pluginmgr - Manage a plugin
This package provides commands and objects for the generic management of plugins which can be loaded into an application.
To avoid the implementation of yet another system to locate Tcl code the system provides by this package is built on top of the regular package management system. Each plugin is considered as a package and a simple invokation of package require is enough to locate and load it, if it exists. The only time we will need additional paths is when a plugin manager is part of a wrapped application and has to be able to search for plugins existing outside of that application. For this situation the package provides a command to create a general set of such paths based on names for the plugin manager and/or application in question.
The main contribution of this package is a generic framework which allows the easy declaration of
How to translate a plugin name to the name of the package implementing it, and vice versa.
The list of commands a plugin has to provide as API, and also of more complex checks as code.
The list of commands expected by the plugin from the environment.
This then allows the easy generation of plugin managers customized to particular types of plugins for an application.
It should be noted that all plugin code is considered untrusted and will always be executed within a safe interpreter. The interpreter is enabled enough to allow plugins the loading of all additional packages they may need.
This command creates a new plugin manager object with an associated Tcl command whose name is objectName. This object command is explained in full detail in the sections OBJECT COMMAND and OBJECT METHODS. The object command will be created under the current namespace if the objectName is not fully qualified, and in the specified namespace otherwise.
The options and their values coming after the name of the object are used to set the initial configuration of the mamager object, specifying the applicable plugins and their API.
This utility command adds a set of paths to the specified object, based on the given names. It will search for:
The environment variable name_PLUGINS. Its contents will be interpreted as a list of package paths. The entries have to be separated by either : (unix) or ; (windows).
The name will be converted to upper-case letters.
The registry entry "HKEY_LOCAL_MACHINE\SOFTWARE\name\PLUGINS". Its contents will be interpreted as a list of package paths. The entries have to be separated by ;. This item is considered only when on Windows (tm).
The casing of letters is not changed.
The registry entry "HKEY_CURRENT_USER\SOFTWARE\name\PLUGINS". Its contents will be interpreted as a list of package paths. The entries have to be separated by ;. This item is considered only when on Windows (tm).
The casing of letters is not changed.
The directory "~/.name/plugin".
The directory "~/.name/plugins".
The casing of letters is not changed.
and add all the paths found that way to the list of package paths maintained by the object.
If name is namespaced each item in the list will be repeated per prefix of name, with conversion of :-sequences into the proper separator (underscore for environment variables, backslash for registry entries, and / for directories).
Examples:
::pluginmgr::paths ::obj docidx => env DOCIDX_PLUGINS reg HKEY_LOCAL_MACHINE\SOFTWARE\docidx\PLUGINS reg HKEY_CURRENT_USER\SOFTWARE\docidx\PLUGINS path ~/.docidx/plugins ::pluginmgr::paths ::obj doctools::idx => env DOCTOOLS_PLUGINS env DOCTOOLS_IDX_PLUGINS reg HKEY_LOCAL_MACHINE\SOFTWARE\doctools\PLUGINS reg HKEY_LOCAL_MACHINE\SOFTWARE\doctools\idx\PLUGINS reg HKEY_CURRENT_USER\SOFTWARE\doctools\PLUGINS reg HKEY_CURRENT_USER\SOFTWARE\doctools\idx\PLUGINS path ~/.doctools/plugin path ~/.doctools/idx/plugin
All commands created by the command ::pluginmgr (See section PACKAGE COMMANDS) have the following general form and may be used to invoke various operations on their plugin manager object.
The method method and its arg'uments determine the exact behavior of the command. See section OBJECT METHODS for the detailed specifications.
This method creates a new plugin management object and returns the associated object command. The generated object is a clone of the object the method was invoked on. I.e. the new object will have the same configuration as the current object. With regard to state, if the current object has a plugin loaded then this plugin and all associated state is moved to the generated clone and the current object is reset into the base state (no plugin loaded). In this manner a configured plugin manager is also a factory for loaded plugins.
The method returns a list of all known options and their current values when called without any arguments.
The method behaves like the method cget when called with a single argument and returns the value of the option specified by said argument.
The method reconfigures the specified options of the object, setting them to the associated values, when called with an even number of arguments, at least two.
The legal options are described in the section OBJECT CONFIGURATION.
This method expects a legal configuration option as argument and will return the current value of that option for the object the method was invoked for.
The legal configuration options are described in section OBJECT CONFIGURATION.
This method destroys the object it is invoked for.
This method interprets its list of arguments as the words of a command and invokes this command in the execution context of the plugin. The result of the invoked command is made the result of the method. The call will fail with an error if no valid plugin has been loaded into the manager object.
This method returns the handle of the safe interpreter the current plugin is loaded into. An empty string as return value signals that the manager currently has no valid plugin loaded.
This method returns the name of the plugin currently loaded. An empty string as return value signals that the manager currently has no valid plugin loaded.
This method loads, validates, and initializes a named plugin into the manager object.
The algorithm to locate and load the plugin employed is:
If the string contains the path to an existing file then this file is taken as the implementation of the plugin.
Otherwise the plugin name is translated into a package name via the value of the option -pattern and then loaded through the regular package management.
The load fails.
The algorithm to validate and initialize the loaded code is:
If the option -api is non-empty introspection commands are used to ascertain that the plugin provides the listed commands.
If the option -check is non-empty the specified command prefix is called.
If either of the above fails the candidate plugin is unloaded again
Otherwise all the commands specified via the option -cmds are installed in the plugin.
A previously loaded plugin is discarded, but only if the new plugin was found and sucessfully validated and initialized. Note that there will be no intereference between old and new plugin as both will be put into separate safe interpreters.
This method unloads the currently loaded plugin. It returns the empty string. The call will be silently ignored if no plugin is loaded at all.
This method uses the contents of the option -pattern to find all packages which can be plugins under the purview of this manager object. It translates their names into plugin names and returns a list containing them.
This methods adds the specified path to the list of additional package paths to look at when searching for a plugin. It returns the empty string. Duplicate paths are ignored, i.e. each path is added only once. Paths are made absolute, but are not normalized.
This method returns a list containing all additional paths which have been added to the plugin manager object since its creation.
All plugin manager objects understand the following configuration options:
This document, and the package it describes, will undoubtedly contain bugs and other problems. Please report such in the category pluginmgr of the Tcllib Trackers. Please also report any ideas for enhancements you may have for either package and/or documentation.
When proposing code changes, please provide unified diffs, i.e the output of diff -u.
Note further that attachments are strongly preferred over inlined patches. Attachments can be made by going to the Edit form of the ticket immediately after its creation, and then using the left-most button in the secondary navigation bar.
Programming tools
Copyright © 2005 Andreas Kupries <andreas_kupries@users.sourceforge.net>


All-in-1 Calc
2D Shapes
3D Shapes
Trignometry
Trignometry
This part of the Web Page is still under development


public abstract class Storage
extends java.lang.Object
| Modifier | Constructor and Description |
|---|---|
protected |
Storage() |
| Modifier and Type | Method and Description |
|---|---|
void |
addUser(User user)
Adds new user to storage.
|
void |
allowType(java.lang.String type)
Allow file with provided type to be uploaded to storage.
|
protected abstract Directory |
buildRoot(java.lang.String path)
Should be overridden in
Storage implementation to handle building root from provided path. |
void |
connect(java.lang.String uid,
java.lang.String path,
User user)
Template method for connecting currently active user to storage
represented by unique id that located on path.
|
void |
disconnect(User user)
Template method for disconnecting currently active user to storage.
|
void |
forbidType(java.lang.String type)
Forbid file with provided type to be uploaded to storage.
|
User |
getActiveUser()
Returns currently active user.
|
java.util.List<java.lang.String> |
getForbiddenTypes() |
User |
getOwner()
Returns storage owner.
|
java.lang.String |
getRegistryPath()
Returns registry file path.
|
Directory |
getRoot()
Returns abstract root directory representation.
|
java.lang.String |
getRootPath()
Returns root path.
|
java.lang.String |
getUid()
Returns currently active user.
|
java.util.List<User> |
getUsers()
Returns list of all storage users.
|
static Storage |
instance()
Returns single live
Storage instance. |
protected abstract void |
onConnect()
Should be overridden in
Storage implementation to handle connect operation. |
protected abstract void |
onDisconnect()
Should be overridden in
Storage implementation to handle disconnect operation. |
void |
removeUser(User user)
Removes existing user from storage.
|
public static Storage instance() throws DriverNotRegisteredException
Storage
instance.DriverNotRegisteredException
- if storage driver implementation has not been registered.public final void connect(java.lang.String uid, java.lang.String path, User user) throws StorageException
uid
- Storage unique identifier.path
- Storage location, i.e., registry file parent directory.user
- Currently active user.StorageException
- if registry file could not be loaded.public final void disconnect(User user) throws StorageException
user
- Currently active user.StorageException
- if registry file could not be saved.protected abstract void onConnect()
Storage
implementation to handle connect operation.protected abstract void onDisconnect()
Storage
implementation to handle disconnect operation.protected abstract Directory buildRoot(java.lang.String path)
Storage
implementation to handle building root from provided path.public final java.lang.String getUid()
public final Directory getRoot()
public final java.lang.String getRootPath()
public final java.lang.String getRegistryPath()
public final User getOwner()
public final User getActiveUser()
public final void addUser(User user) throws PrivilegeException
user
- Newly created user to be added.PrivilegeException
- if currently active user does not have privilege to manage other users.public final void removeUser(User user) throws PrivilegeException
user
- Existing user to be removed.PrivilegeException
- if currently active user does not have privilege to manage other users.public final java.util.List<User> getUsers()
public final void forbidType(java.lang.String type)
type
- Type to be forbidden, i.e., file extension.public final void allowType(java.lang.String type)
type
- Type to be allowed, i.e., file extension.public final java.util.List<java.lang.String> getForbiddenTypes()


