├── .gitignore
├── 0_TEST.ipynb
├── 1_Welcome.ipynb
├── 2_Intro_to_Python_and_SciPy.ipynb
├── 3_Basic_PyART.ipynb
├── 4a_PyART_IO_in_detail.ipynb
├── 4b_PyART_data_model.ipynb
├── 5_Simple_proccessing_and_adding_a_field.ipynb
├── 6_Texture_and_Dealiasing.ipynb
├── 7_Mapping_to_a_cartesian_grid.ipynb
├── 8a_artview_demo_ams37.ipynb
├── 8b_plot_current_nexrad_open_artview.ipynb
├── 9a_CSU_RadarTools_Demo-AMS_OSRSC.ipynb
├── 9b_PyTDA_Demo-AMS_OSRSC.ipynb
├── LICENSE
├── README.md
├── course_outline.rst
├── data
    ├── README.md
    └── provision_data.sh
├── images
    ├── ams.jpg
    ├── arm_logo.png
    ├── freebeer.png
    ├── jupyter-logo.svg
    ├── matplotlib_logo.png
    ├── numpylogo.svg
    ├── python-logo.png
    └── scipy.png
└── testme.py


/.gitignore:
--------------------------------------------------------------------------------
1 | .ipynb_checkpoints
2 | data/
3 | # ignore netCDF files created output by notebooks
4 | *.nc
5 | 


--------------------------------------------------------------------------------
/0_TEST.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "markdown",
 5 |    "metadata": {},
 6 |    "source": [
 7 |     "This notebook will test your system to check if Anaconda and Py-ART have been installed correctly.  Select the cell below and execute by hitting shift+enter.  If **Ready to go** is printed as the first line, everything is installed correctly.  If **ISSUES** is printed or you get an error, something is mis-configured with your system, ask one of the instructors for help."
 8 |    ]
 9 |   },
10 |   {
11 |    "cell_type": "code",
12 |    "execution_count": null,
13 |    "metadata": {
14 |     "collapsed": false
15 |    },
16 |    "outputs": [],
17 |    "source": [
18 |     "import pyart\n",
19 |     "\n",
20 |     "if pyart.__version__ == \"1.7.0\":\n",
21 |     "    if hasattr(pyart.graph, 'RadarMapDisplay'):\n",
22 |     "        print \"Ready to go\"\n",
23 |     "    else:\n",
24 |     "        print \"ISSUES\\n\\nMissing\\Broken Basemap\\n\"\n",
25 |     "        pyart._debug_info()\n",
26 |     "else:\n",
27 |     "    print \"ISSUES\\n\\nWrong Py-ART version\\n\"\n",
28 |     "    pyart._debug_info()"
29 |    ]
30 |   }
31 |  ],
32 |  "metadata": {
33 |   "kernelspec": {
34 |    "display_name": "Python 3",
35 |    "language": "python",
36 |    "name": "python3"
37 |   },
38 |   "language_info": {
39 |    "codemirror_mode": {
40 |     "name": "ipython",
41 |     "version": 3
42 |    },
43 |    "file_extension": ".py",
44 |    "mimetype": "text/x-python",
45 |    "name": "python",
46 |    "nbconvert_exporter": "python",
47 |    "pygments_lexer": "ipython3",
48 |    "version": "3.5.2"
49 |   }
50 |  },
51 |  "nbformat": 4,
52 |  "nbformat_minor": 0
53 | }
54 | 


--------------------------------------------------------------------------------
/1_Welcome.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {
  6 |     "slideshow": {
  7 |      "slide_type": "slide"
  8 |     }
  9 |    },
 10 |    "source": [
 11 |     "# <span class=\"hgviolet\">Welcome to the AMS Short Course on Open Source Radar Software</span>"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "markdown",
 16 |    "metadata": {
 17 |     "slideshow": {
 18 |      "slide_type": "notes"
 19 |     }
 20 |    },
 21 |    "source": [
 22 |     "Stuff"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "markdown",
 27 |    "metadata": {
 28 |     "slideshow": {
 29 |      "slide_type": "fragment"
 30 |     }
 31 |    },
 32 |    "source": [
 33 |     "\n",
 34 |     "- Scott Collis, Argonne National Laboratory\n",
 35 |     "- Jonathan Helmus, Argonne National Laboratory\n",
 36 |     "- Nick Guy, University of Wyoming\n",
 37 |     "- Timothy Lang, NASA Marshall Space Flight Center \n",
 38 |     "- Joseph Hardin, Pacific Northwest National Laboratory\n",
 39 |     "- Marcus van Lier-Walqui, NASA Goddard Institute of Space Studies\n",
 40 |     "\n",
 41 |     "<div>\n",
 42 |     "    <img class=\"logo\" src=\"./images/python-logo.png\" height=\"100\" width=\"250\" align=\"left\"/>\n",
 43 |     "    <img class=\"logo\" src=\"./images/arm_logo.png\" height=\"100\" width=\"250\" align=\"right\"/>\n",
 44 |     "</div>\n",
 45 |     "<img class=\"logo\" src=\"./images/ams.jpg\" height=\"150\" align='center'/>\n"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "markdown",
 50 |    "metadata": {
 51 |     "slideshow": {
 52 |      "slide_type": "slide"
 53 |     }
 54 |    },
 55 |    "source": [
 56 |     "#What Is Open Source?\n"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "markdown",
 61 |    "metadata": {
 62 |     "slideshow": {
 63 |      "slide_type": "fragment"
 64 |     }
 65 |    },
 66 |    "source": [
 67 |     "###\"Free software is a matter of liberty, not price. To understand the concept, you should think of free as in free speech, not as in free beer.\"\n",
 68 |     "  #####—Richard Stallman\n",
 69 |     "<img src='./images/freebeer.png'> \n",
 70 |     "######<h5><a href=\"https://commons.wikimedia.org/wiki/File:Isummit_2008,_Japan,_free_beer_crop.png#/media/File:Isummit_2008,_Japan,_free_beer_crop.png\">Isummit 2008, Japan, free beer crop</a>\" by shaddim - <a class=\"external free\" href=\"https://commons.wikimedia.org/wiki/File:Isummit_2008,_Japan,_free_beer.jpg\">https://commons.wikimedia.org/wiki/File:Isummit_2008,_Japan,_free_beer.jpg</a>. Licensed under <a title=\"Creative Commons Attribution-Share Alike 2.5\" href=\"http://creativecommons.org/licenses/by-sa/2.5\">CC BY-SA 2.5</a> via <a href=\"https://commons.wikimedia.org/wiki/\">Commons</a></h5>"
 71 |    ]
 72 |   },
 73 |   {
 74 |    "cell_type": "markdown",
 75 |    "metadata": {
 76 |     "slideshow": {
 77 |      "slide_type": "slide"
 78 |     }
 79 |    },
 80 |    "source": [
 81 |     "#Course Objectives "
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "markdown",
 86 |    "metadata": {
 87 |     "slideshow": {
 88 |      "slide_type": "fragment"
 89 |     }
 90 |    },
 91 |    "source": [
 92 |     "- To give a familiarity with Python and the Scientific Python ecosystem and IPython and project Jupyter"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "markdown",
 97 |    "metadata": {
 98 |     "slideshow": {
 99 |      "slide_type": "fragment"
100 |     }
101 |    },
102 |    "source": [
103 |     "- To introduce the Python ARM Radar Toolkit and reveal the data model used"
104 |    ]
105 |   },
106 |   {
107 |    "cell_type": "markdown",
108 |    "metadata": {
109 |     "slideshow": {
110 |      "slide_type": "fragment"
111 |     }
112 |    },
113 |    "source": [
114 |     "- To show how to plot a variety of different radar data with Py-ART"
115 |    ]
116 |   },
117 |   {
118 |    "cell_type": "markdown",
119 |    "metadata": {
120 |     "slideshow": {
121 |      "slide_type": "fragment"
122 |     }
123 |    },
124 |    "source": [
125 |     "- Demonstrate how to add a field so you can do processing on your own data"
126 |    ]
127 |   },
128 |   {
129 |    "cell_type": "markdown",
130 |    "metadata": {
131 |     "slideshow": {
132 |      "slide_type": "fragment"
133 |     }
134 |    },
135 |    "source": [
136 |     "- Introduce you to several tools in Py-ART including texture retrieval, dealiasing and gridding"
137 |    ]
138 |   },
139 |   {
140 |    "cell_type": "markdown",
141 |    "metadata": {
142 |     "slideshow": {
143 |      "slide_type": "slide"
144 |     }
145 |    },
146 |    "source": [
147 |     "#Course objectives\n",
148 |     "- Introduce you to ART-View"
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "markdown",
153 |    "metadata": {
154 |     "slideshow": {
155 |      "slide_type": "fragment"
156 |     }
157 |    },
158 |    "source": [
159 |     "- Introduce you to an array of tools built on and independant of Py-ART for working with radar data"
