|
| 1 | +from math import sqrt |
| 2 | + |
| 3 | +import numpy as np |
| 4 | +from numba import jit, prange |
| 5 | + |
| 6 | +from spatialpandas.utils import ngjit |
| 7 | + |
| 8 | + |
| 9 | +@ngjit |
| 10 | +def bounds_interleaved(values): |
| 11 | + """ |
| 12 | + compute bounds |
| 13 | + """ |
| 14 | + xmin = np.inf |
| 15 | + ymin = np.inf |
| 16 | + xmax = -np.inf |
| 17 | + ymax = -np.inf |
| 18 | + |
| 19 | + for i in range(0, len(values), 2): |
| 20 | + x = values[i] |
| 21 | + if np.isfinite(x): |
| 22 | + xmin = min(xmin, x) |
| 23 | + xmax = max(xmax, x) |
| 24 | + |
| 25 | + y = values[i + 1] |
| 26 | + if np.isfinite(y): |
| 27 | + ymin = min(ymin, y) |
| 28 | + ymax = max(ymax, y) |
| 29 | + |
| 30 | + return (xmin, ymin, xmax, ymax) |
| 31 | + |
| 32 | + |
| 33 | +@ngjit |
| 34 | +def bounds_interleaved_1d(values, offset): |
| 35 | + """ |
| 36 | + compute bounds |
| 37 | + """ |
| 38 | + vmin = np.inf |
| 39 | + vmax = -np.inf |
| 40 | + |
| 41 | + for i in range(0, len(values), 2): |
| 42 | + v = values[i + offset] |
| 43 | + if np.isfinite(v): |
| 44 | + vmin = min(vmin, v) |
| 45 | + vmax = max(vmax, v) |
| 46 | + |
| 47 | + return (vmin, vmax) |
| 48 | + |
| 49 | + |
| 50 | +@ngjit |
| 51 | +def compute_line_length(values, value_offsets): |
| 52 | + total_len = 0.0 |
| 53 | + for offset_ind in range(len(value_offsets) - 1): |
| 54 | + start = value_offsets[offset_ind] |
| 55 | + stop = value_offsets[offset_ind + 1] |
| 56 | + x0 = values[start] |
| 57 | + y0 = values[start + 1] |
| 58 | + |
| 59 | + for i in range(start + 2, stop, 2): |
| 60 | + x1 = values[i] |
| 61 | + y1 = values[i + 1] |
| 62 | + |
| 63 | + if (np.isfinite(x0) and np.isfinite(y0) and |
| 64 | + np.isfinite(x1) and np.isfinite(y1)): |
| 65 | + total_len += sqrt((x1 - x0) ** 2 + (y1 - y0) ** 2) |
| 66 | + |
| 67 | + x0 = x1 |
| 68 | + y0 = y1 |
| 69 | + |
| 70 | + return total_len |
| 71 | + |
| 72 | + |
| 73 | +@ngjit |
| 74 | +def compute_area(values, value_offsets): |
| 75 | + area = 0.0 |
| 76 | + |
| 77 | + for offset_ind in range(len(value_offsets) - 1): |
| 78 | + start = value_offsets[offset_ind] |
| 79 | + stop = value_offsets[offset_ind + 1] |
| 80 | + poly_length = stop - start |
| 81 | + |
| 82 | + if poly_length < 6: |
| 83 | + # A degenerate polygon, zero area |
| 84 | + continue |
| 85 | + |
| 86 | + for k in range(start, stop - 4, 2): |
| 87 | + i, j = k + 2, k + 4 |
| 88 | + ix = values[i] |
| 89 | + jy = values[j + 1] |
| 90 | + ky = values[k + 1] |
| 91 | + |
| 92 | + area += ix * (jy - ky) |
| 93 | + |
| 94 | + # wrap-around term for polygon |
| 95 | + firstx = values[start] |
| 96 | + secondy = values[start + 3] |
| 97 | + lasty = values[stop - 3] |
| 98 | + area += firstx * (secondy - lasty) |
| 99 | + |
| 100 | + return area / 2.0 |
| 101 | + |
| 102 | + |
| 103 | +@jit(nogil=True, nopython=True, parallel=True) |
| 104 | +def geometry_map_nested1( |
| 105 | + fn, result, result_offset, values, value_offsets, missing |
| 106 | +): |
| 107 | + assert len(value_offsets) == 1 |
| 108 | + value_offsets0 = value_offsets[0] |
| 109 | + n = len(value_offsets0) - 1 |
| 110 | + for i in prange(n): |
| 111 | + if not missing[i]: |
| 112 | + result[i + result_offset] = fn(values, value_offsets0[i:i + 2]) |
| 113 | + |
| 114 | + |
| 115 | +@jit(nogil=True, nopython=True, parallel=True) |
| 116 | +def geometry_map_nested2( |
| 117 | + fn, result, result_offset, values, value_offsets, missing |
| 118 | +): |
| 119 | + assert len(value_offsets) == 2 |
| 120 | + value_offsets0 = value_offsets[0] |
| 121 | + value_offsets1 = value_offsets[1] |
| 122 | + n = len(value_offsets0) - 1 |
| 123 | + for i in prange(n): |
| 124 | + if not missing[i]: |
| 125 | + start = value_offsets0[i] |
| 126 | + stop = value_offsets0[i + 1] |
| 127 | + result[i + result_offset] = fn(values, value_offsets1[start:stop + 1]) |
| 128 | + |
| 129 | + |
| 130 | +@jit(nogil=True, nopython=True, parallel=True) |
| 131 | +def geometry_map_nested3( |
| 132 | + fn, result, result_offset, values, value_offsets, missing |
| 133 | +): |
| 134 | + assert len(value_offsets) == 3 |
| 135 | + value_offsets0 = value_offsets[0] |
| 136 | + value_offsets1 = value_offsets[1] |
| 137 | + value_offsets2 = value_offsets[2] |
| 138 | + n = len(value_offsets0) - 1 |
| 139 | + for i in prange(n): |
| 140 | + if not missing[i]: |
| 141 | + start = value_offsets1[value_offsets0[i]] |
| 142 | + stop = value_offsets1[value_offsets0[i + 1]] |
| 143 | + result[i + result_offset] = fn(values, value_offsets2[start:stop + 1]) |
| 144 | + |
| 145 | + |
| 146 | +@jit(nopython=True, nogil=True) |
| 147 | +def _lexographic_lt0(a1, a2): |
| 148 | + """ |
| 149 | + Compare two 1D numpy arrays lexographically |
| 150 | + Parameters |
| 151 | + ---------- |
| 152 | + a1: ndarray |
| 153 | + 1D numpy array |
| 154 | + a2: ndarray |
| 155 | + 1D numpy array |
| 156 | +
|
| 157 | + Returns |
| 158 | + ------- |
| 159 | + comparison: |
| 160 | + True if a1 < a2, False otherwise |
| 161 | + """ |
| 162 | + for e1, e2 in zip(a1, a2): |
| 163 | + if e1 < e2: |
| 164 | + return True |
| 165 | + elif e1 > e2: |
| 166 | + return False |
| 167 | + return len(a1) < len(a2) |
| 168 | + |
| 169 | + |
| 170 | +def _lexographic_lt(a1, a2): |
| 171 | + if a1.dtype != np.object and a1.dtype != np.object: |
| 172 | + # a1 and a2 primitive |
| 173 | + return _lexographic_lt0(a1, a2) |
| 174 | + elif a1.dtype == np.object and a1.dtype == np.object: |
| 175 | + # a1 and a2 object, process recursively |
| 176 | + for e1, e2 in zip(a1, a2): |
| 177 | + if _lexographic_lt(e1, e2): |
| 178 | + return True |
| 179 | + elif _lexographic_lt(e2, e1): |
| 180 | + return False |
| 181 | + return len(a1) < len(a2) |
| 182 | + elif a1.dtype != np.object: |
| 183 | + # a2 is object array, a1 primitive |
| 184 | + return True |
| 185 | + else: |
| 186 | + # a1 is object array, a2 primitive |
| 187 | + return False |
| 188 | + |
| 189 | + |
| 190 | +@ngjit |
| 191 | +def _extract_isnull_bytemap(bitmap, bitmap_length, bitmap_offset, dst_offset, dst): |
| 192 | + """ |
| 193 | + Note: Copied from fletcher: See NOTICE for license info |
| 194 | +
|
| 195 | + (internal) write the values of a valid bitmap as bytes to a pre-allocatored |
| 196 | + isnull bytemap. |
| 197 | +
|
| 198 | + Parameters |
| 199 | + ---------- |
| 200 | + bitmap: pyarrow.Buffer |
| 201 | + bitmap where a set bit indicates that a value is valid |
| 202 | + bitmap_length: int |
| 203 | + Number of bits to read from the bitmap |
| 204 | + bitmap_offset: int |
| 205 | + Number of bits to skip from the beginning of the bitmap. |
| 206 | + dst_offset: int |
| 207 | + Number of bytes to skip from the beginning of the output |
| 208 | + dst: numpy.array(dtype=bool) |
| 209 | + Pre-allocated numpy array where a byte is set when a value is null |
| 210 | + """ |
| 211 | + for i in range(bitmap_length): |
| 212 | + idx = bitmap_offset + i |
| 213 | + byte_idx = idx // 8 |
| 214 | + bit_mask = 1 << (idx % 8) |
| 215 | + dst[dst_offset + i] = (bitmap[byte_idx] & bit_mask) == 0 |
| 216 | + |
| 217 | + |
| 218 | +def extract_isnull_bytemap(list_array): |
| 219 | + """ |
| 220 | + Note: Copied from fletcher: See NOTICE for license info |
| 221 | +
|
| 222 | + Extract the valid bitmaps of a chunked array into numpy isnull bytemaps. |
| 223 | +
|
| 224 | + Parameters |
| 225 | + ---------- |
| 226 | + chunked_array: pyarrow.ChunkedArray |
| 227 | +
|
| 228 | + Returns |
| 229 | + ------- |
| 230 | + valid_bytemap: numpy.array |
| 231 | + """ |
| 232 | + result = np.zeros(len(list_array), dtype=bool) |
| 233 | + |
| 234 | + offset = 0 |
| 235 | + chunk = list_array |
| 236 | + valid_bitmap = chunk.buffers()[0] |
| 237 | + if valid_bitmap: |
| 238 | + buf = memoryview(valid_bitmap) |
| 239 | + _extract_isnull_bytemap(buf, len(chunk), chunk.offset, offset, result) |
| 240 | + else: |
| 241 | + return np.full(len(list_array), False) |
| 242 | + |
| 243 | + return result |
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