This file is indexed.

/usr/include/liggghts/probability_distribution.h is in libliggghts-dev 2.3.8-1build1.

This file is owned by root:root, with mode 0o644.

The actual contents of the file can be viewed below.

  1
  2
  3
  4
  5
  6
  7
  8
  9
 10
 11
 12
 13
 14
 15
 16
 17
 18
 19
 20
 21
 22
 23
 24
 25
 26
 27
 28
 29
 30
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
/* ----------------------------------------------------------------------
   LIGGGHTS - LAMMPS Improved for General Granular and Granular Heat
   Transfer Simulations

   LIGGGHTS is part of the CFDEMproject
   www.liggghts.com | www.cfdem.com

   Christoph Kloss, christoph.kloss@cfdem.com
   Copyright 2009-2012 JKU Linz
   Copyright 2012-     DCS Computing GmbH, Linz

   LIGGGHTS is based on LAMMPS
   LAMMPS - Large-scale Atomic/Molecular Massively Parallel Simulator
   http://lammps.sandia.gov, Sandia National Laboratories
   Steve Plimpton, sjplimp@sandia.gov

   This software is distributed under the GNU General Public License.

   See the README file in the top-level directory.
------------------------------------------------------------------------- */

#ifndef LMP_PROBABILITY_DISTRIBUTION_H
#define LMP_PROBABILITY_DISTRIBUTION_H

#include "math.h"
#include "stdio.h"
#include "string.h"
#include "random_park.h"
#include "error.h"
#include "pointers.h"

enum{RANDOM_CONSTANT,RANDOM_UNIFORM,RANDOM_GAUSSIAN,RANDOM_LOGNORMAL};

namespace LMP_PROBABILITY_NS {

   #define MATH_E 2.718281828459

  class PDF
  {
    public:
      PDF(LAMMPS_NS::Error *error)
      {
          mu_ = sigma_ = min_ = max_ = 0.;
          h1_ = h2_ = 0.;
          mass_shift_ = 0;
          this->error = error;
      }
      ~PDF(){}

      int rand_style_;

      double mu_,sigma_;
      double min_,max_;

      // helper
      double h1_,h2_;

      // if 1, pdf is shifted from number to mass based pdf
      int mass_shift_;

      LAMMPS_NS::Error *error;

      inline int rand_style()
      { return rand_style_; }

      inline void set_min_max(double min,double max)
      {
          min_ = min;
          max_ = max;
      }

      inline void activate_mass_shift()
      { mass_shift_ = 1; }

      template<int RAND_STYLE> void set_params(double)
      { error->all(FLERR,"Faulty usage of Probability::set_params"); }

      template<int RAND_STYLE> void set_params(double,double)
      { error->all(FLERR,"Faulty usage of Probability::set_params"); }
  };

  inline double pdf_max(PDF *pdf)
  {
      return pdf->max_;
  }

  inline double pdf_min(PDF *pdf)
  {
      return pdf->min_;
  }

  template <int RAND_STYLE> inline double expectancy_value(PDF *pdf)
  {
      pdf->error->all(FLERR,"Faulty usage of Probability::expectancy");
      return 0.;
  }

  template <int RAND_STYLE> inline double cubic_expectancy_value(PDF *pdf)
  {
      pdf->error->all(FLERR,"Faulty usage of Probability::volume_expectancy");
      return 0.;
  }

  template <int RAND_STYLE> inline double rand_value(PDF *pdf,LAMMPS_NS::RanPark *rp)
  {
      pdf->error->all(FLERR,"Faulty usage of Probability::rand");
      return 0.;
  }

  //------------------------------------------------------------------------------
  // CONSTANT
  //------------------------------------------------------------------------------

  template<> inline void PDF::set_params<RANDOM_CONSTANT>(double val)
  {
         rand_style_ = RANDOM_CONSTANT;
         mu_ = val;
         set_min_max(mu_,mu_);
  }

  template<> inline double cubic_expectancy_value<RANDOM_CONSTANT>(PDF *pdf)
  {

     return pdf->mu_*pdf->mu_*pdf->mu_;
  }

  template<> inline double expectancy_value<RANDOM_CONSTANT>(PDF *pdf)
  {

     return pdf->mu_;
  }

  template<> inline double rand_value<RANDOM_CONSTANT>(PDF *pdf,LAMMPS_NS::RanPark *rp)
  {
     return pdf->mu_;
  }

