Merge pull request #343 from jrleeman/post_jsu_cleanup

Post jsu cleanup

.travis.yml

@@ -17,6 +17,7 @@ env:
   matrix:
     - Python=3.5
     - Python=3.6
+    - Python=3.7
 
 before_install:
   # Taken from: http://conda.pydata.org/docs/travis.html
@@ -33,7 +34,7 @@ before_install:
   - conda update -q conda
   - conda install -q pyyaml
   - conda info -a
-  - sed -i -e "s/python=3.6/python=$Python/" environment.yml
+  - sed -i -e "s/python=3/python=$Python/" environment.yml
 
 install:
   - conda env create -q -f environment.yml

appveyor.yml

@@ -6,6 +6,7 @@ environment:
 
   matrix:
     - PYTHON_VERSION: "3.6"
+    - PYTHON_VERSION: "3.7"
 
 platform:
     - x64

environment.yml

@@ -2,8 +2,9 @@
  channels:
    - conda-forge
  dependencies:
-   - python=3.6
+   - python=3
    - numpy
+   - nomkl
    - matplotlib
    - cartopy
    - jupyter

notebooks/MetPy_Advanced/Isentropic Analysis.ipynb

@@ -201,7 +201,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "isen_level = np.array([295]) * units.kelvin\n",
+    "isen_level = np.array([320]) * units.kelvin\n",
     "isen_press, isen_u, isen_v = mpcalc.isentropic_interpolation(isen_level, press,\n",
     "                                                             temperature, u, v)"
    ]
@@ -234,6 +234,7 @@
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature\n",
     "\n",
     "# Create a plot and basic map projection\n",
     "fig = plt.figure(figsize=(14, 8))\n",
@@ -245,18 +246,24 @@
     "levels = np.arange(300, 1000, 25)\n",
     "cntr = ax.contour(lon, lat, isen_press, transform=ccrs.PlateCarree(),\n",
     "                  colors='black', levels=levels)\n",
-    "ax.clabel(cntr, fmt='%.0f')\n",
+    "ax.clabel(cntr, fmt='%d')\n",
     "\n",
     "# Set up slices to subset the wind barbs--the slices below are the same as `::5`\n",
     "# We put these here so that it's easy to change and keep all of the ones below matched\n",
     "# up.\n",
     "lon_slice = slice(None, None, 5)\n",
-    "lat_slice = slice(None, None, 5)\n",
+    "lat_slice = slice(None, None, 3)\n",
     "ax.barbs(lon[lon_slice], lat[lat_slice],\n",
-    "         isen_u[lon_slice, lat_slice].to('knots').magnitude,\n",
-    "         isen_v[lon_slice, lat_slice].to('knots').magnitude,\n",
+    "         isen_u[lat_slice, lon_slice].to('knots').magnitude,\n",
+    "         isen_v[lat_slice, lon_slice].to('knots').magnitude,\n",
     "         transform=ccrs.PlateCarree(), zorder=2)\n",
     "\n",
+    "ax.add_feature(cfeature.LAND)\n",
+    "ax.add_feature(cfeature.OCEAN)\n",
+    "ax.add_feature(cfeature.COASTLINE)\n",
+    "ax.add_feature(cfeature.BORDERS, linewidth=2)\n",
+    "ax.add_feature(cfeature.STATES, linestyle=':')\n",
+    "\n",
     "ax.set_extent((-120, -70, 25, 55), crs=ccrs.PlateCarree())"
    ]
   },
@@ -313,19 +320,26 @@
     "levels = np.arange(300, 1000, 25)\n",
     "cntr = ax.contour(lon, lat, isen_press, transform=ccrs.PlateCarree(),\n",
     "                  colors='black', levels=levels)\n",
-    "ax.clabel(cntr, fmt='%.0f')\n",
+    "ax.clabel(cntr, fmt='%d')\n",
     "\n",
     "lon_slice = slice(None, None, 8)\n",
     "lat_slice = slice(None, None, 8)\n",
     "ax.barbs(lon[lon_slice], lat[lat_slice],\n",
-    "         isen_u[lon_slice, lat_slice].to('knots').magnitude,\n",
-    "         isen_v[lon_slice, lat_slice].to('knots').magnitude,\n",
+    "         isen_u[lat_slice, lon_slice].to('knots').magnitude,\n",
+    "         isen_v[lat_slice, lon_slice].to('knots').magnitude,\n",
     "         transform=ccrs.PlateCarree(), zorder=2)\n",
     "\n",
     "#\n",
     "# YOUR CODE: Contour/Contourf the mixing ratio values\n",
     "#\n",
     "\n",
+    "\n",
+    "ax.add_feature(cfeature.LAND)\n",
+    "ax.add_feature(cfeature.OCEAN)\n",
+    "ax.add_feature(cfeature.COASTLINE)\n",
+    "ax.add_feature(cfeature.BORDERS, linewidth=2)\n",
+    "ax.add_feature(cfeature.STATES, linestyle=':')\n",
+    "\n",
     "ax.set_extent((-120, -70, 25, 55), crs=ccrs.PlateCarree())"
    ]
   },
@@ -367,20 +381,26 @@
     "levels = np.arange(300, 1000, 25)\n",
     "cntr = ax.contour(lon, lat, isen_press, transform=ccrs.PlateCarree(),\n",
     "                  colors='black', levels=levels)\n",
-    "ax.clabel(cntr, fmt='%.0f')\n",
+    "ax.clabel(cntr, fmt='%d')\n",
     "\n",
     "lon_slice = slice(None, None, 8)\n",
     "lat_slice = slice(None, None, 8)\n",
     "ax.barbs(lon[lon_slice], lat[lat_slice],\n",
-    "         isen_u[lon_slice, lat_slice].to('knots').magnitude,\n",
-    "         isen_v[lon_slice, lat_slice].to('knots').magnitude,\n",
+    "         isen_u[lat_slice, lon_slice].to('knots').magnitude,\n",
+    "         isen_v[lat_slice, lon_slice].to('knots').magnitude,\n",
     "         transform=ccrs.PlateCarree(), zorder=2)\n",
     "\n",
     "\\# Contourf the mixing ratio values\n",
     "mixing_levels = [0.001, 0.002, 0.004, 0.006, 0.010, 0.012, 0.014, 0.016, 0.020]\n",
     "ax.contourf(lon, lat, isen_mixing, transform=ccrs.PlateCarree(),\n",
     "            levels=mixing_levels, cmap='YlGn')\n",
     "\n",
+    "ax.add_feature(cfeature.LAND)\n",
+    "ax.add_feature(cfeature.OCEAN)\n",
+    "ax.add_feature(cfeature.COASTLINE)\n",
+    "ax.add_feature(cfeature.BORDERS, linewidth=2)\n",
+    "ax.add_feature(cfeature.STATES, linestyle=':')\n",
+    "\n",
     "ax.set_extent((-120, -70, 25, 55), crs=ccrs.PlateCarree())\n",
     "</pre></code>\n",
     "</div>"
@@ -482,7 +502,7 @@
     "\n",
     "levels = np.arange(300, 1000, 25)\n",
     "cntr = ax.contour(lon, lat, isen_press, transform=ccrs.PlateCarree(), colors='black', levels=levels)\n",
-    "ax.clabel(cntr, fmt='%.0f')\n",
+    "ax.clabel(cntr, fmt='%d')\n",
     "\n",
     "lon_slice = slice(None, None, 5)\n",
     "lat_slice = slice(None, None, 5)\n",
@@ -500,6 +520,13 @@
     "</pre></code>\n",
     "</div>"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

