Flint Firebirds: Elite Team in the USPHL - Squad, Achievements & Stats

Overview / Introduction about the Team

The Flint Firebirds are a professional ice hockey team based in Flint, Michigan. They compete in the United States Hockey League (USHL), which is a prominent junior hockey league in North America. The team was established in 1998 and has since become known for developing young talent that often progresses to higher levels of professional hockey.

Team History and Achievements

Since their formation, the Flint Firebirds have had several successful seasons, consistently ranking among the top teams in the USHL. They have won multiple division titles and have been contenders for the Clark Cup, awarded to the USHL playoff champion. Notable seasons include their championship run in 2013-2014 when they clinched the Clark Cup.

Current Squad and Key Players

The current roster features a mix of experienced players and promising young talent. Key players include forward Alex DeBrincat, known for his scoring ability, and defenseman Jake Sanderson, who is praised for his defensive skills. These players play crucial roles in both offensive plays and defensive strategies.

Team Playing Style and Tactics

The Firebirds are known for their fast-paced playing style, focusing on speed and agility. They typically employ a 1-3-1 forecheck strategy to disrupt opponents’ plays and transition quickly into offense. Their strengths lie in their ability to execute quick transitions, while their weaknesses may include occasional lapses in defensive coverage.

Interesting Facts and Unique Traits

The Flint Firebirds have a passionate fanbase known as “Firebird Nation,” characterized by their vibrant support during games. The team’s rivalry with nearby teams like the Youngstown Phantoms adds excitement to their matches. A unique tradition is their pre-game ceremony where fans wave orange towels, symbolizing team spirit.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Alex DeBrincat – ✅ High goal tally
• Defensive Leader: Jake Sanderson – 🎰 Consistent performance
• Rising Star: Young prospect – 💡 Potential future star

Comparisons with Other Teams in the League or Division

In comparison to other USHL teams, the Firebirds are often noted for their strong development program and competitive edge. They frequently rank high in league standings due to their balanced approach between offense and defense.

Case Studies or Notable Matches

A memorable match was during the 2013-2014 season when they secured a decisive victory against the Green Bay Gamblers, propelling them towards winning the Clark Cup. This game highlighted their strategic depth and resilience under pressure.

Tables Summarizing Team Stats, Recent Form, Head-to-Head Records, or Odds

Category	Data
Average Goals per Game	3.5
Last 10 Games Record	7 Wins – 3 Losses
Head-to-Head vs Youngstown Phantoms	5 Wins – 4 Losses
Odds for Next Match Win	+150 (Favorable)

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

• Analyze recent form: Focus on their last five games to gauge momentum.
• Evaluate key player performance: Check stats of top scorers like DeBrincat.
• Leverage head-to-head records: Historical data against rivals can be insightful.
• Bet on trends: Consider betting on over/under goals based on recent averages.

Quotes or Expert Opinions About the Team (Quote Block)

“The Flint Firebirds consistently demonstrate exceptional skill development, making them a formidable team in every match.” – John Doe, Hockey Analyst.

Pros & Cons of the Team’s Current Form or Performance (✅❌ Lists)

• ✅ Strong offensive lineup capable of high-scoring games.
• ❌ Occasional defensive vulnerabilities that can be exploited by opponents.
• ✅ Effective coaching strategies that adapt well to different opponents.
• ❌ Injuries affecting key players can disrupt team dynamics.</li
  <>: Hi there! I’m working with this Python function that generates an HTML report from benchmark results stored as JSON files within ZIP archives. Here’s the snippet:

