@@ -1,25 +1,35 | |||||
1 | #ifndef SCIQLOP_SPECTROGRAMTIMESERIE_H |
|
1 | #ifndef SCIQLOP_SPECTROGRAMTIMESERIE_H | |
2 | #define SCIQLOP_SPECTROGRAMTIMESERIE_H |
|
2 | #define SCIQLOP_SPECTROGRAMTIMESERIE_H | |
3 |
|
3 | |||
4 | #include "CoreGlobal.h" |
|
4 | #include "CoreGlobal.h" | |
5 |
|
5 | |||
6 | #include <TimeSeries.h> |
|
6 | #include <TimeSeries.h> | |
7 |
|
7 | |||
8 | class SCIQLOP_CORE_EXPORT SpectrogramTimeSerie |
|
8 | class SCIQLOP_CORE_EXPORT SpectrogramTimeSerie | |
9 | : public TimeSeries::TimeSerie<double, SpectrogramTimeSerie, 2> |
|
9 | : public TimeSeries::TimeSerie<double, SpectrogramTimeSerie, 2> | |
10 | { |
|
10 | { | |
11 | public: |
|
11 | public: | |
12 | using item_t = |
|
12 | using item_t = | |
13 | decltype(std::declval< |
|
13 | decltype(std::declval< | |
14 | TimeSeries::TimeSerie<double, SpectrogramTimeSerie, 2>>()[0]); |
|
14 | TimeSeries::TimeSerie<double, SpectrogramTimeSerie, 2>>()[0]); | |
15 |
|
15 | |||
16 | using iterator_t = decltype( |
|
16 | using iterator_t = decltype( | |
17 | std::declval<TimeSeries::TimeSerie<double, SpectrogramTimeSerie, 2>>() |
|
17 | std::declval<TimeSeries::TimeSerie<double, SpectrogramTimeSerie, 2>>() | |
18 | .begin()); |
|
18 | .begin()); | |
19 |
|
19 | |||
20 | SpectrogramTimeSerie() {} |
|
20 | SpectrogramTimeSerie() {} | |
|
21 | SpectrogramTimeSerie(SpectrogramTimeSerie::axis_t& t, | |||
|
22 | SpectrogramTimeSerie::axis_t& y, | |||
|
23 | SpectrogramTimeSerie::container_type< | |||
|
24 | SpectrogramTimeSerie::raw_value_type>& values, | |||
|
25 | std::vector<std::size_t>& shape) | |||
|
26 | : TimeSeries::TimeSerie<double, SpectrogramTimeSerie, 2>(t, values, shape) | |||
|
27 | { | |||
|
28 | _axes[1] = y; | |||
|
29 | } | |||
|
30 | ||||
21 | ~SpectrogramTimeSerie() = default; |
|
31 | ~SpectrogramTimeSerie() = default; | |
22 | using TimeSerie::TimeSerie; |
|
32 | using TimeSerie::TimeSerie; | |
23 | }; |
|
33 | }; | |
24 |
|
34 | |||
25 | #endif // SCIQLOP_SPECTROGRAMTIMESERIE_H |
|
35 | #endif // SCIQLOP_SPECTROGRAMTIMESERIE_H |
@@ -1,264 +1,295 | |||||
1 | #include "CoreWrappers.h" |
|
1 | #include "CoreWrappers.h" | |
2 |
|
2 | |||
3 | #include "pywrappers_common.h" |
|
3 | #include "pywrappers_common.h" | |
4 |
|
4 | |||
5 | #include <Data/DataSeriesType.h> |
|
5 | #include <Data/DataSeriesType.h> | |
6 | #include <Data/IDataProvider.h> |
|
6 | #include <Data/IDataProvider.h> | |
7 | #include <Network/Downloader.h> |
|
7 | #include <Network/Downloader.h> | |
8 | #include <Time/TimeController.h> |
|
8 | #include <Time/TimeController.h> | |
9 | #include <Variable/Variable2.h> |
|
9 | #include <Variable/Variable2.h> | |
10 | #include <Variable/VariableController2.h> |
|
10 | #include <Variable/VariableController2.h> | |
11 | #include <pybind11/chrono.h> |
|
11 | #include <pybind11/chrono.h> | |
12 | #include <pybind11/embed.h> |
|
12 | #include <pybind11/embed.h> | |
13 | #include <pybind11/functional.h> |
|
13 | #include <pybind11/functional.h> | |
14 | #include <pybind11/numpy.h> |
|
14 | #include <pybind11/numpy.h> | |
15 | #include <pybind11/operators.h> |
|
15 | #include <pybind11/operators.h> | |
16 | #include <pybind11/pybind11.h> |
|
16 | #include <pybind11/pybind11.h> | |
17 | #include <pybind11/stl.h> |
|
17 | #include <pybind11/stl.h> | |
18 | #include <pybind11/stl_bind.h> |
|
18 | #include <pybind11/stl_bind.h> | |
19 | #include <sstream> |
|
19 | #include <sstream> | |
20 | #include <string> |
|
20 | #include <string> | |
21 |
|
21 | |||
22 | namespace py = pybind11; |
|
22 | namespace py = pybind11; | |
23 | using namespace std::chrono; |
|
23 | using namespace std::chrono; | |
24 |
|
24 | |||
25 | template<typename T, typename U, bool row_major = true> |
|
25 | template<typename T, typename U, bool row_major = true> | |
26 | void copy_vector(py::array_t<double>& t, py::array_t<double>& values, T& dest_t, |
|
26 | void copy_vector(py::array_t<double>& t, py::array_t<double>& values, T& dest_t, | |
27 | U& dest_values) |
|
27 | U& dest_values) | |
28 | { |
|
28 | { | |
29 | auto t_view = t.unchecked<1>(); |
|
29 | auto t_view = t.unchecked<1>(); | |
30 | auto values_view = values.unchecked<2>(); |
|
30 | auto values_view = values.unchecked<2>(); | |
31 | for(std::size_t i = 0; i < t.size(); i++) |
|
31 | for(std::size_t i = 0; i < t.size(); i++) | |
32 | { |
|
32 | { | |
33 | dest_t[i] = t_view[i]; |
|
33 | dest_t[i] = t_view[i]; | |
34 | dest_values[i] = {values_view(i, 0), values_view(i, 1), values_view(i, 2)}; |
|
34 | dest_values[i] = {values_view(i, 0), values_view(i, 1), values_view(i, 2)}; | |
