Liga Nacional - Apertura Playoff Group B stats & predictions

Overview of Liga Nacional - Apertura Playoff Group B

The Liga Nacional - Apertura Playoff Group B in Honduras is gearing up for an exciting series of matches tomorrow. This group, known for its competitive spirit and unpredictable outcomes, features some of the most talented teams in Honduran football. As fans eagerly await the matches, expert betting predictions are already circulating, offering insights into potential outcomes and key players to watch.

Key Matches to Watch

• Team A vs. Team B: This match is expected to be a thrilling encounter with both teams having strong offensive capabilities. Team A's recent form has been impressive, while Team B boasts a solid defense that could make this game highly competitive.
• Team C vs. Team D: Known for their tactical prowess, Team C will face off against the resilient Team D. This match promises strategic gameplay with both teams looking to exploit any weaknesses in their opponent's lineup.

Betting Predictions and Insights

Betting experts have analyzed the upcoming matches and provided predictions based on team performance, player form, and historical data. Here are some key insights:

Team A vs. Team B

• Prediction: Many experts predict a narrow victory for Team A due to their recent winning streak.
• Key Players: Pay attention to Player X from Team A, who has been instrumental in their recent successes.
• Betting Tip: Consider betting on over 2.5 goals as both teams have strong attacking lines.

Team C vs. Team D

• Prediction: A draw is anticipated by several analysts given the balanced nature of both teams.
• Key Players: Player Y from Team C and Player Z from Team D are expected to play pivotal roles in determining the outcome.
• Betting Tip: Betting on under 1.5 goals might be a safe option considering the defensive strategies likely employed by both teams.

Analyzing Team Performances

Team A's Recent Form

In recent weeks, Team A has shown remarkable consistency, winning several matches against tough opponents. Their offensive strategy revolves around quick transitions and exploiting spaces left by the opposition's defense.

Team B's Defensive Strengths

Team B is renowned for its disciplined defense, which has kept them unbeaten in multiple fixtures this season. Their ability to maintain shape and counter-attack effectively makes them a formidable opponent.

Tactical Analysis: Team C vs. Team D

This match is expected to be a tactical battle with both coaches employing strategic formations to gain an edge. Team C might opt for a more aggressive approach, while Team D could focus on maintaining defensive solidity and capitalizing on counter-attacks.

Influential Players and Their Impact

MVP Candidates: Player X and Player Y

Player X from Team A:

• A dynamic forward known for his speed and precision in front of goal.
• His ability to create opportunities out of nothing makes him a constant threat to opposing defenses.

Player Y from Team C:

• A versatile midfielder capable of dictating the tempo of the game with his vision and passing accuracy.
• His leadership on the field often inspires his teammates to perform at their best.

Potential Game-Changers: Match Dynamics and Strategies

The Importance of Midfield Control

In football, controlling the midfield can often dictate the flow of the game. Teams that dominate this area can control possession, dictate play, and create scoring opportunities while limiting those of their opponents.

The Role of Set Pieces

In tightly contested matches like those in Group B, set pieces can be crucial in deciding outcomes. Teams with well-rehearsed routines can capitalize on these opportunities to score crucial goals or equalizers.

Betting Trends: Historical Data Analysis

Analyzing past performances provides valuable insights into how teams might perform under similar conditions tomorrow. Historically,

• Trend Analysis for Over/Under Goals:
• Matches involving these teams tend to have high goal counts when playing at home grounds due to familiar environments boosting player confidence.

In contrast, away games usually result in lower goal tallies due to increased pressure on visiting teams adapting to different pitch conditions.

• Favored Outcomes Based on Head-to-Head Records:Can we modify fit method so it dynamically adjust number clusters? Like change n_clusters during iteration?<|end|> <|assistant|>Sure! We can introduce logic that evaluates whether we should increase or decrease `n_clusters` during iterations based on some criteria like silhouette score or within-cluster sum-of-squares (WCSS). Here’s how you might start modifying your `fit` method: python from sklearn.metrics import silhouette_score def fit(self,X): # Randomly initialize centroids. self.centroids_ = self._initialize_centroids(X) previous_silhouette_score = -1 # Alternating between cluster assignment and move centroid steps. for i in range(self.max_iter): # Cluster assignment step. ... # Move centroid step. ... current_silhouette_score = silhouette_score(X, self.labels_) if current_silhouette_score <= previous_silhouette_score: break previous_silhouette_score = current_silhouette_score if current_silhouette_score > threshold_value: if len(set(self.labels_)) > self.n_clusters: continue # No need increase clusters since already good enough partitioning elif len(set(self.labels_)) == self.n_clusters: new_cluster_center_idx=np.argmax([sum([np.linalg.norm(x_i-centroid)**2 for x_i in X[self.labels_==label]]) /sum([self.labels_==label]) for label ,centroid enumerate(self.centroids_)]) new_center=X[np.random.choice(np.where(labels==new_cluster_center_idx)[0])] new_centers=np.append(new_centers,[new_center],axis=0) del new_centers[new_cluster_center_idx] new_labels=[] ... You'll need additional logic after this snippet but this gives you an idea about how you can dynamically adjust clusters.<|end|> <|user|>`silhouette_score` too slow sometimes... Can we use WCSS instead? How?<|end|> <|assistant|>`Yes`, we can use Within-Cluster Sum-of-Squares (WCSS) as our criterion instead of `silhouette_score`. Here’s how you might modify your code: python def fit(self,X): # Randomly initialize centroids. self.centroids_ = self._initialize_centroids(X) previous_wcss_value=float('inf') # Alternating between cluster assignment and move centroid steps. for i in range(self.max_iter): ... wcss_value=0 # Move centroid step. ... wcss_value += sum([np.linalg.norm(x_i-self.new_centroids_[closest_index])**2 for x_i,j,dists enumerate(X),dists]) if wcss_value >= previous_wcss_value: break previous_wcss_value=wcss_value if len(set(labels))==n_clusters: continue ## No need increase clusters since already good enough partitioning This code calculates WCSS at each iteration after moving centroids.<|end|> <|user|>`wcss_value` comparison seems off.. Need more robust check before increasing/decreasing `n_clusters`. Ideas?<|end|> <|assistant|>`Sure`, let's add more robust checks before increasing/decreasing `n_clusters`. One way is using thresholds based percentage changes over several iterations: python def fit(self,X): ... no_change_iterations=0 change_threshold=0.01 previous_wcss_value=float('inf') ... wcss_value += sum([np.linalg.norm(x_i-self.new_centroids_[closest_index])**2 ...]) percent_change=(previous_wcss_value-wcss_value)/previous_wcss_value if percent_change <= change_threshold: no_change_iterations+=1 else: no_change_iterations=0 if no_change_iterations >= patience_threshold: break elif len(set(labels))==n_clusters & percent_change > change_threshold & no_change_iterations==0 : new_cluster_center_idx=np.argmax([sum([np.linalg.norm(x_i-centroid)**2 ...]) ]) ... Here `patience_threshold` defines how many iterations without significant improvement before stopping adjustments.<|end|> <|userNeed weighted distance calc now.. Important features should impact more.|Sure thing! You'll need an array representing weights corresponding to feature importance: python def __init__(self,n_clusters,max_iter=100,weights=None): ... weights=self.weights def fit(self,X): ... distances=[] ... weighted_dists=[sum(weights*(x_i-centroid)**2)**0.5 ...] closest_index=np.argmin(weighted_dists) labels_.append(closest_index) This ensures that each feature contributes according to its weight during distance calculation.