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{
"cells": [
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python AWIPS Data Access Framework\n",
"\n",
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"The [`python-awips`](http://www.github.com/Unidata/python-awips) package provides a Data Acccess Framework (DAF) for requesting data from a remote AWIPS II EDEX server. \n",
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"\n",
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"The [AWIPS II Python Stack](http://) installed via RPM contains the DAF, matplotlib, numpy, scipy, basemap, pint, shapely, and other packages. \n",
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"\n",
"## Listing a Grid Inventory"
]
},
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{
"cell_type": "code",
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"execution_count": 1,
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"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"AVN211\n",
"AVN225\n",
"DGEX\n",
"ECMF-Global\n",
"ECMF1\n",
"ECMF10\n",
"ECMF11\n",
"ECMF12\n",
"ECMF2\n",
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"ECMF3\n",
"ECMF4\n",
"ECMF5\n",
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"ECMF6\n",
"ECMF7\n",
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"ECMF8\n",
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"ECMF9\n",
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"ETA\n",
"GFS\n",
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"GFS40\n",
"GFSGuide\n",
"GFSLAMP5\n",
"HPCGuide\n",
"HPCqpfNDFD\n",
"HRRR\n",
"LAMP2p5\n",
"MRMS_1000\n",
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"NAM12\n",
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"NAM40\n",
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"NCWF\n",
"NOHRSC-SNOW\n",
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"NamDNG\n",
"NamDNG5\n",
"QPE-MSR\n",
"RAP13\n",
"RAP40\n",
"RTMA\n",
"RTMA5\n",
"URMA25\n",
"estofsPR\n",
"estofsUS\n"
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]
}
],
"source": [
"from awips.dataaccess import DataAccessLayer\n",
"\n",
"# Set host\n",
"DataAccessLayer.changeEDEXHost(\"edex-cloud.unidata.ucar.edu\")\n",
"\n",
"# Init data request\n",
"request = DataAccessLayer.newDataRequest()\n",
"\n",
"# Set datatype\n",
"request.setDatatype(\"grid\")\n",
"\n",
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"# Get a list of all available models\n",
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"available_grids = DataAccessLayer.getAvailableLocationNames(request)\n",
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"\n",
"# Sort\n",
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"available_grids.sort()\n",
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"\n",
"# Print\n",
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"for grid in available_grids:\n",
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" print grid"
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]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
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"**LocationNames** is different for different plugins\n",
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"- radar is icao\n",
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"- satellite is sector\n",
"\n",
"\n",
"## Requesting a Grid"
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]
},
{
"cell_type": "code",
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"execution_count": 2,
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"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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"Time : 2016-02-23 15:00:00 (12)\n",
"Model: RAP40\n",
"Unit : %\n",
"Parm : RH\n",
"(151, 113)\n",
"[[ 93.05456543 93.05456543 87.05456543 ..., 73.05456543 72.05456543\n",
" 71.05456543]\n",
" [ 70.05456543 70.05456543 67.05456543 ..., 69.05456543 46.05456924\n",
" 37.05456924]\n",
" [ 40.05456924 56.05456924 68.05456543 ..., 51.05456924 73.05456543\n",
" 74.05456543]\n",
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" ..., \n",
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" [ 65.05456543 62.05456924 63.05456924 ..., 67.05456543 65.05456543\n",
" 46.05456924]\n",
" [ 48.05456924 59.05456924 62.05456924 ..., 4.05456877 5.05456877\n",
" 5.05456877]\n",
" [ 7.05456877 8.05456829 10.05456829 ..., 91.05456543 95.05456543\n",
" 95.05456543]]\n",
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"lat array = [[ 54.24940109 54.35071945 54.45080566 ..., 57.9545517 57.91926193\n",
" 57.88272858]\n",
" [ 57.84495163 57.80593109 57.76566696 ..., 58.07667542 58.08861542\n",
" 58.09931183]\n",
" [ 58.10876846 58.11697769 58.12394714 ..., 56.40270996 56.46187973\n",
" 56.51980972]\n",
" ..., \n",
" [ 19.93209648 19.89832115 19.86351395 ..., 20.054636 20.06362152\n",
" 20.07156372]\n",
" [ 20.0784626 20.08431816 20.08912849 ..., 18.58354759 18.63155174\n",
" 18.67854691]\n",
" [ 18.72453308 18.76950836 18.81346893 ..., 17.49624634 17.42861557\n",
" 17.36001205]]\n",
"lon array = [[-139.83120728 -139.32348633 -138.81448364 ..., -79.26060486\n",
" -78.70166016 -78.14326477]\n",
" [ -77.58544922 -77.02822876 -76.47161865 ..., -100.70157623\n",
" -100.13801575 -99.57427216]\n",
" [ -99.01037598 -98.44634247 -97.88218689 ..., -121.69165039\n",
" -121.15060425 -120.60871887]\n",
" ..., \n",
" [ -82.65139008 -82.26644897 -81.88170624 ..., -98.52494049\n",
" -98.13802338 -97.75105286]\n",
" [ -97.36403656 -96.97698212 -96.58989716 ..., -113.07767487\n",
" -112.69831085 -112.31866455]\n",
" [-111.93874359 -111.5585556 -111.17810822 ..., -69.85433197\n",
" -69.48160553 -69.10926819]]\n"
]
}
],
"source": [
