regenerated docs, now with clickable examples

Author: haesleinhuepf

md/applyVectorFieldMD/readme.md

@@ -56,10 +56,10 @@ Ext.CLIJ2_push(shiftY);
 for (i = 0; i < 36; i += 6) {
 
 	// change the shift from slice to slice
-	Ext.CLIJ2_affineTransform2D(shiftX, rotatedShiftX, "-center rotate=" + (i * 10) + " center");
+	Ext.[CLIJ2_affineTransform2D](https://clij.github.io/clij2-docs/reference_affineTransform2D)(shiftX, rotatedShiftX, "-center rotate=" + (i * 10) + " center");
 
 	// apply transform
-	Ext.CLIJ2_applyVectorField2D(input, rotatedShiftX, shiftY, transformed);
+	Ext.[CLIJ2_applyVectorField2D](https://clij.github.io/clij2-docs/reference_applyVectorField2D)(input, rotatedShiftX, shiftY, transformed);
 
 	// get result back from GPU
 	Ext.CLIJ2_pull(transformed);

md/basics/readme.md

@@ -62,7 +62,7 @@ If we blur the input image, another `result` image is allocated in GPU memory.
 sigma = 5;
 //// you can, but you don't have to define the result image name:
 //result = "blurred_image"; 
-Ext.CLIJ2_gaussianBlur2D(input_image, result, sigma, sigma);
+Ext.[CLIJ2_gaussianBlur2D](https://clij.github.io/clij2-docs/reference_gaussianBlur2D)(input_image, result, sigma, sigma);
 ```
 
 ## Showing a result image

md/benchmarking/readme.md

@@ -86,7 +86,7 @@ run("Close All");
 // Local mean filter in GPU
 for (i = 1; i <= 10; i++) {
 	time = getTime();
-	Ext.CLIJ2_mean3DBox(input, blurred, 3, 3, 3);
+	Ext.[CLIJ2_mean3DBox](https://clij.github.io/clij2-docs/reference_mean3DBox)(input, blurred, 3, 3, 3);
 	print("CLIJ2 GPU mean filter no " + i + " took " + (getTime() - time) + " msec");
 }
 ```

md/blur/readme.md

@@ -37,7 +37,7 @@ run("Close All");
 Apply a Gaussian blur filter in GPU
 
 ```java
-Ext.CLIJ2_gaussianBlur3D(input, blurred, 5, 5, 1);
+Ext.[CLIJ2_gaussianBlur3D](https://clij.github.io/clij2-docs/reference_gaussianBlur3D)(input, blurred, 5, 5, 1);
 
 // Get results back from GPU
 Ext.CLIJ2_pull(blurred);

md/compare_workflows/readme.md

@@ -56,12 +56,12 @@ Ext.CLIJ2_push(input_clij);
 // subtract background in the image
 radius = 25;
 background_subtracted_clij = "background_subtracted_clij";
-Ext.CLIJ2_topHatBox(input_clij, background_subtracted_clij, 25, 25, 0);
+Ext.[CLIJ2_topHatBox](https://clij.github.io/clij2-docs/reference_topHatBox)(input_clij, background_subtracted_clij, 25, 25, 0);
 Ext.CLIJ2_pull(background_subtracted_clij);
 
 // threshold the image
 thresholded_clij = "thresholded_clij";
-Ext.CLIJ2_automaticThreshold(background_subtracted_clij, thresholded_clij, "Default");
+Ext.[CLIJ2_automaticThreshold](https://clij.github.io/clij2-docs/reference_automaticThreshold)(background_subtracted_clij, thresholded_clij, "Default");
 Ext.CLIJ2_pullBinary(thresholded_clij);
 
 end_time_clij = getTime();

md/filtering_in_graphs/readme.md

@@ -30,7 +30,7 @@ Ext.CLIJ2_create2D(pointlist, number_of_points, number_of_dimensions, bit_depth)
 random_min = 0;
 random_max = 100;
 seed = getTime();
-Ext.CLIJ2_setRandom(pointlist, random_min, random_max, seed);
+Ext.[CLIJ2_setRandom](https://clij.github.io/clij2-docs/reference_setRandom)(pointlist, random_min, random_max, seed);
 Ext.CLIJ2_pull(pointlist);
 zoom(10);
 
@@ -40,7 +40,7 @@ zoom(10);
 ## Draw the spots labelled (1, 2, 3, ...) in 2D space
 
