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file_io.py
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file_io.py
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# -*- coding: utf-8 -*-
# Copyright (c) 2016-2021 by University of Kassel and Fraunhofer Institute for Energy Economics
# and Energy System Technology (IEE), Kassel. All rights reserved.
import json
import os
import pickle
from warnings import warn
import numpy
import pandas as pd
from packaging import version
import pandapower.io_utils as io_utils
from pandapower.auxiliary import pandapowerNet
from pandapower.convert_format import convert_format
from pandapower.create import create_empty_network
def 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
"""
if hasattr(filename, 'write'):
pickle.dump(dict(net), filename, protocol=2)
return
if not filename.endswith(".p"):
raise Exception("Please use .p to save pandapower networks!")
save_net = io_utils.to_dict_with_coord_transform(net, ["bus_geodata"], ["line_geodata"])
with open(filename, "wb") as f:
pickle.dump(save_net, f, protocol=2) # use protocol 2 for py2 / py3 compatibility
def 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
"""
writer = pd.ExcelWriter(filename, engine='xlsxwriter')
dict_net = io_utils.to_dict_of_dfs(net, include_results=include_results,
include_empty_tables=include_empty_tables)
for item, table in dict_net.items():
table.to_excel(writer, sheet_name=item)
writer.save()
def 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")
"""
json_string = json.dumps(net, cls=io_utils.PPJSONEncoder, indent=2)
if encryption_key is not None:
json_string = io_utils.encrypt_string(json_string, encryption_key)
if filename is None:
return json_string
if hasattr(filename, 'write'):
filename.write(json_string)
else:
with open(filename, "w") as fp:
fp.write(json_string)
def to_sql(net, con, include_results=True):
dodfs = io_utils.to_dict_of_dfs(net, include_results=include_results)
for name, data in dodfs.items():
data.to_sql(name, con, if_exists="replace")
def to_sqlite(net, filename, include_results=True):
import sqlite3
conn = sqlite3.connect(filename)
to_sql(net, conn, include_results)
conn.close()
def 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
"""
net = pandapowerNet(io_utils.get_raw_data_from_pickle(filename))
io_utils.transform_net_with_df_and_geo(net, ["bus_geodata"], ["line_geodata"])
if convert:
convert_format(net)
return net
def 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
"""
if not os.path.isfile(filename):
raise UserWarning("File %s does not exist!" % filename)
pd_version = version.parse(pd.__version__)
if pd_version < version.parse("0.21"):
xls = pd.ExcelFile(filename).parse(sheetname=None)
elif pd_version < version.parse("0.24"):
xls = pd.ExcelFile(filename).parse(sheet_name=None)
else:
xls = pd.read_excel(filename, sheet_name=None, index_col=0, engine="openpyxl")
try:
net = io_utils.from_dict_of_dfs(xls)
except:
net = _from_excel_old(xls)
if convert:
convert_format(net)
return net
def _from_excel_old(xls):
par = xls["parameters"]["parameter"]
name = None if pd.isnull(par.at["name"]) else par.at["name"]
net = create_empty_network(name=name, f_hz=par.at["f_hz"])
net.update(par)
for item, table in xls.items():
if item == "parameters":
continue
elif item.endswith("std_types"):
item = item.split("_")[0]
for std_type, tab in table.iterrows():
net.std_types[item][std_type] = dict(tab)
elif item == "line_geodata":
points = int(len(table.columns) / 2)
for i, coords in table.iterrows():
coord = [(coords["x%u" % nr], coords["y%u" % nr]) for nr in range(points)
if pd.notnull(coords["x%u" % nr])]
net.line_geodata.loc[i, "coords"] = coord
else:
net[item] = table
return net
def 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")
"""
if hasattr(filename, 'read'):
json_string = filename.read()
elif not os.path.isfile(filename):
raise UserWarning("File {} does not exist!!".format(filename))
else:
with open(filename, "r") as fp:
json_string = fp.read()
return from_json_string(json_string, convert=convert, encryption_key=encryption_key)
def from_json_string(json_string, convert=False, encryption_key=None):
"""
Load a pandapower network from a JSON string.
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:
**json_string** (string) - The json string representation of the network
**convert** (bool, False) - If True, converts the format of the net loaded from json_string from the
older version of pandapower to the newer version format
**encrytion_key** (string, "") - If given, key to decrypt an encrypted json_string
OUTPUT:
**net** (dict) - The pandapower format network
EXAMPLE:
>>> net = pp.from_json_string(json_str)
"""
if encryption_key is not None:
json_string = io_utils.decrypt_string(json_string, encryption_key)
net = json.loads(json_string, cls=io_utils.PPJSONDecoder)
# this can be removed in the future
# now net is saved with "_module", "_class", "_object"..., so json.load already returns
# pandapowerNet. Older files don't have it yet, and are loaded as dict.
# After some time, this part can be removed.
if not isinstance(net, pandapowerNet):
warn("This net is saved in older format, which will not be supported in future.\r\n"
"Please resave your grid using the current pandapower version.",
DeprecationWarning)
net = from_json_dict(net)
if convert:
convert_format(net)
return net
def from_json_dict(json_dict):
"""
Load a pandapower network from a JSON string.
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:
**json_dict** (json) - The json object representation of the network
OUTPUT:
**net** (dict) - The pandapower format network
EXAMPLE:
>>> net = pp.pp.from_json_dict(json.loads(json_str))
"""
name = json_dict["name"] if "name" in json_dict else None
f_hz = json_dict["f_hz"] if "f_hz" in json_dict else 50
net = create_empty_network(name=name, f_hz=f_hz)
if "parameters" in json_dict:
for par, value in json_dict["parameters"]["parameter"].items():
net[par] = value
for key in sorted(json_dict.keys()):
if key == 'dtypes':
continue
if key in net and isinstance(net[key], pd.DataFrame) and isinstance(json_dict[key], dict) \
or key == "piecewise_linear_cost" or key == "polynomial_cost":
net[key] = pd.DataFrame.from_dict(json_dict[key], orient="columns")
net[key].set_index(net[key].index.astype(numpy.int64), inplace=True)
else:
net[key] = json_dict[key]
return net
def from_sql(con):
cursor = con.cursor()
cursor.execute("SELECT name FROM sqlite_master WHERE type='table';")
dodfs = dict()
for t, in cursor.fetchall():
table = pd.read_sql_query("SELECT * FROM %s" % t, con, index_col="index")
table.index.name = None
dodfs[t] = table
net = io_utils.from_dict_of_dfs(dodfs)
return net
def from_sqlite(filename, netname=""):
import sqlite3
con = sqlite3.connect(filename)
net = from_sql(con)
con.close()
return net