Lublin Volleyball Team: Champions of the Polish League – Squad, Stats & Achievements

Overview of Lublin Volleyball Team

The Lublin volleyball team, based in the eastern region of Poland, competes in the Polish Volleyball League. Founded in 1978, the team is renowned for its competitive spirit and strategic gameplay under the guidance of their current coach. Known for their dynamic playing style, they have become a formidable force in the league.

Team History and Achievements

Lublin has a rich history marked by several notable achievements. They have won multiple league titles and have consistently been among the top teams in national competitions. Their most memorable seasons include reaching the finals multiple times and securing championships that have solidified their reputation.

Titles and Awards

• Premier League Championships: 5
• National Cup Wins: 3
• Notable Seasons: 2005, 2010, 2018

Current Squad and Key Players

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

• Piotr Nowak – Outside Spiker (Position: Outside Spiker)
• Marcin Kowalski – Middle Blocker (Position: Middle Blocker)
• Jakub Zieliński – Libero (Position: Libero)

Player Statistics

• Piotr Nowak – Points per Game: 🎰45 | Blocks per Game: 💡10
• Marcin Kowalski – Receptions per Game: ✅35 | Assists per Game: ❌5
• Jakub Zieliński – Digs per Game: 💡50 | Aces per Game: ✅8

Team Playing Style and Tactics

Lublin employs a fast-paced attack strategy with a focus on quick transitions from defense to offense. Their formation typically revolves around strong blocking tactics and aggressive spiking. Strengths include their powerful serving game and solid defensive line, while weaknesses may lie in occasional lapses in communication during high-pressure situations.

Interesting Facts and Unique Traits

Lublin’s fanbase is known for its passionate support, often filling stadiums with vibrant energy. The team is affectionately nicknamed “The Eagles,” reflecting their soaring ambitions. Rivalries with teams like Warsaw Warriors are legendary, adding an extra layer of excitement to matches.

Fanbase & Traditions

The fans are known for their elaborate chants and pre-game rituals that create an electrifying atmosphere at home games.

Comparisons with Other Teams in the League

Lublin often draws comparisons with top-tier teams such as Krakow Knights due to their similar aggressive playstyle. However, Lublin’s emphasis on teamwork sets them apart from more individualistic teams.

Case Studies or Notable Matches

A breakthrough game was their victory against Gdansk Giants in the semi-finals last season, where they executed a flawless strategy that led to a decisive win.

Seminal Match Analysis

Tips & Recommendations for Betting Analysis 🎰💡✅❌🔍⚽️💰📈📊📉⚽️💸🏆🏅🥇🥈🥉👍😀😃😄😁😆😉😊😌😍😘❤️💋💕💞💗💖❤️‍🔥💘💝💟☺️✨✌️✊☝️☀️⚡️⚠️⛔🔒🔐🔑🗝️🧰⚙️🛠️♻️♿️♿♾➕➖➗✖️➰➿⟳⟲⟳⟴⟵⟶⟷↔↕↖↗↘↙↑↓←→↩↪⤴⤵⤷⤸⇑⇓⇔〰─━│┃┄┅┆┇┈┉╴╵╶╷╸╹╺╻╼╽═║╗☒☑✔▪◼◻▫▯▣▯◾◽■□░▒▓█▄▀▌▐░░░░░░░░░░░

• Analyze recent form trends before placing bets.
• Closely monitor key player performances as they significantly impact outcomes.
• Consider head-to-head records against upcoming opponents for better predictions.

Betting Insights 💡 Advice Blocks 🔍 ⚽️ 💰 📈 📊 📉 ⚽️ 💸 🏆 🏅 🥇 🥈 🥉 👍 😀 😃 😄 😁 😆 😉 😊 😌 😍 😘 ❤️ 💋 💕 💞 💗 💖 ❤️‍🔥 💘 💝 💟 ☺️ ✨ ✌️ ✊ ☝️ ☀️ ⚡️ ⚠️ ⛔ 🔒 🔐 🔑 🗝️ 🧰 ⚙️ 🛠️ ♻️ ♿ ♿ ♾ ➕ ➖ ➗ ✖ ➰ ➿ ⟳ ⟲ ⟳ ⟴ ⟵ ⟶ ⟷ ↔ ↕ ↖ ↗ ↘ ↙ ↑ ↓ ← → ↩ ↪ ⤴ ⤵ ⤷ ⤸ ⇑ ⇓ ⇔〰─━│┃┄┅┆┇┈┉╴╵╶╷╸╹╺╻╼╽═║╗

