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
- convert (bool, True) - If True, converts the format of the net loaded from pickle from the older
version of pandapower to the newer version format
- 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.
- convert (bool, True) - If True, converts the format of the net loaded from excel from
the older version of pandapower to the newer version format
- OUTPUT:
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, encryption_key=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, None) - The absolute or relative path to the output file
or a file-like object, if ‘None’ the function returns a json string
- encrytion_key (string, None) - If given, the pandapower network is stored as an
encrypted json string
EXAMPLE:
>>> pp.to_json(net, "example.json")
-
pandapower.from_json(filename, convert=True, encryption_key=None)¶ 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
- convert (bool, True) - If True, converts the format of the net loaded from json from the older
version of pandapower to the newer version format
encrytion_key (string, “”) - If given, key to decrypt an encrypted pandapower network
- OUTPUT:
net (dict) - The pandapower format network
EXAMPLE:
>>> net = pp.from_json("example.json")