pm_distGamma::setGammaRand Interface Reference

Return a scalar or array of arbitrary rank of Gamma-distributed random values with the specified shape and scale parameters \((\kappa, \sigma)\) of the Gamma distribution corresponding to the procedure arguments (kappa, sigma). More...

Detailed Description

Return a scalar or array of arbitrary rank of Gamma-distributed random values with the specified shape and scale parameters \((\kappa, \sigma)\) of the Gamma distribution corresponding to the procedure arguments (kappa, sigma).

See the documentation of pm_distGamma for more information on the Probability Density Function (PDF) of the Gamma distribution.

Parameters

[in,out]	rng	: The input/output scalar that can be an object of, type rngf_type, implying the use of intrinsic Fortran uniform RNG for Gamma RNG. type xoshiro256ssw_type, implying the use of xoshiro256** uniform RNG for Gamma RNG. (optional, default = rngf_type, implying the use of the intrinsic Fortran URNG.)
[out]	rand	: The output scalar or array of rank 1, or array of arbitrary rank if the rng argument is missing or set to rngf_type, or of, type real of kind any supported by the processor (e.g., RK, RK32, RK64, or RK128). On output, it contains Gamma-distributed random value(s).
[in]	kappa	: The input scalar (or array of the same shape as other array-like arguments) of the same type and kind as rand, representing the shape parameter of the Gamma distribution.
[in]	sigma	: The input scalar (or array of the same shape as other array-like arguments) of the same type and kind as rand, representing the scale parameter of the Gamma distribution.

Possible calling interfaces ⛓

use pm_distGamma, only: setGammaRand
 
call setGammaRand(rand, kappa, sigma)
call setGammaRand(rand(..), kappa, sigma)
call setGammaRand(rng, rand, kappa, sigma)
call setGammaRand(rng, rand(:), kappa, sigma)

Warning

The condition 0 < kappa must hold for the corresponding input arguments.
The condition 0 < sigma 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.

The procedures under discussion are recursive.

Note

For repeated Gamma RNG with fixed kappa, it is best to pass a vector of rand to be filled with random numbers rather than calling the procedures with scalar rand argument repeatedly.
In addition to avoiding procedure call overhead, vectorized RGN in this particular case also avoids an unnecessary division and square-root operation.

See also

getGammaLogPDF
setGammaLogPDF
getGammaCDF
setGammaCDF

Example usage ⛓

program example
 
    use pm_kind, only: SK, IK
    use pm_kind, only: RKG => RKS ! all real kinds are supported.
    use pm_distGamma, only: setGammaRand
    use pm_arraySpace, only: setLinSpace
    use pm_arraySpace, only: setLogSpace
    use pm_io, only: display_type
 
    implicit none
 
    integer(IK), parameter  :: NP = 1000_IK
    real(RKG), dimension(NP) :: Kappa, Sigma, rand
 
    type(display_type) :: disp
    disp = display_type(file = "main.out.F90")
 
    call setLogSpace(Kappa, logx1 = log(0.1_RKG), logx2 = log(10._RKG))
    call setLogSpace(Sigma, logx1 = log(0.1_RKG), logx2 = log(10._RKG))
 
    call disp%skip()
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("! Generate random numbers from the Gamma distribution.")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%skip()
 
    call disp%skip()
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("! Gamma random value given integer shape and real inverse rate parameters.")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%skip()
 
    call disp%skip()
    call disp%show("Kappa(1)")
    call disp%show( Kappa(1) )
    call disp%show("Sigma(1)")
    call disp%show( Sigma(1) )
    call disp%show("call setGammaRand(rand(1:2), 1._RKG, sigma = Sigma(1))")
                    call setGammaRand(rand(1:2), 1._RKG, sigma = Sigma(1))
    call disp%show("rand(1)")
    call disp%show( rand(1) )
    call disp%skip()
 
    call disp%skip()
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("! Gamma random value given real shape and real inverse rate parameters.")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%skip()
 
    call disp%skip()
    call disp%show("Kappa(1)")
    call disp%show( Kappa(1) )
    call disp%show("Sigma(1)")
    call disp%show( Sigma(1) )
    call disp%show("call setGammaRand(rand(1:2), Kappa(1), sigma = Sigma(1))")
                    call setGammaRand(rand(1:2), Kappa(1), sigma = Sigma(1))
    call disp%show("rand(1)")
    call disp%show( rand(1) )
    call disp%skip()
 
