I am trying to draw a trend line connecting previous pivot highs and previous pivot lows IF the last pivot high is higher than the previous pivot high AND the last pivot low is lower than the previous pivot low. This is to help spot broadening formations.
I am trying to accomplish this with a for loop:
'''
index=0
result=0
for i = 0 to 50
if ta.pivothigh(10,10)[index] > ta.pivothigh(10,10)[index+1] and ta.pivotlow(10,10)[index] < ta.pivotlow(10,10)[index+1]
index := index
break
result := result+1
index := index+1
bf_upper = line.new(bar_index[index+1], ta.pivothigh(10,10)[index+1], bar_index[index], ta.pivothigh(10,10)[index], extend=extend.right)
line.delete(bf_upper[1])
bf_lower = line.new(bar_index[index+1], ta.pivotlow(10,10)[index+1], bar_index[index], ta.pivotlow(10,10)[index], extend=extend.right)
line.delete(bf_lower[1])
'''
Not able to get it working properly and would appreciate some help how I can achieve this.
Related
I have an array of 1 x 400, where all element values are above 1500. However, I have some elements that have values<50 which are wrong measures and I would like to have the mean of the elements before and after the wrong measured data points and replace it in the main array.
For instance, element number 17 is below 50 so I want to take the mean of elements 16 and 18 and replace element 17 with the new mean.
Can someone help me, please? many thanks in advance.
No language is specified in the question, but for Python you could work with List Comprehension:
# array with 400 values, some of which are incorrect
arr = [...]
arr = [arr[i] if arr[i] >= 50 else (arr[i-1]+arr[i+1])/2 for i in range(len(arr))]
That is, if arr[i] is less than 50, it'll be replaced by the average value of the element before and after it. There are two issues with this approach.
If i is the first or last element, then one of the two values will be undefined, and no mean can be obtained. This can be fixed by just using the value of the available neighbour, as specified below
If two values in a row are very low, the leftmost one will use the rightmost one to calculate its value, which will result in a very low value. This is a problem that may not occur for you in practice, but it is an inherent result of the way you wish to recalculate values, and you might want to keep it in mind.
Improved version, keeping in mind the edge cases:
# don't alter the first and last item, even if they're low
arr = [arr[i] if arr[i] >= 50 or i == 0 or i+1 == len(arr) else (arr[i-1]+arr[i+1])/2 for i in range(len(arr))]
# replace the first and last element if needed
if arr[0] < 50:
arr[0] = arr[1]
if arr[len(arr)-1] < 50:
arr[len(arr)-1] = arr[len(arr)-2]
I hope this answer was useful for you, even if you intend to use another language or framework than python.
This question is a slightly varied version of this one...
Now I'm using Measures instead of Calculated columns and the date is static instead of having it based on a dropdown list.
Here's the Power BI test .pbix file:
https://drive.google.com/open?id=1OG7keqhdvDUDYkFQFMHyxcpi9Zi6Pn3d
This printscreen describes what I'm trying to accomplish:
Basically the date in P6 Update table is used as a cut date and will be fixed\static. It's imported from an Excel sheet where the user can customize it however they want.
Here's what should happen when a matching row in Test data table is found for P6 Update date:
column Earned Daily - must have its value summed with the next row if there's one;
column Earned Cum - must grab the next row's value;
all the previous rows should remain intact, that is, their values won't change;
all subsequent rows must have their values assigned 0.
So for example:
If P6 Update is 1-May-2018, this is the expected result:
1-May 7,498 52,106
2-May 0 0
If P6 Update is 30-Apr-2018, this is the expected result:
30-Apr 13,173 50,699
1-May 0 0
2-May 0 0
If P6 Update is 29-Apr-2018, this is the expected result:
29-Apr 11,906 44,608
30-Apr 0 0
1-May 0 0
2-May 0 0
and so on...
Hope this makes sense.
This is easier in Excel, but trying to do this in Power BI is making me go nuts.
I will ignore previously asked related questions and start from scratch.
