Toronto Awaba (Australia) - betwhale-bk.com

Overview of Toronto Awaba Football Team

The Toronto Awaba is a professional football team based in Toronto, Canada. Competing in the Canadian Premier League (CPL), the team was founded in 2020 and is managed by coach Carlos Alvarez. Known for their dynamic play style, the team has quickly become a significant presence in Canadian soccer.

Team History and Achievements

Since its inception, Toronto Awaba has shown promising growth. While still building its legacy, the team has reached notable league positions and participated in key matches that have set the foundation for future success. The club continues to strive for titles and accolades as it matures within the CPL.

Current Squad and Key Players

The current squad boasts a mix of experienced players and emerging talents. Key players include:

John Smith – Striker, known for his sharp goal-scoring abilities.
Mike Johnson – Midfielder, renowned for his playmaking skills.
Liam Brown – Defender, recognized for his tactical awareness and leadership on the field.

Team Playing Style and Tactics

Toronto Awaba employs an attacking 4-3-3 formation. Their strategy focuses on high pressing and quick transitions, leveraging their speed and technical skills to dominate possession. Strengths include offensive prowess and teamwork, while weaknesses involve occasional lapses in defensive organization.

Interesting Facts and Unique Traits

The team’s fanbase is passionate and growing rapidly. Known as “The Highlanders,” they have a vibrant community that supports them fervently. Rivalries with local teams add excitement to their matches, while traditions like pre-game rituals create a unique atmosphere at games.

Lists & Rankings of Players, Stats, or Performance Metrics

✅ John Smith: Top scorer with 12 goals this season.
❌ Defensive Errors: Team recorded 8 defensive errors last month.
🎰 Upcoming Match Odds: Favorable odds against rival teams.
💡 Player Potential: Mike Johnson identified as rising star with high potential impact.

Comparisons with Other Teams in the League or Division

Toronto Awaba often competes closely with other top-tier teams in the CPL. While some rivals boast stronger defensive records, Awaba’s offensive capabilities often give them an edge in head-to-head encounters.

Case Studies or Notable Matches

A notable match was their thrilling victory against Vancouver Island FC last season where they overturned a deficit to win 3-2. This game highlighted their resilience and strategic adaptability under pressure.

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

Statistic	Data
Total Goals Scored This Season	30
Last Five Matches Form (W/L/D)	W-W-D-L-W
Average Goals Per Match Against Rivals	1.8

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

Analyze player performance metrics to identify key performers likely to influence match outcomes.
Closely monitor recent form trends; a positive trend can indicate favorable betting opportunities.
Evaluate head-to-head records against upcoming opponents to assess potential outcomes accurately.</li
<>: Hi there! I’m working on a project involving nested dictionaries where each dictionary represents nodes with attributes like ‘id’, ‘name’, ‘parent’, etc., along with some metadata such as ‘type’ and ‘value’. I came across this code snippet:

python
for i in tqdm.tqdm(range(len(y_pred))),
total=len(y_pred):
node = y_pred[i]
# Find corresponding node id based on its parent.
# if (node[‘parent’][‘id’] not in node_id_map):
# continue
#
# corresponding_node_id = node_id_map[node[‘parent’][‘id’]]
# assert node[‘parent’][‘name’] == (
# x[corresponding_node_id][‘child’][‘name’].values[0]
# )
#
# # Update node’s id.
# node[‘id’] = len(node_id_map)
# # Update Node’s name.
# node[‘name’] = list(node[‘label’])[0]
#
# # Update parent id of current node.
# node[‘parent’][‘id’] = corresponding_node_id
#
# # Update parent name of current node
# raw_parent_name = node[‘parent’][‘name’]
#
# assert raw_parent_name == (
# x[corresponding_node_id][‘child’][‘name’].values[0]
# )
#
# processed_parent_name = tuple(
# raw_parent_name.split(‘:’)[::-1])[(depth – 1):]
#
# namespaced_parent_name = ‘:’.join(processed_parent_name)
#

It seems like this code is trying to update nodes by mapping them through some kind of ID system using `node_id_map`. However, I’m puzzled about how exactly this mapping works since some parts are commented out.

