Improve detector
This commit is contained in:
jaseg
parent
4c7c927f3c
commit
13bd8d0f2d
5 changed files with 363 additions and 82 deletions
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@ -25,7 +25,7 @@ FMEAS_SAMPLING_RATE ?= $(shell echo $(FMEAS_ADC_SAMPLING_RATE) / \($(FMEAS_FFT_
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DSSS_GOLD_CODE_NBITS ?= 5
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DSSS_DECIMATION ?= 10
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# TODO maybe auto adjust this based on detection rate?
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DSSS_THESHOLD_FACTOR ?= 6.0f
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DSSS_THESHOLD_FACTOR ?= 5.0f
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DSSS_WAVELET_WIDTH ?= 7.3
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DSSS_WAVELET_LUT_SIZE ?= 69
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DSSS_FILTER_FC ?= 3e-3
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@ -23,10 +23,8 @@ struct iir_biquad cwt_filter_bq[DSSS_FILTER_CLEN] = {DSSS_FILTER_COEFF};
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void debug_print_vector(const char *name, size_t len, const float *data, size_t stride, bool index, bool debug);
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static float gold_correlate_step(const size_t ncode, const float a[DSSS_CORRELATION_LENGTH], size_t offx, bool debug);
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static float cwt_convolve_step(const float v[DSSS_WAVELET_LUT_SIZE], size_t offx);
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#if 0
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static float run_iir(const float x, const int order, const struct iir_biquad q[order], struct iir_biquad_state st[order]);
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static float run_biquad(float x, const struct iir_biquad *const q, struct iir_biquad_state *const restrict st);
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#endif
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static void matcher_init(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE]);
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static void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE],
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uint64_t ts, int peak_ch, float peak_ampl);
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@ -41,7 +39,7 @@ void debug_print_vector(const char *name, size_t len, const float *data, size_t
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if (index) {
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DEBUG_PRINTN(" %16s [", "");
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for (size_t i=0; i<len; i++)
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DEBUG_PRINTN("%8zd ", i);
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DEBUG_PRINTN("%8zu ", i);
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DEBUG_PRINTN("]\n");
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}
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@ -62,6 +60,8 @@ void dsss_demod_init(struct dsss_demod_state *st) {
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void dsss_demod_step(struct dsss_demod_state *st, float new_value, uint64_t ts) {
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//const float hole_patching_threshold = 0.01 * DSSS_CORRELATION_LENGTH;
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bool log = false;
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bool log_groups = true;
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st->signal[st->signal_wpos] = new_value;
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st->signal_wpos = (st->signal_wpos + 1) % ARRAY_LENGTH(st->signal);
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@ -80,6 +80,7 @@ void dsss_demod_step(struct dsss_demod_state *st, float new_value, uint64_t ts)
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for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++)
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avg += fabsf(cwt[i]);
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avg /= (float)DSSS_GOLD_CODE_COUNT;
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if (log) DEBUG_PRINTN("%6zu: %f ", ts, avg);
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/* FIXME fix this filter */
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//avg = run_iir(avg, ARRAY_LENGTH(cwt_filter_bq), cwt_filter_bq, st->cwt_filter.st);
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@ -89,18 +90,21 @@ void dsss_demod_step(struct dsss_demod_state *st, float new_value, uint64_t ts)
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bool found = false;
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for (size_t i=0; i<DSSS_GOLD_CODE_COUNT; i++) {
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float val = cwt[i] / avg;
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if (log) DEBUG_PRINTN("%f ", cwt[i]);
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if (log) DEBUG_PRINTN("%f ", val);
