Metadata-Version: 2.1
Name: psyneulink-fastkde
Version: 1.0.19
Summary: Tools for fast and robust univariate and multivariate kernel density estimation
Home-page: https://github.com/LBL-EESA/fastkde
Author: Travis A. O'Brien
Author-email: obrienta@iu.edu
License: UNKNOWN
Download-URL: https://github.com/LBL-EESA/fastkde/archive/v1.0.17tar.gz
Description: fastKDE
        =======
        
        Software Overview
        -----------------
        
        fastKDE calculates a kernel density estimate of arbitrarily dimensioned
        data; it does so rapidly and robustly using recently developed KDE
        techniques. It does so with statistical skill that is as good as
        state-of-the-science 'R' KDE packages, and it does so 10,000 times
        faster for bivariate data (even better improvements for higher
        dimensionality).
        
        *Please cite the following papers when using this method:*
        
          `O’Brien, T. A., Kashinath, K., Cavanaugh, N. R., Collins, W. D. & O’Brien, J. P. A fast and objective multidimensional kernel density estimation method: fastKDE. Comput. Stat. Data Anal. 101, 148–160 (2016). <http://dx.doi.org/10.1016/j.csda.2016.02.014>`__
        
          `O’Brien, T. A., Collins, W. D., Rauscher, S. A. & Ringler, T. D. Reducing the computational cost of the ECF using a nuFFT: A fast and objective probability density estimation method. Comput. Stat. Data Anal. 79, 222–234 (2014). <http://dx.doi.org/10.1016/j.csda.2014.06.002>`__
        
        Example usage:
        
        For a standard PDF
        ~~~~~~~~~~~~~~~~~~
        
        ::
        
            #!python
             
            import numpy as np
            from fastkde import fastKDE
            import pylab as PP
        
            #Generate two random variables dataset (representing 100000 pairs of datapoints)
            N = 2e5
            var1 = 50*np.random.normal(size=N) + 0.1
            var2 = 0.01*np.random.normal(size=N) - 300
              
            #Do the self-consistent density estimate
            myPDF,axes = fastKDE.pdf(var1,var2)
        
            #Extract the axes from the axis list
            v1,v2 = axes
        
            #Plot contours of the PDF should be a set of concentric ellipsoids centered on
            #(0.1, -300) Comparitively, the y axis range should be tiny and the x axis range
            #should be large
            PP.contour(v1,v2,myPDF)
            PP.show()
        
        
        
        For a conditional PDF
        ~~~~~~~~~~~~~~~~~~~~~
        
        The following code generates samples from a non-trivial joint
        distribution
        
        .. code:: python
        
        	from fastkde import fastKDE
        	import pylab as PP
        	import numpy as np
        
        	#***************************
        	# Generate random samples
        	#***************************
        	# Stochastically sample from the function underlyingFunction() (a sigmoid):
        	# sample the absicissa values from a gamma distribution
        	# relate the ordinate values to the sample absicissa values and add
        	# noise from a normal distribution
        
        	#Set the number of samples
        	numSamples = int(1e6)
        
        	#Define a sigmoid function
        	def underlyingFunction(x,x0=305,y0=200,yrange=4):
        	     return (yrange/2)*np.tanh(x-x0) + y0
        
        	xp1,xp2,xmid = 5,2,305  #Set gamma distribution parameters
        	yp1,yp2 = 0,12          #Set normal distribution parameters (mean and std)
        
        	#Generate random samples of X from the gamma distribution
        	x = -(np.random.gamma(xp1,xp2,int(numSamples))-xp1*xp2) + xmid
        	#Generate random samples of y from x and add normally distributed noise
        	y = underlyingFunction(x) + np.random.normal(loc=yp1,scale=yp2,size=numSamples)
        
        **Now that we have the x,y samples, the following code calcuates the
        conditional**
        
        .. code:: python
        
            #***************************
            # Calculate the conditional
            #***************************
            pOfYGivenX,axes = fastKDE.conditional(y,x)
        
        The following plot shows the results:
        
        .. code:: python
        
            #***************************
            # Plot the conditional
            #***************************
            fig,axs = PP.subplots(1,2,figsize=(10,5))
        
            #Plot a scatter plot of the incoming data
            axs[0].plot(x,y,'k.',alpha=0.1)
            axs[0].set_title('Original (x,y) data')
        
            #Set axis labels
            for i in (0,1):
                axs[i].set_xlabel('x')
                axs[i].set_ylabel('y')
        
