280 lines
11 KiB
Python
280 lines
11 KiB
Python
from enum import Enum, unique
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from itertools import groupby
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from typing import Dict, Iterable, Tuple
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from ereuse_devicehub.resources.action.models import BenchmarkDataStorage, BenchmarkProcessor, \
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BenchmarkProcessorSysbench, RateComputer, VisualTest
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from ereuse_devicehub.resources.action.rate.rate import BaseRate
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from ereuse_devicehub.resources.device.models import Computer, DataStorage, Processor, \
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RamModule
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class RateAlgorithm(BaseRate):
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"""The algorithm that generates the Rate v1.0.
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Rate v1.0 rates only computers, counting their processor, ram,
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data storage, appearance, and functionality. This rate is only
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triggered by a Snapshot from Workbench that has a VisualTest.
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The algorithm is as follows:
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1. Specialized subclasses of :class:`BaseRate` compute a rating
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for each component. To perform this, each class normalizes first
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the characteristics and benchmarks of the components between
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0 and 1, and then they merge the values to a resulting score.
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The classes are:
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* :class:`ProcessorRate`, using cores, speed, and ``BenchmarkProcessor``.
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* :class:`RamRate`, using the total of RAM size and speed.
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* :class:`DataStorageRate`, using the total of disk capacity,
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and ``BenchmarkDataStorage``.
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2. Merge the components individual rates into a single rate for
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all components, using a weighted harmonic mean of
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50% for the processor rating, 20% for the data storage rating,
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and 30% for the RAM rating.
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3. Merge the rate for the components with the appearance and
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functionality from :class:`VisualTest`. ``Final Rate =
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Components Rate + Functionality Rate + Appearance Rate``. The
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value is between 0 and 4.7, included.
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"""
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@unique
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class Appearance(Enum):
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Z = 0.5
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A = 0.3
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B = 0
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C = -0.2
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D = -0.5
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E = -1.0
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@unique
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class Functionality(Enum):
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A = 0.4
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B = -0.5
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C = -0.75
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D = -1
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def __init__(self) -> None:
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super().__init__()
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self.RATES = {
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# composition: type: (field, compute class)
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Processor.t: ('processor', ProcessorRate()),
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RamModule.t: ('ram', RamRate()),
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DataStorage.t: ('data_storage', DataStorageRate())
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} # type: Dict[str, Tuple[str, BaseRate]]
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def compute(self, device: Computer) -> RateComputer:
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"""Generates a new
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:class:`ereuse_devicehub.resources.action.models.RateComputer`
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for the passed-in device.
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Do not call directly this class, but use
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:meth:`ereuse_devicehub.resources.action.models.RateComputer.compute`,
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which then calls this.
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"""
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assert isinstance(device, Computer), 'Can only rate computers'
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try:
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visual_test = device.last_action_of(VisualTest)
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except LookupError:
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raise CannotRate('You need a visual test.')
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rate = RateComputer()
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rate.processor = rate.data_storage = rate.ram = 1 # Init
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# Group cpus, rams, storage and compute their rate
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# Treat the same way with HardDrive and SolidStateDrive like (DataStorage)
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clause = lambda x: DataStorage.t if isinstance(x, DataStorage) else x.t
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c = (c for c in device.components if clause(c) in set(self.RATES.keys()))
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for type, components in groupby(sorted(c, key=clause), key=clause):
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if type == Processor.t: # ProcessorRate.compute expects only 1 processor
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components = next(components)
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field, rate_cls = self.RATES[type]
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result = rate_cls.compute(components)
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if result:
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setattr(rate, field, result)
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rate_components = self.harmonic_mean_rates(rate.processor, rate.data_storage, rate.ram)
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rate.appearance = self.Appearance[visual_test.appearance_range.name].value
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rate.functionality = self.Functionality[visual_test.functionality_range.name].value
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rate.rating = rate_components + rate.functionality + rate.appearance
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device.actions_one.add(rate)
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assert 0 <= rate.rating <= 4.7
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return rate
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class ProcessorRate(BaseRate):
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"""
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Calculate a ProcessorRate of all Processor devices
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"""
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# processor.xMin, processor.xMax
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PROCESSOR_NORM = 3196.17, 17503.81
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DEFAULT_CORES = 1
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DEFAULT_SPEED = 1.6
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DEFAULT_SCORE = 4000
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def compute(self, processor: Processor):
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""" Compute processor rate
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We assume always exists a Benchmark Processor
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Obs: cores and speed are possible NULL value
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:return: result is a rate (score) of Processor characteristics
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"""
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cores = processor.cores or self.DEFAULT_CORES
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speed = processor.speed or self.DEFAULT_SPEED
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benchmark_cpu = next(
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e for e in reversed(processor.actions)
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if isinstance(e, BenchmarkProcessor) and not isinstance(e, BenchmarkProcessorSysbench)
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)
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benchmark_cpu = benchmark_cpu.rate or self.DEFAULT_SCORE
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# STEP: Fusion components
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processor_rate = (benchmark_cpu + speed * 2000 * cores) / 2
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# STEP: Normalize values
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processor_norm = max(self.norm(processor_rate, *self.PROCESSOR_NORM), 0)
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# STEP: Compute rate/score from every component
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# Calculate processor_rate