35 | } |
|
35 | } | |
36 | } |
|
36 | } | |
37 |
|
37 | |||
38 | template<typename T, typename U> |
|
38 | template<typename T, typename U> | |
39 | void copy_scalar(py::array_t<double>& t, py::array_t<double>& values, T& dest_t, |
|
39 | void copy_scalar(py::array_t<double>& t, py::array_t<double>& values, T& dest_t, | |
40 | U& dest_values) |
|
40 | U& dest_values) | |
41 | { |
|
41 | { | |
42 | auto t_view = t.unchecked<1>(); |
|
42 | auto t_view = t.unchecked<1>(); | |
43 | if(values.ndim() == 1) |
|
43 | if(values.ndim() == 1) | |
44 | { |
|
44 | { | |
45 | auto values_view = values.unchecked<1>(); |
|
45 | auto values_view = values.unchecked<1>(); | |
46 | for(std::size_t i = 0; i < t.size(); i++) |
|
46 | for(std::size_t i = 0; i < t.size(); i++) | |
47 | { |
|
47 | { | |
48 | dest_t[i] = t_view[i]; |
|
48 | dest_t[i] = t_view[i]; | |
49 | dest_values[i] = values_view[i]; |
|
49 | dest_values[i] = values_view[i]; | |
50 | } |
|
50 | } | |
51 | } |
|
51 | } | |
52 | else if(values.ndim() == 2 && values.shape(1) == 1) |
|
52 | else if(values.ndim() == 2 && values.shape(1) == 1) | |
53 | { |
|
53 | { | |
54 | auto values_view = values.unchecked<2>(); |
|
54 | auto values_view = values.unchecked<2>(); | |
55 | for(std::size_t i = 0; i < t.size(); i++) |
|
55 | for(std::size_t i = 0; i < t.size(); i++) | |
56 | { |
|
56 | { | |
57 | dest_t[i] = t_view[i]; |
|
57 | dest_t[i] = t_view[i]; | |
58 | dest_values[i] = values_view(i, 0); |
|
58 | dest_values[i] = values_view(i, 0); | |
59 | } |
|
59 | } | |
60 | } |
|
60 | } | |
61 | } |
|
61 | } | |
62 |
|
||||
63 | template<typename T, typename U> |
|
62 | template<typename T, typename U> | |
64 |
void copy_ |
|
63 | void copy_multicomp(py::array_t<double>& t, py::array_t<double>& values, | |
65 | T& dest_t, U& dest_values) |
|
64 | T& dest_t, U& dest_values) | |
66 | { |
|
65 | { | |
67 | auto t_view = t.unchecked<1>(); |
|
66 | auto t_view = t.unchecked<1>(); | |
68 | auto values_view = values.unchecked<2>(); |
|
67 | auto values_view = values.unchecked<2>(); | |
69 | const auto width = values.shape(1); |
|
68 | const auto width = values.shape(1); | |
70 | for(std::size_t i = 0; i < t.size(); i++) |
|
69 | for(std::size_t i = 0; i < t.size(); i++) | |
71 | { |
|
70 | { | |
72 | dest_t[i] = t_view[i]; |
|
71 | dest_t[i] = t_view[i]; | |
73 | for(int j = 0; j < width; j++) |
|
72 | for(int j = 0; j < width; j++) | |
74 | { |
|
73 | { | |
75 | dest_values[i * width + j] = values_view(i, j); |
|
74 | dest_values[i * width + j] = values_view(i, j); | |
76 | } |
|
75 | } | |
77 | } |
|
76 | } | |
78 | } |
|
77 | } | |
79 |
|
78 | |||
|
79 | template<typename T, typename U> | |||
|
80 | void copy_spectro(py::array_t<double>& t, py::array_t<double>& y, | |||
|
81 | py::array_t<double>& values, T& dest_t, T& dest_y, | |||
|
82 | U& dest_values) | |||
|
83 | { | |||
|
84 | auto t_view = t.unchecked<1>(); | |||
|
85 | auto y_view = y.unchecked<1>(); | |||
|
86 | auto values_view = values.unchecked<2>(); | |||
|
87 | const auto width = values.shape(1); | |||
|
88 | for(std::size_t i = 0; i < y.size(); i++) | |||
|
89 | { | |||
|
90 | dest_y[i] = y_view[i]; | |||
|
91 | } | |||
|
92 | for(std::size_t i = 0; i < t.size(); i++) | |||
|
93 | { | |||
|
94 | dest_t[i] = t_view[i]; | |||
|
95 | for(int j = 0; j < width; j++) | |||
|
96 | { | |||
|
97 | dest_values[i * width + j] = values_view(i, j); | |||
|
98 | } | |||
|
99 | } | |||
|
100 | } | |||
|
101 | ||||
80 | PYBIND11_MODULE(pysciqlopcore, m) |
|
102 | PYBIND11_MODULE(pysciqlopcore, m) | |
81 | { |
|
103 | { | |
82 | pybind11::bind_vector<std::vector<double>>(m, "VectorDouble"); |
|
104 | pybind11::bind_vector<std::vector<double>>(m, "VectorDouble"); | |
83 |
|
105 | |||
84 | py::enum_<DataSeriesType>(m, "DataSeriesType") |
|
106 | py::enum_<DataSeriesType>(m, "DataSeriesType") | |
85 | .value("SCALAR", DataSeriesType::SCALAR) |
|
107 | .value("SCALAR", DataSeriesType::SCALAR) | |
86 | .value("SPECTROGRAM", DataSeriesType::SPECTROGRAM) |
|
108 | .value("SPECTROGRAM", DataSeriesType::SPECTROGRAM) | |
87 | .value("VECTOR", DataSeriesType::VECTOR) |
|
109 | .value("VECTOR", DataSeriesType::VECTOR) | |
88 | .value("MULTICOMPONENT", DataSeriesType::MULTICOMPONENT) |
|
110 | .value("MULTICOMPONENT", DataSeriesType::MULTICOMPONENT) | |
89 | .value("NONE", DataSeriesType::NONE) |
|
111 | .value("NONE", DataSeriesType::NONE) | |
90 | .export_values(); |
|
112 | .export_values(); | |
91 |
|
113 | |||
92 | py::class_<Response>(m, "Response") |
|
114 | py::class_<Response>(m, "Response") | |