First, create a measure:
Current Earn =
CALCULATE (
SUM( 'Test data'[Value]),
'Test data'[Act Rem] = "Actual Units",
'Test data'[Type] = "Current"
)
This measure will be used in other measures, to save you from typing all these conditions ("Actual Units" and "Current") again and again. It's a great practice to re-use measures in other measures - saves work, makes code cleaner and easier to refactor.
Create another measure:
Cut Date = SELECTEDVALUE('P6 Update'[Date])
We will use this measure whenever we need a cut off date. Please note that it does not have to be hard-coded - if P6 table contains a list of dates, you can create a pull-down slicer from the dates, and can choose the cut-off date dynamically. The formula will work properly.
Create third measure:
Next Earn =
VAR Cut_Date = [Cut Date]
VAR Current_Date = MAX ( 'Test data'[Date] )
VAR Next_Date = Current_Date + 1
VAR Current_Earn = [Current Earn]
VAR Next_Earn = CALCULATE ( [Current Earn], 'Test data'[Date] = Next_Date )
RETURN
SWITCH (
TRUE,
Current_Date < Cut_Date, Current_Earn,
Current_Date = Cut_Date, Current_Earn + Next_Earn,
BLANK ()
)
I am not sure if "Next Earn" is a good name for it, hopefully you will find a more intuitive name. The way it works: we save all necessary inputs into variables, and then use SWITCH function to define the results. Hopefully it's self-explanatory. (Note: if you need 0 above Cut Date, replace BLANK() with 0).
Finally, we define a measure for cumulative earn. It does not require any special logic, because previous measure takes care of it properly:
Cum Earn =
VAR Current_Date = MAX('Test data'[Date])
RETURN
CALCULATE(
[Next Earn],
FILTER(ALL('Test data'[Date]), 'Test data'[Date] <= Current_Date))
Result:
I am trying to fir a partial db-RDA with field.ID to correct for the repeated measurements character of the samples. However including Condition(field.ID) leads to Disappearance of the centroids of the main factor of interest from the plot (left plot below).
The Design: 12 fields have been sampled for species data in two consecutive years, repeatedly. Additionally every year 3 samples from reference fields have been sampled. These three fields have been changed in the second year, due to unavailability of the former fields.
Additionally some environmental variables have been sampled (Nitrogen, Soil moisture, Temperature). Every field has an identifier (field.ID).
Using field.ID as Condition seem to erroneously remove the F1 factor. However using Sampling campaign (SC) as Condition does not. Is the latter the rigth way to correct for repeated measurments in partial db-RDA??
set.seed(1234)
df.exp <- data.frame(field.ID = factor(c(1:12,13,14,15,1:12,16,17,18)),
SC = factor(rep(c(1,2), each=15)),
F1 = factor(rep(rep(c("A","B","C","D","E"),each=3),2)),
Nitrogen = rnorm(30,mean=0.16, sd=0.07),
Temp = rnorm(30,mean=13.5, sd=3.9),
Moist = rnorm(30,mean=19.4, sd=5.8))
df.rsp <- data.frame(Spec1 = rpois(30, 5),
Spec2 = rpois(30,1),
Spec3 = rpois(30,4.5),
Spec4 = rpois(30,3),
Spec5 = rpois(30,7),
Spec6 = rpois(30,7),
Spec7 = rpois(30,5))
data=cbind(df.exp, df.rsp)
dbRDA <- capscale(df.rsp ~ F1 + Nitrogen + Temp + Moist + Condition(SC), df.exp); ordiplot(dbRDA)
dbRDA <- capscale(df.rsp ~ F1 + Nitrogen + Temp + Moist + Condition(field.ID), df.exp); ordiplot(dbRDA)
You partial out variation due to ID and then you try to explain variable aliased to this ID, but it was already partialled out. The key line in the printed output was this:
Some constraints were aliased because they were collinear (redundant)
And indeed, when you ask for details, you get
> alias(dbRDA, names=TRUE)
[1] "F1B" "F1C" "F1D" "F1E"
The F1? variables were constant within ID which already was partialled out, and nothing was left to explain.
