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# Licensed to the Apache Software Foundation (ASF) under one

# or more contributor license agreements. See the NOTICE file

# distributed with this work for additional information

# regarding copyright ownership. The ASF licenses this file

# to you under the Apache License, Version 2.0 (the

# "License"); you may not use this file except in compliance

# with the License. You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing,

# software distributed under the License is distributed on an

# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY

# KIND, either express or implied. See the License for the

# specific language governing permissions and limitations

# under the License.

import datetime

import numpy as np

import pyarrow as pa

import pyarrow.csv

# used to write parquet files

import pyarrow.parquet as pq

def data():

np.random.seed(1)

data = np.concatenate(

[

np.random.normal(0, 0.01, size=50),

np.random.normal(50, 0.01, size=50),

]

)

return pa.array(data)

def data_with_nans():

np.random.seed(0)

data = np.random.normal(0, 0.01, size=50)

mask = np.random.randint(0, 2, size=50)

data[mask == 0] = np.nan

return data

def data_datetime(f):

data = [

datetime.datetime.now(),

datetime.datetime.now() - datetime.timedelta(days=1),

datetime.datetime.now() + datetime.timedelta(days=1),

]

return pa.array(data, type=pa.timestamp(f), mask=np.array([False, True, False]))

def data_date32():

data = [

datetime.date(2000, 1, 1),

datetime.date(1980, 1, 1),

datetime.date(2030, 1, 1),

]

return pa.array(data, type=pa.date32(), mask=np.array([False, True, False]))

def data_timedelta(f):

data = [

datetime.timedelta(days=100),

datetime.timedelta(days=1),

datetime.timedelta(seconds=1),

]

return pa.array(data, type=pa.duration(f), mask=np.array([False, True, False]))

def data_binary_other():

return np.array([1, 0, 0], dtype="u4")

def write_parquet(path, data):

table = pa.Table.from_arrays([data], names=["a"])

pq.write_table(table, path)

return str(path)