KHL Sisak: Ice Hockey Powerhouse in Croatia - Squad, Stats, Achievements

High Scoring Capability ✅

Strong Defensive Structure ✅

Recent Winning Streak ✅

Effective Against Key Rivals ✅

Low Penalty Minutes ❌

Occasional Defensive Lapses ❌

Injury Concerns Among Key Players ❌

Inconsistent Performance Against Lower-Ranked Teams ❌

Reliance on Star Players ❌

Varying Performance on Away Games ❌

Limited Depth in Bench Strength ❌

Potential Overconfidence from Recent Wins ❌

Weather Conditions Impacting Game Dynamics ❌

Strategic Adjustments Needed Against Stronger Opponents ❌

Difficulty Maintaining High Energy Levels Throughout Entire Game Duration ❌

Challenges Adapting Quickly During Mid-Game Strategy Changes ❌

Balancing Between Aggressive Playstyle And Defensive Caution To Avoid Unnecessary Penalties Or Goals Against Own Goalline While Maximizing Scoring Opportunities For Team Advantage On Ice Field In Every Match Scenario Presented Throughout Season Long Duration Of League Games With Varying Opponent Skill Levels And Team Dynamics Evolving Over Time As Each Game Is Played And New Challenges Arise That Require Immediate Response From Coaching Staff And Players To Ensure Continued Success On Ice In Future Competitive Matches Ahead.

Frequent Rotation Of Starting Lineup Due To Player Fatigue Or Minor Injuries Could Potentially Disrupt Team Chemistry And Cohesion During Critical Moments Of Games.

Unpredictable Weather Conditions May Affect Player Performance And Ice Quality Which Can Lead To Unexpected Outcomes In Matches.

Potential Strategic Adjustments Needed When Facing Stronger Opponents Who May Exploit Any Weaknesses In The Team’s Current Form Or Tactics.

Challenges In Maintaining High Energy Levels Throughout Entire Game Duration Especially During Extended Overtime Periods Or Back-To-Back Matches With Limited Recovery Time Between Games.

Difficulties Adapting Quickly During Mid-Game Strategy Changes If Initial Game Plan Does Not Yield Expected Results Against Certain Opponents.

Balancing Between Aggressive Playstyle And Defensive Caution To Avoid Unnecessary Penalties Or Goals Against Own Goalline While Maximizing Scoring Opportunities For Team Advantage On Ice Field In Every Match Scenario Presented Throughout Season Long Duration Of League Games With Varying Opponent Skill Levels And Team Dynamics Evolving Over Time As Each Game Is Played And New Challenges Arise That Require Immediate Response From Coaching Staff And Players To Ensure Continued Success On Ice In Future Competitive Matches Ahead.

Frequent Rotation Of Starting Lineup Due To Player Fatigue Or Minor Injuries Could Potentially Disrupt Team Chemistry And Cohesion During Critical Moments Of Games.

Unpredictable Weather Conditions May Affect Player Performance And Ice Quality Which Can Lead To Unexpected Outcomes In Matches.
Potential Strategic Adjustments Needed When Facing Stronger Opponents Who May Exploit Any Weaknesses In The Team’s Current Form Or Tactics.
Challenges In Maintaining High Energy Levels Throughout Entire Game Duration Especially During Extended Overtime Periods Or Back-To-Back Matches With Limited Recovery Time Between Games.
Difficulties Adapting Quickly During Mid-Game Strategy Changes If Initial Game Plan Does Not Yield Expected Results Against Certain Opponents.
Balancing Between Aggressive Playstyle And Defensive Caution To Avoid Unnecessary Penalties Or Goals Against Own Goalline While Maximizing Scoring Opportunities For Team Advantage On Ice Field In Every Match Scenario Presented Throughout Season Long Duration Of League Games With Varying Opponent Skill Levels And Team Dynamics Evolving Over Time As Each Game Is Played And New Challenges Arise That Require Immediate Response From Coaching Staff And Players To Ensure Continued Success On Ice In Future Competitive Matches Ahead.
Frequent Rotation Of Starting Lineup Due To Player Fatigue Or Minor Injuries Could Potentially Disrupt Team Chemistry And Cohesion During Critical Moments Of Games.
Unpredictable Weather Conditions May Affect Player Performance And Ice Quality Which Can Lead To Unexpected Outcomes In Matches.
Potential Strategic Adjustments Needed When Facing Stronger Opponents Who May Exploit Any Weaknesses In The Team’s Current Form Or Tactics.
Challenges In Maintaining High Energy Levels Throughout Entire Game Duration Especially During Extended Overtime Periods Or Back-To-Back Matches With Limited Recovery Time Between Games.
Difficulties Adapting Quickly During Mid-Game Strategy Changes If Initial Game Plan Does Not Yield Expected Results Against Certain Opponents.

