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, cls=PPJSONDecoder)
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, cls=PPJSONDecoder)
try:
pd_dicts = dicts_to_pandas(data)
net = from_dict_of_dfs(pd_dicts)
if convert:
convert_format(net)
return net
except UserWarning:
# Can be deleted in the future, maybe now
return from_json_dict(data, convert=convert)
def from_json_string(json_string, convert=True):
"""
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
OUTPUT:
**convert** (bool) - use the convert format function to
**net** (dict) - The pandapower format network
EXAMPLE:
>>> net = pp.from_json_string(json_str)
"""
data = json.loads(json_string, cls=PPJSONDecoder)
try:
pd_dicts = dicts_to_pandas(data)
net = from_dict_of_dfs(pd_dicts)
if convert:
convert_format(net)
return net
except UserWarning:
# Can be deleted in the future, maybe now
return from_json_dict(data, convert=convert)
def from_pickle(filename, convert=True):
"""
Load a pandapower format Network from pickle file
INPUT:
**filename** (string) - The absolute or relative path to the input file.
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
"""
if not os.path.isfile(filename):
raise UserWarning("File %s does not exist!!" % filename)
with open(filename, "rb") as f:
if sys.version_info >= (3, 0):
net = pickle.load(f,
encoding='latin1') # with encoding in python 3
else:
net = pickle.load(f) # without encoding in python 2
net = pandapowerNet(net)
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.
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)
try:
# pandas < 0.21
xls = pd.ExcelFile(filename).parse(sheetname=None)
except TypeError:
# pandas 0.21
xls = pd.ExcelFile(filename).parse(sheet_name=None)
try:
net = from_dict_of_dfs(xls)
except:
net = _from_excel_old(xls)
if convert:
convert_format(net)
return net
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) - 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:
>>> net = pp.from_json("example.json")
"""
with open(filename) as data_file:
data = json.load(data_file)
net = create_empty_network(name=data["name"], f_hz=data["f_hz"])
# checks if field exists in empty network and if yes, matches data type
def check_equal_type(name):
if name in net:
if isinstance(net[name], type(data[name])):
return True
elif isinstance(net[name], pd.DataFrame) and isinstance(
data[name], dict):
return True
else:
return False
return True
for k in sorted(data.keys()):
if not check_equal_type(k):
raise UserWarning(
"Different data type for existing pandapower field")
if isinstance(data[k], dict):
if isinstance(net[k], pd.DataFrame):
columns = net[k].columns
net[k] = pd.DataFrame.from_dict(data[k], orient="columns")
net[k].set_index(net[k].index.astype(numpy.int64),
inplace=True)
net[k] = net[k][columns]
else:
net[k] = data[k]
else:
net[k] = data[k]
if convert:
convert_format(net)
return net
return None
def from_json_dict(json_dict, convert=True):
"""
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:
**convert** (bool) - use the convert format function to
**net** (dict) - The pandapower format network
EXAMPLE:
>>> net = pp.pp.from_json_dict(json.loads(json_str))
"""
net = create_empty_network(name=json_dict["name"], f_hz=json_dict["f_hz"])
# checks if field exists in empty network and if yes, matches data type
def check_equal_type(name):
if name in net:
if isinstance(net[name], type(json_dict[name])):
return True
elif isinstance(net[name], pd.DataFrame) and isinstance(
json_dict[name], dict):
return True
else:
return False
return True
for k in sorted(json_dict.keys()):
if not check_equal_type(k):
raise UserWarning(
"Different data type for existing pandapower field")
if isinstance(json_dict[k], dict):
if isinstance(net[k], pd.DataFrame):
columns = net[k].columns
net[k] = pd.DataFrame.from_dict(json_dict[k], orient="columns")
net[k].set_index(net[k].index.astype(numpy.int64),
inplace=True)
net[k] = net[k][columns]
else:
net[k] = json_dict[k]
else:
net[k] = json_dict[k]
if convert:
convert_format(net)
return net
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
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
"""
def read(f):
if sys.version_info >= (3,0):
return pickle.load(f, encoding='latin1')
else:
return pickle.load(f)
if hasattr(filename, 'read'):
net = read(filename)
elif not os.path.isfile(filename):
raise UserWarning("File %s does not exist!!" % filename)
else:
with open(filename, "rb") as f:
net = read(f)
net = pandapowerNet(net)
for key, item in net.items():
if isinstance(item, dict) and "DF" in item:
df_dict = item["DF"]
if "columns" in item["DF"]:
net[key] = pd.DataFrame(columns=df_dict["columns"],
index=df_dict["index"],
data=df_dict["data"])
else:
net[key] = pd.DataFrame.from_dict(item["DF"])
if "columns" in item:
net[key] = net[key].reindex_axis(item["columns"], axis=1)
if "dtypes" in item:
try:
#only works with pandas 0.19 or newer
net[key] = net[key].astype(item["dtypes"])
except:
#works with pandas <0.19
for column in net[key].columns:
net[key][column] = net[key][column].astype(item["dtypes"][column])
if convert:
convert_format(net)
return net
def test_convert_format():
""" Testing a very simple network without transformer for voltage
constraints with OPF """
# boundaries:
vm_max = 1.05
vm_min = 0.95
# create net
net = pp.create_empty_network()
pp.create_bus(net, max_vm_pu=vm_max, min_vm_pu=vm_min, vn_kv=10.)
