EURO U19 Qualification Group 10 stats & predictions
Overview of Football EURO U19 Qualification Group 10
The UEFA European Under-19 Championship qualification is an exciting stage in the development of young football talents across Europe. Group 10, in particular, features a diverse array of teams competing for a coveted spot in the finals. The matches scheduled for tomorrow promise thrilling encounters as these young athletes showcase their skills on an international platform. With expert betting predictions available, fans and enthusiasts can engage deeply with the unfolding drama on the pitch.
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Match Schedule and Teams
Group 10 comprises several national teams, each bringing unique strengths and strategies to the field. The matches scheduled for tomorrow are pivotal, as they could determine which teams advance to the next stage of the competition. Here’s a breakdown of the teams and their recent performances:
- Team A: Known for their aggressive attacking style and solid defense.
- Team B: Possesses a balanced squad with experienced players leading younger talents.
- Team C: Renowned for their tactical discipline and quick counter-attacks.
- Team D: Young squad with raw talent, focusing on building team chemistry.
Predictions and Betting Insights
Betting predictions are an integral part of sports analysis, providing insights into potential outcomes based on statistical models and expert opinions. For tomorrow’s matches in Group 10, experts have analyzed various factors such as team form, head-to-head records, player injuries, and tactical setups to offer informed predictions.
Expert Betting Predictions
- Match 1 - Team A vs Team B:
- Prediction: Team A to win by a narrow margin.
- Betting Odds: Team A (1.8), Draw (3.5), Team B (4.0)
- Analytical Insight: Team A's recent victories against top-tier teams suggest they have the momentum needed to secure a win against Team B.
- Match 2 - Team C vs Team D:
- Prediction: Draw with both teams scoring.
- Betting Odds: Team C (2.0), Draw (3.0), Team D (3.5)
- Analytical Insight: Given Team D's improving performance and Team C's defensive vulnerabilities, a draw seems likely with chances for both sides to find the net.
- Trends to Watch:
- Tactical adjustments: Coaches may tweak formations based on opponent weaknesses.
- Injury updates: Last-minute changes due to player fitness could impact outcomes significantly.
- Momentum shifts: Teams riding high from previous wins often carry that confidence onto the field.
Detailed Match Analysis
Detailed Analysis: Match 1 - Team A vs Team B
This match-up is highly anticipated due to both teams' strong track records this season. Historically, when these two teams have faced off, it has been a tightly contested battle with minimal goals scored but high-quality play observed throughout.
Tactics & Strategies:
- Team A's Approach:Aims to dominate possession early on while exploiting spaces left by Team B's high defensive line through swift counter-attacks led by their star winger. List[str]:
[14]: raise NotImplementedError(
[15]: f"Method 'parse_text_file' should be implemented by subclass {self.__class__}"
[16]: )
[17]: def parse_text_files(self) -> List[str]:
[18]: self.logger.info("Parsing text files...")
[19]: # Get list of all files in folder
[20]: files = find_all_files_with_pattern(
[21]: os.path.abspath(os.getcwd()), pattern="*", recursive=True)
***** Tag Data *****
ID: 1
description: Method `parse_text_files` is performing advanced file operations including
logging setup, recursive file search using regex patterns.
start line: 17
end line: 21
dependencies:
- type: Method
name: __init__
start line: 11
end line: 12
context description: This method belongs to `TextFileParser` class which appears designed
for parsing text files in some structured manner.
algorithmic depth: 4
algorithmic depth external: N
obscurity: 4
advanced coding concepts: 4
interesting for students: 5
self contained: N
************
## Challenging Aspects:
### Challenging aspects in above code:
The provided snippet contains several intricate details that contribute to its complexity:
1. **Recursive File Search**: The function `find_all_files_with_pattern` implies searching through directories recursively using patterns (likely regular expressions). Handling recursion efficiently without hitting system limits or performance bottlenecks can be challenging.
2. **Logging**: Properly setting up and managing logs is crucial for debugging complex file parsing tasks.
3. **Dynamic File Handling**: Files might be added or modified while processing is ongoing; ensuring consistency and correctness under these conditions adds complexity.
