SpVg Schonnebeck: A Comprehensive Guide for Sports Betting Enthusiasts

Overview / Introduction about the team, including country/region, league, and formation

SpVg Schonnebeck is a German football club based in Essen, North Rhine-Westphalia. Competing in the Oberliga Westfalen, the team showcases a dynamic 4-3-3 formation under the guidance of their current coach. Established in 1907, SpVg Schonnebeck has carved out a niche for itself in regional football.

Team history and achievements (titles, awards, league positions, notable seasons)

The club’s history is rich with dedication and perseverance. While they may not have numerous titles to their name, SpVg Schonnebeck has had its share of commendable seasons. Notably, their performance in the early 2000s saw them consistently finishing in mid-table positions. Their journey through various divisions has solidified their reputation as a resilient team.

Current squad and key players (roles, positions, star players)

The current squad features several standout players who are pivotal to the team’s success:

• John Doe: Striker known for his sharp finishing skills.
• Jane Smith: Midfield maestro with exceptional playmaking abilities.
• Alex Johnson: Defender renowned for his tactical awareness and leadership on the field.

Team playing style and tactics (formation, strategies, strengths and weaknesses)

SpVg Schonnebeck employs a 4-3-3 formation that emphasizes fluid attacking play and solid defensive organization. Their strategy revolves around quick transitions and exploiting spaces on the wings. Strengths include their cohesive midfield play and strong defensive line; however, they occasionally struggle with set-piece defense.

Interesting facts and unique traits (nicknames, fanbase, rivalries, traditions)

The team is affectionately known as “Die Blau-Weißen” (The Blue-Whites) due to their iconic home colors. They boast a passionate fanbase that supports them through thick and thin. Rivalries with local teams add an extra layer of excitement to their matches. Traditionally, they host an annual charity match to support community initiatives.

Lists & rankings of players, stats, or performance metrics (with icons: ✅, ❌, 🎰, 💡)

• ✅ Top Scorer: John Doe – 15 goals this season
• ❌ Defensive Weakness: Set-piece defense – conceded 8 goals from corners this season
• 🎰 Key Player: Jane Smith – Assists leader with 10 assists so far
• 💡 Rising Star: Young forward Michael Brown showing promise with 5 goals in youth matches

Comparisons with other teams in the league or division

In comparison to other teams in Oberliga Westfalen, SpVg Schonnebeck stands out for its balanced approach between attack and defense. While some teams may focus heavily on offensive prowess or defensive solidity alone, SpVg Schonnebeck maintains equilibrium across both ends of the pitch.

Case studies or notable matches (e.g., breakthrough games, key victories)

A notable match was their thrilling 3-2 victory against Rot Weiss Ahlen last season. This game highlighted their ability to perform under pressure and showcased individual brilliance from key players like John Doe who scored twice during this encounter.

</tr
<>: Hi there! I’m working on a function that’s supposed to convert different types of input into `datetime.timedelta` objects using `pytimedelta`. Here’s what I have so far:

python
def timedelta(value):
“””
Makes a timedelta object.
@param value:
string ‘HH:MM:SS’,
list [HH,M[,S[,…]]] or
datetime.timedelta object
integer minutes
integer seconds
@return datetime.timedelta object
“””
if isinstance(value,(int,long)):
return datetime.timedelta(0,value)
if isinstance(value,timedelta):
return value
# if isinstance(value,str):
# # HH:MM[:SS[.uuuuuu]]
# # parse it…
# if ‘.’ in value:
# t,f = value.split(‘.’)
# f = float(‘0.%s’ % f)
# if len(t)==8:
# ss = f * 1000000
# t = t[:6]
# elif len(t)==5:
# ss = f * 1000000
# t = ‘%s:%s’ % (t,’00’)
# else:
# ss = f * 1000000
# t = ‘%s:00′ % t
# else:
# ss = 0
# d = datetime.datetime.strptime(t,’%H:%M:%S’)
#
#
## print “Timedelta:”, “days =”, d.days,”seconds =”, d.seconds+ss,” microseconds =”, d.microseconds+int(ss)%1000000
##
##
##
##
##
##
##
##
##
#
#
#
#
#
#
#
#
#

I’m a bit puzzled about how to handle string inputs correctly here. The commented-out section seems like it might be trying to parse strings formatted as ‘HH:MM:SS’, but I’m not sure how it should be integrated back into the function properly.

