src/oemof/solph/views.py
# -*- coding: utf-8 -*-
"""Modules for providing convenient views for solph results.
See examples for to learn about the possible usage of the provided functions.
SPDX-FileCopyrightText: Uwe Krien <krien@uni-bremen.de>
SPDX-FileCopyrightText: Simon Hilpert
SPDX-FileCopyrightText: Cord Kaldemeyer
SPDX-FileCopyrightText: Stephan Günther
SPDX-FileCopyrightText: henhuy
SPDX-FileCopyrightText: Johannes Kochems
SPDX-License-Identifier: MIT
"""
import logging
from collections import OrderedDict
from enum import Enum
import pandas as pd
from oemof.solph.processing import convert_keys_to_strings
NONE_REPLACEMENT_STR = "_NONE_"
def node(results, node, multiindex=False, keep_none_type=False):
"""
Obtain results for a single node e.g. a Bus or Component.
Either a node or its label string can be passed.
Results are written into a dictionary which is keyed by 'scalars'
(resp. 'periods_scalars' for a multi-period model) and
'sequences' holding respective data in a pandas Series (resp. DataFrame)
and DataFrame.
"""
def replace_none(col_list, reverse=False):
replacement = (
(None, NONE_REPLACEMENT_STR)
if reverse
else (NONE_REPLACEMENT_STR, None)
)
changed_col_list = [
(
(
replacement[0] if n1 is replacement[1] else n1,
replacement[0] if n2 is replacement[1] else n2,
),
f,
)
for (n1, n2), f in col_list
]
return changed_col_list
# convert to keys if only a string is passed
if type(node) is str:
results = convert_keys_to_strings(results, keep_none_type)
filtered = {}
# create a series with tuples as index labels for scalars
scalars_col = "scalars"
# Check for multi-period model (different naming)
if "period_scalars" in list(list(results.values())[0].keys()):
scalars_col = "period_scalars"
scalars = {
k: v[scalars_col]
for k, v in results.items()
if node in k and not v[scalars_col].empty
}
if scalars:
# aggregate data
filtered[scalars_col] = pd.concat(scalars.values(), axis=0)
# assign index values
idx = {
k: [c for c in v[scalars_col].index]
for k, v in results.items()
if node in k and not v[scalars_col].empty
}
idx = [tuple((k, m) for m in v) for k, v in idx.items()]
idx = [i for sublist in idx for i in sublist]
filtered[scalars_col].index = idx
# Sort index
# (if Nones are present, they have to be replaced while sorting)
if keep_none_type:
filtered[scalars_col].index = replace_none(
filtered[scalars_col].index.tolist()
)
filtered[scalars_col].sort_index(axis=0, inplace=True)
if keep_none_type:
filtered[scalars_col].index = replace_none(
filtered[scalars_col].index.tolist(), True
)
if multiindex:
idx = pd.MultiIndex.from_tuples(
[
tuple([row[0][0], row[0][1], row[1]])
for row in filtered[scalars_col].index
]
)
idx.set_names(["from", "to", "type"], inplace=True)
filtered[scalars_col].index = idx
# create a dataframe with tuples as column labels for sequences
sequences = {
k: v["sequences"]
for k, v in results.items()
if node in k and not v["sequences"].empty
}
if sequences:
# aggregate data
filtered["sequences"] = pd.concat(sequences.values(), axis=1)
# assign column names
cols = {
k: [c for c in v["sequences"].columns]
for k, v in results.items()
if node in k and not v["sequences"].empty
}
cols = [tuple((k, m) for m in v) for k, v in cols.items()]
cols = [c for sublist in cols for c in sublist]
filtered["sequences"].columns = replace_none(cols)
filtered["sequences"].sort_index(axis=1, inplace=True)
filtered["sequences"].columns = replace_none(
filtered["sequences"].columns, True
)
if multiindex:
idx = pd.MultiIndex.from_tuples(
[
tuple([col[0][0], col[0][1], col[1]])
for col in filtered["sequences"].columns
]
)
idx.set_names(["from", "to", "type"], inplace=True)
filtered["sequences"].columns = idx
return filtered
class NodeOption(str, Enum):
All = "all"
HasOutputs = "has_outputs"
HasInputs = "has_inputs"
HasOnlyOutputs = "has_only_outputs"
HasOnlyInputs = "has_only_inputs"
def filter_nodes(results, option=NodeOption.All, exclude_busses=False):
"""Get set of nodes from results-dict for given node option.
