/usr/share/doc/pyxplot/html/sect0253.html is in pyxplot-doc 0.9.2-4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 | <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
<head>
<meta name="generator" content="plasTeX" />
<meta content="text/html; charset=utf-8" http-equiv="content-type" />
<title>PyXPlot Users' Guide: The stats module</title>
<link href="sect0254.html" title="The time module" rel="next" />
<link href="sect0252.html" title="The random module" rel="prev" />
<link href="ch-function_list.html" title="List of in-built functions" rel="up" />
<link rel="stylesheet" href="styles/styles.css" />
</head>
<body>
<div class="navigation">
<table cellspacing="2" cellpadding="0" width="100%">
<tr>
<td><a href="sect0252.html" title="The random module"><img alt="Previous: The random module" border="0" src="icons/previous.gif" width="32" height="32" /></a></td>
<td><a href="ch-function_list.html" title="List of in-built functions"><img alt="Up: List of in-built functions" border="0" src="icons/up.gif" width="32" height="32" /></a></td>
<td><a href="sect0254.html" title="The time module"><img alt="Next: The time module" border="0" src="icons/next.gif" width="32" height="32" /></a></td>
<td class="navtitle" align="center">PyXPlot Users' Guide</td>
<td><a href="index.html" title="Table of Contents"><img border="0" alt="" src="icons/contents.gif" width="32" height="32" /></a></td>
<td><a href="sect0288.html" title="Index"><img border="0" alt="" src="icons/index.gif" width="32" height="32" /></a></td>
<td><img border="0" alt="" src="icons/blank.gif" width="32" height="32" /></td>
</tr>
</table>
</div>
<div class="breadcrumbs">
<span>
<span>
<a href="index.html">PyXPlot Users' Guide</a> <b>:</b>
</span>
</span><span>
<span>
<a href="sect0089.html">Reference Manual</a> <b>:</b>
</span>
</span><span>
<span>
<a href="ch-function_list.html">List of in-built functions</a> <b>:</b>
</span>
</span><span>
<span>
<b class="current">The <tt class="ttfamily">stats</tt> module</b>
</span>
</span>
<hr />
</div>
<div><h2 id="a0000000254">2.9 The <tt class="ttfamily">stats</tt> module</h2>
<p>The <tt class="ttfamily">stats</tt> module contains statistical functions: </p><p> <big class="large"><b class="bf">stats.binomialCDF(<img src="images/img-0719.png" alt="$k,p,n$" style="vertical-align:-4px;
width:45px;
height:17px" class="math gen" />)</b></big> <br />The stats.binomialCDF(<img src="images/img-0719.png" alt="$k,p,n$" style="vertical-align:-4px;
width:45px;
height:17px" class="math gen" />) function evaluates the probability of getting fewer than or exactly <img src="images/img-0654.png" alt="$k$" style="vertical-align:0px;
width:8px;
height:13px" class="math gen" /> successes out of <img src="images/img-0025.png" alt="$n$" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> trials in a binomial distribution with success probability <img src="images/img-0270.png" alt="$p$" style="vertical-align:-4px;
width:10px;
height:12px" class="math gen" />. <img src="images/img-0654.png" alt="$k$" style="vertical-align:0px;
width:8px;
height:13px" class="math gen" /> and <img src="images/img-0025.png" alt="$n$" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> must be positive real integers. <img src="images/img-0270.png" alt="$p$" style="vertical-align:-4px;
width:10px;
height:12px" class="math gen" /> must be a real number in the range <img src="images/img-0720.png" alt="$0\leq p \leq 1$" style="vertical-align:-4px;
width:74px;
height:16px" class="math gen" />. <a name="a0000001601" id="a0000001601"></a> </p><p> <big class="large"><b class="bf">stats.binomialPDF(<img src="images/img-0719.png" alt="$k,p,n$" style="vertical-align:-4px;
width:45px;
height:17px" class="math gen" />)</b></big> <br />The stats.binomialPDF(<img src="images/img-0719.png" alt="$k,p,n$" style="vertical-align:-4px;
width:45px;
height:17px" class="math gen" />) function evaluates the probability of getting <img src="images/img-0654.png" alt="$k$" style="vertical-align:0px;
width:8px;
height:13px" class="math gen" /> successes out of <img src="images/img-0025.png" alt="$n$" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> trials in a binomial distribution with success probability <img src="images/img-0270.png" alt="$p$" style="vertical-align:-4px;
width:10px;
height:12px" class="math gen" />. <img src="images/img-0654.png" alt="$k$" style="vertical-align:0px;
width:8px;
height:13px" class="math gen" /> and <img src="images/img-0025.png" alt="$n$" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> must be positive real integers. <img src="images/img-0270.png" alt="$p$" style="vertical-align:-4px;
width:10px;
height:12px" class="math gen" /> must be a real number in the range <img src="images/img-0720.png" alt="$0\leq p \leq 1$" style="vertical-align:-4px;
width:74px;
height:16px" class="math gen" />. <a name="a0000001602" id="a0000001602"></a> </p><p> <big class="large"><b class="bf">stats.chisqCDF(<img src="images/img-0721.png" alt="$x,\nu $" style="vertical-align:-4px;
