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1 | Subproject commit 39bf3ff40b41fc01170241f3e471c708c866118b | |
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1 | Subproject commit 698d7cfa01b05427c2377ce2799f1290b9eab2ca | |
| @@ -271,8 +271,8 struct PlottablesUpdater<T, | |||
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271 | 271 | { |
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272 | 272 | std::for_each( |
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273 | 273 | std::begin(*serie), std::end(*serie), [&minValue, &maxValue](const auto& v) { |
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274 |
minValue = std::min( |
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275 |
maxValue = std::max( |
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274 | minValue = std::min(minValue, std::min_element(v.begin(), v.end())->v()); | |
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275 | maxValue = std::max(maxValue, std::max_element(v.begin(), v.end())->v()); | |
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276 | 276 | }); |
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277 | 277 | } |
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278 | 278 | plot.yAxis->setRange(QCPRange { minValue, maxValue }); |
| @@ -329,25 +329,58 struct PlottablesUpdater<T, | |||
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329 | 329 | // { |
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330 | 330 | // plot.yAxis->setRange(QCPRange { min, max }); |
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331 | 331 | // } |
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332 | double minValue = 0., maxValue = 0.; | |
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333 | if (auto serie = dynamic_cast<SpectrogramTimeSerie*>(&dataSeries)) | |
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334 | { | |
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335 | auto& yAxis = serie->axis(1); | |
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336 | if (yAxis.size()) | |
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337 | { | |
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338 | minValue = *std::min_element(std::cbegin(yAxis), std::cend(yAxis)); | |
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339 | maxValue = *std::max_element(std::cbegin(yAxis), std::cend(yAxis)); | |
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340 | } | |
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341 | } | |
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342 | plot.yAxis->setRange(QCPRange { minValue, maxValue }); | |
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332 | 343 | } |
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333 | 344 | |
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334 | 345 | static void updatePlottables( |
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335 | 346 | T& dataSeries, PlottablesMap& plottables, const DateTimeRange& range, bool rescaleAxes) |
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336 | 347 | { |
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337 | 348 | // TODO |
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338 |
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339 |
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340 |
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341 |
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342 | // associated"); | |
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343 | // return; | |
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344 | // } | |
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349 | if (plottables.empty()) | |
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350 | { | |
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351 | qCDebug(LOG_VisualizationGraphHelper()) | |
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352 | << QObject::tr("Can't update spectrogram: no colormap has been associated"); | |
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353 | return; | |
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354 | } | |
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345 | 355 | |
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346 | // // Gets the colormap to update (normally there is only one colormap) | |
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347 | // Q_ASSERT(plottables.size() == 1); | |
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348 | // auto colormap = dynamic_cast<QCPColorMap*>(plottables.at(0)); | |
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349 | // Q_ASSERT(colormap != nullptr); | |
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350 | 356 | |
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357 | // // Gets the colormap to update (normally there is only one colormap) | |
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358 | Q_ASSERT(plottables.size() == 1); | |
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359 | auto colormap = dynamic_cast<QCPColorMap*>(plottables.at(0)); | |
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360 | Q_ASSERT(colormap != nullptr); | |
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361 | if (auto serie = dynamic_cast<SpectrogramTimeSerie*>(&dataSeries)) | |
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362 | { | |
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363 | colormap->data()->setSize(serie->shape()[0], serie->shape()[1]); | |
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364 | if (serie->size(0)) | |
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365 | { | |
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366 | colormap->data()->setRange( | |
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367 | QCPRange { serie->begin()->t(), (serie->end() - 1)->t() }, | |
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368 | QCPRange { 1., 1000. }); | |
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369 | for (int x_index = 0; x_index < serie->shape()[0]; x_index++) | |
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370 | { | |
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371 | auto pixline = (*serie)[x_index]; | |
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372 | for (int y_index = 0; y_index < serie->shape()[1]; y_index++) | |
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373 | { | |
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374 | auto value = pixline[y_index]; | |
