mirror of
https://github.com/bec-project/bec_widgets.git
synced 2025-07-13 19:21:50 +02:00
feat: simulation stream with Gaussian peak in 1st quadrant
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@ -10,13 +10,15 @@ from PyQt5.QtCore import pyqtSlot, pyqtSignal
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from PyQt5.QtWidgets import QWidget
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from pyqtgraph.Qt import uic
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from scipy.stats import multivariate_normal
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class EigerPlot(QWidget):
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update_signale = pyqtSignal()
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def __init__(self, parent=None):
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super().__init__(parent)
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pg.setConfigOptions(background="w", foreground="k", antialias=True)
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# pg.setConfigOptions(background="w", foreground="k", antialias=True)
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current_path = os.path.dirname(__file__)
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uic.loadUi(os.path.join(current_path, "eiger_plot.ui"), self)
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@ -55,6 +57,9 @@ class EigerPlot(QWidget):
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self.doubleSpinBox_hist_min.valueChanged.connect(self.update_hist)
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self.doubleSpinBox_hist_max.valueChanged.connect(self.update_hist)
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# ComboBoxes
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self.comboBox_rotation.currentIndexChanged.connect(lambda k: self.rotate_data(k))
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# Signal/Slots
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self.update_signale.connect(self.on_image_update)
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@ -69,6 +74,25 @@ class EigerPlot(QWidget):
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self.hist_levels[1] + 0.1 * self.hist_levels[1],
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)
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def rotate_data(self, k: int = 0) -> None:
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"""Rotate image by 90 degrees k times.
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Args:
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k(int): Number of times to rotate image by 90 degrees.
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"""
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self.image = np.rot90(self.image, k=k)
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def transpose_data(self):
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self.image = np.transpose(self.image)
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@pyqtSlot()
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def on_image_update(self):
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self.imageItem.setImage(self.image, autoLevels=False)
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###############################
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# ZMQ Consumer
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###############################
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def start_zmq_consumer(self):
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consumer_thread = threading.Thread(target=self.zmq_consumer, daemon=True).start()
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@ -90,10 +114,6 @@ class EigerPlot(QWidget):
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receiver.disconnect(live_stream_url)
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receiver.context.term()
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@pyqtSlot()
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def on_image_update(self):
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self.imageItem.setImage(self.image, autoLevels=False)
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###############################
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# just simulations from here
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###############################
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@ -103,20 +123,31 @@ class EigerPlot(QWidget):
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def sim_stream(self):
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for i in range(100):
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self.image = np.random.rand(100, 100) * 10
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# Generate 100x100 image of random noise
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self.image = np.random.rand(100, 100) * 0.2
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# Define Gaussian parameters
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x, y = np.mgrid[0:50, 0:50]
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pos = np.dstack((x, y))
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# Center at (25, 25) longer along y-axis
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rv = multivariate_normal(mean=[25, 25], cov=[[25, 0], [0, 80]])
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# Generate Gaussian in the first quadrant
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gaussian_quadrant = rv.pdf(pos) * 40
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# Place Gaussian in the first quadrant
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self.image[0:50, 0:50] += gaussian_quadrant * 10
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self.update_signale.emit()
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time.sleep(0.1)
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if __name__ == "__main__":
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import sys
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from PyQt5.QtWidgets import QApplication
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app = QApplication(sys.argv)
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plot = EigerPlot()
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plot.show()
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sys.exit(app.exec_())
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