Suite2p wang lab1 #3
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The thresholds are determined based on the mean and std of the image stack. Four thresholds are introduced: 1. thresh_peak_norm: the threshold calculated using mean+3*sd of mov_norm. It is used to identify active frames for each newly added ROI. 2. thresh_act_pix: the threshold is also calculated using mean+scaling*sd of mov_norm. It should be lower than thresh_peak_norm, and used to determine how much an ROI can expand. This parameter shares the same name as the original parameter used for a similar purpose but without threshold determination based on the imaging stack. 3. thresh_peak: the threshold calculated based on the down-sampled movie at the selected spatial scale. It is calculated using mean+3*sd of mov_norm_down[spatial_scale]. It is used to threshold the mov_norm_down and then calculating their sd to get mov_norm_sd_down. 4. thresh_peak_sd_down: the threshold calculated using mean + scaling*sd of mov_norm_sd_down[spatial_scale]. It is used to peak the next ROI location in mov_norm_sd_down[spatial_scale] that is larger than this threshold.
…ilter and normalization This is to ensure that the mov_norm has a zero mean for each pixel. Also, it seems that normalizing with the max value is easier to visualize the hotspots than normalizing with sd.
1. Added median filter in addition to max and mean filter 2. Since mov_norm has 0 mean, for pixels with high std, they typically have both high positive and negative values. When calculating lam values in iter_extend and selecting active frames, should consider both the positive and negative changes
This detect function is in the file detect.py. Adding the option to use "wang:bin_size" to control binning frames before processing.
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