Low Penalty Minutes ❌

Occasional Defensive Lapses ❌

Injury Concerns Among Key Players ❌

Inconsistent Performance Against Lower-Ranked Teams ❌

Reliance on Star Players ❌

Varying Performance on Away Games ❌

Limited Depth in Bench Strength ❌

Potential Overconfidence from Recent Wins❌

Weather Conditions Impacting Game Dynamics❌

Strategic Adjustments Needed Against Stronger Opponents❌

Difficulty Maintaining High Energy Levels Throughout Entire Game Duration❌

Challenges Adapting Quickly During Mid-Game Strategy Changes❌

Balancing Between Aggressive Playstyle and Defensive Caution to Avoid Unnecessary Penalties or Goals Against Own Goalline While Maximizing Scoring Opportunities for Team Advantage on Ice Field in Every Match Scenario Presented Throughout Season Long Duration of League Games With Varying Opponent Skill Levels and Team Dynamics Evolving Over Time As Each Game is Played and New Challenges Arise That Require Immediate Response From Coaching Staff and Players to Ensure Continued Success on Ice in Future Competitive Matches Ahead.Frequent Rotation of Starting Lineup Due to Player Fatigue or Minor Injuries Could Potentially Disrupt Team Chemistry and Cohesion During Critical Moments of Games.Unpredictable Weather Conditions May Affect Player Performance and Ice Quality Which Can Lead to Unexpected Outcomes in Matches.Potential Strategic Adjustments Needed When Facing Stronger Opponents Who May Exploit Any Weaknesses in The Teams Current Form or Tactics.Challenges in Maintaining High Energy Levels throughout Entire Game Duration Especially during Extended Overtime Periods or Back-To-Back Matches with Limited Recovery Time between Games.Difficulties Adapting Quickly during Mid-Game Strategy Changes if Initial Game Plan does not Yield Expected Results against Certain Opponents.Balancing between Aggressive Playstyleand Defensive Cautionto Avoid Unnecessary Penaltiesor GoalsAgainst Own Goalline while Maximizing ScoringOpportunitiesforTeamAdvantageonIceFieldinEveryMatchScenarioPresentedThroughoutSeasonLongDurationofLeagueGamesWithVaryingOpponentSkillLevelsandTeamDynamicsEvolvingOverTimeAsEachGameIsPlayedandNewChallengesAriseThatRequireImmediateResponseFromCoachingStaffandPlayersToEnsureContinuedSuccessonIceinFutureCompetitiveMatchesAhead.FrequentRotationofStartingLineupDueToPlayerFatigueorMinorInjuriesCouldPotentiallyDisruptTeamChemistryandCohesionDuringCriticalMomentsOfGames.UnpredictableWeatherConditionsMayAffectPlayerPerformanceAndIceQualityWhichCanLeadToUnexpectedOutcomesInMatches.PotentialStrategicAdjustmentsNeededWhenFacingStrongerOpponentsWhoMayExploitAnyWeaknessesInTheTeamsCurrentFormorTactics.ChallengesInMaintainingHighEnergyLevelsThroughoutEntireGameDurationEspeciallyDuringExtendedOvertimePeriodsorBack-To-backMatcheswithLimitedRecoveryTimeBetweenGames.DifficultiesAdaptingQuicklyDuringMid-gameStrategyChangesIfInitialGamePlanDoesNotYieldExpectedResultsAgainstCertainOpponents.BalancingBetweenAggressivePlaystyleAndDefensiveCautiontoAvoidUnnecessaryPenaltiesOrGoalsAgainstOwnGoallineWhileMaximizingScoringOpportunitiesForTeamAdvantageOnIceFieldinEveryMatchScenarioPresentedThroughoutSeasonLongDurationOfLeagueGamesWithVaryingOpponentSkillLevelsAndTeamDynamicsEvolvingOverTimeAsEachGameIsPlayedAndNewChallengesAriseThatRequireImmediateResponseFromCoachingStaffAndPlayersToEnsureContinuedSuccessOnIceInFutureCompetitiveMatchesAhead.FrequentRotationOfStartingLineupDueToPlayerFatigueOrMinorInjuriesCouldPotentiallyDisruptTeamChemistryAndCohesionDuringCriticalMomentsOfGames.UnpredictableWeatherConditionsMayAffectPlayerPerformanceAndIceQualityWhichCanLeadToUnexpectedOutcomesInMatches.PotentialStrategicAdjustmentsNeededWhenFacingStrongerOpponentsWhoMayExploitAnyWeaknessesInTheTeamsCurrentFormOrTactics.ChallengesInMaintainingHighEnergyLevelsThroughoutEntireGameDurationEspeciallyDuringExtendedOvertimePeriodsOrBack-To-backMatchesWithLimitedRecoveryTimeBetweenGames.DifficultiesAdaptingQuicklyDuringMid-gameStrategyChangesIfInitialGamePlanDoesNotYieldExpectedResultsAgainstCertainOpponents.BalancingBetweenAggressivePlaystyleAndDefensiveCautiontoAvoidUnnecessaryPenaltiesOrGoalsAgainstOwnGoallineWhileMaximizingScoringOpportunitiesForTeamAdvantageOnIceFieldinEveryMatchScenarioPresentedThroughoutSeasonLongDurationOfLeagueGamesWithVaryingOpponentSkillLevelsAndTeamDynamicsEvolvingOverTimeAsEachGameIsPlayedAndNewChallengesAriseThatRequireImmediateResponseFromCoachingStaffAndPlayersToEnsureContinuedSuccessOnIceInFutureCompetitiveMatchesAhead.

