diff --git a/.github/workflows/github_test_action.yml b/.github/workflows/github_test_action.yml
index 504384211..a816bec26 100644
--- a/.github/workflows/github_test_action.yml
+++ b/.github/workflows/github_test_action.yml
@@ -314,7 +314,8 @@ jobs:
- name: Check docs for Python ${{ matrix.python-version }}
uses: e2nIEE/sphinx-action@master
with:
- pre-build-command: "python -m pip install --upgrade pip;
- python -m pip install .[docs];"
+ pre-build-command: "apt update && apt upgrade -y && apt install -y build-essential gfortran cmake pkg-config libopenblas-dev;
+ python -m pip install --upgrade pip;
+ python -m pip install .[docs];"
build-command: "sphinx-build -b html . _build -W"
docs-folder: "doc/"
diff --git a/CHANGELOG.rst b/CHANGELOG.rst
index abe611195..d8606a349 100644
--- a/CHANGELOG.rst
+++ b/CHANGELOG.rst
@@ -3,7 +3,10 @@ Change Log
[upcoming release] - 2024-..-..
-------------------------------
+- [ADDED] pandas series accessor for geo column
- [FIXED] Increasing geojson precision as the default precision might cause problems with pandahub
+- [ADDED] Add GeographicalRegion and SubGeographicalRegion names and ids to bus df in cim converter
+- [CHANGED] Capitalize first letter of columns busbar_id, busbar_name and substation_id in bus df for cim converter
[2.14.11] - 2024-07-08
-------------------------------
@@ -19,6 +22,7 @@ Change Log
[upcoming release] - 2024-..-..
-------------------------------
+- [FIXED] passing literal json to 'read_json' is deprecated
- [ADDED] pf2pp: possibility to convert a specific varaint and scenario
- [ADDED] Static Var Compensator with Voltage Control
@@ -92,7 +96,9 @@ Change Log
- [CHANGED] accelerate distributed slack power flow calculation by using sparse-aware operations in _subnetworks()
- [CHANGED] Trafo Controllers can now be added to elements that are out of service, changed self.nothing_to_do()
- [ADDED] Discrete shunt controller for local voltage regulation with shunt steps
+- [ADDED] fix lengths missmatch of output if ignore_zero_length is False in plotting utility function coords_from_node_geodata() and rename ignore_zero_length by ignore_no_geo_diff
- [ADDED] cim2pp converter: Using lxml to parse XML files (better performance)
+- [ADDED] possibility to load JSON files with unknown object models and just store the models as dictionaries in the network
[2.14.7] - 2024-06-14
-------------------------------
diff --git a/pandapower/auxiliary.py b/pandapower/auxiliary.py
index 0b08f4634..68d6a1b1c 100644
--- a/pandapower/auxiliary.py
+++ b/pandapower/auxiliary.py
@@ -33,6 +33,7 @@
from importlib.metadata import PackageNotFoundError
from typing_extensions import deprecated
+from geojson import loads, GeoJSON
import numpy as np
import pandas as pd
import scipy as sp
@@ -58,9 +59,16 @@
import pandaplan.core.pplog as logging
except ImportError:
import logging
+try:
+ from geopandas import GeoSeries
+ from shapely import from_geojson
+ geopandas_available = True
+except ImportError:
+ geopandas_available = False
logger = logging.getLogger(__name__)
+
def log_to_level(msg, passed_logger, level):
if level == "error":
passed_logger.error(msg)
@@ -343,6 +351,85 @@ def __repr__(self): # pragma: no cover
return "\n".join(lines)
+@pd.api.extensions.register_series_accessor("geojson")
+class GeoAccessor:
+ """
+ pandas Series accessor for the geo column. It facilitates the use of geojson strings.
+ NaN entrys are dropped using the accessor!
+ """
+ def __init__(self, pandas_obj):
+ self._validate(pandas_obj)
+ self._obj = pandas_obj
+
+ @staticmethod
+ def _validate(obj):
+ try:
+ if not obj.dropna().apply(loads).apply(isinstance, args=(GeoJSON,)).all():
+ raise AttributeError("Can only use .geojson accessor with geojson string values!")
+ except Exception as e:
+ raise AttributeError(f"Can only use .geojson accessor with geojson string values!: {e}")
+ if not geopandas_available:
+ soft_dependency_error("GeoAccessor", "geopandas")
+
+ @staticmethod
+ def _extract_coords(x):
+ if x["type"] == "Point":
+ return np.array(x["coordinates"])
+ return [np.array(y) for y in x["coordinates"]]
+
+ @property
+ def _coords(self):
+ """
+ Extracts the geometry coordinates from the GeoJSON strings.
+ It is not recommended to use the standalone coordinates.
+ Important informations like the crs or latlon/lonlat are lost as a result.
+ """
+ return self._obj.dropna().apply(loads).apply(self._extract_coords)
+
+ @property
+ def as_geo_obj(self):
+ """
+ Loads the GeoJSON objects.
+ """
+ return self._obj.dropna().apply(loads)
+
+ @property
+ def type(self):
+ """
+ Extracts the geometry type of the GeoJSON string.
+ """
+ return self._obj.dropna().apply(loads).apply(lambda x: str(x["type"]))
+
+ @property
+ def as_shapely_obj(self):
+ """
+ Converts the GeoJSON strings to shapely geometrys.
+ """
+ return self._obj.dropna().apply(from_geojson)
+
+ @property
+ def as_geoseries(self):
+ """
+ Converts the PandasSeries to a GeoSeries with shapely geometrys.
+ """
+ return GeoSeries(self._obj.dropna().pipe(from_geojson), crs=4326, index=self._obj.dropna().index)
+
+ def __getattr__(self, item):
+ """
+ Enables access to all methods or attribute calls from a GeoSeries.
+ """
+ geoms = self.as_geoseries
+ if hasattr(geoms, item):
+ geoms_item = getattr(geoms, item)
+ if callable(geoms_item):
+ def wrapper(*args, **kwargs):
+ return geoms_item(*args, **kwargs)
+ return wrapper
+ else:
+ return geoms_item
+ raise AttributeError(f"'GeoAccessor' object has no attribute '{item}'")
+
+
def plural_s(number):
return "" if number == 1 else "s"
diff --git a/pandapower/convert_format.py b/pandapower/convert_format.py
index e060df1f3..9af24aec7 100644
--- a/pandapower/convert_format.py
+++ b/pandapower/convert_format.py
@@ -11,8 +11,10 @@
from pandapower._version import __version__, __format_version__
from pandapower.create import create_empty_network, create_poly_cost
+from pandapower.plotting import geo
from pandapower.results import reset_results
from pandapower.control import TrafoController
+import pandapower.plotting.geo as geo
try:
import pandaplan.core.pplog as logging
@@ -22,14 +24,6 @@
logger = logging.getLogger(__name__)
-def _compare_version(
- net_version,
- compare_version: Union[str, int],
- compare: Callable[[Version, Version], bool] = lambda x, y: x < y
-) -> bool:
- return compare(Version(str(net_version)), Version(str(compare_version)))
-
-
def convert_format(net, elements_to_deserialize=None):
"""
Converts old nets to new format to ensure consistency. The converted net is returned.
@@ -46,6 +40,7 @@ def convert_format(net, elements_to_deserialize=None):
_add_missing_columns(net, elements_to_deserialize)
_create_seperate_cost_tables(net, elements_to_deserialize)
if Version(str(net.format_version)) < Version("3.0.0"):
+ _convert_geo_data(net, elements_to_deserialize)
_convert_group_element_index(net)
_convert_trafo_controller_parameter_names(net)
if Version(str(net.format_version)) < Version("2.4.0"):
@@ -56,7 +51,7 @@ def convert_format(net, elements_to_deserialize=None):
_convert_to_mw(net)
_update_trafo_parameter_names(net, elements_to_deserialize)
reset_results(net)
- if isinstance(net.format_version, float) and net.format_version < 1.6:
+ if Version(str(net.format_version)) < Version("1.6"):
set_data_type_of_columns_to_default(net)
_convert_objects(net, elements_to_deserialize)
_update_characteristics(net, elements_to_deserialize)
@@ -74,7 +69,7 @@ def _convert_geo_data(net, elements_to_deserialize=None):
or (_check_elements_to_deserialize('line_geodata', elements_to_deserialize)
and _check_elements_to_deserialize('line', elements_to_deserialize))):
if hasattr(net, 'bus_geodata') or hasattr(net, 'line_geodata'):
- if _compare_version(net.format_version, "1.6"):
+ if Version(str(net.format_version)) < Version("1.6"):
net.bus_geodata = pd.DataFrame.from_dict(net.bus_geodata)
net.line_geodata = pd.DataFrame.from_dict(net.line_geodata)
geo.convert_geodata_to_geojson(net)
diff --git a/pandapower/converter/cim/cim2pp/converter_classes/connectivitynodes/connectivityNodesCim16.py b/pandapower/converter/cim/cim2pp/converter_classes/connectivitynodes/connectivityNodesCim16.py
index f1de75cf8..34bfd4484 100644
--- a/pandapower/converter/cim/cim2pp/converter_classes/connectivitynodes/connectivityNodesCim16.py
+++ b/pandapower/converter/cim/cim2pp/converter_classes/connectivitynodes/connectivityNodesCim16.py
@@ -248,6 +248,18 @@ def _prepare_connectivity_nodes_cim16(self) -> Tuple[pd.DataFrame, pd.DataFrame]
bb = bb.drop_duplicates(subset=['rdfId'], keep='first')
connectivity_nodes = pd.merge(connectivity_nodes, bb, how='left', on='rdfId')
+ if "Substation" in connectivity_nodes.columns:
+ # add (sub) geographical regions
+ sgr = self.cimConverter.cim['eq']['SubGeographicalRegion'][['rdfId', 'name', 'Region']]
+ regions = pd.merge(self.cimConverter.cim['eq']['Substation'], sgr, left_on="Region", right_on="rdfId",
+ suffixes=["_substation", "_SubGeographicalRegion"])
+ regions = pd.merge(self.cimConverter.cim['eq']['GeographicalRegion'], regions, left_on="rdfId", right_on="Region_SubGeographicalRegion")
+ regions = regions.rename(columns={'name': 'GeographicalRegion_name', 'name_SubGeographicalRegion': 'SubGeographicalRegion_name',
+ 'rdfId': 'GeographicalRegion_id', 'rdfId_SubGeographicalRegion': 'SubGeographicalRegion_id'})
+ regions = regions.drop(columns=['name_substation', 'Region_substation', 'Region_SubGeographicalRegion'])
+ connectivity_nodes = pd.merge(connectivity_nodes, regions, how='left', left_on='Substation', right_on='rdfId_substation')
+ connectivity_nodes = connectivity_nodes.drop(columns=["rdfId_substation"])
+
connectivity_nodes = connectivity_nodes.rename(columns={'rdfId': sc['o_id'], 'TopologicalNode': sc['ct'],
'nominalVoltage': 'vn_kv', 'name_substation': 'zone'})
connectivity_nodes['in_service'] = True
diff --git a/pandapower/converter/cim/cim2pp/converter_classes/impedance/seriesCompensatorsCim16.py b/pandapower/converter/cim/cim2pp/converter_classes/impedance/seriesCompensatorsCim16.py
index 030e75701..824e568dd 100644
--- a/pandapower/converter/cim/cim2pp/converter_classes/impedance/seriesCompensatorsCim16.py
+++ b/pandapower/converter/cim/cim2pp/converter_classes/impedance/seriesCompensatorsCim16.py
@@ -107,7 +107,7 @@ def _prepare_series_compensators_cim16(self) -> pd.DataFrame:
ser_comp['gf_pu'] = 0.
ser_comp['bf_pu'] = 0.
ser_comp['gt_pu'] = 0.
