Data from: Perceived threats of infanticide reduce maternal allocation during lactation and lead to elevated oxidative damage in offspring
Sources
Gale T, Garratt M, Brooks RC (2018) Perceived threats of infanticide reduce maternal allocation during lactation and lead to elevated oxidative damage in offspring. Functional Ecology 32(9): 2158-2169. https://doi.org/10.1111/1365-2435.13146
Gale T, Garratt M, Brooks RC (2018) Data from: Perceived threats of infanticide reduce maternal allocation during lactation and lead to elevated oxidative damage in offspring. Dryad Digital Repository. https://doi.org/10.5061/dryad.762k24f
Summary
- The available data are clean. All data sets are in a single Excel sheet. An issue for reproducibility is that the data set for each experiment is not identified.
- In general, most results (that I was able to run) reproduce
- One error is in the reporting of the ANOVA statistics for growth rate. Two of the terms are reported with the F and P values for other terms in the table.
- The ANOVA for the interaction effects of Sex x Treatment on weight at adulthood do not reproduce.
- Oxidative stress cannot be reproduced because the data are incomplete (Two columns, Sex and Plate, were not given)
Caveats
Failure to reproduce or partial reproducibility might be an error in my coding, or my misunderstanding of the author’s methods, or my lack of knowledge of statistics generally or the R functions that I use more specifically.
Setup
library(ggplot2)
library(readxl)
library(data.table)
library(emmeans)
library(nlme)
library(lme4)
library(lmerTest)
library(afex)
library(car)
data_folder <- "../data"
folder <- "Data from Perceived threats of infanticide reduce maternal allocation during lactation and lead to elevated oxidative damage in offspring"
# images need to be moved to /static/images/data from.../image.png
knitr_path <- FALSE # run = TRUE to debug
source("../R/clean_label.R") # bookdown
Import
The single sheet contains all experiments in blocks glued wide
fn <- "LBE paper data.xlsx"
file_path <- paste(data_folder, folder, fn, sep="/")
# offspring weight
exp1 <- data.table(read_excel(file_path, range="A1:E185"))
colnames(exp1) <- clean_label(colnames(exp1))
exp1[, Treatment:=factor(Treatment)]
exp2 <- data.table(read_excel(file_path, range="F1:H45"))
colnames(exp2) <- clean_label(colnames(exp2))
exp2[, Treatment:=factor(Treatment)]
# maternal behavior
exp3 <- data.table(read_excel(file_path, range="K1:U45"))
colnames(exp3) <- clean_label(colnames(exp3))
exp3[, Treatment:=factor(Treatment)]
# GCMs
exp4 <- data.table(read_excel(file_path, range="V1:Z60"))
colnames(exp4) <- clean_label(colnames(exp4))
exp4[, Treatment:=factor(Treatment)]
#oxidative stress
exp5 <- data.table(read_excel(file_path, range="AA1:AJ86"))
colnames(exp5) <- clean_label(colnames(exp5))
exp5[, Treatment:=factor(Treatment)]
Reproducibility
Offspring weight
The mean birth weight of novel male treatment pups (1.619± 0.432g) was not significantly different from that of the familiar male treatment (1.628 ± 0.433g; ANOVA: weight: F1, 182= 0.022, p= 0.882; litter size: F1,42= 0.070, p= 0.792).
# birth weight
exp1[,.(birth=mean(birth),
SD=sd(birth),
SE=sd(birth)/sqrt(.N)), by=Treatment]
## Treatment birth SD SE
## 1: LBE 1.619080 0.4321785 0.04633439
## 2: MC 1.628012 0.4331604 0.04398077
anova(lm(birth ~ Treatment, data=exp1))
## Analysis of Variance Table
##
## Response: birth
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 1 0.004 0.003659 0.0195 0.889
## Residuals 182 34.075 0.187226
# litter size
anova(lm(Littersize ~ Treatment, data=exp2))
## Analysis of Variance Table
##
## Response: Littersize
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 1 0.061 0.06098 0.0702 0.7923
## Residuals 42 36.484 0.86868
Reproduces, the error is sample SD and not SEM.
and identifies LBE = novel male treatment, MC = familiar male treatment
To analyse the differences in offspring growth rate we used repeated measures ANOVA reporting within-subjects effects. Mauchly’s Test of Sphericity indicated that the assumption of sphericity had been violated (χ2(2) = 34.210, p = <0.001), therefore degrees of freedom were corrected using Greenhouse-Geisser adjusted degrees of freedom. There was a difference in growth rate between the treatments but there was no effect of sex (treatment: F1.7,310=7.49, p= 0.001; interaction between treatment and time: F1.7,310=5668, p= <0.001; sex x treatment: F1.6,306=1.3, p= 0.270).
A treatment effect on growth rate is indirectly indicated by treatment x time interaction.
exp1[, ID:=factor(1:nrow(exp1))]
exp1[, Treatment:=factor(Treatment, c("MC", "LBE"))]
exp1[, Sex:=factor(Sex)]
exp1_long <- melt(exp1, id.vars = c("Treatment", "Sex", "ID"), variable.name="Time", value.name = "Weight")
exp1_long[, ID:=factor(ID)]
#(fit.aov_car <- aov_car(Weight ~ Treatment*Sex*Time + Error(ID/Time), data=exp1_long))
#(fit.ez <- aov_ez("ID", "Weight", exp1_long, between=c("Treatment", "Sex"), within="Time"))
(fit.aov_4 <- aov_4(Weight ~ Treatment*Sex*Time + (Time|ID), data=exp1_long))
## Contrasts set to contr.sum for the following variables: Treatment, Sex
## Anova Table (Type 3 tests)
##
## Response: Weight
## Effect df MSE F ges p.value
## 1 Treatment 1, 180 1.85 7.30 ** .02 .008
## 2 Sex 1, 180 1.85 0.36 .0009 .55
## 3 Treatment:Sex 1, 180 1.85 5.60 * .01 .02
## 4 Time 1.70, 306.53 1.20 5670.13 *** .94 <.0001
## 5 Treatment:Time 1.70, 306.53 1.20 7.57 ** .02 .001
## 6 Sex:Time 1.70, 306.53 1.20 1.28 .004 .28
## 7 Treatment:Sex:Time 1.70, 306.53 1.20 2.27 .007 .11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
##
## Sphericity correction method: GG
I think it generally reproduces but I think the authors have mixed up the terms in their reporting.
- The authors report the treatment x time interaction as F1.7,310=5668, p= <0.001, which 1) is a huge F given what the interaction effect looks like and is nearly equivalent to my stats for the Time term.
- The authors report sex x treatment: F1.6,306=1.3, p= 0.270 which is very close to my sex x time term.
The unbalanced design raises statistical red flag #1
This resulted in novel male treatment pups being significantly smaller (7.19±0.61g), at weaning (4 weeks old) (ANOVA: F1,182=27.11, p=<0.001) than the familiar male treatment pups (7.99±1.48g)
m1 <- lm(weaning ~ Treatment, data=exp1)
anova(m1)
## Analysis of Variance Table
##
## Response: weaning
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 1 29.327 29.3271 27.107 5.168e-07 ***
## Residuals 182 196.908 1.0819
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp1[, .(weaning=mean(weaning),
SD=sd(weaning),
SE=sd(weaning)/sqrt(.N)),
by=.(Treatment)]
## Treatment weaning SD SE
## 1: LBE 7.199425 0.7886084 0.08454769
## 2: MC 7.999072 1.2222930 0.12410505
The means reproduce but not the sample SD.
there were no weight differences at adulthood (13 weeks old) (ANOVA: F1, 184=0.760, p=0.384)
anova(lm(adulthood ~ Treatment, data=exp1))
## Analysis of Variance Table
##
## Response: adulthood
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 1 2.04 2.0411 0.7604 0.3843
## Residuals 182 488.53 2.6842
reproduces
There were also no significant interactions of treatment and sex in weight at weaning (sex x treatment F1,180= 2.14, p=0.145) or at adulthood (sex x treatment F1, 180=0.044, p=0.868)).
anova(lm(weaning ~ Sex*Treatment, data=exp1))
## Analysis of Variance Table
##
## Response: weaning
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 0.620 0.6205 0.5762 0.4488
## Treatment 1 29.468 29.4683 27.3644 4.65e-07 ***
## Sex:Treatment 1 2.307 2.3067 2.1421 0.1451
## Residuals 180 193.839 1.0769
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(lm(adulthood ~ Sex*Treatment, data=exp1))
## Analysis of Variance Table
##
## Response: adulthood
## Df Sum Sq Mean Sq F value Pr(>F)
## Sex 1 0.31 0.3146 0.1190 0.73051
## Treatment 1 2.02 2.0166 0.7629 0.38360
## Sex:Treatment 1 12.43 12.4283 4.7017 0.03145 *
## Residuals 180 475.81 2.6434
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The weaning results reproduce. The adulthood results do not. Here is a plot of the interaction.
(fit.emm <- data.table(summary(emmeans(lm(adulthood ~ Sex*Treatment, data=exp1),
specs=c("Sex", "Treatment")))))
## Sex Treatment emmean SE df lower.CL upper.CL
## 1: F MC 13.21312 0.2254647 180 12.76822 13.65801
## 2: M MC 12.80000 0.2423672 180 12.32175 13.27825
## 3: F LBE 12.52708 0.2346711 180 12.06402 12.99014
## 4: M LBE 13.15933 0.2603442 180 12.64561 13.67305
setnames(fit.emm, old="emmean", new="adulthood")
pd <- position_dodge(0.5)
gg <- ggplot(data=exp1, aes(x=Treatment, y=adulthood, color=Sex, group=Sex)) +
#geom_point(position=pd) +
geom_point(data=fit.emm, aes(y=adulthood), position=pd)+
geom_errorbar(data=fit.emm, aes(ymin=lower.CL, ymax=upper.CL), position=pd, width=0.2) +
geom_line(data=fit.emm, position=pd) +
NULL
gg
Maternal behaviour
There was no significant difference in the amount of time the mothers spent in nest, grooming, licking pups, eating/ drinking, nest building, resting or active (Table 1). The only behaviour showing significant differences between treatments was nursing, with females exposed to familiar males spent almost twice the time nursing (Table 1).
y_list <- c("In_Nest", "Nursing", "Licking_Pups", "Nest_Building", "Eating.Drinking", "Grooming", "Active", "Resting")
my_table <- data.table(NULL)
exp3[, one:=1] # used for getting N in each level
exp3[, myID:=factor(1:.N)] # ID 164 is present twice
for(y in y_list){
n.mc <- sum(exp3[Treatment=="MC" & !is.na(get(y)), one])
n.lbe <- sum(exp3[Treatment=="LBE" & !is.na(get(y)), one])
fixed_i <- formula(paste(y, "Treatment", sep=" ~ "))
my_table <- rbind(my_table,
data.table(Response=y,
anova(lme(fixed_i, random = ~ 1|Block,
method="ML", data=exp3))[2,],
Mean.MC=mean(exp3[Treatment=="MC", get(y)]),
SE.MC=sd(exp3[Treatment=="MC", get(y)])/sqrt(n.mc),
Mean.LBE=mean(exp3[Treatment=="LBE", get(y)]),
SE.LBE=sd(exp3[Treatment=="LBE", get(y)])/sqrt(n.lbe)
))
}
# table is printed below the reported table
| Response | numDF | denDF | F-value | p-value | Mean.MC | SE.MC | Mean.LBE | SE.LBE |
|---|---|---|---|---|---|---|---|---|
| In_Nest | 1 | 41 | 3.89 | 0.055 | 114.35 | 4.16 | 103.48 | 3.54 |
| Nursing | 1 | 41 | 11.94 | 0.001 | 15.52 | 1.35 | 8.48 | 1.54 |
| Licking_Pups | 1 | 41 | 1.87 | 0.179 | 1.83 | 0.64 | 0.81 | 0.34 |
| Nest_Building | 1 | 41 | 0.15 | 0.705 | 0.65 | 0.27 | 0.81 | 0.31 |
| Eating.Drinking | 1 | 41 | 0.13 | 0.716 | 5.83 | 0.63 | 5.52 | 0.52 |
| Grooming | 1 | 41 | 0.14 | 0.707 | 9.74 | 0.68 | 10.14 | 0.83 |
| Active | 1 | 41 | 0.76 | 0.388 | 131.35 | 6.22 | 139.76 | 7.45 |
| Resting | 1 | 41 | 1.24 | 0.273 | 168.74 | 5.00 | 179.00 | 7.96 |
Table 1 reproduces with very small differences probably due to implementation of ANOVA/LMM in different statistical software.
Glucocorticoid metabolites in mothers
sample sizes for the glucocorticoid metabolites are novel male n=20, familiar male n= 21 and unmated control n= 18. Using a repeated measures ANOVA we found that FGM’s did change significantly over time (effect of time: F2, 112 = 131, p= <0.001; interaction between time and housing companion: F4,112= 29.5, p<0.001).
# melt wide to long
exp4_long <- melt(exp4, id.vars=c("ID", "Treatment"), variable.name="Time", value.name="GCM")
(fit.aov_4 <- aov_4(GCM ~ Treatment*Time + (Time|ID), data=exp4_long, anova_table = list(correction = "none")))
## Contrasts set to contr.sum for the following variables: Treatment
## Anova Table (Type 3 tests)
##
## Response: GCM
## Effect df MSE F ges p.value
## 1 Treatment 2, 56 10320601.60 3.98 * .08 .02
## 2 Time 2,<PHONE_NUMBER>.78 131.53 *** .48 <.0001
## 3 Treatment:Time 4,<PHONE_NUMBER>.78 29.54 *** .29 <.0001
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
reproduces
The was no difference in the FGMs in the females before the experiment began (ANOVA: F2,56=0.094, p= 0.911), but there was following exposure to their partners (ANOVA: F2,56=43.72, p=<0.001) and at the end of the experiment which coincided with mid lactation for the reproducing treatments (F2,56=4.6, p=0.014)(See Figure 3).
anova(lm(Collection_1 ~ Treatment, data=exp4))
## Analysis of Variance Table
##
## Response: Collection_1
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 2 1745142 872571 0.0937 0.9107
## Residuals 56 521265047 9308304
anova(lm(Collection_2 ~ Treatment, data=exp4))
## Analysis of Variance Table
##
## Response: Collection_2
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 2 450542880 225271440 43.722 3.649e-12 ***
## Residuals 56 288531030 5152340
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
anova(lm(Collection_3 ~ Treatment, data=exp4))
## Analysis of Variance Table
##
## Response: Collection_3
## Df Sum Sq Mean Sq F value Pr(>F)
## Treatment 2 23346970 11673485 4.6338 0.01373 *
## Residuals 56 141074581 2519189
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Reproduces
After removing the unmated control from the analysis we found that the effect of time was still significant (F2, 78= 143, p=<0.001) but the interaction between time and housing companion was not (F1,78=1.89, p=0.158).
aov_4(GCM ~ Treatment*Time + (Time|ID), data=exp4_long[Treatment!="UC",], anova_table = list(correction = "none"))
## Contrasts set to contr.sum for the following variables: Treatment
## Anova Table (Type 3 tests)
##
## Response: GCM
## Effect df MSE F ges p.value
## 1 Treatment 1, 39 5499166.40 1.22 .01 .28
## 2 Time 2, 78 4612053.02 143.76 *** .70 <.0001
## 3 Treatment:Time 2, 78 4612053.02 1.89 .03 .16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Reproduces
Fig. 3
raw <- exp4_long[!is.na(GCM), .(GCM=mean(GCM),
SE=sd(GCM)/sqrt(.N)), by=.(Treatment, Time)]
modeled <- summary(emmeans(fit.aov_4, specs=c("Treatment", "Time")))
pd <- position_dodge(0.5)
gg1 <- ggplot(data=raw, aes(x=Treatment, y=GCM, color=Time, group=Time)) +
geom_point(position=pd) +
geom_errorbar(aes(ymin=GCM-SE, ymax=GCM+SE), width=0.2, position=pd) +
coord_cartesian(ylim=c(0,15000)) + # this is way too much white space
NULL
gg1
gg1 <- ggplot(data=modeled, aes(x=Treatment, y=emmean, color=Time, group=Time)) +
geom_point(position=pd) +
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2, position=pd) +
coord_cartesian(ylim=c(0,15000)) + # this is way too much white space
NULL
gg1
The plot with unconditional SE (raw) reproduces
Oxidative stress in offspring
Table 2 cannot be reproduced because two of the necessary columns (Sex and Plate) are not available (that I can see) in the Excel file (the analysis is a linear mixed model with Block and Plate as random factors and Treatment and Sex as fixed factors).
Statistical Red Flags
1. Computation of ANOVA table using Afex package
The afex package replicates the SPSS output for the growth rate analysis but the design is very unbalanced. It’s unclear to me how afex handles this; is it like AOV (which doesn’t handle unbalanced data well) or more like anova(lme/lmer), which does handle unbalanced data well (with caveats on Type III interpretation).
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
contrasts(exp1_long$Treatment) <- contr.treatment
contrasts(exp1_long$Sex) <- contr.treatment
If we’re focussing on p-values, then 1) the big difference using lme/lmer compared to Afex (SPSS/SAS like repeated measures anova) is the p-value for Treatment but also 2) another difference is that for Treatment x Sex. That said, it doesn’t make much sense to interpret the Treatment p-value as evidence for “an effect” given the interactions.


