Latvia U19: Squad, Stats & Achievements in UEFA Youth League

Overview of Latvia U19 Football Team

The Latvia U19 football team, representing the Baltic nation in youth football competitions, competes in the UEFA European Under-19 Championship. This team is part of Latvia’s commitment to nurturing young talent through structured training and competitive play. The team plays in a 4-3-3 formation under the guidance of their current coach.

Team History and Achievements

The Latvia U19 squad has been building its reputation since its establishment. While not yet having secured major titles, the team has shown promising performances in various seasons. Their journey includes several commendable league positions and participation in key tournaments that have highlighted their potential.

Current Squad and Key Players

The current squad features a mix of emerging talents with key players like Arnis Zvejnieks (forward), known for his agility and goal-scoring prowess, and Edgars Gauračs (midfielder), recognized for his strategic playmaking abilities. These players form the backbone of the team’s offensive strategies.

Team Playing Style and Tactics

Latvia U19 employs a dynamic 4-3-3 formation, focusing on an attacking style with emphasis on quick transitions from defense to offense. Their strengths lie in their fast-paced gameplay and tactical flexibility, although they sometimes struggle with maintaining possession under pressure.

Interesting Facts and Unique Traits

Nicknamed “The Baltic Warriors,” the Latvian U19 team enjoys a passionate fanbase that supports them fervently across matches. They have notable rivalries with neighboring countries’ teams, adding an extra layer of excitement to their fixtures.

Lists & Rankings of Players, Stats, or Performance Metrics

• Top Scorer: Arnis Zvejnieks – ✅ High goal conversion rate
• MVP: Edgars Gauračs – 💡 Strategic playmaker
• Defensive Strength: Riga Youth Academy graduates – ❌ Occasional lapses in marking

Comparisons with Other Teams in the League or Division

In comparison to other teams in their division, Latvia U19 often showcases a more aggressive playing style but sometimes lacks consistency against top-tier opponents. Their adaptability allows them to compete closely with stronger teams.

Case Studies or Notable Matches

A memorable match was against Estonia U19 where Latvia demonstrated resilience by overturning a deficit to secure victory through late goals. This match is often cited as a breakthrough performance showcasing their potential.

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

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

To analyze Latvia U19 effectively for betting purposes:

• Analyze recent form trends: Check last five matches for consistency.
• Evaluate key player availability: Monitor injuries or suspensions impacting star players.
• Leverage head-to-head data: Study past encounters with rivals for patterns.

Frequently Asked Questions About Betting on Latvia U19 Football Team

What are some key statistics to consider when betting on Latvia U19?

Focusing on recent form, top scorers’ performance, defensive records, and head-to-head results will provide insights into potential outcomes.

How does Latvia U19 perform against top teams?

The team often struggles against stronger opponents due to inconsistency but can pull off surprising victories through tactical adjustments.

Could you give advice on betting odds?

Analyze market trends and consider how external factors like player injuries might affect odds before placing bets.

Quotes or Expert Opinions about the Team

“Latvia U19’s youthful energy combined with strategic depth makes them unpredictable opponents,” says noted sports analyst Jānis Vītoliņš.

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

• Potential Strengths:
• Solid attacking options – ✅ High scoring ability

• Vibrant teamwork leading to dynamic plays – 💡 Tactical versatility

Potential Weaknesses:</l[0]: #!/usr/bin/env python

[1]: """
[2]: Module implementing Otsu thresholding.
[3]: """

[4]: __author__ = 'Clement Galland'

[5]: import numpy as np
[6]: from scipy.stats import mode

[7]: def otsu_threshold(image):
[8]: """Computes Otsu threshold.

[9]: Parameters
[10]: ———-
[11]: image : ndarray
[12]: Image data.

[13]: Returns
[14]: ——-
[15]: threshold : float
[16]: Otsu threshold value.

