visdom 0.0.4

Visualization tool for Live and Rich Data

# **Visdom**

![visdom_big](https://lh3.googleusercontent.com/-bqH9UXCw-BE/WL2UsdrrbAI/AAAAAAAAnYc/emrxwCmnrW4_CLTyyUttB0SYRJ-i4CCiQCLcB/s0/Screen+Shot+2017-03-06+at+10.51.02+AM.png"visdom_big")

A flexible tool for creating, organizing, and sharing visualizations of live, rich data. Designed for Numpy or Torch.

* [Overview](#overview)
* [Concepts](#concepts)
* [Setup](#setup)
* [Usage](#launch)
* [Visualization API](#visualization-api)
* [To Do](#to-do)
* [Contributing](#contributing)


## Overview

Visdom aims to facilitate visualization of (remote) data with an emphasis on supporting scientific experimentation.

<p align="center"><img src="https://lh3.googleusercontent.com/-h3HuvbU2V0SfgqgXGiK3LPghE5vqvS0pzpObS0YgG_LABMFk62JCa3KVu_2NV_4LJKaAa5-tg=s0" width="500" /></p>

Broadcast visualizations of plots, images, and text for yourself and your collaborators.

<p align="center"><img src="https://thumbs.gfycat.com/SlipperySecondhandGemsbuck-size_restricted.gif" width="500" /></p>

Organize your visualization space programmatically or through the UI to create dashboards for live data, inspect results of experiments, or debug experimental code.

<p align="center"><img align="center" src="https://lh3.googleusercontent.com/-IHexvZ-FMtk/WLTXBgQlijI/AAAAAAAAm_s/514LM8R1XFgyNKPVMf4tNwYluZsHsC63wCLcB/s0/Screen+Shot+2017-02-27+at+3.15.27+PM.png" width="500" /></p>

<br/>

## Concepts
Visdom has a simple set of features that can be composed for various use-cases.

#### Panes
<p align="center"><img align="center" src="https://lh3.googleusercontent.com/-kLnogsg9RCs/WLx34PEsGWI/AAAAAAAAnSs/7t_62pbfmfoEBnkcbKTXIqz0WM8pQJHVQCLcB/s0/Screen+Shot+2017-03-05+at+3.34.43+PM.png" width="500" /></p>


The UI begins as a blank slate -- you can populate it with plots, images, and text. These appear in panes that you can drag, drop, resize, and destroy. The panes live in `envs` and the state of `envs` is stored across sessions. You can download the content of panes -- including your plots in `svg`.



> **Tip**: You can use the zoom of your browser to adjust the scale of the UI.



#### Environments
<p align="center"><img align="center" src="https://lh3.googleusercontent.com/-1wRSpNIoFeo/WLXacodRTMI/AAAAAAAAnEo/sTr5jSnQviA0uLqFIvwPGledmxcpupdkgCLcB/s0/Screen+Shot+2017-02-28+at+2.54.13+PM.png" width="300" /></p>

You can partition your visualization space with `envs`. By default, every user will have an env called `main`. New envs can be created in the UI or programmatically. The state of envs is chronically saved.

You can access a specific env via url: `http://localhost.com:8097/env/main`. If your server is hosted, you can share this url so others can see your visualizations too.

>**Managing Envs:**
>Your envs are loaded at initialization of the server, by default from `$HOME/.visdom/`. Custom paths can be passed as a cmd-line argument. Envs are removed by deleting the corresponding `.json` file from the env dir.

#### State
Once you've created a few visualizations, state is maintained. The server automatically caches your visualizations -- if you reload the page, your visualizations reappear.

<p align="center"><img align="center" src="https://lh3.googleusercontent.com/-ZKeFJfMe5S4/WLXebiNgFwI/AAAAAAAAnFI/AH2cGsf40hEWbH6UeclYQcZPS0YZbcayQCLcB/s0/env_fork_2.gif" width="400" /></p>

* **Save:** You can manually do so with the `save` button. This will serialize the env's state (to disk, in JSON), including window positions. You can save an `env` programmatically.
<br/>This is helpful for more sophisticated visualizations in which configuration is meaningful, e.g. a data-rich demo, a model training dashboard, or systematic experimentation. This also makes them easy to share and reuse.


* **Fork:** If you enter a new env name, saving will create a new env -- effectively **forking** the previous env.


## Setup

Requires Python 2.7/3 (and optionally Torch7)

```bash
# Download
cd /your/clone/path
git clone git://github.com/facebookresearch/visdom.git

# Install
pip install py/requirements
python setup.py install

# Torch client
luarocks make th/visdom-scm-1.rockspec

```

## Launch

Start the server (probably in a `screen` or `tmux`) :

```bash
python -m visdom.server
```

Visdom now can be accessed by going to `http://localhost:8097` in your browser, or your own host address if specified.

