From b8aa31a0bb159e1285270907fdec46809a425ddd Mon Sep 17 00:00:00 2001
From: Danilo Ferreira de Lima <danilo.enoque.ferreira.de.lima@xfel.de>
Date: Mon, 9 Oct 2023 18:19:08 +0200
Subject: [PATCH] Update instructions.

---
 README.md | 33 +++++++++++++++------------------
 1 file changed, 15 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 9af2e50..52af12e 100644
--- a/README.md
+++ b/README.md
@@ -6,7 +6,20 @@ results.
 
 The concept is to collect both results simultaneously during a training phase and use it to learn a model that may be used later, under the same conditions, but without the high-resolution, invasive spectrometer. The idea is that minimum tuning of the parameters of this methods are needed, so that if the data for training is available, no fine-tuning is required.
 
-## Installation in Maxwell
+## Simple usage in Maxwell
+
+The dependencies in the standard kernel `xfel (current)` are enough to run the example notebook. All one needs to do is copy the main code in this package to a given directory.
+This can be done by typing this command in a Maxwell machine:
+
+```
+git clone https://git.xfel.eu/machineLearning/pes_to_spec.git
+cd pes_to_spec
+```
+
+Afterwards open a Jupyter Lab instance (max-jhub.desy.de), go to the directory where the package has been copied and open the example notebook in `notebook/Example offline analysis.ipynb`.
+One should be able to run the example analysis in the notebook.
+
+## Full installation of dependencies from scratch
 
 The dependencies can be installed in a separate environment and used from Juyter Lab in Maxwell, by executing the following commands in Maxwell:
 
@@ -37,23 +50,7 @@ python -m ipykernel install --user --name pes_to_spec_env
 
 After this, open the example notebook from the `notebook` directory in a Jupyter Lab instance (max-jhub.desy.de) and set the kernel to `pes_to_spec_env`.
 
-## Installation in an existing environment
-
-If one just wants to install it standalone, simply type:
-`pip install pes_to_spec`
-
-While the dependencies should be automatically used, using the Intel-optimized `numpy` and `scipy` packages is recommended, as they are much faster.
-This has been tested with `scipy==1.7.3`, but it should work with any version of scipy and numpy.
-```
-# install the optimized numpy and scipy versions
-pip install --force-reinstall --index-url https://pypi.anaconda.org/intel/simple --no-dependencies numpy scipy==1.7.3
-pip install numpy scipy==1.7.3
-# install PyTorch
-pip install torch --index-url https://download.pytorch.org/whl/cpu
-pip install joblib scikit-learn
-```
-
-## Usage
+## API usage
 
 The API may be used as follows:
 
-- 
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