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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:
 Coverage characterization using GenomeCov class (sequana.bedtools module)
<center>http
Step1:  Read a Coverage file in BED format
Step2:  Select one chromosome (there is only one in this case)
Step3:  Compute the running median and plot the results
Step4:  Interactive view of the effects of the running window
Step5:  Region of interests
Step6:  Some statistics
Step7:  GC correlation
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Python Code:
%pylab inline
from sequana import GenomeCov, sequana_data
rcParams['figure.figsize'] = (10,6)
Explanation: Coverage characterization using GenomeCov class (sequana.bedtools module)
<center>http://sequana.readthedocs.org</center>
Author: Thomas Cokelaer 2016-2018
Illustrative example of the Coverage module with interactive widget to see effect of the running median window length
First, let us import the functions of interest (sequana_data is optional and used to import some data from sequana)
End of explanation
gc = GenomeCov(sequana_data("virus.bed", "data"), low_threshold=-2.5, high_threshold=2.5)
Explanation: Read a Coverage file in BED format
End of explanation
chrom = gc[0]
Explanation: Select one chromosome (there is only one in this case)
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N = 4001
chrom.running_median(N, circular=True)
chrom.compute_zscore()
chrom.plot_coverage()
Explanation: Compute the running median and plot the results
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from ipywidgets import interact, interactive, fixed
import ipywidgets as widgets
def f(N):
    chrom.running_median(N, circular=True)
    chrom.compute_zscore()
    chrom.plot_coverage()
    ylim([1000,5500])
    plt.show()
    # plt.show is to fix issue reported in :
    # https://stackoverflow.com/questions/44329068/jupyter-notebook-interactive-plot-with-widgets
interact(f, N=widgets.IntSlider(min=501,max=8001, step=200))
Explanation: Interactive view of the effects of the running window
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chrom.running_median(4101)
chrom.compute_zscore()
chrom.get_rois().get_low_rois()
Explanation: Region of interests
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print(chrom)
chrom.get_centralness()
print(chrom.get_stats())
Explanation: Some statistics
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filename = sequana_data("JB409847.bed")
reference = sequana_data("JB409847.fasta")
gc = GenomeCov(filename)
gc.compute_gc_content(reference)
chrom = gc[0]
chrom.get_gc_correlation()
chrom.plot_gc_vs_coverage(cmap="BrBG", Nlevels=0, bins=[80,50])
Explanation: GC correlation
End of explanation
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