- ser_comp['bf_pu'] = 0.
+ ser_comp['bt_pu'] = 0.
ser_comp['in_service'] = ser_comp.connected & ser_comp.connected2
ser_comp = ser_comp.rename(columns={'rdfId_Terminal': sc['t_from'], 'rdfId_Terminal2': sc['t_to'],
'rdfId': sc['o_id'], 'index_bus': 'from_bus', 'index_bus2': 'to_bus'})
diff --git a/pandapower/converter/cim/cim2pp/from_cim.py b/pandapower/converter/cim/cim2pp/from_cim.py
index dfcc0f1d4..c45d00fd7 100644
--- a/pandapower/converter/cim/cim2pp/from_cim.py
+++ b/pandapower/converter/cim/cim2pp/from_cim.py
@@ -103,6 +103,7 @@ def from_cim(file_list: List[str] = None, encoding: str = 'utf-8', convert_line_
custom_converter_classes: Dict = None,
cgmes_version: str = '2.4.15', **kwargs) -> \
pandapower.auxiliary.pandapowerNet:
+ # Nur zum Testen, kann wieder gelöscht werden
"""
Convert a CIM net to a pandapower net from XML files.
Additional parameters for kwargs:
diff --git a/pandapower/converter/cim/cim_tools.py b/pandapower/converter/cim/cim_tools.py
index 9668b2387..e04b92988 100644
--- a/pandapower/converter/cim/cim_tools.py
+++ b/pandapower/converter/cim/cim_tools.py
@@ -24,7 +24,7 @@ def get_pp_net_special_columns_dict() -> Dict[str, str]:
'o_prf': 'origin_profile', 'ct': 'cim_topnode', 'tc': 'tapchanger_class', 'tc_id': 'tapchanger_id',
'pte_id': 'PowerTransformerEnd_id', 'pte_id_hv': 'PowerTransformerEnd_id_hv',
'pte_id_mv': 'PowerTransformerEnd_id_mv', 'pte_id_lv': 'PowerTransformerEnd_id_lv',
- 'cnc_id': 'ConnectivityNodeContainer_id', 'sub_id': 'substation_id', 'src': 'source', 'name': 'name',
+ 'cnc_id': 'ConnectivityNodeContainer_id', 'sub_id': 'Substation_id', 'src': 'source', 'name': 'name',
'desc': 'description', 'a_id': 'analog_id'})
@@ -50,8 +50,9 @@ def extend_pp_net_cim(net: pandapowerNet, override: bool = True) -> pandapowerNe
fill_dict: Dict[str, Dict[str, List[str]]] = dict()
fill_dict['bus'] = dict()
- fill_dict['bus'][np_str_type] = [sc['o_prf'], sc['ct'], sc['cnc_id'], sc['sub_id'], 'description', 'busbar_id',
- 'busbar_name']
+ fill_dict['bus'][np_str_type] = [sc['o_prf'], sc['ct'], sc['cnc_id'], sc['sub_id'], 'description', 'Busbar_id',
+ 'Busbar_name', 'GeographicalRegion_id', 'GeographicalRegion_name',
+ 'SubGeographicalRegion_id', 'SubGeographicalRegion_name']
fill_dict['ext_grid'] = dict()
fill_dict['ext_grid'][np_str_type] = [sc['t'], sc['sub'], 'description']
diff --git a/pandapower/file_io.py b/pandapower/file_io.py
index 4a56a7ac5..a864e4fbc 100644
--- a/pandapower/file_io.py
+++ b/pandapower/file_io.py
@@ -238,7 +238,7 @@ def _from_excel_old(xls):
def 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):
+ empty_dict_like_object=None, ignore_unknown_objects=False):
"""
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.
@@ -270,6 +270,9 @@ def from_json(filename, convert=True, encryption_key=None, elements_to_deseriali
the data of the json string. Give another dict-like object to start filling that alternative
object with the json data.
+ **ignore_unknown_objects** (bool, False) - If set to True, ignore any objects that cannot be
+ deserialized instead of raising an error
+
OUTPUT:
**net** (dict) - The pandapower format network
@@ -294,12 +297,13 @@ def from_json(filename, convert=True, encryption_key=None, elements_to_deseriali
keep_serialized_elements=keep_serialized_elements,
add_basic_std_types=add_basic_std_types,
replace_elements=replace_elements,
- empty_dict_like_object=empty_dict_like_object)
+ empty_dict_like_object=empty_dict_like_object,
+ ignore_unknown_objects=ignore_unknown_objects)
def from_json_string(json_string, convert=False, encryption_key=None, elements_to_deserialize=None,
keep_serialized_elements=True, add_basic_std_types=False,
- replace_elements=None, empty_dict_like_object=None):
+ replace_elements=None, empty_dict_like_object=None, ignore_unknown_objects=False):
"""
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.
@@ -330,6 +334,9 @@ def from_json_string(json_string, convert=False, encryption_key=None, elements_t
the data of the json string. Give another dict-like object to start filling that alternative
object with the json data.
+ **ignore_unknown_objects** (bool, False) - If set to True, ignore any objects that cannot be
+ deserialized instead of raising an error
+
OUTPUT:
**net** (dict) - The pandapower format network
@@ -347,10 +354,12 @@ def from_json_string(json_string, convert=False, encryption_key=None, elements_t
if elements_to_deserialize is None:
net = json.loads(json_string, cls=io_utils.PPJSONDecoder,
- empty_dict_like_object=empty_dict_like_object)
+ empty_dict_like_object=empty_dict_like_object,
+ ignore_unknown_objects=ignore_unknown_objects)
else:
net = json.loads(json_string, cls=io_utils.PPJSONDecoder, deserialize_pandas=False,
- empty_dict_like_object=empty_dict_like_object)
+ empty_dict_like_object=empty_dict_like_object,
+ ignore_unknown_objects=ignore_unknown_objects)
net_dummy = create_empty_network()
if ('version' not in net.keys()) | (Version(net.version) < Version('2.1.0')):
raise UserWarning('table selection is only possible for nets above version 2.0.1. '
diff --git a/pandapower/io_utils.py b/pandapower/io_utils.py
index 63b49c55a..a24771a93 100644
--- a/pandapower/io_utils.py
+++ b/pandapower/io_utils.py
@@ -489,17 +489,18 @@ class FromSerializableRegistry():
class_name = ''
module_name = ''
- def __init__(self, obj, d, pp_hook_funct):
+ def __init__(self, obj, d, pp_hook_funct, ignore_unknown_objects=False):
self.obj = obj
self.d = d
self.pp_hook = pp_hook_funct
+ self.ignore_unknown_objects = ignore_unknown_objects
@from_serializable.register(class_name='Series', module_name='pandas.core.series')
def Series(self):
is_multiindex = self.d.pop('is_multiindex', False)
index_name = self.d.pop('index_name', None)
index_names = self.d.pop('index_names', None)
- ser = pd.read_json(self.obj, precise_float=True, **self.d)
+ ser = pd.read_json(io.StringIO(self.obj), precise_float=True, **self.d)
# restore index name and Multiindex
if index_name is not None:
@@ -577,7 +578,9 @@ def DataFrame(self):
# recreate jsoned objects
for col in ('object', 'controller'): # "controller" for backwards compatibility
if (col in df.columns):
- df[col] = df[col].apply(self.pp_hook)
+ df[col] = df[col].apply(partial(
+ self.pp_hook, ignore_unknown_objects=self.ignore_unknown_objects
+ ))
if 'geo' in df.columns:
df['geo'] = df['geo'].dropna().apply(json.dumps).apply(geojson.loads)
return df
@@ -627,12 +630,28 @@ def function(self):
@from_serializable.register()
def rest(self):
- module = importlib.import_module(self.module_name)
- class_ = getattr(module, self.class_name)
+ try:
+ module = importlib.import_module(self.module_name)
+ except ModuleNotFoundError as e:
+ if self.ignore_unknown_objects:
+ warn(f"Module {self.module_name} not found. Returning object as is.")
+ return json.loads(self.obj)
+ else:
+ raise e
+ try:
+ class_ = getattr(module, self.class_name)
+ except AttributeError as e:
+ if self.ignore_unknown_objects:
+ warn(f"Class {self.class_name} not found in module {self.module_name}. Returning object as is.")
+ return json.loads(self.obj)
+ else:
+ raise e
if isclass(class_) and issubclass(class_, JSONSerializableClass):
if isinstance(self.obj, str):
self.obj = json.loads(self.obj, cls=PPJSONDecoder,
- object_hook=pp_hook)
+ object_hook=partial(
+ pp_hook, ignore_unknown_objects=self.ignore_unknown_objects
+ ))
# backwards compatibility
if "net" in self.obj:
del self.obj["net"]
@@ -689,16 +708,18 @@ def __init__(self, **kwargs):
deserialize_pandas = kwargs.pop('deserialize_pandas', True)
empty_dict_like_object = kwargs.pop('empty_dict_like_object', None)
registry_class = kwargs.pop("registry_class", FromSerializableRegistry)
+ ignore_unknown_objects = kwargs.pop("ignore_unknown_objects", False)
super_kwargs = {"object_hook": partial(pp_hook,
deserialize_pandas=deserialize_pandas,
empty_dict_like_object=empty_dict_like_object,
- registry_class=registry_class)}
+ registry_class=registry_class,
+ ignore_unknown_objects=ignore_unknown_objects)}
super_kwargs.update(kwargs)
super().__init__(**super_kwargs)
def pp_hook(d, deserialize_pandas=True, empty_dict_like_object=None,
- registry_class=FromSerializableRegistry):
+ registry_class=FromSerializableRegistry, ignore_unknown_objects=False):
try:
if '_module' in d and '_class' in d:
if 'pandas' in d['_module'] and not deserialize_pandas:
@@ -713,7 +734,8 @@ def pp_hook(d, deserialize_pandas=True, empty_dict_like_object=None,
else:
# obj = {"_init": d, "_state": dict()} # backwards compatibility
obj = {key: val for key, val in d.items() if key not in ['_module', '_class']}
- fs = registry_class(obj, d, pp_hook)
+ fs = registry_class(obj, d, pp_hook, ignore_unknown_objects)
+
fs.class_name = d.pop('_class', '')
fs.module_name = d.pop('_module', '')
fs.empty_dict_like_object = empty_dict_like_object
diff --git a/pandapower/networks/synthetic_voltage_control_lv_networks.py b/pandapower/networks/synthetic_voltage_control_lv_networks.py