>If the above does not work, try using a SSH tunnel to your server by adding the following line to your local `~/.ssh/config`:
```LocalForward 127.0.0.1:8097 127.0.0.1:8097```.

#### Python example
```python
import visdom
vis = visdom.Visdom()
vis.text('Hello, world!')
vis.image(np.ones((10, 10, 3)))
```

#### Torch example
```lua
vis = require 'visdom'()
vis:text{text = 'Hello, world!'}
vis:image{img = image.fabio()}
```



### Demos

```bash
python example/demo.py
th example/demo1.lua
th example/demo2.lua
```


## Visualization API
The following API is currently supported. Visualizations are powered by Plotly.
- `vis.scatter`: 2D or 3D scatter plots
- `vis.line` : line plots
- `vis.stem` : stem plots
- `vis.heatmap`: heatmap plots
- `vis.bar` : bar graphs
- `vis.hist` : histograms
- `vis.boxplot`: boxplots
- `vis.surf` : surface plots
- `vis.contour`: contour plots
- `vis.quiver` : quiver plots
- `vis.image` : images
- `vis.text` : text box
- `vis.save` : serialize state

Further details on each of these functions are given below. For a quick introduction into the capabilities of `visdom`, have a look at the `example` directory, or read the details below.

The exact inputs into the plotting functions vary, although most of them take as input a tensor `X` than contains the data and an (optional) tensor `Y` that contains optional data variables (such as labels or timestamps). All plotting functions take as input a optional `win` that can be used to plot into a specific window; each plotting function also returns the `win` of the window it plotted in. One can also specify the `env` to which the visualization should be added.

![visdom_big](https://lh3.googleusercontent.com/-bqH9UXCw-BE/WL2UsdrrbAI/AAAAAAAAnYc/emrxwCmnrW4_CLTyyUttB0SYRJ-i4CCiQCLcB/s0/Screen+Shot+2017-03-06+at+10.51.02+AM.png"visdom_big")

#### plot.scatter
This function draws a 2D or 3D scatter plot. It takes as input an `Nx2` or
`Nx3` tensor `X` that specifies the locations of the `N` points in the
scatter plot. An optional `N` tensor `Y` containing discrete labels that
range between `1` and `K` can be specified as well -- the labels will be
reflected in the colors of the markers.
The following `options` are supported:

- `options.colormap` : colormap (`string`; default = `'Viridis'`)
- `options.markersymbol`: marker symbol (`string`; default = `'dot'`)
- `options.markersize` : marker size (`number`; default = `'10'`)
- `options.markercolor` : color per marker. (`torch.*Tensor`; default = `nil`)
- `options.legend` : `table` containing legend names

`options.markercolor` is a Tensor with Integer values. The tensor can be of size `N` or `N x 3` or `K` or `K x 3`.

- Tensor of size `N`: Single intensity value per data point. 0 = black, 255 = red
- Tensor of size `N x 3`: Red, Green and Blue intensities per data point. 0,0,0 = black, 255,255,255 = white
- Tensor of size `K` and `K x 3`: Instead of having a unique color per data point, the same color is shared for all points of a particular label.


#### plot.line
This function draws a line plot. It takes as input an `N` or `NxM` tensor
`Y` that specifies the values of the `M` lines (that connect `N` points)
to plot. It also takes an optional `X` tensor that specifies the
corresponding x-axis values; `X` can be an `N` tensor (in which case all
lines will share the same x-axis values) or have the same size as `Y`.

The following `options` are supported:

- `options.fillarea` : fill area below line (`boolean`)
- `options.colormap` : colormap (`string`; default = `'Viridis'`)
- `options.markers` : show markers (`boolean`; default = `false`)
- `options.markersymbol`: marker symbol (`string`; default = `'dot'`)
- `options.markersize` : marker size (`number`; default = `'10'`)
- `options.legend` : `table` containing legend names

#### plot.stem
This function draws a stem plot. It takes as input an `N` or `NxM` tensor
`X` that specifies the values of the `N` points in the `M` time series.
An optional `N` or `NxM` tensor `Y` containing timestamps can be specified
as well; if `Y` is an `N` tensor then all `M` time series are assumed to
have the same timestamps.

The following `options` are supported:

- `options.colormap`: colormap (`string`; default = `'Viridis'`)
- `options.legend` : `table` containing legend names

#### plot.heatmap
This function draws a heatmap. It takes as input an `NxM` tensor `X` that
specifies the value at each location in the heatmap.

The following `options` are supported:

- `options.colormap` : colormap (`string`; default = `'Viridis'`)
- `options.xmin` : clip minimum value (`number`; default = `X:min()`)
- `options.xmax` : clip maximum value (`number`; default = `X:max()`)
- `options.columnnames`: `table` containing x-axis labels
- `options.rownames` : `table` containing y-axis labels