Getting a sound to play using dotsPlayable is fairly straightforward. At its core it relies on Matlab's built in audioplayer function.
A user is not expected to create a dotsPlayable object by itself but to use one of the three sub-classes, dotsPlayableTone, dotsPlayableNote, and dotsPlayableFile. The majority of audio properties are defined within the dotsPlayable classdef, with the individual sub-classes having only a few unique properties.
The Playables demo has a pretty good sample of the individual features that can be adjusted. However, some properties are not automatically defined so a user will need to input some properties values before a sound can play.
Lets start with creating a simple tone.
toney = dotsPlayableTone
You should see something like this in the command window.
toney =
dotsPlayableTone with properties:
frequency: []
player: []
isAudible: 1
intensity: 1
sampleFrequency: 44100
bitsPerSample: 16
duration: []
soundsPath: '~'
waveform: []
side: ''
lastPlayTime: []
However, there are a few steps before a tone will be played. A user must set the frequency and duration before a tone can be played. The tone MUST be prepared before it can be played as well. If you do not set these properties you will probably see an error like this.
Error using .*
Matrix dimensions must agree.
Error in linspace (line 31)
y = d1 + (0:n1).*(d2 - d1)/n1;
Error in dotsPlayableTone/prepareToPlay (line 25)
rads = linspace(0, nCycles*2*pi, nSamples);
These properties can be set just like any other snow dots class object.
toney.frequency = 500;
toney.duration = 1;
We can then tell dotsPlayableTone to compute the waveform to play based on the parameters given.
toney.prepareToPlay;
Finally we can play the sound itself.
toney.mayPlayNow;
You should hear a 1 second long tone that sounds like….a tone. We can continually play the sound using mayPlayNow without having to also call prepareToPlay, but any changes made to the audio properties of the tone will not be reflected in the tone until prepareToPlay is called again.
dotsPlayable Properties
isAudible- True or false property that will mute or unmute a sound when it is called to be played. The functions unMute and mute are shorthand functions to set isAudible to true or false respectively. The default value of isAudible is true.
intensity- The default is 1. Essentially how loud the sound is. It's the scale factor to apply to the waveform during playback. If you look into the sub-class sound scripts, you can see during the prepareToPlay how intensity is used to adjust the waveform.
sampleFrequency- The frequency in Hz of sound samples stored in the waveform. Altering the frequency property in dotsPlayableTone is what you will need to do to actually change the frequency of your tone.
bitsPerSample- bit-depth of each sound sample in waveform. The bit-depth can alter the audio data type. The Matlab Read Audio File page may be worth looking at if you are really need to adjust this property.
duration- The duration of the sound to play.
soundsPath- This is the system path to locate the sound files used with a dotsPlayableFile object.
waveform- The waveform is actual value of sound samples to be played back.
side- If a sound should only play on the left side or right side of a set of speakers/headphones. It expects either an empty-string (default) for playback in both ears or a string argument of 'left' or 'right'.
dotsPlayableFile Properties
fileName- The only extra step to read and play back an audio file is simply making sure the file is within your Matlab path and that you enter the name in correctly. If you have the resources downloaded from this git repo, than you should have Coin.wav in your files somewhere. You can simply enter
coiny = dotsPlayableFile;
coiny.fileName = 'Coin.wav';
coiny.prepareToPlay;
coiny.mayPlayNow;
isBlocking - Whether to play synchronously (true) or asynchronously. Using isBlocking = false can lead to errors because Matlab's audio player class is limited.
dotsPlayableNote Properties
dotsPlayableNote is constructed using the Psychtoolbox audio player but can be used the same as the other sound functions. The difference between dotsPlayableTone and Note is that dotsPlayableNote uses an amplitude-envelop concept that causes the tone to smoothly come on and off, unlike the more abrupt onset and offset of dotsPlayableTone.
frequency- The frequency of the dotsPlayableNote can be adjusted to raise or lower the tone
waitTime- Default at 0. waitTime is simply the time, measured in second, that it takes the sound to come on.
ampenv- This is the amplitude envelope. The class will automatically create the envelop for the tone.
noise- Default at 0. The noise is a static-esq noise that plays simultaneously with the created tone. The higher the value the higher the static noise that plays along with the tone.


street
{{ address?.street }}
apt / unit
123
city
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state
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postal code
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country
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I am Carolin, PhD student in the Software Engineering Research Group at TU Delft. Currently, I am looking into how we can visualize amplified JUnit tests for developers.
If you are interested in software testing research, we're still looking for some lovely colleagues in the TestShift Project!
I studied Software Engineering at the TU Munich, University of Augsburg, and LMU Munich.
In the research world, I am fascinated by everything software quality, programming languages and most things that have to do with models 😊
Recently I learned that bots are a easy cross-plattform user interfaces for a huge amount of stuff 😄 Find my Telegram bots for Workflowy and Complice on GitHub.
I love emoij and have a custom keyboard layout to have them at my fingertips, beneath easing coding and typing Japanese 🐳🧐🤗
When mountains are close: Skiing ⛷ and Hiking ⛰
On the weekend I am probably tidying up and reorganizing something 🥰


