Merge pull request #307 from Breakthrough-Energy/jon/plots

Simplify process to generate plotting documentation

docs/README.md

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+This guide will demonstrate how to regenerate the plots used in the documentation here.
+The requirements are
+1) Have docker installed
+2) Download the [PlotData] zip file containing the scenarios used
+
+Next, extract the data to your home directory, or change the expected location in the
+docker-compose.yml file.
+
+
+Start the container, which will provide a notebook environment for running the code
+snippets.
+```
+docker compose up
+```
+
+After copying the notebook url from the output, start the `run_snippets.ipynb` notebook
+and run through it. For the most part, no interaction is required. The exception is any
+bokeh plots which must be manually saved as png (currently only the powerflow snapshot). 
+
+One may notice that the first part of the notebook contains code to generate the cells
+used subsequently. This is due to (as far as I know) lack of support in jupyterlab for
+programmatically adding new cells, so as a result, any changes to the configuration may
+require a developer to rerun this part and copy/paste the output as needed. In most
+cases, one can simply run it with no changes, since the notebook is pre-populated.
+
+The output from each snippet will be saved in the `img2/` directory, similar to `img/` which 
+is checked into git. From here, it's up to the user to compare results and commit the new plots if they look
+good.
+
+[PlotData]: https://besciences.blob.core.windows.net/snapshots/PlotData.zip

docs/docker-compose.yml

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+version: '3.7'
+
+services:
+  postreise:
+    container_name: postreise
+    hostname: postreise
+    image: ghcr.io/breakthrough-energy/postreise:latest
+    stdin_open: true # docker run -i
+    tty: true        # docker run -t
+    working_dir: /app
+    volumes:
+      - ~/PlotData:/mnt/bes/pcm
+      - ./:/app
+    ports:
+      - "10000:10000"
+    environment:
+      - DEPLOYMENT_MODE=1

docs/helper.py

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+mpl_config = {
+    "single": {
+        "curtailment_eastern": (
+            "curtailment_solar_eastern_ts.png",
+            "curtailment_wind_eastern_ts.png",
+        ),
+        "capacity_vs_cf_solar_western_scatter": "capacity_vs_cf_solar_western_scatter.png",
+        "capacity_vs_cost_curve_slope_coal_eastern_scatter": "capacity_vs_cost_curve_slope_coal_eastern_scatter.png",
+        "capacity_vs_curtailment_solar_western_scatter": "capacity_vs_curtailment_solar_western_scatter.png",
+        "curtailment_usa_heatmap": "curtailment_usa_heatmap.png",
+        "generation_stack_western_ts": "generation_stack_western_ts.png",
+    },
+    "comp": {
+        "capacity_vs_generation_bar": (
+            "capacity_vs_generation_ca_bar.png",
+            "capacity_vs_generation_western_bar.png",
+        ),
+        "capacity_vs_generation_pie": (
+            "capacity_vs_generation_wa_pie.png",
+            "capacity_vs_generation_western_pie.png",
+        ),
+        "energy_emission_stack_bar": "energy_emission_stack_bar.png",
+        "shortfall_nv": "shortfall_nv.png",
+        "emission_bar": "emission_bar.png",
+        "shortfall_nv": "shortfall_nv.png",
+    },
+}
+
+bokeh_config = {
+    "single": {
+        "lmp_usa_map": "lmp_usa_map.html",
+        "utilization_map": "utilization_map.html",
+        "emission_map": "emission_map.html",
+        "pf_snapshot_map": "pf_snapshot_map.png",
+    },
+    "other": {"interconnection_map": "interconnection_map.html"},
+    "comp": {"emission_map_carbon_diff": "emission_map.html"},
+}
+
+
+def save_matplotlib(result, filename):
+    for i, output in enumerate(result.outputs):
+        with open(filename[i], "wb") as f:
+            f.write(output.data["image/png"])