDqsdjy9yYrmpByLuE/9WgmAM/NZZVcCiqfiC\n0ZHFIeuJfRvWIRuApbPzuXzpOFwumec3HmPTrnY8fh2HVSHDbaW+xUcsPsjxyTqSYSUn04ymatS1\neHuDHGKicTIliAkrp3oLi80iEQidvPMTx6bENYyTFiuyhDEg4ASXXcPr14n35kV5sZ2v3bSEvEw7\nvoDOp7+7jh6fDuZ2iNoT5yHiPpF3pzj/bqdGNBLDHxr63Hzk4jIWTSvm3kc2J/bTRzKwmhUKs1w0\ntgcIDJWGEkHNP4oppx7UCPGIhtRdwPzMxTQ0xjje6KHvyNNdZsK6cSKtfmUQyUgZLLqdGkY0NnS5\nsndgmfwmKDEkCeJxwJAJHZmOVlCD7PAgSYlV4zEw2gtRWqYTCsrYzBL52VYa20IEw7HUdGWdvLwI\nvu40fIHIgN2mUH2gBiCUM2DR3TdXUFaUxv2PbeVIg2fAPlIGxqaeRD6cXEb7lVu7ReGCimI+cN44\nIvEwzx16id0du/BH/JiwEm0rIFA7Dg0T08ozqartxh86C68TGqIdeMdGMhgYzQGD1gn6wIuZX7u5\ngqq6bv61+Tj+UBRVgmicQWO1ahCLG4QjJ7XDss5HVs2iqT3AyzsaQAugle8aUIajbTlE62ee6AOG\n4LKbuO+284A4G3c18uKmoynlXAJKC518/prZFGY76fKEeO61o2zcWX/qMnOq/B9kWVGOA+JQ3+ZL\nXT7IuukuMx9cVs7LO+s51ujpzYPdyI6eZP2O+dzoR2ZDPJLaNw0TiyxJxOJxHFaFSDROOBIbetuT\nlOY5uevmCl7bVc8fX6oa8XayBMvnFWE3q7yyq+FEW9JXf0ZSlvv3W6oPDRcmVcEfiuByKlwwt5QV\n84v50o9fwRjskOTe9vw0J+EFORZuuWQGVovCb57fz/Em72ltD6QcX8WUHCDO9oNtA+KTJYjFwWYy\ns2pBKeXjNJbNmMTRxk5+9tQeapq7iccVJAkmFqfxlRsrgDiHarr44ZM7Bhz3NatK6fCE2fpWCwE9\nesbaC7Ojk4vnVrBqYT7xmMr//W4rrV0jeEVD/z7uFHVHVWSiUQnNlHjQih6JYbfInD+viA+tmMju\n48d56I9HBqbby2kz4R2uvxosht7PZBli5mbQ3YO273aLym0fnEG6y4zNouJ2K/T0GDy1tpot+1tG\nlA/pLjORiDR8v3qqC0eSwR1r5nOooZVXG1/FcNcgmSKYsDAjfQ7bNjiI6GqyXR1RXzVIvkiApkqE\njcHLkFWTqZxdwJ5jbTR3BN5eOes9TkmCC+YX84mrZuB4lzwnRYrH40OMghPuvPNO7rvvPhyOkT1+\n9u677+YrX/nKaX1jCLBlyxY+9rGPDfhGcuXKlXz2s5/luuuuAxIP0Fm9ejXr16+nsLBw2HTvu+8+\nMjMz+dznPjfiWOrr61m1ahXr1q2jqKjotI7jTPPpfr70z/voCnkGLJORmJc3m7fa9qEbicqoKSZW\nlS3lvMKFtHcHUVUozckiz5nDpsP7OBo8zIajr+PV/VhlM25LGr6IF18kMCB9K06CcS9xSA5gTmZX\nrHx56WeYmFUGQLO/mTxHNgC6DvXeOkyqQnnmOJq7u2jrCvDPrYfYHvgncatvQLoyEvPzZ/PhmR8k\n15FFV7iTBzf+ilpPQ8p6Ja5CvrLsU+T27Sti0OX38vieP7GtcXfKum6zk+tnXsFjO/9K2DgxOdIU\njbuXf5a6tjamFY4nz5HN8c5G/m/jTwlET+SHQ7XxmYpbKM8oo6GrhZkl5XT2BMlwW2n2tqIHNX78\n5G6q6jqIm8JoE3ai2L2J1gcYn1bMp+bfTI4tB4dNo7ajlb++socdu3V6Io3YrS7OnzGOq1dO5D81\nG5LnRwLigF21smrCMtItLh7b9dfBT0SfOETbConUTQHDxKSSND537SzS0sGnB8iwutGjBnosDHEZ\nLW7nSGsThwK7U8rFBRMqGecuYk9zFW80bkuWL1VSWD1hGZdOWE2uKyP5olxf2MfG6u38bu9fiDN4\nkyIBUzLLuWj8KpaWz8UX9hExojx/aB0vH9s0aBkssOfzpWWfINeehaZq+EJBNDUxYfZFPDS09bC7\ncwfPHf7PqfNlCOkWN9FYFK/ux6nZmZI5kVl5U/jTW8/hNwbGc8uca7ls8ir21B4BOUZ32MNPtz+a\negp6D19qz+d7a24nw62iKYkOwaf7cWh2Gro6+On2R2jwNKXkVjx+oq7F40AMFEUmxokRkoLMXcs/\nS4m7AAmJr697kLZA54BYMy1pfPuiu8iwpgGgR3U6PUFquupYVD4jEU9ARzMpaCYleS71iIEeMfjr\n+ir+s7UWj96G0+pmxlT4cOVSJGuA/93w45TzpWHDqK7A12nD7dC4cEEJs6c6mViUga7D39YdY92e\ng/g9YLdoXLJoAtdeMJF6bx1ppiz+tamel7ZXE8x4C1NmG6gRNDSWlS7mo/OuRJMs6BEDvx7g+2v/\nwvHwXlAjENUos87gxoUrcWmZlOSks7X6IJkuJ7nuRFn3RXxkaNloGmhq4jw0d/hwWDX+ur6Ktdtq\n6fEHsTojrJw5kUg8zGtV+wl2uHDbHCyfU0DhuDBmZ5hH3vxj8nz1nedQ1XRKXLl8+oMLmVyYn9xH\nl9/LC9Vr2XB0E17dB1GNSGshWjCf4kkeupTjeHUfTs2OTbXREkgdWLs0B9dM/QC/3f3U4IW3n88u\nvAmrbGFR6TyONrZTlJ1Op9dHXoYbParT7GsjYkRIt7rRFBOabEUzKeyo2c+80mn4wj4c5hN9fGeg\niwxberJMdPYEcdgVunwB/rjvKfa0HMIX8fceUz6mUC6rZ8wimlnF63Vb0QkSj2goPcWcl3ceKxdl\nk+aws+HYJl4+/gY+3Y9JVokD0VgUu2qlomAWc7LnsnTC7GQcelSn1dfBl/79v4O2K331JR4H4hA7\nMh/Jl0cYD9bSYygZrRjo2FUby0uXcNX0C8mwpiXbEd3Q0Y0IrV1eitLzTpT5cCdWt8Gls+YzcVIc\nl83MzKKJg+b9oZp2stPsZLit+AI6DpuWqEOGzr6mKuYWTU95mfiuxn38ZMtv8en+lHSsioVsJlF7\nyE7Qo+Cyupk0NU6V/B/02NCDbbOiETZ07KqV80oqqMhayK+fe4vapiBxVcc8/gByv/4IQMNB7PAi\nvAEvbi2PlfOK+NCKCYk+rcNHXqaD5u4uuvV2yjJKk2W6M9CFhp2fPL+BN3cG0KPx5KDZpEqcNzeX\nq5dNpKqzik376zjQcYS4qwHJFEHFzOrySq6b9QEcmj2Rd61H+cW2P9Doaxry+E7m0OycP24x10xP\npFPb3YCmmMhzJi7g7a2pY1PrK7xaszXZb/VxanYqciu4qeIKiJp48qVDvLyjHk8giKZKGIaSvMhr\nNilcvLiUi5flEg2pHA3t4+HtA5+xIQFXTLmI83Ir+emT+zhS35O4EAxYnTpBPYCa1Y6aXY9kiiTL\nrIbKRZNWMDmzDLtm5YWDL/NW234ivedaQeaiicu5aurF6JEoHaEO7n/15yljGOIScUNBUqPEI+AK\nT+FTKy5iQn4eDT1NFNrG47BpyfLX4m3l73vW80bTFoLREHbVytLShYDEprptA8pkUlQheGAhNs3C\nqjnlXLK0GFkx+Pm2xznSWZOsmxMyxvHFJbdiVx109ej4pVam5EykM9BFTXsL/zq0iX3du5LlWVNM\nLCtZTHl6MRdOXEZnT2IyHoh5eHDTz2nytfbmsYRTysZbPYFAtxVryRFM2U1ECKOhojP4BWYVhSip\nF8NcZif/c/7nCfsVirLd+HwS//vEP2mKHUXJakAy6ciGmYX5FXxw6kXoiofNdW+ysSaRPy6zg8UF\nC7i4bBW5aU7qe1r4d/UGXqvdRiSWiEOTTawYv5Rrpl6KXbMlPjMpyfrT1+ceaqrnsb1PcKTzeGKc\njUR5RilfXHJrcmw7Fgw3Jxp2InmuDDaR9Hg8LFiwgGeeeYapU6cmP58/fz4PPPAAq1atGjbNj3/8\n48lvSd1uNw899NCwsYylieT9rzzEjuZ9oxrDWHb5pFUUufL55fY/jHYoY055WjGNnlaCsbN3S6wZ\nE2GGuaIojFk3z7maioLZ1HTWMz1vEpqioaka/6neyKP9Jk0jdefiT/HU/uep8zSe0TinZ09gYdE8\nfrvzL+8oHU1SuaC8kjUzL6cr0MNvd/6Zva2Hz1CUJ0zJKqPJ107PIBcAxzJZkonFT1y0sMkWxmUU\nc6C96rTLwjulSDJGfOTfFp4rN8+5movKz09c2Ar70BQNn+5HU0z49QC/2PoE+9vPfJk62y6ftIpx\n7kIe2/V3vBHfiLeTgBJ3EXU99Yz0bOU5smn2tQ2/4lmSbnFx36ov89TeF3ijfkdywqkgc17xApYU\nV/Dwjt/TfZr1t9RVRI1n7D3TIsuWTnuga7TDeN+xSWa+suIz/GjTr+kJn/gm3212Ulm8gBeq1w+6\nnYTETy775piZTL6rJ5JNTU2sWLGCtWvXpvzmcuXKldxxxx1cddVVZyWWsTSRXPPnz4zq/gVBEARB\nEARBODckJO6/6GuMTy8Z7VCGnROdkTddVlVVUVFRcSaSSmG3J25/8PlSr455PJ4R32r7bqZH9eFX\nEgRBEARBEAThPSFOnLte+g7HumpHO5RhnZGJJIDfP8S91e+Ay+WioKCAfftO3NpZW1uLz+dj8uTJ\nZ3x/Y01dz5m9PUwQBEEQBEEQhLHv268M/3O80XbGJpJvl2EYhMNhIpHEPerhcJhwOEzfHbdr1qzh\nkUceoa6uDq/Xy4MPPkhlZeWo33J6LhS7C0Y7BEEQBEEQBEEQzjFP+G08JfkcG9HrPz71qU8xY8YM\nZsyYwfTp08nJGfh4+7fr2Wef5e67707+e9asWQDJe3Fvu+02PB4P1157Lbqus3TpUh588MEztv+x\nrO9JaYIgCIIgCIIgvL90BXtIt57iFUejbEQTyVgsxp/+9Cc6OjqQJIns7GymT5/O9OnTmTlzJpr2\n9ic8V199NVdfffWQyxVF4a677uKuu+562/t4N5uTO41dLfuHX1EQBEEQBEEQhPcMk3LqdwWPthFN\nJB955BEAWlpa2Lt3L/v27WPv3r08+eSTyddpSEO9aFB4R24/71ZuffrLox2GIAiCIAiCIAjnkKP3\nXZRj1Ygmkn1yc3PJzc1NeX9jS0sLe/bsSXlth3DmODQ7s3OnsrvlwGiHIgiCIAiCIAjCOVDkGvvP\nShn2YTubN28mGo0OuTw3N5cLL7yQ22+/HYBt27ah6+K1FWfSF8/7+GiHIAiCIAiCIAjCOXLXsk+P\ndgjDGnYieeutt+LxeEac4Kc+9SlaWlreUVBCKodm5/bFHxvtMN71tFBstEMYkrsr8ra2k4ALyyrP\nbDBnkNvk5Lur7+ZXV97PNZMvOSf7dJpO/Y7Z2xfdirgR/9S0UAyr38DhGfoioiAIb58ETMksP6Np\nTsoYf0bTG87b7bcEQRjevSvvINeRPdphDGvYW1vj8TgPPvggZrN5RAmKbyPPjsrShVhUMw+89stT\nrqdE4xjq4MNkSwhidhO6EcGMCRSJsHHifGmyCVVRCERCWGUzFYVzuH7WZdg0G88eeIn1Rzfh1X04\nTDbOK6ngYGs1td7Euy61UAzdMvh1CUWSqcyfzw0VV5NuTaPV107EiGDTbKRb3fj0ABEjQsSIkOPI\nYnvD7uRx9k+3JO7ikorL+f22vxCUBw5wnSYHdy3/DG6Lkwc3/opaTwNp3REufd1DVo+RmDxI0JZp\n5vnz7HgdieIvIVFZsoAb53yIcDBACIMCdx6aYuJw+1F+u/MvHOmsAcDqN1CMOD7XwKrj8ERTPu9/\nLqx+g4hTIxozkssKm0NcudGLHE8MKuJATILnljmpLbLy2YU3osomfvLGb4c423D/RXczPr2E2xZ8\nlDdqd/CDzY8MuS7AXZWfZnL2BP6053nWH3mNaNxIWV6WXsLnF92CJ+RjZ/M+1h55DX8kgMvsYOX4\n81hdXsmGI5t4fteLGKo0oKypKHxu0c3MLpgx4L7+6+dcxfVzrmJf6yG+ueFHQ8a4KGc6B3pq8IR9\nmDERZujBitPk4Osrv0ChKx8ArfdH6T7dz1N7X+D12m14wokyu6q8kivKVuBypjMxaxw/3vwbqjuP\np6RnU60UuHJp8bXh1f24zA4WFs7hSGcNx7vriZN4LVGhI5+vnf8ZPEEfE7PH49MDyeNt8bVx/6s/\np8HbnJK2hJTc3uo3cJosLC5bxPmTltLe1cGM/EmEzQqaYqJh32600kJMigmbZuXul+6nxd8+ZD5A\nokxlqnaumHsFZRkl/LPqFbYefmNAvXSGVKJGmKBdSf3cF+Xyl7vI9sRTJtpxICrD0ytdNOVaACh2\nFVLszmdT3fYBMTi8UXrSUx8OYPUbBO1K8v+nYjfZyLKmU+NpSG5rKNKg7Uu+Ixdv2IcvMrL3GPfV\nyb52RYnGMSsaX6j8BDPzp6IbEWJhHZczja5gD9097UiaRpYjk4gRoSfowaZZyXFkoRuJcqkbETTF\nRHewh7qeRn6y6TcEY2Hg1O3iUIpc+dR7mgZ8bpJU7ph9AwsmL0l+5tMDNLbXse7oJnY176XLCGDG\nRASDGOf2wpkWiqEY8VOe30xLGnct+wwF7vxkvvn1AOlWNzurtvPwW3+mI+pLrm9XrHx64Y2Mzygh\noAexaVbsHX7spaW8uOff/G7/MyllSonGUaLxZJ4Xuwq4df4afvrG7+gMdifXcXdH6Mwa/CGB07LK\nKXTns6V+F56wL6Xe9unftvelByTTzGjX6czSMIfihC2pbaQ9buLCqSu5aupFODQ7kChDbb52bJqN\ne/5zPx29sZ5MicaT/6+csIQbF1xHT9BDoTs/uc6xrlrueuk7Q27/8cUfZbyWxbe3PYovEhh0vVMp\nqQ8O2m+9uMjO0bLE8SiSghE/0dcZqjTo2ERTEuMRh8nGhSWLuXLWpVjjKkHJwK/7+eWzP2af2pHc\n1uo30M3ygHTMaJRnlXKgvQq5335kSWZV8SJWT16B2+rml1t/z67m/QPO51Ak4JKJK7movJLqpmqK\nbTl41TjlWSX8ff+/2HxgI4FgAN2cKG8qMoXufOqCrURiQ1+E+2rlp6konM3xzjrGZRQnP/fpAby+\nLr635dfUe5oGjVKJxpFMJr645L8Yn15Kk7eVn2753ZCviTi5zQOYklVOVfsxjCHaiL5y1pePp6rb\n/cc9fXXx5HavsC5IQ7H1tNtDm8nKPcs/T113I/84vI5GbzOWk/YhIVHsLuQrlbeRbk1DU0y0+Nr5\nw66/s6f1IIFIMJne22mPR2JcWjHfWPlFNEXjeFcd31z3QyKknv9Tjc1PNiFjHF9ccuu7YhIJIMX7\nXtg4hJtuuum0E/3+979/Rl8Rcq7V19ezatWq5CtIxhJ/TQ2+TBsPvvYrmlvqAJBkuHhfnOLjXtSg\nTkCDI5PcpJ+/jKUTFtHxi8fxVVVD76m2ji9l2t13Een9plnPSYOqGtJmziRQW4uSnYVJUfEcOoRq\nsyGbTDgnTgQg0N2FLS0dgJYNL9P49LMEauuSadvKxlN8/XVIqoJclE/TY0/i2bkLIxhEdTnJWbmC\n7OWVOCZMSB4PgL20FG9VFc6JEwm1tHDoez9Miflkss2Ks3IRZTfeSFiTk49GNsJh4pEIB77/Qzw7\ndp0yL/M+8wkyJ03C6k6j4ZnnaXv5FaIeD4rTQe4FK3HPnom1oICeffs48shvIRQakMbupUVMfaMe\nk3GiU5VkGcmiEQ8MXF82m5FUFcN/6oHvlK/fTeaCCoxwmJruOu5/4xG6Q4nzZfUb5BpmPrHoo0ya\ntZiI14vJ6QQSE6hfb3qCbTVvohjxZKdrVjS+tvyzTM+ZjBFODHIVszk5cI6qEuawkUzHCIdRei8e\nhQJ+LDY7oZYWDnznAQLHjifjjAH7yszsnZ/NeVMquXT8cky+EDFdT5aZcEcHUZ+PmK5jycvD5HRS\nu/k1NjfuZl3sCN2xIAUemUsabKQfaSPq8SI77GQtr2T8R28gEI/w/NGX2bllHZ1SmCxDY/KsxVxf\ncTUOzZ4Sa/+/+wR6uqj/ze/p3LoNIxBInt/CD15JPB4niIFxrBb7tKlIwRCqw4FiNhMK+OnZ/iZZ\nixahmM0Y4TBByRjyh+9GOExM15E1LRmDT08M1ByajZ6DB9n/re8Q8/oG3X4ohTffSNYVF/HCxr/y\nWtMOWkw6LrOD8wvmc8WEC2j53R9p3/g6xPoNCkwmiEboG4mYigowQmFi7R2pidus1N+8gt3Hd3Lp\nv5qH/aa27P+/h/y581PyOdDTRdWPHsLXr77FSZSNuNmEGk69EBAH/Co8c2Ea3ZkWVsvlrFj4Adzu\nLDLdiY7Td/QYe++9D+Oku2GUgjwm/fftyJnpOB1uoj4foZYWLLm5BGwqds1GqKkZe3YO7REPjzz/\nEA2d9Sx6y0dxWxRL6nWTFJb8fKJeL1GfDxQFWVWIhXWwWcmaP4/yT9+G6jj1t90AoZYWDj7wffxH\njibbL/uEckpu+DCxrDRM/hDewnR+vvVxotv2U1dqxRyOU1BcxheX3EpG3IKsaQQlA00x0VpTTccv\nHjvRHkoStnGlWPLz6Ny2HSL9BiyqSv4lF1HyketRHQ5afe3kOLIA2HN0J/+37VGsHh1NjxG0KmR0\n6nQUOvny3JuYNWUhbW0NpFlcyJpGV3NDou2w2LGi4Dl8GHnaBKIK/GjzrznaWYu7W+fKV7pI85NS\ndiIWlecr3fhlg84sDU0xsaqskmsmrsblTE+u1/DPf9L64r8TfUg/9gXzmHHHF4l4vVjz82l7fROH\nv/8jMAY/gX29RP8Y7OVllH3y45jS3IRaW6l95jm8O3Yl1+nbpvBT/0XayuXYNVvyQhRAsKkJJSeL\neLcHWdPw+T20/OOftKxbjxpIXN5ST9rnULRxxaRPnkLHps1EvT4Up4Oc85eTd/FqbCUlRLxeAnV1\nOMrLaTm4j43Nu9jcvIsuI0RJj8TSVivOw01w8s+MZJnc1aso+fAatIyM5MfHumr5zqs/ozvkwazH\nqNgXYPqRIFY9tT+1Tygn86brSZ82FampjbCmkJFflJzkQ2ob1vCPFzj+yG+GPM44oJWVkH3pJRzY\n9RqWN/Zjjvb2i4DidKAumc+cW25FdTjwVh+hdeNG2taux/CN7GJQH9u82eR++FoKJk8j6vNR/7en\naV23nkiPByxm7MVFhJtbiHp9mNwuclZdQNE1H0J1ONCNSMqFDK21m+6QD39TA4XjJ6NkZxFqbsbq\nTqPuT0/RsnZdavuqyGCc+kJNzmWXkHn+MuxZ2UScVtobjlOYVYysaeidnVjzE5P/cEcHitlM7Z//\nSuu69Rh+fzLetCsuIS0jm6jPx+Enn6DnlU3EvD5Ul5PcC1dRdM2HCHd0YMnLQzGb6ehpI17bjNXt\npvGf/6b9lY3EThpr2MrGM/Hzn8EIh1HtdvyZicm/qbGDrldeo/mltcRP8YWQ4nLSUDmJrH+9iRLr\nN+45jXPXNwaNmTVUpwMlGiOm64TNCl09bTgcaSmvu/AdPca+e79J1DNwsuyYNJHJX/5vLLm5yfw0\nZ2Yml3sa6zn2w4dS2k9raQmZN64hr2wicasFi82ecjG4oacJk2IiPW7GUGRCQT9WFFSHgzp/KyZF\npamjiUlaNvbsxFyn/xik79/tbY1U//lPxF/fQSwQRLLbyFyyCOXCpUyYOhefHsDkDSZj6Gyqx52R\nPWAMM9qGmxMNO5F8PxprE8mObds5+J0HhuxE3+/MBfk4J06gffMWeA99Iy5p2ikb9JPJDgcx3+CT\nlMxlS1GtNlpffpm43juwV+TEVYhBBifEYqDISJJMPBoFRRmT5U9S1UR8qpro0KJRMGsUXnYpktVC\n/RN/OrM7tFoouGg1uRdeQLy3U2p45nna1m8YcNEje+X5ZC5ZRPtrm2jfun3QCxHvRqrLmejQLWaQ\nFQic/rcap5K2sILurduHX3GUjPvUJyi89AN4q6qIBgK4pkwh6vOhmM0cffS3tG14+YztK33xQrq3\nv0k8epp1T5KY/r/fxJKTlZjUVh85YzEBYLVCMDj8ev3IFjOxUBhUBU73eM4BrSCfkuuu4egvHib2\nbuxHNBN5qy4g87zFOCdPJtTcjN7dzcEf/ZRYZ9dppzX7u9/BUTaellc2Uv2Doe8geTcxZWcx61vf\nxJKXR+3f/k7d40+MdkinpLrdTLrjCxz87veIvUf6j5FQHHbyLlqNlpXJsYd/PaJtZJuNWP++SNMo\n/uhHqPvtYyPa3jppIrPu/TpRv3/ABfMR6xs7vVOSRPbK8yn7+MdGdPHybBMTybdhLE0kO7Zt5+C3\nBr9NRRAEQRAEQRCE9xZTejrzHvrRqE8mh5sTnfmbhYUz6uC37x/tEARBEARBEHQRQnEAACAASURB\nVARBOEciXV1U//LUz70YC8REcqwTXxgLgiAIgiAIwvtKx8bXRjuEYYmJ5BjW9yAaQRAEQRAEQRDe\nX8b6b7bFRHIMG+37ogVBEARBEARBGB1612k+LOscExPJMaz/I4wFQRAEQRAEQXj/6Hu1yVg18K3q\n7yHt7e18/vOfx2QyEQqFuPPOO1myZMnwG44hit0+7DsHBUEQBEEQBEF475DG2DslB/Oenkimp6fz\nxBNPoCgKdXV13HHHHfztb38b7bBOy4xvfZPd//3l0Q5DEARBEARBEIRzZNo3/me0QxjWe/rWVkVR\nUBQFAI/Hw+TJk0c5otPnKBtP6cc/NtphCIIgCIIgCIJwDkz5+t2kzZg+2mEMa9Qnki+88AI33HAD\n8+bNY9q0aQOWG4bBd7/7XRYvXszcuXP5whe+QGdn54jTr6ur4yMf+Qgf//jHWb169ZkM/ZwpuvJy\npnz97tEO413HkJTRDkEYQ2Snc7RDGMhiwZyff273KY+s2T/d+uNTUx8OpsvaaW0/KhQF1Pf0jTnv\nKtJYrKPvMu+KejdC5oIC1DFeJqZ/+z4W/PYRpvx/d2MrLRntcIZknzjh7CSsDN5PmPPzkK3WU246\nXB8TVKwDyrMua2d8bGdIyjtK8+S+70yY/u37yFxQccbTPRtGvQd1uVzccMMNhEIhvvGNbwxY/vDD\nD7N+/Xqeeuop0tLSuOeee/jqV7/Ko48+CsCaNWsGbDNnzhzuueceAIqLi3nyySepq6vjlltuYeXK\nlWf3gM6SzAUVLH02cVtuoL6eaCBA2JBw5OUQ93uJRGO4x5UQ03UCDQ107X6L2sf+ALHYiUQkiSn/\n8zWs+XmoNlvyqbAxXSfU2sb+b36LSHd3cnVTejoTv/gFmv/zHzpf35wSjyEpKHEDn+pgwk1r6Hjm\naTp8Bq6IJ7ntpC//N65JEzEkhZ6GVlR/J20bXqHt5VeJRyKJhBQZIyYl/owbA47bNnMmM7/2ZWK6\njqxpHPjJz/Fs2QIkGhlDUnBEfcl4opqN486pNLonEZHNWG0qcyqKmDsjjbqf/ojAseMnskNRyFm9\nioIrLiMWiaC53WgZGeidnUQDASw5Ocn99on6fGgZGcnPQy0t6D09aG439X99mpZ16zBiErqsocV0\nLAV5zLr3f7Dk5vbmcyvE4zS9+C/aN75G1OtDcTqIyGbkno5Bz71ksRAPhVI+y75gJXmXrObwjx4i\n3Ng4YBtLUSHRYJBox4mLLn151Ndo9uW3ISlomkz5V75K1pRyfDU1qDYb0UCA1nUbaN/4GnE9cb5k\ns4Z7SSWho1UEa+tS9qkW5DPh4/9F/Z//iq+qOvkOVFNhITPu+SqWnJxk/gW7emh57jla/rOOqNeL\n7LCTfeFq8lZUornd6LKGEjcwmRRCrW3UP/0Mra+9gRKLDppHieDk1PLeT8Enb2P85Rcnz2HU70fv\n6cE1aRKew4exFRRw6Mc/pWfXW8T7HrVtUsm/5GIks0bjX59OyTMgeY77/x1UrFiN4IB1IdExqHYb\nqs2WLA/9y1ZM15OP+VYdDqI+H3V/+Rut6zcQ9XoHPa6+zk+JG+gmC9O+dAfZ82cja1oyfb2zM1G+\n09ORNY2oz0f9356mZe16oh4PhqQk4+7SMmjPmkZH+gRCIQMlGqTIW01p1x5MsYGPIO80Z7O78CJi\nkgqS1HvO44CU/Lcr3M4c/5uULl9AzmWXEe7owFVSlHoODh3iyC8eTqmfSYoCRiIvu7U0nBFvIm9l\nmazLLmf/f94gM9xBULHiiPoAKPzIGlxTJpMxZw5Rny/Z1kV9PvTubmxFRSn53/d3/8/0zk68R45y\n8DsPJPc/WP6ffJ771zNFBllViIV1JLuNtJkz6d6xk4CRqINRWU0eT1+Z70uv3ZJFergr+e++AZTV\nCOIxuShbMpv8Ky7FVlBAz8GD1D7+BIGa2kHjPDnWghtvovgDFxL1+zn0wA/wHTmSrK9SQRGla64h\nUluTrJ9xhxNFgogvMOB4ZYeD6d/4HxzjxyX6n8ZGZNXE/v/9NpHOUz9tsC8mc34ec7733ZQ+yVNV\nRefmLbRteIWoz4dkt+GePo3uN3emng9JYsKX7iB9+jRqn/wLLevWgZHaDigOO9nLKslcthRbfj5a\nRgYtG16m+me/hEgkWQf6ys+gJDAkFV0yYTWCAGSvPJ9xN9+IlpGRKFtdXVT95Gf4Dled8rgxmSAS\nSda7PmpaGtO+/S2cRYmLS6GWFpr/9RKt69YT6fGA00Xh6gsouuZDAFT9/Jd0vr6ZgGpnb94KvOas\nlHo3vfllbFH/wHKqKEy/716ijgyyxuXjOXwYS1YWh3/2Kzrf3JlcTZe1lPgMk8aM/7kL18QJyXMV\namlJeRhIX3sa03W0jIzkZ65Jkwi1tHDgOw8QOF4zoH/oXyf719k+gbo6Wte/nGy3JLsNe2kpvv0H\nTp3Xg+hfl/rI5VNILy+h7Y1tyJ4uVJeTrKXnkX/5pcgmU/IYO3ftwpafP+ABKJkZGWRWJAb+ybLw\no4dS6patbDw5l15CuKaWlnUbiAUCye0lu428lSvwHa/Fu3dvStr9z19fX5PS9vfri2SzmZiuYyAn\n1imbzJKvfiYZb0zXaV63nmO/enTAe8qlrExm3PVlbAUFyfzv3rcP18SJyfbRU1VF2vTpybT6t6F9\n/+7fr+mdnTQ+/wLN/1mL4fURkTVq0mfS6JxARLViigYp8FZT6j2ASQ/g0dLYWXARUcWaKMsA8ThS\nPEq8r58BiMco8BxmQseOAX2Ta94cPHv3YURiKeU+ffFCALre3EnEkDiaMZtG1yRisimRd7EoBZ7D\nlHbtQY4baDGd9MpKurdtJRyBkGJJjnEbrCUczl+W0vdJ8ShzGl7CrXcOaCfTF1TQc+AAMZ8/5Zx6\nTK5kmgCmtDSm3ft1HGXjebeQ4vGx8cb7LVu28LGPfYz9+/enfL5y5Uo++9nPct111wFQW1vL6tWr\nWb9+PYWFhadMU9d1tN4C3dnZyc0338w//vGPYWOpr69n1apVrFu3jqKiord5RGdHV4efpx7fTnO9\nZ8AyRYV5i0pZcckUrLbEcUciBo37joArHYfLSiigU1iSkVxmMinJvwMBHZNJQQ35kh1AJJKoDOG6\nWnbf910OKGU0OseDYkk0Qn2V+iRXXD+LqgOtHHyrOeVzkwaXXjObcePS2PBSFft3NxONJjp9KRYl\n31NNnrcKT/Ec6i3jCYWiWGwqGVkOutr9BAO9E9C+wSok4zBpMhF98IkEgCTDjDn5zJiRTfnUfGRN\no6G2k5x8dyI2k0IgoGOzafT0BDGZlGT+9C2LRAyiEQOX20okYhAM6IQCOk63jbX/2MfeHSdN6iSY\nNruAydNzmDKzAJNJoaWpB9WkIEsSf3t8O40NnsThAHkFDs6/aCKFjgiO8YmGJBBINJK1++sompCL\nP5g4J2pvbO2tXspLnHh6gkQjMeJmCz5viPyidLyeELs3HWPH1jr0yMiqem6BkzX/tYD0TDuRiMHR\nqlbycuxsfb2GHVvrCYei2BwaU2fksnBhPumZdlSHI5k/bnfiKmTU5yNuTuSTzabR2eFnx+Yadm2r\nI+DT0SwyU2fkQzxO1aF2Ar7B35WUnefA79UJ+HXMSows71HKm7diIoJ7xSr0WZXMrJxGW00rnlfW\n0v7Kq0g9nfjs2VSXXUxHUE2ei/xCN9fePB+z1ZQ8zzabljzPkOgQIxEDWdOoOthMY03PiZhNoJk1\nfEPE2p+qwpw5ecxYNI6SsmwAenoSAxe320pHe2LQmpnlSJa7ofTvsOsONrB983EOHOomGhlY3mVZ\nYsa8ApavnkRGloNIxEiegz7BgM7Tf9xJ9YHWYY8jkSZcd8s8Jk7KxtvQRNerr7Lvld28mbZ8yDbg\nZIoiYRgnymDZpCyu/PAcvD1BHL1lxu22UnuwAbPbQbrZQHK62bj2EK+tPTKifST2A5evmcPsimIg\nkeftLR7yi9IBsNm0U+Z3JGLQ2e5L5l3fNp37D9K6eRs1r2zhuHk8HfZSoooZhSg5PdWARJujlKhi\noa99khWYv7iU5avK8XgjPP7L1wkHB2mjUtrSfm1b3zIYcvk1N89j+uxEXxj1+aj9019of3UjkR4P\nIcXKwayFeKx5RFQrxCKoJhNRAzSLTPG4DFZfPg2n28qGf+xj147GZJmSFcjNc9HZ4SccOjEokmRY\nuKiQJUuKcBXmDMi7vrrUd8Gidd162oIyWRaD7OXL8HjD7DgcpsVcSES1YpZjzF0yjmWXTE32WycL\n+4PJgXPfeQvU12PKTUy4Em2fQW6+m0BAp+FYG+NL3agOR0rd6R9fMKDzwt/eYv+upgH7u+yyUoqm\nlpKdaaXbG2HDi/vZtyu1L7M7NG745CLcGbZkXH3pHzncQo4tRse6tTSte5mQN4DNYca+dAVpqy7m\nz3/Yjd+nJ85jPI4pFqIoQ6Yl6iDgj2BzaEyels2FV8wgEonxxitH2LW1jlAwsWzOgmImTcsmO8/N\nXx/fzrGqwS9CAqhSjGhcRo0GKQrXMmlGHq+3Zffu/8Sx5BW5OF7VkVJHAYjHkeNRZFUhGpOxOTRm\nzitk+epJBAJ6sv0ymRQ6233k9vangYBOV7svOd7on/+BgE53YwfmNCeZWY4B56b/3/23hURfHNN1\nQtHE375jxzn4f98l0t2dvLitEEMzwpjsFtIvWM2E6z+ENyzxxMNv0NkeYCTyCpysvnIqGTmuZJ82\nWFx9x37y5316eoLYFSNlYtyXTt9xKHGDUJTkfgD8PX5efnYX+w57CQUjgyU9QHqWhYxMB0cOtQ+6\n/EMfnUP5lNyUti9QX4+WlkbcbB0wzsnMciTHQsHecz1YHgylL28gca78PX4e+clmPN3BAevaHCYu\n/sBEnn5qHynt3zAUFW799EKc6XYAdENix+Yadm6pJRiIYLUqTJ+dT0VlWbKf6erw8/ff76CrY2Rl\n4e1QFZgxPYPKy2ZjsWk01XfhcFlZ//QOqqo6QTKdaPfjcZDgQ2umM2XeuBHn77ky3JxoTE8kPR4P\nCxYs4JlnnmHq1KnJz+fPn88DDzzAqlWrTpnm9u3b+eEPf4gsy0SjUW6//fYRPbV1rE4k33ilmpee\nG+HVN4nk5OTtrGe2KESjMYzomCgewrtQ8guqd4lpcwqI6FGq9o9scnU25BW6uO6WChwuS3IyYzIp\nHK9u4w8Pv0Fs8C/GRsydbqGnKzT8iu8T2Xl2PvjhudhdVv7x1C6qD7SNdkhv25RZeVzywRmAxOO/\nfJ3O1rM3SOqzfPUEZi0oYfPLRziwuylxsceqUDYxi9x8Fy//e5hv5k7ykU9U4EqzoZoUejoD7N/d\nyI436gasZ7EqhIIjqwxLV5Xh6Q5zeF8L4d4Lk0XFbqoPDT35Es48VZWIDjOeUFQJSZKIRmLYHBrj\nJ2Sg6zGOVbUPetFsNJitClNm5FNzpJPuzhN1TJYl5iwsZvlFk+ho8/H0H3bi84aTyy1WhQlTczl6\nqJ2Af+gLkSYNZs4rYscb9Wf1ONKzzHzgg7M4fqSTHVtqCAVOcbfPScxmhSkzc1m2ejJOd2IC2tHu\nIxTQ6erw89q6I7Q2DX4XjTA8RZGYv6SU8y+ePOTFtXPpXT2RbGpqYsWKFaxdu5bi4uLk5ytXruSO\nO+7gqquuOiuxjLWJZDCg8+ffbaX2yNh+KakgCIIgCIIgCO9MWrqVT965fNQnk8PNiUb9YTunYrcn\nvqr2+VJ/t+DxeHA4zvyPW8eiYEDnlw++LCaRgiAIgiAIgvA+0N0V5KVn9412GMMa0xNJl8tFQUEB\n+/adyMja2lp8Pt+78lUeb8cLf9uD1xMefkVBEARBEARBEN4Tdr95dm9xPhNGfSJpGAbhcJhI71M8\nw+Ew4XCYvjtu16xZwyOPPEJdXR1er5cHH3yQysrKMXHL6bmwf9fAJ3IKgiAIgiAIgvAeFodo5B0+\nHOEsG/XXfzz77LPcffeJdyTOmjULIHkv7m233YbH4+Haa69F13WWLl3Kgw8+OFrhnlORMV54BEEQ\nBEEQBEE4O0IBPfnE2bFo1CeSV199NVdfffWQyxVF4a677uKuu+46h1GNDZ6egY9IFgRBEARBEATh\nvc8yBp7ceiqjfmurMDR1jL1LRhAEQRAEQRCEc2OszwXERHIMc4/hr7IFQRAEQRAEQTg7ZlWM/efB\niInkGGexjvrdx4IgCIIgCIIgnCNWu8rFV00f7TCGJSaSY9xNn17yjtOQ5bf/0J53sq0wNikmWHXZ\nlNEO45wRZXjkZi0o4gv3XMDchWf3KqhqklBN0lndx1h30ZVTcbjMox2GIAhv02j1LaoGmdn2lM9y\nCxxcf+sCrr+1ggXnlWK2KGhaaFTiO1PsjjP028Cz1NUUlWUwYWo2ZuuZv/W0fHIWn//aKqxj/PeR\nMAYetiOcWn5RGp/872U8/svXCQdjA5Zf+eFZzFlQCkAwoLP2+X28taMRiTATy2opLGzFrEWQVRvZ\nRQsx2Sp4ZW0tB/c0A6BpIaJRE7HYiYqgqhEmlNVRVNCC2RwhHDZR35hL9dFiolHTgBhk2WDcpDwy\nMmzs311LwB/H5tCYNC2HmRVFjC/PJmZE8HpCvPFKDXt2NhDw6didMqXluVx85XScbivRiEF3pw9V\nVeho8/P0k7sIBYIpscmyQXZeGhddXoQrI4snf72DzjZ/ajwKfOBDM9iyYSftHSfidbjMXHdLBdk5\nDhrqOnny19shHk1J/3TYHBpzFpSw9IJyGuu6cKdZUFWFYDDKk7/eiu8dvv9TVmDG3EKWXlBOdq4b\nAF9PkGjUIC3TQcyIoIfjWGwavp4gqkkhGjGIRg0cThkkM6pJob6mg6xsJ9GIkfLkr3mLS3l9/T52\nbmkhGIhgtZtwuix0tPkwovHkehVLS5k1v4A3N9Xx1o564gOL4ZA0s4IeNrDYVCZNyyWv0EX98W6O\nV3cQ8OuoJhmQiEYS68yYU8CSFeWkZzqS5cFi0ag73snU2YWJ44sYHKs+jqI4KShKw+G2Ul/Tgc0W\nwWbPIBTUMZkMWmteprttB7FogFjMRG1DAYcOF2Ay25g5t5Dxk7JoqOnitXVHBsS9eFkJC5eVk9Yb\nR3NjN0WlmbS39ACQluFANSm0t/Rgs6noejx5TmTFRCigE40Y+HwhbFY/Hq+VtDQbshwjFpNprO/m\nH3/dQ8AXQJYNotHBOwtV1Ydclp5l5cbblmCxmvjFAxvwefWU5Tabj0DAMeS5mTorn0s+OB2TSUHt\n/a/PFdfP5Yrr5ybzu7vLj8WisfnlI+zd1Ug4FMJqh/ETi7n4yinEYzIWm5Y8vpqj7VTtb2PHlhqi\nkRMFZt6SHM6/cDLOtDQg8Vhz1aTQ3eHDYtWoPtjCM0/uInZSGTNbFG745GKKx2VQc6SNqgPt7NxS\nQzAQSamHfm8Qh9NKLOqjqSlCbi5oFjeaxUJ3R6Jt6e4OQBx+97NNA/bTn8OpDchTgGtvnkfJuAxi\nsQg2u5qsZ3ooRMAfJS3TkXJs9TUdhENRzKoPiyOLrN66DLD4/AlAol47etvAvrIGiaf1dXf62bOj\nkd3b6wn4dTSLzJyKUtrbvBw91D70AQDX3TSP8ZOyOVa1j43rPLS3tKeUJ0mCeDx1G7tTo2xiNtUH\nWwkGIr37K2H2gmL+9OutKe811rQQum4ZsF9ZNk7ZriqqzPS5BYSDUQ7tbR6wXDVBPCZhGCeCs9pN\nBP2RAeuWT8vB5VSoq66mvcOOYgK7rRtPT9op86ZPdp6Na2+eRXq6HVWz0d7SQ1qGg1jURyxuTbab\n7S09ZOW6qalu4y+PvYnTVkNnd1ZKfp5cXy2WAKGQLdkOIgG9h5SW1kmMHMIhCIeiaBaZCVNysVhN\n7N/ViB4Oo5hUpkzPZsqsbMaXF2GxaRze15Rs9+BEOev7f99nDbWd/P33O/D7gwPOhaxC5QUTeG1t\ndUodkGWJ6/5rPgBP/e5NYrHUwnHZtTPYtbWepvqOlDTNFoUpM7Kor/XT0eobUb5Pn1vApVfPpKPN\nixGNse31Gvbvbhpy/VO1hR/66BzSM220Nnp4841amuo9g26TlWvnA9fMwqRKFJVmJvPM1xMkEo3x\nt9+/SWNdT29eDF6GVTXCpPLjFOS3946PFOob85Pjo75z3re9poWw2QL4fA5kOZZSXyw2mWtunM+6\nFw7R3OAZ8tg1s8JHb1tMerp1wNM7QwE95WEsfk8DobanyTn/xHlQTA6wXs4ra7vo6Up9iOPJx6mY\nICfXTVNDT7KsZuZYyc51U32wNaU9PxXNLDNv0TgWVJbidFk5ergVIxojt8CGy21OtpvN9V3s3dXM\nrq21RCMeFJOFmXPHcf7Fk1MmUc0NXaiqnGw/+9pznzfE5lePceRgW2JMocG8heNYtHw8VquWkjf9\n29n+T0Lt7vCz7fXj7Nt1GE+PijstitOdTndnAJ/XwGSWmL9oPMtWT0SCAf2lHuxBVnvHBM0edm1v\nYOeWGsKhECazhqoo+H0n+pLCkjSuvnEe6Zn2lGPp7g6QV5A25n8TeTIpHj+5GxHq6+tZtWpV8hUk\nY0l7Sx3gShmMRPUAqmZL/B0JUH/4RToatgy6vWbNJLtkOU3V/yRmpF6tSs+bS2ZBBUff+jOx6MBG\nTbNmMHH+57HYnEQjAWr2/xtv526MiB9QkGSZeCyCotpIz51JTkklTUfX0d22j3isdwAgqVgduejB\nToxoEFmxkl28iLzxK1FNtt4KaaL52Aba67diRANIshWzLZ2Qrx0YOLBLz5tD7rjLiUQUZKmF6h2/\nIR7rv57CxIpP4sooT+ZRQ9W/6GzeTSwaIKyrNDVnU9eQjd1VwmXXTGP/W8ep2nOUjk4zFpuVKTMy\nmb84h8wsN5rVjR4KoZoUQr52bO78lHj6JhNRPUAoKGFzqMSMCKpmo7ujA1eai5gRofHYMVxZpcmB\nJ5zoFPoPCjwdR7E581A1W+L8HnqRnra9RCN+VJOd9NzZZBTMR9XsHNn1GCHfic7Y5iqmcNIVuDLG\n99tHB8feeoKApy75mdVZTNnsj2KxZfbmUeJKqyzHkJUTk/GYESEWk3snfok4fZ4gmiYhKyY0s0Rn\nexdZuTnJbaIRg1jUh6rZUtLqbK1GM2nYXPnEYjLEwyfKcW+Z7l+2e1oPcmzfn3rLWx8VWVGIGf0n\n7WZkVSUWTb3A0GfSws9jNqchKyZUzUbI146smKirDZGfF0s5nzEjQsyIEPK3Y7FnpdSzhqp/0d3y\nFtGIH0lSQYJ4LIqi2jDbsnvzd2CnK8mW3vLcTLKnBvwBE29sm4Msx5g76yBulz850O/ucbDjralk\n5dhYdXEZuYU5WBxZJ7b1+tm49jXa6w4zeVItknRikiBJEkXTriW3aGHigoMaxWJ3EtUDyfOhBxOD\nJ83qTpbVPt1th9E0J7KqUXfoOTztB1LiTjhR/0EjI38m+WUXYrFn4emopfbAXwgHWlK2sDjyKZ9z\nS7LM9RfytfP/2rvz+KjKe/HjnzmzZGYy2TeyAgkYQpA9LIIsAmJ/olYR2irWW65V6/ayVWurP31p\n7e9q1epVrK3bxateawu9QsUFREFRi7Lvi0DIQshOZjKZfeb8/ggzZDITIBiSAb/v14uX5mzzfc55\nnuc833NmzmluasJgNJOelY/HacXjsmJJKQi1r/bjcKKdtNmOcGDza/g8rVGPe0L6EAov/AmooDOY\nqavZz47NbrZ+UxNK0IaNzGX6ZYPRapyh/etobcPh8JGcogcNx/vXDeHHVqOAGkCnjycttyzUn1kb\n9nFw2xvh/ZFGT7+iS8ktnBaaFPB7Q/9VtPqwdtJxGVdbK8b4hLD5Pq+f5sZaUtP7Ub6/iqRUPZnZ\nuTRUb6By99+j7gtTQj4Dhs0n4PNgMGWiKAFsVhuJSYlhx97jcmEwGsPqirVhPxV7luL3nhio6gwJ\npOZdheqrovnoxuNtVE9i5jC0uoHkFQ3FYEpqj7VhD5k5w8LK63G5sLc6McfHo9P5ThxjTfsd2+Ax\ndtkb8fl0GOMTcLbWUnPoQ+zN+6KWMSg55wcUDbskNNhrabaTmAgup5OqvW+H9ZfHDxDh9TuOQWN/\nhsmUTtX+D2ip2xzlUzqv02muYmDg8Btx2Y9Qc+CDiPnxyYUUDr8Ov8dJ49ENNB3ZiN/niFjOlJBL\nv6LZJKcNwuO0hupocH+Fzj1eR/s59MgG/N42PB4dVUf6UVNXxLDRRUy6pIiWhk3EW7IwmtOw2Qgb\nU3RsY8EEOrjNpiMb8Hnb0OrjScm6EFVVOFa3mYDPhVZnJj1vHCn9JqPVmTDEadovsDh8GPQaPF4V\nnbaNxNQ8XPZGjJb0sLrvdHhY//lB9myvwmaFhEQ/48ceQK9tv2AS6gu3laAofvQGhSt/dDEDL8gB\n2vuxgOqjfPtbOGwnXuRuMOdQNOIneFw2FEWH0ZyGwXSivEHB82tL/Q78Pkf7+TV7LJkFUwEP+ze+\nitcV/eJNcCR9ot898d/Oy3m8Ci6mMfGSE3ecPE4r9lYrloQk7HYnDoe+/eIjLaH95HFaQ310x/g9\nTitedys15auxNeyKGh/A4LG3gqpBq0+jrmItTTUbUTQu3G6F2oZ8kjInM27yABKSk/F5HHjcKuaE\nE3c+g+d/V1sjRnMCdls9jQ06cvJS0Om1BPxeHPYqElOLQ202GFvDkfUR/abB3I+8C35Axa4lYf0J\nQGr2GLILZ2IwJkX0h2HnAI+jQ733YzAa2y/m2hvxuG0Rx9rncYS2oTOYcdhqOLjtdXye6BdANIqO\ntJyxZBfORKc/MYZpsx3hwKZX8XXqBwcOvx5b495QOwmO0TL7X4yjzUFyWnZY7O3rmSPKFUtOlRNJ\nIhlFrCWStuaDUZIj0BtT8LqOdZhiBiJPPj1Ja0iiYMhVHN75N9TAd7vj1qM0RgpKrqBy95IuF9Ea\n2hM41X92XqtiSb0Ah7WKwBlsP3fINWTmjg39XXPgE+oqPunJ8NDqLeQW4VYi9AAAIABJREFUX0nl\nzre7XCYz/xIyCsbRUP0VTTUb8XsdgB5zQhZuVzN+rwNFayIlaxgZ+RdRc3A1rccOoPqj1AVtIgqe\niAsWQnQ0cOS/YTZnYWs+SNXepae5lpb03LFkF81CUfQ0Hd1K9b53zziGOEsuBn08rcf2n/E2wgW/\n7OM74y2k5pSRXTiDih1LsFsj75onpBbTZqsg4Ov59qVoTahqIDb6eMUCAQfRLsqISIrWRMDvAbr/\ntUtL6mCctiPHk1gN8amDyCyYjN/joHL333o81lhwwbg7aG0u5+iBD5E6Fp3elIbX2cKZ1KmeYkkb\nQpzBQkvD7qgXWc59ChAIXYwJXoyMBZJInoFYSiRtzQf5duNf+jQGIYQQQgghxNmnaOMomfjLqN/Y\n6W2nyonkYTsx7tuNr/R1CEIIIYQQQoheEPC72fXF0/i8sX/3VRLJmCdPnBRCCCGEEOL7w8fhPWf+\nk43eIolkDGt/uIwQQgghhBDi+8Rau7WvQzglSSSFEEIIIYQQIsYEn2wcqySRjGEdH+8vhBBCCCGE\n+P7wuLp+x2gsOO8TyWPHjlFWVsby5cv7OpQzo9GdehkhhBBCCCHEecUY3/dPbj2Z8z6R/POf/8yY\nMWP6OowzNnjMTX0dghBCCCGEEKIXaRRDX4dwSud1IllRUUFLSwulpaV9HcoZS0wtInPAjL4OQwgh\nhBBCCNFLBo1e2NchnFKfJpLvv/8+1113HaNHj2bo0KER8/1+P3/4wx+YMGECo0aN4s4776S5ufm0\nt//8889zxx139GTIfSL/gsvoP+y6vg5DnIN8qravQ4gZroBB9sdJ2P3GLufJfjszrkDsX00WseH7\n0MYafZa+DqFvaM7/Y9tZX51vXQHDafW7LT7zSc95PRHHd6MweOytJKYW9Ug8Z1Of/gAvMTGR6667\nDpfLxcMPPxwx/+WXX+bTTz9lyZIlJCcn88ADD/DrX/+aV199FYD58+dHrDNy5EgeeOABNm/eTHJy\nMgUFBWe9HL0hPWcU6Tmj8HkdVO19D2vDLvw+Z2i+T9Wi00S+czIupRiLOYWmI+sj5pkScsgruQaf\nu42jBz/CZT8aNj/YCRjiErE5nVi0rpNEqCE1ZwyJedNoO7qeY7VbaHH5SYpTMCXkYT+2P2Lb9f4E\ncnQt3dgLkJY3AYeSirPyI3yqJqLMPlWL3R+HThPAovOQmD4EW8Pu095+V/vxBIWE9GLszQdQAz33\nJK1Gn4V0nf2ky7gCBoyKJ/R3i89Mss5L/rBrSc0YwpH9H9Jcu41Wr8oOtZi9aiEujICLkZpDjFJ2\nE6fpOuaOZbf7jRHHu/Pnd7Vud5xsvY6fF1zOp+o45E9nqKmVlH6j6DdwGl6PnW83vYLf2xZaV9FZ\nGDD8x1S7LLx72EaV7URb0RDgAg4xWrOTJK0z7DM7xtM5tq6OkaI1kZx1IbmDLsVgTCLg92L3+DBr\nVawelTRLPAD2YxXs2/oWeE/UeY02noEXzqf20Cc4bJXY/UZ0mgApSVlk9L+Ymv3v4fO0ho6HVp+A\nzpKD+9i+09/JGh0aDagBH6AnMXMIrY17UAM+Gv1JfKBOw4EJ0IBPxYyT/6NZS4LiYEtgKPvUIpzE\nYdErXGg6xlD3F+j9NrQ6MwZTCs7Wo0Cgy48PHkefqm3vUzQ6fIEAOk0gan0K7nevkoA+0NpxTwOB\n02orGm0cqt8dEUNQx20E+4xknYMWn5kWzCTjIFnniLpuV9sMxm0LmFkdmEQ9ae37NKCSSRMzlS9J\nVIIvlzYAnpNuv7t8qh7dSdp35oAZ1B9eAwRwBQyh9lR4wWwUnYHqvUu7+XladIoKaiBsmiugb+87\nNDoSUgdR21hFvOZE29Qoerz+ADqN/3gfFv7CbZ0xi4SUXFob9+HztgFaEjJKSM66EGNcEoe2/XfY\nue/E/tMAapTpnWLu0KbzhsxFG5eALi6N5OR+7ct42uNRtHp8HgeKVo+j9SjfbvpLF3tCg09V8KkG\n+hdOID1vPK62Rg5uefWU+9Ct6tkSGBrqqw24GKo5RJm5lpIR82iu2UTTkQ2oqi+yTBotqCf6qmAd\njia4Xsd+3adqsSRk0n/Yj2iz1VB3aDVeV+SF+tPp2xWdGb+qQeM/cZyD6x32ZbGSqagoBPsYCDCL\ndRTpjna5zWhxAGGxnCo2jaJDDfjQ6swkpA8hZ+B06iq/pK56w/H6H7n+gJE/o7V+F3U1W3H5FYyK\nN2wZnSEBv9+L1+c9rXNexqArSc8ZRXPl5zQd+eZ4nY5eP/2KBafX18VYS0dixhBsDTtPo8/QUDDs\nJ6RkFFO+cwm2hp1dLtnVPjydfsmnatFrtaiB9uWa/Ql8qk6kkVTa22P7+XYIB5mg3RZ1/BE8rkf9\nSeTrTv9GUUe2gJmPAhfTTErocwmopHGM2co67AEDOboWanxpvMd0VHTty6mAD8w6hduHpTEoqx8B\nvxevu5Xyne/Q0NJ4inHvifhdAT0BjXIaff/JaQ0pDBl3C0ZzbP8usiONqqrqqRc7u77++mt+9rOf\nsXt3+IB/+vTp3HbbbcybNw+AyspKZs2axaeffkpubu5Jt/nGG2+watUq4uLiqKysxGQy8eijjzJq\n1KhTxlNdXc2MGTP45JNPyMvLO/OCnQXNzvYGa9AqNLTa+aqmmfVV9bgwoMVFKeUMU/azUy1mvzoQ\nF3FogZJ0CzMKUhmSagagyaOQZWm/GmP3+Gjz+HB4fCTo/HxwuIX1Nc14A5FVI0Gn4dZh6aEGV9Vi\nA52JN3fVUN3qJFplMgD3jO+Pv/4b3iwPcJR+hBr78TWm8C8ysVE6aCSpBVMI+LyYTRaOWB1kWIzU\n2t08v/kwrR5f2LaNCvTnCO6Aj8MUdNhuu3i9lh8PyUDfvBWaNmJ3e0k2aknpNwpn8liKs3Opba7j\n3Z372Gk34MFAHG5GJ7qYlNJKS+12dH4rWp0Zc7/xZPafjFs1kGIy4PK4aWxzkWMOsHvz23jbjqDT\n+EMn64A2HnPWOAYOmoLZaKHB2kJFq4vSRKir+pJvKqpYzaQTJ1jaT7CX8Rn9tHYK+o8hkDqc1/bY\nOdrmpisGRcP8kmx2N9rZ39yG3dvVyU2ljF1cNjAJNWMSWYkJtLi8fF7dwlfVjbT5ApwYiLXHY8ZP\ngtlEnePECSA/XmHOgAQKEsysrQ/wVXUjrR4/Rg1MykvhoiwDBZnt7fOb8sMM6ZdFoslE07E6dh38\nGp11F1s8+exVi3ARRxx+SrTlXKhuJ6DEs0aZxlFPXKdIopuam8r8YQUYtO1frvA4rey2Bnhl62E8\nUepvNGl6GJiWxN4mO3avv/0Uo4A30HG4H+6OMQMZkZVCwO9F0eppdnpodXt5a1cVh62RJwyDokGn\nVXB4/Vj0WiZmJzCpoB8eXwCtVsPT6/fj9IfHmxqnxeVXcfhODNSD+8KkwBBzK2N8X+Byu/Br9CTr\nXKgBL4rWRFJ2GQWDZ6Aoelo8KqnH66ui1VNvd5GXZKbS2sZjX3aVkKqYcOHEFDEnUa/wyMVDaHT4\nGJhqoc7uQus+xqE97+JsPYpR8WILmFmrTjhxQkWlc9sMyo43cENpHpuPNvJVjQ1Hp+qbY9IyMS+D\nd7+tDUtXNcDlAzKYPjAds1bF7rChM6djMbRfH91UXc/75U1d9kvBcob3RdFjBMg2aflRaX/e3V9D\nhe3E4CLRoMUXAIfPj0kBZxc5tQY/szSbKCqZRVpyP97aWcFhmzO0fzI4xqWGzSQEGoATAzmjpR9+\nnxuv61jEYLrel8QhpZA96mDcqpYEg44xGRYm5qaSmWhBcTcRiEvDYtDR4HDx4qZDVLdGDoxyLXFc\nOTiL0dnpOFrraHBryU9JorXpIId3L8HnsdHiM6PRaNinLWUfg2jzqVj0WkZnJWJ1+9jW0Bqx3Y6M\nwIjsFLbXHYvYRwZFw20jc0m3JBJv0IWO4c7aBnKSkkLt26BVsLbZ2HNgPSvr9NSryaH9NyDRxLUl\neSzdXUVFqzt0zNN10N/Uxq5WHS7i0OFmRIKXq4YP58ujbXxZ3YTd4yNepzC5IIOJOSkkGQ2hGOwe\nHwatgsNl51jVF9Qf2YzP60RnMLNZfzFbWy1h9TIvwchtYwrxO1uo3LsMbdvB0AUifUI+6YOuJCcl\njdo2Hy9uPsAxd/T++qfDcri4oB8VLW28tbPyeF1plxNv4N9HDuTdvUfY2RR+UUUB/m1IGv0zMlm8\n9RCHW7s+dyQYdJSkJXDVBdlkxhuptjroZ4LmNgfLD9vY3diK3evHhJv+VDBRu4eCgePpN3Aah5qs\nZCVY+OBwC19VN+HyB9ADuUlmGhxu2rx+9MDJLrdqCFCq+ZZRml1YAyYGpSaSOWQ+SeYEHH4Nuxta\n2NHQxub6VnzHh6oKPpJoxYkZF3GYFCjLiuOSooHkJprZWdtAtsVMnCGOFqcHj9dNZmICDq+Pz6ua\nWFfZcLxPDbZ3F0OpYGyCjcGlP+T9Sgfra451ihNKUuNYMLyIDw/UsbmuhTavP9TeLhvUj7R4c2j5\nvVX72NRi4ssjTaFxlEHRMDo7mRqbk6pWV6h+5sQbSDPq2dHUFvaZccAdo3MY0q8fLo+bZneAxdsr\nws4vBRYDN5bmYDFbSDUZcLTWhdp7tdWBXts+zrN7fGjdTZTvfAdna/WJCxgMxqXqMSlwcUE6I1N1\n/P2AjcO2E58xIMnMTwdbsNVvx163maNuLVVKIQcoxBHQYtZqyLSYOGpz4D7JKdesgdtH9uOC7Bw2\n1TSxoaaRTfVtnZZq38A4NjBUqaI5YEZLgDRtGz5VS5s+n+ILryAhIYNErY+A38tRt47ff9WNC6sn\nMXtABkUpFt7YWRk2jjIrcM/EYnJM7eeHFpeXVRXH+LyqmdO9fP5/L7qAjDioanWTG+fCbMlE0eoB\nqKg7Qm5KCjqD+RRb6RunyoliNpG02WyUlZWxbNkySkpKQtPHjBnDk08+yYwZp/+7wUWLFlFQUMBV\nV111WsvHWiJZaW3jmfX7afOf7qE6+YBIiPORSafg9HV9Z6ynxQOqVoPjtNtl7zDqFFy9uB9EzzJq\nNbg61Kk0PQxItbC1zoY/9GuUjpdZhBCxQBe84Rqjci1xjMtO4r0D9TEdZyyL02pw99A5v/07NuFu\nGVXA2OzYevXfqXKimH23RFtb+5UKiyX8O/WJiYnY7Sf/WlNnd955Z4/F1dtOftegKzK4EN8/vZlE\nArQBxFgSCUgSeY5zdapTTV5oqrMT/kgD6eOFiDWxnpwdsbt599v6vg7jnNZTSSRE/0HIS1sqeWlL\nJSadwr3jB1OQFN9jn3e2xOxTW+Pjj/+uqFPSaLPZIpLL89kfv/62r0MQQgghhBBC9AKnL8BjX+6j\n0tr567+xJ2YTycTERHJycti1a1doWmVlJXa7neLi4j6MrHc55O6CEEIIIYQQ3yv/ueFAX4dwSn2a\nSPr9ftxuN15v+8+x3W43breb4M8258+fzyuvvEJVVRWtra089dRTTJ48OSZ+t9gbgg/WEUIIIYQQ\nQnx/tHq6/zT83tanv5Fcvnw5v/3tb0N/Dx8+HCD0g86bb74Zm83Gtddei8fjYdKkSTz11FN9FW6v\nc3R6QqkQQgghhBDi+8Hq9JBkit13EvdpInnNNddwzTXXdDlfq9Vy//33c//99/diVLEjLyk2HwUs\nhBBCCCGEOLv02pj9FSIQw7+RFO3k2XxCCCGEEEJ8/5gNMfuCDUASyZh3+5iBfR2CEEIIIYQQohfl\nWuL6OoRTkkQyxo3ISmFW/9h6OakQQgghhBDi7NAAt48t6uswTkkSyXPA/NICbhpR0NdhCCGEEEII\nIc6iBIOW/zdtKBlmY1+Hckqx/cVbETI+N53xue13Jvc1WtlSZ2N9TTNtXj9GDVzcP5MRWYm8tLmc\nVu+JxwVrgLLsZPY127G6T+8psHrgnomDKEi0UGV18KfNh7BFeYKsSYGJeRlclJ9C/yQL+xqt7Gy0\n83lFPQ6/etLP0ABjs5OoaXVxxO4OTc8261E1GmrbTv/VJzoNoNHgC6iYlPZ9pdVq+PpIM3avH4te\ny7CMRA4ca6XRGbtPwtUB0aKbkJvC7nobNu+pHwNtAJ6cNRyDotDi9vD8hgPd2pffhUkBjaKc8t2n\nw9LjmZyfSv+EBNItRnbVW3l122Hsp1E+gIFJZn5Ukstr2ytocHS/bOkmA01OD8Ea2j/RxA0X5mPQ\naPmsqpHPqxrxBtrnKkB+ool6hxunL4BFr+WC1Hj2N7edNN78BCO/GFOI0+vnP785ENYmozEpcFF+\nBtvqrTT28Wt/NECeJY4fleaSqDdg1utIMhn4pqqR/95VhSdwom0rwM2jCsiJN9Pq9vHW7kqOnqK+\nLSjNI9mo50+byuncSwzPSGDukDw+PVzPptqW064T0Vj0CneOLaKipY2l+46Gxd0xfp2iiTqvK8Ey\nl6QlA+1P165pdfJf2w/Tdop+r6fplPZ+77uuHwekWeI4andHHJOge8cX0s9s5rDVzivbKnH7o7fz\ndJOuR/vZLLMeu9dPm/fM3qk8LjuZnQ2tOHx+4nUKZdmp+NQAX9ccC7Xz3qAB5hXnMG1AJhuONPLW\nrmq8nT5+aJqZQSkW/nmgPmL9oWkWLspL5b+2VxItbK0GTrf6nWxZDXRZB7pLA5j1Wtq8fkwKTB2Q\nxZDUeN7ZU31a5yWDouHignRm9M8gI95Io90FtD98xOsPkG4xUtlix+XzkRVvDk0LPuXS6w+wpaY5\not/S0P6zoT2NdtZVNYbmKcCIrARuHD6QJoeb5zceDBs3GYFEs4H6MzjvdJSkwM/LCilOS6ayxQ5A\ndoKZWpuTdUea+LrmGA5f7732waKFO8YNIt1oxOr2UJBsoc3r46ODdaw9XIerG03PpNUwOT+D6QPS\n+ehAHV9UN9F5dZNWg16rDRtXDkwyM7swjTHZGVidntCx9PoDHDrWyvaGVr6qburynGAE3ESvu6lG\nHRekWthUaw21eS2g7aLvP1X7yIqPo8XtxeULYFJgXG46cwb341CzlSHpKTQ6XBQkW/Ae7yM9gQBL\n91SzubbltN4Nf9OIgtB4/1ygUYMvbRQh1dXVzJgxI/Qakljm9QeiPtHJ6vRgNuhC84KdwpfVTbR6\nfFj0WsbnpBJQVTbWtoSmTc5P57KiLOL1kdcYOj6CuKvP7RyXw+PDbNDh8PhCnb9eq0SsG1wu2rRG\nuwuHz0dBsiU0PbiNjnFEiynaNIfHh9cfCJWl86OVHR4fVpeXZd8eYf8xB3aPD7NWw5T+mUzJTyMj\n3hi23eD2OsYUpNcqNLa50CsK6RZjKKbg8sFlO5Y9WL7g+hD9+GWY42hwuLF7/SQYdEzKS+vy2Dk8\nvtDrZIKdc/Dz99RbeWN3VdgJMylOx80j+5Og10fE3nF/B7fZ+dHUnetK8O9TPX0suNxRm4PsRHMo\n7nSLMaKOdN4nnfdBZYs91Jk3trnITgx/CnK0OhcUPC4d441WlzqWM7gvutpmsE1anR70WiXUPjtu\nt6syTcxNISehPf7GNhefVTVFXSbDbAxt0+rysPpwQ9hAKTgwu6wwC43aXiazQReK+WT7pKNGuytU\nJ6LpvC+CxyKaozYH6fHG6P3Y8TJEO8Z1rU621NvC2sS47FSm9k8L7auu4o52fIPHMngMvqhuwn78\nM8v6pYQumJ2K9fiFgGB/oNcqHLU50GuVsOMWr1PIjDdGbcNOty9sQNyxTwgeo2j9XmWLnewEM3qt\nQpvXxwcHakODr2D/PjY7iRyLOer6wXX+daT5pOeEaPtoQk4qlw/uF1ou2O471pNgn6HXKlTb7FyQ\nnhyx/4P9I0S2pc7tLRh3o8PNf22voMLqCA0kByaZ+fmoAaEr+l2dH4LHKjigL0i2sL/Bxl/3VlPd\n2p64aIABx7eXHNfej3Ue7Abb0lGbgyTjif7Q6vJE9D0dNdpdXfaNHY9nZx37yI776ajNyVc1zWH1\n7OKCDC4rysLnC0Sc66K1/Y77qtHuCuuvOp9/oX2g3Pn81LHedDVeCPbvwfJ37LODx6UnddVvnerz\nor1+oeN+CPbtwW0F/7/jesF6cboPTvH6A7S4Pbyy5TCHO9XrfxteQE5C+PmxYzlqbU6+qWsJ6zeL\nU+OpbXOH6jSAooHJeWlcMyQ36rihYyydx3Mdyxssc1fH+WTLnO45J+hk5wSgvV+qauxyXHSyvj8Y\nS1fn4RkDMkju0LZPNQ6OJrhO53IYNTBtYFaXY7i+dKqcSBLJKM6lRPJMnG7CJdrF2r7pHE9Pxhfr\n7yvqSqwdo55wOmU63XKfrYFZbzpZWc/W8e+t7Z7t+vtdBjxnY9tnU3cHpr29vd7UF8cm1urD+eJM\n62FXF9OjXdA/1/TGOaE36nOst5lT5UTnZu8ovpNoFTaWK3Ffi7V90zmenozvXEwiIfaOUU84nTKd\nbrnPh/1zsjKcrfL11nbP9vE5k+2fq3Wrp5O+czWJhL45NrFWH84XZ1oPox2Pc7lOd9Qb54TeqM/n\neps5P2pTD/P727+DXVtb28eRCCGEEEIIIUTvC+ZCwdyoM0kko2hoaADg+uuv7+NIhBBCCCGEEKLv\nNDQ00L9//4jp8hvJKFwuFzt37iQjIwOtVtvX4QghhBBCCCFEr/L7/TQ0NDBs2DCMxsiHVUkiKYQQ\nQgghhBCiW87tX3gKIYQQQgghhOh1kkgKIYQQQgghhOgWSSSFEEIIIYQQQnSLJJJCCCGEEEIIIbpF\nEkkhhBBCCCGEEN0iiaQQQgghhBBCiG6RRPIcsXv3bq699lpGjhzJVVddxdatW/s6JBHjNm7cyLx5\n8xgzZgwzZ87knXfeAcBqtXL77bczZswYpk2bxpIlS0LrqKrKH//4RyZMmEBZWRm///3v8fv9ofkr\nVqxgxowZjBw5kltuuYXGxsZeL5eILY2NjUycOJE1a9YAUr9Ez6mtreWWW25h9OjRTJkyhTfeeAOQ\nOiZ6xubNm7nmmmsYPXo0s2fP5r333gOkfonvZvv27UyePDn099mqTzGTF6gi5rlcLvXiiy9W/+d/\n/kf1eDzqkiVL1AkTJqh2u72vQxMxqqWlRS0rK1P/+c9/qn6/X925c6daVlamfvnll+qdd96p3nvv\nvarL5VK3bdumjhs3Tt2yZYuqqqr65ptvqnPmzFHr6urU+vp69eqrr1ZffvllVVVVdc+ePero0aPV\nrVu3qk6nU33ggQfUm266qS+LKWLAzTffrA4ZMkT99NNPVVVVpX6JHhEIBNSrr75afeKJJ1SPx6Pu\n379fLSsrUzdt2iR1THxnPp9PnTBhgvrhhx+qqqqqGzZsUIcOHapWVVVJ/RJnJBAIqEuWLFHHjBmj\njhs3LjT9bNSnWMoL5I7kOWD9+vUoisJ1112HXq/n2muvJT09nc8++6yvQxMxqqamhqlTp3LFFVeg\nKAqlpaWMHz+ezZs3s3r1au666y7i4uIYPnw4c+bMYdmyZQAsX76cG2+8kczMTDIyMrjlllt49913\nAXjvvfeYMWMGI0aMwGg0cu+997Ju3Tq54vo99te//hWTyUR2djYAbW1tUr9Ej9i2bRv19fXce++9\n6PV6Bg8ezDvvvENWVpbUMfGd2Ww2mpub8fv9qKqKRqNBr9ej1Wqlfokz8pe//IU33niDW2+9NTTt\nbJ0TYykvkETyHFBeXk5RUVHYtIEDB3Lo0KE+ikjEupKSEp566qnQ31arlY0bNwKg0+nIz88PzetY\nlw4dOsSgQYPC5pWXl6OqasS8lJQUkpKSKC8vP9vFETGovLycxYsX88gjj4SmVVRUSP0SPWLXrl0M\nHjyYp556ikmTJjF79my2bduG1WqVOia+s5SUFK677jp+9atfUVpayvXXX89DDz3EsWPHpH6JMzJ3\n7lyWL1/OhRdeGJp2ts6JsZQXSCJ5DnA4HJhMprBpRqMRl8vVRxGJc0lrayu33npr6K6k0WgMm9+x\nLjmdzrD5JpOJQCCAx+OJmBec73Q6z34hREzx+Xz8+te/5sEHHyQ5OTk03eFwSP0SPcJqtfL111+T\nkpLCmjVrePzxx3nsscekjokeEQgEMBqNPPfcc2zdupW//OUv/Md//Ad2u13qlzgjmZmZaDSasGln\nq7+KpbxAEslzgMlkiqgcLpcLs9ncRxGJc0VVVRU//vGPSUpK4oUXXsBsNuN2u8OW6ViXjEZj2Hyn\n04lOpyMuLi5qJ+V0OqUefg+9+OKLlJSUMHXq1LDpJpNJ6pfoEQaDgaSkJG655RYMBkPogSjPP/+8\n1DHxna1atYrt27dz2WWXYTAYmDZtGtOmTWPRokVSv0SPOVvnxFjKCySRPAcUFhZGfDWivLw87Ja3\nEJ3t2rWL+fPnM3nyZF588UWMRiP9+/fH6/VSU1MTWq5jXSoqKgqra+Xl5RQWFkad19zcjNVqjfh6\nhTj/ffDBB7z//vuMHTuWsWPHUlNTw69+9SvWrl0r9Uv0iIEDB+L3+8OeYOj3+xk6dKjUMfGdHT16\nFI/HEzZNp9NRWloq9Uv0mLM15oqlvEASyXPAxIkT8Xg8vPnmm3i9XpYuXUpjY2PY44WF6KixsZGb\nbrqJn/3sZ/z2t79FUdqbusViYcaMGfzxj3/E6XSyfft2VqxYwRVXXAHAlVdeyWuvvUZtbS2NjY28\n9NJLXHXVVQDMmTOHVatWsXHjRtxuN8888wxTpkwhJSWlz8op+sZHH33Epk2b2LhxIxs3biQnJ4dn\nnnmG22+/XeqX6BGTJk3CaDTywgsv4PP52Lx5Mx9//DGXXXaZ1DHxnV100UXs2bOHf/zjH6iqyjff\nfMPHH3/M5ZdfLvVL9JizNeaKqbyg158TK87Inj171B/96EfqyJEj1auuuir06GAhovnzn/+sXnDB\nBerIkSPD/j3zzDPqsWPH1LvuukstKytTp06dqi5ZsiS0ns/nU5955hl10qRJ6rhx49THHntM9fl8\nofnvv/++eumll6qjRo1Sf/7zn6uNjY19UTwRY6ZPnx56/YfUL9EqZq8PAAAI2klEQVRTDh8+rC5c\nuFAtKytTp0+fri5dulRVValjomd88skn6pVXXqmOGjVKvfzyy9VVq1apqir1S3w369evD3v9x9mq\nT7GSF2hUVVV7P30VQgghhBBCCHGukq+2CiGEEEIIIYToFkkkhRBCCCGEEEJ0iySSQgghhBBCCCG6\nRRJJIYQQQgghhBDdIomkEEIIIYQQQohukURSCCGEEEIIIUS3SCIphBDivFZcXExxcTH79++PmLd9\n+3aKi4u54YYbwqbX19fzu9/9junTpzN8+HB+8IMf8Oqrr+Lz+ULL3HDDDRQXF/PWW29FbNfr9VJW\nVkZxcXHYdI/Hw8svv8ycOXMYMWIE06ZN49FHH6W5ubmHSnt2/eY3v+GXv/xlX4chhBAiBkgiKYQQ\n4ryn1+tZvXp1xPRVq1ah0WjCplVXVzN37lxqamp46qmneP/997njjjtYvHgxDzzwwGlt91//+het\nra1h07xeLwsXLmT58uXcfffdrFixgieeeIIdO3bw05/+FLvd3gMlFUIIIXqHJJJCCCHOe+PGjYua\n8H388ceMHDkybNojjzxCYWEhL774ImPHjiU/P5/LL7+cJ598kuXLl7N9+/aw7W7YsAGbzXbK7S5e\nvJgDBw7w5ptvMnPmTPLz85kwYQKvvPIKNTU1vP322z1YYiGEEOLskkRSCCHEeW/mzJns3r2b2tra\n0LR9+/Zht9sZPXp0aFpdXR1ffPEFCxcuRFHCT5GTJk3i9ddfZ/DgwaFpQ4cOJSsrizVr1oSmBQIB\nPvnkE2bPnh22/j/+8Q/mzp1Lampq2PSUlBRef/115s6dGzX2uro6fv7znzN69GjGjRvHfffdF3a3\n89VXX2XWrFkMGzaM8ePH8/DDD+P1egFYtGgRd999N08++SSjR49m8uTJvPvuu6xdu5ZZs2YxatQo\n7rvvvtBXdhctWsQdd9zBo48+yqhRo5g+fTp/+9vfutyva9as4YorrmD48OFcccUVrFixostlhRBC\nnF8kkRRCCHHey8vLo7i4OOyu5Mcff8zMmTPDEsa9e/eiqirDhw+Pup2JEydiMpnCps2YMYNPPvkk\n9PfGjRtJSkqiqKgoNM3lcnH48OEutzt8+HDS0tKiznv00UfRaDQsXbqUxYsXs2vXLhYtWgTA8uXL\nefnll3nooYdYuXIljzzyCMuWLeOjjz4Krb969Wp8Ph/Lli1j9uzZPPLII7z44os8++yzPP3003z4\n4YesWrUqtPzatWtpampiyZIl/OIXv+B3v/sdn376aURc+/bt45e//CU33ngjK1as4N///d95+OGH\n+eyzz6KWQwghxPlFEkkhhBDfC7NmzQpLJFetWhVx1zD4FdWEhIRubXfdunW43e7Qdi+99NKwZaxW\na7e3G1RdXU1CQgJ5eXmUlpayaNEi5s2bB0BWVhaPP/44U6ZMITc3lx/84AeUlpZy4MCB0Prx8fHc\nf//9FBQU8JOf/ASXy8UvfvELhg0bxowZMygpKYlY/oknnmDQoEHMnz+fyy+/POpdyddee42rr76a\na6+9loKCAn74wx+yYMECFi9e3O0yCiGEOPdIIimEEOJ7YebMmaHfM1ZUVFBXV8e4cePClklJSQGI\n+M3jyYwZMwaj0chXX32FqqqsXr06IkENbjeYUHbHzTffzMqVKxk/fjy33347O3bsoLCwEIAJEyaQ\nmZnJs88+y5133sns2bPZsmULfr8/tH5ubi5arRYAo9EIQH5+fmi+0WjE4/GE/i4pKcFsNof+vvDC\nCzl48GBEXAcOHGDJkiWMGjUq9G/x4sWUl5d3u4xCCCHOPZJICiGE+F4YMmQIOTk5rFmzho8//phL\nLrkEnU4XtkxpaSmKooQ9UKeju+66K+KhPVqtlksuuYTVq1ezY8cOtFotQ4cODVvGYDBQXFzc5XYX\nLVrEn/70p6jz5syZw+eff84DDzyAoig8/PDD3HvvvUD77y4XLFiA3W5n2rRpLFq0KOw3n0BEGYGI\nJ9V2Lk9Hfr8/Ylpw+o033siyZctC/1asWBH1dShCCCHOP5JICiGE+N6YOXMma9asiXrXENrvHE6d\nOpXXXnuNQCAQNm/dunWsXLky4mE50P711s8++yzq11qDfvjDH/K///u/Ee+MrK+v580334yarAE8\n++yzHDlyhHnz5rFo0SIef/xxPvroI1RV5fXXX+fmm2/moYceYu7cuRQWFlJZWYmqqqe7SyLs378/\n7H2ZO3bsiHgfJkBRURFVVVX0798/9G/t2rUsXbr0jD9bCCHEuUMSSSGEEN8bwYTv4MGDXHTRRVGX\n+c1vfsOBAwe47bbb2LRpExUVFSxZsoR77rmHefPmRdzxA7joootwOBy8/fbbURNUgAULFlBQUMCC\nBQtYvXo1VVVVfPbZZyxcuJCcnBxuuOGGqOuVl5fz2GOPsWvXLsrLy1m5ciUlJSVoNBoyMzNZv349\nBw8eZO/evdx33300NDSEfVW1u+rr6/n973/PoUOHePvtt1m5cmXU2BYuXMjq1at5+eWXqaio4J//\n/CdPP/002dnZZ/zZQgghzh2R33cRQgghzlMjR44kPj6e8ePHYzAYoi4zYMAA3nnnHV544QXuvvtu\nbDYb+fn53HbbbSxYsCDqOgaDgSlTprB582ZGjBjR5TKLFy/mpZde4g9/+AN1dXWkp6dzySWXcPvt\ntxMfHx91vUceeYTHHnuMhQsX4na7KSsr47nnngPgwQcf5MEHH+Saa64hMTGRadOmcf3117Nr164z\n2Dvthg4dSiAQ4Oqrr6Zfv348/fTTlJWVRSw3bNgwnnvuOZ5//nmef/55MjMzueeee/jxj398xp8t\nhBDi3KFRv8v3X4QQQghx3li0aBHr1q3j73//e1+HIoQQIsbJV1uFEEIIIYQQQnSLJJJCCCGEEEII\nIbpFvtoqhBBCCCGEEKJb5I6kEEIIIYQQQohukURSCCGEEEIIIUS3SCIphBBCCCGEEKJbJJEUQggh\nhBBCCNEtkkgKIYQQQgghhOgWSSSFEEIIIYQQQnTL/wfO09PvqeuV4QAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11aff7490>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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01ctKjVll+/L7ssKSn9fqL7h/miWvMa+VEOT7ADZCTMrsa0uUQAAAAt0p+ucYk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\nnYe60AAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11c92a7d0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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Vzi78At/gL8PWbx25VREXw2iAiorep9AFuPvzwQ/z9999Yv349goODcfToUaip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\n8zhxCVu6YyEAAAAASUVORK5CYII=\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11978ea50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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QOC07tCavPLSih/65ra1Xr5CcQzEVuQYlaXwaAwmIJBDxuNUlEIBAIZWhbrqfT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mTx5co3FWZcI1oZW/pBAIBAIBAKBQCC4pdAGRd5oESrEJ6fRZCp/aK9Op+PLL78kLCysxoX6q2K1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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11333be90>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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EREZEMJotEREREREQkg8kiERERERERyWCySERERERERDKYLBIREREREZEMJotE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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1196f7310>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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ZxKCnRPv8imd5IVVw90FLfiN40G1l9Vb02WL1/ZK/XRq7r4RpknSoXjLAjKNG7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\n7AD2E+mRYgAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x113b17110>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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V1CdNpSvDhy+RR6FTetSLTWmNj7fj1mBWftTHRJ6mQcy0onPVcTFxMb6+fRQpL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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x112fa2ad0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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ahmL4nZ86cYeTIkTz//PP07NlTl3EKIUQ4BdSbqzeNGzfmmmuuYfbs2ZJICiFECERFVX+4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\nIYQQQgghhKiw/w8hXH5v1a/bsgAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x119d14fd0>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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/erHPnzun06dP67LPPdOnSJV29elXR0dGZAuGNGzd08uRJ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\nEAIAAACAkyIQAgAAAICT+n8Z9jLORoAelgAAAABJRU5ErkJggg==\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11310c950>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""image/png"": 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vttxk+fDiJiYk8+eSTzJgxAzs7O+bOncsHH3xAkyZNcHBwoHv37vTq1eu+dYSE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\ngAAAAJX4Arr9d9SDtjI3AAAAAElFTkSuQmCC\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x11a6b4e50>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""from assaytools import plots\n"",
    ""plots.plot_mcmc_results(Ltot, Ptot, path_length, mcmc)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Part II""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {
    ""collapsed"": true
   },
   ""source"": [
    ""The first thing we'd like to see is if we can reproduce problems with predicting the delta G for ligands with high affinity compared to the protein concentration used.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 26,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# For this we're just going to define a range of Kd's, with some qualitative variable names.\n"",
    ""# Note here that our protein concentration is: Ptot = 1e-9 # M\n"",
    ""\n"",
    ""Kd_high = 0.2e-9 # M\n"",
    ""Kd_really_high = 0.02e-9 # M\n"",
    ""Kd_low = 20e-9 # M\n"",
    ""Kd_really_low = 200e-9 # M""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 27,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""# Will need an F_PL_i for all of these\n"",
    ""[L_high, P_high, PL_high] = two_component_binding(Kd_high, Ptot, Ltot)\n"",
    ""[L_really_high, P_really_high, PL_really_high] = two_component_binding(Kd_really_high, Ptot, Ltot)\n"",
    ""[L_low, P_low, PL_low] = two_component_binding(Kd_low, Ptot, Ltot)\n"",
    ""[L_really_low, P_really_low, PL_really_low] = two_component_binding(Kd_really_low, Ptot, Ltot)\n"",
    ""\n"",
    ""F_PL_high_i = F_background + ((1400/1e-9)*PL_high + sigma * np.random.randn(npoints)) + ((.4/1e-8)*L_high + sigma * np.random.randn(npoints))\n"",
    ""F_PL_really_high_i = F_background + ((1400/1e-9)*PL_really_high + sigma * np.random.randn(npoints)) + ((.4/1e-8)*L_really_high + sigma * np.random.randn(npoints))\n"",
    ""F_PL_low_i = F_background + ((1400/1e-9)*PL_low + sigma * np.random.randn(npoints)) + ((.4/1e-8)*L_low + sigma * np.random.randn(npoints))\n"",
    ""F_PL_really_low_i = F_background + ((1400/1e-9)*PL_really_low + sigma * np.random.randn(npoints)) + ((.4/1e-8)*L_really_low + sigma * np.random.randn(npoints))\n""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 28,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""pymc_model_high = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""    top_complex_fluorescence=F_PL_high_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 29,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""mcmc_high = pymcmodels.run_mcmc(pymc_model_high)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 30,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": [
    ""pymc_model_really_high = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""    top_complex_fluorescence=F_PL_really_high_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')\n"",
    ""mcmc_really_high = pymcmodels.run_mcmc(pymc_model_really_high)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 31,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""pymc_model_low = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""    top_complex_fluorescence=F_PL_low_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')\n"",
    ""mcmc_low = pymcmodels.run_mcmc(pymc_model_low)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 32,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": [
    ""pymc_model_really_low = pymcmodels.make_model(Ptot, dPstated, Ltot, dLstated,\n"",
    ""    top_complex_fluorescence=F_PL_really_low_i,\n"",
    ""    top_ligand_fluorescence=F_L_i,\n"",
    ""    use_primary_inner_filter_correction=True,\n"",
    ""    use_secondary_inner_filter_correction=True,\n"",
    ""    assay_volume=assay_volume, DG_prior='uniform')\n"",
    ""mcmc_really_low = pymcmodels.run_mcmc(pymc_model_really_low)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 53,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [
    {
     ""data"": {
      ""image/png"": 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OWhZqUhJKQgKIop9pMiUQikfyB8Xq9dOzY8VSbIfmTc8qeWH/88Ue6du0a+f6f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\n"",
      ""text/plain"": [
       ""<matplotlib.figure.Figure at 0x1297f5510>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""plt.figure(figsize=(18,4));\n"",
    ""\n"",
    ""plt.subplot(121)\n"",
    ""property_name = 'top_complex_fluorescence'\n"",
    ""complex = getattr(pymc_model, property_name)\n"",
    ""complex_high = getattr(pymc_model_high, property_name)\n"",
    ""complex_really_high = getattr(pymc_model_really_high, property_name)\n"",
    ""complex_low = getattr(pymc_model_low, property_name)\n"",
    ""complex_really_low = getattr(pymc_model_really_low, property_name)\n"",
    ""property_name = 'top_ligand_fluorescence'\n"",
    ""ligand = getattr(pymc_model, property_name)\n"",
    ""\n"",
    ""for top_complex_fluorescence_model in mcmc.top_complex_fluorescence_model.trace()[::10]:\n"",
    ""    plt.semilogx(Ltot, top_complex_fluorescence_model, marker='.',color='lightcoral', alpha=0.2, hold=True)\n"",
    ""for top_complex_fluorescence_model in mcmc_high.top_complex_fluorescence_model.trace()[::10]:\n"",
    ""    plt.semilogx(Ltot, top_complex_fluorescence_model, marker='.',color='cornflowerblue', alpha=0.2, hold=True)\n"",
    ""for top_complex_fluorescence_model in mcmc_really_high.top_complex_fluorescence_model.trace()[::10]:\n"",
    ""    plt.semilogx(Ltot, top_complex_fluorescence_model, marker='.',color='powderblue', alpha=0.2, hold=True)\n"",
    ""for top_complex_fluorescence_model in mcmc_low.top_complex_fluorescence_model.trace()[::10]:\n"",
    ""    plt.semilogx(Ltot, top_complex_fluorescence_model, marker='.',color='darkseagreen', alpha=0.2, hold=True)\n"",
    ""for top_complex_fluorescence_model in mcmc_really_low.top_complex_fluorescence_model.trace()[::10]:\n"",
    ""    plt.semilogx(Ltot, top_complex_fluorescence_model, marker='.',color='palegreen', alpha=0.2, hold=True)\n"",
    ""for top_ligand_fluorescence_model in mcmc.top_ligand_fluorescence_model.trace()[::10]:\n"",
    ""    plt.semilogx(Ltot, top_ligand_fluorescence_model, marker='.',color='silver', alpha=0.2, hold=True)\n"",
    ""    \n"",
    ""plt.semilogx(Ltot, complex_really_high.value, color='cyan',marker='o',linestyle='None',label='0.02 nM', hold=True)\n"",
    ""plt.semilogx(Ltot, complex_high.value, 'bo',label='0.2 nM', hold=True)\n"",
    ""plt.semilogx(Ltot, complex.value, 'ro',label='2 nM', hold=True)\n"",
    ""plt.semilogx(Ltot, complex_low.value, color='green',marker='o',linestyle='None',label='20 nM', hold=True)\n"",
    ""plt.semilogx(Ltot, complex_really_low.value, color='yellowgreen',marker='o',linestyle='None',label='200 nM', hold=True)\n"",
    ""plt.semilogx(Ltot, ligand.value, 'ko',label='ligand', hold=True)\n"",
    ""plt.xlabel('$[L]_T$ (M)');\n"",
    ""plt.ylabel('$Fluorescence$');\n"",
    ""plt.title('Simulated complex fluorescence varying ligand affinity')\n"",
    ""plt.legend(loc=0);\n"",
    ""\n"",
    ""plt.subplot(122)\n"",
    ""plt.hist(mcmc_really_high.DeltaG.trace(),color='cyan', hold=True)\n"",
    ""plt.axvline(x=np.log(Kd_really_high),color='cyan',label='0.02 nM', hold=True)\n"",
    ""plt.hist(mcmc_high.DeltaG.trace(),color='blue', hold=True)\n"",
    ""plt.axvline(x=np.log(Kd_high),color='blue',label='0.2 nM', hold=True)\n"",
    ""plt.hist(mcmc.DeltaG.trace(),color='red', hold=True)\n"",
    ""plt.axvline(x=np.log(Kd),color='red',label='2 nM', hold=True)\n"",
    ""plt.hist(mcmc_low.DeltaG.trace(),color='green', hold=True)\n"",
    ""plt.axvline(x=np.log(Kd_low),color='green',label='20 nM', hold=True)\n"",
    ""plt.hist(mcmc_really_low.DeltaG.trace(),color='yellowgreen', hold=True)\n"",
    ""plt.axvline(x=np.log(Kd_really_low),color='yellowgreen',label='200 nM', hold=True)\n"",
    ""plt.axvline(x=np.log(Ptot[0]),color='0.7',linestyle='--',label='log(protein conc)', hold=True)\n"",
    ""plt.xlabel('$\\Delta G$ ($k_B T$)',fontsize=16);\n"",
    ""plt.ylabel('$P(\\Delta G)$',fontsize=16);\n"",
    ""plt.legend(loc=2);""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": false
   },
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {
    ""collapsed"": true
   },
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""anaconda-cloud"": {},
  ""kernelspec"": {
   ""display_name"": ""Python [default]"",
   ""language"": ""python"",
   ""name"": ""python2""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 2
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython2"",
   ""version"": ""2.7.12""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 0
}
","Unknown"
"Assay","choderalab/assaytools","examples/autoprotocol/single-wavelength-competition-assay.py",".py","2296","46","""""""
Analyze Abl:Gefinitinib singlet (single fluorescent inhibitor) assay.

""""""

from autoprotocol.unit import Unit
from assaytools.experiments import SingleWavelengthAssay

#
# Single-wavelength competition assay
#

params = {
    'd300_xml_filename' : 'data/Src_Bos_Ima_96well_Mar2015 2015-03-07 1736.DATA.xml', # HP D300 dispense simulated DATA file
    'infinite_xml_filename' : 'data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate_1.xml', # Tecan Infinite plate reader output data
    'dmso_stocks_csv_filename' : 'data/DMSOstocks-Sheet1.csv', # CSV file of DMSO stock inventory
    'hpd300_fluids' : ['GEF001', 'IMA001', 'DMSO'], # uuid of DMSO stocks from dmso_stocks_csv_filename (or 'DMSO' for pure DMSO) used to define HP D300 XML <Fluids> block
    'hpd300_plate_index' : 1, # plate index for HP D300 dispense script
    'receptor_species' : 'Abl(D382N)', # receptor name (just used for convenience)
    'protein_absorbance' : 4.24, # absorbance reading of concentrated protein stock before dilution
    'protein_extinction_coefficient' : Unit(49850, '1/(moles/liter)/centimeter'), # 1/M/cm extinction coefficient for protein
    'protein_molecular_weight' : Unit(41293.2, 'daltons'), # g/mol protein molecular weight
    'protein_stock_volume' : Unit(165.8, 'microliters'), # uL protein stock solution used to make 1 uM protein stock
    'buffer_volume' : Unit(14.0, 'milliliters'), # mL buffer used to make 1 uM protein stock
    'rows_to_analyze' : ['A', 'B'], # rows to analyze
    'assay_volume' : Unit(100.0, 'microliters'), # quantity of protein or buffer dispensed into plate
    'measurements_to_analyze' : ['fluorescence top'], # which measurements to analyze (if specified -- this is optional)
    'wavelengths_to_analyze' : ['280:nanometers', '480:nanometers'], # which wavelengths to analyze (if specified -- this is optional)
}

# Create a single-wavelength assay.
assay = SingleWavelengthAssay(**params)

# Fit the maximum a posteriori (MAP) estimate
map_fit = assay.experiment.map_fit()

# Run some MCMC sampling and return the MCMC object
trace = assay.experiment.run_mcmc(map_fit=map_fit)

# Show summary
assay.experiment.show_summary(mcmc, map_fit)

# Generate plots
plots_filename = 'plots.pdf'
assay.experiment.generate_plots(trace, pdf_filename=plots_filename)
","Python"
"Assay","choderalab/assaytools","examples/autoprotocol/single-wavelength-probe-assay.py",".py","2237","43","""""""
Analyze Abl:Gefinitinib singlet (single fluorescent inhibitor) assay.

""""""

from autoprotocol.unit import Unit
from assaytools.experiments import SingleWavelengthAssay

#
# Single-wavelength competition assay, but only the data for a single probe will be used
#

params = {
    'd300_xml_filename' : 'data/Src_Bos_Ima_96well_Mar2015 2015-03-07 1736.DATA.xml', # HP D300 dispense simulated DATA file
    'infinite_xml_filename' : 'data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate_1.xml', # Tecan Infinite plate reader output data
    'dmso_stocks_csv_filename' : 'data/DMSOstocks-Sheet1.csv', # CSV file of DMSO stock inventory
    'hpd300_fluids' : ['GEF001', 'IMA001', 'DMSO'], # uuid of DMSO stocks from dmso_stocks_csv_filename (or 'DMSO' for pure DMSO) used to define HP D300 XML <Fluids> block
    'hpd300_plate_index' : 1, # plate index for HP D300 dispense script
    'receptor_species' : 'Abl(D382N)', # receptor name (just used for convenience)
    'protein_absorbance' : 4.24, # absorbance reading of concentrated protein stock before dilution
    'protein_extinction_coefficient' : Unit(49850, '1/(moles/liter)/centimeter'), # 1/M/cm extinction coefficient for protein
    'protein_molecular_weight' : Unit(41293.2, 'daltons'), # g/mol protein molecular weight
    'protein_stock_volume' : Unit(165.8, 'microliters'), # uL protein stock solution used to make 1 uM protein stock
    'buffer_volume' : Unit(14.0, 'milliliters'), # mL buffer used to make 1 uM protein stock
    'rows_to_analyze' : ['A', 'B'], # rows to analyze
    'assay_volume' : Unit(100.0, 'microliters'), # quantity of protein or buffer dispensed into plate
    'measurements_to_analyze' : ['fluorescence top'], # which measurements to analyze (if specified -- this is optional)
    'wavelengths_to_analyze' : ['280:nanometers', '480:nanometers'], # which wavelengths to analyze (if specified -- this is optional)
}

# Create a single-wavelength assay.
assay = SingleWavelengthAssay(**params)

# Run some MCMC sampling and return the MCMC object
mcmc = assay.experiment.run_mcmc()

# Show summary
assay.experiment.show_summary(mcmc)

# Generate plots
plots_filename = 'plots.pdf'
assay.experiment.generate_plots(mcmc, pdf_filename=plots_filename)
","Python"
"Assay","choderalab/assaytools","examples/autoprotocol/competition-assay.py",".py","3769","75","from autoprotocol.container import Well, WellGroup, Container
from autoprotocol.container_type import ContainerType
from autoprotocol.unit import Unit
import numpy as np

# Define the container type for 4titude 4ti-0223.
# info: http://4ti.co.uk/microplates/black-clear-bottom/96-well/
# drawing: http://4ti.co.uk/files/1614/0542/7662/4ti-0223_Marketing_Drawing.pdf
# All arguments to ContainerType are required!
capabilities = ['pipette', 'spin', 'absorbance', 'fluorescence', 'luminescence', 'incubate', 'gel_separate', 'cover', 'seal', 'stamp', 'dispense']
well_diameter = Unit(6.30, ""millimeters"")
well_area = np.pi * (well_diameter/2)**2
container_type = ContainerType(name='4titude 4ti-0223', is_tube=False, well_count=96, well_depth_mm=Unit(11.15, 'millimeter'), well_volume_ul=Unit(300, 'milliliter'), well_coating='polystyrene', sterile=False, capabilities=capabilities, shortname='4ti-0223', col_count=12, dead_volume_ul=Unit(20,'milliliter'), safe_min_volume_ul=Unit(50, 'milliliter'))

# Generate a unique container ID
import uuid
id = str(uuid.uuid4())

# Define the container
container = Container(name=""assay-plate"", id=id, container_type=container_type)

#
# Assay Parameters
#

Lstated = [ Unit(x, ""moles/liter"") for x in [20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0] ] # gefitinib concentration
Pstated = Unit(0.5e-6, ""moles/liter"")
well_volume = Unit(100, ""milliliter"")
receptor_name = 'Src'
ligand_name = 'gefitinib'

# Load data into well format.
filename = ""../ipynbs/data-analysis/binding-assay/data/Gef_WIP_SMH_SrcBos_Extend_013015_mdfx_20150220_18.xml""
from assaytools import platereader
data = platereader.read_icontrol_xml(filename)

# Define wells for fluorescence assay (the verbose way; we'd provide convenience methods to format the plate)
ncolumns = 12
well_group = WellGroup([])
for column in range(ncolumns):
    for row in ['A', 'B']:
        well_name = row + str(column+1)
        well = container.well(well_name)
        well.set_volume(well_volume)
        well.set_properties({'area' : well_area})

        # Set concentrations of well components
        if row == 'A':
            well.set_properties({'concentrations' : {receptor_name : Pstated, ligand_name : Lstated[column]} })
            well.set_properties({'concentration_uncertainties' : {receptor_name  : 0.10 * Pstated, ligand_name : 0.08 * Lstated[column]} })
        elif row == 'B':
            well.set_properties({'concentrations' : {ligand_name : Lstated[column]} })
            well.set_properties({'concentration_uncertainties' : {ligand_name : 0.08 * Lstated[column]} })

        # Attach plate reader data
        for wavelength in ['280', '350', '480']:
            well.set_properties({' absorbance_' + wavelength + 'nm' : data['Abs_' + wavelength]['well_data'][well_name] })
        emission_wavelength = '480'
        for excitation_wavelength in ['280', '350']:
            well.set_properties({'fluorescence_top_ex' + excitation_wavelength + 'nm_em' + emission_wavelength + 'nm' : data[excitation_wavelength + '_TopRead']['well_data'][well_name]})
            well.set_properties({'fluorescence_bottom_ex' + excitation_wavelength + 'nm_em' + emission_wavelength + 'nm' : data[excitation_wavelength + '_BottomRead']['well_data'][well_name]})

        # Add to well group
        well_group.append(well)

# TODO: Analyze the well group.
# This is just a non-working stub for now.
# Create a model
from assaytools.analysis import CompetitiveBindingAnalysis
model = CompetitiveBindingAnalysis(wells=well_group, receptor=receptor_name, ligands=[ligand_name])
# fit the maximum a posteriori (MAP) estimate
map = model.map_fit()
# run some MCMC sampling and return the MCMC object
mcmc = model.run_mcmc()
","Python"
"Assay","choderalab/assaytools","examples/autoprotocol/single-wavelength-competition-assay-emcee.py",".py","3466","85","""""""
Analyze Abl:Gefinitinib singlet (single fluorescent inhibitor) assay.

""""""

from autoprotocol.unit import Unit
from assaytools.experiments import SingleWavelengthAssay

#
# Single-wavelength competition assay
#

params = {
    'd300_xml_filename' : 'data/Src_Bos_Ima_96well_Mar2015 2015-03-07 1736.DATA.xml', # HP D300 dispense simulated DATA file
    'infinite_xml_filename' : 'data/Abl Gef gain 120 bw1020 2016-01-19 15-59-53_plate_1.xml', # Tecan Infinite plate reader output data
    'dmso_stocks_csv_filename' : 'data/DMSOstocks-Sheet1.csv', # CSV file of DMSO stock inventory
    'hpd300_fluids' : ['GEF001', 'IMA001', 'DMSO'], # uuid of DMSO stocks from dmso_stocks_csv_filename (or 'DMSO' for pure DMSO) used to define HP D300 XML <Fluids> block
    'hpd300_plate_index' : 1, # plate index for HP D300 dispense script
    'receptor_species' : 'Abl(D382N)', # receptor name (just used for convenience)
    'protein_absorbance' : 4.24, # absorbance reading of concentrated protein stock before dilution
    'protein_extinction_coefficient' : Unit(49850, '1/(moles/liter)/centimeter'), # 1/M/cm extinction coefficient for protein
    'protein_molecular_weight' : Unit(41293.2, 'daltons'), # g/mol protein molecular weight
    'protein_stock_volume' : Unit(165.8, 'microliters'), # uL protein stock solution used to make 1 uM protein stock
    'buffer_volume' : Unit(14.0, 'milliliters'), # mL buffer used to make 1 uM protein stock
    'rows_to_analyze' : ['A', 'B'], # rows to analyze
    'assay_volume' : Unit(100.0, 'microliters'), # quantity of protein or buffer dispensed into plate
    'measurements_to_analyze' : ['fluorescence top'], # which measurements to analyze (if specified -- this is optional)
    'wavelengths_to_analyze' : ['280:nanometers', '480:nanometers'], # which wavelengths to analyze (if specified -- this is optional)
}

# Create a single-wavelength assay.
assay = SingleWavelengthAssay(**params)

# Try emcee
import numpy as np
import emcee
import pymc
# This is the likelihood function for emcee
model = assay.experiment.model
from pymc.Node import ZeroProbability
from numpy import Inf
def lnprob(vals): # vals is a vector of parameter values to try
    # Set each random variable of the pymc model to the value
    # suggested by emcee
    for val, var in zip(vals, model.stochastics):
        var.value = val

    # Calculate logp
    try:
        logp = model.logp
    #except ZeroProbability as e:
    except Exception as e:
        print(e)
        logp = -Inf
    return logp

# emcee parameters
ndim = len(model.stochastics)
nwalkers = 2*ndim
# Find MAP
print('Finding MAP estimate...')
pymc.MAP(model).fit(iterlim=5)
start = np.empty(ndim)
for i, var in enumerate(model.stochastics):
    start[i] = var.value
# sample starting points for walkers around the MAP
p0 = np.random.randn(ndim * nwalkers).reshape((nwalkers, ndim)) + start
# instantiate sampler passing in the pymc likelihood function
print('Creating emcee sampler...')
sampler = emcee.EnsembleSampler(nwalkers, ndim, lnprob)
# burn-in
print('Burn-in...')
pos, prob, state = sampler.run_mcmc(p0, 10)
sampler.reset()
# sample 10 * 500 = 5000
print('Production...')
sampler.run_mcmc(pos, 1000)
# Save samples back to pymc model
print('Copying back into PyMC model')
model = pymc.MCMC(model)
model.sample(1) # This call is to set up the chains
for i, var in enumerate(model.stochastics):
    var.trace._trace[0] = sampler.flatchain[:, i]
pymc.Matplot.plot(model)
","Python"
"Assay","choderalab/assaytools","docs/conf.py",".py","11094","334","# -*- coding: utf-8 -*-
#
# assaytools documentation build configuration file, created by
# sphinx-quickstart on Sun Feb  8 21:57:25 2015.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

import sys
import os

#import assaytools
#import assaytools.version

import sys
try:
    from unittest.mock import MagicMock
except ImportError:
    from mock import Mock as MagicMock

class Mock(MagicMock):
    @classmethod
    def __getattr__(cls, name):
            return MagicMock()

MOCK_MODULES = ['numpy', 'scipy', 'numpy.linalg', 'scipy.special', 'scipy.stats', 'pymc', 'pint', 'pandas']
sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)

# Use mock to make our code think that numpy and scipy are available, even though they might not be available on readthedocs
#import mock
#
#MOCK_MODULES = ['numpy', 'scipy', 'numpy.linalg', 'scipy.special', 'scipy.stats', 'pymc', 'pint']
#for mod_name in MOCK_MODULES:
#    sys.modules[mod_name] = mock.Mock()

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#sys.path.insert(0, os.path.abspath('.'))

# -- General configuration ------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
#extensions = [
#    'sphinx.ext.autodoc',
#    'sphinx.ext.mathjax',
#    'sphinx.ext.viewcode',
#]

# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx',
              'sphinx.ext.todo', 
              #'sphinx.ext.pngmath', 
              'sphinx.ext.ifconfig',
              'numpydoc', 'sphinx.ext.inheritance_diagram',
              'sphinx.ext.autosummary', 'sphinx.ext.extlinks', ]
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.

intersphinx_mapping = {'python': ('http://docs.python.org/2', None),
                       'numpy': ('http://docs.scipy.org/doc/numpy/', None),
                       'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
                       'matplotlib': ('http://matplotlib.sourceforge.net/', None),
                       'pymc': ('http://pymc-devs.github.io/pymc/', None)}

autosummary_generate = True
autodoc_default_flags = ['members', 'inherited-members']

html_context = {
    'github_user': 'choderalab',
    'display_github': True,
    'github_repo': 'assaytools',
    'github_version': 'master',
    'conf_py_path': '/docs/',
    'html_logo' : 'assaytools_logo-small.png',
    'base_url' : 'http://assaytools.choderalab.org'
}

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']

# The suffix of source filenames.
source_suffix = '.rst'

# The encoding of source files.
#source_encoding = 'utf-8-sig'

# The master toctree document.
master_doc = 'index'

# General information about the project.
project = u'assaytools'
copyright = u'2015, John Chodera and Sonya Hanson'

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '0.1'
# The full version, including alpha/beta/rc tags.
release = '0.1.0'

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#language = None

# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']

# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None

# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True

# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True

# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'

# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []

# If true, keep warnings as ""system message"" paragraphs in the built documents.
#keep_warnings = False


# -- Options for HTML output ----------------------------------------------

# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
#html_theme = ""default""
on_rtd = os.environ.get('READTHEDOCS', None) == 'True'

if not on_rtd:  # only import and set the theme if we're building docs locally
    import sphinx_rtd_theme
    html_theme = 'sphinx_rtd_theme'
    html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.
#html_theme_options = {}

# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []

# The name for this set of Sphinx documents.  If None, it defaults to
# ""<project> v<release> documentation"".
#html_title = None

# A shorter title for the navigation bar.  Default is the same as html_title.
#html_short_title = None

# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
html_logo = '_static/assaytools_logo-small.jpeg'

# The name of an image file (within the static path) to use as favicon of the
# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named ""default.css"" will overwrite the builtin ""default.css"".
html_static_path = ['_static']

# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []

# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'

# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True

# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}

# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}

# If false, no module index is generated.
#html_domain_indices = True

# If false, no index is generated.
#html_use_index = True

# If true, the index is split into individual pages for each letter.
#html_split_index = False

# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True

# If true, ""Created using Sphinx"" is shown in the HTML footer. Default is True.
#html_show_sphinx = True

# If true, ""(C) Copyright ..."" is shown in the HTML footer. Default is True.
#html_show_copyright = True

# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it.  The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''

# This is the file name suffix for HTML files (e.g. "".xhtml"").
#html_file_suffix = None

# Output file base name for HTML help builder.
htmlhelp_basename = 'assaytoolsdoc'


# -- Options for LaTeX output ---------------------------------------------

latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',

# The font size ('10pt', '11pt' or '12pt').
'pointsize': '10pt',

# Additional stuff for the LaTeX preamble.
#'preamble': '',
# Additional stuff for the LaTeX preamble.
    'preamble': r'''
        \usepackage{sourcesanspro}
        \renewcommand*\familydefault{\sfdefault} %% Only if the base font of the document is to be sans serif
        \usepackage[T1]{fontenc}

        % \usepackage{charter}
        % \usepackage[defaultsans]{lato}
        \usepackage{inconsolata}
    ''',
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
#  author, documentclass [howto, manual, or own class]).
latex_documents = [
  ('index', 'assaytools.tex', u'assaytools Documentation',
   u'John Chodera and Sonya Hanson', 'manual'),
]

# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None

# For ""manual"" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False

# If true, show page references after internal links.
#latex_show_pagerefs = False

# If true, show URL addresses after external links.
#latex_show_urls = False

# Documents to append as an appendix to all manuals.
#latex_appendices = []

# If false, no module index is generated.
#latex_domain_indices = True


# -- Options for manual page output ---------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
    ('index', 'assaytools', u'assaytools Documentation',
     [u'John Chodera and Sonya Hanson'], 1)
]

# If true, show URL addresses after external links.
#man_show_urls = False


# -- Options for Texinfo output -------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
#  dir menu entry, description, category)
texinfo_documents = [
  ('index', 'assaytools', u'assaytools Documentation',
   u'John Chodera and Sonya Hanson', 'assaytools', 'One line description of project.',
   'Miscellaneous'),
]

# Documents to append as an appendix to all manuals.
#texinfo_appendices = []

# If false, no module index is generated.
#texinfo_domain_indices = True

# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'

# If true, do not generate a @detailmenu in the ""Top"" node's menu.
#texinfo_no_detailmenu = False
","Python"
"Assay","choderalab/assaytools","docs/sphinxext/notebook_sphinxext.py",".py","7820","218","# Copied from the yt_project, commit e8fb57e
# yt/doc/extensions/notebook_sphinxext.py
#  https://bitbucket.org/yt_analysis/yt/src/e8fb57e66ca42e26052dadf054a5c782740abec9/doc/extensions/notebook_sphinxext.py?at=yt

from __future__ import print_function

import time
import os, shutil, glob, re
from sphinx.util.compat import Directive
from docutils import nodes
from docutils.parsers.rst import directives
from IPython.nbconvert import html, python
from IPython.nbformat.current import read, write
from runipy.notebook_runner import NotebookRunner, NotebookError

class NotebookDirective(Directive):
    """"""Insert an evaluated notebook into a document

    This uses runipy and nbconvert to transform a path to an unevaluated notebook
    into html suitable for embedding in a Sphinx document.
    """"""
    required_arguments = 1
    optional_arguments = 1
    option_spec = {'skip_exceptions' : directives.flag}
    final_argument_whitespace = True

    def run(self): # check if there are spaces in the notebook name
        nb_path = self.arguments[0]
        if ' ' in nb_path: raise ValueError(
            ""Due to issues with docutils stripping spaces from links, white ""
            ""space is not allowed in notebook filenames '{0}'"".format(nb_path))
        # check if raw html is supported
        if not self.state.document.settings.raw_enabled:
            raise self.warning('""%s"" directive disabled.' % self.name)

        # get path to notebook
        source_dir = os.path.dirname(
            os.path.abspath(self.state.document.current_source))
        nb_filename = self.arguments[0]
        nb_basename = os.path.basename(nb_filename)
        rst_file = self.state_machine.document.attributes['source']
        rst_dir = os.path.abspath(os.path.dirname(rst_file))
        nb_dir = os.path.join(setup.confdir, '..')
        nb_abs_path = os.path.abspath(os.path.join(nb_dir, nb_filename))

        # Move files around.
        rel_dir = os.path.relpath(rst_dir, setup.confdir)
        rel_path = os.path.join(rel_dir, nb_basename)
        dest_dir = os.path.join(setup.app.builder.outdir, rel_dir)
        dest_path = os.path.join(dest_dir, nb_basename)

        if not os.path.exists(dest_dir):
            os.makedirs(dest_dir)

        # Copy unevaluated script
        try:
            shutil.copyfile(nb_abs_path, dest_path)
        except IOError:
            raise RuntimeError(""Unable to copy notebook to build destination. ""
                               ""%s -> %s"" % (nb_abs_path, dest_path))

        dest_path_eval = dest_path.replace('.ipynb', '_evaluated.ipynb')
        dest_path_script = dest_path.replace('.ipynb', '.py')
        rel_path_eval = nb_basename.replace('.ipynb', '_evaluated.ipynb')
        rel_path_script = nb_basename.replace('.ipynb', '.py')

        # Create python script vesion
        unevaluated_text = nb_to_html(nb_abs_path)
        script_text = nb_to_python(nb_abs_path)
        f = open(dest_path_script, 'wb')
        f.write(script_text.encode('utf8'))
        f.close()

        skip_exceptions = 'skip_exceptions' in self.options

        print('[NotebookDirective] Evaluating %s' % nb_filename)
        start = time.time()
#        evaluated_text = evaluate_notebook(nb_abs_path, dest_path_eval,
#                                           skip_exceptions=skip_exceptions)
        evaluated_text = ''
        print('[NotebookDirective] Took %8.3fs seconds' %
              (time.time() - start))

        # Create link to notebook and script files
        link_rst = ""("" + \
                   formatted_link(nb_basename) + ""; "" + \
                   formatted_link(rel_path_eval) + ""; "" + \
                   formatted_link(rel_path_script) + \
                   "")""

        self.state_machine.insert_input([link_rst], rst_file)

        # create notebook node
        attributes = {'format': 'html', 'source': 'nb_path'}

        use_evaluated = False

        if use_evaluated:
            nb_node = notebook_node('', evaluated_text, **attributes)
        else:
            nb_node = notebook_node('', unevaluated_text, **attributes)

        (nb_node.source, nb_node.line) = \
            self.state_machine.get_source_and_line(self.lineno)

        # add dependency
        self.state.document.settings.record_dependencies.add(nb_abs_path)

        # clean up png files left behind by notebooks.
        png_files = glob.glob(""*.png"")
        fits_files = glob.glob(""*.fits"")
        h5_files = glob.glob(""*.h5"")
        for file in png_files:
            os.remove(file)

        return [nb_node]


class notebook_node(nodes.raw):
    pass

def nb_to_python(nb_path):
    """"""convert notebook to python script""""""
    exporter = python.PythonExporter()
    output, resources = exporter.from_filename(nb_path)
    return output

def nb_to_html(nb_path):
    """"""convert notebook to html""""""
    exporter = html.HTMLExporter(template_file='full')
    output, resources = exporter.from_filename(nb_path)
    header = output.split('<head>', 1)[1].split('</head>',1)[0]
    body = output.split('<body>', 1)[1].split('</body>',1)[0]

    # http://imgur.com/eR9bMRH
    header = header.replace('<style', '<style scoped=""scoped""')
    header = header.replace('body {\n  overflow: visible;\n  padding: 8px;\n}\n', '')
    header = header.replace(""code,pre{"", ""code{"")

    header = header.replace('\n', '')
    comp = re.compile('<style.*?</style>', re.MULTILINE)
    header = comp.sub('', header)

    # Filter out styles that conflict with the sphinx theme.
    filter_strings = [
        'navbar',
        'body{',
        'alert{',
        'uneditable-input{',
        'collapse{',
    ]
    filter_strings.extend(['h%s{' % (i+1) for i in range(6)])

    line_begin_strings = [
        'pre{',
        'p{margin'
        ]

    header_lines = filter(
        lambda x: not any([s in x for s in filter_strings]), header.split('\n'))
    header_lines = filter(
        lambda x: not any([x.startswith(s) for s in line_begin_strings]), header_lines)

    header = '\n'.join(header_lines)

    # concatenate raw html lines
    lines = ['<div class=""ipynotebook"">']
    lines.append(header)
    lines.append(body)
    lines.append('</div>')
    return '\n'.join(lines)

def evaluate_notebook(nb_path, dest_path=None, skip_exceptions=False):
    # Create evaluated version and save it to the dest path.
    # Always use --pylab so figures appear inline
    # perhaps this is questionable?
    notebook = read(open(nb_path), 'json')
    nb_runner = NotebookRunner(notebook, pylab=True, mpl_inline=True)
    try:
        nb_runner.run_notebook(skip_exceptions=skip_exceptions)
    except NotebookError as e:
        print('\n', '-'*80)
        print(e)
        print('-'*80)
        raise
        # Return the traceback, filtering out ANSI color codes.
        # http://stackoverflow.com/questions/13506033/filtering-out-ansi-escape-sequences
        # return 'Notebook conversion failed with the following traceback: \n%s' % \
        # re.sub(r'\\033[\[\]]([0-9]{1,2}([;@][0-9]{0,2})*)*[mKP]?', '', str(e))
    if dest_path is None:
        dest_path = 'temp_evaluated.ipynb'
    write(nb_runner.nb, open(dest_path, 'w'), 'json')
    ret = nb_to_html(dest_path)
    if dest_path is 'temp_evaluated.ipynb':
        os.remove(dest_path)
    return ret

def formatted_link(path):
    return ""`%s <%s>`__"" % (os.path.basename(path), path)

def visit_notebook_node(self, node):
    self.visit_raw(node)

def depart_notebook_node(self, node):
    self.depart_raw(node)

def setup(app):
    setup.app = app
    setup.config = app.config
    setup.confdir = app.confdir

    app.add_stylesheet('css/ipynb.css')

    app.add_node(notebook_node,
                 html=(visit_notebook_node, depart_notebook_node))

    app.add_directive('notebook', NotebookDirective)
","Python"
"Assay","choderalab/assaytools","scripts/get_mean.py",".py","2089","68","# This script allows you to get the mean of a parameter,
# for example F_PL, from a previous run of quickmodel.py

# Usage:
#  python get_mean.py --input 'Src-Bosutinib-AB_mcmc_1.pickle'
#  or
#  python get_mean.py --input 'Src-Bosutinib-AB_mcmc_1.pickle' --parameter 'F_PL'

# Sonya M. Hanson

import json
import cPickle
import os

def get_mean(data_file, parameter='F_PL'):
    """"""
    Quick model for both spectra and single wavelength experiments
    ----------
    input_file : file_name
        File_name of pickle file to get mean from.
    parameter : string, default='F_PL'
        Parameter to get mean of. 
    """"""

    # load data from pickle file
    with open(r'%s'%data_file,'rb') as my_file:
        data = cPickle.load(my_file)

    # get t_equil from json file
    base_name = os.path.splitext(data_file)[0]
    json_base = base_name.replace('_mcmc','')
    json_file = json_base + '.json'

    with open(json_file) as f:
        json_data = json.load(f)
   
    t_equil = json_data['t_equil']

    # find mean of desired parameter removing data before equilibration
    my_mean = data[parameter][0][t_equil:].mean()

    # print and save this for later use
    print('The mean of parameter %s from data file %s is:' %(parameter,data_file))
    print(my_mean)

    mean_dict = {}
    mean_dict['data_file'] = data_file
    mean_dict[parameter] = my_mean

    with open('mean-%s.json'%parameter, 'w') as outfile:
                json.dump(mean_dict, outfile, sort_keys = True, indent = 4, ensure_ascii=False)

    print('Output file: mean-%s.json'%parameter)

def entry_point():

    import argparse

    parser = argparse.ArgumentParser(description=""""""Get mean from quickmodel run. Default parameter is F_PL."""""")
    parser.add_argument(""--input"", help=""The pickle file output by quickmodel for your experiment."",default=None)
    parser.add_argument(""--parameter"", help=""The parameter you want to get the mean for."",default='F_PL')
    args = parser.parse_args()

    get_mean(data_file=args.input,parameter=args.parameter)

if __name__ == '__main__':
    entry_point()
","Python"
"Assay","choderalab/assaytools","scripts/plot_trace.py",".py","1323","45","import pymc
from pymc import MCMC
import assaytools
from pymc.Matplot import plot
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns

def entry_point():

    import sys

    #This allows us to import local inputs.py
    sys.path.append('./')

    import argparse

    # Define argparse stuff

    parser = argparse.ArgumentParser(description=""""""Plot the trace from the pickled object written by quickmodel """""")
    parser.add_argument(""--input"", help=""The pickle object you'd like to plot"",required=True)
    parser.add_argument(""--name"", help=""Name and extension of file that will be saved"",required=True)
    args = parser.parse_args()

    # Define inputs
    # If an inputs###.py is defined, it is run and used. An inputs.json is also created.
    inputs_file = args.input
    filename = args.name

    db = pymc.database.pickle.load(inputs_file)
    ntraces = len(db.trace_names[0])

    sns.set_style('white')
    sns.set_palette('muted', 12)
    fig = plt.figure(figsize=(6, 4 * ntraces))
    for i, trace_name in enumerate(db.trace_names[0]):
        plt.subplot(ntraces, 1, i + 1)
        plt.plot(range(len(db.trace(trace_name)[:])), db.trace(trace_name)[:])
        plt.title(trace_name)
    fig.savefig(filename, dpi=500, bbox_inches='tight')


if __name__ == '__main__':
    entry_point()
","Python"
"Assay","choderalab/assaytools","scripts/inputs_example.py",".py","1250","24","import json
import numpy as np
import os
from glob import glob

inputs = {
    'xml_file_path' :  ""%s/fluorescence-assay-manuscript/data/singlet/DMSO-backfill/""%os.environ['GITHUB_PATH'],
    'file_set'      :  {'p38': glob(""%s/fluorescence-assay-manuscript/data/singlet/DMSO-backfill/*.xml""%os.environ['GITHUB_PATH'])},
    'section'       :  '280_480_TOP_120',
    'ligand_order'  :  ['Bosutinib','Bosutinib Isomer','Erlotinib','Gefitinib','Bosutinib','Bosutinib Isomer','Erlotinib','Gefitinib'],
    'Lstated'       :  np.array([20.0e-6,14.0e-6,9.82e-6,6.88e-6,4.82e-6,3.38e-6,2.37e-6,1.66e-6,1.16e-6,0.815e-6,0.571e-6,0.4e-6,0.28e-6,0.196e-6,0.138e-6,0.0964e-6,0.0676e-6,0.0474e-6,0.0320e-6,0.0240e-6,0.0160e-6,0.0120e-6,0.008e-6,0.0], np.float64), # ligand concentration, M
    'Pstated'       :  0.5e-6 * np.ones([24],np.float64), # protein concentration, M
    'P_error'       :  0.15,
    'L_error'       :  0.08,
    'assay_volume'  :  50e-6, # assay volume, L
    'well_area'     :  0.1369, # well area, cm^2 for 4ti-0203 [http://4ti.co.uk/files/3113/4217/2464/4ti-0201.pdf]
    }

inputs['Lstated'] = inputs['Lstated'].tolist()
inputs['Pstated'] = inputs['Pstated'].tolist()

with open('inputs.json', 'w') as fp:
    json.dump(inputs, fp)
","Python"
"Assay","choderalab/assaytools","scripts/ipnbdoctest.py",".py","15516","451","#!/usr/bin/env python
""""""
simple example script for running and testing notebooks.

Usage: `ipnbdoctest.py foo.ipynb [bar.ipynb [...]]`

Each cell is submitted to the kernel, and the outputs are compared with those stored in the notebook.

The original is found in a gist under https://gist.github.com/minrk/2620735

TODO: It would be nice to add some more output about failed diffs. So far I leave it this way
""""""

import os,sys
import base64
import re
import argparse

from Queue import Empty
#import difflib

try:
    from IPython.kernel import KernelManager
except ImportError:
    from IPython.zmq.blockingkernelmanager import BlockingKernelManager as KernelManager

from IPython.nbformat.current import reads, NotebookNode

class TravisConsole(object):
    """"""
    A wrapper class to allow easier output to the console especially for travis
    """"""
    def __init__(self):
        self.stream = sys.stdout
        self.linebreak = '\n'
        self.fold_count = dict()
        self.fold_stack = dict()

    def fold_open(self, name):
        if name not in self.fold_count:
            self.fold_count[name] = 0
            self.fold_stack[name] = []

        self.fold_count[name] += 1
        fold_name = name.lower() + '.' + str(self.fold_count[name])
        self.fold_stack[name].append(fold_name)

        self.writeln(""travis_fold:start:"" + fold_name)

    def fold_close(self, name):
        fold_name = self.fold_stack[name].pop()
        self.writeln(""travis_fold:end:"" + fold_name)

    def _indent(self, s, num = 4):
        lines = s.splitlines(True)
        lines = map(lambda s: ' ' * num + s, lines)
        return ''.join(lines)

    def writeln(self, s, indent = 0):
        self.write(s, indent)
        if s[-1] != '\n':
            # make a line break if there is none present
            self.br()

    def br(self):
        """"""
        Write a linebreak
        """"""
        self.stream.write(self.linebreak)

    def write(self, s, indent = 0):
        if indent > 0:
            self.stream.write(self._indent(s, indent))
        else:
            self.stream.write(s)


    def compare_png(a64, b64):
        """"""compare two b64 PNGs (incomplete)""""""
        try:
            import Image
        except ImportError:
            pass
        adata = base64.decodestring(a64)
        bdata = base64.decodestring(b64)
        return True

    def red(self, s):
        RED = '\033[31m'
        DEFAULT = '\033[39m'
        return RED + s + DEFAULT

    def green(self, s):
        GREEN = '\033[32m'
        DEFAULT = '\033[39m'
        return GREEN + s + DEFAULT

    def blue(self, s):
        BLUE = '\033[36m'
        DEFAULT = '\033[39m'
        return BLUE + s + DEFAULT

class IPyTestConsole(TravisConsole):
    """"""
    Add support for different output results
    """"""
    def __init__(self):
        super(IPyTestConsole, self).__init__()

        self.default_results = {
            'success' : True,       # passed without differences
            'kernel' : False,       # kernel (IPYTHON) error occurred
            'error' : False,        # errors during execution
            'timeout' : True,       # kernel run timed out
            'diff' : True,          # passed, but with differences in the output
            'skip' : True,          # cell has been skipped, not even tried to execute
            'ignore' : True         # cell has been executed, but not compared
        }

        self.pass_count = 0
        self.fail_count = 0

        self.result_count = { key : 0 for key in self.default_results.keys() }

    def write_result(self, result, okay_list = None):

        my_list = self.default_results.copy()
        if okay_list is not None:
            my_list.update(okay_list)

        if my_list[result]:
            self.write(self.green('ok'))
            self.pass_count += 1
        else:
            self.write(self.red('fail'))
            self.fail_count += 1

        self.writeln(' [' + result + ']')
        self.result_count[result] += 1


class IPyKernel(object):
    """"""
    A simple wrapper class to run cells in an IPython Notebook.

    Notes
    -----
    Use `with` construct to properly instantiate
    """"""

    def __init__(self, console = None):
        # default timeout time is 60 seconds
        self.default_timeout = 60
        self.extra_arguments = ['--pylab=inline']

    def __enter__(self):
        self.km = KernelManager()
        self.km.start_kernel(extra_arguments=self.extra_arguments, stderr=open(os.devnull, 'w'))

        try:
            self.kc = self.km.client()
            self.kc.start_channels()
            self.iopub = self.kc.iopub_channel
        except AttributeError:
            # IPython 0.13
            self.kc = self.km
            self.kc.start_channels()
            self.iopub = self.kc.sub_channel

        self.shell = self.kc.shell_channel

        # run %pylab inline, because some notebooks assume this
        # even though they shouldn't
        self.shell.execute(""pass"")
        self.shell.get_msg()
        while True:
            try:
                self.iopub.get_msg(timeout=1)
            except Empty:
                break

        return self

    def __exit__(self, exc_type, exc_val, exc_tb):
        self.kc.stop_channels()
        self.km.shutdown_kernel()
        del self.km

    def run(self, cell, timeout = None):
        use_timeout = self.default_timeout
        if timeout is not None:
            use_timeout = timeout
        self.shell.execute(cell.input)
        self.shell.get_msg(timeout=use_timeout)
        outs = []

        while True:
            try:
                msg = self.iopub.get_msg(timeout=0.5)
            except Empty:
                break
            msg_type = msg['msg_type']
            if msg_type in ('status', 'pyin'):
                continue
            elif msg_type == 'clear_output':
                outs = []
                continue

            content = msg['content']
            out = NotebookNode(output_type=msg_type)

            if msg_type == 'stream':
                out.stream = content['name']
                out.text = content['data']
            elif msg_type in ('display_data', 'pyout'):
                out['metadata'] = content['metadata']
                for mime, data in content['data'].iteritems():
                    attr = mime.split('/')[-1].lower()
                    # this gets most right, but fix svg+html, plain
                    attr = attr.replace('+xml', '').replace('plain', 'text')
                    setattr(out, attr, data)
                if msg_type == 'pyout':
                    out.prompt_number = content['execution_count']
            elif msg_type == 'pyerr':
                out.ename = content['ename']
                out.evalue = content['evalue']
                out.traceback = content['traceback']
            else:
                print(""unhandled iopub msg:"", msg_type)

            outs.append(out)
        return outs

    def sanitize(self, s):
        """"""sanitize a string for comparison.

        fix universal newlines, strip trailing newlines, and normalize likely random values (memory addresses and UUIDs)
        """"""
        if not isinstance(s, basestring):
            return s
        # normalize newline:
        s = s.replace('\r\n', '\n')

        # ignore trailing newlines (but not space)
        s = s.rstrip('\n')

        # normalize hex addresses:
        s = re.sub(r'0x[a-f0-9]+', '0xFFFFFFFF', s)

        # normalize UUIDs:
        s = re.sub(r'[a-f0-9]{8}(\-[a-f0-9]{4}){3}\-[a-f0-9]{12}', 'U-U-I-D', s)

        return s

    def compare_outputs(self, test, ref, skip_compare=('png', 'traceback', 'latex', 'prompt_number', 'svg', 'html')):
        for key in ref:
            if key not in test:
    #            print ""missing key: %s != %s"" % (test.keys(), ref.keys())
                return False
            elif key not in skip_compare:
                s1 = self.sanitize(test[key])
                s2 = self.sanitize(ref[key])
                if s1 != s2:

    #                print ""mismatch %s:"" % key
                    expected=s1.splitlines(1)
                    actual=s2.splitlines(1)
    #                diff=difflib.unified_diff(expected, actual)

    #                print ''.join(diff)
                    return False
        return True

    def get_commands(self, cell):
        commands = {}
        if hasattr(cell, 'input'):
            lines = cell.input.splitlines()
            if len(lines) > 0:
                first_line = lines[0]
                if first_line.startswith('#!'):
                    txt = first_line[2:].strip()

                    parts = txt.split(',')
                    for part in parts:
                        subparts = part.split(':')
                        if len(subparts) == 1:
                            commands[subparts[0].strip().lower()] = True
                        elif len(subparts) == 2:
                            commands[subparts[0].strip().lower()] = subparts[1]

        return commands

def entry_point():
    parser = argparse.ArgumentParser(
        description='Run all cell in an ipython notebook as a test and check whether these successfully execute and ' +
                    'compares their output to the one inside the notebook')

    parser.add_argument('file', metavar='file.ipynb', help='the notebook to be checked', type=str)

    parser.add_argument('--timeout', dest='timeout',
                   type=int, default=300,
                   help='the default timeout time in seconds for a cell evaluation. Default is 300s.')

    parser.add_argument('--strict', dest='strict', action='store_true',
                   default=False,
                   help='if set to true then the default test is that cell have to match otherwise a diff will not be considered a failed test')

    parser.add_argument('--fail-if-timeout', dest='no_timeout', action='store_true',
                   default=False,
                   help='if set to true then a timeout is considered a failed test')

    args = parser.parse_args()
    ipynb = args.file

    tv = IPyTestConsole()

    if args.strict:
        tv.default_results['diff'] = False

    if args.no_timeout:
        tv.default_results['timeout'] = False

    tv.writeln('testing ipython notebook : ""%s""' % ipynb)
    tv.write(""starting kernel ... "")

    with open(ipynb) as f:
        nb = reads(f.read(), 'json')

    with IPyKernel() as ipy:
        ipy.default_timeout = args.timeout
        tv.writeln(""ok"")

        nbs = ipynb.split('.')
        nb_class_name = nbs[1] + '.' + nbs[0].replace("" "", ""_"")

        tv.br()

        for ws in nb.worksheets:
            for cell in ws.cells:
                if cell.cell_type == 'markdown':
                    for line in cell.source.splitlines():
                        # only tv.writeln(headlines in markdown
                        if line.startswith('#'):
                            tv.writeln(line)

                if cell.cell_type == 'heading':
                    tv.writeln('#' * cell.level + ' ' + cell.source)

                if cell.cell_type != 'code':
                    continue

                # If code cell then continue with checking it

                if hasattr(cell, 'prompt_number'):
                    tv.write(nb_class_name + '.' + 'In [%3i]' % cell.prompt_number + ' ... ')
                else:
                    tv.write(nb_class_name + '.' + 'In [???]' + ' ... ')

                commands = ipy.get_commands(cell)

                result = 'success'

                timeout = ipy.default_timeout

                if 'skip' in commands:
                    tv.write_result('skip')
                    continue

                try:
                    if 'timeout' in commands:
                        outs = ipy.run(cell, timeout=int(commands['timeout']))
                    else:
                        outs = ipy.run(cell)

                except Exception as e:
                    # Internal IPython error occurred (might still be that the cell did not execute correctly)
                    if repr(e) == 'Empty()':
                        # Assume it has been timed out!
                        tv.write_result('timeout')
                        # tv.writeln('>>> TimeOut (%is)' % args.timeout)
                    else:
                        tv.write_result('kernel')
                        tv.fold_open('ipynb.kernel')
                        tv.writeln('>>> ' + out.ename + ' (""' + out.evalue + '"")')
                        tv.writeln(repr(e), indent=4)
                        tv.fold_close('ipynb.kernel')
                    continue

                failed = False
                diff = False
                for out, ref in zip(outs, cell.outputs):
                    if out.output_type == 'pyerr':
                        # An python error occurred. Cell is not completed correctly
                        tv.write_result('error')
                        tv.fold_open('ipynb.fail')
                        tv.write('>>> ' + out.ename + ' (""' + out.evalue + '"")')
                        for idx, trace in enumerate(out.traceback):
                            tv.writeln(trace, indent=4)
                        tv.fold_close('ipynb.fail')
                        failed = True
                    else:
                        if not ipy.compare_outputs(out, ref):
                            # Output is different than the one in the notebook. This might be okay.
                            diff = True

                if diff:
                    if 'strict' in commands:
                        # strict mode means a difference will fail the test
                        tv.write_result('diff', okay_list={ 'diff' : False })
                    elif 'ignore' in commands:
                        # ignore mode means a difference will pass the test
                        tv.write_result('diff', okay_list={ 'diff' : True })
                    else:
                        # use defaults
                        tv.write_result('diff')

                if not failed and not diff:
                    tv.write_result('success')

        tv.br()
        tv.writeln(""testing results"")
        tv.writeln(""==============="")
        if tv.pass_count > 0:
            tv.writeln(""    %3i cells passed ["" % tv.pass_count + tv.green('ok') + ""]"" )
        if tv.fail_count > 0:
            tv.writeln(""    %3i cells failed ["" % tv.fail_count + tv.red('fail') + ""]"" )

        tv.br()
        tv.writeln(""    %3i cells successfully replicated [success]"" % tv.result_count['success'])
        tv.writeln(""    %3i cells had mismatched outputs [diff]"" % tv.result_count['diff'])
        tv.writeln(""    %3i cells timed out during execution [time]"" % tv.result_count['timeout'])
        tv.writeln(""    %3i cells ran with python errors [fail]"" % tv.result_count['error'])
        tv.writeln(""    %3i cells have been executed without comparison [ignore]"" % tv.result_count['ignore'])
        tv.writeln(""    %3i cells failed to even execute (IPython error) [kernel]"" % tv.result_count['kernel'])
        tv.writeln(""    %3i cells have been skipped [skip]"" % tv.result_count['skip'])

        tv.br()
        tv.write(""shutting down kernel ... "")

    tv.writeln('ok')

    if tv.fail_count != 0:
        tv.writeln(tv.red('some tests not passed.'))
        exit(1)
    else:
        tv.writeln(tv.green('all tests passed.'))
        exit(0)

if __name__ == '__main__':
    entry_point()
","Python"
"Assay","choderalab/assaytools","scripts/calculate_Lstated_array.py",".py","4370","89","# Generate Numpy array of stated ligand concentration(Lstated)-corrected for true stock concentration- for logarithmic dilution along a row.
# 
# Example output:
# np.array([20.0e-6,9.15e-6,4.18e-6,1.91e-6,0.875e-6,0.4e-6,0.183e-6,0.0837e-6,0.0383e-6,0.0175e-6,0.008e-6,0.0001e-6], np.float64)
#
# Usage when target stock concentration and true stock concentrations are known:
#    python calculate_Lstated_array.py --n_wells 12 --h_conc 8e-06 --l_conc 2.53e-09 --target_stock_conc 0.010 --true_stock_conc 0.010034 --dilution logarithmic
#    python calculate_Lstated_array.py --n_wells 6 --h_conc 100 --l_conc 10 --target_stock_conc 0.010 --true_stock_conc 0.0100453 --dilution linear
#
# Usage based on only target stock concentration when true stock concentration unknown and assumed to be equal to target:
#    python calculate_Lstated_array.py --n_wells 12 --h_conc 20e-06 --l_conc 8e-09 --target_stock_conc 0.010 --dilution logarithmic
#    python calculate_Lstated_array.py --n_wells 12 --h_conc 20e-06 --l_conc 8e-09 --target_stock_conc 0.010 --dilution linear
#

from __future__ import print_function, division
import numpy as np
import argparse
import sys

# Argument parser
parser = argparse.ArgumentParser(prog=""calculate_Lstated_array"")
parser.add_argument(""--n_wells"", dest=""n_wells"", help=""Enter target number of different concentrations."", type=int)
parser.add_argument(""--h_conc"", dest= ""highest_conc"", help=""The highest concentration in the series (Unit: M)."", type=float)
parser.add_argument(""--l_conc"", dest=""lowest_conc"", help=""The lowest concentration in the series (Unit: M)."", type=float)
parser.add_argument(""--target_stock_conc"", dest=""target_stock_conc"", default=0.01, help=""Enter target concentration of ligand stock solution (Unit: M)."", type=float)
parser.add_argument(""--true_stock_conc"", dest=""true_stock_conc"", help=""Enter true concentration of ligand stock solution (Unit: M). If not provided, true stock concentration will be assumed equal to target stock concentration"", type=float)
parser.add_argument(""--dilution"", dest=""dilution_method"", help=""Defines logarithmic or linear dilution.Enter 'logarithmic' or 'linear.'."", type=str)
args = parser.parse_args()


# true_stock_conc is an optional argument
# If true stock concentration is not known, assume true_stock_conc is equal to target_stock_concentration
if args.true_stock_conc == None:
    print(""True stock concentration is not provided. "")
    print(""Ligand concentration array will be calculated assuming true stock concentration is equal to target stock concentration."")
    args.true_stock_conc = args.target_stock_conc


# Use true stock concentration and target stock concentration to create a scaling factor for true ligand concentrations
SF = args.true_stock_conc/args.target_stock_conc
print(""Stock concentration scaling factor: {}"".format(SF))

n = args.n_wells
print(""Number of wells: "", n)
highest_conc = (args.highest_conc*SF)
print(""Highest concentration (M): "", highest_conc)
lowest_conc = (args.lowest_conc*SF)
print(""Lowest concentration (M): "", lowest_conc)
dilution_method = args.dilution_method
print(""Dilution method is {}."".format(dilution_method))


if dilution_method == ""logarithmic"":
   
     # Calculate dilution factor
    DF = (highest_conc / lowest_conc)**(1.0/(n-1))
    print(""Dilution factor: {}"".format(DF))

    # Calculate concentration of solute in each well
    Ltot = np.zeros(n)
    for i, conc  in enumerate(Ltot):
        Ltot[i] = highest_conc/(DF**i)

elif dilution_method == ""linear"":
   
    # Calculate interval, i.e. concentration difference between consecutive dilution points
    interval = (highest_conc - lowest_conc)/(n-1)
    print(""Linear concentration interval(M): {} "".format(interval) )

    # Calculate linear dilution array
    Ltot = np.zeros(n)
    for i, conc  in enumerate(Ltot):
        Ltot[i] = highest_conc - i*interval

else:
    
    sys.exit(""Error in --dilution argument! It must be specified as '--dilution linear' or '--dilution logarithmic' ."")