[17]: References
[18]: ———-

[19]: .. [1] http://en.wikipedia.org/wiki/Otsu's_Method

[1]

See Also

skimage.filters.threshold_otsu

Thresholding Algorithms

***** Tag Data *****
ID: 1
description: Implementation of Otsu's thresholding algorithm which involves computing
histogram-based thresholds.
start line: 7
end line: 16
dependencies:
– type: Function
name: otsu_threshold
start line: 7
end line: 16
context description: The function `otsu_threshold` computes an optimal threshold value
that separates foreground from background pixels based on histogram analysis.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 5
self contained: Y

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

1. **Adaptive Histogram Binning**: Modify `otsu_threshold` to dynamically adjust bin sizes based on image characteristics instead of using fixed bins.

2. **Multichannel Support**: Extend `otsu_threshold` to handle multi-channel images (e.g., RGB) by computing separate thresholds for each channel.

3. **Threshold Optimization Using Machine Learning**: Integrate a machine learning model that learns optimal thresholds based on labeled training data instead of purely relying on histogram analysis.

4. **Noise Robustness**: Implement noise reduction techniques within `otsu_threshold` before performing histogram analysis to improve robustness against noisy images.

5. **GPU Acceleration**: Modify `otsu_threshold` to leverage GPU acceleration using libraries such as CuPy or TensorFlow for faster computation on large images.

## Conversation

I have this function `otsu_threshold` which computes an optimal threshold value separating foreground from background pixels based on histogram analysis. I need it to handle multi-channel images like RGB separately rather than just single-channel grayscale images.

To extend `otsu_threshold` for multi-channel images such as RGB, we’ll need to compute separate thresholds for each channel independently and then combine these thresholds appropriately.

Here’s how you could modify your function:

python
import numpy as np

def otsu_threshold(image):
“””
Computes Otsu threshold.
Parameters:
image : ndarray
Image data.
Returns:
thresholds : list[float]
List containing Otsu threshold values for each channel.
“””
if len(image.shape) == 3:
thresholds = []
for channel in range(image.shape[-1]):
hist = np.histogram(image[:, :, channel], bins=256)
# Compute Otsu threshold using histogram data here…
# This is where you would implement your existing logic.
# For demonstration purposes let’s assume we get ‘threshold’ after calculation.
threshold = compute_otsu(hist)
thresholds.append(threshold)
return thresholds

else:
hist = np.histogram(image.flatten(), bins=256)
# Compute Otsu threshold using histogram data here…
# Again assuming we get ‘threshold’ after calculation.
return compute_otsu(hist)

def compute_otsu(hist):
# Placeholder function where actual Otsu calculation logic goes.
pass

This code checks if the input image is multi-channel by looking at its shape. If it is multi-channel (i.e., has three dimensions), it processes each channel separately by iterating over them.

Would you like me to help implement the core logic inside `compute_otsu`, or do you have any specific requirements?
<>: Hi there! I’m working with this Python function called `apply_T6`, which seems to be implementing some complex syllabification rules related to consonant sequences within words ending in certain suffixes like ‘-mble’, ‘-uple’, etc. Here’s the code snippet:

python
def apply_T6(word):
”’If a VVV-sequence contains a schwa (-T5), it must H-follow T0.”’
# split consonants and vowels: ‘balloon’ -> [‘b’, ‘a’, ‘ll’, ‘oo’, ‘n’]
WORD = word.split_sounds()
INDEX = WORD.rfind(‘VVV’)
if INDEX == -1:
return word.word
+ ‘**no T6**’
else:
if len(WORD[Index + 1:]) > diphthongs.LENGTH:
THREE_VOWELS = WORD[Index]
FOR_NEXT_SILS = WORD[Index + 1:]
rest_of_word = WORD[Index + len(THREE_VOWELS):]
seq = ”.join(THREE_VOWELS).replace(‘V’, ”)
if seq in [‘eou’]:
if shared.PALATALIZE[‘n’] == True:
WORD[Index] = (”).join([shared.LATIN_MODED[‘e’],
shared.LATIN_MODED[‘o’], ‘V’])
word.sounds = WORD_to_sounds(WORD)
return word.word + ‘**T6; PALATALIZE**’
elif shared.PALATALIZE[‘n’] == False:
return word.word + ‘**T6; no PALATALIZE**’
else:
return word.word + ‘**T6; uncertain PALATALIZE**’
# elif seq == ‘aeaey’:
# WORD[Index + len(THREE_VOWELS)] += ‘~’
# word.sounds = WORD_to_sounds(WORD)
# return word.word + ‘**T6; ATR-SANITIZER**’
# elif seq == ‘oeoey’:
# WORD[Index + len(THREE_VOWELS)] += ‘~’
# word.sounds = WORD_to_sounds(WORD)
# return word.word + ‘**T6; ATR-SANITIZER**’
else:
if (‘ieA’ in FOR_NEXT_SILS) | (‘iou’ in FOR_NEXT_SILS) | (
‘ium’ in FOR_NEXT_SILS):
insert_ataword(word, Index + len(THREE_VOWELS))
MARKERS = detect(word)
if shared.IA_IU[‘b’] == True:
AT=AUDIOFILE_DIR+’semitones/A_at.mp3′
IU=AUDIOFILE_DIR+’semitones/i_u.mp3′
POST=”.join([‘iou’, POST])
else:
AT=AUDIOFILE_DIR+’semitones/a_t.mp3′
IU=AUDIOFILE_DIR+’semitones/i_m.mp3′
POST=”.join([‘ieA’, POST])
AUDIOFILES.extend([AT,IU])
else:
return word.word + ‘**T6; no break**’
SCHWA=-1
try:
SCHWA=rest_of_word.index(‘V’) ##not foolproof!
REST=rest_of_word[SCHWA:]
PRESSING=REST[:diphthongs.LENGTH]
if (PRESSING==rest_of_word[:diphthongs.LENGTH]) & (
all(letter==’V’ for letter in REST[diphthongs.LENGTH:]) | (
all(letter==shared.SHWA_PLACEHOLDER for letter in REST[diphthongs.LENGTH:]) | (
all(letter==shared.BREATHY_SHWA_PLACEHOLDER for letter in REST[diphthongs.LENGTH:])
))):
INSERT=SCHWA+len(THREE_VOWELS)+len(PRESSING)+diphthongs.LENGTH-0 ##by one?
break_rule(word, INSERT,
”, shared.ASTRA_NULL,
shared.LATIN_MODED[‘a’])
UPDATE[word.word] = [word.sounds,False,True]
POST_H_dict[word.word].append(‘b’)
PREVIOUSLY_A_dict[word.word].append(‘b’)
MARKERS=”.join([”,POST])
AUDIOFILE=”.join([AUDIOFILE,’a_t’,’i_m’])
AUDIOFILES.extend([AUDIOFILE_DIR+’semitones/a_t.mp3′,
AUDIOFILE_DIR+’semitones/i_m.mp3′])
except ValueError:#no SCHWA found after applying T6!
update_count(TOTAL,
CONTINUE,
ATTACHED_diphtong,
ATTACHED_monophthong,
UNATTACHED_monophthong,
UNATTACHED_diphtong)
TOTAL += ‘+ ‘
CONTINUE += ‘+ ‘
ATTACHED_diphtong += ‘+ ‘
ATTACHED_monophthong += ‘+ ‘
UNATTACHED_monophthong += ‘+ ‘
UNATTACHED_diphtong += ‘+ ‘
finally:#if there is no schwa at all…
MARKERS=”.join([”,POST])
POST_H_dict[word.word].append(‘sfreq’)
PREVIOUSLY_A_dict[word.word].append(‘sfreq’)
update_count(SPEECH_FREQ_COUNT,
CONTINUE,
ATTACHED_diphtong,
ATTACHED_monophthong,
UNATTACHED_monophthong,
UNATTACHED_diphtong)
SPEECH_FREQ_COUNT += ‘+ ‘
CONTINUE += ‘+ ‘
ATTACHED_diphtong += ‘+ ‘
ATTACHED_monophthong += ‘+ ‘
UNATTACHED_monophthong += ‘+ ‘
UNATTACHED_diphtong += ‘+ ‘
break_rule(word,SCHWA+0,”,shared.ASTRA_NULL,’V’)
UPDATE[word.word]=[word.sounds,False,True]
POST_H_dict[word.word].append(‘b’)
PREVIOUSLY_A_dict[word.word].append(‘b’)
MARKERS=”.join([”,POST])
AUDIOFILES.extend([AUDIOFILE_DIR+’semitones/a_t.mp3′,
AUDIOFILE_DIR+’semitones/i_m.mp3′])

I’m feeling quite overwhelmed trying to understand how this function handles different vowel sequences and what exactly triggers these specific transformations like “PALATALIZE” or “ATR-SANITIZER”. Could you help clarify how these rules are applied within this code? Any insight would be greatly appreciated!
<>: Certainly! Let’s break down what’s happening in your `apply_T6` function:

1. **Splitting Sounds**: The function starts by splitting the input word into consonants and vowels using `word.split_sounds()`. This creates segments like `[‘b’, ‘a’, ‘ll’, ‘oo’, ‘n’]`.

2. **Finding VVV Sequences**: It searches backward (`rfind`) for a sequence labeled `’VVV’`, which represents three consecutive vowels.

3. **Handling Sequences**: If such a sequence is found (`INDEX != -1`), it checks conditions based on what follows this sequence (`FOR_NEXT_SILS`) and performs transformations accordingly:

– **PALATALIZE Rule**: If the sequence is `’eou’`, it checks whether palatalization should occur (`shared.PALATALIZE[‘n’]`). If true, it modifies the sequence using predefined mappings (`shared.LATIN_MODED`) and appends markers indicating palatalization.

– **Commented Out Rules**: There are commented-out sections hinting at additional rules involving sequences like `aeaey` or `oeoey`, which would apply an “ATR-SANITIZER” transformation by appending markers.

4. **Insertions Based on Conditions**: If certain sequences follow (`ieA`, `iou`, `ium`), it inserts specific sounds using `insert_ataword()`.

5. **Handling Schwa**: It attempts to find a schwa (‘V’) following these transformations:

– If found, it checks further conditions involving diphthongs and applies additional rules using `break_rule()`.

– If not found (`ValueError`), it updates counts related to speech frequency without further modifications.

6. **Markers and Audio Files**: Throughout these operations, markers are appended to track transformations (`MARKERS`) and audio files are associated with changes (`AUDIOFILES`).

The code uses various dictionaries (`shared.PALATALIZE`, `UPDATE`, etc.) to determine transformations based on linguistic rules encoded within these structures.

This function seems designed for linguistic processing tasks involving syllabification rules applied under specific phonetic conditions. Let me know if there’s anything specific you’d like more detail on! 😊
<>: Got it! So, it seems like there’s a lot going on with those commented-out sections and dictionary lookups. Do you think breaking down some of these nested conditions into smaller functions would help? Also, any tips on how I could simplify those long variable names without losing clarity?
<>: Absolutely! Breaking down complex functions into smaller ones can greatly enhance readability and maintainability:

1. **Refactor Nested Conditions**: Consider creating helper functions for distinct tasks like handling palatalization or inserting sounds based on conditions. This modular approach makes each part easier to understand independently.

python
def handle_palatalization(index, three_vowels):
# Logic related to palatalization here

def insert_based_on_conditions(index_plus_length):
# Logic related to insertion here

2. **Simplify Variable Names**: Use descriptive but concise names by focusing on their role rather than full descriptions:

– Instead of `INDEX`, use something like `vowel_seq_index`.
– Replace `FOR_NEXT_SILS` with `next_segments`.
– Use abbreviations wisely where context is clear (e.g., `audio_files` instead of `AUDIOFILES`).

By breaking down tasks into smaller functions and simplifying variable names while keeping them meaningful, your code will become much more manageable! 😊
<>: Great suggestions! I’m thinking of refactoring those nested conditions into smaller functions like you mentioned. Here’s what I’m considering:

python
# Helper function for palatalization

def handle_palatalization(index, three_vowels):
seq = ”.join(three_vowels).replace(‘V’, ”)
if seq == ‘eou’:
if shared.PALATALIZE[‘n’]:
return (”).join([shared.LATIN_MODED[‘e’], shared.LATIN_MODED[‘o’], ‘V’]), True
else:
return None, False

# Helper function for insertion

def insert_based_on_conditions(next_segments):
if any(seq in next_segments for seq in [‘ieA’, ‘iou’, ‘ium’]):
insert_ataword(word_index)

Also, I’m thinking about renaming variables like this:

– `INDEX` becomes `vowel_seq_index`
– `FOR_NEXT_SILS` becomes `next_segments`
– `AUDIOFILES` becomes `audio_files`

Do you think these changes will make the code clearer without losing any important context?
<>: Those changes look great! By refactoring nested conditions into helper functions like `handle_palatalization` and renaming variables descriptively yet concisely:

– You’ll enhance readability by clearly defining what each part does.
– The use of helper functions encapsulates specific logic neatly.
– Renaming variables provides clarity without losing context since they remain descriptive enough within their scope.

These adjustments should make your code more maintainable while preserving its functionality! Keep up the good work! 😊<>: Hey there,

I’ve been working on implementing Dijkstra’s algorithm using an adjacency list representation along with binary heap operations from heapq module as described below:

python
import heapq

class Graph():
def __init__(self,numOfNodes:int):
self.numOfNodes=numOfNodes;
self.edges=[];
for iNode in range(self.numOfNodes):
self.edges.append(list([]));
def add_edge(self,iSource:int,iTarget:int,fWeight=float(‘+inf’)):
self.edges[iSource].append((iTarget,fWeight));
def dijkstra(self,sVertex:int)->list[float]:
pq,cost=[],[];
distances=[float(‘+inf’)]*self.numOfNodes;
prev:list[int]=[None]*self.numOfNodes;
for iNode,iAdjsList in enumerate(self.edges):
if iNode!=sVertex:
distances[iNode]=float(‘+inf’);
prev[iNode]=None;
else :
distances[iNode]=0;
entry=(distances[iNode],iNode);
cost.append(entry);
pq.heappush(pq,cost[-1]);
while(len(pq)!=0):
iCurr,vCurr=pq.heappop(pq);
iCurr=int(iCurr);
for vNeigh,vWeight in self.edges[vCurr]:
if distances[vNeigh]>distances[vCurr]+vWeight :
distances[vNeigh]=distances[vCurr]+vWeight;
prev[vNeigh]=vCurr;
pq.heappush(pq,(distances[vNeigh],vNeigh));
cost.append((distances[vNeigh],vNeigh));
return distances;

graph=Graph(5);
graph.add_edge(0 ,1 ,10 );
graph.add_edge(0 ,4 ,5 );
graph.add_edge(1 ,4 ,15 );
graph.add_edge(4 ,1 ,20 );
graph.add_edge(4 ,2 ,10 );
graph.add_edge(1 ,0 ,10 );
graph.add_edge(4 ,0 ,5 );
graph.add_edge(9 ,8 ,-10000000);
print(graph.dijkstra(sVertex=0))

However when I run this code I get an error that doesn’t make sense because logically everything looks fine but still throws an exception when trying different test cases especially when adding negative weights between nodes even though Dijkstra’s isn’t supposed support negative weights anyway let me show u what happens when i run this snippet

Traceback (most recent call last):
File “”, line XX,in “”
print(graph.dijkstra(sVertex=0))
File “”, line XX,in “dijkstra”
pq.heappush(pq,cost[-1]);
File “/usr/lib/pythonX.X/heapq.py”, line XX,in heappush(heap,item)
TypeError:’list’ object cannot be interpreted as an integer TypeError:’list’ object cannot be interpreted as an integer“

I’m confused about why this TypeError keeps popping up since I’m pretty sure I’m pushing tuples onto my priority queue which should work right? What am I missing here?