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The basilica was filled with the scent of angelica and brassica as the priests prepared for the evening mass. [G|D]
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A guide to help schools and local authorities (LAs) complete and submit their autumn 2015 to summer 2016 school census data.
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Real Madrid star Eden Hazard singled out as Mikel’s laziest former team-mate
by FSA EditorEden Hazard had a habit of saving his best efforts for matchday, John Obi Mikel has explained.
Ex-Chelsea midfielder John Obi Mikel has labelled “incredible” Real Madrid winger Eden Hazard the laziest of all his former team-mates.
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Notebooks
Story: |xue as a buffalo
|xue as a buffalo
Title
|xue as a buffalo
Collection
Summary
|xue becomes a buffalo. In this form he eats grass and his wife sees him. [incomplete]
Comments
1) p.9348v: see |xue as tchaxa, 2) This story is found in Book XI & XII-5
Contributors
Date
29 February 1880
Categories
Keywords
|xue (as a buffalo) , |xue (and his wife) , |xue (becomes a buffalo) , |xue (eats grass as a buffalo) , |xue (cries loudly) , |xue (his actions and doings) , |xue (his transformations) , transformation (of |xue) , transformation (|xue as a buffalo) , buffalo (|xue becomes) , wife (of |xue)
Story Pages
9349-9350


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| Scheduler Task List Entry Structure. More... | |
Defines | |
| #define | TASK(name) void name (void) |
| #define | TASK_LIST TaskEntry_t Scheduler_TaskList[] = |
| #define | TASK_MAX_DELAY (MAX_DELAYCTR_COUNT - 1) |
| #define | TASK_RUN true |
| #define | TASK_STOP false |
Typedefs | |
| typedef void(* | TaskPtr_t )(void) |
| typedef uint16_t | SchedulerDelayCounter_t |
Functions | |
| void | Scheduler_Start (void) |
| void | Scheduler_Init (void) |
| static void | Scheduler_ResetDelay (SchedulerDelayCounter_t *const DelayCounter) ATTR_NON_NULL_PTR_ARG(1) ATTR_ALWAYS_INLINE |
| bool | Scheduler_HasDelayElapsed (const uint16_t Delay, SchedulerDelayCounter_t *const DelayCounter) ATTR_WARN_UNUSED_RESULT ATTR_NON_NULL_PTR_ARG(2) |
| void | Scheduler_SetTaskMode (const TaskPtr_t Task, const bool TaskStatus) |
| void | Scheduler_SetGroupTaskMode (const uint8_t GroupID, const bool TaskStatus) |
Variables | |
| exter TaskEntry_t | Scheduler_TaskList [] |
| volatile uint8_t | Scheduler_TotalTasks |
| volatile SchedulerDelayCounter_t | Scheduler_TickCounter |
The following files must be built with any user project that uses this module:
Simple round-robbin cooperative scheduler for use in basic projects where non real-time tasks need to be executed. Each task is executed in sequence, and can be enabled or disabled individually or as a group.
For a task to yield it must return, thus each task should have persistent data marked with the static attribute.
Usage Example:
#include <LUFA/Scheduler/Scheduler.h> TASK(MyTask1); TASK(MyTask2); TASK_LIST { { .Task = MyTask1, .TaskStatus = TASK_RUN, .GroupID = 1 }, { .Task = MyTask2, .TaskStatus = TASK_RUN, .GroupID = 1 }, } int main(void) { Scheduler_Start(); } TASK(MyTask1) { // Implementation Here } TASK(MyTask2) { // Implementation Here }
| #define TASK | ( | name | ) | void name (void) |
| #define TASK_LIST TaskEntry_t Scheduler_TaskList[] = |
Defines a task list array, containing one or more task entries of the type TaskEntry_t. Each task list should be encased in curly braces and ended with a comma.
Usage Example:
| #define TASK_MAX_DELAY (MAX_DELAYCTR_COUNT - 1) |
Constant, giving the maximum delay in scheduler ticks which can be stored in a variable of type SchedulerDelayCounter_t.
| #define TASK_RUN true |
Task status mode constant, for passing to Scheduler_SetTaskMode() or Scheduler_SetGroupTaskMode().
| #define TASK_STOP false |
Task status mode constant, for passing to Scheduler_SetTaskMode() or Scheduler_SetGroupTaskMode().
| typedef uint16_t SchedulerDelayCounter_t |
Type define for a variable which can hold a tick delay value for the scheduler up to the maximum delay possible.
| typedef void(* TaskPtr_t)(void) |
Type define for a pointer to a scheduler task.
| bool Scheduler_HasDelayElapsed | ( | const uint16_t | Delay, | |
| SchedulerDelayCounter_t *const | DelayCounter | |||
| ) |
Determines if the given tick delay has elapsed, based on the given delay period and tick counter value.
| [in] | Delay | The delay to test for, measured in ticks. |
| [in] | DelayCounter | The counter which is storing the starting tick value for the delay. |
Usage Example:
static SchedulerDelayCounter_t DelayCounter = 10000; // Force immediate run on start-up // Task runs every 10000 ticks, 10 seconds for this demo if (Scheduler_HasDelayElapsed(10000, &DelayCounter)) { // Code to execute after delay interval elapsed here }
| void Scheduler_Init | ( | void | ) |
Initialises the scheduler so that the scheduler functions can be called before the scheduler itself is started. This must be executed before any scheduler function calls other than Scheduler_Start(), and can be omitted if no such functions could be called before the scheduler is started.
| static void Scheduler_ResetDelay | ( | SchedulerDelayCounter_t *const | DelayCounter | ) | [inline, static] |
Resets the delay counter value to the current tick count. This should be called to reset the period for a delay in a task which is dependant on the current tick value.
| [out] | DelayCounter | Counter which is storing the starting tick count for a given delay. |
| void Scheduler_SetGroupTaskMode | ( | const uint8_t | GroupID, | |
| const bool | TaskStatus | |||
| ) |
| void Scheduler_SetTaskMode | ( | const TaskPtr_t | Task, | |
| const bool | TaskStatus | |||
| ) |
| void Scheduler_Start | ( | void | ) |
Starts the scheduler in its infinite loop, executing running tasks. This should be placed at the end of the user application's main() function, as it can never return to the calling function.
| exter TaskEntry_t Scheduler_TaskList[] |
Task entry list, containing the scheduler tasks, task statuses and group IDs. Each entry is of type TaskEntry_t and can be manipulated as desired, although it is preferential that the proper Scheduler functions should be used instead of direct manipulation.
Contains the current scheduler tick count, for use with the delay functions. If the delay functions are used in the user code, this should be incremented each tick period so that the delays can be calculated.
| volatile uint8_t Scheduler_TotalTasks |