pp.create_bus(net, max_vm_pu=vm_max, min_vm_pu=vm_min, vn_kv=.4)
pp.create_gen(net,
1,
p_kw=-100,
controllable=True,
max_p_kw=-5,
min_p_kw=-150,
max_q_kvar=50,
min_q_kvar=-50)
net.gen["cost_per_kw"] = 100
pp.create_ext_grid(net, 0)
pp.create_load(net, 1, p_kw=20, controllable=False)
pp.create_line_from_parameters(net,
0,
1,
50,
name="line2",
r_ohm_per_km=0.876,
c_nf_per_km=260.0,
max_i_ka=0.123,
x_ohm_per_km=0.1159876,
max_loading_percent=100 * 690)
# run OPF
convert_format(net)
for init in ["pf", "flat"]:
pp.runopp(net, verbose=False, init=init)
assert net["OPF_converged"]
# check and assert result
logger.debug("test_simplest_voltage")
logger.debug("res_gen:\n%s" % net.res_gen)
logger.debug("res_ext_grid:\n%s" % net.res_ext_grid)
logger.debug("res_bus.vm_pu: \n%s" % net.res_bus.vm_pu)
assert max(net.res_bus.vm_pu) < vm_max
assert min(net.res_bus.vm_pu) > vm_min
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
"""
xls = pd.ExcelFile(filename).parse(sheetname=None)
par = xls["parameters"]["parameters"]
name = None if pd.isnull(par.at["name"]) else par.at["name"]
net = create_empty_network(name=name, f_hz=par.at["f_hz"])
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
# net.line.geodata.coords.
if convert:
convert_format(net)
return net
def from_json_dict(json_dict, convert=True):
"""
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:
**convert** (bool) - use the convert format function to
**net** (dict) - The pandapower format network
EXAMPLE:
>>> net = pp.pp.from_json_dict(json.loads(json_str))
"""
warn(
"This function is deprecated and will be removed in a future release.\r\n"
"Please resave your grid using the current pandapower version.",
DeprecationWarning)
net = create_empty_network(name=json_dict["name"], f_hz=json_dict["f_hz"])
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):
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]
if convert:
convert_format(net)
return net
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'):
net = json.load(filename, cls=PPJSONDecoder)
elif not os.path.isfile(filename):
raise UserWarning("File %s does not exist!!" % filename)
else:
with open(filename) as fp:
net = json.load(fp, cls=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_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
"""
def read(f):
if sys.version_info >= (3, 0):
return pickle.load(f, encoding='latin1')
else:
return pickle.load(f)
if hasattr(filename, 'read'):
net = read(filename)
elif not os.path.isfile(filename):
raise UserWarning("File %s does not exist!!" % filename)
else:
with open(filename, "rb") as f:
net = read(f)
net = pandapowerNet(net)
try:
epsg = net.gis_epsg_code
except AttributeError:
epsg = None
for key, item in net.items():
if isinstance(item, dict) and "DF" in item:
df_dict = item["DF"]
if "columns" in df_dict:
# make sure the index is Int64Index
try:
df_index = pd.Int64Index(df_dict['index'])
except TypeError:
df_index = df_dict['index']
if GEOPANDAS_INSTALLED and "geometry" in df_dict["columns"] \
and epsg is not None:
# convert primitive data-types to shapely-objects
if key == "bus_geodata":
data = {
"x": [row[0] for row in df_dict["data"]],
"y": [row[1] for row in df_dict["data"]]
}
geo = [
Point(row[2][0], row[2][1])
for row in df_dict["data"]
]
elif key == "line_geodata":
data = {"coords": [row[0] for row in df_dict["data"]]}
geo = [LineString(row[1]) for row in df_dict["data"]]
net[key] = GeoDataFrame(data,
crs=from_epsg(epsg),
geometry=geo,
index=df_index)
else:
net[key] = pd.DataFrame(columns=df_dict["columns"],
index=df_index,
data=df_dict["data"])
else:
# TODO: is this legacy code?
net[key] = pd.DataFrame.from_dict(df_dict)
if "columns" in item:
try:
net[key] = net[key].reindex(item["columns"], axis=1)
except TypeError: # legacy for pandas <0.21
net[key] = net[key].reindex_axis(item["columns"],
axis=1)
if "dtypes" in item:
if "columns" in df_dict and "geometry" in df_dict["columns"]:
pass
else:
try:
# only works with pandas 0.19 or newer
net[key] = net[key].astype(item["dtypes"])
except:
# works with pandas <0.19
for column in net[key].columns:
net[key][column] = net[key][column].astype(
item["dtypes"][column])
if convert:
convert_format(net)
return net