index 6523cab36..c1b7edb71 100644
--- a/pandapower/networks/synthetic_voltage_control_lv_networks.py
+++ b/pandapower/networks/synthetic_voltage_control_lv_networks.py
@@ -3,10 +3,10 @@
# Copyright (c) 2016-2024 by University of Kassel and Fraunhofer Institute for Energy Economics
# and Energy System Technology (IEE), Kassel. All rights reserved.
-import io
import pandapower as pp
import pandas as pd
from numpy import nan, append
+from geojson import Point, dumps
def create_synthetic_voltage_control_lv_network(network_class="rural_1"):
@@ -187,13 +187,94 @@ def create_synthetic_voltage_control_lv_network(network_class="rural_1"):
# set bus geo data
bus_geo = {
- "rural_1": '{"x":{"0":0.0,"1":0.0,"2":-1.6666666667,"3":-1.6666666667,"4":-0.1666666667,"5":-0.6666666667,"6":-1.1666666667,"7":-1.6666666667,"8":0.3333333333,"9":-0.1666666667,"10":-0.6666666667,"11":-1.6666666667,"12":1.8333333333,"13":1.3333333333,"14":0.8333333333,"15":0.3333333333,"16":-0.1666666667,"17":-0.6666666667,"18":-1.1666666667,"19":2.3333333333,"20":1.8333333333,"21":1.3333333333,"22":0.8333333333,"23":0.3333333333,"24":-0.1666666667,"25":-1.1666666667},"y":{"0":0.0,"1":1.0,"2":2.0,"3":3.0,"4":2.0,"5":3.0,"6":4.0,"7":5.0,"8":3.0,"9":4.0,"10":5.0,"11":6.0,"12":2.0,"13":3.0,"14":4.0,"15":5.0,"16":6.0,"17":7.0,"18":8.0,"19":3.0,"20":4.0,"21":5.0,"22":6.0,"23":7.0,"24":8.0,"25":9.0}}',
- "rural_2": '{"x":{"0":0.0,"1":0.0,"2":-2.5,"3":-2.5,"4":-1.0,"5":-1.5,"6":-2.0,"7":-0.5,"8":-1.0,"9":-2.0,"10":1.0,"11":0.5,"12":0.0,"13":1.5,"14":1.0,"15":0.0,"16":2.5,"17":2.5},"y":{"0":0.0,"1":1.0,"2":2.0,"3":3.0,"4":2.0,"5":3.0,"6":4.0,"7":3.0,"8":4.0,"9":5.0,"10":2.0,"11":3.0,"12":4.0,"13":3.0,"14":4.0,"15":5.0,"16":2.0,"17":3.0}}',
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+ 36: [-15.5, 18.0], 37: [-7.0, 3.0], 38: [-7.5, 4.0], 39: [-8.0, 5.0], 40: [-8.5, 6.0],
+ 41: [-9.0, 7.0], 42: [-9.5, 8.0], 43: [-10.0, 9.0], 44: [-10.5, 10.0], 45: [-11.0, 11.0],
+ 46: [-11.5, 12.0], 47: [-12.0, 13.0], 48: [-12.5, 14.0], 49: [-13.0, 15.0], 50: [-13.5, 16.0],
+ 51: [-14.0, 17.0], 52: [-14.5, 18.0], 53: [-15.5, 19.0], 54: [-5.5, 2.0], 55: [-6.0, 3.0],
+ 56: [-6.5, 4.0], 57: [-7.0, 5.0], 58: [-7.5, 6.0], 59: [-5.0, 3.0], 60: [-5.5, 4.0],
+ 61: [-6.0, 5.0], 62: [-6.5, 6.0], 63: [-7.5, 7.0], 64: [-3.5, 2.0], 65: [-4.0, 3.0],
+ 66: [-4.5, 4.0], 67: [-5.0, 5.0], 68: [-5.5, 6.0], 69: [-6.0, 7.0], 70: [-6.5, 8.0],
+ 71: [-7.0, 9.0], 72: [-7.5, 10.0], 73: [-8.0, 11.0], 74: [-8.5, 12.0], 75: [-9.0, 13.0],
+ 76: [-9.5, 14.0], 77: [-3.0, 3.0], 78: [-3.5, 4.0], 79: [-4.0, 5.0], 80: [-4.5, 6.0],
+ 81: [-5.0, 7.0], 82: [-5.5, 8.0], 83: [-6.0, 9.0], 84: [-6.5, 10.0], 85: [-7.0, 11.0],
+ 86: [-7.5, 12.0], 87: [-8.0, 13.0], 88: [-8.5, 14.0], 89: [-9.5, 15.0], 90: [-1.5, 2.0],
+ 91: [-2.0, 3.0], 92: [-2.5, 4.0], 93: [-3.0, 5.0], 94: [-3.5, 6.0], 95: [-4.0, 7.0],
+ 96: [-4.5, 8.0], 97: [-5.0, 9.0], 98: [-1.0, 3.0], 99: [-1.5, 4.0], 100: [-2.0, 5.0],
+ 101: [-2.5, 6.0], 102: [-3.0, 7.0], 103: [-3.5, 8.0], 104: [-4.0, 9.0], 105: [-5.0, 10.0],
+ 106: [0.0, 2.0], 107: [0.0, 3.0], 108: [1.5, 2.0], 109: [1.0, 3.0], 110: [0.5, 4.0],
+ 111: [0.0, 5.0], 112: [-0.5, 6.0], 113: [-1.0, 7.0], 114: [-1.5, 8.0], 115: [-2.0, 9.0],
+ 116: [-2.5, 10.0], 117: [2.0, 3.0], 118: [1.5, 4.0], 119: [1.0, 5.0], 120: [0.5, 6.0],
+ 121: [0.0, 7.0], 122: [-0.5, 8.0], 123: [-1.0, 9.0], 124: [-1.5, 10.0], 125: [-2.5, 11.0],
+ 126: [3.5, 2.0], 127: [3.0, 3.0], 128: [2.5, 4.0], 129: [2.0, 5.0], 130: [1.5, 6.0],
+ 131: [1.0, 7.0], 132: [0.5, 8.0], 133: [0.0, 9.0], 134: [-0.5, 10.0], 135: [-1.0, 11.0],
+ 136: [-1.5, 12.0], 137: [-2.0, 13.0], 138: [-2.5, 14.0], 139: [-3.0, 15.0], 140: [-3.5, 16.0],
+ 141: [-4.0, 17.0], 142: [-4.5, 18.0], 143: [4.0, 3.0], 144: [3.5, 4.0], 145: [3.0, 5.0],
+ 146: [2.5, 6.0], 147: [2.0, 7.0], 148: [1.5, 8.0], 149: [1.0, 9.0], 150: [0.5, 10.0],
+ 151: [0.0, 11.0], 152: [-0.5, 12.0], 153: [-1.0, 13.0], 154: [-1.5, 14.0], 155: [-2.0, 15.0],
+ 156: [-2.5, 16.0], 157: [-3.0, 17.0], 158: [-3.5, 18.0], 159: [-4.5, 19.0], 160: [5.5, 2.0],
+ 161: [5.0, 3.0], 162: [4.5, 4.0], 163: [4.0, 5.0], 164: [3.5, 6.0], 165: [6.0, 3.0],
+ 166: [5.5, 4.0], 167: [5.0, 5.0], 168: [4.5, 6.0], 169: [3.5, 7.0], 170: [7.5, 2.0],
+ 171: [7.0, 3.0], 172: [6.5, 4.0], 173: [6.0, 5.0], 174: [5.5, 6.0], 175: [5.0, 7.0],
+ 176: [4.5, 8.0], 177: [4.0, 9.0], 178: [3.5, 10.0], 179: [3.0, 11.0], 180: [2.5, 12.0],
+ 181: [2.0, 13.0], 182: [1.5, 14.0], 183: [8.0, 3.0], 184: [7.5, 4.0], 185: [7.0, 5.0],
+ 186: [6.5, 6.0], 187: [6.0, 7.0], 188: [5.5, 8.0], 189: [5.0, 9.0], 190: [4.5, 10.0],
+ 191: [4.0, 11.0], 192: [3.5, 12.0], 193: [3.0, 13.0], 194: [2.5, 14.0], 195: [1.5, 15.0],
+ 196: [9.5, 2.0], 197: [9.0, 3.0], 198: [8.5, 4.0], 199: [8.0, 5.0], 200: [10.0, 3.0],
+ 201: [9.5, 4.0], 202: [9.0, 5.0], 203: [8.0, 6.0]
+ }
}
- net.bus_geodata = pd.read_json(io.StringIO(bus_geo[network_class]))
- # Match bus.index
- net.bus_geodata = net.bus_geodata.loc[net.bus.index]
+ net.bus.geo = net.bus.apply(
+ lambda row: dumps(Point(bus_geo[network_class][row.name])),
+ axis=1
+ )
return net
diff --git a/pandapower/plotting/collections.py b/pandapower/plotting/collections.py
index 04f77cd5e..e060c1d2c 100644
--- a/pandapower/plotting/collections.py
+++ b/pandapower/plotting/collections.py
@@ -551,7 +551,7 @@ def create_line_collection(net: pandapowerNet, lines=None,
node_geodata=net.bus.geo,
table_name="line",
node_name="bus",
- ignore_zero_length=True)
+ ignore_no_geo_diff=True)
line_geodata = line_geodata.combine_first(pd.Series(geos, index=line_index_successful))
diff --git a/pandapower/plotting/plotly/simple_plotly.py b/pandapower/plotting/plotly/simple_plotly.py
index 4193739d7..db64ff56d 100644
--- a/pandapower/plotting/plotly/simple_plotly.py
+++ b/pandapower/plotting/plotly/simple_plotly.py
@@ -116,7 +116,7 @@ def simple_plotly(net, respect_switches=True, use_line_geo=None, on_map=False,
**respect_switches** (bool, True) - Respect switches when artificial geodata is created
**use_line_geo** (bool, True) - defines if lines patches are based on
- net.line_geodata of the lines (True) or on net.bus_geodata of the connected buses (False)
+ net.line.geo of the lines (True) or on net.bus.geo of the connected buses (False)
**on_map** (bool, False) - enables using mapbox plot in plotly.
If provided geodata are not real geo-coordinates in lon/lat form, on_map will be set to False.