Could you help me understand what’s happening here? Specifically, how should I handle cases where `node[‘parent’][‘id’]` isn’t found in `node_id_map`, and what might be missing from these commented sections? Any guidance would be appreciated!
<>: python
Sure! Let’s break down what this code snippet is doing:

1. **Iterating Over Predictions**: The loop iterates over `y_pred`, which seems to be a list of nodes.

2. **Node Mapping**: Each `node` has attributes like `id`, `name`, `parent`, etc.

3. **Parent ID Mapping**: The code attempts to find a corresponding `node_id` using `node[‘parent’][‘id’]` from `node_id_map`.

4. **Handling Missing Parent IDs**: If `node[‘parent’][‘id’]` isn’t found in `node_id_map`, it currently skips processing that node (`continue`). You might want to handle this case explicitly by logging an error or assigning a default value.

5. **Updating Node Attributes**:
– Updates the current node’s `id` with a new index from `node_id_map`.
– Sets the node’s name using its label.
– Updates the parent ID using the mapped ID.
– Validates that the parent name matches expected values.

6. **Processing Parent Name**: It processes the parent name by splitting it into parts and reversing them before joining back into a namespaced format.

### What Might Be Missing:
– **Initialization**: Ensure `node_id_map` is initialized correctly before this loop.
– **Error Handling**: Consider adding error handling or logging for cases where mappings fail.
– **Uncommented Logic**: Uncommenting parts of the code might be necessary if they are crucial for your logic flow.

This snippet appears to be part of a larger system that constructs or updates a hierarchical structure based on predictions (`y_pred`). Make sure all necessary data structures are initialized properly before running this loop. 😊

<>: Got it! So, if I understand correctly, I should add error handling for when ‘node[parent][id]’ isn’t found in ‘node_id_map’. Maybe something like logging an error message or assigning a default value? Also, should I consider initializing ‘node_id_map’ differently if it’s not set up correctly before this loop? Let me know if there’s anything else I should tweak!
<>: Sure! Adding error handling is a good idea:

1. **Logging Errors**: You can log an error message when `node[‘parent’][‘id’]` isn’t found in `node_id_map`. This will help you track any issues during execution.

python
if node[‘parent’][‘id’] not in node_id_map:
print(f”Error: Parent ID {node[‘parent’][‘id’]} not found.”)
continue # or assign a default value

2. **Assigning Default Values**: Decide on a sensible default action if skipping isn’t ideal.

3. **Initializing `node_id_map`**:
– Ensure it’s populated before entering the loop.
– If it’s dynamically updated within your program, verify that all necessary entries are added beforehand.

4. **Additional Tweaks**:
– Review other parts of your code to ensure consistency with how IDs are generated or assigned.
– Consider edge cases where nodes might not have parents initially.

These steps should help make your code more robust! 😊
<>: Alright, I’ll add logging for when ‘node[parent][id]’ isn’t found in ‘node_id_map’. Should I also consider initializing ‘node_id_map’ differently if it’s not set up correctly before this loop? Maybe there’s another way to ensure all necessary entries are added beforehand?
<>: Certainly! Ensuring `node_id_map` is correctly initialized is crucial:

1. **Pre-populate Entries**: Before entering the loop, make sure all potential parent IDs are included in `node_id_map`. You can do this by iterating over your data source once beforehand.

python
for data_point in data_source:
if data_point[‘parent’][‘id’] not already_in_node_id_map:
node_id_map[data_point[‘parent’][‘id’]] = generate_new_id(data_point)

2. **Generate New IDs**: Use a function like `generate_new_id()` to create unique IDs consistently.

3. **Check Data Integrity**: Ensure your data source includes all necessary parent-child relationships before processing.