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375 | colormap->data()->setCell(x_index, y_index, value); | |
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376 | if (std::isnan(value)) | |
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377 | { | |
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378 | colormap->data()->setAlpha(x_index, y_index, 0); | |
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379 | } | |
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380 | } | |
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381 | } | |
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382 | } | |
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383 | } | |
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351 | 384 | // dataSeries.lockRead(); |
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352 | 385 | |
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353 | 386 | // // Processing spectrogram data for display in QCustomPlot |
| @@ -389,12 +422,12 struct PlottablesUpdater<T, | |||
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389 | 422 | // } |
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390 | 423 | |
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391 | 424 | // // Rescales axes |
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392 |
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393 | ||
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394 |
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395 |
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396 |
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397 |
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425 | auto plot = colormap->parentPlot(); | |
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426 | setPlotYAxisRange(dataSeries, {}, *plot); | |
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427 | if (rescaleAxes) | |
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428 | { | |
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429 | plot->rescaleAxes(); | |
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430 | } | |
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398 | 431 | } |
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399 | 432 | }; |
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400 | 433 | |
| @@ -4,7 +4,6 from datetime import datetime, timedelta, timezone | |||
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4 | 4 | import PythonProviders |
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5 | 5 | import pysciqlopcore |
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6 | 6 | import numpy as np |
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7 | import pandas as pds | |
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8 | 7 | import requests |
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9 | 8 | import copy |
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10 | 9 | from spwc.amda import AMDA |
| @@ -27,10 +26,8 def get_sample(metadata,start,stop): | |||
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27 | 26 | default_ctor_args = (0,2) |
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28 | 27 | tstart=datetime.fromtimestamp(start, tz=timezone.utc) |
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29 | 28 | tend=datetime.fromtimestamp(stop, tz=timezone.utc) |
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30 |
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31 | t = np.array([d.timestamp() for d in df.index]) | |
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32 | values = df.values | |
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33 | return ts_type(t,values) | |
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29 | var = amda.get_parameter(start_time=tstart, stop_time=tend, parameter_id=param_id, method="REST") | |
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30 | return ts_type(var.time,var.data) | |
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34 | 31 | except Exception as e: |
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35 | 32 | print(traceback.format_exc()) |
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36 | 33 | print("Error in amda.py ",str(e)) |
| @@ -55,7 +52,10 for key,parameter in parameters.items(): | |||
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55 | 52 | if n_components is '3': |
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56 | 53 | metadata.append(("type","vector")) |
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57 | 54 | elif n_components !=0: |
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58 | metadata.append(("type","multicomponent")) | |
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55 | if parameter.get('display_type','')=="spectrogram": | |
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56 | metadata.append(("type","spectrogram")) | |
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57 | else: | |
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58 | metadata.append(("type","multicomponent")) | |
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59 | 59 | else: |
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60 | 60 | metadata.append(("type","scalar")) |
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61 | 61 | products.append( (path, components, metadata)) |
| @@ -8,25 +8,30 from spwc.cache import _cache | |||
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8 | 8 | from spwc.common.datetime_range import DateTimeRange |
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9 | 9 | from functools import partial |
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10 | 10 | from datetime import datetime, timedelta, timezone |
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11 | ||
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12 | someglobal = 1 | |
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11 | from spwc.common.variable import SpwcVariable | |
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13 | 12 | |
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14 | 13 | def make_scalar(x): |
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15 | 14 | y = np.cos(x/10.) |
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16 | return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=y) | |
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15 | return SpwcVariable(time=x, data=y) | |
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17 | 16 | |
