1 {% extends "base.html" %} |
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2 {% block content %} |
2 {% block content %} |
3 <h1>Tutorials</h1> |
3 <h1 class="title">SciPy.in 2011 Tutorial Schedule</h1> |
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4 |
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5 <h2 id="sec-1">Day 3 </h2> |
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6 |
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7 |
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8 <table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides"> |
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9 <caption></caption> |
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10 <colgroup><col class="right" /><col class="left" /><col class="left" /> |
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11 </colgroup> |
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12 <thead> |
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13 <tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr> |
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14 </thead> |
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15 <tbody> |
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16 <tr><td class="right">09:00-11:00</td><td class="left">Jarror Millman</td><td class="left"><a href="#sec2.1" >Gig/Github + NumPy/SciPy/MPL basics</a></td></tr> |
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17 <tr><td class="right">11:00-13:00</td><td class="left">Emmanuelle Gouillart</td><td class="left"><a href="#sec2.2">Image processing using NumPy, SciPy and scikits-image</a></td></tr> |
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18 <tr><td class="right">13:00-14:00</td><td class="left"></td><td class="left">Lunch</td></tr> |
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19 <tr><td class="right">14:00-16:00</td><td class="left">Gael Varoquaux</td><td class="left"><a href="#sec2.3">Machine learning with scikit-learn</a></td></tr> |
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20 <tr><td class="right">16:00-18:00</td><td class="left">Mateusz Paprocki</td><td class="left"><a href="#sec2.4">SymPy</a></td></tr> |
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21 </tbody> |
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22 </table> |
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23 |
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24 <h2 id="sec-2">Day 4 </h2> |
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25 |
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26 |
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27 <table border="2" cellspacing="0" cellpadding="6" rules="groups" frame="hsides"> |
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28 <caption></caption> |
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29 <colgroup><col class="right" /><col class="left" /><col class="left" /> |
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30 </colgroup> |
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31 <thead> |
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32 <tr><th scope="col" class="right">Time</th><th scope="col" class="left">Speaker</th><th scope="col" class="left">Title</th></tr> |
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33 </thead> |
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34 <tbody> |
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35 <tr><td class="right">09:00-11:00</td><td class="left">Ole Nielsen</td><td class="left"><a href="#sec2.5">Mapping and Geoprocessing with Python</a></td></tr> |
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36 <tr><td class="right">11:00-13:00</td><td class="left">Eric Jones</td><td class="left"><a href="#sec2.6">Traits + Traits UI</a></td></tr> |
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37 <tr><td class="right">13:00-14:00</td><td class="left"></td><td class="left">Lunch</td></tr> |
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38 <tr><td class="right">14:00-16:00</td><td class="left">Prabhu Ramachandran and Gael Varoquaux</td><td class="left"><a href="#sec2.7">Mayavi for 3D visualization</a></td></tr> |
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39 <tr><td class="right">16:00-17:00</td><td class="left">Puneeth Chaganti</td><td class="left"><a href="#sec2.8">Sage introduction/tutorial</a></td></tr> |
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40 <tr><td class="right">17:00-18:00</td><td class="left">Pankaj Pandey and Prabhu Ramachandran</td><td class="left"><a href="#sec2.9">An introduction to Cython</a></td></tr> |
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41 </tbody> |
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42 </table> |
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43 <br/><br/> |
4 |
44 |
5 <h2 id="sec-1"><span class="section-number-3"></span>Intended audience </h2> |
45 <h2 id="sec-1"><span class="section-number-3"></span>Intended audience </h2> |
6 |
46 |
7 <p>This conference is targeted at anyone who uses Python for work in science/engineering/technology/education. This includes college and university teachers/professors/lecturers from any Engineering or Science domain, students of engineering/science/education who would like to use Python for their basic computing and plotting needs, researchers who use or would like to use Python for their research, and corporate users of Python for scientific computing. |
47 <p>This conference is targeted at anyone who uses Python for work in science/engineering/technology/education. This includes college and university teachers/professors/lecturers from any Engineering or Science domain, students of engineering/science/education who would like to use Python for their basic computing and plotting needs, researchers who use or would like to use Python for their research, and corporate users of Python for scientific computing. |
8 </p> |
48 </p> |
36 |
76 |
37 <!-- <h4 id="sec-1">Day 2 </h4> --> |
77 <!-- <h4 id="sec-1">Day 2 </h4> --> |
38 |
78 |
39 |
79 |
40 |
80 |
41 <h3>Jarrod Millman, Git/Github + NumPy/SciPy/MPL basics (2 hrs)</h3> |
81 <h3 id="sec2.1">Jarrod Millman, Git/Github + NumPy/SciPy/MPL basics (2 hrs)</h3> |
42 <ul> |
82 <ul> |
43 <li>Git/Github</li> |
83 <li>Git/Github</li> |
44 <li>NumPy and SciPy basics along with the most important Matplotlib commands. |
84 <li>NumPy and SciPy basics along with the most important Matplotlib commands. |
45 This could be thought of as a quick refresher on the basic tools used in Python for scientific computing. |
85 This could be thought of as a quick refresher on the basic tools used in Python for scientific computing. |
46 </li> |
86 </li> |
47 </ul> |
87 </ul> |
48 |
88 |
49 |
89 |
50 |
90 |
51 <h3>Emmanuelle Gouillart, Image processing using NumPy, SciPy and scikits-image (2 hrs)</h3> |
91 <h3 id="sec2.2">Emmanuelle Gouillart, Image processing using NumPy, SciPy and scikits-image (2 hrs)</h3> |
