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

# #### New to Plotly?
# Plotly's Python library is free and open source! [Get started](https://plotly.com/python/getting-started/) by downloading the client and [reading the primer](https://plotly.com/python/getting-started/).
# <br>You can set up Plotly to work in [online](https://plotly.com/python/getting-started/#initialization-for-online-plotting) or [offline](https://plotly.com/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plotly.com/python/getting-started/#start-plotting-online).
# <br>We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!

# #### Version Check
# Note: Linear fits are available in version <b>1.9.2+</b><br>
# Run  `pip install plotly --upgrade` to update your Plotly version

# In[1]:


import plotly
plotly.__version__


# ### Linear Fit

# In[2]:


# Learn about API authentication here: https://plotly.com/python/getting-started
# Find your api_key here: https://plotly.com/settings/api

import plotly.plotly as py
import plotly.graph_objs as go

# Scientific libraries
from numpy import arange,array,ones
from scipy import stats


xi = arange(0,9)
A = array([ xi, ones(9)])

# (Almost) linear sequence
y = [19, 20, 20.5, 21.5, 22, 23, 23, 25.5, 24]

# Generated linear fit
slope, intercept, r_value, p_value, std_err = stats.linregress(xi,y)
line = slope*xi+intercept

# Creating the dataset, and generating the plot
trace1 = go.Scatter(
                  x=xi, 
                  y=y, 
                  mode='markers',
                  marker=go.Marker(color='rgb(255, 127, 14)'),
                  name='Data'
                  )

trace2 = go.Scatter(
                  x=xi, 
                  y=line, 
                  mode='lines',
                  marker=go.Marker(color='rgb(31, 119, 180)'),
                  name='Fit'
                  )

annotation = go.Annotation(
                  x=3.5,
                  y=23.5,
                  text='$R^2 = 0.9551,\\Y = 0.716X + 19.18$',
                  showarrow=False,
                  font=go.Font(size=16)
                  )
layout = go.Layout(
                title='Linear Fit in Python',
                plot_bgcolor='rgb(229, 229, 229)',
                  xaxis=go.XAxis(zerolinecolor='rgb(255,255,255)', gridcolor='rgb(255,255,255)'),
                  yaxis=go.YAxis(zerolinecolor='rgb(255,255,255)', gridcolor='rgb(255,255,255)'),
                  annotations=[annotation]
                )

data = [trace1, trace2]
fig = go.Figure(data=data, layout=layout)

py.plot(fig, filename='Linear-Fit-in-python')


# In[4]:


from IPython.display import display, HTML

display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))

get_ipython().system(' pip install git+https://github.com/plotly/publisher.git --upgrade')
import publisher
publisher.publish(
    'linear-fits.ipynb', 'python/linear-fits/', 'Linear Fit',
    'Create a linear fit / regression in Python and add a line of best fit to your chart.',
    title = 'Linear Fit',
    name = 'Linear Fit',
    has_thumbnail='true', thumbnail='thumbnail/linear_fit.jpg', 
    language='python', page_type='example_index',
    display_as='statistics', order=10,
    ipynb= '~notebook_demo/139')


# In[ ]: