coring.cpp

/*
Copyright (c) 2015-2019, Florian Sittel (www.lettis.net) and Daniel Nagel
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.

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 HOLDER 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.
*/

#include "coring.hpp"

#include "logger.hpp"
#include "tools.hpp"

#include <iostream>
#include <fstream>
#include <algorithm>
#include <vector>

#include <omp.h>

namespace Clustering {
namespace Coring {
  WTDMap
  compute_wtd(std::list<std::size_t> streaks) {
    WTDMap wtd;
    if (streaks.size() > 0) {
      streaks.sort(std::greater<std::size_t>());
      std::size_t max_streak = streaks.front();
      for (std::size_t i=0; i <= max_streak; ++i) {
        float n_steps = 0.0f;
        for (auto s: streaks) {
          if (i > s) {
            break;
          }
          n_steps += 1.0f;
        }
        wtd[i] = n_steps / ((float) streaks.size());
      }
    }
    return wtd;
  }

  void
  main(boost::program_options::variables_map args) {
    using namespace Clustering::Tools;
    namespace b_po = boost::program_options;
    // load states
    Clustering::logger(std::cout) << "~~~ reading files\n"
                                  << "    trajectory from: " << args["states"].as<std::string>()
                                  << std::endl;
    std::vector<std::size_t> states = Clustering::Tools::read_clustered_trajectory(args["states"].as<std::string>());
    std::set<std::size_t> state_names(states.begin(), states.end());
    std::size_t n_frames = states.size();
    std::string header_comment = args["header"].as<std::string>();
    std::map<std::string,float> commentsMap = args["commentsMap"].as<std::map<std::string,float>>();
    // read previously used parameters
    read_comments(args["states"].as<std::string>(), commentsMap);
    if (args.count("output") || args.count("distribution") || args.count("cores")) {
      // load concatenation limits to treat concatenated trajectories correctly
      // when performing dynamical corrections
      std::vector<std::size_t> concat_limits;
      if (args.count("concat-limits")) {
        Clustering::logger(std::cout) << "    limits from: "
                                      << args["concat-limits"].as<std::string>() << std::endl;
        concat_limits = Clustering::Tools::read_concat_limits(args["concat-limits"].as<std::string>());
      } else if (args.count("concat-nframes")) {
        std::size_t n_frames_per_subtraj = args["concat-nframes"].as<std::size_t>();
        for (std::size_t i=n_frames_per_subtraj; i <= n_frames; i += n_frames_per_subtraj) {
          concat_limits.push_back(i);
        }
      } else {
        concat_limits = {n_frames};
      }
      // check if concat_limits are well definied
      Clustering::Tools::check_concat_limits(concat_limits, n_frames);
      // load window size information
      Clustering::logger(std::cout) << "\n\n~~~ coring windows:\n    from file: "
                                    << args["windows"].as<std::string>() << std::endl;
      std::map<std::size_t, std::size_t> coring_windows;
      {
        std::ifstream ifs(args["windows"].as<std::string>());
//        std::string buf1, buf2;
        std::string buf;
        std::size_t state, window;
        std::size_t size_for_all = 1;
        if (ifs.fail()) {
          std::cerr << "error: cannot open file '"
                    << args["windows"].as<std::string>()
                    << "'" << std::endl;
          exit(EXIT_FAILURE);
        } else {
          while (ifs.good()) {
            if (std::isdigit(ifs.peek())) {
              ifs >> state;
              ifs >> window;
              if ( ! ifs.fail()) {
                coring_windows[state] = window;
              } else {
                std::cerr << "error: file not correctly formated." << std::endl;
              }
            } else if (ifs.peek() == '*') {
              ifs >> buf;
              ifs >> window;
              if ( ! ifs.fail()) {
                size_for_all = window;
              } else {
                std::cerr << "error: file not correctly formated." << std::endl;
              }
            } else {
              ifs.ignore(std::numeric_limits<std::streamsize>::max(), '\n');
            }
          }
//          while (ifs.good()) {
//            ifs >> buf1;
//            ifs >> buf2;
//            if (ifs.good()) {
//              if (buf1 == "*") {
//                size_for_all = string_to_num<std::size_t>(buf2);
//              } else {
//                coring_windows[string_to_num<std::size_t>(buf1)] = string_to_num<std::size_t>(buf2);
//              }
//            }
//          }
        }
        // fill remaining, not explicitly defined states with common window size
        std::size_t undefined_windows = 0;
        for (std::size_t name: state_names) {
          if ( ! coring_windows.count(name)){
            coring_windows[name] = size_for_all;
            ++undefined_windows;
          }
        }
        // if only single coring window is used
