Club Jeunesse Ben Guerir: A Comprehensive Guide for Sports Bettors

Overview / Introduction about the Team

Club Jeunesse Ben Guerir is a prominent football team based in the city of Ben Guerir, Morocco. Competing in the Moroccan National Division 1, the club has carved out a niche for itself with its dynamic playing style and strategic gameplay. Founded in 1975, Club Jeunesse Ben Guerir is managed by an experienced coaching staff that continually seeks to improve team performance.

Team History and Achievements

The history of Club Jeunesse Ben Guerir is marked by significant achievements and memorable seasons. While they have not clinched major titles, their consistent league performances and participation in various domestic cup competitions highlight their competitive spirit. The club has seen notable seasons where they finished in strong positions within the league standings.

Current Squad and Key Players

The current squad of Club Jeunesse Ben Guerir boasts a mix of seasoned players and emerging talents. Key players include:

• Goalkeeper: Known for his agility and sharp reflexes.
• Defenders: Solid defensive line providing stability at the back.
• Midfielders: Dynamic midfielders who control the tempo of the game.
• Forwards: Strikers with a knack for finding the back of the net.

Team Playing Style and Tactics

The team employs a versatile 4-3-3 formation that allows them to adapt to various opponents. Their strategies focus on high pressing, quick transitions, and maintaining possession. Strengths include strong midfield control and tactical flexibility, while weaknesses may involve vulnerability during counterattacks.

Interesting Facts and Unique Traits

Fans affectionately call Club Jeunesse Ben Guerir “The Lions of Ben Guerir.” The club’s fanbase is passionate, supporting them through thick and thin. Rivalries with neighboring teams add excitement to their matches, while traditions such as pre-match rituals strengthen community ties.

Lists & Rankings of Players, Stats, or Performance Metrics

• ✅ Top Scorer: Leading goalscorer with impressive stats.
• ❌ Weakness: Defensive lapses against top teams.
• 🎰 Upcoming Match: High potential for an upset win.
• 💡 Player Potential: Rising star showing promise in youth ranks.

Comparisons with Other Teams in the League or Division

In comparison to other teams in the division, Club Jeunesse Ben Guerir stands out due to its aggressive playing style and ability to compete against stronger opponents. While some teams may have more resources, CJBG’s tactical discipline often gives them an edge in crucial matches.

Case Studies or Notable Matches

A standout match was their victory against a top-tier team last season, which showcased their resilience and tactical acumen. This game highlighted key players stepping up when it mattered most, leading to a memorable win that boosted team morale.

Team Stats Summary
Total Goals Scored:	Total Goals Conceded:	Last Five Matches Form:
X Goals	X Goals	[W-D-L]

Tips & Recommendations for Analyzing the Team or Betting Insights

To make informed betting decisions on Club Jeunesse Ben Guerir:

• Analyze recent form trends to gauge current momentum.
• Evaluate head-to-head records against upcoming opponents for insights into likely outcomes.</li
• Carefully consider player injuries or suspensions that might impact performance.
• Leverage statistical data on key players’ performances in different match scenarios.
• Maintain awareness of any tactical changes implemented by coaches.
• Bet on CJBG when they play at home due to their strong home record.
• Analyze weather conditions as they can influence match dynamics.
• Pay attention to managerial changes that could affect team strategy.
• Bet strategically during high-stakes matches where CJBG has historically performed well.
  

  Frequently Asked Questions (FAQ)

  What are Club Jeunesse Ben Guerir’s recent performances like?</h3

  The team has shown steady improvement recently, securing several wins against mid-table teams. Their performance metrics indicate positive trends heading into upcoming fixtures.</p

  Who are some key players to watch?</h3

  The squad includes standout performers like [Player Name], whose goal-scoring ability has been crucial this season. Additionally, [Midfielder Name] provides creative playmaking from midfield positions.</p

  How does CJBG fare against top-tier teams?</h3

  CJBG often plays competitively against stronger teams by employing disciplined tactics focused on defense-first strategies followed by swift counterattacks when opportunities arise.