160 |    ]
161 |   },
162 |   {
163 |    "cell_type": "markdown",
164 |    "metadata": {
165 |     "slideshow": {
166 |      "slide_type": "slide"
167 |     }
168 |    },
169 |    "source": [
170 |     "8:30 a.m.\tARRIVAL AND INTRODUCTIONS AND INSTALLATION OF SOFTWARE AND DATA. Collis and Helmus\n",
171 |     "<br>9:15 a.m.\tAN INTRODUCTION TO PYTHON AND IPYTHON Jonathan Helmus\t \n",
172 |     "9:30 a.m.\tBASIC PY-ART AND THE PY-ART DATA MODEL Jonathan Helmus\n",
173 |     "<br>10:00 a.m.\tCOFFEE BREAK "
174 |    ]
175 |   },
176 |   {
177 |    "cell_type": "markdown",
178 |    "metadata": {
179 |     "slideshow": {
180 |      "slide_type": "fragment"
181 |     }
182 |    },
183 |    "source": [
184 |     "10:15 a.m.\tBASIC PY-ART AND THE PY-ART DATA MODEL (CONT) Jonathan Helmus\n",
185 |     "<br>11:30 a.m.\tEXAMPLES OF RADAR PROCESSING USING PY-ART Scott Collis\n",
186 |     "<br>12:00 a.m.\tLUNCH (included)"
187 |    ]
188 |   },
189 |   {
190 |    "cell_type": "markdown",
191 |    "metadata": {
192 |     "slideshow": {
193 |      "slide_type": "slide"
194 |     }
195 |    },
196 |    "source": [
197 |     "1:30 p.m.\tEXAMPLES OF RADAR PROCESSING USING PY-ART (CONT) Scott Collis\n",
198 |     "<br>2:00 p.m.\tPy-ART TOOLS, TEXTURE, DEALIAS AND GRID Scott Collis\n",
199 |     "<br>2:30 p.m.\tPYTHON TOOLS 1: ART-VIEW Nick Guy.\n",
200 |     "<br>3:00 p.m\tCOFFEE BREAK"
201 |    ]
202 |   },
203 |   {
204 |    "cell_type": "markdown",
205 |    "metadata": {
206 |     "slideshow": {
207 |      "slide_type": "fragment"
208 |     }
209 |    },
210 |    "source": [
211 |     "3:30 p.m.\tPYTHON TOOLS 2: RADAR MET TOOLS. Tim Lang\n",
212 |     "<br>4:15 p.m.\tWRAP UP AND FUTHER DISCUSSION. ALL\n",
213 |     "<br>5:00 p.m.\tAdjourn. "
214 |    ]
215 |   },
216 |   {
217 |    "cell_type": "markdown",
218 |    "metadata": {
219 |     "collapsed": true,
220 |     "slideshow": {
221 |      "slide_type": "slide"
222 |     }
223 |    },
224 |    "source": [
225 |     "#This is a living course"
226 |    ]
227 |   },
228 |   {
229 |    "cell_type": "markdown",
230 |    "metadata": {
231 |     "collapsed": true,
232 |     "slideshow": {
233 |      "slide_type": "fragment"
234 |     }
235 |    },
236 |    "source": [
237 |     "- Data is at: <a href=\"http://figshare.com/articles/Data_for_AMS_Short_Course_on_Open_Source_Radar_Software/1537461\"> This figshare repository</a>\n"
238 |    ]
239 |   },
240 |   {
241 |    "cell_type": "markdown",
242 |    "metadata": {
243 |     "slideshow": {
244 |      "slide_type": "fragment"
245 |     }
246 |    },
247 |    "source": [
248 |     "- Materials live <a href=\"https://github.com/openradar/AMS-Short-Course-on-Open-Source-Radar-Software\">This GitHub Repository</a>"
249 |    ]
250 |   },
251 |   {
252 |    "cell_type": "code",
253 |    "execution_count": null,
254 |    "metadata": {
255 |     "collapsed": true
256 |    },
257 |    "outputs": [],
258 |    "source": []
259 |   }
260 |  ],
261 |  "metadata": {
262 |   "celltoolbar": "Slideshow",
263 |   "kernelspec": {
264 |    "display_name": "Python 2",
265 |    "language": "python",
266 |    "name": "python2"
267 |   },
268 |   "language_info": {
269 |    "codemirror_mode": {
270 |     "name": "ipython",
271 |     "version": 2
272 |    },
273 |    "file_extension": ".py",
274 |    "mimetype": "text/x-python",
275 |    "name": "python",
276 |    "nbconvert_exporter": "python",
277 |    "pygments_lexer": "ipython2",
278 |    "version": "2.7.10"
279 |   }
280 |  },
281 |  "nbformat": 4,
282 |  "nbformat_minor": 0
283 | }
284 | 


--------------------------------------------------------------------------------
/2_Intro_to_Python_and_SciPy.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {
   6 |     "slideshow": {
   7 |      "slide_type": "slide"
   8 |     }
   9 |    },
  10 |    "source": [
  11 |     "<img class=\"logo\" src=\"images/python-logo.png\" height=100 align='right'/>\n",
  12 |     "\n",
  13 |     "### Python\n",
  14 |     "\n",
  15 |     "* General-purpose.\n",
  16 |     "* Interpreted.\n",
  17 |     "* Focuses on readability.\n",
  18 |     "* Excellent for interfacing with C, C++ and Fortran code. \n",
  19 |     "* Comprehesive standard library.\n",
  20 |     "* Extended with a large number of third-party packages.\n",
  21 |     "* Widely used in scientific programming."
  22 |    ]
  23 |   },
  24 |   {
  25 |    "cell_type": "markdown",
  26 |    "metadata": {
  27 |     "slideshow": {
  28 |      "slide_type": "notes"
  29 |     }
  30 |    },
  31 |    "source": [
  32 |     "This presentation will give a brief into to some key features of Python and the Scientific Python ecosystem to help those not familar with the language with the remainder of the class.  This is in no way a comprehensive introduction to either topic.  Excellent tutorials on Python and Scientific Python can be found online.\n",
  33 |     "\n",
  34 |     "We will be using IPython for this class which is a package which allows Python code to be run inside a browser.  This is in no way the only way to run python, the Python/IPython shell, scripts and various IDEs can also be used but will not be coverted.\n",
  35 |     "\n",
  36 |     "The notebook for this materials is available if you wish to follow along on your own computer, but we will be moving fast..."
  37 |    ]
  38 |   },
  39 |   {
  40 |    "cell_type": "markdown",
  41 |    "metadata": {
  42 |     "slideshow": {
  43 |      "slide_type": "slide"
  44 |     }
  45 |    },
  46 |    "source": [
  47 |     "## Variables\n",
  48 |     "\n",
  49 |     "* ### Integers"
  50 |    ]
  51 |   },
  52 |   {
  53 |    "cell_type": "code",
  54 |    "execution_count": null,
  55 |    "metadata": {
  56 |     "collapsed": false,
  57 |     "slideshow": {
  58 |      "slide_type": "fragment"
  59 |     }
  60 |    },
  61 |    "outputs": [],
  62 |    "source": [
  63 |     "a = 1"
  64 |    ]
  65 |   },
  66 |   {
  67 |    "cell_type": "code",
  68 |    "execution_count": null,
  69 |    "metadata": {
  70 |     "collapsed": false,
  71 |     "slideshow": {
  72 |      "slide_type": "-"
  73 |     }
  74 |    },
  75 |    "outputs": [],
  76 |    "source": [
  77 |     "a + 1"
  78 |    ]
  79 |   },
  80 |   {
  81 |    "cell_type": "markdown",
  82 |    "metadata": {
  83 |     "slideshow": {
  84 |      "slide_type": "fragment"
  85 |     }
  86 |    },
  87 |    "source": [
  88 |     "* ### Floating point numbers"
  89 |    ]
  90 |   },
  91 |   {
  92 |    "cell_type": "code",
  93 |    "execution_count": null,
  94 |    "metadata": {
  95 |     "collapsed": false,
  96 |     "slideshow": {
  97 |      "slide_type": "fragment"
  98 |     }
  99 |    },
 100 |    "outputs": [],
 101 |    "source": [
 102 |     "b = 2.1"
 103 |    ]
 104 |   },
 105 |   {
 106 |    "cell_type": "code",
 107 |    "execution_count": null,
 108 |    "metadata": {
 109 |     "collapsed": false,
 110 |     "slideshow": {
 111 |      "slide_type": "-"
 112 |     }
 113 |    },
 114 |    "outputs": [],
 115 |    "source": [
 116 |     "b + 1"
 117 |    ]
 118 |   },
 119 |   {
 120 |    "cell_type": "code",
 121 |    "execution_count": null,
 122 |    "metadata": {
 123 |     "collapsed": false,
 124 |     "slideshow": {
 125 |      "slide_type": "-"
 126 |     }
 127 |    },
 128 |    "outputs": [],
 129 |    "source": [
 130 |     "a + b"
 131 |    ]
 132 |   },
 133 |   {
 134 |    "cell_type": "code",
 135 |    "execution_count": null,
 136 |    "metadata": {
 137 |     "collapsed": false,
 138 |     "slideshow": {
 139 |      "slide_type": "-"
 140 |     }
 141 |    },
 142 |    "outputs": [],
 143 |    "source": [
 144 |     "type(a + b)"
 145 |    ]
 146 |   },
 147 |   {
