/* * Copyright (C) 2016 The Android Open Source Project * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ #include "calibration/accelerometer/accel_cal.h" #include #include #include #include #include #include "calibration/util/cal_log.h" // clang-format off #define KSCALE \ 0.101936799f // Scaling from m/s^2 to g (0.101 = 1/(9.81 m/s^2)). #define KSCALE2 9.81f // Scaling from g to m/s^2. #define PHI 0.707f // = 1/sqrt(2) gives a 45 degree angle for sorting data. #define PHIb -0.707f #define PHIZ 0.866f // smaller Z sphere cap, opening angle is 30 degrees. #define PHIZb -0.866f #define G_NORM_MAX \ 1.38f // Norm during stillness should be 1 g, checking from max min values. #define G_NORM_MIN 0.68f #define MAX_OFF 0.1f // Will not accept offsets that are larger than 100 mg. #define MIN_TEMP 20.0f // No Data is collected below 20 degree C. #define MAX_TEMP 45.0f // No Data is collected above 45 degree C. #define TEMP_CUT 30 // Separation point for temperature buckets 30 degree C. #define EIGEN_RATIO 0.35f // EIGEN_RATIO (must be greater than 0.35). #define EIGEN_MAG 0.97f // Eigen value magnitude (must be greater than 0.97). #define ACCEL_NEW_BIAS_THRESHOLD (0.0f) // Bias update detection threshold. #ifdef ACCEL_CAL_DBG_ENABLED #define TEMP_HIST_LOW \ 16 // Putting all Temp counts in first bucket for temp < 16 degree C. #define TEMP_HIST_HIGH \ 62 // Putting all Temp counts in last bucket for temp > 62 degree C. #define HIST_COUNT 9 #endif #ifdef IMU_TEMP_DBG_ENABLED #define IMU_TEMP_DELTA_TIME_NANOS \ 5000000000 // Printing every 5 seconds IMU temp. #endif // clang-format on /////////// Start Debug ////////////////////// #ifdef ACCEL_CAL_DBG_ENABLED // Total bucket Counter. static void accelStatsCounter(struct AccelStillDet *asd, struct AccelStatsMem *adf) { // Sorting the data in the different buckets // x bucket ntx. if (PHI < asd->mean_x) { adf->ntx += 1; } // Negative x bucket ntxb. if (PHIb > asd->mean_x) { adf->ntxb += 1; } // Y bucket nty. if (PHI < asd->mean_y) { adf->nty += 1; } // Negative y bucket ntyb. if (PHIb > asd->mean_y) { adf->ntyb += 1; } // Z bucket ntz. if (PHIZ < asd->mean_z) { adf->ntz += 1; } // Negative z bucket ntzb. if (PHIZb > asd->mean_z) { adf->ntzb += 1; } // The leftover bucket ntle. if (PHI > asd->mean_x && PHIb < asd->mean_x && PHI > asd->mean_y && PHIb < asd->mean_y && PHIZ > asd->mean_z && PHIZb < asd->mean_z) { adf->ntle += 1; } } // Temp histogram generation. static void accelTempHisto(struct AccelStatsMem *adf, float temp) { int index = 0; // Take temp at every stillness detection. adf->start_time_nanos = 0; if (temp <= TEMP_HIST_LOW) { adf->t_hist[0] += 1; return; } if (temp >= TEMP_HIST_HIGH) { adf->t_hist[TEMP_HISTOGRAM - 1] += 1; return; } index = (int)(((temp - TEMP_HIST_LOW) / 2) + 1); adf->t_hist[index] += 1; } #endif ///////// End Debug //////////////////// // Stillness detector reset. static void asdReset(struct AccelStillDet *asd) { asd->nsamples = 0; asd->start_time = 0; asd->acc_x = asd->acc_y = asd->acc_z = 0.0f; asd->acc_xx = asd->acc_yy = asd->acc_zz = 0.0f; } // Stillness detector init. static void accelStillInit(struct AccelStillDet *asd, uint32_t t0, uint32_t n_s, float th) { memset(asd, 0, sizeof(struct AccelStillDet)); asd->var_th = th; asd->min_batch_window = t0; asd->max_batch_window = t0 + 100000000; asd->min_batch_size = n_s; asd->n_still = 0; } // Good data reset. static void agdReset(struct AccelGoodData *agd) { agd->nx = agd->nxb = 0; agd->ny = agd->nyb = 0; agd->nz = agd->nzb = 0; agd->nle = 0; agd->acc_t = agd->acc_tt = 0; agd->e_x = agd->e_y = agd->e_z = 0; } // Good data init. static void accelGoodDataInit(struct AccelGoodData *agd, uint32_t fx, uint32_t fxb, uint32_t fy, uint32_t fyb, uint32_t fz, uint32_t fzb, uint32_t fle) { memset(agd, 0, sizeof(struct AccelGoodData)); agd->nfx = fx; agd->nfxb = fxb; agd->nfy = fy; agd->nfyb = fyb; agd->nfz = fz; agd->nfzb = fzb; agd->nfle = fle; agd->var_t = 0; agd->mean_t = 0; } // Accel cal algo init (ready for temp buckets). static void accelCalAlgoInit(struct AccelCalAlgo *acc, uint32_t fx, uint32_t fxb, uint32_t fy, uint32_t fyb, uint32_t fz, uint32_t fzb, uint32_t fle) { accelGoodDataInit(&acc->agd, fx, fxb, fy, fyb, fz, fzb, fle); kasaInit(&acc->akf); } // Returns true when a new accel calibration is available. bool accelCalNewBiasAvailable(struct AccelCal *acc) { return fabsf(acc->x_bias - acc->x_bias_new) > ACCEL_NEW_BIAS_THRESHOLD || fabsf(acc->y_bias - acc->y_bias_new) > ACCEL_NEW_BIAS_THRESHOLD || fabsf(acc->z_bias - acc->z_bias_new) > ACCEL_NEW_BIAS_THRESHOLD; } // Accel cal init. void accelCalInit(struct AccelCal *acc, const struct AccelCalParameters *parameters) { // Init core accel data. accelCalAlgoInit(&acc->ac1[0], parameters->fx, parameters->fxb, parameters->fy, parameters->fyb, parameters->fz, parameters->fzb, parameters->fle); accelCalAlgoInit(&acc->ac1[1], parameters->fx, parameters->fxb, parameters->fy, parameters->fyb, parameters->fz, parameters->fzb, parameters->fle); // Stillness Reset. accelStillInit(&acc->asd, parameters->t0, parameters->n_s, parameters->th); // Debug data init. #ifdef ACCEL_CAL_DBG_ENABLED memset(&acc->adf, 0, sizeof(struct AccelStatsMem)); #endif acc->x_bias = acc->y_bias = acc->z_bias = 0; acc->x_bias_new = acc->y_bias_new = acc->z_bias_new = 0; acc->average_temperature_celsius = 0; #ifdef IMU_TEMP_DBG_ENABLED acc->temp_time_nanos = 0; #endif } // Stillness time check. static int stillnessBatchComplete(struct AccelStillDet *asd, uint64_t sample_time_nanos) { int complete = 0; // Checking if enough data is accumulated to calc Mean and Var. if ((sample_time_nanos - asd->start_time > asd->min_batch_window) && (asd->nsamples > asd->min_batch_size)) { if (sample_time_nanos - asd->start_time < asd->max_batch_window) { complete = 1; } else { // Checking for too long batch window, if yes reset and start over. asdReset(asd); return complete; } } else if (sample_time_nanos - asd->start_time > asd->min_batch_window && (asd->nsamples < asd->min_batch_size)) { // Not enough samples collected in max_batch_window during sample window. asdReset(asd); } return complete; } // Releasing Memory. void accelCalDestroy(struct AccelCal *acc) { (void)acc; } // Stillness Detection. static int accelStillnessDetection(struct AccelStillDet *asd, uint64_t