Metadata-Version: 2.1
Name: gwdama
Version: 0.2.4
Summary: A GW data manager package and more
Home-page: https://gwdama.readthedocs.io/en/latest/index.html
Author: Pisa Detchar Group
Author-email: francesco.direnzo@df.unipi.it
License: UNKNOWN
Download-URL: https://gitlab.com/gwprojects/gwdama
Description: # gwdama
        
        GW Data Manager
        
        ## 0. Clone this repo
        In your working directory, clone this repository:
        ```bash
        $ git clone https://gitlab.com/gwprojects/gwdama.git
        ```
        This will create a `gwdma` repository. Change directory to this one and check the available branches:
        ```bash
        $ cd gwdama
        $ git branch -a
        ```
        By default, only the master directory is synchronised. If you want to try different branches, like `fradev`:
        ```bash
        $ git checkout fradev
        ```
        Now you are good to go!
        
        ## 1. Getting started
        ### Environment setup
        
        The following installation procedure works correctly on Virgo farm machines. Remember to put the resulting `env` directory
         into a `.gitignore` file in order to avoid pushing it!
        
        1. Create a Python3 environment called `env`, or whatever (in this case, remember to substitute it to `env` in the following commands). This should be done `without-pip`, which is installed in the next step.
            ```bash
            $ python3 -m venv --without-pip env
            ```
        2. Activate the environment, which is going to be empty (really!):
            ```bash
            $ source env/bin/activate
            ```
        3. Get pip, setuptools and wheel from the web:
            ```bash
            $ curl https://bootstrap.pypa.io/get-pip.py | python
            ```
        4. Deactivate and reactivate the environment and check if the previous packages are installed and up-to dated. Check also the versions atc.:
            ```bash
            $ deactivate
            $ source env/bin/activate
            $ python --version
            $ pip list
            ```
        5. Install the required modules. The procedure varies depending on whether a `requirements.txt` file is available 
        (provided somebody has created one with `pip freeze > requirements.txt`) or not:
            1. install the packages from the requirements:
                ```bash 
                $ pip install -r requirements.txt
                ```
                 
           5. install everything manually:
                ```bash
                $ pip install numpy, scipy, matplotlib, pandas, jupyter, scikit-learn, gwpy   
                ```
                Also, it will be necessary to install ` python-ldas-tools-framecpp` to use the method
                `read` of GWpy TimeSeries:
                ```bash
                $ pip install lalsuite
                ```
        6. Check that the previous steps have been completed successfully: entering the following command shouldn't return any arror, warning etc.
            ```bash
            $ python -c "import numpy, matplotlib, pandas, sklearn, scipy"
            ```
        **Notice:** for code developing and benchmark tests, it could also be useful to install the `line_profiler` and
        `memory_profiler` packages. These are not included in the `requirements.txt` file but you can install them easily,
        within the environment, typing:
        ```bash
        $ pip install line_profiler memory_profiler
        ```
        Then, you can exploit the IPython megic commands:
        - `%prun`: Run code with the profiler
        - `%lprun`: Run code with the line-by-line profiler
        - `%memit`: Measure the memory use of a single statement
        - `%mprun`: Run code with the line-by-line memory profiler
           
        ### Install the package (locally)
        We can install the package locally (for use on our system), and import it anywhere else.
        Passing the parameter `-e`, we can install the package with a symlink, so that changes
        to the source files will be immediately available to other users of the package on our system.
        From the main directory containing the package:
        ```bash
        $ pip install -e .
        ```
        Done! You are all set up now, and go testing with some jupyter notebook.
        
        ## 2. Play with data
        There are some test and development notebooks:
        - [Example_1](notebooks/Example1_VirgoFarm.ipynb): with basic example about how to fetch data from inside a Virgo farm machine;
        - [Example2](notebooks/Example2_PCuniverse2.ipynb): how to use it from PC Universe 2, with cernCV data;
        - Under development...
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
