dispatch.nodes_from_excel() - Code Metrics - oemof/oemof-solph - Measure and Improve Code Quality continuously with Scrutinizer

dispatch.nodes_from_excel() A
last analyzed 2025-11-04 19:41 UTC

↳ Parent: dispatch

Complexity

Conditions

Size

Total Lines	42
Code Lines	19

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	19
dl	0
loc	42
rs	9.45
c	0
b	0
f	0
cc	3
nop	1

# -*- coding: utf-8 -*-

"""
General description
-------------------
As the csv-reader was removed with version 0.2 this example shows how to create
an excel-reader. The example is equivalent to the old csv-reader example.
Following the example one can customise the excel reader to ones own needs.

The pandas package supports the '.xls' and the '.xlsx' format but one can
create read and adept the files with open source software such as libreoffice,
openoffice, gnumeric, ...

Code
----
Download source code: :download:`dispatch.py </../examples/excel_reader/dispatch.py>`

.. dropdown:: Click to display code

    .. literalinclude:: /../examples/excel_reader/dispatch.py
        :language: python
        :lines: 57-437

Data
----
Download data: :download:`scenario.xlsx </../examples/excel_reader/scenario.xlsx>`

Installation requirements
-------------------------
This example requires oemof.solph (at least v0.5.0), install by:

.. code:: bash

    pip install oemof.solph>=0.5
    pip install openpyxl


If you want to plot the energy system's graph, you have to install pygraphviz
using:

.. code:: bash

    pip install pygraphviz

For pygraphviz under Windows, some hints are available in the oemof Wiki:
https://github.com/oemof/oemof/wiki/Windows---general

License
-------
Uwe Krien <[email protected]>
Jonathan Amme <[email protected]>

`MIT license <https://github.com/oemof/oemof-solph/blob/dev/LICENSE>`_

"""

import logging
import os

import networkx as nx
import pandas as pd
from matplotlib import pyplot as plt
from oemof.network.graph import create_nx_graph
from oemof.tools import logger

from oemof import solph


def nodes_from_excel(filename):
    """Read node data from Excel sheet

    Parameters
    ----------
    filename : :obj:`str`
        Path to excel file

    Returns
    -------
    :obj:`dict`
        Imported nodes data
    """

    # does Excel file exist?
    if not filename or not os.path.isfile(filename):
        raise FileNotFoundError(
            "Excel data file {} not found.".format(filename)
        )

    xls = pd.ExcelFile(filename)

    nodes_data = {
        "buses": xls.parse("buses"),
        "commodity_sources": xls.parse("commodity_sources"),
        "converters": xls.parse("converters"),
        "renewables": xls.parse("renewables"),
        "demand": xls.parse("demand"),
        "storages": xls.parse("storages"),
        "powerlines": xls.parse("powerlines"),
        "timeseries": xls.parse("time_series"),
    }

    # set datetime index
    nodes_data["timeseries"].set_index("timestamp", inplace=True)
    nodes_data["timeseries"].index = pd.to_datetime(
        nodes_data["timeseries"].index
    )

    print("Data from Excel file {} imported.".format(filename))

    return nodes_data


def create_nodes(nd=None):
    """Create nodes (oemof objects) from node dict

    Parameters
    ----------
    nd : :obj:`dict`
        Nodes data

    Returns
    -------
    nodes : `obj`:dict of :class:`nodes <oemof.network.Node>`
    """

    if not nd:
        raise ValueError("No nodes data provided.")

    nodes = []

    # Create Bus objects from buses table
    busd = {}

    for i, b in nd["buses"].iterrows():
        if b["active"]:
            bus = solph.Bus(label=b["label"])
            nodes.append(bus)

            busd[b["label"]] = bus
            if b["excess"]:
                nodes.append(
                    solph.components.Sink(
                        label=b["label"] + "_excess",
                        inputs={
                            busd[b["label"]]: solph.Flow(
                                variable_costs=b["excess costs"]
                            )
                        },
                    )
                )
            if b["shortage"]:
                nodes.append(
                    solph.components.Source(
                        label=b["label"] + "_shortage",
                        outputs={
                            busd[b["label"]]: solph.Flow(
                                variable_costs=b["shortage costs"]
                            )
                        },
                    )
                )

