gwangjinkim

# Remarks:
#
# The export is done using the automated testing framework [Selenium](https://
# de.wikipedia.org/wiki/Selenium) which results in opening a browser window
# (Google Chrome) that might has to be closed by hand. Other than Plotly's
# own `export()` function this one also allows to set the `width` and `height`
# of the exported plot (in the former it's hardcoded to 800x600 pixels). If
# `incl_PDF_copy`/`incl_PNG_copy` is set to `TRUE`, the exported SVG additionally
# gets converted to a PDF/PNG using the R package [`rsvg`](https://github.com/
# jeroen/rsvg/tree/40576ac326621b40224db344b09158f4ff717433) which relies on

gwangjinkim

# TimSort by https://quinston.com/code-snippets/timsort-algorthm-search-code/

import random

def InsertionSort(array):

    for x in range (1, len(array)):
        for i in range(x, 0, -1):
            if array[i] < array[i - 1]:
                t = array[i]

gwangjinkim

This gist I write, because I couldn't find step by step instructions 
how to install and start postgresql locally (using conda within a conda environment - with the result
that you can run it without sudo/admin rights on your machine!)
and not globally in the operating system (which requires sudo/admin rights on that machine).

I hope, this will help especially people new to postgresql (and those who don't have sudo/admin rights on a specific machine but want
to run postgresql there)!

####################################
# create conda environment

gwangjinkim

#################################################
# solution mainly using pandas functionalities
# reading-in returns an ordered dict (which contains sheet_names as key) -> close to R functionality
# appending to excel like this might be slower
#################################################

def xlsx2dfs(fpath, **kwargs):
    return pd.from_excel(fpath, sheet_name=None, **kwargs)

gwangjinkim

import pandas as pd

df = pd.read_csv("XXXXX_20180101_20171231_start.csv", sep=";", header=None)

df_target = pd.read_csv("XXXX_20180101_20171231_test2_target.csv", sep=";", header=None)

"""
inspect by:

df.head()

gwangjinkim

#################################################################
# How to install Arch in your Android (into the App TermuxArch)
# and be able to use your Android device like a personal computer
# even running a X server to have GUI
#################################################################


###############################
# Install Termux

gwangjinkim

"""
Lisp languages support an image-based development cycle at the REPL.

R - having its roots in Lisp - it was first implemented in Scheme - has overtaken this.
R's base command `save.image(filepath)` can save the state of an entire REPL session in a file,
and next time you start R, you can start where you stopped, by hitting `load(filepath)`.

I was asking myself, since Python's REPL is ipython, what supports 
similar functionailities for pythonistas.

gwangjinkim

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; notes from the book "Vom Problem zum Programm" by Herbert Klaeren and Michael Sperber
;; on object oriented programming
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;
;; object oriented programming = message passing style + state + self + inheritance
;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;

;; object = active container in message passing style

gwangjinkim

# flagstat.fpath <- "~/testing/br-mgd-srt-srt.txt"

# content of the file (flagstat output):

51712430 + 0 in total (QC-passed reads + QC-failed reads)
0 + 0 secondary
0 + 0 supplementary
0 + 0 duplicates
46380832 + 0 mapped (89.69% : N/A)

gwangjinkim

# install IGV viewer for visualization of alignments

wget http://data.broadinstitute.org/igv/projects/downloads/2.4/IGV_2.4.8.zip
unzip IGV_2.4.8.zip
cd IGV_2.4.8
bash igv.sh