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.
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:
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:
The dependencies can be installed in a separate environment and used from Juyter Lab in Maxwell, by executing the following commands in Maxwell:
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`.
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
## API usage
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.