# Create a string that codes for Lstated array:
array_string = ""np.array([""
for i in range(n-1):
    array_string = array_string + str(Ltot[i]) + "",""
array_string = array_string + str(Ltot[n-1]) + ""], np.float64)""

# Print with matching format for input.py file
print(""Copy this line to Lstated section of assaytools input.py file: "")
print(array_string)
","Python"
"Assay","choderalab/assaytools","scripts/__init__.py",".py","1","2","
","Python"
"Assay","choderalab/assaytools","scripts/xml2png.py",".py","17751","507","# A somewhat ugly, utilitarian script takes xml data file output from the Tecan Infinite m1000 Pro
# plate reader and allows for the quick visual inspection of raw data.
#
# Usage: python xml2png.py *.xml

# import math, xml, and dataframe libraries
import numpy as np
from lxml import etree
import pandas as pd
import string

# import libraries for making figures
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import seaborn

#import assaytools helper
from assaytools import platereader

# import libraries for interacting with arguments
import sys
import os
import argparse

# Define argparse stuff

parser = argparse.ArgumentParser(description=""""""Visualize your raw data by making a png from your xml:
> python xml2png.py --type spectra *.xml"""""")
parser.add_argument(""files"", nargs='*', help=""xml file(s) to analyze"")
parser.add_argument(""--type"", help=""type of data file (spectra, singlet_96, singlet_384, scan)"", choices=['spectra', 'singlet_96', 'singlet_384','scan'],default='singlet_96')
args = parser.parse_args()
print(args.files)
print(""*** --type: analyzing %s file(s) ***"" % args.type)

### Define extract function that extracts parameters

def extract(taglist, parameters): #[""Mode"", ""Wavelength Start"", ""Wavelength End"", ""Wavelength Step Size""]
    result = []
    for p in taglist:
        print(""Attempting to extract tag '%s'..."" % p)
        try:
            param = parameters.xpath(""*[@Name='"" + p + ""']"")[0]
            result.append( p + '=' + param.attrib['Value'])
        except:
            ### tag not found
            result.append(None)

    return result

#############################################
 ###               singlet_96            ###
#############################################

# Define get_wells_from_section function that extracts the data from each Section.
# It is written sort of strangely to ensure data is connected to the correct well.

def get_wells_from_section(path):
    reads = path.xpath(""*/Well"")
    wellIDs = [read.attrib['Pos'] for read in reads]

    def convert_value(text):
        try:
            return float(text)
        except ValueError as e:
            # OVER
            return 0.0

    data = [(convert_value(s.text), r.attrib['Pos'])
         for r in reads
         for s in r]

    datalist = {
      well : value
      for (value, well) in data
    }

    welllist = [
                [
                 datalist[chr(64 + row) + str(col)]
                 if chr(64 + row) + str(col) in datalist else None
                 for row in range(1,17)
                ]
                for col in range(1,24)
                ]

    return welllist

def process_files_five(xml_files):
    """"""
    Main entry point.
    """"""

    so_many = len(xml_files)
    print(""****This script is about to make png files for %s xml files. ****""  % so_many)

    for file in xml_files:

        # Parse XML file.

        root = etree.parse(file)

        # Remove extension from xml filename.

        file_name = os.path.splitext(file)[0]

        # Define Sections.

        Sections = root.xpath(""/*/Section"")
        much = len(Sections)
        print(""****The xml file "" + file + "" has %s data sections:****"" % much)
        for sect in Sections:
            print(sect.attrib['Name'])

        data = []

        for i, sect in enumerate(Sections):

           # Extract Parameters for this section.

            path = ""/*/Section[@Name='"" + sect.attrib['Name'] + ""']/Parameters""
            parameters = root.xpath(path)[0]

            # Parameters are extracted slightly differently depending on Absorbance or Fluorescence read.

            if  parameters[0].attrib['Value'] == ""Absorbance"":
                result = extract([""Mode"", ""Wavelength"", ""Part of Plate""], parameters)
                title = '%s, %s, %s' % tuple(result)

            else:
                result = extract([""Gain"", ""Excitation Wavelength"", ""Emission Wavelength"", ""Part of Plate"", ""Mode""], parameters)
                title = '%s, %s, %s, \n %s, %s' % tuple(result)

            print(""****The %sth section has the parameters:****"" % i)
            print(title)

            # Extract Reads for this section.

            Sections = root.xpath(""/*/Section"")

            welllist = get_wells_from_section(sect)

            data.append(
                {
                        'filename' : file_name,
                        'title' : title,
                        'dataframe' : pd.DataFrame(welllist, columns=list('ABCDEFGHIJKLMNOP'))
                }
            )

        # Make plot, complete with subfigure for each section.
        seaborn.set_palette(""Paired"", 10)
        seaborn.set_context(""notebook"", rc={""lines.linewidth"": 2.5})

        fig, axes = plt.subplots(nrows=1,ncols=len(Sections), figsize=(60,4.5))

        for i, sect in enumerate(data):
            sect['dataframe'].plot(title = sect['title'] , ax = axes[i] )

        fig.tight_layout()
        fig.subplots_adjust(top=0.8)
        fig.suptitle(""%s"" % file_name, fontsize=18)

        plt.savefig('%s.png' % file_name)
        print('Look at how pretty your data is: %s.png' % file_name)

    return

#############################################
 ###               SPECTRA               ###
#############################################

### Define an initial set of dataframes, one per each section

large_dataframe0 = pd.DataFrame()
large_dataframe1 = pd.DataFrame()
large_dataframe2 = pd.DataFrame()
large_dataframe3 = pd.DataFrame()
large_dataframe4 = pd.DataFrame()
large_dataframe5 = pd.DataFrame()

def process_files_spectra(xml_files):
    """"""
    Main entry point.
    """"""

    so_many = len(xml_files)
    print(""****This script is about to make png files for %s xml files. ****""  % so_many)

    for file in xml_files:

        ### Parse XML file.

        root = etree.parse(file)

        ### Remove extension from xml filename.

        file_name = os.path.splitext(file)[0]

        ### Extract plate type and barcode.

        plate = root.xpath(""/*/Header/Parameters/Parameter[@Name='Plate']"")[0]
        plate_type = plate.attrib['Value']

        try:
            bar = root.xpath(""/*/Plate/BC"")[0]
            barcode = bar.text
        except:
            barcode = 'no barcode'

        ### Define Sections.

        Sections = root.xpath(""/*/Section"")
        much = len(Sections)
        print(""****The xml file "" + file + "" has %s data sections:****"" % much)
        for sect in Sections:
            print(sect.attrib['Name'])

        for i, sect in enumerate(Sections):

            ### Extract Parameters for this section.

            path = ""/*/Section[@Name='"" + sect.attrib['Name'] + ""']/Parameters""
            parameters = root.xpath(path)[0]

            ### Parameters are extracted slightly differently depending on Absorbance or Fluorescence read.
            # Attach these to title1, title2, or title3, depending on section which will be the same for all 4 files.

            if  parameters[0].attrib['Value'] == ""Absorbance"":
                result = extract([""Mode"", ""Wavelength Start"", ""Wavelength End"", ""Wavelength Step Size""], parameters)
                globals()[""title""+str(i)] = '%s, %s, %s, %s' % tuple(result)

            else:
                result = extract([""Gain"", ""Excitation Wavelength"", ""Emission Wavelength"", ""Part of Plate"", ""Mode""], parameters)
                globals()[""title""+str(i)] = '%s, %s, %s, \n %s, %s' % tuple(result)

            print(""****The %sth section has the parameters:****"" % i)
            print(globals()[""title""+str(i)])

            ### Extract Reads for this section.

            Sections = root.xpath(""/*/Section"")

            reads = root.xpath(""/*/Section[@Name='"" + sect.attrib['Name'] + ""']/*/Well"")

            wellIDs = [read.attrib['Pos'] for read in reads]

            data = [(s.text, float(s.attrib['WL']), r.attrib['Pos'])
                     for r in reads
                     for s in r]

            dataframe = pd.DataFrame(data, columns=['fluorescence','wavelength (nm)','Well'])

            ### dataframe_rep replaces 'OVER' (when fluorescence signal maxes out) with '3289277', an arbitrarily high number

            dataframe_rep = dataframe.replace({'OVER':'3289277'})

            dataframe_rep[['fluorescence']] = dataframe_rep[['fluorescence']].astype('float')

            ### Create large_dataframe1, large_dataframe2, and large_dataframe3 that collect data for each section
            ### as we run through cycle through sections and files.

            globals()[""dataframe_pivot""+str(i)] = pd.pivot_table(dataframe_rep, index = 'wavelength (nm)', columns= ['Well'])

            print('The max fluorescence value in this dataframe is %s'% globals()[""dataframe_pivot""+str(i)].values.max())

            globals()[""large_dataframe""+str(i)] = pd.concat([globals()[""large_dataframe""+str(i)],globals()[""dataframe_pivot""+str(i)]])

            #print globals()[""large_dataframe""+str(i)]

    ### Plot, making a separate png for each section.

    for i, sect in enumerate(Sections):

        section_name = sect.attrib['Name']

        path = ""/*/Section[@Name='"" + sect.attrib['Name'] + ""']/Parameters""
        parameters = root.xpath(path)[0]

        if  parameters[0].attrib['Value'] == ""Absorbance"":
            section_ylim = [0,0.2]
        else:
            section_ylim = [0,50000]

        Alphabet = ['A','B','C','D','E','F','G','H']

        fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))
        for j,A in enumerate(Alphabet):
            for k in range(1,12):
                try:
                    x1,x2 = int(j/3)%3,j%3
                    globals()[""large_dataframe""+str(i)].fluorescence.get(A + str(k)).plot(ax=axes[x1,x2], title=A, c=cm.hsv(k*15), ylim=section_ylim, xlim=[300,600])
                except Exception as e:
                    print(""****No row %s.**** : exception: %s"" %(A, str(e)))

        fig.suptitle('%s \n %s \n Barcode = %s' % (globals()[""title""+str(i)], plate_type, barcode), fontsize=14)
        fig.subplots_adjust(hspace=0.3)
        plt.savefig('%s_%s.png' % (file_name, section_name))

    return

#############################################
 ###               SCAN                  ###
#############################################

def process_files_scan(xml_files):

    so_many = len(xml_files)
    print(""****This script is about to make png files for %s xml files. ****""  % so_many)

    for file in xml_files:

        # Parse XML file.
        root = etree.parse(file)

        # Remove extension from xml filename.
        file_name = os.path.splitext(file)[0]

        # Extract plate type and barcode.
        plate = root.xpath(""/*/Header/Parameters/Parameter[@Name='Plate']"")[0]
        plate_type = plate.attrib['Value']

        bar = root.xpath(""/*/Plate/BC"")[0]
        barcode = bar.text

        # Define Sections.
        Sections = root.xpath(""/*/Section"")
        much = len(Sections)
        print(""****The xml file "" + file + "" has %s data sections:****"" % much)
        for sect in Sections:
            print(sect.attrib['Name'])

        data = []

        for i, sect in enumerate(Sections):

            # Extract Parameters for this section.
            path = ""/*/Section[@Name='"" + sect.attrib['Name'] + ""']/Parameters""
            parameters = root.xpath(path)[0]

            # Parameters are extracted slightly differently depending on Absorbance or Fluorescence read.
            if  parameters[0].attrib['Value'] == ""Absorbance"":
                result = extract([""Mode"", ""Wavelength Start"", ""Wavelength End"", ""Wavelength Step Size""], parameters)
                title = '%s, %s, %s, %s' % tuple(result)

            else:
                result = extract([""Gain"", ""Excitation Wavelength"", ""Emission Wavelength"", ""Part of Plate"", ""Mode""], parameters)
                title = '%s, %s, %s, \n %s, %s' % tuple(result)

            print(""****The %sth section has the parameters:****"" %i)
            print(title)

            # Extract Reads for this section.
            Sections = root.xpath(""/*/Section"")

            reads = root.xpath(""/*/*/*/Well"")

            wellIDs = [read.attrib['Pos'] for read in reads]

            data = [(float(s.text), float(s.attrib['WL']), r.attrib['Pos'])
                    for r in reads
                    for s in r]

            dataframe = pd.DataFrame(data, columns=['fluorescence','wavelength (nm)','Well'])

            dataframe_pivot = pd.pivot_table(dataframe, index = 'wavelength (nm)', columns = ['Well'])

            # Make plot, complete with separate png for each section.
            section_name = sect.attrib['Name']

            fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))
            for i in range(1,12):
                dataframe_pivot.fluorescence.get('A' + str(i)).plot(ax=axes[0,0], title='A', c=cm.hsv(i*15))
            for i in range(1,12):
                dataframe_pivot.fluorescence.get('B' + str(i)).plot(ax=axes[0,1], title='B', c=cm.hsv(i*15))
            for i in range(1,12):
                dataframe_pivot.fluorescence.get('C' + str(i)).plot(ax=axes[0,2], title='C', c=cm.hsv(i*15))
            for i in range(1,12):
                dataframe_pivot.fluorescence.get('D' + str(i)).plot(ax=axes[1,0], title='D', c=cm.hsv(i*15))
            for i in range(1,12):
                dataframe_pivot.fluorescence.get('E' + str(i)).plot(ax=axes[1,1], title='E', c=cm.hsv(i*15))
            for i in range(1,12):
                dataframe_pivot.fluorescence.get('F' + str(i)).plot(ax=axes[1,2], title='F', c=cm.hsv(i*15))
            for i in range(1,12):
                dataframe_pivot.fluorescence.get('G' + str(i)).plot(ax=axes[2,0], title='G', c=cm.hsv(i*15))
            for i in range(1,12):
                dataframe_pivot.fluorescence.get('H' + str(i)).plot(ax=axes[2,1], title='H', c=cm.hsv(i*15))
            fig.suptitle('%s \n %s \n Barcode = %s' % (title, plate_type, barcode), fontsize=14)
            fig.subplots_adjust(hspace=0.3)
            plt.savefig('%s_%s.png' % (file_name, section_name))

    return

#############################################
 ###               singlet_384            ###
#############################################

def plot_singlet_one_section(data, section):

    #This assumes protein data is in row above buffer data

    fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(12, 12))

    axes = axes.ravel()

    ALPHABET = string.ascii_uppercase

    well = dict()
    for j in string.ascii_uppercase:
        for i in range(1,25):
            well['%s' %j + '%s' %i] = i

    for i in range(0,15,2):
        protein_row = ALPHABET[i]
        buffer_row = ALPHABET[i+1]

        try:
            part1_data_protein = platereader.select_data(data, section, protein_row)
            part1_data_buffer = platereader.select_data(data, section, buffer_row)

            reorder_protein = reorder2list(part1_data_protein,well)
            reorder_buffer = reorder2list(part1_data_buffer,well)
            
        except Exception as e:
            print('***no %s%s data*** : exception is %s' %(protein_row,buffer_row, str(e)) )
            continue    
        
        axes[int(i/2)].set_prop_cycle('color',['black','red'])

        if i/2 == 1:
            axes[int(i/2)].plot(reorder_protein,marker='o',linestyle='None',label='protein+ligand')
            axes[int(i/2)].plot(reorder_buffer,marker='o',linestyle='None',label='buffer+ligand')
            axes[int(i/2)].legend(frameon=True)

        axes[int(i/2)].plot(reorder_protein,marker='o',linestyle='None')
        axes[int(i/2)].plot(reorder_buffer,marker='o',linestyle='None')
        axes[int(i/2)].set_xticklabels(range(-4,25,5))
        axes[int(i/2)].set_xlabel('Column Index', horizontalalignment='right',position=(1,1),fontsize=8)
        axes[int(i/2)].set_ylabel('Fluorescence')
        axes[int(i/2)].set_title('%s,%s' %(protein_row,buffer_row))

    fig.suptitle('%s' %section)

def reorder2list(data,well):

    sorted_keys = sorted(well.keys(), key=lambda k:well[k])

    reorder_data = []

    for key in sorted_keys:
        try:
            reorder_data.append(data[key])
        except:
            pass

    reorder_data = [r.replace('OVER','70000') for r in reorder_data]

    reorder_data = np.asarray(reorder_data,np.float64)

    return reorder_data

def process_files_singlet(xml_files):

    so_many = len(xml_files)
    print(""****This script is about to make png files for %s xml files. ****""  % so_many)

    for file in xml_files:

        file_name = os.path.splitext(file)[0]

        data = platereader.read_icontrol_xml(file)

        print(""****The xml file "" + file + "" has %s data sections:****"" % len(data.keys()))
        print(data.keys())

        for key in data.keys():
            plot_singlet_one_section(data,key)

            #fig.tight_layout()
            #fig.suptitle(""%s_%s"" % (file_name,key), fontsize=18)

            plt.savefig('%s_%s.png' % (file_name,key))
            print('Look at how pretty your data is: %s_%s.png' % (file_name,key))
    return


def entry_point():
    xml_files = args.files
    if args.type == 'singlet_384':
        process_files_singlet(xml_files)
    if args.type == 'singlet_96':
        process_files_five(xml_files)
    if args.type == 'spectra':
        process_files_spectra(xml_files)
    if args.type == 'scan':
        process_files_scan(xml_files)

if __name__ == '__main__':
    xml_files = args.files
    if args.type == 'singlet_384':
        process_files_singlet(xml_files)
    if args.type == 'singlet_96':
        process_files_five(xml_files)
    if args.type == 'spectra':
        process_files_spectra(xml_files)
    if args.type == 'scan':
        process_files_scan(xml_files)
","Python"
"Assay","choderalab/assaytools","scripts/quickmodel.py",".py","13003","281","# A somewhat ugly, utilitarian script takes xml data file output from the Tecan Infinite m1000 Pro
# plate reader and allows for the quick visual inspection of raw data.
#
#    Usage:
#        python quickmodel.py --inputs 'inputs_example'
#        or
#        python quickmodel.py --inputs 'inputs_spectra_example' --type 'spectra'


from assaytools import parser
import matplotlib.pyplot as plt
import string
from glob import glob
import os
import json
import numpy as np
import traceback

import seaborn as sns
import pymbar


def quick_model(inputs, nsamples=1000, nthin=20):
    """"""
    Quick model for both spectra and single wavelength experiments

    Parameters
    ----------
    inputs : dict
        Dictionary of input information
    nsamples : int, optional, default=1000
        Number of MCMC samples to collect
    nthin : int, optional, default=20
        Thinning interval ; number of MCMC steps per sample collected
    """"""


    [complex_fluorescence, ligand_fluorescence] = parser.get_data_using_inputs(inputs)  

    for name in complex_fluorescence.keys():

            print(name)

            metadata = {}
            metadata = dict(inputs)

            #these need to be changed so they are TAKEN FROM INPUTS!!!

            # Uncertainties in protein and ligand concentrations.
            try:
                 dPstated = inputs['P_error'] * inputs['Pstated'] # protein concentration uncertainty               
            except:
                 dPstated = 0.35 * inputs['Pstated'] # protein concentration uncertainty
            
            try:
                 dLstated = inputs['L_error'] * inputs['Lstated']
            except:
                 dLstated = 0.08 * inputs['Lstated'] # ligand concentraiton uncertainty (due to gravimetric preparation and HP D300 dispensing)

            from assaytools import pymcmodels
            pymc_model = pymcmodels.make_model(inputs['Pstated'], dPstated, inputs['Lstated'], dLstated,
               top_complex_fluorescence=complex_fluorescence[name],
               top_ligand_fluorescence=ligand_fluorescence[name],
               use_primary_inner_filter_correction=True,
               use_secondary_inner_filter_correction=True,
               assay_volume=inputs['assay_volume'], DG_prior='uniform')

            import datetime
            my_datetime = datetime.datetime.now().strftime(""%Y-%m-%d %H:%M"")
            my_datetime_filename = datetime.datetime.now().strftime(""%Y-%m-%d %H%M"")

            nburn = 0 # no longer need burn-in since we do automated equilibration detection
            niter = nthin*nsamples # former total simulation time
            # Note that nsamples and niter are not the same: Here nsamples is 
            # multiplied by nthin (default 20) so that your final mcmc samples will be the same 
            # as your nsamples, but the actual niter will be 20x that!
            mcmc = pymcmodels.run_mcmc(pymc_model,
                nthin=nthin, nburn=nburn, niter=niter,
                db = 'pickle', dbname = '%s_mcmc-%s.pickle'%(name,my_datetime))

            map = pymcmodels.map_fit(pymc_model)

            # Save the trace for easy plotting later

            DeltaG_map = map.DeltaG.value
            DeltaG = mcmc.trace('DeltaG')[:].mean()
            dDeltaG = mcmc.trace('DeltaG')[:].std()

            ## DEFINE EQUILIBRATION
            #Calculate a mean and std from DeltaG trace after equil

            [t,g,Neff_max] = pymbar.timeseries.detectEquilibration(mcmc.trace('DeltaG')[:])
            DeltaG_equil = mcmc.trace('DeltaG')[t:].mean()
            dDeltaG_equil = mcmc.trace('DeltaG')[t:].std()

            #This is so plotting works on the cluster
            plt.switch_backend('agg')

            ## PLOT MODEL
            #from assaytools import plots
            #figure = plots.plot_measurements(Lstated, Pstated, pymc_model, mcmc=mcmc)
            #Code below inspired by import above, but didn't quite work to import it...

            plt.clf()
            plt.figure(figsize=(8,8))

            plt.subplot(311)
            property_name = 'top_complex_fluorescence'
            complex = getattr(pymc_model, property_name)
            property_name = 'top_ligand_fluorescence'
            ligand = getattr(pymc_model, property_name)
            for top_complex_fluorescence_model in mcmc.trace('top_complex_fluorescence_model')[::10]:
                plt.semilogx(inputs['Lstated'], top_complex_fluorescence_model, marker='.',color='silver')
            for top_ligand_fluorescence_model in mcmc.trace('top_ligand_fluorescence_model')[::10]:
                plt.semilogx(inputs['Lstated'], top_ligand_fluorescence_model, marker='.',color='lightcoral', alpha=0.2)
            plt.semilogx(inputs['Lstated'], complex.value, 'ko',label='complex')
            plt.semilogx(inputs['Lstated'], ligand.value, marker='o',color='firebrick',linestyle='None',label='ligand')

            plt.xlabel('$[L]_T$ (M)');
            plt.ylabel('fluorescence units');
            plt.legend(loc=0);

            ## PLOT HISTOGRAM
            import matplotlib.patches as mpatches
            import matplotlib.lines as mlines

            interval = np.percentile(a=mcmc.trace('DeltaG')[t:], q=[2.5, 50.0, 97.5])
            [hist,bin_edges] = np.histogram(mcmc.trace('DeltaG')[t:],bins=40,normed=True)
            binwidth = np.abs(bin_edges[0]-bin_edges[1])

            #Print summary
            print( 'Delta G (95% credibility interval after equilibration):')
            print( '   %.3g [%.3g,%.3g] k_B T' %(interval[1],interval[0],interval[2]))
            print( 'Delta G (mean and std after equil):')
            print('   %.3g +- %.3g k_B T' %(DeltaG_equil,dDeltaG_equil) )

            #set colors for 95% interval
            clrs = [(0.7372549019607844, 0.5098039215686274, 0.7411764705882353) for xx in bin_edges]
            idxs = bin_edges.argsort()
            idxs = idxs[::-1]
            gray_before = idxs[bin_edges[idxs] < interval[0]]
            gray_after = idxs[bin_edges[idxs] > interval[2]]
            for idx in gray_before:
                clrs[idx] = (.5,.5,.5)
            for idx in gray_after:
                clrs[idx] = (.5,.5,.5)

            plt.subplot(312)

            plt.bar(bin_edges[:-1],hist,binwidth,color=clrs, edgecolor = ""white"");
            sns.kdeplot(mcmc.trace('DeltaG')[t:],bw=.4,color=(0.39215686274509803, 0.7098039215686275, 0.803921568627451),shade=False)
            plt.axvline(x=interval[0],color=(0.5,0.5,0.5),linestyle='--')
            plt.axvline(x=interval[1],color=(0.5,0.5,0.5),linestyle='--')
            plt.axvline(x=interval[2],color=(0.5,0.5,0.5),linestyle='--')
            plt.axvline(x=DeltaG_map,color='black')
            plt.xlabel('$\Delta G$ ($k_B T$)',fontsize=16);
            plt.ylabel('$P(\Delta G)$',fontsize=16);
            plt.xlim(-20,-8)
            hist_legend = mpatches.Patch(color=(0.7372549019607844, 0.5098039215686274, 0.7411764705882353),
                        label = '$\Delta G$ =  %.3g [%.3g,%.3g] $k_B T$'
                        %(interval[1],interval[0],interval[2]) )
            map_legend = mlines.Line2D([],[],color='black',label=""MAP = %.1f $k_B T$""%DeltaG_map)
            plt.legend(handles=[hist_legend,map_legend],fontsize=14,loc=0,frameon=True)

            ## PLOT TRACE
            plt.subplot(313)
            plt.plot(range(0,t),mcmc.trace('DeltaG')[:t], 'go',label='equil. at %s'%t);
            plt.plot(range(t,len(mcmc.trace('DeltaG')[:])),mcmc.trace('DeltaG')[t:], 'o');
            plt.xlabel('MCMC sample');
            plt.ylabel('$\Delta G$ ($k_B T$)');
            plt.legend(loc=2);

            plt.suptitle(""%s: %s"" % (name, my_datetime))
            plt.tight_layout()

            fig1 = plt.gcf()
            fig1.savefig('delG_%s-%s.png'%(name, my_datetime_filename))

            plt.close('all') # close all figures

            Kd = np.exp(DeltaG_equil)
            dKd = np.exp(mcmc.trace('DeltaG')[t:]).std()
            Kd_interval = np.exp(interval)

            if (Kd < 1e-12):
                Kd_summary_interval = '%.1f [%.1f,%.1f] fM' %(Kd_interval[1]/1e-15,Kd_interval[0]/1e-15,Kd_interval[2]/1e-15)
                Kd_summary = ""%.1f fM +- %.1f fM"" % (Kd/1e-15, dKd/1e-15)
            elif (Kd < 1e-9):
                Kd_summary_interval = '%.1f [%.1f,%.1f] pM' %(Kd_interval[1]/1e-12,Kd_interval[0]/1e-12,Kd_interval[2]/1e-12)
                Kd_summary = ""%.1f pM +- %.1f pM"" % (Kd/1e-12, dKd/1e-12)
            elif (Kd < 1e-6):
                Kd_summary_interval = '%.1f [%.1f,%.1f] nM' %(Kd_interval[1]/1e-9,Kd_interval[0]/1e-9,Kd_interval[2]/1e-9)
                Kd_summary = ""%.1f nM +- %.1f nM"" % (Kd/1e-9, dKd/1e-9)
            elif (Kd < 1e-3):
                Kd_summary_interval = '%.1f [%.1f,%.1f] uM' %(Kd_interval[1]/1e-6,Kd_interval[0]/1e-6,Kd_interval[2]/1e-6)
                Kd_summary = ""%.1f uM +- %.1f uM"" % (Kd/1e-6, dKd/1e-6)
            elif (Kd < 1):
                Kd_summary_interval = '%.1f [%.1f,%.1f] mM' %(Kd_interval[1]/1e-3,Kd_interval[0]/1e-3,Kd_interval[2]/1e-3)
                Kd_summary = ""%.1f mM +- %.1f mM"" % (Kd/1e-3, dKd/1e-3)
            else:
                Kd_summary_interval = '%.3e [%.3e,%.3e] M' %(Kd_interval[1],Kd_interval[0],Kd_interval[2])
                Kd_summary = ""%.3e M +- %.3e M"" % (Kd, dKd)

            print('Kd (95% credibility interval after equilibration):')
            print('   %s' %Kd_summary_interval)
            print('Kd (mean and std after equil):')
            print('   %s' %Kd_summary)

            outputs = {
                #'raw_data_file'   : my_file,
                'complex_fluorescence' : complex_fluorescence[name],
                'ligand_fluorescence'  : ligand_fluorescence[name],
                't_equil'              : t,
                'name'                 : name,
                'analysis'             : 'pymcmodels', #right now this is hardcoded, BOOO
                'outfiles'             : '%s_mcmc-%s.pickle, delG_%s-%s.pdf,DeltaG_%s-%s.npy,DeltaG_trace_%s-%s.npy'%(name,my_datetime,name,my_datetime,name,my_datetime,name,my_datetime),
                'DeltaG_cred_int'      : '$\Delta G$ =  %.3g [%.3g,%.3g] $k_B T$'  %(interval[1],interval[0],interval[2]),
                'DeltaG'               : ""DeltaG = %.1f +- %.1f kT, MAP estimate = %.1f"" % (DeltaG, dDeltaG, DeltaG_map),
                'Kd'                   : Kd_summary,
                'bin_edges'            : bin_edges,
                'hist'                 : hist,
                'binwidth'             : binwidth,
                'clrs'                 : clrs,
                'datetime'             : my_datetime
            }

            metadata.update(outputs)

            metadata['ligand_fluorescence'] = metadata['ligand_fluorescence'].tolist()
            metadata['complex_fluorescence'] = metadata['complex_fluorescence'].tolist()
            metadata['t_equil'] = metadata['t_equil'].tolist()
            metadata['bin_edges'] = metadata['bin_edges'].tolist()
            metadata['hist'] = metadata['hist'].tolist()
            metadata['Pstated'] = metadata['Pstated'].tolist()
            metadata['Lstated'] = metadata['Lstated'].tolist()

            import json
            with open('%s-%s.json'%(name,my_datetime), 'w') as outfile:
                json.dump(metadata, outfile, sort_keys = True, indent = 4, ensure_ascii=False)

def entry_point():

    import sys

    #This allows us to import local inputs.py
    sys.path.append('./')

    import argparse

    # Define argparse stuff

    parser = argparse.ArgumentParser(description=""""""Analyze your fluorescence binding data by running assaytools on your xml files:
    > python quickmodel.py --inputs 'inputs_example' """""")
    parser.add_argument(""--inputs"", help=""inputs file (python script, .py not needed)"",default=None)
    parser.add_argument(""--type"", help=""type of data (spectra, singlet)"",choices=['spectra','singlet'],default='singlet')
    parser.add_argument(""--nsamples"", help=""number of samples"",default=1000, type=int)
    parser.add_argument(""--nthin"", help=""thinning interval"",default=20, type=int)
    args = parser.parse_args()

    # Define inputs
    # If an inputs###.py is defined, it is run and used. An inputs.json is also created.
    if args.inputs!=None:
        inputs_file = args.inputs
        name = 'inputs'
        #this is the equivalent of from inputs_file import name as inputs
        inputs = getattr(__import__(inputs_file, fromlist=[name]), name)
    if args.inputs==None:
        with open('inputs.json', 'r') as fp:
            inputs = json.load(fp)

    inputs['Lstated'] = np.asarray(inputs['Lstated'])
    inputs['Pstated'] = np.asarray(inputs['Pstated'])

    if args.type == 'singlet':
        quick_model(inputs, nsamples=args.nsamples, nthin=args.nthin)
    if args.type == 'spectra':
        quick_model(inputs, nsamples=args.nsamples, nthin=args.nthin)

if __name__ == '__main__':
    entry_point()
","Python"
"Assay","choderalab/assaytools","scripts/trace_plots.py",".py","1843","54","import matplotlib
matplotlib.use('Agg')

# NOTE right now this does not work for py3 because pickle files 

import cPickle
import matplotlib.pyplot as plt
import numpy as np

data_file = 'p38-Erlotinib-EF_mcmc-2017-05-05 13:16.pickle'
###ABOVE INPUT PICKLE FILE NAME NEEDS MANUAL EDITTING

with open(r'%s'%data_file,'rb') as my_file:
    data = cPickle.load(my_file)

import traceback
import matplotlib
colors = matplotlib.cm.viridis(np.linspace(0, 0.85, len(data.keys())))

fig, axs = plt.subplots(len(data.keys())/2,2, figsize=(20, 80), facecolor='w', edgecolor='k')
axs = axs.ravel()

for i, key in enumerate(data.keys()):
    try:
        axs[i].plot(data['DeltaG'][0],data[key][0],color=colors[i],marker='.',linestyle = 'None');
        axs[i].set_title('%s'%key,fontsize=16);
        plt.tight_layout()
    except Exception as e:
        # There is often an error here for _state_, which it can't plot.
        # It is fine, just uncomment the line below if you want to see it.
        #print(traceback.print_exc())
        print('%s has no [0]'%key)

    plt.savefig('trace_p38-Erlotinib-EF_May5.png')
    ###ABOVE OUTPUT PNG NAME NEEDS MANUAL EDITTING

plt.clf()
fig, axs = plt.subplots(len(data.keys())/2,2, figsize=(20, 80), facecolor='w', edgecolor='k')
axs = axs.ravel()
for i, key in enumerate(data.keys()):
    try:
        axs[i].plot(data[key][0],color=colors[i],marker='.',linestyle = 'None');
        axs[i].set_title('%s'%key,fontsize=16);
        plt.tight_layout()
    except Exception as e:
        # There is often an error here for _state_, which it can't plot.
        # It is fine, just uncomment the line below if you want to see it.
        #print(traceback.print_exc())
        print('%s has no [0]'%key)

    plt.savefig('DeltaGvall_p38-Erlotinib-EF_May5.png')
    ###ABOVE OUTPUT PNG NAME NEEDS MANUAL EDITTING

","Python"
"Assay","SouthGreenPlatform/TOGGLE","test/allTestModules.sh",".sh","2309","56","#!/bin/sh

###################################################################################################################################
#
# Copyright 2014-2018 IRD-CIRAD-INRA-ADNid
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, see <http://www.gnu.org/licenses/> or
# write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston,
# MA 02110-1301, USA.
#
# You should have received a copy of the CeCILL-C license with this program.
#If not see <http://www.cecill.info/licences/Licence_CeCILL-C_V1-en.txt>
#
# Intellectual property belongs to IRD, CIRAD and South Green developpement plateform for all versions also for ADNid for v2 and v3 and INRA for v3
# Version 1 written by Cecile Monat, Ayite Kougbeadjo, Christine Tranchant, Cedric Farcy, Mawusse Agbessi, Maryline Summo, and Francois Sabot
# Version 2 written by Cecile Monat, Christine Tranchant, Cedric Farcy, Enrique Ortega-Abboud, Julie Orjuela-Bouniol, Sebastien Ravel, Souhila Amanzougarene, and Francois Sabot
# Version 3 written by Cecile Monat, Christine Tranchant, Laura Helou, Abdoulaye Diallo, Julie Orjuela-Bouniol, Sebastien Ravel, Gautier Sarah, and Francois Sabot
#
###################################################################################################################################

cd modules

################################ TEST MODULE
for file in *.t;
do
	cmd=""prove -v $file"";
	echo ""
#########################################################
###########   $cmd   #################
#########################################################"";

	$cmd;


	echo "" 
######################################################### 
###########   $cmd DONE  ##############  
#########################################################"";
done




","Shell"
"Assay","SouthGreenPlatform/TOGGLE","test/downloadTest.sh",".sh","2483","61","#!/bin/sh

###################################################################################################################################
#
# Copyright 2014-2019 IRD-CIRAD-INRA-ADNid
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, see <http://www.gnu.org/licenses/> or
# write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston,
# MA 02110-1301, USA.
#
# You should have received a copy of the CeCILL-C license with this program.
#If not see <http://www.cecill.info/licences/Licence_CeCILL-C_V1-en.txt>
#
# Intellectual property belongs to IRD, CIRAD and South Green developpement plateform for all versions also for ADNid for v2 and v3 and INRA for v3
# Version 1 written by Cecile Monat, Ayite Kougbeadjo, Christine Tranchant, Cedric Farcy, Mawusse Agbessi, Maryline Summo, and Francois Sabot
# Version 2 written by Cecile Monat, Christine Tranchant, Cedric Farcy, Enrique Ortega-Abboud, Julie Orjuela-Bouniol, Sebastien Ravel, Souhila Amanzougarene, and Francois Sabot
# Version 3 written by Cecile Monat, Christine Tranchant, Laura Helou, Abdoulaye Diallo, Julie Orjuela-Bouniol, Sebastien Ravel, Gautier Sarah, and Francois Sabot
#
###################################################################################################################################


echo """"""
#########################################################
###########     Download test datas     #################
#########################################################"""""";

wget http://toggle.ird.fr/files/data.zip && unzip data.zip && rm -Rf data.zip

if [ -d ../dataTest ]; then
	rm -r ../dataTest
	mkdir ../dataTest
else
	mkdir ../dataTest
fi

if [ -d ../data ]; then
	rm -r ../data
	mv data ../
else
	mv data ../
fi



echo """"""
######################################################### 
###########              DONE              ##############  
#########################################################"""""";
","Shell"
"Assay","KwangSun-Ryu/ADMET-AGI-Toxicity-Converter--","tox_prediction.py",".py","10273","238","# ============================================
# 안정화 버전: LightGBM / GBDT(sklearn) / CatBoost
# - 모델별 SelectFromModel(importance median)로 서로 다른 피처셋
# - 100회 반복 AUC 전부 기록(누락 방지)
# - 반복별 Feature Importance 저장
# - (옵션) Top-10 피처 빈도(%) 히트맵
# ============================================
import os, glob, numpy as np, pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import roc_auc_score
from sklearn.impute import SimpleImputer
from sklearn.feature_selection import SelectFromModel
from sklearn.ensemble import GradientBoostingClassifier
import lightgbm as lgb
from catboost import CatBoostClassifier, Pool

# (옵션) 시각화
import matplotlib.pyplot as plt
import seaborn as sns

# -----------------------------
# 0) 경로 & 출력 폴더
# -----------------------------
DATA_PATH = r""C:\Users\nicep\project\Canonicalized_Tox21_with_rdkit_descriptors.csv""
OUT_MODELS, OUT_RESULTS = ""models"", ""results""
os.makedirs(OUT_MODELS, exist_ok=True); os.makedirs(OUT_RESULTS, exist_ok=True)

# -----------------------------
# 1) 데이터 로드 & 피처/타깃 분리
# -----------------------------
df = pd.read_csv(DATA_PATH)

drop_cols = [""ASSAY_NAME"",""LABEL"",""SMILES"",""Can_SMILES""]
X = df.drop(columns=[c for c in drop_cols if c in df.columns], errors=""ignore"")
X = X.apply(pd.to_numeric, errors=""coerce"").replace([np.inf, -np.inf], np.nan)
X = X.dropna(axis=1, how=""all"")
y = pd.to_numeric(df[""LABEL""], errors=""coerce"")

# LABEL 결측 제거
if y.isna().any():
    valid_idx = ~y.isna()
    X = X.loc[valid_idx]
    y = y.loc[valid_idx]

# (안정화 A) 극단값/inf 처리 + winsorize(1~99%) → 폭주/오버플로 방지
X = X.mask(np.abs(X) > 1e10)  # 너무 큰 절대값은 NaN
low = X.quantile(0.01, numeric_only=True)
high = X.quantile(0.99, numeric_only=True)
X = X.clip(lower=low, upper=high, axis=1)

# -----------------------------
# 2) 모델 팩토리
# -----------------------------
def get_model(name, seed):
    if name == ""LightGBM"":
        return lgb.LGBMClassifier(
            n_estimators=300, learning_rate=0.05,
            subsample=0.8, colsample_bytree=0.8,
            max_depth=-1, random_state=seed, n_jobs=-1
        )
    if name == ""GBDT"":  # sklearn GradientBoosting
        return GradientBoostingClassifier(
            n_estimators=300, learning_rate=0.05,
            max_depth=3, subsample=0.8,  # stochastic GBDT로 일반화/안정성↑
            random_state=seed
        )
    if name == ""CatBoost"":
        return CatBoostClassifier(
            iterations=300, depth=6, learning_rate=0.05,
            loss_function=""Logloss"", verbose=False, random_state=seed
        )
    raise ValueError(name)

models = [""LightGBM"", ""GBDT"", ""CatBoost""]

# -----------------------------
# 3) 100회 반복 - 모델별 SelectFromModel + 학습/평가 + 저장
# -----------------------------
auc_records = []
imputer_strategy = ""median""

for run in range(100):
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, stratify=y, test_size=0.3, random_state=run
    )

    imputer = SimpleImputer(strategy=imputer_strategy)
    X_train = pd.DataFrame(imputer.fit_transform(X_train), columns=X.columns, index=X_train.index)
    X_test  = pd.DataFrame(imputer.transform(X_test), columns=X.columns, index=X_test.index)

    X_train = X_train.replace([np.inf, -np.inf], np.nan)
    X_test  = X_test.replace([np.inf, -np.inf], np.nan)
    if X_train.isna().any().any():
        X_train = pd.DataFrame(SimpleImputer(strategy=""median"").fit_transform(X_train),
                               columns=X_train.columns, index=X_train.index)
    if X_test.isna().any().any():
        X_test = pd.DataFrame(SimpleImputer(strategy=""median"").fit_transform(X_test),
                              columns=X_test.columns, index=X_test.index)

    for name in models:
        try:
            selector_est = get_model(name, seed=run)
            train_est = get_model(name, seed=run)

            selector_est.fit(X_train, y_train)

            if hasattr(selector_est, ""feature_importances_""):
                base_importance = selector_est.feature_importances_
            elif hasattr(selector_est, ""coef_""):
                base_importance = np.abs(selector_est.coef_[0])
            else:
                base_importance = np.ones(X_train.shape[1])

            selector = SelectFromModel(selector_est, threshold=""median"", prefit=True)
            X_train_sel = selector.transform(X_train)
            X_test_sel  = selector.transform(X_test)
            mask = selector.get_support()
            selected_features = X.columns[mask]

            X_train_sel = pd.DataFrame(X_train_sel, columns=selected_features, index=X_train.index)
            X_test_sel  = pd.DataFrame(X_test_sel,  columns=selected_features, index=X_test.index)

            if X_train_sel.shape[1] == 0:
                bi = np.asarray(base_importance, dtype=float)
                if np.all((~np.isfinite(bi)) | (bi == 0)):
                    # 모두 0/NaN이면 표준편차 최대 컬럼 선택
                    stds = X_train.to_numpy().std(axis=0)
                    top_idx = int(np.nanargmax(stds))
                else:
                    top_idx = int(np.nanargmax(bi))
                selected_features = X.columns[[top_idx]]
                X_train_sel = pd.DataFrame(X_train.iloc[:, [top_idx]].to_numpy(),
                                           columns=selected_features, index=X_train.index)
                X_test_sel  = pd.DataFrame(X_test.iloc[:, [top_idx]].to_numpy(),
                                           columns=selected_features, index=X_test.index)

     
            train_est.fit(X_train_sel, y_train)

  
            if hasattr(train_est, ""predict_proba""):
                proba = train_est.predict_proba(X_test_sel)
                if proba.ndim == 2 and proba.shape[1] >= 2:
                    y_prob = proba[:, 1]
                else:
                    y_prob = np.zeros(len(y_test), dtype=float)
            elif hasattr(train_est, ""decision_function""):
                scores = train_est.decision_function(X_test_sel)
                y_prob = (scores - scores.min()) / (scores.max() - scores.min() + 1e-12)
            else:
                y_prob = train_est.predict(X_test_sel).astype(float)

            auc = np.nan if len(np.unique(y_test)) < 2 else roc_auc_score(y_test, y_prob)

            auc_records.append({
                ""run"": run, ""model"": name, ""auc"": auc, ""n_features"": len(selected_features)
            })

            if name == ""CatBoost"":
                pool = Pool(X_train_sel, y_train, feature_names=list(selected_features))
                fi_vals = train_est.get_feature_importance(pool)
            elif hasattr(train_est, ""feature_importances_""):
                fi_vals = train_est.feature_importances_
            elif hasattr(train_est, ""coef_""):
                fi_vals = np.abs(train_est.coef_[0])
            else:
                fi_vals = np.ones(len(selected_features))

            fi_df = pd.DataFrame({
                ""run"": run, ""model"": name,
                ""feature"": selected_features, ""importance"": fi_vals
            }).sort_values(""importance"", ascending=False)
            fi_df.to_csv(os.path.join(OUT_RESULTS, f""feature_importance_{name}_run{run}.csv""),
                         index=False)

            print(f""[run {run:03d}] {name}: AUC={auc if pd.notna(auc) else np.nan:.4f} | ""
                  f""n_feat={len(selected_features)} | FI saved."")

        except Exception as e:
            auc_records.append({""run"": run, ""model"": name, ""auc"": np.nan, ""n_features"": 0})
            print(f""[run {run:03d}] {name}: ERROR -> {e}"")

# -----------------------------
# 4) AUC 저장 + 요약 통계
# -----------------------------
auc_df = pd.DataFrame(auc_records)
auc_df.to_csv(os.path.join(OUT_RESULTS, ""auc_by_run.csv""), index=False)

summary = (auc_df.groupby(""model"")[""auc""]
           .agg(auc_mean=lambda s: np.nanmean(s),
                auc_std=lambda s: np.nanstd(s),
                auc_min=lambda s: np.nanmin(s) if (~s.isna()).any() else np.nan,
                auc_median=lambda s: np.nanmedian(s),
                auc_max=lambda s: np.nanmax(s) if (~s.isna()).any() else np.nan)
           .reset_index())
summary.to_csv(os.path.join(OUT_RESULTS, ""auc_summary.csv""), index=False)
print(""\n=== AUC Summary (100 runs) ==="")
print(summary.round(4))

# -----------------------------
# 5) (옵션) 모델별 Top-10 피처 '빈도(%)' 히트맵
# -----------------------------
fi_files = []
for name in models:
    fi_files += glob.glob(os.path.join(OUT_RESULTS, f""feature_importance_{name}_run*.csv""))

if fi_files:
    dfs = []
    for fp in fi_files:
        df_fi = pd.read_csv(fp)  # run, model, feature, importance
        df_fi[""rank""] = df_fi.groupby([""run"",""model""])[""importance""]\
                             .rank(ascending=False, method=""first"")
        dfs.append(df_fi)
    fi_all = pd.concat(dfs, ignore_index=True)

    topk = fi_all[fi_all[""rank""] <= 10].copy()
    freq = topk.groupby([""feature"",""model""]).size().unstack(fill_value=0)
    freq = freq.reindex(columns=models, fill_value=0)

    iters = auc_df.groupby(""model"")[""run""].nunique().reindex(models).fillna(0)
    pct = freq.div(iters.replace(0, np.nan), axis=1) * 100
    pct = pct.fillna(0).round(1)
    pct = pct.loc[pct.mean(1).sort_values(ascending=False).index]

    plt.figure(figsize=(12, max(8, 0.35*len(pct))))
    ax = sns.heatmap(pct, cmap='Blues', vmin=0, vmax=100,
                     linewidths=.4, linecolor='black', cbar=True)
    cbar = ax.collections[0].colorbar
    cbar.set_ticks([0, 20, 40, 60, 80, 100])

    ax.set_title(""Top-10 Feature Frequency (%) by Model (100 runs)"", pad=12)
    ax.set_xlabel(""Model""); ax.set_ylabel(""Feature"")
    ax.set_xticklabels(models, rotation=0)
    ax.set_yticklabels(ax.get_yticklabels(), rotation=0)
    plt.tight_layout(); plt.show()
else:
    print(""no FI"")
","Python"
"Assay","KwangSun-Ryu/ADMET-AGI-Toxicity-Converter--","auto_commit.py",".py","2720","90","import subprocess
import os
import time
import random


# 항상 이 경로에서 실행 (WSL)
BASE_DIR = ""/mnt/e/Google Drive/External SSD/HealthCare/ADMET/코드/Repository/Development/ADMET-AGI-Toxicity-Converter--""

# commit 메시지 파일(권장: BASE_DIR 기준 절대경로)
TEXT_PATH = os.path.join(BASE_DIR, ""commit_messages.txt"")

TOTAL_SECONDS = 5 * 60 * 60 # 12 hours
N = 200

REMOTE = ""origin""
BRANCH = ""main""

def read_messages(file_path):
    with open(file_path, ""r"", encoding=""utf-8"") as f:
        lines = [line.strip() for line in f.readlines()]
    # 빈 줄 제거 (빈 메시지 커밋 방지)
    lines = [m for m in lines if m]
    return lines

def run_cmd(args):
    # args: [""git"", ""status""] 처럼 리스트로 전달 (따옴표/특수문자 안전)
    return subprocess.run(
        args,
        cwd=BASE_DIR,
        capture_output=True,
        text=True
    )

def git_commit_allow_empty(message):
    return run_cmd([""git"", ""commit"", ""--allow-empty"", ""-m"", message])

def git_push(remote, branch):
    return run_cmd([""git"", ""push"", remote, branch])

def generate_offsets():
    return sorted(random.uniform(0, TOTAL_SECONDS) for _ in range(N))

def ensure_repo_ready():
    result = run_cmd([""git"", ""rev-parse"", ""--show-toplevel""])
    if result.returncode != 0:
        raise RuntimeError(f""git repo 확인 실패:\n{result.stderr}"")

def run():
    start = time.monotonic()
    offsets = generate_offsets()

    messages = read_messages(TEXT_PATH)
    if len(messages) < N:
        raise ValueError(f""commit_messages.txt 메시지가 {len(messages)}개입니다. 최소 {N}개가 필요합니다."")

    ensure_repo_ready()

    for idx, off in enumerate(offsets, 1):
        target = start + off
        sleep_sec = target - time.monotonic()
        if sleep_sec > 0:
            time.sleep(sleep_sec)

        message = messages[idx - 1]
        result = git_commit_allow_empty(message)

        if result.returncode != 0:
            print(f""[{idx}/{N}] COMMIT FAILED (code={result.returncode})"", flush=True)
            if result.stderr:
                print(result.stderr, flush=True)
        else:
            print(f""[{idx}/{N}] COMMIT OK"", flush=True)
            if result.stdout:
                print(result.stdout, flush=True)

    # 마지막에 한 번만 push
    push_result = git_push(REMOTE, BRANCH)
    if push_result.returncode != 0:
        print(f""[PUSH] FAILED (code={push_result.returncode})"", flush=True)
        if push_result.stderr:
            print(push_result.stderr, flush=True)
    else:
        print(""[PUSH] OK"", flush=True)
        if push_result.stdout:
            print(push_result.stdout, flush=True)

if __name__ == ""__main__"":
    run()
","Python"
"Assay","fmicompbio/mutscan","NEWS.md",".md","2981","106","# mutscan 0.99.1

* Fix non-ASCII characters in CITATION file

# mutscan 0.99.0

* Prepare for Bioconductor submission
* Remove deprecated arguments variableCollapseMaxDist, variableCollapseMinReads and variableCollapseMinRatio from digestFastqs (deprecated in mutscan 0.3.0)

# mutscan 0.3.4

* Allow use of scattermore/scattermost in plotPairs

# mutscan 0.3.3

* Bugfix in mergeReadPairPartial for situation where specified minMergedLength/maxMergedLength is larger than the total length of the two merged sequences

# mutscan 0.3.1

* Adapt summarizeExperiment to include errorStatistics in metadata(se)
* Modify plotPairs to ignore NAs rather than give an error when calculating correlations

# mutscan 0.3.0

* Collapsing of variable sequences is no longer supported by digestFastqs. Use collapseMutantsBySimilarity() instead

# mutscan 0.2.37

* accommodate changes in edgeR version 3.42 (Bioconductor 3.17)
* mutscan manuscript published and available at https://doi.org/10.1186/s13059-023-02967-0

# mutscan 0.2.36

* Check that reads don't exceed the maximal allowed length
* Add parameter to specify maximal read length

# mutscan 0.2.35

* Add alternative names for variants (including HGVS identifiers)

# mutscan 0.2.34

* Expand examples in function documentation

# mutscan 0.2.33

* Replace Matrix.utils::aggregate.Matrix (removed from CRAN) by DelayedArray::rowsum

# mutscan 0.2.32

* Expand FASTQ file paths automatically
* Filter out reads where the variable region is longer than the best matching WT sequence

# mutscan 0.2.31

* Add option to include identity line in pairs plots
* Select points to label in result plots based on nominal p-value
* Add option to manually set the correlation range for coloring pairs plots

# mutscan 0.2.30

* Swap to MIT license

# mutscan 0.2.29

* Include degrees of freedom in output from calculateRelativeFC

# mutscan 0.2.28

* Rename calculatePPIScore to calculateFitnessScore
* Add amino acid-related information (AA sequence, mutant name, number of mutated amino acid) to digestFastqs output, propagate through summarizeExperiment and collapseMutantsByAA. 

# mutscan 0.2.27

* Add linkMultipleVariants function

# mutscan 0.2.26

* Use arithmetic instead of geometric mean for PPI calculations

# mutscan 0.2.25

* Only add names to WT sequences if they are unnamed

# mutscan 0.2.24

* Added function to calculate nearest string distances

# mutscan 0.2.23

* Changed default value of forbidden codons to """"
* Make result plots more flexible
* Propagate information about the number of mutated bases/codons in `summarizeExperiment()`

# mutscan 0.2.22

* Added argument to specify the title of the QC report.
* Internal refactoring of argument checking.

# mutscan 0.2.21

* Added a `NEWS.md` file to track changes to the package.
* Added the `generateQCReport()` function.
* Added functions to plot results from `calculateRelativeFC()`.
* Added functions to plot distributions of counts as well as total counts.
","Markdown"
"Assay","fmicompbio/mutscan","LICENSE.md",".md","1111","22","# MIT License

Copyright (c) 2022 Friedrich Miescher Institute for Biomedical Research

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:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED ""AS IS"", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
","Markdown"
"Assay","fmicompbio/mutscan","inst/scripts/generateValuesForUnitTestsTrans.R",".R","39358","864","suppressPackageStartupMessages({
  library(Biostrings)
  library(ShortRead)
  library(dplyr)
})

collapse_seqs <- function(seqs, umis, reads, maxdist, umimaxdist, minreads, minratio) {
  tokeep <- list()
  while (length(seqs) > 0) {
    if (reads[1] >= minreads) {
      d <- stringdist::stringdist(seqs[1], seqs, method = ""hamming"")
      w <- union(1, which(d <= maxdist & reads[1] >= minratio * reads))
    } else {
      w <- 1
    }
    tokeep[[seqs[1]]] <- paste(umis[w], collapse = "","")
    seqs <- seqs[-w]
    umis <- umis[-w]
    reads <- reads[-w]
  }
  tokeep <- lapply(tokeep, function(tk) {
    tk <- sort(unique(strsplit(tk, "","")[[1]]))
    if (length(tk) == 1) {
      tk
    } else {
      k <- c()
      while (length(tk) > 0) {
        d <- stringdist::stringdist(tk[1], tk, method = ""hamming"")
        w <- which(d <= umimaxdist)
        k <- c(k, tk[1])
        tk <- tk[-w]
      }
      k
    }
  })
  tokeep
}


processReadsTrans <- function(Ldef) {
  ## Read data
  fq1 <- Biostrings::readQualityScaledDNAStringSet(Ldef$fastqForward)
  if (!is.null(Ldef$fastqReverse)) {
    fq2 <- Biostrings::readQualityScaledDNAStringSet(Ldef$fastqReverse)
  }
  
  ## Number of reads
  message(""Number of reads: "", length(fq1))  ## 1000
  
  ## Adapter sequences
  adapt <- Biostrings::vcountPattern(Ldef$adapterForward, fq1) > 0
  if (!is.null(Ldef$fastqReverse)) {
    adapt2 <- Biostrings::vcountPattern(Ldef$adapterReverse, fq2) > 0
    adapt <- adapt | adapt2
  }
  message(""Number of reads with adapters: "", sum(adapt))  ## 314
  fq1 <- fq1[!adapt]
  if (!is.null(Ldef$fastqReverse)) {
    fq2 <- fq2[!adapt]
  }
  
  ## Average quality in variable sequences too low
  varForward <-  Biostrings::subseq(fq1, start = Ldef$skipForward + Ldef$umiLengthForward + 
                                      Ldef$constantLengthForward + 1, width = Ldef$variableLengthForward)
  avgQualForward <- ShortRead::alphabetScore(Biostrings::quality(varForward))/
    BiocGenerics::width(varForward)
  lowqual <- avgQualForward < Ldef$avePhredMinForward
  if (!is.null(Ldef$fastqReverse)) {
    varReverse <-  Biostrings::subseq(fq2, start = Ldef$skipReverse + Ldef$umiLengthReverse + 
                                        Ldef$constantLengthReverse + 1, width = Ldef$variableLengthReverse)
    avgQualReverse <- ShortRead::alphabetScore(Biostrings::quality(varReverse))/
      BiocGenerics::width(varReverse)
    lowqual <- lowqual | avgQualReverse < Ldef$avePhredMinReverse
  }
  message(""Number of reads with low average quality: "", sum(lowqual))  # 7
  fq1 <- fq1[!lowqual]
  if (!is.null(Ldef$fastqReverse)) {
    fq2 <- fq2[!lowqual]
  }
  
  ## Too many Ns in variable sequences
  varForward <- Biostrings::subseq(fq1, start = Ldef$skipForward + Ldef$umiLengthForward + 
                                     Ldef$constantLengthForward + 1, width = Ldef$variableLengthForward)
  nbrNForward <- Biostrings::vcountPattern(""N"", varForward, fixed = TRUE)
  toomanynvar <- nbrNForward > Ldef$variableNMaxForward
  if (!is.null(Ldef$fastqReverse)) {
    varReverse <- Biostrings::subseq(fq2, start = Ldef$skipReverse + Ldef$umiLengthReverse + 
                                       Ldef$constantLengthReverse + 1, width = Ldef$variableLengthReverse)
    nbrNReverse <- Biostrings::vcountPattern(""N"", varReverse, fixed = TRUE)
    toomanynvar <- toomanynvar | nbrNReverse > Ldef$variableNMaxReverse
  }
  message(""Number of reads with too many N in variable sequence: "", sum(toomanynvar))  # 0
  fq1 <- fq1[!toomanynvar]
  if (!is.null(Ldef$fastqReverse)) {
    fq2 <- fq2[!toomanynvar]
  }
  
  ## Too many Ns in UMIs
  umiForward <- Biostrings::subseq(fq1, start = Ldef$skipForward + 1, width = Ldef$umiLengthForward)
  if (!is.null(Ldef$fastqReverse)) {
    umiReverse <- Biostrings::subseq(fq2, start = Ldef$skipReverse + 1, width = Ldef$umiLengthReverse)
    umis <- Biostrings::xscat(umiForward, umiReverse)
  } else {
    umis <- umiForward
  }
  nbrNumi <- Biostrings::vcountPattern(""N"", umis, fixed = TRUE)
  toomanynumi <- nbrNumi > Ldef$umiNMax
  message(""Number of reads with too many N in UMI: "", sum(toomanynumi))  # 0
  fq1 <- fq1[!toomanynumi]
  if (!is.null(Ldef$fastqReverse)) {
    fq2 <- fq2[!toomanynumi]
  }
  
  ## Compare to wildtype, forward
  mutCodons <- NULL
  uniqueMutCodons <- NULL
  mutBases <- NULL
  uniqueMutBases <- NULL
  nbrMutBasesTot <- NULL
  if (Ldef$wildTypeForward != """") {
    varForward <- Biostrings::subseq(fq1, start = Ldef$skipForward + Ldef$umiLengthForward + 
                                       Ldef$constantLengthForward + 1, width = Ldef$variableLengthForward)
    p <- DNAStringSet(rep(Ldef$wildTypeForward, length(varForward)))
    pattern_width <- width(p)
    subject_width <- width(varForward)
    unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(varForward)))
    breakpoints <- cumsum(pattern_width)
    ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                         breakpoints) + 1L,
                            nbins = length(p))
    skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
    nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
    offsets <- c(0L, breakpoints[-length(breakpoints)])
    mismatchPositions <- nucleotideMismatches - offsets
    ## First check, excluding reads with more than 3x(max nbr mut codons) mismatches
    baseMismatches <- mismatchPositions
    nbrMutBases <- S4Vectors::elementNROWS(unique(baseMismatches))
    toomanybases <- nbrMutBases > 3 * Ldef$nbrMutatedCodonsMaxForward
    message(""Number of reads with too many mutated codons, forward (1): "", sum(toomanybases))
    fq1 <- fq1[!toomanybases]
    if (!is.null(Ldef$fastqReverse)) {
      fq2 <- fq2[!toomanybases]
    }
    mismatchPositions <- mismatchPositions[!toomanybases]
    varForward <- varForward[!toomanybases]
    
    mismatchQualities <- Biostrings::quality(varForward)[mismatchPositions]
    lowqualmm <- min(as(mismatchQualities, ""IntegerList"")) < Ldef$mutatedPhredMinForward
    message(""Number of reads with low-quality mismatch bases, forward: "", sum(lowqualmm))  ## 0
    fq1 <- fq1[!lowqualmm]
    if (!is.null(Ldef$fastqReverse)) {
      fq2 <- fq2[!lowqualmm]
    }
    mismatchPositions <- mismatchPositions[!lowqualmm]
    varForward <- varForward[!lowqualmm]
    ## Number of mutated codons
    codonMismatches <- (mismatchPositions - 1) %/% 3 + 1
    nbrMutCodons <- S4Vectors::elementNROWS(unique(codonMismatches))
    toomanycodons <- nbrMutCodons > Ldef$nbrMutatedCodonsMaxForward
    message(""Number of reads with too many mutated codons, forward: "", sum(toomanycodons))  ## 287
    fq1 <- fq1[!toomanycodons]
    if (!is.null(Ldef$fastqReverse)) {
      fq2 <- fq2[!toomanycodons]
    }
    codonMismatches <- codonMismatches[!toomanycodons]
    varForward <- varForward[!toomanycodons]
    nbrMutCodons <- nbrMutCodons[!toomanycodons]
    nbrMutBases <- S4Vectors::elementNROWS(unique(mismatchPositions[!toomanycodons]))
    ## Forbidden codons
    uniqueMutCodons <- unique(codonMismatches)
    mutCodons <- S4Vectors::endoapply(uniqueMutCodons, 
                                      function(v) rep(3 * v, each = 3) + c(-2, -1, 0))
    mutCodons <- as(varForward, ""DNAStringSet"")[mutCodons]
    matchForbidden <- Biostrings::vmatchPattern(
      pattern = Ldef$forbiddenMutatedCodonsForward, 
      as(mutCodons, ""DNAStringSet""), 
      fixed = FALSE)
    forbiddencodon <- sapply(BiocGenerics::relist(start(unlist(matchForbidden)) %% 3==1, 
                                                  matchForbidden), function(i) any(i))
    message(""Number of reads with forbidden codons, forward: "", sum(forbiddencodon))  ## 6
    fq1 <- fq1[!forbiddencodon]
    if (!is.null(Ldef$fastqReverse)) {
      fq2 <- fq2[!forbiddencodon]
    }
    mutCodons <- mutCodons[!forbiddencodon]
    nbrMutCodonsTot <- nbrMutCodons[!forbiddencodon]
    nbrMutBasesTot <- nbrMutBases[!forbiddencodon]
    uniqueMutCodons <- uniqueMutCodons[!forbiddencodon]
    splitMutCodons <- as(strsplit(gsub(""([^.]{3})"", ""\\1\\."", 
                                       as.character(mutCodons)), ""."", fixed = TRUE), 
                         ""CharacterList"")
    encodedMutCodonsForward <- S4Vectors::unstrsplit(BiocGenerics::relist(
      paste0(""f"", unlist(uniqueMutCodons), unlist(splitMutCodons)), 
      uniqueMutCodons),
      sep = ""_"")
  } else {
    encodedMutCodonsForward <- """"
  }
  
  ## Compare to wildtype, reverse
  if (!is.null(Ldef$fastqReverse) && Ldef$wildTypeReverse != """") {
    varReverse <- Biostrings::subseq(fq2, start = Ldef$skipReverse + Ldef$umiLengthReverse + 
                                       Ldef$constantLengthReverse + 1, width = Ldef$variableLengthReverse)
    p <- DNAStringSet(rep(Ldef$wildTypeReverse, length(varReverse)))
    pattern_width <- width(p)
    subject_width <- width(varReverse)
    unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(varReverse)))
    breakpoints <- cumsum(pattern_width)
    ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                         breakpoints) + 1L,
                            nbins = length(p))
    skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
    nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
    offsets <- c(0L, breakpoints[-length(breakpoints)])
    mismatchPositions <- nucleotideMismatches - offsets
    
    ## First check, excluding reads with more than 3x(max nbr mut codons) mismatches
    baseMismatches <- mismatchPositions
    nbrMutBases <- S4Vectors::elementNROWS(unique(baseMismatches))
    toomanybases <- nbrMutBases > 3 * Ldef$nbrMutatedCodonsMaxReverse
    message(""Number of reads with too many mutated codons, reverse (1): "", sum(toomanybases))
    fq1 <- fq1[!toomanybases]
    if (!is.null(Ldef$fastqReverse)) {
      fq2 <- fq2[!toomanybases]
    }
    mismatchPositions <- mismatchPositions[!toomanybases]
    varReverse <- varReverse[!toomanybases]
    encodedMutCodonsForward <- encodedMutCodonsForward[!toomanybases]
    nbrMutBasesTot <- nbrMutBasesTot[!toomanybases]
    nbrMutCodonsTot <- nbrMutCodonsTot[!toomanybases]
    
    mismatchQualities <- Biostrings::quality(varReverse)[mismatchPositions]
    lowqualmm <- min(as(mismatchQualities, ""IntegerList"")) < Ldef$mutatedPhredMinReverse
    message(""Number of reads with low-quality mismatch bases, reverse: "", sum(lowqualmm))  ## 0
    fq1 <- fq1[!lowqualmm]
    fq2 <- fq2[!lowqualmm]
    mismatchPositions <- mismatchPositions[!lowqualmm]
    varReverse <- varReverse[!lowqualmm]
    encodedMutCodonsForward <- encodedMutCodonsForward[!lowqualmm]
    nbrMutBasesTot <- nbrMutBasesTot[!lowqualmm]
    nbrMutCodonsTot <- nbrMutCodonsTot[!lowqualmm]
    ## Number of mutated codons
    codonMismatches <- (mismatchPositions - 1) %/% 3 + 1
    nbrMutCodons <- S4Vectors::elementNROWS(unique(codonMismatches))
    toomanycodons <- nbrMutCodons > Ldef$nbrMutatedCodonsMaxReverse
    message(""Number of reads with too many mutated codons, reverse: "", sum(toomanycodons))  ## 105
    fq1 <- fq1[!toomanycodons]
    fq2 <- fq2[!toomanycodons]
    codonMismatches <- codonMismatches[!toomanycodons]
    varReverse <- varReverse[!toomanycodons]
    encodedMutCodonsForward <- encodedMutCodonsForward[!toomanycodons]
    nbrMutBasesTot <- nbrMutBasesTot[!toomanycodons]
    nbrMutCodonsTot <- nbrMutCodonsTot[!toomanycodons]
    nbrMutCodons <- nbrMutCodons[!toomanycodons]
    nbrMutBases <- S4Vectors::elementNROWS(unique(mismatchPositions[!toomanycodons]))
    ## Forbidden codons
    uniqueMutCodons <- unique(codonMismatches)
    mutCodons <- S4Vectors::endoapply(uniqueMutCodons, 
                                      function(v) rep(3 * v, each = 3) + c(-2, -1, 0))
    mutCodons <- as(varReverse, ""DNAStringSet"")[mutCodons]
    matchForbidden <- Biostrings::vmatchPattern(
      pattern = Ldef$forbiddenMutatedCodonsReverse, 
      as(mutCodons, ""DNAStringSet""), 
      fixed = FALSE)
    forbiddencodon <- sapply(BiocGenerics::relist(start(unlist(matchForbidden)) %% 3==1, 
                                                  matchForbidden), function(i) any(i))
    message(""Number of reads with forbidden codons, reverse: "", sum(forbiddencodon))  ## 2
    fq1 <- fq1[!forbiddencodon]
    fq2 <- fq2[!forbiddencodon]
    encodedMutCodonsForward <- encodedMutCodonsForward[!forbiddencodon]
    mutCodons <- mutCodons[!forbiddencodon]
    nbrMutCodonsTot <- nbrMutCodonsTot[!forbiddencodon]
    nbrMutBasesTot <- nbrMutBasesTot[!forbiddencodon]
    nbrMutCodons <- nbrMutCodons[!forbiddencodon]
    nbrMutBases <- nbrMutBases[!forbiddencodon]
    uniqueMutCodons <- uniqueMutCodons[!forbiddencodon]
    splitMutCodons <- as(strsplit(gsub(""([^.]{3})"", ""\\1\\."", 
                                       as.character(mutCodons)), ""."", fixed = TRUE), 
                         ""CharacterList"")
    encodedMutCodonsReverse <- S4Vectors::unstrsplit(BiocGenerics::relist(
      paste0(""r"", unlist(uniqueMutCodons), unlist(splitMutCodons)), 
      uniqueMutCodons),
      sep = ""_"")
    mutNames <- paste(encodedMutCodonsForward, encodedMutCodonsReverse, sep = ""_"")
    
    nbrMutCodonsTot <- nbrMutCodonsTot + nbrMutCodons
    nbrMutBasesTot <- nbrMutBasesTot + nbrMutBases
    
  } else {
    mutNames <- encodedMutCodonsForward
  }
  
  ## Constant sequence filtering
  keep <- rep(TRUE, length(fq1))
  if (!all(Ldef$constantForward == """") && Ldef$constantMaxDistForward > (-1)) {
    constForward <- Biostrings::subseq(fq1, start = Ldef$skipForward + Ldef$umiLengthForward + 1, 
                                       width = Ldef$constantLengthForward)
    dists <- as.matrix(pwalign::stringDist(
      c(DNAStringSet(structure(Ldef$constantForward, 
                               names = paste0(""C"", length(Ldef$constantForward)))),
        DNAStringSet(constForward)
      )
    ))
    dists <- dists[-(1:length(Ldef$constantForward)), 1:length(Ldef$constantForward), drop = FALSE]
    keep <- keep & (apply(dists, 1, min) <= Ldef$constantMaxDistForward)
  }
  if (!all(Ldef$constantReverse == """") && Ldef$constantMaxDistReverse > (-1)) {
    constReverse <- Biostrings::subseq(fq2, start = Ldef$skipReverse + Ldef$umiLengthReverse + 1, 
                                       width = Ldef$constantLengthReverse)
    dists <- as.matrix(pwalign::stringDist(
      c(DNAStringSet(structure(Ldef$constantReverse, 
                               names = paste0(""C"", length(Ldef$constantReverse)))),
        DNAStringSet(constReverse)
      )
    ))
    dists <- dists[-(1:length(Ldef$constantReverse)), 1:length(Ldef$constantReverse), drop = FALSE]
    keep <- keep & (apply(dists, 1, min) <= Ldef$constantMaxDistReverse)
  }
  message(""Number of reads with too many mismatches in constant seq: "", sum(!keep))
  fq1 <- fq1[keep]
  if (!is.null(Ldef$fastqReverse)) {
    fq2 <- fq2[keep]
  }
  mutCodons <- mutCodons[keep]
  uniqueMutCodons <- uniqueMutCodons[keep]
  mutNames <- mutNames[keep]

  if (!is.null(nbrMutBasesTot)) {
    nbrMutBasesTot <- nbrMutBasesTot[keep]
    nbrMutCodonsTot <- nbrMutCodonsTot[keep]
    ## Number of mutated bases and codons
    varForwardFinal <- Biostrings::subseq(
      fq1, start = Ldef$skipForward + Ldef$umiLengthForward + 
        Ldef$constantLengthForward + 1, width = Ldef$variableLengthForward)
    if (!is.null(Ldef$fastqReverse)) {
      varReverseFinal <- Biostrings::subseq(
        fq2, start = Ldef$skipReverse + Ldef$umiLengthReverse + 
          Ldef$constantLengthReverse + 1, width = Ldef$variableLengthReverse)
    } else {
      varReverseFinal <- """"
    }
    message(""Number of mutated codons per retained read: "")
    print(table(nbrMutCodonsTot))
    message(""Number of different reads with each number of mismatched codons: "")
    print(table(nbrMutCodonsTot[!duplicated(paste0(varForwardFinal, ""_"", varReverseFinal))]))
    message(""Number of mutated bases per retained read: "")
    print(table(nbrMutBasesTot))
    message(""Number of different reads with each number of mismatched bases: "")
    print(table(nbrMutBasesTot[!duplicated(paste0(varForwardFinal, ""_"", varReverseFinal))]))
    
    ## Number of mutated amino acids
    ## Forward
    p_aa <- Biostrings::translate(DNAStringSet(rep(Ldef$wildTypeForward, length(varForwardFinal))))
    pattern_width_aa <- width(p_aa)
    varForwardFinal_aa <- Biostrings::translate(varForwardFinal)
    subject_width_aa <- width(varForwardFinal_aa)
    unlisted_ans_aa <- which(as.raw(unlist(p_aa)) != as.raw(unlist(varForwardFinal_aa)))
    breakpoints_aa <- cumsum(pattern_width_aa)
    ans_eltlens_aa <- tabulate(findInterval(unlisted_ans_aa - 1L,
                                            breakpoints_aa) + 1L,
                               nbins = length(p_aa))
    skeleton_aa <- IRanges::PartitioningByEnd(cumsum(ans_eltlens_aa))
    aaMismatchesForward <- BiocGenerics::relist(unlisted_ans_aa, skeleton_aa)
    ## Reverse
    if (!is.null(Ldef$fastqReverse)) {
      p_aa <- Biostrings::translate(DNAStringSet(rep(Ldef$wildTypeReverse, length(varReverseFinal))))
      pattern_width_aa <- width(p_aa)
      varReverseFinal_aa <- Biostrings::translate(varReverseFinal)
      subject_width_aa <- width(varReverseFinal_aa)
      unlisted_ans_aa <- which(as.raw(unlist(p_aa)) != as.raw(unlist(varReverseFinal_aa)))
      breakpoints_aa <- cumsum(pattern_width_aa)
      ans_eltlens_aa <- tabulate(findInterval(unlisted_ans_aa - 1L,
                                              breakpoints_aa) + 1L,
                                 nbins = length(p_aa))
      skeleton_aa <- IRanges::PartitioningByEnd(cumsum(ans_eltlens_aa))
      aaMismatchesReverse <- BiocGenerics::relist(unlisted_ans_aa, skeleton_aa)
     
      message(""Number of mutated aas per retained read: "")
      print(table(lengths(aaMismatchesForward) + lengths(aaMismatchesReverse)))
      message(""Number of different reads with each number of mismatched aas: "")
      print(table((lengths(aaMismatchesForward) + lengths(aaMismatchesReverse))[!duplicated(paste0(varForwardFinal, ""_"", varReverseFinal))]))
    } else {
      message(""Number of mutated aas per retained read: "")
      print(table(lengths(aaMismatchesForward)))
      message(""Number of different reads with each number of mismatched aas: "")
      print(table(lengths(aaMismatchesForward)[!duplicated(varForwardFinal)])) 
      
    }
  }

  mutNames <- gsub(""^_"", """", gsub(""_$"", """", mutNames))
  mutNames[mutNames == """"] <- ""WT""
  message(""Number of variants seen twice: "", sum(table(mutNames) == 2))  ## 2
  message(""Variants seen twice: "")
  print(which(table(mutNames) == 2))
  
  ## Retained reads
  message(""Number of retained reads: "", length(fq1))  ## 279
  
  ## Error statistics
  ## Forward
  if (length(Ldef$constantForward) == 1) {
    constForward <- Biostrings::subseq(fq1, start = Ldef$skipForward + Ldef$umiLengthForward + 1, 
                                       width = Ldef$constantLengthForward)
    p <- DNAStringSet(rep(Ldef$constantForward, length(constForward)))
    pattern_width <- width(p)
    subject_width <- width(constForward)
    
    unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(constForward)))
    breakpoints <- cumsum(pattern_width)
    ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                         breakpoints) + 1L,
                            nbins = length(p))
    skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
    nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
    offsets <- c(0L, breakpoints[-length(breakpoints)])
    mismatchPositions <- nucleotideMismatches - offsets
    mismatchQualities <- unlist(as(quality(constForward)[mismatchPositions], ""IntegerList""))
    
    unlisted_ans <- which(as.raw(unlist(p)) == as.raw(unlist(constForward)))
    breakpoints <- cumsum(pattern_width)
    ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                         breakpoints) + 1L,
                            nbins = length(p))
    skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
    nucleotideMatches <- BiocGenerics::relist(unlisted_ans, skeleton)
    offsets <- c(0L, breakpoints[-length(breakpoints)])
    matchPositions <- nucleotideMatches - offsets
    matchQualities <- unlist(as(quality(constForward)[matchPositions], ""IntegerList""))
    
    message(""Match qualities, forward:"")
    print(table(matchQualities))
    message(""Mismatch qualities, forward:"")
    print(table(mismatchQualities))
  }
  
  ## Reverse
  if (!is.null(Ldef$fastqReverse) && length(Ldef$constantReverse) == 1) {
    constReverse <- Biostrings::subseq(fq2, start = Ldef$skipReverse + Ldef$umiLengthReverse + 1,
                                       width = Ldef$constantLengthReverse)
    #constReverse <- Biostrings::reverseComplement(constReverse)
    p <- DNAStringSet(rep(Ldef$constantReverse, length(constReverse)))
    pattern_width <- width(p)
    subject_width <- width(constReverse)
    
    unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(constReverse)))
    breakpoints <- cumsum(pattern_width)
    ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                         breakpoints) + 1L,
                            nbins = length(p))
    skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
    nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
    offsets <- c(0L, breakpoints[-length(breakpoints)])
    mismatchPositions <- nucleotideMismatches - offsets
    mismatchQualities <- unlist(as(quality(constReverse)[mismatchPositions], ""IntegerList""))
    
    unlisted_ans <- which(as.raw(unlist(p)) == as.raw(unlist(constReverse)))
    breakpoints <- cumsum(pattern_width)
    ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                         breakpoints) + 1L,
                            nbins = length(p))
    skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
    nucleotideMatches <- BiocGenerics::relist(unlisted_ans, skeleton)
    offsets <- c(0L, breakpoints[-length(breakpoints)])
    matchPositions <- nucleotideMatches - offsets
    matchQualities <- unlist(as(quality(constReverse)[matchPositions], ""IntegerList""))
    
    message(""Match qualities, reverse:"")
    print(table(matchQualities))
    message(""Mismatch qualities, reverse:"")
    print(table(mismatchQualities))
  }
  
  ## Collapsing
  if (Ldef$wildTypeForward == """" && Ldef$wildTypeReverse == """") {
    umicoll <- list()
    if (!is.null(Ldef$fastqReverse)) {
      fqseq <- paste0(as.character(varForward), ""_"", as.character(varReverse))
    } else {
      fqseq <- as.character(varForward)
    }
    tbl <- as.data.frame(table(fqseq)) |> dplyr::arrange(desc(Freq), fqseq) |>
      dplyr::mutate(fqseq = as.character(fqseq))
    tbl$umis <- """"
    for (i in seq_len(nrow(tbl))) {
      tbl$umis[i] <- paste(umis[fqseq == tbl$fqseq[i]], collapse = "","")
    }
    collseqs <- collapse_seqs(tbl$fqseq, tbl$umis, tbl$Freq, 
                              maxdist = Ldef$variableCollapseMaxDist,
                              umimaxdist = Ldef$umiCollapseMaxDist,
                              minreads = Ldef$variableCollapseMinReads, 
                              minratio = Ldef$variableCollapseMinRatio)
    message(""Number of unique sequences: "", nrow(tbl))
    message(""Number of collapsed sequences: "", length(collseqs))
    message(""Total UMI count: "", length(unlist(collseqs)))
    return(tbl)
  }
  return(NULL)
}

## ----------------------------------------------------------------------------
## Generate values for comparison in unit tests - trans experiment
fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
## default arguments
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  variableCollapseMaxDist = 0, umiCollapseMaxDist = 0, 
  variableCollapseMinReads = 0, variableCollapseMinRatio = 0,
  constantMaxDistForward = -1, constantMaxDistReverse = -1,
  verbose = FALSE
)
processReadsTrans(Ldef)

## ----------------------------------------------------------------------------
## Change some parameter values
fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 30.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNA"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 25.0, mutatedPhredMinReverse = 0.0,
  variableCollapseMaxDist = 0, umiCollapseMaxDist = 0, 
  variableCollapseMinReads = 0, variableCollapseMinRatio = 0,
  constantMaxDistForward = -1, constantMaxDistReverse = -1,
  verbose = FALSE
)
processReadsTrans(Ldef)

## ----------------------------------------------------------------------------
## Only one input file
fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
Ldef <- list(
  fastqForward = fqt1, fastqReverse = NULL, 
  mergeForwardReverse = FALSE, revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = """", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 30.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNA"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 25.0, mutatedPhredMinReverse = 0.0,
  variableCollapseMaxDist = 0, umiCollapseMaxDist = 0, 
  variableCollapseMinReads = 0, variableCollapseMinRatio = 0,
  constantMaxDistForward = -1, constantMaxDistReverse = -1,
  verbose = FALSE
)
processReadsTrans(Ldef)

## Test collapsing
fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
## default arguments
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, 
  minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
  revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  primerForward = """",
  primerReverse = """",
  wildTypeForward = """",
  wildTypeReverse = """", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  mutNameDelimiter = ""."",
  maxNReads = -1, variableCollapseMaxDist = 6, umiCollapseMaxDist = 4, 
  variableCollapseMinReads = 0, variableCollapseMinRatio = 0,
  constantMaxDistForward = -1, constantMaxDistReverse = -1,
  verbose = FALSE
)
processReadsTrans(Ldef)

fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
## default arguments
Ldef <- list(
  fastqForward = fqt1, fastqReverse = NULL, 
  mergeForwardReverse = FALSE, 
  minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
  revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  primerForward = """",
  primerReverse = """",
  wildTypeForward = """",
  wildTypeReverse = """", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  mutNameDelimiter = ""."",
  variableCollapseMaxDist = 10, umiCollapseMaxDist = 5, 
  variableCollapseMinReads = 0, variableCollapseMinRatio = 0,
  constantMaxDistForward = -1, constantMaxDistReverse = -1,
  maxNReads = -1, verbose = FALSE
)
processReadsTrans(Ldef)

## don't add anything between the above and below runs
## same as above but with new collapsing approach
Ldef$variableCollapseMaxDist <- 0
Ldef$umiCollapseMaxDist <- 5 
Ldef$variableCollapseMinReads <- 0
Ldef$variableCollapseMinRatio <- 0
v <- processReadsTrans(Ldef)
cseq <- collapse_seqs(v$fqseq, v$umis, v$Freq, maxdist = 10, 
                      umimaxdist = 0, minreads = 0, minratio = 0)
length(cseq)

## Collapse only highly abundant features
fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
## default arguments
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, 
  minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
  revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  primerForward = """",
  primerReverse = """",
  wildTypeForward = """",
  wildTypeReverse = """", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  mutNameDelimiter = ""."",
  maxNReads = -1, variableCollapseMaxDist = 2, umiCollapseMaxDist = 0, 
  variableCollapseMinReads = 2, variableCollapseMinRatio = 0,
  constantMaxDistForward = -1, constantMaxDistReverse = -1,
  verbose = FALSE
)
v <- processReadsTrans(Ldef)

## don't add anything between the above and below runs
## same as above but with new collapsing approach
Ldef$variableCollapseMaxDist <- 0
Ldef$umiCollapseMaxDist <- 0 
Ldef$variableCollapseMinReads <- 0
Ldef$variableCollapseMinRatio <- 0
v <- processReadsTrans(Ldef)
cseq <- collapse_seqs(v$fqseq, v$umis, v$Freq, maxdist = 2, 
                      umimaxdist = 0, minreads = 1.5, minratio = 0)
length(cseq)

## Collapse only features with high enough count ratio
fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
## default arguments
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, 
  minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
  revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  primerForward = """",
  primerReverse = """",
  wildTypeForward = """",
  wildTypeReverse = """", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  mutNameDelimiter = ""."",
  maxNReads = -1, variableCollapseMaxDist = 3, umiCollapseMaxDist = 0, 
  variableCollapseMinReads = 1, variableCollapseMinRatio = 1.5,
  constantMaxDistForward = -1, constantMaxDistReverse = -1,
  verbose = FALSE
)
v <- processReadsTrans(Ldef)

## don't add anything between the above and below runs
## same as above but with new collapsing approach
Ldef$variableCollapseMaxDist <- 0
Ldef$umiCollapseMaxDist <- 0 
Ldef$variableCollapseMinReads <- 0
Ldef$variableCollapseMinRatio <- 0
v <- processReadsTrans(Ldef)
cseq <- collapse_seqs(v$fqseq, v$umis, v$Freq, maxdist = 3, 
                      umimaxdist = 0, minreads = 1, minratio = 1.5)
length(cseq)

## filter based on constant sequences
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  variableCollapseMaxDist = 0, umiCollapseMaxDist = 0, 
  variableCollapseMinReads = 0, variableCollapseMinRatio = 0,
  constantMaxDistForward = 0, constantMaxDistReverse = 0,
  verbose = FALSE
)
processReadsTrans(Ldef)

## 1 mismatch
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  variableCollapseMaxDist = 0, umiCollapseMaxDist = 0, 
  variableCollapseMinReads = 0, variableCollapseMinRatio = 0,
  constantMaxDistForward = 1, constantMaxDistReverse = 1,
  verbose = FALSE
)
processReadsTrans(Ldef)

## Multiple constant sequences
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 8, 
  constantLengthForward = 18, constantLengthReverse = 20, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
  constantForward = c(""AACCGGAGGAGGGAGCTG"", ""AACCGGCGGAGGGAGCTG""), 
  constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  variableCollapseMaxDist = 0, umiCollapseMaxDist = 0, 
  variableCollapseMinReads = 0, variableCollapseMinRatio = 0,
  constantMaxDistForward = 0, constantMaxDistReverse = 0,
  verbose = FALSE
)
processReadsTrans(Ldef)
","R"
"Assay","fmicompbio/mutscan","inst/scripts/generateValuesForUnitTestsLeuJun.R",".R","20669","271","suppressPackageStartupMessages({
  library(Biostrings)
  library(ShortRead)
})

processReadsLeuJun <- function(Ldef) {
  ## Read data
  fq1 <- Biostrings::readQualityScaledDNAStringSet(Ldef$fastqForward)
  fq2 <- Biostrings::readQualityScaledDNAStringSet(Ldef$fastqReverse)
  
  ## Number of reads
  message(""Number of reads: "", length(fq1))  ## 1000
  
  ## Primer sequences
  prim <- Biostrings::vcountPattern(Ldef$primerForward, fq1) == 0 |
    Biostrings::vcountPattern(Ldef$primerReverse, fq2) == 0
  message(""Number of reads without primers: "", sum(prim))  ## 126
  fq1 <- fq1[!prim]
  fq2 <- fq2[!prim]
  
  ## Average quality in variable sequences too low
  endprimFwd <- unlist(end(Biostrings::vmatchPattern(Ldef$primerForward, fq1)))
  varForward <- Biostrings::subseq(fq1, start = endprimFwd + 1, width = width(fq1) - endprimFwd)
  endprimRev <- unlist(end(Biostrings::vmatchPattern(Ldef$primerReverse, fq2)))
  varReverse <- Biostrings::subseq(fq2, start = endprimRev + 1, width = Ldef$variableLengthReverse)
  avgQualForward <- ShortRead::alphabetScore(Biostrings::quality(varForward))/
    BiocGenerics::width(varForward)
  avgQualReverse <- ShortRead::alphabetScore(Biostrings::quality(varReverse))/
    BiocGenerics::width(varReverse)
  lowqual <- avgQualForward < Ldef$avePhredMinForward | avgQualReverse < Ldef$avePhredMinReverse
  message(""Number of reads with low average quality: "", sum(lowqual))  # 0
  fq1 <- fq1[!lowqual]
  fq2 <- fq2[!lowqual]
  varForward <- varForward[!lowqual]
  varReverse <- varReverse[!lowqual]
  
  ## Too many Ns in variable sequences
  nbrNForward <- Biostrings::vcountPattern(""N"", varForward, fixed = TRUE)
  nbrNReverse <- Biostrings::vcountPattern(""N"", varReverse, fixed = TRUE)
  toomanynvar <- nbrNForward > Ldef$variableNMaxForward | nbrNReverse > Ldef$variableNMaxReverse
  message(""Number of reads with too many N in variable sequence: "", sum(toomanynvar))  # 76
  fq1 <- fq1[!toomanynvar]
  fq2 <- fq2[!toomanynvar]
  varForward <- varForward[!toomanynvar]
  varReverse <- varReverse[!toomanynvar]
  
  ## Compare to wildtype, forward
  bestmatch <- DNAStringSet()
  for (i in seq_along(varForward)) {
    s <- DNAStringSet(Ldef$wildTypeForward)
    s <- Biostrings::subseq(s, start = 1, width = length(varForward[[i]]))
    p <- DNAStringSet(rep(as.character(varForward[[i]]), length(s)))
    pattern_width <- width(p)
    subject_width <- width(s)
    unlisted_ans <- which(as.raw(unlist(p)) == as.raw(unlist(s)))
    breakpoints <- cumsum(pattern_width)
    ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                         breakpoints) + 1L,
                            nbins = length(p))
    skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
    nucleotideMatches <- BiocGenerics::relist(unlisted_ans, skeleton)
    offsets <- c(0L, breakpoints[-length(breakpoints)])
    matchPositions <- nucleotideMatches - offsets
    bestmatch <- c(bestmatch, s[which.max(sapply(matchPositions, length))])
  }
  p <- bestmatch
  pattern_width <- width(p)
  subject_width <- width(varForward)
  unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(varForward)))
  breakpoints <- cumsum(pattern_width)
  ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                       breakpoints) + 1L,
                          nbins = length(p))
  skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
  nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
  offsets <- c(0L, breakpoints[-length(breakpoints)])
  mismatchPositions <- nucleotideMismatches - offsets
  mismatchQualities <- Biostrings::quality(varForward)[mismatchPositions]
  lowqualmm <- min(as(mismatchQualities, ""IntegerList"")) < Ldef$mutatedPhredMinForward
  message(""Number of reads with low-quality mismatch bases, forward: "", sum(lowqualmm))  ## 0
  fq1 <- fq1[!lowqualmm]
  fq2 <- fq2[!lowqualmm]
  varForward <- varForward[!lowqualmm]
  varReverse <- varReverse[!lowqualmm]
  mismatchPositions <- mismatchPositions[!lowqualmm]
  p <- p[!lowqualmm]
  ## Number of mutated codons
  codonMismatches <- (mismatchPositions - 1) %/% 3 + 1
  nbrMutCodons <- S4Vectors::elementNROWS(unique(codonMismatches))
  toomanycodons <- nbrMutCodons > Ldef$nbrMutatedCodonsMaxForward
  message(""Number of reads with too many mutated codons, forward: "", sum(toomanycodons))  ## 58
  fq1 <- fq1[!toomanycodons]
  fq2 <- fq2[!toomanycodons]
  codonMismatches <- codonMismatches[!toomanycodons]
  varForward <- varForward[!toomanycodons]
  varReverse <- varReverse[!toomanycodons]
  p <- p[!toomanycodons]
  ## Forbidden codons
  # uniqueMutCodons <- unique(codonMismatches)
  # mutCodons <- S4Vectors::endoapply(uniqueMutCodons, 
  #                                   function(v) rep(3 * v, each = 3) + c(-2, -1, 0))
  # mutCodons <- as(varForward, ""DNAStringSet"")[mutCodons]
  # matchForbidden <- Biostrings::vmatchPattern(
  #   pattern = Ldef$forbiddenMutatedCodonsForward, 
  #   as(mutCodons, ""DNAStringSet""), 
  #   fixed = FALSE)
  # forbiddencodon <- sapply(BiocGenerics::relist(start(unlist(matchForbidden)) %% 3==1, 
  #                                               matchForbidden), function(i) any(i))
  # message(""Number of reads with forbidden codons, forward: "", sum(forbiddencodon))  ## 6
  # fq1 <- fq1[!forbiddencodon]
  # fq2 <- fq2[!forbiddencodon]
  # mutCodons <- mutCodons[!forbiddencodon]
  # uniqueMutCodons <- uniqueMutCodons[!forbiddencodon]
  # splitMutCodons <- as(strsplit(gsub(""([^.]{3})"", ""\\1\\."", 
  #                                    as.character(mutCodons)), ""."", fixed = TRUE), 
  #                      ""CharacterList"")
  # encodedMutCodonsForward <- S4Vectors::unstrsplit(BiocGenerics::relist(
  #   paste0(""f"", unlist(uniqueMutCodons), unlist(splitMutCodons)), 
  #   uniqueMutCodons),
  #   sep = ""_"")
  encodedMutCodonsForward <- names(p)
  
  
  ## Compare to wildtype, reverse
  p <- DNAStringSet(rep(Ldef$wildTypeReverse, length(varReverse)))
  pattern_width <- width(p)
  subject_width <- width(varReverse)
  unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(varReverse)))
  breakpoints <- cumsum(pattern_width)
  ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                       breakpoints) + 1L,
                          nbins = length(p))
  skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
  nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
  offsets <- c(0L, breakpoints[-length(breakpoints)])
  mismatchPositions <- nucleotideMismatches - offsets
  mismatchQualities <- Biostrings::quality(varReverse)[mismatchPositions]
  lowqualmm <- min(as(mismatchQualities, ""IntegerList"")) < Ldef$mutatedPhredMinReverse
  message(""Number of reads with low-quality mismatch bases, reverse: "", sum(lowqualmm))  ## 0
  fq1 <- fq1[!lowqualmm]
  fq2 <- fq2[!lowqualmm]
  mismatchPositions <- mismatchPositions[!lowqualmm]
  varForward <- varForward[!lowqualmm]
  varReverse <- varReverse[!lowqualmm]
  encodedMutCodonsForward <- encodedMutCodonsForward[!lowqualmm]
  ## Number of mutated codons
  codonMismatches <- (mismatchPositions - 1) %/% 3 + 1
  nbrMutCodons <- S4Vectors::elementNROWS(unique(codonMismatches))
  toomanycodons <- nbrMutCodons > Ldef$nbrMutatedCodonsMaxReverse
  message(""Number of reads with too many mutated codons, reverse: "", sum(toomanycodons))  ## 137
  fq1 <- fq1[!toomanycodons]
  fq2 <- fq2[!toomanycodons]
  codonMismatches <- codonMismatches[!toomanycodons]
  varForward <- varForward[!toomanycodons]
  varReverse <- varReverse[!toomanycodons]
  encodedMutCodonsForward <- encodedMutCodonsForward[!toomanycodons]
  ## Forbidden codons
  uniqueMutCodons <- unique(codonMismatches)
  mutCodons <- S4Vectors::endoapply(uniqueMutCodons, 
                                    function(v) rep(3 * v, each = 3) + c(-2, -1, 0))
  mutCodons <- as(varReverse, ""DNAStringSet"")[mutCodons]
  matchForbidden <- Biostrings::vmatchPattern(
    pattern = Ldef$forbiddenMutatedCodonsReverse, 
    as(mutCodons, ""DNAStringSet""), 
    fixed = FALSE)
  forbiddencodon <- sapply(BiocGenerics::relist(start(unlist(matchForbidden)) %% 3==1, 
                                                matchForbidden), function(i) any(i))
  message(""Number of reads with forbidden codons, reverse: "", sum(forbiddencodon))  ## 3
  fq1 <- fq1[!forbiddencodon]
  fq2 <- fq2[!forbiddencodon]
  encodedMutCodonsForward <- encodedMutCodonsForward[!forbiddencodon]
  mutCodons <- mutCodons[!forbiddencodon]
  uniqueMutCodons <- uniqueMutCodons[!forbiddencodon]
  splitMutCodons <- as(strsplit(gsub(""([^.]{3})"", ""\\1\\."", 
                                     as.character(mutCodons)), ""."", fixed = TRUE), 
                       ""CharacterList"")
  encodedMutCodonsReverse <- S4Vectors::unstrsplit(BiocGenerics::relist(
    paste0(""r"", unlist(uniqueMutCodons), unlist(splitMutCodons)), 
    uniqueMutCodons),
    sep = ""_"")
  mutNames <- paste(encodedMutCodonsForward, encodedMutCodonsReverse, sep = ""_"")
  mutNames <- gsub(""^_"", """", gsub(""_$"", """", mutNames))
  mutNames[mutNames <- """"] <- ""WT""
  message(""Number of variants seen twice: "", sum(table(mutNames) == 2))  ## 5
  message(""Variants seen twice: "")
  print(which(table(mutNames) == 2))  ## ATF6B_r13CCC, ATF7_r26CTC, CEBPA, FOSB, FOSL1_r3GGC
  
  ## Retained reads
  message(""Number of retained reads: "", length(fq1))  ## 600
}

## ----------------------------------------------------------------------------
leu <- c(ATF2 = ""GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGCTGAAGACTTGAGTTCATTAAATGGTCAGCTGCAGAGTGAAGTCACCCTGCTGAGAAATGAAGTGGCACAGCTGAAACAGCTTCTTCTGGCT"",
         ATF7 = ""GATCCAGATGAGCGACGGCAGCGCTTTCTGGAGCGCAACCGGGCTGCAGCCTCCCGCTGCCGCCAAAAGCGAAAGCTGTGGGTGTCCTCCCTAGAGAAGAAGGCCGAAGAACTCACTTCTCAGAACATTCAGCTGAGTAATGAAGTCACATTACTACGCAATGAGGTGGCCCAGTTGAAACAGCTACTGTTAGCT"",
         CREB5 = ""GATCCGGACGAGAGGCGGCGGAAATTTCTGGAACGGAACCGGGCAGCTGCCACCCGCTGCAGACAGAAGAGGAAGGTCTGGGTGATGTCATTGGAAAAGAAAGCAGAAGAACTCACCCAGACAAACATGCAGCTTCAGAATGAAGTGTCTATGTTGAAAAATGAGGTGGCCCAGCTGAAACAGTTGTTGTTAACA"",
         ATF3 = ""GAAGAAGATGAAAGGAAAAAGAGGCGACGAGAAAGAAATAAGATTGCAGCTGCAAAGTGCCGAAACAAGAAGAAGGAGAAGACGGAGTGCCTGCAGAAAGAGTCGGAGAAGCTGGAAAGTGTGAATGCTGAACTGAAGGCTCAGATTGAGGAGCTCAAGAACGAGAAGCAGCATTTGATATACATGCTCAACCTT"",
         JDP2 = ""GAGGAAGAGGAGCGAAGGAAAAGGCGCCGGGAGAAGAACAAAGTCGCAGCAGCCCGATGCCGGAACAAGAAGAAGGAGCGCACGGAGTTTCTGCAGCGGGAATCCGAGCGGCTGGAACTCATGAACGCAGAGCTGAAGACCCAGATTGAGGAGCTGAAGCAGGAGCGGCAGCAGCTCATCCTGATGCTGAACCGA"",
         ATF4 = ""GAGAAACTGGATAAGAAGCTGAAAAAAATGGAGCAAAACAAGACAGCAGCCACTAGGTACCGCCAGAAGAAGAGGGCGGAGCAGGAGGCTCTTACTGGTGAGTGCAAAGAGCTGGAAAAGAAGAACGAGGCTCTAAAAGAGAGGGCGGATTCCCTGGCCAAGGAGATCCAGTACCTGAAAGATTTGATAGAAGAG"",
         ATF5 = ""ACCCGAGGGGACCGCAAGCAAAAGAAGAGAGACCAGAACAAGTCGGCGGCTCTGAGGTACCGCCAGCGGAAGCGGGCAGAGGGTGAGGCCCTGGAGGGCGAGTGCCAGGGGCTGGAGGCACGGAATCGCGAGCTGAAGGAACGGGCAGAGTCCGTGGAGCGCGAGATCCAGTACGTCAAGGACCTGCTCATCGAG"",
         CREBZF = ""AGTCCCCGGAAGGCGGCGGCGGCCGCTGCCCGCCTTAATCGACTGAAGAAGAAGGAGTACGTGATGGGGCTGGAGAGTCGAGTCCGGGGTCTGGCAGCCGAGAACCAGGAGCTGCGGGCCGAGAATCGGGAGCTGGGCAAACGCGTACAGGCACTGCAGGAGGAGAGTCGCTACCTACGGGCAGTCTTAGCCAAC"",
         BATF2 = ""CCCAAGGAGCAACAAAGGCAGCTGAAGAAGCAGAAGAACCGGGCAGCCGCCCAGCGAAGCCGGCAGAAGCACACAGACAAGGCAGACGCCCTGCACCAGCAGCACGAGTCTCTGGAAAAAGACAACCTCGCCCTGCGGAAGGAGATCCAGTCCCTGCAGGCCGAGCTGGCGTGGTGGAGCCGGACCCTGCACGTG"",
         BATF3 = ""GAGGATGATGACAGGAAGGTCCGAAGGAGAGAAAAAAACCGAGTTGCTGCTCAGAGAAGTCGGAAGAAGCAGACCCAGAAGGCTGACAAGCTCCATGAGGAATATGAGAGCCTGGAGCAAGAAAACACCATGCTGCGGAGAGAGATCGGGAAGCTGACAGAGGAGCTGAAGCACCTGACAGAGGCACTGAAGGAG"",
         CEBPE = ""AAAGATAGCCTTGAGTACCGGCTGAGGCGGGAGCGCAACAACATCGCCGTGCGCAAGAGCCGAGACAAGGCCAAGAGGCGCATTCTGGAGACGCAGCAGAAGGTGCTGGAGTACATGGCAGAGAACGAGCGCCTCCGCAGCCGCGTGGAGCAGCTCACCCAGGAGCTAGACACCCTCCGCAACCTCTTCCGCCAG"",
         BACH1 = ""CTGGATTGTATCCATGATATTCGAAGAAGAAGTAAAAACAGAATTGCTGCACAGCGCTGTCGCAAGAGAAAACTTGACTGTATACAGAATCTTGAATCAGAAATTGAGAAGCTGCAAAGTGAAAAGGAGAGCTTGTTGAAGGAAAGAGATCACATTTTGTCAACTCTGGGTGAGACAAAGCAGAACCTAACTGGA"",
         BACH2 = ""TTAGAGTTTATTCATGATGTCCGACGGCGCAGCAAGAACCGCATCGCGGCCCAGCGCTGCCGCAAAAGGAAACTGGACTGTATTCAGAATTTAGAATGTGAAATCCGCAAATTGGTGTGTGAGAAAGAGAAACTGTTGTCAGAGAGGAATCAACTGAAAGCATGCATGGGGGAACTGTTGGACAACTTCTCCTGC"",
         NFE2L1 = ""CTGAGCCTCATCCGAGACATCCGGCGCCGGGGCAAGAACAAGATGGCGGCGCAGAACTGCCGCAAGCGCAAGCTGGACACCATCCTGAATCTGGAGCGTGATGTGGAGGACCTGCAGCGTGACAAAGCCCGGCTGCTGCGGGAGAAAGTGGAGTTCCTGCGCTCCCTGCGACAGATGAAGCAGAAGGTCCAGAGC"",
         NFE2 = ""CTAGCGCTAGTCCGGGACATCCGACGACGGGGCAAAAACAAGGTGGCAGCCCAGAACTGCCGCAAGAGGAAGCTGGAAACCATTGTGCAGCTGGAGCGGGAGCTGGAGCGGCTGACCAATGAACGGGAGCGGCTTCTCAGGGCCCGCGGGGAGGCAGACCGGACCCTGGAGGTCATGCGCCAACAGCTGACAGAG"",
         NFIL3 = ""AAGAAAGATGCTATGTATTGGGAAAAAAGGCGGAAAAATAATGAAGCTGCCAAAAGATCTCGTGAGAAGCGTCGACTGAATGACCTGGTTTTAGAGAACAAACTAATTGCACTGGGAGAAGAAAACGCCACTTTAAAAGCTGAGCTGCTTTCACTAAAATTAAAGTTTGGTTTAATTAGCTCCACAGCATATGCT"",
         FOS = ""GAAGAAGAAGAGAAAAGGAGAATCCGAAGGGAAAGGAATAAGATGGCTGCAGCCAAATGCCGCAACCGGAGGAGGGAGCTGACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTAGAGTTCATCCTGGCAGCT"",
         FOSB = ""GAGGAAGAGGAGAAGCGAAGGGTGCGCCGGGAACGAAATAAACTAGCAGCAGCTAAATGCAGGAACCGGCGGAGGGAGCTGACCGACCGACTCCAGGCGGAGACAGATCAGTTGGAGGAAGAAAAAGCAGAGCTGGAGTCGGAGATCGCCGAGCTCCAAAAGGAGAAGGAACGTCTGGAGTTTGTGCTGGTGGCC"",
         FOSL1 = ""GAGGAAGAGGAGCGCCGCCGAGTAAGGCGCGAGCGGAACAAGCTGGCTGCGGCCAAGTGCAGGAACCGGAGGAAGGAACTGACCGACTTCCTGCAGGCGGAGACTGACAAACTGGAAGATGAGAAATCTGGGCTGCAGCGAGAGATTGAGGAGCTGCAGAAGCAGAAGGAGCGCCTAGAGCTGGTGCTGGAAGCC"",
         FOSL2 = ""GAAGAGGAGGAGAAGCGTCGCATCCGGCGGGAGAGGAACAAGCTGGCTGCAGCCAAGTGCCGGAACCGACGCCGGGAGCTGACAGAGAAGCTGCAGGCGGAGACAGAGGAGCTGGAGGAGGAGAAGTCAGGCCTGCAGAAGGAGATTGCTGAGCTGCAGAAGGAGAAGGAGAAGCTGGAGTTCATGTTGGTGGCT"",
         MAFB = ""GTGATCCGCCTGAAGCAGAAGCGGCGGACCCTGAAGAACCGGGGCTACGCCCAGTCTTGCAGGTATAAACGCGTCCAGCAGAAGCACCACCTGGAGAATGAGAAGACGCAGCTCATTCAGCAGGTGGAGCAGCTTAAGCAGGAGGTGTCCCGGCTGGCCCGCGAGAGAGACGCCTACAAGGTCAAGTGCGAGAAA"",
         JUN = ""CAGGAGCGGATCAAGGCGGAGAGGAAGCGCATGAGGAACCGCATCGCTGCCTCCAAGTGCCGAAAAAGGAAGCTGGAGAGAATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTTAAACAGAAAGTCATGAAC"",
         JUNB = ""CAAGAGCGCATCAAAGTGGAGCGCAAGCGGCTGCGGAACCGGCTGGCGGCCACCAAGTGCCGGAAGCGGAAGCTGGAGCGCATCGCGCGCCTGGAGGACAAGGTGAAGACGCTCAAGGCCGAGAACGCGGGGCTGTCGAGTACCGCCGGCCTCCTCCGGGAGCAGGTGGCCCAGCTCAAACAGAAGGTCATGACC"",
         JUND = ""CAGGAGCGCATCAAGGCGGAGCGCAAGCGGCTGCGCAACCGCATCGCCGCCTCCAAGTGCCGCAAGCGCAAGCTGGAGCGCATCTCGCGCCTGGAAGAGAAAGTGAAGACCCTCAAGAGTCAGAACACGGAGCTGGCGTCCACGGCGAGCCTGCTGCGCGAGCAGGTGGCGCAGCTCAAGCAGAAAGTCCTCAGC"",
         CREB3 = ""GAACAAATTCTGAAACGTGTGCGGAGGAAGATTCGAAATAAAAGATCTGCTCAAGAGAGCCGCAGGAAAAAGAAGGTGTATGTTGGGGGTTTAGAGAGCAGGGTCTTGAAATACACAGCCCAGAATATGGAGCTTCAGAACAAAGTACAGCTTCTGGAGGAACAGAATTTGTCCCTTCTAGATCAACTGAGGAAA"",
         HLF = ""CTGAAGGATGACAAGTACTGGGCAAGGCGCAGAAAGAACAACATGGCAGCCAAGCGCTCCCGCGACGCCCGGAGGCTGAAAGAGAACCAGATCGCCATCCGGGCCTCGTTCCTGGAGAAGGAGAACTCGGCCCTCCGCCAGGAGGTGGCTGACTTGAGGAAGGAGCTGGGCAAATGCAAGAACATACTTGCCAAG"",
         MAFG = ""ATCGTCCAGCTGAAGCAGCGCCGGCGCACGCTCAAGAACCGCGGCTACGCTGCCAGCTGCCGCGTGAAGCGGGTGACGCAGAAGGAGGAGCTGGAGAAGCAGAAGGCGGAGCTGCAGCAGGAGGTGGAGAAGCTGGCCTCAGAGAACGCCAGCATGAAGCTGGAGCTCGACGCGCTGCGCTCCAAGTACGAGGCG"",
         MAFK = ""GTGACCCGCCTGAAGCAGCGTCGGCGCACACTCAAGAACCGCGGCTACGCGGCCAGCTGCCGCATCAAGCGGGTGACGCAGAAGGAGGAGCTGGAGCGGCAGCGCGTGGAGCTGCAGCAGGAGGTGGAGAAGCTGGCGCGTGAGAACAGCAGCATGCGGCTGGAGCTGGACGCCCTGCGCTCCAAGTACGAGGCG"",
         XBP1 = ""AGCCCCGAGGAGAAGGCGCTGAGGAGGAAACTGAAAAACAGAGTAGCAGCTCAGACTGCCAGAGATCGAAAGAAGGCTCGAATGAGTGAGCTGGAACAGCAAGTGGTAGATTTAGAAGAAGAGAACCAAAAACTTTTGCTAGAAAATCAGCTTTTACGAGAGAAAACTCATGGCCTTGTAGTTGAGAACCAGGAG"",
         ATF6 = ""ATTGCTGTGCTAAGGAGACAGCAACGTATGATAAAAAATCGAGAATCCGCTTGTCAGTCTCGCAAGAAGAAGAAAGAATATATGCTAGGGTTAGAGGCGAGATTAAAGGCTGCCCTCTCAGAAAACGAGCAACTGAAGAAAGAAAATGGAACACTGAAGCGGCAGCTGGATGAAGTTGTGTCAGAGAACCAGAGG"",
         ATF6B = ""GCAAAGCTGCTGAAGCGGCAGCAGCGAATGATCAAGAACCGGGAGTCAGCCTGCCAGTCCCGGAGAAAGAAGAAAGAGTATCTGCAGGGACTGGAGGCTCGGCTGCAAGCAGTACTGGCTGACAACCAGCAGCTCCGCCGAGAGAATGCTGCCCTCCGGCGGCGGCTGGAGGCCCTGCTGGCTGAAAACAGCGAG"",
         CEBPA = ""AAGAACAGCAACGAGTACCGGGTGCGGCGCGAGCGCAACAACATCGCGGTGCGCAAGAGCCGCGACAAGGCCAAGCAGCGCAACGTGGAGACGCAGCAGAAGGTGCTGGAGCTGACCAGTGACAATGACCGCCTGCGCAAGCGGGTGGAACAGCTGAGCCGCGAACTGGACACGCTGCGGGGCATCTTCCGCCAG"",
         CEBPB = ""AAGCACAGCGACGAGTACAAGATCCGGCGCGAGCGCAACAACATCGCCGTGCGCAAGAGCCGCGACAAGGCCAAGATGCGCAACCTGGAGACGCAGCACAAGGTCCTGGAGCTCACGGCCGAGAACGAGCGGCTGCAGAAGAAGGTGGAGCAGCTGTCGCGCGAGCTCAGCACCCTGCGGAACTTGTTCAAGCAG"",
         CEBPD = ""CGCGGCAGCCCCGAGTACCGGCAGCGGCGCGAGCGCAACAACATCGCCGTGCGCAAGAGCCGCGACAAGGCCAAGCGGCGCAACCAGGAGATGCAGCAGAAGTTGGTGGAGCTGTCGGCTGAGAACGAGAAGCTGCACCAGCGCGTGGAGCAGCTCACGCGGGACCTGGCCGGCCTCCGGCAGTTCTTCAAGCAG"",
         CEBPG = ""CGAAACAGTGACGAGTATCGGCAACGCCGAGAGAGGAACAACATGGCTGTGAAAAAGAGCCGGTTGAAAAGCAAGCAGAAAGCACAAGACACACTGCAGAGAGTCAATCAGCTCAAAGAAGAGAATGAACGGTTGGAAGCAAAAATCAAATTGCTGACCAAGGAATTAAGTGTACTCAAAGATTTGTTTCTTGAG"",
         CREB1 = ""GAAGCAGCACGAAAGAGAGAGGTCCGTCTAATGAAGAACAGGGAAGCAGCTCGAGAGTGTCGTAGAAAGAAGAAAGAATATGTGAAATGTTTAGAAAACAGAGTGGCAGTGCTTGAAAATCAAAACAAGACATTGATTGAGGAGCTAAAAGCACTTAAGGACCTTTACTGCCACAAATCAGAT"",
         CREB3L1 = ""GAGAAGGCCTTGAAGAGAGTCCGGAGGAAAATCAAGAACAAGATCTCAGCCCAGGAGAGCCGTCGTAAGAAGAAGGAGTATGTGGAGTGTCTAGAAAAGAAGGTGGAGACATTTACATCTGAGAACAATGAACTGTGGAAGAAGGTGGAGACCCTGGAGAATGCCAACAGGACCCTGCTCCAGCAGCTGCAGAAA"",
         CREB3L2 = ""GAGAAGGCCCTGAAGAAAATTCGGAGGAAGATCAAGAATAAGATTTCTGCTCAGGAAAGTAGGAGAAAGAAGAAAGAATACATGGACAGCCTGGAGAAAAAAGTGGAGTCTTGTTCAACTGAGAACTTGGAGCTTCGGAAGAAGGTAGAGGTTCTAGAGAACACTAATAGGACTCTCCTTCAGCAACTCCAGAAG"",
         CREB3L3 = ""GAGCGAGTGCTGAAAAAAATCCGCCGGAAAATCCGGAACAAGCAGTCGGCGCAAGAAAGCAGGAAGAAGAAGAAGGAATATATCGATGGCCTGGAGACTCGGATGTCAGCTTGCACTGCTCAGAATCAGGAGTTACAGAGGAAAGTCTTGCATCTCGAGAAGCAAAACCTGTCCCTCTTGGAGCAACTGAAGAAA"",
         CREB3L4 = ""GAGAGGGTCCTCAAGAAGGTCAGGAGGAAAATCCGTAACAAGCAGTCAGCTCAGGACAGTCGGCGGCGGAAGAAGGAGTACATTGATGGGCTGGAGAGCAGGGTGGCAGCCTGTTCTGCACAGAACCAAGAATTACAGAAAAAAGTCCAGGAGCTGGAGAGGCACAACATCTCCTTGGTAGCTCAGCTCCGCCAG"",
         CREBL2 = ""CCAGCCAAAATTGACTTGAAAGCAAAACTTGAGAGGAGCCGGCAGAGTGCAAGAGAATGCCGAGCCCGAAAAAAGCTGAGATATCAGTATTTGGAAGAGTTGGTATCCAGTCGAGAAAGAGCTATATGTGCCCTCAGAGAGGAACTGGAAATGTACAAGCAGTGGTGCATGGCAATGGACCAAGGAAAAATCCCT"",
         CREBRF = ""CCCTTAACAGCCCGACCAAGGTCAAGGAAGGAAAAAAATAAGCTGGCTTCCAGAGCTTGTCGGTTAAAGAAGAAAGCCCAGTATGAAGCTAATAAAGTGAAATTATGGGGCCTCAACACAGAATATGATAATTTATTGTTTGTAATCAACTCCATCAAGCAAGAGATTGTAAACCGGGTACAGAATCCAAGAGAT"",
         DBP = ""CAGAAGGATGAGAAATACTGGAGCCGGCGGTACAAGAACAACGAGGCAGCCAAGCGGTCCCGTGACGCCCGGCGGCTCAAGGAGAACCAGATATCGGTGCGGGCGGCCTTCCTGGAGAAGGAGAACGCCCTGCTGCGGCAGGAAGTTGTGGCCGTGCGCCAGGAGCTGTCCCACTACCGCGCCGTGCTGTCCCGA"",
         NFE2L2 = ""CTTGCATTAATTCGGGATATACGTAGGAGGGGTAAGAATAAAGTGGCTGCTCAGAATTGCAGAAAAAGAAAACTGGAAAATATAGTAGAACTAGAGCAAGATTTAGATCATTTGAAAGATGAAAAAGAAAAATTGCTCAAAGAAAAAGGAGAAAATGACAAAAGCCTTCACCTACTGAAAAAACAACTCAGCACC"",
         NFE2L3 = ""GTCTCACTTATCCGTGACATCAGACGAAGAGGGAAAAATAAAGTTGCTGCGCAGAACTGTCGTAAACGCAAATTGGACATAATTTTGAATTTAGAAGATGATGTATGTAACTTGCAAGCAAAGAAGGAAACTCTTAAGAGAGAGCAAGCACAATGTAACAAAGCTATTAACATAATGAAACAGAAACTGCATGAC"",
         TEF = ""CAGAAGGATGAAAAGTACTGGACAAGACGCAAGAAGAACAACGTGGCAGCTAAACGGTCACGGGATGCCCGGCGCCTGAAAGAGAATCAGATCACCATCCGGGCAGCCTTCCTGGAGAAGGAGAACACAGCCCTGCGGACGGAGGTGGCCGAGCTACGCAAGGAGGTGGGCAAGTGCAAGACCATCGTGTCCAAG""
)
## Generate values for comparison in unit tests - trans experiment
fqt1 <- system.file(""extdata/leujunt0_1.fastq.gz"", package = ""mutscan"")
fqt2 <- system.file(""extdata/leujunt0_2.fastq.gz"", package = ""mutscan"")
## default arguments
Ldef <- list(
  fastqForward = fqt1, fastqReverse = fqt2, 
  mergeForwardReverse = FALSE, revComplForward = FALSE, revComplReverse = FALSE,
  skipForward = -1, skipReverse = -1, 
  umiLengthForward = -1, umiLengthReverse = -1, 
  constantLengthForward = -1, constantLengthReverse = -1, 
  variableLengthForward = -1, variableLengthReverse = 96,
  adapterForward = """", 
  adapterReverse = """",
  primerForward = ""GTCAGGTGGAGGCGGATCC"",
  primerReverse = ""GAAAAAGGAAGCTGGAGAGA"",
  wildTypeForward = leu,
  wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
  constantForward = """", 
  constantReverse = """", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 0,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = """",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  mutNameDelimiter = ""."", verbose = FALSE
)
processReadsLeuJun(Ldef)

","R"
"Assay","fmicompbio/mutscan","inst/scripts/generateExampleDataForLinkMultipleVariants.R",".R","6785","201","## Generate test data for linkMultipleVariants
## Construct: C(6) - V(24) - C(10) - V(30) - C(10) - V(40) - C(6)
## Forward read length 90
## Reverse read length 70

nReads <- 100
status <- rep(""OK"", nReads)

## First constant region
c1 <- rep(""ACGTGA"", nReads)
set.seed(1)
i1 <- sample(seq_len(nReads), 4)  ## reads with mutation
c1[i1] <- ""ACTTGA""
status[i1] <- gsub(""OK,"", """", paste0(status[i1], "",C1mut""))

## Second constant region
c2 <- rep(""TTGATCCCGG"", nReads)
set.seed(2)
i2 <- sample(seq_len(nReads), 5)  ## reads with mutation
c2[i2] <- ""TTGATCGCGG""
status[i2] <- gsub(""OK,"", """", paste0(status[i2], "",C2mut""))

## Third constant region
c3 <- rep(""TAAATACCGG"", nReads)
set.seed(3)
i3 <- sample(seq_len(nReads), 5)  ## reads with mutation
c3[i3] <- ""TAGATACCGG""
status[i3] <- gsub(""OK,"", """", paste0(status[i3], "",C3mut""))

## Fourth constant region
c4 <- rep(""ACCTGA"", nReads)
set.seed(4)
i4 <- sample(seq_len(nReads), 4)  ## reads with mutation
c4[i4] <- ""ACCCGA""
status[i4] <- gsub(""OK,"", """", paste0(status[i4], "",C4mut""))

## First variable region (barcodes)
tmpstatus <- rep("""", nReads)
set.seed(1)
v1true <- rep(c(paste(sample(c(""A"", ""C"", ""G"", ""T""), 24, replace = TRUE), collapse = """"),
                paste(sample(c(""A"", ""C"", ""G"", ""T""), 24, replace = TRUE), collapse = """"),
                paste(sample(c(""A"", ""C"", ""G"", ""T""), 24, replace = TRUE), collapse = """")),
              c(floor(nReads/3), floor(nReads/3), nReads - 2 * floor(nReads/3)))
v1 <- v1true
i1 <- sample(seq_len(nReads), 10)  ## reads with mutation
for (i in i1) {
    tmp <- strsplit(v1[i], """")[[1]]
    i0 <- sample(seq_along(tmp), 1)
    tmp[i0] <- ifelse(tmp[i0] == ""A"", ""T"", ""A"")
    v1[i] <- paste(tmp, collapse = """")
    tmpstatus[i] <- "",V1mut""
}
i2 <- sample(seq_len(nReads), 4)  ## reads with N
for (i in i2) {
    tmp <- strsplit(v1[i], """")[[1]]
    tmp[sample(seq_along(tmp), 1)] <- ""N""
    v1[i] <- paste(tmp, collapse = """")
    tmpstatus[i] <- paste0(tmpstatus[i], "",V1N"")
}
ordr <- sample(seq_along(v1true), length(v1true))
v1true <- v1true[ordr]
v1 <- v1[ordr]
tmpstatus <- tmpstatus[ordr]
status <- gsub(""OK,"", """", paste0(status, tmpstatus))

## Second variable region (multiple WT sequences)
tmpstatus <- rep("""", nReads)
set.seed(2)
V2v1 <- paste(sample(c(""A"", ""C"", ""G"", ""T""), 30, replace = TRUE), collapse = """")
V2v2 <- paste(sample(c(""A"", ""C"", ""G"", ""T""), 30, replace = TRUE), collapse = """")
V2v3 <- paste(sample(c(""A"", ""C"", ""G"", ""T""), 30, replace = TRUE), collapse = """")
v2true <- rep(c(V2v1, V2v2, V2v3),
              c(floor(nReads/3), floor(nReads/3), nReads - 2 * floor(nReads/3)))
n2true <- rep(c(""V2v1"", ""V2v2"", ""V2v3""),
              c(floor(nReads/3), floor(nReads/3), nReads - 2 * floor(nReads/3)))
v2 <- v2true
i1 <- sample(seq_len(nReads), 20)   ## reads with mutation
for (i in i1) {
    tmp <- strsplit(v2[i], """")[[1]]
    i0 <- sample(seq_along(tmp), 1)
    repl <- ifelse(tmp[i0] == ""A"", ""T"", ""A"")
    tmp[i0] <- repl
    v2[i] <- paste(tmp, collapse = """")
    tmpstatus[i] <- paste0("","", n2true[i], ""."", i0, ""."", repl)
}
i3 <- sample(seq_len(nReads), 3)   ## reads with deletion
## don't modify the reads here, since that won't generate an invalid overlap 
## (the modification will be in both the fwd and rev reads). Just record
## which reads to modify
for (i in i3) {
    tmpstatus[i] <- paste0(tmpstatus[i], "",V2del"")
}
ordr <- sample(seq_along(v2true), length(v2true))
v2true <- v2true[ordr]
n2true <- n2true[ordr]
v2 <- v2[ordr]
tmpstatus <- tmpstatus[ordr]
status <- gsub(""OK,"", """", paste0(status, tmpstatus))

## Third variable region (one WT sequence)
tmpstatus <- rep("""", nReads)
set.seed(3)
V3 <- paste(sample(c(""A"", ""C"", ""G"", ""T""), 40, replace = TRUE), collapse = """")
v3true <- rep(V3, nReads)
v3 <- v3true
i1 <- sample(seq_len(nReads), 30)   ## reads with mutation
for (i in i1) {
    tmp <- strsplit(v3[i], """")[[1]]
    i0 <- sample(seq_along(tmp), 1)
    repl <- ifelse(tmp[i0] == ""A"", ""T"", ""A"")
    tmp[i0] <- repl
    v3[i] <- paste(tmp, collapse = """")
    tmpstatus[i] <- paste0("",V3."", i0, ""."", repl)
}
ordr <- sample(seq_along(v3true), length(v3true))
v3true <- v3true[ordr]
v3 <- v3[ordr]
tmpstatus <- tmpstatus[ordr]
status <- gsub(""OK,"", """", paste0(status, tmpstatus))

## Final reads
reads <- paste0(c1, v1, c2, v2, c3, v3, c4)
qualities <- rep(paste(rep(""I"", 126), collapse = """"), nReads)

readsR1 <- substr(reads, start = 1, stop = 90)
readsR2 <- substr(reads, start = 57, stop = 126)
qualitiesR1 <- substr(qualities, start = 1, stop = 90)
qualitiesR2 <- substr(qualities, start = 57, stop = 126)

## Add the deletion to the forward reads
idx <- grep(""V2del"", status)
for (i in idx) {
    tmp <- strsplit(readsR1[i], """")[[1]]
    tmp[41] <- """"
    tmp <- c(tmp, substr(reads[i], start = 91, stop = 91))
    tmp <- paste(tmp, collapse = """")
    readsR1[i] <- tmp
}


fastqR1 <- Biostrings::QualityScaledDNAStringSet(
    x = Biostrings::DNAStringSet(
        x = structure(readsR1, names = paste0(""R"", seq_len(nReads)))
    ), 
    quality = Biostrings::PhredQuality(
        Biostrings::BStringSet(
            x = structure(qualitiesR1, names = paste0(""R"", seq_len(nReads)))
        )
    )
)
fastqR2 <- Biostrings::QualityScaledDNAStringSet(
    x = Biostrings::reverseComplement(
        Biostrings::DNAStringSet(
            x = structure(readsR2, names = paste0(""R"", seq_len(nReads)))
        )
    ), 
    quality = Biostrings::PhredQuality(
        Biostrings::BStringSet(
            x = structure(qualitiesR2, names = paste0(""R"", seq_len(nReads)))
        )
    )
)

Biostrings::writeQualityScaledXStringSet(fastqR1, filepath = ""multipleVariableRegions_R1.fastq.gz"", compress = TRUE)
Biostrings::writeQualityScaledXStringSet(fastqR2, filepath = ""multipleVariableRegions_R2.fastq.gz"", compress = TRUE)

## True sequences
barcode <- v1true
V2var <- vapply(strsplit(status, "",""), function(x) {
    i <- grep(""V2.*\\."", x)
    if (length(i) > 0) {
        x[i]
    } else {
        NA_character_
    }}, NA_character_)
V2var[is.na(V2var)] <- paste0(n2true[is.na(V2var)], "".0.WT"")
V3var <- vapply(strsplit(status, "",""), function(x) {
    i <- grep(""V3.*\\."", x)
    if (length(i) > 0) {
        x[i]
    } else {
        NA_character_
    }}, NA_character_)
V3var[is.na(V3var)] <- ""V3.0.WT""
WTV2 <- c(V2v1 = V2v1, V2v2 = V2v2, V2v3 = V2v3)
WTV3 <- c(V3 = V3)
constFwd <- ""ACGTGATTGATCCCGGTAAATACCGG""
constRev <- ""TAAATACCGGACCTGA""

truth <- data.frame(read = paste0(""R"", seq_len(nReads)),
                    trueBarcode = barcode,
                    trueV2 = V2var,
                    trueV3 = V3var,
                    obsBarcode = v1,
                    status = status)

saveRDS(list(truth = truth, WTV2 = WTV2, WTV3 = WTV3,
             constFwd = constFwd, constRev = constRev),
        ""multipleVariableRegions_truth.rds"")

","R"
"Assay","fmicompbio/mutscan","inst/scripts/generateExampleData.R",".R","5270","65","suppressPackageStartupMessages({
  library(ShortRead)
  library(BiocParallel)
})

## Trans, Input
r1 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/SRR5952429_1.fastq.gz"", withIds = TRUE)
r2 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/SRR5952429_2.fastq.gz"", withIds = TRUE)
ShortRead::writeFastq(r1[1:1000], file = ""transInput_1.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)
ShortRead::writeFastq(r2[1:1000], file = ""transInput_2.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)

## Trans, Output
r1 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/SRR5952432_1.fastq.gz"", withIds = TRUE)
r2 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/SRR5952432_2.fastq.gz"", withIds = TRUE)
ShortRead::writeFastq(r1[1:1000], file = ""transOutput_1.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)
ShortRead::writeFastq(r2[1:1000], file = ""transOutput_2.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)

## Cis, Input
r1 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/SRR5952435_1.fastq.gz"", withIds = TRUE)
r2 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/SRR5952435_2.fastq.gz"", withIds = TRUE)
ShortRead::writeFastq(r1[1:1000], file = ""cisInput_1.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)
ShortRead::writeFastq(r2[1:1000], file = ""cisInput_2.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)

## Cis, Output
r1 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/SRR5952438_1.fastq.gz"", withIds = TRUE)
r2 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/SRR5952438_2.fastq.gz"", withIds = TRUE)
ShortRead::writeFastq(r1[1:1000], file = ""cisOutput_1.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)
ShortRead::writeFastq(r2[1:1000], file = ""cisOutput_2.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)

## Count matrix, cis
meta <- read.delim(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/meta/GSE102901_cis_coldata.tsv"", header = TRUE, as.is = TRUE)
sampleIds <- meta$Name
names(sampleIds) <- sampleIds
allSamples <- lapply(sampleIds, function(id) {
  print(id)
  mutscan::digestFastqs(fastqForward = paste0(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/"", id, ""_1.fastq.gz""),
                        fastqReverse = paste0(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/GSE102901/FASTQ/"", id, ""_2.fastq.gz""),
                        mergeForwardReverse = TRUE, minOverlap = 0, maxOverlap = 0,
                        maxFracMismatchOverlap = 1, greedyOverlap = TRUE, 
                        revComplForward = FALSE, revComplReverse = TRUE,
                        elementsForward = ""SUCV"", elementsReverse = ""SUCVS"",
                        elementLengthsForward = c(1, 10, 18, 96),
                        elementLengthsReverse = c(1, 7, 17, 96, -1),
                        constantForward = ""AACCGGAGGAGGGAGCTG"",
                        constantReverse = ""GAGTTCATCCTGGCAGC"", adapterForward = ""GGAAGAGCACACGTC"", 
                        adapterReverse = ""GGAAGAGCGTCGTGT"", primerForward = """",
                        primerReverse = """", 
                        avePhredMinForward = 20, avePhredMinReverse = 20,
                        variableNMaxForward = 0, variableNMaxReverse = 0, umiNMax = 0,
                        wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"", 
                        wildTypeReverse = """", 
                        nbrMutatedCodonsMaxForward = 1, nbrMutatedCodonsMaxReverse = 1,
                        forbiddenMutatedCodonsForward = ""NNW"", forbiddenMutatedCodonsReverse = ""NNW"", 
                        mutNameDelimiter = ""."", maxNReads = -1, verbose = TRUE, nThreads = 6)
})
se <- mutscan::summarizeExperiment(x = allSamples, coldata = meta, countType = ""umis"")
seaa <- mutscan::collapseMutantsByAA(se)
saveRDS(se, file = ""GSE102901_cis_se.rds"")

## Leu+Jun
r1 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/LEU_JUN_6rep_3tp/FASTQ/t0/GD09_26674_AACGGT_read1.fastq.gz"", withIds = TRUE)
r2 <- ShortRead::readFastq(""/tungstenfs/groups/gbioinfo/sonechar/FMIgroups/GroupDiss/Diss_deep_mutational_scanning/data/LEU_JUN_6rep_3tp/FASTQ/t0/GD09_26674_AACGGT_read2.fastq.gz"", withIds = TRUE)
ShortRead::writeFastq(r1[22501:23500], file = ""leujunt0_1.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)
ShortRead::writeFastq(r2[22501:23500], file = ""leujunt0_2.fastq.gz"", mode = ""w"", full = FALSE, compress = TRUE)
","R"
"Assay","fmicompbio/mutscan","inst/scripts/generateValuesForUnitTestsCis.R",".R","15055","315","suppressPackageStartupMessages({
  library(Biostrings)
  library(ShortRead)
})

processReadsCis <- function(Ldef) {
  ## Read data
  fq1 <- Biostrings::readQualityScaledDNAStringSet(Ldef$fastqForward)
  fq2 <- Biostrings::readQualityScaledDNAStringSet(Ldef$fastqReverse)
  
