Merge pull request #50 from rl-institut/features/plot-scalar-resources

Features/plot scalar resources

Snakefile

@@ -9,6 +9,10 @@ examples = [
 
 scenario_list_example = ['examples']
 
+resources = ['conv_pp_scalar']
+
+scenarios = ["toy-scenario", "toy-scenario-2"]
+
 # Target rules
 rule plot_grouped_scenarios:
     input:
@@ -30,6 +34,10 @@ rule report_all_examples:
     input:
         expand("results/{scenario}/report/", scenario=examples)
 
+rule plot_all_resources:
+    input:
+        expand("results/_resources/plots/{resource}.png", resource=resources)
+
 rule clean:
     shell:
         """
@@ -56,12 +64,11 @@ rule prepare_conv_pp:
         opsd="raw/conventional_power_plants_DE.csv",
         gpkg="raw/boundaries_germany_nuts3.gpkg",
         b3_regions="raw/b3_regions.yaml",
-        scalar_template="oemof_b3/schema/scalars.csv",
         script="scripts/prepare_conv_pp.py"
     output:
         "results/_resources/conv_pp_scalar.csv"
     shell:
-        "python scripts/prepare_conv_pp.py {input.opsd} {input.gpkg} {input.b3_regions} {input.scalar_template} {output}"
+        "python scripts/prepare_conv_pp.py {input.opsd} {input.gpkg} {input.b3_regions} {output}"
 
 rule prepare_feedin:
     input:
@@ -148,6 +155,15 @@ rule plot_dispatch:
     shell:
         "python scripts/plot_dispatch.py {input} {output}"
 
+rule plot_conv_pp_scalars:
+    input:
+        data="results/_resources/{resource}.csv",
+        script="scripts/plot_conv_pp_scalars.py"
+    output:
+        "results/_resources/plots/{resource}_var_{var_name}.png"
+    shell:
+        "python {input.script} {input.data} {wildcards.var_name} {output}"
+
 rule report:
     input:
         template="report/report.md",

docs/model_pipeline.rst

@@ -47,4 +47,4 @@ Plotting
    :maxdepth: 1
    :glob:
 
-   plotting/*
+   plotting/*

docs/plotting/plot_scalar_resources.rst

@@ -0,0 +1,6 @@
+.. _plot_scalar_resources_label:
+
+plot_scalar_resources
+=====================
+
+.. automodule:: plot_conv_pp

oemof_b3/__init__.py

@@ -8,6 +8,8 @@
 
 labels_dict = load_yaml(os.path.join(dir_name, "config", "labels.yml"))
 
+labels_to_german = load_yaml(os.path.join(dir_name, "config", "labels_to_german.yml"))
+
 colors_csv = pd.read_csv(
     os.path.join(dir_name, "config", "colors.csv"), header=[0], index_col=[0]
 )

oemof_b3/config/colors.csv

@@ -30,12 +30,18 @@ H2 Backpressure CHP,magenta
 CH4 Backpressure CHP,brown
 CH4 Extraction CHP,darkred
 
-Heat demand,#000000
-Heat excess,#7f7f7f
-Heat shortage,#7f7f7f
-Heat storage,#9467bd
-
-CH4 boiler,#d62728
+Heat cen. demand,#000000
+Heat cen. excess,#7f7f7f
+Heat cen. shortage,#7f7f7f
+Heat cen. storage in,#9467bd
+Heat cen. storage out,#9467bd
+Heat dec. demand,#000000
+Heat dec. excess,#7f7f7f
+Heat dec. shortage,#7f7f7f
+Heat dec. storage out,#9467bd
+Heat dec. storage in,#9467bd
+
+CH4 Boiler,#d62728
 Heatpump,#2ca02c
 res. PtH,#ff7f0e
 

