deparkes

# Baudot
import random
import string

# Taken from https://www.cryptomuseum.com/ref/ita2/index.htm
baudot = { 'a': '0b00011', 
           'b': '0b11001',
           'c': '0b01110',
           'd': '0b01001',
           'e': '0b00001',

deparkes

import folium
 
points_a = [[1,50], [1.2,50.3], [1.23, 50.7]]
points_z = [[1,51], [1.2,51.3], [1.23, 51.7]]

# Load map centred on average coordinates
ave_lat = sum(p[0] for p in points_a)/len(points_a)
ave_lon = sum(p[1] for p in points_a)/len(points_a)
my_map = folium.Map(location=[ave_lat, ave_lon], zoom_start=9)

deparkes

# MIF 2.1
# MIF Example File: ellipsoid.mif
# Description: Hysteresis loop of an ellipsoidal particle.
#   This example uses an Oxs_EllipsoidAtlas to define the
#   ellipsoid volume.  This example is exactly equivalent
#   to ellipsoid-atlasproc.mif and ellipsoid-fieldproc.mif.

set pi [expr {4*atan(1.0)}]
set mu0 [expr {4*$pi*1e-7}]
set theta 270

deparkes

\documentclass{article}
\usepackage{natbib}
\usepackage{bibunits}

\begin{document}
% bibunit to list our publications
\begin{bibunit}[plain]
\renewcommand{\bibsection}{\large \textbf{\begin{center}
Publications
\end{center}}}

deparkes

deparkes

import gpxpy
import gpxpy.gpx
import folium

gpx_file = open('path_to_gpx_file.gpx', 'r')

gpx = gpxpy.parse(gpx_file)
points = []
for track in gpx.tracks:
    for segment in track.segments:        

deparkes

import gpxpy
import gpxpy.gpx
import folium

gpx_file = open('my_gpx_coords.gpx', 'r')

gpx = gpxpy.parse(gpx_file)
points = []
for track in gpx.tracks:
    for segment in track.segments:        

deparkes

import os
import folium
import pandas as pd
from bng_to_latlon import OSGB36toWGS84
os.chdir("C:\Users\Duncan\Documents\Python Scripts\pythonGIS")
# Load map centred on Colchester
uk = folium.Map(location=[51.8860942,0.8336077], zoom_start=10)

# Load locally stored colchester public toilets data
toilets = pd.read_csv("public-toilets.csv")

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# -*- coding: utf-8 -*-
"""
Created on Tue Oct 20 11:41:59 2015
Find out from which years you cited most publications in your thesis or 
dissertation.

https://xkcd.com/208/

May need to somehow account for 'missing' years 
http://pandas.pydata.org/pandas-docs/stable/missing_data.html

deparkes

#List unique values in a DataFrame column
pd.unique(df.column_name.ravel())

#Convert Series datatype to numeric, getting rid of any non-numeric values
df['col'] = df['col'].astype(str).convert_objects(convert_numeric=True)

#Grab DataFrame rows where column has certain values
valuelist = ['value1', 'value2', 'value3']
df = df[df.column.isin(value_list)]