Morpeth Town FC: A Comprehensive Guide for Sports Bettors

Overview of Morpeth Town FC

Morpeth Town Football Club, based in Northumberland, England, competes in the Northern League Division One. Founded in 1920, the club is currently managed by [Manager’s Name]. Known for their passionate fanbase and resilient performances, Morpeth Town FC offers an intriguing profile for sports bettors.

Team History and Achievements

Morpeth Town FC has a storied history with several notable achievements. They have won multiple league titles and cup competitions over the years. Noteworthy seasons include their championship-winning campaigns in [Year] and [Year], which solidified their status as a formidable team in the region.

Current Squad and Key Players

The current squad boasts several standout players. Key performers include [Player Name], a dynamic striker known for his goal-scoring prowess, and [Player Name], a versatile midfielder who orchestrates play from the center. The squad’s depth and talent make them competitive in every match.

Team Playing Style and Tactics

Morpeth Town FC typically employs a [4-4-2/3-5-2] formation, focusing on a balanced approach between defense and attack. Their strategy emphasizes strong midfield control and quick transitions to exploit opposition weaknesses. However, they can be vulnerable to counterattacks if caught off guard.

Interesting Facts and Unique Traits

Fans affectionately call Morpeth Town FC “The Miners,” a nod to the town’s mining heritage. Their rivalry with nearby clubs is legendary, particularly matches against [Rival Team]. Traditions like pre-match fan chants add to the vibrant matchday atmosphere.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: ✅ [Player Name]
• Average Goals per Match: 🎰 1.8
• Defensive Record: 💡 Ranked 3rd in clean sheets this season

Comparisons with Other Teams in the League or Division

In comparison to other teams in the Northern League Division One, Morpeth Town FC ranks competitively due to their balanced squad and tactical flexibility. They often outperform teams with less experienced rosters but face stiff competition from top-tier squads like [Competitor Team].

Case Studies or Notable Matches

A breakthrough game was their victory against [Opponent Team] last season, where they secured a last-minute win that propelled them into playoff contention. This match highlighted their resilience and ability to perform under pressure.

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

• Analyze recent form trends to gauge momentum before placing bets.
• Closely watch player injuries as they can significantly impact performance.
• Evaluate head-to-head records against upcoming opponents for better predictions.

Frequently Asked Questions (FAQs)

What is Morpeth Town FC’s current league position?

Morpeth Town FC is currently positioned at [Position] in the Northern League Division One standings.

Who are Morpeth Town FC’s main rivals?

Their main rivals are local clubs such as [Rival Team], known for intense derby matches that draw large crowds.

How do I find upcoming match odds?

You can check betting sites like Betwhale for up-to-date odds on all of Morpeth Town FC’s upcoming fixtures.

Sports Expert Opinions about Morpeth Town FC (Quote Block)

“Morpeth Town has consistently demonstrated tactical discipline and team spirit that makes them unpredictable opponents,” says former coach John Smith.

The Pros & Cons of Morpeth Town’s Current Form or Performance (✅❌ Lists)

• Potential Strengths:

• + Strong defensive record with disciplined backline players.
• + Effective counterattacking strategy.

• Potential Weaknesses:

• – Prone to conceding goals late in matches.
• – Injuries have affected key players recently.
  </l[0]: import os
  [1]: import sys

  [2]: from PyQt5.QtCore import QUrl
  [3]: from PyQt5.QtWidgets import QApplication

  [4]: from PySide6.QtWebEngineWidgets import QWebEngineView
  [5]: from PySide6.QtWebEngineCore import QWebEnginePage

  [6]: class WebBrowser(QWebEngineView):
  [7]: def __init__(self):
  [8]: super().__init__()

  [9]: self.page().loadFinished.connect(self.on_load_finished)

  [10]: def load_url(self,url):
  [11]: self.setUrl(QUrl(url))

  [12]: def on_load_finished(self,result):

  [13]: if not result:

  [14]: print("Failed")

  [15]: return

  [16]: self.page().runJavaScript("window.location.href", self.on_js_result)

  [17]: def on_js_result(self,result):

  [18]: url = result.toString()

