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/**
 * @file data_validator.c
 *
 * A data validation class to identify anomalies in data streams
 *
 * @author Lorenz Meier <lorenz@px4.io>
 */

#include "data_validator.h"

#include <ecl.h>

void
DataValidator::put(uint64_t timestamp, float val, uint64_t error_count_in, int priority_in)
{
	float data[dimensions] = { val }; //sets the first value and all others to 0

	put(timestamp, data, error_count_in, priority_in);
}

void
DataValidator::put(uint64_t timestamp, const float val[dimensions], uint64_t error_count_in, int priority_in)
{
	_event_count++;

	if (error_count_in > _error_count) {
		_error_density += (error_count_in - _error_count);

	} else if (_error_density > 0) {
		_error_density--;
	}

	_error_count = error_count_in;
	_priority = priority_in;

	for (unsigned i = 0; i < dimensions; i++) {
		if (_time_last == 0) {
			_mean[i] = 0;
			_lp[i] = val[i];
			_M2[i] = 0;

		} else {
			float lp_val = val[i] - _lp[i];

			float delta_val = lp_val - _mean[i];
			_mean[i] += delta_val / _event_count;
			_M2[i] += delta_val * (lp_val - _mean[i]);
			_rms[i] = sqrtf(_M2[i] / (_event_count - 1));

			if (fabsf(_value[i] - val[i]) < 0.000001f) {
				_value_equal_count++;

			} else {
				_value_equal_count = 0;
			}
		}

		_vibe[i] = _vibe[i] * 0.99f + 0.01f * fabsf(val[i] - _lp[i]);

		// XXX replace with better filter, make it auto-tune to update rate
		_lp[i] = _lp[i] * 0.99f + 0.01f * val[i];

		_value[i] = val[i];
	}

	_time_last = timestamp;
}

float
DataValidator::confidence(uint64_t timestamp)
{
	float ret = 1.0f;

	/* check if we have any data */
	if (_time_last == 0) {
		_error_mask |= ERROR_FLAG_NO_DATA;
		ret = 0.0f;

	} else if (timestamp - _time_last > _timeout_interval) {
		/* timed out - that's it */
		_error_mask |= ERROR_FLAG_TIMEOUT;
		ret = 0.0f;

	} else if (_value_equal_count > _value_equal_count_threshold) {
		/* we got the exact same sensor value N times in a row */
		_error_mask |= ERROR_FLAG_STALE_DATA;
		ret = 0.0f;

	} else if (_error_count > NORETURN_ERRCOUNT) {
		/* check error count limit */
		_error_mask |= ERROR_FLAG_HIGH_ERRCOUNT;
		ret = 0.0f;

	} else if (_error_density > ERROR_DENSITY_WINDOW) {
		/* cap error density counter at window size */
		_error_mask |= ERROR_FLAG_HIGH_ERRDENSITY;
		_error_density = ERROR_DENSITY_WINDOW;

	}

	/* no critical errors */
	if (ret > 0.0f) {
		/* return local error density for last N measurements */
		ret = 1.0f - (_error_density / ERROR_DENSITY_WINDOW);

		if (ret > 0.0f) {
			_error_mask = ERROR_FLAG_NO_ERROR;
		}
	}

	return ret;
}

void
DataValidator::print()
{
	if (_time_last == 0) {
		ECL_INFO("\tno data");
		return;
	}

	for (unsigned i = 0; i < dimensions; i++) {
		ECL_INFO("\tval: %8.4f, lp: %8.4f mean dev: %8.4f RMS: %8.4f conf: %8.4f",
			 (double) _value[i], (double)_lp[i], (double)_mean[i],
			 (double)_rms[i], (double)confidence(ecl_absolute_time()));
	}
}