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Komodo
talk-trading
A channel to discuss trading. For useful links and more info click the pinned messages to the right.
100 messages
Before 05-Dec-19 12:00 AM
bpkdasbaum
24-Oct-18 07:26 PM
to trick other bots lulz
Alice
24-Oct-18 07:26 PM
@bpkdasbaum
the 0.01s are there to move other trades out of sight
jl777c
24-Oct-18 07:26 PM
my guess is these bots use the same algo across all the altcoins during bear
and when it flips to bull marketmode will be pushing prices up
bpkdasbaum
24-Oct-18 07:26 PM
@Alice
oh that's sneaky
jl777c
24-Oct-18 07:27 PM
yes, bots might have a finite unfiltered window of recent trades
if they are all sells, it could bias the other bots to sell
Alice
24-Oct-18 07:27 PM
its not unusual in hft
jl777c
24-Oct-18 07:28 PM
bots vs bots vs bro-whale
1
Alice
24-Oct-18 07:28 PM
bots vs bots vs bro-whale-bots
most volume is from bots
siu - Chainmakers
24-Oct-18 07:34 PM
the 0.01 bots detected a key level and traded based on that
bpkdasbaum
24-Oct-18 07:37 PM
https://imgflip.com/i/2kvgqr
Pathetic Spidey
2
3
1
1
Llama
24-Oct-18 07:44 PM
Selling KMDICE at multiple rates: 0-5k: 0.4 KMD each 5k-15k: 0.35 KMD each 15k+: 0.325 KMD each
Cryptoon
24-Oct-18 07:45 PM
Didnt really read all comments
But saw that jl said whalebro
Day cant get any better than this tbh
2
1
1
https://twitter.com/jacobstoon/status/1055184469785804804?s=21
CrypToon (@Jacobstoon)
Whalebro playing the binance bots, smart retail investors better set the sails for mooncity
$KMD
!kmdtip
@bpkdasbaum
0.777 great picture
1
KMD TipBot
BOT
24-Oct-18 07:50 PM
TipBot
@Cryptoon
(Cryptoon#3362) You tipped
@bpkdasbaum
0.777 KMD
Alice
24-Oct-18 07:52 PM
sell your fang stocks and buy komodo!
grewalsatinder
24-Oct-18 07:55 PM
whalebro - >
https://img.memecdn.com/come-at-me-bro_o_161491.gif
Cryptoon
24-Oct-18 07:59 PM
Needs a komodo dragon gif
grewalsatinder
24-Oct-18 07:59 PM
https://i.imgflip.com/2kvjbg.jpg
2
2
bpkdasbaum
24-Oct-18 08:34 PM
0.2.42c is most current agama right?
Cryptoon
24-Oct-18 08:41 PM
Yes
1
Hollowman9000
24-Oct-18 08:57 PM
Missed that
@FishyGuts
Too quick on the trigger
FishyGuts
24-Oct-18 08:58 PM
it was a private one
So how bout these 1 m apples.
That is a purty chart to me.
but i dont chart so I dont know what I am lookjing at
ph03nix
24-Oct-18 09:02 PM
https://media.giphy.com/media/26xBEamXwaMSUbV72/giphy.gif
Product Hunt
FishyGuts
24-Oct-18 09:02 PM
Except that KMD is ... yep that.
I hope.
If we get KMD trading pairs you oafs better use them.
I will but I'm done buying coins for now
siu - Chainmakers
24-Oct-18 09:19 PM
Wow, btc is less volatile than stocks now
Tek
24-Oct-18 09:19 PM
buttcoin
I stocked up some Dash
come on Venezuela make it moon
buy XMR
insta regret
siu - Chainmakers
24-Oct-18 09:20 PM
Also kmd is actuqlly getting better returns than most markets
Tek
24-Oct-18 09:21 PM
kmd is pretty good
yeah
thanks to whale bro
sometimes I just want to send my entire savings fund and just
buy
in one time
but
I know I might get rekt so I have to DCA and send a little every month in stead
tolken
24-Oct-18 09:24 PM
Trade some on bdex please
Need feed bots something to play with.
FishyGuts
24-Oct-18 10:09 PM
https://twitter.com/BitcoinNewsBull/status/1055209553170718720
Bitcoin News Bull (@BitcoinNewsBull)
Stratis (STRAT) Certified Microsoft ISV – 20% Surge -
https://t.co/8Kibigtp80
#Bitcoin #Cryptocurrency #Blockchain
https://twitter.com/PirateChain/status/1055210119271567361
The Dreaded PirateChain (@PirateChain)
@BitcoinNewsBull How about a gain of 20% quietly over the course of a month without people noticing....
actionslacks
24-Oct-18 10:20 PM
Kmd has been approved for an advanced AWS technology partnership as well.
But who cares right?
Hollowman9000
24-Oct-18 10:22 PM
So, moving on
NPC service shutting down... c:\ >
(edited)
FishyGuts
24-Oct-18 10:42 PM
@actionslacks
like hyperledger and eth being utility’s chains on AWS?
actionslacks
24-Oct-18 10:45 PM
@FishyGuts
idk about that, you should ask in
#dev-scalability
jspook16
24-Oct-18 10:50 PM
Kmd inching closer to waves..
ComputerGenie 👴👴👴
24-Oct-18 10:59 PM
...and we are co-ming in waves!
jspook16
24-Oct-18 11:41 PM
Invasion of the 'Modos
Llama
24-Oct-18 11:48 PM
Selling KMDICE at multiple rates: 0-5k: 0.4 KMD each 5k-15k: 0.35 KMD each 15k+: 0.325 KMD each
jspook16
25-Oct-18 12:09 AM
?! Why so cheap?
Vertigo
25-Oct-18 12:25 AM
I don't think whale bro is buying to dump $0.05 cents higher. He may stay a while with all that KMD probably waiting for 10k
(edited)
At least
Alice
25-Oct-18 12:32 AM
@Vertigo
true
approaching resistance at 23k
Vertigo
25-Oct-18 12:35 AM
Who cares I'm here for the tech.
Ahhh my I'm here for the tech meme is not uploading....
 - Mahatma Gandhi quotes from BrainyQuote.com
Gordon Komodo
25-Oct-18 01:20 AM
yeah, it will be on tuesday
ComputerGenie 👴👴👴
25-Oct-18 01:38 AM
@Flexatron
3/4 of what it was a year ago isn't anywhere near
Vertigo
25-Oct-18 02:01 AM
Is a step in the right direction
BlockTech
25-Oct-18 09:32 AM
@actionslacks
nice quotes, I think KMD is between laugh and fight, other project start to fear us ^^.
Cryptoon
25-Oct-18 09:42 AM
are the tipbot tx all onchain?
Or first handled on a centralized tipbot ledger and only on chain when you withdraw/deposit?
siu - Chainmakers
25-Oct-18 09:49 AM
Tipbot is centralized software afaik
1
bpkdasbaum
25-Oct-18 10:33 AM
kmd is not fast enough to handle a tipbot onchain
Llama
25-Oct-18 10:34 AM
Selling KMDICE at multiple rates: 0-5k: 0.4 KMD each 5k-15k: 0.35 KMD each 15k+: 0.325 KMD each
Cryptoon
25-Oct-18 10:54 AM
Aha because for the ARRRbot I thought it said something like wait a moment for the tx to process, so I figured because it was onchain
sφldat
25-Oct-18 10:58 AM
https://www.tradingview.com/x/XUl5WD3t/
Deleted User
25-Oct-18 10:59 AM
Selling kmdice @ 0.38 kmd each.
Mark81
25-Oct-18 11:34 AM
Is kmd in process of testing 21ksat to become support?
devpro
25-Oct-18 11:47 AM
wts 80k vrsc @0.4kmd pm me
benohanlon
25-Oct-18 12:07 PM
@PTYX
think i want something even smaller
bpkdasbaum
25-Oct-18 12:36 PM
@Mark81
we already bounced on 22
also 0.618 fib retrace, which is bullish
https://www.tradingview.com/x/CIHAegta/
up to 2289 and see if we can break that
jl777c
25-Oct-18 12:54 PM
ARRR tipbot is onchain
this creates more ztx and increases the anon set
3


Have you participated in ____________ in the last year?
Classical
Jazz
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Plays
Opera
Ballet
Dance
Craft Fairs
Art Museums
Books
Loading Visualisation Choropleth


Graphs the theoretical light arrival time delays and radial velocities of pulsators in binary systems, given supplied variables. If the periastron is supplied as BJD, then the range of dates over which to calculate is also required.
Controls: Highlight region of graph to zoom in, double click to reset.
TD Viewer is a short javascript program that can calculate theoretical light arrival time delays and radial velocities of pulsators in binary systems, written by Daniel Hey (danielhey at outlook) and Dr. Simon Murphy, 2017
Based on the papers by Murphy S. and Shibahashi H., Finding binaries from phase modulation of pulsating stars with Kepler. IV. Detection limits and radial velocity verification, 2016,
TD Viewer makes use of the excellent skeleton CSS framework, and the Dygraphs charting library.
| Parameter | Value |
|---|---|
| a (ls) | |
| a1 (ls) | |
| a2 (ls) | |
| Mass function (SM) | |
| Mass function (kg) | |
| Mass function (orbit) |


