Rating workflow

One of the main aims of nanite is to simplify data analysis by sorting out bad curves automatically based on a user defined rating scheme. Nanite allows to automate the rating process using machine learning, based on scikit-learn. In short, an estimator is trained with a sample dataset that was manually rated by a user. This estimator is then applied to new data and, in an optimal scenario, reproduces the rating scheme that the user intended when he rated the training dataset. For a more detailed analysis, please refer to [MAM+19].

Nanite already comes with a default training set that is based on AFM data recorded for zebrafish spinal cord sections, called zef18. The original zef18 dataset is available online [MMG18]. Download links: 1

With nanite, you can also create your own training set. The required steps to do so are described in the following.

Rating experimental data manually

In the rating step, experimental data are fitted and manually rated by the user. The raw data, the preprocessed data, the fit, all parameters, and the manual rating are then stored in a rating container (an HDF5 file).

First, set up a fitting profile using nanite-setup-profile if you have not already done so in the fitting guide. You can run the command nanite-setup-profile again to verify that all settings are correct.

To start manual rating, use the command nanite-rate. The first argument is a folder containing experimental force-distance curves and the second argument is a path to a rating container (nameXY.h5). If the rating container already exists, new data will be appended (nothing is overridden).

nanite-rate path/to/data/directory path/to/nameXY.h5

This will open a graphical user interface that displays the preprocessed and fitted experimental data:


Fig. 3 Graphical user interface (GUI) for rating. The inset shows a close-up of the indentation part and the fitted parameters. The user name (defaults to login name) is used to assign a rating to a user (not mandatory). The rating (integer from 0/bad to 10/good or -1/invalid) and a comment can be defined for each curve. The shortcuts ALT+Left and ALT+Right can be used to navigate within the dataset while keeping the cursor focused in the rating field. While navigating, the data are stored in the rating container and the GUI can be closed without data loss.

For the subsequent steps, it is irrelevant whether you create many small rating containers or one global rating container. Many small containers have the advantage that the effect of individual rating sessions could be analyzed separately, while a global rating container keeps all data in one place.

Generating the training set

The training set consists only of the samples (features of each force-distance curve) and the manual ratings. It is stored as a set of small text files on disk. As described earlier, nanite comes with the predefined zef18 training set. In this step, a user-defined training set will be generated for use with nanite.

Use the command nanite-generate-trainining-set to convert the rating container(s) to a training set:

nanite-generate-trainining-set path/to/nameXY.h5 path/to/training_set/

This will create the folder path/to/training_set/ts_nameXY containing several text files, one for each feature and one for the manual rating.

Applying the training set

To apply the training set when rating curves with nanite-fit, you will have to update the profile using nanite-setup-profile again (see fitting guide). The relevant program output will look like this:


Select training set:
training set (path or name) (currently 'zef18'): path/to/training_set/ts_nameXY

Select rating regressor:
  1: AdaBoost
  2: Decision Tree
  3: Extra Trees
  4: Gradient Tree Boosting
  5: Random Forest
  6: SVR (RBF kernel)
  7: SVR (linear kernel)
(currently '3'):

Done. You may edit all parameters in '/home/user/.config/nanite/cli_profile.cfg'.

When running nanite-fit data_path output_path now, the new training set is used for rating. The new ratings are stored in output_path/statistics.tsv and can be used for further analysis, e.g. quality assessment or sorting.

If you would like to employ a user-defined training set in a Python script, you may do so by specifying the training set path as an argument to nanite.Indentation.rate_quality.


The SHA256 checksum of zef18.h5 is 63d89a8aa911a255fb4597b2c1801e30ea14810feef1bb42c11ef10f02a1d055.