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fixed inconsistencies in returned objects: missing channels, is_clipped
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@@ -98,6 +98,7 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
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self.y.append(numpy.zeros((samples,), dtype="int16"))
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self.x = numpy.zeros((samples,), dtype="float32")
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self.nChannels = channels
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self.index.append((0, samples-1))
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self.cont_data = True
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@@ -231,6 +232,8 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
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job_id=self.job_id,
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job_date=self.job_date,
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)
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r.nChannels = self.nChannels
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r.is_clipped = self.is_clipped
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finally:
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self.lock.release()
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@@ -274,6 +277,8 @@ class ADC_Result(Resultable, Drawable, DamarisFFT, Signalpath):
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tmp_y.append(self.y[i][start:end+1].copy())
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r = ADC_Result(x = tmp_x, y = tmp_y, index = [(0,len(tmp_y[0])-1)], sampl_freq = self.sampling_rate, desc = self.description.copy() if self.description is not None else None, job_id = self.job_id, job_date = self.job_date)
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r.nChannels = self.nChannels
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r.is_clipped = self.is_clipped
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self.lock.release()
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return r
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@@ -111,21 +111,22 @@ class Accumulation(Errorable, Drawable, DamarisFFT, Signalpath):
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self.lock.release()
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raise
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tmp_x = self.x[start:end+1]
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tmp_x = self.x[start:end+1].copy()
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tmp_y = []
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for i in range(self.get_number_of_channels()):
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tmp_y.append(self.y[i][start:end+1])
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tmp_y.append(self.y[i][start:end+1].copy())
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r = Accumulation(x = tmp_x, y = tmp_y, n = self.n, index = [(0,len(tmp_y[0])-1)], sampl_freq = self.sampling_rate, error = self.use_error)
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r = Accumulation(x = tmp_x, y = tmp_y, n = self.n, index = [(0,len(tmp_y[0])-1)], sampl_freq = self.sampling_rate, error = self.use_error)
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if self.uses_statistics():
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r.y_square = []
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for i in range(self.get_number_of_channels()):
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r.y_square.append(self.y_square[i][start:end+1])
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r.y_square.append(self.y_square[i][start:end+1].copy())
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if self.common_descriptions is not None:
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r.common_descriptions = self.common_descriptions.copy()
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r.time_period = self.time_period[:]
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r.job_ids = self.job_ids.copy()
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r.is_clipped = self.is_clipped
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self.lock.release()
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return r
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@@ -231,7 +231,6 @@ class ResultReader:
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for i in range(nChannels):
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# split interleaved data
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self.result.y[i][tmp_index:tmp_index+tmp_size] = tmp_part[i::nChannels]
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self.result.y[i][tmp_index:tmp_index+tmp_size] = tmp_part[i::nChannels]
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if self.result.index != []:
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self.result.index.append((tmp_index, tmp_index+tmp_size-1))
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