  ## Number of reads
  message(""Number of reads: "", length(fq1))  ## 1000
  
  ## Adapter sequences
  adapt <- Biostrings::vcountPattern(Ldef$adapterForward, fq1) > 0 |
    Biostrings::vcountPattern(Ldef$adapterReverse, fq2) > 0
  message(""Number of reads with adapters: "", sum(adapt))  ## 126
  fq1 <- fq1[!adapt]
  fq2 <- fq2[!adapt]
  
  ## Merge sequences
  varForward <-  Biostrings::subseq(fq1, start = Ldef$skipForward + Ldef$umiLengthForward + 
                                      Ldef$constantLengthForward + 1, width = Ldef$variableLengthForward)
  varReverse <-  Biostrings::subseq(fq2, start = Ldef$skipReverse + Ldef$umiLengthReverse + 
                                      Ldef$constantLengthReverse + 1, width = Ldef$variableLengthReverse)
  varReverse <- Biostrings::reverseComplement(varReverse)
  replaceAt <- as(ShortRead::FastqQuality(Biostrings::quality(varReverse)), ""matrix"") > 
    as(ShortRead::FastqQuality(Biostrings::quality(varForward)), ""matrix"")
  replaceAtList <- as(lapply(seq_len(nrow(replaceAt)),
                             function(x) which(replaceAt[x, ])), ""IntegerList"")
  consensusSeq <- Biostrings::replaceLetterAt(x = varForward, at = replaceAt, 
                                              letter = varReverse[replaceAtList])
  qualsForward <- as(ShortRead::FastqQuality(Biostrings::quality(varForward)), ""matrix"")
  qualsReverse <- as(ShortRead::FastqQuality(Biostrings::quality(varReverse)), ""matrix"")
  qualsForward[replaceAt] <- qualsReverse[replaceAt]
  phredQuals <- apply(qualsForward, 1, function(v) seqTools::ascii2char(v + 33))
  consensusQuals <- Biostrings::PhredQuality(phredQuals)
  varForward <- Biostrings::QualityScaledDNAStringSet(consensusSeq, consensusQuals)
  
  ## Average quality in variable sequences too low
  avgQualForward <- ShortRead::alphabetScore(Biostrings::quality(varForward))/
    BiocGenerics::width(varForward)
  lowqual <- avgQualForward < Ldef$avePhredMinForward
  message(""Number of reads with low average quality: "", sum(lowqual))  # 0
  fq1 <- fq1[!lowqual]
  fq2 <- fq2[!lowqual]
  varForward <- varForward[!lowqual]
  
  ## Too many Ns in variable sequences
  nbrNForward <- Biostrings::vcountPattern(""N"", varForward, fixed = TRUE)
  toomanynvar <- nbrNForward > Ldef$variableNMaxForward
  message(""Number of reads with too many N in variable sequence: "", sum(toomanynvar))  # 44
  fq1 <- fq1[!toomanynvar]
  fq2 <- fq2[!toomanynvar]
  varForward <- varForward[!toomanynvar]
  
  ## Too many Ns in UMIs
  umiForward <- Biostrings::subseq(fq1, start = Ldef$skipForward + 1, width = Ldef$umiLengthForward)
  umiReverse <- Biostrings::subseq(fq2, start = Ldef$skipReverse + 1, width = Ldef$umiLengthReverse)
  umis <- Biostrings::xscat(umiForward, umiReverse)
  nbrNumi <- Biostrings::vcountPattern(""N"", umis, fixed = TRUE)
  toomanynumi <- nbrNumi > Ldef$umiNMax
  message(""Number of reads with too many N in UMI: "", sum(toomanynumi))  # 0
  fq1 <- fq1[!toomanynumi]
  fq2 <- fq2[!toomanynumi]
  varForward <- varForward[!toomanynumi]
  
  ## Compare to wildtype, forward
  p <- DNAStringSet(rep(Ldef$wildTypeForward, length(varForward)))
  pattern_width <- width(p)
  subject_width <- width(varForward)
  unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(varForward)))
  breakpoints <- cumsum(pattern_width)
  ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                       breakpoints) + 1L,
                          nbins = length(p))
  skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
  nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
  offsets <- c(0L, breakpoints[-length(breakpoints)])
  mismatchPositions <- nucleotideMismatches - offsets
  mismatchQualities <- Biostrings::quality(varForward)[mismatchPositions]
  lowqualmm <- min(as(mismatchQualities, ""IntegerList"")) < Ldef$mutatedPhredMinForward
  message(""Number of reads with low-quality mismatch bases: "", sum(lowqualmm))  ## 0
  fq1 <- fq1[!lowqualmm]
  fq2 <- fq2[!lowqualmm]
  mismatchPositions <- mismatchPositions[!lowqualmm]
  varForward <- varForward[!lowqualmm]
  ## Number of mutated codons/bases
  codonMismatches <- (mismatchPositions - 1) %/% 3 + 1
  nbrMutCodons <- S4Vectors::elementNROWS(unique(codonMismatches))
  baseMismatches <- mismatchPositions
  nbrMutBases <- S4Vectors::elementNROWS(unique(baseMismatches))
  if (Ldef$nbrMutatedCodonsMaxForward != (-1)) {
    toomanycodons <- nbrMutCodons > Ldef$nbrMutatedCodonsMaxForward
  } else {
    toomanycodons <- nbrMutBases > Ldef$nbrMutatedBasesMaxForward
  }
  message(""Number of reads with too many mutated codons: "", sum(toomanycodons))  ## 581
  fq1 <- fq1[!toomanycodons]
  fq2 <- fq2[!toomanycodons]
  codonMismatches <- codonMismatches[!toomanycodons]
  nbrMutCodons <- nbrMutCodons[!toomanycodons]
  baseMismatches <- baseMismatches[!toomanycodons]
  nbrMutBases <- nbrMutBases[!toomanycodons]
  varForward <- varForward[!toomanycodons]
  ## Forbidden codons
  # if (Ldef$nbrMutatedCodonsMaxForward != (-1)) {
  uniqueMutCodons <- unique(codonMismatches)
  uniqueMutBases <- unique(baseMismatches)
  mutBases <- S4Vectors::endoapply(uniqueMutBases,
                                    function(v) v)
  mutBases <- as(varForward, ""DNAStringSet"")[mutBases]
  mutCodons <- S4Vectors::endoapply(uniqueMutCodons, 
                                    function(v) rep(3 * v, each = 3) + c(-2, -1, 0))
  mutCodons <- as(varForward, ""DNAStringSet"")[mutCodons]
  matchForbidden <- Biostrings::vmatchPattern(
    pattern = Ldef$forbiddenMutatedCodonsForward, 
    as(mutCodons, ""DNAStringSet""), 
    fixed = FALSE)
  forbiddencodon <- sapply(BiocGenerics::relist(start(unlist(matchForbidden)) %% 3==1, 
                                                matchForbidden), function(i) any(i))
  message(""Number of reads with forbidden codons: "", sum(forbiddencodon))  ## 82
  fq1 <- fq1[!forbiddencodon]
  fq2 <- fq2[!forbiddencodon]
  varForward <- varForward[!forbiddencodon]
  mutCodons <- mutCodons[!forbiddencodon]
  mutBases <- mutBases[!forbiddencodon]
  uniqueMutCodons <- uniqueMutCodons[!forbiddencodon]
  uniqueMutBases <- uniqueMutBases[!forbiddencodon]
  nbrMutCodons <- nbrMutCodons[!forbiddencodon]
  nbrMutBases <- nbrMutBases[!forbiddencodon]
  if (Ldef$nbrMutatedCodonsMaxForward != (-1)) {
    splitMutCodons <- as(strsplit(gsub(""([^.]{3})"", ""\\1\\."", 
                                       as.character(mutCodons)), ""."", fixed = TRUE), 
                         ""CharacterList"")
    encodedMutCodonsForward <- S4Vectors::unstrsplit(BiocGenerics::relist(
      paste0(""f"", unlist(uniqueMutCodons), unlist(splitMutCodons)), 
      uniqueMutCodons),
      sep = ""_"")
  } else {
    splitMutBases <- as(strsplit(gsub(""([^.]{1})"", ""\\1\\."",
                                      as.character(mutBases)), ""."", fixed = TRUE),
                        ""CharacterList"")
    encodedMutCodonsForward <- S4Vectors::unstrsplit(BiocGenerics::relist(
      paste0(""f"", unlist(uniqueMutBases), unlist(splitMutBases)), 
      uniqueMutBases),
      sep = ""_"")
  }
  
  mutNames <- encodedMutCodonsForward
  mutNames <- gsub(""^_"", """", gsub(""_$"", """", mutNames))
  mutNames[mutNames <- """"] <- ""WT""
  message(""Number of variants seen thrice: "", sum(table(mutNames) == 3))  ## 5
  message(""Variants seen thrice: "")
  print(which(table(mutNames) == 3))  
  
  ## Retained reads
  message(""Number of retained reads: "", length(fq1))  ## 167
  
  ## Number of mutated bases and codons
  message(""Number of mutated codons per retained read: "")
  print(table(nbrMutCodons))
  message(""Number of different reads with each number of mismatched codons: "")
  print(table(nbrMutCodons[!duplicated(varForward)]))
  message(""Number of mutated bases per retained read: "")
  print(table(nbrMutBases))
  message(""Number of different reads with each number of mismatched bases: "")
  print(table(nbrMutBases[!duplicated(varForward)]))
  
  ## Number of mutated amino acids
  p_aa <- Biostrings::translate(DNAStringSet(rep(Ldef$wildTypeForward, length(varForward))))
  pattern_width_aa <- width(p_aa)
  varForward_aa <- Biostrings::translate(varForward)
  subject_width_aa <- width(varForward_aa)
  unlisted_ans_aa <- which(as.raw(unlist(p_aa)) != as.raw(unlist(varForward_aa)))
  breakpoints_aa <- cumsum(pattern_width_aa)
  ans_eltlens_aa <- tabulate(findInterval(unlisted_ans_aa - 1L,
                                       breakpoints_aa) + 1L,
                          nbins = length(p_aa))
  skeleton_aa <- IRanges::PartitioningByEnd(cumsum(ans_eltlens_aa))
  aaMismatches <- BiocGenerics::relist(unlisted_ans_aa, skeleton_aa)
  message(""Number of mutated aas per retained read: "")
  print(table(lengths(aaMismatches)))  ## 97, 70
  message(""Number of different reads with each number of mismatched aas: "")
  print(table(lengths(aaMismatches)[!duplicated(varForward)]))  ## 14, 53
  
  ## Error statistics
  ## Forward
  constForward <- Biostrings::subseq(fq1, start = Ldef$skipForward + Ldef$umiLengthForward + 1, 
                                     width = Ldef$constantLengthForward)
  p <- DNAStringSet(rep(Ldef$constantForward, length(constForward)))
  pattern_width <- width(p)
  subject_width <- width(constForward)
  
  unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(constForward)))
  breakpoints <- cumsum(pattern_width)
  ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                       breakpoints) + 1L,
                          nbins = length(p))
  skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
  nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
  offsets <- c(0L, breakpoints[-length(breakpoints)])
  mismatchPositions <- nucleotideMismatches - offsets
  mismatchQualities <- unlist(as(quality(constForward)[mismatchPositions], ""IntegerList""))
  
  unlisted_ans <- which(as.raw(unlist(p)) == as.raw(unlist(constForward)))
  breakpoints <- cumsum(pattern_width)
  ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                       breakpoints) + 1L,
                          nbins = length(p))
  skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
  nucleotideMatches <- BiocGenerics::relist(unlisted_ans, skeleton)
  offsets <- c(0L, breakpoints[-length(breakpoints)])
  matchPositions <- nucleotideMatches - offsets
  matchQualities <- unlist(as(quality(constForward)[matchPositions], ""IntegerList""))
  
  message(""Match qualities, forward:"")
  print(table(matchQualities))
  message(""Mismatch qualities, forward:"")
  print(table(mismatchQualities))
  
  ## Reverse
  constReverse <- Biostrings::subseq(fq2, start = Ldef$skipReverse + Ldef$umiLengthReverse + 1,
                                     width = Ldef$constantLengthReverse)
  constReverse <- Biostrings::reverseComplement(constReverse)
  p <- DNAStringSet(rep(Ldef$constantReverse, length(constReverse)))
  pattern_width <- width(p)
  subject_width <- width(constReverse)
  
  unlisted_ans <- which(as.raw(unlist(p)) != as.raw(unlist(constReverse)))
  breakpoints <- cumsum(pattern_width)
  ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                       breakpoints) + 1L,
                          nbins = length(p))
  skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
  nucleotideMismatches <- BiocGenerics::relist(unlisted_ans, skeleton)
  offsets <- c(0L, breakpoints[-length(breakpoints)])
  mismatchPositions <- nucleotideMismatches - offsets
  mismatchQualities <- unlist(as(quality(constReverse)[mismatchPositions], ""IntegerList""))
  
  unlisted_ans <- which(as.raw(unlist(p)) == as.raw(unlist(constReverse)))
  breakpoints <- cumsum(pattern_width)
  ans_eltlens <- tabulate(findInterval(unlisted_ans - 1L,
                                       breakpoints) + 1L,
                          nbins = length(p))
  skeleton <- IRanges::PartitioningByEnd(cumsum(ans_eltlens))
  nucleotideMatches <- BiocGenerics::relist(unlisted_ans, skeleton)
  offsets <- c(0L, breakpoints[-length(breakpoints)])
  matchPositions <- nucleotideMatches - offsets
  matchQualities <- unlist(as(quality(constReverse)[matchPositions], ""IntegerList""))
  
  message(""Match qualities, reverse:"")
  print(table(matchQualities))
  message(""Mismatch qualities, reverse:"")
  print(table(mismatchQualities))
}  

## ----------------------------------------------------------------------------
## Generate values for comparison in unit tests - cis experiment
fqc1 <- system.file(""extdata/cisInput_1.fastq.gz"", package = ""mutscan"")
fqc2 <- system.file(""extdata/cisInput_2.fastq.gz"", package = ""mutscan"")
## default arguments
Ldef <- list(
  fastqForward = fqc1, fastqReverse = fqc2, 
  mergeForwardReverse = TRUE, revComplForward = FALSE, revComplReverse = TRUE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 7, 
  constantLengthForward = 18, constantLengthReverse = 17, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = """", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAGTTCATCCTGGCAGC"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  verbose = FALSE
)
processReadsCis(Ldef)

## Limit number of mismatching bases rather than codons
Ldef <- list(
  fastqForward = fqc1, fastqReverse = fqc2, 
  mergeForwardReverse = TRUE, revComplForward = FALSE, revComplReverse = TRUE,
  skipForward = 1, skipReverse = 1, 
  umiLengthForward = 10, umiLengthReverse = 7, 
  constantLengthForward = 18, constantLengthReverse = 17, 
  variableLengthForward = 96, variableLengthReverse = 96,
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = """", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAGTTCATCCTGGCAGC"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = -1,
  nbrMutatedCodonsMaxReverse = -1,
  nbrMutatedBasesMaxForward = 2,
  nbrMutatedBasesMaxReverse = 2,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  verbose = FALSE
)
processReadsCis(Ldef)
","R"
"Assay","fmicompbio/mutscan","src/RcppExports.cpp",".cpp","26407","340","// Generated by using Rcpp::compileAttributes() -> do not edit by hand
// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#include <Rcpp.h>

using namespace Rcpp;

#ifdef RCPP_USE_GLOBAL_ROSTREAM
Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();
Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
#endif

// calcNearestStringDist
IntegerVector calcNearestStringDist(std::vector<std::string> x, std::string metric, int nThreads);
RcppExport SEXP _mutscan_calcNearestStringDist(SEXP xSEXP, SEXP metricSEXP, SEXP nThreadsSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< std::vector<std::string> >::type x(xSEXP);
    Rcpp::traits::input_parameter< std::string >::type metric(metricSEXP);
    Rcpp::traits::input_parameter< int >::type nThreads(nThreadsSEXP);
    rcpp_result_gen = Rcpp::wrap(calcNearestStringDist(x, metric, nThreads));
    return rcpp_result_gen;
END_RCPP
}
// complement
char complement(char n);
RcppExport SEXP _mutscan_complement(SEXP nSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< char >::type n(nSEXP);
    rcpp_result_gen = Rcpp::wrap(complement(n));
    return rcpp_result_gen;
END_RCPP
}
// compareCodonPositions
bool compareCodonPositions(std::string a, std::string b, const char mutNameDelimiter);
RcppExport SEXP _mutscan_compareCodonPositions(SEXP aSEXP, SEXP bSEXP, SEXP mutNameDelimiterSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< std::string >::type a(aSEXP);
    Rcpp::traits::input_parameter< std::string >::type b(bSEXP);
    Rcpp::traits::input_parameter< const char >::type mutNameDelimiter(mutNameDelimiterSEXP);
    rcpp_result_gen = Rcpp::wrap(compareCodonPositions(a, b, mutNameDelimiter));
    return rcpp_result_gen;
END_RCPP
}
// translateString
std::string translateString(const std::string& s);
RcppExport SEXP _mutscan_translateString(SEXP sSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< const std::string& >::type s(sSEXP);
    rcpp_result_gen = Rcpp::wrap(translateString(s));
    return rcpp_result_gen;
END_RCPP
}
// makeBaseHGVS
std::string makeBaseHGVS(const std::vector<std::string> mutationsSorted, const std::string mutNameDelimiter, const std::string wtSeq, const std::string varSeq);
RcppExport SEXP _mutscan_makeBaseHGVS(SEXP mutationsSortedSEXP, SEXP mutNameDelimiterSEXP, SEXP wtSeqSEXP, SEXP varSeqSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< const std::vector<std::string> >::type mutationsSorted(mutationsSortedSEXP);
    Rcpp::traits::input_parameter< const std::string >::type mutNameDelimiter(mutNameDelimiterSEXP);
    Rcpp::traits::input_parameter< const std::string >::type wtSeq(wtSeqSEXP);
    Rcpp::traits::input_parameter< const std::string >::type varSeq(varSeqSEXP);
    rcpp_result_gen = Rcpp::wrap(makeBaseHGVS(mutationsSorted, mutNameDelimiter, wtSeq, varSeq));
    return rcpp_result_gen;
END_RCPP
}
// test_makeAAHGVS
std::string test_makeAAHGVS(const std::vector<std::string> mutationsSorted, const std::string mutNameDelimiter, const std::string wtSeq);
RcppExport SEXP _mutscan_test_makeAAHGVS(SEXP mutationsSortedSEXP, SEXP mutNameDelimiterSEXP, SEXP wtSeqSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< const std::vector<std::string> >::type mutationsSorted(mutationsSortedSEXP);
    Rcpp::traits::input_parameter< const std::string >::type mutNameDelimiter(mutNameDelimiterSEXP);
    Rcpp::traits::input_parameter< const std::string >::type wtSeq(wtSeqSEXP);
    rcpp_result_gen = Rcpp::wrap(test_makeAAHGVS(mutationsSorted, mutNameDelimiter, wtSeq));
    return rcpp_result_gen;
END_RCPP
}
// test_compareToWildtype
Rcpp::List test_compareToWildtype(const std::string varSeq, const std::string wtSeq, const std::vector<int> varIntQual, const std::vector<std::string> forbiddenCodons_vect, const double mutatedPhredMin, const int nbrMutatedCodonsMax, const std::string codonPrefix, const int nbrMutatedBasesMax, const std::string mutNameDelimiter, const bool collapseToWT);
RcppExport SEXP _mutscan_test_compareToWildtype(SEXP varSeqSEXP, SEXP wtSeqSEXP, SEXP varIntQualSEXP, SEXP forbiddenCodons_vectSEXP, SEXP mutatedPhredMinSEXP, SEXP nbrMutatedCodonsMaxSEXP, SEXP codonPrefixSEXP, SEXP nbrMutatedBasesMaxSEXP, SEXP mutNameDelimiterSEXP, SEXP collapseToWTSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< const std::string >::type varSeq(varSeqSEXP);
    Rcpp::traits::input_parameter< const std::string >::type wtSeq(wtSeqSEXP);
    Rcpp::traits::input_parameter< const std::vector<int> >::type varIntQual(varIntQualSEXP);
    Rcpp::traits::input_parameter< const std::vector<std::string> >::type forbiddenCodons_vect(forbiddenCodons_vectSEXP);
    Rcpp::traits::input_parameter< const double >::type mutatedPhredMin(mutatedPhredMinSEXP);
    Rcpp::traits::input_parameter< const int >::type nbrMutatedCodonsMax(nbrMutatedCodonsMaxSEXP);
    Rcpp::traits::input_parameter< const std::string >::type codonPrefix(codonPrefixSEXP);
    Rcpp::traits::input_parameter< const int >::type nbrMutatedBasesMax(nbrMutatedBasesMaxSEXP);
    Rcpp::traits::input_parameter< const std::string >::type mutNameDelimiter(mutNameDelimiterSEXP);
    Rcpp::traits::input_parameter< const bool >::type collapseToWT(collapseToWTSEXP);
    rcpp_result_gen = Rcpp::wrap(test_compareToWildtype(varSeq, wtSeq, varIntQual, forbiddenCodons_vect, mutatedPhredMin, nbrMutatedCodonsMax, codonPrefix, nbrMutatedBasesMax, mutNameDelimiter, collapseToWT));
    return rcpp_result_gen;
END_RCPP
}
// test_decomposeRead
List test_decomposeRead(const std::string sseq, const std::string squal, const std::string elements, const std::vector<int> elementLengths, const std::vector<std::string> primerSeqs, std::string umiSeq, std::string varSeq, std::string varQual, std::vector<int> varLengths, std::string constSeq, std::string constQual, int nNoPrimer, int nReadWrongLength);
RcppExport SEXP _mutscan_test_decomposeRead(SEXP sseqSEXP, SEXP squalSEXP, SEXP elementsSEXP, SEXP elementLengthsSEXP, SEXP primerSeqsSEXP, SEXP umiSeqSEXP, SEXP varSeqSEXP, SEXP varQualSEXP, SEXP varLengthsSEXP, SEXP constSeqSEXP, SEXP constQualSEXP, SEXP nNoPrimerSEXP, SEXP nReadWrongLengthSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< const std::string >::type sseq(sseqSEXP);
    Rcpp::traits::input_parameter< const std::string >::type squal(squalSEXP);
    Rcpp::traits::input_parameter< const std::string >::type elements(elementsSEXP);
    Rcpp::traits::input_parameter< const std::vector<int> >::type elementLengths(elementLengthsSEXP);
    Rcpp::traits::input_parameter< const std::vector<std::string> >::type primerSeqs(primerSeqsSEXP);
    Rcpp::traits::input_parameter< std::string >::type umiSeq(umiSeqSEXP);
    Rcpp::traits::input_parameter< std::string >::type varSeq(varSeqSEXP);
    Rcpp::traits::input_parameter< std::string >::type varQual(varQualSEXP);
    Rcpp::traits::input_parameter< std::vector<int> >::type varLengths(varLengthsSEXP);
    Rcpp::traits::input_parameter< std::string >::type constSeq(constSeqSEXP);
    Rcpp::traits::input_parameter< std::string >::type constQual(constQualSEXP);
    Rcpp::traits::input_parameter< int >::type nNoPrimer(nNoPrimerSEXP);
    Rcpp::traits::input_parameter< int >::type nReadWrongLength(nReadWrongLengthSEXP);
    rcpp_result_gen = Rcpp::wrap(test_decomposeRead(sseq, squal, elements, elementLengths, primerSeqs, umiSeq, varSeq, varQual, varLengths, constSeq, constQual, nNoPrimer, nReadWrongLength));
    return rcpp_result_gen;
END_RCPP
}
// test_mergeReadPairPartial
List test_mergeReadPairPartial(std::string seqF, std::vector<int> qualF, std::string seqR, std::vector<int> qualR, std::vector<int> lenF, std::vector<int> lenR, size_t minOverlap, size_t maxOverlap, size_t minMergedLength, size_t maxMergedLength, double maxFracMismatchOverlap, bool greedy);
RcppExport SEXP _mutscan_test_mergeReadPairPartial(SEXP seqFSEXP, SEXP qualFSEXP, SEXP seqRSEXP, SEXP qualRSEXP, SEXP lenFSEXP, SEXP lenRSEXP, SEXP minOverlapSEXP, SEXP maxOverlapSEXP, SEXP minMergedLengthSEXP, SEXP maxMergedLengthSEXP, SEXP maxFracMismatchOverlapSEXP, SEXP greedySEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< std::string >::type seqF(seqFSEXP);
    Rcpp::traits::input_parameter< std::vector<int> >::type qualF(qualFSEXP);
    Rcpp::traits::input_parameter< std::string >::type seqR(seqRSEXP);
    Rcpp::traits::input_parameter< std::vector<int> >::type qualR(qualRSEXP);
    Rcpp::traits::input_parameter< std::vector<int> >::type lenF(lenFSEXP);
    Rcpp::traits::input_parameter< std::vector<int> >::type lenR(lenRSEXP);
    Rcpp::traits::input_parameter< size_t >::type minOverlap(minOverlapSEXP);
    Rcpp::traits::input_parameter< size_t >::type maxOverlap(maxOverlapSEXP);
    Rcpp::traits::input_parameter< size_t >::type minMergedLength(minMergedLengthSEXP);
    Rcpp::traits::input_parameter< size_t >::type maxMergedLength(maxMergedLengthSEXP);
    Rcpp::traits::input_parameter< double >::type maxFracMismatchOverlap(maxFracMismatchOverlapSEXP);
    Rcpp::traits::input_parameter< bool >::type greedy(greedySEXP);
    rcpp_result_gen = Rcpp::wrap(test_mergeReadPairPartial(seqF, qualF, seqR, qualR, lenF, lenR, minOverlap, maxOverlap, minMergedLength, maxMergedLength, maxFracMismatchOverlap, greedy));
    return rcpp_result_gen;
END_RCPP
}
// findClosestRefSeq
int findClosestRefSeq(std::string& varSeq, std::vector<std::string>& wtSeq, size_t upperBoundMismatch, int& sim);
RcppExport SEXP _mutscan_findClosestRefSeq(SEXP varSeqSEXP, SEXP wtSeqSEXP, SEXP upperBoundMismatchSEXP, SEXP simSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< std::string& >::type varSeq(varSeqSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string>& >::type wtSeq(wtSeqSEXP);
    Rcpp::traits::input_parameter< size_t >::type upperBoundMismatch(upperBoundMismatchSEXP);
    Rcpp::traits::input_parameter< int& >::type sim(simSEXP);
    rcpp_result_gen = Rcpp::wrap(findClosestRefSeq(varSeq, wtSeq, upperBoundMismatch, sim));
    return rcpp_result_gen;
END_RCPP
}
// findClosestRefSeqEarlyStop
int findClosestRefSeqEarlyStop(std::string& varSeq, std::vector<std::string>& wtSeq, size_t upperBoundMismatch, int& sim);
RcppExport SEXP _mutscan_findClosestRefSeqEarlyStop(SEXP varSeqSEXP, SEXP wtSeqSEXP, SEXP upperBoundMismatchSEXP, SEXP simSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< std::string& >::type varSeq(varSeqSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string>& >::type wtSeq(wtSeqSEXP);
    Rcpp::traits::input_parameter< size_t >::type upperBoundMismatch(upperBoundMismatchSEXP);
    Rcpp::traits::input_parameter< int& >::type sim(simSEXP);
    rcpp_result_gen = Rcpp::wrap(findClosestRefSeqEarlyStop(varSeq, wtSeq, upperBoundMismatch, sim));
    return rcpp_result_gen;
END_RCPP
}
// groupSimilarSequences
Rcpp::DataFrame groupSimilarSequences(std::vector<std::string> seqs, std::vector<double> scores, double collapseMaxDist, double collapseMinScore, double collapseMinRatio, bool verbose);
RcppExport SEXP _mutscan_groupSimilarSequences(SEXP seqsSEXP, SEXP scoresSEXP, SEXP collapseMaxDistSEXP, SEXP collapseMinScoreSEXP, SEXP collapseMinRatioSEXP, SEXP verboseSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< std::vector<std::string> >::type seqs(seqsSEXP);
    Rcpp::traits::input_parameter< std::vector<double> >::type scores(scoresSEXP);
    Rcpp::traits::input_parameter< double >::type collapseMaxDist(collapseMaxDistSEXP);
    Rcpp::traits::input_parameter< double >::type collapseMinScore(collapseMinScoreSEXP);
    Rcpp::traits::input_parameter< double >::type collapseMinRatio(collapseMinRatioSEXP);
    Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP);
    rcpp_result_gen = Rcpp::wrap(groupSimilarSequences(seqs, scores, collapseMaxDist, collapseMinScore, collapseMinRatio, verbose));
    return rcpp_result_gen;
END_RCPP
}
// digestFastqsCpp
List digestFastqsCpp(std::vector<std::string> fastqForwardVect, std::vector<std::string> fastqReverseVect, bool mergeForwardReverse, size_t minOverlap, size_t maxOverlap, size_t minMergedLength, size_t maxMergedLength, double maxFracMismatchOverlap, bool greedyOverlap, bool revComplForward, bool revComplReverse, std::string elementsForward, std::vector<int> elementLengthsForward, std::string elementsReverse, std::vector<int> elementLengthsReverse, std::string adapterForward, std::string adapterReverse, std::vector<std::string> primerForward, std::vector<std::string> primerReverse, std::vector<std::string> wildTypeForward, std::vector<std::string> wildTypeForwardNames, std::vector<std::string> wildTypeReverse, std::vector<std::string> wildTypeReverseNames, std::vector<std::string> constantForward, std::vector<std::string> constantReverse, double avePhredMinForward, double avePhredMinReverse, int variableNMaxForward, int variableNMaxReverse, int umiNMax, int nbrMutatedCodonsMaxForward, int nbrMutatedCodonsMaxReverse, int nbrMutatedBasesMaxForward, int nbrMutatedBasesMaxReverse, CharacterVector forbiddenMutatedCodonsForward, CharacterVector forbiddenMutatedCodonsReverse, bool useTreeWTmatch, bool collapseToWTForward, bool collapseToWTReverse, double mutatedPhredMinForward, double mutatedPhredMinReverse, std::string mutNameDelimiter, int constantMaxDistForward, int constantMaxDistReverse, double umiCollapseMaxDist, std::string filteredReadsFastqForward, std::string filteredReadsFastqReverse, int maxNReads, bool verbose, int nThreads, int chunkSize, size_t maxReadLength);
RcppExport SEXP _mutscan_digestFastqsCpp(SEXP fastqForwardVectSEXP, SEXP fastqReverseVectSEXP, SEXP mergeForwardReverseSEXP, SEXP minOverlapSEXP, SEXP maxOverlapSEXP, SEXP minMergedLengthSEXP, SEXP maxMergedLengthSEXP, SEXP maxFracMismatchOverlapSEXP, SEXP greedyOverlapSEXP, SEXP revComplForwardSEXP, SEXP revComplReverseSEXP, SEXP elementsForwardSEXP, SEXP elementLengthsForwardSEXP, SEXP elementsReverseSEXP, SEXP elementLengthsReverseSEXP, SEXP adapterForwardSEXP, SEXP adapterReverseSEXP, SEXP primerForwardSEXP, SEXP primerReverseSEXP, SEXP wildTypeForwardSEXP, SEXP wildTypeForwardNamesSEXP, SEXP wildTypeReverseSEXP, SEXP wildTypeReverseNamesSEXP, SEXP constantForwardSEXP, SEXP constantReverseSEXP, SEXP avePhredMinForwardSEXP, SEXP avePhredMinReverseSEXP, SEXP variableNMaxForwardSEXP, SEXP variableNMaxReverseSEXP, SEXP umiNMaxSEXP, SEXP nbrMutatedCodonsMaxForwardSEXP, SEXP nbrMutatedCodonsMaxReverseSEXP, SEXP nbrMutatedBasesMaxForwardSEXP, SEXP nbrMutatedBasesMaxReverseSEXP, SEXP forbiddenMutatedCodonsForwardSEXP, SEXP forbiddenMutatedCodonsReverseSEXP, SEXP useTreeWTmatchSEXP, SEXP collapseToWTForwardSEXP, SEXP collapseToWTReverseSEXP, SEXP mutatedPhredMinForwardSEXP, SEXP mutatedPhredMinReverseSEXP, SEXP mutNameDelimiterSEXP, SEXP constantMaxDistForwardSEXP, SEXP constantMaxDistReverseSEXP, SEXP umiCollapseMaxDistSEXP, SEXP filteredReadsFastqForwardSEXP, SEXP filteredReadsFastqReverseSEXP, SEXP maxNReadsSEXP, SEXP verboseSEXP, SEXP nThreadsSEXP, SEXP chunkSizeSEXP, SEXP maxReadLengthSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< std::vector<std::string> >::type fastqForwardVect(fastqForwardVectSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type fastqReverseVect(fastqReverseVectSEXP);
    Rcpp::traits::input_parameter< bool >::type mergeForwardReverse(mergeForwardReverseSEXP);
    Rcpp::traits::input_parameter< size_t >::type minOverlap(minOverlapSEXP);
    Rcpp::traits::input_parameter< size_t >::type maxOverlap(maxOverlapSEXP);
    Rcpp::traits::input_parameter< size_t >::type minMergedLength(minMergedLengthSEXP);
    Rcpp::traits::input_parameter< size_t >::type maxMergedLength(maxMergedLengthSEXP);
    Rcpp::traits::input_parameter< double >::type maxFracMismatchOverlap(maxFracMismatchOverlapSEXP);
    Rcpp::traits::input_parameter< bool >::type greedyOverlap(greedyOverlapSEXP);
    Rcpp::traits::input_parameter< bool >::type revComplForward(revComplForwardSEXP);
    Rcpp::traits::input_parameter< bool >::type revComplReverse(revComplReverseSEXP);
    Rcpp::traits::input_parameter< std::string >::type elementsForward(elementsForwardSEXP);
    Rcpp::traits::input_parameter< std::vector<int> >::type elementLengthsForward(elementLengthsForwardSEXP);
    Rcpp::traits::input_parameter< std::string >::type elementsReverse(elementsReverseSEXP);
    Rcpp::traits::input_parameter< std::vector<int> >::type elementLengthsReverse(elementLengthsReverseSEXP);
    Rcpp::traits::input_parameter< std::string >::type adapterForward(adapterForwardSEXP);
    Rcpp::traits::input_parameter< std::string >::type adapterReverse(adapterReverseSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type primerForward(primerForwardSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type primerReverse(primerReverseSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type wildTypeForward(wildTypeForwardSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type wildTypeForwardNames(wildTypeForwardNamesSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type wildTypeReverse(wildTypeReverseSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type wildTypeReverseNames(wildTypeReverseNamesSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type constantForward(constantForwardSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type constantReverse(constantReverseSEXP);
    Rcpp::traits::input_parameter< double >::type avePhredMinForward(avePhredMinForwardSEXP);
    Rcpp::traits::input_parameter< double >::type avePhredMinReverse(avePhredMinReverseSEXP);
    Rcpp::traits::input_parameter< int >::type variableNMaxForward(variableNMaxForwardSEXP);
    Rcpp::traits::input_parameter< int >::type variableNMaxReverse(variableNMaxReverseSEXP);
    Rcpp::traits::input_parameter< int >::type umiNMax(umiNMaxSEXP);
    Rcpp::traits::input_parameter< int >::type nbrMutatedCodonsMaxForward(nbrMutatedCodonsMaxForwardSEXP);
    Rcpp::traits::input_parameter< int >::type nbrMutatedCodonsMaxReverse(nbrMutatedCodonsMaxReverseSEXP);
    Rcpp::traits::input_parameter< int >::type nbrMutatedBasesMaxForward(nbrMutatedBasesMaxForwardSEXP);
    Rcpp::traits::input_parameter< int >::type nbrMutatedBasesMaxReverse(nbrMutatedBasesMaxReverseSEXP);
    Rcpp::traits::input_parameter< CharacterVector >::type forbiddenMutatedCodonsForward(forbiddenMutatedCodonsForwardSEXP);
    Rcpp::traits::input_parameter< CharacterVector >::type forbiddenMutatedCodonsReverse(forbiddenMutatedCodonsReverseSEXP);
    Rcpp::traits::input_parameter< bool >::type useTreeWTmatch(useTreeWTmatchSEXP);
    Rcpp::traits::input_parameter< bool >::type collapseToWTForward(collapseToWTForwardSEXP);
    Rcpp::traits::input_parameter< bool >::type collapseToWTReverse(collapseToWTReverseSEXP);
    Rcpp::traits::input_parameter< double >::type mutatedPhredMinForward(mutatedPhredMinForwardSEXP);
    Rcpp::traits::input_parameter< double >::type mutatedPhredMinReverse(mutatedPhredMinReverseSEXP);
    Rcpp::traits::input_parameter< std::string >::type mutNameDelimiter(mutNameDelimiterSEXP);
    Rcpp::traits::input_parameter< int >::type constantMaxDistForward(constantMaxDistForwardSEXP);
    Rcpp::traits::input_parameter< int >::type constantMaxDistReverse(constantMaxDistReverseSEXP);
    Rcpp::traits::input_parameter< double >::type umiCollapseMaxDist(umiCollapseMaxDistSEXP);
    Rcpp::traits::input_parameter< std::string >::type filteredReadsFastqForward(filteredReadsFastqForwardSEXP);
    Rcpp::traits::input_parameter< std::string >::type filteredReadsFastqReverse(filteredReadsFastqReverseSEXP);
    Rcpp::traits::input_parameter< int >::type maxNReads(maxNReadsSEXP);
    Rcpp::traits::input_parameter< bool >::type verbose(verboseSEXP);
    Rcpp::traits::input_parameter< int >::type nThreads(nThreadsSEXP);
    Rcpp::traits::input_parameter< int >::type chunkSize(chunkSizeSEXP);
    Rcpp::traits::input_parameter< size_t >::type maxReadLength(maxReadLengthSEXP);
    rcpp_result_gen = Rcpp::wrap(digestFastqsCpp(fastqForwardVect, fastqReverseVect, mergeForwardReverse, minOverlap, maxOverlap, minMergedLength, maxMergedLength, maxFracMismatchOverlap, greedyOverlap, revComplForward, revComplReverse, elementsForward, elementLengthsForward, elementsReverse, elementLengthsReverse, adapterForward, adapterReverse, primerForward, primerReverse, wildTypeForward, wildTypeForwardNames, wildTypeReverse, wildTypeReverseNames, constantForward, constantReverse, avePhredMinForward, avePhredMinReverse, variableNMaxForward, variableNMaxReverse, umiNMax, nbrMutatedCodonsMaxForward, nbrMutatedCodonsMaxReverse, nbrMutatedBasesMaxForward, nbrMutatedBasesMaxReverse, forbiddenMutatedCodonsForward, forbiddenMutatedCodonsReverse, useTreeWTmatch, collapseToWTForward, collapseToWTReverse, mutatedPhredMinForward, mutatedPhredMinReverse, mutNameDelimiter, constantMaxDistForward, constantMaxDistReverse, umiCollapseMaxDist, filteredReadsFastqForward, filteredReadsFastqReverse, maxNReads, verbose, nThreads, chunkSize, maxReadLength));
    return rcpp_result_gen;
END_RCPP
}
// mergeValues
DataFrame mergeValues(std::vector<std::string> mutNamesIn, std::vector<std::string> valuesIn, char delimiter);
RcppExport SEXP _mutscan_mergeValues(SEXP mutNamesInSEXP, SEXP valuesInSEXP, SEXP delimiterSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< std::vector<std::string> >::type mutNamesIn(mutNamesInSEXP);
    Rcpp::traits::input_parameter< std::vector<std::string> >::type valuesIn(valuesInSEXP);
    Rcpp::traits::input_parameter< char >::type delimiter(delimiterSEXP);
    rcpp_result_gen = Rcpp::wrap(mergeValues(mutNamesIn, valuesIn, delimiter));
    return rcpp_result_gen;
END_RCPP
}
// levenshtein_distance
int levenshtein_distance(const std::string& str1, const std::string& str2, int ignored_variable);
RcppExport SEXP _mutscan_levenshtein_distance(SEXP str1SEXP, SEXP str2SEXP, SEXP ignored_variableSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< const std::string& >::type str1(str1SEXP);
    Rcpp::traits::input_parameter< const std::string& >::type str2(str2SEXP);
    Rcpp::traits::input_parameter< int >::type ignored_variable(ignored_variableSEXP);
    rcpp_result_gen = Rcpp::wrap(levenshtein_distance(str1, str2, ignored_variable));
    return rcpp_result_gen;
END_RCPP
}
// hamming_distance
int hamming_distance(const std::string& str1, const std::string& str2, int ignored_variable);
RcppExport SEXP _mutscan_hamming_distance(SEXP str1SEXP, SEXP str2SEXP, SEXP ignored_variableSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< const std::string& >::type str1(str1SEXP);
    Rcpp::traits::input_parameter< const std::string& >::type str2(str2SEXP);
    Rcpp::traits::input_parameter< int >::type ignored_variable(ignored_variableSEXP);
    rcpp_result_gen = Rcpp::wrap(hamming_distance(str1, str2, ignored_variable));
    return rcpp_result_gen;
END_RCPP
}
// hamming_shift_distance
int hamming_shift_distance(const std::string& str1, const std::string& str2, int max_abs_shift);
RcppExport SEXP _mutscan_hamming_shift_distance(SEXP str1SEXP, SEXP str2SEXP, SEXP max_abs_shiftSEXP) {
BEGIN_RCPP
    Rcpp::RObject rcpp_result_gen;
    Rcpp::RNGScope rcpp_rngScope_gen;
    Rcpp::traits::input_parameter< const std::string& >::type str1(str1SEXP);
    Rcpp::traits::input_parameter< const std::string& >::type str2(str2SEXP);
    Rcpp::traits::input_parameter< int >::type max_abs_shift(max_abs_shiftSEXP);
    rcpp_result_gen = Rcpp::wrap(hamming_shift_distance(str1, str2, max_abs_shift));
    return rcpp_result_gen;
END_RCPP
}

RcppExport SEXP _rcpp_module_boot_mod_BKtree();

static const R_CallMethodDef CallEntries[] = {
    {""_mutscan_calcNearestStringDist"", (DL_FUNC) &_mutscan_calcNearestStringDist, 3},
    {""_mutscan_complement"", (DL_FUNC) &_mutscan_complement, 1},
    {""_mutscan_compareCodonPositions"", (DL_FUNC) &_mutscan_compareCodonPositions, 3},
    {""_mutscan_translateString"", (DL_FUNC) &_mutscan_translateString, 1},
    {""_mutscan_makeBaseHGVS"", (DL_FUNC) &_mutscan_makeBaseHGVS, 4},
    {""_mutscan_test_makeAAHGVS"", (DL_FUNC) &_mutscan_test_makeAAHGVS, 3},
    {""_mutscan_test_compareToWildtype"", (DL_FUNC) &_mutscan_test_compareToWildtype, 10},
    {""_mutscan_test_decomposeRead"", (DL_FUNC) &_mutscan_test_decomposeRead, 13},
    {""_mutscan_test_mergeReadPairPartial"", (DL_FUNC) &_mutscan_test_mergeReadPairPartial, 12},
    {""_mutscan_findClosestRefSeq"", (DL_FUNC) &_mutscan_findClosestRefSeq, 4},
    {""_mutscan_findClosestRefSeqEarlyStop"", (DL_FUNC) &_mutscan_findClosestRefSeqEarlyStop, 4},
    {""_mutscan_groupSimilarSequences"", (DL_FUNC) &_mutscan_groupSimilarSequences, 6},
    {""_mutscan_digestFastqsCpp"", (DL_FUNC) &_mutscan_digestFastqsCpp, 52},
    {""_mutscan_mergeValues"", (DL_FUNC) &_mutscan_mergeValues, 3},
    {""_mutscan_levenshtein_distance"", (DL_FUNC) &_mutscan_levenshtein_distance, 3},
    {""_mutscan_hamming_distance"", (DL_FUNC) &_mutscan_hamming_distance, 3},
    {""_mutscan_hamming_shift_distance"", (DL_FUNC) &_mutscan_hamming_shift_distance, 3},
    {""_rcpp_module_boot_mod_BKtree"", (DL_FUNC) &_rcpp_module_boot_mod_BKtree, 0},
    {NULL, NULL, 0}
};

RcppExport void R_init_mutscan(DllInfo *dll) {
    R_registerRoutines(dll, NULL, CallEntries, NULL, NULL);
    R_useDynamicSymbols(dll, FALSE);
}
","C++"
"Assay","fmicompbio/mutscan","src/digestFastqs.cpp",".cpp","103294","2386","#include <Rcpp.h>
#include <cerrno>
#include <zlib.h>
#include <string>
#include <vector>
#include <numeric>
#include <map>
#include <algorithm>    // std::sort
#include ""BKtree_utils.h""

#ifdef _OPENMP
#include <omp.h>
// [[Rcpp::plugins(openmp)]]
#endif

// #define BUFFER_SIZE 65536 // maximum length of a single read + 2

// define constants that are used below
#define NO_SIMILAR_REF    -1 // no similar enough wildtype sequence was found
#define TOO_MANY_BEST_REF -2 // too many equally good hits among the WT sequences
#define QUALITY_OFFSET    33

// FastqBuffer_utils needs the BUFFER_SIZE to be defined
#include ""FastqBuffer_utils.h""

using namespace std::placeholders;
using namespace Rcpp;

// check if the last gzgets reached end of file
// return either:
// - true (reached end of file)
// - false (not yet reached end of file, but reading was ok)
// - fall back to R with an error (reading was not ok)
bool reached_end_of_file(gzFile file, char *ret) {
  static int err;
  if (ret == Z_NULL) {
    if (gzeof(file)) {
      return true;
    } else {
      const char *error_string; // # nocov start
      error_string = gzerror(file, &err);
      if (err) {
        stop(error_string);
      } // # nocov end
    }
  }
  // Check if we have read until a newline character. Otherwise, the read is
  // too long -> break
  if (std::string(ret).back() != '\n') {
    stop(""Encountered a read exceeding the maximal allowed length"");
  }
  return false;
}

// read next four lines from gzipped file and store
// second and fourth in *seq and *qual
// return either:
// - true (reached end of file, I am done)
// - false (not yet reached end of file, not done yet)
// - nothing (encountered an error, fall back to R from reached_end_of_file())
bool get_next_seq(gzFile file, char *seq, char *qual, size_t BUFFER_SIZE) {
  // sequence identifier
  if (reached_end_of_file(file, gzgets(file, seq, BUFFER_SIZE))) {
    return true;
  }
  // sequence
  if (reached_end_of_file(file, gzgets(file, seq, BUFFER_SIZE))) {
    return true; // # nocov
  }
  // quality identifier
  if (reached_end_of_file(file, gzgets(file, qual, BUFFER_SIZE))) {
    return true; // # nocov
  }
  // quality
  if (reached_end_of_file(file, gzgets(file, qual, BUFFER_SIZE))) {
    return true; // # nocov
  }

  return false;
}

// create the complement of a base
// [[Rcpp::export]]
char complement(char n) {
  switch(n) {
    case 'A':
    case 'a':
      return 'T';
    case 'T':
    case 't':
      return 'A';
    case 'G':
    case 'g':
      return 'C';
    case 'C':
    case 'c':
      return 'G';
    case 'N':
    case 'n':
      return 'N';
  }
  stop(""Invalid DNA base character in sequence - aborting"");
}

// initialize IUPAC code table
std::map<char,std::vector<char>> initializeIUPAC() {
  std::map<char,std::vector<char>> IUPAC;
  IUPAC['A'] = std::vector<char>({'A'});
  IUPAC['C'] = std::vector<char>({'C'});
  IUPAC['G'] = std::vector<char>({'G'});
  IUPAC['T'] = std::vector<char>({'T'});
  IUPAC['M'] = std::vector<char>({'A','C'});
  IUPAC['R'] = std::vector<char>({'A','G'});
  IUPAC['W'] = std::vector<char>({'A','T'});
  IUPAC['S'] = std::vector<char>({'C','G'});
  IUPAC['Y'] = std::vector<char>({'C','T'});
  IUPAC['K'] = std::vector<char>({'G','T'});
  IUPAC['V'] = std::vector<char>({'A','C','G'});
  IUPAC['H'] = std::vector<char>({'A','C','T'});
  IUPAC['D'] = std::vector<char>({'A','G','T'});
  IUPAC['B'] = std::vector<char>({'C','G','T'});
  IUPAC['N'] = std::vector<char>({'A','C','G','T'});
  return IUPAC;
}

// initialize one- to three-letter amino acid conversion table
std::map<char,std::string> initializeThreeAA() {
    std::map<char,std::string> threeAA;
    threeAA['A'] = std::string(""Ala"");
    threeAA['R'] = std::string(""Arg"");
    threeAA['N'] = std::string(""Asn"");
    threeAA['D'] = std::string(""Asp"");
    threeAA['C'] = std::string(""Cys"");
    threeAA['Q'] = std::string(""Gln"");
    threeAA['E'] = std::string(""Glu"");
    threeAA['G'] = std::string(""Gly"");
    threeAA['H'] = std::string(""His"");
    threeAA['I'] = std::string(""Ile"");
    threeAA['L'] = std::string(""Leu"");
    threeAA['K'] = std::string(""Lys"");
    threeAA['M'] = std::string(""Met"");
    threeAA['F'] = std::string(""Phe"");
    threeAA['P'] = std::string(""Pro"");
    threeAA['S'] = std::string(""Ser"");
    threeAA['T'] = std::string(""Thr"");
    threeAA['W'] = std::string(""Trp"");
    threeAA['Y'] = std::string(""Tyr"");
    threeAA['V'] = std::string(""Val"");
    threeAA['*'] = std::string(""*"");
    return threeAA;
}

// split string by delimiter (code from https://www.fluentcpp.com/2017/04/21/how-to-split-a-string-in-c/)
std::vector<std::string> split(const std::string& s, char delimiter) {
  std::vector<std::string> tokens;
  std::string token;
  std::istringstream tokenStream(s);
  while (std::getline(tokenStream, token, delimiter)) {
    tokens.push_back(token);
  }
  return tokens;
}

// compare position of codons of the form (std::string)""x.123.NNN_""
// [[Rcpp::export]]
bool compareCodonPositions(std::string a, std::string b, const char mutNameDelimiter) {
  int posa = std::stoi(split(a, mutNameDelimiter)[1]);
  int posb = std::stoi(split(b, mutNameDelimiter)[1]);
  return (posa < posb);
}

// translate sequence
// [[Rcpp::export]]
std::string translateString(const std::string& s) {
  const char* tr = ""KQE*TPASRRG*ILVLNHDYTPASSRGCILVFKQE*TPASRRGWMLVLNHDYTPASSRGCILVF"";
  std::string aa = """";
  size_t i = 0, j = 0;
  const int pow4[] = {1, 4, 16};
  int codonVal = 0;

  for (i = 0; i < s.size(); i++) {
    if (s[i] == '_') {
      j = 0;
      aa.push_back('_');
    } else {
      switch(s[i]) {
        case 'A':
        case 'a':
          // codonVal += 0 * pow4[j];
          break;

        case 'C':
        case 'c':
          codonVal += 1 * pow4[j];
          break;

        case 'G':
        case 'g':
          codonVal += 2 * pow4[j];
          break;

        case 'T':
        case 't':
        case 'u':
        case 'U':
          codonVal += 3 * pow4[j];
          break;

        default:
          codonVal += 64;
          break;
      }

      if (j == 2) {
        if (codonVal > 63) {
          aa.push_back('X');
        } else {
          aa.push_back(tr[codonVal]);
        }
        j = 0;
        codonVal = 0;
      } else {
        j++;
      }

    }

  }
  return aa;
}

// check if string ends with another string
// see https://stackoverflow.com/questions/874134/find-out-if-string-ends-with-another-string-in-c
bool hasEnding (std::string const &fullString, std::string const &ending) {
    if (fullString.length() >= ending.length()) {
        return (0 == fullString.compare (fullString.length() - ending.length(), ending.length(), ending));
    } else {
        return false;
    }
}

// Helper function to create the ID for a single mutation group (delins or
// single mutation)
std::string makeSingleBaseHGVSid(int posMin, int posMax, const std::string wtSeq,
                                 const std::string varSeq) {
    std::string id = """";
    if (posMax > posMin) {
        // delins
        id += std::to_string(posMin) + ""_"" + std::to_string(posMax) +
            ""delins"" +
            varSeq.substr((size_t)(posMin - 1), (size_t)(posMax - posMin + 1)) + "";"";
    } else {
        // single base mutation
        id += std::to_string(posMin) + wtSeq[posMin - 1] +
            "">"" + varSeq[posMin - 1] + "";"";
    }
    return id;
}

// [[Rcpp::export]]
std::string makeBaseHGVS(const std::vector<std::string> mutationsSorted,
                         const std::string mutNameDelimiter,
                         const std::string wtSeq, const std::string varSeq) {
  int prevPosMin = -5, prevPosMax = -5; // something far enough from the first mutation position to not cause a delins
  bool moreThanOne = false;
  int pos;
  std::string mutId = """";

  if (mutationsSorted.size() == 0) {
    // no mutations - return only _ (all variants should end with this character,
    // as it will be stripped off later)
    return std::string(""_"");
  } else {
    for (size_t i = 0; i < mutationsSorted.size(); i++) {
      // get the mutated position
      pos = std::stoi(split(mutationsSorted[i], *(mutNameDelimiter.c_str()))[1]);
      if (pos - prevPosMax > 2) {
        // need to start a new mutation group
        // first finish the previous one and add to mutId
        if (prevPosMin > 0) {
          // an actual mutation (the initialization value was negative)
          if (mutId != """") {
            // not the first mutation we add
            moreThanOne = true;
          }
          mutId += makeSingleBaseHGVSid(prevPosMin, prevPosMax, wtSeq, varSeq);
        }
        prevPosMin = pos;
        prevPosMax = pos;
      } else {
        prevPosMax = pos;
      }
    }
    // process last mutation
    if (mutId != """") {
      // not the first mutation we add
      moreThanOne = true;
    }
    mutId += makeSingleBaseHGVSid(prevPosMin, prevPosMax, wtSeq, varSeq);
    mutId.pop_back(); // remove final ;
    if (moreThanOne) {
      mutId = ""["" + mutId + ""]_"";
    } else {
      mutId += ""_"";
    }
  }
  return mutId;
}

std::string makeAAHGVS(const std::vector<std::string> mutationsSorted,
                       const std::string mutNameDelimiter,
                       const std::string wtSeq,
                       std::map<char, std::string> &threeAA) {
  int pos;
  std::string mutAA;
  char wtAA;
  std::string mutId = """";

  if (mutationsSorted.size() == 0) {
    // no mutations - return only _ (all variants should end with this character,
    // as it will be stripped off later)
    return std::string(""_"");
  } else {
    if (mutationsSorted.size() > 1) {
      mutId += ""["";
    }
    for (size_t i = 0; i < mutationsSorted.size(); i++) {
      // get the mutated position and the mutated amino acid
      pos = std::stoi(split(mutationsSorted[i], *(mutNameDelimiter.c_str()))[1]);
      mutAA = split(mutationsSorted[i], *(mutNameDelimiter.c_str()))[2];
      // get the corresponding wt amino acid
      wtAA = wtSeq[pos - 1];
      // assemble mutation identifier
      mutId += ""("" + threeAA[wtAA] + std::to_string(pos) + threeAA[*(mutAA.c_str())] + "");"";
    }
    mutId.pop_back(); // remove final ;
    if (mutationsSorted.size() > 1) {
      mutId += ""]"";
    }
    mutId += ""_"";
    return mutId;
  }
}

// [[Rcpp::export]]
std::string test_makeAAHGVS(const std::vector<std::string> mutationsSorted,
                            const std::string mutNameDelimiter,
                            const std::string wtSeq) {
    std::map<char,std::string> threeAA = initializeThreeAA();
    return makeAAHGVS(mutationsSorted, mutNameDelimiter, wtSeq, threeAA);
}

// compare read to wildtype sequence,
// identify mutated bases/codons, filter, update counters
// and add to the name (mutantName* may be empty if no mutations are found,
// except mutantName*HGVS will be set to the nearest wildtype name := `codonPrefix`)
// returns true if the read needs to be filtered out
// (and a counter has been incremented)
// - instead of conditionally creating mutantName,
//   build mutantNameBase and mutantNameCodon in parallel and
//   conditionally set to mutantName base at the end
// - at the end, take mutantNameBase and mutantNameAA and
//   create mutantNameBaseHGVS and mutantNameAAHGVS from it (helper function
//   that takes name and wt/mutant sequences as input):
//   * always using `codonPrefix` as the reference sequence name
//   * if it contains no ':c', add it for mutantNameBaseHGVS
//   * replace ':c' by ':p' for mutantNameAAHGVS (or add if there is no ':c')
//   * use `threeAA` for mapping one- to three-letter aa
//   * follow future standard: encode base substitutions individually, combine
//     all that are spaced less than 2nt appart into a delins
//   * make sure all names (including *HGVS) always end with a final '_'
//     (except non-HGVS names if there are no mutations)
bool compareToWildtype(const std::string varSeq, const std::string wtSeq,
                       const std::vector<int> varIntQual, const double mutatedPhredMin,
                       const int nbrMutatedCodonsMax, const std::set<std::string> &forbiddenCodons,
                       const std::string codonPrefix, const int nbrMutatedBasesMax,
                       int &nMutQualTooLow, int &nTooManyMutCodons, int &nForbiddenCodons,
                       int &nTooManyMutBases, std::string &mutantName,
                       std::string &mutantNameBase, std::string &mutantNameCodon,
                       std::string &mutantNameBaseHGVS, std::string &mutantNameAA,
                       std::string &mutantNameAAHGVS, int &nMutBases,
                       int &nMutCodons, int &nMutAAs, std::set<std::string> &mutationTypes,
                       const std::string mutNameDelimiter,
                       const bool collapseToWT,
                       std::map<char, std::string> &threeAA) {
  // exactly one of nbrMutatedCodonsMax or nbrMutatedBasesMax should be -1 (checked in the R code).
  // the one that is not -1 will be used for filtering and naming the mutant

  std::set<std::string> mutatedBases, mutatedCodons, mutatedAAs;
  std::set<std::string>::iterator mutatedCodonIt;
  bool hasLowQualMutation, hasForbidden;
  std::string varCodon, wtCodon, varAA, wtAA;

  // create prefixes for HGVS identifiers
  // if codonPrefix doesn't end with :c, add it -> basePrefixHGVS
  // replace :c by :p in basePrefixHGVS -> aaPrefixHGVS
  std::string basePrefixHGVS = codonPrefix, aaPrefixHGVS = codonPrefix;
  if (!hasEnding(codonPrefix, "":c"")) {
      basePrefixHGVS = basePrefixHGVS + "":c"";
      aaPrefixHGVS = aaPrefixHGVS + "":p"";
  } else {
      aaPrefixHGVS.pop_back();
      aaPrefixHGVS = aaPrefixHGVS + ""p"";
  }

  // filter if there are too many mutated codons
  // mutatedCodons.clear();
  hasLowQualMutation = false;
  for (size_t i = 0; i < varSeq.length(); i++) {
    if (varSeq[i] != wtSeq[i]) { // found mismatching base
      // record if the mutated base quality is below a threshold
      if (varIntQual[i] < mutatedPhredMin) {
        hasLowQualMutation = true;
        break;
      }
      // add codon or base (depending on which to consider) to mutatedCodonsOrBases
      // if (nbrMutatedCodonsMax != (-1)) {
      // consider codons
      varCodon = varSeq.substr((int)(i / 3) * 3, 3);
      wtCodon = wtSeq.substr((int)(i / 3) * 3, 3);
      mutatedCodons.insert(codonPrefix + mutNameDelimiter +
        std::to_string((int)(i / 3) + 1) + mutNameDelimiter +
        varCodon +
        std::string(""_""));
      // } else {
        // nbrMutatedBasesMax != -1
        // consider individual bases
      mutatedBases.insert(codonPrefix + mutNameDelimiter +
        std::to_string((int)(i) + 1) + mutNameDelimiter +
        varSeq.substr((int)(i), 1) +
        std::string(""_""));
      // }
      varAA = translateString(varCodon);
      wtAA = translateString(wtCodon);
      if (varAA != wtAA) {
        // add to mutatedAA
        mutatedAAs.insert(codonPrefix + mutNameDelimiter +
          std::to_string((int)(i / 3) + 1) + mutNameDelimiter +
          varAA +
          std::string(""_""));
        // nonsynonymous or stop
        if (varAA == ""*"") {
          mutationTypes.insert(""stop"");
        } else {
          mutationTypes.insert(""nonsynonymous"");
        }
      } else {
        // synonymous
        mutationTypes.insert(""silent"");
      }
    }
  }
  if (hasLowQualMutation) {
#ifdef _OPENMP
    #pragma omp atomic
#endif
    nMutQualTooLow++;
    return true;
  }
  // check if there are too many mutated codons/bases
  if (nbrMutatedCodonsMax != (-1)) {
    // consider codons
    if ((int)mutatedCodons.size() > nbrMutatedCodonsMax) {
#ifdef _OPENMP
      #pragma omp atomic
#endif
      nTooManyMutCodons++;
      return true;
    }
  } else {
    //consider bases
    if ((int)mutatedBases.size() > nbrMutatedBasesMax) {
#ifdef _OPENMP
      #pragma omp atomic
#endif
      nTooManyMutBases++;
      return true;
    }
  }

  // check if there are forbidden codons
  // if (nbrMutatedCodonsMax != (-1)) {
  hasForbidden = false;
  for (mutatedCodonIt = mutatedCodons.begin(); mutatedCodonIt != mutatedCodons.end(); mutatedCodonIt++) {
    if (forbiddenCodons.find((*mutatedCodonIt).substr((*mutatedCodonIt).length() - 4, 3)) != forbiddenCodons.end()) { // found forbidden codon
      hasForbidden = true;
      break;
    }
  }
  if (hasForbidden) {
#ifdef _OPENMP
    #pragma omp atomic
#endif
    nForbiddenCodons++;
    return true;
  }
  // }

  nMutBases += (int)mutatedBases.size();
  nMutCodons += (int)mutatedCodons.size();
  nMutAAs += (int)mutatedAAs.size();

  // create name for mutant
  if (collapseToWT) {
    mutantNameBase += codonPrefix + ""_"";
    mutantNameCodon += codonPrefix + ""_"";
    mutantName += codonPrefix + ""_"";
    mutantNameAA += codonPrefix + ""_"";
    mutantNameBaseHGVS += basePrefixHGVS + ""_"";
    mutantNameAAHGVS += aaPrefixHGVS + ""_"";
  } else {
    std::vector<std::string> mutatedBasesSorted, mutatedCodonsSorted, mutatedAAsSorted;

    mutatedBasesSorted.assign(mutatedBases.begin(), mutatedBases.end());
    std::sort(mutatedBasesSorted.begin(), mutatedBasesSorted.end(),
              std::bind(compareCodonPositions, _1, _2, *(mutNameDelimiter.c_str())));

    mutatedCodonsSorted.assign(mutatedCodons.begin(), mutatedCodons.end());
    std::sort(mutatedCodonsSorted.begin(), mutatedCodonsSorted.end(),
              std::bind(compareCodonPositions, _1, _2, *(mutNameDelimiter.c_str())));

    mutatedAAsSorted.assign(mutatedAAs.begin(), mutatedAAs.end());
    std::sort(mutatedAAsSorted.begin(), mutatedAAsSorted.end(),
              std::bind(compareCodonPositions, _1, _2, *(mutNameDelimiter.c_str())));

    for (size_t i = 0; i < mutatedBasesSorted.size(); i++) {
      mutantNameBase += mutatedBasesSorted[i];
      if (nbrMutatedCodonsMax == (-1)) {
        mutantName += mutatedBasesSorted[i];
      }
    }
    for (size_t i = 0; i < mutatedCodonsSorted.size(); i++) {
      mutantNameCodon += mutatedCodonsSorted[i];
      if (nbrMutatedCodonsMax != (-1)) {
        mutantName += mutatedCodonsSorted[i];
      }
    }
    for (size_t i = 0; i < mutatedAAsSorted.size(); i++) {
      mutantNameAA += mutatedAAsSorted[i];
    }
    // if no mutant codons, name as <codonPrefix>.0.WT
    if (mutatedBasesSorted.size() == 0) {
      mutantNameBase += codonPrefix + mutNameDelimiter + ""0"" + mutNameDelimiter + ""WT_"";
      mutantNameCodon += codonPrefix + mutNameDelimiter + ""0"" + mutNameDelimiter + ""WT_"";
      mutantName += codonPrefix + mutNameDelimiter + ""0"" + mutNameDelimiter + ""WT_"";
      mutantNameBaseHGVS += basePrefixHGVS + ""_"";
    } else {
      mutantNameBaseHGVS += basePrefixHGVS + ""."" +
          makeBaseHGVS(mutatedBasesSorted, mutNameDelimiter, wtSeq, varSeq);
    }
    if (mutatedAAsSorted.size() == 0) {
      mutantNameAA += codonPrefix + mutNameDelimiter + ""0"" + mutNameDelimiter + ""WT_"";
      mutantNameAAHGVS += aaPrefixHGVS + ""_"";
    } else {
      mutantNameAAHGVS += aaPrefixHGVS + ""."" +
          makeAAHGVS(mutatedAAsSorted, mutNameDelimiter,
                     translateString(wtSeq), threeAA);
    }
  }

  return false;
}

// [[Rcpp::export]]
Rcpp::List test_compareToWildtype(
        const std::string varSeq,
        const std::string wtSeq,
        const std::vector<int> varIntQual,
        const std::vector<std::string> forbiddenCodons_vect,
        const double mutatedPhredMin = 0.0,
        const int nbrMutatedCodonsMax = -1,
        const std::string codonPrefix = ""c"",
        const int nbrMutatedBasesMax = -1,
        const std::string mutNameDelimiter = ""."",
        const bool collapseToWT = false) {
    // allocate C++ only variables
    std::map<char,std::string> threeAA = initializeThreeAA();
    std::set<std::string> forbiddenCodons(forbiddenCodons_vect.begin(),
                                          forbiddenCodons_vect.end());
    int nMutQualTooLow = 0, nTooManyMutCodons = 0, nForbiddenCodons = 0;
    int nTooManyMutBases = 0, nMutBases = 0, nMutCodons = 0, nMutAAs = 0;
    std::string mutantName(""""), mutantNameBase("""");
    std::string mutantNameCodon(""""), mutantNameBaseHGVS("""");
    std::string mutantNameAA(""""), mutantNameAAHGVS("""");
    std::set<std::string> mutationTypes;

    // call compareToWildtype
    if (compareToWildtype(varSeq, wtSeq, varIntQual, mutatedPhredMin,
                          nbrMutatedCodonsMax, forbiddenCodons,
                          codonPrefix, nbrMutatedBasesMax, nMutQualTooLow,
                          nTooManyMutCodons, nForbiddenCodons,
                          nTooManyMutBases, mutantName,
                          mutantNameBase, mutantNameCodon,
                          mutantNameBaseHGVS, mutantNameAA,
                          mutantNameAAHGVS, nMutBases,
                          nMutCodons, nMutAAs, mutationTypes,
                          mutNameDelimiter, collapseToWT, threeAA)) {
        // read should be filtered out -> return empty list
        return Rcpp::List::create();
    } else {
        std::vector<std::string> mutationTypes_vect(mutationTypes.begin(),
                                                    mutationTypes.end());
        return Rcpp::List::create(Rcpp::_[""nMutQualTooLow""] = nMutQualTooLow,
                                  Rcpp::_[""nTooManyMutCodons""] = nTooManyMutCodons,
                                  Rcpp::_[""nForbiddenCodons""] = nForbiddenCodons,
                                  Rcpp::_[""nTooManyMutBases""] = nTooManyMutBases,
                                  Rcpp::_[""nMutBases""] = nMutBases,
                                  Rcpp::_[""nMutCodons""] = nMutCodons,
                                  Rcpp::_[""nMutAAs""] = nMutAAs,
                                  Rcpp::_[""mutantName""] = mutantName,
                                  Rcpp::_[""mutantNameBase""] = mutantNameBase,
                                  Rcpp::_[""mutantNameCodon""] = mutantNameCodon,
                                  Rcpp::_[""mutantNameBaseHGVS""] = mutantNameBaseHGVS,
                                  Rcpp::_[""mutantNameAA""] = mutantNameAA,
                                  Rcpp::_[""mutantNameAAHGVS""] = mutantNameAAHGVS,
                                  Rcpp::_[""mutationTypes""] = mutationTypes_vect);
    }
}

// compare constSeq to constant sequence and update
// counter by match/mismatch and by base Phred quality
// return true
bool tabulateBasesByQual(const std::string constSeq, const std::string constant,
                         const std::vector<int> constIntQual,
                         std::vector<size_t> &nPhredCorrect, std::vector<size_t> &nPhredMismatch) {
  for (size_t i = 0; i < constSeq.length(); i++) { // for each base
    if (constSeq[i] != constant[i]) { // found mismatch
#ifdef _OPENMP
      #pragma omp atomic
#endif
      nPhredMismatch[constIntQual[i]]++;
    } else {                          // found match
#ifdef _OPENMP
      #pragma omp atomic
#endif
      nPhredCorrect[constIntQual[i]]++;
    }
  }
  return true;
}


// stores information about retained mutants
struct mutantInfo {
  std::set<std::string> umi;      // set of unique UMIs observed for read pairs of that mutant
  int nReads;                     // number of reads
  int maxNReads;                  // maximum number of reads for an individual mutant, in case of collapsing multiple
                                  // mutants into one entry
  std::set<int> nMutBases;
  std::set<int> nMutCodons;
  std::set<int> nMutAAs;
  std::set<std::string> mutationTypes;
  std::set<std::string> sequence; // set of sequences for that mutant
  std::set<std::string> mutantNameBase;
  std::set<std::string> mutantNameCodon;
  std::set<std::string> mutantNameBaseHGVS;
  std::set<std::string> mutantNameAA;
  std::set<std::string> mutantNameAAHGVS;
  std::set<std::string> sequenceAA;
  std::string varLengths;         // lengths of individual variable segments (e.g. ""10,20"" or ""10,20_20,10"")
};

// open fastq file and check if it worked
gzFile  openFastq(std::string filename, const char* mode = ""rb"") {
  gzFile file = gzopen(filename.c_str(), mode);
  if (!file) {
    if (errno) { // # nocov start
      stop(""Failed to open file '"", filename,  ""': "",
           strerror(errno), "" (errno="", errno, "")"");
    } else {
      stop(""Failed to open file '"", filename, ""': zlib out of memory"");
    } // # nocov end
  }
  return file;
}

// given a vector of codons (potentially containing IUPAC ambiguous bases),
// enumerate all non-ambiguous codons that match to them
std::set<std::string> enumerateCodonsFromIUPAC(CharacterVector forbiddenMutatedCodons,
                                               std::map<char,std::vector<char>> IUPAC, bool verbose) {
  std::set<std::string> forbiddenCodons;
  std::string codon(""NNN"");
  for (int i = 0; i < forbiddenMutatedCodons.length(); i++) {
    std::vector<char> B1s = IUPAC[forbiddenMutatedCodons[i][0]];
    std::vector<char> B2s = IUPAC[forbiddenMutatedCodons[i][1]];
    std::vector<char> B3s = IUPAC[forbiddenMutatedCodons[i][2]];
    for (std::vector<char>::iterator b1 = B1s.begin(); b1 != B1s.end(); b1++) {
      codon[0] = (*b1);
      for (std::vector<char>::iterator b2 = B2s.begin(); b2 != B2s.end(); b2++) {
        codon[1] = (*b2);
        for (std::vector<char>::iterator b3 = B3s.begin(); b3 != B3s.end(); b3++) {
          codon[2] = (*b3);
          forbiddenCodons.insert(codon);
        }
      }
    }
  }
  if (verbose) {
    Rcout << ""done enumerating forbidden codons ("" << forbiddenCodons.size() << "")"" << std::endl;
  }
  return forbiddenCodons;
}

// given a read and information about its 'composition', extract UMI, constant sequence,
// variable sequence
// S - skip
// U - umi
// V - variable
// C - constant
// P - primer
bool decomposeRead(const std::string sseq,
                   const std::string squal,
                   const std::string elements,
                   const std::vector<int> elementLengths,
                   const std::vector<std::string> primerSeqs,
                   std::string &umiSeq,
                   std::string &varSeq, std::string &varQual,
                   std::vector<int> &varLengths,
                   std::string &constSeq, std::string &constQual,
                   int &nNoPrimer, int &nReadWrongLength) {
  // check if elements contains a P (primer)
  size_t pPos = elements.find('P');  // location of P in the elements string
  if (pPos != std::string::npos) {

    // search for the primer
    bool foundprimer = false;
    for (size_t j = 0; j < primerSeqs.size(); j++) {
      size_t primerPos = sseq.find(primerSeqs[j]); // primer position in read
      if (primerPos != std::string::npos) {
        foundprimer = true;
        // primer found - split read in parts before/after primer and check separately
        // none of them can have a P in the elements substring, since only one P is allowed in
        // the original elements string
        // part before the primer
        std::vector<int> elementsPre = std::vector<int>(elementLengths.begin(),
                                                        elementLengths.begin() + pPos);
        bool pre = decomposeRead(sseq.substr(0, primerPos), squal.substr(0, primerPos),
                                 elements.substr(0, pPos), elementsPre, primerSeqs,
                                 umiSeq, varSeq, varQual, varLengths, constSeq,
                                 constQual, nNoPrimer, nReadWrongLength);
        if (!pre) {
          return false;
        }
        // part after the primer
        std::vector<int> elementsPost = std::vector<int>(elementLengths.begin() + pPos + 1,
                                                         elementLengths.end());
        bool post = decomposeRead(sseq.substr(primerPos + primerSeqs[j].size(), sseq.size()),
                                  squal.substr(primerPos + primerSeqs[j].size(), squal.size()),
                                  elements.substr(pPos + 1, elements.size()),
                                  elementsPost, primerSeqs,
                                  umiSeq, varSeq, varQual, varLengths, constSeq,
                                  constQual, nNoPrimer, nReadWrongLength);
        if (!post) {
          return false; // # nocov
        }
        break;
      }
    }
    // no primer found - read should not be considered further
    if (!foundprimer) {
#ifdef _OPENMP
      #pragma omp atomic
#endif
      nNoPrimer++;
      return false;
    }
  } else {
    // no primer, extract read parts from given lengths
    // iterate through elements

    // sum of the element lengths (including -1), will be
    // used to derive the length if not specified
    int totalElementLength = std::accumulate(elementLengths.begin(),
                                             elementLengths.end(), 0);
    size_t currentPos = 0; // current position in the string
    size_t currentLength; // length of the current element

    // check that the length is ok
    if (elementLengths.size() > 0) { // don't check if there are no elements,
                                     // e.g. before the primer
      if (std::find(elementLengths.begin(), elementLengths.end(), -1) == elementLengths.end()) {
        // no -1's - sum of element lengths must be equal to the sequence length
        if (totalElementLength != (int)sseq.size()) {
#ifdef _OPENMP
          #pragma omp atomic
#endif
          nReadWrongLength++;
          return false;
        }
      } else {
        // -1 present - sum of element lengths must be < sequence length
        if (totalElementLength >= (int)sseq.size()) {
#ifdef _OPENMP
          #pragma omp atomic
#endif
          nReadWrongLength++;
          return false;
        }
      }
    }

    for (size_t i = 0; i < elements.size(); i++) { // go through elements
      if (elementLengths[i] == -1) {
        // get element length as the total read length minus the lengths of the specified parts
        currentLength = sseq.size() - totalElementLength - 1;
      } else {
        currentLength = elementLengths[i];
      }
      if (elements[i] == 'U') { // add to umiSeq
        umiSeq += sseq.substr(currentPos, currentLength);
      } else if (elements[i] == 'C') { // add to constant seq
        constSeq += sseq.substr(currentPos, currentLength);
        constQual += squal.substr(currentPos, currentLength);
      } else if (elements[i] == 'V') { // add to variable seq
        varLengths.push_back((int)currentLength);
        varSeq += sseq.substr(currentPos, currentLength);
        varQual += squal.substr(currentPos, currentLength);
      }
      currentPos += currentLength;
    }
  }

  return true; // keep read
}

// wrapper around decomposeRead used in unit testing
// (needed because decomposeRead merges in-place and returns only 'true' or 'false',
//  and R passes copies of the arguments, so that the results of the extraction cannot be ""seen"")
// [[Rcpp::export]]
List test_decomposeRead(const std::string sseq,
                        const std::string squal,
                        const std::string elements,
                        const std::vector<int> elementLengths,
                        const std::vector<std::string> primerSeqs,
                        std::string umiSeq,
                        std::string varSeq, std::string varQual,
                        std::vector<int> varLengths,
                        std::string constSeq, std::string constQual,
                        int nNoPrimer, int nReadWrongLength) {
  decomposeRead(sseq, squal, elements, elementLengths, primerSeqs,
                umiSeq, varSeq, varQual, varLengths, constSeq, constQual,
                nNoPrimer, nReadWrongLength);
  List L = List::create(Named(""umiSeq"") = umiSeq,
                        Named(""varSeq"") = varSeq,
                        Named(""varQual"") = varQual,
                        Named(""varLengths"") = varLengths,
                        Named(""constSeq"") = constSeq,
                        Named(""constQual"") = constQual,
                        Named(""nNoPrimer"") = nNoPrimer,
                        Named(""nReadWrongLength"") = nReadWrongLength);
  return L;
}


// merge forward and reverse sequences (partially overlapping)
// - don't count N's as a mismatch
// - default values for minOverlap,maxOverlap are zero, which will:
//      set minOverlap,maxOverlap to the length of the shorter read
// - first find optimal overlap without indels
//      for greedy = true, pick the longest overlap with less than maxMismatch errors
//      for greedy = false, pick the one with the highest score := number of overlap bases - number of mismatches in overlap
// - merge the reads, at each overlap position keeping the base with maximal Phred quality
// - check if the merging position (based on comparing sequences) is compatible with
//      the variable segment lengths (break points are aligned) and update varLengthsForward accordingly
// - store the results into varSeqForward and varIntQualForward
// - if no valid overlap is found within the scope of minOverlap/maxOverlap/maxMismatch, do nothing
//   (varSeqForward and varIntQualForward still correspond to the input values)
// - return false if a valid merge was found and performed, and return true otherwise
bool mergeReadPairPartial(std::string &varSeqForward, std::vector<int> &varIntQualForward,
                          std::string &varSeqReverse, std::vector<int> &varIntQualReverse,
                          std::vector<int> &varLengthsForward, std::vector<int> &varLengthsReverse,
                          size_t minOverlap = 0, size_t maxOverlap = 0,
                          size_t minMergedLength = 0, size_t maxMergedLength = 0,
                          double maxFracMismatchOverlap = 0,
                          bool greedy = true) {
  // initialize overlap parameters
  size_t lenF = varSeqForward.length(), lenR = varSeqReverse.length();
  if (minOverlap == 0) {
    minOverlap = lenF;
    if (minOverlap > lenR) {
      minOverlap = lenR;
    }
  }
  // if maxMergedLength is 0, set it to the sum of lenF and lenR
  // (the merged sequence can't be longer than that)
  if (maxMergedLength == 0) {
    maxMergedLength = lenF + lenR;
  }
  if (maxMergedLength > lenF + lenR) {
    maxMergedLength = lenF + lenR;
  }
  // if maxMergedLength is specified, adjust allowed minOverlap
  if (minOverlap < (lenF + lenR - maxMergedLength)) {
    minOverlap = (lenF + lenR - maxMergedLength);
  }
  if (minOverlap > lenF || minOverlap > lenR) {
    return true; //  no valid overlap possible
  }

  if (maxOverlap == 0) {
    maxOverlap = lenF;
    if (maxOverlap > lenR) {
      maxOverlap = lenR;
    }
  }
  // if minMergedLength is 0, set it to the larger of lenF and lenR
  // (the merged sequence can't be shorter than that)
  if (minMergedLength == 0) {
    minMergedLength = lenF;
    if (minMergedLength < lenR) {
      minMergedLength = lenR;
    }
  }
  if (minMergedLength > lenF + lenR) {
    return true; // no valid overlap possible
  }
  // if minMergedLength is specified, adjust allowed maxOverlap
  if (maxOverlap > (lenF + lenR - minMergedLength)) {
    maxOverlap = (lenF + lenR - minMergedLength);
  }
  if (maxOverlap < minOverlap) {
    return true; //  no valid overlap possible
  }

  // find overlap (score := number of overlap bases - number of mismatches in overlap)
  size_t o, i, j;
  size_t cumpos = 0, bestO = 0, matchedlen = 0;
  int bestScore = -1, score;
  double fracmm;

  for (o = maxOverlap; o >= minOverlap; o--) {
    // calculate score for overlap of o bases
    score = 0;
    for (i = lenF - o, j = 0; i < lenF; i++, j++) {
      if (varSeqForward[i] == varSeqReverse[j] || varSeqForward[i] == 'N' || varSeqReverse[j] == 'N') {
        score++;
      }
    }
    // if valid, store overlap
    fracmm = ((double)o - (double)score)/(double)o;
    if (fracmm <= maxFracMismatchOverlap && score > bestScore) {
      bestO = o;
      bestScore = score;
      // if greedy, stop checking for overlaps
      if (greedy) {
        break;
      }
    }
  }

  // if a valid overlap has been found, merge reads into varSeqForward, varIntQualForward
  if (bestO > 0) {
    // ... check if variable segments lengths' breaks are aligned
    o = 0;
    for (i = 0; i < varLengthsForward.size(); i++) {
      cumpos += varLengthsForward[i];
      if (cumpos > lenF - bestO) { // break within overlap
        if (cumpos < lenF &&
            (int)(cumpos - (lenF - bestO) - matchedlen) != varLengthsReverse[i - o]) {
          return true; // no valid overlap possible
        } else if (cumpos < lenF) {
          matchedlen += varLengthsReverse[i - o];
        }
      } else { // before overlap -> increase break offset for reverse (forward[i] <=> reverse[i - o])
        o++;
      }
    }
    // ... ... is the next reverse read break still in the overlap region?
    i = varLengthsForward.size() - 1;
    if (varLengthsReverse.size() > i - o && matchedlen + varLengthsReverse[i - o] < bestO) {
      return true;
    }
    // ... ... all variable lengths breaks are aligned -> create segment lengths vector for the merged
    varLengthsForward.resize(o); // keep the first o element from forward (before the overlap)
    varLengthsForward.push_back(varLengthsReverse[0] +
        (lenF - bestO - std::accumulate(varLengthsForward.begin(),varLengthsForward.end(), 0)));
    for (i = 1; i < varLengthsReverse.size(); i++) {
      varLengthsForward.push_back(varLengthsReverse[i]);
    }
    // ... grow varSeqForward, varIntQualForward
    varSeqForward.resize(lenF + lenR - bestO);
    varIntQualForward.resize(lenF + lenR - bestO);
    // ... at each overlap position, keep base with higher quality) and store in forward read
    for (i = lenF - bestO, j = 0; i < lenF; i++, j++) {
      if (varIntQualReverse[j] > varIntQualForward[i]) {
        varSeqForward[i] = varSeqReverse[j];
        varIntQualForward[i] = varIntQualReverse[j];
      }
    }
    // ... add non-overlapping part of varSeqReverse, varIntQualReverse
    for (i = lenF, j = bestO; i < lenF + lenR - bestO; i++, j++) {
      varSeqForward[i] = varSeqReverse[j];
      varIntQualForward[i] = varIntQualReverse[j];
    }

    return false;

  } else {
    return true;
  }
}

// wrapper around mergeReadPairPartial used in unit testing
// (needed because mregeReadPairPartial merges in-place and returns only 'true',
//  and R passes copies of the arguments, so that the results of the merging cannot be ""seen"")
// [[Rcpp::export]]
List test_mergeReadPairPartial(std::string seqF, std::vector<int> qualF,
                               std::string seqR, std::vector<int> qualR,
                               std::vector<int> lenF, std::vector<int> lenR,
                               size_t minOverlap = 0, size_t maxOverlap = 0,
                               size_t minMergedLength = 0, size_t maxMergedLength = 0,
                               double maxFracMismatchOverlap = 0,
                               bool greedy = true) {
  bool result = mergeReadPairPartial(seqF, qualF, seqR, qualR, lenF, lenR,
                                     minOverlap, maxOverlap,
                                     minMergedLength, maxMergedLength,
                                     maxFracMismatchOverlap, greedy);
  List L = List::create(Named(""return"") = result,
                        Named(""mergedSeq"") = seqF,
                        Named(""mergedQual"") = qualF,
                        Named(""mergedLengths"") = lenF);
  return L;
}


void removeEOL(std::string &seq) {
  if (seq.back() == '\n') {
    seq.pop_back();
  }
  if (seq.back() == '\r') {
    seq.pop_back(); // # nocov
  }
}

// Find closest wild type sequence to a variable sequence
// Here, 'closest' is defined as the sequence with the largest number of matching bases
// Assumes that the start of varSeq coincides with the start of each wtSeq
// [[Rcpp::export]]
int findClosestRefSeq(std::string &varSeq, std::vector<std::string> &wtSeq,
                      size_t upperBoundMismatch, int &sim) {
  // return index of most similar sequence
  int idx = NO_SIMILAR_REF;
  int maxsim = 0;
  int nbrbesthits = 0;
  int currsim;
  size_t minl;
  for (size_t i = 0; i < wtSeq.size(); i++) {
    currsim = 0;
    std::string currSeq = wtSeq[i];
    minl = std::min(varSeq.size(), currSeq.size());
    for (size_t j = 0; j < minl; j++) {
      currsim += (currSeq[j] == varSeq[j]);
    }
    if (((int)varSeq.size() - currsim <= (int)upperBoundMismatch)) {
      if (currsim == maxsim) {
        nbrbesthits++;
      } else if (currsim > maxsim) {
        nbrbesthits = 1;
        idx = (int)i;
        maxsim = currsim;
      }
    }
  }
  sim = maxsim;
  if (nbrbesthits > 1) {
    return TOO_MANY_BEST_REF;
  } else {
    return idx;
  }
}

// Find closest wild type sequence to a variable sequence
// Similar to findClosestRefSeq, but implements an early stopping
// criterion if it is clear that the similarity is not going
// be large enough
// [[Rcpp::export]]
int findClosestRefSeqEarlyStop(std::string &varSeq, std::vector<std::string> &wtSeq,
                               size_t upperBoundMismatch, int &sim) {
  // return index of most similar sequence
  int idx = NO_SIMILAR_REF;
  int maxsim = -1;
  int nbrbesthits = 0;
  int currsim;
  size_t minl;
  for (size_t i = 0; i < wtSeq.size(); i++) {
    currsim = 0;
    std::string currSeq = wtSeq[i];
    minl = std::min(varSeq.size(), currSeq.size());
    for (size_t j = 0; j < minl; j++) {
      // if currsim + (minl - j) >= varSeq.size() - upperBoundMismatch, we can still continue looking
      // if varSeq is longer than minl, the additional positions are considered mismatches
      if (currsim < (int)(j - minl + varSeq.size() - upperBoundMismatch)) {
        // no chance to reach the minimal similarity - break
        break;
      }
      currsim += (currSeq[j] == varSeq[j]);
    }
    if (((int)varSeq.size() - currsim <= (int)upperBoundMismatch)) {
      if (currsim == maxsim) {
        nbrbesthits++;
      } else if (currsim > maxsim) {
        nbrbesthits = 1;
        idx = (int)i;
        maxsim = currsim;
      }
    }
  }
  sim = maxsim;
  if (nbrbesthits > 1) {
    return TOO_MANY_BEST_REF;
  } else {
    return idx;
  }
}

// Find closest wild type sequence to a variable sequence
// Here, 'closest' is defined as the sequence with the largest number of matching bases
// Assumes that the start of varSeq coincides with the start of each wtSeq
// Input: a variable sequence, a BKtree and a map (mapping entries in the tree to
// their indexes in the original vector)
int findClosestRefSeqTree(std::string &varSeq, BKtree &wtTree,
                          std::map<std::string, int> &wtTreeIdx,
                          size_t upperBoundMismatch, int &sim) {
  // return index of most similar sequence
  std::string idx;
  std::vector<std::string> searchResults;
  for (size_t i = 0; i < std::min(upperBoundMismatch + 1, varSeq.size()); i++) {
    // find all sequences in the tree within distance i of varSeq
    searchResults = wtTree.search(varSeq, i);
    if (searchResults.size() > 0) {
      sim = varSeq.size() - i;
      // if we find multiple best hits, skip the read (return TOO_MANY_BEST_REF)
      if (searchResults.size() > 1) {
        return TOO_MANY_BEST_REF;
      } else {
        idx = searchResults[0];
        return wtTreeIdx[idx];
      }
    }
  }
  return NO_SIMILAR_REF;
}

// Convert mutant summary to DataFrame
DataFrame mutantSummaryToDataFrame(std::map<std::string, mutantInfo> mutantSummary) {
    std::map<std::string, mutantInfo>::iterator mutantSummaryIt;

    size_t dfLen = mutantSummary.size();
    std::vector<std::string> dfSeq(dfLen, """"), dfName(dfLen, """");
    std::vector<int> dfReads(dfLen, 0), dfUmis(dfLen, 0), dfMaxReads(dfLen, 0);
    std::vector<std::string> dfMutBases(dfLen, """"), dfMutCodons(dfLen, """");
    std::vector<std::string> dfMutAAs(dfLen, """"), dfVarLengths(dfLen, """");
    std::vector<std::string> dfMutantNameBase(dfLen, """"), dfMutantNameCodon(dfLen, """");
    std::vector<std::string> dfMutantNameBaseHGVS(dfLen, """"), dfMutantNameAA(dfLen, """");
    std::vector<std::string> dfMutantNameAAHGVS(dfLen, """"), dfSeqAA(dfLen, """");
    std::vector<std::string> dfMutationTypes(dfLen, """");
    int j = 0;
    for (mutantSummaryIt = mutantSummary.begin(); mutantSummaryIt != mutantSummary.end(); mutantSummaryIt++) {
        // collapse all sequences associated with the mutant
        std::vector<std::string> sequenceVector((*mutantSummaryIt).second.sequence.begin(),
                                                (*mutantSummaryIt).second.sequence.end());
        std::string collapsedSequence = """";
        for (size_t i = 0; i < sequenceVector.size(); i++) {
            collapsedSequence += sequenceVector[i] + "","";
        }
        if (!collapsedSequence.empty()) {
            collapsedSequence.pop_back(); // remove final "",""
        }

        // mutantNameBase
        std::vector<std::string> mutantNameBaseVector((*mutantSummaryIt).second.mutantNameBase.begin(),
                                                      (*mutantSummaryIt).second.mutantNameBase.end());
        std::string collapsedMutantNameBase = """";
        for (size_t i = 0; i < mutantNameBaseVector.size(); i++) {
            collapsedMutantNameBase += mutantNameBaseVector[i] + "","";
        }
        if (!collapsedMutantNameBase.empty()) {
            collapsedMutantNameBase.pop_back(); // remove final "",""
        }

        // mutantNameCodon
        std::vector<std::string> mutantNameCodonVector((*mutantSummaryIt).second.mutantNameCodon.begin(),
                                                       (*mutantSummaryIt).second.mutantNameCodon.end());
        std::string collapsedMutantNameCodon = """";
        for (size_t i = 0; i < mutantNameCodonVector.size(); i++) {
            collapsedMutantNameCodon += mutantNameCodonVector[i] + "","";
        }
        if (!collapsedMutantNameCodon.empty()) {
            collapsedMutantNameCodon.pop_back(); // remove final "",""
        }

        // mutantNameBaseHGVS
        std::vector<std::string> mutantNameBaseHGVSVector((*mutantSummaryIt).second.mutantNameBaseHGVS.begin(),
                                                          (*mutantSummaryIt).second.mutantNameBaseHGVS.end());
        std::string collapsedMutantNameBaseHGVS = """";
        for (size_t i = 0; i < mutantNameBaseHGVSVector.size(); i++) {
            collapsedMutantNameBaseHGVS += mutantNameBaseHGVSVector[i] + "","";
        }
        if (!collapsedMutantNameBaseHGVS.empty()) {
            collapsedMutantNameBaseHGVS.pop_back(); // remove final "",""
        }

        // mutantNameAA
        std::vector<std::string> mutantNameAAVector((*mutantSummaryIt).second.mutantNameAA.begin(),
                                                    (*mutantSummaryIt).second.mutantNameAA.end());
        std::string collapsedMutantNameAA = """";
        for (size_t i = 0; i < mutantNameAAVector.size(); i++) {
            collapsedMutantNameAA += mutantNameAAVector[i] + "","";
        }
        if (!collapsedMutantNameAA.empty()) {
            collapsedMutantNameAA.pop_back(); // remove final "",""
        }

        // mutantNameAAHGVS
        std::vector<std::string> mutantNameAAHGVSVector((*mutantSummaryIt).second.mutantNameAAHGVS.begin(),
                                                        (*mutantSummaryIt).second.mutantNameAAHGVS.end());
        std::string collapsedMutantNameAAHGVS = """";
        for (size_t i = 0; i < mutantNameAAHGVSVector.size(); i++) {
            collapsedMutantNameAAHGVS += mutantNameAAHGVSVector[i] + "","";
        }
        if (!collapsedMutantNameAAHGVS.empty()) {
            collapsedMutantNameAAHGVS.pop_back(); // remove final "",""
        }

        // mutationTypes
        std::vector<std::string> mutationTypesVector((*mutantSummaryIt).second.mutationTypes.begin(),
                                                     (*mutantSummaryIt).second.mutationTypes.end());
        std::string collapsedMutationTypes = """";
        for (size_t i = 0; i < mutationTypesVector.size(); i++) {
            collapsedMutationTypes += mutationTypesVector[i] + "","";
        }
        if (!collapsedMutationTypes.empty()) {
            collapsedMutationTypes.pop_back(); // remove final "",""
        }

        // collapse all aa sequences associated with the mutant
        std::vector<std::string> sequenceAAVector((*mutantSummaryIt).second.sequenceAA.begin(),
                                                  (*mutantSummaryIt).second.sequenceAA.end());
        std::string collapsedSequenceAA = """";
        for (size_t i = 0; i < sequenceAAVector.size(); i++) {
            collapsedSequenceAA += sequenceAAVector[i] + "","";
        }
        if (!collapsedSequenceAA.empty()) {
            collapsedSequenceAA.pop_back(); // remove final "",""
        }

        // collapse all observed nMutBases/nMutCodons
        std::vector<int> nMutBasesVector((*mutantSummaryIt).second.nMutBases.begin(),
                                         (*mutantSummaryIt).second.nMutBases.end());
        std::string collapsedNMutBases = """";
        for (size_t i = 0; i < nMutBasesVector.size(); i++) {
            collapsedNMutBases += (std::to_string(nMutBasesVector[i]) + "","");
        }
        if (!collapsedNMutBases.empty()) {
            collapsedNMutBases.pop_back();
        }

        // codons
        std::vector<int> nMutCodonsVector((*mutantSummaryIt).second.nMutCodons.begin(),
                                          (*mutantSummaryIt).second.nMutCodons.end());
        std::string collapsedNMutCodons = """";
        for (size_t i = 0; i < nMutCodonsVector.size(); i++) {
            collapsedNMutCodons += std::to_string(nMutCodonsVector[i]) + "","";
        }
        if (!collapsedNMutCodons.empty()) {
            collapsedNMutCodons.pop_back();
        }

        // AAs
        std::vector<int> nMutAAsVector((*mutantSummaryIt).second.nMutAAs.begin(),
                                       (*mutantSummaryIt).second.nMutAAs.end());
        std::string collapsedNMutAAs = """";
        for (size_t i = 0; i < nMutAAsVector.size(); i++) {
            collapsedNMutAAs += std::to_string(nMutAAsVector[i]) + "","";
        }
        if (!collapsedNMutAAs.empty()) {
            collapsedNMutAAs.pop_back();
        }

        dfName[j] = (*mutantSummaryIt).first;
        dfSeq[j] = collapsedSequence;
        dfReads[j] = (*mutantSummaryIt).second.nReads;
        dfMaxReads[j] = (*mutantSummaryIt).second.maxNReads;
        dfUmis[j] = (*mutantSummaryIt).second.umi.size();
        dfMutBases[j] = collapsedNMutBases;
        dfMutCodons[j] = collapsedNMutCodons;
        dfMutAAs[j] = collapsedNMutAAs;
        dfMutantNameBase[j] = collapsedMutantNameBase;
        dfMutantNameCodon[j] = collapsedMutantNameCodon;
        dfMutantNameBaseHGVS[j] = collapsedMutantNameBaseHGVS;
        dfMutantNameAA[j] = collapsedMutantNameAA;
        dfMutantNameAAHGVS[j] = collapsedMutantNameAAHGVS;
        dfMutationTypes[j] = collapsedMutationTypes;
        dfSeqAA[j] = collapsedSequenceAA;
        dfVarLengths[j] = (*mutantSummaryIt).second.varLengths;
        j++;
    }

    DataFrame df = DataFrame::create(Named(""mutantName"") = dfName,
                                     Named(""sequence"") = dfSeq,
                                     Named(""nbrReads"") = dfReads,
                                     Named(""maxNbrReads"") = dfMaxReads,
                                     Named(""nbrUmis"") = dfUmis,
                                     Named(""nbrMutBases"") = dfMutBases,
                                     Named(""nbrMutCodons"") = dfMutCodons,
                                     Named(""nbrMutAAs"") = dfMutAAs,
                                     Named(""varLengths"") = dfVarLengths,
                                     Named(""mutantNameBase"") = dfMutantNameBase,
                                     Named(""mutantNameCodon"") = dfMutantNameCodon,
                                     Named(""mutantNameBaseHGVS"") = dfMutantNameBaseHGVS,
                                     Named(""mutantNameAA"") = dfMutantNameAA,
                                     Named(""mutantNameAAHGVS"") = dfMutantNameAAHGVS,
                                     Named(""mutationTypes"") = dfMutationTypes,
                                     Named(""sequenceAA"") = dfSeqAA,
                                     Named(""stringsAsFactors"") = false);

    return df;
}

//' Create a conversion table for collapsing similar sequences
//' @param seqs Character vector with nucleotide sequences (or pairs of
//' sequences concatenated with ""_"") to be collapsed. The sequences must
//' all be of the same length.
//' @param scores Numeric vector of ""scores"" for the sequences. Typically
//' the total read/UMI count. A higher score will be preferred when
//' deciding which sequence to use as the representative for a group of
//' collapsed sequences.
//' @param collapseMaxDist Numeric scalar defining the tolerance for collapsing
//' similar sequences. If the value is in [0, 1), it defines the maximal
//' Hamming distance in terms of a fraction of sequence length:
//' (\code{round(collapseMaxDist * nchar(sequence))}).
//' A value greater or equal to 1 is rounded and directly used as the maximum
//' allowed Hamming distance. Note that sequences can only be
//' collapsed if they are all of the same length. The default value is 0.
//' @param collapseMinScore Numeric scalar, indicating the minimum score
//' required for a sequence to be considered as a representative for a
//' group of similar sequences (i.e., to allow other sequences to be
//' collapsed into it). The default value is 0.
//' @param collapseMinRatio Numeric scalar. During collapsing of
//' similar sequences, a low-frequency sequence will be collapsed
//' with a higher-frequency sequence only if the ratio between the
//' high-frequency and the low-frequency scores is at least this
//' high. A value of 0 indicates that no such check is performed.
//' @param verbose Logical scalar, whether to print progress messages.
//'
//' @return A data.frame with two columns, containing the input sequences
//' and the representatives for the groups resulting from grouping similar
//' sequences, respectively.
//'
//' @examples
//' seqs <- c(""AACGTAGCA"", ""ACCGTAGCA"", ""AACGGAGCA"", ""ATCGGAGCA"", ""TGAGGCATA"")
//' scores <- c(5, 1, 3, 1, 8)
//' groupSimilarSequences(seqs = seqs, scores = scores,
//'                       collapseMaxDist = 1, collapseMinScore = 0,
//'                       collapseMinRatio = 0, verbose = FALSE)
//'
//' @export
//' @author Michael Stadler, Charlotte Soneson
// [[Rcpp::export]]
Rcpp::DataFrame groupSimilarSequences(std::vector<std::string> seqs,
                                      std::vector<double> scores,
                                      double collapseMaxDist=0.0,
                                      double collapseMinScore=0.0,
                                      double collapseMinRatio=0.0,
                                      bool verbose=false) {

    // combine seqs and scores
    std::map<std::string, double> seqsScores;
    std::map<std::string, double>::iterator seqsScoresIt, seqsScoresSimIt;
    for (size_t i = 0; i < seqs.size(); i++) {
        seqsScores.insert(std::pair<std::string, double>(seqs[i], scores[i]));
    }

    // get sequence length
    size_t seqlen = seqs[0].length();

    // calculate Hamming distance tolerance
    double tol;
    if (collapseMaxDist >= 1.0) {
        tol = collapseMaxDist;
    } else {
        tol = (collapseMaxDist *
            (seqs[0].find(""_"") != std::string::npos ? seqlen-1 : seqlen));
    }

    if (verbose) {
        Rcout << ""start collapsing sequences (tolerance: "" << tol << "")..."";
    }

    // sort seqsScores decreasingly by scores
    // ... create an empty intermediate vector
    std::vector<std::pair<std::string, double>> vec;
    std::vector<std::pair<std::string, double>>::iterator vecIt;
    // copy key-value pairs from seqsScores to vec
    std::copy(seqsScores.begin(), seqsScores.end(),
              std::back_inserter<std::vector<std::pair<std::string, double>>>(vec));
    // ... sort vec by decreasing order of pair.second
    //     (if second values are equal, order by the pair's first value)
    std::sort(vec.begin(), vec.end(),
              [](const std::pair<std::string, double>& l,
                 const std::pair<std::string, double>& r) {
                  if (l.second != r.second)
                      return l.second > r.second;
                  return l.first < r.first;
              });

    // store sequences in BK tree
    BKtree tree;
    for (vecIt = vec.begin(); vecIt != vec.end(); vecIt++) {
        if ((*vecIt).first.length() != seqlen) {
            warning(""Skipping sequence collapsing because reads are not all of the same length"");
            tree.remove_all();
            break;
        } else {
            tree.insert((*vecIt).first);
        }
    }
    vec.clear(); // remove temporary vector

    std::string querySeq, representativeSeq;
    std::vector<std::string> simSeqs;
    std::map<std::string, std::string> single2representative;

    // start querying in the order of tree.items (ordered decreasingly by scores)
    size_t start_size = (double)tree.size;
    while (tree.size > 0) {
        querySeq = tree.first();
        // check in seqsScores if score for querySeq is < collapseMinScore
        seqsScoresIt = seqsScores.find(querySeq);
        if (collapseMinScore > 0 &&
            seqsScoresIt != seqsScores.end() &&
            (*seqsScoresIt).second < collapseMinScore) {
            // in that case, score < collapseMinScore for all other
            // sequences in the tree as well
            // if so, extract all remaining sequences in the tree and
            // add them to single2representative, each mapping to itself
            std::vector<std::string> rest = tree.get_all();
            for (size_t i = 0; i < rest.size(); i++) {
                single2representative[rest[i]] = rest[i];
            }
            tree.remove_all();
            // after that, tree.size = 0 so the loop will stop
        } else {
            simSeqs = tree.search(querySeq, tol);
            for (size_t i = 0; i < simSeqs.size(); i++) {
                // check that the score for querySeq is high enough compared to that of simSeqs[i]
                // if not, don't collapse simSeqs[i] with querySeq
                // must check explicitly if simSeqs[i] == querySeq, since the ratio in
                // that case will always be 1, and the function will loop indefinitely
                // if the querySeq is not removed
                if (((seqsScoresSimIt = seqsScores.find(simSeqs[i])) != seqsScores.end() &&
                    (*seqsScoresIt).second >= collapseMinRatio * (*seqsScoresSimIt).second) ||
                    querySeq == simSeqs[i]) {
                    single2representative[simSeqs[i]] = querySeq;
                    tree.remove(simSeqs[i]);
                }
            }

            // check for user interruption and print progress
            if ((start_size - tree.size) % 2000 == 0) { // every 2,000 queries (every ~1-2s)
                Rcpp::checkUserInterrupt(); // ... check for user interrupt # nocov start
                // ... and give an update
                if (verbose && (start_size - tree.size) % 2000 == 0) {
                    Rcout << ""    "" << std::setprecision(4) <<
                        (100.0 * ((double)(start_size - tree.size) / (double)start_size)) <<
                            ""% done"" << std::endl; // # nocov end
                }
            }
        }
    }

    // return grouping results as a DataFrame
    std::vector<std::string> repseqs(seqs.size());
    std::unordered_set<std::string> uniqueRepseqs;
    for (size_t i = 0; i < seqs.size(); i++) {
        repseqs[i] = single2representative[seqs[i]];
        uniqueRepseqs.insert(repseqs[i]);
    }
    Rcpp::DataFrame df = DataFrame::create(Named(""sequence"") = seqs,
                                           Named(""representative"") = repseqs);

    if (verbose) {
        Rcout << ""done (reduced from "" << seqs.size() << "" to "" << uniqueRepseqs.size() << "")"" << std::endl;
    }
    uniqueRepseqs.clear();

    return df;
}



// [[Rcpp::export]]
List digestFastqsCpp(std::vector<std::string> fastqForwardVect,
                     std::vector<std::string> fastqReverseVect,
                     bool mergeForwardReverse,
                     size_t minOverlap, size_t maxOverlap,
                     size_t minMergedLength, size_t maxMergedLength,
                     double maxFracMismatchOverlap, bool greedyOverlap,
                     bool revComplForward, bool revComplReverse,
                     std::string elementsForward,
                     std::vector<int> elementLengthsForward,
                     std::string elementsReverse,
                     std::vector<int> elementLengthsReverse,
                     std::string adapterForward, std::string adapterReverse,
                     std::vector<std::string> primerForward,
                     std::vector<std::string> primerReverse,
                     std::vector<std::string> wildTypeForward,
                     std::vector<std::string> wildTypeForwardNames,
                     std::vector<std::string> wildTypeReverse,
                     std::vector<std::string> wildTypeReverseNames,
                     std::vector<std::string> constantForward,
                     std::vector<std::string> constantReverse,
                     double avePhredMinForward = 20.0, double avePhredMinReverse = 20.0,
                     int variableNMaxForward = 0, int variableNMaxReverse = 0,
                     int umiNMax = 0,
                     int nbrMutatedCodonsMaxForward = 1,
                     int nbrMutatedCodonsMaxReverse = 1,
                     int nbrMutatedBasesMaxForward = -1,
                     int nbrMutatedBasesMaxReverse = -1,
                     CharacterVector forbiddenMutatedCodonsForward = ""NNW"",
                     CharacterVector forbiddenMutatedCodonsReverse = ""NNW"",
                     bool useTreeWTmatch = false,
                     bool collapseToWTForward = false,
                     bool collapseToWTReverse = false,
                     double mutatedPhredMinForward = 0.0,
                     double mutatedPhredMinReverse = 0.0,
                     std::string mutNameDelimiter = ""."",
                     int constantMaxDistForward = -1,
                     int constantMaxDistReverse = -1,
                     double umiCollapseMaxDist = 0.0,
                     std::string filteredReadsFastqForward = """",
                     std::string filteredReadsFastqReverse = """",
                     int maxNReads = -1, bool verbose = false,
                     int nThreads = 1, int chunkSize = 100000,
                     size_t maxReadLength = 1024) {

  // See https://github.com/Rdatatable/data.table/issues/3300#issuecomment-457017735 for
  // a discussion on limiting the number of threads available to OpenMP
#ifdef _OPENMP
  if (nThreads > std::min(omp_get_max_threads(), omp_get_thread_limit())) {
    warning(""'nThreads' is larger than the number of available threads. Reducing 'nThreads' to %i"", std::min(omp_get_max_threads(), omp_get_thread_limit()));
    nThreads = std::min(omp_get_max_threads(), omp_get_thread_limit());
  }
#else
  if (nThreads > 1) {
    warning(""OpenMP parallelization not available. Ignoring 'nThreads'."");
  }
#endif

  // Biostrings::IUPAC_CODE_MAP
  std::map<char,std::vector<char>> IUPAC = initializeIUPAC();
  std::map<char,std::string> threeAA = initializeThreeAA();

  // --------------------------------------------------------------------------
  // declare variables
  // --------------------------------------------------------------------------
  size_t BUFFER_SIZE = maxReadLength + 2;
  FastqBuffer *chunkBuffer = new FastqBuffer((size_t)chunkSize, BUFFER_SIZE, fastqReverseVect[0].compare("""") != 0);

  int nTot = 0, nAdapter = 0, nNoPrimer = 0, nReadWrongLength = 0, nTooManyMutConstant = 0;
  int nNoValidOverlap = 0, nAvgVarQualTooLow = 0, nTooManyNinVar = 0, nTooManyNinUMI = 0;
  int nTooManyMutCodons = 0, nTooManyMutBases = 0, nForbiddenCodons = 0, nMutQualTooLow = 0;
  int nTooManyBestWTHits = 0, nTooManyBestConstantHits = 0, nRetain = 0;

  std::map<std::string, mutantInfo> mutantSummary;
  std::map<std::string, mutantInfo>::iterator mutantSummaryIt, mutantSummarySimIt;
  std::vector<size_t> nPhredCorrectForward(100, 0), nPhredMismatchForward(100, 0);
  std::vector<size_t> nPhredCorrectReverse(100, 0), nPhredMismatchReverse(100, 0);

  // enumerate forbidden codons based on forbiddenMutatedCodons
  std::set<std::string> forbiddenCodonsForward = enumerateCodonsFromIUPAC(forbiddenMutatedCodonsForward, IUPAC, verbose);
  std::set<std::string> forbiddenCodonsReverse = enumerateCodonsFromIUPAC(forbiddenMutatedCodonsReverse, IUPAC, verbose);

  // max number of mismatches for constant sequences
  size_t upperBoundMismatchConstForward = 0, upperBoundMismatchConstReverse = 0;

  // build BKtree(s) for wildtype sequences
  bool useTreeWTmatchForward = useTreeWTmatch, useTreeWTmatchReverse = useTreeWTmatch;
  BKtree wtTreeForward, wtTreeReverse;
  std::map<std::string, int> wtTreeForwardIdx, wtTreeReverseIdx;
  size_t upperBoundMismatchForward = 0, upperBoundMismatchReverse = 0;
  // forward
  if (wildTypeForward[0].compare("""") != 0 && useTreeWTmatch) {
    std::vector<std::string>::iterator wtTreeForwardVecIt;
    size_t wtForwardLen = wildTypeForward[0].length();
    for (wtTreeForwardVecIt = wildTypeForward.begin();
         wtTreeForwardVecIt != wildTypeForward.end(); wtTreeForwardVecIt++) {
      if ((*wtTreeForwardVecIt).length() != wtForwardLen) {
        // not all WT sequences are of the same length ->
        // don't use the tree for finding the most similar WT sequence
        wtTreeForward.remove_all();
        useTreeWTmatchForward = false;
        break;
      } else {
        wtTreeForward.insert(*wtTreeForwardVecIt);
      }
    }
    for (size_t i = 0; i < wildTypeForward.size(); i++) {
      wtTreeForwardIdx[wildTypeForward[i]] = i;
    }
  }
  // determine the upper bound for the number of base mismatches in the tree search
  if (nbrMutatedCodonsMaxForward != (-1)) {
    upperBoundMismatchForward = 3 * nbrMutatedCodonsMaxForward;
  } else {
    upperBoundMismatchForward = nbrMutatedBasesMaxForward;
  }

  if (wildTypeReverse[0].compare("""") != 0 && useTreeWTmatch) {
    std::vector<std::string>::iterator wtTreeReverseVecIt;
    size_t wtReverseLen = wildTypeReverse[0].length();
    for (wtTreeReverseVecIt = wildTypeReverse.begin();
         wtTreeReverseVecIt != wildTypeReverse.end(); wtTreeReverseVecIt++) {
      if ((*wtTreeReverseVecIt).length() != wtReverseLen) {
        // not all WT sequences are of the same length ->
        // don't use the tree for finding the most similar WT sequence
        wtTreeReverse.remove_all();
        useTreeWTmatchReverse = false;
        break;
      } else {
        wtTreeReverse.insert(*wtTreeReverseVecIt);
      }
    }
    for (size_t i = 0; i < wildTypeReverse.size(); i++) {
      wtTreeReverseIdx[wildTypeReverse[i]] = i;
    }
  }
  // determine the upper bound for the number of base mismatches in the tree search
  if (nbrMutatedCodonsMaxForward != (-1)) {
    upperBoundMismatchReverse = 3 * nbrMutatedCodonsMaxReverse;
  } else {
    upperBoundMismatchReverse = nbrMutatedBasesMaxReverse;
  }

  // open files for outputting filtered reads
  gzFile outfile1 = NULL, outfile2 = NULL;
  if (filteredReadsFastqForward.compare("""") != 0) {
    outfile1 = openFastq(filteredReadsFastqForward, ""wb"");
    if (filteredReadsFastqReverse.compare("""") != 0) {
      outfile2 = openFastq(filteredReadsFastqReverse, ""wb"");
    }
  }

  // --------------------------------------------------------------------------
  // iterate over fastq files
  // --------------------------------------------------------------------------
  for (size_t f = 0; f < fastqForwardVect.size(); f++) {
    // if maxNReads has been reached in the previous file, break
    bool done;
    if (maxNReads != (-1) && nTot >= maxNReads) {
      done = true; // # nocov
    } else {
      done = false;
    }
    std::string fastqForward = fastqForwardVect[f];
    std::string fastqReverse = fastqReverseVect[f];

    // --------------------------------------------------------------------------
    // open fastq file(s)
    // --------------------------------------------------------------------------
    gzFile file1 = openFastq(fastqForward, ""rb"");
    gzFile file2 = NULL;
    if (fastqReverse.compare("""") != 0) {
      file2 = openFastq(fastqReverse, ""rb"");
    }

    // iterate over sequences
    if (verbose) {
      Rcout << ""start reading sequences for file "" <<
               (fastqReverse.compare("""") != 0 ? ""pair "" : """") << (f + 1) <<
               "" of "" << fastqForwardVect.size() << ""..."" << std::endl;
    }

    // counter for reads being added to the chunk vector
    size_t iChunk = 0;
    while (done == false) {
      done = get_next_seq(file1,
                          chunkBuffer->seq1 + iChunk * BUFFER_SIZE,
                          chunkBuffer->qual1 + iChunk * BUFFER_SIZE,
                          BUFFER_SIZE);
      if (fastqReverse.compare("""") != 0) {
        done = (done || get_next_seq(file2,
                                     chunkBuffer->seq2 + iChunk * BUFFER_SIZE,
                                     chunkBuffer->qual2 + iChunk * BUFFER_SIZE,
                                     BUFFER_SIZE));
      }
      if (done == false) {
        iChunk++;
        nTot++;
      }
      if (nTot % 200000 == 0) { // every 200,000 reads (every ~1.6 seconds)
        Rcpp::checkUserInterrupt(); // ... check for user interrupt # nocov
      }

      // if maxNReads has been reached, break
      if (maxNReads != (-1) && nTot >= maxNReads) {
        done = true;
      }

      // process reads in the chunk
      if ((int)iChunk == chunkSize || done) {
#ifdef _OPENMP
#pragma omp parallel for schedule(guided) num_threads(nThreads)
#endif
        for (size_t ci = 0; ci < iChunk; ci++) { // for each read in chunk
          // while (done == false) {
          // initialize read parts as empty strings (since they will be added onto
          // when extracting the respective parts from the reads)
          std::string varSeqForward = """", varSeqReverse = """", varQualForward = """";
          std::string varQualReverse = """", umiSeq = """", constSeqForward = """";
          std::string constSeqReverse = """", constQualForward = """", constQualReverse = """";
          std::string mutantName = """", mutantNameBase = """", mutantNameCodon = """",
              mutantNameBaseHGVS = """", mutantNameAA = """", mutantNameAAHGVS = """";
          std::vector<int> varLengthsForward, varLengthsReverse;
          std::string varLengthsForwardStr = """", varLengthsReverseStr = """";
          int nMutBases = 0, nMutCodons = 0, nMutAAs = 0;
          std::set<std::string> mutationTypes;
          int maxSim = 0;
          int constantLengthForward, constantLengthReverse;
          std::map<std::string, mutantInfo>::iterator mutantSummaryParIt;

          // convert C char* to C++ string
          std::string sseq1(chunkBuffer->seq1 + ci * BUFFER_SIZE);
          std::string squal1(chunkBuffer->qual1 + ci * BUFFER_SIZE);
          std::string sseq2, squal2;
          if (fastqReverse.compare("""") != 0) {
            sseq2 = chunkBuffer->seq2 + ci * BUFFER_SIZE;
            squal2 = chunkBuffer->qual2 + ci * BUFFER_SIZE;
          }

          // search for adapter sequences and filter read pairs
          if ((adapterForward.compare("""") != 0 && sseq1.find(adapterForward) != std::string::npos) ||
              (fastqReverse.compare("""") != 0 && adapterReverse.compare("""") != 0 && sseq2.find(adapterReverse) != std::string::npos)) {
#ifdef _OPENMP
            #pragma omp atomic
#endif
            nAdapter++;
            chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""adapter"");
            continue;
          }

          // check if the last character(s) are new line, if so remove them
          removeEOL(sseq1);
          removeEOL(squal1);
          if (fastqReverse.compare("""") != 0) {
            removeEOL(sseq2);
            removeEOL(squal2);
          }

          // Extract the components of the reads
          // UMI, constant sequence, variable sequence, forward/reverse
          // if processing of either forward or reverse read signals 'break', go to the next read pair
          if (!decomposeRead(sseq1, squal1, elementsForward, elementLengthsForward,
                             primerForward, umiSeq, varSeqForward, varQualForward,
                             varLengthsForward, constSeqForward, constQualForward,
                             nNoPrimer, nReadWrongLength)) {
            chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""noPrimer_readWrongLength"");
            continue;
          }
          if (fastqReverse.compare("""") != 0 &&
              !decomposeRead(sseq2, squal2, elementsReverse, elementLengthsReverse,
                             primerReverse, umiSeq, varSeqReverse, varQualReverse,
                             varLengthsReverse, constSeqReverse, constQualReverse,
                             nNoPrimer, nReadWrongLength)) {
            chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""noPrimer_readWrongLength"");
            continue;
          }

          // reverse complement if requested
          if (revComplForward) {
            transform(
              begin(varSeqForward), end(varSeqForward),
              begin(varSeqForward), complement);
            reverse(begin(varSeqForward), end(varSeqForward));
            reverse(begin(varQualForward), end(varQualForward));
            std::reverse(varLengthsForward.begin(), varLengthsForward.end());
          }
          if (fastqReverse.compare("""") != 0 && revComplReverse) {
            transform(
              begin(varSeqReverse), end(varSeqReverse),
              begin(varSeqReverse), complement);
            reverse(begin(varSeqReverse), end(varSeqReverse));
            reverse(begin(varQualReverse), end(varQualReverse));
            std::reverse(varLengthsReverse.begin(), varLengthsReverse.end());
          }

          // convert qualities to int
          std::vector<int> varIntQualForward(varSeqForward.length(), 0);
          for (size_t i = 0; i < varSeqForward.length(); i++) {
            varIntQualForward[i] = int(varQualForward[i]) - QUALITY_OFFSET;
          }

          std::vector<int> varIntQualReverse;
          if (fastqReverse.compare("""") != 0) {
            varIntQualReverse.resize(varSeqReverse.length()); // set number of elements
            std::fill(varIntQualReverse.begin(), varIntQualReverse.end(), 0); // fill with zero
            for (size_t i = 0; i < varSeqReverse.length(); i++) {
              varIntQualReverse[i] = int(varQualReverse[i]) - QUALITY_OFFSET;
            }
          }

          // if requested, fuse forward and reverse reads
          if (mergeForwardReverse) {
            if (mergeReadPairPartial(varSeqForward, varIntQualForward,
                                     varSeqReverse, varIntQualReverse,
                                     varLengthsForward, varLengthsReverse,
                                     minOverlap, maxOverlap,
                                     minMergedLength, maxMergedLength,
                                     maxFracMismatchOverlap, greedyOverlap)) {
              // read should be filtered out - no valid overlap found
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nNoValidOverlap++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""noValidOverlap"");
              continue;
            }
          }
          // if no reverse sequence was provided, hereafter it is identical to the situation
          // where forward and reverse reads were merged
          bool noReverse = mergeForwardReverse || fastqReverse.compare("""") == 0;

          // filter if the average quality in variable region is too low
          if (std::accumulate(varIntQualForward.begin(), varIntQualForward.end(), 0.0) <
            avePhredMinForward * varSeqForward.length() ||
            (!noReverse && std::accumulate(varIntQualReverse.begin(), varIntQualReverse.end(), 0.0) <
              avePhredMinReverse * varSeqReverse.length())) {
#ifdef _OPENMP
            #pragma omp atomic
#endif
            nAvgVarQualTooLow++;
            chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""avgVarQualTooLow"");
            continue;
          }

          // filter if there are too many N's in variable regions
          if (std::count(varSeqForward.begin(), varSeqForward.end(), 'N') > variableNMaxForward ||
              (!noReverse && std::count(varSeqReverse.begin(), varSeqReverse.end(), 'N') > variableNMaxReverse)) {
#ifdef _OPENMP
            #pragma omp atomic
#endif
            nTooManyNinVar++;
            chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""tooManyNinVar"");
            continue;
          }

          if (umiSeq != """" && std::count(umiSeq.begin(), umiSeq.end(), 'N') > umiNMax) {
#ifdef _OPENMP
            #pragma omp atomic
#endif
            nTooManyNinUMI++;
            chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""tooManyNinUMI"");
            continue;
          }

          // if wildTypeForward is available...
          if (wildTypeForward[0].compare("""") != 0) {
            int idxForward;
            if (useTreeWTmatchForward) {
              idxForward = findClosestRefSeqTree(varSeqForward, wtTreeForward,
                                                 wtTreeForwardIdx, upperBoundMismatchForward, maxSim);
            } else {
              idxForward = findClosestRefSeqEarlyStop(varSeqForward, wildTypeForward,
                                                      upperBoundMismatchForward, maxSim);
            }
            // if idxForward = NO_SIMILAR_REF (defined above), no similar enough wildtype sequence was found - skip the read
            if (idxForward == NO_SIMILAR_REF) {
              if (nbrMutatedCodonsMaxForward != (-1)) {
#ifdef _OPENMP
                #pragma omp atomic
#endif
                nTooManyMutCodons++;
              } else {
#ifdef _OPENMP
                #pragma omp atomic
#endif
                nTooManyMutBases++;
              }
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""mutQualTooLow_tooManyMutCodons_forbiddenCodons"");
              continue;
            }
            // if idxForward = TOO_MANY_BEST_REF (defined above), too many equally good hits among the WT sequences - skip the read
            if (idxForward == TOO_MANY_BEST_REF) {
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nTooManyBestWTHits++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""tooManyBestWTHits"");
              continue;
            }
            std::string wtForward = wildTypeForward[idxForward];
            std::string wtNameForward = wildTypeForwardNames[idxForward];

            // if the variable region is longer than the (best matching) WT sequence,
            // skip the read (consider it to have the wrong length)
            if (varSeqForward.length() > wtForward.length()) {
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nReadWrongLength++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""noPrimer_readWrongLength"");
              continue;
            }

            if (compareToWildtype(varSeqForward, wtForward, varIntQualForward,
                                  mutatedPhredMinForward, nbrMutatedCodonsMaxForward, forbiddenCodonsForward,
                                  wtNameForward, nbrMutatedBasesMaxForward, nMutQualTooLow,
                                  nTooManyMutCodons, nForbiddenCodons, nTooManyMutBases,
                                  mutantName, mutantNameBase, mutantNameCodon,
                                  mutantNameBaseHGVS, mutantNameAA, mutantNameAAHGVS,
                                  nMutBases, nMutCodons,
                                  nMutAAs, mutationTypes, mutNameDelimiter, collapseToWTForward,
                                  threeAA)) {
              // read is to be filtered out
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""mutQualTooLow_tooManyMutCodons_forbiddenCodons"");
              continue;
            }
          } else if (varSeqForward.length() > 0) { // variable seq, but no reference -> add variable seq to mutantName
            mutantName += (varSeqForward + std::string(""_""));
            mutantNameBase += (varSeqForward + std::string(""_""));
            mutantNameCodon += (varSeqForward + std::string(""_""));
            mutantNameAA += (translateString(varSeqForward) + std::string(""_""));
            // don't set mutantNameBaseHGVS, mutantNameAAHGVS if there is no wt
          }
          // convert varLengthsForward to string
          if (varLengthsForward.size() > 0) {
            for (size_t i = 0; i < varLengthsForward.size(); i++) {
                varLengthsForwardStr += (std::to_string(varLengthsForward[i]) + "","");
            }
            varLengthsForwardStr.pop_back();
          }

          // if wildTypeReverse is available...
          if (!noReverse && wildTypeReverse[0].compare("""") != 0) {
            int idxReverse;
            if (useTreeWTmatchReverse) {
              idxReverse = findClosestRefSeqTree(varSeqReverse, wtTreeReverse,
                                                 wtTreeReverseIdx, upperBoundMismatchReverse, maxSim);
            } else {
              idxReverse = findClosestRefSeqEarlyStop(varSeqReverse, wildTypeReverse,
                                                      upperBoundMismatchReverse, maxSim);
            }
            // if idxReverse = NO_SIMILAR_REF (defined above), no similar enough wildtype sequence was found - skip the read
            if (idxReverse == NO_SIMILAR_REF) {
              if (nbrMutatedCodonsMaxReverse != (-1)) {
#ifdef _OPENMP
                #pragma omp atomic
#endif
                nTooManyMutCodons++;
              } else {
#ifdef _OPENMP
                #pragma omp atomic
#endif
                nTooManyMutBases++;
              }
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""mutQualTooLow_tooManyMutCodons_forbiddenCodons"");
              continue;
            }
            // if idxReverse = TOO_MANY_BEST_REF (defined above), too many equally good hits among the WT sequences - skip the read
            if (idxReverse == TOO_MANY_BEST_REF) {
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nTooManyBestWTHits++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""tooManyBestWTHits"");
              continue;
            }
            std::string wtReverse = wildTypeReverse[idxReverse];
            std::string wtNameReverse = wildTypeReverseNames[idxReverse];

            // if the variable region is longer than the (best matching) WT sequence,
            // skip the read (consider it to have the wrong length)
            if (varSeqReverse.length() > wtReverse.length()) {
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nReadWrongLength++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""noPrimer_readWrongLength"");
              continue;
            }

            if (compareToWildtype(varSeqReverse, wtReverse, varIntQualReverse,
                                  mutatedPhredMinReverse, nbrMutatedCodonsMaxReverse, forbiddenCodonsReverse,
                                  wtNameReverse, nbrMutatedBasesMaxReverse, nMutQualTooLow,
                                  nTooManyMutCodons, nForbiddenCodons, nTooManyMutBases,
                                  mutantName, mutantNameBase, mutantNameCodon,
                                  mutantNameBaseHGVS, mutantNameAA, mutantNameAAHGVS,
                                  nMutBases, nMutCodons,
                                  nMutAAs, mutationTypes, mutNameDelimiter, collapseToWTReverse,
                                  threeAA)) {
              // read is to be filtered out
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""mutQualTooLow_tooManyMutCodons_forbiddenCodons"");
              continue;
            }
          } else if (!noReverse && varSeqReverse.length() > 0) { // variable seq, but no reference -> add variable seq to mutantName
            mutantName += (varSeqReverse + std::string(""_""));
            mutantNameBase += (varSeqReverse + std::string(""_""));
            mutantNameCodon += (varSeqReverse + std::string(""_""));
            mutantNameAA += (translateString(varSeqReverse) + std::string(""_""));
            // don't set mutantNameBaseHGVS, mutantNameAAHGVS if there is no wt
          }
          // convert varLengthsReverse to string
          if (varLengthsReverse.size() > 0) {
              for (size_t i = 0; i < varLengthsReverse.size(); i++) {
                  varLengthsReverseStr += (std::to_string(varLengthsReverse[i]) + "","");
              }
              varLengthsReverseStr.pop_back();
          }

          // for retained reads, count numbers of (mis-)matching bases in constant seq by Phred quality
          constantLengthForward = (int) constSeqForward.length();
          constantLengthReverse = (int) constSeqReverse.length();
          std::vector<int> constIntQualForward(constantLengthForward, 0);
          std::vector<int> constIntQualReverse(constantLengthReverse,0);
          int idxConstForward = 0;
          int idxConstReverse = 0;

          if (constantForward[0].compare("""") != 0) {
            // reverse complement if requested
            if (revComplForward) {
              transform(begin(constSeqForward), end(constSeqForward),
                        begin(constSeqForward), complement);
              reverse(constSeqForward.begin(), constSeqForward.end());
              reverse(constQualForward.begin(), constQualForward.end());
            }

            // populate an integer vector of base qualities
            for (size_t i = 0; i < (size_t) constantLengthForward; i++) {
              constIntQualForward[i] = int(constQualForward[i]) - QUALITY_OFFSET;
            }

            // find closest constant sequence and tabulate mismatches by quality
            maxSim = 0;
            // define upperBoundMismatch for this read
            if (constantMaxDistForward != (-1)) {
              upperBoundMismatchConstForward = (size_t)constantMaxDistForward;
            } else {
              upperBoundMismatchConstForward = constSeqForward.size();
            }
            idxConstForward = findClosestRefSeqEarlyStop(constSeqForward, constantForward,
                                                         upperBoundMismatchConstForward, maxSim);
            if (idxConstForward == NO_SIMILAR_REF) {
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nTooManyMutConstant++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""tooManyMutConstant"");
              continue;
            }
            // more than one equally good best hit among the constant sequences - skip read
            if (idxConstForward == TOO_MANY_BEST_REF) {
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nTooManyBestConstantHits++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""tooManyBestConstantHits"");
              continue;
            }
          }

          if (fastqReverse.compare("""") != 0 &&
              constantReverse[0].compare("""") != 0) {
            // reverse (complement) sequence and quality string
            if (revComplReverse) {
              transform(begin(constSeqReverse), end(constSeqReverse),
                        begin(constSeqReverse), complement);
              reverse(constSeqReverse.begin(), constSeqReverse.end());
              reverse(constQualReverse.begin(), constQualReverse.end());
            }

            // populate an integer vector of base qualities
            for (size_t i = 0; i < (size_t) constantLengthReverse; i++) {
              constIntQualReverse[i] = int(constQualReverse[i]) - QUALITY_OFFSET;
            }

            // find closest constant sequence and tabulate mismatches by quality
            maxSim = 0;
            // define upperBoundMismatch for this read
            if (constantMaxDistReverse != (-1)) {
              upperBoundMismatchConstReverse = (size_t)constantMaxDistReverse;
            } else {
              upperBoundMismatchConstReverse = constSeqReverse.size();
            }
            idxConstReverse = findClosestRefSeqEarlyStop(constSeqReverse, constantReverse,
                                                         upperBoundMismatchConstReverse, maxSim);
            if (idxConstReverse == NO_SIMILAR_REF) {
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nTooManyMutConstant++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""tooManyMutConstant"");
              continue;
            }
            // more than one equally good best hit among the constant sequences - skip read
            if (idxConstReverse == TOO_MANY_BEST_REF) {
#ifdef _OPENMP
              #pragma omp atomic
#endif
              nTooManyBestConstantHits++;
              chunkBuffer->write_seq(ci, outfile1, outfile2, nTot-(int)iChunk+(int)ci, ""tooManyBestConstantHits"");
              continue;
            }
          }

          if (constantForward[0].compare("""") != 0) {
            tabulateBasesByQual(constSeqForward, constantForward[idxConstForward],
                                constIntQualForward,
                                nPhredCorrectForward, nPhredMismatchForward);
          }
          if (fastqReverse.compare("""") != 0 &&
              constantReverse[0].compare("""") != 0) {
            tabulateBasesByQual(constSeqReverse, constantReverse[idxConstReverse],
                                constIntQualReverse,
                                nPhredCorrectReverse, nPhredMismatchReverse);
          }

          // store the read pair
#ifdef _OPENMP
          #pragma omp atomic
#endif
          nRetain++;
          // ... create final mutant name
          if (mutantName.length() > 0) { // we have a least one mutation, or sequence-based name
            mutantName.pop_back(); // remove '_' at the end
            mutantNameBase.pop_back(); // remove '_' at the end
            mutantNameCodon.pop_back(); // remove '_' at the end
          } else {
            // will we ever go in here? // # nocov start
            if (wildTypeForward[0].compare("""") != 0 ||
                (!noReverse && wildTypeReverse[0].compare("""") != 0)) {
              mutantName = ""WT"";
              mutantNameBase = ""WT"";
              mutantNameCodon = ""WT"";
            } // # nocov end
          }
          if (mutantNameBaseHGVS.length() > 0) { // we have a (closest) wildtype name
            mutantNameBaseHGVS.pop_back(); // remove '_' at the end
            mutantNameAAHGVS.pop_back(); // remove '_' at the end
          }
          if (mutantNameAA.length() > 0) { // we have a least one mutation, or sequence-based name
            mutantNameAA.pop_back(); // remove '_' at the end
          } else {
            // will we ever go in here? // # nocov start
            if (wildTypeForward[0].compare("""") != 0 ||
                (!noReverse && wildTypeReverse[0].compare("""") != 0)) {
              mutantNameAA = ""WT"";
            } // # nocov end
          }
          if (!noReverse) { // ""trans"" experiment
            varSeqForward += (std::string(""_"") + varSeqReverse);
            varLengthsForwardStr += (std::string(""_"") + varLengthsReverseStr);
          }
          // ... check if mutant already exists in mutantSummary
#ifdef _OPENMP
#pragma omp critical
#endif
{
          if ((mutantSummaryParIt = mutantSummary.find(mutantName)) != mutantSummary.end()) {
            // ... ... update existing mutantInfo
            (*mutantSummaryParIt).second.nReads++;
            (*mutantSummaryParIt).second.maxNReads++;
            if (umiSeq != """") {
              (*mutantSummaryParIt).second.umi.insert(umiSeq);
            }
            (*mutantSummaryParIt).second.sequence.insert(varSeqForward);
            (*mutantSummaryParIt).second.nMutBases.insert(nMutBases);
            (*mutantSummaryParIt).second.nMutCodons.insert(nMutCodons);
            (*mutantSummaryParIt).second.nMutAAs.insert(nMutAAs);
            (*mutantSummaryParIt).second.mutationTypes.insert(mutationTypes.begin(), mutationTypes.end());
            (*mutantSummaryParIt).second.mutantNameBase.insert(mutantNameBase);
            (*mutantSummaryParIt).second.mutantNameCodon.insert(mutantNameCodon);
            (*mutantSummaryParIt).second.mutantNameBaseHGVS.insert(mutantNameBaseHGVS);
            (*mutantSummaryParIt).second.mutantNameAA.insert(mutantNameAA);
            (*mutantSummaryParIt).second.mutantNameAAHGVS.insert(mutantNameAAHGVS);
            (*mutantSummaryParIt).second.sequenceAA.insert(translateString(varSeqForward));
          } else {
            // ... ... create mutantInfo instance for this mutant and add it to mutantSummary
            mutantInfo newMutant;
            newMutant.nReads = 1;
            newMutant.maxNReads = 1;
            newMutant.nMutBases.insert(nMutBases);
            newMutant.nMutCodons.insert(nMutCodons);
            newMutant.nMutAAs.insert(nMutAAs);
            if (umiSeq != """") {
              newMutant.umi.insert(umiSeq);
            }
            newMutant.sequence.insert(varSeqForward);
            newMutant.varLengths = varLengthsForwardStr;
            newMutant.mutationTypes.insert(mutationTypes.begin(), mutationTypes.end());
            newMutant.mutantNameBase.insert(mutantNameBase);
            newMutant.mutantNameCodon.insert(mutantNameCodon);
            newMutant.mutantNameBaseHGVS.insert(mutantNameBaseHGVS);
            newMutant.mutantNameAA.insert(mutantNameAA);
            newMutant.mutantNameAAHGVS.insert(mutantNameAAHGVS);
            newMutant.sequenceAA.insert(translateString(varSeqForward));
            mutantSummary.insert(std::pair<std::string,mutantInfo>(mutantName, newMutant));
          }
}
        } // iterate over individual sequence pairs
        iChunk = 0;

        // ... and give an update
        if (verbose) {
          Rcout << ""    "" << nTot << "" read pairs processed (""
                << std::setprecision(3) << (100*((double)nRetain/nTot)) << ""% retained)"" << std::endl;
        }
      }
      // check if end-of-file was reached
      if (done) {
        break;
      }
    }

    // clean up
    gzclose(file1);
    if (fastqReverse.compare("""") != 0) {
      gzclose(file2);
    }
  } // iterate over fastq files

  // close output files
  if (outfile1 != NULL) {
    gzclose(outfile1);
  }
  if (outfile2 != NULL) {
    gzclose(outfile2);
  }

  if (verbose) {
    Rcout << ""done reading sequences"" << std::endl;
    Rcout << ""retained "" << mutantSummary.size() << "" unique features"" << std::endl;
  }

