Merge pull request #254 from jrleeman/Improve_Solution_Checks

Improve Solution Automation

notebooks/Animation/Creating Animations.ipynb

@@ -476,52 +476,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %load solutions/animation.py\n",
-    "from netCDF4 import num2date\n",
-    "\n",
-    "# Load data\n",
-    "cat = TDSCatalog('http://thredds-test.unidata.ucar.edu/thredds/catalog/casestudies/irma/model/gfs/catalog.xml')\n",
-    "best_ds = cat.datasets['Best GFS Half Degree Forecast Time Series']\n",
-    "\n",
-    "# Access the best dataset using the subset service and request data\n",
-    "ncss = best_ds.subset()\n",
-    "\n",
-    "# Set up query\n",
-    "query = ncss.query().accept('netcdf4')\n",
-    "query.lonlat_box(west=-90, east=-55, south=15, north=30)\n",
-    "query.variables('Pressure_surface', 'Wind_speed_gust_surface')\n",
-    "query.time_range(datetime(2017, 9, 6, 12), datetime(2017, 9, 11, 12))\n",
-    "\n",
-    "# Pull useful pieces out of nc\n",
-    "nc = ncss.get_data(query)\n",
-    "lon = nc.variables['longitude'][:]\n",
-    "lat = nc.variables['latitude'][:]\n",
-    "press = nc.variables['Pressure_surface']\n",
-    "winds = nc.variables['Wind_speed_gust_surface']\n",
-    "time_var = nc.variables['time1']\n",
-    "times = num2date(time_var[:], time_var.units)\n",
-    "\n",
-    "# Create a figure for plotting\n",
-    "proj = ccrs.LambertConformal(central_longitude=-70)\n",
-    "fig = plt.figure(figsize=(10, 5))\n",
-    "ax = fig.add_subplot(1, 1, 1, projection=proj)\n",
-    "ax.coastlines()\n",
-    "add_metpy_logo(fig, x=15, y=15)\n",
-    "\n",
-    "# Setup up the animation, looping over data to do the plotting that we want\n",
-    "pressure_levels = np.arange(95000, 105000, 800)\n",
-    "wind_levels = np.arange(0., 100., 10.)\n",
-    "artists = []\n",
-    "\n",
-    "for press_slice, wind_slice, time in zip(press, winds, times):\n",
-    "    press_contour = ax.contour(lon, lat, press_slice, pressure_levels,\n",
-    "                               transform=ccrs.PlateCarree(), colors='black')\n",
-    "    wind_contour = ax.contour(lon, lat, wind_slice, wind_levels,\n",
-    "                              transform=ccrs.PlateCarree(), colors='blue')\n",
-    "    text = ax.text(0.5, 1.01, time, transform=ax.transAxes, ha='center')\n",
-    "    artists.append(press_contour.collections + wind_contour.collections + [text])\n",
-    "\n",
-    "manimation.ArtistAnimation(fig, artists, interval=100)\n"
+    "# %load solutions/animation.py"
    ]
   },
   {
@@ -535,9 +490,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:unidata-workshop]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-unidata-workshop-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/CartoPy/CartoPy.ipynb

@@ -396,34 +396,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %load solutions/map.py\n",
-    "fig = plt.figure(figsize=(10, 8))\n",
-    "ax = fig.add_subplot(1, 1, 1, projection=ccrs.Mercator())\n",
-    "\n",
-    "ax.add_feature(cfeat.COASTLINE)\n",
-    "ax.add_feature(cfeat.LAND, facecolor='tab:brown')\n",
-    "ax.add_feature(cfeat.OCEAN, facecolor='tab:cyan')\n",
-    "ax.add_feature(cfeat.BORDERS, linewidth=2)\n",
-    "ax.add_feature(state_borders, linestyle=\"--\", edgecolor='black')\n",
-    "\n",
-    "ax.plot(-101.877, 33.583, marker='o', color='tab:green', transform=ccrs.PlateCarree())\n",
-    "\n",
-    "ax.set_extent([-108, -93, 25, 37])"
+    "# %load solutions/map.py"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python [conda env:unidata-workshop]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-unidata-workshop-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {

notebooks/Satellite_Data/Working with Satellite Data.ipynb

@@ -88,7 +88,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "list(cat.datasets)[-5:]"
+    "cat.datasets[-5:]"
    ]
   },
   {
@@ -115,7 +115,7 @@
     "<div class=\"alert alert-success\">\n",
     "    <b>EXERCISE</b>:\n",
     "     <ul>\n",
-    "      <li>Using the link to the Hurricane Irma archive above, get data from September 10, 2017 at 15Z for the mid-level water vapor channel (9) in the Mesoscale-1 sector. You could search through the datasets list manually, but instead try the `cat.datasets.filter_time_nearest` functinoality!</li>\n",
+    "      <li>Using the link to the Hurricane Irma archive above, get data from September 10, 2017 at 15Z for the mid-level water vapor channel (9) in the Mesoscale-1 sector. You could search through the datasets list manually, but instead try the `cat.datasets.filter_time_nearest` functionality!</li>\n",
     "    </ul>\n",
     "</div>"
    ]
@@ -135,11 +135,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# %load solutions/satellite_data.py\n",
-    "cat = TDSCatalog('http://thredds-test.unidata.ucar.edu/thredds/catalog/casestudies/irma/goes16/Mesoscale-1/Channel09/20170910/catalog.xml')\n",
-    "ds = cat.datasets.filter_time_nearest(datetime(2017, 9, 10, 0))\n",
-    "print(ds.name)\n",
-    "ds = ds.remote_access(service='OPENDAP')"
+    "# %load solutions/satellite_data.py"
    ]
   },
   {
@@ -175,7 +171,7 @@
    "source": [
     "Our goal is to plot the imagery, so we'll be using the `Sectorized_CMI` variable from the `.variables` dictionary.\n",
     "Rather than just giving back the raw array of data, this gives back a `Variable` object; from here not only\n",
-    "can we get the raw data values, but there is useful metadata as well. We can see just what additional information\n",
+    "can we get the raw data values, but there are useful metadata as well. We can see just what additional information\n",
     "is present by printing out the `Variable` object:"
    ]
   },
@@ -342,7 +338,7 @@
    "metadata": {},
    "source": [
     "Now that we know how to properly reference the imagery data, we can plot\n",
-    "the data. CartoPy's projections are designed to interface with matplotlib, so they can just be passed as the `projection` keyword argument when creating an `Axes` using the `add_subplot` method. Since the x and y coordinates, as well as the image data, are referenced in the lambert conformal projection, we can pass all of them directly to plotting methods (such as `imshow`) with no additional information. The `extent` keyword argument to `imshow` is used to specify the bounds of the image data being plotted. It is **especially important** to specify that the `origin` is at the the upper left of the image (standard practice in imagery). If your forget this, your image will be flipped. Try it!"
+    "the data. CartoPy's projections are designed to interface with matplotlib, so they can just be passed as the `projection` keyword argument when creating an `Axes` using the `add_subplot` method. Since the x and y coordinates, as well as the image data, are referenced in the lambert conformal projection, we can pass all of them directly to plotting methods (such as `imshow`) with no additional information. The `extent` keyword argument to `imshow` is used to specify the bounds of the image data being plotted. It is **especially important** to specify that the `origin` is at the upper left of the image (standard practice in imagery). If your forget this, your image will be flipped. Try it!"
    ]
   },
   {
@@ -370,7 +366,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This is a nice start, but it would be nice to have better geographic references for the image. For example, where are the states? Cartopy's `feature` module has support for adding geographic features to plots ,with many features are built in. The `BORDERS` built-in feature contains country borders. There is also support for creating \"custom\" features from the [Natural Earth](http://www.naturalearthdata.com/) set of free vector and raster map data (CartoPy will automatically download the necessary data and cache it locally). Here we create a feature for states/provinces."
+    "This is a nice start, but it would be nice to have better geographic references for the image. For example, where are the states? Cartopy's `feature` module has support for adding geographic features to plots, with many features are built in. The `BORDERS` built-in feature contains country borders. There is also support for creating \"custom\" features from the [Natural Earth](http://www.naturalearthdata.com/) set of free vector and raster map data (CartoPy will automatically download the necessary data and cache it locally). Here we create a feature for states/provinces."
    ]
   },
   {
@@ -552,8 +548,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Well, that's a good idea, but the text is invisible in some places! White text and black text have issues, but we can outline the text using matplotlib's [path effects](http://matplotlib.org/users/patheffects_guide.html). More details on path effects can be found in the [documentation](http://matplotlib.org/users/patheffects_guide.html) if you want to know more.\n",
-    "\n"
+    "Well, that's a good idea, but the text is invisible in some places! White text and black text have issues, but we can outline the text using matplotlib's [path effects](http://matplotlib.org/users/patheffects_guide.html). More details on path effects can be found in the [documentation](http://matplotlib.org/users/patheffects_guide.html) if you want to know more."
    ]
   },
   {
@@ -649,7 +644,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "First we'll import the animation support from matplotlib. We also tell it that we want it to render the animations to HTML using the HTML5 video tag:"
+    "First, we'll import the animation support from matplotlib. We also tell it that we want it to render the animations to HTML using the HTML5 video tag:"
    ]
   },
   {
@@ -690,7 +685,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Then we loop over a bunch of the datasets. For each one we pull out the data and plot both the timestamp and the image. The `ArtistAnimation` class takes the `Figure` instance and a list as required arguments. The contents of this list is a collection of matplotlib artists for each frame of the animation. In the loop below, we populate this list with the `Text` instance created when adding the timestamp as well as the image that results from plotting the data."
+    "Then we loop over a bunch of the datasets. For each one we pull out the data and plot both the timestamp and the image. The `ArtistAnimation` class takes the `Figure` instance and a list as required arguments. The contents of this list are a collection of matplotlib artists for each frame of the animation. In the loop below, we populate this list with the `Text` instance created when adding the timestamp as well as the image that results from plotting the data."
    ]
   },
   {