            #Draw a contour plot of the conditional
            axs[1].contourf(axes[0],axes[1],pOfYGivenX,64)
            #Overplot the original underlying relationship
            axs[1].plot(axes[0],underlyingFunction(axes[0]),linewidth=3,linestyle='--',alpha=0.5)
            axs[1].set_title('P(y|x)')
        
            #Set axis limits to be the same
            xlim = [np.amin(axes[0]),np.amax(axes[0])]
            ylim = [np.amin(axes[1]),np.amax(axes[1])]
            axs[1].set_xlim(xlim)
            axs[1].set_ylim(ylim)
            axs[0].set_xlim(xlim)
            axs[0].set_ylim(ylim)
        
            fig.tight_layout()
        
            PP.savefig('conditional_demo.png')
            PP.show()
        
        .. figure:: conditional_demo.png
           :alt: Conditional PDF
        
           Conditional PDF
        
        How do I get set up?
        --------------------
        
        A standard python build: ``python setup.py install``
        
        or
        
        ``pip install fastkde``
        
        Download the source
        ~~~~~~~~~~~~~~~~~~~
        
        Please contact Travis A. O'Brien TAOBrien@lbl.gov to obtain the latest
        version of the source.
        
        Install pre-requisites
        ~~~~~~~~~~~~~~~~~~~~~~
        
        This code requires the following software:
        
        -  Python >= 2.7.3
        -  Numpy >= 1.7
        -  scipy
        -  cython
        
        Copyright Information
        ---------------------
        
        ::
        
            LAWRENCE BERKELEY NATIONAL LABORATORY
            RESEARCH & DEVELOPMENT, NON-COMMERCIAL USE ONLY, LICENSE
             
            Copyright (c) 2015, The Regents of the University of California, through
            Lawrence Berkeley National Laboratory (subject to receipt of any required
            approvals from the U.S. Dept. of Energy).  All rights reserved.
             
            Redistribution and use in source and binary forms, with or without
            modification, are permitted provided that the following conditions are met:
             
            (1) Redistributions of source code must retain the above copyright notice,
            this list of conditions and the following disclaimer.
             
            (2) Redistributions in binary form must reproduce the above copyright
            notice, this list of conditions and the following disclaimer in the
            documentation and/or other materials provided with the distribution.
             
            (3) Neither the name of the University of California, Lawrence Berkeley
            National Laboratory, U.S. Dept. of Energy nor the names of its contributors
            may be used to endorse or promote products derived from this software
            without specific prior written permission.
             
            (4) Use of the software, in source or binary form is FOR RESEARCH
            & DEVELOPMENT, NON-COMMERCIAL USE, PURPOSES ONLY. All commercial use rights
            for the software are hereby reserved. A separate commercial use license is
            available from Lawrence Berkeley National Laboratory.
        
            (5) In the event you create any bug fixes, patches, upgrades, updates,
            modifications, derivative works or enhancements to the source code or
            binary code of the software ("Enhancements") you hereby grant The Regents of
            the University of California and the U.S. Government a paid-up,
            non-exclusive, irrevocable, worldwide license in the Enhancements to
            reproduce, prepare derivative works, distribute copies to the public,
            perform publicly and display publicly, and to permit others to do so.  THIS
            SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
            ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
            IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
            ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
            LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
            CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
            SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
            INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
            CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
            ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
            POSSIBILITY OF SUCH DAMAGE.  *** Copyright Notice *** FastKDE v1.0,
            Copyright (c) 2015, The Regents of the University of California, through
            Lawrence Berkeley National Laboratory (subject to receipt of any required
            approvals from the U.S. Dept. of Energy).  All rights reserved.
            If you have questions about your rights to use or distribute this software,
            please contact Berkeley Lab's Innovation & Partnerships Office at
            IPO@lbl.gov.
            NOTICE.  This software was developed under funding from the U.S. Department of Energy.  As such,
            the U.S. Government has been granted for itself and others acting on its
            behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
            Software to reproduce, prepare derivative works, and perform publicly and
            display publicly.  Beginning five (5) years after the date permission to
            assert copyright is obtained from the U.S. Department of Energy, and
            subject to any subsequent five (5) year renewals, the U.S. Government is
            granted for itself and others acting on its behalf a paid-up, nonexclusive,
            irrevocable, worldwide license in the Software to reproduce, prepare
            derivative works, distribute copies to the public, perform publicly and
            display publicly, and to permit others to do so.
            ****************************
        
        
        
Keywords: statistics,probability,KDE,kernel density estimation
Platform: UNKNOWN
Provides-Extra: test
Provides-Extra: dev
Provides-Extra: all
