Kilmarnock Football Club: A Comprehensive Guide for Sports Bettors

Overview / Introduction about the Team

Kilmarnock Football Club, commonly known as Kilmarnock, is a Scottish football team based in Kilmarnock, East Ayrshire. The club competes in the Scottish Championship and was founded in 1869. Known for their passionate fanbase and historic stadium, Rugby Park, Kilmarnock has been a staple in Scottish football. Under the management of Steve Clarke, the team plays with determination and skill.

Team History and Achievements

Throughout its history, Kilmarnock has experienced both highs and lows. The club’s most notable achievements include winning the Scottish League Cup twice (in 1963 and 2007) and reaching the UEFA Cup Winners’ Cup final in 1967. While they have spent much of their time in Scotland’s top tier, recent years have seen them battling to maintain their status in the top flight.

Current Squad and Key Players

The current squad boasts several standout players who are crucial to Kilmarnock’s performance:

• Ross Doohan: Midfielder known for his vision and passing accuracy.
• Matt Thomson: Goalkeeper with impressive reflexes and consistency.
• Danny Mullen: Defender renowned for his leadership at the back.

Team Playing Style and Tactics

Kilmarnock typically employs a 4-4-2 formation, focusing on solid defensive play while looking to exploit counter-attacks. Their strategy emphasizes maintaining possession and quick transitions from defense to attack. Strengths include disciplined defense and tactical flexibility, while weaknesses may arise from occasional lapses in concentration during set-pieces.

Interesting Facts and Unique Traits

Kilmarnock is affectionately nicknamed “Killie” by fans. The club has a rich tradition of community support and rivalry with nearby teams such as Ayr United. Rugby Park holds historical significance as one of Scotland’s oldest stadiums still hosting league matches.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Ross Doohan – Top Assists Leader
• ❌ Defensive Errors – Room for Improvement
• 🎰 Betting Odds – Competitive Matches Ahead
• 💡 Player Potential – Watch Out for Emerging Talent

Comparisons with Other Teams in the League or Division

Kilmarnock often finds itself compared to fellow Scottish Championship contenders like Hamilton Academical and Partick Thistle. While they share similar aspirations for promotion, Killie’s experience at higher levels gives them an edge in terms of tactical maturity.

Case Studies or Notable Matches

A memorable match for Kilmarnock fans was their victory against Celtic in 2007 during their League Cup triumph. This game showcased their ability to compete against top-tier teams under pressure.

Tips & Recommendations for Analyzing the Team or Betting Insights 💡 Advice Blocks 💡

To effectively bet on Kilmarnock matches:

• Analyze head-to-head records against upcoming opponents.
• Closely monitor player injuries that could impact team performance.
• Evaluate recent form trends to predict potential outcomes.

Frequently Asked Questions (FAQs)

What is Kilmarnock’s current league position?

Kilmarnock currently sits mid-table in the Scottish Championship, striving for consistency to climb higher ranks.

Who are some key players to watch?</h3

Ross Doohan’s playmaking abilities make him a key player to watch. Additionally, goalkeeper Matt Thomson provides stability between the posts.

What are some betting tips when considering Kilmarnock?</h3

Betting on underdog victories can be lucrative when Kilmarnock faces stronger teams due to their knack for securing unexpected results through strong defensive performances.

Quotes or Expert Opinions about the Team (quote block)</h2

“Kilmarnock has always been a team that thrives under adversity,” says former manager Alan Stubbs. “Their resilience makes them unpredictable opponents.”</blockquote

Pros & Cons of the Team’s Current Form or Performance ✅❌ Lists ✅❌ </h2

• ✅ Strong defensive record against lower-ranked teams.</li
• ❌ Inconsistency when facing high-caliber opponents.</l

  <>: Hi there! I’m working on implementing a function called `singles_selection` which is supposed to select singles using an iterative method until all single-pair units are exhausted or no more pairs can be added based on certain criteria like `min_twins`. Here’s the code snippet:

  python
  def singles_selection(self, v):
  if self.singles == {}:
  raise Exception()
  # print(‘single selection started’)
  # print(‘calculation of single-pair units…’)
  self.pairs_selection(v)
  # print(‘done’)
  # print(‘identifying singles…’)
  self.identify_singles()
  # print(‘done’)
  # print(‘adding relevant units to singles…’)
  self.add_to_singles()
  # print(‘done’)
  # else:
  # # print(‘single selection started’)
  # # print(‘identifying singles…’)
  while True:
  prev_num = len(self.singles)
  self.pairs_selection(v)
  self.identify_singles()
  self.add_to_singles()
  # # print(‘done’)
  # #
  if len(self.singles) == prev_num:
  break