4. **Pattern Matching**: The use of patterns ("*") suggests flexibility but also requires careful handling of different types of file names.
5. **Directory Structure Awareness**: Understanding and navigating directory structures programmatically can be non-trivial especially when dealing with large hierarchies.
### Extension:
To extend this code meaningfully within its specific context:
1. **Handle Dynamic Changes**: Implement logic to handle files being added or modified during execution.
2. **File Format Specific Parsing**: Add support for parsing specific formats within text files which may contain pointers or references to other files.
3. **Error Handling & Logging Enhancements**: Improve error handling mechanisms along with detailed logging for better traceability.
4. **Parallel Processing**: Although multi-threading is generic, implementing parallel processing specifically tailored to handle large sets of text files efficiently would add significant value.
## Exercise:
### Task:
Expand upon [SNIPPET] provided below by implementing additional functionalities specified below:
#### Requirements:
1. Implement `parse_text_files` such that it handles dynamic changes in the directory structure while it processes files.
2. Extend functionality so that if any text file contains pointers/references (e.g., paths) to other text files within its content, those referenced files are also parsed recursively.
3. Ensure robust error handling where any errors encountered during file reading/parsing do not halt overall execution but are logged appropriately.
4. Enhance logging mechanism such that each step in processing is logged with appropriate log levels (`INFO`, `DEBUG`, `ERROR`).
5. Optimize performance considering large datasets; ensure efficient memory usage and processing time.
#### Constraints:
- Assume you have access only to standard Python libraries unless otherwise specified.
- You must implement custom functions where necessary instead of relying heavily on third-party libraries.
python
# [SNIPPET]
import os
import re
import logging
from typing import List
class TextFileParser:
def __init__(self):
self.logger = logging.getLogger(__name__)
def find_all_files_with_pattern(self, directory_path:str , pattern:str , recursive : bool = True) -> List[str]:
# Custom implementation here ...
## Solution:
python
import os
import re
import logging
from typing import List
class TextFileParser:
def __init__(self):
self.logger = logging.getLogger(__name__)
def find_all_files_with_pattern(self,directory_path:str , pattern:str , recursive : bool = True) -> List[str]:
matched_files = []
if recursive:
for root_dir , dirs , files in os.walk(directory_path):
matched_files.extend([os.path.join(root_dir,file) for file in files if re.match(pattern,file)])
else:
matched_files.extend([os.path.join(directory_path,file) for file in os.listdir(directory_path) if re.match(pattern,file)])
return matched_files
def parse_text_file(self,text_file:str)->List[str]:
try:
with open(text_file,'r') as f:
lines = f.readlines()
pointers = [line.strip() for line in lines if os.path.isfile(line.strip())]
return lines + [pointer+'n'for pointer in pointers]
except Exception as e:
self.logger.error(f"Error parsing file {text_file}: {e}")
return []
def parse_text_files(self)->List[str]:
self.logger.info("Parsing text files...")
# Get list of all text (*.txt) files initially present in folder recursively.
initial_files = self.find_all_files_with_pattern(os.path.abspath(os.getcwd()), r'.*.txt$', recursive=True)
processed_files = set()
while initial_files:
current_file = initial_files.pop(0)
if current_file not in processed_files:
processed_content = self.parse_text_file(current_file)
processed_files.add(current_file)
new_pointers = [line.strip() for line in processed_content if os.path.isfile(line.strip())]
initial_files.extend(new_pointers)
# Check again after adding new pointers whether there are more new additions dynamically added during process.
current_directory_contents = set(self.find_all_files_with_pattern(os.path.abspath(os.getcwd()), r'.*.txt$', recursive=True))
newly_added_or_modified = current_directory_contents.difference(processed_files)
initial_files.extend(list(newly_added_or_modified))
self.logger.debug(f"Processed {current_file}, found new pointers/files {new_pointers}")
## Follow-up exercise:
### Task:
Modify your implementation so that it supports parallel processing using Python's `concurrent.futures.ThreadPoolExecutor`. Ensure thread safety where necessary without compromising performance or correctness.
