[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -8,7 +8,7 @@ exclude: "^LICENSES"
 
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.4.0
+  rev: v6.0.0
   hooks:
   - id: check-merge-conflict
   - id: end-of-file-fixer
@@ -19,51 +19,51 @@ repos:
 
 # Sort package imports alphabetically
 - repo: https://github.com/PyCQA/isort
-  rev: 5.10.1
+  rev: 7.0.0
   hooks:
   - id: isort
     args: ["--profile", "black", "--filter-files"]
 
 # Find common spelling mistakes in comments and docstrings
 - repo: https://github.com/codespell-project/codespell
-  rev: v2.2.2
+  rev: v2.4.1
   hooks:
   - id: codespell
     args: ['--ignore-regex="\b[A-Z]+\b"'] # Ignore capital case words, e.g. country codes
     types_or: [python, rst, markdown]
     files: ^(actions|doc)/
 
 # Formatting with "black" coding style
-- repo: https://github.com/psf/black
-  rev: 22.10.0
+- repo: https://github.com/psf/black-pre-commit-mirror
+  rev: 26.1.0
   hooks:
   # Format Python files
   - id: black
   # Format Jupyter Python notebooks
   - id: black-jupyter
 
 - repo: https://github.com/nbQA-dev/nbQA
-  rev: 1.5.3
+  rev: 1.9.1
   hooks:
     # Apply isort to Jupyter Python notebooks
   - id: nbqa-isort
 
 # Do YAML formatting (before the linter checks it for misses)
 - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
-  rev: v2.4.0
+  rev: v2.16.0
   hooks:
   - id: pretty-format-yaml
     args: [--autofix, --indent, '2', --preserve-quotes]
 
 # Format Snakemake rule / workflow files
 - repo: https://github.com/snakemake/snakefmt
-  rev: v0.7.0
+  rev: v0.11.4
   hooks:
   - id: snakefmt
 
 # Check for FSFE REUSE compliance (licensing)
 - repo: https://github.com/fsfe/reuse-tool
-  rev: v1.0.0
+  rev: v6.2.0
   hooks:
   - id: reuse
 

actions/create_network.py.ipynb

@@ -314,7 +314,6 @@
     "def scale_transportation_with_distance(\n",
     "    n, link_types=[\"HVDC overhead\", \"HVDC submarine\", \"pipeline\", \"submarine pipeline\"]\n",
     "):\n",
-    "\n",
     "    \"\"\"Scales the cost and efficiency of specific links (transport options) by distance.\n",
     "\n",
     "    Only implemented for:\n",
@@ -752,9 +751,9 @@
     "                & (loaded_unloaded_ratio[\"from\"] == \"LOHC (loaded)\")\n",
     "                & (loaded_unloaded_ratio[\"to\"] == \"LOHC (used)\")\n",
     "            ][\"efficiency\"].item()\n",
-    "            n.links.loc[\n",
-    "                f\"{ship_bus} trip demand & losses\", \"bus2\"\n",
-    "            ] = f\"{ship_bus} LOHC (used)\"\n",
+    "            n.links.loc[f\"{ship_bus} trip demand & losses\", \"bus2\"] = (\n",
+    "                f\"{ship_bus} LOHC (used)\"\n",
+    "            )\n",
     "            n.links.loc[f\"{ship_bus} trip demand & losses\", \"efficiency2\"] = (\n",
     "                1 - shipping_efficiency\n",
     "            ) * loaded_unloaded_ratio\n",

actions/solve_network.py.ipynb

@@ -144,7 +144,7 @@
     "        (\"links\", \"pipeline\", \"CAPEX_pipeline\"),\n",
     "        (\"links\", \"methanolisation\", \"CAPEX_MeOHSynthesis\"),\n",
     "    ]\n",
-    "    for (components_name, search_string, modifier_name) in mapping:\n",
+    "    for components_name, search_string, modifier_name in mapping:\n",
     "        c = getattr(n, components_name)\n",
     "        c.loc[c.index.str.contains(search_string), \"capital_cost\"] *= scenario[\n",
     "            \"modifiers\"\n",