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if processor_norm >= self.CEXP:
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processor_rate = self.rate_exp(processor_norm)
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if self.CLIN <= processor_norm < self.CLOG:
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processor_rate = self.rate_lin(processor_norm)
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if processor_norm >= self.CLOG:
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processor_rate = self.rate_log(processor_norm)
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return processor_rate
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class RamRate(BaseRate):
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"""
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Calculate a RamRate of all RamModule devices
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"""
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# ram.size.xMin; ram.size.xMax
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SIZE_NORM = 256, 8192
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RAM_SPEED_NORM = 133, 1333
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# ram.speed.factor
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RAM_SPEED_FACTOR = 3.7
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# ram.size.weight; ram.speed.weight;
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RAM_WEIGHTS = 0.7, 0.3
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def compute(self, ram_devices: Iterable[RamModule]):
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"""
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If ram speed or ram size, we assume default values before declared
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:return: result is a rate (score) of all RamModule components
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"""
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size = 0.0
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speed = 0.0
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# STEP: Filtering, data cleaning and merging of component parts
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for ram in ram_devices:
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_size = ram.size or 0
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size += _size
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if ram.speed:
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speed += (ram.speed or 0) * _size
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else:
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speed += (_size / self.RAM_SPEED_FACTOR) * _size
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# STEP: Fusion components
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# To guarantee that there will be no 0/0
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if size:
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speed /= size
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# STEP: Normalize values
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size_norm = max(self.norm(size, *self.SIZE_NORM), 0)
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ram_speed_norm = max(self.norm(speed, *self.RAM_SPEED_NORM), 0)
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# STEP: Compute rate/score from every component
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# Calculate size_rate
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if self.CEXP <= size_norm < self.CLIN:
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size_rate = self.rate_exp(size_norm)
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if self.CLIN <= size_norm < self.CLOG:
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size_rate = self.rate_lin(size_norm)
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if size_norm >= self.CLOG:
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size_rate = self.rate_log(size_norm)
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# Calculate ram_speed_rate
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if self.CEXP <= ram_speed_norm < self.CLIN:
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ram_speed_rate = self.rate_exp(ram_speed_norm)
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if self.CLIN <= ram_speed_norm < self.CLOG:
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ram_speed_rate = self.rate_lin(ram_speed_norm)
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if ram_speed_norm >= self.CLOG:
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ram_speed_rate = self.rate_log(ram_speed_norm)
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# STEP: Fusion Characteristics
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return self.harmonic_mean(self.RAM_WEIGHTS, rates=(size_rate, ram_speed_rate))
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class DataStorageRate(BaseRate):
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"""
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Calculate the rate of all DataStorage devices
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"""
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# drive.size.xMin; drive.size.xMax
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SIZE_NORM = 4, 265000
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READ_SPEED_NORM = 2.7, 109.5
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WRITE_SPEED_NORM = 2, 27.35
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# drive.size.weight; drive.readingSpeed.weight; drive.writingSpeed.weight;
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DATA_STORAGE_WEIGHTS = 0.5, 0.25, 0.25
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def compute(self, data_storage_devices: Iterable[DataStorage]):
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"""
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Obs: size != NULL and 0 value & read_speed and write_speed != NULL
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:return: result is a rate (score) of all DataStorage devices
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"""
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size = 0
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read_speed = 0
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write_speed = 0
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# STEP: Filtering, data cleaning and merging of component parts
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for storage in data_storage_devices:
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# We assume all hdd snapshots have BenchmarkDataStorage
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benchmark = storage.last_action_of(BenchmarkDataStorage)
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# prevent NULL values
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_size = storage.size or 0
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size += _size
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read_speed += benchmark.read_speed * _size
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write_speed += benchmark.write_speed * _size
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# STEP: Fusion components
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# Check almost one storage have size, try catch exception 0/0
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if size:
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read_speed /= size
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write_speed /= size
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# STEP: Normalize values
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size_norm = max(self.norm(size, *self.SIZE_NORM), 0)
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read_speed_norm = max(self.norm(read_speed, *self.READ_SPEED_NORM), 0)
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write_speed_norm = max(self.norm(write_speed, *self.WRITE_SPEED_NORM), 0)
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# STEP: Compute rate/score from every component
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# Calculate size_rate
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if size_norm >= self.CLOG:
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size_rate = self.rate_log(size_norm)
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elif self.CLIN <= size_norm < self.CLOG:
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size_rate = self.rate_lin(size_norm)
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elif self.CEXP <= size_norm < self.CLIN:
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size_rate = self.rate_exp(size_norm)
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# Calculate read_speed_rate
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if read_speed_norm >= self.CLOG:
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read_speed_rate = self.rate_log(read_speed_norm)
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elif self.CLIN <= read_speed_norm < self.CLOG:
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read_speed_rate = self.rate_lin(read_speed_norm)
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elif self.CEXP <= read_speed_norm < self.CLIN:
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read_speed_rate = self.rate_exp(read_speed_norm)
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# write_speed_rate
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if write_speed_norm >= self.CLOG:
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write_speed_rate = self.rate_log(write_speed_norm)
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elif self.CLIN <= write_speed_norm < self.CLOG:
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write_speed_rate = self.rate_lin(write_speed_norm)
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elif self.CEXP <= write_speed_norm < self.CLIN:
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write_speed_rate = self.rate_exp(write_speed_norm)
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# STEP: Fusion Characteristics
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return self.harmonic_mean(self.DATA_STORAGE_WEIGHTS,
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rates=(size_rate, read_speed_rate, write_speed_rate))
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rate_algorithm = RateAlgorithm()
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class CannotRate(Exception):
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pass
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