"# Grid request\n",
"request.setLocationNames('RAP40')\n",
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"request.setParameters(\"RH\")\n",
"request.setLevels(\"850MB\")\n",
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"\n",
"# Get available times\n",
"t = DataAccessLayer.getAvailableTimes(request)\n",
"\n",
"# Select last available time [-1]\n",
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"response = DataAccessLayer.getGridData(request, [t[0]])\n",
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"data = response[0]\n",
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"lon,lat = data.getLatLonCoords()\n",
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"\n",
"# Print info\n",
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"print 'Time :', t[-1]\n",
"print 'Model:', data.getLocationName()\n",
"print 'Unit :', data.getUnit()\n",
"print 'Parm :', data.getParameter()\n",
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"\n",
"# Print data array\n",
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"print data.getRawData().shape\n",
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"print data.getRawData()\n",
"print \"lat array =\", lat\n",
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"print \"lon array =\", lon\n"
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]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"## Plotting a Grid using Basemap\n",
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"\n",
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"Using **matplotlib**, **numpy**, and **basemap**:"
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]
},
{
"cell_type": "code",
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"execution_count": 3,
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"metadata": {
"collapsed": false
},
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"outputs": [
{
"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f9435e09810>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
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"source": [
"%matplotlib inline\n",
"import matplotlib.tri as mtri\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.transforms import offset_copy\n",
"from mpl_toolkits.basemap import Basemap, cm\n",
"import numpy as np\n",
"from numpy import linspace, transpose\n",
"from numpy import meshgrid\n",
"\n",
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"plt.figure(figsize=(12, 12), dpi=100)\n",
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"lons,lats = data.getLatLonCoords()\n",
"\n",
"map = Basemap(projection='cyl',\n",
" resolution = 'c',\n",
" llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n",
" urcrnrlon =lons.max(), urcrnrlat = lats.max()\n",
")\n",
"map.drawcoastlines()\n",
"map.drawstates()\n",
"map.drawcountries()\n",
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"\n",
"# \n",
"# We have to reproject our grid, see https://stackoverflow.com/questions/31822553/m\n",
"#\n",
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"x = linspace(0, map.urcrnrx, data.getRawData().shape[1])\n",
"y = linspace(0, map.urcrnry, data.getRawData().shape[0])\n",
"xx, yy = meshgrid(x, y)\n",
"ngrid = len(x)\n",
"rlons = np.repeat(np.linspace(np.min(lons), np.max(lons), ngrid),\n",
" ngrid).reshape(ngrid, ngrid)\n",
"rlats = np.repeat(np.linspace(np.min(lats), np.max(lats), ngrid),\n",
" ngrid).reshape(ngrid, ngrid).T\n",
"tli = mtri.LinearTriInterpolator(mtri.Triangulation(lons.flatten(),\n",
" lats.flatten()), data.getRawData().flatten())\n",
"rdata = tli(rlons, rlats)\n",
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"cs = map.contourf(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')\n",
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"\n",
"# add colorbar.\n",
"cbar = map.colorbar(cs,location='bottom',pad=\"5%\")\n",
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"cbar.set_label(data.getParameter() + data.getUnit() )\n",
"\n",
"# Show plot\n",
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"plt.show()\n"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"or use `pcolormesh` instead of `contourf`"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f94401cbfd0>"
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12, 12), dpi=100)\n",
"map = Basemap(projection='cyl',\n",
" resolution = 'c',\n",
" llcrnrlon = lons.min(), llcrnrlat = lats.min(),\n",
" urcrnrlon =lons.max(), urcrnrlat = lats.max()\n",
")\n",
"map.drawcoastlines()\n",
"map.drawstates()\n",
"map.drawcountries()\n",
"cs = map.pcolormesh(rlons, rlats, rdata, latlon=True, vmin=0, vmax=100, cmap='YlGn')"
]
},
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{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plotting a Grid using Cartopy\n"
]
},
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{
"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
"name": "stderr",
"output_type": "stream",
"text": [
"/awips2/python/lib/python2.7/site-packages/matplotlib/artist.py:221: MatplotlibDeprecationWarning: This has been deprecated in mpl 1.5, please use the\n",
"axes property. A removal date has not been set.\n",
" warnings.warn(_get_axes_msg, mplDeprecation, stacklevel=1)\n"
]
},
{
"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAq8AAAIICAYAAABAeAfLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS7Unol5Cy8hsmDvBVBJ
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f9434111ad0>"
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]
},
"metadata": {},
"output_type": "display_data"
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}
],
"source": [
"import os\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
