pm_distPower::getPowerLogRand Interface Reference

Generate and return a scalar (or array of arbitrary rank) of the natural logarithm(s) of random value(s) from the (Truncated) Power distribution with parameters \((\alpha, x_\mathrm{min}, x_\mathrm{max})\).
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Detailed Description

Generate and return a scalar (or array of arbitrary rank) of the natural logarithm(s) of random value(s) from the (Truncated) Power distribution with parameters \((\alpha, x_\mathrm{min}, x_\mathrm{max})\).

See the documentation of pm_distPower for more information on the (Truncated) Power distribution.

Parameters

[in]	alpha	: The input scalar (or array of the same rank, shape, and size as other array like arguments), of type real of kind any supported by the processor (e.g., RK, RK32, RK64, or RK128), containing the shape parameter ( \(\alpha\)) of the distribution.
[in]	logMinX	: The input scalar (or array of the same rank, shape, and size as other array like arguments), of the same type and kind as alpha, containing the natural logarithm of the first scale parameter of the distribution, representing the minimum of the support of the distribution. (optional, default = 0. It can be present if and only if logMaxX is also present.)
[in]	logMaxX	: The input scalar (or array of the same rank, shape, and size as other array like arguments), of the same type and kind as alpha, containing the natural logarithm of the second scale parameter of the distribution, representing the maximum of the support of the distribution.

Returns

logRand : The output scalar (or array of the same rank, shape, and size as other array like arguments), of the same type and kind as alpha, containing the random value(s) from the specified distribution.

Possible calling interfaces ⛓

use pm_distPower, only: getPowerLogRand
 
logRand = getPowerLogRand(alpha, logMinX) ! Power distribution.
logRand = getPowerLogRand(alpha, logMinX, logMaxX) ! Truncated Power distribution.
!

Warning

The condition alpha > 0 must hold for the corresponding input arguments.
The conditions logMinX < logMaxX must hold for the corresponding input arguments.
These conditions are verified only if the library is built with the preprocessor macro CHECK_ENABLED=1.

Remarks

The procedures under discussion are impure.

The procedures under discussion are elemental.

See also

setPowerLogRand

Example usage ⛓

program example
 
    use pm_kind, only: SK, IK, LK
    use pm_io, only: display_type
    use pm_distPower, only: getPowerLogRand
 
    implicit none
 
    real                    :: logx(3)
 
    type(display_type)      :: disp
    disp = display_type(file = "main.out.F90")
 
    call disp%skip()
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("! Compute random value(s) from the Power distribution.")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%skip()
 
    call disp%skip()
    call disp%show("logx(1) = getPowerLogRand(alpha = +2., logMaxX = -2.) ! Power distribution.")
                    logx(1) = getPowerLogRand(alpha = +2., logMaxX = -2.) ! Power distribution.
    call disp%show("logx(1)")
    call disp%show( logx(1) )
    call disp%skip()
 
    call disp%skip()
    call disp%show("logx(1:3) = getPowerLogRand(alpha = +[+2., +3., +4.], logMaxX = -2.) ! Power distribution.")
                    logx(1:3) = getPowerLogRand(alpha = +[+2., +3., +4.], logMaxX = -2.) ! Power distribution.
    call disp%show("logx(1:3)")
    call disp%show( logx(1:3) )
    call disp%skip()
 
    call disp%skip()
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("! Compute random value(s) from the Truncated Power distribution.")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%skip()
 
    call disp%skip()
    call disp%show("logx(1) = getPowerLogRand(alpha = +1., logMinX = -2., logMaxX = 5.) ! Truncated Power distribution.")
                    logx(1) = getPowerLogRand(alpha = +1., logMinX = -2., logMaxX = 5.) ! Truncated Power distribution.
    call disp%show("logx(1)")
    call disp%show( logx(1) )
    call disp%skip()
 
    call disp%skip()
    call disp%show("logx(1:3) = getPowerLogRand(alpha = +[+1., +2., +3.], logMinX = -2., logMaxX = 5.) ! Truncated Power distribution.")
                    logx(1:3) = getPowerLogRand(alpha = +[+1., +2., +3.], logMinX = -2., logMaxX = 5.) ! Truncated Power distribution.
    call disp%show("logx(1:3)")
    call disp%show( logx(1:3) )
    call disp%skip()
 
    !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    ! Output an example array for visualization.
    !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    block
        use pm_arraySpace, only: setLinSpace
        real :: alpha(3), logMinX(3), logMaxX(3), logx(3)
        integer(IK) :: fileUnit, i
        alpha = [.1, 2., .5]
        logMinX = log([3., tiny(0.), tiny(0.)])
        logMaxX = log([10., 5., 8.])
        open(newunit = fileUnit, file = "getPowerLogRand.RK.txt")
        do i = 1, 2000_IK
            logx(1) = getPowerLogRand(alpha(1), logMinX(1), logMaxX(1))
            logx(2:3) = getPowerLogRand(alpha(2:3), logMaxX(2:3))
            write(fileUnit, "(*(g0,:,', '))") exp(logx)
        end do
        close(fileUnit)
    end block
 
end program example

Example Unix compile command via Intel ifort compiler ⛓

#!/usr/bin/env sh
rm main.exe
ifort -fpp -standard-semantics -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
./main.exe