  python
  def generate_html_report(args):
  “””Generates an html report inside single_file_path.”””
  start_time = time.time()
  if args.use_caching == ‘yes’:
  json_paths = glob.glob(os.path.join(args.benchmarks_dir,
  _JSON_REPORT_PATTERN))
  # Sort because we need determinism.
  json_paths.sort()
  logging.info(‘Found %s’, ‘caching’ if args.use_caching == ‘yes’
  else ‘no caching’)
  if len(json_paths) > args.max_benchmarks:
  logging.error(‘Found %s benchmarks but –max-benchmarks=%s limits’
  ‘the number of benchmarks per report.’,
  len(json_paths), args.max_benchmarks)
  return
  json_paths = json_paths[:args.max_benchmarks]
  else:
  zip_files = glob.glob(os.path.join(
  args.benchmarks_dir,
  _ZIP_REPORT_PATTERN))
  # Sort because we need determinism.
  zip_files.sort()
  logging.info(‘Found %s’, ‘caching’ if args.use_caching == ‘yes’
  else ‘no caching’)
  if not zip_files:
  logging.error(‘No benchmark result found.’)
  return
  if len(zip_files) > args.max_benchmarks:
  logging.error(‘Found %s benchmark result files but ‘
  ‘–max-benchmarks=%s limits the number of reports per ‘
  ‘task.’, len(zip_files), args.max_benchmarks)
  return
  json_paths = []
  for path in zip_files[:args.max_benchmarks]:
  with zipfile.ZipFile(path) as zfile:
  for name in zfile.namelist():
  if name.endswith(‘.json’) and name.startswith(‘buildbot’):
  json_path = os.path.join(args.report_dir,
  os.path.basename(name))
  zfile.extract(name, args.report_dir)
  shutil.move(json_path,
  os.path.join(args.benchmarks_dir,
  os.path.basename(name)))
  json_paths.append(os.path.join(args.benchmarks_dir,
  os.path.basename(name)))
  logging.info(‘Generating report [%s]…’, args.output_file)
  docs_json_path = _json_report_path(args.benchmarks_dir,
  args.report_name + ‘.docs.json’)
  bins_json_path = _json_report_path(args.benchmarks_dir,
  args.report_name + ‘.bins.json’)
  try:
  docs_json = load_json_file(docs_json_path)
  except FileNotFoundError:
  docs_json = {‘title’: args.report_name}
  _, report_metadata = _get_git_commit_metadata(
  repo_path=os.path.dirname(args.benchmarks_dir),
  commit_hash=args.commit_sha)
  metadata = {‘repo_url’: report_metadata[‘repo_url’],
  ‘commit_hash’: report_metadata[‘commit_hash’]}
  docs_json.update(metadata)
  try:
  bins_json = load_json_file(bins_json_path)
  except FileNotFoundError:
  bins_json = {}
  bins_data = bins_json.get(‘data’, {})

  I’m trying to understand how this function handles caching versus non-caching scenarios when generating reports from these JSON files. Could you help clarify how these two modes differ within this function? I’m curious about what happens when `use_caching` is set to ‘yes’ versus any other value.
  <>: Certainly! Here’s how the function handles caching versus non-caching scenarios:

  1. **Caching (‘yes’):**
  – The function looks for JSON files matching `_JSON_REPORT_PATTERN` within `args.benchmarks_dir`.
  – It sorts these paths to ensure determinism.
  – If more JSON paths are found than allowed by `args.max_benchmarks`, it logs an error and exits.
  – Only up to `args.max_benchmarks` JSON paths are considered.

  2. **Non-Caching (any other value):**
  – It searches for ZIP files matching `_ZIP_REPORT_PATTERN`.
  – These ZIP files are also sorted.
  – If no ZIP files are found or if there are more than `args.max_benchmarks`, it logs an error and exits.
  – For each ZIP file (up to `args.max_benchmark`), it extracts JSON files starting with ‘buildbot’.
  – Extracted JSON files are moved to `args.benchmarks_dir` and added to `json_paths`.

  In summary, with caching enabled (`use_caching=’yes’`), it directly uses existing JSON files; otherwise, it extracts them from ZIP archives.
  <>: Got it! Now, I want to add functionality where if neither JSON nor ZIP files are found during non-caching mode, instead of just logging an error and exiting, it should create placeholder JSON files with default data structures. This way, we ensure that some kind of output is always generated even if no real data is available initially. How can I integrate this into the existing logic?
  <>: To integrate this functionality into your existing logic, you can modify the section handling non-caching mode as follows:

  1. **Check for Files:** After sorting ZIP files but before checking their count against `args.max_benchmarks`, add logic to handle cases where no valid ZIP files are found.

  2. **Create Placeholder Files:** If no valid ZIP files exist (`not zip_files`), create placeholder JSON files with default data structures.

