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