Richard Law alpha-beta-soup

## interpolatedMedian.py
# interpolatedMedian.py

# Author: Richard Law

# Calculates the interpolated median of a list,
# which is a form of median that retains the
# important properties of a median (isn't swayed by
# unusually large or small values) but gives a better
# indication of the ditstribution of values

## NZ-post-street-types.csv

          
            Full
            Abbreviation

            
              Access
              Accs

            
              Dune
              Dune

            
              Accessway
              Accswy

            
              Elm
              Elm

            
              Alley
              Aly

            
              End
              End

            
              Anchorage
              Ancg

            
              Entrance
              Ent

            
              Approach
              App

## ranova.py
# Author: d.engemann@fz-juelich.de
#
# License: BSD (3-clause)
# XXX clafify licensing

import numpy as np
from collections import namedtuple
from scipy.signal import detrend
from scipy import stats

## obtain_corners.py
from osgeo import gdal, osr
# Click on your layer in the TOC
alayer = qgis.utils.iface.activeLayer()
bag = gdal.Open(alayer.source())
bag_gtrn = bag.GetGeoTransform()
bag_proj = bag.GetProjectionRef()
bag_srs = osr.SpatialReference(bag_proj)
geo_srs =bag_srs.CloneGeogCS()
transform = osr.CoordinateTransformation( bag_srs, geo_srs)

## zoom2me.py
import qgis.utils
import pycurl
import StringIO
import json

buffer = StringIO.StringIO()
c = pycurl.Curl()
c.setopt(c.URL, 'http://ipinfo.io/')
c.setopt(c.WRITEDATA, buffer)
c.perform()

## pint_convert_list.py
import pint

ur = pint.UnitRegistry()
Q = ur.Quantity

def convert(array, source, target):
    '''
    Convert a list in one unit to a list in another, using Pint.
    Simple but saves me from writing this repeatedly in ipython.
    <source> and <target> must be Pint units, e.g.

## unit.py
import pint

ureg = pint.UnitRegistry()

def is_str_valid_pint_unit(unit):
    try:
        ureg(unit)
    except pint.errors.UndefinedUnitError:
        return False
    return True

## Makefile
build/ESRI_Census_Based_2013_NZTM.zip:
	mkdir -p $(dir $@)
	curl -o $@ http://www3.stats.govt.nz/digitalboundaries/census/$(notdir $@)

build/ESRI_Census_Based_2013_NZTM.shp: build/ESRI_Census_Based_2013_NZTM.zip
	unzip -od $(dir $@) $<
	touch $@

build/wards.geojson: build/ESRI\ shapefile\ Output/2013\ Digital\ Boundaries\ Generalised\ Clipped/WARD2013_GV_Clipped.shp
	ogr2ogr -f GeoJSON build/wards.geojson build/ESRI\ shapefile\ Output/2013\ Digital\ Boundaries\ Generalised\ Clipped/WARD2013_GV_Clipped.shp -s_srs EPSG:2193 -t_srs EPSG:2193

## transitHeadways.py
import numpy as np

def headway_array(arrivals_array, maxseconds=24*60*60, daysinweek=7):
    '''
    Given a numpy.array object with...
    > the number of rows equal to the number of days in a week, <daysinweek>
    > the number of rows equal to the number of trips made on a public transport
      route
    > the array values equal to the time at which the vehicle arrives at the
      stop, expressed in seconds since midnight for each day. None values can be

## Makefile
INPUT := a.tif b.tif c.tif d.tif e.tif

# This is how you could use gdal_merge.py to combine all the input files
# BUT: gdal_merge.py loads them all into memory first, so it can exceed your system memory very easily!
.ONESHELL:
all.gdal_merge.tif : $(INPUT)
 	gdal_merge.py -o $@ -of GTiff -co TILED=YES -co COMPRESS=LZW -co TFW=YES -co BLOCKXSIZE=128 -co BLOCKYSIZE=128 -co NUM_THREADS=ALL_CPUS -ot UInt16 -n 0 $^