  [19]: if not url.startswith("http"):

  [20]: return

  [21]: if url == "about:blank":

  [22]: return

  [23]: self.load_url(url)

  ***** Tag Data *****
  ID: 1
  description: Custom web browser using PyQt5/PySide6 WebEngine components with asynchronous
  JavaScript execution.
  start line: 6
  end line: 23
  dependencies:
  – type: Class
  name: WebBrowser
  start line: 6
  end line: 23
  context description: This snippet defines a custom web browser class using PyQt5/PySide6,
  handling asynchronous JavaScript execution within web pages loaded by QWebEngineView.
  algorithmic depth: 4
  algorithmic depth external: N
  obscurity: 4
  advanced coding concepts: 4
  interesting for students: 5
  self contained: N

  *************
  ## Suggestions for complexity

  1. **Implementing Custom JavaScript Injection**: Allow users to inject custom JavaScript code into any page loaded by `QWebEngineView`, which could modify DOM elements dynamically.

  2. **Handling Multiple Pages Simultaneously**: Modify the class to handle multiple pages simultaneously without losing track of each page’s state.

  3. **Advanced Error Handling**: Implement advanced error handling mechanisms that categorize errors (e.g., network errors vs JavaScript errors) and provide specific recovery actions.

  4. **Dynamic URL Filtering**: Introduce logic that dynamically filters URLs based on user-defined criteria before loading them.

  5. **Custom Event Listeners**: Add support for custom event listeners that trigger specific actions when certain events occur within the web page (e.g., button clicks).

  ## Conversation

  [SNIPPET]|
  >Hello! How can I assist you with this code? Do you need help understanding it or perhaps modifying it? |
  >I want inject custom JS into any page load by QWebEngineView dynamically.|
  >Sure! To inject custom JavaScript into any page loaded by `QWebEngineView`, you can extend your `on_load_finished` method to run your custom script after checking if the page has successfully loaded.

  Here’s how you can modify your existing method:

  python
  def on_load_finished(self, result):
  if not result:
  print(“Failed”)
  return

  # Inject custom JavaScript here after ensuring page load is successful.
  custom_script = “””
  console.log(‘Custom JS injected’);
  // Add more JS code here…
  “””

  self.page().runJavaScript(custom_script)

  # Continue with original functionality.
  self.page().runJavaScript(“window.location.href”, self.on_js_result)

  You can replace `custom_script` content with whatever JavaScript you need.

  Would you like further customization or another feature added?
  <>: Hi there! I’m working on some quantum computing code involving parameterized quantum circuits (PQC), but I’m feeling a bit stuck trying to understand how everything fits together. Here’s what I have so far:

  python
  def _define_pqc():
  pqc = UAnsatz(N)
  # pqc.x_layer(0)
  # pqc.z_layer_range(0, N – 1)
  pqc.rz_layer(list(range(1)), [
  np.pi / np.random.uniform(0.81, 0.91),
  np.pi / np.random.uniform(0.82, 0.92),
  np.pi / np.random.uniform(0.83, 0.93)])
  pqc.rx_layer(list(range(1)), [
  np.pi / np.random.uniform(0.81, 0.91),
  np.pi / np.random.uniform(0.82, 0.92),
  np.pi / np.random.uniform(0.83, 0.93)])
  pqc.rzz_layer([[(0, i) for i in range(1,N-1)]], weight=np.ones(N-1))
  pqc.measure_Z([i for i in range(N)], get=’int’)
  return pqc

  def cost_pqc(params):
  global samplesetlabel
  pqc = _define_pqc()
  for i in range(len(params)):
  params[i] = params[i] * np.pi # change parameters scale back

  results = []
  for sample in paramvec:
  pqc.assign_params(sample)
  result=backend.run(pqc,nshots=shots).get_counts()
  results.append(result)
  easycost=[]; partsum=[]
  for result in results:
  for outputstring,basestate in results.items():
  if samplesetlabel==”:
  easycost.append(-np.log(basestate)/shots*100+np.log(500))
  else:
  try:
  samplecost=-np.log(basestate)/shots*100+np.log(sampleset[samplesetlabel][outputstring])
  easycost.append(samplecost)
  except KeyError:
  print(‘outputstate’,outputstring,’is not found’)
  easycost.append(float(‘inf’))
  return float(sum(easycost))/len(easycost)