analysis/plot_paper_figures.ipynb

@@ -51,6 +51,8 @@
    "source": [
     "# Take a list of labels and rename the ESCs from that list\n",
     "# to more prettier (and shorter) names\n",
+    "\n",
+    "\n",
     "def prettiefy_esc_names(labels):\n",
     "\n",
     "    prettier_names = {\n",
@@ -628,8 +630,8 @@
     "fig = plt.figure(figsize=(12, 4))\n",
     "ax = plt.gca()\n",
     "\n",
-    "for (esc, x) in zip(esc_markers.keys(), xs):\n",
-    "    for ((idx, row), xx) in zip(df.loc[esc].sort_values(\"value\").iterrows(), xxs):\n",
+    "for esc, x in zip(esc_markers.keys(), xs):\n",
+    "    for (idx, row), xx in zip(df.loc[esc].sort_values(\"value\").iterrows(), xxs):\n",
     "        ax.bar(\n",
     "            x + xx * width - (xxs.shape[0] - 1) * width / 2,\n",
     "            row[\"value\"],\n",
@@ -694,8 +696,8 @@
     "fig = plt.figure(figsize=(10, 6))\n",
     "ax = plt.gca()\n",
     "\n",
-    "for (esc, x) in zip(esc_markers.keys(), xs):\n",
-    "    for (year, xx) in zip(years, xxs):\n",
+    "for esc, x in zip(esc_markers.keys(), xs):\n",
+    "    for year, xx in zip(years, xxs):\n",
     "        for idx, row in df.loc[esc, year].iterrows():\n",
     "            # Single value dots\n",
     "            ax.scatter(\n",
@@ -777,8 +779,8 @@
     "fig = plt.figure(figsize=(10, 6))\n",
     "ax = plt.gca()\n",
     "\n",
-    "for (esc, x) in zip(esc_markers.keys(), xs):\n",
-    "    for (year, xx) in zip(years, xxs):\n",
+    "for esc, x in zip(esc_markers.keys(), xs):\n",
+    "    for year, xx in zip(years, xxs):\n",
     "        for idx, row in df.loc[esc, year].iterrows():\n",
     "            # Individual dots\n",
     "            ax.scatter(\n",
@@ -888,8 +890,8 @@
     "ax = plt.gca()\n",
     "\n",
     "# Plot each vertical year line and each scatter point separately\n",
-    "for (esc, x) in zip(conversion_factors.keys(), xs):\n",
-    "    for (year, xx) in zip(years, xxs):\n",
+    "for esc, x in zip(conversion_factors.keys(), xs):\n",
+    "    for year, xx in zip(years, xxs):\n",
     "        for idx, row in df.loc[esc, year].iterrows():\n",
     "            # each scatter/data point. Marked by color of exporter\n",
     "            ax.scatter(\n",
@@ -1000,8 +1002,8 @@
     "ax = plt.gca()\n",
     "\n",
     "# Plot each vertical year line and each scatter point separately\n",
-    "for (esc, x) in zip(conversion_factors.keys(), xs):\n",
-    "    for (year, xx) in zip(years, xxs):\n",
+    "for esc, x in zip(conversion_factors.keys(), xs):\n",
+    "    for year, xx in zip(years, xxs):\n",
     "        for idx, row in df.loc[esc, year].iterrows():\n",
     "            # each scatter/data point. Marked by color of exporter\n",
     "            ax.scatter(\n",

