Plot input costs

apply_conversion(df, target_unit_installation, target_unit_storage)

Apply unit conversion to each row in the DataFrame.

Parameters:

Name	Type	Description	Default
df	DataFrame	Input DataFrame containing cost data	required
target_unit_installation	str	Target unit for installation costs (e.g. "eur/kW")	required
target_unit_storage	str	Target unit for storage costs (e.g. "eur/kWh")	required

Returns:

Type	Description
DataFrame	pd.DataFrame: DataFrame with converted units - Preserves original unit in 'original_unit' column - Updates 'unit' column with new unit - Converts all numeric values to target units

Source code in workflow/scripts/plot_input_costs.py

def apply_conversion(df: pd.DataFrame, 
                    target_unit_installation: str, 
                    target_unit_storage: str) -> pd.DataFrame:
    """Apply unit conversion to each row in the DataFrame.

    Args:
        df (pd.DataFrame): Input DataFrame containing cost data
        target_unit_installation (str): Target unit for installation costs (e.g. "eur/kW")
        target_unit_storage (str): Target unit for storage costs (e.g. "eur/kWh")

    Returns:
        pd.DataFrame: DataFrame with converted units
            - Preserves original unit in 'original_unit' column
            - Updates 'unit' column with new unit
            - Converts all numeric values to target units
    """
    # Ensure essential columns exist
    for col in ["technology", "reference", "unit"]:
        if col not in df.columns:
            df[col] = f"Default_{col}"

    # Keep the original unit in a separate column if not present
    if "original_unit" not in df.columns:
        df["original_unit"] = df["unit"].copy()

    # Convert row by row
    return df.apply(
        convert_row_units,
        axis=1,
        args=(target_unit_installation, target_unit_storage)
    )

convert_row_units(row, target_unit_installation, target_unit_storage)

Convert row's numeric columns to target units.

Parameters:

Name	Type	Description	Default
row	Series	Input row containing cost data	required
target_unit_installation	str	Target unit for installation costs (e.g. "eur/kW")	required
target_unit_storage	str	Target unit for storage costs (e.g. "eur/kWh")	required

Returns:

Type	Description
Series	pd.Series: Row with converted values

Source code in workflow/scripts/plot_input_costs.py

def convert_row_units(row: pd.Series, 
                     target_unit_installation: str,
                     target_unit_storage: str) -> pd.Series:
    """Convert row's numeric columns to target units.

    Args:
        row (pd.Series): Input row containing cost data
        target_unit_installation (str): Target unit for installation costs (e.g. "eur/kW")
        target_unit_storage (str): Target unit for storage costs (e.g. "eur/kWh")

    Returns:
        pd.Series: Row with converted values
    """
    if "unit" not in row or pd.isna(row["unit"]):
        return row

    # Parse unit string
    currency_part, capacity_part = parse_unit_string(row["unit"])
    if not currency_part or not capacity_part:
        return row

    # Determine if storage unit and get target unit
    is_storage = is_storage_unit(capacity_part)
    target_unit = target_unit_storage if is_storage else target_unit_installation
    tgt_currency, tgt_capacity = parse_unit_string(target_unit)

    # Get currency conversion factor
    orig_currency = CURRENCY_ALIASES.get(currency_part.lower(), "unknown")
    currency_factor = EXCHANGE_RATES.get((orig_currency, tgt_currency), 1.0)

    # Get capacity conversion factor
    norm_capacity = normalize_capacity_unit(capacity_part)
    norm_target = normalize_capacity_unit(tgt_capacity)

    if norm_capacity in ["kw", "mw", "kwh", "mwh"] and norm_target in ["kw", "mw", "kwh", "mwh"]:
        cap_factor = get_capacity_factor(norm_capacity, norm_target)
        new_capacity_part = norm_target
    else:
        cap_factor = 1.0
        new_capacity_part = capacity_part

    # Calculate total conversion factor
    factor = currency_factor * cap_factor

    # Convert numeric columns
    numeric_cols = get_numeric_columns(row)
    for col in numeric_cols:
        val = row[col]
        if pd.notna(val):
            try:
                row[col] = float(val) * factor
            except ValueError:
                pass

    # Update unit string
    row["unit"] = f"{tgt_currency.upper()}/{new_capacity_part}"

    return row

detect_file_encoding(file_path)

Detect the encoding of a file.

Parameters:

Name	Type	Description	Default
file_path	str	Path to the file whose encoding needs to be detected.	required

Returns:

Name	Type	Description
str	str	The detected encoding of the file.

Source code in workflow/scripts/plot_input_costs.py

def detect_file_encoding(file_path: str) -> str:
    """Detect the encoding of a file.

    Args:
        file_path: Path to the file whose encoding needs to be detected.

    Returns:
        str: The detected encoding of the file.
    """
    with open(file_path, 'rb') as f:
        result = chardet.detect(f.read())
    encoding = result['encoding']
    return encoding

filter_investment_parameter(df)

Filter DataFrame to keep only rows where parameter is 'investment'.

Parameters:

Name	Type	Description	Default
df	DataFrame	Input DataFrame containing cost data with a 'parameter' column.	required

Returns:

Type	Description
DataFrame	pd.DataFrame: Filtered DataFrame containing only investment parameter rows. If 'parameter' column does not exist, returns the original DataFrame unchanged.

Source code in workflow/scripts/plot_input_costs.py

def filter_investment_parameter(df: pd.DataFrame) -> pd.DataFrame:
    """Filter DataFrame to keep only rows where parameter is 'investment'.

    Args:
        df (pd.DataFrame): Input DataFrame containing cost data with a 'parameter' column.

    Returns:
        pd.DataFrame: Filtered DataFrame containing only investment parameter rows.
            If 'parameter' column does not exist, returns the original DataFrame unchanged.
    """
    if "parameter" not in df.columns:
        logging.warning("Warning: 'parameter' column not found.")
        return df
    # Filter for rows that have parameter == "investment"
    mask = df["parameter"].str.lower() == "investment"
    df_investment = df[mask].copy()
    return df_investment

filter_technologies_by_config(df, techs_dict)

Filter and categorize technologies based on the provided technology dictionary.

Parameters:

Name	Type	Description	Default
df	DataFrame	Input DataFrame containing technology data	required
techs_dict	Dict[str, List[str]]	Dictionary containing technology lists with keys: - 'vre_techs': List of variable renewable energy technologies - 'conv_techs': List of conventional technologies - 'store_techs': List of storage technologies	required

Returns:

Type	Description
DataFrame	pd.DataFrame: Filtered DataFrame with mapped technologies and categories - 'mapped_technology': Standardized technology names - 'category': Technology category (VRE, Conventional, Storage, Solar Thermal)

Source code in workflow/scripts/plot_input_costs.py

def filter_technologies_by_config(df: pd.DataFrame, 
                                techs_dict: Dict[str, List[str]]) -> pd.DataFrame:
    """Filter and categorize technologies based on the provided technology dictionary.

    Args:
        df (pd.DataFrame): Input DataFrame containing technology data
        techs_dict (Dict[str, List[str]]): Dictionary containing technology lists with keys:
            - 'vre_techs': List of variable renewable energy technologies
            - 'conv_techs': List of conventional technologies
            - 'store_techs': List of storage technologies

    Returns:
        pd.DataFrame: Filtered DataFrame with mapped technologies and categories
            - 'mapped_technology': Standardized technology names
            - 'category': Technology category (VRE, Conventional, Storage, Solar Thermal)
    """
    try:
        # Validate input DataFrame
        if df.empty:
            logging.warning("Warning: Input DataFrame is empty")
            return pd.DataFrame()

        if "technology" not in df.columns:
            logging.error("Error: 'technology' column not found in DataFrame")
            return pd.DataFrame()

        # Example aliases
        alias_map: Dict[str, List[str]] = {
            "solar thermal": ["central solar thermal", "decentral solar thermal"],
            "hydroelectricity": ["hydro"],
            "heat pump": ["central air heat pump", "decentral air heat pump"],
            "resistive heater": ["central resistive heater", "decentral resistive heater"],
            "Sabatier": ["methanation"],
            "H2 CHP": ["central hydrogen CHP"],
            "OCGT gas": ["OCGT"],
            "CHP gas": ["central gas CHP", "decentral CHP"],
            "gas boiler": ["central gas boiler", "decentral gas boiler"],
            "coal boiler": ["central coal boiler", "decentral coal boiler"],
            "coal power plant": ["coal"],
            "CHP coal": ["central coal CHP"],
            "H2": ["H2 pipeline", "hydrogen storage tank type 1"],
            "battery": ["battery storage"],
            "water tanks": ["central water tank storage", "decentral water tank storage"]
        }

        # Reverse alias map for easier lookup
        tech_aliases: Dict[str, str] = {}
        for main_tech, aliases in alias_map.items():
            for alias in aliases:
                tech_aliases[alias.lower()] = main_tech

        # get all techs
        vre_techs = techs_dict.get("vre_techs", [])
        conv_techs = techs_dict.get("conv_techs", [])
        store_techs = techs_dict.get("store_techs", [])
        solar_thermal = ["solar thermal"]  # special case for solar thermal

        # combine all techs
        all_techs = vre_techs + conv_techs + store_techs + solar_thermal

        # create tech to category mapping
        tech_categories: Dict[str, str] = {}
        for tech in vre_techs:
            if tech != "solar thermal":  # exclude solar thermal
                tech_categories[tech] = "VRE Technologies"
        for tech in conv_techs:
            tech_categories[tech] = "Conventional Technologies"
        for tech in store_techs:
            tech_categories[tech] = "Storage Technologies"
        tech_categories["solar thermal"] = "Solar Thermal"

        # Create a copy of the DataFrame
        df_filtered = df.copy()

        # Direct mapping
        df_filtered["mapped_technology"] = df_filtered["technology"].where(
            df_filtered["technology"].isin(all_techs)
        )

        # Alias mapping
        df_filtered["mapped_technology"].fillna(
            df_filtered["technology"].str.lower().map(tech_aliases),
            inplace=True
        )

        # Log unmapped technologies
        unmapped_techs = df_filtered[df_filtered["mapped_technology"].isna()]["technology"].unique()
        if len(unmapped_techs) > 0:
            logging.warning(
                f"Warning: The following technologies could not be mapped: {unmapped_techs}"
            )

        # Drop rows where no match was found
        df_filtered = df_filtered.dropna(subset=["mapped_technology"])

        if df_filtered.empty:
            logging.warning("Warning: No matching technologies found!")
            return pd.DataFrame()

        # Add category based on mapped technology
        df_filtered["category"] = df_filtered["mapped_technology"].map(tech_categories)

        return df_filtered

    except KeyError as e:
        logging.error(f"Error: Missing key in technology dictionary - {str(e)}")
        return pd.DataFrame()
    except AttributeError as e:
        logging.error(f"Error: Invalid DataFrame structure - {str(e)}")
        return pd.DataFrame()
    except Exception as e:
        logging.error(f"Unexpected error in technology filtering: {str(e)}")
        return pd.DataFrame()

get_capacity_factor(orig_cap, target_cap)

Calculate the capacity conversion factor.

Parameters:

Name	Type	Description	Default
orig_cap	str	Original capacity unit	required
target_cap	str	Target capacity unit	required

Returns:

Name	Type	Description
float	float	Conversion factor between capacity units

Source code in workflow/scripts/plot_input_costs.py

def get_capacity_factor(orig_cap: str, target_cap: str) -> float:
    """Calculate the capacity conversion factor.

    Args:
        orig_cap (str): Original capacity unit
        target_cap (str): Target capacity unit

    Returns:
        float: Conversion factor between capacity units
    """
    capacity_factors: Dict[Tuple[str, str], float] = {
        ("kw", "kw"): 1.0,
        ("mw", "kw"): 1/1000.0,
        ("kwh", "kwh"): 1.0,
        ("mwh", "kwh"): 1/1000.0,
        ("kw", "mw"): 1000.0,
        ("mw", "mw"): 1.0,
        ("kwh", "mwh"): 1000.0,
        ("mwh", "mwh"): 1.0
    }
    return capacity_factors.get((orig_cap.lower(), target_cap.lower()), 1.0)

get_numeric_columns(row)

Get list of numeric columns in the row.

Parameters:

Name	Type	Description	Default
row	Series	Input row	required

Returns:

Type	Description
List[str]	List[str]: List of column names containing numeric values - Year columns (e.g., "2020", "2025") - Cost columns (e.g., "cost_2020", "cost_2025")

Source code in workflow/scripts/plot_input_costs.py

def get_numeric_columns(row: pd.Series) -> List[str]:
    """Get list of numeric columns in the row.

