Note

You can download this example as a Jupyter notebook or start it in interactive mode.

Demonstrate usage of logging

PyPSA uses the Python standard library logging. This notebook shows how to use it and control the logging messages from different modules.

One can set the logging level to different values like ERROR, WARNING, INFO, DEBUG. This works independently for separate module.

We start by setting the basic logging level to ERROR.

import pypsa, os
import logging

logging.basicConfig(level=logging.ERROR)

ERROR 1: PROJ: proj_create_from_database: Open of /home/docs/checkouts/readthedocs.org/user_builds/pypsa/conda/latest/share/proj failed

network = pypsa.examples.ac_dc_meshed(from_master=True)

out = network.optimize()

/home/docs/checkouts/readthedocs.org/user_builds/pypsa/conda/latest/lib/python3.11/site-packages/pypsa/optimization/optimize.py:357: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  n.df(c)[attr + "_opt"].update(df)

Now turn on infos just for OPF module.

pypsa.opf.logger.setLevel(logging.INFO)

out = network.optimize()

/home/docs/checkouts/readthedocs.org/user_builds/pypsa/conda/latest/lib/python3.11/site-packages/pypsa/optimization/optimize.py:357: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  n.df(c)[attr + "_opt"].update(df)

Now turn on warnings just for OPF module

pypsa.opf.logger.setLevel(logging.WARNING)

out = network.optimize()

/home/docs/checkouts/readthedocs.org/user_builds/pypsa/conda/latest/lib/python3.11/site-packages/pypsa/optimization/optimize.py:357: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  n.df(c)[attr + "_opt"].update(df)

Now turn on all messages for the PF module

pypsa.pf.logger.setLevel(logging.DEBUG)

out = network.lpf()

DEBUG:pypsa.pf:Slack bus for sub-network 0 is Manchester

DEBUG:pypsa.pf:Slack bus for sub-network 1 is Norwich DC

DEBUG:pypsa.pf:Slack bus for sub-network 2 is Frankfurt

DEBUG:pypsa.pf:No slack generator found in sub-network 3, using Norway Wind as the slack generator

DEBUG:pypsa.pf:Slack bus for sub-network 3 is Norway

DEBUG:pypsa.pf:Slack bus for sub-network 0 is Manchester

INFO:pypsa.pf:Performing linear load-flow on AC sub-network SubNetwork 0 for snapshot(s) DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',
               '2015-01-01 02:00:00', '2015-01-01 03:00:00',
               '2015-01-01 04:00:00', '2015-01-01 05:00:00',
               '2015-01-01 06:00:00', '2015-01-01 07:00:00',
               '2015-01-01 08:00:00', '2015-01-01 09:00:00'],
              dtype='datetime64[ns]', name='snapshot', freq=None)

DEBUG:pypsa.pf:Slack bus for sub-network 1 is Norwich DC

INFO:pypsa.pf:Performing linear load-flow on DC sub-network SubNetwork 1 for snapshot(s) DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',
               '2015-01-01 02:00:00', '2015-01-01 03:00:00',
               '2015-01-01 04:00:00', '2015-01-01 05:00:00',
               '2015-01-01 06:00:00', '2015-01-01 07:00:00',
               '2015-01-01 08:00:00', '2015-01-01 09:00:00'],
              dtype='datetime64[ns]', name='snapshot', freq=None)

DEBUG:pypsa.pf:Slack bus for sub-network 2 is Frankfurt

INFO:pypsa.pf:Performing linear load-flow on AC sub-network SubNetwork 2 for snapshot(s) DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',
               '2015-01-01 02:00:00', '2015-01-01 03:00:00',
               '2015-01-01 04:00:00', '2015-01-01 05:00:00',
               '2015-01-01 06:00:00', '2015-01-01 07:00:00',
               '2015-01-01 08:00:00', '2015-01-01 09:00:00'],
              dtype='datetime64[ns]', name='snapshot', freq=None)

DEBUG:pypsa.pf:Slack bus for sub-network 3 is Norway

INFO:pypsa.pf:Performing linear load-flow on AC sub-network SubNetwork 3 for snapshot(s) DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 01:00:00',
               '2015-01-01 02:00:00', '2015-01-01 03:00:00',
               '2015-01-01 04:00:00', '2015-01-01 05:00:00',
               '2015-01-01 06:00:00', '2015-01-01 07:00:00',
               '2015-01-01 08:00:00', '2015-01-01 09:00:00'],
              dtype='datetime64[ns]', name='snapshot', freq=None)

Now turn off all messages for the PF module again

pypsa.pf.logger.setLevel(logging.ERROR)

out = network.lpf()