GimmeMotifs documentation
Simon van Heeringen
Mar 3, 2011
Contents
1 Introduction
2 What's included?
3 Installation
3.1 Installation packages
3.1.1 Installation on Ubuntu or Debian
3.1.2 Installation on Fedora
3.2 Installation from source
3.2.1 Prerequisites
3.2.2 Required packages (Python)
3.2.3 Other required packages
3.2.4 Additional motif prediction programs
3.2.5 Building from source
3.3 Configuration
3.3.1 Data sources
3.3.2 Indexing the genomes
3.3.3 Adding gene files
3.3.4 The easy way: add_organism.py
3.3.5 Adding motif prediction tools
3.3.6 MotifSampler configuration
3.3.7 Other configuration options
4 Usage
4.1 Quick GimmeMotifs example
4.2 GimmeMotifs example
4.3 Using GimmeMotifs: best practices and tips
4.3.1 GimmeMotifs is multi-threaded
4.3.2 Running time
4.3.3 Intermediate results
4.3.4 Running on FASTA files
4.3.5 Small input sets
4.4 Detailed options
5 Other scripts
5.1 Input formats
5.2 Descriptions
6 Acknowledgements
1 Introduction
GimmeMotifs is a de novo motif prediction pipeline, especially suited for ChIP-seq datasets. It incorporates several existing motif prediction algorithms in an ensemble method to predict motifs and clusters these motifs using the WIC similarity scoring metric. It is freely available for download and use under the MIT license. If you find GimmeMotifs useful, please cite:
- van Heeringen SJ and Veenstra GJC, GimmeMotifs: a de novo motif prediction pipeline for ChIP-sequencing experiments, Bioinformatics Advance Access published November 15, 2010,
doi:10.1093/bioinformatics/btq636.
This document describes how to install and use GimmeMotifs, for theoretical details, please see our publication [1].
Hopefully this document explains at least the basics of installation and usage, but it's probably far from complete. If you have any further question, please don't hesistate to contact me<EMAIL_ADDRESS>2 What's included?
GimmeMotifs is dependent upon the great work of others: motif prediction algorithms. GimmeMotifs includes several tools by default:
All these tools are installed automatically with GimmeMotifs. To use these programs no additional steps have to be taken (except for MotifSampler see 3.3.6). Please note: all these programs include their own license and most are free for academic or non-commercial use only! For commercial use of any of these programs, please consult the respective author!
3 Installation
GimmeMotifs runs on Linux. Definitely not on Windows, sorry. Mac OS X should work in theory, but as I don't have the means to test this, I'm not completely sure. I have tried to make installation of GimmeMotifs as easy as possible. There are installation packages available for Fedora, Ubuntu and Debian. It's also possible to install GimmeMotifs from source, but it depends on quite some external packages. Please make sure all prerequisites are installed before installing GimmeMotifs from source.
3.1 Installation packages
3.1.1 Installation on Ubuntu or Debian
Download the latest GimmeMotifs .deb package for your distribution from http://www.ncmls.nl/bioinfo/gimmemotifs/ (available for 32-bit and 64-bit systems). Please note: due to a different Python version, the Debian and Ubuntu .deb packages are different! Please use the correct one for your distribution.
These packages have been tested on up-to-date versions of Karmic Koala (9.10), Lucid Lynx (10.04) and Debian Lenny (5.05).
Open a terminal and install GimmeMotifs as follows. Go to the directory where you downloaded GimmeMotifs, for example:
cd ~/Downloads
Start the install (substitute the package name for the .deb package you downloaded):
sudo dpkg -i gimmemotifs_0.63-1_i386.deb
Likely, dpkg will complain about some missing dependencies. Install all dependencies with:
sudo apt-get -f install
Complete the GimmeMotifs installation with:
sudo dpkg -i gimmemotifs_0.63-1_i386.deb
Currently, there is a bug with the versions of the Parallel Python (python-pp) and Numpy (python-numpy) in the Ubuntu and Debian repositories. Therefore the package python-pp is not installed as a dependency. This can be fixed by installing the latest version of Parallel Python from the Python Package Index:
sudo easy_install pp
Now you should have a working version of GimmeMotifs! The next steps are to install additional motif tools (optional, see section 3.3.5) and to do some configuration (required, see section 3.3 ). You can also directly try the quick example (section 4.1), if you're impatient (but don't forget to perform the additional steps!)
3.1.2 Installation on Fedora
Download the latest GimmeMotifs .rpm package from http://www.ncmls.nl/bioinfo/gimmemotifs/ (available for 32-bit and 64-bit systems). This package has been tested on an up-to-date version of Fedora 13. Install GimmeMotifs as follows (substitute the package name for the .rpm package you downloaded):
sudo yum install --nogpgcheck gimmemotifs_0.63-1_i386.rpm
GimmeMotifs doesn't play nice with SELinux enabled on Fedora, sorry. To turn it off:
sudo setenforce 0
Now you should have a working version of GimmeMotifs. The next steps are to install additional motif tools (optional, see section 3.3.5) and to do some configuration (required, see section 3.3 ). You can also directly try the quick example (section 4.1), if you're impatient (but don't forget to perform the additional steps!)
3.2 Installation from source
3.2.1 Prerequisites
Before you can install GimmeMotifs you'll need:
- some Python modules and other packages
- motif prediction tools
3.2.2 Required packages (Python)
- Python 2.5, 2.6 or 2.7 (not Python 3) http://www.python.org
- Scipy http://www.scipy.org/
SciPy is the fundamental package needed for scientific computing with Python.
- matplotlib (0.98 or higher) http://matplotlib.sourceforge.net/
A python 2D plotting library. All figures and plots produced by GimmeMotifs are made using matplotlib.
- parallel python 1.6.0 http://www.parallelpython.com/
A python module which provides mechanism for parallel execution of python code. This Python library is used for parallel execution of for instance the motif finding tools.
- kid http://www.kid-templating.org/
A simple template language for XML based vocabularies; used to produce the HTML reports.
3.2.3 Other required packages
- gsl http://www.gnu.org/software/gsl/
The GNU Scientific Library.
This library might already be installed on your system, but you'll also need the development headers to compile GimmeMotifs!.
3.2.4 Additional motif prediction programs
A lot of motif prediction tools are compiled and/or installed with GimmeMotifs. The following tools have to be installed seperately:
Please consult the respective manuals regarding installation of these tools. It's always possible to install these programs after installation of GimmeMotifs and update the configuration files to include the new tools (see section 3.3.5). However, during installation, GimmeMotifs will try to find any installed tools and add them automatically, so that's the easiest option.
3.2.5 Building from source
You can download the lastest version of GimmeMotifs at:
http://www.ncmls.eu/bioinfo/gimmemotifs/.
Start by unpacking the source archive
tar xvzf gimmemotifs-1.00.tar.gz
cd gimmemotifs-1.00
You can build GimmeMotifs with the following command:
python setup.py build
Run the tests to check if the basics work correctly:
python run_tests.py
If you encounter no errors, go ahead with installing GimmeMotifs (root privileges required):
sudo python setup.py install
During installation GimmeMotifs will try to locate the tools you have installed. If you have recently installed them, running an updatedb will be necessary. Using this option GimmeMotifs will create a configuration file, the default is:
/usr/share/gimmemotifs/gimmemotifs.cfg
This is a system-wide configuration that can be used by all users.
It is also possible to run the setup.py install command with the -prefix, -home, or -install-data options, to install in GimmeMotifs in a different location (for instance, in your own home directory). This should be fine, however, these alternative methods of installing GimmeMotifs have not been extensively tested. Please note that in this case the configuration file will be created, but every user will have to put this configuration file in his/her home directory: /.gimmemotifs.cfg. The install script will also inform you of this during install. Please contact me if you run into problems with the installation.
Once the installation is finished, you can try the quick example (section 4.1), or continue with the configuration in the next section.
3.3 Configuration
3.3.1 Data sources
You will need some genome fasta files for any motif-prediction if you want to run GimmeMotifs with BED files as input (which is recommended, although GimmeMotifs will also work on FASTA files). To get from a BED file to the sequence information these genomic fasta files are absolutely required. The fasta files should be organized in one directory with one file per chromosome or scaffold, with the filename being the chromosome name with an extension of .fa, .fsa or .fasta. No exceptions, no different layouts. A good source is the UCSC Genome Browser database [12]. For instance, the human hg18 files needed to run the examples included with GimmeMotifs can be downloaded here:
ftp://hgdownload.cse.ucsc.edu/goldenPath/hg18/bigZips/chromFa.zip
All fasta files need to be indexed before GimmeMotifs can use them, see section 3.3.2.
3.3.2 Indexing the genomes
All the genomes that you want to use with GimmeMotifs will need to be indexed for (relatively) fast retrieval of sequences. You can do this, once you have installed GimmeMotifs, by running the following command (as root or with sudo):
create_genome_index.py -f /dir/to/fasta/files/ -n genome_name
For instance, if I wanted to index the human genome (version hg18) on my computer, where all fasta files are located in the directory /usr/share/genome/ I would run the following command:
sudo create_genome_index.py -f /usr/share/genome/hg18/ -n hg18
Repeat this step for every additional genome or organism that you want to use GimmeMotifs with. Please note: for Weeder, currently only hg18, hg19, mm9, sacCer2 and xenTro2 are supported as organism names (following the UCSC naming convention). This will be fixed as a configuration file in a later release.
3.3.3 Adding gene files
When using the genomic_matched background setting (which is the default), there needs to be a file describing genes in BED format in the gene_dir, which is defined in the configuration file. By default this is: /usr/share/gimmemotifs/genes/. The file needs to be named <index_name>.bed, so for instance hg18.bed. By default hg18.bed, mm9.bed and xenTro2.bed are included.
3.3.4 The easy way: add_organism.py
The script add_organism.py combines the previous two steps (indexing the fasta files, and adding a gene file), and makes sure the gene BED file is in the correct place with the correct name. This is the easiest way to add a new genome/organism for use with GimmeMotifs.
3.3.5 Adding motif prediction tools
Please note that these steps are only necessary when you have installed any of these tools after you have installed GimmeMotifs.
Weeder
After installing Weeder the following section needs to be added to the GimmeMotifs configuration file:
[Weeder]
bin = /usr/share/Weeder/weederTFBS.out
dir = /usr/share/Weeder/
All other Weeder binaries should be present in the same directory as weederTFBS.out. The directory specified by dir should contain the FreqFiles directory included with Weeder.
In addition Weeder should be added to the line in the params section of the configuration file. For instance
tools = MDmodule,MEME,MotifSampler,trawler,Improbizer,BioProspector
needs to be changed to:
tools = MDmodule,MEME,MotifSampler,trawler,Improbizer,BioProspector,Weeder
3.3.6 MotifSampler configuration
If you want to use MotifSampler there is one more step that you'll have to take after installation of GimmeMotifs. For every organism, you'll need a MotifSampler background. Note that human (hg18), mouse (mm9) and Xenopus (xenTro2) background models are included, so for these organisms MotifSampler will work out of the box. For other organisms the necessary background files can be created with CreateBackgroundModel (which is included with GimmeMotifs or can be downloaded from the same site as MotifSampler). The background model file needs to be saved in the directory /usr/share/gimmemotifs/MotifSampler and it should be named <organism_index_name>.bg. So, for instance, if I downloaded the human epd background (epd_homo_sapiens_499_chromgenes_non_split_3.bg), this file should be saved as /usr/share/gimmemotifs/MotifSampler/hg18.bg.
3.3.7 Other configuration options
All of GimmeMotifs configuration is stored in /usr/share/gimmemotifs/gimmemotifs.cfg or /.gimmemotifs.cfg. If the file /.gimmemotifs.cfg exists in your home directory this will always have precedence over the system-wide configuration.
The configuraton file is created at installation time with all defaults set, but you can always edit it afterwards. It contains two sections main and params that take care of paths, file locations, parameter settings etc. Additionally, every motif tool has it's own section.
Let's have a look at the options.
[main]
index_dir = /usr/share/gimmemotifs/genome_index
template_dir = /usr/share/gimmemotifs/templates
seqlogo = /usr/local/bin/seqlogo
score_dir = /usr/share/gimmemotifs/score_dists
motif_databases = /usr/share/gimmemotifs/motif_databases
gene_dir = /usr/share/gimmemotifs/genes
tools_dir = /usr/share/gimmemotifs/tools
- index_dir The location of the indeces of the genome fasta-files.
- template_dir The location of the KID html templates, used to generate the reports.
- seqlogo The seqlogo executable.
- score_dir To generate p-values, a pre-calculated file with mean and sd of score distributions is needed. These are located here.
- motif_databases For now contains only the JASPAR motifs.
- gene_dir Directory with bed-files containing gene locations for every indexed organism. This is needed to create the matched genomic background.
- tools_dir Here all tools included with GimmeMotifs are stored.
[params]
background = genomic_matched,random
use_strand = False
tools = MDmodule,Weeder,MotifSampler
