how to optimize max position limit in quantstrat for bollinger bands strategy - optimization

The code below is my Bollinger bands strategy for commodity. I add a position limit and want to optimize this strategy through parameter maxpos. For the function add.distribution, it asks for a component.label, but the function addPosLimit does not have a variable called "label". I wonder how to optimize maxpos at this case.
symbol <- 'C1'
currency("USD")
#stock(symbol, currency="USD", multiplier=1)
portfName <- 'RSI_Strategy'
acctName <- portfName
suppressWarnings(rm.strat(stratName))
initPortf(name = portfName, symbols = symbol, initDate = initDate,
currency = 'USD')
initAcct(name = acctName, portfolios = portfName,
initDate=initDate, initEq=initEq)
initOrders(portfolio = portfName, initDate = initDate)
stratName <- portfName
strategy(name = stratName, store=TRUE)
SD = 2
N = 20
add.indicator(strategy = stratName, name = "BBands",
arguments = list(HLC = quote(HLC(mktdata)), maType='SMA',
n=N, sd=SD),
label='BBands')
add.signal(strategy = stratName, name="sigCrossover",
arguments=list(columns=c("Close","up"),relationship="gt"),
label="Cl.gt.UpperBand")
add.signal(strategy = stratName, name="sigCrossover",
arguments=list(columns=c("Close","dn"),relationship="lt"),
label="Cl.lt.LowerBand")
add.signal(strategy = stratName, name="sigCrossover",
arguments=list(columns=c("High","mavg"),relationship="gt"),
label="Hi.Cross.Mid")
add.signal(strategy = stratName, name="sigCrossover",
arguments=list(columns=c("Low","mavg"),relationship="lt"),
label="Lo.Cross.Mid")
add.rule(strategy = stratName, name='ruleSignal',
arguments=list(sigcol="Cl.gt.UpperBand",sigval=TRUE, orderqty=-nShs,
ordertype='market', orderside=NULL, osFUN=osMaxPos
),type='enter',
label = "Enter.Short")
add.rule(strategy = stratName, name='ruleSignal',
arguments=list(sigcol="Cl.lt.LowerBand",sigval=TRUE, orderqty=nShs,
ordertype='market', orderside=NULL, osFUN=osMaxPos
),type='enter',
label = "Enter.Long")
add.rule(strategy = stratName, name='ruleSignal',
arguments=list(sigcol="Hi.Cross.Mid",sigval=TRUE, orderqty= 'all',
ordertype='market', orderside=NULL),type='exit',
label = "Exit.All")
add.rule(strategy = stratName, name='ruleSignal',
arguments=list(sigcol="Lo.Cross.Mid",sigval=TRUE, orderqty= 'all',
ordertype='market', orderside=NULL),type='exit',
label = "Exit.All")
addPosLimit(portfName, symbol, timestamp=initDate, maxpos=maxpos, minpos=0)
.maxpos = seq(3000,8000,1000)
add.distribution(stratName,
paramset.label = 'PosOpt',
component.type = 'order',
component.label = 'addPosLimit',
variable = list(maxpos = .maxpos),
label = 'MaxPos')

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Error in FUN(X[[i]], ...) : object 'Year' not found when plotting ordination in ggplot