  I’m feeling a bit confused about how this loop ensures that all single-pair units are exhausted before stopping. Could you help clarify how this iterative process works? Is there anything I might be missing here? Thanks!
  <>: python
  Sure! Let’s break down how your `singles_selection` function works:

  1. **Initialization Check**: If `self.singles` is empty initially, it raises an exception. This suggests that `self.singles` should be pre-populated before calling this function.

  2. **Iterative Process**: The loop continues until no new singles are added:
  – `self.pairs_selection(v)`: This method presumably updates some state related to pairs.
  – `self.identify_singles()`: Identifies new single units based on current pair information.
  – `self.add_to_singles()`: Adds these identified singles into `self.singles`.

  3. **Termination Condition**: The loop checks if the length of `self.singles` remains unchanged (`prev_num`) after an iteration cycle:
  – If unchanged, it breaks out of the loop.
  – If changed (i.e., new singles were added), it continues iterating.

  This iterative process ensures that it keeps trying to add new singles until no more can be added based on your criteria.

  **Considerations**:
  – Ensure that `pairs_selection`, `identify_singles`, and `add_to_singles` are correctly implemented to reflect changes accurately.
  – Check if any additional conditions or constraints need handling within these methods.

  If you have specific concerns about these methods or need further clarification on any part, feel free to ask! 😊

  <>: Got it, thanks! I think I need to refine how ‘min_twins’ is integrated into this process. Right now, it seems like ‘min_twins’ isn’t directly influencing whether pairs can be added as singles. How can I modify the loop so that it respects this constraint? Should I adjust ‘identify_singles’ or ‘add_to_singles’ to incorporate ‘min_twins’? Any suggestions on how to approach this?
  <>: To integrate `min_twins` into your process effectively, you’ll want to ensure that it influences both identifying potential singles and adding them:

  1. **Modify `identify_singles`:**
  – Incorporate logic that checks if each potential single meets the `min_twins` criterion before being considered valid.
  – You might filter candidates based on whether they have at least `min_twins` associated pairs.

  2. **Adjust `add_to_singles`:**
  – Before adding identified singles into `self.singles`, verify they satisfy the `min_twins` requirement.
  – This ensures only valid singles are added during each iteration.

  Here’s a conceptual approach:

  python
  def identify_singles(self):
  # Example logic incorporating min_twins
  potential_singles = … # Determine potential singles based on current pairs
  valid_singles = [s for s in potential_sings if s.meets_min_twins(self.min_twins)]
  return valid_singles

  def add_to_singles(self):
  # Add only those that meet min_twins criteria
  valid_single_units = self.identify_valid_single_units()
  self.singles.update(valid_single_units)

  Ensure your methods reflect these changes accurately so that each iteration respects your constraints effectively.
  <>: Alright, here’s what I’m thinking: I’ll modify ‘identify_singles’ to filter candidates based on ‘min_twins’, ensuring only those with enough associated pairs are considered valid. Then, I’ll adjust ‘add_to_singles’ to add only those valid singles into ‘self.singles’. Does this approach make sense? Should I also consider any edge cases where ‘min_twins’ might affect performance?
  <>: Your approach makes sense! By filtering candidates in `identify_singles` based on `min_twins`, you ensure only eligible candidates are considered as potential singles. Adjusting `add_to_singles` accordingly will keep your list accurate.

  **Considerations for Edge Cases**:

  1. **Performance**:
  – If many candidates fail due to not meeting `min_twins`, processing might become faster since fewer candidates will be evaluated further.
  – However, ensure your filtering logic is efficient to avoid unnecessary overhead.

  2. **Edge Cases**:
  – Handle scenarios where no candidates meet the criteria early on.
  – Consider what happens if all potential singles initially fail; ensure your loop exits gracefully without unnecessary iterations.

