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This commit is contained in:
8
.idea/.gitignore
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vendored
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8
.idea/.gitignore
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# Default ignored files
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/shelf/
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/workspace.xml
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# Editor-based HTTP Client requests
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/httpRequests/
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# Datasource local storage ignored files
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/dataSources/
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/dataSources.local.xml
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6
.idea/inspectionProfiles/profiles_settings.xml
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.idea/inspectionProfiles/profiles_settings.xml
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<component name="InspectionProjectProfileManager">
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<settings>
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<option name="USE_PROJECT_PROFILE" value="false" />
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<version value="1.0" />
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</settings>
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</component>
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.idea/misc.xml
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.idea/misc.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8" project-jdk-type="Python SDK" />
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</project>
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8
.idea/modules.xml
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8
.idea/modules.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="ProjectModuleManager">
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<modules>
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<module fileurl="file://$PROJECT_DIR$/.idea/python_project.iml" filepath="$PROJECT_DIR$/.idea/python_project.iml" />
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</modules>
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</component>
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</project>
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8
.idea/python_project.iml
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8
.idea/python_project.iml
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<?xml version="1.0" encoding="UTF-8"?>
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<module type="PYTHON_MODULE" version="4">
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<component name="NewModuleRootManager">
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<content url="file://$MODULE_DIR$" />
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<orderEntry type="inheritedJdk" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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.idea/vcs.xml
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6
.idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$" vcs="Git" />
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</component>
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</project>
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2815
1_disturb.csv
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2815
1_disturb.csv
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BIN
TurbineExhaustTemperatureTheromcouple-FourWorkingCondition.xlsx
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TurbineExhaustTemperatureTheromcouple-FourWorkingCondition.xlsx
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algorithm.py
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algorithm.py
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from abc import ABC, abstractmethod
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class algorithm(ABC):
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@abstractmethod
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def set_config(self, config):
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pass
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@abstractmethod
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def config(self):
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pass
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@abstractmethod
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def eval(self, value):
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pass
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algorithm_py(1).zip
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algorithm_py(1).zip
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cycle.py
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cycle.py
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from algorithm import algorithm
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import json
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import math
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class cycle(algorithm):
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def __init__(self):
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self.config_dict_ = None
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self.config_ = None
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self.amplitude_base_ = 0.0
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self.time_base_ = 10.0
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self.start_angle_base_ = 0.0
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def set_config(self, config):
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self.config_ = config
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self.config_dict_ = json.loads(self.config_)
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self.amplitude_base_ = self.config_dict_["CYCLE_AMPLITUDE_BASE"]
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self.time_base_ = self.config_dict_["CYCLE_TIME_BASE"]
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self.start_angle_base_ = self.config_dict_["CYCLE_START_ANGLE_BASE"]
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def config(self):
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return self.config_
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def eval(self, value, times):
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return value + self.amplitude_base_ * math.sin((2 * math.pi * times / self.time_base_) + self.start_angle_base_)
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cycle_test.py
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cycle_test.py
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import pandas as pd
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from cycle import cycle
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filepath = "D:/python_project/TurbineExhaustTemperatureTheromcouple-FourWorkingCondition.xlsx"
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origin_data = pd.read_excel(filepath,sheet_name='s4_ttxd')
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row_len = origin_data.shape[0]
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cow_len = origin_data.shape[1]
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disturb_data = origin_data
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algorithm_step = cycle()
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cow_name = "ttxd_12_1"
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algorithm_step.set_config('{"CYCLE_AMPLITUDE_BASE": 200 , "CYCLE_TIME_BASE": 5 ,"CYCLE_START_ANGLE_BASE": 3.14}')
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# algorithm_step.set_config('{"CYCLE_TIME_BASE": 5 }')
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for i in range(0,row_len):
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disturb_data.loc[i,cow_name]=algorithm_step.eval(origin_data.loc[i,cow_name],i)
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print(disturb_data)
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# 以下均为测试性能用
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print(algorithm_step.config_)
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cycle_test_csv.py
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cycle_test_csv.py
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import pandas as pd
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from cycle import cycle
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filepath = "D:/python_project/1.csv"
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origin_data = pd.read_csv(filepath)
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row_len = origin_data.shape[0]
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cow_len = origin_data.shape[1]