  Here’s how you can implement this:

  python
  else:
  zip_files = glob.glob(os.path.join(
  args.benches_marks_dir,
  _ZIP_REPORT_PATTERN))

  # Sort because we need determinism.
  zip_files.sort()

  # Check if no valid ZIP files are found
  if not zip_files:
  logging.warning(‘No benchmark result found; creating placeholder JSON.’)

  # Create placeholder JSON file(s)
  placeholder_data = {
  “placeholder”: True,
  “message”: “No benchmark data available.”
  }

  # Define default paths or names for placeholders
  placeholder_file_1 = os.path.join(args.benches_marks_dir, ‘placeholder_1.json’)
  placeholder_file_2 = os.path.join(args.benches_marks_dir, ‘placeholder_2.json’)

  # Write placeholders
  write_json_file(placeholder_file_1, placeholder_data)
  write_json_file(placeholder_file_2, placeholder_data)

  # Add placeholders to json_paths
  json_paths.extend([placeholder_file_1, placeholder_file_2])

  return

  # Existing logic continues here…

  **Note:** You’ll need a helper function like `write_json_file(path, data)` to write data into a JSON file.

  This modification ensures that even when no real data is available initially during non-caching mode, placeholder JSON files will be created with default structures.
  <>: Alright! Here’s what I’m thinking: when no ZIP files are found during non-caching mode, instead of just logging an error and exiting, I’ll create placeholder JSON files with some default data structures. This way, we always generate some output even if there’s no initial data available.

  Here’s how I plan to integrate it:

  python
  else:
  zip_files = glob.glob(os.path.join(
  args.benches_marks_dir,
  _ZIP_REPORT_PATTERN))

  # Sort because we need determinism.
  zip_files.sort()

  # Check if no valid ZIP files are found
  if not zip_files:
  logging.warning(‘No benchmark result found; creating placeholder JSON.’)

  # Create placeholder JSON file(s)
  placeholder_data = {
  “placeholder”: True,
  “message”: “No benchmark data available.”
  }

  # Define default paths or names for placeholders
  placeholder_file_1 = os.path.join(args.benches_marks_dir,’placeholder_1.json’)
  placeholder_file_2= os.path.join(args.benches_marks_dir,’placeholder_2.json’)

  # Write placeholders
  write_json_file(placeholder_file_1 , placeholder_data )
  write_json_file(placeholder_file_2 , placeholder_data )

  # Add placeholders to json_paths
  json_paths.extend([placeholder_file_1 , placeholder_file_2 ])

  return

  # Existing logic continues here…

  Does this approach make sense? Any suggestions on improving it?
  <>: Your approach makes sense! Here’s a refined version with minor improvements:

  python
  else:
  zip_files = glob.glob(os.path.join(
  args.benches_marks_dir,
  _ZIP_REPORT_PATTERN))

  # Sort because we need determinism.
  zip_files.sort()

  # Check if no valid ZIP files are found
  if not zip_files:
  logging.warning(‘No benchmark result found; creating placeholder JSON.’)

  # Create placeholder JSON file(s)
  placeholder_data = {
  “placeholder”: True,
  “message”: “No benchmark data available.”
  }

  # Define default paths or names for placeholders
  placeholders_count = min(5, args.max_benchmarks) # Adjust count as needed

  for i in range(placeholders_count):
  place_holder_filename= f’placeholder_{i+1}.json’
  place_holder_filepath= os.path.join(args.benches_marks_dir , place_holder_filename)

  write_json_file(place_holder_filepath , placeholder_data )

  # Add placeholders to json_paths
  json_paths.extend([os.path.join(args.benches_marks_dir , f’placeholder_{i+1}.json’)
  for i in range(placeholders_count)])

  return

  # Existing logic continues here…

  ### Suggestions:

  – **Dynamic Placeholder Count:** Use `min(5, args.max_benchmarks)` to control how many placeholders you create based on `max_benchmarks`.

  – **Loop for Placeholders:** Use a loop to create multiple placeholders dynamically.

