Replace missing opentps.core.examples imports with local helper modules

[Copilot]

Multiple example scripts imported from opentps.core.examples.{syntheticData,showStuff} which no longer exists in the opentps package.

Changes

Created local helper modules in examples/:

• syntheticData.py - synthetic CT/4DCT generation (createSynthetic3DCT, createSynthetic4DCT, getPhasesPositions)
• showStuff.py - visualization utilities (showModelWithAnimatedFields, dose/DVH plotting functions)

Updated 14 example scripts with direct imports:

# Before
from opentps.core.examples.syntheticData import *

# After  
import sys, os
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..'))
from syntheticData import *

Design notes:

• Helper modules excluded from sphinx-gallery via existing filename_pattern: r'run_.*\.py$' config
• No __init__.py files required
• Standard library path manipulation (sys, os)

Affected: dynamicData (6 files), imageProcessing (4 files), Segmentation, multiplePythonEnv (2 files), registration (2 files)

Original prompt

examples/dynamicData/run_exampleMidP.py
examples/imageProcessing/exampleTransform3DCupy.py
examples/dynamicData/exampleInterFractionChanges.py
examples/Segmentation/run_Segmentation.py
examples/imageProcessing/cupyVSsitkTransforms.py

requires some opentps.core.example module that is no longer availiable. However, her is the script that they were using:

showStuff.py

from matplotlib import pyplot as plt
from matplotlib.animation import FuncAnimation
from scipy.ndimage import binary_dilation
import numpy as np

from opentps.core.processing.doseCalculation.doseCalculationConfig import DoseCalculationConfig
from opentps.core.processing.doseCalculation.protons.mcsquareDoseCalculator import MCsquareDoseCalculator
from opentps.core.io import mcsquareIO
from opentps.core.io.scannerReader import readScanner
from opentps.core.data import DVH
from opentps.core.processing.planOptimization.tools import evaluateClinical
from opentps.core.processing.imageProcessing.resampler3D import resampleImage3DOnImage3D, resampleImage3D

def showModelWithAnimatedFields(model):

for field in model.deformationList:
    field.resample(spacing=model.midp.spacing, gridSize=model.midp.gridSize, origin=model.midp.origin)

y_slice = int(model.midp.gridSize[1] / 2)

plt.figure()
fig = plt.gcf()

def updateAnim(imageIndex):
    fig.clear()
    compX = model.deformationList[imageIndex].velocity.imageArray[:, y_slice, :, 0]
    compZ = model.deformationList[imageIndex].velocity.imageArray[:, y_slice, :, 2]
    plt.imshow(model.midp.imageArray[:, y_slice, :][::5, ::5], cmap='gray')
    plt.quiver(compZ[::5, ::5], compX[::5, ::5], alpha=0.2, color='red', angles='xy', scale_units='xy', scale=5)

anim = FuncAnimation(fig, updateAnim, frames=len(model.deformationList), interval=300)

# anim.save('D:/anim.gif')
plt.show()

def show2DMaskBorder(filledMaskSlice, color='red'):

dilatedROI = binary_dilation(filledMaskSlice)
border = np.logical_xor(dilatedROI, filledMaskSlice)

return border

def simulateAndShowOptiDoseDVH(ct, plan, roi, refSolver, figName='', outPutPath='', show=True):
# MCsquare simulation
ctCalibration = readScanner(DoseCalculationConfig().scannerFolder)
bdl = mcsquareIO.readBDL(DoseCalculationConfig().bdlFile)

# Configure MCsquare
mc2 = MCsquareDoseCalculator()
mc2.beamModel = bdl
mc2.ctCalibration = ctCalibration
mc2.nbPrimaries = 1e7
doseImage = mc2.computeDose(ct, plan)

# Compute DVH on resampled contour
target_DVH = DVH(roi, doseImage)
# lung_DVH = DVH()
print('D5 - D95 =  {} Gy'.format(target_DVH.D5 - target_DVH.D95))
clinROI = [roi.name, roi.name]
clinMetric = ["Dmin", "Dmax"]
clinLimit = [59., 61.]
clinObj = {'ROI': clinROI, 'Metric': clinMetric, 'Limit': clinLimit}
print('Clinical evaluation')
evaluateClinical(doseImage, [roi], clinObj)

# center of mass
roi = resampleImage3DOnImage3D(roi, ct)
COM_coord = roi.centerOfMass
COM_index = roi.getVoxelIndexFromPosition(COM_coord)
X_coord = COM_index[0]
Y_coord = COM_index[1]
Z_coord = COM_index[2]

contourTargetMask = roi.getBinaryContourMask()

img_ct1 = ct.imageArray[:, :, Z_coord].transpose(1, 0)
img_mask1 = contourTargetMask.imageArray[:, :, Z_coord].transpose(1, 0)
img_dose1 = resampleImage3DOnImage3D(doseImage, ct)
img_dose1 = img_dose1.imageArray[:, :, Z_coord].transpose(1, 0)

img_ct2 = ct.imageArray[X_coord, :, :].transpose(1, 0)
img_mask2 = contourTargetMask.imageArray[X_coord, :, :].transpose(1, 0)
img_dose2 = resampleImage3DOnImage3D(doseImage, ct)
img_dose2 = img_dose2.imageArray[X_coord, :, :].transpose(1, 0)

img_ct3 = ct.imageArray[:, Y_coord, :].transpose(1, 0)
img_mask3 = contourTargetMask.imageArray[:, Y_coord, :].transpose(1, 0)
img_dose3 = resampleImage3DOnImage3D(doseImage, ct)
img_dose3 = img_dose3.imageArray[:, Y_coord, :].transpose(1, 0)

# Display dose
fig, ax = plt.subplots(2, 3, figsize=(16, 9))
fig.suptitle(figName)

ax[0, 0].axes.get_xaxis().set_visible(False)
ax[0, 0].axes.get_yaxis().set_visible(False)
ax[0, 0].imshow(img_ct1, cmap='gray')
ax[0, 0].imshow(img_mask1, alpha=.2, cmap='binary')  # PTV
dose = ax[0, 0].imshow(img_dose1, cmap='jet', alpha=.2)
plt.colorbar(dose, ax=ax[0, 0])

ax[0, 1].axes.get_xaxis().set_visible(False)
ax[0, 1].axes.get_yaxis().set_visible(False)
ax[0, 1].imshow(img_ct2, cmap='gray')
ax[0, 1].imshow(img_mask2, alpha=.2, cmap='binary')  # PTV
dose = ax[0, 1].imshow(img_dose2, cmap='jet', alpha=.2)
plt.colorbar(dose, ax=ax[0, 1])

ax[0, 2].axes.get_xaxis().set_visible(False)
ax[0, 2].axes.get_yaxis().set_visible(False)
ax[0, 2].imshow(img_ct3, cmap='gray')
ax[0, 2].imshow(img_mask3, alpha=.2, cmap='binary')  # PTV
dose = ax[0, 2].imshow(img_dose3, cmap='jet', alpha=.2)
plt.colorbar(dose, ax=ax[0, 2])

...

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