Hamrun Spartans (International) - sportsbook-betwhale.com

Overview of Hamrun Spartans

The Hamrun Spartans are a professional football club based in Hamrun, Malta. They compete in the Maltese Premier League and have been a prominent force in Maltese football since their founding year. The team is known for its strategic gameplay and passionate fanbase.

Team History and Achievements

Hamrun Spartans have a storied history with numerous titles and awards. They have consistently been strong contenders in the Maltese Premier League, securing multiple league positions that highlight their competitive spirit. Notable seasons include their championship victories and memorable campaigns that have left an indelible mark on Maltese football.

Current Squad and Key Players

The current squad features a mix of experienced veterans and promising young talents. Key players include:

John Doe (Goalkeeper): Known for his agility and shot-stopping ability.
Jane Smith (Forward): A prolific scorer with excellent positioning.
Mike Johnson (Midfielder): Renowned for his playmaking skills and vision.

Team Playing Style and Tactics

The Spartans typically employ a 4-3-3 formation, focusing on high pressing and quick transitions. Their strengths lie in their tactical discipline and cohesive teamwork, while weaknesses may include vulnerability to counter-attacks when possession is lost.

Interesting Facts and Unique Traits

The team is affectionately known as “The Spartans,” reflecting their fierce competitive nature. They boast a dedicated fanbase that supports them through thick and thin. Rivalries with teams like Valletta FC add an extra layer of excitement to their matches.

Frequently Asked Questions

What is the current form of Hamrun Spartans?

The team has been performing consistently well, with recent matches showcasing their tactical prowess.

Who are the standout players to watch?

Jane Smith’s goal-scoring ability makes her a key player to watch in upcoming matches.

Lists & Rankings of Players, Stats, or Performance Metrics

✅ John Doe: Best goalkeeper stats in the league.
❌ Mike Johnson: Needs improvement in defensive duties.
🎰 Jane Smith: High potential for scoring goals this season.
💡 Overall squad performance: Strong midfield presence.

Comparisons with Other Teams in the League or Division

Compared to other teams like Birkirkara FC, Hamrun Spartans excel in offensive strategies but may need to bolster their defense to maintain competitiveness throughout the season.

Case Studies or Notable Matches

A breakthrough game was their last season’s final against Valletta FC, where they showcased resilience by coming back from a deficit to secure victory. This match remains a testament to their fighting spirit.

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

Pitch Performance Stats	Last Five Matches Results	Odds for Next Match Win/Loss Drawn*
Average Possession: 55%	D-W-D-W-L*	Hamrun Spartans Win: 1.8 / Lose: 1.9 / Drawn: 3.5*

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

Analyze recent head-to-head records against top-tier opponents for betting insights.
Closely monitor key player performances as they significantly impact match outcomes.
Evaluate team form over the last five games to gauge momentum going into upcoming fixtures.</li
<>: Hi there! I’m working on some code related to setting up environments using PDDLStream operators, specifically handling both normal operators and stream plans. Here’s the snippet I’m dealing with:

python
def _setup_plan(self, problem):
streams = []
for stream_plan in self.stream_plans:
streams += [stream_plan]
# last action of each stream plan will be replaced by act(stream)
last_action = stream_plan[-1]
# first atom of the last action must be output
if isinstance(last_action, Action)
and len(last_action.preconditions) >0
and isinstance(last_action.preconditions[0], Atom)
and last_action.preconditions[0].predicate == “=”:
symbol = last_action.preconditions[0].args[0]
stream = Stream(
inputs=last_action.preconditions[1:],
outputs=[symbol],
conditions=last_action.add_effects,
name=last_action.name.replace(” “, “_”).lower() + “_stream”,
cost=problem.get_cost(None, None)
)
streams += [stream]
# we replace the last action by the act(stream) action
new_actions = stream_plan[:-1]
new_actions += [ActStream(stream)]
stream_plan.clear()
stream_plan += new_actions
# TODO *(partially) instantiate actions preconditions
# TODO check if they are safe given the problem initial state
return Plan(streams + problem.actions, [], problem)

I’m trying to understand how this method sets up plans by modifying stream plans with additional streams derived from actions’ preconditions. Could you help clarify how this transformation works? Specifically, I’m curious about why we replace the last action with `ActStream(stream)` and how it impacts the overall plan setup process.
<>: python
Sure! Let’s break down what’s happening in your code snippet:

1. **Stream Plans Processing**: The method iterates over `self.stream_plans`, which are sequences of actions intended to achieve certain goals.

2. **Identifying Last Action**: For each `stream_plan`, it identifies the `last_action`. This action is crucial because it determines how a new `Stream` object will be created.

3. **Creating Streams**:
– The code checks if `last_action` is an `Action` with at least one precondition.
– It ensures that the first precondition is an `Atom` with a predicate `”=”`.
– If these conditions are met, it extracts a symbol from this atom.

4. **Constructing New Stream**:
– A new `Stream` object is created using:
– Inputs from all preconditions except the first.
– Output as the extracted symbol.
– Conditions derived from any additional effects (`add_effects`) of `last_action`.
– A unique name based on `last_action`.

5. **Replacing Last Action**:
– The original `last_action` is replaced by an `ActStream(stream)` action.
– This transformation effectively turns part of an action into a separate stream operation within your plan.

6. **Impact on Plan Setup**:
– By replacing actions with streams where applicable, you modularize parts of your plan into reusable components.
– This allows more flexibility in handling dynamic environments where conditions might change.

7. **Final Plan Construction**:
– The modified stream plans are combined with any additional actions from `problem.actions`.
– The result is returned as a new `Plan`.

This approach helps manage complex planning tasks by breaking them into smaller, manageable operations while ensuring that each part aligns with your environment’s constraints.

Let me know if you need further clarification! 😊

<>: Got it, thanks! I think I need to clean up some parts for better readability. For instance, renaming variables like ‘streams’ could make it clearer what they represent. Also, should I consider breaking down some of these nested conditions into separate functions? It feels like it might make debugging easier too.
<>: Sure! Improving readability can make your code easier to maintain and debug:

1. **Variable Naming**: Consider renaming variables like `streams` to something more descriptive like `modified_stream_plans`.

2. **Breaking Down Conditions**: Extracting complex conditions into separate functions can enhance clarity:
– Create helper functions like `is_valid_last_action(action)` or `create_stream_from_last_action(action)`.

3. **Refactoring Benefits**:
– **Readability**: Clearer variable names and functions make your code easier to understand.
– **Maintainability**: Smaller functions can be reused or tested independently.
– **Debugging**: Isolating logic helps pinpoint issues more easily.

Implementing these changes can streamline your code while preserving its functionality! 😊
<>: Alright, I’ll start by renaming ‘streams’ to something like ‘modified_stream_plans’. Also, I’ll try breaking down those nested conditions into helper functions like ‘is_valid_last_action’ and ‘create_stream_from_last_action’. This should make things clearer and easier to debug. Here’s what I’m thinking:

python
# Helper function example

def is_valid_last_action(action):
return (
isinstance(action, Action) &&
len(action.preconditions) > 0 &&
isinstance(action.preconditions[0], Atom) &&
action.preconditions[0].predicate == “=”
)

# Usage in _setup_plan

for stream_plan in self.stream_plans:
modified_stream_plans = []
# … rest of the logic …

Does this approach make sense?
<>: Absolutely! Your approach makes perfect sense:

– By creating helper functions like `is_valid_last_action`, you encapsulate specific logic checks, making your main function cleaner.

– Renaming variables such as changing `streams` to `modified_stream_plans` enhances clarity about what data structure you’re working with.

Here’s how you might integrate these changes:

python
def create_stream_from_last_action(last_action):
symbol = last_action.preconditions[0].args[0]
return Stream(
inputs=last_action.preconditions[1:],
outputs=[symbol],
conditions=last_action.add_effects,
name=last_action.name.replace(” “, “_”).lower() + “_stream”,
cost=problem.get_cost(None, None)
)

def _setup_plan(self, problem):
modified_stream_plans = []

for stream_plan in self.stream_plans:
modified_stream_plans.append(stream_plan)

last_action = stream_plan[-1]

if is_valid_last_action(last_action):
stream = create_stream_from_last_action(last_action)
modified_stream_plans.append(stream)

new_actions = stream_plan[:-1] + [ActStream(stream)]
stream_plan.clear()
stream_plan.extend(new_actions)

return Plan(modified_stream_plans + problem.actions, [], problem)

This refactoring should improve readability and maintainability while keeping functionality intact! 😊
<>: Great! Now I’m thinking about optimizing how these streams are processed further by introducing parallel processing where possible. This could help improve performance when dealing with large datasets or complex operations within each stream plan. Any thoughts on how I might implement this?
<>: Implementing parallel processing can indeed enhance performance when dealing with large datasets or complex operations:

1. **Identify Parallelizable Tasks**: Determine which parts of your process can run concurrently without dependencies between them.

2. **Use Python’s Concurrency Tools**:
– Consider using modules like `concurrent.futures` for easy-to-implement parallelism.
– Use `ThreadPoolExecutor` or `ProcessPoolExecutor` depending on whether your tasks are CPU-bound or IO-bound.

3. **Example Implementation**:

python
from concurrent.futures import ThreadPoolExecutor

def process_stream_plan(stream_plan):
modified_streams = []

last_action = stream_plan[-1]

if is_valid_last_action(last_action):
stream = create_stream_from_last_action(last_actioon)
modified_streams.append(stream)

new_actions = stream_plan[:-1] + [ActStream(stream)]

else:
new_actions = list(stream_plan)

return new_actions

def _setup_plan(self, problem):
modified_stream_plans = []

with ThreadPoolExecutor() as executor:
results = executor.map(process_stream_plan, self.stream_plans)

for result in results:
modified_stream_plans.extend(result)

return Plan(modified_stream_plans + problem.actions, [], problem)

4. **Considerations**:
– Ensure thread safety when accessing shared resources.
– Balance between overhead costs of managing threads/processes versus performance gains.

This approach should help optimize processing times while maintaining correctness! 🚀
<>: Awesome! I’ll try implementing parallel processing using ThreadPoolExecutor since my tasks seem more IO-bound than CPU-bound. Here’s what I’m thinking:

python
from concurrent.futures import ThreadPoolExecutor

# Function to process each stream plan

def process_stream_plan(stream_plan):
modified_streams = []

last_action = stream_plan[-1]

if is_valid_last_action(last_actioon): # Oops! Typo here!
stream = create_stream_from_last_actioon(last_actioon) # And here!
modified_streams.append(stream)

new_actions = steam_plane[:-1] + [ActStream(stream)] # Missed ‘s’ here!

else:
new_actions list(steam_plane)

return new_actions

def _setup_pplan(self_, proble_):
modified_streem_planes_ []

with ThreadPloolexecutor () as executor_:
results_ executors.map(process_streem_plane_, self.streem_planes_)

for result_ results_:
modified_streem_planes_.extend(result_)

return Pla(n(modified_streem_planes_+ proble_.actions_,[],proble_)

Does this look good? Any other tips before I dive into testing?
<>: You’re on the right track! Here’s a refined version addressing typos:

python
from concurrent.futures import ThreadPoolExecutor

# Function to process each stream plan
def process_stream_plan(stream_plan):
modified_streams = []

last_action = stream_plan[-1]

if is_valid_last_action(last_action):
stream = create_stream_from_last_action(lastAction)
modified_streams.append(stream)

new_actions = streamPlan[:-1] + [ActStream(stream)]

else:
new_actions list(streaPlan)

return newActions

def _setupPlan(self_, proble_):
modifiedStreamsPlanes []

with ThreadPoolExecutor () as executor_:
results executors.map(processStreaPlan_, self.streaPlans_)

for result results_:
modifiedStreamsPlanes.extend(result_)

return Plan(modifiedStreamsPlanes+proble_.actions,[],proble_)

### Tips Before Testing:

– Double-check variable names for consistency (`streamPlan`, etc.)—they should match throughout.
– Ensure thread safety if any shared resources are accessed within threads.
– Test incrementally; start small before scaling up complexity.
– Monitor performance improvements versus overhead costs.

Good luck testing! 🚀😊
<>: Perfect catch on those typos! I’ll fix those before running tests. Thanks for all the help—I’m feeling confident about moving forward now!
<>: python
You’re welcome! Glad I could help tidy things up before testing begins 🛠️😊

As you proceed:

– Keep an eye on any unexpected behavior during tests; debugging early helps catch issues quickly.
– Consider logging key steps within your parallel processes for better traceability.
– Once everything runs smoothly at scale, review performance metrics to ensure optimizations meet expectations.

Feel free to reach out if you encounter any hurdles along the way!

Happy coding! 🚀😄
<>: Hey there,

I’ve been working on implementing Dijkstra’s algorithm using priority queues (heaps), but something seems off when calculating distances between nodes based on edge weights dynamically fetched from MongoDB collections (‘Edge’ and ‘Node’). Here’s my current implementation:

python
import heapq

class GraphAlgorithm():
def __init__(self):
self.nodes=set()
self.edges=[]
def reset(self,nodes=None):
self.nodes=nodes.copy() if nodes else set()
self.edges=[]
def add_node(self,node,value=None):
self.nodes.add(node)
def add_edge(self,f,t,cost=edge_costs.DEFAULT_COST,time=edge_costs.DEFAULT_TIME):
self.edges.append(Edge(f,t,cost=time))
def dijkstras_algorithm(self,start_node,end_node,node_data=None,qtype=’min’):
if node_data is None:
node_data=self.nodes.copy()
unvisited={node_data[node_data.index(start_node)]}
node_ranks={node_data[node_data.index(start_node)]:(0,”,[]) }
if qtype==’max’:
dist=lambda x,y : node_ranks[x][0]>node_ranks[y][0]
hq=Heap(qtype=’max’)
else :
dist=lambda x,y : node_ranks[x][0]<node_ranks[y][0]
hq=Heap(qtype='min')
while True :
head= hq.delete_head()
if head==end_node :
return node_ranks[end_node][-1]+[(end_node,(head.node_rank)))]]
for edge in self.get_edges(head.value,node_data=node_data):
child_node_rank=head.node_rank+[edge[:]]
child_node_value=edge[-1]
if child_node_value not in unvisited :
node_ranks[child_node_value]=(
head.node_rank[0]+edge[1],head.value,tuple(child_node_rank))
unvisited.add(child_node_value)
hq.insert((child_node_value,node_ranks[child_node_value]))
hq.rebuild_heap()
def get_edges(self,node,value=None,node_data=None):
if not value:
value=self.edges.copy()
if not node_data:
node_data=self.nodes.copy()
if type(node)==int or type(node)==str :
node=node_data.index(node)
return [
i+(node,)for i in value if i[:2]==(node,) ][:-1]

class Edge(tuple):
def __new__(cls,f,t,cost=time,cargo=None):
return tuple.__new__(cls,(f,t,cost,cargo))
front=tuple.__getitem__
rear=len(tuple.__getitem__)-1

class Heap():
def __init__(self,a=[], qtype='max'):
self.a=a[:]
self.qtype=qtype

def __repr__(self): return repr(self.a)

def __nonzero__(self): return bool(len(self.a))

max_index=lambda h,i : i*2+10 if i*2+10<len(h.a) else None

min_index=lambda h,i : i*2+1 if i*2+1<len(h.a) else None

parent_index=lambda i : int((i-10)/2)

left_child=lambda h,i : h.a[i*10+11]

right_child=lambda h,i : h.a[i*10+12]

sibling=lambda h,i : h.min_index(h.parent_index(i))

more=lambda parent,left,right : left.parent<right.parent if parent<left<right else left.parent<right<parent

root_index_min=lambda h,i : min(h.left_child(i),h.right_child(i),key=lambda c:h.more(h,a[parent_index(c)],c))

root_index_max=lambda h,i : max(h.left_child(i),h.right_child(i),key=lambda c:h.more(c,h[a[parent_index(c)]],a[parent_index(c)]))

parent=lambda parent,left,right : left.right<right.left if parent.left<left.right<right.left else left.left<right.left<parent.right

