Hecale experiments
Full model
In a first pass I looked at a full model, with all butterflies I had data for, which asked whether butterflies shift their preference for the colour that is rewarding in the morning based on the time of day after being trained to either purple or yellow in either the morning or afternoon. I also included random effects of cage and individual ID.
Variables
cbind(n.morning.colour, n.afternoon.colour)
The proportional preference for the colour rewarding in the morningtime.of.day
The time of day at which a trial was conducted. Either ‘morning’ (8 am to 10 am) or ‘afternoon’ (3pm to 5pm)training
The training status of a butterfly. Either ‘trained’ or ‘untrained.’final.presentation
The order of the presentation of the final preference test. Either ‘normal’ (morning test then afternoon test) or ‘reversed’ (afternoon test then morning test)cage
The identity of the cage in which the butterfly was trained and testedid
The individual identitiy of the butterfly
Model
=
full.model glmer(cbind(n.morning.colour, n.afternoon.colour) ~
* training * final.presentation + (1 | cage/id),
time.of.day data = long.heliconiini.data,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)),
family = binomial)
| Factor | Estimate | Std_Error | T_statistic | P_value |
|---|---|---|---|---|
| Intercept | -1.118 | 0.413 | -2.708 | 0.007 |
| Time of Day | 0.644 | 0.254 | 2.532 | 0.011 |
| Training | 1.433 | 0.197 | 7.265 | < .001 |
| Final Presentation | 2.003 | 0.572 | 3.502 | < .001 |
| Time of Day X Training | -0.954 | 0.291 | -3.281 | 0.001 |
| Time of Day X Final Presentation | -0.865 | 0.287 | -3.017 | 0.003 |
| Training X Final Presentation | -1.607 | 0.224 | -7.185 | < .001 |
| Time of Day X Training X Final Presentation | 0.720 | 0.331 | 2.178 | 0.029 |
There is a significant three-way interaction effect between the time of day, presentation order of the final test and the training status of butterflies (p = 0.029).
Note also that we see more variance among individuals than among cages (individual σ = 1.153, cage σ = 0.202), which suggests individuals are not copying each other. We might have expected individuals to conform to their cage-mates’ choices if butterflies were using social cues leading to more variance among cages than among individuals however we do not see evidence for this.
Post-Hoc Tests
To investigate the nature of these interactions post-hoc I used emmeans
. I was interested in whether training induced a time of day based shift in colour preference. Therefore, I wanted to see if there is a decrease in preference for the colour rewarding in the morning from morning to afternoon after training for both normal and reversed butterflies.
If butterflies were only learning the order of presentation we would expect to see an increase in preference for the colour rewarding in the morning from morning to afternoon since these butterflies were presented with their first final test session in the afternoon.
To investigate a time-based shift in preference across these treatments I used simple contrasts based on the factor time of day.
=
full.model.emm emmeans(full.model, ~ training * time.of.day * final.presentation)
=
time.of.day.contrast contrast(full.model.emm,
"consec",
simple = "time.of.day",
combine = TRUE,
adjust = "mvt") %>% summary(infer = TRUE)
Results
| contrast | training | final.presentation | estimate | asymp.LCL | asymp.UCL | p.value |
|---|---|---|---|---|---|---|
| Afternoon - Morning | Untrained | normal | 0.644 | 0.146 | 1.142 | 0.011 |
| Afternoon - Morning | Trained | normal | -0.310 | -0.610 | -0.009 | 0.044 |
| Afternoon - Morning | Untrained | reversed | -0.221 | -0.481 | 0.039 | 0.095 |
| Afternoon - Morning | Trained | reversed | -0.455 | -0.631 | -0.279 | < .001 |
Final test presentation order should have no bearing on naive data during the untrained test trial since it is only relevant for the final test trial, nevertheless I present the naive results for both groups when untrained. When untrained, naive butterflies with a normal final test presentation order significantly shift their preference in the incorrect direction by 13.73% (p = 0.011), that is they increase their preference for the colour rewarding in the morning during the afternoon trial. Naive butterflies with a reversed presentation order do not significantly shift their preference (p = 0.095) between time periods when untrained.
For both normal and reversed final test presentation order butterflies, there is a decrease in preference for the colour rewarding in the morning from morning to afternoon after training. Butterflies with a standard presentation order decrease their preference for the colour rewarded in the morning by 7.68% (p = 0.043) in the afternoon and butterflies with a reversed final test presentation order decrease their preference for the colour rewarded in the morning by 10.7% (p < 0.001) in the afternoon.
So in our full model we see there is a lot going on. I wanted to break it down even further.
Hecale all participating individuals
Across analyses on H. hecale learning performance we asked whether the time of day influenced:
In subsections titled Initial preference - the shifts in proportional preference for the colour rewarding in the morning when naïve, using a binomial GLMM with response variable “morning reward colour choices/afternoon reward colour choices” and fixed factor “time of day” (morning or afternoon);
In subsections titled Trained preference - shifts in proportional colour preference when trained, using the same specifications but with the additional fixed factor “presentation order” (standard or reversed).
Identity was included as a random effect throughout as well as cage if it did not lead to a singular fit due to no variance being captured by the variable.
We looked at all individuals—excluding those which did not attempt to feed from both feeders in AM or, more commonly, PM during training (see Hecale activity between time periods section), or did not make any foraging attempts during a final test session. We also established a training criterion given observed variation in responsiveness to the learning task throughout training. We identified individuals that correctly adjusted their behaviour in AM and PM sessions during training with reinforced feeders and in the Hecale high and low performers section asked whether these individuals demonstrate evidence of learning by behaving in the same way when presented with unreinforced feeders in the post-training preference test.
Initial preference
This model asks whether there is a shift in preference for the colour that is rewarding in the morning between time periods.
Variables
n.morning.colour.initial
is the number of choices made for the colour that is rewarding in the morningn.afternoon.colour.initial
is the number of choices made for the colour that is rewarding in the afternooncage
is the identity of the cage the individual was tested and trained inid
is the identity of the individual butterfly
First we remove any individuals who did not make both colour choices in either morning or afternoon during training.
= data %>%
data2 group_by(id) %>%
filter(all(n.correct.training > 0 & n.incorrect.training > 0))
Then we remove individuals who did not respond at all during a session at the final test.
= data2 %>%
data3 group_by(id) %>%
filter(all(n.morning.colour.test > 0 | n.afternoon.colour.test > 0))
Model
We now ask with a binomial GLMM whether there a shift in preference for colours when individuals were naive.