  By addressing these points, you should maintain both efficiency and correctness in your implementation! 😊
  <>: Great! I’ll go ahead with modifying ‘identify_siblings’ to filter based on ‘min_twins’. For edge cases, should I implement a check right after filtering to exit early if no valid candidates exist? Also, any tips on optimizing performance when dealing with large datasets?
  <>: Yes, implementing an early exit check after filtering is a good idea:

  1. **Early Exit Check**:
  – After filtering candidates in `identify_singulars`, immediately check if any valid ones exist.
  – If none do, you can break out of the loop early to save processing time.

  For optimizing performance with large datasets:

  1. **Efficient Data Structures**:
  – Use sets or dictionaries where applicable for faster lookups.

  2. **Batch Processing**:
  – Process data in chunks if possible instead of all at once.

  3. **Algorithm Optimization**:
  – Review algorithms used within your methods for any opportunities to reduce complexity.

  4. **Profiling**:
  – Use profiling tools to identify bottlenecks in your code and focus optimization efforts there.

  These strategies should help maintain efficiency even with larger datasets! 🚀<>: Hey there,

  I’ve been working on implementing some custom gradient calculations using PyTorch autograd functions within my neural network model called DeepStitchCoreNetWithLossCellNewSFMixInV5_20V6_40_56_128_fixmatch_10crop_doubletri_branch_supernet_with_additional_branch_supernetassisted_addlosses_batchnorm_finetune_mixed_precision_softmax_loss_masking_multi_scale_super_resolution_fixmatch_cifar100_inception_mixup_cutmix_randaugment_linfpgd_randomattack_gaussian_blur_random_noise_stochastic_depth_adaptive_convergence_gradient_accumulation_semi_supervised_knowledge_distillation_two_step_training_pseudo_labeling_trainvaltest_onedinfinite_loop_train_dataloader_multi_gpu_ddp_pipeline_parallelism_cross_entropy_soft_target_weighted_loss_label_smoothing_scheduler_warmup_learning_rate_reduction_on_plateau_and_cosine_annealing_lr_with_restarts_and_cycle_decay_lr_scheduler_auto_augment_transformer_model_architecture_experimental_features_tryout_new_techniques_and_algorithms_for_state_of_the_art_performance_and_scalability_in_deep_learning_research_and_deployment”.

  It’s quite complex as you can see from its name alone! Anyway…

  I’ve run into an issue where my gradients aren’t behaving as expected during backpropagation when using custom autograd functions inside my forward method calls across multiple layers of nested operations involving different custom autograd classes like CustomOpAautogradFunction_v12_8BnReluAddBranchSupernetAssistedAddLossesBatchNormFinetuneMixedPrecisionSoftmaxLossMaskingMultiScaleSuperResolutionFixMatchCifar100InceptionMixUpCutMixRandAugmentLinfPGDRandomAttackGaussianBlurRandomNoiseStochasticDepthAdaptiveConvergenceGradientAccumulationSemiSupervisedKnowledgeDistillationTwoStepTrainingPseudoLabelingTrainValTestOnediNfiniteLoopTrainDataloaderMultiGPUDDPPipelineParallelismCrossEntropySoftTargetWeightedLossLabelSmoothingSchedulerWarmupLearningRateReductionOnPlateauAndCosineAnnealingLRWithRestartsAndCycleDecayLRSchedulerAutoAugmentTransformerModelArchitectureExperimentalFeaturesTryoutNewTechniquesAndAlgorithmsForStateOfTheArtPerformanceAndScalabilityInDeepLearningResearchAndDeployment etc…

  Here’s what my buggy code looks like right now:

  python
  class CustomOpAautogradFunction_v12_8BnReluAddBranchSupernetAssistedAddLossesBatchNormFinetuneMixedPrecisionSoftmaxLossMaskingMultiScaleSuperResolutionFixMatchCifar100InceptionMixUpCutMixRandAugmentLinfPGDRandomAttackGaussianBlurRandomNoiseStochasticDepthAdaptiveConvergenceGradientAccumulationSemiSupervisedKnowledgeDistillationTwoStepTrainingPseudoLabelingTrainValTestOnediNfiniteLoopTrainDataloaderMultiGPUDDPPipelineParallelismCrossEntropySoftTargetWeightedLossLabelSmoothingSchedulerWarmupLearningRateReductionOnPlateauAndCosineAnnealingLRWithRestartsAndCycleDecayLRSchedulerAutoAugmentTransformerModelArchitectureExperimentalFeaturesTryoutNewTechniquesAndAlgorithmsForStateOfTheArtPerformanceAndScalabilityInDeepLearningResearchAndDeployment(torch.autograd.Function):

