File size: 7,452 Bytes
d2f661a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 | from bisect import bisect_left
import multiprocessing
import dask
from numba import njit, prange, types
from numba.typed import Dict
import numpy as np
from .patches import unpack_patches
class EqualFrequencySampler:
def __init__(
self, bins, patch_data, patch_index,
sample_shape, time_range_valid, time_range_sampling=None,
timestep_secs=5*60,
random_seed=None, preselected_samples=None
):
binned_patches = bin_classify_patches_parallel(
bins,
*unpack_patches(patch_data),
zero_value=patch_data.get("zero_value", 0),
scale=patch_data.get("scale")
)
complete_ind = indices_with_complete_sample(
patch_index, sample_shape, time_range_valid, timestep_secs
)
if time_range_sampling is None:
time_range_sampling = time_range_valid
self.starting_ind = [
starting_indices_for_centers(
p, complete_ind, sample_shape, time_range_sampling, timestep_secs
)
for p in binned_patches
]
self.num_bins = len(self.starting_ind)
self.rng = np.random.RandomState(seed=random_seed)
self.preselected_samples = preselected_samples
self.current_ind = np.array([len(ind) for ind in self.starting_ind])
def get_bin_sample(self, bin_ind):
patches = self.starting_ind[bin_ind]
sample_ind = self.current_ind[bin_ind]
if sample_ind >= patches.shape[0]:
self.rng.shuffle(patches)
sample_ind = self.current_ind[bin_ind] = 0
else:
self.current_ind[bin_ind] += 1
return patches[sample_ind,:]
def __call__(self, num):
# sample each bin with equal probability
bins = self.rng.randint(self.num_bins, size=num)
coords = np.stack(
[self.get_bin_sample(b) for b in bins],
axis=0
)
return coords
def bin_classify_patches(
bins, patches, patch_coords, patch_times,
zero_patch_coords, zero_patch_times,
zero_value=0, metric_func=None,
scale=None,
):
if metric_func is None:
def metric_func(x):
xm = np.percentile(x, 99, axis=(1,2))
if np.issubdtype(x.dtype, np.integer):
xm = xm.round()
return xm.astype(x.dtype)
binned_patches = [[] for _ in range(len(bins)+1)]
def find_bin(value):
return bisect_left(bins, value)
zero_bin = find_bin(zero_value if scale is None else scale[zero_value])
for (t,(pi,pj)) in zip(zero_patch_times, zero_patch_coords):
binned_patches[zero_bin].append((t,pi,pj))
patch_metrics = metric_func(patches)
if scale is not None:
patch_metrics = scale[patch_metrics]
for (metric,t,(pi,pj)) in zip(patch_metrics, patch_times, patch_coords):
patch_bin = find_bin(metric)
binned_patches[patch_bin].append((t,pi,pj))
for i in range(len(binned_patches)):
if binned_patches[i]:
binned_patches[i] = np.array(binned_patches[i])
else:
binned_patches[i] = np.zeros((0,3), dtype=np.int64)
return binned_patches
def bin_classify_patches_parallel(
bins, patches, patch_coords, patch_times,
zero_patch_coords, zero_patch_times,
zero_value=0, metric_func=None,
scale=None,
):
num_patches = patches.shape[0]
num_zeros = zero_patch_coords.shape[0]
num_procs = multiprocessing.cpu_count()
tasks = []
for p in range(num_procs):
pk0 = int(round(num_patches*p/num_procs))
pk1 = int(round(num_patches*(p+1)/num_procs))
zk0 = int(round(num_zeros*p/num_procs))
zk1 = int(round(num_zeros*(p+1)/num_procs))
task = dask.delayed(bin_classify_patches)(
bins,
patches[pk0:pk1,...], patch_coords[pk0:pk1,...],
patch_times[pk0:pk1],
zero_patch_coords[zk0:zk1,...], zero_patch_times[zk0:zk1],
zero_value=zero_value, metric_func=metric_func,
scale=scale
)
tasks.append(task)
chunked_bins = dask.compute(tasks, scheduler="threads")[0]
n_bins = len(chunked_bins[0])
binned_patches = [
np.concatenate([cb[i] for cb in chunked_bins], axis=0)
for i in range(n_bins)
]
return binned_patches
def indices_with_complete_sample(
patch_index, sample_shape, time_range, timestep_secs
):
"""Check which locations will give a sample without missing data.
"""
ind = np.array(list(patch_index.patch_index.keys()))
t0 = ind[:,0]
i0 = ind[:,1]
j0 = ind[:,2]
n = ind.shape[0]
complete = np.ones(n, dtype=bool)
# we use this dict like a set - numba doesn't support typed sets
complete_ind = Dict.empty(
key_type=types.UniTuple(types.int64, 3),
value_type=types.uint8
)
@njit(parallel=True) # many nested loops, numba optimization needed
def check_complete(index, complete, complete_ind):
for k in prange(n):
for ts in range(*time_range):
t = t0[k] + ts*timestep_secs
for di in range(sample_shape[0]):
i = i0[k] + di
for dj in range(sample_shape[1]):
j = j0[k] + dj
if (t,i,j) not in index:
complete[k] = False
for k in range(n): # no prange: can't set dict items in parallel
if complete[k]:
complete_ind[(t0[k],i0[k],j0[k])] = np.uint8(0)
check_complete(patch_index.patch_index, complete, complete_ind)
return complete_ind
def starting_indices_for_centers(
centers, complete_ind, sample_shape, time_range, timestep_secs
):
"""Determine a complete list of sample indices that
contain one or more of the centerpoints.
"""
@njit
def find_indices(centers, starting_ind, complete_ind):
for k in range(centers.shape[0]):
t0 = centers[k,0]
i0 = centers[k,1]
j0 = centers[k,2]
for ts in range(*time_range):
t = t0 - ts*timestep_secs # note minus signs in (t,i,j)
for di in range(sample_shape[0]):
i = i0 - di
for dj in range(sample_shape[1]):
j = j0 - dj
if (t,i,j) in complete_ind:
starting_ind[(t,i,j)] = np.uint8(0)
num_chunks = multiprocessing.cpu_count()
@dask.delayed
def chunk(i):
starting_ind = Dict.empty(
key_type=types.UniTuple(types.int64, 3),
value_type=types.uint8
)
k0 = int(round(centers.shape[0] * (i / num_chunks)))
k1 = int(round(centers.shape[0] * ((i+1) / num_chunks)))
find_indices(centers[k0:k1,...], starting_ind, complete_ind)
return starting_ind
jobs = [chunk(i) for i in range(num_chunks)]
starting_ind = dask.compute(jobs, scheduler='threads')[0]
starting_ind = np.concatenate(
[np.array(list(st_ind.keys())) for st_ind in starting_ind if st_ind],
axis=0
)
return starting_ind
|