analysis = medium
pvalue = 0.001
width = 200
fraction = 0.2
genome = hg18
lwidth = 500
cluster_threshold = 0.95
available_tools = Weeder,MDmodule,MotifSampler,gadem,meme,trawler
abs_max = 1000
enrichment = 1.5
max_time = None
This section specifies all the default GimmeMotifs parameters. Most of these can also be specified at the command-line when running GimmeMotifs, in which case they will override the parameters specified here.
4 Usage
4.1 Quick GimmeMotifs example
You can try GimmeMotifs with a small example dataset included in the examples directory, included with GimmeMotifs. This example does not require any additional configuration if GimmeMotifs is installed correctly.
Change to a directory where you have write permissions and run the following command (substitute the filename with the location of the file on your system):
gimme_motifs.py -i /usr/share/gimmemotifs/examples/TAp73alpha.fa -n p73
The -n or -name option defines the name of the output directory that is created. All output files are stored in this directory.
Depending on your computer you may have to wait some minutes for your results. Once GimmeMotifs is finished you can open p73/p73_motif_report.html in your browser.
4.2 GimmeMotifs example
This example is the same as above, except it will start from a BED file. This example does require you to have hg18 present and indexed.
Change to a directory where you have write permissions and run the following command (substitute the filename with the location of the file on your system):
gimme_motifs.py -i /usr/share/gimmemotifs/examples/TAp73alpha.bed -n example
The -n or -name option defines the name of the output directory that is created. All output files are stored in this directory.
Depending on your computer you may have to wait some minutes for your results. Once GimmeMotifs is finished you can open example/example_motif_report.html in your browser.
4.3 Using GimmeMotifs: best practices and tips
4.3.1 GimmeMotifs is multi-threaded
GimmeMotifs runs multi-threaded and uses all the CPU's in the system. This means that all the programs will be run in parallel as much as possible. Of course some programs are still single-threaded, and will not benefit from this. Because GimmeMotifs uses all the available CPU's it does not make much sense to start multiple GimmeMotifs jobs at the same time.
4.3.2 Running time
The running time of GimmeMotifs greatly depends on which tools you use for prediction and how large the dataset is. Some of the tools might take a very long time and two of them, GADEM and MoAn, are not added to the default tools because of this reason. You can always use them for an analysis (by specifying the -t command-line option), but it is recommended to only do this for a small dataset (say, less than 5000 peaks). Weeder in combination with the xl analysis can also take a very long time, so be prepared. In general a small analysis will be the quickest, and a xl analysis will be the slowest.
While GimmeMotifs is developed specifically for ChIP-seq datasets, most motif prediction tools are not. In practice this means that it does not make much sense to predict motifs on a large amount of sequences, as this will usually not result in higher quality motifs. Therefore GimmeMotifs uses an absolute limit for the prediction set. By default 20% of the sequences are used as input for motif prediction, but with an absolute maximum. This is controlled by the abs_max parameter in the configuration file, which is set to 1000 by default. In general, if you have a large amount of peaks, you can also consider to run GimmeMotifs on the top sequences of your input, for instance the 5000 highest peaks.
There are two options that you can use to control the running time of GimmeMotifs. First, you can set an absolute time limit with the max_time option. This option (in hours) determines the maximum time used for motif prediction. If some programs take longer, the running jobs will be terminated, and the program will continue with all the motifs that have been predicted so far. The other option is kind of an emergency button: when you think that GimmeMotifs has been running long enough, you can press Ctrl+C once, and only once!. This will signal GimmeMotifs to terminate the running jobs and continue with the analysis. Please note that this works almost always, but still, there is a small chance that program might be in a function where the Ctrl-C option screws up, and GimmeMotifs will not be able to handle the result gracefully.
4.3.3 Intermediate results
GimmeMotifs produces a lot of intermediate results, such as all predicted motifs, fasta-files used for validation and so on. These are deleted by default (as they can get quite large), but if you are interested in them, you can specify the -k option.
4.3.4 Running on FASTA files
It is also possible to run GimmeMotifs on a FASTA file as input instead of a BED file. This is detected automatically if you're inputfile correctly formatted according to FASTA specifications. In this case it is not possible to generate a genomic matched background, so only the random Markov background will be used. Please note that for best results, all the sequences should be of the same length. This is not necessary for motif prediction, but the statistics and positional preference plots will be wrong if sequences have different lengths. Also see the next section.
4.3.5 Small input sets
Keep in mind that GimmeMotifs is developed for larger datasets, where you have the luxury to use a large fraction of your input for validation. So, at least several hundred sequences would be optimal. If you want to run GimmeMotifs on a small input dataset, it might be worthwile to increase the fraction used for prediction (with the -f parameter.
4.4 Detailed options
- -i or -inputfile
This is the only mandatory option. The inputfile needs to be in BED or FASTA format. BED-fomatted files need to contain at least three tab-seperated columns describing chromosome name, start and end. The fourth column is optional, if specified it will be used by MDmodule to sort the features before motif prediction. GimmeMotifs will take the center of these features, and subsequently extend those to the width specified by the width parameter (see below).
-
-n or -name
The name of your analysis. All outputfiles will be stored in a directory named as given by this parameter. By default this will be gimmemotifs_dd_mm_yyyy, where d,m and y are the current day, month and year respectively.
-
-a or -analysis
The size of motifs to look for: small (5-8), medium (5-12), large (6-15) or xl (6-20). The larger the motifs, the longer GimmeMotifs will run. The 'xl' can take a very long time!
-
-g or -genome
Name of the genome (index) to use. For instance, for the example in section 3.3.2 this would be hg18.
-
-s or -singlestrand
Only use the + strand for prediction (off by default).
-
-f or -fraction
This parameter controls the fraction of the sequences used for prediction. This 0.2 by default, so in this case a randomly chosen 20% of the sequences will be used for prediction. The remaining sequences will be used for validation (enrichment, ROC curves etc.). If you have a large set of sequences (ie. most ChIP-seq peak sets), this is fine. However, if your set is smaller, it might be worthwile to increase this prediction fraction.
-
-w or -width
This is the width of the sequences used for motif prediction. Smaller sequences will result in a faster analysis, but you are of course limited by the accuracy of your data. For the tested ChIP-seq data sets 200 performs fine.
-
-e or -enrichment
All motifs should have an absolute enrichment of at least this parameter compared to background to be called significant.
-
-p or -pvalue
All motifs should have a pvalue of at most this parameter (hypergeometric enrichment compared to background) to be called significant.
-
-b or -background
Type of background to use. By default random (1st order Markov model, similar dinucleotide frequencies as your sequences) and matched_genomic (randomly chosen from the genome with a similar distribution respective to the TSS of genes) are used.
-
-l or -localization_width
Width used in the positional preference plots.
-
-t or -tools
A comma-seperated list of all the motif prediction tools to use. By default all installed tools that are specified in the GimmeMotifs configuration file are used.
-
-max_time
Time limit for motif prediction in hours. Use this to control the maximum number of hours that GimmeMotifs uses for motif prediction. After this time, all jobs that are still running will be terminated, and GimmeMotifs will continue with the motifs that are predicted so far.
5 Other scripts
In addition to gimme_motifs.py the GimmeMotifs package contains several other tools that can perform the various substeps of GimmeMotifs, as well as other useful tools. Run them to see the options.
5.1 Input formats
Most tools in this section take a file in PWM format as input. This is actually a file with Position Specific Scoring Matrices (PSSMs) containing frequencies. It looks like this:
>motif1
0.3611 0.0769 0.4003 0.1664
0.2716 0.0283 0.5667 0.1381
0.6358 0.0016 0.3344 0.0330
0.0016 0.9859 0.0016 0.0157
0.8085 0.0063 0.0502 0.1397
>motif2
0.2276 0.0157 0.0330 0.7284
0.0031 0.0016 0.9984 0.0016
0.0377 0.3799 0.0016 0.5856
0.0816 0.7096 0.0173 0.1962
0.1350 0.4035 0.0675 0.3987
The frequencies are seperated by tabs, and in the order A,C,G,T.
5.2 Descriptions
closest_motif_match.py
Taking an input file with motifs, find the best matching file in another file of motifs (according to the WIC metric).
create_genome_index.py
Creates an index to use with GimmeMotifs. See section 3.3 for details.
generate_background_sequences.py
Generate random sequences according to one of two methods: random or matched_genomic. With the argument type set to random, and an input file in FASTA format, this script will generate sequences with the same dinucleotide distribution as the input sequences according to a 1st order Markov model trained on the input sequences. The -n options is set to 10 by default. The length distribution of the sequences in the output file will be similar as the inputfile. The Markov model can be changed with option -m.
If the type is specified as matched_genomic the inputfile needs to be in BED format, and the script will select genomic regions with a similar distribution relative to the transcription start of genes as the input file. Make sure to select the correct genome. The length of the sequences in the output file will be set to the median of the features in the input file.
motif_cluster.py
Cluster a set of motifs with the WIC metric.
motif_localization_plots.py
Create the positional preference plots for all the motifs in the input PWM file. This will give best results if all the sequences in the FASTA-formatted inputfile have the same length. Keep in mind that this only makes sense if the sequences are centered around a similar feature (transcription start site, highest point in a peak, etc.). The default threshold for motif scanning is 0.95, see pwmscan.py for more details.
motif_roc.py
Given a sample (positives, peaks) and a background file (random sequences, random promoters or similar), creates a ROC plot for all the motifs in an input PWM file. All the motifs will be plotted in the same graph, you can select one or more specific motifs to plot with the -i option.
motif_roc_metrics.py
Given a sample (positives, peaks) and a background file (random sequences, random promoters or similar), calculated several statistics for all the motifs in an input PWM file. You can select one or more specific motifs with the -i option. The ROC area under curve (ROC_AUC), Mean Normalized Conditional Probability (MNCP) and maximum f-measure are reported for each motif.
pwm2logo.py
Convert the motifs in a PWM file to a logo using weblogo.
pwmscan.py
Scan a set of sequences with a set of motifs, and give the resulting matches in GFF or BED format. The threshold is based on the maximum and minimum possible score for each motif. So, 0.95 means that the score of a motif should be at least 95% of the (maximum score - minimum score). This should probably not be set much lower than 0.8, and should be generally at least 0.9 for good specificity. Keep in mind that the optimal threshold might be different for each motif!
track2fasta.py
Convert a set of BED formatted sequences to a FASTA file. The genome needs to be indexed for GimmeMotifs using create_genome_index.py.
6 Acknowledgements
We are grateful to Waseem Akhtar, Robert Akkers, Max Koeppel, Evelyn Kouwenhoven, Marion Lohrum, Leonie Smeenk and Jo Zhou for providing data and feedback during GimmeMotifs development. Also we would like to thank Stefanie Bartels, Adalberto Costessi, Joost Martens and Nagesha Rao for testing and helpful discussion.
Of course GimmeMotifs by itself wouldn't be able to do anything, if there wasn't such a number of excellent tools available. Therefore, a big thanks to all the authors of the motif prediction programs for making their software publicly available and allowing me to distribute them with GimmeMotifs!
In addition, I would like to thank Wolfgang Lugmayr and Aaron Statham for various bugfixes.
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URL http://dx.doi.org/10.1038/nmeth1061.
- [11]
-
Giulio Pavesi, Paolo Mereghetti, Giancarlo Mauri, and Graziano Pesole.
Weeder web: discovery of transcription factor binding sites in a set
of sequences from co-regulated genes.
Nucl. Acids Res., 32 (suppl_2): W199-203,
July 2004.
doi: 10.1093/nar/gkh465.
URL
http://nar.oxfordjournals.org.proxy.ubn.ru.nl:8080/cgi/content/abstract/32/suppl_2/W199.
- [12]
-
Brooke Rhead, Donna Karolchik, Robert M. Kuhn, Angie S. Hinrichs, Ann S. Zweig,
Pauline A. Fujita, Mark Diekhans, Kayla E. Smith, Kate R. Rosenbloom,
Brian J. Raney, Andy Pohl, Michael Pheasant, Laurence R. Meyer, Katrina
Learned, Fan Hsu, Jennifer Hillman-Jackson, Rachel A. Harte, Belinda
Giardine, Timothy R. Dreszer, Hiram Clawson, Galt P. Barber, David Haussler,
and W. James Kent.
The UCSC genome browser database: update 2010.
Nucl. Acids Res., 38 (suppl_1): D613-619,
January 2010.
doi: 10.1093/nar/gkp939.
File translated from
TEX
by
TTH,
version 3.77.
On 3 Mar 2011, 11:00.


