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@ -265,11 +265,11 @@ class Parser(object): |
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# The images for the various channels are interleaved within the same array. For example, the second image |
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# of a four image group will be composed of bytes 2, 6, 10, etc. If you understand why someone would design |
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# a data structure that way, please send the author of this library a message. |
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number_of_true_channels = int(len(image_group_data[image_data_start:]) / (height * width)) |
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number_of_true_channels = int(len(image_group_data[4:]) / (height * width)) |
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try: |
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image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, width)) |
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except ValueError: |
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image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, int(len(image_group_data[image_data_start::number_of_true_channels])/height))) |
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image_data = np.reshape(image_group_data[image_data_start::number_of_true_channels], (height, int(round(len(image_group_data[image_data_start::number_of_true_channels])/height)))) |
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# Skip images that are all zeros! This is important, since NIS Elements creates blank "gap" images if you |
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# don't have the same number of images each cycle. We discovered this because we only took GFP images every |
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