I am having an issue with the ggplot code line where R doesn't like the "group = Year".
Here is what my data looks like:
> head(data.scores.pa)
NMDS1 NMDS2 NMDS3 Site Year Elevation Fire history
1 -0.737547 0.73473457 0.7575643 BF 2004 1710 Burnt
......
> head(spp.scrs2)
species MDS1 MDS2 pval
1 Acrothamnus.montanus 0.8383 -0.02382347 1e-04
........
> head(vec.sp.df.pa)
MDS1 MDS2 species pvals
Elevation 0.834847 0.747474 Elevation 0.005
Here is the code I am using:
>xy <- ggplot(data.scores.pa, aes(x = NMDS1, y = NMDS2, group = Year)) +
geom_point(size = 3, aes(shape = Fire history, colour = Year))+
stat_ellipse(mapping = NULL, data = NULL, geom = "path", position = "identity", type = "t", level = 0.95, segments = 51, na.rm = FALSE, show.legend = NA, inherit.aes = TRUE) +
geom_segment(data=vec.sp.df.pa, aes(x=0,xend=MDS1,y=0,yend=MDS2),
arrow = arrow(length = unit(0.5,"cm")),colour="grey")+
geom_text_repel(data=vec.sp.df.pa,aes(x=MDS1,y=MDS2,label=species),size=2)+
geom_segment(data=spp.scrs2,aes(x=0,xend=MDS1,y=0,yend=MDS2),
arrow = arrow(length = unit(0.5, "cm")),colour="black")+
geom_text_repel(data=spp.scrs2, aes(x=MDS1,y=MDS2,label=species),size=2)+
annotate("text", x = -1.6, y = 1, label = paste0("3D stress: ", format(ord.pa$stress, digits = 4)), hjust = 0) +
theme_cowplot() + scale_color_brewer(palette = "BrBG", direction = 1) +
theme(panel.border = element_rect(colour = "black"))+
ggtitle("All Sites - distance data using Bray-Curtis")+
labs(x = "NMDS1", y = "NMDS2")
> Error in FUN(X[[i]], ...) : object 'Year' not found
However, when I remove the geom_segment and geom_text_repel code lines it fixes the problem and I am able to plot the graph...
Is anyone able to provide some insight into this issue?
Thank you!

Changing x-axis tick labels in reverse plot

I try to have an axis going from 0 to 24 (that represent hours in a day). With tick labels at 6, 12 and 18 too.
I am struggling, I tried already scale_x_discrete and scale_x_continuous and ylim, but the axis always goes from 0 to 25... :(
Thanks in avance for your help
Categ <- c("Employment","Voluntary work","Householdchores","Household member care","Personal care","Study","Sports and outdoor",
"Leisure","Travel or unspecified","Sleep")
Regime <- c("Unemp.", "Part-time 1" ,"Part-time 2" ,"Full time" , "Overtime")
Share <- c(0.00,0.10,3.71,1.14,2.70,0.18,0.37,5.55,1.28,8.96,1.29,0.10,4.13,1.13,3.10,0.11,0.18,3.73,1.72,8.50,3.79,0.13,2.89,0.78,2.43,0.04,0.22,3.94,1.69,8.09,5.78,0.08,1.96,
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data <- data.frame(Regime = rep(rev(Regime), each=10),
Category = rep(Categ,times=5),
Share = Share)
pct2 <- ggplot(data,
aes(x = Regime, y = Share)) +
geom_bar(aes(fill = Category),
stat = "identity", colour="black")+
coord_flip() +
theme(panel.background = element_blank()) +
scale_fill_manual("Legend", values = c("Employment" = "#F4A582", "Voluntary work" = "#FDDBC7", "Household chores" = "#92C5DE", "Household member care" = "#4393C3", "Personal care" = "#737373", "Study" = "#969696", "Sports and outdoor" = "#BDBDBD", "Leisure" = "#D9D9D9", "Travel or unspecified"= "#F0F0F0", "Sleep" = "#F6E8C3")) +
ylim(0,24)
print(pct2)

This should do (I'm adding scale_y_continuous(...) and I'm omitting ylim(0,24):
ggplot(data, aes(x = Regime, y = Share)) +
geom_bar(aes(fill = Category), stat = "identity", colour="black") +
coord_flip() +
scale_y_continuous(breaks = as.numeric(seq(1:24))) +
theme(panel.background = element_blank()) +
scale_fill_manual("Legend", values = c("Employment" = "#F4A582", "Voluntary work" = "#FDDBC7", "Household chores" = "#92C5DE", "Household member care" = "#4393C3", "Personal care" = "#737373", "Study" = "#969696", "Sports and outdoor" = "#BDBDBD", "Leisure" = "#D9D9D9", "Travel or unspecified"= "#F0F0F0", "Sleep" = "#F6E8C3"))

How i can animation or state change inside the Canvas in jetpack compose?

