@@ -7,6 +7,7 | |||
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7 | 7 | #include <DataSource/datasources.h> |
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8 | 8 | |
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9 | 9 | #include <QPair> |
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10 | #include <QList> | |
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10 | 11 | #include <SqpApplication.h> |
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11 | 12 | // must be included last because of Python/Qt definition of slots |
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12 | 13 | #include "numpy_wrappers.h" |
@@ -51,14 +52,14 MultiComponentTimeSerie* make_multi_comp(T& t, T& y) | |||
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51 | 52 | } |
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52 | 53 | |
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53 | 54 | template <typename T> |
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54 | SpectrogramTimeSerie* make_spectro(T& t, T& y) | |
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55 | SpectrogramTimeSerie* make_spectro(T& t, T& y, T& z) | |
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55 | 56 | { |
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56 |
auto |
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57 | auto z_size = z.flat_size(); | |
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57 | 58 | auto t_size = t.flat_size(); |
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58 |
if (t_size && ( |
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59 | if (t_size && (z_size % t_size) == 0) | |
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59 | 60 | { |
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60 | return new SpectrogramTimeSerie { std::move(t.data), std::move(y.data), | |
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61 |
{ t_size, |
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61 | return new SpectrogramTimeSerie { std::move(t.data), std::move(y.data), std::move(z.data), | |
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62 | { t_size, z_size / t_size }, std::nan("1"), std::nan("1") }; | |
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62 | 63 | } |
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63 | 64 | return nullptr; |
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64 | 65 | } |
@@ -75,7 +76,7 public: | |||
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75 | 76 | |
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76 | 77 | virtual ~PyDataProvider() {} |
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77 | 78 | |
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78 |
virtual QPair<QPair<NpArray, |
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79 | virtual QPair<QPair<QPair<NpArray,NpArray>,NpArray>, DataSeriesType> get_data( | |
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79 | 80 | const QMap<QString, QString>& key, double start_time, double stop_time) |
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80 | 81 | { |
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81 | 82 | (void)key, (void)start_time, (void)stop_time; |
@@ -94,20 +95,21 public: | |||
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94 | 95 | auto [data, type] |
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95 | 96 | = get_data(metadata, parameters.m_Range.m_TStart, parameters.m_Range.m_TEnd); |
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96 | 97 | |
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97 |
auto& [ |
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98 | auto& [axes, values] = data; | |
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99 | auto& [x, y] = axes; | |
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98 | 100 | switch (type) |
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99 | 101 | { |
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100 | 102 | case DataSeriesType::SCALAR: |
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101 |
ts = make_scalar( |
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103 | ts = make_scalar(x, values); | |
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102 | 104 | break; |
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103 | 105 | case DataSeriesType::VECTOR: |
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104 |
ts = make_vector( |
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106 | ts = make_vector(x, values); | |
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105 | 107 | break; |
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106 | 108 | case DataSeriesType::MULTICOMPONENT: |
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107 |
ts = make_multi_comp( |
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109 | ts = make_multi_comp(x, values); | |
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108 | 110 | break; |
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109 | 111 | case DataSeriesType::SPECTROGRAM: |
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110 |
ts = make_spectro( |
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112 | ts = make_spectro(x, y, values); | |
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111 | 113 | break; |
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112 | 114 | default: |
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113 | 115 | break; |
@@ -9,48 +9,45 from spwc.amda import AMDA | |||
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9 | 9 | |
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10 | 10 | amda = AMDA() |
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11 | 11 | |
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12 | ||
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12 | 13 | def amda_make_scalar(var=None): |
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13 | 14 | if var is None: |
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14 | return ((np.array(), np.array()), DataSeriesType.SCALAR) | |
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15 | return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SCALAR) | |
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15 | 16 | else: |
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16 | return ((var.time,var.data), DataSeriesType.SCALAR) | |
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17 | return (((var.time, np.array([])), var.data), DataSeriesType.SCALAR) | |
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18 | ||
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17 | 19 | |
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18 | 20 | def amda_make_vector(var=None): |
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19 | 21 | if var is None: |
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20 | return ((np.array(), np.array()), DataSeriesType.VECTOR) | |
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22 | return (((np.array([]), np.array([])), np.array([])), DataSeriesType.VECTOR) | |
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21 | 23 | else: |