# gdalwarp does not suffer from this limitation, but is a bit harder to use, so here's how I do it in a Makefile
all.gdalwarp.tif : $(INPUT)
	# interpolatedMedian.py

	# Author: Richard Law

	# Calculates the interpolated median of a list,
	# which is a form of median that retains the
	# important properties of a median (isn't swayed by
	# unusually large or small values) but gives a better
	# indication of the ditstribution of values
	Full	Abbreviation
	Access	Accs
	Dune	Dune
	Accessway	Accswy
	Elm	Elm
	Alley	Aly
	End	End
	Anchorage	Ancg
	Entrance	Ent
	Approach	App
	# Author: d.engemann@fz-juelich.de
	#
	# License: BSD (3-clause)
	# XXX clafify licensing

	import numpy as np
	from collections import namedtuple
	from scipy.signal import detrend
	from scipy import stats
	from osgeo import gdal, osr
	# Click on your layer in the TOC
	alayer = qgis.utils.iface.activeLayer()
	bag = gdal.Open(alayer.source())
	bag_gtrn = bag.GetGeoTransform()
	bag_proj = bag.GetProjectionRef()
	bag_srs = osr.SpatialReference(bag_proj)
	geo_srs =bag_srs.CloneGeogCS()
	transform = osr.CoordinateTransformation( bag_srs, geo_srs)
	import qgis.utils
	import pycurl
	import StringIO
	import json

	buffer = StringIO.StringIO()
	c = pycurl.Curl()
	c.setopt(c.URL, 'http://ipinfo.io/')
	c.setopt(c.WRITEDATA, buffer)
	c.perform()
	import pint

	ur = pint.UnitRegistry()
	Q = ur.Quantity

	def convert(array, source, target):
	'''
	Convert a list in one unit to a list in another, using Pint.
	Simple but saves me from writing this repeatedly in ipython.
	<source> and <target> must be Pint units, e.g.
	import pint

	ureg = pint.UnitRegistry()

	def is_str_valid_pint_unit(unit):
	try:
	ureg(unit)
	except pint.errors.UndefinedUnitError:
	return False
	return True
	build/ESRI_Census_Based_2013_NZTM.zip:
	mkdir -p $(dir $@)
	curl -o $@ http://www3.stats.govt.nz/digitalboundaries/census/$(notdir $@)

	build/ESRI_Census_Based_2013_NZTM.shp: build/ESRI_Census_Based_2013_NZTM.zip
	unzip -od $(dir $@) $<
	touch $@

	build/wards.geojson: build/ESRI\ shapefile\ Output/2013\ Digital\ Boundaries\ Generalised\ Clipped/WARD2013_GV_Clipped.shp
	ogr2ogr -f GeoJSON build/wards.geojson build/ESRI\ shapefile\ Output/2013\ Digital\ Boundaries\ Generalised\ Clipped/WARD2013_GV_Clipped.shp -s_srs EPSG:2193 -t_srs EPSG:2193
	import numpy as np

	def headway_array(arrivals_array, maxseconds=246060, daysinweek=7):
	'''
	Given a numpy.array object with...
	> the number of rows equal to the number of days in a week, <daysinweek>
	> the number of rows equal to the number of trips made on a public transport
	route
	> the array values equal to the time at which the vehicle arrives at the
	stop, expressed in seconds since midnight for each day. None values can be
	INPUT := a.tif b.tif c.tif d.tif e.tif

	# This is how you could use gdal_merge.py to combine all the input files
	# BUT: gdal_merge.py loads them all into memory first, so it can exceed your system memory very easily!
	.ONESHELL:
	all.gdal_merge.tif : $(INPUT)
	gdal_merge.py -o $@ -of GTiff -co TILED=YES -co COMPRESS=LZW -co TFW=YES -co BLOCKXSIZE=128 -co BLOCKYSIZE=128 -co NUM_THREADS=ALL_CPUS -ot UInt16 -n 0 $^

	# gdalwarp does not suffer from this limitation, but is a bit harder to use, so here's how I do it in a Makefile
	all.gdalwarp.tif : $(INPUT)