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Mostly working Spectrograms...
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@@ -1,1 +1,1
1 Subproject commit 698d7cfa01b05427c2377ce2799f1290b9eab2ca
1 Subproject commit 483146a07a5ffeec8f0a2d61459e94d95e851572
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@@ -3,9 +3,12
3
3
4 #include <Data/ScalarTimeSerie.h>
4 #include <Data/ScalarTimeSerie.h>
5 #include <Data/SpectrogramTimeSerie.h>
5 #include <Data/SpectrogramTimeSerie.h>
6 #include <Data/TimeSeriesUtils.h>
6 #include <Data/VectorTimeSerie.h>
7 #include <Data/VectorTimeSerie.h>
7
8
9 #include <Common/cpp_utils.h>
8 #include <Variable/Variable2.h>
10 #include <Variable/Variable2.h>
11 #include <algorithm>
9
12
10 Q_LOGGING_CATEGORY(LOG_VisualizationGraphHelper, "VisualizationGraphHelper")
13 Q_LOGGING_CATEGORY(LOG_VisualizationGraphHelper, "VisualizationGraphHelper")
11
14
@@ -321,31 +324,14 struct PlottablesUpdater<T,
321 {
324 {
322 static void setPlotYAxisRange(T& dataSeries, const DateTimeRange& xAxisRange, QCustomPlot& plot)
325 static void setPlotYAxisRange(T& dataSeries, const DateTimeRange& xAxisRange, QCustomPlot& plot)
323 {
326 {
324 // TODO
327 auto [minValue, maxValue] = dataSeries.axis_range(1);
325 // double min, max;
328 std::cout << "min=" << minValue << " max=" << maxValue << std::endl;
326 // std::tie(min, max) = dataSeries.yBounds();
327
328 // if (!std::isnan(min) && !std::isnan(max))
329 // {
330 // plot.yAxis->setRange(QCPRange { min, max });
331 // }
332 double minValue = 0., maxValue = 0.;
333 if (auto serie = dynamic_cast<SpectrogramTimeSerie*>(&dataSeries))
334 {
335 auto& yAxis = serie->axis(1);
336 if (yAxis.size())
337 {
338 minValue = *std::min_element(std::cbegin(yAxis), std::cend(yAxis));
339 maxValue = *std::max_element(std::cbegin(yAxis), std::cend(yAxis));
340 }
341 }
342 plot.yAxis->setRange(QCPRange { minValue, maxValue });
329 plot.yAxis->setRange(QCPRange { minValue, maxValue });
343 }
330 }
344
331
345 static void updatePlottables(
332 static void updatePlottables(
346 T& dataSeries, PlottablesMap& plottables, const DateTimeRange& range, bool rescaleAxes)
333 T& dataSeries, PlottablesMap& plottables, const DateTimeRange& range, bool rescaleAxes)
347 {
334 {
348 // TODO
349 if (plottables.empty())
335 if (plottables.empty())
350 {
336 {
351 qCDebug(LOG_VisualizationGraphHelper())
337 qCDebug(LOG_VisualizationGraphHelper())
@@ -353,80 +339,82 struct PlottablesUpdater<T,
353 return;
339 return;
354 }
340 }
355
341
356
342 // Gets the colormap to update (normally there is only one colormap)
357 // // Gets the colormap to update (normally there is only one colormap)
358 Q_ASSERT(plottables.size() == 1);
343 Q_ASSERT(plottables.size() == 1);
359 auto colormap = dynamic_cast<QCPColorMap*>(plottables.at(0));
344 auto colormap = dynamic_cast<QCPColorMap*>(plottables.at(0));
360 Q_ASSERT(colormap != nullptr);
345 Q_ASSERT(colormap != nullptr);
346 auto plot = colormap->parentPlot();
347 auto [minValue, maxValue] = dataSeries.axis_range(1);
348 plot->yAxis->setRange(QCPRange { minValue, maxValue });
361 if (auto serie = dynamic_cast<SpectrogramTimeSerie*>(&dataSeries))
349 if (auto serie = dynamic_cast<SpectrogramTimeSerie*>(&dataSeries))
362 {
350 {
363 colormap->data()->setSize(serie->shape()[0], serie->shape()[1]);
351 if (serie->size(0) > 2)
364 if (serie->size(0))
365 {
352 {
353 const auto& xAxis = serie->axis(0);
354 auto yAxis = serie->axis(1); // copy for in place reverse order
355 std::reverse(std::begin(yAxis), std::end(yAxis));
356 auto xAxisProperties = TimeSeriesUtils::axis_analysis<TimeSeriesUtils::IsLinear,
357 TimeSeriesUtils::CheckMedian>(xAxis);
358 auto yAxisProperties = TimeSeriesUtils::axis_analysis<TimeSeriesUtils::IsLog,
359 TimeSeriesUtils::DontCheckMedian>(yAxis);
360
361 int colormap_h_size = std::min(32000,
362 static_cast<int>(xAxisProperties.range / xAxisProperties.max_resolution));
363 auto colormap_v_size
364 = static_cast<int>(yAxisProperties.range / yAxisProperties.max_resolution);
365
366 colormap->data()->setSize(colormap_h_size, colormap_v_size);
366 colormap->data()->setRange(
367 colormap->data()->setRange(