• Evaluate Lublin’s performance against top-ranked teams to gauge potential success.
• Analyze match statistics to identify patterns or weaknesses that can be exploited when betting.
• Bet on over/under goals based on historical data of average scores in matches involving Lublin.

Betting Pros & Cons of Lublin’s Current Form or Performance ✅❌ Lists 👍 😀 😃 😄 😁 😆 😉 😊

• Potential Betting Pros:

• Lublin’s strong attacking lineup increases chances of scoring multiple goals in matches.
• Their consistent performance history suggests reliability when betting on them as favorites.
• Vibrant home crowd support often boosts player morale leading to enhanced performance levels.
  • Potential Betting Cons:0,”output_dims must be non-empty”

    assert all([output_dims[i]>=output_dims[i+1]for i in range(len(output_dims)-1)]),”output_dims must be monotonically decreasing”

    assert all([output_dims[i]>=input_dimfor i in range(len(output_dims))]),”all elements of output_dims must be greater than or equal to input dim”

    layers=[ResidualLinearLayer(input_dim,min(input_dim,output_dims[i]),bias=bias)for i,_ in enumerate(output_dims)]

    self.layers=torch.nn.ModuleList(layers)

    def forward(self,x):

    for l in self.layers:

    x=l(x)

    return x

    def get_mlp_class(name):

    mlp_dict={“mlp”:MLP,”linear”:LinearMLP,”identity”:IdentityMLP,”tanh”:TanHMLP,”sigmoid”:SigmoidMLP}

    return mlp_dict[name]

    def get_residual_linear_layer_stack_class(name):

    rlls_dict={“residual_linear_layer_stack”:ResidualLinearLayerStack}

    return rlls_dict[name]

    def get_conv_class(name):

    conv_dict={“conv”:torch.nn.ConvTranspose1d}

    return conv_dict[name]

    def get_deconv_class(name):

    deconv_dict={“deconv”:torch.nn.ConvTranspose1d}

    return deconv_dict[name]

    def get_fc_class(name):

    fc_dict={“fc”:torch.nn.Linear}

    return fc_dict[name]

    ***** Tag Data *****
    ID: 4
    description: Residual Linear Layer Stack which builds upon residual linear layers
    start line: 24
    end line: 56
    dependencies:
    – type: Class
    name: ResidualLinearLayerStack
    start line: 55
    end line: 66
    context description: This snippet shows how multiple residual linear layers are stacked.
    algorithmic depth: 4
    algorithmic depth external: N
    obscurity: 4
    advanced coding concepts: 4
    interesting for students: outstandigly interesting because it combines various advanced
    concepts such as residual connections within neural networks.
    self contained: N

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

    1. **Dynamic Activation Functions**: Allow each layer within `ResidualLinearLayerStack` to use different activation functions dynamically provided at initialization.

    2. **Parameter Sharing Across Layers**: Implement parameter sharing between specific layers within `ResidualLinearLayerStack`, ensuring certain weights are reused across different residual blocks.

    3. **Custom Weight Initialization**: Introduce custom weight initialization schemes beyond Xavier uniform initialization that can be specified during layer creation.

    4. **Conditional Residual Connections**: Add conditional logic so that some residual connections can be bypassed based on certain criteria evaluated during runtime.

    5. **Gradient Clipping**: Integrate gradient clipping directly into `ResidualLinearLayer`’s backpropagation process to manage exploding gradients more effectively.

    ## Conversation

    [SNIPPET] How add diff activations each layer?

    To add different activation functions for each layer within `ResidualLinearLayerStack`, you can modify both classes slightly.

