I am using MATPOWER for optimal power flow of IEEE30 bus system. I am changing the real power generation of a particular bus 12 times and want to save the result 12 times also. But while doing so only the result of last run is saved in the result struct. The code is given:
P=xlsread('C:\Users\User\Documents\MATLAB\output\sp.xlsx');
for h=1:12
P(h);
**mpc.gen(NG,PG)=P(h);**
mpopt = mpoption('pf.alg', 'NR', 'verbose', 1, 'out.all', 0);
results= runopf(mpc,mpopt);
end
You could store the result struct you get from each opf run in a struct array like so:
for h=1:12
P(h);
**mpc.gen(NG,PG)=P(h);**
mpopt = mpoption('pf.alg', 'NR', 'verbose', 1, 'out.all', 0);
results(h) = runopf(mpc,mpopt);
end
Adressing results should then be possible by calling e.g. results(3).branch or whatever you want to evaluate.
Related
I had the task to code the following:
Take a list of integers and returns the value of these numbers added up, but only if they are odd.
Example input: [1,5,3,2]
Output: 9
I did the code below and it worked perfectly.
numbers = [1,5,3,2]
print(numbers)
add_up_the_odds = []
for number in numbers:
if number % 2 == 1:
add_up_the_odds.append(number)
print(add_up_the_odds)
print(sum(add_up_the_odds))
Then I tried to re-code it using function definition / return:
def add_up_the_odds(numbers):
odds = []
for number in range(1,len(numbers)):
if number % 2 == 1:
odds.append(number)
return odds
numbers = [1,5,3,2]
print (sum(odds))
But I couldn’t make it working, anybody can help with that?
Note: I'm going to assume Python 3.x
It looks like you're defining your function, but never calling it.
When the interpreter finishes going through your function definition, the function is now there for you to use - but it never actually executes until you tell it to.
Between the last two lines in your code, you need to call add_up_the_odds() on your numbers array, and assign the result to the odds variable.
i.e. odds = add_up_the_odds(numbers)
I am using a data set called sleep (found here: https://drive.google.com/file/d/15ZnsWtzbPpUBQN9qr-KZCnyX-0CYJHL5/view) to run a three way within subject ANOVA comparing Performance based on Stimulation, Deprivation, and Time. I have successfully done this before using anova_test from rstatix. I want to look at the sphericity output but it doesn't appear in the output. I have got it to come up with other three way within subject datasets, so I'm not sure why this is happening. Here is my code:
anova_test(data = sleep, dv = Performance, wid = Subject, within = c(Stimulation, Deprivation, Time))
I also tried to save it to an object and use get_anova_table, but that didn't look any different.
sleep_aov <- anova_test(data = sleep, dv = Performance, wid = Subject, within = c(Stimulation, Deprivation, Time))
get_anova_table(sleep_aov, correction = "GG")
This is an ideal dataset I pulled from the internet, so I'm starting to think the data had a W of 1 (perfect sphericity) and so rstatix is skipping this output. Is this something anova_test does?
Here also is my code using a dataset that does return Mauchly's:
weight_loss_long <- pivot_longer(data = weightloss, cols = c(t1, t2, t3), names_to = "time", values_to = "loss")
weight_loss_long$time <- factor(weight_loss_long$time)
anova_test(data = weight_loss_long, dv = loss, wid = id, within = c(diet, exercises, time))
Not an expert at all, but it might be because your factors have only two levels.
From anova_summary() help:
"Value
return an object of class anova_test a data frame containing the ANOVA table for independent measures ANOVA. However, for repeated/mixed measures ANOVA, it is a list containing the following components are returned:
ANOVA: a data frame containing ANOVA results
Mauchly's Test for Sphericity: If any within-Ss variables with more than 2 levels are present, a data frame containing the results of Mauchly's test for Sphericity. Only reported for effects that have more than 2 levels because sphericity necessarily holds for effects with only 2 levels.
Sphericity Corrections: If any within-Ss variables are present, a data frame containing the Greenhouse-Geisser and Huynh-Feldt epsilon values, and corresponding corrected p-values. "
I want to calculate test statistics for a fb prophet forecast in a function because I want to average the test stats over different forecasts and cutoff points after using the fb-prophet cross_validation to get df_cv. I created a function that I apply to the dataframe after grouping by the cutoff points, in order to receive a measure per cutoff point. Then I calculate the mean over all these values.
