"""
Generative DNA φ-Harmonic Spiral — volumetric, genome-driven cells & division
Requirements: pip install vispy pyqt6 numpy
Run: python generative_spiral_cells.py
"""

import os
import numpy as np
import itertools
from vispy import scene, app
from vispy.scene.visuals import Line, Markers, Text
from vispy.color import Color

# ---------- CONFIG ----------
phi = (1 + np.sqrt(5)) / 2
golden_angle_deg = 360 / (phi ** 2)  # 137.507°

bases = ['A','T','G','C']
all_mappings = [dict(zip(bases,p)) for p in itertools.permutations([1,2,3,4])]
print(f"Total mappings: {len(all_mappings)}")  # 24

geometries = [
    (1, 'C', 'red',          'Point',        0.015269, 1),
    (2, 'D', 'green',        'Line',         0.008262, 2),
    (3, 'E', 'violet',       'Triangle',     0.110649, 3),
    (4, 'F', 'mediumpurple', 'Tetrahedron', -0.083485, 4),
    (5, 'G', 'blue',         'Pentachoron',  0.025847, 5),
    (6, 'A', 'indigo',       'Hexacross',   -0.045123, 12),
    (7, 'B', 'purple',       'Heptacube',    0.067891, 14),
    (8, 'C', 'white',        'Octacube',     0.012345, 16),
]

angles = [i * golden_angle_deg for i in range(8)]
core_radius = 15.0
strand_sep = 0.6
N = 600

MAX_POINTS = 8000
MAX_ORGANELLES = 200

frame = 0

# ---------- FASTA LOADER ----------

# ---------- AUTO-DETECT AND LOAD GENOME ----------
def find_covid_fasta():
    """Automatically find the COVID-19 FASTA file in the ncbi_dataset"""
    import glob
    possible_paths = [
        r"ncbi_dataset\data\GCF_009858895.2\*.fna",
        r"ncbi_dataset\data\GCA_009858895.3\*.fna",
        r"ncbi_dataset\data\*\*.fna",
    ]

    for pattern in possible_paths:
        files = glob.glob(pattern)
        if files:
            return files[0]

    raise FileNotFoundError("Could not find COVID-19 FASTA file in ncbi_dataset directory")


def load_genome(fasta_file):
    seq = []
    with open(fasta_file) as f:
        for line in f:
            if line.startswith(">"): continue
            seq.extend(list(line.strip().upper()))
    return seq

# EDIT THIS PATH IF NEEDED
fasta_path = find_covid_fasta()
if not os.path.exists(fasta_path):
    raise FileNotFoundError(f"FASTA not found: {fasta_path}")
genome_seq = load_genome(fasta_path)
genome_len = len(genome_seq)
print("Genome length:", genome_len)

# ---------- VISPY SCENE ----------
comp_canvas = scene.SceneCanvas(keys='interactive', size=(1200,800),
                                bgcolor='#000011', title="Generative DNA Cells (FASTA-driven)")
comp_view = comp_canvas.central_widget.add_view()
comp_view.camera = 'turntable'

strand_vis_1 = Line(pos=np.zeros((1,3)), color=(0,1,1,0.9), width=2, parent=comp_view.scene)
strand_vis_2 = Line(pos=np.zeros((1,3)), color=(1,0.5,0,0.9), width=2, parent=comp_view.scene)

organelles = []
echo_shells = []
labels = []
accum_s1 = []
accum_s2 = []

# ---------- HELPERS ----------
def consensus_dim_for_base(b):
    dim_counts = [m.get(b,1)-1 for m in all_mappings]
    return int(np.round(np.mean(dim_counts)))

def organelle_params_from_base(b):
    d = consensus_dim_for_base(b)
    spawn_prob = 0.02 + (d / 16.0) * 0.12
    cluster_size = 6 + (d % 5) * 3
    radial_bias = 0.2 + (d / 16.0) * 0.9
    noise_scale = 0.08 + (d / 16.0) * 0.4
    return spawn_prob, cluster_size, radial_bias, noise_scale

def spawn_organelle(center, color_rgb, size, n=12, persistence=180):
    pts = center + np.random.normal(scale=0.3, size=(n,3)) * size
    rgba = list(Color(color_rgb).rgba)
    rgba[3] = 0.95
    mark = Markers(pos=pts, face_color=rgba, edge_color='white', size=6, parent=comp_view.scene)
    return {"marker": mark, "age": 0, "life": persistence}

def make_echo_shell(center, scale, color_rgb, alpha=0.12):
    shell_pts = center + (np.random.normal(scale=0.4, size=(max(8,int(12*scale)),3)) * scale)
    rgba = list(Color(color_rgb).rgba)
    rgba[3] = alpha
    shell = Markers(pos=shell_pts, face_color=rgba, edge_color=None, size=3, parent=comp_view.scene)
    return {"marker": shell, "age": 0, "life": 300}

# ---------- PROGRESS TEXT ----------
progress_text = Text("0%", pos=[0,0,20], color='white', font_size=24,
                     anchor_x='center', parent=comp_view.scene)

# ---------- UPDATE LOOP ----------
def update(ev):
    global frame, accum_s1, accum_s2, organelles, echo_shells

    frame += 1
    idx = frame % genome_len
    base = genome_seq[idx]

    # POSITIVE FIELD: φ-spiral composite
    composite_s1 = np.zeros(3)
    composite_s2 = np.zeros(3)
    for mapping in all_mappings:
        dim = mapping.get(base,1)-1
        _, _, col, _, alpha, verts = geometries[dim]
        r = core_radius * (1 - (idx/genome_len)**1.4)
        r = max(r,0.5)
        theta = idx * np.radians(golden_angle_deg)
        z = np.sin(idx/genome_len * np.pi * 4) * 1.6 + (idx/genome_len)*6.0
        a1, a2 = np.radians(angles[dim]), np.radians(-angles[dim])
        p1 = np.array([r*np.cos(theta)*np.cos(a1)-r*np.sin(theta)*np.sin(a1),
                       r*np.sin(theta)*np.cos(a1)+r*np.cos(theta)*np.sin(a1),
                       z])
        p2 = np.array([r*np.cos(theta)*np.cos(a2)-r*np.sin(theta)*np.sin(a2)+strand_sep,
                       r*np.sin(theta)*np.cos(a2)+r*np.cos(theta)*np.sin(a2)-strand_sep,
                       z])
        composite_s1 += p1
        composite_s2 += p2
    composite_s1 /= len(all_mappings)
    composite_s2 /= len(all_mappings)

    # NEGATIVE / NOISE FIELD
    noise = (np.random.rand(3)-0.5) * 0.5 * (1 - r/core_radius)
    composite_s1 += noise
    composite_s2 -= noise

    # ACCUMULATE
    accum_s1.append(composite_s1.copy())
    accum_s2.append(composite_s2.copy())
    if len(accum_s1) > MAX_POINTS:
        drop = len(accum_s1)//3
        accum_s1, accum_s2 = accum_s1[drop:], accum_s2[drop:]

    # ORGANELLE NUCLEATION
    spawn_prob, cluster_size, radial_bias, noise_scale = organelle_params_from_base(base)
    if np.random.rand() < spawn_prob:
        local_center = (composite_s1 + composite_s2)/2.0
        dim = consensus_dim_for_base(base)
        color_rgb = geometries[min(dim,len(geometries)-1)][2]
        organelles.append(spawn_organelle(local_center, color_rgb, size=0.8+radial_bias, n=cluster_size, persistence=260))
        echo_shells.append(make_echo_shell(local_center, scale=0.6+radial_bias, color_rgb=color_rgb, alpha=0.08))

    # UPDATE VISUALS
    strand_vis_1.set_data(np.array(accum_s1))
    strand_vis_2.set_data(np.array(accum_s2))

    # FADE & CLEANUP
    def update_entities(entity_list, max_count=MAX_ORGANELLES):
        new_list = []
        for ent in entity_list:
            ent['age'] += 1
            alpha = max(0.0, 1.0 - ent['age']/ent['life'])
            rgba = list(ent.get('base_rgba',(1,1,1,1)))
            rgba[3] = alpha
            try: ent['marker'].set_data(pos=ent['marker']._pos, face_color=rgba)
            except: pass
            if ent['age'] < ent['life']:
                ent['base_rgba'] = rgba
                new_list.append(ent)
            else:
                try: ent['marker'].parent = None
                except: pass
        return new_list[:max_count]

    organelles[:] = update_entities(organelles)
    echo_shells[:] = update_entities(echo_shells, max_count=MAX_ORGANELLES*2)

    # CAMERA ROTATION
    comp_view.camera.azimuth = frame*0.12
    comp_view.camera.elevation = 15 + 8*np.sin(frame*0.002)

    # PROGRESS
    progress_text.text = f"{min(100,frame/genome_len*100):.1f}%"
    comp_canvas.update()

# ---------- START ----------
timer = app.Timer(interval=0.02, connect=update, start=True)
if __name__ == "__main__":
    comp_canvas.show()
    app.run()
