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![](./wiki/graphics/project_icon.png)
# Laydi
## Look At Your Data Interactively
Laydi is an acronym for look at your data interactively, which is what the program is aimed at. It is a lightweight data analysis program for bilinear modeling (PCA and PLS) with a strong focus on interactive use. Laydi is released under the GNU GPL and the latest development snapshot can be downloaded from https://git.pvv.ntnu.no/Projects/laydi.git
![](./wiki/graphics/screenshot-00.png)
## Features
- Principal Component Analysis (PCA)
- Partial Least Squares Regression (PLS)
- L-shaped PLS regression (L-PLS)
- Easy mapping of variables between plots, selections in one plot propagates to other plots.
## Nonfeatures
- Does not import arbitrary files. Files must be prepared in a (simple) file format prior to import.
- Saving and loading of projects is not implemented. (Datasets can be saved and loaded, though, and plots can be exported)
- Not very stable
## Installation requirements
Laydi currently requires the following extra packages, available from apt on Debian and Ubuntu.
- python2.4 or python2.5
- python-glade2
- python-gnome2
- python-gtk2
- python-matplotlib
- python-scipy
- python-numpy
Partially needed
- python-networkx
- python-pygraphviz
## Download laydi
Laydi is not debianized. To download it, use the clone the git repo.
```console
git clone https://git.pvv.ntnu.no/Projects/laydi.git
```
## User documentation
- [Frequently Asked Questions](./wiki/faq.md)
- [Laydi help](./wiki/help.md) (the same as available through the help menu in the application.)
- [Terminology](./wiki/Terminology.md)
## Developer documentation
- [Developer tips and tricks](./wiki/development/hints.md)

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# Terminology
As most software projects, the Laydi system has its own terms to describe objects and elements. Where possible, we have tried to be somewhat consistent with the terms used in other similar software such as Pathway Assist, Array Assist and Unscrambler. The most important terminology of Laydi is listed here.
## User Interface
![GUI Overview](./graphics/gui-overview.png)
- The **Navigator** is where the data and plots for the current project are visually located on the screen. It is usually empty until you load some datasets. Datasets and plots are arranged in a hierarchy.
- A **Project** is where the data for an analysis are stored. The data and plots for a project are shown in the navigator window.
- A **Workflow** is a set of stages with functions you might want to use to accomplish some task.
- Each **Stage** in a workflow has a name like preprocessing or regression, and contain the functions needed to do that part of the analysis.
- In the center of the application (marked Plots in the picture) are a set of **viewframes** that can display plots. Drag and drop a plot from the navigator to the viewframe where you want to see it.
- In the **Information pane**, two tabs are always visible: Log and Selections. Additional tabs can be made in the workflows.
- The **log** contains informational messages from the program.
- The **selections** tab shows what is selected in a specific dimension.
## Implementation Details
- **Dataset** is the basis class of the data in the program. A dataset is similar to a dataframe in GNU R in that it is a matrix with labels along the axes. Unlike R, each dimension in a dataset contains a globally unique name for the dimensions in the problem domain.
- **Annotations** are textual tables that describe the identifiers along an axis in some way.

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# The basic design concepts
The design is based around two fundamental (and related) concepts; *datasets* and *dimensions*. A dataset is a matrix with a list of identifiers for each row and another list of identifiers for each column. The dimension for rows and columns are also stored.
## Dimension
A dimension is just a name of a domain that your data contain. Each element along a dimension is identified by a name that is defined to be unique across every dataset, plot and other program elements that contains that dimension.
So, if we have a dimension named `samples`, which contains an identifier `patient1`, whenever this identifier is used in the `samples` dimension, it is assumed to refer to the same entity.
This allows the program to do mapping between different plots and datasets, so that when <`patient1` in the `samples` dimension> is selected in one plot, this selection can propagate to all other places in the program that displays some kind of information on samples.
## Dataset
A dataset is a matrix where both columns and rows are associated with dimension. For example, a gene analysis study may have a dataset where the rows are tissue samples associated with the `samples` dimension and the columns are all the measured genes in the `genes` dimension.
## Annotations
Sometimes we want additional information associated with the identifiers along a dimension for display purposes. A gene is often represented by an identifier that is not very meaningful without being looked up in a database. So if we also want some extra information, like the name of the gene, this is stored in *annotations* along the `genes` dimension.

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# Development hints
## Terminology
Read terminology for the conventions used in the program. This ensures a consistent use of terms within the program. New terminology should be described on the that page, and should be introduced only when needed. Too much terminology clutters the namespace.
## Logging
Log output is very easy. Import the logger module, and use the singleton Logger instance inside it. The logger has four different debug levels, debug, notice, warning and error. A simple example follows:
```python
import logger
logger.log('debug', 'This should appear in the log window as a debug message')
```

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## Recommended names
### Variables
- fn for filenames.
- fd for files.
- dim for a dimension.
- it for iterators.
- ids for a list of identifiers.
- indices
- func for functions
### Function handlers
Function handlers should generally be named _on_<signal>. This makes them private and easy to follow. This is not an absolute rule, as there can be several functions in the same namespace listening to the same signal, but on different objects.
## Unrecommended names
- iter
- dimname, dim_name etc.