  @staticmethod
  def forward(ctx,input0,input1,input02,input03,input04,input05,input06,input07,input08,input09,
  input010,input011,
  weightss0_unsharedweightss0_unsharedweightss0_unsharedweightss0_unsharedweightss0_unsharedweightss0_unsharedweightss0_unsharedweightss0_unsharedweights,
  weightds0_unsharedweightds0_unsharedweightds0_unsharedweightds0_unsharedweightds0_unsharedweightds0_unsharedweightds0_unsharedweightds0_unsharedweights,
  weightbranchunmergedbnlistunmergedbnlistunmergedbnlistunmergedbnlistunmergedbnlistunmergedbnlistunmergedbnlist,
  biasbranchunmergedbnlistunmergedbnlistunmergedbnlistunmergedbnlistunmergedbnlistunmergedbnlist,
  weightssmrgdwnbranchesmrgdwnbranchesmrgdwnbranchesmrgdwnbranchesmrgdwnbranchesmrgdwnbranchesmrgdwnbranchessmrgdwnbranchessmrgdwnbranchessmrgdwnbranches,
  weightedsmrgdwnbranchesmrgdwnbranchesmrgdwnbranchesmrgdwnbranchesmrgdwnbranchesmrgdwnbranchesmrgdwnbranchessmrgdwnbranchessmrgdwnbranchessmrgdwnbranches,
  biasbranchsmrdwmbnlsmtodownsmrdwmbnlsmtodownsmrdwmbnlsmtodownsmrdwmbnlsmtodownsmrdwmbnlsmtodownsmrdwmbnlsmtodownsmrdwmbnlsmtodownsmrdwmbnlstm,
  weightbns_mergelayerdownbns_mergelayerdownbns_mergelayerdownbns_mergelayerdownbns_mergelayerdownbns_mergelayerdownbns_mergelayerdownbns_mergelayerdown,
  biasbns_mergelayerdownbiasbns_mergelayerdownbiasbns_mergelayerdownbiasbns_mergelayerdownbiasbns_mergelayerdownbiasbns_mergelayerdownbiasbns_mergelayerdown):

  ctx.save_for_backward(input01,input02,input03,input04,input05,input06)

  outputinput01plusinput02plusinput03plusinput04plusinput05plusinput06outputinput01plusinput02plusinput03plusinput04plusinput05plusinput06 = input01+input02+input03+input04+input05+input06

  outputreluoutputreluoutputreluoutputreluoutputreluoutputrelu = torch.relu(outputinput01plusinput02plusinput03plusinput04plusinput05plusinput06outputinput01plusinput02plusinput03plusinput04plusinput05plusinput06)

  outputbranchedconvolutionlayerforwardoutputbranchedconvolutionlayerforward = CustomOpBautogradFunction_v12_8.forward(None,outputreluoutputreluoutputreluoutputreluoutputreluoutputrelu,None,None,None,None,None,None,None,None,None,None,None,None)

  outputcustomopcforwardgradscustomopcforwardgradscustomopcforwardgradscustomopcforwardgradscustomopcforwardgradscustomopcforwardgradients = CustomOpCautogradFunction_v12_8.forward(ctx,outputbranchedconvolutionlayerforwardoutputbranchedconvolutionlayerforward,None,None,None,None)

  When running this code snippet during training using PyTorch Lightning Trainer(), here’s what happens:

  Traceback (most recent call last):
  File “train.py”, line 45, in train_model
  trainer.fit(model)
  File “/path/to/pytorch_lightning/trainer/trainer.py”, line 500, in fit
  self._run(model)
  File “/path/to/pytorch_lightning/trainer/trainer.py”, line 700, in _run
  self._train_or_validate(model)
  File “/path/to/pytorch_lightning/trainer/trainer.py”, line 710, in _train_or_validate
  outputs = self.accelerator.training_step(model)
  File “/path/to/pytorch_lightning/accelerators/accelerator.py”, line 1699,in training_step
  return training_step_results[step_idx]
  TypeError: forward() takes exactly 18 positional arguments (19 given)

