visdom 0.1.8

A tool for visualizing live, rich data for Torch and Numpy

# **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. Supports Torch and Numpy.

* [Overview](#overview)
* [Concepts](#concepts)
* [Setup](#setup)
* [Usage](#usage)
* [API](#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.

### Windows
<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 windows that you can drag, drop, resize, and destroy. The windows live in `envs` and the state of `envs` is stored across sessions. You can download the content of windows -- including your plots in `svg`.



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

##### Callbacks

The python Visdom implementation supports callbacks on a window. The demo shows an example of this in the form of an editable text pad. The functionality of these callbacks allows the Visdom object to receive and react to events that happen in the frontend.

You can subscribe a window to events by adding a function to the event handlers dict for the window id you want to subscribe by calling `viz.register_event_handler(handler, win_id)` with your handler and the window id. Multiple handlers can be registered to the same window. You can remove all event handlers from a window using `viz.clear_event_handlers(win_id)`. When an event occurs to that window, your callbacks will be called on a dict containing:

- `event_type`: one of the below event types
- `pane_data`: all of the stored contents for that window including layout and content.
- `eid`: the current environment id
- `target`: the window id the event is called on

Additional parameters are defined below.

Right now two callback events are supported:

1. `Close` - Triggers when a window is closed. Returns a dict with only the aforementioned fields.
2. `KeyPress` - Triggers when a key is pressed. Contains additional parameters:
- `key` - A string representation of the key pressed (applying state modifiers such as SHIFT)
- `key_code` - The javascript event keycode for the pressed key (no modifiers)

### Environments
<p align="center"><img align="center" src="https://user-images.githubusercontent.com/1276867/34618198-fc63976c-f20b-11e7-9c0d-060132fdb37e.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. Environments are able to keep entirely different pools of plots.

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.

Environments are automatically hierarchically organized by the first `_`.

#### Selecting Environments
<p align="center"><img align="center" src="https://user-images.githubusercontent.com/1276867/34618242-261d55d4-f20c-11e7-820d-c16731248b26.png" width="300" /></p>

From the main page it is possible to toggle between different environments using the environment selector. Selecting a new environment will query the server for the plots that exist in that environment. The environment selector allows for searching and filtering for the new enironment.

#### Comparing Environments

From the main page it is possible to compare different environments using the environment selector. Selecting multiple environments in the check box will query the server for the plots with the same titles in all environments and plot them in a single plot. An additional compare legend pane is created with a number corresponding to each selected environment. Individual plots are updated with legends corresponding to "x_name" where `x` is a number corresponding with the compare legend pane and `name` is the original name in the legend.

#### Clearing Environments
You can use the eraser button to remove all of the current contents of an environment. This closes the plot windows for that environment but keeps the empty environment for new plots.

#### Managing Environments
<p align="center"><img align="center" src="https://user-images.githubusercontent.com/1276867/34618262-3bb635c8-f20c-11e7-9370-9facfde0cfb7.png" width="400" /></p>

Pressing the folder icon opens a dialog that allows you to fork or force save the current environment, or delete any of your existing environments. Use of this feature is fully described in the **State** section.

>**Env Files:**
>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 using the delete button or 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.

> **Tip**: Fork an environment before you begin to make edits to ensure that your changes are saved seperately.

### Filter
You can use the `filter` to dynamically sift through windows present in an env -- just provide a regular expression with which to match titles of window you want to show. This can be helpful in use cases involving an env with many windows e.g. when systematically checking experimental results.

<p align="center"><img align="center" src="https://user-images.githubusercontent.com/1276867/34618118-b86cb138-f20b-11e7-834d-b7d7039313f0.png" width="300" /></p>

> **Note**: If you have saved your current view, the view will be restored after clearing the filter.
> <p align="center"><img align="center" src="https://user-images.githubusercontent.com/1276867/34849912-f0693f30-f6f1-11e7-90b6-2a39f83280e8.gif" width="500" /></p>

### Views
<p align="center"><img align="center" src="https://user-images.githubusercontent.com/1276867/34618173-e2546f40-f20b-11e7-9969-16267891fb53.png" width="300" /></p>

It is possible to manage the views simply by dragging the tops of windows around, however additional features exist to keep views organized and save common views. View management can be useful for saving and switching between multiple common organizations of your windows.

#### Saving/Deleting Views
Using the folder icon, a dialog window opens where views can be forked in the same way that envs can be. Saving a view will retain the position and sizes of all of the windows in a given environment. Views are saved in `$HOME/.visdom/view/layouts.json` in the visdom filepath.

