Merge branch 'master' of https://github.com/C-CINA/focus

apps/focus/AutoImportWindow.cpp

@@ -773,8 +773,8 @@ void AutoImportWindow::importImage() {
     conf->set("TLTANG", "-", false);
     conf->set("TAXA", "-", false);
     conf->set("TANGL", "-", false);
-    conf->set("defocus", "-", false);
-    conf->set("defocus_defocus", "-", false);
+    conf->set("defocus", "0.0,0.0,0.0", false);
+    conf->set("defocus_defocus", "0.0,0.0,0.0", false);
 
     //Set the parameters from filename
     for(QString param : fileNameParams.keys()) {

apps/src/widgets/ImageViewer.cpp

@@ -49,7 +49,15 @@ void ImageViewer::loadFile(const QString &fileName, const QString& extension, bo
     if(fileName.isEmpty() || extension.isEmpty()) return;
 
     if (!QFileInfo(fileName).exists()) {
-        setText(notFoundMessage_); 
+        if (extension_ == "mrc" && QFileInfo(fileName+".png").exists()) {
+            QImage image;
+            image = QImage(fileName+".png");
+            imageLabel->setPixmap(QPixmap::fromImage(image));
+            resizeWidgets();
+        }
+        else {
+            setText(notFoundMessage_); 
+        }
         return;
     }
 

scripts/proc/focus_utilities.py

@@ -95,6 +95,21 @@ def RotationalAverage( img ):
 
 	return rotavg
 
+def RadialProfile( img ):
+# Compute the 1D radial profile of a 2D image or 3D volume:
+
+	rmesh = RadialIndices( img.shape, rounding=True )[0]
+
+	profile = np.zeros( len( np.unique( rmesh ) ) )
+	j = 0
+	for r in np.unique( rmesh ):
+
+		idx = rmesh == r
+		profile[j] = img[idx].mean()
+		j += 1
+
+	return profile
+
 def RadialFilter( img, filt, return_filter = False ):
 # Given a list of factors 'filt', radially multiplies the Fourier Transform of 'img' by the corresponding term in 'filt'
 
@@ -121,11 +136,11 @@ def RadialFilter( img, filt, return_filter = False ):
 
 	if not return_filter:
 
-		return np.fft.irfftn( ft )
+		return np.fft.irfftn( ft, s=img.shape )
 
 	else:
 
-		return np.fft.irfftn( ft ), filter2d
+		return np.fft.irfftn( ft, s=img.shape ), filter2d
 
 def SoftMask( imsize = [100, 100], radius = 0.5, width = 6.0, rounding=False, xyz=[0,0,0] ):
 # Generates a circular or spherical mask with a soft cosine edge
@@ -204,7 +219,7 @@ def FilterGauss( img, apix=1.0, lp=-1, hp=-1, return_filter=False ):
 
 	ft = np.fft.rfftn( img )
 
-	filtered = np.fft.irfftn( ft * bandpass )
+	filtered = np.fft.irfftn( ft * bandpass, s=img.shape )
 
 	if return_filter:
 
@@ -228,7 +243,7 @@ def FilterBfactor( img, apix=1.0, B=0.0, return_filter=False ):
 
 	ft = np.fft.rfftn( img )
 
-	filtered = np.fft.irfftn( ft * bfac )
+	filtered = np.fft.irfftn( ft * bfac, s=img.shape )
 
 	if return_filter:
 
@@ -309,7 +324,7 @@ def HighResolutionNoiseSubstitution( img, lp = -1, apix = 1.0 ):
 
 	ftnew = amps * ( np.cos( phases ) + 1j*np.sin( phases ) )
 
-	return np.fft.irfftn( ftnew )
+	return np.fft.irfftn( ftnew, s=img.shape )
 
 def Resample( img, newsize=None, apix=1.0, newapix=None ):
 # Resizes a real image or volume by cropping/padding its Fourier Transform, i.e. resampling.
@@ -343,7 +358,7 @@ def NormalizeImg( img, mean=0.0, std=1.0, radius = -1 ):
 
 	return (img - m + mean) * std / s
 
-def FCC( volume1, volume2, phiArray = [0.0] ):
+def FCC( volume1, volume2, phiArray = [0.0], invertCone = False, xy_only = False, z_only = False ):
 	"""
 	Fourier conic correlation
 
@@ -366,6 +381,13 @@ def FCC( volume1, volume2, phiArray = [0.0] ):
 			[M,N,P] = volume1.shape
 			[zmesh, ymesh, xmesh] = np.mgrid[ -M/2:M/2, -N/2:N/2, -P/2:P/2  ]
 			rhomax = np.int( np.ceil( np.sqrt( M*M/4.0 + N*N/4.0 + P*P/4.0) ) + 1 )
+			if xy_only:
+				zmesh *= 0
+				rhomax = np.int( np.ceil( np.sqrt( N*N/4.0 + P*P/4.0) ) + 1 )
+			if z_only:
+				xmesh *= 0
+				ymesh *= 0
+				rhomax = rhomax = np.int( np.ceil( np.sqrt( M*M/4.0 ) ) + 1 )
 			rhomesh = np.sqrt( xmesh*xmesh + ymesh*ymesh + zmesh*zmesh )
 			phimesh = np.arccos( zmesh / rhomesh )
 			phimesh[M/2,N/2,P/2] = 0.0
@@ -402,6 +424,8 @@ def FCC( volume1, volume2, phiArray = [0.0] ):
 			rhoround_conic = rhoround
 		else:
 			conic = np.ravel( (phimesh <= phiAngle ) + ( (np.abs(phimesh - np.pi)) <= phiAngle )  )
+			if invertCone:
+				conic = np.invert( conic )
 			rhoround_conic = rhoround[conic]
 			fft1_conic = fft1[conic]
 			conj_fft2_conic = conj_fft2[conic]
@@ -410,7 +434,7 @@ def FCC( volume1, volume2, phiArray = [0.0] ):
 		Norm1 = np.bincount( rhoround_conic, np.abs(fft1_conic)*np.abs(fft1_conic) )
 		Norm2 = np.bincount( rhoround_conic, np.abs(conj_fft2_conic)*np.abs(conj_fft2_conic) )
 
-		goodIndices = np.argwhere( (Norm1 * Norm2) > 0.0 )
+		goodIndices = np.argwhere( (Norm1 * Norm2) > 0.0 )[:-1]
 		FCC_normed[goodIndices,J] = FCC[goodIndices] / np.sqrt( Norm1[goodIndices] * Norm2[goodIndices] )
 
 	return FCC_normed