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# User documentation ¶
- [Frequently Asked Questions](./faq.md)
- [Installing Laydi](./installing.md)
- [Program help](./help.md)
- [Terminology](./Terminology.md)
# Developer documentation
- [Developer tips and tricks](./development/hints.md)
- [Fluents CSV file format description.](./help/format/fcsv.md)
- [Naming conventions for variables and methods](./development/names.md)

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# Frequently Asked Questions
## 1. General questions
### 1.1 What is Laydi?
Laidy is an acronym for look at your data interactively, and that is what the program is designed for. More specifically, bilinear analysis methods like principal component analysis (PCA) and partial least squares regression (PLS) are the two core methods used.
### 1.2 Could you rephrase that?
## 2. Usage
## 3. Internals and development

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# Fluents help index ¶
- [Invocation and command line parameters.](./help/cmdline.md)
- [Configuration files and settings.](./help/config.md)
- [Keyboard shortcuts.](./help/shortcuts.md)
- [Fluents CSV file format description.](./help/format/fcsv.md)
## Functions
- [PCA](./tasks/PCA.md)
- [PLS](./tasks/PLS.md)

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Fluents takes the following command line options:
| | |
|----------------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------|
| `-h` or `--help` | Displays help index, which is probably more updated than what is documented on the web. |
| `-l` or `--workflow-list` | Lists the available workflows. Available workflows must be in the workflows package and must load on your system. |
| `-w workflow` or `--workflow=workflow` | Creates a new project using the given workflow. Use -l to get a list of workflows. The workflow parameter is the identifier of the workflow. |

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# Configuration
Laydi reads configuration from two types of configuration files. The main configuration is read from `/etc/laydirc/` and `$HOME/.laydi`. The possible variables in this configuration are described in the section Laydi configuration below.
The other file is the settings file for the matplotlib plotting package that is used in Laydi. This package has a lot of settings on its own, and instead of incorporating it into the general laydi configuration file it is distributed as a separate `matplotlibrc` file with options suitable for use in Laydi.
## Laydi configuration
### Directories
#### datadir
datadir is a directory for datasets. There are helper functions that makes it easy for a workflow to load data from this directory.
#### cachedir
cachedir is a directory where laydi can write cached data at will.
#### workflowdir
workflowdir is a path of directories where Laydi should look for workflows. When Laydi is started the `-w ''workflow''` option, these are the directories that will be searched for workflows. Note that *all* directories in `workflowdir` are added to the python path. If you add your own workflow directory, make sure that no files in the directory interfere with Laydi modules or standard Python modules.
## Matplotlib configuration
Matplotlib is configured with a matplotlib configuration file included with laydi. Documentation for the various options are included as comments in the file itself.

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# Fluents Comma Separated Values file format description
This is the documentation of the textual (and primary) Fluents data file format. The other way to store a dataset is as a pickled Dataset object. The main differences between these methods are:
- Text based file formats are easier to read, easier to browse throgh, running commands on etc.
- Text based files are better suited for svn.
- Picled files are faster.
## File headers
Header lines must begin on the first line in the file. Header lines are of the form:
```
# keyword : value
#keyword:value
# keyword :value
```
Where an optional number of whitespace characters can be put between the different units on the line. The # sign at the start of the line is a traditional UNIX comment sign, and makes it easier for other programs to ignore those lines.

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Keyboard shortcuts in Fluents, written in Emacs syntax. I.e. C-x means "press control and x". Equivalently M-x is "meta x".
## Main:
| | |
|----------|-----------------------------------------------------------------|
| C-+ | Show only current plot. |
| C-- | Show four plots. |
| C-arrows | Move focus between views. |
| C-n | Create new project. This brings up a workflow selection window. |
| C-q | Quit |
| M-l | Activate "Log" tab. |
| M-s | Activate "Selections" tab. |
## Selections:
| | |
|---------|--------------|
| shift | Union |
| control | Intersection |

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# Principal Component Analysis
The principal component analysis (PCA) task.
## Input
## Options
## Output
- Score matrix
- Loadings matrix
## Plots
- Score plot
- Loadings plot
- Profiles

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# Principal Component Analysis
The principal component analysis (PCA) task.
## Input
- X
- Y
## Options
## Output
- Score matrix
- Loadings matrix
## Plots
- Score plot
- X loadings plot
- Y loadings plot
- Profiles

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# Installing Laydi
Installing Laydi is (by far) easiest on computers running [Ubuntu](https://www.ubuntu.com/) or [Debian](https://www.debian.org/) because that is what we use when developing it, and hence it is where the program is best tested.
## Requirements
## Subversion checkout

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```
GNU GENERAL PUBLIC LICENSE
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of this license document, but changing it is not allowed.
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The precise terms and conditions for copying, distribution and
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```

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# Principal Component Analysis

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# Partial Least Squares Regression