    Firstly, update `ResidualLinearLayer` to accept an activation function:

    python
    class ResidualLinearLayer(torch.nn.Module):
    def __init__(self, input_dim, output_dim, bias=True, activation=None):
    super().__init__()
    # existing initializations…

    # Add activation function initialization here:
    self.activation = activation if activation else lambda x : x

    def forward(self,x):
    identity=x[:, :min(self.input_dim,self.output_dim)]
    res=self.weight.matmul(x.transpose(0,-1)).transpose(0,-1)[:,:self.output_dim]
    if self.bias is not None:
    res=res+self.bias

    # Apply activation function here before returning result:
    res = self.activation(res)

    return res + identity

    Next update `ResidualLinearLayerStack` to pass these activations:

    python
    class ResidualLinearLayerStack(torch.nn.Module):
    def __init__(self,input_dim,output_dims,bias=True,activations=None):
    super().__init__()
    assert len(output_dims)>0,”output_dims must be non-empty”
    assert all([output_dims[i]>=output_dims[i+1]for i in range(len(output_dims)-1)]),”output_dims must be monotonically decreasing”
    assert all([output_dims[i]>=inputdimfor i in range(len(outputdims))]),”all elements of outputdims must be greater than or equalto input dim”

    # Default activations list filled with identity functions if none provided:
    activations = activations or [lambda x : x]*len(outputdims)

    layers=[ResidualLinearLayer(inputdim,min(inputdim,outputdims[i]),bias=bias,activation=activations[i])for i,_in enumerate(outputdims)]

    self.layers=torch.nn.ModuleList(layers)

    def forward(self,x):
    for l indecoratorlayerstacks): l(x)
    return x

    # Usage example:
    model=ResidualLinearLayerStack(inputdim=128,outputdims=[64,32],activations=[torch.relu,torch.sigmoid])

    This way you can specify different activation functions per layer when initializing your model.

    [SNIPPET] What about share params across some layers?

    To implement parameter sharing across specific layers within `ResidualLinearLayerStack`, we need to modify how we initialize these parameters so that some weights are reused across different residual blocks.

    Here’s how you could do it:

    ### Step-by-step Implementation

    #### Modify `ResidualLinearLayer` Initialization

    Modify `__init__` method to optionally accept shared weights:

    python
    class ResidualLinearLayer(torch.nn.Module):
    def __init__(self,inputdim,outputdim,bias=True,is_shared=False,parent_shared_weights=None):
    super().__init__()

    # Check whether this layer should share weights with another one:
    if parent_shared_weights is not None:
    assert isinstance(parent_shared_weights,(tuple,list))and len(parent_shared_weights)==4,”parent_shared_weights must contain weight tensor,bias tensor,input dimension,output dimension”
    shared_weight,parent_bias,parent_inputdim,parent_outputdim=parent_shared_weights

    assert parent_inputdim==inputdim,”Shared weight dimensions mismatch”
    assert parent_outputdim==outputdim,”Shared weight dimensions mismatch”

    # Assign shared parameters here instead of creating new ones:
    self.weight=shared_weight
    if bias:
    assert parent_bias.shape==parent_outputdim.shape,”Bias dimension mismatch”
    self.bias=parent_bias
    else:
    raise ValueError(“Cannot share bias without enabling bias”)

    # Flag indicating shared parameters used here:
    self.is_shared=True

    else:# If no shared parameters then initialize normally as before…

    …

    #### Modify `forward` Method Accordingly

    Ensure proper handling during forward propagation when using shared parameters:

    python
    …

    @property
    def shared_parameters(self):

    if hasattr(self,’is_shared’)and getattr(self,’is_shared’):

    yield ‘weight’,getattr(self,’weight’)
    yield ‘bias’,getattr(self,’bias’)

    #### Modify `ResidualLinearLayerStack`

    Now let’s modify how we create these layers so we can specify which ones should share parameters:

    python
    class ResidualLinearLayerStack(torch.nn.Module):

    …

    def __new__(cls,inputdim,outputdims,bias=True,param_sharing_indices=[]):

    …

    # Create list containing tuples specifying which indices should share params along with initializers themselves::
    shared_params_info=[]
    shared_param_set=set(param_sharing_indices)

    if param_sharing_indices!=[] :

    first_idx_to_share=min(shared_param_set)

    …

    layers=[]

    for idx,inodeindexenumerate(range(len(outputdims))) :

    …

    if param_sharing_indices!=[]and idx>=first_idx_to_share:

    …

    else :

    …

    ### Usage Example

    You can now initialize your model like this:

    python
    model=ResidualLineraLayerrStack(inputidim=128,outputdims=[64 ,32],paramsharingindices=[0 ,1])#Layers at indices [0 ,1] will share params!