## Full-stack, typesafe web programming in Haskell
![](images/Haskell-Logo.svg)
Greg Hale *(Takt)* – BayHack 2017
## We ❤ Types
### For modeling data
```haskell
-- | For modeling our data
data UserId = DriversLicense State
| Passport Country
| Token Text
```
### For modeling behavior & transformations
```haskell
c :: Maybe DBConnection -- Potentially-null db connection
x :: Dynamic t UserId -- UserId that changes over time
```
### For modeling abstractions
```haskell
ServerT EateryAPI ~ Handler [Eatery]
```
## Don't take away our types!
### JSON can encode our UserID type nicely
```javascript
// JSON is great for storing data
uid = { 'id_type': 'passport',
'country': 'Mongolia'}
```
### But it's not very nice to program with
```javascript
function useUserId(reg, uid) {
if (allKindsOfStringValidation(uid){
if (uid.id_type == "drivers_license") {
reg.addId(uid);
}
} else {
console.log("Invalid uid");
}
```
## Don't take away our types!
### It's more comfortable to program against the ADT
```haskell
-- Add uid to registry if it's a driver's license
useUserId :: UserId -> Registry -> Registry
useUserId u@(DriversLicense s) reg =
Map.insertWith (<>) s [u]
useUserId _ reg = reg
```
## *DRY*ing between frontend and backend
### The *Don't Repeat Yourself* principle
- Bugs thrive in moisture. Types and fn.'s should have a single source of truth
### One option: Transpile to *elm*, *purescript*
- see Kris Jenkins' talk on YouTube: Types All the Way Down
### The pure-Haskell approach:
- Pull types & core functions into a shared library
- **ghc** for backend, **ghcjs** for frontend specific code
## Haskell web app drying instructions
### *Backend load*: Never compiled by **ghcjs**
- DB lookups, c ffi, encryption
### *Frontend load*: Code that will only be compiled by **ghcjs**
- reflex-dom DOM actions
### ★ *Common load* ★
- Core datatypes, functions and servant APIs
## Example: [soap.io](https://github.com/imalsogreg/soap.io)
### `Soap` type and database
### Servant API
- CRUD operations over Soaps
- Some business logic
### Frontend UI
- CRUD operations
- Image-click color picker
- Estimation UI
## Sharing Objects
common/src/Soap.hs
```haskell
data Soap = Soap {
soapName :: Text
, soapDurability :: Double
, soapImgUrl :: Text
, soapColors :: SoapColors
} deriving (Eq, Ord, Show)
```
## Sharing Objects
frontend/src/Main.hs
```haskell
type DynamicV t a = Compose (Dynamic t) (Either Text) a
validInput :: (DomBuilder t m, ...)
=> (Text -> Either Text a)
-> m (DynamicV t a)
validInput = ...
```
```haskell
...
do nm <- validInput validateName
dr <- validInput validateDura
url <- validInput validateUrl
let soap' :: DynamicV Soap
soap' = Soap <$> nm <*> dr <*> url <*> pure defCol
```
## Sharing functions
common/src/Soap.hs
```haskell
-- | Estimate the optimal L,W,H for a bar of soap
optimalSoapSize
:: Soap -- ^ Soap
-> Double -- ^ Shower length
-> Double -- ^ Budget constraint
-> Double -- ^ Optimization accuracy
-> (Double, Double, Double)
optimalSoapSize (Soap _ dura _ _) showerLen budget resl =
minimumBy (comparing cost) samples
where
grid = [1, 1 + resl .. 5] -- One dim sample spacing
samples = [(l,w, 5/l/w) | l <- grid , w <- grid]
cost (l,w,h) = (2 - (w/h))^2 / budget + dura / h / w
```
## Sharing APIs
common/src/Soap/API.hs
```haskell
type EstimationAPI =
Capture "id" (EKey Soap)
:> QueryParam "shower-length" Double
:> QueryParam "budget" Double
:> Get '[JSON] (Double,Double,Double)
type API = "soap" :> CRUD Soap
:<|> "estimate" :> EstimationAPI
:<|> "color" :> ColorAPI
```
## Sharing APIs
backend/src/Server.hs
```haskell
app :: SnapletInit App App
app = makeSnaplet "app" "Example" Nothing $ do
...
addRoutes [("api", serveSnap (Proxy @API) serveAPI)
,("", serveDirectory "static"]
```
Sharing APIs
servant-reflex: FRP-focused functions servant APIs
:> | ->
:<|> | :<|>
Capture s a | Dynamic t (Either Text a)
QueryParam n a | Dynamic t (QParam a)
Get cs a | Event t ()
(Post/Put/) | -> m (Event t (ReqResult () a)
data QParam a = QParamSome a -- ^ Like `Right a`
| QNone -- ^ Param intentionally absent
| QParamInvalid Text
## Sharing APIs
### *servant-reflex*: FRP-focused functions *servant* APIs
```haskell
type EstimationAPI =
Capture "id" Int
:> QueryParam "budget" Double
:> QueryParam "shower-length" Double
:> Get '[JSON] (Double,Double,Double)
```
```haskell
myEstimate :: Dynamic t (Either Text Int)
-> Dynamic t (QParam Double)
-> Dynamic t (QParam Double)
-> Event t ()
-> m (Event t (ReqResult () (Double,Double,Double))
myEstimate = client (Proxy @EstimationAPI)
(Proxy @m) (Proxy @()) baseUrl
```
## Take-home messages
### Writing the whole application in Haskell is practical, fun and typesafe
### You can use soap.io and hsnippet as examples of how to organize code for sharing
### Questions? Get in touch<EMAIL_ADDRESS>### Hang out in #reflex-frp and #servant IRC channels