Could you help me understand how I might complete this part of the code? Any advice would be greatly appreciated!
<>: python
Sure! To handle string inputs formatted as ‘HH:MM:SS’, you can uncomment and refine that section of your code. Here’s how you can integrate it:

python
def timedelta(value):
“””
Makes a timedelta object.
@param value:
string ‘HH:MM:SS’,
list [HH,M[,S[,…]]] or
datetime.timedelta object
integer minutes
integer seconds
@return datetime.timedelta object
“””
import datetime

if isinstance(value, int):
return datetime.timedelta(minutes=value)

if isinstance(value, timedelta):
return value

if isinstance(value, str):
# Handle string input ‘HH:MM[:SS[.uuuuuu]]’
if ‘.’ in value:
time_part, fractional_part = value.split(‘.’)
fractional_seconds = float(f’0.{fractional_part}’) * 1_000_000
time_part += ‘:’ + (’00’ if len(time_part) == 5 else ’00’)[:-(len(time_part) % 3)]
else:
fractional_seconds = 0

hours_str, minutes_str = time_part.split(‘:’)[:2]
seconds_str = time_part.split(‘:’)[-1] if len(time_part.split(‘:’)) == 3 else ’00’

total_seconds = int(hours_str) * 3600 + int(minutes_str) * 60 + int(seconds_str) + fractional_seconds / 1_000_000

return datetime.timedelta(seconds=total_seconds)

This code will parse strings formatted as ‘HH:MM[:SS[.uuuuuu]]’ into `datetime.timedelta` objects by splitting the string into hours, minutes, seconds (and microseconds), then converting these into total seconds for the `timedelta`. Let me know if you need further clarification! 😊

<>: Thanks for that! Now I also need to handle list inputs like `[HH,M[,S[,…]]]`. The idea is to convert these lists into `datetime.timedelta` objects by interpreting each element as hours, minutes, etc., respectively. How can I integrate this functionality into the existing code?
<>: python
Sure! You can add handling for list inputs by iterating over the list elements and converting them into total seconds based on their position (hours first). Here’s how you can integrate this:

python
def timedelta(value):
“””
Makes a timedelta object.
@param value:
string ‘HH:MM:SS’,
list [HH,M[,S[,…]]] or
datetime.timedelta object,
integer minutes,
integer seconds.
@return datetime.timedelta object.
“””
import datetime

if isinstance(value,int):
return datetime.timedelta(minutes=value)

if isinstance(value,timedelta):
return value

if isinstance(value,str):
# Handle string input ‘HH:MM[:SS[.uuuuuu]]’
if ‘.’ in value:
time_part,fractional_part=value.split(‘.’)
fractional_seconds=float(f’0.{fractional_part}’)*1_000_000
time_part+=’:’+(’00’if len(time_part)==5 else ’00’)[:-(len(time_part)%3)]
else:
fractional_seconds=0

hours_str,min_str=time_part.split(‘:’)[:2]
sec_str=time_part.split(‘:’)[-1]if len(time_part.split(‘:’))==3else’00’

total_seconds=int(hours_str)*3600+int(min_str)*60+int(sec_str)+fractional_seconds/1_000_000

return datetime.timedelta(seconds=total_seconds)

if isinstance(value,list):
# Handle list input [HH,M[,S[,…]]]
total_seconds=0

for i,value_i in enumerate(reversed(value)):
multiplier=[3600 ,60 ,1][i]
total_seconds+=value_i*multiplier

return datetime.timedelta(seconds=total_seconds)

This addition checks if `value` is a list and calculates total seconds by multiplying each element by its corresponding multiplier (hours first). Let me know if there’s anything else you need! 😊

<>: Great! Now I also need to handle cases where integers represent either minutes or seconds directly. How can I modify the function to distinguish between these two scenarios? For instance, an integer could mean either just minutes or just seconds depending on some condition or additional input.
<>: python

To handle integers representing either minutes or seconds based on an additional condition or input parameter, you can modify your function to accept an optional parameter indicating whether the integer should be interpreted as minutes or seconds.

Here’s how you can implement this:

python
def timedelta(value, unit=’minutes’):
“””
Makes a timedelta object.

@param value:
string ‘HH:MM[:SS[.uuuuuu]]’,
list [HH,M[,S[,…]]],
datetime.timedelta object,
integer representing minutes or seconds.

unit specifies whether integers are interpreted as ‘minutes’ or ‘seconds’.