  //------------------------------------------------------------------------------
  // UNIFORM
  //------------------------------------------------------------------------------

  template<> inline void PDF::set_params<RANDOM_UNIFORM>(double min, double max)
  {
         rand_style_ = RANDOM_UNIFORM;
         set_min_max(min,max);
         h1_ = 2./(1./(min_*min_)-1./(max_*max_));
         h2_ = h1_/(2.*min_*min_);
  }

  template<> inline double cubic_expectancy_value<RANDOM_UNIFORM>(PDF *pdf)
  {
     if(pdf->mass_shift_)
        /*return 0.5 * (pdf->min_ + pdf->max_);*/
        pdf->error->all(FLERR,"mass distribution not implemented for uniform");
     else
        return 0.25*(pdf->max_*pdf->max_*pdf->max_+
                     pdf->max_*pdf->max_*pdf->min_+
                     pdf->max_*pdf->min_*pdf->min_+
                     pdf->min_*pdf->min_*pdf->min_);
  }

  template<> inline double expectancy_value<RANDOM_UNIFORM>(PDF *pdf)
  {
     if(!pdf->mass_shift_)
        return pow(0.5 * (pdf->min_ + pdf->max_),3.);
     else
        return sqrt(pdf->h1_/(2.*(pdf->h2_-0.5)));
  }

  template<> inline double rand_value<RANDOM_UNIFORM>(PDF *pdf,LAMMPS_NS::RanPark *rp)
  {
     double rn =  rp->uniform();
     if(!pdf->mass_shift_)
        return (pdf->min_) + rn * (pdf->max_ - pdf->min_);
     else
        return sqrt(pdf->h1_/(2.*(pdf->h2_-rn)));
  }

  //------------------------------------------------------------------------------
  // GAUSSIAN
  //------------------------------------------------------------------------------

  template<> inline void PDF::set_params<RANDOM_GAUSSIAN>(double mu, double sigma)
  {
         rand_style_ = RANDOM_GAUSSIAN;
         mu_ = mu;
         sigma_ = sigma;

         // set min-max to +- 3 sigma (99.73% of all values)
         set_min_max(mu_-3.*sigma_, mu_+3.*sigma_);

         if(min_ < 0.)
            error->all(FLERR,"Probablity distribution: mu-3*sigma < 0, please increase mu or decrease sigma");
  }

  template<> inline double cubic_expectancy_value<RANDOM_GAUSSIAN>(PDF *pdf)
  {
     if(pdf->mass_shift_) pdf->error->all(FLERR,"mass distribution not implemented for gaussian");
     return pdf->mu_*(pdf->mu_*pdf->mu_+3*pdf->sigma_*pdf->sigma_);
  }

  template<> inline double expectancy_value<RANDOM_GAUSSIAN>(PDF *pdf)
  {
     if(pdf->mass_shift_) pdf->error->all(FLERR,"mass distribution not implemented for gaussian");
     return pdf->mu_;
  }

  template<> inline double rand_value<RANDOM_GAUSSIAN>(PDF *pdf,LAMMPS_NS::RanPark *rp)
  {
     if(pdf->mass_shift_) pdf->error->all(FLERR,"mass distribution not implemented for gaussian");
     double value;
     do
     {
         value = pdf->mu_ + rp->gaussian() * pdf->sigma_;
     } while (value < pdf->min_ || value > pdf->max_);
     return value;
  }

  //------------------------------------------------------------------------------
  // LOGNORMAL
  //------------------------------------------------------------------------------

  template<> inline void PDF::set_params<RANDOM_LOGNORMAL>(double mu, double sigma)
  {
      error->all(FLERR,"lognormal distribution currently deactivated");

      rand_style_ = RANDOM_LOGNORMAL;
      mu_ = mu;
      sigma_ = sigma;