width:28px;
height:12px" class="math gen" />)</b></big> <br />The stats.chisqCDF(<img src="images/img-0721.png" alt="$x,\nu $" style="vertical-align:-4px;
width:28px;
height:12px" class="math gen" />) function returns the cumulative probability density at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> in a <img src="images/img-0272.png" alt="$\chi $" style="vertical-align:-4px;
width:11px;
height:12px" class="math gen" />-squared distribution with <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> degrees of freedom. <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> must be a positive real dimensionless integer. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> must be a positive real dimensionless number. <a name="a0000001603" id="a0000001603"></a> </p><p> <big class="large"><b class="bf">stats.chisqCDFi(<img src="images/img-0722.png" alt="$P,\nu $" style="vertical-align:-4px;
width:30px;
height:16px" class="math gen" />)</b></big> <br />The stats.chisqCDFi(<img src="images/img-0722.png" alt="$P,\nu $" style="vertical-align:-4px;
width:30px;
height:16px" class="math gen" />) function returns the point <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> at which the cumulative probability density in a <img src="images/img-0272.png" alt="$\chi $" style="vertical-align:-4px;
width:11px;
height:12px" class="math gen" />-squared distribution with <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> degrees of freedom is <img src="images/img-0723.png" alt="$P$" style="vertical-align:0px;
width:14px;
height:12px" class="math gen" />. <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> must be a positive real dimensionless integer. <img src="images/img-0723.png" alt="$P$" style="vertical-align:0px;
width:14px;
height:12px" class="math gen" /> must be a real number in the range <img src="images/img-0720.png" alt="$0\leq p \leq 1$" style="vertical-align:-4px;
width:74px;
height:16px" class="math gen" />. <a name="a0000001604" id="a0000001604"></a> </p><p> <big class="large"><b class="bf">stats.chisqPDF(<img src="images/img-0721.png" alt="$x,\nu $" style="vertical-align:-4px;
width:28px;
height:12px" class="math gen" />)</b></big> <br />The stats.chisqPDF(<img src="images/img-0721.png" alt="$x,\nu $" style="vertical-align:-4px;
width:28px;
height:12px" class="math gen" />) function returns the probability density at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> in a <img src="images/img-0272.png" alt="$\chi $" style="vertical-align:-4px;
width:11px;
height:12px" class="math gen" />-squared distribution with <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> degrees of freedom. <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> must be a positive real dimensionless integer. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> must be a positive real dimensionless number. <a name="a0000001605" id="a0000001605"></a> </p><p> <big class="large"><b class="bf">stats.gaussianCDF(<img src="images/img-0701.png" alt="$x,\sigma $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />)</b></big> <br />The stats.gaussianCDF(<img src="images/img-0701.png" alt="$x,\sigma $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />) function evaluates the Gaussian cumulative distribution function of standard deviation <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" />. The distribution is centred upon <img src="images/img-0135.png" alt="$x=0$" style="vertical-align:0px;
width:43px;
height:12px" class="math gen" />. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> and <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> must both be real, but may have any physical dimensions so long as they match. <a name="a0000001606" id="a0000001606"></a> </p><p> <big class="large"><b class="bf">stats.gaussianCDFi(<img src="images/img-0701.png" alt="$x,\sigma $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />)</b></big> <br />The stats.gaussianCDFi(<img src="images/img-0701.png" alt="$x,\sigma $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />) function evaluates the inverse Gaussian cumulative distribution function of standard deviation <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" />. The distribution is centred upon <img src="images/img-0135.png" alt="$x=0$" style="vertical-align:0px;
width:43px;
height:12px" class="math gen" />. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> and <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> must both be real, but may have any physical dimensions so long as they match. <a name="a0000001607" id="a0000001607"></a> </p><p> <big class="large"><b class="bf">stats.gaussianPDF(<img src="images/img-0701.png" alt="$x,\sigma $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />)</b></big> <br />The stats.gaussianPDF(<img src="images/img-0701.png" alt="$x,\sigma $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />) function evaluates the Gaussian probability density function of standard deviation <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" />. The distribution is centred upon <img src="images/img-0135.png" alt="$x=0$" style="vertical-align:0px;