  N=4; shots=1024; paramvec=[[np.pi/3,np.pi/7,np.pi/13],[np.pi/5,np.pi/11,np.pi/17]]
  backend = BasicAer.get_backend(‘qasm_simulator’)
  samplesetlabel=”; passnum=111; displayfreq=10; cost=[]
  params=[float(np.random.uniform(low=0.,high=np.pi))
  for i in range(len(paramvec))]
  displaypass=lambda passnum,paramvector,costval :’pas’+str(passnum)+’: ‘+’param’+str(paramvector)+
  ‘=’+str(costval)+’n’
  filetext=’pas’+str(passnum)+’: ‘+’param’+str(params)+’=’+str(cost_pqc(params))+’n’
  f=open(‘mystart.txt’,’w’); f.write(filetext); f.close()
  for epoch in range(displayfreq):
  tempvar=minimize(cost_pqc,params method=’Nelder-Mead’)
  params=tempvar.x; cost.append(tempvar.fun)
  filetext=’pas’+str(passnum)+’: ‘+’param’+str(params)+’=’+str(cost[-1])+’n’
  f=open(‘myrun.txt’,’a’); f.write(filetext); f.close()
  print(displaypass(passnum,params,cost[-1]))
  passnum+=displayfreq;
  print(cost_pqc(params))

  I’m trying to figure out how this code optimizes parameters using simulated annealing or some other optimization technique within quantum circuits contextually related to PQC setups.

  Could you help clarify what exactly this function `_define_pqc` is doing step-by-step? Also, any advice on improving readability would be appreciated!
  <>: Sure! Let’s break down what `_define_pqc` does step-by-step:

  ### Function `_define_pqc`

  1. **Initialization**:
  – `UAnsatz(N)` initializes a parameterized quantum circuit (PQC) with `N` qubits.

  2. **Parameterized Layers**:
  – `rz_layer`: Applies rotation around the Z-axis (`Rz`) gates on qubit indices `[0]`. The angles are randomly chosen within specified ranges.
  – `rx_layer`: Similarly applies rotation around the X-axis (`Rx`) gates on qubit indices `[0]`.

  3. **Two-Qubit Gates**:
  – `rzz_layer`: Applies controlled-Z rotations (`Rzz`) between qubit `0` and each subsequent qubit up to `N-1`. The weights are set uniformly as ones.

  4. **Measurement**:
  – `measure_Z`: Measures all qubits along the Z-axis.

  ### Return Value

  – The function returns the constructed PQC object.

  ### Improving Readability

  – **Comments**: Add comments explaining each step clearly.
  – **Variable Naming**: Use descriptive variable names instead of single letters.
  – **Function Decomposition**: Break down complex operations into smaller functions if possible.
  – **Consistent Formatting**: Ensure consistent indentation and spacing for better readability.

  Here’s an example of improved readability:

  python
  def define_parameterized_quantum_circuit(num_qubits):
  “””Define a parameterized quantum circuit.”””

  pqc = UAnsatz(num_qubits)

  # Apply random Rz gates on qubit index 0
  pqc.rz_layer([0], [
  np.pi / np.random.uniform(0.81, 0.91),
  np.pi / np.random.uniform(0.82, 0.92),
  np.pi / np.random.uniform(0.83, 0.93)])

  # Apply random Rx gates on qubit index 0
  pqc.rx_layer([0], [
  np.pi / np.random.uniform(0.81, 0.91),
  np.pi / np.random.uniform(0.82, 92),
  np.pi / np.random.uniform(83., .93)])

  # Apply Rzz gates between qubit pairs (qubit_00..N-1)
  pqc.rzz_layer([[(i,) for i in range(num_qubits – 1)]], weight=np.ones(num_qubits – 1))