    # Create Source objects from table 'commodity sources'
    for i, cs in nd["commodity_sources"].iterrows():
        if cs["active"]:
            nodes.append(
                solph.components.Source(
                    label=cs["label"],
                    outputs={
                        busd[cs["to"]]: solph.Flow(
                            variable_costs=cs["variable costs"]
                        )
                    },
                )
            )

    # Create Source objects with fixed time series from 'renewables' table
    for i, re in nd["renewables"].iterrows():
        if re["active"]:
            # set static outflow values
            outflow_args = {"nominal_capacity": re["capacity"]}
            # get time series for node and parameter
            for col in nd["timeseries"].columns.values:
                if col.split(".")[0] == re["label"]:
                    outflow_args[col.split(".")[1]] = nd["timeseries"][col]

            # create
            nodes.append(
                solph.components.Source(
                    label=re["label"],
                    outputs={busd[re["to"]]: solph.Flow(**outflow_args)},
                )
            )

    # Create Sink objects with fixed time series from 'demand' table
    for i, de in nd["demand"].iterrows():
        if de["active"]:
            # set static inflow values
            inflow_args = {"nominal_capacity": de["nominal value"]}
            # get time series for node and parameter
            for col in nd["timeseries"].columns.values:
                if col.split(".")[0] == de["label"]:
                    inflow_args[col.split(".")[1]] = nd["timeseries"][col]

            # create
            nodes.append(
                solph.components.Sink(
                    label=de["label"],
                    inputs={busd[de["from"]]: solph.Flow(**inflow_args)},
                )
            )

    # Create Converter objects from 'converters' table
    for i, t in nd["converters"].iterrows():
        if t["active"]:
            # set static inflow values
            inflow_args = {"variable_costs": t["variable input costs"]}
            # get time series for inflow of converter
            for col in nd["timeseries"].columns.values:
                if col.split(".")[0] == t["label"]:
                    inflow_args[col.split(".")[1]] = nd["timeseries"][col]
            # create
            nodes.append(
                solph.components.Converter(
                    label=t["label"],
                    inputs={busd[t["from"]]: solph.Flow(**inflow_args)},
                    outputs={
                        busd[t["to"]]: solph.Flow(
                            nominal_capacity=t["capacity"]
                        )
                    },
                    conversion_factors={busd[t["to"]]: t["efficiency"]},
                )
            )

    for i, s in nd["storages"].iterrows():
        if s["active"]:
            nodes.append(
                solph.components.GenericStorage(
                    label=s["label"],
                    inputs={
                        busd[s["bus"]]: solph.Flow(
                            nominal_capacity=s["capacity inflow"],
                            variable_costs=s["variable input costs"],
                        )
                    },
                    outputs={
                        busd[s["bus"]]: solph.Flow(
                            nominal_capacity=s["capacity outflow"],
                            variable_costs=s["variable output costs"],
                        )
                    },
                    nominal_capacity=s["nominal capacity"],
                    loss_rate=s["capacity loss"],
                    initial_storage_level=s["initial capacity"],
                    max_storage_level=s["capacity max"],
                    min_storage_level=s["capacity min"],
                    inflow_conversion_factor=s["efficiency inflow"],
                    outflow_conversion_factor=s["efficiency outflow"],
                )
            )

    for i, p in nd["powerlines"].iterrows():
        if p["active"]:
            bus1 = busd[p["bus_1"]]
            bus2 = busd[p["bus_2"]]
            nodes.append(
                solph.components.Converter(
                    label="powerline" + "_" + p["bus_1"] + "_" + p["bus_2"],
                    inputs={bus1: solph.Flow()},
                    outputs={bus2: solph.Flow()},
                    conversion_factors={bus2: p["efficiency"]},
                )
            )
            nodes.append(
                solph.components.Converter(
                    label="powerline" + "_" + p["bus_2"] + "_" + p["bus_1"],
                    inputs={bus2: solph.Flow()},
                    outputs={bus1: solph.Flow()},
                    conversion_factors={bus1: p["efficiency"]},
                )
            )

    return nodes


def draw_graph(
    grph,
    edge_labels=True,
    node_color="#AFAFAF",
    edge_color="#CFCFCF",
    plot=True,
    node_size=2000,
    with_labels=True,
    arrows=True,
    layout="neato",
):
    """
    Parameters
    ----------
    grph : networkxGraph
        A graph to draw.
    edge_labels : boolean
        Use nominal capacities of flow as edge label
    node_color : dict or string
        Hex color code oder matplotlib color for each node. If string, all
        colors are the same.