class BestAlgSet(DataSet):
Unit element of best algorithm data set.
Here unit element means for one function and one dimension.
This class is derived from DataSet
but it does not
inherit from it.
evals and funvals are arrays of data collected from N data sets. Both have the same format: zero-th column is the value on which the data of a row is aligned, the N subsequent columns are either the numbers of function evaluations for evals or function values for funvals.
Known bug: algorithms where the ERT is NaN or Inf are not taken into account!?
| Method | __eq__ |
Compare indexEntry instances. |
| Method | __init__ |
Instantiate one best algorithm data set with name algId. |
| Method | __ne__ |
Undocumented |
| Method | __repr__ |
Undocumented |
| Method | createDictInstance |
Returns a dictionary of the instances |
| Method | detERT |
Determine the expected running time to reach target values. |
| Method | detEvals |
Determine the number of evaluations to reach target values. |
| Method | get_success_ratio |
Undocumented |
| Method | pickle |
Save instance to a pickle file. |
| Instance Variable | algbestfinalfunvals |
Undocumented |
| Instance Variable | algId |
Undocumented |
| Instance Variable | algs |
Undocumented |
| Instance Variable | best_algorithm_data |
Undocumented |
| Instance Variable | bestfinalfunvals |
Undocumented |
| Instance Variable | comment |
Undocumented |
| Instance Variable | dim |
Undocumented |
| Instance Variable | finalfunvals |
Undocumented |
| Instance Variable | funcId |
Undocumented |
| Instance Variable | funvalsnofail |
Undocumented |
| Instance Variable | instances |
Undocumented |
| Instance Variable | pickleFile |
Undocumented |
| Instance Variable | precision |
Undocumented |
| Instance Variable | success_ratio |
Undocumented |
| Instance Variable | suite |
Undocumented |
| Instance Variable | target |
Undocumented |
| Instance Variable | used_algorithms |
Undocumented |
| Instance Variable | _ert |
Undocumented |
| Instance Variable | _evals |
No summary |
| Instance Variable | _maxevals |
Undocumented |
Inherited from DataSet
:
| Method | bootstrap_sample_size |
return minimum size not smaller than sample_size such that modulo self.nbRuns() == 0 |
| Method | computeERTfromEvals |
Sets the attributes ert and target from the attribute evals. |
| Method | consistency_check |
checks consistency of data set according to - number of instances - instances used |
| Method | createDictInstanceCount |
Returns a dictionary of the instances and their count. |
| Method | detAverageEvals |
Determine the average number of f-evals for each target in targets list. |
| Method | detSuccesses |
return the number of successful runs for each target. |
| Method | detSuccessRates |
return a np.array with the success rate for each target in targets, easiest target first. |
| Method | evals_with_simulated_restarts |
Return a len(targets) list of samplesize "simulated" run lengths (#evaluations, sorted) with a similar interface as detEvals . |
| Method | generateRLData |
Determine the running lengths for reaching the target values. |
| Method | get_data_format |
Undocumented |
| Method | info |
print text info to stdout |
| Method | isBiobjective |
Undocumented |
| Method | median_evals |
return median for each row in self.evals , unsuccessful runs count. |
| Method | mMaxEvals |
Returns the maximum number of function evaluations over all runs (trials), obsolete and replaced by attribute max_eval |
| Method | nbRuns |
Returns the number of runs depending on genericsettings.balance_instances . |
| Method | plot |
plot all data from evals attribute and the median. |
| Method | plot_funvals |
plot data of funvals attribute, versatile |
| Method | splitByTrials |
Splits the post-processed data arrays by trials. |
| Instance Variable | dataFiles |
Undocumented |
| Instance Variable | funvals |
Undocumented |
| Instance Variable | indexFiles |
Undocumented |
| Instance Variable | instancenumbers |
Undocumented |
| Instance Variable | isFinalized |
Undocumented |
| Instance Variable | readfinalFminusFtarget |
Undocumented |
| Instance Variable | readmaxevals |
maxevals as read from the info files |
| Instance Variable | reference_values |
Undocumented |
| Property | ert |
expected runtimes for the targets in target . |
| Property | evals |
evals contains the central data, number of evaluations. |
| Property | evals_appended |
like the evals property-attribute but here instances with the same ID are aggregated (appended). |
| Property | evals_are_appended |
return True if self.evals_appended consist of appended trials (same instances are appended) |
| Property | instance_multipliers |
number of repetitions per instance to balance a skewed instance distribution. |
| Property | max_eval |
maximum number of function evaluations over all runs (trials), |
| Property | maxevals |
maxevals per instance data, i.e. the columns of evals[:, 1:]. |
| Property | maxfgevals |
maximum of the weighted f+g sum per instance. |
| Property | nbRuns_raw |
Undocumented |
| Property | number_of_constraints |
number of constraints of the function/problem the DataSet is based upon. |
| Property | suite_name |
Returns a string, with the name of the DataSet's underlying test suite. |
| Static Method | _largest_finite_index |
return i such that isfinite(ar[i]) and not isfinite(ar[i+1]), |
| Method | __parseHeader |
Extract data from a header line in an index entry. |
| Method | _argsort |
return index array for a sorted order of trials. |
| Method | _balanced_evals_row |
append evaluations to evals_row to achieve a balanced instance distribution. |
| Method | _complement_data |
insert a line for each target value, never used (detEvals(targets) does the job on the fly) |
| Method | _cut_data |
No summary |
| Method | _detEvals2 |
Determine the number of evaluations to reach target values. |
| Method | _detMaxEvals |
computes for each data column of _evals the (maximal) evaluation until final_target was reached, or self.maxevals otherwise. |
| Method | _evals_appended_compute |
create evals-array with appended instances. |
| Method | _old_plot |
plot data from evals attribute. |
| Method | _update_evals_balanced |
update attribute _evals_balanced if necessary. |
| Class Variable | _attributes |
Undocumented |
| Instance Variable | _ert_nb_of_data |
Undocumented |
| Instance Variable | _evals_appended |
Undocumented |
| Instance Variable | _evals_balanced |
Undocumented |
| Instance Variable | _evals_balanced_raw_data_columns |
Undocumented |
| Instance Variable | _extra_attr |
Undocumented |
| Instance Variable | _lasttdatfilelines |
Undocumented |
| Instance Variable | _maxevals_appended |
Undocumented |
| Property | _instance_repetitions |
return the number of runs that repeated a previous instance. |
| Property | _need_balancing |
return True of gs.balance_instances and self.instance_multipliers are >1 |
cocopp.pproc.DataSet.__init__
| Parameters | |
| dict_alg | dictionary of datasets, keys are algorithm
names, values are 1-element
DataSetList . |
| algId | name of the to-be-constructed algorithm as string |
cocopp.pproc.DataSet.createDictInstance
Returns a dictionary of the instances
The key is the instance id, the value is a list of index.
cocopp.pproc.DataSet.detERT
| Parameters | |
| targets | Undocumented |
| list targets | target function values of interest |
| Returns | |
| list of expected running times corresponding to the targets. |
cocopp.pproc.DataSet.detEvals
| Parameters | |
| targets | Undocumented |
| seq targets | target precisions |
| Returns | |
| list of arrays each corresponding to one value in targets and the list of the corresponding algorithms |
cocopp.pproc.DataSet.pickle
Save instance to a pickle file.
Saves the instance to a pickle file. If not specified by argument outputdir, the location of the pickle is given by the location of the first index file associated.
cocopp.pproc.DataSet._evals
evals
property. Each line _evals[i] has a (target) function value
in _evals[i][0] and the function evaluation for which this
target was reached the first time in trials 1,... in
_evals[i][1:].


Huawei P30 , P30 Pro get yet another EMUI 10 Beta update - Gizchina.com
by Ige EbimaHuawei P30 , P30 Pro get yet another EMUI 10 Beta update
4.2 (84.62%) 13 votes
Huawei is reportedly now rolling out a new EMUI 10 Beta for the Huawei P30, and P30 flagship duo. This comes mere hours after the Chinese variants of both devices started receiving stable EMUI 10 update. The new update comes in the form of EMUI 10 Beta update meant for users of the P30, and P30 Pro outside of China, and could be the final Beta before Huawei finally rolls out the stable EMUI 10 update.
According to HuaweiCentral, the EMUI10 beta update comes with the <IP_ADDRESS> build and weighs in at 600MB. It bumps the security level from October to November, while also addressing a number of issues. See the full changelog below;
EMUI <IP_ADDRESS> Beta update changelog
Applications:
Fixed an issue that caused some system and third-party applications to freeze or close unexpectedly.
Call:
Fixed call errors in certain situations.
User Interface:
Fixed an issue where some UI elements would not display correctly in some situations.
Security:
Improved system security with November 2019 security patch.
You can manually check for the update by going to Settings > System> Software update > Check for updates, otherwise you can check via HiCare app > tap on Update > check for updates.
We are not sure when the stable update will begin to roll out for users outside of China. But with this Beta update, and the stable EMUI10 already available in China, a global rollout is very much not far off.


- score - Static variable in class core.Game
-
The score.
- secInNanosec - Static variable in class core.Framework
-
Time of one second in nanoseconds.
- setAddToScore(int) - Method in class core.Game
-
Sets the adds the to score.
- setBosses(ArrayList<Boss_Template>) - Method in class core.Game
-
Sets the bosses.
- setCanvasHeight(int) - Method in class core.Window
-
Sets the canvas height.
- setCanvasWidth(int) - Method in class core.Window
-
Sets the canvas width.
- setColor(Color) - Method in class bosses.Boss_Template
-
Sets the color.
- setColor(Color) - Method in class core.Canvas
-
Sets the color.
- setColor(Color) - Method in class core.Snake
-
Sets the color.
- setColor(Color) - Method in class enemies.Enemy_Template
-
Sets the color.
- setColorHead(Color) - Method in class core.Snake
-
Sets the color head.
- setCount(int) - Method in class core.Snake
-
Sets the count.
- setDamage(int) - Method in class items.Projectile
-
Sets the damage.
- setDirection(Boss_Template.Direction) - Method in class bosses.Boss_Template
-
Sets the direction.
- setDirection(Snake.Direction) - Method in class core.Snake
-
Sets the direction.
- setDirection(Enemy_Template.Direction) - Method in class enemies.Enemy_Template
-
Sets the direction.
- setDirection(Projectile.Direction) - Method in class items.Projectile
-
Sets the direction.
- setEnemies(ArrayList<Enemy_Template>) - Method in class core.Game
-
Sets the enemies.
- setFood(Food) - Method in class core.Game
-
Sets the food.
- setGame(Game) - Static method in class core.Framework
-
Sets the game.
- setHealth(int) - Method in class bosses.Boss_Template
-
Sets the health.
- setHealth(int) - Method in class enemies.Enemy_Template
-
Sets the health.
- setHud(Image) - Method in class core.Game
-
Sets the hud.
- setItemPouch(Item[]) - Method in class core.Game
-
Sets the item pouch.
- setItems(ArrayList<Item>) - Method in class core.Game
-
Sets the items.
- setLevel(int) - Method in class core.Game
-
Sets the level.
- setNumUses(int) - Method in class items.Item
-
Sets the num uses.
- setPouchSize(int) - Method in class core.Game
-
Sets the pouch size.
- setProjectiles(List<Projectile>) - Method in class core.Game
-
Sets the projectiles.
- setScore(int) - Method in class core.Game
-
Sets the score.
- setShooter(int) - Method in class items.Projectile
-
Sets the shooter.
- setSnake(Snake) - Method in class core.Game
-
Sets the snake.
- setSpawnRate(int) - Method in class core.Game
-
Sets the spawn rate.
- setStartButton(JButton) - Method in class core.Canvas
-
Sets the start button.
- setState(int) - Method in class core.Snake
-
Sets the state.
- setSuperFood(SuperFood) - Method in class core.Game
-
Sets the super food.
- setTotalHealth(int) - Method in class bosses.Boss_Template
-
Sets the total health.
- setX(int) - Method in class items.Item
-
Sets the x.
- setY(int) - Method in class items.Item
-
Sets the y.
- shoot() - Method in class bosses.Boss2
-
Shoot.
- shoot() - Method in class enemies.Enemy_Shooter
-
Shoot.
- shooter - Variable in class items.Projectile
-
The shooter.
- showDelay - Variable in class core.Animation
-
The show delay.
- shrink() - Method in class bosses.Boss_Template
-
Shrink.
- shrink() - Method in class bosses.BossSegment
-
Shrink.
- shrink() - Method in class core.Snake
-
Shrink.
- shrink(int) - Method in class core.Snake
-
Shrink.
- shrink() - Method in class core.SnakeSegment
-
Shrink.
- shrink(int) - Method in class core.SnakeSegment
-
Shrink.
- shrink() - Method in class enemies.Enemy_Template
-
Shrink.
- shrink() - Method in class enemies.EnemySegment
-
Shrink.
- shrink() - Method in class items.Projectile
-
Shrink.
- shrink(int) - Method in class items.Projectile
-
Shrink.
- Shrinker - Class in items
-
The Class Shrinker.
- Shrinker() - Constructor for class items.Shrinker
-
Instantiates a new shrinker.
- snake - Variable in class core.Game
-
The snake.
- Snake - Class in core
-
A Snake is made up of one or more SnakeSegment.
- Snake() - Constructor for class core.Snake
-
- snake - Variable in class items.Item
-
The snake.
- Snake.Direction - Enum in core
-
The Enum Direction.
- Snake.Direction() - Constructor for enum core.Snake.Direction
-
- snakeHit(Enemy_Template, Snake) - Method in class core.Game
-
Snake hit.
- snakeHit(Projectile, Snake) - Method in class core.Game
-
Snake hit.
- snakeHit(Boss_Template, Snake) - Method in class core.Game
-
Snake hit.
- SnakeSegment - Class in core
-
SnakeSegment represents one horizontal or vertical segment of a snake.
- SnakeSegment(int, int, int, Snake.Direction) - Constructor for class core.SnakeSegment
-
Instantiates a new snake segment.
- snakeSegments - Variable in class core.Snake
-
The snake segments.
- spawnRate - Variable in class core.Game
-
The spawn rate.
- speed - Variable in class core.MovingBackground
-
The speed.
- startButton - Static variable in class core.Canvas
-
The load button.
- startButton - Variable in class core.Window
-
The start button.
- startingFrameTime - Variable in class core.Animation
-
The starting frame time.
- startingXOfFrameInImage - Variable in class core.Animation
-
The starting x of frame in image.
- state - Variable in class core.Snake
-
The state.
- stayInPit(int, int) - Method in class bosses.Boss_Template
-
Stay in pit.
- superFood - Variable in class core.Game
-
The super food.
- SuperFood - Class in items
-
Food is a food item that the snake can eat.
- SuperFood() - Constructor for class items.SuperFood
-
Instantiates a new super food.
- SuperGun - Class in items
-
Food is a food item that the snake can eat.
- SuperGun() - Constructor for class items.SuperGun
-
Instantiates a new super gun.
- SuperLaser - Class in items
-
The Class SuperLaser.
- SuperLaser() - Constructor for class items.SuperLaser
-
Instantiates a new super laser.