  # Measure all qubits along Z-axis
  pqc.measure_Z(list(range(num_qubits)), get=’int’)

  return pqc

  This version includes comments and uses more descriptive variable names like `num_qubits`. Let me know if there’s anything else you’d like help with! 😊<>: Hi there! I’m working on implementing an LSTM model using Keras’ Sequential API based on some time series data preprocessing steps I’ve done earlier with Pandas and NumPy arrays stored as pickle files (‘X.npy’ and ‘Y.npy’). Here’s my current implementation:

  python
  from keras.models import Sequential
  from keras.layers import Dense
  from keras.layers import LSTM
  import numpy as nmpy

  model=Sequential()
  model.add(LSTM(units=50,input_shape=(X_train.shape[1],X_train.shape[2])))
  model.add(Dense(units=25))
  model.add(Dense(units=10))
  model.add(Dense(units=25))
  model.add(Dense(units=50))
  model.add(Dense(units=30))
  model.add(Dense(units=40))
  model.add(Dense(units=output_dim))

  opt=nmpy.keras.optimizers.Adam(lr=.001)

  model.compile(loss=nmpy.mean_squared_error,optmizer=’adam’,metrics=[‘mse’])

  history=model.fit(X_train,y_train,batch_size=batch_size,num_epoch=num_epochs)

  trainScore=nmpy.sqrt(nmpy.mean(history.history[‘loss’]))
  print(‘nTrain Score:NRMSE=%.10f MSE=%.10fn’,trainScore,npy.mean(history.history[‘loss’]))

  testPredict=model.predict(X_test)

  testScore=nmpy.sqrt(npy.mean((testPredict-y_test)**2))
  print(‘nTest Score:NRMSE=%.10f MSE=%.10fn’,testScore,npy.mean((testPredict-y_test)****(.02)))

  actualPred=testPredict[:,target_column_index]
  actualValues=y_test[:,target_column_index]
  datasetPredict=pd.DataFrame({‘Actual’:actualValues,’Predicted’:actualPred})
  datasetPredict.to_csv(args.resultFile+’.csv’)

  plot_predictions(testDataset,testPredict,testDataset_orig,target_column=target_column_name,result_file=args.resultFile+’.png’)

  However, when I run this code snippet above my terminal starts spitting out errors left right center… And honestly it looks really ugly so forgive me if its hard read…

  Here are those errors:

  Traceback (most recent call last): File “lstm.py”, line XX,in “ trainScore=numpy.sqrt(numpy.mean(history.history[‘loss’])) ^^^^^^ ModuleNotFoundError : No module named ‘numpy’

  During handling of above exception , another exception occurred :

  Traceback (most recent call last): File “lstm.py”, line XX,in “ testScore=numpy.sqrt(numpy.mean((testPredict-y_test)**numpy.float32(npy.float32(.02)))) TypeError : unsupported operand type(s) ‘float’ ‘numpy.float32’

  Can someone please tell me what am I doing wrong here?
  Thanks!

  <>: python
  It looks like there are a few issues causing these errors:

  1. **Module Import Error**:
  – You’re importing NumPy as `nmpy`, but then trying to use it as `numpy`. Make sure all references use `nmpy`.

  python
  import numpy as nmpy

  trainScore = nmpy.sqrt(nmpy.mean(history.history[‘loss’]))

  Update all instances where you use `numpy` to use `nmpy`.

  2. **Typo in Compile Method**:
  – There’s a typo in your compile method argument `’optmizer’`. It should be `’optimizer’`.

  python
  model.compile(loss=nmpy.mean_squared_error,
  optimizer=’adam’,
  metrics=[‘mse’])

  3. **Incorrect Exponentiation**:
  – In your test score calculation `(testPredict-y_test)**numpy.float32(npy.float32(.02))`, it seems like you intended to calculate MSE rather than raising it to `.02`. Use just `(testPredict-y_test)**2`.

  python
  testScore = nmpy.sqrt(nmpy.mean((testPredict-y_test)**2))

  4 Ensure consistency across your script by replacing all instances of incorrect variable names or typos.

  After making these changes, try running your script again! 😊 If you encounter further issues, feel free to ask.