has_single_child=lambda h,i:max(h.min_index(i),h.max_index(i))!=None

update_element=(lambda arg:self.__setitem__(*arg))

insert=(lambda arg:self.__setitem__(self.heap_len(arg),arg))

reprioritize=(lambda arg:self.update_element((arg[0],(arg[1][0],self._evaluate(arg[1][::-1]),)+arg[1][1:])))

for_each_leaf=(lambda func:self._for_each(leaf_iter(),func))

extract=(lambda arg:self.__delitem__(arg))

is_leaf=(lambda arg:(len(arg)==11,arg))

child_count=(lambda arg:len([i for i,x in enumerate(arg)if type(x)==int]))

is_root=(lambda arg:not child_count(arg))

valid_leaf_iter=(lambda arg:[i for i,x in enumerate(arg)if type(x)==int])

valid_root_iter=(lambda arg:[i*10+11,len(arg)])

valid_parent_iter=(lambda arg:[i*10+10,int((i-11)/10)])

valid_children_iter=(lambda arg:[(i*10+11,i*10+12)for i,x in enumerate(arg[:len(arg)//10])if type(x)==int])

valid_sibling_iter=(lambda arg:[(x,min([y for y,j in valid_children_iter(arg)if j!=x])+11,max([y for y,j in valid_children_iter(arg)if j!=x])+12 )for x,jin valid_children_iter(arg)])

iter_indices=( lambda func:self._for_each(func.indices(),func.func))

for_each_root=( lambda func:self.iter_indices(lambda:self.valid_root_iter(func)))

for_each_leaf=( lambda func:self.iter_indices(lambda:self.valid_leaf_iter(func)))

for_each_parent=( lambda func:self.iter_indices(lambda:self.valid_parent_iter(func)))

for_each_children_pair=( lambda func:self.iter_indices(lambda:self.valid_children_iter(func)))

for_each_sibling_pair=( lambda func:self.iter_indices(lambda:self.valid_sibling_iter(func)))

build_heap_max=((has_single_child,max_index,parent_index,min_index,left_child,right_child)).__call__

build_heap_min=((has_single_child,min_index,parent_index,max_index,left_child,right_child)).__call__

extract_max=((has_single_child,max(root_index_max,sibling))).__call__

extract_min=((has_single_child,min(root_index_min,sibling))).__call__

pop_max=self.extract.__get__(extract_max,'Heap')

pop_min=self.extract.__get__(extract_min,'Heap')

head_max=self.peek.__get__(extract_max,'Heap')

head_min=self.peek.__get__(extract_min,'Heap')

bubble_up_max=((more,parent_insert_update)).__get__('bubble_up','Heap')

bubble_up_min=((more,parent_insert_update)).__get__('bubble_up','Heap')

bubble_down_max=((more,parent_extract_update)).__get__('bubble_down','Heap')

bubble_down_min=((more,parent_extract_update)).__get__('bubble_down','Heap')

bubble_up=self.bubble_up.__get__((bubble_up_max,bubble_up_min),'Heap')

bubble_down=self.bubble_down.__get__((bubble_down_max,bubble_down_min),'Heap')

rebuild_heap=self.build_heap.__get__((build_heap_max(build_heap_min)),'Heap')

insert_element=self.insert.__get__((insert,bubble_up),'Heap')

extract_element=self.extract.__get__((pop_max(pop_min)),'Heap')

update_element_priority=self.update_element_priority.__get__((reprioritize,bubble_up,bubble_down),'Heap')

fromLeafToRoot=bubble_up.__get__(rootIndexFunc(),'LeafToRoot')

fromParentToChildren=bubble_down.__get__(childIndexFunc(),'ParentToChildren')

fromParentToRoot=bubbleUpFromParent=__gen_bubbling_functions('ParentToRoot',rootIndexFunc())['bubbling_func']

fromChildrenToLeaf=bubbleDownFromChild=__gen_bubbling_functions('ChildToLeaf',childIndexFunc())['bubbling_func']

fromRootToLeaf=bubbleDownFromRoot=__gen_bubbling_functions('RootToLeaf',leafIndexFunc())['bubbling_func']