    Args:
        row (pd.Series): Input row

    Returns:
        List[str]: List of column names containing numeric values
            - Year columns (e.g., "2020", "2025")
            - Cost columns (e.g., "cost_2020", "cost_2025")
    """
    numeric_cols = []

    # Check for year columns
    for col in row.index:
        if isinstance(col, str) and col.isdigit():
            numeric_cols.append(col)

    # Check for cost columns
    if not numeric_cols:
        for col in row.index:
            if isinstance(col, str) and "cost_" in col and col.split("_")[-1].isdigit():
                numeric_cols.append(col)

    # Check for specific years
    possible_years = ["2020", "2025", "2030", "2035", "2040", "2045", "2050", "2055", "2060"]
    for year in possible_years:
        if year in row.index and year not in numeric_cols:
            numeric_cols.append(year)

    return numeric_cols

is_storage_unit(capacity_part)

Check if the capacity part indicates a storage unit.

Parameters:

Name	Type	Description	Default
capacity_part	str	Capacity part of the unit string	required

Returns:

Name	Type	Description
bool	bool	True if the unit is for storage (kWh or MWh)

Source code in workflow/scripts/plot_input_costs.py

def is_storage_unit(capacity_part: str) -> bool:
    """Check if the capacity part indicates a storage unit.

    Args:
        capacity_part (str): Capacity part of the unit string

    Returns:
        bool: True if the unit is for storage (kWh or MWh)
    """
    return "kwh" in capacity_part.lower() or "mwh" in capacity_part.lower()

load_and_clean_data(file_path)

Load and clean data from a CSV file.

load a CSV file, detects its encoding, replaces '-' with NaN, and drops the 'link' column if it exists.

Parameters:

Name	Type	Description	Default
file_path	str	Path to the CSV file to be loaded.	required

Returns:

Type	Description
DataFrame	pd.DataFrame: Cleaned DataFrame with standardized data.

Source code in workflow/scripts/plot_input_costs.py

def load_and_clean_data(file_path: str) -> pd.DataFrame:
    """Load and clean data from a CSV file.

    load a CSV file, detects its encoding, replaces '-' with NaN,
    and drops the 'link' column if it exists.

    Args:
        file_path: Path to the CSV file to be loaded.

    Returns:
        pd.DataFrame: Cleaned DataFrame with standardized data.
    """
    encoding = detect_file_encoding(file_path)
    try:
        df = pd.read_csv(file_path, encoding=encoding)
        df.replace("-", np.nan, inplace=True)
        if "link" in df.columns:
            df.drop(columns=["link"], inplace=True)
        return df
    except Exception as e:
        logging.error(f"Error loading file {file_path}: {e}")
        return pd.DataFrame()

load_reference_data(file_path)

Load and process reference data for technology cost comparison.

Parameters:

Name	Type	Description	Default
file_path	str	Path to the reference data CSV file.	required

Returns:

Type	Description
DataFrame	pd.DataFrame: Processed reference data with standardized units.

Source code in workflow/scripts/plot_input_costs.py

def load_reference_data(file_path: str) -> pd.DataFrame:
    """Load and process reference data for technology cost comparison.

    Args:
        file_path: Path to the reference data CSV file.

    Returns:
        pd.DataFrame: Processed reference data with standardized units.
    """
    try:
        ref_df = load_and_clean_data(file_path)
        ref_df = ref_df[ref_df["reference"] != "PyPSA-China"]

        if "parameter" in ref_df.columns:
            ref_df = filter_investment_parameter(ref_df)
        ref_df = apply_conversion(ref_df, "eur/kW", "eur/kWh")
        return ref_df
    except Exception as e:
        logging.error(f"Error loading reference data: {e}")
        return pd.DataFrame()

normalize_capacity_unit(cap)

Normalize capacity unit string.

Parameters:

Name	Type	Description	Default
cap	str	Capacity unit string	required

Returns:

Name	Type	Description
str	str	Normalized capacity unit - Converts to lowercase - Removes spaces - Standardizes square notation

Source code in workflow/scripts/plot_input_costs.py

def normalize_capacity_unit(cap: str) -> str:
    """Normalize capacity unit string.