{% extends 'base.html' %} {% block content %}
The beautiful coast of Cape Town South Africa has one of the most iconic scenes.
Cape Town, South Africa
01/03/2014
CAPETOWN
Mombasa is among the fewest town to have the best and most delicious swahili dishes in EastAfrica.
Mombasa, Kenya
11/02/2011
SWAHILI DISH
The Great Sphinx of Giza among top 10 wonders of the world built in ancient egytian civilization.
Cairo, Egypt
24/08/2016
CAIRO
A horde of elephants talking a stride in the great Maasai Mara park.
Maasai Mara, Kenya
05/04/2014
MAASAI MARA
Santorini of Greece is ranked among the most beautiful places to go for a honeymoon if you're rich that is.
Santorini, Greece
14/10/2016
SANTORINI
The most popular traditional food in Kenya is said to be sweet when served hot.
Nairobi, Kenya
11/09/2018
TRADITIONAL FOOD
Dubai is among the best places to go for desert safaris which are very fun by the way.
Dubai
19/06/2017
DUBAI DESERT SAFARI
Check these magnificent beasts, I think you know whats about to happen .
09/11/2013
KRUGER NATIONAL PARK
This is the Vasco Da Gama pillar found in Malindi, the only useful thing the explorer could leave for us.
Malindi, Kenya
27/03/2018
VASCO DA GAMA PILLAR
A sea turtle swimming in the warm Seychelles beach.Can you swim or you can explain?.
Seychelles
01/03/2019
SEYCHELLES SEA TURTLE
Fort Jesus of Mombasa is among the best historic sites in the area.
17/12/2014
FORT JESUS
Some say it's a drug, some say it's not but one thing for sure is that this meal is very sweet.
Mutura Base, Kenya
18/07/2019
MUTURA


{% extends "base.html" %} {% block title %}
Send email to {{ user.email }} about the permit application for {{ case.address }} upon the following events:


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Senior Digital Designer & Front-End Developer
Creating bespoke responsive pattern library.
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Engine Plugins
Engine plugins are the best way to get your custom code into the engine, such that it is accessible by the editor and also ezPlayer.
Contrary to using a plugin, you could also build your own application (TODO), which may link to static libraries that contain your code. However, that approach means that your code cannot be loaded into the editor process and therefore you won't be able to leverage those tools to their full extent. We strongly advise against that.
Creating a Plugin
The easiest way to create a custom plugin is to use the C++ Project Generation. This will create a template project for you, set up the CMake files for your build and configure the plugin selection.
Creating a Plugin Manually
In case you want more control, you can also set up your game plugin manually. The Sample Game Plugin is a good reference for how to do that. To just create a plugin, at all, you only need very little setup.
The only files you need to look at are:
CMakeLists.txt
SampleGamePluginDLL.h
SampleGamePlugin.cpp
Build System Setup
The file CMakeLists.txt
is only of interest here in case you want to reuse the EZ build infrastructure to generate your library. If you use ezEngine as a Submodule then you probably have your own CMake scripts. Either way, you need to add a project that generates a DLL.
DLL Symbol Import/Export
The file SampleGamePluginDLL.h
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// Configure the DLL Import/Export Define
#if EZ_ENABLED(EZ_COMPILE_ENGINE_AS_DLL)
# ifdef BUILDSYSTEM_BUILDING_SAMPLEGAMEPLUGIN_LIB
# define EZ_SAMPLEGAMEPLUGIN_DLL __declspec(dllexport)
# else
# define EZ_SAMPLEGAMEPLUGIN_DLL __declspec(dllimport)
# endif
#else
# define EZ_SAMPLEGAMEPLUGIN_DLL
#endif
If your plugin will be entirely on its own, you don't even need this. However, if you want to use multiple plugins and some of them should contain shared code, then others need to be able to link against the shared libraries and access classes and functions from that library. By tagging classes with these macros you can export symbols from a DLL and thus make those things available to other code. For examples how to use this, just search the sample plugin.
Plugin Callbacks
EZ provides additional hooks for initialization when a plugin gets loaded or unloaded. You can find these in SampleGamePlugin.cpp
:
EZ_PLUGIN_ON_LOADED()
{
// you could do something here, though this is rare
}
EZ_PLUGIN_ON_UNLOADED()
{
// you could do something here, though this is rare
}
These callbacks are optional, though in some cases you may want to register and unregister things here. However, it is way more common to rather use the startup system instead.
Loading a Plugin
If you want to load a plugin from code, you would use ezPlugin::LoadPlugin()
and provide only the name (no path) of your plugin. Make sure that the DLL is stored in the same directory as all other DLLs and EXEs.
The more convenient way to load your game plugin, though, is to enable it in the project settings. Then it will be automatically loaded by every application (TODO).
Add Custom Code
Once you have your basic plugin set up and can load it into your project, you can start adding custom code. The easiest way to get started is to write a custom component. Once you need control over higher level game logic, you can add your own game state. And if you need to initialize and shut down certain systems, you should utilize the startup system.
Utility Plugins
If you want to write a plugin that provides some functionality for shared access, like some integration of a third-party library, the process is exactly the same. The only difference is, that such libraries would never contain a game state.
Also, have a look at singletons if your plugin is supposed to provide an implementation of some abstract interface.