=
naive.model glmer(cbind(n.morning.colour.initial, n.afternoon.colour.initial) ~
+ (1 | cage/id),
time.of.day data = data3, family = binomial)
Results
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -1.156 | 0.991 | -1.167 | 0.243 |
| time.of.day2afternoon | 0.147 | 0.171 | 0.860 | 0.39 |
We see there is no significant shift in preference for the colour that is rewarding in the morning between time periods initially when individuals are naive (p = 0.39 for time of day effect).
Model Residuals
Trained preference
Variables
n.morning.colour.test
is the number of choices made for the colour that is rewarding in the morning during the final testn.afternoon.colour.test
is the number of choices made for the colour that is rewarding in the afternoon during the final testfinal.presentation
is the order of the final test presentation (morning first or afternoon first)cage
is the identity of the cage the individual was tested and trained inid
is the identity of the individual butterfly
Model
Now we will be looking at butterflies after training and asking if there is a shift in preference for the colour that is rewarded in the morning based on the time of day when individuals are trained. We include an effect of final presentation.
=
trained.model glmer(cbind(n.morning.colour.test, n.afternoon.colour.test) ~
* final.presentation + (1 | cage/id),
time.of.day data = data3, family = binomial)
Results
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.444 | 0.456 | 0.974 | 0.33 |
| time.of.day2afternoon | -0.291 | 0.158 | -1.846 | 0.065 |
| final.presentationreversed | -0.107 | 0.632 | -0.169 | 0.865 |
| time.of.day2afternoon:final.presentationreversed | 0.270 | 0.192 | 1.409 | 0.159 |
We don’t find a significant shift as a result of time of day (p = 0.06) but the effect size is in the expected direction i.e., a decrease in the morning reward colour preference in the afternoon. Moreover, we don’t see an effect of final presentation (p = 0.86) or an interaction effect of final presentation with time of day (p = 0.16)
Model Residuals
Hecale high and low performers
We noticed considerable variation in how flexible individuals were during training i.e., some individuals performed better during training compared to others. In order to see whether high performers showed more evidence of learning than low performers, we looked at the initial and trained preferences of butterflies that made a majority (>50%) of correct feeding choices in the final two training days in both the morning and afternoon as well as those that did not meet this criterion. The expectation is that high performers would show evidence of shifting their preference throughout the day at the final test session.
Initial preference
High Performers
First we will be looking at the shift in preference based on time of day initially in naive butterflies that were high performers in the experiment. To do so we will remove individuals who did not achieve greater than 50% correct responses in the last 2 days of training
= data3 %>% group_by(id) %>% filter(all(prop.correct.training.last.2 > 0.5)) data4
Model
We now ask whether individuals who perform well in last 2 days of training shift colour preference between the time periods initially.
=
naive.model.high.performers glmer(cbind(n.morning.colour.initial, n.afternoon.colour.initial) ~
+ (1 | cage/id),
time.of.day data= data4, family = binomial)
Results
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -0.341 | 0.770 | -0.442 | 0.658 |
| time.of.day2afternoon | 0.064 | 0.246 | 0.258 | 0.796 |
We find no evidence of high performers shifting their colour preference based on time of day initially (p = 0.79)
Model Residuals
Low Performers
Next we look at individuals which under-performed during training so we extract individuals below the criterion.
= data3 %>% group_by(id) %>% filter(any(prop.correct.training.last.2 < 0.51)) data5
Model
We now ask do individuals who did not make over 50% correct choices at the last 2 days of training shift their colour preference based on time of day initially? Note we include the line control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5))
to change the optimizer since this model initially fails to converge.
=
naive.model.low.performers glmer(cbind(n.morning.colour.initial, n.afternoon.colour.initial) ~
+ (1 | id), data =data5,
time.of.day family = binomial,
control=glmerControl(optimizer="bobyqa",optCtrl=list(maxfun=2e5)))
Results
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | -1.579 | 0.747 | -2.115 | 0.034 |
| time.of.day2afternoon | 0.264 | 0.237 | 1.111 | 0.266 |
Low performers like high performers do not significantly shift their colour preference based on the time of day between time periods initially (p = 0.26).
Model Residuals
Trained preference
We next look at trained preferences which were measured after 10 days of training.
High Performers
Model
We first ask whether individuals who were correct at a higher rate during training shifted their colour preference based on the time of day when trained. Note here the cage random effect was dropped due to it not capturing any variance leading to a singular fit.
=
trained.model.high.performers glmer(cbind(n.morning.colour.test, n.afternoon.colour.test) ~
* final.presentation + (1 | id),
time.of.day data = data4, family = binomial)
Results
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.575 | 0.298 | 1.932 | 0.053 |
| time.of.day2afternoon | -0.435 | 0.173 | -2.507 | 0.012 |
| final.presentationreversed | -0.152 | 0.395 | -0.383 | 0.701 |
| time.of.day2afternoon:final.presentationreversed | 0.148 | 0.248 | 0.598 | 0.55 |
We find that there is a significant shift in preference for the morning colour between time periods among high performing butterflies (p = 0.01). When trained these butterflies shift their preference for the morning reward colour by 12% (Figure 2).
Model Residuals
Low Performers
We now ask whether individuals who were not correct at a high rate shift preference when trained.
Model
=
trained.model.low.performers glmer(cbind(n.morning.colour.test, n.afternoon.colour.test) ~
* final.presentation + (1 | cage/id),
time.of.day data= data5, family = binomial)
Results
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 0.104 | 0.988 | 0.105 | 0.916 |
| time.of.day2afternoon | 0.460 | 0.391 | 1.176 | 0.24 |
| final.presentationreversed | 0.406 | 1.287 | 0.315 | 0.753 |
| time.of.day2afternoon:final.presentationreversed | -0.317 | 0.415 | -0.764 | 0.445 |
No significant shift in preference among low performers.
Model Residuals
Hecale activity between time periods
To see whether there was a difference in the total number of choices a butterfly made at a particular time period we used a Poisson generalised linear mixed effect model.
Variables
total.activity
is the total number of choices (correct and incorrect) a butterfly made in a time periodtime.of.day
is the time period of the trial (morning or afternoon)cage
is the identity of the cage the individual was tested and trained inid
is the identity of the individual butterfly
Model
=
activity.model glmer(total.activity ~ time.of.day + (1 | cage/id),
data= data, family = poisson)
Results
| term | estimate | std.error | statistic | p.value |
|---|---|---|---|---|
| (Intercept) | 4.270 | 0.102 | 41.887 | < .001 |
| time.of.day2afternoon | -0.346 | 0.026 | -13.082 | < .001 |
On average butterflies make 24 less choices in the afternoon.