  // reading of reads is done - don't need the chunkBuffer anymore
  delete chunkBuffer;

  // collapse similar UMI sequences in each variable sequence mutantSummary
  if (umiCollapseMaxDist > 0.0) {
    // calculate Hamming distance tolerance
    int tol;
    if (umiCollapseMaxDist >= 1.0) {
      tol = (int)umiCollapseMaxDist;
    } else {
      tol = (int)(umiCollapseMaxDist * (*(*mutantSummary.begin()).second.umi.begin()).length());
    }

    if (verbose) {
      Rcout << ""start collapsing UMIs (tolerance: "" << tol << "")..."";
    }

    // store sequences (from names) in BK tree
    BKtree tree;
    std::set<std::string>::iterator umiIt;
    int mutCounter = 0;
    for (mutantSummaryIt = mutantSummary.begin(); mutantSummaryIt != mutantSummary.end(); mutantSummaryIt++) {
      if ((*mutantSummaryIt).second.umi.size() == 1) {
        continue;
      } else {
        tree.remove_all();
        for (umiIt = (*mutantSummaryIt).second.umi.begin(); umiIt != (*mutantSummaryIt).second.umi.end(); umiIt++) {
          tree.insert((*umiIt));
        }

        std::vector<std::string> simSeqs;
        std::set<std::string> collapsedUmis;
        while (tree.size > 0) {
          simSeqs = tree.search(tree.first(), tol);
          collapsedUmis.insert(tree.first());
          for (size_t i = 0; i < simSeqs.size(); i++) {
            tree.remove(simSeqs[i]);
          }
        }

        (*mutantSummaryIt).second.umi = collapsedUmis;
      }
      mutCounter++;
      // check for user interruption and print progress
      if (mutCounter % 200 == 0) { // every 200 queries
        Rcpp::checkUserInterrupt(); // ... check for user interrupt # nocov
      }
    }

    if (verbose) {
      Rcout << ""done"" << std::endl;
    }
  }

  // return results
  // ... parameters
  // put back wildtype sequences and names in Rcpp::StringVector
  Rcpp::StringVector wildTypeForwardRcpp(wildTypeForward.size());
  wildTypeForwardRcpp = wildTypeForward;
  wildTypeForwardRcpp.attr(""names"") = wildTypeForwardNames;
  Rcpp::StringVector wildTypeReverseRcpp(wildTypeReverse.size());
  wildTypeReverseRcpp = wildTypeReverse;
  wildTypeReverseRcpp.attr(""names"") = wildTypeReverseNames;

  std::vector<std::string> forbiddenCodonsUsedForward(forbiddenCodonsForward.begin(), forbiddenCodonsForward.end());
  std::vector<std::string> forbiddenCodonsUsedReverse(forbiddenCodonsReverse.begin(), forbiddenCodonsReverse.end());
  List param;
  param.push_back(fastqForwardVect, ""fastqForward"");
  param.push_back(fastqReverseVect, ""fastqReverse"");
  param.push_back(mergeForwardReverse, ""mergeForwardReverse"");
  param.push_back(minOverlap, ""minOverlap"");
  param.push_back(maxOverlap, ""maxOverlap"");
  param.push_back(minMergedLength, ""minMergedLength"");
  param.push_back(maxMergedLength, ""maxMergedLength"");
  param.push_back(maxFracMismatchOverlap, ""maxFracMismatchOverlap"");
  param.push_back(greedyOverlap, ""greedyOverlap"");
  param.push_back(revComplForward, ""revComplForward"");
  param.push_back(revComplReverse, ""revComplReverse"");
  param.push_back(elementsForward, ""elementsForward"");
  param.push_back(elementLengthsForward, ""elementLengthsForward"");
  param.push_back(elementsReverse, ""elementsReverse"");
  param.push_back(elementLengthsReverse, ""elementLengthsReverse"");
  param.push_back(adapterForward, ""adapterForward"");
  param.push_back(adapterReverse, ""adapterReverse"");
  param.push_back(primerForward, ""primerForward"");
  param.push_back(primerReverse, ""primerReverse"");
  param.push_back(wildTypeForwardRcpp, ""wildTypeForward"");
  param.push_back(wildTypeReverseRcpp, ""wildTypeReverse"");
  param.push_back(constantForward, ""constantForward"");
  param.push_back(constantReverse, ""constantReverse"");
  param.push_back(avePhredMinForward, ""avePhredMinForward"");
  param.push_back(avePhredMinReverse, ""avePhredMinReverse"");
  param.push_back(variableNMaxForward, ""variableNMaxForward"");
  param.push_back(variableNMaxReverse, ""variableNMaxReverse"");
  param.push_back(umiNMax, ""umiNMax"");
  param.push_back(nbrMutatedCodonsMaxForward, ""nbrMutatedCodonsMaxForward"");
  param.push_back(nbrMutatedCodonsMaxReverse, ""nbrMutatedCodonsMaxReverse"");
  param.push_back(nbrMutatedBasesMaxForward, ""nbrMutatedBasesMaxForward"");
  param.push_back(nbrMutatedBasesMaxReverse, ""nbrMutatedBasesMaxReverse"");
  param.push_back(forbiddenCodonsUsedForward, ""forbiddenMutatedCodonsForward"");
  param.push_back(forbiddenCodonsUsedReverse, ""forbiddenMutatedCodonsReverse"");
  param.push_back(useTreeWTmatch, ""useTreeWTmatch"");
  param.push_back(collapseToWTForward, ""collapseToWTForward"");
  param.push_back(collapseToWTReverse, ""collapseToWTReverse"");
  param.push_back(mutatedPhredMinForward, ""mutatedPhredMinForward"");
  param.push_back(mutatedPhredMinReverse, ""mutatedPhredMinReverse"");
  param.push_back(mutNameDelimiter, ""mutNameDelimiter"");
  param.push_back(constantMaxDistForward, ""constantMaxDistForward"");
  param.push_back(constantMaxDistReverse, ""constantMaxDistReverse"");
  param.push_back(umiCollapseMaxDist, ""umiCollapseMaxDist"");
  param.push_back(filteredReadsFastqForward, ""filteredReadsFastqForward"");
  param.push_back(filteredReadsFastqReverse, ""filteredReadsFastqReverse"");
  param.push_back(maxNReads, ""maxNReads"");
  param.push_back(nThreads, ""nThreads"");
  param.push_back(chunkSize, ""chunkSize"");
  param.push_back(maxReadLength, ""maxReadLength"");

  // ... error statistics
  DataFrame err = DataFrame::create(Named(""PhredQuality"") = seq_len(100) - 1,
                                    Named(""nbrMatchForward"") = nPhredCorrectForward,
                                    Named(""nbrMismatchForward"") = nPhredMismatchForward,
                                    Named(""nbrMatchReverse"") = nPhredCorrectReverse,
                                    Named(""nbrMismatchReverse"") = nPhredMismatchReverse);

  // ... filter statistics
  DataFrame filt = DataFrame::create(Named(""nbrTotal"") = nTot,
                                     Named(""f1_nbrAdapter"") = nAdapter,
                                     Named(""f2_nbrNoPrimer"") = nNoPrimer,
                                     Named(""f3_nbrReadWrongLength"") = nReadWrongLength,
                                     Named(""f4_nbrNoValidOverlap"") = nNoValidOverlap,
                                     Named(""f5_nbrAvgVarQualTooLow"") = nAvgVarQualTooLow,
                                     Named(""f6_nbrTooManyNinVar"") = nTooManyNinVar,
                                     Named(""f7_nbrTooManyNinUMI"") = nTooManyNinUMI,
                                     Named(""f8_nbrTooManyBestWTHits"") = nTooManyBestWTHits,
                                     Named(""f9_nbrMutQualTooLow"") = nMutQualTooLow,
                                     Named(""f10a_nbrTooManyMutCodons"") = nTooManyMutCodons,
                                     Named(""f10b_nbrTooManyMutBases"") = nTooManyMutBases,
                                     Named(""f11_nbrForbiddenCodons"") = nForbiddenCodons,
                                     Named(""f12_nbrTooManyMutConstant"") = nTooManyMutConstant,
                                     Named(""f13_nbrTooManyBestConstantHits"") = nTooManyBestConstantHits,
                                     Named(""nbrRetained"") = nRetain);

  // ... main data frame
  DataFrame df = mutantSummaryToDataFrame(mutantSummary);

  // ... pack into list
  List L = List::create(Named(""parameters"") = param,
                        Named(""filterSummary"") = filt,
                        Named(""summaryTable"") = df,
                        Named(""errorStatistics"") = err);
  return L;
}
","C++"
"Assay","fmicompbio/mutscan","src/mergeEntriesForSummarization.cpp",".cpp","2294","62","#include <string>
#include <vector>
#include <numeric>
#include <map>
#include <Rcpp.h>
using namespace Rcpp;

// Split a string at a delimiter and return a set
std::set<std::string> splitSet(const std::string& s, char delimiter) {
    std::set<std::string> tokens;
    std::string token;
    std::istringstream tokenStream(s);
    while (std::getline(tokenStream, token, delimiter)) {
        tokens.insert(token);
    }
    return tokens;
}

// Merge entries (e.g., sequences) corresponding to the same mutant
// [[Rcpp::export]]
DataFrame mergeValues(std::vector<std::string> mutNamesIn, std::vector<std::string> valuesIn,
                      char delimiter = ',') {
    std::map<std::string, std::set<std::string>> valueSet;
    std::map<std::string, std::set<std::string>>::iterator valueSetIt;

    for (size_t i=0; i<mutNamesIn.size(); i++) {
        if ((valueSetIt = valueSet.find(mutNamesIn[i])) != valueSet.end()) {
            // mutant already present
            std::set<std::string> sst = splitSet(valuesIn[i], delimiter);
            (*valueSetIt).second.insert(sst.begin(), sst.end());
        } else {
            // mutant not yet present
            valueSet.insert(std::pair<std::string,std::set<std::string>>(mutNamesIn[i], 
                                                                         splitSet(valuesIn[i], delimiter)));
        }
    }
    
    size_t dfLen = valueSet.size();
    std::vector<std::string> dfValue(dfLen, """"), dfName(dfLen, """");
    
    int j = 0;
    for (valueSetIt = valueSet.begin(); valueSetIt != valueSet.end(); valueSetIt++) {
        std::vector<std::string> valueVector((*valueSetIt).second.begin(),
                                             (*valueSetIt).second.end());
        std::string collapsedValue = """";
        for (size_t i = 0; i < valueVector.size(); i++) {
            collapsedValue += valueVector[i] + std::string(1, delimiter);
        }
        if (!collapsedValue.empty()) {
            collapsedValue.pop_back(); // remove final "",""
        }
        dfName[j] = (*valueSetIt).first;
        dfValue[j] = collapsedValue;
        j++;
    }
    
    DataFrame df = DataFrame::create(Named(""mutantNameColl"") = dfName,
                                     Named(""valueColl"") = dfValue);
    
    return df;
} 
","C++"
"Assay","fmicompbio/mutscan","src/stringdist.h",".h","384","13","#ifndef STRINGDIST_HPP
#define STRINGDIST_HPP

#include <Rcpp.h>
#include <string>

// prototypes for string distance functions, e.g. used by the BKtree class
int levenshtein_distance(const std::string &, const std::string &, int);
int hamming_distance(const std::string &, const std::string &, int);
int hamming_shift_distance(const std::string &, const std::string &, int);


#endif","Unknown"
"Assay","fmicompbio/mutscan","src/calcNearestStringDist.cpp",".cpp","2817","90","#include <Rcpp.h>
#include <string>
#include <vector>
#include <climits>
#include ""stringdist.h""

#ifdef _OPENMP
#include <omp.h>
// [[Rcpp::plugins(openmp)]]
#endif

using namespace Rcpp;

//' Calculate distances to the nearest string
//' 
//' Given a character vector, calculate the distance for each element
//' to the nearest neighbor amongst all the other elements.
//' 
//' @param x A character vector.
//' @param metric A character scalar defining the string distance metric. One
//'   of \code{""hamming""} (default), \code{""hamming_shift""} or
//'   \code{""levenshtein""}.
//' @param nThreads numeric(1), number of threads to use for parallel processing.
//' 
//' @return An integer vector of the same length as \code{x}.
//' 
//' @examples
//' calcNearestStringDist(c(""lazy"", ""hazy"", ""crazy""))
//' calcNearestStringDist(c(""lazy"", ""hazy"", ""crazy""), metric = ""hamming_shift"")
//' calcNearestStringDist(c(""lazy"", ""hazy"", ""crazy""), metric = ""levenshtein"")
//' 
//' @export
// [[Rcpp::export]]
IntegerVector calcNearestStringDist(std::vector<std::string> x,
                                    std::string metric = ""hamming"",
                                    int nThreads = 1) {
    // declare variables
    size_t i, j, n = x.size();
    int dist1;
    int (*distance)(const std::string&, const std::string&, int); // pointer to function to calcluate string distance
    IntegerVector dists(n, INT_MAX);
    
    // set distance function pointer
    if (metric == ""hamming"") {
        distance = &hamming_distance;
        
    } else if (metric == ""hamming_shift"") {
        distance = &hamming_shift_distance;
        
    } else if (metric == ""levenshtein"") {
        distance = &levenshtein_distance;
        
    } else {
        stop(""unknown distance metric '%s'"", metric);
    }
    
    // choose algorithm depending on number of threads
    if (nThreads < 3) { // serial version: O(n*log(n))
        for (i = 0; i < n - 1; i++) {
            for (j = i + 1; j < n; j++) {
                dist1 = distance(x[i], x[j], -1);
                if (dists[i] > dist1)
                    dists[i] = dist1;
                if (dists[j] > dist1)
                    dists[j] = dist1;
            }
        }
    } else {             // parallel version (O(n^2))
#ifdef _OPENMP
#pragma omp parallel for schedule(static) num_threads(nThreads) private(i,j,dist1) shared(dists)
#endif
        for (i = 0; i < n; i++) {
            int mindist = INT_MAX;
            for (j = 0; j < n; j++) {
                if (i == j)
                    continue;
                dist1 = distance(x[i], x[j], -1);
                if (mindist > dist1)
                    mindist = dist1;
            }
#ifdef _OPENMP
            #pragma omp critical
#endif
            dists[i] = mindist;
        }
    }
    
    return dists;
}
","C++"
"Assay","fmicompbio/mutscan","src/stringdist.cpp",".cpp","3609","110","#include ""stringdist.h""

// distance metrices used by the BKtree class:
// calculate levenshtein distance between pair of strings
// [[Rcpp::export]]
int levenshtein_distance(const std::string &str1, const std::string &str2,
                         int ignored_variable = -1){
    size_t m = str1.length(), n = str2.length();
    
    int** dn = new int*[m + 1];
    for(size_t i = 0; i < m + 1; ++i) {
        dn[i] = new int[n + 1];
    }
    
    for (size_t i = 0; i < m + 1; ++i) {
        for (size_t j = 0; j < n + 1; ++j) {
            if (i == 0) {
                dn[0][j] = j; // deletions at the start of str2
                
            } else if (j == 0) {
                dn[i][0] = i; // deletions at the start of str1
                
            } else if (str1[i-1] == str2[j-1]) { // match of str1[i-1] and str2[j-1]
                dn[i][j] = dn[i-1][j-1];
                
            } else { // mismatch between str1[i-1] and str2[j-1] -> find minimal source
                dn[i][j] = 1 + std::min(
                    dn[i-1][j],          // deletion in str1
                           std::min(dn[i][j-1], // deletion in str2
                                    dn[i-1][j-1])        // mismatch
                );
            }
        }
    }
    
    int d = dn[m][n];
    for (size_t i = 0; i < m + 1; ++i) {
        delete [] dn[i];
    }
    delete [] dn;
    
    return d;
}

// calculate hamming distance between pair of strings of equal lengths
// int hamming_distance(const std::string &str1, const std::string &str2,
//                      int ignored_variable = -1){
//   int d = 0;
//   
//   for (size_t i = 0; i <= str1.length(); i++) {
//     d += (str1[i] != str2[i]);
//   }
//   
//   return d;
// }
// [[Rcpp::export]]
int hamming_distance(const std::string &str1, const std::string &str2,
                     int ignored_variable = -1){
    const char *a = str1.data(), *b = str2.data();
    
    return std::inner_product(a, a + str1.length(), b, 0,
                              std::plus<int>(), std::not_equal_to<int>());
}

// calculate hamming distance + shifts between pair of strings of equal lengths
// int hamming_shift_distance(const std::string &str1, const std::string &str2,
//                            int max_abs_shift = -1){
//   int d = str1.size(), ds = 0;
//   if (max_abs_shift < 0) {
//     max_abs_shift = (int)str1.size() - 1;
//   }
// 
//   d = str1.size();
//   for (int s = -max_abs_shift; s <= max_abs_shift; s++) {
//     ds = std::abs(s) + std::abs(s); // overhangs are treated as mismatches
//     for (size_t i = std::max(0, s), j = std::max(0, -s);
//          i < str1.length() + std::min(0, s); i++, j++) {
//       ds += (str1[i] != str2[j]);
//     }
//     if (ds < d) {
//       d = ds;
//     }
//   }
// 
//   return d;
// }
// [[Rcpp::export]]
int hamming_shift_distance(const std::string &str1, const std::string &str2,
                           int max_abs_shift = -1){
    int d = str1.size(), ds = 0;
    const char *a = str1.data(), *b = str2.data();
    if (max_abs_shift < 0) {
        max_abs_shift = (int)str1.size() - 1;
    }
    
    for (int s = -max_abs_shift; s <= max_abs_shift; s++) {
        ds = std::abs(s) + std::abs(s) + // overhangs are treated as mismatches
            std::inner_product(a + std::max(0, s),
                               a + str1.length() + std::min(0, s),
                               b + std::max(0, -s), 0,
                               std::plus<int>(), std::not_equal_to<int>());
        if (ds < d) {
            d = ds;
        }
    }
    
    return d;
}

","C++"
"Assay","fmicompbio/mutscan","src/BKtree_utils.h",".h","14579","461","#ifndef BKTREE_UTILS_HPP
#define BKTREE_UTILS_HPP

#include <Rcpp.h>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include <map>
#include <utility>
#include <iostream>
#include ""stringdist.h""

using namespace Rcpp;


// simple implementation of a BK tree (https://en.wikipedia.org/wiki/BK-tree)
// for std::string elements and Hamming (+ Shifts) or Levenshtein distance
// based on implementation by @talhasaruhan:
//   https://github.com/talhasaruhan/fuzzy-search/blob/1ddcefc772c8764351d2c679c266a97a58729eef/bktree.hpp
//   available under MIT license (see https://github.com/talhasaruhan/fuzzy-search/issues/2)
class BKtree {
public:

  // tree node
  class node {
  public:
    int index; // items[index] in tree contains string of this node
    std::unordered_map<int, node*> children;

    // node constructor / destructor
    node(int i): index(i) {}
    ~node() {
      for (const std::pair<const int, BKtree::node*>&p : children) {
        delete p.second;
      }
    }

    // node capacity (memory consumption of node and its children)
    int capacity(){
      int sz = sizeof(index);
      sz += ((sizeof(int) + sizeof(node*)) * children.size()) + sizeof(children);
      for (std::pair<const int, node*>&child : children) {
        sz += child.second->capacity();
      }
      return sz;
    }
  };

  size_t size; // number of strings in tree

  // tree constructors
  BKtree(const std::string dmetric = ""hamming"", int mshift = -1) { // empty tree
    size = 0;
    metric = dmetric;
    max_absolute_shift = mshift;
    _set_distance_function_pointer();
    root = nullptr;
  }

  BKtree(const std::vector<std::string>& v,
         const std::string dmetric = ""hamming"", int mshift = -1) { // from a vector of strings
    metric = dmetric;
    max_absolute_shift = mshift;
    _set_distance_function_pointer();
    items = v;
    size = v.size();
    _build_from_items(v);
  }

  // add a new string into to tree
  void insert(std::string str) {
    size++;

    // remove it from deleted map if necessary
    if (deleted.find(str) != deleted.end()){
      deleted.erase(str);
      return;
    }

    // add it to items and get its index (zero-based)
    int index = items.size();
    items.push_back(str);
    // ... and try to add it to the tree as a new child node
    if(!_add_from_items(index)) {
      // if new string str was already in the tree, remove it again from items and size
      size--;
      items.pop_back();
    }
  }

  // remove a string from the tree
  // - string is only remembered as deleted, but not yet removed from the tree
  // - if too many deleted strings exist, rebuild whole tree from scratch
  void remove(std::string str) {
    if(deleted.find(str) != deleted.end() || !has(str)) {
      // string is already deleted or not in tree, nothing to do
      return;
    }

    // add string to deleted map and decrease size
    deleted.insert(str);
    size--;
    if (deleted.size() >= size/2) {
      if (size > 0) {
        // deleted is now quite large -> rebuild tree and release memory
        _rebuild();
      } else {
        // tree is empty now
        delete root;
        root = nullptr;
        items.clear();
        deleted.clear();
      }
    }
  }

  // remove all elements from tree
  void remove_all() {
    if (size > 0) {
      delete root;
      root = nullptr;
      size = 0;
      items.clear();
      deleted.clear();
    }
  }

  // get all (non-deleted) elements from the tree
  std::vector<std::string> get_all() {
    std::vector<std::string> all;
    for (size_t i = 0; i < items.size(); i++) {
      if (deleted.find(items[i]) == deleted.end()) {
        // item[i] is not deleted -> copy to all
        all.push_back(items[i]);
      }
    }
    return all;
  }

  // search elements within distance tol
  std::vector<std::string> search(std::string str, int tol) {
    if (size <= 0) {
      // tree is empty, nothing to return
      return std::vector<std::string>(0);
    }
    std::vector<std::string> vec; // results vector
    _search(str, tol, root, vec); // start recursive search at the root
    return vec;
  }

  // does the tree contain a string within distance tol of str?
  bool has(std::string str, int tol = 0) {
    if (size <= 0) {
      // tree is empty, return false
      return false;
    }
    return _has(str, tol, root); // start  recursive search at the root
  }

  // get first non-deleted element (in the order of items)
  std::string first() {
    std::string res = """";
    if (size > 0) {
      for (size_t i = 0; i < size; i++) {
        if (deleted.find(items[i]) == deleted.end()) {
          res = items[i];
          break;
        }
      }
    }
    return res;
  }

  // print tree on stdout
  // void print(node* r = nullptr, node* n = nullptr, int depth = 0) {
  void print() {
    Rcout << size << ""\n"";
    if (size > 0) {
      _print(root, root, 0); // recursive node printing starting from root at zero indentation
    }
  }

  // tree capacity (memory consumption of tree plus root node and its children)
  int capacity() {
    int sz = sizeof(size) + sizeof(node*) + sizeof(items) + sizeof(deleted);
    for (std::string &s: items) {
      sz += s.size() * sizeof(char);
    }
    for (const std::string &s: deleted) {
      sz += s.size() * sizeof(char);
    }
    if (size > 0) {
      sz += root->capacity();
    }
    return sz;
  }

  // return the distance metric used by a tree instance
  std::string distance_metric() {
    return metric;
  }

  // return the maximal shift used in hamming_shift_distance()
  int maximal_absolute_shift() {
    return max_absolute_shift;
  }

  inline std::vector<std::string>::iterator begin() noexcept { return items.begin(); }
  inline std::vector<std::string>::const_iterator cbegin() const noexcept { return items.cbegin(); }
  inline std::vector<std::string>::iterator end() noexcept { return items.end(); }
  inline std::vector<std::string>::const_iterator cend() const noexcept { return items.end(); }

private:
  std::vector<std::string> items;            // all strings added to the tree in the order of addition
                                             // (including potentially deleted ones)
  node* root;                                // pointer to root node
  std::unordered_set<std::string> deleted;   // nodes deleted from the tree but not yet removed
  std::string metric;                        // name of the distance metric to use
  int (*distance)(const std::string&, const std::string&, int); // pointer to function to calcluate string distance
  int max_absolute_shift;                    // maximum shift (only used for metric=""hamming_shift"")

  // set the distance function pointer according to metric
  void _set_distance_function_pointer() {
    if (metric == ""hamming"") {
      distance = &hamming_distance;

    } else if (metric == ""hamming_shift"") {
      distance = &hamming_shift_distance;

    } else if (metric == ""levenshtein"") {
      distance = &levenshtein_distance;

    } else {
      stop(""unknown distance metric '%s'"", metric);
    }
  }

  // build new tree from string elements in vector v
  void _build_from_items(const std::vector<std::string>& v) {
    if (size < 1) {
      // empty vector, just create an empty tree
      root = nullptr;
      return;

    } else if (size == 1) {
      // single element -> root at index zero
      root = new node(0);
      return;

    } else {
      // multiple elements -> create root and add rest sequentially
      root = new node(0);
      for (size_t i = 1; i < size; ++i) {
        if (!_add_from_items(i)) { // attach item[i] as new child
          // item[i] was already in the tree -> put it to the end of items
          swap(items[i], items[size-1]);
          // and forget about it
          size--;
          i--;
        }
      }
      // truncate items to unique items
      items.resize(size);
    }
  }

  // rebuild tree from items and release elements stored in deleted
  void _rebuild() {
    // reorder items (move deleted items to the end)
    // remark: this implementation perturbs the original order in items
    /*
    size_t i = 0, j = items.size() - 1;
    while (i <= j) {
      if (deleted.find(items[i]) != deleted.end()) {
        // item[i] is deleted -> move it to the end of items
        swap(items[i], items[j--]);
      } else {
        i++;
      }
    }
    // truncate items to just the non-deleted elements
    items.resize(i);
    */

    // alternative implementation (preserving order in items)
    std::vector<std::string> newitems;
    for (size_t i = 0; i < items.size(); i++) {
      if (deleted.find(items[i]) == deleted.end()) {
        // item[i] is not deleted -> copy to newitems
        newitems.push_back(items[i]);
      }
    }
    items.clear();
    items.insert(items.begin(), newitems.begin(), newitems.end());
    // items = newitems;

    // ... and empty deleted
    deleted.clear();

    // build up new tree from cleaned items
    delete root;
    size = items.size();
    _build_from_items(items);
  }

  // add items[index] as a new node to tree
  bool _add_from_items(int index) {
    std::string str = items[index];
    bool res = false;

    if(root == nullptr) { // tree is empty -> make it root
      root = new node(index);
      res = true;

    } else {
      node* t = root; // start with root
      node* new_node = new node(index); // create new node

      // distance of new item to current node
      int dist = distance(items[t->index], str, max_absolute_shift);

      // while current node t already has a child at that distance dist
      while (t->children.find(dist) != t->children.end()) {
        // descend to the children of that child
        t = t->children.find(dist)->second;
        // ... and calculate new distance
        dist = distance(items[t->index], str, max_absolute_shift);
      }

      // t now points to a node without children at distance dist
      if (dist > 0) { // non-zero distance -> insert new string as child at distance dist
        t->children.insert(std::pair<int, node*>(dist, new_node));
        res = true;

      } else { // zero distance -> found new string already in the tree: don't insert and return false
        res = false;
      }
    }

    return res;
  }

  // recursively search for elements within tol of str in subtree of node r, add results to vec
  void _search(std::string str, int tol, node* r, std::vector<std::string>& vec) {
    std::string r_val = items[r->index];
    int dist = distance(r_val, str, max_absolute_shift);

    if (dist <= tol) { // found element within tolerance
      // only add to results if not deleted
      if (deleted.find(r_val) == deleted.end()) {
        vec.push_back(r_val);
      }
    }

    // apply BK tree triangle inequality to
    // calculate boundaries for potential further hits relative to current hit
    int gte = dist - tol, lte = dist + tol;

    for (const std::pair<const int, BKtree::node*>& p: r->children) {
      if(p.first >= gte && p.first <= lte) {
        // ... and recurse search on children in that range
        _search(str, tol, p.second, vec);
      }
    }
  }

  // recursively check if subtree of node r has an element within tol of str
  bool _has(std::string str, int tol, node* r) {
    std::string r_val = items[r->index];
    int dist = distance(r_val, str, max_absolute_shift);

    if (dist <= tol) { // found an element within tolerance
      // only return true if not deleted
      if (deleted.find(r_val) == deleted.end()) {
        return true;
      }
    }

    // apply BK tree triangle inequality to
    // calculate boundaries for potential further hits relative to current hit
    int gte = dist - tol, lte = dist + tol;

    for (const std::pair<const int, BKtree::node*>&p: r->children) {
      if (p.first >= gte && p.first <= lte) {
        // ... and start recursive search on children in that range
        if (_has(str, tol, p.second)) {
          return true;
        }
      }
    }

    return false;
  }

  // recursively print subtree underneath node n:
  // first print node n (child of node r), indented by depth tabs
  // and the recurse on all children of n
  void _print(node* r = nullptr, node* n = nullptr, int depth = 0) {
    if (n == nullptr) {
      n = root; // # nocov
    }
    if (r == nullptr) {
      r = root; // # nocov
    }

    for (int i = 0; i < depth; ++i) {
      Rcout << ""\t"";
    }

    std::string n_val = items[n->index], r_val = items[r->index];
    if(deleted.find(n_val) != deleted.end()) {
      // if node n is deleted, prefix it with D*
      Rcout << ""D*"";
    }
    Rcout << distance(r_val, n_val, max_absolute_shift) << "": "" << n_val << std::endl;
    for (const std::pair<const int, BKtree::node*>&x: n->children) {
      // recursively print all children of n
      _print(n, x.second, depth + 1);
    }
  }
};



// exposing the BKtree class to R using Rcpp-modules
// currently only used for unit testing
RCPP_MODULE(mod_BKtree) {
  class_<BKtree>( ""BKtree"" )

  .constructor()
  .constructor<std::string>()
  .constructor<std::string, int>()
  .constructor<std::vector<std::string>>()
  .constructor<std::vector<std::string>, std::string>()
  .constructor<std::vector<std::string>, std::string, int>()

  .field_readonly( ""size"", &BKtree::size, ""number of elements stored in tree"" )

  .method( ""insert"", &BKtree::insert, ""insert a new element into tree"" )
  .method( ""remove"", &BKtree::remove, ""remove an element from tree"" )
  .method( ""remove_all"", &BKtree::remove_all, ""remove all elements from tree"" )
  .method( ""get_all"", &BKtree::get_all, ""get all elements stored in tree"" )
  .method( ""print"", &BKtree::print, ""print tree on console"" )
  .method( ""search"", &BKtree::search, ""search for elements within distance tolerance"" )
  .method( ""has"", &BKtree::has, ""check if there are elements within distance toelrance"" )
  .method( ""first"", &BKtree::first, ""get first (non-deleted) element from tree"" )
  .method( ""capacity"", &BKtree::capacity, ""calculate tree memory usage"" )
  .method( ""distance_metric"", &BKtree::distance_metric, ""return distance metric used by tree"")
  .method( ""maximal_absolute_shift"", &BKtree::maximal_absolute_shift, ""return maximal shift for metric='hamming_shift'"")

  ;
}

// TODO: Attempting to export this function here gives a warning:
// Invalid parameter: 'std::string&' for Rcpp::export attribute at BKtree_utils.hpp:467
// For now, don't export it.
int findClosestRefSeqFaster(std::string&, BKtree&, std::map<std::string, int>&, int&);

#endif
","Unknown"
"Assay","fmicompbio/mutscan","src/FastqBuffer_utils.h",".h","3106","103","#include <Rcpp.h>
#include <string.h>
#include <zlib.h>

using namespace Rcpp;

class FastqBuffer {
private:
  size_t nentries; // number of fastq entries allocated (BUFFER_SIZE-1 max length)
  size_t BUFFER_SIZE; // buffer size
  bool paired;  // paired read entries?
  char *buffer; // single block of memory
  //  each entry e has 2-times (4-times for paired=true) BUFFER_SIZE bytes available
  //  entries e1, e2, ... are stored in buffer in the following order:
  //   e1.seq1, e2.seq1, ..., e1.qual1, e2.qual2, ..., e1.seq2, e2.seq2, ..., e1.qual2, e2.qual2, ...
  //   (space for seq2 and qual2 is only allocated for paired=true)
public:
  char *seq1, *qual1, *seq2, *qual2;

  // constructor
  FastqBuffer(size_t n, size_t b, bool p = true) {
    nentries = n;
    BUFFER_SIZE = b;
    paired = p;
    buffer = new char[nentries * BUFFER_SIZE * (paired ? 4 : 2)];
    seq1 = buffer;
    qual1 = buffer + (nentries * BUFFER_SIZE);
    if (paired) {
      seq2 = buffer + (2 * nentries * BUFFER_SIZE);
      qual2 = buffer + (3 * nentries * BUFFER_SIZE);
    } else {
      seq2 = NULL;
      qual2 = NULL;
    }
  }

  // copy constructor
  FastqBuffer(const FastqBuffer& fqb) {
    paired = fqb.paired;
    nentries = fqb.nentries;
    BUFFER_SIZE = fqb.BUFFER_SIZE;
    buffer = new char[nentries * BUFFER_SIZE * (paired ? 4 : 2)];
    strncpy(buffer, fqb.buffer, nentries * BUFFER_SIZE * (paired ? 4 : 2));
    seq1 = buffer;
    qual1 = buffer + (nentries * BUFFER_SIZE);
    if (paired) {
      seq2 = buffer + (2 * nentries * BUFFER_SIZE);
      qual2 = buffer + (3 * nentries * BUFFER_SIZE);
    } else {
      seq2 = NULL;
      qual2 = NULL;
    }
  }

  // destructor
  ~FastqBuffer() {
    delete[] buffer;
  }

  // Write one (pair of) filtered reads to output fastq file(s)
  bool write_seq(size_t i, gzFile file1, gzFile file2, const int n, const char* label) {
    bool success = (i < nentries);
#ifdef _OPENMP
#pragma omp critical
#endif
{
    if (success && file1 != NULL) {
      std::string read_id = ""@S"" + std::to_string(n) + ""_"" + label + "" 1\n"";
      if (success && gzputs(file1, read_id.c_str()) == (-1)) {
        success = false; // # nocov
      }
      if (success && gzputs(file1, seq1 + (i * BUFFER_SIZE)) == (-1)) {
        success = false; // # nocov
      }
      if (success && gzputs(file1, ""+\n"") == (-1)) {
        success = false; // # nocov
      }
      if (success && gzputs(file1, qual1 + (i * BUFFER_SIZE)) == (-1)) {
        success = false; // # nocov
      }
    }

    if (success && file2 != NULL) {
      std::string read_id = ""@S"" + std::to_string(n) + ""_"" + label + "" 2\n"";
      if (success && gzputs(file2, read_id.c_str()) == (-1)) {
        success = false; // # nocov
      }
      if (success && gzputs(file2, seq2 + (i * BUFFER_SIZE)) == (-1)) {
        success = false; // # nocov
      }
      if (success && gzputs(file2, ""+\n"") == (-1)) {
        success = false; // # nocov
      }
      if (success && gzputs(file2, qual2 + (i * BUFFER_SIZE)) == (-1)) {
        success = false; // # nocov
      }
    }
}
    return success;
  }

};
","Unknown"
"Assay","fmicompbio/mutscan","R/mutscan-package.R",".R","310","11","## usethis namespace: start
#' @useDynLib mutscan, .registration = TRUE
#' @importFrom Rcpp sourceCpp
## usethis namespace: end
NULL

# global variables definition for R CMD check
# (avoid NOTE about ""no visible binding for global variable"")
#' @importFrom utils globalVariables
utils::globalVariables(c("".""))
","R"
"Assay","fmicompbio/mutscan","R/plotPairs.R",".R","13570","314","#' Make pairs plot of selected assay from a SummarizedExperiment object
#'
#' Construct a pairs plot of all columns of a given assay. The lower-triangular
#' panels display the scatter plots, the upper-triangular ones print out
#' the (Pearson or Spearman) correlations, and the diagonal panels show
#' histograms of the respective columns.
#'
#' @author Charlotte Soneson
#'
#' @export
#'
#' @return A \code{ggplot} object.
#'
#' @param se A SummarizedExperiment object, e.g. the output of
#'     \code{summarizeExperiment}
#' @param selAssay Character scalar, the assay to use as the basis for the
#'     pairs plot.
#' @param doLog Logical scalar, whether or not to log-transform the values
#'     before plotting.
#' @param pseudocount Numeric scalar, the pseudocount to add to the values
#'     before log-transforming (if \code{doLog} is \code{TRUE}).
#' @param corMethod Either ""pearson"" or ""spearman"", the type of correlation
#'     to calculate.
#' @param histBreaks Numeric scalar, the number of breaks in the histograms
#'     to put in the diagonal panels.
#' @param pointsType Either ""points"", ""smoothscatter"", ""scattermore"" or 
#'     ""scattermost"" (the latter two require the ""scattermore"" package to be 
#'     installed), determining the type of plots that will be made.
#' @param corSizeMult,corSizeAdd Numeric scalars determining how the
#'     absolute correlation value is transformed into a font size. The
#'     transformation is corSizeMult * abs(corr) + corSizeAdd.
#' @param pointSize,pointAlpha Numeric scalars determining the size and
#'     opacity of points in the plot.
#' @param colorByCorrelation Logical scalar, indicating whether the correlation
#'     panels should be colored according to the correlation value.
#' @param corrColorRange Numeric vector of length 2, providing the lower and 
#'     upper limits of the color scale when coloring by correlation. Both 
#'     values should be positive; the same range is used for negative 
#'     correlations. If \code{NULL} (the default), the range is inferred from 
#'     the data.
#' @param addIdentityLine Logical scalar, indicating whether the identity line 
#'     should be added (only used if \code{pointsType = ""points""}).
#'
#' @importFrom GGally eval_data_col ggpairs
#' @importFrom ggplot2 ggplot annotate theme_void ylim stat_density2d
#'     scale_fill_continuous geom_point theme_bw theme element_blank aes
#'     geom_histogram scale_x_continuous scale_y_continuous geom_abline
#'     after_stat element_rect
#' @importFrom stats cor
#' @importFrom SummarizedExperiment assayNames assay
#' @importFrom grDevices hcl.colors rgb colorRamp
#' @importFrom rlang .data
#'
#' @examples
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"", 
#'                           package = ""mutscan""))[1:200, ]
#' plotPairs(se)
#' 
plotPairs <- function(se, selAssay = ""counts"", doLog = TRUE, pseudocount = 1,
                      corMethod = ""pearson"", histBreaks = 40,
                      pointsType = ""points"", corSizeMult = 5,
                      corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                      colorByCorrelation = TRUE, corrColorRange = NULL,
                      addIdentityLine = FALSE) {
    
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertScalar(x = selAssay, type = ""character"",
                  validValues = assayNames(se))
    .assertScalar(x = doLog, type = ""logical"")
    .assertScalar(x = pseudocount, type = ""numeric"", rngIncl = c(0, Inf))
    .assertScalar(x = corMethod, type = ""character"",
                  validValues = c(""pearson"", ""spearman""))
    .assertScalar(x = histBreaks, type = ""numeric"", rngExcl = c(0, Inf))
    .assertScalar(x = pointsType, type = ""character"",
                  validValues = c(""smoothscatter"", ""points"", ""scattermore"",
                                  ""scattermost""))
    .assertScalar(x = corSizeMult, type = ""numeric"", rngExcl = c(0, Inf))
    .assertScalar(x = corSizeAdd, type = ""numeric"", rngIncl = c(0, Inf))
    .assertScalar(x = pointSize, type = ""numeric"", rngExcl = c(0, Inf))
    .assertScalar(x = pointAlpha, type = ""numeric"", rngExcl = c(0, Inf))
    .assertScalar(x = colorByCorrelation, type = ""logical"")
    .assertVector(x = corrColorRange, type = ""numeric"", rngIncl = c(0, 1), 
                  len = 2, allowNULL = TRUE)
    if (!is.null(corrColorRange)) {
        stopifnot(corrColorRange[2] >= corrColorRange[1])
    }
    .assertScalar(x = addIdentityLine, type = ""logical"")
    if (pointsType %in% c(""scattermore"", ""scattermost"")) {
        .assertPackagesAvailable(""scattermore"")
    }
    
    ## --------------------------------------------------------------------- ##
    ## Define shared theme elements
    ## --------------------------------------------------------------------- ##
    ggtheme <- list(
        theme_bw(),
        theme(panel.grid.major = element_blank(),
              panel.grid.minor = element_blank())
    )
    
    ## --------------------------------------------------------------------- ##
    ## Correlations
    ## --------------------------------------------------------------------- ##
    ## Define function to calculate and display correlations 
    ## (for use with ggpairs)
    cor_fcn <- function(data, mapping, ...) {
        ## Get data
        xData <- eval_data_col(data, mapping$x)
        yData <- eval_data_col(data, mapping$y)
        
        ## Calculate correlation
        mainCor <- cor(xData, yData, method = corMethod, 
                       use = ""pairwise.complete.obs"")
        transfCor <- (abs(mainCor) - min(corRange)) / 
            (max(corRange) - min(corRange))
        ## Protect against numerical imprecision leading to values outside
        ## the allowed range
        transfCor <- min(1.0, transfCor)
        transfCor <- max(0.0, transfCor)
        
        ## Determine the color
        if (colorByCorrelation) {
            if (mainCor >= 0) {
                cols <- hcl.colors(n = 11, palette = ""RdBu"")[6:2]
                col <- rgb(colorRamp(
                    cols)(transfCor),
                    maxColorValue = 255)
            } else {
                # nocov start
                cols <- hcl.colors(n = 11, palette = ""RdBu"")[6:10]
                col <- rgb(colorRamp(
                    cols)(transfCor),
                    maxColorValue = 255)
                # nocov end
            }
        } else {
            col <- ""white"" #nocov
        }
        
        ## Construct plot
        ggplot(data = data, mapping = mapping) +
            annotate(x = 0.5, y = 0.5, 
                     label = round(mainCor, digits = 3),
                     geom = ""text"",
                     size = abs(mainCor) * corSizeMult + corSizeAdd) +
            ggtheme + ylim(c(0, 1)) +
            theme(
                panel.background = element_rect(fill = col)
            )
    }
    
    ## --------------------------------------------------------------------- ##
    ## Scatter plots
    ## --------------------------------------------------------------------- ##
    # All the point plotting functions are tested, but for some reason covr 
    # doesn't pick it up - ignore these definitions in the coverage tests
    # nocov start
    if (pointsType == ""smoothscatter"") {
        ## Define function to create smoothscatter-like plot 
        ## (for use with ggpairs)
        smoothscat <- function(data, mapping, ...) {
            ggplot(data = data, mapping = mapping) +
                stat_density2d(
                    aes(fill = after_stat(
                        .data$density)^0.25), geom = ""tile"",
                    contour = FALSE, n = 200) +
                scale_fill_continuous(low = ""white"", 
                                      high = ""darkgreen"") +
                ggtheme +
                scale_x_continuous(expand = c(0, 0)) +
                scale_y_continuous(expand = c(0, 0))
        }
    } else if (pointsType == ""points"") {
        ## Define function to create scatter plot (for use with ggpairs)
        if (addIdentityLine) {
            plotpoints <- function(data, mapping, ...) {
                ggplot(data = data, mapping = mapping) +
                    geom_abline(slope = 1, intercept = 0, 
                                linetype = ""dashed"", color = ""grey"") + 
                    geom_point(alpha = pointAlpha, size = pointSize) +
                    ggtheme
            }
        } else {
            plotpoints <- function(data, mapping, ...) {
                ggplot(data = data, mapping = mapping) +
                    geom_point(alpha = pointAlpha, size = pointSize) +
                    ggtheme
            }
        }
    } else if (pointsType == ""scattermore"") {
        ## Define function to create scattermore plot (for use with ggpairs)
        if (addIdentityLine) {
            plotscattermore <- function(data, mapping, ...) {
                ggplot(data = data, mapping = mapping) +
                    geom_abline(slope = 1, intercept = 0, 
                                linetype = ""dashed"", color = ""grey"") + 
                    scattermore::geom_scattermore(alpha = pointAlpha, 
                                                  pointsize = pointSize) +
                    ggtheme
            }
        } else {
            plotscattermore <- function(data, mapping, ...) {
                ggplot(data = data, mapping = mapping) +
                    scattermore::geom_scattermore(alpha = pointAlpha, 
                                                  pointsize = pointSize) +
                    ggtheme
            }
        }
    } else if (pointsType == ""scattermost"") {
        ## Define function to create scattermost plot (for use with ggpairs)
        if (addIdentityLine) {
            plotscattermost <- function(data, mapping, ...) {
                ## Get data
                xData <- eval_data_col(data, mapping$x)
                yData <- eval_data_col(data, mapping$y)
                
                ggplot(data = data, mapping = mapping) +
                    geom_abline(slope = 1, intercept = 0, 
                                linetype = ""dashed"", color = ""grey"") + 
                    scattermore::geom_scattermost(xy = cbind(xData, yData), 
                                                  pointsize = pointSize) +
                    ggtheme
            }
        } else {
            plotscattermost <- function(data, mapping, ...) {
                ## Get data
                xData <- eval_data_col(data, mapping$x)
                yData <- eval_data_col(data, mapping$y)
                
                ggplot(data = data, mapping = mapping) +
                    scattermore::geom_scattermost(xy = cbind(xData, yData), 
                                                  pointsize = pointSize) +
                    ggtheme
            }
        }
    }
    # nocov end
    
    ## Define the function to use for the plots
    if (pointsType == ""smoothscatter"") {
        lower <- list(continuous = smoothscat)
    } else if (pointsType == ""points"") {
        lower <- list(continuous = plotpoints)
    } else if (pointsType == ""scattermore"") {
        lower <- list(continuous = plotscattermore)
    } else if (pointsType == ""scattermost"") {
        lower <- list(continuous = plotscattermost)
    }
    
    ## --------------------------------------------------------------------- ##
    ## Histogram
    ## --------------------------------------------------------------------- ##
    # nocov start
    diaghist <- function(data, mapping, ...) {
        gg <- ggplot(data = data, mapping = mapping) +
            geom_histogram(fill = ""#F5C710"", color = ""grey50"", 
                           bins = histBreaks) +
            ggtheme
        if (pointsType == ""smoothscatter"") {
            gg <- gg + scale_x_continuous(expand = c(0, 0))
        }
        gg
    }
    # nocov end
    
    ## --------------------------------------------------------------------- ##
    ## Combined plot
    ## --------------------------------------------------------------------- ##
    ## Prepare the data and plot title, depending on whether to log-transform 
    ## or not
    if (doLog) {
        mat <- log10(as.matrix(assay(se, selAssay)) + 
                         pseudocount)
        title <- paste0(""log10("", selAssay,
                        ifelse(pseudocount > 0, 
                               paste0("" + "", pseudocount), """"),
                        "")"")
    } else {
        mat <- as.matrix(assay(se, selAssay))
        title <- selAssay
    }
    mat <- as.data.frame(mat)
    title <- paste0(title, "", "", toupper(substr(corMethod, 1, 1)),
                    substr(corMethod, 2, nchar(corMethod)), "" correlation"")
    
    ## Calculate correlations and get range
    allCors <- cor(mat, method = corMethod, 
                   use = ""pairwise.complete.obs"")
    if (!is.null(corrColorRange)) {
        corRange <- corrColorRange
    } else {
        corRange <- range(abs(allCors[upper.tri(allCors)]))
    }
    if (length(unique(corRange)) == 1) {
        ## Having a range with width 0 leads to problems in the rescaling of 
        ## the correlations
        corRange <- c(0, 1)
    }
    
    ## Construct the pairs plot
    ggpairs(
        data = mat,
        mapping = NULL,
        columns = seq_len(ncol(mat)),
        title = title, xlab = NULL, ylab = NULL,
        upper = list(continuous = cor_fcn),
        lower = lower,
        diag = list(continuous = diaghist),
        progress = FALSE,
        axisLabels = ""show""
    )
}

","R"
"Assay","fmicompbio/mutscan","R/generateQCReport.R",".R","3571","91","#' Generate QC report
#'
#' @param se A \code{SummarizedExperiment} object, typically generated with
#'     \code{summarizeExperiment()}.
#' @param outFile Character string providing the name of the output file.
#'     Should have the extension \code{.html}.
#' @param reportTitle Character string specifying the title of the QC report.
#' @param forceOverwrite Logical scalar, indicating whether an existing file
#'     with the same name as \code{outFile} should be overwritten.
#' @param ... Additional parameters to be forwarded to
#'     \code{\link[rmarkdown]{render}}, for example \code{quiet = TRUE}.
#'
#' @export
#' @author Charlotte Soneson
#'
#' @returns Invisibly, the path to the generated html file.
#'
#' @seealso \code{\link[rmarkdown]{render}} used to render the html output file.
#'
#' @importFrom rmarkdown render
#' @importFrom xfun Rscript_call
#' @importFrom DT datatable
#' @importFrom SummarizedExperiment colData
#' @importFrom dplyr bind_rows full_join
#' @importFrom S4Vectors metadata
#' @importFrom tools file_ext
#'
#' @examples
#' ## Load SummarizedExperiment object
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"",
#'                           package = ""mutscan""))
#' ## Define output file
#' outfile <- tempfile(fileext = "".html"")
#'
#' ## Generate QC report
#' generateQCReport(se, outfile)
#'
generateQCReport <- function(se, outFile, reportTitle = ""mutscan QC report"",
                             forceOverwrite = FALSE, ...) {

    ## --------------------------------------------------------------------- ##
    ## Check that input arguments are valid
    ## --------------------------------------------------------------------- ##
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertScalar(x = outFile, type = ""character"")
    .assertScalar(x = forceOverwrite, type = ""logical"")
    if (file_ext(outFile) != ""html"") {
        stop(""'outFile' must have the file extension '.html'."")
    }
    outDir <- dirname(outFile)
    if (!dir.exists(outDir)) {
        dir.create(outDir, recursive = TRUE)
    }
    if (file.exists(outFile)) {
        if (!forceOverwrite) {
            stop(outFile, "" already exists and forceOverwrite = FALSE, "", 
                 ""stopping."")
        } else {
            message(outFile, "" already exists but forceOverwrite = TRUE, "", 
                    ""overwriting."")
        }
    }

    ## --------------------------------------------------------------------- ##
    ## Render the Rmd file
    ## --------------------------------------------------------------------- ##
    args <- list()
    args$input <- system.file(""templates"", ""qc_report_template.Rmd"",
                              package = ""mutscan"")
    args$output_format <- ""html_document""
    args$output_file <- basename(outFile)
    args$output_dir <- outDir
    args$intermediates_dir <- outDir
    args$run_pandoc <- TRUE
    args$params <- list(se = se, title = reportTitle)
    if (any(names(args) %in% ...names())) {
        warning(""Will ignore arguments provided in '...' that are set "",
                ""internally by generateQCReport: "",
                paste(intersect(names(args), ...names()), collapse = "", ""))
    }
    args <- c(args, list(...)[setdiff(...names(), names(args))])

    outputReport <- Rscript_call(
        render, args
    )

    ## --------------------------------------------------------------------- ##
    ## Return (invisibly) the path to the rendered html file
    ## --------------------------------------------------------------------- ##
    invisible(outFile)
}","R"
"Assay","fmicompbio/mutscan","R/plotTotals.R",".R","2707","77","#' Plot the column totals of a selected assay
#' 
#' @param se A \code{SummarizedExperiment} object, typically generated by 
#'     \code{summarizeExperiment()}.
#' @param selAssay Character scalar specifying the assay in \code{se} to 
#'     use for the plotting.
#' @param groupBy Character scalar indicating a column in 
#'     \code{rowData(se)} to group the features by before calculating the 
#'     column sums.
#' 
#' @export
#' @author Charlotte Soneson
#' 
#' @return A ggplot object.
#' 
#' @importFrom ggplot2 ggplot theme_minimal theme element_text labs 
#'     geom_bar scale_fill_discrete aes
#' @importFrom SummarizedExperiment assay rowData assayNames
#' @importFrom rlang .data
#' 
#' @examples 
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"", 
#'                           package = ""mutscan""))[1:200, ]
#' plotTotals(se)
#' 
plotTotals <- function(se, selAssay = ""counts"", groupBy = NULL) {
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertScalar(x = selAssay, type = ""character"",
                  validValues = assayNames(se))
    if (!is.null(groupBy)) {
        .assertScalar(
            x = groupBy, type = ""character"",
            validValues = colnames(rowData(se)))
    }
    
    ## Extract the assay matrix and reformat relevant parts of it for plotting
    selAssayMat <- assay(se, selAssay)
    if (!is.null(groupBy)) {
        df <- do.call(
            rbind, 
            lapply(unique(rowData(se)[[groupBy]]), 
                   function(rn) {
                       data.frame(
                           names = colnames(se),
                           category = rn,
                           total = colSums(
                               selAssayMat[rowData(se)[[groupBy]] == rn, ])
                       )
                   })
        )
    } else {
        df <- data.frame(names = colnames(se),
                         category = ""all"",
                         total = colSums(selAssayMat))
    }
    
    gg <- ggplot(df, aes(x = .data$names, y = .data$total)) + 
        theme_minimal() + 
        theme(
            axis.text.x = element_text(angle = 90, hjust = 1, 
                                       vjust = 0.5, size = 12),
            axis.text.y = element_text(size = 12),
            axis.title = element_text(size = 14)) + 
        labs(x = """", y = paste0(""Total ("", selAssay, "")""))
    if (!is.null(groupBy)) {
        gg <- gg + 
            geom_bar(stat = ""identity"", position = ""stack"", 
                     aes(fill = .data$category)) + 
            scale_fill_discrete(name = groupBy)
    } else {
        gg <- gg + 
            geom_bar(stat = ""identity"", position = ""stack"")
    }
    
    gg
}
","R"
"Assay","fmicompbio/mutscan","R/linkMultipleVariants.R",".R","14540","294","#' Process an experiment with multiple variable sequences
#'
#' This function enables the processing of data sets with multiple
#' variable sequences, which should potentially be handled in different
#' ways. For example, a barcode association experiment
#' with two variable sequences (the barcode and the biological variant)
#' that need to be processed differently, e.g. in terms of matching to
#' wildtype sequences or collapsing of similar sequences.
#' In contrast, while \code{digestFastqs} allow the specification
#' of multiple variable sequences (within each of the forward and reverse
#' reads), they will be concatenated and processed as a single unit.
#'
#' linkMultipleVariants will process the input in the following way:
#' \itemize{
#' \item First, run \code{digestFastqs} with the parameters provided
#'     in \code{combinedDigestParams}. Typically, this will be a
#'     ""naive"" counting run, where the frequencies of all observed
#'     variants are tabulated. The variable sequences
#'     within the forward and reverse reads, respectively, will be
#'     processed as a single sequence. 
#' \item Next, run \code{digestFastqs} with each of the additional
#'     parameter sets provided (\code{...}). Each of these should
#'     correspond to a single variable sequence from the combined
#'     run (i.e., if there are two Vs in the element specifications
#'     in the combined run, there should be two additional
#'     parameter sets provided, each corresponding to the
#'     processing of one variable sequence part). It is assumed
#'     that the order of the additional arguments correspond to the
#'     order of the variable sequences in the combined run, in such a way
#'     that if the variable sequences extracted in each of the separate
#'     runs are concatenated in the order that the parameter sets are
#'     provided to \code{linkMultipleVariants}, they will form the variable
#'     sequence extracted in the combined run.
#' \item The result of each of the separate runs is a 'conversion table',
#'     containing the final set of identified sequence variants as well
#'     as all individual sequences corresponding to each of them. This
#'     is then combined with the count table from the combined, ""naive""
#'     run in order to create an aggregated count table. More precisely,
#'     each sequence in the combined run is split into the constituent
#'     variable sequences, and
#'     each variable sequence is then matched to the output from the right
#'     separate run, from which the final feature ID (mutant name, or
#'     collapsed sequence) will be extracted and used to replace the original
#'     sequence in the combined count table. Once all the matches are done,
#'     rows with NAs (where no match could be found in the separate run)
#'     are removed and the counts are aggregated across all identical
#'     combinations of variable sequences.
#' }
#'
#' In order to define the \code{elementsForward} and \code{elementsReverse}
#' arguments for the separate runs, a strategy that often works is to simply
#' copy the arguments from the combined run, and successively replace all
#' but one of the 'V's by 'S'. This will effectively process one variable
#' sequence at the time, while keeping all other elements of the reads
#' consistent (since this can affect e.g. filtering criteria). Note that
#' to process individual variable sequences in the reverse read, you also
#' need to swap the 'forward' and 'reverse' specifications (since
#' \code{digestFastqs} requires a forward read).
#'
#' @param combinedDigestParams A named list of arguments to
#'     \code{digestFastqs} for the combined (""naive"") run.
#' @param ... Additional arguments providing arguments to \code{digestFastqs}
#'     for the separate runs (processing each variable sequence in turn).
#'     Each argument must be a named list of arguments to \code{digestFastqs}.
#'     In addition, arguments \code{collapseMaxDist}, \code{collapseMinScore}
#'     and \code{collapseMinRatio} can be specified, and will be passed on 
#'     to \code{collapseMutantsBySimilarity}.
#'
#' @author Charlotte Soneson, Michael Stadler
#'
#' @export
#'
#' @return A list with the following elements: 
#' \itemize{
#' \item countAggregated - a \code{tibble} with columns corresponding to 
#'     each of the variable sequences, and a column with the total observed 
#'     read count for the combination.
#' \item convSeparate - a list of conversion tables from the respective 
#'     separate runs.
#' \item outCombined - the \code{digestFastqs} output for the combined run.
#' }
#' 
#' @examples 
#' fqFile <- system.file(""extdata"", ""cisInput_1.fastq.gz"", 
#'                       package = ""mutscan"")
#' out <- linkMultipleVariants(
#'     combinedDigestParams = list(fastqForward = fqFile, 
#'                                 elementsForward = ""SVCV"", 
#'                                 elementLengthsForward = c(1, 10, 18, 96)),
#'     # the first variable sequence is the UMI
#'     umi = list(fastqForward = fqFile, elementsForward = ""SVCS"",
#'                elementLengthsForward = c(1, 10, 18, 96)),
#'     # the second variable sequence is the amplicon variant
#'     var = list(fastqForward = fqFile, elementsForward = ""SSCV"",
#'                elementLengthsForward = c(1, 10, 18, 96), 
#'                collapseMaxDist = 3, collapseMinScore = 1)
#' )
#' # conversion tables
#' lapply(out$convSeparate, head)
#' # aggregated count table
#' head(out$countAggregated)
#' 
#' @importFrom dplyr select rename group_by summarize across matches mutate
#' @importFrom tidyr separate separate_rows
#' @importFrom rlang .data
#'
linkMultipleVariants <- function(combinedDigestParams = list(), ...) {

    ## Process additional arguments
    paramsSeparate <- list(...)

    collapseParams <- c(""collapseMaxDist"",
                        ""collapseMinScore"", ""collapseMinRatio"")
    
    ## --------------------------------------------------------------------- ##
    ## Initial checks
    ## --------------------------------------------------------------------- ##
    ## combinedDigestParams and each element of paramsSeparate must be named
    ## lists
    .assertVector(x = combinedDigestParams, type = ""list"")
    defaults <- formals(digestFastqs)
    .assertVector(x = names(combinedDigestParams), type = ""character"",
                  allowNULL = FALSE, validValues = names(defaults))
    for (parms in paramsSeparate) {
        .assertVector(x = parms, type = ""list"")
        .assertVector(x = names(parms), type = ""character"", allowNULL = FALSE,
                      validValues = c(names(defaults), collapseParams))
    }

    ## Fill the parameter lists with default values for all arguments
    ## that are not provided, to avoid having to check for possible
    ## NULL values downstream
    paramsSeparate <- lapply(paramsSeparate, function(parm) {
        c(parm, as.list(defaults[!(names(defaults) %in% names(parm))]))
    })
    combinedDigestParams <- 
        c(combinedDigestParams,
          as.list(defaults[!(names(defaults) %in%
                                 names(combinedDigestParams))]))

    ## --------------------------------------------------------------------- ##
    ## Checks
    ## --------------------------------------------------------------------- ##
    ## We can't have a fwd and a rev V in the same run unless they are merged
    ## since this would disagree with the order in the concatenated V
    ## Currently, each separate run can only have one V in the fwd and/or one 
    ## in the rev (both allowed if they are matched); otherwise the lengths
    ## extracted from the combined run don't match with those from the 
    ## separate runs
    for (parm in paramsSeparate) {
        nbrFwdV <- sum(strsplit(parm$elementsForward, """")[[1]] == ""V"")
        nbrRevV <- sum(strsplit(parm$elementsReverse, """")[[1]] == ""V"")
        if (nbrFwdV == 0 && nbrRevV == 0) {
            stop(""Each separate run must contain at least one variable "",
                 ""segment."")
        }
        if (nbrFwdV > 0 && nbrRevV > 0 && 
            !parm$mergeForwardReverse) {
            stop(""A separate run can not have variable sequences in both "",
                 ""the forward and reverse reads unless 'mergeForwardReverse' "",
                 ""is TRUE."")
        }
        if (nbrFwdV > 1 || nbrRevV > 1) {
            stop(""A separate run can have at most one variable segment "",
                 ""in each of the forward and reverse reads, respectively."")
        }
    }
    
    ## Summing UMI counts after the processing may not be accurate - will
    ## always use read counts
    if (length(grep(""U"", paste0(combinedDigestParams$elementsForward,
                                combinedDigestParams$elementsReverse))) > 0) {
        warning(""Aggregating UMI counts may not be accurate - will use "",
                ""read counts instead"")
    }

    ## --------------------------------------------------------------------- ##
    ## Combined quantification - just tabulate combined variable sequences
    ## --------------------------------------------------------------------- ##
    outCombined <- do.call(digestFastqs, combinedDigestParams)

    ## Get count matrix with ""raw"" (uncorrected) sequences
    countCombined <- outCombined$summaryTable |>
        dplyr::select(""sequence"", ""nbrReads"", ""varLengths"") |>
        mutate(idx = paste0(""I"", seq_along(.data$sequence)))

    ## If applicable, separate into forward and reverse sequences
    if (any(grepl(""_"", countCombined$sequence))) {
        countCombined <- countCombined |>
            separate(.data$sequence, into = c(""sequenceForward"", 
                                                     ""sequenceReverse""), 
                            sep = ""_"") |>
            separate(.data$varLengths, into = c(""varLengthsForward"", 
                                                       ""varLengthsReverse""),
                            sep = ""_"") |>
            mutate(
                nCompForward = vapply(strsplit(.data$varLengthsForward, "",""), 
                                      length, 0),
                nCompReverse = vapply(strsplit(.data$varLengthsReverse, "",""), 
                                      length, 0))
    } else {
        countCombined <- countCombined |>
            rename(sequenceForward = ""sequence"",
                          varLengthsForward = ""varLengths"") |>
            mutate(
                nCompForward = vapply(strsplit(.data$varLengthsForward, "",""), 
                                      length, 0))
    }
    
    offsetForward <- 0
    ## Split forward and reverse sequences, respectively
    if (""sequenceForward"" %in% colnames(countCombined)) {
        stopifnot(""varLengthsForward"" %in% colnames(countCombined))
        ## Check that all sequences have the same number of components
        stopifnot(length(unique(countCombined$nCompForward)) == 1)
        offsetForward <- countCombined$nCompForward[1]
        tmp <- split(countCombined, countCombined$varLengthsForward)
        countCombined <- unsplit(lapply(tmp, function(df) {
            w <- as.numeric(strsplit(df$varLengthsForward[1], "","")[[1]])
            df <- df |>
                separate(
                    .data$sequenceForward, 
                    into = names(paramsSeparate)[seq_along(w)],
                    # into = paste0(""V"", seq_along(w)),
                    sep = cumsum(w[seq_len(length(w) - 1)]))
        }), countCombined$varLengthsForward)
    }
    if (""sequenceReverse"" %in% colnames(countCombined)) {
        stopifnot(""varLengthsReverse"" %in% colnames(countCombined))
        ## Check that all sequences have the same number of components
        stopifnot(length(unique(countCombined$nCompReverse)) == 1)
        tmp <- split(countCombined, countCombined$varLengthsReverse)
        countCombined <- unsplit(lapply(tmp, function(df) {
            w <- as.numeric(strsplit(df$varLengthsReverse[1], "","")[[1]])
            df <- df |>
                separate(
                    .data$sequenceReverse, 
                    into = names(paramsSeparate)[offsetForward + seq_along(w)],
                    # into = paste0(""V"", offsetForward + seq_along(w)),
                    sep = cumsum(w[seq_len(length(w) - 1)]))
        }), countCombined$varLengthsReverse)
    }
    
    ## --------------------------------------------------------------------- ##
    ## Quantify each variable sequence separately
    ## --------------------------------------------------------------------- ##
    outSeparate <- lapply(paramsSeparate, function(ps) {
        tmp <- do.call(digestFastqs, ps[!names(ps) %in% collapseParams])
        if (any(collapseParams %in% names(ps))) {
            ## Collapse this variable region -> need to call 
            ## groupSimilarSequences
            tbl <- do.call(groupSimilarSequences, 
                           c(list(seqs = tmp$summaryTable$sequence,
                                  scores = tmp$summaryTable$nbrReads,
                                  verbose = FALSE),
                             ps[names(ps) %in% collapseParams]))
            colnames(tbl)[colnames(tbl) == ""representative""] <- ""mutantName""
            tmp$summaryTable <- tbl
        }
        tmp
    })

    ## Filter tables
    filtSeparate <- lapply(outSeparate, function(out) out$filterSummary)
    
    ## Conversion tables
    convSeparate <- lapply(outSeparate, function(out) {
        out$summaryTable |>
            dplyr::select(""mutantName"", ""sequence"") |>
            separate_rows(""sequence"", sep = "","")
    })

    ## --------------------------------------------------------------------- ##
    ## Replace naive sequences with corrected ones
    ## --------------------------------------------------------------------- ##
    for (i in names(paramsSeparate)) {
        countCombined[[i]] <- 
            convSeparate[[i]]$mutantName[match(countCombined[[i]],
                                               convSeparate[[i]]$sequence)]
    }

    ## Filter out rows with NAs (the sequence was not retained)
    countCombined <- countCombined[rowSums(is.na(countCombined)) == 0, ,
                                   drop = FALSE]

    ## Aggregate counts
    countAggregated <- countCombined |>
        group_by(across(names(paramsSeparate))) |>
        summarize(nbrReads = sum(.data$nbrReads), .groups = ""drop"")

    list(countAggregated = countAggregated, convSeparate = convSeparate,
         outCombined = outCombined, filtSeparate = filtSeparate)
}
","R"
"Assay","fmicompbio/mutscan","R/summarizeExperiment.R",".R","10118","228",".hasReadComponent <- function(composition, component) {
    tmp <- gregexpr(pattern = component, text = composition, fixed = TRUE)[[1]]
    if (length(tmp) == 1 && tmp == -1) {
        FALSE
    } else {
        TRUE
    }
}

#' Summarize and collapse multiple mutational scanning experiments
#'
#' Combine multiple sequence lists (as returned by \code{\link{digestFastqs}}
#' into a \code{\link[SummarizedExperiment]{SummarizedExperiment}}, with
#' observed variable sequences (sequence pairs) in rows and samples in columns.
#'
#' @param x A named list of objects returned by \code{\link{digestFastqs}}.
#'     Names are used to link the objects to the metadata provided in
#'     \code{coldata}.
#' @param coldata A \code{data.frame} with at least one column ""Name"", which
#'     will be used to link to objects in \code{x}. A potentially subset and
#'     reordered version of \code{coldata} is stored in the \code{colData} of 
#'     the returned \code{\link[SummarizedExperiment]{SummarizedExperiment}}.
#' @param countType Either ""reads"" or ""umis"". If ""reads"", the ""count"" assay of
#'     the returned object will contain the observed number of reads for each
#'     sequence (pair). If ""umis"", the ""count"" assay will contain the number of
#'     unique UMIs observed for each sequence (pair).
#'
#' @return A \code{\link[SummarizedExperiment]{SummarizedExperiment}} \code{x}
#'     with
#'     \describe{
#'         \item{assays(x)$counts}{containing the observed number of sequences 
#'         or sequence pairs (if \code{countType} = ""reads""), or the observed 
#'         number of unique UMIs for each sequence or sequence pair (if 
#'         \code{countType} = ""umis"").}
#'         \item{rowData(x)}{containing the unique sequences or sequence 
#'         pairs.}
#'         \item{colData(x)}{containing the metadata provided by 
#'         \code{coldata}.}
#'     }
#'
#' @author Michael Stadler, Charlotte Soneson
#'
#' @export
#'
#' @importFrom SummarizedExperiment SummarizedExperiment
#' @importFrom S4Vectors DataFrame
#' @importFrom IRanges IntegerList
#' @importFrom methods is new as
#' @importFrom dplyr bind_rows distinct left_join mutate filter group_by
#'     summarize
#' @importFrom rlang .data
#' @importFrom stats setNames
#' 
#' @examples 
#' ## Input sample
#' inp <- digestFastqs(
#'     fastqForward = system.file(""extdata"", ""cisInput_1.fastq.gz"", 
#'                                package = ""mutscan""), 
#'     elementsForward = ""SUCV"", elementLengthsForward = c(1, 10, 18, 96), 
#'     constantForward = ""AACCGGAGGAGGGAGCTG"", 
#'     wildTypeForward = c(FOS = paste0(
#'         ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTC"", 
#'         ""TGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")),
#'     nbrMutatedCodonsMaxForward = 1
#' )
#' ## Output sample
#' outp <- digestFastqs(
#'     fastqForward = system.file(""extdata"", ""cisOutput_1.fastq.gz"", 
#'                                package = ""mutscan""), 
#'     elementsForward = ""SUCV"", elementLengthsForward = c(1, 10, 18, 96), 
#'     constantForward = ""AACCGGAGGAGGGAGCTG"", 
#'     wildTypeForward = c(FOS = paste0(
#'         ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTC"", 
#'         ""TGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")),
#'     nbrMutatedCodonsMaxForward = 1
#' )
#' ## Combine
#' se <- summarizeExperiment(
#'     x = list(r1inp = inp, r1outp = outp), 
#'     coldata = data.frame(Name = c(""r1inp"", ""r1outp""), 
#'                          Condition = c(""input"", ""output""), 
#'                          Replicate = c(""rep1"", ""rep1"")),
#'     countType = ""umis""
#' )
#' se
#'
summarizeExperiment <- function(x, coldata, countType = ""umis"") {
    ## ------------------------------------------------------------------------
    ## Pre-flight checks
    ## ------------------------------------------------------------------------
    if (!is(x, ""list"") || is.null(names(x))) {
        stop(""'x' must be a named list"")
    }
    if (any(duplicated(names(x)))) {
        stop(""duplicated names in 'x' (e.g. technical replicated to be "", 
             ""merged) is not supported yet"")
    }
    if (!is(coldata, ""data.frame"") || !(""Name"" %in% colnames(coldata))) {
        stop(""'coldata' must be a data.frame with at least one column, named "",
             ""'Name'."")
    }
    .assertScalar(x = countType, type = ""character"",
                  validValues = c(""umis"", ""reads""))
    ## If no UMI sequences were given, then countType = ""umis"" should not be 
    ## allowed
    if (countType == ""umis"" &&
        !(all(vapply(x, function(w) {
            .hasReadComponent(w$parameters$elementsForward, ""U"") ||
                .hasReadComponent(w$parameters$elementsReverse, ""U"")
        }, FALSE)))) {
        stop(""'countType' is set to 'umis', but no UMI sequences "",
             ""were provided when quantifying. "",
             ""Set 'countType' to 'reads' instead."")
    }
    ## Get the mutNameDelimiters, and make sure that they are the same
    mutnamedel <- unique(vapply(x, 
                                function(w) w$parameters$mutNameDelimiter, """"))
    if (length(mutnamedel) > 1) {
        stop(""All samples must have the same 'mutNameDelimiter'"")
    }
    
    coldata$Name <- as.character(coldata$Name)
    
    ## ------------------------------------------------------------------------
    ## Link elements in x with coldata
    ## ------------------------------------------------------------------------
    nms <- intersect(names(x), coldata$Name)
    if (length(nms) == 0) {
        stop(""names in 'x' do not match 'coldata$Name'"")
    } else if (length(nms) < length(x)) {
        nmsNotUsed <- setdiff(names(x), nms)
        warning(""skipping "", length(nmsNotUsed), "" elements in 'x' ("",
                paste(nmsNotUsed, collapse = "", ""), "") because they did not "",
                ""match any 'coldata$Name'."")
    }
    x <- x[nms]
    coldata <- coldata[match(nms, coldata$Name), , drop = FALSE]
    
    ## All sample names
    allSamples <- names(x)
    names(allSamples) <- allSamples
    
    ## ------------------------------------------------------------------------
    ## Get all observed sequences for each mutant name
    ## For a given sample, the same mutant name can correspond to multiple 
    ## sequences, separated by ,
    ## ------------------------------------------------------------------------
    tmpdf <- do.call(bind_rows, lapply(x, function(w) w$summaryTable))
    
    allSequences <- mergeValues(tmpdf$mutantName, tmpdf$sequence) |>
        setNames(c(""mutantName"", ""sequence""))
    allSequences <- DataFrame(allSequences)
    
    ## ------------------------------------------------------------------------
    ## Add info about nbr mutated bases/codons/AAs,
    ## sequenceAA, mutantNameAA, mutationTypes, varLengths
    ## Also here, each column can contain multiple values separated with ,
    ## (e.g. if variable sequences were collapsed to WT in digestFastqs)
    ## ------------------------------------------------------------------------
    for (v in intersect(c(""nbrMutBases"", ""nbrMutCodons"", ""nbrMutAAs"", 
                          ""mutantNameBase"", ""mutantNameBaseHGVS"",
                          ""mutantNameCodon"", ""mutantNameAA"", 
                          ""mutantNameAAHGVS"", ""sequenceAA"", ""mutationTypes"",
                          ""varLengths""),
                        colnames(tmpdf))) {
        tmp <- mergeValues(tmpdf$mutantName, tmpdf[[v]]) |>
            setNames(c(""mutantName"", v))
        allSequences[[v]] <- tmp[[v]][match(allSequences$mutantName,
                                            tmp$mutantName)]
        
        if (v %in% c(""nbrMutBases"", ""nbrMutCodons"", ""nbrMutAAs"")) {
            tmpList <- as(
                lapply(strsplit(allSequences[[v]], "",""), 
                       function(w) sort(as.integer(w))),
                ""IntegerList"")
            allSequences[[paste0(""min"", sub(""^n"", ""N"", v))]] <- min(tmpList)
            allSequences[[paste0(""max"", sub(""^n"", ""N"", v))]] <- max(tmpList)
        }
    }
    
    ## ------------------------------------------------------------------------
    ## Create a sparse matrix
    ## ------------------------------------------------------------------------
    countCol <- ifelse(countType == ""umis"", ""nbrUmis"", ""nbrReads"")
    tmp <- do.call(bind_rows, lapply(allSamples, function(s) {
        st <- x[[s]]$summaryTable
        data.frame(i = match(st$mutantName, allSequences$mutantName),
                   j = match(s, allSamples),
                   x = as.numeric(st[, countCol]))
    }))
    countMat <- new(""dgTMatrix"", i = tmp$i - 1L, j = tmp$j - 1L,
                    x = tmp$x, Dim = c(nrow(allSequences), 
                                       length(allSamples)))
    ## Convert to dgCMatrix for easier processing downstream
    countMat <- as(countMat, ""CsparseMatrix"")
    
    ## ------------------------------------------------------------------------
    ## Create the colData
    ## ------------------------------------------------------------------------
    addMeta <- do.call(bind_rows, lapply(allSamples, function(s) {
        x[[s]]$filterSummary |> mutate(Name = s)
    }))
    coldata <- left_join(coldata, addMeta, by = ""Name"")
    
    ## ------------------------------------------------------------------------
    ## Create SummarizedExperiment object
    ## ------------------------------------------------------------------------
    se <- SummarizedExperiment(
        assays = list(counts = countMat),
        colData = coldata[match(allSamples, coldata$Name), , drop = FALSE],
        rowData = allSequences,
        metadata = list(
            parameters = lapply(allSamples, 
                                function(w) x[[w]]$parameters),
            errorStatistics = lapply(allSamples, 
                                     function(w) x[[w]]$errorStatistics),
            countType = countType,
            mutNameDelimiter = mutnamedel)
    )
    
    if (!any(allSequences$mutantName == """")) {
        rownames(se) <- allSequences$mutantName
    }
    colnames(se) <- allSamples
    
    return(se)
}
","R"
"Assay","fmicompbio/mutscan","R/zzz.R",".R","114","5",".onUnload <- function (libpath) {
    # nocov start
    library.dynam.unload(""mutscan"", libpath)
    # nocov end
}","R"
"Assay","fmicompbio/mutscan","R/collapseMutantsByAA.R",".R","8948","224","#' Collapse mutants by similarity
#' 
#' These functions can be used to collapse variants, either by similarity or 
#' according to a pre-defined grouping. The functions \code{collapseMutants} 
#' and \code{collapseMutantsByAA} assume that a grouping variable is available 
#' as a column in \code{rowData(se)} (\code{collapseMutantsByAA} is a 
#' convenience function for the case when this column is ""mutantNameAA"", and 
#' is provided for backwards compatibility). The 
#' \code{collapseMutantsBySimilarity} will generate the grouping variable 
#' based on user-provided thresholds on the sequence similarity (defined by 
#' the Hamming distance), and subsequently collapse based on the derived 
#' grouping. 
#' 
#' @param se A \code{\link[SummarizedExperiment]{SummarizedExperiment}}
#'     generated by \code{\link{summarizeExperiment}}
#' @param assayName The name of the assay that will be used to calculate a 
#'     ""score"" (typically derived from the read counts) for each variant.
#' @param scoreMethod Character scalar giving the approach used to calculate 
#'     ranking scores from the assay defined by \code{assayName}. Currently, 
#'     this can be one of \code{""rowSum""} or \code{""rowMean""}. All filtering
#'     criteria will be applied to these scores.
#' @param sequenceCol Character scalar giving the name of the column in 
#'     \code{rowData(se)} that contains the nucleotide sequence of the 
#'     variants. 
#' @param collapseMaxDist Numeric scalar defining the tolerance for collapsing 
#'     similar sequences. If the value is in [0, 1), it defines the maximal 
#'     Hamming distance in terms of a fraction of sequence length:
#'     (\code{round(collapseMaxDist * nchar(sequence))}).
#'     A value greater or equal to 1 is rounded and directly used as the 
#'     maximum allowed Hamming distance. Note that sequences can only be
#'     collapsed if they are all of the same length.
#' @param collapseMinScore Numeric scalar, indicating the minimum score for the 
#'     sequence to be considered for collapsing with similar sequences.
#' @param collapseMinRatio Numeric scalar. During collapsing of
#'     similar sequences, a low-frequency sequence will be collapsed 
#'     with a higher-frequency sequence only if the ratio between the 
#'     high-frequency and the low-frequency scores is at least this 
#'     high. The default value of 0 indicates that no such check is performed.
#' @param verbose Logical, whether to print progress messages.
#' 
#' @author Charlotte Soneson, Michael Stadler
#' 
#' @export
#' 
#' @importFrom SummarizedExperiment assayNames assay rowData
#' @importFrom Matrix rowSums rowMeans
#' 
#' @examples
#' library(SummarizedExperiment)
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"",
#'                           package = ""mutscan""))[1:200, ]
#' ## The rows of this object correspond to individual codon variants
#' dim(se)
#' head(rownames(se))
#' 
#' ## Collapse by amino acid
#' sec <- collapseMutantsByAA(se)
#' ## The rows of the collapsed object correspond to amino acid variants
#' dim(sec)
#' head(rownames(sec))
#' ## The mutantName column contains the individual codon variants that were 
#' ## collapsed
#' head(rowData(sec))
#' 
#' ## Collapse similar sequences
#' sec2 <- collapseMutantsBySimilarity(
#'     se = se, assayName = ""counts"", scoreMethod = ""rowSum"",
#'     sequenceCol = ""sequence"", collapseMaxDist = 2,
#'     collapseMinScore = 0, collapseMinRatio = 0)
#' dim(sec2)
#' head(rownames(sec2))
#' head(rowData(sec2))
#' ## collapsed count matrix
#' assay(sec2, ""counts"")
#'
collapseMutantsBySimilarity <- function(se, assayName, scoreMethod = ""rowSum"",
                                        sequenceCol = ""sequence"", 
                                        collapseMaxDist = 0,
                                        collapseMinScore = 0, 
                                        collapseMinRatio = 0,
                                        verbose = TRUE) {
    ## Check input arguments
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertScalar(x = assayName, type = ""character"", 
                  validValues = assayNames(se))
    .assertScalar(x = scoreMethod, type = ""character"",
                  validValues = c(""rowSum"", ""rowMean""))
    .assertScalar(x = sequenceCol, type = ""character"",
                  validValues = colnames(rowData(se)))
    .assertScalar(x = collapseMaxDist, type = ""numeric"")
    .assertScalar(x = collapseMinScore, type = ""numeric"")
    .assertScalar(x = collapseMinRatio, type = ""numeric"")
    .assertScalar(x = verbose, type = ""logical"")
    
    ## All sequences must be the same length, and can only consist of ACGT_
    seqs <- rowData(se)[, sequenceCol]
    if (length(unique(nchar(seqs))) != 1) {
        stop(""All entries in the sequence column must have the same length"")
    }
    if (!all(vapply(seqs, 
                    function(x) grepl(""^[ACGT_]*$"", x), FALSE))) {
        stop(""All entries in the sequence column must consist of [ACGT_]"")
    }
    
    ## Get matching table
    if (scoreMethod == ""rowSum"") {
        scores <- rowSums(assay(se, assayName))
    } else if (scoreMethod == ""rowMean"") {
        scores <- rowMeans(assay(se, assayName))
    }
    matchTable <- groupSimilarSequences(
        seqs = seqs,
        scores = scores,
        collapseMaxDist = collapseMaxDist,
        collapseMinScore = collapseMinScore,
        collapseMinRatio = collapseMinRatio,
        verbose = verbose
    )
    matchTable$representative <- rownames(se)[
        match(matchTable$representative, 
              rowData(se)[, sequenceCol])]
    
    ## Add grouping variable
    rowData(se)$collapseCol <- 
        matchTable$representative[
            match(rowData(se)[, sequenceCol], 
                  matchTable$sequence)]
    
    ## Collapse
    se <- collapseMutants(se, nameCol = ""collapseCol"")
    
    se
}

#' @rdname collapseMutantsBySimilarity
#' @export
#' 
collapseMutantsByAA <- function(se) {
    collapseMutants(se = se, nameCol = ""mutantNameAA"")
}

#' Collapse counts by mutated amino acid
#'
#' @param se A \code{\link[SummarizedExperiment]{SummarizedExperiment}}
#'     generated by \code{\link{summarizeExperiment}}
#' @param nameCol A character scalar providing the column of 
#'     \code{rowData(se)} that contains the amino acid mutant names (that will 
#'     be the new row names).
#'
#' @return A \code{\link[SummarizedExperiment]{SummarizedExperiment}} where
#'     counts have been aggregated by the mutated amino acid(s).
#'
#' @author Charlotte Soneson, Michael Stadler
#'
#' @export
#'
#' @importFrom DelayedArray rowsum
#' @importFrom S4Vectors metadata DataFrame
#' @importFrom SummarizedExperiment assays rowData SummarizedExperiment colData
#'     rowData<-
#' @importFrom dplyr across group_by summarize full_join
#' @importFrom stats setNames
#'
#' @rdname collapseMutantsBySimilarity
#' 
collapseMutants <- function(se, nameCol) {
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertScalar(x = nameCol, type = ""character"",
                  validValues = colnames(rowData(se)))
    
    ## Collapse assays
    aList <- lapply(assays(se), function(a) {
        rowsum(
            x = a,
            group = factor(rowData(se)[[nameCol]])
        )
    })
    
    ## Collapse rowData - simple columns
    rd <- mergeValues(rowData(se)[[nameCol]],
                      rowData(se)$sequence) |>
        setNames(c(nameCol, ""sequence""))
    for (v in setdiff(
        intersect(c(""mutantName"", ""mutantNameBase"", ""mutantNameBaseHGVS"",
                    ""mutantNameCodon"", ""mutantNameAA"", ""mutantNameAAHGVS"",
                    ""sequenceAA"", ""mutationTypes"",
                    ""nbrMutBases"", ""nbrMutCodons"", ""nbrMutAAs"", ""varLengths""),
                  colnames(rowData(se))),
        nameCol)) {
        tmp <- mergeValues(rowData(se)[[nameCol]],
                           rowData(se)[[v]])
        rd[[v]] <- tmp$valueColl[match(rd[[nameCol]], tmp$mutantNameColl)]
    }
    rd0 <- as.data.frame(rowData(se)) |>
        group_by(.data[[nameCol]]) |>
        summarize(
            across(c(""minNbrMutBases"", ""minNbrMutCodons"",
                     ""minNbrMutAAs""),
                   function(x) min(x)),
            across(c(""maxNbrMutBases"", ""maxNbrMutCodons"",
                     ""maxNbrMutAAs""),
                   function(x) max(x)))
    rd <- rd |>
        full_join(rd0, by = nameCol)
    
    rd <- DataFrame(rd)
    rownames(rd) <- rd[[nameCol]]
    
    ## Create SummarizedExperiment
    rn1 <- rownames(aList[[1]])
    cn1 <- colnames(aList[[1]])
    for (i in seq_along(aList)) {
        stopifnot(all(rownames(aList[[i]]) == rn1))
        stopifnot(all(colnames(aList[[i]]) == cn1))
    }
    stopifnot(cn1 == rownames(colData(se)))
    SummarizedExperiment(
        assays = aList,
        colData = colData(se),
        metadata = metadata(se),
        rowData = rd[rn1, ]
    )
}
","R"
"Assay","fmicompbio/mutscan","R/plotDistributions.R",".R","5963","154","#' Plot distribution of observed values
#' 
#' @param se A \code{SummarizedExperiment} object, typically generated by 
#'     \code{summarizeExperiment()}.
#' @param selAssay Character scalar specifying the assay in \code{se} to 
#'     use for the plotting.
#' @param groupBy Character scalar specifying a column from 
#'     \code{colData(se)} to use for coloring or stratifying the plots. 
#' @param plotType Character scalar specifying the type of plot to construct. 
#'     Either \code{'density'}, \code{'histogram'} or \code{'knee'}.
#' @param facet Logical scalar, indicating whether or not to facet the plot
#'     by the values specified in the \code{groupBy} column.
#' @param pseudocount Numeric scalar, representing the number to add to the 
#'     observed values in the \code{selAssay} assay before plotting. 
#' 
#' @export
#' @author Charlotte Soneson
#' 
#' @return A ggplot object.
#' 
#' @importFrom tibble rownames_to_column
#' @importFrom tidyr gather
#' @importFrom dplyr group_by arrange mutate desc ungroup left_join
#' @importFrom SummarizedExperiment colData assay assayNames
#' @importFrom ggplot2 ggplot scale_x_log10 scale_y_log10 labs geom_line 
#'     facet_wrap geom_density geom_histogram theme_minimal theme 
#'     element_text aes
#' @importFrom rlang .data
#' 
#' @examples 
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"", 
#'                           package = ""mutscan""))[1:200, ]
#' plotDistributions(se)
#' 
plotDistributions <- function(se, selAssay = ""counts"", 
                              groupBy = NULL, plotType = ""density"", 
                              facet = FALSE, pseudocount = 0) {
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertScalar(x = selAssay, type = ""character"", 
                  validValues = assayNames(se))
    if (!is.null(groupBy)) {
        .assertScalar(
            x = groupBy, type = ""character"",
            validValues = colnames(colData(se)))
    }
    .assertScalar(x = plotType, type = ""character"", 
                  validValues = c(""density"", ""knee"", ""histogram""))
    .assertScalar(x = facet, type = ""logical"")
    .assertScalar(x = pseudocount, type = ""numeric"", rngIncl = c(0, Inf))
    
    ## Define a common theme to use for the plots
    commonTheme <- list(
        theme_minimal(),
        theme(axis.text = element_text(size = 12),
              axis.title = element_text(size = 14))
    )
    
    df <- as.data.frame(as.matrix(
        assay(se, selAssay, withDimnames = TRUE)
    )) |>
        rownames_to_column(""feature"") |>
        gather(key = ""Name"", value = ""value"", -""feature"") |>
        group_by(.data$Name) |>
        arrange(desc(.data$value)) |>
        mutate(idx = seq_along(.data$value), 
               value = .data$value + pseudocount) |>
        ungroup() |>
        left_join(as.data.frame(colData(se)),
                  by = ""Name"")
    
    ## If the user doesn't explicitly group by any variable, impose grouping
    ## by the sample ID. In that case, don't color by sample ID if facetting
    ## is used (only one curve per facet). If a variable to group by is 
    ## specified, color by sample ID even if facetting is used. 
    if (is.null(groupBy)) {
        groupBy <- ""Name""
        colorFacetByName <- FALSE
    } else {
        colorFacetByName <- TRUE
    }
    
    ## Specify plot depending on desired type
    if (plotType == ""knee"") {
        gg <- ggplot(df, aes(x = .data$idx, 
                             y = .data$value)) + 
            scale_x_log10() + scale_y_log10() + 
            labs(x = ""Feature (sorted)"", 
                 y = paste0(selAssay, 
                            ifelse(pseudocount == 0, 
                                   """", paste0("" + "", pseudocount))))
        if (facet) {
            if (colorFacetByName) {
                gg <- gg + geom_line(aes(color = .data$Name))
            } else {
                gg <- gg + geom_line(aes(group = .data$Name))
            }
            gg <- gg + 
                facet_wrap(~ .data[[groupBy]])
        } else {
            gg <- gg + 
                geom_line(aes(group = .data$Name, 
                              color = .data[[groupBy]]))
        }
    } else if (plotType == ""density"") {
        gg <- ggplot(df, aes(x = .data$value)) + 
            scale_x_log10() + 
            labs(x = paste0(selAssay, 
                            ifelse(pseudocount == 0, 
                                   """", paste0("" + "", pseudocount))),
                 y = ""Density"")
        if (facet) {
            if (colorFacetByName) {
                gg <- gg + 
                    geom_density(aes(color = .data$Name))
            } else {
                gg <- gg + 
                    geom_density(aes(group = .data$Name))
            }
            gg <- gg + 
                facet_wrap(~ .data[[groupBy]])
        } else {
            gg <- gg + 
                geom_density(aes(group = .data$Name, 
                                 color = .data[[groupBy]]))
        }
    } else if (plotType == ""histogram"") {
        gg <- ggplot(df, aes(x = .data$value)) + 
            scale_x_log10() + 
            labs(x = paste0(selAssay, 
                            ifelse(pseudocount == 0, 
                                   """", paste0("" + "", pseudocount))),
                 y = ""Count"")
        if (facet) {
            if (colorFacetByName) {
                gg <- gg + 
                    geom_histogram(aes(fill = .data$Name), 
                                   bins = 50)
            } else {
                gg <- gg + 
                    geom_histogram(aes(group = .data$Name), 
                                   bins = 50)
            }
            gg <- gg +
                facet_wrap(~ .data[[groupBy]])
        } else {
            gg <- gg + 
                geom_histogram(aes(group = .data$Name,
                                   fill = .data[[groupBy]]))
        }
    }
    
    gg + commonTheme
}
","R"
"Assay","fmicompbio/mutscan","R/digestFastqs.R",".R","40915","789","#' Read, filter and digest sequences from fastq file(s).
#'
#' Read sequences for one or a pair of fastq files and digest them (extract
#' umis, constant and variable parts, filter, extract mismatch information from
#' constant and count the observed unique variable parts). Alternatively, 
#' primer sequences could be specified, in which case the sequence immediately
#' following the primer will be considered the variable sequence.
#'
#' The processing of a read pair goes as follows:
#' \enumerate{
#'  \item Search for perfect matches to forward/reverse adapter sequences,
#'  filter out the read pair if a match is found in either the forward or
#'  reverse read.
#'  \item If primer sequences are provided, search for perfect matches, and
#'  filter out the read pair if not all provided primer sequences can be found.
#'  \item Extract the UMI, constant and variable sequence from forward and
#'  reverse reads, based on the definition of the respective read composition.
#'  \item If requested, collapse forward and reverse variable regions by
#'  retaining, for each position, the base with the highest reported base
#'  quality.
#'  \item Filter out the read (pair) if the average quality in the variable 
#'  region is below \code{avePhredMinForward}/\code{avePhredMinReverse}, in 
#'  either the forward or reverse read (or the merged read).
#'  \item Filter out the read (pair) if the number of Ns in the variable region
#'  exceeds \code{variableNMaxForward}/\code{variableNMaxReverse}.
#'  \item Filter out the read (pair) if the number of Ns in the combined 
#'  forward and reverse UMI sequence exceeds \code{umiNMax}
#'  \item If one or more wild type sequences (for the variable region) are
#'  provided, find the mismatches between the (forward/reverse) variable region
#'  and the provided wild type sequence (if more than one wild type sequence is
#'  provided, first find the one that is closest to the read).
#'  \item Filter out the read (pair) if any mutated base has a quality below
#'  \code{mutatedPhredMinForward}/\code{mutatedPhredMinReverse}.
#'  \item Filter out the read (pair) if the number of mutated codons exceeds
#'  \code{nbrMutatedCodonsMaxForward}/\code{nbrMutatedCodonsMaxReverse}.
#'  \item Filter out the read (pair) if any of the mutated codons match any of 
#'  the codons encoded by
#'  \code{forbiddenMutatedCodonsForward}/\code{forbiddenMutatedCodonsReverse}.
#'  \item Assign a 'mutation name' to the read (pair). This name is a 
#'  combination of parts of the form XX\{.\}YY\{.\}NNN, where XX is the name of 
#'  the most similar reference sequence, YY is the mutated codon number, and 
#'  NNN is the mutated codon. \{.\} is a delimiter, specified via 
#'  \code{mutNameDelimiter}. If no wildtype sequences are provided, the
#'  variable sequence will be used as the mutation name'.
#'}
#'
#' Based on the retained reads following this filtering process, count the
#' number of reads, and the number of unique UMIs, for each variable sequence
#' (or pair of variable sequences).
#'
#' @param fastqForward,fastqReverse Character vectors, paths to gzipped FASTQ 
#'     files corresponding to forward and reverse reads, respectively. If more 
#'     than one forward/reverse sequence file is given, they need to be 
#'     provided in the same order. Note that if multiple fastq files are 
#'     provided, they are all assumed to correspond to the same sample, and 
#'     will effectively be concatenated.
#' @param mergeForwardReverse Logical scalar, whether to fuse the forward and
#'     reverse variable sequences.
#' @param minOverlap,maxOverlap Numeric scalar, the minimal and maximal allowed
#'     overlap between the forward and reverse reads when merging. Only used if
#'     \code{mergeForwardReverse} is \code{TRUE}. If set to 0, only overlaps
#'     covering the full length of the shortest of the two reads will be
#'     considered.
#' @param minMergedLength,maxMergedLength Numeric scalar, the minimal and 
#'     maximal allowed total length of the merged product (if 
#'     \code{mergeForwardReverse} is \code{TRUE}). If set to 0, any length is 
#'     allowed.
#' @param maxFracMismatchOverlap Numeric scalar, maximal mismatch rate in the
#'     overlap. Only used if \code{mergeForwardReverse} is \code{TRUE}.
#' @param greedyOverlap Logical scalar. If \code{TRUE}, the first overlap 
#'     satisfying \code{minOverlap}, \code{maxOverlap}, \code{minMergedLength},
#'     \code{maxMergedLength} and \code{maxFracMismatchOverlap} will
#'     be retained. If \code{FALSE}, all valid overlaps will be scored and the 
#'     one with the highest score (largest number of matches) will be retained.
#' @param revComplForward,revComplReverse Logical scalar, whether to reverse
#'     complement the forward/reverse variable and constant sequences, 
#'     respectively.
#' @param elementsForward,elementsReverse Character scalars representing the
#'     composition of the forward and reverse reads, respectively. The strings 
#'     should consist only of the letters S (skip), C (constant), U (umi), 
#'     P (primer), V (variable), and cover the full extent of the read. Most 
#'     combinations are allowed (and a given letter can appear multiple times), 
#'     but there can be at most one occurrence of P. If a given letter is 
#'     included multiple times, the corresponding sequences will be 
#'     concatenated in the output.
#' @param elementLengthsForward,elementLengthsReverse Numeric vectors 
#'     containing the lengths of each read component from 
#'     \code{elementsForward}/\code{elementsReverse}, respectively. If the 
#'     length of one element is set to -1, it will be inferred from the other 
#'     lengths (as the remainder of the read). At most one number (or one 
#'     number on each side of the primer P) can be set to -1. The indicated
#'     length of the primer is not used (instead it's inferred from the 
#'     provided primer sequence) and can also be set to -1.
#' @param adapterForward,adapterReverse Character scalars, the adapter sequence 
#'     for forward/reverse reads, respectively. If a forward/reverse read 
#'     contains the corresponding adapter sequence, the sequence pair will be 
#'     filtered out. If set to \code{NULL}, no adapter filtering is performed. 
#'     The number of filtered read pairs are reported in the return value.
#' @param primerForward,primerReverse Character vectors, representing the 
#'     primer sequence(s) for forward/reverse reads, respectively. Only read 
#'     pairs that contain perfect matches to both the forward and reverse 
#'     primers (if given) will be retained. Multiple primers can be specified - 
#'     they will be considered in order and the first match will be used.
#' @param wildTypeForward,wildTypeReverse Character scalars or named character
#'     vectors, the wild type sequence for the forward and reverse variable 
#'     region. If given as a single string, the reference sequence will be 
#'     named 'f' (for forward) or 'r' (for reverse).
#' @param constantForward,constantReverse Character vectors giving,
#'     the expected constant forward and reverse sequences. If more than one 
#'     sequence is provided, they must all have the same length.
#' @param avePhredMinForward,avePhredMinReverse Numeric scalar, the minimum 
#'     average Phred score in the variable region for a read to be retained. 
#'     If a read pair contains both forward and reverse variable regions, the 
#'     minimum average Phred score has to be achieved in both for a read pair 
#'     to be retained.
#' @param variableNMaxForward,variableNMaxReverse Numeric scalar, the maximum 
#'     number of Ns allowed in the variable region for a read to be retained.
#' @param umiNMax Numeric scalar, the maximum number of Ns allowed in the UMI 
#'     for a read to be retained.
#' @param nbrMutatedCodonsMaxForward,nbrMutatedCodonsMaxReverse Numeric
#'     scalar, the maximum number of mutated codons that are allowed. Note that 
#'     for the forward and reverse sequence, respectively, exactly one of
#'     \code{nbrMutatedCodonsMax} and \code{nbrMutatedBasesMax} must be -1,
#'     and the other must be a non-negative number. The one that is not -1
#'     will be used to filter and name the identified mutants.
#' @param nbrMutatedBasesMaxForward,nbrMutatedBasesMaxReverse Numeric scalar,
#'     the maximum number of mutated bases that are allowed. Note that for the
#'     forward and reverse sequence, respectively, exactly one of
#'     \code{nbrMutatedCodonsMax} and \code{nbrMutatedBasesMax} must be -1,
#'     and the other must be a non-negative number. The one that is not -1
#'     will be used to filter and name the identified mutants.
#' @param forbiddenMutatedCodonsForward,forbiddenMutatedCodonsReverse Character
#'     vector of codons (can contain ambiguous IUPAC characters, see
#'     \code{\link[Biostrings]{IUPAC_CODE_MAP}}). If a read pair contains a
#'     mutated codon matching this pattern, it will be filtered out.
#' @param useTreeWTmatch Logical scalar. Should a tree-based matching
#'     to wild type sequences be used if possible? If the number of allowed
#'     mismatches is small, and the number of wild type sequences is large,
#'     this is typically faster.
#' @param collapseToWTForward,collapseToWTReverse Logical scalar, indicating
#'     whether to just represent the observed variable sequence by the
#'     closest wildtype sequence rather than retaining the information about
#'     the mutations.
#' @param mutatedPhredMinForward,mutatedPhredMinReverse Numeric scalar, the 
#'     minimum Phred score of a mutated base for the read to be retained. If 
#'     any mutated base has a Phred score lower than \code{mutatedPhredMin}, 
#'     the read (pair) will be discarded.
#' @param mutNameDelimiter Character scalar, the delimiter used in the naming 
#'     of mutants. Generally, mutants will be named as XX\{.\}YY\{.\}NNN, where 
#'     XX is the closest provided reference sequence, YY is the mutated base or 
#'     codon number (depending on whether \code{nbrMutatedBases*} or 
#'     \code{nbrMutatedCodons*} is specified), and NNN is the
#'     mutated base or codon. Here, \{.\} is the provided 
#'     \code{mutNameDelimiter}. The delimiter must be a single character 
#'     (not ""_""), and can not appear in any of the provided reference sequence 
#'     names.
#' @param constantMaxDistForward,constantMaxDistReverse Numeric scalars, the 
#'     maximum allowed Hamming distance between the extracted and expected 
#'     constant sequence. If multiple constant sequences are provided, the most 
#'     similar one is used. Reads with a larger distance to the expected 
#'     constant sequence are discarded. If set to -1, no filtering is done.
#' @param umiCollapseMaxDist Numeric scalar defining 
#'     the tolerances for collapsing similar UMI sequences. If the value is 
#'     in [0, 1), it defines the maximal Hamming distance in terms of a 
#'     fraction of sequence length:
#'     (\code{round(umiCollapseMaxDist * nchar(umiSeq))}).
#'     A value greater or equal to 1 is rounded and directly used as the 
#'     maximum allowed Hamming distance. 
#' @param filteredReadsFastqForward,filteredReadsFastqReverse Character 
#'     scalars, the names of a (pair of) FASTQ file(s) where filtered-out reads 
#'     will be written. The name(s) should end in .gz (the output will always 
#'     be compressed). If empty, filtered reads will not be written to a file.
#' @param maxNReads Integer scalar, the maximum number of reads to process. 
#'     The first \code{maxNReads} read (pairs) in the FASTQ file(s) will be 
#'     used. If set to -1, all reads in the FASTQ file(s) will be processed.
#' @param verbose Logical scalar, whether to print out progress messages.
#' @param nThreads Numeric scalar, the number of threads to use for parallel 
#'     processing.
#' @param chunkSize Numeric scalar, the number of read (pairs) to keep in 
#'     memory for parallel processing. Reduce from the default value if you 
#'     run out of memory.
#' @param maxReadLength Numeric scalar, the maximum allowed read length. Longer 
#'     read lengths lead to higher memory allocation, and may require 
#'     the \code{chunkSize} to be decreased. 
#'
#' @return A list with four entries:
#' \describe{
#' \item{summaryTable}{A \code{data.frame} that contains, for each observed
#' mutation combination, the corresponding variable region sequences (or pair 
#' of sequences), the number of observed such sequences, and the number of 
#' unique UMIs observed for the sequence. It also has additional columns: 
#' 'maxNbrReads' contains the number of reads for the most frequent observed 
#' sequence represented by the feature (only relevant if similar variable 
#' regions are collapsed). 'nbrMutBases', 'nbrMutCodons' and 'nbrMutAAs' give 
#' the number of mutated bases, codons or amino acids in each variant. 
#' Alternative variant names based on base, codon or amino acid sequence are 
#' provided in columns mutantNameBase', 'mutantNameCodon', 'mutantNameAA'. In 
#' addition, mutantNameBaseHGVS' and 'mutantNameAAHGVS' give base- and amino 
#' acid-based names following the HGVS nomenclature 
#' (https://varnomen.hgvs.org/). Please note that the provided reference 
#' sequence names are used for the HGVS sequence identifiers. It is up to the 
#' user to use appropriately named reference sequences in order to obtain 
#' valid HGVS variant names.}
#' \item{filterSummary}{A \code{data.frame} that contains the number of input
#' reads, the number of reads filtered out in the processing, and the number of
#' retained reads. The filters are named according to the convention
#' ""fxx_filter"", where ""xx"" indicates the order in which the filters were
#' applied, and ""filter"" indicates the type of filter. Note that filters are
#' applied successively, and the reads filtered out in one step are not
#' considered for successive filtering steps.}
#' \item{errorStatistics}{A \code{data.frame} that contains, for each Phred
#' quality score between 0 and 99, the number of bases in the extracted 
#' constant sequences with that quality score that match/mismatch with the 
#' provided reference constant sequence.}
#' \item{parameters}{A \code{list} with all parameter settings that were used 
#' in the processing. Also contains the version of the package and the time of
#' processing.}
#' }
#' 
#' @examples
#' ## See the vignette for complete worked-out examples for different types of 
#' ## data sets
#' 
#' ## ---------------------------------------------------------------------- ## 
#' ## Process a single-end data set, assume that the full read represents  
#' ## the variable region
#' out <- digestFastqs(
#'     fastqForward = system.file(""extdata"", ""cisInput_1.fastq.gz"", 
#'                                package = ""mutscan""), 
#'     elementsForward = ""V"", elementLengthsForward = -1
#' )
#' ## Table with read counts and mutant information
#' head(out$summaryTable)
#' ## Filter summary
#' out$filterSummary
#' 
#' ## ---------------------------------------------------------------------- ## 
#' ## Process a single-end data set, specify the read as a combination of 
#' ## UMI, constant region and variable region (skip the first base)
#' out <- digestFastqs(
#'     fastqForward = system.file(""extdata"", ""cisInput_1.fastq.gz"", 
#'                                package = ""mutscan""), 
#'     elementsForward = ""SUCV"", elementLengthsForward = c(1, 10, 18, 96), 
#'     constantForward = ""AACCGGAGGAGGGAGCTG""
#' )
#' ## Table with read counts and mutant information
#' head(out$summaryTable)
#' ## Filter summary
#' out$filterSummary
#' ## Error statistics
#' out$errorStatistics
#' 
#' ## ---------------------------------------------------------------------- ## 
#' ## Process a single-end data set, specify the read as a combination of 
#' ## UMI, constant region and variable region (skip the first base), provide 
#' ## the wild type sequence to compare the variable region to and limit the 
#' ## number of allowed mutated codons to 1
#' out <- digestFastqs(
#'     fastqForward = system.file(""extdata"", ""cisInput_1.fastq.gz"", 
#'                                package = ""mutscan""), 
#'     elementsForward = ""SUCV"", elementLengthsForward = c(1, 10, 18, 96), 
#'     constantForward = ""AACCGGAGGAGGGAGCTG"", 
#'     wildTypeForward = c(FOS = paste0(
#'         ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTC"", 
#'         ""TGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")),
#'     nbrMutatedCodonsMaxForward = 1
#' )
#' ## Table with read counts and mutant information
#' head(out$summaryTable)
#' ## Filter summary
#' out$filterSummary
#' ## Error statistics
#' out$errorStatistics
#' 
#' ## ---------------------------------------------------------------------- ## 
#' ## Process a paired-end data set where both the forward and reverse reads 
#' ## contain the same variable region and thus should be merged to generate 
#' ## the final variable sequence, specify the reads as a combination of 
#' ## UMI, constant region and variable region (skip the first and/or last
#' ## base), provide the wild type sequence to compare the variable region to 
#' ## and limit the number of allowed mutated codons to 1
#' out <- digestFastqs(
#'     fastqForward = system.file(""extdata"", ""cisInput_1.fastq.gz"", 
#'                                package = ""mutscan""),
#'     fastqReverse = system.file(""extdata"", ""cisInput_2.fastq.gz"",
#'                                package = ""mutscan""), 
#'     mergeForwardReverse = TRUE, 
#'     revComplForward = FALSE, revComplReverse = TRUE, 
#'     elementsForward = ""SUCV"", elementLengthsForward = c(1, 10, 18, 96),
#'     elementsReverse = ""SUCVS"", elementLengthsReverse = c(1, 7, 17, 96, -1),
#'     constantForward = ""AACCGGAGGAGGGAGCTG"", 
#'     constantReverse = ""GAGTTCATCCTGGCAGC"",
#'     wildTypeForward = c(FOS = paste0(
#'         ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTC"", 
#'         ""TGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")),
#'     nbrMutatedCodonsMaxForward = 1
#' )
#' ## Table with read counts and mutant information
#' head(out$summaryTable)
#' ## Filter summary
#' out$filterSummary
#' ## Error statistics
#' out$errorStatistics
#' 
#' ## ---------------------------------------------------------------------- ## 
#' ## Process a paired-end data set where the forward and reverse reads 
#' ## contain variable regions corresponding to different proteins, and thus 
#' ## should not be merged, specify the reads as a combination of 
#' ## UMI, constant region and variable region (skip the first base), provide 
#' ## the wild type sequence to compare the variable region to and limit the 
#' ## number of allowed mutated codons to 1
#' out <- digestFastqs(
#'     fastqForward = system.file(""extdata"", ""transInput_1.fastq.gz"", 
#'                                package = ""mutscan""),
#'     fastqReverse = system.file(""extdata"", ""transInput_2.fastq.gz"",
#'                                package = ""mutscan""), 
#'     mergeForwardReverse = FALSE,  
#'     elementsForward = ""SUCV"", elementLengthsForward = c(1, 10, 18, 96),
#'     elementsReverse = ""SUCV"", elementLengthsReverse = c(1, 8, 20, 96),
#'     constantForward = ""AACCGGAGGAGGGAGCTG"", 
#'     constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
#'     wildTypeForward = c(FOS = paste0(
#'         ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTC"", 
#'         ""TGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")),
#'     wildTypeReverse = c(JUN = paste0(
#'         ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTC"", 
#'         ""GGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"")), 
#'     nbrMutatedCodonsMaxForward = 1,
#'     nbrMutatedCodonsMaxReverse = 1
#' )
#' ## Table with read counts and mutant information
#' head(out$summaryTable)
#' ## Filter summary
#' out$filterSummary
#' ## Error statistics
#' out$errorStatistics
#' 
#' @export
#' @importFrom utils packageVersion
digestFastqs <- function(fastqForward, fastqReverse = NULL,
                         mergeForwardReverse = FALSE, 
                         minOverlap = 0, maxOverlap = 0,
                         minMergedLength = 0, maxMergedLength = 0,
                         maxFracMismatchOverlap = 1, greedyOverlap = TRUE,
                         revComplForward = FALSE, revComplReverse = FALSE,
                         adapterForward = """", adapterReverse = """",
                         elementsForward = """",
                         elementLengthsForward = numeric(0),
                         elementsReverse = """",
                         elementLengthsReverse = numeric(0),
                         primerForward = c(""""), primerReverse = c(""""),
                         wildTypeForward = """", wildTypeReverse = """",
                         constantForward = c(""""), constantReverse = c(""""),
                         avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
                         variableNMaxForward = 0, variableNMaxReverse = 0,
                         umiNMax = 0,
                         nbrMutatedCodonsMaxForward = 1,
                         nbrMutatedCodonsMaxReverse = 1,
                         nbrMutatedBasesMaxForward = -1,
                         nbrMutatedBasesMaxReverse = -1,
                         forbiddenMutatedCodonsForward = """",
                         forbiddenMutatedCodonsReverse = """",
                         useTreeWTmatch = FALSE,
                         collapseToWTForward = FALSE,
                         collapseToWTReverse = FALSE,
                         mutatedPhredMinForward = 0.0,
                         mutatedPhredMinReverse = 0.0,
                         mutNameDelimiter = ""."",
                         constantMaxDistForward = -1,
                         constantMaxDistReverse = -1,
                         umiCollapseMaxDist = 0.0,
                         filteredReadsFastqForward = """",
                         filteredReadsFastqReverse = """",
                         maxNReads = -1, verbose = FALSE,
                         nThreads = 1, chunkSize = 100000,
                         maxReadLength = 1024) {
    ## pre-flight checks ------------------------------------------------------
    ## fastq files exist
    if (length(fastqForward) < 1 || !all(file.exists(fastqForward)) ||
        (!is.null(fastqReverse) && 
         (length(fastqReverse) != length(fastqForward) ||
          !all(file.exists(fastqReverse))))) {
        stop(""'fastqForward' and 'fastqReverse' must point to one or "", 
             ""several matching existing files"")
    }
    if (is.null(fastqReverse)) {
        fastqReverse <- rep("""", length(fastqForward))
    }
    ## Convert file paths to canonical form, expand ~ and complete 
    ## relative paths
    fastqForward <- normalizePath(fastqForward, mustWork = FALSE)
    fastqReverse <- normalizePath(fastqReverse, mustWork = FALSE)
    
    ## merging/rev complementing arguments are ok
    .assertScalar(x = mergeForwardReverse, type = ""logical"")
    .assertScalar(x = revComplForward, type = ""logical"")
    .assertScalar(x = revComplReverse, type = ""logical"")
    if (any(fastqReverse == """") && mergeForwardReverse) {
        stop(""Both forward and reverse FASTQ files must be given in order to "",
             ""merge forward and reverse reads"")
    }
    
    ## check numeric inputs
    .assertVector(x = elementLengthsForward, type = ""numeric"",
                  rngIncl = c(0, Inf), validValues = -1)
    .assertVector(x = elementLengthsReverse, type = ""numeric"",
                  rngIncl = c(0, Inf), validValues = -1)
    .assertScalar(x = minOverlap, type = ""numeric"", rngIncl = c(0, Inf))
    .assertScalar(x = maxOverlap, type = ""numeric"", rngIncl = c(0, Inf))
    .assertScalar(x = minMergedLength, type = ""numeric"", rngIncl = c(0, Inf))
    .assertScalar(x = maxMergedLength, type = ""numeric"", rngIncl = c(0, Inf))
    .assertScalar(x = avePhredMinForward, type = ""numeric"", 
                  rngIncl = c(0, Inf))
    .assertScalar(x = avePhredMinReverse, type = ""numeric"", 
                  rngIncl = c(0, Inf))
    .assertScalar(x = variableNMaxForward, type = ""numeric"", 
                  rngIncl = c(0, Inf))
    .assertScalar(x = variableNMaxReverse, type = ""numeric"", 
                  rngIncl = c(0, Inf))
    .assertScalar(x = umiNMax, type = ""numeric"", rngIncl = c(0, Inf))
    .assertScalar(x = nbrMutatedCodonsMaxForward, type = ""numeric"",
                  rngIncl = c(0, Inf), validValues = -1)
    .assertScalar(x = nbrMutatedCodonsMaxReverse, type = ""numeric"",
                  rngIncl = c(0, Inf), validValues = -1)
    .assertScalar(x = nbrMutatedBasesMaxForward, type = ""numeric"",
                  rngIncl = c(0, Inf), validValues = -1)
    .assertScalar(x = nbrMutatedBasesMaxReverse, type = ""numeric"",
                  rngIncl = c(0, Inf), validValues = -1)
    .assertScalar(x = mutatedPhredMinForward, type = ""numeric"", 
                  rngIncl = c(0, Inf))
    .assertScalar(x = mutatedPhredMinReverse, type = ""numeric"", 
                  rngIncl = c(0, Inf))
    .assertScalar(x = constantMaxDistForward, type = ""numeric"",
                  rngIncl = c(0, Inf), validValues = -1)
    .assertScalar(x = constantMaxDistReverse, type = ""numeric"",
                  rngIncl = c(0, Inf), validValues = -1)
    .assertScalar(x = umiCollapseMaxDist, type = ""numeric"", 
                  rngIncl = c(0, Inf))
    .assertScalar(x = maxNReads, type = ""numeric"", rngIncl = c(0, Inf),
                  validValues = -1)
    .assertScalar(x = nThreads, type = ""numeric"", rngExcl = c(0, Inf))
    .assertScalar(x = chunkSize, type = ""numeric"", rngExcl = c(0, Inf))
    .assertScalar(x = maxReadLength, type = ""numeric"", rngExcl = c(0, Inf))
    
    ## If a wildtype sequence is provided, 
    ## it must be unambiguous how to identify and name mutants
    if (any(wildTypeForward != """")) {
        if ((nbrMutatedCodonsMaxForward == (-1) && 
             nbrMutatedBasesMaxForward == (-1)) ||
            (nbrMutatedCodonsMaxForward != (-1) && 
             nbrMutatedBasesMaxForward != (-1))) {
            stop(""Exactly one of 'nbrMutatedCodonsMaxForward' and "", 
                 ""'nbrMutatedBasesMaxForward' must be -1"")
        }
    }
    if (any(wildTypeReverse != """")) {
        if ((nbrMutatedCodonsMaxReverse == (-1) && 
             nbrMutatedBasesMaxReverse == (-1)) ||
            (nbrMutatedCodonsMaxReverse != (-1) && 
             nbrMutatedBasesMaxReverse != (-1))) {
            stop(""Exactly one of 'nbrMutatedCodonsMaxReverse' and "", 
                 ""'nbrMutatedBasesMaxReverse' must be -1"")
        }
    }
    
    ## adapters must be strings, valid DNA characters
    if (length(adapterForward) != 1 || !is.character(adapterForward) ||
        !grepl(""^[AaCcGgTt]*$"", adapterForward) || 
        length(adapterReverse) != 1 ||
        !is.character(adapterReverse) || 
        !grepl(""^[AaCcGgTt]*$"", adapterReverse)) {
        stop(""Adapters must be character strings, only containing "", 
             ""valid DNA characters"")
    } else {
        adapterForward <- toupper(adapterForward)
        adapterReverse <- toupper(adapterReverse)
    }
    
    ## Check provided read composition
    if (!(length(elementsForward) == 1 && is.character(elementsForward))) {
        stop(""'elementsForward' must be a character scalar"")
    }
    if (!(length(elementsReverse) == 1 && is.character(elementsReverse))) {
        stop(""'elementsReverse' must be a character scalar"")
    }
    
    if (nchar(elementsForward) == 0) {
        stop(""'elementsForward' must be a non-empty character scalar"")
    }
    if (any(fastqReverse != """") && nchar(elementsReverse) == 0) {
        stop(""'elementsReverse' must be a non-empty character scalar"")
    }
    
    if (!all(strsplit(elementsForward, """")[[1]] %in% 
             c(""C"", ""U"", ""S"", ""V"", ""P""))) {
        stop(""'elementsForward' can only contain letters 'CUSVP'"")
    }
    if (!all(strsplit(elementsReverse, """")[[1]] %in% 
             c(""C"", ""U"", ""S"", ""V"", ""P""))) {
        stop(""'elementsReverse' can only contain letters 'CUSVP'"")
    }
    
    if (nchar(elementsForward) != length(elementLengthsForward)) {
        stop(""'elementsForward' and 'elementsLengthsForward' must "", 
             ""have the same length"")
    }
    if (nchar(elementsReverse) != length(elementLengthsReverse)) {
        stop(""'elementsReverse' and 'elementsLengthsReverse' must "", 
             ""have the same length"")
    }
    
    ## Max one 'P'
    PposFwd <- gregexpr(pattern = ""P"", elementsForward, fixed = TRUE)[[1]]
    PposRev <- gregexpr(pattern = ""P"", elementsReverse, fixed = TRUE)[[1]]
    
    if (length(PposFwd) > 1) {
        stop(""'elementsForward' can contain max one 'P'"")
    }
    if (length(PposRev) > 1) {
        stop(""'elementsReverse' can contain max one 'P'"")
    }
    
    ## If no 'P', max one -1 length
    if (length(PposFwd) == 1 && PposFwd == -1 &&
        sum(elementLengthsForward == -1) > 1) {
        stop(""Max one element length (forward) can be -1"")
    }
    if (length(PposRev) == 1 && PposRev == -1 &&
        sum(elementLengthsReverse == -1) > 1) {
        stop(""Max one element length (reverse) can be -1"")
    }
    
    ## If a 'P', max one -1 length on each side
    if (length(PposFwd) == 1 && PposFwd != -1) {
        if ((PposFwd != 1 && 
             sum(elementLengthsForward[seq_len(PposFwd - 1)] == -1) > 1) ||
            (PposFwd != nchar(elementsForward) &&
             sum(elementLengthsForward[(PposFwd + 1):nchar(elementsForward)] == -1) > 1)) {
            stop(""Max one element length (forward) on each side "", 
                 ""of the primer can be -1"")
        }
    }
    if (length(PposRev) == 1 && PposRev != -1) {
        if ((PposRev != 1 && 
             sum(elementLengthsReverse[seq_len(PposRev - 1)] == -1) > 1) ||
            (PposRev != nchar(elementsReverse) &&
             sum(elementLengthsReverse[(PposRev + 1):nchar(elementsReverse)] == -1) > 1)) {
            stop(""Max one element length (reverse) on each side "", 
                 ""of the primer can be -1"")
        }
    }
    
    if (!is.character(primerForward) || length(primerForward) < 1 ||
        !all(grepl(""^[AaCcGgTt]*$"", primerForward)) || 
        !is.character(primerReverse) ||
        length(primerReverse) < 1 || 
        !all(grepl(""^[AaCcGgTt]*$"", primerReverse))) {
        stop(""Primers must be character vectors, "", 
             ""only containing valid DNA characters"")
    } else {
        primerForward <- vapply(primerForward, toupper, """")
        primerReverse <- vapply(primerReverse, toupper, """")
    }
    
    ## if wild type sequence is a string, make it into a vector
    if (length(wildTypeForward) == 1 && is.null(names(wildTypeForward))) {
        wildTypeForward <- c(f = wildTypeForward)
    }
    if (length(wildTypeReverse) == 1 && is.null(names(wildTypeReverse))) {
        wildTypeReverse <- c(r = wildTypeReverse)
    }
    
    ## wild type sequences must be given in named vectors
    if (any(is.null(names(wildTypeForward))) || 
        any(names(wildTypeForward) == """") ||
        any(is.null(names(wildTypeReverse))) || 
        any(names(wildTypeReverse) == """")) {
        stop('wild type sequences must be given in named vectors')
    }
    
    ## wild type sequences must be strings, valid DNA characters
    if (!all(vapply(wildTypeForward, is.character, FALSE)) || 
        !all(vapply(wildTypeForward, length, 0) == 1) ||
        !all(vapply(wildTypeForward, 
                    function(w) grepl(""^[AaCcGgTt]*$"", w), FALSE)) ||
        !all(vapply(wildTypeReverse, is.character, FALSE)) || 
        !all(vapply(wildTypeReverse, length, 0) == 1) ||
        !all(vapply(wildTypeReverse, 
                    function(w) grepl(""^[AaCcGgTt]*$"", w), FALSE))) {
        stop(""wild type sequences must be character strings, "",
             ""only containing valid DNA characters"")
    } else {
        wildTypeForward <- toupper(wildTypeForward)
        wildTypeReverse <- toupper(wildTypeReverse)
    }
    
    ## all wild type sequences should be unique
    if (any(duplicated(wildTypeForward)) ||
        any(duplicated(wildTypeReverse))) {
        stop(""Duplicated wild type sequences are not allowed"")
    }
    
    ## cis experiment - should not have wildTypeReverse
    if (mergeForwardReverse && any(vapply(wildTypeReverse, nchar, 1) > 0)) {
        warning(""Ignoring 'wildTypeReverse' when forward and "", 
                ""reverse reads are merged"")
        wildTypeReverse <- c(r = """")
    }
    
    ## if both constantForward and constantLengthForward are given, check that
    ## the lengths inferred from the two are consistent
    if (!is.character(constantForward) || !is.character(constantReverse) ||
        length(constantForward) < 1 || length(constantReverse) < 1 ||
        !all(grepl(""^[AaCcGgTt]*$"", constantForward)) ||
        !all(grepl(""^[AaCcGgTt]*$"", constantReverse))) {
        stop(""constant sequences must be character strings, "",
             ""only containing valid DNA characters."")
    } else {
        constantForward <- toupper(constantForward)
        constantReverse <- toupper(constantReverse)
    }
    
    if (!all(vapply(constantForward, nchar, 0L) == 
             nchar(constantForward[1])) ||
        !all(vapply(constantReverse, nchar, 0L) == 
             nchar(constantReverse[1]))) {
        stop(""All constant sequences must be of the same length"")
    }
    
    CposFwd <- gregexpr(pattern = ""C"", elementsForward)[[1]]
    if (nchar(constantForward[1]) > 0 &&
        sum(CposFwd) != -1 && 
        sum(elementLengthsForward[CposFwd]) != nchar(constantForward[1])) {
        stop(""The sum of the constant sequence lengths in elementsForward ("",
             sum(elementLengthsForward[CposFwd]),
             "") does not correspond to the length of the given "", 
             ""'constantForward' ("",
             nchar(constantForward[1]), "")"")
    }
    CposRev <- gregexpr(pattern = ""C"", elementsReverse)[[1]]
    if (nchar(constantReverse[1]) > 0 &&
        sum(CposRev) != -1 && 
        sum(elementLengthsReverse[CposRev]) != nchar(constantReverse[1])) {
        stop(""The sum of the constant sequence lengths in elementsReverse ("",
             sum(elementLengthsReverse[CposRev]),
             "") does not correspond to the length of the given "", 
             ""'constantReverse' ("",
             nchar(constantReverse[1]), "")"")
    }
    
    if (!all(is.character(forbiddenMutatedCodonsForward)) ||
        !all(grepl(""^[ACGTMRWSYKVHDBN]{3}$"", 
                   toupper(forbiddenMutatedCodonsForward)) |
             forbiddenMutatedCodonsForward == """") ||
        !all(is.character(forbiddenMutatedCodonsReverse)) ||
        !all(grepl(""^[ACGTMRWSYKVHDBN]{3}$"", 
                   toupper(forbiddenMutatedCodonsReverse)) |
             forbiddenMutatedCodonsReverse == """")) {
        stop(""All elements of 'forbiddenMutatedCodonsForward' and "", 
             ""'forbiddenMutatedCodonsReverse' must be "",
             ""character strings consisting of three valid IUPAC letters."")
    } else {
        forbiddenMutatedCodonsForward <- toupper(forbiddenMutatedCodonsForward)
        forbiddenMutatedCodonsReverse <- toupper(forbiddenMutatedCodonsReverse)
    }
    
    if (!is.logical(useTreeWTmatch) || length(useTreeWTmatch) != 1) {
        stop(""'useTreeWTmatch' must be a logical scalar."")
    }
    
    if (!is.logical(collapseToWTForward) || length(collapseToWTForward) != 1) {
        stop(""'collapseToWTForward' must be a logical scalar."")
    }
    if (!is.logical(collapseToWTReverse) || length(collapseToWTReverse) != 1) {
        stop(""'collapseToWTReverse' must be a logical scalar."")
    }
    
    ## mutNameDelimiter must be a single character, and can not 
    ## appear in any of the WT sequence names
    if (!is.character(mutNameDelimiter) || length(mutNameDelimiter) != 1 ||
        nchar(mutNameDelimiter) != 1 || mutNameDelimiter == ""_"") {
        stop(""'mutNameDelimiter' must be a single-letter character scalar, "", 
             ""not equal to '_'"")
    }
    if (any(grepl(mutNameDelimiter, 
                  c(names(wildTypeForward), names(wildTypeReverse)), 
                  fixed = TRUE))) {
        stop(""'mutNameDelimiter' can not appear in the name of any of "", 
             ""the provided wild type sequences."")
    }
    
    if (!is.character(filteredReadsFastqForward) || 
        !is.character(filteredReadsFastqReverse) ||
        length(filteredReadsFastqForward) != 1 || 
        length(filteredReadsFastqReverse) != 1 ||
        (filteredReadsFastqForward != """" && 
         !grepl(""\\.gz$"", filteredReadsFastqForward)) ||
        (filteredReadsFastqReverse != """" && 
         !grepl(""\\.gz$"", filteredReadsFastqReverse))) {
        stop(""'filteredReadsFastqForward' and 'filteredReadsFastqReverse' "", 
             ""must be character scalars ending with .gz."")
    }
    ## If path is """", it will still be """"
    filteredReadsFastqForward <- 
        normalizePath(filteredReadsFastqForward, mustWork = FALSE)
    filteredReadsFastqReverse <- 
        normalizePath(filteredReadsFastqReverse, mustWork = FALSE)
    
    if ((any(fastqReverse == """") && filteredReadsFastqReverse != """") ||
        (all(fastqReverse != """") && filteredReadsFastqForward != """" && 
         filteredReadsFastqReverse == """") ||
        (all(fastqForward != """") && filteredReadsFastqForward == """" && 
         filteredReadsFastqReverse != """")) {
        stop(""The pairing of the output FASTQ files must be compatible "", 
             ""with that of the input files."")
    }
    
    .assertScalar(x = verbose, type = ""logical"")
    
    ## call digestFastqsCpp ---------------------------------------------------
    ## Represent the wildtype sequences as pairs of vectors (one with 
    ## sequences, one with names), to make things faster on the C++ side
    wildTypeForwardNames <- names(wildTypeForward)
    wildTypeForward <- unname(wildTypeForward)
    wildTypeReverseNames <- names(wildTypeReverse)
    wildTypeReverse <- unname(wildTypeReverse)
    
    res <- digestFastqsCpp(
        fastqForwardVect = fastqForward,
        fastqReverseVect = fastqReverse,
        mergeForwardReverse = mergeForwardReverse,
        minOverlap = minOverlap,
        maxOverlap = maxOverlap,
        minMergedLength = minMergedLength,
        maxMergedLength = maxMergedLength,
        maxFracMismatchOverlap = maxFracMismatchOverlap,
        greedyOverlap = greedyOverlap,
        revComplForward = revComplForward,
        revComplReverse = revComplReverse,
        elementsForward = elementsForward,
        elementLengthsForward = as.numeric(elementLengthsForward),
        elementsReverse = elementsReverse,
        elementLengthsReverse = as.numeric(elementLengthsReverse),
        adapterForward = adapterForward,
        adapterReverse = adapterReverse,
        primerForward = primerForward,
        primerReverse = primerReverse,
        wildTypeForward = wildTypeForward,
        wildTypeForwardNames = wildTypeForwardNames,
        wildTypeReverse = wildTypeReverse,
        wildTypeReverseNames = wildTypeReverseNames,
        constantForward = constantForward,
        constantReverse = constantReverse,
        avePhredMinForward = avePhredMinForward,
        avePhredMinReverse = avePhredMinReverse,
        variableNMaxForward = variableNMaxForward,
        variableNMaxReverse = variableNMaxReverse,
        umiNMax = umiNMax,
        nbrMutatedCodonsMaxForward = nbrMutatedCodonsMaxForward,
        nbrMutatedCodonsMaxReverse = nbrMutatedCodonsMaxReverse,
        nbrMutatedBasesMaxForward = nbrMutatedBasesMaxForward,
        nbrMutatedBasesMaxReverse = nbrMutatedBasesMaxReverse,
        forbiddenMutatedCodonsForward = forbiddenMutatedCodonsForward,
        forbiddenMutatedCodonsReverse = forbiddenMutatedCodonsReverse,
        useTreeWTmatch = useTreeWTmatch,
        collapseToWTForward = collapseToWTForward,
        collapseToWTReverse = collapseToWTReverse,
        mutatedPhredMinForward = mutatedPhredMinForward,
        mutatedPhredMinReverse = mutatedPhredMinReverse,
        mutNameDelimiter = mutNameDelimiter,
        constantMaxDistForward = constantMaxDistForward,
        constantMaxDistReverse = constantMaxDistReverse,
        umiCollapseMaxDist = umiCollapseMaxDist,
        filteredReadsFastqForward = filteredReadsFastqForward,
        filteredReadsFastqReverse = filteredReadsFastqReverse,
        maxNReads = maxNReads,
        verbose = verbose,
        nThreads = as.integer(nThreads),
        chunkSize = as.integer(chunkSize),
        maxReadLength = maxReadLength)
    
    ## Add package version and processing date --------------------------------
    res$parameters$processingInfo <- paste0(
        ""Processed by mutscan v"", utils::packageVersion(""mutscan""), "" on "",
        Sys.time()
    )
    res
}
","R"
"Assay","fmicompbio/mutscan","R/RcppExports.R",".R","8351","130","# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' Calculate distances to the nearest string
#' 
#' Given a character vector, calculate the distance for each element
#' to the nearest neighbor amongst all the other elements.
#' 
#' @param x A character vector.
#' @param metric A character scalar defining the string distance metric. One
#'   of \code{""hamming""} (default), \code{""hamming_shift""} or
#'   \code{""levenshtein""}.
#' @param nThreads numeric(1), number of threads to use for parallel processing.
#' 
#' @return An integer vector of the same length as \code{x}.
#' 
#' @examples
#' calcNearestStringDist(c(""lazy"", ""hazy"", ""crazy""))
#' calcNearestStringDist(c(""lazy"", ""hazy"", ""crazy""), metric = ""hamming_shift"")
#' calcNearestStringDist(c(""lazy"", ""hazy"", ""crazy""), metric = ""levenshtein"")
#' 
#' @export
calcNearestStringDist <- function(x, metric = ""hamming"", nThreads = 1L) {
    .Call(`_mutscan_calcNearestStringDist`, x, metric, nThreads)
}

complement <- function(n) {
    .Call(`_mutscan_complement`, n)
}

compareCodonPositions <- function(a, b, mutNameDelimiter) {
    .Call(`_mutscan_compareCodonPositions`, a, b, mutNameDelimiter)
}

translateString <- function(s) {
    .Call(`_mutscan_translateString`, s)
}

makeBaseHGVS <- function(mutationsSorted, mutNameDelimiter, wtSeq, varSeq) {
    .Call(`_mutscan_makeBaseHGVS`, mutationsSorted, mutNameDelimiter, wtSeq, varSeq)
}

test_makeAAHGVS <- function(mutationsSorted, mutNameDelimiter, wtSeq) {
    .Call(`_mutscan_test_makeAAHGVS`, mutationsSorted, mutNameDelimiter, wtSeq)
}

test_compareToWildtype <- function(varSeq, wtSeq, varIntQual, forbiddenCodons_vect, mutatedPhredMin = 0.0, nbrMutatedCodonsMax = -1L, codonPrefix = ""c"", nbrMutatedBasesMax = -1L, mutNameDelimiter = ""."", collapseToWT = FALSE) {
    .Call(`_mutscan_test_compareToWildtype`, varSeq, wtSeq, varIntQual, forbiddenCodons_vect, mutatedPhredMin, nbrMutatedCodonsMax, codonPrefix, nbrMutatedBasesMax, mutNameDelimiter, collapseToWT)
}

test_decomposeRead <- function(sseq, squal, elements, elementLengths, primerSeqs, umiSeq, varSeq, varQual, varLengths, constSeq, constQual, nNoPrimer, nReadWrongLength) {
    .Call(`_mutscan_test_decomposeRead`, sseq, squal, elements, elementLengths, primerSeqs, umiSeq, varSeq, varQual, varLengths, constSeq, constQual, nNoPrimer, nReadWrongLength)
}

test_mergeReadPairPartial <- function(seqF, qualF, seqR, qualR, lenF, lenR, minOverlap = 0L, maxOverlap = 0L, minMergedLength = 0L, maxMergedLength = 0L, maxFracMismatchOverlap = 0, greedy = TRUE) {
    .Call(`_mutscan_test_mergeReadPairPartial`, seqF, qualF, seqR, qualR, lenF, lenR, minOverlap, maxOverlap, minMergedLength, maxMergedLength, maxFracMismatchOverlap, greedy)
}

findClosestRefSeq <- function(varSeq, wtSeq, upperBoundMismatch, sim) {
    .Call(`_mutscan_findClosestRefSeq`, varSeq, wtSeq, upperBoundMismatch, sim)
}

findClosestRefSeqEarlyStop <- function(varSeq, wtSeq, upperBoundMismatch, sim) {
    .Call(`_mutscan_findClosestRefSeqEarlyStop`, varSeq, wtSeq, upperBoundMismatch, sim)
}

#' Create a conversion table for collapsing similar sequences
#' @param seqs Character vector with nucleotide sequences (or pairs of
#' sequences concatenated with ""_"") to be collapsed. The sequences must
#' all be of the same length.
#' @param scores Numeric vector of ""scores"" for the sequences. Typically
#' the total read/UMI count. A higher score will be preferred when
#' deciding which sequence to use as the representative for a group of
#' collapsed sequences.
#' @param collapseMaxDist Numeric scalar defining the tolerance for collapsing
#' similar sequences. If the value is in [0, 1), it defines the maximal
#' Hamming distance in terms of a fraction of sequence length:
#' (\code{round(collapseMaxDist * nchar(sequence))}).
#' A value greater or equal to 1 is rounded and directly used as the maximum
#' allowed Hamming distance. Note that sequences can only be
#' collapsed if they are all of the same length. The default value is 0.
#' @param collapseMinScore Numeric scalar, indicating the minimum score
#' required for a sequence to be considered as a representative for a
#' group of similar sequences (i.e., to allow other sequences to be
#' collapsed into it). The default value is 0.
#' @param collapseMinRatio Numeric scalar. During collapsing of
#' similar sequences, a low-frequency sequence will be collapsed
#' with a higher-frequency sequence only if the ratio between the
#' high-frequency and the low-frequency scores is at least this
#' high. A value of 0 indicates that no such check is performed.
#' @param verbose Logical scalar, whether to print progress messages.
#'
#' @return A data.frame with two columns, containing the input sequences
#' and the representatives for the groups resulting from grouping similar
#' sequences, respectively.
#'
#' @examples
#' seqs <- c(""AACGTAGCA"", ""ACCGTAGCA"", ""AACGGAGCA"", ""ATCGGAGCA"", ""TGAGGCATA"")
#' scores <- c(5, 1, 3, 1, 8)
#' groupSimilarSequences(seqs = seqs, scores = scores,
#'                       collapseMaxDist = 1, collapseMinScore = 0,
#'                       collapseMinRatio = 0, verbose = FALSE)
#'
#' @export
#' @author Michael Stadler, Charlotte Soneson
groupSimilarSequences <- function(seqs, scores, collapseMaxDist = 0.0, collapseMinScore = 0.0, collapseMinRatio = 0.0, verbose = FALSE) {
    .Call(`_mutscan_groupSimilarSequences`, seqs, scores, collapseMaxDist, collapseMinScore, collapseMinRatio, verbose)
}

digestFastqsCpp <- function(fastqForwardVect, fastqReverseVect, mergeForwardReverse, minOverlap, maxOverlap, minMergedLength, maxMergedLength, maxFracMismatchOverlap, greedyOverlap, revComplForward, revComplReverse, elementsForward, elementLengthsForward, elementsReverse, elementLengthsReverse, adapterForward, adapterReverse, primerForward, primerReverse, wildTypeForward, wildTypeForwardNames, wildTypeReverse, wildTypeReverseNames, constantForward, constantReverse, avePhredMinForward = 20.0, avePhredMinReverse = 20.0, variableNMaxForward = 0L, variableNMaxReverse = 0L, umiNMax = 0L, nbrMutatedCodonsMaxForward = 1L, nbrMutatedCodonsMaxReverse = 1L, nbrMutatedBasesMaxForward = -1L, nbrMutatedBasesMaxReverse = -1L, forbiddenMutatedCodonsForward = ""NNW"", forbiddenMutatedCodonsReverse = ""NNW"", useTreeWTmatch = FALSE, collapseToWTForward = FALSE, collapseToWTReverse = FALSE, mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0, mutNameDelimiter = ""."", constantMaxDistForward = -1L, constantMaxDistReverse = -1L, umiCollapseMaxDist = 0.0, filteredReadsFastqForward = """", filteredReadsFastqReverse = """", maxNReads = -1L, verbose = FALSE, nThreads = 1L, chunkSize = 100000L, maxReadLength = 1024L) {
    .Call(`_mutscan_digestFastqsCpp`, fastqForwardVect, fastqReverseVect, mergeForwardReverse, minOverlap, maxOverlap, minMergedLength, maxMergedLength, maxFracMismatchOverlap, greedyOverlap, revComplForward, revComplReverse, elementsForward, elementLengthsForward, elementsReverse, elementLengthsReverse, adapterForward, adapterReverse, primerForward, primerReverse, wildTypeForward, wildTypeForwardNames, wildTypeReverse, wildTypeReverseNames, constantForward, constantReverse, avePhredMinForward, avePhredMinReverse, variableNMaxForward, variableNMaxReverse, umiNMax, nbrMutatedCodonsMaxForward, nbrMutatedCodonsMaxReverse, nbrMutatedBasesMaxForward, nbrMutatedBasesMaxReverse, forbiddenMutatedCodonsForward, forbiddenMutatedCodonsReverse, useTreeWTmatch, collapseToWTForward, collapseToWTReverse, mutatedPhredMinForward, mutatedPhredMinReverse, mutNameDelimiter, constantMaxDistForward, constantMaxDistReverse, umiCollapseMaxDist, filteredReadsFastqForward, filteredReadsFastqReverse, maxNReads, verbose, nThreads, chunkSize, maxReadLength)
}

mergeValues <- function(mutNamesIn, valuesIn, delimiter = ',') {
    .Call(`_mutscan_mergeValues`, mutNamesIn, valuesIn, delimiter)
}

levenshtein_distance <- function(str1, str2, ignored_variable = -1L) {
    .Call(`_mutscan_levenshtein_distance`, str1, str2, ignored_variable)
}

hamming_distance <- function(str1, str2, ignored_variable = -1L) {
    .Call(`_mutscan_hamming_distance`, str1, str2, ignored_variable)
}

hamming_shift_distance <- function(str1, str2, max_abs_shift = -1L) {
    .Call(`_mutscan_hamming_shift_distance`, str1, str2, max_abs_shift)
}

","R"
"Assay","fmicompbio/mutscan","R/calculateRelativeFC.R",".R","8321","196","#' Calculate logFCs relative to WT using edgeR
#'
#' Calculate logFCs and associated p-values for a given comparison, using
#' either limma or the Negative Binomial quasi-likelihood framework of edgeR.
#' The observed counts for the WT variants can be used as offsets in the model.
#'
#' @param se SummarizedExperiment object.
#' @param design Design matrix. The rows of the design matrix must be in the
#'     same order as the columns in \code{se}.
#' @param coef Coefficient(s) to test with edgeR or limma.
#' @param contrast Numeric contrast to test with edgeR or limma.
#' @param WTrows Vector of row names that will be used as the reference when
#'     calculating logFCs and statistics. If more than one value is provided, 
#'     the sum of the corresponding counts is used to generate offsets. If 
#'     \code{NULL}, offsets will be defined as the effective library sizes 
#'     (using TMM normalization factors).
#' @param selAssay Assay to select from \code{se} for the analysis.
#' @param pseudocount Pseudocount to add when calculating log-fold changes.
#' @param method Either 'edgeR' or 'limma'. If set to 'limma', voom is used to
#'     transform the counts and estimate observation weights before applying
#'     limma. In this case, the results also contain the standard errors of the
#'     logFCs.
#' @param normMethod Character scalar indicating which normalization method
#'     should be used to calculate size factors. Should be either \code{""TMM""}
#'     or \code{""csaw""} when \code{WTrows} is \code{NULL}, and \code{""geomean""}
#'     or \code{""sum""} when \code{WTrows} is provided.
#'
#' @author Charlotte Soneson, Michael Stadler
#'
#' @export
#'
#' @return A \code{data.frame} with output from the statistical testing 
#' framework (edgeR or limma). 
#' 
#' @importFrom edgeR DGEList scaleOffset estimateDisp glmQLFit glmQLFTest
#'     topTags predFC topTags calcNormFactors getNormLibSizes
#' @importFrom SummarizedExperiment colData assay assayNames assays
#' @importFrom limma voom eBayes topTable lmFit contrasts.fit
#' @importFrom csaw normOffsets
#'
#' @examples
#' library(SummarizedExperiment)
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"",
#'                           package = ""mutscan""))[1:200, ]
#' design <- model.matrix(~ Replicate + Condition,
#'                        data = colData(se))
#'                               
#' ## Calculate ""absolute"" log-fold changes with edgeR
#' res <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"", 
#'                            method = ""edgeR"")
#' head(res)
#' ## Calculate log-fold changes relative to the WT sequence with edgeR
#' stopifnot(""f.0.WT"" %in% rownames(se))
#' res <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"", 
#'                            method = ""edgeR"", WTrows = ""f.0.WT"")
#' head(res)
#' 
#' ## Calculate ""absolute"" log-fold changes with limma
#' res <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"", 
#'                            method = ""limma"")
#' head(res)
#' ## Calculate log-fold changes relative to the WT sequence with limma
#' stopifnot(""f.0.WT"" %in% rownames(se))
#' res <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"", 
#'                            method = ""limma"", WTrows = ""f.0.WT"")
#' head(res)
#'
calculateRelativeFC <- function(se, design, coef = NULL, contrast = NULL,
                                WTrows = NULL, selAssay = ""counts"",
                                pseudocount = 1, method = ""edgeR"",
                                normMethod = ifelse(is.null(WTrows),
                                                    ""TMM"", ""sum"")) {
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertScalar(x = selAssay, type = ""character"")
    if (!(selAssay %in% assayNames(se))) {
        if (is.null(assayNames(se)) &&
            length(assays(se)) == 1) {
            warning(""No assayNames provided in 'se', but only one "",
                    ""assay present - using that."")
            selAssay <- 1L
        } else {
            stop(""The provided 'selAssay' not present in 'se'."")
        }
    }
    
    if (!is.null(WTrows)) {
        .assertVector(x = WTrows, type = ""character"", 
                      validValues = rownames(se))
    }
    
    if (nrow(design) != ncol(se)) {
        stop(""The number of rows in 'design' ("", nrow(design),
             "") is not equal to the number"",
             "" of columns in 'se' ("", ncol(se), "")."")
    }
    
    .assertScalar(x = pseudocount, type = ""numeric"", rngIncl = c(0, Inf))
    
    .assertScalar(x = method, type = ""character"",
                  validValues = c(""edgeR"", ""limma""))
    
    if (normMethod %in% c(""csaw"", ""TMM"") && !is.null(WTrows)) {
        stop(""normMethod = '"", normMethod,
             ""' can only be used when WTrows is NULL."")
    }
    
    if (normMethod %in% c(""sum"", ""geomean"") && is.null(WTrows)) {
        stop(""normMethod = '"", normMethod,
             ""' can only be used when WTrows is not NULL."")
    }
    
    if (normMethod == ""csaw"" && method == ""limma"") {
        stop(""normMethod = 'csaw' can only be used with method = 'edgeR'."")
    }
    
    .assertScalar(x = normMethod, type = ""character"",
                  validValues = c(""csaw"", ""TMM"", ""geomean"", ""sum""))
    
    if (is.null(coef) && is.null(contrast)) {
        stop(""'coef' and 'contrast' can not both be NULL."")
    }
    
    if (!is.null(contrast) && !is.null(dim(contrast))) {
        stop(""'contrast' must be a vector."")
    }
    
    ## Create DGEList from SummarizedExperiment
    dge <- DGEList(
        counts = as.matrix(assay(se, selAssay)),
        samples = colData(se))
    if (normMethod == ""csaw"") {
        ## csaw normalization - also calculate normalization factors since
        ## aveLogCPM does not use provided offsets
        ## In this case, we know that WTrows is NULL, so all features
        ## will be used for the normalization
        dge <- calcNormFactors(dge)
        dge <- normOffsets(dge)
    } else if (normMethod == ""TMM"") {
        ## TMM normalization, with all features
        dge <- calcNormFactors(dge)
    } else if (normMethod == ""geomean"") {
        ## Use size factors (offsets) derived from the geometric mean
        ## of the WT rows
        tmp0 <- dge$counts[WTrows, , drop = FALSE]
        tmp0 <- tmp0[apply(tmp0, 1, min) > 0, , drop = FALSE]
        logoffsets <- apply(tmp0, 2, function(s) mean(log(s)))
        dge <- scaleOffset(dge, logoffsets)
    } else if (normMethod == ""sum"") {
        ## Use size factors (offsets) derived from the sum of the
        ## WT rows
        tmp0 <- dge$counts[WTrows, , drop = FALSE]
        dge <- scaleOffset(dge, log(colSums(tmp0)))
    }
    
    ## Fit model and perform test
    if (method == ""edgeR"") {
        dge <- estimateDisp(dge, design = design)
        fit <- glmQLFit(dge, design = design)
        qlf <- glmQLFTest(fit, coef = coef, contrast = contrast)
        tt <- topTags(qlf, n = Inf, sort.by = ""none"")$table
        ## Calculate shrunken fold changes. Only when testing
        ## a single coefficient or contrast
        predfc <- predFC(dge, design = design, 
                         prior.count = pseudocount)
        if (length(coef) == 1 && is.null(contrast)) {
            tt$logFC_shrunk <- predfc[, coef]
        } else if (!is.null(contrast)) {
            tt$logFC_shrunk <- c(predfc %*% cbind(contrast))
        }
        tt$df.total <- qlf$df.total
        tt$df.prior <- qlf$df.prior
        tt$df.test <- qlf$df.test
    } else if (method == ""limma"") {
        if (!is.null(dge$offset)) {
            vm <- voom(dge, design = design, lib.size = exp(dge$offset))
        } else {
            vm <- voom(dge, design = design,
                       lib.size = getNormLibSizes(dge))
        }
        fit <- lmFit(vm, design = design)
        if (!is.null(contrast)) {
            fit <- contrasts.fit(fit, contrasts = contrast)
            coef <- 1
        }
        fit <- eBayes(fit)
        tt <- topTable(fit, coef = coef,
                       confint = TRUE, number = Inf, sort.by = ""none"")
        if (length(coef) == 1) {
            tt$se.logFC <- sqrt(fit$s2.post) * fit$stdev.unscaled[, coef]
        }
        tt$df.total <- fit$df.total
        tt$df.prior <- fit$df.prior
    }
    tt
}
","R"
"Assay","fmicompbio/mutscan","R/utils.R",".R","8348","216","# This script is provided as a utility via the swissknife package 
# (https://github.com/fmicompbio/swissknife). This script is provided under 
# the MIT license, and package authors are permitted to 
# include the code as-is in other packages, as long as this note and the 
# information provided below crediting the authors of the respective 
# functions is retained. 

