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"""Stage runner with an input-hash cache."""
from __future__ import annotations
import hashlib
import importlib
import os
import time
from dataclasses import dataclass, field
from pathlib import Path
import numpy as np
from . import config as C
from .grid import Grid
STAGES = ["grid", "sketch", "plates", "crust", "elevation", "erosion", "climate",
"hydrology", "seabed", "environment", "ice", "fields", "render"]
class StageError(RuntimeError):
pass
@dataclass
class Ctx:
root: Path
cfg: dict
tect: dict
res: int
data: dict = field(default_factory=dict)
grid: Grid | None = None
out_dir: Path | None = None # render: out/r<res> unless set (an era writes out/r<res>/eras/<era>)
preview_dir: Path | None = None
low_memory: bool = False # trade speed for a lower memory peak; never changes the results
@property
def seed(self) -> int:
return int(self.cfg["build"]["seed"])
def need(self, *keys):
missing = [k for k in keys if k not in self.data]
if missing:
raise StageError(f"missing inputs {missing}: run the stage that produces them first")
return [self.data[k] for k in keys]
def inputs_key(root: Path, res: int) -> str:
h = hashlib.sha256(str(res).encode())
files = []
for sub, pat in (("config", "*.toml"), ("masks", "*.png"), ("sketch", "*.png")):
files += sorted(p for p in (root / sub).glob(pat) if p.name != C.ERAS_FILE) # eras: their own key
files += sorted(Path(__file__).resolve().parent.glob("*.py"))
for p in files:
h.update(p.name.encode())
h.update(p.read_bytes())
return h.hexdigest()[:16]
ALIGN = 64
def save_npz_aligned(path, /, **arrays) -> None:
"""np.savez(path, **arrays), but each array's data starts at a multiple of ALIGN bytes in the file (the local zip
header gets a padding extra field, as zipalign does): an ordinary .npz for np.load, whose arrays npz_maps can
map in place. Alignment matters for more than speed: numpy sums 8-byte-misaligned data in buffered chunks,
i.e. in another order — mapped misaligned inputs would change the last bits of results."""
import io
import struct
import zipfile
from numpy.lib import format as F
with zipfile.ZipFile(path, "w", compression=zipfile.ZIP_STORED, allowZip64=True) as zf:
for name, v in arrays.items():
v = np.asarray(v)
if v.dtype.hasobject:
raise ValueError(f"save_npz_aligned: {name} has object dtype")
head = io.BytesIO()
d = F.header_data_from_array_1_0(v)
try:
F.write_array_header_1_0(head, d)
except ValueError:
head = io.BytesIO()
F.write_array_header_2_0(head, d)
info = zipfile.ZipInfo(f"{name}.npy", date_time=(1980, 1, 1, 0, 0, 0))
info.compress_type = zipfile.ZIP_STORED
start = zf.fp.tell() + 30 + len(info.filename.encode()) + 20 + len(head.getvalue()) # 20: zip64 field
pad = -start % ALIGN
if 0 < pad < 4: # an extra field is at least its 4-byte header
pad += ALIGN
if pad:
info.extra = struct.pack("<HH", 0xA1A1, pad - 4) + bytes(pad - 4)
with zf.open(info, "w", force_zip64=True) as m:
m.write(head.getvalue())
_write_data(m, v)
def _write_data(m, v) -> None:
"""The array's bytes (C order, or Fortran order for an F-contiguous array, as np.save) in 16 MB pieces."""
flat = v.T.reshape(-1) if (v.flags.f_contiguous and not v.flags.c_contiguous) else np.ascontiguousarray(v).reshape(-1)
step = max(1, (16 << 20) // max(v.itemsize, 1))
for i in range(0, flat.size, step):
m.write(flat[i:i + step].tobytes())
def npz_maps(f: Path) -> dict:
"""The arrays of an uncompressed .npz (np.savez) mapped from the file, copy-on-write: plain writable arrays with
the same values, whose pages the OS reads on use and can drop again (writes stay private, the file never
changes). Members that can't be mapped (compressed, object dtype) are read into memory as np.load would."""
import mmap
import struct
import zipfile
out = {}
with open(f, "rb") as fh, zipfile.ZipFile(fh) as zf:
mm = mmap.mmap(fh.fileno(), 0, access=mmap.ACCESS_COPY)
for info in zf.infolist():
name = info.filename[:-4] if info.filename.endswith(".npy") else info.filename
ok = info.compress_type == zipfile.ZIP_STORED
if ok:
fh.seek(info.header_offset)
local = fh.read(30)
n_name, n_extra = struct.unpack("<HH", local[26:30])
fh.seek(info.header_offset + 30 + n_name + n_extra)
version = np.lib.format.read_magic(fh)
read_header = {(1, 0): np.lib.format.read_array_header_1_0,
(2, 0): np.lib.format.read_array_header_2_0}.get(version)
ok = read_header is not None
if ok:
shape, fortran, dtype = read_header(fh, max_header_size=1 << 20)
ok = not dtype.hasobject
ok = fh.tell() % ALIGN == 0 # misaligned: read (see save_npz_aligned)
if ok:
count = int(np.prod(shape, dtype=np.int64))
a = np.frombuffer(mm, dtype=dtype, count=count, offset=fh.tell()) if count else np.empty(0, dtype)
out[name] = a.reshape(shape, order="F" if fortran else "C")
else:
with zf.open(info) as m:
out[name] = np.lib.format.read_array(m, allow_pickle=False)
return out
def _after(ctx: Ctx, name: str) -> None:
if name == "grid":
ctx.grid = Grid.from_arrays(ctx.data, ctx.res, float(ctx.cfg["planet"]["radius_km"]))
def build(root: Path, res: int, start: str | None = None, stop: str | None = None, log=print,
low_memory: bool = False) -> Ctx:
cfg, tect = C.load(root)
ctx = Ctx(root, cfg, tect, res, low_memory=low_memory)
cache = root / "out" / "cache" / f"r{res}"
cache.mkdir(parents=True, exist_ok=True)
key = inputs_key(root, res)
stages = STAGES[: STAGES.index(stop) + 1] if stop else STAGES
forced = False
for name in stages:
forced = forced or name == start
f = cache / f"{name}.npz"
t0 = time.time()
if not forced and f.exists():
with np.load(f, allow_pickle=False) as z:
hit = str(z["_key"]) == key
if hit and not ctx.low_memory:
ctx.data.update({k: z[k] for k in z.files if k != "_key"})
if hit:
if ctx.low_memory:
ctx.data.update({k: v for k, v in npz_maps(f).items() if k != "_key"})
_after(ctx, name)
log(f"{name}: cached")
continue
forced = True
out = importlib.import_module(f"mapgen.{name}").run(ctx)
tmp = f.with_name(f"{f.stem}.tmp-{os.getpid()}.npz")
save_npz_aligned(tmp, _key=np.array(key), **out)
os.replace(tmp, f) # never truncated in place: maps of the old file stay valid
if ctx.low_memory: # fields kept on disk from here on (the cache file just written)
maps = npz_maps(f)
out = {k: maps.get(k, v) if isinstance(v, np.ndarray) else v for k, v in out.items()}
ctx.data.update(out)
_after(ctx, name)
log(f"{name}: {time.time() - t0:.1f}s")
return ctx
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