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Tour of Ceylon

Ceylon Walkthrough

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Reference

Language module API

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Ceylon IDE for Eclipse

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A function or class initializer has a parameter list declaring the parameters of the function or class. A parameter list may contain zero or more parameters .

You have the choice between 'inline' style:

And 'reference' style:

The second style is preferred when the parameters are annotated.

Syntactically, a parameter list is a comma separated list enclosed in parentheses following the type parameter list . Each parameter is either:

A parameter with its own parameter list is called a callable parameter , and its type is a function type. A function with no parameter list is called a value parameter .

A default argument for a parameter may be specified.

For a value parameter, the default argument expression follows the parameter name, separated with an equals sign:

For a callable parameter, the default argument expression follows the parameter's parameter list, separated with a fat arrow ( => ):

A defaulted parameter allows the function or class to be invoked without an argument for that parameter.

Parameters with default values may be called optional parameters (not to be confused with an optional type T? ) or defaulted parameters . Parameters without default values may be called required parameters . The default argument expression may be called the default argument .

To avoid ambiguity, defaulted parameters are only permitted after all the required parameters in the parameter list.

Note: In Ceylon, functions which accept an Iterable parameter are generally preferred over variadic functions, since Iterable s may be evaluated lazily by the called function or class, whereas the arguments to a variadic parameter must be evaluated and packaged into a sequence before the function or class is called.

A variadic function has a variadic parameter as the last parameter in the parameter list.

We solve this problem only in a limited way, by using as gold-standards partial genome alignments implied by multiple alignments of proteins and of structural RNAs. Thus we measure alignment accuracy only in the parts of genomes that encode these molecules, and our conclusions might not apply to other parts of genomes. Our assessment is nevertheless useful. Although protein- and RNA-coding regions are thought to comprise only a small minority of large (e.g. mammalian) genomes, these regions are the focus of many downstream studies that use genome alignments. Moreover, small genomes (e.g. yeasts) consist mostly of coding sequence, as do the alignable parts of large but distantly related genomes (e.g. mammal versus non-mammal). Finally, we submit that there is little justification for genome alignment parameters used up till now, and a limited assessment is better than none.

Alignment accuracy can be measured at two levels: correctness of whole (local) alignments, or correctness of aligned bases. For genome alignment, the latter is arguably more relevant. This is because many downstream analyses, such as RNA structure prediction or detection of positively selected sites, depend on the base-level accuracy of the alignments [ 17 ]. In such analyses, more information is obtained from long alignments than from short alignments, making it inappropriate to weight the correctness of long and short alignments equally. This stands in contrast to protein database searches (e.g. BLAST), where residue-level accuracy is not always important, since we may wish to know only whether a protein is evolutionarily related to another protein. Unfortunately, the classic theory of alignment statistics has been developed from the standpoint of database searches and alignment-level accuracy [ 9 ].

This study does not address the problem of distinguishing orthologous from paralogous alignments. This important problem is very different from that of aligning homologous bases, and requires its own specialized methods.

In this study we empirically assess many parameter choices on whole genome alignments of several organisms. We were able to perform many thousands of genome alignments only by using our new alignment software, LAST. Our results give a practical guide for choosing repeat masking strategy, substitution and gap costs, and X-drop parameter - along with empirical estimates of false and true positive rates. We find that the best parameters significantly outperform current standard practice, often decreasing false positives by a factor of two or more at the same true positive rate.

Figure 1

E-values of reverse genome alignments with six repeat-masking methods . In each column, the second-named genome was reversed and then aligned to the first-named genome twenty times, using twenty different scoring schemes. The red lines show the theoretically expected number of alignments at each E-value threshold, and the black lines show the observed number. Alignments in rows 1-5 and 7 were performed with LAST, and those in row 6 were done with BLASTZ, using BLASTZ's internal entropy-masking method. "TRFs": Tandem Repeats Finder with standard parameters; "TRF": Tandem Repeats Finder with non-standard parameters; "hard": hard-masking; "soft": soft-masking.

Figure 2

A spurious similarity caused by tandem repeats . The upper sequence is from the genome and the lower sequence is from the reversed genome. DustMasker fails to mask these sequences.

We obtained the non-standard TRF parameters by trial and error: we simply lowered TRF's mismatch cost, gap cost, and score cutoff until it worked. It is likely that a more principled and effective repeat-masking method than this can be found.

H_SV_TEST: Boot-up Servo Test Cycles

Number of cycles to run servo test on boot-up

H_RSC_SLEWRATE: Throttle servo slew rate

This controls the maximum rate at which the throttle output can change, as a percentage per second. A value of 100 means the throttle can change over its full range in one second. A value of zero gives unlimited slew rate.

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Throttle Servo Position for 0 percent collective. This is on a scale from 0 to 1000, where 1000 is full throttle and 0 is zero throttle. Actual PWM values are controlled by SERVOX_MIN and SERVOX_MAX. The 0 percent collective is defined by H_COL_MIN and 100 percent collective is defined by H_COL_MAX.

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Throttle Servo Position for 25 percent collective. This is on a scale from 0 to 1000, where 1000 is full throttle and 0 is zero throttle. Actual PWM values are controlled by SERVOX_MIN and SERVOX_MAX. The 0 percent collective is defined by H_COL_MIN and 100 percent collective is defined by H_COL_MAX.

H_RSC_THRCRV_50: Throttle Servo Position for 50 percent collective

Throttle Servo Position for 50 percent collective. This is on a scale from 0 to 1000, where 1000 is full throttle and 0 is zero throttle. Actual PWM values are controlled by SERVOX_MIN and SERVOX_MAX. The 0 percent collective is defined by H_COL_MIN and 100 percent collective is defined by H_COL_MAX.

H_RSC_THRCRV_75: Throttle Servo Position for 75 percent collective

Throttle Servo Position for 75 percent collective. This is on a scale from 0 to 1000, where 1000 is full throttle and 0 is zero throttle. Actual PWM values are controlled by SERVOX_MIN and SERVOX_MAX. The 0 percent collective is defined by H_COL_MIN and 100 percent collective is defined by H_COL_MAX.

H_RSC_THRCRV_100: Throttle Servo Position for 100 percent collective

Throttle Servo Position for 100 percent collective. This is on a scale from 0 to 1000, where 1000 is full throttle and 0 is zero throttle. Actual PWM values are controlled by SERVOX_MIN and SERVOX_MAX. The 0 percent collective is defined by H_COL_MIN and 100 percent collective is defined by H_COL_MAX.

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Helicopter’s minimum collective pitch setting at zero throttle input in Stabilize mode

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Helicopter’s collective pitch setting at mid-low throttle input in Stabilize mode

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Helicopter’s collective pitch setting at mid-high throttle input in Stabilize mode

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Helicopter’s maximum collective pitch setting at full throttle input in Stabilize mode

Used to soften collective pitch inputs near center point in Acro mode.

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