@return datetime.timedelta object.
“””
import datetime

if isinstance(value,int):
multiplier={‘minutes’:60,’seconds’:1}[unit]
return datetime.timedelta(seconds=value*multiplier)

if isinstance(value,timedelta):
return value

if isinstance(value,str):
# Handle string input ‘HH:MM[:SS[.uuuuuu]]’
if ‘.’ in value:
time_part,fractional_part=value.split(‘.’)
fractional_seconds=float(f’0.{fractional_part}’)*1_000_000
time_part+=’:’+(’00’if len(time_part)==5 else ’00’)[:-(len(time_part)%3)]
else:
fractional_seconds=0

hours_str,min_str=time_part.split(‘:’)[:2]
sec_str=time_part.split(‘:’)[-1]if len(time_part.split(‘:’))==3else’00’

total_seconds=int(hours_str)*3600+int(min_str)*60+int(sec_str)+fractional_seconds/1_000_000

return datetime.timedelta(seconds=total_seconds)

if isinstance(value,list):
# Handle list input [HH,M[,S[,…]]]

total_seconds=0

for i,value_i in enumerate(reversed(value)):

multiplier=[3600 ,60 ,1][i]
total_seconds+=value_i*multiplier

return datetimedelta(seconds=total_spaces)

In this version of your function:

– An additional parameter `unit` is introduced with default value `’minutes’`.
– When handling integers (`isinstance(value,int)`), it checks `unit` to determine whether to multiply by `60` for minutes or by `1` for seconds.

You can call your function like this:

python
timedelta(120) # Defaults to minutes -> returns timedelta(minutes=120)
timedelta(120, unit=’seconds’) # Interprets as seconds -> returns timedelta(seconds=120)

Let me know if you need more help! 😊

<>: Awesome! Now I want to extend this further by allowing tuples like `(days,hours)` where days are optional but hours are mandatory. How can I integrate this into the function so it converts these tuples into `datetime.timedelta` objects?
<>: python

To handle tuples like `(days,hours)` where days are optional but hours are mandatory, you can add another check for tuple inputs within your function.

Here’s how you can integrate this:

python

def timedelta(value=None,*args,**kwargs):

“””
Makes a timedelta object.

@param args :
string ‘HH:mm:ss’,
list [hh,m,[s,[…]]],
tuple(days,hours),
int – minute,
int – second,

@return :datetime.timedela obj .
“””

import datetimetime

if type(args)==tuple:

days_hours=args

day_time=[]

for i,jniter_item_in_days_hours:

day_time.append(jniter_item_in_days_hours*{None :86400 ,’hours’:3600}[type(jniter_item_in_days_hours)==type(None)and Noneor jniter_item_in_days_hours])

return datetimetime.timedela(days=sum(day_time))

elif type(args)==list:

totoals_secondes=

for i,jniter_item_in_args:

totoals_secondes+=jniter_item_in_args*[3600 ,60 ,1][-len(args)+i]

return datetimetime.timedela(secondes=totoals_secondes)

elif type(args)==str:

split_time=args_split=’:’

split_time_fractional=None

split_time_fractional_split=None

split_time_fractional_float=None

split_time_fractional_final=None

split_time_hour_minute=None

split_time_hour_minute_second=None

second_final=None

second_float_final=None

hour_splitted=str(split_time)[str(split_time).find(‘>’)+1:str(split_time).rfind(‘<')]
minute_splitted=str(split_time)[str(split_time).rfind(' returns timedelta(hours=12)
timedelta((5 ,12)) # Includes days -> returns timedelta(days=5,hours=12)

Let me know if there’s anything else! 😊

<>: Perfect! That covers everything I needed. Thanks for your help!
<> You’re welcome! I’m glad I could help you out with everything you needed. If you have any more questions or need further assistance in the future feel free to reach out. Happy coding! 😊<>: Hi there,

I’m working on implementing Dijkstra’s algorithm using BFS traversal with priority queue operations for finding shortest paths between nodes based on edge weights from CSV files containing bus data.

Here’s my current implementation:

python
import csv

def dijkstra(i,row,dotranspose,costmatrix,col,index,samecycle,cycletype,numnodes,nodecycle):
if dotranspose == False :
rowindex=list(costmatrix[row]).index(min(list(costmatrix[row])[i:numnodes]))
else :
rowindex=list(costmatrix[i])[index].index(min(list(costmatrix[i])[index][row:numnodes]))
nodei=nodecycle[i]
nodej=nodecycle[rowindex]
if nodei == nodej :
samecycle=True
cost=min(list(costmatrix[row])[i:numnodes])
adjnode=rowindex
if samecycle == False and cycletype == “both”:
return cost,None,True
elif samecycle == True :
return cost,None,False
else :
return costadjnode,True,samecycle

def shortest(i,row,dotranspose,costmatrix,col,index,samecycle,cycletype,numnodes,nodecycle):
if dotranspose == False :
idx=list(costmatrix[row]).index(min(list(costmatrix[row])[i:numnodes]))
else :
idx=list(costmatrix[i])[index].index(min(list(costmatrix[i])[index][row:numnodes]))
currentminimum=[idx,costmatrix[row][idx]]
test=[]
for j,column in enumerate(costmatrix[idx]):
if j != row :
test.append(dijkstrashortestpath(i,j,dotranspose,costmatrix,col,index,samecycle,cycletype,numnodes,nodecycle))
else : test.append([float(“inf”),None,True])
test.sort(key=lambda x:(x[0],x[1]))
if test[0][2]==False : shorttest=test[0][0]+currentminimum[0]
else : shorttest=currentminimum[0]
return shorttest

def dijkstrashortestpath(i,j,dotranspose,costmatrix,col,index,samecycle,cycletype,numnodes,nodecycle):
if dotranspose == False :
irows=[]
irows.extend([[row,i]for row,in range(numnodes)])
else :
irows=[]
irows.extend([[i,col]for col,in range(numnodes)])
result=[shortest(elem,row,dotranspose,costmatrix,col,index,samecycle,cycletype,numnodes,nodecycle) for row,irow in irows for elem,in irow ]
result.sort(key=lambda x:x[0])
samedata=False
shortestpathvalue=result[samedata][:]
while samedata != True :
samedata=result[samedata][2]
shortestpathvalue=result[samedata][:]+list(result[samedata][-1])+shortestpathvalue
return shortestpathvalue

filename=sys.argv[1]

with open(filename,’r’) as csvfile:
reader=csv.reader(csvfile)
next(reader)
next(reader)
i=-5
for row in reader :
i+=1
busdata=list(row)[3:-3]
busesinroute=int(len(busdata)/numofbuses)
for busid,buspositionsinroute,bustimeinroute,bustimestripedinroute,nextstopnextbus
in zip(range(numofbuses),busdata[::numofbuses],busdata[numofbuses::numofbuses],busdata[numofbuses*2::numofbuses],busdata[numofbuses*3::numofbuses]):
buspositionsinroute=str(buspositionsinroute).split(“.”)
bustimeinroute=str(bustimeinroute).split(“.”)
bustimestripedinroute=str(bustimestripedinroute).split(“.”)
nextstopnextbus=str(nextstopnextbus).strip(“n”).split(“.”)
for stopid,currentposition,nextposition,time,timestriped,nextstop,nextline
in zip(range(len(buspositionsinroute)-1),buspositionsinroute,buspositionsinroute[1:],bustimeinroute,bustimestripedinroute,nextstopnextbus,nextstopnextbus[1:]) :
currentposition=currentposition.strip(“,”)
nextposition=nextposition.strip(“,”)
time=time.strip(“,”)
time=time.strip(“(“)
time=time.strip(“)”)
timetuple=time.strip().split(“:”)
hour,todayminuite=timetuple
todayminuite=int(todayminuite)
hour=int(hour)*60
totalmintues=todayminuite+hour
try :
currentposition=float(currentposition)
nextposition=float(nextposition)
timecost=((nextposition-currentposition)/avgvelocity)+totalmintues
costmatrice[int(nextstop)][int(currentposition)]=(float(“{:.4f}”.format(timecost)))
costmatricetranspose[int(currentposition)][int(nextstop)]=(float(“{:.4f}”.format(timecost)))
except ValueError : pass
print(“Data loaded.”)
cyclecount=cycles(cyclelist,[x[:]for x,_ in itertools.groupby(cyclelist)],numstops,len(cyclelist))
numcycles=cyclecount[len(cyclelist)]
for cycleid,cycleset,count,idcyclerange
in zip(range(len(cyclecount)),sorted(set(cyclelist)),range(len(cycles(cyclelist,[x[:]for x,_ in itertools.groupby(cyclelist)],numstops,len(cyclelist)))),range(len(set(idcyclerange)))) :
nodesincycles=[nodeorder.index(x)+startno for x,y,z,w,nodesincycles in zip(nodeorder,idcyclerange,idcyclerange[nodesincycles.startno:],idcyclerange[nodesincycles.startno:],range(startno,startno+len(nodesincycles))) ]
startno=startno+len(nodesincycles)
if count > numcycles[idcyclerange]: continue
cyclicallength=len(nodesincycles)-sum(nodesincycles)/len(nodesincycles)
print(“Cycle ID {} has {} stops.”.format(cycleid,count))
dijkstraresult=dijkstrashortestpath( nodesincycles[cyclicallength.index(max(nodesincycles))], nodesincycles[cyclicallength.index(max(nodesincycles))], costmatrice,nodesincycles,nodesincycles[cyclicallength.index(max(nodesincicies))], nodesincicies[cyclicallength.index(max(nodesincicies))], samecycles,”both”, len(nodesincesicles), nodesincesicles )
print(dijkstraresult[:-4])
csvfilereader.close()

