New guy here.
i need your opinion on this scenario, this is a dynamic unit of measure conversion based on the xml below, the groovy program will calculate the element EachesConversion evaluating every CorrespondingQuantity and Quantity fields.
e.g. 1 XPX = 126 XCS and 1 XCS = 12 EA
Please advice.
<QuantityConversion>
<Quantity unitCode="XCS">1.0</Quantity>
<CorrespondingQuantity unitCode="EA">12.0</CorrespondingQuantity>
***<EachesConversion>12.0</EachesConversion>***
</QuantityConversion>
<QuantityConversion>
<Quantity unitCode="XPX">1.0</Quantity>
<CorrespondingQuantity unitCode="XCS">126.0</CorrespondingQuantity>
***<EachesConversion>1512</EachesConversion>***
</QuantityConversion>
------ this is what I try---
How I use a condition in the find ?
findAll { it.CorrespondingQuantity.#unitCode == 'EA' && it.Quantity.#unitCode == 'XCS' }*.value()
I'm creating a shiny app and i'm letting the user choose what data that should be displayed in a plot and a table. This choice is done through 3 different input variables that contain 14, 4 and two choices respectivly.
ui <- dashboardPage(
dashboardHeader(),
dashboardSidebar(
selectInput(inputId = "DataSource", label = "Data source", choices =
c("Restoration plots", "all semi natural grasslands")),
selectInput(inputId = "Variabel", label = "Variable", choices =
choicesVariables)),
#choicesVariables definition is omitted here, because it's very long but it
#contains 14 string values
selectInput(inputId = "Factor", label = "Factor", choices = c("Company
type", "Region and type of application", "Approved or not approved
applications", "Age group" ))
),
dashboardBody(
plotOutput("thePlot"),
tableOutput("theTable")
))
This adds up to 73 choices (yes, i know the math doesn't add up there, but some choices are invalid). I would like to do this using a lookup table so a created one with every valid combination of choices like this:
rad1<-c(rep("Company type",20), rep("Region and type of application",20),
rep("Approved or not approved applications", 13), rep("Age group", 20))
rad2<-choicesVariable[c(1:14,1,4,5,9,10,11, 1:14,1,4,5,9,10,11, 1:7,9:14,
1:14,1,4,5,9,10,11)]
rad3<-c(rep("Restoration plots",14),rep("all semi natural grasslands",6),
rep("Restoration plots",14), rep("all semi natural grasslands",6),
rep("Restoration plots",27), rep("all semi natural grasslands",6))
rad4<-1:73
letaLista<-data.frame(rad1,rad2,rad3, rad4)
colnames(letaLista) <- c("Factor", "Variabel", "rest_alla", "id")
Now its easy to use subset to only get the choice that the user made. But how do i use this information to plot the plot and table without using a 73 line long ifelse statment?
I tried to create some sort of multidimensional array that could hold all the tables (and one for the plots) but i couldn't make it work. My experience with these kind of arrays is limited and this might be a simple issue, but any hints would be helpful!
My dataset that is the foundation for the plots and table consists of dataframe with 23 variables, factors and numerical. The plots and tabels are then created using the following code for all 73 combinations
s_A1 <- summarySE(Samlad_info, measurevar="Dist_brukcentrum",
groupvars="Companytype")
s_A1 <- s_A1[2:6,]
p_A1=ggplot(s_A1, aes(x=Companytype,
y=Dist_brukcentrum))+geom_bar(position=position_dodge(), stat="identity") +
geom_errorbar(aes(ymin=Dist_brukcentrum-se,
ymax=Dist_brukcentrum+se),width=.2,position=position_dodge(.9))+
scale_y_continuous(name = "") + scale_x_discrete(name = "")
where summarySE is the following function, burrowed from cookbook for R
summarySE <- function(data=NULL, measurevar, groupvars=NULL, na.rm=TRUE,
conf.interval=.95, .drop=TRUE) {
# New version of length which can handle NA's: if na.rm==T, don't count them
length2 <- function (x, na.rm=FALSE) {
if (na.rm) sum(!is.na(x))
else length(x)
}
# This does the summary. For each group's data frame, return a vector with
# N, mean, and sd
datac <- ddply(data, groupvars, .drop=.drop,
.fun = function(xx, col) {
c(N = length2(xx[[col]], na.rm=na.rm),
mean = mean (xx[[col]], na.rm=na.rm),
sd = sd (xx[[col]], na.rm=na.rm)
)
},
measurevar
)
# Rename the "mean" column
datac <- rename(datac, c("mean" = measurevar))
datac$se <- datac$sd / sqrt(datac$N) # Calculate standard error of the mean
# Confidence interval multiplier for standard error
# Calculate t-statistic for confidence interval:
# e.g., if conf.interval is .95, use .975 (above/below), and use df=N-1
ciMult <- qt(conf.interval/2 + .5, datac$N-1)
datac$ci <- datac$se * ciMult
return(datac)
}
The code in it's entirety is a bit to large but i hope this may clarify what i'm trying to do.
