Better unit tests

test/test_01_loading.py

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+def test_01_loading_test1_csv():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+
+def test_01_loading_test1_csv_check_data():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.checkData()
+
+def test_01_loading_test1_csv_column_values():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.columnValues("responseTime")
+
+def test_01_loading_test1_xlxs():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.xlsx", "test1")

test/test_02_processing.py

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+def test_02_processing_test1_csv():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dp = dr.process()
+
+def test_02_processing_test1_csv_print_pivots_rates():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dp = dr.process()
+    dp.printPivot()
+    dp.printRates()
+
+def test_02_processing_test1_csv_descriptive_stats():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dp = dr.process()
+    dp.printDescriptiveStats()
+
+def test_02_processing_test1_csv_response_time():
+    import pyWitness
+    drRAC = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    drRAC.collapseContinuousData(column="responseTime",
+                                 bins=[0, 5000, 10000, 15000, 20000, 99999],
+                                 labels=[1, 2, 3, 4, 5])
+    dpRAC = drRAC.process(reverseConfidence=True, dependentVariable="responseTime")

test/test_03_plotting.py

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+def test_03_plotting_test1_csv_plot_roc():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dp = dr.process()
+    dp.plotROC()
+
+def test_03_plotting_test1_csv_plot_cac():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dp = dr.process()
+    dp.plotCAC()
+
+def test_03_plotting_test1_csv_plot_rac():
+    import pyWitness
+    drRAC = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    drRAC.collapseContinuousData(column="responseTime",
+                                 bins=[0, 5000, 10000, 15000, 20000, 99999],
+                                 labels=[1, 2, 3, 4, 5])
+    dpRAC = drRAC.process(reverseConfidence=True, dependentVariable="responseTime")
+    dpRAC.plotCAC()

test/test_04_collapsing.py

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+def test_04_collapsing_categorical_data():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseCategoricalData(column='confidence',
+                               map={0: 30, 10: 30, 20: 30, 30: 30, 40: 30, 50: 30, 60: 30,
+                                    70: 75, 80: 75,
+                                    90: 95, 100: 95})
+    dp = dr.process()
+    dp.plotCAC()
+
+def test_04_collapsing_continuous_data():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=[1, 2, 3])
+    dp = dr.process()
+    dp.plotROC()
+
+def test_04_collapsing_continuous_data_pauc():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=[1, 2, 3])
+    dp = dr.process()
+    print(dp.pAUC)

test/test_05_fitting.py

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+def test_05_fitting_test1_csv_indep_obs_eqvar():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=[1, 2, 3])
+    dp = dr.process()
+    mf = pyWitness.ModelFitIndependentObservation(dp)
+    mf.setEqualVariance()
+    mf.fit()
+
+def test_05_fitting_test1_csv_indep_obs_uneqvar_print_parameters():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=[1, 2, 3])
+    dp = dr.process()
+    mf = pyWitness.ModelFitIndependentObservation(dp)
+    mf.printParameters()
+
+def test_05_fitting_test1_csv_best_rest():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence")
+    dp = dr.process()
+    mf_br = pyWitness.ModelFitBestRest(dp)
+
+def test_05_fitting_test1_csv_ensemble():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence")
+    dp = dr.process()
+    mf_en = pyWitness.ModelFitEnsemble(dp)
+
+def test_05_fitting_test1_csv_integration():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence")
+    dp = dr.process()
+    mf_in = pyWitness.ModelFitIntegration(dp)
+
+def test_05_fitting_test1_csv_set_parameters_plot_hit_v_false():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=[1, 2, 3])
+    dp = dr.process()
+    dp.plotHitVsFalseAlarmRate()
+
+def test_05_fitting_test1_csv_set_parameters_print_parameters():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=[1, 2, 3])
+    dp = dr.process()
+    mf = pyWitness.ModelFitIndependentObservation(dp)
+    mf.printParameters()
+
+def test_05_fitting_test1_csv_set_parameters_set_equal_var():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=[1, 2, 3])
+    dp = dr.process()
+    mf = pyWitness.ModelFitIndependentObservation(dp)
+    mf.setEqualVariance()
+    mf.setParameterEstimates()
+    mf.printParameters()
+
+def test_05_fitting_test1_csv_plot_fit_roc():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=None)
+    dp = dr.process()
+    dp.calculateConfidenceBootstrap(nBootstraps=200)
+    mf = pyWitness.ModelFitIndependentObservation(dp)
+    mf.setEqualVariance()
+    mf.fit()
+    dp.plotROC(label="Data")
+    mf.plotROC(label="Indep. obs. fit")
+
+def test_05_fitting_test1_csv_plot_fit():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=None)
+    dp = dr.process()
+    dp.calculateConfidenceBootstrap(nBootstraps=200)
+    mf = pyWitness.ModelFitIndependentObservation(dp)
+    mf.setEqualVariance()
+    mf.fit()
+    dp.plotROC(label="Data")
+    mf.plotROC(label="Indep. obs. fit")
+    mf.plotFit()
+
+def test_05_fitting_test1_csv_plot_fit_cac():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=None)
+    dp = dr.process()
+    dp.calculateConfidenceBootstrap(nBootstraps=200)
+    mf = pyWitness.ModelFitIndependentObservation(dp)
+    mf.setEqualVariance()
+    mf.fit()
+    dp.plotCAC(label="Data")
+    mf.plotCAC(label="Indep. obs. fit")

