Save and Load Networks

  Advantage Disadvantage
Example: saving
case9241pegase
pickle
Allows storing of objects
- large filesize
- Stored objects might become
incompatible when loading
with different versions
- Savetime: 1.2s
- Loadtime: 0.65s
- Filesize: 18.4 MB
Excel
Human readable
- Long time to save and load
- Needs libraries that are not part of
standard python distribution
- Savetime: 23.9s
- Loadtime: 10.9s
- Filesize: 4.9 MB
SQL    
- Savetime: 1.32s
- Loadtime: 0.6s
- Filesize: 5.1 MB
json
can be interpreted in
other languages
potential insecurity with additional
translation in json notation
-Savetime: 0.19s
-Loadtime: 0.79s
- Filesize: 5.3 MB

pickle

pandapower.to_pickle(net, filename)

Saves a pandapower Network with the pickle library.

INPUT:

net (dict) - The pandapower format network

filename (string) - The absolute or relative path to the output file or an writable file-like objectxs

EXAMPLE:

>>> pp.to_pickle(net, os.path.join("C:", "example_folder", "example1.p"))  # absolute path
>>> pp.to_pickle(net, "example2.p")  # relative path
pandapower.from_pickle(filename, convert=True)

Load a pandapower format Network from pickle file

INPUT:
filename (string or file) - The absolute or relative path to the input file or file-like object
OUTPUT:
net (dict) - The pandapower format network

EXAMPLE:

>>> net1 = pp.from_pickle(os.path.join("C:", "example_folder", "example1.p")) #absolute path
>>> net2 = pp.from_pickle("example2.p") #relative path

Excel

pandapower.to_excel(net, filename, include_empty_tables=False, include_results=True)

Saves a pandapower Network to an excel file.

INPUT:

net (dict) - The pandapower format network

filename (string) - The absolute or relative path to the output file

OPTIONAL:

include_empty_tables (bool, False) - empty element tables are saved as excel sheet

include_results (bool, True) - results are included in the excel sheet

EXAMPLE:

>>> pp.to_excel(net, os.path.join("C:", "example_folder", "example1.xlsx"))  # absolute path
>>> pp.to_excel(net, "example2.xlsx")  # relative path
pandapower.from_excel(filename, convert=True)

Load a pandapower network from an excel file

INPUT:
filename (string) - The absolute or relative path to the input file.
OUTPUT:

convert (bool) - use the convert format function to

net (dict) - The pandapower format network

EXAMPLE:

>>> net1 = pp.from_excel(os.path.join("C:", "example_folder", "example1.xlsx")) #absolute path
>>> net2 = pp.from_excel("example2.xlsx") #relative path

Json

pandapower.to_json(net, filename=None)

Saves a pandapower Network in JSON format. The index columns of all pandas DataFrames will be saved in ascending order. net elements which name begins with “_” (internal elements) will not be saved. Std types will also not be saved.

INPUT:

net (dict) - The pandapower format network

filename (string or file) - The absolute or relative path to the output file or file-like object

EXAMPLE:

>>> pp.to_json(net, "example.json")
pandapower.from_json(filename, convert=True)

Load a pandapower network from a JSON file. The index of the returned network is not necessarily in the same order as the original network. Index columns of all pandas DataFrames are sorted in ascending order.

INPUT:
filename (string or file) - The absolute or relative path to the input file or file-like object
OUTPUT:

convert (bool) - use the convert format function to

net (dict) - The pandapower format network

EXAMPLE:

>>> net = pp.from_json("example.json")

SQL

pandapower.to_sqlite(net, filename, include_results=True)
pandapower.from_sqlite(filename, netname='')