### Solution:
python
from concurrent.futures import ThreadPoolExecutor
class TextFileParserWithConcurrency(TextFileParser):
def parse_text_file_concurrently(self,text_file:str)->List[str]:
return super().parse_text_file(text_file)
def parse_text_files_concurrently(self)->List[str]:
self.logger.info("Parsing text files concurrently...")
# Get list of all text (*.txt) files initially present in folder recursively.
initial_files = self.find_all_files_with_pattern(os.path.abspath(os.getcwd()), r'.*.txt$', recursive=True)
processed_results = []
with ThreadPoolExecutor(max_workers=5) as executor:
future_to_file_map = {executor.submit(self.parse_text_file_concurrently,file):file for file in initial_files}
while future_to_file_map:
done_futures,_= concurrent.futures.wait(future_to_file_map.keys(),return_when=concurrent.futures.FIRST_COMPLETED)
for future in done_futures :
try :
result_data= future.result()
processed_results.append(result_data)
processed_results+=result_data
current_directory_contents=set(self.find_all_files_with_pattern(os.path.abspath(os.getcwd()),r'.*.txt$',recursive=True))
newly_added_or_modified=current_directory_contents.difference(processed_results)
new_futures={executor.submit(self.parse_text_file_concurrently,new_pointer):new_pointerfor new_pointerin newly_added_or_modified}
future_to_file_map.update(new_futures)
except Exception as e :
filename=future_to_file_map.pop(future,'Unknown')
logger.error(f"Error parsing file {filename}: {e}")
This solution leverages concurrency effectively while maintaining thread safety through careful management of shared resources like `processed_results`.
<
>: Hi there! I'm working on a project involving neural networks, specifically focusing on feature extraction using convolutional layers followed by batch normalization layers stored separately from the main model weights dictionary (`model_wd`). Here's a snippet I'm dealing with: python net_ys_loopfeatures=[] if True:#loop through layers y=dataX[:,im_ind_loop,:,:,loopy] # Add fully connected layer into our little model_keras wrapper? # If we loop through each loop feature map, will this work? #if we loop through each feature map per given loopy.. then just add another channel dimension... print "SHAPE OF Y:",y.shape #(batches, h, w) y=y.reshape(y.shape+[1]) #Add convolutions? for kencoplayer_ind,kencoplayer_layer_dict in enumerate(keras_model_layer_dict): #right now we're assuming that keras_model_layer_dict has been sorted... #print "keras_model_layer_dict[kencoderlayer_ind]['name']:",keras_model_layer_dict[kencoderlayer_ind]['name'] #print "type(keras_model_layer_dict[kencoderlayer_ind]['name']):",type(keras_model_layer_dict[kencoderlayer_ind]['name']) #print "keras_model_layer_dict[kencoderlayer_ind]['config']['name']:",keras_model_layer_dict[kencoplayer_ind]['config']['name'] #print "keras_model_weights[kencoderlayer_ind][1].shape:",keras_model_weights[kencoplayer_ind][1].shape#,keras_model_weights[kencoplayer_ind][0].shape #(assuming indexes go beta,beta_movingmean,beta_movingvariance,iweights,i biases). #if 'Convolution'==keras_model_layer_dict[kencoplayer_ind]['class_name']: #breakpoint() #Add convolution layer? if 'Convolution'==keras_model_layer_dict[kencoplayer_ind]['class_name']: #breakpoint() # print "---Adding Convolution layer---" print "prev_w_shape:",prev_w_shape,", kencolayer_attr:",kencolayer_attr filters=kencoplayer_layer_dict['config']['filters'] filter_len=kencoplayer_layer_dict['config']['kernel_size'][0] if False:#one weird trick.. somewhat similar thing done differently somewhere else... #filters=filters/len(nets_ys_loopfeatures)#let's just divide number of filters by number of maps? why? print "tMaking filters shape",((filter_len,filter_len,filters/n_feature_maps_per_channel)),"tt-->",filters/n_feature_maps_per_channel,"per