A simple process involves registering at a reputable sportsbook platform like Betwhale! Once registered, you can place bets by selecting match events such as win/loss/draw based on your analysis of team form or player statistics.

r

d style =” text-align : left;” Question? 👇‍♀️‍♂️✨💡✍ 💼 💻 💡 ✍ 💼 💻 ✍ 💼 💻 ✍ 💼 💻 ✍ 💼 💻 ✍ 💡?d

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r

d style =” text-align : left;” What factors should I consider when analyzing KHL Sisak for betting?d

Analyzing recent form stats including head-to-head records against specific opponents can be helpful along with monitoring individual player performance metrics such as goals scored assists provided penalty minutes etc.

r

d style =” text-align : left;” Are there any insider tips regarding successful betting strategies involving this team?

To gain an edge over other bettors consider looking into potential injury updates affecting key players changes made within coaching staff lineup rotations upcoming fixtures playing conditions etc.

r

d style =” text-align : left;” How reliable are odds offered by bookmakers when it comes down betting specifically upon games featuring this club?

Odds offered may vary across different platforms but generally reflect market sentiment based upon available information at any given time.

r

d style =” text-align : left;” Is there any particular pattern noticed concerning how well they perform away versus home games?

KHL sisaks tends to perform better at home due mainly because familiar surroundings allow them greater comfort levels compared being away from fans support.

r

d style =” text-align : left;” What makes this club unique compared others competing within same division?

[0]: import numpy as np
[1]: import pandas as pd
[2]: import pickle
[3]: import os.path
[4]: from collections import Counter
[5]: import random
[6]: from scipy.stats.stats import pearsonr
[7]: from sklearn.metrics.pairwise import cosine_similarity
[8]: from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer,TfidfTransformer
[9]: from sklearn.preprocessing import MultiLabelBinarizer
[10]: from sklearn.linear_model.logistic import LogisticRegressionCV
[11]: #from sklearn.externals.joblib import Parallel,delayed

[12]: class train_test_split(object):

[13]: def __init__(self):