 148 |    "cell_type": "markdown",
 149 |    "metadata": {
 150 |     "slideshow": {
 151 |      "slide_type": "slide"
 152 |     }
 153 |    },
 154 |    "source": [
 155 |     "## Variables\n",
 156 |     "\n",
 157 |     "* ### Complex numbers"
 158 |    ]
 159 |   },
 160 |   {
 161 |    "cell_type": "code",
 162 |    "execution_count": null,
 163 |    "metadata": {
 164 |     "collapsed": true,
 165 |     "slideshow": {
 166 |      "slide_type": "fragment"
 167 |     }
 168 |    },
 169 |    "outputs": [],
 170 |    "source": [
 171 |     "c = 1.5 + 0.5j  # complex numbers"
 172 |    ]
 173 |   },
 174 |   {
 175 |    "cell_type": "code",
 176 |    "execution_count": null,
 177 |    "metadata": {
 178 |     "collapsed": false,
 179 |     "slideshow": {
 180 |      "slide_type": "-"
 181 |     }
 182 |    },
 183 |    "outputs": [],
 184 |    "source": [
 185 |     "print c.real\n",
 186 |     "print c.imag"
 187 |    ]
 188 |   },
 189 |   {
 190 |    "cell_type": "markdown",
 191 |    "metadata": {
 192 |     "slideshow": {
 193 |      "slide_type": "fragment"
 194 |     }
 195 |    },
 196 |    "source": [
 197 |     "  * ### Booleans"
 198 |    ]
 199 |   },
 200 |   {
 201 |    "cell_type": "code",
 202 |    "execution_count": null,
 203 |    "metadata": {
 204 |     "collapsed": true,
 205 |     "slideshow": {
 206 |      "slide_type": "fragment"
 207 |     }
 208 |    },
 209 |    "outputs": [],
 210 |    "source": [
 211 |     "d = 3 > 4 "
 212 |    ]
 213 |   },
 214 |   {
 215 |    "cell_type": "code",
 216 |    "execution_count": null,
 217 |    "metadata": {
 218 |     "collapsed": false,
 219 |     "slideshow": {
 220 |      "slide_type": "-"
 221 |     }
 222 |    },
 223 |    "outputs": [],
 224 |    "source": [
 225 |     "print d"
 226 |    ]
 227 |   },
 228 |   {
 229 |    "cell_type": "code",
 230 |    "execution_count": null,
 231 |    "metadata": {
 232 |     "collapsed": false,
 233 |     "slideshow": {
 234 |      "slide_type": "-"
 235 |     }
 236 |    },
 237 |    "outputs": [],
 238 |    "source": [
 239 |     "type(d)"
 240 |    ]
 241 |   },
 242 |   {
 243 |    "cell_type": "markdown",
 244 |    "metadata": {
 245 |     "slideshow": {
 246 |      "slide_type": "slide"
 247 |     }
 248 |    },
 249 |    "source": [
 250 |     "## Variables\n",
 251 |     "\n",
 252 |     "* ### Strings"
 253 |    ]
 254 |   },
 255 |   {
 256 |    "cell_type": "code",
 257 |    "execution_count": null,
 258 |    "metadata": {
 259 |     "collapsed": false,
 260 |     "slideshow": {
 261 |      "slide_type": "fragment"
 262 |     }
 263 |    },
 264 |    "outputs": [],
 265 |    "source": [
 266 |     "s = \"Hello everyone\"\n",
 267 |     "type(s)"
 268 |    ]
 269 |   },
 270 |   {
 271 |    "cell_type": "code",
 272 |    "execution_count": null,
 273 |    "metadata": {
 274 |     "collapsed": false,
 275 |     "slideshow": {
 276 |      "slide_type": "fragment"
 277 |     }
 278 |    },
 279 |    "outputs": [],
 280 |    "source": [
 281 |     "a = \"Hello \" \n",
 282 |     "b = \"World\"\n",
 283 |     "print a + b"
 284 |    ]
 285 |   },
 286 |   {
 287 |    "cell_type": "markdown",
 288 |    "metadata": {
 289 |     "slideshow": {
 290 |      "slide_type": "slide"
 291 |     }
 292 |    },
 293 |    "source": [
 294 |     "## Variables can be cast from one type to another"
 295 |    ]
 296 |   },
 297 |   {
 298 |    "cell_type": "code",
 299 |    "execution_count": null,
 300 |    "metadata": {
 301 |     "collapsed": false,
 302 |     "slideshow": {
 303 |      "slide_type": "fragment"
 304 |     }
 305 |    },
 306 |    "outputs": [],
 307 |    "source": [
 308 |     "a = 1\n",
 309 |     "print a\n",
 310 |     "print type(a)"
 311 |    ]
 312 |   },
 313 |   {
 314 |    "cell_type": "code",
 315 |    "execution_count": null,
 316 |    "metadata": {
 317 |     "collapsed": false,
 318 |     "slideshow": {
 319 |      "slide_type": "-"
 320 |     }
 321 |    },
 322 |    "outputs": [],
 323 |    "source": [
 324 |     "b = float(a)\n",
 325 |     "print b\n",
 326 |     "print type(b)"
 327 |    ]
 328 |   },
 329 |   {
 330 |    "cell_type": "code",
 331 |    "execution_count": null,
 332 |    "metadata": {
 333 |     "collapsed": false,
 334 |     "slideshow": {
 335 |      "slide_type": "fragment"
 336 |     }
 337 |    },
 338 |    "outputs": [],
 339 |    "source": [
 340 |     "s = \"1.23\"\n",
 341 |     "print s\n",
 342 |     "print type(s)"
 343 |    ]
 344 |   },
 345 |   {
 346 |    "cell_type": "code",
 347 |    "execution_count": null,
 348 |    "metadata": {
 349 |     "collapsed": false,
 350 |     "slideshow": {
 351 |      "slide_type": "-"
 352 |     }
 353 |    },
 354 |    "outputs": [],
 355 |    "source": [
 356 |     "f = float(s)\n",
 357 |     "print f\n",
 358 |     "print type(f)"
 359 |    ]
 360 |   },
 361 |   {
 362 |    "cell_type": "markdown",
 363 |    "metadata": {
 364 |     "slideshow": {
 365 |      "slide_type": "slide"
 366 |     }
 367 |    },
 368 |    "source": [
 369 |     "## Containers"
 370 |    ]
 371 |   },
 372 |   {
 373 |    "cell_type": "markdown",
 374 |    "metadata": {
 375 |     "slideshow": {
 376 |      "slide_type": "-"
 377 |     }
 378 |    },
 379 |    "source": [
 380 |     "* ### Lists"
 381 |    ]
 382 |   },
 383 |   {
 384 |    "cell_type": "code",
 385 |    "execution_count": null,
 386 |    "metadata": {
 387 |     "collapsed": false,
 388 |     "slideshow": {
 389 |      "slide_type": "fragment"
 390 |     }
 391 |    },
 392 |    "outputs": [],
 393 |    "source": [
 394 |     "l = ['red', 'blue', 'green', 'black', 'white']"
 395 |    ]
 396 |   },
 397 |   {
 398 |    "cell_type": "code",
 399 |    "execution_count": null,
 400 |    "metadata": {
 401 |     "collapsed": false,
 402 |     "slideshow": {
 403 |      "slide_type": "fragment"
 404 |     }
 405 |    },
 406 |    "outputs": [],
 407 |    "source": [
 408 |     "len(l)"
 409 |    ]
 410 |   },
 411 |   {
 412 |    "cell_type": "markdown",
 413 |    "metadata": {
 414 |     "slideshow": {
 415 |      "slide_type": "slide"
 416 |     }
 417 |    },
 418 |    "source": [
 419 |     "### Indexing"
 420 |    ]
 421 |   },
 422 |   {
 423 |    "cell_type": "code",
 424 |    "execution_count": null,
 425 |    "metadata": {
 426 |     "collapsed": false,
 427 |     "slideshow": {
 428 |      "slide_type": "fragment"
 429 |     }
 430 |    },
 431 |    "outputs": [],
 432 |    "source": [
 433 |     "l"
 434 |    ]
 435 |   },
 436 |   {
 437 |    "cell_type": "code",
 438 |    "execution_count": null,
 439 |    "metadata": {
 440 |     "collapsed": false,
 441 |     "slideshow": {
 442 |      "slide_type": "fragment"
 443 |     }
 444 |    },
 445 |    "outputs": [],
 446 |    "source": [
 447 |     "print l[0]\n",
 448 |     "print l[1]\n",
 449 |     "print l[2]"
 450 |    ]
 451 |   },
 452 |   {
 453 |    "cell_type": "code",
 454 |    "execution_count": null,
 455 |    "metadata": {
 456 |     "collapsed": false,
 457 |     "slideshow": {
 458 |      "slide_type": "fragment"
 459 |     }
 460 |    },
 461 |    "outputs": [],
 462 |    "source": [
 463 |     "print l[-1]   # last element\n",
 464 |     "print l[-2]"
 465 |    ]
 466 |   },
 467 |   {
 468 |    "cell_type": "code",
 469 |    "execution_count": null,
 470 |    "metadata": {
 471 |     "collapsed": false,
 472 |     "slideshow": {
 473 |      "slide_type": "fragment"
 474 |     }
 475 |    },
 476 |    "outputs": [],
 477 |    "source": [
 478 |     "l[0] = 'orange'\n",
 479 |     "print l"
 480 |    ]
 481 |   },
 482 |   {
 483 |    "cell_type": "markdown",
 484 |    "metadata": {
 485 |     "slideshow": {
 486 |      "slide_type": "slide"
 487 |     }
 488 |    },
 489 |    "source": [
 490 |     "### Slicing"
 491 |    ]
