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Merge pull request #39 from stompsjo/rename-RadClass
2 parents 0177640 + 3b022a0 commit ee05b92

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Lines changed: 62 additions & 63 deletions

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‎README.md‎

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@@ -38,12 +38,12 @@ RadClass is designed to be modular, allowing a user to use analysis modules as n
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![RadClass Workflow](/images/RadClass_workflow.png)
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An HDF5 data file is specified as input, which is processed by `DataSet`. The user can specify a type of `AnalysisParameters`. For example, `H0` for hypothesis testing, `BackgroundEstimator` for background estimation, etc.
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`RadClass` then uses `DataSet` and the user specified `AnalysisParameters` to run, storing the results for use by the user.
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To see examples of how a user can initialize and run `RadClass`, review /tests/.
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`Processor` then uses `DataSet` and the user specified `AnalysisParameters` to run, storing the results for use by the user.
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To see examples of how a user can initialize and run `Processor`, review /tests/.
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## Data Format
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`RadClass` expects a data structure as follows:
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`RadClass.Processor` expects a data structure as follows:
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![File Structure](/images/file_structure.png)
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RadClass/RadClass.py renamed to RadClass/Processor.py

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@@ -7,7 +7,7 @@
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import RadClass.DataSet as ds
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class RadClass:
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class Processor:
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'''
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Bulk handler class. Contains most functions needed for processing data
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stream files and pass along to an analysis object.

‎images/RadClass_workflow.png‎

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‎tests/test_BackgroundEstimator.py‎

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@@ -3,7 +3,7 @@
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import os
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from datetime import datetime, timedelta
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from RadClass.RadClass import RadClass
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from RadClass.Processor import Processor
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from RadClass.BackgroundEstimator import BackgroundEstimator
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import tests.test_data as test_data
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@@ -40,8 +40,8 @@ def test_estimation():
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confidence = 0.8
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bckg = BackgroundEstimator(confidence=confidence)
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# run handler script
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classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, store_data=True, analysis=bckg)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, store_data=True, analysis=bckg)
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classifier.run_all()
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bckg.estimate()
@@ -69,8 +69,8 @@ def test_write():
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confidence = 0.8
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bckg = BackgroundEstimator(confidence=confidence)
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# run handler script
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classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, store_data=True, analysis=bckg)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, store_data=True, analysis=bckg)
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classifier.run_all()
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ofilename = 'bckg_test'

‎tests/test_DiffSpectra.py‎

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@@ -3,7 +3,7 @@
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import os
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from datetime import datetime, timedelta
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from RadClass.RadClass import RadClass
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from RadClass.Processor import Processor
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from RadClass.DiffSpectra import DiffSpectra
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import tests.test_data as test_data
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@@ -34,9 +34,9 @@ def test_difference():
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# small stride since there are less data samples in test_data
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diff_stride = 2
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post_analysis = DiffSpectra(stride=diff_stride)
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classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, post_analysis=post_analysis,
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store_data=True)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, post_analysis=post_analysis,
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store_data=True)
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classifier.run_all()
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diff_spectra = post_analysis.diff_spectra
@@ -81,9 +81,9 @@ def test_write():
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# small stride since there are less data samples in test_data
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diff_stride = 2
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post_analysis = DiffSpectra(stride=diff_stride)
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classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, post_analysis=post_analysis,
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store_data=True)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, post_analysis=post_analysis,
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store_data=True)
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classifier.run_all()
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post_analysis.write(filename)
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‎tests/test_H0.py‎

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@@ -3,7 +3,7 @@
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import os
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from datetime import datetime, timedelta
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from RadClass.RadClass import RadClass
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from RadClass.Processor import Processor
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from RadClass.H0 import H0
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import tests.test_data as test_data
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# run handler script with analysis parameter
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analysis = H0()
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classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, analysis=analysis)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, analysis=analysis)
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classifier.run_all()
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obs_timestamp = analysis.triggers[0][0]
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# run handler script with analysis parameter
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analysis = H0(gross=False, energy_bins=test_data.energy_bins)
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classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, analysis=analysis)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, analysis=analysis)
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classifier.run_all()
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obs_timestamp = analysis.triggers[0][0]
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# run handler script with analysis parameter
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analysis = H0()
97-
classifier = RadClass(stride, integration, test_data.datapath,
98-
test_data.filename, analysis=analysis)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, analysis=analysis)
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classifier.run_all()
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analysis.write(filename)
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# run handler script with analysis parameter
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analysis = H0(gross=False, energy_bins=test_data.energy_bins)
118-
classifier = RadClass(stride, integration, test_data.datapath,
119-
test_data.filename, analysis=analysis)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, analysis=analysis)
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classifier.run_all()
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analysis.write(filename)
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‎tests/test_RadClass.py‎