sample_time_nanos, float x, float y, float z) { float inv = 0.0f; int complete = 0.0f; float g_norm = 0.0f; // Accumulate for mean and VAR. asd->acc_x += x; asd->acc_xx += x * x; asd->acc_y += y; asd->acc_yy += y * y; asd->acc_z += z; asd->acc_zz += z * z; // Setting a new start time and wait until T0 is reached. if (++asd->nsamples == 1) { asd->start_time = sample_time_nanos; } if (stillnessBatchComplete(asd, sample_time_nanos)) { // Getting 1/#samples and checking asd->nsamples != 0. if (0 < asd->nsamples) { inv = 1.0f / asd->nsamples; } else { // Something went wrong resetting and start over. asdReset(asd); return complete; } // Calculating the VAR = sum(x^2)/n - sum(x)^2/n^2. asd->var_x = (asd->acc_xx - (asd->acc_x * asd->acc_x) * inv) * inv; asd->var_y = (asd->acc_yy - (asd->acc_y * asd->acc_y) * inv) * inv; asd->var_z = (asd->acc_zz - (asd->acc_z * asd->acc_z) * inv) * inv; // Checking if sensor is still. if (asd->var_x < asd->var_th && asd->var_y < asd->var_th && asd->var_z < asd->var_th) { // Calcluating the MEAN = sum(x) / n. asd->mean_x = asd->acc_x * inv; asd->mean_y = asd->acc_y * inv; asd->mean_z = asd->acc_z * inv; // Calculating g_norm^2. g_norm = asd->mean_x * asd->mean_x + asd->mean_y * asd->mean_y + asd->mean_z * asd->mean_z; // Magnitude check, still passsing when we have worse case offset. if (g_norm < G_NORM_MAX && g_norm > G_NORM_MIN) { complete = 1; asd->n_still += 1; } } asdReset(asd); } return complete; } // Good data detection, sorting and accumulate the data for Kasa. static int accelGoodData(struct AccelStillDet *asd, struct AccelCalAlgo *ac1, float temp) { int complete = 0; float inv = 0.0f; // Sorting the data in the different buckets and accum // x bucket nx. if (PHI < asd->mean_x && ac1->agd.nx < ac1->agd.nfx) { ac1->agd.nx += 1; ac1->agd.acc_t += temp; ac1->agd.acc_tt += temp * temp; kasaAccumulate(&ac1->akf, asd->mean_x, asd->mean_y, asd->mean_z); } // Negative x bucket nxb. if (PHIb > asd->mean_x && ac1->agd.nxb < ac1->agd.nfxb) { ac1->agd.nxb += 1; ac1->agd.acc_t += temp; ac1->agd.acc_tt += temp * temp; kasaAccumulate(&ac1->akf, asd->mean_x, asd->mean_y, asd->mean_z); } // Y bucket ny. if (PHI < asd->mean_y && ac1->agd.ny < ac1->agd.nfy) { ac1->agd.ny += 1; ac1->agd.acc_t += temp; ac1->agd.acc_tt += temp * temp; kasaAccumulate(&ac1->akf, asd->mean_x, asd->mean_y, asd->mean_z); } // Negative y bucket nyb. if (PHIb > asd->mean_y && ac1->agd.nyb < ac1->agd.nfyb) { ac1->agd.nyb += 1; ac1->agd.acc_t += temp; ac1->agd.acc_tt += temp * temp; kasaAccumulate(&ac1->akf, asd->mean_x, asd->mean_y, asd->mean_z); } // Z bucket nz. if (PHIZ < asd->mean_z && ac1->agd.nz < ac1->agd.nfz) { ac1->agd.nz += 1; ac1->agd.acc_t += temp; ac1->agd.acc_tt += temp * temp; kasaAccumulate(&ac1->akf, asd->mean_x, asd->mean_y, asd->mean_z); } // Negative z bucket nzb. if (PHIZb > asd->mean_z && ac1->agd.nzb < ac1->agd.nfzb) { ac1->agd.nzb += 1; ac1->agd.acc_t += temp; ac1->agd.acc_tt += temp * temp; kasaAccumulate(&ac1->akf, asd->mean_x, asd->mean_y, asd->mean_z); } // The leftover bucket nle. if (PHI > asd->mean_x && PHIb < asd->mean_x && PHI > asd->mean_y && PHIb < asd->mean_y && PHIZ > asd->mean_z && PHIZb < asd->mean_z && ac1->agd.nle < ac1->agd.nfle) { ac1->agd.nle += 1; ac1->agd.acc_t += temp; ac1->agd.acc_tt += temp * temp; kasaAccumulate(&ac1->akf, asd->mean_x, asd->mean_y, asd->mean_z); } // Checking if all buckets are full. if (ac1->agd.nx == ac1->agd.nfx && ac1->agd.nxb == ac1->agd.nfxb && ac1->agd.ny == ac1->agd.nfy && ac1->agd.nyb == ac1->agd.nfyb && ac1->agd.nz == ac1->agd.nfz && ac1->agd.nzb == ac1->agd.nfzb) { // Check if akf->nsamples is zero. if (ac1->akf.nsamples == 0) { agdReset(&ac1->agd); kasaReset(&ac1->akf); complete = 0; return complete; } // Normalize the data to the sample numbers. kasaNormalize(&ac1->akf); // Calculate the temp VAR and MEAN. inv = 1.0f / ac1->akf.nsamples; ac1->agd.var_t = (ac1->agd.acc_tt - (ac1->agd.acc_t * ac1->agd.acc_t) * inv) * inv; ac1->agd.mean_t = ac1->agd.acc_t * inv; complete = 1; } // If any of the buckets has a bigger number as specified, reset and start // over. if (ac1->agd.nx > ac1->agd.nfx || ac1->agd.nxb > ac1->agd.nfxb || ac1->agd.ny > ac1->agd.nfy || ac1->agd.nyb > ac1->agd.nfyb || ac1->agd.nz > ac1->agd.nfz || ac1->agd.nzb > ac1->agd.nfzb) { agdReset(&ac1->agd); kasaReset(&ac1->akf); complete = 0; return complete; } return complete; } // Eigen value magnitude and ratio test. static int accEigenTest(struct KasaFit *akf, struct AccelGoodData *agd) { // covariance matrix. struct Mat33 S; S.elem[0][0] = akf->acc_xx - akf->acc_x * akf->acc_x; S.elem[0][1] = S.elem[1][0] = akf->acc_xy - akf->acc_x * akf->acc_y; S.elem[0][2] = S.elem[2][0] = akf->acc_xz - akf->acc_x * akf->acc_z; S.elem[1][1] = akf->acc_yy - akf->acc_y * akf->acc_y; S.elem[1][2] = S.elem[2][1] = akf->acc_yz - akf->acc_y * akf->acc_z; S.elem[2][2] = akf->acc_zz - akf->acc_z * akf->acc_z; struct Vec3 eigenvals; struct Mat33 eigenvecs; mat33GetEigenbasis(&S, &eigenvals, &eigenvecs); float evmax = (eigenvals.x > eigenvals.y) ? eigenvals.x : eigenvals.y; evmax = (eigenvals.z > evmax) ? eigenvals.z : evmax; float evmin = (eigenvals.x < eigenvals.y) ? eigenvals.x : eigenvals.y; evmin = (eigenvals.z < evmin) ? eigenvals.z : evmin; float eigenvals_sum = eigenvals.x + eigenvals.y + eigenvals.z; // Testing for negative number. float evmag = (eigenvals_sum > 0) ? sqrtf(eigenvals_sum) : 0; // Passing when evmin/evmax> EIGEN_RATIO. int eigen_pass = (evmin > evmax * EIGEN_RATIO) && (evmag > EIGEN_MAG); agd->e_x = eigenvals.x; agd->e_y = eigenvals.y; agd->e_z = eigenvals.z; return eigen_pass; } // Updating the new bias and save to pointers. Return true if the bias changed. bool accelCalUpdateBias(struct AccelCal *acc, float *x, float *y, float *z) { *x = acc->x_bias_new; *y = acc->y_bias_new; *z = acc->z_bias_new; // Check to see if the bias changed since last call to accelCalUpdateBias. // Compiler does not allow us to use "==" and "!