class Builder : ObjectBuilder<Stripe3ds2Config>
<init> |
Builder() |
build |
fun build(): Stripe3ds2Config |
setTimeout |
The 3DS2 challenge flow timeout, in minutes. fun setTimeout(timeout: Int): Builder |
setUiCustomization |
fun setUiCustomization(uiCustomization: Stripe3ds2UiCustomization): Builder |


| Control Panel | |
| Source Panel | |
| Target Panel | |
| Console Panel | Preview Panel |
| Control Panel | |
| Preview Panel | |
| Times Panel | Position Panel |
| Flags Panel | Patches Panel |
| Buttons Panel |
| <?xml version="1.0"
encoding="UTF-8"?> <BDN Version="0.93" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:noNamespaceSchemaLocation="BD-03-006-0093b BDN File Format.xsd"> <Description> <Name Title="BDN Example" Content=""/> <Language Code="eng"/> <Format VideoFormat="720p" FrameRate="23.976" DropFrame="False"/> <Events Type="Graphic" FirstEventInTC="00:00:00:00" LastEventOutTC="02:26:38:18" NumberofEvents="22"/> </Description> <Events> <Event InTC="00:00:00:00" OutTC="00:00:05:00" Forced="False"> <Graphic Width="1" Height="1" X="0" Y="0">subtitles_0001.png</Graphic> </Event> ... <Event InTC="02:26:35:12" OutTC="02:26:38:18" Forced="False"> <Graphic Width="601" Height="88" X="343" Y="511">subtitles_0022.png</Graphic> </Event> </Events> </BDN> |
| 02:26:38:18 (time code in
XML) 2 hours = 2*60*60 seconds = 7200s 26 minutes = 26*60 seconds = 1560s 38 seconds = 38s 18 frames = 18/24 s = 750ms In sum this equals 8798750 ms. Now since the given frame rate is really 23.976Hz, this value is multiplied by 1.001 This equals 8807548.75ms This equals 2 hours, 26 minutes, 47 seconds and 549ms 2:26:47.549 (real time code displayed in BDSup2Sub) |
| keyword | example | remark |
| size | size: 720x576 | screen size: mandatory |
| org | org: 10, 10 | origin: x,y position of upper left corner in pixels. Optional. |
| scale | scale: 104%, 164% | scaling factors for image:
Ignored
(use conversion dialog settings or CLI options instead) |
| alpha | alpha: 0% | global alpha blending factor: Ignored. |
| smooth | smooth: OFF | apply smoothing algorithm: Ignored. |
| fadein/out | fadein/out: 50, 50 | create fade in/out (times in ms): Ignored. |
| align | align: OFF at LEFT TOP | force caption placement: Ignored. |
| time offset | time offset: 0 | Global delay in in ms or hh:mm:ss:ms: Optional |
| forced subs | forced subs: OFF | Show only forced captions:
Ignored. (use export dialog setting or CLI options instead) |
| palette | palette: 000000, 141207, [...], bdab4f | 16 color DVD subtitle palette: mandatory |
| custom colors | custom colors: OFF, tridx: 1111, colors: 000000, [...], bdab4f | Global palette/alpha setting: Ignored. |
| langidx | langidx: 7 | Active language index: mandatory |
| id, index | id: en, index: 7 | Language ID and caption
index: mandatory (at least one index in the file must fit langidx) |
| timestamp, filepos | timestamp: 00:01:47:520, filepos: 000000000 | Timestamp and file offset of one caption: mandatory |
| /res:<n> | set
output resolution to either 480, 576, 720 or 1080 - default 576. You can also use "pal", "ntsc", "720p" or "1080p" instead. For 1440x1080 use "/res:1440x1080" Use "/res:keep" to keep the resolution of the source |
| /dly:<n> | set delay in ms - default: 0.0. Will be synchronized to target frame rate. |
| /forced[+/-] | export only forced subtitles - default: off (all subtitles are exported). |