 492 |   },
 493 |   {
 494 |    "cell_type": "code",
 495 |    "execution_count": null,
 496 |    "metadata": {
 497 |     "collapsed": false,
 498 |     "slideshow": {
 499 |      "slide_type": "fragment"
 500 |     }
 501 |    },
 502 |    "outputs": [],
 503 |    "source": [
 504 |     "print l[2:5]"
 505 |    ]
 506 |   },
 507 |   {
 508 |    "cell_type": "code",
 509 |    "execution_count": null,
 510 |    "metadata": {
 511 |     "collapsed": false,
 512 |     "slideshow": {
 513 |      "slide_type": "fragment"
 514 |     }
 515 |    },
 516 |    "outputs": [],
 517 |    "source": [
 518 |     "print l[2:-1]"
 519 |    ]
 520 |   },
 521 |   {
 522 |    "cell_type": "code",
 523 |    "execution_count": null,
 524 |    "metadata": {
 525 |     "collapsed": false,
 526 |     "slideshow": {
 527 |      "slide_type": "fragment"
 528 |     }
 529 |    },
 530 |    "outputs": [],
 531 |    "source": [
 532 |     "print l[1:6:2]"
 533 |    ]
 534 |   },
 535 |   {
 536 |    "cell_type": "code",
 537 |    "execution_count": null,
 538 |    "metadata": {
 539 |     "collapsed": false,
 540 |     "slideshow": {
 541 |      "slide_type": "fragment"
 542 |     }
 543 |    },
 544 |    "outputs": [],
 545 |    "source": [
 546 |     "l[::-1]"
 547 |    ]
 548 |   },
 549 |   {
 550 |    "cell_type": "markdown",
 551 |    "metadata": {
 552 |     "slideshow": {
 553 |      "slide_type": "slide"
 554 |     }
 555 |    },
 556 |    "source": [
 557 |     "### Lists can store different type of variable in each element"
 558 |    ]
 559 |   },
 560 |   {
 561 |    "cell_type": "code",
 562 |    "execution_count": null,
 563 |    "metadata": {
 564 |     "collapsed": false,
 565 |     "slideshow": {
 566 |      "slide_type": "fragment"
 567 |     }
 568 |    },
 569 |    "outputs": [],
 570 |    "source": [
 571 |     "ll = [5, 22.9, 14.8+1j, 'hello', [1,2,3]]"
 572 |    ]
 573 |   },
 574 |   {
 575 |    "cell_type": "code",
 576 |    "execution_count": null,
 577 |    "metadata": {
 578 |     "collapsed": false,
 579 |     "slideshow": {
 580 |      "slide_type": "fragment"
 581 |     }
 582 |    },
 583 |    "outputs": [],
 584 |    "source": [
 585 |     "ll"
 586 |    ]
 587 |   },
 588 |   {
 589 |    "cell_type": "code",
 590 |    "execution_count": null,
 591 |    "metadata": {
 592 |     "collapsed": false,
 593 |     "slideshow": {
 594 |      "slide_type": "fragment"
 595 |     }
 596 |    },
 597 |    "outputs": [],
 598 |    "source": [
 599 |     "print ll[0]\n",
 600 |     "print ll[1]\n",
 601 |     "print ll[2]\n",
 602 |     "print ll[3]\n",
 603 |     "print ll[4]"
 604 |    ]
 605 |   },
 606 |   {
 607 |    "cell_type": "markdown",
 608 |    "metadata": {
 609 |     "slideshow": {
 610 |      "slide_type": "slide"
 611 |     }
 612 |    },
 613 |    "source": [
 614 |     "## Containers\n",
 615 |     "\n",
 616 |     "* ### Dictionaries"
 617 |    ]
 618 |   },
 619 |   {
 620 |    "cell_type": "code",
 621 |    "execution_count": null,
 622 |    "metadata": {
 623 |     "collapsed": false,
 624 |     "slideshow": {
 625 |      "slide_type": "fragment"
 626 |     }
 627 |    },
 628 |    "outputs": [],
 629 |    "source": [
 630 |     "d = {'name': 'Jonathan', 'id': 223984, 'location': 'USA'}"
 631 |    ]
 632 |   },
 633 |   {
 634 |    "cell_type": "code",
 635 |    "execution_count": null,
 636 |    "metadata": {
 637 |     "collapsed": false,
 638 |     "slideshow": {
 639 |      "slide_type": "fragment"
 640 |     }
 641 |    },
 642 |    "outputs": [],
 643 |    "source": [
 644 |     "d.keys()"
 645 |    ]
 646 |   },
 647 |   {
 648 |    "cell_type": "code",
 649 |    "execution_count": null,
 650 |    "metadata": {
 651 |     "collapsed": false,
 652 |     "slideshow": {
 653 |      "slide_type": "fragment"
 654 |     }
 655 |    },
 656 |    "outputs": [],
 657 |    "source": [
 658 |     "d.values()"
 659 |    ]
 660 |   },
 661 |   {
 662 |    "cell_type": "code",
 663 |    "execution_count": null,
 664 |    "metadata": {
 665 |     "collapsed": false,
 666 |     "slideshow": {
 667 |      "slide_type": "fragment"
 668 |     }
 669 |    },
 670 |    "outputs": [],
 671 |    "source": [
 672 |     "d['name']"
 673 |    ]
 674 |   },
 675 |   {
 676 |    "cell_type": "code",
 677 |    "execution_count": null,
 678 |    "metadata": {
 679 |     "collapsed": false,
 680 |     "slideshow": {
 681 |      "slide_type": "fragment"
 682 |     }
 683 |    },
 684 |    "outputs": [],
 685 |    "source": [
 686 |     "d['id']"
 687 |    ]
 688 |   },
 689 |   {
 690 |    "cell_type": "code",
 691 |    "execution_count": null,
 692 |    "metadata": {
 693 |     "collapsed": false,
 694 |     "slideshow": {
 695 |      "slide_type": "fragment"
 696 |     }
 697 |    },
 698 |    "outputs": [],
 699 |    "source": [
 700 |     "d['id'] = 1234"
 701 |    ]
 702 |   },
 703 |   {
 704 |    "cell_type": "code",
 705 |    "execution_count": null,
 706 |    "metadata": {
 707 |     "collapsed": false,
 708 |     "slideshow": {
 709 |      "slide_type": "fragment"
 710 |     }
 711 |    },
 712 |    "outputs": [],
 713 |    "source": [
 714 |     "d['id']"
 715 |    ]
 716 |   },
 717 |   {
 718 |    "cell_type": "markdown",
 719 |    "metadata": {
 720 |     "slideshow": {
 721 |      "slide_type": "slide"
 722 |     }
 723 |    },
 724 |    "source": [
 725 |     "## Containers\n",
 726 |     "\n",
 727 |     "* ### Tuples"
 728 |    ]
 729 |   },
 730 |   {
 731 |    "cell_type": "code",
 732 |    "execution_count": null,
 733 |    "metadata": {
 734 |     "collapsed": false,
 735 |     "slideshow": {
 736 |      "slide_type": "fragment"
 737 |     }
 738 |    },
 739 |    "outputs": [],
 740 |    "source": [
 741 |     "t = ('red', 'blue', 'green')"
 742 |    ]
 743 |   },
 744 |   {
 745 |    "cell_type": "code",
 746 |    "execution_count": null,
 747 |    "metadata": {
 748 |     "collapsed": false,
 749 |     "slideshow": {
 750 |      "slide_type": "fragment"
 751 |     }
 752 |    },
 753 |    "outputs": [],
 754 |    "source": [
 755 |     "t[0]"
 756 |    ]
 757 |   },
 758 |   {
 759 |    "cell_type": "code",
 760 |    "execution_count": null,
 761 |    "metadata": {
 762 |     "collapsed": false,
 763 |     "slideshow": {
 764 |      "slide_type": "fragment"
 765 |     }
 766 |    },
 767 |    "outputs": [],
 768 |    "source": [
 769 |     "t[1:3]"
 770 |    ]
 771 |   },
 772 |   {
 773 |    "cell_type": "code",
 774 |    "execution_count": null,
 775 |    "metadata": {
 776 |     "collapsed": false,
 777 |     "slideshow": {
 778 |      "slide_type": "fragment"
 779 |     }
 780 |    },
 781 |    "outputs": [],
 782 |    "source": [
 783 |     "t[1] = 'orange'"
 784 |    ]
 785 |   },
 786 |   {
 787 |    "cell_type": "markdown",
 788 |    "metadata": {
 789 |     "slideshow": {
 790 |      "slide_type": "slide"
 791 |     }
 792 |    },
 793 |    "source": [
 794 |     "## Flow control\n",
 795 |     "\n",
 796 |     "* ### conditional (if, else, elif)"
 797 |    ]
 798 |   },
 799 |   {
 800 |    "cell_type": "code",
 801 |    "execution_count": null,
 802 |    "metadata": {
 803 |     "collapsed": false,
 804 |     "slideshow": {
 805 |      "slide_type": "fragment"
 806 |     }
 807 |    },
 808 |    "outputs": [],
 809 |    "source": [
 810 |     "a = 10\n",
 811 |     "if a == 10:\n",
 812 |     "    print \"a is 10\""
 813 |    ]
 814 |   },
 815 |   {
 816 |    "cell_type": "code",
 817 |    "execution_count": null,
 818 |    "metadata": {
 819 |     "collapsed": false,
 820 |     "slideshow": {
 821 |      "slide_type": "fragment"
 822 |     }
 823 |    },
 824 |    "outputs": [],
 825 |    "source": [
 826 |     "a = 10\n",
 827 |     "if a > 10:\n",
 828 |     "    print \"a is larger than 10\"\n",
 829 |     "else:\n",