notebooks/Metpy_Introduction/Introduction to MetPy.ipynb

@@ -167,7 +167,7 @@
    "metadata": {},
    "source": [
     "### Temperature\n",
-    "Temperature units are actually relatively tricky (more like absolutely tricky as you'll see). Temperature is a non-multiplicative unit - they are in a system with a reference point. That means that not only is there a scaling factor, but also an offset. This makes the math and unit book-keeping a little more complex. Imagine adding 10 degrees Celsius to 100 degrees Celsius. Is the answer 110 degrees Celsius or 383.15 degrees Celsius (283.15 K + 272.15 K)? That's why there are delta degrees units in the unit registry for offset units. For more examples and explanation you can watch [MetPy Monday #13](https://www.youtube.com/watch?v=iveJCqxe3Z4).\n",
+    "Temperature units are actually relatively tricky (more like absolutely tricky as you'll see). Temperature is a non-multiplicative unit - they are in a system with a reference point. That means that not only is there a scaling factor, but also an offset. This makes the math and unit book-keeping a little more complex. Imagine adding 10 degrees Celsius to 100 degrees Celsius. Is the answer 110 degrees Celsius or 383.15 degrees Celsius (283.15 K + 373.15 K)? That's why there are delta degrees units in the unit registry for offset units. For more examples and explanation you can watch [MetPy Monday #13](https://www.youtube.com/watch?v=iveJCqxe3Z4).\n",
     "\n",
     "Let's take a look at how this works and fails:"
    ]

