Andor iQ Python IDE script to calculate Yokogawa excitation uniformity

$Drop Off.py

#SHELL

"""
Calculate Spinning Disk uniformity and compare with Andor's specification.

Usage:
Start the Python IDE from iQ from under Plugins.  Run this Plugin from
the iQ menu and select the "Progress" tab.
Run a protocol with just "Snap" and loop protocol enabled to collect images.
The bar will update to show how close the image drop-off.
Note if you are using the Clara, you have to edit the baseline
variable below.

Installation:
Copy this file to the Python Plugins directory, usually:
c:/Program Files/Andor Bioimaging/PythonEngine/Plugins/

Written by Pariksheet Nanda <p.nanda@andor.com> October 10, 2012
"""

from iqapp.observers import AcquisitionObserver
from iqtools.dialogs import progressSet
from numpy import array


class UniformityCheck(AcquisitionObserver):

    # FIXME: Need a way of finding the active camera model is a Clara
    # to set the baseline to 500

    # Baseline for iXon and CMOS cameras are 100.  Clara is the odd
    # one out at 500
    baseline = 100

    def event_newFrame(self, n):
        self.imf = self.im.frame(n) # Get current frame
        center = self.roi_mean_intensity()
        top_left = self.roi_mean_intensity([0.1, 0.1])
        top_right = self.roi_mean_intensity([0.9, 0.1])
        bottom_left = self.roi_mean_intensity([0.1, 0.9])
        bottom_right = self.roi_mean_intensity([0.9, 0.9])
        sides = (top_left + top_right + 
                 bottom_right + bottom_right) /4
        drop_off = (center - sides) / center * 100  # percent
        output = "C:{0} S:{1} TL:{2} TR:{3} BL:{4} " + \
            "BR:{5}"
        print output.format(center, sides, top_left, top_right,
                            bottom_left, bottom_right)
        status = '{0:6.2f}%'.format(drop_off)
        progressSet("Drop Off", percentage=drop_off,
                    status=status)
    
    def roi_mean_intensity(self, position_percent=(0.5,0.5)):
        """
        Get the mean value of a 5% rectangle ROI at a specific image
        position.

        @param position_percent: relative x and y position in image
        @type: tuple
        @rtype: float
        """
        imf_shape = array(self.imf.shape)
        roi_shape = imf_shape * 0.05
        roi_shape = roi_shape.astype(int)
        x, y = roi_shape
        roi_center = imf_shape * position_percent
        x_offset, y_offset = roi_center - roi_shape / 2
        roi = self.imf[x_offset:x_offset+x, y_offset:y_offset+y]
        return roi.mean() - self.baseline


obs = UniformityCheck()
obs.register()