 830 |     "    print \"a is less than 10... or maybe equal too\""
 831 |    ]
 832 |   },
 833 |   {
 834 |    "cell_type": "code",
 835 |    "execution_count": null,
 836 |    "metadata": {
 837 |     "collapsed": false,
 838 |     "slideshow": {
 839 |      "slide_type": "fragment"
 840 |     }
 841 |    },
 842 |    "outputs": [],
 843 |    "source": [
 844 |     "a = 4\n",
 845 |     "if a > 10:\n",
 846 |     "    print \"a is larger than 10\"\n",
 847 |     "elif a < 10:\n",
 848 |     "    print \"a is less than 10\"\n",
 849 |     "else:\n",
 850 |     "    print \"a is equal to 10\""
 851 |    ]
 852 |   },
 853 |   {
 854 |    "cell_type": "markdown",
 855 |    "metadata": {
 856 |     "slideshow": {
 857 |      "slide_type": "slide"
 858 |     }
 859 |    },
 860 |    "source": [
 861 |     "## Flow control\n",
 862 |     "\n",
 863 |     "* ### Loops"
 864 |    ]
 865 |   },
 866 |   {
 867 |    "cell_type": "code",
 868 |    "execution_count": null,
 869 |    "metadata": {
 870 |     "collapsed": false,
 871 |     "slideshow": {
 872 |      "slide_type": "fragment"
 873 |     }
 874 |    },
 875 |    "outputs": [],
 876 |    "source": [
 877 |     "for i in range(10):\n",
 878 |     "    print i"
 879 |    ]
 880 |   },
 881 |   {
 882 |    "cell_type": "code",
 883 |    "execution_count": null,
 884 |    "metadata": {
 885 |     "collapsed": false,
 886 |     "slideshow": {
 887 |      "slide_type": "fragment"
 888 |     }
 889 |    },
 890 |    "outputs": [],
 891 |    "source": [
 892 |     "for color in ['red', 'blue', 'orange']:\n",
 893 |     "    print \"My favorite color is\", color"
 894 |    ]
 895 |   },
 896 |   {
 897 |    "cell_type": "markdown",
 898 |    "metadata": {
 899 |     "slideshow": {
 900 |      "slide_type": "slide"
 901 |     }
 902 |    },
 903 |    "source": [
 904 |     "## Functions"
 905 |    ]
 906 |   },
 907 |   {
 908 |    "cell_type": "code",
 909 |    "execution_count": null,
 910 |    "metadata": {
 911 |     "collapsed": false,
 912 |     "slideshow": {
 913 |      "slide_type": "fragment"
 914 |     }
 915 |    },
 916 |    "outputs": [],
 917 |    "source": [
 918 |     "def func():\n",
 919 |     "    print \"Hello world\""
 920 |    ]
 921 |   },
 922 |   {
 923 |    "cell_type": "code",
 924 |    "execution_count": null,
 925 |    "metadata": {
 926 |     "collapsed": false,
 927 |     "slideshow": {
 928 |      "slide_type": "-"
 929 |     }
 930 |    },
 931 |    "outputs": [],
 932 |    "source": [
 933 |     "func()"
 934 |    ]
 935 |   },
 936 |   {
 937 |    "cell_type": "code",
 938 |    "execution_count": null,
 939 |    "metadata": {
 940 |     "collapsed": false,
 941 |     "slideshow": {
 942 |      "slide_type": "fragment"
 943 |     }
 944 |    },
 945 |    "outputs": [],
 946 |    "source": [
 947 |     "def func2(name):\n",
 948 |     "    print \"Hello\", name"
 949 |    ]
 950 |   },
 951 |   {
 952 |    "cell_type": "code",
 953 |    "execution_count": null,
 954 |    "metadata": {
 955 |     "collapsed": false,
 956 |     "slideshow": {
 957 |      "slide_type": "-"
 958 |     }
 959 |    },
 960 |    "outputs": [],
 961 |    "source": [
 962 |     "func2(\"Jonathan\")"
 963 |    ]
 964 |   },
 965 |   {
 966 |    "cell_type": "code",
 967 |    "execution_count": null,
 968 |    "metadata": {
 969 |     "collapsed": false,
 970 |     "slideshow": {
 971 |      "slide_type": "fragment"
 972 |     }
 973 |    },
 974 |    "outputs": [],
 975 |    "source": [
 976 |     "def times2(x):\n",
 977 |     "    return x * 2"
 978 |    ]
 979 |   },
 980 |   {
 981 |    "cell_type": "code",
 982 |    "execution_count": null,
 983 |    "metadata": {
 984 |     "collapsed": false,
 985 |     "slideshow": {
 986 |      "slide_type": "fragment"
 987 |     }
 988 |    },
 989 |    "outputs": [],
 990 |    "source": [
 991 |     "y = times2(2)\n",
 992 |     "print y"
 993 |    ]
 994 |   },
 995 |   {
 996 |    "cell_type": "code",
 997 |    "execution_count": null,
 998 |    "metadata": {
 999 |     "collapsed": false,
1000 |     "slideshow": {
1001 |      "slide_type": "slide"
1002 |     }
1003 |    },
1004 |    "outputs": [],
1005 |    "source": [
1006 |     "def times_something(x, y=2):\n",
1007 |     "    print x*y"
1008 |    ]
1009 |   },
1010 |   {
1011 |    "cell_type": "code",
1012 |    "execution_count": null,
1013 |    "metadata": {
1014 |     "collapsed": false,
1015 |     "slideshow": {
1016 |      "slide_type": "-"
1017 |     }
1018 |    },
1019 |    "outputs": [],
1020 |    "source": [
1021 |     "times_something(3)"
1022 |    ]
1023 |   },
1024 |   {
1025 |    "cell_type": "code",
1026 |    "execution_count": null,
1027 |    "metadata": {
1028 |     "collapsed": false,
1029 |     "slideshow": {
1030 |      "slide_type": "-"
1031 |     }
1032 |    },
1033 |    "outputs": [],
1034 |    "source": [
1035 |     "times_something(3, 3)"
1036 |    ]
1037 |   },
1038 |   {
1039 |    "cell_type": "markdown",
1040 |    "metadata": {
1041 |     "slideshow": {
1042 |      "slide_type": "slide"
1043 |     }
1044 |    },
1045 |    "source": [
1046 |     "## Classes"
1047 |    ]
1048 |   },
1049 |   {
1050 |    "cell_type": "code",
1051 |    "execution_count": null,
1052 |    "metadata": {
1053 |     "collapsed": false,
1054 |     "slideshow": {
1055 |      "slide_type": "fragment"
1056 |     }
1057 |    },
1058 |    "outputs": [],
1059 |    "source": [
1060 |     "class Car(object):\n",
1061 |     "    \n",
1062 |     "    engine = 'V4'    # class attribute\n",
1063 |     "    \n",
1064 |     "    def start(self):  # class method\n",
1065 |     "        print \"Starting the car with a\", self.engine, \"engine\""
1066 |    ]
1067 |   },
1068 |   {
1069 |    "cell_type": "code",
1070 |    "execution_count": null,
1071 |    "metadata": {
1072 |     "collapsed": false,
1073 |     "slideshow": {
1074 |      "slide_type": "-"
1075 |     }
1076 |    },
1077 |    "outputs": [],
1078 |    "source": [
1079 |     "mycar = Car()"
1080 |    ]
1081 |   },
1082 |   {
1083 |    "cell_type": "code",
1084 |    "execution_count": null,
1085 |    "metadata": {
1086 |     "collapsed": false,
1087 |     "slideshow": {
1088 |      "slide_type": "-"
1089 |     }
1090 |    },
1091 |    "outputs": [],
1092 |    "source": [
1093 |     "type(mycar)"
1094 |    ]
1095 |   },
1096 |   {
1097 |    "cell_type": "code",
1098 |    "execution_count": null,
1099 |    "metadata": {
1100 |     "collapsed": false,
1101 |     "slideshow": {
1102 |      "slide_type": "-"
1103 |     }
1104 |    },
1105 |    "outputs": [],
1106 |    "source": [
1107 |     "mycar.engine"
1108 |    ]
1109 |   },
1110 |   {
1111 |    "cell_type": "code",
1112 |    "execution_count": null,
1113 |    "metadata": {
1114 |     "collapsed": false,
1115 |     "slideshow": {
1116 |      "slide_type": "-"
1117 |     }
1118 |    },
1119 |    "outputs": [],
1120 |    "source": [
1121 |     "mycar.start()"
1122 |    ]
1123 |   },
1124 |   {
1125 |    "cell_type": "code",
1126 |    "execution_count": null,
1127 |    "metadata": {
1128 |     "collapsed": false,
1129 |     "slideshow": {
1130 |      "slide_type": "fragment"
1131 |     }
1132 |    },
1133 |    "outputs": [],
1134 |    "source": [
1135 |     "mycar.engine = 'V6'"
1136 |    ]
1137 |   },
1138 |   {
1139 |    "cell_type": "code",
1140 |    "execution_count": null,
1141 |    "metadata": {
1142 |     "collapsed": false,
1143 |     "slideshow": {
1144 |      "slide_type": "-"
1145 |     }
1146 |    },
1147 |    "outputs": [],
1148 |    "source": [
1149 |     "mycar.engine"
1150 |    ]
1151 |   },
1152 |   {
1153 |    "cell_type": "code",
1154 |    "execution_count": null,
1155 |    "metadata": {
1156 |     "collapsed": false,
1157 |     "slideshow": {
1158 |      "slide_type": "-"
1159 |     }