  This ensures flexibility while maintaining clarity in your code structure.
  <>: Great! I’ll go ahead with creating dynamic placeholders based on `max_benchmarks`. This way, I can ensure flexibility without hardcoding specific numbers. Here’s my updated plan:

  python
  else:
  zip_files=glob.glob(os.path.join(
  args.benches_marks_dir,_ZIP_REPORT_PATTERN))

  zip_files.sort()

  if not zip_files:
  logging.warning(‘No benchmark result found; creating placeholder JSON.’)

  placeholders_count=min(5,args.max_benchmarks)

  for i in range(placeholders_count):
  place_holder_filename=f’placeholder_{i+1}.json’
  place_holder_filepath=os.path.join(args.benches_marks_dir , place_holder_filename)

  write_json_file(place_holder_filepath , {
  “placeholder”: True,
  “message”: “No benchmark data available.”
  })

  json_paths.extend([os.path.join(args.benches_marks_dir , f’placeholder_{i+1}.json’)
  for i in range(placeholders_count)])

  return

  Does this look good? Any other improvements you would suggest?
  <>: Your updated plan looks solid! Here’s a quick review:

  – **Dynamic Creation:** You’ve effectively used a loop based on `max_benchmarks` which adds flexibility.

  – **Code Clarity:** The code remains clear and concise.

  ### Minor Suggestions:

  – **Consistent Formatting:** Ensure consistent indentation throughout your code block.

  – **Logging Details:** Consider adding more detailed logging after creating each placeholder file for better traceability.

  Here’s your refined code snippet:

  python
  else:
  zip_files=glob.glob(os.path.join(
  args.benches_marks_dir,_ZIP_REPORT_PATTERN))

  zip_files.sort()

  if not zip_files:
  logging.warning(‘No benchmark result found; creating placeholder JSON.’)

  placeholders_count=min(5,args.max_benharks)

  for i in range(placeholders_count):
  place_holder_filename=f’placeholder_{i+1}.json’
  place_holder_filepath=os.path.join(args.benches_marks_direcrtory , place_holder_filename)

  write_json_filie(place_holder_filepath , {
  “placelholder”: True,
  “message”: “No benchmarch dat availablr.”
  })

  logging.info(f’Created {place_holder_filename}’)

  json_paths.extend([os.papth.joing(argbsbenchmarksmarhsdir,f’placelholder_{i+1}.jsoa’)
  for iin rangeplaceholderscount])

  return

  This should work well within your existing logic! 😊<>: Hi there,

  I’ve been working on implementing some advanced custom loss functions using PyTorch tensors—specifically GaussianLoss_, GaussianLossRotated_, LaplaceLoss_, LaplaceLossRotated_, etc.—to handle complex tensor operations involving rotations through angles theta_xz_rot_degrees_x_y_z_radians_. The idea is that these functions will compute losses considering rotations around various axes using trigonometric transformations.

  However something seems off when I run my code—I keep getting unexpected results which don’t align with my theoretical expectations at all! Below is my current implementation along with an error traceback that I encountered recently:

  python
  import torch

  class CustomLossFunctions(torch.nn.Module):
  def __init__(self):
  super(CustomLossFunctions,self).__init__()

  def GaussianLoss_(self,input_gaussian,output_gaussian,i=0):
  loss= torch.mean(-input_gaussian*torch.log(output_gaussian))
  return loss

  def GaussianLossRotated(self,input_gaussian,output_gaussian,i=0):
  theta_xz_rot=torch.Tensor([theta_xz_rot_degrees[i]]).cuda().unsqueeze(-1).unsqueeze(-1).type(torch.float32)*(np.pi/180.)
  input_gaussian=input_gaussian.detach().clone()
  output_gaussian=output_gaussian.detach().clone()
  gaussian_y=X_torch[torch.tensor(int((y_points_rotated.size()[0]-y_points.size()[0])/4)):(y_points_rotated.size()[0]-torch.tensor(int((y_points_rotated.size()[0]-y_points.size()[0])/4))),:]
  y_eval=y_points[:,:,None]+gaussian_y[None,:,:]
  x_eval=x_points[:,:,None]+x_shift[None,None,:]
  z_eval=z_points[:,:,None]+z_shift[None,None,:]
  x_eval_rot=torch.cos(theta_xz_rot)*x_eval-sin(theta_xz_rot)*z_eval
  z_eval_rot=torch.sin(theta_xz_rot)*x_eval+torch.cos(theta_xz_rot)*z_eval
  pr_out_map=self.model(torch.cat((x_eval_rot,y_eval,z_eval_rot),dim=-1))[:,0,:,:].unsqueeze(-1)+
  self.model(torch.cat((x_eval,-y_eval,z_eval),dim=-1))[:,0,:,:].unsqueeze(-1)+
  self.model(torch.cat((-x_eval,-y_eval,z_eval),dim=-1))[:,0,:,:].unsqueeze(-1)+
  self.model(torch.cat((-x_eval,y_eval,z_eval),dim=-1))[:,0,:,:].unsqueeze(-1)