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@@ -1,11 +1,10 @@
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from multiprocessing.sharedctypes import Value
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import numpy as np
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import h5py
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import pytest
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import os
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from datetime import datetime, timedelta
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from RadClass.RadClass import RadClass
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from RadClass.Processor import Processor
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import tests.test_data as test_data
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# initialize sample data
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integration = int(test_data.timesteps/10)
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# run handler script
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classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, analysis=NullAnalysis(),
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store_data=False)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, analysis=NullAnalysis(),
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store_data=False)
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classifier.run_all()
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np.testing.assert_equal(True, classifier.analysis.changed)
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cache_size = 10000
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stop_time = 2e9
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58-
classifier = RadClass(stride=stride, integration=integration,
59-
datapath=test_data.datapath,
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filename=test_data.filename, store_data=store_data,
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cache_size=cache_size, stop_time=stop_time,
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labels=test_data.labels)
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classifier = Processor(stride=stride, integration=integration,
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datapath=test_data.datapath,
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filename=test_data.filename, store_data=store_data,
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cache_size=cache_size, stop_time=stop_time,
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labels=test_data.labels)
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np.testing.assert_equal(stride, classifier.stride)
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np.testing.assert_equal(integration, classifier.integration)
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7574
integration = int(test_data.timesteps/10)
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# run handler script
78-
classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, store_data=True)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, store_data=True)
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classifier.run_all()
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# the resulting 1-hour observation should be:
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cache_size = 100
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# run handler script
97-
classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename, store_data=True,
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cache_size=cache_size)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, store_data=True,
98+
cache_size=cache_size)
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classifier.run_all()
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# the resulting 1-hour observation should be:
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integration = 5
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# run handler script
116-
classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename)
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classifier.run_all()
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# the resulting 1-hour observation should be:
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137136
filename = 'test_results'
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# run handler script
140-
classifier = RadClass(stride, integration, test_data.datapath,
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test_data.filename)
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classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename)
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classifier.run_all()
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classifier.write(filename)
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184183
start_time = timestamps[integration]
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# run handler script
187-
classifier = RadClass(stride, integration, test_data.datapath,
188-
test_data.filename, store_data=True,
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cache_size=cache_size, start_time=start_time)
186+
classifier = Processor(stride, integration, test_data.datapath,
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test_data.filename, store_data=True,
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cache_size=cache_size, start_time=start_time)
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classifier.run_all()
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192191
integration_val = (((2*integration)*(2*integration-1)/2) -
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212211
stop_time = timestamps[integration*(periods-1)+1]
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# run handler script
215-
classifier = RadClass(stride, integration, test_data.datapath,
216-
test_data.filename, store_data=True,
217-
cache_size=cache_size, stop_time=stop_time)
214+
classifier = Processor(stride, integration, test_data.datapath,
215+
test_data.filename, store_data=True,
216+
cache_size=cache_size, stop_time=stop_time)
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classifier.run_all()
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integration_val = (((integration*(periods-1)) *
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238237
stop_time = timestamps[-1]+1000.0
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# run handler script
241-
classifier = RadClass(stride, integration, test_data.datapath,
242-
test_data.filename, store_data=True,
243-
stop_time=stop_time)
240+
classifier = Processor(stride, integration, test_data.datapath,
241+
test_data.filename, store_data=True,
242+
stop_time=stop_time)
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classifier.run_all()
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# the resulting 1-hour observation should be:
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259258
# Checks if start_time > stop_time
260259
start_time = timestamps[1]
261260
stop_time = timestamps[0]
262-
classifier = RadClass(stride, integration, test_data.datapath,
263-
test_data.filename, start_time=start_time,
264-
stop_time=stop_time)
261+
classifier = Processor(stride, integration, test_data.datapath,
262+
test_data.filename, start_time=start_time,
263+
stop_time=stop_time)
265264
with pytest.raises(ValueError):
266265
classifier.queue_file()
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268267
# checks if start_time > last timestamp
269268
start_time = timestamps[-1] + 1000.0
270-
classifier = RadClass(stride, integration, test_data.datapath,
271-
test_data.filename, start_time=start_time)
269+
classifier = Processor(stride, integration, test_data.datapath,
270+
test_data.filename, start_time=start_time)
272271
with pytest.raises(ValueError):
273272
classifier.queue_file()
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275274
# checks if stop_time < first timestamp
276275
stop_time = timestamps[0] - 1000.0
277-
classifier = RadClass(stride, integration, test_data.datapath,
278-
test_data.filename, stop_time=stop_time)
276+
classifier = Processor(stride, integration, test_data.datapath,
277+
test_data.filename, stop_time=stop_time)
279278
with pytest.raises(ValueError):
280279
classifier.queue_file()
281280

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287286
# Checks if start_time = stop_time
288287
start_time = timestamps[0]
289288
stop_time = timestamps[0]
290-
classifier = RadClass(stride, integration, test_data.datapath,
291-
test_data.filename, start_time=start_time,
292-
stop_time=stop_time)
289+
classifier = Processor(stride, integration, test_data.datapath,
290+
test_data.filename, start_time=start_time,
291+
stop_time=stop_time)
293292
with pytest.raises(RuntimeWarning):
294293
classifier.queue_file()

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