    Args:
        cap (str): Capacity unit string

    Returns:
        str: Normalized capacity unit
            - Converts to lowercase
            - Removes spaces
            - Standardizes square notation
    """
    if not cap:
        return ""
    cap = cap.lower()
    cap = cap.replace(" ", "")
    cap = cap.replace("²", "2")
    return cap

parse_unit_string(unit_str)

Parse a unit string into currency and capacity parts.

Parameters:

Name	Type	Description	Default
unit_str	str	Unit string in format "currency/capacity"	required

Returns:

Type	Description
Tuple[str | None, str | None]	Tuple[str | None, str | None]: - First element: currency part (e.g., "eur", "usd", "cny") - Second element: capacity part (e.g., "kw", "kwh") Returns (None, None) if invalid format

Source code in workflow/scripts/plot_input_costs.py

def parse_unit_string(unit_str: str) -> Tuple[str | None, str | None]:
    """Parse a unit string into currency and capacity parts.

    Args:
        unit_str (str): Unit string in format "currency/capacity"

    Returns:
        Tuple[str | None, str | None]: 
            - First element: currency part (e.g., "eur", "usd", "cny")
            - Second element: capacity part (e.g., "kw", "kwh")
            Returns (None, None) if invalid format
    """
    if pd.isna(unit_str) or "/" not in unit_str:
        return None, None
    currency_part, capacity_part = unit_str.split("/", 1)
    return currency_part.strip().lower(), capacity_part.strip().lower()

plot_technologies_by_category(costs_df, ref_df=None, tech_colors=None, font_size=14, plot_reference=True)

Plot technology cost trends with literature comparison.

Parameters:

Name	Type	Description	Default
costs_df	DataFrame	DataFrame containing the main cost data	required
ref_df	DataFrame | None	DataFrame containing reference data for comparison	None
tech_colors	Dict[str, str] | None	Dictionary mapping technology names to colors	None
font_size	int	Font size for plot elements	14
plot_reference	bool	Whether to plot reference data	True

Returns:

Type	Description
Figure	plt.Figure: The generated plot figure

Source code in workflow/scripts/plot_input_costs.py

def plot_technologies_by_category(
    costs_df: pd.DataFrame,
    ref_df: pd.DataFrame | None = None,
    tech_colors: Dict[str, str] | None = None,
    font_size: int = 14,
    plot_reference: bool = True
) -> plt.Figure:
    """Plot technology cost trends with literature comparison.

    Args:
        costs_df (pd.DataFrame): DataFrame containing the main cost data
        ref_df (pd.DataFrame | None): DataFrame containing reference data for comparison
        tech_colors (Dict[str, str] | None): Dictionary mapping technology names to colors
        font_size (int): Font size for plot elements
        plot_reference (bool): Whether to plot reference data

    Returns:
        plt.Figure: The generated plot figure
    """
    if costs_df.empty:
        logging.error("Error: The costs DataFrame is empty; cannot plot.")
        return None

    if tech_colors is None:
        tech_colors = {}

    # Get year columns
    year_cols = [col for col in costs_df.columns if col.isdigit()]
    if not year_cols:
        year_cols = [col.split("_")[-1] for col in costs_df.columns 
                    if "cost_" in col and col.split("_")[-1].isdigit()]
    if not year_cols:
        possible_years = ["2020", "2025", "2030", "2035", "2040", 
                         "2045", "2050", "2055", "2060"]
        year_cols = [year for year in possible_years if year in costs_df.columns]
    if not year_cols:
        logging.error("Error: Unable to identify year columns in costs data.")
        return None
    year_cols = sorted(year_cols)

    ref_year_cols = [col for col in ref_df.columns if col.isdigit()] if ref_df is not None else []
    if plot_reference and ref_df is not None and not ref_year_cols:
        logging.error("Error: Unable to identify year columns in reference data.")
        return None
    ref_year_cols = sorted(ref_year_cols)