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if (fabsf(val) > fabsf(max_val)) {
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max_val = val;
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max_ch = i;
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max_ts = ts;
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if (fabsf(val) > DSSS_THESHOLD_FACTOR)
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found = true;
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}
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}
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/* FIXME: skipped sample handling here */
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if (fabsf(val) > DSSS_THESHOLD_FACTOR)
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found = true;
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}
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if (log) DEBUG_PRINTN("%f %d ", max_val, found);
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if (log) DEBUG_PRINTN("\n");
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matcher_tick(st->matcher_cache, ts, max_ch, max_val);
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if (found) {
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@ -116,6 +120,7 @@ void dsss_demod_step(struct dsss_demod_state *st, float new_value, uint64_t ts)
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/* We're between groups */
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return;
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if (log_groups) DEBUG_PRINTN("GROUP: %zu %d %f\n", st->group.max_ts, st->group.max_ch, st->group.max);
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/* A group ended. Process result. */
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group_received(st);
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@ -152,7 +157,9 @@ void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE], u
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const float score_depreciation = 0.1f; /* 0.0 -> no depreciation, 1.0 -> complete disregard */
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const int current_phase = ts % DSSS_CORRELATION_LENGTH;
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const int max_skips = TRANSMISSION_SYMBOLS/4*3;
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bool debug = true;
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bool header_printed = false;
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for (size_t i=0; i<DSSS_MATCHER_CACHE_SIZE; i++) {
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if (states[i].last_phase == -1)
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continue; /* Inactive entry */
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@ -161,6 +168,11 @@ void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE], u
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/* Skip sampling */
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float score = fabsf(peak_ampl) * (1.0f - skip_sampling_depreciation);
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if (score > states[i].candidate_score) {
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if (debug && !header_printed) {
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header_printed = true;
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DEBUG_PRINTN("windows %zu\n", ts);
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}
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if (debug) DEBUG_PRINTN(" skip %d old=%f new=%f\n", i, states[i].candidate_score, score);
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/* We win, update candidate */
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assert(i < DSSS_MATCHER_CACHE_SIZE);
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states[i].candidate_score = score;
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@ -175,7 +187,15 @@ void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE], u
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* process a group a couple ticks after its peak. We have to make sure the window is still open at this point.
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* This means we have to match against group_phase_tolerance should a little bit loosely.
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*/
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if (abs(states[i].last_phase - current_phase) == group_phase_tolerance + DSSS_DECIMATION) {
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int phase_delta = current_phase - states[i].last_phase;
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if (phase_delta < 0)
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phase_delta += DSSS_CORRELATION_LENGTH;
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if (phase_delta == group_phase_tolerance + DSSS_DECIMATION) {
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if (debug && !header_printed) {
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header_printed = true;