      // also here, take +- 3 sigma as min/max
      // change in expectancy considered negligable
      double min =  pow(MATH_E,mu_ - 3. * sigma_);
      double max =  pow(MATH_E,mu_ + 3. * sigma_);
      set_min_max(min, max);
      if(min_ < 0.)
            error->all(FLERR,"Probablity distribution: exp(mu-3*sigma) < 0, please increase mu or decrease sigma");
  }

  template<> inline double cubic_expectancy_value<RANDOM_LOGNORMAL>(PDF *pdf)
  {
     if(pdf->mass_shift_) pdf->error->all(FLERR,"mass distribution not implemented for lognormal");
     return pow(MATH_E,3.*pdf->mu_+4.5*pdf->sigma_*pdf->sigma_);
  }

  template<> inline double expectancy_value<RANDOM_LOGNORMAL>(PDF *pdf)
  {
     if(pdf->mass_shift_) pdf->error->all(FLERR,"mass distribution not implemented for lognormal");
     return pow(MATH_E,pdf->mu_ + 0.5 * pdf->sigma_ * pdf->sigma_);
  }

  template<> inline double rand_value<RANDOM_LOGNORMAL>(PDF *pdf,LAMMPS_NS::RanPark *rp)
  {
     if(pdf->mass_shift_) pdf->error->all(FLERR,"mass distribution not implemented for lognormal");
     double value;
     do
     {
         value = pow(MATH_E,pdf->mu_ + rp->gaussian() * pdf->sigma_);
     } while (value < pdf->min_ || value > pdf->max_);
     return value;
  }

  //------------------------------------------------------------------------------
  // MASTER FUNCTIONS
  //------------------------------------------------------------------------------

  inline double expectancy(PDF *pdf)
  {
      if(pdf->rand_style_ == RANDOM_CONSTANT) return expectancy_value<RANDOM_CONSTANT>(pdf);
      else if(pdf->rand_style_ == RANDOM_UNIFORM) return expectancy_value<RANDOM_UNIFORM>(pdf);
      else if(pdf->rand_style_ == RANDOM_GAUSSIAN) return expectancy_value<RANDOM_GAUSSIAN>(pdf);
      else if(pdf->rand_style_ == RANDOM_LOGNORMAL) return expectancy_value<RANDOM_LOGNORMAL>(pdf);
      else pdf->error->all(FLERR,"Faulty implemantation in Probability::expectancy");
      return 0.;
  }

  inline double cubic_expectancy(PDF *pdf)
  {
      if(pdf->rand_style_ == RANDOM_CONSTANT) return cubic_expectancy_value<RANDOM_CONSTANT>(pdf);
      else if(pdf->rand_style_ == RANDOM_UNIFORM) return cubic_expectancy_value<RANDOM_UNIFORM>(pdf);
      else if(pdf->rand_style_ == RANDOM_GAUSSIAN) return cubic_expectancy_value<RANDOM_GAUSSIAN>(pdf);
      else if(pdf->rand_style_ == RANDOM_LOGNORMAL) return cubic_expectancy_value<RANDOM_LOGNORMAL>(pdf);
      else pdf->error->all(FLERR,"Faulty implemantation in Probability::expectancy");
      return 0.;
  }

  inline double rand(PDF *pdf,LAMMPS_NS::RanPark *rp)
  {
      if(pdf->rand_style_ == RANDOM_CONSTANT) return rand_value<RANDOM_CONSTANT>(pdf,rp);
      else if(pdf->rand_style_ == RANDOM_UNIFORM) return rand_value<RANDOM_UNIFORM>(pdf,rp);
      else if(pdf->rand_style_ == RANDOM_GAUSSIAN) return rand_value<RANDOM_GAUSSIAN>(pdf,rp);
      else if(pdf->rand_style_ == RANDOM_LOGNORMAL) return rand_value<RANDOM_LOGNORMAL>(pdf,rp);
      else pdf->error->all(FLERR,"Faulty implemantation in Probability::rand");
      return 0.;
  }

};

#endif