I'm writing a program to play blackjack and one of the functions calculates the score. It takes in an input which is a structure array of cards and one of the attributes is value (for an ace the value is 11). My function is supposed to determine if the total of the values is over 21 and if 1 of the cards is an ace, then the ace's value is changed to 1. Can anyone help me figure this out please?
for index=1:length(input)
if(input(input).value == 11)
input(index).value = 1;
end;
end;
You're not actually summing the cards in your original snippet. You also seem to have a typo in input(input), I think this should be input(index). If you wanted to do it with a for loop like you have, you'd do something like this:
total = 0;
for index=1:length(input)
if(input(index).value == 11)
input(index).value = 1;
end;
total = total + input(index);
end;
The more MATLAB way of doing things would be to avoid loops by using the sum in-built command.
I'm looking for a function or solution to the following:
For the chart in SQL Reporting i need to multiply values from a Column A. For summation i would use =SUM(COLUMN_A) for the chart. But what can i use for multiplication - i was not able to find a solution so far?
Currently i am calculating the value of the stacked column as following:
=ROUND(SUM(Fields!Value_Is.Value)/SUM(Fields!StartValue.Value),3)
Instead of SUM i need something to multiply the values.
Something like that:
=ROUND(MULTIPLY(Fields!Value_Is.Value)/MULTIPLY(Fields!StartValue.Value),3)
EDIT #1
Okay tried to get this thing running.
The expression for the chart looks like this:
=Exp(Sum(Log(IIf(Fields!Menge_Ist.Value = 0, 10^-306, Fields!Menge_Ist.Value)))) / Exp(Sum(Log(IIf(Fields!Startmenge.Value = 0, 10^-306, Fields!Startmenge.Value))))
If i calculate my 'needs' manually i have to get the following result:
In my SQL Report i get the following result:
To make it easier, these are the raw values:
and you have the possibility to group the chart by CW, CQ or CY
(The values from the first pictures are aggregated Sum values from the raw values by FertStufe)
EDIT #2
Tried your expression, which results in this:
Just to make it clear:
The values in the column
=Value_IS / Start_Value
in the first picture are multiplied against each other
0,9947 x 1,0000 x 0,59401 = 0,58573
Diffusion Calenderweek 44 Sums
Startvalue: 1900,00 Value Is: 1890,00 == yield:0,99474
Waffer unbestrahlt Calenderweek 44 Sums
Startvalue: 620,00 Value Is: 620,00 == yield 1,0000
Pellet Calenderweek 44 Sums
Startvalue: 271,00 Value Is: 160,00 == yield 0,59041
yield Diffusion x yield Wafer x yield Pellet = needed Value in chart = 0,58730
EDIT #3
The raw values look like this:
The chart ist grouped - like in the image - on these fields
CY (Calendar year), CM (Calendar month), CW (Calendar week)
You can download the data as xls here:
https://www.dropbox.com/s/g0yrzo3330adgem/2013-01-17_data.xls
The expression i use (copy / past from the edit window)
=Exp(Sum(Log(Fields!Menge_Ist.Value / Fields!Startmenge.Value)))
I've exported the whole report result to excel, you can get it here:
https://www.dropbox.com/s/uogdh9ac2onuqh6/2013-01-17_report.xls
it's actually a workaround. But I am pretty sure is the only solution for this infamous problem :D
This is how I did:
Exp(∑(Log(X))), so what you should do is:
Exp(Sum(Log(Fields!YourField.Value)))
Who said math was worth nothing? =D
EDIT:
Corrected the formula.
By the way, it's tested.
Addressing Ian's concern:
Exp(Sum(Log(IIf(Fields!YourField.Value = 0, 10^-306, Fields!YourField.Value))))
The idea is change 0 with a very small number. Just an idea.
EDIT:
Based on your updated question this is what you should do:
Exp(Sum(Log(Fields!Value_IS.Value / Fields!Start_Value.Value)))
I just tested the above code and got the result you hoped for.