#' Utility function to check validity of scalar variable values.
#' 
#' This function provides a convenient way e.g. to check that provided
#' arguments to functions satisfy required criteria. 
#'  
#' @param x The variable to be checked.
#' @param type The desired type of \code{x}.
#' @param rngIncl The allowed range of the (numeric) variable \code{x}, 
#'     including the endpoints.
#' @param rngExcl The allowed range of the (numeric) variable \code{x}, 
#'     excluding the endpoints.
#' @param validValues A vector with the allowed values of \code{x}.
#' @param allowNULL Logical, whether or not \code{NULL} is an acceptable 
#'     value for \code{x}.
#' 
#' @author Michael Stadler, Charlotte Soneson
#' @noRd
#' @keywords internal
#' @importFrom methods is
.assertScalar <- function(x,
                          type = NULL,
                          rngIncl = NULL,
                          rngExcl = NULL,
                          validValues = NULL,
                          allowNULL = FALSE) {
    
    .assertVector(x = x, type = type, rngIncl = rngIncl,
                  rngExcl = rngExcl, validValues = validValues,
                  len = 1, rngLen = NULL, allowNULL = allowNULL)
    
}

#' Utility function to check validity of vector variable values.
#' 
#' This function provides a convenient way e.g. to check that provided
#' arguments to functions satisfy required criteria. 
#'  
#' @param x The variable to be checked
#' @param type The desired type of \code{x}.
#' @param rngIncl The allowed range of the (numeric) variable \code{x}, 
#'     including the endpoints.
#' @param rngExcl The allowed range of the (numeric) variable \code{x}, 
#'     excluding the endpoints.
#' @param validValues A vector with the allowed values of \code{x}.
#' @param len The required length of \code{x}.
#' @param rngLen The allowed range for the length of \code{x}.
#' @param allowNULL Logical, whether or not \code{NULL} is an acceptable 
#'     value for \code{x}.
#' 
#' @author Michael Stadler, Charlotte Soneson
#' @noRd
#' @keywords internal
#' @importFrom methods is
.assertVector <- function(x,
                          type = NULL,
                          rngIncl = NULL,
                          rngExcl = NULL,
                          validValues = NULL,
                          len = NULL, 
                          rngLen = NULL,
                          allowNULL = FALSE) {
    sc <- sys.calls()
    mycall <- sc[[length(sc)]]
    if (length(sc) >= 2 &&
        identical(as.character(sc[[length(sc) - 1]])[1], "".assertScalar"")) {
        mycall <- sc[[length(sc) - 1]]
    }
    args <- lapply(mycall, as.character)[-1]
    xname <- if (""x"" %in% names(args)) args$x else ""argument""
    
    ## Check arguments
    stopifnot(is.null(type) || (length(type) == 1L && is.character(type)))
    stopifnot(is.null(rngIncl) || (length(rngIncl) == 2L && 
                                       is.numeric(rngIncl)))
    stopifnot(is.null(rngExcl) || (length(rngExcl) == 2L && 
                                       is.numeric(rngExcl)))
    stopifnot(is.null(len) || (length(len) == 1L && is.numeric(len)))
    stopifnot(is.null(rngLen) || (length(rngLen) == 2L && is.numeric(rngLen)))
    stopifnot(is.logical(allowNULL) && length(allowNULL) == 1L)
    if (!is.null(rngIncl) && !is.null(rngExcl)) {
        stop(""'rngIncl' and 'rngExcl' can not both be specified"")
    }
    
    ## If there are too many valid values, print only the first 15
    if (length(validValues) > 15) {
        vvPrint <- paste(c(validValues[seq_len(15)], 
                           ""...(truncated)""),
                         collapse = "", "")
    } else {
        vvPrint <- paste(validValues, collapse = "", "")
    }
    
    if (is.null(x)) {
        if (allowNULL) {
            return(invisible(TRUE))
        } else {
            stop(""'"", xname, ""' must not be NULL"", call. = FALSE)
        }
    }
    
    if (is.null(type) && (!is.null(rngIncl) || !is.null(rngExcl))) {
        type <- ""numeric""
    }
    
    if (!is.null(type) && !is(x, type)) {
        stop(""'"", xname, ""' must be of class '"", type, ""'"", call. = FALSE)
    }
    
    if (!is.null(rngIncl)) {
        if (!is.null(validValues)) {
            if (any((x < rngIncl[1] | x > rngIncl[2]) & 
                    !(x %in% validValues))) {
                stop(""'"", xname, ""' must be within ["", rngIncl[1], "","", 
                     rngIncl[2], ""] (inclusive), or one of: "", vvPrint,
                     call. = FALSE)
            }
        } else {
            if (any(x < rngIncl[1] | x > rngIncl[2])) {
                stop(""'"", xname, ""' must be within ["", rngIncl[1], "","", 
                     rngIncl[2], ""] (inclusive)"", call. = FALSE)
            }
        }
    } else if (!is.null(rngExcl)) {
        if (!is.null(validValues)) {
            if (any((x <= rngExcl[1] | x >= rngExcl[2]) & 
                    !(x %in% validValues))) {
                stop(""'"", xname, ""' must be within ("", rngExcl[1], "","", 
                     rngExcl[2], "") (exclusive), or one of: "", vvPrint,
                     call. = FALSE)
            }
        } else {
            if (any(x <= rngExcl[1] | x >= rngExcl[2])) {
                stop(""'"", xname, ""' must be within ("", rngExcl[1], "","", 
                     rngExcl[2], "") (exclusive)"", call. = FALSE)
            }
        }
    } else {
        if (!is.null(validValues) && !all(x %in% validValues)) {
            stop(""All values in '"", xname, ""' must be one of: "", vvPrint,
                 call. = FALSE)
        }
    }
    
    
    if (!is.null(len) && length(x) != len) {
        stop(""'"", xname, ""' must have length "", len, call. = FALSE)
    }
    
    if (!is.null(rngLen) && (length(x) < rngLen[1] || length(x) > rngLen[2])) {
        stop(""length of '"", xname, ""' must be within ["", rngLen[1], "","", 
             rngLen[2], ""] (inclusive)"", call. = FALSE)
    }
    
    return(invisible(TRUE))
}

#' Utility function that makes sure that packages are available
#' 
#' The function tries loading the namespaces of the packages given in
#' \code{pkgs}, and throws an exception with an informative error message if
#' that is not the case.
#' 
#' @param pkgs Character vector with package names. Can be either just a
#'   package name or a string of the form \code{""githubuser/packagename""} for
#'   packages hosted on GitHub.
#' @param suggestInstallation Logical scalar. If \code{TRUE}, include an
#'   expression to install the missing package(s) as part of the generated
#'   error message.
#' 
#' @author Michael Stadler, Charlotte Soneson
#' 
#' @noRd
#' @keywords internal
.assertPackagesAvailable <- function(pkgs, suggestInstallation = TRUE) {
    stopifnot(exprs = {
        is.character(pkgs)
        is.logical(suggestInstallation)
        length(suggestInstallation) == 1L
    })
    
    avail <- unlist(lapply(sub(""^[^/]+/"", """", pkgs),
                           function(pkg) {
                               requireNamespace(pkg, quietly = TRUE)
                           }))
    
    if (any(!avail)) {
        caller <- deparse(sys.calls()[[sys.nframe() - 1]])
        callerfunc <- sub(""\\(.+$"", """", caller)
        haveBioc <- requireNamespace(""BiocManager"", quietly = TRUE)
        msg <- paste0(
            ""The package"", ifelse(sum(!avail) > 1, ""s '"", "" '""),
            paste(sub(""^[^/]+/"", """", pkgs[!avail]), collapse = ""', '""),
            ""' "",
            ifelse(sum(!avail) > 1, ""are"", ""is""), "" required for "",
            callerfunc, ""(), but not installed.\n"")
        if (suggestInstallation) {
            msg <- paste0(
                msg,
                ""Install "", ifelse(sum(!avail) > 1, ""them"", ""it""), "" using:\n"",
                ifelse(haveBioc, """", ""install.packages(\""BiocManager\"")\n""),
                ""BiocManager::install(c(\"""",
                paste(pkgs[!avail], collapse = ""\"", \""""), ""\""))"")
        }
        stop(msg, call. = FALSE)
    }
    
    invisible(TRUE)
}
","R"
"Assay","fmicompbio/mutscan","R/plotFiltering.R",".R","7270","171","#' Visualize the filtering procedure 
#' 
#' Display the number (or fraction) of reads remaining after each step 
#' of the internal \code{mutscan} filtering. 
#' 
#' The function assumes that the number of reads filtered out in each step 
#' are provided as columns of \code{colData(se)}, with column names 
#' of the form \code{f[0-9]_filteringreason}, and that all filtering columns 
#' occur between the columns named \code{nbrTotal} and \code{nbrRetained}. 
#' 
#' @author Charlotte Soneson
#' 
#' @export
#' 
#' @return A \code{ggplot} object. 
#' 
#' @param se A \code{SummarizedExperiment} object, e.g. from 
#'     \code{summarizeExperiment}.
#' @param valueType Either ""reads"" or ""fractions"", indicating whether to 
#'     plot the number of reads, or the fraction of the total number of reads,
#'     that are retained after/filtered out in each filtering step.
#' @param onlyActiveFilters Logical scalar, whether to only include the 
#'     active filters (i.e., where any read was filtered out in any of the 
#'     samples). Defaults to \code{TRUE}. 
#' @param displayNumbers Logical scalar, indicating whether to display the 
#'     number (or fraction) of reads retained at every filtering step. 
#' @param numberSize Numeric scalar, indicating the size of the displayed 
#'     numbers (if \code{displayNumbers} is \code{TRUE}). 
#' @param plotType Character scalar, indicating what to show in the plot. 
#'     Either \code{""remaining""} or \code{""filtered""}.
#' @param facetBy Character scalar, indicating the variable by which the plots 
#'     should be facetted. Either \code{""sample""} or \code{""step""}.
#' 
#' @importFrom tidyselect matches
#' @importFrom dplyr select mutate group_by summarize pull ungroup filter
#' @importFrom tibble rownames_to_column 
#' @importFrom tidyr gather
#' @importFrom ggplot2 ggplot aes geom_bar facet_wrap theme theme_bw labs
#'     geom_text element_text
#' @importFrom rlang .data
#' 
#' @examples 
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"", 
#'                           package = ""mutscan""))[1:200, ]
#' plotFiltering(se)
#' 
plotFiltering <- function(se, valueType = ""reads"", onlyActiveFilters = TRUE,
                          displayNumbers = TRUE, numberSize = 4,
                          plotType = ""remaining"", facetBy = ""sample"") {
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertScalar(x = valueType, type = ""character"", 
                  validValues = c(""reads"", ""fractions""))
    .assertScalar(x = onlyActiveFilters, type = ""logical"")
    .assertScalar(x = displayNumbers, type = ""logical"")
    .assertScalar(x = numberSize, type = ""numeric"", rngExcl = c(0, Inf))
    .assertScalar(x = plotType, type = ""character"",
                  validValues = c(""remaining"", ""filtered""))
    .assertScalar(x = facetBy, type = ""character"", 
                  validValues = c(""sample"", ""step""))
    
    ## --------------------------------------------------------------------- ##
    ## Extract relevant columns from colData(se)
    ## --------------------------------------------------------------------- ##
    cd <- as.data.frame(colData(se)) |>
        dplyr::select(""nbrTotal"":""nbrRetained"") |>
        dplyr::select(c(""nbrTotal"", matches(""^f.*_""), 
                        ""nbrRetained""))
    
    ## Remove inactive filters if desired
    if (onlyActiveFilters) {
        cd <- cd[, colSums(cd) > 0, drop = FALSE]
    }
    
    ## Check that filtering columns are in the right order
    nbrs <- gsub(""^f"", """", vapply(strsplit(colnames(cd), ""_""), .subset, 
                                  FUN.VALUE = """", 1))
    nbrs <- nbrs[-c(1, length(nbrs))]
    nbrs <- gsub(""a$|b$"", """", nbrs)
    stopifnot(all(diff(as.numeric(nbrs)) >= 0))
    
    ## Check that all columns are numeric
    stopifnot(all(apply(cd, 2, is.numeric)))
    
    ## --------------------------------------------------------------------- ##
    ## Reshape into long format
    ## --------------------------------------------------------------------- ##
    cd <- cd |> 
        rownames_to_column(""sample"") |>
        gather(key = ""step"", value = ""nbrReads"", -""sample"") |>
        mutate(step = factor(.data$step, levels = colnames(cd)))
    
    ## Check that numbers add up (total - all filters = retained)
    stopifnot(all(
        cd |> 
            group_by(sample) |>
            summarize(
                remdiff = .data$nbrReads[.data$step == ""nbrTotal""] - 
                    sum(.data$nbrReads[grepl(""^f"", .data$step)]),
                remlist = .data$nbrReads[.data$step == ""nbrRetained""]) |>
            mutate(obsdiff = .data$remdiff - .data$remlist) |>
            pull(.data$obsdiff) == 0)) 
    
    ## --------------------------------------------------------------------- ##
    ## Calculate number of remaining reads at each step
    ## --------------------------------------------------------------------- ##
    cd <- cd |>
        group_by(sample) |>
        mutate(fracReads = signif(
            .data$nbrReads / 
                .data$nbrReads[.data$step == ""nbrTotal""],
            digits = 3)) |>
        mutate(cumulsum = vapply(seq_along(.data$step), function(i) {
            sum(.data$nbrReads[as.numeric(.data$step) <= 
                                   as.numeric(.data$step[i]) & 
                                   .data$step != ""nbrTotal""])
        }, NA_real_)) |>
        mutate(nbrRemaining = .data$nbrReads[.data$step == ""nbrTotal""] - 
                   .data$cumulsum) |>
        mutate(fracRemaining = signif(
            .data$nbrRemaining /
                .data$nbrReads[.data$step == ""nbrTotal""],
            digits = 3)) |>
        filter(.data$step != ""nbrRetained"") |>
        ungroup()
    
    ## --------------------------------------------------------------------- ##
    ## Create plot
    ## --------------------------------------------------------------------- ##
    
    yvar <- switch(
        paste0(valueType, ""_"", plotType),
        reads_remaining = ""nbrRemaining"",
        fractions_remaining = ""fracRemaining"",
        reads_filtered = ""nbrReads"",
        fractions_filtered = ""fracReads""
    )
    ylab1 <- switch(
        valueType,
        reads = ""Number of reads"",
        fractions = ""Fraction of reads""
    )
    ylab2 <- switch(
        plotType,
        remaining = "" remaining after"",
        filtered = "" filtered out in""
    )
    if (plotType == ""filtered"") {
        cd <- cd |> 
            filter(.data$step != ""nbrTotal"")
    }
    gg <- ggplot(cd, aes(
        x = .data[[setdiff(c(""step"", ""sample""), facetBy)]], 
        y = .data[[yvar]], 
        label = .data[[yvar]])) + 
        geom_bar(stat = ""identity"") + 
        facet_wrap(~ .data[[facetBy]], ncol = 1, scales = ""free_y"") + 
        theme_bw() + 
        theme(
            axis.text.x = element_text(angle = 90, hjust = 1, 
                                       vjust = 0.5, size = 12),
            axis.text.y = element_text(size = 12),
            axis.title = element_text(size = 14)) + 
        labs(x = """", y = ylab1, 
             title = paste0(ylab1, ylab2, "" each filtering step""))
    
    if (displayNumbers) {
        gg <- gg + geom_text(vjust = 1.5, color = ""white"", 
                             size = numberSize)
    }
    gg
}","R"
"Assay","fmicompbio/mutscan","R/plotResults.R",".R","14225","313","#' Construct an (interactive) scatterplot
#'
#' This function provides a convenience wrapper for constructing a scatter
#' plot of two columns in a data.frame, and color the points by whether
#' the values in a third column are below a provided value.
#' Users will interact with the function via the higher-level wrappers
#' \code{plotVolcano()} and \code{plotMeanDiff()}, which both call this
#' function internally.
#'
#' @param res A \code{data.frame} with values to plot.
#' @param xCol,yCol Character scalars defining the columns to plot on the
#'     x- and y-axis, respectively.
#' @param xLabel,yLabel Character scalars providing the x- and y-axis
#'     titles, respectively.
#' @param colorCol Character scalar defining the column to use to color
#'     the points (typically the adjusted p-value).
#' @param colorThreshold Numeric scalar. If the value in \code{colorCol}
#'     is lower than colorThreshold, the point will be indicated in red.
#' @param labelCol Character scalar defining the column to use to label
#'     individual points in the plot.
#' @param labelValues Character vector defining the points to highlight with
#'     labels. Must correspond to values in the \code{labelCol} column.
#' @param centerAxis Character vector, a subset of c(""x"", ""y"") or \code{NULL}.
#'     Indicates the axis that should be centered (i.e., where the axis
#'     limits should be extended to put 0 in the center).
#' @param pointSize Character scalar, either \code{""small""} or \code{""large""},
#'     defining the style to use to plot the points.
#' @param interactivePlot Logical scalar, whether to return an interactive
#'     plot.
#'
#' @return If \code{interactivePlot} is \code{TRUE}, a \code{plotly}
#'     object. If \code{interactivePlot} is \code{FALSE}, a \code{ggplot2}
#'     object.
#'
#' @author Charlotte Soneson
#' @keywords internal
#' @noRd
#'
#' @importFrom ggplot2 ggplot theme_minimal coord_cartesian theme labs
#'     element_text geom_point aes
#' @importFrom rlang .data
#' @importFrom ggrepel geom_text_repel
.plotScatter <- function(res, xCol, yCol, xLabel = xCol, yLabel = yCol,
                         colorCol, colorThreshold,
                         labelCol = NULL, labelValues = c(), centerAxis = ""x"",
                         pointSize = ""small"", interactivePlot = FALSE) {
    .assertVector(x = res, type = ""data.frame"")
    .assertScalar(x = xCol, type = ""character"", validValues = colnames(res))
    .assertScalar(x = yCol, type = ""character"", validValues = colnames(res))
    .assertScalar(x = xLabel, type = ""character"")
    .assertScalar(x = yLabel, type = ""character"")
    .assertScalar(x = colorCol, type = ""character"",
                  validValues = colnames(res))
    .assertScalar(x = colorThreshold, type = ""numeric"")
    .assertScalar(x = labelCol, type = ""character"",
                  validValues = colnames(res), allowNULL = TRUE)
    .assertVector(x = labelValues, type = ""character"", allowNULL = TRUE)
    .assertVector(x = centerAxis, type = ""character"",
                  validValues = c(""x"", ""y""), allowNULL = TRUE)
    .assertScalar(x = pointSize, type = ""character"",
                  validValues = c(""small"", ""large""))
    .assertScalar(x = interactivePlot, type = ""logical"")
    
    if (interactivePlot && !requireNamespace(""plotly"", quietly = TRUE)) {
        stop(""The 'plotly' package is required to make interactive plots."") # nocov
    }
    xr <- range(res[[xCol]], na.rm = TRUE)
    yr <- range(res[[yCol]], na.rm = TRUE)
    if (""x"" %in% centerAxis) {
        xr <- c(-max(abs(xr), na.rm = TRUE), max(abs(xr), na.rm = TRUE))
    }
    if (""y"" %in% centerAxis) {
        yr <- c(-max(abs(yr), na.rm = TRUE), max(abs(yr), na.rm = TRUE))
    }
    gg <- ggplot(res, aes(x = .data[[xCol]], 
                          y = .data[[yCol]])) +
        theme_minimal() + 
        coord_cartesian(xlim = xr, ylim = yr) +
        theme(axis.text = element_text(size = 12),
              axis.title = element_text(size = 14),
              title = element_text(size = 14)) +
        labs(x = xLabel, y = yLabel)
    if (!is.null(labelCol)) {
        gg <- gg +
            aes(label = .data[[labelCol]])
    }
    if (pointSize == ""large"") {
        gg <- gg +
            geom_point(fill = ""lightgrey"", color = ""grey"", 
                       pch = 21, size = 1.5)
    } else {
        gg <- gg +
            geom_point(color = ""black"", size = 0.25, alpha = 0.5)
    }
    if (any(res[[colorCol]] < colorThreshold)) {
        gg <- gg +
            labs(subtitle = paste0(""Points are indicated in red if "",
                                   colorCol, ""<"", colorThreshold))
        if (pointSize == ""large"") {
            gg <- gg +
                geom_point(
                    data = res[res[[colorCol]] < colorThreshold, , 
                               drop = FALSE],
                    fill = ""red"", color = ""grey"", pch = 21, size = 1.5
                )
        } else {
            gg <- gg +
                geom_point(
                    data = res[res[[colorCol]] < colorThreshold, , 
                               drop = FALSE],
                    color = ""red"", size = 0.25
                )
        }
    }
    if (interactivePlot) {
        plotly::ggplotly(gg) # nocov
    } else {
        if (!is.null(labelCol) && length(labelValues) > 0) {
            .assertVector(x = labelValues, type = ""character"", 
                          validValues = res[[labelCol]])
            gg <- gg +
                geom_text_repel(
                    data = res[res[[labelCol]] %in% labelValues, , 
                               drop = FALSE],
                    max.overlaps = Inf, size = 4, min.segment.length = 0.1)
        }
        gg
    }
}

.getColName <- function(res, validValues, aspect) {
    .assertVector(x = res, type = ""data.frame"")
    colName <- grep(paste(paste0(""^"", validValues, ""$""), collapse = ""|""),
                    colnames(res), value = TRUE)
    if (length(colName) == 0) {
        stop(""No suitable column found for "", aspect, "", one of the "",
             ""following expected: "", paste(validValues, collapse = "", ""))
    }
    if (length(colName) > 1) {
        warning(""Multiple suitable columns found for "", aspect,
                ""; choosing the first: "", colName[1])
        colName <- colName[1]
    }
    colName
}

#' Construct an MA (mean-difference) plot
#'
#' @param res \code{data.frame} (typically output from
#'     \code{calculateRelativeFC()}) with columns corresponding to the
#'     average abundance (\code{logCPM} or \code{AveExpr}), log-fold
#'     change (\code{logFC}) and significance (\code{FDR} or
#'     \code{adj.P.Val}).
#' @param meanCol,logFCCol,pvalCol,padjCol Character scalars indicating the
#'     columns from \code{res} that will be used to represent the
#'     mean value (x-axis), logFC (y-axis), nominal p-value (used to find
#'     the top features to label) and adjusted p-value (used
#'     for coloring). If \code{NULL} (default), pre-specified values
#'     will be used depending on the available columns
#'     (\code{""logCPM""} or \code{""AveExpr""}, \code{""logFC""},
#'     \code{""PValue""} or \code{""P.Value""}, and
#'     \code{""FDR""} or \code{""adj.P.Val""}, respectively).
#' @param padjThreshold Numeric scalar indicating the adjusted p-value
#'     threshold to use for coloring the points. All features with
#'     adjusted p-value below the treshold will be shown in red.
#' @param pointSize Either \code{""small""} or \code{""large""}, indicating
#'     which of the two available plot styles that will be used.
#' @param interactivePlot Logical scalar, indicating whether an
#'     interactive plot should be returned, in which one can hover
#'     over the individual points and obtain further information.
#' @param nTopToLabel Numeric scalar, indicating the number of points that
#'     should be labeled in the plot. The points will be ranked by the
#'     \code{pvalCol} column, and the top \code{nTopToLabel} values will
#'     be labeled by the corresponding row names. Only used if
#'     \code{interactivePlot} is \code{FALSE}.
#'
#' @return If \code{interactivePlot} is \code{TRUE}, a \code{plotly}
#'     object. If \code{interactivePlot} is \code{FALSE}, a \code{ggplot2}
#'     object.
#'
#' @author Charlotte Soneson
#' @export
#'
#' @examples
#' library(SummarizedExperiment)
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"",
#'                           package = ""mutscan""))[1:200, ]
#' design <- model.matrix(~ Replicate + Condition,
#'                        data = colData(se))
#' res <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"")
#' plotMeanDiff(res, pointSize = ""large"", nTopToLabel = 3)
#'
plotMeanDiff <- function(res, meanCol = NULL, logFCCol = NULL, pvalCol = NULL,
                         padjCol = NULL, padjThreshold = 0.05, 
                         pointSize = ""small"",
                         interactivePlot = FALSE, nTopToLabel = 0) {
    .assertScalar(x = nTopToLabel, type = ""numeric"", rngIncl = c(0, Inf))
    .assertVector(x = res, type = ""data.frame"")
    ## Get the columns to use
    if (is.null(meanCol)) {
        meanCol <- .getColName(res, validValues = c(""logCPM"", ""AveExpr""),
                               aspect = ""x-axis"")
    }
    if (is.null(logFCCol)) {
        logFCCol <- .getColName(res, validValues = c(""logFC""),
                                aspect = ""y-axis"")
    }
    if (is.null(pvalCol)) {
        pvalCol <- .getColName(res, validValues = c(""PValue"", ""P.Value""),
                               aspect = ""labeling"")
    }
    if (is.null(padjCol)) {
        padjCol <- .getColName(res, validValues = c(""FDR"", ""adj.P.Val""),
                               aspect = ""highlighting"")
    }
    .assertScalar(pvalCol, type = ""character"", validValues = colnames(res))
    res$feature <- rownames(res)
    if (nTopToLabel > 0) {
        labelValues <- res[order(res[[pvalCol]]), 
                           ""feature""][seq_len(nTopToLabel)]
    } else {
        labelValues <- c()
    }
    
    .plotScatter(res = res, xCol = meanCol, yCol = logFCCol,
                 xLabel = meanCol, yLabel = logFCCol,
                 colorCol = padjCol,
                 colorThreshold = padjThreshold,
                 labelCol = ""feature"", labelValues = labelValues,
                 centerAxis = ""y"", pointSize = pointSize,
                 interactivePlot = interactivePlot)
}

#' Construct a volcano plot
#'
#' @param res \code{data.frame} (typically output from
#'     \code{calculateRelativeFC()}) with columns corresponding to the
#'     log-fold change (\code{logFC}), p-value (\code{PValue} or
#'     \code{P.Value}) and significance (\code{FDR} or \code{adj.P.Val}).
#' @param logFCCol,pvalCol,padjCol Character scalars indicating the
#'     columns from \code{res} that will be used to represent the
#'     logFC (x-axis), p-value (y-axis) and adjusted p-value (used
#'     for coloring). If \code{NULL} (default), pre-specified values
#'     will be used depending on the available columns
#'     (\code{""logFC""}, \code{""PValue""} or \code{""P.Value""}, and
#'     \code{""FDR""} or \code{""adj.P.Val""}, respectively).
#' @param padjThreshold Numeric scalar indicating the adjusted p-value
#'     threshold to use for coloring the points. All features with
#'     adjusted p-value below the treshold will be shown in red.
#' @param pointSize Either \code{""small""} or \code{""large""}, indicating
#'     which of the two available plot styles that will be used.
#' @param interactivePlot Logical scalar, indicating whether an
#'     interactive plot should be returned, in which one can hover
#'     over the individual points and obtain further information.
#' @param nTopToLabel Numeric scalar, indicating the number of points that
#'     should be labeled in the plot. The points will be ranked by the
#'     \code{pvalCol} column, and the top \code{nTopToLabel} values will
#'     be labeled by the corresponding row names. Only used if
#'     \code{interactivePlot} is \code{FALSE}.
#'
#' @return If \code{interactivePlot} is \code{TRUE}, a \code{plotly}
#'     object. If \code{interactivePlot} is \code{FALSE}, a \code{ggplot2}
#'     object.
#'
#' @author Charlotte Soneson
#' @export
#'
#' @examples
#' library(SummarizedExperiment)
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"",
#'                           package = ""mutscan""))[1:200, ]
#' design <- model.matrix(~ Replicate + Condition,
#'                        data = colData(se))
#' res <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"")
#' plotVolcano(res, pointSize = ""large"", nTopToLabel = 3)
#'
plotVolcano <- function(res, logFCCol = NULL, pvalCol = NULL, padjCol = NULL,
                        padjThreshold = 0.05, pointSize = ""small"",
                        interactivePlot = FALSE, nTopToLabel = 0) {
    .assertScalar(x = nTopToLabel, type = ""numeric"", rngIncl = c(0, Inf))
    .assertVector(x = res, type = ""data.frame"")
    ## Get the columns to use
    if (is.null(logFCCol)) {
        logFCCol <- .getColName(res, validValues = c(""logFC""),
                                aspect = ""x-axis"")
    }
    if (is.null(pvalCol)) {
        pvalCol <- .getColName(res, validValues = c(""PValue"", ""P.Value""),
                               aspect = ""y-axis"")
    }
    if (is.null(padjCol)) {
        padjCol <- .getColName(res, validValues = c(""FDR"", ""adj.P.Val""),
                               aspect = ""highlighting"")
    }
    .assertScalar(pvalCol, type = ""character"", validValues = colnames(res))
    res$negLog10P <- -log10(res[[pvalCol]])
    res$feature <- rownames(res)
    if (nTopToLabel > 0) {
        labelValues <- res[order(res[[pvalCol]]),
                           ""feature""][seq_len(nTopToLabel)]
    } else {
        labelValues <- c()
    }
    
    .plotScatter(res = res, xCol = logFCCol, yCol = ""negLog10P"",
                 xLabel = logFCCol, yLabel = paste0(""-log10("", pvalCol, "")""),
                 colorCol = padjCol,
                 colorThreshold = padjThreshold,
                 labelCol = ""feature"", labelValues = labelValues,
                 centerAxis = ""x"", pointSize = pointSize,
                 interactivePlot = interactivePlot)
}
","R"
"Assay","fmicompbio/mutscan","R/calculateFitnessScore.R",".R","6390","140","#' Calculate fitness scores.
#' 
#' Using sequence counts before and after selection, calculate fitness scores 
#' as described by Diss and Lehner (2018).
#' 
#' @param se SummarizedExperiment object as returned by 
#'     \code{\link{summarizeExperiment}}.
#' @param pairingCol Name of column in \code{colData(se)} with 
#'     replicate/pairing information. Samples with the same value in this 
#'     column will be paired.
#' @param ODCols Name(s) of column(s) in \code{colData(se)} with OD values 
#'     (numeric), used to normalize for different numbers of cells.
#' @param comparison 3-element character vector of the form 
#'     \code{(column, numerator, denominator)}. \code{column} is the name of 
#'     the column in \code{colData(se)} with experimental conditions. 
#'     \code{numerator} and \code{denominator} define the comparison, 
#'     e.g. \code{c(""cond"", ""output"", ""input"")} will look in the 
#'     \code{""cond""} column and calculate fitness for the ratio of 
#'     \code{""output""} over \code{""input""} counts.
#' @param WTrows Vector of row names that will be used as the reference when
#'     calculating fitness scores. If more than one value is provided, the 
#'     average of the corresponding fitness scores is used as a reference. 
#'     If NULL, no division by WT scores will be done.
#' @param selAssay Assay to select from \code{se} for the analysis.
#'   
#' @return A numeric vector with fitness scores.
#' 
#' @author Michael Stadler and Charlotte Soneson
#' 
#' @references ""The genetic landscape of a physical interaction.""
#'     Diss G and Lehner B. Elife. 2018;7:e32472. doi: 10.7554/eLife.32472.
#' 
#' @importFrom methods is
#' @importFrom SummarizedExperiment colData assay
#' @importFrom Matrix colSums
#' 
#' @export
#' 
#' @examples 
#' se <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"", 
#'                           package = ""mutscan""))
#' ## Check that the wildtype sequence is present in the data
#' stopifnot(""f.0.WT"" %in% rownames(se))
#' ## Calculate PPI scores as defined in Diss & Lehner (2018)
#' ppis <- calculateFitnessScore(
#'     se = se, pairingCol = ""Replicate"", 
#'     ODCols = c(""OD1"", ""OD2""),
#'     comparison = c(""Condition"", ""cis_output"", ""cis_input""),
#'     WTrows = ""f.0.WT"")
#' ## Matrix with PPI scores for each replicate
#' head(ppis)
#' 
calculateFitnessScore <- function(se, pairingCol, ODCols, comparison, WTrows,
                                  selAssay = ""counts"") {
    ## ------------------------------------------------------------------------
    ## pre-flight checks
    ## ------------------------------------------------------------------------
    .assertVector(x = se, type = ""SummarizedExperiment"")
    
    ## pairingCol is in colData(se)
    .assertScalar(x = pairingCol, type = ""character"", 
                  validValues = colnames(colData(se)))
    
    ## ODCols are all in colData(se) and contain numeric values
    .assertVector(x = ODCols, type = ""character"", rngLen = c(1, Inf),
                  validValues = colnames(colData(se)))
    for (odc in ODCols) {
        .assertVector(x = colData(se)[[odc]], 
                      type = ""numeric"")
    }
    
    ## comparison is length(3)-character with column and values in colData(se)
    .assertVector(x = comparison, type = ""character"", len = 3)
    .assertScalar(x = comparison[1], type = ""character"", 
                  validValues = colnames(colData(se)))
    .assertVector(
        x = comparison[2:3], type = ""character"", 
        validValues = colData(se)[[comparison[1]]])
    
    ## there is exactly one observation per pairing and condition
    if (any(table(colData(se)[colData(se)[, comparison[1]] %in% 
                              comparison[2:3], pairingCol],
                  colData(se)[colData(se)[, comparison[1]] %in% 
                              comparison[2:3], comparison[1]]) != 1)) {
        stop(""There must be exactly one sample for every "", 
             ""replicate-condition combination "",
             ""defined by 'pairingCol' and 'comparison'"")
    }
    
    ## WTrows exist in the SE
    .assertVector(x = WTrows, type = ""character"", validValues = rownames(se), 
                  allowNULL = TRUE)
    
    ## ------------------------------------------------------------------------
    ## subset se and reorder samples by shared replicates
    ## ------------------------------------------------------------------------
    se_numerator <- se[, colData(se)[, comparison[1]] == comparison[2]]
    se_denominator <- se[, colData(se)[, comparison[1]] == comparison[3]]
    
    shared_repl <- intersect(colData(se_numerator)[, pairingCol], 
                             colData(se_denominator)[, pairingCol])
    se_numerator <- se_numerator[, match(shared_repl, 
                                         colData(se_numerator)[, pairingCol])]
    se_denominator <- 
        se_denominator[, match(shared_repl, 
                               colData(se_denominator)[, pairingCol])]
    
    ## ------------------------------------------------------------------------
    ## calculate normalized counts (n_i)
    ## ------------------------------------------------------------------------
    norm_counts_numerator <- sweep(
        as.matrix(assay(se_numerator, selAssay)), MARGIN = 2, 
        STATS = apply(colData(se_numerator)[, ODCols, drop = FALSE], 
                      MARGIN = 1, prod) /
            colSums(assay(se_numerator, selAssay)), 
        FUN = ""*"")
    norm_counts_denominator <- sweep(
        as.matrix(assay(se_denominator, selAssay)), MARGIN = 2, 
        STATS = apply(colData(se_denominator)[, ODCols, drop = FALSE], 
                      MARGIN = 1, prod) /
            colSums(assay(se_denominator, selAssay)), 
        FUN = ""*"")
    n <- log2(norm_counts_numerator/norm_counts_denominator)
    n[!is.finite(n)] <- NA
    colnames(n) <- paste0(comparison[2], ""_vs_"", comparison[3],
                          ""_repl"", shared_repl)
    
    
    ## ------------------------------------------------------------------------
    ## calculate fitness = n_i / n_WT
    ## ------------------------------------------------------------------------
    if (is.null(WTrows)) {
        nWT <- rep(1, ncol(n))
    } else {
        nWT <- colMeans(n[WTrows, , drop = FALSE])
    }
    fitness <- sweep(n, MARGIN = 2, STATS = nWT, FUN = ""/"")
    return(fitness)
}
","R"
"Assay","fmicompbio/mutscan","R/relabelMutPositions.R",".R","2776","63","#' Relabel the positions of mutations in the designated ID
#' 
#' @param se SummarizedExperiment object, with row names of the form
#'     XX\{.\}AA\{.\}NNN, where XX is the name of the reference sequence, 
#'     AA is the position of the mutated codon, and NNN is the mutated codon 
#'     or amino acid. \{.\} is the delimiter, to be specified in the 
#'     \code{mutNameDelimiter} argument. For rows corresponding to sequences 
#'     with multiple mutated codons, the row names contain multiple names of 
#'     the form above in a single string, separated by ""_"".
#' @param conversionTable \code{data.frame} with at least three columns:
#'     \itemize{
#'     \item seqname The reference sequence name (should match XX in the 
#'     mutation name)
#'     \item position The codon position (should match AA in the mutation name)
#'     \item name The new name for the codon (will replace AA in the mutation 
#'     name, if the reference sequence matches seqname)
#'     } 
#' @param mutNameDelimiter The delimiter used in the mutation name 
#'     (\{.\} above).
#' 
#' @author Charlotte Soneson
#' @export
#' 
#' @return A SummarizedExperiment object with modified row names.
#' 
#' @importFrom S4Vectors unstrsplit
#' @importFrom utils relist
#' @importFrom BiocGenerics rownames rownames<-
#' 
#' @examples
#' x <- readRDS(system.file(""extdata"", ""GSE102901_cis_se.rds"",
#'                          package = ""mutscan""))
#' conversionTable <- data.frame(seqname = ""f"", position = 0:32) 
#' conversionTable$name = paste0((conversionTable$position - 1) %/% 7 + 1, 
#'                               c("""", rep(letters[1:7], 6))[1:33])
#' out <- relabelMutPositions(x, conversionTable)
#' 
relabelMutPositions <- function(se, conversionTable, mutNameDelimiter = ""."") {
    .assertVector(x = se, type = ""SummarizedExperiment"")
    .assertVector(x = colnames(conversionTable), type = ""character"")
    .assertScalar(x = mutNameDelimiter, type = ""character"")
    stopifnot(all(c(""position"", ""name"", ""seqname"") %in% 
                      colnames(conversionTable)))
    
    conversionTable$position <- as.character(conversionTable$position)
    spl <- strsplit(rownames(se), ""_"")
    unl <- unlist(spl)
    unl <- lapply(strsplit(unl, mutNameDelimiter, fixed = TRUE), 
                  function(w) {
                      idx <- which(conversionTable$seqname == w[1] & 
                                       conversionTable$position == w[2])
                      if (length(idx) > 0) {
                          w[2] <- conversionTable$name[idx]
                      }
                      w
                  })
    unl <- unstrsplit(unl, sep = mutNameDelimiter)
    spl <- relist(unl, skeleton = spl)
    spl <- unstrsplit(spl, ""_"")
    rownames(se) <- spl
    se
}
","R"
"Assay","fmicompbio/mutscan","tests/testthat.R",".R","58","5","library(testthat)
library(mutscan)

test_check(""mutscan"")
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_digestFastqs_helpers.R",".R","29252","613","## ----------------------------------------------------------------------------
## complement
## ----------------------------------------------------------------------------
test_that(""complement works"", {
    expect_identical(complement(""a""), ""T"")
    expect_identical(complement(""A""), ""T"")
    expect_identical(complement(""c""), ""G"")
    expect_identical(complement(""C""), ""G"")
    expect_identical(complement(""g""), ""C"")
    expect_identical(complement(""G""), ""C"")
    expect_identical(complement(""t""), ""A"")
    expect_identical(complement(""T""), ""A"")
    expect_identical(complement(""n""), ""N"")
    expect_identical(complement(""N""), ""N"")
    expect_error(complement(""B""), ""Invalid DNA base"")
    expect_error(complement(""Q""), ""Invalid DNA base"")
})

## ----------------------------------------------------------------------------
## compareCodonPositions
## ----------------------------------------------------------------------------
test_that(""compareCodonPositions works"", {
  expect_true(compareCodonPositions(""f.1.AAA_"", ""f.2.ACT_"", "".""))
  expect_true(compareCodonPositions(""f.2.ACT_"", ""f.10.TCA_"", "".""))
})

## ----------------------------------------------------------------------------
## findClosestRefSeq
## ----------------------------------------------------------------------------
test_that(""findClosestRefSeq works"", {
  expect_equal(findClosestRefSeq(varSeq = ""ACGT"", wtSeq = c(""ATTT"", ""ACTT""),
                                 upperBoundMismatch = 4L, sim = 0L), 1L)
  expect_equal(findClosestRefSeq(varSeq = ""ACGT"", wtSeq = c(""ACGT"", ""ACTT""),
                                 upperBoundMismatch = 4L, sim = 0L), 0L)
  expect_equal(findClosestRefSeq(varSeq = ""ACGT"", wtSeq = c(""ACG"", ""ACGT""),
                                 upperBoundMismatch = 4L, sim = 0L), 1L)
  expect_equal(findClosestRefSeq(varSeq = ""ACGT"", wtSeq = c(""AACGT"", ""ACCTA""),
                                 upperBoundMismatch = 4L, sim = 0L), 1L)
  expect_equal(findClosestRefSeq(varSeq = ""ACGT"", wtSeq = c(""ATTT"", ""ACTT""),
                                 upperBoundMismatch = 0L, sim = 0L), -1L)
  expect_equal(findClosestRefSeq(varSeq = ""ACGT"", wtSeq = c(""ATGT"", ""ACTT""),
                                 upperBoundMismatch = 1L, sim = 0L), -2L)

  expect_equal(findClosestRefSeqEarlyStop(varSeq = ""ACGT"", wtSeq = c(""ATTT"", ""ACTT""),
                                          upperBoundMismatch = 4L, sim = 0L), 1L)
  expect_equal(findClosestRefSeqEarlyStop(varSeq = ""ACGT"", wtSeq = c(""ACGT"", ""ACTT""),
                                          upperBoundMismatch = 4L, sim = 0L), 0L)
  expect_equal(findClosestRefSeqEarlyStop(varSeq = ""ACGT"", wtSeq = c(""ACG"", ""ACGT""),
                                          upperBoundMismatch = 4L, sim = 0L), 1L)
  expect_equal(findClosestRefSeqEarlyStop(varSeq = ""ACGT"", wtSeq = c(""AACGT"", ""ACCTA""),
                                          upperBoundMismatch = 4L, sim = 0L), 1L)
  expect_equal(findClosestRefSeqEarlyStop(varSeq = ""ACGT"", wtSeq = c(""ATTT"", ""ACTT""),
                                          upperBoundMismatch = 0L, sim = 0L), -1L)
  expect_equal(findClosestRefSeqEarlyStop(varSeq = ""ACGT"", wtSeq = c(""ATGT"", ""ACTT""),
                                          upperBoundMismatch = 1L, sim = 0L), -2L)
})

## ----------------------------------------------------------------------------
## decomposeRead
## ----------------------------------------------------------------------------
test_that(""decomposeRead works"", {
  ## All element lengths specified, no primer
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""USCVCUSV"",
    elementLengths = c(2, 1, 3, 3, 1, 2, 1, 1), primerSeqs = """",
    varSeq = """", varQual = """", varLengths = integer(0L), umiSeq = """",
    constSeq = """", constQual = """", nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""ACCT"")
  expect_equal(res$varSeq, ""AAGG"")
  expect_equal(res$varQual, ""gABG"")
  expect_equal(res$varLengths, c(3L, 1L))
  expect_equal(res$constSeq, ""TTAC"")
  expect_equal(res$constQual, ""defC"")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## All element lengths specified, no primer, 0 lengths included
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTG"", squal = ""abcdefgABCDEF"", elements = ""SUSCVUCUS"",
    elementLengths = c(0, 2, 1, 3, 3, 0, 1, 2, 1), primerSeqs = """",
    varSeq = """", varQual = """", varLengths = integer(0L), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""ACCT"")
  expect_equal(res$varSeq, ""AAG"")
  expect_equal(res$varQual, ""gAB"")
  expect_equal(res$varLengths, 3L)
  expect_equal(res$constSeq, ""TTAC"")
  expect_equal(res$constQual, ""defC"")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Derive variable length from other lengths
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTG"", squal = ""abcdefgABCDEF"", elements = ""USCVCUS"",
    elementLengths = c(2, 1, 3, -1, 1, 2, 1), primerSeqs = """",
    varSeq = """", varQual = """", varLengths = integer(0L), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""ACCT"")
  expect_equal(res$varSeq, ""AAG"")
  expect_equal(res$varQual, ""gAB"")
  expect_equal(res$varLengths, 3L)
  expect_equal(res$constSeq, ""TTAC"")
  expect_equal(res$constQual, ""defC"")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Primer
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""USCVPS"",
    elementLengths = c(2, 1, 3, 3, 3, -1), primerSeqs = c(""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0L), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""AC"")
  expect_equal(res$varSeq, ""AAG"")
  expect_equal(res$varQual, ""gAB"")
  expect_equal(res$varLengths, 3L)
  expect_equal(res$constSeq, ""TTA"")
  expect_equal(res$constQual, ""def"")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Only variable sequence
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""V"",
    elementLengths = -1, primerSeqs = c(""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0L), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, """")
  expect_equal(res$varSeq, ""ACGTTAAAGCCTGG"")
  expect_equal(res$varQual, ""abcdefgABCDEFG"")
  expect_equal(res$varLengths, 14L)
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Primer + variable sequence
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""PV"",
    elementLengths = c(-1, -1), primerSeqs = c(""ACG""),
    varSeq = """", varQual = """", varLengths = integer(0L), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, """")
  expect_equal(res$varSeq, ""TTAAAGCCTGG"")
  expect_equal(res$varQual, ""defgABCDEFG"")
  expect_equal(res$varLengths, 11L)
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Variable sequence + primer
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""VP"",
    elementLengths = c(-1, -1), primerSeqs = c(""ACG""),
    varSeq = ""A"", varQual = ""E"", varLengths = 1L, umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, """")
  expect_equal(res$varSeq, ""A"")
  expect_equal(res$varQual, ""E"")
  expect_equal(res$varLengths, c(1L, 0L))
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Primer, not the right length of sequence
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""VPU"",
    elementLengths = c(10, 3, 2), primerSeqs = c(""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0L), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, """")
  expect_equal(res$varSeq, """")
  expect_equal(res$varQual, """")
  expect_equal(res$varLengths, integer(0))
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 1)

  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""VPU"",
    elementLengths = c(8, 3, 2), primerSeqs = c(""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, """")
  expect_equal(res$varSeq, """")
  expect_equal(res$varQual, """")
  expect_equal(res$varLengths, integer(0))
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 1)

  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""CVPU"",
    elementLengths = c(10, -1, 3, 2), primerSeqs = c(""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, """")
  expect_equal(res$varSeq, """")
  expect_equal(res$varQual, """")
  expect_equal(res$varLengths, integer(0))
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 1)

  ## Primer, but don't explicitly specify that the part after the primer should be skipped
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""USCVP"",
    elementLengths = c(2, 1, 3, 3, 3), primerSeqs = c(""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""AC"")
  expect_equal(res$varSeq, ""AAG"")
  expect_equal(res$varQual, ""gAB"")
  expect_equal(res$varLengths, 3L)
  expect_equal(res$constSeq, ""TTA"")
  expect_equal(res$constQual, ""def"")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Multiple primers, only one present
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""USCVP"",
    elementLengths = c(2, 1, 3, 3, 3), primerSeqs = c(""TTT"", ""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""AC"")
  expect_equal(res$varSeq, ""AAG"")
  expect_equal(res$varQual, ""gAB"")
  expect_equal(res$varLengths, 3L)
  expect_equal(res$constSeq, ""TTA"")
  expect_equal(res$constQual, ""def"")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Multiple primers, none present
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""USCVP"",
    elementLengths = c(2, 1, 3, 3, 3), primerSeqs = c(""TTT"", ""GGG""),
    varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, """")
  expect_equal(res$varSeq, """")
  expect_equal(res$varQual, """")
  expect_equal(res$varLengths, integer(0))
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 1)
  expect_equal(res$nReadWrongLength, 0)

  ## Multiple primers, all present (use the first one)
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""USCVP"",
    elementLengths = c(2, 1, 3, 3, 3), primerSeqs = c(""CCT"", ""TTA""),
    varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""AC"")
  expect_equal(res$varSeq, ""AAG"")
  expect_equal(res$varQual, ""gAB"")
  expect_equal(res$varLengths, 3L)
  expect_equal(res$constSeq, ""TTA"")
  expect_equal(res$constQual, ""def"")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Primer, infer length of sequences
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""UPV"",
    elementLengths = c(-1, 3, -1), primerSeqs = c(""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""ACGTTAAAG"")
  expect_equal(res$varSeq, ""GG"")
  expect_equal(res$varQual, ""FG"")
  expect_equal(res$varLengths, 2L)
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Primer, don't specify primer length
  res <- mutscan:::test_decomposeRead(
    sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""UPV"",
    elementLengths = c(-1, -1, -1), primerSeqs = c(""CCT""),
    varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
    constSeq = """", constQual = """",
    nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, ""ACGTTAAAG"")
  expect_equal(res$varSeq, ""GG"")
  expect_equal(res$varQual, ""FG"")
  expect_equal(res$varLengths, 2L)
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)

  ## Multiple variable segments with different lengths
  res <- mutscan:::test_decomposeRead(
      sseq = ""ACGTTAAAGCCTGG"", squal = ""abcdefgABCDEFG"", elements = ""VPV"",
      elementLengths = c(-1, -1, -1), primerSeqs = c(""CCT""),
      varSeq = """", varQual = """", varLengths = integer(0), umiSeq = """",
      constSeq = """", constQual = """",
      nNoPrimer = 0, nReadWrongLength = 0)
  expect_equal(res$umiSeq, """")
  expect_equal(res$varSeq, ""ACGTTAAAGGG"")
  expect_equal(res$varQual, ""abcdefgABFG"")
  expect_equal(res$varLengths, c(9L, 2L))
  expect_equal(res$constSeq, """")
  expect_equal(res$constQual, """")
  expect_equal(res$nNoPrimer, 0)
  expect_equal(res$nReadWrongLength, 0)
})

## ----------------------------------------------------------------------------
## mergeReadPairsPartial
## ----------------------------------------------------------------------------
test_that(""mergeReadPairsPartial works"", {
  ## don't count N as mismatch, pick base with higher quality
  sF1 <- ""AAAANA""; qF1 <- rep(40L, nchar(sF1)); lF1 <- 6L
  sR1 <- ""AACCCC""; qR1 <- rep(42L, nchar(sR1)); lR1 <- 6L
  res1a <- mutscan:::test_mergeReadPairPartial(sF1, qF1, sR1, qR1, lF1, lR1, 1, 6, 0, 0, 1/4, TRUE)
  res1b <- mutscan:::test_mergeReadPairPartial(sF1, qF1, sR1, qR1, lF1, lR1, 1, 6, 0, 0, 1/4, FALSE)
  res1c <- mutscan:::test_mergeReadPairPartial(sF1, qR1, sR1, qF1, lF1, lR1, 2, 2, 0, 0, 1/4, TRUE)
  res1d <- mutscan:::test_mergeReadPairPartial(sF1, qF1, sR1, qR1, lF1, lR1, 2, 2, 0, 0, 0, TRUE)
  expect_type(res1a, ""list"")
  expect_type(res1b, ""list"")
  expect_type(res1c, ""list"")
  expect_type(res1d, ""list"")
  expect_identical(res1a$mergedSeq, ""AAAACCCC"")
  expect_identical(res1a$mergedQual, rep(c(40L, 42L), c(2, 6)))
  expect_identical(res1a$mergedLengths, 8L)
  expect_identical(res1a, res1b)
  expect_identical(res1c$mergedSeq, ""AAAANACCCC"")
  expect_identical(res1d$mergedSeq, ""AAAAAACCCC"")
  expect_identical(res1c$mergedLengths, 10L)
  expect_identical(res1d$mergedLengths, 10L)

  ## no valid overlap, multiple possible overlaps with single mismatch
  sF2 <- ""TTACACG""; qF2 <- rep(10L, nchar(sF2)); lF2 <- 7L
  sR2 <- ""ACACACA""; qR2 <- rep(40L, nchar(sR2)); lR2 <- 7L
  res2a <- mutscan:::test_mergeReadPairPartial(sF2, qF2, sR2, qR2, lF2, lR2, 0, 0, 0, 0, maxFracMismatchOverlap = 3/7, TRUE)
  res2b <- mutscan:::test_mergeReadPairPartial(sF2, qF2, sR2, qR2, lF2, lR2, 1, 7, 0, 0, 1/5, TRUE)
  expect_type(res2a, ""list"")
  expect_type(res2b, ""list"")
  expect_identical(res2a$mergedSeq, sR2)
  expect_identical(res2a$mergedQual, qR2)
  expect_identical(res2a$mergedLengths, 7L)
  expect_identical(res2b$mergedSeq, ""TTACACACA"")
  expect_identical(res2b$mergedQual, rep(c(10L,40L), c(2,7)))
  expect_identical(res2b$mergedLengths, 9L)

  ## specify min/max merged length instead
  res2c <- mutscan:::test_mergeReadPairPartial(sF2, qF2, sR2, qR2, lF2, lR2, 1, 7, 9, 9, 1, FALSE)
  res2d <- mutscan:::test_mergeReadPairPartial(sF2, qF2, sR2, qR2, lF2, lR2, 1, 7, 11, 11, 1, FALSE)
  res2e <- mutscan:::test_mergeReadPairPartial(sF2, qF2, sR2, qR2, lF2, lR2, 1, 7, 12, 12, 1, FALSE)
  expect_type(res2c, ""list"")
  expect_type(res2d, ""list"")
  expect_type(res2e, ""list"")
  expect_identical(res2c$mergedSeq, ""TTACACACA"")
  expect_identical(res2c$mergedQual, rep(c(10L, 40L), c(2, 7)))
  expect_identical(res2c$mergedLengths, 9L)
  expect_identical(res2d$mergedSeq, ""TTACACACACA"")
  expect_identical(res2d$mergedQual, rep(c(10L, 40L), c(4, 7)))
  expect_identical(res2d$mergedLengths, 11L)
  expect_identical(res2e$mergedSeq, ""TTACAACACACA"")
  expect_identical(res2e$mergedQual, rep(c(10L, 40L), c(5, 7)))
  expect_identical(res2e$mergedLengths, 12L)

  ## invalid overlaps specified/overlaps modified internally
  ## minOverlap > lenF -> no valid overlap
  res0a <- mutscan:::test_mergeReadPairPartial(sF2, qF2, sR2, qR2, lF2, lR2, 8, 7, 9, 9, 1, FALSE)
  expect_type(res0a, ""list"")
  expect_identical(res0a$mergedSeq, sF2)
  expect_identical(res0a$mergedQual, qF2)
  expect_identical(res0a$mergedLengths, 7L)
  expect_true(res0a$return)
  ## minOverlap = 0, lenF > lenR -> minOverlap = lenR
  sF0 <- ""TTACACG""; qF0 <- rep(10L, nchar(sF0)); lF0 <- nchar(sF0)
  sR0 <- ""ACACG""; qR0 <- rep(40L, nchar(sR0)); lR0 <- nchar(sR0)
  res0b <- mutscan:::test_mergeReadPairPartial(sF0, qF0, sR0, qR0, lF0, lR0, 0, 7, 5, 9, 1, FALSE)
  expect_type(res0b, ""list"")
  expect_identical(res0b$mergedSeq, sF0)
  expect_identical(res0b$mergedQual, rep(c(10L, 40L), c(2, 5)))
  expect_identical(res0b$mergedLengths, 7L)
  expect_false(res0b$return)
  ## maxOverlap = 0, lenF > lenR -> maxOverlap = lenR
  res0c <- mutscan:::test_mergeReadPairPartial(sF0, qF0, sR0, qR0, lF0, lR0, 1, 0, 5, 9, 1, FALSE)
  expect_type(res0c, ""list"")
  expect_identical(res0c$mergedSeq, sF0)
  expect_identical(res0c$mergedQual, rep(c(10L, 40L), c(2, 5)))
  expect_identical(res0c$mergedLengths, 7L)
  expect_false(res0c$return)
  ## minOverlap > maxOverlap -> no valid overlap
  res0d <- mutscan:::test_mergeReadPairPartial(sF2, qF2, sR2, qR2, lF2, lR2, 4, 3, 9, 9, 1, FALSE)
  expect_type(res0d, ""list"")
  expect_identical(res0d$mergedSeq, sF2)
  expect_identical(res0d$mergedQual, qF2)
  expect_identical(res0d$mergedLengths, 7L)
  expect_true(res0d$return)

  ## minMergedLength > lenF + lenR -> no valid overlap
  res0e <- mutscan:::test_mergeReadPairPartial(sF2, qF2, sR2, qR2, lF2, lR2, 1, 7, 15, 9, 1, FALSE)
  expect_type(res0e, ""list"")
  expect_identical(res0e$mergedSeq, sF2)
  expect_identical(res0e$mergedQual, qF2)
  expect_identical(res0e$mergedLengths, 7L)
  expect_true(res0e$return)
  ## maxMergedLength > lenF + lenR -> maxMergedLength = lenF + lenR
  res0f <- mutscan:::test_mergeReadPairPartial(sF0, qF0, sR0, qR0, lF0, lR0, 1, 0, 5, 19, 1, FALSE)
  expect_type(res0f, ""list"")
  expect_identical(res0f$mergedSeq, sF0)
  expect_identical(res0f$mergedQual, rep(c(10L, 40L), c(2, 5)))
  expect_identical(res0f$mergedLengths, 7L)
  expect_false(res0f$return)

  ## padded reads
  for (i in 1:10) {
    sF <- paste(rep(c(""C"",""A""), c(i, 6)), collapse = """")
    qF <- rep(30L, nchar(sF))
    sR <- paste(rep(c(""A"",""C""), c(6, i)), collapse = """")
    qR <- rep(30L, nchar(sR))
    lF <- i + 6
    lR <- 6 + i
    res <- mutscan:::test_mergeReadPairPartial(sF, qF, sR, qR, lF, lR, 6, 6, 0, 0, 0)
    expect_identical(res$mergedSeq, paste(rep(c(""C"",""A"",""C""), c(i, 6, i)), collapse = """"))
    expect_equal(res$mergedLengths, i + 6 + i)
  }

  ## reads of unequal length
  sR <- paste(rep(""A"", 6), collapse = """")
  qR <- rep(30L, nchar(sR))
  lR <- 6L
  for (i in 1:10) {
    sF <- paste(rep(c(""C"",""A""), c(i, 6)), collapse = """")
    qF <- rep(30L, nchar(sF))
    lF <- i + 6
    res <- mutscan:::test_mergeReadPairPartial(sF, qF, sR, qR, lF, lR, 6, 6, 0, 0, 0)
    expect_identical(res$mergedSeq, sF)
    expect_equal(res$mergedLengths, lF)
    res <- mutscan:::test_mergeReadPairPartial(sR, qR, sF, qF, lR, lF, 6, 6, 0, 0, 0)
    expect_identical(res$mergedSeq, sR)
    expect_equal(res$mergedLengths, lR)
  }

  ## test variable segment lengths merging
  ## ... that must work
  sF3 <- ""AAAAAAAAACGTCCCAAC"";   qF3 <- rep(40L, nchar(sF3)); lF3 <- c(13L,  5L)
  sR3 <- ""CGTCCCAACCCGGGGGGGGG""; qR3 <- rep(42L, nchar(sR3)); lR3 <- c( 4L, 16L)
  res3a <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, lF3, lR3, 1, 0, 1, 0, 0.1, FALSE)
  res3b <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, c(5L, 13L), 20L, 1, 0, 1, 0, 0.1, FALSE)
  res3c <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, c(5L, 13L), c(14L, 6L), 1, 0, 1, 0, 0.1, FALSE)
  res3d <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, c(5L, 8L, 5L), c(4L, 13L, 3L), 1, 0, 1, 0, 0.1, FALSE)
  res3e <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, c(3L, 2L, 8L, 2L, 3L), c(4L, 2L, 11L, 3L), 1, 0, 1, 0, 0.1, FALSE)
  res3f <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, c(5L, 13L), c(9L, 5L, 6L), 1, 0, 1, 0, 0.1, FALSE)
  expect_false(res3a$return)
  expect_identical(res3a$mergedSeq, ""AAAAAAAAACGTCCCAACCCGGGGGGGGG"")
  expect_equal(res3a$mergedLengths, c(13L, 16L))
  expect_identical(res3b$mergedSeq, ""AAAAAAAAACGTCCCAACCCGGGGGGGGG"")
  expect_equal(res3b$mergedLengths, c(5L, 24L))
  expect_identical(res3c$mergedSeq, ""AAAAAAAAACGTCCCAACCCGGGGGGGGG"")
  expect_equal(res3c$mergedLengths, c(5L, 18L, 6L))
  expect_identical(res3d$mergedSeq, ""AAAAAAAAACGTCCCAACCCGGGGGGGGG"")
  expect_equal(res3d$mergedLengths, c(5L, 8L, 13L, 3L))
  expect_identical(res3e$mergedSeq, ""AAAAAAAAACGTCCCAACCCGGGGGGGGG"")
  expect_equal(res3e$mergedLengths, c(3L, 2L, 8L, 2L, 11L, 3L))
  expect_identical(res3f$mergedSeq, ""AAAAAAAAACGTCCCAACCCGGGGGGGGG"")
  expect_equal(res3f$mergedLengths, c(5L, 13L, 5L, 6L))
  ## ... that must fail
  res3g <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, c(12L, 6L), c( 4L, 16L), 1, 0, 1, 0, 0.1, FALSE)
  res3h <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, c(5L, 13L), c(2L, 18L), 1, 0, 1, 0, 0.1, FALSE)
  res3i <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sR3, qR3, c(12L, 6L), c(3L, 4L, 2L, 11L), 1, 0, 1, 0, 0.1, FALSE)
  expect_identical(res3g$mergedSeq, sF3)
  expect_true(res3g$return)
  expect_identical(res3h$mergedSeq, sF3)
  expect_true(res3h$return)
  expect_identical(res3i$mergedSeq, sF3)
  expect_true(res3i$return)
  ## ... that completely overlaps and must work
  res3j <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sF3, qF3, 18L, 18L, 1, 0, 1, 0, 0.1, FALSE)
  res3k <- mutscan:::test_mergeReadPairPartial(sF3, qF3, sF3, qF3, c(5L, 13L), c(5L, 13L), 1, 0, 1, 0, 0.1, FALSE)
  expect_identical(res3j$mergedSeq, sF3)
  expect_false(res3j$return)
  expect_equal(res3j$mergedLengths, 18L)
  expect_identical(res3k$mergedSeq, sF3)
  expect_false(res3k$return)
  expect_equal(res3k$mergedLengths, c(5L, 13L))
})

## ----------------------------------------------------------------------------
## makeBaseHGVS/makeAAHGVS
## ----------------------------------------------------------------------------
test_that(""makeBaseHGVS works"", {
    expect_equal(makeBaseHGVS(c(""FOS.1.A"", ""FOS.2.T""), ""."", ""TAG"", ""ATG""), ""1_2delinsAT_"")
    expect_equal(makeBaseHGVS(c(""f.1.A"", ""r.4.A""), ""."", ""TAGT"", ""AAGA""), ""[1T>A;4T>A]_"")
    expect_equal(makeBaseHGVS(c(""f.1.A"", ""r.4.A"", ""f.6.C""), ""."", ""TAGTGTAGTCCGT"", ""AAGAGCAGTCCGT""),
                 ""[1T>A;4_6delinsAGC]_"")
    expect_equal(makeBaseHGVS(c(""r.4.A""), ""."", ""TAGTGTAGTCCGT"", ""TAGAGTAGTCCGT""), ""4T>A_"")
    expect_equal(makeBaseHGVS(character(0), ""."", ""AAA"", ""AAA""), ""_"")
    expect_equal(makeBaseHGVS(c(""f.1.A"", ""r.4.A"", ""r.8.A""), ""."",
                              ""TACTTTAT"", ""AAGAAAAA""),
                 ""[1T>A;4T>A;8T>A]_"")
})

test_that(""makeAAHGVS works"", {
    expect_equal(test_makeAAHGVS(""FOS.1.H"", ""."", ""TDTLQAETDQLEDEKSALQTEIANLLKEKEKL""),
                 ""(Thr1His)_"")
    expect_equal(test_makeAAHGVS(c(""f.1.L"", ""r.4.M""), ""."", ""TDTLQAETDQLEDEKSALQTEIANLLKEKEKL""),
                 ""[(Thr1Leu);(Leu4Met)]_"")
    expect_equal(test_makeAAHGVS(c(""f.1.L"", ""f.2.M""), ""."", ""TDTLQAETDQLEDEKSALQTEIANLLKEKEKL""),
                 ""[(Thr1Leu);(Asp2Met)]_"")
    expect_equal(test_makeAAHGVS(c(""f.1.L"", ""f.2.M"", ""f.7.M""), ""."", ""TDTLQAETDQLEDEKSALQTEIANLLKEKEKL""),
                 ""[(Thr1Leu);(Asp2Met);(Glu7Met)]_"")
    expect_equal(test_makeAAHGVS(character(0), ""."", ""TDTL""), ""_"")
})

## ----------------------------------------------------------------------------
## compareToWildtype
## ----------------------------------------------------------------------------
test_that(""compareToWildtype works"", {
    # read is kept
    expect_identical(
        test_compareToWildtype(varSeq = ""AAAGGACGA"", wtSeq = ""AATGGACGT"",
                               varIntQual = rep(32L, 9L),
                               forbiddenCodons_vect = character(0),
                               mutatedPhredMin = 0.0,
                               nbrMutatedCodonsMax = 3L,
                               codonPrefix = ""xyz"",
                               nbrMutatedBasesMax = 3L,
                               mutNameDelimiter = ""."",
                               collapseToWT = TRUE),
        list(nMutQualTooLow = 0L, nTooManyMutCodons = 0L, nForbiddenCodons = 0L,
             nTooManyMutBases = 0L, nMutBases = 2L, nMutCodons = 2L,
             nMutAAs = 1L, mutantName = ""xyz_"", mutantNameBase = ""xyz_"",
             mutantNameCodon = ""xyz_"", mutantNameBaseHGVS = ""xyz:c_"",
             mutantNameAA = ""xyz_"", mutantNameAAHGVS = ""xyz:p_"",
             mutationTypes = c(""nonsynonymous"", ""silent"")))
    expect_identical(
        test_compareToWildtype(varSeq = ""AAAGGACGA"", wtSeq = ""AATGGACGT"",
                               varIntQual = rep(32L, 9L),
                               forbiddenCodons_vect = character(0),
                               mutatedPhredMin = 0.0,
                               nbrMutatedCodonsMax = 3L,
                               codonPrefix = ""xyz:c"",
                               nbrMutatedBasesMax = 3L,
                               mutNameDelimiter = ""."",
                               collapseToWT = TRUE),
        list(nMutQualTooLow = 0L, nTooManyMutCodons = 0L, nForbiddenCodons = 0L,
             nTooManyMutBases = 0L, nMutBases = 2L, nMutCodons = 2L,
             nMutAAs = 1L, mutantName = ""xyz:c_"", mutantNameBase = ""xyz:c_"",
             mutantNameCodon = ""xyz:c_"", mutantNameBaseHGVS = ""xyz:c_"",
             mutantNameAA = ""xyz:c_"", mutantNameAAHGVS = ""xyz:p_"",
             mutationTypes = c(""nonsynonymous"", ""silent"")))
    # read is filtered out
    expect_identical(
        test_compareToWildtype(varSeq = ""CGTCGTCGA"", wtSeq = ""CGTCGTCGT"",
                               varIntQual = rep(32L, 9L),
                               forbiddenCodons_vect = character(0),
                               mutatedPhredMin = 33.0, # <---
                               nbrMutatedCodonsMax = 3L,
                               codonPrefix = ""xyz"",
                               nbrMutatedBasesMax = 3L,
                               mutNameDelimiter = ""."",
                               collapseToWT = TRUE),
        list())
    expect_identical(
        test_compareToWildtype(varSeq = ""CGACGACGA"", wtSeq = ""CGTCGTCGT"",
                               varIntQual = rep(32L, 9L),
                               forbiddenCodons_vect = character(0),
                               mutatedPhredMin = 0.0,
                               nbrMutatedCodonsMax = -1L,
                               codonPrefix = ""xyz"",
                               nbrMutatedBasesMax = 2L, # <---
                               mutNameDelimiter = ""."",
                               collapseToWT = TRUE),
        list())
})

## ----------------------------------------------------------------------------
## groupSimilarSequences
## ----------------------------------------------------------------------------
test_that(""groupSimilarSequences works"", {
    expect_identical(
        groupSimilarSequences(c(""AA"", ""AT"", ""AC"", ""TT""), 1:4, collapseMaxDist = 0),
        data.frame(sequence = c(""AA"", ""AT"", ""AC"", ""TT""),
                   representative = c(""AA"", ""AT"", ""AC"", ""TT""))
    )
    expect_identical(
        groupSimilarSequences(c(""AA"", ""AT"", ""AC"", ""TT""), 1:4, collapseMaxDist = 1),
        data.frame(sequence = c(""AA"", ""AT"", ""AC"", ""TT""),
                   representative = c(""AC"", ""TT"", ""AC"", ""TT""))
    )
    expect_warning(groupSimilarSequences(c(""AA"", ""AT"", ""AC"", ""TTT""), 1:4,
                                       collapseMaxDist = 1),
                 ""not all of the same length"")
})","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_BKtree_utils.R",".R","9927","268","test_that(""levenshtein_distance() works"", {
  # ground truth e.g. from:
  # stringdist::stringdist(s1, s2, method = ""lv"")

  s1 <- ""AAAAAAACCC""
  s2 <- ""AAAAAACCCC""
  s3 <- ""ACGTACGTACGT""
  s4 <- ""CGTACGTACGTA""

  expect_error(levenshtein_distance(c(s1, s1), s2))
  expect_error(levenshtein_distance(s1, c(s2, s2)))

  expect_identical(levenshtein_distance(s1, s1), 0L)
  expect_identical(levenshtein_distance(s2, s2), 0L)
  expect_identical(levenshtein_distance(s3, s3), 0L)
  expect_identical(levenshtein_distance(s4, s4), 0L)
  expect_identical(levenshtein_distance(s1, s2), 1L)
  expect_identical(levenshtein_distance(s1, s3), 8L)
  expect_identical(levenshtein_distance(s1, s4), 9L)
  expect_identical(levenshtein_distance(s2, s3), 9L)
  expect_identical(levenshtein_distance(s2, s4), 9L)
  expect_identical(levenshtein_distance(s3, s4), 2L)
})


test_that(""hamming_distance() works"", {
  # ground truth e.g. from:
  # stringdist::stringdist(s1, s2, method = ""hamming"")

  s1 <- ""AAAAAAACCC""
  s2 <- ""AAAAAACCCC""
  s3 <- ""ACGTACGTACGT""
  s4 <- ""CGTACGTACGTA""

  expect_error(hamming_distance(c(s1, s1), s2))
  expect_error(hamming_distance(s1, c(s2, s2)))

  expect_identical(hamming_distance(s1, s1),  0L)
  expect_identical(hamming_distance(s2, s2),  0L)
  expect_identical(hamming_distance(s3, s3),  0L)
  expect_identical(hamming_distance(s4, s4),  0L)
  expect_identical(hamming_distance(s1, s2),  1L)
  expect_identical(hamming_distance(s2, s1),  1L)
  expect_identical(hamming_distance(s3, s4), 12L)
  expect_identical(hamming_distance(s4, s3), 12L)
})




test_that(""hamming_shift_distance() works"", {
  # ground truth e.g. from:
  # s <- 1; stringdist::stringdist(substr(s1, 1, 15 - s), substr(s2, 1 + s, 15), ""hamming"") + 2 * s
  # s <- 1; stringdist::stringdist(substr(s2, 1, 15 - s), substr(s1, 1 + s, 15), ""hamming"") + 2 * s

  s1 <- ""AAACAAAACGTTGCC""
  s2 <- ""ACAAAACGTTGCCCC""
  s3 <- ""AGTCATGCTTAGAAA""
  s4 <- ""AAAAGTCATGCTTAG""

  expect_error(hamming_shift_distance(c(s1, s1), s2))
  expect_error(hamming_shift_distance(s1, c(s2, s2)))
  expect_error(hamming_shift_distance(s1, s2, c(1L, -1L)))

  expect_identical(hamming_shift_distance(s1, s1),  0L)
  expect_identical(hamming_shift_distance(s2, s2),  0L)
  expect_identical(hamming_shift_distance(s3, s3),  0L)
  expect_identical(hamming_shift_distance(s4, s4),  0L)
  expect_identical(hamming_shift_distance(s1, s2,  0),  9L)
  expect_identical(hamming_shift_distance(s1, s2,  1),  9L)
  expect_identical(hamming_shift_distance(s1, s2,  2),  4L)
  expect_identical(hamming_shift_distance(s1, s2,  3),  4L)
  expect_identical(hamming_shift_distance(s1, s2, -1),  4L)
  expect_identical(hamming_shift_distance(s3, s4,  0), 11L)
  expect_identical(hamming_shift_distance(s3, s4,  1), 11L)
  expect_identical(hamming_shift_distance(s3, s4,  2), 11L)
  expect_identical(hamming_shift_distance(s3, s4,  3),  6L)
  expect_identical(hamming_shift_distance(s3, s4, -1),  6L)
})


# using Rcpp-module that exposes the BKtree class to R
test_that(""low-level BKtree wrapper functions work as expected"", {
  # create random k-mers
  set.seed(42)
  k <- 30L
  n <- 20
  alph <- c(""A"",""C"",""G"",""T"",""N"")
  seqs <- unlist(lapply(seq_len(n - length(alph) + 1),
                        function(i) paste(sample(alph, size = k, replace = TRUE),
                                          collapse = """")))
  seqs <- c(seqs, paste0(setdiff(alph, substr(seqs[1], 1, 1)),
                         substr(seqs[1], 2, k)))

  # load module and create tree instances
  bk <- Rcpp::Module(""mod_BKtree"", PACKAGE = ""mutscan"")
  BKtree <- bk$BKtree
  expect_error(new(BKtree, ""error""))
  # expect_error(new(BKtree, seqs, ""error"")) # triggers an unrecoverable crash in BioC 3.17 -> deactivate
  expect_error(new(BKtree, seqs, ""hamming_shift"", c(1L, -1L)))
  tree <- new(BKtree)
  tree2 <- new(BKtree, ""levenshtein"")
  tree3 <- new(BKtree, seqs[c(seq.int(n), seq.int(n))], ""hamming"", -1)
  tree4 <- new(BKtree, character(0), ""hamming"", -1)
  tree5 <- new(BKtree, ""hamming_shift"", 5L)
  tree6 <- new(BKtree, ""hamming_shift"", 0L)

  expect_s4_class(bk, ""Module"")
  expect_s4_class(BKtree, ""C++Class"")
  expect_s4_class(tree, ""Rcpp_BKtree"")
  expect_s4_class(tree2, ""Rcpp_BKtree"")
  expect_s4_class(tree3, ""Rcpp_BKtree"")
  expect_s4_class(tree4, ""Rcpp_BKtree"")
  expect_s4_class(tree5, ""Rcpp_BKtree"")
  expect_s4_class(tree6, ""Rcpp_BKtree"")
  expect_identical(tree$distance_metric(), ""hamming"")
  expect_identical(tree2$distance_metric(), ""levenshtein"")
  expect_identical(tree3$distance_metric(), ""hamming"")
  expect_identical(tree4$distance_metric(), ""hamming"")
  expect_identical(tree5$distance_metric(), ""hamming_shift"")
  expect_identical(tree6$distance_metric(), ""hamming_shift"")
  expect_identical(tree5$maximal_absolute_shift(), 5L)
  expect_identical(tree6$maximal_absolute_shift(), 0L)

  # add sequences
  expect_error(tree$insert(seqs))
  expect_identical(tree$size, 0)
  expect_identical(tree2$size, 0)
  for (s in seqs) {
    tree$insert(s)
    tree2$insert(s)
  }
  expect_identical(tree$size, n)
  expect_identical(tree2$size, n)
  expect_identical(tree3$size, n)
  tree$insert(seqs[1])
  tree2$insert(seqs[1])
  expect_identical(tree$size, n)
  expect_identical(tree2$size, n)

  # check presence
  expect_true(tree$has(seqs[1], 0))
  expect_true(tree2$has(seqs[1], 0))
  expect_true(tree$has(seqs[1], k))
  expect_true(tree2$has(seqs[1], k))
  expect_false(tree$has(""non_existing"", 0))
  expect_false(tree2$has(""non_existing"", 0))

  # get first element
  expect_identical(tree$first(), seqs[1])
  expect_identical(tree2$first(), seqs[1])

  # memory size
  expect_type(m <- tree$capacity(), ""integer"")
  expect_true(m > 0 && m < n * k * 8)
  expect_identical(tree2$capacity(), m)
  expect_identical(tree3$remove(seqs[n]), NULL)
  expect_type(m3 <- tree3$capacity(), ""integer"")
  expect_true(m3 > 0 && m3 < (n - 1) * k * 8)
  expect_identical(tree3$insert(seqs[n]), NULL)

  # print tree
  expect_output(tree$print(), paste0(""^"", n, "".+0: "", seqs[1]))
  expect_output(tree2$print(), paste0(""^"", n, "".+0: "", seqs[1]))
  expect_identical(tree3$remove(seqs[n]), NULL)
  expect_output(tree3$print(), paste0(""^"", n - 1, "".+0: "", seqs[1]))
  expect_identical(tree3$insert(seqs[n]), NULL)
  expect_output(tree4$print(), ""^0$"")

  # search  similar sequences
  # ... hamming distances
  # ground truth from: sum(stringdist::stringdist(seqs[1], seqs, method = ""hamming"") <= 24)
  res <- tree$search(seqs[1], 0)
  expect_type(res, ""character"")
  expect_length(res, 1L)
  expect_length(tree$search(seqs[1],  1),  5L)
  expect_length(tree$search(seqs[1], 24), 14L)
  expect_length(tree$search(seqs[2],  2),  1L)
  expect_length(tree$search(seqs[2], 17),  1L)
  expect_length(tree$search(seqs[2], 23),  9L)
  expect_length(tree$search(seqs[2], 26), 16L)
  expect_length(tree$search(seqs[2], 27), 20L)
  expect_length(tree$search(seqs[2], 30), 20L)
  expect_true(all(tree$search(seqs[2], 23) %in% seqs))
  # ... hamming+shift distances
  expect_identical(tree4$size, 0)
  expect_identical(tree5$size, 0)
  expect_identical(tree6$size, 0)
  for (s in c(""CGATCGATGCAA"", ""AACGATCGATGC"")) {
    tree4$insert(s)
    tree5$insert(s)
    tree6$insert(s)
  }
  expect_identical(tree4$size, 2)
  expect_identical(tree5$size, 2)
  expect_identical(tree6$size, 2)
  expect_length(tree4$search(""CGATCGATGCAA"",  0), 1L)
  expect_length(tree4$search(""CGATCGATGCAA"", 11), 1L)
  expect_length(tree4$search(""CGATCGATGCAA"", 12), 2L)
  expect_length(tree5$search(""CGATCGATGCAA"",  0), 1L)
  expect_length(tree5$search(""CGATCGATGCAA"",  3), 1L)
  expect_length(tree5$search(""CGATCGATGCAA"",  4), 2L)
  expect_length(tree6$search(""CGATCGATGCAA"",  0), 1L)
  expect_length(tree6$search(""CGATCGATGCAA"", 11), 1L)
  expect_length(tree6$search(""CGATCGATGCAA"", 12), 2L)
  # ... levenshtein distances
  # ground truth from: sum(stringdist::stringdist(seqs[1], seqs, method = ""lv"") <= 24)
  res <- tree2$search(seqs[1], 0)
  expect_type(res, ""character"")
  expect_length(res, 1L)
  expect_length(tree2$search(seqs[1],  1),  5L)
  expect_length(tree2$search(seqs[1], 18),  8L)
  expect_length(tree2$search(seqs[2],  2),  1L)
  expect_length(tree2$search(seqs[2], 17),  3L)
  expect_length(tree2$search(seqs[2], 18),  6L)
  expect_length(tree2$search(seqs[2], 19), 13L)
  expect_length(tree2$search(seqs[2], 20), 18L)
  expect_length(tree2$search(seqs[2], 30), 20L)
  expect_true(all(tree2$search(seqs[2], 19) %in% seqs))

  # remove sequences
  expect_identical(tree$remove(""non_existing""), NULL)
  expect_identical(tree2$remove(""non_existing""), NULL)
  expect_identical(tree$size, n)
  expect_identical(tree2$size, n)
  r <- ceiling(n * 0.7)
  for (i in seq_len(r)) {
    tree$remove(seqs[i])
    tree2$remove(seqs[i])
  }
  expect_identical(tree$size, n - r)
  expect_identical(tree2$size, n - r)
  expect_identical(tree$remove_all(), NULL)
  expect_identical(tree2$remove_all(), NULL)
  expect_identical(tree$size, 0)
  expect_identical(tree2$size, 0)
  expect_identical(tree3$size, n)
  expect_type(tree3$remove(seqs[1]), ""NULL"")
  expect_identical(tree3$size, n - 1)
  expect_type(tree3$insert(seqs[1]), ""NULL"")
  expect_identical(tree3$size, n)

  # check existing elements
  expect_identical(tree$remove_all(), NULL)
  expect_identical(tree2$remove_all(), NULL)
  expect_identical(tree$search(seqs[1], 0), character(0))
  expect_identical(tree2$has(seqs[1], 0), FALSE)
  for (s in seqs) {
    tree$insert(s)
    tree2$insert(s)
  }
  expect_type(res <- tree$get_all(), ""character"")
  expect_type(res2 <- tree2$get_all(), ""character"")
  expect_identical(seqs, res)
  expect_identical(seqs, res2)
  set.seed(43)
  i <- sample(length(seqs), round(length(seqs) / 3))
  for (ii in i) {
    tree$remove(seqs[ii])
    tree2$remove(seqs[ii])
  }
  expect_type(res <- tree$get_all(), ""character"")
  expect_type(res2 <- tree2$get_all(), ""character"")
  expect_identical(seqs[-i], res)
  expect_identical(seqs[-i], res2)

})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_digestFastqs_trans.R",".R","78348","1414","test_that(""digestFastqs works as expected for trans experiments"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCVV"", elementsReverse = ""SUCVV"",
    elementLengthsForward = c(1, 10, 18, 80, 16),
    elementLengthsReverse = c(1, 8, 20, 16, 80),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)

  L <- Ldef; L$fastqForward <- c(fqt1, fqt1); L$fastqReverse <- c(fqt2, fqt2)
  res2 <- do.call(digestFastqs, L)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 192L + 95L + 68L + 37L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 279L)

  expect_equal(res$filterSummary * 2, res2$filterSummary)
  expect_equal(res$summaryTable$nbrReads * 2, res2$summaryTable$nbrReads)
  expect_equal(res$summaryTable$nbrUmis, res2$summaryTable$nbrUmis)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
  expect_equal(sum(res$summaryTable$nbrReads == 2), 2L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""f.13.GAG_r.0.WT"", ""f.26.TAG_r.8.AGG"")))
  expect_equal(sort(res$summaryTable$mutantNameCodon[res$summaryTable$nbrReads == 2]),
               sort(c(""f.13.GAG_r.0.WT"", ""f.26.TAG_r.8.AGG"")))
  expect_equal(sort(res$summaryTable$mutantNameBase[res$summaryTable$nbrReads == 2]),
               sort(c(""f.39.G_r.0.WT"", ""f.76.T_f.77.A_r.22.A_r.23.G"")))
  expect_equal(sort(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$nbrReads == 2]),
               sort(c(""f:c.39T>G_r:c"", ""f:c.76_77delinsTA_r:c.22_23delinsAG"")))
  expect_equal(sort(res$summaryTable$mutantNameAAHGVS[res$summaryTable$nbrReads == 2]),
               sort(c(""f:p.(Asp13Glu)_r:p"", ""f:p.(Leu26*)_r:p.(Val8Arg)"")))
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))

  ## Check the number of reads with a given number of mismatches
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 1]), 4L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 2]), 14L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 3]), 52L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 4]), 83L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 5]), 87L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 6]), 39L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 1]), 15L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 2]), 264L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 0]), 1L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 1]), 37L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 2]), 241L)

  ## Similarly but for the number of unique sequences
  expect_equal(sum(res$summaryTable$nbrMutBases == 1), 3L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 2), 14L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 3), 52L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 4), 82L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 5), 87L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 6), 39L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 1), 14L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 2), 263L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 0), 1L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 1), 36L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 2), 240L)

  ## check that variable segment lengths are reported correctly
  expect_true(all(res$summaryTable$varLengths == ""80,16_16,80""))

  ## Check that mutant naming worked (compare to manual matching)
  example_seq <- paste0(""ACTGATACAACCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTG"",
                        ""CAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCGCCCGGCT"",
                        ""GGAGGAAAAAGTGGGCACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGC"",
                        ""CAACATGCTCAGGGAACAGGTGGCACAGCTT"")
  expect_equal(res$summaryTable$mutantName[res$summaryTable$sequence == example_seq],
               ""f.4.ACC_r.9.GGC"")
  expect_equal(res$summaryTable$mutantNameAA[res$summaryTable$sequence == example_seq],
               ""f.4.T_r.9.G"")
  expect_equal(res$summaryTable$mutantNameCodon[res$summaryTable$sequence == example_seq],
               ""f.4.ACC_r.9.GGC"")
  expect_equal(res$summaryTable$mutantNameBase[res$summaryTable$sequence == example_seq],
               ""f.10.A_f.11.C_r.25.G_r.26.G_r.27.C"")
  expect_equal(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$sequence == example_seq],
               ""f:c.10_11delinsAC_r:c.25_27delinsGGC"")
  expect_equal(res$summaryTable$mutantNameAAHGVS[res$summaryTable$sequence == example_seq],
               ""f:p.(Leu4Thr)_r:p.(Lys9Gly)"")
  
  expect_equal(sum(grepl(""WT"", res$summaryTable$mutantNameAA) & res$summaryTable$nbrMutBases > 0), 37L)
  expect_true(all(grepl(""silent"", res$summaryTable$mutationTypes[grepl(""WT"", res$summaryTable$mutantNameAA) & grepl(""f\\.[1-9]"", res$summaryTable$mutantName) & grepl(""r\\.[1-9]"", res$summaryTable$mutantName)])))
  expect_true(all(grepl(""nonsynonymous|stop"", res$summaryTable$mutationTypes[grepl(""\\.[1-9][0-9]*\\."", res$summaryTable$mutantNameAA)])))
  expect_true(all(grepl(""stop"", res$summaryTable$mutationTypes[grepl(""TAG|TAA|TGA"", res$summaryTable$mutantName)])))

  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(Ldef$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(Ldef$constantReverse[1]) * res$filterSummary$nbrRetained)

  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 160L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 52L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 302L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 472L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 4020L)

  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 14], 11L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 27], 4L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 37], 1L)

  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 14], 204L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 22], 14L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 27], 474L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 33], 462L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 37], 4405L)

  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 14], 17L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 27], 3L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 33], 1L)

})

## ----------------------------------------------------------------------------
## Constant seq mismatch filtering
## ----------------------------------------------------------------------------
test_that(""digestFastqs works as expected for trans experiments, filter based on constant seq mismatches"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = 0,
    constantMaxDistReverse = 0,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 287L + 105L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 28L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 251L)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  

  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))
  expect_equal(sum(res$summaryTable$nbrReads == 2), 1L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""f.26.TAG_r.8.AGG"")))

  ## Check the number of reads with a given number of mismatches
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 1]), 3L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 2]), 14L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 3]), 46L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 4]), 76L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 5]), 75L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 6]), 37L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 1]), 14L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 2]), 237L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 0]), 1L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 1]), 35L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 2]), 215L)
  
  ## Similarly but for the number of unique sequences
  expect_equal(sum(res$summaryTable$nbrMutBases == 1), 3L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 2), 14L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 3), 46L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 4), 75L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 5), 75L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 6), 37L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 1), 14L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 2), 236L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 0), 1L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 1), 35L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 2), 214L)
  
  
  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(Ldef$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(Ldef$constantReverse[1]) * res$filterSummary$nbrRetained)

  expect_equal(res$errorStatistics$nbrMismatchForward, rep(0L, nrow(res$errorStatistics)))
  expect_equal(res$errorStatistics$nbrMismatchReverse, rep(0L, nrow(res$errorStatistics)))

  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 126L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 43L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 248L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 398L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 3703L)

  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 14], 137L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 22], 11L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 27], 380L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 33], 407L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 37], 4085L)

  ## --------------------------------------------------------------------------
  ## Allow 1 mismatch
  ## --------------------------------------------------------------------------
  L <- Ldef; L$constantMaxDistForward <- 1L; L$constantMaxDistReverse <- 1L
  res <- do.call(digestFastqs, L)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 287L + 105L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 4L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 275L)

  for (nm in setdiff(names(L), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], L[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(L[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))
  expect_equal(sum(res$summaryTable$nbrReads == 2), 2L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""f.13.GAG_r.0.WT"", ""f.26.TAG_r.8.AGG"")))

  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(L$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(L$constantReverse[1]) * res$filterSummary$nbrRetained)

  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 147L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 49L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 288L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 459L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 3994L)

  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 14], 9L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 27], 3L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 37], 1L)

  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 14], 178L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 22], 12L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 27], 450L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 33], 454L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 37], 4394L)

  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 14], 10L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 27], 1L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 33], 1L)

  ## --------------------------------------------------------------------------
  ## Provide 2 constant sequences
  ## --------------------------------------------------------------------------
  L <- Ldef; L$constantForward <- c(""AACCGGAGGAGGGAGCTG"", ""AACCGGCGGAGGGAGCTG"")
  res <- do.call(digestFastqs, L)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 287L + 105L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 26L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 253L)

  for (nm in setdiff(names(L), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], L[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(L[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  

  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))
  expect_equal(sum(res$summaryTable$nbrReads == 2), 1L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""f.26.TAG_r.8.AGG"")))

  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(L$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(L$constantReverse[1]) * res$filterSummary$nbrRetained)

  ## --------------------------------------------------------------------------
  ## Split constant sequence in two and let the function concatenate them
  ## --------------------------------------------------------------------------
  L <- Ldef; L$elementsForward <- ""SUCCV""; L$elementsReverse <- ""SUCCV""
  L$elementLengthsForward <- c(1, 10, 10, 8, 96); L$elementLengthsReverse <- c(1, 8, 9, 11, 96)

  res <- do.call(digestFastqs, L)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 287L + 105L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 28L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 251L)

  for (nm in setdiff(names(L), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], L[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(L[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  

  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))
  expect_equal(sum(res$summaryTable$nbrReads == 2), 1L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""f.26.TAG_r.8.AGG"")))

  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(L$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(L$constantReverse[1]) * res$filterSummary$nbrRetained)

  expect_equal(res$errorStatistics$nbrMismatchForward, rep(0L, nrow(res$errorStatistics)))
  expect_equal(res$errorStatistics$nbrMismatchReverse, rep(0L, nrow(res$errorStatistics)))

  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 126L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 43L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 248L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 398L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 3703L)

  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 14], 137L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 22], 11L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 27], 380L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 33], 407L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 37], 4085L)

})

## ----------------------------------------------------------------------------
## Read composition specification
## ----------------------------------------------------------------------------
test_that(""digestFastqs works as expected for trans experiments - no UMI specified"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SSCV"", elementsReverse = ""SSCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 500, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 287L + 105L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 279L)

  expect_equal(res$summaryTable$nbrUmis, rep(0, nrow(res$summaryTable)))
})

test_that(""digestFastqs works as expected for trans experiments, when variable sequence length is not given"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, -1),
    elementLengthsReverse = c(1, 8, 20, -1),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 700, 
    maxReadLength = 125
  )

  res <- do.call(digestFastqs, Ldef)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 287L + 105L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 279L)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  

  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
  expect_equal(sum(res$summaryTable$nbrReads == 2), 2L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""f.13.GAG_r.0.WT"", ""f.26.TAG_r.8.AGG"")))
  expect_equal(sort(res$summaryTable$mutantNameCodon[res$summaryTable$nbrReads == 2]),
               sort(c(""f.13.GAG_r.0.WT"", ""f.26.TAG_r.8.AGG"")))
  expect_equal(sort(res$summaryTable$mutantNameBase[res$summaryTable$nbrReads == 2]),
               sort(c(""f.39.G_r.0.WT"", ""f.76.T_f.77.A_r.22.A_r.23.G"")))
  expect_equal(sort(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$nbrReads == 2]),
               sort(c(""f:c.39T>G_r:c"", ""f:c.76_77delinsTA_r:c.22_23delinsAG"")))
  expect_equal(sort(res$summaryTable$mutantNameAAHGVS[res$summaryTable$nbrReads == 2]),
               sort(c(""f:p.(Asp13Glu)_r:p"", ""f:p.(Leu26*)_r:p.(Val8Arg)"")))
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))
  
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))

  ## Check that mutant naming worked (compare to manual matching)
  example_seq <- paste0(""ACTGATACAACCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTG"",
                        ""CAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCGCCCGGCT"",
                        ""GGAGGAAAAAGTGGGCACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGC"",
                        ""CAACATGCTCAGGGAACAGGTGGCACAGCTT"")
  expect_equal(res$summaryTable$mutantName[res$summaryTable$sequence == example_seq],
               ""f.4.ACC_r.9.GGC"")
  expect_equal(res$summaryTable$mutantNameAA[res$summaryTable$sequence == example_seq],
               ""f.4.T_r.9.G"")
  expect_equal(res$summaryTable$mutantNameCodon[res$summaryTable$sequence == example_seq],
               ""f.4.ACC_r.9.GGC"")
  expect_equal(res$summaryTable$mutantNameBase[res$summaryTable$sequence == example_seq],
               ""f.10.A_f.11.C_r.25.G_r.26.G_r.27.C"")
  expect_equal(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$sequence == example_seq],
               ""f:c.10_11delinsAC_r:c.25_27delinsGGC"")
  expect_equal(res$summaryTable$mutantNameAAHGVS[res$summaryTable$sequence == example_seq],
               ""f:p.(Leu4Thr)_r:p.(Lys9Gly)"")

  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(Ldef$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(Ldef$constantReverse[1]) * res$filterSummary$nbrRetained)

  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 160L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 52L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 302L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 472L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 4020L)

  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 14], 11L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 27], 4L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 37], 1L)

  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 14], 204L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 22], 14L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 27], 474L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 33], 462L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 37], 4405L)

  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 14], 17L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 27], 3L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 33], 1L)

})

## ----------------------------------------------------------------------------
## Multiple wildtype sequences
## ----------------------------------------------------------------------------
test_that(""digestFastqs works as expected for trans experiments when multiple reference sequences are provided"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = c(forward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",  ## this is the right one
                        wrong = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT""),
    wildTypeReverse = c(wrong = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
                        reverse1 = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT""), ## this is the right one
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 30.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNA"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 25.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""="",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = TRUE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )

  expect_output(res <- do.call(digestFastqs, Ldef))

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 88L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 189L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 149L + 5L + 34L + 25L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 1L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 193L)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""nThreads"",
                                    ""forbiddenMutatedCodonsReverse"", ""verbose"",
                                    ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  

  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
  expect_equal(sum(res$summaryTable$nbrReads == 2), 1L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""forward=26=TAG_reverse1=8=AGG"")))
  expect_equal(sort(res$summaryTable$mutantNameCodon[res$summaryTable$nbrReads == 2]),
               sort(c(""forward=26=TAG_reverse1=8=AGG"")))
  expect_equal(sort(res$summaryTable$mutantNameBase[res$summaryTable$nbrReads == 2]),
               sort(c(""forward=76=T_forward=77=A_reverse1=22=A_reverse1=23=G"")))
  expect_equal(sort(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$nbrReads == 2]),
               sort(c(""forward:c.76_77delinsTA_reverse1:c.22_23delinsAG"")))
  expect_equal(sort(res$summaryTable$mutantNameAAHGVS[res$summaryTable$nbrReads == 2]),
               sort(c(""forward:p.(Leu26*)_reverse1:p.(Val8Arg)"")))
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))

  ## Check that mutant naming worked (compare to manual matching)
  example_seq <- paste0(""ACTGATACAACCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTG"",
                        ""CAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA_ATCGCCCGGCT"",
                        ""GGAGGAAAAAGTGGGCACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGC"",
                        ""CAACATGCTCAGGGAACAGGTGGCACAGCTT"")
  expect_equal(res$summaryTable$mutantName[res$summaryTable$sequence == example_seq],
               ""forward=4=ACC_reverse1=9=GGC"")
  expect_equal(res$summaryTable$mutantNameAA[res$summaryTable$sequence == example_seq],
               ""forward=4=T_reverse1=9=G"")
  expect_equal(res$summaryTable$mutantNameCodon[res$summaryTable$sequence == example_seq],
               ""forward=4=ACC_reverse1=9=GGC"")
  expect_equal(res$summaryTable$mutantNameBase[res$summaryTable$sequence == example_seq],
               ""forward=10=A_forward=11=C_reverse1=25=G_reverse1=26=G_reverse1=27=C"")
  expect_equal(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$sequence == example_seq],
               ""forward:c.10_11delinsAC_reverse1:c.25_27delinsGGC"")
  expect_equal(res$summaryTable$mutantNameAAHGVS[res$summaryTable$sequence == example_seq],
               ""forward:p.(Leu4Thr)_reverse1:p.(Lys9Gly)"")

  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(Ldef$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(Ldef$constantReverse[1]) * res$filterSummary$nbrRetained)

  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 96L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 26L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 179L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 302L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 2865L)

  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 14], 2L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 27], 3L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 37], 1L)

  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 14], 116L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 22], 5L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 27], 308L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 33], 322L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 37], 3101L)

  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 14], 6L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 27], 1L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 33], 1L)

  ## Test that we get the same results with useTreeWTmatch = TRUE
  Ldef$useTreeWTmatch <- TRUE
  expect_output(res2 <- do.call(digestFastqs, Ldef))

  expect_equal(res$filterSummary$nbrTotal, res2$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res2$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res2$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res2$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res2$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res2$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res2$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res2$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res2$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res2$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res2$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res2$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res2$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res2$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res2$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res2$filterSummary$nbrRetained)

  expect_equal(res$summaryTable$nbrReads, res2$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res2$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res2$summaryTable$sequence)

})

## ----------------------------------------------------------------------------
## Only forward read
## ----------------------------------------------------------------------------
test_that(""digestFastqs works as expected for experiments with only forward read"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = NULL,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = """",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = numeric(0),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = """",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = """",
    avePhredMinForward = 20.0, avePhredMinReverse = 30.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNA"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 25.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 300, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)

  L <- Ldef; L$fastqForward <- c(fqt1, fqt1); L$fastqReverse <- NULL
  res2 <- do.call(digestFastqs, L)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 297L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 0L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 211L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 212L + 7L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 1L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
    expect_equal(res$filterSummary$nbrRetained, 272L)
    
    expect_equal(res$filterSummary * 2, res2$filterSummary)
    expect_equal(res$summaryTable$nbrReads * 2, res2$summaryTable$nbrReads)
    expect_equal(res$summaryTable$nbrUmis, res2$summaryTable$nbrUmis)
    
    for (nm in setdiff(names(Ldef), c(""fastqReverse"", ""forbiddenMutatedCodonsForward"",
                                      ""forbiddenMutatedCodonsReverse"", ""verbose"",
                                      ""elementLengthsReverse"", ""fastqForward""))) {
        expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
    }
    expect_equal(res$parameters[[""fastqReverse""]], """", ignore_attr = TRUE)
    expect_equal(res$parameters[[""fastqForward""]], 
                 normalizePath(Ldef[[""fastqForward""]], mustWork = FALSE), 
                 ignore_attr = TRUE)

    
    expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
    expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
    expect_equal(sum(res$summaryTable$nbrReads == 2), 29L)
    
    ## Check the number of reads with a given number of mismatches
    expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 0]), 10L)
    expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 1]), 47L)
    expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 2]), 123L)
    expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 3]), 92L)
    expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 0]), 10L)
    expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 1]), 262L)
    expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 0]), 21L)
    expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 1]), 251L)
    
    ## Similarly but for the number of unique sequences
    expect_equal(sum(res$summaryTable$nbrMutBases == 0), 1L)
    expect_equal(sum(res$summaryTable$nbrMutBases == 1), 43L)
    expect_equal(sum(res$summaryTable$nbrMutBases == 2), 102L)
    expect_equal(sum(res$summaryTable$nbrMutBases == 3), 78L)
    expect_equal(sum(res$summaryTable$nbrMutCodons == 0), 1L)
    expect_equal(sum(res$summaryTable$nbrMutCodons == 1), 223L)
    expect_equal(sum(res$summaryTable$nbrMutAAs == 0), 10L)
    expect_equal(sum(res$summaryTable$nbrMutAAs == 1), 214L)
    
    
    ## Check that mutant naming worked (compare to manual matching)
    example_seq <- paste0(""ACTGATACAACCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTG"",
                          ""CAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")
    expect_equal(res$summaryTable$mutantName[res$summaryTable$sequence == example_seq],
                 ""f.4.ACC"")
    
    expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
                 nchar(Ldef$constantForward[1]) * res$filterSummary$nbrRetained)
    expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
                 nchar(Ldef$constantReverse[1]) * res$filterSummary$nbrRetained)
    
    expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 187L)
    expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 47L)
    expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 314L)
    expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 469L)
    expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 3868L)
    
    expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 14], 6L)
    expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 27], 4L)
    expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 37], 1L)
    
})

## ----------------------------------------------------------------------------
## Only forward read, provide a too short WT sequence
## ----------------------------------------------------------------------------
test_that(""digestFastqs works as expected when the WT sequence is too short"", {
    fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
    fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
    ## default arguments
    Ldef <- list(
        fastqForward = fqt1, fastqReverse = NULL,
        mergeForwardReverse = FALSE,
        minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
        revComplForward = FALSE, revComplReverse = FALSE,
        elementsForward = ""SUCV"", elementsReverse = """",
        elementLengthsForward = c(1, 10, 18, 96),
        elementLengthsReverse = numeric(0),
        adapterForward = ""GGAAGAGCACACGTC"",
        adapterReverse = """",
        primerForward = """",
        primerReverse = """",
        wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACT"",
        wildTypeReverse = """",
        constantForward = ""AACCGGAGGAGGGAGCTG"",
        constantReverse = """",
        avePhredMinForward = 20.0, avePhredMinReverse = 30.0,
        variableNMaxForward = 0, variableNMaxReverse = 0,
        umiNMax = 0,
        nbrMutatedCodonsMaxForward = 1,
        nbrMutatedCodonsMaxReverse = 1,
        nbrMutatedBasesMaxForward = -1,
        nbrMutatedBasesMaxReverse = -1,
        forbiddenMutatedCodonsForward = ""NNA"",
        forbiddenMutatedCodonsReverse = ""NNW"",
        useTreeWTmatch = FALSE,
        mutatedPhredMinForward = 25.0, mutatedPhredMinReverse = 0.0,
        mutNameDelimiter = ""."",
        constantMaxDistForward = -1,
        constantMaxDistReverse = -1,
        umiCollapseMaxDist = 0,
        filteredReadsFastqForward = """",
        filteredReadsFastqReverse = """",
        maxNReads = -1, verbose = FALSE,
        nThreads = 1, chunkSize = 300, 
        maxReadLength = 1024
    )
    
    res <- do.call(digestFastqs, Ldef)
    
    expect_equal(res$filterSummary$nbrTotal, 1000L)
    expect_equal(res$filterSummary$f1_nbrAdapter, 297L)
    expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
    expect_equal(res$filterSummary$f3_nbrReadWrongLength, 284L) ## generated as 1000-297-419 (all var seqs are too long)
    expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
    expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 0L)
    expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
    expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
    expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
    expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
    expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 419L) ## this value was generated and checked manually
    expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
    expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
    expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
    expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
    expect_equal(res$filterSummary$nbrRetained, 0L)
    
    expect_equal(nrow(res$summaryTable), 0L)
    
    for (nm in setdiff(names(Ldef), c(""fastqReverse"", ""forbiddenMutatedCodonsForward"",
                                      ""forbiddenMutatedCodonsReverse"", ""verbose"",
                                      ""elementLengthsReverse"", ""fastqForward""))) {
        expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
    }
    expect_equal(res$parameters[[""fastqReverse""]], """", ignore_attr = TRUE)
    expect_equal(res$parameters[[""fastqForward""]], 
                 normalizePath(Ldef[[""fastqForward""]], mustWork = FALSE), 
                 ignore_attr = TRUE)
    
})

## ----------------------------------------------------------------------------
## Save filtered reads to fastq files
## ----------------------------------------------------------------------------
test_that(""writing filtered reads to file works"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")

  outfile1 <- file.path(tempdir(), ""mutscan_test_1.fastq.gz"")
  outfile2 <- gsub(""_1.fastq"", ""_2.fastq"", outfile1)

  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = outfile1,
    filteredReadsFastqReverse = outfile2,
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 287L + 105L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 279L)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse"",
                                    ""filteredReadsFastqForward"", 
                                    ""filteredReadsFastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"", ""filteredReadsFastqForward"",
               ""filteredReadsFastqReverse"")) {
    expect_equal(normalizePath(res$parameters[[nm]], mustWork = FALSE), 
                 normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  

  # Biostrings 2.63.1 on arm64 produces the following warning:
  # Warning message:
  # In XStringSet(""DNA"", x, start = start, end = end, width = width,  :
  #   metadata columns on input DNAStringSet object were dropped
  suppressWarnings({
      out1 <- Biostrings::readQualityScaledDNAStringSet(outfile1)
      out2 <- Biostrings::readQualityScaledDNAStringSet(outfile2)
  })

  expect_equal(length(out1), length(out2))
  expect_equal(names(out1), gsub("" 2"", "" 1"", names(out2)))
  expect_equal(length(out1), res$filterSummary$nbrTotal - res$filterSummary$nbrRetained)
  expect_equal(length(grep(""adapter"", names(out1))), res$filterSummary$f1_nbrAdapter)
  expect_equal(length(grep(""noPrimer"", names(out1))), res$filterSummary$f2_nbrNoPrimer)
  expect_equal(length(grep(""readWrongLength"", names(out1))), res$filterSummary$f3_nbrReadWrongLength)
  expect_equal(length(grep(""noValidOverlap"", names(out1))), res$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(length(grep(""avgVarQualTooLow"", names(out1))), res$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(length(grep(""tooManyNinVar"", names(out1))), res$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(length(grep(""tooManyNinUMI"", names(out1))), res$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(length(grep(""tooManyBestWTHits"", names(out1))), res$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(length(grep(""mutQualTooLow"", names(out1))),
               res$filterSummary$f9_nbrMutQualTooLow + res$filterSummary$f10a_nbrTooManyMutCodons +
                 res$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(length(grep(""tooManyMutConstant"", names(out1))), res$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(length(grep(""tooManyBestConstantHits"", names(out1))), res$filterSummary$f13_nbrTooManyBestConstantHits)

  ## Test that we get the same results with useTreeWTmatch = TRUE
  Ldef$useTreeWTmatch <- TRUE
  res2 <- do.call(digestFastqs, Ldef)

  expect_equal(res$filterSummary$nbrTotal, res2$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res2$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res2$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res2$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res2$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res2$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res2$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res2$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res2$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res2$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res2$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res2$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res2$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res2$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res2$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res2$filterSummary$nbrRetained)

  expect_equal(res$summaryTable$nbrReads, res2$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res2$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res2$summaryTable$sequence)

})

test_that(""digestFastqs works as expected for trans experiments, if collapsing to WT"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCVV"", elementsReverse = ""SUCVV"",
    elementLengthsForward = c(1, 10, 18, 80, 16),
    elementLengthsReverse = c(1, 8, 20, 16, 80),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    collapseToWTForward = TRUE,
    collapseToWTReverse = TRUE, 
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )
  
  res <- do.call(digestFastqs, Ldef)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 192L + 95L + 68L + 37L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 279L)
  
  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(res$summaryTable$nbrReads, res$summaryTable$maxNbrReads)
  expect_equal(nrow(res$summaryTable), 1L)
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))
  
  ## Check the number of reads with a given number of mismatches
  expect_equal(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == ""1,2,3,4,5,6""], 279L)
  expect_equal(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == ""1,2""], 279L)
  expect_equal(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == ""0,1,2""], 279L)
  expect_equal(res$summaryTable$mutationTypes, ""nonsynonymous,silent,stop"")
  expect_equal(res$summaryTable$mutantNameAA, ""f_r"")
  expect_equal(res$summaryTable$mutantNameBase, ""f_r"")
  expect_equal(res$summaryTable$mutantNameCodon, ""f_r"")
  expect_equal(res$summaryTable$mutantNameBaseHGVS, ""f:c_r:c"")
  expect_equal(res$summaryTable$mutantNameAAHGVS, ""f:p_r:p"")
  
  ## check that variable segment lengths are reported correctly
  expect_equal(res$summaryTable$varLengths, ""80,16_16,80"")
})

test_that(""reads are filtered out if there are too many constant hits"", {
    fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
    fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
    ## default arguments
    Ldef <- list(
        fastqForward = fqt1, fastqReverse = fqt2,
        mergeForwardReverse = FALSE,
        minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
        revComplForward = FALSE, revComplReverse = FALSE,
        elementsForward = ""SUCVV"", elementsReverse = ""SUCVV"",
        elementLengthsForward = c(1, 10, 18, 80, 16),
        elementLengthsReverse = c(1, 8, 20, 16, 80),
        adapterForward = ""GGAAGAGCACACGTC"",
        adapterReverse = ""GGAAGAGCGTCGTGT"",
        primerForward = """",
        primerReverse = """",
        wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
        wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
        constantForward = ""AACCGGAGGAGGGAGCTG"",
        constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
        avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
        variableNMaxForward = 0, variableNMaxReverse = 0,
        umiNMax = 0,
        nbrMutatedCodonsMaxForward = 1,
        nbrMutatedCodonsMaxReverse = 1,
        nbrMutatedBasesMaxForward = -1,
        nbrMutatedBasesMaxReverse = -1,
        forbiddenMutatedCodonsForward = ""NNW"",
        forbiddenMutatedCodonsReverse = ""NNW"",
        useTreeWTmatch = FALSE,
        collapseToWTForward = TRUE,
        collapseToWTReverse = TRUE, 
        mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
        mutNameDelimiter = ""."",
        constantMaxDistForward = -1,
        constantMaxDistReverse = -1,
        umiCollapseMaxDist = 0,
        filteredReadsFastqForward = """",
        filteredReadsFastqReverse = """",
        maxNReads = -1, verbose = FALSE,
        nThreads = 1, chunkSize = 1000, 
        maxReadLength = 1024
    )
    
    ## Forward
    Ldef1 <- Ldef; Ldef1$constantForward <- c(c1 = ""AACCGGAGGAGGGAGCTA"", c2 = ""AACCGGAGGAGGGAGCTC"")
    res1 <- do.call(digestFastqs, Ldef1)
    
    expect_equal(res1$filterSummary$nbrTotal, 1000L)
    expect_equal(res1$filterSummary$f1_nbrAdapter, 314L)
    expect_equal(res1$filterSummary$f2_nbrNoPrimer, 0L)
    expect_equal(res1$filterSummary$f3_nbrReadWrongLength, 0L)
    expect_equal(res1$filterSummary$f4_nbrNoValidOverlap, 0L)
    expect_equal(res1$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
    expect_equal(res1$filterSummary$f6_nbrTooManyNinVar, 0L)
    expect_equal(res1$filterSummary$f7_nbrTooManyNinUMI, 0L)
    expect_equal(res1$filterSummary$f8_nbrTooManyBestWTHits, 0L)
    expect_equal(res1$filterSummary$f9_nbrMutQualTooLow, 0L)
    expect_equal(res1$filterSummary$f10a_nbrTooManyMutCodons, 192L + 95L + 68L + 37L)
    expect_equal(res1$filterSummary$f10b_nbrTooManyMutBases, 0L)
    expect_equal(res1$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
    expect_equal(res1$filterSummary$f12_nbrTooManyMutConstant, 0L)
    expect_equal(res1$filterSummary$f13_nbrTooManyBestConstantHits, 279L)
    expect_equal(res1$filterSummary$nbrRetained, 0L)
    
    ## Reverse
    Ldef1 <- Ldef; Ldef1$constantReverse <- c(c1 = ""GAAAAAGGAAGCTGGAGAGG"", c2 = ""GAAAAAGGAAGCTGGAGAGT"")
    res1 <- do.call(digestFastqs, Ldef1)
    
    expect_equal(res1$filterSummary$nbrTotal, 1000L)
    expect_equal(res1$filterSummary$f1_nbrAdapter, 314L)
    expect_equal(res1$filterSummary$f2_nbrNoPrimer, 0L)
    expect_equal(res1$filterSummary$f3_nbrReadWrongLength, 0L)
    expect_equal(res1$filterSummary$f4_nbrNoValidOverlap, 0L)
    expect_equal(res1$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
    expect_equal(res1$filterSummary$f6_nbrTooManyNinVar, 0L)
    expect_equal(res1$filterSummary$f7_nbrTooManyNinUMI, 0L)
    expect_equal(res1$filterSummary$f8_nbrTooManyBestWTHits, 0L)
    expect_equal(res1$filterSummary$f9_nbrMutQualTooLow, 0L)
    expect_equal(res1$filterSummary$f10a_nbrTooManyMutCodons, 192L + 95L + 68L + 37L)
    expect_equal(res1$filterSummary$f10b_nbrTooManyMutBases, 0L)
    expect_equal(res1$filterSummary$f11_nbrForbiddenCodons, 6L + 2L)
    expect_equal(res1$filterSummary$f12_nbrTooManyMutConstant, 0L)
    expect_equal(res1$filterSummary$f13_nbrTooManyBestConstantHits, 279L)
    expect_equal(res1$filterSummary$nbrRetained, 0L)
})

test_that(""digestFastqs runs with revComplForward = TRUE"", {
    fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
    fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
    ## default arguments
    Ldef <- list(
        fastqForward = fqt1, fastqReverse = fqt2,
        mergeForwardReverse = FALSE,
        minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
        revComplForward = TRUE, revComplReverse = FALSE,
        elementsForward = ""SUCVV"", elementsReverse = ""SUCVV"",
        elementLengthsForward = c(1, 10, 18, 80, 16),
        elementLengthsReverse = c(1, 8, 20, 16, 80),
        adapterForward = ""GGAAGAGCACACGTC"",
        adapterReverse = ""GGAAGAGCGTCGTGT"",
        primerForward = """",
        primerReverse = """",
        wildTypeForward = ""TAGTTTTTCCTTCTCCTTCAGCAGGTTGGCAATCTCGGTCTGCAAAGCAGACTTCTCATCTTCTAGTTGGTCTGTCTCCGCTTGGAGTGTATCAGT"",
        wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
        constantForward = ""CAGCTCCCTCCTCCGGTT"",
        constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
        avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
        variableNMaxForward = 0, variableNMaxReverse = 0,
        umiNMax = 0,
        nbrMutatedCodonsMaxForward = 1,
        nbrMutatedCodonsMaxReverse = 1,
        nbrMutatedBasesMaxForward = -1,
        nbrMutatedBasesMaxReverse = -1,
        forbiddenMutatedCodonsForward = """",
        forbiddenMutatedCodonsReverse = """",
        useTreeWTmatch = FALSE,
        collapseToWTForward = TRUE,
        collapseToWTReverse = TRUE, 
        mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
        mutNameDelimiter = ""."",
        constantMaxDistForward = -1,
        constantMaxDistReverse = -1,
        umiCollapseMaxDist = 0,
        filteredReadsFastqForward = """",
        filteredReadsFastqReverse = """",
        maxNReads = -1, verbose = FALSE,
        nThreads = 1, chunkSize = 1000, 
        maxReadLength = 1024
    )
    
    res <- do.call(digestFastqs, Ldef)

    expect_equal(res$filterSummary$nbrTotal, 1000L)
    expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
    expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
    expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
    expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
    expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
    expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
    expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
    expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
    expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
    expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 394L)
    expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
    expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
    expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
    expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
    expect_equal(res$filterSummary$nbrRetained, 285L)
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_collapseMutantsByAA.R",".R","16335","313","## groupSimilarSequences
seqs <- c(""AACGTAGCA"", ""ACCGTAGCA"", ""AACGGAGCA"", ""ATCGGAGCA"", ""TGAGGCATA"")
scores <- c(5, 1, 3, 1, 8)
gr <- groupSimilarSequences(seqs = seqs, scores = scores, collapseMaxDist = 1, 
                            collapseMinScore = 0, collapseMinRatio = 0, verbose = FALSE)
expect_equal(gr$sequence, seqs)
expect_equal(gr$representative, seqs[c(1, 1, 1, 4, 5)])

gr <- groupSimilarSequences(seqs = seqs, scores = scores, collapseMaxDist = 0, 
                            collapseMinScore = 0, collapseMinRatio = 0, verbose = FALSE)
expect_equal(gr$sequence, seqs)
expect_equal(gr$representative, seqs[c(1, 2, 3, 4, 5)])

gr <- groupSimilarSequences(seqs = seqs, scores = scores, collapseMaxDist = 1, 
                            collapseMinScore = 0, collapseMinRatio = 2, verbose = FALSE)
expect_equal(gr$sequence, seqs)
expect_equal(gr$representative, seqs[c(1, 1, 3, 3, 5)])

gr <- groupSimilarSequences(seqs = seqs, scores = scores, collapseMaxDist = 1, 
                            collapseMinScore = 4, collapseMinRatio = 2, verbose = FALSE)
expect_equal(gr$sequence, seqs)
expect_equal(gr$representative, seqs[c(1, 1, 3, 4, 5)])

gr <- groupSimilarSequences(seqs = seqs, scores = scores, collapseMaxDist = 9, 
                            collapseMinScore = 4, collapseMinRatio = 2, verbose = FALSE)
expect_equal(gr$sequence, seqs)
expect_equal(gr$representative, seqs[c(1, 5, 5, 5, 5)])


## collapseMutants
Ldef <- list(
  mergeForwardReverse = TRUE, 
  minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
  revComplForward = FALSE, revComplReverse = TRUE,
  elementsForward = ""SUCV"", elementsReverse = ""SUCVS"",
  elementLengthsForward = c(1, 10, 18, 96),
  elementLengthsReverse = c(1, 7, 17, 96, -1),
  adapterForward = ""GGAAGAGCACACGTC"", 
  adapterReverse = ""GGAAGAGCGTCGTGT"",
  primerForward = """",
  primerReverse = """",
  wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
  wildTypeReverse = """", 
  constantForward = ""AACCGGAGGAGGGAGCTG"", 
  constantReverse = ""GAGTTCATCCTGGCAGC"", 
  avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
  variableNMaxForward = 0, variableNMaxReverse = 0, 
  umiNMax = 0,
  nbrMutatedCodonsMaxForward = 1,
  nbrMutatedCodonsMaxReverse = 1,
  nbrMutatedBasesMaxForward = -1,
  nbrMutatedBasesMaxReverse = -1,
  forbiddenMutatedCodonsForward = ""NNW"",
  forbiddenMutatedCodonsReverse = ""NNW"",
  useTreeWTmatch = FALSE,
  mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
  mutNameDelimiter = ""."",
  constantMaxDistForward = -1,
  constantMaxDistReverse = -1,
  umiCollapseMaxDist = 0,
  filteredReadsFastqForward = """",
  filteredReadsFastqReverse = """",
  maxNReads = -1, 
  verbose = FALSE,
  nThreads = 1, chunkSize = 1000, 
  maxReadLength = 1024
)
Ldef1 <- c(
  list(fastqForward = system.file(""extdata/cisInput_1.fastq.gz"", package = ""mutscan""),
       fastqReverse = system.file(""extdata/cisInput_2.fastq.gz"", package = ""mutscan"")
  ), Ldef)
Ldef2 <- c(
  list(fastqForward = system.file(""extdata/cisOutput_1.fastq.gz"", package = ""mutscan""),
       fastqReverse = system.file(""extdata/cisOutput_2.fastq.gz"", package = ""mutscan"")
  ), Ldef)

out1 <- do.call(digestFastqs, Ldef1)
out2 <- do.call(digestFastqs, Ldef2)

coldata <- data.frame(Name = c(""sample1"", ""sample2""), 
                      Condition = c(""input"", ""output""),
                      Replicate = c(1, 1), OD = c(0.05, 1.5),
                      stringsAsFactors = FALSE)

se <- summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                          coldata = coldata, countType = ""umis"")

test_that(""collapseMutantsByAA fails with incorrect arguments"", {
    expect_error(collapseMutantsByAA(se = 1))
    expect_error(collapseMutantsByAA(se = coldata))
    expect_error(collapseMutantsByAA(se = out1))
    expect_error(collapseMutantsByAA(se = list(sample1 = out1, sample2 = out2)))
    expect_error(collapseMutantsByAA(se = se, nameCol = 1))
    expect_error(collapseMutantsByAA(se = se, nameCol = c(""sequence"", ""mutantNameAA"")))
    expect_error(collapseMutantsByAA(se = se, nameCol = ""missing""))
})

test_that(""collapseMutantsBySimilarity fails with incorrect arguments"", {
    expect_error(collapseMutantsBySimilarity(se = 1))
    expect_error(collapseMutantsBySimilarity(se = coldata))
    expect_error(collapseMutantsBySimilarity(se = out1))
    expect_error(collapseMutantsBySimilarity(se = list(sample1 = out1, sample2 = out2)))
    expect_error(collapseMutantsBySimilarity(se = se, assayName = 1),
                 ""'assayName' must be of class 'character'"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""random""),
                 ""All values in 'assayName'"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = c(""counts"", ""counts"")),
                 ""'assayName' must have length 1"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = 1),
                 ""'scoreMethod' must be of class 'character'"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = c(""rowSum"", ""rowMean"")),
                 ""'scoreMethod' must have length 1"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""random""),
                 ""All values in 'scoreMethod' must be one of"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = 1),
                 ""'sequenceCol' must be of class 'character'"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", 
                                             sequenceCol = c(""sequence"", ""sequence"")),
                 ""'sequenceCol' must have length 1"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""abc""),
                 ""All values in 'sequenceCol' must be one of"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = ""1""),
                 ""'collapseMaxDist' must be of class 'numeric'"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = c(1, 2)),
                 ""'collapseMaxDist' must have length 1"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = 1, collapseMinScore = ""1""),
                 ""'collapseMinScore' must be of class 'numeric'"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = 1, collapseMinScore = c(1, 2)),
                 ""'collapseMinScore' must have length 1"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = 1, collapseMinScore = 1,
                                             collapseMinRatio = ""1""),
                 ""'collapseMinRatio' must be of class 'numeric'"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = 1, collapseMinScore = 1,
                                             collapseMinRatio = c(1, 2)),
                 ""'collapseMinRatio' must have length 1"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = 1, collapseMinScore = 1,
                                             collapseMinRatio = 0, verbose = ""TRUE""),
                 ""'verbose' must be of class 'logical'"")
    expect_error(collapseMutantsBySimilarity(se = se, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = 1, collapseMinScore = 1,
                                             collapseMinRatio = 0, verbose = c(TRUE, FALSE)),
                 ""'verbose' must have length 1"")
    
    se0 <- se
    SummarizedExperiment::rowData(se0)$sequence[1] <- ""ACGT""
    expect_error(collapseMutantsBySimilarity(se = se0, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = 1, collapseMinScore = 1,
                                             collapseMinRatio = 0, verbose = TRUE),
                 ""All entries in the sequence column must have the same length"")
    se0 <- se
    SummarizedExperiment::rowData(se0)$sequence[1] <- gsub(""A"", ""B"", rowData(se0)$sequence[1])
    expect_error(collapseMutantsBySimilarity(se = se0, assayName = ""counts"", 
                                             scoreMethod = ""rowSum"", sequenceCol = ""sequence"",
                                             collapseMaxDist = 1, collapseMinScore = 1,
                                             collapseMinRatio = 0, verbose = TRUE),
                 ""All entries in the sequence column must consist of"")
})


test_that(""collapseMutantsByAA works as expected"", {
  secoll <- collapseMutantsByAA(se)
  expect_s4_class(secoll, ""SummarizedExperiment"")
  expect_equal(SummarizedExperiment::colData(se), 
               SummarizedExperiment::colData(secoll))
  expect_equal(S4Vectors::metadata(se), 
               S4Vectors::metadata(secoll))
  expect_equal(colnames(se), colnames(secoll))
  
  aapos <- unique(unlist(rowData(se)$mutantNameAA))
  expect_equal(nrow(secoll), length(aapos))
  expect_equal(sort(rownames(secoll)), sort(unique(aapos)))
  
  tmp <- table(rep(unlist(SummarizedExperiment::rowData(se)$mutantNameAA), 
                   SummarizedExperiment::assay(se, ""counts"")[, 1]))
  tmp <- tmp[rownames(secoll)]
  tmp[is.na(tmp)] <- 0
  expect_equal(SummarizedExperiment::assay(secoll, ""counts"")[, 1],
               as.numeric(tmp), ignore_attr = TRUE)
  
  tmp <- table(rep(unlist(SummarizedExperiment::rowData(se)$mutantNameAA), 
                   SummarizedExperiment::assay(se, ""counts"")[, 2]))
  tmp <- tmp[rownames(secoll)]
  tmp[is.na(tmp)] <- 0
  expect_equal(SummarizedExperiment::assay(secoll, ""counts"")[, 2],
               as.numeric(tmp), ignore_attr = TRUE)
  
  for (mn in rownames(secoll)) {
    expect_equal(SummarizedExperiment::assay(secoll, ""counts"")[mn, ],
                 colSums(SummarizedExperiment::assay(se, ""counts"")[SummarizedExperiment::rowData(se)$mutantNameAA == mn, , drop = FALSE]))
  }
  expect_equal(min(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutBases, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$minNbrMutBases)
  expect_equal(min(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutCodons, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$minNbrMutCodons)
  expect_equal(min(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutAAs, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$minNbrMutAAs)
  expect_equal(max(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutBases, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$maxNbrMutBases)
  expect_equal(max(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutCodons, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$maxNbrMutCodons)
  expect_equal(max(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutAAs, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$maxNbrMutAAs)
})

test_that(""collapseMutantsByAA works as expected - collapseToWT"", {
  Ldef1$collapseToWTForward <- TRUE
  Ldef2$collapseToWTForward <- TRUE
  out1 <- do.call(digestFastqs, Ldef1)
  out2 <- do.call(digestFastqs, Ldef2)
  
  se <- summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                            coldata = coldata, countType = ""umis"")
  
  secoll <- collapseMutantsByAA(se)
  expect_equal(SummarizedExperiment::colData(se), 
               SummarizedExperiment::colData(secoll))
  expect_equal(S4Vectors::metadata(se), 
               S4Vectors::metadata(secoll))
  expect_equal(colnames(se), colnames(secoll))
  
  aapos <- unique(unlist(rowData(se)$mutantNameAA))
  expect_equal(nrow(secoll), length(aapos))
  expect_equal(sort(rownames(secoll)), sort(unique(aapos)))
  
  tmp <- table(rep(unlist(SummarizedExperiment::rowData(se)$mutantNameAA), 
                   SummarizedExperiment::assay(se, ""counts"")[, 1]))
  tmp <- tmp[rownames(secoll)]
  tmp[is.na(tmp)] <- 0
  expect_equal(SummarizedExperiment::assay(secoll, ""counts"")[, 1],
               as.numeric(tmp), ignore_attr = TRUE)
  
  tmp <- table(rep(unlist(SummarizedExperiment::rowData(se)$mutantNameAA), 
                   SummarizedExperiment::assay(se, ""counts"")[, 2]))
  tmp <- tmp[rownames(secoll)]
  tmp[is.na(tmp)] <- 0
  expect_equal(SummarizedExperiment::assay(secoll, ""counts"")[, 2],
               as.numeric(tmp), ignore_attr = TRUE)
  
  for (mn in rownames(secoll)) {
    expect_equal(SummarizedExperiment::assay(secoll, ""counts"")[mn, ],
                 colSums(SummarizedExperiment::assay(se, ""counts"")[SummarizedExperiment::rowData(se)$mutantNameAA == mn, , drop = FALSE]))
  }
  expect_equal(min(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutBases, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$minNbrMutBases)
  expect_equal(min(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutCodons, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$minNbrMutCodons)
  expect_equal(min(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutAAs, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$minNbrMutAAs)
  expect_equal(max(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutBases, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$maxNbrMutBases)
  expect_equal(max(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutCodons, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$maxNbrMutCodons)
  expect_equal(max(methods::as(
      lapply(strsplit(SummarizedExperiment::rowData(se)$nbrMutAAs, "",""), function(w) sort(as.integer(w))),
      ""IntegerList"")), 
      SummarizedExperiment::rowData(se)$maxNbrMutAAs)
  
  expect_equal(nrow(secoll), 1L)
  expect_equal(ncol(secoll), 2L)
  expect_equal(SummarizedExperiment::rowData(secoll)$nbrMutBases[[1]], ""0,1,2"")
  expect_equal(SummarizedExperiment::rowData(secoll)$nbrMutCodons[[1]], ""0,1"")
  expect_equal(SummarizedExperiment::rowData(secoll)$nbrMutAAs[[1]], ""0,1"")
  expect_equal(SummarizedExperiment::rowData(secoll)$mutationTypes, 
               ""nonsynonymous,silent,stop"")
})



