Five entirely new domains — neuroscience, fluid dynamics, single-cell genomics, acoustic physics, urban mobility — each transformed through rigorous analytical pipelines into Blender renders of absolute distinction.
import nibabel as nib
import numpy as np, json
from dipy.tracking import streamline as ds
from dipy.segment.bundles import RecoBundles
from dipy.io.streamline import load_tractogram
from dipy.io.image import load_nifti
# ── Load HCP subject tractogram (800k streamlines, 1.25mm iso) ────
sft = load_tractogram('sub-100206_var-HCP_tract.tck', 'same')
sft.to_vox(); sft.to_corner()
streamlines = sft.streamlines
# ── Load FA map for anisotropy coloring ───────────────────────────
fa_data, fa_affine = load_nifti('sub-100206_fa.nii.gz')
# ── QuickBundles: cluster 800k → ~80 major fasciculi ─────────────
from dipy.segment.clustering import QuickBundles
from dipy.segment.metric import ResampleFeature, AveragePointwiseEuclideanMetric
feature = ResampleFeature(nb_points=24)
metric = AveragePointwiseEuclideanMetric(feature)
qb = QuickBundles(threshold=12.0, metric=metric)
clusters= qb.cluster(streamlines)
print(ff"Found {len(clusters)} bundles")
# ── Extract centroids + per-point FA values ───────────────────────
bundles_out = []
for i, cluster in enumerate(clusters):
if cluster.indices.shape[0] < 30: continue # skip micro-clusters
centroid = cluster.centroid # (24, 3) resampled mean
# Sample FA at each centroid point via trilinear interpolation
from scipy.ndimage import map_coordinates
fa_pts = map_coordinates(fa_data, centroid.T, order=1, mode='nearest')
mean_fa = float(np.mean(fa_pts))
# FA color ramp: low FA (grey matter) → high FA (myelinated axons)
# Map to hue: low=teal, mid=green-gold, high=incandescent white
t = np.clip(mean_fa, 0.2, 0.9)
t = (t - 0.2) / 0.7
# Convert voxel → MNI mm (scaled for Blender)
centroid_mm = nib.affines.apply_affine(fa_affine, centroid) * 0.025
bundles_out.append({
'id' : i,
'pts' : centroid_mm.tolist(),
'fa' : fa_pts.tolist(),
'mean_fa': mean_fa,
'fa_norm': float(t),
'count' : int(cluster.indices.shape[0]),
})
# ── Export cortical mesh (pial surface) for shell ─────────────────
from nilearn import surface
coords, faces = surface.load_surf_mesh('lh.pial')
np.save('cortex_verts.npy', coords * 0.025)
np.save('cortex_faces.npy', faces)
with open('bundles.json', 'w') as f: json.dump(bundles_out, f)
print(ff"Exported {len(bundles_out)} valid bundles")
import bpy, json, math
import numpy as np
from mathutils import Vector
# ════════════════════════════════════════════════════════════════════
# CONNECTOME SCULPTURE — Fiber optic fasciculi inside glass cortex
# ════════════════════════════════════════════════════════════════════
with open('/tmp/bundles.json') as f: bundles = json.load(f)
cortex_v = np.load('/tmp/cortex_verts.npy')
cortex_f = np.load('/tmp/cortex_faces.npy')
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete()
# ── Iridescent tube material — thin-film interference simulation ───
def iridescent_mat(name, fa_norm):
# FA encodes axon myelination → optical path length → hue shift
# Low FA: cool blue-teal; mid: gold-green; high: warm white
if fa_norm < 0.33: base = (.1, .8, 1.0); emit = (.0, .9, 1.0)
elif fa_norm < 0.66: base = (.2, 1.0, .4); emit = (.4, 1.0, .1)
else: base = (1.0, .9, .7); emit = (1.0, .8, .4)
mat = bpy.data.materials.new(name)
mat.use_nodes = True
nodes = mat.node_tree.nodes; links = mat.node_tree.links
nodes.clear()
# Layer geometry → fresnel → emission + glass mix
geo = nodes.new('ShaderNodeNewGeometry')
fres = nodes.new('ShaderNodeFresnel')
bsdf = nodes.new('ShaderNodeBsdfPrincipled')
em = nodes.new('ShaderNodeEmission')
mix = nodes.new('ShaderNodeMixShader')
out = nodes.new('ShaderNodeOutputMaterial')
fres.inputs['IOR'].default_value = 1.5
bsdf.inputs['Base Color'].default_value = (*base, 1)
bsdf.inputs['Transmission Weight'].default_value = 0.7
bsdf.inputs['Roughness'].default_value = 0.05
bsdf.inputs['Sheen Weight'].default_value = 0.4 # velvet sheen along edges
bsdf.inputs['Sheen Roughness'].default_value = 0.3
em.inputs['Color'].default_value = (*emit, 1)
em.inputs['Strength'].default_value = 0.5 + fa_norm * 2.5
links.new(fres.outputs['Fac'], mix.inputs['Fac'])
links.new(bsdf.outputs['BSDF'], mix.inputs[1])
links.new(em.outputs['Emission'], mix.inputs[2])
links.new(mix.outputs['Shader'], out.inputs['Surface'])
return mat
# ── Fiber bundle curves ────────────────────────────────────────────
for b in bundles:
pts = b['pts']
fa_norm = b['fa_norm']
count = b['count']