1160 |    },
1161 |    "outputs": [],
1162 |    "source": [
1163 |     "mycar.start()"
1164 |    ]
1165 |   },
1166 |   {
1167 |    "cell_type": "markdown",
1168 |    "metadata": {
1169 |     "slideshow": {
1170 |      "slide_type": "slide"
1171 |     }
1172 |    },
1173 |    "source": [
1174 |     "## The Scientific Python ecosystem \n",
1175 |     "\n",
1176 |     "* ### NumPy\n",
1177 |     "\n",
1178 |     "![NumPy logo](images/numpylogo.svg)"
1179 |    ]
1180 |   },
1181 |   {
1182 |    "cell_type": "code",
1183 |    "execution_count": null,
1184 |    "metadata": {
1185 |     "collapsed": false,
1186 |     "slideshow": {
1187 |      "slide_type": "slide"
1188 |     }
1189 |    },
1190 |    "outputs": [],
1191 |    "source": [
1192 |     "import numpy as np"
1193 |    ]
1194 |   },
1195 |   {
1196 |    "cell_type": "code",
1197 |    "execution_count": null,
1198 |    "metadata": {
1199 |     "collapsed": false,
1200 |     "slideshow": {
1201 |      "slide_type": "fragment"
1202 |     }
1203 |    },
1204 |    "outputs": [],
1205 |    "source": [
1206 |     "a = np.array([0, 1, 2, 3, 4, 5, 6, 7])"
1207 |    ]
1208 |   },
1209 |   {
1210 |    "cell_type": "code",
1211 |    "execution_count": null,
1212 |    "metadata": {
1213 |     "collapsed": false,
1214 |     "slideshow": {
1215 |      "slide_type": "-"
1216 |     }
1217 |    },
1218 |    "outputs": [],
1219 |    "source": [
1220 |     "a"
1221 |    ]
1222 |   },
1223 |   {
1224 |    "cell_type": "code",
1225 |    "execution_count": null,
1226 |    "metadata": {
1227 |     "collapsed": false,
1228 |     "slideshow": {
1229 |      "slide_type": "-"
1230 |     }
1231 |    },
1232 |    "outputs": [],
1233 |    "source": [
1234 |     "a.shape"
1235 |    ]
1236 |   },
1237 |   {
1238 |    "cell_type": "code",
1239 |    "execution_count": null,
1240 |    "metadata": {
1241 |     "collapsed": false,
1242 |     "slideshow": {
1243 |      "slide_type": "-"
1244 |     }
1245 |    },
1246 |    "outputs": [],
1247 |    "source": [
1248 |     "a.ndim"
1249 |    ]
1250 |   },
1251 |   {
1252 |    "cell_type": "code",
1253 |    "execution_count": null,
1254 |    "metadata": {
1255 |     "collapsed": false,
1256 |     "slideshow": {
1257 |      "slide_type": "-"
1258 |     }
1259 |    },
1260 |    "outputs": [],
1261 |    "source": [
1262 |     "a.dtype"
1263 |    ]
1264 |   },
1265 |   {
1266 |    "cell_type": "code",
1267 |    "execution_count": null,
1268 |    "metadata": {
1269 |     "collapsed": false,
1270 |     "slideshow": {
1271 |      "slide_type": "slide"
1272 |     }
1273 |    },
1274 |    "outputs": [],
1275 |    "source": [
1276 |     "a[0::2]"
1277 |    ]
1278 |   },
1279 |   {
1280 |    "cell_type": "code",
1281 |    "execution_count": null,
1282 |    "metadata": {
1283 |     "collapsed": false,
1284 |     "slideshow": {
1285 |      "slide_type": "-"
1286 |     }
1287 |    },
1288 |    "outputs": [],
1289 |    "source": [
1290 |     "a[a>3]"
1291 |    ]
1292 |   },
1293 |   {
1294 |    "cell_type": "code",
1295 |    "execution_count": null,
1296 |    "metadata": {
1297 |     "collapsed": false,
1298 |     "slideshow": {
1299 |      "slide_type": "-"
1300 |     }
1301 |    },
1302 |    "outputs": [],
1303 |    "source": [
1304 |     "a * 2 + 100"
1305 |    ]
1306 |   },
1307 |   {
1308 |    "cell_type": "code",
1309 |    "execution_count": null,
1310 |    "metadata": {
1311 |     "collapsed": false,
1312 |     "slideshow": {
1313 |      "slide_type": "-"
1314 |     }
1315 |    },
1316 |    "outputs": [],
1317 |    "source": [
1318 |     "a.mean()"
1319 |    ]
1320 |   },
1321 |   {
1322 |    "cell_type": "markdown",
1323 |    "metadata": {
1324 |     "slideshow": {
1325 |      "slide_type": "slide"
1326 |     }
1327 |    },
1328 |    "source": [
1329 |     "### Arrays can be multi-dimensional"
1330 |    ]
1331 |   },
1332 |   {
1333 |    "cell_type": "code",
1334 |    "execution_count": null,
1335 |    "metadata": {
1336 |     "collapsed": false,
1337 |     "slideshow": {
1338 |      "slide_type": "fragment"
1339 |     }
1340 |    },
1341 |    "outputs": [],
1342 |    "source": [
1343 |     "b = np.arange(12).reshape(3,4)"
1344 |    ]
1345 |   },
1346 |   {
1347 |    "cell_type": "code",
1348 |    "execution_count": null,
1349 |    "metadata": {
1350 |     "collapsed": false,
1351 |     "slideshow": {
1352 |      "slide_type": "-"
1353 |     }
1354 |    },
1355 |    "outputs": [],
1356 |    "source": [
1357 |     "b.shape"
1358 |    ]
1359 |   },
1360 |   {
1361 |    "cell_type": "code",
1362 |    "execution_count": null,
1363 |    "metadata": {
1364 |     "collapsed": false,
1365 |     "slideshow": {
1366 |      "slide_type": "-"
1367 |     }
1368 |    },
1369 |    "outputs": [],
1370 |    "source": [
1371 |     "b"
1372 |    ]
1373 |   },
1374 |   {
1375 |    "cell_type": "code",
1376 |    "execution_count": null,
1377 |    "metadata": {
1378 |     "collapsed": false,
1379 |     "slideshow": {
1380 |      "slide_type": "-"
1381 |     }
1382 |    },
1383 |    "outputs": [],
1384 |    "source": [
1385 |     "b[1,2]"
1386 |    ]
1387 |   },
1388 |   {
1389 |    "cell_type": "code",
1390 |    "execution_count": null,
1391 |    "metadata": {
1392 |     "collapsed": false,
1393 |     "slideshow": {
1394 |      "slide_type": "-"
1395 |     }
1396 |    },
1397 |    "outputs": [],
1398 |    "source": [
1399 |     "b[0:2, ::-1]"
1400 |    ]
1401 |   },
1402 |   {
1403 |    "cell_type": "markdown",
1404 |    "metadata": {
1405 |     "slideshow": {
1406 |      "slide_type": "slide"
1407 |     }
1408 |    },
1409 |    "source": [
1410 |     "## The Scientific Python ecosystem \n",
1411 |     "\n",
1412 |     "* ### SciPy\n",
1413 |     "\n",
1414 |     "![SciPy logo](images/scipy.png)"
1415 |    ]
1416 |   },
1417 |   {
1418 |    "cell_type": "code",
1419 |    "execution_count": null,
1420 |    "metadata": {
1421 |     "collapsed": true,
1422 |     "slideshow": {
1423 |      "slide_type": "slide"
1424 |     }
1425 |    },
1426 |    "outputs": [],
1427 |    "source": [
1428 |     "import scipy"
1429 |    ]
1430 |   },
1431 |   {
1432 |    "cell_type": "code",
1433 |    "execution_count": null,
1434 |    "metadata": {
1435 |     "collapsed": false,
1436 |     "slideshow": {
1437 |      "slide_type": "-"
1438 |     }
1439 |    },
1440 |    "outputs": [],
1441 |    "source": [
1442 |     "print scipy.__doc__"
1443 |    ]
1444 |   },
1445 |   {
1446 |    "cell_type": "markdown",
1447 |    "metadata": {
1448 |     "slideshow": {
1449 |      "slide_type": "slide"
1450 |     }
1451 |    },
1452 |    "source": [
1453 |     "## The Scientific Python ecosystem \n",
1454 |     "\n",
1455 |     "* ### matplotlib\n",
1456 |     "\n",
1457 |     "![matplotlib logo](images/matplotlib_logo.png)"
1458 |    ]
1459 |   },
1460 |   {
1461 |    "cell_type": "code",
1462 |    "execution_count": null,
1463 |    "metadata": {
1464 |     "collapsed": false,
1465 |     "slideshow": {
1466 |      "slide_type": "slide"
1467 |     }
1468 |    },
1469 |    "outputs": [],
1470 |    "source": [
1471 |     "%pylab inline"
1472 |    ]
1473 |   },
1474 |   {
1475 |    "cell_type": "code",
1476 |    "execution_count": null,
1477 |    "metadata": {
1478 |     "collapsed": false,
1479 |     "slideshow": {
1480 |      "slide_type": "-"
1481 |     }
1482 |    },
1483 |    "outputs": [],
1484 |    "source": [
1485 |     "plot([1,2,3])"
1486 |    ]
1487 |   },
1488 |   {
1489 |    "cell_type": "code",
1490 |    "execution_count": null,
1491 |    "metadata": {
1492 |     "collapsed": false,
1493 |     "slideshow": {
1494 |      "slide_type": "slide"
1495 |     }
1496 |    },
1497 |    "outputs": [],
1498 |    "source": [
1499 |     "a = np.random.rand(30, 30)\n",
1500 |     "imshow(a)\n",
1501 |     "colorbar()"
1502 |    ]
1503 |   }
1504 |  ],
