def processed_data(cls, tid, shape, *,
gen='random',
dtype=config['SOURCE_PROC_IMAGE_DTYPE'],
roi_histogram=False,
histogram=False,
correlation=False,
binning=False,
**kwargs):
processed = ProcessedData(tid)
imgs = cls._gen_images(gen, shape, dtype)
processed.image = ImageData.from_array(imgs, **kwargs)
if roi_histogram:
pass
if histogram:
hist = processed.hist
hist.hist = np.arange(10)
hist.bin_centers = np.arange(10) / 100.
hist.mean, hist.median, hist.std = 1., 0, 0.1
if correlation:
pass
if binning:
pass
return processed
def testGeneral(self):
# ---------------------
# pulse-resolved data
# ---------------------
data = ProcessedData(1234)
self.assertEqual(1234, data.tid)
self.assertEqual(0, data.n_pulses)
data.image = ImageData.from_array(np.zeros((1, 2, 2)))
self.assertEqual(1, data.n_pulses)
data = ProcessedData(1235)
data.image = ImageData.from_array(np.zeros((3, 2, 2)))
self.assertEqual(3, data.n_pulses)
# ---------------------
# train-resolved data
# ---------------------
data = ProcessedData(1236)
data.image = ImageData.from_array(np.zeros((2, 2)))
self.assertEqual(1236, data.tid)
self.assertEqual(1, data.n_pulses)
def testBin1dHist(self):
from extra_foam.gui.windows.binning_w import Bin1dHist
widget = Bin1dHist()
widget._data = ProcessedData(1)
widget.refresh()
def testBulletinView(self):
processed = ProcessedData(1357)
processed.image = ImageData.from_array(np.ones((10, 4, 4), np.float32))
processed.image.dark_count = 99
processed.image.n_dark_pulses = 10
processed.pidx.mask([1, 3, 5, 6])
self.gui._queue.append(processed)
self.image_tool.updateWidgetsF()
view = self.image_tool._bulletin_view
self.assertEqual(1357, int(view._latest_tid.intValue()))
self.assertEqual(10, int(view._n_total_pulses.intValue()))
self.assertEqual(6, int(view._n_kept_pulses.intValue()))
self.assertEqual(99, int(view._dark_train_counter.intValue()))
self.assertEqual(10, int(view._n_dark_pulses.intValue()))
def testGeneral(self):
from extra_foam.gui.windows.correlation_w import CorrelationPlot
for i in range(2):
widget = CorrelationPlot(0)
widget._data = ProcessedData(1)
widget.refresh()
def processed_data(cls,
tid,
shape,
*,
gen='random',
dtype=config['SOURCE_PROC_IMAGE_DTYPE'],
roi_histogram=False,
histogram=False,
correlation=False,
binning=False,
**kwargs):
processed = ProcessedData(tid)
imgs = cls._gen_images(gen, shape, dtype)
processed.image = ImageData.from_array(imgs, **kwargs)
if roi_histogram:
pass
if histogram:
hist = processed.hist
hist.hist = np.arange(10)
hist.bin_centers = np.arange(10) / 100.
hist.mean, hist.median, hist.std = 1., 0, 0.1
if correlation:
corr_resolution = 2
for i in range(2):
corr = processed.corr[i]
if i == 0:
data = SimplePairSequence()
else:
data = OneWayAccuPairSequence(corr_resolution)
for j in range(5):
data.append((j, 5 * j))
corr.x, corr.y = data.data()
corr.source = f"abc - {i}"
corr.resolution = 0 if i == 0 else corr_resolution
if binning:
pass
return processed
def testHeatmap1D(self):
from extra_foam.gui.windows.binning_w import Bin1dHeatmap
widget = Bin1dHeatmap()
widget._data = ProcessedData(1)
# test "Auto level" reset
widget._auto_level = True
widget.refresh()
self.assertFalse(widget._auto_level)
def testHeatmap2D(self):
from extra_foam.gui.windows.binning_w import Bin2dHeatmap
for is_count in [False, True]:
widget = Bin2dHeatmap(count=is_count)
widget._data = ProcessedData(1)
# test "Auto level" reset
widget._auto_level = True
widget.refresh()
self.assertFalse(widget._auto_level)
def testBulletinView(self):
processed = ProcessedData(1357)
processed.image = ImageData.from_array(np.ones((10, 4, 4), np.float32))
processed.image.dark_count = 99
processed.image.n_dark_pulses = 10
processed.pidx.mask([1, 3, 5, 6])
self.gui._queue.append(processed)
self.image_tool.updateWidgetsF()
view = self.image_tool._bulletin_view
self.assertEqual(1357, int(view._displayed_tid.intValue()))
self.assertEqual(10, int(view._n_total_pulses.intValue()))
self.assertEqual(6, int(view._n_kept_pulses.intValue()))
self.assertEqual(99, int(view._dark_train_counter.intValue()))
self.assertEqual(10, int(view._n_dark_pulses.intValue()))
with patch.object(view._mon, "reset_process_count") as reset:
view._reset_process_count_btn.clicked.emit()
reset.assert_called_once()
def testFomHist(self):
from extra_foam.gui.windows.histogram_w import FomHist
widget = FomHist()
# empty data
widget._data = ProcessedData(1)
widget.refresh()
# non-empty data
widget._data = self.processed_data(1001, (4, 2, 2), histogram=True)
widget.refresh()
def testPoiRoiHist(self):
from extra_foam.gui.windows.pulse_of_interest_w import PoiRoiHist
widget = PoiRoiHist(0)
# empty data
data = ProcessedData(1)
widget.updateF(data)
# non-empty data
data = self.processed_data(1001, (4, 2, 2), histogram=True)
widget.updateF(data)