=" when comparing floats, so // just use "<" and ">". if ((acc->x_bias < acc->x_bias_new) || (acc->x_bias > acc->x_bias_new) || (acc->y_bias < acc->y_bias_new) || (acc->y_bias > acc->y_bias_new) || (acc->z_bias < acc->z_bias_new) || (acc->z_bias > acc->z_bias_new)) { acc->x_bias = acc->x_bias_new; acc->y_bias = acc->y_bias_new; acc->z_bias = acc->z_bias_new; return true; } return false; } // Set the (initial) bias. void accelCalBiasSet(struct AccelCal *acc, float x, float y, float z) { acc->x_bias = acc->x_bias_new = x; acc->y_bias = acc->y_bias_new = y; acc->z_bias = acc->z_bias_new = z; } // Removing the bias. void accelCalBiasRemove(struct AccelCal *acc, float *x, float *y, float *z) { *x = *x - acc->x_bias; *y = *y - acc->y_bias; *z = *z - acc->z_bias; } // Accel Cal Runner. void accelCalRun(struct AccelCal *acc, uint64_t sample_time_nanos, float x, float y, float z, float temp) { // Scaling to 1g, better for the algorithm. x *= KSCALE; y *= KSCALE; z *= KSCALE; // DBG: IMU temp messages every 5s. #ifdef IMU_TEMP_DBG_ENABLED if ((sample_time_nanos - acc->temp_time_nanos) > IMU_TEMP_DELTA_TIME_NANOS) { CAL_DEBUG_LOG("IMU Temp Data: ", ", " CAL_FORMAT_3DIGITS ", %" PRIu64 ", " CAL_FORMAT_6DIGITS_TRIPLET " \n", CAL_ENCODE_FLOAT(temp, 3), sample_time_nanos, CAL_ENCODE_FLOAT(acc->x_bias_new, 6), CAL_ENCODE_FLOAT(acc->y_bias_new, 6), CAL_ENCODE_FLOAT(acc->z_bias_new, 6)); acc->temp_time_nanos = sample_time_nanos; } #endif int temp_gate = 0; // Temp GATE. if (temp < MAX_TEMP && temp > MIN_TEMP) { // Checking if accel is still. if (accelStillnessDetection(&acc->asd, sample_time_nanos, x, y, z)) { #ifdef ACCEL_CAL_DBG_ENABLED // Creating temp hist data. accelTempHisto(&acc->adf, temp); #endif // Two temp buckets. if (temp < TEMP_CUT) { temp_gate = 0; } else { temp_gate = 1; } #ifdef ACCEL_CAL_DBG_ENABLED accelStatsCounter(&acc->asd, &acc->adf); #endif // If still -> pass the averaged accel data (mean) to the // sorting, counting and accum function. if (accelGoodData(&acc->asd, &acc->ac1[temp_gate], temp)) { // Running the Kasa fit. struct Vec3 bias; float radius; // Grabbing the fit from the MAG cal. kasaFit(&acc->ac1[temp_gate].akf, &bias, &radius, G_NORM_MAX, G_NORM_MIN); // If offset is too large don't take. if (fabsf(bias.x) < MAX_OFF && fabsf(bias.y) < MAX_OFF && fabsf(bias.z) < MAX_OFF) { // Eigen Ratio Test. if (accEigenTest(&acc->ac1[temp_gate].akf, &acc->ac1[temp_gate].agd)) { // Storing the new offsets and average temperature. acc->x_bias_new = bias.x * KSCALE2; acc->y_bias_new = bias.y * KSCALE2; acc->z_bias_new = bias.z * KSCALE2; acc->average_temperature_celsius = acc->ac1[temp_gate].agd.mean_t; } #ifdef ACCEL_CAL_DBG_ENABLED //// Debug /////// acc->adf.noff += 1; // Resetting the counter for the offset history. if (acc->adf.n_o > HIST_COUNT) { acc->adf.n_o = 0; } // Storing the Debug data. acc->adf.x_o[acc->adf.n_o] = bias.x; acc->adf.y_o[acc->adf.n_o] = bias.y; acc->adf.z_o[acc->adf.n_o] = bias.z; acc->adf.e_x[acc->adf.n_o] = acc->ac1[temp_gate].agd.e_x; acc->adf.e_y[acc->adf.n_o] = acc->ac1[temp_gate].agd.e_y; acc->adf.e_z[acc->adf.n_o] = acc->ac1[temp_gate].agd.e_z; acc->adf.var_t[acc->adf.n_o] = acc->ac1[temp_gate].agd.var_t; acc->adf.mean_t[acc->adf.n_o] = acc->ac1[temp_gate].agd.mean_t; acc->adf.cal_time[acc->adf.n_o] = sample_time_nanos; acc->adf.rad[acc->adf.n_o] = radius; acc->adf.n_o += 1; #endif } else { #ifdef ACCEL_CAL_DBG_ENABLED acc->adf.noff_max += 1; #endif } /////////////// // Resetting the structs for a new accel cal run. agdReset(&acc->ac1[temp_gate].agd); kasaReset(&acc->ac1[temp_gate].akf); } } } } #ifdef ACCEL_CAL_DBG_ENABLED // Local helper macro for printing log messages. #ifdef CAL_NO_FLOAT_FORMAT_STRINGS #define CAL_FORMAT_ACCEL_HISTORY \ "%s%d.