#### plot.bar
This function draws a regular, stacked, or grouped bar plot. It takes as
input an `N` or `NxM` tensor `X` that specifies the height of each of the
bars. If `X` contains `M` columns, the values corresponding to each row
are either stacked or grouped (dependending on how `options.stacked` is
set). In addition to `X`, an (optional) `N` tensor `Y` can be specified
that contains the corresponding x-axis values.

The following plot-specific `options` are currently supported:

- `options.columnnames`: `table` containing x-axis labels
- `options.stacked` : stack multiple columns in `X`
- `options.legend` : `table` containing legend labels

#### plot.histogram
This function draws a histogram of the specified data. It takes as input
an `N` tensor `X` that specifies the data of which to construct the
histogram.

The following plot-specific `options` are currently supported:

- `options.numbins`: number of bins (`number`; default = 30)

#### plot.boxplot
This function draws boxplots of the specified data. It takes as input
an `N` or an `NxM` tensor `X` that specifies the `N` data values of which
to construct the `M` boxplots.

The following plot-specific `options` are currently supported:

- `options.legend`: labels for each of the columns in `X`

#### plot.surf
This function draws a surface plot. It takes as input an `NxM` tensor `X`
that specifies the value at each location in the surface plot.

The following `options` are supported:

- `options.colormap`: colormap (`string`; default = `'Viridis'`)
- `options.xmin` : clip minimum value (`number`; default = `X:min()`)
- `options.xmax` : clip maximum value (`number`; default = `X:max()`)

#### plot.contour
This function draws a contour plot. It takes as input an `NxM` tensor `X`
that specifies the value at each location in the contour plot.

The following `options` are supported:

- `options.colormap`: colormap (`string`; default = `'Viridis'`)
- `options.xmin` : clip minimum value (`number`; default = `X:min()`)
- `options.xmax` : clip maximum value (`number`; default = `X:max()`)

#### plot.quiver
This function draws a quiver plot in which the direction and length of the
arrows is determined by the `NxM` tensors `X` and `Y`. Two optional `NxM`
tensors `gridX` and `gridY` can be provided that specify the offsets of
the arrows; by default, the arrows will be done on a regular grid.

The following `options` are supported:

- `options.normalize`: length of longest arrows (`number`)
- `options.arrowheads`: show arrow heads (`boolean`; default = `true`)

#### plot.image
This function draws an img. It takes as input an `CxWxH` tensor `img`
that contains the image.

The following `options` are supported:

- `options.jpgquality`: JPG quality (`number` 0-100; default = 100)

#### plot.text
This function prints text in a box. It takes as input an `text` string.
No specific `options` are currently supported.


### Customizing plots

The plotting functions take an optional `options` table as input that can be used to change (generic or plot-specific) properties of the plots. All input arguments are specified in a single table; the input arguments are matches based on the keys they have in the input table.

The following `options` are generic in the sense that they are the same for all visualizations (except `plot.image` and `plot.text`):

- `options.title` : figure title
- `options.width` : figure width
- `options.height` : figure height
- `options.showlegend` : show legend (`true` or `false`)
- `options.xtype` : type of x-axis (`'linear'` or `'log'`)
- `options.xlabel` : label of x-axis
- `options.xtick` : show ticks on x-axis (`boolean`)
- `options.xtickmin` : first tick on x-axis (`number`)
- `options.xtickmax` : last tick on x-axis (`number`)
- `options.xtickstep` : distances between ticks on x-axis (`number`)
- `options.ytype` : type of y-axis (`'linear'` or `'log'`)
- `options.ylabel` : label of y-axis
- `options.ytick` : show ticks on y-axis (`boolean`)
- `options.ytickmin` : first tick on y-axis (`number`)
- `options.ytickmax` : last tick on y-axis (`number`)
- `options.ytickstep` : distances between ticks on y-axis (`number`)
- `options.marginleft` : left margin (in pixels)
- `options.marginright` : right margin (in pixels)
- `options.margintop` : top margin (in pixels)
- `options.marginbottom`: bottom margin (in pixels)


The other options are visualization-specific, and are described in the
documentation of the functions.

## To Do

- [ ] Command line tool for systematic {logs -> plots}
- [ ] Filtering through panes with regex by title (or meta field)
- [ ] Compiling react by python server at runtime
- [ ] Option to have static DOM for scaling to thousands of images

## Contributing
See guidelines for contributing [here.](./CONTRIBUTING.md)