HG_REPOSITORIES/LPP/SciQLOP_Repos/SciQLop Commit - r1464:dce5077d4598

Some added fake specro and switched to new spwc getting rid of DataFrames...

jeandet -

r1464:dce5077d4598

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r1464:dce5077d4598

core +1 -1

	@@ -1,1 +1,1
	1	Subproject commit 39bf3ff40b41fc01170241f3e471c708c866118b		1	Subproject commit 698d7cfa01b05427c2377ce2799f1290b9eab2ca

gui/src/Visualization/VisualizationGraphHelper.cpp +50 -17

@@ -329,25 +329,58 struct PlottablesUpdater<T,
329	// {	329	// {
330	// plot.yAxis->setRange(QCPRange { min, max });	330	// plot.yAxis->setRange(QCPRange { min, max });
331	// }	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 });
332	}	343	}
333		344
334	static void updatePlottables(	345	static void updatePlottables(
335	T& dataSeries, PlottablesMap& plottables, const DateTimeRange& range, bool rescaleAxes)	346	T& dataSeries, PlottablesMap& plottables, const DateTimeRange& range, bool rescaleAxes)
336	{	347	{
337	// TODO	348	// TODO
338	// if (plottables.empty())	349	if (plottables.empty())
339	// {	350	{
340	// qCDebug(LOG_VisualizationGraphHelper())	351	qCDebug(LOG_VisualizationGraphHelper())
341	// << QObject::tr("Can't update spectrogram: no colormap has been	352	<< QObject::tr("Can't update spectrogram: no colormap has been associated");
342	// associated");	353	return;
343	// return;	354	}
344	// }
345		355
346	// // Gets the colormap to update (normally there is only one colormap)
347	// Q_ASSERT(plottables.size() == 1);
348	// auto colormap = dynamic_cast<QCPColorMap*>(plottables.at(0));
349	// Q_ASSERT(colormap != nullptr);
350		356
		357	// // Gets the colormap to update (normally there is only one colormap)
		358	Q_ASSERT(plottables.size() == 1);
		359	auto colormap = dynamic_cast<QCPColorMap*>(plottables.at(0));
		360	Q_ASSERT(colormap != nullptr);
		361	if (auto serie = dynamic_cast<SpectrogramTimeSerie*>(&dataSeries))
		362	{
		363	colormap->data()->setSize(serie->shape()[0], serie->shape()[1]);
		364	if (serie->size(0))
		365	{
		366	colormap->data()->setRange(
		367	QCPRange { serie->begin()->t(), (serie->end() - 1)->t() },
		368	QCPRange { 1., 1000. });
		369	for (int x_index = 0; x_index < serie->shape()[0]; x_index++)
		370	{
		371	auto pixline = (*serie)[x_index];
		372	for (int y_index = 0; y_index < serie->shape()[1]; y_index++)
		373	{
		374	auto value = pixline[y_index];
		375	colormap->data()->setCell(x_index, y_index, value);
		376	if (std::isnan(value))
		377	{
		378	colormap->data()->setAlpha(x_index, y_index, 0);
		379	}
		380	}
		381	}
		382	}
		383	}
351	// dataSeries.lockRead();	384	// dataSeries.lockRead();
352		385
353	// // Processing spectrogram data for display in QCustomPlot	386	// // Processing spectrogram data for display in QCustomPlot
@@ -389,12 +422,12 struct PlottablesUpdater<T,
389	// }	422	// }
390		423
391	// // Rescales axes	424	// // Rescales axes
392	// auto plot = colormap->parentPlot();	425	auto plot = colormap->parentPlot();
393		426	setPlotYAxisRange(dataSeries, {}, *plot);
394	// if (rescaleAxes)	427	if (rescaleAxes)
395	// {	428	{
396	// plot->rescaleAxes();	429	plot->rescaleAxes();
397	// }	430	}
398	}	431	}
399	};	432	};
400		433

plugins/python_providers/resources/amda.py +5 -5

             import PythonProviders
             import pysciqlopcore
             import numpy as np
-            import pandas as pds
             import requests
             import copy
             from spwc.amda import AMDA
                                 default_ctor_args = (0,2)
                     tstart=datetime.fromtimestamp(start, tz=timezone.utc)
                     tend=datetime.fromtimestamp(stop, tz=timezone.utc)
-                    df = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST")
+                    var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST")
-                    t = np.array([d.timestamp() for d in df.index])
+                    return ts_type(var.time,var.data)
-                    values = df.values
-                    return ts_type(t,values)
                 except Exception as e:
                     print(traceback.format_exc())
                     print("Error in amda.py ",str(e))
                 if n_components is '3':
                     metadata.append(("type","vector"))
                 elif n_components !=0:
+                    if parameter.get('display_type','')=="spectrogram":
+                        metadata.append(("type","spectrogram"))
+                    else:
                         metadata.append(("type","multicomponent"))
                 else:
                     metadata.append(("type","scalar"))

plugins/python_providers/resources/test.py +19 -10

             from spwc.common.datetime_range import DateTimeRange
             from functools import partial
             from datetime import datetime, timedelta, timezone
+            from spwc.common.variable import SpwcVariable
-            someglobal = 1
             def make_scalar(x):
                 y = np.cos(x/10.)
-                return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=y)
+                return SpwcVariable(time=x, data=y)
             def make_vector(x):
                 v=np.ones((len(x),3))
                 for i in range(3):
                     v.transpose()[:][i] = np.cos(x/10. + float(i)) + (100. * np.cos(x/10000. + float(i)))
-                return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=v)
+                return SpwcVariable(time=x, data=v)
             def make_multicomponent(x):
                 v=np.ones((len(x),4))
                 for i in range(4):
                     v.transpose()[:][i] = float(i+1) * np.cos(x/10. + float(i))
-                return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=v)
+                return SpwcVariable(time=x, data=v)
+            def make_spectrogram(x):
+                v=np.ones((len(x),32))
+                for i in range(32):
+                    v.transpose()[:][i] = 100.*(2.+ float(i+1) * np.cos(x/1024. + float(i)))
+                return SpwcVariable(time=x, data=v)
             def _get_data(p_type, start, stop):
                     return make_vector(x)
                 if p_type == 'multicomponent':
                     return make_multicomponent(x)
+                if p_type == 'spectrogram':
+                    return make_spectrogram(np.arange(math.ceil(start), math.floor(stop),15.))
                 return None
             def get_data(metadata,start,stop):
                             elif value == 'multicomponent':
                                 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),values)
+                                default_ctor_args = (0,2)
                         if key == 'cache' and value == 'true':
                             use_cache = True
                     if use_cache:
                         cache_product = f"tests/{p_type}"
-                        df = _cache.get_data(cache_product, DateTimeRange(datetime.fromtimestamp(start, tz=timezone.utc), datetime.fromtimestamp(stop, tz=timezone.utc)),
+                        var = _cache.get_data(cache_product, DateTimeRange(datetime.fromtimestamp(start, tz=timezone.utc), datetime.fromtimestamp(stop, tz=timezone.utc)),
                                                  partial(_get_data, p_type),
                                                  fragment_hours=24)
                     else:
                         print("No Cache")
-                        df = _get_data(p_type, start, stop)
+                        var = _get_data(p_type, start, stop)
-                    t = np.array([d.timestamp() for d in df.index])
+                    return ts_type(var.time,var.data)
-                    values = df.values
-                    return ts_type(t,values)
                 except Exception as e:
                     print(traceback.format_exc())
                     print("Error in test.py ",str(e))
                 ("/tests/without_cache/scalar",[],[("type","scalar")]),
                 ("/tests/without_cache/vector",[],[("type","vector")]),
                 ("/tests/without_cache/multicomponent",[],[("type","multicomponent"),('size','4')]),
+                ("/tests/without_cache/spectrogram",[],[("type","spectrogram"),('size','32')]),
                 ("/tests/with_cache/scalar",[],[("type","scalar"), ("cache","true")]),
                 ("/tests/with_cache/vector",[],[("type","vector"), ("cache","true")]),
                 ("/tests/with_cache/multicomponent",[],[("type","multicomponent"),('size','4'), ("cache","true")])

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