","R"
"Assay","fmicompbio/mutscan","tests/testthat/test-translate.R",".R","736","16","test_that(""translateString works"", {
  code <- Biostrings::GENETIC_CODE
  for (i in seq_along(code)) {
    expect_identical(translateString(names(code)[i]), unname(code[i]))
  }
  expect_identical(translateString(paste(names(code), collapse = ""_"")),
                   paste(code, collapse = ""_""))
  expect_identical(translateString(paste(names(code), collapse = """")),
                   paste(code, collapse = """"))
  expect_identical(translateString(tolower(paste(names(code), collapse = """"))),
                   paste(code, collapse = """"))
  expect_identical(translateString(paste(gsub(""T"", ""U"", names(code)), collapse = """")),
                   paste(code, collapse = """"))
  expect_identical(translateString(""AAN,AAA.A""), ""XXX"")
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test-relabelMutPositions.R",".R","2838","55","test_that(""relabeling mutants works"", {
    se <- SummarizedExperiment::SummarizedExperiment(
        assays = list(exprs = matrix(rnorm(14), nrow = 7))
    )
    rownames(se) <- c(""f.0.WT"", ""f.1.CGG_r.2.AGT"", ""f.1.AGT_f.3.GTA"",
                      ""f.2.G"", ""f.3.G_r.2.A"", ""f.3.G_r.0.WT"",
                      ""f.2.GGT_f.3.AGT_r.1.GAG"")
    colnames(se) <- c(""sample1"", ""sample2"")
    
    convTable <- data.frame(seqname = c(""f"", ""f"", ""f"", ""f"", ""r"", ""r"", ""r""),
                            position = c(0, 1, 2, 3, 0, 1, 2),
                            name = LETTERS[seq_len(7)])
    
    ## Fails with the wrong arguments
    expect_error(relabelMutPositions(se = 1, conversionTable = convTable,
                                     mutNameDelimiter = "".""),
                 ""'se' must be of class 'SummarizedExperiment'"")
    expect_error(relabelMutPositions(se = se, conversionTable = 1,
                                     mutNameDelimiter = "".""),
                 ""'colnamesconversionTable' must not be NULL"")
    expect_error(relabelMutPositions(se = se, conversionTable = convTable[, -1],
                                     mutNameDelimiter = "".""),
                 ""%in% colnames(conversionTable)) is not TRUE"", fixed = TRUE)
    expect_error(relabelMutPositions(se = se, conversionTable = convTable[, -2],
                                     mutNameDelimiter = "".""),
                 ""%in% colnames(conversionTable)) is not TRUE"", fixed = TRUE)
    expect_error(relabelMutPositions(se = se, conversionTable = convTable[, -3],
                                     mutNameDelimiter = "".""),
                 ""%in% colnames(conversionTable)) is not TRUE"", fixed = TRUE)
    expect_error(relabelMutPositions(se = se, conversionTable = convTable,
                                     mutNameDelimiter = 1),
                 ""'mutNameDelimiter' must be of class 'character'"")
    expect_error(relabelMutPositions(se = se, conversionTable = convTable,
                                     mutNameDelimiter = c(""."", ""_"")),
                 ""'mutNameDelimiter' must have length 1"")
    
    ## Works with the right arguments
    out <- relabelMutPositions(se, convTable, ""."")
    expect_equal(rownames(out),
                 c(""f.A.WT"", ""f.B.CGG_r.G.AGT"", ""f.B.AGT_f.D.GTA"",
                   ""f.C.G"", ""f.D.G_r.G.A"", ""f.D.G_r.E.WT"", 
                   ""f.C.GGT_f.D.AGT_r.F.GAG""))
    expect_equal(colnames(out), colnames(se))
    
    ## Missing entries in conversion table
    convTable <- convTable[c(2, 4, 6, 7), ]
    
    out <- relabelMutPositions(se, convTable, ""."")
    expect_equal(rownames(out),
                 c(""f.0.WT"", ""f.B.CGG_r.G.AGT"", ""f.B.AGT_f.D.GTA"",
                   ""f.2.G"", ""f.D.G_r.G.A"", ""f.D.G_r.0.WT"", 
                   ""f.2.GGT_f.D.AGT_r.F.GAG""))
    expect_equal(colnames(out), colnames(se))
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_plotResults.R",".R","7210","151","se <- readRDS(system.file(""extdata/GSE102901_cis_se.rds"", package = ""mutscan""))
se <- se[1:200, ]
design <- stats::model.matrix(~ Replicate + Condition, 
                              data = SummarizedExperiment::colData(se))
res0 <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                            contrast = NULL, WTrows = ""f.0.WT"", selAssay = ""counts"",
                            pseudocount = 1, method = ""edgeR"",
                            normMethod = ""sum"")

test_that(""plotResults functions fail with incorrect input"", {

    expect_error(plotMeanDiff(as.matrix(res0)))
    expect_error(plotVolcano(as.matrix(res0)))

    expect_error(plotMeanDiff(res0, meanCol = 1))
    expect_error(plotMeanDiff(res0, meanCol = ""missing""))
    expect_error(plotMeanDiff(res0, meanCol = c(""AveExpr"", ""logCPM"")))
    expect_error(plotMeanDiff(res0, logFCCol = 1))
    expect_error(plotMeanDiff(res0, logFCCol = ""missing""))
    expect_error(plotMeanDiff(res0, logFCCol = c(""AveExpr"", ""logFC"")))
    expect_error(plotMeanDiff(res0, pvalCol = 1))
    expect_error(plotMeanDiff(res0, pvalCol = ""missing""))
    expect_error(plotMeanDiff(res0, pvalCol = c(""PValue"", ""P.Value"")))
    expect_error(plotMeanDiff(res0, padjCol = 1))
    expect_error(plotMeanDiff(res0, padjCol = ""missing""))
    expect_error(plotMeanDiff(res0, padjCol = c(""FDR"", ""adj.P.Val"")))

    expect_error(plotVolcano(res0, logFCCol = 1))
    expect_error(plotVolcano(res0, logFCCol = ""missing""))
    expect_error(plotVolcano(res0, logFCCol = c(""AveExpr"", ""logFC"")))
    expect_error(plotVolcano(res0, pvalCol = 1))
    expect_error(plotVolcano(res0, pvalCol = ""missing""))
    expect_error(plotVolcano(res0, pvalCol = c(""PValue"", ""P.Value"")))
    expect_error(plotVolcano(res0, padjCol = 1))
    expect_error(plotVolcano(res0, padjCol = ""missing""))
    expect_error(plotVolcano(res0, padjCol = c(""FDR"", ""adj.P.Val"")))

    expect_error(plotMeanDiff(res0, padjThreshold = ""0.05""))
    expect_error(plotMeanDiff(res0, padjThreshold = c(0.01, 0.05)))
    expect_error(plotVolcano(res0, padjThreshold = ""0.05""))
    expect_error(plotVolcano(res0, padjThreshold = c(0.01, 0.05)))

    expect_error(plotMeanDiff(res0, pointSize = 1))
    expect_error(plotMeanDiff(res0, pointSize = c(""small"", ""large"")))
    expect_error(plotMeanDiff(res0, pointSize = ""wrong""))
    expect_error(plotVolcano(res0, pointSize = 1))
    expect_error(plotVolcano(res0, pointSize = c(""small"", ""large"")))
    expect_error(plotVolcano(res0, pointSize = ""wrong""))

    expect_error(plotMeanDiff(res0, interactivePlot = 1))
    expect_error(plotMeanDiff(res0, interactivePlot = c(TRUE, FALSE)))
    expect_error(plotMeanDiff(res0, interactivePlot = ""TRUE""))
    expect_error(plotVolcano(res0, interactivePlot = 1))
    expect_error(plotVolcano(res0, interactivePlot = c(TRUE, FALSE)))
    expect_error(plotVolcano(res0, interactivePlot = ""TRUE""))

    expect_error(plotMeanDiff(res0, nTopToLabel = ""1""))
    expect_error(plotMeanDiff(res0, nTopToLabel = c(1, 2)))
    expect_error(plotMeanDiff(res0, nTopToLabel = -1))
    expect_error(plotVolcano(res0, nTopToLabel = ""1""))
    expect_error(plotVolcano(res0, nTopToLabel = c(1, 2)))
    expect_error(plotVolcano(res0, nTopToLabel = -1))

    res1 <- res0
    colnames(res1)[colnames(res1) == ""logCPM""] <- ""wrong""
    expect_error(plotMeanDiff(res1),
                 ""No suitable column found for x-axis"")

    res1 <- res0
    colnames(res1)[colnames(res1) == ""logFC""] <- ""wrong""
    expect_error(plotMeanDiff(res1),
                 ""No suitable column found for y-axis"")
    expect_error(plotVolcano(res1),
                 ""No suitable column found for x-axis"")

    res1 <- res0
    colnames(res1)[colnames(res1) == ""FDR""] <- ""wrong""
    expect_error(plotMeanDiff(res1),
                 ""No suitable column found for highlighting"")
    expect_error(plotVolcano(res1),
                 ""No suitable column found for highlighting"")

    res1 <- res0
    colnames(res1)[colnames(res1) == ""PValue""] <- ""wrong""
    expect_error(plotVolcano(res1),
                 ""No suitable column found for y-axis"")

    res1 <- res0
    res1$AveExpr <- res1$logCPM
    expect_warning(plotMeanDiff(res1),
                   ""Multiple suitable columns found for x-axis"")

    res1 <- res0
    res1$P.Value <- res1$PValue
    expect_warning(plotVolcano(res1),
                   ""Multiple suitable columns found for y-axis"")

    res1 <- res0
    res1$adj.P.Val <- res1$FDR
    expect_warning(plotMeanDiff(res1),
                   ""Multiple suitable columns found for highlighting"")
    expect_warning(plotVolcano(res1),
                   ""Multiple suitable columns found for highlighting"")
})

test_that(""plotResults functions work as expected"", {
    res1 <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                                contrast = NULL, WTrows = ""f.0.WT"", selAssay = ""counts"",
                                pseudocount = 1, method = ""edgeR"",
                                normMethod = ""sum"")
    res2 <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                                contrast = NULL, WTrows = ""f.0.WT"", selAssay = ""counts"",
                                pseudocount = 1, method = ""limma"",
                                normMethod = ""sum"")

    expect_true(ggplot2::is_ggplot(plotMeanDiff(res1)))
    expect_true(ggplot2::is_ggplot(plotMeanDiff(res2)))
    expect_true(ggplot2::is_ggplot(plotVolcano(res1)))
    expect_true(ggplot2::is_ggplot(plotVolcano(res2)))

    expect_true(ggplot2::is_ggplot(
        plotMeanDiff(res1, meanCol = ""logCPM"", logFCCol = ""logFC"",
                     padjCol = ""FDR"", padjThreshold = 0.1)))
    expect_true(ggplot2::is_ggplot(
        plotMeanDiff(res2, meanCol = ""AveExpr"", logFCCol = ""logFC"",
                     padjCol = ""adj.P.Val"", padjThreshold = 0.1)))
    expect_true(ggplot2::is_ggplot(
        plotVolcano(res1, logFCCol = ""logFC"", pvalCol = ""PValue"",
                    padjCol = ""FDR"", padjThreshold = 0.1)))
    expect_true(ggplot2::is_ggplot(
        plotVolcano(res2, logFCCol = ""logFC"", pvalCol = ""P.Value"",
                    padjCol = ""adj.P.Val"", padjThreshold = 0.1)))

    expect_true(ggplot2::is_ggplot(plotMeanDiff(res1, nTopToLabel = 5)))
    expect_true(ggplot2::is_ggplot(plotMeanDiff(res2, nTopToLabel = 5)))
    expect_true(ggplot2::is_ggplot(plotVolcano(res1, nTopToLabel = 5)))
    expect_true(ggplot2::is_ggplot(plotVolcano(res2, nTopToLabel = 5)))

    expect_true(ggplot2::is_ggplot(plotMeanDiff(res1, pointSize = ""large"")))
    expect_true(ggplot2::is_ggplot(plotMeanDiff(res2, pointSize = ""large"")))
    expect_true(ggplot2::is_ggplot(plotVolcano(res1, pointSize = ""large"")))
    expect_true(ggplot2::is_ggplot(plotVolcano(res2, pointSize = ""large"")))

    ## These tests won't run if the X11 display connection can not be opened
    # skip_if_not_installed(""plotly"")
    # expect_s3_class(plotMeanDiff(res1, interactivePlot = TRUE), ""plotly"")
    # expect_s3_class(plotMeanDiff(res2, interactivePlot = TRUE), ""plotly"")
    # expect_s3_class(plotVolcano(res1, interactivePlot = TRUE), ""plotly"")
    # expect_s3_class(plotVolcano(res2, interactivePlot = TRUE), ""plotly"")
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_digestFastqs_primers.R",".R","26873","379","test_that(""digestFastqs works as expected for primer experiments"", {
  fqt1 <- system.file(""extdata/leujunt0_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/leujunt0_2.fastq.gz"", package = ""mutscan"")
  leu <- c(ATF2 = ""GATCCTGATGAAAAAAGGAGAAAGTTTTTAGAGCGAAATAGAGCAGCAGCTTCAAGATGCCGACAAAAAAGGAAAGTCTGGGTTCAGTCTTTAGAGAAGAAAGCTGAAGACTTGAGTTCATTAAATGGTCAGCTGCAGAGTGAAGTCACCCTGCTGAGAAATGAAGTGGCACAGCTGAAACAGCTTCTTCTGGCT"",
           ATF7 = ""GATCCAGATGAGCGACGGCAGCGCTTTCTGGAGCGCAACCGGGCTGCAGCCTCCCGCTGCCGCCAAAAGCGAAAGCTGTGGGTGTCCTCCCTAGAGAAGAAGGCCGAAGAACTCACTTCTCAGAACATTCAGCTGAGTAATGAAGTCACATTACTACGCAATGAGGTGGCCCAGTTGAAACAGCTACTGTTAGCT"",
           CREB5 = ""GATCCGGACGAGAGGCGGCGGAAATTTCTGGAACGGAACCGGGCAGCTGCCACCCGCTGCAGACAGAAGAGGAAGGTCTGGGTGATGTCATTGGAAAAGAAAGCAGAAGAACTCACCCAGACAAACATGCAGCTTCAGAATGAAGTGTCTATGTTGAAAAATGAGGTGGCCCAGCTGAAACAGTTGTTGTTAACA"",
           ATF3 = ""GAAGAAGATGAAAGGAAAAAGAGGCGACGAGAAAGAAATAAGATTGCAGCTGCAAAGTGCCGAAACAAGAAGAAGGAGAAGACGGAGTGCCTGCAGAAAGAGTCGGAGAAGCTGGAAAGTGTGAATGCTGAACTGAAGGCTCAGATTGAGGAGCTCAAGAACGAGAAGCAGCATTTGATATACATGCTCAACCTT"",
           JDP2 = ""GAGGAAGAGGAGCGAAGGAAAAGGCGCCGGGAGAAGAACAAAGTCGCAGCAGCCCGATGCCGGAACAAGAAGAAGGAGCGCACGGAGTTTCTGCAGCGGGAATCCGAGCGGCTGGAACTCATGAACGCAGAGCTGAAGACCCAGATTGAGGAGCTGAAGCAGGAGCGGCAGCAGCTCATCCTGATGCTGAACCGA"",
           ATF4 = ""GAGAAACTGGATAAGAAGCTGAAAAAAATGGAGCAAAACAAGACAGCAGCCACTAGGTACCGCCAGAAGAAGAGGGCGGAGCAGGAGGCTCTTACTGGTGAGTGCAAAGAGCTGGAAAAGAAGAACGAGGCTCTAAAAGAGAGGGCGGATTCCCTGGCCAAGGAGATCCAGTACCTGAAAGATTTGATAGAAGAG"",
           ATF5 = ""ACCCGAGGGGACCGCAAGCAAAAGAAGAGAGACCAGAACAAGTCGGCGGCTCTGAGGTACCGCCAGCGGAAGCGGGCAGAGGGTGAGGCCCTGGAGGGCGAGTGCCAGGGGCTGGAGGCACGGAATCGCGAGCTGAAGGAACGGGCAGAGTCCGTGGAGCGCGAGATCCAGTACGTCAAGGACCTGCTCATCGAG"",
           CREBZF = ""AGTCCCCGGAAGGCGGCGGCGGCCGCTGCCCGCCTTAATCGACTGAAGAAGAAGGAGTACGTGATGGGGCTGGAGAGTCGAGTCCGGGGTCTGGCAGCCGAGAACCAGGAGCTGCGGGCCGAGAATCGGGAGCTGGGCAAACGCGTACAGGCACTGCAGGAGGAGAGTCGCTACCTACGGGCAGTCTTAGCCAAC"",
           BATF2 = ""CCCAAGGAGCAACAAAGGCAGCTGAAGAAGCAGAAGAACCGGGCAGCCGCCCAGCGAAGCCGGCAGAAGCACACAGACAAGGCAGACGCCCTGCACCAGCAGCACGAGTCTCTGGAAAAAGACAACCTCGCCCTGCGGAAGGAGATCCAGTCCCTGCAGGCCGAGCTGGCGTGGTGGAGCCGGACCCTGCACGTG"",
           BATF3 = ""GAGGATGATGACAGGAAGGTCCGAAGGAGAGAAAAAAACCGAGTTGCTGCTCAGAGAAGTCGGAAGAAGCAGACCCAGAAGGCTGACAAGCTCCATGAGGAATATGAGAGCCTGGAGCAAGAAAACACCATGCTGCGGAGAGAGATCGGGAAGCTGACAGAGGAGCTGAAGCACCTGACAGAGGCACTGAAGGAG"",
           CEBPE = ""AAAGATAGCCTTGAGTACCGGCTGAGGCGGGAGCGCAACAACATCGCCGTGCGCAAGAGCCGAGACAAGGCCAAGAGGCGCATTCTGGAGACGCAGCAGAAGGTGCTGGAGTACATGGCAGAGAACGAGCGCCTCCGCAGCCGCGTGGAGCAGCTCACCCAGGAGCTAGACACCCTCCGCAACCTCTTCCGCCAG"",
           BACH1 = ""CTGGATTGTATCCATGATATTCGAAGAAGAAGTAAAAACAGAATTGCTGCACAGCGCTGTCGCAAGAGAAAACTTGACTGTATACAGAATCTTGAATCAGAAATTGAGAAGCTGCAAAGTGAAAAGGAGAGCTTGTTGAAGGAAAGAGATCACATTTTGTCAACTCTGGGTGAGACAAAGCAGAACCTAACTGGA"",
           BACH2 = ""TTAGAGTTTATTCATGATGTCCGACGGCGCAGCAAGAACCGCATCGCGGCCCAGCGCTGCCGCAAAAGGAAACTGGACTGTATTCAGAATTTAGAATGTGAAATCCGCAAATTGGTGTGTGAGAAAGAGAAACTGTTGTCAGAGAGGAATCAACTGAAAGCATGCATGGGGGAACTGTTGGACAACTTCTCCTGC"",
           NFE2L1 = ""CTGAGCCTCATCCGAGACATCCGGCGCCGGGGCAAGAACAAGATGGCGGCGCAGAACTGCCGCAAGCGCAAGCTGGACACCATCCTGAATCTGGAGCGTGATGTGGAGGACCTGCAGCGTGACAAAGCCCGGCTGCTGCGGGAGAAAGTGGAGTTCCTGCGCTCCCTGCGACAGATGAAGCAGAAGGTCCAGAGC"",
           NFE2 = ""CTAGCGCTAGTCCGGGACATCCGACGACGGGGCAAAAACAAGGTGGCAGCCCAGAACTGCCGCAAGAGGAAGCTGGAAACCATTGTGCAGCTGGAGCGGGAGCTGGAGCGGCTGACCAATGAACGGGAGCGGCTTCTCAGGGCCCGCGGGGAGGCAGACCGGACCCTGGAGGTCATGCGCCAACAGCTGACAGAG"",
           NFIL3 = ""AAGAAAGATGCTATGTATTGGGAAAAAAGGCGGAAAAATAATGAAGCTGCCAAAAGATCTCGTGAGAAGCGTCGACTGAATGACCTGGTTTTAGAGAACAAACTAATTGCACTGGGAGAAGAAAACGCCACTTTAAAAGCTGAGCTGCTTTCACTAAAATTAAAGTTTGGTTTAATTAGCTCCACAGCATATGCT"",
           FOS = ""GAAGAAGAAGAGAAAAGGAGAATCCGAAGGGAAAGGAATAAGATGGCTGCAGCCAAATGCCGCAACCGGAGGAGGGAGCTGACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTAGAGTTCATCCTGGCAGCT"",
           FOSB = ""GAGGAAGAGGAGAAGCGAAGGGTGCGCCGGGAACGAAATAAACTAGCAGCAGCTAAATGCAGGAACCGGCGGAGGGAGCTGACCGACCGACTCCAGGCGGAGACAGATCAGTTGGAGGAAGAAAAAGCAGAGCTGGAGTCGGAGATCGCCGAGCTCCAAAAGGAGAAGGAACGTCTGGAGTTTGTGCTGGTGGCC"",
           FOSL1 = ""GAGGAAGAGGAGCGCCGCCGAGTAAGGCGCGAGCGGAACAAGCTGGCTGCGGCCAAGTGCAGGAACCGGAGGAAGGAACTGACCGACTTCCTGCAGGCGGAGACTGACAAACTGGAAGATGAGAAATCTGGGCTGCAGCGAGAGATTGAGGAGCTGCAGAAGCAGAAGGAGCGCCTAGAGCTGGTGCTGGAAGCC"",
           FOSL2 = ""GAAGAGGAGGAGAAGCGTCGCATCCGGCGGGAGAGGAACAAGCTGGCTGCAGCCAAGTGCCGGAACCGACGCCGGGAGCTGACAGAGAAGCTGCAGGCGGAGACAGAGGAGCTGGAGGAGGAGAAGTCAGGCCTGCAGAAGGAGATTGCTGAGCTGCAGAAGGAGAAGGAGAAGCTGGAGTTCATGTTGGTGGCT"",
           MAFB = ""GTGATCCGCCTGAAGCAGAAGCGGCGGACCCTGAAGAACCGGGGCTACGCCCAGTCTTGCAGGTATAAACGCGTCCAGCAGAAGCACCACCTGGAGAATGAGAAGACGCAGCTCATTCAGCAGGTGGAGCAGCTTAAGCAGGAGGTGTCCCGGCTGGCCCGCGAGAGAGACGCCTACAAGGTCAAGTGCGAGAAA"",
           JUN = ""CAGGAGCGGATCAAGGCGGAGAGGAAGCGCATGAGGAACCGCATCGCTGCCTCCAAGTGCCGAAAAAGGAAGCTGGAGAGAATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTTAAACAGAAAGTCATGAAC"",
           JUNB = ""CAAGAGCGCATCAAAGTGGAGCGCAAGCGGCTGCGGAACCGGCTGGCGGCCACCAAGTGCCGGAAGCGGAAGCTGGAGCGCATCGCGCGCCTGGAGGACAAGGTGAAGACGCTCAAGGCCGAGAACGCGGGGCTGTCGAGTACCGCCGGCCTCCTCCGGGAGCAGGTGGCCCAGCTCAAACAGAAGGTCATGACC"",
           JUND = ""CAGGAGCGCATCAAGGCGGAGCGCAAGCGGCTGCGCAACCGCATCGCCGCCTCCAAGTGCCGCAAGCGCAAGCTGGAGCGCATCTCGCGCCTGGAAGAGAAAGTGAAGACCCTCAAGAGTCAGAACACGGAGCTGGCGTCCACGGCGAGCCTGCTGCGCGAGCAGGTGGCGCAGCTCAAGCAGAAAGTCCTCAGC"",
           CREB3 = ""GAACAAATTCTGAAACGTGTGCGGAGGAAGATTCGAAATAAAAGATCTGCTCAAGAGAGCCGCAGGAAAAAGAAGGTGTATGTTGGGGGTTTAGAGAGCAGGGTCTTGAAATACACAGCCCAGAATATGGAGCTTCAGAACAAAGTACAGCTTCTGGAGGAACAGAATTTGTCCCTTCTAGATCAACTGAGGAAA"",
           HLF = ""CTGAAGGATGACAAGTACTGGGCAAGGCGCAGAAAGAACAACATGGCAGCCAAGCGCTCCCGCGACGCCCGGAGGCTGAAAGAGAACCAGATCGCCATCCGGGCCTCGTTCCTGGAGAAGGAGAACTCGGCCCTCCGCCAGGAGGTGGCTGACTTGAGGAAGGAGCTGGGCAAATGCAAGAACATACTTGCCAAG"",
           MAFG = ""ATCGTCCAGCTGAAGCAGCGCCGGCGCACGCTCAAGAACCGCGGCTACGCTGCCAGCTGCCGCGTGAAGCGGGTGACGCAGAAGGAGGAGCTGGAGAAGCAGAAGGCGGAGCTGCAGCAGGAGGTGGAGAAGCTGGCCTCAGAGAACGCCAGCATGAAGCTGGAGCTCGACGCGCTGCGCTCCAAGTACGAGGCG"",
           MAFK = ""GTGACCCGCCTGAAGCAGCGTCGGCGCACACTCAAGAACCGCGGCTACGCGGCCAGCTGCCGCATCAAGCGGGTGACGCAGAAGGAGGAGCTGGAGCGGCAGCGCGTGGAGCTGCAGCAGGAGGTGGAGAAGCTGGCGCGTGAGAACAGCAGCATGCGGCTGGAGCTGGACGCCCTGCGCTCCAAGTACGAGGCG"",
           XBP1 = ""AGCCCCGAGGAGAAGGCGCTGAGGAGGAAACTGAAAAACAGAGTAGCAGCTCAGACTGCCAGAGATCGAAAGAAGGCTCGAATGAGTGAGCTGGAACAGCAAGTGGTAGATTTAGAAGAAGAGAACCAAAAACTTTTGCTAGAAAATCAGCTTTTACGAGAGAAAACTCATGGCCTTGTAGTTGAGAACCAGGAG"",
           ATF6 = ""ATTGCTGTGCTAAGGAGACAGCAACGTATGATAAAAAATCGAGAATCCGCTTGTCAGTCTCGCAAGAAGAAGAAAGAATATATGCTAGGGTTAGAGGCGAGATTAAAGGCTGCCCTCTCAGAAAACGAGCAACTGAAGAAAGAAAATGGAACACTGAAGCGGCAGCTGGATGAAGTTGTGTCAGAGAACCAGAGG"",
           ATF6B = ""GCAAAGCTGCTGAAGCGGCAGCAGCGAATGATCAAGAACCGGGAGTCAGCCTGCCAGTCCCGGAGAAAGAAGAAAGAGTATCTGCAGGGACTGGAGGCTCGGCTGCAAGCAGTACTGGCTGACAACCAGCAGCTCCGCCGAGAGAATGCTGCCCTCCGGCGGCGGCTGGAGGCCCTGCTGGCTGAAAACAGCGAG"",
           CEBPA = ""AAGAACAGCAACGAGTACCGGGTGCGGCGCGAGCGCAACAACATCGCGGTGCGCAAGAGCCGCGACAAGGCCAAGCAGCGCAACGTGGAGACGCAGCAGAAGGTGCTGGAGCTGACCAGTGACAATGACCGCCTGCGCAAGCGGGTGGAACAGCTGAGCCGCGAACTGGACACGCTGCGGGGCATCTTCCGCCAG"",
           CEBPB = ""AAGCACAGCGACGAGTACAAGATCCGGCGCGAGCGCAACAACATCGCCGTGCGCAAGAGCCGCGACAAGGCCAAGATGCGCAACCTGGAGACGCAGCACAAGGTCCTGGAGCTCACGGCCGAGAACGAGCGGCTGCAGAAGAAGGTGGAGCAGCTGTCGCGCGAGCTCAGCACCCTGCGGAACTTGTTCAAGCAG"",
           CEBPD = ""CGCGGCAGCCCCGAGTACCGGCAGCGGCGCGAGCGCAACAACATCGCCGTGCGCAAGAGCCGCGACAAGGCCAAGCGGCGCAACCAGGAGATGCAGCAGAAGTTGGTGGAGCTGTCGGCTGAGAACGAGAAGCTGCACCAGCGCGTGGAGCAGCTCACGCGGGACCTGGCCGGCCTCCGGCAGTTCTTCAAGCAG"",
           CEBPG = ""CGAAACAGTGACGAGTATCGGCAACGCCGAGAGAGGAACAACATGGCTGTGAAAAAGAGCCGGTTGAAAAGCAAGCAGAAAGCACAAGACACACTGCAGAGAGTCAATCAGCTCAAAGAAGAGAATGAACGGTTGGAAGCAAAAATCAAATTGCTGACCAAGGAATTAAGTGTACTCAAAGATTTGTTTCTTGAG"",
           CREB1 = ""GAAGCAGCACGAAAGAGAGAGGTCCGTCTAATGAAGAACAGGGAAGCAGCTCGAGAGTGTCGTAGAAAGAAGAAAGAATATGTGAAATGTTTAGAAAACAGAGTGGCAGTGCTTGAAAATCAAAACAAGACATTGATTGAGGAGCTAAAAGCACTTAAGGACCTTTACTGCCACAAATCAGAT"",
           CREB3L1 = ""GAGAAGGCCTTGAAGAGAGTCCGGAGGAAAATCAAGAACAAGATCTCAGCCCAGGAGAGCCGTCGTAAGAAGAAGGAGTATGTGGAGTGTCTAGAAAAGAAGGTGGAGACATTTACATCTGAGAACAATGAACTGTGGAAGAAGGTGGAGACCCTGGAGAATGCCAACAGGACCCTGCTCCAGCAGCTGCAGAAA"",
           CREB3L2 = ""GAGAAGGCCCTGAAGAAAATTCGGAGGAAGATCAAGAATAAGATTTCTGCTCAGGAAAGTAGGAGAAAGAAGAAAGAATACATGGACAGCCTGGAGAAAAAAGTGGAGTCTTGTTCAACTGAGAACTTGGAGCTTCGGAAGAAGGTAGAGGTTCTAGAGAACACTAATAGGACTCTCCTTCAGCAACTCCAGAAG"",
           CREB3L3 = ""GAGCGAGTGCTGAAAAAAATCCGCCGGAAAATCCGGAACAAGCAGTCGGCGCAAGAAAGCAGGAAGAAGAAGAAGGAATATATCGATGGCCTGGAGACTCGGATGTCAGCTTGCACTGCTCAGAATCAGGAGTTACAGAGGAAAGTCTTGCATCTCGAGAAGCAAAACCTGTCCCTCTTGGAGCAACTGAAGAAA"",
           CREB3L4 = ""GAGAGGGTCCTCAAGAAGGTCAGGAGGAAAATCCGTAACAAGCAGTCAGCTCAGGACAGTCGGCGGCGGAAGAAGGAGTACATTGATGGGCTGGAGAGCAGGGTGGCAGCCTGTTCTGCACAGAACCAAGAATTACAGAAAAAAGTCCAGGAGCTGGAGAGGCACAACATCTCCTTGGTAGCTCAGCTCCGCCAG"",
           CREBL2 = ""CCAGCCAAAATTGACTTGAAAGCAAAACTTGAGAGGAGCCGGCAGAGTGCAAGAGAATGCCGAGCCCGAAAAAAGCTGAGATATCAGTATTTGGAAGAGTTGGTATCCAGTCGAGAAAGAGCTATATGTGCCCTCAGAGAGGAACTGGAAATGTACAAGCAGTGGTGCATGGCAATGGACCAAGGAAAAATCCCT"",
           CREBRF = ""CCCTTAACAGCCCGACCAAGGTCAAGGAAGGAAAAAAATAAGCTGGCTTCCAGAGCTTGTCGGTTAAAGAAGAAAGCCCAGTATGAAGCTAATAAAGTGAAATTATGGGGCCTCAACACAGAATATGATAATTTATTGTTTGTAATCAACTCCATCAAGCAAGAGATTGTAAACCGGGTACAGAATCCAAGAGAT"",
           DBP = ""CAGAAGGATGAGAAATACTGGAGCCGGCGGTACAAGAACAACGAGGCAGCCAAGCGGTCCCGTGACGCCCGGCGGCTCAAGGAGAACCAGATATCGGTGCGGGCGGCCTTCCTGGAGAAGGAGAACGCCCTGCTGCGGCAGGAAGTTGTGGCCGTGCGCCAGGAGCTGTCCCACTACCGCGCCGTGCTGTCCCGA"",
           NFE2L2 = ""CTTGCATTAATTCGGGATATACGTAGGAGGGGTAAGAATAAAGTGGCTGCTCAGAATTGCAGAAAAAGAAAACTGGAAAATATAGTAGAACTAGAGCAAGATTTAGATCATTTGAAAGATGAAAAAGAAAAATTGCTCAAAGAAAAAGGAGAAAATGACAAAAGCCTTCACCTACTGAAAAAACAACTCAGCACC"",
           NFE2L3 = ""GTCTCACTTATCCGTGACATCAGACGAAGAGGGAAAAATAAAGTTGCTGCGCAGAACTGTCGTAAACGCAAATTGGACATAATTTTGAATTTAGAAGATGATGTATGTAACTTGCAAGCAAAGAAGGAAACTCTTAAGAGAGAGCAAGCACAATGTAACAAAGCTATTAACATAATGAAACAGAAACTGCATGAC"",
           TEF = ""CAGAAGGATGAAAAGTACTGGACAAGACGCAAGAAGAACAACGTGGCAGCTAAACGGTCACGGGATGCCCGGCGCCTGAAAGAGAATCAGATCACCATCCGGGCAGCCTTCCTGGAGAAGGAGAACACAGCCCTGCGGACGGAGGTGGCCGAGCTACGCAAGGAGGTGGGCAAGTGCAAGACCATCGTGTCCAAG""
  )
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2, 
    mergeForwardReverse = FALSE, 
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SPV"", elementsReverse = ""SPVS"",
    elementLengthsForward = c(-1, -1, -1),
    elementLengthsReverse = c(-1, -1, 96, -1),
    adapterForward = """", 
    adapterReverse = """",
    primerForward = ""GTCAGGTGGAGGCGGATCC"",
    primerReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    wildTypeForward = leu,
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
    constantForward = """", 
    constantReverse = """", 
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0, 
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 0,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = """",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."", 
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )
  
  res <- do.call(digestFastqs, Ldef)
  
  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 0L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 126L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 0L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 76L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 58L + 137L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 3L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 600L)
  
  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(sum(res$summaryTable$nbrReads == 2), 11L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""BACH2.0.WT_r.6.CCC"", ""BATF2.0.WT_r.21.TGG"", ""BATF2.0.WT_r.7.GGG"", ""CEBPB.0.WT_r.12.CTC"",
                      ""CEBPD.0.WT_r.13.CCC"", ""CEBPD.0.WT_r.23.GAG"", ""CREB3L3.0.WT_r.22.GGG"",
                      ""FOSL1.0.WT_r.22.CCC"", ""FOSL2.0.WT_r.13.GGG"", ""JUNB.0.WT_r.0.WT"", ""XBP1.0.WT_r.0.WT"")))
  expect_equal(sort(res$summaryTable$mutantNameCodon[res$summaryTable$nbrReads == 2]),
               sort(c(""BACH2.0.WT_r.6.CCC"", ""BATF2.0.WT_r.21.TGG"", ""BATF2.0.WT_r.7.GGG"", ""CEBPB.0.WT_r.12.CTC"",
                      ""CEBPD.0.WT_r.13.CCC"", ""CEBPD.0.WT_r.23.GAG"", ""CREB3L3.0.WT_r.22.GGG"",
                      ""FOSL1.0.WT_r.22.CCC"", ""FOSL2.0.WT_r.13.GGG"", ""JUNB.0.WT_r.0.WT"", ""XBP1.0.WT_r.0.WT"")))
  expect_equal(sort(res$summaryTable$mutantNameBase[res$summaryTable$nbrReads == 2]),
               sort(c(""BACH2.0.WT_r.16.C_r.17.C_r.18.C"", ""BATF2.0.WT_r.61.T_r.62.G"", 
                      ""BATF2.0.WT_r.19.G_r.20.G_r.21.G"", ""CEBPB.0.WT_r.34.C_r.35.T_r.36.C"",
                      ""CEBPD.0.WT_r.37.C_r.39.C"", ""CEBPD.0.WT_r.67.G_r.69.G"", 
                      ""CREB3L3.0.WT_r.65.G_r.66.G"", ""FOSL1.0.WT_r.64.C"", 
                      ""FOSL2.0.WT_r.38.G_r.39.G"", ""JUNB.0.WT_r.0.WT"", ""XBP1.0.WT_r.0.WT"")))
  expect_equal(sort(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$nbrReads == 2]),
               sort(c(""BACH2:c_r:c.16_18delinsCCC"", ""BATF2:c_r:c.61_62delinsTG"", 
                      ""BATF2:c_r:c.19_21delinsGGG"", ""CEBPB:c_r:c.34_36delinsCTC"",
                      ""CEBPD:c_r:c.37_39delinsCCC"", ""CEBPD:c_r:c.67_69delinsGAG"", 
                      ""CREB3L3:c_r:c.65_66delinsGG"", ""FOSL1:c_r:c.64G>C"", 
                      ""FOSL2:c_r:c.38_39delinsGG"", ""JUNB:c_r:c"", ""XBP1:c_r:c"")))
  expect_equal(sort(res$summaryTable$mutantNameAAHGVS[res$summaryTable$nbrReads == 2]),
               sort(c(""BACH2:p_r:p.(Glu6Pro)"", ""BATF2:p_r:p.(Thr21Trp)"", 
                      ""BATF2:p_r:p.(Lys7Gly)"", ""CEBPB:p_r:p.(Lys12Leu)"",
                      ""CEBPD:p_r:p.(Ala13Pro)"", ""CEBPD:p_r:p.(Asn23Glu)"",
                      ""CREB3L3:p_r:p.(Ala22Gly)"", ""FOSL1:p_r:p.(Ala22Pro)"", 
                      ""FOSL2:p_r:p.(Ala13Gly)"", ""JUNB:p_r:p"", ""XBP1:p_r:p"")))
  expect_equal(res$summaryTable$nbrUmis, rep(0L, nrow(res$summaryTable))) ## no UMIs in this experiment
  
  ## ---------------------------------------------------------------------------
  ## Test that we get the same results with useTreeWTmatch = TRUE
  ## ---------------------------------------------------------------------------
  Ldef$useTreeWTmatch <- TRUE
  res2 <- do.call(digestFastqs, Ldef)
  
  expect_equal(res$filterSummary$nbrTotal, res2$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res2$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res2$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res2$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res2$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res2$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res2$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res2$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res2$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res2$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res2$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res2$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res2$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res2$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res2$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res2$filterSummary$nbrRetained)
  
  expect_equal(res$summaryTable$nbrReads, res2$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res2$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res2$summaryTable$sequence)
  
  ## ---------------------------------------------------------------------------
  ## Test aggregating across groups of wildtype sequences
  ## ---------------------------------------------------------------------------
  Ldef <- list(
      fastqForward = fqt1, fastqReverse = fqt2, 
      mergeForwardReverse = FALSE, 
      minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
      revComplForward = FALSE, revComplReverse = FALSE,
      elementsForward = ""SPV"", elementsReverse = ""SPSS"",
      elementLengthsForward = c(-1, -1, -1),
      elementLengthsReverse = c(-1, -1, 96, -1),
      adapterForward = """", 
      adapterReverse = """",
      primerForward = ""GTCAGGTGGAGGCGGATCC"",
      primerReverse = ""GAAAAAGGAAGCTGGAGAGA"",
      wildTypeForward = leu,
      wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
      constantForward = """", 
      constantReverse = """", 
      avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
      variableNMaxForward = 0, variableNMaxReverse = 0, 
      umiNMax = 0,
      nbrMutatedCodonsMaxForward = 0,
      nbrMutatedCodonsMaxReverse = 0,
      nbrMutatedBasesMaxForward = -1,
      nbrMutatedBasesMaxReverse = -1,
      forbiddenMutatedCodonsForward = """",
      forbiddenMutatedCodonsReverse = ""NNW"",
      useTreeWTmatch = FALSE,
      mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
      mutNameDelimiter = ""."", 
      constantMaxDistForward = -1,
      constantMaxDistReverse = -1,
      umiCollapseMaxDist = 0,
      filteredReadsFastqForward = """",
      filteredReadsFastqReverse = """",
      maxNReads = -1, verbose = FALSE,
      nThreads = 1, chunkSize = 1000, 
      maxReadLength = 1024
  )
  res1 <- do.call(digestFastqs, Ldef)
  leu2 <- leu
  names(leu2)[1:5] <- ""ATF2""
  Ldef$wildTypeForward <- leu2
  res2 <- do.call(digestFastqs, Ldef)
  
  expect_identical(res1$filterSummary, res2$filterSummary)
  expect_identical(res1$errorStatistics, res2$errorStatistics)
  expect_equal(nrow(res1$summaryTable), nrow(res2$summaryTable) + 4)
  idx <- which(!res1$summaryTable$mutantName %in% paste0(names(leu)[1:5], "".0.WT_r.0.WT""))
  expect_identical(res1$summaryTable[idx, ], 
                   res2$summaryTable[match(res1$summaryTable$mutantName[idx], 
                                           res2$summaryTable$mutantName), ],
                   ignore_attr = TRUE)
  expect_equal(sum(res1$summaryTable[-idx, ""nbrReads""]), 
               res2$summaryTable[res2$summaryTable$mutantName == 
                                     paste0(names(leu2)[1], "".0.WT_r.0.WT""), ""nbrReads""])
  
  ## ---------------------------------------------------------------------------
  ## Swap forward and reverse reads, specify max nbr of mutated bases
  ## ---------------------------------------------------------------------------
  Ldef <- list(
      fastqForward = fqt2, fastqReverse = fqt1, 
      mergeForwardReverse = FALSE, 
      minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
      revComplForward = FALSE, revComplReverse = FALSE,
      elementsForward = ""SPSS"", elementsReverse = ""SPV"", 
      elementLengthsForward = c(-1, -1, 96, -1),
      elementLengthsReverse = c(-1, -1, -1),
      adapterForward = """", 
      adapterReverse = """",
      primerForward = ""GAAAAAGGAAGCTGGAGAGA"",
      primerReverse = ""GTCAGGTGGAGGCGGATCC"",
      wildTypeForward = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
      wildTypeReverse = leu,
      constantForward = """", 
      constantReverse = """", 
      avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
      variableNMaxForward = 0, variableNMaxReverse = 0, 
      umiNMax = 0,
      nbrMutatedCodonsMaxForward = -1,
      nbrMutatedCodonsMaxReverse = -1,
      nbrMutatedBasesMaxForward = 0,
      nbrMutatedBasesMaxReverse = 0,
      forbiddenMutatedCodonsForward = ""NNW"",
      forbiddenMutatedCodonsReverse = """",
      useTreeWTmatch = FALSE,
      mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
      mutNameDelimiter = ""."", 
      constantMaxDistForward = -1,
      constantMaxDistReverse = -1,
      umiCollapseMaxDist = 0,
      filteredReadsFastqForward = """",
      filteredReadsFastqReverse = """",
      maxNReads = -1, verbose = FALSE,
      nThreads = 1, chunkSize = 1000, 
      maxReadLength = 1024
  )
  res1 <- do.call(digestFastqs, Ldef)
  leu2 <- leu
  names(leu2)[1:5] <- ""ATF2""
  Ldef$wildTypeReverse <- leu2
  res2 <- do.call(digestFastqs, Ldef)
  
  expect_identical(res1$filterSummary, res2$filterSummary)
  expect_identical(res1$errorStatistics, res2$errorStatistics)
  expect_equal(nrow(res1$summaryTable), nrow(res2$summaryTable) + 4)
  idx <- which(!res1$summaryTable$mutantName %in% paste0(""f.0.WT_"", names(leu)[1:5], "".0.WT""))
  expect_identical(res1$summaryTable[idx, ], 
                   res2$summaryTable[match(res1$summaryTable$mutantName[idx], 
                                           res2$summaryTable$mutantName), ],
                   ignore_attr = TRUE)
  expect_equal(sum(res1$summaryTable[-idx, ""nbrReads""]), 
               res2$summaryTable[res2$summaryTable$mutantName == 
                                     paste0(""f.0.WT_"", names(leu2)[1], "".0.WT""), ""nbrReads""])
  
  ## ---------------------------------------------------------------------------
  ## Too many WT hits, reverse reads
  ## ---------------------------------------------------------------------------
  Ldef <- list(
      fastqForward = fqt1, fastqReverse = fqt2, 
      mergeForwardReverse = FALSE, 
      minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
      revComplForward = FALSE, revComplReverse = FALSE,
      elementsForward = ""SPV"", elementsReverse = ""SPSS"",
      elementLengthsForward = c(-1, -1, -1),
      elementLengthsReverse = c(-1, -1, 96, -1),
      adapterForward = """", 
      adapterReverse = """",
      primerForward = ""GTCAGGTGGAGGCGGATCC"",
      primerReverse = ""GAAAAAGGAAGCTGGAGAGA"",
      wildTypeForward = leu,
      wildTypeReverse = c(A = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", B = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTTA""), 
      constantForward = """", 
      constantReverse = """", 
      avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
      variableNMaxForward = 0, variableNMaxReverse = 0, 
      umiNMax = 0,
      nbrMutatedCodonsMaxForward = 0,
      nbrMutatedCodonsMaxReverse = 0,
      nbrMutatedBasesMaxForward = -1,
      nbrMutatedBasesMaxReverse = -1,
      forbiddenMutatedCodonsForward = """",
      forbiddenMutatedCodonsReverse = ""NNW"",
      useTreeWTmatch = FALSE,
      mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
      mutNameDelimiter = ""."", 
      constantMaxDistForward = -1,
      constantMaxDistReverse = -1,
      umiCollapseMaxDist = 0,
      filteredReadsFastqForward = """",
      filteredReadsFastqReverse = """",
      maxNReads = -1, verbose = FALSE,
      nThreads = 1, chunkSize = 1000, 
      maxReadLength = 1024
  )
  res1 <- do.call(digestFastqs, Ldef)
  
  expect_identical(res1$filterSummary$f8_nbrTooManyBestWTHits, 809L)
  
  ## ---------------------------------------------------------------------------
  ## Too short WT hit, reverse reads
  ## ---------------------------------------------------------------------------
  Ldef <- list(
      fastqForward = fqt1, fastqReverse = fqt2, 
      mergeForwardReverse = FALSE, 
      minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
      revComplForward = FALSE, revComplReverse = FALSE,
      elementsForward = ""SPV"", elementsReverse = ""SPVS"",
      elementLengthsForward = c(-1, -1, -1),
      elementLengthsReverse = c(-1, -1, 96, -1),
      adapterForward = """", 
      adapterReverse = """",
      primerForward = ""GTCAGGTGGAGGCGGATCC"",
      primerReverse = ""GAAAAAGGAAGCTGGAGAGA"",
      wildTypeForward = leu,
      wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCT"", 
      constantForward = """", 
      constantReverse = """", 
      avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
      variableNMaxForward = 0, variableNMaxReverse = 0, 
      umiNMax = 0,
      nbrMutatedCodonsMaxForward = 0,
      nbrMutatedCodonsMaxReverse = 1,
      nbrMutatedBasesMaxForward = -1,
      nbrMutatedBasesMaxReverse = -1,
      forbiddenMutatedCodonsForward = """",
      forbiddenMutatedCodonsReverse = ""NNW"",
      useTreeWTmatch = FALSE,
      mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
      mutNameDelimiter = ""."", 
      constantMaxDistForward = -1,
      constantMaxDistReverse = -1,
      umiCollapseMaxDist = 0,
      filteredReadsFastqForward = """",
      filteredReadsFastqReverse = """",
      maxNReads = -1, verbose = FALSE,
      nThreads = 1, chunkSize = 1000, 
      maxReadLength = 1024
  )
  res1 <- do.call(digestFastqs, Ldef)
  
  expect_identical(res1$filterSummary$f3_nbrReadWrongLength, 439L)
  expect_identical(res1$filterSummary$nbrRetained, 0L)
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_calcNearestStringDist.R",".R","2340","52","test_that(""calcNearestStringDist fails with incorrect arguments"", {
    expect_error(calcNearestStringDist(), ""argument \""x\"" is missing"")
    expect_error(calcNearestStringDist(x = 1:3), ""Expecting a string vector"")
    expect_error(calcNearestStringDist(x = letters, metric = ""error""), ""unknown distance metric 'error'"")
    expect_error(calcNearestStringDist(x = letters, nThreads = c(1L, 2L)), ""Expecting a single value"")
})

test_that(""calcNearestStringDist works as expected"", {
    # strs1 <- c(""lazy"", ""hazy"", ""craz"")  # same lengths
    # strs2 <- c(""lazy"", ""hazy"", ""crazy"") # unequal lengths

    set.seed(42)
    strs1 <- sample(x = Biostrings::mkAllStrings(alphabet = Biostrings::DNA_BASES,
                                                 width = 7),
                    size = 200)
    strs2 <- c(sample(x = Biostrings::mkAllStrings(alphabet = Biostrings::DNA_BASES,
                                                   width = 7),
                      size = 200),
               sample(x = Biostrings::mkAllStrings(alphabet = Biostrings::DNA_BASES,
                                                   width = 8),
                      size = 200))

    d0 <- pwalign::stringDist(x = strs1, method = ""hamming"")
    e0 <- pwalign::stringDist(x = strs2, method = ""levenshtein"")

    dm <- as.matrix(d0)
    em <- as.matrix(e0)

    diag(dm) <- diag(em) <- NA

    d <- as.integer(unname(apply(dm, 1, min, na.rm = TRUE)))
    e <- as.integer(unname(apply(em, 1, min, na.rm = TRUE)))

    expect_type(res1 <- calcNearestStringDist(x = strs1, metric = ""hamming"", nThreads = 1L), ""integer"")
    expect_type(res2 <- calcNearestStringDist(x = strs1, metric = ""hamming"", nThreads = 4L), ""integer"")
    expect_type(res3 <- calcNearestStringDist(x = strs2, metric = ""levenshtein"", nThreads = 1L), ""integer"")
    expect_type(res4 <- calcNearestStringDist(x = strs2, metric = ""levenshtein"", nThreads = 4L), ""integer"")
    expect_type(res5 <- calcNearestStringDist(x = strs1, metric = ""hamming_shift"", nThreads = 1L), ""integer"")
    expect_type(res6 <- calcNearestStringDist(x = strs1, metric = ""hamming_shift"", nThreads = 4L), ""integer"")

    expect_identical(d, res1)
    expect_identical(e, res3)
    expect_true(all(res5 <= res1))
    expect_identical(res1, res2)
    expect_identical(res3, res4)
    expect_identical(res5, res6)
})




","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_summarizeExperiment.R",".R","18605","337","Ldef <- list(
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    collapseToWTForward = FALSE,
    collapseToWTReverse = FALSE, 
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    maxNReads = -1, verbose = FALSE
)
Ldef1 <- c(
    list(fastqForward = system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan""),
         fastqReverse = system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
    ), Ldef)
Ldef2 <- c(
    list(fastqForward = system.file(""extdata/transOutput_1.fastq.gz"", package = ""mutscan""),
         fastqReverse = system.file(""extdata/transOutput_2.fastq.gz"", package = ""mutscan"")
    ), Ldef)

out1 <- do.call(digestFastqs, Ldef1)
out2 <- do.call(digestFastqs, Ldef2)

coldata <- data.frame(Name = c(""sample1"", ""sample2""),
                      Condition = c(""input"", ""output""),
                      Replicate = c(1, 1), OD = c(0.05, 1.5),
                      stringsAsFactors = FALSE)

test_that(""summarizeExperiment fails with incorrect arguments"", {
    ## x must be a named list, with names matching coldata$Name
    expect_error(summarizeExperiment(x = 1, coldata = coldata))
    expect_error(summarizeExperiment(x = list(out1), coldata = coldata))
    expect_error(summarizeExperiment(x = out1, coldata = coldata))
    expect_error(summarizeExperiment(x = list(s1 = out1, s1 = out2), coldata = coldata))

    ## coldata must be a data.frame with a column Name
    expect_error(summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                                     coldata = 1))
    expect_error(summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                                     coldata = coldata[, c(""Condition"", ""OD"")]))
    expect_error(summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                                     coldata = as.list(coldata)))

    ## countType must be reads or umis
    expect_error(summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                                     coldata = coldata, countType = 1))
    expect_error(summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                                     coldata = coldata, countType = ""umi""))
    expect_error(summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                                     coldata = coldata, countType = c(""reads"", ""umis"")))

    ## names must match
    expect_error(summarizeExperiment(x = list(s1 = out1, s2 = out2),
                                     coldata = coldata, countType = ""umis""))
    expect_warning(summarizeExperiment(x = list(sample1 = out1, sample2 = out2,
                                                sample3 = out1),
                                       coldata = coldata, countType = ""umis""))

    ## samples must have the same mutNameDelimiter
    tmpout2 <- out2
    tmpout2$parameters$mutNameDelimiter <- "":""
    expect_error(summarizeExperiment(x = list(sample1 = out1, sample2 = tmpout2),
                                     coldata = coldata, countType = ""reads""))
})

test_that(""summarizeExperiment works as expected with reads output"", {
    se <- summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                              coldata = coldata, countType = ""reads"")
    expect_equal(nrow(se), length(union(out1$summaryTable$mutantName,
                                        out2$summaryTable$mutantName)))
    expect_equal(ncol(se), 2)
    expect_equal(sort(rownames(se)),
                 sort(union(out1$summaryTable$mutantName, out2$summaryTable$mutantName)))
    expect_equal(Matrix::colSums(SummarizedExperiment::assay(se, ""counts"")),
                 c(out1$filterSummary$nbrRetained, out2$filterSummary$nbrRetained),
                 ignore_attr = TRUE)
    expect_equal(Matrix::colSums(SummarizedExperiment::assay(se, ""counts"")),
                 c(sum(out1$summaryTable$nbrReads), sum(out2$summaryTable$nbrReads)),
                 ignore_attr = TRUE)

    for (cn in colnames(out1$filterSummary)) {
        expect_equal(SummarizedExperiment::colData(se)[""sample1"", cn],
                     out1$filterSummary[, cn])
        expect_equal(SummarizedExperiment::colData(se)[""sample2"", cn],
                     out2$filterSummary[, cn])
    }

    for (cn in colnames(coldata)) {
        expect_equal(SummarizedExperiment::colData(se)[""sample1"", cn],
                     coldata[1, cn])
        expect_equal(SummarizedExperiment::colData(se)[""sample2"", cn],
                     coldata[2, cn])
    }

    expect_equal(S4Vectors::metadata(se)$parameters[[""sample1""]], out1$parameters)
    expect_equal(S4Vectors::metadata(se)$parameters[[""sample2""]], out2$parameters)
    expect_equal(S4Vectors::metadata(se)$errorStatistics[[""sample1""]], out1$errorStatistics)
    expect_equal(S4Vectors::metadata(se)$errorStatistics[[""sample2""]], out2$errorStatistics)

    expect_true(all(c(""mutantName"", ""sequence"", ""nbrMutBases"",
                      ""minNbrMutBases"", ""maxNbrMutBases"",
                      ""nbrMutCodons"", ""minNbrMutCodons"", ""maxNbrMutCodons"",
                      ""nbrMutAAs"", ""minNbrMutAAs"", ""maxNbrMutAAs"",
                      ""sequenceAA"", ""mutantNameAA"", ""mutationTypes"",
                      ""varLengths"") %in% 
                        colnames(SummarizedExperiment::rowData(se))))
    
    ## Check that the number of mutated codons are correct (=equal to the number of
    ## entries in the mutant name that don't contain WT)
    expect_equal(sapply(strsplit(SummarizedExperiment::rowData(se)$mutantName, ""_""),
                        function(w) length(w[!grepl(""WT"", w)])),
                 SummarizedExperiment::rowData(se)$minNbrMutCodons, ignore_attr = TRUE)
    expect_equal(sapply(strsplit(SummarizedExperiment::rowData(se)$mutantName, ""_""),
                        function(w) length(w[!grepl(""WT"", w)])),
                 SummarizedExperiment::rowData(se)$maxNbrMutCodons, ignore_attr = TRUE)
    
    ## Check that the number of mutated bases is not larger than 3x the number of mutated codons
    expect_true(all(SummarizedExperiment::rowData(se)$maxNbrMutBases <=  
                        3 * SummarizedExperiment::rowData(se)$maxNbrMutCodons))
    expect_true(all(SummarizedExperiment::rowData(se)$maxNbrMutAAs <= 
                        SummarizedExperiment::rowData(se)$maxNbrMutCodons))
    
    ## variable lengths
    expect_equal(SummarizedExperiment::rowData(se)$varLengths, 
                 rep(""96_96"", nrow(se)), ignore_attr = TRUE)
    
    ## All variants with no mutated AAs must have a WT in the name
    expect_true(all(grepl(""WT"", SummarizedExperiment::rowData(se)$mutantNameAA[SummarizedExperiment::rowData(se)$maxNbrMutAAs == 0])))
    ## No mutation for the complete WT
    expect_equal(SummarizedExperiment::rowData(se)[""f.0.WT_r.0.WT"", ]$mutationTypes, """")
    ## Mutation types for variants with no mutated AAs, but mutated bases, should be silent
    expect_true(all(SummarizedExperiment::rowData(se)$mutationTypes[SummarizedExperiment::rowData(se)$maxNbrMutAAs == 0 & SummarizedExperiment::rowData(se)$maxNbrMutBases > 0] == ""silent""))
    ## check translation
    expect_equal(SummarizedExperiment::rowData(se)$sequenceAA[3], 
                 mutscan:::translateString(SummarizedExperiment::rowData(se)$sequence[3]))
    
    ## Spot checks
    expect_equal(SummarizedExperiment::rowData(se)$minNbrMutBases[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.CTC""], 3)  ## WT: GCT
    expect_equal(SummarizedExperiment::rowData(se)$minNbrMutCodons[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.CTC""], 1)  ## WT: GCT
    expect_equal(SummarizedExperiment::rowData(se)$minNbrMutAAs[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.CTC""], 1)  ## WT: A
    expect_equal(SummarizedExperiment::rowData(se)$mutantNameAA[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.CTC""], ""f.0.WT_r.13.L"")  ## WT: A
    expect_equal(SummarizedExperiment::rowData(se)$mutationTypes[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.CTC""], ""nonsynonymous"")  ## WT: A
    
    expect_equal(SummarizedExperiment::rowData(se)$minNbrMutBases[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.GCG""], 1)  ## WT: GCT
    expect_equal(SummarizedExperiment::rowData(se)$minNbrMutCodons[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.GCG""], 1)  ## WT: GCT
    expect_equal(SummarizedExperiment::rowData(se)$minNbrMutAAs[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.GCG""], 0)  ## WT: A
    expect_equal(SummarizedExperiment::rowData(se)$mutantNameAA[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.GCG""], ""f.0.WT_r.0.WT"")  ## WT: A
    expect_equal(SummarizedExperiment::rowData(se)$mutationTypes[SummarizedExperiment::rowData(se)$mutantName == ""f.0.WT_r.13.GCG""], ""silent"")  ## WT: A
    
})

test_that(""summarizeExperiment works as expected with umis output"", {
    se <- summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                              coldata = coldata, countType = ""umis"")
    expect_equal(nrow(se), length(union(out1$summaryTable$mutantName,
                                        out2$summaryTable$mutantName)))
    expect_equal(ncol(se), 2)
    expect_equal(sort(rownames(se)),
                 sort(union(out1$summaryTable$mutantName, out2$summaryTable$mutantName)))
    expect_equal(Matrix::colSums(SummarizedExperiment::assay(se, ""counts"")),
                 c(sum(out1$summaryTable$nbrUmis), sum(out2$summaryTable$nbrUmis)),
                 ignore_attr = TRUE)

    for (cn in colnames(out1$filterSummary)) {
        expect_equal(SummarizedExperiment::colData(se)[""sample1"", cn],
                     out1$filterSummary[, cn])
        expect_equal(SummarizedExperiment::colData(se)[""sample2"", cn],
                     out2$filterSummary[, cn])
    }

    for (cn in colnames(coldata)) {
        expect_equal(SummarizedExperiment::colData(se)[""sample1"", cn],
                     coldata[1, cn])
        expect_equal(SummarizedExperiment::colData(se)[""sample2"", cn],
                     coldata[2, cn])
    }

    expect_equal(S4Vectors::metadata(se)$parameters[[""sample1""]], out1$parameters)
    expect_equal(S4Vectors::metadata(se)$parameters[[""sample2""]], out2$parameters)
    expect_equal(S4Vectors::metadata(se)$errorStatistics[[""sample1""]], out1$errorStatistics)
    expect_equal(S4Vectors::metadata(se)$errorStatistics[[""sample2""]], out2$errorStatistics)
})

test_that(""summarizeExperiment orders samples equally in count matrix/colData"", {
    se1 <- summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                               coldata = coldata, countType = ""umis"")
    se2 <- summarizeExperiment(x = list(sample2 = out2, sample1 = out1),
                               coldata = coldata, countType = ""umis"")
    m1 <- se1[, match(coldata$Name, colnames(se1))]
    m2 <- se2[rownames(m1), match(coldata$Name, colnames(se2))]
    expect_equal(SummarizedExperiment::colData(m1), SummarizedExperiment::colData(m2))
    expect_equal(SummarizedExperiment::assay(m1, ""counts""),
                 SummarizedExperiment::assay(m2, ""counts""))
})

test_that(""summarizeExperiment recognizes the presence of UMI counts correctly"", {
    L1 <- Ldef1; L1$elementsForward <- ""SSCV""; L1$elementsReverse <- ""SSCV""
    L2 <- Ldef2; L2$elementsForward <- ""SSCV""; L2$elementsReverse <- ""SSCV""
    outl1 <- do.call(digestFastqs, L1)
    outl2 <- do.call(digestFastqs, L2)
    expect_error(summarizeExperiment(x = list(sample1 = outl1, sample2 = outl2),
                                     coldata = coldata, countType = ""umis""))

})

test_that(""summarizeExperiment works as expected when collapsing to WT"", {
    ## Also test that errorStatistics is empty if there is no constant sequence
    Ldef1$collapseToWTForward <- TRUE
    Ldef2$collapseToWTForward <- TRUE
    Ldef1$elementsForward = ""SUSV""
    Ldef1$elementsReverse = ""SUSV""
    Ldef2$elementsForward = ""SUSV""
    Ldef2$elementsReverse = ""SUSV""
    
    out1 <- do.call(digestFastqs, Ldef1)
    out2 <- do.call(digestFastqs, Ldef2)
    
    se <- summarizeExperiment(x = list(sample1 = out1, sample2 = out2),
                              coldata = coldata, countType = ""reads"")
    expect_equal(nrow(se), length(union(out1$summaryTable$mutantName,
                                        out2$summaryTable$mutantName)))
    expect_equal(ncol(se), 2)
    expect_equal(sort(rownames(se)),
                 sort(union(out1$summaryTable$mutantName, out2$summaryTable$mutantName)))
    expect_equal(Matrix::colSums(SummarizedExperiment::assay(se, ""counts"")),
                 c(out1$filterSummary$nbrRetained, out2$filterSummary$nbrRetained),
                 ignore_attr = TRUE)
    expect_equal(Matrix::colSums(SummarizedExperiment::assay(se, ""counts"")),
                 c(sum(out1$summaryTable$nbrReads), sum(out2$summaryTable$nbrReads)),
                 ignore_attr = TRUE)
    
    for (cn in colnames(out1$filterSummary)) {
        expect_equal(SummarizedExperiment::colData(se)[""sample1"", cn],
                     out1$filterSummary[, cn])
        expect_equal(SummarizedExperiment::colData(se)[""sample2"", cn],
                     out2$filterSummary[, cn])
    }
    
    for (cn in colnames(coldata)) {
        expect_equal(SummarizedExperiment::colData(se)[""sample1"", cn],
                     coldata[1, cn])
        expect_equal(SummarizedExperiment::colData(se)[""sample2"", cn],
                     coldata[2, cn])
    }
    
    expect_equal(S4Vectors::metadata(se)$parameters[[""sample1""]], out1$parameters)
    expect_equal(S4Vectors::metadata(se)$parameters[[""sample2""]], out2$parameters)
    expect_equal(S4Vectors::metadata(se)$errorStatistics[[""sample1""]], out1$errorStatistics)
    expect_equal(S4Vectors::metadata(se)$errorStatistics[[""sample2""]], out2$errorStatistics)
    expect_equal(sum(S4Vectors::metadata(se)$errorStatistics[[""sample1""]][, c(""nbrMatchForward"", ""nbrMismatchForward"", ""nbrMatchReverse"", ""nbrMismatchReverse"")]), 0)
    expect_equal(sum(S4Vectors::metadata(se)$errorStatistics[[""sample2""]][, c(""nbrMatchForward"", ""nbrMismatchForward"", ""nbrMatchReverse"", ""nbrMismatchReverse"")]), 0)
    
    expect_true(all(c(""mutantName"", ""sequence"", ""nbrMutBases"",
                      ""minNbrMutBases"", ""maxNbrMutBases"",
                      ""nbrMutCodons"", ""minNbrMutCodons"", ""maxNbrMutCodons"",
                      ""nbrMutAAs"", ""minNbrMutAAs"", ""maxNbrMutAAs"",
                      ""sequenceAA"", ""mutantNameAA"", ""mutationTypes"",
                      ""varLengths"") %in% 
                        colnames(SummarizedExperiment::rowData(se))))
    
    ## variable lengths
    expect_equal(SummarizedExperiment::rowData(se)$varLengths, 
                 rep(""96_96"", nrow(se)), ignore_attr = TRUE)
    
    expect_false(any(grepl(""^,"", SummarizedExperiment::rowData(se)$mutationTypes)))
    expect_false(any(grepl("",$"", SummarizedExperiment::rowData(se)$mutationTypes)))
    expect_equal(SummarizedExperiment::rowData(se)$sequenceAA[3], 
                 mutscan:::translateString(SummarizedExperiment::rowData(se)$sequence[3]))
    expect_type(SummarizedExperiment::rowData(se)$nbrMutBases, ""character"")
    expect_type(SummarizedExperiment::rowData(se)$nbrMutCodons, ""character"")
    expect_type(SummarizedExperiment::rowData(se)$nbrMutAAs, ""character"")
    expect_equal(SummarizedExperiment::rowData(se)$nbrMutBases[[which(SummarizedExperiment::rowData(se)$mutantName == ""f_r.0.WT"")]], ""0,1,2,3"")
    expect_equal(SummarizedExperiment::rowData(se)$nbrMutCodons[[which(SummarizedExperiment::rowData(se)$mutantName == ""f_r.0.WT"")]], ""0,1"")
    expect_equal(SummarizedExperiment::rowData(se)$nbrMutAAs[[which(SummarizedExperiment::rowData(se)$mutantName == ""f_r.0.WT"")]], ""0,1"")
    
    expect_true(all(grepl(""stop"", SummarizedExperiment::rowData(se)$mutationTypes[grep(""\\*"", SummarizedExperiment::rowData(se)$mutantNameAA)])))
})

test_that(""mergeValues works"", {
    res <- mergeValues(c(""A"", ""B"", ""C"", ""A"", ""D"", ""B""),
                       c(""a,b"", ""b,c"", ""c"", ""b,c"", ""b,a"", ""d""))
    expect_s3_class(res, ""data.frame"")
    expect_named(res, c(""mutantNameColl"", ""valueColl""))
    expect_equal(res$mutantNameColl, c(""A"", ""B"", ""C"", ""D""))
    expect_equal(res$value, c(""a,b,c"", ""b,c,d"", ""c"", ""a,b""))
    
    res <- mergeValues(c(""B"", ""A"", ""C"", ""D""),
                       c(""a,b"", ""c"", ""c"", ""d""))
    expect_s3_class(res, ""data.frame"")
    expect_named(res, c(""mutantNameColl"", ""valueColl""))
    expect_equal(res$mutantNameColl, c(""A"", ""B"", ""C"", ""D""))
    expect_equal(res$value, c(""c"", ""a,b"", ""c"", ""d""))
    
    res <- mergeValues(c(""A"", ""B"", ""C"", ""A"", ""D"", ""B""),
                       c(""a:b"", ""b:c"", ""c"", ""b:c"", ""b:a"", ""d""),
                       delimiter = "":"")
    expect_s3_class(res, ""data.frame"")
    expect_named(res, c(""mutantNameColl"", ""valueColl""))
    expect_equal(res$mutantNameColl, c(""A"", ""B"", ""C"", ""D""))
    expect_equal(res$value, c(""a:b:c"", ""b:c:d"", ""c"", ""a:b""))
    
    res <- mergeValues(c(""A"", ""B"", ""C"", ""A"", ""D"", ""B""),
                       c(""a,b"", ""b:c"", ""c"", ""b:c"", ""b:a"", ""d""),
                       delimiter = "":"")
    expect_s3_class(res, ""data.frame"")
    expect_named(res, c(""mutantNameColl"", ""valueColl""))
    expect_equal(res$mutantNameColl, c(""A"", ""B"", ""C"", ""D""))
    expect_equal(res$value, c(""a,b:b:c"", ""b:c:d"", ""c"", ""a:b""))
})




","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_plotPairs.R",".R","10660","181","se <- readRDS(system.file(""extdata/GSE102901_cis_se.rds"", package = ""mutscan""))
se <- se[1:1000, 1:3]

test_that(""plotPairs fails with incorrect arguments"", {
    expect_error(plotPairs(se = 1))
    expect_error(plotPairs(se = ""x""))
    expect_error(plotPairs(se = matrix(1:6), 2, 3))

    expect_error(plotPairs(se = se, selAssay = 1))
    expect_error(plotPairs(se = se, selAssay = c(""counts"", ""logcounts"")))
    expect_error(plotPairs(se = se, selAssay = ""logcounts""))

    expect_error(plotPairs(se = se, selAssay = ""counts"", doLog = 1))
    expect_error(plotPairs(se = se, selAssay = ""counts"", doLog = ""TRUE""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", doLog = c(TRUE, FALSE)))

    expect_error(plotPairs(se = se, selAssay = ""counts"", pseudocount = ""1""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", pseudocount = c(1, 2)))
    expect_error(plotPairs(se = se, selAssay = ""counts"", pseudocount = -1))

    expect_error(plotPairs(se = se, selAssay = ""counts"", corMethod = 1))
    expect_error(plotPairs(se = se, selAssay = ""counts"",
                           corMethod = c(""spearman"", ""pearson"")))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corMethod = ""pearman""))

    expect_error(plotPairs(se = se, selAssay = ""counts"", histBreaks = ""1""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", histBreaks = c(1, 2)))
    expect_error(plotPairs(se = se, selAssay = ""counts"", histBreaks = 0))

    expect_error(plotPairs(se = se, selAssay = ""counts"", pointsType = 1))
    expect_error(plotPairs(se = se, selAssay = ""counts"",
                           pointsType = c(""smoothscatter"", ""points"")))
    expect_error(plotPairs(se = se, selAssay = ""counts"", pointsType = ""oints""))

    expect_error(plotPairs(se = se, selAssay = ""counts"", corSizeMult = ""1""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corSizeMult = c(1, 2)))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corSizeMult = 0))

    expect_error(plotPairs(se = se, selAssay = ""counts"", corSizeAdd = ""1""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corSizeAdd = c(1, 2)))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corSizeAdd = -1))

    expect_error(plotPairs(se = se, selAssay = ""counts"", pointSize = ""1""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", pointSize = c(1, 2)))
    expect_error(plotPairs(se = se, selAssay = ""counts"", pointSize = 0))

    expect_error(plotPairs(se = se, selAssay = ""counts"", pointAlpha = ""1""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", pointAlpha = c(1, 2)))
    expect_error(plotPairs(se = se, selAssay = ""counts"", pointAlpha = 0))

    expect_error(plotPairs(se = se, selAssay = ""counts"", colorByCorrelation = 1))
    expect_error(plotPairs(se = se, selAssay = ""counts"", colorByCorrelation = ""TRUE""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", colorByCorrelation = c(TRUE, FALSE)))
    
    expect_error(plotPairs(se = se, selAssay = ""counts"", corrColorRange = 1))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corrColorRange = c(-1, 0.5)))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corrColorRange = c(1, 0.5)))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corrColorRange = c(""0.5"", ""1"")))
    expect_error(plotPairs(se = se, selAssay = ""counts"", corrColorRange = c(0, 2)))
    
    expect_error(plotPairs(se = se, selAssay = ""counts"", addIdentityLine = 1))
    expect_error(plotPairs(se = se, selAssay = ""counts"", addIdentityLine = ""TRUE""))
    expect_error(plotPairs(se = se, selAssay = ""counts"", addIdentityLine = c(TRUE, FALSE)))
})

test_that(""plotPairs works with correct arguments"", {
    ## Defaults
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL,
                              addIdentityLine = FALSE), ""ggmatrix"")

    ## Not log-transformed
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = FALSE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")

    ## No pseudocount
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 0, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")

    ## Spearman correlation
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""spearman"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")

    ## Fewer breaks
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 5, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")

    ## smoothScatter
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""smoothscatter"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")
    
    ## scattermore
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""scattermore"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 3.5, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")
    
    ## scattermost
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""scattermost"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 3.5, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")

    ## Change font size to correlation relation
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 2,
                              corSizeAdd = 0, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")

    ## Point size, alpha
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 1, pointAlpha = 3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")

    ## Don't color by correlation
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = FALSE, corrColorRange = NULL, 
                              addIdentityLine = FALSE), ""ggmatrix"")
    
    ## Change color range
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = c(0.7, 1), 
                              addIdentityLine = FALSE), ""ggmatrix"")
    
    ## Add identity line
    expect_s3_class(plotPairs(se = se, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL, 
                              addIdentityLine = TRUE), ""ggmatrix"")

    ## Make one correlation negative
    seneg <- se
    SummarizedExperiment::assay(seneg, 1)[, 2] <-
        1/(SummarizedExperiment::assay(seneg, 1)[, 2] + 1)
    expect_s3_class(plotPairs(se = seneg, selAssay = ""counts"", doLog = TRUE,
                              pseudocount = 1, corMethod = ""pearson"",
                              histBreaks = 40, pointsType = ""points"", corSizeMult = 5,
                              corSizeAdd = 2, pointSize = 0.1, pointAlpha = 0.3,
                              colorByCorrelation = TRUE, corrColorRange = NULL,
                              addIdentityLine = FALSE), ""ggmatrix"")
})","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_digestFastqs_trans_collapse.R",".R","33021","671","test_that(""digestFastqs works as expected for trans experiments, when similar sequences are collapsed"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = """",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 4,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = TRUE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)
  
  ## Summarize single sample and collapse
  se <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                            countType = ""reads"")
  secoll <- collapseMutantsBySimilarity(se, assayName = ""counts"", 
                                        collapseMaxDist = 6, 
                                        collapseMinScore = 0, collapseMinRatio = 0, 
                                        verbose = FALSE)
  seumi <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                               countType = ""umis"")
  secollumi <- collapseMutantsBySimilarity(seumi, assayName = ""counts"", 
                                           scoreMethod = ""rowMean"",
                                           collapseMaxDist = 6, 
                                           collapseMinScore = 0, collapseMinRatio = 0, 
                                           verbose = TRUE)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 0L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 679L)
  
  expect_equal(SummarizedExperiment::colData(secoll)$nbrTotal, 1000L)
  expect_equal(SummarizedExperiment::colData(secoll)$f1_nbrAdapter, 314L)
  expect_equal(SummarizedExperiment::colData(secoll)$f2_nbrNoPrimer, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f3_nbrReadWrongLength, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f4_nbrNoValidOverlap, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(SummarizedExperiment::colData(secoll)$f6_nbrTooManyNinVar, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f7_nbrTooManyNinUMI, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f9_nbrMutQualTooLow, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f10a_nbrTooManyMutCodons, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f10b_nbrTooManyMutBases, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f11_nbrForbiddenCodons, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f12_nbrTooManyMutConstant, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(SummarizedExperiment::colData(secoll)$nbrRetained, 679L)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(sum(SummarizedExperiment::assay(secoll, ""counts"")), 
               res$filterSummary$nbrRetained)
  expect_equal(nrow(res$summaryTable), 677L)
  expect_equal(sum(res$summaryTable$nbrUmis), 679L)
  expect_equal(nrow(secoll), 294L)
  expect_equal(nrow(secollumi), 294L)
  expect_true(all(res$summaryTable$varLengths == ""96_96""))
})

test_that(""digestFastqs works as expected for trans experiments, when similar sequences are collapsed (relative UMI distance)"", {
    fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
    fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
    ## default arguments
    Ldef <- list(
        fastqForward = fqt1, fastqReverse = fqt2,
        mergeForwardReverse = FALSE,
        minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
        revComplForward = FALSE, revComplReverse = FALSE,
        elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
        elementLengthsForward = c(1, 10, 18, 96),
        elementLengthsReverse = c(1, 8, 20, 96),
        adapterForward = ""GGAAGAGCACACGTC"",
        adapterReverse = ""GGAAGAGCGTCGTGT"",
        primerForward = """",
        primerReverse = """",
        wildTypeForward = """",
        wildTypeReverse = """",
        constantForward = ""AACCGGAGGAGGGAGCTG"",
        constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
        avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
        variableNMaxForward = 0, variableNMaxReverse = 0,
        umiNMax = 0,
        nbrMutatedCodonsMaxForward = 1,
        nbrMutatedCodonsMaxReverse = 1,
        nbrMutatedBasesMaxForward = -1,
        nbrMutatedBasesMaxReverse = -1,
        forbiddenMutatedCodonsForward = ""NNW"",
        forbiddenMutatedCodonsReverse = ""NNW"",
        useTreeWTmatch = FALSE,
        mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
        mutNameDelimiter = ""."",
        constantMaxDistForward = -1,
        constantMaxDistReverse = -1,
        umiCollapseMaxDist = 0.23,
        filteredReadsFastqForward = """",
        filteredReadsFastqReverse = """",
        maxNReads = -1, verbose = TRUE,
        nThreads = 1, chunkSize = 1000, 
        maxReadLength = 1024
    )
    
    res <- do.call(digestFastqs, Ldef)
    
    ## Summarize single sample and collapse
    se <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                              countType = ""reads"")
    secoll <- collapseMutantsBySimilarity(se, assayName = ""counts"", 
                                          collapseMaxDist = 6, 
                                          collapseMinScore = 0, collapseMinRatio = 0, 
                                          verbose = FALSE)
    seumi <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                                 countType = ""umis"")
    secollumi <- collapseMutantsBySimilarity(seumi, assayName = ""counts"", 
                                             scoreMethod = ""rowMean"",
                                             collapseMaxDist = 6, 
                                             collapseMinScore = 0, collapseMinRatio = 0, 
                                             verbose = TRUE)
    
    expect_equal(res$filterSummary$nbrTotal, 1000L)
    expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
    expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
    expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
    expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
    expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
    expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
    expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
    expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
    expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
    expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 0L)
    expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
    expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
    expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
    expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
    expect_equal(res$filterSummary$nbrRetained, 679L)
    
    expect_equal(SummarizedExperiment::colData(secoll)$nbrTotal, 1000L)
    expect_equal(SummarizedExperiment::colData(secoll)$f1_nbrAdapter, 314L)
    expect_equal(SummarizedExperiment::colData(secoll)$f2_nbrNoPrimer, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f3_nbrReadWrongLength, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f4_nbrNoValidOverlap, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f5_nbrAvgVarQualTooLow, 7L)
    expect_equal(SummarizedExperiment::colData(secoll)$f6_nbrTooManyNinVar, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f7_nbrTooManyNinUMI, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f8_nbrTooManyBestWTHits, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f9_nbrMutQualTooLow, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f10a_nbrTooManyMutCodons, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f10b_nbrTooManyMutBases, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f11_nbrForbiddenCodons, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f12_nbrTooManyMutConstant, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$f13_nbrTooManyBestConstantHits, 0L)
    expect_equal(SummarizedExperiment::colData(secoll)$nbrRetained, 679L)
    
    for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
        expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
    }
    for (nm in c(""fastqForward"", ""fastqReverse"")) {
        expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                     ignore_attr = TRUE)
    }
    
    expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
    expect_equal(sum(SummarizedExperiment::assay(secoll, ""counts"")), 
                 res$filterSummary$nbrRetained)
    expect_equal(nrow(res$summaryTable), 677L)
    expect_equal(sum(res$summaryTable$nbrUmis), 679L)
    expect_equal(nrow(secoll), 294L)
    expect_equal(nrow(secollumi), 294L)
    expect_true(all(res$summaryTable$varLengths == ""96_96""))
})

test_that(""digestFastqs works as expected for trans experiments, when similar sequences are collapsed - extreme case"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = """",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 100,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 125
  )

  res <- do.call(digestFastqs, Ldef)
  se <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                            countType = ""reads"")
  secoll <- collapseMutantsBySimilarity(se, assayName = ""counts"", 
                                        collapseMaxDist = 500, 
                                        collapseMinScore = 0, collapseMinRatio = 0, 
                                        verbose = FALSE)
  seumi <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                               countType = ""umis"")
  secollumi <- collapseMutantsBySimilarity(seumi, assayName = ""counts"", 
                                           collapseMaxDist = 500, 
                                           collapseMinScore = 0, collapseMinRatio = 0, 
                                           verbose = FALSE)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 0L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 679L)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(nrow(res$summaryTable), 677L)
  expect_equal(sum(res$summaryTable$nbrUmis), 677L)
  expect_equal(nrow(secoll), 1L)
  expect_equal(nrow(secollumi), 1L)
})

test_that(""digestFastqs works as expected for trans experiments, when similar sequences are collapsed - only forward read"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = NULL,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = """",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 5,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)
  se <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                            countType = ""reads"")
  secoll <- collapseMutantsBySimilarity(se, assayName = ""counts"", 
                                        collapseMaxDist = 10, 
                                        collapseMinScore = 0, collapseMinRatio = 0, 
                                        verbose = FALSE)
  seumi <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                               countType = ""umis"")
  secollumi <- collapseMutantsBySimilarity(seumi, assayName = ""counts"", 
                                           collapseMaxDist = 10, 
                                           collapseMinScore = 0, collapseMinRatio = 0, 
                                           verbose = FALSE)
  
  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 297L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 0L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 0L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 703L)

  for (nm in setdiff(names(Ldef), c(""fastqReverse"", ""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(nrow(res$summaryTable), 613L)
  expect_equal(sum(res$summaryTable$nbrUmis), 687L)
  expect_equal(nrow(se), 613L)
  expect_equal(nrow(secoll), 52L)
  expect_equal(nrow(seumi), 613L)
  expect_equal(nrow(secollumi), 52L)
})

test_that(""digestFastqs works as expected for trans experiments, when similar sequences are collapsed (only abundant ones)"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = """",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 500, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)
  se <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                            countType = ""reads"")
  secoll <- collapseMutantsBySimilarity(se, assayName = ""counts"", 
                                        collapseMaxDist = 2, 
                                        collapseMinScore = 1.5, collapseMinRatio = 0, 
                                        verbose = FALSE)
  seumi <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                               countType = ""umis"")
  secollumi <- collapseMutantsBySimilarity(seumi, assayName = ""counts"", 
                                           collapseMaxDist = 2, 
                                           collapseMinScore = 1.5, collapseMinRatio = 0, 
                                           verbose = FALSE)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 0L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 679L)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""nThreads"",
                                    ""forbiddenMutatedCodonsReverse"", ""verbose"",
                                    ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }

  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(nrow(res$summaryTable), 677L)
  expect_equal(sum(res$summaryTable$nbrUmis), 679L)
  expect_equal(nrow(secoll), 673L)
  expect_equal(nrow(secollumi), 673L)
  
  ## Don't consider mutations here since we're collapsing (i.e., we have no wildtype)
  expect_equal(sum(res$summaryTable$nbrMutBases == 0), nrow(res$summaryTable))
  expect_equal(sum(res$summaryTable$nbrMutCodons == 0), nrow(res$summaryTable))
  expect_equal(sum(res$summaryTable$nbrMutAAs == 0), nrow(res$summaryTable))
})

test_that(""digestFastqs works as expected for trans experiments, when similar sequences are collapsed (only high ratio)"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = """",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )

  res <- do.call(digestFastqs, Ldef)
  se <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                            countType = ""reads"")
  secoll <- collapseMutantsBySimilarity(se, assayName = ""counts"", 
                                        collapseMaxDist = 3, 
                                        collapseMinScore = 1, collapseMinRatio = 1.5, 
                                        verbose = FALSE)
  seumi <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                               countType = ""umis"")
  secollumi <- collapseMutantsBySimilarity(seumi, assayName = ""counts"", 
                                           collapseMaxDist = 3, 
                                           collapseMinScore = 1, collapseMinRatio = 1.5, 
                                           verbose = FALSE)
  
  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 0L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 679L)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(nrow(res$summaryTable), 677L)
  expect_equal(sum(res$summaryTable$nbrUmis), 679L)
  expect_equal(nrow(secoll), 656L)
  expect_equal(nrow(secollumi), 656L)
})

test_that(""digestFastqs works as expected for trans experiments, when similar sequences are collapsed (only high ratio), specify distance threshold as ratio"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2,
    mergeForwardReverse = FALSE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = """",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )
  
  res <- do.call(digestFastqs, Ldef)
  se <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                            countType = ""reads"")
  secoll <- collapseMutantsBySimilarity(se, assayName = ""counts"", 
                                        collapseMaxDist = 0.016, 
                                        collapseMinScore = 1, collapseMinRatio = 1.5, 
                                        verbose = FALSE)
  seumi <- summarizeExperiment(list(s1 = res), coldata = data.frame(Name = ""s1""), 
                               countType = ""umis"")
  secollumi <- collapseMutantsBySimilarity(seumi, assayName = ""counts"", 
                                           collapseMaxDist = 0.016, 
                                           collapseMinScore = 1, collapseMinRatio = 1.5, 
                                           verbose = FALSE)
  
  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 314L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 7L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 0L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 0L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 0L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 679L)
  
  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE), 
                 ignore_attr = TRUE)
  }
  
  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(nrow(res$summaryTable), 677L)
  expect_equal(sum(res$summaryTable$nbrUmis), 679L)
  expect_equal(nrow(secoll), 656L)
  expect_equal(nrow(secollumi), 656L)
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_calculateRelativeFC.R",".R","10513","189","se <- readRDS(system.file(""extdata/GSE102901_cis_se.rds"", package = ""mutscan""))
se <- se[1:200, ]
design <- stats::model.matrix(~ Replicate + Condition, 
                              data = SummarizedExperiment::colData(se))

test_that(""calculateRelativeFC fails with incorrect arguments"", {
    expect_error(calculateRelativeFC(se = NULL, design = design,
                                     coef = ""Conditioncis_output""),
                 ""'se' must not be NULL"")
    expect_error(calculateRelativeFC(se = as.matrix(assay(se)), design = design,
                                     coef = ""Conditioncis_output""),
                 ""'se' must be of class 'SummarizedExperiment'"")
    expect_error(calculateRelativeFC(se = se, design = NULL,
                                     coef = ""Conditioncis_output""),
                 ""argument is of length zero"")
    expect_error(calculateRelativeFC(se = se, design = design[1:3, ],
                                     coef = ""Conditioncis_output""),
                 ""The number of rows in"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""missing""),
                 ""One or more named coef arguments"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = NULL, contrast = NULL),
                 ""can not both be NULL"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = NULL,
                                     contrast = matrix(sample(c(0, 1),
                                                              ncol(se) * 2,
                                                              replace = TRUE),
                                                       ncol = 2)),
                 ""'contrast' must be a vector"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     WTrows = ""missing""),
                 ""All values in 'WTrows' must be"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     selAssay = ""missing""),
                 ""The provided 'selAssay' not present"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     selAssay = 1),
                 ""'selAssay' must be of class 'character'"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     pseudocount = -1),
                 ""must be within"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     method = ""missing""),
                 ""'method' must be one of: edgeR, limma"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     WTrows = NULL, normMethod = ""sum""),
                 ""normMethod = 'sum' can only be used when WTrows is not NULL"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     WTrows = NULL, normMethod = ""geomean""),
                 ""normMethod = 'geomean' can only be used when WTrows is not NULL"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     WTrows = rownames(se)[1], normMethod = ""TMM""),
                 ""normMethod = 'TMM' can only be used when WTrows is NULL"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     WTrows = rownames(se)[1], normMethod = ""csaw""),
                 ""normMethod = 'csaw' can only be used when WTrows is NULL"")
    expect_error(calculateRelativeFC(se = se, design = design,
                                     coef = ""Conditioncis_output"",
                                     WTrows = NULL, normMethod = ""csaw"",
                                     method = ""limma""),
                 ""normMethod = 'csaw' can only be used with method = 'edgeR'"")
})

test_that(""calculateRelativeFC works"", {
    ## With a single WT row, normMethod = ""sum"" or ""geomean"" should be identical

    res1a <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                                 contrast = NULL, WTrows = ""f.0.WT"",
                                 selAssay = ""counts"", pseudocount = 1,
                                 method = ""edgeR"", normMethod = ""sum"")
    res1b <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                                 contrast = NULL, WTrows = ""f.0.WT"",
                                 selAssay = ""counts"", pseudocount = 1,
                                 method = ""edgeR"", normMethod = ""geomean"")
    ## If there's only one assay, it doesn't need to be named
    senoname <- SummarizedExperiment(
        assays = list(assay(se, ""counts"")),
        rowData = rowData(se),
        colData = colData(se),
        metadata = metadata(se)
    )
    expect_warning(
        res1c <- calculateRelativeFC(senoname, design, coef = ""Conditioncis_output"",
                             contrast = NULL, WTrows = ""f.0.WT"", selAssay = ""counts"",
                             pseudocount = 1, method = ""edgeR"",
                             normMethod = ""sum""),
        ""No assayNames provided in 'se'"")
    expect_equal(res1a$logFC, res1b$logFC)
    expect_equal(res1a$logFC, res1c$logFC)
    expect_equal(res1a$logFC_shrunk, res1b$logFC_shrunk)
    expect_equal(res1a$logFC_shrunk, res1c$logFC_shrunk)
    expect_equal(res1a$logCPM, res1b$logCPM)
    expect_equal(res1a$logCPM, res1c$logCPM)
    expect_equal(res1a$F, res1b$F)
    expect_equal(res1a$F, res1c$F)
    expect_equal(res1a$PValue, res1b$PValue)
    expect_equal(res1a$PValue, res1c$PValue)
    expect_equal(res1a$FDR, res1b$FDR)
    expect_equal(res1a$FDR, res1c$FDR)

    ## Specifying the coef or contrast should be equivalent
    res2b <- calculateRelativeFC(se, design, coef = NULL,
                                 contrast = as.numeric(colnames(design) == ""Conditioncis_output""),
                                 WTrows = ""f.0.WT"", selAssay = ""counts"",
                                 pseudocount = 1, method = ""edgeR"",
                                 normMethod = ""sum"")
    expect_equal(res1a$logFC, res2b$logFC)
    expect_equal(res1a$logFC_shrunk, res2b$logFC_shrunk)
    expect_equal(res1a$logCPM, res2b$logCPM)
    expect_equal(res1a$F, res2b$F)
    expect_equal(res1a$PValue, res2b$PValue)
    expect_equal(res1a$FDR, res2b$FDR)

    ## With a single WT row, that one should not show any change
    expect_equal(res1a[""f.0.WT"", ""logFC""], 0, tolerance = 1e-4)
    expect_equal(res1a[""f.0.WT"", ""logFC_shrunk""], 0, tolerance = 1e-4)
    expect_equal(res1a[""f.0.WT"", ""PValue""], 1, tolerance = 1e-4)
    expect_equal(res1a[""f.0.WT"", ""FDR""], 1, tolerance = 1e-4)
    expect_named(res1a, c(""logFC"", ""logCPM"", ""F"", ""PValue"", ""FDR"",
                          ""logFC_shrunk"", ""df.total"", ""df.prior"", ""df.test""))

    ## Should also work with limma
    res3a <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                                 contrast = NULL, WTrows = ""f.0.WT"", selAssay = ""counts"",
                                 pseudocount = 1, method = ""limma"",
                                 normMethod = ""sum"")
    expect_equal(res3a[""f.0.WT"", ""logFC""], 0, tolerance = 1e-4)
    expect_equal(res3a[""f.0.WT"", ""P.Value""], 1, tolerance = 1e-4)
    expect_equal(res3a[""f.0.WT"", ""adj.P.Val""], 1, tolerance = 1e-4)
    expect_named(res3a, c(""logFC"", ""CI.L"", ""CI.R"", ""AveExpr"", ""t"",
                          ""P.Value"", ""adj.P.Val"", ""B"", ""se.logFC"", 
                          ""df.total"", ""df.prior""))
    
    ## Also with contrast
    res3b <- calculateRelativeFC(se, design, coef = NULL,
                                 contrast = as.numeric(colnames(design) == ""Conditioncis_output""),
                                 WTrows = ""f.0.WT"", selAssay = ""counts"",
                                 pseudocount = 1, method = ""limma"",
                                 normMethod = ""sum"")
    expect_equal(res3b[""f.0.WT"", ""logFC""], 0, tolerance = 1e-4)
    expect_equal(res3b[""f.0.WT"", ""P.Value""], 1, tolerance = 1e-4)
    expect_equal(res3b[""f.0.WT"", ""adj.P.Val""], 1, tolerance = 1e-4)
    expect_named(res3b, c(""logFC"", ""CI.L"", ""CI.R"", ""AveExpr"", ""t"",
                          ""P.Value"", ""adj.P.Val"", ""B"", ""se.logFC"",
                          ""df.total"", ""df.prior""))
    
    ## Coef and contrast should be equivalent also for limma
    expect_equal(res3a$logFC, res3b$logFC)
    expect_equal(res3a$CI.L, res3b$CI.L)
    expect_equal(res3a$CI.R, res3b$CI.R)
    expect_equal(res3a$AveExpr, res3b$AveExpr)
    expect_equal(res3a$t, res3b$t)
    expect_equal(res3a$P.Value, res3b$P.Value)
    expect_equal(res3a$adj.P.Val, res3b$adj.P.Val)
    expect_equal(res3a$B, res3b$B)
    expect_true(all(res3a$se.logFC >= 0))
    expect_equal(res3a$se.logFC, res3b$se.logFC)
    
    ## Correlation between edgeR and limma logFCs should be high
    expect_gt(cor(res1a$logFC, res3a$logFC), 0.98)

    ## Run without WTrows
    res4a <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                                 contrast = NULL, WTrows = NULL, selAssay = ""counts"",
                                 pseudocount = 1, method = ""edgeR"",
                                 normMethod = ""TMM"")
    res4b <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                                 contrast = NULL, WTrows = NULL, selAssay = ""counts"",
                                 pseudocount = 1, method = ""limma"",
                                 normMethod = ""TMM"")
    res4c <- calculateRelativeFC(se, design, coef = ""Conditioncis_output"",
                                 contrast = NULL, WTrows = NULL, selAssay = ""counts"",
                                 pseudocount = 1, method = ""edgeR"",
                                 normMethod = ""csaw"")
    expect_gt(cor(res4a$logFC, res4b$logFC), 0.98)
})

","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_linkMultipleVariants.R",".R","33910","595","test_that(""linkMultipleVariants works"", {
    ## Fails with the wrong input
    ## ------------------------------------------------------------------------
    expect_error(linkMultipleVariants(combinedDigestParams = 1),
                 ""'combinedDigestParams' must be of class 'list'"")
    expect_error(linkMultipleVariants(combinedDigestParams = list(1)),
                 ""'namescombinedDigestParams' must not be NULL"")
    expect_error(linkMultipleVariants(combinedDigestParams = list(unknown = 1)),
                 ""All values in 'namescombinedDigestParams' must be one of"")
    expect_error(linkMultipleVariants(combinedDigestParams = list(fastqForward = 1),
                                      secondArg = 1),
                 ""'parms' must be of class 'list'"")
    expect_error(linkMultipleVariants(combinedDigestParams = list(fastqForward = 1),
                                      secondArg = list(1)),
                 ""'namesparms' must not be NULL"")
    expect_error(linkMultipleVariants(combinedDigestParams = list(fastqForward = 1),
                                      secondArg = list(unknown = 1)),
                 ""All values in 'namesparms' must be one of"")
    
    expect_error(linkMultipleVariants(combinedDigestParams = list(fastqForward = 1),
                                      secondArg = list(elementsForward = ""CC"")),
                 ""Each separate run must contain at least one variable"")
    expect_error(linkMultipleVariants(combinedDigestParams = list(fastqForward = 1),
                                      secondArg = list(elementsForward = ""V"",
                                                       elementsReverse = ""V"")),
                 ""A separate run can not have variable sequences in both"")
    expect_error(linkMultipleVariants(combinedDigestParams = list(fastqForward = 1),
                                      secondArg = list(elementsForward = ""VV"")),
                 ""A separate run can have at most one variable segment"")
    
    ## Works with correct input
    ## ------------------------------------------------------------------------
    ## Read truth
    truth <- readRDS(system.file(""extdata"", ""multipleVariableRegions_truth.rds"",
                                 package = ""mutscan""))
    verbose <- FALSE
    
    ## ------------------------------------------------------------------------
    ## Don't filter by mutations in constant sequence
    res <- linkMultipleVariants(
        combinedDigestParams = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCVCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCV"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 20, 
            maxOverlap = 30, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            verbose = verbose),
        barcode = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCSCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            avePhredMinForward = 20, 
            collapseMaxDist = 1, collapseMinScore = 1, collapseMinRatio = 1,
            verbose = verbose),
        V2 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCVCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CSCV"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 10, 
            maxOverlap = 20, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV2,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
            verbose = verbose, useTreeWTmatch = TRUE),
        V3 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCSCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCS"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 5, 
            maxOverlap = 15, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV3,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
            verbose = verbose, useTreeWTmatch = TRUE)
    )
    
    ## Filter out only reads with N/deletions in the variable sequence
    keep <- !grepl(""N|del"", truth$truth$status)
    
    ## Truth
    correct <- truth$truth[keep, ] |>
        dplyr::group_by(trueBarcode, trueV2, trueV3) |> dplyr::tally() |>
        dplyr::arrange(dplyr::desc(n), trueBarcode, trueV2, trueV3)
    
    ## Obs
    obs <- res$countAggregated |> dplyr::arrange(desc(nbrReads), barcode, V2, V3)
    
    expect_equal(sum(obs$nbrReads), sum(keep))
    expect_equal(correct$trueBarcode, obs$barcode)
    expect_equal(correct$trueV2, obs$V2)
    expect_equal(correct$trueV3, obs$V3)
    expect_equal(correct$n, obs$nbrReads)
    expect_equal(res$outCombined$filterSummary[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))  ## del
    expect_equal(res$outCombined$filterSummary[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""N"", truth$truth$status)))   ## N
    expect_equal(res$outCombined$filterSummary[, ""nbrRetained""], 
                 sum(!grepl(""N|del"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""nbrRetained""], 
                 sum(!grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""nbrRetained""], 
                 sum(!grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""nbrRetained""], 
                 sum(!grepl(""del"", truth$truth$status)))
    
    ## ------------------------------------------------------------------------
    ## Filter out mutations in constant sequences
    res <- linkMultipleVariants(
        combinedDigestParams = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCVCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCV"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 20, 
            maxOverlap = 30, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            constantForward = truth$constFwd,
            constantReverse = truth$constRev,
            constantMaxDistForward = 0,
            constantMaxDistReverse = 0,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            verbose = verbose),
        barcode = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCSCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            constantForward = truth$constFwd,
            constantMaxDistForward = 0,
            avePhredMinForward = 20,
            collapseMaxDist = 1, collapseMinScore = 1, collapseMinRatio = 1,
            verbose = verbose),
        V2 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCVCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CSCV"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 10, 
            maxOverlap = 20, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            constantForward = truth$constFwd,
            constantReverse = truth$constRev,
            constantMaxDistForward = 0,
            constantMaxDistReverse = 0,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV2,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
            verbose = verbose, useTreeWTmatch = TRUE),
        V3 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCSCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCS"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 5, 
            maxOverlap = 15, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            constantForward = truth$constFwd,
            constantReverse = truth$constRev,
            constantMaxDistForward = 0,
            constantMaxDistReverse = 0,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV3,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
            verbose = verbose, useTreeWTmatch = TRUE)
    )
    
    ## Filter out reads with N/deletions in the variable sequence, or mutations in constant
    keep <- !grepl(""C.*mut|N|del"", truth$truth$status)
    
    ## Truth
    correct <- truth$truth[keep, ] |>
        dplyr::group_by(trueBarcode, trueV2, trueV3) |> dplyr::tally() |>
        dplyr::arrange(dplyr::desc(n), trueBarcode, trueV2, trueV3)
    
    ## Obs
    obs <- res$countAggregated |> dplyr::arrange(desc(nbrReads), barcode, V2, V3)
    
    expect_equal(sum(obs$nbrReads), sum(keep))
    expect_equal(correct$trueBarcode, obs$barcode)
    expect_equal(correct$trueV2, obs$V2)
    expect_equal(correct$trueV3, obs$V3)
    expect_equal(correct$n, obs$nbrReads)

    ## first filter out reads with deletion -> no valid overlap
    ## then reads with N in barcode -> too many N
    ## then reads with mutation in constant
    expect_equal(res$outCombined$filterSummary[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$outCombined$filterSummary[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""N"", truth$truth$status)))
    expect_equal(res$outCombined$filterSummary[, ""f12_nbrTooManyMutConstant""], 
                 sum(grepl(""C.*mut"", truth$truth$status) & 
                         !grepl(""N"", truth$truth$status) & 
                         !grepl(""del"", truth$truth$status)))
    expect_equal(res$outCombined$filterSummary[, ""nbrRetained""], 
                 sum(!grepl(""C.*mut|N|del"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""f12_nbrTooManyMutConstant""], 
                 sum(grepl(""C[1-3]mut|del"", truth$truth$status) & 
                         !grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""nbrRetained""], 
                 sum(!grepl(""V1N|C[1-3]mut|del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""f12_nbrTooManyMutConstant""], 
                 sum(grepl(""C.*mut"", truth$truth$status) & 
                         !grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""nbrRetained""], 
                 sum(!grepl(""C.*mut|del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""f12_nbrTooManyMutConstant""], 
                 sum(grepl(""C.*mut"", truth$truth$status) & 
                         !grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""nbrRetained""], 
                 sum(!grepl(""C.*mut|del"", truth$truth$status)))

    ## ------------------------------------------------------------------------
    ## Collapse to WT
    res <- linkMultipleVariants(
        combinedDigestParams = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCVCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCV"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 20, 
            maxOverlap = 30, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            verbose = verbose),
        barcode = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCSCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            avePhredMinForward = 20, 
            collapseMaxDist = 1, collapseMinScore = 1, collapseMinRatio = 1,
            verbose = verbose),
        V2 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCVCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CSCV"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 10, 
            maxOverlap = 20, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV2,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = TRUE,
            verbose = verbose, useTreeWTmatch = TRUE),
        V3 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCSCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCS"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 5, 
            maxOverlap = 15, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV3,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = TRUE,
            verbose = verbose, useTreeWTmatch = TRUE)
    )
    
    ## Filter out only reads with N/deletions in the variable sequence
    keep <- !grepl(""N|del"", truth$truth$status)
    
    ## Truth
    correct <- truth$truth[keep, ] |>
        dplyr::mutate(trueV2 = gsub(""\\..*"", """", trueV2),
                      trueV3 = gsub(""\\..*"", """", trueV3)) |>
        dplyr::group_by(trueBarcode, trueV2, trueV3) |> dplyr::tally() |>
        dplyr::arrange(dplyr::desc(n), trueBarcode, trueV2, trueV3)
    
    ## Obs
    obs <- res$countAggregated |> dplyr::arrange(desc(nbrReads), barcode, V2, V3)
    
    expect_equal(sum(obs$nbrReads), sum(keep))
    expect_equal(correct$trueBarcode, obs$barcode)
    expect_equal(correct$trueV2, obs$V2)
    expect_equal(correct$trueV3, obs$V3)
    expect_equal(correct$n, obs$nbrReads)
    expect_equal(res$outCombined$filterSummary[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))  ## del
    expect_equal(res$outCombined$filterSummary[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""N"", truth$truth$status)))   ## N
    expect_equal(res$outCombined$filterSummary[, ""nbrRetained""], 
                 sum(!grepl(""N|del"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""nbrRetained""], 
                 sum(!grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""nbrRetained""], 
                 sum(!grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""nbrRetained""], 
                 sum(!grepl(""del"", truth$truth$status)))
    
    ## ------------------------------------------------------------------------
    ## Don't merge fwd and rev
    res <- linkMultipleVariants(
        combinedDigestParams = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCVCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCS"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = FALSE,
            revComplForward = FALSE, revComplReverse = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            verbose = verbose),
        barcode = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCSCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            avePhredMinForward = 20, 
            collapseMaxDist = 1, collapseMinScore = 1, collapseMinRatio = 1,
            verbose = verbose),
        V2 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCVCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            mergeForwardReverse = FALSE,
            revComplForward = FALSE, revComplReverse = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV2,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
            verbose = verbose, useTreeWTmatch = TRUE),
        V3 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCS"", elementLengthsForward = c(6, 40, 10, -1),
            mergeForwardReverse = FALSE,
            revComplForward = TRUE,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV3,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
            verbose = verbose, useTreeWTmatch = TRUE)
    )
    
    ## Filter out only reads with N/deletions in the variable sequence
    keep <- !grepl(""N|del"", truth$truth$status)
    
    ## Truth
    correct <- truth$truth[keep, ] |>
        dplyr::group_by(trueBarcode, trueV2, trueV3) |> dplyr::tally() |>
        dplyr::arrange(dplyr::desc(n), trueBarcode, trueV2, trueV3)
    
    ## Obs
    obs <- res$countAggregated |> dplyr::arrange(desc(nbrReads), barcode, V2, V3)
    
    expect_equal(sum(obs$nbrReads), sum(keep))
    expect_equal(correct$trueBarcode, obs$barcode)
    expect_equal(correct$trueV2, obs$V2)
    expect_equal(correct$trueV3, obs$V3)
    expect_equal(correct$n, obs$nbrReads)
    expect_equal(res$outCombined$filterSummary[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""N"", truth$truth$status)))
    expect_equal(res$outCombined$filterSummary[, ""nbrRetained""], 
                 sum(!grepl(""N"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""nbrRetained""], 
                 sum(!grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""f10b_nbrTooManyMutBases""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""nbrRetained""], 
                 sum(!grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""nbrRetained""], 100)
    
    ## ------------------------------------------------------------------------
    ## Don't allow any mutations
    res <- linkMultipleVariants(
        combinedDigestParams = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCVCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCV"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 20, 
            maxOverlap = 30, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            constantForward = truth$constFwd,
            constantReverse = truth$constRev,
            constantMaxDistForward = 0,
            constantMaxDistReverse = 0,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            verbose = verbose),
        barcode = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CVCSCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            constantForward = truth$constFwd,
            constantMaxDistForward = 0,
            avePhredMinForward = 20, 
            collapseMaxDist = 0, collapseMinScore = 1, collapseMinRatio = 1,
            verbose = verbose),
        V2 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCVCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CSCV"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 10, 
            maxOverlap = 20, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            constantForward = truth$constFwd,
            constantReverse = truth$constRev,
            constantMaxDistForward = 0,
            constantMaxDistReverse = 0,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV2,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 0, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
            verbose = verbose, useTreeWTmatch = TRUE),
        V3 = list(
            fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                       package = ""mutscan""),
            fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                       package = ""mutscan""),
            elementsForward = ""CSCSCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
            elementsReverse = ""CVCS"", elementLengthsReverse = c(6, 40, 10, -1),
            mergeForwardReverse = TRUE, minOverlap = 5, 
            maxOverlap = 15, maxFracMismatchOverlap = 0,
            revComplForward = FALSE, revComplReverse = TRUE,
            constantForward = truth$constFwd,
            constantReverse = truth$constRev,
            constantMaxDistForward = 0,
            constantMaxDistReverse = 0,
            avePhredMinForward = 20, avePhredMinReverse = 20,
            wildTypeForward = truth$WTV3,
            nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 0, 
            forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
            verbose = verbose, useTreeWTmatch = TRUE)
    )
    
    ## Filter out reads with N/deletions in the variable sequence, or mutations in constant
    keep <- !grepl(""C.*mut|N|del|V2|V3"", truth$truth$status)
    
    ## Truth
    correct <- truth$truth[keep, ] |>
        dplyr::group_by(obsBarcode, trueV2, trueV3) |> dplyr::tally() |>
        dplyr::arrange(dplyr::desc(n), obsBarcode, trueV2, trueV3)
    
    ## Obs
    obs <- res$countAggregated |> dplyr::arrange(desc(nbrReads), barcode, V2, V3)
    
    expect_equal(sum(obs$nbrReads), sum(keep))
    expect_equal(correct$obsBarcode, obs$barcode)
    expect_equal(correct$trueV2, obs$V2)
    expect_equal(correct$trueV3, obs$V3)
    expect_equal(correct$n, obs$nbrReads)
    
    expect_equal(res$outCombined$filterSummary[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$outCombined$filterSummary[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""N"", truth$truth$status)))
    expect_equal(res$outCombined$filterSummary[, ""f12_nbrTooManyMutConstant""], 
                 sum(grepl(""C.*mut"", truth$truth$status) & 
                         !grepl(""N"", truth$truth$status) & 
                         !grepl(""del"", truth$truth$status)))
    expect_equal(res$outCombined$filterSummary[, ""nbrRetained""], 
                 sum(!grepl(""C.*mut|N|del"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""f6_nbrTooManyNinVar""], 
                 sum(grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""f12_nbrTooManyMutConstant""], 
                 sum(grepl(""C[1-3]mut|del"", truth$truth$status) & 
                         !grepl(""V1N"", truth$truth$status)))
    expect_equal(res$filtSeparate$barcode[, ""nbrRetained""], 
                 sum(!grepl(""V1N|C[1-3]mut|del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""f10b_nbrTooManyMutBases""],
                 sum(grepl(""V2v[0-9]\\."", truth$truth$status) & 
                         !grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""f12_nbrTooManyMutConstant""], 
                 sum(grepl(""C.*mut"", truth$truth$status) & 
                         !grepl(""del"", truth$truth$status) & 
                         !grepl(""V2v[0-9]\\."", truth$truth$status)))
    expect_equal(res$filtSeparate$V2[, ""nbrRetained""], 
                 sum(!grepl(""C.*mut|del|V2v[0-9]\\."", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""f4_nbrNoValidOverlap""], 
                 sum(grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""f10b_nbrTooManyMutBases""],
                 sum(grepl(""V3\\."", truth$truth$status) & 
                         !grepl(""del"", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""f12_nbrTooManyMutConstant""], 
                 sum(grepl(""C.*mut"", truth$truth$status) & 
                         !grepl(""del"", truth$truth$status) & 
                         !grepl(""V3\\."", truth$truth$status)))
    expect_equal(res$filtSeparate$V3[, ""nbrRetained""], 
                 sum(!grepl(""C.*mut|del|V3\\."", truth$truth$status)))
    
    # --------------------------------------------------------------------------
    # Get warning when summing UMIs
    expect_warning({
        res <- linkMultipleVariants(
            combinedDigestParams = list(
                fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                           package = ""mutscan""),
                fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                           package = ""mutscan""),
                elementsForward = ""UVCVCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
                elementsReverse = ""CVCV"", elementLengthsReverse = c(6, 40, 10, -1),
                mergeForwardReverse = TRUE, minOverlap = 20, 
                maxOverlap = 30, maxFracMismatchOverlap = 0,
                revComplForward = FALSE, revComplReverse = TRUE,
                avePhredMinForward = 20, avePhredMinReverse = 20,
                verbose = verbose),
            barcode = list(
                fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                           package = ""mutscan""),
                elementsForward = ""CVCSCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
                avePhredMinForward = 20, 
                collapseMaxDist = 1, collapseMinScore = 1, collapseMinRatio = 1,
                verbose = verbose),
            V2 = list(
                fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                           package = ""mutscan""),
                fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                           package = ""mutscan""),
                elementsForward = ""CSCVCS"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
                elementsReverse = ""CSCV"", elementLengthsReverse = c(6, 40, 10, -1),
                mergeForwardReverse = TRUE, minOverlap = 10, 
                maxOverlap = 20, maxFracMismatchOverlap = 0,
                revComplForward = FALSE, revComplReverse = TRUE,
                avePhredMinForward = 20, avePhredMinReverse = 20,
                wildTypeForward = truth$WTV2,
                nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
                forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
                verbose = verbose, useTreeWTmatch = TRUE),
            V3 = list(
                fastqForward = system.file(""extdata"", ""multipleVariableRegions_R1.fastq.gz"",
                                           package = ""mutscan""),
                fastqReverse = system.file(""extdata"", ""multipleVariableRegions_R2.fastq.gz"",
                                           package = ""mutscan""),
                elementsForward = ""CSCSCV"", elementLengthsForward = c(6, 24, 10, 30, 10, -1),
                elementsReverse = ""CVCS"", elementLengthsReverse = c(6, 40, 10, -1),
                mergeForwardReverse = TRUE, minOverlap = 5, 
                maxOverlap = 15, maxFracMismatchOverlap = 0,
                revComplForward = FALSE, revComplReverse = TRUE,
                avePhredMinForward = 20, avePhredMinReverse = 20,
                wildTypeForward = truth$WTV3,
                nbrMutatedCodonsMaxForward = -1, nbrMutatedBasesMaxForward = 1, 
                forbiddenMutatedCodonsForward = """", collapseToWTForward = FALSE,
                verbose = verbose, useTreeWTmatch = TRUE)
        )}, ""Aggregating UMI counts"")
})


","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_plotTotals.R",".R","1184","29","se <- readRDS(system.file(""extdata/GSE102901_cis_se.rds"", package = ""mutscan""))
se <- se[1:1000, 1:3]
SummarizedExperiment::rowData(se)$categ <- sample(LETTERS[1:3], nrow(se), replace = TRUE)

test_that(""plotTotals fails with incorrect arguments"", {
    expect_error(plotTotals(se = 1))
    expect_error(plotTotals(se = ""x""))
    expect_error(plotTotals(se = matrix(1:6), 2, 3))
    
    expect_error(plotTotals(se = se, selAssay = 1))
    expect_error(plotTotals(se = se, selAssay = c(""counts"", ""counts"")))
    expect_error(plotTotals(se = se, selAssay = TRUE))
    expect_error(plotTotals(se = se, selAssay = ""wrong""))
    
    expect_error(plotTotals(se = se, groupBy = 1))
    expect_error(plotTotals(se = se, groupBy = ""wrong""))
    expect_error(plotTotals(se = se, groupBy = c(""Name"", ""Condition"")))
})

test_that(""plotTotals works as expected"", {
    ## Defaults
    expect_true(ggplot2::is_ggplot(plotTotals(se = se, selAssay = ""counts"",
                                              groupBy = NULL)))

    ## Group by column
    expect_true(ggplot2::is_ggplot(plotTotals(se = se, selAssay = ""counts"",
                                              groupBy = ""categ"")))
    
})","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_digestFastqs_parallelization.R",".R","16082","267","test_that(""parallel processing works (cis)"", {
  fqc1 <- system.file(""extdata/cisInput_1.fastq.gz"", package = ""mutscan"")
  fqc2 <- system.file(""extdata/cisInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqc1, fastqReverse = fqc2, 
    mergeForwardReverse = TRUE, 
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 1, greedyOverlap = TRUE, 
    revComplForward = FALSE, revComplReverse = TRUE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCVS"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 7, 17, 96, -1),
    adapterForward = ""GGAAGAGCACACGTC"", 
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = c(wt1 = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
                        wt2 = ""CCTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
                        wt3 = ""ACTGATACACTCCAAGCGGAGACTGACCAACTAGAAGATGAGAAGTCTTCTTTGCAGACCGAGATTGCCAACGTGCTGAAGGAGAAGGAAAAACTA""),
    wildTypeReverse = """", 
    constantForward = ""AACCGGAGGAGGGAGCTG"", 
    constantReverse = ""GAGTTCATCCTGGCAGC"", 
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0, 
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = -1,
    nbrMutatedCodonsMaxReverse = -1,
    nbrMutatedBasesMaxForward = 2,
    nbrMutatedBasesMaxReverse = 2,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1, 
    maxReadLength = 1024
  )
  
  res <- do.call(digestFastqs, Ldef)
  
  ## Increase number of threads
  Ldef1 <- Ldef; Ldef1$nThreads <- 3
  ## Capture warnings from missing OpenMP/not enough threads
  a <- capture_warnings({
      res1 <- do.call(digestFastqs, Ldef1)
  })
  expect_equal(sum(grepl(""OpenMP|available"", a)), length(a))

  expect_equal(res$filterSummary$nbrTotal, res1$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res1$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res1$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res1$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res1$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res1$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res1$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res1$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res1$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res1$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res1$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res1$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res1$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res1$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res1$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res1$filterSummary$nbrRetained)
  
  expect_equal(res$summaryTable$nbrReads, res1$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res1$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res1$summaryTable$sequence)
  
  ## Increase chunk size
  Ldef1 <- Ldef; Ldef1$nThreads <- 3; Ldef1$chunkSize <- 100
  ## Capture warnings from missing OpenMP/not enough threads
  a <- capture_warnings({
      res1 <- do.call(digestFastqs, Ldef1)
  })
  expect_equal(sum(grepl(""OpenMP|available"", a)), length(a))
  
  expect_equal(res$filterSummary$nbrTotal, res1$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res1$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res1$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res1$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res1$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res1$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res1$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res1$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res1$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res1$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res1$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res1$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res1$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res1$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res1$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res1$filterSummary$nbrRetained)
  
  expect_equal(res$summaryTable$nbrReads, res1$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res1$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res1$summaryTable$sequence)
  
  ## With tree matching
  Ldef1 <- Ldef; Ldef1$useTreeWTmatch <- TRUE; Ldef1$nThreads <- 3; Ldef1$chunkSize <- 100
  ## Capture warnings from missing OpenMP/not enough threads
  a <- capture_warnings({
      res1 <- do.call(digestFastqs, Ldef1)
  })
  expect_equal(sum(grepl(""OpenMP|available"", a)), length(a))
  
  expect_equal(res$filterSummary$nbrTotal, res1$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res1$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res1$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res1$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res1$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res1$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res1$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res1$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res1$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res1$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res1$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res1$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res1$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res1$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res1$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res1$filterSummary$nbrRetained)
  
  expect_equal(res$summaryTable$nbrReads, res1$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res1$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res1$summaryTable$sequence)
})

test_that(""parallel processing works (trans)"", {
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2, 
    mergeForwardReverse = FALSE, 
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"", 
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
    constantForward = ""AACCGGAGGAGGGAGCTG"", 
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0, 
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE, 
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = 0,
    constantMaxDistReverse = 0,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1, 
    maxReadLength = 1024
  )
  
  res <- do.call(digestFastqs, Ldef)
  
  ## Increase number of threads
  Ldef1 <- Ldef; Ldef1$nThreads <- 3
  ## Capture warnings from missing OpenMP/not enough threads
  a <- capture_warnings({
      res1 <- do.call(digestFastqs, Ldef1)
  })
  expect_equal(sum(grepl(""OpenMP|available"", a)), length(a))
  
  expect_equal(res$filterSummary$nbrTotal, res1$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res1$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res1$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res1$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res1$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res1$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res1$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res1$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res1$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res1$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res1$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res1$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res1$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res1$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res1$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res1$filterSummary$nbrRetained)
  
  expect_equal(res$summaryTable$nbrReads, res1$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res1$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res1$summaryTable$sequence)
  
  ## Increase chunk size
  Ldef1 <- Ldef; Ldef1$nThreads <- 3; Ldef1$chunkSize <- 100
  ## Capture warnings from missing OpenMP/not enough threads
  a <- capture_warnings({
      res1 <- do.call(digestFastqs, Ldef1)
  })
  expect_equal(sum(grepl(""OpenMP|available"", a)), length(a))
  
  expect_equal(res$filterSummary$nbrTotal, res1$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res1$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res1$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res1$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res1$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res1$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res1$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res1$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res1$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res1$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res1$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res1$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res1$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res1$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res1$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res1$filterSummary$nbrRetained)
  
  expect_equal(res$summaryTable$nbrReads, res1$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res1$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res1$summaryTable$sequence)
  
  ## With tree matching
  Ldef1 <- Ldef; Ldef1$useTreeWTmatch <- TRUE; Ldef1$nThreads <- 3; Ldef1$chunkSize <- 100
  ## Capture warnings from missing OpenMP/not enough threads
  a <- capture_warnings({
      res1 <- do.call(digestFastqs, Ldef1)
  })
  expect_equal(sum(grepl(""OpenMP|available"", a)), length(a))
  
  expect_equal(res$filterSummary$nbrTotal, res1$filterSummary$nbrTotal)
  expect_equal(res$filterSummary$f1_nbrAdapter, res1$filterSummary$f1_nbrAdapter)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, res1$filterSummary$f2_nbrNoPrimer)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, res1$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, res1$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, res1$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, res1$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, res1$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, res1$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, res1$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, res1$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, res1$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, res1$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, res1$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, res1$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res$filterSummary$nbrRetained, res1$filterSummary$nbrRetained)
  
  expect_equal(res$summaryTable$nbrReads, res1$summaryTable$nbrReads)
  expect_equal(res$summaryTable$mutantName, res1$summaryTable$mutantName)
  expect_equal(res$summaryTable$sequence, res1$summaryTable$sequence)
  
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_plotDistributions.R",".R","6324","130","se <- readRDS(system.file(""extdata/GSE102901_cis_se.rds"", package = ""mutscan""))
se <- se[1:1000, 1:3]

test_that(""plotDistributions fails with incorrect arguments"", {
    expect_error(plotDistributions(se = 1))
    expect_error(plotDistributions(se = ""x""))
    expect_error(plotDistributions(se = matrix(1:6), 2, 3))
    
    expect_error(plotDistributions(se = se, selAssay = 1))
    expect_error(plotDistributions(se = se, selAssay = c(""counts"", ""counts"")))
    expect_error(plotDistributions(se = se, selAssay = TRUE))
    expect_error(plotDistributions(se = se, selAssay = ""wrong""))
    
    expect_error(plotDistributions(se = se, groupBy = 1))
    expect_error(plotDistributions(se = se, groupBy = ""wrong""))
    expect_error(plotDistributions(se = se, groupBy = c(""Name"", ""Condition"")))

    expect_error(plotDistributions(se = se, plotType = 1))
    expect_error(plotDistributions(se = se, plotType = TRUE))
    expect_error(plotDistributions(se = se, plotType = c(""density"", ""histogram"")))
    expect_error(plotDistributions(se = se, plotType = ""wrong""))
    
    expect_error(plotDistributions(se = se, facet = 1))
    expect_error(plotDistributions(se = se, facet = c(TRUE, FALSE)))
    expect_error(plotDistributions(se = se, facet = ""wrong""))
    
    expect_error(plotDistributions(se = se, pseudocount = TRUE))
    expect_error(plotDistributions(se = se, pseudocount = ""1""))
    expect_error(plotDistributions(se = se, pseudocount = c(0, 1)))
    expect_error(plotDistributions(se = se, pseudocount = -1))
})

test_that(""plotDistributions works as expected"", {
    ## Defaults
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = NULL, plotType = ""density"",
                          facet = FALSE, pseudocount = 0)))

    ## Change plot type
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = NULL, plotType = ""histogram"",
                          facet = FALSE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = NULL, plotType = ""knee"",
                          facet = FALSE, pseudocount = 0)))
    
    ## groupBy Name
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Name"", plotType = ""density"",
                          facet = FALSE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Name"", plotType = ""histogram"",
                          facet = FALSE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Name"", plotType = ""knee"",
                          facet = FALSE, pseudocount = 0)))
    
    ## groupBy Condition
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""density"",
                          facet = FALSE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""histogram"",
                          facet = FALSE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""knee"",
                          facet = FALSE, pseudocount = 0)))
    
    ## Facet
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = NULL, plotType = ""density"",
                          facet = TRUE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = NULL, plotType = ""histogram"",
                          facet = TRUE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = NULL, plotType = ""knee"",
                          facet = TRUE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Name"", plotType = ""density"",
                          facet = TRUE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Name"", plotType = ""histogram"",
                          facet = TRUE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Name"", plotType = ""knee"",
                          facet = TRUE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""density"",
                          facet = TRUE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""histogram"",
                          facet = TRUE, pseudocount = 0)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""knee"",
                          facet = TRUE, pseudocount = 0)))
    
    ## Increase pseudocount
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""density"",
                          facet = FALSE, pseudocount = 2)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""histogram"",
                          facet = FALSE, pseudocount = 3)))
    expect_true(ggplot2::is_ggplot(
        plotDistributions(se = se, selAssay = ""counts"",
                          groupBy = ""Condition"", plotType = ""knee"",
                          facet = FALSE, pseudocount = 4)))
    
})","R"
"Assay","fmicompbio/mutscan","tests/testthat/tests-utils.R",".R","6657","112","library(testthat)

## ------------------------------------------------------------------------- ##
## Misspecified arguments
## ------------------------------------------------------------------------- ##
expect_error(.assertScalar(1, type = TRUE))
expect_error(.assertScalar(1, type = 1))
expect_error(.assertScalar(1, type = c(""numeric"", ""character"")))
expect_error(.assertScalar(1, type = ""numeric"", rngIncl = TRUE))
expect_error(.assertScalar(1, type = ""numeric"", rngIncl = ""rng""))
expect_error(.assertScalar(1, type = ""numeric"", rngIncl = 1))
expect_error(.assertScalar(1, type = ""numeric"", rngIncl = 1:3))
expect_error(.assertScalar(1, type = ""numeric"", rngExcl = TRUE))
expect_error(.assertScalar(1, type = ""numeric"", rngExcl = ""rng""))
expect_error(.assertScalar(1, type = ""numeric"", rngExcl = 1))
expect_error(.assertScalar(1, type = ""numeric"", rngExcl = 1:3))
expect_error(.assertScalar(1, type = ""numeric"", rngIncl = c(0, 2), rngExcl = c(0, 2)))
expect_error(.assertScalar(1, type = ""numeric"", allowNULL = 1))
expect_error(.assertScalar(1, type = ""numeric"", allowNULL = ""rng""))
expect_error(.assertScalar(1, type = ""numeric"", allowNULL = NULL))
expect_error(.assertScalar(1, type = ""numeric"", allowNULL = c(TRUE, FALSE)))

expect_error(.assertVector(1, type = TRUE))
expect_error(.assertVector(1, type = 1))
expect_error(.assertVector(1, type = c(""numeric"", ""character"")))
expect_error(.assertVector(1, type = ""numeric"", rngIncl = TRUE))
expect_error(.assertVector(1, type = ""numeric"", rngIncl = ""rng""))
expect_error(.assertVector(1, type = ""numeric"", rngIncl = 1))
expect_error(.assertVector(1, type = ""numeric"", rngIncl = 1:3))
expect_error(.assertVector(1, type = ""numeric"", rngExcl = TRUE))
expect_error(.assertVector(1, type = ""numeric"", rngExcl = ""rng""))
expect_error(.assertVector(1, type = ""numeric"", rngExcl = 1))
expect_error(.assertVector(1, type = ""numeric"", rngExcl = 1:3))
expect_error(.assertVector(1, type = ""numeric"", rngIncl = c(0, 2), rngExcl = c(0, 2)))
expect_error(.assertVector(1, type = ""numeric"", allowNULL = 1))
expect_error(.assertVector(1, type = ""numeric"", allowNULL = ""rng""))
expect_error(.assertVector(1, type = ""numeric"", allowNULL = NULL))
expect_error(.assertVector(1, type = ""numeric"", allowNULL = c(TRUE, FALSE)))
expect_error(.assertVector(1, type = ""numeric"", len = TRUE))
expect_error(.assertVector(1, type = ""numeric"", len = ""rng""))
expect_error(.assertVector(1, type = ""numeric"", len = 1:3))
expect_error(.assertVector(1, type = ""numeric"", rngLen = TRUE))
expect_error(.assertVector(1, type = ""numeric"", rngLen = ""rng""))
expect_error(.assertVector(1, type = ""numeric"", rngLen = 1))
expect_error(.assertVector(1, type = ""numeric"", rngLen = 1:3))

## ------------------------------------------------------------------------- ##
## Checks, .assertScalar
## ------------------------------------------------------------------------- ##
expect_true(.assertScalar(1, type = ""numeric"", rngIncl = c(1, 3)))
expect_error(.assertScalar(1, type = ""numeric"", rngExcl = c(1, 3)))
expect_true(.assertScalar(1, type = ""numeric"", rngExcl = c(1, 3), validValues = 1))
expect_true(.assertScalar(-1, type = ""numeric"", rngIncl = c(1, 3), validValues = c(-1, 0)))
expect_error(.assertScalar(-1, type = ""numeric"", rngIncl = c(1, 3), validValues = 0))
expect_true(.assertScalar(-1, type = ""numeric"", validValues = c(-1, 0)))
expect_error(.assertScalar(-1, type = ""numeric"", validValues = c(-2, 0)))
expect_true(.assertScalar(NA_real_, type = ""numeric"", rngIncl = c(1, 2), validValues = NA_real_))
expect_error(.assertScalar(NA, type = ""numeric"", rngIncl = c(1, 2), validValues = NA_real_))
expect_true(.assertScalar(NA_real_, type = ""numeric"", rngIncl = c(1, 2), validValues = NA))
expect_true(.assertScalar(1, type = ""numeric"", rngIncl = c(0, 3), validValues = 3))
expect_true(.assertScalar(1, rngIncl = c(0, 3), validValues = 3))
expect_true(.assertScalar(1, type = ""numeric"", rngIncl = c(0, 1)))
expect_error(.assertScalar(1, type = ""numeric"", rngExcl = c(0, 1)))
expect_true(.assertScalar(1, type = ""numeric"", rngExcl = c(0, 1), validValues = 1))
expect_error(.assertScalar(1, type = ""numeric"", rngExcl = c(0, 1), validValues = 3:4))
expect_true(.assertScalar(NULL, type = ""numeric"", allowNULL = TRUE))
expect_error(.assertScalar(NULL, type = ""numeric"", allowNULL = FALSE))
expect_error(.assertScalar(1, type = ""character""))
expect_error(.assertScalar(""x"", type = ""numeric""))
expect_error(.assertScalar(FALSE, type = ""character""))
expect_error(.assertScalar(c(1, 2), type = ""numeric""))
test <- ""text""
expect_error(.assertScalar(x = test, type = ""numeric""),
             ""'test' must be of class 'numeric"")

## ------------------------------------------------------------------------- ##
## Checks, .assertVector
## ------------------------------------------------------------------------- ##
expect_true(.assertVector(c(1, 2), type = ""numeric"", rngIncl = c(1, 3)))
expect_error(.assertVector(c(1, 2), type = ""numeric"", rngIncl = c(1, 1.5)))
expect_error(.assertVector(c(1, 2), type = ""numeric"", rngExcl = c(1, 3)))
expect_true(.assertVector(c(1, 2), type = ""numeric"", rngExcl = c(1, 3), validValues = 1))
expect_error(.assertVector(c(1, 2), type = ""numeric"", validValues = c(1, 3)))
expect_true(.assertVector(c(1, 2), type = ""numeric"", validValues = c(1, 2)))
expect_error(.assertVector(c(1, 2), type = ""numeric"", len = 1))
expect_true(.assertVector(c(1, 2), type = ""numeric"", len = 2))
expect_error(.assertVector(c(1, 2), type = ""numeric"", rngLen = c(3, 5)))
expect_true(.assertVector(c(1, 2), type = ""numeric"", rngLen = c(2, 5)))
expect_true(.assertVector(c(1, 2), type = ""numeric"", rngLen = c(1, 2)))
expect_error(.assertVector(c(""a"", ""b""), type = ""character"", validValues = c(""A"", ""B"")))
expect_true(.assertVector(LETTERS[1:2], type = ""character"", validValues = LETTERS))
test <- ""text""
expect_error(.assertVector(x = test, type = ""numeric""),
             ""'test' must be of class 'numeric"")

## -------------------------------------------------------------------------- ##
## Checks, .assertPackagesAvailable
## -------------------------------------------------------------------------- ##
test_that("".assertPackagesAvailable works"", {
    testfunc <- function(...) .assertPackagesAvailable(...)
    expect_error(testfunc(1L))
    expect_error(testfunc(""test"", ""error""))
    expect_error(testfunc(""test"", c(TRUE, FALSE)))
    
    expect_true(testfunc(""base""))
    expect_true(testfunc(""githubuser/base""))
    expect_true(testfunc(c(""base"", ""methods"")))
    expect_error(testfunc(c(""error"", ""error2"")), ""BiocManager"")
    expect_error(testfunc(""error1"", suggestInstallation = FALSE), ""installed.\n$"")
    rm(testfunc)
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_digestFastqs_input.R",".R","12216","288","test_that(""digestFastqs fails with incorrect arguments"", {
  ## example ""trans"" fastq files 
  fqt1 <- system.file(""extdata/transInput_1.fastq.gz"", package = ""mutscan"")
  fqt2 <- system.file(""extdata/transInput_2.fastq.gz"", package = ""mutscan"")
  
