Workflow Steps and Code Snippets

249 tagged steps and code snippets that match keyword scipy

In this repository, we present the code for the analysis of study of the transcription's impact on Escherichia coli chromosome.

import bacchus.directional as bcd
import cooler
import matplotlib.pyplot as plt
import numpy as np
import os
import scipy.sparse as sp

mat_file = snakemake.input.mat
res = snakemake.params.res
cmap = snakemake.params.cmap
macro_output = str(snakemake.output.macrodomain)
CID_output = str(snakemake.output.cid)
out_file = str(snakemake.output.compare)

# Create outdir if necessary.
os.makedirs(str(snakemake.params.outdir), exist_ok=True)

mat = cooler.Cooler(f"{mat_file}::/resolutions/{res}").matrix(
    balance=True, sparse=True
)[:]

# Positions of the CIDs borders from Lioy et al., Cell, 2018.
lioy = [
    40,
    145,
    220,
    440,
    510,
    760,
    980,
    1145,
    1195,
    1300,
    1585,
    1670,
    1795,
    1845,
    2100,
    2255,
    2380,
    2525,
    2720,
    2895,
    2990,
    3165,
    3295,
    3430,
    3645,
    3800,
    3935,
    4030,
    4160,
    4205,
    4465,
]

# Length of E coli chromosome in kb
n = 4642

# Comput min max size
max_cid = n - lioy[-1] + lioy[0]
min_cid = n - lioy[-1] + lioy[0]
prev_cid = lioy[0]
for i in lioy[1:]:
    size = i - prev_cid
    prev_cid = i
    if size > max_cid:
        max_cid = size
    if size < min_cid:
        min_cid = size

print(f"Longest CID- Lioy: {max_cid}kb")
print(f"Shortest CID - Lioy: {min_cid}kb")

# Compute macrodomains on our matrix based on directional index.
di_macro = bcd.directional_index(mat, 80)
plt.subplots(figsize=(12, 1))
plt.fill_between(
    x=np.arange(0, len(di_macro) * 5, 5),
    y1=0,
    y2=di_macro,
    where=di_macro > 0,
    color="g",
)
plt.fill_between(
    x=np.arange(0, len(di_macro) * 5, 5),
    y1=0,
    y2=di_macro,
    where=di_macro <= 0,
    color="r",
)
plt.ylim(-2, 2)
plt.xlim(0, n)
plt.savefig(macro_output)

borders_macro = bcd.di_borders(di_macro)
print("Numbers of CIDs:", len(borders_macro))

# Compute CIDs on our matrix based on directional index.
di_CIDs = bcd.directional_index(mat, 20)
plt.subplots(figsize=(12, 2))
plt.fill_between(
    x=np.arange(0, len(di_CIDs) * 5, 5),
    y1=0,
    y2=di_CIDs,
    where=di_CIDs > 0,
    color="g",
)
plt.fill_between(
    x=np.arange(0, len(di_CIDs) * 5, 5),
    y1=0,
    y2=di_CIDs,
    where=di_CIDs <= 0,
    color="r",
)
plt.ylim(-2, 2)
plt.xlim(0, n)
plt.savefig(CID_output)

borders_CIDs = bcd.di_borders(di_CIDs)
print("Numbers of CIDs:", len(borders_CIDs))

from matplotlib.patches import Patch
from matplotlib.lines import Line2D

# Plot the CID on a matrix at 5kb with their rspectives positions marked with
# stars.
axis = "kb"
start = 0
title = "WT E. coli domains - binning 5kb"
dpi = 300
vmax = 99

fig, ax = plt.subplots(
    3,
    1,
    figsize=(11, 13),
    dpi=dpi,
    gridspec_kw={"height_ratios": [10, 1, 1]},
    sharex=True,
)

# Axis values
scaling_factor = res // 1000
end = n // scaling_factor

# Display plots
im = ax[0].imshow(
    mat.toarray()[start:end, start:end],
    cmap=cmap,
    vmin=0,
    vmax=np.percentile(mat.toarray(), vmax),
    extent=(
        start * scaling_factor,
        end * scaling_factor,
        end * scaling_factor,
        start * scaling_factor,
    ),
)

# Legend
ax[2].set_xlabel(f"Genomic coordinates ({axis:s})", fontsize=16)
ax[0].set_ylabel(f"Genomic coordinates ({axis:s})", fontsize=16)
ax[0].tick_params(axis="both", labelsize=16)
ax[1].tick_params(axis="both", labelsize=16)
ax[2].tick_params(axis="both", labelsize=16)
ax[2].tick_params(axis="x", which="major", pad=15)
ax[1].set_ylabel("DI\n(400kb)", fontsize=16)
ax[2].set_ylabel("DI\n(100kb)", fontsize=16)
ax[0].set_title(title, size=18)
ax[1].set_title("Macrodomains", size=16)
ax[2].set_title("CIDs", size=16)

# Colorbar
cbar = plt.colorbar(im, ax=ax.ravel().tolist(), shrink=0.33, anchor=(0, 0.7))
cbar.ax.tick_params(labelsize=16)

# Add DI plots
ax[1].fill_between(
    x=np.arange(0, len(di_macro) * 5, 5),
    y1=0,
    y2=di_macro,
    where=di_macro > 0,
    color="#33a02c",
)
ax[1].fill_between(
    x=np.arange(0, len(di_macro) * 5, 5),
    y1=0,
    y2=di_macro,
    where=di_macro <= 0,
    color="#e31a1c",
)
ax[1].set_ylim(-2, 2)
ax[1].set_xlim(start, end * scaling_factor)
for i in borders_macro:
    if i > start and i < end:
        ax[1].text(x=(i * 5) - 25, y=-3, s="*", color="k", fontweight="bold")

ax[2].fill_between(
    x=np.arange(0, len(di_CIDs) * 5, 5),
    y1=0,
    y2=di_CIDs,
    where=di_CIDs >= 0,
    color="#33a02c",
    interpolate=True,
)
ax[2].fill_between(
    x=np.arange(0, len(di_CIDs) * 5, 5),
    y1=0,
    y2=di_CIDs,
    where=di_CIDs <= 0,
    color="#e31a1c",
    interpolate=True,
)
ax[2].set_ylim(-2, 2)
ax[2].set_xlim(start * scaling_factor, end * scaling_factor)
for i in borders_CIDs:
    if i > start and i < end:
        ax[0].axvline(x=i * 5, linestyle="dashed", color="k")
        ax[2].text(x=(i * 5) - 25, y=-3, s="*", color="k", fontweight="bold")
for i in [133, 176, 394, 713, 872]:
    if i > start and i < end:
        ax[2].text(
            x=(i * 5) - 25, y=-3, s="*", color="#33a02c", fontweight="bold"
        )
for i in [102, 239, 317, 369, 420, 505, 633, 729, 832]:
    if i > start and i < end:
        ax[2].text(
            x=(i * 5) - 25, y=-3, s="*", color="#e31a1c", fontweight="bold"
        )
# Add legend of stars
legend_elements = [
    Line2D(
        [],
        [],
        color="k",
        marker="*",
        linestyle="None",
        markersize=10,
        label="Conserved borders",
    ),
    Line2D(
        [],
        [],
        color="#33a02c",
        marker="*",
        linestyle="None",
        markersize=10,
        label="New borders",
    ),
    Line2D(
        [],
        [],
        color="#e31a1c",
        marker="*",
        linestyle="None",
        markersize=10,
        label="Missing borders",
    ),
]
ax[2].legend(handles=legend_elements, bbox_to_anchor=(1.3, 1.05))

# Savefig
if out_file is not None:
    plt.savefig(out_file, dpi=dpi)

Python numpy matplotlib scipy cooler cooler bacchus From line 4 of scripts/CIDs_analysis_Lioy.py

import bacchus.directional as bcd
import bacchus.insulation as bci
import bacchus.io as bcio
import cooler
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.collections as mc
import numpy as np
import os
import pandas as pd
import scipy.sparse as sp
import scipy.stats as st
import seaborn as sns
from random import choices

mat_file = snakemake.input.mat
rna_file = snakemake.input.rna
annotation_file = snakemake.input.annotation
cmap = snakemake.params.cmap
dpi = snakemake.params.dpi
text_file = str(snakemake.output.text_file)
full_mat_file = str(snakemake.output.full_mat)
zoom_1_file = str(snakemake.output.zoom1)
zoom_2_file = str(snakemake.output.zoom2)
full_mat_di_file = str(snakemake.output.full_mat_di)
zoom_1_di_file = str(snakemake.output.zoom1_di)
zoom_2_di_file = str(snakemake.output.zoom2_di)
out_files_mat = [full_mat_file, zoom_1_file, zoom_2_file]
out_files_mat_di = [full_mat_di_file, zoom_1_di_file, zoom_2_di_file]
bor_trans = str(snakemake.output.bor_trans)
trans_bor = str(snakemake.output.trans_bor)

# Create outdir if necessary.
os.makedirs(str(snakemake.params.outdir), exist_ok=True)


def write_stats(text_file, text, writing_type="a"):
    with open(text_file, writing_type) as out:
        out.write(f"{text}\n")


# Compute CIDs at 5kb resolution with 100kb window size.
M5000 = cooler.Cooler(f"{mat_file}::/resolutions/{5000}").matrix(
    balance=True, sparse=True
)[:]
di5000 = bcd.directional_index(M5000, 20)
l5000 = bcd.di_borders(di5000)
write_stats(
    text_file,
    f"Numbers of CIDs at 5kb with 100kb window size: {len(l5000)}",
    "w",
)

# Compute CIDs at 2kb resolution with 50kb window size.
M2000 = cooler.Cooler(f"{mat_file}::/resolutions/{2000}").matrix(
    balance=True, sparse=True
)[:]
di2000 = bcd.directional_index(M2000, 25)
l2000 = bcd.di_borders(di2000)
write_stats(
    text_file, f"Numbers of CIDs at 2kb with 50kb window size: {len(l2000)}"
)

# Compute CIDs at 1kb resolution using insulation score.
M1000 = cooler.Cooler(f"{mat_file}::/resolutions/{1000}").matrix(
    balance=True, sparse=True
)[:]
di1000 = bcd.directional_index(M1000, 20)
l1000 = bcd.di_borders(di1000)
write_stats(
    text_file, f"Numbers of CIDs at 1kb with 50kb window size: {len(l1000)}"
)

mat = cooler.Cooler(f"{mat_file}::/resolutions/{1000}").matrix(
    balance=True, sparse=False
)[:]
mat[np.isnan(mat)] = 0
final_borders_1000, lri_1000 = bci.get_insulation_score(
    mat, [10, 15, 20, 25, 30]
)
write_stats(
    text_file,
    f"{len(final_borders_1000)} CIDs have been detected with insulation score at 1kb resolution.",
)


rna, _ = bcio.extract_big_wig(rna_file, ztransform=False)

annotation = pd.DataFrame(
    columns=["type", "start", "end", "strand", "gene_name", "rpkm"]
)

n = 0
with open(annotation_file, "r") as file:
    for line in file:
        if line.startswith("#"):
            continue
        elif line.startswith(">"):
            break
        else:
            line = line.split("\t")
            if line[2] in ["gene", "tRNA", "rRNA"]:
                if line[2] == "gene":
                    name = line[8].split("Name=")[-1].split(";")[0]
                    annot = {
                        "type": line[2],
                        "start": int(line[3]),
                        "end": int(line[4]),
                        "strand": line[6],
                        "gene_name": name,
                        "rpkm": np.mean(rna[int(line[3]) : int(line[4])]),
                    }
                    annotation = annotation.append(annot, ignore_index=True)


###
# Print matrices with CIDs borders (Fig 1e).
###
starts = [0, 2500, 3000]
ends = [len(mat), 3000, 3500]
col = ["blue", "lime", "cyan"]

for i in range(3):
    out_file, start, end = out_files_mat[i], starts[i], ends[i]

    vmax = 99
    title = None
    fig, ax = plt.subplots(1, 1, figsize=(7, 7), dpi=dpi)

    # Axis values
    scaling_factor = 1.0
    axis = "kb"

    # No end values given.
    if end == 0:
        end = len(mat)

    # Display plots
    im = ax.imshow(
        mat[start:end, start:end],
        cmap=cmap,
        vmin=0,
        vmax=np.nanpercentile(mat, vmax),
        extent=(
            start * scaling_factor,
            end * scaling_factor,
            end * scaling_factor,
            start * scaling_factor,
        ),
    )

    # Legend
    ax.set_xlabel(f"Genomic coordinates ({axis:s})", fontsize=16)
    ax.set_ylabel(f"Genomic coordinates ({axis:s})", fontsize=16)
    ax.tick_params(axis="both", labelsize=16)

    # Title
    if title is not None:
        ax.set_title(title, size=18)

    # Colorbar
    cbar = plt.colorbar(im, shrink=0.33, anchor=(0, 0.5))
    cbar.ax.tick_params(labelsize=16)

    first = True
    for i in [x * 5 for x in l5000]:
        if i in np.arange(start, end):
            #         plt.axvline(i, lw=1, ls='dashed', c='blue')
            if first:
                s = i
                first = False
                ax.axvline(
                    s,
                    ymin=(end - s) / (end - start),
                    ymax=1,
                    c=col[0],
                    lw=1,
                    zorder=0.5,
                )
                ax.axhline(
                    s,
                    xmin=0,
                    xmax=1 - ((end - s) / (end - start)),
                    c=col[0],
                    lw=1,
                    zorder=0.5,
                )
            else:
                e = i
                ax.add_patch(
                    patches.Rectangle(
                        (s, s), e - s, e - s, edgecolor=col[0], fill=False
                    )
                )
                s = i
    ax.axvline(
        s, ymin=(end - s) / (end - start), ymax=0, c=col[0], lw=1, zorder=0.5
    )
    ax.axhline(
        s,
        xmin=1,
        xmax=1 - ((end - s) / (end - start)),
        c=col[0],
        lw=1,
        zorder=0.5,
    )

    first = True
    for i in [x * 5 for x in l2000]:
        if i in np.arange(start, end):
            #         plt.axvline(i, lw=1, ls='dashed', c='cyan')
            if first:
                s = i
                first = False
                ax.axvline(
                    s,
                    ymin=(end - s) / (end - start),
                    ymax=1,
                    c=col[1],
                    lw=1,
                    zorder=0.5,
                )
                ax.axhline(
                    s,
                    xmin=0,
                    xmax=1 - ((end - s) / (end - start)),
                    c=col[1],
                    lw=1,
                    zorder=0.5,
                )
            else:
                e = i
                ax.add_patch(
                    patches.Rectangle(
                        (s, s), e - s, e - s, edgecolor=col[1], fill=False
                    )
                )
                s = i
    ax.axvline(
        s, ymin=(end - s) / (end - start), ymax=0, c=col[1], lw=1, zorder=0.5
    )
    ax.axhline(
        s,
        xmin=1,
        xmax=1 - ((end - s) / (end - start)),
        c=col[1],
        lw=1,
        zorder=0.5,
    )

    first = True
    for i in final_borders_1000:
        if i in np.arange(start, end):
            #         plt.axvline(i, lw=1, ls='dashed', c='magenta')
            if first:
                s = i
                first = False
                ax.axvline(
                    s,
                    ymin=(end - s) / (end - start),
                    ymax=1,
                    c=col[2],
                    lw=1,
                    zorder=0.5,
                )
                ax.axhline(
                    s,
                    xmin=0,
                    xmax=1 - ((end - s) / (end - start)),
                    c=col[2],
                    lw=1,
                    zorder=0.5,
                )
            else:
                e = i
                ax.add_patch(
                    patches.Rectangle(
                        (s, s), e - s, e - s, edgecolor=col[2], fill=False
                    )
                )
                s = i
    ax.axvline(
        s, ymin=(end - s) / (end - start), ymax=0, c=col[2], lw=1, zorder=0.5
    )
    ax.axhline(
        s,
        xmin=1,
        xmax=1 - ((end - s) / (end - start)),
        c=col[2],
        lw=1,
        zorder=0.5,
    )

    # Savefig
    plt.savefig(out_file, dpi=dpi, bbox_inches="tight")
    plt.close()


###
# Print full matrices with CIDs borders using DI only (Supp Fig 1f).
###

for i in range(3):
    out_file, start, end = out_files_mat_di[i], starts[i], ends[i]

    vmax = 99
    title = None
    fig, ax = plt.subplots(1, 1, figsize=(7, 7), dpi=dpi)

    # Axis values
    scaling_factor = 1.0
    axis = "kb"

    # No end values given.
    if end == 0:
        end = len(mat)

    # Display plots
    im = ax.imshow(
        mat[start:end, start:end],
        cmap=cmap,
        vmin=0,
        vmax=np.percentile(mat, vmax),
        extent=(
            start * scaling_factor,
            end * scaling_factor,
            end * scaling_factor,
            start * scaling_factor,
        ),
    )

    # Legend
    ax.set_xlabel(f"Genomic coordinates ({axis:s})", fontsize=16)
    ax.set_ylabel(f"Genomic coordinates ({axis:s})", fontsize=16)
    ax.tick_params(axis="both", labelsize=16)

    # Title
    if title is not None:
        ax.set_title(title, size=18)

    # Colorbar
    cbar = plt.colorbar(im, shrink=0.33, anchor=(0, 0.5))
    cbar.ax.tick_params(labelsize=16)

    first = True
    for i in [x * 5 for x in l5000]:
        if i in np.arange(start, end):
            #         plt.axvline(i, lw=1, ls='dashed', c='blue')
            if first:
                s = i
                first = False
                ax.axvline(
                    s,
                    ymin=(end - s) / (end - start),
                    ymax=1,
                    c=col[0],
                    lw=1,
                    zorder=0.5,
                )
                ax.axhline(
                    s,
                    xmin=0,
                    xmax=1 - ((end - s) / (end - start)),
                    c=col[0],
                    lw=1,
                    zorder=0.5,
                )
            else:
                e = i
                ax.add_patch(
                    patches.Rectangle(
                        (s, s), e - s, e - s, edgecolor=col[0], fill=False
                    )
                )
                s = i
    ax.axvline(
        s, ymin=(end - s) / (end - start), ymax=0, c=col[0], lw=1, zorder=0.5
    )
    ax.axhline(
        s,
        xmin=1,
        xmax=1 - ((end - s) / (end - start)),
        c=col[0],
        lw=1,
        zorder=0.5,
    )

    first = True
    for i in [x * 5 for x in l2000]:
        if i in np.arange(start, end):
            # plt.axvline(i, lw=1, ls='dashed', c='cyan')
            if first:
                s = i
                first = False
                ax.axvline(
                    s,
                    ymin=(end - s) / (end - start),
                    ymax=1,
                    c=col[1],
                    lw=1,
                    zorder=0.5,
                )
                ax.axhline(
                    s,
                    xmin=0,
                    xmax=1 - ((end - s) / (end - start)),
                    c=col[1],
                    lw=1,
                    zorder=0.5,
                )
            else:
                e = i
                ax.add_patch(
                    patches.Rectangle(
                        (s, s), e - s, e - s, edgecolor=col[1], fill=False
                    )
                )
                s = i
    ax.axvline(
        s, ymin=(end - s) / (end - start), ymax=0, c=col[1], lw=1, zorder=0.5
    )
    ax.axhline(
        s,
        xmin=1,
        xmax=1 - ((end - s) / (end - start)),
        c=col[1],
        lw=1,
        zorder=0.5,
    )

    first = True
    for i in [x * 5 for x in l1000]:
        if i in np.arange(start, end):
            # plt.axvline(i, lw=1, ls='dashed', c='cyan')
            if first:
                s = i
                first = False
                ax.axvline(
                    s,
                    ymin=(end - s) / (end - start),
                    ymax=1,
                    c=col[2],
                    lw=1,
                    zorder=0.5,
                )
                ax.axhline(
                    s,
                    xmin=0,
                    xmax=1 - ((end - s) / (end - start)),
                    c=col[2],
                    lw=1,
                    zorder=0.5,
                )
            else:
                e = i
                ax.add_patch(
                    patches.Rectangle(
                        (s, s), e - s, e - s, edgecolor=col[2], fill=False
                    )
                )
                s = i
    ax.axvline(
        s, ymin=(end - s) / (end - start), ymax=0, c=col[2], lw=1, zorder=0.5
    )
    ax.axhline(
        s,
        xmin=1,
        xmax=1 - ((end - s) / (end - start)),
        c=col[2],
        lw=1,
        zorder=0.5,
    )

    # Savefig
    plt.savefig(out_file, dpi=dpi, bbox_inches="tight")
    plt.close()

###
# Transcription at borders.
###


def get_borders_transcriptions(annotation, borders, size, step):
    borders_transcriptions = []
    for i in annotation.index:
        if annotation.loc[i, "strand"] == "-":
            pos = annotation.loc[i, "end"]
        else:
            pos = annotation.loc[i, "start"]
        for j in borders:
            a = j * size + step
            if abs(pos - a) < 2500:
                borders_transcriptions.append(annotation.loc[i, "rpkm"])
    return borders_transcriptions


borders_trans_5000 = get_borders_transcriptions(annotation, l5000, 5000, 2500)
borders_trans_2000 = get_borders_transcriptions(annotation, l2000, 5000, 1000)
borders_trans_1000 = get_borders_transcriptions(
    annotation, final_borders_1000, 1000, 500
)
data = {
    "RPKM (log)": np.log(
        list(annotation.rpkm)
        + list(np.log(borders_trans_5000))
        + list(np.log(borders_trans_2000))
        + list(np.log(borders_trans_1000))
    ),
    "Genes": list(np.repeat(f"All genes\nn={len(annotation)}", len(annotation)))
    + list(
        np.repeat(f"5kb\nn={len(borders_trans_5000)}", len(borders_trans_5000))
    )
    + list(
        np.repeat(f"2kb\nn={len(borders_trans_2000)}", len(borders_trans_2000))
    )
    + list(
        np.repeat(f"1kb\nn={len(borders_trans_1000)}", len(borders_trans_1000))
    ),
}
data = pd.DataFrame(data)
data.replace([np.inf, -np.inf, np.nan], 0, inplace=True)

sns.violinplot(x="Genes", y="RPKM (log)", data=data, palette="tab10")
plt.xlabel("Resolution of the matrix to detect the borders", size=14)
plt.ylabel("RPKM (log)", size=14)
plt.title(
    "Genes transcription of genes at less\nthan 5kb of a detected border",
    size=16,
)
plt.xticks(size=12)
plt.yticks(size=12)
plt.savefig(bor_trans, bbox_inches="tight")
plt.close()

write_stats(
    text_file,
    f"p-value between 5kb borders and whole genome: {st.mannwhitneyu(np.log(list(annotation.rpkm)), np.log(borders_trans_5000))[1]}",
)
write_stats(
    text_file,
    f"p-value between 2kb borders and whole genome: {st.mannwhitneyu(np.log(list(annotation.rpkm)), np.log(borders_trans_2000))[1]}",
)
write_stats(
    text_file,
    f"p-value between 1kb borders and whole genome: {st.mannwhitneyu(np.log(list(annotation.rpkm)), np.log(borders_trans_1000))[1]}",
)

##
# Transcription depending on distance borders
###

annotation["dist_5kb"] = 0
annotation["dist_2kb"] = 0
annotation["dist_1kb"] = 0
annotation["log1p_rpkm"] = np.log1p(annotation["rpkm"])
for i in annotation.index:
    value = np.inf
    if annotation.loc[i, "strand"] == "+":
        for x in l5000:
            pos = annotation.loc[i, "start"]
            if pos < x * 5000:
                a = x * 5000 - pos
            elif pos > (x + 1) * 5000:
                a = pos - (x + 1) * 5000
            else:
                a = -1
            value = min(a, value)
    else:
        for x in l5000:
            pos = annotation.loc[i, "end"]
            if pos < x * 5000:
                a = x * 5000 - pos
            elif pos > (x + 1) * 5000:
                a = pos - (x + 1) * 5000
            else:
                a = -1
            value = min(a, value)
    annotation.loc[i, "dist_5kb"] = value

for i in annotation.index:
    value = np.inf
    if annotation.loc[i, "strand"] == "+":
        for x in l2000:
            pos = annotation.loc[i, "start"]
            if pos < x * 5000:
                a = x * 5000 - pos
            elif pos > (x + 0.4) * 5000:
                a = pos - (x + 0.4) * 5000
            else:
                a = -1
            value = min(a, value)
    else:
        for x in l2000:
            pos = annotation.loc[i, "end"]
            if pos < x * 5000:
                a = x * 5000 - pos
            elif pos > (x + 0.4) * 5000:
                a = pos - (x + 0.4) * 5000
            else:
                a = -1
            value = min(a, value)
    annotation.loc[i, "dist_2kb"] = value

for i in annotation.index:
    value = np.inf
    if annotation.loc[i, "strand"] == "+":
        for x in final_borders_1000:
            pos = annotation.loc[i, "start"]
            if pos < x * 1000:
                a = x * 1000 - pos
            elif pos > (x + 1) * 1000:
                a = pos - (x + 1) * 1000
            else:
                a = -1
            value = min(a, value)
    else:
        for x in final_borders_1000:
            pos = annotation.loc[i, "end"]
            if pos < x * 1000:
                a = x * 1000 - pos
            elif pos > (x + 1) * 1000:
                a = pos - (x + 1) * 1000
            else:
                a = -1
            value = min(a, value)
    annotation.loc[i, "dist_1kb"] = value

bin_means_l3, bin_edges_l3, binnumber_l3 = st.binned_statistic(
    annotation.dist_1kb,
    annotation.rpkm,
    statistic="mean",
    bins=41,
    range=(-5000, 200000),
)
bin_means_l2, bin_edges_l2, binnumber_l2 = st.binned_statistic(
    annotation.dist_2kb,
    annotation.rpkm,
    statistic="mean",
    bins=41,
    range=(-5000, 200000),
)
bin_means_l1, bin_edges_l1, binnumber_l1 = st.binned_statistic(
    annotation.dist_5kb,
    annotation.rpkm,
    statistic="mean",
    bins=41,
    range=(-5000, 200000),
)


def bootstrap_down(data):
    l = np.zeros((10000))
    for i in range(10000):
        l[i] = np.mean(choices(data, k=len(data)))
    return np.percentile(l, 5)


def bootstrap_up(data):
    l = np.zeros((10000))
    for i in range(10000):
        l[i] = np.mean(choices(data, k=len(data)))
    return np.percentile(l, 95)


bin_perc5_l1, bin_edges_l1, binnumber_l1 = st.binned_statistic(
    annotation.dist_5kb,
    annotation.rpkm,
    statistic=bootstrap_down,
    bins=41,
    range=(-5000, 200000),
)
bin_perc95_l1, bin_edges_l1, binnumber_l1 = st.binned_statistic(
    annotation.dist_5kb,
    annotation.rpkm,
    statistic=bootstrap_up,
    bins=41,
    range=(-5000, 200000),
)
bin_perc5_l2, bin_edges_l2, binnumber_l2 = st.binned_statistic(
    annotation.dist_2kb,
    annotation.rpkm,
    statistic=bootstrap_down,
    bins=41,
    range=(-5000, 200000),
)
bin_perc95_l2, bin_edges_l2, binnumber_l2 = st.binned_statistic(
    annotation.dist_2kb,
    annotation.rpkm,
    statistic=bootstrap_up,
    bins=41,
    range=(-5000, 200000),
)
bin_perc5_l3, bin_edges_l3, binnumber_l3 = st.binned_statistic(
    annotation.dist_1kb,
    annotation.rpkm,
    statistic=bootstrap_down,
    bins=41,
    range=(-5000, 200000),
)
bin_perc95_l3, bin_edges_l3, binnumber_l3 = st.binned_statistic(
    annotation.dist_1kb,
    annotation.rpkm,
    statistic=bootstrap_up,
    bins=41,
    range=(-5000, 200000),
)

plt.plot(bin_edges_l1[1:] / 1000, bin_means_l1, label="5kb")
plt.fill_between(
    bin_edges_l1[1:] / 1000, bin_perc5_l1, bin_perc95_l1, alpha=0.5
)
plt.plot(bin_edges_l2[1:] / 1000, bin_means_l2, label="2kb")
plt.fill_between(
    bin_edges_l2[1:] / 1000, bin_perc5_l2, bin_perc95_l2, alpha=0.5
)
plt.plot(bin_edges_l3[1:] / 1000, bin_means_l3, label="1kb")
plt.fill_between(
    bin_edges_l3[1:] / 1000, bin_perc5_l3, bin_perc95_l3, alpha=0.5
)
plt.xlabel("Distance from the closest border (kb)", size=14)
plt.ylabel("Mean RPKM", size=14)
plt.xlim(0, 50)
plt.title(
    "Genes transcription depending on\nthe distance to the closest border",
    size=16,
)
plt.xticks(size=12)
plt.yticks(size=12)
plt.legend()
plt.savefig(trans_bor, bbox_inches="tight")
plt.close()

Python Pandas numpy matplotlib seaborn scipy cooler cooler bacchus From line 4 of scripts/CIDs_analysis_resolution.py

import bacchus.io as bcio
import bacchus.plot as bcp
import bacchus.hic as bch
import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import os
from os.path import dirname

# Make sure that the figures directory exists.
os.makedirs(dirname(snakemake.output.mat_fig), exist_ok=True)
os.makedirs(dirname(snakemake.output.corr_fig), exist_ok=True)

# Import parameters
binning = int(snakemake.params.binning)
label = str(snakemake.params.species)
circular = str(snakemake.params.circular)

# Import contact map as a dense matrix.
M = bcio.build_map(
    matrix_files=[str(snakemake.input.hic)],
    fragment_file="none",
    bin_size=binning,
    mat_format="cool",
    normalize=True,
    subsample=0,
)

# Plot the contact map.
bcp.contact_map(
    M,
    axis="Mb",
    binning=binning,
    cmap="Reds",
    dpi=500,
    out_file=str(snakemake.output.mat_fig),
    title=label,
    vmax=99,
)

# Import RNA tracks.
rna, chrom_start = bcio.extract_big_wig(
    file=str(snakemake.input.rna),
    binning=binning,
    circular=circular,
    sigma=None,
    ztransform=None,
)
# Ugly loop to have the chrom length...
chrom_start_size = {}
for i, name in enumerate(chrom_start):
    if i != 0:
        chrom_start_size[prev_name] = {
            "start": start,
            "length": chrom_start[name] - start,
        }
    prev_name = name
    start = chrom_start[name]
chrom_start_size[prev_name] = {
    "start": start,
    "length": chrom_start[name] - start,
}

# Compute HiC signal.
hic = bch.compute_hic_signal(M, binning=binning, start=5000, stop=10000)
print(st.spearmanr(hic, rna[:-2])[0])

# Plot correlation between HiC signal and RNAseq.
fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.scatter(hic, np.log(rna[:-2]))
ax.set_xlabel("HiC signal")
ax.set_ylabel("Transcription (log)")
ax.set_title(label)
ax.text(
    x=np.nanpercentile(hic, 99),
    y=np.nanpercentile(np.log(rna), 1),
    s=f"corr={st.spearmanr(hic, rna[:-2])[0]:.2f}",
)
plt.savefig(snakemake.output.corr_fig, dpi=100)

Python numpy matplotlib scipy bacchus From line 4 of scripts/corr_hic_rna.py

import bacchus.hic as bch
import bacchus.io as bcio
import cooler
import matplotlib.pyplot as plt
import numpy as np
import os
import scipy.stats as st

# Import snakemake.
mat_t7 = snakemake.input.mat_t7
mat_t7_2p100 = snakemake.input.mat_t7_2p100
mat_t7_2p60 = snakemake.input.mat_t7_2p60
mat_t7_2p100_dv = snakemake.input.mat_t7_2p100_dv
mat_t7_2p60_dv = snakemake.input.mat_t7_2p60_dv
mat_t7_2p100_cv = snakemake.input.mat_t7_2p100_cv
rna_t7 = snakemake.input.rna_t7
rna_t7_2p100 = snakemake.input.rna_t7_2p100
rna_t7_2p60 = snakemake.input.rna_t7_2p60
rna_t7_2p100_dv = snakemake.input.rna_t7_2p100_dv
rna_t7_2p60_dv = snakemake.input.rna_t7_2p60_dv
rna_t7_2p100_cv = snakemake.input.rna_t7_2p100_cv
chip_RNA_t7 = snakemake.input.chip_RNA_t7
chip_RNA_t7_2p100 = snakemake.input.chip_RNA_t7_2p100
chip_RNA_t7_2p60 = snakemake.input.chip_RNA_t7_2p60
chip_RNA_t7_2p100_dv = snakemake.input.chip_RNA_t7_2p100_dv
chip_RNA_t7_2p60_dv = snakemake.input.chip_RNA_t7_2p60_dv
chip_RNA_t7_2p100_cv = snakemake.input.chip_RNA_t7_2p100_cv
chip_gapr_t7 = snakemake.input.chip_gapr_t7
chip_gapr_t7_2p100 = snakemake.input.chip_gapr_t7_2p100
chip_gapr_t7_2p60 = snakemake.input.chip_gapr_t7_2p60
chip_gapr_t7_2p100_dv = snakemake.input.chip_gapr_t7_2p100_dv
chip_gapr_t7_2p60_dv = snakemake.input.chip_gapr_t7_2p60_dv
chip_gapr_t7_2p100_cv = snakemake.input.chip_gapr_t7_2p100_cv
chip_gapr_control = snakemake.input.chip_gapr_control
cmap = snakemake.params.cmap
res = snakemake.params.res
tracks_res = snakemake.params.tracks_res 
outdir = snakemake.params.outdir 
out_plot = str(snakemake.output.plot)
out_txt = str(snakemake.output.txt)

# Make sure output directory exists.
os.makedirs(outdir, exist_ok=True)

# Import matrices
mat_t7 = cooler.Cooler(f"{mat_t7}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7[np.isnan(mat_t7)] = 0
mat_t7_2p100 = cooler.Cooler(f"{mat_t7_2p100}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_2p100[np.isnan(mat_t7_2p100)] = 0
mat_t7_2p60 = cooler.Cooler(f"{mat_t7_2p60}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_2p60[np.isnan(mat_t7_2p60)] = 0
mat_t7_2p100_dv = cooler.Cooler(f"{mat_t7_2p100_dv}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_2p100_dv[np.isnan(mat_t7_2p100_dv)] = 0
mat_t7_2p60_dv = cooler.Cooler(f"{mat_t7_2p60_dv}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_2p60_dv[np.isnan(mat_t7_2p60_dv)] = 0
mat_t7_2p100_cv = cooler.Cooler(f"{mat_t7_2p100_cv}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_2p100_cv[np.isnan(mat_t7_2p100_cv)] = 0

# Import RNA tracks
rna_t7, _ = bcio.extract_big_wig(rna_t7, tracks_res)
rna_t7_2p100, _ = bcio.extract_big_wig(rna_t7_2p100, tracks_res)
rna_t7_2p60, _ = bcio.extract_big_wig(rna_t7_2p60, tracks_res)
rna_t7_2p100_dv, _ = bcio.extract_big_wig(rna_t7_2p100_dv, tracks_res)
rna_t7_2p60_dv, _ = bcio.extract_big_wig(rna_t7_2p60_dv, tracks_res)
rna_t7_2p100_cv, _ = bcio.extract_big_wig(rna_t7_2p100_cv, tracks_res)

# Import chip RNA pol T7
chip_RNA_t7, _ = bcio.extract_big_wig(chip_RNA_t7, res)
chip_RNA_t7_2p100, _ = bcio.extract_big_wig(chip_RNA_t7_2p100, res)
chip_RNA_t7_2p60, _ = bcio.extract_big_wig(chip_RNA_t7_2p60, res)
chip_RNA_t7_2p100_dv, _ = bcio.extract_big_wig(chip_RNA_t7_2p100_dv, res)
chip_RNA_t7_2p60_dv, _ = bcio.extract_big_wig(chip_RNA_t7_2p60_dv, res)
chip_RNA_t7_2p100_cv, _ = bcio.extract_big_wig(chip_RNA_t7_2p100_cv, res)

# Import chip GapR
chip_gapr_t7, _ = bcio.extract_big_wig(chip_gapr_t7, res)
chip_gapr_t7_2p100, _ = bcio.extract_big_wig(chip_gapr_t7_2p100, res)
chip_gapr_t7_2p60, _ = bcio.extract_big_wig(chip_gapr_t7_2p60, res)
chip_gapr_t7_2p100_dv, _ = bcio.extract_big_wig(chip_gapr_t7_2p100_dv, res)
chip_gapr_t7_2p60_dv, _ = bcio.extract_big_wig(chip_gapr_t7_2p60_dv, res)
chip_gapr_t7_2p100_cv, _ = bcio.extract_big_wig(chip_gapr_t7_2p100_cv, res)

# Control GapR
chip_gapr_control, _ = bcio.extract_big_wig(chip_gapr_control, res)

# Compute the log fold change between signal
chip_gapr_t7 = np.log2(chip_gapr_t7) - np.log2(chip_gapr_control) 
chip_gapr_t7_2p100 = np.log2(chip_gapr_t7_2p100) - np.log2(chip_gapr_control)
chip_gapr_t7_2p60 = np.log2(chip_gapr_t7_2p60) - np.log2(chip_gapr_control)
chip_gapr_t7_2p100_dv = np.log2(chip_gapr_t7_2p100_dv) - np.log2(chip_gapr_control)
chip_gapr_t7_2p60_dv = np.log2(chip_gapr_t7_2p60_dv) - np.log2(chip_gapr_control)
chip_gapr_t7_2p100_cv = np.log2(chip_gapr_t7_2p100_cv) - np.log2(chip_gapr_control) 

# Compute HiC signal
hic_t7 = bch.compute_hic_signal(mat_t7, 1000, 0, 5000)
hic_t7_2p100 = bch.compute_hic_signal(mat_t7_2p100, 1000, 0, 5000)
hic_t7_2p60 = bch.compute_hic_signal(mat_t7_2p60, 1000, 0, 5000)
hic_t7_2p100_dv = bch.compute_hic_signal(mat_t7_2p100_dv, 1000, 0, 5000)
hic_t7_2p60_dv = bch.compute_hic_signal(mat_t7_2p60_dv, 1000, 0, 5000)
hic_t7_2p100_cv = bch.compute_hic_signal(mat_t7_2p100_cv, 1000, 0, 5000)

list_rna = [
    rna_t7,
    rna_t7_2p100,
    rna_t7_2p60,
    rna_t7_2p100_dv,
    rna_t7_2p60_dv,
    rna_t7_2p100_cv,
]
list_chip_T7 = [
    chip_RNA_t7,
    chip_RNA_t7_2p100,
    chip_RNA_t7_2p60,
    chip_RNA_t7_2p100_dv,
    chip_RNA_t7_2p60_dv,
    chip_RNA_t7_2p100_cv,
]
list_chip_gapr = [
    chip_gapr_t7,
    chip_gapr_t7_2p100,
    chip_gapr_t7_2p60,
    chip_gapr_t7_2p100_dv,
    chip_gapr_t7_2p60_dv,
    chip_gapr_t7_2p100_cv,
]
list_hic_signal = [
    hic_t7,
    hic_t7_2p100,
    hic_t7_2p60,
    hic_t7_2p100_dv,
    hic_t7_2p60_dv,
    hic_t7_2p100_cv,
]
list_hic = [
    mat_t7,
    mat_t7_2p100,
    mat_t7_2p60,
    mat_t7_2p100_dv,
    mat_t7_2p60_dv,
    mat_t7_2p100_cv,
]

def z_transform(values):
    return (values - np.nanmean(values) / np.nanstd(values))

# for i in range(6):
#     list_rna[i] = z_transform(list_rna[i])
#     list_chip_T7[i] = z_transform(list_chip_T7[i])
#     list_chip_gapr[i] = z_transform(list_chip_gapr[i])
#     list_hic_signal[i] = z_transform(list_hic_signal[i])

# Make the plots 
start, end = 0, 750
binning = 10

fig, ax = plt.subplots(4,6 , figsize=(20,15), sharex=True, gridspec_kw={'height_ratios': [8, 4, 4, 4]})

# Define max values for x_lim
max_rna = 15.5
min_gapr = -4

# Define color
col = ["k", "#fdbf6f", "#1f78b4", '#e31a1c']

for i in range(6):
    # Hicmap
    im = ax[0, i].imshow(
        list_hic[i][start:end, start:end],
        cmap="Reds",
        vmax=np.nanpercentile(list_hic[0], 99.9),
        extent=(start, end, end, start),
    )
    ax[0, i].get_xaxis().set_visible(False)
    ax[0, i].tick_params(axis='both', labelsize=16)
    # ax[0, i].set_ylabel("Genomic coordinates (kb)", size=16)

    # Settings plot 1, 2
    # ax[1, i].set_xlabel("Genomic coordinates (kb)", size=16)
    ax[1, 0].set_ylabel("Transcription (CPM)", size=16)
    ax[1, i].spines['top'].set_visible(False)
    ax[1, i].spines['right'].set_visible(False)
    ax[1, i].set_ylim(-1, 3000)
    ax[1, i].tick_params(axis='both', labelsize=16, color=col[0], labelcolor=col[0])
    # ax[2, i].set_xlabel("Genomic coordinates (kb)", size=16)
    ax[2, 0].set_ylabel("T7 RNA pol signal (CPM)", size=16)
    ax[2, i].spines['top'].set_visible(False)
    ax[2, i].spines['right'].set_visible(False)
    ax[2, i].set_ylim(-0.05, 1)
    ax[2, i].tick_params(axis='both', labelsize=16, color=col[0], labelcolor=col[0])
    ax[3, i].set_xlabel("Genomic coordinates (kb)", size=16)
    ax[3, 0].set_ylabel("GapR signal (log2 fold change)", size=16)
    ax[3, i].spines['top'].set_visible(False)
    ax[3, i].spines['right'].set_visible(False)
    ax[3, i].set_ylim(-0.05, 1)
    ax[3, i].tick_params(axis='both', labelsize=16, color=col[0], labelcolor=col[0])

    # RNAseq
    if list_rna[i].all() != None:
        p1 = ax[1, i].fill_between(
            np.arange(start, end, 1 / binning),
            list_rna[i][int(start * binning):int(end * binning)],
            color=col[0],
            label="RNAseq",
            linewidth=1,
        )

    # HiC signal
    p2 = ax[2, i].plot(
        np.arange(start, end, 1),
        list_hic_signal[i][start:end],
        color=col[3],
        label="HiC signal",
        linewidth=1,
        alpha=.7,
    )
    p2 = ax[3, i].plot(
        np.arange(start, end, 1),
        list_hic_signal[i][start:end],
        color=col[3],
        label="HiC signal",
        linewidth=1,
        alpha=.7,
    )

    # T7 ChIPseq
    ax3 = ax[2, i].twinx()
    if list_chip_T7[i].all() != None:
        p3 = ax3.plot(
            np.arange(start, end, 1),
            list_chip_T7[i][start:end],
            color=col[2],
            label="T7 RNA pol ChIPseq",
            alpha=.7,
            linewidth=1,
        )
        ax3.set_ylim(-100, 6000)

    # GapR ChIPseq
    ax4 = ax[3, i].twinx()
    if list_chip_gapr[i].all() != None:
        p4 = ax4.plot(
            np.arange(start, end, 1),
            list_chip_gapr[i][start:end],
            color=col[1],
            label="GapR ChIPseq",
            alpha=1,
            linewidth=1,
        )
        # ax[3, i].set_ylim(-7.5, 10)
        ax4.set_ylim(-1.5, 1.5)

# Colorbar
cbar = fig.colorbar(im, ax=ax.ravel().tolist(), shrink=.2, anchor=(1.2, .89))
cbar.ax.tick_params(labelsize=16)

# Legend
leg = fig.legend(
    labels=["RNAseq", "GapR ChIPseq", "T7 RNA pol ChIPseq", "HiC signal"],
    loc=(0.9, 0.25),
)
for i in range(4):
    leg.legendHandles[i].set_color(col[i])

# Adjust space between plot
plt.subplots_adjust(wspace=.4, hspace=.2, left=0.05, right=0.88)
plt.savefig(out_plot, dpi=250)


# Correlation between tracks
start=220 #kb
end=580 #kb

def get_binning(values, size=10):
    n = len(values) // size
    new_values = np.zeros(n + 1)
    for i in range(n):
        new_values[i] = np.nanmean(values[i * size:(i + 1) * size])
    new_values[n] = np.nanmean(values[n * size:])
    new_values
    return new_values

for i in range(6):
    list_rna[i] = get_binning(list_rna[i])
    # list_chip_T7[i] = get_binning(list_chip_T7[i])
    # list_chip_gapr[i] = get_binning(list_chip_gapr[i])


with open(out_txt, 'w') as out:
    out.write('Correlation between signal:\n')
    label=["T7", "T7_CL_100", "T7_CL_60", "T7_DV_100", "T7_DV_60", "T7_CV"]
    for i in range(6):
    #     out.write(f"{label[i]};RNA/T7 Pearson correlation: {st.pearsonr(list_rna[i][start:end], list_chip_T7[i][start:end])[0]:.2f}\n")
    #     out.write(f"{label[i]};RNA/GapR Pearson correlation: {st.pearsonr(list_rna[i][start:end], list_chip_gapr[i][start:end])[0]:.2f}\n")
    #     out.write(f"{label[i]};RNA/HiC Pearson correlation: {st.pearsonr(list_rna[i][start:end], list_hic_signal[i][start:end])[0]:.2f}\n")
    #     out.write(f"{label[i]};T7/GapR Pearson correlation: {st.pearsonr(list_chip_T7[i][start:end], list_chip_gapr[i][start:end])[0]:.2f}\n")
    #     out.write(f"{label[i]};T7/HiC Pearson correlation: {st.pearsonr(list_chip_T7[i][start:end], list_hic_signal[i][start:end])[0]:.2f}\n")
    #     out.write(f"{label[i]};GapR/HiC Pearson correlation: {st.pearsonr(list_chip_gapr[i][start:end], list_hic_signal[i][start:end])[0]:.2f}\n")
        out.write(f"{label[i]};RNA/T7 Spearmann correlation: {st.spearmanr(list_rna[i][start:end], list_chip_T7[i][start:end])[0]:.2f}\n")    
        out.write(f"{label[i]};RNA/GapR Spearmann correlation: {st.spearmanr(list_rna[i][start:end], list_chip_gapr[i][start:end])[0]:.2f}\n")
        out.write(f"{label[i]};RNA/HiC Spearmann correlation: {st.spearmanr(list_rna[i][start:end], list_hic_signal[i][start:end])[0]:.2f}\n")
        out.write(f"{label[i]};T7/GapR Spearmann correlation: {st.spearmanr(list_chip_T7[i][start:end], list_chip_gapr[i][start:end])[0]:.2f}\n")
        out.write(f"{label[i]};T7/HiC Spearmann correlation: {st.spearmanr(list_chip_T7[i][start:end], list_hic_signal[i][start:end])[0]:.2f}\n")  
        out.write(f"{label[i]};GapR/HiC Spearmann correlation: {st.spearmanr(list_chip_gapr[i][start:end], list_hic_signal[i][start:end])[0]:.2f}\n")

Python numpy matplotlib scipy cooler cooler bacchus From line 4 of scripts/multi_prom_gapr.py

import bacchus.hic as bch
import cooler
import hicstuff.hicstuff as hcs
import matplotlib.pyplot as plt
import numpy as np
import copy 
import os
from scipy import ndimage
import serpentine as srp

# Import snakemake.
mat_t7_ara_file = snakemake.input.mat_ara
mat_t7_novo_file = snakemake.input.mat_novo
mat_t7_ara_rif_file = snakemake.input.mat_ara_rif
mat_t7_novo_rif_file = snakemake.input.mat_novo_rif
cmap = snakemake.params.cmap
res = snakemake.params.res
res_ratio = snakemake.params.res_ratio
width = snakemake.params.width
cpus = snakemake.threads

outfile = str(snakemake.output.matrices)
outfile_ratio = str(snakemake.output.ratio)
outfile_ratio_rif = str(snakemake.output.ratio_rif)
# out_zoom = str(snakemake.output.zoom)
out_zoom_ratio = str(snakemake.output.zoom_ratio)

# Make sure output directory exists.
os.makedirs(str((snakemake.params.outdir)), exist_ok=True)

# Import matrices
subsample = 11675151
files = [mat_t7_ara_file, mat_t7_novo_file, mat_t7_ara_rif_file, mat_t7_novo_rif_file]
hic = [0, 0, 0, 0]
for i, file in enumerate(files):
    clr = cooler.Cooler(f"{file}::/resolutions/{res}")
    mat = clr.matrix(balance=False, sparse=True)[:, :]
    mat = hcs.subsample_contacts(mat, subsample)
    mat = hcs.normalize_sparse(mat, norm='ICE', iterations=100, n_mad=10)
    hic[i] = mat.toarray()

# Ratio
subsample = 10251593
hic_ratio = [0, 0, 0, 0]
for i, file in enumerate(files):
    clr = cooler.Cooler(f"{file}::/resolutions/{res_ratio}")
    mat = clr.matrix(balance=False, sparse=True)[:, :]
    print(mat.sum())
    mat = hcs.subsample_contacts(mat, subsample)
    mat = hcs.normalize_sparse(mat, norm='ICE', iterations=100, n_mad=10)
    hic_ratio[i] = mat.toarray()

ratio = np.log10(hic_ratio[1]) - np.log10(hic_ratio[0])
ratio[np.isnan(ratio)] =  0
ratio[ratio == np.inf] =  0
ratio[ratio == -np.inf] =  0
ratio_rif = np.log10(hic_ratio[3]) - np.log10(hic_ratio[2])
ratio_rif[np.isnan(ratio_rif)] =  0
ratio_rif[ratio_rif == np.inf] =  0
ratio_rif[ratio_rif == -np.inf] =  0

###
# PLOT MATRICES
###

title = ["novo- Rif-", "novo+ Rif-", "novo- Rif+", "novo+ Rif+"]
ratios = [ratio, ratio_rif]
start = 0
end = len(hic[0])
end_ratio = end * res // res_ratio 

fig, ax = plt.subplots(3, 2, figsize=(10, 10))
for i in range(2):
    for j in range(2):
        ax[i, j].imshow(
            hic[i + 2 * j][start:end, start:end],
            vmin=0,
            vmax=0.0012,
            cmap="Reds",
            extent=(start, end, end, start),
        )
    ax[i, j].set_title(title[i + 2 * j])
    ax[2, i].imshow(
        ratios[i][start:end_ratio, start:end_ratio],
        vmin=-2,
        vmax=2,
        cmap="seismic",
        extent=(start, end, end, start),
    )
    ax[2, i].set_title("novo+/novo-")

plt.savefig(outfile)
plt.close()

###
# Plot only ratio.
###

fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.imshow(
    ratios[0][start:end_ratio, start:end_ratio],
    vmin=-2,
    vmax=2,
    cmap="seismic",
    extent=(start, end, end, start),
)
ax.set_title("novo+/novo-")

plt.savefig(outfile_ratio, dpi=300)
plt.close()

fig, ax = plt.subplots(1, 1, figsize=(10, 10))
ax.imshow(
    ratios[1][start:end_ratio, start:end_ratio],
    vmin=-2,
    vmax=2,
    cmap="seismic",
    extent=(start, end, end, start),
)
ax.set_title("novo+ Rif+/novo- Rif+")

plt.savefig(outfile_ratio_rif, dpi=300)
plt.close()

###
# Plot zoom without RNAseq.
###

# Rotate matrices.
# hic_rot = copy.copy(hic)
# for i in range(len(hic)):
#     hic_rot[i] = bch.interpolate_white_lines(hic_rot[i])
#     hic_rot[i][np.isnan(hic_rot[i])] = 0
#     hic_rot[i] = ndimage.rotate(hic_rot[i], 45, reshape=True)

# fig, ax = plt.subplots(
#     1, 4, figsize=(20, 10)
# )

# start = 300_000
# end = 480_000

# row1 = len(hic_rot[0]) // 2 - int(np.sqrt(2) * (width / res))
# row2 = len(hic_rot[0]) // 2 + int(np.sqrt(2) * (width / res))
# col1 = int((start // res) * np.sqrt(2))
# col2 = int((end // res) * np.sqrt(2))

# for i in range(len(hic)):
#     # Hicmap
#     im = ax[i].imshow(
#         hic_rot[i][row1:row2, col1:col2],
#         cmap="Reds",
#         vmin=0,
#         vmax=0.002,
#         extent=(col1 / np.sqrt(2), col2 / np.sqrt(2), width // res, -width // res),
#     )
#     ax[i].get_xaxis().set_visible(False)
#     ax[i].set_title(title[i], size=18)
#     if i == 0:
#         ax[i].tick_params(axis="both", labelsize=16)
#         ax[i].set_ylabel("Genomic coordinates (kb)", size=16)
#     else:
#         ax[i].get_yaxis().set_visible(False)

# # Colorbar
# cbar = fig.colorbar(
#     im,
#     ax=ax.ravel().tolist(),
#     shrink=0.5,
#     anchor=(1., 0.5)
# )
# cbar.ax.tick_params(labelsize=16)

# # Adjust space between plot
# plt.subplots_adjust(wspace=0.4, hspace=0.0, left=0.05, right=0.88)
# plt.savefig(out_zoom)
# plt.close()

###
# Plot zoom ratio.
###

start, end = 0, 750
end_ratio = end * res // res_ratio 

fig, ax = plt.subplots(3, 2, figsize=(10, 10))
for i in range(2):
    for j in range(2):
        ax[i, j].imshow(
            hic[i + 2 * j][start:end, start:end],
            vmin=0,
            vmax=0.0012,
            cmap="Reds",
            extent=(start, end, end, start),
        )
    ax[i, j].set_title(title[i + 2 * j])
for i in range(2):
    ax[2, i].imshow(
        ratios[i][start:end_ratio, start:end_ratio],
        vmin=-1,
        vmax=1,
        cmap="seismic",
        extent=(start, end, end, start),
    )
    ax[2, i].set_title("novo+/novo-")

plt.savefig(out_zoom_ratio)
plt.close() 

Python numpy matplotlib scipy cooler cooler bacchus hicstuff Serpentine From line 4 of scripts/novobiocin_matrices.py

import bacchus.io as bcio
import bacchus.hic as bch
import bacchus.plot as bcp
import bacchus.transcription as bct
import cooler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
import scipy.stats as sst
from os.path import dirname, join

# Make sure that the figures directory exists.
os.makedirs(dirname(snakemake.output.pileup), exist_ok=True)
os.makedirs(dirname(snakemake.output.pileup_zoom), exist_ok=True)

# Import parameters
binning = int(snakemake.params.binning)
label = str(snakemake.params.species)
circular = str(snakemake.params.circular)
window = int(snakemake.params.window)
threshold = int(snakemake.params.threshold)
unit_length = int(snakemake.params.unit_length)

# Import RNA tracks.
rna, chrom_start = bcio.extract_big_wig(
    file=str(snakemake.input.rna),
    binning=binning,
    circular=circular,
    sigma=None,
    ztransform=None,
)
# Ugly loop to have the chrom length...
chrom_start_size = {}
for i, name in enumerate(chrom_start):
    if i != 0:
        chrom_start_size[prev_name] = {
            "start": start,
            "length": chrom_start[name] - start,
        }
    prev_name = name
    start = chrom_start[name]
chrom_start_size[prev_name] = {
    "start": start,
    "length": len(rna) - start,
}

# RNA at 1bp.
rna_1bp, chrom_start_1bp = bcio.extract_big_wig(
    file=str(snakemake.input.rna),
    binning=1,
    circular=circular,
    sigma=None,
    ztransform=None,
)

# Extract annotation from GFF.
annotation = pd.DataFrame(
    columns=["type", "chr", "start", "end", "strand", "name", "tss", "tts", "rpkm"]
)
with open(snakemake.input.annotation, "r") as file:
    for line in file:
        if line.startswith("#"):
            continue
        # Stop if fasta sequence at the end or empty line.
        elif line.startswith(">") or (line.startswith("\n")):
            break
        else:
            line = line.split("\t")
            if line[2] == "CDS":
                name = line[8].split("Name=")[-1].split(";")[0]
                annot = {
                    "type": line[2],
                    "chr": line[0],
                    "start": int(line[3]),
                    "end": int(line[4]),
                    "strand": line[6],
                    "name": name,
                }
                annotation = annotation.append(annot, ignore_index=True)

print(annotation)

# Find TSS/TTS positions.
for i in annotation.index:
    if annotation.loc[i, "strand"] == "+":
        annotation.loc[i, "tss"] = annotation.loc[i, "start"]
        annotation.loc[i, "tts"] = annotation.loc[i, "end"]
    else:
        annotation.loc[i, "tts"] = annotation.loc[i, "start"]
        annotation.loc[i, "tss"] = annotation.loc[i, "end"]

# Compute the RPKM values from the genes.
for i in annotation.index:
    chr_start = chrom_start_1bp[annotation.loc[i, "chr"]]
    annotation.loc[i, "rpkm"] = np.nanmean(
        rna_1bp[
            chr_start
            + annotation.loc[i, "start"] : chr_start
            + annotation.loc[i, "end"]
        ]
    )

# Compute coding density.
n = 0
for i in annotation.index:
    n += annotation.loc[i, "end"] - annotation.loc[i, "start"]
coding_density = n / len(rna_1bp)

# Plot RPKM distribution.
plt.hist(annotation.rpkm, bins=25)
plt.text(
    x=np.nanpercentile(annotation.rpkm, 80),
    y=len(annotation.rpkm) // 25,
    s=f"corr={coding_density:.2f}",
)
plt.savefig(snakemake.output.rpkm)

# Compute pileup
for tu_length in [0, 3000]:
    for threshold2 in np.arange(5, 30, 5):
        rna_pileup_pos, _, pileup_pos, _ = bct.pileup_genes(
            clr=cooler.Cooler(f"{snakemake.input.hic}::/resolutions/{binning}"),
            annotation=annotation,
            rna=rna,
            chrom_start_size=chrom_start_size,
            window_size=25000,
            binning=binning,
            threshold=threshold2,
            neg="detrend",
            tu_length=tu_length,
            operation="mean",
            circular=circular,
        )

        # Plot pileup
        # Parameters
        ax_kb = 1000
        window_plot = 25000 // ax_kb

        # Plot
        fig, ax = plt.subplots(
            2, 1, figsize=(8, 13), gridspec_kw={"height_ratios": [7, 3]}
        )
        # RNA
        ax[1].axvline(
            0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4
        )
        ax[1].tick_params(axis="both", labelsize=14)
        ax[1].plot(
            np.arange(
                -window_plot,
                window_plot + (1 * binning / ax_kb),
                binning / ax_kb,
            ),
            rna_pileup_pos,
        )
        ax[1].set_ylabel("Transcription (CPM)", fontsize=15)
        ax[1].set_xlabel("Genomic distance (kb)", fontsize=15)
        # HiC
        if tu_length == 0:
            vmax = 0.012
        else:
            vmax = 0.008
        ax[0].get_xaxis().set_visible(False)
        im = ax[0].imshow(
            pileup_pos**0.8,
            cmap="Reds",
            vmin=0,
            vmax=vmax,
            extent=[-window_plot, window_plot, window_plot, -window_plot],
        )
        # ax[0].axvline(0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4)
        # ax[0].axhline(0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4)
        ax[0].tick_params(axis="both", labelsize=14)
        ax[0].set_ylabel("Genomic distance (kb)", fontsize=15)

        # Add colorbar, title and savefig
        fig.colorbar(
            im, ax=ax.ravel().tolist(), shrink=0.33, anchor=(1.3, 0.75)
        )
        plt.subplots_adjust(hspace=0.1)
        ax[0].set_title(label, fontsize=20)
        plt.savefig(
            join(
                snakemake.params.out_dir,
                f"pileup_pos_{threshold2}_TU{tu_length}.pdf",
            ),
            dpi=200,
        )

        hic_signal = bch.compute_hic_signal(
            pileup_pos, binning=500, start=0, stop=5000
        )
        print(
            f"{label} (Gene) - Pearson correlation: {sst.pearsonr(hic_signal[10:40], rna_pileup_pos[10:40])[0]:.2f}"
        )
        print(
            f"{label} (Gene) - p-value: {sst.pearsonr(hic_signal[10:40], rna_pileup_pos[10:40])[1]:.5f}"
        )

# Log ratio pileup
rna_pileup_pos, rna_pileup_neg, pileup_pos, pileup_neg = bct.pileup_genes(
    clr=cooler.Cooler(f"{snakemake.input.hic}::/resolutions/{binning}"),
    annotation=annotation,
    rna=rna,
    chrom_start_size=chrom_start_size,
    window_size=window,
    binning=binning,
    threshold=threshold,
    neg="random-neighbor",
    tu_length=unit_length,
    operation="mean",
    circular=circular,
)

# Print correlation:
hic_signal = bch.compute_hic_signal(pileup_pos, binning=500, start=0, stop=5000)
print(
    f"{label} (TU) - Pearson correlation: {sst.pearsonr(hic_signal[10:window//500 - 10], rna_pileup_pos[10:window//500 - 10])[0]:.2f}"
)
print(
    f"{label} (TU) - p-value: {sst.pearsonr(hic_signal[10:window//500 - 10], rna_pileup_pos[10:window//500 - 10])[1]:.5f}"
)

# Plot pileup pos.
# Plot pileup
# Parameters
ax_kb = 1000
window_plot = 25000 // ax_kb

# Plot pos
fig, ax = plt.subplots(
    2, 1, figsize=(8, 13), gridspec_kw={"height_ratios": [7, 3]}
)
# RNA
ax[1].axvline(0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4)
ax[1].tick_params(axis="both", labelsize=14)
ax[1].plot(
    np.arange(
        -window_plot,
        window_plot + (1 * binning / ax_kb),
        binning / ax_kb,
    ),
    rna_pileup_pos[window // 500 - 50 : window // 500 + 51],
)
ax[1].set_ylabel("Transcription (CPM)", fontsize=15)
ax[1].set_xlabel("Genomic distance (kb)", fontsize=15)
# HiC
ax[0].get_xaxis().set_visible(False)
im = ax[0].imshow(
    pileup_pos[
        window // 500 - 50 : window // 500 + 51,
        window // 500 - 50 : window // 500 + 51,
    ]
    ** 0.8,
    cmap="Reds",
    vmin=0.003,
    vmax=0.007,
    extent=[-window_plot, window_plot, window_plot, -window_plot],
)
# ax[0].axvline(0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4)
# ax[0].axhline(0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4)
ax[0].tick_params(axis="both", labelsize=14)
ax[0].set_ylabel("Genomic distance (kb)", fontsize=15)

# Add colorbar, title and savefig
fig.colorbar(im, ax=ax.ravel().tolist(), shrink=0.33, anchor=(1.3, 0.75))
plt.subplots_adjust(hspace=0.1)
ax[0].set_title(label, fontsize=20)
plt.savefig(snakemake.output.pileup_pos_TU, dpi=200)

# Plot neg
fig, ax = plt.subplots(
    2, 1, figsize=(8, 13), gridspec_kw={"height_ratios": [7, 3]}
)
# RNA
ax[1].axvline(0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4)
ax[1].tick_params(axis="both", labelsize=14)
ax[1].plot(
    np.arange(
        -window_plot,
        window_plot + (1 * binning / ax_kb),
        binning / ax_kb,
    ),
    rna_pileup_neg[window // 500 - 50 : window // 500 + 51],
)
ax[1].set_ylabel("Transcription (CPM)", fontsize=15)
ax[1].set_xlabel("Genomic distance (kb)", fontsize=15)
# HiC
ax[0].get_xaxis().set_visible(False)
im = ax[0].imshow(
    pileup_neg[
        window // 500 - 50 : window // 500 + 51,
        window // 500 - 50 : window // 500 + 51,
    ]
    ** 0.8,
    cmap="Reds",
    vmin=0,
    vmax=np.nanpercentile(
        pileup_pos[
            window // 500 - 50 : window // 500 + 51,
            window // 500 - 50 : window // 500 + 51,
        ]
        ** 0.8,
        99,
    ),
    extent=[-window_plot, window_plot, window_plot, -window_plot],
)
# ax[0].axvline(0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4)
# ax[0].axhline(0, color="black", linestyle="dashed", linewidth=1.5, alpha=0.4)
ax[0].tick_params(axis="both", labelsize=14)
ax[0].set_ylabel("Genomic distance (kb)", fontsize=15)

# Add colorbar, title and savefig
fig.colorbar(im, ax=ax.ravel().tolist(), shrink=0.33, anchor=(1.3, 0.75))
plt.subplots_adjust(hspace=0.1)
ax[0].set_title(label, fontsize=20)
plt.savefig(snakemake.output.pileup_neg_TU, dpi=200)

# Plot pileup genes

# Plot the pileup ratio.
bcp.pileup_plot(
    pileup=pileup_pos,
    pileup_control=pileup_neg,
    gen_tracks=[rna_pileup_pos],
    gen_tracks_control=[rna_pileup_neg],
    binning=binning,
    window=50000,
    ratio="log",
    out_file=snakemake.output.pileup,
    title=label,
    dpi=200,
    vmax=0.2,
)
bcp.pileup_plot(
    pileup=pileup_pos,
    pileup_control=pileup_neg,
    gen_tracks=[rna_pileup_pos],
    gen_tracks_control=[rna_pileup_neg],
    binning=binning,
    window=25000,
    ratio="log",
    out_file=snakemake.output.pileup_zoom,
    title=label,
    dpi=200,
    vmax=0.2,
)
bcp.pileup_plot(
    pileup=pileup_pos,
    pileup_control=pileup_neg,
    gen_tracks=[rna_pileup_pos],
    gen_tracks_control=[rna_pileup_neg],
    binning=binning,
    window=10000,
    ratio="log",
    out_file=snakemake.output.pileup_zoom2,
    title=label,
    dpi=200,
    vmax=0.2,
)

Python Pandas numpy matplotlib scipy cooler cooler bacchus From line 4 of scripts/pileup_species.py

import bacchus.hic as bch
import bacchus.io as bcio
import bacchus.plot as bcp
import cooler
import copy
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
import os
from os.path import join
import pandas as pd
from scipy import ndimage
import seaborn as sns

# Import snakemake parameters.
annotation_file = snakemake.input.annotation
mat_wt_file = str(snakemake.input.cool_wt)
mat_rf_file = str(snakemake.input.cool_rf)
rna_wt_file = snakemake.input.rna_wt
rna_rf_file = snakemake.input.rna_rf
cov_wt_file = snakemake.input.cov_wt
cov_rf_file = snakemake.input.cov_rf
gc_file = snakemake.input.gc
epod_file = snakemake.input.EPODs
res = int(snakemake.params.res)
cmap = snakemake.params.cmap
outdir = str(snakemake.params.outdir)
width = snakemake.params.width
pos = snakemake.params.positions

# Create outdir if necessary.
os.makedirs(outdir, exist_ok=True)


def import_annotation_gff(annotation_file):
    """Function to create a table of the gene positions from the gff file."""
    annotation = pd.DataFrame(
        columns=["type", "start", "end", "strand", "name"]
    )
    with open(annotation_file, "r") as file:
        for line in file:
            # Header.
            if line.startswith("#"):
                continue
            # Stop at the fasta sequences.
            elif line.startswith(">"):
                break
            else:
                line = line.split("\t")
                if line[2] in ["gene", "tRNA", "rRNA"]:
                    if line[2] == "gene":
                        name = line[8].split("Name=")[-1].split(";")[0]
                        # Extract gene position.
                        annot = {
                            "type": line[2],
                            "start": int(line[3]),
                            "end": int(line[4]),
                            "strand": line[6],
                            "gene_name": name,
                        }
                        annotation = annotation.append(annot, ignore_index=True)
    return annotation


# Import files.
annotation = import_annotation_gff(annotation_file)
mat_wt = cooler.Cooler(f"{mat_wt_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_wt[np.isnan(mat_wt)] = 0
mat_rf = cooler.Cooler(f"{mat_rf_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_rf[np.isnan(mat_rf)] = 0
rna_wt, _ = bcio.extract_big_wig(rna_wt_file, binning=100)
rna_rf, _ = bcio.extract_big_wig(rna_rf_file, binning=100)
cov_wt, _ = bcio.extract_big_wig(cov_wt_file, binning=1000)
cov_rf, _ = bcio.extract_big_wig(cov_wt_file, binning=1000)
gc_content = pd.read_csv(gc_file, sep="\t", header=None).iloc[:, 1]
epod = pd.read_csv(epod_file, sep="\t", header=None)

# Make the rotation of the matrix to plot them
mat_wt_rot = copy.copy(mat_wt)
mat_wt_rot = bch.interpolate_white_lines(mat_wt_rot)
mat_wt_rot[np.isnan(mat_wt_rot)] = 0
mat_wt_rot = ndimage.rotate(mat_wt_rot, 45, reshape=True)
mat_rf_rot = copy.copy(mat_rf)
mat_rf_rot = bch.interpolate_white_lines(mat_rf_rot)
mat_rf_rot[np.isnan(mat_rf_rot)] = 0
mat_rf_rot = ndimage.rotate(mat_rf_rot, 45, reshape=True)


def plot_region(
    M1,
    M1_rot,
    M2,
    M2_rot,
    rna1,
    rna2,
    cov1,
    cov2,
    annotation,
    binning,
    width,
    zoom_ini,
    outfile,
    split=False,
):
    rna_binning = 100
    pal = sns.color_palette("Paired")

    # Defined values to specify the borders of the matrices and tables
    zoom = [zoom // binning for zoom in zoom_ini]
    width = width / 1000
    row1 = len(M1_rot) // 2 - int(np.sqrt(2) * width)
    row2 = len(M1_rot) // 2 + int(np.sqrt(2) * width)
    col1 = int(zoom[0] * np.sqrt(2))
    col2 = int(zoom[1] * np.sqrt(2))
    annotation_zoom = annotation[
        np.logical_and(
            annotation.start > zoom_ini[0], annotation.end < zoom_ini[1]
        )
    ]
    ymax = np.nanmax(
        np.concatenate(
            (
                rna1[zoom_ini[0] // rna_binning : zoom_ini[1] // rna_binning],
                rna2[zoom_ini[0] // rna_binning : zoom_ini[1] // rna_binning],
            )
        )
    )
    max_cov = np.nanmax(cov1) * 1.02
    min_cov = np.nanmin(cov1) * 0.98

    # Define panels depending on split or not.
    if split and ymax > 1500:
        a = 4
        fig, ax = plt.subplots(
            7,
            2,
            figsize=(16, 12),
            gridspec_kw={"height_ratios": [1, 30, 1, 6, 12, 3, 3]},
            sharex=False,
        )

        # Parameters to merge panel 2 and 3.
        d = 0.02
        D = 0.04
        for j in range(2):
            ax[a - 1, j].set_xlim(zoom_ini[0] // 1000, zoom_ini[1] // 1000)
            ax[a - 1, j].tick_params(axis="both", labelsize=15)
            ax[a - 1, j].spines["bottom"].set_visible(False)
            ax[a - 1, j].spines["top"].set_visible(False)
            ax[a - 1, j].spines["right"].set_visible(False)
            ax[a - 1, j].set_ylim(ymax - 525, ymax)
            ax[a - 1, j].get_xaxis().set_visible(False)

            # Add the small cut between the two panels
            kwargs = dict(
                transform=ax[a - 1, j].transAxes, color="k", clip_on=False
            )
            ax[a - 1, j].plot((-d, +d), (-D, +D), **kwargs)  # top-left diagonal
            kwargs.update(
                transform=ax[a, j].transAxes
            )  # switch to the bottom axes
            ax[a, j].plot(
                (-d, +d), (1 - d, 1 + d), **kwargs
            )  # bottom-left diagonal

        # Plot the RNA on the split panel
        ax[a - 1, 0].fill_between(
            np.arange(
                zoom_ini[0] // 1000, zoom_ini[1] // 1000, rna_binning / 1000
            ),
            rna1[zoom_ini[0] // rna_binning : zoom_ini[1] // rna_binning],
            color="black",
        )
        ax[a - 1, 1].fill_between(
            np.arange(
                zoom_ini[0] // 1000, zoom_ini[1] // 1000, rna_binning / 1000
            ),
            rna2[zoom_ini[0] // rna_binning : zoom_ini[1] // rna_binning],
            color="black",
        )

    else:
        a = 3
        fig, ax = plt.subplots(
            6,
            2,
            figsize=(16, 12),
            gridspec_kw={"height_ratios": [1, 30, 1, 15, 3, 3]},
            sharex=False,
        )

    # Plot expressed genes - blue are forward - red are reversed
    for j in range(2):
        ax[0, j].set_xlim(zoom_ini[0], zoom_ini[1])
        ax[0, j].get_xaxis().set_visible(False)
        ax[0, j].get_yaxis().set_visible(False)
        ax[2, j].set_xlim(zoom_ini[0], zoom_ini[1])
        ax[2, j].get_xaxis().set_visible(False)
        ax[2, j].get_yaxis().set_visible(False)
    pos = 1
    for i in range(len(annotation_zoom)):
        # Extract annotation information
        annot = annotation_zoom.iloc[
            i,
        ]
        strand = annot.strand
        start = annot.start // rna_binning
        end = annot.end // rna_binning
        name = annot.gene_name
        # Defined color depending on the strand
        if strand == "+":
            color = pal.as_hex()[1]
        else:
            color = pal.as_hex()[5]
        # Print it only if it transcribed (10% most transcribed genes)
        if np.mean(rna1[start:end]) >= 120.7628998139441:
            pos = pos * -1
            for j in range(2):
                ax[0, j].add_patch(
                    patches.Rectangle(
                        (start * 100, 0),
                        (end - start) * 100,
                        1,
                        edgecolor=color,
                        facecolor=color,
                        fill=True,
                    )
                )
                ax[0, j].text(
                    x=(start + ((end - start) / 2)) * 100,
                    y=pos + 0.5,
                    s=name,
                    rotation=90 * pos,
                    wrap=True,
                )
    color = pal.as_hex()[3]
    for i in epod.index:
        start = epod.loc[i, 3]
        end = epod.loc[i, 4]
        if (
            (start > zoom_ini[0])
            and (start < zoom_ini[1])
            or (end > zoom_ini[0])
            and (end < zoom_ini[1])
        ):
            start = max(start, zoom_ini[0]) // rna_binning
            end = min(end, zoom_ini[1]) // rna_binning
            for j in range(2):
                ax[2, j].add_patch(
                    patches.Rectangle(
                        (start * 100, 0),
                        (end - start) * 100,
                        1,
                        edgecolor=color,
                        facecolor=color,
                        fill=True,
                    )
                )
    #                 ax[2, j].text(
    #                     x=(start + ((end - start) / 2)) * 100,
    #                     y=pos + 0.5,
    #                     s="EPOD",
    #                     rotation=90 * pos,
    #                     wrap=True,
    #                 )

    # Plot the matrices
    ax[1, 0].set_ylabel("Genomic distance (kb)", fontsize=15)
    for j in range(2):
        ax[1, j].get_xaxis().set_visible(False)
        ax[1, j].tick_params(axis="both", labelsize=15)

    # Plot the matrice 1
    im = ax[1, 0].imshow(
        M1_rot[row1:row2, col1:col2],
        cmap=cmap,
        interpolation="none",
        vmin=0,
        vmax=np.nanpercentile(M1[zoom[0] : zoom[1], zoom[0] : zoom[1]], 97),
        extent=(col1 / np.sqrt(2), col2 / np.sqrt(2), width, -width),
    )

    # Plot the matrice 2
    ax[1, 1].imshow(
        M2_rot[row1:row2, col1:col2],
        cmap=cmap,
        interpolation="none",
        vmin=0,
        vmax=np.nanpercentile(M1[zoom[0] : zoom[1], zoom[0] : zoom[1]], 97),
        extent=(col1 / np.sqrt(2), col2 / np.sqrt(2), width, -width),
    )

    # RNAseq legends and plot at the bottom panel.
    ax[a, 0].set_ylabel("RNA count (CPM)", fontsize=15)
    for j in range(2):
        #         ax[a, j].set_xlabel("Genomic coordinates (kb)", size=15)
        ax[a, j].get_xaxis().set_visible(False)
        ax[a, j].tick_params(axis="both", labelsize=15)
        ax[a, j].set_xlim(zoom_ini[0] // 1000, zoom_ini[1] // 1000)
        ax[a, j].spines["top"].set_visible(False)
        ax[a, j].spines["right"].set_visible(False)
        if split:
            if ymax > 1500:
                ax[a, j].set_ylim(0, 1050)
            else:
                ax[a, j].set_ylim(0, 1500)
        else:
            ax[a, j].set_ylim(0, ymax)

    # Plot RNA at the bottom panel
    ax[a, 0].fill_between(
        np.arange(zoom_ini[0] // 1000, zoom_ini[1] // 1000, rna_binning / 1000),
        rna1[zoom_ini[0] // rna_binning : zoom_ini[1] // rna_binning],
        color="black",
    )
    ax[a, 1].fill_between(
        np.arange(zoom_ini[0] // 1000, zoom_ini[1] // 1000, rna_binning / 1000),
        rna2[zoom_ini[0] // rna_binning : zoom_ini[1] // rna_binning],
        color="black",
    )

    # GC content
    gc_binning = 100
    for j in range(2):
        ax[a + 1, j].plot(
            np.arange(
                zoom_ini[0] // 1000, zoom_ini[1] // 1000, gc_binning / 1000
            ),
            gc_content[
                (zoom_ini[0] + 250)
                // gc_binning : (zoom_ini[1] + 250)
                // gc_binning
            ],
            color="black",
        )
        ax[a + 1, j].set_xlim(zoom_ini[0] // 1000, zoom_ini[1] // 1000)
        ax[a + 1, j].tick_params(axis="both", labelsize=15)
        ax[a + 1, j].get_xaxis().set_visible(False)
    ax[a + 1, 0].set_ylabel("GC", fontsize=15)

    # Coverage
    cov_binning = 1000
    ax[a + 2, 0].plot(
        np.arange(zoom_ini[0] // 1000, zoom_ini[1] // 1000, cov_binning / 1000),
        cov1[
            (zoom_ini[0] + 250)
            // cov_binning : (zoom_ini[1] + 250)
            // cov_binning
        ],
        color="black",
    )
    ax[a + 2, 1].plot(
        np.arange(zoom_ini[0] // 1000, zoom_ini[1] // 1000, cov_binning / 1000),
        cov2[
            (zoom_ini[0] + 250)
            // cov_binning : (zoom_ini[1] + 250)
            // cov_binning
        ],
        color="black",
    )
    for j in range(2):
        ax[a + 2, j].set_xlim(zoom_ini[0] // 1000, zoom_ini[1] // 1000)
        ax[a + 2, j].set_ylim(min_cov, max_cov)
        ax[a + 2, j].set_xlabel("Genomic coordinates (kb)", size=15)
        ax[a + 2, j].tick_params(axis="both", labelsize=15)
    ax[a + 2, 0].set_ylabel("HiC\ncoverage\n(CPM)", fontsize=15)

    # Colorbar
    cbar = fig.colorbar(
        im, ax=ax.ravel().tolist(), shrink=0.3, anchor=(1.2, 0.75)
    )
    cbar.ax.tick_params(labelsize=15)

    # Save the fig and adjust margins
    if split and ymax > 1500:
        plt.subplots_adjust(wspace=0.2, hspace=0.1)
    else:
        plt.subplots_adjust(wspace=0.2, hspace=0.1)
    plt.savefig(outfile, bbox_inches="tight", dpi=200)


for split in [True, False]:
    if split:
        outfile = join(
            outdir,
            f"region_{pos[0]}_{pos[1]}_split.pdf",
        )
    else:
        outfile = join(
            outdir,
            f"region_{pos[0]}_{pos[1]}.pdf",
        )
    position = [int(p) * 1000 for p in pos]
    plot_region(
        mat_wt,
        mat_wt_rot,
        mat_rf,
        mat_rf_rot,
        rna_wt,
        rna_rf,
        cov_wt,
        cov_rf,
        annotation,
        res,
        width,
        position,
        outfile,
        split=split,
    )

Python Snakemake Pandas numpy matplotlib seaborn scipy cooler cooler bacchus From line 4 of scripts/plot_zoom.py

import bacchus.hic as bch
import cooler
import hicstuff.hicstuff as hcs
import matplotlib.pyplot as plt
import numpy as np
import copy 
import os
from scipy import ndimage
import bacchus.plot as bcp

# Import snakemake.
wt_file = snakemake.input.mat_wt
pompA_file = snakemake.input.mat_pompA
prpsM_file = snakemake.input.mat_prpsM
cmap = snakemake.params.cmap
res = snakemake.params.res
width = snakemake.params.width

out_wt = str(snakemake.output.wt)
out_pompA = str(snakemake.output.pompA)
out_prpsM = str(snakemake.output.prpsM)

# Make sure output directory exists.
os.makedirs(str((snakemake.params.outdir)), exist_ok=True)

files = [wt_file, pompA_file, prpsM_file]
outs = [out_wt, out_pompA, out_prpsM]
title = ['WT', "pompA", "prpsM"]
hic = [0, 0, 0]
start = 300000 // res
end = 420000 // res
subsample = 24593739
for i, file in enumerate(files):
    clr = cooler.Cooler(f"{file}::/resolutions/{res}")
    mat = clr.matrix(balance=False, sparse=True)[: :]
    print(mat.sum())
    mat = hcs.subsample_contacts(mat, subsample) 
    mat = hcs.normalize_sparse(mat, norm='ICE', iterations=100, n_mad=10)
    hic[i] = mat.toarray()
    bcp.contact_map(
        hic[i],
        binning=res,
        title=title[i],
        zmax=0.002,
        out_file=outs[i],
        start=start,
        end=end,
    )

Python numpy matplotlib scipy cooler cooler bacchus hicstuff From line 4 of scripts/prom_matrices.py

from pickletools import markobject
import bacchus.hic as bch
import bacchus.io as bcio
import cooler
import copy
import hicstuff.hicstuff as hcs
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy import ndimage
import serpentine as srp

# Import snakemake.
mat_t7_glu_file = snakemake.input.mat_t7_glu
mat_t7_ara_file = snakemake.input.mat_t7_ara
mat_t7_glu_rif_file = snakemake.input.mat_t7_glu_rif
mat_t7_ara_rif_file = snakemake.input.mat_t7_ara_rif
rna_t7_glu = snakemake.input.rna_t7_glu
rna_t7_ara = snakemake.input.rna_t7_ara
rna_t7_glu_rif = snakemake.input.rna_t7_glu_rif
rna_t7_ara_rif = snakemake.input.rna_t7_ara_rif

cmap = snakemake.params.cmap
res = snakemake.params.res
res_ratio = snakemake.params.res_ratio
width = snakemake.params.width
cpus = snakemake.threads

outfile = str(snakemake.output.matrices)
out_zoom = str(snakemake.output.zoom)
out_zoom_ratio = str(snakemake.output.zoom_ratio)
out_rna = str(snakemake.output.rna_only)

# Make sure output directory exists.
os.makedirs(str(snakemake.params.outdir), exist_ok=True)

# Import matrices
mat_t7_glu = cooler.Cooler(f"{mat_t7_glu_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_glu[np.isnan(mat_t7_glu)] = 0
mat_t7_ara = cooler.Cooler(f"{mat_t7_ara_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_ara[np.isnan(mat_t7_ara)] = 0
mat_t7_glu_rif = cooler.Cooler(f"{mat_t7_glu_rif_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_glu_rif[np.isnan(mat_t7_glu_rif)] = 0
mat_t7_ara_rif = cooler.Cooler(f"{mat_t7_ara_rif_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_ara_rif[np.isnan(mat_t7_ara_rif)] = 0

# Do the ratio
def contact_map_ratio(
    M1,
    M2,
    iterations=10,
    threshold=10,
    cpus=16,
):
    """Function to compute the log ratio of two matrices and use serpentine to
    return a smoother log ratio.
    """
    M1 = M1.tocoo()
    M2 = M2.tocoo()
    m1 = M1.sum()
    m2 = M2.sum()
    if m1 < m2:
        M2 = hcs.subsample_contacts(M2, int(m1))
    else:
        M1 = hcs.subsample_contacts(M1, int(m2))
    subsample = min(m1, m2)
    print("Matrices have been subsampled to {0} contacts.".format(subsample))
    # M1 = M1.tocsr() - M1.tocsr().multiply(
    #     get_win_density(M1, win_size=3, sym_upper=True) < 2 / 9
    # )
    # M2 = M2.tocsr() - M2.tocsr().multiply(
    #     get_win_density(M2, win_size=3, sym_upper=True) < 2 / 9
    # )
    M1 = bch.get_symmetric(M1).toarray()
    M2 = bch.get_symmetric(M2).toarray()
    if threshold == "auto":
        trend, threshold = srp.MDbefore(M1, M2, show=False)
    else:
        threshold = int(threshold)
    M1_serp, M2_serp, ratio_srp = srp.serpentin_binning(
        M1,
        M2,
        parallel=cpus,
        triangular=False,
        threshold=threshold,
        minthreshold=threshold / 5,
        iterations=iterations,
        verbose=False,
    )
    ratio_log = np.log2(M2) - np.log2(M1)
    return M1_serp, M2_serp, ratio_log, ratio_srp, subsample

mat_t7_glu_sparse = cooler.Cooler(f"{mat_t7_glu_file}::/resolutions/{res_ratio}").matrix(
    balance=False, sparse=True
)[:]
mat_t7_ara_sparse = cooler.Cooler(f"{mat_t7_ara_file}::/resolutions/{res_ratio}").matrix(
    balance=False, sparse=True
)[:]
mat_t7_glu_rif_sparse = cooler.Cooler(f"{mat_t7_glu_rif_file}::/resolutions/{res_ratio}").matrix(
    balance=False, sparse=True
)[:]
mat_t7_ara_rif_sparse = cooler.Cooler(f"{mat_t7_ara_rif_file}::/resolutions/{res_ratio}").matrix(
    balance=False, sparse=True
)[:]

(
    _,
    _,
    ratio_log_glu,
    ratio_srp_glu,
    subsample,
) = contact_map_ratio(mat_t7_glu_sparse, mat_t7_ara_sparse, iterations=25, threshold=100, cpus=cpus)
(
    _,
    _,
    ratio_log_ara,
    ratio_srp_ara,
    subsample,
) = contact_map_ratio(mat_t7_glu_rif_sparse, mat_t7_ara_rif_sparse, iterations=25, threshold=100, cpus=cpus)

###
# PLOT MATRICES
###

title = ["Glucose", "Arabinose", "Glucose + Rif", "Arabinose + Rif"]
hic = [mat_t7_glu, mat_t7_ara, mat_t7_glu_rif, mat_t7_ara_rif]
ratio = [
    ratio_srp_glu - ratio_srp_glu.mean(),
    ratio_srp_ara - ratio_srp_ara.mean(),
]
start = 0
end = len(hic[0])

fig, ax = plt.subplots(2, 3, figsize=(10, 10))
for i in range(2):
    for j in range(2):
        ax[i, j].imshow(
            hic[i + 2 * j][start:end, start:end],
            vmin=0,
            vmax=np.nanpercentile(hic[0], 99),
            cmap="Reds",
            extent=(start, end, end, start),
        )
        ax[i, j].set_title(title[i + 2 * j])
    ax[i, 2].imshow(
        ratio[i][start:end, start:end],
        vmin=-2,
        vmax=2,
        cmap="seismic",
        extent=(start, end, end, start),
    )
    ax[i, 2].set_title("Rif+/Rif-")

plt.savefig(outfile)
plt.close()

###
# Plot zoom ratio.
###

start, end = 260 // 5, 560 // 5
fig, ax = plt.subplots(2, 1, figsize=(5, 10))
for i in range(2):
    ax[i].imshow(
        ratio[i][start:end, start:end],
        vmin=-2,
        vmax=2,
        cmap="seismic",
        extent=(start, end, end, start),
    )
plt.savefig(out_zoom_ratio)
plt.close()

###
# Plot zoom with RNAseq.
###

# Rotate matrices.
hic_rot = copy.copy(hic)
for i in range(len(hic)):
    hic_rot[i] = bch.interpolate_white_lines(hic_rot[i])
    hic_rot[i][np.isnan(hic_rot[i])] = 0
    hic_rot[i] = ndimage.rotate(hic_rot[i], 45, reshape=True)

# Import RNA
rna_binning = 100
rna_t7_glu, _ = bcio.extract_big_wig(rna_t7_glu, rna_binning)
rna_t7_ara, _ = bcio.extract_big_wig(rna_t7_ara, rna_binning)
rna_t7_glu_rif, _ = bcio.extract_big_wig(rna_t7_glu_rif, rna_binning)
rna_t7_ara_rif, _ = bcio.extract_big_wig(rna_t7_ara_rif, rna_binning)
rna = [rna_t7_glu, rna_t7_ara, rna_t7_glu_rif, rna_t7_ara_rif]

fig, ax = plt.subplots(
    2, 4, figsize=(20, 10), gridspec_kw={"height_ratios": [8, 4]}, sharex=False
)

start = 300_000
end = 480_000

row1 = len(hic_rot[0]) // 2 - int(np.sqrt(2) * (width / res))
row2 = len(hic_rot[0]) // 2 + int(np.sqrt(2) * (width / res))
col1 = int((start // res) * np.sqrt(2))
col2 = int((end // res) * np.sqrt(2))

for i in range(4):
    # Hicmap
    im = ax[0, i].imshow(
        hic_rot[i][row1:row2, col1:col2],
        cmap="Reds",
        vmin=0,
        vmax=np.nanpercentile(hic[0], 99),
        extent=(col1 / np.sqrt(2), col2 / np.sqrt(2), width // res, -width // res),
    )
    ax[0, i].get_xaxis().set_visible(False)
    ax[0, i].set_title(title[i], size=18)
    if i == 0:
        ax[0, i].tick_params(axis="both", labelsize=16)
        ax[0, i].set_ylabel("Genomic coordinates (kb)", size=16)
    else:
        ax[0, i].get_yaxis().set_visible(False)

    # RNAseq
    ax[1, i].set_xlabel("Genomic coordinates (kb)", size=16)
    if i == 0:
        ax[0, i].tick_params(axis="both", labelsize=16)
        ax[1, i].set_ylabel("Transcription (CPM)", size=16)
    else:
        ax[0, i].get_yaxis().set_visible(False)

    ax_rna = ax[1, i]
    p1 = ax_rna.plot(
        np.arange(start / 1000, end / 1000, rna_binning / 1000),
        rna[i][int(start / rna_binning) : int(end / rna_binning)],
        color="k",
        label="RNAseq",
        alpha=0.7,
        linewidth=1,
    )
    ax_rna.set_ylim(0, 8000)
    ax_rna.tick_params(axis="both", labelsize=16, color="k", labelcolor="k")
    ax_rna.spines["top"].set_visible(False)
    ax_rna.spines["right"].set_visible(False)

# Colorbar
cbar = fig.colorbar(im, ax=ax.ravel().tolist(), shrink=0.5, anchor=(1.2, 0.8))
cbar.ax.tick_params(labelsize=16)

# Adjust space between plot
plt.subplots_adjust(wspace=0.4, hspace=0.0, left=0.05, right=0.88)
plt.savefig(out_zoom)
plt.close()

# Plot RNAseq only
start = 300_000
end = 560_000

fig, ax = plt.subplots(1, 1, figsize=(10, 5))
ax.set_xlabel("Genomic coordinates (kb)", size=16)
ax.set_ylabel("Transcription (CPM)", size=16)
for i in [0, 1, 3]:
    ax.plot(
        np.arange(start / 1000, end / 1000, rna_binning / 1000),
        rna[i][int(start / rna_binning) : int(end / rna_binning)],
        label=title[i],
        alpha=0.7,
        linewidth=1,
    )
ax.set_ylim(0, 1500)
ax.tick_params(axis="both", labelsize=16, color="k", labelcolor="k")
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
plt.legend()
plt.savefig(out_rna)
plt.close()

Python numpy matplotlib scipy cooler cooler bacchus hicstuff Serpentine From line 4 of scripts/rifampicine_for.py

from pickletools import markobject
import bacchus.hic as bch
import bacchus.io as bcio
import cooler
import copy
import hicstuff.hicstuff as hcs
import matplotlib.pyplot as plt
import numpy as np
import os
from scipy import ndimage
import serpentine as srp

# Import snakemake.
mat_t7_glu_file = snakemake.input.mat_t7_glu
mat_t7_ara_file = snakemake.input.mat_t7_ara
mat_t7_glu_rif_file = snakemake.input.mat_t7_glu_rif
mat_t7_ara_rif_file = snakemake.input.mat_t7_ara_rif
rna_t7_glu = snakemake.input.rna_t7_glu
rna_t7_ara = snakemake.input.rna_t7_ara
rna_t7_glu_rif = snakemake.input.rna_t7_glu_rif
rna_t7_ara_rif = snakemake.input.rna_t7_ara_rif
chip_t7_glu = snakemake.input.chip_t7_glu
chip_t7_ara = snakemake.input.chip_t7_ara
chip_t7_glu_rif = snakemake.input.chip_t7_glu_rif
chip_t7_ara_rif_R1 = snakemake.input.chip_t7_ara_rif_R1
chip_t7_ara_rif_R2 = snakemake.input.chip_t7_ara_rif_R2

cmap = snakemake.params.cmap
res = snakemake.params.res
res_ratio = snakemake.params.res_ratio
width = snakemake.params.width
cpus = snakemake.threads

outdir = str(snakemake.params.outdir)
outfile = str(snakemake.output.matrices)
out_zoom = str(snakemake.output.zoom)
out_zoom_ratio = str(snakemake.output.zoom_ratio)

# Make sure output directory exists.
os.makedirs(outdir, exist_ok=True)

# Import matrices
mat_t7_glu = cooler.Cooler(f"{mat_t7_glu_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_glu[np.isnan(mat_t7_glu)] = 0
mat_t7_ara = cooler.Cooler(f"{mat_t7_ara_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_ara[np.isnan(mat_t7_ara)] = 0
mat_t7_glu_rif = cooler.Cooler(f"{mat_t7_glu_rif_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_glu_rif[np.isnan(mat_t7_glu_rif)] = 0
mat_t7_ara_rif = cooler.Cooler(f"{mat_t7_ara_rif_file}::/resolutions/{res}").matrix(
    balance=True, sparse=False
)[:]
mat_t7_ara_rif[np.isnan(mat_t7_ara_rif)] = 0

# Do the ratio
def contact_map_ratio(
    M1,
    M2,
    iterations=10,
    threshold=10,
    cpus=16,
):
    """Function to compute the log ratio of two matrices and use serpentine to
    return a smoother log ratio.
    """
    M1 = M1.tocoo()
    M2 = M2.tocoo()
    m1 = M1.sum()
    m2 = M2.sum()
    if m1 < m2:
        M2 = hcs.subsample_contacts(M2, int(m1))
    else:
        M1 = hcs.subsample_contacts(M1, int(m2))
    subsample = min(m1, m2)
    print("Matrices have been subsampled to {0} contacts.".format(subsample))
    # M1 = M1.tocsr() - M1.tocsr().multiply(
    #     get_win_density(M1, win_size=3, sym_upper=True) < 2 / 9
    # )
    # M2 = M2.tocsr() - M2.tocsr().multiply(
    #     get_win_density(M2, win_size=3, sym_upper=True) < 2 / 9
    # )
    M1 = bch.get_symmetric(M1).toarray()
    M2 = bch.get_symmetric(M2).toarray()
    if threshold == "auto":
        trend, threshold = srp.MDbefore(M1, M2, show=False)
    else:
        threshold = int(threshold)
    M1_serp, M2_serp, ratio_srp = srp.serpentin_binning(
        M1,
        M2,
        parallel=cpus,
        triangular=False,
        threshold=threshold,
        minthreshold=threshold / 5,
        iterations=iterations,
        verbose=False,
    )
    ratio_log = np.log2(M2) - np.log2(M1)
    return M1_serp, M2_serp, ratio_log, ratio_srp, subsample

mat_t7_glu_sparse = cooler.Cooler(f"{mat_t7_glu_file}::/resolutions/{res_ratio}").matrix(
    balance=False, sparse=True
)[:]
mat_t7_ara_sparse = cooler.Cooler(f"{mat_t7_ara_file}::/resolutions/{res_ratio}").matrix(
    balance=False, sparse=True
)[:]
mat_t7_glu_rif_sparse = cooler.Cooler(f"{mat_t7_glu_rif_file}::/resolutions/{res_ratio}").matrix(
    balance=False, sparse=True
)[:]
mat_t7_ara_rif_sparse = cooler.Cooler(f"{mat_t7_ara_rif_file}::/resolutions/{res_ratio}").matrix(
    balance=False, sparse=True
)[:]

(
    _,
    _,
    ratio_log_glu,
    ratio_srp_glu,
    subsample,
) = contact_map_ratio(mat_t7_glu_sparse, mat_t7_ara_sparse, iterations=25, threshold=100, cpus=cpus)
(
    _,
    _,
    ratio_log_ara,
    ratio_srp_ara,
    subsample,
) = contact_map_ratio(mat_t7_glu_rif_sparse, mat_t7_ara_rif_sparse, iterations=25, threshold=100, cpus=cpus)


###
# PLOT MATRICES
###

title = ["Glucose", "Arabinose", "Glucose + Rif", "Arabinose + Rif"]
hic = [mat_t7_glu, mat_t7_ara, mat_t7_glu_rif, mat_t7_ara_rif]
ratio = [
    ratio_srp_glu - ratio_srp_glu.mean(),
    ratio_srp_ara - ratio_srp_ara.mean(),
]
start = 0
end = len(hic[0])

fig, ax = plt.subplots(2, 3, figsize=(10, 10))
for i in range(2):
    for j in range(2):
        ax[i, j].imshow(
            hic[i + 2 * j][start:end, start:end],
            vmin=0,
            vmax=np.nanpercentile(hic[0], 99),
            cmap="Reds",
            extent=(start, end, end, start),
        )
        ax[i, j].set_title(title[i + 2 * j])
    ax[i, 2].imshow(
        ratio[i][start:end, start:end],
        vmin=-2,
        vmax=2,
        cmap="seismic",
        extent=(start, end, end, start),
    )
    ax[i, 2].set_title("Rif+/Rif-")

plt.savefig(outfile, dpi=300)
plt.close()

###
# Plot zoom ratio.
###

start, end = 260 // 5, 560 // 5
fig, ax = plt.subplots(2, 1, figsize=(5, 10))
for i in range(2):
    ax[i].imshow(
        ratio[i][start:end, start:end],
        vmin=-2,
        vmax=2,
        cmap="seismic",
        extent=(start, end, end, start),
    )
plt.savefig(out_zoom_ratio)
plt.close()

###
# Plot zoom.
###

# Rotate matrices.
hic_rot = copy.copy(hic)
for i in range(len(hic)):
    hic_rot[i] = bch.interpolate_white_lines(hic_rot[i])
    hic_rot[i][np.isnan(hic_rot[i])] = 0
    hic_rot[i] = ndimage.rotate(hic_rot[i], 45, reshape=True)

start = 240_000
end = 420_000

row1 = len(hic_rot[0]) // 2 - int(np.sqrt(2) * (width / res))
row2 = len(hic_rot[0]) // 2 + int(np.sqrt(2) * (width / res))
col1 = int((start // res) * np.sqrt(2))
col2 = int((end // res) * np.sqrt(2))

# Import RNA
rna_binning = 100
rna_t7_glu, _ = bcio.extract_big_wig(rna_t7_glu, rna_binning)
rna_t7_ara, _ = bcio.extract_big_wig(rna_t7_ara, rna_binning)
rna_t7_glu_rif, _ = bcio.extract_big_wig(rna_t7_glu_rif, rna_binning)
rna_t7_ara_rif, _ = bcio.extract_big_wig(rna_t7_ara_rif, rna_binning)
rna = [rna_t7_glu, rna_t7_glu_rif, rna_t7_ara, rna_t7_ara_rif]

# Import ChIP
chip_binning = 100
chip_t7_glu, _ = bcio.extract_big_wig(chip_t7_glu, chip_binning)
chip_t7_ara, _ = bcio.extract_big_wig(chip_t7_ara, chip_binning)
chip_t7_glu_rif, _ = bcio.extract_big_wig(chip_t7_glu_rif, chip_binning)
chip_t7_ara_rif_R1, _ = bcio.extract_big_wig(chip_t7_ara_rif_R1, chip_binning)
chip_t7_ara_rif_R2, _ = bcio.extract_big_wig(chip_t7_ara_rif_R2, chip_binning)
T7_pol = [chip_t7_glu, chip_t7_glu_rif, chip_t7_ara, chip_t7_ara_rif_R1]

fig, ax = plt.subplots(
    3,
    4,
    figsize=(20, 6),
    gridspec_kw={"height_ratios": [15, 7, 7]},
    sharex=True,
)
for j in range(4):

    # HiC
    im = ax[0, j].imshow(
        hic_rot[j][row1:row2, col1:col2],
        cmap="Reds",
        vmax=0.002,
        vmin=0,
        extent=(col1 / np.sqrt(2), col2 / np.sqrt(2), width // res, -width // res),
    )

    # RNAseq
    ax[1, j].fill_between(
        np.arange(start / res, end / res, 0.1), rna[j][start // 100 : end // 100], color="k"
    )
    ax[1, j].set_ylim(0, 5000)

    # T7 pol Chip_seq
    ax[2, j].plot(
        np.arange(start / res, end / res, 0.1),
        T7_pol[j][start  // 100:end // 100],
    )
    ax[2, j].set_ylim(0, 5000)

    # Legend
    ax[0, j].set_title(title[j], size=16)
    ax[0, j].tick_params(axis="both", labelsize=12)
    ax[2, j].tick_params(axis="both", labelsize=12)
    if j != 0:
        ax[0, j].get_yaxis().set_visible(False)

    ax[1, j].set_xlabel("Genomic coordinates (kb)", fontsize=14)
ax[0, 0].set_ylabel("Genomic\ndistance (kb)", fontsize=14)
ax[1, 0].set_ylabel("Transcription\n(CPM)", fontsize=14)


# Colorbar
cbar = fig.colorbar(im, ax=ax.ravel().tolist(), shrink=0.3, anchor=(-0.2, 0.4))
cbar.ax.tick_params(labelsize=15)
plt.savefig(out_zoom)

Python numpy matplotlib scipy cooler cooler bacchus hicstuff Serpentine From line 4 of scripts/rifampicine_rev.py

tool / pypi

scipy

.. image:: https://raw.githubusercontent.com/scipy/scipy/main/doc/source/_static/logo.svg :target: https://scipy.org :width: 110 :height: 110 :align: left .. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A :target: https://numfocus.org .. image:: https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads :target: https://pypi.org/project/scipy/ .. image:: https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads :target: https://anaconda.org/conda-forge/scipy .. image:: https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg :target: https://stackoverflow.com/questions/tagged/scipy .. image:: https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue :target: https://www.nature.com/articles/s41592-019-0686-2 SciPy (pronounced "Sigh Pie") is an open-source software for mathematics, science, and engineering. It includes modules for statistics, optimization, integration, li

References:

https://pypi.python.org/pypi/scipy

https://scipy.org/

[email protected]

scipy

Metadata:

library: python:scipy

license: Copyright (c) 2001-2002 Enthought, Inc. 2003-2023, SciPy Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ---- This binary distribution of SciPy also bundles the following software: Name: GCC runtime library Files: .dylibs/* Description: dynamically linked to files compiled with gcc Availability: https://gcc.gnu.org/viewcvs/gcc/ License: GPLv3 + runtime exception Copyright (C) 2002-2017 Free Software Foundation, Inc. Libgfortran is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3, or (at your option) any later version. Libgfortran is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. Under Section 7 of GPL version 3, you are granted additional permissions described in the GCC Runtime Library Exception, version 3.1, as published by the Free Software Foundation. You should have received a copy of the GNU General Public License and a copy of the GCC Runtime Library Exception along with this program; see the files COPYING3 and COPYING.RUNTIME respectively. If not, see <http://www.gnu.org/licenses/>. ---- Full text of license texts referred to above follows (that they are listed below does not necessarily imply the conditions apply to the present binary release): ---- GCC RUNTIME LIBRARY EXCEPTION Version 3.1, 31 March 2009 Copyright (C) 2009 Free Software Foundation, Inc. <http://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. This GCC Runtime Library Exception ("Exception") is an additional permission under section 7 of the GNU General Public License, version 3 ("GPLv3"). It applies to a given file (the "Runtime Library") that bears a notice placed by the copyright holder of the file stating that the file is governed by GPLv3 along with this Exception. When you use GCC to compile a program, GCC may combine portions of certain GCC header files and runtime libraries with the compiled program. The purpose of this Exception is to allow compilation of non-GPL (including proprietary) programs to use, in this way, the header files and runtime libraries covered by this Exception. 0. Definitions. A file is an "Independent Module" if it either requires the Runtime Library for execution after a Compilation Process, or makes use of an interface provided by the Runtime Library, but is not otherwise based on the Runtime Library. "GCC" means a version of the GNU Compiler Collection, with or without modifications, governed by version 3 (or a specified later version) of the GNU General Public License (GPL) with the option of using any subsequent versions published by the FSF. "GPL-compatible Software" is software whose conditions of propagation, modification and use would permit combination with GCC in accord with the license of GCC. "Target Code" refers to output from any compiler for a real or virtual target processor architecture, in executable form or suitable for input to an assembler, loader, linker and/or execution phase. Notwithstanding that, Target Code does not include data in any format that is used as a compiler intermediate representation, or used for producing a compiler intermediate representation. The "Compilation Process" transforms code entirely represented in non-intermediate languages designed for human-written code, and/or in Java Virtual Machine byte code, into Target Code. Thus, for example, use of source code generators and preprocessors need not be considered part of the Compilation Process, since the Compilation Process can be understood as starting with the output of the generators or preprocessors. A Compilation Process is "Eligible" if it is done using GCC, alone or with other GPL-compatible software, or if it is done without using any work based on GCC. For example, using non-GPL-compatible Software to optimize any GCC intermediate representations would not qualify as an Eligible Compilation Process. 1. Grant of Additional Permission. You have permission to propagate a work of Target Code formed by combining the Runtime Library with Independent Modules, even if such propagation would otherwise violate the terms of GPLv3, provided that all Target Code was generated by Eligible Compilation Processes. You may then convey such a combination under terms of your choice, consistent with the licensing of the Independent Modules. 2. No Weakening of GCC Copyleft. The availability of this Exception does not imply any general presumption that third-party software is unaffected by the copyleft requirements of the license of GCC. ---- GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things. To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. Therefore, you have certain responsibilities if you distribute copies of the software, or if you modify it: responsibilities to respect the freedom of others. For example, if you distribute copies of such a program, whether gratis or for a fee, you must pass on to the recipients the same freedoms that you received. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. Developers that use the GNU GPL protect your rights with two steps: (1) assert copyright on the software, and (2) offer you this License giving you legal permission to copy, distribute and/or modify it. For the developers' and authors' protection, the GPL clearly explains that there is no warranty for this free software. For both users' and authors' sake, the GPL requires that modified versions be marked as changed, so that their problems will not be attributed erroneously to authors of previous versions. Some devices are designed to deny users access to install or run modified versions of the software inside them, although the manufacturer can do so. This is fundamentally incompatible with the aim of protecting users' freedom to change the software. The systematic pattern of such abuse occurs in the area of products for individuals to use, which is precisely where it is most unacceptable. Therefore, we have designed this version of the GPL to prohibit the practice for those products. If such problems arise substantially in other domains, we stand ready to extend this provision to those domains in future versions of the GPL, as needed to protect the freedom of users. Finally, every program is threatened constantly by software patents. States should not allow patents to restrict development and use of software on general-purpose computers, but in those that do, we wish to avoid the special danger that patents applied to a free program could make it effectively proprietary. To prevent this, the GPL assures that patents cannot be used to render the program non-free. The precise terms and conditions for copying, distribution and modification follow. TERMS AND CONDITIONS 0. Definitions. "This License" refers to version 3 of the GNU General Public License. "Copyright" also means copyright-like laws that apply to other kinds of works, such as semiconductor masks. "The Program" refers to any copyrightable work licensed under this License. Each licensee is addressed as "you". "Licensees" and "recipients" may be individuals or organizations. To "modify" a work means to copy from or adapt all or part of the work in a fashion requiring copyright permission, other than the making of an exact copy. The resulting work is called a "modified version" of the earlier work or a work "based on" the earlier work. A "covered work" means either the unmodified Program or a work based on the Program. To "propagate" a work means to do anything with it that, without permission, would make you directly or secondarily liable for infringement under applicable copyright law, except executing it on a computer or modifying a private copy. Propagation includes copying, distribution (with or without modification), making available to the public, and in some countries other activities as well. To "convey" a work means any kind of propagation that enables other parties to make or receive copies. Mere interaction with a user through a computer network, with no transfer of a copy, is not conveying. An interactive user interface displays "Appropriate Legal Notices" to the extent that it includes a convenient and prominently visible feature that (1) displays an appropriate copyright notice, and (2) tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion. 1. Source Code. The "source code" for a work means the preferred form of the work for making modifications to it. "Object code" means any non-source form of a work. A "Standard Interface" means an interface that either is an official standard defined by a recognized standards body, or, in the case of interfaces specified for a particular programming language, one that is widely used among developers working in that language. The "System Libraries" of an executable work include anything, other than the work as a whole, that (a) is included in the normal form of packaging a Major Component, but which is not part of that Major Component, and (b) serves only to enable use of the work with that Major Component, or to implement a Standard Interface for which an implementation is available to the public in source code form. A "Major Component", in this context, means a major essential component (kernel, window system, and so on) of the specific operating system (if any) on which the executable work runs, or a compiler used to produce the work, or an object code interpreter used to run it. The "Corresponding Source" for a work in object code form means all the source code needed to generate, install, and (for an executable work) run the object code and to modify the work, including scripts to control those activities. However, it does not include the work's System Libraries, or general-purpose tools or generally available free programs which are used unmodified in performing those activities but which are not part of the work. For example, Corresponding Source includes interface definition files associated with source files for the work, and the source code for shared libraries and dynamically linked subprograms that the work is specifically designed to require, such as by intimate data communication or control flow between those subprograms and other parts of the work. The Corresponding Source need not include anything that users can regenerate automatically from other parts of the Corresponding Source. The Corresponding Source for a work in source code form is that same work. 2. Basic Permissions. All rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law. You may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. You may convey covered works to others for the sole purpose of having them make modifications exclusively for you, or provide you with facilities for running those works, provided that you comply with the terms of this License in conveying all material for which you do not control copyright. Those thus making or running the covered works for you must do so exclusively on your behalf, under your direction and control, on terms that prohibit them from making any copies of your copyrighted material outside their relationship with you. Conveying under any other circumstances is permitted solely under the conditions stated below. Sublicensing is not allowed; section 10 makes it unnecessary. 3. Protecting Users' Legal Rights From Anti-Circumvention Law. No covered work shall be deemed part of an effective technological measure under any applicable law fulfilling obligations under article 11 of the WIPO copyright treaty adopted on 20 December 1996, or similar laws prohibiting or restricting circumvention of such measures. When you convey a covered work, you waive any legal power to forbid circumvention of technological measures to the extent such circumvention is effected by exercising rights under this License with respect to the covered work, and you disclaim any intention to limit operation or modification of the work as a means of enforcing, against the work's users, your or third parties' legal rights to forbid circumvention of technological measures. 4. Conveying Verbatim Copies. You may convey verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice; keep intact all notices stating that this License and any non-permissive terms added in accord with section 7 apply to the code; keep intact all notices of the absence of any warranty; and give all recipients a copy of this License along with the Program. You may charge any price or no price for each copy that you convey, and you may offer support or warranty protection for a fee. 5. Conveying Modified Source Versions. You may convey a work based on the Program, or the modifications to produce it from the Program, in the form of source code under the terms of section 4, provided that you also meet all of these conditions: a) The work must carry prominent notices stating that you modified it, and giving a relevant date. b) The work must carry prominent notices stating that it is released under this License and any conditions added under section 7. This requirement modifies the requirement in section 4 to "keep intact all notices". c) You must license the entire work, as a whole, under this License to anyone who comes into possession of a copy. This License will therefore apply, along with any applicable section 7 additional terms, to the whole of the work, and all its parts, regardless of how they are packaged. This License gives no permission to license the work in any other way, but it does not invalidate such permission if you have separately received it. d) If the work has interactive user interfaces, each must display Appropriate Legal Notices; however, if the Program has interactive interfaces that do not display Appropriate Legal Notices, your work need not make them do so. A compilation of a covered work with other separate and independent works, which are not by their nature extensions of the covered work, and which are not combined with it such as to form a larger program, in or on a volume of a storage or distribution medium, is called an "aggregate" if the compilation and its resulting copyright are not used to limit the access or legal rights of the compilation's users beyond what the individual works permit. Inclusion of a covered work in an aggregate does not cause this License to apply to the other parts of the aggregate. 6. Conveying Non-Source Forms. You may convey a covered work in object code form under the terms of sections 4 and 5, provided that you also convey the machine-readable Corresponding Source under the terms of this License, in one of these ways: a) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by the Corresponding Source fixed on a durable physical medium customarily used for software interchange. b) Convey the object code in, or embodied in, a physical product (including a physical distribution medium), accompanied by a written offer, valid for at least three years and valid for as long as you offer spare parts or customer support for that product model, to give anyone who possesses the object code either (1) a copy of the Corresponding Source for all the software in the product that is covered by this License, on a durable physical medium customarily used for software interchange, for a price no more than your reasonable cost of physically performing this conveying of source, or (2) access to copy the Corresponding Source from a network server at no charge. c) Convey individual copies of the object code with a copy of the written offer to provide the Corresponding Source. This alternative is allowed only occasionally and noncommercially, and only if you received the object code with such an offer, in accord with subsection 6b. d) Convey the object code by offering access from a designated place (gratis or for a charge), and offer equivalent access to the Corresponding Source in the same way through the same place at no further charge. You need not require recipients to copy the Corresponding Source along with the object code. If the place to copy the object code is a network server, the Corresponding Source may be on a different server (operated by you or a third party) that supports equivalent copying facilities, provided you maintain clear directions next to the object code saying where to find the Corresponding Source. Regardless of what server hosts the Corresponding Source, you remain obligated to ensure that it is available for as long as needed to satisfy these requirements. e) Convey the object code using peer-to-peer transmission, provided you inform other peers where the object code and Corresponding Source of the work are being offered to the general public at no charge under subsection 6d. A separable portion of the object code, whose source code is excluded from the Corresponding Source as a System Library, need not be included in conveying the object code work. A "User Product" is either (1) a "consumer product", which means any tangible personal property which is normally used for personal, family, or household purposes, or (2) anything designed or sold for incorporation into a dwelling. In determining whether a product is a consumer product, doubtful cases shall be resolved in favor of coverage. For a particular product received by a particular user, "normally used" refers to a typical or common use of that class of product, regardless of the status of the particular user or of the way in which the particular user actually uses, or expects or is expected to use, the product. A product is a consumer product regardless of whether the product has substantial commercial, industrial or non-consumer uses, unless such uses represent the only significant mode of use of the product. "Installation Information" for a User Product means any methods, procedures, authorization keys, or other information required to install and execute modified versions of a covered work in that User Product from a modified version of its Corresponding Source. The information must suffice to ensure that the continued functioning of the modified object code is in no case prevented or interfered with solely because modification has been made. If you convey an object code work under this section in, or with, or specifically for use in, a User Product, and the conveying occurs as part of a transaction in which the right of possession and use of the User Product is transferred to the recipient in perpetuity or for a fixed term (regardless of how the transaction is characterized), the Corresponding Source conveyed under this section must be accompanied by the Installation Information. But this requirement does not apply if neither you nor any third party retains the ability to install modified object code on the User Product (for example, the work has been installed in ROM). The requirement to provide Installation Information does not include a requirement to continue to provide support service, warranty, or updates for a work that has been modified or installed by the recipient, or for the User Product in which it has been modified or installed. Access to a network may be denied when the modification itself materially and adversely affects the operation of the network or violates the rules and protocols for communication across the network. Corresponding Source conveyed, and Installation Information provided, in accord with this section must be in a format that is publicly documented (and with an implementation available to the public in source code form), and must require no special password or key for unpacking, reading or copying. 7. Additional Terms. "Additional permissions" are terms that supplement the terms of this License by making exceptions from one or more of its conditions. Additional permissions that are applicable to the entire Program shall be treated as though they were included in this License, to the extent that they are valid under applicable law. If additional permissions apply only to part of the Program, that part may be used separately under those permissions, but the entire Program remains governed by this License without regard to the additional permissions. When you convey a copy of a covered work, you may at your option remove any additional permissions from that copy, or from any part of it. (Additional permissions may be written to require their own removal in certain cases when you modify the work.) You may place additional permissions on material, added by you to a covered work, for which you have or can give appropriate copyright permission. Notwithstanding any other provision of this License, for material you add to a covered work, you may (if authorized by the copyright holders of that material) supplement the terms of this License with terms: a) Disclaiming warranty or limiting liability differently from the terms of sections 15 and 16 of this License; or b) Requiring preservation of specified reasonable legal notices or author attributions in that material or in the Appropriate Legal Notices displayed by works containing it; or c) Prohibiting misrepresentation of the origin of that material, or requiring that modified versions of such material be marked in reasonable ways as different from the original version; or d) Limiting the use for publicity purposes of names of licensors or authors of the material; or e) Declining to grant rights under trademark law for use of some trade names, trademarks, or service marks; or f) Requiring indemnification of licensors and authors of that material by anyone who conveys the material (or modified versions of it) with contractual assumptions of liability to the recipient, for any liability that these contractual assumptions directly impose on those licensors and authors. All other non-permissive additional terms are considered "further restrictions" within the meaning of section 10. If the Program as you received it, or any part of it, contains a notice stating that it is governed by this License along with a term that is a further restriction, you may remove that term. If a license document contains a further restriction but permits relicensing or conveying under this License, you may add to a covered work material governed by the terms of that license document, provided that the further restriction does not survive such relicensing or conveying. If you add terms to a covered work in accord with this section, you must place, in the relevant source files, a statement of the additional terms that apply to those files, or a notice indicating where to find the applicable terms. Additional terms, permissive or non-permissive, may be stated in the form of a separately written license, or stated as exceptions; the above requirements apply either way. 8. Termination. You may not propagate or modify a covered work except as expressly provided under this License. Any attempt otherwise to propagate or modify it is void, and will automatically terminate your rights under this License (including any patent licenses granted under the third paragraph of section 11). However, if you cease all violation of this License, then your license from a particular copyright holder is reinstated (a) provisionally, unless and until the copyright holder explicitly and finally terminates your license, and (b) permanently, if the copyright holder fails to notify you of the violation by some reasonable means prior to 60 days after the cessation. Moreover, your license from a particular copyright holder is reinstated permanently if the copyright holder notifies you of the violation by some reasonable means, this is the first time you have received notice of violation of this License (for any work) from that copyright holder, and you cure the violation prior to 30 days after your receipt of the notice. Termination of your rights under this section does not terminate the licenses of parties who have received copies or rights from you under this License. If your rights have been terminated and not permanently reinstated, you do not qualify to receive new licenses for the same material under section 10. 9. Acceptance Not Required for Having Copies. You are not required to accept this License in order to receive or run a copy of the Program. Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so. 10. Automatic Licensing of Downstream Recipients. Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License. An "entity transaction" is a transaction transferring control of an organization, or substantially all assets of one, or subdividing an organization, or merging organizations. If propagation of a covered work results from an entity transaction, each party to that transaction who receives a copy of the work also receives whatever licenses to the work the party's predecessor in interest had or could give under the previous paragraph, plus a right to possession of the Corresponding Source of the work from the predecessor in interest, if the predecessor has it or can get it with reasonable efforts. You may not impose any further restrictions on the exercise of the rights granted or affirmed under this License. For example, you may not impose a license fee, royalty, or other charge for exercise of rights granted under this License, and you may not initiate litigation (including a cross-claim or counterclaim in a lawsuit) alleging that any patent claim is infringed by making, using, selling, offering for sale, or importing the Program or any portion of it. 11. Patents. A "contributor" is a copyright holder who authorizes use under this License of the Program or a work on which the Program is based. The work thus licensed is called the contributor's "contributor version". A contributor's "essential patent claims" are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, "control" includes the right to grant patent sublicenses in a manner consistent with the requirements of this License. Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor's essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version. In the following three paragraphs, a "patent license" is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To "grant" such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party. If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either (1) cause the Corresponding Source to be so available, or (2) arrange to deprive yourself of the benefit of the patent license for this particular work, or (3) arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. "Knowingly relying" means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient's use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid. If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it. A patent license is "discriminatory" if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license (a) in connection with copies of the covered work conveyed by you (or copies made from those copies), or (b) primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007. Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law. 12. No Surrender of Others' Freedom. If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program. 13. Use with the GNU Affero General Public License. Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such. 14. Revised Versions of this License. The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License "or any later version" applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation. If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy's public statement of acceptance of a version permanently authorizes you to choose that version for the Program. Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version. 15. Disclaimer of Warranty. THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 16. Limitation of Liability. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. 17. Interpretation of Sections 15 and 16. If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. <one line to give the program's name and a brief idea of what it does.> Copyright (C) <year> <name of author> This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: <program> Copyright (C) <year> <name of author> This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <http://www.gnu.org/licenses/>. The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <http://www.gnu.org/philosophy/why-not-lgpl.html>.

version: 1.11.2

106 253 0 0

Are you the author of this tool? Add a badge to your website with a link to this page.

Insert the following code to your website to add

with a link to this page.