bkamapantula

repository for images

bkamapantula

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

# In[1]:


import pandas as pd
import geopandas
import matplotlib.pyplot as plt
import matplotlib.image as image

bkamapantula

[
  {
    "Region": "South",
    "Name": "Andhra Pradesh",
    "Seats": 175,
    "Central-Govt-Party": 0,
    "Each": 1
  },
  {
    "Region": "NE",

bkamapantula

  // Available variables:
  // - Machine
  // - interpret
  // - assign
  // - send
  // - sendParent
  // - spawn
  // - raise
  // - actions

bkamapantula

This chart is created as a response to the call to redesign the OLPC map by Curran Kelleher.

Original map at OLPC website.

Source data.

bkamapantula

About data

Aadhaar enrollments on a given date and pincode combination are grouped together and represented in the dataset.

Here, the enrollment data is divided into X no. of buckets by the aadhaar data operator. Each row represents information about a single enrollment (of a person) except the last four columns. The value in mobile_number_provided and email_provided for a row will always be less than or equal to the value in aadhaar_generated or rejected column. Consider the following example row from the dataset:

20150420,Allahabad Bank,A-Onerealtors Pvt Ltd,Uttar Pradesh,Ambedkar Nagar,Akbarpur,224155,F,22,5,0,0,5

Here, the last four columns represent: aadhaar_generated, rejected, mobile_number_provided, email_provided. 5 for aadhaar_generated field represents 5 aadhaar generations, 0 rejections, 0 enrollments with mobile number and 5 enrollments with email information.

bkamapantula

# requires GEOquery package to be installed
# writes output to stdout
# for more metadata, check the respective classes at http://rileylab.bio.umb.edu/sites/g/files/g1314676/f/201403/GEOquery.pdf
library(GEOquery)

gsm_ids <- c("GSM409307", "GSM1220601", "GSM1120314", "GSM1120315", "GSM1112815", "GSM1112816", "GSM1112817", "GSM1112818", "GSM410808")

for(iter in seq_along(gsm_ids)) {
    gds <- getGEO(gsm_ids[iter])
    cat(gsm_ids[iter], Meta(gds)$title)

bkamapantula

bkamapantula

import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt

# %matplotlib inline
mpl.style.use('ggplot')

df = pd.read_csv("chr_19_H2BK12ac_result.txt", sep="\t")

plt.pcolor(df[['run_1', 'run_2', 'run_3', 'run_4', 'run_5']], cmap='RdBu', norm=mpl.colors.LogNorm())

bkamapantula

# gets fileName.ext from /path/to/fileName.ext. if it's /path/to, it gets 'to'
file_name_w_ext=$(basename "$1")
# gets fileName from the given file
file_name="${file_name_w_ext%.*}"

# gets directory path 
file-path=$(dirname "$1")

# collapse multiple columns into a single column (leaving the first column)
awk '{for(i=2;i<=NF;i++) {print $i}}' infile.txt > infile_col_collapse.txt