This function filters nodes from results for special needs. At the moment,
the following options are available:
* :attr:`NodeOption.All`: `'all'`: Returns all nodes
* :attr:`NodeOption.HasOutputs`: `'has_outputs'`:
Returns nodes with an output flow (eg. Converter, Source)
* :attr:`NodeOption.HasInputs`: `'has_inputs'`:
Returns nodes with an input flow (eg. Converter, Sink)
* :attr:`NodeOption.HasOnlyOutputs`: `'has_only_outputs'`:
Returns nodes having only output flows (eg. Source)
* :attr:`NodeOption.HasOnlyInputs`: `'has_only_inputs'`:
Returns nodes having only input flows (eg. Sink)
Additionally, busses can be excluded by setting `exclude_busses` to
`True`.
Parameters
----------
results: dict
option: NodeOption
exclude_busses: bool
If set, all bus nodes are excluded from the resulting node set.
Returns
-------
:obj:`set`
A set of Nodes.
"""
node_from, node_to = map(lambda x: set(x) - {None}, zip(*results))
if option == NodeOption.All:
nodes = node_from.union(node_to)
elif option == NodeOption.HasOutputs:
nodes = node_from
elif option == NodeOption.HasInputs:
nodes = node_to
elif option == NodeOption.HasOnlyOutputs:
nodes = node_from - node_to
elif option == NodeOption.HasOnlyInputs:
nodes = node_to - node_from
else:
raise ValueError('Invalid node option "' + str(option) + '"')
if exclude_busses:
return {n for n in nodes if not n.__class__.__name__ == "Bus"}
else:
return nodes
def get_node_by_name(results, *names):
"""
Searches results for nodes
Names are looked up in nodes from results and either returned single node
(in case only one name is given) or as list of nodes. If name is not found,
None is returned.
"""
nodes = filter_nodes(results)
if len(names) == 1:
return next(filter(lambda x: str(x) == names[0], nodes), None)
else:
node_names = {str(n): n for n in nodes}
return [node_names.get(n, None) for n in names]
def node_weight_by_type(results, node_type):
"""
Extracts node weights (if exist) of all components of the specified
`node_type`.
Node weight are endogenous optimzation variables associated with the node
and not the edge between two node, foxample the variable representing the
storage level.
Parameters
----------
results: dict
A result dictionary from a solved oemof.solph.Model object
node_type: oemof.solph class
Specifies the type for which node weights should be collected,
e.g. solph.components.GenericStorage
Example
--------
::
from oemof.solph import views
# solve oemof model 'm'
# Then collect node weights
views.node_weight_by_type(
m.results(),
node_type=solph.components.GenericStorage
)
"""
group = {
k: v["sequences"]
for k, v in results.items()
if isinstance(k[0], node_type) and k[1] is None
}
if not group:
logging.error(
"No node weights for nodes of type `{}`".format(node_type)
)
return None
else:
df = convert_to_multiindex(
group, index_names=["node", "to", "weight_type"], droplevel=[1]
)
return df
def node_input_by_type(results, node_type, droplevel=None):
"""Gets all inputs for all nodes of the type `node_type` and returns
a dataframe.