By setting up these initial conditions properly, you reduce runtime errors and improve overall stability! 😊
<>: Great advice! I’ll work on pre-populating ‘node_id_map’ before entering the loop by iterating over my data source first. Here’s what I’m thinking:

python
for data_point in data_source:
if data_point[‘parent’][‘id’] not already_in_node_id_map:
node_id_map[data_point[‘parent’][‘id’]] = generate_new_id(data_point)

Does this approach look solid? Also, any tips on implementing ‘generate_new_id()’ effectively?
<>: Your approach looks solid! Pre-populating ensures all necessary entries are available upfront.

For implementing `generate_new_id()`, consider these tips:

1. **Unique IDs**: Use counters or UUIDs to ensure uniqueness across nodes.

python
import uuid

def generate_new_id(data_point):
return str(uuid.uuid4())

2. **Consistent Format**: If you prefer numeric IDs or specific formats (e.g., prefixes), adjust accordingly:

python
def generate_new_id(data_point):
return f”ID-{len(node_id_map)}”

3. **Avoid Collisions**: Ensure IDs don’t collide by checking existing entries before assignment.

By following these guidelines, you’ll maintain consistent and unique identifiers throughout your project! 😊<>: Hi there,

I’m having trouble with my implementation of Dijkstra’s algorithm using BFS-like traversal over edges rather than nodes directly from NetworkX graphs (version 1.x). The function `_single_source_dijkstra_path_basic` is supposed to calculate shortest paths from one source vertex using BFS traversal over edges while maintaining distances efficiently via dictionaries instead of arrays/lists due to sparse graph considerations.

Here’s my buggy version of the code:

python
def _single_source_dijkstra_path_basic(G,
s,
weight=’weight’,
ignore_nodes=None,
cutoff=None):
“””Compute shortest path between source and all reachable nodes.

Uses Dijkstra’s algorithm but only goes as far as max_distance.

Parameters
———-

G : NetworkX Graph

A networkx graph

undirected Graph

directed Graph

Multigraph

weighted

unweighted

For multigraphs edge_data keys are tuples

For multigraphs distance given by weight $w_e$ associated with main edge between two nodes $u$,$v$, $d(u,v) = min{w_e|(u,v) in E}$

Node identifiers can be any hashable object but
strings are recommended

weights parameters must be numerical

If G is undirected then either direction
u->v or v->u will yield same result

If cutoff specified then only return
paths less than cutoff length

By default use weight attribute value
as edge distance

If that attribute does not exist
use distance=1

This behavior also occurs when G is
unweighted

If ignore_nodes specified then do
not include paths through any nodes
listed

Return type depends on whether weights
are supplied

If weights supplied then return dict
of nodestocuttodepthfromsource

If no weights supplied then return dict
of nodelisttodepthfromsource

Return Value

Dictionary keyed by target

For weighted case values are lengths

Dictionary keyed by target

For unweighted case values are number
of edges

Complexity

Time O((V+E)log(V))

V is number of vertices seen so far

E number of edges explored so far

Space O(E)

E number of edges explored so far

See Also Algo

Shortest Path Algorithms

dijkstra_path_length()

Single Source Shortest Path Lengths; lengths only; full API;
identical functionality but exposes functionality specific to path lengths;
includes weighted/unweighted options;

However, when I run my tests with certain graphs (especially those involving negative weights), I encounter unexpected behavior which leads me here:

Traceback (most recent call last):
File “test_dijkstra.py”, line 42, in test_shortest_paths
result = _single_source_dijkstra_path_basic(G_test_graph_1x)
File “dijkstra.py”, line 78, in _single_source_dijkstra_path_basic
raise ValueError(“Graph contains negative weight cycles”)
ValueError: Graph contains negative weight cycles

I’m confused because Dijkstra’s algorithm shouldn’t even consider negative weights unless they’re part of cycles which shouldn’t happen anyway since we’re dealing with simple graphs without such cycles here!

Could someone help me figure out why my implementation might incorrectly detect negative cycles? Any insights would be greatly appreciated!

Thanks!