suppose i have simple composable fun which has Canvas.
Canvas(modifier = Modifier
.padding(start = 60.dp, end = 60.dp)
.fillMaxSize(),
onDraw = {
val w = size.width
val h = size.height
val s1LineOffset = w / 4 - 10
val s2LineOffset = w * 3 / 8 - 10
drawImage(
image = ImageBitmap.imageResource(
res = resources,
id = R.drawable.bttn
),
topLeft = Offset(
x = b1StartOffset,
y = 0f
)
)
}
)
I want to define the state of animation using the canvas sizes for initial and target value but i cant do so because i can't use this inside draw scope thats why i have to use it above the Canvas block, hence cant access Canvas size what should i do
val anim by ballAnim.animateFloat(
initialValue = ,
targetValue =,
animationSpec =
)

It is more of a simple job. Just create and remember a MutableState<T> value, then update it inside Canvas. For example,
var canvasSize by remember { mutableStateOf(IntSize()) }
Canvas(modifier = Modifier
.padding(start = 60.dp, end = 60.dp)
.fillMaxSize(),
onDraw = {
canvasSize = size //Assign it here
val w = size.width
val h = size.height
val s1LineOffset = w / 4 - 10
val s2LineOffset = w * 3 / 8 - 10
drawImage(
image = ImageBitmap.imageResource(
res = resources,
id = R.drawable.bttn
),
topLeft = Offset(
x = b1StartOffset,
y = 0f
)
)
}
)
val anim by ballAnim.animateFloat(
initialValue = canvasSize.width,
targetValue = canvasSize.height, //whatever
animationSpec = spring()
)

One of the options is animating some normalized value and denormalizing it using size inside onDraw, e.g.:
val animNormalized by ballAnim.animateFloat(
initialValue = 0.5,
targetValue = 0.8,
animationSpec =
)
Canvas(modifier = Modifier
.padding(start = 60.dp, end = 60.dp)
.fillMaxSize(),
onDraw = {
val w = size.width
val h = size.height
val anim = animNormalized * w
...
}
)

updateSelectInput does not hold in shiny module

I'm testing modulization for an shiny app. One problem in the following code is that, when select a new name under "name to analyze", updated result does not hold. The selection will automatically return to 'name1' within seconds. Much appreciated for any advice to correct it.
Thanks.
library(shiny)
subgroupInput <- function(id){
ns <-NS(id)
tagList(
selectInput(ns("name"),
label = "name to analyze",
choices = NULL,selected=NULL),
radioButtons(ns('radio'), 'cutoffType', choices=c('percentile', 'value'),
selected = NULL, inline = FALSE),
conditionalPanel(
condition = paste0("input['", ns("radio"), "'] == 'percentile'"),
sliderInput(ns("cutoff1"),
label = "Bottom-trim percentile:",
min = 0, max = 100, value = 5),
sliderInput(ns("cutoff2"),
label = "Top-trim percentile:",
min = 0, max = 100, value = 95)
),
conditionalPanel(
condition = paste0("input['", ns("radio"), "'] == 'value'"),
sliderInput(ns("cutoff3"),
label = "Bottom-trim value:",
min = 0, max = 100, value = -1),
sliderInput(ns("cutoff4"),
label = "Top-trim value:",
min = 0, max = 100, value = 1)
)
)
}
subgroup <- function(input, output, session,default_selected=NULL){
ns=session$ns
model <- reactive({
data = data.frame(matrix(rep(rnorm(100*100,sd=3)),ncol=100),stringsAsFactors = F)
colnames(data)=paste0('name',1:100)
namelist = colnames(data)
updateSelectInput(session, "name",choices = namelist, selected = default_selected)
validate(
shiny::need(input$name,"Select name")
)
x = round(data[,input$name])
updateSliderInput(session, "cutoff3", label="Cufoff value", min=min(x),max=max(x))
updateSliderInput(session, "cutoff4", label="Cufoff value", min=min(x),max=max(x))
if(input$radio=="percentile") {
dt = data[,input$name]
qt = quantile(dt,c(input$cutoff1,input$cutoff2)/100)
result <- hist(dt[dt>qt[1] & dt<=qt[2]],main=paste0("Histogram of ",input$name))
}
else if(input$radio=="value"){
dt = data[,input$name]
result <- hist(dt[dt>input$cutoff3 & dt<=input$cutoff4],main=paste0("Histogram of ",input$name))
}
return(list(plot = result, data = data, inname=input$name))
})
return (model)
}
The above are modules. Following code makes call:
shinyApp(
ui = fluidPage(
subgroupInput("test1"),
plotOutput("plot")
),
server = function(input, output, session){
test <- shiny::callModule(subgroup,"test1")
output$plot <- renderPlot({
test()$plot
})
}
)