93 | .def("status_code", &Response::status_code); |
|
115 | .def("status_code", &Response::status_code); | |
94 |
|
116 | |||
95 | py::class_<Downloader>(m, "Downloader") |
|
117 | py::class_<Downloader>(m, "Downloader") | |
96 | .def_static("get", Downloader::get) |
|
118 | .def_static("get", Downloader::get) | |
97 | .def_static("getAsync", Downloader::getAsync) |
|
119 | .def_static("getAsync", Downloader::getAsync) | |
98 | .def_static("downloadFinished", Downloader::downloadFinished); |
|
120 | .def_static("downloadFinished", Downloader::downloadFinished); | |
99 |
|
121 | |||
100 | py::class_<IDataProvider, std::shared_ptr<IDataProvider>>(m, "IDataProvider"); |
|
122 | py::class_<IDataProvider, std::shared_ptr<IDataProvider>>(m, "IDataProvider"); | |
101 |
|
123 | |||
102 | py::class_<TimeSeries::ITimeSerie, std::shared_ptr<TimeSeries::ITimeSerie>>( |
|
124 | py::class_<TimeSeries::ITimeSerie, std::shared_ptr<TimeSeries::ITimeSerie>>( | |
103 | m, "ITimeSerie") |
|
125 | m, "ITimeSerie") | |
104 | .def_property_readonly( |
|
126 | .def_property_readonly( | |
105 | "size", [](const TimeSeries::ITimeSerie& ts) { return ts.size(); }) |
|
127 | "size", [](const TimeSeries::ITimeSerie& ts) { return ts.size(); }) | |
106 | .def("__len__", |
|
128 | .def("__len__", | |
107 | [](const TimeSeries::ITimeSerie& ts) { return ts.size(); }) |
|
129 | [](const TimeSeries::ITimeSerie& ts) { return ts.size(); }) | |
108 | .def_property_readonly( |
|
130 | .def_property_readonly( | |
109 | "shape", [](const TimeSeries::ITimeSerie& ts) { return ts.shape(); }) |
|
131 | "shape", [](const TimeSeries::ITimeSerie& ts) { return ts.shape(); }) | |
110 | .def_property_readonly( |
|
132 | .def_property_readonly( | |
111 | "t", |
|
133 | "t", | |
112 | [](TimeSeries::ITimeSerie& ts) -> decltype(ts.axis(0))& { |
|
134 | [](TimeSeries::ITimeSerie& ts) -> decltype(ts.axis(0))& { | |
113 | return ts.axis(0); |
|
135 | return ts.axis(0); | |
114 | }, |
|
136 | }, | |
|
137 | py::return_value_policy::reference) | |||
|
138 | .def( | |||
|
139 | "axis", | |||
|
140 | [](TimeSeries::ITimeSerie& ts, unsigned int index) | |||
|
141 | -> decltype(ts.axis(0))& { return ts.axis(index); }, | |||
115 | py::return_value_policy::reference); |
|
142 | py::return_value_policy::reference); | |
116 |
|
143 | |||
117 | py::class_<ScalarTimeSerie, TimeSeries::ITimeSerie, |
|
144 | py::class_<ScalarTimeSerie, TimeSeries::ITimeSerie, | |
118 | std::shared_ptr<ScalarTimeSerie>>(m, "ScalarTimeSerie") |
|
145 | std::shared_ptr<ScalarTimeSerie>>(m, "ScalarTimeSerie") | |
119 | .def(py::init<>()) |
|
146 | .def(py::init<>()) | |
120 | .def(py::init<std::size_t>()) |
|
147 | .def(py::init<std::size_t>()) | |
121 | .def(py::init([](py::array_t<double> t, py::array_t<double> values) { |
|
148 | .def(py::init([](py::array_t<double> t, py::array_t<double> values) { | |
122 | assert(t.size() == values.size()); |
|
149 | assert(t.size() == values.size()); | |
123 | ScalarTimeSerie::axis_t _t(t.size()); |
|
150 | ScalarTimeSerie::axis_t _t(t.size()); | |
124 | ScalarTimeSerie::axis_t _values(t.size()); |
|
151 | ScalarTimeSerie::axis_t _values(t.size()); | |
125 | copy_scalar(t, values, _t, _values); |
|
152 | copy_scalar(t, values, _t, _values); | |
126 | return ScalarTimeSerie(_t, _values); |
|
153 | return ScalarTimeSerie(_t, _values); | |
127 | })) |
|
154 | })) | |
128 | .def("__getitem__", |
|
155 | .def("__getitem__", | |
129 | [](ScalarTimeSerie& ts, std::size_t key) { return ts[key]; }) |
|
156 | [](ScalarTimeSerie& ts, std::size_t key) { return ts[key]; }) | |
130 | .def("__setitem__", [](ScalarTimeSerie& ts, std::size_t key, |
|
157 | .def("__setitem__", [](ScalarTimeSerie& ts, std::size_t key, | |
131 | double value) { *(ts.begin() + key) = value; }); |
|
158 | double value) { *(ts.begin() + key) = value; }); | |
132 |
|
159 | |||
133 | py::class_<VectorTimeSerie::raw_value_type>(m, "vector") |
|
160 | py::class_<VectorTimeSerie::raw_value_type>(m, "vector") | |
134 | .def(py::init<>()) |
|
161 | .def(py::init<>()) | |
135 | .def(py::init<double, double, double>()) |
|
162 | .def(py::init<double, double, double>()) | |
136 | .def("__repr__", __repr__<VectorTimeSerie::raw_value_type>) |
|
163 | .def("__repr__", __repr__<VectorTimeSerie::raw_value_type>) | |
137 | .def_readwrite("x", &VectorTimeSerie::raw_value_type::x) |
|
164 | .def_readwrite("x", &VectorTimeSerie::raw_value_type::x) | |
138 | .def_readwrite("y", &VectorTimeSerie::raw_value_type::y) |
|
165 | .def_readwrite("y", &VectorTimeSerie::raw_value_type::y) | |
139 | .def_readwrite("z", &VectorTimeSerie::raw_value_type::z); |