    call disp%skip()
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("! Gamma random numbers with a fixed set of parameters.")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%skip()
 
    call disp%skip()
    call disp%show("Kappa(1:NP:NP/3)")
    call disp%show( Kappa(1:NP:NP/3) )
    call disp%show("Sigma(1:NP:NP/3)")
    call disp%show( Sigma(1:NP:NP/3) )
    call disp%show("call setGammaRand(rand(1:NP:NP/3), Kappa(1), sigma = Sigma(1))")
                    call setGammaRand(rand(1:NP:NP/3), Kappa(1), sigma = Sigma(1))
    call disp%show("rand(1:NP:NP/3)")
    call disp%show( rand(1:NP:NP/3) )
    call disp%skip()
 
    call disp%skip()
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("! Gamma random numbers for a range of parameters.")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%skip()
 
    call disp%skip()
    call disp%show("Kappa(1:NP:NP/3)")
    call disp%show( Kappa(1:NP:NP/3) )
    call disp%show("Sigma(1:NP:NP/3)")
    call disp%show( Sigma(1:NP:NP/3) )
    call disp%show("call setGammaRand(rand(1:NP:NP/3), Kappa(1:NP:NP/3), sigma = Sigma(1:NP:NP/3))")
                    call setGammaRand(rand(1:NP:NP/3), Kappa(1:NP:NP/3), sigma = Sigma(1:NP:NP/3))
    call disp%show("rand(1:NP:NP/3)")
    call disp%show( rand(1:NP:NP/3) )
    call disp%skip()
 
    call disp%skip()
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%show("! Test the mean of a random sample against the analytic answer.")
    call disp%show("!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%")
    call disp%skip()
 
    block
        use pm_sampleMean, only: getMean
        use pm_distExp, only: getExpRand
        real(RKG) :: kappa, omega, sigma, mean
        integer(IK) :: itry
        do itry = 1, 30
            call disp%skip()
            call disp%show("kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)")
                            kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
            call disp%show("[kappa, sigma]")
            call disp%show( [kappa, sigma] )
            call disp%show("call setGammaRand(rand, kappa, sigma)")
                            call setGammaRand(rand, kappa, sigma)
            call disp%show("mean = kappa * sigma")
                            mean = kappa * sigma
            call disp%show("[getMean(rand), mean]")
            call disp%show( [getMean(rand), mean] )
            call disp%skip()
        end do
    end block
 
    !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
    ! Output an example rand array for visualization.
    !%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
    block
        use pm_io, only: getErrTableWrite
        real(RKG):: rand(5000, 3)
        call setGammaRand(rand(:, 1), +0.8_RKG, sigma = 2._RKG)
        call setGammaRand(rand(:, 2), +1.0_RKG, sigma = 2._RKG)
        call setGammaRand(rand(:, 3), +5.0_RKG, sigma = 2._RKG)
        if (0 /= getErrTableWrite(SK_"setGammaRand.RK.txt", rand)) error stop "Table writing failed."
    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 ⛓