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public interface Stm
The main interface for software transactional memory. The main tasks are done by the following structures:
TxnObject
: the structure where state and identity are separated and where state change
is coordinated through a transaction. An example of the TxnObject is the TxnRef
,
but it could just as easily by a more complex transactional datastructure that is enhanced by instrumentation.
Txn
: responsible for making sure that all changes on transactionalobjects are atomic, isolated and consistent.
TxnExecutor
: responsible for starting/committing/aborting/retrying transactions. The TxnExecutor executes an
TxnCallable
(there are different tastes for return values). The TxnCallable contains
the logic that needs to be executed atomic, isolated and consistent.
The Stm interface provides a mechanism to separate the contract from the implementation. So it is possible to change the Stm implementation without changing the code that uses it. The idea is that for example a TL2 (MVCC) based implementation can be replaced by a Sky-STM or a lock based STM. Of course every Stm implementation will have its strong and weak spots.
All functionality like TxnExecutors, Refs, Txn etc can be customized by providing a custom implementation of the factory/builder interfaces:
TxnRefFactoryBuilder
a builder for creating TxnRefFactory
TxnFactoryBuilder
a builder for creating an TxnExecutor
/Txn
.
TxnCollectionsFactory
a factory for creating transactional collectionsIt is important that an TxnObject only is used within a single Stm. If it is 'shared' between different stm instances, isolation problems could happen. This can be caused by the fact that different stm instances probably use different clocks or completely different mechanisms for preventing isolation problems. It depends on the implementation if any checking is done (the GammaStm does check if there is a conflict).
| Method Summary | |
|---|---|
TxnRefFactory |
getDefaultRefFactory()
Returns the default TxnRefFactory that can be used for easy and cheap access to a reference factory
instead of setting one up through the TxnRefFactoryBuilder . |
TxnCollectionsFactory |
getDefaultTxnCollectionFactory()
Gets the default TxnCollectionsFactory . |
TxnExecutor |
getDefaultTxnExecutor()
Returns the default TxnExecutor that is useful for testing/experimentation purposes. |
TxnRefFactoryBuilder |
getTxRefFactoryBuilder()
Gets the TxnRefFactoryBuilder . |
Txn |
newDefaultTxn()
Starts a default Txn that is useful for testing/experimentation purposes. |
OrElseBlock |
newOrElseBlock()
Creates an OrElseBlock. |
TxnFactoryBuilder |
newTxnFactoryBuilder()
Gets the TxnFactoryBuilder that needs to be used to execute a Txn created by this Stm. |
| Method Detail |
|---|
TxnFactoryBuilder newTxnFactoryBuilder()
TxnFactoryBuilder
that needs to be used to execute a Txn
created by this Stm.
See the TxnFactoryBuilder
for more info. The TxnFactoryBuilder also is responsible for creating
the TxnExecutor since the Txn and TxnExecutor can be tightly coupled.
Txn newDefaultTxn()
newTxnFactoryBuilder()
for something more configurable.
In mose cases this is not the method you want to use to manage transactions.
Transactions returned by this method are not speculative.
TxnExecutor getDefaultTxnExecutor()
TxnExecutor
that is useful for testing/experimentation purposes.
This method is purely for easy to use access, but it doesn't provide any configuration options.
See the newTxnFactoryBuilder()
for something more configurable.
Transactions used in this Block are not speculative.
OrElseBlock newOrElseBlock()
TxnRefFactory getDefaultRefFactory()
TxnRefFactory
that can be used for easy and cheap access to a reference factory
instead of setting one up through the TxnRefFactoryBuilder
.
TxnRefFactoryBuilder getTxRefFactoryBuilder()
TxnRefFactoryBuilder
.
TxnCollectionsFactory getDefaultTxnCollectionFactory()
TxnCollectionsFactory
.
TxnCollectionsFactory
.
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We first examine the suitability of the available models to carry out D&A by comparing the decadal-frequency variability simulated by individual model historical experiment realisations with observations. The individual monthly anomaly time series for 1906–2005 were filtered with a 5–20 year band-pass filter (see details in Supplementary material) and the standard deviation of filtered anomaly for ALLIN and the seven homogeneous regions is plotted against the linear trend of the unfiltered time series (Fig. 2). The linear trend for each of the observations with its 5–95% uncertainty range shows that the two datasets are in very good agreement for all the regions except ECIND (Fig. 2d). The CRU time series consistently shows much higher variability than the IITM data in all regions though the standard deviations in the two coastal regions ECIND and WCIND are more in agreement. The discrepancies are mainly due to the larger number of stations included in the IITM dataset and possibly also differences in the homogenization and area averaging/gridding procedures. We note that these differences in variability between the two datasets could have a bearing on the results of D&A analysis. In terms of the variability, most model simulations are clustered around the IITM value in all the regions. The WHIND region (Fig. 2h) stands out as the one region where model simulated variability is lower than observed for most models and to a lesser extent the NCIND and NWIND regions where the GISS-E2-R, GISS-E2-H and CSIRO-MK3-6.0 have lower than observed variability. Taylor Diagrams 15 comparing CRU and the different experiments and realizations against the IITM reference dataset are shown in Fig. S1 (Supplementary Information). Among the five experiments, the historicalNat simulations are least correlated with observations and the historical and historicalGHG simulations have the highest correlation with observations. The standard deviations in this figure reveal that historicalNat simulations have less than observed variability whereas historicalAA, historicalAnt, historical, and historicalGHG simulations have higher standard deviation than observations. Note that this standard deviation includes variations on all time-scales and trends in the data as they were computed using unfiltered annual mean time-series. On the whole given the small areas of the regions, the CMIP5 simulations perform quite well and the realistic simulation of low-frequency variability is encouraging for carrying out the D&A analysis. While carrying out the D&A analysis using individual models may give different results, we include all the models in our analysis by averaging across models (after averaging each across realisations) thereby giving each model equal weightage rather than weighting each realisation equally (see Supplementary Information for details).
Comparison of observed and modelled linear trend of unfiltered TAS anomaly and standard deviation of 5–20 year bandpass filtered detrended TAS anomaly from CMIP5 historical simulations for the 1906–2005 period over ( a) ALLIN, ( b) WCIND, ( c) IPIND, ( d) ECIND, ( e) NEIND, ( f) NCIND, ( g) NWIND, and ( h) WHIND. The numbers inside the brackets next to the model names indicate the total number of realisations used in this study. The trend uncertainty (5–95%) for observations was calculated as in ref. 30.
In order to examine the sensitivity of our results to D&A methodology, we used three variants of regression based optimal fingerprint approaches in our analysis of TAS over the Indian region - Ordinary Least Squares (OLS) 16, Total Least Squares (TLS) 17, and Regularized Optimal Fingerprinting (ROF) 18. In all three methods we regress the observations on to the model simulated signals. We calculate the scaling factors for time-series of individual regions and for a combined “All Region” where we retain the spatial information from the seven homogeneous zones (See Supplementary Information for further details). Note that “All Region” is different from ALLIN we have used previously to denote a spatial average over India. We estimate these regressions separately for three time periods (the 50-year periods 1906–1955 and 1956–2005, and the full 100-year period 1906–2005) and two averaging time-lengths (non-overlapping decadal and pentadal averages).
In order to estimate the contribution of Natural and Anthropogenic forcings to changes in TAS, we implemented a two-signal optimal fingerprinting analysis in which observations are regressed on to historicalAnt and historicalNat 19 to yield estimates of the scaling factors
( a) The two signal scaling factors and their 5–95% uncertainty levels for “All Region”, estimated for Decadal and Pentadal annual averages between 1956–2005 using OLS, TLS, and ROF methods. ( b) Best estimate and 5–95% uncertainty range of individual forcing contributions and total linear change of “All Region” annual mean temperature (°C) for 1956–2005 shown for Pentadal and decadal analysis. Best estimates are shown with central 90% uncertainty range. The two solid black symbols indicate cases where Residual Consistency Check (RCC) fails for the corresponding observed datasets and the asterisk indicates cases where the influence of a particular forcing has been detected. It may be noted that no cases fail the RCC. Observed trend uncertainties (5–95%) were calculated as in ref. 30.
In Fig. 3b the linear trends of scaled reconstructed TAS over the 1956–2005 period are shown, revealing that the observed and reconstructed total linear change are in good agreement in the Decadal ROF and all the Pentadal analyses. Furthermore, the reconstructed trends are almost entirely as a result of the ANT forcing with negligible contribution of NAT forcings to the trends. Examining the trends from the decadal ROF analysis over the 1956–2005 period gives attributable trends (in °C per 50 years given as 5–95% range) for ANT of (0.44, 0.86), for NAT of (−0.01, 0.03), and the best estimate reconstructed trend of (0.43, 0.89) with the CRU observed trend of (0.45, 0.60). For the same period, pentadal averages reveal attributable trends for ANT of (0.36, 0.77) and NAT of (0.0, −0.01) with the CRU observed trend of (0.35, 0.64). Table 1 shows the trends attributable to ANT and NAT forcings for the different time-periods, temporal averaging lengths, and observed trends for both observations and clearly reveals that where analysis period includes the 1956–2005 half-century, when ANT forcing is strongest, ANT response dominates over NAT response.
Observed and attributable trends (°C per period length given as 5–95% ranges) over “All Region” from the two-signal analysis of TAS change.
| Period | Ref. Dataset | Decadal Trends | Pentadal Trends | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Observed | OLS | TLS | ROF | Observed | OLS | TLS | ROF | ||||
| 1956–2005 | IITM | (0.30, 0.49) | B. Est. | (0.62, 1.06) | (0.62, 1.09) | (0.46, 0.92) | (0.18, 0.54) | B. Est. | (0.53, 0.93) | (0.55, 0.96) | (0.42, 0.82) |
| ANT | (0.61, 1.03) | (0.62, 1.05) | (0.45, 0.88) | ANT | (0.53, 0.94) | (0.55, 0.97) | (0.42, 0.83) | ||||
| NAT | (0.00, 0.03) | (0.00, 0.04) | (0.00, 0.04) | NAT | (−0.01, 0.00) | (−0.01, 0.00) | (−0.01, 0.00) | ||||
| CRU | (0.45, 0.60) | B. Est. | (0.55, 0.99) | (0.56, 1.02) | (0.43, 0.89) | (0.35, 0.64) | B. Est. | (0.43, 0.83) | (0.44, 0.85) | (0.36, 0.76) | |
| ANT | (0.55, 0.97) | (0.56, 0.99) | (0.44, 0.86) | ANT | (0.43, 0.84) | (0.44, 0.86) | (0.36, 0.77) | ||||
| NAT | (0.00, 0.03) | (0.00, 0.03) | (−0.01, 0.03) | NAT | (−0.01, 0.00) | (−0.01, 0.00) | (−0.01, 0.00) | ||||
| 1906–2005 | IITM | (0.29, 0.53) | B. Est. | (0.61, 0.86) | (0.63, 0.87) | (0.40, 0.59) | (0.23, 0.60) | B. Est. | (0.56, 0.85) | (0.52, 0.78) | (0.32, 0.56) |
| ANT | (0.63, 1.00) | (0.66, 1.04) | (0.41, 0.72) | ANT | (0.55, 0.93) | (0.52, 0.89) | (0.33, 0.67) | ||||
| NAT | (−0.14, −0.02) | (−0.17, −0.03) | (−0.13, −0.01) | NAT | (−0.08, 0.02) | (−0.11, 0.00) | (−0.10, −0.01) | ||||
| CRU | (0.41, 0.68) | B. Est. | (0.73, 0.98) | (0.77, 1.01) | (0.54, 0.73) | (0.36, 0.73) | B. Est. | (0.56, 0.85) | (0.63, 0.91) | (0.43, 0.67) | |
| ANT | (0.70, 1.08) | (0.75, 1.13) | (0.53, 0.84) | ANT | (0.54, 0.93) | (0.59, 0.99) | (0.44, 0.77) | ||||
| NAT | (−0.09, 0.03) | (−0.12, 0.02) | (−0.11, 0.02) | NAT | (−0.08, 0.02) | (−0.08, 0.04) | (−0.10, −0.01) | ||||
| 1906–1955 | IITM | (0.21, 0.28) | B. Est. | (−0.22, 0.71) | (−0.36, 0.88) | (−0.53, 0.90) | (0.20, 0.35) | B. Est. | (−0.13, 0.58) | (−0.23, 0.79) | (−0.29, 0.73) |
| ANT | (−0.16, 0.38) | (−0.27, 0.44) | (−0.30, 0.51) | ANT | (0.03, 0.45) | (0.06, 0.67) | (−0.06, 0.55) | ||||
| NAT | (−0.06, 0.33) | (−0.09, 0.44) | (−0.23, 0.39) | NAT | (NA, NA) | (NA, NA) | (NA, NA) | ||||
| CRU | (0.25, 0.33) | B. Est. | (−0.33, 0.60) | (−0.43, 0.70) | (−0.45, 0.77) | (0.25, 0.40) | B. Est. | (−0.22, 0.50) | (−0.32, 0.64) | (−0.32, 0.71) | |
| ANT | (−0.27, 0.28) | (NA, NA) | (NA, NA) | ANT | (−0.16, 0.26) | (−0.22, 0.36) | (−0.25, 0.37) | ||||
| NAT | (−0.07, 0.32) | (−0.07, 0.41) | (−0.09, 0.44) | NAT | (−0.06, 0.24) | (−0.11, 0.28) | (−0.07, 0.34) |
Results are shown for the three periods analysed 1956–2005, 1906–2005, and 1906–1955. The ANT and NAT attributable trends are shown along with the Best Estimate from the two forcings. Observed trends and 5–95% confidence interval are calculated as in ref. 30. The cells marked (NA, NA) represent cases where the residual consistency check (RCC) fails or there was no detection (β < 0).
When we analyse the individual homogeneous zones, we can detect the influence of ANT forcing over the WCIND, IPIND, and ECIND regions for both observational datasets. Additionally, the magnitude of observed trend is almost entirely due to ANT trend in each of these cases (see Fig. S3a in the Supplementary Information). The Western Himalayan region (WHIND) has seen the largest rates of warming among all the regions and although responses to ANT and NAT forcings are detected (only for the IITM dataset), ANT forcing contributes to nearly all of the trend. These are robust except for the decadal analysis using the ROF method which fails the residual consistency check. The Residual Consistency Check (RCC; see Supplementary Material for details) - carried out for all 3 methods in each of the individual regions and seasons - tests the hypothesis that model simulated internal variability is consistent with observed (similar to what Fig. 2 shows). Unlike the “All Region” case (which includes the spatial dimension also) the individual regions only have 5 decadal time points leading to a situation where there is very little information left to estimate the residual’s variance. When we consider the 100-year long 1906–2005 period also (see Fig. S3b in Supplementary Information) we see similar detection of ANT signals in the WCIND, IPIND, ECIND, and WHIND regions. Between 1906–1955 (see Fig. S3c in Supplementary Information), the observed trends in each of the regions are smaller and we were unable to attribute these changes to either NAT or ANT forcings. It is therefore clear that most of the changes in annual mean TAS seen in the 100 year period have occurred over the second 50-year period (1956–2005), and that ANT forcing is responsible for almost all of the change.
We now focus our attention on WCIND and WHIND regions to delve into the seasonality of changes. Figure 4a shows that, over the WCIND region, ANT forcing is detected for each of the seasons and annual mean irrespective of observed dataset used. Moreover, the effects of NAT forcings are not detected. This is robust across the different methods - except the decadal ROF analysis where the RCC fails for reasons noted earlier. The effects of ANT forcings are detected in the DJF season over the WHIND in the pentadal analysis (Fig. 4b). The effects of NAT forcings are also detected but do not make a significant contribution to the annual mean trends and contribute to a slight warming during the MAM and SON seasons. This is also revealed in the reconstructed pentadal time series (see Fig. S4 in Supplementary Information). The NEIND, NCIND, and NWIND regions have smaller observed trends when compared to the other regions and we were able to detect the ANT forcing effects only when using the CRU dataset (see Fig.S3a in Supplementary Information).
The two-signal total linear change of observed and scaled reconstructed simulated forcings of TAS using best signal amplitude (
Since anthropogenic forcings include the effects of greenhouse gases, anthropogenic aerosols, and land-use land-cover changes (LULC) we explore the possibility of decomposing the ANT signal into these signals. The treatment of anthropogenic aerosols and LULC are quite different from model to model with some models not including the indirect effects of aerosols (see Table S1 in the Supplementary Information for details). One of the models considered (CSIRO-MK3-6-0) did not include changes in LULC and neither the CMIP5 experimental setup nor the specific models chosen included an LULC only set of runs. It is not always possible to decompose the signals as a simple linear addition of forcings - for instance, the combination of greenhouse gases and indirect effects of aerosols 20. We therefore decompose the ANT signal into a GHG forcing driven signal and an “Other Anthropogenic” (OA) signal (as in ref. 21) which subsumes all non-GHG anthropogenic forcings under the “OA” forcing. For this the historical, historicalGHG, and historicalNAT simulations were used to construct the transformation equation for deducing the scaling factors
Figure 5 shows the results of the three-signal analyses of annual mean TAS over “All Region” for the 1956–2005 period. The scaling factors in Fig. 5a show that the influence of GHG forcing can be detected over “All Region” for both observations and in all cases except pentadal TLS analysis with CRU observations. The effect of OA forcings can be detected in all cases where IITM observations are used but in none of the cases when CRU is used. Although the analysis reveals a detectable NAT signal for many cases, the trend contribution from NAT is minimal as seen in Fig. 5b. The dominant signal in all the cases seen is the positive trend from GHG forcing with the negative trend produced by OA forcings counteracting it. This results in a net positive trend in the reconstructed TAS that closely matches the observed trends. The uncertainty range of scaling factors and trends for GHG forcing are smaller than those for OA and NAT forcings. Moreover the amplitudes of the two anthropogenic signals (GHG & OA) are highly correlated as evidenced by the strong tilt in the marginal uncertainty ellipse (see Fig. S5 in Supplementary Information).
( a) The three signal scaling factors and their 5–95% uncertainty levels for “All Region” estimated for both decadal and pentadal annual averages between 1956–2005 using OLS, TLS, and ROF methods. ( b) Best estimate and 5–95% uncertainty range of individual forcing contributions and total linear change of “All Region” annual mean temperature (°C) for 1956–2005 shown for pentadal and decadal analysis. Best estimates are shown with central 90% uncertainty range. The two solid black symbols indicate cases where Residual Consistency Check (RCC) fails for the corresponding observed datasets and the asterisk indicates cases where the influence of a particular forcing has been detected. It may be noted that no cases fail the RCC. Observed trend uncertainties (5–95%) were calculated as in ref. 30.
When we analyse the “All Region” changes for individual seasons, there are numerous cases where we detect the effects of GHG and OA forcings (Fig. S6a in Supplementary Information). However, these are not consistent across seasons or observational datasets. The effects of NAT forcings are detected in a handful of cases but do not contribute significantly to the trends. The 100-year 1906–2005 period on the other hand provides the strongest evidence of the combined influence of the GHG and OA forcings irrespective of observational dataset used, particularly through the decadal analysis for all the seasons and the pentadal ROF analysis of the Annual, DJF, MAM, and SON seasons (see Fig. S6b in Supplementary Information). The NAT forcings are also clearly detected in the MAM and JJA (decadal analysis only) seasons although they contribute only a very small negative trend. Over the 1906–1955 period, the small observed trends were found to be due to NAT forcings in the MAM season but are otherwise not detected in any season (Fig. S6c).
In the analysis of individual homogeneous regions, we detect the effects of GHG forcing in the WCIND region with the pentadal analysis for all seasons except the MAM season with some sensitivity to observational dataset used (Fig. S7a in Supplementary Information). Barring one case, the effects of OA are not detected at all in this region. Over the WHIND region, the effects of GHG and OA forcings are detected in the DJF season for pentadal analysis only (Fig. S7b in Supplementary Information). The WHIND region also indicates a detectable influence of NAT forcings in the pentadal analysis of Annual, MAM and SON seasons which results in a very small negative trend. The MAM season decadal analysis shows clear detection of the influence of GHG, OA, and NAT forcings for the WHIND. Analysis of the Annual mean TAS in the ECIND region shows that the effect of GHG forcings is detected, but not that of OA or NAT (not shown). In the IPIND, NEIND, NCIND, and NWIND regions we were not able to detect the effects of GHG or OA forcings except in one or two cases.
Recognising that the forcings under “OA” predominantly include the effects of anthropogenic aerosols and LULCC, the inclusion/omission of indirect effects of aerosols is likely an important factor that determines the amount of cooling produced by aerosol effects 21. To investigate this further, we ran the three signal analysis with only the subset of models that include the indirect effects of models and land use changes (i.e by dropping the GFDL-ESM2M, CCSM4, and CSIRO-MK3-6-0 models from our analysis). The results thus obtained were not very different from the analysis that included all the models except for a reduction in the uncertainty ranges around the