    # Create subplots
    technologies = costs_df["technology"].unique()
    num_techs = len(technologies)
    num_cols = 6
    num_rows = (num_techs + num_cols - 1) // num_cols

    fig, axs = uplt.subplots(
        nrows=num_rows,
        ncols=num_cols,
        figwidth=6 * num_cols,
        sharex=True,
        sharey=False
    )
    if not isinstance(axs, np.ndarray):
        axs = np.array([axs])
    axs = axs.flatten()

    dash_styles = ['--', '-.', ':', (0, (3, 1, 1, 1))]

    # Plot each technology
    for i, tech in enumerate(technologies):
        if i >= len(axs):
            logging.warning(f"Warning: Exceeded subplot limit, skipping {tech}")
            continue

        ax = axs[i]
        tech_df = costs_df[costs_df["technology"] == tech]
        ref_tech_df = ref_df[ref_df["technology"] == tech] if ref_df is not None else None
        color = tech_colors.get(tech, "#999999")

        # Plot main data
        tech_years, tech_values = [], []
        for year in year_cols:
            values = pd.to_numeric(tech_df[year], errors='coerce').dropna().values
            if values.size > 0:
                tech_years.append(int(year))
                tech_values.append(np.median(values))

        legend_handles = []
        legend_labels = []
        if tech_years:
            line, = ax.plot(
                tech_years,
                tech_values,
                linewidth=2.5,
                color=color,
                linestyle='-',
                label=tech
            )
            legend_handles.append(line)
            legend_labels.append(tech)

        # Plot reference data
        if plot_reference and ref_tech_df is not None and ref_tech_df.shape[0] > 0:
            for j, (ref_name, ref_group) in enumerate(ref_tech_df.groupby("reference")):
                ref_years, ref_values = [], []
                for year in ref_year_cols:
                    vals = pd.to_numeric(ref_group[year], errors='coerce').dropna().values
                    if vals.size > 0:
                        ref_years.append(int(year))
                        ref_values.append(np.median(vals))

                if ref_years:
                    dash_style = dash_styles[j % len(dash_styles)]
                    ref_line, = ax.plot(
                        ref_years,
                        ref_values,
                        linewidth=2,
                        color=color,
                        linestyle=dash_style,
                        label=ref_name
                    )
                    legend_handles.append(ref_line)
                    legend_labels.append(ref_name)

        # Set labels and formatting
        if "unit" in tech_df.columns and not tech_df["unit"].isna().all():
            unit = tech_df["unit"].iloc[0]
            unit_parts = unit.split('/')
            if len(unit_parts) == 2:
                currency, capacity = unit_parts
                if 'eur' in currency.lower():
                    ax.set_ylabel(f"EUR/{capacity.upper()}", fontsize=font_size)
                else:
                    ax.set_ylabel(f"{unit}", fontsize=font_size)
            else:
                ax.set_ylabel(f"{unit}", fontsize=font_size)
        else:
            ax.set_ylabel("Cost (EUR)", fontsize=font_size)

        ax2 = ax.twinx()
        if "unit" in tech_df.columns and not tech_df["unit"].isna().all():
            unit = tech_df["unit"].iloc[0]
            unit_parts = unit.split('/')
            if len(unit_parts) == 2:
                _, capacity = unit_parts
                ax2.set_ylabel(f"CNY/{capacity.upper()}", fontsize=font_size)
            else:
                ax2.set_ylabel("Cost (CNY)", fontsize=font_size)
        else:
            ax2.set_ylabel("Cost (CNY)", fontsize=font_size)

        y_min, y_max = ax.get_ylim()
        ax2.set_ylim(y_min * 7.8, y_max * 7.8)

        ax.set_title(f"{tech}", fontsize=font_size)
        ax.grid(True, linestyle='--', alpha=0.7)
        ax.tick_params(axis='y', labelsize=font_size)
        ax2.tick_params(axis='y', labelsize=font_size)
        ax.legend(
            legend_handles,
            legend_labels,
            loc='upper right',
            fontsize=font_size-2,
            ncol=1
        )

    # Hide unused subplots
    total_plots = num_rows * num_cols
    for i in range(num_techs, total_plots):
        if i < len(axs):
            axs[i].set_visible(False)

    axs[0].figure.format(
        suptitle="Technology Cost Trends with Literature Comparison",
        abc=True,
        abcloc="ul",
        xlabel="Year",
        fontsize=font_size
    )

    return fig