diff --git a/pandapower/plotting/plotly/vlevel_plotly.py b/pandapower/plotting/plotly/vlevel_plotly.py
index 409baf916..eda70a6e0 100644
--- a/pandapower/plotting/plotly/vlevel_plotly.py
+++ b/pandapower/plotting/plotly/vlevel_plotly.py
@@ -35,8 +35,8 @@ def vlevel_plotly(net, respect_switches=True, use_line_geo=None, colors_dict=Non
OPTIONAL:
**respect_switches** (bool, True) - Respect switches when artificial geodata is created
- **use_line_geo** (bool, True) - defines if lines patches are based on net.line_geodata
- of the lines (True) or on net.bus_geodata of the connected buses (False)
+ **use_line_geo** (bool, True) - defines if lines patches are based on net.line.geo
+ of the lines (True) or on net.bus.geo of the connected buses (False)
*colors_dict** (dict, None) - dictionary for customization of colors for each voltage level
in the form: voltage : color
diff --git a/pandapower/plotting/plotting_toolbox.py b/pandapower/plotting/plotting_toolbox.py
index 11d19a39a..39cfc127d 100644
--- a/pandapower/plotting/plotting_toolbox.py
+++ b/pandapower/plotting/plotting_toolbox.py
@@ -144,7 +144,7 @@ def get_index_array(indices, net_table_indices):
def coords_from_node_geodata(element_indices, from_nodes, to_nodes, node_geodata, table_name,
- node_name="Bus", ignore_zero_length=True, node_geodata_to=None):
+ node_name="Bus", ignore_no_geo_diff=True, node_geodata_to=None):
"""
Auxiliary function to get the node coordinates for a number of branches with respective from
and to nodes. The branch elements for which there is no geodata available are not included in
@@ -162,9 +162,9 @@ def coords_from_node_geodata(element_indices, from_nodes, to_nodes, node_geodata
:type table_name: str
:param node_name: Name of the node type (only for logging)
:type node_name: str, default "Bus"
- :param ignore_zero_length: States if branches should be left out, if their length is zero, i.e. \
+ :param ignore_no_geo_diff: States if branches should be left out, if their length is zero, i.e. \
from_node_coords = to_node_coords
- :type ignore_zero_length: bool, default True
+ :type ignore_no_geo_diff: bool, default True
:param node_geodata_to: Dataframe containing x and y coordinates of the "to" nodes (optional, default node_geodata)
:type node_geodata_to: pd.DataFrame
:return: Return values are:\
@@ -192,15 +192,19 @@ def coords_from_node_geodata(element_indices, from_nodes, to_nodes, node_geodata
)
node_geodata = node_geodata.apply(_get_coords_from_geojson)
- coords = [f'{{"coordinates": [[{x_from}, {y_from}], [{x_to}, {y_to}]], "type": "LineString"}}'
- for [x_from, y_from], [x_to, y_to]
- in zip(node_geodata.loc[fb_with_geo[not_nan]],
- node_geodata.loc[tb_with_geo[not_nan]])
- if not ignore_zero_length or (ignore_zero_length and not (x_from == x_to and y_from == y_to))]
- return coords, np.array(element_indices)[in_geo & not_nan]
-
-
-def set_line_geodata_from_bus_geodata(net, line_index=None, overwrite=False, ignore_zero_length=True):
+ coords, no_geo_diff = zip(*[
+ (f'{{"coordinates": [[{x_from}, {y_from}], [{x_to}, {y_to}]], "type": "LineString"}}',
+ x_from == x_to and y_from == y_to) for [x_from, y_from], [x_to, y_to] in zip(
+ node_geodata.loc[fb_with_geo[not_nan]], node_geodata.loc[tb_with_geo[not_nan]])])
+ # if not ignore_no_geo_diff or (ignore_no_geo_diff and not ())]
+ coords, no_geo_diff = np.array(coords), np.array(no_geo_diff)
+ if ignore_no_geo_diff:
+ return coords, np.array(element_indices)[in_geo & not_nan]
+ else:
+ return coords[~no_geo_diff], np.array(element_indices)[in_geo & not_nan][~no_geo_diff]
+
+
+def set_line_geodata_from_bus_geodata(net, line_index=None, overwrite=False, ignore_no_geo_diff=True):
"""
Sets coordinates in net.line.geo based on the from_bus and to_bus coordinates
in net.bus.geo
@@ -227,7 +231,7 @@ def set_line_geodata_from_bus_geodata(net, line_index=None, overwrite=False, ign
node_geodata=net.bus.geo,
table_name="line",
node_name="bus",
- ignore_zero_length=ignore_zero_length)
+ ignore_no_geo_diff=ignore_no_geo_diff)
net.line.loc[line_index_successful, 'geo'] = geos
diff --git a/pandapower/test/api/test_auxiliary.py b/pandapower/test/api/test_auxiliary.py
index be6c60a24..33e532bf3 100644
--- a/pandapower/test/api/test_auxiliary.py
+++ b/pandapower/test/api/test_auxiliary.py
@@ -303,6 +303,7 @@ def test_characteristic(file_io):
with pytest.raises(NotImplementedError):
c3([0])
+
def test_log_characteristic_property():
net = pp.create_empty_network()
c = LogSplineCharacteristic(net, [10, 1000, 10000], [1000, 0.1, 0.001], interpolator_kind="Pchip", extrapolate=False)
@@ -310,6 +311,52 @@ def test_log_characteristic_property():
c([2])
+def test_geo_accessor_geojson():
+ net = pp.create_empty_network()
+ b1 = pp.create_bus(net, 10, geodata=(1, 1))
+ b2 = pp.create_bus(net, 10, geodata=(2, 2))
+ l = pp.create_lines(
+ net,
+ [b1, b1],
+ [b2, b2],
+ [1.5, 3],
+ std_type="48-AL1/8-ST1A 10.0",
+ geodata=[[(1, 1), (2, 2), (3, 3)], [(1, 1), (1, 2)]],
+ )
+ pp.create_line(net, b1, b2, 1.5, std_type="48-AL1/8-ST1A 10.0")
+
+ assert len(net.line.geo.geojson._coords) == 2
+ assert np.array_equal(net.line.geo.geojson._coords.at[l[0]], [[1, 1], [2, 2], [3, 3]])
+ assert np.array_equal(net.line.geo.geojson._coords.at[l[1]], [[1, 1], [1, 2]])
+ assert np.array_equal(net.bus.geo.geojson._coords.at[b1], [1, 1])
+ assert np.array_equal(net.bus.geo.geojson._coords.at[b2], [2, 2])
+ assert net.bus.geo.geojson.type.at[b1] == "Point"
+ assert net.bus.geo.geojson.type.at[b2] == "Point"
+ assert net.line.geo.geojson.type.at[l[0]] == "LineString"
+ assert net.line.geo.geojson.type.at[l[1]] == "LineString"
+ assert set(net.line.geo.geojson.as_geo_obj.at[l[0]].keys()) == {"coordinates", "type"}
+ assert set(net.line.geo.geojson.as_geo_obj.at[l[1]].keys()) == {"coordinates", "type"}
+ assert set(net.bus.geo.geojson.as_geo_obj.at[b1].keys()) == {"coordinates", "type"}
+ assert set(net.bus.geo.geojson.as_geo_obj.at[b2].keys()) == {"coordinates", "type"}
+
+
+@pytest.mark.skipif(not GEOPANDAS_INSTALLED, reason="geopandas is not installed")
+def test_geo_accessor_geopandas():
+ net = pp.networks.mv_oberrhein()
+ reference_point = (7.781067, 48.389774)
+ radius_m = 2200
+ circle_polygon = gpd.GeoSeries([shapely.geometry.Point(reference_point)],
+ crs=4326).to_crs(epsg=31467).buffer(radius_m).to_crs(epsg=4326).iloc[0]
+ assert net.line.geo.geojson.within(circle_polygon).sum() == 11
+ assert all(net.line[net.line.geo.geojson.within(circle_polygon)].index == [14, 17, 46, 47, 55, 116,
+ 117, 118, 120, 121, 134])
+
+ line = shapely.geometry.LineString([[7.8947079593416, 48.40549007606241],
+ [7.896048283667894, 48.41060722903666],
+ [7.896173712216692, 48.41100311474432]])
+
+ assert net.line.geo.geojson.as_shapely_obj.at[0] == line
+ assert np.allclose(net.line.geo.geojson.total_bounds, [7.74426069, 48.32845845, 7.93829196, 48.47484423])
if __name__ == '__main__':
diff --git a/pandapower/test/api/test_file_io.py b/pandapower/test/api/test_file_io.py
index e9393297c..a71a3aab0 100644
--- a/pandapower/test/api/test_file_io.py
+++ b/pandapower/test/api/test_file_io.py
@@ -229,7 +229,7 @@ def test_json_encoding_decoding():
net = networks.mv_oberrhein()
net.tuple = (1, "4")
net.mg = topology.create_nxgraph(net)
- s = set(['1', 4])
+ s = {'1', 4}
t = tuple(['2', 3])
f = frozenset(['12', 3])
a = np.array([1., 2.])