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DEBUG_PRINTN("windows %zu\n", ts);
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}
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if (debug) DEBUG_PRINTN(" %d ", i);
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/* Process window results */
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assert(i < DSSS_MATCHER_CACHE_SIZE);
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assert(0 <= states[i].data_pos && states[i].data_pos < TRANSMISSION_SYMBOLS);
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@ -183,17 +203,21 @@ void matcher_tick(struct matcher_state states[static DSSS_MATCHER_CACHE_SIZE], u
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states[i].data_pos = states[i].data_pos + 1;
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states[i].last_score = score_depreciation * states[i].last_score +
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(1.0f - score_depreciation) * states[i].candidate_score;
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if (debug) DEBUG_PRINTN("commit pos=%d val=%d score=%f ", states[i].data_pos, states[i].candidate_data, states[i].last_score);
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states[i].candidate_score = 0.0f;
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states[i].last_skips += states[i].candidate_skips;
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if (states[i].last_skips > max_skips) {
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if (debug) DEBUG_PRINTN("expire ");
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states[i].last_phase = -1; /* invalidate entry */
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} else if (states[i].data_pos == TRANSMISSION_SYMBOLS) {
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if (debug) DEBUG_PRINTN("match ");
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/* Frame received completely */
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handle_dsss_received(states[i].data);
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states[i].last_phase = -1; /* invalidate entry */
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}
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if (debug) DEBUG_PRINTN("\n");
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}
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}
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}
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@ -211,6 +235,7 @@ static float score_group(const struct group *g, int phase_delta) {
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}
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void group_received(struct dsss_demod_state *st) {
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bool debug = true;
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const int group_phase = st->group.max_ts % DSSS_CORRELATION_LENGTH;
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/* This is the score of a decoding starting at this group (with no context) */
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float base_score = score_group(&st->group, 0);
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@ -234,6 +259,7 @@ void group_received(struct dsss_demod_state *st) {
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float group_score = score_group(&st->group, phase_delta);
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if (st->matcher_cache[i].candidate_score < group_score) {
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assert(i < DSSS_MATCHER_CACHE_SIZE);
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if (debug) DEBUG_PRINTN(" appending %zu %d score=%f pd=%d\n", i, decode_peak(st->group.max_ch, st->group.max), group_score, phase_delta);
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/* Append to entry */
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st->matcher_cache[i].candidate_score = group_score;
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st->matcher_cache[i].candidate_phase = group_phase;
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@ -243,7 +269,8 @@ void group_received(struct dsss_demod_state *st) {
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}
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/* Search for weakest entry */
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float score = st->matcher_cache[i].last_score;
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/* TODO figure out this fitness function */
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float score = st->matcher_cache[i].last_score * (1.5f + 1.0f/st->matcher_cache[i].data_pos);
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if (score < min_score) {
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min_idx = i;
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min_score = score;
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@ -252,6 +279,7 @@ void group_received(struct dsss_demod_state *st) {
