SIMER | Data Simulation for Life Science and Breeding
kandi X-RAY | SIMER Summary
kandi X-RAY | SIMER Summary
Data Simulation for Life Science and Breeding
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QUESTION
My express.js is unable to start upon typing npm start
and I don't understand why it's happening, here's my error log:
ANSWER
Answered 2022-Jan-29 at 12:23You need to install Your package
- http-errors in root project directory.
npm install http-errors
- Try to run again
npm start
QUESTION
I have a dataframe which consist a column named Url. There are around 500K Urls. I want to delete all the rows where the Urls consist of some domain name like amazon.com, ebay.com, bestbuy.com and so on.
Those Urls can be like the below:
https://www.homedepot.com/b/Plumbing-Water-Pumps-Sump-Pumps-Submersible-Sump-Pumps/N-5yc1vZbqj6
https://images.homedepot-static.com/catalog/pdfImages/ba/ba1bd2c2-82ea-4510-85c8-333392e70a23.pdf
https://us.amazon.com/Simer-A5000-Hole-Battery-System/dp/B000DZKXC2
https://www.amazon.com/Hydromatic-DPS41-Diaphragm-Switch-Range/dp/B009S0NS0C
So the domain can be present as subdomain too. It may or may not consist with http, https, www, top level country domain name like co.uk, .co.nz and so on.
So I need an universal solution to delete any URL when the domain name is present in the exclude-sites list.
I already created a function for it which is working fine for smaller data set. But it couldn't clean the data for the 500K rows even after running it for straight 5 hours.
Here is the function I am using:
...ANSWER
Answered 2022-Jan-13 at 20:09exclude_sites_escaped = [x.replace('.', '\.') for x in exclude_sites]
df[~df['Url'].str.contains('|'.join(exclude_sites_escaped), regex = True)]
QUESTION
I'm trying to create a database for a flask market website using models (User and Item).
When I go into my python terminal and execute the following commands:
...ANSWER
Answered 2021-Dec-21 at 00:16I was executing all those database commands in the PyCharm repl. I believe it has something to do with memory which is why it won't work in the python console on Pycharm. It works fine in a command prompt terminal and in a python file.
QUESTION
I know AWS IOT supports QOS 0 and QOS 1. But I did not find anything regarding quality of service in node SDK. Can anyone tell me how can I publish with QOS 1.
...ANSWER
Answered 2020-Mar-18 at 06:57The syntax looks like you are using the node MQTT.js library.
From https://www.npmjs.com/package/mqtt#publish the third parameter is a set of options that include the QOS level.
To publish with QOS 1:
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After obtaining genotypic map data and genotype data, we can start our simulation. num.gen, number of generations in simulation replication, replication index of simulation verbose, whether to print detail mrk.dense, whether markers are dense, it is TRUE when sequencing data out, prefix of output file name outpath, path of output files out.format, format of output, "numeric" or "plink" seed.geno, random seed of a simulation process seed.map, random seed of map file out.geno.gen, indice of generations of output genotype out.pheno.gen, indice of generations of output phenotype rawgeno1, extrinsic genotype matrix1 rawgeno2, extrinsic genotype matrix2 rawgeno3, extrinsic genotype matrix3 rawgeno4, extrinsic genotype matrix4 num.ind, population size of base population prob, weight of "0" and "1" in genotype matrix, the sum of elements in vector equal to 1 input.map, map that should be input, the marker number should be consistent in both map file and genotype data len.block, length of every blocks range.hot, range of exchages in hot spot block range.cold, range of exchages in cold spot block rate.mut, mutation rate between 1e-8 and 1e-6 cal.model, phenotype model with "A", "AD", "ADI" FR, list of fixed effects, random effects, and their combination h2.tr1, heritability vector of a single trait, every element are corresponding to a, d, aXa, aXd, dXa, dXd respectively num.qtn.tr1, integer or integer vector, the number of QTN in a single trait sd.tr1, standard deviation of different effects, the last 5 vector elements are corresponding to d, aXa, aXd, dXa, dXd respectively and the rest elements are corresponding to a dist.qtn.tr1, distribution of QTN's effects with options: "normal", "geometry" and "gamma", vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively eff.unit.tr1, unit effect of geometric distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively shape.tr1, shape of gamma distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively scale.tr1, scale of gamma distribution of a single trait, vector elements are corresponding to a, d, aXa, aXd, dXa, dXd respectively multrait, whether applying pair traits with overlapping, TRUE represents applying, FALSE represents not num.qtn.trn, QTN distribution matrix, diagnal elements are total QTN number of the trait, non-diagnal are QTN number of overlop qtn eff.sd, a matrix with the standard deviation of QTN effects gnt.cov, genetic covariance matrix among all traits env.cov, environment covariance matrix among all traits qtn.spot, QTN probability in every blocks maf, Minor Allele Frequency, markers selection range is from maf to 0.5 sel.crit, selection criteria with options: "TGV", "TBV", "pEBVs", "gEBVs", "ssEBVs", "pheno" sel.on, whether to add selection mtd.reprod, different reproduction methods with options: "clone", "dh", "selfpol", "singcro", "tricro", "doubcro", "backcro","randmate", "randexself" and "userped" userped, user-designed pedigree to control mating process num.prog, litter size of dams ratio, ratio of males in all individuals prog.tri, litter size of the first single cross process in trible cross process prog.doub, litter size of the first two single cross process in double cross process prog.back, a vector with litter size in every generation of back-cross ps, fraction selected in selection decr, whether to sort by decreasing sel.multi, selection method of multiple traits with options: "tdm", "indcul" and "index" index.wt, economic weights of selection index method, its length should equals to the number of traits index.tdm, index represents which trait is being selected goal.perc, percentage of goal more than mean of scores of individuals pass.perc, percentage of expected excellent individuals sel.sing, selection method of single trait with options: "ind", "fam", "infam" and "comb".
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