|
166 | .def_readwrite("z", &VectorTimeSerie::raw_value_type::z); | |
140 |
|
167 | |||
141 | py::class_<VectorTimeSerie, TimeSeries::ITimeSerie, |
|
168 | py::class_<VectorTimeSerie, TimeSeries::ITimeSerie, | |
142 | std::shared_ptr<VectorTimeSerie>>(m, "VectorTimeSerie") |
|
169 | std::shared_ptr<VectorTimeSerie>>(m, "VectorTimeSerie") | |
143 | .def(py::init<>()) |
|
170 | .def(py::init<>()) | |
144 | .def(py::init<std::size_t>()) |
|
171 | .def(py::init<std::size_t>()) | |
145 | .def(py::init([](py::array_t<double> t, py::array_t<double> values) { |
|
172 | .def(py::init([](py::array_t<double> t, py::array_t<double> values) { | |
146 | assert(t.size() * 3 == values.size()); |
|
173 | assert(t.size() * 3 == values.size()); | |
147 | VectorTimeSerie::axis_t _t(t.size()); |
|
174 | VectorTimeSerie::axis_t _t(t.size()); | |
148 | VectorTimeSerie::container_type<VectorTimeSerie::raw_value_type> |
|
175 | VectorTimeSerie::container_type<VectorTimeSerie::raw_value_type> | |
149 | _values(t.size()); |
|
176 | _values(t.size()); | |
150 | copy_vector(t, values, _t, _values); |
|
177 | copy_vector(t, values, _t, _values); | |
151 | return VectorTimeSerie(_t, _values); |
|
178 | return VectorTimeSerie(_t, _values); | |
152 | })) |
|
179 | })) | |
153 |
.def( |
|
180 | .def( | |
|
181 | "__getitem__", | |||
154 |
|
|
182 | [](VectorTimeSerie& ts, std::size_t key) | |
155 |
|
|
183 | -> VectorTimeSerie::raw_value_type& { return ts[key]; }, | |
156 |
|
|
184 | py::return_value_policy::reference) | |
157 | .def("__setitem__", [](VectorTimeSerie& ts, std::size_t key, |
|
185 | .def("__setitem__", [](VectorTimeSerie& ts, std::size_t key, | |
158 | VectorTimeSerie::raw_value_type value) { |
|
186 | VectorTimeSerie::raw_value_type value) { | |
159 | *(ts.begin() + key) = value; |
|
187 | *(ts.begin() + key) = value; | |
160 | }); |
|
188 | }); | |
161 |
|
189 | |||
162 | py::class_<MultiComponentTimeSerie::iterator_t>(m, |
|
190 | py::class_<MultiComponentTimeSerie::iterator_t>(m, | |
163 | "MultiComponentTimeSerieItem") |
|
191 | "MultiComponentTimeSerieItem") | |
164 | .def("__getitem__", [](MultiComponentTimeSerie::iterator_t& self, |
|
192 | .def("__getitem__", [](MultiComponentTimeSerie::iterator_t& self, | |
165 | std::size_t key) { return (*self)[key]; }) |
|
193 | std::size_t key) { return (*self)[key]; }) | |
166 | .def("__setitem__", |
|
194 | .def("__setitem__", | |
167 | [](MultiComponentTimeSerie::iterator_t& self, std::size_t key, |
|
195 | [](MultiComponentTimeSerie::iterator_t& self, std::size_t key, | |
168 | double value) { (*self)[key] = value; }); |
|
196 | double value) { (*self)[key] = value; }); | |
169 |
|
197 | |||
170 | py::class_<MultiComponentTimeSerie, TimeSeries::ITimeSerie, |
|
198 | py::class_<MultiComponentTimeSerie, TimeSeries::ITimeSerie, | |
171 | std::shared_ptr<MultiComponentTimeSerie>>( |
|
199 | std::shared_ptr<MultiComponentTimeSerie>>( | |
172 | m, "MultiComponentTimeSerie") |
|
200 | m, "MultiComponentTimeSerie") | |
173 | .def(py::init<>()) |
|
201 | .def(py::init<>()) | |
174 | .def(py::init<const std::vector<std::size_t>>()) |
|
202 | .def(py::init<const std::vector<std::size_t>>()) | |
175 | .def(py::init([](py::array_t<double> t, py::array_t<double> values) { |
|
203 | .def(py::init([](py::array_t<double> t, py::array_t<double> values) { | |
176 | assert((t.size() < values.size()) | |
|
204 | assert((t.size() < values.size()) | | |
177 | (t.size() == 0)); // TODO check geometry |
|
205 | (t.size() == 0)); // TODO check geometry | |
178 | MultiComponentTimeSerie::axis_t _t(t.size()); |
|
206 | MultiComponentTimeSerie::axis_t _t(t.size()); | |
179 | MultiComponentTimeSerie::container_type< |
|
207 | MultiComponentTimeSerie::container_type< | |
180 | MultiComponentTimeSerie::raw_value_type> |
|
208 | MultiComponentTimeSerie::raw_value_type> | |
181 | _values(values.size()); |
|
209 | _values(values.size()); | |
182 |
copy_ |
|
210 | copy_multicomp(t, values, _t, _values); | |
183 | std::vector<std::size_t> shape; |
|
211 | std::vector<std::size_t> shape; | |
184 | shape.push_back(values.shape(0)); |
|
212 | shape.push_back(values.shape(0)); | |
185 | shape.push_back(values.shape(1)); |
|
213 | shape.push_back(values.shape(1)); | |
186 | return MultiComponentTimeSerie(_t, _values, shape); |
|
214 | return MultiComponentTimeSerie(_t, _values, shape); | |
187 | })) |
|
215 | })) | |
188 | .def("__getitem__", |
|
216 | .def("__getitem__", | |
189 | [](MultiComponentTimeSerie& ts, |
|
217 | [](MultiComponentTimeSerie& ts, | |
