Merge pull request #385 from Breakthrough-Energy/develop

Merge develop into master for 0.5.3 release

.github/workflows/coverage.yml

@@ -11,10 +11,10 @@ jobs:
 
     name: Generate coverage report
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - name: Set up Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: 3.8
 

.github/workflows/lint.yml

@@ -7,15 +7,15 @@ jobs:
     if: "!contains(github.event.head_commit.message, 'skip_ci')"
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
-      - uses: actions/setup-python@v2
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
       - run: python -m pip install --upgrade tox
       - run: tox -e checkformatting
   flake8:
     if: "!contains(github.event.head_commit.message, 'skip_ci')"
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
-      - uses: actions/setup-python@v2
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
       - run: python -m pip install --upgrade tox
       - run: tox -e flake8

.github/workflows/packages.yml

@@ -8,7 +8,7 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - name: Pipenv lock
         id: pipenv_lock

.github/workflows/release.yml

@@ -10,8 +10,8 @@ jobs:
     name: Publish python package
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
-      - uses: actions/setup-python@v2
+      - uses: actions/checkout@v3
+      - uses: actions/setup-python@v4
       - run: python -m pip install --upgrade build
       - run: python -m build
 
@@ -33,4 +33,4 @@ jobs:
           repo: plug
           workflow_id: 12413223
           token: ${{ secrets.CI_TOKEN_CLONE_REPO }}
-          inputs: '{"imageTags": "stable"}'
+          inputs: '{"imageTag": "stable"}'

.github/workflows/test.yml

@@ -13,10 +13,10 @@ jobs:
         python-version: ['3.8', '3.9', '3.10']
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - name: Set up Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python-version }}
 

Pipfile

@@ -8,7 +8,7 @@ bokeh = "~=2.0"
 matplotlib = "~=3.3"
 numpy = "~=1.20"
 pandas = "~=1.4.0"
-powersimdata = "~=0.5.4"
+powersimdata = "~=0.5.5"
 pyproj = "~=3.0"
 
 [dev-packages]

README.md

@@ -10,7 +10,8 @@
 ![GitHub last commit (branch)](https://img.shields.io/github/last-commit/Breakthrough-Energy/PostREISE/develop?logo=GitHub)
 ![GitHub pull requests](https://img.shields.io/github/issues-pr/Breakthrough-Energy/PostREISE?logo=GitHub)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
-[![Code of Conduct](https://img.shields.io/badge/code%20of-conduct-ff69b4.svg?style=flat)](https://breakthrough-energy.github.io/docs/communication/code_of_conduct.html)
+[![Code of Conduct](https://img.shields.io/badge/Code_of_conduct-ff69b4.svg)](https://breakthrough-energy.github.io/docs/communication/code_of_conduct.html)
+[![Slack](https://img.shields.io/badge/Community_Slack-sign_up-1f425f.svg?logo=slack)](https://science.breakthroughenergy.org/#get-updates)
 
 
 # PostREISE

docs/README.md

@@ -1,30 +1,36 @@
-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
+### Requirements
+This guide will demonstrate how to regenerate the plots used in the
+[documentation][docs]. The only requirement is that docker is installed. The
+scenarios used will be downloaded from blob storage, which may take a while but
+should happen automatically.
 
-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.
+### Usage
+First, go to this directory, then start the container, which will provide a
+notebook environment for running the code snippets.
 ```
+cd docs
 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). 
+Access the notebook in your browser using the localhost url given by jupyter.
+NOTE: if the host port is already in use by another container, it can be
+changed to one that is available (e.g. 10001). In this case, you'll need to
+change the port in the url to match, since the one given by jupyter is the port
+inside the container.
+
+Now simply run the notebook to regenerate the plots. 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.
+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.
+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]: https://breakthrough-energy.github.io/docs/postreise/index.html

docs/analyze.rst

@@ -216,8 +216,8 @@ Generator Capacity and Generation
 
 
       data = os.path.join(os.path.dirname(inspect.getfile(postreise)), "data")
-        hist_gen = pd.read_csv(
-        os.path.join(data, "2016_Historical_USA_TAMU_Generation_GWh.csv"), index_col=0
+      hist_gen = pd.read_csv(
+          os.path.join(data, "2016_Historical_USA_TAMU_Generation_GWh.csv"), index_col=0
       ).T
       historical_generation = summarize_hist_gen(hist_gen, hist_gen.columns.to_list())
 

docs/code/curtailment_eastern.py

@@ -10,7 +10,7 @@
 plot_curtailment_time_series(
     scenario,
     "Eastern",
-    ["wind", "solar"],
+    ["solar", "wind"],
     time_freq="D",
     t2c=t2c,
     label_fontsize=30,

docs/docker-compose.yml

@@ -1,17 +1,15 @@
-version: '3.7'
-
 services:
   postreise:
-    container_name: postreise
-    hostname: postreise
+    container_name: postreise_docs
+    hostname: postreise_docs
     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
+      - ~/ScenarioData:/mnt/bes/pcm
       - ./:/app
     ports:
-      - "10000:10000"
+      - 10000:10000
     environment:
       - DEPLOYMENT_MODE=1

docs/helper.py

@@ -1,3 +1,5 @@
+import base64
+
 mpl_config = {
     "single": {
         "curtailment_eastern": (
@@ -41,4 +43,5 @@
 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"])
+            content = output.data["image/png"].encode()
+            f.write(base64.decodebytes(content))