 ```java
-Ext.CLIJ2_pointlistToLabelledSpots(pointlist, spots_image);
+Ext.[CLIJ2_pointlistToLabelledSpots](https://clij.github.io/clij2-docs/reference_pointlistToLabelledSpots)(pointlist, spots_image);
 Ext.CLIJ2_pull(spots_image);
 zoom(4);
 run("glasbey_on_dark");
@@ -68,7 +68,7 @@ for (i = 0; i < number_of_points; i += 1) {
 }
 measurement_array[number_of_points / 2] = 20;
 
-Ext.CLIJ2_pushArray(measurement, measurement_array, number_of_points, 1, 1);
+Ext.[CLIJ2_pushArray](https://clij.github.io/clij2-docs/reference_pushArray)(measurement, measurement_array, number_of_points, 1, 1);
 Ext.CLIJ2_pull(measurement);
 zoom(10);
 
@@ -78,7 +78,7 @@ zoom(10);
 ## Make a parametric image showing measurements in 2D space
 
 ```java
-Ext.CLIJ2_replaceIntensities(labelled_voronoi, measurement, parametric_image);
+Ext.[CLIJ2_replaceIntensities](https://clij.github.io/clij2-docs/reference_replaceIntensities)(labelled_voronoi, measurement, parametric_image);
 Ext.CLIJ2_pull(parametric_image);
 zoom(4);
 setMinAndMax(0, 20);
@@ -92,13 +92,13 @@ run("Fire");
 ```java
 
 // determine the touch matrix
-Ext.CLIJ2_generateTouchMatrix(labelled_voronoi, touch_matrix);
+Ext.[CLIJ2_generateTouchMatrix](https://clij.github.io/clij2-docs/reference_generateTouchMatrix)(labelled_voronoi, touch_matrix);
 
 /// determine median value of each nodes neighbors
-Ext.CLIJ2_medianOfTouchingNeighbors(measurement, touch_matrix, median_measurement);
+Ext.[CLIJ2_medianOfTouchingNeighbors](https://clij.github.io/clij2-docs/reference_medianOfTouchingNeighbors)(measurement, touch_matrix, median_measurement);
 
 // draw the median measurement, again as parametric image
-Ext.CLIJ2_replaceIntensities(labelled_voronoi, median_measurement, parametric_image);
+Ext.[CLIJ2_replaceIntensities](https://clij.github.io/clij2-docs/reference_replaceIntensities)(labelled_voronoi, median_measurement, parametric_image);
 Ext.CLIJ2_pull(parametric_image);
 zoom(4);
 setMinAndMax(0, 20);

md/labeling/readme.md

@@ -39,10 +39,10 @@ run("Close All");
 Create a mask using a fixed threshold and label connected components
 
 ```java
-Ext.CLIJ2_automaticThreshold(input, mask, "Otsu");
+Ext.[CLIJ2_automaticThreshold](https://clij.github.io/clij2-docs/reference_automaticThreshold)(input, mask, "Otsu");
 
 // label
-Ext.CLIJ2_connectedComponentsLabelingBox(mask, labelmap);
+Ext.[CLIJ2_connectedComponentsLabelingBox](https://clij.github.io/clij2-docs/reference_connectedComponentsLabelingBox)(mask, labelmap);
 
 // show result
 Ext.CLIJ2_pull(mask);

md/matrix_multiply/readme.md

@@ -25,8 +25,8 @@ array1 = newArray(1, 2, 3, 4, 5);
 array2 = newArray(6, 7, 8, 9, 10);
 
 // push arrays to GPU
-Ext.CLIJ2_pushArray(vector1, array1, 5, 1, 1);
-Ext.CLIJ2_pushArray(vector2, array2, 5, 1, 1);
+Ext.[CLIJ2_pushArray](https://clij.github.io/clij2-docs/reference_pushArray)(vector1, array1, 5, 1, 1);
+Ext.[CLIJ2_pushArray](https://clij.github.io/clij2-docs/reference_pushArray)(vector2, array2, 5, 1, 1);
 
 ```
 
@@ -35,7 +35,7 @@ Therefore, we transpose one of our vectors
 
 ```java
 // transpose first input vector and show it
-Ext.CLIJ2_transposeXY(vector1, vector1transposed);
+Ext.[CLIJ2_transposeXY](https://clij.github.io/clij2-docs/reference_transposeXY)(vector1, vector1transposed);
 Ext.CLIJ2_pull(vector1transposed);
 zoom(100);
 
@@ -50,7 +50,7 @@ zoom(100);
 ## Matrix multiplication
 
