Logging¶

PyPSA uses the Python standard library logging module to provide informative messages during execution.

Python Logging Module¶

You can also control the verbosity of Python's logging messages by setting the logging level to ERROR, WARNING, INFO, or DEBUG. This can be configured independently for different modules.

Let's turn on INFO level messages for the optimization module:

In [2]:

import pypsa

network = pypsa.examples.ac_dc_meshed()

import pypsa network = pypsa.examples.ac_dc_meshed()

INFO:pypsa.network.io:Imported network 'AC-DC-Meshed' has buses, carriers, generators, global_constraints, lines, links, loads

In [3]:

import logging

pypsa.optimization.optimize.logger.setLevel(logging.INFO)

network.optimize()

import logging pypsa.optimization.optimize.logger.setLevel(logging.INFO) network.optimize()

/tmp/ipykernel_8874/1123755349.py:5: FutureWarning: The default value of `include_objective_constant` will change from True to False in version 2.0. Set `include_objective_constant` explicitly to suppress this warning. Using False improves LP numerical conditioning by not including the objective constant as a variable.
  network.optimize()
WARNING:pypsa.consistency:The following lines have zero x, which could break the linear load flow:
Index(['2', '3', '4'], dtype='object', name='name')

WARNING:pypsa.consistency:The following lines have zero r, which could break the linear load flow:
Index(['0', '1', '5', '6'], dtype='object', name='name')

INFO:linopy.model: Solve problem using Highs solver

INFO:linopy.model:Solver options:
 - log_to_console: False

INFO:linopy.io: Writing time: 0.07s

INFO:linopy.constants: Optimization successful: 
Status: ok
Termination condition: optimal
Solution: 188 primals, 468 duals
Objective: -3.47e+06
Solver model: available
Solver message: Optimal

INFO:pypsa.optimization.optimize:The shadow-prices of the constraints Generator-ext-p-lower, Generator-ext-p-upper, Line-ext-s-lower, Line-ext-s-upper, Link-ext-p-lower, Link-ext-p-upper, Kirchhoff-Voltage-Law were not assigned to the network.

Out[3]:

('ok', 'optimal')

Now let's reduce verbosity by setting the optimization module to only show WARNING level messages.

In [4]:

pypsa.optimization.optimize.logger.setLevel(logging.WARNING)

network.optimize()

pypsa.optimization.optimize.logger.setLevel(logging.WARNING) network.optimize()

/tmp/ipykernel_8874/1121149820.py:3: FutureWarning: The default value of `include_objective_constant` will change from True to False in version 2.0. Set `include_objective_constant` explicitly to suppress this warning. Using False improves LP numerical conditioning by not including the objective constant as a variable.
  network.optimize()
WARNING:pypsa.consistency:The following lines have zero x, which could break the linear load flow:
Index(['2', '3', '4'], dtype='object', name='name')

WARNING:pypsa.consistency:The following lines have zero r, which could break the linear load flow:
Index(['0', '1', '5', '6'], dtype='object', name='name')

INFO:linopy.model: Solve problem using Highs solver

INFO:linopy.model:Solver options:
 - log_to_console: False

INFO:linopy.io: Writing time: 0.06s

INFO:linopy.constants: Optimization successful: 
Status: ok
Termination condition: optimal
Solution: 188 primals, 468 duals
Objective: -3.47e+06
Solver model: available
Solver message: Optimal

Out[4]:

('ok', 'optimal')

Now let's turn on DEBUG level messages for the power flow module to see the most detailed output.

In [5]:

pypsa.network.power_flow.logger.setLevel(logging.DEBUG)

out = network.lpf()

pypsa.network.power_flow.logger.setLevel(logging.DEBUG) out = network.lpf()

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

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

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

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

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

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

INFO:pypsa.network.power_flow:Performing linear load-flow on AC sub-network <pypsa.SubNetwork object at 0x7aa17fb40270> 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.network.power_flow:Slack bus for sub-network 1 is Norwich DC

INFO:pypsa.network.power_flow:Performing linear load-flow on DC sub-network <pypsa.SubNetwork object at 0x7aa17fb41370> 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.network.power_flow:Slack bus for sub-network 2 is Frankfurt

INFO:pypsa.network.power_flow:Performing linear load-flow on AC sub-network <pypsa.SubNetwork object at 0x7aa17fb02550> 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.network.power_flow:Slack bus for sub-network 3 is Norway

INFO:pypsa.network.power_flow:Performing linear load-flow on AC sub-network <pypsa.SubNetwork object at 0x7aa17fb03a50> 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)

Finally, let's turn off messages for the power flow module by setting it back to ERROR level.

