HG_REPOSITORIES/LPP/SciQLOP_Repos/SciQLop Commit - r1467:da44adcd99e4

Spectrogram segfault should be fixed now, added ability to provide Spectrogram min sampling time to avoid computation when possible...

jeandet -

r1467:da44adcd99e4

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r1467:da44adcd99e4

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core +1 -1

		@@ -1,1 +1,1
1		Subproject commit 483146a07a5ffeec8f0a2d61459e94d95e851572
	1	Subproject commit 1e3f92d40b0ec24443e067aaa9e7bca385ec27b2

gui/src/TimeWidget/TimeWidget.cpp +1 -1

              void TimeWidget::onTimeUpdateRequested()
              {
                  auto dateTime = timeRange();
-                 emit timeUpdated(std::move(dateTime));
+                 emit timeUpdated(dateTime);
                  sqpApp->timeController().setDateTimeRange(dateTime);
              }

gui/src/Visualization/VisualizationGraphHelper.cpp +92 -34

              #include <Common/cpp_utils.h>
              #include <Variable/Variable2.h>
              #include <algorithm>
+             #include <cmath>
              Q_LOGGING_CATEGORY(LOG_VisualizationGraphHelper, "VisualizationGraphHelper")
                      double minValue = 0., maxValue = 0.;
                      if (auto serie = dynamic_cast<MultiComponentTimeSerie*>(&dataSeries))
                      {
-                         std::for_each(
-                             std::begin(*serie), std::end(*serie), [&minValue, &maxValue](const auto& v) {
+                         std::for_each(std::begin(*serie), std::end(*serie), [&minValue, &maxValue](auto& v) {
-                                 minValue = std::min(minValue, std::min_element(v.begin(), v.end())->v());
-                                 maxValue = std::max(maxValue, std::max_element(v.begin(), v.end())->v());
-                             });
                  }
              };
+             /*=============================================================*/
+             // TODO move this to dedicated srcs
+             /*=============================================================*/
+             struct ColomapProperties
+             {
+                 int h_size_px;
+                 int v_size_px;
+                 double h_resolutuon;
+                 double v_resolutuon;
+             };
+             inline ColomapProperties CMAxisAnalysis(const TimeSeriesUtils::axis_properties& xAxisProperties,
+                 const TimeSeriesUtils::axis_properties& yAxisProperties)
+             {
+                 int colormap_h_size
+                     = std::min(32000, static_cast<int>(xAxisProperties.range / xAxisProperties.max_resolution));
+                 int colormap_v_size = static_cast<int>(yAxisProperties.range / yAxisProperties.max_resolution);
+                 double colormap_h_resolution = xAxisProperties.range / static_cast<double>(colormap_h_size);
+                 double colormap_v_resolution = yAxisProperties.range / static_cast<double>(colormap_v_size);
+                 return ColomapProperties { colormap_h_size, colormap_v_size, colormap_h_resolution,
+                     colormap_v_resolution };
+             }
+             inline std::vector<std::pair<int, int>> build_access_pattern(const std::vector<double>& axis,
+                 const TimeSeriesUtils::axis_properties& axisProperties,
+                 const ColomapProperties& colormap_properties)
+             {
+                 std::vector<std::pair<int, int>> access_pattern;
+                 for (int index = 0, cel_index = 0; index < colormap_properties.v_size_px; index++)
+                 {
+                     double current_y = pow(10., (axisProperties.max_resolution * index) + axisProperties.min);
+                     if (current_y > axis[cel_index])
+                         cel_index++;
+                     access_pattern.push_back({ index, axis.size() - 1 - cel_index });
+                 }
+                 return access_pattern;
+             }
+             /*=============================================================*/
              /**
               * Specialization of PlottablesUpdater for spectrograms
               * @sa SpectrogramSeries
                              auto yAxis = serie->axis(1); // copy for in place reverse order
                              std::reverse(std::begin(yAxis), std::end(yAxis));
                              auto xAxisProperties = TimeSeriesUtils::axis_analysis<TimeSeriesUtils::IsLinear,
-                                 TimeSeriesUtils::CheckMedian>(xAxis);
+                                 TimeSeriesUtils::CheckMedian>(xAxis, serie->min_sampling);
                              auto yAxisProperties = TimeSeriesUtils::axis_analysis<TimeSeriesUtils::IsLog,
                                  TimeSeriesUtils::DontCheckMedian>(yAxis);
+                             auto colormap_properties = CMAxisAnalysis(xAxisProperties, yAxisProperties);
-                             int colormap_h_size = std::min(32000,
-                                 static_cast<int>(xAxisProperties.range / xAxisProperties.max_resolution));
-                             auto colormap_v_size
-                                 = static_cast<int>(yAxisProperties.range / yAxisProperties.max_resolution);
-                             colormap->data()->setSize(colormap_h_size, colormap_v_size);
+                             colormap->data()->setSize(
+                                 colormap_properties.h_size_px, colormap_properties.v_size_px);
                              colormap->data()->setRange(
-                                 QCPRange { serie->begin()->t(), (serie->end() - 1)->t() },
-                                 { minValue, maxValue });
+                                 QCPRange { xAxisProperties.min, xAxisProperties.max }, { minValue, maxValue });
-                             std::vector<std::pair<int, int>> y_access_pattern;
-                             for (int y_index = 0, cel_index = 0; y_index < colormap_v_size; y_index++)
+                             {
-                                 double current_y = pow(
-., (yAxisProperties.max_resolution * y_index) + std::log10(minValue));
-                                 if (current_y > yAxis[cel_index])
-                                     cel_index++;
-                                 y_access_pattern.push_back({ y_index, yAxis.size() - 1 - cel_index });
+                             }
+                             auto y_access_pattern
+                                 = build_access_pattern(yAxis, yAxisProperties, colormap_properties);
                              auto line = serie->begin();
-                             double current_time = xAxis[0];
+                             auto next_line = line + 1;
+                             double current_time = xAxisProperties.min;
                              int x_index = 0;
-                             while (x_index < colormap_h_size)
+                             auto x_min_resolution
+                                 = std::fmin(2. * serie->max_sampling, xAxisProperties.max_resolution * 100.);
+                             std::vector<double> line_values(serie->size(1));
+                             double avg_coef = 0.;
+                             bool has_nan = false;
+                             while (x_index < colormap_properties.h_size_px)
                              {
-                                 if (current_time > (line + 1)->t())
+                                 if (next_line != std::end(*serie) and current_time >= next_line->t())
                                  {
-                                     line++;
+                                     line = next_line;
+                                     next_line++;
                                  }
-                                 if ((current_time - xAxis[0])
-                                     > (x_index * xAxisProperties.range / colormap_h_size))
+                                 if ((current_time - xAxisProperties.min)
+                                     > (static_cast<double>(x_index + 1) * colormap_properties.h_resolutuon))
                                  {
+                                     std::for_each(std::cbegin(y_access_pattern), std::cend(y_access_pattern),
+                                         [&colormap, &line_values, x_index, avg_coef](const auto& acc) {
+                                             colormap->data()->setCell(
+                                                 x_index, acc.first, line_values[acc.second] / avg_coef);
+                                         });
+                                     std::fill(std::begin(line_values), std::end(line_values), 0.);
                                      x_index++;
+                                     avg_coef = 0.;
+                                     has_nan = false;
                                  }
-                                 if (line->t() <= (current_time + xAxisProperties.max_resolution))
+                                 if (line->t() + x_min_resolution > current_time)
                                  {
-                                     std::for_each(std::cbegin(y_access_pattern), std::cend(y_access_pattern),
-                                         [&colormap, &line, x_index](const auto& acc) {
-                                             colormap->data()->setCell(x_index, acc.first, (*line)[acc.second]);
-                                         });
+                                     if (has_nan)
+                                     {
+                                         std::transform(std::begin(*line), std::end(*line),
+                                             std::begin(line_values),
+                                             [](const auto& input) { return input.v(); });
+                                         has_nan = false;
                                  }
                                  else
                                  {
-                                     for (int y_index = 0; y_index < colormap_v_size; y_index++)
+                                         std::transform(std::begin(*line), std::end(*line),
+                                             std::cbegin(line_values), std::begin(line_values),
+                                             [](const auto& input, auto output) { return input.v() + output; });
+                                     }
+                                     avg_coef += 1.;
+                                 }
+                                 else
-                                     {
-                                         colormap->data()->setCell(x_index, y_index, std::nan(""));
+                                     for (int y_index = 0; y_index < colormap_properties.v_size_px; y_index++)
+                                     {
+                                         if (avg_coef > 0.)
+                                         {
+                                             has_nan = true;
+                                             std::fill(
+                                                 std::begin(line_values), std::end(line_values), std::nan(""));
+                                         }
                                      }
                                  }
                                  current_time += xAxisProperties.max_resolution;

plugins/python_providers/resources/amda.py +3 -1

                  if var is None:
                      return pysciqlopcore.SpectrogramTimeSerie((0,2))
                  else:
+                     min_sampling = float(var.meta.get("DATASET_MIN_SAMPLING","nan"))
+                     max_sampling = float(var.meta.get("DATASET_MAX_SAMPLING","nan"))
                      if "PARAMETER_TABLE_MIN_VALUES[1]" in var.meta:
                          min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[1]"].split(',') ])
                          max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[1]"].split(',') ])
                          y = (max_v + min_v)/2.
                      else:
                          y = np.logspace(1,3,var.data.shape[1])[::-1]
-                     return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data)
+                     return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data,min_sampling,max_sampling)
              def amda_get_sample(metadata,start,stop):
                  ts_type = amda_make_scalar

plugins/python_providers/resources/test.py +1 -1

                                  ts_type = pysciqlopcore.MultiComponentTimeSerie
                                  default_ctor_args = (0,2)
                              elif value == 'spectrogram':
-                                 ts_type = lambda t,values: pysciqlopcore.SpectrogramTimeSerie(t,np.logspace(1,3,32)[::-1],values)
+                                 ts_type = lambda t,values: pysciqlopcore.SpectrogramTimeSerie(t,np.logspace(1,3,32)[::-1],values,np.nan,np.nan)
                                  default_ctor_args = (0,2)
                          if key == 'cache' and value == 'true':
                              use_cache = True

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