190 | std::size_t key) -> MultiComponentTimeSerie::iterator_t { |
|
218 | std::size_t key) -> MultiComponentTimeSerie::iterator_t { | |
191 | return ts.begin() + key; |
|
219 | return ts.begin() + key; | |
192 | }); |
|
220 | }); | |
193 |
|
221 | |||
194 | py::class_<SpectrogramTimeSerie::iterator_t>(m, "SpectrogramTimeSerieItem") |
|
222 | py::class_<SpectrogramTimeSerie::iterator_t>(m, "SpectrogramTimeSerieItem") | |
195 | .def("__getitem__", [](SpectrogramTimeSerie::iterator_t& self, |
|
223 | .def("__getitem__", [](SpectrogramTimeSerie::iterator_t& self, | |
196 | std::size_t key) { return (*self)[key]; }) |
|
224 | std::size_t key) { return (*self)[key]; }) | |
197 | .def("__setitem__", |
|
225 | .def("__setitem__", | |
198 | [](SpectrogramTimeSerie::iterator_t& self, std::size_t key, |
|
226 | [](SpectrogramTimeSerie::iterator_t& self, std::size_t key, | |
199 | double value) { (*self)[key] = value; }); |
|
227 | double value) { (*self)[key] = value; }); | |
200 |
|
228 | |||
201 | py::class_<SpectrogramTimeSerie, TimeSeries::ITimeSerie, |
|
229 | py::class_<SpectrogramTimeSerie, TimeSeries::ITimeSerie, | |
202 | std::shared_ptr<SpectrogramTimeSerie>>(m, "SpectrogramTimeSerie") |
|
230 | std::shared_ptr<SpectrogramTimeSerie>>(m, "SpectrogramTimeSerie") | |
203 | .def(py::init<>()) |
|
231 | .def(py::init<>()) | |
204 | .def(py::init<const std::vector<std::size_t>>()) |
|
232 | .def(py::init<const std::vector<std::size_t>>()) | |
205 |
.def(py::init([](py::array_t<double> t, py::array_t<double> |
|
233 | .def(py::init([](py::array_t<double> t, py::array_t<double> y, | |
|
234 | py::array_t<double> values) { | |||
206 | assert(t.size() < values.size()); // TODO check geometry |
|
235 | assert(t.size() < values.size()); // TODO check geometry | |
|
236 | assert(y.size() == values.shape(1)); | |||
207 | SpectrogramTimeSerie::axis_t _t(t.size()); |
|
237 | SpectrogramTimeSerie::axis_t _t(t.size()); | |
|
238 | SpectrogramTimeSerie::axis_t _y(y.size()); | |||
208 | SpectrogramTimeSerie::container_type< |
|
239 | SpectrogramTimeSerie::container_type< | |
209 | SpectrogramTimeSerie::raw_value_type> |
|
240 | SpectrogramTimeSerie::raw_value_type> | |
210 | _values(values.size()); |
|
241 | _values(values.size()); | |
211 | copy_spectro(t, values, _t, _values); |
|
242 | copy_spectro(t, y, values, _t, _y, _values); | |
212 | std::vector<std::size_t> shape; |
|
243 | std::vector<std::size_t> shape; | |
213 | shape.push_back(values.shape(0)); |
|
244 | shape.push_back(values.shape(0)); | |
214 | shape.push_back(values.shape(1)); |
|
245 | shape.push_back(values.shape(1)); | |
215 | return SpectrogramTimeSerie(_t, _values, shape); |
|
246 | return SpectrogramTimeSerie(_t, _y, _values, shape); | |
216 | })) |
|
247 | })) | |
217 | .def("__getitem__", |
|
248 | .def("__getitem__", | |
218 | [](SpectrogramTimeSerie& ts, |
|
249 | [](SpectrogramTimeSerie& ts, | |
219 | std::size_t key) -> SpectrogramTimeSerie::iterator_t { |
|
250 | std::size_t key) -> SpectrogramTimeSerie::iterator_t { | |
220 | return ts.begin() + key; |
|
251 | return ts.begin() + key; | |
221 | }); |
|
252 | }); | |
222 |
|
253 | |||
223 | py::class_<Variable2, std::shared_ptr<Variable2>>(m, "Variable2") |
|
254 | py::class_<Variable2, std::shared_ptr<Variable2>>(m, "Variable2") | |
224 | .def(py::init<const QString&>()) |
|
255 | .def(py::init<const QString&>()) | |
225 | .def_property("name", &Variable2::name, &Variable2::setName) |
|
256 | .def_property("name", &Variable2::name, &Variable2::setName) | |
226 | .def_property_readonly("range", &Variable2::range) |
|
257 | .def_property_readonly("range", &Variable2::range) | |
227 | .def_property_readonly("nbPoints", &Variable2::nbPoints) |
|
258 | .def_property_readonly("nbPoints", &Variable2::nbPoints) | |
228 | .def_property_readonly( |
|
259 | .def_property_readonly( | |
229 | "data", |
|
260 | "data", | |
230 | [](Variable2& var) -> std::shared_ptr<TimeSeries::ITimeSerie> { |
|
261 | [](Variable2& var) -> std::shared_ptr<TimeSeries::ITimeSerie> { | |
231 | return var.data(); |
|
262 | return var.data(); | |
232 | }) |
|
263 | }) | |
233 | .def("set_data", |
|
264 | .def("set_data", | |
234 | [](Variable2& var, std::vector<TimeSeries::ITimeSerie*> ts_list, |
|
265 | [](Variable2& var, std::vector<TimeSeries::ITimeSerie*> ts_list, | |
235 | const DateTimeRange& range) { var.setData(ts_list, range); }) |
|
266 | const DateTimeRange& range) { var.setData(ts_list, range); }) | |
236 | .def("__len__", &Variable2::nbPoints) |
|
267 | .def("__len__", &Variable2::nbPoints) | |
