# -*- coding: utf-8 -*-
"""Modules for providing a convenient data structure for solph results.
Information about the possible usage is provided within the examples.
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-FileCopyrightText: Patrik Schönfeldt <patrik.schoenfeldt@dlr.de>
SPDX-License-Identifier: MIT
"""
import itertools
import numbers
import operator
import sys
from collections import abc
from itertools import groupby
from typing import Dict
from typing import Tuple
import numpy as np
import pandas as pd
from oemof.network.network import Entity
from pyomo.core.base.piecewise import IndexedPiecewise
from pyomo.core.base.var import Var
from oemof.solph.components._generic_storage import GenericStorage
from ._plumbing import _FakeSequence
from .helpers import flatten
PERIOD_INDEXES = ("invest", "total", "old", "old_end", "old_exo")
[docs]
def get_tuple(x):
"""Get oemof tuple within iterable or create it
Tuples from Pyomo are of type `(n, n, int)`, `(n, n)` and `(n, int)`.
For single nodes `n` a tuple with one object `(n,)` is created.
"""
for i in x:
if isinstance(i, tuple):
return i
elif issubclass(type(i), Entity):
return (i,)
# for standalone variables, x is used as identifying tuple
if isinstance(x, tuple):
return x
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def get_timestep(x):
"""Get the timestep from oemof tuples
The timestep from tuples `(n, n, int)`, `(n, n)`, `(n, int)` and (n,)
is fetched as the last element. For time-independent data (scalars)
zero ist returned.
"""
if all(issubclass(type(n), Entity) for n in x):
return 0
else:
return x[-1]
[docs]
def remove_timestep(x):
"""Remove the timestep from oemof tuples
The timestep is removed from tuples of type `(n, n, int)` and `(n, int)`.
"""
if all(issubclass(type(n), Entity) for n in x):
return x
else:
return x[:-1]
[docs]
def create_dataframe(om):
"""Create a result DataFrame with all optimization data
Results from Pyomo are written into one common pandas.DataFrame where
separate columns are created for the variable index e.g. for tuples
of the flows and components or the timesteps.
"""
# get all pyomo variables including their block
block_vars = list(
set([bv.parent_component() for bv in om.component_data_objects(Var)])
)
var_dict = {}
for bv in block_vars:
# Drop the auxiliary variables introduced by pyomo's Piecewise
parent_component = bv.parent_block().parent_component()
if not isinstance(parent_component, IndexedPiecewise):
try:
idx_set = getattr(bv, "_index_set")
except AttributeError:
# To make it compatible with Pyomo < 6.4.1
idx_set = getattr(bv, "_index")
for i in idx_set:
key = (str(bv).split(".")[0], str(bv).split(".")[-1], i)
value = bv[i].value
var_dict[key] = value
# use this to create a pandas dataframe
df = pd.DataFrame(list(var_dict.items()), columns=["pyomo_tuple", "value"])
df["variable_name"] = df["pyomo_tuple"].str[1]
# adapt the dataframe by separating tuple data into columns depending
# on which dimension the variable/parameter has (scalar/sequence).
# columns for the oemof tuple and timestep are created
df["oemof_tuple"] = df["pyomo_tuple"].map(get_tuple)
df = df[df["oemof_tuple"].map(lambda x: x is not None)]
df["timestep"] = df["oemof_tuple"].map(get_timestep)
df["oemof_tuple"] = df["oemof_tuple"].map(remove_timestep)
# Use another call of remove timestep to get rid of period not needed
df.loc[df["variable_name"] == "flow", "oemof_tuple"] = df.loc[
df["variable_name"] == "flow", "oemof_tuple"
].map(remove_timestep)
# order the data by oemof tuple and timestep
df = df.sort_values(["oemof_tuple", "timestep"], ascending=[True, True])
# drop empty decision variables
df = df.dropna(subset=["value"])
return df
[docs]
def divide_scalars_sequences(df_dict, k):
"""Split results into scalars and sequences results
Parameters
----------
df_dict: dict
dict of pd.DataFrames, keyed by oemof tuples
k: tuple
oemof tuple for results processing
"""
try:
condition = df_dict[k][:-1].isnull().any()
scalars = df_dict[k].loc[:, condition].dropna().iloc[0]
sequences = df_dict[k].loc[:, ~condition]
return {"scalars": scalars, "sequences": sequences}
except IndexError:
error_message = (
"Cannot access index on result data. "
+ "Did the optimization terminate"
+ " without errors?"
