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)
+0.192789733E-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)
+0.146627855E-1
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! 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)
+0.461255433E-3, +0.232378849E-8, +0.125766145E-1, +0.796557309E-8
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! 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)
+0.411371235E-3, +0.387056149E-2, +6.75889826, +67.2632828
 
 
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
! Test the mean of a random sample against the analytic answer.
!%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.25593460, +0.843398452
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.04580116, +1.05925333
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+6.53944302, +0.282809496
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.85777009, +1.84941661
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+2.10668516, +0.558247901E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.113393411, +0.117605254
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.587499440, +0.686475076E-2
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.378300971E-2, +0.403303700E-2
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.425436169, +3.09141994
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+1.27251959, +1.31520188
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.10502982, +0.595415011E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.632723644E-1, +0.657951310E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.617607117, +0.463864766E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.284711700E-1, +0.286486186E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.138399601, +0.781810284E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.140611725E-1, +0.108202230E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.10488963, +0.944579691E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.103835016, +0.104365632
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.355049670, +1.53014612
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.565379083, +0.543277860
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.223521013E-1, +0.381012291
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.842913240E-2, +0.851642527E-2
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.10672128, +0.162377611
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.170151368, +0.179706752
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+3.66845751, +0.119955830
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.434006631, +0.440052867
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.546247542, +1.16398180
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.624200761, +0.635822177
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.434742302, +0.262964517E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.115999999E-1, +0.114321802E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.17992485, +0.268622100
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.310573727, +0.316953897
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.26637924, +0.804037899E-1
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.101973012, +0.101821691
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.750454143E-1, +0.877367198
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.559935495E-1, +0.658423826E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.199832991, +1.17387259
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.258224994, +0.234578475
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.46304011, +1.60915339
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+2.44233465, +2.35425591
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.942411721, +0.190167740
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.175109044, +0.179216310
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.186331034, +0.210274681
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.406572744E-1, +0.391806997E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.235560864, +0.924122155
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.240402922, +0.217687011
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.400287092, +1.22469091
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.487759829, +0.490227968
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.504582047, +0.302653670
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.162026063, +0.152713612
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+1.15946388, +2.42746019
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+2.78323388, +2.81455231
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.349314623E-1, +1.38247859
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.453141257E-1, +0.482919998E-1
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+2.19591928, +1.53773582
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+3.42718101, +3.37674379
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.522154808, +0.927167535
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.489555836, +0.484124988
 
 
kappa = getExpRand(1._RKG); sigma = getExpRand(1._RKG)
[kappa, sigma]
+0.391054988, +0.755496800
call setGammaRand(rand, kappa, sigma)
mean = kappa * sigma
[getMean(rand), mean]
+0.293998480, +0.295440793
 
 

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