#!/usr/bin/env python
# coding: utf-8

# JupyterLab
# ==========
# 
# Sergey Zagoruyko @ WILLOW

# Next iteration of Jupyter, ready to use!
# 
# My experience for keeping track of experiments:
#  * Jupyter since 2015 (3 years+)
#  * JupyterLab since 2018 (about 1 year)
# 
# The most powerful feature:
#  * **Extensions**

# ## Install
# 
# a bit messy but installs nodejs:
# 
# ```bash
# conda install -c conda-forge jupyterlab
# ```
# 
# otherwise:
# 
# ```bash
# pip install jupyterlab
# ```

# ## Vim mode
# 
# Text editing (e.g. python code or yaml files) supports by default Sublime, Emacs and Vim.
# 
# To edit Jupyter cells there is Vim extension: <https://github.com/jwkvam/jupyterlab-vim>
#         
# Install with:
# 
# ```
# jupyter labextension install jupyterlab_vim
# ```
# 
# <img src=https://user-images.githubusercontent.com/86304/38079432-b7596fd8-32f3-11e8-9ebd-4b9e7823f5f9.gif>

# ## Kernels
# 
# * IPyKernel
# * IJulia
# * IHaskell
# * Matlab Kernel
# * IOctave
# 
# and others <https://github.com/jupyter/jupyter/wiki/Jupyter-kernels>

# ## UI improvements
# 
# * Built-in terminals
# * Moving cells
# * Panes

# ## Visual Studio Code integration
# 
# Microsoft Python extension allows importing and editing jupyter files:
# 
# <img src=https://msdnshared.blob.core.windows.net/media/2018/11/RunCells.gif>

# ## Visualizations
# 
# ### Videos

# In[10]:


from IPython.display import HTML

youtube_id = 'ctOM-Gza04Y'

HTML(f'<iframe width="560" height="315" src="https://www.youtube.com/embed/{youtube_id}?rel=0&amp;controls=0&amp;showinfo=0" frameborder="0" allowfullscreen></iframe>')


# ### Pandas

# In[11]:


import pandas as pd
pd.read_json('/sequoia/data1/szagoruy/tmp/47179/exp_args.json')


# ## Matplotlib

# In[12]:


get_ipython().run_line_magic('pylab', 'inline')
get_ipython().run_line_magic('config', "InlineBackend.figure_format = 'retina'")
import seaborn as sns
import torch
sns.set()


# In[13]:


a = torch.rand(64, 64, 3)
plt.imshow(a)


# In[14]:


pd.DataFrame({'x': torch.randn(128).cumsum(0),
              'y': torch.randn(128).cumsum(0),
             }).plot.hist(figsize=(16,8), alpha=0.8);


# ### Graphs

# In[15]:


import  torch
from torch import nn
from torchviz import make_dot


# In[16]:


lstm_cell = nn.LSTMCell(128, 128)
x = torch.randn(1, 128)
make_dot(lstm_cell(x), params=dict(list(lstm_cell.named_parameters())))


# ### LaTex rendering
# 
# Besides writing equations in jupyter cells like that: $$D_{KL} = \sum_i p(i) \log \frac{p(i)}{q(i)},$$
# it is possible to write latex files that get compiled and rendered in Jupyter lab.

# ## ipynb sharing
# * https://gist.github.com
# * https://nbviewer.jupyter.org

# ## Debugging
# 
# `%pdb on` / `%pdb off` magick turns on debugging on errors

# ## Downsides
# 
# **Not a complete IDE!**:
# * Git integration is pain (although there are attemps to improve it)
# * No linter
# * Execution order is mess
# * Code completion is not as good as VSCode or Atom (no completion outside of kernels)

# ## My workflow
# 
# With JupyterLab:
# * Keep track of experiments (converge curves, notes)
# * Visualizations (debugging, share notebooks with other people)
# * Start new jobs on cluster
# 
# For writing code I use IDE (mostly vim, sometimes VSCode).

# In[ ]: