1. Installing
After the software has been downloaded and extracted, it is necessary to build shared libraries.
This is achieved by running
	make
This has been tested on a Mac.
If problems are encountered, please contact [email protected].

2. Testing
To test the setup, run
	python runTests.py
This may take a while depending on the speed of the computer and the number of processors available.

3. Using
To use SPROID, trajectory data should be entered in a YAML format (see http://yaml.org).
The format used by SPROID is (see test.yaml)
	Trajectories:
	- [ [0, 1], [.1, 2], [.5, 1] ]
	- [ [0, 1, 2], [1, 2, 3], [2, 1, 4], [3, 2, 5], [4, 1, 6] ]
	- [ [0.5, 3, 6], [1.5, 1, 0], [2.5, -1, -4], [3.5, 3, 7] ]
The file consists of a list of trajectories.
Each trajectory is represented as a list of points in space-time.
The point may be in a space with arbitrary dimension, but the time component must be listed first (that is, [t, x, y, ...]).
After creating a file with the trajectory data, SPROID can be utilized as follows
	#import sproid code
	from sproid import *

	#load trajectory data
	s = Sproid(filename='filename.yaml')

	#Now we create a number of stochastic processes with unknown parameters
	#At present SPROID only supports 1D processes
	#Brownian motion with diffusion coefficient between 1e-3 and 1
	bm = BrownianMotion1D([1e-3], [1e0])
	#fractional Brownian with diffusion coefficient between 1e-3 and 1; and Hurst exponent between 0.2 and 0.9
	fbm = FractionalBrownianMotion1D([1e-3, 0.2], [1e0, 0.9])
	#symmmemtric Levy motion with alpha between 0.5 and 1.999 and diffusion coefficient between 1e-3 and 1
	slm = SymmetricLevyMotion1D([0.5, 1e-3], [1.999, 1e0])
	#Brownian motion with a power-law clock with diffusion coefficient between 1e-3 and 1; and power-law (for the clock) between 0.25 and 2.0
	bmplc = StochasticProcessWithNonlinearClock(BrownianMotion1D, power_law_clock, [1e-3, 0.25], [1e0, 2.0])

	#analyze the trajectories using the stochastic processes
	#analyze the first spatial coordinate of the trajectories
	s.getResults(plot=False, sps=[bm, fbm, slm, bmplc], posIndex=0)
	#analyze the second spatial coordinate of the trajectories
	s.getResults(plot=False, sps=[bm, fbm, slm, bmplc], posIndex=1)
Different sets of stochastic processes and parameters may be used.
Note that getResults(...) runs in parallel by default using 4 processes.