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Spectrograms works again, needs more polish......
jeandet -
r1498:d1a5badbcf0e
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@@ -7,6 +7,7
7 7 #include <DataSource/datasources.h>
8 8
9 9 #include <QPair>
10 #include <QList>
10 11 #include <SqpApplication.h>
11 12 // must be included last because of Python/Qt definition of slots
12 13 #include "numpy_wrappers.h"
@@ -51,14 +52,14 MultiComponentTimeSerie* make_multi_comp(T& t, T& y)
51 52 }
52 53
53 54 template <typename T>
54 SpectrogramTimeSerie* make_spectro(T& t, T& y)
55 SpectrogramTimeSerie* make_spectro(T& t, T& y, T& z)
55 56 {
56 auto y_size = y.flat_size();
57 auto z_size = z.flat_size();
57 58 auto t_size = t.flat_size();
58 if (t_size && (y_size % t_size) == 0)
59 if (t_size && (z_size % t_size) == 0)
59 60 {
60 return new SpectrogramTimeSerie { std::move(t.data), std::move(y.data),
61 { t_size, y_size / t_size } };
61 return new SpectrogramTimeSerie { std::move(t.data), std::move(y.data), std::move(z.data),
62 { t_size, z_size / t_size }, std::nan("1"), std::nan("1") };
62 63 }
63 64 return nullptr;
64 65 }
@@ -75,7 +76,7 public:
75 76
76 77 virtual ~PyDataProvider() {}
77 78
78 virtual QPair<QPair<NpArray, NpArray>, DataSeriesType> get_data(
79 virtual QPair<QPair<QPair<NpArray,NpArray>,NpArray>, DataSeriesType> get_data(
79 80 const QMap<QString, QString>& key, double start_time, double stop_time)
80 81 {
81 82 (void)key, (void)start_time, (void)stop_time;
@@ -94,20 +95,21 public:
94 95 auto [data, type]
95 96 = get_data(metadata, parameters.m_Range.m_TStart, parameters.m_Range.m_TEnd);
96 97
97 auto& [t, y] = data;
98 auto& [axes, values] = data;
99 auto& [x, y] = axes;
98 100 switch (type)
99 101 {
100 102 case DataSeriesType::SCALAR:
101 ts = make_scalar(t, y);
103 ts = make_scalar(x, values);
102 104 break;
103 105 case DataSeriesType::VECTOR:
104 ts = make_vector(t, y);
106 ts = make_vector(x, values);
105 107 break;
106 108 case DataSeriesType::MULTICOMPONENT:
107 ts = make_multi_comp(t, y);
109 ts = make_multi_comp(x, values);
108 110 break;
109 111 case DataSeriesType::SPECTROGRAM:
110 ts = make_spectro(t, y);
112 ts = make_spectro(x, y, values);
111 113 break;
112 114 default:
113 115 break;
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@@ -9,43 +9,40 from spwc.amda import AMDA
9 9
10 10 amda = AMDA()
11 11
12
12 13 def amda_make_scalar(var=None):
13 14 if var is None:
14 return ((np.array(), np.array()), DataSeriesType.SCALAR)
15 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SCALAR)
15 16 else:
16 return ((var.time,var.data), DataSeriesType.SCALAR)
17 return (((var.time, np.array([])), var.data), DataSeriesType.SCALAR)
18
17 19
18 20 def amda_make_vector(var=None):
19 21 if var is None:
20 return ((np.array(), np.array()), DataSeriesType.VECTOR)
22 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.VECTOR)
21 23 else:
22 return ((var.time,var.data), DataSeriesType.VECTOR)
24 return (((var.time, np.array([])), var.data), DataSeriesType.VECTOR)
25
23 26
24 27 def amda_make_multi_comp(var=None):
25 28 if var is None:
26 return ((np.array(), np.array()), DataSeriesType.MULTICOMPONENT)
29 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.MULTICOMPONENT)
27 30 else:
28 return ((var.time,var.data), DataSeriesType.MULTICOMPONENT)
31 return (((var.time, np.array([])), var.data), DataSeriesType.MULTICOMPONENT)
32
29 33
30 34 def amda_make_spectro(var=None):
31 35 if var is None:
32 return ((np.array(), np.array()), DataSeriesType.SPECTROGRAM)
36 return (((np.array([]), np.array([])), np.array([])), DataSeriesType.SPECTROGRAM)
33 37 else:
34 38 min_sampling = float(var.meta.get("DATASET_MIN_SAMPLING","nan"))
35 39 max_sampling = float(var.meta.get("DATASET_MAX_SAMPLING","nan"))
36 if "PARAMETER_TABLE_MIN_VALUES[1]" in var.meta:
37 min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[1]"].split(',') ])
38 max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[1]"].split(',') ])
39 y = (max_v + min_v)/2.
40 elif "PARAMETER_TABLE_MIN_VALUES[0]" in var.meta:
41 min_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MIN_VALUES[0]"].split(',') ])
42 max_v = np.array([ float(v) for v in var.meta["PARAMETER_TABLE_MAX_VALUES[0]"].split(',') ])
43 y = (max_v + min_v)/2.
44 else:
45 y = np.logspace(1,3,var.data.shape[1])[::-1]
46 return ((var.time,var.data), DataSeriesType.SPECTROGRAM)
40 if var.y is None and len(var.data):
41 var.y = np.logspace(1, 3, var.data.shape[1])[::-1]
42 return (((var.time, var.y), var.data), DataSeriesType.SPECTROGRAM)
47 43 #return pysciqlopcore.SpectrogramTimeSerie(var.time,y,var.data,min_sampling,max_sampling,True)
48 44
45
49 46 def amda_get_sample(metadata,start,stop):
50 47 ts_type = amda_make_scalar
51 48 try:
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@@ -82,11 +82,11 class MyProvider(PyDataProvider):
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)
83 83 else:
84 84 var = _get_data(p_type, start, stop)
85 return ((var.time,var.data), ts_type)
85 return (((var.time, np.array([])),var.data), ts_type)
86 86 except Exception as e:
87 87 print(traceback.format_exc())
88 88 print("Error in test.py ",str(e))
89 return ((np.array(), np.array()), ts_type)
89 return (((np.array([]), np.array([])), np.array([])), ts_type)
90 90
91 91
92 92 t=MyProvider()
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@@ -1,1 +1,1
1 Subproject commit 9f4e10a342eece28498d9b732bf59f8d1908b716
1 Subproject commit 3bce297cb13e8337d71adceae6737c104c66ad52
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