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"import iris\n",
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"import cartopy.crs as ccrs\n",
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"from cartopy import config\n",
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"\n",
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"lon,lat = data.getLatLonCoords()\n",
"plt.figure(figsize=(12, 12), dpi=100)\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"cs = plt.contourf(rlons, rlats, rdata, 60, transform=ccrs.PlateCarree(), vmin=0, vmax=100, cmap='YlGn')\n",
"ax.coastlines()\n",
"ax.gridlines()\n",
"\n",
"# add colorbar\n",
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"cbar = plt.colorbar(orientation='horizontal')\n",
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"cbar.set_label(data.getParameter() + data.getUnit() )\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Plotting a Sounding with MetPy"
]
},
{
"cell_type": "code",
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"execution_count": 123,
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"metadata": {
"collapsed": false
},
"outputs": [
{
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"data": {
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7f94210a1d90>"
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]
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"metadata": {},
"output_type": "display_data"
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}
],
"source": [
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"from metpy.calc import get_wind_components, lcl, dry_lapse, parcel_profile\n",
"from metpy.calc import tools\n",
"from metpy.plots import SkewT, Hodograph\n",
"from metpy.units import units, concatenate\n",
"from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
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"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
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"\n",
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"# Change default to be better for skew-T\n",
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"plt.rcParams['figure.figsize'] = (9, 9)\n",
"#fig = plt.figure(figsize=(9, 9))\n",
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"\n",
"# Request BUFR UAIR\n",
"request.setDatatype(\"bufrua\")\n",
"request.setParameters(\"tpMan\",\"tdMan\",\"prMan\",\"htMan\",\"wdMan\",\"wsMan\")\n",
"request.setLocationNames(\"72240\")\n",
"datatimes = DataAccessLayer.getAvailableTimes(request)\n",
"response = DataAccessLayer.getGeometryData(request,times=datatimes[-1].validPeriod)\n",
"\n",
"# Initialize data arrays\n",
"tpMan,tdMan,prMan,htMan,wdMan,wsMan = [],[],[],[],[],[]\n",
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"\n",
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"# Build ordered arrays\n",
"for ob in response:\n",
" tpMan.append(float(ob.getString(\"tpMan\")))\n",
" tdMan.append(float(ob.getString(\"tdMan\")))\n",
" prMan.append(float(ob.getString(\"prMan\")))\n",
" htMan.append(float(ob.getString(\"htMan\")))\n",
" wdMan.append(float(ob.getString(\"wdMan\")))\n",
" wsMan.append(float(ob.getString(\"wsMan\")))\n",
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"\n",
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"# Convert temps to C\n",
"T = np.array(tpMan)-273.15\n",
"Td = np.array(tdMan)-273.15\n",
"p = np.array(prMan)/100\n",
"height = np.array(htMan)\n",
"direc = np.array(wdMan)\n",
"spd = np.array(wsMan)\n",
"u, v = get_wind_components(spd, np.deg2rad(direc))\n",
"p = p * units.mbar\n",
"T = T * units.degC\n",
"Td = Td * units.degC\n",
"spd = spd * units.knot\n",
"direc = direc * units.deg\n",
"\n",
"# Create a skewT plot\n",
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"skew = SkewT()\n",
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"\n",
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"# Plot the data using normal plotting functions, in this case using\n",
"# log scaling in Y, as dictated by the typical meteorological plot\n",
"skew.plot(p, T, 'r')\n",
"skew.plot(p, Td, 'g')\n",
"skew.plot_barbs(p, u, v)\n",
"skew.ax.set_ylim(1000, 100)\n",
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"skew.ax.set_xlim(-40, 60)\n",
"\n",
"# Calculate LCL height and plot as black dot\n",
"l = lcl(p[0], T[0], Td[0])\n",
"lcl_temp = dry_lapse(concatenate((p[0], l)), T[0])[-1].to('degC')\n",
"skew.plot(l, lcl_temp, 'ko', markerfacecolor='black')\n",
"\n",
"# Calculate full parcel profile and add to plot as black line\n",
"prof = parcel_profile(p, T[0], Td[0]).to('degC')\n",
"skew.plot(p, prof, 'k', linewidth=2)\n",
"\n",
"# Example of coloring area between profiles\n",
"skew.ax.fill_betweenx(p, T, prof, where=T>=prof, facecolor='blue', alpha=0.4)\n",
"skew.ax.fill_betweenx(p, T, prof, where=T<prof, facecolor='red', alpha=0.4)\n",
"\n",
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"# An example of a slanted line at constant T -- in this case the 0 isotherm\n",
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"l = skew.ax.axvline(0, color='c', linestyle='--', linewidth=2)\n",
"\n",
"# Draw hodograph\n",
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"ax_hod = inset_axes(skew.ax, '30%', '30%', loc=3)\n",
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"h = Hodograph(ax_hod, component_range=50.)\n",
"h.add_grid(increment=20)\n",
"h.plot_colormapped(u, v, spd)\n",
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"\n",
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"# Show the plot\n",
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"plt.show()"
]
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},
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