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/src/oemof/solph/processing.py

# -*- 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")


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


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]


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]


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


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)


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])


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!")


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["variable_name"] = "soc"
    soc_ts["timestep"] = range(len(soc_ts))

    # Disaggregate segments by linear interpolation and remove
    # last timestep afterwards (only needed for interpolation)
    interpolated_soc = soc_ts.interpolate()
    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,
    )


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 meta_results(om, undefined=False):
    """
    Fetch some metadata from the Solver. Feel free to add more keys.

    Valid keys of the resulting dictionary are: 'objective', 'problem',
    'solver'.

    om : oemof.solph.Model
        A solved Model.
    undefined : bool
        By default (False) only defined keys can be found in the dictionary.
        Set to True to get also the undefined keys.

    Returns
    -------
    dict
    """
    meta_res = {"objective": om.objective()}

    for k1 in ["Problem", "Solver"]:
        k1 = k1.lower()
        meta_res[k1] = {}
        for k2, v2 in om.es.results[k1][0].items():
            try:
                if str(om.es.results[k1][0][k2]) == "<undefined>":
                    if undefined:
                        meta_res[k1][k2] = str(om.es.results[k1][0][k2])
                else:
                    meta_res[k1][k2] = om.es.results[k1][0][k2]
            except TypeError:
                if undefined:
                    msg = "Cannot fetch meta results of type {0}"
                    meta_res[k1][k2] = msg.format(
                        type(om.es.results[k1][0][k2])
                    )

    return meta_res


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


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

oemof / oemof-solph / 15487666987

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