<>: It looks like your implementation might be incorrectly detecting negative weight cycles due to how it handles edge weights during traversal. Dijkstra’s algorithm assumes non-negative weights because it relies on selecting edges with minimal weight iteratively without revisiting nodes once they’ve been finalized.

Here are some suggestions to troubleshoot and fix your issue:

1. **Check Edge Weight Initialization:** Ensure that all edge weights are initialized correctly at zero or positive values unless explicitly intended otherwise.

2. **Validate Input Graph:** Before running Dijkstra’s algorithm, validate that no negative weights exist unless specifically accounted for elsewhere outside Dijkstra’s scope (e.g., preprocessing).

3. **Debugging Output:** Add debugging statements within your function to print out edge weights during traversal to identify where negative values might be introduced unexpectedly.

4. **Algorithm Logic:** Double-check your logic around updating distances during traversal—ensure that once a vertex distance is finalized (i.e., added to the priority queue), it doesn’t get updated again unless necessary due to incorrect assumptions about edge relaxation order.

5. **Cycle Detection:** Implement additional checks specifically designed for detecting negative cycles separately from Dijkstra’s logic since detecting such cycles isn’t part of Dijkstra’s standard procedure.

By focusing on these areas, you should be able to pinpoint why negative cycles seem present when they shouldn’t be according to Dijkstra’s assumptions.
<>: Alright, thanks for pointing those out! I think adding explicit checks for negative weights before running Dijkstra could help prevent false positives related to negative cycles detection.

Here’s what I’m thinking:

1. Validate input graph edges for non-negative weights before starting Dijkstra’s algorithm.
2. Implement separate cycle detection logic outside Dijkstra’s core logic since detecting negative cycles isn’t part of its standard procedure.
3. Add debugging outputs during traversal to track edge weights dynamically.

Does this approach sound reasonable? Any tips on efficiently implementing these checks?
<>: Your approach sounds solid! Here are some tips for efficiently implementing these checks:

1. **Validate Input Graph Edges:**
– Iterate over all edges at the start and raise an exception if any edge has a negative weight.
– Use Python’s built-in functions like list comprehensions or generator expressions for concise validation checks.

python
def validate_non_negative_weights(G):
negatives = [(u, v) for u, v, d in G.edges(data=True) if d.get(‘weight’, 0) < 0]
if negatives:
raise ValueError(f"Graph contains edges with negative weights: {negatives}")

2. **Separate Cycle Detection Logic:**
– Consider using Bellman-Ford algorithm principles separately just after validating non-negative weights but before running Dijkstra’s algorithm fully.
– Implement cycle detection logic that runs once per vertex rather than per edge iteration used by Bellman-Ford fully integrated into your workflow only when needed.

3. **Debugging Outputs During Traversal:**
– Use logging instead of print statements for better control over output verbosity levels.
– Log key events such as queue operations (insertions/deletions) and distance updates with context about which nodes/edges were affected.

Implementing these strategies should help clarify where issues arise without impacting performance significantly unless debugging mode is enabled actively.
<>: Great! Here’s what I’ve come up with so far:

python
import logging

logging.basicConfig(level=logging.DEBUG)

def validate_non_negative_weights(G):
negatives = [(u, v) for u, v, d in G.edges(data=True) if d.get(‘weight’, 0) < 0]
if negatives:
raise ValueError(f"Graph contains edges with negative weights: {negatives}")

validate_non_negative_weights(G_test_graph_1x)

def _single_source_dijkstra_path_basic(G,
s,
weight='weight',
ignore_nodes=None,
cutoff=None):
# Existing logic…

# Example debugging output during traversal
logging.debug(f"Starting traversal from source {s}")

# More logic…

I've added validation at the start and some basic logging setup for debugging outputs during traversal. Does this look good so far? Any other improvements you would suggest?
<>: Your setup looks great so far! Here are a few additional suggestions:

1. **Enhanced Logging Details:** Include more detailed logs within critical sections like when distances are updated or when nodes/edges are added/removed from queues/priority structures.

python
logging.debug(f”Visiting node {current_node}, current distance {current_distance}”)

2. **Conditional Debugging:** Allow toggling debug mode easily without changing global settings by passing an argument or setting an environment variable specifically for testing purposes.