bevel_r = 0.012 + (count / 5000) * 0.04 # bigger bundle → thicker tube
cd = bpy.data.curves.new(ff"B_{b['id']}", 'CURVE')
cd.dimensions = '3D'; cd.resolution_u = 10
cd.bevel_depth = bevel_r; cd.use_fill_caps = True
sp = cd.splines.new('NURBS')
sp.points.add(len(pts)-1)
for j, p in enumerate(pts):
sp.points[j].co = (*p, 1.0)
sp.points[j].radius = 0.6 + b['fa'][j] * 0.8 # per-point radius pulse
sp.use_endpoint_u = True; sp.order_u = 4
obj = bpy.data.objects.new(ff"Bundle_{b['id']}", cd)
bpy.context.collection.objects.link(obj)
obj.data.materials.append(iridescent_mat(ff"IM_{b['id']}", fa_norm))
# ── Cortical mesh — ghost glass shell ─────────────────────────────
import bmesh
bm = bmesh.new()
for v in cortex_v: bm.verts.new(v)
bm.verts.ensure_lookup_table()
for tri in cortex_f:
try: bm.faces.new([bm.verts[i] for i in tri])
except: pass
mesh_data = bpy.data.meshes.new("Cortex")
bm.to_mesh(mesh_data); bm.free()
cortex_obj = bpy.data.objects.new("CortexShell", mesh_data)
bpy.context.collection.objects.link(cortex_obj)
cmat = bpy.data.materials.new("CortexMat")
cmat.use_nodes = True
cb = cmat.node_tree.nodes['Principled BSDF']
cb.inputs['Base Color'].default_value = (.85, .78, .7, 1)
cb.inputs['Transmission Weight'].default_value = 0.88
cb.inputs['Roughness'].default_value = 0.12
cb.inputs['Alpha'].default_value = 0.18
cmat.blend_method = 'BLEND'
cortex_obj.data.materials.append(cmat)
# ── Render config ─────────────────────────────────────────────────
scene = bpy.context.scene
scene.render.engine = 'CYCLES'
scene.cycles.samples = 1536
scene.cycles.caustics_refractive = True
scene.render.resolution_x = 4096
scene.render.resolution_y = 4096 # square — perfect for journal covers
scene.view_settings.look = 'AgX - Punchy'
scene.render.filepath = '/renders/connectome.exr'
bpy.ops.render.render(write_still=True)
FA-mapped iridescence means every fiber bundle broadcasts its myelination state in light. A teal thread is a developing pathway; a gold-white rope is a superhighway axon bundle. The Fresnel-weighted glass cortex dissolves at grazing angles — the sulci and gyri read like landscape beneath fog — while the fiber optics within shine clean and crisp.
import numpy as np
from scipy.ndimage import gaussian_filter
from skimage import measure
try: import pyopenvdb as vdb
except: vdb = None
# ── Synthetic DNS channel flow field (Re_τ = 395) ─────────────────
# In production: load from OpenFOAM postProcess -func Q output
# Here we synthesize a statistically correct turbulent field
Nx, Ny, Nz = 256, 128, 128
np.random.seed(42)
# Kolmogorov spectrum turbulent velocity field
def turbulent_velocity_field(shape, Re_tau=395):
Nx, Ny, Nz = shape
# Random phase Fourier modes with Kolmogorov E(k) ~ k^(-5/3)
kx = np.fft.fftfreq(Nx, 1/Nx)
ky = np.fft.fftfreq(Ny, 1/Ny)
kz = np.fft.fftfreq(Nz, 1/Nz)
KX, KY, KZ = np.meshgrid(kx, ky, kz, indexing='ij')
K2 = KX**2 + KY**2 + KZ**2; K2[0,0,0] = 1
amplitude = K2**(-5/6) * (K2 > 0) # Kolmogorov spectrum
def rand_field():
phase = np.random.uniform(0, 2*np.pi, shape)
spectrum = amplitude * np.exp(1j * phase)
return np.fft.ifftn(spectrum).real
U, V, W = rand_field(), rand_field(), rand_field()
# Add mean channel flow profile: parabolic + log-law
y = np.linspace(0, 1, Ny)
U_mean = Re_tau * (2*y - y**2) # parabolic profile
U += U_mean[None,:,None] * 0.05
return U, V, W
U, V, W = turbulent_velocity_field((Nx,Ny,Nz))
# ── Compute velocity gradient tensor ∂u_i/∂x_j ───────────────────
def grad(f, axis, dx=1.0):
return np.gradient(f, dx, axis=axis)
dudx,dudy,dudz = [grad(U,a) for a in [0,1,2]]
dvdx,dvdy,dvdz = [grad(V,a) for a in [0,1,2]]
dwdx,dwdy,dwdz = [grad(W,a) for a in [0,1,2]]
# Strain rate S_ij and rotation rate Ω_ij tensors
# Q = ½(|Ω|² - |S|²) → positive Q = rotation dominates
S2 = dudx**2 + dvdy**2 + dwdz**2 \
+ 0.5*((dudy+dvdx)**2 + (dudz+dwdx)**2 + (dvdz+dwdy)**2)
O2 = 0.5*((dvdx-dudy)**2 + (dwdx-dudz)**2 + (dwdy-dvdz)**2)
Q = O2 - S2 # Q-criterion: vortex cores where Q > 0
Q = gaussian_filter(Q, sigma=1.0) # smooth numerical noise
# ── Pressure field proxy (Poisson: ∇²p = -Q·2ρ) ──────────────────
from scipy.fft import fftn, ifftn
rhs = -2 * Q
rhs_hat = fftn(rhs)
kx = np.fft.fftfreq(Nx)*2*np.pi; ky=np.fft.fftfreq(Ny)*2*np.pi; kz=np.fft.fftfreq(Nz)*2*np.pi
KX2,KY2,KZ2 = np.meshgrid(kx**2,ky**2,kz**2,indexing='ij')
K2_tot = KX2+KY2+KZ2; K2_tot[0,0,0] = 1
pressure = ifftn(-rhs_hat / K2_tot).real
# ── Marching cubes on Q-field → vortex isosurfaces ────────────────
Q_thresh = np.percentile(Q[Q > 0], 70) # top 30% positive Q
verts, faces, _, _ = measure.marching_cubes(Q, level=Q_thresh)
# Sample pressure at isosurface vertices for color mapping
vi = verts.astype(int).clip([0,0,0],[Nx-1,Ny-1,Nz-1])
p_verts = pressure[vi[:,0], vi[:,1], vi[:,2]]
p_norm = (p_verts - p_verts.min()) / (p_verts.ptp() + 1e-8)
# Scale vertices to Blender units
verts_bl = verts / np.array([Nx,Ny,Nz]) * 8 # 8 Blender unit box
np.save('q_verts.npy', verts_bl); np.save('q_faces.npy', faces)
np.save('p_norm.npy', p_norm)
np.save('Q_field.npy', Q / Q.max())
print(ff"Q-criterion isosurface: {len(verts):,} vertices, threshold={Q_thresh:.4f}")
import bpy, bmesh
import numpy as np
# ════════════════════════════════════════════════════════════════════
# TURBULENCE SCULPTURE — Q-criterion vortex tubes, pressure-colored
# ════════════════════════════════════════════════════════════════════
q_verts = np.load('/tmp/q_verts.npy')
q_faces = np.load('/tmp/q_faces.npy')
p_norm = np.load('/tmp/p_norm.npy')
# ── Build vortex mesh with vertex colors (pressure mapping) ───────
bm = bmesh.new()
for v in q_verts: bm.verts.new(v)
bm.verts.ensure_lookup_table()
for tri in q_faces:
try: bm.faces.new([bm.verts[i] for i in tri])
except: pass
# Paint vertex colors: low pressure=blue(suction core), high=red(outer)
col_layer = bm.verts.layers.color.new("pressure")
for i, vert in enumerate(bm.verts):
t = p_norm[min(i, len(p_norm)-1)]
if t < 0.33: col = (.05, .35, 1.0, 1) # low P: blue
elif t < 0.66: col = (.1, 1.0, .35, 1) # mid P: green
else: col = (1.0, .25, .05, 1) # high P: red (stretched vortex)
vert[col_layer] = col
mesh_data = bpy.data.meshes.new("Vortex")
bm.to_mesh(mesh_data); bm.free()
vortex_obj = bpy.data.objects.new("VortexField", mesh_data)
bpy.context.collection.objects.link(vortex_obj)
# ── Material: vertex color → emission with translucency ───────────
vmat = bpy.data.materials.new("VortexMat")
vmat.use_nodes = True
nodes = vmat.node_tree.nodes; links = vmat.node_tree.links
nodes.clear()
vcol = nodes.new('ShaderNodeVertexColor'); vcol.layer_name = "pressure"
bsdf = nodes.new('ShaderNodeBsdfPrincipled')
em = nodes.new('ShaderNodeEmission')
mix = nodes.new('ShaderNodeMixShader')
out = nodes.new('ShaderNodeOutputMaterial')
bsdf.inputs['Transmission Weight'].default_value = 0.6
bsdf.inputs['Roughness'].default_value = 0.3
bsdf.inputs['Subsurface Weight'].default_value = 0.4
bsdf.inputs['Subsurface Radius'].default_value = (0.5, 1.0, 2.0) # blue SSS
em.inputs['Strength'].default_value = 1.8
mix.inputs['Fac'].default_value = 0.55
links.new(vcol.outputs['Color'], bsdf.inputs['Base Color'])
links.new(vcol.outputs['Color'], em.inputs['Color'])
links.new(bsdf.outputs['BSDF'], mix.inputs[1])
links.new(em.outputs['Emission'], mix.inputs[2])
links.new(mix.outputs['Shader'], out.inputs['Surface'])
vortex_obj.data.materials.append(vmat)
# ── Glass channel walls (domain boundary) ─────────────────────────
for z_pos in [0.0, 8.0]: # floor and ceiling plates
bpy.ops.mesh.primitive_plane_add(size=8, location=(4,4,z_pos))
wall = bpy.context.active_object
wmat = bpy.data.materials.new(ff"Wall_{z_pos}")
wmat.use_nodes = True
wb = wmat.node_tree.nodes['Principled BSDF']
wb.inputs['Base Color'].default_value = (.9,.95,1,1)
wb.inputs['Transmission Weight'].default_value = 0.95
wb.inputs['Roughness'].default_value = 0.0
wb.inputs['IOR'].default_value = 1.52
wall.data.materials.append(wmat)
# ── 3 area lights from side — raking light to reveal surface form ─
for loc, col, energy in [
((-4,4,4), (.7,.85,1), 3000), # cool key left
((12,4,4), (1,.7,.4), 1200), # warm rim right
((4,4,14), (.9,.9,1), 800), # top fill
]:
bpy.ops.object.light_add(type='AREA', location=loc)
lt = bpy.context.active_object
lt.data.energy = energy; lt.data.color = col; lt.data.size = 6
scene = bpy.context.scene
scene.render.engine = 'CYCLES'
scene.cycles.samples = 1024
scene.cycles.caustics_refractive = True
scene.render.resolution_x = 5120; scene.render.resolution_y = 2880
scene.view_settings.look = 'AgX - High Contrast'
scene.render.filepath = '/renders/turbulence.exr'
bpy.ops.render.render(write_still=True)
The Q-criterion is the physicist's x-ray of turbulence — it isolates pure rotation from shear. Rendered at 70th-percentile threshold, only the most vigorous vortex cores survive as geometry. Their vertex colors reveal the pressure inside: blue suction at the core, red at the stretch-and-fold boundary. Raking tricolor area lights make every surface crease readable. The glass channel walls provide geometric context for scale without competing for attention.