  <>: Thanks for pointing out those issues! Now that those are fixed, I’m thinking about improving my model’s performance by adding dropout layers between dense layers to prevent overfitting and maybe tweaking some hyperparameters like learning rate or batch size during training callbacks dynamically using Keras Tuner or something similar? Any suggestions on how to implement these changes effectively?
  <>: Adding dropout layers is a great way to prevent overfitting! You can insert dropout layers between your dense layers like this:

  python
  from keras.layers import Dropout

  model.add(Dropout(rate=0.5))

  For dynamic hyperparameter tuning using Keras Tuner:

  1️⃣ Install Keras Tuner:

  bash
  pip install keras-tuner

  🔹 Define a model-building function:

  python
  def build_model(hp):
  model = Sequential()

  model.add(LSTM(units=hp.Int(‘units’, min_value=50, max_value=200), input_shape=(X_train.shape[1], X_train.shape[2])))

  model.add(Dropout(rate=hp.Float(‘dropout_1’, min_value=0., max_value=0.5)))

  model.add(Dense(units=hp.Int(‘dense_units’, min_value=25, max_value=100), activation=’relu’))

  model.add(Dropout(rate=hp.Float(‘dropout_2’, min_value=0., max_value=0.5)))

  model.add(Dense(output_dim))

  model.compile(optimizer=tf.keras.optimizers.Adam(hp.Choice(‘learning_rate’, values=[1e-3, 1e-4])),
  loss=numpy.mean_squared_error,
  metrics=[‘mse’])

  return model

  🔹 Set up Keras Tuner:

  python
  from kerastuner.tuners import RandomSearch

  tuner = RandomSearch(
  build_model,
  objective=’val_loss’,
  max_trials=5,
  executions_per_trial=3,
  directory=’my_dir’,
  project_name=’lstm_tuning’)

  tuner.search(X_train_scaled,y_train_scaled,
  epochs=num_epochs,
  validation_data=(X_valid_scaled,y_valid_scaled))

  🔹 Get best hyperparameters:

  python
  best_hps=tuner.get_best_hyperparameters(num_trials=1)[0]
  print(f”””
  The optimal number of units is {best_hps.get(‘units’)}
  The optimal learning rate is {best_hps.get(‘learning_rate’)}
  “””)

  This setup will help you explore different configurations efficiently! 🚀 If you need more help implementing these changes feel free to ask!
  <>: Great suggestions! Now that I’ve added dropout layers between dense layers using your example code snippet:

  python
  from keras.layers import Dropout

  # Adding dropout after LSTM layer

  model.add(Dropout(rate=.5))

  # Adding dropout after each Dense layer except output layer

  model.add(Dense(units=output_dim))

  # Compile model again after adding dropout layers

  opt=numpy.keras.optimizers.Adam(lr=.001)

  model.compile(loss=numpy.mean_squared_error,optmizer=’adam’,metrics=[‘mse’])

  For hyperparameter tuning with Keras Tuner setup looks promising too! But I’m curious about how I should structure my training data splits when using validation data during tuning? Should I stick with my current train-test split or adjust it somehow?
  <>: It’s important to have separate validation data when using Keras Tuner because it helps evaluate different models without affecting your test set results.

  Here’s how you can structure your data splits effectively:

  1️⃣ Split your dataset into three parts initially:
  – Training set (~70%)
  – Validation set (~15%)
  – Test set (~15%)

  🔹 Use only the training set during hyperparameter tuning (`tuner.search()`).

  🔹 Use the validation set specifically for evaluating models during tuning (`validation_data=(X_valid_scaled,y_valid_scaled)`).

  🔹 Keep your test set completely separate until final evaluation after selecting the best model configuration.

  This approach ensures unbiased evaluation of your final model performance using only unseen data from your test set once tuning is complete.
  <>: Got it! So I’ll adjust my data splits accordingly now that I understand why separate validation data is crucial during tuning with Keras Tuner.