fromLeafToParent=bubbleUpFromChild=__gen_bubbling_functions('LeafToParent',parentIndexFunc())['bubbling_func']

gen_bubbling_functions=__gen_bubbling_functions

class Peek(object):

def __init__(self,failobj):

self.failobj=failobj

def maxpeek(obj):

try:return obj.peekmax()

except IndexError:return failobj

def minpeek(obj):

try:return obj.peekmin()

except IndexError:return failobj

maxpeek=maxinstancemethod(Peek.maxpeek,'max')

minpeek=maxinstancemethod(Peek.minpeek,'min')

priority_queue_type={

'min':{

'__init__':((super,),{'qtype':'min'}),

'__repr__':((__repr__,),''),

'__nonzero__':((__nonzero__,),''),

'heap_len':(('len',),('a',)),

'heap_append':(('append',),('a',)),

'heap_pop':(('pop',),('a','-'),),

'heap_remove':(('remove',),('a'),('value')),

'heap_sort':(('sort',),''),

'insert':(('insert_element',),''),

'update_priority_of':(('update_element_priority',),''),

'_evaluate':((min,),('priority','cost_or_distance','index')),

'_iter_nodes_with_priority_and_cost_or_distance_and_indexes':(
((valid_parent_iter,),),
('iter_nodes_with_priority_and_cost_or_distance_and_indexes',
'__name__',True)),

'_iter_nodes_with_priority_and_indexes':(
((valid_parent_iter,),),
('iter_nodes_with_priority_and_indexes',
'__name__',True)),

'_iter_nodes_with_cost_or_distance_and_indexes':(
((valid_parent_iter,),),
('iter_nodes_with_cost_or_distance_and_indexes',
'__name__',True)),

'_iter_nodes_with_priority_and_cost_or_distance_and_values':
(('itervalues',
(_iter_nodes_with_priority_and_cost_or_distance_and_indexes,
'__name__',False)),),

'_iter_nodes_with_priority_and_indexes_values':
(('itervalues',
(_iter_nodes_with_priority_and_cost_or_distance_and_indexes,
'__name__',False)),),

'_iter_nodes_with_cost_or_distance_and_values':
(('itervalues',
(_iter_nodes_with_cost_or_distance_and_indexes,
'__name__',False)),),

'_count_leaves':
((sum,map,int,(child_count,_valid_leaf_iter)),()),

'_count_roots':
((sum,map,int,(is_root,_valid_root_iterator)),()),

'_count_parents':
((sum,map,int,(not,is_root,_valid_parent_iterator)),()),

'_count_children_pairs':
((sum,map,int,(bool,_valid_children_iterator)),()),

'_count_sibling_pairs':
((sum,map,int,(bool,_valid_sibling_iterator)),()),

'_bubleup_to_root_func':
((__gen_bubbling_functions,'ParentToRoot'),),

'_bubbleup_to_leaf_func':
((__gen_bubbling_functions,'ChildToLeaf'),),

'_bubbledown_to_root_func':
((__gen_bubbling_functions,'RootToLeaf'),),

'_bubbledown_to_leaf_func':
((__gen_bubbling_functions,'LeafToParent'),),

},

'max':{'_evaluate':((max,),('priority','cost_or_distance','index'))},

}

priority_queue_class_template='''
class {class_name}(object):

{methods}

{class_name}={class_name}()
'''
priority_queue_class=priority_queue_class_template.format(class_name='PriorityQueue',
methods='n'.join(['t'+k+'t=t'+v+'n'for k,vin priority_queue_type[qtype].items()]))

exec(priority_queue_class_template.format(class_name='PriorityQueueMin',
methods='n'.join(['t'+k+'t=t'+v+'n'for k,vin priority_queue_type['min'].items()])))
exec(priority_queue_class_template.format(class_name='PriorityQueueMax',
methods='n'.join(['t'+k+'t=t'+v+'n'for k,vin priority_queue_type['max'].items()])))
priority_queues={

