### Mapping out the train routes in India

I had nothing better to do on a Sunday morning so I made a map. If you have been following my blog, you will know that I am trying to make sense of the Indian Railways, why the trains runs late  and if there's anyway we can learn about the cause of the delays, based on patters in delay.

Towards that goal, I posted two blog posts that described how and where I was collecting my data from and a first look at the data I was gathering. Even from the preliminary look, it can be seen that there are specific routes/stations that are causing a delay along a train's route. And the delays were induced on multiple runs at the same station, meaning that it wasn't simply a one time thing.

Moving on, another way to look at the problem is to understand how crowded the railway lines are. By mapping all train movement in India, we will be able to understand how crowded specific routes are and if they're crowded at a specific time of the day. By adding delay information from multiple trains to the map, we will also be able to prove with good certainty about the routes that are crowded and are leading to large delays.

I took a small step in that direction today by mapping out the routes of a few trains. Below you will find one such map/image, where the red lines correspond to the routes of a few trains. Note that this isn't the complete roster of trains that are run by the Indian Railways, it is but a very very small subset. But the process by which such a map can be made is scalable, the small subset to make this proof of concept.

While this image isn't interactive, running the code actually creates an interactive image, which displays station codes when the user hovers over the route. I modified an available example that produced a USA flight paths map using plotly.  The code can be found below.

import glob
import plotly.plotly as py
import pandas as pd

files = glob.glob('routes/*')

df_stations = pd.DataFrame(columns=['station', 'lat', 'long'])
df_station_paths = pd.DataFrame(columns=['start_lon', 'start_lat', 'end_lon', 'end_lat'])

for file in files:
names=['station', 'lat', 'long'],
na_values=[0.0])
df_stations_temp = df_stations_temp.dropna(axis=0, how='any')
df_station_paths_temp = pd.DataFrame([[df_stations_temp.iloc[i]['long'],
df_stations_temp.iloc[i]['lat'],
df_stations_temp.iloc[i+1]['long'],
df_stations_temp.iloc[i+1]['lat']]
for i in range(len(df_stations_temp)-1)],
columns=['start_lon', 'start_lat', 'end_lon', 'end_lat'])
df_stations = pd.concat([df_stations, df_stations_temp], ignore_index=True)
df_station_paths = pd.concat([df_station_paths, df_station_paths_temp], ignore_index=True)

stations = [ dict(
type = 'scattergeo',
locationmode = 'India',
lon = df_stations['long'],
lat = df_stations['lat'],
hoverinfo = 'text',
text = df_stations['station'],
mode = 'markers',
marker = dict(
size=2,
color='rgb(255, 0, 0)',
line = dict(
width=3,
color='rgba(68, 68, 68, 0)'
)
))]

station_paths = []
for i in range( len( df_station_paths ) ):
station_paths.append(
dict(
type = 'scattergeo',
locationmode = 'India',
lon = [ df_station_paths['start_lon'][i], df_station_paths['end_lon'][i] ],
lat = [ df_station_paths['start_lat'][i], df_station_paths['end_lat'][i] ],
mode = 'lines',
line = dict(
width = 1,
color = 'red',
),
opacity = 1.,
)
)

layout = dict(
showlegend = False,
height=1000,
geo = dict(
scope='India',
projection=dict( type='azimuthal equal area' ),
showland = True,
landcolor = 'rgb(243, 243, 243)',
countrycolor = 'rgb(204, 204, 204)',
),
)

fig = dict( data=station_paths+stations, layout=layout )
py.iplot( fig, filename='d3-station-paths' )


To briefly go over the code, the route files for individual trains were stored in routes/train_number.csv and each file contained three columns - station code along route, lat, long. Note that the locations and station codes along the route of a specific train were acquired using RailwayAPI. From the file, the above code first creates a Pandas DataFrame, which is then manipulated to create a new DataFrame that contains the train's path/route. These two DataFrames are finally modified and passed onto plotly, which creates the above map.

The map is far from perfect. For starters, like I mentioned earlier, it is but a small subset of all trains available. Secondly, the lat/long data seems to be faulty, because there seem to be stray lines that deviate from a train's actual route in the map.

I am trying to look for a better source of information than RailwayAPI. I am trying to get a list of all trains run by Indian Railways. I am trying to find an official source of information, from the Indian Railways. I am trying to find an easier way to make such a map, and make it interactive. If there's something I can do to make the map/processing better, point it out to me! I'd love to hear comments/feedback.

Until the next time ...

### Animation using GNUPlot

Animation using GNUPlotI've been trying to create an animation depicting a quasar spectrum moving across the 5 SDSS pass bands with respect to redshift. It is important to visualise what emission lines are moving in and out of bands to be able to understand the color-redshift plots and the changes in it.
I've tried doing this using the animate function in matplotlib, python but i wasn't able to make it work - meaning i worked on it for a couple of days and then i gave up, not having found solutions for my problems on the internet.
And then i came across this site, where the gunn-peterson trough and the lyman alpha forest have been depicted - in a beautiful manner. And this got me interested in using js and d3 to do the animations and make it dynamic - using sliders etc.
In the meanwhile, i thought i'd look up and see if there was a way to create animations in gnuplot and whoopdedoo, what do i find but nirvana!

In the image, you see 5 static curves and one dynam…

### on MOOCs.

For those of you who don't know, MOOC stands for Massively Open Online Course.

The internet is an awesome thing. It's making education free for all. Well, mostly free. But it's surprising at the width and depth of courses being offered online. And it looks like they are also having an impact on students, especially those from universities that are not top ranked. Students in all parts of the world can now get a first class education experience, thanks to courses offered by Stanford, MIT, Caltech, etc.

I'm talking about MOOCs because one of my new year resolutions is to take online courses, atleast 2 per semester (6 months). And I've chosen the following two courses on edX - Analyzing Big Data with Microsoft R Server and Data Science Essentials for now. I looked at courses on Coursera but I couldn't find any which was worthy and free. There are a lot more MOOC providers out there but let's start here. And I feel like the two courses are relevant to where I …

### On programmers.

I just watched this brilliant keynote today. It's a commentary on Programmers and the software development industry/ecosystem as a whole.

I am not going to give you a tl;dr version of the talk because it is a talk that I believe everyone should watch, that everyone should learn from. Instead, I am going to give my own parallel-ish views on programmers and programming.
As pointed out in the talk, there are mythical creatures in the software development industry who are revered as gods. Guido Van Rossum, the creator of Python, was given the title Benevolent Dictator For Life (BDFL). People flock around the creators of popular languages or libraries. They are god-like to most programmers and are treated like gods. By which, I mean to say, we assume they don't have flaws. That they are infallible. That they are perfect.
And alongside this belief in the infallibility of these Gods, we believe that they were born programmers. That programming is something that people are born wit…