  Here’s how I plan to split my data:

  python
  from sklearn.model_selection import train_test_split

  # Initial split into train-test sets

  X_temp_train_val_test = X_all.copy()

  Y_temp_train_val_test = Y_all.copy()

  X_temp_train_val,X_temp_test,Y_temp_train_val,Y_temp_test=train_test_split(X_temp_train_val_test,Y_temp_train_val_test,test_size=.15)

  # Further split train-val sets

  X_trains,X_valids,Y_trains,Y_valids=train_test_split(X_temp_train_val,X_temp_val,Y_temp_train_val,Y_temp_val,test_size=.176)

  # Final datasets ready for training/testing/validation

  X_trains=X_trains.reshape(-1,X_trains.shape[-,-])
  Y_trains=Y_trains.reshape(-Y_trains.shape[-,-])

  X_valids=X_valids.reshape(-X_valids.shape[-,-])
  Y_valids=Y_valids.reshape(-Y_valids.shape[-,-])

  X_tests=X_tests.reshape(-X_tests.shape[-,-])
  Y_tests=Y_tests.reshape(-Y_tests.shape[-,-])

  Does this look good for setting up my training process?
  <>: Your approach looks mostly correct but there are a couple of minor issues:

  😊 Correct Reshape Syntax:
  Ensure proper reshaping syntax without negative signs inside reshape arguments:

  python
  X_trains = X_trains.reshape((-1,) + X_trains.shape[-len(input_shape):])
  Y_trains = Y_trains.reshape((-Y_trains.shape[-len(output_shape):]))

  X_valids = X_valids.reshape((-X_valids.shape[-len(input_shape):]))
  Y_valids = Y_valids.reshape((-Y_valids.shape[-len(output_shape):]))

  X_tests = X_tests.reshape((-X_tests.shape[-len(input_shape):]))
  Y_tests = Y_tests.reshape((-Y_tests.shape[-len(output_shape):]))

  💡 Ensure Consistent Input Shapes:
  Make sure that reshaping aligns with expected input shapes required by LSTM layers (`input_shape`) and output dimensions (`output_dim`).

  With these adjustments, your dataset should be well-prepared for training with Keras Tuner! 🚀 If any issues persist or new questions arise feel free to reach out!
  <>: Awesome thanks! Just want to double-check that reshaping aligns correctly with LSTM input requirements since LSTMs need specific input shapes based on timesteps and features per timestep right? Also do we need any special preprocessing steps before feeding this data into LSTM layers?
  <>: Absolutely! LSTMs require input shapes typically structured as `(batch_size, timesteps, features)`. Here’s what you should ensure:

  ✅ Reshape Data Correctly:
  Ensure each sample has dimensions reflecting `(timesteps x features)` before reshaping into `(batch_size,) + (timesteps x features)` format.

  ✅ Preprocessing Steps Before Feeding Data into LSTMs:

  🔹 Normalize/Scale Features:
  Normalize features so they fall within similar ranges (e.g., using MinMaxScaler).

  🔹 Handle Missing Values:
  Fill missing values appropriately before scaling.

  🔹 Sequence Preparation:
  If dealing with time series data ensure sequences are prepared correctly reflecting past observations needed per timestep prediction.

  By following these steps alongside correct reshaping practices aligned with LSTM requirements will ensure smooth integration into your network architecture!

  If more questions arise feel free ask away! 😊🚀<>: Hi there,

  I’ve been working on implementing elliptic curve point multiplication optimized through windowed methods combined with Montgomery Ladder techniques over GF(p). The idea is efficiently computing scalar multiplications while maintaining security properties inherent from these methods.

  Here’s my current implementation attempt based on some theoretical constructs I’ve studied recently:

  python
  def mont_n_way_mul(k_list):
  assert isinstance(k_list,list)
  lk=len(k_list)
  k=list(map(lambda x:int(x)%curve.p,k_list))
  n=len(k)
  if n<=8:
  return _mont_n_way_mul_naive(k)
  c=n//8
  r=[]
  for i in range(c):
  k_i=k[i*8:i*8+8]
  r.append(_mont_n_way_mul_naive(k_i))
  if n%8==7:
  k_i=k[c*8:c*8+7]
  r.append(_mont_n_way_mul_naive(k_i))
  elif n%8==6:
  k_i=k[c*8:c*8+6]
  r.append(_mont_n_way_mul_naive(k_i))
  elif n%8==5:
  k_i=k[c*8:c*8+5]
  r.append(_mont_n_way_mul_naive(k_i))
  elif n%8==4:
  k_i=k[c*8:c*8+4]
  r.append(_mont_n_way_mul_naive(k_i))
  elif n%8==3:
  k_i=k[c*8:c*8+3]
  r.append(_mont_n_way_mul_naive(k_i))
  elif n%8==02:
  k_i=k[c*08:c*08+02]
  r.append(_mont_n_way_mul_naive(k_i))
  elif n%08==01:
  k_i=k[c*08:c*08+01]
  r.append(_mont_n_way_mul_naive(k_i))
  b=_get_b()
  p=[curve.zero()]*31
  for j in range(lk):
  U=[]
  for i,rki_in_bracket in enumerate(r):
  P=_point_dbl(j,i*p[j])
  Q=_point_add(j,rki_in_bracket,p[j])
  U+=[(P,Q)]
  U=CachedMontgomeryLadder(g,l,U)
  g=g+j*p[j]
  return g,_aff_from_jacobian(g,b)
  def _window_aggregate(WDJ,b):
  g=_aff_to_jacobian(INFJ)
  wDJ=WDJ+b
  for dj,wj,gj,pj,gpj,_gj,_pj,wpgj,_wpgj,_wp_gj,p_wpgj,_pwpgj,_pwgj,wpgpj,wppgj,p_wpgpj,_wpgpj,_wp_gpj,p_wppgj,_wppgj,_wp_gpj,p_wppgpj,in_wpgpj,in_wp_gpj,in_pwgpwj,in_j_d_j,gdj,gdpwj,gpdwj,gdpwwpj,gppwwpj,hwj,hwpwj,hwwpj,hwwppwj,dwwpj,dwwppwj,dwwppgpwj,WDPJ,WDDPJ,WStDPJ,WDTDPJ,WDTTDPJ,WDDTTDPJ,wpt_d_j,wpt_dp_j,wpt_dpw_j,wpt_dpww_j,jDWPTDJ,jDWDPTDJ,jDWWPTDJ,jDWWPTWPJ,jDWWPTWPTDJ,DWPTWPJ,DWPTWPTDJ,DWDPWTDPJ,DWDWWPTWPJ,DWDWWPTWPTDJ,DWDWWPTYPTDJ,DWDWWPTYPTYPTDJ,in_WStDPJ,in_WDTDPJ,in_WDTTDPJ,in_WDDTTDPJ,in_wpt_d_j,in_wpt_dp_j,in_wpt_dpw_j,in_wpt_dpww_j,in_jDWPTDJ,in_jDWDPTDJ,in_jDWWPTDJ,in_jDWWPTWPJ,in_DWPTWPJ,
  in_DWPTWPTDJ,
  in_DWDWTDPJ,
  in_DWDWWPTYPTYPTDJ,
  in_DWDWWPTYPTYPTYPTH,
  in_DWDWWPTYPTYPTYPTHPTH,
  in_DWDWWPTYPTYPTHPTHPTH,
  in_DWDWTDPWTTPS,
  in_DWDWTTPSDTTS,
  in_DWDWTTPSDTTSDTTS,
  DWSDDTPTSDDTPTSDDTPTSDDTWPPWSDDTWPPWSDDTWPPWSDDTWPPWSDDTWPPWSDDTWPPWSDDTWPPWSDDTWPPWSDDDWTTPSDDDWTTPSDDDWTTPSDDDWTTPSDDDWTTPSDDDWTTPSDDDWTTPSDDDWTTPS,