> **Note**: Saved views are static, and editing a saved view copies that view over to the `current` view where editing can occur.

#### Re-Packing
Using the repack icon (9 boxes), visdom will attempt to pack your windows in a way that they best fit while retaining row/column ordering.

> **Note**: Due to the reliance on row/column ordering and `ReactGridLayout` the final layout might be slightly different than what might be expected. We're working on improving that experience or providing alternatives that give more fine-tuned control.

#### Reloading Views
<p align="center"><img align="center" src="https://user-images.githubusercontent.com/1276867/34621042-9c6c05f6-f215-11e7-92c7-60afe2bf7e1e.gif" width="600" /></p>

Using the view dropdown it is possible to select previously saved views, restoring the locations and sizes of all of the windows within the current environment to the places they were when that view was saved last.

## Setup

Requires Python 2.7/3 (and optionally Torch7)

```bash
# Install Python server and client from pip
# (STABLE VERSION, NOT ALL CURRENT FEATURES ARE SUPPORTED)
pip install visdom

# Install Torch client
# (STABLE VERSION, NOT ALL CURRENT FEATURES ARE SUPPORTED)
luarocks install visdom

```

```bash
# Install python from source
pip install -e .
# If the above runs into issues, you can try the below
easy_install .

# Install Torch client from source (from th directory)
luarocks make

```

## Usage

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 an 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```.

#### Command Line Options

The following options can be provided to the server:

1. `-port` : The port to run the server on.
2. `-env_path` : The path to the serialized session to reload.
3. `-logging_level` : Logging level (default = INFO). Accepts both standard text and numeric logging values.
4. `-readonly` : Flag to start server in readonly mode.


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

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

Some users have reported issues when connecting Lua clients to the Visdom server.
A potential work-around may be to switch off IPv6:
```
vis = require 'visdom'()
vis.ipv6 = false -- switches off IPv6
vis:text{text = 'Hello, world!'}
```


### Demos

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


## API
For a quick introduction into the capabilities of `visdom`, have a look at the `example` directory, or read the details below.

### Basics
Visdom offers the following basic visualization functions:
- [`vis.image`](#visimage) : image
- [`vis.images`](#visimages) : list of images
- [`vis.text`](#vistext) : arbitrary HTML
- [`vis.audio`](#visaudio) : audio
- [`vis.video`](#visvideo) : videos
- [`vis.svg`](#vissvg) : SVG object
- [`vis.matplot`](#vismatplot) : matplotlib plot
- [`vis.save`](#vissave) : serialize state server-side

### Plotting
We have wrapped several common plot types to make creating basic visualizations easily. These visualizations are powered by [Plotly](https://plot.ly/).

The following API is currently supported:
- [`vis.scatter`](#visscatter) : 2D or 3D scatter plots
- [`vis.line`](#visline) : line plots
- [`vis.stem`](#visstem) : stem plots
- [`vis.heatmap`](#visheatmap) : heatmap plots
- [`vis.bar`](#visbar) : bar graphs
- [`vis.histogram`](#vishistogram) : histograms
- [`vis.boxplot`](#visboxplot) : boxplots
- [`vis.surf`](#vissurf) : surface plots
- [`vis.contour`](#viscontour) : contour plots
- [`vis.quiver`](#visquiver) : quiver plots
- [`vis.mesh`](#vismesh) : mesh plots

### Generic Plots
Note that the server API adheres to the Plotly convention of `data` and `layout` objects, such that you can produce your own arbitrary `Plotly` visualizations:

```python
import visdom
vis = visdom.Visdom()

trace = dict(x=[1, 2, 3], y=[4, 5, 6], mode="markers+lines", type='custom',
marker={'color': 'red', 'symbol': 104, 'size': "10"},
text=["one", "two", "three"], name='1st Trace')
layout = dict(title="First Plot", xaxis={'title': 'x1'}, yaxis={'title': 'x2'})

vis._send({'data': [trace], 'layout': layout, 'win': 'mywin'})
```

### Others
- [`vis.close`](#visclose) : close a window by id
- [`vis.win_exists`](#viswin_exists) : check if a window already exists by id
- [`vis.get_window_data`](#visget_window_data): get current data for a window
- [`vis.check_connection`](#vischeck_connection): check if the server is connected

## Details
![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")

### Basics

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

The following `opts` are supported:

- `opts.jpgquality`: JPG quality (`number` 0-100; default = 100)
- `opts.caption`: Caption for the image

#### vis.images

This function draws a list of `images`. It takes an input `B x C x H x W` tensor or a `list of images` all of the same size. It makes a grid of images of size (B / nrow, nrow).

