Merge branch 'master' of https://code.ill.fr/fernandez-ruiz/scatteringimage

# Conflicts:
#	doc_files/Sphere/suggest.csv

README.md

@@ -42,17 +42,20 @@ All the scripts in this folder, should also be in the same directory in order to
 Among all the scripts, we are going to define the main ones. They call the other scripts in the folder. 
 * **save_sim_sphere:** script developed by ILL scientists to generate simulated scattered images based on instrumental and 
 sample parameters. Next scripts are based on this one.
-* **create_suggested_images:** create images specified in [suggest.csv](doc_files/suggest.csv). 
+* **create_suggested_images:** create images whose description is specified in [suggest.csv](doc_files/Sphere/suggest.csv). 
 * **create_img_param:** script used in *regression_radius_multip.py* for generating individual scattered images.
 
 ## doc_files
 It contains template files that are generated in python scripts.
 * **results.csv:** file (csv format) produced in *label_classify_image_folder.py*. It contains info of classified images
 (dist, collimation, wavelength, CNN prediction for this image, and the average prediction). It is used in 
-*regression_radius_multip.py*.
+[regression_radius_multip.py](python/regression_radius_multip.py) and [regression_radius_deep.py](python/regression_radius_deep.py).
 * **suggest.csv:** file (csv format). It contains: *dist*, *col*, *wavelength* suggested, and the *original radius*.
 It is created in *regression_radius_multip.py* and it is used in 
-[create_suggested_images.m](doc_files/create_suggested_images.m).
+[create_suggested_images.m](matlab/create_suggested_images.m).
+* **Original_misclassified.txt:** file (txt format) produced in *retrain.py* when option *--print_misclassified_images* is active. 
+It is used in [misclassified_images.py](python/misclassified_images.py) to reclassify images. You can move the images from their previous ubication to the one 
+that the CNN decides.
 
 ## imgs
 It contains two folders: one with simulated scattered images and other with real ones. All of them follows a *Sphere 

python_packages_required.md

 # Python packages to install:
+* **argparse:** makes it easy to write user-friendly command-line interfaces 
+* **csv:** csv files treatment
+* **h5py:** read nxs file
+* **keras:** makes it easy to build Convolutional Neural Network
+* **lightgbm:** type of regression model
+* **matlab.engine:** execute Matlab code. It is not easy to install. Follow 
+[this link](https://www.scivision.dev/matlab-engine-callable-from-python-how-to-install-and-setup/). 
+Pay attention if your computer is 32 or 64 bits and follow the path where the 
+package is installed. You have to check the path where *Python* is looking for the packages installed.
+* **matplotlib.pyplot:** plot figures. Problem to install it in GPU
 * **numpy:** array computing
-* **os:** move files
 * **opencv-python:** (cv2) image processing
-* **matplotlib.pyplot:** plot figures
+* **os:** move files
+* **pandas:** dataframe treatment
+* **re:** regular expression.
+* **shutil:** create and delete directories
 * **sklearn:** data mining and data analysis
 * **skimage:**  image processing and computer vision.
-* **argparse:** makes it easy to write user-friendly command-line interfaces 
-* **tensorflow:** open source machine learning library for research and production
-* **keras:** makes it easy to build Convolutional Neural Network
-* **csv:** csv files treatment
-* **matlab.engine:** execute Matlab code 
+* **statsmodels.api:** create a summary for models
 * **subprocess:** execute command line code
-* **re:** regular expression.
+* **tensorflow:** open source machine learning library for research and production
 * **time:** time measurement
-* **pandas:** dataframe treatment
-* **shutil:** create and delete directories
-* **xgboost:** type of model
-* **lightgbm:** type of model
-* **statsmodels.api:** create a summary for models
+* **xgboost:** type of regression model
 * **\_\_future\_\_**