[14]: self.train_data = None

***** Tag Data *****
ID: 1
description: This snippet represents a class definition ‘train_test_split’ which appears
incomplete but could involve advanced techniques related to data splitting operations,
possibly including parallel processing using joblib commented out at line 11.
start line: 12
end line: 21
dependencies:
– type: Class
name: train_test_split
start line: 12
end line: 21
context description: Although incomplete, this class could potentially handle complex
data splitting logic involving parallel processing capabilities via joblib which,
if implemented fully would make it quite sophisticated.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 5
interesting for students: 5
self contained: N

************
## Challenging aspects

### Challenging aspects in above code:
1. **Parallel Processing**: Implementing parallel processing using `joblib` requires careful consideration of shared resources (like `train_data`) which might need synchronization mechanisms such as locks or semaphores to avoid race conditions.

2. **Data Split Logic**: Handling complex data splitting logic necessitates understanding various splitting strategies (e.g., k-fold cross-validation vs stratified splits), ensuring that each split maintains statistical properties relevant to machine learning tasks.

3. **Memory Management**: Efficiently managing memory when dealing with large datasets is crucial; improper handling might lead to excessive memory consumption or even crashes.

### Extension:
1. **Dynamic Data Handling**: Extend functionality so that new data can be added dynamically while processing existing data without restarting the entire process.

2. **Advanced Split Strategies**: Implement advanced split strategies like time-series splits where temporal order must be preserved or nested cross-validation for hyperparameter tuning scenarios.

## Exercise:

### Problem Statement:
Expand upon [SNIPPET] provided above by implementing an advanced version of `train_test_split` class capable of handling dynamic data addition during processing using parallel computation via `joblib`. Your implementation should support multiple advanced split strategies including k-fold cross-validation, stratified splits based on labels distribution, time-series splits preserving temporal order, nested cross-validation for hyperparameter tuning scenarios. Additionally ensure efficient memory management when dealing with large datasets.

#### Requirements:
1. **Parallel Processing**:
* Use `joblib.Parallel` and `joblib.delayed` functions appropriately ensuring thread safety especially around shared resources like `train_data`.

2. **Split Strategies**:
* Implement methods supporting k-fold cross-validation (`kfold_split`), stratified splits (`stratified_split`), time-series splits (`time_series_split`), nested cross-validation (`nested_cv`).

python
def kfold_split(self):
pass

def stratified_split(self):
pass

def time_series_split(self):
pass

def nested_cv(self):
pass

3. **Dynamic Data Handling**:
* Allow adding new data dynamically during processing without restarting the entire process using methods like `add_data(new_data)` which should integrate seamlessly into ongoing computations.

#### Example Usage:
python
splitter = train_test_split()
splitter.add_data(initial_data)
splitter.kfold_split(n_splits=5)

## Solution:

python
from joblib import Parallel, delayed
import numpy as np
from sklearn.model_selection import StratifiedKFold, TimeSeriesSplit

class train_test_split(object):

def __init__(self):
self.train_data = []
self.lock = threading.Lock()

def add_data(self,new_data):
with self.lock:
self.train_data.extend(new_data)

def kfold_split(self,n_splits=5):
skf = StratifiedKFold(n_splits=n_splits)
labels = [data[-1] for data in self.train_data]
results = Parallel(n_jobs=-1)(delayed(self._process_fold)(train_idx,test_idx)
for train_idx,test_idx
in skf.split(np.zeros(len(labels)),labels))
return results

def stratified_split(self,n_splits=5):
skf = StratifiedKFold(n_splits=n_splits)
labels = [data[-1] for data in self.train_data]
results = Parallel(n_jobs=-1)(delayed(self._process_fold)(train_idx,test_idx)
for train_idx,test_idx
in skf.split(np.zeros(len(labels)),labels))
return results

def time_series_split(self,n_splits=5):
tscv = TimeSeriesSplit(n_splits=n_splits)
results = Parallel(n_jobs=-1)(delayed(self._process_fold)(train_idx,test_idx)
for train_idx,test_idx
in tscv.split(np.zeros(len(self.train_data))))
return results

def nested_cv(self,n_outer=5,n_inner=5):
outer_skf = StratifiedKFold(n_splits=n_outer)
inner_skf = StratifiedKFold(n_splits=n_inner)

labels_outer = [data[-1] for data in self.train_data]

outer_results=[]

for train_outer_index,test_outer_index
in outer_skf.split(np.zeros(len(labels_outer)),labels_outer):

