sminot

from functools import lru_cache
import os
import pandas as pd

@lru_cache(maxsize=16)
def read_fasta(fasta_fp):
    assert os.path.exists(fasta_fp)
    
    fasta = {}
    header = None

sminot

from functools import lru_cache
import pandas as pd

@lru_cache(maxsize=128)
def parse_gff(gff_fp):
    df = pd.read_csv(
        gff_fp, 
        sep="\t", 
        comment="#", 
        header=None,

sminot

#!/usr/bin/env python3

import boto3
import argparse
from datetime import datetime

parser = argparse.ArgumentParser()
parser.add_argument("job_id")

# Add the arguments

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# Plot the distribution of CAG sizes
def plot_cag_size(hdf_fp, pdf=None, min_size=5, alpha=0.25):
    cag_annot = pd.read_hdf(hdf_fp, "/annot/cag/all").set_index("CAG")
    
    # Calculate the log10 size (number of genes per CAG)
    cag_annot = cag_annot.assign(
        size_log10 = cag_annot["size"].apply(np.log10)
    )
    
    # Filter by CAG size

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# Plot the number of genes detected and the proportion of reads aligned
def plot_specimen_summary(hdf_fp, pdf=None, alpha = 0.85):
    
    specimen_summary = pd.read_hdf(hdf_fp, "/summary/all").set_index("specimen")
    
    specimen_summary = specimen_summary.assign(
        prop_reads = specimen_summary["aligned_reads"] / specimen_summary["n_reads"]
    )
    
    for col_name, axis_title in [

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# Read in data from the HDF store
assert os.path.exists(hdf_fp)
cag_annot = pd.read_hdf(hdf_fp, "/annot/cag/all").set_index("CAG")
cag_abund = pd.read_hdf(hdf_fp, "/abund/cag/wide").set_index("CAG")
corncob_df = pd.read_hdf(hdf_fp, "/stats/cag/corncob")
betta_df = pd.read_hdf(hdf_fp, "/stats/enrichment/betta")
manifest = pd.read_hdf(hdf_fp, "/manifest").set_index("specimen")
specimen_summary = pd.read_hdf(hdf_fp, "/summary/all").set_index("specimen")

sminot

from functools import lru_cache
from collections import defaultdict
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import matplotlib.patches as mpatches
from scipy import stats
import seaborn as sns
import os

sminot

jupyter nbconvert --ExecutePreprocessor.timeout=-1 --to notebook --inplace --execute NOTEBBOOK

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paste -d '' <(bunzip2 -c Raw_Read2_Barcodes.fq.bz2 | awk '{if(NR % 4 == 2 || NR % 4 == 4){print}else{print ""}}') <(bunzip2 -c Raw_Read1.fq.bz2) | fastx_barcode_splitter.pl --bcfile barcodes.tsv --prefix PREFIX --suffix _R1.fastq --bol

sminot

import io
import boto3
import pandas as pd

def read_csv_from_s3(s3_url, s3=None, sep=","):
    assert s3_url.startswith("s3://")
    bucket_name, key_name = s3_url[5:].split("/", 1)

    if s3 is None:
        s3 = boto3.client('s3')