test/test_06_adv_stats.py

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+def test_adv_stats_test1_csv_bootstrap():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=None)
+    dp = dr.process()
+    dp.calculateConfidenceBootstrap(nBootstraps=200, cl=95)
+    dp.printPivot()
+    dp.printRates()
+
+def test_adv_stats_test2_csv_processing_two_conditions():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test2.csv")
+    dr.cutData(column="previouslyViewedVideo", value=1, option="keep")
+    dpControl = dr.process(column="group", condition="Control")
+    dpVerbal = dr.process(column="group", condition="Verbal")
+    minRate = min(dpControl.liberalTargetAbsentSuspectId, dpVerbal.liberalTargetAbsentSuspectId)
+
+def test_adv_stats_test2_csv_compare_two_pauc():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test2.csv")
+    dr.cutData(column="previouslyViewedVideo", value=1, option="keep")
+    dpControl = dr.process(column="group", condition="Control")
+    dpVerbal = dr.process(column="group", condition="Verbal")
+    minRate = min(dpControl.liberalTargetAbsentSuspectId, dpVerbal.liberalTargetAbsentSuspectId)
+    dpControl = dr.process("group", "Control", pAUCLiberal=minRate)
+    dpControl.calculateConfidenceBootstrap(nBootstraps=200)
+    dpVerbal = dr.process("group", "Verbal", pAUCLiberal=minRate)
+    dpVerbal.calculateConfidenceBootstrap(nBootstraps=200)
+    dpControl.comparePAUC(dpVerbal)
+
+def test_adv_stats_test1_csv_generate_sdt_data():
+    import pyWitness
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=None)
+    dp = dr.process()
+    mf = pyWitness.ModelFitIndependentObservation(dp, debug=True)
+    mf.setEqualVariance()
+    mf.fit()
+    dr1 = mf.generateRawData(nGenParticipants=10000)
+
+    # Need to process the synthetic data
+    dp1 = dr1.process()
+
+    # calculate uncertainties using bootstrap
+    dp.calculateConfidenceBootstrap()
+    dp1.calculateConfidenceBootstrap()
+
+def test_adv_stats_test1_csv_power_analysis():
+    import pyWitness
+    import numpy
+    dr = pyWitness.DataRaw("../data/tutorial/test1.csv")
+    dr.collapseContinuousData(column="confidence", bins=[-1, 60, 80, 100], labels=None)
+    dp = dr.process()
+    mf = pyWitness.ModelFitIndependentObservation(dp, debug=False)
+    mf.setEqualVariance()
+    mf.fit()
+
+    for nGen in numpy.linspace(500, 5000, 9 + 1):
+        drSimulated = mf.generateRawData(nGenParticipants=nGen)
+        dpSimulated = drSimulated.process()
+        dpSimulated.calculateConfidenceBootstrap(nBootstraps=2000)
+        print(nGen, dpSimulated.liberalTargetAbsentSuspectId, dpSimulated.pAUC, dpSimulated.pAUC_low,
+              dpSimulated.pAUC_high)