 ```java
-Ext.CLIJ2_multiplyMatrix(vector1transposed, vector2, matrix);
+Ext.[CLIJ2_multiplyMatrix](https://clij.github.io/clij2-docs/reference_multiplyMatrix)(vector1transposed, vector2, matrix);
 Ext.CLIJ2_pull(matrix);
 zoom(100);
 
@@ -62,15 +62,15 @@ zoom(100);
 ```java
 
 // generate another matrix of the same size with random values
-Ext.CLIJ2_getDimensions(matrix, width, height, depth);
+Ext.[CLIJ2_getDimensions](https://clij.github.io/clij2-docs/reference_getDimensions)(matrix, width, height, depth);
 bitDepth_float = 32;// 32-bit float type
 Ext.CLIJ2_create2D(another_matrix, width, height, bitDepth_float); 
 
 // random values between 0 and 1; seed is 5
-Ext.CLIJ2_setRandom(another_matrix, 0, 1, 5); 
+Ext.[CLIJ2_setRandom](https://clij.github.io/clij2-docs/reference_setRandom)(another_matrix, 0, 1, 5); 
 
 // element wise multiplication
-Ext.CLIJ2_multiplyImages(matrix, another_matrix, matrix_element_wise_multiplied);
+Ext.[CLIJ2_multiplyImages](https://clij.github.io/clij2-docs/reference_multiplyImages)(matrix, another_matrix, matrix_element_wise_multiplied);
 Ext.CLIJ2_pull(matrix_element_wise_multiplied);
 zoom(100);
 
@@ -80,7 +80,7 @@ zoom(100);
 ## Element wise multiplication of the matrix with a scalar
 
 ```java
-Ext.CLIJ2_multiplyImageAndScalar(matrix, elements_times_2, 2);
+Ext.[CLIJ2_multiplyImageAndScalar](https://clij.github.io/clij2-docs/reference_multiplyImageAndScalar)(matrix, elements_times_2, 2);
 Ext.CLIJ2_pull(elements_times_2);
 zoom(100);
 

md/maximumProjection/readme.md

@@ -38,18 +38,18 @@ close();
 We can use the classical maximum intensity projection in Z:
 
 ```java
-Ext.CLIJ2_maximumZProjection(input, maximum_z_projected);
+Ext.[CLIJ2_maximumZProjection](https://clij.github.io/clij2-docs/reference_maximumZProjection)(input, maximum_z_projected);
 Ext.CLIJ2_pull(maximum_z_projected);
 ```
 <a href="image_1587404232000.png"><img src="image_1587404232000.png" width="250" alt="CLIJ2_maximumZProjection_result71"/></a>
 
 But we can also project in X and Y direction:
 
 ```java
-Ext.CLIJ2_maximumYProjection(input, maximum_y_projected);
+Ext.[CLIJ2_maximumYProjection](https://clij.github.io/clij2-docs/reference_maximumYProjection)(input, maximum_y_projected);
 Ext.CLIJ2_pull(maximum_y_projected);
 
-Ext.CLIJ2_maximumXProjection(input, maximum_x_projected);
+Ext.[CLIJ2_maximumXProjection](https://clij.github.io/clij2-docs/reference_maximumXProjection)(input, maximum_x_projected);
 Ext.CLIJ2_pull(maximum_x_projected);
 ```
 <a href="image_1587404232083.png"><img src="image_1587404232083.png" width="250" alt="CLIJ2_maximumYProjection_result72"/></a>
@@ -60,7 +60,7 @@ Furthermore, we can frame the range from which the projection is drawn:
 ```java
 min_z = 90;
 max_z = 100;
-Ext.CLIJ2_maximumZProjectionBounded(input, bound_projection, min_z, max_z);
+Ext.[CLIJ2_maximumZProjectionBounded](https://clij.github.io/clij2-docs/reference_maximumZProjectionBounded)(input, bound_projection, min_z, max_z);
 Ext.CLIJ2_pull(bound_projection);
 ```
 <a href="image_1587404232142.png"><img src="image_1587404232142.png" width="250" alt="CLIJ2_maximumZProjectionBounded_result74"/></a>

md/mean_of_touching_neighbors/readme.md

@@ -37,7 +37,7 @@ run("Close All");
 Create a mask using the Otsu threshold algorithm
 
 ```java
-Ext.CLIJ2_thresholdOtsu(input, mask);
+Ext.[CLIJ2_thresholdOtsu](https://clij.github.io/clij2-docs/reference_thresholdOtsu)(input, mask);
 Ext.CLIJ2_pull(mask);
 