        if (coring_windows.size() == 0) {
          commentsMap["single_coring_time"] = size_for_all;
        }
        header_comment.append(Clustering::Tools::stringprintf(
                "#\n# coring specific parameters: \n"
                "#    %i state-specific coring windows were read\n"
                "#    %i frames is used for reamining states\n",
                state_names.size() - undefined_windows, size_for_all));
        Clustering::logger(std::cout) << "    " << state_names.size() - undefined_windows
                                      << " state-specific coring windows were read" << std::endl;
        if (size_for_all > 1) {
        Clustering::logger(std::cout) << "    default window was set to " << size_for_all
                                      << " frames" << std::endl;
        }
      }
      // core trajectory
      Clustering::logger(std::cout) << "\n~~~ coring trajectory" << std::endl;
      std::vector<std::size_t> cored_traj(n_frames);
      std::size_t current_core = states[0];
      std::vector<long> cores(n_frames);
      std::size_t changed_frames = 0;
      // honour concatenation limits, i.e. treat every concatenated trajectory-part on its own
      std::size_t last_limit = 0;
      for (std::size_t next_limit: concat_limits) {
        current_core = states[last_limit];
        std::size_t next_limit_corrected = std::min(next_limit, n_frames);
        for (std::size_t i=last_limit; i < next_limit_corrected; ++i) {
          // coring window
          std::size_t w = std::min(i+coring_windows[states[i]], next_limit);
          bool is_in_core = true;
          for (std::size_t j=i+1; j < w; ++j) {
            if (states[j] != states[i]) {
              is_in_core = false;
              break;
            }
          }
          if (is_in_core) {
            current_core = states[i];
            cores[i] = current_core;
          } else {
            ++changed_frames;
            cores[i] = -1;
          }
          cored_traj[i] = current_core;
        }
        last_limit = next_limit_corrected;
      }
      float changed_frames_perc = (float) 100*changed_frames / n_frames;
      Clustering::logger(std::cout) << Clustering::Tools::stringprintf("    %.2f", changed_frames_perc)
                                    << "% of frames were changed\n    " << changed_frames
                                    << " frames in total"
                                    << "\n    store result in: " << args["output"].as<std::string>()
                                    << std::endl;
      // write cored trajectory to file
      std::string header_coring = header_comment
                  + Clustering::Tools::stringprintf("#    %.2f", changed_frames_perc)
                  + "% of frames were changed\n";
      if (args.count("output")) {
        Clustering::Tools::write_clustered_trajectory(args["output"].as<std::string>(),
                                                      cored_traj,
                                                      header_coring,
                                                      commentsMap);
      }
      // write core information to file
      if (args.count("cores")) {
        append_commentsMap(header_coring, commentsMap);
        Clustering::Tools::write_single_column<long>(args["cores"].as<std::string>(),
                                                     cores,
                                                     header_coring,
                                                     false);
      }
      // compute/save escape time distributions
      if (args.count("distribution")) {
        Clustering::logger(std::cout) << "~~~ generating distribution" << std::endl;
        std::map<std::size_t, std::list<std::size_t>> streaks;
        std::size_t current_state = cored_traj[0];
        long n_counts = 0;
        for (std::size_t state: cored_traj) {
          if (state == current_state) {
            ++n_counts;
          } else {
            streaks[current_state].push_back(n_counts);
            current_state = state;
            n_counts = 1;
          }
        }
        streaks[current_state].push_back(n_counts);

        // append values to header comment
        append_commentsMap(header_comment, commentsMap);

        std::map<std::size_t, WTDMap> etds;
        for (std::size_t state: state_names) {
          etds[state] = compute_wtd(streaks[state]);
        }
        // write WTDs to file
        Clustering::logger(std::cout) << "    storing..." << std::endl;
        for (auto state_etd: etds) {
          std::string fname = Clustering::Tools::stringprintf(args["distribution"].as<std::string>() + "_%d", state_etd.first);
          Clustering::Tools::write_map<std::size_t, float>(fname, state_etd.second, header_comment);
        }
      }
    } else {
      std::cerr << "\n" << "error (coring): nothing to do! please define '--output', '--distribution' or both!" << "\n\n";
      exit(EXIT_FAILURE);
    }
  }
} // end namespace Coring
} // end namespace Clustering