I am a software engineer with a passion for solving problems. I have many years experience in a variety of fields with a variety of languages. Let me help you solve your problems.
This is just a sampling of some of the stills I have.
Contact me at my e-mail below.
cheryl.a.orcutt AT gmail.com


FILM TITLE: ERENDIRA DIRECTOR: RUY GUERRA COUNTRY: MEXICO 1983 MUSIC: MAURICE LECOEUR CAST: Irene Pappas, Claudia Ohana, Michael Lonsdale, Oliver Wehe, Rufus, Blanca Guerra SUPER FEATURES: Film from the weird film series.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
Erendira is not a bad film. But it does show that the Mexican community has a knack for doing something strange, or weird, or just plain bad. This film fits into the area of the Jodorowsky material, or some of the very odd things that get made. It isn't a great film, but it is rather unusually written with its many biblical references, and an undertone that one has a hard time deciding if it is worth laughing, or crying.
But it does try to do something with its material, and one wonders if it is Irene Pappas that tries to save, what would otherwise be a bad film. It comes off, as a colorfully bad Luis Bunuel film, with the themes right in front of us, but we never see them.
It is the story, based on Gabriel Garcia Marquez' novel, of a young woman who is kept by her mother, and is forced into prostitution to pay for everything that the girl has ever done wrong, as a way to pay her debt to the mother. But as is often the case, the young girl does get attached to one young man, and an impossible situation arises. She can't pay her debts, and the mother does not wish the daughter to leave, or she would be all alone in the "desert" which is where most of the film takes place. But the daughter has her clumsy moments, and these are the excuse that keeps her trapped.
In between, is a bunch of stuff that makes little sense, it seems, as the mother goes into a trance like state during her sleep, and tells many stories that range from the weird to the very strange. One thing is obvious, they are some of the things that went through her mind when she was a young girl, and also forced into prostitution. A daydream coming alive. The young girl thinks that the mother is losing it, which she is.
And to save their love affair, the young one decides the young man has to do a deed for her, and this deed, is unfortunately what is also going to get her cursed into becoming exactly what her mother was. In the end, nothing has changed it seems..... in between, there are these rather amusing, and gypsy like settings in the beach and the desert which keeps our attention on what is going on, which is not much, but makes us wonder.
Irene Pappas is not bad, although the way many film makers have used her, has not made her look attractive, or strong as an actress. This film has enough dialogue for her, but it is one of those situations where it is really hard to figure out whether she is helping the whole project or not. I venture to say that she makes it better, but that may not be the case.
A strange film, well photographed, very colorful, but otherwise dull.
3 GIBLOONS Copyright (c) Pedro Sena 1996. All Rights Reserved. Member of the Internet Movie Critics Association
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
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Related links: index of all rec.arts.movies.reviews reviews


Contents
% launch brainstorm, with no gui (but only if is not already running) if ~brainstorm('status') brainstorm %nogui end
Error using evalin Undefined function or variable 'script_MEGHEM_TF_Second_fast_slow_Step3a_resample_perm_t'.
SET EXPORT FOLDER FOR REPORTS
export_main_folder='/Users/giorgioarcara/Documents/Lavori San Camillo/MEGHEM analisi marzo 2016/MEGHEM_analysis_reports/'; export_folder='resample'; if ~exist([export_main_folder, '/' export_folder]) mkdir([export_main_folder, '/' export_folder]) % create folder if it does not exist end;
SET PROTOCOL
ProtocolName = 'Meghem_analisi_3'; % get the protocol index, knowing the name iProtocol = bst_get('Protocol', ProtocolName); % set the current protocol gui_brainstorm('SetCurrentProtocol', iProtocol); % check info ProtocolInfo=bst_get('ProtocolInfo') % get the subject list my_subjects = bst_get('ProtocolSubjects') SubjectNames={my_subjects.Subject(2:end).Name}; % NOTE! the 2-end, to exclude the intra trials
SELECT TRIALS
my_sFiles_string='Second_Corr' % make the first selection with bst process my_sFiles_ini = bst_process('CallProcess', 'process_select_files_timefreq', [], [], ... 'subjectname', 'Group_analysis', ... 'includeintra', 1,... 'tag', my_sFiles_string);
DIVIDE BY CONDITION
Conditions={'Fast18', 'Slow18'}; Condition_grouped=group_by_str_bst( {my_sFiles_ini.FileName}, Conditions); Condition_grouped{1}=sort_by_fragment(Condition_grouped{1}, 'MH0..'); Condition_grouped{2}=sort_by_fragment(Condition_grouped{2}, 'MH0..');
set frequencies
Freqs={[5 7]; [8 12]}; Freq_names={'theta' 'alpha'}; % NOTE: to make permutation on ROI i first extract values, isolating one % frequency. % in this way, results for one frequency are treated as a source object and % I can use the permutation function for sources (that allows to specify % scouts). for iFreq=1:length(Freqs);
for iCond=1:length(Conditions); for iSubj=1:length(SubjectNames);
IN this loop I extract values separately for each subject
this is necessary cause extract values try always to concatenate time or subjects. Only using just on subject at the time I obtain what I want. a Process: Extract values: [-300ms,600ms] 5-7Hz
curr_file = bst_process('CallProcess', 'process_extract_values', Condition_grouped{iCond}{iSubj}, [], ... 'timewindow', [-0.3, 0.6], ... 'freqrange', [Freqs{iFreq}(1), Freqs{iFreq}(2)], ... 'rows', '', ... 'isabs', 0, ... 'avgtime', 0, ... 'avgrow', 0, ... 'avgfreq', 0, ... 'matchrows', 0, ... 'dim', 2, ... % Concatenate time (dimension 2) 'Comment', ''); % Process: Resample: 300Hz curr_file = bst_process('CallProcess', 'process_resample', curr_file, [], ... 'freq', 300, ... 'overwrite', 1); % Process: Add tag to name. curr_file = bst_process('CallProcess', 'process_add_tag', curr_file, [], ... 'tag', [SubjectNames{iSubj}, '_', my_sFiles_string, '_', Conditions{iCond}, 'resample'] , ... 'output', 2); % Add to name % create a cell with n Condition elements. Each one contain the % struct related to a condition extract_all_files{iCond}(iSubj)=curr_file;
end; end; bst_report('Start', extract_all_files{1}); % notice that the order of subjects of group 1 and 2 is correct (i.e., paired) cause % it reflects the order of Grouped_condition, that were sorted % by fragment before % Process: Perm t-test paired [0ms, 400ms] H0:(A=B), H1:(A<>B) Res = bst_process('CallProcess', 'process_test_permutation2p', extract_all_files{1}, extract_all_files{2}, ... 'timewindow', [0, 0.4], ... 'scoutsel', {'Destrieux', {'G_temporal_middle R', 'G_temporal_middle L', 'G_front_middle L', 'G_front_middle R', 'G_pariet_inf-Angular L', 'G_pariet_inf-Angular R', 'G_pariet_inf-Supramar L', 'G_pariet_inf-Supramar R', 'G_parietal_sup L', 'G_parietal_sup R', 'S_intrapariet_and_P_trans L', 'S_intrapariet_and_P_trans R'}}, ... 'scoutfunc', 1, ... % Mean 'isnorm', 0, ... 'avgtime', 0, ... 'iszerobad', 1, ... 'Comment', '', ... 'test_type', 'ttest_paired', ... % Paired Student's t-test T = mean(A-B) / std(A-B) * sqrt(n) 'randomizations', 1000, ... 'tail', 'two'); % Two-tailed
delete manually all files
for myfile=1:length(extract_all_files{1}) % the line below retrive the whole path to the file (from % protocol info, and from file name). delete(fullfile(ProtocolInfo.STUDIES, extract_all_files{1}(myfile).FileName)); end; for myfile=1:length(extract_all_files{2}) % the line below retrive the whole path to the file (from % protocol info, and from file name). delete(fullfile(ProtocolInfo.STUDIES, extract_all_files{2}(myfile).FileName)); end; % reload all conditions of current subjects to update GUI. db_reload_conditions(1); %export_matlab(Res, %% Cannot USE DELETE PROCESS TO DELETE (due to a probable bug) % also the results of the perm-test is deleted. I should add here some lines to delete manually the files % and then reload files in brainstorm % alternativly you can temporary store the dataset and then % re_import_it with db_add_data % Process: Delete selected files (delete the imported epoch in the % first step, to save space). %Del = bst_process('CallProcess', 'process_delete', extract_all_files{1}, [], ... % 'target', 1); % Delete selected files (group 1) %Del = bst_process('CallProcess', 'process_delete', extract_all_files{2}, [], ... % 'target', 1); % Delete selected files (group 2) % Save and display report ReportFile = bst_report('Save', Res); bst_report('Export', ReportFile, [export_main_folder, '/' export_folder]);
end;


