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.
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
"""
if not os.path.isfile(filename):
raise UserWarning("File %s does not exist!" % filename)
xls = pd.ExcelFile(filename).parse(sheetname=None)
try:
net = from_dict_of_dfs(xls)
restore_all_dtypes(net, xls["dtypes"])
except:
net = _from_excel_old(xls)
if convert:
convert_format(net)
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 = from_dict_of_dfs(dodfs)
restore_all_dtypes(net, dodfs["dtypes"])
return net
def test_restore_all_dtypes():
net = create_test_network()
pp.runpp(net)
net['res_test'] = pd.DataFrame(columns=['test'], data=[1, 2, 3])
net['test'] = pd.DataFrame(columns=['test'], data=[1, 2, 3])
net.line['test'] = 123
net.res_line['test'] = 123
net.bus['test'] = 123
net.res_bus['test'] = 123
net.res_load['test'] = 123
dtdf = collect_all_dtypes_df(net)
restore_all_dtypes(net, dtdf)
def 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")
"""
if hasattr(filename, 'read'):
data = json.load(filename)
elif not os.path.isfile(filename):
raise UserWarning("File %s does not exist!!" % filename)
else:
with open(filename) as data_file:
data = json.load(data_file)
try:
pd_dicts = dicts_to_pandas(data)
net = from_dict_of_dfs(pd_dicts)
restore_all_dtypes(net, pd_dicts["dtypes"])
if convert:
convert_format(net)
return net
except UserWarning:
# Can be deleted in the future, maybe now
return from_json_dict(data, convert=convert)