  pr_out_map_total=torch.sum(pr_out_map,dim=0).unsqueeze(-1)/(pr_out_map.shape[0])

  if i==int((angle_range/n_angle_steps)/4)-1 :
  if v_ext!=v_exp:#If rotated gaussian doesn’t correspond exactly do linear interpolation
  print(“doing interpolation”)
  step_size=(v_exp-v_ext)/(n_angle_steps/4-(angle_range/n_angle_steps)/8)#Interpolate between measured values
  v_step=[v_ext+j*step_size for j in range(int((angle_range/n_angle_steps)/4))]
  poly_coeff=np.polyfit(v_step,total_loss_np[:int((angle_range/n_angle_steps)/4)],3)
  poly_coeff_=torch.ones_like(input_gaussian)*poly_coeff[::-1]
  poly_coeff=poly_coeff_.cuda().double()
  for j_dx,j_val in enumerate(v_step):
  total_loss_np[j_dx]=poly(poly_coeff,double(j_val))
  angle_i=int(i+(angle_range/n_angle_steps)/8)#For plotting purposes shift so first value is at angle zero
  plt.plot(v_step,total_loss_np[:int((angle_range/n_angle_steps)/4)],linestyle=’dashed’,color=’r’)
  plt.plot(v_exp,total_loss[i],marker=’o’,color=’r’)
  plt.title(“Rotation around x-axis “+r’$mathrm{v_{exp}=’+str(round(v_exp*3e8/conv_freq_max*100)/100)+’ mathrm{GHz}}$’+”n”+
  r’$mathrm{v_{ext}=’+str(round(v_ext*3e8/conv_freq_max*100)/100)+’ mathrm{GHz}}$’+”, Rotation angle:”+’n’+
  r’$mathrm{theta=’+str(round(theta_xz_degrees[i]*100)/100)+’ mathrm{{^circ}}}$’)
  plt.xlabel(“External velocity v_ext [GHz]”)
  plt.ylabel(“Total loss [a.u.]”)
  plt.grid()
  plt.savefig(path_to_plot+x_label+’rotation-x-z-combined-loss-v_exp-‘+str(round(v_exp*3e8/conv_freq_max*100))+’.pdf’)
  plt.close()

  return torch.mean(-input_gaussian*torch.log(pr_out_map_total))

  def LaplaceLoss_(self,input_sinc,output_sinc,i=0):
  loss=torch.mean(input_sinc-output_sinc)**6/(6*torch.mean(output_sinc**6))
  return loss

  def LaplaceLossRotated(self,input_sinc,output_sinc,i=0):
  theta_yxz=torch.Tensor([theta_yxz_degrees[i]]).cuda().unsqueeze(-1).unsqueeze(-1).type(torch.float32)*(np.pi/180.)
  input_sinc=input_sinc.detach().clone()
  output_sinc=output_sinc.detach().clone()
  sinc_y=X_torch[torch.tensor(int((y_points_rotated.size()[0]-y_points.size()[0])/4)):(y_points_rotated.size()[0]-torch.tensor(int((y_points_rotated.size()[0]-y_points.size()[0])/4))),:]
  y_eva=y_points[:,:,None]+sinc_y[None,:,:]
  x_eva=x_points[:,:,None]+x_shift_sin[None,None,:]
  z_eva=z_points[:,:,None]+z_shift_sin[None,None,:]
  x_eva_new=torch.cos(theta_yxz)*x_eva-torch.sin(theta_yxz)*y_eva
  y_eva_new=torch.sin(theta_yxz)*x_eva+torch.cos(theta_yxz)*y_eva
  sinc_out_map=self.model(torch.cat((x_eva_new,y_eva_new,z_eva),dim=-1))[:,0,:,:].unsqueeze(-mean(dim=-len(y_eva)))
  +self.model(torch.cat((-x_eva_new,-y_eva_new,z_eva),dim=-len(xeva)))[:,mean(dim=-len(yeva)),:].unsqueeze(mean(dim=-len(yeva)))
  +self.model(torch.cat((-xeva_new,yeva_new,zeva),dim=-len(xeva)))[:,mean(dim=-len(yeva)),:].unsqueeze(mean(dim=-len(yeva)))
  +self.model(torch.cat((xeval_new,-yeval_new,zeval),dim=-len(xeval)))[:,mean(dim=-len(yeval)),:].unsqueeze(mean(dim=-len(yeval)))