notebooks/Skew_T/solutions/skewt_bulk_shear.py

@@ -0,0 +1,2 @@
+shr_u, shr_v = mpcalc.bulk_shear(p, u, v, depth=200 * units.hPa)
+print(shr_u, shr_v)

notebooks_preprocess.py

@@ -9,7 +9,10 @@ def format_script_for_cell(path):
     """Read and format a .py file to be inserted into the json for a cell."""
     header = '\n# Cell content replaced by load magic replacement.\n'
     with open(str(path), encoding='utf8') as f:
-        return header + f.read()
+        solution = f.read()
+        if not solution:
+            raise RuntimeError('Solution {} has no content.'.format(path))
+        return header + solution
 
 
 def find_load_magics_in_cell(cell):
@@ -27,6 +30,14 @@ def get_cell_content_as_string(cell):
     return ''.join(cell['source']) + '\n'
 
 
+def find_extra_content(cell_text):
+    """Find and non load magic or blank lines in a solution cell."""
+    for line in cell_text.split('\n'):
+        m = re.match('#\s?%load.*', line)
+        if not m and line:
+            raise RuntimeError('Solution cell has extra content: {}'.format(cell_text))
+
+
 def process_cell(path, cell):
     """Append the data from the load magics into the cell content."""
     modified = False
@@ -39,6 +50,7 @@ def process_cell(path, cell):
         script_path = path.parent / magic_string.split('load ')[1]
         formatted_script = format_script_for_cell(script_path)
         cell_str = get_cell_content_as_string(cell)
+        find_extra_content(cell_str)
         cell['source'] = cell_str + formatted_script
         modified = True
     return modified