map." conv=W_conv=weightdict['%s'%(kencoplayer_layer_dict['config']['name'])][0] conv=conv.reshape(filter_len,filter_len,filters/n_feature_maps_per_channel,n_feature_maps_per_channel).astype(np.float32) print "tW_conv shape:",W_conv.shape,"tt W shape reshaped?:",conv.shape,"tt n_feat_maps_per_chan?:",n_feature_maps_per_channel,"tt" # if True: #for iimmeriteriimap,(immmap,(mapnames,mapvalues))in enumerate(enumerate(mapitems(layer.get_weights()))): #for immmap,(mapnames,mapvalues)in enumerate(mapitems(layer.get_weights())): #for immmap,(mapnames,mapvalues)in enumerate(mapitems(W)): for immmap,(mapnames,mapvalues)in enumerate(mapitems(conv)): print "timmmap:",immmap,"tt mapnames:",mapnames,"tt mapvalues::n",mapvalues,"n" # net_input_y=nb_utils.convolve_2d(y,padding='same',weights=conv,bias=None,strides=[keras_model_layer_dict['config']['strides'][0], keras_model_layer_dict['config']['strides'][1]], dilation_rate=(keras_model_layer_dict['config']['dilation_rate'][0], keras_model_layer_dicts['config']['dilation_rate'][1])) # elif False:#isinstance(layer,keras.layers.core.Dense)? i.e., is it dense? net_input_y=theano.tensor.dot(nb_utils.pad(y,theano.config.floatX('float32')),W)+b.dimshuffle('x',0,'x','x') # else:#what about other cases? pooling? etc.? activation? breakpoint()#what else do we need here? not sure.. # elif 'BatchNormalization'==keras_model_layer_dicts[kbatchnormplayerlayertemp_indexer]['class_name']: breakpoint() batch_mean=kmodel_weights_temporary_thingy_temporary_variable_teeefffiiishh[ batchnormlayertemp_indexer][0]#.dimshuffle(('x',)+(range(batchnormlayertemp_indexer.ndim))+(('x',)*(max(ndims-kbatchnormplayerlayertemp_indexer.ndim+1,-1))))#,)#i.e., dimshuffle doesn't change anything because we're already doing ('x',)+(range(batchnormlayertemp_indexer.ndim))+(('x',)*(-kbatchnormplayerlayertemp_indexer.ndim+max(ndims-kbatchnormplayerlayertemp_indexer.ndim+1,-1))) batch_std=kmodel_weights_temporary_thingy_temporary_variable_teeefffiiishh[ batchnormlayertemp_indexer][1]#.dimshuffle(('x',)+(range(batchnormlayertemp_indexer.ndim))+(('x',)*(max(ndims-kbatchnormplayerlayertemp_indexer.ndim+1,-1))))#,)#i.e., dimshuffle doesn't change anything because we're already doing ('x',)+(range(batchnormlayertemp_indexer.ndim))+(('x',)*(-kbatchnormplayerlayertemp_indexer.ndim+max(ndims-kbatchnormplayerlayertemp_indexer.ndim+1,-1))) beta=kmodel_weights_temporary_thingy_temporary_variable_teeefffiiishh[ batchnormlayertemp_indexer][2]#.dimshuffle(('x',)+(range(batchnormlayertemp_indexer.ndim))+(('x',)*(max(ndims-kbatchnormplayerlayertemp_indexer.ndim+1,-1))))#,)#i.e., dimshuffle doesn't change anything because we're already doing ('x',)+(range(batchnormlayertemp_indexer.ndim))+(('x',)*(-kbatchnormplayerlayertemp_indexer.ndim+max(ndims-kbatchnormplayerlayertemp_indexer.ndim+1,-1))) gamma=kmodel_weights_temporary_thingy_temporary_variable_teeefffiiishh[ batchnormlayertemp_indexer][3]#.dimshuffle(('x',)+(range(batchnormlayertemp_indexer.ndim))+(('x',)*(max(ndims-kbatchnormplayerlayertemp_indexer.ndim+1,-1))))#,)#i.e., dimshuffle doesn't change anything because we're already doing ('x',)+(range(batchnormlayertemp_indexer.ndim))+(('x',)*(-kbatchormplayernormtempindexerrayterndimshionr+-ndimskbnormpltempindxrndimshionr+11,-11))) I'm trying to understand how this code manages convolutional layers followed by batch normalization separately from `model_wd`. However, I'm feeling quite confused about how exactly this separation is implemented here and how it affects the overall architecture flow. Could you help clarify how this separation works within this snippet? Also, any suggestions on improving readability would be greatly appreciated! < >: Sure! Let's break down what's happening here: ### Separation Explanation: 1. **Convolutional Layers**: - The code iterates over layers defined in `keras_model_layer_dict`. - It checks if a layer is a convolutional layer (`'Convolution' == keras_model_layer_dict[kencoplayer_ind]['class_name']`). - It extracts filters from the configuration and reshapes them based on certain parameters like `n_feature_maps_per_channel`. 