Red-Green Detection, Multiple Stim

def diff_img_pic(self):
    """ Differential images for each simulation.

    """
    cell_contour = measure.find_contours(self.proc_mask, level=0.5)

    stim_num = self.wt_up_mask_list.shape[0]
    fig = plt.figure(figsize= (30, 30))
    for i in range(stim_num):
        ax0 = fig.add_subplot(2, stim_num, i+1)
        ax0.text(40, 40, f'WT ({int(np.max(self.wt_up_label_list[i]))} ROIs)', fontsize=20, color='white')
        ax0.imshow(self.wt_diff_img_list[i], cmap=plot.CMaps().cmap_red_green, vmax=1, vmin=-1)
        for ce_c in cell_contour:
            ax0.plot(ce_c[:, 1], ce_c[:, 0], linewidth=1, color='w')
        ax0.axis('off')
        ax0.axis('off')

        ax1 = fig.add_subplot(2, stim_num, i+1+stim_num)
        ax1.text(40, 40, f'Mut. ({int(np.max(self.mut_up_label_list[i]))} ROIs)', fontsize=20, color='white')
        ax1.imshow(self.mut_diff_img_list[i], cmap=plot.CMaps().cmap_red_green, vmax=1, vmin=-1)
        for ce_c in cell_contour:
            ax1.plot(ce_c[:, 1], ce_c[:, 0], linewidth=2, color='w')
        ax1.axis('off')
    plt.tight_layout()
    plt.show()

def mask_diff_pic(self):
    """ WT vs. mutant up masks comparison with overlay (red - WT, green - mutant).

    """
    cell_contour = measure.find_contours(self.proc_mask, level=0.5)

    stim_num = self.wt_up_mask_list.shape[0]
    fig = plt.figure(figsize= (20, 20))
    for i in range(stim_num):
        wt_mask = np.asarray(self.wt_up_mask_list[i], dtype=int) 
        mut_mask = np.asarray(self.mut_up_mask_list[i], dtype=int)
        sum_mask = wt_mask + mut_mask
        tot_mask = sum_mask != 0

        # up_mask_diff = plot.toRGB(r_img=wt_mask,
        #                           g_img=mut_mask,
        #                           b_img=np.zeros_like(wt_mask))
        ax0 = plt.subplot(1, stim_num, i+1)
        ax0.imshow(np.mean(self.mut_img, axis=0)*-1, cmap='Greys')
        ax0.imshow(ma.masked_where(~tot_mask, sum_mask)*-1, cmap=plot.CMaps().cmap_red_green)
        ax0.text(100, 100, f'WT {int(np.max(self.wt_up_label_list[i]))} ROIs\nMut. {int(np.max(self.mut_up_label_list[i]))} ROIs',
                 fontsize=20, color='white')
        for ce_c in cell_contour:
            ax0.plot(ce_c[:, 1], ce_c[:, 0], linewidth=2, color='w')
        ax0.axis('off')

    plt.tight_layout()
    plt.show()

@staticmethod
def up_mask_calc(input_img_series:np.ndarray, input_img_mask:np.ndarray,
                 sd_tolerance:int=2, stim_list:list[int]=[10],
                 base_frames:int=2, stim_win:int=1, stim_shift:int=0):
    """ Function for generating a set of insertion regions mask
    for image series with multiple stimuli/applications using the differential image method.

    Could be used in stand-alone mode as a static method of the WTvsMut class.

    Parameters
    ----------
    input_img_series: ndarray [t,x,y]
        image time series
    input_img_mask: ndarray [x,y]
        cell region boolean mask
    sd_tolerance: int
        insertion ("up") region detection threshold: the number
        of standard deviations of extracellular noise
        (measured in the area outside of `input_img_mask`)
    base_frames: int
        number of frames from the beginning
        of the image series used to create
        an image of basal fluorescence
    stim_list: list[int]
        indexes of the frames where stimulations begins
    stim_win: int
        stimulation window, the number of frames following each `stim_list` indexes
        that are used to create set of images displaying maximal insertions after each stimuli

    Returns
    -------
    up_mask_filt_list: ndarray [stimuli, x,y]
        boolean masks of intensity increase regions
    up_label_list: ndarray [stimuli, x,y]
        label images of intensity increase regions  
    img_diff_list: ndarray [stimuli, x,y]
        differential images of intensity changes after stimulation