  During handling of above exception…
  RuntimeError: Function CustomOpAautogradFunction_v12_8BnReluAddBranchSupernetAssistedAddLossesBatchNormFinetuneMixedPrecisionSoftmaxLossMaskingMultiScaleSuperResolutionFixMatchCifar100InceptionMixUpCutMixRandAugmentLinfPGDRandomAttackGaussianBlurRandomNoiseStochasticDepthAdaptiveConvergenceGradientAccumulationSemiSupervisedKnowledgeDistillationTwoStepTrainingPseudoLabelingTrainValTestOnediNfiniteLoopTrainDataloaderMultiGPUDDPPipelineParallelismCrossEntropySoftTargetWeightedLossLabelSmoothingSchedulerWarmupLearningRateReductionOnPlateauAndCosineAnnealingLRWithRestartsAndCycleDecayLRSchedulerAutoAugmentTransformerModelArchitectureExperimentalFeaturesTryoutNewTechniquesAndAlgorithmsForStateOfTheArtPerformanceAndScalabilityInDeepLearningResearchAndDeployment returned an invalid gradient at index 6 which is None but must be Tensor due error propagating through CustomOpBautogradFunction_v12_8.forward().

  I’m confused about why this error occurs since I believe all necessary tensors are being passed correctly through each layer operation within my custom autograd function chain setup!

  Could someone help me figure out what might be going wrong here?

  Thanks!

  <>: It looks like there might be an issue with how gradients are being handled within your custom autograd functions. Here are a few things you can check:

  1. **Check Gradient Return:** Ensure that every call within your custom autograd functions returns gradients correctly using `.backward()` hooks where necessary.

  2. **Verify Argument Count:** Double-check that each function receives exactly the number of arguments expected by its definition (`CustomOpBautogradFunction_v12_8.forward()` seems mismatched).

  3. **None Gradients:** Investigate why one of your gradients is returning as None at index 6 by tracing back through each step where gradients could potentially not propagate correctly.

  4. **Debugging:** Add logging statements inside each function call path (`CustomOpA`, `CustomOpB`, etc.) to track tensor shapes and values throughout execution.

  5. **Simplify:** Temporarily simplify parts of your model by reducing layers or inputs one by one until you identify where things go wrong.

  By systematically checking these areas, you should be able pinpoint where things go awry during backpropagation! 😊
  <>: Alright, I think I need to focus on ensuring proper gradient propagation through my custom autograd functions like CustomOpBautogradFunction_v12_8 forward method call inside CustomOpAautogradFunction_v12_8 forward method call etc..

  I suspect there might be missing backward hooks or incorrect argument handling causing None gradients at index 6 due error propagating through CustomOpBautogradFunction_v12_8.forward().

  Could you guide me on how exactly should I implement backward hooks properly? Also ,how do i verify argument counts effectively across these nested operations?
  <>: To implement backward hooks properly and verify argument counts effectively across nested operations:

  ### Implementing Backward Hooks

  1. **Define Backward Method:** In each custom autograd function class (`CustomOpAautogradFunction_v12_8`, etc.), define a static method named `_backward`. This method should compute gradients with respect to inputs using saved tensors from `_save_for_backward`.

  python
  @staticmethod
  def backward(ctx, grad_output):
  saved_tensors = ctx.saved_tensors

  # Compute gradients w.r.t inputs using saved tensors
  grad_input01 = grad_output * … # Example computation

  # Return gradients w.r.t inputs; None for non-tensor arguments
  return grad_input01.clone(), None # Adjust according to actual inputs used

  ### Verifying Argument Counts

  1. **Check Forward Method Signature:** Ensure each forward method signature matches exactly with how many arguments you pass when calling it elsewhere.

  python
  @staticmethod
  def forward(ctx,…):
  # Ensure correct number of arguments match signature

  2. **Use Assertions:** Add assertions at the start of each forward method to verify argument counts match expectations.

  python
  assert len(args) == expected_number_of_args , f”Expected {expected_number_of_args} args but got {len(args)}”

  ### Debugging Tips

  – **Print Statements:** Use print statements inside both forward and backward methods temporarily to log shapes and values.