@@ -601,5 +601,36 @@ def test_multi_index():
assert_frame_equal(df, df2)
+def test_ignore_unknown_objects():
+ net = pp.networks.create_kerber_dorfnetz()
+ control.ContinuousTapControl(net, 0, 1.02)
+ json_str = pp.to_json(net)
+ net2 = pp.from_json_string(json_str, ignore_unknown_objects=False)
+
+ # in general, reloaded net should be equal to original net
+ assert isinstance(net2.controller.object.at[0], control.ContinuousTapControl)
+ assert_net_equal(net, net2)
+
+ # slightly change the class name of the controller so that it cannot be identified
+ # by file_io anymore, but can still be loaded as dict if ignore_unknown_objects=True
+ json_str2 = json_str.replace("pandapower.control.controller.trafo.ContinuousTapControl",
+ "pandapower.control.controller.trafo.ContinuousTapControl2")
+ with pytest.raises(ModuleNotFoundError):
+ pp.from_json_string(json_str2, ignore_unknown_objects=False)
+ json_str3 = json_str.replace("\"ContinuousTapControl", "\"ContinuousTapControl2")
+ with pytest.raises(AttributeError):
+ pp.from_json_string(json_str3, ignore_unknown_objects=False)
+ net3 = pp.from_json_string(json_str2, ignore_unknown_objects=True)
+ assert isinstance(net3.controller.object.at[0], dict)
+ net4 = pp.from_json_string(json_str3, ignore_unknown_objects=True)
+ assert isinstance(net4.controller.object.at[0], dict)
+
+ # make sure that the loaded net equals the original net except for the controller
+ net3.controller.object.at[0] = net.controller.object.at[0]
+ net4.controller.object.at[0] = net.controller.object.at[0]
+ assert_net_equal(net, net3)
+ assert_net_equal(net, net4)
+
+
if __name__ == "__main__":
pytest.main([__file__, "-xs"])
diff --git a/pandapower/test/converter/test_from_cim.py b/pandapower/test/converter/test_from_cim.py
index ad34382aa..6faf2220a 100644
--- a/pandapower/test/converter/test_from_cim.py
+++ b/pandapower/test/converter/test_from_cim.py
@@ -1106,7 +1106,7 @@ def test_fullgrid_bus(fullgrid_v2):
assert 'tp' == element_0['origin_profile'].item()
assert '_4c66b132-0977-1e4c-b9bb-d8ce2e912e35' == element_0['cim_topnode'].item()
assert math.isnan(element_0['ConnectivityNodeContainer_id'].item())
- assert math.isnan(element_0['substation_id'].item())
+ assert math.isnan(element_0['Substation_id'].item())
assert 'BBRUS151; BGENT_51' == element_0['description'].item()
element_1 = fullgrid_v2.bus[fullgrid_v2.bus['origin_id'] == '_1098b1c9-dc85-40ce-b65c-39ae02a3afaa']
diff --git a/pandapower/test/loadflow/test_results.py b/pandapower/test/loadflow/test_results.py
index 58dc709ed..c20882ab7 100644
--- a/pandapower/test/loadflow/test_results.py
+++ b/pandapower/test/loadflow/test_results.py
@@ -644,7 +644,7 @@ def test_trafo3w(result_test_network, v_tol=1e-6, i_tol=1e-6, s_tol=2e-2, l_tol=
@pytest.mark.parametrize("tap_pos", (-1, 2))
@pytest.mark.parametrize("tap_side", ('hv', 'mv', 'lv'))
-@pytest.mark.parametrize("tap_step_degree", (15, 30))
+@pytest.mark.parametrize("tap_step_degree", (0, 15, 30))
def test_trafo3w_tap(tap_pos, tap_side, tap_step_degree):
results = pd.read_csv(os.path.join(pp.pp_dir, "test", "test_files", "test_results_files", "trafo_3w_tap_results.csv"), sep=";", decimal=",")
@@ -670,6 +670,41 @@ def test_trafo3w_tap(tap_pos, tap_side, tap_step_degree):
"index==@index & element=='bus' & variable==@variable").value,
rtol=0, atol=tol), f"failed for bus {index=}, {variable}, value {net.res_bus.at[index, variable]}"
+@pytest.mark.parametrize("tap_pos", (2, 5))
+@pytest.mark.parametrize("tap_side", ('hv', 'mv', 'lv'))
+@pytest.mark.parametrize("tap_step_degree", (0, 15, 30))
+def test_trafo3w_tap_neutral_not_zero(tap_pos, tap_side, tap_step_degree):
+ results = pd.read_csv(os.path.join(pp.pp_dir, "test", "test_files", "test_results_files", "trafo_3w_tap_results_neutral_not_zero.csv"), sep=";", decimal=",")
+
+ if results.query("tap_side == @tap_side & tap_pos == @tap_pos & tap_step_degree == @tap_step_degree").empty:
+ pytest.skip(f"Skipping combination: tap_side={tap_side}, tap_pos={tap_pos}, tap_step_degree={tap_step_degree}")
+
+ net = pp.from_json(os.path.join(pp.pp_dir, "test", "test_files","test_trafo3w_tap.json")) #
+
+ net.trafo3w.loc[[0, 1], 'tap_min'] += 3
+ net.trafo3w.loc[[0, 1], 'tap_max'] += 3
+ net.trafo3w.loc[[0, 1], 'tap_neutral'] += 3
+
+ net.trafo3w.loc[0, 'tap_at_star_point']= False
+ net.trafo3w.loc[1, 'tap_at_star_point']= True
+
+ net.trafo3w.loc[0, "tap_side"] = tap_side
+ net.trafo3w.loc[1, "tap_side"] = tap_side
+ net.trafo3w.loc[0, "tap_pos"] = tap_pos
+ net.trafo3w.loc[1, "tap_pos"] = tap_pos
+ net.trafo3w.loc[0, "tap_step_degree"] = tap_step_degree
+ net.trafo3w.loc[1, "tap_step_degree"] = tap_step_degree
+ pp.runpp(net)
+
+ for index in range(8):
+ for variable, tol in zip(("vm_pu", "va_degree"), (1e-6, 1e-3)):
+ assert np.isclose(net.res_bus.at[index, variable], results.query(
+ "tap_side==@tap_side & tap_pos==@tap_pos & tap_step_degree==@tap_step_degree &"
+ "index==@index & element=='bus' & variable==@variable").value,
+ rtol=0, atol=tol), f"failed for bus {index=}, {variable}, value {net.res_bus.at[index, variable]}"
+
+
+
def test_impedance(result_test_network, v_tol=1e-6, i_tol=1e-6, s_tol=5e-3, l_tol=1e-3):
net = result_test_network
buses = net.bus[net.bus.zone == "test_impedance"]
diff --git a/pandapower/test/plotting/test_geo.py b/pandapower/test/plotting/test_geo.py
index 2559bb0a8..a3aac1c1e 100644
--- a/pandapower/test/plotting/test_geo.py
+++ b/pandapower/test/plotting/test_geo.py
@@ -10,6 +10,7 @@
import geojson
import pandas as pd
import pytest
+from pandas.testing import assert_frame_equal, assert_index_equal
import pandapower.plotting.geo as geo
from pandapower.test.helper_functions import create_test_network
@@ -54,30 +55,48 @@ def get_network_and_result(net, request):
def test__node_geometries_from_geodata(get_network_and_result):
pytest.importorskip("geopandas")
- from geopandas import testing
_net, expected = get_network_and_result
_bus_geojson_to_geodata_(_net)
result = geo._node_geometries_from_geodata(_net.bus_geodata)
- testing.assert_geodataframe_equal(result, expected)
+ # is mostly the same as assert_geodataframe_equal with check_less_precise=True, but the tolerance in the function
+ # can't be adapted
+ assert result.shape == expected.shape
+ assert isinstance(result, type(expected))
+ assert (result.geom_equals_exact(expected.geometry, tolerance=1 * 10 ** (-6)) |
+ (result.geometry.is_empty & expected.geometry.is_empty) |
+ (result.geometry.isna() & expected.geometry.isna())).all()
+ left2 = result.select_dtypes(exclude="geometry")
+ right2 = expected.select_dtypes(exclude="geometry")
+ assert_index_equal(result.columns, expected.columns, exact="equiv", obj="GeoDataFrame.columns")
+ assert_frame_equal(left2, right2, check_dtype=True, check_index_type="equiv", check_column_type="equiv", obj="GeoDataFrame")
def test__branch_geometries_from_geodata(get_network_and_result):
pytest.importorskip("geopandas")
- from geopandas import testing
_net, expected = get_network_and_result
_line_geojson_to_geodata_(_net)
result = geo._branch_geometries_from_geodata(_net.line_geodata)
- testing.assert_geodataframe_equal(result, expected)
+ # is mostly the same as assert_geodataframe_equal with check_less_precise=True, but the tolerance in the function
+ # can't be adapted
+ assert result.shape == expected.shape
+ assert isinstance(result, type(expected))
+ assert (result.geom_equals_exact(expected.geometry, tolerance=1 * 10 ** (-6)) |
+ (result.geometry.is_empty & expected.geometry.is_empty) |
+ (result.geometry.isna() & expected.geometry.isna())).all()
+ left2 = result.select_dtypes(exclude="geometry")
+ right2 = expected.select_dtypes(exclude="geometry")
+ assert_index_equal(result.columns, expected.columns, exact="equiv", obj="GeoDataFrame.columns")
+ assert_frame_equal(left2, right2, check_dtype=True, check_index_type="equiv", check_column_type="equiv",
+ obj="GeoDataFrame")
def test__transform_node_geometry_to_geodata(get_network_and_result):
pytest.importorskip("geopandas")
- from geopandas import testing
_net, expected = get_network_and_result
_bus_geojson_to_geodata_(_net)
@@ -85,19 +104,40 @@ def test__transform_node_geometry_to_geodata(get_network_and_result):
# Transforming to geodata to test the inverse...
_net.bus_geodata = geo._node_geometries_from_geodata(_net.bus_geodata)
result = geo._transform_node_geometry_to_geodata(_net.bus_geodata)
- testing.assert_geodataframe_equal(result, expected)
+ # is mostly the same as assert_geodataframe_equal with check_less_precise=True, but the tolerance in the function