The following arguments and `opts` are supported:

- `nrow`: Number of images in a row
- `padding`: Padding around the image, equal padding around all 4 sides
- `opts.jpgquality`: JPG quality (`number` 0-100; default = 100)
- `opts.caption`: Caption for the image

#### vis.text
This function prints text in a box. You can use this to embed arbitrary HTML.
It takes as input a `text` string.
No specific `opts` are currently supported.

#### vis.audio
This function plays audio. It takes as input the filename of the audio
file or an `N` tensor containing the waveform (use an `Nx2` matrix for stereo
audio). The function does not support any plot-specific `opts`.

The following `opts` are supported:

- `opts.sample_frequency`: sample frequency (`integer` > 0; default = 44100)

Known issue: Visdom uses scipy to convert tensor inputs to wave files. Some
versions of Chrome are known not to play these wave files (Firefox and Safari work fine).

#### vis.video
This function plays a video. It takes as input the filename of the video
`videofile` or a `LxHxWxC`-sized
`tensor` containing all the frames of the video as input. The
function does not support any plot-specific `opts`.

The following `opts` are supported:

- `opts.fps`: FPS for the video (`integer` > 0; default = 25)

Note: Using `tensor` input requires that ffmpeg is installed and working.
Your ability to play video may depend on the browser you use: your browser has
to support the Theano codec in an OGG container (Chrome supports this).

#### vis.svg
This function draws an SVG object. It takes as input a SVG string `svgstr` or
the name of an SVG file `svgfile`. The function does not support any specific
`opts`.

#### vis.matplot
This function draws a Matplotlib `plot`. The function does not support
any plot-specific `opts`.

> **Note**: `matplot` is not rendered using the same backend as plotly plots, and is somewhat less efficient. Using too many matplot windows may degrade visdom performance.

#### vis.save
This function saves the `envs` that are alive on the visdom server. It takes input a list (in python) or table (in lua) of env ids to be saved.

### Plotting
Further details on the wrapped plotting functions are given 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 an 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.

#### vis.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.

`update` can be used to efficiently update the data of an existing plot. Use 'append' to append data, 'replace' to use new data, or 'remove' to remove the trace specified by `name`. If updating a single trace, use `name` to specify the name of the trace to be updated. Update data that is all NaN is ignored (can be used for masking update).

The following `opts` are supported:

- `opts.colormap` : colormap (`string`; default = `'Viridis'`)
- `opts.markersymbol`: marker symbol (`string`; default = `'dot'`)
- `opts.markersize` : marker size (`number`; default = `'10'`)
- `opts.markercolor` : color per marker. (`torch.*Tensor`; default = `nil`)
- `opts.legend` : `table` containing legend names
- `opts.textlabels` : text label for each point (`list`: default = `None`)
- `opts.layoutopts` : dict of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.
- `opts.traceopts` : dict mapping trace names or indices to dicts of additional options that the graph backend accepts. For example `traceopts = {'plotly': {'myTrace': {'mode': 'markers'}}}`.

`opts.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.


#### vis.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`.

`update` can be used to efficiently update the data of an existing plot. Use 'append' to append data, 'replace' to use new data, or 'remove' to remove the trace specified by `name`. If updating a single trace, use `name` to specify the name of the trace to be updated. Update data that is all NaN is ignored (can be used for masking update).

The following `opts` are supported:

- `opts.fillarea` : fill area below line (`boolean`)
- `opts.colormap` : colormap (`string`; default = `'Viridis'`)
- `opts.markers` : show markers (`boolean`; default = `false`)
- `opts.markersymbol`: marker symbol (`string`; default = `'dot'`)
- `opts.markersize` : marker size (`number`; default = `'10'`)
- `opts.legend` : `table` containing legend names
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.
- `opts.traceopts` : `dict` mapping trace names or indices to `dict`s of additional options that plot.ly accepts for a trace.


