# Copyright (c) 2019, MD2K Center of Excellence
# - Nasir Ali <nasir.ali08@gmail.com>
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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from datetime import datetime, timedelta
from cerebralcortex.core.metadata_manager.stream.metadata import Metadata, DataDescriptor, ModuleMetadata
from cerebralcortex.core.util.spark_helper import get_or_create_sc
[docs]def gen_phone_battery_data2()->object:
"""
Create pyspark dataframe with some sample phone battery data
Returns:
DataFrame: pyspark dataframe object with columns: ["timestamp", "offset", "battery_level", "ver", "user"]
"""
column_name = ["timestamp", "battery_level","bat2", "version", "user"]
sample_data = []
timestamp = datetime(2019, 1, 9, 11, 34, 59)
tmp = 1
sample = 100
sample2 = 70
sqlContext = get_or_create_sc("sqlContext")
for row in range(1000, 1, -1):
tmp += 1
if tmp == 100:
sample = sample - 1
sample2 = sample2 - 2
tmp = 1
timestamp = timestamp + timedelta(0, 1)
sample_data.append((timestamp, sample, sample2, 1, "dfce1e65-2882-395b-a641-93f31748591b"))
df = sqlContext.createDataFrame(sample_data, column_name)
return df
[docs]def gen_phone_battery_data()->object:
"""
Create pyspark dataframe with some sample phone battery data
Returns:
DataFrame: pyspark dataframe object with columns: ["timestamp", "offset", "battery_level", "ver", "user"]
"""
column_name = ["timestamp", "battery_level", "version", "user"]
sample_data = []
timestamp = datetime(2019, 1, 9, 11, 34, 59)
tmp = 1
sample = 100
sqlContext = get_or_create_sc("sqlContext")
for row in range(1000, 1, -1):
tmp += 1
if tmp == 100:
sample = sample - 1
tmp = 1
timestamp = timestamp + timedelta(0, 1)
sample_data.append((timestamp, sample, 1, "dfce1e65-2882-395b-a641-93f31748591b"))
df = sqlContext.createDataFrame(sample_data, column_name)
return df