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Some refac on Spectrograms...
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@@ -1,1 +1,1
1 Subproject commit 70d6748a06620f865b683c86c89cc27e1cb0d4b9
1 Subproject commit c6cf2dba079da680390ae2de0522a39964c5e629
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@@ -337,21 +337,112 inline ColomapProperties CMAxisAnalysis(const TimeSeriesUtils::axis_properties&
337 colormap_v_resolution };
337 colormap_v_resolution };
338 }
338 }
339
339
340
341 template <bool condition, typename T, typename U>
342 std::enable_if_t<condition, T> constexpr conditional_v(T first, U second)
343 {
344 return first;
345 }
346
347 template <bool condition, typename T, typename U>
348 std::enable_if_t<!condition, U> constexpr conditional_v(T first, U second)
349 {
350 return second;
351 }
352
353 template <bool reversedAxis = true, bool reversedData = true>
340 inline std::vector<std::pair<int, int>> build_access_pattern(const std::vector<double>& axis,
354 inline std::vector<std::pair<int, int>> build_access_pattern(const std::vector<double>& axis,
341 const TimeSeriesUtils::axis_properties& axisProperties,
355 const TimeSeriesUtils::axis_properties& axisProperties,
342 const ColomapProperties& colormap_properties)
356 const ColomapProperties& colormap_properties)
343 {
357 {
344 std::vector<std::pair<int, int>> access_pattern;
358 std::vector<std::pair<int, int>> access_pattern;
345 for (int index = 0, cel_index = axis.size() - 1; index < colormap_properties.v_size_px; index++)
359 for (int index = 0, axis_index = conditional_v<reversedAxis>(axis.size() - 1, 0),
360 data_index = conditional_v<reversedData>(axis.size() - 1, 0);
361 index < colormap_properties.v_size_px; index++)
346 {
362 {
347 double current_y = pow(10., (axisProperties.max_resolution * index) + axisProperties.min);
363 double current_y = (axisProperties.max_resolution * index) + axisProperties.min;
348 if (current_y > axis[cel_index])
364 if (current_y > axis[axis_index])
349 cel_index--;
365 {
350 access_pattern.push_back({ index, cel_index });
366 conditional_v<reversedAxis>(
367 [&axis_index]() { axis_index--; }, [&axis_index]() { axis_index++; })();
368 conditional_v<reversedData>(
369 [&data_index]() { data_index--; }, [&data_index]() { data_index++; })();
370 }
371 access_pattern.push_back({ index, data_index });
351 }
372 }
352 return access_pattern;
373 return access_pattern;
353 }
374 }
354
375
376 inline bool is_log(const std::vector<double>& axis)
377 {
378 if (axis.size() > 2)
379 {
380 auto first = axis.front(), midle = axis[axis.size() / 2], last = axis.back();
381 auto error_linear = (midle - (last + first) / 2) / midle;
382 first = log10(first);
383 midle = log10(midle);
384 last = log10(last);
385 auto error_log = (midle - (last + first) / 2) / midle;
386 return error_log < error_linear;
387 }
388 return false;
389 }
390
391 template <typename accessPattern_t, typename colomapT, typename axProp_t, typename cmProp_t>
392 inline void fill_data(SpectrogramTimeSerie* serie, colomapT* colormap,
393 const accessPattern_t& y_access_pattern, const axProp_t& xAxisProperties,
394 const cmProp_t& colormap_properties)
395 {
396 auto line = serie->begin();
397 auto next_line = line + 1;
398 double current_time = xAxisProperties.min;
399 int x_index = 0;
400 auto x_min_resolution
401 = std::fmin(2. * serie->max_sampling, xAxisProperties.max_resolution * 100.);
402 std::vector<double> line_values(serie->size(1));
403 double avg_coef = 0.;
404 while (x_index < colormap_properties.h_size_px)
405 {
406 if (next_line != std::end(*serie) and current_time >= next_line->t())
407 {
408 line = next_line;
409 next_line++;
410 }
411 if ((current_time - xAxisProperties.min)
412 > (static_cast<double>(x_index + 1) * colormap_properties.h_resolutuon))
413 {
414 std::for_each(std::cbegin(y_access_pattern), std::cend(y_access_pattern),
415 [&colormap, &line_values, x_index, avg_coef](const auto& acc) {
416 colormap->data()->setCell(
417 x_index, acc.first, line_values[acc.second] / avg_coef);
418 });
419 std::fill(std::begin(line_values), std::end(line_values), 0.);
420 x_index++;
421 avg_coef = 0.;
422 }
423 if (line->t() + x_min_resolution > current_time)
424 {
425 {
426 std::transform(std::begin(*line), std::end(*line), std::cbegin(line_values),
427 std::begin(line_values),
428 [](const auto& input, auto output) { return input.v() + output; });
429 }
430 avg_coef += 1.;
431 }
432 else
433 {
434 for (int y_index = 0; y_index < colormap_properties.v_size_px; y_index++)
435 {
436 if (avg_coef > 0.)
