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MythTV s User jobs interface provides a simple way to make any of these commands available for MythTV to use. The same technique used to make all of the scripts covered in this section run as post-processing jobs for MythTV works for all third-party components. Run mythtvsetup and navigate to the last page of the General section. Several tokens have special meaning within the job command lines. There s more or less one of these for every important (and for some less important) piece of metadata associated with a recording. It may be necessary for you to pass some of these tokens to your post-processing job in order for it to work properly. The important ones needed by nuvexport and myth2ipod include %FILE% , which is the filename for the recording to which the job applies, and %DIR% , which is the directory path where the recording is stored. A full listing appears in the official MythTV documentation at www.mythtv.org/modules.php name=MythInstall. You might find the following commands useful, shown as both plaintext below and within the appropriate MythTV configuration dialog in Figure 17-4. Transcode for iPod:

Listing A-4 (continued)

A lengthy runtime configuration routine executes before delivering you to the KnoppMyth desktop. Following the successful completion of the prior pre-installation process, you are presented with one last series of prompts before being turned over to the MythTV setup utility. A prompt that contains the line Please provide the root password: in a console window means you have arrived safely. Type the administrator password you specified earlier during the pre-installation process. Next comes a series of questions, another round of time zone and date settings, and a last request for an IP address. Then the software performs some automated configuration tasks before stopping to ask you to enable a BootSplash screen. That text is reproduced here, verbatim:

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The STM has a size of 20 action perception pairs in all the experiments Figure 1614 displays the evolution of the mean squared error provided by the current models (communication, world, and satisfaction) predicting the STM as iterations of the MDB take place in both physical agents The error clearly decreases in all cases and in a very similar way for both agents (except at the beginning, where the STM is being lled up) This means that the MDB works similarly on two very different real platforms and that the MDB is able to provide real modeling of the environment, the communication, and the satisfaction of the physical agent As the error values show in Figure 16.

TEMPERAMENT AND PERSONALITY TRAITS 86 EXTRAVERSION/SOCIABILITY 88 Cortical Arousal 88 Cortical Arousability 89 Monoamines 91 Monoamine Oxidase 93 Hormones 93 Summary 94 NEUROTICISM/ANXIETY/HARM AVOIDANCE 94 Autonomic Arousal 95 Brain Arousal 95 Monoamines 95 Hormones 96 Molecular Genetics 97 Summary 97 PSYCHOTICISM/IMPULSIVITY/SENSATION SEEKING/CONSCIENTIOUSNESS/CONSTRAINT

WorkbookNewSheet: Occurs when a new worksheet is added. SheetDeactivate event: Occurs when the active worksheet is deactivated. SheetActivate event: Occurs when the newly added worksheet is activated.

outcome research and, 223, 225, 230 Task Force on Promotion and Dissemination of Psychological Procedures, 225 Task Force on Statistical Inference, 33, 51 Amnesia: ERP methods and 304 305 focal retrograde, 293 Amos: data analysis and, 92, 93, 96 growth curve analysis and, 453 multiple regression and, 105 106 Amplitude, 421 Analogue study, 206 Analysis of covariance (ANCOVA): multivariate ANCOVA (MANCOVA), 231 outcome research and, 231 program evaluation and, 332, 333, 335 causal analysis, 329 Analysis of variance (ANOVA), 29 31, 138 139 cell means and, 16 cluster analysis and, 179 EDA and, 34, 35 36 regression analyses, 37 two-way t and, 50 experimental design and, 3 4 growth curve analysis and, 451 John Tukey and, 34 measuring personality and, 397 meta-analysis and, 540 multiple linear regression and, 492, 498 multivariate ANOVA (MANOVA), 57, 116, 117, 127, 179 normality and, 120 ordinal analysis and, 654 ordinal analysis and, 645, 649, 653, 657, 658 power and, 65 preparatory data analysis and, 116, 127 additivity, 125, 126 covariance, 127 heterogeneity of variance and, 134 homogeneity of variance, 124 125 independence of errors, 125, 126 sample size, unequal, 124 125 sphericity, 126 regression model and, 14 15 SEM and, 623, 628 survival analysis and, 572 time series analysis and, 591 See also Structural equation modeling (SEM) Animal learning: apparatus, choosing an, 274 boxes and, 272, 273 274 case study, 285 286 computers in, use of, 281 282 archives, 285 data analysis, 284, 285 experimental design, 282 283

The on-screen Roomba icon can be drawn at any rotation and position, in order to match the motion of the real robot. The icon version will also display sensor information. By using the Processing line(), ellipse(), and rect() drawing commands, it s possible to produce a little iconic Roomba outline as shown in Figure 7-5. The outer circle represents the entire Roomba shape, while the inner thick line segments each represent a different sensor, described by the nearest text. When the sensor isn t detecting anything, the corresponding line segment stays gray, but if it is detecting, it will change to a bright color, usually red. The code to draw the virtual robot is shown in Listing 7-5. It uses many Processing tricks and gives an example of how you can go about drawing a detailed object in Processing. The color change on sensor detection is accomplished by changing the line color with the stroke() command.

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