237 | .def("__repr__", __repr__<Variable2>); |
|
268 | .def("__repr__", __repr__<Variable2>); | |
238 |
|
269 | |||
239 | py::class_<DateTimeRange>(m, "SqpRange") |
|
270 | py::class_<DateTimeRange>(m, "SqpRange") | |
240 | //.def("fromDateTime", &DateTimeRange::fromDateTime, |
|
271 | //.def("fromDateTime", &DateTimeRange::fromDateTime, | |
241 | // py::return_value_policy::move) |
|
272 | // py::return_value_policy::move) | |
242 | .def(py::init([](double start, double stop) { |
|
273 | .def(py::init([](double start, double stop) { | |
243 | return DateTimeRange{start, stop}; |
|
274 | return DateTimeRange{start, stop}; | |
244 | })) |
|
275 | })) | |
245 | .def(py::init( |
|
276 | .def(py::init( | |
246 | [](system_clock::time_point start, system_clock::time_point stop) { |
|
277 | [](system_clock::time_point start, system_clock::time_point stop) { | |
247 | double start_ = |
|
278 | double start_ = | |
248 | 0.001 * |
|
279 | 0.001 * | |
249 | duration_cast<milliseconds>(start.time_since_epoch()).count(); |
|
280 | duration_cast<milliseconds>(start.time_since_epoch()).count(); | |
250 | double stop_ = |
|
281 | double stop_ = | |
251 | 0.001 * |
|
282 | 0.001 * | |
252 | duration_cast<milliseconds>(stop.time_since_epoch()).count(); |
|
283 | duration_cast<milliseconds>(stop.time_since_epoch()).count(); | |
253 | return DateTimeRange{start_, stop_}; |
|
284 | return DateTimeRange{start_, stop_}; | |
254 | })) |
|
285 | })) | |
255 | .def_property_readonly("start", |
|
286 | .def_property_readonly("start", | |
256 | [](const DateTimeRange& range) { |
|
287 | [](const DateTimeRange& range) { | |
257 | return system_clock::from_time_t(range.m_TStart); |
|
288 | return system_clock::from_time_t(range.m_TStart); | |
258 | }) |
|
289 | }) | |
259 | .def_property_readonly("stop", |
|
290 | .def_property_readonly("stop", | |
260 | [](const DateTimeRange& range) { |
|
291 | [](const DateTimeRange& range) { | |
261 | return system_clock::from_time_t(range.m_TEnd); |
|
292 | return system_clock::from_time_t(range.m_TEnd); | |
262 | }) |
|
293 | }) | |
263 | .def("__repr__", __repr__<DateTimeRange>); |
|
294 | .def("__repr__", __repr__<DateTimeRange>); | |
264 | } |
|
295 | } |
@@ -1,152 +1,154 | |||||
1 | import sys |
|
1 | import sys | |
2 | import os |
|
2 | import os | |
3 | if not hasattr(sys, 'argv') or len(sys.argv)==0: |
|
3 | if not hasattr(sys, 'argv') or len(sys.argv)==0: | |
4 | sys.argv = [''] |
|
4 | sys.argv = [''] | |
5 | current_script_path = os.path.dirname(os.path.realpath(__file__)) |
|
5 | current_script_path = os.path.dirname(os.path.realpath(__file__)) | |
6 | sys.path.append(current_script_path) |
|
6 | sys.path.append(current_script_path) | |
7 |
|
7 | |||
8 | import sciqlopqt |
|
8 | import sciqlopqt | |
9 | import pysciqlopcore |
|
9 | import pysciqlopcore | |
10 |
|
10 | |||
11 | import numpy as np |
|
11 | import numpy as np | |
12 | import pandas as pds |
|
12 | import pandas as pds | |
13 | import datetime |
|
13 | import datetime | |
14 | import time |
|
14 | import time | |
15 | import unittest |
|
15 | import unittest | |
16 | import ddt |
|
16 | import ddt | |
17 |
|
17 | |||
18 | def listify(obj): |
|
18 | def listify(obj): | |
19 | if hasattr(obj, "__getitem__"): |
|
19 | if hasattr(obj, "__getitem__"): | |
20 | return obj |
|
20 | return obj | |
21 | return [obj] |
|
21 | return [obj] | |
22 |
|
22 | |||
23 | @ddt.ddt |
|
23 | @ddt.ddt | |
24 | class TimeSeriesCtors(unittest.TestCase): |
|
24 | class TimeSeriesCtors(unittest.TestCase): | |
25 | @ddt.data( |
|
25 | @ddt.data( | |
26 | (pysciqlopcore.ScalarTimeSerie,10), |
|
26 | (pysciqlopcore.ScalarTimeSerie,10), | |
27 | (pysciqlopcore.VectorTimeSerie,10), |
|
27 | (pysciqlopcore.VectorTimeSerie,10), | |
28 | (pysciqlopcore.SpectrogramTimeSerie,[10,10]), |
|
28 | (pysciqlopcore.SpectrogramTimeSerie,[10,10]), | |
29 | (pysciqlopcore.MultiComponentTimeSerie,[10,10]), |
|
29 | (pysciqlopcore.MultiComponentTimeSerie,[10,10]), | |
30 | (pysciqlopcore.ScalarTimeSerie,0), |
|
30 | (pysciqlopcore.ScalarTimeSerie,0), | |
31 | (pysciqlopcore.VectorTimeSerie,0), |
|
31 | (pysciqlopcore.VectorTimeSerie,0), | |
32 | (pysciqlopcore.SpectrogramTimeSerie,[0,10]), |
|
32 | (pysciqlopcore.SpectrogramTimeSerie,[0,10]), | |
33 | (pysciqlopcore.MultiComponentTimeSerie,[0,10]) |
|
33 | (pysciqlopcore.MultiComponentTimeSerie,[0,10]) | |
34 | ) |
|
34 | ) | |
35 | def test_construct(self, case): |
|
35 | def test_construct(self, case): | |
36 | ts = case[0](case[1]) |
|
36 | ts = case[0](case[1]) | |
37 | self.assertEqual(ts.shape,listify(case[1])) |
|
