nucleus | DEPRECATED : See scireum/sirius

Here is a general approach that will handle GO, SMART, and potentially KEGG, though it is impossible to say without any information about KEGG.

The function f below takes as arguments

• x, a character vector
• split, the delimiter separating items in lists
• sep, the delimiter separating identifiers and terms within items

and returns a logical vector indexing the elements of x with at least one non-duplicated term.

This is a bug in Greengrass which means that it can only be used on English language systems right now. We now have a pull request to fix it so it will work with any language. https://github.com/aws-greengrass/aws-greengrass-nucleus/pull/1150

I did not test the full code given below, you probably will have to make adjustments

Since I don't know what your coding background is, I'll try to be as extensive as I can. Keep in mind I never developed something with Puppeteer and I don't have 30+ years of exp. in JS, so what I'm going to write is probably not best optimised.

First, I'd make minDiscount an Int instead of a string, to properly compare two numbers.

You could assign each droplet to a particular nucleus by checking its cetroid against the bounding box limits:

• Direct use of seaborn.FacetGrid is deprecate. In this case it's better to use seaborn.displot, which is a figure-level plot.

• Iterate through the column names for df1_sample, and use the column name to get df1_Sample[col] and df1_Control[col], which assumes both dataframes have the same column names, as shown in the OP.

  • Use for c1, c2 in zip(df1_Sample.columns[:-1], df1_Control.columns[:-1]): with df1_Sample[c1] and df1_Control[c2] if the column names are not the same, however the columns of both dataframes need to be ordered.

• Tested in python 3.8.11, pandas 1.3.2, matplotlib 3.4.3, seaborn 0.11.2

• For each column pair, to plot the histogram for each compound separately, it will be easier to combine the data into a long dataframe, and then plot with seaborn.displot.

This issue should be fixed in the next release, version 5.2021.7. There has already been an issue raised on GitHub here and an accompanying PR here

For long form data collection, I think it is easy to prepare an empty data frame, get the stock data sequentially, and add it to the empty data frame.

Your batch size is 4, and you are using the default value for bs when calling IOU, which means bs=15, not 4. Therefore call IOU by passing the batch size: IOU(output, y, bs=bs). Better yet, you could remove the bs argument, and define bs as y.size(0) inside the function.

I can't give you a sure-bet smoking gun answer without being able to test it myself, but my guess is the problem is that you're building your library with -fPIC, but your main application is not.

Keep in mind that when you ask the compiler to generate position independent code, you're also asking it to generate position independent data. In your case the global variable g_appconfig is going to be at a fixed location determined by the linker.

Try taking out the -fPIC option on your library build. Since you're only linking statically you don't really need it, and it's more likely to hurt your performance than help.