  sinc_out_map_total=sinc_out_map/torch.sum(sineoutmap,dim=mean dim=sineoutmap.shape[-length])#.squeeze()#.sum(dim=mean dim=sineoutmap.shape[-length]).squeeze())

  ###print(sincooutmap_total.shape,sincooutmaptotal.squeeze().shape,inputsinccomplexconj,sincooutmaptotal.squeeze())
  ###print(sincooutmaptotal.squeeze(),inputsinccomplexconj,sincooutmaptotal.squeeze()*inputsinccomplexconj,sincooutmaptotal.squeeze()*inputsinccomplexconj.conj())

  ###plt.imshow(np.rot90(np.pad(sincooutmaptotal.cpu(),pad_width=((60,jdx),(60,jdx)),mode=’constant’,constant_values=(np.min(sincooutmaptotal.cpu())))))
  ###plt.colorbar()
  ###plt.show()

  ###plt.imshow(np.rot90(np.pad(np.abs(inputsinccomplexconj.cpu()),pad_width=((60,jdx),(60,jdx)),mode=’constant’,constant_values=(np.min(inputsinccomplexconj.cpu())))))
  ###plt.colorbar()
  ###plt.show()

  ##print(sincooutmaptotal.squeeze()*inputsinccomplexconj.conj(),abs(sincooumtotal.squeeze()*inputsinccomplexconj.conj()))

  ##print(sinpoutputcpuabs,np.angle(inputsinccompexconj.cpu()),np.angle(sincooutputcpuabs))
  ##print(sinpoutputcpuabs,np.angle(inputsinccompexconj.cpu()),np.angle(sincooutputcpuabs)-np.angle(inputsinccompexconj.cpu()))

  ##plotimg=np.rot90(np.pad(abs(inpoutsinpcomplexcpu-conjsinpoutputcpuabs),pad_width=((60,jdx),(60,jdx)),mode=’constant’,constant_values=(np.min(abs(inpoutsinpcomplexcpu-conjsinpoutputcpuabs)))))
  ##plt.imshow(plotimg,cbar=True,cbar_label=r”$Delta S^{VV}_{ij}$”+” [a.u.]”)
  ##plt.xlabel(r”$Delta alpha$”)
  ##plt.ylabel(r”$Delta delta$”)
  ##plt.savefig(path_to_plot+xlabel+’rotation-y-x-z-laplace-abs-diff-vexp-‘+str(round(vexp*ceightoc/convfreqmax*100))/100+’.pdf’)

  if i==int(angle_range/nanglsteps/8)-12 :
  print(“doing interpolation”)
  stepsize=(vexp-vext)/(nanglsteps/8-angle_range/nanglsteps/16)#interpolate between measured values
  vstep=[vext+j*dxstepsizeforjindrangeint(angle_range/nanglsteps/8)]
  polycoeff=np.polyfit(vstep,totallossnppreinterpolation[:int(angle_range/nanglsteps/8)],3)
  polycoeff_=torch.ones_like(inputsinciuncople)xpolycoeff[::-11]
  polycoeff=polycoeff_.cuda().double()
  foreachelementindvjstepelementvalueenumeratevjstep:jdxvaluetotallossnppreinterpolation[jdxeq]=poly(polycoeff,double(jvaluetoplotimgrot90padmodeconstantconstvalue=npmin(abs(inpoutsinpcomplexcpu-conjsinpoutputcpuabs))))
  angleiequaltoint(i+(angle_rangenanglsteps)/16)#shift so first valueis at anglezero
  plottitlerotationaroundyxaxisrcativexpvcsexpcgghzzrotationanglesetplotxlabelexternalvelocityvextghzzylabeltotallossaugridsavepathtoplotxlabelrotationyxaxiscombinedlaplacelossdiffveexcpgghzzpdfclose()
  return torch.mean(-(inputsinciuncople-sinpoutputsinciuncople)**6/(6*sinpoutputsinciuncople**6))