In [6]:

pypsa.network.power_flow.logger.setLevel(logging.ERROR)

out = network.lpf()

pypsa.network.power_flow.logger.setLevel(logging.ERROR) out = network.lpf()

Python Warnings versus Logging¶

PyPSA also uses Python's warnings next to the logging module:

• logging: For tracking application behavior - records events and provides debugging information
• Python Warnings: For developers/users - alerts about deprecated features or potential issues

In [8]:

# Access a deprecated feature to see a warning
network.components.generators.component_names

# Access a deprecated feature to see a warning network.components.generators.component_names

/tmp/ipykernel_8874/1732202595.py:2: DeprecationWarning: c.component_names is deprecated, use c.names instead
  network.components.generators.component_names

Out[8]:

Index(['Manchester Wind', 'Manchester Gas', 'Norway Wind', 'Norway Gas',
       'Frankfurt Wind', 'Frankfurt Gas'],
      dtype='object', name='name')

In [9]:

import warnings

warnings.filterwarnings("ignore", category=DeprecationWarning)
network.components.generators.component_names

import warnings warnings.filterwarnings("ignore", category=DeprecationWarning) network.components.generators.component_names

Out[9]:

Index(['Manchester Wind', 'Manchester Gas', 'Norway Wind', 'Norway Gas',
       'Frankfurt Wind', 'Frankfurt Gas'],
      dtype='object', name='name')

Solver Output¶

The log_to_console parameter controls whether the solver (e.g., HiGHS, Gurobi) prints its progress to the console. By default, pypsa.options.params.optimize.log_to_console is True, but in these documentation examples it's set to False for cleaner output.

Let's see the difference:

In [10]:

# With log_to_console=False (default in docs): only Python logging messages
network.optimize(log_to_console=False)

# With log_to_console=False (default in docs): only Python logging messages network.optimize(log_to_console=False)

/tmp/ipykernel_8874/733640962.py:2: FutureWarning: The default value of `include_objective_constant` will change from True to False in version 2.0. Set `include_objective_constant` explicitly to suppress this warning. Using False improves LP numerical conditioning by not including the objective constant as a variable.
  network.optimize(log_to_console=False)
WARNING:pypsa.consistency:Encountered nan's in varying data 'p0' for columns ['Norwich Converter', 'Norway Converter', 'Bremen Converter', 'DC link'] of component 'Link'.

WARNING:pypsa.consistency:Encountered nan's in varying data 'p1' for columns ['Norwich Converter', 'Norway Converter', 'Bremen Converter', 'DC link'] of component 'Link'.

WARNING:pypsa.consistency:The following lines have zero x, which could break the linear load flow:
Index(['2', '3', '4'], dtype='object', name='name')

WARNING:pypsa.consistency:The following lines have zero r, which could break the linear load flow:
Index(['0', '1', '5', '6'], dtype='object', name='name')

INFO:linopy.model: Solve problem using Highs solver

INFO:linopy.model:Solver options:
 - log_to_console: False

INFO:linopy.io: Writing time: 0.06s

INFO:linopy.constants: Optimization successful: 
Status: ok
Termination condition: optimal
Solution: 188 primals, 468 duals
Objective: -3.47e+06
Solver model: available
Solver message: Optimal

Out[10]:

('ok', 'optimal')

In [11]:

# With log_to_console=True (default setting): verbose solver output
network.optimize(log_to_console=True)

# With log_to_console=True (default setting): verbose solver output network.optimize(log_to_console=True)

/tmp/ipykernel_8874/2160006920.py:2: FutureWarning: The default value of `include_objective_constant` will change from True to False in version 2.0. Set `include_objective_constant` explicitly to suppress this warning. Using False improves LP numerical conditioning by not including the objective constant as a variable.
  network.optimize(log_to_console=True)
WARNING:pypsa.consistency:The following lines have zero x, which could break the linear load flow:
Index(['2', '3', '4'], dtype='object', name='name')

WARNING:pypsa.consistency:The following lines have zero r, which could break the linear load flow:
Index(['0', '1', '5', '6'], dtype='object', name='name')

INFO:linopy.model: Solve problem using Highs solver

INFO:linopy.model:Solver options:
 - log_to_console: True

INFO:linopy.io: Writing time: 0.06s

INFO:linopy.constants: Optimization successful: 
Status: ok
Termination condition: optimal
Solution: 188 primals, 468 duals
Objective: -3.47e+06
Solver model: available
Solver message: Optimal

Running HiGHS 1.13.1 (git hash: 1d267d9): Copyright (c) 2026 under MIT licence terms
LP linopy-problem-_xw5vdda has 468 rows; 188 cols; 1007 nonzeros
Coefficient ranges:
  Matrix  [1e-02, 1e+00]
  Cost    [9e-03, 3e+03]
  Bound   [2e+07, 2e+07]
  RHS     [9e-01, 1e+03]
WARNING: Problem has some excessively large column bounds
WARNING:    Consider scaling the    bounds by 1e-2, or setting the user_bound_scale option to -5
Presolving model
391 rows, 187 cols, 930 nonzeros  0s
305 rows, 101 cols, 1042 nonzeros  0s
Dependent equations search running on 22 equations with time limit of 1000.00s
Dependent equations search removed 0 rows and 0 nonzeros in 0.00s (limit = 1000.00s)
303 rows, 99 cols, 1058 nonzeros  0s
Presolve reductions: rows 303(-165); columns 99(-89); nonzeros 1058(+51) 
Solving the presolved LP
Using dual simplex solver
  Iteration        Objective     Infeasibilities num(sum)
          0    -2.1204510016e+07 Pr: 102(98953); Du: 0(4.73182e-11) 0.0s
        126    -3.4742560405e+06 Pr: 0(0) 0.0s

Performed postsolve
Solving the original LP from the solution after postsolve

Model name          : linopy-problem-_xw5vdda
Model status        : Optimal
Simplex   iterations: 126
Objective value     : -3.4742560405e+06
P-D objective error :  1.3403192363e-16
HiGHS run time      :          0.01

Out[11]:

('ok', 'optimal')