Clean Coimbatore Conclave starts today
by Special CorrespondentCoimbatore Corporation will hold a two-day conclave, ‘Clean Coimbatore Conclave 2019’, on November 16 and 17 at Suguna Kalyana Mandapam to offer waste management solutions for bulk waste generators.
A communication from the civic body said the conclave would also have stalls by companies offering solutions or ideas for rainwater harvesting, alternatives for plastics and showcase home composting techniques.
The conclave would begin at 9.30 a.m. with Municipal Administration Minister S.P. Velumani inaugurating the event.


Get settings from atcOverrides
2018-09-05T12:00:00.000Z
2018-09-05T18:00:00.000Z
Europe/London
Star Wars Day Party
May the force be with you
Tatooine
Luke Skywalker
<EMAIL_ADDRESS>Get settings from atcOverrides
with overrides by data-*
attributes
2018-09-05T12:00:00.000Z
2018-09-05T18:00:00.000Z
Europe/London
Star Wars Day Party
May the force be with you
Tatooine
Luke Skywalker
luke@starwars.com


Helion Developer Platform Leadership Offsite - March 11, 2015
Engineering Hotness Goals
- hot fix release in 24 hours
- all releases are done like hotfixes
- one build process to rule them all!
- updates succeed 100% of the time build to build and from last release to build
- efficency improvements in envs
- access to esoteric env in under 24 hours
- acceess to standard in under 10 min
- env mangagement tops out at 3 people
- Installer works 100% of the time in 3 hours or less
- 90% cumulitive code coverage, 100% scenario coverage as release criteria
- 80% found will be at or before the gate
- measure bugs found AT the gate
- RCA all bugs that escape the gate
- 50% of bugs cuaght in dev
- 20% caught by CI/CD
- 20% caught by Nightly
- 10% by release activities
- feature branch checkin: 70%
- Only successful passing all gates can code be merged to MASTER
- net new Code does not get checked in with code coverage at 70%
- Every checkin increases the code coverage
- Last release stats: 127 bugs were not caught at or before the gate
- In 3 months, the ratio of bugs caught by CI/CD to be 50/50
- In 6 months the ratio is 5 to 1
- A developer can gain 80% reasonable confidence that change will not regress the system in under 2 hours
- at all times be able to report the overall capacity of the org for internal and external focused projects
- NPS for developer productivity up over current baseline by 20 points in 3 months
- Do not increase ratio of non customer facing engineering systems and products
Implementation Details / Prerequisites
- intentional usage of branches and preservation of MASTER
- Predictable, scalable CDN capabilities
- Fully automated sub set of tests that can be run by one person
- HOS labs and environments managed by Brad need to be HDP compliant
- Nightly tests should be able to be run against all of the esoteric environments
- All installations need to be push button (automated)
- Installs need to be composable to be consumed by the installer CLI
- automated quality verification in build pipeline
- What google does
- functional team all the way up called SRE
- SREs are parachuted in to services teams to help
- services compete for SRE time
- HDP SRE mods
- Funnel Mangement
- Construct a CI/CD system: 1 person
- people working on environments (trust brad and his team)
- HOS one box - Brads
- HDP / ALS
- Program Management assistence (is this the release manager?) YES
- Tooling around templates
- quality engineering
- frameworks
- tooling
- instrumentation
- Platform lifecycle management / Common Components: Install / Update / Upgrade
Parking Lot
- how to defend against the CI/CD system becoming a crutch
How to stay out of the black hole of shame
- don't rathole
- collect underpants
- profit!


@(currentPage: Page[models.ModuleView], currentOrderBy: Int, nameFilter: String, versionFilter: Long, tagFilter: List[String])(implicit request: play.api.mvc.RequestHeader)
@import tag._
@**
Helper generating table headers to reorder the table by a field
@param newOrderBy new ordering for results
@param title the title to display in the header
@param nameFilter current filter being applied to the names
@param versionFilter current filter being applied to the versions
@param tagListFilter current filter being applied to the tags
@param currentOrderBy current order being applied to the resultset
@param call the 'list' method of a Controller that we will call to load the page
*@
@headerEntryMod(newOrderBy: Int, title: String, nameFilter: String, versionFilter: Long, tagListFilter: List[String], currentOrderBy: Int, call: (Int, Int, String, Long, List[String]) => Call) = {
}
@**
Helper generating navigation links between pages
@param newPage destination page
@param nameFilter current filter being applied to the names
@param versionFilter current filter being applied to the versions
@param tagListFilter current filter being applied to the tags
@param currentOrderBy current order being applied to the resultset
@param call the 'list' method of a Controller that we will call to load the page.
@param newOrderBy new ordering for results. Default: None
*@
@pageLinkMod(newPage: Int, nameFilter: String, versionFilter: Long, tagListFilter: List[String], currentOrderBy: Int, call: (Int, Int, String, Long, List[String]) => Call, newOrderBy: Option[Int] = None) = {
@{
call(newPage,
newOrderBy.map { orderBy =>
if(orderBy == scala.math.abs(currentOrderBy)) {
if(currentOrderBy > 0) {
-currentOrderBy
} else {
orderBy
}
} else {
orderBy
}
}.getOrElse(currentOrderBy),
nameFilter, versionFilter, tagListFilter)
}
}
@**
Helper generating the pagination footer for list views
@param currentPage the page currently being displayed
@param pageSize number of elements in this page
@param nameFilter current filter being applied to the names
@param versionFilter current filter being applied to the versions
@param tagListFilter current filter being applied to the tags
@param currentOrderBy current order being applied to the resultset
@param call the 'list' method of a Controller that we will call to load the page
*@
@paginationFooterMod(currentPage: Page[Any], pageSize: Int, nameFilter: String, versionFilter: Long, tagListFilter: List[String], currentOrderBy: Int, call: (Int, Int, String, Long, List[String]) => Call) = {


COOKIES BAKED PER HOUR
based on number of customers per hour and number average number of cookies per sale
Input New Store Information
Location of Store
Minimmum Customers
Maximum Customers
Average Cookies
ADD NEW STORE


In dance/movement therapy, movement is the therapeutic tool used to process feelings and emotions. The client is encouraged to experience, observe, and process behaviors and thoughts through body sensations, non-verbal communication, and body language. We use the body to recharge, refocus, and even re-pattern the mind.
We use the body to assess, observe, and intervene in the therapeutic relationship. When words alone may not be expressing what someone is experiencing, dance/movement therapy can help to validate and support each individual.
This holistic alternative to traditional talk therapy is a wonderful way to treat mind, body, and spirit. Using movement we can connect to our subconscious, enhance our awareness, and learn to be more present.
Why choose Chicago Dance Therapy?
Founder/CEO
Therapist
Therapist
Therapist
Therapist
"The therapy has a direct impact on my mother's level of involvement and expression. She's developing new neural pathways to have the ability to speak, and she travels freely in the city...She's all over the place and it's amazing."
"During your sessions they [residents] are more alert and happy...it's like the sadness or confusion floats away and they are dancing away at a place where they are safe and joyful."
"Erica, your presentation was one of the most meaningful that I have ever heard on the subject of dementia. I am going to share this with...family and friends. Duplicating your delivery will be difficult, but the impact of this piece is incredibly powerful. Thanks again for sharing and for your insight and compassion for patients and families going on this rough ride."


