KaneAI - World’s First E2E Software Testing Agent.Book a Demo Join TestMu Conference

How to use test_experimental method in pytest-play

Best Python code snippet using pytest-play_python

imageStats.py

Source:imageStats.py

1import sys, copy2import numpy as np3import itk4import numpy as np5import pandas as pd6from scipy import ndimage7CPUImageType = itk.Image[itk.F,2]8ReaderType = itk.ImageFileReader[CPUImageType]9reader = ReaderType.New();10reader.SetFileName(sys.argv[1]);11reader.Update();12reference_CT_slice=itk.GetArrayFromImage(reader.GetOutput());13reader.SetFileName(sys.argv[2]);14reader.Update();15simulated_CT_slice=itk.GetArrayFromImage(reader.GetOutput());16roi_length = 4017reference_fibre_in_centre = reference_CT_slice[429 - roi_length:430 + roi_length, 520 - roi_length:521 + roi_length];18test_fibre_in_centre = simulated_CT_slice[429 - roi_length:430 + roi_length, 520 - roi_length:521 + roi_length];19def create_circular_mask(h, w, center=None, radius=None):20    if center is None: # use the middle of the image21        center = (int(w/2), int(h/2))22    if radius is None: # use the smallest distance between the center and image walls23        radius = min(center[0], center[1], w-center[0], h-center[1])24    Y, X = np.ogrid[:h, :w]25    dist_from_center = np.sqrt((X - center[0])**2 + (Y-center[1])**2)26    mask = dist_from_center <= radius27    return np.array(mask, dtype=bool);28def createMasks(mask_shape):29    fibre_radius_in_px = int(108 / 1.9) / 230    core_radius_in_px = int(16 / 1.9) / 231    core_mask = create_circular_mask(mask_shape[1], mask_shape[0], None, core_radius_in_px);32    fibre_mask = create_circular_mask(mask_shape[1], mask_shape[0], None, fibre_radius_in_px);33    matrix_mask = np.logical_not(fibre_mask);34    #fibre_mask = np.subtract(fibre_mask, core_mask);35    fibre_mask = np.bitwise_xor(fibre_mask, core_mask);36    #TypeError: numpy boolean subtract, the `-` operator, is not supported, use the bitwise_xor, the `^` operator, or the logical_xor function instead.37    return core_mask, fibre_mask, matrix_mask38mask_shape = reference_fibre_in_centre.shape;39core_mask, fibre_mask, matrix_mask = createMasks(mask_shape);40core_mask = ndimage.binary_erosion(core_mask).astype(core_mask.dtype);41for i in range(4):42    fibre_mask = ndimage.binary_erosion(fibre_mask).astype(fibre_mask.dtype);43    matrix_mask = ndimage.binary_erosion(matrix_mask, border_value=1).astype(matrix_mask.dtype);44core_mask.shape = [core_mask.shape[0], core_mask.shape[1]]45fibre_mask.shape = [fibre_mask.shape[0], fibre_mask.shape[1]]46matrix_mask.shape = [matrix_mask.shape[0], matrix_mask.shape[1]]47def getMuStatistics(reference_fibre_in_centre, test_fibre_in_centre, core_mask, fibre_mask, matrix_mask):48    data = [];49    index = np.nonzero(core_mask);50    data.append(["Theorical",51                "Core",52                "W",53                341.61,54                341.61,55                341.61,56                0.0]);57    data.append(["Experimental",58                "Core",59                "W",60                np.min(reference_fibre_in_centre[index]),61                np.max(reference_fibre_in_centre[index]),62                np.mean(reference_fibre_in_centre[index]),63                np.std(reference_fibre_in_centre[index])]);64    data.append(["Simulated",65                "Core",66                "W",67                np.min(test_fibre_in_centre[index]),68                np.max(test_fibre_in_centre[index]),69                np.mean(test_fibre_in_centre[index]),70                np.std(test_fibre_in_centre[index])]);71    index = np.nonzero(fibre_mask);72    data.append(["Theorical",73                "Fibre",74                "SiC",75                2.736,76                2.736,77                2.736,78                0.0]);79    data.append(["Experimental",80                "Fibre",81                "SiC",82                np.min(reference_fibre_in_centre[index]),83                np.max(reference_fibre_in_centre[index]),84                np.mean(reference_fibre_in_centre[index]),85                np.std(reference_fibre_in_centre[index])]);86    data.append(["Simulated",87                "Fibre",88                "SiC",89                np.min(test_fibre_in_centre[index]),90                np.max(test_fibre_in_centre[index]),91                np.mean(test_fibre_in_centre[index]),92                np.std(test_fibre_in_centre[index])]);93    index = np.nonzero(matrix_mask);94    data.append(["Theorical",95                "Matrix",96                "Ti90Al6V4",97                13.1274,98                13.1274,99                13.1274,100                0.0]);101    data.append(["Experimental",102                "Matrix",103                "Ti90Al6V4",104                np.min(reference_fibre_in_centre[index]),105                np.max(reference_fibre_in_centre[index]),106                np.mean(reference_fibre_in_centre[index]),107                np.std(reference_fibre_in_centre[index])]);108    data.append(["Simulated",109                "Matrix",110                "Ti90Al6V4",111                np.min(test_fibre_in_centre[index]),112                np.max(test_fibre_in_centre[index]),113                np.mean(test_fibre_in_centre[index]),114                np.std(test_fibre_in_centre[index])]);115    return pd.DataFrame(data,116            index=None,117            columns=['CT', 'Structure', "Composition", 'min', 'max', 'mean', 'stddev'])118df = getMuStatistics(reference_fibre_in_centre, test_fibre_in_centre, core_mask, fibre_mask, matrix_mask)119test_experimental=df["CT"] == "Experimental";120test_simulated=df["CT"] == "Simulated";121test_W=df["Composition"] == "W"122test_SiC=df["Composition"] == "SiC"123test_Ti90Al6V4=df["Composition"] == "Ti90Al6V4"124print(df[test_experimental & test_W]["mean"].astype(float)[1],125    df[test_experimental & test_W]["stddev"].astype(float)[1],126    df[test_simulated & test_W]["mean"].astype(float)[2],127    df[test_simulated & test_W]["stddev"].astype(float)[2],128    df[test_experimental & test_SiC]["mean"].astype(float)[4],129    df[test_experimental & test_SiC]["stddev"].astype(float)[4],130    df[test_simulated & test_SiC]["mean"].astype(float)[5],131    df[test_simulated & test_SiC]["stddev"].astype(float)[5],132    df[test_experimental & test_Ti90Al6V4]["mean"].astype(float)[7],133    df[test_experimental & test_Ti90Al6V4]["stddev"].astype(float)[7],134    df[test_simulated & test_Ti90Al6V4]["mean"].astype(float)[8],135    df[test_simulated & test_Ti90Al6V4]["stddev"].astype(float)[8])136# MEAN_CORE_SIM=`grep "After noise CORE SIMULATED (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 13`137# STDDEV_CORE_SIM=`grep "After noise CORE SIMULATED (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 14`138#139# MEAN_FIBRE_REF=`grep "After noise FIBRE REF (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 13`140# STDDEV_FIBRE_REF=`grep "After noise FIBRE REF (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 14`141#142# MEAN_FIBRE_SIM=`grep "After noise FIBRE SIMULATED (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 13`143# STDDEV_FIBRE_SIM=`grep "After noise FIBRE SIMULATED (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 14`144#145# MEAN_MATRIX_REF=`grep "After noise MATRIX REF (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 13`146# STDDEV_MATRIX_REF=`grep "After noise MATRIX REF (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 14`147#148# MEAN_MATRIX_SIM=`grep "After noise MATRIX SIMULATED (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 13`149# STDDEV_MATRIX_SIM=`grep "After noise MATRIX SIMULATED (MIN, MEDIAN, MAX, MEAN, STDDEV)" run_SCW_$i/optimisation-$i.out | cut -d " " -f 14`...

test_experimental.py

Source:test_experimental.py

2# Simon Hulse3# simon.hulse@chem.ox.ac.uk4# Last Edited: Thu 13 Jan 2022 17:31:26 GMT5from nmr_sims. experimental import Experimental6def test_experimental():7    experimental = Experimental(8        channels=["1H", "13C"],9        sweep_widths=[10000, 100000],10        field="800MHz",11        temperature="25C",...

test_experimental_data.py

Source:test_experimental_data.py

1from neuromodcell.experimental_data import Experimental2def test_experimental():3    exp_trial = Experimental()4    exp_trial.define_exp(mean = 2)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.