1505 |  "metadata": {
1506 |   "celltoolbar": "Slideshow",
1507 |   "kernelspec": {
1508 |    "display_name": "Python 2",
1509 |    "language": "python",
1510 |    "name": "python2"
1511 |   },
1512 |   "language_info": {
1513 |    "codemirror_mode": {
1514 |     "name": "ipython",
1515 |     "version": 2
1516 |    },
1517 |    "file_extension": ".py",
1518 |    "mimetype": "text/x-python",
1519 |    "name": "python",
1520 |    "nbconvert_exporter": "python",
1521 |    "pygments_lexer": "ipython2",
1522 |    "version": "2.7.10"
1523 |   }
1524 |  },
1525 |  "nbformat": 4,
1526 |  "nbformat_minor": 0
1527 | }
1528 | 


--------------------------------------------------------------------------------
/8b_plot_current_nexrad_open_artview.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "<b>Import needed libraries</b>"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "code",
 12 |    "execution_count": 3,
 13 |    "metadata": {
 14 |     "collapsed": false
 15 |    },
 16 |    "outputs": [
 17 |     {
 18 |      "name": "stderr",
 19 |      "output_type": "stream",
 20 |      "text": [
 21 |       "/Users/guy/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:1318: UserWarning:  This call to matplotlib.use() has no effect\n",
 22 |       "because the backend has already been chosen;\n",
 23 |       "matplotlib.use() must be called *before* pylab, matplotlib.pyplot,\n",
 24 |       "or matplotlib.backends is imported for the first time.\n",
 25 |       "\n",
 26 |       "  warnings.warn(_use_error_msg)\n"
 27 |      ]
 28 |     }
 29 |    ],
 30 |    "source": [
 31 |     "import os\n",
 32 |     "import urllib2\n",
 33 |     "import pyart\n",
 34 |     "#import numpy as np\n",
 35 |     "import matplotlib.pyplot as plt\n",
 36 |     "import artview\n",
 37 |     "\n",
 38 |     "%matplotlib inline"
 39 |    ]
 40 |   },
 41 |   {
 42 |    "cell_type": "markdown",
 43 |    "metadata": {},
 44 |    "source": [
 45 |     "<b>Create a class for accessing and plotting latest radar image</b>"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 4,
 51 |    "metadata": {
 52 |     "collapsed": true
 53 |    },
 54 |    "outputs": [],
 55 |    "source": [
 56 |     "class Get88D(object):\n",
 57 |     "    \"\"\"\n",
 58 |     "    A class mentod to retrieve and plot NEXRAD data.\n",
 59 |     "    \n",
 60 |     "    The metar retrieval is a modification of code found at:\n",
 61 |     "    https://github.com/akrherz/iem/blob/master/scripts/asos/iem_scraper_example.py\n",
 62 |     "    \n",
 63 |     "    It is dependent upon the Iowa State Mesonet database.\n",
 64 |     "    \"\"\"\n",
 65 |     "    def __init__(self, radarID):\n",
 66 |     "        '''Initialize the class'''\n",
 67 |     "\n",
 68 |     "        # Set date formats to be used with datetime\n",
 69 |     "        self.d_fmt = \"%Y-%m-%d %H:%M\"\n",
 70 |     "        self.dout_fmt = \"%Y-%m-%d_%H:%M\"\n",
 71 |     "        \n",
 72 |     "        # Use passed arguments\n",
 73 |     "        self.radarID = radarID\n",
 74 |     "\n",
 75 |     "    def get_data(self):\n",
 76 |     "        '''Function to return metar data and create output text file'''\n",
 77 |     "        # Query the radarID directory to get file list\n",
 78 |     "        SERVICE = \"http://nomads.ncep.noaa.gov/pub/data/nccf/radar/nexrad_level2\"\n",
 79 |     "        nexurl = '%s/%s/'%(SERVICE, self.radarID)\n",
 80 |     "        response = urllib2.urlopen(\"%s%s\"%(nexurl, \"dir.list\"))\n",
 81 |     "        self.list88D = response.read().split(\"\\n\")\n",
 82 |     "        \n",
 83 |     "        # At this point you have a list of data files, BUT there are 2 columns\n",
 84 |     "        # col 1 = file size, col2 = filename\n",
 85 |     "        \n",
 86 |     "        # Now grab the latest data file and save it locally to open\n",
 87 |     "        data = urllib2.urlopen(\"%s%s\"%(nexurl,self.list88D[-2].split(\" \")[1]))\n",
 88 |     "        with open(\"latest88D.bz\", \"wb\") as code:\n",
 89 |     "            code.write(data.read())\n",
 90 |     "\n",
 91 |     "    def plot_nexrad(self, vmin=None, vmax=None, xlims=None, ylims=None):\n",
 92 |     "        '''Create a plot'''\n",
 93 |     "        # Create a PyArt radar instance\n",
 94 |     "        fig, ax = plt.subplots()\n",
 95 |     "        #ax = plt.axes()\n",
 96 |     "        self.r=pyart.io.read_nexrad_archive(\"latest88D.bz\")\n",
 97 |     "        d=pyart.graph.RadarDisplay(self.r)\n",
 98 |     "        d.plot('reflectivity',0, vmin=vmin, vmax=vmax, cmap=\"pyart_Carbone42\")\n",
 99 |     "        if xlims is None:\n",
100 |     "            xlims = (-250., 250)\n",
101 |     "        if ylims is None:\n",
102 |     "            ylims = (-250., 250)\n",
103 |     "        d.set_limits(xlims, ylims)\n",
104 |     "        \n",
105 |     "    def remove_bz(self):\n",
106 |     "        os.remove(\"latest88D.bz\")"
107 |    ]
108 |   },
109 |   {
110 |    "cell_type": "markdown",
111 |    "metadata": {},
112 |    "source": [
113 |     "<b>Now lets make a current plot!</b>"
114 |    ]
115 |   },
116 |   {
117 |    "cell_type": "code",
118 |    "execution_count": 6,
119 |    "metadata": {
120 |     "collapsed": false
121 |    },
122 |    "outputs": [
123 |     {
124 |      "data": {
125 |       "image/png": 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jbQ9Wxq9EJgvqxbM//cDGQXSL7ManHfsy/WA6CxMWEj4jHH+bP0m7EwlsEgRw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PCU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\n",
126 |       "text/plain": [
127 |        "<matplotlib.figure.Figure at 0x11a594b10>"
128 |       ]
129 |      },
130 |      "metadata": {},
131 |      "output_type": "display_data"
132 |     }
133 |    ],
134 |    "source": [
135 |     "radarID = \"KABR\"#KCYS\"\n",
136 |     "Radar = Get88D(radarID)\n",
137 |     "Radar.get_data()\n",
138 |     "Radar.plot_nexrad(vmin=-10, vmax=60.)\n",
139 |     "Radar.remove_bz()"
140 |    ]
141 |   },
142 |   {
143 |    "cell_type": "markdown",
144 |    "metadata": {},
145 |    "source": [
146 |     "<b> Now we can open using ARTView to access features via GUI</b>"
147 |    ]
148 |   },
149 |   {
150 |    "cell_type": "code",
151 |    "execution_count": 8,
152 |    "metadata": {
153 |     "collapsed": false
154 |    },
155 |    "outputs": [
156 |     {
157 |      "name": "stdout",
158 |      "output_type": "stream",
159 |      "text": [
160 |       "Changed Scan types, reinitializing\n"
161 |      ]
162 |     }
163 |    ],
164 |    "source": [
165 |     "artview.view.view(Radar.r)"
166 |    ]
167 |   },
168 |   {
169 |    "cell_type": "code",
170 |    "execution_count": null,
171 |    "metadata": {
172 |     "collapsed": true
173 |    },
174 |    "outputs": [],
175 |    "source": []
176 |   }
177 |  ],
178 |  "metadata": {
179 |   "kernelspec": {
180 |    "display_name": "Python 2",
181 |    "language": "python",
182 |    "name": "python2"
183 |   },
184 |   "language_info": {
185 |    "codemirror_mode": {
186 |     "name": "ipython",
187 |     "version": 2
188 |    },
189 |    "file_extension": ".py",
190 |    "mimetype": "text/x-python",
191 |    "name": "python",
192 |    "nbconvert_exporter": "python",
193 |    "pygments_lexer": "ipython2",
194 |    "version": "2.7.10"
195 |   }
196 |  },
197 |  "nbformat": 4,
198 |  "nbformat_minor": 0
199 | }
200 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | Copyright (c) 2015, 
 2 | All rights reserved.
 3 | 
 4 | Redistribution and use in source and binary forms, with or without
 5 | modification, are permitted provided that the following conditions are met:
 6 | 
 7 | * Redistributions of source code must retain the above copyright notice, this
 8 |   list of conditions and the following disclaimer.