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22 | return ((var.time,var.data), DataSeriesType.VECTOR) | |
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24 | return (((var.time, np.array([])), var.data), DataSeriesType.VECTOR) | |
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25 | ||
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23 | 26 | |
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24 | 27 | def amda_make_multi_comp(var=None): |
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25 | 28 | if var is None: |
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26 | return ((np.array(), np.array()), DataSeriesType.MULTICOMPONENT) | |
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29 | return (((np.array([]), np.array([])), np.array([])), DataSeriesType.MULTICOMPONENT) | |
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27 | 30 | else: |
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28 | return ((var.time,var.data), DataSeriesType.MULTICOMPONENT) | |
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31 | return (((var.time, np.array([])), var.data), DataSeriesType.MULTICOMPONENT) | |
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32 | ||
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29 | 33 | |
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30 | 34 | def amda_make_spectro(var=None): |
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31 | 35 | if var is None: |
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32 | return ((np.array(), np.array()), DataSeriesType.SPECTROGRAM) | |
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36 | return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SPECTROGRAM) | |
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33 | 37 | else: |
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34 | min_sampling = float(var.meta.get("DATASET_MIN_SAMPLING","nan")) | |
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35 | max_sampling = float(var.meta.get("DATASET_MAX_SAMPLING","nan")) | |
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36 | if "PARAMETER_TABLE_MIN_VALUES[1]" in var.meta: | |
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37 | min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[1]"].split(',') ]) | |
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38 | max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[1]"].split(',') ]) | |
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39 | y = (max_v + min_v)/2. | |
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40 | elif "PARAMETER_TABLE_MIN_VALUES[0]" in var.meta: | |
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41 | min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[0]"].split(',') ]) | |
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42 | max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[0]"].split(',') ]) | |
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43 | y = (max_v + min_v)/2. | |
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44 | else: | |
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45 | y = np.logspace(1,3,var.data.shape[1])[::-1] | |
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46 | return ((var.time,var.data), DataSeriesType.SPECTROGRAM) | |
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38 | min_sampling = float(var.meta.get("DATASET_MIN_SAMPLING", "nan")) | |
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39 | max_sampling = float(var.meta.get("DATASET_MAX_SAMPLING", "nan")) | |
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40 | if var.y is None and len(var.data): | |
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41 | var.y = np.logspace(1, 3, var.data.shape[1])[::-1] | |
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42 | return (((var.time, var.y), var.data), DataSeriesType.SPECTROGRAM) | |
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47 | 43 | #return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data,min_sampling,max_sampling,True) |
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48 | 44 | |
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49 | def amda_get_sample(metadata,start,stop): | |
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45 | ||
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46 | def amda_get_sample(metadata, start, stop): | |
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50 | 47 | ts_type = amda_make_scalar |
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51 | 48 | try: |
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52 | 49 | param_id = None |
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53 | for key,value in metadata: | |
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50 | for key, value in metadata: | |
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54 | 51 | if key == 'xml:id': |
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55 | 52 | param_id = value |
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56 | 53 | elif key == 'type': |
@@ -60,48 +57,48 def amda_get_sample(metadata,start,stop): | |||
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60 | 57 | ts_type = amda_make_multi_comp |
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61 | 58 | elif value == 'spectrogram': |
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62 | 59 | ts_type = amda_make_spectro |
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63 | tstart=datetime.fromtimestamp(start, tz=timezone.utc) | |
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64 | tend=datetime.fromtimestamp(stop, tz=timezone.utc) | |
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60 | tstart = datetime.fromtimestamp(start, tz=timezone.utc) | |
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61 | tend = datetime.fromtimestamp(stop, tz=timezone.utc) | |
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65 | 62 | var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST") |
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66 | 63 | return ts_type(var) |
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67 | 64 | except Exception as e: |
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68 | 65 | print(traceback.format_exc()) |
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69 | print("Error in amda.py ",str(e)) | |
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66 | print("Error in amda.py ", str(e)) | |
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70 | 67 | return ts_type() |
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71 | 68 | |
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72 | 69 | |
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73 | 70 | class AmdaProvider(PyDataProvider): |
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74 | 71 | def __init__(self): |
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75 | super(AmdaProvider,self).__init__() | |
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72 | super(AmdaProvider, self).__init__() | |
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76 | 73 | if len(amda.component) is 0: |
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77 | 74 | amda.update_inventory() |