    edge_color : string
        Hex color code oder matplotlib color for edge color.

    plot : boolean
        Show matplotlib plot.

    node_size : integer
        Size of nodes.

    with_labels : boolean
        Draw node labels.

    arrows : boolean
        Draw arrows on directed edges. Works only if an optimization_model has
        been passed.
    layout : string
        networkx graph layout, one of: neato, dot, twopi, circo, fdp, sfdp.
    """
    if isinstance(node_color, dict):
        node_color = [node_color.get(g, "#AFAFAF") for g in grph.nodes()]

    # set drawing options
    options = {
        "with_labels": with_labels,
        "node_color": node_color,
        "edge_color": edge_color,
        "node_size": node_size,
        "arrows": arrows,
    }

    # try to use pygraphviz for graph layout
    try:
        import pygraphviz

        pos = nx.drawing.nx_agraph.graphviz_layout(grph, prog=layout)
    except ImportError:
        logging.error("Module pygraphviz not found, I won't plot the graph.")
        return

    # draw graph
    nx.draw(grph, pos=pos, **options)

    # add edge labels for all edges
    if edge_labels is True and plt:
        labels = nx.get_edge_attributes(grph, "weight")
        nx.draw_networkx_edge_labels(grph, pos=pos, edge_labels=labels)

    # show output
    if plot is True:
        plt.show()


def main(optimize=True):
    logger.define_logging()
    datetime_index = pd.date_range(
        "2016-01-01 00:00:00", "2016-01-01 23:00:00", freq="60min"
    )

    # model creation and solving
    logging.info("Starting optimization")

    # initialisation of the energy system
    esys = solph.EnergySystem(
        timeindex=datetime_index, infer_last_interval=False
    )

    # read node data from Excel sheet
    excel_nodes = nodes_from_excel(
        os.path.join(
            os.path.dirname(os.path.abspath(__file__)),
            "scenario.xlsx",
        )
    )

    # create nodes from Excel sheet data
    my_nodes = create_nodes(nd=excel_nodes)

    # add nodes and flows to energy system
    esys.add(*my_nodes)

    print("*********************************************************")
    print("The following objects have been created from excel sheet:")
    for n in esys.nodes:
        oobj = (
            str(type(n)).replace("<class 'oemof.solph.", "").replace("'>", "")
        )
        print(oobj + ":", n.label)
    print("*********************************************************")

    if optimize is False:
        return esys

    # creation of a least cost model from the energy system
    om = solph.Model(esys)
    om.receive_duals()

    # solving the linear problem using the given solver
    om.solve(solver="cbc")

    # create graph of esys
    # You can use argument filename='/home/somebody/my_graph.graphml'
    # to dump your graph to disc. You can open it using e.g. yEd or gephi
    graph = create_nx_graph(esys)

    # plot esys graph
    draw_graph(
        grph=graph,
        plot=True,
        layout="neato",
        node_size=1000,
        node_color={"R1_bus_el": "#cd3333", "R2_bus_el": "#cd3333"},
    )

    # print and plot some results
    results = solph.processing.results(om)

    region2 = solph.views.node(results, "R2_bus_el")
    region1 = solph.views.node(results, "R1_bus_el")

    print(region2["sequences"].sum())
    print(region1["sequences"].sum())

    fig, ax = plt.subplots(figsize=(10, 5))
    region1["sequences"].plot(ax=ax)
    ax.legend(
        loc="upper center", prop={"size": 8}, bbox_to_anchor=(0.5, 1.4), ncol=3
    )
    fig.subplots_adjust(top=0.7)
    plt.show()
    logging.info("Done!")


if __name__ == "__main__":
    main()