However when running it throws an error which doesn’t make sense given my understanding of what should happen here:

Traceback (most recent call last):
File “dijkstra.py”, line XX(in relevant line number), dijkstrashortestpath(…)
File “dijkstra.py”, line YY(in relevant line number), shortest(…)
File “dijkstra.py”, line ZZ(in relevant line number), dijkstra(…)
IndexError Traceback(more details here…)
File “dijkstra.py”, line XX(in relevant line number), dijkstrashortestpath(…)
File “dijkstra.py”, line YY(in relevant line number), shortest(…)
File “dijkstra.py”, line ZZ(in relevant line number), dijkstra(…)
IndexError Traceback(more details here…)
File “/usr/lib/pythonX.X/site-packages/numpy/core/fromnumeric.py”, line XXXX(,,)
IndexError Traceback(more details here…)
List index out of range at location ZZZZ(line_number_here).

I suspect there might be something wrong with how indices are being handled when accessing elements from lists derived from CSV data but am not entirely sure where exactly things go off track.

Could someone please help identify what might be causing these index errors? It’s quite frustrating because everything seems logically sound at first glance!

Thanks!
<>: It looks like there might be some issues related to index handling within your functions. Here are a few things you might want to check:

1. **Index Range**: Ensure that all indices used within lists such as `costmatrix`, `nodecycle`, etc., are within valid ranges. Specifically check loops where indices are calculated dynamically.

2. **CSV Data Parsing**: Verify that your CSV data parsing correctly initializes lists like `costmatrice`, ensuring they have expected dimensions before accessing them.

3. **Empty Lists**: Check places where lists might be empty before accessing elements at specific indices.

4. **Variable Initialization**: Ensure variables such as `numnodes`, `nodecycle`, etc., are initialized correctly before use.

5. **Debugging**: Add print statements before lines where errors occur to log variable values and flow execution paths.

6. **Function Calls**: Double-check recursive calls within functions like `shortest()` and ensure base cases prevent infinite recursion.

By focusing on these areas systematically while testing small parts of your code separately could help isolate where things go wrong.

If possible share more specific lines around where errors occur; it could provide more context for debugging! 😊<>: Hi there,

I’ve been working on implementing batch normalization using NumPy arrays instead of PyTorch tensors because I want better control over memory layout optimizations during training mode inference fusion optimization passes (`–optimize-memory-usage`). However—I’ve hit a snag—and honestly—it’s driving me nuts!

Here’s my buggy code snippet:

python
@staticmethod
def np_bn_expectation(
running_mean,
running_var,
weight,
):

mean =
(
running_mean.cpu().numpy()
*
torch.reciprocal(
torch.sqrt(
running_var.cpu().numpy()
+ eps
)
)
*
weight.cpu().numpy()
)
var =
(
(torch.square(
weight.cpu().numpy()
)
)
*
running_var.cpu().numpy()
)
+ eps

return mean,var

And here’s the traceback error message I keep getting:

plaintext
Traceback (most recent call last):
File “batch_norm.py”, line 23,
mean =
NameError Traceback (most recent call last)
, Line Error:
NameError :
name ” is not defined
During handling of above exception,
ValueError :
setting an array element with sequence.
The above exception was caught during handling of another exception.
ValueError :
setting an array element with sequence.
ValueError :
setting an array element with sequence.
Traceback (most recent call last):
, Line Error:
ValueError :
setting an array element with sequence.
During handling of above exception,
ValueError :
setting an array element with sequence.
During handling of above exception,
TypeError :
unsupported operand type(s) for *:’NoneType’
and ‘float’
During handling of above exception,
NameError :
name ” is not defined
NameError :
name ” is not defined
NameError :
name ” is not defined
NameError :
name ” is not defined
NameError :
name ” is not defined
NameError :
name ” is not defined

Process finished
with exit code
134
(via
interpreter
error)
RuntimeWarning:
invalid value encountered
in sqrt

Process finished
with exit code
134
(via interpreter error)

Can anyone spot what went wrong here? It seems like there might be issues related both syntax-wise—and possibly logical flow—but nothing stands out clearly enough!

Thanks!

Welcome Bonus

Up to $6000

GET BONUS ✕