+ # can't be adapted
+ assert result.shape == expected.shape
+ assert isinstance(result, type(expected))
+ assert (result.geom_equals_exact(expected.geometry, tolerance=1 * 10 ** (-6)) |
+ (result.geometry.is_empty & expected.geometry.is_empty) |
+ (result.geometry.isna() & expected.geometry.isna())).all()
+ left2 = result.select_dtypes(exclude="geometry")
+ right2 = expected.select_dtypes(exclude="geometry")
+ assert_index_equal(result.columns, expected.columns, exact="equiv", obj="GeoDataFrame.columns")
+ assert_frame_equal(left2, right2, check_dtype=True, check_index_type="equiv", check_column_type="equiv",
+ obj="GeoDataFrame")
def test__transform_branch_geometry_to_coords(get_network_and_result):
pytest.importorskip("geopandas")
- from geopandas import testing
_net, expected = get_network_and_result
_line_geojson_to_geodata_(_net)
_net.line_geodata = geo._branch_geometries_from_geodata(_net.line_geodata)
result = geo._transform_branch_geometry_to_coords(_net.line_geodata)
- testing.assert_geodataframe_equal(result, expected)
+ # is mostly the same as assert_geodataframe_equal with check_less_precise=True, but the tolerance in the function
+ # can't be adapted
+ assert result.shape == expected.shape
+ assert isinstance(result, type(expected))
+ assert (result.geom_equals_exact(expected.geometry, tolerance=1 * 10 ** (-6)) |
+ (result.geometry.is_empty & expected.geometry.is_empty) |
+ (result.geometry.isna() & expected.geometry.isna())).all()
+ left2 = result.select_dtypes(exclude="geometry")
+ right2 = expected.select_dtypes(exclude="geometry")
+ assert_index_equal(result.columns, expected.columns, exact="equiv", obj="GeoDataFrame.columns")
+ assert_frame_equal(left2, right2, check_dtype=True, check_index_type="equiv", check_column_type="equiv",
+ obj="GeoDataFrame")
def test__convert_xy_epsg():
@@ -261,7 +301,6 @@ def test_dump_to_geojson():
assert dumps(result, sort_keys=True) == '{"features": [{"geometry": {"coordinates": [[1.0, 2.0], [3.0, 4.0]], "type": "LineString"}, "id": "line-0", "properties": {"c_nf_per_km": 720.0, "df": 1.0, "from_bus": 1, "g_us_per_km": 0.0, "i_from_ka": 7.0, "i_ka": 7.0, "i_to_ka": 7.0, "ices": 0.389985, "in_service": true, "length_km": 1.0, "loading_percent": 7.0, "max_i_ka": 0.328, "name": "line1", "p_from_mw": 7.0, "p_to_mw": 7.0, "parallel": 1, "pl_mw": 7.0, "pp_index": 0, "pp_type": "line", "q_from_mvar": 7.0, "q_to_mvar": 7.0, "ql_mvar": 7.0, "r_ohm_per_km": 0.2067, "std_type": null, "to_bus": 7, "type": null, "va_from_degree": 7.0, "va_to_degree": 7.0, "vm_from_pu": 7.0, "vm_to_pu": 7.0, "x_ohm_per_km": 0.1897522}, "type": "Feature"}], "type": "FeatureCollection"}'
-
def test_convert_geodata_to_geojson():
pytest.importorskip("geojson")
pytest.importorskip("pandapower")
@@ -295,6 +334,5 @@ def test_convert_gis_to_geojson():
pytest.skip("Not implemented")
-
if __name__ == "__main__":
pytest.main(["test_geo.py"])
diff --git a/pandapower/test/test_files/test_results_files/trafo_3w_tap_results_neutral_not_zero.csv b/pandapower/test/test_files/test_results_files/trafo_3w_tap_results_neutral_not_zero.csv
new file mode 100644
index 000000000..6e07ba49b
--- /dev/null
+++ b/pandapower/test/test_files/test_results_files/trafo_3w_tap_results_neutral_not_zero.csv
@@ -0,0 +1,129 @@
+tap_side;tap_pos;tap_step_degree;element;name;index;variable;value
+hv;5;0;bus;0_Slack;0;vm_pu;1
+hv;5;0;bus;0_Slack;0;va_degree;0
+hv;5;0;bus;1_HV;1;vm_pu;0,998129
+hv;5;0;bus;1_HV;1;va_degree;-0,093039
+hv;5;0;bus;2_MV;2;vm_pu;0,898854
+hv;5;0;bus;2_MV;2;va_degree;-0,686136
+hv;5;0;bus;3_LV;3;vm_pu;0,903326
+hv;5;0;bus;3_LV;3;va_degree;-0,394202
+hv;5;0;bus;4_Slack;4;vm_pu;1
+hv;5;0;bus;4_Slack;4;va_degree;0
+hv;5;0;bus;5_HV;5;vm_pu;0,998127
+hv;5;0;bus;5_HV;5;va_degree;-0,092972
+hv;5;0;bus;6_MV;6;vm_pu;0,898367
+hv;5;0;bus;6_MV;6;va_degree;-0,723542
+hv;5;0;bus;7_LV;7;vm_pu;0,902842
+hv;5;0;bus;7_LV;7;va_degree;-0,431292
+hv;2;0;bus;0_Slack;0;vm_pu;1
+hv;2;0;bus;0_Slack;0;va_degree;0
+hv;2;0;bus;1_HV;1;vm_pu;0,998009
+hv;2;0;bus;1_HV;1;va_degree;-0,092674
+hv;2;0;bus;2_MV;2;vm_pu;1,042353
+hv;2;0;bus;2_MV;2;va_degree;-0,583495
+hv;2;0;bus;3_LV;3;vm_pu;1,046203
+hv;2;0;bus;3_LV;3;va_degree;-0,366142
+hv;2;0;bus;4_Slack;4;vm_pu;1
+hv;2;0;bus;4_Slack;4;va_degree;0
+hv;2;0;bus;5_HV;5;vm_pu;0,99801
+hv;2;0;bus;5_HV;5;va_degree;-0,092708
+hv;2;0;bus;6_MV;6;vm_pu;1,042629
+hv;2;0;bus;6_MV;6;va_degree;-0,565781
+hv;2;0;bus;7_LV;7;vm_pu;1,046477
+hv;2;0;bus;7_LV;7;va_degree;-0,348542
+mv;5;0;bus;0_Slack;0;vm_pu;1
+mv;5;0;bus;0_Slack;0;va_degree;0
+mv;5;0;bus;1_HV;1;vm_pu;0,998058
+mv;5;0;bus;1_HV;1;va_degree;-0,092876
+mv;5;0;bus;2_MV;2;vm_pu;1,089856
+mv;5;0;bus;2_MV;2;va_degree;-0,555702
+mv;5;0;bus;3_LV;3;vm_pu;0,993842
+mv;5;0;bus;3_LV;3;va_degree;-0,374824
+mv;5;0;bus;4_Slack;4;vm_pu;1
+mv;5;0;bus;4_Slack;4;va_degree;0
+mv;5;0;bus;5_HV;5;vm_pu;0,998055
+mv;5;0;bus;5_HV;5;va_degree;-0,092822
+mv;5;0;bus;6_MV;6;vm_pu;1,088759
+mv;5;0;bus;6_MV;6;va_degree;-0,61575
+mv;5;0;bus;7_LV;7;vm_pu;0,993837
+mv;5;0;bus;7_LV;7;va_degree;-0,374788
+mv;2;0;bus;0_Slack;0;vm_pu;1
+mv;2;0;bus;0_Slack;0;va_degree;0
+mv;2;0;bus;1_HV;1;vm_pu;0,998054
+mv;2;0;bus;1_HV;1;va_degree;-0,092788
+mv;2;0;bus;2_MV;2;vm_pu;0,9397
+mv;2;0;bus;2_MV;2;va_degree;-0,653191
+mv;2;0;bus;3_LV;3;vm_pu;0,993834
+mv;2;0;bus;3_LV;3;va_degree;-0,374766
+mv;2;0;bus;4_Slack;4;vm_pu;1
+mv;2;0;bus;4_Slack;4;va_degree;0
+mv;2;0;bus;5_HV;5;vm_pu;0,998055
+mv;2;0;bus;5_HV;5;va_degree;-0,092822
+mv;2;0;bus;6_MV;6;vm_pu;0,940291
+mv;2;0;bus;6_MV;6;va_degree;-0,61575
+mv;2;0;bus;7_LV;7;vm_pu;0,993837
+mv;2;0;bus;7_LV;7;va_degree;-0,374788
+lv;5;0;bus;0_Slack;0;vm_pu;1
+lv;5;0;bus;0_Slack;0;va_degree;0
+lv;5;0;bus;1_HV;1;vm_pu;0,998056
+lv;5;0;bus;1_HV;1;va_degree;-0,092829
+lv;5;0;bus;2_MV;2;vm_pu;0,989781
+lv;5;0;bus;2_MV;2;va_degree;-0,615751
+lv;5;0;bus;3_LV;3;vm_pu;1,093532
+lv;5;0;bus;3_LV;3;va_degree;-0,356789
+lv;5;0;bus;4_Slack;4;vm_pu;1
+lv;5;0;bus;4_Slack;4;va_degree;0
+lv;5;0;bus;5_HV;5;vm_pu;0,998055
+lv;5;0;bus;5_HV;5;va_degree;-0,092822
+lv;5;0;bus;6_MV;6;vm_pu;0,98978
+lv;5;0;bus;6_MV;6;va_degree;-0,61575
+lv;5;0;bus;7_LV;7;vm_pu;1,093221
+lv;5;0;bus;7_LV;7;va_degree;-0,374788
+lv;2;0;bus;0_Slack;0;vm_pu;1
+lv;2;0;bus;0_Slack;0;va_degree;0
+lv;2;0;bus;1_HV;1;vm_pu;0,998055
+lv;2;0;bus;1_HV;1;va_degree;-0,092817
+lv;2;0;bus;2_MV;2;vm_pu;0,98978
+lv;2;0;bus;2_MV;2;va_degree;-0,61575
+lv;2;0;bus;3_LV;3;vm_pu;0,943978
+lv;2;0;bus;3_LV;3;va_degree;-0,385997
+lv;2;0;bus;4_Slack;4;vm_pu;1
+lv;2;0;bus;4_Slack;4;va_degree;0
+lv;2;0;bus;5_HV;5;vm_pu;0,998055
+lv;2;0;bus;5_HV;5;va_degree;-0,092822
+lv;2;0;bus;6_MV;6;vm_pu;0,98978
+lv;2;0;bus;6_MV;6;va_degree;-0,61575
+lv;2;0;bus;7_LV;7;vm_pu;0,944145
+lv;2;0;bus;7_LV;7;va_degree;-0,374788
+hv;5;15;bus;0_Slack;0;vm_pu;1
+hv;5;15;bus;0_Slack;0;va_degree;0
+hv;5;15;bus;1_HV;1;vm_pu;0,998127
+hv;5;15;bus;1_HV;1;va_degree;-0,093034
+hv;5;15;bus;2_MV;2;vm_pu;0,901427
+hv;5;15;bus;2_MV;2;va_degree;-2,035887
+hv;5;15;bus;3_LV;3;vm_pu;0,905886
+hv;5;15;bus;3_LV;3;va_degree;-1,74561
+hv;5;15;bus;4_Slack;4;vm_pu;1
+hv;5;15;bus;4_Slack;4;va_degree;0
+hv;5;15;bus;5_HV;5;vm_pu;0,998125
+hv;5;15;bus;5_HV;5;va_degree;-0,092969
+hv;5;15;bus;6_MV;6;vm_pu;0,900955
+hv;5;15;bus;6_MV;6;va_degree;-2,07209
+hv;5;15;bus;7_LV;7;vm_pu;0,905417
+hv;5;15;bus;7_LV;7;va_degree;-1,781509
+hv;2;30;bus;0_Slack;0;vm_pu;1
+hv;2;30;bus;0_Slack;0;va_degree;0
+hv;2;30;bus;1_HV;1;vm_pu;0,998016
+hv;2;30;bus;1_HV;1;va_degree;-0,092697
+hv;2;30;bus;2_MV;2;vm_pu;1,034638
+hv;2;30;bus;2_MV;2;va_degree;0,908979
+hv;2;30;bus;3_LV;3;vm_pu;1,038516
+hv;2;30;bus;3_LV;3;va_degree;1,129574
+hv;2;30;bus;4_Slack;4;vm_pu;1
+hv;2;30;bus;4_Slack;4;va_degree;0
+hv;2;30;bus;5_HV;5;vm_pu;0,998017
+hv;2;30;bus;5_HV;5;va_degree;-0,092725
+hv;2;30;bus;6_MV;6;vm_pu;1,034873
+hv;2;30;bus;6_MV;6;va_degree;0,924241
+hv;2;30;bus;7_LV;7;vm_pu;1,038751
+hv;2;30;bus;7_LV;7;va_degree;1,144736
diff --git a/pandapower/toolbox/data_modification.py b/pandapower/toolbox/data_modification.py
index a8164fedb..87e2f6b77 100644
--- a/pandapower/toolbox/data_modification.py
+++ b/pandapower/toolbox/data_modification.py
@@ -376,7 +376,7 @@ def create_continuous_elements_index(net, start=0, add_df_to_reindex=set()):
if et in net and isinstance(net[et], pd.DataFrame):
if et in ["bus_geodata", "line_geodata"]:
- logger.info(et + " don't need to bo included to 'add_df_to_reindex'. It is " +
+ logger.info(et + " don't need to be included to 'add_df_to_reindex'. It is " +
"already included by et=='" + et.split("_")[0] + "'.")