Example Windows Batch compile command via Intel ifort compiler ⛓

del main.exe
set PATH=..\..\..\lib;%PATH%
ifort /fpp /standard-semantics /O3 /I:..\..\..\include main.F90 ..\..\..\lib\libparamonte*.lib /exe:main.exe
main.exe

Example Unix / MinGW compile command via GNU gfortran compiler ⛓

#!/usr/bin/env sh
rm main.exe
gfortran -cpp -ffree-line-length-none -O3 -Wl,-rpath,../../../lib -I../../../inc main.F90 ../../../lib/libparamonte* -o main.exe
./main.exe

Example output ⛓

 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Compute random value(s) from the Power distribution.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
logx(1) = getPowerLogRand(alpha = +2., logMaxX = -2.) ! Power distribution.
logx(1)
-2.01484036
 
 
logx(1:3) = getPowerLogRand(alpha = +[+2., +3., +4.], logMaxX = -2.) ! Power distribution.
logx(1:3)
-2.17148566, -2.33558464, -3.08002019
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Compute random value(s) from the Truncated Power distribution.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
logx(1) = getPowerLogRand(alpha = +1., logMinX = -2., logMaxX = 5.) ! Truncated Power distribution.
logx(1)
+4.64728928
 
 
logx(1:3) = getPowerLogRand(alpha = +[+1., +2., +3.], logMinX = -2., logMaxX = 5.) ! Truncated Power distribution.
logx(1:3)
+4.66115046, +4.87632084, +4.68037033
 
 

Postprocessing of the example output ⛓

#!/usr/bin/env python
 
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import glob
import sys
 
linewidth = 2
fontsize = 17
 
marker ={ "CK" : "-"
        , "IK" : "."
        , "RK" : "-"
        }
xlab =  { "CK" : "Random Value ( Real / Imaginary ))"
        , "IK" : "Random Value ( Integer )"
        , "RK" : "Random Value ( Real )"
        }
legends =   [ r"$\alpha = +.1, x_{min} = +3., x_{max} = +10$"
            , r"$\alpha = +2., x_{min} = +0., x_{max} = +5$"
            , r"$\alpha = +.5, x_{min} = +0., x_{max} = +8$"
            ]
 
for kind in ["IK", "CK", "RK"]:
 
    pattern = "*." + kind + ".txt"
    fileList = glob.glob(pattern)
    if len(fileList) == 1:
 
        df = pd.read_csv(fileList[0], delimiter = ", ")
 
        fig = plt.figure(figsize = 1.25 * np.array([6.4, 4.8]), dpi = 200)
        ax = plt.subplot()
 
        if kind == "CK":
            ax.hist ( df.values[:, 0]
                    , bins = 30
                    , histtype = "stepfilled"
                    , density = True
                    , alpha = 0.7
                    )
            ax.hist ( df.values[:, 1]
                    , bins = 30
                    , histtype = "stepfilled"
                    , density = True
                    , alpha = 0.7
                    )
        else:
            ax.hist ( df.values[:,:]
                    , bins = 50
                    , histtype = "stepfilled"
                    , density = True
                    , alpha = 0.7
                    )
            ax.legend   ( legends[::-1]
                        , fontsize = fontsize
                        )
 
        plt.xticks(fontsize = fontsize - 2)
        plt.yticks(fontsize = fontsize - 2)
        ax.set_xlabel(xlab[kind], fontsize = 17)
        ax.set_ylabel("Density", fontsize = 17)
        ax.set_title("Histogram of {} randomly generated values".format(len(df.values)), fontsize = fontsize)
        #ax.set_yscale("log")
        #ax.set_xscale("log")
 
        plt.grid(visible = True, which = "both", axis = "both", color = "0.85", linestyle = "-")
        ax.tick_params(axis = "y", which = "minor")
        ax.tick_params(axis = "x", which = "minor")
        ax.set_axisbelow(True)
 
        plt.tight_layout()
        plt.savefig(fileList[0].replace(".txt",".png"))
 
    elif len(fileList) > 1:
 
        sys.exit("Ambiguous file list exists.")

Visualization of the example output ⛓

Test:

test_pm_distPower

Todo:

Very Low Priority: This generic interface can be extended to complex arguments.

Final Remarks ⛓

---

If you believe this algorithm or its documentation can be improved, we appreciate your contribution and help to edit this page's documentation and source file on GitHub.
For details on the naming abbreviations, see this page.
For details on the naming conventions, see this page.
This software is distributed under the MIT license with additional terms outlined below.

1. If you use any parts or concepts from this library to any extent, please acknowledge the usage by citing the relevant publications of the ParaMonte library.
   
2. If you regenerate any parts/ideas from this library in a programming environment other than those currently supported by this ParaMonte library (i.e., other than C, C++, Fortran, MATLAB, Python, R), please also ask the end users to cite this original ParaMonte library.
   

This software is available to the public under a highly permissive license.
Help us justify its continued development and maintenance by acknowledging its benefit to society, distributing it, and contributing to it.

Copyright

Computational Data Science Lab

Author:

Amir Shahmoradi, Oct 16, 2009, 11:14 AM, Michigan

Definition at line 1725 of file pm_distPower.F90.

---

The documentation for this interface was generated from the following file:

• src/fortran/main/pm_distPower.F90