HG_REPOSITORIES/LPP/SciQLOP_Repos/SciQLop Commit - r1432:db5304cf6c8c

MultiComponent TS almost complete...

jeandet -

r1432:db5304cf6c8c

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r1432:db5304cf6c8c

core +1 -1

		@@ -1,1 +1,1
1		Subproject commit 41273d8529cddde1c8b6bcea03d71d9cd1a18c06
	1	Subproject commit 9a080a34054c21a67236f761e8843005c07ff0ba

gui/src/Visualization/AxisRenderingUtils.cpp +5 -1

              template <typename T>
              struct AxisSetter<T,
                  typename std::enable_if_t<std::is_base_of<ScalarTimeSerie, T>::value
-                     or std::is_base_of<VectorTimeSerie, T>::value>>
+                     or std::is_base_of<VectorTimeSerie, T>::value
+                     or std::is_base_of<MultiComponentTimeSerie, T>::value>>
              {
                  static void setProperties(QCustomPlot&, SqpColorScale&)
                  {
                      case DataSeriesType::VECTOR:
                          return std::make_unique<AxisHelper<VectorTimeSerie>>(
                              std::dynamic_pointer_cast<VectorTimeSerie>(variable.data()));
+                     case DataSeriesType::MULTICOMPONENT:
+                         return std::make_unique<AxisHelper<MultiComponentTimeSerie>>(
+                             std::dynamic_pointer_cast<MultiComponentTimeSerie>(variable.data()));
                      default:
                          // Creates default helper
                          break;

gui/src/Visualization/PlottablesRenderingUtils.cpp +4 -1

              template <typename T>
              struct PlottablesSetter<T,
                  typename std::enable_if_t<std::is_base_of<ScalarTimeSerie, T>::value
-                     or std::is_base_of<VectorTimeSerie, T>::value>>
+                     or std::is_base_of<VectorTimeSerie, T>::value
+                     or std::is_base_of<MultiComponentTimeSerie, T>::value>>
              {
                  static void setProperties(PlottablesMap& plottables)
                  {
                          return std::make_unique<PlottablesHelper<SpectrogramTimeSerie>>();
                      case DataSeriesType::VECTOR:
                          return std::make_unique<PlottablesHelper<VectorTimeSerie>>();
+                     case DataSeriesType::MULTICOMPONENT:
+                         return std::make_unique<PlottablesHelper<MultiComponentTimeSerie>>();
                      default:
                          // Returns default helper
                          break;

gui/src/Visualization/VisualizationGraphHelper.cpp +15 -7

              template <typename T>
-             struct PlottablesUpdater<T, typename std::enable_if_t<std::is_base_of<MultiComponentTimeSerie, T>::value>>
+             struct PlottablesUpdater<T,
+                 typename std::enable_if_t<std::is_base_of<MultiComponentTimeSerie, T>::value>>
              {
                  static void setPlotYAxisRange(T& dataSeries, const DateTimeRange& xAxisRange, QCustomPlot& plot)
                  {
                      if (auto serie = dynamic_cast<MultiComponentTimeSerie*>(&dataSeries))
                      {
                          // TODO
-             //            std::for_each(
-             //                std::begin(*serie), std::end(*serie), [&minValue, &maxValue](const auto& v) {
-             //                    minValue = std::min({ minValue, std::min_element(v.begin(),v.end()) });
-             //                    maxValue = std::max({ maxValue, std::max_element(v.begin(),v.end()) });
-             //                });
+                         //            std::for_each(
+                         //                std::begin(*serie), std::end(*serie), [&minValue, &maxValue](const
+                         //                auto& v) {
+                         //                    minValue = std::min({ minValue,
+                         //                    std::min_element(v.begin(),v.end()) }); maxValue = std::max({
+                         //                    maxValue, std::max_element(v.begin(),v.end()) });
+                         //                });
                      }
                      plot.yAxis->setRange(QCPRange { minValue, maxValue });
                              auto dataContainer = QSharedPointer<SqpDataContainer>::create();
                              if (auto serie = dynamic_cast<MultiComponentTimeSerie*>(&dataSeries))
                              {
-             // TODO
+                                 // TODO
+                                 std::for_each(std::begin(*serie), std::end(*serie),
+                                     [&dataContainer, component = plottable.first](const auto& value) {
+                                         dataContainer->appendGraphData(
+                                             QCPGraphData(value.t(), value[component]));
+                                     });
                              }
                              graph->setData(dataContainer);
                          }