notebooks/Model_Output/Downloading model fields with NCSS.ipynb

@@ -186,7 +186,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ds['time']"
+    "ds['time1']"
    ]
   },
   {
@@ -257,13 +257,6 @@
     "cax.set_label(var.attrs['units'])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -481,9 +474,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from metpy.calc import lcl, dry_lapse, parcel_profile\n",
     "from metpy.plots import SkewT\n",
-    "from metpy.units import concatenate\n",
     "\n",
     "# Create a new figure. The dimensions here give a good aspect ratio\n",
     "fig = plt.figure(figsize=(9, 9))\n",

notebooks/NumPy/Numpy Basics.ipynb

@@ -585,7 +585,9 @@
     }
    },
    "source": [
-    "Indexing in Python is 0-based, so the command below looks for the 2nd item along the first dimension (row) and the 3rd along the second dimension (column)."
+    "Indexing in Python is 0-based, so the command below looks for the 2nd item along the first dimension (row) and the 3rd along the second dimension (column).\n",
+    "\n",
+    "![](array_index.png)"
    ]
   },
   {
@@ -670,7 +672,7 @@
     "Slicing syntax is written as `start:stop[:step]`, where all numbers are optional.\n",
     "- defaults: \n",
     "  - start = 0\n",
-    "  - end = len(dim)\n",
+    "  - stop = len(dim)\n",
     "  - step = 1\n",
     "- The second colon is also optional if no step is used.\n",
     "\n",
@@ -753,10 +755,15 @@
     "The goal of this tutorial is to provide an overview of the use of the NumPy library. It tries to hit all of the important parts, but it is by no means comprehensive. For more information, try looking at the:\n",
     "- [Tentative NumPy Tutorial](http://wiki.scipy.org/Tentative_NumPy_Tutorial)\n",
     "- [NumPy User Guide](http://docs.scipy.org/doc/numpy/user/)\n",
-    "- [Introduction to NumPy from SAM](http://www.sam.math.ethz.ch/~raoulb/teaching/PythonTutorial/intro_numpy.html)\n",
-    "- [Matplotlib Documentation](http://matplotlib.org)\n",
-    "- [Matplotlib `plot` documentation](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot)"
+    "- [Introduction to NumPy from SAM](http://www.sam.math.ethz.ch/~raoulb/teaching/PythonTutorial/intro_numpy.html)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -775,7 +782,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,

notebooks/Satellite_Data/Working with Satellite Data.ipynb

@@ -49,7 +49,7 @@
     "\n",
     "The first step is to find the satellite data. Normally, we would browse over to http://thredds.ucar.edu/thredds/ and find the top-level [THREDDS Data Server (TDS)](https://www.unidata.ucar.edu/software/thredds/current/tds/TDS.html) catalog. From there we can drill down to find satellite data products.\n",
     "\n",
-    "For this tutorial, we want to utilize the new GOES-16 imagery. This is found at http://thredds-test.unidata.ucar.edu/. For current data, you could navigate to the `Test Datasets` directory, then `GOES-16 Products` and `GOES-16`. There are subfolders for the CONUS, full disk, mesoscale sector images, and other products. In each of these is a folder for each [channel of the ABI](http://www.goes-r.gov/education/ABI-bands-quick-info.html). In each channel there is a folder for every day in the month-long rolling archive. As you can see, there is a massive amount of data coming down from GOES-16! For the workshop we will be using archived case study data from Irma located at [http://thredds-test.unidata.ucar.edu/thredds/catalog/casestudies/irma/goes16](http://thredds-test.unidata.ucar.edu/thredds/catalog/casestudies/irma/goes16).\n",
+    "For this tutorial, we want to utilize the new GOES-16 imagery. This is found at http://thredds-test.unidata.ucar.edu/. For current data, you could navigate to the `Test Datasets` directory, then `GOES-16 Products` and `GOES-16`. There are subfolders for the CONUS, full disk, mesoscale sector images, and other products. In each of these is a folder for each [channel of the ABI](http://www.goes-r.gov/education/ABI-bands-quick-info.html). In each channel there is a folder for every day in the month-long rolling archive. As you can see, there is a massive amount of data coming down from GOES-16! For the workshop we will be using archived case study data from Irma located at [http://thredds-test.unidata.ucar.edu/thredds/catalog/casestudies/irma/goes16/catalog.html](http://thredds-test.unidata.ucar.edu/thredds/catalog/casestudies/irma/goes16/catalog.html).\n",
     "\n",
     "We could download the files to our computers from here, but that can become tedious for downloading many files, requires us to store them on our computer, and does not lend itself to automation.\n",
     "\n",