1			# -- coding: utf-8 --
2
3			"""
4			General description
5			-------------------
6			As the csv-reader was removed with version 0.2 this example shows how to create
7			an excel-reader. The example is equivalent to the old csv-reader example.
8			Following the example one can customise the excel reader to ones own needs.
9
10			The pandas package supports the '.xls' and the '.xlsx' format but one can
11			create read and adept the files with open source software such as libreoffice,
12			openoffice, gnumeric, ...
13
14			Code
15			----
16			Download source code: :download:`dispatch.py </../examples/excel_reader/dispatch.py>`
17
18			.. dropdown:: Click to display code
19
20			.. literalinclude:: /../examples/excel_reader/dispatch.py
21			:language: python
22			:lines: 57-437
23
24			Data
25			----
26			Download data: :download:`scenario.xlsx </../examples/excel_reader/scenario.xlsx>`
27
28			Installation requirements
29			-------------------------
30			This example requires oemof.solph (at least v0.5.0), install by:
31
32			.. code:: bash
33
34			pip install oemof.solph>=0.5
35			pip install openpyxl
36
37
38			If you want to plot the energy system's graph, you have to install pygraphviz
39			using:
40
41			.. code:: bash
42
43			pip install pygraphviz
44
45			For pygraphviz under Windows, some hints are available in the oemof Wiki:
46			https://github.com/oemof/oemof/wiki/Windows---general
47
48			License
49			-------
50			Uwe Krien <[email protected]>
51			Jonathan Amme <[email protected]>
52
53			`MIT license <https://github.com/oemof/oemof-solph/blob/dev/LICENSE>`_
54
55			"""
56
57			import logging
58			import os
59
60			import networkx as nx
61			import pandas as pd
62			from matplotlib import pyplot as plt
63			from oemof.network.graph import create_nx_graph
64			from oemof.tools import logger
65
66			from oemof import solph
67
68
69			def nodes_from_excel(filename):
70			"""Read node data from Excel sheet
71
72			Parameters
73			----------
74			filename : :obj:`str`
75			Path to excel file
76
77			Returns
78			-------
79			:obj:`dict`
80			Imported nodes data
81			"""
82
83			# does Excel file exist?
84			if not filename or not os.path.isfile(filename):
85			raise FileNotFoundError(
86			"Excel data file {} not found.".format(filename)
87			)
88
89			xls = pd.ExcelFile(filename)
90
91			nodes_data = {
92			"buses": xls.parse("buses"),
93			"commodity_sources": xls.parse("commodity_sources"),
94			"converters": xls.parse("converters"),
95			"renewables": xls.parse("renewables"),
96			"demand": xls.parse("demand"),
97			"storages": xls.parse("storages"),
98			"powerlines": xls.parse("powerlines"),
99			"timeseries": xls.parse("time_series"),
100			}
101
102			# set datetime index
103			nodes_data["timeseries"].set_index("timestamp", inplace=True)
104			nodes_data["timeseries"].index = pd.to_datetime(
105			nodes_data["timeseries"].index
106			)
107
108			print("Data from Excel file {} imported.".format(filename))
109
110			return nodes_data
111
112
113			def create_nodes(nd=None):
114			"""Create nodes (oemof objects) from node dict
115
116			Parameters
117			----------
118			nd : :obj:`dict`
119			Nodes data
120
121			Returns
122			-------
123			nodes : `obj`:dict of :class:`nodes <oemof.network.Node>`
124			"""
125
126			if not nd:
127			raise ValueError("No nodes data provided.")
128
129			nodes = []
130
131			# Create Bus objects from buses table
132			busd = {}
133
134			for i, b in nd["buses"].iterrows():
135			if b["active"]:
136			bus = solph.Bus(label=b["label"])
137			nodes.append(bus)
138
139			busd[b["label"]] = bus
140			if b["excess"]:
141			nodes.append(
142			solph.components.Sink(
143			label=b["label"] + "_excess",
144			inputs={
145			busd[b["label"]]: solph.Flow(
146			variable_costs=b["excess costs"]
147			)
148			},
149			)
150			)
151			if b["shortage"]:
152			nodes.append(
153			solph.components.Source(
154			label=b["label"] + "_shortage",
155			outputs={
156			busd[b["label"]]: solph.Flow(
157			variable_costs=b["shortage costs"]
158			)
159			},
160			)
161			)
162
163			# Create Source objects from table 'commodity sources'