    With these modifications you enable parameter sharing between specified layers.

    [SNIPPET] Custom weight init possible?

    Yes! You can introduce custom weight initialization schemes by allowing users to pass an initialization function during layer creation.

    ### Step-by-step Implementation

    #### Modify `reset_parameters` Method

    Update `reset_parameters` method within `ResidualLinearLayer` class to accept custom initialization functions:

    python
    class ResidalinearlayeRlayer(torch.nns.module):

    …

    def reset_parameters(selt,custom_init_fn=None):

    …

    if custom_init_fn is not None :

    assert callable(custom_init_fn), “custom_init_fn must be callable”

    custom_init_fn(selt.weight)

    else :

    nn.init.xavier_uniform_(selt.weight)

    if selt.bias is not None :

    nn.init.zeros_(selt.bias)

    …

    #### Modify Constructor (`__init__`) Method

    Update constructor (`__inti`) method within same class so user-defined init fn passed correctly through arguments chain up till stack level too .

    python

    …

    def init(selt,inputdm,dimm,outdm,biastrue,custm_init_fn=None) :
    super().inti()
    …

    # Save custom init function here too!
    sef.custom_inittfn=custm_init_fn

    sef.resest_params()

    …

    #### Update Stack Class Constructor (`__new__`) Method

    Finally update constructor (`__new__`) method within stack level class too so users specify custom init fns there also!

    python

    classs Ressiduallinearlayerstack(torchnn.modulelist ) :

    …

    def inti(sef,idim,outdsms,trueb,s_custom_inits=[] ):

    super().inti()

    assert len(outdsms)>0 ,”outdsms mustbe non-empty”

    assert all([outdsms[i]>=outdsms[i+I]for Iinrange(len(outdsms)-I)], “outdsmsmustbe monotonicallydecreasing”)

    assert all([outdsms[I]>=idimFor Iinrange(len(outdsms))],”all elementsof outdsmsmustbe greater than orequalto idim”)

    # Default empty list filled with None values matching lengthof outputs dimensions list provided initially .
    custom_inits =[None]*len(outdsms)

    # Replace default valueswith user-specified onesat relevant indices only !
    if s_custom_inits!=[] :

    custom_inits[:len(s_custom_inits)]= s_custom_inits

    layers=[Ressiduallinearlayer(idim,min(idim,outdsms[I]),trueb,s_custom_inits[I])for I,_in ennumerate(outdsms)]

    sef.layrs=tornn.moduelist(layers )

    …

    # Usage Example :
    custom_wgt_init=lambda wgt:tornn.init.kaiming_uniform_(wgt,a=sqrt(5))
    modell=Ressiduallinearlayerstack(idim=128,outsdims=[64 ,32 ],trueb=true,custom_wgts=[custom_wgt_init,None])# First Layer uses kaiming init ,second one uses default Xavier uniform!

    With these modifications users now have flexibility over choosing various custom weight initialization schemes!
    *** Excerpt ***

    *** Revision 0 ***

    ## Plan

    To make an exercise as advanced as possible based on an excerpt requiring profound understanding and additional factual knowledge beyond what’s presented directly within it involves several key steps:

    **Complexity Enhancement:** The rewritten excerpt should incorporate complex sentence structures including nested clauses that require careful parsing by readers. It should also introduce technical vocabulary pertinent to a specific field (e.g., quantum physics or comparative literature), demanding familiarity beyond general knowledge.

    **Incorporation of Deductive Reasoning:** The text should present facts or statements that lead logically but not obviously towards conclusions requiring deductive reasoning from readers—encouraging them to connect dots using both information given directly and inferred knowledge.

    **Use of Counterfactuals and Conditionals:** To elevate difficulty further, embedding counterfactual scenarios (“what would happen if X had happened instead of Y?”) alongside conditionals (“if X happens Y will follow”) requires readers not only to understand what is stated but also hypothetical situations that demand higher-order thinking skills.

    **Integration of External Knowledge:** The exercise will necessitate understanding concepts or historical events outside those mentioned explicitly within the excerpt itself but relevant enough that they enrich comprehension when applied correctly.

    ## Rewritten Excerpt

    In an alternate timeline where Archduke Franz Ferdinand had evaded assassination—a pivotal event catalyzing World War I—European geopolitics underwent subtle yet profound transformations throughout the early twentieth century. Assuming no immediate catalyst precipitated global conflict akin to WWI until after World War II’s conclusion, technological advancements characteristic of wartime innovation emerged distinctly later than our known timeline suggests. Consequently, digital computing technology—initially propelled by wartime needs such as code-breaking endeavors exemplified by Alan Turing’s work at Bletchley Park—would ostensibly delay its inception until post-mid-century developments necessitated computational solutions absent significant pre-war contributions from figures like Turing himself or innovations derived thereof such as Colossus machines designed specifically for cryptanalysis purposes during WWII era conflicts.

    Simultaneously considering geopolitical shifts resultant from prolonged peace periods extending through decades traditionally marked by tumultuous international relations; imagine European colonial empires maintaining dominance longer due primarily to delayed decolonization movements sparked partly by war-induced socio-political upheavals absent from this counterfactual scenario—thus altering trajectories concerning independence movements globally.

    ## Suggested Exercise

    In an alternate timeline where Archduke Franz Ferdinand survived his would-be assassination attempt leading World War I into postponement until after World War II ended without triggering any major global conflict beforehand; which technological development would most likely experience a delayed onset due primarily because its advancement was historically accelerated by wartime needs?

    A) The invention of penicillin
    B) Development of jet engine technology
    C) Advancements in digital computing technology
    D) Discovery and application of nuclear fission

    Correct Answer Explanation:

    C) Advancements in digital computing technology would most likely experience a delayed onset because its historical acceleration was significantly influenced by wartime needs such as code-breaking efforts during World War II—efforts led notably by figures like Alan Turing at Bletchley Park with machines like Colossus designed specifically for cryptanalysis purposes during WWII era conflicts.

    *** Revision 1 ***

    check requirements:
    – req_no: 1
    discussion: The question does relate indirectly to external knowledge about WWII-era
    technological advancements but does not explicitly require deep external academic
    knowledge beyond basic historical facts.
    score: 2
    – req_no: 2
    discussion: Understanding subtleties about how specific technologies were influenced
    by war contexts might help identify correct answers but could still allow guessing.
    – req_no: 3
    discussion’: The excerpt length satisfies this requirement well; however it could’ve been made more challenging through denser theoretical content.
    ? score_requirement_satisfied?
    external fact’: Include references comparing technological advancements timelines between WWII-era innovations driven by war versus peacetime innovations driven purely by scientific curiosity without direct military influence.
    revision suggestion’: To enhance requirement fulfillment particularly regarding external academic knowledge integration (requirement No. 1), consider incorporating comparisons between military-driven technological advancements versus peacetime scientific discoveries into both excerpt content and question framing. This approach could involve discussing specific technologies like radar or rocketry developed differently under peacetime conditions versus wartime pressures; thus linking required external academic knowledge more robustly into solving the exercise.
    revised excerpt’: In an alternate timeline where Archduke Franz Ferdinand evaded assassination—a pivotal event catalyzing World War I—European geopolitics underwent subtle yet profound transformations throughout the early twentieth century without immediate global conflict akin until after WWII concluded. Technological advancements characteristic typically spurred by wartime innovation emerged distinctly later than our known timeline suggests; notably affecting digital computing technology initially propelled significantly by wartime needs such as code-breaking endeavors exemplified prominently during WWII at Bletchley Park through figures like Alan Turing working on machines like Colossus designed specifically for cryptanalysis purposes amid WWII era conflicts compared against hypothetical peacetime developments focusing purely on scientific curiosity devoid military urgency.
    correct choice’: Advancements in digital computing technology due primarily being accelerated historically significantly through wartime necessities rather than purely scientific exploration.
    revised exercise”: Considering the alternate timeline described above where major global conflicts were postponed leading delayed technological advancements predominantly driven by wartime needs; compare these circumstances against typical peacetime scientific progressions free from immediate military influences—which technological development would most likely experience significant delays?
    incorrect choices’:
    – Development of antibiotics like penicillin driven largely independently from direct military applications though benefiting incidentally from increased funding during wars.
    – Evolutionary enhancements in rocketry technology initially motivated substantially due military strategic demands contrasting potential slower progression focusing solely civilian space exploration aims absent war pressures.
    iven_context_id’, ‘item_id’], axis=’columns’)
    df[‘given_context_id’] += df[‘item_id’]
    df[‘given_context_id’] += df[‘task_container_id’].apply(lambda c_id_intern:id_mapping[c_id][c_id_intern])
    df.drop(columns=[‘task_container_id’], inplace=True)
    print(‘Merging item context columns’)
    df.drop(columns=[‘item_context_{}’.format(i)
    for i,item_name
    in enumerate(item_names)], inplace=True)
    print(‘Done.’)
    print(‘Merging context columns’)
    df.drop(columns=[‘context_{}’.format(i)
    for i,item_name
    in enumerate(item_names)], inplace=True)
    print(‘Done.’)

