Checking out thresholding

rfplot.py

@@ -8,6 +8,10 @@ import matplotlib.dates as mdates
 from matplotlib.backend_bases import MouseButton
 from matplotlib.widgets  import RectangleSelector
 import matplotlib as mpl
+from astropy.visualization import (ZScaleInterval,  ImageNormalize,SqrtStretch)
+import imageio
+from skimage.morphology import binary_dilation, remove_small_objects
+
 mpl.rcParams['keymap.save'].remove('s')
 mpl.rcParams['keymap.fullscreen'].remove('f')
 
@@ -42,11 +46,43 @@ if __name__ == "__main__":
     def line_select_callback(eclick, erelease):
         x1, y1 = eclick.xdata, eclick.ydata
         x2, y2 = erelease.xdata, erelease.ydata
-        array = mark.get_offsets()
-        maskx = np.logical_and(array[:,0] >= min(x1,x2),  array[:,0] <= max(x1,x2))
-        masky = np.logical_and(array[:,1] >= min(y1,y2),  array[:,1] <= max(y1,y2))
-        mask = np.logical_and(maskx, masky)
-        mark.set_offsets(array[np.logical_not(mask),:])
+
+        t1_ind = round(len(s.t) * (x1 - tmin) / (tmax - tmin))
+        t2_ind = round(len(s.t) * (x2 - tmin) / (tmax - tmin))
+        f1_ind = round(len(s.freq) * (y1 - fmin) / (fmax - fmin))
+        f2_ind = round(len(s.freq) * (y2 - fmin) / (fmax - fmin))
+        
+        
+        submat = s.z[f1_ind:f2_ind,t1_ind:t2_ind]
+        
+        signal = submat - np.median(submat, axis=0)
+        background = np.copy(signal)
+        filter = np.ones(50)/50
+        for i in range(signal.shape[1]):
+            background[:,i] = np.convolve(signal[:,i], filter, mode="same")
+
+        sig_without_background = signal - background
+        mask = sig_without_background > 3 * np.std(sig_without_background, axis=0)
+        sig_without_background[mask] = background[mask]
+        sig_without_background[np.logical_not(mask)] = signal[np.logical_not(mask)]
+
+        for i in range(signal.shape[1]):
+            background[:,i] = np.convolve(sig_without_background[:,i], filter, mode="same")
+
+        sig_without_background = signal - background
+        mask = (sig_without_background > 3 * np.std(sig_without_background, axis=0)).astype(np.uint8)
+        mask = binary_dilation(mask)  
+        remove_small_objects(mask, min_size=16, in_place=True)
+        mask = np.flipud(mask)
+
+        imageio.imwrite(f'test3.png', 255 * mask)
+
+        print(s.z[f1_ind:f2_ind,t1_ind:t2_ind].shape)
+        # array = mark.get_offsets()
+        # maskx = np.logical_and(array[:,0] >= min(x1,x2),  array[:,0] <= max(x1,x2))
+        # masky = np.logical_and(array[:,1] >= min(y1,y2),  array[:,1] <= max(y1,y2))
+        # mask = np.logical_and(maskx, masky)
+        # mark.set_offsets(array[np.logical_not(mask),:])
         fig.canvas.draw()
         print(f"select over {x1},{y1},{x2},{y2}")