164			for i, cs in nd["commodity_sources"].iterrows():
165			if cs["active"]:
166			nodes.append(
167			solph.components.Source(
168			label=cs["label"],
169			outputs={
170			busd[cs["to"]]: solph.Flow(
171			variable_costs=cs["variable costs"]
172			)
173			},
174			)
175			)
176
177			# Create Source objects with fixed time series from 'renewables' table
178			for i, re in nd["renewables"].iterrows():
179			if re["active"]:
180			# set static outflow values
181			outflow_args = {"nominal_capacity": re["capacity"]}
182			# get time series for node and parameter
183			for col in nd["timeseries"].columns.values:
184			if col.split(".")[0] == re["label"]:
185			outflow_args[col.split(".")[1]] = nd["timeseries"][col]
186
187			# create
188			nodes.append(
189			solph.components.Source(
190			label=re["label"],
191			outputs={busd[re["to"]]: solph.Flow(**outflow_args)},
192			)
193			)
194
195			# Create Sink objects with fixed time series from 'demand' table
196			for i, de in nd["demand"].iterrows():
197			if de["active"]:
198			# set static inflow values
199			inflow_args = {"nominal_capacity": de["nominal value"]}
200			# get time series for node and parameter
201			for col in nd["timeseries"].columns.values:
202			if col.split(".")[0] == de["label"]:
203			inflow_args[col.split(".")[1]] = nd["timeseries"][col]
204
205			# create
206			nodes.append(
207			solph.components.Sink(
208			label=de["label"],
209			inputs={busd[de["from"]]: solph.Flow(**inflow_args)},
210			)
211			)
212
213			# Create Converter objects from 'converters' table
214			for i, t in nd["converters"].iterrows():
215			if t["active"]:
216			# set static inflow values
217			inflow_args = {"variable_costs": t["variable input costs"]}
218			# get time series for inflow of converter
219			for col in nd["timeseries"].columns.values:
220			if col.split(".")[0] == t["label"]:
221			inflow_args[col.split(".")[1]] = nd["timeseries"][col]
222			# create
223			nodes.append(
224			solph.components.Converter(
225			label=t["label"],
226			inputs={busd[t["from"]]: solph.Flow(**inflow_args)},
227			outputs={
228			busd[t["to"]]: solph.Flow(
229			nominal_capacity=t["capacity"]
230			)
231			},
232			conversion_factors={busd[t["to"]]: t["efficiency"]},
233			)
234			)
235
236			for i, s in nd["storages"].iterrows():
237			if s["active"]:
238			nodes.append(
239			solph.components.GenericStorage(
240			label=s["label"],
241			inputs={
242			busd[s["bus"]]: solph.Flow(
243			nominal_capacity=s["capacity inflow"],
244			variable_costs=s["variable input costs"],
245			)
246			},
247			outputs={
248			busd[s["bus"]]: solph.Flow(
249			nominal_capacity=s["capacity outflow"],
250			variable_costs=s["variable output costs"],
251			)
252			},
253			nominal_capacity=s["nominal capacity"],
254			loss_rate=s["capacity loss"],
255			initial_storage_level=s["initial capacity"],
256			max_storage_level=s["capacity max"],
257			min_storage_level=s["capacity min"],
258			inflow_conversion_factor=s["efficiency inflow"],
259			outflow_conversion_factor=s["efficiency outflow"],
260			)
261			)
262
263			for i, p in nd["powerlines"].iterrows():
264			if p["active"]:
265			bus1 = busd[p["bus_1"]]
266			bus2 = busd[p["bus_2"]]
267			nodes.append(
268			solph.components.Converter(
269			label="powerline" + "_" + p["bus_1"] + "_" + p["bus_2"],
270			inputs={bus1: solph.Flow()},
271			outputs={bus2: solph.Flow()},
272			conversion_factors={bus2: p["efficiency"]},
273			)
274			)
275			nodes.append(
276			solph.components.Converter(
277			label="powerline" + "_" + p["bus_2"] + "_" + p["bus_1"],
278			inputs={bus2: solph.Flow()},
279			outputs={bus1: solph.Flow()},
280			conversion_factors={bus1: p["efficiency"]},
281			)
282			)
283
284			return nodes
285
286
287			def draw_graph(
288			grph,
289			edge_labels=True,
290			node_color="#AFAFAF",
291			edge_color="#CFCFCF",
292			plot=True,
293			node_size=2000,
294			with_labels=True,
295			arrows=True,
296			layout="neato",
297			):
298			"""
299			Parameters
300			----------
301			grph : networkxGraph
302			A graph to draw.
303			edge_labels : boolean
304			Use nominal capacities of flow as edge label
305			node_color : dict or string
