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SEP 1 -- Improved and automated logging

Abstract

This Snakemake Enhancement Proposal (SEP) suggests several enhancements of Snakemake's logging and report generating capabilities.

Introduction

Bioinformaticians often have an enormous library of homemade scripts

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import pandas as pd

mir_miR_correspondence = "/local/home/annata/mirna.mature.offset0.txt"

mirna_example_file = "/local/home/annata/SHORTREADS/OFFCONTROL/offcontrol-start/Demux.SRhi10002.Adipocyte%20-%20omental%2c%20donor3.SRhi10002_hg19.11475-119C8.GTGAAA.fastq.gz.filter.shortreads"

### READ FILES

mirna_df = pd.read_table(mirna_example_file, sep="\s+", header=None,
                         names="id1 id2 nb1 mirna_seq score nb2 short_read_seq type end offset".split(), index_col=0)

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grouping <- c("A","A","B","B")
design<-model.matrix(~factor(grouping))


#############################################################
### csaw, with its combined window methodology.
############################################################

xparam <- readParam(dedup=FALSE)

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switchTab(n) -> {{ RUNTIME('goToTab', {index: n - 1}); }}
map 1 :call switchTab(1)<CR>
map 2 :call switchTab(2)<CR>
map 3 :call switchTab(3)<CR>
map 4 :call switchTab(4)<CR>
map 5 :call switchTab(5)<CR>
map 6 :call switchTab(6)<CR>
map 7 :call switchTab(7)<CR>
map 8 :call switchTab(8)<CR>
map 9 :call switchTab(9)<CR>

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import pandas as pd

# A combined P-value was computed for each peak cluster using Simes’ method
# (19). For a cluster containing n windows, the combined P-value is defined as
# p{s}=min{np{r}/r;r=1,2…,n} where the p{r} are the individual window P-values sorted
# in increasing order. This provides weak control of the family-wise error rate
# across the set of null hypotheses for all windows in the cluster. In other
# words, p{s} represents evidence against the global null hypothesis, i.e. that
# no windows in the cluster are DB.

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{
    "global": {
        "check_for_updates_on_startup": true,
        "show_in_menu_bar": true,
        "show_profile_name_in_menu_bar": false
    },
    "profiles": [
        {

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# Works on very large datasets.

import pandas as pd

try:
    import mkl
    mkl.set_num_threads(1)
except:
    pass

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# wget http://big.databio.org/example_data/AIList/AIListTestData.tgz

nrows = 1.5e6

from ncls import NCLS
from ailist import AIList

import numpy as np
import pandas as pd

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# ctypedef struct ailist_t:
#     int64_t nr, mr  					# Number of regions
#     interval_t *interval_list			# Regions data
#     uint32_t first, last				# Record range of intervals
#     int nc, lenC[10], idxC[10]
#     uint32_t *maxE
# ...
#     uint32_t binary_search(interval_t* As, uint32_t idxS, uint32_t idxE, uint32_t qe) nogil

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# Author: [email protected]
# License: simplified BSD (3 clause)
# Note: code is based on scipy.stats.pearsonr

def ss(a, axis):
    return np.sum(a * a, axis=axis)

def compute_corr(x, y):
    x = np.asarray(x)
    y = np.asarray(y)