 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Generate random numbers from the Gamma distribution.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Gamma random value given integer shape and real inverse rate parameters.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
Kappa(1)
+0.999999940E-1
Sigma(1)
+0.999999940E-1
call setGammaRand(rand(1:2), 1._RKG, sigma = Sigma(1))
rand(1)
+0.350463316E-1
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Gamma random value given real shape and real inverse rate parameters.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
Kappa(1)
+0.999999940E-1
Sigma(1)
+0.999999940E-1
call setGammaRand(rand(1:2), Kappa(1), sigma = Sigma(1))
rand(1)
+0.111616500E-3
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Gamma random numbers with a fixed set of parameters.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
Kappa(1:NP:NP/3)
+0.999999940E-1, +0.464158833, +2.15443444, +10.0000010
Sigma(1:NP:NP/3)
+0.999999940E-1, +0.464158833, +2.15443444, +10.0000010
call setGammaRand(rand(1:NP:NP/3), Kappa(1), sigma = Sigma(1))
rand(1:NP:NP/3)
+0.161919191E-21, +0.506589494E-25, +0.241757636E-8, +0.531675629E-1
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Gamma random numbers for a range of parameters.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
Kappa(1:NP:NP/3)
+0.999999940E-1, +0.464158833, +2.15443444, +10.0000010
Sigma(1:NP:NP/3)
+0.999999940E-1, +0.464158833, +2.15443444, +10.0000010
call setGammaRand(rand(1:NP:NP/3), Kappa(1:NP:NP/3), sigma = Sigma(1:NP:NP/3))
rand(1:NP:NP/3)
+0.101767357E-3, +0.392729677E-1, +8.54112053, +105.588081
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Test the mean of a random sample against the analytic answer.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.00546825, +0.434241235
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.452220440, +0.436615765
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.27718508, +0.509940147
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.638982892, +0.651287973
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.541436449E-1, +0.261118054
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.135476189E-1, +0.141378837E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.16025317, +0.122660562
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.135582343, +0.142317310
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+2.52769852, +0.642927110
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.59376228, +1.62512589
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.20331061, +0.945478261
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.10778260, +1.13770401
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+4.17782593, +0.967857659
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+4.15142822, +4.04354095
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.632569134, +0.426450968E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.269864183E-1, +0.269759726E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.345118403, +0.251940582E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.878522173E-2, +0.869493280E-2
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.117207125, +1.42077124
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.178616077, +0.166524515
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.721429884, +1.66105318
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.27041173, +1.19833338
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.35084963, +0.686897561E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.941719338E-1, +0.927895308E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.72837472, +0.325220264E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.586105883E-1, +0.562102497E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.67769408, +1.59111452
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+2.67735529, +2.66940331
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.46930611, +0.460368961
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.663441002, +0.676422954
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.653058171, +2.33908558
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.54577231, +1.52755892
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+2.02758336, +0.965503991
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.92626476, +1.95763981
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.80686975, +1.13532996
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+2.12197185, +2.05139327
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.553639650, +0.412180930
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.226465300, +0.228199705
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.348401934, +0.112492777
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.399899110E-1, +0.391927026E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.20892048, +1.10068536
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.30128932, +1.33064103
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+4.07920074, +4.94486237
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+20.1848640, +20.1710854
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+2.30473351, +0.521049500E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.119311512, +0.120088026
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+2.73516870, +0.332421422
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.914106131, +0.909228683
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.86366689, +0.101797506
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.189615786, +0.189716637
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.771563172, +0.594291687
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.440484166, +0.458533585
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.243205577, +0.294669449
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.726856515E-1, +0.716652498E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.509388626, +0.730737209
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.367854625, +0.372229218
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.74241698, +0.101324260
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.178830653, +0.176549107
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.36058033, +0.411827981
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.529349804, +0.560325027
 
 

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" : "Gamma Random Value ( real/imaginary components )"
        , "IK" : "Gamma Random Value ( integer-valued )"
        , "RK" : "Gamma Random Value ( real-valued )"
        }
legends =   [ r"$\kappa = 0.8,~\sigma = 2$"
            , r"$\kappa = 1.0,~\sigma = 2$"
            , r"$\kappa = 5.0,~\sigma = 2$"
            ]
 
for kind in ["IK", "CK", "RK"]:
 
    pattern = "*." + kind + ".txt"
    fileList = glob.glob(pattern)
    if len(fileList) == 1:
 
        df = pd.read_csv(fileList[0], delimiter = ",", header = None)
 
        fig = plt.figure(figsize = 1.25 * np.array([6.4, 4.8]), dpi = 200)
        ax = plt.subplot()
 
        for j in range(len(df.values[0,:])):
            if kind == "CK":
                plt.hist( df.values[:,j]
                        , histtype = "stepfilled"
                        , alpha = 0.5
                        , bins = 75
                        )
            else:
                plt.hist( df.values[:,j]
                        , histtype = "stepfilled"
                        , alpha = 0.5
                        , bins = 75
                        )
                ax.legend   ( legends
                            , fontsize = fontsize
                            )
        plt.xticks(fontsize = fontsize - 2)
        plt.yticks(fontsize = fontsize - 2)
        ax.set_xlabel(xlab[kind], fontsize = 17)
        ax.set_ylabel("Count", fontsize = 17)
        ax.set_title("Histograms of {} Gamma random values".format(len(df.values[:, 0])), fontsize = 17)
 
        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")
 
        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_distGamma

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 1193 of file pm_distGamma.F90.

---

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

• src/fortran/main/pm_distGamma.F90