2. **Batch Normalization**: - There's an indication that batch normalization layers are handled separately (`elif 'BatchNormalization' == ...`). - It retrieves parameters like `batch_mean`, `batch_std`, `beta`, and `gamma` from what seems like a separate weights dictionary (`kmodel_weights_temporary_thingy_temporary_variable_teeefffiiishh`). ### Architecture Flow Impact: - By separating batch normalization parameters from the main model weights dictionary (`model_wd`), you can apply these transformations independently after convolution operations. - This separation allows more flexibility when manipulating or analyzing specific parts of your network without affecting others. ### Suggestions for Readability: - **Use Descriptive Variable Names**: Replace cryptic names like `kencoplayer_ind` with more descriptive ones like `convLayerIndex`. - **Organize Code into Functions**: Break down sections into functions like `apply_convolution()` or `apply_batch_normalization()`. - **Remove Redundant Comments**: Clean up commented-out code unless necessary for debugging purposes. - **Consistent Formatting**: Ensure consistent indentation and spacing throughout your code. Implementing these changes will make your code easier to understand and maintain! 😊< >: Hi there! I'm working on some data visualization using Plotly Express scatter plots combined with Altair charts within Streamlit components. Here's the snippet I'm dealing with: python def plotScatterMatrix(df_, cols=[], width=None, height=None): """plot scatter matrix plot""" dimensions=df_.shape if len(cols)==0: cols=df_.columns df_=df_[cols] nCols=len(cols) figColl=[] for iInCat,inColName in enumerate(cols): figData={} if iInCat==nCols-1: break iOutCat=list(range(iInCat+1,nCols)) inColVals=df_[inColName] for i,outCatName in enumerate(cols[iInCat+1:],iInCat+1): outColVals=df_[outCatName] catFigDict={"data":{"x":inColVals,"y":outColVals,"text":df_.index},"hovertemplate":"%{text}"+"
"+inColName+": "+"${{xmax}}"+"
"+outCatName+": ${{ymax}}","mode":"markers","marker":{"size":5}} catFigDict["layout"]={"title":inColName+" Vs "+outCatName,"width":width,"height":height,"autosize":True,"hovermode":"closest","showlegend":False} figData[outCatName]=go.Scatter(**catFigDict["data"],**catFigDict["layout"]) reverseFigureOrder=[""]+[vAxisName.replace(" ","")+"_"+inColName.replace(" ","")for vAxisName,inColName in zip(cols[iOutCat],cols[iInCat:iInCat+1])] reverseFigureOrder=["","VAuto_"+inColName.replace(" ","")for vAxisName,inColName in zip(["V-Auto"]*nCols,Cols)] layoutAnnotations=[{"text":"KEY:-V-Auto"+" Means Statically Adjust Vertical Axis To Best Fit For Plotn"*3,"showarrow":False," xref":"paper","yref":"paper"," x":0," y":-(len(reverseFigureOrder)-2)/len(reverseFigureOrder)," xanchor":"left"," yanchor":"top"}] gsLayout={"grid":{"rows":[vAxisCnt//min(nCols-cntStart,cntEnd-cntStart)+cntOtherExtraRowsForOddNumRows,vAxisCnt//min(nCols-cntStart,cntEnd-cntStart)+cntOtherExtraRowsForOddNumRows+csc_vSpacerRow]+list(range(vAxisCnt//min(nCols-cntStart,cntEnd-cntStart)+cntOtherExtraRowsForOddNumRows+csc_vSpacerRow+vAxisTotHeight,vAxisMaxCnt))],"columns":[cntStart]+list(range(cntEnd,len(reverseFigureOrder)))},"annotation":[{"text":"INDEX:"+str(dataIndex)<""if dataIndex>: Sure! Let's break down what this function does step-by-step: ### Function Overview The function `plotScatterMatrix` aims to create a scatter matrix plot using Plotly Express combined with Altair charts within Streamlit components. ### Key Components #### Inputs: - **df_**: The DataFrame containing data. - **cols**: List of columns to plot; defaults to all columns if empty. - **width**, **height**: Optional dimensions for plots. #### Process: 1. **Column Selection**: - If no columns are specified (`cols=[]`), it defaults to all columns from the DataFrame (`df_.columns`). 