VILLASnode/common/lib/hist.cpp

/* Histogram class.
 *
 * Author: Steffen Vogel <post@steffenvogel.de>
 * SPDX-FileCopyrightText: 2014-2023 Institute for Automation of Complex Power Systems, RWTH Aachen University
 * SPDX-License-Identifier: Apache-2.0
 */

#include <algorithm>
#include <cmath>

#include <villas/config.hpp>
#include <villas/exceptions.hpp>
#include <villas/hist.hpp>
#include <villas/table.hpp>
#include <villas/utils.hpp>

using namespace villas;
using namespace villas::utils;

namespace villas {

Hist::Hist(int buckets, Hist::cnt_t wu)
    : resolution(0), high(0), low(0),
      highest(std::numeric_limits<double>::min()),
      lowest(std::numeric_limits<double>::max()), last(0), total(0), warmup(wu),
      higher(0), lower(0), data(buckets, 0), _m{0, 0}, _s{0, 0} {}

void Hist::put(double value) {
  last = value;

  // Update min/max
  if (value > highest)
    highest = value;
  if (value < lowest)
    lowest = value;

  if (data.size()) {
    if (total < warmup) {
      // We are still in warmup phase... Waiting for more samples...
    } else if (data.size() && total == warmup && warmup != 0) {
      low = getMean() - 3 * getStddev();
      high = getMean() + 3 * getStddev();
      resolution = (high - low) / data.size();
    } else if (data.size() && (total == warmup) && (warmup == 0)) {
      // There is no warmup phase
      // TODO resolution = ?
    } else {
      idx_t idx = std::round((value - low) / resolution);

      // Check bounds and increment
      if (idx >= (idx_t)data.size())
        higher++;
      else if (idx < 0)
        lower++;
      else
        data[idx]++;
    }
  }

  total++;

  // Online / running calculation of variance and mean
  //  by Donald Knuth’s Art of Computer Programming, Vol 2, page 232, 3rd edition
  if (total == 1) {
    _m[1] = _m[0] = value;
    _s[1] = 0.0;
  } else {
    _m[0] = _m[1] + (value - _m[1]) / total;
    _s[0] = _s[1] + (value - _m[1]) * (value - _m[0]);

    // Set up for next iteration
    _m[1] = _m[0];
    _s[1] = _s[0];
  }
}

void Hist::reset() {
  total = 0;
  higher = 0;
  lower = 0;

  highest = std::numeric_limits<double>::min();
  lowest = std::numeric_limits<double>::max();

  for (auto &elm : data)
    elm = 0;
}

double Hist::getMean() const {
  return total > 0 ? _m[0] : std::numeric_limits<double>::quiet_NaN();
}

double Hist::getVar() const {
  return total > 1 ? _s[0] / (total - 1)
                   : std::numeric_limits<double>::quiet_NaN();
}

double Hist::getStddev() const { return sqrt(getVar()); }

void Hist::print(Logger logger, bool details, std::string prefix) const {
  if (total > 0) {
    Hist::cnt_t missed = total - higher - lower;

    logger->info("{}Counted values: {} ({} between {} and {})", prefix, total,
                 missed, low, high);
    logger->info("{}Highest:  {:g}", prefix, highest);
    logger->info("{}Lowest:   {:g}", prefix, lowest);
    logger->info("{}Mu:       {:g}", prefix, getMean());
    logger->info("{}1/Mu:     {:g}", prefix, 1.0 / getMean());
    logger->info("{}Variance: {:g}", prefix, getVar());
    logger->info("{}Stddev:   {:g}", prefix, getStddev());

    if (details && total - higher - lower > 0) {
      char *buf = dump();
      logger->info("{}Matlab: {}", prefix, buf);
      free(buf);

      plot(logger);
    }
  } else
    logger->info("{}Counted values: {}", prefix, total);
}

void Hist::plot(Logger logger) const {
  // Get highest bar
  Hist::cnt_t max = *std::max_element(data.begin(), data.end());

  std::vector<TableColumn> cols = {
      {-9, TableColumn::Alignment::RIGHT, "Value", "%+9.3g"},
      {-6, TableColumn::Alignment::RIGHT, "Count", "%6ju"},
      {0, TableColumn::Alignment::LEFT, "Plot", "%s", "occurrences"}};

  Table table = Table(logger, cols);

  // Print plot
  table.header();

  for (size_t i = 0; i < data.size(); i++) {
    double value = low + (i)*resolution;
    Hist::cnt_t cnt = data[i];
    int bar = cols[2].getWidth() * ((double)cnt / max);

    char *buf = strf("%s", "");
    for (int i = 0; i < bar; i++)
      buf = strcatf(&buf, "\u2588");

    table.row(3, value, cnt, buf);

    free(buf);
  }
}

char *Hist::dump() const {
  char *buf = new char[128];
  if (!buf)
    throw MemoryAllocationError();

  memset(buf, 0, 128);

  strcatf(&buf, "[ ");

  for (auto elm : data)
    strcatf(&buf, "%ju ", elm);

  strcatf(&buf, "]");

  return buf;
}

json_t *Hist::toJson() const {
  json_t *json_buckets, *json_hist;

  json_hist = json_pack("{ s: f, s: f, s: i }", "low", low, "high", high,
                        "total", total);

  if (total > 0) {
    json_object_update(json_hist,
                       json_pack("{ s: i, s: i, s: f, s: f, s: f, s: f, s: f }",
                                 "higher", higher, "lower", lower, "highest",
                                 highest, "lowest", lowest, "mean", getMean(),
                                 "variance", getVar(), "stddev", getStddev()));
  }

  if (total - lower - higher > 0) {
    json_buckets = json_array();

    for (auto elm : data)
      json_array_append(json_buckets, json_integer(elm));

    json_object_set(json_hist, "buckets", json_buckets);
  }

  return json_hist;
}

int Hist::dumpJson(FILE *f) const {
  json_t *j = Hist::toJson();

  int ret = json_dumpf(j, f, 0);

  json_decref(j);

  return ret;
}

int Hist::dumpMatlab(FILE *f) const {
  fprintf(f, "struct(");
  fprintf(f, "'low', %f, ", low);
  fprintf(f, "'high', %f, ", high);
  fprintf(f, "'total', %ju, ", total);
  fprintf(f, "'higher', %ju, ", higher);
  fprintf(f, "'lower', %ju, ", lower);
  fprintf(f, "'highest', %f, ", highest);
  fprintf(f, "'lowest', %f, ", lowest);
  fprintf(f, "'mean', %f, ", getMean());
  fprintf(f, "'variance', %f, ", getVar());
  fprintf(f, "'stddev', %f, ", getStddev());

  if (total - lower - higher > 0) {
    char *buf = dump();
    fprintf(f, "'buckets', %s", buf);
    free(buf);
  } else
    fprintf(f, "'buckets', zeros(1, %zu)", data.size());

  fprintf(f, ")");

  return 0;
}

} // namespace villas