Sync develop to test/smokeApp/rc1

.gitignore

@@ -0,0 +1,3 @@
+.vscode*
+.DS_Store*
+smoke/.vscode/

README.md

@@ -1,5 +1,23 @@
-# app-space-template
+# smoke
+Hamish Gordon's group space for development and deployment of C3 AI DTI applications.
 
-### Template Repo for new C3ai application
+### Main components
+The `smoke` space in C3's IDS, which lives in DTI RC01, currently has one application (`smokeApp`) and one branch (`dev01`). The latter corresponds to a C3 tag that can be access via the vanity URL https://dev01-smokeapp.devrc01.c3aids.cloud/static/console/
 
-more
+### Typical development workflow
+This GitHub repository always contains at least three branches: 
+
+- an intrinsic `main` branch
+- a C3 `develop` branch that hosts the application code
+- a `dev01` branch that represents what is deployed to the tag above
+
+The typical development workflow then goes as follows:
+
+1. Clone the repository to your local environment (we highly encourage using VS Code)
+2. Create a new branch **from** `dev01` and give it a meaningful name, e.g. `username-dev`
+3. Develop code, regularly commit, and push back to GitHub when you want the code provisioned to the tag
+4. Open a pull request **from** `username-dev` **to** `dev01`
+5. Github Actions that DTI DevOps have running will then trigger the provisioning of the package to the `dev01` tag
+
+As long as the pull request is open, pushing code to GitHub will always trigger the provisioning workflow. 
+For that reason, if you have multiple developers in your team, we recommend that you consider more ephemeral pull requests to tackle simple tasks at a time. You are welcome to use GitHub Projects to give them tags and other details, as well as using GitHub Issues to describe the steps of more complex development efforts that can then be broken into several small tasks, each one with its own PR (open, close, next). 

climateInformatics2023/notebooks/MissingnessMapPlot.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "651c359f",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-03-29T18:08:35.554369Z",
+     "start_time": "2023-03-29T18:08:35.166169Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.interpolate import griddata\n",
+    "\n",
+    "import sys\n",
+    "try:\n",
+    "    import cartopy.crs as ccrs\n",
+    "except ImportError:\n",
+    "    pass\n",
+    "\n",
+    "if 'cartopy.crs' not in sys.modules:\n",
+    "    get_cartopy = False\n",
+    "    print('You have not imported the {} module'.format('cartopy.crs'))\n",
+    "else:\n",
+    "    get_cartopy = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "92b0eca9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-03-29T18:09:03.792088Z",
+     "start_time": "2023-03-29T18:09:03.748109Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"observations\", \"r\") as f:\n",
+    "    obs_df = pd.read_csv(f, index_col=0)\n",
+    "f.close()\n",
+    "\n",
+    "with open(\"outliers\", \"r\") as f:\n",
+    "    outliers = pd.read_csv(f)\n",
+    "f.close()\n",
+    "\n",
+    "my_outliers = outliers[outliers.time<=np.min(outliers.time)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "303ba60d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2023-03-29T18:38:00.271521Z",
+     "start_time": "2023-03-29T18:37:59.883525Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "projection = ccrs.PlateCarree(central_longitude=0)\n",
+    "fig = plt.figure(figsize=(10,10))\n",
+    "\n",
+    "# Draw island\n",
+    "ax = fig.add_subplot(1, 1, 1, projection=projection)\n",
+    "ax.coastlines()\n",
+    "\n",
+    "# Produce gridlines, coordinate labels\n",
+    "ax.set_extent([-45, 40, -30, 10], ccrs.PlateCarree())\n",
+    "ax.gridlines(draw_labels=True, crs=projection)\n",
+    "\n",
+    "# Add points along flight path and color for altitude\n",
+    "measurements = ax.scatter(\n",
+    "    my_outliers[(my_outliers.missing==False) & (my_outliers.outlier==False)].longitude,\n",
+    "    my_outliers[(my_outliers.missing==False) & (my_outliers.outlier==False)].latitude,\n",
+    "    zorder=1, alpha=1,\n",
+    "    c=my_outliers[(my_outliers.missing==False) & (my_outliers.outlier==False)].meanResponse, cmap=\"Blues\"\n",
+    ")\n",
+    "\n",
+    "cbar = plt.colorbar(measurements, orientation = 'horizontal')\n",
+    "cbar.set_label('Measured AOD', fontsize = 14)\n",
+    "\n",
+    "ax.scatter(\n",
+    "    my_outliers[my_outliers.missing==True].longitude,\n",
+    "    my_outliers[my_outliers.missing==True].latitude,\n",
+    "    zorder=1, alpha=0.5,\n",
+    "    c=\"r\", marker=\"s\", s=50,\n",
+    "    label='Missing measurement'\n",
+    ")\n",
+    "ax.scatter(\n",
+    "    my_outliers[my_outliers.outlier==True].longitude,\n",
+    "    my_outliers[my_outliers.outlier==True].latitude,\n",
+    "    zorder=1, alpha=0.5,\n",
+    "    c=\"g\", marker=\"s\", s=50,\n",
+    "    label='Outlier'\n",
+    ")\n",
+    "\n",
+    "plt.legend(loc = 'upper center', bbox_to_anchor = (0.5, -0.1), ncol = 2, fontsize = 14)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "has_local_update": true,
+  "is_local": true,
+  "is_remote": true,
+  "kernelspec": {
+   "display_name": "py-gordon_1_0_0",
+   "language": "Python",
+   "name": "py-gordon_1_0_0"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  },
+  "last_sync_time": "2023-03-29T19:11:40.222296"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}