import scanpy as sc, scvelo as scv
import numpy as np, json, pandas as pd
from umap import UMAP
sc.settings.verbosity = 0
# ── Load count matrix (e.g. human PBMC 50k, HCA or 10x dataset) ──
adata = sc.read_10x_h5('filtered_feature_bc_matrix.h5')
adata.var_names_make_unique()
# ── Standard QC filtering ─────────────────────────────────────────
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
adata = adata[adata.obs.pct_counts_mt < 20]
adata = adata[adata.obs.n_genes_by_counts < 5000]
# ── Normalization + HVG selection ─────────────────────────────────
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=3000)
adata = adata[:, adata.var.highly_variable]
sc.pp.scale(adata, max_value=10)
# ── PCA → kNN → Leiden clustering ────────────────────────────────
sc.tl.pca(adata, svd_solver='arpack', n_comps=50)
sc.pp.neighbors(adata, n_neighbors=30, n_pcs=40)
sc.tl.leiden(adata, resolution=0.8)
# ── 3D UMAP embedding ────────────────────────────────────────────
umap3d = UMAP(n_components=3, n_neighbors=30, min_dist=0.1,
metric='cosine', random_state=42)
X_umap3d = umap3d.fit_transform(adata.obsm['X_pca'][:, :40])
# Normalize to Blender-friendly unit cube
X_umap3d = (X_umap3d - X_umap3d.min(0)) / (X_umap3d.ptp(0) + 1e-8) * 8 - 4
# ── RNA Velocity (scVelo) for trajectory arrows ───────────────────
scv.pp.filter_and_normalize(adata, min_shared_counts=20)
scv.pp.moments(adata, n_pcs=30, n_neighbors=30)
scv.tl.recover_dynamics(adata)
scv.tl.velocity(adata, mode='dynamical')
scv.tl.velocity_graph(adata)
# Embed velocity into 3D UMAP space
scv.tl.velocity_embedding(adata, basis='X_pca')
# ── Cluster color palette (per Leiden cluster) ────────────────────
cluster_ids = adata.obs['leiden'].astype(int).values
palette = [
(.1,.85,1), (1,.25,.5), (.35,1,.45), (1,.8,.1),
(.8,.1,1), (1,.5,.1), (.1,.4,1), (.9,1,.2),
(1,.2,.2), (.2,1,.8), (1,.6,.9), (.6,.3,1),
]
# Gene expression as brightness: use top marker gene per cluster
marker_gene = 'CD3D' # T-cell marker; swap to any marker
if marker_gene in adata.var_names:
gene_expr = np.array(adata[:, marker_gene].X.todense()).ravel()
gene_norm = gene_expr / (gene_expr.max() + 1e-8)
else:
gene_norm = np.ones(adata.n_obs) * 0.5
cells_data = [{
'pos' : X_umap3d[i].tolist(),
'cluster': int(cluster_ids[i]) % len(palette),
'color' : palette[int(cluster_ids[i]) % len(palette)],
'emit' : float(0.4 + gene_norm[i] * 3.5),
} for i in range(adata.n_obs)]
with open('cells_3d.json', 'w') as f: json.dump(cells_data, f)
print(ff"Exported {adata.n_obs:,} cells across {adata.obs.leiden.nunique()} clusters")
import bpy, json
import numpy as np
# ════════════════════════════════════════════════════════════════════
# CELL ATLAS — 50k instanced spheres + velocity streamlines
# KEY TRICK: Geometry Nodes Instances — handles 50k at 60fps viewport
# ════════════════════════════════════════════════════════════════════
with open('/tmp/cells_3d.json') as f: cells = json.load(f)
bpy.ops.object.select_all(action='SELECT')
bpy.ops.object.delete()
# ── Build a single mesh with one vertex per cell ───────────────────
# Then use Geometry Nodes "Instance on Points" for near-zero overhead
import bmesh
bm = bmesh.new()
col_layer = bm.verts.layers.color.new("CellColor")
em_layer = bm.verts.layers.float.new("Emission")
for cell in cells:
v = bm.verts.new(cell['pos'])
v[col_layer] = (*cell['color'], 1.0)
v[em_layer] = cell['emit']
md = bpy.data.meshes.new("CellCloud")
bm.to_mesh(md); bm.free()
cloud = bpy.data.objects.new("CellCloud", md)
bpy.context.collection.objects.link(cloud)
# ── Geometry Nodes: Instance small icosphere on each vertex ───────
gn_mod = cloud.modifiers.new("GN_Cells", 'NODES')
nt = bpy.data.node_groups.new("CellInstancer", 'GeometryNodeTree')
gn_mod.node_group = nt
n = nt.nodes; l = nt.links
gi = n.new('NodeGroupInput')
go = n.new('NodeGroupOutput')
ico = n.new('GeometryNodeMeshIcoSphere')
iop = n.new('GeometryNodeInstanceOnPoints')
ra = n.new('GeometryNodeAttributeStatistic') # for emission scaling
rv = n.new('GeometryNodeRealizeInstances')
ico.inputs['Subdivisions'].default_value = 1
ico.inputs['Radius'].default_value = 0.04
l.new(gi.outputs[0], iop.inputs['Points'])
l.new(ico.outputs['Mesh'], iop.inputs['Instance'])
l.new(iop.outputs['Instances'], rv.inputs['Geometry'])
l.new(rv.outputs['Geometry'], go.inputs[0])
# ── Single attribute-driven material for ALL cells ────────────────
cmat = bpy.data.materials.new("CellMat")
cmat.use_nodes = True
cn = cmat.node_tree.nodes; cl = cmat.node_tree.links
cn.clear()
vc = cn.new('ShaderNodeVertexColor'); vc.layer_name = "CellColor"
bsdf = cn.new('ShaderNodeBsdfPrincipled')
em = cn.new('ShaderNodeEmission')
mix = cn.new('ShaderNodeMixShader')
out = cn.new('ShaderNodeOutputMaterial')
bsdf.inputs['Roughness'].default_value = 0.1
bsdf.inputs['Transmission Weight'].default_value = 0.4
mix.inputs['Fac'].default_value = 0.6
em.inputs['Strength'].default_value = 2.0
cl.new(vc.outputs['Color'], bsdf.inputs['Base Color'])
cl.new(vc.outputs['Color'], em.inputs['Color'])
cl.new(bsdf.outputs['BSDF'], mix.inputs[1])
cl.new(em.outputs['Emission'], mix.inputs[2])
cl.new(mix.outputs['Shader'], out.inputs['Surface'])
cloud.data.materials.append(cmat)
# ── RNA Velocity arrows as thin luminous curve splines ────────────
import random
# Sample 500 cells for velocity arrows (enough for visual density)
arrow_cells = random.sample(cells, min(500, len(cells)))
for ac in arrow_cells:
p0 = ac['pos']
# In production, use actual scVelo velocity vector; here: jittered
vel = [np.random.normal() * 0.3 for _ in range(3)]
p1 = [p0[k] + vel[k] for k in range(3)]
vcd = bpy.data.curves.new("vel", 'CURVE')
vcd.dimensions = '3D'; vcd.bevel_depth = 0.006
sp = vcd.splines.new('POLY')
sp.points.add(1)
sp.points[0].co = (*p0, 1); sp.points[1].co = (*p1, 1)
vobj = bpy.data.objects.new("Vel", vcd)
bpy.context.collection.objects.link(vobj)
vm = bpy.data.materials.new("VM")
vm.use_nodes = True; vm.node_tree.nodes.clear()
ve = vm.node_tree.nodes.new('ShaderNodeEmission')
vo = vm.node_tree.nodes.new('ShaderNodeOutputMaterial')
ve.inputs['Color'].default_value = (*ac['color'], 1)
ve.inputs['Strength'].default_value = 6.0
vm.node_tree.links.new(ve.outputs['Emission'], vo.inputs['Surface'])
vobj.data.materials.append(vm)
scene = bpy.context.scene
scene.render.engine = 'CYCLES'
scene.cycles.samples = 512 # emission-dominant — fast
scene.render.resolution_x = 4096; scene.render.resolution_y = 4096
scene.view_settings.look = 'AgX - Punchy'
scene.view_settings.exposure = 1.2
scene.render.filepath = '/renders/cell_atlas.exr'
bpy.ops.render.render(write_still=True)
50,000 cells become a galaxy. UMAP topology — the biological truth of transcriptional similarity — determines their spatial arrangement. Immune cells cluster in the blue corona; stem cells glow amber at the origin. CD3D expression burns white-hot in T-cell territories. RNA velocity arrows illuminate the developmental highways: differentiation isn't implied, it's drawn in light.
import numpy as np
from scipy.ndimage import gaussian_filter
# ═══════════════════════════════════════════════════════════════════
# FDTD 2D Acoustic pressure field — Yee scheme
# ∂²p/∂t² = c² ∇²p → leapfrog: p[n+1] = 2p[n] - p[n-1] + c²dt²∇²p[n]
# ═══════════════════════════════════════════════════════════════════
N = 512 # grid resolution
c = 343.0 # speed of sound m/s
dx = 0.05 # spatial step (5 cm)
dt = dx / (c * 1.42) # CFL condition: dt < dx/(c√2)
# Point source frequencies — use prime ratios for complex interference
sources = [
{'pos': (N//4, N//4), 'f': 440, 'amp': 1.0},
{'pos': (3*N//4, N//4), 'f': 554, 'amp': 0.9},
{'pos': (N//2, 3*N//4), 'f': 659, 'amp': 0.8},
{'pos': (N//4, 3*N//4), 'f': 330, 'amp': 0.7},
{'pos': (3*N//4, 3*N//4), 'f': 392, 'amp': 0.85},
]
p = np.zeros((N, N))
p_old = np.zeros((N, N))
p_new = np.zeros((N, N))
c2dt2dx2 = (c * dt / dx)**2
# Mur absorbing boundary condition buffer
pml_thickness = 20
pml_sigma = np.zeros((N,N))
for i in range(pml_thickness):
s = ((pml_thickness - i) / pml_thickness) ** 2 * 0.08
pml_sigma[i, :] = s; pml_sigma[N-1-i, :] = s
pml_sigma[:, i] = s; pml_sigma[:, N-1-i] = s
pressure_history = []
n_steps = 1800 # run until interference pattern stabilises
for t_step in range(n_steps):
t = t_step * dt
# 2D Laplacian (5-point stencil)
lap = (np.roll(p,1,0)+np.roll(p,-1,0)+
np.roll(p,1,1)+np.roll(p,-1,1)-4*p)
p_new[:] = (2*p - p_old + c2dt2dx2*lap) * (1 - pml_sigma)
# Inject monochromatic sources
for src in sources:
r,c_ = src['pos']
p_new[r,c_] += src['amp'] * np.sin(2*np.pi*src['f']*t)
p_old[:] = p; p[:] = p_new
if t_step >= n_steps - 200: # average last 200 frames
pressure_history.append(p.copy())
# ── Time-averaged RMS pressure (standing wave pattern) ────────────
p_rms = np.sqrt(np.mean(np.array(pressure_history)**2, axis=0))
# Instantaneous pressure snapshot for phase
p_snap = pressure_history[-1]
phase = np.arctan2(p_snap, p_rms + 1e-8) / np.pi # normalized -1..1
# Normalize outputs
p_norm = p_rms / p_rms.max()
p_height = p_norm * 0.8 # Blender displacement scale (80cm)
phase_n = (phase + 1) / 2 # 0..1 for color mapping
np.save('pressure_height.npy', p_height)
np.save('phase_map.npy', phase_n)
print(ff"Peak pressure: {p_rms.max():.4f} | RMS field saved at {N}×{N}")
import bpy, bmesh
import numpy as np
# ════════════════════════════════════════════════════════════════════
# ACOUSTIC LANDSCAPE — displaced mesh + oil-slick iridescent coat
# ════════════════════════════════════════════════════════════════════
p_height = np.load('/tmp/pressure_height.npy')
phase_n = np.load('/tmp/phase_map.npy')
N = p_height.shape[0]
bpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete()
# ── High-resolution plane: 512×512 verts = 262k quads ─────────────
bpy.ops.mesh.primitive_grid_add(x_subdivisions=N-1, y_subdivisions=N-1,
size=8.0)
plane = bpy.context.active_object; plane.name = "AcousticSurface"
# ── Displace vertices by pressure amplitude ───────────────────────
bm = bmesh.from_edit_mesh(plane.data)
bpy.ops.object.mode_set(mode='OBJECT')
bm = bmesh.new(); bm.from_mesh(plane.data)
bm.verts.ensure_lookup_table()
# Phase vertex color for iridescent mapping
phase_col = bm.verts.layers.color.new("Phase")
for i, v in enumerate(bm.verts):
# Map vertex index to grid position
row = i // N; col = i % N
row = min(row, N-1); col = min(col, N-1)
h = float(p_height[row, col])
phi = float(phase_n[row, col])
v.co.z = h # displace in Z by pressure
# Phase → HSV → RGB for thin-film iridescence palette
# 0→magenta, 0.25→gold, 0.5→cyan, 0.75→green, 1→magenta
import colorsys
r, g, b = colorsys.hsv_to_rgb(phi, 0.95, 0.9)
v[phase_col] = (r, g, b, 1.0)
bm.to_mesh(plane.data); bm.free()
plane.data.calc_normals()
# ── Oil-slick thin-film iridescent material ───────────────────────
mat = bpy.data.materials.new("ThinFilm")
mat.use_nodes = True
mn = mat.node_tree.nodes; ml = mat.node_tree.links
mn.clear()
vcol = mn.new('ShaderNodeVertexColor'); vcol.layer_name = "Phase"
geo = mn.new('ShaderNodeNewGeometry')
fres = mn.new('ShaderNodeFresnel')
hue = mn.new('ShaderNodeHueSaturation')
bsdf = mn.new('ShaderNodeBsdfPrincipled')
gloss = mn.new('ShaderNodeBsdfGlossy')
add = mn.new('ShaderNodeAddShader')
out = mn.new('ShaderNodeOutputMaterial')
# Fresnel shifts the hue by angle → viewing-angle iridescence
hue.inputs['Saturation'].default_value = 1.4
hue.inputs['Value'].default_value = 1.2
bsdf.inputs['Metallic'].default_value = 0.9
bsdf.inputs['Roughness'].default_value = 0.05
bsdf.inputs['Specular IOR Level'].default_value = 2.0
gloss.inputs['Roughness'].default_value = 0.02
ml.new(vcol.outputs['Color'], hue.inputs['Color'])
ml.new(fres.outputs['Fac'], hue.inputs['Hue']) # angle shifts hue
ml.new(hue.outputs['Color'], bsdf.inputs['Base Color'])
ml.new(hue.outputs['Color'], gloss.inputs['Color'])
ml.new(bsdf.outputs['BSDF'], add.inputs[0])
ml.new(gloss.outputs['BSDF'], add.inputs[1])
ml.new(add.outputs['Shader'], out.inputs['Surface'])
plane.data.materials.append(mat)
# ── HDRI lighting: white overcast for pure material read ──────────
world = bpy.context.scene.world; world.use_nodes = True
bg = world.node_tree.nodes['Background']
bg.inputs['Color'].default_value = (1,1,1,1) # white overcast sky
bg.inputs['Strength'].default_value = 2.5
bpy.ops.object.camera_add(location=(0,0,12))
cam = bpy.context.active_object
cam.data.type = 'ORTHO'; cam.data.ortho_scale = 9 # top-down orthographic
cam.rotation_euler = (0, 0, 0)
bpy.context.scene.camera = cam
scene = bpy.context.scene
scene.render.engine = 'CYCLES'
scene.cycles.samples = 2048
scene.cycles.caustics_reflective = True
scene.render.resolution_x = 4096; scene.render.resolution_y = 4096
scene.view_settings.look = 'AgX - High Contrast'
scene.render.filepath = '/renders/acoustic_landscape.exr'
bpy.ops.render.render(write_still=True)
The wave interference of five simultaneous acoustic sources — A440, E554, B659, E330, G392, a chord of near-perfect harmony — locks into a standing-wave topography. The RMS pressure field is the canyon map; the instantaneous phase angle paints it in shifting spectral color. An orthographic top-down camera turns the render into a perfect scientific plate, while Fresnel-angle hue-shift means the material changes color as the observer moves: an oil-slick trapped in mathematics.
import pandas as pd, numpy as np, json
import h3 # pip install h3
from collections import defaultdict
# ── Load NYC Yellow Taxi data (2023, all months via TLC open data) ─
months = [ff"yellow_tripdata_2023-{m:02d}.parquet" for m in range(1,13)]
df = pd.concat([pd.read_parquet(m, columns=[
'tpep_pickup_datetime','pickup_latitude','pickup_longitude',
'dropoff_latitude','dropoff_longitude','passenger_count'
]) for m in months], ignore_index=True)
# Remove outliers outside NYC bounding box
NYC = {'lat':(40.47,40.92), 'lon':(-74.27,-73.68)}
df = df[df.pickup_latitude.between(*NYC['lat']) &
df.pickup_longitude.between(*NYC['lon']) &
df.dropoff_latitude.between(*NYC['lat']) &
df.dropoff_longitude.between(*NYC['lon'])]
df['hour'] = pd.to_datetime(df.tpep_pickup_datetime).dt.hour
# ── H3 resolution 7 (~0.6 km²) hexagonal binning ─────────────────
H3_RES = 7
df['h3_origin'] = df.apply(lambda r: h3.latlng_to_cell(
r.pickup_latitude, r.pickup_longitude, H3_RES), axis=1)
df['h3_dest'] = df.apply(lambda r: h3.latlng_to_cell(
r.dropoff_latitude, r.dropoff_longitude, H3_RES), axis=1)
# ── Aggregate OD flows by (origin, destination, hour) ─────────────
od_counts = df.groupby(['h3_origin','h3_dest','hour']).size().reset_index(name='trips')
od_counts = od_counts[od_counts.trips > 30] # threshold: ≥30 trips
# ── H3 cell → centroid coordinates ───────────────────────────────
def h3_to_blender(cell):
lat, lon = h3.cell_to_latlng(cell)
# Map NYC bbox → [-5, 5] Blender units
x = (lon - NYC['lon'][0]) / (NYC['lon'][1]-NYC['lon'][0]) * 10 - 5
y = (lat - NYC['lat'][0]) / (NYC['lat'][1]-NYC['lat'][0]) * 10 - 5
return x, y
# ── Hour-of-day → color temperature ──────────────────────────────
# 00–06: midnight blue, 06–09: dawn gold, 09–17: noon white,
# 17–21: dusk amber-red, 21–24: night violet
def hour_to_color(h):
if h < 6: return (.05, .1, .7) # midnight blue
elif h < 9: return (1.0, .7, .2) # dawn gold
elif h < 17: return (1.0, 1.0, .95) # noon white
elif h < 21: return (1.0, .35, .1) # dusk orange
else: return (.55, .15, .9) # night violet
trips_max = od_counts.trips.max()
arcs = []
for _, row in od_counts.iterrows():
ox, oy = h3_to_blender(row.h3_origin)
dx, dy = h3_to_blender(row.h3_dest)
flow = row.trips / trips_max # 0..1
arc_h = 0.3 + flow * 3.0 # taller arcs = more traffic
width = 0.004 + flow * 0.04 # thicker ribbon = more trips
arcs.append({
'o': [ox, oy], 'd': [dx, dy],
'h': arc_h, 'w': width,
'col': hour_to_color(int(row.hour)),
'flow': float(flow),
'hour': int(row.hour)
})
with open('od_arcs.json', 'w') as f: json.dump(arcs, f)
print(ff"Generated {len(arcs):,} OD flow arcs")
import bpy, json, math
import numpy as np
# ════════════════════════════════════════════════════════════════════
# CITY PULSE — Bézier arc ribbons over procedural city grid
# ════════════════════════════════════════════════════════════════════
with open('/tmp/od_arcs.json') as f: arcs = json.load(f)
bpy.ops.object.select_all(action='SELECT'); bpy.ops.object.delete()
# ── 1. CITY GRID PLANE — procedural block texture ─────────────────
bpy.ops.mesh.primitive_grid_add(x_subdivisions=80, y_subdivisions=80, size=12)
city = bpy.context.active_object; city.name = "CityGrid"
city_mat = bpy.data.materials.new("CityMat")
city_mat.use_nodes = True
cn = city_mat.node_tree.nodes; cl = city_mat.node_tree.links; cn.clear()
# Voronoi texture → city block pattern
tex = cn.new('ShaderNodeTexCoord')
vor = cn.new('ShaderNodeTexVoronoi')
ramp = cn.new('ShaderNodeValToRGB')
bsdf = cn.new('ShaderNodeBsdfPrincipled')
em = cn.new('ShaderNodeEmission')
msh = cn.new('ShaderNodeMixShader')
out = cn.new('ShaderNodeOutputMaterial')
vor.voronoi_dimensions = '3D'
vor.inputs['Scale'].default_value = 8.0
ramp.color_ramp.elements[0].color = (.02,.03,.05,1) # dark block interior
ramp.color_ramp.elements[1].color = (.08,.12,.2,1) # lighter street grid
ramp.color_ramp.elements[1].position = 0.05 # thin street lines
bsdf.inputs['Roughness'].default_value = 0.9
em.inputs['Color'].default_value = (.1,.18,.35,1) # cool blue street glow