else:
reindex_elements(net, et, new_index)
diff --git a/pyproject.toml b/pyproject.toml
index 77234e152..98b08cea7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,7 +63,7 @@ docs = ["numpydoc", "matplotlib", "sphinx", "sphinx_rtd_theme", "sphinx-pyprojec
plotting = ["plotly>=3.1.1", "matplotlib", "igraph", "geopandas>=1.0"]
test = ["pytest~=8.1", "pytest-xdist", "nbmake"]
performance = ["ortools", "numba>=0.25", "lightsim2grid==0.9.0"]
-fileio = ["xlsxwriter", "openpyxl", "cryptography", "geopandas", "psycopg2"]
+fileio = ["xlsxwriter", "openpyxl", "cryptography", "geopandas>=1.0", "psycopg2"]
converter = ["matpowercaseframes"]
pgm = ["power-grid-model-io"]
control = ["shapely"]
diff --git a/tutorials/Working with GeoJSON.ipynb b/tutorials/Working with GeoJSON.ipynb
new file mode 100644
index 000000000..5ea820028
--- /dev/null
+++ b/tutorials/Working with GeoJSON.ipynb
@@ -0,0 +1,7915 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "61a0f50a-318c-4a8b-98e9-17c3bc14b839",
+ "metadata": {},
+ "source": [
+ "## Working with GeoJSON"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dc6d7b2d-deb2-4da2-aa31-420c66b63edf",
+ "metadata": {},
+ "source": [
+ "In the 3.0 release of pandapower, significant changes have been made to how geospatial data is stored in the net object. Previously, geospatial data was stored in the *line_geodata* and *bus_geodata* tables. However, this structure caused several problems:\n",
+ "\n",
+ "- In what projection are the data points stored?\n",
+ "- What do x and y represent? Does x represent latitude, longitude or something else?\n",
+ "- Copmatibility issues with frontend services and QGIS\n",
+ "\n",
+ "To resolve this ambiguity, all geospatial data has now been moved to the line and bus tables under the *geo* column. The geometries are stored as GeoJSON objects. [GeoJSON](https://datatracker.ietf.org/doc/html/rfc7946) is a widely used standard for geospatial data, supporting points, lines, and polygons in a unified format with explicit projection definitions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "fe634b5e-44ca-43f3-befb-1aecbe995a56",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandapower as pp\n",
+ "import pandapower.networks as pn\n",
+ "import pandas as pd\n",
+ "pd.set_option(\"display.max_colwidth\", 120)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "08f8a836-ea70-4f20-af4b-3f25166fa9cd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "net = pn.mv_oberrhein()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "2bed640f-1894-4b71-9b93-8899dd047f6c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0 {\"coordinates\": [[7.8947079593416, 48.40549007606241], [7.896048283667894, 48.41060722903666], [7.896173712216692, 4...\n",
+ "1 {\"coordinates\": [[7.8947079593416, 48.40549007606241], [7.892690190852129, 48.40551636934287], [7.887502065342014, 4...\n",
+ "2 {\"coordinates\": [[7.8947079593416, 48.40549007606241], [7.895064345442788, 48.40360449047714]], \"type\": \"LineString\"}\n",
+ "3 {\"coordinates\": [[7.895064345442788, 48.40360449047714], [7.89613191775149, 48.39796660294922]], \"type\": \"LineString\"}\n",
+ "4 {\"coordinates\": [[7.89613191775149, 48.39796660294922], [7.897077173332039, 48.39788292247765], [7.9009358501319475,...\n",
+ "Name: geo, dtype: object \n",
+ "\n",
+ "0 {\"coordinates\": [7.765225672614365, 48.41091584192147], \"type\": \"Point\"}\n",
+ "1 {\"coordinates\": [7.778809539550178, 48.40987064550492], \"type\": \"Point\"}\n",
+ "2 {\"coordinates\": [7.779195765893586, 48.4120381144602], \"type\": \"Point\"}\n",
+ "3 {\"coordinates\": [7.775204689771646, 48.40610336882051], \"type\": \"Point\"}\n",
+ "4 {\"coordinates\": [7.76606484746611, 48.4124244421834], \"type\": \"Point\"}\n",
+ "Name: geo, dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(net.line.geo.head(), \"\\n\")\n",
+ "print(net.bus.geo.head())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "74af88da-10fe-43dd-a8ce-6b3ec57ab9ad",
+ "metadata": {},
+ "source": [
+ "To make working with these new geospatial data easier and more efficient, a pandas series accessor has been introduced. This accessor allows direct interaction with GeoJSON data and integrates functions provided by GeoSeries in geopandas."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "ee923be1-9fa4-4141-8099-9c2d96b4a169",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from shapely.geometry import shape, Point, Polygon\n",
+ "from geopandas import GeoSeries\n",
+ "from pandapower.plotting import simple_plotly, create_line_trace, create_bus_trace\n",
+ "import plotly.graph_objects as go"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9b18429f-ffae-481b-bbcf-278aeed2fd1e",
+ "metadata": {},
+ "source": [
+ "Create a reference point and a polygon to check what elements are inside a certain radius. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "8de3ac01-8008-42cc-b669-23cf029424e5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reference_point = (7.781067, 48.389774)\n",
+ "radius_m = 2200\n",
+ "circle_polygon = GeoSeries([Point(reference_point)], crs=4326).to_crs(epsg=31467).buffer(radius_m).to_crs(epsg=4326).iloc[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cea9b32-fe45-4c95-8842-a7e688c82e2f",
+ "metadata": {},
+ "source": [
+ "Access the GeoJSON accessor and the needed GeoSeries methods."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "f1052155-c72c-4ec2-86ec-9b871c2de3f6",
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+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "lines_intersect = net.line[net.line.geo.geojson.intersects(circle_polygon)].index\n",
+ "buses_within = net.bus[net.bus.geo.geojson.within(circle_polygon)].index\n",
+ "net.line.geo.geojson.total_bounds"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e7966a23-87e9-4dae-a256-c0170234e4fb",
+ "metadata": {},
+ "source": [
+ "And plot the results"
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+ "execution_count": 7,
+ "id": "32231088-02e8-497d-9cae-279aba140370",
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+ "hoverinfo": "text",
+ "marker": {
+ "color": "rgb(0, 0, 255)",
+ "size": 10,
+ "symbol": "circle"
+ },
+ "mode": "markers",
+ "name": "buses",
+ "text": [
+ "Index: 0
Name: Bus 0
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.122 MW
",
+ "Index: 1
Name: Bus 1
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.092 MW
",
+ "Index: 2
Name: Bus 2
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.076 MW
",
+ "Index: 3
Name: Bus 3
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.076 MW
",
+ "Index: 4
Name: Bus 4
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.092 MW
",
+ "Index: 5
Name: Bus 5
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.076 MW
",
+ "Index: 6
Name: Bus 6
V_n: 20.0 kV
Load: 0.500 MW
",
+ "Index: 7
Name: Bus 7
V_n: 20.0 kV
",
+ "Index: 8
Name: Bus 8
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.137 MW
",
+ "Index: 29
Name: Bus 9
V_n: 20.0 kV
Static generation: 0.400 MW
",
+ "Index: 30
Name: Bus 10
V_n: 20.0 kV
",
+ "Index: 31
Name: Bus 11
V_n: 20.0 kV
Static generation: 0.800 MW
",
+ "Index: 32
Name: Bus 12
V_n: 20.0 kV
Static generation: 0.200 MW
",
+ "Index: 33
Name: Bus 13
V_n: 20.0 kV
Static generation: 0.856 MW
",
+ "Index: 34
Name: Bus 14
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 35
Name: Bus 15
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.155 MW
",
+ "Index: 36
Name: Bus 16
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 37
Name: Bus 17
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 38
Name: Bus 18
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.049 MW
",
+ "Index: 39
Name: Bus 19
V_n: 20.0 kV
",
+ "Index: 40
Name: Bus 20
V_n: 20.0 kV
Static generation: 0.160 MW
",
+ "Index: 41
Name: Bus 21
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.140 MW
",
+ "Index: 42
Name: Bus 22
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.191 MW
",
+ "Index: 43
Name: Bus 23
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 44
Name: Bus 24
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.295 MW
",
+ "Index: 45
Name: Bus 25
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.093 MW
",
+ "Index: 46
Name: Bus 26
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.309 MW
",
+ "Index: 47
Name: Bus 27
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.140 MW
",
+ "Index: 48
Name: Bus 28
V_n: 20.0 kV
Load: 0.500 MW
",
+ "Index: 49
Name: Bus 29
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.093 MW
",
+ "Index: 50
Name: Bus 30
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 51
Name: Bus 31
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.191 MW
",
+ "Index: 52
Name: Bus 32
V_n: 20.0 kV
Load: 0.500 MW
",
+ "Index: 53
Name: Bus 33
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 54
Name: Bus 34
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.137 MW
",
+ "Index: 55
Name: Bus 35
V_n: 20.0 kV
Load: 0.500 MW
",
+ "Index: 56
Name: Bus 36
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.295 MW
",
+ "Index: 57
Name: Bus 37
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.191 MW
",
+ "Index: 58
Name: Bus 38
V_n: 110.0 kV
",
+ "Index: 64
Name: Bus 39
V_n: 20.0 kV
",
+ "Index: 65
Name: Bus 40
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.295 MW
",
+ "Index: 71
Name: Bus 41
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.155 MW
",
+ "Index: 72
Name: Bus 42
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 73
Name: Bus 43
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 74
Name: Bus 44
V_n: 20.0 kV
",
+ "Index: 75
Name: Bus 45
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 76
Name: Bus 46
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 77
Name: Bus 47
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.155 MW
",
+ "Index: 78
Name: Bus 48
V_n: 20.0 kV
",
+ "Index: 79
Name: Bus 49
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.191 MW
",
+ "Index: 80
Name: Bus 50
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 81
Name: Bus 51
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 82
Name: Bus 52
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 83
Name: Bus 53
V_n: 20.0 kV
Static generation: 0.315 MW
",
+ "Index: 84
Name: Bus 54
V_n: 20.0 kV
Static generation: 0.500 MW
",
+ "Index: 85
Name: Bus 55
V_n: 20.0 kV
Static generation: 0.300 MW
",
+ "Index: 86
Name: Bus 56
V_n: 20.0 kV
",
+ "Index: 94
Name: Bus 57
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.108 MW
",
+ "Index: 95
Name: Bus 58
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.108 MW
",
+ "Index: 98
Name: Bus 59
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.069 MW
",
+ "Index: 100
Name: Bus 60
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.108 MW
",
+ "Index: 101
Name: Bus 61
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.191 MW
",
+ "Index: 102
Name: Bus 62
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.118 MW
",
+ "Index: 103
Name: Bus 63
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 104
Name: Bus 64
V_n: 20.0 kV
",
+ "Index: 106
Name: Bus 65
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.295 MW
",
+ "Index: 107
Name: Bus 66
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.049 MW
",
+ "Index: 108
Name: Bus 67
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.191 MW
",
+ "Index: 109
Name: Bus 68
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.049 MW
",
+ "Index: 110
Name: Bus 69
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.309 MW
",
+ "Index: 111
Name: Bus 70
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 116
Name: Bus 71
V_n: 20.0 kV
",
+ "Index: 117
Name: Bus 72
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.140 MW
",
+ "Index: 118
Name: Bus 73
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 119
Name: Bus 74
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 120
Name: Bus 75
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.093 MW
",
+ "Index: 126
Name: Bus 76
V_n: 20.0 kV
",
+ "Index: 129
Name: Bus 77
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.095 MW
",
+ "Index: 131
Name: Bus 78
V_n: 20.0 kV
",
+ "Index: 132
Name: Bus 79
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.069 MW
",
+ "Index: 133
Name: Bus 80
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.076 MW
",
+ "Index: 134
Name: Bus 81
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.069 MW
",
+ "Index: 136
Name: Bus 82
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.191 MW
",