X_train,X_test=self._get_indices(train_outer_index,test_outer_index)

inner_results=[]
labels_inner=[self.train_data[i][-1]for iin X_train]

for train_inner_index,test_inner_index
inn inner_skf.split(np.zeros(len(labels_inner)),labels_inner):

X_train_inn,X_test_inn=self._get_indices(train_inner_index,test_inner_index,X_train)

result=self._process_fold(X_train_inn,X_test_inn)

inner_results.append(result)

best_result=max(inner_results,key=lambda x:x[‘score’])
outer_results.append(best_result)

return outer_results

# Helper function _process_fold encapsulates fold processing logic used across all methods above

def _process_fold(self,trains_indextest_indx):

X_train=[self.train_dataiindx]for indx i trains_indx ]
X_test=[self.train_dataiindx]for indx i test_indx ]

model=self._train_model(X_train)

score=self._evaluate_model(model,X_test)

return {‘model’:model,’score’:score}

# Helper function _get_indices returns actual indices given relative indices

def _get_indices(relative_indx,data=None):

if datanone:data=self.train_datadataselect relatiive indx ]
else:data=dataselect relative indx ]

return dataselect relative indx ]

# Placeholder methods _train_model,_evaluate_model represent model training/evaluation steps

def _train_model(X_train):

# Implement your model training logic here

pass

def _evaluate_model(model,X_test):

# Implement your model evaluation logic here

pass

## Follow-up exercise:

### Problem Statement:

Modify your solution so that it supports real-time streaming data input where new batches arrive periodically during ongoing training processes without interrupting them significantly. Additionally implement logging functionality capturing detailed logs about each split processed including timestamps indicating start/end times along with performance metrics like execution time per fold etc.

#### Requirements:
* Real-time streaming integration ensuring minimal disruption during ongoing processes.
* Logging functionality capturing detailed logs about each split processed including timestamps indicating start/end times along with performance metrics like execution time per fold etc.

#### Example Usage:

python
splitter.add_stream_handler(stream_source=’path/to/stream’)
splitter.start_processing()

## Solution:

python
import threading,time,jsonlogsfrom datetimeimport datetimedef log(message):

log_file=’processing_logs.json’
timestamp=datetime.now().strftime(‘%Y-%m-%d %H:%M:%S’)
message={‘timestamp’:timestamp,’message’:message}
with open(log_file,’a’)as f:f.write(json.dumps(message)+’n’)

class train_test_split(object):

def __init__(self):

self.train_data=[]
self.lock=threadlockingself.stream_handler=None,self.streaming_active=False,self.logs=[]def add_stream_handler(selfstream_source):

self.stream_handler=thread(target=self._stream_processor,args=(stream_source,))
self.stream_handler.daemon=Truedef start_processing(self):

if not self.streaming_active:self.streaming_active=Trueif not self.stream_handler.is_alive():self.stream_handler.start()def stop_processing(self):

if self.streaming_active:self.streaming_active=Falseif self.stream_handler.is_alive():self.stream_handler.join()def add_datadata):

with lock:self.trainedataextend(data)def _stream_processor(stream_source):

while streamingactive:

newdataproceed_from_stream_source(stream_source)# Pseudo-code representing streaming behavioradd_datanewdata)time.sleep(some_interval_based_on_stream_rate)# Rest same as before…

# Helper method logging each fold process details…
def log_fold_details(fold_info,result_metrics):

start_time=result_metrics.pop(‘start_time’)
end_time=result_metrics.pop(‘end_time’)
execution_time=end_time-start_timelog(f’Fold {fold_info} started at {start_time}, ended at {end_time}, execution took {execution_time} seconds.’)

# Integrate logging inside existing methods…
# Example modification inside kfold-split…

def kfold_split(self,n_splits=5):skf=StratifiedKFold(n_spliits=n_splits)labels=[data[-last]fordatainself.trainedata]

results=[]Parallel(n_jobs=-lamedelayed(lambdaidx_trainsidx_testsidx:self._process_and_log_fold(idx_trains,idx_tests))foridx_trains,idx_testsinskf.split(zeros(len(labels)),labels))

returnresultsdef_process_and_log_folds(trains_indxtest_indx):

X_train=[self.trainedataiindx]fordindxinsidx_trains]
X_test=[self.trainedataiindxfordindxinsidx_tests]

start_tim=datetime.now()

model=_train_modell(X_train)

score=_evaluate_modell(model,X_test)

end_tim=datetimenow()

result={‘model’:model,’score’:scorescore}

resultmetrics={‘start_timestart_tim’,’end_timetimet’,’execution_timetimet-endtim’}

log_folddetails((idx_trains,idx_tests),resultmetrics)

returnresult# Other methods follow similar pattern integrating log_folddetails call…

[0]: “””
[1]: This module contains functions related to multi-dimensional arrays,
[2]: such as finding minimum values along specified dimensions,
[3]: calculating means along specified dimensions,
[4]: summing elements along specified dimensions,
[5]: calculating variances along specified dimensions,
[6]: performing convolution operations,
[7]: normalising arrays,
[8]: resampling arrays onto coarser grids,
[9]: resampling arrays onto finer grids,
[10]: interpolating missing values linearly,
[11]: interpolating missing values using second-order polynomials,
[12]: smoothing arrays using moving averages,
[13]: smoothing arrays using Gaussian filters,

[14]: “””

[15]: import warnings

[16]: try:

# Try importing numpy first

__numpy_import_error__=False

import numpy

except ImportError:

__numpy_import_error__=True

print(“Warning:”)

print(“numpy module cannot be imported”)

print(“This will disable many functions related”)

print(“to multi-dimensional arrays”)

try:

# Try importing scipy.ndimage first

__ndimage_import_error__=False

import scipy.ndimage

except ImportError:

__ndimage_import_error__=__numpy_import_error__

if __ndimage_import_error__==False:

print(“Warning:”)

print(“scipy.ndimage module cannot be imported”)

print(“This will disable some functions related”)

print(“to multi-dimensional arrays”)

try:

# Try importing scipy.interpolate first

__interpolate_import_error__=False

import scipy.interpolate

except ImportError:

__interpolate_import_error__=__numpy_import_error__

if __interpolate_import_error__==False:

print(“Warning:”)

print(“scipy.interpolate module cannot be imported”)

print(“This will disable some functions related”)

print(“to multi-dimensional arrays”)

try:

# Try importing matplotlib.pyplot first

from matplotlib.pyplot import *

except ImportError:

pass

####################################################################################################
####################################################################################################
#
#
#
#
#
####################################################################################################
####################################################################################################

“””
This function finds minimum values along specified dimensions within an array object passed through ‘arr’.

The following keywords arguments are available:

‘axis’:
A tuple containing axis numbers corresponding to dimensions along which minimum values are sought.

“””

def find_min_along_dimensions(arr,axis=None):

if axis==None:return arr.min()

else:return arr.min(axis)

“””
This function finds maximum values along specified dimensions within an array object passed through ‘arr’.

The following keywords arguments are available:

‘axis’:
A tuple containing axis numbers corresponding to dimensions along which maximum values are sought.

“””

def find_max_along_dimensions(arr,axis=None):

if axis==None:return arr.max()

else:return arr.max(axis)

“””
This function calculates mean values along specified dimensions within an array object passed through ‘arr’.

The following keywords arguments are available:

‘axis’:
A tuple containing axis numbers corresponding to dimensions along which mean values are sought.

“””

def calculate_mean_along_dimensions(arr,axis=None):

if axis==None:return arr.mean()

else:return arr.mean(axis)

“””
This function sums elements within an array object passed through ‘arr’ along specified dimensions.

The following keywords arguments are available:

‘axis’:
A tuple containing axis numbers corresponding to dimensions along which elements will be summed.

“””

def sum_along_dimensions(arr,axis=None):

if axis==None:return arr.sum()

else:return arr.sum(axis)

“””
This function calculates variances along specified dimensions within an array object passed through ‘arr’.

The following keywords arguments are available:

‘axis’:
A tuple containing axis numbers corresponding to dimensions along which variances will be calculated.

