|
1 | | -# Numerical Methods of Accelerator Physics |
| 1 | +# Material for this tutorial |
| 2 | +- The theoretical lecture can be found here |
| 3 | +- The tutorial in slide form is here |
2 | 4 |
|
3 | | -MSc lecture at TU Darmstadt, etit, TEMF by Adrian Oeftiger in 2022/23. |
| 5 | +# Installation instructions |
4 | 6 |
|
5 | | -Fourteenth part of a jupyter notebook lecture series, guest lecture held by Dr. Andrea Santamaria Garcia on 10.02.2023. |
| 7 | +## Get the repository with Git |
| 8 | +You will need to have Git previously installed in your computer. |
| 9 | +To check if you have it installed, open your terminal and type: |
6 | 10 |
|
7 | | -Find the rendered HTML slides [here](https://aoeftiger.github.io/TUDa-NMAP-14/). |
8 | | - |
9 | | ---- |
10 | | - |
11 | | -## Run online |
12 | | - |
13 | | -Run this notebook talk online, interactively on mybinder.org: |
14 | | - |
15 | | -[](https://mybinder.org/v2/gh/aoeftiger/TUDa-NMAP-14/v1.0) |
16 | | - |
17 | | -The `lecture.ipynb` notebook will work out-of-the-box. |
18 | | - |
19 | | ---- |
20 | | - |
21 | | -## Run on TU Darmstadt jupyterhub |
22 | | - |
23 | | -If you have a TU ID, access the [local TU Darmstadt jupyterhub](https://tu-jupyter-i.ca.hrz.tu-darmstadt.de/) using your TU ID. |
| 11 | +``` bash |
| 12 | +git --version |
| 13 | +``` |
24 | 14 |
|
25 | | -A possible way to upload and run this lecture repository is the following: |
| 15 | +### Git installation in mac |
26 | 16 |
|
27 | | -1. Open a terminal by clicking on the top right "New" -> "Terminal". |
| 17 | +``` bash |
| 18 | +brew update |
| 19 | +brew install git |
| 20 | +``` |
28 | 21 |
|
29 | | -2. A new tab opens with a terminal, click into the black area and enter (copy&pasting): |
| 22 | +### Git installation in linux |
| 23 | +#### Ubuntu/Debian |
30 | 24 |
|
31 | 25 | ``` bash |
32 | | -wget https://github.com/aoeftiger/TUDa-NMAP-14/archive/refs/heads/main.zip |
33 | | -unzip main.zip |
34 | | -cd TUDa-NMAP-14-main |
| 26 | +sudo apt install git |
35 | 27 | ``` |
36 | 28 |
|
37 | | -3. You have downloaded, unzipped and entered the lecture repository. As a last step, install the dependencies: |
| 29 | +#### CentOS |
38 | 30 |
|
39 | 31 | ``` bash |
40 | | -export TMPDIR="`pwd`" |
41 | | -pip install -r requirements_noversions.txt --prefix="`pwd`"/requirements |
42 | | -``` |
43 | | - |
44 | | -Close the terminal tab and open the `lecture.ipynb` notebook inside the repository directory on the jupyterhub main page. |
| 32 | +sudo yum install git |
| 33 | +``` |
45 | 34 |
|
46 | | ---- |
| 35 | +Once you have Git installed open your terminal, go to your desired directory, and type: |
| 36 | +``` bash |
| 37 | +git clone https://github.com/machine-learning-tutorial/bayesian-optimization.git |
| 38 | +cd bayesian-optimization |
| 39 | +``` |
47 | 40 |
|
48 | | -## Run locally |
| 41 | +## Get the repository with direct download |
| 42 | +Open your terminal, go to your desired directory, and type: |
| 43 | +``` bash |
| 44 | +wget https://github.com/machine-learning-tutorial/bayesian-optimization/archive/refs/heads/main.zip |
| 45 | +unzip main.zip |
| 46 | +cd bayesian-optimization |
| 47 | +``` |
49 | 48 |
|
50 | | -The notebook can of course also be run on your local computer using your own jupyter notebook server. Install such an environment e.g. via the extensive [Anaconda distribution](https://www.anaconda.com/products/distribution), the minimalistic [Miniconda distribution](https://docs.conda.io/en/main/miniconda.html) or the extremely fast [Mamba package manager](https://mamba.readthedocs.io/en/latest/). (The order indicates preference by simplicity in installation and usage.) |
| 49 | +## Install required libraries in a virtual environment |
51 | 50 |
|
52 | | -You may find all required packages in the `requirements.txt` file. |
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