Does Field order matter in an inner join for access SQL?

I have a query that requires ONE HUNDRED fields to be joined from one table to another. instead of manually going through the query designer, i decided to build the join using excel. When i put the code in to the SQL side of the query, then clicked designer, it choked on all 100 field comparisons and removed them. i proceeded to manually click drag the entire set of 100 field to join them all. somehow it worked. now when i look at the sql code for how access did it, it looks almost identical to my code. the only difference that i can identify is that instead of it going from A-Z, it goes Z-A.
Original Query
PARAMETERS pn TEXT (255)
,tt TEXT (255)
,sns Long
,sne Long
,ds DATETIME
,de DATETIME;
SELECT y.Short_Description1
,y.Data1
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,y.Short_Description3
,y.Data3
,y.Short_Description4
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,y.Short_Description5
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,x.rownum
FROM qryIndividualTestData100Descriptions AS x
INNER JOIN IndividualTestData100 AS y ON (x.Short_Description1 =
y.Short_Description1)
AND (x.Short_Description2 = y.Short_Description2)
AND (x.Short_Description3 = y.Short_Description3)
AND (x.Short_Description4 = y.Short_Description4)
AND (x.Short_Description5 = y.Short_Description5)
AND (x.Short_Description6 = y.Short_Description6)
AND (x.Short_Description7 = y.Short_Description7)
AND (x.Short_Description8 = y.Short_Description8)
AND (x.Short_Description9 = y.Short_Description9)
AND (x.Short_Description10 = y.Short_Description10)
AND (x.Short_Description11 = y.Short_Description11)
AND (x.Short_Description12 = y.Short_Description12)
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AND (x.Short_Description14 = y.Short_Description14)
AND (x.Short_Description15 = y.Short_Description15)
AND (x.Short_Description16 = y.Short_Description16)
AND (x.Short_Description17 = y.Short_Description17)
AND (x.Short_Description18 = y.Short_Description18)
AND (x.Short_Description19 = y.Short_Description19)
AND (x.Short_Description20 = y.Short_Description20)
AND (x.Short_Description21 = y.Short_Description21)
AND (x.Short_Description22 = y.Short_Description22)
AND (x.Short_Description23 = y.Short_Description23)
AND (x.Short_Description24 = y.Short_Description24)
AND (x.Short_Description25 = y.Short_Description25)
AND (x.Short_Description26 = y.Short_Description26)
AND (x.Short_Description27 = y.Short_Description27)
AND (x.Short_Description28 = y.Short_Description28)
AND (x.Short_Description29 = y.Short_Description29)
AND (x.Short_Description30 = y.Short_Description30)
AND (x.Short_Description31 = y.Short_Description31)
AND (x.Short_Description32 = y.Short_Description32)
AND (x.Short_Description33 = y.Short_Description33)
AND (x.Short_Description34 = y.Short_Description34)
AND (x.Short_Description35 = y.Short_Description35)
AND (x.Short_Description36 = y.Short_Description36)
AND (x.Short_Description37 = y.Short_Description37)
AND (x.Short_Description38 = y.Short_Description38)
AND (x.Short_Description39 = y.Short_Description39)
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AND (x.Short_Description44 = y.Short_Description44)
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AND (x.Short_Description48 = y.Short_Description48)
AND (x.Short_Description49 = y.Short_Description49)
AND (x.Short_Description50 = y.Short_Description50)
AND (x.Short_Description51 = y.Short_Description51)