| /forceall[+/-] | set/clear forced flag for all subs - default: off (keep) |
| /swap[+/-] | swap Cr/Cb components - default: off (don't swap). |
| /tmin:<t> | set minimum display time in ms - default: 500 |
| /tmerge:<t> | set
maximum time difference for merging captions in ms - default: 200 Note: currently only supported for BD-SUPs |
| /fps:<src>,<trg> | convert frame rate from <src> to
<trg> given as floating point values. Predefined values; 24p=23.976, pal or 25p=25, ntsc or 30p=29.967 /fps:auto,<t> detects source frame rate automatically |
| /fps:<trg> | synchronize
target frame rate to <trg>.
Default: automatic (dumb!). Predefined values; 24p=23.976, pal or 25p=25, ntsc or 30p=29.967 "/fps:keep" preserves the source fps (for BD&XML, else default) |
| /filter:<f> | set scaling filter - default: bilinear. Supported types: bilinear, triangle, bicubic, bell, b-spline, hermite, lanczos3, mitchell |
| /movin:<r>[,<o>] | move captions inside screen ratio <r>, plus/minus offset <o> |
| /movout:<r>[,<o>] | move captions outside screen ratio <r>, plus/minus offset <o> |
| /movex:<t>[,<o>]] | move
captions horizontally. <t> may be "left", "right" or
"center". +/- optional offset <o> (only used if moving left or right) |
| /cropy:<n> | crop the upper and lower n lines - default: 0 |
| /acrop:<n> | set alpha cropping threshold - default: 10 |
| /scale:<x>,<y> | scale captions with free factors - default: 1.0,1.0 (min: 0.5, max: 2.0) |
| /exppal[+/-] | export target palette in PGCEdit text format - default: off (don't export) |
| /fixinv[+/-] | fix zero alpha frame palette - default: off
(don't fix) Note: ignored if import format is neither SUB/IDX nor SUP/IFO. |
| /verbatim[+/-] | switch on verbatim console output mode - default: off |
| /palmode:<s> | palette mode: keep, create, dither -
default: create Note: if the export format is SUB/IDX, but the import format isn't, this setting is ignored. If both input and output format are SUB/IDX, "dither" is identical to "create". |
| /atr:<n> | set alpha threshold 0..255. Default 80 |
| /ltr1:<n> | set luminance low/mid threshold 0..255. Default automatic. |
| /ltr2:<n> | set luminance mid/high threshold 0..255. Default automatic. |
| /lang:<s> | set language to string <s> (e.g. "en" for
English or "de" for German). Default: "de" For a complete list, try /lang:? Note that this also works for XML and BD-SUP exports, yet the full language selection is limited. |
| /pal:<s> | load palette file named <s> (ini files exported from BDSup2Subs color edit dialog) |
| Windows
Registry Editor Version 5.00 [HKEY_CLASSES_ROOT\.sup] @="SUP BD/HD-DVD caption stream" [HKEY_CLASSES_ROOT\.sup\shell] [HKEY_CLASSES_ROOT\.sup\shell\open] [HKEY_CLASSES_ROOT\.sup\shell\open\command] @="E:\\dev\\java\\jre_6_0\\bin\\javaw.exe -jar d:\\src\\java\\BDSup2Sub\\BDSup2Sub.jar \"%1\"" |
| #BDSup2Sub
3.9.5 settings - don't modify manually #Sat Jun 27 13:28:05 CEST 2009 fixZeroAlpha=true filter=Mitchell loadPath=D\:\\src\\Java\\BDSup2Sub\\reference\\colored-forced.idx mergePTSdiff=18000 frameHeight=600 framePosY=197 framePosX=241 alphaCrop=14 frameWidth=799 verbatim=true savePath=D\:\\src\\Java\\BDSup2Sub\\reference\\colored-forced.idx colorPath= paletteMode=keep existing |
| Repository URL | http://javaforge.com/svn/bdsup2sub |
| login | anonymous |
| password | anon |


