Crispy copy number input file and CRISPR screen input file format question about crispy HOT 3 CLOSED

import crispy as cy
import matplotlib.pyplot as plt
from crispy.CRISPRData import ReadCounts, Library

"""
Import sample data
"""
rawcounts, copynumber = cy.Utils.get_example_data()

"""
Import CRISPR-Cas9 library

Important:
      Library has to have the following columns: "Chr", "Start", "End", "Approved_Symbol"
      Library and segments have to have consistent "Chr" formating: "Chr1" or "chr1" or "1"
      Gurantee that "Start" and "End" columns are int
"""
lib = Library.load_library("Yusa_v1.1.csv.gz")

lib = lib.rename(
    columns=dict(start="Start", end="End", chr="Chr", Gene="Approved_Symbol")
).dropna(subset=["Chr", "Start", "End"])

lib["Chr"] = "chr" + lib["Chr"]

lib["Start"] = lib["Start"].astype(int)
lib["End"] = lib["End"].astype(int)

"""
Calculate fold-change
"""
plasmids = ["ERS717283"]
rawcounts = ReadCounts(rawcounts).remove_low_counts(plasmids)
sgrna_fc = rawcounts.norm_rpm().foldchange(plasmids)

"""
Correct CRISPR-Cas9 sgRNA fold changes
"""
crispy = cy.Crispy(
    sgrna_fc=sgrna_fc.mean(1), copy_number=copynumber, library=lib.loc[sgrna_fc.index]
)

# Fold-changes and correction integrated funciton.
# Output is a modified/expanded BED formated data-frame with sgRNA and segments information
#   n_sgrna: represents the minimum number of sgRNAs required per segment to consider in the fit.
#            Recomended default values range between 4-10.
bed_df = crispy.correct(n_sgrna=10)
print(bed_df.head())

# Gaussian Process Regression is stored
crispy.gpr.plot(x_feature="ratio", y_feature="fold_change")
plt.show()