Features/plot scalar resources

Snakefile

@@ -4,6 +4,8 @@ examples = [
     'more_renewables_less_fossil'
 ]
 
+resources = ['conv_pp_scalar']
+
 # Target rules
 
 rule run_all_examples:
@@ -18,6 +20,10 @@ 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:
         """
@@ -82,6 +88,15 @@ rule plot_dispatch:
     shell:
         "python scripts/plot_dispatch.py {input} {output}"
 
+rule plot_scalar_resources:
+    input:
+        data="results/_resources/{resource}.csv",
+        script="scripts/plot_scalar_resources.py"
+    output:
+        "results/_resources/plots/{resource}.png"
+    shell:
+        "python {input.script} {input.data} {output}"
+
 rule report:
     input:
         template="report/report.md",

scripts/plot_scalar_resources.py

@@ -0,0 +1,137 @@
+r"""
+Inputs
+-------
+resources : str
+    path of input file with data in scalar resources template format as .csv
+target : str
+    path of output file with prepared data as .csv
+
+Outputs
+---------
+matplotlib.figure.Figure
+    plotted scalar resources
+
+Description
+-------------
+The script takes data in the format of the scalar resources template, prepares the
+data and plots the data.
+"""
+import sys
+import os
+import pandas as pd
+import matplotlib.pyplot as plt
+import oemoflex.tools.plots as plots
+from oemoflex.tools.plots import colors_odict
+
+if __name__ == "__main__":
+    resources = sys.argv[1]
+
+    target = sys.argv[2]
+
+    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)
+
+    def load_scalars(path):
+        df = pd.read_csv(path, sep=",", index_col=0)
+        return df
+
+    # Load scalar data
+    scalars = load_scalars(resources)
+
+    # GENERAL
+    unit_dict = {"capacity_net_el": "W"}
+    carrier_dict = {
+        "biomass": "Biomass",
+        "ch4": "CH4",
+        "hard coal": "Hard coal",
+        "oil": "Oil",
+        "lignite": "Lignite",
+        "other": "Other",
+    }
+
+    german_labels = {
+        "Biomass": "Biomasse",
+        "CH4": "Erdgas",
+        "Hard coal": "Steinkohle",
+        "Oil": "Öl",
+        "Lignite": "Braunkohle",
+        "Other": "Andere",
+    }
+
+    # User input
+    var_name = "capacity_net_el"
+    conv_number = 1000 * 1000
+
+    def prepare_conv_pp_scalars(var_name, conv_number, carrier_dict=carrier_dict):
+        # select var_name to be plotted
+        selected_df = scalars[scalars["var_name"] == var_name].copy()
+
+        # CONV_PP specific
+        selected_df.loc[(selected_df["region"] != "Berlin"), "region"] = "Brandenburg"
+        # capitalize carrier names
+        selected_df["carrier"].replace(carrier_dict, inplace=True)
+        # aggregate carriers in regions
+        selected_df_agg = selected_df.groupby(
+            ["scenario", "region", "carrier", "var_name"]
+        ).sum()
+        selected_df_agg.reset_index(inplace=True)
+
+        # apply pivot table
+        df_pivot = pd.pivot_table(
+            selected_df_agg,
+            index=["region"],
+            columns="carrier",
+            values="var_value",
+        )
+
+        # colors
+        color_keys = df_pivot.columns
+        german_color_dict = {}
+        for key in color_keys:
+            german_color_dict[german_labels[key]] = colors_odict[key]
+        df_pivot.rename(columns=german_labels, inplace=True)
+        german_color_keys = df_pivot.columns
+        # convert to SI unit:
+        if conv_number:
+            df_pivot *= conv_number
+
+        return df_pivot, german_color_keys, german_color_dict
+
+    def plot_scalar_resources(df, color_keys, color_dict, unit_dict):
+        alpha = 0.3
+        fontsize = 14
+        plt.rcParams.update({"font.size": fontsize})
+
+        fig, ax = plt.subplots(figsize=(12, 6))
+        # apply EngFormatter if power is plotted
+        if unit_dict[var_name] == "W":
+            ax = plots.eng_format(ax, "W")
+        df.plot.bar(
+            ax=ax,
+            color=[color_dict[key] for key in color_keys],
+            width=0.8,
+            zorder=2,
+            stacked=False,
+            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(labels=german_labels)
+        plt.legend(title=None, frameon=True, framealpha=1)
+
+        plt.tight_layout()
+        plt.savefig(target, bbox_inches="tight")
+
+    df_pivot, german_color_keys, german_color_dict = prepare_conv_pp_scalars(
+        var_name=var_name, conv_number=conv_number
+    )
+    plot_scalar_resources(df_pivot, german_color_keys, german_color_dict, unit_dict)