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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Simulate a really big data set for testing
This is will be used to test memory limits in ipyrad, and also for comparing run times with other software. RAD data sets have three size dimensions that greatly affect run times
Step1:  Load the data set with ipyrad
Step2:  Demultiplex the data files
All of the software methods we are comparing will use the same demultiplexed data files, so we will not count this step towards the total run times comparisons.
Step3:  The data files
These are the demultiplexed data files that each method will analyze.
Step4:  Assemble the data set with ipyrad
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Python Code:
## import Python libraries
import ipyrad as ip
%%bash
## this will take about XX minutes to run, sorry, the code is not parallelized
## we simulate 360 tips by using the default 12 taxon tree and requesting 40
## individuals per taxon. Default is theta=0.002. Crown age= 5*2Nu (check this)
simrrls -L 100000 -f pairddrad -l 150 -n 30 -o Big_i360_L100K
## because it takes a long time to simulate this data set, you can alternatively
## just download the data set that we already simulated using the code above. 
## The data set is hosted on anaconda, just run the following to get it. 
# conda download -c ipyrad bigData
Explanation: Simulate a really big data set for testing
This is will be used to test memory limits in ipyrad, and also for comparing run times with other software. RAD data sets have three size dimensions that greatly affect run times: the number of taxa, the number of loci, and the length of reads. Here we will set all three to quite large values, by simulating paired-end 150bp reads for 300 taxa for 100K loci. For simplicity we do not simulate any missing data, but this is not expected to affect run times or memory limits except to the extent that it increased the total number of loci in the data set.
End of explanation
data = ip.Assembly("bigHorsePaired")
data.set_params("project_dir", "bigdata")
data.set_params("raw_fastq_path", "bigHorsePaired_R*_.fastq.gz")
data.set_params("barcodes_path", "bigHorsePaired_barcodes.txt")
data.set_params("datatype", "pairddrad")
data.set_params("restriction_overhang", ("TGCAG", "CCGG"))
data.get_params()
Explanation: Load the data set with ipyrad
End of explanation
data.run('1')
Explanation: Demultiplex the data files
All of the software methods we are comparing will use the same demultiplexed data files, so we will not count this step towards the total run times comparisons.
End of explanation
ls -l bigdata/bigHorsePaired_fastqs/
Explanation: The data files
These are the demultiplexed data files that each method will analyze.
End of explanation
data.run("234567")
Explanation: Assemble the data set with ipyrad
End of explanation
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