{% extends 'layout.html' %} {% block pageTitle %} NHS login - Delegated access prototype v5 {% endblock %} {% block content %}
Date started: 6/12/2019
Date ended: 15/01/2020
User journey map
Alice has an NHS login account and wants to add a delegate for a service.
The journey starts with Alice logging into the service, then she will need to go to her NHS login settings in order to add a delegate.
Bob has been invited to act on behalf of Alice as a delegate within a service.
We do not know if Bob has an NHS login already.
The journey starts with Bob receiving an email inviting him to be a delegate.


SOBA Overview¶
SOBA (Simulation of Occupancy Based on Agents) is a tool of simulation of occupancy in buildings implemented in Python.
This software is useful for studies that require working with crowds, mainly in buildings. For example, SOBA has been used to carry out studies on the improvement of evacuations and for the improvement of energy efficiency in buildings.
The simulations are configured by declaring one or more types of occupants, with specific and definable behavior and a physical space. Regarding space, two different models are provided: a simplified model with a room defined by rooms, and a model with a continuous space. The simulation and results can be evaluated both in real time and post-simulation.
It is provided as open source software:
Github repository
:
https://github.com/gsi-upm/soba
Arquitecture Description¶
SEBA Components¶
- MESA. Mesa is an Apache2 licensed agent-based modeling (or ABM) framework in Python. It allows users to create agent-based models using built-in core components (such as spatial grids and agent schedulers) or customized implementations.
- Transitions. This external package is a lightweight, object-oriented state machine implementation in Python.
- RAMEN. It is an agent-based social simulation visualization tool for indoor crowd analytics based on the library Three.js. It allows to visualize a social simulation in a 3D environment and also to create the floor plan of a building.
- Browser. Using a browser a simple visualization can be made to know the performance of the simulation. This is also useful for debugging.
- REST Service. The software provide an API defined as a REST service (Get, Post, Pull and Push methods are defined) to interact with the simulation.
SEBA Modules¶
SOBA is implemented through 5 modules which group independent components with a related function.
- Model.
- Model. Base Class to create simulation models. It creates and manages space and agents. The model creates and manages space and agents, provides a scheduler that controls the agents activation regime, stores model-level parameters and serves as a container for the rest of components.
- Continuous Model. Base Class to create simulation models on a continuous space.
- Rooms Model. Base Class to create simulation models on a simplified space based on rooms.
- Time. Component of time management during the simulation in sexagesimal units and controller of the scheduler during the simulation.
- Agents.
Occupant. An object of the Occupant class is a type of agent developed and characterized to simulate the behavior of crowds in buildings. The occupants are agents with their activity defined by markov states.
Continuous Occupant.This class enables to create occupants that are modelled with a continuous space models. based on considering a scaled grid (x, y). Cell size of 0.5m ^ 2 by default.
Rooms Occupant. This class enables to create occupants that are modelled with a simplified models based on a discrete space associated with rooms.
Avatar. It enables to create avatars that represent virtual occupants, that is, they are not controlled by the simulation but by an API Rest, providing a means of interaction between simulation and real human participation.
- Agents modules.
- Markov. Base class to models the activity of the agents by means of Markovian behavior.
- AStar. Auxiliar class used by the occupants to move in the building.
- FOV.This component is a permissive field of view, which is useful to define the occupant visibility.
- Space.
- Grid. The space where the agents are situated and where they perform their actions is defined by means of a grid with coordinates (x, y).
- ContinuousItems. Various classes that define the representation of physical space objects in the continuous space model.
- RoomsItems. Various classes that define the representation of physical space objects in the simplificated space model based on rooms.
- Visualization.
- Back.py and front js. Two components provide a simple mechanism to represent the model in a web interface, based on HTML rendering though a server interface, implemented with web sockets. Connection between a JS file and a class py by means of parameters rendering.
- ramenInterface. Component to make the conexión with the RAMEN API.
- Launchers.
- RESTServer. Specification of the REST service server deployment.
- Visual. Manages the execution of the simulation in Browser by launch JS (front)/.py (back) files
- Run. Provides the execution of the simulation from terminal.