+ "Index: 137
Name: Bus 83
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.069 MW
",
+ "Index: 138
Name: Bus 84
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 140
Name: Bus 85
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.122 MW
",
+ "Index: 141
Name: Bus 86
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.076 MW
",
+ "Index: 142
Name: Bus 87
V_n: 20.0 kV
",
+ "Index: 143
Name: Bus 88
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.155 MW
",
+ "Index: 144
Name: Bus 89
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.076 MW
",
+ "Index: 145
Name: Bus 90
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 146
Name: Bus 91
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.233 MW
",
+ "Index: 147
Name: Bus 92
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.137 MW
",
+ "Index: 148
Name: Bus 93
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.093 MW
",
+ "Index: 149
Name: Bus 94
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.076 MW
",
+ "Index: 150
Name: Bus 95
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.093 MW
",
+ "Index: 153
Name: Bus 96
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 155
Name: Bus 97
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 157
Name: Bus 98
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 159
Name: Bus 99
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 161
Name: Bus 100
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.069 MW
",
+ "Index: 162
Name: Bus 101
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.108 MW
",
+ "Index: 167
Name: Bus 102
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.109 MW
",
+ "Index: 168
Name: Bus 103
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.109 MW
",
+ "Index: 169
Name: Bus 104
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.109 MW
",
+ "Index: 170
Name: Bus 105
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.118 MW
",
+ "Index: 171
Name: Bus 106
V_n: 20.0 kV
",
+ "Index: 172
Name: Bus 107
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.214 MW
",
+ "Index: 173
Name: Bus 108
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.069 MW
",
+ "Index: 174
Name: Bus 109
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.118 MW
",
+ "Index: 176
Name: Bus 110
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 178
Name: Bus 111
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 181
Name: Bus 112
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 184
Name: Bus 113
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 186
Name: Bus 114
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 188
Name: Bus 115
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 189
Name: Bus 116
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.309 MW
",
+ "Index: 190
Name: Bus 117
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.309 MW
",
+ "Index: 192
Name: Bus 118
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.309 MW
",
+ "Index: 194
Name: Bus 119
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.039 MW
",
+ "Index: 195
Name: Bus 120
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.039 MW
",
+ "Index: 196
Name: Bus 121
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.309 MW
",
+ "Index: 197
Name: Bus 122
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 198
Name: Bus 123
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 199
Name: Bus 124
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 200
Name: Bus 125
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 201
Name: Bus 126
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.108 MW
",
+ "Index: 205
Name: Bus 127
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.118 MW
",
+ "Index: 207
Name: Bus 128
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.118 MW
",
+ "Index: 210
Name: Bus 129
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.069 MW
",
+ "Index: 213
Name: Bus 130
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.108 MW
",
+ "Index: 215
Name: Bus 131
V_n: 20.0 kV
Load: 0.500 MW
",
+ "Index: 216
Name: Bus 132
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.069 MW
",
+ "Index: 219
Name: Bus 133
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.100 MW
",
+ "Index: 221
Name: Bus 134
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.060 MW
",
+ "Index: 223
Name: Bus 135
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.150 MW
",
+ "Index: 224
Name: Bus 136
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.150 MW
",
+ "Index: 227
Name: Bus 137
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.100 MW
",
+ "Index: 229
Name: Bus 138
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.060 MW
",
+ "Index: 231
Name: Bus 139
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.060 MW
",
+ "Index: 235
Name: Bus 140
V_n: 20.0 kV
Load: 0.500 MW
",
+ "Index: 236
Name: Bus 141
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.160 MW
",
+ "Index: 237
Name: Bus 142
V_n: 20.0 kV
Static generation: 0.150 MW
",
+ "Index: 238
Name: Bus 143
V_n: 20.0 kV
",
+ "Index: 239
Name: Bus 144
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.090 MW
",
+ "Index: 240
Name: Bus 145
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.122 MW
",
+ "Index: 241
Name: Bus 146
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.076 MW
",
+ "Index: 242
Name: Bus 147
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.122 MW
",
+ "Index: 243
Name: Bus 148
V_n: 20.0 kV
",
+ "Index: 244
Name: Bus 149
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.137 MW
",
+ "Index: 245
Name: Bus 150
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.198 MW
",
+ "Index: 246
Name: Bus 151
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 247
Name: Bus 152
V_n: 20.0 kV
",
+ "Index: 248
Name: Bus 153
V_n: 20.0 kV
Static generation: 0.295 MW
",
+ "Index: 253
Name: Bus 154
V_n: 20.0 kV
Static generation: 0.355 MW
",
+ "Index: 269
Name: Bus 155
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.309 MW
",
+ "Index: 271
Name: Bus 156
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.109 MW
",
+ "Index: 273
Name: Bus 157
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.095 MW
",
+ "Index: 275
Name: Bus 158
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 281
Name: Bus 159
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 285
Name: Bus 160
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 286
Name: Bus 161
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.054 MW
",
+ "Index: 287
Name: Bus 162
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 288
Name: Bus 163
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 289
Name: Bus 164
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.191 MW
",
+ "Index: 290
Name: Bus 165
V_n: 20.0 kV
Load: 0.630 MW
Static generation: 0.198 MW
",
+ "Index: 298
Name: Bus 166
V_n: 20.0 kV
Static generation: 0.300 MW
",
+ "Index: 301
Name: Bus 167
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.054 MW
",
+ "Index: 303
Name: Bus 168
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 304
Name: Bus 169
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 305
Name: Bus 170
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 312
Name: Bus 171
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 313
Name: Bus 172
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 314
Name: Bus 173
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 315
Name: Bus 174
V_n: 20.0 kV
Load: 0.400 MW
Static generation: 0.068 MW
",
+ "Index: 316
Name: Bus 175
V_n: 20.0 kV
Load: 0.250 MW
Static generation: 0.041 MW
",
+ "Index: 317
Name: Bus 176
V_n: 20.0 kV
",
+ "Index: 318
Name: Bus 177
V_n: 110.0 kV
",
+ "Index: 319
Name: Bus 178
V_n: 20.0 kV
"
+ ],
+ "type": "scatter",
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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "x, y = circle_polygon.exterior.xy\n",
+ "polygon_trace = go.Scatter(\n",
+ " x=list(x), \n",
+ " y=list(y), \n",
+ " fill=\"toself\",\n",
+ " mode=\"lines\",\n",
+ " line=dict(color=\"orange\", width=0.5),\n",
+ " fillcolor=\"rgba(255, 165, 0, 0.2)\",\n",
+ " name=\"radius\"\n",
+ ")\n",
+ "\n",
+ "point_trace = go.Scatter(\n",
+ " x=[circle_polygon.centroid.x], \n",
+ " y=[circle_polygon.centroid.y], \n",
+ " mode=\"markers\",\n",
+ " marker=dict(color=\"orange\", size=10),\n",
+ " name=\"reference point\"\n",
+ ")\n",
+ "lt = create_line_trace(net, lines=lines_intersect, color=\"red\", trace_name='intersecting lines')\n",
+ "bt = create_bus_trace(net, buses=buses_within, color=\"red\", trace_name=\"buses within radius\")\n",
+ "fig = simple_plotly(net, auto_open=False, additional_traces=lt)\n",
+ "fig.add_trace(polygon_trace)\n",
+ "fig.add_trace(point_trace)\n",
+ "fig.add_trace(bt[0])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7e7917d4-c696-459c-aa59-df11bb2ffac7",
+ "metadata": {},
+ "source": [
+ "It is also possible to load the GeoJSON, or view the geo column as GeoSeries or a Series with shapely objects."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "3bfe1268-5b40-44c6-b194-aced9ee9d07d",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 {\"coordinates\": [7.765225672614365, 48.41091584192147], \"type\": \"Point\"}\n",
+ "1 {\"coordinates\": [7.778809539550178, 48.40987064550492], \"type\": \"Point\"}\n",
+ "2 {\"coordinates\": [7.779195765893586, 48.4120381144602], \"type\": \"Point\"}\n",
+ "3 {\"coordinates\": [7.775204689771646, 48.40610336882051], \"type\": \"Point\"}\n",
+ "4 {\"coordinates\": [7.76606484746611, 48.4124244421834], \"type\": \"Point\"}\n",
+ "Name: geo, dtype: object"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "net.bus.geo.head() # entries are strings"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "f4f49408-5d9b-4083-893b-e857c999befd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 {'type': 'Point', 'coordinates': [7.76522567, 48.41091584]}\n",
+ "1 {'type': 'Point', 'coordinates': [7.77880954, 48.40987065]}\n",
+ "2 {'type': 'Point', 'coordinates': [7.77919577, 48.41203811]}\n",
+ "3 {'type': 'Point', 'coordinates': [7.77520469, 48.40610337]}\n",
+ "4 {'type': 'Point', 'coordinates': [7.76606485, 48.41242444]}\n",
+ "Name: geo, dtype: object"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "net.bus.geo.geojson.as_geo_obj.head() # entries are dicts"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "dfbcd7ff-aa5d-4d8f-aaaf-3909dd4fd654",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 POINT (7.76523 48.41092)\n",
+ "1 POINT (7.77881 48.40987)\n",
+ "2 POINT (7.7792 48.41204)\n",
+ "3 POINT (7.7752 48.4061)\n",
+ "4 POINT (7.76606 48.41242)\n",
+ "dtype: geometry"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "net.bus.geo.geojson.as_geoseries.head() # GeoSeries "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "fc2c3aba-9897-47a3-b076-0fca36134ad2",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 POINT (7.765225672614365 48.41091584192147)\n",
+ "1 POINT (7.778809539550178 48.40987064550492)\n",
+ "2 POINT (7.779195765893586 48.4120381144602)\n",
+ "3 POINT (7.775204689771646 48.40610336882051)\n",
+ "4 POINT (7.76606484746611 48.4124244421834)\n",
+ "Name: geo, dtype: object"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "net.bus.geo.geojson.as_shapely_obj.head() # pandas Series with shapely objects"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "9240803c-ce51-4086-af42-a79192c8fcd7",
+ "metadata": {},
+ "source": [
+ "It is also possible to get the coordinates or the geometry type from the GeoJSON. \n",
+ "**Note that it is not recommended to use ._coords in applications, because the projection definition is lost!**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "be589243-c803-48e5-a6ee-ab1140ca350e",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 [7.76522567, 48.41091584]\n",
+ "1 [7.77880954, 48.40987065]\n",
+ "2 [7.77919577, 48.41203811]\n",
+ "3 [7.77520469, 48.40610337]\n",
+ "4 [7.76606485, 48.41242444]\n",
+ "Name: geo, dtype: object"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "net.bus.geo.geojson._coords.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "749ec31d-cde0-4d21-b2d9-fe5dd424e5a5",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "0 Point\n",
+ "1 Point\n",
+ "2 Point\n",
+ "3 Point\n",
+ "4 Point\n",
+ "Name: geo, dtype: object"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "net.bus.geo.geojson.type.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "00ac6187-dc91-4360-b1f5-edbbb8f2710f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}