)
raise IndexError(error_message)
[docs]
def set_result_index(df_dict, k, result_index):
"""Define index for results
Parameters
----------
df_dict: dict
dict of pd.DataFrames, keyed by oemof tuples
k: tuple
oemof tuple for results processing
result_index: pd.Index
Index to use for results
"""
try:
df_dict[k].index = result_index
except ValueError:
try:
df_dict[k] = df_dict[k][:-1]
df_dict[k].index = result_index
except ValueError as e:
msg = (
"\nFlow: {0}-{1}. This could be caused by NaN-values "
"in your input data."
)
raise type(e)(
str(e) + msg.format(k[0].label, k[1].label)
).with_traceback(sys.exc_info()[2])
[docs]
def set_sequences_index(df, result_index):
try:
df.index = result_index
except ValueError:
try:
df = df[:-1]
df.index = result_index
except ValueError:
raise ValueError("Results extraction failed!")
[docs]
def results(model, remove_last_time_point=False):
"""Create a nested result dictionary from the result DataFrame
The already rearranged results from Pyomo from the result DataFrame are
transferred into a nested dictionary of pandas objects.
The first level key of that dictionary is a node (denoting the respective
flow or component).
The second level keys are "sequences" and "scalars" for a *standard model*:
* A pd.DataFrame holds all results that are time-dependent, i.e. given as
a sequence and can be indexed with the energy system's timeindex.
* A pd.Series holds all scalar values which are applicable for timestep 0
(i.e. investments).
For a *multi-period model*, the second level key for "sequences" remains
the same while instead of "scalars", the key "period_scalars" is used:
* For sequences, see standard model.
* Instead of a pd.Series, a pd.DataFrame holds scalar values indexed
by periods. These hold investment-related variables.
Examples
--------
* *Standard model*: `results[idx]['scalars']`
and flows `results[n, n]['sequences']`.
* *Multi-period model*: `results[idx]['period_scalars']`
and flows `results[n, n]['sequences']`.
Parameters
----------
model : oemof.solph.Model
A solved oemof.solph model.
remove_last_time_point : bool
The last time point of all TIMEPOINT variables is removed to get the
same length as the TIMESTEP (interval) variables without getting
nan-values. By default, the last time point is removed if it has not
been defined by the user in the EnergySystem but inferred. If all
time points have been defined explicitly by the user the last time
point will not be removed by default. In that case all interval
variables will get one row with nan-values to have the same index
for all variables.
"""
# Extraction steps that are the same for both model types
df = create_dataframe(model)
# create a dict of dataframes keyed by oemof tuples
df_dict = {
k if len(k) > 1 else (k[0], None): v[
["timestep", "variable_name", "value"]
]
for k, v in df.groupby("oemof_tuple")
}
# Define index
if model.es.tsa_parameters:
for p, period_data in enumerate(model.es.tsa_parameters):
if p == 0:
if model.es.periods is None:
timeindex = model.es.timeindex
else:
timeindex = model.es.periods[0]
result_index = _disaggregate_tsa_timeindex(
timeindex, period_data
)
else:
result_index = result_index.union(
_disaggregate_tsa_timeindex(
model.es.periods[p], period_data
)
)
else:
if model.es.timeindex is None:
result_index = list(range(len(model.es.timeincrement) + 1))
else:
result_index = model.es.timeindex
if model.es.tsa_parameters is not None:
df_dict = _disaggregate_tsa_result(df_dict, model.es.tsa_parameters)
# create final result dictionary by splitting up the dataframes in the
# dataframe dict into a series for scalar data and dataframe for sequences
result = {}
# Standard model results extraction
if model.es.periods is None:
result = _extract_standard_model_result(
df_dict, result, result_index, remove_last_time_point
)
scalars_col = "scalars"
# Results extraction for a multi-period model
else:
period_indexed = ["invest", "total", "old", "old_end", "old_exo"]
result = _extract_multi_period_model_result(
model,
df_dict,
period_indexed,
result,
result_index,
remove_last_time_point,
)
scalars_col = "period_scalars"
# add dual variables for bus constraints
if model.dual is not None:
grouped = groupby(
sorted(model.BusBlock.balance.iterkeys()), lambda t: t[0]
)
for bus, timestep in grouped:
duals = [
model.dual[model.BusBlock.balance[bus, t]] for _, t in timestep
]
if model.es.periods is None:
df = pd.DataFrame({"duals": duals}, index=result_index[:-1])
# TODO: Align with standard model
else:
df = pd.DataFrame({"duals": duals}, index=result_index)
if (bus, None) not in result.keys():
result[(bus, None)] = {
"sequences": df,
scalars_col: pd.Series(dtype=float),
}
else:
result[(bus, None)]["sequences"]["duals"] = duals
return result
def _extract_standard_model_result(
df_dict, result, result_index, remove_last_time_point
):
"""Extract and return the results of a standard model
* Optionally remove last time point or include it elsewise.