The problem is that my function returns not only the value I am looking for but also a 0 as well as an information of the dtype. I can still do calculations with the returned value but when I want to plot etc. later it is very inconvenient. How can I strip these unnecessary values from the output?
def compute_avg_stats(df_cv,perf_measure):
measures = {'mse':mean_squared_error,'mae':mean_absolute_error,'mape':mean_absolute_percentage_error,'rmse':mean_squared_error}
performance_stats = {}
if perf_measure == 'rmse':
measure = np.sqrt(measures[perf_measure](y_true=df_cv['y'],y_pred=df_cv['yhat']))
else:
measure = measures[perf_measure](y_true=df_cv['yu'],y_pred=df_cv['yhat'])
return measure
df_cv.groupby('cutoff').apply(compute_avg_stats,perf_measure='rmse').to_frame().mean()
I think .mean() returns a Series. Try with .mean()[0]
My process looks like:
select some data 50 rows per select,
do sth with data (set some values)
transform row to object of another table
call batchInsert(myListOfRecords).execute()
My problem is how to set up when data should be inserted ? In my current setup data is only inserted at the end of my loop. This is some kind of problem for me because i want process much more data then i do in my tests. So if i will agree with this then my proccess will end with exception (OutOfMemory). Where i should define max amount of data in batch to call instert?
The important thing here is to not fetch all the rows you want to process into memory in one go. When using jOOQ, this is done using ResultQuery.fetchLazy() (possibly along with ResultQuery.fetchSize(int)). You can then fetch the next 50 rows using Cursor.fetchNext(50) and proceed with your insertion as follows:
try (Cursor<?> cursor = ctx
.select(...)
.from(...)
.fetchSize(50)
.fetchLazy()) {
Result<?> batch;
for (;;) {
batch = cursor.fetchNext(50);
if (batch.isEmpty())
break;
// Do your stuff here
// Do your insertion here
ctx.batchInsert(...);
}
}
I want to avoid importing different modules as that is mostly what I have found while looking online. I am stuck with this bit of code and I don't really know how to fix it or improve on it. Here's what I've got so far.
def avg(lst):
'''lst is a list that contains lists of numbers; the
function prints, one per line, the average of each list'''
for i[0:-1] in lst:
return (sum(i[0:-1]))//len(i)
Again, I'm quite new and this for loops jargon is quite confusing to me, so if someone could help me get it so the output of, say, a list of grades would be different lines containing the averages. So if for lst I inserted grades = [[95,92,86,87], [66,54], [89,72,100], [33,0,0]], it would have 4 lines that all had the averages of those sublists. I also am to assume in the function that the sublists could have any amount of grades, but I can assume that the lists have non-zero values.
Edit1: # jramirez, could you explain what that is doing differently than mine possible? I don't doubt that it is better or that it will work but I still don't really understand how to recreate this myself... regardless, thank you.
I think this is what you want:
def grade_average(grades):
for grade in grades:
avg = 0
for num in grade:
avg += num
avg = avg / len(grade)
print ("Average for " + str(grade) + " is = " + str(avg))
if __name__ == '__main__':
grades = [[95,92,86,87],[66,54],[89,72,100],[33,0,0]]
grade_average(grades)
Result:
Average for [95, 92, 86, 87] is = 90.0
Average for [66, 54] is = 60.0
Average for [89, 72, 100] is = 87.0
Average for [33, 0, 0] is = 11.0
Problems with your code: the extraneous indexing of i; the use of // to truncate he averate (use round if you want to round it); and the use of return in the loop, so it would stop after the first average. Your docstring says 'print' but you return instead. This is actually a good thing. Functions should not print the result they calculate, as that make the answer inaccessible to further calculation. Here is how I would write this, as a generator function.
def averages(gradelists):
'''Yield average for each gradelist.'''
for glist in gradelists:
yield sum(glist) /len(glist)
print(list(averages(
[[95,92,86,87], [66,54], [89,72,100], [33,0,0]])))
[90.0, 60.0, 87.0, 11.0]
To return a list, change the body of the function to (beginner version)
ret = []
for glist in gradelists:
ret.append(sum(glist) /len(glist))
return ret
or (more advanced, using list comprehension)
return [sum(glist) /len(glist) for glist in gradelists]
However, I really recommend learning about iterators, generators, and generator functions (defined with yield).