lukauskas

%config InlineBackend.figure_format = 'retina'
%matplotlib inline

from matplotlib import pyplot as plt
PIE_SLICES = [
    [10, 20, 30, 40], # Pie 1 (arbitrary units, matplotlib will transform them to pct total.)
    [1, 1, 2, 2], # Pie 2
    [0.1, 0.8, 0.1], # Pie 3
]

lukauskas

%config InlineBackend.figure_format = 'retina'
%matplotlib inline

from matplotlib import pyplot as plt
PIE_SLICES = [
    [10, 20, 30, 40], # Pie 1 (arbitrary units, matplotlib will transform them to pct total.)
    [1, 1, 2, 2], # Pie 2
    [0.1, 0.8, 0.1], # Pie 3
]

lukauskas

%config InlineBackend.figure_format = 'retina'
%matplotlib inline

from matplotlib import pyplot as plt
PIE_SLICES = [
    [10, 20, 30, 40], # Pie 1 (arbitrary units, matplotlib will transform them to pct total.)
    [1, 1, 2, 2], # Pie 2
    [0.1, 0.8, 0.1], # Pie 3
]

lukauskas

from rpy2.robjects import pandas2ri
from rpy2.robjects.packages import importr

import rpy2.robjects
import fa2
import gc

import pandas as pd
import numpy as np

lukauskas

#!/bin/bash
qstat -u '*' -ext

lukauskas

lukauskas

lukauskas

lukauskas

Initialisation of notebook (suitable for print)

%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import os
import pandas as pd
import numpy as np
import seaborn as sns
from matplotlib import pyplot as plt

lukauskas

#  This doesn't work nicely as it uses bioclite to install dependdencies
# which is why I'm not submitting it to AUR
# but it's useful if one compiles it themselves.
pkgname=phantompeakqualtools-git
pkgver=r30.b9267ea

pkgrel=1
pkgdesc="Computes quick but highly informative enrichment and quality measures for ChIP-seq/DNase-seq/FAIRE-seq/MNase-seq data. It can also be used to obtain robust estimates of the predominant fragment length or characteristic tag shift values in these assays."
arch=('i686' 'x86_64')
url="https://github.com/kundajelab/phantompeakqualtools"