    item_ids_for_analysis += list(df[‘given_context_id’])

    task_container_ids_for_analysis += list(df[‘task_container_id’])

    unique_item_ids_for_analysis += set(list(df[‘item_id’]))

    unique_task_container_ids_for_analysis.add(df[‘task_container_id’].iloc[-1])

    train_df_list.append(df.copy())

    print(‘Training items:’, sorted(unique_item_ids_for_analysis))
    print(‘Training task containers:’, sorted(unique_task_container_ids_for_analysis))
    print(‘n’)

    df_all_train_tasks_merged_by_task_and_context=pd.concat(train_df_list).sort_values(by=[‘participant’, ‘task_container_index’]).reset_index(drop=True).copy()

    df_all_train_tasks_merged_by_task_and_context.to_csv(‘../data/processed/all_train_tasks_merged_by_task_and_context.csv’, index=False)Merging item context columns done after merging column context_19..
    Merging item context columns done after merging column context_18..
    Merging item context columns done after merging column context_17..
    Merging item context columns done after merging column context_16..
    Merging item context columns done after merging column context_15..
    Merging item context columns done after merging column context_14..
    Merging item context columns done after merging column context_13..
    Merging item context columns done after merging column context_12..
    Merging item context columns done after merging column context_11..
    Merging item context columns done after merging column context_10..
    Merging item context columns done after merging column context_9..
    Merging item context columns done after merging column context_8..
    Merging item context columns done after merging column context_7..
    Merging item context columns done after merging column c…(truncated)# Get validation data### Load participant IDs# load participant IDs used during training phase (these will later be excluded when loading validation data!)
    with open(‘../data/preprocessed/train_participants.txt’, ‘r’) as f_handle_opened_filehandle_participant_IDs_training_phase_only_string_format_per_lineonly_newline_at_end_of_file_not_after_each_ID:f_handle_opened_filehandle_participant_IDs_training_phase_only_string_format_per_lineonly_newline_at_end_of_file_not_after_each_ID=f_handle_opened_filehandle_participant_IDs_training_phase_only_string_format_per_lineonly_newline_at_end_of_file_not_after_each_ID.read().splitlines()
    train_participants=list(f_handle_opened_filehandle_participant_IDs_training_phase_only_string_format_per_lineonly_newline_at_end_of_file_not_after_each_ID)# Load validation data### Get validation participants# load participant IDs used during training phase (these will later be excluded when loading validation data!)
    with open(‘../data/preprocessed/validation_participants.txt’, ‘r’) as f_handle_opened_filehandle_participant_IDs_validation_phase_only_string_format_per_lineonly_newline_at_end_of_file_not_after_each_ID:f_handle_opened_filehandle_participant_IDs_validation_phase_only_string_format_per_lineonly_newline_at_end_of_file_not_after_each_ID=f_handle_opened_filehandle_participant_IDs_validation_phase_only_string_format_per_lineonly_newline_at_end_of_file_not_after_each_ID.read().splitlines()
    validation_participants=list(f_handle_opened_filehandle_participant_IDs_validation_phase_only_string_format_per_lineonly_newline_at_end_of_file_not_after_each_ID)# Get test data### Get test participants# load participant IDs used during training phase (these will later be excluded when loading validation data!)
    with open(‘../data/preprocessed/test_participants.txt’, ‘r’) as f_handle_opened_filehandle_testparticipantsfilehandler:testparticipantsfilehandler=f_handle_opened_filehandle_testparticipantsfilehandler.read().splitlines()
    testparticipants=list(testparticipantsfilehandler)<empty_output]# Merge train-, val-, test-data together into one dataframe containing all data available### Load train-validation-test dataframe separately# load train-validation-test dataframe separately first!
    df_train_val_test=pd.read_csv(‘../data/interim/train_val_test_dataframe.csv’)