* Set index to timeindex and pivot results such that values are displayed
for the respective variables. Reindex with the energy system's timeindex.
* Filter for columns with nan values to retrieve scalar variables. Split
up the DataFrame into sequences and scalars and return it.
Parameters
----------
df_dict : dict
dictionary of results DataFrames
result : dict
dictionary to store the results
result_index : pd.DatetimeIndex
timeindex to use for the results (derived from EnergySystem)
remove_last_time_point : bool
if True, remove the last time point
Returns
-------
result : dict
dictionary with results stored
"""
if remove_last_time_point:
# The values of intervals belong to the time at the beginning of the
# interval.
for k in df_dict:
df_dict[k].set_index("timestep", inplace=True)
df_dict[k] = df_dict[k].pivot(
columns="variable_name", values="value"
)
set_result_index(df_dict, k, result_index[:-1])
result[k] = divide_scalars_sequences(df_dict, k)
else:
for k in df_dict:
df_dict[k].set_index("timestep", inplace=True)
df_dict[k] = df_dict[k].pivot(
columns="variable_name", values="value"
)
# Add empty row with nan at the end of the table by adding 1 to the
# last value of the numeric index.
df_dict[k].loc[df_dict[k].index[-1] + 1, :] = np.nan
set_result_index(df_dict, k, result_index)
result[k] = divide_scalars_sequences(df_dict, k)
return result
def _extract_multi_period_model_result(
model,
df_dict,
period_indexed=None,
result=None,
result_index=None,
remove_last_time_point=False,
):
"""Extract and return the results of a multi-period model
Difference to standard model is in the way, scalar values are extracted
since they now depend on periods.
Parameters
----------
model : oemof.solph.models.Model
The optimization model
df_dict : dict
dictionary of results DataFrames
period_indexed : list
list of variables that are indexed by periods
result : dict
dictionary to store the results
result_index : pd.DatetimeIndex
timeindex to use for the results (derived from EnergySystem)
remove_last_time_point : bool
if True, remove the last time point
Returns
-------
result : dict
dictionary with results stored
"""
for k in df_dict:
df_dict[k].set_index("timestep", inplace=True)
df_dict[k] = df_dict[k].pivot(columns="variable_name", values="value")
# Split data set
period_cols = [
col for col in df_dict[k].columns if col in period_indexed
]
# map periods to their start years for displaying period results
d = {
key: val + model.es.periods[0].min().year
for key, val in enumerate(model.es.periods_years)
}
period_scalars = df_dict[k].loc[:, period_cols].dropna()
sequences = df_dict[k].loc[
:, [col for col in df_dict[k].columns if col not in period_cols]
]
if remove_last_time_point:
set_sequences_index(sequences, result_index[:-1])
else:
set_sequences_index(sequences, result_index)
if period_scalars.empty:
period_scalars = pd.DataFrame(index=d.values())
try:
period_scalars.rename(index=d, inplace=True)
period_scalars.index.name = "period"
result[k] = {
"period_scalars": period_scalars,
"sequences": sequences,
}
except IndexError:
error_message = (
"Some indices seem to be not matching.\n"
"Cannot properly extract model results."
)
raise IndexError(error_message)
return result
def _disaggregate_tsa_result(df_dict, tsa_parameters):
"""
Disaggregate timeseries aggregated by TSAM
All component flows are disaggregated using mapping order of original and
typical clusters in TSAM parameters. Additionally, storage SOC is
disaggregated from inter and intra storage contents.
Multi-period indexes are removed from results up front and added again
after disaggregation.