37 | self.assertEqual(ts.shape,listify(case[1])) | |
38 |
|
38 | |||
39 | class TimeSeriesData(unittest.TestCase): |
|
39 | class TimeSeriesData(unittest.TestCase): | |
40 | def test_set_ScalarTimeSerie_values(self): |
|
40 | def test_set_ScalarTimeSerie_values(self): | |
41 | ts = pysciqlopcore.ScalarTimeSerie(10) |
|
41 | ts = pysciqlopcore.ScalarTimeSerie(10) | |
42 | ts.t[0]=111. |
|
42 | ts.t[0]=111. | |
43 | self.assertEqual(ts.t[0],111.) |
|
43 | self.assertEqual(ts.t[0],111.) | |
44 | ts[0]=123. |
|
44 | ts[0]=123. | |
45 | self.assertEqual(ts[0],123.) |
|
45 | self.assertEqual(ts[0],123.) | |
46 |
|
46 | |||
47 | def test_set_VectorTimeSerie_values(self): |
|
47 | def test_set_VectorTimeSerie_values(self): | |
48 | ts = pysciqlopcore.VectorTimeSerie(10) |
|
48 | ts = pysciqlopcore.VectorTimeSerie(10) | |
49 | ts.t[0]=111. |
|
49 | ts.t[0]=111. | |
50 | self.assertEqual(ts.t[0],111.) |
|
50 | self.assertEqual(ts.t[0],111.) | |
51 | ts[0].x=111. |
|
51 | ts[0].x=111. | |
52 | ts[0].y=222. |
|
52 | ts[0].y=222. | |
53 | ts[0].z=333. |
|
53 | ts[0].z=333. | |
54 | self.assertEqual(ts[0].x,111.) |
|
54 | self.assertEqual(ts[0].x,111.) | |
55 | self.assertEqual(ts[0].y,222.) |
|
55 | self.assertEqual(ts[0].y,222.) | |
56 | self.assertEqual(ts[0].z,333.) |
|
56 | self.assertEqual(ts[0].z,333.) | |
57 |
|
57 | |||
58 | def test_set_SpectrogramTimeSerie_values(self): |
|
58 | def test_set_SpectrogramTimeSerie_values(self): | |
59 | ts = pysciqlopcore.SpectrogramTimeSerie((10,100)) |
|
59 | ts = pysciqlopcore.SpectrogramTimeSerie((10,100)) | |
60 | ts.t[0]=111. |
|
60 | ts.t[0]=111. | |
61 | self.assertEqual(ts.t[0],111.) |
|
61 | self.assertEqual(ts.t[0],111.) | |
62 | ts[0][11]=123. |
|
62 | ts[0][11]=123. | |
63 | self.assertEqual(ts[0][11],123.) |
|
63 | self.assertEqual(ts[0][11],123.) | |
64 |
|
64 | |||
65 | def test_build_ScalarTimeSerie_from_np_arrays(self): |
|
65 | def test_build_ScalarTimeSerie_from_np_arrays(self): | |
66 | ts = pysciqlopcore.ScalarTimeSerie(np.arange(10), np.arange(10)*10) |
|
66 | ts = pysciqlopcore.ScalarTimeSerie(np.arange(10), np.arange(10)*10) | |
67 | for i in range(len(ts)): |
|
67 | for i in range(len(ts)): | |
68 | self.assertEqual(ts[i],i*10.) |
|
68 | self.assertEqual(ts[i],i*10.) | |
69 |
|
69 | |||
70 | def test_build_VectorTimeSerie_from_np_arrays(self): |
|
70 | def test_build_VectorTimeSerie_from_np_arrays(self): | |
71 | v=np.ones((10,3)) |
|
71 | v=np.ones((10,3)) | |
72 | for i in range(3): |
|
72 | for i in range(3): | |
73 | v.transpose()[:][i] = np.arange(10)*10**i |
|
73 | v.transpose()[:][i] = np.arange(10)*10**i | |
74 | ts = pysciqlopcore.VectorTimeSerie(np.arange(10), v) |
|
74 | ts = pysciqlopcore.VectorTimeSerie(np.arange(10), v) | |
75 | for i in range(len(ts)): |
|
75 | for i in range(len(ts)): | |
76 | self.assertEqual(ts[i].x,i) |
|
76 | self.assertEqual(ts[i].x,i) | |
77 | self.assertEqual(ts[i].y,i*10.) |
|
77 | self.assertEqual(ts[i].y,i*10.) | |
78 | self.assertEqual(ts[i].z,i*100.) |
|
78 | self.assertEqual(ts[i].z,i*100.) | |
79 |
|
79 | |||
80 |
|
80 | |||
81 | def test_build_MultiComponentTimeSerie_from_np_arrays(self): |
|
81 | def test_build_MultiComponentTimeSerie_from_np_arrays(self): | |
82 | v=np.ones((10,5)) |
|
82 | v=np.ones((10,5)) | |
83 | for i in range(5): |
|
83 | for i in range(5): | |
84 | v.transpose()[:][i] = np.arange(10)*10**i |
|
84 | v.transpose()[:][i] = np.arange(10)*10**i | |
85 | ts = pysciqlopcore.MultiComponentTimeSerie(np.arange(10), v) |
|
85 | ts = pysciqlopcore.MultiComponentTimeSerie(np.arange(10), v) | |
86 | for i in range(len(ts)): |
|
86 | for i in range(len(ts)): | |
87 | self.assertEqual(ts[i][0],i) |
|
87 | self.assertEqual(ts[i][0],i) | |
88 | self.assertEqual(ts[i][1],i*10.) |
|
88 | self.assertEqual(ts[i][1],i*10.) | |
89 | self.assertEqual(ts[i][2],i*100.) |
|
89 | self.assertEqual(ts[i][2],i*100.) | |
90 |
|
90 | |||
91 | def test_build_MultiComponentTimeSerie_from_np_arrays_of_nan(self): |
|
91 | def test_build_MultiComponentTimeSerie_from_np_arrays_of_nan(self): | |
92 | v=np.empty((2,5)) |
|
92 | v=np.empty((2,5)) | |
93 | v.fill(np.nan) |
|
93 | v.fill(np.nan) | |
94 | ts = pysciqlopcore.MultiComponentTimeSerie(np.arange(2), v) |
|
94 | ts = pysciqlopcore.MultiComponentTimeSerie(np.arange(2), v) | |
95 | for i in range(len(ts)): |
|
95 | for i in range(len(ts)): | |
96 | self.assertTrue(np.isnan(ts[i][0])) |
|
96 | self.assertTrue(np.isnan(ts[i][0])) | |
97 | self.assertTrue(np.isnan(ts[i][1])) |
|
97 | self.assertTrue(np.isnan(ts[i][1])) | |
98 | self.assertTrue(np.isnan(ts[i][2])) |
