Batch Processing - Generate Cases

This notebook demonstrates creating cases in batch and running them in parallel.

Create Cases in Batch

The approach to create cases in batch following this procedure:

• Load the base case from file

• For each desired case output:

  • Alter parameters to the desired value

  • Save each system to a new case file

import andes
import numpy as np
from andes.utils.paths import get_case

andes.config_logger(stream_level=30)  # brief logging

# create directory for output cases
!rm -rf batch_cases
!mkdir -p batch_cases

kundur = get_case('kundur/kundur_full.xlsx')
ss = andes.load(kundur)

We demonstrate running the Kundur's system under different loading conditions.

Cases are created by modifying the p0 of PQ with idx == PQ_0.

As always, input parameters can be inspected by accessing Model.as_df(vin=True).

p0_base = ss.PQ.get('p0', "PQ_0")

Create 3 cases so that the load increases from p0_base to 1.2 * p0_base.

N_CASES = 3  # Note: increase `N_CASES` as necessary

p0_values = np.linspace(p0_base, 1.2 * p0_base, N_CASES)

for value in p0_values:
    ss.PQ.alter('p0', 'PQ_0', value)
    file_name = f'batch_cases/kundur_p_{value:.2f}.xlsx'
    
    andes.io.dump(ss, 'xlsx', file_name, overwrite=True)

Parallel Simulation

Parallel simulation is easy with the command line tool.

Change directory to batch_cases:

import os

# change the Python working directory
os.chdir('batch_cases')

!ls -la

total 56
drwxr-xr-x 2 hacui hacui  4096 Apr 19 20:31 .
drwxr-xr-x 6 hacui hacui  4096 Apr 19 20:31 ..
-rw-r--r-- 1 hacui hacui 14841 Apr 19 20:31 kundur_p_11.59.xlsx
-rw-r--r-- 1 hacui hacui 14842 Apr 19 20:31 kundur_p_12.75.xlsx
-rw-r--r-- 1 hacui hacui 14840 Apr 19 20:31 kundur_p_13.91.xlsx

Running from Command line

!andes run *.xlsx -r tds

    _           _         | Version 1.6.4.post10.dev0+gd1a4589d
   /_\  _ _  __| |___ ___ | Python 3.9.10 on Linux, 04/19/2022 08:31:06 PM
  / _ \| ' \/ _` / -_|_-< | 
 /_/ \_\_||_\__,_\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.

Working directory: "/home/hacui/repos/andes/examples/batch_cases"
-> Processing 3 jobs on 12 CPUs.
Process 0 for "kundur_p_11.59.xlsx" started.
Process 1 for "kundur_p_12.75.xlsx" started.
Process 2 for "kundur_p_13.91.xlsx" started.
<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.                      
<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.                      
<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.                      
100%|########################################| 100.0/100 [00:01<00:00, 89.55%/s]
100%|########################################| 100.0/100 [00:01<00:00, 87.15%/s]
100%|########################################| 100.0/100 [00:01<00:00, 83.73%/s]
Log saved to "/tmp/andes-4fbaduja/andes.log".
-> Multiprocessing finished in 1.7566 seconds.

Number of CPUs

In some cases, you don't want the simulatino to use up all resources.

ANDES allows to control the number of processes to run in parallel through --ncpu NCPU, where NCPU is the maximum number of processes (equivalent to the number of CPU cores) allowed.

!andes run *.xlsx -r tds --ncpu 4

    _           _         | Version 1.6.4.post10.dev0+gd1a4589d
   /_\  _ _  __| |___ ___ | Python 3.9.10 on Linux, 04/19/2022 08:31:09 PM
  / _ \| ' \/ _` / -_|_-< | 
 /_/ \_\_||_\__,_\___/__/ | This program comes with ABSOLUTELY NO WARRANTY.

Working directory: "/home/hacui/repos/andes/examples/batch_cases"
-> Processing 3 jobs on 4 CPUs.
Process 0 for "kundur_p_11.59.xlsx" started.
Process 1 for "kundur_p_12.75.xlsx" started.
Process 2 for "kundur_p_13.91.xlsx" started.
<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.                      
<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.                      
<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.                      
100%|########################################| 100.0/100 [00:01<00:00, 88.46%/s]
100%|########################################| 100.0/100 [00:01<00:00, 86.36%/s]
100%|########################################| 100.0/100 [00:01<00:00, 83.39%/s]
Log saved to "/tmp/andes-rlpeyxo9/andes.log".
-> Multiprocessing finished in 1.7693 seconds.

Running with APIs

Setting pool = True allows returning all system instances in a list.

This comes with a penalty in computation time but can be helpful if you want to extract data directly.

systems = andes.run('*.xlsx', routine='tds', pool=True, verbose=10)

Working directory: "/home/hacui/repos/andes/examples/batch_cases"
Found files: ['kundur_p_11.59.xlsx', 'kundur_p_12.75.xlsx', 'kundur_p_13.91.xlsx']
-> Processing 3 jobs on 12 CPUs.

Cases are processed in the following order:
"kundur_p_11.59.xlsx"
"kundur_p_12.75.xlsx"
"kundur_p_13.91.xlsx"
<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.

<Toggle 1>: Line.Line_8 status changed to 0 at t=2.0 sec.
Log saved to "/tmp/andes-lmohp4sw/andes.log".
-> Multiprocessing finished in 3.2217 seconds.

systems[0]

<andes.system.System at 0x7fbaa050a070>

systems

[<andes.system.System at 0x7fbaa050a070>,
 <andes.system.System at 0x7fba7bca2c70>,
 <andes.system.System at 0x7fba7bc9db80>]

Example plots

Plotting or data analyses can be carried out as usual.

ss = systems[0]

systems[0].TDS.plotter.plot(ss.GENROU.omega, latex=False)

(<Figure size 480x320 with 1 Axes>, <AxesSubplot:xlabel='Time [s]'>)

systems[2].TDS.plotter.plot(ss.GENROU.omega, latex=False)

(<Figure size 480x320 with 1 Axes>, <AxesSubplot:xlabel='Time [s]'>)

!andes misc -C
!rm -rf batch_cases

No output file found in the working directory.