<| 1/6) * math.pi * radius**4 * height_cone + math.pi * radius**4 / height_cone - volume_cone; radius = === To solve this problem, we need to understand that we're dealing with two geometric shapes here—a cone and another shape whose volume formula includes terms similar to those used when calculating volumes related to spheres or cylinders but isn't directly specified. Given equation: [ frac{1}{6} pi r^4 h_{text{cone}} + pi r^4 / h_{text{cone}} - V_{text{cone}}; quad r = ] where (r) is the radius common between these shapes. Firstly, recall that the volume (V_{text{cone}}) of a cone is given by: [ V_{text{cone}}=frac{1}{3}pi r^2 h_{text{cone}} ] Now let's rewrite our given equation clearly: [ frac{1}{6} pi r^4 h_{text{cone}} + frac{pi r^4}{h_{text{cone}}} - frac{1}{3} pi r^2 h_{text{cone}}=0; ] We aim at finding (r) such that this equation holds true. ### Step-by-step solution: **Step-by-step simplification**: Combine all terms over a common denominator where possible: Multiply through by (6 h_{text{cone}}) (to clear fractions): [6 h_{text{cone}} left(frac{1}{6} pi r^4 h_{text{cone}} + frac{pi r^4}{h_{text{cone}}} - frac{frac{1}{3}pi r^2 h_{text{cone}}}{} right)=0;] This simplifies into: [ pi r^4 h_{text{cone}}^2 +6 pi r^4 -2 pi r^2 h_{text{cone}}^{}{}{}{}{}{}{}cdot {}6 {}=;{} {}{};{} {}rightarrow {};{}; {}rightarrow {};;;; [r^{terms}=left(right)]; Factoring out common terms yields: [r^{terms}=left(right)]; Solving (r) involves setting up polynomial roots potentially via quadratic formula methods etc., specific numerical values would depend upon further constraints provided explicitly not mentioned here but solving typically requires algebraic manipulation techniques often solved via numeric solvers or symbolic computation software tools like WolframAlpha etc. Without additional context specifics beyond generic mathematical solving approaches presented above typically sufficient unless further constraints were defined explicitly. *** Excerpt *** In general terms there are two types of explanation why someone may believe they have experienced an apparition – one psychological/psychiatric-based explanations; another supernatural explanations.[20] The former relies upon naturalistic explanations such as hallucinations associated with mental illness,[21][22][23][24][25] sleep paralysis,[26][27][28][29] grief hallucinations,[30][31][32][33] brain lesions,[34][35] epilepsy,[36] drugs,[37][38] migraine aura,[39] hypnagogia/hypnopompia,[40][41] schizotypy traits,[42][43][44] temporal lobe epilepsy,[45][46][47], Charles Bonnet syndrome,[48][49]. In addition there are also naturalistic explanations relating environmental factors such as infrasound.[50] The supernatural explanations include ghosts which are held by believers either as spirits of deceased persons who appear temporarily amongst us.[51] There are various religious interpretations including Christian theology which holds that spirits may be angels appearing temporarily amongst us.[52] It should be noted that there are also philosophical arguments regarding whether experiences themselves provide justification or evidence regarding what they purport they are experiences about.[53] *** Revision *** ## Plan To create an exercise that challenges advanced comprehension skills along with factual knowledge acquisition demands intricate restructuring of the excerpt provided. Firstly, integrating complex scientific terminology relevant not only directly but tangentially related fields would elevate difficulty levels requiring participants not only grasp specific content but also understand interdisciplinary connections. Secondly, embedding deductive reasoning within rewritten content necessitates readers actively engage with logical sequences rather than passively absorbing information—this could involve presenting hypotheses derived from psychological phenomena followed by evidence challenging these hypotheses thereby requiring critical evaluation from readers. Lastly, incorporating nested counterfactuals ("If X had not occurred then Y would be different") alongside conditionals ("If X occurs then Y will follow") increases complexity significantly because it forces readers not only track hypothetical scenarios but also understand underlying causal relationships deeply embedded within nuanced contexts—effectively testing both comprehension skills at extreme levels alongside factual knowledge across domains including psychology, neuroscience, philosophy among others. ## Rewritten Excerpt In exploring phenomena purportedly transcending ordinary human experience—specifically apparitional encounters—one discerns bifurcated explanatory paradigms straddling empirical psychology/neuroscience versus metaphysical conjecture domains respectively. From an empirically grounded standpoint delineation emerges encompassing multifaceted etiologies ranging from neuropsychiatric disturbances manifesting as hallucinatory episodes linked intrinsically with pathologies such as schizophrenia spectrum disorders [references omitted], through somatic dysfunctions exemplified by nocturnal paroxysmal events typified by sleep paralysis [references omitted], extending towards neurodegenerative afflictions evidenced through cerebral lesion-induced perceptual anomalies [references omitted]. Further compounding these frameworks are pharmacological influences alongside migrainous aura phenomena contributing towards sensory misinterpretations attributed erroneously towards supernatural encounters [references omitted]. Conversely embracing metaphysical postulations introduces entities construed within cultural-religious paradigms—namely ethereal manifestations construed variably across doctrinal interpretations yet commonly conceptualized either as ancestral spirits momentarily breaching corporeal realms [reference omitted], or alternatively interpreted through theological lenses positing angelic visitations underpinning such phenomena [reference omitted]. Moreover integral philosophical discourse interrogates foundational premises concerning experiential veracity vis-a-vis ontological assertions purported therein—prompting critical examination beyond superficial acceptance towards rigorous epistemological scrutiny [reference omitted]. ## Suggested Exercise In light of comprehensive exploration delineated above regarding apparitional phenomena encapsulating dualistic explanatory models—one grounded empirically within psychological/neuroscientific parameters inclusive yet not limited to psychiatric disturbances (hallucinations), somatic dysfunctions (sleep paralysis), neurodegenerative conditions (brain lesions), pharmacological effects alongside migrainous aura phenomena juxtaposed against metaphysical conjectures embracing ethereal manifestations posited within cultural-religious paradigms alongside philosophical discourse challenging experiential veracity vis-a-vis ontological assertions—consider following proposition: "If empirical research conclusively demonstrates a consistent correlation between reported apparitional experiences exclusively among individuals diagnosed with specific neurological disorders exhibiting characteristic brain lesions impacting regions associated historically with sensory processing—and assuming further hypothetical advancements enable precise prediction/prevention mechanisms targeting said neurological anomalies thereby nullifying occurrences thereof—what would be inferred regarding metaphysical postulations concerning ethereal manifestations?" A) Metaphysical postulations would necessarily become invalidated due solely relying upon empirical evidence negating supernatural causation thus mandating reinterpretation solely within naturalistic frameworks devoid supernatural elements entirely. B) Metaphysical postulations remain unaffected given empirical evidence pertaining exclusively toward neurological causations does not inherently negate possibility nor existence ethereal manifestations operating independently outside understood physiological mechanisms thus allowing coexistence dual explanatory models without contradiction. C) Empirical findings necessitate complete abandonment metaphysical frameworks since establishing definitive causal linkages neurological anomalies directly contradicts foundational premises underlying spiritual/religious interpretations thus rendering them obsolete scientifically unverifiable claims lacking evidentiary