Parameters
----------
df_dict : dict
Raw results from oemof model
tsa_parameters : list-of-dicts
TSAM parameters holding order, occurrences and timsteps_per_period for
each period
Returns
-------
dict: Disaggregated sequences
"""
periodic_dict = {}
flow_dict = {}
for key, data in df_dict.items():
periodic_values = data[data["variable_name"].isin(PERIOD_INDEXES)]
if not periodic_values.empty:
periodic_dict[key] = periodic_values
flow_dict[key] = data[~data["variable_name"].isin(PERIOD_INDEXES)]
# Find storages and remove related entries from flow dict:
storages, storage_keys = _get_storage_soc_flows_and_keys(flow_dict)
for key in storage_keys:
del flow_dict[key]
# Find multiplexer and remove related entries from flow dict:
multiplexer, multiplexer_keys = _get_multiplexer_flows_and_keys(flow_dict)
for key in multiplexer_keys:
del flow_dict[key]
# Disaggregate flows
for flow in flow_dict:
disaggregated_flow_frames = []
period_offset = 0
for tsa_period in tsa_parameters:
for k in tsa_period["order"]:
flow_k = flow_dict[flow].iloc[
period_offset
+ k * tsa_period["timesteps"] : period_offset
+ (k + 1) * tsa_period["timesteps"]
]
# Disaggregate segmentation
if "segments" in tsa_period:
flow_k = _disaggregate_segmentation(
flow_k, tsa_period["segments"], k
)
disaggregated_flow_frames.append(flow_k)
period_offset += tsa_period["timesteps"] * len(
tsa_period["occurrences"]
)
ts = pd.concat(disaggregated_flow_frames)
ts.timestep = range(len(ts))
ts = ts.set_index("timestep") # Have to set and reset index as
# interpolation in pandas<2.1.0 cannot handle NANs in index
flow_dict[flow] = ts.ffill().reset_index("timestep")
# Add storage SOC flows:
for storage, soc in storages.items():
flow_dict[(storage, None)] = _calculate_soc_from_inter_and_intra_soc(
soc, storage, tsa_parameters
)
# Add multiplexer boolean actives values:
for multiplexer, values in multiplexer.items():
flow_dict[(multiplexer, None)] = _calculate_multiplexer_actives(
values, multiplexer, tsa_parameters
)
# Add periodic values (they get extracted in period extraction fct)
for key, data in periodic_dict.items():
flow_dict[key] = pd.concat([flow_dict[key], data])
return flow_dict
def _disaggregate_segmentation(
df: pd.DataFrame,
segments: Dict[Tuple[int, int], int],
current_period: int,
) -> pd.DataFrame:
"""Disaggregate segmentation
For each segment values are reindex by segment length holding None values,
which are interpolated in a later step (as storages need linear
interpolation while flows need padded interpolation).
Parameters
----------
df : pd.Dataframe
holding values for each segment
segments : Dict[Tuple[int, int], int]
Segmentation dict from TSAM, holding segmentation length for each
timestep in each typical period
current_period: int
Typical period the data belongs to, needed to extract related segments
Returns
-------
pd.Dataframe
holding values for each timestep instead of each segment.
Added timesteps contain None values and are interpolated later.
"""
current_segments = list(
v for ((k, s), v) in segments.items() if k == current_period
)
df.index = range(len(current_segments))
segmented_index = itertools.chain.from_iterable(
[i] + list(itertools.repeat(None, s - 1))
for i, s in enumerate(current_segments)
)
disaggregated_data = df.reindex(segmented_index)
return disaggregated_data
def _calculate_soc_from_inter_and_intra_soc(soc, storage, tsa_parameters):
"""Calculate resulting SOC from inter and intra SOC flows"""
soc_frames = []
i_offset = 0
t_offset = 0
for p, tsa_period in enumerate(tsa_parameters):
for i, k in enumerate(tsa_period["order"]):
inter_value = soc["inter"].iloc[i_offset + i]["value"]
# Self-discharge has to be taken into account for calculating
# inter SOC for each timestep in cluster
t0 = t_offset + i * tsa_period["timesteps"]
# Add last timesteps of simulation in order to interpolate SOC for
# last segment correctly:
is_last_timestep = (
p == len(tsa_parameters) - 1
and i == len(tsa_period["order"]) - 1
)
timesteps = (
tsa_period["timesteps"] + 1
if is_last_timestep
else tsa_period["timesteps"]
)
inter_series = (
pd.Series(
