Daniel Buscombe dbuscombe-usgs

🎯

Focusing

ML/Geospatial/Algorithm/Software environmental analytics and research, specializing in coastal, marine, and rivers @MARDAScience

dbuscombe-usgs / dat2mat.py

Created October 16, 2015 16:39

Utility for converting PyHum's memory mapped numpy arrays into matlab/octave format files

	from Tkinter import Tk
	from tkFileDialog import askopenfilename, askdirectory
	import tkMessageBox
	import os
	from scipy.io import loadmat, savemat
	import numpy as np

	def get_mmap_data(filename, dtype, shape):
	with open(filename, 'r') as ff:
	fp = np.memmap(ff, dtype=dtype, mode='r', shape=shape)

dbuscombe-usgs / plot_merge_crop.py

Created September 17, 2015 18:37

plot a merged port and sidescan, cropping to a user-specified maximum range

	import matplotlib.pyplot as plt
	import os
	import numpy as np
	from scipy.io import loadmat

	sonpath = '/home/mydata/sonpath'
	humfile = '/home/mydata/humfile.DAT'

	# maximum range in m to shwo
	maxy = 35

dbuscombe-usgs / nonsmooth_kml.py

Created September 9, 2015 18:27

creating a trackline kml in PyHum using non-smoothed easting/northings

	from scipy.io import loadmat
	import simplekml
	import pyproj
	import numpy as np

	my_meta_file = '/home/dbuscombe/pyhum_test/testclipmeta.mat'

	cs2cs_args="epsg:26949"

	metadat = loadmat(my_meta_file)

dbuscombe-usgs / hillshade.py

Created March 27, 2015 12:48

shaded relief algorithm in python

	import numpy as np

	def cart2pol(x, y):
	'''
	cartesian to polar coordinates
	'''
	theta = np.arctan2(y, x)
	rho = np.sqrt(x2 + y2)
	return (theta, rho)

dbuscombe-usgs / running_stats.py

Last active August 29, 2015 14:02

example application of using RunningStats class for running mean calcs

	import numpy as np
	import matplotlib.pylab as plt

	# generate some data
	x = np.random.rand(1000,1)

	# get the class
	import RunningStats

	# get an instance of the class

dbuscombe-usgs / conv_stats.py

Created June 8, 2014 22:27

example to show convergence on global mean using runningstats

	import numpy as np
	import matplotlib.pylab as plt

	# generate some data
	x = np.random.rand(1000,1)

	# get the class
	import RunningStats

	# get an instance of the class

dbuscombe-usgs / simple_stats.py

Created June 8, 2014 22:24

simple implementation of runningstats class

	import numpy as np

	# generate some data
	x = np.random.rand(1000,1)

	# get the class
	import RunningStats

	# get an instance of the class
	rs = RunningStats.RunningStats()

dbuscombe-usgs / swig_runningstats.sh

Created June 8, 2014 22:20

swig command to create RunningStats module

swig -c++ -python RunningStats.i

dbuscombe-usgs / RunningStats.i

Created June 8, 2014 22:19

SWIG file for RunningStats.cpp

dbuscombe-usgs / setup.py

Created June 8, 2014 22:16

distutils setup for for compiling RunningStats C++ module (http://www.johndcook.com/standard_deviation.html) using SWIG

	"""
	setup.py file for RunningStats
	"""

	from distutils.core import setup, Extension

	RunningStats_module = Extension('_RunningStats',
	sources=['RunningStats_wrap.cxx', 'RunningStats.cpp'],
	)