notebooks/Siphon/Siphon Overview.ipynb

@@ -118,7 +118,7 @@
    "outputs": [],
    "source": [
     "from datetime import datetime, timedelta\n",
-    "request_time = datetime(date.year, date.month, date.day, 18, 30)\n",
+    "request_time = date.replace(hour=18, minute=30)\n",
     "ds = cat.datasets.filter_time_nearest(request_time)\n",
     "ds"
    ]
@@ -172,7 +172,7 @@
     "<code><pre>\n",
     "date = datetime.utcnow() - timedelta(days=1)\n",
     "cat = TDSCatalog('http://thredds.ucar.edu/thredds/catalog/satellite/SFC-T/SUPER-NATIONAL_1km/{dt:%Y%m%d}/catalog.xml'.format(dt=date))\n",
-    "request_time = datetime(date.year, date.month, date.day, 12, 0)\n",
+    "request_time = date.replace(hour=12, minute=0, second=0)\n",
     "datasets = cat.datasets.filter_time_range(request_time, request_time + timedelta(hours=6))\n",
     "print(datasets)\n",
     "</pre></code>\n",

notebooks/Skew_T/Hodograph.ipynb

@@ -41,7 +41,7 @@
     "<a name=\"upperairdata\"></a>\n",
     "## Obtain upper air data\n",
     "\n",
-    "Just as we learned in the siphon basics and upper air and skew-T notebook, we need to obtain upperair data to plot. We are going to stick with September 10, 2017 at 00Z for Key West, Fl. If you need a review on obtaining upper air data, please review those lessons."
+    "Just as we learned in the siphon basics and upper air and skew-T notebook, we need to obtain upperair data to plot. We are going to get a sounding from October 4, 1998 00Z at Norman, OK. If you need a review on obtaining upper air data, please review those lessons."
    ]
   },
   {

notebooks/Surface_Data/Surface Data with Siphon and MetPy.ipynb

@@ -166,7 +166,7 @@
    "source": [
     "import numpy as np\n",
     "\n",
-    "from metpy.calc import wind_components\n",
+    "import metpy.calc as mpcalc\n",
     "from metpy.units import units\n",
     "\n",
     "# Since we used the CSV data, this is just a dictionary of arrays\n",
@@ -177,7 +177,7 @@
     "alt = data['altimeter_setting']\n",
     "\n",
     "# Convert wind to components\n",
-    "u, v = wind_components(data['wind_speed'], data['wind_direction'] * units.degree)\n",
+    "u, v = mpcalc.wind_components(data['wind_speed'], data['wind_direction'] * units.degree)\n",
     "\n",
     "# Need to handle missing (NaN) and convert to proper code\n",
     "cloud_cover = 8 * data['sky_coverage'] / 100.\n",
@@ -220,8 +220,7 @@
     "import cartopy.feature as cfeature\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "from metpy.plots import StationPlot\n",
-    "from metpy.plots.wx_symbols import sky_cover\n",
+    "from metpy.plots import StationPlot, sky_cover\n",
     "\n",
     "# Set up a plot with map features\n",
     "fig = plt.figure(figsize=(12, 12))\n",
@@ -258,14 +257,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from metpy.calc import reduce_point_density\n",
-    "\n",
     "# Project points so that we're filtering based on the way the stations are laid out on the map\n",
     "proj = ccrs.Stereographic(central_longitude=-95, central_latitude=35)\n",
     "xy = proj.transform_points(ccrs.PlateCarree(), lons, lats)\n",
     "\n",
     "# Reduce point density so that there's only one point within a 200km circle\n",
-    "mask = reduce_point_density(xy, 200000)"
+    "mask = mpcalc.reduce_point_density(xy, 200000)"
    ]
   },
   {
@@ -351,7 +348,7 @@
     "<div id=\"sol1\" class=\"collapse\">\n",
     "<code><pre>\n",
     "# Use reduce_point_density\n",
-    "mask = reduce_point_density(xy, 75000)\n",
+    "mask = mpcalc.reduce_point_density(xy, 75000)\n",
     "\n",
     "\\# Set up a plot with map features\n",
     "fig = plt.figure(figsize=(12, 12))\n",