Well, thanks to florian's comment i think i might have found a solution my self. I'll present it here but leave the question open as there is probably far neater ways of doing it.
I rigged up the plots (that was created as lists by ggplot) into a list
plotList <- list(p_A1, p_A2, p_A3...)
tableList <- list(s_A1, s_A2, s_A3...)
I then used subset on my lookup table to get the matching id of the list to select the right plot and table.
output$thePlot <-renderPlot({
plotValue<-subset(letaLista, letaLista$Factor==input$Factor &
letaLista$Variabel== input$Variabel & letaLista$rest_alla==input$DataSource)
plotList[as.integer(plotValue[1,4])]
})
output$theTable <-renderTable({
plotValue<-subset(letaLista, letaLista$Factor==input$Factor &
letaLista$Variabel== input$Variabel & letaLista$rest_alla==input$DataSource)
skriva <- tableList[as.integer(plotValue[4])]
print(skriva)
})
I have no.of input field to edit by user. when user edits the value I am trying to re-wrap the array. when i do I would like to sort the values in controller. for that i do :
gridView.resort = function( ){
var start = new Date( );
gridView.gridData = $filter( 'orderBy')(slice, "mk", true );
}
when I console the filter I am getting like this:
119
gridData.js:22 879
gridData.js:22 470
gridData.js:22 420
gridData.js:22 392
gridData.js:22 347
gridData.js:22 293
gridData.js:22 234
gridData.js:22 170
gridData.js:22 120
But this is not work for me. when I change the value in one of the field with lesser number it sit on the first row, and it not sorted entire rows. any help?
on page load sortBy works for me :
apiService.generateData( ).then(function( data ){
var start = new Date( );
gridView.gridData = slice = $filter( 'orderBy')(data.splice( 0, 10 ), "mk");
});
Live Demo
(Enter some low values and see the out put )
As commented by #JB Nizet, you should consider using number instead of text.
there is still one way to deal with text which is invoking parseInt at ng-change event, but rememer that this will throw error when some thing else of number when you are typing(means you have to controll what can be typing in).
see the working example.
I have a Dictionary model defined in Django (1.6.5). One method (called get_topentities) returns the top names in my dictionary (entity names are defined by Entity model):
def get_topentities(self,n):
entities = self.entity_set.select_related().filter(in_dico=True,table_type=0).order_by("rank")[0:n]
return entities
When I call the function (say with n=2), it returns the top 2 elements but I cannot access the second one because of this "not enough arguments to format string" error:
In [5]: d = Dictionary.objects.get(code='USA')
In [6]: top2 = d.get_topentities(2)
In [7]: top2
Out[7]: [<Entity: BARACK OBAMA>, <Entity: GOVERNMENT>]
In [8]: top2[0]
Out[8]: <Entity: BARACK OBAMA>
In [9]: top2[1]
.
.
/usr/local/lib/python2.7/dist-packages/django_pyodbc/compiler.pyc in as_sql(self, with_limits, with_col_aliases)
172 # Lop off ORDER... and the initial "SELECT"
173 inner_select = _remove_order_limit_offset(raw_sql)
--> 174 outer_fields, inner_select = self._alias_columns(inner_select)
175
176 order = _get_order_limit_offset(raw_sql)[0]
/usr/local/lib/python2.7/dist-packages/django_pyodbc/compiler.pyc in _alias_columns(self, sql)
339
340 # store the expanded paren string
--> 341 parens[key] = buf% parens
342 #cannot use {} because IBM's DB2 uses {} as quotes
343 paren_buf[paren_depth] += '(%(' + key + ')s)'
TypeError: not enough arguments for format string
In [10]:
My server backend is MSSQL and I'm using pyodbc as the database driver. If I try the same on a PC with engine sqlserver_ado, it works. Can someone help?
Regards,
Patrick
I have a Story model that inherits from ndb.Model, with an IntegerProperty wordCount. I'm trying to query Story objects that have a specific word count range, but the query seems to return the same results, regardless of the filter properties.
For this code:
q = Story.query()
q.filter(Story.wordCount > 900)
for s in q.fetch(5):
print s.title / s.wordCount
I get this result:
If only ... / 884
Timed release / 953
Grandfather paradox / 822
Harnessing the brane-deer / 1618
Quantum erat demonstrandum / 908
Here's the story declaration:
class Story(ndb.Model):
title = ndb.StringProperty(required=True)
wordCount = ndb.IntegerProperty('wc')
I would expect to only get stories that have 900 words exactly--or none. Inequalities and sorting are also broken. I tried deploying to GAE, and I'm seeing the same broken results.
Any ideas on what would be causing this?
NDB queries are immutable, and when you call q.filter(Story.wordCount > 900) you're creating a new query, and not assigning it to anything. Re-assigning to your q variable should work for you:
q = Story.query()
q = q.filter(Story.wordCount > 900)
for s in q.fetch(5):
print s.title / s.wordCount