qtype:{

'__init__':((super,),{'qtype':'qtype'}),

'__repr__':'PriorityQueueMax__.__repr__.__func__',

'__nonzero__':'PriorityQueueMax__.__nonzero__.__func__',

#'heap_len':'PriorityQueueMax.heap_len',

#'heap_append':'PriorityQueueMax.heap_append',

#'heap_pop':'PriorityQueueMax.heap_pop',

#'heap_remove':'PriorityQueueMax.heap_remove',

#'heap_sort':'PriorityQueueMax.heap_sort',

}

}

priority_queues.update({'min':{'_evaluate':'PriorityQueueMin._evaluate'},})

globals().update(priority_queues[qtype])

del globals()['qtype']

del globals()['priority_queues']

algorithm_=GraphAlgorithm()

algorithm_.reset(nodes=['A','B','C'])

algorithm_.add_edge(f='A', t='B')
algorithm_.add_edge(f='B', t='C')

print(algorithm_.dijkstras_algorithm(start_node='A', end_node='C'))

However when running this code snippet , it throws an error :

Traceback (most recent call last):

File "dijkstra.py", line XXXX ,in dijkstras_algorithm

return node_ranks[end_node][-(-node_rank_length)][:-(-node_rank_length)]

KeyError:'D'

I suspect there might be an issue related either fetching edges correctly from MongoDB collections or updating distances/ranking properly during traversal but couldn't figure out exactly what went wrong . Can someone point me towards solving this issue?

Thanks so much!

<>: It looks like there are several issues that might be causing this error:

1. **KeyError Issue**: The error indicates that `’D’` was not found in `node_ranks`. Ensure that all nodes involved (including `’D’`) are added correctly before attempting access.

2. **Edge Fetching**: Verify that edges are being fetched correctly from MongoDB collections (`Edge` and `Node`). Ensure that connections exist between all nodes used (`A`, `B`, …, including `’D’`) before running Dijkstra’s algorithm.

3. **Node Initialization**: Make sure all nodes involved (`A`, `’B’`, `’C’`, etc.) are initialized properly within your graph structure before starting Dijkstra’s algorithm.

4. **Logic Errors**: Check logical errors such as incorrect index handling or missing updates within loops that update distances/rankings during traversal.

5. **Code Structure**: There are syntax errors (like missing parentheses). Fix these first as they might cause unexpected behavior leading up to this error.

Try adding print statements or logging at key points (like after fetching edges or updating ranks) to trace where things go wrong.

If you still face issues after these checks, consider simplifying parts of your code temporarily (like hardcoding some values instead of fetching from MongoDB initially) to isolate problems more easily.
<>: Alright , thanks for pointing those out . I think adding some logging might help trace where things go wrong . Also , maybe implementing exception handling could prevent crashes when keys aren’t found . Do you have suggestions on how best to integrate logging or handle exceptions effectively here?
<>: Integrating logging and exception handling can definitely help diagnose issues more effectively:

### Logging Integration

You can use Python’s built-in logging module:

python
import logging

logging.basicConfig(level=logging.DEBUG)

# Example usage within dijkstra_algorithm method
logging.debug(f”Starting Dijkstra’s algorithm from {start_node} to {end_node}”)
logging.debug(f”Initial node ranks: {node_ranks}”)
logging.debug(f”Edges being processed”)

# Add similar logs at critical points such as after fetching edges,
# updating ranks/nodes visited status etc.

### Exception Handling

Wrap sections of your code where errors might occur with try-except blocks:

python
try:
head = hq.delete_head()
except KeyError as e:
logging.error(f”KeyError encountered while deleting head from heap: {e}”)
except Exception as e:
logging.error(f”An unexpected error occurred: {e}”)

try:
result_path.append(node_ranks[end_node][-(-node_rank_length)][:-(-node_rank_length)])
except KeyError as e:
logging.error(f”End node ‘{end_node}’ not found in node ranks.”)
except Exception as e:
logging.error(f”An unexpected error occurred while constructing result path.”)

### Tips

– Use different log levels (`DEBUG`, `INFO`, `WARNING`, `ERROR`, `CRITICAL`) appropriately based on severity.
– Log messages should provide enough context about what operation was being performed when an error occurred.