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78 | 75 | parameters = copy.deepcopy(amda.parameter) |
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79 | for name,component in amda.component.items(): | |
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76 | for name, component in amda.component.items(): | |
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80 | 77 | if 'components' in parameters[component['parameter']]: |
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81 | 78 | parameters[component['parameter']]['components'].append(component) |
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82 | 79 | else: |
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83 | 80 | parameters[component['parameter']]['components']=[component] |
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84 | 81 | |
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85 | 82 | products = [] |
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86 | for key,parameter in parameters.items(): | |
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83 | for key, parameter in parameters.items(): | |
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87 | 84 | path = f"/AMDA/{parameter['mission']}/{parameter.get('observatory','')}/{parameter['instrument']}/{parameter['dataset']}/{parameter['name']}" |
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88 | 85 | components = [component['name'] for component in parameter.get('components',[])] |
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89 |
metadata = { |
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90 | n_components = parameter.get('size',0) | |
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86 | metadata = {key: item for key, item in parameter.items() if key is not 'components'} | |
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87 | n_components = parameter.get('size', 0) | |
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91 | 88 | if n_components == '3': |
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92 | metadata["type"]="vector" | |
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93 | elif parameter.get('display_type','')=="spectrogram": | |
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94 | metadata["type"]="spectrogram" | |
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95 | elif n_components !=0: | |
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96 | metadata["type"]="multicomponent" | |
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89 | metadata["type"] = "vector" | |
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90 | elif parameter.get('display_type', '')=="spectrogram": | |
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91 | metadata["type"] = "spectrogram" | |
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92 | elif n_components != 0: | |
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93 | metadata["type"] = "multicomponent" | |
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97 | 94 | else: |
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98 | metadata["type"]="scalar" | |
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99 |
products.append( |
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95 | metadata["type"] = "scalar" | |
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96 | products.append(Product(path, components, metadata)) | |
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100 | 97 | self.register_products(products) |
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101 | 98 | for mission in amda.mission: |
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102 | 99 | self.set_icon(f'/AMDA/{mission}','satellite') |
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103 | 100 | |
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104 | def get_data(self,metadata,start,stop): | |
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101 | def get_data(self, metadata, start, stop): | |
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105 | 102 | ts_type = amda_make_scalar |
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106 | 103 | try: |
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107 | 104 | param_id = metadata['xml:id'] |
@@ -112,13 +109,13 class AmdaProvider(PyDataProvider): | |||
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112 | 109 | ts_type = amda_make_multi_comp |
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113 | 110 | elif ts_type_str == 'spectrogram': |
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114 | 111 | ts_type = amda_make_spectro |
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115 | tstart=datetime.fromtimestamp(start, tz=timezone.utc) | |
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116 | tend=datetime.fromtimestamp(stop, tz=timezone.utc) | |
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112 | tstart = datetime.fromtimestamp(start, tz=timezone.utc) | |
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113 | tend = datetime.fromtimestamp(stop, tz=timezone.utc) | |
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117 | 114 | var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST") |
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118 | 115 | return ts_type(var) |
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119 | 116 | except Exception as e: |
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120 | 117 | print(traceback.format_exc()) |
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121 | print("Error in amda.py ",str(e)) | |
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118 | print("Error in amda.py ", str(e)) | |
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122 | 119 | return ts_type() |
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123 | 120 | |
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124 | 121 | _amda = AmdaProvider() |
@@ -82,11 +82,11 class MyProvider(PyDataProvider): | |||
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82 | 82 | 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) |
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83 | 83 | else: |
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84 | 84 | var = _get_data(p_type, start, stop) |
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85 | return ((var.time,var.data), ts_type) | |
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85 | return (((var.time, np.array([])),var.data), ts_type) | |
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86 | 86 | except Exception as e: |
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87 | 87 | print(traceback.format_exc()) |
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88 | 88 | print("Error in test.py ",str(e)) |
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89 | return ((np.array(), np.array()), ts_type) | |
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89 | return (((np.array([]), np.array([])), np.array([])), ts_type) | |
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90 | 90 | |
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91 | 91 | |
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92 | 92 | t=MyProvider() |
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