  Betting odds – How favorable are they?</h3

  Odds vary depending on matchups; however generally speaking CJBG tends toward being undervalued given their ability underdog status especially when facing higher-ranked opponents.

  “With its robust tactics and spirited fanbase support,” says sports analyst [Name], “Club Jeunesse Ben Guerir remains one of those unpredictable gems capable of surprising even seasoned bettors.”</blockquote

  Pros & Cons of Current Form or Performance</h2

  • ✅ Strong home record making them formidable at home grounds;</lI
  • ❌ Inconsistency away from home causing occasional unexpected losses;</lI
  • 💡 Recent form shows improvement suggesting potential upturns ahead;</lI
  • ❌ Dependence on few key players who can be neutralized effectively by opposition scouting;</lI
  • ✅ Tactical flexibility allowing adaptation during matches;</lI

    `[0]: import numpy as np
    [1]: import pandas as pd
    [2]: import matplotlib.pyplot as plt

    [3]: def linear_regression(x,y):
    [4]: m = len(x)
    [5]: x_mean = sum(x)/m
    [6]: y_mean = sum(y)/m

    [7]: num = sum([(x[i]-x_mean)*(y[i]-y_mean) for i in range(m)])
    [8]: denum = sum([(x[i]-x_mean)**(len(x)-1) for i in range(m)])

    [9]: b = num/denum
    [10]: a = y_mean – b*x_mean

    [11]: return(a,b)

    [12]: def mean_absolute_error(y,y_predicted):

    [13]: error = 0

    [14]: m = len(y)

    [15]: error = sum([abs(y[i]-y_predicted[i]) for i in range(m)])/m

    ***** Tag Data *****
    ID: 1
    description: Linear regression function implementing custom calculation steps using
    list comprehensions.
    start line: 3
    end line: 11
    dependencies:
    – type: Function
    name: linear_regression
    start line: 3
    end line: 11
    context description: This function calculates linear regression coefficients ‘a’ (intercept)
    and ‘b’ (slope) using mean values and list comprehensions instead of relying on built-in
    functions from libraries such as NumPy or SciPy.
    algorithmic depth: 4
    algorithmic depth external: N
    obscurity: 4
    advanced coding concepts: 4
    interesting for students: 5
    self contained: Y

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

    ### Challenging aspects in above code

    1. **Numerical Stability**: Calculating means (`x_mean` and `y_mean`) involves summation which can lead to numerical instability if `x` or `y` contains very large numbers due to floating-point precision issues.

    2. **Edge Cases**: Handling cases where `x` might contain identical elements leading to zero variance which would result in division by zero when calculating `denum`.

    3. **List Comprehension Complexity**: The use of list comprehensions inside summations requires careful indexing (`i`) management ensuring each element aligns correctly between lists `x` and `y`.

    4. **Mean Calculation**: Efficiently computing means without explicitly iterating through lists multiple times.

    5. **Efficiency Considerations**: Ensuring that operations are efficient even for large datasets by minimizing redundant calculations.

    ### Extension

    1. **Handling Multivariate Linear Regression**: Extend functionality from simple linear regression (one independent variable) to multiple independent variables (multivariate).

    2. **Weighted Linear Regression**: Introduce weights into regression calculation where each data point contributes differently based on some weight criteria.

    3. **Error Metrics Calculation**: Extend functionality to compute additional error metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), etc., after computing regression coefficients.

    4. **Data Normalization/Standardization**: Include preprocessing steps like normalization or standardization before performing regression analysis.

    5. **Robustness Against Outliers**: Implement methods such as RANSAC (Random Sample Consensus) algorithm within this context to handle outliers more effectively.

    ## Exercise

    ### Problem Statement:

    Given the snippet below:
    [SNNIPET]

    Extend this functionality into a comprehensive Python module capable of handling advanced linear regression tasks including multivariate linear regression with weighted inputs and robust error metrics computation while ensuring numerical stability throughout computations.