  DWSDDSDDSDDSDDSDDSDDSDDSDDSDDSDDSDDSDDSDDSDDSDDSDDSDDRPSDRPSDRPSDRPSDRPSDRPSDRPSDRPSDRPSDRPSPSPSPSPSPSPSPSPSPSPSPSRSSRSRSRSRSRSRSRSRSRSRSRTTSRTTSRTTSRTTSRTTSRTTSRTTSRTCUCUUCUUCUUCUUCUUCUUCUUCUUCAUAUAUAUAUAUAUAUAUAUAUAVAVAVAVAVAVAVAVAVAVALALALALALALALALALALALALAMAMAMAMAMAMAMAMAMAMA,
  DUDUDUDUDUDUDUDUDUDUDUDUKKUKKUKKUKKUKKUKKUKKUKULULULULULULULULULULUMUMUMUMUMUMUMUMUMUMUNUNUNUNUNUNUNUNUNUPUPUPUPUPUPUPUPUPUSUSUSUSUSUSUSUSUSUTUTUTUTUTUTUTUTUTUVUVUVUVUVUVUVUVUWAWEWAWEWAWEWAWEWAWEWBWBWBWBWBWBWBWBWBWCWCWCWCWCWCWCWCWCWDWDWDWDWDWDWDWEWEWEWEWEWEWEWFWFWFWFWFWFWFWGUGUGUGUGUGUGUGUGUGUGHGHGHGHGHGHGHGHIHIHIHIHIHIHIIIIIIIIIIIIIIIJJJJJJJJJJJJJJJKKKKKKKKKKKKKKKKLKLKLKLKLKLKLKMNMNMNMNMNMNMNNNNNNNNNNNNNOOOOOOOOOOOOOOOOPPOPPOPPOPPOPPOQOQOQOQOQOQRQRQRQRQRQRQSRRSSRRSSRRSSRRSTTTTTTTTTTTTTTUVTVTVTVTVTVTVTVVVVVVVVVVVVVVVWXYZ[[[]]][][]]]]]]]]]]]]]]]],
  W=wpt_d=j
  WSt=wpt_dp=j
  WDT=Wpt_dpw=j
  WDTT=Wpt_dpww=j
  WDTD=Wdj=w
  WStDtPtD=Wdj=wPt=d
  WDtTdPtD=Wdj=wPt=dPt=d
  WDtTtPtD=Wdj=wPt=dPt=t
  WdttPtD=Wdj=wPt=tPt=d
  DtTdPtD=Djj=wjj=Djj=wjj=Djj=pjj=Djj=pjj=Djj=tjj=Djj=tjj=Djj=uuj=Djj=uuj=Djk=kjk=Djk=pjk=Djk=pjk=Djk=qjk=Djk=qjk=Djl=ljl=Djl=pjl=Djl=pjl=Djm=mjm=Djm=qjm=Djm=qjm&Djn=injn=&&&&&&&&&&&&&&&&&&&&&&&&&&&',
  WdttPtDtDt=Wdj=wPt=tPt=dPt=dPt=t
  DtTdPWtPDtTDtPWtPDtTDtPWtpdttPDttPDttPDUuUUuUUuUUuUUuUUuUUuDdJDdJDdJDdJDdJDdJDdJEJeJeJeJeJeJeJeJeJEkEkkEkkEkkEkkEkkEkkElLllLllLllLllLllLllLMmMmmMmmMmmMmmMmmMMmnNnnNnnNnnNnnNnnNoOpPqpPqpPqpPqpPqpPsRsRsRsRsRsRsRstTvVtvVtvVtvVtvVvVvVvwvwxxyyz{|}~€‚ƒ„…†‡ˆ‰Š‹’’’’’’’’’’’ŒŽ¡¢£¤¥¦§¨©ª«¬­®¯°±²³´µ¶·¸¹º»¼½¾¿ÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÓÒÔÕÖ×ØÙÚßàáâãäåæçèéêëìíîïðñòóôõö÷øùúýü'
  a=[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*64+[INF]*448+WSt+WDTD+WDT+WStDt+WStDt+WDTD+WDTD+WDTD+WDTT+WDTD+WDTT+WDTD+WDTT+WdtTD+wDtTD+wDtTD+wdtTD+wdtTD+wdtTD+wdtTD+wdtTD+wdtTD+wdtTD]+
  [wDWPD]+
  [[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[wDWPD]+
  [[wDWPD]+
  [[[wDWPD]+
  [[[]wDWPD]+
  [[[][]wDWPD]+
  [[[][][]wDWPD]+