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

# ## <center> Impact Factors for a few Nonlinear Science  Journals </center>

# Using  `pandas` and `plotly` Python libraries, we extract and plot the impact factors  of the following journals:

# - Celestial Mechanics and Dynamical Astronomy
# - Chaos
# - Chaos, Solitons, and Fractals
# - Communications In Nonlinear Science And Numerical Simulation
# - Discrete and Continuous Dynamical Systems, Series A
# - Discrete and Continuous Dynamical Systems, Series B
# - International Journal of Bifurcation and Chaos (IJBC)
# - Journal of Physics A: Mathematical and Theoretical
# - Nonlinearity
# - Physica D: Nonlinear Phenomena
# - Regular & Chaotic Dynamics    
# - SIAM Journal on Applied Dynamical Systems

# The table of impact factors for each journal, starting from 2008,  can be found through the *Journal Impact Factor Search Engine*
#   [http://www.bioxbio.com/if/html/](http://www.bioxbio.com/if/)

# For example, at 
# [http://www.bioxbio.com/if/html/CHAOS.html](http://www.bioxbio.com/if/html/CHAOS.html) is displayed   the table for  *Chaos*:

# We read the table   as a `pandas.DataFrame`:

# In[36]:


import pandas as pd
import numpy as np


# In[37]:


url=r'http://www.bioxbio.com/if/html/CHAOS.html'
dfr = pd.read_html(url, header=0)#returns a list of tables
dfr0=dfr[0]
dfr0


# In order to  read succesively the tables corresponding to the chosen journals, we define a list
# of strings that are appended to the above URL:

# In[38]:


ur=['CELEST-MECH-DYN-ASTR', 'CHAOS', 'CHAOS-SOLITON-FRACT','COMMUN-NONLINEAR-SCI', \
    'DISCRETE-CONT-DYN-A', 'DISCRETE-CONT-DYN-B','INT-J-BIFURCAT-CHAOS',\
    'J-PHYS-A-MATH-THEOR', 'NONLINEARITY','PHYSICA-D','REGUL-CHAOTIC-DYN',\
    'SIAM-J-APPL-DYN-SYST']


# `stitle` is the tuple of titles for the plotly plots that will be  generated from recorded data:

# In[39]:


stitle=tuple(['Celest Mech Dyn Astr', 'Chaos', 'Chaos, Solitons, Fractals',\
        'Commun Nonl Science', 'Discrete Cont Dyn A', 'Discrete Cont Dyn B',\
        'Int J Bifurcat Chaos', 'J Phys A Math Theor', '   Nonlinearity', \
        'Physica D', ' Regular Chaotic Dyn', ' SIAM J Appl Dyn Syst'])


# In[40]:


dfs=[]
for  u in ur:
    url=r'http://www.bioxbio.com/if/html/'+u+'.html'
    df = pd.read_html(url, header=0)
    df0=df[0]
    df0=df0.replace('-', '0', regex=True)
    dfn=list(df0['Impact Factor (IF)'])
    dfs.append(dfn)


# The Journal *Communications in Nonlinear Science and Numerical Simulation* is monitored only since 2010. 
# We insert 0 as its impact for 2008 and 2009:

# In[41]:


impacts=[]

for d in dfs:
    d=map(float, d)
    impacts.append(d[::-1])#revert the impacts such that to get their list starting with 2008


# The impact factors of each journal are now displayed in a `plotly` scatter plot:

# In[42]:


import plotly.plotly as py    
import plotly.tools as tls 
from plotly.graph_objs import *


# In[43]:


def make_Scatter(sbplt, y ):
        return Scatter(
        x=[2008, 2009, 2010, 2011, 2012, 2013, 2014],       
        y=y,  
        mode='markers+lines',
            
        marker=Marker(
        color='rgb(0, 143, 213)',
        size=6
        ),
        line=Line(
        color= 'rgb(0, 143, 213)',   #'#2c7fb8',
        width=2),
        name=' ',
        xaxis= 'x{}'.format(sbplt),  
        yaxis= 'y{}'.format(sbplt),   
       )
    


# In[44]:


axis_style = dict(
    zeroline=False,      
    showgrid=True, 
    gridwidth=1,
    gridcolor='#FFFFFF')  
    
def make_XAxis():
    xaxis = XAxis(axis_style, range=[2008, 2014],
                   nticks=7,
                   dtick=1,
                   showticklabels=False
                   )   
    return xaxis


def make_YAxis():
    yaxis = YAxis(axis_style, range=[0,3.4])                      
    return yaxis


# In[45]:


figure = tls.make_subplots(rows=3, cols=4, subplot_titles=stitle,
    horizontal_spacing=0.05,
    vertical_spacing=0.055)


# In[46]:


sbp=range(1,13)


# In[47]:


pl_width=1000 
pl_height=675

figure['layout'].update(plot_bgcolor='#EFECEA',
                        showlegend=False,     
                        hovermode='closest',  
                        autosize=False,       
                        width=pl_width,       
                        height=pl_height)    

title = 'Impact Factors for a few  Journals in Nonlinear Science <br> (updated July 1, 2015)'
figure['layout'].update(title=title,                                 
                        font= Font(family="Open Sans, sans-serif"))  


# In[48]:


anno_text="Data source:\
<a href='http://www.bioxbio.com/if/'> [1]</a>,  Code:\
<a href='http://nbviewer.ipython.org/gist/empet/eeb8bbe354e709bf590b'> [2]</a>"


figure['layout']['annotations']+=[
    Annotation(
            showarrow=False, 
            text=anno_text,  
            xref='paper',     
            yref='paper',     
            x=0,  
            y=-0.15,  
            xanchor='left',   
            yanchor='bottom',  
            font=Font(
            size=12 )
            )
]
for sbplt in sbp: #change the default font size for  subplots title
    figure['layout']['annotations'][sbplt-1]['font']= {'size': 12}


# In[49]:


for s in sbp:
    k=sbp.index(s)
    y=impacts[k]
    figure['data'] += [make_Scatter(s,impacts[k])]
    figure['layout'].update({'xaxis{}'.format(s): make_XAxis()})
    figure['layout'].update({'yaxis{}'.format(s): make_YAxis()})
  


# In[50]:


for s in range(9,13):
    xaxis_splt = figure['layout']['xaxis{}'.format(s)] 
    xaxis_splt.update(showticklabels=True,
                      dtick=1,
                      tickangle=-45,
                      ticks='outside',
                      nticks=6)
                      
for s in range(1,10,4):
    yaxis_splt = figure['layout']['yaxis{}'.format(s)] 
    yaxis_splt.update(showticklabels=True,
                      dtick=0.5,
                      ticks='outside')  


# In[51]:


py.sign_in("empet", "my_api_key")


# In[ ]:


#py.plot(figure, filename='Impact-Factors-NSJ', width=1000, height=700, #world_readable=False)#https://plot.ly/1290/~empet/ 
py.plot(figure, filename='Impact-Factors-DS', width=1000, height=700, world_readable=False) 


# In[55]:


from IPython.core.display import HTML
def  css_styling():
    styles = open("./custom.css", "r").read()
    return HTML(styles)
css_styling()