 9 | 
10 | * Redistributions in binary form must reproduce the above copyright notice,
11 |   this list of conditions and the following disclaimer in the documentation
12 |   and/or other materials provided with the distribution.
13 | 
14 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
15 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
16 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
17 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
18 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
19 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
20 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
21 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
22 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
23 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
24 | 
25 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # AMS Short Course on Open Source Radar Software
 2 | Welcome to the AMS Short Course on Open Source Radar Software
 3 | 
 4 | This living repository was first created for the <a href="https://www2.ametsoc.org/ams/index.cfm/meetings-events/ams-meetings/37th-conference-on-radar-meteorology/"> 37th conference on Radar Meteorology</a>
 5 | 
 6 | ### Pre-course install instructions
 7 | 
 8 | Prior to the course it would be extremely helpful if all attenders could
 9 | install Anaconda and Py-ART using the following steps.
10 | 
11 | 
12 | 1.  Download and install Anaconda from Continuum Analytics using the 
13 |     instructions on the site, http://continuum.io/downloads.  
14 |     Add Anaconda to your path if/when asked.
15 | 
16 | 2.  Open a terminal or command prompt and install basemap and netcdf using the
17 |     command:
18 |     
19 |     ```
20 |     conda install basemap netcdf4 
21 |     ```
22 |    
23 |     Answer yes to any prompts.
24 | 
25 | 3.  In the same terminal or command prompt install Py-ART using the command:
26 | 
27 |     ```
28 |     conda install -c https://conda.anaconda.org/jjhelmus pyart
29 |     ```
30 | 
31 |     Answer yes to any prompts. 
32 | 
33 | ### Contents
34 | 
35 | > <a href="https://github.com/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/course_outline.rst"> Course Outline</a>
36 | > 
37 | > <a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/1_Welcome.ipynb"> Welcome Slides </a>
38 | >
39 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/2_Intro_to_Python_and_SciPy.ipynb">Intro to Python and SciPy</a>
40 | >
41 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/3_Basic_PyART.ipynb">Basic Py-ART</a>
42 | >
43 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/4a_PyART_IO_in_detail.ipynb">Py-ART IO in Detail</a>
44 | >
45 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/4b_PyART_data_model.ipynb">Py-ART Data Model</a>
46 | >
47 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/5_Simple_proccessing_and_adding_a_field.ipynb">Simple proccessing and adding a field </a>
48 | >
49 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/6_Texture_and_Dealiasing.ipynb">Texture and Dealiasing</a>
50 | >
51 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/7_Mapping_to_a_cartesian_grid.ipynb">Gridding radars to a Cartesian Grid</a>
52 | >
53 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/8a_artview_demo_ams37.ipynb">ARTView Demo</a>
54 | >
55 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/8b_plot_current_nexrad_open_artview.ipynb">Plot Current Nexrad in ARTView</a>
56 | >
57 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/9a_CSU_RadarTools_Demo-AMS_OSRSC.ipynb">CSU Radar Tools</a>
58 | >
59 | ><a href="http://nbviewer.ipython.org/github/openradar/AMS-Short-Course-on-Open-Source-Radar-Software/blob/master/9b_PyTDA_Demo-AMS_OSRSC.ipynb">PyTDA Demo</a>
60 | >
61 | 
62 | 
63 | ###<a href='http://figshare.com/articles/Data_for_AMS_Short_Course_on_Open_Source_Radar_Software/1537461'>FigShare data repository</a>
64 | 
65 | 
66 | 


--------------------------------------------------------------------------------
/course_outline.rst:
--------------------------------------------------------------------------------
  1 | AMS Short Course on Open Source Radar Software
  2 | =============================
  3 | 
  4 | Render this document as pdf by ``rst2pdf course_outline.rst``
  5 | 
  6 | **08:30 - 09:15** `ARRIVAL AND INTRODUCTIONS AND INSTALLATION OF SOFTWARE AND DATA`_::
  7 | 
  8 |    Taking seats, booting notebooks...
  9 | 
 10 |    Intro slides by Scott.
 11 | 
 12 | **09:15 - 9:30** `AN INTRODUCTION TO PYTHON AND IPYTHON`_::
 13 | 
 14 |     Python will be the key to use the presented radar software tools.
 15 | 
 16 |     Learn how to use Python for writing scripts and for interactive data analysis.
 17 | 
 18 |     Scipy, Numpy and Matplotlib
 19 | 
 20 |     What is IPython and the IPython notebook? 
 21 | 
 22 | **9:30 - 10:00** `BASIC PY-ART AND THE PY-ART DATA MODEL`_::
 23 | 
 24 |     How Py-ART reads data into Python and how to access the raw data
 25 |   
 26 | **10:00 - 10:15** Coffee break
 27 | 
 28 | **10:15 - 11:30** `BASIC PY-ART AND THE PY-ART DATA MODEL`_::
 29 | 
 30 |    Plotting in Py-ART
 31 | 
 32 | **11:30 - 12:00** `EXAMPLES OF RADAR PROCESSING USING PY-ART`_::
 33 | 
 34 |    Example based study from Miami of how to view data, modify data and add the
 35 |    data to a radar object
 36 | 
 37 | **12:00 - 13:30** Lunch break
 38 | 
 39 | 
 40 | **13:30 - 14:00** `EXAMPLES OF RADAR PROCESSING USING PY-ART`_::
 41 | 
 42 |     Example based study from Miami of how to view data, modify data and add the
 43 |     data to a radar object
 44 | 
 45 | 
 46 | **14:00 - 14:30** `Py-ART TOOLS, TEXTURE, DEALIAS AND GRID`_::
 47 | 
 48 |    The Gatefilter and how to use it.
 49 | 
 50 |    Processing: Texture and dealiasing.
 51 | 
 52 |    Gridding: Gridding one or more radars. 
 53 | 
 54 | **14:30 - 15:00**  `PYTHON TOOLS 1: ART-VIEW`_::
 55 | 
 56 |      How to use the ARTview visualization package.
 57 |      
 58 |      How to launch a "quick" ARTview window from command line during interactive data analysis.
 59 | 
 60 | 
 61 | **15:00 - 15:30** Coffee break
 62 | 
 63 | **15:30 - 16:15**  `PYTHON TOOLS 2: RADAR MET TOOLS`_::
 64 | 
 65 |     PyTDMA and CSU Radar Tools
 66 | 
 67 | **16:15 - 17:00** `WRAP UP AND FUTHER DISCUSSION`_::
 68 | 
 69 |    Questions and comments.
 70 | 
 71 |    Feedback.
 72 | 
 73 | 
 74 | .. raw:: pdf
 75 | 
 76 |       PageBreak
 77 | 
 78 | 
 79 | ARRIVAL AND INTRODUCTIONS AND INSTALLATION OF SOFTWARE AND DATA
 80 | ---------------------------------------------------------------
 81 | 
 82 | Time to settle in and say hello. Course tutors will open the course and introduce themselves and their projects, as well as the general structure and concept of the short course.
 83 | 
 84 | 
 85 | AN INTRODUCTION TO PYTHON AND IPYTHON
 86 | -------------------------------------
 87 | 
 88 | Run By Jonathan
 89 | 
 90 | BASIC PY-ART AND THE PY-ART DATA MODEL
 91 | --------------------------------------
 92 | 
 93 | Run By Jonathan
 94 | 
 95 | 
 96 | EXAMPLES OF RADAR PROCESSING USING PY-ART
 97 | -----------------------------------------
 98 | 
 99 | Investigating data from the Miami Nexrad by Scott.
100 | 
101 | 
102 | Py-ART TOOLS, TEXTURE, DEALIAS AND GRID
103 | ---------------------------------------
104 | 
105 | All Oklahoma centric run by Scott
106 | 
107 | 
108 | PYTHON TOOLS 1: ART-VIEW
109 | ------------------------
110 | 
111 | Nick will show us ART View!
112 | 
113 | 
114 | PYTHON TOOLS 2: RADAR MET TOOLS
115 | -------------------------------
116 | 
117 | The Great work from CSU and NASA MSFC by Tim
118 | 
119 | 
120 | WRAP UP AND FUTHER DISCUSSION
121 | -----------------------------
122 | 
123 | Feedback and further discussion
124 | ALL
125 | 
126 | 


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/data/provision_data.sh:
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1 | #!/bin/bash
2 | wget \
3 |     https://ndownloader.figshare.com/articles/1537461/versions/6 \
4 |     -O pyart_course_data.zip
5 | unzip -o pyart_course_data.zip
6 | mv 3402575_KAMX_20140417_1056 KAMX_20140417_1056
7 | rm -f pyart_course_data.zip
8 | 
9 | 


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/testme.py:
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 1 | #!/usr/bin/env python
 2 | import pyart
 3 | from matplotlib import pyplot as plt
 4 | import numpy as np
 5 | from scipy import ndimage, signal
 6 | import time
 7 | 
 8 | if __name__ == "__main__":
 9 |     radar = pyart.io.read('./data/csapr_test_case.nc')
10 |     display = pyart.graph.RadarMapDisplay(radar)
11 |     fig = plt.figure(figsize = [10,8])
12 |     display.plot_ppi_map('reflectivity', sweep = 2, resolution = 'i',
13 |                     vmin = -10, vmax = 64, mask_outside = False,
14 |                     cmap = pyart.graph.cm.NWSRef)
15 |     plt.savefig('./awesome.png')
16 | 
17 | 


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