stdev | Computes the standard deviation | Architecture library
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kandi X-RAY | stdev Summary
stdev === [NPM version][npm-image]][npm-url] [Build Status][travis-image]][travis-url] [Coverage Status][coveralls-image]][coveralls-url] [Dependencies][dependencies-image]][dependencies-url].
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QUESTION
I'm trying to create a horizontal bar chart that visualizes abundance at specified depth intervals at both day and night. Essentially for those who are familiar with diel vertical migration (dvm), I'm trying to visualize that.
Here's a link to an example of what I'm talking about, specifically the bottom part of the figure. And another one.
I've been able to get this so far
but I'd like to make the duplicates go side by side instead with the middle divider indicating the change in day vs. night. So instead of it going 5, 4, 3, 2, 1, 5, 4, 3, 2, 1 on the y-axis it will have one set of 5-1 on the left and another set on the right, with the columns being directly side-by-side. Having one side shaded and another white would be super useful also! I'm also wondering how to add the error bars for the standard deviation too.
Here's the code that I have so far:
...ANSWER
Answered 2021-Jun-06 at 09:45I think this is what you're looking for :-)
The ggplot2
and dplyr
libraries would be useful to you! You can use dplyr::filter
to extract all the Fish data instead of manually doing so for each variable.
Then, you can use ggplot
to make the graph. I plotted Day with correct Average values, and the Night data with negative Average values. Then, flipped the coordinates (so the graph is verticle). Then, relabeled the y-axis since you won't want to show negative values.
QUESTION
I have two questions:
- I wanna replace
redCandlesCounter
with barssince, because it looks better. The thing is when I do that:if (barssince(not isCandleGreen and close > middleBand and open < upperBand) > 2 and isLong)
kinda breaks the logic by selling on the next candle, which is not what I want. By the way, the tests were done on GTO/USDT (Binance) 30m interval, pictures are at 30 Mar '21 18:30.
- A sexier way to replace
isCandleGreen = close > open
?
ANSWER
Answered 2021-Apr-15 at 14:15I guess, you can try to use barssince like this:
QUESTION
I am using the following code to make a small example of a function I intend to use on a big data set.
I calculate statistical features incrementally for each ID
in which the unit is month.
ANSWER
Answered 2021-May-28 at 20:33Here's the same logic implemented in a vectorized form (which is generally more efficient on large datasets):
QUESTION
I have a custom function that creates a scatterplot:
...ANSWER
Answered 2021-May-17 at 12:59The documentation highlights why this behavior happens.
By calling fig=plt.figure(1)
you create a new figure if there is no other figure with the same identifier. In your case, this means the 1
. In your case, the figure with the identifier with the 1
is always restored and no new figure is created.
https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html#matplotlib.pyplot.figure
From the documentation:
QUESTION
My goal is to have draw 500 sample points, take its mean, and then do 6000 times from a distribution. Basically:
Take sample lengths ranging from N = 1 to 500. For each sample length, draw 6000 samples and estimate the mean from each of the samples. Calculate the standard deviation from these means for each sample length, and show graphically that the decrease in standard deviation corresponds to a square root reduction.
I am trying to do this on a gamma distribution, but all of my standard deviations are coming out as zero... and I'm not sure why.
This is the program so far:
...ANSWER
Answered 2021-May-13 at 09:49The problem is with the line stdevs.append(np.std(sample.mean(axis=0)))
This takes the standard deviation of a single value i.e. the mean of your sample
array, so it will always be 0
.
You need to pass np.std()
all the values in your sample not just its mean.
stdevs.append(np.std(sample))
will give you your array of standard deviations for each sampling.
QUESTION
corr is the correlation coeficient value, meaning I have 3 correlation values and std_mev are standard deviation value meaning I also have 3 standard deviation values. I want my code to check if the conditions in the code below are met and if they are met it should execute the statement that is true and compute the induc values as shown in the code. Unfortunately no error or even an execution is being performed to produce the induc values please assist, I have a attached a picture to show this. the code is below:
...ANSWER
Answered 2021-May-16 at 12:32Everything seems correct, but the print in the last few lines are inside the else condition which is not executed. that's why your code doesn't print anything. Try to indent them back to be outside the condition like this:
QUESTION
this morning I received a mal containing, among other things, a file with the extension .wsf, on which I inadvertently clicked.
I immediately realized that I had made a mistake ... but too late :( Can you tell me if it is malicious code?
Here is the code:
...ANSWER
Answered 2021-May-10 at 13:47The fact it obfuscates itself is a good indicator it maybe malicious, if you want to see what it's attempting to run you can;
Comment out this line (like below);
QUESTION
I'm trying to calculate ZSCORE basen on a measure created in DAX.
Measure would be:
...ANSWER
Answered 2021-May-04 at 07:21I've found a solution by creating a calculate table using DAX:
QUESTION
I'm trying to use the train_test_split
function by providing the labels array that is a 2-d array for stratifying, with only 0 or 1 values (i.e. [0,0], [0,1], [1,0] or [1,1] are the four possible labels). I cannot rename labels (e.g. to 1,2,3,4 for instance) for code compatibility reasons.
Hereafter the code of the function where train_test_split
is used
ANSWER
Answered 2021-May-03 at 15:34This is caused by conflicting types, and how pandas
internally handles lists. Case 3 reproduces the error, and then shows how to fix it in.
Case 1: train_test_split
can handle stratified 2D labels:
QUESTION
I have a dataset upon which I would like to implement the Naïve Bayes algorithm but it is triggering an error on line 107; str_column_to_float(dataset, i) as follows; "could not convert string to float:''" I thought it was because of the headers for the various columns but even after I removed them and run the code, it is still giving me the same error. Any help will very much be appreciated. The link to the dataset is as follows; [Dataset][1] The code is below
...ANSWER
Answered 2021-May-02 at 01:19The ValueError is being raised because float() is trying to cast a word to a string.
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