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"))
>>> net2 = pp.from_excel("example2.xlsx") #relative path

Json

pandapower.to_json(net, filename=None, encryption_key=None, store_index_names=False)

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

store_index_names (bool, False) - If True, an additional dict “index_names” is stored into the json string which includes the index names of the dataframes within the net. Since pandapower does usually not use net[elm].index.name, the default is False.

EXAMPLE:

>>> pp.to_json(net, "example.json")
pandapower.from_json(filename, convert=True, encryption_key=None, elements_to_deserialize=None, keep_serialized_elements=True, add_basic_std_types=False, replace_elements=None, empty_dict_like_object=None, restore_index_names=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

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

elements_to_deserialize (list, None) - Deserialize only certain pandapower elements. If None all elements are deserialized.

keep_serialized_elements (bool, True) - Keep serialized elements if given. Default: Serialized elements are kept.

add_basic_std_types (bool, False) - Add missing standard-types from pandapower standard type library.

replace_elements (dict, None) - Keys are replaced by values found in json string. Both key and value are supposed to be strings.

empty_dict_like_object (dict/pandapower.pandapowerNet/…, None) - If None, the output of pandapower.create_empty_network() is used as an empty element to be filled by the data of the json string. Give another dict-like object to start filling that alternative object with the json data.

restore_index_names (bool, True) - If True, the index names of dataframes in the net are restored using information of a dict “index_names” (if this dict is available).

OUTPUT:

net (dict) - The pandapower format network

EXAMPLE:

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

SQL

pandapower.to_sqlite(net, filename, include_results=False)

Saves pandapowerNet an SQLite format

Parameters
netgrid model

pandapowerNet

filenamepath to a text file where the data will be stored

str

include_resultswhether result tables should be included

bool

pandapower.from_sqlite(filename)

Loads a grid model from SQLite format

Parameters
filenamepath to the text file where the data are stored
Returns
netthe grid model

pandapowerNet

PostgreSQL

pandapower.to_postgresql(net, host, user, password, database, schema, include_results=False, grid_id=None, grid_id_column='grid_id', grid_catalogue_name='grid_catalogue', index_name=None)

Uploads a pandapowerNet to a PostgreSQL database. The database must exist, the element tables are created if they do not exist. JSON serialization (e.g. for controller objects) is not implemented yet.

Parameters
netpandapowerNet

the grid model to be uploaded to the database

hoststr

hostname for connecting to the database

userstr

username for logging in

password
databasestr

name of the database

schemastr

name of the database schema (e.g. ‘postgres’)

include_resultsbool

specify whether the power flow results are included when the grid is uploaded, default=False

grid_idint

unique grid_id that will be used to identify the data for the grid model, default None. If None, it will be set automatically by PostgreSQL

grid_id_columnstr

name of the column for “grid_id” in the PosgreSQL tables, default=”grid_id”.

grid_catalogue_namestr

name of the catalogue table that includes all grid_id values and the timestamp when the grid data were added

index_namestr

name of the custom column to be used inplace of index in the element tables if it is not the standard DataFrame index

Returns
grid_id: int

returns either the user-specified grid_id or the automatically generated grid_id of the grid model

pandapower.from_postgresql(host, user, password, database, schema, grid_id, grid_id_column='grid_id', grid_catalogue_name='grid_catalogue')
Parameters
hoststr

hostname for connecting to the database

userstr

username for logging in

password
databasestr

name of the database

schemastr

name of the database schema (e.g. ‘postgres’)

grid_idint

unique grid_id that will be used to identify the data for the grid model, default None. If None, it will be set automatically by PostgreSQL

grid_id_columnstr

name of the column for “grid_id” in the PosgreSQL tables, default=”grid_id”.

grid_catalogue_namestr

name of the catalogue table that includes all grid_id values and the timestamp when the grid data were added

Returns
netpandapowerNet
pandapower.delete_postgresql_net(host, user, password, database, schema, grid_id, grid_id_column='grid_id', grid_catalogue_name='grid_catalogue')

Removes a grid model from the PostgreSQL database.

Parameters
hoststr

hostname for the DB, e.g. “localhost”

userstr
passwordstr
databasestr

name of the database

schemastr

name of the database schema (e.g. ‘postgres’)

grid_idint

unique grid_id that will be used to identify the data for the grid model

grid_id_columnstr

name of the column for “grid_id” in the PosgreSQL tables, default=”grid_id”.

grid_catalogue_namestr

name of the catalogue table that includes all grid_id values and the timestamp when the grid data were added

Returns