oemof_b3/config/labels.yml

@@ -12,11 +12,16 @@ electricity-pth: res. PtH
 electricity-shortage: Shortage
 electricity-transmission: Transmission
 h2-gt: H2 GT
-heat-demand: Heat demand
+heat_central-demand: Heat cen. demand
+heat_decentral-demand: Heat dec. demand
 hydro-ror: Hydro ROR
 solar-pv: PV
 wind-onshore: Wind on
 h2-bpchp: H2 Backpressure CHP
 ch4-excess: CH4 Excess
-heat-excess: Heat Excess
-heat-storage: Heat Storage
+heat_central-excess: Heat cen. excess
+heat_central-storage: Heat cen. storage
+heat_central-shortage: Heat cen. shortage
+heat_decentral-excess: Heat dec. excess
+heat_decentral-storage: Heat dec. storage
+heat_decentral-shortage: Heat dec. shortage

oemof_b3/config/labels_to_german.yml

@@ -0,0 +1,33 @@
+Battery in: Batterie in
+Battery out: Batterie out
+Battery: Batterie
+Biomass GT: Biomass GT
+Biomass ST: Biomass ST
+Biomass: Biomasse
+CH4 Backpressure CHP: CH4 Backpressure CHP
+CH4 Boiler: CH4 Boiler
+CH4 Excess: CH4 Excess
+CH4 Extraction CHP: CH4 Extraction CHP
+CH4 GT: CH4 GT
+CH4: CH4
+Curtailment: Curtailment
+El. demand: El. demand
+El. in: Batterie in
+Electrolyzer: Electrolyzer
+H2 GT: H2 GT
+Hard coal: Steinkohle
+Heat demand: Heat demand
+Heat Excess: Heat Excess
+Heat Storage: Heat Storage
+Hydro reservoir: Reservoir
+Hydro ROR: Hydro ROR
+Lignite: Braunkohle
+Nuclear ST: Nuclear ST
+Oil: Öl
+Other: Andere
+PV: PV
+res. PtH: res. PtH
+Shortage: Shortage
+Transmission: Transmission
+Wind off: Wind off
+Wind on: Wind on