width:43px;
height:12px" class="math gen" />. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> and <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> must both be real, but may have any physical dimensions so long as they match. <a name="a0000001608" id="a0000001608"></a> </p><p> <big class="large"><b class="bf">stats.lognormalCDF(<img src="images/img-0724.png" alt="$x,\zeta ,\sigma $" style="vertical-align:-4px;
width:46px;
height:17px" class="math gen" />)</b></big> <br />The stats.lognormalCDF(<img src="images/img-0724.png" alt="$x,\zeta ,\sigma $" style="vertical-align:-4px;
width:46px;
height:17px" class="math gen" />) function evaluates the log normal cumulative distribution function of standard deviation <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" />, centred upon <img src="images/img-0275.png" alt="$\zeta $" style="vertical-align:-4px;
width:9px;
height:17px" class="math gen" />, at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" />. <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> must be real, positive and dimensionless. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> and <img src="images/img-0275.png" alt="$\zeta $" style="vertical-align:-4px;
width:9px;
height:17px" class="math gen" /> must both be real, but may have any physical dimensions so long as they match. <a name="a0000001609" id="a0000001609"></a> </p><p> <big class="large"><b class="bf">stats.lognormalCDFi(<img src="images/img-0724.png" alt="$x,\zeta ,\sigma $" style="vertical-align:-4px;
width:46px;
height:17px" class="math gen" />)</b></big> <br />The stats.lognormalCDFi(<img src="images/img-0724.png" alt="$x,\zeta ,\sigma $" style="vertical-align:-4px;
width:46px;
height:17px" class="math gen" />) function evaluates the inverse log normal cumulative distribution function of standard deviation <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" />, centred upon <img src="images/img-0275.png" alt="$\zeta $" style="vertical-align:-4px;
width:9px;
height:17px" class="math gen" />, at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" />. <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> must be real, positive and dimensionless. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> and <img src="images/img-0275.png" alt="$\zeta $" style="vertical-align:-4px;
width:9px;
height:17px" class="math gen" /> must both be real, but may have any physical dimensions so long as they match. <a name="a0000001610" id="a0000001610"></a> </p><p> <big class="large"><b class="bf">stats.lognormalPDF(<img src="images/img-0724.png" alt="$x,\zeta ,\sigma $" style="vertical-align:-4px;
width:46px;
height:17px" class="math gen" />)</b></big> <br />The stats.lognormalPDF(<img src="images/img-0724.png" alt="$x,\zeta ,\sigma $" style="vertical-align:-4px;
width:46px;
height:17px" class="math gen" />) function evaluates the log normal probability density function of standard deviation <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" />, centred upon <img src="images/img-0275.png" alt="$\zeta $" style="vertical-align:-4px;
width:9px;
height:17px" class="math gen" />, at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" />. <img src="images/img-0273.png" alt="$\sigma $" style="vertical-align:0px;
width:11px;
height:8px" class="math gen" /> must be real, positive and dimensionless. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> and <img src="images/img-0275.png" alt="$\zeta $" style="vertical-align:-4px;
width:9px;
height:17px" class="math gen" /> must both be real, but may have any physical dimensions so long as they match. <a name="a0000001611" id="a0000001611"></a> </p><p> <big class="large"><b class="bf">stats.poissonCDF(<img src="images/img-0725.png" alt="$x,\mu $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />)</b></big> <br />The stats.poissonCDF(<img src="images/img-0725.png" alt="$x,\mu $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />) function returns the probability of getting <img src="images/img-0726.png" alt="$\leq x$" style="vertical-align:-3px;
width:28px;
height:15px" class="math gen" /> from a Poisson distribution with mean <img src="images/img-0727.png" alt="$\mu $" style="vertical-align:-4px;
width:11px;
height:12px" class="math gen" />, where <img src="images/img-0727.png" alt="$\mu $" style="vertical-align:-4px;
width:11px;
height:12px" class="math gen" /> must be real, positive and dimensionless and <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> must be real and dimensionless. <a name="a0000001612" id="a0000001612"></a> </p><p> <big class="large"><b class="bf">stats.poissonPDF(<img src="images/img-0725.png" alt="$x,\mu $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />)</b></big> <br />The stats.poissonPDF(<img src="images/img-0725.png" alt="$x,\mu $" style="vertical-align:-4px;