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18 | 17 | def make_vector(x): |
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19 | 18 | v=np.ones((len(x),3)) |
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20 | 19 | for i in range(3): |
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21 | 20 | v.transpose()[:][i] = np.cos(x/10. + float(i)) + (100. * np.cos(x/10000. + float(i))) |
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22 | return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=v) | |
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21 | return SpwcVariable(time=x, data=v) | |
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23 | 22 | |
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24 | 23 | |
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25 | 24 | def make_multicomponent(x): |
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26 | 25 | v=np.ones((len(x),4)) |
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27 | 26 | for i in range(4): |
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28 | 27 | v.transpose()[:][i] = float(i+1) * np.cos(x/10. + float(i)) |
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29 | return pds.DataFrame(index=[datetime.fromtimestamp(t, tz=timezone.utc) for t in x], data=v) | |
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28 | return SpwcVariable(time=x, data=v) | |
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29 | ||
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30 | def make_spectrogram(x): | |
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31 | v=np.ones((len(x),32)) | |
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32 | for i in range(32): | |
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33 | v.transpose()[:][i] = 100.*(2.+ float(i+1) * np.cos(x/1024. + float(i))) | |
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34 | return SpwcVariable(time=x, data=v) | |
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30 | 35 | |
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31 | 36 | |
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32 | 37 | def _get_data(p_type, start, stop): |
| @@ -40,6 +45,8 def _get_data(p_type, start, stop): | |||
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40 | 45 | return make_vector(x) |
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41 | 46 | if p_type == 'multicomponent': |
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42 | 47 | return make_multicomponent(x) |
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48 | if p_type == 'spectrogram': | |
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49 | return make_spectrogram(np.arange(math.ceil(start), math.floor(stop),15.)) | |
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43 | 50 | return None |
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44 | 51 | |
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45 | 52 | def get_data(metadata,start,stop): |
| @@ -56,19 +63,20 def get_data(metadata,start,stop): | |||
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56 | 63 | elif value == 'multicomponent': |
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57 | 64 | ts_type = pysciqlopcore.MultiComponentTimeSerie |
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58 | 65 | default_ctor_args = (0,2) |
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66 | elif value == 'spectrogram': | |
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67 | ts_type = lambda t,values: pysciqlopcore.SpectrogramTimeSerie(t,np.logspace(1,3,32),values) | |
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68 | default_ctor_args = (0,2) | |
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59 | 69 | if key == 'cache' and value == 'true': |
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60 | 70 | use_cache = True |
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61 | 71 | if use_cache: |
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62 | 72 | cache_product = f"tests/{p_type}" |
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63 |
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73 | var = _cache.get_data(cache_product, DateTimeRange(datetime.fromtimestamp(start, tz=timezone.utc), datetime.fromtimestamp(stop, tz=timezone.utc)), | |
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64 | 74 | partial(_get_data, p_type), |
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65 | 75 | fragment_hours=24) |
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66 | 76 | else: |
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67 | 77 | print("No Cache") |
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68 |
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69 | t = np.array([d.timestamp() for d in df.index]) | |
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70 | values = df.values | |
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71 | return ts_type(t,values) | |
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78 | var = _get_data(p_type, start, stop) | |
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79 | return ts_type(var.time,var.data) | |
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72 | 80 | except Exception as e: |
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73 | 81 | print(traceback.format_exc()) |
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74 | 82 | print("Error in test.py ",str(e)) |
| @@ -78,6 +86,7 products = [ | |||
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78 | 86 | ("/tests/without_cache/scalar",[],[("type","scalar")]), |
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79 | 87 | ("/tests/without_cache/vector",[],[("type","vector")]), |
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80 | 88 | ("/tests/without_cache/multicomponent",[],[("type","multicomponent"),('size','4')]), |
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89 | ("/tests/without_cache/spectrogram",[],[("type","spectrogram"),('size','32')]), | |
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81 | 90 | ("/tests/with_cache/scalar",[],[("type","scalar"), ("cache","true")]), |
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82 | 91 | ("/tests/with_cache/vector",[],[("type","vector"), ("cache","true")]), |
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83 | 92 | ("/tests/with_cache/multicomponent",[],[("type","multicomponent"),('size','4'), ("cache","true")]) |
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