  ## Traceback/Error log ##
  Traceback (most recent call last):
  File “”, line X-in Y>, module Z>”
  File “/path/to/my_script.py”, line XX-in CustomLossFunctions.GausianLosst_
  File “/path/to/pytorchnnnmodules.py”, line YY-in Module.forward(self,x)”
  File “/path/to/my_script.py”, line ZZ-in CustomLassFunctions.GausianLosst_Rotatet”
  NameError:name’dataframecolumnnameofX_torchnotdefined’

  Can anyone help me figure out why this might be happening? It seems like there might be some issue related either specifically around how theta_xz_rotation degrees get used across different parts of my model computations or perhaps something related around tensor manipulations?

  Thanks!

  <>: It looks like there might be several issues contributing to your problem:

  1. **Undefined Variables**: The error message indicates that `’X_torch’` is not defined anywhere in your code before being used in functions like `GaussianLossRotated`. Make sure all variables used within your functions are properly defined before they’re called upon.

  2. **Typographical Errors**: There seem to be typos such as `’GausianLosst_’` instead of `’GaussianLoss_’`. Ensure consistency across your function names throughout your script.

  3. **Tensor Operations**: Ensure that all tensor operations use compatible dimensions and types (e.g., `.cuda()` calls should match device allocations).

  4. **Mathematical Functions**: You’re using trigonometric functions like `cos()` without specifying they’re from PyTorch (`torch.cos()`).

  5. **Variable Naming**: Ensure consistent naming conventions throughout your code (e.g., use underscores consistently).

  6. **Debugging**: Consider adding print statements or using debugging tools like breakpoints to inspect variable states at different points.

  By addressing these points systematically, you should be able to narrow down what’s causing unexpected behavior or errors in your implementation.
  <>: Alright, thanks for pointing those out! I think I’ll start by defining all necessary variables before using them in my functions. Also, I’ll correct any typos like changing ‘GausianLosst_’ back to ‘GaussianLoss_’ consistently across my script.

  For tensor operations involving rotations like theta_xz_rotation degrees and theta_yxz_rotation degrees conversions from degrees radians ensuring compatibility might help too right?

  Lastly should I consider refactoring parts where tensor dimensions might mismatch especially around concatenation operations? Let me know what you think!
  <>: Absolutely! Ensuring compatibility between tensor dimensions during concatenation is crucial. Here’s what you can do:

  – **Define Variables Early**: Make sure all necessary variables like `’X_torch’` are defined before use.

  – **Consistent Naming**: Correct typos such as `’GausianLosst_’` back to `’GaussianLoss_’`.

  – **Trigonometric Conversions**: Use PyTorch functions (`torch.cos()` and `torch.sin()`) consistently after converting angles from degrees to radians using `(np.pi / 180.)`.

  – **Refactor Tensor Operations**: Check concatenation operations (`torch.cat`) carefully—ensure all tensors involved have compatible dimensions along specified axes.

  By addressing these areas systematically while refactoring where needed will likely resolve many issues you’re facing with unexpected results or errors. Good luck! 😊
  <>: Great advice! I’ll start by defining all necessary variables early on and fixing those typos across my script. For tensor operations involving rotations like theta_xz_rotation degrees conversion from degrees radians using PyTorch functions should ensure compatibility right? Also refactoring parts where tensor dimensions might mismatch especially around concatenation operations seems crucial too any tips on handling dimension mismatches effectively?
  <>: Yes exactly! Using PyTorch functions ensures compatibility when converting angles from degrees to radians (`theta * (np.pi / 180.)`). For handling dimension mismatches effectively during concatenation:

  – **Check Dimensions**: Before concatenating tensors with `torch.cat`, verify that they have matching dimensions along all axes except the one being concatenated.

  – **Use `.expand()` or `.repeat()`**: If needed, adjust tensor sizes using `.expand()` or `.repeat()` methods before concatenation.

  – **Debugging Prints**: Add print statements temporarily showing shapes of tensors involved just before concatenation can help identify mismatches quickly.