itertools.accumulate(
(
(
(1 - storage.loss_rate[t])
** tsa_period["segments"][(k, t - t0)]
if "segments" in tsa_period
else 1 - storage.loss_rate[t]
)
for t in range(
t0,
t0 + timesteps - 1,
)
),
operator.mul,
initial=1,
)
)
* inter_value
)
intra_series = soc["intra"][(p, k)].iloc[0:timesteps]
soc_frame = pd.DataFrame(
intra_series["value"].values
+ inter_series.values, # Neglect indexes, otherwise none
columns=["value"],
)
# Disaggregate segmentation
if "segments" in tsa_period:
soc_disaggregated = _disaggregate_segmentation(
soc_frame[:-1] if is_last_timestep else soc_frame,
tsa_period["segments"],
k,
)
if is_last_timestep:
soc_disaggregated.loc[len(soc_disaggregated)] = (
soc_frame.iloc[-1]
)
soc_frame = soc_disaggregated
soc_frames.append(soc_frame)
i_offset += len(tsa_period["order"])
t_offset += i_offset * tsa_period["timesteps"]
soc_ts = pd.concat(soc_frames)
soc_ts["timestep"] = range(len(soc_ts))
interpolated_soc = soc_ts.interpolate()
interpolated_soc["variable_name"] = "soc"
return interpolated_soc.iloc[:-1]
def _calculate_multiplexer_actives(values, multiplexer, tsa_parameters):
"""Calculate multiplexer actives"""
actives_frames = []
for p, tsa_period in enumerate(tsa_parameters):
for i, k in enumerate(tsa_period["order"]):
timesteps = tsa_period["timesteps"]
actives_frames.append(
pd.DataFrame(
values[(p, k)].iloc[0:timesteps], columns=["value"]
)
)
actives_frames_ts = pd.concat(actives_frames)
actives_frames_ts["variable_name"] = values[(p, k)][
"variable_name"
].values[0]
actives_frames_ts["timestep"] = range(len(actives_frames_ts))
return actives_frames_ts
def _get_storage_soc_flows_and_keys(flow_dict):
"""Detect storage flows in flow dict"""
storages = {}
storage_keys = []
for oemof_tuple, data in flow_dict.items():
if not isinstance(oemof_tuple[0], GenericStorage):
continue # Skip components other than Storage
if oemof_tuple[1] is not None and not isinstance(oemof_tuple[1], int):
continue # Skip storage output flows
# Here we have either inter or intra storage index,
# depending on oemof tuple length
storage_keys.append(oemof_tuple)
if oemof_tuple[0] not in storages:
storages[oemof_tuple[0]] = {"inter": 0, "intra": {}}
if len(oemof_tuple) == 2:
# Must be filtered for variable name "inter_storage_content",
# otherwise "init_content" variable (in non-multi-period approach)
# interferes with SOC results
storages[oemof_tuple[0]]["inter"] = data[
data["variable_name"] == "inter_storage_content"
]
if len(oemof_tuple) == 3:
storages[oemof_tuple[0]]["intra"][
(oemof_tuple[1], oemof_tuple[2])
] = data
return storages, storage_keys
def _get_multiplexer_flows_and_keys(flow_dict):
"""Detect multiplexer flows in flow dict"""
multiplexer = {}
multiplexer_keys = []
for oemof_tuple, data in flow_dict.items():
if oemof_tuple[1] is not None and not isinstance(oemof_tuple[1], int):
continue
if "multiplexer_active" in data["variable_name"].values[0]:
multiplexer.setdefault(oemof_tuple[0], {})
multiplexer_keys.append(oemof_tuple)
multiplexer[oemof_tuple[0]][
(oemof_tuple[1], oemof_tuple[2])
] = data
return multiplexer, multiplexer_keys
def _disaggregate_tsa_timeindex(period_index, tsa_parameters):
"""Disaggregate aggregated period timeindex by using TSA parameters"""
return pd.date_range(
start=period_index[0],
periods=tsa_parameters["timesteps_per_period"]
* len(tsa_parameters["order"]),
freq=period_index.freq,
)
[docs]
def convert_keys_to_strings(result, keep_none_type=False):
"""
Convert the dictionary keys to strings.
All (tuple) keys of the result object e.g. results[(pp1, bus1)] are
converted into strings that represent the object labels
e.g. results[('pp1','bus1')].
"""
if keep_none_type:
converted = {
(
tuple([str(e) if e is not None else None for e in k])
if isinstance(k, tuple)
else str(k) if k is not None else None
): v
for k, v in result.items()
}
else:
converted = {
tuple(map(str, k)) if isinstance(k, tuple) else str(k): v
for k, v in result.items()
}
return converted
def __separate_attrs(
system, exclude_attrs, get_flows=False, exclude_none=True
):
"""
Create a dictionary with flow scalars and series.