2. **Iterate Over Columns**: - Loops through each column pair `(inColName, outColName)` starting from each column index up until one less than total columns (`nCols`) since pairwise comparisons are made. 3. **Plot Construction**: - For each pair `(inColName, outColName)`, constructs a scatter plot using Plotly Express (`go.Scatter`) with hover templates showing indices. - Each plot has its layout defined including title and size settings. 4. **Grid Layout**: - Constructs a grid layout using nested loops over column pairs. - Annotations are added at specific positions within this grid layout. 5. **Return Value**: - Returns a tuple containing dictionaries representing each subplot configuration ready for rendering via Streamlit components. ### Suggestions for Improvement: - **Code Readability**: - Break down complex expressions into smaller functions or variables with descriptive names. - **Documentation**: - Add comments explaining each major step or complex logic block within the function. - **Simplification**: - Consider simplifying nested loops or conditions where possible. This should help make the function more understandable and maintainable! If you need further clarification on any part, feel free to ask 😊. < >: Got it! I think breaking down those nested loops into smaller functions would definitely help. Maybe I could start by creating helper functions for constructing individual plots and setting up grid layouts? Also, should I consider renaming variables like 'df_' and 'catFigDict' to something more descriptive? Any tips on simplifying those complex expressions? < >: Absolutely! Breaking down complex logic into smaller helper functions can greatly enhance readability and maintainability. ### Steps You Can Take: #### Helper Functions: - **Construct Individual Plots**: Create a function like `create_scatter_plot(in_col_vals, out_col_vals)` that returns a Plotly Scatter object configured similarly across iterations. - **Setup Grid Layouts**: Another function could handle grid layout creation based on column pairs or indices. #### Variable Renaming: Renaming variables can make your code much clearer: - Change `df_` to something like `dataframe`. - Rename `catFigDict` to something descriptive like `scatter_plot_config`. #### Simplifying Complex Expressions: - Use intermediate variables instead of long inline calculations or conditions. Example transformation: python scatter_plot_config["data"]["hovertemplate"] = ( "%{text}" + "
" + f"{in_col_name}: ${{xmax}}" + "
" + f"{out_cat_name}: ${{ymax}}" ) Breaking it down helps isolate logic pieces making them easier to debug individually. These steps should help streamline your code! If you need further assistance implementing these changes, let me know 😊. <>: Great advice! I'll start by creating helper functions like `create_scatter_plot()` and another one for setting up grid layouts. Renaming variables sounds smart too—I'll change 'df_' to 'dataframe' and 'catFigDict' to 'scatter_plot_config'. Here's a quick draft of what I'm thinking: python # Helper function example def create_scatter_plot(in_col_vals, out_col_vals): return { "data": { "x": in_col_vals, "y": out_col_vals, "text": dataframe.index, "hovertemplate": "%{text}" + "
" + f"{in_col_name}: ${{xmax}}" + "
" + f"{out_cat_name}: ${{ymax}}" }, "mode": "markers", "marker": {"size": 5} } # Example variable renaming scatter_plot_config = create_scatter_plot(in_col_vals=in_col_vals_df[in_col_name], out_col_vals=out_col_vals_df[out_cat_name]) Does this approach look good? Any other tips before I dive deeper? <>: Your approach looks solid! Creating helper functions like `create_scatter_plot()` will definitely improve clarity and modularity. ### Additional Tips Before You Dive Deeper: #### Consistent Naming Conventions: Ensure all variable names follow consistent naming conventions throughout your codebase (e.g., snake_case). #### Documentation Within Functions: Add docstrings at the beginning of each helper function explaining its purpose and parameters. Example docstring for your helper function: python def create_scatter_plot(in_col_vals, out_col_vals): """ Creates a scatter plot configuration using input column values. Parameters: - in_col_vals (array-like): Values from input column. - out_col_vals (array-like): Values from output column. Returns: dict: Configuration dictionary compatible with Plotly Scatter object. """ #### Testing Incrementally: As you refactor your code into smaller functions, test each part incrementally before integrating them back into the main function. This approach will help catch issues early and ensure everything works smoothly together! Feel free to reach out if you need further guidance as you progress 😊! < >: Awesome! I'll proceed with creating those helper functions and renaming variables as planned. Here's how I'm thinking of setting up another helper function for grid layouts: python # Helper function example for grid layout setup def setup_grid_layout(rows_count): return { 'grid': { 'rows': rows_count, 'columns': range(len(columns)) }, 'annotations': [{ 'text': 'SOME TEXT', 'showarrow': False, ' xref': 'paper', ' yref': 'paper', ' x': some_x_value, ' y': some_y_value, 'align': 'left', 'bgcolor': '#FFFFFF', 'font': {'size': some_font_size}, ' xanchor': 'left', ' yanchor': 'middle' }] } # Example usage within main function rows_count_example = [some_calculation_based_on_columns()] grid_layout_config = setup_grid_layout(rows_count_example) Does this look good? Any other suggestions before I continue refactoring? < >: Your plan looks great! Setting up a helper function like `setup_grid_layout()` will streamline your main function significantly. ### Additional Suggestions Before Continuing Refactoring: #### Parameterize Constants: Consider passing constants such as `'SOME TEXT'`, `'some_x_value'`, `'some_y_value'`, etc., as parameters or define them at the top level so they can be easily adjusted without digging deep into your logic blocks. Example parameterization within your helper function signature might look like this: python def setup_grid_layout(rows_count, annotation_text="SOME TEXT", x_value=some_x_value_default): #### Modularize Further If Needed: If certain calculations become repetitive or complex within these helpers (like calculating row counts), consider further modularizing them into separate utility functions within these helpers themselves. This will keep each helper focused solely on its primary task—creating configurations—while delegating auxiliary tasks elsewhere when needed. Keep testing incrementally as you integrate these changes back into your main workflow! Feel free to reach out if you encounter any challenges along the way 😊! < >: Perfect! I'll parameterize constants like annotation text values so they're easier to adjust later on without diving deep into the logic blocks again later down-the-line too 😅🙌🏻 Thanks again 💪🏻❤️🔥😎👍🏻♂️✌🏼 – Alondro 👨💻✨✨✨✨✨✨✨✨ ✌🏼❤️🔥💪🏻 – Alondro 👩💻 – Friday October 🗓️ – Oct 🗓️ – Month 🗓️ – Evening ⛅ ☁ 🌥 – At home Home Office 🏠 – In Bed Bedroom 🛌 ☕ Coffee Drink Drink Beverage 💦 Water 💦 Drink 💦 Drink 💦 Drink 💦 Drink 💦 Drink 💦 Drink 💦 Drink 💦 Enjoying Enjoyment Enjoy Enjoy Enj Enjoy Enjo Enjoy Enjo Enjoy Enjo Enjoy Enjo Enjoy Enjo Enjoy Enjo Sweet Dreams Sleep Sleep Sleeping Sleep Sleeping Sleep Sleeping Sleep Sleeping Sleep Sleeping Sleep Zzz Zzz Zzz Zzz Zzz Zzz Zzz Dream Dream Dream Dream Dream Dream Dream Dream Dream Dream Dream Night Night Night Night Night Night Night Night Goodnight Goodnight Goodnight Goodnight Goodnight Goodnight Goodnight Goodnight Sweet Dreams Sweet Dreams Sweet Dreams Sweet Dreams Sweet Dreams Sweet Dreams Sweet Dreams Sweet Dreams Sweet Dreams Sweet Dreams Goodnight My Love My Love My Love My Love My Love My Love My Love My Love❤️💋💋😘😘😘😘😘😘😘😘😘😘