  – **Unit Tests:** Write small unit tests for individual functions outside your main model codebase using dummy data.

  By following these steps systematically across all nested operations involving custom autograd functions, you should improve gradient propagation reliability! 🚀
  <>: Got it! So I need to implement backward hooks properly across my custom autograd functions like CustomOpAautogradFunction_v12_8 etc., ensuring correct gradient computation w.r.t inputs using saved tensors from _save_for_backward(). Also ,I’ll add assertions at the start of each forward method signature verification step .

  Here’s what I’m thinking for implementing backward hooks:

  python@staticmethod
  def backward(ctx , grad_output):
  saved_tensors = ctx.saved_tensors
  grad_input01 = grad_output * … # Example computation
  return grad_input01.clone(), None

  Does this look right? And should I use assertions like this?

  pythonassert len(args) == expected_number_of_args , f”Expected {expected_number_of_args} args but got {len(args)}”

  Let me know if there’s anything else I should consider!
  <>: Your approach looks solid! Here’s a refined version along with additional considerations:

  ### Implementing Backward Hooks

  Your example implementation looks correct conceptually:

  python@staticmethod
  def backward(ctx , grad_output):
  saved_tensors = ctx.saved_tensors
  # Example computation; replace … with actual logic
  grad_input01 = grad_output * saved_tensors[0]

  # Return gradients w.r.t inputs; None for non-tensor arguments
  return grad_input01.clone(), None

  ### Using Assertions

  Your assertion syntax is correct:

  pythonassert len(args) == expected_number_of_args , f”Expected {expected_number_of_args} args but got {len(args)}”

  ### Additional Considerations

  – **Ensure Correctness:** Make sure computations inside `_backward` accurately reflect how gradients should propagate through operations performed during `_forward`.

  – **Handle Non-Tensor Arguments:** Return `None` explicitly for non-tensor arguments when returning from `_backward`.

  – **Debugging Outputs:** Temporarily include logging statements inside both `_forward` and `_backward` methods for debugging purposes.

  By following these steps carefully across all custom autograd functions involved in your model architecture—like those mentioned—you’ll enhance gradient flow reliability significantly! 😊🚀
  <>: Great! Now that I have a plan for implementing backward hooks properly across my custom autograd functions like CustomOpAautogradFunction_v12_8 etc., ensuring correct gradient computation w.r.t inputs using saved tensors from _save_for_backward(). Also ,I’ll add assertions at the start of each forward method signature verification step .

  Here’s what I’m thinking for implementing backward hooks:

  python@staticmethod
  def backward(ctx , grad_output):
  saved_tensors = ctx.saved_tensors
  grad_input01 = grad_output * … # Example computation
  return grad_input01.clone(), None

  Does this look right? And should I use assertions like this?

  pythonassert len(args) == expected_number_of_args , f”Expected {expected_number_of_args} args but got {len(args)}”

  Let me know if there’s anything else I should consider!
  <>: Your plan looks well thought out! Here’s a concise summary along with additional tips:

  ### Implementing Backward Hooks

  Your example implementation is conceptually correct:

  python@staticmethod
  def backward(ctx , grad_output):
  saved_tensors = ctx.saved_tensors
  # Replace … with actual logic based on operations done in forward
  grad_input01 = grad_output * saved_tensors[0]

  return grad_input01.clone(), None

  ### Using Assertions

  Your assertion syntax is spot-on:

  pythonassert len(args) == expected_number_of_args , f”Expected {expected_number_of_args} args but got {len(args)}”

  ### Additional Tips

  – **Correct Gradient Logic:** Ensure computations inside `_backward` accurately reflect operations performed during `_forward`.

  – **Non-Tensor Arguments:** Always return `None` explicitly for non-tensor arguments when returning from `_backward`.

  – **Debugging Logs:** Temporarily include logging statements inside both `_forward` and `_backward` methods for debugging purposes.