306			Hex color code oder matplotlib color for each node. If string, all
307			colors are the same.
308
309			edge_color : string
310			Hex color code oder matplotlib color for edge color.
311
312			plot : boolean
313			Show matplotlib plot.
314
315			node_size : integer
316			Size of nodes.
317
318			with_labels : boolean
319			Draw node labels.
320
321			arrows : boolean
322			Draw arrows on directed edges. Works only if an optimization_model has
323			been passed.
324			layout : string
325			networkx graph layout, one of: neato, dot, twopi, circo, fdp, sfdp.
326			"""
327			if isinstance(node_color, dict):
328			node_color = [node_color.get(g, "#AFAFAF") for g in grph.nodes()]
329
330			# set drawing options
331			options = {
332			"with_labels": with_labels,
333			"node_color": node_color,
334			"edge_color": edge_color,
335			"node_size": node_size,
336			"arrows": arrows,
337			}
338
339			# try to use pygraphviz for graph layout
340			try:
341			import pygraphviz
342
343			pos = nx.drawing.nx_agraph.graphviz_layout(grph, prog=layout)
344			except ImportError:
345			logging.error("Module pygraphviz not found, I won't plot the graph.")
346			return
347
348			# draw graph
349			nx.draw(grph, pos=pos, **options)
350
351			# add edge labels for all edges
352			if edge_labels is True and plt:
353			labels = nx.get_edge_attributes(grph, "weight")
354			nx.draw_networkx_edge_labels(grph, pos=pos, edge_labels=labels)
355
356			# show output
357			if plot is True:
358			plt.show()
359
360
361			def main(optimize=True):
362			logger.define_logging()
363			datetime_index = pd.date_range(
364			"2016-01-01 00:00:00", "2016-01-01 23:00:00", freq="60min"
365			)
366
367			# model creation and solving
368			logging.info("Starting optimization")
369
370			# initialisation of the energy system
371			esys = solph.EnergySystem(
372			timeindex=datetime_index, infer_last_interval=False
373			)
374
375			# read node data from Excel sheet
376			excel_nodes = nodes_from_excel(
377			os.path.join(
378			os.path.dirname(os.path.abspath(__file__)),
379			"scenario.xlsx",
380			)
381			)
382
383			# create nodes from Excel sheet data
384			my_nodes = create_nodes(nd=excel_nodes)
385
386			# add nodes and flows to energy system
387			esys.add(*my_nodes)
388
389			print("*********************************************************")
390			print("The following objects have been created from excel sheet:")
391			for n in esys.nodes:
392			oobj = (
393			str(type(n)).replace("<class 'oemof.solph.", "").replace("'>", "")
394			)
395			print(oobj + ":", n.label)
396			print("*********************************************************")
397
398			if optimize is False:
399			return esys
400
401			# creation of a least cost model from the energy system
402			om = solph.Model(esys)
403			om.receive_duals()
404
405			# solving the linear problem using the given solver
406			om.solve(solver="cbc")
407
408			# create graph of esys
409			# You can use argument filename='/home/somebody/my_graph.graphml'
410			# to dump your graph to disc. You can open it using e.g. yEd or gephi
411			graph = create_nx_graph(esys)
412
413			# plot esys graph
414			draw_graph(
415			grph=graph,
416			plot=True,
417			layout="neato",
418			node_size=1000,
419			node_color={"R1_bus_el": "#cd3333", "R2_bus_el": "#cd3333"},
420			)
421
422			# print and plot some results
423			results = solph.processing.results(om)
424
425			region2 = solph.views.node(results, "R2_bus_el")
426			region1 = solph.views.node(results, "R1_bus_el")
427
428			print(region2["sequences"].sum())
429			print(region1["sequences"].sum())
430
431			fig, ax = plt.subplots(figsize=(10, 5))
432			region1["sequences"].plot(ax=ax)
433			ax.legend(
434			loc="upper center", prop={"size": 8}, bbox_to_anchor=(0.5, 1.4), ncol=3
435			)
436			fig.subplots_adjust(top=0.7)
437			plt.show()
438			logging.info("Done!")
439
440
441			if __name__ == "__main__":
442			main()
443

oemof / oemof-solph

dispatch.nodes_from_excel() A last analyzed 2025-11-04 19:41 UTC

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like

dispatch.nodes_from_excel() A
last analyzed 2025-11-04 19:41 UTC