Episcript - Story editor for the web
Load:
Select one...
Mode:
Select one...
Edit
View
Story -
https://github.com/ledlogic/episcript


The ForwardEulerSolver plugin implements the Forward Euler method to solve ODEs and it can be customised through the following property:
1


After getting seriously distracted looking up examples of CSS design, I've come up with a wishlist of features I want to add:
This week we’re learning about the relationship between HTML, CSS and the Document Object Model (“the DOM”). We’ve been asked to think of an analogy to describe these concepts. I like the (admittedly loose) analogy of making coffee:
I guess in this analogy the web developer would be the barista... You get the idea.
“Boxifying” involves visualising the layout of a webpage as a series of different shaped boxes. You start by breaking down the page into big boxes for the general layout, then go smaller and smaller for the more detailed pieces of design. Designing with boxes has the advantage that if you want to move things around at a later date, the parts will fit together more easily than if everything item had a unique footprint. I think of it like moving furniture around a room. It’s easier to change the layout if the walls are straight instead of curved, and the furniture has flat edges instead of pointy bits.
The elements of a website can be broken down further. At the heart of each box is content, which is nestled in padding and surrounded by a border. At the very outside of the box is the margin. This is the “box model”, depicted below. Resizing a certain aspect of the box can change the total footprint of the element.


{{#each item.post_tags}}
{{/each}}
Optional. Tags are used to help applicants find
your post quicker. It is also used to help us market specific topics
more accurately. Only the first 5 tags will be displayed.
{{#if errors.post_tags}}
{{errors.post_tags}}
{{/if}}
{{#if item.post_resume}}
View Resume
{{/if}}
{{#if errors.post_resume}}
{{errors.post_resume}}
{{/if}}
{{#if item.post_banner}}
See your current banner.
{{/if}}
Optional. Promotional images are used to visually
attract potential applicants to your post. Suggested size:
313 x 85px
{{#if errors.post_banner}}
{{errors.post_banner}}
{{/if}}
{{#if errors.post_detail}}
{{errors.post_detail}}
{{/if}}


a76
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674
22 1000
Tuning Joins for ThroughputTo specify that the join operation be optimized for maximum throughput, specify the USE_MERGE hint to force the optimizer to choose a sort-merge join. Most likely, this is the optimization approach the optimizer would choose by default anyway, unless another approach was defined with the Oracle initialization parameters. Tuning Joins for Response TimeTo specify that the join operation be optimized for response time, specify the USE_NL hint to force the optimizer to choose a nested-loops join. Remember that with the USE_NL hint, you must specify the inner table in the nested loop. On its own, the optimizer would not choose this approach.
LockingA book on Oracle performance is not complete without a discussion about locking. With Oracle version 6, the concept of row-level locking was introduced. Although row-level locking improves performance, there are conditions in which table-level locking is necessary. What Is Locking?In multiuser systems or servers, many users may update the same information at the same time. Locking allows this to occur and ensures that data you are currently modifying cannot be modified at the same time by a different user. The basic idea of locking is that when a user accesses data through a transaction, that data is locked (that is, it is exclusively owned by that transaction) until the transaction is committed or rolled back. Because data cannot be accessed by other transactions until the lock is released, it is to the benefit of the system that you reduce both the time the data is locked and the amount of data that is locked. Oracle provides two basic types of locks: the row-level lock and the table-level lock. When you use a row-level lock, only the row in the table that is being modified is locked. Other users can access data and update other rows in that table; they just cant update the particular row being modified. All rows in the table can be viewedeven the one being modified. Other users who view the row being modified see the old version of the data (through the rollback segment) until the changes are committed. Row-level locking is more efficient than table-level locking because it does not keep other transactions from running. OLTP systems benefit from row-level locking because of the large number of concurrent users who access the system. When you use a table-level lock, the entire table being modified is locked. Other users cant modify any rows in the table until the lock is released. As with the row-level lock, other users can still view data being modified; they see the old version of the data (throu ac1 gh the rollback segments) until the changes are committed. Serializable ReadsIf your application requires serializable reads, you must change the parameters SERIALIZABLE and ROW_LOCKING in the Oracle initialization file. By changing these parameters, you alter the performance of the system dramatically. Oracle recommends that users implement applications using the default row locking. When you set the Oracle initialization parameter SERIALIZABLE to TRUE, queries acquire table-level read locks, thus preventing updates to the objects being read until the transaction containing the query has been committed. This mode provides for repeatable reads and ensures that two queries for the same data in the same transaction see the same data. This parameter provides for ANSI-degree-three consistencybut at a considerable cost in concurrency. I recommend that you do not set SERIALIZABLE unless it is absolutely necessary to correctly run older applications. In most cases, row-level locking is optimal.
|
| 6fc |
|


When I woke up, I noticed the Modafinil pills had arrived. I took a half, since was going to be my first time.
My plan was to go to a symposium right after. When I arrived I was still myself, a little bit of anxiety took over because I just took something that I have never taken before. While sitting on the chair and listening to many different stories, suddenly I feel something coming up. I was getting really heavy, and my heart rate was going up. Kind of anxiety attack. Every time I started breathing out, I felt a rush. This continued for about 15-30 minutes. At one point I didn't want to stay at the symposium at all, I just left while they were still going on to go for a walk outside. Then I decided to go back again. It was so hard to sit still and focus on one point, I was looking around me all the time. Then at the break I talked to a few people, I noticed I got more compassionate with people, and I also noticed that when a conversation was leading nowhere I changed the subject really fast or just walked away politely. During the come-up of the drug, my body started feeling really tired.
I didn't stay at the symposium till the end. I left to go to a friend of mine, then I went home. But before that, I went to the supermarket. And I didn't exactly know what I wanted, I was thinking really logical. And I decided to not buy some things because it will just be too much etcetera. When I was walking around in the supermarket I was getting a little bit paranoia, felt like people were watching me all the time. And also when I was outside this was the same sometimes.
It was a weird experience. I was nicer to people, I managed to do things faster instead of leaving it to be. I was feeling a higher temperature and heart rate. Some anxiety and paranoia were there too.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Payment ID Generator
Random Payment ID
Genrated random Payment ID
Custom Payment ID
Provide hex values [0-9a-f]
Generated Payment ID
Custom ASCII encoded Payment ID
Provide ASCII characters
Generated Payment ID
ASCII Payment ID can be decoded, for example
here
.


{% extends "public.html" %} {% block scripts %} {% assets filters='rjsmin', output='__bundle__/core/index.js', 'core/js/index.js' %} {% endassets %} {% endblock %} {% block page %}
Easy to set up and lightweight, LNbits can run on any lightning-network funding source, currently supporting LND, c-lightning, OpenNode, lntxbot, LNPay and even LNbits itself!
You can run LNbits for yourself, or easily offer a custodian solution for others.
Each wallet has its own API keys and there is no limit to the number of wallets you can make. Being able to partition funds makes LNbits a useful tool for money management and as a development tool.
Extensions add extra functionality to LNbits so you can experiment with a range of cutting-edge technologies on the lightning network. We have made developing extensions as easy as possible, and as a free and open-source project, we encourage people to develop and submit their own.


--- layout: toolbox title: Week number favicon: favicon.weeknum.ico ---
Week number of the this week. It can be used for naming weekly directory.


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InfoWars boss Alex Jones 'storms' Apple factory in a 'battle tank'
The controversial right-wing shock jock Alex Jones has ‘stormed’ an Apple factory in the ‘InfoWars Battle Tank’.
Jones, who is banned from Facebook, Twitter and Youtube, rocked up outside a facility in Texas which builds Mac Pro computers.
The factory employs thousands of people and is part of Apple’s $1 billion plan to make more of its products in the USA.
‘When you build it in the United States, you don’t have to worry about tariffs,’ Trump said after touring the building.
Outside, Alex Jones squared off against anti-Trump protesters.
Driving an armoured car and yelling through a bullhorn, Alex Jones slammed left-wingers and called for free speech to be protected.
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It’s hard to hear exactly what he shouted, but it sounded something like: ‘We have come here in an armoured vehicle to protect us from the left.
‘They are the same as the communist Chinese who ran over people in tanks to suppress their speech.
‘We are here to promote free speech.’
He also yelled: ‘America is back. Donald Trump is fighting corruption. Donald Trump is innocent and America loves Trump.;