Update README.md

Adding pipeline.ipynb to the description.

README.md

@@ -17,17 +17,18 @@ To install the dependencies, it is recomended to use Anaconda or Mamba. An envir
 
 The results described in the paper where obtained by execute the following steps:
 
-1. `aggregate_results.ipynb`: Collect, aggregate and evaluate causal links. Produces causal (correlation) matrix plots.
-2. Find the appropriate threshold to filter spurious links, using SHERPA.
+1. `pipeline.ipynb`: Run PC1 within the PCMCI framework. This yields a set of causal drivers for each output at every grid column of SPCAM.
+2. `aggregate_results.ipynb`: Collect, aggregate and evaluate causal links. Produces causal (correlation) matrix plots.
+3. Find the appropriate threshold to filter spurious links, using SHERPA.
     1. `SHERPA_threshold_GridSearch.ipynb`: Best general threshold.
     2. `notebooks_SHERPA_thrs_optimization_per_output/Create_optimized_numparents_dict_mse.ipynb`: Best threshold for each output.
-3. Creation and training of neural networks (NN).
+4. Creation and training of neural networks (NN).
     1. `NN_Creation.ipynb`: Can create both Causally-informed NN that use the best general threshold (from 2.1) and Non-causal NN that use all inputs.
     2. `NN_Creation_optimized_threshold.ipynb`: Causally-informed NN that use the best threshold for each output (from 2.2).
     3. `NN_Creation_random_links.ipynb`: NN using random links
-4. Evaluation
+5. Evaluation
     1. `notebooks_evaluate_CausalNNs_r2/evaluate_nonlinearities_in_SPCAM.ipynb`: Comparation between the different types of NN
     2. `notebooks_online_evaluation`: Comparation with SPCAM
         1. `cross_section_online_evaluation.ipynb`
         2. `latitudinal_2Dfields_online_evaluation.ipynb`
-5. `notebooks_xai/shap_xai.ipynb`: Use explainable AI to evaluate the importance of the inputs in each NN
+6. `notebooks_xai/shap_xai.ipynb`: Use explainable AI to evaluate the importance of the inputs in each NN