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/* If we found empty entries, replace one by a new decoding starting at this group */
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if (empty_idx >= 0) {
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if (debug) DEBUG_PRINTN(" empty %zd %d\n", empty_idx, decode_peak(st->group.max_ch, st->group.max));
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assert(0 <= empty_idx && empty_idx < DSSS_MATCHER_CACHE_SIZE);
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st->matcher_cache[empty_idx].last_phase = group_phase;
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st->matcher_cache[empty_idx].candidate_score = base_score;
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@ -264,6 +292,7 @@ void group_received(struct dsss_demod_state *st) {
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/* If the weakest decoding in cache is weaker than a new decoding starting here, replace it */
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} else if (min_score < base_score && min_idx >= 0) {
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if (debug) DEBUG_PRINTN(" min %zd %d\n", min_idx, decode_peak(st->group.max_ch, st->group.max));
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assert(0 <= min_idx && min_idx < DSSS_MATCHER_CACHE_SIZE);
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st->matcher_cache[min_idx].last_phase = group_phase;
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st->matcher_cache[min_idx].candidate_score = base_score;
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@ -276,7 +305,6 @@ void group_received(struct dsss_demod_state *st) {
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}
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}
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#if 0
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float run_iir(const float x, const int order, const struct iir_biquad q[order], struct iir_biquad_state st[order]) {
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float intermediate = x;
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for (int i=0; i<(order+1)/2; i++)
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@ -292,7 +320,6 @@ float run_biquad(float x, const struct iir_biquad *const q, struct iir_biquad_st
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st->reg[0] = intermediate;
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return out;
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}
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#endif
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float cwt_convolve_step(const float v[DSSS_WAVELET_LUT_SIZE], size_t offx) {
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float sum = 0.0f;
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@ -216,23 +216,14 @@ int main(void)
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con_printf("Booted.\r\n");
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while (23) {
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if (adc_fft_buf_ready_idx != -1) {
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for (int i=0; i<168*1000*2; i++)
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asm volatile ("nop");
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GPIOA->BSRR = 1<<11;
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//adc_fft_buf_ready_idx = !adc_fft_buf_ready_idx; /* DEBUG */
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//DEBUG:
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//memcpy(adc_fft_buf[!adc_fft_buf_ready_idx], adc_fft_buf[adc_fft_buf_ready_idx] + FMEAS_FFT_LEN/2, sizeof(adc_fft_buf[0][0]) * FMEAS_FFT_LEN/2);
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memcpy(adc_fft_buf[!adc_fft_buf_ready_idx], adc_fft_buf[adc_fft_buf_ready_idx] + FMEAS_FFT_LEN/2, sizeof(adc_fft_buf[0][0]) * FMEAS_FFT_LEN/2);
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GPIOA->BSRR = 1<<11<<16;
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for (int i=0; i<168*1000*2; i++)
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asm volatile ("nop");
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/* BEGIN DEBUG */
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con_printf_blocking("\r\n%06d: ", freq_sample_ts);
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int old_idx = adc_fft_buf_ready_idx;
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for (int i=0; i<FMEAS_FFT_LEN/2; i++)
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con_printf_blocking("%03x ", adc_fft_buf[old_idx][FMEAS_FFT_LEN/2 + i]);
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adc_fft_buf_ready_idx = -1;
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freq_sample_ts++; /* TODO: also increase in case of freq measurement error? */
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GPIOA->BSRR = 1<<11<<16;
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continue;
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/* END DEBUG */
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GPIOA->BSRR = 1<<11;