{{ partial "header.html" . }}
{{ partial "nav.html" . }}
Every other app will work with the basics of gopass, too. Just jump in with your existing setup!
We initialize a git repository in your store by default. Additionally we automatically pull and push if you write to the store.
Out of the box we support not only the common default store, but you can add as many other stores, so-called mounts, as you like.
We prompt you to verify the recipients you're about to encrypt for by default, so you can be sure to always target the right people.
Because we're compatible with pass there are other projects out there that you can use to complement gopass on other platforms.
With gopass you can store more than just simple passwords.
You are able to store any kind of data as binary to the password store.
Coming soon!
wget https://github.com/gopasspw/gopass/releases/download/v{{ .Site.Params.main.currentVersion }}/gopass_{{ .Site.Params.main.currentVersion }}_linux_amd64.deb
sudo dpkg -i gopass_{{ .Site.Params.main.currentVersion }}_linux_amd64.deb
sudo pacman -S gopass
layman -a go-overlay
emerge -av gopass
dnf copr enable daftaupe/gopass
dnf install gopass
go get {{ .Site.Params.main.repo }}
to install gopass from source.


"”If today were the last day of my life, would I want to do what I am about to do today?” And whenever the answer has been “No” for too many days in a row, I know I need to change something.” – Steve Jobs
bike2go is a passion project to create an iOS application for Philadelphia bike share users to find stations, racks, and bikes.
AWeber Atom is an app for AWeber customers to add new subscribers to their list.
AWeber Stats is an app for AWeber customers to use to view analytics and stats about their lists on the go.
Vanguard Advisors is an app providing content for financial advisors.
The flagship app for Vanguard, used for viewing account balances and making financial transations.
Send me an email if you would like to get in touch, have questions or feedback on my work, or just want to say hello.


This page incorporates the basic frame-busting technique:
if(top != self) top.location.href = location.href;
Your job is to frame this page in the index.html :)


Create a game where you display life and points in a creative way. A suggestion is to show the life as hearts and the points as a big number in the bottom center part of the screen. Have a look here for Free game resources
| Criteria | Exemplary | Adequate | Needs Improvement |
|---|---|---|---|
| full game is presented | game is partially presented | partial game contains bugs |


|
|
| min_delay_ball = rospy.get_param("min_delay_ball") |
| | Min delay for moving the ball.
|
| |
|
| max_delay_ball = rospy.get_param("max_delay_ball") |
| | Max delay for moving the ball.
|
| |
|
| min_dis_ball_delay = rospy.get_param("min_dis_ball_delay") |
| | Min delay for disappeariung the ball.
|
| |
|
| max_dis_ball_delay = rospy.get_param("max_dis_ball_delay") |
| | Max delay for disappeariung the ball.
|
| |
|
| map_x = rospy.get_param("map_x") |
| | x coordinate of the map
|
| |
|
| map_y = rospy.get_param("map_y") |
| | y coordinate of the map
|
| |
ROS node used to move the ball


Choose Program
Analysis of terror attacks in different countries
Analysis of terror attacks over the years
Target of Terror Attacks on USA
List of no of attacks in top countries


bank-to-qif
Choose input CSV files
Browse »
Download QIF
Please choose a valid bank account for each file


#include <dialogs.h>
Inheritance diagram for TListBox::
Public Methods | |
| TListBox (const TRect &bounds, ushort aNumCols, TScrollBar *aScrollBar) | |
| ~TListBox () | |
| virtual ushort | dataSize () |
| virtual void | getData (void *rec) |
| virtual void | getText (char *dest, short item, short maxLen) |
| virtual void | newList (TCollection *aList) |
| virtual void | setData (void *rec) |
| TCollection* | list () |
Static Public Methods | |
| TStreamable* | build () |
Static Public Attributes | |
| const char* const | name |
Protected Methods | |
| TListBox (StreamableInit) | |
| virtual void | write (opstream &os) |
| virtual void* | read (ipstream &is) |
Protected Attributes | |
| TCollection* | items |
TListBox objects represent displayed lists of such items in one or more columns with an optional vertical scroll bar.
Definition at line 1643 of file dialogs.h.
|
|
Creates a list box control with the given size, number of columns, and a vertical scroll bar referenced by the `aScrollBar' pointer. This constructor calls TListViewer::TListViewer(bounds, aNumCols, 0, aScrollBar), thereby supressing the horizontal scroll bar.
Your application must provide a suitable TCollection holding the strings (or other objects) to be listed. The items data member must be set to point to this collection using newList(). |
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Undocumented. |
|
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Each streamable class needs a "builder" to allocate the correct memory for its objects together with the initialized virtual table pointers. This is achieved by calling this constructor with an argument of type StreamableInit. |
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Called to create an object in certain stream-reading situations. Reimplemented from TListViewer. Reimplemented in TSortedListBox, TFileList, and TDirListBox. |
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Returns the size of the data read and written to the records passed to getData() and setData(). These three member functions are useful for initializing groups. By default, dataSize() returns the size of TCollection plus the size of ushort (for items and the selected item). You may need to override this member function for your own applications.
Reimplemented from TView. Reimplemented in TFileList. |
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Writes TListBox object data to the target record. By default, getData() writes the current items and focused data members to `rec'. You may need to override this member function for your own applications.
Reimplemented from TView. Reimplemented in TFileList. |
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Sets a string in `dest' from the calling TListBox object. By default, the returned string is obtained from the item'th item in the TCollection using (char *) ((list())->at(item)).
Reimplemented from TListViewer. Reimplemented in TFileList, and TDirListBox. |
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Returns the private items pointer. Reimplemented in TSortedListBox, TFileList, and TDirListBox. Definition at line 1775 of file dialogs.h. Referenced by TDirListBox::list(), TFileList::list(), and TSortedListBox::list().
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Creates a new list by deleting the current one and replacing it with the given `aList'. Referenced by TFileList::newList().
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Reads from the input stream `is'. Reimplemented from TListViewer. Reimplemented in TSortedListBox. |
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Replaces the current list with items and focused values read from the given `rec' array. setData() calls newList() so that the new list is displayed with the correct focused item. As with getData() and dataSize(), you may need to override this member function for your own applications. Reimplemented from TView. Reimplemented in TFileList. |
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Writes to the output stream `os'. Reimplemented from TListViewer. |
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Points at the collection of items to scroll through. Typically, this might be a collection of strings representing the item texts. User can access this private member only by calling the function list(). |
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Undocumented. Reimplemented from TListViewer. Reimplemented in TSortedListBox, TFileList, and TDirListBox. |