def _get_data(self, pulse_resolved=True):
processed = ProcessedData(1001)
if pulse_resolved:
processed.pulse.roi.roi1.fom = [
10, 20, 30, 40, 20, 30, 20, 10, 40, 10
]
else:
processed.roi.roi1.fom = 10
data = {'tid': 1001, 'processed': processed, 'raw': dict()}
return data, processed
def testCorrectedView(self):
from extra_foam.gui.image_tool.corrected_view import CorrectedView
# pulse-resolved
view = CorrectedView(pulse_resolved=True, parent=self.gui)
# empty data
data = ProcessedData(1)
view._corrected.updateF(data)
view._roi_hist.updateF(data)
# non-empty data
data = self.processed_data(1001, (2, 2), roi_histogram=True)
view._roi_hist.updateF(data)
# train-resolved
view = CorrectedView(pulse_resolved=False, parent=self.gui)
# empty data
data = ProcessedData(1)
view._roi_hist.updateF(data)
# non-empty data
data = self.processed_data(1001, (2, 2), roi_histogram=True)
view._roi_hist.updateF(data)
def testCorrectedView(self):
# pulse-resolved
view = CorrectedView(pulse_resolved=True, parent=self.gui)
# empty data
data = ProcessedData(1)
view.updateF(data, True)
# non-empty data
data = self.processed_data(1001, (2, 2), roi_histogram=True)
view.updateF(data, True)
# train-resolved
view = CorrectedView(pulse_resolved=False, parent=self.gui)
# empty data
data = ProcessedData(1)
view.updateF(data, True)
# non-empty data
data = self.processed_data(1001, (2, 2), roi_histogram=True)
view.updateF(data, True)
def testAzimuthalIntegrationView(self):
# pulse-resolved
view = AzimuthalInteg1dView(pulse_resolved=True, parent=self.gui)
# empty data
data = ProcessedData(1)
view.updateF(data, True)
# non-empty data
data = self.processed_data(1001, (2, 2))
data.ai.x = np.arange(10)
data.ai.y = np.arange(10)
view.updateF(data, True)
data.ai.peaks = np.arange(10)
with patch.object(view._azimuthal_integ_1d_curve,
"setAnnotationList") as mocked:
view._azimuthal_integ_1d_curve.updateF(data)
mocked.assert_called_once()
def testRoiImageView(self):
widget = RoiImageView(1)
widget.setImage = MagicMock()
processed = ProcessedData(1)
processed.image.masked_mean = np.ones((3, 3))
# invalid ROI rect
self.assertListEqual(RectRoiGeom.INVALID, list(processed.roi.geom1.geometry))
widget.updateF(processed)
widget.setImage.assert_not_called()
# invalid ROI rect
processed.roi.geom1.geometry = [0, 0, -1, 0]
widget.updateF(processed)
widget.setImage.assert_not_called()
# valid ROI rect
processed.roi.geom1.geometry = [0, 0, 2, 2]
widget.updateF(processed)
widget.setImage.assert_called_once()
def testPoiImageView(self):
from extra_foam.gui.windows.pulse_of_interest_w import PoiImageView
widget = PoiImageView(0)
data = ProcessedData(1)
widget.updateF(data)
def data_with_assembled(cls, tid, shape, *,
src_type=DataSource.BRIDGE,
dtype=config['SOURCE_PROC_IMAGE_DTYPE'],
gen='random',
slicer=None,
with_xgm=False,
with_digitizer=False,
**kwargs):
imgs = cls._gen_images(gen, shape, dtype)
processed = ProcessedData(tid)
processed.image = ImageData.from_array(imgs, **kwargs)
if imgs.ndim == 2:
slicer = None
else:
slicer = slice(None, None) if slicer is None else slicer
src_list = [('Foo', 'oof'), ('Bar', 'rab'), ('karaboFAI', 'extra_foam')]
src_name, key_name = random.choice(src_list)
catalog = SourceCatalog()
ctg = 'ABCD'
src = f'{src_name} {key_name}'
catalog.add_item(SourceItem(ctg, src_name, [], key_name, slicer, None))
catalog._main_detector = src
n_pulses = processed.n_pulses
if with_xgm:
# generate XGM data
processed.pulse.xgm.intensity = np.random.rand(n_pulses)
processed.xgm.intensity = random.random()
processed.xgm.x = random.random()
processed.xgm.y = random.random()
if with_digitizer:
# generate digitizer data
digitizer = processed.pulse.digitizer
digitizer.ch_normalizer = 'B'
for ch in digitizer:
digitizer[ch].pulse_integral = np.random.rand(n_pulses)
data = {
'processed': processed,
'catalog': catalog,
'meta': {
src: {
'timestamp.tid': tid,
'source_type': src_type,
}
},
'raw': {
src: dict()
},
'assembled': {
'data': imgs,
}
}
if imgs.ndim == 2:
data['assembled']['sliced'] = imgs
else:
data['assembled']['sliced'] = imgs[slicer]
return data, processed
def testPumpProbeImageView(self):
from extra_foam.gui.windows.pump_probe_w import PumpProbeImageView
widget = PumpProbeImageView()
data = ProcessedData(1)
widget.updateF(data)
def testPumpProbeVFomPlot(self):
from extra_foam.gui.windows.pump_probe_w import PumpProbeVFomPlot
widget = PumpProbeVFomPlot()
data = ProcessedData(1)
widget.updateF(data)
def testInTrainFomPlot(self):
from extra_foam.gui.windows.histogram_w import InTrainFomPlot
widget = InTrainFomPlot()
data = ProcessedData(1)
widget.updateF(data)
def testPumpProbeFomPlot(self):
from extra_foam.gui.windows.pump_probe_w import PumpProbeFomPlot
widget = PumpProbeFomPlot()
widget._data = ProcessedData(1)
widget.refresh()