width:29px;
height:12px" class="math gen" />) function returns the probability of getting <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> from a Poisson distribution with mean <img src="images/img-0727.png" alt="$\mu $" style="vertical-align:-4px;
width:11px;
height:12px" class="math gen" />, where <img src="images/img-0727.png" alt="$\mu $" style="vertical-align:-4px;
width:11px;
height:12px" class="math gen" /> must be real, positive and dimensionless and <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> must be a real dimensionless integer. <a name="a0000001613" id="a0000001613"></a> </p><p> <big class="large"><b class="bf">stats.tdistCDF(<img src="images/img-0721.png" alt="$x,\nu $" style="vertical-align:-4px;
width:28px;
height:12px" class="math gen" />)</b></big> <br />The stats.tdistCDF(<img src="images/img-0721.png" alt="$x,\nu $" style="vertical-align:-4px;
width:28px;
height:12px" class="math gen" />) function returns the cumulative probability density at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> in a <img src="images/img-0065.png" alt="$t$" style="vertical-align:0px;
width:6px;
height:12px" class="math gen" />-distribution with <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> degrees of freedom. <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> must be a positive real dimensionless integer. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> must be a positive real dimensionless number. <a name="a0000001614" id="a0000001614"></a> </p><p> <big class="large"><b class="bf">stats.tdistCDFi(<img src="images/img-0722.png" alt="$P,\nu $" style="vertical-align:-4px;
width:30px;
height:16px" class="math gen" />)</b></big> <br />The stats.tdistCDFi(<img src="images/img-0722.png" alt="$P,\nu $" style="vertical-align:-4px;
width:30px;
height:16px" class="math gen" />) function returns the point <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> at which the cumulative probability density in a <img src="images/img-0065.png" alt="$t$" style="vertical-align:0px;
width:6px;
height:12px" class="math gen" />-distribution with <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> degrees of freedom is <img src="images/img-0723.png" alt="$P$" style="vertical-align:0px;
width:14px;
height:12px" class="math gen" />. <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> must be a positive real dimensionless integer. <img src="images/img-0723.png" alt="$P$" style="vertical-align:0px;
width:14px;
height:12px" class="math gen" /> must be a real number in the range <img src="images/img-0720.png" alt="$0\leq p \leq 1$" style="vertical-align:-4px;
width:74px;
height:16px" class="math gen" />. <a name="a0000001615" id="a0000001615"></a> </p><p> <big class="large"><b class="bf">stats.tdistPDF(<img src="images/img-0721.png" alt="$x,\nu $" style="vertical-align:-4px;
width:28px;
height:12px" class="math gen" />)</b></big> <br />The stats.tdistPDF(<img src="images/img-0721.png" alt="$x,\nu $" style="vertical-align:-4px;
width:28px;
height:12px" class="math gen" />) function returns the probability density at <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> in a <img src="images/img-0065.png" alt="$t$" style="vertical-align:0px;
width:6px;
height:12px" class="math gen" />-distribution with <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> degrees of freedom. <img src="images/img-0271.png" alt="$\nu $" style="vertical-align:0px;
width:9px;
height:8px" class="math gen" /> must be a positive real dimensionless integer. <img src="images/img-0030.png" alt="$x$" style="vertical-align:0px;
width:10px;
height:8px" class="math gen" /> must be a positive real dimensionless number. <a name="a0000001616" id="a0000001616"></a> </p></div>
<div class="navigation">
<table cellspacing="2" cellpadding="0" width="100%">
<tr>
<td><a href="sect0252.html" title="The random module"><img alt="Previous: The random module" border="0" src="icons/previous.gif" width="32" height="32" /></a></td>
<td><a href="ch-function_list.html" title="List of in-built functions"><img alt="Up: List of in-built functions" border="0" src="icons/up.gif" width="32" height="32" /></a></td>
<td><a href="sect0254.html" title="The time module"><img alt="Next: The time module" border="0" src="icons/next.gif" width="32" height="32" /></a></td>
<td class="navtitle" align="center">PyXPlot Users' Guide</td>
<td><a href="index.html" title="Table of Contents"><img border="0" alt="" src="icons/contents.gif" width="32" height="32" /></a></td>
<td><a href="sect0288.html" title="Index"><img border="0" alt="" src="icons/index.gif" width="32" height="32" /></a></td>
<td><img border="0" alt="" src="icons/blank.gif" width="32" height="32" /></td>
</tr>
</table>
</div>
<script language="javascript" src="icons/imgadjust.js" type="text/javascript"></script>
</body>
</html>
|