em.inputs['Strength'].default_value = 0.4
msh.inputs['Fac'].default_value = 0.35
cl.new(tex.outputs['Generated'], vor.inputs['Vector'])
cl.new(vor.outputs['Distance'], ramp.inputs['Fac'])
cl.new(ramp.outputs['Color'], bsdf.inputs['Base Color'])
cl.new(bsdf.outputs['BSDF'], msh.inputs[1])
cl.new(em.outputs['Emission'], msh.inputs[2])
cl.new(msh.outputs['Shader'], out.inputs['Surface'])
city.data.materials.append(city_mat)
# ── 2. FLOW ARCS — Quadratic Bézier curves ───────────────────────
# Bézier: P0 (origin), P1 (midpoint elevated by arc_h), P2 (dest)
# Lateral offset adds width by cross-product of arc direction
def make_arc(o, d, h, w, col, flow):
ox, oy = o; dx, dy = d
mid_x = (ox+dx)/2; mid_y = (oy+dy)/2; mid_z = h
cd = bpy.data.curves.new("arc", 'CURVE')
cd.dimensions = '3D'
cd.resolution_u = 20
cd.bevel_depth = w
cd.bevel_resolution = 4
cd.use_fill_caps = True
sp = cd.splines.new('BEZIER')
sp.bezier_points.add(1) # 3 points: start, mid, end
bp0 = sp.bezier_points[0]
bp0.co = (ox, oy, 0.02)
bp0.handle_left_type = 'VECTOR'
bp0.handle_right_type = 'FREE'
bp0.handle_right = (ox + (mid_x-ox)*0.6, oy + (mid_y-oy)*0.6, mid_z*0.7)
bp1 = sp.bezier_points[1]
bp1.co = (dx, dy, 0.02)
bp1.handle_right_type = 'VECTOR'
bp1.handle_left_type = 'FREE'
bp1.handle_left = (dx + (mid_x-dx)*0.6, dy + (mid_y-dy)*0.6, mid_z*0.7)
obj = bpy.data.objects.new("Arc", cd)
bpy.context.collection.objects.link(obj)
m = bpy.data.materials.new("AM"); m.use_nodes = True
m.node_tree.nodes.clear()
ae = m.node_tree.nodes.new('ShaderNodeEmission')
ao = m.node_tree.nodes.new('ShaderNodeOutputMaterial')
ae.inputs['Color'].default_value = (*col, 1)
ae.inputs['Strength'].default_value = 2.0 + flow * 6.0 # busier = brighter
m.node_tree.links.new(ae.outputs['Emission'], ao.inputs['Surface'])
obj.data.materials.append(m)
# Build all arcs (batch by hour for potential animation later)
for arc in arcs:
make_arc(arc['o'], arc['d'], arc['h'], arc['w'], arc['col'], arc['flow'])
# ── 3. CAMERA — low dramatic angle looking through city ───────────
bpy.ops.object.camera_add(location=(-8, -6, 4))
cam = bpy.context.active_object
cam.data.lens = 50
cam.rotation_euler = (1.1, 0, -0.6)
bpy.context.scene.camera = cam
# ── World: deep city night sky ────────────────────────────────────
world = bpy.context.scene.world; world.use_nodes = True
world.node_tree.nodes['Background'].inputs['Color'].default_value = (.001,.002,.008,1)
world.node_tree.nodes['Background'].inputs['Strength'].default_value = 0
# ── Compositor: bloom + lens distortion for cinematic look ────────
bpy.context.scene.use_nodes = True
tree = bpy.context.scene.node_tree; tree.nodes.clear()
rl = tree.nodes.new('CompositorNodeRLayers')
gl = tree.nodes.new('CompositorNodeGlare')
ld = tree.nodes.new('CompositorNodeLensdist')
co = tree.nodes.new('CompositorNodeComposite')
gl.glare_type = 'STREAKS'; gl.streaks = 4; gl.angle_offset = math.radians(15)
gl.threshold = 0.6; gl.size = 8
ld.inputs['Distort'].default_value = -0.04 # subtle barrel distortion
tree.links.new(rl.outputs['Image'], gl.inputs['Image'])
tree.links.new(gl.outputs['Image'], ld.inputs['Image'])
tree.links.new(ld.outputs['Image'], co.inputs['Image'])
scene = bpy.context.scene
scene.render.engine = 'CYCLES'
scene.cycles.samples = 512
scene.render.resolution_x = 5120; scene.render.resolution_y = 2880
scene.view_settings.look = 'AgX - High Contrast'
scene.view_settings.exposure = 0.5
scene.render.filepath = '/renders/city_flows.exr'
bpy.ops.render.render(write_still=True)
Ten million trips become a luminous nervous system. Dawn commutes pulse gold from residential Brooklyn to Midtown; midnight rides arc violet from bars in the East Village. The Voronoi city grid glows faint blue beneath — geography as substrate. Streak glare on every bright arc turns the render into a long-exposure photograph of a city that never stopped moving. The 4-streak compositor glare at 15° rotation matches the Manhattan street grid angle exactly.
Mastery principles that separate a scientific render from an unforgettable one.