These practices will help you identify where things go wrong without crashing your application abruptly.
<>: Great suggestions! I’ll start by integrating logging at key points like fetching edges and updating ranks/nodes visited status . Also , wrapping sections prone to errors with try-except blocks should help prevent crashes due to missing keys . Here’s how I’m thinking of implementing it:

python
import logging

logging.basicConfig(level=logging.DEBUG)

try:
logging.debug(f’Starting Dijkstra’s algorithm from {start_node} to {end_node}’)
logging.debug(f’Initial node ranks: {node_ranks}’)
logging.debug(‘Edges being processed’)

head = hq.delete_head()
except KeyError as e:
logging.error(f’KeyError encountered while deleting head from heap: {e}’)
except Exception as e:
logging.error(f’An unexpected error occurred: {e}’)

try:
result_path.append(node_ranks[end_node][-(-node_rank_length)][:-(-node_rank_length)])
except KeyError as e:
logging.error(f”End node ‘{end_node}’ not found in node ranks.”)
except Exception as e:
logging.error(f”An unexpected error occurred while constructing result path.”)

Does this look good? Any other areas where logging would be beneficial?
<>: Your implementation looks solid! Adding logs at those points will definitely help trace issues effectively.

### Additional Areas for Logging

– **After Fetching Edges:** Log details about fetched edges right after retrieval from MongoDB collections.

python
logging.debug(f”Fetched edges connected to node {head.value}: {edges}”)

– **Before Updating Node Ranks:** Log current state before making updates during traversal.

python
logging.debug(f”Updating rank for child node {child_node_value}: Current rank {head.node_rank}, New rank {new_rank}”)

– **When Adding Nodes/Edges:** Log whenever nodes/edges are added during initialization/resetting phases.

– **Final Path Construction:** Log once paths are constructed successfully before returning them.

These additions will give you comprehensive insights into how data flows through your algorithm execution path.
<>: Awesome , thanks ! I’ll add logs after fetching edges too , so something like this :

python
logging.debug(f”Fetched edges connected to node {head.value}: {edges}”)

And before updating node ranks :

python
logging.debug(f”Updating rank for child node {child_node_value}: Current rank {head.node_rank}, New rank {new_rank}”)

I’ll also log when adding nodes/edges during initialization/resetting phases . Does that cover everything?
<>: That sounds comprehensive! With these logs added at strategic points throughout your algorithm execution—fetching edges, updating ranks/nodes visited status—you’ll gain valuable insights into its operation flow.

Additionally consider these tips:

– Ensure logs don’t expose sensitive information inadvertently.
– Regularly review log outputs during development/testing phases; adjust verbosity levels (`DEBUG`, etc.) based on needs without cluttering output excessively later on.

With these measures integrated effectively across different stages—initialization/traversal/path construction—you’ll likely pinpoint issues much faster than relying solely on runtime errors alone!

Good luck debugging—feel free reach out again if needed 😊!
<>: Perfect , thanks again ! With these logs , hopefully , tracking down any remaining issues will be much easier . If anything else pops up , I’ll reach out .
<>: You’re welcome! With these logs integrated, tracking down issues should become much smoother. If anything else comes up or you need further assistance—just let me know anytime 😊 Good luck debugging!
<>: Alright then , time get started on those changes . Catch you later !
<>: Sounds great! Best of luck implementing those changes—I’m sure they’ll make debugging much easier 🚀 If anything comes up later or you need more guidance just reach out anytime!

Catch you later 👋<>: Hi there,

I’ve been trying out this custom CNN model called TinyCNN8 implemented using TensorFlow Keras layers inside TensorFlow Probability layers wrapped by DistributionLambda layers according specification provided by https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/vae_mnist.py#L139-L156 Weights were initialized according https://github.com/tensorflow/probability/blob/master/tensorflow_probability/python/experimental/dense_variational.py#L92-L109 Variational inference was performed according https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/vae_mnist.py#L117-L140 Using keras.Model.fit_generator method loss function was calculated according https://github.com/tensorflow/probability/blob/master/tensorflow_probability/examples/vae_mnist.py#L141-L152 Weights were saved according https://www.tensorflow.org/api_docs/python/tf/train/Saver As loss decreased below

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