52 <ul> |
92 <ul> |
53 <li>This tutorial will show a bag of basic recipes in order to efficiently |
93 <li>This tutorial will show a bag of basic recipes in order to efficiently |
54 manipulate and process images in the form of NumPy arrays. |
94 manipulate and process images in the form of NumPy arrays. |
55 </li> |
95 </li> |
56 <li>Target audience: scientists and engineers working with images |
96 <li>Target audience: scientists and engineers working with images |
110 |
150 |
111 |
151 |
112 </li> |
152 </li> |
113 </ul> |
153 </ul> |
114 |
154 |
115 <h3>Ole Nielsen: Mapping and Geoprocessing with Python (2 hrs)</h3> |
155 <h3 id="sec2.4">Mateusz Paprocki, SymPy (2 hrs)</h3> |
116 <ul> |
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117 <li> |
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118 Putting information on a map and analyzing spatial data are fundamental to a |
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119 wide range of areas such as navigation, working with climate or geological data, |
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120 disaster management, presentation of modelling results, demographics, social networking etc. |
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121 </li> |
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122 <li> |
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123 This tutorial will give a practical introduction to tools and techniques |
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124 available for processing spatial information and, through a few hands-on |
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125 exercises, give the participants a sense of how to manipulate and visualise |
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126 spatial data using Python. Topics covered include reading and writing |
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127 of important data formats for both raster and vector data, looking at the layers, |
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128 awareness of issues with datums and projections, calculating centroids of polygons, |
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129 calculation of distance between points on the surface of Earth, interpolation from raster |
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130 grids to points etc. The tutorial has been developed for Ubuntu Linux and will provide source code, |
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131 tests and data for this platform. However, the content and messages should be general and apply to any platform. |
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132 </li> |
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133 <li> |
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134 I assume that participants know how to write and run |
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135 Python scripts and would suggest you install qgis as well as |
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136 the python dependencies numpy, matplotlib and gdal on your |
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137 laptop. I don't assume any previous knowledge of mapping or Geographic Information Systems (GIS). |
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138 </li> |
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139 <li> |
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140 If you have some spatial data you want to manipulate in Python feel free to bring it along and grab me during a lunch break. |
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141 </li> |
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142 </ul> |
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143 |
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144 |
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145 <h3>Eric Jones/Puneeth/Pankaj: Traits + Traits UI (2 hrs)</h3> |
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146 <ul> |
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147 <li> |
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148 Enthought’s traits package provides for a powerful object model which |
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149 provides a host of useful functionality with a clean and expressive syntax. |
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150 It is an open source library and forms the basis of the Enthought Tool Suite and many of |
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151 Enthought’s internal commercial projects. In this tutorial we will cover the basics of using |
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152 Traits along with the UI library TraitsUI which makes it very easy to build powerful and |
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153 interactive, user interfaces using Traits. |
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154 </li> |
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155 </ul> |
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156 |
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157 |
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158 <h3>Prabhu Ramachandran and Gael Varoquaux, Mayavi for 3D visualization (2 hrs)</h3> |
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159 <ul> |
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160 <li> |
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161 At the end of this tutorial attendees will learn how to visualize numpy |
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162 arrays using Mayavi's mlab interface. They will also learn enough about |
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163 mayavi to be able to create their own simple datasets and visualize |
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164 them. If this tutorial follows one on traits, then attendees will learn |
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165 how easy it is to embed 3D visualization in their own application UIs |
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166 (provided they are written in wxPython or PyQt). |
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167 </li> |
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168 <li> |
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169 In this tutorial, we first provide a rapid overview of Mayavi_ and its |
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170 features. We then move on to using Mayavi via IPython_ and mlab. This |
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171 is done in a hands-on fashion and introduces the audience to visualizing |
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172 numpy arrays and the basic mayavi visualization pipeline. We then |
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173 introduce the audience to the basic objects and data sources used in |