 ```
@@ -47,9 +47,9 @@ Draw a Voronoi diagram and invert it
 
 ```java
 
-Ext.CLIJ2_voronoiOctagon(mask, voronoi_diagram);
+Ext.[CLIJ2_voronoiOctagon](https://clij.github.io/clij2-docs/reference_voronoiOctagon)(mask, voronoi_diagram);
 // invert
-Ext.CLIJ2_binaryNot(voronoi_diagram, inverted_voronoi);
+Ext.[CLIJ2_binaryNot](https://clij.github.io/clij2-docs/reference_binaryNot)(voronoi_diagram, inverted_voronoi);
 
 Ext.CLIJ2_pullBinary(voronoi_diagram);
 Ext.CLIJ2_pullBinary(inverted_voronoi);
@@ -61,10 +61,10 @@ Ext.CLIJ2_pullBinary(inverted_voronoi);
 Generate a label map and extend it to make labels touch
 
 ```java
-Ext.CLIJ2_connectedComponentsLabelingBox(inverted_voronoi, labelled);
+Ext.[CLIJ2_connectedComponentsLabelingBox](https://clij.github.io/clij2-docs/reference_connectedComponentsLabelingBox)(inverted_voronoi, labelled);
 
 // Extend labels so that they touch
-Ext.CLIJ2_maximum2DBox(labelled, labelled_extended, 2, 2);
+Ext.[CLIJ2_maximum2DBox](https://clij.github.io/clij2-docs/reference_maximum2DBox)(labelled, labelled_extended, 2, 2);
 
 Ext.CLIJ2_pull(labelled);
 Ext.CLIJ2_pull(labelled_extended);
@@ -76,7 +76,7 @@ Ext.CLIJ2_pull(labelled_extended);
 Determine touch matrix
 
 ```java
-Ext.CLIJ2_generateTouchMatrix(labelled_extended, touch_matrix);
+Ext.[CLIJ2_generateTouchMatrix](https://clij.github.io/clij2-docs/reference_generateTouchMatrix)(labelled_extended, touch_matrix);
 Ext.CLIJ2_pullBinary(touch_matrix);
 
 ```
@@ -87,16 +87,16 @@ Do statistics on the label map
 
 ```java
 run("Clear Results");
-Ext.CLIJ2_statisticsOfBackgroundAndLabelledPixels(input, labelled_extended);
+Ext.[CLIJ2_statisticsOfBackgroundAndLabelledPixels](https://clij.github.io/clij2-docs/reference_statisticsOfBackgroundAndLabelledPixels)(input, labelled_extended);
 
-Ext.CLIJ2_resultsTableColumnToImage(intensity_values, "MEAN_INTENSITY");
+Ext.[CLIJ2_resultsTableColumnToImage](https://clij.github.io/clij2-docs/reference_resultsTableColumnToImage)(intensity_values, "MEAN_INTENSITY");
 
 ```
 
 Show Mean intensity per label as parametric image
 
 ```java
-Ext.CLIJ2_replaceIntensities(labelled_extended, intensity_values, intensity_map);
+Ext.[CLIJ2_replaceIntensities](https://clij.github.io/clij2-docs/reference_replaceIntensities)(labelled_extended, intensity_values, intensity_map);
 Ext.CLIJ2_pull(intensity_map);
 rename("label intensity");
 
@@ -107,8 +107,8 @@ Determine mean (mean) intensity of local neighbors and draw another parametric i
 
 ```java
 
-Ext.CLIJ2_meanOfTouchingNeighbors(intensity_values, touch_matrix, local_mean_intensity_values);
-Ext.CLIJ2_replaceIntensities(labelled_extended, local_mean_intensity_values, local_mean_intensity_map);
+Ext.[CLIJ2_meanOfTouchingNeighbors](https://clij.github.io/clij2-docs/reference_meanOfTouchingNeighbors)(intensity_values, touch_matrix, local_mean_intensity_values);
+Ext.[CLIJ2_replaceIntensities](https://clij.github.io/clij2-docs/reference_replaceIntensities)(labelled_extended, local_mean_intensity_values, local_mean_intensity_map);
 Ext.CLIJ2_pull(local_mean_intensity_map);
 rename("mean neighbor intensity");
 