The dictionary is structured with flows as tuples and nested dictionaries
holding the scalars and series e.g.
{(node1, node2): {'scalars': {'attr1': scalar, 'attr2': 'text'},
'sequences': {'attr1': iterable, 'attr2': iterable}}}
system:
A solved oemof.solph.Model or oemof.solph.Energysystem
exclude_attrs: List[str]
List of additional attributes which shall be excluded from
parameter dict
get_flows: bool
Whether to include flow values or not
exclude_none: bool
If set, scalars and sequences containing None values are excluded
Returns
-------
dict
"""
def detect_scalars_and_sequences(com):
scalars = {}
sequences = {}
default_exclusions = [
"__",
"_",
"registry",
"inputs",
"outputs",
"Label",
"input",
"output",
"constraint_group",
]
# Must be tuple in order to work with `str.startswith()`:
exclusions = tuple(default_exclusions + exclude_attrs)
attrs = [
i
for i in dir(com)
if not (i.startswith(exclusions) or callable(getattr(com, i)))
]
for a in attrs:
attr_value = getattr(com, a)
# Iterate trough investment and add scalars and sequences with
# "investment" prefix to component data:
if attr_value.__class__.__name__ == "Investment":
invest_data = detect_scalars_and_sequences(attr_value)
scalars.update(
{
"investment_" + str(k): v
for k, v in invest_data["scalars"].items()
}
)
sequences.update(
{
"investment_" + str(k): v
for k, v in invest_data["sequences"].items()
}
)
continue
if isinstance(attr_value, str):
scalars[a] = attr_value
continue
# If the label is a tuple it is iterable, therefore it should be
# converted to a string. Otherwise, it will be a sequence.
if a == "label":
attr_value = str(attr_value)
if isinstance(attr_value, abc.Iterable):
sequences[a] = attr_value
elif isinstance(attr_value, _FakeSequence):
scalars[a] = attr_value.value
else:
scalars[a] = attr_value
sequences = flatten(sequences)
com_data = {
"scalars": scalars,
"sequences": sequences,
}
move_undetected_scalars(com_data)
if exclude_none:
remove_nones(com_data)
com_data = {
"scalars": pd.Series(com_data["scalars"]),
"sequences": pd.DataFrame(com_data["sequences"]),
}
return com_data
def move_undetected_scalars(com):
for ckey, value in list(com["sequences"].items()):
if isinstance(value, (str, numbers.Number)):
com["scalars"][ckey] = value
del com["sequences"][ckey]
elif isinstance(value, _FakeSequence):
com["scalars"][ckey] = value.value
del com["sequences"][ckey]
elif len(value) == 0:
del com["sequences"][ckey]
def remove_nones(com):
for ckey, value in list(com["scalars"].items()):
if value is None:
del com["scalars"][ckey]
for ckey, value in list(com["sequences"].items()):
if len(value) == 0 or value[0] is None:
del com["sequences"][ckey]
# Check if system is es or om:
if system.__class__.__name__ == "EnergySystem":
components = system.flows() if get_flows else system.nodes
else:
components = system.flows if get_flows else system.es.nodes
data = {}
for com_key in components:
component = components[com_key] if get_flows else com_key
component_data = detect_scalars_and_sequences(component)
comkey = com_key if get_flows else (com_key, None)
data[comkey] = component_data
return data
[docs]
def parameter_as_dict(system, exclude_none=True, exclude_attrs=None):
"""
Create a result dictionary containing node parameters.
Results are written into a dictionary of pandas objects where
a Series holds all scalar values and a dataframe all sequences for nodes
and flows.
The dictionary is keyed by flows (n, n) and nodes (n, None), e.g.
`parameter[(n, n)]['sequences']` or `parameter[(n, n)]['scalars']`.
Parameters
----------
system: energy_system.EnergySystem
A populated energy system.
exclude_none: bool
If True, all scalars and sequences containing None values are excluded
exclude_attrs: Optional[List[str]]
Optional list of additional attributes which shall be excluded from
parameter dict
Returns
-------
dict: Parameters for all nodes and flows
"""
if exclude_attrs is None:
exclude_attrs = []
flow_data = __separate_attrs(
system, exclude_attrs, get_flows=True, exclude_none=exclude_none
)
node_data = __separate_attrs(
system, exclude_attrs, get_flows=False, exclude_none=exclude_none
)
flow_data.update(node_data)
return flow_data