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

# # Jupyter
# 
# This notebook [on nbviewer](https://nbviewer.jupyter.org/gist/minrk/8ec83e6cb7a55be361e4)
# 
# - Started as part of IPython
# - IPython since 2001
# - Document Format: [nbformat](https://nbformat.readthedocs.org/en/latest/)
# - Interactive Computing Protocol: [jupyter-client](https://jupyter-client.readthedocs.org)
# - Interactive Environment: [notebook](https://jupyter-notebook.readthedocs.org/en/latest/)
# - Language-agnostic (> 50 language kernels)
# - Multi-user notebook server: [JupyterHub](https://github.com/juptyer/jupyterhub)
# - https://jupyter.org

# # Notebook with code and prose
# Here is an example that uses [SymPy](http://sympy.org/en/index.html) to factor polynomials.

# In[12]:


from ipywidgets import interact
from sympy import Symbol, Eq, factor
from sympy.interactive import init_printing
init_printing(use_latex='mathjax')


# In[13]:


x = Symbol('x')
x


# In[14]:


@interact
def factorit(n=12):
    return Eq(x**n-1, factor(x**n-1))


# ## Plotting
# 
# Inline plotting with matplotlib

# In[15]:


get_ipython().run_line_magic('matplotlib', 'inline')
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns


# We want to plot:
# 
# \begin{equation}
# y = sin(\omega t)
# \end{equation}

# In[16]:


def plot_sinωt(ω=3, T=5):
    t = np.linspace(0, T, 1000)
    y = np.sin(ω * t)
    plt.plot(t, y)
    plt.title(r"$\omega = %s$" % ω)

plot_sinωt(5, 5)
# interact(plot_sinωt)


# ### Interactive graphics with Bokeh

# In[17]:


import time
from bokeh.io import output_notebook, show
from bokeh.charts import Scatter
from bokeh.sampledata.iris import flowers

output_notebook()


# In[18]:


# fill a data frame with the data of interest and create a groupby object
df = flowers[["petal_length", "petal_width", "species"]]
xyvalues = df.groupby("species")

TOOLS="resize,crosshair,pan,wheel_zoom,box_zoom,reset,previewsave"

scatter = Scatter(xyvalues, tools=TOOLS, ylabel='petal_width')

show(scatter)


# # Contributing to Jupyter & IPython
# 
# - Several repos
#   - [jupyter/notebook](https://github.com/jupyter/notebook)
#   - [ipython/ipython](https://github.com/ipython/ipython)
#   - [jupyter/nbconvert](https://github.com/jupyter/nbconvert)
#   - [...](https://github.com/jupyter)
# - All BSD licensed
# - Mostly Python (some JavaScript)
# - All on GitHub via Pull Requests
# - [Jupyter Mailing List](https://groups.google.com/forum/#!forum/jupyter)
# - ~5 full-time funded core developers, hiring more in USA
# - ~10 industry-provides contributors (Rackspace, Continuum, Bloomberg, Google, IBM)
# - Weekly short report meetings [online](https://www.youtube.com/user/ipythondev)