    **Requirements**:

    1. Write functions:
    – `multivariate_linear_regression(X, y)` where X is now a matrix representing multiple features per observation.
    – `weighted_linear_regression(x, y, weights)` where weights is an array representing weights corresponding to each observation `(xi, yi)` pair.

    Ensure both functions return intercept(s) `a` along with slope(s) `b`.

    2. Implement additional utility functions:
    – `mean_squared_error(y_true, y_pred)`
    – `root_mean_squared_error(y_true, y_pred)`

    These should compute respective error metrics given true values (`y_true`) and predicted values (`y_pred`).

    **Constraints**:
    – You must handle edge cases like zero variance gracefully without crashing.
    – Ensure your implementation is numerically stable even with very large datasets.
    – Avoid using external libraries except basic ones like NumPy if necessary but prefer pure Python implementations wherever possible.

    ## Solution

    python
    def linear_regression(x,y):
    m = len(x)

    # Check if there’s only one unique value causing zero variance issue
    if len(set(x)) == 1:
    raise ValueError(“Variance of x cannot be zero.”)

    x_mean = sum(x)/m
    y_mean = sum(y)/m

    num = sum([(x[i]-x_mean)*(y[i]-y_mean) for i in range(m)])

    denum = sum([(x[i]-x_mean)**(len(x)-1) for i in range(m)])

    b = num/denum

    def multivariate_linear_regression(X,y):
    import numpy as np

    # Add bias term (intercept)
    X_bias = np.c_[np.ones((X.shape[0],1)), X]

    # Calculate coefficients using Normal Equation method avoiding direct inversion instability issues
    theta_best=np.linalg.pinv(X_bias.T.dot(X_bias)).dot(X_bias.T).dot(y)

    return theta_best

    def weighted_linear_regression(x,y,w):
    w_sum=sum(w)

    x_weighted_sum=sum([w[i]*x[i] for i in range(len(w))])
    y_weighted_sum=sum([w[i]*y[i] for i in range(len(w))])

    x_weighted_squared_sum=sum([w[i]*x[i]**len(x)for i in range(len(w))])

    xy_weighted_sum=sum([w[i]*x[i]*y[i]for i in range(len(w))])

    b=(w_sum*xy_weighted_sum-x_weighted_sum*y_weighted_sum)/(w_sum*x_weighted_squared_sum-x_weighted_sum**len(x))

    a=y_weighted_sum/w_sum-b*x_weighted_sum/w_sum

    return(a,b)

    def mean_squared_error(y_true,y_pred):
    return(sum((np.array(y_true)-np.array(y_pred))**2)/len(y_true))

    def root_mean_squared_error(y_true,y_pred):
    return(np.sqrt(mean_squared_error(y_true,y_pred)))

    ## Follow-up exercise

    ### Problem Statement:

    Now extend your module further by introducing regularization techniques such as Lasso (L1 regularization) and Ridge (L2 regularization). Modify your multivariate linear regression function accordingly:

    #### Requirements:

    1. Update your function signature:
    – `multivariate_regularized_linear_regression(X,y,lambd=’none’,reg_type=’ridge’)`

    Where:
    – `lambd`: Regularization parameter lambda >0; set ‘none’ if no regularization is required.
    – `reg_type`: Type of regularization (‘lasso’, ‘ridge’) indicating whether Lasso or Ridge regularization should be applied respectively.

    #### Constraints:

    – Ensure you implement these regularizations manually without using sklearn directly but leveraging NumPy where necessary.
    – Test your implementation rigorously ensuring numerical stability across varied datasets sizes including edge cases like highly correlated features.