%06d,%s%d.%06d,%s%d.%06d,%s%d.%06d,%s%d.%06d,%s%d.%06d,%s%d.%06d," \ "%s%d.%06d,%s%d.%06d,%s%d.%06d" #else #define CAL_FORMAT_ACCEL_HISTORY \ "%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f,%.6f" #endif // CAL_NO_FLOAT_FORMAT_STRINGS // Debug Print Output void accelCalDebPrint(struct AccelCal *acc, float temp) { static int32_t kk = 0; if (++kk == 1000) { // X offset history last 10 values. CAL_DEBUG_LOG("[ACCEL_CAL]", "{11," CAL_FORMAT_ACCEL_HISTORY "}(x_off history)\n", CAL_ENCODE_FLOAT(acc->adf.x_o[0], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[1], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[2], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[3], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[4], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[5], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[6], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[7], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[8], 6), CAL_ENCODE_FLOAT(acc->adf.x_o[9], 6)); // Y offset history last 10 values. CAL_DEBUG_LOG("[ACCEL_CAL]", "{12," CAL_FORMAT_ACCEL_HISTORY "}(y_off history)\n", CAL_ENCODE_FLOAT(acc->adf.y_o[0], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[1], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[2], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[3], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[4], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[5], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[6], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[7], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[8], 6), CAL_ENCODE_FLOAT(acc->adf.y_o[9], 6)); // Z offset history last 10 values. CAL_DEBUG_LOG("[ACCEL_CAL]", "{13," CAL_FORMAT_ACCEL_HISTORY "}(z_off history)\n", CAL_ENCODE_FLOAT(acc->adf.z_o[0], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[1], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[2], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[3], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[4], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[5], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[6], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[7], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[8], 6), CAL_ENCODE_FLOAT(acc->adf.z_o[9], 6)); // Temp history variation VAR of offset. CAL_DEBUG_LOG("[ACCEL_CAL]", "{14," CAL_FORMAT_ACCEL_HISTORY "}(VAR temp history)\n", CAL_ENCODE_FLOAT(acc->adf.var_t[0], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[1], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[2], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[3], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[4], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[5], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[6], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[7], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[8], 6), CAL_ENCODE_FLOAT(acc->adf.var_t[9], 6)); // Temp mean history of offset. CAL_DEBUG_LOG("[ACCEL_CAL]", "{15," CAL_FORMAT_ACCEL_HISTORY "}(MEAN Temp history)\n", CAL_ENCODE_FLOAT(acc->adf.mean_t[0], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[1], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[2], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[3], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[4], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[5], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[6], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[7], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[8], 6), CAL_ENCODE_FLOAT(acc->adf.mean_t[9], 6)); // KASA radius history. CAL_DEBUG_LOG("[ACCEL_CAL]", "{16," CAL_FORMAT_ACCEL_HISTORY "}(radius)\n", CAL_ENCODE_FLOAT(acc->adf.rad[0], 6), CAL_ENCODE_FLOAT(acc->adf.rad[1], 6), CAL_ENCODE_FLOAT(acc->adf.rad[2], 6), CAL_ENCODE_FLOAT(acc->adf.rad[3], 6), CAL_ENCODE_FLOAT(acc->adf.rad[4], 6), CAL_ENCODE_FLOAT(acc->adf.rad[5], 6), CAL_ENCODE_FLOAT(acc->adf.rad[6], 6), CAL_ENCODE_FLOAT(acc->adf.rad[7], 6), CAL_ENCODE_FLOAT(acc->adf.rad[8], 6), CAL_ENCODE_FLOAT(acc->adf.rad[9], 6)); kk = 0; } if (kk == 750) { // Eigen Vector X. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 7," CAL_FORMAT_ACCEL_HISTORY "}(eigen x)\n", CAL_ENCODE_FLOAT(acc->adf.e_x[0], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[1], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[2], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[3], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[4], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[5], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[6], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[7], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[8], 6), CAL_ENCODE_FLOAT(acc->adf.e_x[9], 6)); // Y. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 8," CAL_FORMAT_ACCEL_HISTORY "}(eigen y)\n", CAL_ENCODE_FLOAT(acc->adf.e_y[0], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[1], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[2], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[3], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[4], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[5], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[6], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[7], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[8], 6), CAL_ENCODE_FLOAT(acc->adf.e_y[9], 6)); // Z. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 9," CAL_FORMAT_ACCEL_HISTORY "}(eigen z)\n", CAL_ENCODE_FLOAT(acc->adf.e_z[0], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[1], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[2], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[3], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[4], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[5], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[6], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[7], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[8], 6), CAL_ENCODE_FLOAT(acc->adf.e_z[9], 6)); // Accel Time in ns. CAL_DEBUG_LOG("[ACCEL_CAL]", "{10,%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 ",%" PRIu64 "}(timestamp ns)\n", acc->adf.cal_time[0], acc->adf.cal_time[1], acc->adf.cal_time[2], acc->adf.cal_time[3], acc->adf.cal_time[4], acc->adf.cal_time[5], acc->adf.cal_time[6], acc->adf.cal_time[7], acc->adf.cal_time[8], acc->adf.cal_time[9]); } if (kk == 500) { // Total bucket count. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 0,%2d, %2d, %2d, %2d, %2d, %2d, %2d}(Total Bucket #)\n", (unsigned)acc->adf.ntx, (unsigned)acc->adf.ntxb, (unsigned)acc->adf.nty, (unsigned)acc->adf.ntyb, (unsigned)acc->adf.ntz, (unsigned)acc->adf.ntzb, (unsigned)acc->adf.ntle); // Live bucket count lower. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 1,%2d, %2d, %2d, %2d, %2d, %2d, %2d, %3d}(Bucket # " "lower)\n", (unsigned)acc->ac1[0].agd.nx, (unsigned)acc->ac1[0].agd.nxb, (unsigned)acc->ac1[0].agd.ny, (unsigned)acc->ac1[0].agd.nyb, (unsigned)acc->ac1[0].agd.nz, (unsigned)acc->ac1[0].agd.nzb, (unsigned)acc->ac1[0].agd.nle, (unsigned)acc->ac1[0].akf.nsamples); // Live bucket count hogher. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 2,%2d, %2d, %2d, %2d, %2d, %2d, %2d, %3d}(Bucket # " "higher)\n", (unsigned)acc->ac1[1].agd.nx, (unsigned)acc->ac1[1].agd.nxb, (unsigned)acc->ac1[1].agd.ny, (unsigned)acc->ac1[1].agd.nyb, (unsigned)acc->ac1[1].agd.nz, (unsigned)acc->ac1[1].agd.nzb, (unsigned)acc->ac1[1].agd.nle, (unsigned)acc->ac1[1].akf.nsamples); // Offset used. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 3,"CAL_FORMAT_6DIGITS_TRIPLET", %2d}(updated offset " "x,y,z, total # of offsets)\n", CAL_ENCODE_FLOAT(acc->x_bias, 6), CAL_ENCODE_FLOAT(acc->y_bias, 6), CAL_ENCODE_FLOAT(acc->z_bias, 6), (unsigned)acc->adf.noff); // Offset New. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 4," CAL_FORMAT_6DIGITS_TRIPLET ", " CAL_FORMAT_6DIGITS "}(New offset x,y,z, live temp)\n", CAL_ENCODE_FLOAT(acc->x_bias_new, 6), CAL_ENCODE_FLOAT(acc->y_bias_new, 6), CAL_ENCODE_FLOAT(acc->z_bias_new, 6), CAL_ENCODE_FLOAT(temp, 6)); // Temp Histogram. CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 5,%7d, %7d, %7d, %7d, %7d, %7d, %7d, %7d, %7d, %7d, %7d, " "%7d, %7d}(temp histo)\n", (unsigned)acc->adf.t_hist[0], (unsigned)acc->adf.t_hist[1], (unsigned)acc->adf.t_hist[2], (unsigned)acc->adf.t_hist[3], (unsigned)acc->adf.t_hist[4], (unsigned)acc->adf.t_hist[5], (unsigned)acc->adf.t_hist[6], (unsigned)acc->adf.t_hist[7], (unsigned)acc->adf.t_hist[8], (unsigned)acc->adf.t_hist[9], (unsigned)acc->adf.t_hist[10], (unsigned)acc->adf.t_hist[11], (unsigned)acc->adf.t_hist[12]); CAL_DEBUG_LOG("[ACCEL_CAL]", "{ 6,%7d, %7d, %7d, %7d, %7d, %7d, %7d, %7d, %7d, %7d, %7d, " "%7d}(temp histo)\n", (unsigned)acc->adf.t_hist[13], (unsigned)acc->adf.t_hist[14], (unsigned)acc->adf.t_hist[15], (unsigned)acc->adf.t_hist[16], (unsigned)acc->adf.t_hist[17], (unsigned)acc->adf.t_hist[18], (unsigned)acc->adf.t_hist[19], (unsigned)acc->adf.t_hist[20], (unsigned)acc->adf.t_hist[21], (unsigned)acc->adf.t_hist[22], (unsigned)acc->adf.t_hist[23], (unsigned)acc->adf.t_hist[24]); } } #endif