sampled | sampled instruments , loops and impulse responses | Audio Utils library

I like to create some sampling effort curves for species data. Where are several study areas with a number of sampling plots, resampled over a certain time period. My data set looks similar to this one:

I'm trying to collect on my notebook using xperf. The .etl file is generated. i'm using the "Diag" that includes precise and sampled CPU profiles.

But, when open .etl on WPA, it did not show the "sampled" grap, just precise. Doing some searches, I found this can be related to Hardware Counters used to the sampled timing.

But, my xperf show that pmcsource timing is available:

[![xperf pmcsources output][1]][1]

Does someone have some idea how I can troubleshoot this missing sampled grap? [1]: https://i.stack.imgur.com/fVnNl.png

Getting the below error while trying to install sample data and upgrading the setup in Magento 2.4.2-p1 Error: Unable to apply data patch Magento\CatalogRuleSampleData\Setup\Patch\Data\InstallCatalogRuleSampleData for module Magento_CatalogRuleSampleData. Original exception message: Rolled back transaction has not been completed correctly.

Please help.

I have n points in a 3D space. I want to stochastically sample a subset of points with all nearest-neighbor distances larger than r. The size of the subset m is unknown, but I want the sampled points to be as dense as possible, i.e. maximize m.

There are similar questions, but they are all about generating points, rather than sampling from given points.
Generate random points in 3D space with minimum nearest-neighbor distance

Generate 3-d random points with minimum distance between each of them?

Say I have 300 random 3D points,

https://www.tensorflow.org/tutorials/text/word2vec uses tf.random.log_uniform_candidate_sampler for negative sampling.

The tutorial sets true_classes to context_class.

My experiment shows no matter what I set for true_classes, the function always yields good results.

I want to create a heightfield map that consists of squares of random height. Given an array of NxN, I want that every square of size MxM, where M

0.2, 0.2, 0.6, 0.6, 0.1, 0.1,
0.2, 0.2, 0.6, 0.6, 0.1, 0.1,
0.5, 0.5, 0.3, 0.3, 0.8, 0.8,
0.5, 0.5, 0.3, 0.3, 0.8, 0.8,
0.6, 0.6, 0.4, 0.4, 0.9, 0.9,
0.6, 0.6, 0.4, 0.4, 0.9, 0.9

For now, I've come up with an inefficient way of doing it with 2 nested for loops. I'm sure there must be an efficient and elegant way to do that with NumPy slicing.

I hope this makes sense. I'm starting with a data frame (df) in R that looks like this:

I want to create a 'rolling data frame' that tells me if the sample type "A" or "B" was sampled in the last 7 days with the first column as "sample type", second column as "date", third column as "Sampled in last 7 days". The final column would be populated with a "yes" or a "no".

I can get to a point where I have counts for each sample type per day using:

I'm developing an open source battery model, and I work with datasheets of different cells to use them in the model. The temperature characteristics of a battery look like this :

When the data is sampled to numerical values, the resulting arrays do not have the same lengths :

I'm looking to perform a 2D interpolation in order to determine the voltage of the battery at a given capacity and temperature.

I struggle to find a good way to interpolate this kind of data. I do realize that interpolation between two arrays of unequal lengths might not be a well defined problem, but I'm trying to look for a solution which would provide reasonable results in this case.

I think that regularizing the data to a grid might work, but I suspect that it is not a very good solution in my case because of how uneven the length and shape of the curves is. I think it might cause the interpolation to be performed between two points that are far away and "do not correspond to each other" on the curves if you know what I mean.

Instead, I would be hoping for a kind of solution that would "extend" the triangular part of the dataset.

I would be very thankful if you can provide any idea that could help me find a solution.

EDIT : I will try to clarify the problem, sorry if I wasn't able to express it in a clear manner.

The input are the graphs from the datasheet, which are read into numeric values (let's say Excel/csv for storage and pandas datasheet for the Python code)

The output is a function that for any point inside the domain of definition (x=Temperature, y=Capacity), provides the interpolated value of (z=Voltage)

I do not fully understand the first question, but the confusion might come from the fact that I do not want graphs as outputs and I do not extrapolate any data.

I do not know which would be the best way to share the data, I think 170 lines might be a bit too much to copy-paste. I don't think it's exactly necessary either.

The point is that I sampled the curves on the graph for every 25 mAh of capacity. Since the battery is cut off below a certain voltage, the arrays have varying lengths : the 60°C curve ends around 4200mAh, while the -40°C ends sooner, around 3600mAh

EDIT2 N. Wouda : I hope it's allowed to share links, I uploaded the csv here : https://transferxl.com/08jXjy5T1814kr

Pranav Hosangadi : In such case, I would raise a Value Error

I want to pass data to fragment so when items in recycler adapter clicked it pass item name (sample[position].text1) fetched from firebase to fragment. I tried bundle, interface but getting error in both methods.I searched on internet but not find anything which solve my problem. mainActivity(splash screen) is only Activity in my App rest are fragments.

I used inner class method, I'm getting result but in another fragment where this adapter attached and I don't want it there. Problem: pass sample[position].text1 to fragment so I can pass it to db.collection("here") to fetch data from Firebase.

Adapter