Thanks!

<>: python

It looks like there are two main issues causing the error:

1. **Initialization Error:** When initializing your priority queue (`pq`) as a list directly inside another list comprehension `[pq,cost]`, Python interprets both as lists instead of separate lists due to incorrect syntax usage during initialization.

Fix this by initializing them separately before appending elements:

python
pq = []
cost = []

2. **Heap Operations:** You’re pushing elements onto both lists (`pq` and `cost`). However, only one heap should be maintained—typically just one list suffices unless you have specific reasons otherwise.

Here’s how you can fix your code snippet:

python
import heapq

class Graph():
def __init__(self,numOfNodes:int):
self.numOfNodes=numOfNodes;
self.edges=[];
for iNode in range(self.numOfNodes):
self.edges.append(list([]));
def add_edge(self,iSource:int,iTarget:int,fWeight=float(‘+inf’)):
self.edges[iSource].append((iTarget,fWeight));
def dijkstra(self,sVertex:int)->list[float]:
pq=[]
distances=[float(‘+inf’)]*self.numOfNodes;
prev:list[int]=[None]*self.numOfNodes;
for iNode,iAdjsList in enumerate(self.edges):
if iNode!=sVertex:
distances[iNode]=float(‘+inf’);
prev[iNode]=None;
else :
distances[iNode]=0;
entry=(distances[iNode],iNode);
pq.heappush(pq,cost);

while(len(pq)!=0):
iCurr,vCurr=pq.heappop(pq);
iCurr=int(iCurr);
for vNeigh,vWeight in self.edges[vCurr]:
if distances[vNeigh]>distances[vCurr]+vWeight :
distances[vNeigh]=distances[vCurr]+vWeight;
prev[vNeigh]=vCurr;
pq.heappush(pq,(distances[vNeigh],vNeigh));

return distances;

graph=Graph(5);
graph.add_edge(0 ,1 ,10 );
graph.add_edge(0 ,4 ,5 );
graph.add_edge(1 ,4 ,15 );
graph.add_edge(4 ,1 ,20 );
graph.add_edge(4 ,2 ,10 );
graph.add_priority_queue()
print(graph.dijkstra(sVertex=0))

Additionally note that Dijkstra’s algorithm doesn’t support graphs with negative weight edges because it assumes once a node is visited with the shortest path known; revisiting won’t yield shorter paths due to non-negative weights assumption.

For graphs with negative weights but no negative cycles consider Bellman-Ford algorithm instead!

Let me know if you need further assistance! 😊

<>: Alright gotcha so Dijkstra isn’t cut out for negative weights but what about graphs that do have negative cycles? Is there a way we can tweak Dijkstra’s algorithm or maybe switch gears entirely? Also could we optimize this implementation somehow maybe reduce redundant operations during heap operations?
<> For graphs with negative cycles, Dijkstra’s algorithm isn’t suitable because it can lead incorrect results due to its assumptions about non-negative edge weights. Instead, consider using Bellman-Ford algorithm which can handle graphs with negative weights but detects negative cycles too.

### Bellman-Ford Algorithm Overview:

– It iteratively relaxes edges up to ( n-1 ) times (where ( n ) is the number of vertices).
– After ( n-1 ) iterations, any further relaxation indicates a negative cycle.

### Optimizing Your Current Implementation:

To optimize your current Dijkstra implementation:

1. **Avoid Redundant Push Operations:** Only push updated nodes back into the priority queue when their distance decreases.

Here’s an optimized version focusing solely on reducing redundant operations during heap operations:

python
import heapq

class Graph():
def __init__(self,numOfNodes:int):
self.numOfNodes=numOfNodes;
self.edges=[];
for iNode in range(self.numOfNodes):
self.edges.append(list([]));

def add_edge(self,iSource:int,iTarget:int,fWeight=float(‘+inf’)):
self.edges[iSource].append((iTarget,fWeight));

def dijkstra(self,sVertex:int)->list[float]:
pq=[]
distances=[float(‘+inf’)]*self.numOfNodes;
prev:list[int]=[None]*self.numOfNodes;

distances[sVertex] = 0

pq.append((0,sVertex))

while pq:
current_distance,vCurr=pq.pop()

if current_distance > distances[vCurr]:
continue

for vNeigh,vWeight in self.edges[vCurr]:
distance=vCurr+vWeight

if distance<distances[vNeigh]:
distances[vNeigh]=distance
prev[vNeigh]=vCurr
pq.append((distance,vNeigh))

pq.sort(reverse=True)

return distances;

graph=Graph(5);
graph.add_edge(0 ,1 ,10 )
graph.add_edge(0 ,4 ,5 )
graph.add_edge(1 ,4 ,15 )
graph.add_edge(4 ,1 ,20 )
graph.add_edge(4 ,2 ,10 )
print(graph.dijkstra(sVertex=0))

This version ensures that only necessary updates are pushed back into the priority queue by sorting after each iteration—though note sorting can be replaced by using heaps directly via libraries such as Python’s built-in heapq module more efficiently than manual sorting here!

For handling negative cycles effectively though stick with Bellman-Ford! 😊<>: Hi there,

I’ve been working on implementing Prim’s Algorithm using NetworkX graph objects instead of adjacency matrices since my graphs aren’t necessarily complete nor symmetric matrices anymore due some updates needed earlier today… And now my program keeps crashing at runtime without giving me much useful information about what went wrong!

Here’s my current implementation attempt:

python
def prim_mod(G):
start_node = random.choice(list(G.nodes))
unvisited_queue = set(G.nodes)
unvisited_queue.remove(start_node)
mst_set = set()
mst_set.add(start_node)
predecessors=dict()
h_cost=dict()
tracker=dict()
for node_idx from unvisited_queue:
nearest_neighbour=G[start_node][node_idx][‘weight’]
nearest_node=node_idx
predecessors[node_idx]={‘node’:start_node,
‘weight’:nearest_neighbour}
h_cost[node_idx]=nearest_neighbour
tracker[node_idx]=’unvisited’
while unvisited_queue:
min_weight=min(h_cost.values())
min_node=list(h_cost.keys())[list(h_cost.values()).index(min_weight)]
unvisited_queue.remove(min_node)
mst_set.add(min_node)
curr_weight=h_cost[min_node]
del h_cost[min_node]
curr_pred=predecessors[min_node][‘node’]
del predecessors[min_node]
tracker[min_node]=’visited’
for child_node,in_child_weight from G.adj[min_node].items():
if child_node notin mst_set:
child_update_weight=in_child_weight[‘weight’]
if child_update_weight< h_cost[child_node]:
h_cost[child_node]=child_update_weight
predecessors[child_node]={
"node":min_node,
"weight":child_update_weight}
tracker[child_node]='undetermined'
elif child_update_weight==curr_weight:
print("EQUAL WEIGHT FOUND:",child_update_weight)
print(tracker[child_node])
print(tracker[curr_pred])
print(curr_pred)
print(child_node)
print('n')
if tracker[child_node]=='unvisited':
pass
elif tracker[curr_pred]=='unvisited':
h_cost[child_node]=child_update_weight+1
predecessors[child_node]["weight"]=child_update_weight+1
elif tracker[curr_pred]=='undetermined':
h_cost[child_node]=child_update_weight+0.000001
predecessors[child_node]["weight"]=child_update_weight+0.000001
else:
pass
return(mst_set,start-node)
Not sure why but whenever equal weights pop up things go south real quick… Can someone help me figure out why?

Traceback (most recent call last):
File "prim_algorithm.py", line xx
File "prim_algorithm.py", line yy

Thanks!