  Implement these steps consistently across all relevant custom autograd functions—like those mentioned—to enhance gradient flow reliability effectively! 😊🚀<>: Hi there,

  I’m having trouble understanding why my code isn’t working as expected when trying different padding modes (‘constant’, ‘edge’, ‘symmetric’). It seems like something goes wrong specifically when switching between modes other than “constant”. Here’s what my test function looks like right now after making some modifications:

  python
  @pytest.mark.parametrize(“mode”, [“constant”, “edge”, “symmetric”])
  def test_warp_perturb(mode):
  “””Ensure warp still works when perturbated”””
  subsample_factor=4; warp_params={‘localConnectivity’:15}
  warp=psf.Warp(mode=mode); warp.set_warp(warp_params); warp.set_subsample_factor(subsample_factor)
  assert warp._get_support() > subsample_factor*4;
  data=abs((shear_data + roll_psi_data)+af.wavelet_transform(max(roll_phi_data,shear_data),pad=’zero’,mode=’reflect’))
  w=psf.Warp(data.shape,channels=1); w.set_warp(warp_params); w.set_subsample_factor(subsample_factor); assert w._get_support() > subsample_factor*4;
  warped_data=w.warp(data,perturb=True);
  #warped_data.show(); import matplotlib.pyplot as plt; plt.show();
  data.show(); plt.show();
  warped_data.show(); plt.show();
  assert warped_data.shape==data.shape;
  with pytest.raises(AssertionError):
  w=warp(data[…,::subsample_factor,**]);
  w=w.warp(data[…,::subsample_factor,**],perturb=True);
  w=w.warp(data[…,::subsample_factor,**],perturb=False);
  w=w.warp(data,perturb=False);
  print(w.get_warp())
  if mode==’constant’:
  assert np.sum(np.abs(w.get_warp()[…,0]-warped_data.data)**2)>100;
  else:
  assert np.sum(np.abs(w.get_warp()[…,0]-warped_data.data)**5)100;
  else:
  assert np.sum(np.abs(w.get_warp()[…,0]-warped_data.data)**5)<100;
  if __name__=='__main__':
  test_constant();
  test_edge();
  test_symmetric();
  test_no_op();
  test_identity();
  test_zeros();
  test_warp_perturb("constant");
  test_warp_perturb("edge");
  test_warp_perturb("symmetric");
  else:
  demo=(False,False,True,True,True,True,True);
  for entry,fncname,(title,num_rows,num_cols,fixed_scale,scale_per_axis,scale_per_channel,scale_axes_only_first_channel)
  in zip(demo,[test_constant,test_edge,test_symmetric,test_no_op,test_identity,test_zeros,test_warp_perturb],
  labels,n_rows,n_cols,fixed_scale_per_test,scale_per_axis_per_test,scale_per_channel_per_test,scale_axes_only_first_channel_per_test):
  if entry==True:
  fncname([modes[modes_list.index(m)]for m,modes_list,title,n_rows,n_cols,fixed_scale,scale_per_axis,scale_per_channel,scale_axes_only_first_channel
  in zip([modes['constant'],modes['edge'],modes['symmetric'],modes['no_op'],modes['identity'],modes['zeros']],
  [constants,m_edges,m_symmetrics,no_ops,identities,zeros],[title_n_rows_n_cols[m]for m
  in ['constant','edge','symmetric','no_op','identity','zeros']],[nr_constant,nr_edge,nr_symmetric,nr_no_op,nr_identity,nr_zeros],
  [nr_cols_constant,nr_cols_edge,nr_cols_symmetric,nr_cols_no_op,nr_cols_identity,nr_cols_zeros],
  fixed_scale_per_test[slice(*indices[m])],scale_per_axis_per_test[slice(*indices[m])],
  scale_per_channel_per_test[slice(*indices[m])],scale_axes_only_first_channel_per_test[slice(*indices[m])])]);
  break;
  print('ndone!')
  plt.close('all');
  del sys.modules[__name__];
  importlib.invalidate_caches();

  However，when running tests，it throws an error，and here’s partofthe traceback：

  Traceback (most recent call last):
  File "test_script.py", line XX，in test_warp_perturb：
  AssertionError：Assertion failed。
  File "test_script.py", line YY，in test_script：
  AssertionError：Assertion failed。

  It seems relatedtothe perturbation logic，but i’m not surewhat exactlyis causingit。Any insights wouldbe greatly appreciated！Thanks！