437 {
438 std::fill(std::begin(line_values), std::end(line_values), 0);
439 }
440 }
441 }
442 current_time += xAxisProperties.max_resolution * 0.9;
443 }
444 }
445
355 /*=============================================================*/
446 /*=============================================================*/
356
447
357 /**
448 /**
@@ -365,7 +456,6 struct PlottablesUpdater<T,
365 static void setPlotYAxisRange(T& dataSeries, const DateTimeRange& xAxisRange, QCustomPlot& plot)
456 static void setPlotYAxisRange(T& dataSeries, const DateTimeRange& xAxisRange, QCustomPlot& plot)
366 {
457 {
367 auto [minValue, maxValue] = dataSeries.axis_range(1);
458 auto [minValue, maxValue] = dataSeries.axis_range(1);
368 std::cout << "min=" << minValue << " max=" << maxValue << std::endl;
369 plot.yAxis->setRange(QCPRange { minValue, maxValue });
459 plot.yAxis->setRange(QCPRange { minValue, maxValue });
370 }
460 }
371
461
@@ -390,8 +480,13 struct PlottablesUpdater<T,
390 {
480 {
391 if (serie->size(0) > 2)
481 if (serie->size(0) > 2)
392 {
482 {
483 if (serie->y_is_log)
484 colormap->setDataScaleType(QCPAxis::stLogarithmic);
485 else
486 colormap->setDataScaleType(QCPAxis::stLinear);
393 const auto& xAxis = serie->axis(0);
487 const auto& xAxis = serie->axis(0);
394 auto yAxis = serie->axis(1); // copy for in place reverse order
488 auto yAxis = serie->axis(1); // copy for in place reverse order
489 auto y_is_log = is_log(yAxis);
395 std::reverse(std::begin(yAxis), std::end(yAxis));
490 std::reverse(std::begin(yAxis), std::end(yAxis));
396 auto xAxisProperties = TimeSeriesUtils::axis_analysis<TimeSeriesUtils::IsLinear,
491 auto xAxisProperties = TimeSeriesUtils::axis_analysis<TimeSeriesUtils::IsLinear,
397 TimeSeriesUtils::CheckMedian>(xAxis, serie->min_sampling);
492 TimeSeriesUtils::CheckMedian>(xAxis, serie->min_sampling);
@@ -404,57 +499,9 struct PlottablesUpdater<T,
404 colormap->data()->setRange(
499 colormap->data()->setRange(
405 QCPRange { xAxisProperties.min, xAxisProperties.max }, { minValue, maxValue });
500 QCPRange { xAxisProperties.min, xAxisProperties.max }, { minValue, maxValue });
406
501
407 auto y_access_pattern
502 auto y_access_pattern = build_access_pattern<false, true>(
408 = build_access_pattern(serie->axis(1), yAxisProperties, colormap_properties);
503 yAxis, yAxisProperties, colormap_properties);
409
504 fill_data(serie, colormap, y_access_pattern, xAxisProperties, colormap_properties);
410 auto line = serie->begin();
411 auto next_line = line + 1;
412 double current_time = xAxisProperties.min;
413 int x_index = 0;
414 auto x_min_resolution
415 = std::fmin(2. * serie->max_sampling, xAxisProperties.max_resolution * 100.);
416 std::vector<double> line_values(serie->size(1));
417 double avg_coef = 0.;
418 while (x_index < colormap_properties.h_size_px)
419 {
420 if (next_line != std::end(*serie) and current_time >= next_line->t())
421 {
422 line = next_line;
423 next_line++;
424 }
425 if ((current_time - xAxisProperties.min)
426 > (static_cast<double>(x_index + 1) * colormap_properties.h_resolutuon))
427 {
428 std::for_each(std::cbegin(y_access_pattern), std::cend(y_access_pattern),
429 [&colormap, &line_values, x_index, avg_coef](const auto& acc) {
430 colormap->data()->setCell(
431 x_index, acc.first, line_values[acc.second] / avg_coef);
432 });
433 std::fill(std::begin(line_values), std::end(line_values), 0.);
434 x_index++;
435 avg_coef = 0.;
436 }
437 if (line->t() + x_min_resolution > current_time)
438 {
439 {
440 std::transform(std::begin(*line), std::end(*line),
441 std::cbegin(line_values), std::begin(line_values),
442 [](const auto& input, auto output) { return input.v() + output; });
443 }
444 avg_coef += 1.;
445 }
446 else
447 {
448 for (int y_index = 0; y_index < colormap_properties.v_size_px; y_index++)
449 {
450 if (avg_coef > 0.)
451 {
452 std::fill(std::begin(line_values), std::end(line_values), 0);
453 }
454 }
455 }
456 current_time += xAxisProperties.max_resolution * 0.9;
457 }
458 }
505 }
459 colormap->rescaleDataRange(true);
506 colormap->rescaleDataRange(true);
460 if (rescaleAxes)
507 if (rescaleAxes)
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@@ -44,7 +44,7 def amda_make_spectro(var=None):
44 y = (max_v + min_v)/2.
44 y = (max_v + min_v)/2.
45 else:
45 else:
46 y = np.logspace(1,3,var.data.shape[1])[::-1]
46 y = np.logspace(1,3,var.data.shape[1])[::-1]
47 return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data,min_sampling,max_sampling)
47 return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data,min_sampling,max_sampling,True)
48
48
49 def amda_get_sample(metadata,start,stop):
49 def amda_get_sample(metadata,start,stop):
50 ts_type = amda_make_scalar
50 ts_type = amda_make_scalar
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@@ -64,7 +64,7 def get_data(metadata,start,stop):
64 ts_type = pysciqlopcore.MultiComponentTimeSerie
64 ts_type = pysciqlopcore.MultiComponentTimeSerie
65 default_ctor_args = (0,2)
65 default_ctor_args = (0,2)
66 elif value == 'spectrogram':
66 elif value == 'spectrogram':
67 ts_type = lambda t,values: pysciqlopcore.SpectrogramTimeSerie(t,np.logspace(1,3,32)[::-1],values,np.nan,np.nan)
67 ts_type = lambda t,values: pysciqlopcore.SpectrogramTimeSerie(t,np.logspace(1,3,32)[::-1],values,np.nan,np.nan,True)
68 default_ctor_args = (0,2)
68 default_ctor_args = (0,2)
69 if key == 'cache' and value == 'true':
69 if key == 'cache' and value == 'true':
70 use_cache = True
70 use_cache = True
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