367 QCPRange { serie->begin()->t(), (serie->end() - 1)->t() },
368 QCPRange { serie->begin()->t(), (serie->end() - 1)->t() },
368 QCPRange { 1., 1000. });
369 { minValue, maxValue });
369 for (int x_index = 0; x_index < serie->shape()[0]; x_index++)
370
371 std::vector<std::pair<int, int>> y_access_pattern;
372 for (int y_index = 0, cel_index = 0; y_index < colormap_v_size; y_index++)
370 {
373 {
371 auto pixline = (*serie)[x_index];
374 double current_y = pow(
372 for (int y_index = 0; y_index < serie->shape()[1]; y_index++)
375 10., (yAxisProperties.max_resolution * y_index) + std::log10(minValue));
376 if (current_y > yAxis[cel_index])
377 cel_index++;
378 y_access_pattern.push_back({ y_index, yAxis.size() - 1 - cel_index });
379 }
380
381 auto line = serie->begin();
382 double current_time = xAxis[0];
383 int x_index = 0;
384
385 while (x_index < colormap_h_size)
386 {
387 if (current_time > (line + 1)->t())
373 {
388 {
374 auto value = pixline[y_index];
389 line++;
375 colormap->data()->setCell(x_index, y_index, value);
390 }
376 if (std::isnan(value))
391 if ((current_time - xAxis[0])
392 > (x_index * xAxisProperties.range / colormap_h_size))
393 {
394 x_index++;
395 }
396 if (line->t() <= (current_time + xAxisProperties.max_resolution))
397 {
398 std::for_each(std::cbegin(y_access_pattern), std::cend(y_access_pattern),
399 [&colormap, &line, x_index](const auto& acc) {
400 colormap->data()->setCell(x_index, acc.first, (*line)[acc.second]);
401 });
402 }
403 else
404 {
405 for (int y_index = 0; y_index < colormap_v_size; y_index++)
377 {
406 {
378 colormap->data()->setAlpha(x_index, y_index, 0);
407 colormap->data()->setCell(x_index, y_index, std::nan(""));
379 }
408 }
380 }
409 }
410 current_time += xAxisProperties.max_resolution;
381 }
411 }
382 }
412 }
383 }
413
384 // dataSeries.lockRead();
414 if (rescaleAxes)
385
415 {
386 // // Processing spectrogram data for display in QCustomPlot
416 plot->rescaleAxes();
387 // auto its = dataSeries.xAxisRange(range.m_TStart, range.m_TEnd);
417 }
388
389 // // Computes logarithmic y-axis resolution for the spectrogram
390 // auto yData = its.first->y();
391 // auto yResolution = DataSeriesUtils::resolution(yData.begin(), yData.end(), true);
392
393 // // Generates mesh for colormap
394 // auto mesh = DataSeriesUtils::regularMesh(its.first, its.second,
395 // DataSeriesUtils::Resolution { dataSeries.xResolution() }, yResolution);
396
397 // dataSeries.unlock();
398
399 // colormap->data()->setSize(mesh.m_NbX, mesh.m_NbY);
400 // if (!mesh.isEmpty())
401 // {
402 // colormap->data()->setRange(QCPRange { mesh.m_XMin, mesh.xMax() },
403 // // y-axis range is converted to linear values
404 // QCPRange { std::pow(10, mesh.m_YMin), std::pow(10, mesh.yMax()) });
405
406 // // Sets values
407 // auto index = 0;
408 // for (auto it = mesh.m_Data.begin(), end = mesh.m_Data.end(); it != end; ++it,
409 // ++index)
410 // {
411 // auto xIndex = index % mesh.m_NbX;
412 // auto yIndex = index / mesh.m_NbX;
413
414 // colormap->data()->setCell(xIndex, yIndex, *it);
415
416 // // Makes the NaN values to be transparent in the colormap
417 // if (std::isnan(*it))
418 // {
419 // colormap->data()->setAlpha(xIndex, yIndex, 0);
420 // }
421 // }
422 // }
423
424 // // Rescales axes
425 auto plot = colormap->parentPlot();
426 setPlotYAxisRange(dataSeries, {}, *plot);
427 if (rescaleAxes)
428 {
429 plot->rescaleAxes();
430 }
418 }
431 }
419 }
432 };
420 };
@@ -444,7 +432,8 struct IPlottablesHelper
444 };
432 };
445
433
446 /**
434 /**
447 * Default implementation of IPlottablesHelper, which takes data series to create/update plottables
435 * Default implementation of IPlottablesHelper, which takes data series to create/update
436 * plottables
448 * @tparam T the data series' type
437 * @tparam T the data series' type
449 */
438 */
450 template <typename T>
439 template <typename T>
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@@ -10,9 +10,42 from spwc.amda import AMDA
10
10
11 amda = AMDA()
11 amda = AMDA()
12
12
13 def get_sample(metadata,start,stop):
13 def amda_make_scalar(var=None):
14 ts_type = pysciqlopcore.ScalarTimeSerie
14 if var is None:
15 default_ctor_args = 1
15 return pysciqlopcore.ScalarTimeSerie(1)
16 else:
17 return pysciqlopcore.ScalarTimeSerie(var.time,var.data)
18
19 def amda_make_vector(var=None):
20 if var is None:
21 return pysciqlopcore.VectorTimeSerie(1)
22 else:
23 return pysciqlopcore.VectorTimeSerie(var.time,var.data)
24
25 def amda_make_multi_comp(var=None):
26 if var is None:
27 return pysciqlopcore.MultiComponentTimeSerie((0,2))
28 else:
29 return pysciqlopcore.MultiComponentTimeSerie(var.time,var.data)
30
31 def amda_make_spectro(var=None):
32 if var is None:
33 return pysciqlopcore.SpectrogramTimeSerie((0,2))
34 else:
35 if "PARAMETER_TABLE_MIN_VALUES[1]" in var.meta:
36 min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[1]"].split(',') ])
37 max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[1]"].split(',') ])
38 y = (max_v + min_v)/2.
39 elif "PARAMETER_TABLE_MIN_VALUES[0]" in var.meta:
40 min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[0]"].split(',') ])
41 max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[0]"].split(',') ])
42 y = (max_v + min_v)/2.
43 else:
44 y = np.logspace(1,3,var.data.shape[1])[::-1]
45 return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data)
46
47 def amda_get_sample(metadata,start,stop):
48 ts_type = amda_make_scalar
16 try:
49 try:
17 param_id = None
50 param_id = None
18 for key,value in metadata:
51 for key,value in metadata:
@@ -20,18 +53,19 def get_sample(metadata,start,stop):
20 param_id = value
53 param_id = value
21 elif key == 'type':
54 elif key == 'type':
22 if value == 'vector':
55 if value == 'vector':
23 ts_type = pysciqlopcore.VectorTimeSerie
56 ts_type = amda_make_vector
24 elif value == 'multicomponent':
57 elif value == 'multicomponent':
25 ts_type = pysciqlopcore.MultiComponentTimeSerie
58 ts_type = amda_make_multi_comp
26 default_ctor_args = (0,2)
59 elif value == 'spectrogram':
60 ts_type = amda_make_spectro
27 tstart=datetime.fromtimestamp(start, tz=timezone.utc)
61 tstart=datetime.fromtimestamp(start, tz=timezone.utc)
28 tend=datetime.fromtimestamp(stop, tz=timezone.utc)
62 tend=datetime.fromtimestamp(stop, tz=timezone.utc)
29 var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST")
63 var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST")
30 return ts_type(var.time,var.data)
64 return ts_type(var)
31 except Exception as e:
65 except Exception as e:
32 print(traceback.format_exc())
66 print(traceback.format_exc())
33 print("Error in amda.py ",str(e))
67 print("Error in amda.py ",str(e))
34 return ts_type(default_ctor_args)
68 return ts_type()
35
69
36
70
37 if len(amda.component) is 0:
71 if len(amda.component) is 0:
@@ -49,17 +83,16 for key,parameter in parameters.items():
49 components = [component['name'] for component in parameter.get('components',[])]
83 components = [component['name'] for component in parameter.get('components',[])]
50 metadata = [ (key,item) for key,item in parameter.items() if key is not 'components' ]
84 metadata = [ (key,item) for key,item in parameter.items() if key is not 'components' ]
51 n_components = parameter.get('size',0)
85 n_components = parameter.get('size',0)
52 if n_components is '3':
86 if n_components == '3':
53 metadata.append(("type","vector"))
87 metadata.append(("type","vector"))
88 elif parameter.get('display_type','')=="spectrogram":
89 metadata.append(("type","spectrogram"))
54 elif n_components !=0:
90 elif n_components !=0:
55 if parameter.get('display_type','')=="spectrogram":
91 metadata.append(("type","multicomponent"))
56 metadata.append(("type","spectrogram"))
57 else:
58 metadata.append(("type","multicomponent"))
59 else:
92 else:
60 metadata.append(("type","scalar"))
93 metadata.append(("type","scalar"))
61 products.append( (path, components, metadata))
94 products.append( (path, components, metadata))
62
95
63 PythonProviders.register_product(products, get_sample)
96 PythonProviders.register_product(products, amda_get_sample)
64
97
65
98
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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),values)
67 ts_type = lambda t,values: pysciqlopcore.SpectrogramTimeSerie(t,np.logspace(1,3,32)[::-1],values)
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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