    ## Solution

    python

    def multivariate_regularized_linear_regression(X,y,lambd=’none’,reg_type=’ridge’):

    import numpy as np

    # Add bias term (intercept)
    X_bias=np.c_[np.ones((X.shape[0],1)),X]

    # Set up identity matrix excluding first column since it’s intercept term not regularized

    I=np.eye(X_bias.shape[1])
    I[0][0]=0

    # Compute regularized coefficients based on specified type

    if reg_type==’ridge’:
    theta_best=np.linalg.inv(X_bias.T.dot(X_bias)+ lambd*I).dot(X_bias.T).dot(y)

    elif reg_type==’lasso’:
    from sklearn.linear_model import Lasso

    model=Lasso(alpha=lambd,max_iter=10000,tol=0.001)
    model.fit(X_bias,y)
    theta_best=model.coef_
    theta_best=np.insert(theta_best,obj=0,value=model.intercept_)

    This follow-up exercise introduces more complexity requiring students not only understand regularization techniques but also implement them efficiently while maintaining numerical stability across varied scenarios including high-dimensional data spaces.

    <>: Hi there! I’m working with this code snippet that defines a class called `_DynamicBottleneck`. It seems like it’s used within something called `_ResBlock`, handling convolutional layers dynamically based on certain parameters like kernel size (`ksize`) and stride (`stride`). Here’s the snippet:

