update docs

.gitignore

@@ -233,3 +233,4 @@ Saltykov_output.csv
 ## Notebook checkpoints
 #############
 .ipynb_checkpoints
+grain_size_tools/.ipynb_checkpoints/notebook_example-checkpoint.ipynb

DOCS/getting_started.md

@@ -45,7 +45,7 @@ differential stress via paleopizometers.
 Get a list of the main methods using: get.function_list()
 ```
 
-Alternatively, if you are using a Jupyter notebook you have an example in the link below:
+Alternatively, if you are using a Jupyter notebook you have an example in the link below (you can also find this example notebook on your hard drive inside the ``grain_size_tools`` folder):
 
 https://github.com/marcoalopez/GrainSizeTools/blob/master/grain_size_tools/notebook_example.ipynb
 

DOCS/imageJ_tutorial.md

@@ -12,7 +12,7 @@ Grain size studies in rocks are usually based on measures performed in thin sect
 ![Figure 1. An example of a grain boundary map](https://raw.githubusercontent.com/marcoalopez/GrainSizeTools/master/FIGURES/GBmap.png)  
 *Figure 1. An example of a grain boundary map*
 
-Nowadays, these measures are mostly made on digital images made by pixels (e.g. Heilbronner and Barret 2014), also known as raster graphics image. You can obtain some information on raster graphics [here](https://en.wikipedia.org/wiki/Raster_graphics). For example, in a 8-bit grayscale image -the most used type of grayscale image-, each pixel contains three values: information about its location in the image -their x and y coordinates- and its "grey" value in a range that goes from 0 (black) to 255 (white) (i.e. it allows 256 different grey intensities). In the case of a grain boundary map (Fig. 1), we usually use a binary image where only two possible values exist, 0 for white pixels (the grain boundary) and 1 for black pixels (the grains).
+Nowadays, these measures are mostly made on digital images made by pixels (e.g. Heilbronner and Barret 2014), also known as raster graphics image. You can obtain some information on raster graphics [here](https://en.wikipedia.org/wiki/Raster_graphics). For example, in a 8-bit grayscale image -the most used type of grayscale image-, each pixel contains three values: information about its location in the image -their x and y coordinates- and its "grey" value in a range that goes from 0 (black) to 255 (white) (i.e. it allows 256 different grey intensities). In the case of a grain boundary map (Fig. 1), a binary image is normally used where only two possible values exist, 0 for black pixels (the grains) and 1 for white pixels (the grain boundary).
 
 One of the key points about raster images is that they are resolution dependent, which means that each pixel have a physical dimension. Consequently, the smaller the size of the pixel the higher the resolution. The resolution depends on the number of pixels per unit area or length, and it is usually measured in pixel per (square) inch (PPI) (more information about [Image resolution](https://en.wikipedia.org/wiki/Image_resolution) and [Pixel density](https://en.wikipedia.org/wiki/Pixel_density)). This concept is key since the resolution of the raw image -the image obtained directly from the microscope- will limit the precision of the measures. Known the size of the pixels is therefore essential and it will allow us to set the scale of the image to measure of the areas of the grain profiles. In addition, it will allow us to later make a perimeter correction when calculating the equivalent diameters from the areas of the grain profiles. So be sure about the image resolution at every step, from the raw image you get from the microscope until you get the grain boundary map.
 
@@ -65,13 +65,5 @@ Heilbronner, R., Barret, S., 2014. Image Analysis in Earth Sciences. Springer-Ve
 
 > This book focuses on image analysis related with Earth Sciences putting much emphasis on methods used in structural geology. The first two chapters deals with image processing and grain segmentation techniques using the software Image SXM, which is a different flavour of the ImageJ family applications (see [here](http://fiji.sc/ImageJ)).
 
-Heilbronner, R., 2000. Automatic grain boundary detection and grain size analysis using polarization micrographs or orientation images. *J. Struct. Geol.* 22, 969–981. doi:[10.1016/S0191-8141(00)00014-6](http://www.sciencedirect.com/science/article/pii/S0191814100000146)
-
-> This paper explains a simple procedure for creating grain boundary maps from thin sections using a semi-automatic method implemented in a NIH Image macro named Lazy Grain Boundary (LGB). NIH Image is the predecessor of ImageJ (no longer under active development). The authors compare the results obtained using the LGB method and manual segmentation using digital images obtained from a quartzite under light microscopy using different techniques. Interestingly, all the steps described in the protocol can be automated using the ImageJ software and using more sophisticated segmentation algorithms than those originally implemented in the LGB macro (e.g. using the Canny edge detector instead of the Sobel one or more sophisticated noise reduction filters).
-
-Barraud, J., 2006. The use of watershed segmentation and GIS software for textural analysis of thin sections. *Journal of Volcanology and Geothermal Research* 154, 17–33. doi:[10.1016/j.jvolgeores.2005.09.017](http://dx.doi.org/10.1016%2Fj.jvolgeores.2005.09.017)
-
-> This paper propose a workflow for grain segmentation and grain analysis that differs slightly from other approaches referred here. For the acquisition, it uses three different images from light microscopy with different orientations, combining them in a false-colour RGB image. For grain segmentation it uses the anisotropic diffusion for noise reduction plus watershed methods (both available in ImageJ).
-
 [next section](https://github.com/marcoalopez/GrainSizeTools/blob/master/DOCS/references.md)  
 [table of contents](https://github.com/marcoalopez/GrainSizeTools/blob/master/DOCS/tableOfContents.md)