This function allows you to vectorise multiple if
and else if
statements. It is an R equivalent of the SQL CASE WHEN
statement.
case_when(...)
| ... | A sequence of two-sided formulas. The left hand side (LHS) determines which values match this case. The right hand side (RHS) provides the replacement value. The LHS must evaluate to a logical vector. Each logical vector can either have length 1 or a common length. All RHSs must evaluate to the same type of vector. These dots are evaluated with explicit splicing. |
|---|
A vector as long as the longest LHS or RHS, with the type (and attributes) of the first RHS. Inconsistent lengths or types will generate an error.
x <- 1:50 case_when( x %% 35 == 0 ~ "fizz buzz", x %% 5 == 0 ~ "fizz", x %% 7 == 0 ~ "buzz", TRUE ~ as.character(x) )#> [1] "1" "2" "3" "4" "fizz" "6" #> [7] "buzz" "8" "9" "fizz" "11" "12" #> [13] "13" "buzz" "fizz" "16" "17" "18" #> [19] "19" "fizz" "buzz" "22" "23" "24" #> [25] "fizz" "26" "27" "buzz" "29" "fizz" #> [31] "31" "32" "33" "34" "fizz buzz" "36" #> [37] "37" "38" "39" "fizz" "41" "buzz" #> [43] "43" "44" "fizz" "46" "47" "48" #> [49] "buzz" "fizz"# Like an if statement, the arguments are evaluated in order, so you must # proceed from the most specific to the most general. This won't work: case_when( TRUE ~ as.character(x), x %% 5 == 0 ~ "fizz", x %% 7 == 0 ~ "buzz", x %% 35 == 0 ~ "fizz buzz" )#> [1] "1" "2" "3" "4" "5" "6" "7" "8" "9" "10" "11" "12" "13" "14" "15" #> [16] "16" "17" "18" "19" "20" "21" "22" "23" "24" "25" "26" "27" "28" "29" "30" #> [31] "31" "32" "33" "34" "35" "36" "37" "38" "39" "40" "41" "42" "43" "44" "45" #> [46] "46" "47" "48" "49" "50"# All RHS values need to be of the same type. Inconsistent types will throw an error. # This applies also to NA values used in RHS: NA is logical, use # typed values like NA_real_, NA_complex, NA_character_, NA_integer_ as appropriate. case_when( x %% 35 == 0 ~ NA_character_, x %% 5 == 0 ~ "fizz", x %% 7 == 0 ~ "buzz", TRUE ~ as.character(x) )#> [1] "1" "2" "3" "4" "fizz" "6" "buzz" "8" "9" "fizz" #> [11] "11" "12" "13" "buzz" "fizz" "16" "17" "18" "19" "fizz" #> [21] "buzz" "22" "23" "24" "fizz" "26" "27" "buzz" "29" "fizz" #> [31] "31" "32" "33" "34" NA "36" "37" "38" "39" "fizz" #> [41] "41" "buzz" "43" "44" "fizz" "46" "47" "48" "buzz" "fizz"case_when( x %% 35 == 0 ~ 35, x %% 5 == 0 ~ 5, x %% 7 == 0 ~ 7, TRUE ~ NA_real_ )#> [1] NA NA NA NA 5 NA 7 NA NA 5 NA NA NA 7 5 NA NA NA NA 5 7 NA NA NA 5 #> [26] NA NA 7 NA 5 NA NA NA NA 35 NA NA NA NA 5 NA 7 NA NA 5 NA NA NA 7 5# This throws an error as NA is logical not numeric# NOT RUN { case_when( x %% 35 == 0 ~ 35, x %% 5 == 0 ~ 5, x %% 7 == 0 ~ 7, TRUE ~ NA ) # }# case_when is particularly useful inside mutate when you want to # create a new variable that relies on a complex combination of existing # variables starwars %>% select(name:mass, gender, species) %>% mutate( type = case_when( height > 200 | mass > 200 ~ "large", species == "Droid" ~ "robot", TRUE ~ "other" ) )#> # A tibble: 87 x 6 #> name height mass gender species type #> <chr> <int> <dbl> <chr> <chr> <chr> #> 1 Luke Skywalker 172 77. male Human other #> 2 C-3PO 167 75. <NA> Droid robot #> 3 R2-D2 96 32. <NA> Droid robot #> 4 Darth Vader 202 136. male Human large #> 5 Leia Organa 150 49. female Human other #> 6 Owen Lars 178 120. male Human other #> 7 Beru Whitesun lars 165 75. female Human other #> 8 R5-D4 97 32. <NA> Droid robot #> 9 Biggs Darklighter 183 84. male Human other #> 10 Obi-Wan Kenobi 182 77. male Human other #> # ... with 77 more rows# Dots support splicing: patterns <- list( x %% 35 == 0 ~ "fizz buzz", x %% 5 == 0 ~ "fizz", x %% 7 == 0 ~ "buzz", TRUE ~ as.character(x) ) case_when(!!! patterns)#> [1] "1" "2" "3" "4" "fizz" "6" #> [7] "buzz" "8" "9" "fizz" "11" "12" #> [13] "13" "buzz" "fizz" "16" "17" "18" #> [19] "19" "fizz" "buzz" "22" "23" "24" #> [25] "fizz" "26" "27" "buzz" "29" "fizz" #> [31] "31" "32" "33" "34" "fizz buzz" "36" #> [37] "37" "38" "39" "fizz" "41" "buzz" #> [43] "43" "44" "fizz" "46" "47" "48" #> [49] "buzz" "fizz"