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float out;
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@ -2,9 +2,12 @@
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#define __SR_GLOBAL_H__
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#include <stdint.h>
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#include <sys/types.h>
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#ifndef SIMULATION
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#include <stm32f407xx.h>
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#include <stm32f4_isr.h>
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#include <sys/types.h>
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#endif
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#define UNUSED(x) ((void) x)
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#define ARRAY_LENGTH(x) (sizeof(x) / sizeof(x[0]))
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@ -2,7 +2,7 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -10,6 +10,7 @@
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"import csv\n",
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"import re\n",
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"import math\n",
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"import struct\n",
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"\n",
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"import numpy as np\n",
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"from matplotlib import pyplot as plt\n",
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@ -20,7 +21,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -29,7 +30,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 51,
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"execution_count": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -46,21 +47,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 64,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-64-bdef8329a3e8>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
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" fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "c040d7b8285a4444abc23ec0ef8c0d45",
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"model_id": "57f40afcd2214d0392a5fc7577843454",
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"version_major": 2,
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"version_minor": 0
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},
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@ -97,25 +90,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"def read_raw_log(fn):\n",
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" with open(fn) as f:\n",
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" vals = np.array([ int(x, 16) for line in f for x in line.partition(':')[2].split() ])\n",
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" return vals"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "f5f37f4970ae4d0fb5677c55045c6ebf",
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"model_id": "8b2507e2a7a24129acddcdbb4356f8df",
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"version_major": 2,
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"version_minor": 0
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},
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@ -131,35 +112,218 @@
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"fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n",
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"ax1, ax2, ax3, ax4 = axs.flatten()\n",
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"\n",
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"raw_50hz, raw_silence = read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_50hz_clean.log'), read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_silence_clean3.log')\n",
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"freqs_mod, freqs_clean = read_freq_log('data/meas_sig_audio_test.log'), read_freq_log('/mnt/c/Users/jaseg/shared/dsss_test_50hz_clean_improved.log')\n",
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"\n",
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"ax1.plot(raw_50hz)\n",
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"ax1.plot(freqs_mod)\n",
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"ax1.grid()\n",
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"#for x in range(0, len(raw_50hz), 128):\n",
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"# ax1.axvline(x, color='red', alpha=0.3)\n",
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"\n",
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"ax2.plot(raw_silence)\n",
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"ax2.plot(freqs_clean)\n",
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"ax2.grid()\n",
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"#for x in range(0, len(raw_silence), 128):\n",
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"# ax2.axvline(x, color='red', alpha=0.3)\n",