plugins/python_providers/resources/amda.py +11 -5

              import numpy as np
              import pandas as pds
              import requests
+             import copy
              from spwc.amda import AMDA
              amda = AMDA()
                          elif key == 'type':
                              if value == 'vector':
                                  ts_type = pysciqlopcore.VectorTimeSerie
-                     tstart=datetime.datetime.utcfromtimestamp(start)
-                     tend=datetime.datetime.utcfromtimestamp(stop)
+                             elif value == 'multicomponent':
+                                 ts_type = pysciqlopcore.MultiComponentTimeSerie
+                     tstart=datetime.datetime.fromtimestamp(start, tz=timezone.utc)
+                     tend=datetime.datetime.fromtimestamp(stop, tz=timezone.utc)
                      df = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id)
                      #t = np.array([d.timestamp()-7200 for d in df.index])
                      t = np.array([d.timestamp() for d in df.index])
                      values = df.values
-                     return ts_type(t,values)
+                     return ts_type(t,values.transpose())
                      return ts_type(1)
                  except Exception as e:
                      print(traceback.format_exc())
              if len(amda.component) is 0:
                  amda.update_inventory()
-             parameters = amda.parameter.copy()
+             parameters = copy.deepcopy(amda.parameter)
              for name,component in amda.component.items():
                  if 'components' in parameters[component['parameter']]:
                      parameters[component['parameter']]['components'].append(component)
                  path = f"/AMDA/{parameter['mission']}/{parameter['instrument']}/{parameter['dataset']}/{parameter['name']}"
                  components = [component['name'] for component in parameter.get('components',[])]
                  metadata = [ (key,item) for key,item in parameter.items() if key is not 'components' ]
-                 if parameter.get('size',0) is '3':
+                 n_components = parameter.get('size',0)
+                 if n_components is '3':
                      metadata.append(("type","vector"))
+                 elif n_components !=0:
+                     metadata.append(("type","multicomponent"))
                  else:
                      metadata.append(("type","scalar"))
                  products.append( (path, components, metadata))

plugins/python_providers/resources/test.py +30 -3

              someglobal = 1
-             def test(name,start,stop):
-                 x = np.arange(start, stop)
+             def make_scalar(x):
                  y = np.cos(x/10.)
                  return pysciqlopcore.ScalarTimeSerie(x,y)
+             def make_vector(x):
+                 v=np.ones((3,len(x)))
+                 for i in range(3):
+                     v[:][i] = np.cos(x/10. + float(i))
+                 return pysciqlopcore.VectorTimeSerie(x,v)
+             def make_multicomponent(x):
+                 v=np.ones((4,len(x)))
+                 for i in range(4):
+                     v[:][i] = float(i+1) * np.cos(x/10. + float(i))
+                 return pysciqlopcore.MultiComponentTimeSerie(x,v)
+             def get_data(metadata,start,stop):
+                 x = np.arange(start, stop)
+                 for key,value in metadata:
+                     if key == 'xml:id':
+                         param_id = value
+                     elif key == 'type':
+                         if value == 'vector':
+                             return make_vector(x)
+                         elif value == 'multicomponent':
+                             return make_multicomponent(x)
+                 return make_scalar(x)
-             #PythonProviders.register_product(["/folder1/folder2/product1", "/folder1/folder3/product2", "/folder4/folder5/product3"],test)
+             PythonProviders.register_product([("/tests/scalar",[],[("type","scalar")]), ("/tests/vector",[],[("type","vector")]), ("/tests/multicomponent",[],[("type","multicomponent"),('size','4')])],get_data)

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