gwdama 0.2.4

A GW data manager package and more

gwdama

GW Data Manager

0. Clone this repo

In your working directory, clone this repository:

$ git clone https://gitlab.com/gwprojects/gwdama.git

This will create a gwdma repository. Change directory to this one and check the available branches:

$ cd gwdama
$ git branch -a

By default, only the master directory is synchronised. If you want to try different branches, like fradev:

$ 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.

   $ python3 -m venv --without-pip env
   

2. Activate the environment, which is going to be empty (really!):

   $ source env/bin/activate
   

3. Get pip, setuptools and wheel from the web:

   $ 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.:

   $ 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:

      $ pip install -r requirements.txt
      

   2. install everything manually:

      $ 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:

      $ pip install lalsuite
      

6. Check that the previous steps have been completed successfully: entering the following command shouldn't return any arror, warning etc.

   $ 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:

$ 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:

$ 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: with basic example about how to fetch data from inside a Virgo farm machine;
• Example2: how to use it from PC Universe 2, with cernCV data;
• Under development...