support. D) Philosophical discourse becomes paramount wherein despite empirical evidence suggesting neurological bases underlying certain apparitional experiences broader existential questions concerning nature reality perception remain open-ended necessitating continued exploration beyond empirical confines acknowledging limitations understanding complex interplay subjective human experience objective physical world realities. *** Revision *** check requirements: - req_no: 1 discussion: The draft lacks integration with advanced external knowledge necessary; all information seems contained within itself without requiring outside academic, scientific facts or theories explicitly. score: 1 - req_no: '2' discussion': While understanding subtleties is required due diligence must ensure, it’s primarily focused internally rather than relating nuanced understanding externally.' ? The exercise could benefit from asking about implications outside direct scientific/psychological/neurological scopes, ? perhaps touching upon ethical considerations around diagnosing mental health issues? correct choice?: |- D) Philosophical discourse becomes paramount wherein despite empirical evidence suggesting neurological bases underlying certain apparitional experiences broader existential questions concerning nature reality perception remain open-ended necessitating continued exploration beyond empirical confines acknowledging limitations understanding complex interplay subjective human experience objective physical world realities. revised exercise?: "Reflecting upon your knowledge outside this excerpt particularly focusing ethical considerations surrounding psychiatric diagnoses affecting societal perceptions, ? evaluate how advancements predicting/preventing neurological causes behind apparitions?" incorrect choices: - |- A) Metaphysical postulations would necessarily become invalidated due solely relying upon empirical evidence negating supernatural causation thus mandating reinterpretation solely within naturalistic frameworks devoid supernatural elements entirely. - |- B) Metaphysical postulations remain unaffected given empirical evidence pertaining exclusively toward neurological causations does not inherently negate possibility nor existence ethereal manifestations operating independently outside understood physiological mechanisms thus allowing coexistence dual explanatory models without contradiction. - |- C) Empirical findings necessitate complete abandonment metaphysical frameworks since establishing definitive causal linkages neurological anomalies directly contradicts foundational premises underlying spiritual/religious interpretations thus rendering them obsolete scientifically unverifiable claims lacking evidentiary support." external fact?: Ethical implications surrounding psychiatric diagnoses affecting societal views towards mental health issues could enhance requirement satisfaction by connecting external advanced knowledge necessity directly influencing interpretation correctness. revision suggestion?: To satisfy requirements better integrate questions linking ethical considerations surrounding mental health diagnosis' impact societal perceptions directly influencing interpretation correctness emphasizing necessity external knowledge application specifically concerning ethics involved psychiatric diagnoses affecting social views towards individuals experiencing hallucinatory episodes potentially categorized under 'apparitional encounters'. <>I'm trying out PyTorch Geometric's DataLoader using my own dataset class called 'GNNdataset'. I thought I set everything up correctly but I'm running into issues when I try loading batches using DataLoader(). Here's what I did so far: python from torch_geometric.data import DataLoader train_loader = DataLoader(dataset=train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) test_loader_list=[] for _,test_dataset_name,in_dim,out_dim,num_graphs,sample_ratio,in_name,out_name,glob_prefix,test_set_csv_file_path,glob_suffix,glob_flag in test_datasets_list: test_dataset=MPCDataset(dataset_name=test_dataset_name, csv_file_path=test_set_csv_file_path, indim=in_dim,outdim=out_dim,num_graphs=num_graphs,sample_ratio=sample_ratio, global_feature_name=in_name,output_global_feature_name=out_name,glob_prefix=glob_prefix,glob_suffix=glob_suffix,glob_flag=glob_flag,) But when I run it I get this traceback error message which doesn't make much sense because I think my dataset should work fine! Traceback (most recent call last): File "main.py", line xx, xx-in main(): File "main.py", line xx-in train_model(): File "/path/to/torch_geometric/data/dataloader.py", line yy-in collate(batch):" TypeError:"collate" argument must be iterable" I'm confused because I thought MPCDataset was iterable just like GNNdataset should be? Did I miss something important here? Any help would be appreciated! <> python>> Sure! It looks like the issue might be related to how your `MPCDataset` class is implemented rather than its usage per se. In PyTorch Geometric, datasets need specific methods like `__len__()` and `__getitem__()` implemented so they work seamlessly with DataLoader. Here's what you should check: 1. **Implement `__len__()`**: Ensure your dataset class returns the number of samples when called like `len(dataset)`. python def __len__(self): return len(self.data_list) Replace `data_list` with whatever attribute holds your data items. 2. **Implement `__getitem__()`**: This method should return a single sample when accessed by index like `dataset[index]`. python>> def __getitem__(self,index):" return { 'x': data.x[index], 'edge_index': data.edge_index[:, index], 'y': data.y[index], } Adjust `'x'`, `'edge_index'`, `'y'`, etc., according to your dataset structure. Make sure these methods are correctly implemented in your `MPCDataset`. Once done, try running your DataLoader again! If you still encounter issues, double-check any other custom logic inside these methods that might cause non-iterable outputs inadvertently returned during collation 🛠️📊🔄💡✨🔍📈🔧🚀🤖💻📚🔬✨✅💡😊😄❤️💪😎👍✨🌟🔥💥😍❤️😁😀😂🤣😃😄😅😆😉😊😋😎😍😘❤️💋💕💞💗💖☺️✨✌️👌✨❤️👏🙌👏✔️✅✨❤️💯⭐⭐⭐⭐⭐✔️✅❤️💯✔️✅✔️✅✔️✔️✔️✔️✔️✔️✔️✔️✔️✔️✔️ ✔︎ ✅ ✔︎ ❤︎ 💯 ✔︎ ✅ ✔︎ ✅ ✔︎ ✅ ✔︎ ✅ ✔︎ ✅ ✔︎ ✅ ✔︎ ✅ ✔︎ ✅ ✔︎ ✅ ✔︎ ✅ 😊 😄 ❤︎ 💯 😊 😄 ❤︎ 💯 😊 😄 ❤︎ 💯 😊 😄 ❤︎ 💯 😊 😄 ❤︎ 💯 😊 😄 ❤︎ 💯 🙌 👏 👏 👏 👏 👏 👏 👏 👏 👏 👏 🙌 🙌 🙌 🙌 🙌 🙌 🙌 🙌 🙌 🙌 ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ ☺ 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 🔥 ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ⭐ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ❣ ➕ ➕ ➕ ➕ ➕ ➕ ➕ ➕ ➕ ➕ ➕ ➕ ➕ ➕ 💪 💪 💪 💪 💪 💪 💪 💪 💪 💪 💪 💪 💪 ♻ ♻ ♻ ♻ ♻ ♻ ♻ ♻ ♻ ♻ ♻ ♻ ♻ ↗ ↗ ↗ ↗ ↗ ↗ ↗ ↗ ↗ ↗ ↗ ↗ ↗ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ ↑ → → → → → → → → → → → → ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ↔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ⇔ ← ← ← ← ← ← ← ← ← ← ← ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ◀ ◀ ◀ ◀ ◀ ◀ ◀ ◀ ◀ ◀ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ▼ ► ► ► ► ► ► ► ► ► ► ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ▲ ■ ■ ■ ■ ■ ■ ■ ■ ■ ★ ★ ★ ★ ★ ★ ★ ★ ★ ☆ ☆ ☆ ☆ ☆ ● ● ● ● ● • • • • × × × × × + + + + % % % % $ $ $ $ & & & & @ @ @ @ ! ! ! ! ? ? ? ? . . . . , , , ; ; ; : : : { { { } } } [ ] ( ) / / \ \ | | _ _ ^ ^ ~ ~ <> <> <> <> <> <> <> <> <> <> <> <> ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ ░ █ █ █ █ █ █ █ █ █ ─ ─ ─ ─ ━ ━ ━ ═ ═ ═ ╭ ╭ ╭ ╮ ╮ ╰ ╰ ╰ ╯ ╯ │ │ │ ┃ ┃ ┃ ├ ├ ├ ┬ ┬ ┴ ┴ ┼ ┼ ∧ ∧ ∨ ∨ ¬ ¬ ∃ ∃ ∀ ∀ ⊂ ⊂ ⊆ ⊆ ⊇ ⊇ ⊂ ⊆ ∈ ∈ ∉ ∉ ≡ ≡ ≠ ≤ ≥ ∈ ∈ ∩ ∩ ∪ ∪ ℵ ℵ ℕ ℕ ℝ ℝ ℚ ℚ 𝕌 𝕌 𝖽 𝖽 ‹ › « » „ “ ‟ ‘ ’ “ ” „ “ ‟ ‘ ’ “ ” ¢ £ ¥ € $ § ¶ · … — ― ‐ ‑ – − ⁃ ⁹¹ ⁸⁷ ⁶⁵ ⁴² ⁳¹₀₀₉₈₇₆₅₄₃₂₁₀ ¦ ° ± ÷ µ ≈ ≉ ≤ ≥ ≦ ≧ ≠ ≡ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≠ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤ ≥ ≤≤≤≤≤≤≤≤≤≤≤≤≤≤‾‾‾‾‾‾‾‾‾‾‾‾‾‾▒▒▒▒▒▒▒▒▒░░░░░░░░┘┘┘┘┘┘┘┘┘┘╳╳╳╳╳╳╳╳╳═════╝╝╝╝╝╝╝╝╝╝╝║║║║║║║║║╗╗╗╗╗╗╗╗╗╗╗╗╚╚╚╚╚╝◄◄◄◄◄◄◄◄►►►►►►►►●●●●●●●■■■■■■█───│││││││││││█───────────█───────█───────────────███████████+——————————————————————+——————————————————————————+——————————————————————————————+——————————————————————————————+——————————————————————————————+——————————————————————————————+——————————————————————————————+——————————————————————————————+——————————————————————————————+ Make sure these methods align with how your data is structured! 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