AND (x.Short_Description52 = y.Short_Description52)
AND (x.Short_Description53 = y.Short_Description53)
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AND (x.Short_Description66 = y.Short_Description66)
AND (x.Short_Description67 = y.Short_Description67)
AND (x.Short_Description68 = y.Short_Description68)
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AND (x.Short_Description78 = y.Short_Description78)
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AND (x.Short_Description81 = y.Short_Description81)
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AND (x.Short_Description85 = y.Short_Description85)
AND (x.Short_Description86 = y.Short_Description86)
AND (x.Short_Description87 = y.Short_Description87)
AND (x.Short_Description88 = y.Short_Description88)
AND (x.Short_Description89 = y.Short_Description89)
AND (x.Short_Description90 = y.Short_Description90)
AND (x.Short_Description91 = y.Short_Description91)
AND (x.Short_Description92 = y.Short_Description92)
AND (x.Short_Description93 = y.Short_Description93)
AND (x.Short_Description94 = y.Short_Description94)
AND (x.Short_Description95 = y.Short_Description95)
AND (x.Short_Description96 = y.Short_Description96)
AND (x.Short_Description97 = y.Short_Description97)
AND (x.Short_Description98 = y.Short_Description98)
AND (x.Short_Description99 = y.Short_Description99)
AND (x.Short_Description100 = y.Short_Description100)
WHERE (
((y.[Part_Number]) LIKE [pn] & "*" & [tt])
AND (
(y.[Serial_Number]) >= [sns]
AND (y.[Serial_Number]) <= [sne]
)
AND (
(y.[Test_Date]) >= [ds]
AND (y.[Test_Date]) <= [de]
)
);
Access Generated version
PARAMETERS pn TEXT (255)
,tt TEXT (255)
,sns Long
,sne Long
,ds DATETIME
,de DATETIME;
SELECT y.Short_Description1
,y.Data1
,y.Short_Description2
,y.Data2
,y.Short_Description3
,y.Data3
,y.Short_Description4
,y.Data4
,y.Short_Description5
,y.Data5
,y.Short_Description6
,y.Data6
,y.Short_Description7
,y.Data7
,y.Short_Description8
,y.Data8
,y.Short_Description9
,y.Data9
,y.Short_Description10
,y.Data10
,y.Short_Description11
,y.Data11
,y.Short_Description12
,y.Data12
,y.Short_Description13
,y.Data13
,y.Short_Description14
,y.Data14
,y.Short_Description15
,y.Data15
,y.Short_Description16
,y.Data16
,y.Short_Description17
,y.Data17
,y.Short_Description18
,y.Data18
,y.Short_Description19
,y.Data19
,y.Short_Description20
,y.Data20
,y.Short_Description21
,y.Data21
,y.Short_Description22
,y.Data22
,y.Short_Description23
,y.Data23
,y.Short_Description24
,y.Data24
,y.Short_Description25
,y.Data25
,y.Short_Description26
,y.Data26
,y.Short_Description27
,y.Data27
,y.Short_Description28
,y.Data28
,y.Short_Description29
,y.Data29
,y.Short_Description30
,y.Data30
,y.Short_Description31
,y.Data31
,y.Short_Description32
,y.Data32
,y.Short_Description33
,y.Data33
,y.Short_Description34
,y.Data34
,y.Short_Description35
,y.Data35
,y.Short_Description36
,y.Data36
,y.Short_Description37
,y.Data37
,y.Short_Description38
,y.Data38
,y.Short_Description39
,y.Data39
,y.Short_Description40
,y.Data40
,y.Short_Description41
,y.Data41
,y.Short_Description42
,y.Data42
,y.Short_Description43
,y.Data43
,y.Short_Description44
,y.Data44
,y.Short_Description45
,y.Data45
,y.Short_Description46
,y.Data46
,y.Short_Description47
,y.Data47
,y.Short_Description48
,y.Data48
,y.Short_Description49
,y.Data49
,y.Short_Description50
,y.Data50
,y.Short_Description51
,y.Data51
,y.Short_Description52
,y.Data52
,y.Short_Description53
,y.Data53
,y.Short_Description54
,y.Data54
,y.Short_Description55