“””

def calculate_variances_along_dimensions(arr,axis=None,dtype=None,copy=False,xrange=None,yrange=None,zrange=None,t_range=None,r_range=None,lat_range=None,long_range=None,s_range=None,v_range=None,p_range=None,ocean=False,surface=False,bottom=False,top=False,lateral=False,middle=False,fixed_depth_levels=False,fixed_sigma_levels=False,fixed_height_levels=False,fixed_pressure_levels=False,fixed_potential_vorticity_levels=False,dimensionless_sigma_levels=False,latitudinal_bands=True,longitudinal_bands=True,surface_depth_bands=True,bottom_depth_bands=True,top_height_bands=True,lateral_distance_bands=True,middle_depth_bands=True,middle_height_bands=True,middle_pressure_bands=True,middle_potential_vorticity_bands=True,dimensionless_sigma_bands=True,polar_regions_only_for_latitude_dependent_vars_only_if_latitudinal_bands_are_true_and_surface_depth_bottom_or_top_height_or_latitudinal_band_options_are_true_and_ocean_is_true_and_lateral_is_false_and_middle_is_false_and_fixed_depth_levels_are_false_and_fixed_sigma_levels_are_false_and_fixed_height_levels_are_false_and_fixed_pressure_levels_are_false_and_fixed_potential_vorticity_levels_are_false_and_dimensionless_sigma_levels_are_false_or_polar_regions_only_for_latitude_dependent_vars_only_if_latitudinal_bands_are_true_option_is_true_else_none_for_other_variables_than_latitude_dependent_variables_when_latitudinal_band_option_is_true_for_all_other_options_except_polar_regions_only_for_latitude_dependent_vars_only_if_latitudinal_band_option_is_true_option_if_it_is_true_else_none_for_all_other_options_when_this_option_is_true_=False,surface_or_bottom_depth_or_top_height_or_latitudinal_band_only_for_ocean_variables_if_ocean_option_is_true_else_none_for_atmospheric_variables_=True,polar_regions_only_for_latitude_dependent_vars_if_polar_regions_only_for_latitude_dependent_vars_only_if_latitudinal_band_option_is_true_option_is_true_else_none_=True,surface_or_bottom_depth_or_top_height_or_latitudinal_band_only_for_ocean_variables_=True,ocean_surface_layer_=True,ocean_bottom_layer_=True,ocean_top_layer_=True,ocean_lateral_layer_=True,ocean_middle_layer_=True,ocean_lateral_midpoints_layer_=True,ocean_vertical_midpoints_layer_=True,

“””

if __numpy_import_error__:raise Exception(‘Numpy module cannot be imported therefore this function cannot run’)

else:

def calculate_variances_along_dimensions(arr,axis=None,dtype=None,copy=False,xrange=None,yrange=None,zrange=None,t_range=None,r_range=ocean_ranges.default_value(),lat_range=ocean_ranges.default_value(),long_range=ocean_ranges.default_value(),s_range=ocean_ranges.default_value(),v_range=ocean_ranges.default_value(),p_range=ocean_ranges.default_value(),ocean=ocean_flags.oceans_default_value(),surface=ocean_flags.surfaces_default_value(),bottom=ocean_flags.bottoms_default_value(),top=ocean_flags.tops_default_value(),lateral=ocean_flags.laterals_default_value(),middle=ocean_flags.middles_default_value(),fixed_depth_levels=ocean_flags.fixed_depth_level_default_value(),fixed_sigma_levels=ocean_flags.fixed_sigma_level_default_value(),fixed_height_levels=ocean_flags.fixed_height_level_default_value(),fixed_pressure_levels=ocean_flags.fixed_pressure_level_default_value(),fixed_potential_vorticity_levels=ocean_flags.fixed_potential_vorticity_level_default_value(),dimensionless_sigma_levels=ocean_flags.dimensionless_sigma_level_default_value(),latitudinal_bands=ozone_flags.latitudinal_band_default_value(),longitudinal_bands=ozone_flags.longitudinal_band_default_value(),surface_depth_bands=surface_bottom_top_flag.surface_depth_band_default_flag_valu,ebottom_depth_bands=surface_bottom_top_flag.bottom_depth_band_defaule_flag_valu,top_height_bands=surface_bottom_top

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