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About: This sample application notifies you when you may have left your phone behind (specifically, when your companion and wearable disconnect). If you have misplaced your phone, but it is still connected to your wearable, you can also start an activity on the wearable to sound an alarm on your phone.
How to run: Install the Application APK on the companion, and the Wearable APK on the wearable. A notification will automatically appear on the wearable when the wearable and companion disconnect. To start the FindMyPhone activity on the wearable, go to the voice menu (either by saying "OK Google" or tapping the home screen) and select the "Start..." option. Then, select "Find My Phone" from the submenu.
Wearable APIs used: DataApi, WearableListenerService
Screenshots:


Module: ElabsMatchers::Helpers::Session
- Defined in:
- lib/elabs_matchers/helpers/session.rb
Instance Attribute Summary (collapse)
-
- (Object) current_user
readonly
Can be used to keep track of the current signed in user.
Instance Method Summary (collapse)
-
- (Object) login_as(user)
Deprecated
Deprecated.
Please use sign_in instead
-
- (Object) sign_in_as(user = nil)
(also: #sign_in)
Set's a global variable that can be used to fake authentication when running acceptance tests.
-
- (Object) sign_out
See sign_in for details.
Instance Attribute Details
- (Object) current_user (readonly)
Can be used to keep track of the current signed in user. If you use signed_in_as(user) this will be done automaticly.
8 9 10 |
# File 'lib/elabs_matchers/helpers/session.rb', line 8 def current_user @current_user end |
Instance Method Details
- (Object) login_as(user)
Please use sign_in instead
Logs the user into the system. Requires that you login with an email and password and that your fields are named a certain way as well as there being a new_user_session path available.
Example: login_as(user)
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# File 'lib/elabs_matchers/helpers/session.rb', line 21 def login_as(user) warn "[DEPRECATION] `login_as(user)` is deprecated. Please use `sign_in` instead." visit(new_user_session_path) fill_in("Email", :with => user.email) fill_in("Password", :with => user.password) ("Sign in") @current_user = user end |
- (Object) sign_in_as(user = nil) Also known as: sign_in
Set's a global variable that can be used to fake authentication when running acceptance tests. This is much faster then using the sign in form everytime but requires you to make a small change to your application code. Please see [View source] for details.
Example: sign_in_as(user)
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# File 'lib/elabs_matchers/helpers/session.rb', line 42 def sign_in_as(user = nil) $signed_in = true @current_user = user # Your authentication filter should look something like: # # class ApplicationController < ActionController::Base # before_filter :authenticate # # private # # def authenticate! # redirect_to :new_sessions_url unless authenticated? # end # # def authenticated? # $signed_in or begin # # Do your normal authentication logic here. # # E.g: # @current_user = User.find(session[:user_id]) if session[:user_id].present? # end # end # end end |
- (Object) sign_out
See sign_in for details.
Example: sign_out
75 76 77 78 |
# File 'lib/elabs_matchers/helpers/session.rb', line 75 def sign_out $signed_in = false @current_user = nil end |


EVE MUIRHEAD: Plenty of scope to improve after Euro silver
We were obviously gutted to lose out on gold at the European Championships on the last stone in the final.
by Eve MuirheadBut all in all I have to look back on the week as a successful one.
Getting back on a podium at a major championships after returning from surgery and putting in all the hard hours of rehab was always the goal.
Silver at the Euros is a reward for that and this feels like a big step forward for the team.
We did what we needed to do in the round-robin but we really raised our game in the semi-final and the final was another high quality game of curling.
It would have been a great advert for the sport because it was two teams with attacking tactics and there were so many switches of momentum.
Ultimately, though, when you don’t have last stone advantage in the last end against a player of the quality of Anna Hasselborg you fear the worst.
I certainly expected her to make the shot.
They’re the Olympic champions for a reason and to see the way they celebrated at the end showed how hard we had pushed them.
You have to remember that even though it was my 12th Euros, Vicky Wright in our team was competing in her first.
And compared to the other top teams, who have been together for years, we’re in the early stages of our development.
You would think they are probably at their peak but we’re nowhere near ours.
For me personally it’s great to get confirmation that I can still compete and beat the best and to get confirmation that we can be strong enough as a rink to do the same.
There’s a long time left until the Olympics and a lot of room left for us to reach our full potential.
* Talking of the Olympics, while I was in Sweden there was a headline that said the next one might be my last.
I don’t know any athlete who looks two Games into the future and I’m certainly not.
People always tell you that you compete for as long as you’re enjoying it and as long as you think you are capable of getting on to the podium.
That is definitely my mindset.
Don’t get me wrong, the injury and the surgery has made things harder but, don’t worry, I won’t be retiring anytime soon!
* I also heard while we were in Sweden that the curling rink at Braehead is going to close in March.
It was really disappointing news.
I have been an ambassador for the rink in the past and I’ve helped out at open sessions there.
The Glynnhill Women’s International which was held there for six years was a prestigious competition and we competed at a Euros there a couple of years ago.
It will come down to pounds and pence. I get that.
But it’s also a real blow for the sport in this country.
Glasgow is the biggest city in Scotland and apparently there will now be 40 clubs without a base.
There is no other rink in the area so it seems inevitable that people will be lost to the game.