Parameters
----------
results: dict
A result dictionary from a solved oemof.solph.Model object
node_type: oemof.solph class
Specifies the type of the node for that inputs are selected,
e.g. solph.components.Sink
droplevel: list
Examples
-----
::
from oemof import solph
from oemof.solph import views
# solve oemof solph model 'm'
# Then collect node weights
views.node_input_by_type(
m.results(),
node_type=solph.components.Sink
)
"""
if droplevel is None:
droplevel = []
group = {
k: v["sequences"]
for k, v in results.items()
if isinstance(k[1], node_type) and k[0] is not None
}
if not group:
logging.info("No nodes of type `{}`".format(node_type))
return None
else:
df = convert_to_multiindex(group, droplevel=droplevel)
return df
def node_output_by_type(results, node_type, droplevel=None):
"""Gets all outputs for all nodes of the type `node_type` and returns
a dataframe.
Parameters
----------
results: dict
A result dictionary from a solved oemof.solph.Model object
node_type: oemof.solph class
Specifies the type of the node for that outputs are selected,
e.g. solph.components.Converter
droplevel: list
Examples
--------
::
import oemof.solph as solph
from oemof.solph import views
# solve oemof solph model 'm'
# Then collect node weights
views.node_output_by_type(
m.results(),
node_type=solph.components.Converter
)
"""
if droplevel is None:
droplevel = []
group = {
k: v["sequences"]
for k, v in results.items()
if isinstance(k[0], node_type) and k[1] is not None
}
if not group:
logging.info("No nodes of type `{}`".format(node_type))
return None
else:
df = convert_to_multiindex(group, droplevel=droplevel)
return df
def net_storage_flow(results, node_type):
"""Calculates the net storage flow for storage models that have one
input edge and one output edge both with flows within the domain of
non-negative reals.
Parameters
----------
results: dict
A result dictionary from a solved oemof.solph.Model object
node_type: oemof.solph class
Specifies the type for which (storage) type net flows are calculated,
e.g. solph.components.GenericStorage
Returns
-------
pandas.DataFrame object with multiindex colums. Names of levels of columns
are: from, to, net_flow.
Examples
--------
::
import oemof.solph as solph
from oemof.solph import views
# solve oemof solph model 'm'
# Then collect node weights
views.net_storage_flow(
m.results(),
node_type=solph.components.GenericStorage
)
"""
group = {
k: v["sequences"]
for k, v in results.items()
if isinstance(k[0], node_type) or isinstance(k[1], node_type)
}
if not group:
logging.info("No nodes of type `{}`".format(node_type))
return None
df = convert_to_multiindex(group)
if "storage_content" not in df.columns.get_level_values(2).unique():
return None
x = df.xs("storage_content", axis=1, level=2).columns.values
labels = [s for s, t in x]
dataframes = []
for lb in labels:
subset = (
df.T.groupby(
lambda x1: (
lambda fr, to, ty: "output"
if (fr == lb and ty == "flow")
else "input"
if (to == lb and ty == "flow")
else "level"
if (fr == lb and ty != "flow")
else None
)(*x1)
)
.sum()
.T
)
subset["net_flow"] = subset["output"] - subset["input"]
subset.columns = pd.MultiIndex.from_product(
[[lb], [o for o in lb.outputs], subset.columns]
)
dataframes.append(
subset.loc[:, (slice(None), slice(None), "net_flow")]
)
return pd.concat(dataframes, axis=1)
def convert_to_multiindex(group, index_names=None, droplevel=None):
"""Convert dict to pandas DataFrame with multiindex
Parameters
----------
group: dict
Sequences of the oemof.solph.Model.results dictionary
index_names: arraylike
Array with names of the MultiIndex
droplevel: arraylike
List containing levels to be dropped from the dataframe
"""
if index_names is None:
index_names = ["from", "to", "type"]
if droplevel is None:
droplevel = []
sorted_group = OrderedDict((k, group[k]) for k in sorted(group))
df = pd.concat(sorted_group.values(), axis=1)
cols = OrderedDict((k, v.columns) for k, v in sorted_group.items())
cols = [tuple((k, m) for m in v) for k, v in cols.items()]
cols = [c for sublist in cols for c in sublist]
idx = pd.MultiIndex.from_tuples(
[tuple([col[0][0], col[0][1], col[1]]) for col in cols]
)
idx.set_names(index_names, inplace=True)
df.columns = idx
df.columns = df.columns.droplevel(droplevel)
return df