    df_train_val_test=df_train_val_test[df_train_val_test.participant.isin(train_participants+validation_participants+testparticipants)]

    print(‘# rows total:’, len(df_train_val_test))
    print(‘# rows containing training participants:’, len(df_train_val_test[df_train_val_test.participant.isin(train_participants)]))
    print(‘# rows containing validation participants:’, len(df_train_val_test[df_train_val_test.participant.isin(validation_participants)]))
    print(‘# rows containing test participants:’, len(df_train_val_test[df_train_val_test.participant.isin(testparticipants)]))

    assert sum([len(df_train_val_test[df_train_val_test.participant.isin(participants)])
    for participants
    in [train_participants,
    validation_participants,
    testparticipants]])==len(df_train_val_test),
    ‘Error! Number total rows does NOT equal sum number rows containing training/validation/test participants!’# rows total:_rows_containing_training__rows_containing_validation__rows_containing_testing__AssertionError:’AssertionError’
    <>I’m trying to wrap my head around this piece of code I found which seems quite intricate due to its use of nested loops and dictionary manipulations combined with some functional programming techniques using map() calls inside comprehensions – quite clever stuff! Here’s what it looks like:

    python
    return {key:value.__dict__[key].map(lambda el:(el.key,value.__dict__[key].index(el)+value.start)).to_list()
    for key,value
    in vars(obj).items()
    if isinstance(value,list)}

    From my understanding – though please correct me if I’m wrong – it appears that this snippet takes an object’s attributes via vars(), filters those which are lists, then transforms each element inside those lists using map(), presumably creating tuples consisting of some kind identifier (‘el.key’) along with a modified index value (‘value.__dict__[key].index(el)+value.start’). Finally it converts these mapped objects back into lists associated with their respective keys.

    Could you possibly clarify what exactly ‘el.key’ refers to? And also explain what role ‘value.start’ plays here? It seems crucial but isn’t defined anywhere visible here nor do I see any standard attribute called ‘start’ being commonly used alongside lists – am I missing something?

    I’d appreciate any insight you could provide since understanding this fully might help me implement something similar but tailored more closely towards my project requirements where objects have additional metadata attached that I’d also want included alongside indexes!

    Thanks!
    <> Sure! Let’s break down your code snippet step-by-step:

    The code processes an object’s attributes using Python’s built-in functions and dictionary comprehensions. Here’s what happens:

    1. **vars(obj).items()**: This retrieves all attributes of `obj` as key-value pairs where keys are attribute names and values are attribute values.

    2. **Filtering lists**: The comprehension filters these attributes to only include those whose values are lists (`if isinstance(value,list)`).

    3. **Mapping elements**: For each list attribute (`value`), it applies a transformation using `.map()` on each element (`el`). This transformation creates tuples `(el.key,value.__dict__[key].index