Running iDEA

As it is a python package there are many different ways of running iDEA.

Using the iDEA code directly

Simply edit the parameters file parameters.py and run

python run.py

In order not to overwrite results from different calculations, make sure to choose different run names for different inputs

### Library imports
from __future__ import division
from iDEA.input import InputSection, SystemSection
import numpy as np


### Run parameters
run = InputSection()
run.name = 'run_name'                #: Name to identify run. Note: Do not use spaces or any special characters (.~[]{}<>?/\)
run.time_dependence = False          #: Run time-dependent calculation
run.verbosity = 'default'            #: Output verbosity ('low', 'default', 'high')
run.save = True                      #: Save results to disk when they are generated
run.module = 'iDEA'                  #: Specify alternative folder (in this directory) containing modified iDEA module
run.NON = True                       #: Run Non-Interacting approximation
run.LDA = False                      #: Run LDA approximation
run.HF = False                       #: Run Hartree-Fock approximation
run.HYB = False                      #: Run Hybrid (HF-LDA) calculation
run.EXT = True                       #: Run Exact Many-Body calculation

Using the iDEA package in a python script

Since iDEA is designed as a python package, it can be run from everywhere, if you let your python installation know where the package is located. During the installation of iDEA the idea-public directory should have been added to PYTHONPATH. To test this has worked simply perform the following

cd $test_folder                  # some folder you have created
cp $path_to_iDEA/parameters.py . # where you have downloaded iDEA
cp $path_to_iDEA/run.py .
python run.py

Here, we are running iDEA much in the same way as before but your $test_folder can be located anywhere on your computer.

The main advantage of having an iDEA python package is that you can access its functionality directly in a python script.

The example below uses a python loop to converge the grid spacing for an iDEA calculation of a test system of non-interacting electrons in a harmonic well.

from iDEA.input import Input

# read parameters file
inp = Input.from_python_file('parameters.py')
inp.run.verbosity = 'low'

# Converging xmax parameter
for xmax in [4,6,8,10]:
    # Note: the dependent sys.deltax is automatically updated
    inp.sys.xmax = xmax

    # perform checks on input parameters
    inp.check()
    inp.execute()
    E = inp.results.non.gs_non_E
    print(" xmax = {:4.1f}, E = {:6.4f} Ha".format(xmax,E))

In order to run this example, do

cd $path_to_iDEA/examples/06_convergence
python run.py  # assuming you already added iDEA to your PYTHONPATH

Using the iDEA package in an ipython shell

As iDEA is a python package it can also be run from the interactive python shell (ipython). This is particularly useful as ipython has a convenient auto-complete feature. The following example can be run in an ipython shell line-by-line.

from iDEA.input import Input

# import parameters file
pm = Input.from_python_file('parameters.py')

# change parameters
pm.run.EXT = True
pm.run.NON = True
print(pm.run)

# run jobs
results = pm.execute()

# plot the relevant results
x = pm.space.grid
plt.plot(x, results.ext.gs_ext_den, label='exact')
plt.plot(x, results.non.gs_non_den, label='non-interacting')
plt.legend()
plt.show()