reading_sparse_layouts.py

# reading_sparse_layouts.py
#
# LICENSE
#
# The MIT License
#
# Copyright (c) 2018 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
#
# This is a part of the TileDB tutorial:
#   https://docs.tiledb.io/en/latest/tutorials/reading.html
#
# When run, this program will create a simple 2D sparse array, write some data
# to it, and read a slice of the data back in the layout of the user's choice
# (passed as an argument to the program: "row", "col", or "global").
#


import numpy as np
import sys
import tiledb

# Name of the array to create.
array_name = "reading_sparse_layouts"


def create_array():
    # 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=2, dtype=np.int32),
                        tiledb.Dim(name="cols", domain=(1, 4), tile=2, dtype=np.int32))

    # The array will be sparse with a single attribute "a" so each (i,j) cell can store an integer.
    schema = tiledb.ArraySchema(domain=dom, sparse=True,
                                attrs=[tiledb.Attr(name="a", dtype=np.int32)])

    # Create the (empty) array on disk.
    tiledb.SparseArray.create(array_name, schema)


def write_array():
    # Open the array and write to it.
    with tiledb.SparseArray(array_name, mode='w') as A:
        # To write, the coordinates must be split into two vectors, one per dimension
        I, J = [1, 1, 2, 1, 2, 2], [1, 2, 2, 4, 3, 4]
        data = np.array(([1, 2, 3, 4, 5, 6]));
        A[I, J] = data

def read_array(order):
    # Open the array and read from it.
    with tiledb.SparseArray(array_name, mode='r') as A:
        # Get non-empty domain
        print("Non-empty domain: {}".format(A.nonempty_domain()))

        # Slice only rows 1, 2 and cols 2, 3, 4.
        # NOTE: The `query` syntax is required to specify an order
        # other than the default row-major
        data = A.query(attrs=["a"], order=order, coords=True)[1:3, 2:5]
        a_vals = data["a"]
        coords = data["coords"]

        for i in range(coords.shape[0]):
            print("Cell {} has data {}".format(str(coords[i]), str(a_vals[i])))


# Check if the array already exists.
if tiledb.object_type(array_name) != "array":
    create_array()
    write_array()

layout = ""
if len(sys.argv) > 1:
    layout = sys.argv[1]

order = 'C'
if layout == "col":
    order = 'F'
elif layout == "global":
    order = 'G'
else:
    order = 'C'

read_array(order)