analysis/results-plotting.ipynb

@@ -90,7 +90,7 @@
    "source": [
     "# Plot all ESCs per exporter\n",
     "for name, group in df.groupby(df.index.names[:3]):\n",
-    "    (year, wacc, importer) = name\n",
+    "    year, wacc, importer = name\n",
     "\n",
     "    tmp = group.loc[name].loc[:, :, \"general\", \"Cost per MWh delivered\"]\n",
     "    tmp.reset_index().pivot(index=\"exporter\", columns=\"esc\", values=\"value\").plot(\n",
@@ -118,7 +118,7 @@
    "source": [
     "# Plot per ESC all exporters\n",
     "for name, group in df.groupby(df.index.names[:3]):\n",
-    "    (year, wacc, importer) = name\n",
+    "    year, wacc, importer = name\n",
     "\n",
     "    tmp = group.loc[name].loc[:, :, \"general\", \"Cost per MWh delivered\"]\n",
     "    tmp.reset_index().pivot(index=\"esc\", columns=\"exporter\", values=\"value\").plot(\n",
@@ -146,7 +146,7 @@
    "source": [
     "# Energy surplus factor by ESC for each exporter\n",
     "for name, group in df.groupby(df.index.names[:3]):\n",
-    "    (year, wacc, importer) = name\n",
+    "    year, wacc, importer = name\n",
     "\n",
     "    tmp = group.loc[name].loc[:, :, \"general\", \"Energy surplus factor\"]\n",
     "    tmp.reset_index().pivot(index=\"exporter\", columns=\"esc\", values=\"value\").plot(\n",
@@ -189,7 +189,7 @@
     "cutoff = 0.1\n",
     "\n",
     "for name, group in df.groupby(df.index.names[:4]):\n",
-    "    (year, wacc, importer, esc) = name\n",
+    "    year, wacc, importer, esc = name\n",
     "\n",
     "    group = group.loc[name].loc[:, \"cost\", :]  # Select cost only\n",
     "\n",
@@ -289,7 +289,7 @@
     "}\n",
     "\n",
     "for name, group in df.groupby(df.index.names[:4]):\n",
-    "    (year, wacc, importer, esc) = name\n",
+    "    year, wacc, importer, esc = name\n",
     "\n",
     "    group = group.loc[name].loc[:, \"capacity factor\", :]  # Select CFs only\n",
     "\n",
@@ -359,7 +359,7 @@
     "brk = False\n",
     "\n",
     "for name, group in df.groupby(df.index.names[:4]):\n",
-    "    (year, wacc, importer, esc) = name\n",
+    "    year, wacc, importer, esc = name\n",
     "\n",
     "    group = group.loc[name].loc[:, \"installed capacity\", :]  # Select capacities only\n",
     "\n",

rules/gegis.smk

@@ -14,15 +14,15 @@ if config["GlobalEnergyGIS"].get("init_gegis", False) is True:
         script:
             "../actions/set_GEGIS_base_dir.jl"
 
-# Download auxiliary datasets for GEGIS
+    # Download auxiliary datasets for GEGIS
     rule download_GEGIS_dataset:
         output:
             Path(config["GlobalEnergyGIS"]["base_dir"]) / "protected.jld",  # not full list, only dependencies for rules below (proxy all others)
             Path(config["GlobalEnergyGIS"]["base_dir"]) / "gadm.tif",
         script:
             "../actions/download_GEGIS_datasets.jl"
 
-# Download ERA5 data for wind/solar/synthetic demand for GEGIS
+    # Download ERA5 data for wind/solar/synthetic demand for GEGIS
     rule download_GEGIS_era5:
         output:
             Path(config["GlobalEnergyGIS"]["base_dir"]) / "era5wind{year}.h5",

rules/results.smk

@@ -5,6 +5,7 @@
 # Use paramspace to evaluate which scenarios to run
 scenarios = Paramspace(pd.read_csv("scenarios/default.csv", dtype=str))
 
+
 # Custom pattern for formatting Paramspace, as snakemake
 # does currently not allow for patterns without the wildcard_name included
 # see: https://stackoverflow.com/questions/71293563/custom-patterns-with-snakemakes-paramspace/71296522#71296522