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"\n",
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"w = 16384\n",
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"w = 512\n",
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"\n",
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"ax3.psd(raw_50hz, w, 1e3)\n",
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"\n",
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"ax4.psd(raw_silence, w, 1e3)\n",
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"ax3.psd(freqs_mod[:80000], w, 100/128 * 10)\n",
|
||||
"ax4.psd(freqs_clean[:80000], w, 100/128 * 10)\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"with open('data/dsss_test_demod_fixed_03.bin', 'wb') as f:\n",
|
||||
" for freq in read_freq_log('data/dsss_test_demod_fixed_03.log'):\n",
|
||||
" f.write(struct.pack('f', freq))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "eba72dea09bc45ff9de68cb22b352fb3",
|
||||
"model_id": "d3a944613a184c44be006ef957770e31",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[<matplotlib.lines.Line2D at 0x7f49ad047130>]"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, ax = plt.subplots()\n",
|
||||
"ax.plot(read_freq_log('data/dsss_test_demod_fixed_03.log'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "5d366527c29840da9404ec7b1e1e0665",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(84250, 86000)"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, axs = plt.subplots(2, 1, figsize=(15, 6))\n",
|
||||
"ax1, ax2 = axs.flatten()\n",
|
||||
"\n",
|
||||
"freqs_mod = read_freq_log('data/meas_sig_audio_test.log')\n",
|
||||
"\n",
|
||||
"with open('data/meas_sig_audio_test.bin', 'wb') as f:\n",
|
||||
" for freq in freqs_mod:\n",
|
||||
" f.write(struct.pack('f', freq))\n",
|
||||
" \n",
|
||||
"with open('data/ref_sig_audio_test.bin', 'rb') as f:\n",
|
||||
" freqs_ref = np.array(list(struct.iter_unpack('f', f.read())))\n",
|
||||
"\n",
|
||||
"ax1.plot(freqs_mod)\n",
|
||||
"ax1.set_title('measured')\n",
|
||||
"ax1.grid()\n",
|
||||
"ax2.plot(freqs_ref)\n",
|
||||
"ax2.set_title('reference')\n",
|
||||
"ax2.grid()\n",
|
||||
"\n",
|
||||
"ax1.set_xlim([84250+47, 86000+47])\n",
|
||||
"ax2.set_xlim([84250, 86000])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def read_raw_log(fn):\n",
|
||||
" with open(fn) as f:\n",
|
||||
" vals = np.array([ int(x, 16) for line in f for x in line.partition(':')[2].split() ])\n",
|
||||
" return vals"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "bc4efa3809dd423482f1ef34974192c0",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-19-55f36f700399>:6: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
|
||||
" fig.tight_layout()\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# We see artifacts of the 128sp processing window in the analog readings if we process a buffer immediately after capture. We can reduce these artifacts by deferring the sampling a few milliseconds into the capture period.\n",
|
||||
"# The reason for this is presumably a suboptimal power layout on the cheap f407 devboard leading to CPU load changes dumping noise into our ADC supply.\n",
|
||||
"# In time domain plots we can see frequent high spikes in ADC counts that would be in accordance with this conjecture.\n",
|
||||
"\n",
|
||||
"fig, axs = plt.subplots(2, 2, figsize=(15, 9), sharey='row', gridspec_kw={'hspace': 0.2, 'wspace': 0.05})\n",
|
||||
"fig.tight_layout()\n",
|
||||
"ax1, ax2, ax3, ax4 = axs.flatten()\n",
|
||||
"\n",
|
||||
"raw_left, raw_right = read_raw_log('data/rawlog_silence_clean.log'), read_raw_log('data/rawlog_silence_clean3.log')\n",
|
||||
"raw_left = raw_left[26000:-10000].astype(float)\n",
|
||||
"\n",
|
||||
"raw_right = raw_right[26000:]\n",
|
||||
"raw_right = raw_right[:len(raw_left)].astype(float)\n",
|
||||
"\n",
|
||||
"raw_left -= np.mean(raw_left)\n",
|
||||
"raw_right -= np.mean(raw_right)\n",
|
||||
"\n",
|
||||
"ax1.set_title('Immediate processing')\n",
|
||||
"ax1.plot(raw_left)\n",
|
||||
"ax1.grid()\n",
|
||||
"for x in range(0, len(raw_left), 128):\n",
|
||||
" ax1.axvline(x, color='red', alpha=0.3)\n",
|
||||
"\n",
|
||||
"ax2.set_title('Deferred processing')\n",
|
||||
"ax2.plot(raw_right)\n",
|
||||
"ax2.grid()\n",
|
||||
"for x in range(0, len(raw_right), 128):\n",
|
||||
" ax2.axvline(x, color='red', alpha=0.3)\n",
|
||||
"\n",
|
||||
"ax1.set_xlim([0, 1000])\n",
|
||||
"ax1.set_ylim([-200, 200])\n",
|
||||
"ax2.set_xlim([0, 1000])\n",
|
||||
"ax2.set_ylim([-200, 200])\n",
|
||||
"ax1.set_xlabel('t [ms]')\n",
|
||||
"ax2.set_xlabel('t [ms]')\n",
|
||||
"ax1.set_ylabel('ADC reading [counts]')\n",
|
||||
"\n",
|
||||
"w = 2048\n",
|
||||
"\n",
|
||||
"ax3.psd(raw_left, w, 1e3)\n",
|
||||
"ax4.psd(raw_right, w, 1e3)\n",
|
||||
"ax3.set_ylim([-10, 26])\n",
|
||||
"ax4.set_ylim([-10, 26])\n",
|
||||
"ax4.set_ylabel(None)\n",
|
||||
"None"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "291774359d674902aad182268ec9c469",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -174,8 +338,8 @@
|
|||
"source": [
|
||||
"fig, ax = plt.subplots(figsize=(12, 6))\n",
|
||||
"\n",
|
||||
"raw_silence = read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_silence_clean.log')\n",
|
||||
"raw_silence2 = read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_silence_clean3.log')\n",
|
||||
"raw_silence = read_raw_log('data/rawlog_silence_clean.log')\n",
|
||||
"raw_silence2 = read_raw_log('data/rawlog_silence_clean3.log')\n",
|
||||
"\n",
|
||||
"raw_silence = raw_silence.reshape([-1, 128])\n",
|
||||
"le_mean = raw_silence.mean(axis=0)\n",
|
||||
|
|
@ -189,21 +353,21 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 90,
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<ipython-input-90-5a220009d359>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n"
|
||||
"<ipython-input-40-132a47f1202f>:1: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`).\n",
|
||||
" fig, axs = plt.subplots(4, 1, figsize=(15, 9), sharex=True)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "d9143b226974466aaec05a70592ac69e",
|
||||
"model_id": "2740bc857e6c4296b9ca74ada34fbb1d",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
|
|
@ -216,23 +380,119 @@
|
|||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, axs = plt.subplots(2, 2, figsize=(15, 9))\n",
|
||||
"fig, axs = plt.subplots(4, 1, figsize=(15, 9), sharex=True)\n",
|
||||
"ax1, ax2, ax3, ax4 = axs.flatten()\n",
|
||||
"\n",
|
||||
"raw_d, raw_e = read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_test_d.log'), read_raw_log('/mnt/c/Users/jaseg/shared/rawlog_test_e.log')\n",
|
||||
"#with open('data/meas_sig_audio_test_processed.log') as f:\n",
|
||||
"with open('data/meas_sig_audio_test_fixed_03.log') as f:\n",
|
||||
" lines = f.readlines()\n",
|
||||
" data = np.array([ [float(x) for x in line.split(': ')[1].split()] for line in lines if not line.startswith('GROUP:') ])\n",
|
||||
" groups = [ int(line.split(' ')[2]) for line in lines if line.startswith('GROUP:') ]\n",
|
||||
"\n",
|
||||
"ax1.plot(raw_d)\n",
|
||||
"ax1.plot(data[:,0])\n",
|
||||
"ax1.grid()\n",
|
||||
"ax1.set_xlim([4000, 16000])\n",
|
||||
"ax1.set_ylim([-0.02, 0.1])\n",
|
||||
"\n",
|
||||
"ax2.plot(raw_e)\n",
|
||||
"ax2.plot(data[:,1:-2:2])\n",
|
||||
"ax2.grid()\n",
|
||||
"ax2.set_ylim([-0.6, 0.6])\n",
|
||||
"\n",
|
||||
"w = 16384\n",
|
||||
"ax3.plot(data[:,2:-2:2])\n",
|
||||
"ax3.grid()\n",
|
||||
"\n",
|
||||
"ax3.psd(raw_d, w, 1e3)\n",
|
||||
"ax4.psd(raw_e, w, 1e3)\n",
|
||||
"#ax3.psd(raw_silence, w, 1e3)\n",
|
||||
"None"
|
||||
"ax4.plot(data[:,-2:])\n",
|
||||
"ax4.grid()\n",
|
||||
"\n",
|
||||
"for x in groups:\n",
|
||||
" ax4.axvline(x, color='red', alpha=0.4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<matplotlib.lines.Line2D at 0x7f497186d6d0>"
|
||||
]
|
||||
},
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ax4.axvline(12433)\n",
|
||||
"ax4.axvline(12164)\n",
|
||||
"ax4.axvline(12475)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"application/vnd.jupyter.widget-view+json": {
|
||||
"model_id": "cff56db4807048cda06ae8b15b0c6344",
|
||||
"version_major": 2,
|
||||
"version_minor": 0
|
||||
},
|
||||
"text/plain": [
|
||||
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, axs = plt.subplots(4, 1, figsize=(15, 9), sharex=True)\n",
|
||||
"ax1, ax2, ax3, ax4 = axs.flatten()\n",
|
||||
"\n",
|
||||
"with open('data/ref_sig_audio_test_processed.log') as f:\n",
|
||||
" lines = f.readlines()\n",
|
||||
" data = np.array([ [float(x) for x in line.split(': ')[1].split()] for line in lines if not line.startswith('GROUP:') ])\n",
|
||||
" groups = [ int(line.split(': ')[1]) for line in lines if line.startswith('GROUP:') ]\n",
|
||||
"\n",
|
||||
"ax1.plot(data[:,0])\n",
|
||||
"ax1.grid()\n",
|
||||
"ax1.set_xlim([82000, 95000])\n",
|
||||
"ax1.set_ylim([-0.02, 0.1])\n",
|
||||
"\n",
|
||||
"ax2.plot(data[:,1:-2:2])\n",
|
||||
"ax2.grid()\n",
|
||||
"ax2.set_ylim([-0.6, 0.6])\n",
|
||||
"\n",
|
||||
"ax3.plot(data[:,2:-2:2])\n",
|
||||
"ax3.grid()\n",
|
||||
"\n",
|
||||
"ax4.plot(data[:,-2:])\n",
|
||||
"ax4.grid()\n",
|
||||
"\n",
|
||||
"for x in groups:\n",
|
||||
" ax4.axvline(x, color='red', alpha=0.4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 93,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[516, 518, 4402, 4404, 5537, 7238, 12585, 17497, 17724, 34074, 34076, 41628, 44677, 46287, 49371, 49837, 53877, 54554, 56666, 56949, 60923, 67313, 67744, 67746, 68392, 79601, 84707, 84709, 84711, 85017, 85019, 85021, 85327, 85329, 85637, 85947, 85949, 85951, 86257, 86259, 86261, 86567, 86569, 86571, 86877, 86879, 86881, 87187, 87189, 87191, 87498, 87500, 87807, 87809, 87811, 88117, 88119, 88121, 88427, 88429, 88431, 88488, 88737, 88739, 88741, 89047, 89049, 89051, 89357, 89359, 89361, 89668, 89670, 89730, 89978, 89980, 90287, 90289, 90291, 90598, 90600, 90602, 90907, 90909, 90911, 91217, 91219, 91221, 91527, 91529, 91531, 91837, 91839, 91841, 92148, 92150, 92458, 92460, 92768, 92770, 93078, 93080, 93388, 93390, 93392, 93698, 93700, 93702, 94008, 94010, 94318, 94320]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(groups)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue