File size: 3,311 Bytes
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 | # query_condition_sparse.py
#
# LICENSE
#
# The MIT License
#
# Copyright (c) 2021 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.
#
# This example creates an array with one string-typed attribute,
# writes sample data to the array, and then prints out a filtered
# dataframe using the TileDB QueryCondition feature.
from pprint import pprint
import numpy as np
import tiledb
uri = "query_condition_sparse"
def create_array(path):
# create a sparse array
dom = tiledb.Domain(
tiledb.Dim(name="coords", domain=(1, 10), tile=1, dtype=np.uint32)
)
attrs = [
tiledb.Attr(name="attr1", dtype=np.uint64),
tiledb.Attr(name="attr2", dtype=np.float64),
]
schema = tiledb.ArraySchema(domain=dom, attrs=attrs, sparse=True)
tiledb.Array.create(path, schema, overwrite=True)
# fill array with randomized values
with tiledb.open(path, "w") as arr:
rand = np.random.default_rng()
arr[np.arange(1, 11)] = {
"attr1": rand.integers(low=0, high=10, size=10),
"attr2": rand.random(size=10),
}
def read_array(path):
with tiledb.open(uri) as arr:
print("--- without query condition:")
print()
pprint(arr[:])
print()
with tiledb.open(uri) as arr:
qc = "(2 < attr1 < 6) and (attr2 < 0.5 or attr2 > 0.85)"
print(f"--- with query condition {qc}:")
print()
res = arr.query(cond=qc)[:]
pprint(res)
if __name__ == "__main__":
"""Example output for `python query_condition_sparse.py`:
--- without query condition:
OrderedDict([('attr1', array([2, 4, 4, 3, 4, 7, 5, 2, 2, 8], dtype=uint64)),
('attr2',
array([0.62445071, 0.32415481, 0.39117764, 0.66609931, 0.48122102,
0.93561984, 0.70998524, 0.10322076, 0.28343041, 0.33623958])),
('coords',
array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=uint32))])
--- with query condition (2 < attr1 < 6) and (attr2 < 0.5 or attr2 > 0.85):
OrderedDict([('attr1', array([4, 4, 4], dtype=uint64)),
('attr2', array([0.32415481, 0.39117764, 0.48122102])),
('coords', array([2, 3, 5], dtype=uint32))])
"""
create_array(uri)
read_array(uri)
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