scripts/plot_conv_pp_scalars.py

@@ -0,0 +1,193 @@
+r"""
+Inputs
+-------
+resources : str
+    path of input file with data of prepared conventional power plant data as .csv
+target : str
+    path of output file with plot
+
+Outputs
+---------
+matplotlib.figure.Figure
+    plotted conventional power plant data
+
+Description
+-------------
+The script takes prepared data of conventional power plants from OPSD, prepares the
+data further and plots the data.
+"""
+import os
+import sys
+
+import matplotlib.pyplot as plt
+import oemoflex.tools.plots as plots
+import pandas as pd
+
+import oemof_b3.tools.data_processing as dp
+from oemof_b3 import colors_odict, labels_to_german
+
+# User input
+# converting from MW to W
+conv_number = 1e6
+german_translation = True
+unit = "W"
+
+
+# GENERAL
+c_to_cl = {
+    "biomass": "Biomass",
+    "ch4": "CH4",
+    "hard coal": "Hard coal",
+    "oil": "Oil",
+    "lignite": "Lignite",
+    "other": "Other",
+}
+
+cl_to_gcl = labels_to_german
+
+c_to_gcl = {carrier: cl_to_gcl[cl] for carrier, cl in c_to_cl.items()}
+
+colors_odict_german = {
+    cl_to_gcl[cl]: color for cl, color in colors_odict.items() if cl in cl_to_gcl
+}
+
+
+def prepare_conv_pp_scalars(df_conv_pp_scalars, var_name, conv_number, label_mapping):
+    r"""
+    This function converts scalar data in oeomof-b3 format to a format that can be passed to
+    `plot_grouped_bar` .
+
+    It aggregates power plants with the same carrier.
+
+    Parameters
+    ----------
+    df_conv_pp_scalars: pd.DataFrame
+        contains all conventional power plants in Berlin and Brandenburg
+    var_name: string
+        indicates the var_name which shall be plotted
+    conv_number: int
+         converts the value to SI unit, i.e. W
+
+    Returns
+    -------
+    df_pivot : pandas.DataFrame
+        Unique unit
+    color_dict: dict
+        contains colors for different carriers for plotting
+    """
+    # select var_name to be plotted
+    selected_df = df_conv_pp_scalars[df_conv_pp_scalars["var_name"] == var_name].copy()
+
+    assert not selected_df.empty, f"Variable name '{var_name}' not found in data."
+
+    # CONV_PP specific
+    selected_df.loc[(selected_df["region"] != "Berlin"), "region"] = "Brandenburg"
+
+    # aggregate carriers in regions
+    selected_df_agg = dp.aggregate_scalars(
+        df=selected_df, columns_to_aggregate=["tech"]
+    )
+
+    # convert to SI unit:
+    if conv_number:
+        selected_df_agg.loc[:, "var_value"] *= conv_number
+
+    selected_df_agg["carrier"].replace(label_mapping, inplace=True)
+
+    # apply pivot table
+    df_pivot = pd.pivot_table(
+        selected_df_agg,
+        index=["region"],
+        columns="carrier",
+        values="var_value",
+    )
+
+    return df_pivot
+
+
+def plot_grouped_bar(ax, df, color_dict, unit, stacked=False):
+    r"""
+    This function plots scalar data as grouped bar plot. The index of the DataFrame
+    will be interpreted as groups (e.g. regions), the columns as different categories (e.g. energy
+    carriers) within the groups which will be plotted in different colors.
+
+    Parameters
+    ----------
+    ax: matplotlib Axes object
+        Axes to draw the plot.
+    df: pd.DataFrame
+        DataFrame with an index defining the groups and columns defining the bars of different color
+        with in the group.
+    color_dict: dict
+        Dictionary defining colors of the categories
+    unit: str
+        Unit of the variables
+    stacked : boolean
+        Stack bars of a group. False by default.
+    """
+    alpha = 0.3
+    fontsize = 14
+    plt.rcParams.update({"font.size": fontsize})
+
+    # apply EngFormatter if power is plotted
+    ax = plots._eng_format(ax, unit)
+
+    df.plot.bar(
+        ax=ax,
+        color=[color_dict[key] for key in df.columns],
+        width=0.8,
+        zorder=2,
+        stacked=stacked,
+        rot=0,
+    )
+
+    for spine in ["top", "left", "right"]:
+        ax.spines[spine].set_visible(False)
+    ax.spines["bottom"].set_alpha(alpha)
+    ax.tick_params(axis="both", length=0, pad=7)
+
+    ax.grid(axis="y", zorder=1, color="black", alpha=alpha)
+    ax.set_xlabel(xlabel=None)
+    ax.legend()
+    plt.legend(title=None, frameon=True, framealpha=1)
+
+    plt.tight_layout()
+
+    return ax
+
+
+if __name__ == "__main__":
+    resources = sys.argv[1]
+
+    var_name = sys.argv[2]
+
+    target = sys.argv[3]
+
+    target_dir = os.path.dirname(target)
+
+    # create the directory plotted where all plots are saved
+    if not os.path.exists(target_dir):
+        os.makedirs(target_dir)
+
+    # Load scalar data
+    df_conv_pp_scalars = dp.load_b3_scalars(resources)
+
+    if german_translation:
+        label_mapping = c_to_gcl
+        color_dict = colors_odict_german
+    else:
+        label_mapping = c_to_cl
+        color_dict = colors_odict
+
+    df_pivot = prepare_conv_pp_scalars(
+        df_conv_pp_scalars=df_conv_pp_scalars,
+        var_name=var_name,
+        conv_number=conv_number,
+        label_mapping=label_mapping,
+    )
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+
+    plot_grouped_bar(ax, df_pivot, color_dict, unit)
+
+    plt.savefig(target, bbox_inches="tight")

scripts/plot_dispatch.py

@@ -50,9 +50,11 @@
     ]
 
     # select carrier
-    carrier = "electricity"
+    carriers = ["electricity", "heat_central", "heat_decentral"]
 
-    selected_bus_files = [file for file in bus_files if carrier in file]
+    selected_bus_files = [
+        file for file in bus_files for carrier in carriers if carrier in file
+    ]
 
     for bus_file in selected_bus_files: