from reportlab.lib.pagesizes import A4
from reportlab.lib import colors
from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
from reportlab.lib.units import cm, mm
from reportlab.platypus import (
    SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle,
    HRFlowable, PageBreak, KeepTogether
)
from reportlab.lib.enums import TA_CENTER, TA_LEFT, TA_JUSTIFY
from reportlab.platypus import BaseDocTemplate, Frame, PageTemplate
from reportlab.pdfgen import canvas

OUTPUT = "/tmp/workspace/rodent-guide/Rodent_Research_Selection_Guide.pdf"

# ── Color palette ──────────────────────────────────────────────
NAVY      = colors.HexColor("#1a3558")
BLUE      = colors.HexColor("#2563a8")
TEAL      = colors.HexColor("#0d7377")
LIGHT_BG  = colors.HexColor("#eef3fb")
HEADER_BG = colors.HexColor("#1a3558")
ALT_ROW   = colors.HexColor("#f0f5ff")
WHITE     = colors.white
GOLD      = colors.HexColor("#e8a020")
LIGHT_TEAL= colors.HexColor("#d0f0f0")
MOUSE_BG  = colors.HexColor("#e8f0fb")
RAT_BG    = colors.HexColor("#e8f8f2")

# ── Page numbering callback ─────────────────────────────────────
def add_page_number(canvas_obj, doc):
    canvas_obj.saveState()
    canvas_obj.setFont("Helvetica", 8)
    canvas_obj.setFillColor(colors.HexColor("#888888"))
    w, h = A4
    canvas_obj.drawCentredString(w / 2, 1.2 * cm, f"Page {doc.page}")
    canvas_obj.drawString(2 * cm, 1.2 * cm, "Rodent Model Selection Guide  |  Research Reference")
    canvas_obj.restoreState()

# ── Styles ──────────────────────────────────────────────────────
styles = getSampleStyleSheet()

def S(name, parent="Normal", **kw):
    return ParagraphStyle(name, parent=styles[parent], **kw)

style_title    = S("Title2",  fontSize=28, textColor=WHITE,   alignment=TA_CENTER, spaceAfter=4, fontName="Helvetica-Bold")
style_subtitle = S("Sub",     fontSize=13, textColor=GOLD,    alignment=TA_CENTER, spaceAfter=2, fontName="Helvetica")
style_h1       = S("H1",      fontSize=15, textColor=WHITE,   spaceAfter=4, spaceBefore=6, fontName="Helvetica-Bold")
style_h2       = S("H2",      fontSize=12, textColor=NAVY,    spaceAfter=4, spaceBefore=8, fontName="Helvetica-Bold")
style_h3       = S("H3",      fontSize=10, textColor=TEAL,    spaceAfter=2, spaceBefore=6, fontName="Helvetica-Bold")
style_body     = S("Body2",   fontSize=8.5, leading=12, spaceAfter=3)
style_note     = S("Note",    fontSize=8,  textColor=colors.HexColor("#555555"), leading=11, italics=1)
style_toc_item = S("TOCitem", fontSize=9.5, textColor=BLUE, spaceAfter=3, leftIndent=8)
style_bullet   = S("Bullet2", fontSize=8.5, leading=12, leftIndent=14, spaceAfter=2, bulletIndent=4)
style_th       = S("TH",      fontSize=8.5, textColor=WHITE, fontName="Helvetica-Bold", alignment=TA_CENTER)
style_td       = S("TD",      fontSize=8,   leading=11)
style_td_ctr   = S("TDC",     fontSize=8,   leading=11, alignment=TA_CENTER)

def cell(text, style=None, bg=None):
    """Shorthand for a Paragraph cell."""
    s = style or style_td
    return Paragraph(text, s)

def hdr(text):
    return Paragraph(text, style_th)

def section_banner(title, color=NAVY):
    data = [[Paragraph(title, style_h1)]]
    t = Table(data, colWidths=[17*cm])
    t.setStyle(TableStyle([
        ("BACKGROUND", (0,0), (-1,-1), color),
        ("TOPPADDING",   (0,0), (-1,-1), 7),
        ("BOTTOMPADDING",(0,0), (-1,-1), 7),
        ("LEFTPADDING",  (0,0), (-1,-1), 12),
    ]))
    return t

# ── Document ────────────────────────────────────────────────────
doc = SimpleDocTemplate(
    OUTPUT,
    pagesize=A4,
    leftMargin=2*cm, rightMargin=2*cm,
    topMargin=2.2*cm, bottomMargin=2.2*cm,
    title="Rodent Model Selection Guide",
    author="Research Reference",
)

story = []
W = 17 * cm   # usable width

# ═══════════════════════════════════════════════════════════════
#  COVER PAGE
# ═══════════════════════════════════════════════════════════════
cover_data = [[Paragraph("RODENT MODEL<br/>SELECTION GUIDE", style_title)]]
cover_tbl = Table(cover_data, colWidths=[W])
cover_tbl.setStyle(TableStyle([
    ("BACKGROUND", (0,0),(-1,-1), NAVY),
    ("TOPPADDING",   (0,0),(-1,-1), 30),
    ("BOTTOMPADDING",(0,0),(-1,-1), 10),
]))
story.append(cover_tbl)

sub_data = [[Paragraph("Choosing the right Mice &amp; Rat strains for Biomedical Research", style_subtitle)]]
sub_tbl = Table(sub_data, colWidths=[W])
sub_tbl.setStyle(TableStyle([
    ("BACKGROUND", (0,0),(-1,-1), BLUE),
    ("TOPPADDING",   (0,0),(-1,-1), 8),
    ("BOTTOMPADDING",(0,0),(-1,-1), 8),
]))
story.append(sub_tbl)
story.append(Spacer(1, 0.5*cm))

intro = (
    "This reference guide provides a quick-lookup resource for selecting the most appropriate "
    "mouse and rat models for common biomedical research applications. It covers inbred strains, "
    "outbred stocks, immunodeficient models, genetically engineered models, and disease-specific "
    "selections — organized for rapid use at the bench."
)
story.append(Paragraph(intro, style_body))
story.append(Spacer(1, 0.3*cm))
story.append(HRFlowable(width=W, color=GOLD, thickness=2))
story.append(Spacer(1, 0.3*cm))

# TOC
story.append(Paragraph("Contents", style_h2))
toc_items = [
    "1.  Why Rodents? — Key Selection Principles",
    "2.  Inbred Mouse Strains",
    "3.  Outbred Mouse Stocks",
    "4.  Immunodeficient Mouse Models",
    "5.  Transgenic & Knockout Mouse Models",
    "6.  Specialized Diversity Mouse Panels",
    "7.  Common Rat Stocks & Inbred Strains",
    "8.  Specialty & Disease Rat Models",
    "9.  Quick-Reference: Research Application → Model",
    "10. Mice vs. Rats — Head-to-Head Comparison",
    "11. Key Suppliers",
    "12. Strain Selection Tips & Pitfalls",
]
for item in toc_items:
    story.append(Paragraph(item, style_toc_item))

story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 1 — Why Rodents
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("1.  Why Rodents? — Key Selection Principles"))
story.append(Spacer(1, 0.3*cm))

principles = [
    ("<b>Genetic homology:</b>", "~85% of mouse protein-coding genes have a human orthologue; rat genome is similarly conserved."),
    ("<b>Rapid reproduction:</b>", "21-day gestation; sexually mature at 6-8 weeks — enables fast generational studies."),
    ("<b>Genetic tools:</b>", "CRISPR, Cre-lox, transgenics, and thousands of characterised strains available off-the-shelf."),
    ("<b>Cost & housing:</b>", "Far less expensive to house and maintain than non-human primates or dogs."),
    ("<b>Reproducibility:</b>", "Inbred strains are near-isogenic — minimises biological noise; outbred stocks reflect population diversity."),
    ("<b>Regulatory acceptance:</b>", "Rodent data are accepted by FDA, EMA, and ICH guidelines for IND/NDA submissions."),
]
for bold, desc in principles:
    story.append(Paragraph(f"{bold} {desc}", style_bullet))

story.append(Spacer(1, 0.4*cm))
story.append(Paragraph(
    "<b>Three core questions before choosing a model:</b>",
    style_body
))
q_data = [
    ["Q1", "Do you need genetic uniformity (reproducibility) or diversity (population modelling)?", "→ Inbred strain vs. outbred stock"],
    ["Q2", "Does the background immune system matter (intact vs. compromised)?",                    "→ Immunocompetent vs. immunodeficient"],
    ["Q3", "Is a specific gene, pathway or human disease phenotype required?",                      "→ Transgenic / knockout / spontaneous mutant"],
]
q_tbl = Table(q_data, colWidths=[1.2*cm, 9.5*cm, 6.3*cm])
q_tbl.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (0,-1), BLUE),
    ("TEXTCOLOR",    (0,0), (0,-1), WHITE),
    ("FONTNAME",     (0,0), (0,-1), "Helvetica-Bold"),
    ("BACKGROUND",   (1,0), (-1,-1), LIGHT_BG),
    ("FONTSIZE",     (0,0), (-1,-1), 8),
    ("LEADING",      (0,0), (-1,-1), 11),
    ("LEFTPADDING",  (0,0), (-1,-1), 6),
    ("RIGHTPADDING", (0,0), (-1,-1), 6),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("ROWBACKGROUNDS",(1,0),(-1,-1),[LIGHT_BG, ALT_ROW]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#cccccc")),
    ("VALIGN",       (0,0), (-1,-1), "MIDDLE"),
]))
story.append(q_tbl)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 2 — Inbred Mouse Strains
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("2.  Inbred Mouse Strains   (Mus musculus)", color=BLUE))
story.append(Spacer(1, 0.25*cm))
story.append(Paragraph(
    "Inbred strains are generated by ≥20 generations of brother-sister mating, producing animals that are "
    ">98% genetically identical. They are the gold standard for mechanistic studies requiring reproducibility.",
    style_body
))
story.append(Spacer(1, 0.2*cm))

mouse_inbred_headers = ["Strain", "Coat", "Immune Bias", "Key Characteristics", "Primary Uses"]
mouse_inbred_rows = [
    ["C57BL/6 (B6)", "Black", "Th1", "Most widely used; high genetic stability; susceptible to HFD-induced obesity; good for transgenics", "Immunology, oncology, neuroscience, metabolic disease, knockout backgrounds"],
    ["BALB/c", "Albino", "Th2", "Docile; good antibody producer; high susceptibility to allergy/asthma models", "Allergy, asthma, hybridoma production, infectious disease, cancer (syngeneic)"],
    ["DBA/2", "Dilute brown", "Th1", "Alcohol preference; susceptible to glaucoma; cardiac sensitivity", "Addiction, ophthalmology, cardiology"],
    ["A/J", "Albino", "Th2", "High lung tumour susceptibility; unusual complement activity", "Lung cancer, asthma, complement studies"],
    ["C3H/He", "Agouti", "Th1", "MMTV-infected; high mammary tumour incidence; strong macrophage response", "Mammary oncology, infectious disease"],
    ["FVB/N", "Albino", "Th1", "Large, clear pronuclei → excellent for microinjection; high litter sizes", "Transgenic generation, oncology"],
    ["129 strains", "Varies", "Mixed", "First ES cell isolation; used as knockout founders; poor behavioural phenotype", "Gene targeting, embryonic stem cells"],
    ["SJL/J", "Albino", "Th1", "Highly susceptible to EAE; reticulosarcoma-prone", "Multiple sclerosis (EAE), autoimmunity"],
    ["NOD", "Albino", "Th1", "Spontaneous autoimmune diabetes; defective NK-cell activity", "Type 1 diabetes, autoimmunity, islet biology"],
    ["NZB/NZW F1", "Black/White", "Th2", "Spontaneous lupus-like nephritis; anti-dsDNA antibodies", "Systemic lupus erythematosus (SLE)"],
    ["MRL/lpr", "Albino", "Mixed", "Fas-deficient; lymphoproliferation; severe autoimmunity", "Autoimmunity, rheumatoid arthritis, SLE"],
    ["AKR/J", "Albino", "Th1", "High spontaneous leukaemia incidence (>90% by 1 year)", "Leukaemia, oncology"],
    ["C57BL/10", "Black", "Th1", "Near-isogenic to C57BL/6; useful for congenic studies", "Muscular dystrophy, immunogenetics"],
]

col_w = [2.2*cm, 1.1*cm, 1.3*cm, 6.5*cm, 5.9*cm]
h_row = [hdr(h) for h in mouse_inbred_headers]
tbl_data = [h_row]
for i, row in enumerate(mouse_inbred_rows):
    bg = MOUSE_BG if i % 2 == 0 else WHITE
    tbl_data.append([cell(row[0], style_td), cell(row[1], style_td_ctr),
                     cell(row[2], style_td_ctr), cell(row[3], style_td),
                     cell(row[4], style_td)])

tbl = Table(tbl_data, colWidths=col_w, repeatRows=1)
tbl.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  NAVY),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [MOUSE_BG, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#bbbbbb")),
    ("TOPPADDING",   (0,0), (-1,-1), 4),
    ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5),
    ("RIGHTPADDING", (0,0), (-1,-1), 5),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
]))
story.append(tbl)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 3 — Outbred Mouse Stocks
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("3.  Outbred Mouse Stocks", color=TEAL))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "Outbred stocks maintain genetic diversity within a closed colony. They are called 'stocks' (not strains). "
    "Best suited for toxicology, general pharmacology, and studies requiring population heterogeneity.",
    style_body
))
story.append(Spacer(1, 0.2*cm))

outbred_headers = ["Stock", "Coat", "Key Features", "Best Uses", "Limitations"]
outbred_rows = [
    ["CD-1 (ICR)", "Albino", "Most widely used outbred stock; large litters; docile; broad genetic diversity", "Toxicology, safety/efficacy testing, pharmacology, aging, reproductive studies", "High variability can reduce statistical power"],
    ["Swiss Webster", "Albino", "Hardy; robust breeders; easily maintained", "General purpose, virology, neuroscience, drug screening", "Genetic drift over time between colonies"],
    ["ICR", "Albino", "Similar to CD-1; derived from Swiss mice; used in Asia predominantly", "Pharmacology, toxicology, reproductive biology", "Genetic differences between suppliers"],
    ["NMRI", "Albino", "European outbred; good reproductive performance", "Neuropharmacology, general screening", "Less characterised than CD-1"],
    ["CF-1", "Albino", "Charles River outbred; consistent colony", "Reproductive toxicology, general studies", "Limited genetic characterisation"],
]
col_w2 = [1.9*cm, 1.1*cm, 4.2*cm, 5.3*cm, 4.5*cm]
h_row2 = [hdr(h) for h in outbred_headers]
tbl2_data = [h_row2] + [[cell(r[0]), cell(r[1], style_td_ctr), cell(r[2]), cell(r[3]), cell(r[4])] for r in outbred_rows]
tbl2 = Table(tbl2_data, colWidths=col_w2, repeatRows=1)
tbl2.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  TEAL),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [LIGHT_TEAL, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#bbbbbb")),
    ("TOPPADDING",   (0,0), (-1,-1), 4),
    ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
]))
story.append(tbl2)

# ═══════════════════════════════════════════════════════════════
#  SECTION 4 — Immunodeficient Models
# ═══════════════════════════════════════════════════════════════
story.append(Spacer(1, 0.5*cm))
story.append(section_banner("4.  Immunodeficient Mouse Models", color=colors.HexColor("#7b2d8b")))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "Immunodeficient mice accept xenografts (human tumour or tissue transplants) and are essential for "
    "humanisation experiments and adoptive transfer studies.",
    style_body
))
story.append(Spacer(1, 0.2*cm))

immuno_hdrs = ["Model", "Immune Defect", "Engraftment Capacity", "Primary Uses"]
immuno_rows = [
    ["Nude (Foxn1<sup>nu</sup>)", "No thymus → T-cell deficient; B cells present but dysfunctional", "Moderate (human tumour xenografts)", "Xenograft tumour studies, monoclonal antibody production"],
    ["SCID (CB-17)", "No functional T or B cells (DNA-PKcs mutation)", "Good (human cell/tissue engraftment)", "Human xenografts, hematopoietic reconstitution, HIV research"],
    ["NSG (NOD-SCID gamma)", "No T, B, or NK cells; defective complement; IL-2Rγ null", "Excellent — gold standard for human engraftment", "Humanised mouse models, PDX tumours, CAR-T cell testing, stem cell research"],
    ["NRG (NOD-Rag1-gamma)", "Similar to NSG; Rag1 knockout background", "Excellent", "Human immune reconstitution, PDX models"],
    ["Rag1/Rag2 KO", "No mature T or B cells", "Good", "Adoptive immune transfer, lymphocyte reconstitution"],
    ["Beige (Chediak-Higashi)", "NK cell dysfunction; neutrophil defects", "Moderate", "NK cell biology, innate immunity studies"],
    ["BRG (BALB/c-Rag2-gamma)", "No T, B, NK cells on BALB/c background", "Excellent", "Xenografts, humanisation on BALB/c background"],
]
col_w3 = [2.8*cm, 4.5*cm, 3.5*cm, 6.2*cm]
h_row3 = [hdr(h) for h in immuno_hdrs]
tbl3_data = [h_row3] + [[cell(r[0]), cell(r[1]), cell(r[2]), cell(r[3])] for r in immuno_rows]
tbl3 = Table(tbl3_data, colWidths=col_w3, repeatRows=1)
tbl3.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#7b2d8b")),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [colors.HexColor("#f5eefa"), WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#cccccc")),
    ("TOPPADDING",   (0,0), (-1,-1), 4),
    ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
]))
story.append(tbl3)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 5 — Transgenic / Knockout Models
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("5.  Transgenic & Knockout Mouse Models", color=colors.HexColor("#c0392b")))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "Genetically engineered mouse models (GEMMs) allow precise manipulation of gene function. "
    "Created via traditional pronuclear injection, ES cell targeting, or CRISPR-Cas9.",
    style_body
))
story.append(Spacer(1, 0.2*cm))

gemm_hdrs = ["Model / Name", "Genetic Modification", "Disease Modelled", "Research Use"]
gemm_rows = [
    ["APP/PS1", "Human APP + PSEN1 mutations (transgenic)", "Alzheimer's disease (amyloid plaques)", "Neurodegeneration, drug testing for AD"],
    ["5xFAD", "5 AD mutations in APP & PSEN1", "Aggressive early-onset Alzheimer's", "Amyloid biology, neuroinflammation"],
    ["3xTg-AD", "APP, PSEN1, tau mutations", "Amyloid + tau pathology (full AD spectrum)", "Full AD pathology, tau therapeutics"],
    ["db/db", "Leptin receptor knockout (BKS background)", "Type 2 diabetes + obesity", "Metabolic syndrome, insulin resistance, nephropathy"],
    ["ob/ob", "Leptin gene knockout", "Severe obesity + hyperinsulinaemia", "Obesity, metabolic studies"],
    ["ApoE-KO", "ApoE gene knockout", "Atherosclerosis (on HFD)", "Cardiovascular disease, lipid metabolism"],
    ["LDLR-KO", "LDL receptor knockout", "Hypercholesterolaemia, atherosclerosis", "Cardiovascular, statin testing"],
    ["p53-KO", "Trp53 knockout", "Cancer predisposition model", "Tumour suppressor function, DNA damage"],
    ["PyMT", "MMTV-PyMT transgene", "Spontaneous mammary tumours", "Breast cancer, metastasis research"],
    ["hACE2 transgenic", "Human ACE2 expression", "SARS-CoV-2 susceptibility", "COVID-19 research, antiviral testing"],
    ["Cre-lox (conditional KO)", "Tissue-specific Cre recombinase + floxed gene", "Organ-specific gene deletion", "Cell-type-specific gene function"],
    ["Reporter lines (GFP/Luciferase)", "Fluorescent/bioluminescent reporters", "Cell tracking, lineage tracing", "In vivo imaging, developmental biology"],
]
col_w4 = [3.0*cm, 4.0*cm, 4.0*cm, 6.0*cm]
h_row4 = [hdr(h) for h in gemm_hdrs]
tbl4_data = [h_row4] + [[cell(r[0]), cell(r[1]), cell(r[2]), cell(r[3])] for r in gemm_rows]
tbl4 = Table(tbl4_data, colWidths=col_w4, repeatRows=1)
tbl4.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#c0392b")),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [colors.HexColor("#fdf0ee"), WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#cccccc")),
    ("TOPPADDING",   (0,0), (-1,-1), 4),
    ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
]))
story.append(tbl4)

story.append(Spacer(1, 0.4*cm))
story.append(Paragraph(
    "<b>Note on background strain:</b> The genetic background profoundly influences phenotype. "
    "Most knockouts are backcrossed onto C57BL/6 for uniformity. Always match controls to the exact background strain and generation.",
    style_note
))
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 6 — Diversity Panels
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("6.  Specialised Diversity Mouse Panels", color=colors.HexColor("#2d6a4f")))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "These resources capture broad genetic diversity and are recommended by NIH/Infrafrontier "
    "to improve translational relevance and reduce single-strain bias.",
    style_body
))
story.append(Spacer(1, 0.2*cm))

div_hdrs = ["Panel", "Description", "Founders", "Best Uses"]
div_rows = [
    ["Collaborative Cross (CC)", "Panel of ~60 inbred strains derived from 8 genetically diverse founder strains", "A/J, C57BL/6J, 129S1, NOD, NZO, CAST, PWK, WSB", "Complex trait analysis, infectious disease, cancer susceptibility, population genomics"],
    ["Diversity Outbred (DO)", "Outbred population continuously bred from CC founders; captures maximum allelic diversity", "Same 8 founders as CC", "QTL mapping, drug response variability, microbiome studies, precision medicine modelling"],
    ["BXD strains", "Recombinant inbred panel from C57BL/6 x DBA/2 cross; 100+ strains", "C57BL/6 + DBA/2", "Systems genetics, gene expression QTL, neuroscience, alcohol studies"],
    ["Hybrid F1 (e.g. B6D2F1)", "First-generation cross of two inbred strains; heterozygous but uniform", "Two inbred strains", "Aging studies (NIA aging panel), tumour challenge models"],
]
col_w5 = [3.2*cm, 5.5*cm, 3.8*cm, 4.5*cm]
h_row5 = [hdr(h) for h in div_hdrs]
tbl5_data = [h_row5] + [[cell(r[0]), cell(r[1]), cell(r[2]), cell(r[3])] for r in div_rows]
tbl5 = Table(tbl5_data, colWidths=col_w5, repeatRows=1)
tbl5.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#2d6a4f")),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [colors.HexColor("#d8f3dc"), WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#cccccc")),
    ("TOPPADDING",   (0,0), (-1,-1), 5),
    ("BOTTOMPADDING",(0,0), (-1,-1), 5),
    ("LEFTPADDING",  (0,0), (-1,-1), 5),
    ("VALIGN",       (0,0), (-1,-1), "TOP"),
]))
story.append(tbl5)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 7 — RAT STOCKS AND STRAINS
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("7.  Rat Stocks & Inbred Strains   (Rattus norvegicus)", color=colors.HexColor("#1a6b3c")))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "The rat is the #2 most used research animal. Larger size enables surgical manipulations, "
    "catheter implantation, serial blood sampling, and detailed behavioural phenotyping. "
    "CRISPR has rapidly expanded the transgenic rat toolkit.",
    style_body
))
story.append(Spacer(1, 0.3*cm))

story.append(Paragraph("Outbred Rat Stocks", style_h3))
outbred_rat_hdrs = ["Stock", "Coat", "Key Features", "Primary Research Uses"]
outbred_rat_rows = [
    ["Sprague-Dawley (SD)", "Albino", "Most widely used rat worldwide; docile; excellent breeders; large litters; predictable growth curves", "General pharmacology/toxicology, reproductive toxicology, behavioral studies, surgical models, cardiovascular, neuroscience"],
    ["Wistar", "Albino", "Slightly smaller than SD; originated at Wistar Institute (1906); calmer temperament; uniform growth", "Nutrition, metabolism, cardiovascular research, general purpose, teaching labs"],
    ["Long-Evans", "Hooded (black/white)", "Intact visual system (pigmented retina); good spatial learning; superior vision to albino rats", "Neurobehavioral studies, learning & memory, vision research, addiction models"],
    ["Holtzman", "Albino", "SD-derived; used for neuroscience and drug studies", "Neuroscience, pharmacology"],
]
col_w6 = [2.8*cm, 1.2*cm, 6.0*cm, 7.0*cm]
h_row6 = [hdr(h) for h in outbred_rat_hdrs]
tbl6 = Table([h_row6] + [[cell(r[0]), cell(r[1], style_td_ctr), cell(r[2]), cell(r[3])] for r in outbred_rat_rows],
             colWidths=col_w6, repeatRows=1)
tbl6.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#1a6b3c")),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [RAT_BG, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#cccccc")),
    ("TOPPADDING",   (0,0), (-1,-1), 4), ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5), ("VALIGN", (0,0), (-1,-1), "TOP"),
]))
story.append(tbl6)

story.append(Spacer(1, 0.3*cm))
story.append(Paragraph("Inbred Rat Strains", style_h3))

inbred_rat_hdrs = ["Strain", "Coat", "Key Feature / Phenotype", "Primary Research Uses"]
inbred_rat_rows = [
    ["Fischer 344 (F344)", "Albino", "Most common inbred rat; long lifespan studies; NCI standard toxicology strain; mononuclear cell leukaemia with age", "Aging research, carcinogenicity/toxicology testing (NCI), oncology"],
    ["Lewis (LEW)", "Albino", "Highly susceptible to EAE and adjuvant-induced arthritis; accepts allografts poorly", "Autoimmunity (EAE, RA), transplantation immunology, addiction"],
    ["Brown Norway (BN)", "Agouti", "Resistant to EAE; often used as allograft donor; Th2-biased", "Immunology, transplantation (donor strain), allergy"],
    ["Wistar-Kyoto (WKY)", "Albino", "Normotensive control for SHR; also a model of depression/anxiety", "Cardiovascular control, psychiatric disease models"],
    ["SHR (Spontaneously Hypertensive Rat)", "Albino", "Develops hypertension spontaneously by 6-8 weeks; SHRSP variant is stroke-prone", "Hypertension, cardiovascular disease, stroke"],
    ["Dahl Salt-Sensitive (SS/Jr)", "Albino", "Develops severe hypertension on high-salt diet; renal damage", "Hypertension, renal disease, cardiovascular"],
    ["Zucker Fatty (fa/fa)", "Albino", "Leptin receptor mutation; severely obese; insulin resistant; hyperlipidaemia", "Obesity, type 2 diabetes, metabolic syndrome, cardiovascular"],
    ["Goto-Kakizaki (GK)", "Albino", "Non-obese spontaneous Type 2 diabetic rat; moderate hyperglycaemia", "Type 2 diabetes (non-obese model), pancreatic beta-cell research"],
    ["Brattleboro Rat", "Albino", "Vasopressin (ADH) deficiency; diabetes insipidus phenotype", "Kidney/renal physiology, water balance, ADH research"],
    ["BUF / DA / PVG", "Various", "Elevated multiple sclerosis-like susceptibility", "Neuroimmunology, MS models"],
]
col_w7 = [2.8*cm, 1.2*cm, 6.5*cm, 6.5*cm]
h_row7 = [hdr(h) for h in inbred_rat_hdrs]
tbl7 = Table([h_row7] + [[cell(r[0]), cell(r[1], style_td_ctr), cell(r[2]), cell(r[3])] for r in inbred_rat_rows],
             colWidths=col_w7, repeatRows=1)
tbl7.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#155c34")),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [RAT_BG, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#cccccc")),
    ("TOPPADDING",   (0,0), (-1,-1), 4), ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5), ("VALIGN", (0,0), (-1,-1), "TOP"),
]))
story.append(tbl7)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 8 — Specialty / Immunodeficient Rat Models
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("8.  Specialty & Immunodeficient Rat Models", color=colors.HexColor("#6d2b7a")))
story.append(Spacer(1, 0.2*cm))

spec_rat_hdrs = ["Model", "Defect / Feature", "Primary Uses"]
spec_rat_rows = [
    ["Nude Rat (Foxn1<sup>rnu</sup>)", "Athymic; T-cell deficient; hairless; B cells present", "Human tumour xenografts, cancer biology, monoclonal antibody production"],
    ["SCID Rat", "No functional T or B cells", "Xenotransplantation, immune reconstitution, human cell engraftment"],
    ["hRen/hAGT Rat", "Human renin-angiotensin transgene", "Hypertension, renin-angiotensin system, antihypertensive drug testing"],
    ["SD-Tg(CAG-EGFP)", "Ubiquitous GFP expression", "Cell tracking, transplantation, developmental studies"],
    ["Alzheimer Rat (CRISPR APP/tau)", "Human APP + tau mutations via CRISPR", "Alzheimer's disease, tau biology, neurodegeneration drug testing"],
    ["SHR/cp", "SHR + corpulent (obese) trait", "Metabolic syndrome with hypertension, cardiovascular-metabolic comorbidity"],
]
col_w8 = [3.5*cm, 6.5*cm, 7.0*cm]
h_row8 = [hdr(h) for h in spec_rat_hdrs]
tbl8 = Table([h_row8] + [[cell(r[0]), cell(r[1]), cell(r[2])] for r in spec_rat_rows],
             colWidths=col_w8, repeatRows=1)
tbl8.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#6d2b7a")),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [colors.HexColor("#f5eefa"), WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#cccccc")),
    ("TOPPADDING",   (0,0), (-1,-1), 4), ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5), ("VALIGN", (0,0), (-1,-1), "TOP"),
]))
story.append(tbl8)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 9 — Quick Reference: Research Application → Model
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("9.  Quick-Reference: Research Application → Recommended Model", color=GOLD))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "Use this table when you know your research area and need rapid model selection guidance.",
    style_body
))
story.append(Spacer(1, 0.2*cm))

quick_hdrs = ["Research Area", "First-Choice Mouse", "First-Choice Rat", "Notes"]
quick_rows = [
    ["Oncology — syngeneic tumours",     "C57BL/6 (with B16, LLC, MC38 tumours)\nBALB/c (with CT26, 4T1)",
     "Fischer 344",
     "Match tumour cell line to host strain MHC haplotype"],
    ["Oncology — xenograft (human)",     "NSG or NRG (immunodeficient)",
     "Nude or SCID rat",
     "NSG gives best engraftment; use PDX for translational relevance"],
    ["Immunology — Th1 responses",       "C57BL/6",       "Lewis",         "C57BL/6 is strongly Th1-biased"],
    ["Immunology — Th2/allergy",         "BALB/c or A/J", "Brown Norway",  "BALB/c best for IgE and allergy models"],
    ["Autoimmunity (EAE/MS)",            "SJL/J (relapsing-remitting EAE)\nC57BL/6 (chronic EAE)", "Lewis rat",  "EAE induction protocol varies by strain"],
    ["Type 1 Diabetes",                  "NOD mouse",     "LEW.1AR1/Ztm-iddm", "NOD most studied; monitor for sex differences"],
    ["Type 2 Diabetes / Obesity",        "db/db or ob/ob (B6 background)", "Zucker Fatty (fa/fa) or GK rat", "GK for non-obese T2DM; Zucker for obese T2DM"],
    ["Cardiovascular / Hypertension",    "ApoE-KO or LDLR-KO (on HFD)",  "SHR or Dahl Salt-Sensitive",  "Rats preferred for BP telemetry and cardiac surgery"],
    ["Atherosclerosis",                  "ApoE-KO + high-fat diet",       "LDLR-KO rat (CRISPR)",         "ApoE-KO on C57BL/6 most standard"],
    ["Alzheimer's Disease",              "APP/PS1, 5xFAD, 3xTg-AD",      "hAPP/tau CRISPR rat",           "5xFAD for fast amyloid accumulation; 3xTg for tau"],
    ["Parkinson's Disease",              "C57BL/6 (MPTP, rotenone models)", "Sprague-Dawley (6-OHDA)",    "Rats preferred for motor behaviour readouts"],
    ["Stroke / Brain Injury",            "C57BL/6 (MCAo model)",          "Sprague-Dawley or Wistar",      "Rats preferred — larger brain, better imaging, surgery"],
    ["Addiction / Reward",               "C57BL/6 (high alcohol preference)", "Sprague-Dawley or Long-Evans", "Long-Evans preferred for operant behaviour tasks"],
    ["Depression / Anxiety",             "C57BL/6 (stress models)",       "WKY rat (genetic model); SD (CMS)", "Forced swim, social defeat stress protocols"],
    ["Toxicology / Safety Testing",      "CD-1 (ICR) outbred",            "Sprague-Dawley (ICH guideline)", "ICH S1B/S5 guidelines specify SD and CD-1"],
    ["Reproductive Toxicology",          "CD-1 or Swiss Webster",         "Sprague-Dawley",                "SD timed-pregnant widely used for developmental tox"],
    ["Infectious Disease (bacterial)",   "C57BL/6 or BALB/c",             "Wistar or SD",                  "Strain susceptibility varies by pathogen"],
    ["Infectious Disease (viral)",       "C57BL/6, hACE2 transgenic (COVID)", "Cotton rat for RSV (Sigmodon hispidus)", "Cotton rat is gold standard for RSV research"],
    ["Kidney / Renal Disease",           "C57BL/6 (UUO, cisplatin models)", "Dahl SS, Zucker Diabetic Fatty", "Rats better for surgical renal models"],
    ["Aging Research",                   "C57BL/6, F1 hybrids (B6D2F1)",  "Fischer 344 / F344xBN F1 hybrid", "NIA distributes aged rodents to researchers"],
    ["Humanised Models / PDX",           "NSG → engraft human HSCs or PDX", "NSG rat equivalent",           "NSG + human cytokine support for best reconstitution"],
    ["Gene Function Studies",            "Cre-lox conditional KO on C57BL/6", "CRISPR KO rat",             "Use congenic controls; validate Cre specificity"],
    ["Drug Pharmacokinetics (PK/PD)",    "CD-1 or C57BL/6",               "Sprague-Dawley",                "Rats allow serial sampling; larger blood volumes"],
]
col_w9 = [3.5*cm, 4.3*cm, 4.3*cm, 4.9*cm]
h_row9 = [hdr(h) for h in quick_hdrs]
tbl9_data = [h_row9]
for i, r in enumerate(quick_rows):
    bg = colors.HexColor("#fffbec") if i % 2 == 0 else WHITE
    tbl9_data.append([cell(r[0]), cell(r[1]), cell(r[2]), cell(r[3])])
tbl9 = Table(tbl9_data, colWidths=col_w9, repeatRows=1)
tbl9.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#b77a00")),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [colors.HexColor("#fffbec"), WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#dddddd")),
    ("TOPPADDING",   (0,0), (-1,-1), 4), ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5), ("VALIGN", (0,0), (-1,-1), "TOP"),
]))
story.append(tbl9)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 10 — Mice vs Rats comparison
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("10.  Mice vs. Rats — Head-to-Head Comparison", color=NAVY))
story.append(Spacer(1, 0.2*cm))

comp_hdrs = ["Feature", "Mouse (Mus musculus)", "Rat (Rattus norvegicus)"]
comp_rows = [
    ["Popularity",          "#1 most used lab animal",                  "#2 most used lab animal"],
    ["Body weight",         "20–30 g",                                  "200–500 g"],
    ["Lifespan",            "~2 years",                                 "~2.5–3 years"],
    ["Gestation",           "~21 days",                                 "~22 days"],
    ["Litter size",         "8–12 pups",                                "8–14 pups"],
    ["Cost",                "Lower",                                    "Higher (cage space, feed)"],
    ["Genetic tools",       "Extensive (knockouts, transgenics, CRISPR, 10,000+ JAX strains)", "Rapidly expanding via CRISPR"],
    ["Surgical suitability","Limited by size; feasible",               "Preferred — larger vessels, catheterisation, organ access"],
    ["Behavioural studies", "Good; extensive validated tests",          "Excellent; larger brain, richer repertoire"],
    ["Brain imaging (MRI)", "Challenging (small size)",                 "Much better (larger brain)"],
    ["Serial blood sampling","Difficult (<0.1 mL/draw)",               "Practical (jugular/carotid catheter)"],
    ["Pharmacokinetics",    "Acceptable; volume limitations",           "Preferred for PK/PD studies"],
    ["Husbandry",           "Easier, cheaper",                         "More space, more food"],
    ["Common background",   "Inbred strains (C57BL/6, BALB/c)",        "Outbred stocks (SD, Wistar)"],
    ["Regulatory toxicology","ICH-accepted outbred: CD-1",             "ICH-accepted outbred: Sprague-Dawley"],
    ["Neuroscience preference","Standard for genetics",                 "Preferred for behaviour, physiology, surgical"],
]
col_w10 = [4.0*cm, 6.5*cm, 6.5*cm]
h_row10 = [hdr(h) for h in comp_hdrs]
tbl10_data = [h_row10]
for i, r in enumerate(comp_rows):
    tbl10_data.append([cell(r[0], style_td), cell(r[1], style_td), cell(r[2], style_td)])
tbl10 = Table(tbl10_data, colWidths=col_w10, repeatRows=1)
tbl10.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),   NAVY),
    ("BACKGROUND",   (1,1), (1,-1),   MOUSE_BG),
    ("BACKGROUND",   (2,1), (2,-1),   RAT_BG),
    ("ROWBACKGROUNDS",(0,1),(0,-1),   [LIGHT_BG, ALT_ROW]),
    ("GRID",         (0,0), (-1,-1),  0.3, colors.HexColor("#cccccc")),
    ("TOPPADDING",   (0,0), (-1,-1),  4), ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1),  5), ("VALIGN", (0,0), (-1,-1), "TOP"),
    ("FONTNAME",     (0,1), (0,-1),   "Helvetica-Bold"),
    ("FONTSIZE",     (0,1), (0,-1),   8),
]))
story.append(tbl10)
story.append(PageBreak())

# ═══════════════════════════════════════════════════════════════
#  SECTION 11 — Key Suppliers
# ═══════════════════════════════════════════════════════════════
story.append(section_banner("11.  Key Suppliers & Repositories", color=colors.HexColor("#34495e")))
story.append(Spacer(1, 0.2*cm))

sup_hdrs = ["Supplier", "Speciality", "Notable Strains/Stocks", "Website"]
sup_rows = [
    ["The Jackson Laboratory (JAX)", "Largest mouse repository globally; 10,000+ strains; cryopreservation", "C57BL/6J, BALB/cJ, NOD/ShiLtJ, NSG, 5xFAD, DO, CC strains", "jax.org"],
    ["Charles River Laboratories", "Most widely used commercial supplier; GLP-certified facilities", "CD-1, C57BL/6, BALB/c, Sprague-Dawley, Wistar, Long-Evans", "criver.com"],
    ["Taconic Biosciences", "Custom transgenics; humanised models; germ-free rodents", "NSG, NRG, GEMM service, germ-free C57BL/6", "taconic.com"],
    ["Envigo / Inotiv", "Toxicology-grade animals; GLP stocks", "SD, Wistar, Fischer 344, CD-1, Beagle", "envigo.com"],
    ["Janvier Labs (EU)", "European supplier; SPF-certified; FELASA health monitoring", "C57BL/6J, BALB/cJ, Wistar, SD, RjHan:WI", "janvier-labs.com"],
    ["NIH Rat Resource & Research Center (RRRC)", "Rat strains, cryopreservation, IVF services", "Inbred, hybrid, mutant rat strains", "rrrc.missouri.edu"],
    ["MMRRC (Mouse Mutant Resource & Research Center)", "Mutant and knockout mouse strains; NIH-funded", "Thousands of GEMM lines", "mmrrc.org"],
    ["European Mouse Mutant Archive (EMMA/Infrafrontier)", "EU repository; CC strains, mutants", "CC founder and descendant strains", "infrafrontier.eu"],
]
col_w11 = [3.5*cm, 4.0*cm, 5.5*cm, 4.0*cm]
h_row11 = [hdr(h) for h in sup_hdrs]
tbl11_data = [h_row11] + [[cell(r[0]), cell(r[1]), cell(r[2]), cell(r[3])] for r in sup_rows]
tbl11 = Table(tbl11_data, colWidths=col_w11, repeatRows=1)
tbl11.setStyle(TableStyle([
    ("BACKGROUND",   (0,0), (-1,0),  colors.HexColor("#34495e")),
    ("ROWBACKGROUNDS",(0,1),(-1,-1), [LIGHT_BG, WHITE]),
    ("GRID",         (0,0), (-1,-1), 0.3, colors.HexColor("#cccccc")),
    ("TOPPADDING",   (0,0), (-1,-1), 4), ("BOTTOMPADDING",(0,0), (-1,-1), 4),
    ("LEFTPADDING",  (0,0), (-1,-1), 5), ("VALIGN", (0,0), (-1,-1), "TOP"),
]))
story.append(tbl11)

# ═══════════════════════════════════════════════════════════════
#  SECTION 12 — Tips & Pitfalls
# ═══════════════════════════════════════════════════════════════
story.append(Spacer(1, 0.5*cm))
story.append(section_banner("12.  Strain Selection Tips & Common Pitfalls", color=colors.HexColor("#c0392b")))
story.append(Spacer(1, 0.2*cm))

tips = [
    ("<b>Background strain matters enormously.</b>", "The same gene knockout can produce opposite phenotypes on C57BL/6 vs. BALB/c backgrounds. Always backcross to ≥10 generations onto your target background before publishing."),
    ("<b>Sex is a biological variable.</b>",          "NIH mandates inclusion of both sexes. NOD diabetes incidence differs markedly by sex. SHR males develop hypertension earlier than females."),
    ("<b>Supplier drift is real.</b>",                "C57BL/6J (Jackson) differs genetically from C57BL/6N (other suppliers) — the Nnt mutation affects metabolism. Always specify sub-strain (J vs. N)."),
    ("<b>Age and housing matter.</b>",                "Gut microbiome, circadian rhythm, group housing, and bedding all affect experimental outcomes. Report all husbandry details."),
    ("<b>Always use matched controls.</b>",           "Littermate wild-type controls are preferred over vendor controls. For knockouts, use the same F-generation and cross."),
    ("<b>Albino vs. pigmented.</b>",                  "Albino strains (BALB/c, SD) have poor vision — avoid visual behaviour tasks. Use pigmented strains (C57BL/6, Long-Evans) for vision/retina research."),
    ("<b>Pilot your model.</b>",                      "Phenotype penetrance and severity often vary between facilities. Run a small pilot to validate the phenotype in your specific vivarium."),
    ("<b>Consider sample size and power.</b>",         "Inbred strains reduce variance, requiring fewer animals. Outbred stocks have higher variance, requiring larger cohorts — factor this into power calculations."),
    ("<b>Health status.</b>",                          "Specify SPF (specific pathogen free) status. Helicobacter pylori, Pasteurella, and MHV can confound immunology and GI experiments silently."),
]
for bold, desc in tips:
    story.append(Paragraph(f"• {bold} {desc}", style_bullet))
    story.append(Spacer(1, 0.1*cm))

story.append(Spacer(1, 0.3*cm))
story.append(HRFlowable(width=W, color=GOLD, thickness=2))
story.append(Spacer(1, 0.2*cm))
story.append(Paragraph(
    "<b>References:</b> Wei et al., J Invest Dermatol (2026) · Kong &amp; Buzney, JAMA Dermatol (2026) · "
    "Merck Veterinary Manual (2024) · NIH ORIP Rodent Models Fact Sheet (2025) · "
    "The Jackson Laboratory strain resources (jax.org) · Labome Laboratory Mice and Rats survey",
    style_note
))

# ── Build ──────────────────────────────────────────────────────
doc.build(story, onFirstPage=add_page_number, onLaterPages=add_page_number)
print(f"PDF generated: {OUTPUT}")