  ## default arguments
  Ldef <- list(
    fastqForward = fqt1, fastqReverse = fqt2, 
    mergeForwardReverse = FALSE, 
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
    revComplForward = FALSE, revComplReverse = FALSE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCV"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 8, 20, 96),
    adapterForward = ""GGAAGAGCACACGTC"", 
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = c(""""),
    primerReverse = c(""""),
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"", 
    constantForward = ""AACCGGAGGAGGGAGCTG"", 
    constantReverse = ""GAAAAAGGAAGCTGGAGAGA"", 
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0, 
    variableNMaxForward = 0, variableNMaxReverse = 0, 
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = TRUE,
    collapseToWTForward = FALSE,
    collapseToWTReverse = FALSE,
    mutatedPhredMinForward = 0.0, 
    mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
  )
  
  ## Incorrect specification of merging/rev complementing arguments
  for (var in c(""mergeForwardReverse"", ""revComplForward"", ""revComplReverse"")) {
    L <- Ldef; L[[var]] <- ""str""
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- """"
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- c(TRUE, TRUE)
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- c(TRUE, FALSE)
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- 1
    expect_error(do.call(digestFastqs, L))
  }
  
  ## Nonexistent fastqForward/fastqReverse file
  L <- Ldef; L$fastqForward <- ""nonexistent.fastq.gz""
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L$fastqForward <- """"
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L$fastqForward <- NULL
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L$fastqReverse <- ""nonexistent.fastq.gz""
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L$fastqReverse <- """"
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L$fastqForward <- c(fqt1, fqt2)
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L$fastqReverse <- c(fqt1, fqt2)
  expect_error(do.call(digestFastqs, L))
  
  ## No reverse reads if mergeForwardReverse is TRUE
  L <- Ldef; L[[""fastqReverse""]] <- NULL; L[[""mergeForwardReverse""]] <- TRUE
  expect_error(do.call(digestFastqs, L))
  
  ## Wrong type of numeric argument
  for (var in c(""avePhredMinForward"", ""avePhredMinReverse"", ""variableNMaxForward"",
                ""variableNMaxReverse"", ""umiNMax"", 
                ""nbrMutatedCodonsMaxForward"", ""nbrMutatedCodonsMaxReverse"", 
                ""nbrMutatedBasesMaxForward"", ""nbrMutatedBasesMaxReverse"", 
                ""mutatedPhredMinForward"", ""mutatedPhredMinReverse"",
                ""umiCollapseMaxDist"",
                ""constantMaxDistForward"", ""constantMaxDistReverse"", ""maxNReads"",
                ""nThreads"", ""chunkSize"", ""maxReadLength"")) {
    L <- Ldef; L[[var]] <- ""str""
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- c(1, 2)
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- -2
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- TRUE
    expect_error(do.call(digestFastqs, L))
    if (!(var %in% c(""nbrMutatedCodonsMaxForward"", ""nbrMutatedCodonsMaxReverse"", 
                     ""nbrMutatedBasesMaxForward"", ""nbrMutatedBasesMaxReverse"",
                     ""constantMaxDistForward"", ""constantMaxDistReverse"",
                     ""maxNReads"", ""maxReadLength""))) {
      L <- Ldef; L[[var]] <- -1
      expect_error(do.call(digestFastqs, L))
    }
    if (var %in% c(""nThreads"", ""chunkSize"", ""maxReadLength"")) {
      L <- Ldef; L[[var]] <- 0
      expect_error(do.call(digestFastqs, L))
    }
  }
  
  ## Both or none of max number of codons and bases specified
  ## Note that this should only give an error if a wildtype sequence is specified (which it is here)
  L <- Ldef; L[[""nbrMutatedCodonsMaxForward""]] <- L[[""nbrMutatedBasesMaxForward""]] <- 1
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""nbrMutatedCodonsMaxReverse""]] <- L[[""nbrMutatedBasesMaxReverse""]] <- 1
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""nbrMutatedCodonsMaxForward""]] <- L[[""nbrMutatedBasesMaxForward""]] <- -1
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""nbrMutatedCodonsMaxReverse""]] <- L[[""nbrMutatedBasesMaxReverse""]] <- -1
  expect_error(do.call(digestFastqs, L))
  
  
  for (var in c(""elementLengthsForward"", ""elementLengthsReverse"")) {
    L <- Ldef; L[[var]] <- as.character(L[[var]])
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- as.logical(L[[var]])
    expect_error(do.call(digestFastqs, L))
  }
  
  for (var in c(""elementsForward"", ""elementsReverse"")) {
    L <- Ldef; L[[var]] <- ""ABC""
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- ""UPPV""
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- c(""UPV"", ""CUS"")
    expect_error(do.call(digestFastqs, L))
  }
  
  for (var in c(""Forward"", ""Reverse"")) {
    L <- Ldef; L[[paste0(""elements"", var)]] <- ""CUV""
    L[[paste0(""elementLengths"", var)]] <- c(-1, -1, 4)
    expect_error(do.call(digestFastqs, L))
    L[[paste0(""elementLengths"", var)]] <- c(1, 4)
    expect_error(do.call(digestFastqs, L))
    
    L <- Ldef; L[[paste0(""elementLengths"", var)]][2] <- -2
    expect_error(do.call(digestFastqs, L))
    
    L <- Ldef; L[[paste0(""elements"", var)]] <- """"
    L[[paste0(""elementLengths"", var)]] <- c()
    expect_error(do.call(digestFastqs, L))
    
    L <- Ldef; L[[paste0(""elements"", var)]] <- ""CUVPUV""
    L[[paste0(""elementLengths"", var)]] <- c(-1, -1, 4, 4, -1, 2)
    expect_error(do.call(digestFastqs, L))
    L[[paste0(""elementLengths"", var)]] <- c(-1, 1, 4, 4, -1, -1)
    expect_error(do.call(digestFastqs, L))
    L[[paste0(""elementLengths"", var)]] <- c(1, 4)
    expect_error(do.call(digestFastqs, L))
  }
  
  ## Invalid sequences
  for (var in c(""adapterForward"", ""adapterReverse"", ""wildTypeForward"",
                ""wildTypeReverse"", ""constantForward"", ""constantReverse"")) {
    if (!(var %in% c(""constantForward"", ""constantReverse""))) {
      L <- Ldef; L[[var]] <- c(""ACGT"", ""ACGT"")
      expect_error(do.call(digestFastqs, L))
    }
    L <- Ldef; L[[var]] <- 1
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- ""EF""
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- paste0(L[[var]], "" "")
    expect_error(do.call(digestFastqs, L))
  }
  
  for (var in c(""primerForward"", ""primerReverse"")) {
    L <- Ldef; L[[var]] <- 1
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- ""EF""
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- c(""ACGT"", ""EF"")
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[var]] <- ""ACGT ""
    expect_error(do.call(digestFastqs, L))
  }
  
  ## Wild type sequence not in named vector (or unnamed string)
  L <- Ldef
  L$wildTypeForward <- c(""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"", 
                         ""ATCGCCCGGCTGGAGGAAAAAGTGAAAACCTTGAAAGCTCAGAACTCGGAGCTGGCGTCCACGGCCAACATGCTCAGGGAACAGGTGGCACAGCTT"")
  expect_error(do.call(digestFastqs, L))
  
  ## Duplicated wild type sequences
  L <- Ldef
  L$wildTypeForward <- c(wt1 = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
                         wt2 = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")
  expect_error(do.call(digestFastqs, L))
  
  ## Constant sequence of wrong length
  for (var in c(""constantForward"", ""constantReverse"")) {
    L <- Ldef; L[[var]] <- substr(L[[var]], 1, 10)
    expect_error(do.call(digestFastqs, L))
  }
  
  ## Invalid forbidden codons
  L <- Ldef; L[[""forbiddenMutatedCodonsForward""]] <- c(""EFI"")
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""forbiddenMutatedCodonsReverse""]] <- c(""EFI"")
  expect_error(do.call(digestFastqs, L))
  
  ## Invalid mutNameDelimiter
  L <- Ldef; L[[""mutNameDelimiter""]] <- """"
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""mutNameDelimiter""]] <- ""__""
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""mutNameDelimiter""]] <- ""f""
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""mutNameDelimiter""]] <- ""r""
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""mutNameDelimiter""]] <- 1
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""mutNameDelimiter""]] <- c(""a"", ""B"")
  expect_error(do.call(digestFastqs, L))
  
  ## Invalid value of useTreeWTmatch
  L <- Ldef; L[[""useTreeWTmatch""]] <- 2
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""useTreeWTmatch""]] <- ""TRUE""
  expect_error(do.call(digestFastqs, L))
  
  ## Invalid value of output FASTQ files
  L <- Ldef; L[[""filteredReadsFastqForward""]] <- 1; L[[""filteredReadsFastqReverse""]] <- 1
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""filteredReadsFastqForward""]] <- c(""file1.gz"", ""file2.gz"")
  L[[""filteredReadsFastqReverse""]] <- ""file3.gz""
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""filteredReadsFastqForward""]] <- TRUE; L[[""filteredReadsFastqReverse""]] <- FALSE
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""filteredReadsFastqForward""]] <- ""file1.fastq""
  L[[""filteredReadsFastqReverse""]] <- ""file3.fastq""
  expect_error(do.call(digestFastqs, L))
  
  ## Invalid combination of input and output FASTQ
  L <- Ldef; L[[""fastqReverse""]] <- NULL; L[[""filteredReadsFastqForward""]] <- ""file1.fastq.gz"";
  L[[""filteredReadsFastqReverse""]] <- ""file2.fastq.gz""
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""filteredReadsFastqForward""]] <- ""file1.fastq.gz"";
  L[[""filteredReadsFastqReverse""]] <- """"
  expect_error(do.call(digestFastqs, L))
  L <- Ldef; L[[""filteredReadsFastqForward""]] <- """";
  L[[""filteredReadsFastqReverse""]] <- ""file2.fastq.gz""
  expect_error(do.call(digestFastqs, L))
  
  ## Invalid value of verbose
  for (v in c(""verbose"", ""collapseToWTForward"", ""collapseToWTReverse"")) {
    L <- Ldef; L[[v]] <- 2
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[v]] <- ""TRUE""
    expect_error(do.call(digestFastqs, L))
    L <- Ldef; L[[v]] <- c(TRUE, FALSE)
    expect_error(do.call(digestFastqs, L))
  }
  
  ## Too long read
  expect_error(digestFastqs(
      fastqForward = system.file(""extdata"", ""cisInput_1.fastq.gz"",
                                 package = ""mutscan""),
      elementsForward = ""V"", elementLengthsForward = -1, 
      maxReadLength = 100),
      ""Encountered a read exceeding the maximal allowed length"")
  ## Check border cases
  expect_error(digestFastqs(
      fastqForward = system.file(""extdata"", ""cisInput_1.fastq.gz"",
                                 package = ""mutscan""),
      elementsForward = ""V"", elementLengthsForward = -1, 
      maxReadLength = 124),
      ""Encountered a read exceeding the maximal allowed length"")
  ## Works if the maxReadLength is specified accordingly
  out <- digestFastqs(
      fastqForward = system.file(""extdata"", ""cisInput_1.fastq.gz"",
                                 package = ""mutscan""),
      elementsForward = ""V"", elementLengthsForward = -1, 
      maxReadLength = 125)
  expect_type(out, ""list"")
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_digestFastqs_cis.R",".R","22561","419","test_that(""digestFastqs works as expected for cis experiments"", {
  fqc1 <- system.file(""extdata/cisInput_1.fastq.gz"", package = ""mutscan"")
  fqc2 <- system.file(""extdata/cisInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqc1, fastqReverse = fqc2,
    mergeForwardReverse = TRUE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 1, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = TRUE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCVS"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 7, 17, 96, -1),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAGTTCATCCTGGCAGC"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000,
    maxReadLength = 1024
  )

  ## hit maxNReads
  Ldef0 <- Ldef
  Ldef0$maxNReads <- 10
  res0 <- do.call(digestFastqs, Ldef0)
  expect_identical(res0$filterSummary$nbrTotal, 10L)

  ## hit nTooManyNinUMI
  Ldef0 <- Ldef
  Ldef0$elementsForward <- ""UV""
  Ldef0$elementsReverse <- ""UVS""
  Ldef0$elementLengthsForward <- c(115, 10)
  Ldef0$elementLengthsReverse = c(111, 10, -1)
  Ldef0$adapterForward <- Ldef0$adapterReverse <- """"
  Ldef0$primerForward <- Ldef0$primerReverse <- """"
  Ldef0$wildTypeForward <- ""GGAAAAACTA""
  Ldef0$constantForward <- Ldef0$constantReverse <- """"
  res0 <- do.call(digestFastqs, Ldef0)
  expect_identical(res0$filterSummary$f7_nbrTooManyNinUMI, 497L)

  ## hit nTooManyMutBases
  Ldef0 <- Ldef
  Ldef0$nbrMutatedBasesMaxForward <- 0
  Ldef0$nbrMutatedBasesMaxReverse <- 0
  Ldef0$nbrMutatedCodonsMaxForward <- -1
  Ldef0$nbrMutatedCodonsMaxReverse <- -1
  res0 <- do.call(digestFastqs, Ldef0)
  expect_identical(res0$filterSummary$f10b_nbrTooManyMutBases, 753L)

  ## hit not all WT sequences are of the same length
  Ldef0 <- Ldef
  Ldef0$maxNReads <- 100
  Ldef0$useTreeWTmatch <- TRUE
  Ldef0$mergeForwardReverse <- FALSE
  Ldef0$wildTypeForward <- """"
  Ldef0$wildTypeReverse <- c(w1 = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
                             w2 = ""GACAGACCAACTAGAAGTTACATGAGAAGTCTGCTTTGCAGACCGAGATTGCCA"")
  res0 <- do.call(digestFastqs, Ldef0)
  expect_true(any(grepl(""_w1"", res0$summaryTable$mutantNameBase)))

  ## now run with the unmodified arguments
  res <- do.call(digestFastqs, Ldef)

  ## Specify reverse WT - check that it's ignored
  LdefrevWT <- Ldef
  LdefrevWT$wildTypeReverse <- ""ACGT""
  expect_warning(resrevWT <- do.call(digestFastqs, LdefrevWT),
                 ""Ignoring 'wildTypeReverse'"")

  expect_equal(res$filterSummary, resrevWT$filterSummary)
  expect_equal(res$summaryTable, resrevWT$summaryTable)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 126L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 0L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 44L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 581L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 0L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 82L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 167)

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE),
                 ignore_attr = TRUE)
  }

  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(sum(res$summaryTable$nbrReads == 2), 11L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 2]),
               sort(c(""f.14.AAG"", ""f.15.ATG"", ""f.19.CAC"", ""f.1.ACC"", ""f.20.AAC"", ""f.21.GTG"",
                      ""f.24.AGC"", ""f.4.CGC"", ""f.7.GTG"", ""f.9.GGC"", ""f.9.GTC"")))
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))

  ## Check the number of reads with a given number of mismatches
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 0]), 77L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 1]), 88L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 2]), 2L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 0]), 77L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 1]), 90L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 0]), 97L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 1]), 70L)

  ## Similarly but for the number of unique sequences
  expect_equal(sum(res$summaryTable$nbrMutBases == 0), 1L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 1), 64L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 2), 2L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 0), 1L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 1), 66L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 0), 14L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 1), 53L)

  ## Check that mutant naming worked (compare to manual matching)
  example_seq <- paste0(""ACTGATACACGCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCT"",
                        ""GCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")
  expect_equal(res$summaryTable$mutantName[res$summaryTable$sequence == example_seq],
               ""f.4.CGC"")
  expect_equal(res$summaryTable$mutantNameAA[res$summaryTable$sequence == example_seq],
               ""f.4.R"")
  expect_equal(res$summaryTable$mutantNameBase[res$summaryTable$sequence == example_seq],
               ""f.11.G"")
  expect_equal(res$summaryTable$mutantNameCodon[res$summaryTable$sequence == example_seq],
               ""f.4.CGC"")
  expect_equal(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$sequence == example_seq],
               ""f:c.11T>G"")
  expect_equal(res$summaryTable$mutantNameAAHGVS[res$summaryTable$sequence == example_seq],
               ""f:p.(Leu4Arg)"")

  expect_equal(res$summaryTable$sequenceAA,
               as.character(Biostrings::translate(
                 Biostrings::DNAStringSet(res$summaryTable$sequence))))
  expect_equal(as.numeric(res$summaryTable$varLengths),
               nchar(res$summaryTable$sequence))
  expect_equal(sum(grepl(""WT"", res$summaryTable$mutantNameAA) & res$summaryTable$nbrMutBases > 0), 13)
  expect_true(all(grepl(""silent"", res$summaryTable$mutationTypes[grepl(""WT"", res$summaryTable$mutantNameAA) & res$summaryTable$nbrMutBases > 0])))
  expect_true(all(grepl(""nonsynonymous|stop"", res$summaryTable$mutationTypes[grepl(""\\.[1-9][0-9]*\\."", res$summaryTable$mutantNameAA)])))
  expect_true(all(grepl(""stop"", res$summaryTable$mutationTypes[grepl(""TAG|TAA|TGA"", res$summaryTable$mutantName)])))

  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(Ldef$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(Ldef$constantReverse[1]) * res$filterSummary$nbrRetained)

  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 51L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 19L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 79L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 163L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 2690L)

  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 14], 1L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 27], 1L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 37], 2L)

  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 14], 41L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 27], 56L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 33], 96L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 37], 2620L)

  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 2], 4L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 14], 7L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 33], 1L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 37], 14L)

  ## Add an additional constant sequence of the wrong length - should fail
  Ldefcs <- Ldef
  Ldefcs$constantForward <- c(""AACCGGAGGAGGGAGCTG"", ""AACCGGAGGAGGGAGCT"")
  expect_error(do.call(digestFastqs, Ldefcs),
               ""All constant sequences must be of the same length"")

})

test_that(""digestFastqs works as expected when specifying max nbr of mutated bases rather than codons"", {
  fqc1 <- system.file(""extdata/cisInput_1.fastq.gz"", package = ""mutscan"")
  fqc2 <- system.file(""extdata/cisInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqc1, fastqReverse = fqc2,
    mergeForwardReverse = TRUE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 1, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = TRUE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCVS"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 7, 17, 96, -1),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAGTTCATCCTGGCAGC"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = -1,
    nbrMutatedCodonsMaxReverse = -1,
    nbrMutatedBasesMaxForward = 2,
    nbrMutatedBasesMaxReverse = 2,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000,
    maxReadLength = 125
  )

  res <- do.call(digestFastqs, Ldef)

  expect_equal(res$filterSummary$nbrTotal, 1000L)
  expect_equal(res$filterSummary$f1_nbrAdapter, 126L)
  expect_equal(res$filterSummary$f2_nbrNoPrimer, 0L)
  expect_equal(res$filterSummary$f3_nbrReadWrongLength, 0L)
  expect_equal(res$filterSummary$f4_nbrNoValidOverlap, 0L)
  expect_equal(res$filterSummary$f5_nbrAvgVarQualTooLow, 0L)
  expect_equal(res$filterSummary$f6_nbrTooManyNinVar, 44L)
  expect_equal(res$filterSummary$f7_nbrTooManyNinUMI, 0L)
  expect_equal(res$filterSummary$f8_nbrTooManyBestWTHits, 0L)
  expect_equal(res$filterSummary$f9_nbrMutQualTooLow, 0L)
  expect_equal(res$filterSummary$f10a_nbrTooManyMutCodons, 0L)
  expect_equal(res$filterSummary$f10b_nbrTooManyMutBases, 416L)
  expect_equal(res$filterSummary$f11_nbrForbiddenCodons, 212L)
  expect_equal(res$filterSummary$f12_nbrTooManyMutConstant, 0L)
  expect_equal(res$filterSummary$f13_nbrTooManyBestConstantHits, 0L)
  expect_equal(res$filterSummary$nbrRetained, 202L)

  expect_named(res$summaryTable, c(""mutantName"", ""sequence"",""nbrReads"",
                                   ""maxNbrReads"", ""nbrUmis"", ""nbrMutBases"",
                                   ""nbrMutCodons"", ""nbrMutAAs"", ""varLengths"",
                                   ""mutantNameBase"", ""mutantNameCodon"",
                                   ""mutantNameBaseHGVS"", ""mutantNameAA"",
                                   ""mutantNameAAHGVS"", ""mutationTypes"",
                                   ""sequenceAA""))

  for (nm in setdiff(names(Ldef), c(""forbiddenMutatedCodonsForward"", ""forbiddenMutatedCodonsReverse"", ""verbose"", ""fastqForward"", ""fastqReverse""))) {
    expect_equal(res$parameters[[nm]], Ldef[[nm]], ignore_attr = TRUE)
  }
  for (nm in c(""fastqForward"", ""fastqReverse"")) {
    expect_equal(res$parameters[[nm]], normalizePath(Ldef[[nm]], mustWork = FALSE),
                 ignore_attr = TRUE)
  }

  expect_equal(sum(res$summaryTable$nbrReads), res$filterSummary$nbrRetained)
  expect_equal(sum(res$summaryTable$nbrReads == 3), 5L)
  expect_equal(sort(res$summaryTable$mutantName[res$summaryTable$nbrReads == 3]),
               sort(c(""f.12.G"", ""f.17.T"", ""f.60.G"", ""f.85.T"", ""f.96.G"")))
  expect_true(all(res$summaryTable$nbrReads == res$summaryTable$nbrUmis))

  ## Check the number of reads with a given number of mismatches
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 0]), 77L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 1]), 88L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutBases == 2]), 37L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 0]), 77L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 1]), 90L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutCodons == 2]), 35L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 0]), 98L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 1]), 86L)
  expect_equal(sum(res$summaryTable$nbrReads[res$summaryTable$nbrMutAAs == 2]), 18L)

  ## Similarly but for the number of unique sequences
  expect_equal(sum(res$summaryTable$nbrMutBases == 0), 1L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 1), 64L)
  expect_equal(sum(res$summaryTable$nbrMutBases == 2), 36L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 0), 1L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 1), 66L)
  expect_equal(sum(res$summaryTable$nbrMutCodons == 2), 34L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 0), 15L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 1), 69L)
  expect_equal(sum(res$summaryTable$nbrMutAAs == 2), 17L)

  ## Check that mutant naming worked (compare to manual matching)
  example_seq <- paste0(""ACTGATACACGCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCT"",
                        ""GCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"")
  expect_equal(res$summaryTable$mutantName[res$summaryTable$sequence == example_seq],
               ""f.11.G"")
  expect_equal(res$summaryTable$mutantNameAA[res$summaryTable$sequence == example_seq],
               ""f.4.R"")
  expect_equal(res$summaryTable$mutantNameBase[res$summaryTable$sequence == example_seq],
               ""f.11.G"")
  expect_equal(res$summaryTable$mutantNameCodon[res$summaryTable$sequence == example_seq],
               ""f.4.CGC"")
  expect_equal(res$summaryTable$mutantNameBaseHGVS[res$summaryTable$sequence == example_seq],
               ""f:c.11T>G"")
  expect_equal(res$summaryTable$mutantNameAAHGVS[res$summaryTable$sequence == example_seq],
               ""f:p.(Leu4Arg)"")

  expect_equal(sum(res$errorStatistics$nbrMatchForward + res$errorStatistics$nbrMismatchForward),
               nchar(Ldef$constantForward[1]) * res$filterSummary$nbrRetained)
  expect_equal(sum(res$errorStatistics$nbrMatchReverse + res$errorStatistics$nbrMismatchReverse),
               nchar(Ldef$constantReverse[1]) * res$filterSummary$nbrRetained)

  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 14], 61L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 22], 22L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 27], 97L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 33], 203L)
  expect_equal(res$errorStatistics$nbrMatchForward[res$errorStatistics$PhredQuality == 37], 3249L)

  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 14], 1L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 27], 1L)
  expect_equal(res$errorStatistics$nbrMismatchForward[res$errorStatistics$PhredQuality == 37], 2L)

  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 14], 42L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 27], 63L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 33], 117L)
  expect_equal(res$errorStatistics$nbrMatchReverse[res$errorStatistics$PhredQuality == 37], 3184L)

  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 2], 5L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 14], 7L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 33], 2L)
  expect_equal(res$errorStatistics$nbrMismatchReverse[res$errorStatistics$PhredQuality == 37], 14L)

})

test_that(""digestFastqs gives the same results regardless of how WT matching is done"", {
  fqc1 <- system.file(""extdata/cisInput_1.fastq.gz"", package = ""mutscan"")
  fqc2 <- system.file(""extdata/cisInput_2.fastq.gz"", package = ""mutscan"")
  ## default arguments
  Ldef <- list(
    fastqForward = fqc1, fastqReverse = fqc2,
    mergeForwardReverse = TRUE,
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 1, greedyOverlap = TRUE,
    revComplForward = FALSE, revComplReverse = TRUE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCVS"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 7, 17, 96, -1),
    adapterForward = ""GGAAGAGCACACGTC"",
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = c(wt1 = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
                        wt2 = ""CCTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
                        wt3 = ""ACTGATACACTCCAAGCGGAGACTGACCAACTAGAAGATGAGAAGTCTTCTTTGCAGACCGAGATTGCCAACGTGCTGAAGGAGAAGGAAAAACTA""),
    wildTypeReverse = """",
    constantForward = ""AACCGGAGGAGGGAGCTG"",
    constantReverse = ""GAGTTCATCCTGGCAGC"",
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0,
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = -1,
    nbrMutatedCodonsMaxReverse = -1,
    nbrMutatedBasesMaxForward = 2,
    nbrMutatedBasesMaxReverse = 2,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE,
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, verbose = FALSE,
    nThreads = 1, chunkSize = 1000,
    maxReadLength = 1024
  )

  res1 <- do.call(digestFastqs, Ldef)
  Ldef$useTreeWTmatch <- TRUE
  res2 <- do.call(digestFastqs, Ldef)

  expect_equal(res1$filterSummary$nbrTotal, res2$filterSummary$nbrTotal)
  expect_equal(res1$filterSummary$f1_nbrAdapter, res2$filterSummary$f1_nbrAdapter)
  expect_equal(res1$filterSummary$f2_nbrNoPrimer, res2$filterSummary$f2_nbrNoPrimer)
  expect_equal(res1$filterSummary$f3_nbrReadWrongLength, res2$filterSummary$f3_nbrReadWrongLength)
  expect_equal(res1$filterSummary$f4_nbrNoValidOverlap, res2$filterSummary$f4_nbrNoValidOverlap)
  expect_equal(res1$filterSummary$f5_nbrAvgVarQualTooLow, res2$filterSummary$f5_nbrAvgVarQualTooLow)
  expect_equal(res1$filterSummary$f6_nbrTooManyNinVar, res2$filterSummary$f6_nbrTooManyNinVar)
  expect_equal(res1$filterSummary$f7_nbrTooManyNinUMI, res2$filterSummary$f7_nbrTooManyNinUMI)
  expect_equal(res1$filterSummary$f8_nbrTooManyBestWTHits, res2$filterSummary$f8_nbrTooManyBestWTHits)
  expect_equal(res1$filterSummary$f9_nbrMutQualTooLow, res2$filterSummary$f9_nbrMutQualTooLow)
  expect_equal(res1$filterSummary$f10a_nbrTooManyMutCodons, res2$filterSummary$f10a_nbrTooManyMutCodons)
  expect_equal(res1$filterSummary$f10b_nbrTooManyMutBases, res2$filterSummary$f10b_nbrTooManyMutBases)
  expect_equal(res1$filterSummary$f11_nbrForbiddenCodons, res2$filterSummary$f11_nbrForbiddenCodons)
  expect_equal(res1$filterSummary$f12_nbrTooManyMutConstant, res2$filterSummary$f12_nbrTooManyMutConstant)
  expect_equal(res1$filterSummary$f13_nbrTooManyBestConstantHits, res2$filterSummary$f13_nbrTooManyBestConstantHits)
  expect_equal(res1$filterSummary$nbrRetained, res2$filterSummary$nbrRetained)

  expect_equal(res1$summaryTable$nbrReads, res2$summaryTable$nbrReads)
})

","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_generateQCReport.R",".R","1871","37","se <- readRDS(system.file(""extdata/GSE102901_cis_se.rds"", package = ""mutscan""))
se <- se[1:100, ]
outFile <- tempfile(fileext = "".html"")
outFile2 <- file.path(tempdir(), ""newfolder"", ""newreport.html"")

test_that(""generateQCReport fails with incorrect input"", {
    expect_error(generateQCReport(se = NULL, outFile = outFile))
    expect_error(generateQCReport(se = ""file"", outFile = outFile))

    expect_error(generateQCReport(se = se, outFile = 1))
    expect_error(generateQCReport(se = se, outFile = ""out.pdf""))
    expect_error(generateQCReport(se = se, outFile = c(""out1.html"", ""out2.html"")))

    expect_error(generateQCReport(se = se, outFile = outFile, forceOverwrite = 1))
    expect_error(generateQCReport(se = se, outFile = outFile, forceOverwrite = ""wrong""))
    expect_error(generateQCReport(se = se, outFile = outFile, forceOverwrite = c(TRUE, FALSE)))
})

test_that(""generateQCReport works as expected"", {
    expect_warning(expect_equal(generateQCReport(se = se, outFile = outFile,
                                                 forceOverwrite = FALSE,
                                                 quiet = TRUE,
                                                 run_pandoc = FALSE),
                                outFile),
                   ""Will ignore arguments"")
    expect_true(file.exists(outFile))
    expect_error(generateQCReport(se = se, outFile = outFile, forceOverwrite = FALSE))
    expect_message(expect_equal(generateQCReport(se = se, outFile = outFile,
                                                 forceOverwrite = TRUE,
                                                 quiet = TRUE),
                                outFile),
                   ""overwriting"")
    expect_equal(generateQCReport(se = se, outFile = outFile2,
                                  forceOverwrite = FALSE, quiet = TRUE),
                 outFile2)
})
","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_calculateFitnessScore.R",".R","8642","165","Ldef <- list(
    mergeForwardReverse = TRUE, 
    minOverlap = 0, maxOverlap = 0, maxFracMismatchOverlap = 0, greedyOverlap = TRUE, 
    revComplForward = FALSE, revComplReverse = TRUE,
    elementsForward = ""SUCV"", elementsReverse = ""SUCVS"",
    elementLengthsForward = c(1, 10, 18, 96),
    elementLengthsReverse = c(1, 7, 17, 96, -1),
    adapterForward = ""GGAAGAGCACACGTC"", 
    adapterReverse = ""GGAAGAGCGTCGTGT"",
    primerForward = """",
    primerReverse = """",
    wildTypeForward = ""ACTGATACACTCCAAGCGGAGACAGACCAACTAGAAGATGAGAAGTCTGCTTTGCAGACCGAGATTGCCAACCTGCTGAAGGAGAAGGAAAAACTA"",
    wildTypeReverse = """", 
    constantForward = ""AACCGGAGGAGGGAGCTG"", 
    constantReverse = ""GAGTTCATCCTGGCAGC"", 
    avePhredMinForward = 20.0, avePhredMinReverse = 20.0,
    variableNMaxForward = 0, variableNMaxReverse = 0, 
    umiNMax = 0,
    nbrMutatedCodonsMaxForward = 1,
    nbrMutatedCodonsMaxReverse = 1,
    nbrMutatedBasesMaxForward = -1,
    nbrMutatedBasesMaxReverse = -1,
    forbiddenMutatedCodonsForward = ""NNW"",
    forbiddenMutatedCodonsReverse = ""NNW"",
    useTreeWTmatch = FALSE, 
    mutatedPhredMinForward = 0.0, mutatedPhredMinReverse = 0.0,
    mutNameDelimiter = ""."",
    constantMaxDistForward = -1,
    constantMaxDistReverse = -1,
    umiCollapseMaxDist = 0,
    filteredReadsFastqForward = """",
    filteredReadsFastqReverse = """",
    maxNReads = -1, 
    verbose = FALSE,
    nThreads = 1, chunkSize = 1000, 
    maxReadLength = 1024
)
Ldef1 <- c(
    list(fastqForward = system.file(""extdata/cisInput_1.fastq.gz"", package = ""mutscan""),
         fastqReverse = system.file(""extdata/cisInput_2.fastq.gz"", package = ""mutscan"")
    ), Ldef)
Ldef2 <- c(
    list(fastqForward = system.file(""extdata/cisOutput_1.fastq.gz"", package = ""mutscan""),
         fastqReverse = system.file(""extdata/cisOutput_2.fastq.gz"", package = ""mutscan"")
    ), Ldef)

out1 <- do.call(digestFastqs, Ldef1)
out2 <- do.call(digestFastqs, Ldef2)

coldata <- data.frame(Name = c(""sample1"", ""sample2"", ""sample3"", ""sample4""), 
                      Condition = c(""input"", ""output"", ""input"", ""output""),
                      Replicate = c(1, 1, 2, 2), OD = c(0.05, 1.5, 0.05, 1.5),
                      stringsAsFactors = FALSE)

se <- summarizeExperiment(x = list(sample1 = out1, sample2 = out2,
                                   sample3 = out2, sample4 = out1),
                          coldata = coldata, countType = ""umis"")
secoll <- collapseMutantsByAA(se)

test_that(""calculateFitnessScore fails with incorrect arguments"", {
    expect_error(calculateFitnessScore(se = 1, pairingCol = ""Replicate"", ODCols = ""OD"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = out1, pairingCol = ""Replicate"", ODCols = ""OD"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = list(sample1 = out1, sample2 = out2), 
                                       pairingCol = ""Replicate"", ODCols = ""OD"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""Unknown"", ODCols = ""OD"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = 1, ODCols = ""OD"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""OD"", ODCols = ""OD"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", ODCols = ""Unknown"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", ODCols = ""Condition"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", ODCols = 1,
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", ODCols = ""OD"",
                                       comparison = c(""Unknown"", ""output"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", ODCols = ""OD"",
                                       comparison = c(""Condition"", ""unknown"", ""input""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", ODCols = ""OD"",
                                       comparison = c(""Condition"", ""output"", ""unknown""),
                                       WTrows = ""f.0.NA""))
    expect_error(calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", ODCols = ""OD"",
                                       comparison = c(""Condition"", ""output"", ""input""),
                                       WTrows = ""f.0.NA"", selAssay = ""missing""))
})

test_that(""calculateFitnessScore works as expected"", {
    ppi <- calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", ODCols = ""OD"",
                                 comparison = c(""Condition"", ""output"", ""input""), 
                                 WTrows = ""f.0.WT"")
    
    expect_equal(nrow(ppi), nrow(secoll))
    expect_equal(rownames(ppi), rownames(secoll))
    expect_equal(ncol(ppi), 2)
    expect_equal(colnames(ppi), c(""output_vs_input_repl1"", ""output_vs_input_repl2""))
    expect_equal(ppi[""f.0.WT"", ], c(1, 1), ignore_attr = TRUE)
    
    ## Test ""replicate 1""
    w <- SummarizedExperiment::colData(secoll)$Condition == ""output"" & 
        SummarizedExperiment::colData(secoll)$Replicate == 1
    ncout <- as.matrix(SummarizedExperiment::assay(secoll, ""counts""))[, w]
    ncout <- ncout * SummarizedExperiment::colData(secoll)$OD[w]/sum(ncout)
    
    w <- SummarizedExperiment::colData(secoll)$Condition == ""input"" & 
        SummarizedExperiment::colData(secoll)$Replicate == 1
    ncin <- as.matrix(SummarizedExperiment::assay(secoll, ""counts""))[, w]
    ncin <- ncin * SummarizedExperiment::colData(secoll)$OD[w]/sum(ncin)
    
    ratios <- log2(ncout/ncin)
    ratios[!is.finite(ratios)] <- NA
    ratios <- ratios/ratios[""f.0.WT""]
    
    expect_equal(ppi[, ""output_vs_input_repl1""], ratios)
    
    ## Test ""replicate 2""
    w <- SummarizedExperiment::colData(secoll)$Condition == ""output"" & 
        SummarizedExperiment::colData(secoll)$Replicate == 2
    ncout <- as.matrix(SummarizedExperiment::assay(secoll, ""counts""))[, w]
    ncout <- ncout * SummarizedExperiment::colData(secoll)$OD[w]/sum(ncout)
    
    w <- SummarizedExperiment::colData(secoll)$Condition == ""input"" & 
        SummarizedExperiment::colData(secoll)$Replicate == 2
    ncin <- as.matrix(SummarizedExperiment::assay(secoll, ""counts""))[, w]
    ncin <- ncin * SummarizedExperiment::colData(secoll)$OD[w]/sum(ncin)
    
    ratios <- log2(ncout/ncin)
    ratios[!is.finite(ratios)] <- NA
    
    ## Test performance if WTrows = NULL
    ppinull <- calculateFitnessScore(se = secoll, pairingCol = ""Replicate"",
                                     ODCols = ""OD"",
                                     comparison = c(""Condition"", ""output"", ""input""),
                                     WTrows = NULL)
    expect_equal(ppinull[, ""output_vs_input_repl2""] , ratios)
    
    ## Use the WTrows
    ratios <- ratios/ratios[""f.0.WT""]
    expect_equal(ppi[, ""output_vs_input_repl2""], ratios)
    
    ## Test that PPI scores of input/output gives the same as output/input
    ppirev <- calculateFitnessScore(se = secoll, pairingCol = ""Replicate"", 
                                    ODCols = ""OD"",
                                    comparison = c(""Condition"", ""input"", ""output""), 
                                    WTrows = ""f.0.WT"")
    expect_equal(ppirev[, ""input_vs_output_repl1""], ppi[, ""output_vs_input_repl1""])
    
})

","R"
"Assay","fmicompbio/mutscan","tests/testthat/test_plotFiltering.R",".R","5068","116","se <- readRDS(system.file(""extdata/GSE102901_cis_se.rds"", package = ""mutscan""))
se <- se[1:1000, 1:3]

test_that(""plotFiltering fails with incorrect arguments"", {
    expect_error(plotFiltering(se = 1))
    expect_error(plotFiltering(se = ""x""))
    expect_error(plotFiltering(se = matrix(1:6), 2, 3))
    
    expect_error(plotFiltering(se = se, valueType = 1))
    expect_error(plotFiltering(se = se, valueType = c(""reads"", ""fractions"")))
    expect_error(plotFiltering(se = se, valueType = ""wrong""))
    
    expect_error(plotFiltering(se = se, onlyActiveFilters = 1))
    expect_error(plotFiltering(se = se, onlyActiveFilters = ""TRUE""))
    expect_error(plotFiltering(se = se, onlyActiveFilters = c(TRUE, FALSE)))
    
    expect_error(plotFiltering(se = se, displayNumbers = 1))
    expect_error(plotFiltering(se = se, displayNumbers = ""TRUE""))
    expect_error(plotFiltering(se = se, displayNumbers = c(TRUE, FALSE)))
    
    expect_error(plotFiltering(se = se, numberSize = ""1""))
    expect_error(plotFiltering(se = se, numberSize = TRUE))
    expect_error(plotFiltering(se = se, numberSize = c(1, 2)))
    expect_error(plotFiltering(se = se, numberSize = 0))
    
    expect_error(plotFiltering(se = se, plotType = 1))
    expect_error(plotFiltering(se = se, plotType = TRUE))
    expect_error(plotFiltering(se = se, plotType = c(""remaining"", ""filtered"")))
    expect_error(plotFiltering(se = se, plotType = ""wrong""))
    
    expect_error(plotFiltering(se = se, facetBy = 1))
    expect_error(plotFiltering(se = se, facetBy = TRUE))
    expect_error(plotFiltering(se = se, facetBy = c(""sample"", ""step"")))
    expect_error(plotFiltering(se = se, facetBy = ""wrong""))
})

test_that(""plotFiltering works with correct arguments"", {
    ## Defaults
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""reads"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""remaining"", facetBy = ""sample"")))
    
    ## Fractions
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""fractions"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""remaining"", facetBy = ""sample"")))
    
    ## Only active filters
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""reads"", 
                      onlyActiveFilters = TRUE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""remaining"", facetBy = ""sample"")))
    
    ## Don't display numbers
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""reads"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = FALSE, numberSize = 4,
                      plotType = ""remaining"", facetBy = ""sample"")))
    
    ## Change size of displayed numbers
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""reads"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = FALSE, numberSize = 2,
                      plotType = ""remaining"", facetBy = ""sample"")))
    
    ## Reads + Filtered + Sample
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""reads"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""filtered"", facetBy = ""sample"")))

    ## Reads + Filtered + Step
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""reads"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""filtered"", facetBy = ""step"")))

    ## Reads + Remaining + Step
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""reads"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""remaining"", facetBy = ""step"")))

    ## Fractions + Filtered + Sample
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""fractions"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""filtered"", facetBy = ""sample"")))
    
    ## Fractions + Filtered + Step
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""fractions"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""filtered"", facetBy = ""step"")))
    
    ## Fractions + Remaining + Step
    expect_true(ggplot2::is_ggplot(
        plotFiltering(se = se, valueType = ""fractions"", 
                      onlyActiveFilters = FALSE, 
                      displayNumbers = TRUE, numberSize = 4,
                      plotType = ""remaining"", facetBy = ""step"")))
    
})
","R"
"Assay","bramble50/mmp_kg","setup.py",".py","570","24","# -*- coding: utf-8 -*-
""""""
    Setup file for mmp_kg.
    Use setup.cfg to configure your project.

    This file was generated with PyScaffold 3.2.1.
    PyScaffold helps you to put up the scaffold of your new Python project.
    Learn more under: https://pyscaffold.org/
""""""
import sys

from pkg_resources import VersionConflict, require
from setuptools import setup

try:
    require('setuptools>=38.3')
except VersionConflict:
    print(""Error: version of setuptools is too old (<38.3)!"")
    sys.exit(1)


if __name__ == ""__main__"":
    setup(use_pyscaffold=True)
","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/__init__.py",".py","363","12","# -*- coding: utf-8 -*-
from pkg_resources import get_distribution, DistributionNotFound

try:
    # Change here if project is renamed and does not equal the package name
    dist_name = 'MMP_KG'
    __version__ = get_distribution(dist_name).version
except DistributionNotFound:
    __version__ = 'unknown'
finally:
    del get_distribution, DistributionNotFound
","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/create_graph.py",".py","4795","158","# -*- coding: utf-8 -*-
""""""
This is a skeleton file that can serve as a starting point for a Python
console script. To run this script uncomment the following lines in the
[options.entry_points] section in setup.cfg:

    console_scripts =
         fibonacci = mmp_kg.skeleton:run

Then run `python setup.py install` which will install the command `runner`
inside your current environment.
Besides console scripts, the header (i.e. until _logger...) of this file can
also be used as template for Python modules.

""""""

import argparse
import sys
import logging

from mmp_kg import __version__

__author__ = ""bramble50""
__copyright__ = ""bramble50""
__license__ = ""mit""

_logger = logging.getLogger(__name__)

from mmp_kg.connectors import get_dbase
from mmp_kg.utils import mmpdb_utils
from mmp_kg.utils import neo4j_utils
from mmp_kg import config
import pandas as pd

def create_graph_from_chembl(doc_id, graph_file):
    """"""Create a graph from a chembl document id

    Args:
      doc_id (int): integer
      graph_file (str): string

    Returns:
      graph_db: neo4j graph database object
    """"""
    dir_path = config.temp_dir
    
    # 1) Set ChEBML as the database to use
    chembl = get_dbase('chembl')
    # 2) Get assays for doc_id
    assays = chembl.make_query(template='adme_assays_for_docid', doc_id=doc_id)
    assay_ids = tuple(assays['assay_id'].tolist())
    
    # 3)Get data for all the assays
    assay_data = chembl.make_query(template='get_assay_compounds_from_assay_ids', assay_id_list=assay_ids)
    # 4) Format the data and write to disk
    assay_pivot = pd.DataFrame(assay_data.pivot_table(index='molregno', 
                                                      values='standard_value', 
                                                      columns='assay_concat', 
                                                      fill_value='*'
                                                     ).to_records())
    assay_pivot.rename(columns={'molregno':'ID'}, inplace=True)
    smiles = assay_data[['canonical_smiles','molregno']].drop_duplicates(['molregno'])
    smiles.to_csv('{0}/smiles.csv'.format(dir_path), header=False, index=False)
    assay_pivot.to_csv('{0}/properties.csv'.format(dir_path), sep='\t', index=False)
    
    # 5) Fragment smiles
    
    frag_info = mmpdb_utils.run_smiles_fragment(assay_data)
    
    # 6) Create mmpdb database
    mmpdb_utils.run_mmpdb(assay_data)
    
    # 7) Create Neo4J files
    mmpdb_utils.get_export_mmpkg_files()
    
    # 8) Create Neo4J Graph
    neo_info = neo4j_utils.create_database_locally_dep(graph_file, config.node_edge_list_dict)
    
    return(neo_info)
    
def parse_args(args):
    """"""Parse command line parameters

    Args:
      args ([str]): command line parameters as list of strings

    Returns:
      :obj:`argparse.Namespace`: command line parameters namespace
    """"""
    parser = argparse.ArgumentParser(
        description=""Creates a mmpkg Neo4J database from assays derived from a given ChEMBL document id"")
    parser.add_argument(
        ""--version"",
        action=""version"",
        version=""MMP_KG {ver}"".format(ver=__version__))
    parser.add_argument(
        dest=""doc_id"",
        help=""chembl document id"",
        type=int,
        metavar=""INT"")
    parser.add_argument(
        dest=""graph_file"",
        help=""name of the graph output file"",
        type=str,
        metavar=""STRING"")
    parser.add_argument(
        ""-v"",
        ""--verbose"",
        dest=""loglevel"",
        help=""set loglevel to INFO"",
        action=""store_const"",
        const=logging.INFO)
    parser.add_argument(
        ""-vv"",
        ""--very-verbose"",
        dest=""loglevel"",
        help=""set loglevel to DEBUG"",
        action=""store_const"",
        const=logging.DEBUG)
    return parser.parse_args(args)


def setup_logging(loglevel):
    """"""Setup basic logging

    Args:
      loglevel (int): minimum loglevel for emitting messages
    """"""
    logformat = ""[%(asctime)s] %(levelname)s:%(name)s:%(message)s""
    logging.basicConfig(level=loglevel, stream=sys.stdout,
                        format=logformat, datefmt=""%Y-%m-%d %H:%M:%S"")


def main(args):
    """"""Main entry point allowing external calls

    Args:
      args ([str]): command line parameter list
    """"""
    args = parse_args(args)
    setup_logging(args.loglevel)
    _logger.debug(""Creating graph database..."")
    neo_info = create_graph_from_chembl(args.doc_id, args.graph_file)
    if neo_info == 1:
        print(""Failed"")
    else:
        print(""Graph database has been created from {0} and outputted to filename {1}"".format(args.doc_id, args.graph_file))
        _logger.info(""Done"")


def run():
    """"""Entry point for console_scripts
    """"""
    main(sys.argv[1:])


if __name__ == ""__main__"":
    run()","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/config.py",".py","1645","39","#The filepath to your chembl sqllite database:
chembl_sqlite_db = ""/Users/matthew/code/databases/chembl_25/chembl_25_sqlite/chembl_25.db""

#for parts of the code that can be parallised specify the cpu core limit
cpu_cores = 8

# Temp directory
temp_dir = "".""

# Neo4J path - the directory where your neo4j installation is located 
#(if you don't have a neo4j installation set as ""None"")
path_to_neo4j = ""/Users/matthew/downloads/neo4j-community-3.5.14/""


# The list of edges and nodes you can create
# names must match the query name in mmp_kg.connectors.mmpdb_sql.py
node_edge_list_dict = [{'type':'nodes', 
                        'label':'Compound', 
                        'name':'get_compound_nodes'},
                       {'type':'nodes', 
                        'label':'Fragment', 
                        'name':'get_fragment_nodes'},
#                       {'type':'nodes',
#                        'label':'Environment',
#                        'name':'get_environment_nodes'},
#                       {'type':'edges',
#                        'label':'IS_IN_ENVIRONMENT',
#                        'name':'get_fragment_environment_edges'},
#                       {'type':'edges',
#                        'label':'MMP_RULE_ENVIRONMENT',
#                        'name':'get_fragment_fragment_edges'},
                       {'type':'edges',
                        'label':'IS_MMP_OF',
                        'name':'get_compound_compound_edges'},
                       {'type':'edges',
                        'label':'IS_FRAGMENT_OF',
                        'name':'get_compound_fragment_edges'},
                      ]
","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/utils/neo4j_utils.py",".py","2827","74","# -*- coding: utf-8 -*-
""""""

""""""
import logging

import os
import docker
from mmp_kg import config
from mmp_kg.utils.other_utils import run_and_log_process
    
def build_neo4j_image():
    """"""builds a neo4j docker image running locally""""""
    neo4j_data_dir = config.neo4j_data_dir
    neo4j_log_dir = config.neo4j_log_dir
    # docker run --publish=7474:7474 --publish=7687:7687 --volume=$HOME/neo4j/data:/data --volume=$HOME/neo4j/logs:/logs neo4j:3.5
    client = docker.from_env()

    client.containers.run('neo4j:3.5',
                      ports={'7474':('127.0.0.1', 7474),'7687':('127.0.0.1', 7687)}, 
                      volumes={'/Users/matthew/desktop/neo4j':{'bind':'/data', 'mode': 'rw'},
                               '/Users/matthew/desktop/neo4j/logs':{'bind':'/logs', 'mode': 'rw'}
                              },
                      detach=True,
                      )
    
def create_database_locally_dep(database_file, node_edge_list_dict, path_to_neo4j=None):
    """"""
    node_edge_list_dict should be of the type e.g. [{'type':'nodes', 
                                                'label':""Compound"", 
                                                'file':""get_compound_nodes.csv""
                                                },
                                                {'type':'nodes', 
                                                 'label':'Fragment',
                                                 'file':""get_fragment_nodes.csv""}
                                                ]
    """"""
    database_file = os.path.join(config.temp_dir, database_file)
    if path_to_neo4j == None:
        path_to_neo4j=config.path_to_neo4j
    else:
        path_to_neo4j
    """"""Using the depreciated neo4j import tool to create the graph.db file""""""
    neo4j_tool = os.path.join(path_to_neo4j, 'bin/neo4j-import')
    process_list = [neo4j_tool, 
                     'import', 
                     '--into',
                     database_file,
                     '--id-type',
                     'string']
    
    for ne in node_edge_list_dict:
            ne_type = ne['type']
            ne_label = ne['label']
            ne_file = ne['name']
            process_list.append(f'--{ne_type}:{ne_label}')
            process_list.append(f'{config.temp_dir}/{ne_file}.csv')

    rc = run_and_log_process(process_list)
          
    return rc
    
def create_database_locally(path_to_neo4j=config.path_to_neo4j):
    """"""Using the new neo4j CLI tool to import and create the graph.db file""""""
    neo4j_tool = os.path.join(path_to_neo4j, 'bin/neo4j-admin')
    process_list = [neo4j_tool, 
                     'import', 
                     '--nodes',
                     ###TO FINISH
                    ]
    
    rc = run_and_log_process(process_list)
          
    return rc","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/utils/mmpdb_utils.py",".py","2786","87","# -*- coding: utf-8 -*-
""""""

""""""
import logging

import os
from mmp_kg import config
from mmp_kg.connectors import get_dbase
from mmp_kg.utils.other_utils import run_and_log_process
import pandas as pd

from io import StringIO

def run_smiles_fragment(assay_data):
    """"""
    """"""
    smiles_file = os.path.join(config.temp_dir, ""smiles.csv"")
    fragment_file = os.path.join(config.temp_dir, ""smiles.fragment"")
    cpu_cores = config.cpu_cores
    #Drop duplicates
    smiles = assay_data[['canonical_smiles','molregno']].drop_duplicates(['molregno'])
    #Write to CSV
    smiles.to_csv(smiles_file, header=False, index=False)
    
    command = ['mmpdb', 
               'fragment', 
               f'{smiles_file}',
               '-o',
               f'{fragment_file}',
               '--delimiter',
               'comma',
               '-j',
               f'{cpu_cores}' 
              ]
    
    rc = run_and_log_process(command)
    
    return rc


def run_mmpdb(assay_data, output_filename = None):
    if output_filename = None:
        database_output = os.path.join(config.temp_dir, ""mmp_db.mmpdb"")
    else:
        database_output = os.path.join(config.temp_dir, output_path)
    fragment_file = os.path.join(config.temp_dir, ""smiles.fragment"")
    properties_file = os.path.join(config.temp_dir, ""properties.csv"")
    
    cpu_cores = config.cpu_cores
    
    assay_pivot = pd.DataFrame(assay_data.pivot_table(index='molregno', 
                                                      values='standard_value', 
                                                      columns='assay_concat', 
                                                      fill_value='*'
                                                     ).to_records())
    assay_pivot.rename(columns={'molregno':'ID'}, inplace=True)
    assay_pivot.to_csv(properties_file, sep='\t', index=False)
    command = ['mmpdb', 
               'index', 
               f'{fragment_file}',
               '--properties',
               f'{properties_file}',
               '-o',
               f'{database_output}'
              ]
    
    rc = run_and_log_process(command)
    
    return rc



def get_export_mmpkg_files(node_edge_list_dict=config.node_edge_list_dict, path_to_db = None):
    if path_to_db = None:
        try:
            path_to_db = os.path.join(config.temp_dir, ""mmp_db.mmpdb""
        except Exception:
            logging.exception(""No mmp database can be found..."")
                          
    mmpdb = get_dbase('mmpdb')
    node_edge_list = [d['name'] for d in node_edge_list_dict]
    
    for ne in node_edge_list:
        logging.info(f'getting {ne}...')
        dataframe = mmpdb.make_query(template=ne, path_to_db)
        dataframe.to_csv(f'{config.temp_dir}/{ne}.csv', index=False)","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/utils/other_utils.py",".py","423","17","# -*- coding: utf-8 -*-
""""""

""""""
import logging
from subprocess import Popen, PIPE, STDOUT

def run_and_log_process(cmd):
    process = Popen(cmd, stdout=PIPE, stderr=STDOUT, universal_newlines=True)
    while True:
        output = process.stdout.readline()
        if output == '' and process.poll() is not None:
            break
        if output:
            print(output.strip())
    rc = process.poll()
    return rc","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/connectors/base_con.py",".py","683","26","# -*- coding: utf-8 -*-
""""""

""""""
import logging
from abc import ABC, abstractmethod


class ChemDb(ABC):
    logging.basicConfig(format='%(levelname)s : %(message)s', level=logging.INFO)

    @abstractmethod
    def source_name(self):
        """""" name of data source""""""

    @abstractmethod
    def get_connection(self):
        """""" Return a database python database interface for this database""""""

    @abstractmethod
    def make_query(self, template, **kwargs):
        """""" query the database using a predefined template and keyword args""""""

    @abstractmethod
    def get_available_query_templates():
        """""" Return available templates instantiated in query_template_dict""""""","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/connectors/mmpdb_sql.py",".py","8400","146","# -*- coding: utf-8 -*-
""""""
Holds all key chembl queries
""""""
import logging
import sqlalchemy as sql
import pandas as pd
from mmp_kg import config
from mmp_kg.connectors.base_con import ChemDb

class MmpSqlDb(ChemDb):
    def __init__(self):
        self.name = self.source_name()

    @staticmethod
    def source_name():
        return 'mmpdb'

    def get_connection(path_to_db):
        connection = sql.create_engine(f'sqlite:///{path_to_db}')
        return connection

    def make_query(self, path_to_db, template: str, **kwargs):
        sql = query_template_dict[template](**kwargs)
    
        conn = self.get_connection(path_to_db)
        df = pd.read_sql_query(sql, conn)

        # Add details of query to the dataframe
        query_params = kwargs
        query_params['template'] = template
        df['query'] = str(query_params)
        df['source'] = self.name

        # Log
        logging.info(f'{len(df)}')

        return df
    
    @staticmethod
    def get_available_query_templates():
        return list(query_template_dict.keys())


def return_identity(x):
    return x

def get_fragment_nodes_2():
    """""" get all assays for a single chembl document id""""""
    query = '''SELECT DISTINCT t2.fragment_id as ""fragmentid:ID(Fragment)"", t2.smiles 
                             FROM
                             (SELECT re.environment_fingerprint_id, rs.smiles, rs.id as fragment_id
                             FROM rule_environment re
                             JOIN rule r ON re.rule_id = r.id
                             JOIN rule_smiles rs ON rs.id = r.from_smiles_id
                             UNION
                             SELECT re.environment_fingerprint_id, rs.smiles, rs.id as fragment_id
                             FROM rule_environment re
                             JOIN rule r ON re.rule_id = r.id
                             JOIN rule_smiles rs ON rs.id = r.to_smiles_id)t2'''
    return query

query_template_dict = {
    #Fragment nodes
    'get_fragment_nodes': lambda: '''SELECT DISTINCT t2.fragment_id as ""fragmentid:ID(Fragment)"", t2.smiles 
                                     FROM
                                     (SELECT re.environment_fingerprint_id, rs.smiles, rs.id as fragment_id
                                     FROM rule_environment re
                                     JOIN rule r ON re.rule_id = r.id
                                     JOIN rule_smiles rs ON rs.id = r.from_smiles_id
                                     UNION
                                     SELECT re.environment_fingerprint_id, rs.smiles, rs.id as fragment_id
                                     FROM rule_environment re
                                     JOIN rule r ON re.rule_id = r.id
                                     JOIN rule_smiles rs ON rs.id = r.to_smiles_id)t2''',
    #Compound nodes
    'get_compound_nodes': lambda: '''SELECT c.id as ""compoundid:ID(Compound)"", c.clean_smiles as smiles 
                                     FROM compound c''',
    #Environment nodes
    'get_environment_nodes': lambda: '''SELECT DISTINCT re.environment_fingerprint_id as ""environmentid:ID(Environment)"",  
                                        re.radius as ""radius:int"", 
                                        ef.fingerprint
                                        FROM rule_environment re
                                        JOIN environment_fingerprint ef ON re.environment_fingerprint_id = ef.id
                                        GROUP BY re.environment_fingerprint_id''',
    #Fragment -> Environment edges - IS_IN_ENVIRONMENT
    'get_fragment_environment_edges': lambda: '''SELECT DISTINCT t2.environment_fingerprint_id as "":END_ID(Environment)"",
                                                 t2.id as "":START_ID(Fragment)""
                                                 FROM
                                                 (SELECT re.environment_fingerprint_id, rs.smiles, rs.id
                                                 FROM rule_environment re
                                                 JOIN rule r ON re.rule_id = r.id
                                                 JOIN rule_smiles rs ON rs.id = r.from_smiles_id
                                                 UNION
                                                 SELECT re.environment_fingerprint_id, rs.smiles, rs.id
                                                 FROM rule_environment re
                                                 JOIN rule r ON re.rule_id = r.id
                                                 JOIN rule_smiles rs ON rs.id = r.to_smiles_id)t2''',
    #Fragment -> Fragment - MMP_RULE_ENVIRONMENT
    'get_fragment_fragment_edges': lambda: '''SELECT r.from_smiles_id as "":START_ID(Fragment)"",
                                              r.to_smiles_id as "":END_ID(Fragment)"",
                                              res.id as ""id:int"",
                                              res.rule_environment_id as ""rule_environment_id:int"",
                                              res.property_name_id as ""property_name_id:int"",
                                              pn.name as ""property_name"",
                                              res.count as ""count:int"",
                                              res.avg as ""avg:float"",
                                              res.std as ""std:float"",
                                              res.kurtosis as ""kurtosis:float"",
                                              res.skewness as ""skewness:float"",
                                              res.min as ""min:float"",
                                              res.q1 as ""q1:float"",
                                              res.median as ""median:float"",
                                              res.max as ""max:float"",
                                              res.paired_t as ""paired_t:float"", 
                                              res.p_value as ""p_value:float"",
                                              re.environment_fingerprint_id as ""environment_fingerprint_id:int"",
                                              re.radius as ""radius:int"", ef.fingerprint
                                              FROM rule_environment_statistics res
                                              JOIN rule_environment re ON re.id = res.rule_environment_id
                                              JOIN rule r ON re.rule_id = r.id
                                              JOIN environment_fingerprint ef ON re.environment_fingerprint_id = ef.id
                                              JOIN property_name pn ON res.property_name_id = pn.id
                                              ORDER BY res.count desc''',
    #Compound <-> Compound - IS_MMP_OF
    'get_compound_compound_edges': lambda: '''SELECT DISTINCT c.compound1_id as "":START_ID(Compound)"",
                                              c.compound2_id as "":END_ID(Compound)""
                                              FROM pair c
                                              JOIN compound AS cp1 ON cp1.id = c.compound1_id
                                              JOIN compound AS cp2 ON cp2.id = c.compound2_id''',
    #Compound -> Fragment - IS_FRAGMENT_OF
    'get_compound_fragment_edges': lambda: '''SELECT p.compound1_id as "":START_ID(Compound)"", 
                                              r.from_smiles_id as "":END_ID(Fragment)""
                                              FROM pair p
                                              JOIN rule_environment re ON p.rule_environment_id = re.id
                                              JOIN rule r ON re.rule_id = r.id
                                              JOIN rule_smiles rs ON rs.id = r.from_smiles_id
                                              UNION
                                              SELECT p.compound2_id as ""compound_id"", r.to_smiles_id as ""fragment_id""
                                              FROM pair p
                                              JOIN rule_environment re ON p.rule_environment_id = re.id
                                              JOIN rule r ON re.rule_id = r.id
                                              JOIN rule_smiles rs ON rs.id = r.to_smiles_id''',
    #Custom query
    'custom_query': lambda sql: return_identity(sql),
}","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/connectors/__init__.py",".py","298","14","from mmp_kg.connectors.chembl_sql import ChemSqlDb
from mmp_kg.connectors.mmpdb_sql import MmpSqlDb

source_dict = {
    'chembl': ChemSqlDb(),
    'mmpdb': MmpSqlDb()
              }

def get_dbase(source):
    return source_dict[source]


def get_available_dbases():
    return source_dict.keys()","Python"
"Assay","bramble50/mmp_kg","src/mmp_kg/connectors/chembl_sql.py",".py","2536","78","# -*- coding: utf-8 -*-
""""""
Holds all key chembl queries
""""""
import logging
import sqlalchemy as sql
import pandas as pd
from mmp_kg import config
from mmp_kg.connectors.base_con import ChemDb

class ChemSqlDb(ChemDb):
    def __init__(self):
        self.db_path = config.chembl_sqlite_db
        self.name = self.source_name()

    @staticmethod
    def source_name():
        return 'chembl'

    def get_connection(self):
        path_to_db = self.db_path
        connection = sql.create_engine(f'sqlite:///{path_to_db}')
        return connection

    def make_query(self, template: str, **kwargs):
        sql = query_template_dict[template](**kwargs)
    
        conn = self.get_connection()
        df = pd.read_sql_query(sql, conn)

        # Add details of query to the dataframe
        query_params = kwargs
        query_params['template'] = template
        df['query'] = str(query_params)
        df['source'] = self.name

        # Log
        logging.info(f'Found {len(df)} records for {template}')

        return df
    
    @staticmethod
    def get_available_query_templates():
        return list(query_template_dict.keys())


def return_identity(x):
    return x
    
def get_adme_assays_for_docid(doc_id):
    """""" get all assays for a single chembl document id""""""
    query = f'''SELECT *
               FROM ASSAYS a
               WHERE a.doc_id = {doc_id}
               AND a.assay_type = 'A'
            '''
    return query

def get_assay_compounds(assay_id_list):
    assay_ids = tuple(assay_id_list)
    ''' get all compounds for a list of assay ids'''
    query = f'''SELECT cs.molregno, cs.canonical_smiles, act.standard_relation, 
               act.standard_value, act.standard_units, act.standard_type, 
               a.assay_id, a.description, a.assay_organism, a.assay_strain,
               a.assay_cell_type, a.tid, a.assay_type, a.tissue_id, a.variant_id, 
               a.assay_id || ' ' || act.standard_type ||' ' || act.standard_units || ' ' || a.assay_organism as assay_concat
               FROM activities act
               JOIN compound_structures cs ON act.molregno = cs.molregno
               JOIN assays a ON a.assay_id = act.assay_id
               WHERE act.assay_id IN {assay_ids}
               '''
    return query