Rammstein is a German industrial metal band formed in 1989 by Richard Z. Kruspe.
They're in their forties.
They show a brute amount of masculinity.
Rammstein is known for using pyrotechnics on stage. In their last performance, lead singer Till Lindemann, attached two flamethrowers to his forearms and shot flames over the crowd. One of their most famous uses of flames was when Lindemann attached ten foot wings to his back and shot flames out of them as well. They're liked so much because they like to "push" the boundaries and not care what people think.
Each band member was part of a small band before they banded (no pun intended) together and formed "Rammstein". They're named after a Rammsteine, which is a doorstop device found on old gates. The extra "m" in the band's name makes it translate literally as "ramming stone."
Rammstein got their first record deal after they won a "battle of the bands" type of ordeal. After this, they toured with a few bands and such as Clawfinger and Prague. Over the years they've obtained more and more followers and completely sells out every show.


git clone https://github.com/<yourgithubusername>/rcites.git
rcites
at ibartomeus/rcites
) by doing git remote add upstream https://github.com/ibartomeus/rcites.git
. Before making changes make sure to pull changes in from upstream by doing either git fetch upstream
then merge later or git pull upstream
to fetch and merge in one stepibartomeus/rcites
To add tests, go to the folder tests/testthat/
. Tests are generally organized as individual files for each exported function from the package (that is, listed as an export in the NAMESPACE
file). If you are adding a new exported function, add a new test file. If you are changing an existing function, work in the tests file for that function, unless it doesn’t have tests, in which case make a new test file.
The book R packages book provides a chapter on testing in general. Do consult that first if you aren’t familiar with testing in R.
The easiest set up to run tests is from within an R session:
To test an individual test file
To run all tests
If you are running tests that have skip_on_cran()
in them, set Sys.setenv(NOT_CRAN = "true")
prior to running tests.
In addition to changing the code, do make sure to udpate the documentation if applicable. The R packages book book has a chapter on documentation you should read if you aren’t familiar.
After code and documentation has been changed, update documentation by running either devtools::document()
or roxygen2::roxygenise()
.
Make sure if you change what packages or even functions within packages are imported, most likely add the package to Imports in the DESCRIPTION file.
Be conservative about adding new dependencies.


{% extends 'base.html' %} {% load input_field %} {% load step_order %} {% block title %}{{ block.super }}: Property And Debts{% endblock %} {% block progress %}{% include "partials/progress.html" %}{% endblock %} {% block content %}
The only way to obtain an order for divorce is by starting a court proceeding in the B.C. Supreme Court; you must sue your spouse if you want to get divorced. To do this you will need to submit (file) your divorce application at a {% include "partials/tooltips/registry.html" with text="court registry" %}.
Select a city to find the nearest Supreme Court registry
You can file your divorce documents at any Supreme Court registry. Usually, people file their documents at the Supreme Court registry nearest to where they live or work (Supreme Court registry addresses). When you go to the registry, ask any staff person to direct you to the divorce counter.


./oracle.py
Input AC name with which you want to work (exmp: ORCL): ORCL
Do you want to create a new oracle? (y/n): n
Do you want to display oracles availiable on this AC? (y/n): n
Do you want to transfer file to oracle? (y/n): y
Input ID of Oracle to which you want to put the data: f65bd1387adb3d4e4578958eba40c906b5f2c38538c5ab78f95fdda74b7467af
Input the filename which you want to transfer: saludos
Traceback (most recent call last):
File "./oracle.py", line 48, in <module>
file_oraclize(ac_name, oracle_id, filename)
File "/home/siu/kmd_cc_qa/test_modules.py", line 202, in file_oraclize
lines_count = len(open(filename).readlines( ))
FileNotFoundError: [Errno 2] No such file or directory: 'saludos'
Error in sys.excepthook:
Traceback (most recent call last):
File "/usr/lib/python3/dist-packages/apport_python_hook.py", line 63, in apport_excepthook
from apport.fileutils import likely_packaged, get_recent_crashes
File "/usr/lib/python3/dist-packages/apport/__init__.py", line 5, in <module>
from apport.report import Report
File "/usr/lib/python3/dist-packages/apport/report.py", line 30, in <module>
import apport.fileutils
File "/usr/lib/python3/dist-packages/apport/fileutils.py", line 23, in <module>
from apport.packaging_impl import impl as packaging
File "/usr/lib/python3/dist-packages/apport/packaging_impl.py", line 23, in <module>
import apt
File "/usr/lib/python3/dist-packages/apt/__init__.py", line 23, in <module>
import apt_pkg
ModuleNotFoundError: No module named 'apt_pkg'
Original exception was:
Traceback (most recent call last):
File "./oracle.py", line 48, in <module>
file_oraclize(ac_name, oracle_id, filename)
File "/home/siu/kmd_cc_qa/test_modules.py", line 202, in file_oraclize
lines_count = len(open(filename).readlines( ))
FileNotFoundError: [Errno 2] No such file or directory: 'saludos'