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disturb_data = origin_data
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algorithm_step = cycle()
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cow_name = "G1.TTXD1_3"
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algorithm_step.set_config('{"CYCLE_AMPLITUDE_BASE": 200 , "CYCLE_TIME_BASE": 5 ,"CYCLE_START_ANGLE_BASE": 3.14}')
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# algorithm_step.set_config('{"CYCLE_TIME_BASE": 5 }')
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for i in range(0,row_len):
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disturb_data.loc[i,cow_name]=algorithm_step.eval(origin_data.loc[i,cow_name],i)
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print(disturb_data)
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disturb_data.to_csv("D:/python_project/1_disturb.csv", index=False)
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# 以下均为测试性能用
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print(algorithm_step.config_)
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excursion.py
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excursion.py
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from algorithm import algorithm
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import json
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class excursion(algorithm):
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def __init__(self):
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self.config_dict_ = None
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self.config_ = None
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self.amplitude_base_ = 0.0
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def set_config(self, config):
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self.config_ = config
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self.config_dict_ = json.loads(self.config_)
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self.amplitude_base_ = self.config_dict_["STEP_AMPLITUDE_BASE"]
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def config(self):
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return self.config_
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def eval(self, value):
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return value + self.amplitude_base_
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main.py
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main.py
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# 这是一个示例 Python 脚本。
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# 按 Shift+F10 执行或将其替换为您的代码。
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# 按 双击 Shift 在所有地方搜索类、文件、工具窗口、操作和设置。
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from step import step
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algorithm_step = step()
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algorithm_step.set_config('{"STEP_AMPLITUDE_BASE": 1.3}')
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for x in range(1, 101):
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print("%d %f" % (x, algorithm_step.eval(x)))
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step.py
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step.py
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from algorithm import algorithm
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import json
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class step(algorithm):
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def __init__(self):
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self.config_dict_ = None
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self.config_ = None
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self.amplitude_base_ = 0.0
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def set_config(self, config):
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self.config_ = config
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self.config_dict_ = json.loads(self.config_)
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self.amplitude_base_ = self.config_dict_["STEP_AMPLITUDE_BASE"]
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def config(self):
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return self.config_
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def eval(self, value):
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return value * self.amplitude_base_
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step_test.py
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step_test.py
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import pandas as pd
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from step import step
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filepath = "D:/python_project/TurbineExhaustTemperatureTheromcouple-FourWorkingCondition.xlsx"
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origin_data = pd.read_excel(filepath,sheet_name='s4_ttxd')
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row_len = origin_data.shape[0]
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cow_len = origin_data.shape[1]
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disturb_data = origin_data
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algorithm_step = step()
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cow_name = "ttxd_12_1"
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algorithm_step.set_config('{"STEP_AMPLITUDE_BASE": 1.3}')
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for i in range(0,row_len):
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disturb_data.loc[i,cow_name]=algorithm_step.eval(origin_data.loc[i,cow_name])
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print(disturb_data)
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white_noise.py
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white_noise.py
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from random import random
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import numpy as np
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from algorithm import algorithm
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import json
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class white_noise(algorithm):
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def __init__(self):
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self.config_dict_ = None
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self.config_ = None
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self.standard_deviation_base_ = 0.0
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def set_config(self, config):
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self.config_ = config
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self.config_dict_ = json.loads(self.config_)
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self.standard_deviation_base_ = self.config_dict_["WHITE_NOISE_STANDARD_DEVIATION_BASE"]
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def config(self):
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return self.config_
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def eval(self, value):
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return value + np.random.normal(loc=0.0, scale=self.standard_deviation_base_, size=None)
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white_noise_test.py
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white_noise_test.py
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import pandas as pd
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from white_noise import white_noise
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filepath = "D:/python_project/TurbineExhaustTemperatureTheromcouple-FourWorkingCondition.xlsx"
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origin_data = pd.read_excel(filepath,sheet_name='s4_ttxd')
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row_len = origin_data.shape[0]
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cow_len = origin_data.shape[1]
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disturb_data = origin_data
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algorithm_step = white_noise()
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# cow_name = "ttxd_12_1"
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algorithm_step.set_config('{"WHITE_NOISE_STANDARD_DEVIATION_BASE": 100}')
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# for i in range(0,row_len):
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# disturb_data.loc[i,cow_name]=algorithm_step.eval(origin_data.loc[i,cow_name])
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# print(disturb_data)
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print(algorithm_step.config_)
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