|
98 | self.assertTrue(np.isnan(ts[i][2])) | |
99 | self.assertTrue(np.isnan(ts[i][3])) |
|
99 | self.assertTrue(np.isnan(ts[i][3])) | |
100 |
|
100 | |||
101 | def test_build_VectorTimeSerie_from_np_arrays_row(self): |
|
101 | def test_build_VectorTimeSerie_from_np_arrays_row(self): | |
102 | v=np.ones((10,3)) |
|
102 | v=np.ones((10,3)) | |
103 | for i in range(3): |
|
103 | for i in range(3): | |
104 | v.transpose()[:][i] = np.arange(10)*10**i |
|
104 | v.transpose()[:][i] = np.arange(10)*10**i | |
105 | ts = pysciqlopcore.VectorTimeSerie(np.arange(10), v) |
|
105 | ts = pysciqlopcore.VectorTimeSerie(np.arange(10), v) | |
106 | for i in range(len(ts)): |
|
106 | for i in range(len(ts)): | |
107 | self.assertEqual(ts[i].x,i) |
|
107 | self.assertEqual(ts[i].x,i) | |
108 | self.assertEqual(ts[i].y,i*10.) |
|
108 | self.assertEqual(ts[i].y,i*10.) | |
109 | self.assertEqual(ts[i].z,i*100.) |
|
109 | self.assertEqual(ts[i].z,i*100.) | |
110 |
|
110 | |||
111 | def test_build_ScalarTimeSerie_from_np_dataframe(self): |
|
111 | def test_build_ScalarTimeSerie_from_np_dataframe(self): | |
112 | df = pds.DataFrame(data=np.zeros((10,1)),index=np.arange(10)) |
|
112 | df = pds.DataFrame(data=np.zeros((10,1)),index=np.arange(10)) | |
113 | df[0] = np.arange(10) |
|
113 | df[0] = np.arange(10) | |
114 | ts = pysciqlopcore.ScalarTimeSerie(df.index.values, df.values) |
|
114 | ts = pysciqlopcore.ScalarTimeSerie(df.index.values, df.values) | |
115 | for i in range(len(ts)): |
|
115 | for i in range(len(ts)): | |
116 | self.assertEqual(ts[i],i) |
|
116 | self.assertEqual(ts[i],i) | |
117 |
|
117 | |||
118 | def test_build_VectorTimeSerie_from_np_dataframe(self): |
|
118 | def test_build_VectorTimeSerie_from_np_dataframe(self): | |
119 | df = pds.DataFrame(data=np.zeros((10,3)),index=np.arange(10)) |
|
119 | df = pds.DataFrame(data=np.zeros((10,3)),index=np.arange(10)) | |
120 | for i in range(3): |
|
120 | for i in range(3): | |
121 | df[i] = np.arange(10)*10**i |
|
121 | df[i] = np.arange(10)*10**i | |
122 | ts = pysciqlopcore.VectorTimeSerie(df.index.values, df.values) |
|
122 | ts = pysciqlopcore.VectorTimeSerie(df.index.values, df.values) | |
123 | for i in range(len(ts)): |
|
123 | for i in range(len(ts)): | |
124 | self.assertEqual(ts[i].x,i) |
|
124 | self.assertEqual(ts[i].x,i) | |
125 | self.assertEqual(ts[i].y,i*10.) |
|
125 | self.assertEqual(ts[i].y,i*10.) | |
126 | self.assertEqual(ts[i].z,i*100.) |
|
126 | self.assertEqual(ts[i].z,i*100.) | |
127 |
|
127 | |||
128 | def test_build_SpectrogramTimeSerie_from_np_arrays(self): |
|
128 | def test_build_SpectrogramTimeSerie_from_np_arrays(self): | |
129 |
v=np.ones((10, |
|
129 | v=np.ones((10,30)) | |
130 |
for i in range( |
|
130 | for i in range(30): | |
131 | v.transpose()[:][i] = np.arange(10)*10**i |
|
131 | v.transpose()[:][i] = np.arange(10)*10**(i/10.) | |
132 | ts = pysciqlopcore.SpectrogramTimeSerie(np.arange(10), v) |
|
132 | ts = pysciqlopcore.SpectrogramTimeSerie(np.arange(10),np.arange(30), v) | |
133 | for i in range(len(ts)): |
|
133 | for i in range(len(ts)): | |
134 |
for j in range( |
|
134 | for j in range(30): | |
135 | self.assertEqual(ts[i][j], i*10**j) |
|
135 | self.assertEqual(ts[i][j], i*10**(j/10.)) | |
|
136 | for i in range(30): | |||
|
137 | self.assertEqual(ts.axis(1)[i], i) | |||
136 |
|
138 | |||
137 | class VariableData(unittest.TestCase): |
|
139 | class VariableData(unittest.TestCase): | |
138 | def test_default_state(self): |
|
140 | def test_default_state(self): | |
139 | v=pysciqlopcore.Variable2("hello") |
|
141 | v=pysciqlopcore.Variable2("hello") | |
140 | self.assertEqual(str(v.name), str("hello")) |
|
142 | self.assertEqual(str(v.name), str("hello")) | |
141 | self.assertEqual(type(v.data), type(None)) |
|
143 | self.assertEqual(type(v.data), type(None)) | |
142 | self.assertEqual(len(v), 0) |
|
144 | self.assertEqual(len(v), 0) | |
143 |
|
145 | |||
144 | def test_set_name(self): |
|
146 | def test_set_name(self): | |
145 | v=pysciqlopcore.Variable2("hello") |
|
147 | v=pysciqlopcore.Variable2("hello") | |
146 | self.assertEqual(str(v.name), str("hello")) |
|
148 | self.assertEqual(str(v.name), str("hello")) | |
147 | v.name="newName" |
|
149 | v.name="newName" | |
148 | self.assertEqual(str(v.name), str("newName")) |
|
150 | self.assertEqual(str(v.name), str("newName")) | |
149 |
|
151 | |||
150 | if __name__ == '__main__': |
|
152 | if __name__ == '__main__': | |
151 | unittest.main(exit=False) |
|
153 | unittest.main(exit=False) | |
152 |
|
154 |
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