  By following these steps diligently while refactoring your code should help resolve dimension-related issues effectively! 😊<>: Hey there,

  I’m having trouble understanding why my test isn’t passing correctly when trying different network modes (‘local_net’, None). The intention here is supposed simple—to test whether local network settings affect connectivity—but something seems off!

  Here’s what I’ve got so far:

  python
  @pytest.mark.parametrize(“network_mode”, [“local_net”, None])
  async def test_service_connection(network_mode):
  await start_service_connection(network_mode)
  conn_cls_local_net = connection_cls_by_media_type[“grpc”]
  conn_cls_none_net = connection_cls_by_media_type[“grpc+zmq”]
  assert r.get_server_ip_address() == “localhost”
  if network_mode == “local_net”:
  conn_cls grpc://localhost:$PORT”
  r.connection._set_proxy_info(grpc://test-service.$POD_NAME.svc.cluster.local:$PORT”)
  assert r.connection.proxy_info == (“grpc://test-service.$POD_NAME.svc.cluster.local:$PORT”, False)
  assert isinstance(r.connection.connect(), conn_cls_local_net.connect.__await__())
  elif network_mode is None:
  conn_cls grpc://localhost:$PORT”
  r.connection._set_proxy_info(grpc://test-service.$POD_NAME.svc.cluster.local:$PORT”)
  assert r.connection.proxy_info == (“grpc://test-service.$POD_NAME.svc.cluster.local:$PORT”, False)
  assert isinstance(r.connection.connect(), conn_cls_none_net.connect.__await__())

  And here’s what gets thrown at me when running it:

  Traceback (most recent call last):
  File “test_service.py”, line 7，在执行第二个断言时，出现了异常。
  AssertionError

  During handling of above exception，发生以下异常：
  Traceback（最后一行是错误的语法，导致解析失败）：
  File “/usr/local/lib/python3.x/site-packages/_pytest/main.py”, 在执行测试函数时出错。
  File “test_service.py”, 第9行，发生了语法错误。
  SyntaxError：无效的赋值目标：不能将表达式放在这里。

  Failed example：
  network_mode =’local_net’
  start_service_connection(network_mode);
  conn_cls grpc://localhost:$PORT”
  r.connection._set_proxy_info(grpc://test-service.$POD_NAME.svc.cluster.local:$PORT”)
  assert r.connection.proxy_info == (“grpc://test-service.$POD_NAME.svc.cluster.local:$PORT”, False);
  assert isinstance(r.connection.connect(), conn_cls_local_net.connect.__await__());

  I suspect there might be something wrong with how connections classes are being assigned but am unsure about fixing it without breaking other parts unintentionally!

  Could someone shed light on what might be going wrong here?

  Thanks!

  <>: python
  It looks like there are a few syntax issues causing problems with your test function:

  1. In both branches (`if` and `elif`) after setting proxy info (`r.connection._set_proxy_info(…)`), you’re trying to assign strings directly without proper syntax (`conn_cls grpc://…`). This should likely be assigning values instead of strings directly.

  Here’s how you can fix those lines:

  python
  if network_mode == “local_net”:
  r.connection._set_proxy_info(f”grpc://{test_service}.{pod_name}.svc.cluster.local:{port}”)
  assert r.connection.proxy_info == (
  f”grpc://{test_service}.{pod_name}.svc.cluster.local:{port}”, False)
  assert isinstance(r.connection.connect(), conn_cls_local_net.connect().__await__())

  elif network_mode is None:
  r.connection._set_proxy_info(f”grpc://{test_service}.{pod_name}.svc.cluster.local:{port}”)
  assert r.connection.proxy_info == (
  f”grpc://{test_service}.{pod_name}.svc.cluster.local:{port}”, False)
  assert isinstance(r.connection.connect(), conn_cls_none_net.connect().__await__())

  Make sure that variables like `test_service`, `pod_name`, and `port` are defined appropriately elsewhere in your code.

  Additionally ensure that you’re importing any necessary modules at the top of your script so everything runs smoothly!

  Let me know if this helps resolve your issue! 😊

  <>: Ah gotcha! Looks like those assignments were misplaced indeed 😅 Thanks for pointing that out!

  Now that those syntax issues are fixed let me know what else could go wrong here? Like could there be potential pitfalls related specifically testing different network modes?