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2c3c408 | 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 | # multi_attribute.py
#
# LICENSE
#
# The MIT License
#
# Copyright (c) 2020 TileDB, Inc.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# DESCRIPTION
#
# Please see the TileDB documentation for more information:
# https://docs.tiledb.com/main/how-to/arrays/reading-arrays/multi-range-subarrays
#
# When run, this program will create a simple 2D dense array with two
# attributes, write some data to it, and read a slice of the data back on
# (i) both attributes, and (ii) subselecting on only one of the attributes.
#
import numpy as np
import tiledb
# Name of the array to create.
array_name = "multi_attribute"
def create_array():
# Check if the array already exists.
if tiledb.object_type(array_name) == "array":
return
# The array will be 4x4 with dimensions "rows" and "cols", with domain [1,4].
dom = tiledb.Domain(
tiledb.Dim(name="rows", domain=(1, 4), tile=4, dtype=np.int32),
tiledb.Dim(name="cols", domain=(1, 4), tile=4, dtype=np.int32),
)
# Add two attributes "a1" and "a2", so each (i,j) cell can store
# a character on "a1" and a vector of two floats on "a2".
schema = tiledb.ArraySchema(
domain=dom,
sparse=False,
attrs=[
tiledb.Attr(name="a1", dtype=np.uint8),
tiledb.Attr(
name="a2",
dtype=np.dtype([("", np.float32), ("", np.float32), ("", np.float32)]),
),
],
)
# Create the (empty) array on disk.
tiledb.DenseArray.create(array_name, schema)
def write_array():
# Open the array and write to it.
with tiledb.DenseArray(array_name, mode="w") as A:
data_a1 = np.array(
(
list(
map(
ord,
[
"a",
"b",
"c",
"d",
"e",
"f",
"g",
"h",
"i",
"j",
"k",
"l",
"m",
"n",
"o",
"p",
],
)
)
)
)
data_a2 = np.array(
(
[
(1.1, 1.2, 1.3),
(2.1, 2.2, 2.3),
(3.1, 3.2, 3.3),
(4.1, 4.2, 4.3),
(5.1, 5.2, 5.3),
(6.1, 6.2, 6.3),
(7.1, 7.2, 7.3),
(8.1, 8.2, 8.3),
(9.1, 9.2, 9.3),
(10.1, 10.2, 10.3),
(11.1, 11.2, 11.3),
(12.1, 12.2, 12.3),
(13.1, 13.2, 13.3),
(14.1, 14.2, 14.3),
(15.1, 15.2, 15.3),
(16.1, 16.2, 16.3),
]
),
dtype=[("", np.float32), ("", np.float32), ("", np.float32)],
)
A[:, :] = {"a1": data_a1, "a2": data_a2}
def read_array():
# Open the array and read from it.
with tiledb.DenseArray(array_name, mode="r") as A:
# Slice only rows 1, 2 and cols 2, 3, 4.
data = A[1:3, 2:5]
print("Reading both attributes a1 and a2:")
a1, a2 = data["a1"].flat, data["a2"].flat
for i, v in enumerate(a1):
print(
"a1: '%s', a2: (%.1f,%.1f,%.1f)"
% (chr(v), a2[i][0], a2[i][1], a2[i][2])
)
def read_array_subselect():
# Open the array and read from it.
with tiledb.DenseArray(array_name, mode="r") as A:
# Slice only rows 1, 2 and cols 2, 3, 4, attribute 'a1' only.
# We use the '.query()' syntax which allows attribute subselection.
data = A.query(attrs=["a1"])[1:3, 2:5]
print("Subselecting on attribute a1:")
for a in data["a1"].flat:
print("a1: '%s'" % chr(a))
create_array()
write_array()
read_array()
read_array_subselect()
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