    """
    ref_img_series = filters.gaussian(input_img_series, sigma=1.5, channel_axis=0)

    up_mask_filt_list = []
    up_label_list = []
    img_diff_list = []
    stim_i_pairs_list = []
    for stim_i in stim_list:
        base_i = stim_i-base_frames

        img_base = np.mean(ref_img_series[base_i:stim_i], axis=0) 

        start_i = stim_i+stim_shift
        fin_i = stim_i+stim_win + stim_shift
        img_max = np.mean(ref_img_series[start_i:fin_i], axis=0)

        img_diff = img_max - img_base
        img_diff_norm = img_diff / np.max(np.abs(img_diff))

        diff_sd = np.std(img_diff, where=input_img_mask)
        up_mask = img_diff > diff_sd * sd_tolerance
        # up_mask =  img_diff_norm > sd_tolerance

        # NEW MASK PROCESSING
        up_mask_filt = ndi.binary_fill_holes(up_mask)
        up_mask_filt = morphology.remove_small_objects(up_mask_filt, min_size=20)
        up_mask_filt = segmentation.expand_labels(up_mask_filt, distance=1)

        # OLD MASK PROCESSING
        # up_mask_filt = morphology.opening(up_mask, footprint=morphology.disk(3))
        # up_mask_filt = morphology.dilation(up_mask_filt, footprint=morphology.disk(2))

        up_mask_filt = segmentation.clear_border(up_mask_filt)
        up_label = measure.label(up_mask_filt)

        up_mask_filt_list.append(up_mask_filt)
        up_label_list.append(up_label)
        img_diff_list.append(img_diff_norm)

        stim_i_pairs_list.append([[base_i, stim_i],[start_i, fin_i]])

    print(stim_i_pairs_list)

    return np.asarray(up_mask_filt_list), np.asarray(up_label_list), np.asarray(img_diff_list)

@staticmethod
def up_mask_calc_old(input_img_series:np.ndarray, input_img_mask:np.ndarray,
                 sd_tolerance:int=2, base_frames:int=5,
                 stim_list:list[int]=[10], stim_win:int=5):
    """ Function for generating a set of insertion regions mask
    for image series with multiple stimuli/applications using the differential image method.

    Could be used in stand-alone mode as a static method of the WTvsMut class.

    Parameters
    ----------
    input_img_series: ndarray [t,x,y]
        image time series
    input_img_mask: ndarray [x,y]
        cell region boolean mask
    sd_tolerance: int
        insertion ("up") region detection threshold: the number
        of standard deviations of extracellular noise
        (measured in the area outside of `input_img_mask`)
    base_frames: int
        number of frames from the beginning
        of the image series used to create
        an image of basal fluorescence
    stim_list: list[int]
        indexes of the frames where stimulations begins
    stim_win: int
        stimulation window, the number of frames following each `stim_list` indexes
        that are used to create set of images displaying maximal insertions after each stimuli

    Returns
    -------
    up_mask_filt_list: ndarray [stimuli, x,y]
        boolean masks of intensity increase regions
    up_label_list: ndarray [stimuli, x,y]
        label images of intensity increase regions  
    img_diff_list: ndarray [stimuli, x,y]
        differential images of intensity changes after stimulation

    """
    ref_img_series = filters.gaussian(input_img_series, sigma=1.25, channel_axis=0)

    img_base = np.mean(ref_img_series[:base_frames+1], axis=0)

    up_mask_filt_list = []
    up_label_list = []
    img_diff_list = []
    stim_i_pairs_list = []
    for stim_start in stim_list:
        start_i = stim_start
        stop_i = stim_start+stim_win+1

        stim_i_pairs_list.append([start_i, stop_i-1])

        img_max = np.mean(ref_img_series[start_i:stop_i], axis=0)

        img_diff = img_max - img_base
        img_diff_norm = img_diff/np.max(np.abs(img_diff))

        diff_sd = np.std(img_diff, where=input_img_mask)
        up_mask = img_diff > diff_sd * sd_tolerance
        # up_mask =  img_diff_norm > sd_tolerance

        up_mask_filt = morphology.opening(up_mask, footprint=morphology.disk(2))
        up_mask_filt = morphology.dilation(up_mask_filt, footprint=morphology.disk(1))
        up_mask_filt = segmentation.clear_border(up_mask_filt)
        up_label = measure.label(up_mask_filt)

        up_mask_filt_list.append(up_mask_filt)
        up_label_list.append(up_label)
        img_diff_list.append(img_diff_norm)

    print(stim_i_pairs_list)

    return np.asarray(up_mask_filt_list), np.asarray(up_label_list), np.asarray(img_diff_list)