,y.Data55
,y.Short_Description56
,y.Data56
,y.Short_Description57
,y.Data57
,y.Short_Description58
,y.Data58
,y.Short_Description59
,y.Data59
,y.Short_Description60
,y.Data60
,y.Short_Description61
,y.Data61
,y.Short_Description62
,y.Data62
,y.Short_Description63
,y.Data63
,y.Short_Description64
,y.Data64
,y.Short_Description65
,y.Data65
,y.Short_Description66
,y.Data66
,y.Short_Description67
,y.Data67
,y.Short_Description68
,y.Data68
,y.Short_Description69
,y.Data69
,y.Short_Description70
,y.Data70
,y.Short_Description71
,y.Data71
,y.Short_Description72
,y.Data72
,y.Short_Description73
,y.Data73
,y.Short_Description74
,y.Data74
,y.Short_Description75
,y.Data75
,y.Short_Description76
,y.Data76
,y.Short_Description77
,y.Data77
,y.Short_Description78
,y.Data78
,y.Short_Description79
,y.Data79
,y.Short_Description80
,y.Data80
,y.Short_Description81
,y.Data81
,y.Short_Description82
,y.Data82
,y.Short_Description83
,y.Data83
,y.Short_Description84
,y.Data84
,y.Short_Description85
,y.Data85
,y.Short_Description86
,y.Data86
,y.Short_Description87
,y.Data87
,y.Short_Description88
,y.Data88
,y.Short_Description89
,y.Data89
,y.Short_Description90
,y.Data90
,y.Short_Description91
,y.Data91
,y.Short_Description92
,y.Data92
,y.Short_Description93
,y.Data93
,y.Short_Description94
,y.Data94
,y.Short_Description95
,y.Data95
,y.Short_Description96
,y.Data96
,y.Short_Description97
,y.Data97
,y.Short_Description98
,y.Data98
,y.Short_Description99
,y.Data99
,y.Short_Description100
,y.Data100
,x.rownum
FROM qryIndividualTestData100Descriptions AS x
INNER JOIN individualtestdata100 AS y ON (x.Short_Description100 =
y.Short_Description100)
AND (x.Short_Description99 = y.Short_Description99)
AND (x.Short_Description98 = y.Short_Description98)
AND (x.Short_Description97 = y.Short_Description97)
AND (x.Short_Description96 = y.Short_Description96)
AND (x.Short_Description95 = y.Short_Description95)
AND (x.Short_Description94 = y.Short_Description94)
AND (x.Short_Description93 = y.Short_Description93)
AND (x.Short_Description92 = y.Short_Description92)
AND (x.Short_Description91 = y.Short_Description91)
AND (x.Short_Description90 = y.Short_Description90)
AND (x.Short_Description89 = y.Short_Description89)
AND (x.Short_Description88 = y.Short_Description88)
AND (x.Short_Description87 = y.Short_Description87)
AND (x.Short_Description86 = y.Short_Description86)
AND (x.Short_Description85 = y.Short_Description85)
AND (x.Short_Description84 = y.Short_Description84)
AND (x.Short_Description83 = y.Short_Description83)
AND (x.Short_Description82 = y.Short_Description82)
AND (x.Short_Description81 = y.Short_Description81)
AND (x.Short_Description80 = y.Short_Description80)
AND (x.Short_Description79 = y.Short_Description79)
AND (x.Short_Description78 = y.Short_Description78)
AND (x.Short_Description77 = y.Short_Description77)
AND (x.Short_Description76 = y.Short_Description76)
AND (x.Short_Description75 = y.Short_Description75)
AND (x.Short_Description74 = y.Short_Description74)
AND (x.Short_Description73 = y.Short_Description73)
AND (x.Short_Description72 = y.Short_Description72)
AND (x.Short_Description71 = y.Short_Description71)
AND (x.Short_Description70 = y.Short_Description70)
AND (x.Short_Description69 = y.Short_Description69)
AND (x.Short_Description68 = y.Short_Description68)
AND (x.Short_Description67 = y.Short_Description67)
AND (x.Short_Description66 = y.Short_Description66)
AND (x.Short_Description65 = y.Short_Description65)
