diff options
Diffstat (limited to 'lib')
| -rw-r--r-- | lib/elements/satin_column.py | 8 | ||||
| -rw-r--r-- | lib/stitches/running_stitch.py | 6 | ||||
| -rw-r--r-- | lib/utils/prng.py | 26 |
3 files changed, 20 insertions, 20 deletions
diff --git a/lib/elements/satin_column.py b/lib/elements/satin_column.py index 4028ad27..eba63c6c 100644 --- a/lib/elements/satin_column.py +++ b/lib/elements/satin_column.py @@ -783,7 +783,7 @@ class SatinColumn(EmbroideryElement): # pre-cache ramdomised parameters to avoid property calls in loop if use_random: - seed = prng.joinArgs(self.random_seed, "satin-points") + seed = prng.join_args(self.random_seed, "satin-points") offset_proportional_min = np.array(offset_proportional) - self.random_width_decrease offset_range = (self.random_width_increase + self.random_width_decrease) spacing_sigma = spacing * self.random_zigzag_spacing @@ -857,7 +857,7 @@ class SatinColumn(EmbroideryElement): if to_travel <= 0: if use_random: - roll = prng.uniformFloats(seed, cycle) + roll = prng.uniform_floats(seed, cycle) offset_prop = offset_proportional_min + roll[0:2] * offset_range to_travel = spacing + ((roll[2] - 0.5) * 2 * spacing_sigma) else: @@ -987,7 +987,7 @@ class SatinColumn(EmbroideryElement): if last_point is not None: split_points, _ = self.get_split_points( last_point, a, last_short_point, a_short, max_stitch_length, last_count, - length_sigma, random_phase, min_split_length, prng.joinArgs(seed, 'satin-split', 2*i)) + length_sigma, random_phase, min_split_length, prng.join_args(seed, 'satin-split', 2 * i)) patch.add_stitches(split_points, ("satin_column", "satin_split_stitch")) patch.add_stitch(a_short) @@ -995,7 +995,7 @@ class SatinColumn(EmbroideryElement): split_points, last_count = self.get_split_points( a, b, a_short, b_short, max_stitch_length, None, - length_sigma, random_phase, min_split_length, prng.joinArgs(seed, 'satin-split', 2*i+1)) + length_sigma, random_phase, min_split_length, prng.join_args(seed, 'satin-split', 2 * i + 1)) patch.add_stitches(split_points, ("satin_column", "satin_split_stitch")) patch.add_stitch(b_short) diff --git a/lib/stitches/running_stitch.py b/lib/stitches/running_stitch.py index 8ba53498..1dbfcaaf 100644 --- a/lib/stitches/running_stitch.py +++ b/lib/stitches/running_stitch.py @@ -24,7 +24,7 @@ def split_segment_even_n(a, b, segments: int, jitter_sigma: float = 0.0, random_ splits = np.array(range(1, segments)) / segments if random_seed is not None: - jitters = (prng.nUniformFloats(len(splits), random_seed) * 2) - 1 + jitters = (prng.n_uniform_floats(len(splits), random_seed) * 2) - 1 splits = splits + jitters * (jitter_sigma / segments) # sort the splits in case a bad roll transposes any of them @@ -39,12 +39,12 @@ def split_segment_even_dist(a, b, max_length: float, jitter_sigma: float = 0.0, def split_segment_random_phase(a, b, length: float, length_sigma: float, random_seed: str) -> typing.List[shgeo.Point]: line = shgeo.LineString([a, b]) - progress = length * prng.uniformFloats(random_seed, "phase")[0] + progress = length * prng.uniform_floats(random_seed, "phase")[0] splits = [progress] distance = line.length if progress >= distance: return [] - for x in prng.iterUniformFloats(random_seed): + for x in prng.iter_uniform_floats(random_seed): progress += length * (1 + length_sigma * (x - 0.5) * 2) if progress >= distance: break diff --git a/lib/utils/prng.py b/lib/utils/prng.py index 9face2be..33102205 100644 --- a/lib/utils/prng.py +++ b/lib/utils/prng.py @@ -13,7 +13,7 @@ import numpy as np # Using multiple counters for n-dimentional random streams is also possible and is useful for grid-like structures. -def joinArgs(*args): +def join_args(*args): # Stringifies parameters into a slash-separated string for use in hash keys. # Idempotent and associative. return "/".join([str(x) for x in args]) @@ -22,37 +22,37 @@ def joinArgs(*args): MAX_UNIFORM_INT = 2 ** 32 - 1 -def uniformInts(*args): +def uniform_ints(*args): # Single pseudo-random drawing determined by the joined parameters. # To get a longer sequence of random numbers, call this loop with a counter as one of the parameters. # Returns 8 uniformly random uint32. - s = joinArgs(*args) + s = join_args(*args) # blake2s is python's fastest hash algorithm for small inputs and is designed to be usable as a PRNG. h = blake2s(s.encode()).hexdigest() nums = [] for i in range(0, 64, 8): - nums.append(int(h[i:i+8], 16)) + nums.append(int(h[i:i + 8], 16)) return np.array(nums) -def uniformFloats(*args): +def uniform_floats(*args): # Single pseudo-random drawing determined by the joined parameters. # To get a longer sequence of random numbers, call this loop with a counter as one of the parameters. # Returns an array of 8 floats in the range [0,1] - return uniformInts(*args) / MAX_UNIFORM_INT + return uniform_ints(*args) / MAX_UNIFORM_INT -def nUniformFloats(n: int, *args): +def n_uniform_floats(n: int, *args): # returns a fixed number (which may exceed 8) of floats in the range [0,1] - seed = joinArgs(*args) - nBlocks = ceil(n/8) - blocks = [uniformFloats(seed, x) for x in range(nBlocks)] + seed = join_args(*args) + nBlocks = ceil(n / 8) + blocks = [uniform_floats(seed, x) for x in range(nBlocks)] return np.concatenate(blocks)[0:n] -def iterUniformFloats(*args): +def iter_uniform_floats(*args): # returns an infinite sequence of floats in the range [0,1] - seed = joinArgs(*args) - blocks = map(lambda x: list(uniformFloats(seed, x)), count(0)) + seed = join_args(*args) + blocks = map(lambda x: list(uniform_floats(seed, x)), count(0)) return chain.from_iterable(blocks) |