#### vis.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 `opts` are supported:

- `opts.colormap`: colormap (`string`; default = `'Viridis'`)
- `opts.legend` : `table` containing legend names
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

#### vis.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 `opts` are supported:

- `opts.colormap` : colormap (`string`; default = `'Viridis'`)
- `opts.xmin` : clip minimum value (`number`; default = `X:min()`)
- `opts.xmax` : clip maximum value (`number`; default = `X:max()`)
- `opts.columnnames`: `table` containing x-axis labels
- `opts.rownames` : `table` containing y-axis labels
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

#### vis.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 (depending on how `opts.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 `opts` are currently supported:

- `opts.rownames`: `table` containing x-axis labels
- `opts.stacked` : stack multiple columns in `X`
- `opts.legend` : `table` containing legend labels
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

#### vis.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 `opts` are currently supported:

- `opts.numbins`: number of bins (`number`; default = 30)
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

#### vis.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 `opts` are currently supported:

- `opts.legend`: labels for each of the columns in `X`
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

#### vis.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 `opts` are supported:

- `opts.colormap`: colormap (`string`; default = `'Viridis'`)
- `opts.xmin` : clip minimum value (`number`; default = `X:min()`)
- `opts.xmax` : clip maximum value (`number`; default = `X:max()`)
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

#### vis.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 `opts` are supported:

- `opts.colormap`: colormap (`string`; default = `'Viridis'`)
- `opts.xmin` : clip minimum value (`number`; default = `X:min()`)
- `opts.xmax` : clip maximum value (`number`; default = `X:max()`)
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

#### vis.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 `opts` are supported:

- `opts.normalize`: length of longest arrows (`number`)
- `opts.arrowheads`: show arrow heads (`boolean`; default = `true`)
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

#### vis.mesh
This function draws a mesh plot from a set of vertices defined in an
`Nx2` or `Nx3` matrix `X`, and polygons defined in an optional `Mx2` or
`Mx3` matrix `Y`.

The following `opts` are supported:

- `opts.color`: color (`string`)
- `opts.opacity`: opacity of polygons (`number` between 0 and 1)
- `opts.layoutopts` : `dict` of any additional options that the graph backend accepts for a layout. For example `layoutopts = {'plotly': {'legend': {'x':0, 'y':0}}}`.

### Customizing plots

The plotting functions take an optional `opts` 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 `opts` are generic in the sense that they are the same for all visualizations (except `plot.image`, `plot.text`, `plot.video`, and `plot.audio`):

- `opts.title` : figure title
- `opts.width` : figure width
- `opts.height` : figure height
- `opts.showlegend` : show legend (`true` or `false`)
- `opts.xtype` : type of x-axis (`'linear'` or `'log'`)
- `opts.xlabel` : label of x-axis
- `opts.xtick` : show ticks on x-axis (`boolean`)
- `opts.xtickmin` : first tick on x-axis (`number`)
- `opts.xtickmax` : last tick on x-axis (`number`)
- `opts.xtickvals` : locations of ticks on x-axis (`table` of `number`s)
- `opts.xticklabels` : ticks labels on x-axis (`table` of `string`s)
- `opts.xtickstep` : distances between ticks on x-axis (`number`)
- `opts.xtickfont` : font for x-axis labels (dict of [font information](https://plot.ly/javascript/reference/#layout-font))
- `opts.ytype` : type of y-axis (`'linear'` or `'log'`)
- `opts.ylabel` : label of y-axis
- `opts.ytick` : show ticks on y-axis (`boolean`)
- `opts.ytickmin` : first tick on y-axis (`number`)
- `opts.ytickmax` : last tick on y-axis (`number`)
- `opts.ytickvals` : locations of ticks on y-axis (`table` of `number`s)
- `opts.yticklabels` : ticks labels on y-axis (`table` of `string`s)
- `opts.ytickstep` : distances between ticks on y-axis (`number`)
- `opts.ytickfont` : font for y-axis labels (dict of [font information](https://plot.ly/javascript/reference/#layout-font))
- `opts.marginleft` : left margin (in pixels)
- `opts.marginright` : right margin (in pixels)
- `opts.margintop` : top margin (in pixels)
- `opts.marginbottom`: bottom margin (in pixels)

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

### Others

#### vis.close

This function closes a specific window. It takes input window id `win` and environment id `eid`. Use `win` as `None` to close all windows in an environment.

#### vis.win_exists

This function returns a bool indicating whether or not a window `win` exists on the server already. Returns None if something went wrong.

Optional arguments:
- `env`: Environment to search for the window in. Default is `None`.

#### vis.get_window_data
This function returns the window data for the given window. Returns data for all windows in an env if win is None.

Arguments:
- `env`: Environment to search for the window in.
- `win`: Window to return data for. Set to `None` to retrieve all the windows in an environment.

#### vis.check_connection

This function returns a bool indicating whether or not the server is connected.

## To Do

- [ ] Command line tool for easy systematic plotting from live logs.
- [ ] Filtering through windows with regex by title (or meta field)
- [ ] Compiling react by python server at runtime

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

## Acknowledgments
Visdom was inspired by tools like [display](https://github.com/szym/display) and relies on [Plotly](https://plot.ly/) as a plotting front-end.