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Suggestions for development #6

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Suggestions for development
jbrezmorf Apr 7, 2026
9b0bbb1
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IvaNodyanosh Apr 14, 2026
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38 changes: 33 additions & 5 deletions field_synthesis/field_synthesis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,9 @@ class FieldSynthesis():
"""
Třída pro syntézu prostorových polí pomocí generování kotevních bodů
a jejich následného míchání.
"""
"""

Assumption: The set of points where the input and output fields live is the same.

Inicializuje parametry syntézy pole.

Args:
Expand All @@ -21,13 +22,31 @@ class FieldSynthesis():
num_source (int): Počet dostupných zdrojů (typů polí).
dimension (int): Dimenze prostoru (2D, 3D).
seed (int): Seed pro generátor náhodných čísel.

TODO: add parameter point_coords shape=(K, dimension)

"""

area_size: int
# TODO: compute from point_coords as volume of the axis aligned bounding box.
# must be float.
# next, minimum distance is vol ** (1/dim) / safety_factor , e.g. saftey_factor = 2.0
# should be in interval [1, 2].

count_points: int
# Suggested number of anchor points, could be refined due to minimal distance.

num_source: int
# Number of field samples we will mix;
# TODO: remove this, we will know that only
# after passing the field samples to the mixing function.

dimension: int = 2
# TODO: remove, diven by the shape of point_coords

free_space_ratio: float = 0.4
# TODO: Remove, given indirectly through `count_points`

seed: int = 42

# self.min_distance = self.calc_distance()
Expand Down Expand Up @@ -108,6 +127,9 @@ def spatial_points(self, target_points, k_neighbors=5) -> list:
Poté aplikujeme vektorizovanou masku pro filtrování sousedů, kteří jsou dále než 2 * D.
Args:
target_points (np.ndarray): Pole cílových bodů, pro které chceme naj

TODO: could be refactored into cached_property since target_points == point_coords are given
in constructor.
"""

tree = cKDTree(self.anchor_points)
Expand Down Expand Up @@ -135,19 +157,25 @@ def mix_fields(self, target_points) -> np.ndarray:
Pokud má sousedů více, provedeme průměr jejich hodnot. Pokud žádného
Args:
target_points (np.ndarray): Pole cílových bodů, pro které chceme získat smíšené hodnoty.
TODO: pass in field samples, field_samples shape = (N, K)
N .. number of samples
K ... number of points == len(point_coords)
"""

neighbor_indices_list = self.spatial_points(target_points)
# List of lists of anchor neighbours of each point.

mixed_results = []
for neighbors in neighbor_indices_list:
for i_point, neighbors in enumerate(neighbor_indices_list):
if len(neighbors) == 0:
mixed_results.append(np.nan)
elif len(neighbors) == 1:
mixed_results.append(self.fields_indices[neighbors[0]])
else:
# "Provedeme průměr, pokud jich zbyde více
# TODO: use the field samples and compute mean of their values
# mixed_samples = field_samples[self.fields_indices[neighbors], i_point] # shape (len(neighbours), )
# value = np.mean( mixed_samples)
values = self.fields_indices[neighbors]
mixed_results.append(np.mean(values))

return np.array(mixed_results)
return np.array(mixed_results)
54 changes: 28 additions & 26 deletions field_synthesis/generate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,67 +6,69 @@
from field_synthesis import FieldSynthesis

def load_data(zip_path):
"""Витягує координати та всі сампли з одного ZIP-архіву."""
"""Extrahuje souřadnice a všechny vzorky z jednoho ZIP archivu."""
coords = None
all_values = []

with zipfile.ZipFile(zip_path, 'r') as z:
file_list = z.namelist()

# Načtení souřadnic
coords_file = [f for f in file_list if 'coords' in f and f.endswith('.npz')][0]
with z.open(coords_file) as f:
data = np.load(io.BytesIO(f.read()))
coords = data[data.files[0]] if isinstance(data, np.lib.npyio.NpzFile) else data

# Vybereme první pole z npz souboru
coords = data[data.files[0]]

# Načtení hodnot polí
value_files = [f for f in file_list if 'values' in f and f.endswith('.npz')]
print(f"Found {len(value_files)} source samples in ZIP.")
print(f"Nalezeno {len(value_files)} zdrojových vzorků v ZIPu.")

for name in sorted(value_files):
with z.open(name) as f:
data = np.load(io.BytesIO(f.read()))
val = data[data.files[0]] if isinstance(data, np.lib.npyio.NpzFile) else data
val = data[data.files[0]]
all_values.append(val)

return coords, np.array(all_values)

def main():
parser = argparse.ArgumentParser()
parser.add_argument("archive", help="Path to cond_tensors.zip")
parser.add_argument("-o", "--output", default="synthesis_results.zip")
parser.add_argument("--anchors", type=int, default=100)
parser.add_argument("--count", type=int, default=200)
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("archive", help="Cesta k vstupnímu ZIP archivu (cond_tensors.zip)")
parser.add_argument("-o", "--output", default="synthesis_results.zip", help="Název výstupního souboru")
parser.add_argument("--anchors", type=int, default=100, help="Počet kotevních bodů (anchor points)")
parser.add_argument("--count", type=int, default=200, help="Počet polí k vygenerování")
parser.add_argument("--seed", type=int, default=42, help="Seed pro generátor náhodných čísel")
args = parser.parse_args()

# 1. Načtení dat
coords, samples_raw = load_data(args.archive)

samples = samples_raw[:, :, 0]
# Předpokládáme, že samples_raw má tvar (N, K, 1), převedeme na (N, K)
if samples_raw.ndim == 3:
samples = samples_raw[:, :, 0]
else:
samples = samples_raw

max_range = np.max(coords)
# 2. Inicializace FieldSynthesis
# Souřadnice bodů předáváme přímo do konstruktoru
fs = FieldSynthesis(
area_size=max_range,
point_coords=coords,
count_points=args.anchors,
num_source=len(samples),
dimension=3,
seed=args.seed
)

print(f"Synthesizing {args.count} fields...")
print(f"Syntetizuji {args.count} polí...")

# 3. Generování nových polí
with zipfile.ZipFile(args.output, 'w', compression=zipfile.ZIP_DEFLATED) as out_z:
for i in range(args.count):
res = fs.mix_fields(coords)
# Každé volání mix_fields vytvoří unikátní pole díky vnitřnímu stavu RNG
res = fs.mix_fields(samples)

# Uložení výsledku do paměti (io.BytesIO)
buffer = io.BytesIO()
np.save(buffer, res)

filename = f"field_{i:04d}.npy"
out_z.writestr(filename, buffer.getvalue())

if (i + 1) % 50 == 0:
print(f"Progress: {i + 1}/{args.count}")

print(f"Done! Results in {args.output}")

if __name__ == "__main__":
main()
out_z.writestr(filename, buffer.getvalue())