@@ -120,14 +120,14 @@ Determine min and max (mean) intensity of local neighbors and draw two more para
 ```java
 
 // min
-Ext.CLIJ2_minimumOfTouchingNeighbors(intensity_values, touch_matrix, local_minimum_intensity_values);
-Ext.CLIJ2_replaceIntensities(labelled_extended, local_minimum_intensity_values, local_minimum_intensity_map);
+Ext.[CLIJ2_minimumOfTouchingNeighbors](https://clij.github.io/clij2-docs/reference_minimumOfTouchingNeighbors)(intensity_values, touch_matrix, local_minimum_intensity_values);
+Ext.[CLIJ2_replaceIntensities](https://clij.github.io/clij2-docs/reference_replaceIntensities)(labelled_extended, local_minimum_intensity_values, local_minimum_intensity_map);
 Ext.CLIJ2_pull(local_minimum_intensity_map);
 rename("minimum neighbor intensity");
 
 // max
-Ext.CLIJ2_maximumOfTouchingNeighbors(intensity_values, touch_matrix, local_maximum_intensity_values);
-Ext.CLIJ2_replaceIntensities(labelled_extended, local_maximum_intensity_values, local_maximum_intensity_map);
+Ext.[CLIJ2_maximumOfTouchingNeighbors](https://clij.github.io/clij2-docs/reference_maximumOfTouchingNeighbors)(intensity_values, touch_matrix, local_maximum_intensity_values);
+Ext.[CLIJ2_replaceIntensities](https://clij.github.io/clij2-docs/reference_replaceIntensities)(labelled_extended, local_maximum_intensity_values, local_maximum_intensity_map);
 Ext.CLIJ2_pull(local_maximum_intensity_map);
 rename("maximum neighbor intensity");
 

md/measure_overlap/readme.md

@@ -39,8 +39,8 @@ run("Close All");
 
 ```java
 
-Ext.CLIJ2_automaticThreshold(input, mask1, "Otsu");
-Ext.CLIJ2_automaticThreshold(input, mask2, "MinError");
+Ext.[CLIJ2_automaticThreshold](https://clij.github.io/clij2-docs/reference_automaticThreshold)(input, mask1, "Otsu");
+Ext.[CLIJ2_automaticThreshold](https://clij.github.io/clij2-docs/reference_automaticThreshold)(input, mask2, "MinError");
 
 Ext.CLIJ2_pullBinary(mask1);
 Ext.CLIJ2_pullBinary(mask2);
@@ -52,8 +52,8 @@ Ext.CLIJ2_pullBinary(mask2);
 ## measure overlap between the two masks
 
 ```java
-Ext.CLIJ2_getJaccardIndex(mask1, mask2, jaccardIndex);
-Ext.CLIJ2_getSorensenDiceCoefficient(mask1, mask2, diceIndex);
+Ext.[CLIJ2_getJaccardIndex](https://clij.github.io/clij2-docs/reference_getJaccardIndex)(mask1, mask2, jaccardIndex);
+Ext.[CLIJ2_getSorensenDiceCoefficient](https://clij.github.io/clij2-docs/reference_getSorensenDiceCoefficient)(mask1, mask2, diceIndex);
 
 // output result
 IJ.log("Overlap (Jaccard index): " + (jaccardIndex*100) + "%");

md/measure_statistics/readme.md

@@ -42,10 +42,10 @@ run("Close All");
 ## Segment the image and create a label mask
 
 ```java
-Ext.CLIJ2_thresholdOtsu(input, mask);
+Ext.[CLIJ2_thresholdOtsu](https://clij.github.io/clij2-docs/reference_thresholdOtsu)(input, mask);
 
 // connected components analysis
-Ext.CLIJ2_connectedComponentsLabeling(mask, labelmap);
+Ext.[CLIJ2_connectedComponentsLabeling](https://clij.github.io/clij2-docs/reference_connectedComponentsLabeling)(mask, labelmap);
 
 // show labelling
 Ext.CLIJx_pull(labelmap);
@@ -59,7 +59,7 @@ run("glasbey on dark");
 
 
 ```java
-Ext.CLIJ2_statisticsOfLabelledPixels(input, labelmap);
+Ext.[CLIJ2_statisticsOfLabelledPixels](https://clij.github.io/clij2-docs/reference_statisticsOfLabelledPixels)(input, labelmap);
 
 
 ```

md/rotate_comparison/readme.md

@@ -47,7 +47,7 @@ rotated_cpu = getTitle();
 ## Rotate image on GPU
 
 ```java
-Ext.CLIJ2_affineTransform2D(input, rotated_gpu, "-center rotate=45 center");
+Ext.[CLIJ2_affineTransform2D](https://clij.github.io/clij2-docs/reference_affineTransform2D)(input, rotated_gpu, "-center rotate=45 center");
 
 // show results
 Ext.CLIJ2_pull(rotated_gpu);