Duncan Parkes deparkes

## baudot.py
# 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',

## folium_lines_and_markers.py
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)

## radial_field_example.mif
# 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

## latex_publications.tex
\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}}}

## uk_universities_locations.csv

          
            Name
            lat
            lon

            
              The University of Aberdeen
              57.165019
              -2.099122

            
              University of Abertay Dundee
              56.46334
              -2.973441

            
              Aberystwyth University
              52.403473
              -4.043584

            
              Anglia Ruskin University
              51.741381
              0.474495

            
              Aston University
              52.486637
              -1.890952

            
              Bangor University
              53.229193
              -4.129437

            
              Bath Spa University
              51.373209
              -2.440912

            
              The University of Bath
              51.380441
              -2.330673

            
              University of Bedfordshire
              51.87825
              -0.411539

## folium_plot_lines.py
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:

## GPX_Folium_Map.py
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:

## colch_toilets.py
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")

## RefCount.py
# -*- 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

## useful_pandas_snippets.py
#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)]
	# 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',
	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)
	# 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$pi1e-7}]
	set theta 270
	\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}}}
Name	lat	lon
The University of Aberdeen	57.165019	-2.099122
University of Abertay Dundee	56.46334	-2.973441
Aberystwyth University	52.403473	-4.043584
Anglia Ruskin University	51.741381	0.474495
Aston University	52.486637	-1.890952
Bangor University	53.229193	-4.129437
Bath Spa University	51.373209	-2.440912
The University of Bath	51.380441	-2.330673
University of Bedfordshire	51.87825	-0.411539
	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:
	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")
	# -- 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
	#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)]