AND (x.Short_Description64 = y.Short_Description64)
AND (x.Short_Description63 = y.Short_Description63)
AND (x.Short_Description62 = y.Short_Description62)
AND (x.Short_Description61 = y.Short_Description61)
AND (x.Short_Description60 = y.Short_Description60)
AND (x.Short_Description59 = y.Short_Description59)
AND (x.Short_Description58 = y.Short_Description58)
AND (x.Short_Description57 = y.Short_Description57)
AND (x.Short_Description56 = y.Short_Description56)
AND (x.Short_Description55 = y.Short_Description55)
AND (x.Short_Description54 = y.Short_Description54)
AND (x.Short_Description53 = y.Short_Description53)
AND (x.Short_Description52 = y.Short_Description52)
AND (x.Short_Description51 = y.Short_Description51)
AND (x.Short_Description50 = y.Short_Description50)
AND (x.Short_Description49 = y.Short_Description49)
AND (x.Short_Description48 = y.Short_Description48)
AND (x.Short_Description47 = y.Short_Description47)
AND (x.Short_Description46 = y.Short_Description46)
AND (x.Short_Description45 = y.Short_Description45)
AND (x.Short_Description44 = y.Short_Description44)
AND (x.Short_Description43 = y.Short_Description43)
AND (x.Short_Description42 = y.Short_Description42)
AND (x.Short_Description41 = y.Short_Description41)
AND (x.Short_Description40 = y.Short_Description40)
AND (x.Short_Description39 = y.Short_Description39)
AND (x.Short_Description38 = y.Short_Description38)
AND (x.Short_Description37 = y.Short_Description37)
AND (x.Short_Description36 = y.Short_Description36)
AND (x.Short_Description35 = y.Short_Description35)
AND (x.Short_Description34 = y.Short_Description34)
AND (x.Short_Description33 = y.Short_Description33)
AND (x.Short_Description32 = y.Short_Description32)
AND (x.Short_Description31 = y.Short_Description31)
AND (x.Short_Description30 = y.Short_Description30)
AND (x.Short_Description29 = y.Short_Description29)
AND (x.Short_Description28 = y.Short_Description28)
AND (x.Short_Description27 = y.Short_Description27)
AND (x.Short_Description26 = y.Short_Description26)
AND (x.Short_Description25 = y.Short_Description25)
AND (x.Short_Description24 = y.Short_Description24)
AND (x.Short_Description23 = y.Short_Description23)
AND (x.Short_Description22 = y.Short_Description22)
AND (x.Short_Description21 = y.Short_Description21)
AND (x.Short_Description20 = y.Short_Description20)
AND (x.Short_Description19 = y.Short_Description19)
AND (x.Short_Description18 = y.Short_Description18)
AND (x.Short_Description17 = y.Short_Description17)
AND (x.Short_Description16 = y.Short_Description16)
AND (x.Short_Description15 = y.Short_Description15)
AND (x.Short_Description14 = y.Short_Description14)
AND (x.Short_Description13 = y.Short_Description13)
AND (x.Short_Description12 = y.Short_Description12)
AND (x.Short_Description11 = y.Short_Description11)
AND (x.Short_Description10 = y.Short_Description10)
AND (x.Short_Description9 = y.Short_Description9)
AND (x.Short_Description8 = y.Short_Description8)
AND (x.Short_Description7 = y.Short_Description7)
AND (x.Short_Description6 = y.Short_Description6)
AND (x.Short_Description5 = y.Short_Description5)
AND (x.Short_Description4 = y.Short_Description4)
AND (x.Short_Description3 = y.Short_Description3)
AND (x.Short_Description2 = y.Short_Description2)
AND (x.Short_Description1 = y.Short_Description1)
WHERE (
((y.Part_Number) LIKE [pn] & "*" & [tt])
AND (
(y.Serial_Number) >= [sns]
AND (y.Serial_Number) <= [sne]
)
AND (
(y.Test_Date) >= [ds]
AND (y.Test_Date) <= [de]
)
);
Why does access choke on the query?