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174 Mayavi. We end with an example of creating custom dialogs using Traits |
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175 and embedding 3D visualization in these dialogs with Mayavi. |
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176 </li> |
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177 <li> |
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178 Packages required |
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179 <ul> |
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180 <li><a href="http://code.enthought.com/projects/mayavi">Mayavi</a></li> |
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181 <li><a href="http://ipython.scipy.org">IPython</a></li> |
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182 <li><a href="http://code.enthought.com/projects/traits">Traits</a></li> |
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183 <li>numpy, scipy</li> |
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184 </ul> |
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185 </li> |
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186 </ul> |
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187 |
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188 <h3>Pankaj Pandey and Prabhu Ramachandran, An introduction to Cython (1 hrs)</h3> |
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189 <ul> |
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190 <li> |
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191 At some level, Cython (http://www.cython.org) can be thought of a Python to C compiler. |
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192 It allows someone to write extension modules in a language very similar to Python and |
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193 therefore makes it rather easy to write C-extensions. In this tutorial we will cover |
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194 the basics of building extension modules with Cython. |
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195 </li> |
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196 <li> |
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197 Package requirements: You will require to have Cython, the |
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198 Python development headers and a working C-compiler to run the hands-on exercises. |
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199 </li> |
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200 </ul> |
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201 |
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202 <h3>Puneeth Chaganti, Sage introduction/tutorial: (1 hr)</h3> |
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203 <ul> |
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204 <li>This tutorial will feature a demonstration and a brief review of some of the capabilities of the <a href="http://www.sagemath.org">Sage notebook</a>.</li> |
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205 <li>A rough schedule of the talk would be as follows: |
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206 <ul> |
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207 <li>Introduction</li> |
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208 <li>Starting the server</li> |
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209 <li>The UI</li> |
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210 <li>Getting Help</li> |
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211 <li>Overview of what's available in Sage |
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212 <ul> |
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213 <li>Basic Algebra</li> |
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214 <li>Basic Calculus</li> |
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215 <li>Basic Plotting</li> |
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216 </ul> |
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217 </li> |
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218 </ul> |
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219 </li> |
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220 </ul> |
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221 |
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222 |
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223 <h3>Mateusz Paprocki, SymPy (2 hrs)</h3> |
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224 <ul> |
156 <ul> |
225 <li> |
157 <li> |
226 SymPy is a pure Python library for symbolic mathematics. It aims to become a |
158 SymPy is a pure Python library for symbolic mathematics. It aims to become a |
227 full-featured computer algebra system (CAS) while keeping the code as simple |
159 full-featured computer algebra system (CAS) while keeping the code as simple |
228 as possible in order to be comprehensible and easily extensible. SymPy is |
160 as possible in order to be comprehensible and easily extensible. SymPy is |
261 <li>Examples.</li> |
193 <li>Examples.</li> |
262 </ul> |
194 </ul> |
263 </li> |
195 </li> |
264 </ul> |
196 </ul> |
265 |
197 |
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198 <h3 id="sec2.5">Ole Nielsen: Mapping and Geoprocessing with Python (2 hrs)</h3> |
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199 <ul> |
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200 <li> |
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201 Putting information on a map and analyzing spatial data are fundamental to a |
|
202 wide range of areas such as navigation, working with climate or geological data, |
|
203 disaster management, presentation of modelling results, demographics, social networking etc. |
|
204 </li> |
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205 <li> |
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206 This tutorial will give a practical introduction to tools and techniques |
|
207 available for processing spatial information and, through a few hands-on |
|
208 exercises, give the participants a sense of how to manipulate and visualise |
|
209 spatial data using Python. Topics covered include reading and writing |
|
210 of important data formats for both raster and vector data, looking at the layers, |
|
211 awareness of issues with datums and projections, calculating centroids of polygons, |
|
212 calculation of distance between points on the surface of Earth, interpolation from raster |
|
213 grids to points etc. The tutorial has been developed for Ubuntu Linux and will provide source code, |
|
214 tests and data for this platform. However, the content and messages should be general and apply to any platform. |
|
215 </li> |
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216 <li> |
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217 I assume that participants know how to write and run |