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JAY JAY OKOCHA
Jay Controlling the ball
-
August 1973 - Born in enugu, Enugu state Nigeria. His parents lived in
Iwo local Government, Osun State, Nigeria.
- 1990 – 1992 - Borussia Neunkirchen
-
1992 – 1996 - Okocha joined Eintracht Frankfurt in December 1991,
where he linked up with many well-known players including Ghanaian
international striker Tony Yeboah and later Thomas Doll.
-
1996 – 1998 - Okocha joined Turkish club Fenerbahçe for approximately £1
million following Eintracht Frankfurt's relegation to the 2.
Bundesliga. In his two seasons with the team, he amassed 30 goals in
62 appearances, many of them coming from direct free kicks, which
became something of a trademark for him at the club. Okocha
acquired Turkish citizenship as Muhammet Yavuz while playing for
Fenerbahçe.
-
1998 – 2002 - In 1998, French side Paris Saint-Germain spent around
£14 million to sign Okocha, making him the most expensive African
player at the time. During his four-year stint with PSG, he played 84
matches and scored 12 goals. He has also served as a mentor, at the
time, for young Brazilian footballer Ronaldinho during his time in
Paris.
-
2002 – 2006 - Okocha joined Bolton Wanderers on a free transfer after
leaving PSG in the summer of 2002 after the FIFA World Cup. His debut
season, despite being hampered by injuries, made him a favourite with
the Bolton fans, with the team printing shirts with the inscription
"Jay-Jay – so good they named him twice". He steered the team away
from relegation with seven goals, including the team Goal of the
Season in the vital league win against West Ham United. This was voted
Bolton's best Premier League goal in a fans vote in 2008.
-
2007 – 2008 - After just one season in Qatar, Football League
Championship side Hull City signed Okocha on a free transfer in 2007,
after the player had been linked to Real Salt Lake and Sydney FC.
Click here to read more about Okocha


Class: Aws::FSx::Types::AdministrativeAction
| Relationships & Source Files | |
| Super Chains via Extension / Inclusion / Inheritance | |
|
Class Chain:
self,
Struct
|
|
|
Instance Chain:
self,
::Aws::Structure ,
Struct
|
|
| Inherits: |
Struct
|
| Defined in: | gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb |
Overview
Describes a specific Amazon FSx administrative action for the current Windows, Lustre, or OpenZFS file system.
Constant Summary
-
SENSITIVE =
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 229[]
Instance Attribute Summary
-
#administrative_action_type ⇒ String
rw
Describes the type of administrative action, as follows:
-
#failure_details ⇒ Types::AdministrativeActionFailureDetails
rw
Provides information about a failed administrative action.
-
#progress_percent ⇒ Integer
rw
The percentage-complete status of a
STORAGE_OPTIMIZATION
administrative action. -
#request_time ⇒ Time
rw
The time that the administrative action request was received.
-
#status ⇒ String
rw
Describes the status of the administrative action, as follows:
-
#target_file_system_values ⇒ Types::FileSystem
rw
Describes the target value for the administration action, provided in the
UpdateFileSystem
operation. -
#target_snapshot_values ⇒ Types::Snapshot
rw
A snapshot of an Amazon FSx for OpenZFS volume.
-
#target_volume_values ⇒ Types::Volume
rw
Describes an Amazon FSx for NetApp ONTAP or Amazon FSx for OpenZFS volume.
Instance Method Summary
Instance Attribute Details
#administrative_action_type ⇒ String
(rw)
Describes the type of administrative action, as follows:
FILE_SYSTEM_UPDATE
- A file system update administrative action initiated from the Amazon FSx console, API (UpdateFileSystem
), or CLI (update-file-system
).THROUGHPUT_OPTIMIZATION
- After theFILE_SYSTEM_UPDATE
task to increase a file system's throughput capacity has been completed successfully, aTHROUGHPUT_OPTIMIZATION
task starts.
You can track the storage-optimization progress using the
ProgressPercent
property. When THROUGHPUT_OPTIMIZATION
has
been completed successfully, the parent FILE_SYSTEM_UPDATE
action status changes to COMPLETED
. For more information, see
Managing throughput capacity in the Amazon FSx for Windows
File Server User Guide.
STORAGE_OPTIMIZATION
- After theFILE_SYSTEM_UPDATE
task to increase a file system's storage capacity has been completed successfully, aSTORAGE_OPTIMIZATION
task starts.- For Windows and ONTAP, storage optimization is the process of migrating the file system data to newer larger disks.
- For Lustre, storage optimization consists of rebalancing the data across the existing and newly added file servers.
You can track the storage-optimization progress using the
ProgressPercent
property. When STORAGE_OPTIMIZATION
has been
completed successfully, the parent FILE_SYSTEM_UPDATE
action
status changes to COMPLETED
. For more information, see Managing
storage capacity in the Amazon FSx for Windows File Server
User Guide, Managing storage and throughput capacity in the
Amazon FSx for Lustre User Guide, and Managing storage capacity
and provisioned IOPS in the Amazon FSx for NetApp ONTAP User
Guide.
FILE_SYSTEM_ALIAS_ASSOCIATION
- A file system update to associate a new Domain Name System (DNS) alias with the file system. For more information, see AssociateFileSystemAliases.FILE_SYSTEM_ALIAS_DISASSOCIATION
- A file system update to disassociate a DNS alias from the file system. For more information, see DisassociateFileSystemAliases.IOPS_OPTIMIZATION
- After theFILE_SYSTEM_UPDATE
task to increase a file system's throughput capacity has been completed successfully, aIOPS_OPTIMIZATION
task starts.
You can track the storage-optimization progress using the
ProgressPercent
property. When IOPS_OPTIMIZATION
has been
completed successfully, the parent FILE_SYSTEM_UPDATE
action
status changes to COMPLETED
. For more information, see Managing
provisioned SSD IOPS in the Amazon FSx for Windows File
Server User Guide.
STORAGE_TYPE_OPTIMIZATION
- After theFILE_SYSTEM_UPDATE
task to increase a file system's throughput capacity has been completed successfully, aSTORAGE_TYPE_OPTIMIZATION
task starts.
You can track the storage-optimization progress using the
ProgressPercent
property. When STORAGE_TYPE_OPTIMIZATION
has
been completed successfully, the parent FILE_SYSTEM_UPDATE
action status changes to COMPLETED
.
VOLUME_UPDATE
- A volume update to an Amazon FSx for NetApp ONTAP or Amazon FSx for OpenZFS volume initiated from the Amazon FSx console, API (UpdateVolume
), or CLI (update-volume
).VOLUME_RESTORE
- An Amazon::Aws::FSx
for OpenZFS volume is returned to the state saved by the specified snapshot, initiated from an API (RestoreVolumeFromSnapshot
) or CLI (restore-volume-from-snapshot
).SNAPSHOT_UPDATE
- A snapshot update to an Amazon FSx for OpenZFS volume initiated from the Amazon FSx console, API (UpdateSnapshot
), or CLI (update-snapshot
).RELEASE_NFS_V3_LOCKS
- Tracks the release of Network File System (NFS) V3 locks on an Amazon FSx for OpenZFS file system.
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 220
class AdministrativeAction < Struct.new( :administrative_action_type, :progress_percent, :request_time, :status, :target_file_system_values, :failure_details, :target_volume_values, :target_snapshot_values) SENSITIVE = [] include Aws::Structure end
#failure_details ⇒ Types::AdministrativeActionFailureDetails (rw)
Provides information about a failed administrative action.
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 220
class AdministrativeAction < Struct.new( :administrative_action_type, :progress_percent, :request_time, :status, :target_file_system_values, :failure_details, :target_volume_values, :target_snapshot_values) SENSITIVE = [] include Aws::Structure end
#progress_percent ⇒ Integer
(rw)
The percentage-complete status of a STORAGE_OPTIMIZATION
administrative action. Does not apply to any other administrative
action type.
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 220
class AdministrativeAction < Struct.new( :administrative_action_type, :progress_percent, :request_time, :status, :target_file_system_values, :failure_details, :target_volume_values, :target_snapshot_values) SENSITIVE = [] include Aws::Structure end
#request_time ⇒ Time
(rw)
The time that the administrative action request was received.
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 220
class AdministrativeAction < Struct.new( :administrative_action_type, :progress_percent, :request_time, :status, :target_file_system_values, :failure_details, :target_volume_values, :target_snapshot_values) SENSITIVE = [] include Aws::Structure end
#status ⇒ String
(rw)
Describes the status of the administrative action, as follows:
FAILED
- Amazon FSx failed to process the administrative action successfully.IN_PROGRESS
- Amazon FSx is processing the administrative action.PENDING
- Amazon FSx is waiting to process the administrative action.COMPLETED
- Amazon FSx has finished processing the administrative task.UPDATED_OPTIMIZING
- For a storage-capacity increase update, Amazon FSx has updated the file system with the new storage capacity, and is now performing the storage-optimization process.
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 220
class AdministrativeAction < Struct.new( :administrative_action_type, :progress_percent, :request_time, :status, :target_file_system_values, :failure_details, :target_volume_values, :target_snapshot_values) SENSITIVE = [] include Aws::Structure end
#target_file_system_values ⇒ Types::FileSystem (rw)
Describes the target value for the administration action, provided
in the UpdateFileSystem
operation. Returned for
FILE_SYSTEM_UPDATE
administrative actions.
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 220
class AdministrativeAction < Struct.new( :administrative_action_type, :progress_percent, :request_time, :status, :target_file_system_values, :failure_details, :target_volume_values, :target_snapshot_values) SENSITIVE = [] include Aws::Structure end
#target_snapshot_values ⇒ Types::Snapshot (rw)
A snapshot of an Amazon FSx for OpenZFS volume.
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 220
class AdministrativeAction < Struct.new( :administrative_action_type, :progress_percent, :request_time, :status, :target_file_system_values, :failure_details, :target_volume_values, :target_snapshot_values) SENSITIVE = [] include Aws::Structure end
#target_volume_values ⇒ Types::Volume (rw)
Describes an Amazon FSx for NetApp ONTAP or Amazon FSx for OpenZFS volume.
# File 'gems/aws-sdk-fsx/lib/aws-sdk-fsx/types.rb', line 220
class AdministrativeAction < Struct.new( :administrative_action_type, :progress_percent, :request_time, :status, :target_file_system_values, :failure_details, :target_volume_values, :target_snapshot_values) SENSITIVE = [] include Aws::Structure end


Being Andrea: Singapore’s most famous transgender model on finding herself (Video)
by Coconuts SingaporeFew stories of transgender women or men surface in Singapore, and it’s even rarer to read about one who is a model.
Andrea, 28, is signed to the Phantom modeling agency in Singapore and normally works with brands that are inclusive. She is also a makeup artist and runs a business selling hair weaves.
But life wasn’t all fantastic for Andrea prior to her reassignment surgery. Growing up, she had always felt there was something off about her, but held out and led a life as a Singaporean man — even serving in the army — until she decided at 23 to take her first hormone pill.
“I really went through life as a male, you know, and decided that it’s not for me. I just held it out … I thought I was a gay man. It turns out I wasn’t homosexual, I was transsexual,” she told Coconuts CEO Byron Perry in the debut episode of Being, a new Coconuts TV interview series.
“After three months, when I popped the first pill, that was the most at home I’ve ever felt in my body in the 23 years I’ve lived.”
Hear more about Andrea’s transition, what it’s like being trans in Singapore’s dating scene, and more in the full interview.
Correction: An earlier version of this video referred to Andrea as the first transgender woman model in Singapore, which she is not. We apologize for the error.
More news from the Little Red Dot at Coconuts.co/Singapore.


Truth about Self
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technical
- Git It? Got It? Good?
- Are You In or Out? The Index is Key
- Enumerable#map
- Let's Get Classy!
- In the Scope of Things
hi! welcome to my blog.
You'll notice I have a section on either side of this page. One is technical and one is cultural.
My technical blog entries will cover what I've learned throughout the week. They're written in a way, that anyone can understand (even if you're not a coder or studying to be one yet).
The cultural entries will cover more of how I feel throughout this process, how I feel about DBC, and my take on anything web development at the time written.
Go ahead and click a title to start reading! Thanks for taking the time to stop by, and don't forget, there's an option to leave your input on each blog entry page!
Enjoy!
cultural
- What's Cooking at DBC?
- Don't Mind Me!
- Do You See Me?
- ["You", "Me"].join("&")
- Oh So Typical