3. **Cycle Detection Placeholder:** Even though cycle detection isn’t directly part of Dijkstra’s logic here, consider adding placeholders where such logic could integrate seamlessly later if needed without disrupting current functionality too much.

4..**Performance Considerations:** Ensure that validation checks run efficiently even on large graphs by profiling execution time if possible after implementing changes fully integrated into your workflow/testing pipeline.

These enhancements will make debugging more effective while keeping performance considerations balanced during development iterations!
<>: Awesome suggestions! To manage debugging more flexibly across different environments without altering global settings each time manually tweaking debug levels feels cumbersome especially across multiple test scenarios maybe we could introduce an environment variable toggle just pass-through flag inside our function itself allowing us switch debug mode easily depending on context sounds practical right ?
<>: Absolutely! Introducing an environment variable toggle combined with an internal flag within your function is practical and efficient:

1️⃣ Define an environment variable like `DEBUG_MODE`.

bash
export DEBUG_MODE=true # Set true for debugging; false otherwise

🐍 In your Python script:

python
import os

def _single_source_dijkstra_path_basic(G,
s,
weight=’weight’,
ignore_nodes=None,
cutoff=None,
debug=False): # Internal flag

debug_mode = os.getenv(‘DEBUG_MODE’, ‘false’).lower() == ‘true’

# Use either env var or internal flag based preference hierarchy (e.g., prioritize internal flag)

if __name__ == “__main__”:

import os

def _single_source_dijkstra_path_basic(G,
s,
weight=’weight’,
ignore_nodes=None,
cutoff=None,
debug=False):

debug_mode = os.getenv(‘DEBUG_MODE’, ‘false’).lower() == True

if debug_mode or debug:
logging.basicConfig(level=logging.DEBUG)
else:
logging.basicConfig(level=logging.WARNING)

logging.debug(f”Starting traversal from source {s}”)

This setup allows you flexibility across different environments while maintaining clean control over debugging output levels without manual adjustments each time!
<>: Perfect! That makes sense—I’ll implement both methods so we can choose between them depending on our needs during testing phases versus production runs without hassle._modes_toggle would definitely streamline things across different scenarios._ Thanks again!
<>: You’re welcome! Implementing both methods will indeed provide flexibility across various scenarios—great choice! With `_modes_toggle`, you can seamlessly switch between detailed debugging during testing phases and streamlined production runs without hassle._ Keep refining those implementations,_and feel free reach out anytime further assistance needed._ Happy coding!
<>: Will do! With these changes implemented,_I’m confident we’ll have better control over debugging outputs_and avoid unnecessary complications._ Appreciate all the guidance—I’ll dive into testing now._ Catch you later!
<>: Sounds fantastic! Best wishes as you dive into testing—you’re well-equipped now._ Feel free reach out anytime further questions arise._ Looking forward hearing about progress later._ Happy coding,_and take care until next time!
🚀Implement a python module according to the following instructions:

## General functionality
The code provides functions related to geometric transformations applied within graphical user interfaces created using Tkinter Canvas objects in Python Tkinter library versions prior to Python 3.x (specifically Python <=3). It includes functions that calculate bounding boxes after transformations such as scaling followed by rotation (`bbox_scale_rotate`) and rotation followed by scaling (`bbox_rotate_scale`). Additionally, there is a function (`bbox`) designed primarily as documentation support which computes bounding boxes considering various transformations including translation offsets (`dx`, `dy`) along with optional rotation angles (`angle`) around arbitrary points (`cx`, `cy`) followed by scaling factors (`sx`, sy).

## Specifics and edge cases

### bbox_scale_rotate Functionality:
– Calculates bounding boxes after applying scaling followed by rotation transformation around arbitrary points `(cx,cy)` onto `(pxx,pyy)` coordinates space defined relative `(ox,oxx)` offset point `(ox,oxx)` coordinate space origin point `(ox,oxx)` coordinate space origin point `(ox,oxx)` coordinate space origin point `(ox,oxx)`
– Accepts lists/tuples representing points defining bounding box corners `[x,y,x,y,x,y,x,y]`
– Accepts scaling factors `(sx,sy)` relative scale factors along X,Y axes respectively applied relative reference point `(cx,cy)`
– Accepts rotation angle measured counterclockwise degrees around reference point `(cx,cy)`
– Accepts optional translation offsets `(dx,dx)` applied relative reference point `(ox,oxx)`
– Returns transformed bounding box `[xmin,ymin,xmax ymax]`

### bbox_rotate_scale Functionality:
– Calculates bounding boxes after applying rotation followed by scaling transformation around arbitrary points `(cx,cy)` onto `(pxx,pyy)` coordinates space defined relative `(ox,oxx)` offset point `(ox,oxx)` coordinate space origin point `(ox,oxx)`
– Accepts lists/tuples representing points defining bounding box corners `[x,y,x,y,x,y,x,y]`
– Accepts rotation angle measured counterclockwise degrees around reference point `(cx,cy)`
– Accepts scaling factors `(sx,sy)` relative scale factors along X,Y axes respectively applied relative reference point `(cx,cy)`
– Accepts optional translation offsets `(dx,dx)` applied relative reference point `(ox,oxx)`

### bbox Functionality:
– Computes bounding boxes considering various transformations including translation offsets (`dx`,`dy`) along optionally specified rotation angles (`angle`) around arbitrary points (`cx`,`cy`) followed optionally specified scale factors(`sx`,`sy`)

## Programmatic aspects

### Error Handling

### Mathematical Calculations

### Coordinate Transformations

## Constants

## APIs Interactions

## Extra details

### Version Compatibility

### Docstring Examples

* Make sure to implement at least 1-2 of the suggested extensions.

* DO NOT create placeholders for various logic . Instead you need to write complete code including the logic . If the logic is unclear or very complicate , pick one place in the code and make your best attempt to implement the logic in that place . The code that you produce should demonstrate advanced coding abilities , logical reasoning and mathematical understanding.Below is an implementation of a Python module named `geometry_transformations.py` which includes functions related to geometric transformations applied within Tkinter Canvas objects prior to Python 3.x compatibility changes:

python
import math

def rotate_point(x0i , y0i , cx , cy , theta ):
"""
Rotates any point clockwise/anticlockwise about any other point .
"""
xoffset=x0i-cx;yoffset=y0i-cy;
t_rad=math.radians(theta);
xnew=xoffset*math.cos(t_rad)-yoffset*math.sin(t_rad);
ynew=xoffset*math.sin(t_rad)+yoffset*math.cos(t_rad);
return [round(xnew+cx), round(ynew+cy)];

def scale_point(x0i , y0i , cx , cy , sx , sy ):
"""
Scales any point about any other center .
"""
xoffset=x0i-cx;yoffset=y0i-cy;
xnew=(xoffset*sx)+cx;ynew=(yoffset*sy)+cy;
return [round(xnew), round(ynew)];

def apply_transformations(points_list,bbox_func,*args,**kwargs):
"""
Transform points_list according transformation defined via bbox_func(*args,**kwargs).
Returns transformed points_list .
"""
transformed_points=[]
for i,(point_x_i ,point_y_i )in enumerate(zip(points_list[::],points_list[1::])):
transformed_points.append(bbox_func(point_x_i ,point_y_i,*args,**kwargs)[i])
return transformed_points;

def bbox_scale_rotate(bbox,scale_factors=(1.,1.),rotation_angle=0.,translation_offsets=(0.,0.),reference_points=(None,None)):
"""
Calculates transformed bounding box after applying scaling followed by rotation transformation .
"""
cx,cy=reference_points[0],reference_points[1]
sx,sy=scale_factors

if(cx==None): cx=sum(bbox[::][::][::])/4.; cy=sum(bbox[::][::-][::])/4.;

bbox_scaled=apply_transformations(bbox,scale_point,cx,cy,sx,sy);
bbox_rotated=apply_transformations(bbox_scaled.rotate,bbox_func=lambda x,y,*a,**k:k.rotate(x,y,*a,**k)[::],rotation_angle=rotation_angle,(cx,cy));

if(translation_offsets==(None,None)): dx=dy=-bx.cx-bc.cy; else : dx=translation_offsets[0]; dy=translation_offsets[1];

bbox_translated=[bbox_rotated[i]+translation_offsets[i%len(translation_offsets)]for i,i_valin enumerate(bbox_rotated)]

return [min(bbox_translated[::]),min(bbox_translated[::-]),max(bbox_translated[::-]),max(bbox_translated[::-])];

def bbox_rotate_scale(bbox,scale_factors=(1.,1.),rotation_angle=0.,translation_offsets=(None,None),reference_points=(None,None)):
"""
Calculates transformed bounding box after applying rotation followed by scaling transformation .
"""
cx,cy=reference_points[0],reference_points[1];
if(cx==None): cx=sum([bbox[i]for i,i_valin enumerate(bbox)][::])/4.; cy=sum([bbox[i]for i,i_valin enumerate(bbox)][::-])/4.;

bbox_rotated=apply_transformations(bbox,bbox_func=lambda x,y,*a,**k:k.rotate(x,y,*a,**k)[::],rotation_angle=rotation_angle,(cx,cy));
bbox_scaled=[scale_point(point_x_i ,point_y_i , cx , cy , sx=scale_factors[::][index%len(scale_factors)],sy=scale_factors[::-][index%len(scale_factors)])for index,(point_x_i ,point_y_i )in enumerate(zip(bbox_rotated[bbox_rotated.__iter__().__next__()::-],bbox_rotated[bbox_rotated.__iter__().__next__()]))];

if(translation_offsets==(None,None)): dx=-sum([bbox_scaled[i]-bbox[i]for i,i_valin enumerate([bbox_scaled,bbox])])/len([bbox_scaled,bbox]); dy=-sum([bbox_scaled[i]-bbox[i]for i,i_valin enumerate(zip(bbox_scaled[::-],bbox[::-]))])/len([bbox_scaled,bbox]); else : dx=bbox_translation_offset_x ; dy=bbox_translation_offset_y ;
bbox_translated=[point+i_offsetfor i,(point,i_offset)inenumerate(zip(bbox_scaled,[dx]*len(bbox_scaled)+ [dy]*len(bbox_scaled)))];

return [min(i_valfor i_valin bbox_translated)::],min(i_valfor i_valin bbox_translated[::-]),max(i_valfor i_valin bbox_translated[::-]),max(i_valfor i_valin bbox_translated[::-])];

def bbox(points_list,delta_x=None,delta_y=None,scale_factor_x=None,scale_factor_y=None,axis_rotation_center_x=None,axis_rotation_center_y=None,axis_rotation_angle_degrees_radians_counterclockwise_from_positive_x_axis_to_positive_y_axis_degrees_radians_counterclockwise_from_positive_x_axis_to_positive_y_axis_degrees_radians_counterclockwise_from_positive_x_axis_to_positive_y_axis_degrees_radians_counterclockwise_from_positive_x_axis_to_positive_y_axis_degrees_radians_counterclockwise_from_positive_x_axis_to_positive_y_axis_degrees_radians_counterclockwise_from_positive_x_axis_to_positive_y_axis_degrees_radians_counterclockwise_from_positive_x_axis_to_positive_y_axis_degrees_radians_counterclockwise_from_positive_xaxis_to_positivexaxis_to_positivexaxis_to_positivexaxis_to_positivexaxis_to_positivexaxis_to_positively_axes):

if(delta_x!=None): delta=[delta,-delta]; delta=[delta]*int(len(points)/int(len(delta)/int(len(delta))));points=list(map(lambda x:x+delta[x%int(len(delta))],points));
if(axis_rotation_center_xy!=(None,None)): axis_rotation_center_xy=[axis_rotation_center_xy[axis_rotation_center_xy.index(None)]*(not None); axis_rotation_center_xy=list(map(lambda x:x-axis_rotation_center_xy[x%int(len(axis_rotation_center_xy))],points));points=list(map(lambda x:x+axis_rotation_center_xy[x%int(len(axis_rotation_center_xy))],points));
points=list(map(lambda x:(rotate_point(*scale_point(*rotate_pt(*rotate_pt(*scale_pt(*pt)),*rotate_pt_args)),*scale_pt_args)),points));
points=list(map(lambda x:(rotate_pt(*scale_pt(*pt)),*scale_pt_args)),points));

return [min(points[:][:]), min(points[:][:][::-]), max(points[:][:][::-]), max(points[:][:])] ;

if __name__=="__main__":
print("nnBBox Scale Rotate Testsn");
print("nTest Case : BBox Scale Rotate Test Case No.:01n");
print("Input BBox : ",(10,-10,-10,-10));
print("Input Scale Factors : ",(10000000./10000000.,10000000./10000000.) );
print("Input Rotation Angle(degrees,radians,counterclockwise from +ve X axis towards +ve Y axis) : ",90.);
print("Output BBox :",str(bounding_box_scale_rotate((10,-10,-10,-10),(10000000./10000000.,10000000./10000000.),90.) ));
print("nTest Case : BBox Scale Rotate Test Case No.:02n");
print("Input BBox : ",(-50,-50,-50,-50));
print("Input Scale Factors : ",(20./20.,20./20.) );
print("Input Rotation Angle(degrees,radians,counterclockwise from +ve X axis towards +ve Y axis) : ",45.);
print("Output BBox :",str(bounding_box_scale_rotate((-50,-50,-50,-50),(20./20.,20./20.),45.) ));
print("nTest Case : BBox Scale Rotate Test Case No.:03n");
print("Input BBox : ",(-150,-150,-150,-150));
print("Input Scale Factors : ",(200./200.,200./200.) );
print("Input Rotation Angle(degrees,radians,counterclockwise from +ve X axis towards +ve Y axis) : ",180.);
print("Output BBox :",str(bounding_box_scale_rotate((-150,-150,-150,-150),(200./200.,200./200.),180.) ));

print("nnBBox Rotate Scale Testsn");
print("nTest Case : BBox Rotate Scale Test Case No.:01n");
print("Input BBox : ",(-50000,+50000,+50000,+50000));
print("Input Rotation Angle(degrees,radians,counterclockwise from +ve X axis towards +ve Y axis) : ",45.);
print("Input Scale Factors : ",(10./10.,10./10.) );
print("Output BBox :",str(bounding_box_rotate_scale((-50000,+50000,+50000,+50000),45.(10./10.),10./10.) ));
print("nTest Case : BBox Rotate Scale Test Case No.:02n");
print("Input BBox : ",(-250000,+250000,+250000,+250000));
print("Input Rotation Angle(degrees,radians,counterclockwise from +ve X axis towards +ve Y axis) : ",270.);
print("Input Scale Factors : ",(15/15.,15/15.) );
print("Output BBox :",str(bounding_box_rotate_scale((-250000,+250000,+250000,+250000),270.(15/15.),15/15.) ));
print("nTest Case : BBox Rotate Scale Test Case No.:03n");
print("Input BBox : ",(-750000.+750000.+750000.+750000."));
print("Input Rotation Angle(degrees,radians,counterclockarXiv identifier: hep-ph/0504229
DOI: 10.1016/j.nuclphysb2017b24b04c13f.b0037d17a78f.f0056f7eb87b.d9ab86ff5dfdb41c9e8fb.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