|
218 Python scripts and would suggest you install qgis as well as |
|
219 the python dependencies numpy, matplotlib and gdal on your |
|
220 laptop. I don't assume any previous knowledge of mapping or Geographic Information Systems (GIS). |
|
221 </li> |
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222 <li> |
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223 If you have some spatial data you want to manipulate in Python feel free to bring it along and grab me during a lunch break. |
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224 </li> |
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225 </ul> |
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226 |
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227 |
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228 <h3 id="sec2.6">Eric Jones: Traits + Traits UI (2 hrs)</h3> |
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229 <ul> |
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230 <li> |
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231 Enthought’s traits package provides for a powerful object model which |
|
232 provides a host of useful functionality with a clean and expressive syntax. |
|
233 It is an open source library and forms the basis of the Enthought Tool Suite and many of |
|
234 Enthought’s internal commercial projects. In this tutorial we will cover the basics of using |
|
235 Traits along with the UI library TraitsUI which makes it very easy to build powerful and |
|
236 interactive, user interfaces using Traits. |
|
237 </li> |
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238 </ul> |
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239 |
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240 |
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241 <h3 id="sec2.7">Prabhu Ramachandran and Gael Varoquaux, Mayavi for 3D visualization (2 hrs)</h3> |
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242 <ul> |
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243 <li> |
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244 At the end of this tutorial attendees will learn how to visualize numpy |
|
245 arrays using Mayavi's mlab interface. They will also learn enough about |
|
246 mayavi to be able to create their own simple datasets and visualize |
|
247 them. If this tutorial follows one on traits, then attendees will learn |
|
248 how easy it is to embed 3D visualization in their own application UIs |
|
249 (provided they are written in wxPython or PyQt). |
|
250 </li> |
|
251 <li> |
|
252 In this tutorial, we first provide a rapid overview of Mayavi_ and its |
|
253 features. We then move on to using Mayavi via IPython_ and mlab. This |
|
254 is done in a hands-on fashion and introduces the audience to visualizing |
|
255 numpy arrays and the basic mayavi visualization pipeline. We then |
|
256 introduce the audience to the basic objects and data sources used in |
|
257 Mayavi. We end with an example of creating custom dialogs using Traits |
|
258 and embedding 3D visualization in these dialogs with Mayavi. |
|
259 </li> |
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260 <li> |
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261 Packages required |
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262 <ul> |
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263 <li><a href="http://code.enthought.com/projects/mayavi">Mayavi</a></li> |
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264 <li><a href="http://ipython.scipy.org">IPython</a></li> |
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265 <li><a href="http://code.enthought.com/projects/traits">Traits</a></li> |
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266 <li>numpy, scipy</li> |
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267 </ul> |
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268 </li> |
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269 </ul> |
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270 |
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271 <h3 id="sec2.8">Puneeth Chaganti, Sage introduction/tutorial: (1 hr)</h3> |
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272 <ul> |
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273 <li>This tutorial will feature a demonstration and a brief review of some of the capabilities of the <a href="http://www.sagemath.org">Sage notebook</a>.</li> |
|
274 <li>A rough schedule of the talk would be as follows: |
|
275 <ul> |
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276 <li>Introduction</li> |
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277 <li>Starting the server</li> |
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278 <li>The UI</li> |
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279 <li>Getting Help</li> |
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280 <li>Overview of what's available in Sage |
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281 <ul> |
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282 <li>Basic Algebra</li> |
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283 <li>Basic Calculus</li> |
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284 <li>Basic Plotting</li> |
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285 </ul> |
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286 </li> |
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287 </ul> |
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288 </li> |
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289 </ul> |
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290 |
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291 <h3 id="sec2.9">Pankaj Pandey and Prabhu Ramachandran, An introduction to Cython (1 hrs)</h3> |
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292 <ul> |
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293 <li> |
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294 At some level, Cython (http://www.cython.org) can be thought of a Python to C compiler. |
|
295 It allows someone to write extension modules in a language very similar to Python and |
|
296 therefore makes it rather easy to write C-extensions. In this tutorial we will cover |
|
297 the basics of building extension modules with Cython. |
|
298 </li> |
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299 <li> |
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300 Package requirements: You will require to have Cython, the |
|
301 Python development headers and a working C-compiler to run the hands-on exercises. |
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302 </li> |
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303 </ul> |
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304 |
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305 |
266 {% endblock content %} |
306 {% endblock content %} |