query_template_dict = {
    'adme_assays_for_docid': lambda doc_id: get_adme_assays_for_docid(doc_id),
    'get_assay_compounds_from_assay_ids': lambda assay_id_list: get_assay_compounds(assay_id_list),
    'custom_query': lambda sql: return_identity(sql),
}","Python"
"Assay","bramble50/mmp_kg","notebooks/nodes2020.ipynb",".ipynb","586464","1949","{
 ""cells"": [
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""# Neo4j Demo 2020-10-20\n"",
    ""---""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""This notebook contains the code used at the Neo4J Nodes conference 2020""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""#### Imports""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 1,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""from py2neo import Graph\n"",
    ""import pandas as pd\n"",
    ""\n"",
    ""from rdkit import Chem\n"",
    ""from rdkit.Chem import AllChem\n"",
    ""from rdkit import DataStructs\n"",
    ""from rdkit.Chem import rdDepictor\n"",
    ""from rdkit.Chem import PandasTools\n"",
    ""from rdkit.Chem.Draw import IPythonConsole\n"",
    ""from IPython.core.display import display, HTML\n"",
    ""from rdkit.Chem import MACCSkeys\n"",
    ""rdDepictor.SetPreferCoordGen(True)\n"",
    ""IPythonConsole.ipython_useSVG = True\n"",
    ""from rdkit import RDLogger\n"",
    ""RDLogger.DisableLog('rdApp.*')""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### 1) Connect to the graph""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 2,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""graph_1 = Graph(host=\""localhost\"", password='mmpkg')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 3,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""[{'gds.version()': '1.3.0'}]""
      ]
     },
     ""execution_count"": 3,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""graph_1.run(\""\""\""RETURN gds.version()\""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### 2) Create your in-memory graphs""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 4,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""[{'nodeProjection': {'Fragment': {'properties': {}, 'label': 'Fragment'},\n"",
       ""   'Compound': {'properties': {}, 'label': 'Compound'}},\n"",
       ""  'relationshipProjection': {'IS_FRAGMENT_OF': {'orientation': 'NATURAL',\n"",
       ""    'aggregation': 'DEFAULT',\n"",
       ""    'type': 'IS_FRAGMENT_OF',\n"",
       ""    'properties': {}}},\n"",
       ""  'graphName': 'fragsimgraph',\n"",
       ""  'nodeCount': 5122,\n"",
       ""  'relationshipCount': 7551,\n"",
       ""  'createMillis': 139}]""
      ]
     },
     ""execution_count"": 4,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# This one is needed for compound similarity and fragment->compound degree\n"",
    ""graph_1.run(\""\""\""\n"",
    ""            CALL gds.graph.create('fragsimgraph', ['Compound', 'Fragment'], 'IS_FRAGMENT_OF')\n"",
    ""            \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 5,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""[{'nodeProjection': {'Fragment': {'properties': {}, 'label': 'Fragment'},\n"",
       ""   'Compound': {'properties': {}, 'label': 'Compound'}},\n"",
       ""  'relationshipProjection': {'IS_FRAGMENT_OF': {'orientation': 'REVERSE',\n"",
       ""    'aggregation': 'DEFAULT',\n"",
       ""    'type': 'IS_FRAGMENT_OF',\n"",
       ""    'properties': {}}},\n"",
       ""  'graphName': 'compound_degree_graph',\n"",
       ""  'nodeCount': 5122,\n"",
       ""  'relationshipCount': 7551,\n"",
       ""  'createMillis': 31}]""
      ]
     },
     ""execution_count"": 5,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# This one we'll use for compound->fragment degree\n"",
    ""graph_1.run(\""\""\""\n"",
    ""            CALL gds.graph.create('compound_degree_graph', ['Fragment', 'Compound'], \n"",
    ""            {IS_FRAGMENT_OF: {\n"",
    ""                    type: 'IS_FRAGMENT_OF',\n"",
    ""                    orientation: 'REVERSE'\n"",
    ""            }})\n"",
    ""            \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 6,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""[{'nodeProjection': {'Fragment': {'properties': {}, 'label': 'Fragment'}},\n"",
       ""  'relationshipProjection': {'IS_FRAGMENT_OF': {'orientation': 'NATURAL',\n"",
       ""    'aggregation': 'DEFAULT',\n"",
       ""    'type': 'IS_FRAGMENT_OF',\n"",
       ""    'properties': {'avg': {'property': 'avg',\n"",
       ""      'aggregation': 'DEFAULT',\n"",
       ""      'defaultValue': nan}}},\n"",
       ""   'MMP_RULE_ENVIRONMENT': {'orientation': 'NATURAL',\n"",
       ""    'aggregation': 'DEFAULT',\n"",
       ""    'type': 'MMP_RULE_ENVIRONMENT',\n"",
       ""    'properties': {'avg': {'property': 'avg',\n"",
       ""      'aggregation': 'DEFAULT',\n"",
       ""      'defaultValue': nan}}}},\n"",
       ""  'graphName': 'fraggraph',\n"",
       ""  'nodeCount': 3598,\n"",
       ""  'relationshipCount': 77408,\n"",
       ""  'createMillis': 390}]""
      ]
     },
     ""execution_count"": 6,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""# Fragment only graph with all edges - we'll use this for pagerank\n"",
    ""graph_1.run(\""\""\""\n"",
    ""            CALL gds.graph.create('fraggraph', ['Fragment'],['IS_FRAGMENT_OF','MMP_RULE_ENVIRONMENT'],\n"",
    ""            {relationshipProperties:['avg']})\n"",
    ""            \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 7,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""[{'nodeProjection': {'Fragment': {'properties': {}, 'label': 'Fragment'}},\n"",
       ""  'relationshipProjection': {'MMP_RULE_ENVIRONMENT': {'orientation': 'UNDIRECTED',\n"",
       ""    'aggregation': 'DEFAULT',\n"",
       ""    'type': 'MMP_RULE_ENVIRONMENT',\n"",
       ""    'properties': {'avg': {'property': 'avg',\n"",
       ""      'aggregation': 'DEFAULT',\n"",
       ""      'defaultValue': nan}}}},\n"",
       ""  'graphName': 'fraggraph_2',\n"",
       ""  'nodeCount': 3598,\n"",
       ""  'relationshipCount': 154816,\n"",
       ""  'createMillis': 142}]""
      ]
     },
     ""execution_count"": 7,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#Fragment only graph with fragment-fragment edges only - we'll use this to calculate fragment-fragment degree\n"",
    ""graph_1.run(\""\""\""\n"",
    ""            CALL gds.graph.create('fraggraph_2', ['Fragment'],\n"",
    ""            {MMP_RULE_ENVIRONMENT: {\n"",
    ""                        type: 'MMP_RULE_ENVIRONMENT',\n"",
    ""                        orientation: 'UNDIRECTED',\n"",
    ""                        properties:['avg']}}\n"",
    ""                                 )\n"",
    ""            \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 8,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>creationTime</th>\n"",
       ""      <th>degreeDistribution</th>\n"",
       ""      <th>graphName</th>\n"",
       ""      <th>memoryUsage</th>\n"",
       ""      <th>modificationTime</th>\n"",
       ""      <th>nodeCount</th>\n"",
       ""      <th>nodeProjection</th>\n"",
       ""      <th>nodeQuery</th>\n"",
       ""      <th>relationshipCount</th>\n"",
       ""      <th>relationshipProjection</th>\n"",
       ""      <th>relationshipQuery</th>\n"",
       ""      <th>schema</th>\n"",
       ""      <th>sizeInBytes</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td>2020-10-09T17:08:38.722712000+01:00</td>\n"",
       ""      <td>{'p99': 13, 'min': 0, 'max': 221, 'mean': 1.47...</td>\n"",
       ""      <td>compound_degree_graph</td>\n"",
       ""      <td>1708 KiB</td>\n"",
       ""      <td>2020-10-09T17:08:38.756265000+01:00</td>\n"",
       ""      <td>5122</td>\n"",
       ""      <td>{'Fragment': {'properties': {}, 'label': 'Frag...</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>7551</td>\n"",
       ""      <td>{'IS_FRAGMENT_OF': {'orientation': 'REVERSE', ...</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>{'relationships': {'IS_FRAGMENT_OF': {}}, 'nod...</td>\n"",
       ""      <td>1749152</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td>2020-10-09T17:08:47.605108000+01:00</td>\n"",
       ""      <td>{'p99': 228, 'min': 0, 'max': 1958, 'mean': 21...</td>\n"",
       ""      <td>fraggraph</td>\n"",
       ""      <td>4337 KiB</td>\n"",
       ""      <td>2020-10-09T17:08:48.002522000+01:00</td>\n"",
       ""      <td>3598</td>\n"",
       ""      <td>{'Fragment': {'properties': {}, 'label': 'Frag...</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>77408</td>\n"",
       ""      <td>{'IS_FRAGMENT_OF': {'orientation': 'NATURAL', ...</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>{'relationships': {'IS_FRAGMENT_OF': {'avg': '...</td>\n"",
       ""      <td>4442088</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td>2020-10-09T17:08:04.358976000+01:00</td>\n"",
       ""      <td>{'p99': 17, 'min': 0, 'max': 36, 'mean': 1.474...</td>\n"",
       ""      <td>fragsimgraph</td>\n"",
       ""      <td>1708 KiB</td>\n"",
       ""      <td>2020-10-09T17:08:06.184282000+01:00</td>\n"",
       ""      <td>5122</td>\n"",
       ""      <td>{'Fragment': {'properties': {}, 'label': 'Frag...</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>7551</td>\n"",
       ""      <td>{'IS_FRAGMENT_OF': {'orientation': 'NATURAL', ...</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>{'relationships': {'IS_FRAGMENT_OF': {}}, 'nod...</td>\n"",
       ""      <td>1749152</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td>2020-10-09T17:09:25.036845000+01:00</td>\n"",
       ""      <td>{'p99': 354, 'min': 0, 'max': 2372, 'mean': 43...</td>\n"",
       ""      <td>fraggraph_2</td>\n"",
       ""      <td>2735 KiB</td>\n"",
       ""      <td>2020-10-09T17:09:25.180051000+01:00</td>\n"",
       ""      <td>3598</td>\n"",
       ""      <td>{'Fragment': {'properties': {}, 'label': 'Frag...</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>154816</td>\n"",
       ""      <td>{'MMP_RULE_ENVIRONMENT': {'orientation': 'UNDI...</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>{'relationships': {'MMP_RULE_ENVIRONMENT': {'a...</td>\n"",
       ""      <td>2801632</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""                          creationTime  \\\n"",
       ""0  2020-10-09T17:08:38.722712000+01:00   \n"",
       ""1  2020-10-09T17:08:47.605108000+01:00   \n"",
       ""2  2020-10-09T17:08:04.358976000+01:00   \n"",
       ""3  2020-10-09T17:09:25.036845000+01:00   \n"",
       ""\n"",
       ""                                  degreeDistribution              graphName  \\\n"",
       ""0  {'p99': 13, 'min': 0, 'max': 221, 'mean': 1.47...  compound_degree_graph   \n"",
       ""1  {'p99': 228, 'min': 0, 'max': 1958, 'mean': 21...              fraggraph   \n"",
       ""2  {'p99': 17, 'min': 0, 'max': 36, 'mean': 1.474...           fragsimgraph   \n"",
       ""3  {'p99': 354, 'min': 0, 'max': 2372, 'mean': 43...            fraggraph_2   \n"",
       ""\n"",
       ""  memoryUsage                     modificationTime  nodeCount  \\\n"",
       ""0    1708 KiB  2020-10-09T17:08:38.756265000+01:00       5122   \n"",
       ""1    4337 KiB  2020-10-09T17:08:48.002522000+01:00       3598   \n"",
       ""2    1708 KiB  2020-10-09T17:08:06.184282000+01:00       5122   \n"",
       ""3    2735 KiB  2020-10-09T17:09:25.180051000+01:00       3598   \n"",
       ""\n"",
       ""                                      nodeProjection nodeQuery  \\\n"",
       ""0  {'Fragment': {'properties': {}, 'label': 'Frag...      None   \n"",
       ""1  {'Fragment': {'properties': {}, 'label': 'Frag...      None   \n"",
       ""2  {'Fragment': {'properties': {}, 'label': 'Frag...      None   \n"",
       ""3  {'Fragment': {'properties': {}, 'label': 'Frag...      None   \n"",
       ""\n"",
       ""   relationshipCount                             relationshipProjection  \\\n"",
       ""0               7551  {'IS_FRAGMENT_OF': {'orientation': 'REVERSE', ...   \n"",
       ""1              77408  {'IS_FRAGMENT_OF': {'orientation': 'NATURAL', ...   \n"",
       ""2               7551  {'IS_FRAGMENT_OF': {'orientation': 'NATURAL', ...   \n"",
       ""3             154816  {'MMP_RULE_ENVIRONMENT': {'orientation': 'UNDI...   \n"",
       ""\n"",
       ""  relationshipQuery                                             schema  \\\n"",
       ""0              None  {'relationships': {'IS_FRAGMENT_OF': {}}, 'nod...   \n"",
       ""1              None  {'relationships': {'IS_FRAGMENT_OF': {'avg': '...   \n"",
       ""2              None  {'relationships': {'IS_FRAGMENT_OF': {}}, 'nod...   \n"",
       ""3              None  {'relationships': {'MMP_RULE_ENVIRONMENT': {'a...   \n"",
       ""\n"",
       ""   sizeInBytes  \n"",
       ""0      1749152  \n"",
       ""1      4442088  \n"",
       ""2      1749152  \n"",
       ""3      2801632  ""
      ]
     },
     ""execution_count"": 8,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#View the list of current graphs\n"",
    ""pd.DataFrame(graph_1.run(\""\""\""\n"",
    ""            CALL gds.graph.list()\n"",
    ""            \""\""\"").data())""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>graphName</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td>fragsimgraph_test</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""           graphName\n"",
       ""0  fragsimgraph_test""
      ]
     },
     ""execution_count"": 13,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#Delete a graph\n"",
    ""pd.DataFrame(graph_1.run(\""\""\""\n"",
    ""            CALL gds.graph.drop('fragsimgraph_test') YIELD graphName;\n"",
    ""            \""\""\"").data())""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### 3a) Node Similarity\n"",
    ""##### Find me compounds that are similar to each other by fragments""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 9,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#Fetch data\n"",
    ""node_sim_data = graph_1.run(\""\""\""\n"",
    ""            CALL gds.nodeSimilarity.stream('fragsimgraph')\n"",
    ""            YIELD node1, node2, similarity\n"",
    ""            RETURN gds.util.asNode(node1).smiles AS Compound1, \n"",
    ""                   gds.util.asNode(node2).smiles AS Compound2, \n"",
    ""                   gds.util.asNode(node1).compoundid as Compound_id1,\n"",
    ""                   gds.util.asNode(node2).compoundid as Compound_id2,\n"",
    ""                   similarity\n"",
    ""            ORDER BY similarity DESCENDING, Compound1, Compound2\n"",
    ""            \""\""\"").data()\n"",
    ""#Convert to pandas\n"",
    ""node_sim_df = pd.DataFrame(node_sim_data)\n"",
    ""#Add RDKIT molecules\n"",
    ""PandasTools.AddMoleculeColumnToFrame(node_sim_df, 'Compound1','Compound1')\n"",
    ""PandasTools.AddMoleculeColumnToFrame(node_sim_df, 'Compound2','Compound2')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 10,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>Compound1</th>\n"",
       ""      <th>Compound2</th>\n"",
       ""      <th>Compound_id1</th>\n"",
       ""      <th>Compound_id2</th>\n"",
       ""      <th>similarity</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" 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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>1459</td>\n"",
       ""      <td>1495</td>\n"",
       ""      <td>1.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" 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FAkEvzXBTUxOfzx9pZSWztkYcDuLzkViMCgvR4sUdOnv7bWRkhEaPRjY2KCQESaVqJN8JyvZjTZyIANDx41TFQwihH3/8kZwj0CQO3dMiikycONHNzU3VWXiZTBYfH4+XxgHA29v72rVrcrk8Pj4ebyJycXGRdvl7p6enT58+Hb/E2dk5Pj6+01q4TCb76quvcAfNYrHOb92KKipQUxPi85GJCQL4R2cSCbpzB1G3/4cyYZ08iQDQuHGIqmX+oqIiPFyNiYnRPNrHH38MAH5+fpqH6oXk5GQWi2VoaKhSi5WcnEzu6x87dmxSUhJ+cuLEifhJV1dXcnmj25eTNU6enp6XLl3Cz2dkZEyZMoV8/u+//5bJZHXksJ3FQmvWINq2v1ImrLY2xOMhAPTrr5REa8NbnTRcxiGprq42NTVlsVia77zoiZqaGrz4GBYWpuRLMjMzSW+6ESNGxMTEtLe3Z2VlzZ07Fz9pZ2eHn+w9jlQqjYmJIe31vb298Z42fNcSFxcnl8svXbrk5ua2bvx4RBDI0xOlp2v8E/cGlcUU4eHIzAxFR1MQ6r333gMAR0dHNTZ29gRe0tmwYQNVATuBl6WnT5+uzOJjdXX10qVLyUFSVFSURCJ58OBBUFAQrgXicrkCgUClbW2NjY07duzARugGBgaWlpZbt25taWkpKSlZunQpqbOHZ85Q1q30DJXCamxEPW1qOn8eeXujVavQxx+j6GiUlNR27dq1nu6Wf/75Z4IgDA0N0yn9VOXn57NYLBMTE8WbLKrAy6YWFhb3799X5vqzZ89aWVmZmpqGhobW1tY+evQoNDTU2NgYAIyMjEJCQkQikXqZ1NTUhIaGGhkZWVtbJyQk8Pl8vFPczMyMz+drYRc1hkphXbyIvLzQw4cdX86fj8it6nv2dNx/4H8jR97FHyAjIyN7e/tp06aVl5fjK8vKyvBAOzIyksLcMAsXLgQAPp9Pbdi8vDw80/aD0oshzz33HAD88ccfCKHKyko8mmSz2atXry4tLdU8JVyTgrcbEQSxcuVKoVCoeVjloVJYP/yAAP6ZXRs2DNXWdjwWCtH58+jrr9GOHWjtWrRmTc748eMVT2TFXZ5UKsXVrT4+PnRs9v3jjz/wr7upqYmqmK2trbjuLVBxmvFpYGGRAz4/Pz9vb28Nb34VSUtLA4Dnn3/ey8srMzOTqrDKQ7GwAgKQoyPC+zGxsJYvR+++i3bvRidOoNRUlJ+Pmpv/eYlYLC4sLPwTz84hhPddqbpRWCXwjdLhw4epCojtx52cnFSaaeskLEoWLhXBwnrxxRepDas8FJfYm5nB4cOwYQPk5AAANDXBjz92c9m8eQfKy4/a2dnZ2NjweDxbW9uGhobq6urw8HAWi3XixAn6jpfevHnz8uXLIyMjyWGyJly8eHH//v0GBgbfffddTyuVyoBHVxSCEAKAPvTqod5t5uWXwcMDdu8GADA0hFOnQCiE8nKoqICyMhAKQSiElpa7ubm5ubm5ii8kCIIgiB07dtBqi+Xv7+/k5FRYWHj27Flcrg0ARUVFcrmcx+PhhRQlqampwVuxw8LC8OSI8uA/OaLNTrHPocUfa98+cHMDkQiMjeHJfe7/UFPzeUXF22VlZUKhsKKiorS0tLKysrW1VSAQeHp60pESCZvNfu+990JCQvbu3UsK66OPPjpz5gwAmJiYdHKy7MnSEiEUGBhYWVk5Y8YMXFPUr+jzFovKMVZcHCJrKY4fRwCIukkoKulanbF+/XonJyd8W94TgwcPVlzG/vLLL0GDzc0uLi4AQF/9xV9//QUAXl5eNMV/KlQKS4f497//DQCvvPKKUCgkt9MghEQiUW5ubnJycmxsrEAgIE1BnnnmGQBITk7Gl+Xm5uJO88cff1QvAbqFlZbW7OLSvHRpn32yB+jJb8HBwdHR0VevXsV7i7lcbtfuz8vLi8fjGRoa4pfU1dVhMUkkkjfeeEMsFq9Zs2bZsmXqJUD3GEsmM7tzB2i7BXo6A1RYw4YNq6ioWLhw4e3bt6uqqmpra69du9b1MhaLNWzYMGzqzOPx7OzsbG1tjx8/npOTAwCnT59eu3YtudDLoMgAFRYAmJqapqSkAIBUKq2pqamsrOzq61xaWlpRUVFRUdFthPr6+vj4+P4pLNwU9uHYfeAKi8TQ0NDGxsbGxgbXXCjS3t5eXV1dWloqFAoTEhKwBYgiv/zyC7mPoF/BCKtfY2BgYGtri8dh3faV+fn59+/fxw5sKqH381iMW6uybNmyZejQoV2f//XXX7WfzFPp8xaLEZayWFlZdXtKXv8UFpcL3t7g7Nx3GfTVPIeOsnjx4k6/QHxYkKpx8GbiW7du0ZFkf4BpsVQjOjoauw2SNDc3460EmnDy5ElsUaFhHABITobp06GmpuPLBQugqUnzqCrDCEs1bGxsBAJBpyfV6A0VNVRXV7d582Z/f/8pU6ZcunRJwwwfP4bLl+GDDzq+vH4d2ts1DNk9T/kY9HGLqYPI5fJOpwONGzdO+Ze3tbXFxMSYm5tbWVnNmTOnoKAAP6NYCnHjxg210+t2Vxy11NbWhoaGzps3r5drGGGpQ0lJCfa9IVFyKfrMmTOjR4/GLzEyMgIAQ0PDDRs2VFZWNjc3CwQCvCjJYrECAgLUcMkXCtH336NVq9CFC8jZGbW2dgiLqn2EUqn00KFDeLcci8W6fv16T1cywlKTL774QlFYT3XkzsvLmzdvHr54zJgx8fHxisXyZmZmoaGhdXV1uKoC77MwMjIKCgqqrq5WJh+RCIWGIlNTFBKCVq1CCKE330R8Pho2DIlEaMYMFBCAlKvz6JGUlBRsiQgAU6ZM6b3UhRGWmrS3t2O7EUwvfg01NTUhISHY3sPS0rJTOXx+fj5ZLG9lZSUQCMRiMa4Dwy8xNzfvvbqmpQWFhyMuFwEggkCLFnUI6+FDZGODjIxQWto/Zc9hYerYIBQUFOBTQgCAx+PFxsY+tSKBEZb65OTk4O4MAAYPHtzVahB7MeAKHGzv0dNe/itXrsyaNQuHIitXr127Rh7X6Orqqri9ByOTdTil4donb2+UldX9rrjSUrRiBSIIBIDs7FBsrLKVhdihBP+YHA6Hz+craTOr58KqqanZuXPn999/n5qaSkd8bGiIwbVcJImJiYpeDHl5eU+N1qnWHpfVJycnu7u7d62uTkhICAjYiSXl7o6ebBXrjdRUNGFChwqXLCntvSgIV1cPGTIED6dWrFihUj2m3gpLLpfHxsbi3wveR+Xr66tkNanySCQS8jzirVu34ievXr1KHtHo6ur6yy+/KB+wqztIVlaWXC5XbA4vX76MT4Jhs9mzZ+fFxalQ2IxN14YORVOnBmC5dDuGU/SDmDNnjhp1afoprBs3buCDWABg5syZW7duxSU0uBqYQs9whFBGRgYeDLm5uZWXl5PFP7hwXj2vb4lEQrYW2M8IO3F2dS9Sr26stla2adMmvIeRy+VGRUWRws3Pz1+wYAH+1Y0ePboXM5Le0Tdh1dXVkSPl4cOHk8dklJeXr1ixAv9JRo0ade7cOQrfFDtNAAC+mzM1Nd2+fbvmJ5aJRKIPP/yQvEOcNGkSOZzfuXOn5vELCgpIDTk7O//www8hISH4LtXCwkJt1WL0SliJiYl2dnYAYGBgEBISomh1l5eXJ5fLf//99/Hjx+Nfpa+vb3FxsYbv2NzcHB8fP2/ePGw2gcOqMf/UC9gz0sDA4MUXX1TfIbJnkpKS8NQarm00MDDYuHGj5gXDeiKsgoIC8gbKy8srJydH8btlZWUcDmf69Ok3b96USqVRUVF4HhLbb6jx0W9paTl16pS/vz9Z2MNms7G2rl27Rt2P9Q+5ubllZWWafxK6RSKR7NmzJzIyMiAgQJmbDGXQeWE1Nzfz+Xz8abO0tIyKiup6W56amopd7dhsdnBwsEgkevToUUhICB4M2draKnmwVHt7e3Jy8ooVK/AMAr5dmjZtWlRUVFVVFe4QXVxctObo0p/RbWElJiba29uT98O9NOB1dXXkfAzWX3t7+9WrV8kK5tmzZ/e0iUUmk/31118hISF4NE3e7gkEAkULl9bWVtzPbtq0ifofVdfQVWEVFRXhU0YAYOLEiUo6aeXn57/yyiv4VZMmTcLuieSshKGhYaeRWVZWVkhICLk8jPXE5/N7slm/fv06PkxFpSkGvUT3hNXa2kqaiXG53EOHDnXt+3qHbOcIgsDzfiKRSPFeMiwsbMuWLSNGjCD1NGbMGD6fr0x9aVhYGAA4ObnW1lJ2JKwuonvCys3NHTFiBKkJ9YLgkRlWp4WFRVRUlFQqvX79Op54JPe283i8kJAQlQ4EaG9vX7Lk387O4jffVC81PUH3hIX3QlHi93f37l3yQJFly5YhhORy+fLly8mOUj3zt+JiZG6OAFBcnOY56iq6t4NUKpUCwKRJkzQPNXr06PPnzycmJjo5Oa1btw4ACILAA3AfH5+pU6eq59bi6AgREQAA69dDWZnmaeokuiestrY2ADA0NCwsLDx16tSNGzc0DLhw4cI7d+6QplxYuKRlg3oEBYGvL9TVQWAg6G/tYG/onrDwH97IyOjcuXPLli07efKk5jEVZYSFS+6HUZtvvgFra0hOhkOHNIykk+iesMgWi3xAU3wN4wwZAocPAwCkp2uelO6heyX2ZItFPqApvuah/Pzgjz9gxgzNI+keOtxiUTIY6ik+VXqdMaO/FPppGd0TFtmi0NoVUtgQaq3Qr1+he8Lq1GJR3hXSodeAAEhLg9RUCkP2d3RPWJ1arP48xiIh7e8lEgqj9mt0T1i6NcYiUbS/Hwjo8F0hTQqgKSwo2N8PBHS1xSKnG/rbPFZLC7z+OhQUAADIZHDoEMhkHQZoQ4bAf/4DbW196YemNXRMWDKZTC6Xs9lsFovVP8dYW7ZAXBysXAkAsGsXvPMOxMfD8eMd333rLUAInnmGmlT7MzomLMVWqh9ON/z6Kxw5AsbGcPQoXLsGn38OLBY8KeEZWOjYGEvxrz537txBgwYpbsdTG4lEQp6/pbawampg9WpACHbtglGjwN0dpFL48EOg88ipfkxf79tRjUePHgGAlZUVVQHxjr/Ro0eTR2NiQxU1an9ff/2tKVNOz52LZDIUFIQA0Nix6jhw6Ac6Jizs5W9paUnJyZFnzpzBDR6bzU5MTEQIlZeXY/On4OBglc67P3LkCE6svLz2/HlEEMjYGGVna56jrqJjwmppaREIBARBYDMdteOUlZX5+/vjNnvixIkZGRlSqXTv3r24Ep+sFsSlE09VWFFRET47Pi4urrq62s1t8oQJf+7fr3Z2+oCOCQshlJ6eruhXka1is9DW1hYVFYV1gHe74zow0uxqwYIFsbGxq1evVjygcOLEibt37+62XlQqleIaslWrVsnlclw75OMzn4YjrXUJ3RMWQgjXbOGSB5Ucdi5duuTq6oq1EhAQQNbn4MpVR0fHn3/+mby4vb0dlxMqniPctfxLLpdHRUWNGTOmvr7+4MGDAMDlcik5iV6n0UlhYRQ9wZ5qYlFZWUmagjg7O1+8eFHR5whXFPZ0tH1ra2tiYqJiATSpsKKiInyNRCK5ffu2mZkZaHCIoT6hw8LCFBQU+Pr64j92T7Y7Fy5cwGYNHA5HIBC0tbVlZ2e/+OKL+FWzZs1S8kBKsViMFaZ4rjguib5//76HhwcABAYGUv0j6iQ6LyyMolHY3LlzO5mCVFVVWVhY+Pr63rt3r6mpic/n43ZO0edIJbApSEBAAG6i4MmhS05OTpq7C+kHeiIs9MQ/HY+HDAwMgoKCHj58SH4XD3oSExPx/AKLxQoKClKsplePlpYW3IYZGxsPHjz4s88+0zCg3qA/wsI8fvyYdA/DXnVSqRQhVFRURNamuru7Z2ZmUvu+e/bsAYDly5dTG1Z30TdhYXJycsg6QVdX15UrV+KpKUtLy6NHj6rq9aAM9+/fJwhi0KBB1PpQ6i76KSwMLnHGS0DY60FJM371wMXZihMWAxkd292gEgsXLszLy5swYcLjx4/DwsJOnL5qgusAAAJ6SURBVDjR7UmWVOHn5wcAZ86coe8tdAh9FhYAGBsb40bLxcWF7vfCwkpISJDJZHS/V/9Hz4WlTcaOHTt69Gi5nHvlyoO+zqXv0X9hIYTgyTwT3axc+Xd9/d3/+z9HLbxXP0f/haVN5s59ViaDM2cGqMOMIoywqGTyZBg+HO7fh+zsvk6lr2GERSUsFrz6KgAAc2uo/8JyjIhwz8riPjm0jW78/AAAEhK08279F/0XlpaZPRu4XMjJgcLCvk6lT9GxKh01wMNorZWIGhrCggVQVAR1ddp6y36J/gtL+3z7LbDZfZ1EXzMAukJ866+tsvbkZJg1q7PN2qVLsGXLP9d4e0NtrXbS6TMGgLC0S7c2ayJRh5sD5to1kEr7JDvtwQiLerq1WZPLoa2t499AQP/HWOttbF4bOtT5yR5iLUDarOXk/PPkb7+Bs3PH44YGreXSZ+izsG63tISXlpJf+g8ZssjKSjtv3dVm7aWXICmp47FCwaLeos/CapbJEEK7HDuWhC0MtPrDDiibta7o+RjLhMWyMzbG/wZpZQ6AILq3WVO8K8XX3LwJy5eDvz9ofGhLf4RA+rsQf7WxMbiwEDdUBEDS+PGG/clLb9s22LQJCgogMbHjUCd9Qp+7QgB4nsM5OmZMX2fRPbt3w927sGsXfP55X6dCA3ourP7MxYvw9dfw1VdgZ9fXqdCAPo+xCNDehLsa7N0LYjF88AEcPdrXqdCAPo+xGPoQfW6xGPoQRlgMtMAIi4EWGGEx0AIjLAZaYITFQAuMsBhogREWAy0wwmKgBUZYDLTACIuBFhhhMdACIywGWmCExUALjLAYaIERFgMtMMJioIX/B2XhkrIUCLNVAAAAAElFTkSuQmCC\"" alt=\""Mol\""/></td>\n"",
       ""      <td>1495</td>\n"",
       ""      <td>1459</td>\n"",
       ""      <td>1.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>1357</td>\n"",
       ""      <td>1331</td>\n"",
       ""      <td>1.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>1357</td>\n"",
       ""      <td>1321</td>\n"",
       ""      <td>1.0</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>1357</td>\n"",
       ""      <td>1354</td>\n"",
       ""      <td>1.0</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""                                           Compound1  \\\n"",
       ""0  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""1  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""2  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""3  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""4  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""\n"",
       ""                                           Compound2 Compound_id1  \\\n"",
       ""0  <img data-content=\""rdkit/molecule\"" src=\""data:i...         1459   \n"",
       ""1  <img data-content=\""rdkit/molecule\"" src=\""data:i...         1495   \n"",
       ""2  <img data-content=\""rdkit/molecule\"" src=\""data:i...         1357   \n"",
       ""3  <img data-content=\""rdkit/molecule\"" src=\""data:i...         1357   \n"",
       ""4  <img data-content=\""rdkit/molecule\"" src=\""data:i...         1357   \n"",
       ""\n"",
       ""  Compound_id2  similarity  \n"",
       ""0         1495         1.0  \n"",
       ""1         1459         1.0  \n"",
       ""2         1331         1.0  \n"",
       ""3         1321         1.0  \n"",
       ""4         1354         1.0  ""
      ]
     },
     ""execution_count"": 10,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""node_sim_df.head()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 11,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/plain"": [
       ""[{'nodesCompared': 1524, 'relationshipsWritten': 10149}]""
      ]
     },
     ""execution_count"": 11,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""#Write\n"",
    ""graph_1.run(\""\""\""\n"",
    ""            CALL gds.nodeSimilarity.write('fragsimgraph',\n"",
    ""                {\n"",
    ""                writeRelationshipType: 'SIMILAR_BY_FRAGMENT',\n"",
    ""                writeProperty: 'similarity_score'\n"",
    ""                })\n"",
    ""                YIELD nodesCompared, relationshipsWritten\n"",
    ""                \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### 3b) Degree - Number of fragments per compound node""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 12,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""# Number of nodes per fragment node\n"",
    ""data = graph_1.run(\""\""\""\n"",
    ""                CALL gds.alpha.degree.write('fragsimgraph',{writeProperty: 'fragment_degree'})\n"",
    ""                YIELD nodes, writeProperty\n"",
    ""                \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 13,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""# Number of fragments per compound node\n"",
    ""data = graph_1.run(\""\""\""\n"",
    ""                CALL gds.alpha.degree.write('compound_degree_graph',{writeProperty: 'compound_degree'})\n"",
    ""                YIELD nodes, writeProperty\n"",
    ""                \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 14,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""# Number of fragments per fragment node\n"",
    ""data = graph_1.run(\""\""\""\n"",
    ""                CALL gds.alpha.degree.write('fraggraph_2',{writeProperty: 'frag_frag_degree'})\n"",
    ""                YIELD nodes, writeProperty\n"",
    ""                \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### 3c) Let's compare to other similarity metrics""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#Fetch data\n"",
    ""node_sim_data = graph_1.run(\""\""\""MATCH (n:Compound)-[m]-(p)\n"",
    ""                               WHERE n.fragment_degree>=15\n"",
    ""                               AND m.similarity_score IS NOT null\n"",
    ""                               AND startnode(m) = n\n"",
    ""                               RETURN\n"",
    ""                               DISTINCT n.compoundid AS cid1, \n"",
    ""                                        n.smiles AS Compound1, \n"",
    ""                                        n.fragment_degree,\n"",
    ""                                        p.compoundid AS cid2,\n"",
    ""                                        p.smiles AS Compound2,\n"",
    ""                                        p.fragment_degree,\n"",
    ""                                        m.similarity_score\n"",
    ""                              ORDER BY m.similarity_score DESC\""\""\"").data()\n"",
    ""#Convert to pandas\n"",
    ""node_sim_df = pd.DataFrame(node_sim_data)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#Add RDKIT molecules\n"",
    ""PandasTools.AddMoleculeColumnToFrame(node_sim_df, 'Compound1','Compound1')\n"",
    ""PandasTools.AddMoleculeColumnToFrame(node_sim_df, 'Compound2','Compound2')\n"",
    ""#Calculate Morgan fingerprints for comparison\n"",
    ""fps_c1 = [AllChem.GetMorganFingerprintAsBitVect(x,radius=2, nBits=1024) for x in node_sim_df['Compound1']]\n"",
    ""fps_c2 = [AllChem.GetMorganFingerprintAsBitVect(x,radius=2, nBits=1024) for x in node_sim_df['Compound2']]\n"",
    ""node_sim_df['morgan_1024_r2_sim'] = [DataStructs.FingerprintSimilarity(x, y, metric=DataStructs.TanimotoSimilarity) for x, y in zip(fps_c1, fps_c2)]\n"",
    ""#Calculate MACCS fingerprint similarity for comparison\n"",
    ""maccs1 = [MACCSkeys.GenMACCSKeys(x) for x in node_sim_df['Compound1'].tolist()]\n"",
    ""maccs2 = [MACCSkeys.GenMACCSKeys(x) for x in node_sim_df['Compound2'].tolist()]\n"",
    ""node_sim_df['maccs_sim'] = [DataStructs.FingerprintSimilarity(x, y, metric=DataStructs.TanimotoSimilarity) for x, y in zip(maccs1, maccs2)]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>Compound1</th>\n"",
       ""      <th>Compound2</th>\n"",
       ""      <th>cid1</th>\n"",
       ""      <th>cid2</th>\n"",
       ""      <th>m.similarity_score</th>\n"",
       ""      <th>n.fragment_degree</th>\n"",
       ""      <th>p.fragment_degree</th>\n"",
       ""      <th>morgan_1024_r2_sim</th>\n"",
       ""      <th>maccs_sim</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>544</td>\n"",
       ""      <td>548</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>17.0</td>\n"",
       ""      <td>17.0</td>\n"",
       ""      <td>0.505263</td>\n"",
       ""      <td>0.887324</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>549</td>\n"",
       ""      <td>547</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>15.0</td>\n"",
       ""      <td>15.0</td>\n"",
       ""      <td>0.697674</td>\n"",
       ""      <td>0.971831</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>547</td>\n"",
       ""      <td>549</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>15.0</td>\n"",
       ""      <td>15.0</td>\n"",
       ""      <td>0.697674</td>\n"",
       ""      <td>0.971831</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>548</td>\n"",
       ""      <td>544</td>\n"",
       ""      <td>1.000000</td>\n"",
       ""      <td>17.0</td>\n"",
       ""      <td>17.0</td>\n"",
       ""      <td>0.505263</td>\n"",
       ""      <td>0.887324</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>6</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAX1klEQVR4nO2de1RU19XA9wDDG2GgSBAogiIQxAdRUaNRVEQFW2sEHwl1iQGTaCcrpSnYqjTR6BBtnWUaFas2+CWKMYhQHyGKoqQaEUg1KIFFEgyooAgqojyc2d8fZ7wi8hhm7pEB9u+PrDhz2Zw7/Gbf197nSBARCEJsjLp7AETvhMQiuEBiEVwgsQgukFgEF0gsggskFsEFEovgAolFcIHEIrhAYhFcILEILpBYBBdILIILJBbBBRKL4AKJRXCBxCK4QGIRXCCxCC6QWAQXSCyCCyQWwQUSi+ACiUVwgcQiuEBiEVwgsQgukFgEF0gsggskFsEFEovgAolFcIHEIrhAYhFcILEILpBYBBdILIILJBbBBRKL4AKJRXCBxCK4QGIRXCCxCC6QWAQXSCyCCyQWwQUSi+CCSXcPoOvk5cHBg2BuDgsWwJAh3T0aom2eR8ZCxJKSkqKiIpVKpW+s/Hx44w0YMQKcnSEsDKqqxBggIT7cxSotLQ0JCRk1atSkSZP8/f2PHTumS5Tbt+Htt6GkBA4cgNhYiIiA6GhYuBCOHhV7vIQ4cBTr4cOHq1ev9vPzO378uFQqNTExKSoqmjVr1pw5c0pLS7WNolLB9u3g7Q3btkFsLJSWwrBhmrc8PSljGS7Ih6ysLG9vbwCQSCSRkZG3bt1qbGxUKpX9+vUDAKlUKpfLa2trO4ly4QIGBiIAAmBQEJ48iatW4dq1iIiPHuHvfoeXLnEaP6En4ot1/fr1yMhIZu2wYcMyMzPffvvt06dPs3dv3boll8uNjY0BwN7eXqlUNjc3PxuksrJy8eLFd0aMQAB0d8f9+1GpxH790NUVx47FUaPQ2xvfekv0wRNiIaZYKpUqKSmJ5SRLS8uEhITk5OQXXngBAEaMGKFWq4UtL1++PGPGDCafj4/PkSNHhLeam5s3b95sa2sLAAuHDsVVqzAjA729NXnr1Vfx1i2srMS6OhFHToiOaGLl5+ePGTOGuRIWFpadnR0SEsL+OWHChO+///7ZH8nIyBg0aBDbZtq0aYWFhdnZ2f7+/sIrWVlZl+RyjVI+Pvj112KNluCNCGLV1tYKRzdXV9eUlBSFQmFubg4AMplMqVSqVKr2frapqUmpVLL8JJVKBw8eDACDBg1KS0tTKpXW1tYuNjaPBg7EhARsaNB/qMRzQ1+xrl275uTkxLRYuXLlsWPHfHx8hHP2mzdvahOkqqoqJiaGqfnKK698+eWXnp6eLMiiRYuqrl3Tc5DE80dfsRQKhbu7++jRowsLC6Ojo9lRzN/f/5tvvulqqIsXLzo6OjK92Im/cMpP9Dj0FSswMBAAzp07h4hKpZKdszc2NuoW7ZVXXmFetne1SPQUxHlWKJFIAGDFihWvvvqqq6urznHYM59t27a9/PLLogyM6C70vfOOiPBYLGNjY32sAoBHjx4BgIlJD3w0TjyNmGLpD8tYwmkW0XMxLLFkMmc3N08TEwtRohHdiGEddCorM8rLgRJWL8CwMhar1yKxegEkFsEFwxLr0SMAALoo7AUYVjMFZaxeg77JQdyMFRQE5eVgaSlKMKI7MRSxcnOhthZ27wYA2LcPFi7UM55h0wcajQzlHCsrCyIioLwcAODjj/UMxoXGxsaHDx+KEKhvNBrpJVZZWdn169cBICsrS/+hLF0KcjkAAKL+wSAnJ2fJkiV/+ctfUlJSUL+IVVVVv//972fOnOnh4REfH19XV6djoB9/hEWL4NNP+0SjkW7PrhsbGz/88ENLS0sAkEqlABAcHFxUVKRbtMpKXL8e09Nx6VJMS8OxYxER26o51YqrV6/Onz+fJVH23wkTJuTl5ekQSqVSbdu2zc7ODgDMzMxYNFdX188++6xlpbVWVFfjb36DADhwIP7vf5oXP/0UN2zA3lhwpotY2dnZfn5+zMvQ0NDNmzc7OjrC496bu3fvah+quRmVSrSxwUWLMD0da2pw1CgcNgy/+QYlEgwLw9LSLgysvr5eoVBYW1sDgIWFRVxcXFJSEiu6Z4WHN27c0D7axYuXxo0bx3YzLCysrKwsNzd3/Pjx7JVRo0Z1oebs4EG0t8eYGDQzQ4kEly1DfNxodOIEWllhWBj+9FMXdtXg6ZpYN27ciIyMZF9cLy+v9PT0mpoaRKypqRGqk52dnZOTk7X5Qmdno5+fpqJ9/HhMT0dE3LcPPTwwORmtrREAzcwwPh7v3et8bBkZGQMHDhQ8+Pnnn9nrd+/efe+990xNTQEgOPjdxETstFqsvh4TEvDll8uF3RHeUqvVX3zxxa9//Wsma3h4eFlZWSfhUlNx9Wo0MkJTU4yORgC0sXnSaHToEFpZIQBaWGBCAtbXd76rPQFtxVKpVMnJyQ4ODiwZJCQkpKamuru7L1myRNimoKBAqKMaPXr0+fPn24tWVVUfGYkSCQKglxdmZraxzbVruHixZhtnZ9y3r7S92vn8/PyJEyey3xsQEHDmzJlntykpKQkPXyST3QbAwYPx0KF29zQjA93dEQCNjfFvf/vyXltS19fXJyQksLp+S0vLuLi4una6hu6eOKExadEiBMDwcAwMxP37n2o0qqjA117T7KqbW0laWpePs4aHVmI924ETGhrK/hkYGPjw4UNhS7VaLbR8GRkZsVbVlqGYoE5OTj4+Bewr2uKn2yAvDydMQEdHlY2Ny7PSVFdXC5nSwcFBqVQ+evSog2hZWejvr8mRU6bgxYtPvXvtGkZGat4dORLb/15oKC8vF/K3q6trqzxdX1//+uuvOzk5Nc2ejRIJ/v3vGBuLNTXthsvNxXHjmt3c7MzNx4wZw4pyey6diNWyA8fFxWX37t2seQYA7Ozs2vtD3r9/PyEhgR19WFcq2+zChQujRo1iRkZFxWl5/qRW45df5rMSQolEsnDhwl9++aVVe49cLr9z54420ZqbccsWtLdHAAwIwK++0ry+dy/u3YsAaGWFCgV26OdTfPvtt2PHjmU7NWbMmLNnzyLivXv31q1bN3HiRF9f3+LMTNTSEpXqm88/Z80pRkZG0dHRWnajGCDtisVyT//+/YW/3NGjR1988UX2CYaHh1dVVXUcurCwMCgoSDhCzZs3z8jICADc3NxSU1O7OtD79++vWbPGwsKCHYtZUgSA2bNnl5SUdDVadTUuX47Ll2O/fvjLL4iI48YhIn74oS6XaCqV6l//+pcgxI4dO+bMmbNmzZqysrKmpqauRrtz505sbCz7WtrZ2el2PdvttCvW9evXmVWTJ08+c+aMkPOHDBly/Phx7X9BRkaGu7s7ANjY2JiYmMjl8jbPWrSkoqIiMjLSxMRk6NCh3t7eLVuodWD9enz3XZwzBxE19zj0geVpMzOz0NDQiooKPaOVlJSEh4d7eXk19MyGynbFYi3w7733nlqtjoiIAAArK6vExEQdvoL37993c3MDgAMHDug3Wg0//fRTeXm5/m08z9480x92ZVrapdsk7XP79m1R4jx/2n1WyMrPg4ODJRKJQqGQSCQfffQRu8zWnoaGBnNzcysrKwcHh/LyctborD8eHh6ixGFs3AjTp0NTkzjRWD+ImZmZKNHs7e1FifP8afeRTsuGGQ8Pj5SUlK5aBQAbNmyQSCQffPABiloEIS6FhTB3Lty7J060xsZGEE+snksnGUvPhhnBJ8MUa+VKAID+/eHWLbh5U5yYTCx26t2X0Spj6Yxh+tSKxkYAALFMaGpqAspYHYjVFzIWg51diWUCE4syFt+MJWD4YoliQnNzs1qtlkql7I5dX6avZ6zmZlCrQSoFUUygEyyBdhNSHznHampqnjjx36amUoAlYkSjEywNffqqEAAaG+tycpbZ29uLIhbdaxB4TleFBiyWmAcvOhQK8D3HEjBYscQ9eNGhUECrjFVfX//gwQMdor/xxhvHjx+fP3++zuPjDY+MRWKBlhlr7dq1Q4YM2bFjh1qt7lL0wYMHT5s2beDAgQ0NDWDAGaur+9UeV65cAToUMtp7Oj19+nRbW9vo6Oiamhqh4DgwMJAVsmlPRUUFq8SSy+UG+Ky+urr63XfflUgkQUFB/xOaZ7rOgwcPWG3jr371qxMnTmRlZYk4yJ5Iu2IVFBSwu3xOTk5JSUkpKSmsrAoAwsLCftKipaSpqSkxMdHKygoAbG1tdSjuez6kpqayWn5jY+M333xTh6LNI0eOsIILY2PjFStWXLt2bcaMGREREbt27SouLuYxZsOno9Lk/Px8No0xAPj6+qalpbVqruqg0ysnJ2fo0KGCiOXl5RwGLxq1tbVxcXHs3MjOzk6hUGhZXtdy4aDhw4d/++23wltnz56VSqUWFhZbt95dvRrv38f211HohXTeTJGRkcGm8weAadOmnTx5MiYmhiWzAQMGJCUltSp7Z61gbINBgwZ9JVSVGzzFxcXh4eFsT728vL744osONm61cJBCoWj1OahUqj179sTHrxswAAFwwwb08sK0NLx+nfNuGAZadek8uyJcVlYWO/Hy8PAQunRYmbzQvBoXF/ew4xYcg+T48eNCrp06derFVq08iIj43XffsQnuWT6+evVqe9FyctDWFgMDcdIkBMAVK9DBAcPCev8aU11oWG21ItzmzZuTk5MPHz7M3i0uLp46dSr7rCdPnnzlyhU+A34eNDc3JyUlsW+IiYlJTEyM0DlSX18fFxcntC1pU2x98yZu2YIA6OiIb76JADh8uOatvXsRES9cwMuXNa+06I3t2XS5xb7VinCHDx8WLogA4IUXXtCyDdrwqampiYuLY/slk8kUCsXBgwdZGW1Xu0J+/BHnzcP330dTU5RI0MrqqdagjRtxzx7Nlv7+PHalG9BxUpDU1FRhRTg2Z4aRkdHy5cs7XzS1p3H58uXp06ezPWX34caOHavbjYm//hUBcPTo1q1BGzfiqlWYk4M5OejjI+rouw8dHwXOnTt39uzZW7dujY+Pt7S0dHR03LNnj9C32Zt48cUXMzMzDx8+/Mknn/j4+Hh7ewvXLl1l3ToYORIKCiAwEO7dg0OHnrx1+bKmIIxNltkb0FPMuXPnAsBedrJAaAHrORPm1cFeeijUt7yNnYIY4LMaQ4bNA7dsGeg8hZvhQzNfP29WroS6OujXD2xsND1nf/rTk3cvXequcYmMvhmL5SoUZXbHPgPL7737MyOxugF26i9SRYWB0tebSboFEqtzKGPpQF8QS9+TdxJLB4yMMCAgy8hIAjC1u8fCC7oq7AYkEnVBQbCxsTHAo+4eCy/oUNgNsBv3YtVDGyYkVjfQFz40OhR2D0ZGRmq1Wq1WG9wsDyItIEUZq3tgn5tYR8OGhgbWeaYv4i0gRWJ1D1FRUdbW1iEhId9//70+cWpra+Pj4z09PadOnRoREXH16lW9hnXggFgLSOkrlkwm8/X1Zes0EdoTFRUllUpPnjwZEBAgl8tramq6GqGxsfEf//jH4MGDExMTKysrc3NzDxw44Ofnt27dOl2Wv6upgb17obQUhg3TvOLpCVVVsGsX3LnT5WigR9mMWq2uq6tbunQpAOzcubO2trampqZ31I4+H27fvi2Xy1mvuUwmUyqVWs4DzdbzETpcpkyZkp+f3/EyGR3R3IxJSejoiEZGuHo1rl2L+HgBqZ07EQDt7VGh6HwFoqfRUazDhw8DQGhoaHR0NADs2LGDTZ9fWVmpW8A+S1FR0cyZM5ki2sxc/9///ldYgczX1/ezzz7bsGHD9cetP9nZ2SNGjGDvBgYGtmxHa5sjR9DHR7PMy9SpmJuL48c/WUDq0iUMCtK86+ODGRna75eOYh09ehQAZs6cuWzZMgDYvn27s7MzAFzvI81NYpORkSGUek+bNu2y0FzRgitXrgjdaS4uLtu3b09JSWHTysfExAibCasVwePljNr8thcWFmay1g4AHDJEs/Yao+UCUoiYno7e3giAEsmG11777rvvtNkjHcX66quvACAkJOStt94CgK1bt7q4uACA/isy9FnY6kAte+yE1YEqKipiYmJYa5C1tXVcXNzBgwf9/f2ZZAEBAc929NfV1bFlMtiPJCQkCC24wiHY1Ni4PiAAFQrstDu3qQm3bCmZPp3JGhUVda2zlWF0FOvrr78GgODg4OXLlwPAP//5T7b2xC+s+4TQlZY9dg4ODomJievXr7exsWG2xcTEnD59Wlh6zc3N7dmG4ZawdVPYxl5eXnv37t20aRNrfjExMVmxYkV1dbX2Y2P94i0X0+ugT0lHsU6cOGFn5xEauuCPf2yys1N/8knz0KEv29l5/Pxzu62bhPbk5eWxfmBmmEQimTdv3pkzZ4S8xdrRtOwHPnr0qI+PD7SoIJ8xY4bOjZ9Xr15lVwkmJianTp1qbzMdxTp5EgEwKAjfeQcBUKlET08EwB9/1C0e0Rq1Wq1UKgHAxsbm3LlzpaWlLFWYmZnFxsbWdLDuYVs0NTWtWrXK3Nzc1tb22LFj+g/v0KFDAODs7NzeBjrexxIqip79H0IUJBLJb3/7WwBwcHAYO3bsoEGDgoKCwsPDr1y5smnTJplM1qVoUqk0KiqqoaHB3t5e6DfWB7buZAfPo3R8VtjKJ5WKxBIfdtRTPW41TE9P1+1G9J///OeysrJ33nkHxHuI1OlMovpmLBaZMhYPWoml8+ONEydOHDhwoK6urmU0PeEllqkpyGRgZQVWViCTgakp2NmBTAbUXygircTSMw4igngZi8UR/1A4ejTU1EBuLtTWAnvM5eQECxfqFoxoG2NjM1/f1/v1s9QzDvvzs0tCQ89YjKwsiIiA8nIAgI8/1icS0QbGxnZFRf9XUpKkd5wnGUsssTrNWPpWNyxdCnI5QG9vv+wWWDrQ3wQeh8JOM5a+FaSTJ7eeOIUQC7HE6pZDoQilyeKuqUwIiJuxWK56bifvIhRcy2QQG9ubJ07pLvpuxmJrKgPAggWwYIE+kYg2MDaGmBgRFlLslpN36tIxUNitnKQkAIB9+/S6lePt7X379m1WImHod94J3oh4K2fLli15eXkTJkyAnnIfi+BKm7dybt6EP/wBqqu7HC0zMxMA1Gr1UT16bxgPHjxITk6WyWSurq7tbqR/BQXBAzZV6dKlmJammVw5Lw8RMSpK09+wfbu2a6j88MMPs2bNavlHnz17dmlpqQ6jUqvVn3/++YABAwDA3Ny8g3WHSCwDpdUcuGlpCIBhYXjqFIaEaErVFy4sOX/+fAdBWhUor169etOmTba2tvC4+ln7qeoRsaioKDg4mKn50ksvnTt3roONSSwDhYmFiPv2oYcH7tyJ1tYIgFZWqFDgwYPo5/fIyWmERCKJjIx8NnN00FJx48aNxYsXSyQSI4nkdlAQ7t/f+WhqazEuLnHKFACwt7dXKpWqzrIlidVjKC/HiIgnvVjHjl1ZuXIlm7XawcGhZRvL+fPnhTn3x4wZ02YT2NmzZ1Nfe00TbvJkvHSp7d+qUuGuXdi/PwI0Wlh8EB+v5RoRJFYP49Qp9PPDwYMfSqWWYWFh2dnZM2fOnMMWukCsqKgQ2lZdXFw6aVtVqzE5mUmDJiYYE4O3bj21wYULGBiokW/SpHblawsSq+fR2Igff5zBVhi1trb+6KOPamtrHzx40Go1yTotVxirrUW5HE1M0NQUf/gBL1zAlSvx/fexuBg3bkQAdHbG5GTsYo87idVTKS8vF1q73N3dWcOwRCKZP39+B8vctcvFi7hrF+bl4fDhuH8/7tiBXl5YXo6JibotgUdi9WxOnjzp5+fn4OBgZmY2cuTI06dP6xUuLu7J6itr1uDu3TpHokc6PZugoKCCgoLi4uKGhoaXXnpJ32ncSkufPDzy9IQbN3SORHfeezympqb+/v6jR48WYXLAWbPgP/8BAFCpID0dHrdc64AEqfSTELhzB0JDoakJ6upgyhTYulXnSCQW8QxVVWBlBdbW+sQgsQgu0DkWwQUSi+ACiUVwgcQiuEBiEVwgsQgukFgEF0gsggskFsEFEovgAolFcIHEIrhAYhFcILEILpBYBBdILIILJBbBBRKL4AKJRXCBxCK4QGIRXCCxCC6QWAQXSCyCCyQWwQUSi+ACiUVwgcQiuEBiEVwgsQgukFgEF0gsggskFsEFEovgAolFcIHEIrhAYhFcILEILpBYBBdILIILJBbBBRKL4AKJRXCBxCK4QGIRXCCxCC78P1SJacMHjFfBAAAAAElFTkSuQmCC\"" alt=\""Mol\""/></td>\n"",
       ""      <td>546</td>\n"",
       ""      <td>544</td>\n"",
       ""      <td>0.941176</td>\n"",
       ""      <td>16.0</td>\n"",
       ""      <td>17.0</td>\n"",
       ""      <td>0.698795</td>\n"",
       ""      <td>0.914286</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>7</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>548</td>\n"",
       ""      <td>546</td>\n"",
       ""      <td>0.941176</td>\n"",
       ""      <td>17.0</td>\n"",
       ""      <td>16.0</td>\n"",
       ""      <td>0.697674</td>\n"",
       ""      <td>0.971831</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>544</td>\n"",
       ""      <td>546</td>\n"",
       ""      <td>0.941176</td>\n"",
       ""      <td>17.0</td>\n"",
       ""      <td>16.0</td>\n"",
       ""      <td>0.698795</td>\n"",
       ""      <td>0.914286</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>5</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAY30lEQVR4nO2deVhTVxbAT0IkLAYEQRah7aBQtVZBRVwHUGxdsJtCO84gX+tIq9MvWOvCJ1XoqJWZaRXtwGdq25Fp/WYKxW3cFRwrKu6IIyCVatmKC0GRRRKSM3/cGFNElrz3khDP769n8nJywR/33bycc48IEYEg+EZs7gEQ1gmJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJRQgCiUUIAolFCAKJZQYuX4azZ3XHBw/qDqqqzDUcQSCxzMDu3TB9OiiVAABr1gAAlJXBwIEQHQ0//2zeofEGiWUeZs+GpUsBANjewmfOAABkZcELL8Dq1dDcbM6x8QKJZR5eeQXq6+GHHwAAcnPhyy9hxw6IioLGRli1CubMOfLdd9/16A2tRT169D2UtWthxAgIDIRZs0CrBakUfvgBJBJ4912YNg3+/veG3FwvlaphwoQJqampI0eONPd4jYFmLLPh5QWxsXD7NixZAu+/DwCQlgZz58LUqQ6ff77e09MzLy9v9OjRH3zwgblHahRImA+lEt3cEAAjI/HwYZwxAwEwKEgZEBCQlZWVlJRkZ2e3fv16cw/TGEgsc9LUhKtXo6MjAqCdHX70kSYr6/7kyXL2Nz99+vRDhw61tLSYe5jGQGKZn6oqjItDsRh/+9tLXl5e6enpf/vb3/r06QMAvXr1io+PVyqVvLxRSUlJdXV1Q0MDL9E6hsSyFE6fxilT3mJzVXBw8P79++VyuUQiCQwMdHV1TU1NbW1tNTp4dXV1XFycWCwODg7u379/RkYGjyNvFxLLgtBqtd9++62Pjw+bqyorKy9duvS73/2O2RYYGHjs2LHuxnzw4MG6det69+4NAFKp9JlnnmHRpk6dWlpaKsRPwSCxLI7GxsZVq1YtW7ZM/8ju3bv9/PyYEJGRkdevX+9iqN27dw8YMED/wmvXrmk0moyMDDc3N+auXC6/f/++ED8FidUzaGlpSU1NlclkAGBvb798+fKOhSguLp46dSpTatCgQZmZmfHx8Vu2bGHP1tbWyuVysVgMAD4+PkJcGUmsnkRVVVVMTIxIJAIAtlTSarVtzmHSSCQSAHBxcVm/fn1aWhqbojw9PR88eKA/89y5c6NHj2byTZ48ubi4mMehklg9j9OnT48ZM4YJERoaWlBQwB5Xq9UKhcLd3R0AJBJJXFzcjh07hg8fzs4MCwvTn6mHXRn79u0LALa2tsuXL29ubuZlkCRWj4QJ4eHhAQBisTgmJiY7O/vFF19kDk2aNOnQoUMxMTHsn76+vh1f7AyvjH5+fnv27OE+QhKrB1NXV7do0aJevXqxT3wAMHDgwMzMTHbLHgAcHBySkpK6OAkdP3582LBhbLYrKyvjODYSq8dTVFTk7e0NAO+8805LS0tERAQAiESimJiYqqqqboVSq9UrVqyws7MbM2YMx1GRWNbAzJkzAWD37t2ImJOTM2rUqLy8PONClZaWAoC/vz/HIUk4fYNNWAa2trYAoFKpAGDSpElnzpxhnxy7RXJyclFR0YIFCwBArVZzHBKlzVgDhmIBgBFWAUBubm5WVlZdXR2QWASjjVjGwW59ISIAtLa2chwSiWUN8CIW+3TJoBmLAOBVLDZjkVgEAIlFCASPYmm1WiCxCAaPi3eNRiMSiTQaDTPMaEgsa4BNNhynGRaktbWVGcbxgyGJZQ3weClUq9W8aEpiWQNtxKqvrzciyMCBAyMiIjw8PPRTF5chkVjWAFOBiVVRUfHss8/Gx8d3V6+EhITDhw9HRkbSjEXocHNz8/Pz27lz54kTJ/bt21dfX79p06bBgwdv27YNu7mFwq5dux48eCCVSrdu3cpp/c7xS2zCQpg1axYAiESiuXPnHjx4cNy4cez/Nzg4+PTp012JcPbs2dDQUPYq9m3jiBEjTpw4Ydx4SCwrobGxUZ/f5+jouGrVqq+++sowxfTWrVtPem1FRQWrOgQAV1fXlJSU7Ozs5557jhkWFRVVXl7e3fGQWFZFeXm5PiPZ398/MzPzgw8+YGsmV1fX9PT0NsUX9+/f1+toa2srl8vr6urYU01NTYamJiUlGRZidAqJZYUcOXLkhRdeYHpFRkYeOXJk2rRpABAREaE/R6VSKRQKNqWxaemnn356PFQbU/fu3dvFMZBY1olKpUpNTXVyctJPRdu2bSspKWHPHj58WG/e2LFjO11I5eTkGJralYx4Esua0W/ZAA/rEM+cOaNfoQcEBGRmZnYx1OOm1tfXd3A+iWX9nDhxYsSIEYYf9/r165eenq5Wq7sbqrq6Wl8xO3ToUI1G86QzSaynAo1Go1AonJ2dHR0d4+Li7t69yyXa2bNnBw0aFBAQsGnTpiedQ2I9RXh5eQFAdXU191CbN28GgLi4uCedQFU6TxHcv6vRarWbN29uampiVfkdfO1NYj1FsHwYjl8C/ulPfxKLxf/85z87DkXfFT5FcE9bEIvFNjY2Wq3WxsYGOpyxSKynCF7SFliKDruFQZdCAgBAIrkMABzT2W1tbZubmzsVi2aspwhWOMixFpXNWOxWFolFADwUi/uMBQ/FosU7AQAgkQBwnrEMC6ZpxiIABJixSCwCAMDXF/z8wNaWUxASi/gVly/DwoVQVgYTJz7qF2wEbm5uLi4uJBah4/F+wcZx9OhRpVLJdiulxTsB8Fi/YC6cP38eaMZ6ymlqgtu3AX7dL/jePSOjXb9+ffbs2VFRURKJJDs7+0mnkVjWDKKugTnr4AoAmzZBQgKoVBAUBDNnQkVFN6I1NTUlJycPGTIkOzvbwcEhMTFR352g3fcmrJNTpzAkBAEQAIODMSkJ9+1DRNy8GQMC0MEBAdDJCTdswE4zSbVabWZmJuscxiovbty40fFLSCwrpKKiYv78ByIRAqC3N27dio+nEF+/jq++qtNu2DA8efKJvTbPnTs3fvx4Ng2NHDmyixt9k1hWRWNjY0pKikwmCw3NtrVFuRzv3evo/CNHcNAglMnueXh4x8TE1NTUGD5rWIvh5eWlUCg6SHJvA4llJWi12m3btvn6+rKr1Zw5sTdutG0M1i7NzZiaut/e3h4A+vTps2nTptbWVtbFzrAm517Hhj4GiWUNaLXasLAwdrUyri1FWVlZZGQki+Dv788EBYA33njDuL46JJY18K9//SsoKKhv374KhYJL62jW2dDZ2VkikXh6erLNaoyDbjdYA3l5eRcvXlyxYkVcXBxLGjYOsVh8586dKVOmDB06tKampn///saHMvqVhOXANrJi6yQu6DeccXFx0Yc1MhTHoRCWACKCsS10DGFiabVa/YHxoTgOhbAESCxCECxQLKrSsQaYAcwGLrzi4FAXGAgyGWi1toMHc+kgQGJZA3zNWK4qFRQUwNChUFcHxcVc0uPpUmgN8CUWsDlPqwUWikPeFollDfB1KXzkk94wYyGxrAH+ZyzOYtEayxrgTSwnJ/DzAzc30GrBzw843HElsawB3sSaNg3KyngYEIllRlhnQB4WRjyusQCgpAT27wdHR3j9dXB3NzoMrbHMwN27dxMSEsaNGzdq1Kjk5GSO7eDu3btXUFAAvMxYhYXw5psgk8HNmxAWBg8eGB/K6LwIwgjUanVaWhrbZ1E/wQwbNuzkyZNGRNNoNBkZGawJQGxs7P3797mOb8UK/OYb3fGCBbh/v9GRSCzTkZOTw+o8ASA8PLygoODAgQMDBgxgksXFxSmVdd2Kpi+SGTNmzKlTp3gY4uuv45UruuMNG/CLL4yORGKZgh9//DEqKopJMHDgQMNt+1nLGqlUKpM5DRpUqVCgtrOM4tJS/MMfaliXGz8/v++//563gSoU+OmniIhqNb70ElZWGh2JxBIW1gVJKpXCw1ZHzc3Nj59WVFT09ts7WM3M5MlYWtp+tLo6XLwYbW0RAF9++ZOUlJRuNU7qnNpaDAnB8ePR3x8TErhEIrGEwnABxBq7/fLLLx2cr9ViRga6uyMA9uqFy5ejoYEaDWZkYL9+CIBiMcbEYIfBuPHLL9jYyDEGiSUIR48e1S+AQkJC8vPzu/hCpRLlchSL0cMD8/LwzBnd4+npKBYjAIaH48WLQg2bR0gs/vn888+ZUr/5zW+ysrKMiHDsGO7Zg2vWoJsb1tYiIk6YgCtX4vbtPA9VOETIfecRwoDKysrY2Nj8/PylS5cmJCSwJbZxrF0LlZWgUsFXX8GECZCXx+MwBYdukPJMc3Nzbm6ut7d3cnIyF6sYhvvD9CzoKx2eYTspcryZbsimTTBrFpc8A/NAMxbPsDsLLS0tvEQrL4e8PJgyhetWx6aHxOIZJhYvM1ZiItTWQnQ0qNVw7hz3eCaFxOIZfmcsqRQAgL/rqukwnVhXrsCNG7rjPXtM9ramhq2x+BKLbZ3NUzCTYjqx9u6F/Hzd8cqVJntbUyORSGxsbDQajUaj4R6t585YJv1U+PPPcOmSKd/QPLD+WCqVivtmCkysnjhjmVSsU6fg7l0APraDtmSkUmlzc3NLSwt3sZycLk+ceKlvXxeAGbyMzWSYVKy33oK33gIAOHDAlG9ranhcZiH+ePx4jJvb6z1OLPN8KuSeQ2vJ8HjHgcdQJqYzsUpKYMMG+OIL3Rb0HJg2DQYM0B2vW8cp1OXLcPas7phLWxiB4PGOA783L0xJh2LxmFrPXy8XfkMJAY+XQisV67vvYOlS+OMfYeVKCA2F//4Xamvh/Hmj34zHXi48hiotLY2MjExOTp4yZcqVK1e4hgNwd3e3t7ffunUrl37xjMLCQuiZYnWYj/V4av2CBSgSdTd/sbYWq6txzRrctw9nz8Zjx3D8eCwu7jy5u11qatqGqqvD8nJjQtXV1S1atIi1DO3duzcA2NraLlmypLtbTxuMrWbRokVFRUUsZlBQ0Bl9ql430afJOzs7FxYWGhfEjHQoVpvU+tJSTEjQZVw7OeFf/oKdJVyrVKhQoLs7RkfrbKiuxrFjccQIdHbGUaPwhx+6MdY7d1AuR0dH/PDDR6FCQnDJEmSb5dd1ucjl8bzhoqKi+fPns5IsT0/Pf/zjHxpNl8TXarG+XpORkTF16tQ5c+akpaWp1erc3Nznn39eH7yWZet1jYaGhqSkJJZy00GavIXToVhtUutfegkjInDvXnzlFdYr487LL+/cufNJr96xA/39dU01pkzB5ORHvVyefx69vREARSKMjsbr1zsZZUsL/vWv6OSEACiVYnT0o1AjR2JsLLL2Hn374oYN2NLSSbT8/PyQkBA2YYeFhRUUFOifunDhwsSJEwEgKGjyyJHY8X7pxcVYUIDjx2NUVKuvr6+jo+OlS5f0z+rLb5ipGRkZnQzroe6enp4AIBKJOk2Tt2S6kJrMUuvLytDZWZfov3gx7tqFw4fHDhkCAOHh4Ya/UEQ8f/78m2+WMKUGD8Y9e9qJ2tCAq1bpWgXZ2WFKStmTLkD79xfoBX3tNSwpaeecM2cwLEx3TkCA9t//3q5t70JbWVkZExPDKoZ9fHza/c9mLR7GjbvCvJ87F6ur2448PR1TU3W/CUdHFIkwPb2wsr1iqatXr06aNIlJHB4eXtLu6BER8ejRo4GBgezMkJAQfuoEzUd3ct5v3sT583U5/e7uN7Zu3bhxo6urKwBIJJKFCxfeuXOnqqqKbTUeFDTJ1RVTUjqZPyorMS4Oe/dGb++Qvn37pqamqg1aUZWUlEyfPh0Ahg+/+/zzmJ2NSUkYGIgqVfvRDh/G4cMxNLQAAEaOHJmTk6N/qqmpKSUlhS2kHBwckpKSmpqaOhhYQwMmJqKdHQKgTIYpKXjxoq60YdQoBMCPP0aRCO3tMT4ep017YsEWImq12oyMDDc3NwCwt7dPSkpqU7NVXl7eRvd2/yp6Ft0vprh4EUNDtb17B3p4DB48OCsr6/3335dIJAAwYcIER0dHAJBKpUuWLLl7t7NuZQ+5cKFywoQJ7I916NChBw8eVCqVcrmcLYFdXFwUil0KhZYVP9nYoIEwbWltxa1bs/Q730+bNq2wsHD37t3PPfcceyQyMrLTlmh6yssxJgYBcPx4XL1aV9rw9dcIgD4+OHs2AuDKlV0KVVtbGxcXx+wJCAhg0hsup7qiew/CyCqd4l279P9Vs2fPPnjwoI+Pj/6fxnVfycrK8vPzY0GYoDY2NgsWLDh48GBISMjEiSUAGBqKBiuiJ9LY2Lh27VrWY0i/RcKoUaNOnDhhxMCOHMHCQlyzBt97D995BzUa7N0bAXDxYvzmm+59ts3JyQkICGBLqPDwcP2nh9jY2KqqKiPGZrEYX/7V1NS0evVqZkCvXr3c3d29vb2PHz/OZTQqlSo1NdXe3t7b2zs4OPjw4cPR0dFMi8DAlwwK07tEbW3twoULXVxcnJ2dU1NTuTSZQfzVPY4XX0RXV9y82Zg4KpUqJSXFzs7u2WefBYDg4GDjdLdwuNYVskUV+x3NmzePlzGFhoYCQG5ubmVlpaOjo4ODw/Lly43bSqW0tBQA/P39uY/K8HZJSAhy3Nnl6tWrp0+f3r69/Q8ZVgDXL6G9vb0VCkViYiIAcL/RzEBEALCxsenfv/+3335bUlKiX3d3neXLl0dHR5eXlwNfO5IBAICXF8TGQmsrdHM4bQkICIiPjw8JCeFhUyuLhJ+0GZZ4xEvOJPx6f7rXXnvNuCAHDhwoLCycM2cO8CRWYqLu4N134d13uccDmUxmrVYBX2kzrJWZEGJxDMLg0mlND785+4WFhUqlMjMzk2sgS4UfsdjtBr4uhbyLxcuMxW/OfkNDw/nz56uqqrgGslT4uRTyO2OxOBynGRZEv1zjZWA85uyPPH/+9htv2N+6xU84y8MSxbLMGQt4zdmXHjsm3b4devWCrVs5j8sS4VMsi7oUGlrO14zFW85+UxMMHgxxcSASwbVrMHAgH6OzLPgRi62xrH7G0sP1w9zt248yX4cNI7GeiBBrLEubsZYte3R84QK3WCIRvPqq7tjTk1ssC8USxWKTDUcbWBC2eOd9xuLKZ5/Brl2646IieOMNs45GEPi8FFrUGmvevHk1NTUsz46vNRZvODiAi4vu2NnZrEMRCsudsZhYaWlpO3fu3Lhx45AhQ7oV5OOPPwaA3NxcsMAZa906iI191LLGGuHnNy6VSvv06XP16tV8/T1EY7lw4UJNTQ0A/Oc//2ltbf3ss8+OHDkSFBS0bNmy+vr6boWqqan59NNPPTw8WPq5BcFrXZ2Fwte32cnJyQAgEonefvvtmpoaIyLU1ta+9957bPJjc8yYMWMOHTokl8vZgyzFtCvZL2q1euPGjc7OzgAgk8mUSqUR4xEQ/lrWWCy8icVqB1gypLOzc0pKSkunVQ0PYUUE7u7uACCRSORyeUZGxjPPPMNMjYqK2rt3L6txAICgoKBjx451EO3s2bPBwcHs5MjIyJ9++omPn48P7t/HFStw1SoeW9ZYLDzv837t2jVWDTesX7/WESPwwIHOX3PypHrixGH9+gFAREREUVERe/jxtN3vv//eMMP4+mPFPbW1tXK5nM12Pj4+md3NDBQOjQa//BK9vHSlI599xlfLGotFkAYCe/fuLXv1VV3RTHQ0VlS0f15Njb5068fIyHb32meFBkwmX1/fLVu2JCUlOTg4AMDMmTP1p7E5jxUs2NrayuXyhoYGIX40I1AdPYqBgbrfxtixmJ/PY8sai0WwzhQPHuAnn6CjIwKgoyN+8smvamtaW3HDBl09mZ0dfvRRx81bDDuIhIWFHThwYM6cOf/73//Ys+fOndPXCU6ePLm4uFioH6qbsGrm1HHjdNUXGRm/ypDno2WNxSJwy5OqKoyJQZEIX3gBVSosLsb161GhwJs3MTQUATAiArvmAZuT+vXrBw/Li2/evMmKedjSvn///l0pCjUNdXV1ixcvZruDOMlkd9evR2spv+kiJumlc/QonjqFly7hsGG4ZQv++c84ZAieO9d+JWuHKJXK+Ph4VhYmk8lkMhkASKXSxMTERov569++fTu7KIvF4nnz5lW3KXh9OjBhkyb+PmNfvXp1xowZLi4uvr6+4eHhV/SfsCyD/Px8kUgUFhZ24cIFc4/FbJhwq8jiYvj973XHAQFQUWF0pICAgD179lRUVNjY2Hh7e/MzPP5gfeRGjx5t7oGYExN2//riC7h/Hz78EFpbYcYM+PpreFivTFgfJhRLqYTp00EigVu3YNYsrttFEpaNyfsV1tSAkxM4OJj0TQmTQ40wCUGwsHwSwlogsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhBILEIQSCxCEEgsQhB+D9qlCPTBNNeMgAAAABJRU5ErkJggg==\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>546</td>\n"",
       ""      <td>548</td>\n"",
       ""      <td>0.941176</td>\n"",
       ""      <td>16.0</td>\n"",
       ""      <td>17.0</td>\n"",
       ""      <td>0.697674</td>\n"",
       ""      <td>0.971831</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>9</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>557</td>\n"",
       ""      <td>555</td>\n"",
       ""      <td>0.894737</td>\n"",
       ""      <td>18.0</td>\n"",
       ""      <td>18.0</td>\n"",
       ""      <td>0.637931</td>\n"",
       ""      <td>0.821429</td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>8</th>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td>555</td>\n"",
       ""      <td>557</td>\n"",
       ""      <td>0.894737</td>\n"",
       ""      <td>18.0</td>\n"",
       ""      <td>18.0</td>\n"",
       ""      <td>0.637931</td>\n"",
       ""      <td>0.821429</td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""                                           Compound1  \\\n"",
       ""0  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""3  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""1  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""2  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""6  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""7  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""4  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""5  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""9  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""8  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""\n"",
       ""                                           Compound2 cid1 cid2  \\\n"",
       ""0  <img data-content=\""rdkit/molecule\"" src=\""data:i...  544  548   \n"",
       ""3  <img data-content=\""rdkit/molecule\"" src=\""data:i...  549  547   \n"",
       ""1  <img data-content=\""rdkit/molecule\"" src=\""data:i...  547  549   \n"",
       ""2  <img data-content=\""rdkit/molecule\"" src=\""data:i...  548  544   \n"",
       ""6  <img data-content=\""rdkit/molecule\"" src=\""data:i...  546  544   \n"",
       ""7  <img data-content=\""rdkit/molecule\"" src=\""data:i...  548  546   \n"",
       ""4  <img data-content=\""rdkit/molecule\"" src=\""data:i...  544  546   \n"",
       ""5  <img data-content=\""rdkit/molecule\"" src=\""data:i...  546  548   \n"",
       ""9  <img data-content=\""rdkit/molecule\"" src=\""data:i...  557  555   \n"",
       ""8  <img data-content=\""rdkit/molecule\"" src=\""data:i...  555  557   \n"",
       ""\n"",
       ""   m.similarity_score  n.fragment_degree  p.fragment_degree  \\\n"",
       ""0            1.000000               17.0               17.0   \n"",
       ""3            1.000000               15.0               15.0   \n"",
       ""1            1.000000               15.0               15.0   \n"",
       ""2            1.000000               17.0               17.0   \n"",
       ""6            0.941176               16.0               17.0   \n"",
       ""7            0.941176               17.0               16.0   \n"",
       ""4            0.941176               17.0               16.0   \n"",
       ""5            0.941176               16.0               17.0   \n"",
       ""9            0.894737               18.0               18.0   \n"",
       ""8            0.894737               18.0               18.0   \n"",
       ""\n"",
       ""   morgan_1024_r2_sim  maccs_sim  \n"",
       ""0            0.505263   0.887324  \n"",
       ""3            0.697674   0.971831  \n"",
       ""1            0.697674   0.971831  \n"",
       ""2            0.505263   0.887324  \n"",
       ""6            0.698795   0.914286  \n"",
       ""7            0.697674   0.971831  \n"",
       ""4            0.698795   0.914286  \n"",
       ""5            0.697674   0.971831  \n"",
       ""9            0.637931   0.821429  \n"",
       ""8            0.637931   0.821429  ""
      ]
     },
     ""execution_count"": 17,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""node_sim_df.sort_values(by=['m.similarity_score'], ascending=False).head(10)""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### 4a) Pagerank\n"",
    ""##### Find me fragments which are important, based on their place in the fragment network, weighted by avg MMP change - maybe these are the ones that get changed the most frequently?""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 15,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""pagerank_w = graph_1.run(\""\""\""\n"",
    ""                CALL gds.pageRank.write('fraggraph', {relationshipWeightProperty:'avg', writeProperty:'fragment_pageRank'})\n"",
    ""                \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 16,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""pagerank_uw = graph_1.run(\""\""\""\n"",
    ""                CALL gds.pageRank.write('fraggraph', {writeProperty:'fragment_pageRank_unweighted'})\n"",
    ""                \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 17,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""page_data = graph_1.run(\""\""\""\n"",
    ""                MATCH (n:Fragment)\n"",
    ""                WHERE EXISTS(n.fragment_pageRank)\n"",
    ""                RETURN n.smiles as smiles, n.fragment_pageRank as fragment_pageRank, n.fragment_pageRank_unweighted, n.compound_degree, n.frag_frag_degree,\n"",
    ""                       n.unique_fragment_count\n"",
    ""                ORDER by n.fragment_pageRank DESC\n"",
    ""                \""\""\"").data()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 18,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#Convert to pandas\n"",
    ""page_df = pd.DataFrame(page_data)\n"",
    ""#Add RDKIT molecules\n"",
    ""PandasTools.AddMoleculeColumnToFrame(page_df, 'smiles','Fragment')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 19,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>fragment_pageRank</th>\n"",
       ""      <th>n.compound_degree</th>\n"",
       ""      <th>n.frag_frag_degree</th>\n"",
       ""      <th>n.fragment_pageRank_unweighted</th>\n"",
       ""      <th>n.unique_fragment_count</th>\n"",
       ""      <th>smiles</th>\n"",
       ""      <th>Fragment</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td>4.979573</td>\n"",
       ""      <td>221.0</td>\n"",
       ""      <td>2372.0</td>\n"",
       ""      <td>11.085179</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>[*:1][H]</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td>3.609844</td>\n"",
       ""      <td>68.0</td>\n"",
       ""      <td>1749.0</td>\n"",
       ""      <td>4.971513</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>[*:1]c1ccccc1</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td>3.092455</td>\n"",
       ""      <td>26.0</td>\n"",
       ""      <td>613.0</td>\n"",
       ""      <td>3.544064</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>[*:1]c1ccccc1[*:2]</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td>2.978492</td>\n"",
       ""      <td>1.0</td>\n"",
       ""      <td>24.0</td>\n"",
       ""      <td>1.230628</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>[*:1]c1ccccc1N([*:2])C</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td>2.911339</td>\n"",
       ""      <td>1.0</td>\n"",
       ""      <td>114.0</td>\n"",
       ""      <td>0.766771</td>\n"",
       ""      <td>None</td>\n"",
       ""      <td>[*:1]c1ccccc1N(C)C</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""   fragment_pageRank  n.compound_degree  n.frag_frag_degree  \\\n"",
       ""0           4.979573              221.0              2372.0   \n"",
       ""1           3.609844               68.0              1749.0   \n"",
       ""2           3.092455               26.0               613.0   \n"",
       ""3           2.978492                1.0                24.0   \n"",
       ""4           2.911339                1.0               114.0   \n"",
       ""\n"",
       ""   n.fragment_pageRank_unweighted n.unique_fragment_count  \\\n"",
       ""0                       11.085179                    None   \n"",
       ""1                        4.971513                    None   \n"",
       ""2                        3.544064                    None   \n"",
       ""3                        1.230628                    None   \n"",
       ""4                        0.766771                    None   \n"",
       ""\n"",
       ""                   smiles                                           Fragment  \n"",
       ""0                [*:1][H]  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""1           [*:1]c1ccccc1  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""2      [*:1]c1ccccc1[*:2]  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""3  [*:1]c1ccccc1N([*:2])C  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""4      [*:1]c1ccccc1N(C)C  <img data-content=\""rdkit/molecule\"" src=\""data:i...  ""
      ]
     },
     ""execution_count"": 19,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""page_df.head()""
   ]
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### 5) Link prediction\n"",
    ""\n"",
    ""* It's not useful to predict links between compounds here... i.e. which compounds will be MMP of each other... compounds are either MMP or they are not...\n"",
    ""\n"",
    ""* Instead what is more useful is to predict whether FRAGMENTS will be related (i.e. will changing one fragment for another change a property I am interested in?)\n"",
    ""\n"",
    ""##### We can use Adamic Adar for this\n"",
    ""* Adamic Adar is a measure used to compute the closeness of nodes based on their shared neighbors.\n"",
    ""* This will tell us the likelyhood of a relationship between two fragments - \""if you have this in your molecule have you tried this?\""\n"",
    ""* What it does not tell us is the size of that property or even what property... to do this we'll need more advanced algos that can account for more features of nodes and relationships.""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 23,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""#Fetch data\n"",
    ""aa_data = graph_1.run(\""\""\""MATCH (f1:Fragment), (f2:Fragment)\n"",
    ""                               WHERE NOT (f1)-[:MMP_RULE_ENVIRONMENT]-(f2)\n"",
    ""                               AND f1.compound_degree > 10\n"",
    ""                               AND f2.compound_degree > 10\n"",
    ""                               RETURN gds.alpha.linkprediction.adamicAdar(f1, f2) AS score, \n"",
    ""                                      f1.smiles, \n"",
    ""                                      f2.smiles, \n"",
    ""                                      f1.compound_degree\n"",
    ""                               ORDER BY score DESC\""\""\"").data()\n"",
    ""#Convert to pandas\n"",
    ""aa_df = pd.DataFrame(aa_data)\n"",
    ""#Add RDKIT molecules\n"",
    ""PandasTools.AddMoleculeColumnToFrame(aa_df, 'f1.smiles','Fragment1')\n"",
    ""PandasTools.AddMoleculeColumnToFrame(aa_df, 'f2.smiles','Fragment2')""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 24,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""<div>\n"",
       ""<style scoped>\n"",
       ""    .dataframe tbody tr th:only-of-type {\n"",
       ""        vertical-align: middle;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe tbody tr th {\n"",
       ""        vertical-align: top;\n"",
       ""    }\n"",
       ""\n"",
       ""    .dataframe thead th {\n"",
       ""        text-align: right;\n"",
       ""    }\n"",
       ""</style>\n"",
       ""<table border=\""1\"" class=\""dataframe\"">\n"",
       ""  <thead>\n"",
       ""    <tr style=\""text-align: right;\"">\n"",
       ""      <th></th>\n"",
       ""      <th>f1.compound_degree</th>\n"",
       ""      <th>f1.smiles</th>\n"",
       ""      <th>f2.smiles</th>\n"",
       ""      <th>score</th>\n"",
       ""      <th>Fragment1</th>\n"",
       ""      <th>Fragment2</th>\n"",
       ""    </tr>\n"",
       ""  </thead>\n"",
       ""  <tbody>\n"",
       ""    <tr>\n"",
       ""      <th>0</th>\n"",
       ""      <td>29.0</td>\n"",
       ""      <td>[*:1]C([*:2])C</td>\n"",
       ""      <td>[*:1]C</td>\n"",
       ""      <td>6.190237</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAEwklEQVR4nO3az0tUexzH4TOTSLmqiDRnYS3t1yIJBGnRKlxErVu46e+I/odoV9BOWgWBEAVBrSLBFhkxYIsK0poKSWtchNpC8IY1Y9eZ99W5Pc/qy5kzZz6DL86RM6e0urpaQLuVt3sA/p+ERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIuIrj/cb3x8fGZmpiiKK1eulMtyZBN/GlbU/Pz8tWvX1tbnz58/derU9s5D65qde96+fbuystLo1Y8fP379+rUtQ0xPT7flOOwcDc9Ys7Ozt27d6unpOX78+MmTJ9e3z8/Pv3z58sWLF7VabXh4+Ny5cy1OUK/Xnz171uJB2GkahvX8+fOiKOr1+uTk5OTk5Pr269evr6+np6dbDKter4+Pj3/58qWVg7ADNQxrYGCgVqu9efOm0dVw//79R48e3cJHLi4ufv78+dOnTzMzM69evWpytaVzNQxrcHBwcHBwaWmpWq1OTEz8/Oc/ffr00NBQb2/vFj5vampqYmJiK5PSUTa5cbBnz56enp4NJ5XFxcWtVcXfY5PbDQsLC3fv3l1bVyqVubm5lZWVarU6NTU1NDT06/43btyYnZ1dWx85cmRsbKy949IpmoW1urp6586dpaWloij27t07Njb25MmTR48eFUVx//79gYGBAwcO/PqW367XnThxor+/f8PG27dvLywsNJnk3bt3N2/ebPY9OsfTp0/v3bu33VO0wcGDBz98+NDo1WZhLS8vf//+fW194cKF7u7uM2fOVKvV9+/fl0qlb9++/RrWprq7uw8dOrRh4+7du5uHRcdpFlZXV9fly5cfP35cKpUOHz5cFEW5XL548eLDhw9HR0f37dv3H81IB9rkf6xyuXz27Nmft/T29l66dKnR/seOHevr61vfs/X56FBt/q1wZGSkvQekQ3lOgYgd8XTDpiqVytWrV7d7Cv4FZywihEWEsIgQFhHCIkJYRAiLCGER0eYbpA8ePKjVamvrSqWy4XdG/h5tDuv169dzc3Nr6+Xl5fYenA7iUkiEsIhoc1hdXf9cW3ft2rWFd/28pnOVfvtkOrTIpZAIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYRHxAym/7wQiks8ZAAAAAElFTkSuQmCC\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>1</th>\n"",
       ""      <td>192.0</td>\n"",
       ""      <td>[*:1]C</td>\n"",
       ""      <td>[*:1]C([*:2])C</td>\n"",
       ""      <td>6.190237</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>2</th>\n"",
       ""      <td>70.0</td>\n"",
       ""      <td>[*:1]OC</td>\n"",
       ""      <td>[*:1]O[*:2]</td>\n"",
       ""      <td>5.591355</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>3</th>\n"",
       ""      <td>33.0</td>\n"",
       ""      <td>[*:1]O[*:2]</td>\n"",
       ""      <td>[*:1]OC</td>\n"",
       ""      <td>5.591355</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>4</th>\n"",
       ""      <td>84.0</td>\n"",
       ""      <td>[*:1]F</td>\n"",
       ""      <td>[*:1]c1ccc(F)cc1</td>\n"",
       ""      <td>5.520216</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>5</th>\n"",
       ""      <td>18.0</td>\n"",
       ""      <td>[*:1]c1ccc(F)cc1</td>\n"",
       ""      <td>[*:1]F</td>\n"",
       ""      <td>5.520216</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>6</th>\n"",
       ""      <td>45.0</td>\n"",
       ""      <td>[*:1]CC</td>\n"",
       ""      <td>[*:1]OCC</td>\n"",
       ""      <td>5.192471</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>7</th>\n"",
       ""      <td>18.0</td>\n"",
       ""      <td>[*:1]OCC</td>\n"",
       ""      <td>[*:1]CC</td>\n"",
       ""      <td>5.192471</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAANNUlEQVR4nO3de0xTdx/H8W+hoNxEZOpQ0Q10XBScl8U5LzCFhDnYnBsmi5FlZMHsEqjLNhZdBBfjcNkTW/wH8Mncgn+YkmWbN7YIc4IYcQgCMi7DDoQVUVcZCJZa+D1/tA9FvBU437Z0n1f8o9Zz+RLe9vSUniITQhCA1FzsPQA4J4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC7m9BwAWer3+m2++IaLk5GR3d3fbD4CwnJMQorOz03RjnJv69ddfz5w5Q0RJSUlPP/20lWvhUOhUrly5UlVV1d/fP/xOvV5fVVV15cqVsW2ztrZ2DGvhEcuplJSUXL169eTJk0MPLQUFBS0tLQMDA3PmzAkODh7tBouLi3U63RgmQVhOxdfXd9KkSf39/X/88YfpHtMDlbu7+9SpU63cyJ07d27evNnR0XH58uW2traxTYKwnMqmTZsGBgaamprUavXQna+//npoaKhcbtX3urOzMycnZ/yTICxn4+rq2traOvyelpaWRYsWjVisrq7O9Oze09Pz+eefl3wMhOVsmpqaysvLiei5557z8PAoKSm5ePHi/PnzQ0NDhy926dKl5uZmIvL39x8elre3d1RU1NBfBwYGzp49O4YxJnBYer1eJpO5u7vLZDJ7z+JAtFotEU2bNi02NtbV1bWxsbGzs1Or1Y4I62G8vLyio6OH/mo0Gv9FYQ0ODh4+fDg9PT0qKkqj0ahUqpUrV9p7KEcRHR09e/ZsLy8vNzc3Itq0aZNOp7OyKglNvLBKSkoUCkVVVRURnTx5sqenZ/Xq1W+99dbevXuffPJJe0/nEBYsWDB0e8aMGTNmzLh/mY0bN/b29hKRq6srxwwT6QXS9vb2pKSk6OjoqqqqOXPmfPvtt1qtNiMjw93d/dChQ/Pnz8/MzNTr9fYec2Lw8vIyNefv78+x/YkRVl9fX2Zm5jPPPJOfn+/h4ZGenl5fX5+UlOTt7Z2ZmXn58uXExMTe3t7du3dHREQUFBTYe15w+LCEEAUFBeHh4bt379br9YmJib///ntWVpa3t/fQMsHBwWq1uri4OCIiorm5efPmzevXrx/bDyJAKg4dVkVFxZo1azZv3tza2rps2bKSkhK1Wj1v3rwHLrxu3brKysrc3Nzp06f/8ssvS5cu3bZt240bN2w8M5g4aFharXbbtm0rVqwoKysLCAjIzc29cOHC6tWrH72WXC5PSUlpbGxMTU0lory8vJCQEJVKZTQabTI1WDhcWAaDQaVShYWF5eXlyeXy1NTUhoaGlJQUFxdrR/Xz81OpVLW1tXFxcbdu3VIoFBERET/99BPr2DCCY4V17NixsLAwhULR3d0dHx9fX1+vUqmmTJkyhk2FhoYWFhYePXo0KCiooaHhpZdeSkhI0Gg0ks8MD+QoYdXX18fFxb3yyisajSYsLKywsPDYsWNBQUHj3GxCQkJ9fb1SqZwyZcrx48fDwsLS0tK6u7slmRkewf5h6XS6tLS0iIiIn3/+edq0aUql0nQUk2r77u7uaWlppuOp0WjMzs4ODQ3Ny8sbHByUahcTUVlZ2ZEjR44cOXLixAmO7dszrLt376pUquDg4OzsbBcXF9Pz7rS0NI7Xgk1nAOXl5atWrero6DCdGZw7d07yHdled3f3999/b/rBs/VaWloaGxsbGxv//PNPjqnsFlZRUdGSJUsUCkVXV1dMTIzplYInnniCdafLly8vLS1Vq9Vz586tqKj4YedO2ryZrl5l3Skfg8FQVFSUnZ1dU1Nz7tw5h3oMtkNYTU1NCQkJsbGxdXV1CxYsUKvVp06duv8NQ0xkMlliYmJdXV3mZ5/trKujggJauJCysuje94k7OCFEdXX1gQMHysrKBgYGwsPDk5OTrT9xJiIrF5bJZENLWvlWQfOK47+Kw3pdXV1ZWVn79+83GAxTp0799NNPFQrFpEmTbDbASG1ttHMn5ecTEQUG0p49lJRkt2GsptVqCwsL29vbiWjWrFlxcXGBgYH2HmokG4VleqPLxx9/fP36dRcXly1btnz11VcP/Km7HZw+TQoF1dQQEb34IimVFBlp75kerKenp6ioqKamhoh8fHzWr18fGRnpmG9Hs0VYp0+f3r59e3V1NRFFR0crlcrFixdz73R0Bgfp8GH66CO6cYNcXGjLFvrPf2j6dHuPZWE0GsvLy0tKSgwGg1wuX7Fixdq1a+1yJaqVeMNqa2vbuXNnfn4+EQUGBu7ZsyfJkY81t27Rvn20fz8ZDOTnR+nptH072f2bJ0RPcfF/a2tNL78tXLgwNjbW19fXzlM9luBx+/btjIyMyZMnE5Gnp2dGRsadO3eY9iWxhgaxYYMgEkQiJEScPGnPYS5eFGvWCJnshx07cnJyWlpa7DnMaEgf1uDgoOl8nv5/Ctba2ir5XtgdPSqCg815xceL5mZbD9DRIZKThYuLIBIzZ94pKBgcHLT1DOMgcVgXLlx44YUXTI+Fy5cvLysrk3b7NmUwCKVSTJkiiISbm0hNFf/8Y7v9+vpa9tvVZYv9SkqysNrb27du3Wo6Q5k1a1Zubu7AwIBUG7cnrVakpJgfOQICRG6uYP26Tp0SYWHmR8qYGFFXx7gvThKE1dfXl5WV5ePjQ0Sm9w13d3ePf7OOpaJCrFpl/n4vWybOnn3UwkajqKoSJ06Ir78Whw6J774TpaVCp3vMLkY8tztxQsLxbW+8YR09enTo8yfi4+M1Go0kYzmiwUGhVot58wSRkMlEYqK4/7ljQ4N4910xY4a5j+F/5HIRFSVycoTROHItnU6kpgq5XBAJPz+RlSX6+23zNfEZe1iVlZVr1641JbVkyZIzZ85IOJbj6ukRO3aIyZMFkXj7bcv9RqPYt898/6P/RESI8+fv2eYHH5jLe/99cfOmjb8gJmMJ6+bNm6mpqab3IPj7+yuVSuP9/wudm0Yj3nxTtLVZ7tm2bWRAbm4iMlKsWydmzhz5T15eorTUsu61a2LjRlFba/uvg8/owjIYDEql0vTqnJubW2pqatcEPGGR3t6993SzcqU4f17o9ZYFOjrEgQPCw8OyjI+PcOKnDaMK69SpU+Hh4aZjX0xMTN2EPWGRWEeH5Qjo6ioyMh7wLMqkpsZyxkck3nnHtoPalFVhNTQ0bNiwwZRUSEjI8ePHuceaSD75xNLK7t2PWfjGDfMLVKZj5UR86dg6jwlLp9Olp6ebftjp5+eXlZXVP/FPWKSk0wkfH3Mos2eL3t7Hr/Lll5YQ33uPf0T7eGhYd+/eNV38SUQuLi5bt27t7Oy05WQTQ06OpZJDh6xaRa8Xc+eaV1m0iHc8+3loWNXV1abzvpiYmFrnOmGR0ocfWsLq67N2reRk8yqenpzD2dND32waGRm5a9euxYsXv/rqqxK/ocKZDH3GdUAAeXhYu9bQxwz19VFnJ82cKf1g9vaodzHv2rXLZnNMVENXwI7qEshhn19FGo1ThmX/6wontqErfKz+lQ1ERH5+ltu9vVLO4zAQ1vgMJWIwjGKt4R+ePu6rvR0TwhqfoYPaqH6hSEuL+YZcTnPnSjuRg0BY4zO2sBobzTfmzaPRXKw3gSCs8RkKq6uLKiutWkWnox9/NN+eP59lKgeAsMZnyRLL7c8/t2qVgwepr898+403pB/JMdj0SmjntGYNDX3EflUVPfvsoxa+fZvCw81P3oOCqLERh0J4iC++sNx+7bVHHRD//pvWr7ecEu7a5axVEbFdV/jv8vLLlh/sTJ4sDh4U91+q1dBwz3tmwsMf+u4ap4BDoRQ0Glq3job/zi0vL1q4kBYtIl9fam6m336ja9cs/xoYSKWl9JCPf3YOCEsif/1F8fF06dLjl1y6lNRqGv0vO51Y8BxLIrNnU0UF5eQ86gXPp54ilYouXHD6qgiPWCyqq+n0adJq6fp1IqKZMykggKKi7nltwtkhLGCBQyGwQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrD4H5yDLWo3Tic9AAAAAElFTkSuQmCC\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>8</th>\n"",
       ""      <td>41.0</td>\n"",
       ""      <td>[*:1]c1ccc([*:2])cc1</td>\n"",
       ""      <td>[*:1][H]</td>\n"",
       ""      <td>4.082251</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAQ8klEQVR4nO3dbUxT1x8H8NNJUQqo5UEcDnDIUGEqMnFOZAY10ziyLG6+MiXORcySDZotDl9s612QBTfAopmmxIxhZmIQMTGKc8BABoPZKogKwgrlqSrPlAcrLe39vzh/bzqEPtCetrf9fd5Zzr33h/323nPPueVwaJpGANjaK44uALgmCBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASIgWIAICBYgAoIFiIBgASI8HF0AcIBbt25VVVV98803CxYsMNm4o6NDLpcrlUqVSuXn5xcYGBgfH7948WLjW0Gw3AhN03/88UdkZCReVndiYqK1tdXHxycqKmquTWQy2fXr15l/qlQqhULR1NT08ccfr1q1ysixOLB0r/vo6+uTSCTMO87hcGiaDg8PFwgEs7aXy+UXLlxACIWEhGzatInP5/f29t66dWtqasrX1/fzzz/39PSc61gQLPfy7NkzmUxWWVmJEPL19d23b19YWBiHw5m18c2bN+vr64OCgj799FMul4tfZNL24YcfbtiwYa4DwaXQvfB4vK6uLoRQQEDA4ODg1NQUk6rq6mq5XO7r67t//378Sk9PD0IoKiqKSRVCKCIiwsvLS61W9/X1GTkQ3BWaRafT3b17t7GxUavVOroWq3R3d3d0dMTGxh48eNDLy6u6upr5UX9/f09Pj1KpZF4ZGxtbuHDhihUrDPdA07ROp0MIGabtZXDGMq2qqkooFLa2tnp5eQUEBGRmZjKfadYJDQ09dOhQUFCQp6fngQMH/P39mR/5+Pjw+Xw/Pz/mlS+//PLlPUilUo1GgxAKDg42ciDoYxmjUCiOHj16+fJlhFBwcLCHh0d3dzdCaO/evbm5uatXr3Z0gXZF03RdXV15eTlN0yEhIZ988slcnTMEl8K5TE5OUhQVFRV1+fJlHo8nEona29vlcrlEIgkICCgtLV23bl1aWtro6KijK7UTpVJ57ty5srIymqaXLVv20UcfGUkVQgjR4L/0en1RUVFoaChCiMPh7N+/v6ury7DB0NBQamoqHlr08/MTi8XT09OOqtYO1Gr1lStXKIqiKCojI6O8vFyr1ZrcCoL1H7dv3966dSv+yG3atKmmpmaulg0NDe+++y5uGRMTc+vWLXvWaTc9PT25ubk4VRcvXhwcHDRzQwjW/ymVypSUlFdeeQUhFBwcLJFIdDqdya2uXr36+uuv43glJSV1dHTYoVS7GRoaysrKoigqOzu7tbXVom0hWPSzZ8+ysrJ8fX0RQp6enqmpqWNjY/Pb3MvLKz093aLNnVlxcTFFUSdPnpzHb+TuwbLVKae3t1cgEOD+7IoVKwoLC/V6vW1LtcbAwMDw8LDxNrdv375y5crNmzeZV7KzsymKkkqlqtk8f/7cyN7cd7ihoaFBKBTiEcKYmBixWLx9+3Yr9ymVStPS0urq6hBCcXFxeXl577zzjg1qtcLz589ramrq6+vDwsLmmhPEiouLHz58uGTJEqFQiBAaGRk5deqUkfbbtm3buXPnXD91x+GGoaGhtLS0uLi46upqf39/sVgsk8nMT5WRwfe4uLja2trCwsLly5dLpdL4+Pjk5OSnT5/aqHDL0DQtk8lOnz5dW1ur1+v9/Pz0er2R9vh0i3uZCCHDIfh5Ht59aDQasVi8ZMkShBCXy01NTR0dHbVoD0+ePAkNDc3KyjJ+IZiYmBCJRAsXLkQIeXt7i0Qi4+1tTqFQnD17Ft/N/frrr0+fPrXn0Wm36mOVlZUxDx7t2rXrwYMH89hJdnY23sOaNWtu3LhhvPG///7LTP5EREQUFRXNq3DLqFSqkpISHKnc3NzGxkY7HPRlbhGsR48e7d27F7/BkZGR165ds2ZvZWVl0dHR5ge0oqJi3bp1uP3OnTubmpqsOboRGo2msrLy+PHjFEVlZmZWVlaaM5JJiIsHa3h4OD09HT+Pxufzs7KypqamrN+tpZdUrVaL54IQQh4eHikpKQMDA9aXwdDr9Y2NjfgmjqKooqIiSy/xNueywdLpdIWFhYGBgbhDKhAI+vr6bHuIwcFBZm4H3wQYn9vBc0EeHh7oxVyQTc4ojx49ys/Px5E6d+6cUqm0fp/Wc81gVVRUrF+/Hl99EhMT7927R+5Yd+/eZeZ2Nm7caHJup6WlZc+ePeZ31IzAg2c8Hi8nJycnJ0cmkznP4JmrBcuwvxwSElJYWGif4169enXlypX4uElJSQqFwmT78PBwpn17e7tFh8MPX/B4PISQl5fX+fPnNRrN/KsnwHWChe/wFy1axNzhq9VqexaA53Z8fHzQi7md8fFxI+2npqbEYjH+HpVFU0kzQuycE5SuECy9Xo/HJBFCHA5HIBA8efLEUcVYOrdj0eT3nTt3EhISzL/sOhDrg/XPP/9s2bIF/1/HxcX9/fffjq6Ipv9b1ebNm+vq6oy3l0qlho/r1NbWzmgwMDBg0Y2Cw7E4WE4+74tvS4OCgpjbUuPD33M9YIiHNvAVc36zBQ7BymBNT0+LRCLcdcXPDU9OTjq6qNkZzu34+PiYnNsZGxs7duwYbu/r65ucnMx84fiDDz5oa2uzW+VWYmWwmpub4+LizLz/cgZtbW3Mveobb7xhcm6nu7sbP4mAx1RXr159/fp1+5RqK6wM1uHDhxFCX3/9taMLsUx5efmbb77JzO3cv3/fePv09HSE0JYtWxw4MzNvLH5sJiIiwtElWGbnzp0NDQ14bqeioiI2NvbIkSODg4NztcdT5pGRkXiwnl1YHCw2whOFzc3NKSkper0+Pz8/Kiqqs7PT0XXZHgTLAQIDAyUSyf3793fv3h0dHc2MdroS9p1jEUI0TaMXTzyy19q1a3///XeVSuXoQoiAM5aD4WdvZsXqz49LBevixYtHjhzBf/wJOJZLBaumpiY/P7+5udnRhQDXChZwHqwMFqs7H26ClcGai4sFjtW/jksFCzgPCBYgAoIFiGBlsFjd+XATrAzWXFwscKz+dVwqWMB5QLAAERAsQAQrgzVX54PVnRIXw8pguQlWf04gWIAICBYgAoIFiGBlsKDz7vxYGSw3werPCQQLEAHBAkS4VLBYfe1wMawMFgTI+bEyWG6C1Z8fCBYgAoIFiHCpYLH62uFiWBksCJDzY2Ww3ASrPz8QLEAEK//w2lwSEhI4HM7atWsdXQhwrWAJBALj62kDu2HlpZDVnY8ZpqenHV0CEawMlmvo7e1NTk5OTk6eqwGrPz8udSlki8nJyRMnTmRnZ6vVam9v78ePHwcHBzu6KBuDM5Zd0TR96dKl6OjojIwMtVqdlJT04MED10sVYuMZi6bprq4uhBDr/u7+nTt3hEJhTU0NQig2NjYvL2/btm1G2iuVSnuVRoD9VlexBZlMht+MZcuWOXzNS/M9fvyYWe3y1VdflUgk5ixLvmDBgs2bN8tkMrvVaUOsCdbjx48PHjyI35vly5e///77np6eCKHFixf/+OOPU1NTji5wdobr8+LVBlUqlZH2Go0mLy+Pz+cjhDw8PL744gsnX/ByLiwI1lzvjeG64hERESbXarO/GSuKy+Vy4+1nLA/W1NRknzpJcPZgmXxvKioq1q1bhxvs2LHDSd6M5ubmPXv24KrWrFlTWlpqvL3hgobO+SGxlPMGq6Wlxcz3RqvVSiSSwMBA9GJ5rYGBAXuWaojpHiGE/Pz8xGKx8dUGx8fHmSVYvb29TS7ByhbOGCz83uBF+sx5b6zZyoY0Gg1ei9DMfFu6aDS7OFewdDrdhQsXcNcVd6eGh4ct2sOM89yNGzcIlTpDWVmZRaunVlVVxcTE4PZvv/22yWXuWceJgtXe3n7mzJlvv/2Wy+Wa894YMaNn1t7ebsM6Z7B0veeenh6BQIAnal577bXCwkK9Xk+uPEdximANDg5euHCBoiiKok6fPl1fX2/9Pg3vJT09PU3e58/DyMhIenq6+SvUT05OikQiLy8vhBCPx0tPTx8fH7dtSc7DwcFSq9VlZWUZGRkURf3www+VlZW2HbZRKpXMyGRwcLBEItHpdNbvFnePli1bZmb3SK/XFxUVhYWFIYQ4HM7+/fs7OzutL8OZOSxYer2+sbHxp59+oijq+++/LykpmZiYIHQsqVS6detWfLXatGlTbW2tNXurrKzcsGED3tv27dsbGhpMHj0+Ph63f+utt/766y9rjs4WjgmWQqE4e/YsvvYVFBTYYVoGnzNCQ0OZc0ZXV9c89pOZmYkjsnLlykuXLhlvbHi+xDM5NjlfsoK9g6VSqUpKSnCkcnNzGxsb7dl1nZiYEIlEixYtYgaN1Gq1RXt4+PDhkiVLTG5ohx6ek7NfsHQ6XXl5+fHjxymKyszMrK6utvM4E6O7u5t5gjkkJKSwsNCizU32uO15T+q07HrGKigooCiqqKhodHTUnsed1Z9//rl+/Xr89icmJt67d8/6fTY3N+/evRvvc+3atXYbRXNCdg1Wf39/b2+vPY9oHL65w3NB+Oauv79/frt6eSaHpU8l2IpZwaqqqqIoytL/qd9++62goGA+RdnX8PBweno6fgiHz+dnZWVZ9BCORqMRi8VLly7FswWOnal0HsYeTaZp+ubNmwqFgqZphNDExMTt27fNXCNeoVDI5XL8qKeTw2Fqamrau3fvyMjIsWPH1q9fX1paas625eXlGzduFAqFo6Oju3btamhoYKYL3RwHh2ZWfX19EomEacDhcGiaDg8PN/7dPa1WK5fLS0tLJyYmEEIikci2FRNVXl6empra0tKCEEpKShKLxatWrZq1ZVtb21dffXXt2jWEUGRkZE5OTlJSkl1rdW7GgoUQevbsmUwmq6ysRAj5+vru27cvLCzMyBeS8vLyRkdHDV9hV7AQQlqt9syZM999993Y2BiXy/3ss88yMjLwwAE2OjqalZV18uRJjUazdOnSY8eOCYVCPLEDGCa+pcPj8fDlLCAgYHx8fGpqyjBV1dXVv/zyy6VLl5hX1Go1l8vlcrm4G8tGXC43LS3t0aNHKSkper3+1KlTa9asyc/P1+l0er3+/Pnzq1evPnHixPT0tEAgaG1tZaYLgSETZ6zu7u6CgoLY2NgdO3b8/PPPfD7/8OHDzE+Li4vxgKFQKJyxYUtLS1FREWLhGcuQVCpNS0urq6tDCEVHR2u12ra2NoRQYmKiWCxmRivAy0x8/Ss0NPTQoUNBQUGenp4HDhzw9/c3/KmPjw+fz/fz8yNZoSPFxcXV1tYWFxcfPXq0s7Nzeno6JCTk+PHjRr6+DDATZ6x5c40zFmNycvLu3bs6nW7Lli14RggYx74vrDqEt7d3QkKCo6tgE/iKPSACggWIgGABIiBYgAirOu9SqVSpVPJ4vPfee89WBQHXYNUZq6ur6969e2ZOSwO3YlWw8PQOfqYbAEOkBkiBm4OTDSACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiACggWIgGABIiBYgAgIFiDif7x1mTSb3mwiAAAAAElFTkSuQmCC\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>9</th>\n"",
       ""      <td>221.0</td>\n"",
       ""      <td>[*:1][H]</td>\n"",
       ""      <td>[*:1]c1ccc([*:2])cc1</td>\n"",
       ""      <td>4.082251</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>10</th>\n"",
       ""      <td>15.0</td>\n"",
       ""      <td>[*:1]c1ccc(F)cc1F</td>\n"",
       ""      <td>[*:1]Nc1ccc(F)cc1F</td>\n"",
       ""      <td>3.893880</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>11</th>\n"",
       ""      <td>14.0</td>\n"",
       ""      <td>[*:1]Nc1ccc(F)cc1F</td>\n"",
       ""      <td>[*:1]c1ccc(F)cc1F</td>\n"",
       ""      <td>3.893880</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>12</th>\n"",
       ""      <td>19.0</td>\n"",
       ""      <td>[*:1]c1ccc(Cl)cc1</td>\n"",
       ""      <td>[*:1]Cl</td>\n"",
       ""      <td>3.887705</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>13</th>\n"",
       ""      <td>85.0</td>\n"",
       ""      <td>[*:1]Cl</td>\n"",
       ""      <td>[*:1]c1ccc(Cl)cc1</td>\n"",
       ""      <td>3.887705</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>14</th>\n"",
       ""      <td>13.0</td>\n"",
       ""      <td>[*:1]c1ccc(C)cc1</td>\n"",
       ""      <td>[*:1]C</td>\n"",
       ""      <td>3.765773</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>15</th>\n"",
       ""      <td>192.0</td>\n"",
       ""      <td>[*:1]C</td>\n"",
       ""      <td>[*:1]c1ccc(C)cc1</td>\n"",
       ""      <td>3.765773</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAEwklEQVR4nO3az0tUexzH4TOTSLmqiDRnYS3t1yIJBGnRKlxErVu46e+I/odoV9BOWgWBEAVBrSLBFhkxYIsK0poKSWtchNpC8IY1Y9eZ99W5Pc/qy5kzZz6DL86RM6e0urpaQLuVt3sA/p+ERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIuIrj/cb3x8fGZmpiiKK1eulMtyZBN/GlbU/Pz8tWvX1tbnz58/derU9s5D65qde96+fbuystLo1Y8fP379+rUtQ0xPT7flOOwcDc9Ys7Ozt27d6unpOX78+MmTJ9e3z8/Pv3z58sWLF7VabXh4+Ny5cy1OUK/Xnz171uJB2GkahvX8+fOiKOr1+uTk5OTk5Pr269evr6+np6dbDKter4+Pj3/58qWVg7ADNQxrYGCgVqu9efOm0dVw//79R48e3cJHLi4ufv78+dOnTzMzM69evWpytaVzNQxrcHBwcHBwaWmpWq1OTEz8/Oc/ffr00NBQb2/vFj5vampqYmJiK5PSUTa5cbBnz56enp4NJ5XFxcWtVcXfY5PbDQsLC3fv3l1bVyqVubm5lZWVarU6NTU1NDT06/43btyYnZ1dWx85cmRsbKy949IpmoW1urp6586dpaWloij27t07Njb25MmTR48eFUVx//79gYGBAwcO/PqW367XnThxor+/f8PG27dvLywsNJnk3bt3N2/ebPY9OsfTp0/v3bu33VO0wcGDBz98+NDo1WZhLS8vf//+fW194cKF7u7uM2fOVKvV9+/fl0qlb9++/RrWprq7uw8dOrRh4+7du5uHRcdpFlZXV9fly5cfP35cKpUOHz5cFEW5XL548eLDhw9HR0f37dv3H81IB9rkf6xyuXz27Nmft/T29l66dKnR/seOHevr61vfs/X56FBt/q1wZGSkvQekQ3lOgYgd8XTDpiqVytWrV7d7Cv4FZywihEWEsIgQFhHCIkJYRAiLCGER0eYbpA8ePKjVamvrSqWy4XdG/h5tDuv169dzc3Nr6+Xl5fYenA7iUkiEsIhoc1hdXf9cW3ft2rWFd/28pnOVfvtkOrTIpZAIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYREhLCKERYSwiBAWEcIiQlhECIsIYRHxAym/7wQiks8ZAAAAAElFTkSuQmCC\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>16</th>\n"",
       ""      <td>34.0</td>\n"",
       ""      <td>[*:1]c1ccc([*:2])c([*:3])c1</td>\n"",
       ""      <td>[*:1]c1ccc([*:2])c(Cl)c1</td>\n"",
       ""      <td>3.684324</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>17</th>\n"",
       ""      <td>20.0</td>\n"",
       ""      <td>[*:1]c1ccc([*:2])c(Cl)c1</td>\n"",
       ""      <td>[*:1]c1ccc([*:2])c([*:3])c1</td>\n"",
       ""      <td>3.684324</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAT40lEQVR4nO3da0xT5x8H8If7CsigUBEExBuKw6k4tqn8dSrKpiwxm7zYImTOpcmiKagzLNkMBy8bOpXLhhsEt2jUTNSNwBQWyhrQCUphQbCC3CpQUAulXFpuvfxfPK7pUEsrPe156O/zSsvTc36U73nOcy49j51Wq0UAmJu9tQsA0xMEC9ACggVoAcECtIBgAVpAsAAtIFiAFhAsQAsIFqAFBAvQAoIFaAHBArSAYAFaQLAALSBYgBYQLEALCBagBQQL0AKCBWgBwQK0gGABWkCwAC0gWIAWECxACwgWoAUE6z/6+vpaW1utXcV04GjtApilrq5OIBB89dVXjo6GPhmNRlNTU9PR0dHV1WVnZ+fl5TV//vyVK1c6ODhYrFSGg2AhhJBWqy0uLg4JCdFoNAihwcHBxsZGNze3pUuXPttYo9Fcvny5oaFB94pUKn3w4EFVVdVnn33m4uJiuboZzA4eCoIQevz4cU5ODk6Vzrx58+Li4p5tXFpaevPmTYRQRETEwoULR0dH7927h3P2xhtvbN261TI1Mxz0WAgh5Ovre+DAAaFQWFpaihByd3ePjY0NDAx8bmM8CFu1atXmzZvxK2FhYfn5+bW1tWKx2FIlMx0M3p9ycXHBieFwOENDQwqFws7ODv9IIBDk5uZeuXIFITQ+Pv7o0SOEUGhoqP7bAwICEEJDQ0OWrpupIFhPdXZ2isXi8PDwTz75xNXVFe/sMJlMJpFIOjo6EEKjo6ORkZFr16719/fXf3t7eztCaMGCBRYum7FgV/hUYGDgrl27OByOs7Pzjh072Gy27kceHh5sNnvGjBkIIXd39/Xr1+PXR0ZGBgYGZDJZY2NjXV0dQui5g33bBIP3lycSiS5fvqz775o1a6KioqxYD6PArvDlzZgxY9asWbpzVxUVFeXl5dYtiTmgx5oqlUolkUhu3LjR0tKCENq3bx/eado46LFMo1KpRkdHx8fHda84OjrOmTMnNjbW3t4eIdTU1GS96hgEBu+mEQgEt27d8vPz43K5+q+7uLh4eHjI5XKpVGqt2hgFeizTeHt7I4SkUuno6Kj+68PDwwMDAwghX19f61TGMBCsyVVXV//+++9//vknQmju3Ln29vYqlaqgoEClUuEGSqXy6tWrGo3GwcEhODjYmrUyBuwKJycWi+vr6z08PKKjo728vCIiIm7fvi0SiR4+fDhr1iytViuRSHAHtn79ek9PT2vXywgQrMnhaztqtbq1tTUwMHDz5s0sFqu8vFyhUOAjQYSQm5tbVFTU8uXLrVopg8DpBmNlZWX19PTs3r3bx8cHITQ8PCyVSnt7e52dnb29vX18fAzfwmVr4LMwFt4CdVemWSxWUFBQUFCQVYtiLhi8A1pAsAAtIFiAFhAsY00YYwHDIFiAFhAsQAsIFqAFBMtYMMYyCQQL0AKCBWgBwQK0gGAZC8ZYJoFgAVpAsAAtIFiAFnA/lrHq6+vlcrlSqYSbj40Bd5AaKzg4+OHDh2KxeM6cOdauhQCwKzQWHBWaBIIFaAHBMhb0WCaBYAFaQLCMBT2WSSBYgBYQLGNBj2USCJaxIFgmMSpYMMMMMJVRl3SMnGEGa25ubmpq6urqksvlbDabw+FERkZOg8sg0GOZxFBQTJphBrtz505RUZHuv0NDQ+3t7XV1dR9++GFISIgZ6wYMZ+haoUkzzCCEmpubL1y4gBAKCgqKiIhgs9mdnZ0CgWBkZMTNzW3Pnj2vvPKKeau3JD8/v0ePHnV3d8+aNcvatRDAUI9l0gwzCCH8sChfX98dO3Y4OTkhhPz9/Tkczrlz5xQKhUgkCg8PN3f9gKEmGTPpzzAjlUr1Z5hBCAkEgpaWFk9Pz+3btyOE8KQgS5YswanC5s6d6+bmplAonjx5QstvYCkwxjLJJEeFBmaYQf+dZEaj0fT397NYrNmzZ09YCH5Wp37awLQ3SY9lYIYZ9N9JZuzt7ffv3//sEqqqqvDzOSfMakSQ4eHhzMxMhULh6emZkJCQmZk5c+ZMaxfFdDTe6KfVaisqKvh8vlarDQgI+PTTT4nbj2i12kuXLiUlJeHJvRwcHNRqtZeXV0pKyueffw7PhjSArjPvnZ2dubm5JSUlWq125syZ27dvJy5VNTU169at++ijj9rb21esWFFWViYSiWJiYvr6+ng8XlhY2PXr161dI3OZv8caHh4uLi6+e/cuQsjBwWHVqlXr1q0ja+Pu7u6mKOrMmTNqtdrb2/vgwYN79uzRTcbE5/MTEhJEIhFCKCYmJj09ff78+Vatl5G0ZtXR0XHy5EmKoiiK+vXXX3t7e827fLqNjo6mp6d7eHgghJycnHg8nlwuf7bZ2NhYenr6q6++qmvW399v+WqZzJzB6unpSU1NpSjqxIkTjY2NZlyyZRQUFMybNw9vbzExMc3NzYbb9/T08Hg83JP5+fllZ2er1WrLlMp85gzWlStXKIpKS0sbGBgw42ItQCQSvfvuuzhSixcvvn79uvHvra6ujoyMxO9duXLl33//TV+dBJlSsIRC4W+//VZcXIz/e+LECYqiqqqq+p9nZGRkeHhYqVSao2yz6e3t1fU6bDY7PT19fHzc1IVoNJq8vDz8tTA7O7vY2NiHDx/SUS1BpjR4v3r1Kp5kZu/evX19fZmZmQYar169emxsrL6+/p133omIiMCz+1nR+Pj46dOnKYqSy+VOTk47d+48evQonnXi5SiVyuPHjx87dgxfG/3iiy++/PJLoi+PTsWU/rr6ZxAkEskka7K3l8vlIyMjxcXF2dnZbW1tU1n1FPH5/BUrViQmJsrl8qioqJqamuzs7KmkCiHk6upKUVRTU1NcXJxCoUhJSQkLC9OfNNq2WLiHbGlp+eGHH/Bh48WLF2UymYULaGho2Lp1K/7dQ0JCCgsLTXq7SqXas2fP/fv3DTcTCASvv/46XsuGDRvu3r07hZKJZOlgabVatVpdWVn57bffUhR1+PDhoqKikZERC6xXJpMlJSU5OzsjhDw9PVNTU0dHR01dSE5ODkLIyckJ7/0NtFSr1WfPnuVwOAghR0dHLpcrlUqnUD5hrBAsbHBwsLCwMCUlhaKokydPCoVCjUZD07rGx8ezs7Px39je3j4uLu7x48cvtyg82Mfne/FgX6VSGWgvk8lMaj9tWC1YWFdX15kzZ/CeMScnp6Ojw+yr4PP5ulteN2zYUFtbO/Vl3r9/Pzo6Gi9z+fLlZWVlhts3NDS89957uH1oaKjuOHoas3KwtFqtRqOpra3F5+tTUlLy8/PNdRrswYMHsbGx+M+5YMGCvLw8syxWp6CgYO7cuXj5MTExbW1tk7bXPwHb2tpq3noYxfrBwsbGxgQCwZEjR44ePbp06dLk5OSpDLwGBweTk5NdXFwQQm5ublNcmgH4EhC+cYjFYiUlJQ0ODk7aHl8ywu2JO5lsJKYEC5PJZFlZWXibXrhwoamHbNp/h8x4Knk8nOru7qajVH0SiSQuLg6ffJk9e/bZs2cNjxclEgmXy8Vn8oxpTyJmBQv766+/dMfqGzdurKurM/KNAoFg2bJl+I1vvfVWRUUFrXVOcOfOnbffflu39srKSsPtq6qqVq1ahdu/+eabFq6WbkwMlva/x3HGHKu3t7frvjsUEBBgrT5Ao9FM6C8NH37i9vhrP7j9o0ePLFYtrRgaLMyYY/WhoaHk5GR85cTV1TU5OdnqlyNxSXiEZ8wJM/327u7u9I0ILYnRwcJedGyPN3c/Pz/EyEu/+sekISEh165dM9y+qalJ1/7lxpeMQkCwsAnH6vn5+boBSkREBGNvVikpKXnttddwnVFRUffu3TPcns/nh4WF6drX19dbpk6zIyZYWq1WqVQePnzYzc0Nn0TAw6nz588z/JDK1NtNx8bG0tLS8NMuPDw8CD0fQVKwsI6ODnyP+bZt2xQKhbXLMZapt5s+efLk/fff9/Ly2rlzp8WKNCPygqXVauPj4xFCZ8+etXYhJquurv7f//6H93QrV668efOmgcbXrl1DCG3ZssVi5ZkRkQ9e0xL7bffw8PCysrILFy4EBATgkMXHx5P+8IHnIjJYRLOzs/v4448bGxvxWZL8/Hy1Wv3cluRuP4jQYBH9iWP4dlORSHThwgV8xmSaIel7pNNPcHBwcHDwi35K9PYzrXqs+/fv8/n8Se++BxZAZLBeJC0tbdOmTdPmkQrQY1naiz5xov8S0wyRwbIRRG8nRAYLeizmIzJYNoLo7YTIYEGPxXxEBstGEL2dEBks6LGYj8hg2QiitxMigwU9FvMRGSwbQfR2QmSwoMdiPiKDZSOI3k6IDBb0WMxHZLBsBNHbCZHBgh6L+YgMlo0gejshMljQYzEfkcGyEURvJ0QGC3os5iMyWDaC6O2EyGBBj8V8RAbLRhC9nRAZLOixmI/IYNkIorcTIoMFPRbzERksG0H0dkJksKDHYj4ig2UjiN5OiAwW9FjMR2SwXmSaBYvoX4fIB6+96BPPy8uzRjngOaZVjzXNEN1jERksoj9xfRUVFVeuXLF2FbQgMljTgEQiiY+PX7NmDZfL7enpeW4borefaTXGIoJSqfzuu++OHz+uVCpZLNbu3btZLJa1izI/IoOF4XiRpbCwkMfjicVihFBMTExmZqZuVulnEb39EDnlydGjR4ODg0NDQw1PGcIoQqFQN9kJnp/CcPsHDx5ERkb6+vpyOJyioiLLFGlGRAZrYGAgMDAQIWRnZxcfHy+RSKxdkSFdXV1cLhdPz+Tj4/Pc6Tz1yeXy/fv3Ozs7I4RcXV1xFj/44AOyZr0nMlharVahUCQnJ+PRCZ5YdXh42NpFTaQ/Z70xE8o9O1N6e3u7bgnOzs48Ho+UWeZIDRbW0dGhmz4+MDCQUfOBTZi5s7m52XB7gUCwfPly3H7CTOm4z8Oz3vv7+086JR0TkB0sTH/y+vXr19fW1lq3HpFIpJtrePHixZOOkPBM6XjzMDBTulAoXLNmDV5sRETErVu36CnfPKZDsLT/7kRmzpyJ/p31/smTJ5YvQ3+2Szw7+vj4uIH2ps6UrtFo8vLygoKC8PgyLi6uu7vb3L+EeUyTYGEymSwpKQkPe728vCb9u5rRhPl5uVyuVCo10H5CREyaKR2PL3Ec3dzcmDnr/bQKFtbQ0LBlyxbj90RTV1JSsmTJErxGY2YIv3379tRnSm9ubt62bRteSFJSUmNj40vVTpdpGCysoKAATx2Nx84tLS10rEU/xCEhIX/88Yfh9p2dnbrhlFmG4SUlJTExMcnJyRRFnT9/3nA3aUnTNljaf3dPNB2ry2QyHo/n6OiId7upqamjo6MG2isUitTUVHd3d4QQi8VKSkoyVzFqtVooFB4/fpyiqEOHDhUWFjJhDvbpHCxswrH6i465jDc+Pp6dnc3hcIw8UMDDqTlz5ui6z7a2tqkU8FxKpbKoqOjQoUMURR07dqyysnKKv+YUTf9gYVVVVatXr576sTqfzw8NDcXL2bRp06TDKaFQGBkZqbuSU15e/nLrNZJUKj1//jxFURRF/fTTT2KxmNbVGWArwdKa6Vg9IyMDIbRgwYK8vDzDLSUSia6n9PPzy87ONnwlx4waGxszMjJwvC5evNjX12eZ9eqzoWBh+qeOXuJYHe8HDQ+n9K/k4LGd4Ss5dFCpVJWVld988w1FUUeOHCkpKTFcs9nZXLCw5ubm2NhYvIcypvsxXkFBge5OGPqORo3U399/9epV3HVlZWVZctRlo8HCSktLly5dikOwcePGurq6qSytpqZm7dq1eGmhoaHFxcXmqnOKJBJJbm7ujRs3LLlSmw6W9t9dm4+Pj+4GhJcYkehfyfH29p70xhjL02g0+ifMZDIZ3V2prQcL6+3tnXCNz8hkTLiSw+Px5HI53dVOXVlZGUVRJl3vOnfuXG5urvHt4csUCCHEZrMzMjLq6uqio6NlMlliYmJERER5ebnhd/H5/GXLliUmJvb390dFRf3zzz8ZGRk4ZMyk1WqLiopaWlo0Gg1CaHBwsLKysq6ubtI3trW1tba2dnZ2Gr8uOy2Bd47TqrCwMCEhoa2tDSEUExPz/fffBwcHT2jT0NCwb9++oqIihNCiRYtOnTqlu7DDZI8fP87JycGp0pk3b15cXNyL3jI2NtbU1FRcXDw0NIQQSk5ONnJdEKznGBsb+/HHHw8ePDg4OMhisXg83tdff42vxshkspSUlNOnT6tUKi8vr6SkpL179+L7KYgwMjIiFApLS0sRQu7u7rGxsYGBgS/6vkZaWtrAwID+K8YHC3aFz+Hs7JyQkNDQ0BAXFzcyMnLs2LHFixf/8ssv2dnZixYtyszMRAhxudzGxkbdXTqkcHFxaW1tRQhxOJyhoSGFQqFLlUAgyM3N1f8CrUqlcnFxcXFxwZdETULw17/o5u/vf+7cOS6Xm5iYWF1dvWvXLty7R0dHnzp1SnefDFk6OzvFYnF4ePjGjRuzsrJu3rypu0Ilk8kkEsng4KCu8YEDB/A/GhoaLl26ZNKKIFiTiIyMvHPnzs8//1xbW9vd3b1z586tW7dau6iXFxgYuGvXLg6H4+zsvGPHDjabrfuRh4cHm82eMWOGWVYEYywwOV2PBWMsYGUQLEALCBagBQQL0AKCBWgBpxvAU9XV1e3t7a6urrqvcU8F9FjgKbFYfPfuXZFIZJalQbDAU+Z9whucIAW0gB4L0AKCBWgBwQK0gGABWkCwAC0gWIAWECxACwgWoAUEC9ACggVoAcECtIBgAVpAsAAtIFiAFhAsQAsIFqAFBAvQAoIFaAHBArSAYAFaQLAALSBYgBb/B+KAn2YHY0xaAAAAAElFTkSuQmCC\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>18</th>\n"",
       ""      <td>14.0</td>\n"",
       ""      <td>[*:1]c1ccc(OC)cc1</td>\n"",
       ""      <td>[*:1]OC</td>\n"",
       ""      <td>3.415256</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""    <tr>\n"",
       ""      <th>19</th>\n"",
       ""      <td>70.0</td>\n"",
       ""      <td>[*:1]OC</td>\n"",
       ""      <td>[*:1]c1ccc(OC)cc1</td>\n"",
       ""      <td>3.415256</td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAANaklEQVR4nO3de1BU5R/H8e8uuFwUBURA118oaoSBpah4Sc1LGWhT0+hfilNpzjSjiJaXFAZDJUirVcymqckxzW5MTWZqKZgiBRjgcDG8oyJ3RNlYRGCf3x/QXghkWc6XZXc/r+GPs097nn1W3p1ddg+LTAhBAFKTW3oBYJsQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQuEBSwQFrBAWMACYQELhAUsHC29AMspKKDkZCoooLIyKisjrZYcHMjbm7y9KSCAFiyg6dPJ0Y7/fXpGJoSw9Bp63eef04cf0t9/d3E1Dw+KiKCtW8nDo1eWZVPsLKziYlqxglJSurGLlxclJtLrr7OtyTbZU1iFhTRjBtXWmrNvTAzFxUm9IFtmN2GVlNC0aXT7ttHgkCEUHk5PP03jx9PYsVRaShcu0IULdPYs5eS0n2HHDtq8udfWa/WEPdBqxYQJgsjoKzxcVFR0ev2EBOHo2H6X3NzeXbcVs4+wfvzRqA9nZ5GU1PVef/wh/PyMdnzlFf612gj7eCgMDaWsLP3F/fvp1VdN2rG4mJ54ghob2y7KZJSXR0FBki/Q9tjBC6QpKUZVjR9Py5aZuu+IEbRmjf6iELRtm5Rrs112cMSaO5dSU/UXU1Jozpxu7H7/Po0aRTU1bRflcqqqIk9PKVdoi2z9iNXYSKdP6y+Gh3evKiIaNIg2bdJf1Grp2jVp1mbTbD2sGzfI8JA8f745k0yfbnTx+vUeLck+2HpY7SIYNcqcScaMedSc0BE7C8vf35xJvLzI3b3TOaEj9hSWTEYjR5o5z+jR+u3i4p6syE7Yelh37ui3fXzI2dnMeRwc9NtabY+WZB9sPSxXV/22RmP+PIZHKZxFYwJbD8vwBae6OqquNmeShgaqqNBfRFgmsKewiMx8Cardk6rhw81ejv2w0bCKi+ndd4mIxo41GjcvLMN3hIgoNJSIqLGR4uNJrTZvgbbP0u+CS62+XsTGChcXQSSSk0VZmQSnJ4SE6GeQycTdu0II8d57gkgMHixUKtHcLO2dsAE2FJZWK778UiiV+gj8/UVTkxg92iiLvLzuTXvunFGazzwjhBAVFcLNTT84caJIT+e4T9bLVsI6f15Mn25UQEiISEsTQoht24zGFy3q3syLFhntfvBg2/iRI2LECKP/tHChKC6W+H5ZLesP684dsXKlkMv13+ChQ8Wnn4qWlrYrlJcLhcLooHX+vKmTp6UJBwf9vj4+oqFB/181GpGQIAYM0F/B1VXExgqNRuL7aIWsOazGRqFSGT0kKRQiMlLcv9/+mqtWGR1ahgwRhYVdz3/9uvD2Ntrx6687uFpJiYiIEDKZ/mrDh4sDB4RWK8F9tFpWG9aRI8Lfv/0j0bVrHV9ZrW5/krG7u1CpRFNTp/N/9ZXw8Gh/jvwjZGaKKVOMrj9rlj2fI2+FYV28KObPN/oWBgaK48e72OvMmbYfFQ2/lErxxhvi229FerrIzRW5ueL4cRETIwIC2l8zJETcu9fFTbS0iAMHhI+Pfi+5XEREiPJyqe66FbGqsGpqRGSk0ZMeT89u/LR/7pxwd29fjClfwcGipsbURarVIjZWODkZHR0TEsSDB2bfb2tkJWE9fChUKqMs+vUTK1eKqqruzZOf3/4B69FfcrlYvbqDJ21dunxZLF5sNNXjj4uff+72PFbLGsI6eVI8+aTRN2nePJGfb+ZsWq04fFgEBXWRlJOTWLiwGz8/mr7ygoIezWkl+vYvU1y+TOvW0S+/6EfGjKEdO2jxYgkmLy6mo0fpwgWqrKSKCqqvp8GDydeXfH1p2jQKDyc3NwlupamJ9u6luDi6d69tRKGgyMiHcXEKFxcJ5u+zLF12J2prxcaNRq8/WfUzFeNnh3Xz5iUmJmZkZGht9yWJvnfE0mrp0CFav54qK9tG5HJasoR27iQfH4uurMcuXKA1aygj4+DGjdcdHIjI19c3LCzsscces/TKpNfHwjp9mqKiKC9PP/Lss6RS0VNPWW5NEqtNSzuQnX3//n3dSFBQ0HPPPTdw4EALrkpyfSas27dpyxY6eFA/8r//0fbt3fitZevR3NycmZl59uzZhw8fto44OjqGhobOnDlToVBYdm1S6QNh1dfTzp2UmEgPHrSNuLrS+vW0aZP5p6hbA7VaferUqTyDw7Obm9vcuXPHjRsnk8ksuDBJWDQsISg5md56S/+xVTIZLVpEu3aRLT7t6FBpaenx48dLSkp0I0qlMiwsTKlUWnBVPWexsNQZGW6rV9Nff+mHpkyh3btp8mSLrMeChBB5eXmnTp36559/WkdkMllwcPDzzz/fv39/y67NbBYI686dO++8884fZ84UVlQ4tX5C0LBhFBtLK1aQ3EZPlTZBU1NTenr6uXPnWlpaWkcUCsXUqVNnzJjhYPjLZ1aiV8NqaGjYs2fPjh071Go1ESXOmrUhK4siI2nLFmlejbR+d+/eTUlJuXjxom7E09Nz7ty5Y9udvN/n9VJYQojvvvtuw4YNt27d0g0uX7bs87g48vPrhQVYlxs3bpw4caJS90oekb+//wsvvDBkyBALrqpbeiOsnJycqKiotLQ03cj48eNVKtXMmTO5b9p6abXa3Nzc1NRUzb+/ZyuXyydNmjR79mwnJyfLrs0UvGHV1NTExcV9/PHHuucNgwcPjomJWbVqlTU+b+h9Go0mNTU1JydH920aOXLkMmt4bY8rrKampn379sXGxupeYu7Xr9+bb74ZFxc3aNAgjlu0YdXV1SdOnLh27RoRLV26dJR5H8bUyzjegDx58mRgYKDhrcybN6/QlNPMoXOFhYXHjh2z9CpMJfER69KlS+vWrTt27JhuJCAg4IMPPliwYIGEtwKGDh8+fOXKFSKKiYmR95nXayT761a1tbVbt27dt29fc3Nz64iHh8fGjRvXrl1rM+9/2aHa2to9e/a0br/44osTJkwwcUcJwmpubv7iiy+io6OrqqpaR+Ry+ZIlS3bt2uXt7d3z+eG/bt26NXz48M6OT1VVVS4uLgMGDOj5DeXn55u3Y0/DSk1NjYqKMrz5OXPmfPTRR+PGjevhzNCZ0tLS/fv3u7q6BgUFGf4719bWXrx4saCgoLKycsqUKfPN+yRfAxqNJue/f1PINOaHdfXq1c2bN3///fe6kdGjR8fHxy+W5Lxh6FzrCREajSYrKyvL4JNw9u7dq9vOz8/vYVgajebw4cOG5411izlh1dfX79y5MyEhofHfvwXSv3//t99+e9OmTc42faJLH+Hn51dZWXnz5k1tJx9a6enpad5bQGq1uqamprq6+sqVK1evXu1sflN0LyytVnvo0KENGzZU/PsJdzKZbOnSpe+//76vr6/Zi4BuCQwMDAwMbGhoKCoqOnr0qOG3f9KkSSEhIT5mncOdnZ199OhRqRbZjbAyMzOjoqIyMjJ0I5MnT1apVFOnTpVqNWA6FxcXV1fXdgcVtVptXlWSMymskpKSzZs3Hzp0SPeil1KpjI+Pj4iIsIFzHa1UXV3dTz/91LqtVCrLysq0Wm1RUVF2dnZISEiHu3z22WelpaWt29xvDXURlkajSUpK2r59u+4cNFdX19WrV0dHR0vy0yyYRwjxww8/NDQ0EJG7u/uyZcv+/PPP33//nYh+/fVXPz8/Ly+vDvfqcLtVcHDwsGHD2g1+8803dXV1Zqyw07CEEMnJyevXr79586ZucOHChUlJSSNGjDDjlkBCLS0tTU1NrdsvvfSSQqGYMWNGUVFReXm5TCarr6/vMKxHUygUQ4cObTfo7OwscVi7d+9eu3at7uLEiRNVKtX0dn+uCCzE0dFx+fLlZ86ckclkrf+fy+Xyl19+OSUlJSwszKMPfGB4p+8V1tXVBQQElJeXe3l5RUdH40QXG5Cenl7z7x9e9PHxCW39+OdH+uSTT3TnG0rzls7AgQPj4+MvXbq0ZcsWN5w3bBN68wHnUU/eX3vttV5bB9iYvnKWBdgYhAUsEBawQFjAAmEBC4QFLBAWsEBYwAJhAQvJfv0L+r7ffvtN98afUqmcPXs2320hLDtSXFxcVlbWuq37NA0meCgEFggLWCAsO+LoqH/mY+LZdYa7GG53qQ98HDfYIhyxgAXCAhYIC1ggLGCBsIAFwgIWCAtYICxggbCABcICFggLWCAsYIGwgAXCAhYIC1ggLGCBsIAFwgIWCAtYICxggbCABcICFggLWCAsYIGwgAXCAhYIC1ggLGCBsIAFwgIWCAtYICxggbCABcICFggLWCAsYIGwgAXCAhYIC1ggLGCBsIAFwgIWCAtYICxggbCABcICFggLWCAsYIGwgAXCAhYIC1ggLGCBsIAFwgIWCAtYICxggbCABcICFggLWPwfM7S1/qApMPMAAAAASUVORK5CYII=\"" alt=\""Mol\""/></td>\n"",
       ""      <td><img data-content=\""rdkit/molecule\"" src=\""data:image/png;base64,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\"" alt=\""Mol\""/></td>\n"",
       ""    </tr>\n"",
       ""  </tbody>\n"",
       ""</table>\n"",
       ""</div>""
      ],
      ""text/plain"": [
       ""    f1.compound_degree                    f1.smiles  \\\n"",
       ""0                 29.0               [*:1]C([*:2])C   \n"",
       ""1                192.0                       [*:1]C   \n"",
       ""2                 70.0                      [*:1]OC   \n"",
       ""3                 33.0                  [*:1]O[*:2]   \n"",
       ""4                 84.0                       [*:1]F   \n"",
       ""5                 18.0             [*:1]c1ccc(F)cc1   \n"",
       ""6                 45.0                      [*:1]CC   \n"",
       ""7                 18.0                     [*:1]OCC   \n"",
       ""8                 41.0         [*:1]c1ccc([*:2])cc1   \n"",
       ""9                221.0                     [*:1][H]   \n"",
       ""10                15.0            [*:1]c1ccc(F)cc1F   \n"",
       ""11                14.0           [*:1]Nc1ccc(F)cc1F   \n"",
       ""12                19.0            [*:1]c1ccc(Cl)cc1   \n"",
       ""13                85.0                      [*:1]Cl   \n"",
       ""14                13.0             [*:1]c1ccc(C)cc1   \n"",
       ""15               192.0                       [*:1]C   \n"",
       ""16                34.0  [*:1]c1ccc([*:2])c([*:3])c1   \n"",
       ""17                20.0     [*:1]c1ccc([*:2])c(Cl)c1   \n"",
       ""18                14.0            [*:1]c1ccc(OC)cc1   \n"",
       ""19                70.0                      [*:1]OC   \n"",
       ""\n"",
       ""                      f2.smiles     score  \\\n"",
       ""0                        [*:1]C  6.190237   \n"",
       ""1                [*:1]C([*:2])C  6.190237   \n"",
       ""2                   [*:1]O[*:2]  5.591355   \n"",
       ""3                       [*:1]OC  5.591355   \n"",
       ""4              [*:1]c1ccc(F)cc1  5.520216   \n"",
       ""5                        [*:1]F  5.520216   \n"",
       ""6                      [*:1]OCC  5.192471   \n"",
       ""7                       [*:1]CC  5.192471   \n"",
       ""8                      [*:1][H]  4.082251   \n"",
       ""9          [*:1]c1ccc([*:2])cc1  4.082251   \n"",
       ""10           [*:1]Nc1ccc(F)cc1F  3.893880   \n"",
       ""11            [*:1]c1ccc(F)cc1F  3.893880   \n"",
       ""12                      [*:1]Cl  3.887705   \n"",
       ""13            [*:1]c1ccc(Cl)cc1  3.887705   \n"",
       ""14                       [*:1]C  3.765773   \n"",
       ""15             [*:1]c1ccc(C)cc1  3.765773   \n"",
       ""16     [*:1]c1ccc([*:2])c(Cl)c1  3.684324   \n"",
       ""17  [*:1]c1ccc([*:2])c([*:3])c1  3.684324   \n"",
       ""18                      [*:1]OC  3.415256   \n"",
       ""19            [*:1]c1ccc(OC)cc1  3.415256   \n"",
       ""\n"",
       ""                                            Fragment1  \\\n"",
       ""0   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""1   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""2   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""3   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""4   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""5   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""6   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""7   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""8   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""9   <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""10  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""11  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""12  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""13  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""14  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""15  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""16  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""17  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""18  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""19  <img data-content=\""rdkit/molecule\"" src=\""data:i...   \n"",
       ""\n"",
       ""                                            Fragment2  \n"",
       ""0   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""1   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""2   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""3   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""4   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""5   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""6   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""7   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""8   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""9   <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""10  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""11  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""12  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""13  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""14  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""15  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""16  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""17  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""18  <img data-content=\""rdkit/molecule\"" src=\""data:i...  \n"",
       ""19  <img data-content=\""rdkit/molecule\"" src=\""data:i...  ""
      ]
     },
     ""execution_count"": 24,
     ""metadata"": {},
     ""output_type"": ""execute_result""
    }
   ],
   ""source"": [
    ""aa_df.head(20)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""markdown"",
   ""metadata"": {},
   ""source"": [
    ""### BONUS) Plotting""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 20,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
       ""\n"",
       ""    <div class=\""bk-root\"">\n"",
       ""        <a href=\""https://bokeh.pydata.org\"" target=\""_blank\"" class=\""bk-logo bk-logo-small bk-logo-notebook\""></a>\n"",
       ""        <span id=\""1001\"">Loading BokehJS ...</span>\n"",
       ""    </div>""
      ]
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    },
    {
     ""data"": {
      ""application/javascript"": [
       ""\n"",
       ""(function(root) {\n"",
       ""  function now() {\n"",
       ""    return new Date();\n"",
       ""  }\n"",
       ""\n"",
       ""  var force = true;\n"",
       ""\n"",
       ""  if (typeof root._bokeh_onload_callbacks === \""undefined\"" || force === true) {\n"",
       ""    root._bokeh_onload_callbacks = [];\n"",
       ""    root._bokeh_is_loading = undefined;\n"",
       ""  }\n"",
       ""\n"",
       ""  var JS_MIME_TYPE = 'application/javascript';\n"",
       ""  var HTML_MIME_TYPE = 'text/html';\n"",
       ""  var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n"",
       ""  var CLASS_NAME = 'output_bokeh rendered_html';\n"",
       ""\n"",
       ""  /**\n"",
       ""   * Render data to the DOM node\n"",
       ""   */\n"",
       ""  function render(props, node) {\n"",
       ""    var script = document.createElement(\""script\"");\n"",
       ""    node.appendChild(script);\n"",
       ""  }\n"",
       ""\n"",
       ""  /**\n"",
       ""   * Handle when an output is cleared or removed\n"",
       ""   */\n"",
       ""  function handleClearOutput(event, handle) {\n"",
       ""    var cell = handle.cell;\n"",
       ""\n"",
       ""    var id = cell.output_area._bokeh_element_id;\n"",
       ""    var server_id = cell.output_area._bokeh_server_id;\n"",
       ""    // Clean up Bokeh references\n"",
       ""    if (id != null && id in Bokeh.index) {\n"",
       ""      Bokeh.index[id].model.document.clear();\n"",
       ""      delete Bokeh.index[id];\n"",
       ""    }\n"",
       ""\n"",
       ""    if (server_id !== undefined) {\n"",
       ""      // Clean up Bokeh references\n"",
       ""      var cmd = \""from bokeh.io.state import curstate; print(curstate().uuid_to_server['\"" + server_id + \""'].get_sessions()[0].document.roots[0]._id)\"";\n"",
       ""      cell.notebook.kernel.execute(cmd, {\n"",
       ""        iopub: {\n"",
       ""          output: function(msg) {\n"",
       ""            var id = msg.content.text.trim();\n"",
       ""            if (id in Bokeh.index) {\n"",
       ""              Bokeh.index[id].model.document.clear();\n"",
       ""              delete Bokeh.index[id];\n"",
       ""            }\n"",
       ""          }\n"",
       ""        }\n"",
       ""      });\n"",
       ""      // Destroy server and session\n"",
       ""      var cmd = \""import bokeh.io.notebook as ion; ion.destroy_server('\"" + server_id + \""')\"";\n"",
       ""      cell.notebook.kernel.execute(cmd);\n"",
       ""    }\n"",
       ""  }\n"",
       ""\n"",
       ""  /**\n"",
       ""   * Handle when a new output is added\n"",
       ""   */\n"",
       ""  function handleAddOutput(event, handle) {\n"",
       ""    var output_area = handle.output_area;\n"",
       ""    var output = handle.output;\n"",
       ""\n"",
       ""    // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n"",
       ""    if ((output.output_type != \""display_data\"") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n"",
       ""      return\n"",
       ""    }\n"",
       ""\n"",
       ""    var toinsert = output_area.element.find(\"".\"" + CLASS_NAME.split(' ')[0]);\n"",
       ""\n"",
       ""    if (output.metadata[EXEC_MIME_TYPE][\""id\""] !== undefined) {\n"",
       ""      toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n"",
       ""      // store reference to embed id on output_area\n"",
       ""      output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\""id\""];\n"",
       ""    }\n"",
       ""    if (output.metadata[EXEC_MIME_TYPE][\""server_id\""] !== undefined) {\n"",
       ""      var bk_div = document.createElement(\""div\"");\n"",
       ""      bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n"",
       ""      var script_attrs = bk_div.children[0].attributes;\n"",
       ""      for (var i = 0; i < script_attrs.length; i++) {\n"",
       ""        toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n"",
       ""      }\n"",
       ""      // store reference to server id on output_area\n"",
       ""      output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\""server_id\""];\n"",
       ""    }\n"",
       ""  }\n"",
       ""\n"",
       ""  function register_renderer(events, OutputArea) {\n"",
       ""\n"",
       ""    function append_mime(data, metadata, element) {\n"",
       ""      // create a DOM node to render to\n"",
       ""      var toinsert = this.create_output_subarea(\n"",
       ""        metadata,\n"",
       ""        CLASS_NAME,\n"",
       ""        EXEC_MIME_TYPE\n"",
       ""      );\n"",
       ""      this.keyboard_manager.register_events(toinsert);\n"",
       ""      // Render to node\n"",
       ""      var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n"",
       ""      render(props, toinsert[toinsert.length - 1]);\n"",
       ""      element.append(toinsert);\n"",
       ""      return toinsert\n"",
       ""    }\n"",
       ""\n"",
       ""    /* Handle when an output is cleared or removed */\n"",
       ""    events.on('clear_output.CodeCell', handleClearOutput);\n"",
       ""    events.on('delete.Cell', handleClearOutput);\n"",
       ""\n"",
       ""    /* Handle when a new output is added */\n"",
       ""    events.on('output_added.OutputArea', handleAddOutput);\n"",
       ""\n"",
       ""    /**\n"",
       ""     * Register the mime type and append_mime function with output_area\n"",
       ""     */\n"",
       ""    OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n"",
       ""      /* Is output safe? */\n"",
       ""      safe: true,\n"",
       ""      /* Index of renderer in `output_area.display_order` */\n"",
       ""      index: 0\n"",
       ""    });\n"",
       ""  }\n"",
       ""\n"",
       ""  // register the mime type if in Jupyter Notebook environment and previously unregistered\n"",
       ""  if (root.Jupyter !== undefined) {\n"",
       ""    var events = require('base/js/events');\n"",
       ""    var OutputArea = require('notebook/js/outputarea').OutputArea;\n"",
       ""\n"",
       ""    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n"",
       ""      register_renderer(events, OutputArea);\n"",
       ""    }\n"",
       ""  }\n"",
       ""\n"",
       ""  \n"",
       ""  if (typeof (root._bokeh_timeout) === \""undefined\"" || force === true) {\n"",
       ""    root._bokeh_timeout = Date.now() + 5000;\n"",
       ""    root._bokeh_failed_load = false;\n"",
       ""  }\n"",
       ""\n"",
       ""  var NB_LOAD_WARNING = {'data': {'text/html':\n"",
       ""     \""<div style='background-color: #fdd'>\\n\""+\n"",
       ""     \""<p>\\n\""+\n"",
       ""     \""BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\""+\n"",
       ""     \""may be due to a slow or bad network connection. Possible fixes:\\n\""+\n"",
       ""     \""</p>\\n\""+\n"",
       ""     \""<ul>\\n\""+\n"",
       ""     \""<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\""+\n"",
       ""     \""<li>use INLINE resources instead, as so:</li>\\n\""+\n"",
       ""     \""</ul>\\n\""+\n"",
       ""     \""<code>\\n\""+\n"",
       ""     \""from bokeh.resources import INLINE\\n\""+\n"",
       ""     \""output_notebook(resources=INLINE)\\n\""+\n"",
       ""     \""</code>\\n\""+\n"",
       ""     \""</div>\""}};\n"",
       ""\n"",
       ""  function display_loaded() {\n"",
       ""    var el = document.getElementById(\""1001\"");\n"",
       ""    if (el != null) {\n"",
       ""      el.textContent = \""BokehJS is loading...\"";\n"",
       ""    }\n"",
       ""    if (root.Bokeh !== undefined) {\n"",
       ""      if (el != null) {\n"",
       ""        el.textContent = \""BokehJS \"" + root.Bokeh.version + \"" successfully loaded.\"";\n"",
       ""      }\n"",
       ""    } else if (Date.now() < root._bokeh_timeout) {\n"",
       ""      setTimeout(display_loaded, 100)\n"",
       ""    }\n"",
       ""  }\n"",
       ""\n"",
       ""\n"",
       ""  function run_callbacks() {\n"",
       ""    try {\n"",
       ""      root._bokeh_onload_callbacks.forEach(function(callback) {\n"",
       ""        if (callback != null)\n"",
       ""          callback();\n"",
       ""      });\n"",
       ""    } finally {\n"",
       ""      delete root._bokeh_onload_callbacks\n"",
       ""    }\n"",
       ""    console.debug(\""Bokeh: all callbacks have finished\"");\n"",
       ""  }\n"",
       ""\n"",
       ""  function load_libs(css_urls, js_urls, callback) {\n"",
       ""    if (css_urls == null) css_urls = [];\n"",
       ""    if (js_urls == null) js_urls = [];\n"",
       ""\n"",
       ""    root._bokeh_onload_callbacks.push(callback);\n"",
       ""    if (root._bokeh_is_loading > 0) {\n"",
       ""      console.debug(\""Bokeh: BokehJS is being loaded, scheduling callback at\"", now());\n"",
       ""      return null;\n"",
       ""    }\n"",
       ""    if (js_urls == null || js_urls.length === 0) {\n"",
       ""      run_callbacks();\n"",
       ""      return null;\n"",
       ""    }\n"",
       ""    console.debug(\""Bokeh: BokehJS not loaded, scheduling load and callback at\"", now());\n"",
       ""    root._bokeh_is_loading = css_urls.length + js_urls.length;\n"",
       ""\n"",
       ""    function on_load() {\n"",
       ""      root._bokeh_is_loading--;\n"",
       ""      if (root._bokeh_is_loading === 0) {\n"",
       ""        console.debug(\""Bokeh: all BokehJS libraries/stylesheets loaded\"");\n"",
       ""        run_callbacks()\n"",
       ""      }\n"",
       ""    }\n"",
       ""\n"",
       ""    function on_error() {\n"",
       ""      console.error(\""failed to load \"" + url);\n"",
       ""    }\n"",
       ""\n"",
       ""    for (var i = 0; i < css_urls.length; i++) {\n"",
       ""      var url = css_urls[i];\n"",
       ""      const element = document.createElement(\""link\"");\n"",
       ""      element.onload = on_load;\n"",
       ""      element.onerror = on_error;\n"",
       ""      element.rel = \""stylesheet\"";\n"",
       ""      element.type = \""text/css\"";\n"",
       ""      element.href = url;\n"",
       ""      console.debug(\""Bokeh: injecting link tag for BokehJS stylesheet: \"", url);\n"",
       ""      document.body.appendChild(element);\n"",
       ""    }\n"",
       ""\n"",
       ""    for (var i = 0; i < js_urls.length; i++) {\n"",
       ""      var url = js_urls[i];\n"",
       ""      var element = document.createElement('script');\n"",
       ""      element.onload = on_load;\n"",
       ""      element.onerror = on_error;\n"",
       ""      element.async = false;\n"",
       ""      element.src = url;\n"",
       ""      console.debug(\""Bokeh: injecting script tag for BokehJS library: \"", url);\n"",
       ""      document.head.appendChild(element);\n"",
       ""    }\n"",
       ""  };var element = document.getElementById(\""1001\"");\n"",
       ""  if (element == null) {\n"",
       ""    console.error(\""Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \"")\n"",
       ""    return false;\n"",
       ""  }\n"",
       ""\n"",
       ""  function inject_raw_css(css) {\n"",
       ""    const element = document.createElement(\""style\"");\n"",
       ""    element.appendChild(document.createTextNode(css));\n"",
       ""    document.body.appendChild(element);\n"",
       ""  }\n"",
       ""\n"",
       ""  var js_urls = [\""https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\"", \""https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\"", \""https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\"", \""https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\""];\n"",
       ""  var css_urls = [\""https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\"", \""https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\"", \""https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\""];\n"",
       ""\n"",
       ""  var inline_js = [\n"",
       ""    function(Bokeh) {\n"",
       ""      Bokeh.set_log_level(\""info\"");\n"",
       ""    },\n"",
       ""    \n"",
       ""    function(Bokeh) {\n"",
       ""      \n"",
       ""    },\n"",
       ""    function(Bokeh) {} // ensure no trailing comma for IE\n"",
       ""  ];\n"",
       ""\n"",
       ""  function run_inline_js() {\n"",
       ""    \n"",
       ""    if ((root.Bokeh !== undefined) || (force === true)) {\n"",
       ""      for (var i = 0; i < inline_js.length; i++) {\n"",
       ""        inline_js[i].call(root, root.Bokeh);\n"",
       ""      }if (force === true) {\n"",
       ""        display_loaded();\n"",
       ""      }} else if (Date.now() < root._bokeh_timeout) {\n"",
       ""      setTimeout(run_inline_js, 100);\n"",
       ""    } else if (!root._bokeh_failed_load) {\n"",
       ""      console.log(\""Bokeh: BokehJS failed to load within specified timeout.\"");\n"",
       ""      root._bokeh_failed_load = true;\n"",
       ""    } else if (force !== true) {\n"",
       ""      var cell = $(document.getElementById(\""1001\"")).parents('.cell').data().cell;\n"",
       ""      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n"",
       ""    }\n"",
       ""\n"",
       ""  }\n"",
       ""\n"",
       ""  if (root._bokeh_is_loading === 0) {\n"",
       ""    console.debug(\""Bokeh: BokehJS loaded, going straight to plotting\"");\n"",
       ""    run_inline_js();\n"",
       ""  } else {\n"",
       ""    load_libs(css_urls, js_urls, function() {\n"",
       ""      console.debug(\""Bokeh: BokehJS plotting callback run at\"", now());\n"",
       ""      run_inline_js();\n"",
       ""    });\n"",
       ""  }\n"",
       ""}(window));""
      ],
      ""application/vnd.bokehjs_load.v0+json"": ""\n(function(root) {\n  function now() {\n    return new Date();\n  }\n\n  var force = true;\n\n  if (typeof root._bokeh_onload_callbacks === \""undefined\"" || force === true) {\n    root._bokeh_onload_callbacks = [];\n    root._bokeh_is_loading = undefined;\n  }\n\n  \n\n  \n  if (typeof (root._bokeh_timeout) === \""undefined\"" || force === true) {\n    root._bokeh_timeout = Date.now() + 5000;\n    root._bokeh_failed_load = false;\n  }\n\n  var NB_LOAD_WARNING = {'data': {'text/html':\n     \""<div style='background-color: #fdd'>\\n\""+\n     \""<p>\\n\""+\n     \""BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\""+\n     \""may be due to a slow or bad network connection. Possible fixes:\\n\""+\n     \""</p>\\n\""+\n     \""<ul>\\n\""+\n     \""<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\""+\n     \""<li>use INLINE resources instead, as so:</li>\\n\""+\n     \""</ul>\\n\""+\n     \""<code>\\n\""+\n     \""from bokeh.resources import INLINE\\n\""+\n     \""output_notebook(resources=INLINE)\\n\""+\n     \""</code>\\n\""+\n     \""</div>\""}};\n\n  function display_loaded() {\n    var el = document.getElementById(\""1001\"");\n    if (el != null) {\n      el.textContent = \""BokehJS is loading...\"";\n    }\n    if (root.Bokeh !== undefined) {\n      if (el != null) {\n        el.textContent = \""BokehJS \"" + root.Bokeh.version + \"" successfully loaded.\"";\n      }\n    } else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(display_loaded, 100)\n    }\n  }\n\n\n  function run_callbacks() {\n    try {\n      root._bokeh_onload_callbacks.forEach(function(callback) {\n        if (callback != null)\n          callback();\n      });\n    } finally {\n      delete root._bokeh_onload_callbacks\n    }\n    console.debug(\""Bokeh: all callbacks have finished\"");\n  }\n\n  function load_libs(css_urls, js_urls, callback) {\n    if (css_urls == null) css_urls = [];\n    if (js_urls == null) js_urls = [];\n\n    root._bokeh_onload_callbacks.push(callback);\n    if (root._bokeh_is_loading > 0) {\n      console.debug(\""Bokeh: BokehJS is being loaded, scheduling callback at\"", now());\n      return null;\n    }\n    if (js_urls == null || js_urls.length === 0) {\n      run_callbacks();\n      return null;\n    }\n    console.debug(\""Bokeh: BokehJS not loaded, scheduling load and callback at\"", now());\n    root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n    function on_load() {\n      root._bokeh_is_loading--;\n      if (root._bokeh_is_loading === 0) {\n        console.debug(\""Bokeh: all BokehJS libraries/stylesheets loaded\"");\n        run_callbacks()\n      }\n    }\n\n    function on_error() {\n      console.error(\""failed to load \"" + url);\n    }\n\n    for (var i = 0; i < css_urls.length; i++) {\n      var url = css_urls[i];\n      const element = document.createElement(\""link\"");\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.rel = \""stylesheet\"";\n      element.type = \""text/css\"";\n      element.href = url;\n      console.debug(\""Bokeh: injecting link tag for BokehJS stylesheet: \"", url);\n      document.body.appendChild(element);\n    }\n\n    for (var i = 0; i < js_urls.length; i++) {\n      var url = js_urls[i];\n      var element = document.createElement('script');\n      element.onload = on_load;\n      element.onerror = on_error;\n      element.async = false;\n      element.src = url;\n      console.debug(\""Bokeh: injecting script tag for BokehJS library: \"", url);\n      document.head.appendChild(element);\n    }\n  };var element = document.getElementById(\""1001\"");\n  if (element == null) {\n    console.error(\""Bokeh: ERROR: autoload.js configured with elementid '1001' but no matching script tag was found. \"")\n    return false;\n  }\n\n  function inject_raw_css(css) {\n    const element = document.createElement(\""style\"");\n    element.appendChild(document.createTextNode(css));\n    document.body.appendChild(element);\n  }\n\n  var js_urls = [\""https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.js\"", \""https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.js\"", \""https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.js\"", \""https://cdn.pydata.org/bokeh/release/bokeh-gl-1.2.0.min.js\""];\n  var css_urls = [\""https://cdn.pydata.org/bokeh/release/bokeh-1.2.0.min.css\"", \""https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.2.0.min.css\"", \""https://cdn.pydata.org/bokeh/release/bokeh-tables-1.2.0.min.css\""];\n\n  var inline_js = [\n    function(Bokeh) {\n      Bokeh.set_log_level(\""info\"");\n    },\n    \n    function(Bokeh) {\n      \n    },\n    function(Bokeh) {} // ensure no trailing comma for IE\n  ];\n\n  function run_inline_js() {\n    \n    if ((root.Bokeh !== undefined) || (force === true)) {\n      for (var i = 0; i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }if (force === true) {\n        display_loaded();\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\""Bokeh: BokehJS failed to load within specified timeout.\"");\n      root._bokeh_failed_load = true;\n    } else if (force !== true) {\n      var cell = $(document.getElementById(\""1001\"")).parents('.cell').data().cell;\n      cell.output_area.append_execute_result(NB_LOAD_WARNING)\n    }\n\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\""Bokeh: BokehJS loaded, going straight to plotting\"");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, function() {\n      console.debug(\""Bokeh: BokehJS plotting callback run at\"", now());\n      run_inline_js();\n    });\n  }\n}(window));""
     },
     ""metadata"": {},
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""from bokeh.plotting import figure, show\n"",
    ""from bokeh.io import output_notebook\n"",
    ""from bokeh.models import ColumnDataSource\n"",
    ""from bokeh.models.tools import HoverTool\n"",
    ""from sklearn.linear_model import LinearRegression\n"",
    ""from bokeh.models import Slope\n"",
    ""output_notebook()""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 131,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": [
    ""node_sim_df2 = node_sim_df[['m.similarity_score','maccs_sim','cid1','cid2']]""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": 133,
   ""metadata"": {},
   ""outputs"": [
    {
     ""data"": {
      ""text/html"": [
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       ""\n"",
       ""\n"",
       ""\n"",
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     ""metadata"": {},
     ""output_type"": ""display_data""
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similarity\"",\""formatter\"":{\""id\"":\""6193\"",\""type\"":\""BasicTickFormatter\""},\""ticker\"":{\""id\"":\""5948\"",\""type\"":\""BasicTicker\""}},\""id\"":\""5947\"",\""type\"":\""LinearAxis\""},{\""attributes\"":{\""axis_label\"":\""node similarity\"",\""formatter\"":{\""id\"":\""6191\"",\""type\"":\""BasicTickFormatter\""},\""ticker\"":{\""id\"":\""5943\"",\""type\"":\""BasicTicker\""}},\""id\"":\""5942\"",\""type\"":\""LinearAxis\""},{\""attributes\"":{},\""id\"":\""6193\"",\""type\"":\""BasicTickFormatter\""},{\""attributes\"":{\""callback\"":null,\""tooltips\"":[[\""molecule1\"",\""@cid1\""],[\""molecule2\"",\""@cid2\""],[\""node_sim\"",\""@m.similarity_score\""],[\""maccs_sim\"",\""@maccs_sim\""]]},\""id\"":\""5971\"",\""type\"":\""HoverTool\""},{\""attributes\"":{\""callback\"":null},\""id\"":\""5936\"",\""type\"":\""DataRange1d\""},{\""attributes\"":{\""active_drag\"":\""auto\"",\""active_inspect\"":\""auto\"",\""active_multi\"":null,\""active_scroll\"":\""auto\"",\""active_tap\"":\""auto\"",\""tools\"":[{\""id\"":\""5952\"",\""type\"":\""PanTool\""},{\""id\"":\""5953\"",\""type\"":\""WheelZoomTool\""},{\""id\"":\""5954\"",\""type\"":\""BoxZoomTool\""},{\""id\"":\""5955\"",\""type\"":\""SaveTool\""},{\""id\"":\""5956\"",\""type\"":\""ResetTool\""},{\""id\"":\""5957\"",\""type\"":\""HelpTool\""},{\""id\"":\""5971\"",\""type\"":\""HoverTool\""}]},\""id\"":\""5958\"",\""type\"":\""Toolbar\""},{\""attributes\"":{\""callback\"":null},\""id\"":\""5934\"",\""type\"":\""DataRange1d\""},{\""attributes\"":{\""text\"":\""\""},\""id\"":\""6189\"",\""type\"":\""Title\""},{\""attributes\"":{},\""id\"":\""5957\"",\""type\"":\""HelpTool\""},{\""attributes\"":{},\""id\"":\""5948\"",\""type\"":\""BasicTicker\""}],\""root_ids\"":[\""5933\""]},\""title\"":\""Bokeh Application\"",\""version\"":\""1.2.0\""}};\n"",
       ""  var render_items = [{\""docid\"":\""9657eb93-2213-4c11-9bcb-5b4a526d508f\"",\""roots\"":{\""5933\"":\""f3eccc19-007e-487a-90c8-a0454926dd96\""}}];\n"",
       ""  root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n"",
       ""\n"",
       ""  }\n"",
       ""  if (root.Bokeh !== undefined) {\n"",
       ""    embed_document(root);\n"",
       ""  } else {\n"",
       ""    var attempts = 0;\n"",
       ""    var timer = setInterval(function(root) {\n"",
       ""      if (root.Bokeh !== undefined) {\n"",
       ""        embed_document(root);\n"",
       ""        clearInterval(timer);\n"",
       ""      }\n"",
       ""      attempts++;\n"",
       ""      if (attempts > 100) {\n"",
       ""        console.log(\""Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\"");\n"",
       ""        clearInterval(timer);\n"",
       ""      }\n"",
       ""    }, 10, root)\n"",
       ""  }\n"",
       ""})(window);""
      ],
      ""application/vnd.bokehjs_exec.v0+json"": """"
     },
     ""metadata"": {
      ""application/vnd.bokehjs_exec.v0+json"": {
       ""id"": ""5933""
      }
     },
     ""output_type"": ""display_data""
    }
   ],
   ""source"": [
    ""p = figure()\n"",
    ""p.circle(x='m.similarity_score', y='maccs_sim',\n"",
    ""         source=node_sim_df2,\n"",
    ""         size=5, color='blue')\n"",
    ""p.xaxis.axis_label = \""node similarity\""\n"",
    ""p.yaxis.axis_label = \""maccs similarity\""\n"",
    ""hover = HoverTool()\n"",
    ""hover.tooltips=[\n"",
    ""    ('molecule1', '@cid1'),\n"",
    ""    ('molecule2', '@cid2'),\n"",
    ""    ('node_sim','@m.similarity_score'),\n"",
    ""    ('maccs_sim','@maccs_sim'),\n"",
    ""#    ('fragment_pageRank', '@fragment_pageRank')\n"",
    ""]\n"",
    ""\n"",
    ""p.add_tools(hover)\n"",
    ""\n"",
    ""show(p)""
   ]
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  },
  {
   ""cell_type"": ""code"",
   ""execution_count"": null,
   ""metadata"": {},
   ""outputs"": [],
   ""source"": []
  }
 ],
 ""metadata"": {
  ""kernelspec"": {
   ""display_name"": ""Python [conda env:rdkit-env-1] *"",
   ""language"": ""python"",
   ""name"": ""conda-env-rdkit-env-1-py""
  },
  ""language_info"": {
   ""codemirror_mode"": {
    ""name"": ""ipython"",
    ""version"": 3
   },
   ""file_extension"": "".py"",
   ""mimetype"": ""text/x-python"",
   ""name"": ""python"",
   ""nbconvert_exporter"": ""python"",
   ""pygments_lexer"": ""ipython3"",
   ""version"": ""3.6.8""
  }
 },
 ""nbformat"": 4,
 ""nbformat_minor"": 2
}
","Unknown"
"Assay","bramble50/mmp_kg","docs/conf.py",".py","9154","273","# -*- coding: utf-8 -*-
#
# This file is execfile()d with the current directory set to its containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

import os
import sys
import inspect
import shutil

__location__ = os.path.join(os.getcwd(), os.path.dirname(
    inspect.getfile(inspect.currentframe())))

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.join(__location__, '../src'))

# -- Run sphinx-apidoc ------------------------------------------------------
# This hack is necessary since RTD does not issue `sphinx-apidoc` before running
# `sphinx-build -b html . _build/html`. See Issue:
# https://github.com/rtfd/readthedocs.org/issues/1139
# DON'T FORGET: Check the box ""Install your project inside a virtualenv using
# setup.py install"" in the RTD Advanced Settings.
# Additionally it helps us to avoid running apidoc manually

try:  # for Sphinx >= 1.7
    from sphinx.ext import apidoc
except ImportError:
    from sphinx import apidoc

output_dir = os.path.join(__location__, ""api"")
module_dir = os.path.join(__location__, ""../src/mmp_kg"")
try:
    shutil.rmtree(output_dir)
except FileNotFoundError:
    pass

try:
    import sphinx
    from pkg_resources import parse_version

    cmd_line_template = ""sphinx-apidoc -f -o {outputdir} {moduledir}""
    cmd_line = cmd_line_template.format(outputdir=output_dir, moduledir=module_dir)

    args = cmd_line.split("" "")
    if parse_version(sphinx.__version__) >= parse_version('1.7'):
        args = args[1:]

    apidoc.main(args)
except Exception as e:
    print(""Running `sphinx-apidoc` failed!\n{}"".format(e))

# -- General configuration -----------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
# needs_sphinx = '1.0'

# Add any Sphinx extension module names here, as strings. They can be extensions
# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.todo',
              'sphinx.ext.autosummary', 'sphinx.ext.viewcode', 'sphinx.ext.coverage',
              'sphinx.ext.doctest', 'sphinx.ext.ifconfig', 'sphinx.ext.mathjax',
              'sphinx.ext.napoleon']

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']

# The suffix of source filenames.
source_suffix = '.rst'

# The encoding of source files.
# source_encoding = 'utf-8-sig'

# The master toctree document.
master_doc = 'index'

# General information about the project.
project = u'MMP_KG'
copyright = u'2019, bramble50'

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = ''  # Is set by calling `setup.py docs`
# The full version, including alpha/beta/rc tags.
release = ''  # Is set by calling `setup.py docs`

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
# language = None

# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
# today = ''
# Else, today_fmt is used as the format for a strftime call.
# today_fmt = '%B %d, %Y'

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']

# The reST default role (used for this markup: `text`) to use for all documents.
# default_role = None

# If true, '()' will be appended to :func: etc. cross-reference text.
# add_function_parentheses = True

# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
# add_module_names = True

# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
# show_authors = False

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'

# A list of ignored prefixes for module index sorting.
# modindex_common_prefix = []

# If true, keep warnings as ""system message"" paragraphs in the built documents.
# keep_warnings = False


# -- Options for HTML output ---------------------------------------------------

# The theme to use for HTML and HTML Help pages.  See the documentation for
# a list of builtin themes.
html_theme = 'alabaster'

# Theme options are theme-specific and customize the look and feel of a theme
# further.  For a list of options available for each theme, see the
# documentation.
html_theme_options = {
    'sidebar_width': '300px',
    'page_width': '1200px'
}

# Add any paths that contain custom themes here, relative to this directory.
# html_theme_path = []

# The name for this set of Sphinx documents.  If None, it defaults to
# ""<project> v<release> documentation"".
try:
    from mmp_kg import __version__ as version
except ImportError:
    pass
else:
    release = version

# A shorter title for the navigation bar.  Default is the same as html_title.
# html_short_title = None

# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
# html_logo = """"

# The name of an image file (within the static path) to use as favicon of the
# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
# html_favicon = None

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named ""default.css"" will overwrite the builtin ""default.css"".
html_static_path = ['_static']

# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
# html_last_updated_fmt = '%b %d, %Y'

# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
# html_use_smartypants = True

# Custom sidebar templates, maps document names to template names.
# html_sidebars = {}

# Additional templates that should be rendered to pages, maps page names to
# template names.
# html_additional_pages = {}

# If false, no module index is generated.
# html_domain_indices = True

# If false, no index is generated.
# html_use_index = True

# If true, the index is split into individual pages for each letter.
# html_split_index = False

# If true, links to the reST sources are added to the pages.
# html_show_sourcelink = True

# If true, ""Created using Sphinx"" is shown in the HTML footer. Default is True.
# html_show_sphinx = True

# If true, ""(C) Copyright ..."" is shown in the HTML footer. Default is True.
# html_show_copyright = True

# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it.  The value of this option must be the
# base URL from which the finished HTML is served.
# html_use_opensearch = ''

# This is the file name suffix for HTML files (e.g. "".xhtml"").
# html_file_suffix = None

# Output file base name for HTML help builder.
htmlhelp_basename = 'mmp_kg-doc'


# -- Options for LaTeX output --------------------------------------------------

latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
# 'papersize': 'letterpaper',

# The font size ('10pt', '11pt' or '12pt').
# 'pointsize': '10pt',

# Additional stuff for the LaTeX preamble.
# 'preamble': '',
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title, author, documentclass [howto/manual]).
latex_documents = [
  ('index', 'user_guide.tex', u'MMP_KG Documentation',
   u'bramble50', 'manual'),
]

# The name of an image file (relative to this directory) to place at the top of
# the title page.
# latex_logo = """"

# For ""manual"" documents, if this is true, then toplevel headings are parts,
# not chapters.
# latex_use_parts = False

# If true, show page references after internal links.
# latex_show_pagerefs = False

# If true, show URL addresses after external links.
# latex_show_urls = False

# Documents to append as an appendix to all manuals.
# latex_appendices = []

# If false, no module index is generated.
# latex_domain_indices = True

# -- External mapping ------------------------------------------------------------
python_version = '.'.join(map(str, sys.version_info[0:2]))
intersphinx_mapping = {
    'sphinx': ('http://www.sphinx-doc.org/en/stable', None),
    'python': ('https://docs.python.org/' + python_version, None),
    'matplotlib': ('https://matplotlib.org', None),
    'numpy': ('https://docs.scipy.org/doc/numpy', None),
    'sklearn': ('http://scikit-learn.org/stable', None),
    'pandas': ('http://pandas.pydata.org/pandas-docs/stable', None),
    'scipy': ('https://docs.scipy.org/doc/scipy/reference', None),
}
","Python"
"Assay","bramble50/mmp_kg","tests/test_skeleton.py",".py","290","17","# -*- coding: utf-8 -*-

import pytest
from mmp_kg.skeleton import fib

__author__ = ""bramble50""
__copyright__ = ""bramble50""
__license__ = ""mit""


def test_fib():
    assert fib(1) == 1
    assert fib(2) == 1
    assert fib(7) == 13
    with pytest.raises(AssertionError):
        fib(-10)
","Python"
"Assay","bramble50/mmp_kg","tests/conftest.py",".py","227","11","# -*- coding: utf-8 -*-
""""""
    Dummy conftest.py for mmp_kg.

    If you don't know what this is for, just leave it empty.
    Read more about conftest.py under:
    https://pytest.org/latest/plugins.html
""""""

# import pytest
","Python"
"Assay","cystone/CNSSingleCellR","07_visual.R",".R","1946","48","library(Seurat)
library(tidyverse)
library(patchwork)
rm(list=ls())
dir.create(""07_visual"")
load(file=""./2.results/04_subCl_scRNA.rdata"") 
# RidgePlot山脊图
p1 = RidgePlot(scRNA, features = ""FCN1"")
p2 = RidgePlot(scRNA, features = ""PC_2"")
plotc = p1/p2 + plot_layout(guides = 'collect')
ggsave('07_visual/ridgeplot_eg.png', plotc, width = 8,height = 8)

# VInPlot小提琴图
p1 = VlnPlot(scRNA, features = ""nCount_RNA"", pt.size = 0)
p2 = VlnPlot(scRNA, features = ""CD8A"", pt.size = 0)
plotc = p1/p2 + plot_layout(guides = 'collect')
ggsave('07_visual/vlnplot_eg.png', plotc, width = 8,height = 8)

# FeaturePlot特征图
p1 <- FeaturePlot(scRNA,features = ""CD8A"", reduction = 'umap')
p2 <- FeaturePlot(scRNA,features = ""CD79A"", reduction = 'umap')
plotc = p1|p2
ggsave('07_visual/featureplot_eg.png', plotc, width = 10, height = 4)

# DotPlot点图
genelist = c('LYZ','CD79A','CD8A','CD8B','GZMB','FCGR3A')
p = DotPlot(scRNA, features = genelist)
ggsave('07_visual/dotplot_eg.png', p, width = 7, height = 5)

# DoHeatmap热图
genelist = read.csv(""03_cellIdentity/top10_diff_genes_wilcox.csv"")
genelist <- pull(genelist, gene) %>% as.character
p = DoHeatmap(scRNA, features = genelist, group.by = ""seurat_clusters"")
ggsave('07_visual/heatmap_eg.png', p, width = 12, height = 9)

# FeatureScatter散点图
p1 <- FeatureScatter(scRNA, feature1 = 'PC_1', feature2 = 'PC_2')
p2 <- FeatureScatter(scRNA, feature1 = 'nCount_RNA', feature2 = 'nFeature_RNA')
plotc = p1|p2
ggsave('07_visual/featurescatter_eg.png', plotc, width = 10, height = 4)

# DimPlot降维图
p1 <- DimPlot(scRNA, reduction = 'tsne', group.by = ""celltype"", label=T)
p2 <- DimPlot(scRNA, reduction = 'umap', group.by = ""Phase"", label=T)
p3 <- DimPlot(scRNA, reduction = 'pca', group.by = ""celltype"", label=T)
p4 <- DimPlot(scRNA, reduction = 'umap', group.by = ""seurat_clusters"", label=T)
plotc = (p1|p2)/(p3|p4)
ggsave('07_visual/dimplot_eg.png', plotc, width = 10, height = 8)","R"
"Assay","cystone/CNSSingleCellR","06_enrich.R",".R","3674","80","library(Seurat)
library(tidyverse)
library(patchwork)
library(monocle)
library(clusterProfiler)
library(org.Hs.eg.db)
rm(list=ls())
dir.create(""06_enrich"")

load(file=""./2.results/04_subCl_scRNA.rdata"") 
load(file=""./2.results/05_pseudoTime_mycds.rdata"") 

# 基因差异表达分析
#比较cluster0和cluster1的差异表达基因
dge.cluster <- FindMarkers(scRNA,ident.1 = 0,ident.2 = 1)
sig_dge.cluster <- subset(dge.cluster, p_val_adj<0.01&abs(avg_log2FC)>1)
#比较B_cell和T_cells的差异表达基因
dge.celltype <- FindMarkers(scRNA, ident.1 = 'B_cell', ident.2 = 'T_cells', group.by = 'celltype')
sig_dge.celltype <- subset(dge.celltype, p_val_adj<0.01&abs(avg_log2FC)>1)
#比较拟时State1和State3的差异表达基因
p_data <- subset(pData(mycds),select='State')
scRNAsub <- subset(scRNA, cells=row.names(p_data))
scRNAsub <- AddMetaData(scRNAsub,p_data,col.name = 'State')
dge.State <- FindMarkers(scRNAsub, ident.1 = 1, ident.2 = 3, group.by = 'State')
sig_dge.State <- subset(dge.State, p_val_adj<0.01&abs(avg_log2FC)>1)

#差异基因GO富集分析
ego_ALL <- enrichGO(gene = row.names(sig_dge.celltype),
                    #universe     = row.names(dge.celltype),
                    OrgDb = 'org.Hs.eg.db',
                    keyType       = 'SYMBOL',
                    ont           = ""ALL"",
                    pAdjustMethod = ""BH"",
                    pvalueCutoff  = 0.01,
                    qvalueCutoff  = 0.05)
ego_all <- data.frame(ego_ALL)
write.csv(ego_all,'06_enrich/enrichGO.csv')           
ego_CC <- enrichGO(gene          = row.names(sig_dge.celltype),
                   #universe     = row.names(dge.celltype),
                   OrgDb         = 'org.Hs.eg.db',
                   keyType       = 'SYMBOL',
                   ont           = ""CC"",
                   pAdjustMethod = ""BH"",
                   pvalueCutoff  = 0.01,
                   qvalueCutoff  = 0.05)
ego_MF <- enrichGO(gene          = row.names(sig_dge.celltype),
                   #universe     = row.names(dge.celltype),
                   OrgDb         = 'org.Hs.eg.db',
                   keyType       = 'SYMBOL',
                   ont           = ""MF"",
                   pAdjustMethod = ""BH"",
                   pvalueCutoff  = 0.01,
                   qvalueCutoff  = 0.05)
ego_BP <- enrichGO(gene          = row.names(sig_dge.celltype),
                   #universe     = row.names(dge.celltype),
                   OrgDb         = 'org.Hs.eg.db',
                   keyType       = 'SYMBOL',
                   ont           = ""BP"",
                   pAdjustMethod = ""BH"",
                   pvalueCutoff  = 0.01,
                   qvalueCutoff  = 0.05)           
ego_CC@result$Description <- substring(ego_CC@result$Description,1,70)
ego_MF@result$Description <- substring(ego_MF@result$Description,1,70)
ego_BP@result$Description <- substring(ego_BP@result$Description,1,70)
p_BP <- barplot(ego_BP,showCategory = 10) + ggtitle(""barplot for Biological process"")
p_CC <- barplot(ego_CC,showCategory = 10) + ggtitle(""barplot for Cellular component"")
p_MF <- barplot(ego_MF,showCategory = 10) + ggtitle(""barplot for Molecular function"")
plotc <- p_BP/p_CC/p_MF
ggsave('06_enrich/enrichGO.png', plotc, width = 12,height = 10)


# 差异基kegg富集分析
genelist <- bitr(row.names(sig_dge.celltype), fromType=""SYMBOL"",
                 toType=""ENTREZID"", OrgDb='org.Hs.eg.db')
genelist <- pull(genelist,ENTREZID)               
ekegg <- enrichKEGG(gene = genelist, organism = 'hsa')
p1 <- barplot(ekegg, showCategory=20)
p2 <- dotplot(ekegg, showCategory=20)
plotc = p1/p2
ggsave(""06_enrich/enrichKEGG.png"", plot = plotc, width = 12, height = 10)","R"
"Assay","cystone/CNSSingleCellR","00_jichu.R",".R","641","17","install.packages(""devtools"", dependencies=T)
install.packages(""BiocManager"", dependencies=T)
install.packages(""tidyverse"", dependencies=T)
install.packages('Seurat', dependencies=T)
BiocManager::install(c(""SingleR"",""monocle"", ""DESeq2""),ask = F,update = F) 
BiocManager::install(c(""clusterProfiler"",""DOSE"",""pheatmap""),ask = F,update = F)
BiocManager::install(c(""org.Hs.eg.db"",""org.Mm.eg.db"",""org.Rn.eg.db""),ask = F,
                     update = F)

devtools::install_github('RGLab/MAST', upgrade=F, build_vignettes = F)
library(Seurat)
library(MAST)


BiocManager::install(c(""SingleCellExperiment""),ask = F,
                     update = F)
","R"
"Assay","cystone/CNSSingleCellR","00_pkgs_docs_shared.R",".R","346","13","suppressMessages(library(tidyverse))
suppressMessages(library(pacman))
suppressMessages(library(data.table))

wkPath <- c('./download')
for(i in wkPath){
  wkPathi = i
  # wkPathi = paste0(sectionName, '/', i)
  #每一个子项目都含plot、result、input
  if (!dir.exists(wkPathi)) dir.create(wkPathi)
}
rm(list=c('i', 'wkPathi', 'wkPath'))
","R"
"Assay","cystone/CNSSingleCellR","04_subCluster.R",".R","3721","84","library(Seurat)
library(monocle)
library(tidyverse)
library(patchwork)
rm(list=ls())
dir.create(""04_subCluster"")
load(file=""./2.results/03_cell_id_scRNA.rdata"")
##提取细胞子集
Cells.sub <- subset(scRNA@meta.data, celltype==""T_cells"")
scRNAsub <- subset(scRNA, cells=row.names(Cells.sub))

# 提重新降维聚类



# 因为再聚类的细胞之间差异比较小，所以聚类函数FindClusters()控制分辨率的参数建议调高到resolution = 0.9。

##PCA降维
scRNAsub <- FindVariableFeatures(scRNAsub, selection.method = ""vst"", nfeatures = 2000)
scale.genes <-  rownames(scRNAsub)
scRNAsub <- ScaleData(scRNAsub, features = scale.genes)
scRNAsub <- RunPCA(scRNAsub, features = VariableFeatures(scRNAsub))
ElbowPlot(scRNAsub, ndims=20, reduction=""pca"")
pc.num=1:10
##细胞聚类
scRNAsub <- FindNeighbors(scRNAsub, dims = pc.num) 
scRNAsub <- FindClusters(scRNAsub, resolution = 0.9)
table(scRNAsub@meta.data$seurat_clusters)
metadata <- scRNAsub@meta.data
cell_cluster <- data.frame(cell_ID=rownames(metadata), cluster_ID=metadata$seurat_clusters)
write.csv(cell_cluster,'04_subCluster/cell_cluster.csv',row.names = F)
##非线性降维
#tSNE
scRNAsub = RunTSNE(scRNAsub, dims = pc.num)
embed_tsne <- Embeddings(scRNAsub, 'tsne')
write.csv(embed_tsne,'04_subCluster/embed_tsne.csv')
plot1 = DimPlot(scRNAsub, reduction = ""tsne"") 
ggsave(""04_subCluster/tSNE.pdf"", plot = plot1, width = 8, height = 7)
ggsave(""04_subCluster/tSNE.png"", plot = plot1, width = 8, height = 7)
#UMAP
scRNAsub <- RunUMAP(scRNAsub, dims = pc.num)
embed_umap <- Embeddings(scRNAsub, 'umap')
write.csv(embed_umap,'04_subCluster/embed_umap.csv') 
plot2 = DimPlot(scRNAsub, reduction = ""umap"") 
ggsave(""04_subCluster/UMAP.pdf"", plot = plot2, width = 8, height = 7)
ggsave(""04_subCluster/UMAP.png"", plot = plot2, width = 8, height = 7)
#合并tSNE与UMAP
plotc <- plot1+plot2+ plot_layout(guides = 'collect')
ggsave(""04_subCluster/tSNE_UMAP.pdf"", plot = plotc, width = 10, height = 5)
ggsave(""04_subCluster/tSNE_UMAP.png"", plot = plotc, width = 10, height = 5)


# Cluster差异分析
diff.wilcox = FindAllMarkers(scRNAsub)
all.markers = diff.wilcox %>% select(gene, everything()) %>% subset(p_val<0.05)
top10 = all.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
write.csv(all.markers, ""04_subCluster/diff_genes_wilcox.csv"", row.names = F)
write.csv(top10, ""04_subCluster/top10_diff_genes_wilcox.csv"", row.names = F)

# SingleR细胞鉴定

##细胞类型鉴定
library(SingleR)
refdata <- MonacoImmuneData()
testdata <- GetAssayData(scRNAsub, slot=""data"")
clusters <- scRNAsub@meta.data$seurat_clusters
cellpred <- SingleR(test = testdata, ref = refdata, labels = refdata$label.fine, 
                    method = ""cluster"", clusters = clusters, 
                    assay.type.test = ""logcounts"", assay.type.ref = ""logcounts"")
celltype = data.frame(ClusterID=rownames(cellpred), celltype=cellpred$labels, stringsAsFactors = F)
write.csv(celltype,""04_subCluster/celltype_singleR.csv"",row.names = F)
scRNAsub@meta.data$celltype = ""NA""
for(i in 1:nrow(celltype)){
  scRNAsub@meta.data[which(scRNAsub@meta.data$seurat_clusters == celltype$ClusterID[i]),'celltype'] <- celltype$celltype[i]}
p1 = DimPlot(scRNAsub, group.by=""celltype"", label=T, label.size=5, reduction='tsne')
p2 = DimPlot(scRNAsub, group.by=""celltype"", label=T, label.size=5, reduction='umap')
p3 = plotc <- p1+p2+ plot_layout(guides = 'collect')
ggsave(""04_subCluster/tSNE_celltype.pdf"", p1, width=7 ,height=6)
ggsave(""04_subCluster/UMAP_celltype.pdf"", p2, width=7 ,height=6)
ggsave(""04_subCluster/celltype.pdf"", p3, width=10 ,height=5)
ggsave(""04_subCluster/celltype.png"", p3, width=10 ,height=5)

save(scRNA, file=""./2.results/04_subCl_scRNA.rdata"")
","R"
"Assay","cystone/CNSSingleCellR","02_cl.R",".R","4240","95","library(Seurat)
library(tidyverse)
library(patchwork)
rm(list=ls())
dir.create(""X02_cluster"")
load('./processData/01_QC_scRNA.rdata')
#-------寻找高变基因----------
scRNA <- FindVariableFeatures(scRNA, selection.method = ""vst"", nfeatures = 2000) 
top10 <- head(VariableFeatures(scRNA), 10) 
plot1 <- VariableFeaturePlot(scRNA) 
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE, size=2.5) 
plot <- CombinePlots(plots = list(plot1, plot2),legend=""bottom"") 
ggsave(""X02_cluster/VariableFeatures.pdf"", plot = plot, width = 8, height = 6) 
ggsave(""X02_cluster/VariableFeatures.png"", plot = plot, width = 8, height = 6)
#--------数据中心化-------
##如果内存足够最好对所有基因进行中心化
scale.genes <-  rownames(scRNA)
scRNA <- ScaleData(scRNA, features = scale.genes)
##如果内存不够，可以只对高变基因进行标准化
# scale.genes <-  VariableFeatures(scRNA)
# scRNA <- ScaleData(scRNA, features = scale.genes)
#原始表达矩阵
GetAssayData(scRNA,slot=""counts"",assay=""RNA"")[1:5, 1:5] 
#标准化之后的表达矩阵              
GetAssayData(scRNA,slot=""data"",assay=""RNA"")[1:5, 1:5]
#中心化之后的表达矩阵 
GetAssayData(scRNA,slot=""scale.data"",assay=""RNA"")[1:5, 1:5] 
#----------细胞周期回归-----------
# 细胞周期评分
g2m_genes = cc.genes$g2m.genes
g2m_genes = CaseMatch(search = g2m_genes, match = rownames(scRNA))
s_genes = cc.genes$s.genes
s_genes = CaseMatch(search = s_genes, match = rownames(scRNA))
scRNA <- CellCycleScoring(object=scRNA,  g2m.features=g2m_genes,  
                          s.features=s_genes)

scRNAa <- RunPCA(scRNA, features = c(s_genes, g2m_genes))
p <- DimPlot(scRNAa, reduction = ""pca"", group.by = ""Phase"")
ggsave(""X02_cluster/cellcycle_pca.png"", p, width = 8, height = 6)
##如果需要消除细胞周期的影响
#scRNAb <- ScaleData(scRNA, vars.to.regress = c(""S.Score"", ""G2M.Score""), 
# features = rownames(scRNA))
#-----------降维，提取主成分--------
# PCA降维
scRNA <- RunPCA(scRNA, features = VariableFeatures(scRNA)) 
plot1 <- DimPlot(scRNA, reduction = ""pca"", group.by=""orig.ident"") 
plot2 <- ElbowPlot(scRNA, ndims=20, reduction=""pca"") 
plotc <- plot1+plot2
ggsave(""X02_cluster/orig.ident_pca.pdf"", plot = plotc, width = 8, height = 4) 
ggsave(""X02_cluster/orig.ident_pca.png"", plot = plotc, width = 8, height = 4)

pc.num=1:15
# 获取PCA结果 
# 此部分代码为分析非必须代码，不建议运行！！！
{
  #获取基因在pc轴上的投射值
  Loadings(object = scRNA[[""pca""]])
  #获取各个细胞的pc值
  Embeddings(object = scRNA[[""pca""]])
  #获取各pc轴解释量方差
  Stdev(scRNA)
  #查看决定pc值的top10基因， 此例查看pc1-pc5轴
  print(scRNA[[""pca""]], dims = 1:5, nfeatures = 10) 
  #查看决定pc值的top10基因在500个细胞中的热图，此例查看pc1-pc9轴
  DimHeatmap(scRNA, dims = 1:9, nfeatures=10, cells = 500, balanced = TRUE)
}
#----------细胞聚类------------
scRNA <- FindNeighbors(scRNA, dims = pc.num) 
scRNA <- FindClusters(scRNA, resolution = 0.3)
table(scRNA@meta.data$seurat_clusters)
metadata <- scRNA@meta.data
cell_cluster <- data.frame(cell_ID=rownames(metadata), cluster_ID=metadata$seurat_clusters)
write.csv(cell_cluster,'X02_cluster/cell_cluster.csv',row.names = F)

#-----------非线性降维--------------
#tSNE
scRNA = RunTSNE(scRNA, dims = pc.num)
embed_tsne <- Embeddings(scRNA, 'tsne')
write.csv(embed_tsne,'X02_cluster/embed_tsne.csv')
plot1 = DimPlot(scRNA, reduction = ""tsne"") 
ggsave(""X02_cluster/tSNE.pdf"", plot = plot1, width = 8, height = 7)
ggsave(""X02_cluster/tSNE.png"", plot = plot1, width = 8, height = 7)
#UMAP
scRNA <- RunUMAP(scRNA, dims = pc.num)
embed_umap <- Embeddings(scRNA, 'umap')
write.csv(embed_umap,'X02_cluster/embed_umap.csv') 
plot2 = DimPlot(scRNA, reduction = ""umap"") 
ggsave(""X02_cluster/UMAP.pdf"", plot = plot2, width = 8, height = 7)
ggsave(""X02_cluster/UMAP.png"", plot = plot2, width = 8, height = 7)
#合并tSNE与UMAP
plotc <- plot1+plot2+ plot_layout(guides = 'collect')
ggsave(""X02_cluster/tSNE_UMAP.pdf"", plot = plotc, width = 10, height = 5)
ggsave(""X02_cluster/tSNE_UMAP.png"", plot = plotc, width = 10, height = 5)
##保存数据
save(scRNA, file=""./2.results/X02_cluster_scRNA.rdata"")","R"
"Assay","cystone/CNSSingleCellR","01_QC.R",".R","3367","74","suppressMessages(library(Seurat))
suppressMessages(library(MAST))
suppressMessages(library(tidyverse))
suppressMessages(library(pacman))
suppressMessages(library(data.table))
rm(list=ls())
dir.create(""X01_QC"")
dir.create('./download/01_base_file/', recursive=T)
dir.create('processData')
##----------创建seurat对象------------
inPath = ""./download/01_base_file/filtered_feature_bc_matrix""
scRNA.counts <- Read10X(data.dir = inPath)    
try({scRNA = CreateSeuratObject(scRNA.counts[['Gene Expression']])},silent=T)
if(!exists('scRNA')){
  scRNA = CreateSeuratObject(scRNA.counts,project = ""SeuratProject"")
}
# table(scRNA@meta.data$orig.ident) #查看样本的细胞数量

##--------------计算质控指标-------------
#计算细胞中线粒体基因比例
scRNA[[""percent.mt""]] <- PercentageFeatureSet(scRNA, pattern = ""^MT-"")
#计算红细胞比例
HB.genes <- c(""HBA1"",""HBA2"",""HBB"",""HBD"",""HBE1"",""HBG1"",""HBG2"",""HBM"",""HBQ1"",""HBZ"")
HB_m <- match(HB.genes, rownames(scRNA@assays$RNA)) 
HB.genes <- rownames(scRNA@assays$RNA)[HB_m] 
HB.genes <- HB.genes[!is.na(HB.genes)] 
scRNA[[""percent.HB""]]<-PercentageFeatureSet(scRNA, features=HB.genes) 
#head(scRNA@meta.data)

col.num <- length(levels(scRNA@active.ident))
ind = c(""nFeature_RNA"", ""nCount_RNA"", ""percent.mt"",""percent.HB"")
violin <- VlnPlot(scRNA,
                  features = ind, 
                  cols =rainbow(col.num), 
                  pt.size = 0.01, #不需要显示点，可以设置pt.size = 0
                  ncol = 4) + 
  theme(axis.title.x=element_blank(), axis.text.x=element_blank(), 
        axis.ticks.x=element_blank()) 
ggsave(""X01_QC/vlnplot_before_qc.pdf"", plot = violin, width = 12, height = 6) 
ggsave(""X01_QC/vlnplot_before_qc.png"", plot = violin, width = 12, height = 6)  
plot1 <- FeatureScatter(scRNA, feature1 = ""nCount_RNA"", feature2 = ""percent.mt"")
plot2 <- FeatureScatter(scRNA, feature1 = ""nCount_RNA"", feature2 = ""nFeature_RNA"")
plot3 <- FeatureScatter(scRNA, feature1 = ""nCount_RNA"", feature2 = ""percent.HB"")
pearplot <- CombinePlots(plots = list(plot1, plot2, plot3), nrow=1, legend=""none"") 
ggsave(""X01_QC/pearplot_before_qc.pdf"", plot = pearplot, width = 12, height = 5) 
ggsave(""X01_QC/pearplot_before_qc.png"", plot = pearplot, width = 12, height = 5)


#----------设置质控标准-------------
print(c(""请输入允许基因数和核糖体比例，示例如下："", ""minGene=500"", 
        ""maxGene=4000"", ""pctMT=20""))
minGene=500
maxGene=4000
pctMT=15

#-------------数据质控------------
scRNA <- subset(scRNA, subset = nFeature_RNA > minGene & 
                  nFeature_RNA < maxGene & percent.mt < pctMT)
col.num <- length(levels(scRNA@active.ident))
violin <-VlnPlot(scRNA,
                 features = c(""nFeature_RNA"", ""nCount_RNA"", ""percent.mt"",""percent.HB""), 
                 cols =rainbow(col.num), 
                 pt.size = 0.1, 
                 ncol = 4) + 
  theme(axis.title.x=element_blank(), axis.text.x=element_blank(), 
        axis.ticks.x=element_blank()) 
ggsave(""X01_QC/vlnplot_after_qc.pdf"", plot = violin, width = 12, height = 6) 
ggsave(""X01_QC/vlnplot_after_qc.png"", plot = violin, width = 12, height = 6)
scRNA <- NormalizeData(scRNA, normalization.method = ""LogNormalize"", 
                       scale.factor = 10000)

#----------------保存中间结果-----------------
save(scRNA, file=""./processData/01_QC_scRNA.rdata"")
","R"
"Assay","cystone/CNSSingleCellR","03_cellIdentity.R",".R","3967","79","library(Seurat)
library(tidyverse)
library(patchwork)
library(SingleR)
rm(list=ls())
dir.create(""X03_cellIdentity"")
load('./processData//02_cluster_scRNA.rdata')

##以下方法三选一，建议第一种
#默认wilcox方法
diff.wilcox = FindAllMarkers(scRNA)
all.markers = diff.wilcox %>% dplyr::select(gene, everything()) %>% subset(p_val<0.05)
top10 = all.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
write.csv(all.markers, ""X03_cellIdentity/diff_genes_wilcox.csv"", row.names = F)
write.csv(top10, ""X03_cellIdentity/top10_diff_genes_wilcox.csv"", row.names = F)
# #专为单细胞设计的MAST
# diff.mast = FindAllMarkers(scRNA, test.use = 'MAST')
# all.markers = diff.mast %>% select(gene, everything()) %>% subset(p_val<0.05)
# top10 = all.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
# write.csv(all.markers, ""X03_cellIdentity/diff_genes_mast.csv"", row.names = F)
# write.csv(top10, ""X03_cellIdentity/top10_diff_genes_mast.csv"", row.names = F)
# #bulkRNA经典方法DESeq2
# diff.deseq2 = FindAllMarkers(scRNA, test.use = 'DESeq2', slot = 'counts')
# all.markers = diff.deseq2 %>% select(gene, everything()) %>% subset(p_val<0.05)
# top10 = all.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_log2FC)
# write.csv(all.markers, ""X03_cellIdentity/diff_genes_deseq2.csv"", row.names = F)
# write.csv(top10, ""X03_cellIdentity/top10_diff_genes_deseq2.csv"", row.names = F)
##top10基因绘制热图
top10_genes <- read.csv(""X03_cellIdentity/top10_diff_genes_wilcox.csv"")
top10_genes = CaseMatch(search = as.vector(top10_genes$gene), match = rownames(scRNA)) 
plot1 = DoHeatmap(scRNA, features = top10_genes, group.by = ""seurat_clusters"",
                  group.bar = T, size = 4)
ggsave(""X03_cellIdentity/top10_markers.pdf"", plot=plot1, width=8, height=6) 
ggsave(""X03_cellIdentity/top10_markers.png"", plot=plot1, width=8, height=6)

#挑选部分基因
select_genes <- c('LYZ','CD79A','CD8A','CD8B','GZMB','FCGR3A')
#vlnplot展示
p1 <- VlnPlot(scRNA, features = select_genes, pt.size=0,  ncol=2)
# p1 <- VlnPlot(scRNA, features = select_genes, pt.size=0, group.by=""celltype"", ncol=2)
ggsave(""X03_cellIdentity/selectgenes_VlnPlot.png"", p1, width=6 ,height=8)
#featureplot展示
p2 <- FeaturePlot(scRNA, features = select_genes, reduction = ""umap"", label=T, ncol=2)
ggsave(""X03_cellIdentity/selectgenes_FeaturePlot.png"", p2, width=8 ,height=12)
p3=p1|p2
ggsave(""X03_cellIdentity/selectgenes.png"", p3, width=10 ,height=8)

#----------SingleR鉴定细胞类型----------
library(SingleR)
refFile = '~/database/SingleR_ref/ref_humanPrimaryCell,.rdata'
if(file.exists(refFile)){load(file = refFile)}else{
  refdata <- HumanPrimaryCellAtlasData()
  save(refdata, file=refFile)
}

testdata <- GetAssayData(scRNA, slot=""data"")
clusters <- scRNA@meta.data$seurat_clusters
cellpred <- SingleR(test = testdata, ref = refdata, labels = refdata$label.main, 
                    method = ""cluster"", clusters = clusters, 
                    assay.type.test = ""logcounts"", assay.type.ref = ""logcounts"")
celltype = data.frame(ClusterID=rownames(cellpred), 
                      celltype=cellpred$labels, stringsAsFactors = F)
write.csv(celltype,""X03_cellIdentity/celltype_singleR.csv"",row.names = F)
scRNA@meta.data$celltype = ""NA""
for(i in 1:nrow(celltype)){
  ind = which(scRNA@meta.data$seurat_clusters == celltype$ClusterID[i])
  scRNA@meta.data[ind,'celltype'] <- celltype$celltype[i]}


p1 = DimPlot(scRNA, group.by=""celltype"", label=T, label.size=5, reduction='tsne')
p2 = DimPlot(scRNA, group.by=""celltype"", label=T, label.size=5, reduction='umap')
p3 = plotc <- p1+p2+ plot_layout(guides = 'collect')
ggsave(""X03_cellIdentity/tSNE_celltype.pdf"", p1, width=7 ,height=6)
ggsave(""X03_cellIdentity/UMAP_celltype.pdf"", p2, width=7 ,height=6)
ggsave(""X03_cellIdentity/celltype.pdf"", p3, width=10 ,height=5)
ggsave(""X03_cellIdentity/celltype.png"", p3, width=10 ,height=5)

save(scRNA, file=""./processData/03_cell_id_scRNA.rdata"")
","R"
"Assay","cystone/CNSSingleCellR","05_pseudoTime.R",".R","2729","68","library(Seurat)
library(monocle)
library(tidyverse)
library(patchwork)
rm(list=ls())
dir.create(""05_pseudoTime"")

#--------数据导入与处理---------
load(file=""./2.results/04_subCl_scRNA.rdata"") #scRNAsub是上一节保存的T细胞子集seurat对象
data <- as(as.matrix(scRNA@assays$RNA@counts), 'sparseMatrix')
pd <- new('AnnotatedDataFrame', data = scRNA@meta.data)
fData <- data.frame(gene_short_name = row.names(data), row.names = row.names(data))
fd <- new('AnnotatedDataFrame', data = fData)
mycds <- newCellDataSet(data,
                        phenoData = pd,
                        featureData = fd,
                        expressionFamily = negbinomial.size())


mycds <- estimateSizeFactors(mycds)
mycds <- estimateDispersions(mycds, cores=4, relative_expr = TRUE)
#mycds <- detectGenes(mycds, min_expr = 2)  #很多教程不用

# 提选择代表性基因

##使用clusters差异表达基因
diff.genes <- read.csv('./04_subCluster/diff_genes_wilcox.csv')
diff.genes <- subset(diff.genes,p_val_adj<0.01)$gene
mycds <- setOrderingFilter(mycds, diff.genes)
p1 <- plot_ordering_genes(mycds)
##使用seurat选择的高变基因
var.genes <- VariableFeatures(scRNA)
mycds <- setOrderingFilter(mycds, var.genes)
p2 <- plot_ordering_genes(mycds)
##使用monocle选择的高变基因
disp_table <- dispersionTable(mycds)
disp.genes <- subset(disp_table, mean_expression >= 0.1 & dispersion_empirical >= 1 * dispersion_fit)$gene_id
mycds <- setOrderingFilter(mycds, disp.genes)
p3 <- plot_ordering_genes(mycds)
##结果对比
p1|p2|p3

#---------降维及细胞排序----------
#降维
mycds <- reduceDimension(mycds, max_components = 2, method = 'DDRTree')
#排序
mycds <- orderCells(mycds)
#State轨迹分布图
plot1 <- plot_cell_trajectory(mycds, color_by = ""State"")
ggsave(""05_pseudoTime/State.pdf"", plot = plot1, width = 6, height = 5)
ggsave(""pseudotime/State.png"", plot = plot1, width = 6, height = 5)
##Cluster轨迹分布图
plot2 <- plot_cell_trajectory(mycds, color_by = ""seurat_clusters"")
ggsave(""05_pseudoTime/Cluster.pdf"", plot = plot2, width = 6, height = 5)
ggsave(""05_pseudoTime/Cluster.png"", plot = plot2, width = 6, height = 5)
##Pseudotime轨迹图
plot3 <- plot_cell_trajectory(mycds, color_by = ""Pseudotime"")
ggsave(""05_pseudoTime/Pseudotime.pdf"", plot = plot3, width = 6, height = 5)
ggsave(""05_pseudoTime/Pseudotime.png"", plot = plot3, width = 6, height = 5)
##合并作图
plotc <- plot1|plot2|plot3
ggsave(""05_pseudoTime/Combination.pdf"", plot = plotc, width = 10, height = 3.5)
ggsave(""05_pseudoTime/Combination.png"", plot = plotc, width = 10, height = 3.5)
##保存结果
write.csv(pData(mycds), ""05_pseudoTime/pseudotime.csv"")

save(mycds, file=""./2.results/05_pseudoTime_mycds.rdata"")
","R"