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Kyaw Naing Win knwin

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@knwin
knwin / elevation.py
Created August 24, 2019 14:35 — forked from Alliages/elevation.py
A very simple python script that get elevation from latitude and longitude with google maps API
#
# elevation: A very simple python script that get elevation from latitude and longitude with google maps API by Guillaume Meunier
#
# -----------------------------------
# NO DEPENDANCIES except JSON and URLLIB
# -----------------------------------
#
# Copyright (c) 2016, Guillaume Meunier <alliages@gmail.com>
# GEOJSON_export is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published
@knwin
knwin / my_test.geojson
Last active March 30, 2019 10:02
geojson.io test
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@knwin
knwin / drone-image-locaitons-as-kml.py
Created September 9, 2018 03:23
Extract GPS coordinates from EXIF of images and create point kmls
# Extract Location information from EXIF of drone images and creat a kml
# Kyaw Naing Win
# 2018-09-09
import exifread, os
import datetime
from fastkml import kml
from shapely.geometry import Point
today = str(datetime.date.today())
ctime = str(datetime.datetime.now().time())
today = today + ", " + ctime
@knwin
knwin / fifa_worldcup.csv
Last active October 6, 2015 08:46
Edition Year Host_Country Winner Runner_up Average_attendance Teams Matches Goals_scored Average_goals
1930 World Cup Uruguay 1930 Uruguay Uruguay Argentina 32808 13 18 70 3.9
1934 World Cup Italy 1934 Italy Italy Czechoslovakia 21353 16 17 70 4.1
1938 World Cup France 1938 France Italy Hungary 20872 15 18 84 4.7
1950 World Cup Brazil 1950 Brazil Uruguay Brazil 47511 13 22 88 4
1954 World Cup Switzerland 1954 Switzerland Germany Hungary 29562 16 26 140 5.4
1958 World Cup Sweden 1958 Sweden Brazil Sweden 23423 16 35 126 3.6
1962 World Cup Chile 1962 Chile Brazil Czechoslovakia 27912 16 32 89 2.8
1966 World Cup England 1966 England England Germany 48848 16 32 89 2.8
1970 World Cup Mexico 1970 Mexico Brazil Italy 50124 16 32 95 3
@knwin
knwin / Fund_2015.csv
Last active October 6, 2015 09:09
Funds vs Beneficiaries
Donor Funding_USD Funding_USD_M Ben
Pakistan 95742134 95.74 663708
United States 77839789 77.84 563966
European Commission 29216495 29.22 463953
United Kingdom 20551625 20.55 129208
Carry-over (donors not specified) 19125098 19.13 235505
Japan 18873196 18.87 82367
Germany 12709603 12.71 457447
Allocation of funds by UN agencies 11763602 11.76 972083
Canada 9524536 9.52 157802
@knwin
knwin / Total_Humanitarian_Funding_per_Donor_in_2015.csv
Last active October 6, 2015 08:57
Donor Funding_USD Funding_USD_M
Pakistan 95742134 95.74
United States 77839789 77.84
European Commission 29216495 29.22
United Kingdom 20551625 20.55
Carry-over (donors not specified) 19125098 19.13
Japan 18873196 18.87
Germany 12709603 12.71
Allocation of funds by UN agencies 11763602 11.76
Canada 9524536 9.52
@knwin
knwin / index.html
Last active October 6, 2015 08:41
Top Ten donors
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>D3 Page Template</title>
<script type="text/javascript" src="http://d3js.org/d3.v3.min.js"></script>
</head>
<body>
<h>Top Ten Donor to the humanitarian emergencies in Pakistan - 2015</h>
@knwin
knwin / betterlifeindex.csv
Created September 6, 2015 10:52
Module2: SVG graphic and Data Loading Exercise
We can make this file beautiful and searchable if this error is corrected: It looks like row 6 should actually have 25 columns, instead of 13. in line 5.
Country,Dwellings without basic facilities,Housing expenditure,Rooms per person,Household net adjusted disposable income,Household net financial wealth,Employment rate,Job security,Long-term unemployment rate,Personal earnings,Quality of support network,Educational attainment,Student skills,Years in education,Air pollution,Water quality,Consultation on rule-making,Voter turnout,Life expectancy,Self-reported health,Life satisfaction,Assault rate,Homicide rate,Employees working very long hours,Time devoted to leisure and personal care
Australia,1.1,20,2.3,31197,38482,72,4.4,1.06,46585,93,74,512,18.8,13,93,10.5,93,82,85,7.4,2.1,0.8,14.23,14.41
Austria,1,21,1.6,29256,48125,73,3.4,1.07,43837,95,82,500,16.9,27,95,7.1,75,81.1,69,7.5,3.4,0.5,8.61,14.46
Belgium,1.9,20,2.3,27811,78368,62,4.5,3.37,47276,91,71,509,18.8,21,84,4.5,89,80.5,74,7.1,6.6,1.2,4.41,15.71
Brazil,6.7,21,1.4,10310,6875,67,4.8,2.17,7909,90,43,402,16.3,18,67,4,79,73.4,69,7.2,7.9,25.5,10.74,14.97
Canada,0.2,22,2.5,30212,63261,72,6.6,0.9,44017,94,89,522