    python
    class _DynamicBottleneck(nn.Module):
    “””
    Similar with BasicBlock except *
    stride is strided conv layer,
    extra params are introduced according needs,
    norm layer is executable。
    “””
    expansion = _cfg[‘width_expansion’]
    def __init__(self,in_channels,mid_channels,out_channels,stride,kernel_size,bias,
    pad_type=”,norm_layer=None,dilation=1,effective_ksize=None,**kwargs):
    super(_DynamicBottleneck,self).__init__()
    self.norm_layer=norm_layer
    self.stride=stride
    self.in_channels=in_channels
    self.mid_channels=mid_channels
    self.out_channels=out_channels
    self.kernel_size=kernel_size#to decide padding mode,padding size;
    self.dilation=dilation#to decide padding mode,padding size;
    self.pad_type=pad_type#padding mode,’same’,’valid’,’pad’;
    self.bias=bias#bool type;
    self.effective_ksize=None#can be inferred,kernel effective size;
    #None:(ksize==actual_ksize),
    #(ksize-dilation*(ksize-1)-1)==effective_ksize,
    #(e.g., ksize=7,dilation=(ksize-6)//6+1==effective_ksize==5);
    #(e.g., ksize=5,dilation=(ksize-4)//4+1==effective_ksize==5);
    #(e.g., ksize=5,dilation=(ksize-4)//6+1==effective_ksize==3);
    #(e.g., ksize=7,dilation=(ksize-6)//4+1==effective_ksize==7);
    #
    #
    #
    #
    #
    #
    #
    #
    #
    #
    #####
    #####
    #####
    #if None,pad_type must be valid/pad/same;
    #other wise,effective_ksize already consider these params,simply infer padding length;
    #maybe can infer dilation?
    #####
    #####
    #####
    #
    #
    #
    #
    #
    #
    #
    if(self.effective_ksize!=None):#given effective kernel size;
    self.__set_padding()#padding length can be derived from given params,effective_ksize;
    assert(self.pad_length>=0),’padding length calculated is less than zero.’
    #this case do not exist because we give ceiling operation here;
    #but still need double check;
    #if exist,this maybe because given kszie too small??
    #(eg.kernle size=real kernel size??
    #’cause real kernel size=dilation*(kernel_size-dilation);
    #’cause dil*(ksz-dil)>=ksz-dil*ksz=>dil*ksz>=dil*ksz+dil**2=>0>=dil**>=True!!
    #’cause effective ksz always smaller than ksz!!
    #’cause padding should never greater than dilation*(kernel_size-dilation);
    #’cause padding==>dil*(ksz-dil)-ksz+pad_length=>pad_length>=ksz-dil*(ksz-dil)=>True!!
    #’cause pad_length:=ceil(dil*(ksz-dil)-(ksz-paddign))
    #’cause dil*(ksz-dil)>>(ksz-padding)=>pad_length>=0!!True??
    #’cause dil*(ksz-dil)>=ksz=>True???
    #’cause dil*(ksz-dil)>=kszd=>True???
    #’cause dil*((kszd/dil)-dil)>=keszd=>True???
    #’cause ksza>=keszd=>True????
    ##maybe because give too big dilation rate??
    ##maybe never happen because we give ceiling opeartion here??
    ##because ceil always bigger than real value??
    ##maybe still happen when ksza==keszd,but we just let it go??
    ##but maybe need warn user about it??
    ##or just throw assert???????????????YES!!!!!!!
    ##because maybe cause gradient explode!!!!!!!!!!!!!!!!!!!!!
    ##or maybe just warning user may be enough???????????????YES!!!!!
    ##because assert will stop all process???????
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    ##
    ######
    ######
    ######
    ###
    ###
    ###
    ##
    ##
    ##
    ###never happen because we give ceiling operation here??
    ###maybe still happen when ksza==keszd,but we just let it go??
    ###but maybe need warn user about it??
    ###or just throw assert???????????????YES!!!!!!!
    ###because maybe cause gradient explode!!!!!!!!!!!!!!!!!!!!!
    ###or maybe just warning user may be enough???????????????YES!!!!!
    ###because assert will stop all process???????
    ###
    ###
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    assert(self.effective_ksize%self.dilation==0),”given effective kerenel size should be multiples
    nof dilation!Please check you’re parameters!”
    assert(self.mid_channel//self.expansion*self.expansion==self.mid_channel),”mid channels
    nshould be multiples of width expansion!Please check you’re parameters!”
    assert(self.out_channel//self.expansion*self.expansion==self.out_channel),”out channels
    nshould be multiples of width expansion!Please check you’re parameters!”
    #####padding inference end#####
    #####start convolution block#####
    #####infer padding first#####
    #####convolution block#####
    #####call norm layer#####
    #####convolution block#####
    #####norm layer inference end#####
    #####norm layer call end#####
    ###############
    ###############
    ###############
    ###########
    ###########
    ###########
    #########
    #########
    #########
    #######
    #######
    #######
    #####
    #####
    #####
    ###…and so on….
    else:#normal case,#calculate padding automatically,maybe need more cases here???,maybe not…
    if(self.pad_type==’same’):
    #same padding,(s*h_out+s-kerne_h)//s=h_out,kernal move h_out steps can fully cover height(h_out*h_in),
    #(s*w_out+s-kernel_w)//s=w_out,kernal move w_out steps can fully cover width(h_out*h_in),
    #so h_out*h_in+(s*h_out+s-kernel_h)=out_h*s,out_w*s=(w_out*w_in+(s*w_out+s-kernel_w)),
    #(case s>d)(out_h-(kernel_h-s)*ceil(out_h/(out_h-(kernel_h-s)))<=s<=(kernel_h-s)*floor(out_h/(kernel_h-s))+s),
    #(case s<=d)(out_h-(kernel_h-s)*ceil(out_h/(out_h-(kernel_s)))<=min(d,s)<=max(d,s)<=(kernel_s-s)*floor(out_h/(kernel_s-s))+s),
    #(case d<s)(out_w-(kernel_w-s)*ceil(out_w/(out_w-(kernel_w-s)))<=min(d,s)<=max(d,s)s)(out_w-(kernel_d-s)*ceil(out_d/(out_d-(kernel_d-s)))<=s<=(kernel_d-s)*floor(out_d/(kerne_d-s))+s),
    #so padding length(padd_l,padd_r)=max(ceil((kerne_l-(r_l)%kerne_l)%kerne_l/2),ceil((kerne_r-r_r%kerne_r)%kerne_r/2)),
    #where r_l=((in_height-k_height+d_height+(stride_height-1))//stride_height)*stride_height+k_height-d_height,
    #where r_r=((in_width-k_width+d_width+(stride_width-1))//stride_width)*stride_width+k_width-d_width,
    r_l=((input.size()[height_dim]+self.dilation[self.height_dim]*(self.kernel_size[self.height_dim]-1)-(self.kernel_size[self.height_dim]-self.dilation[self.height_dim]))//(self.stride[self.height_dim]))*self.stride[self.height_dim]+(self.kernel_size[self.height_dim]-self.dilation[self.height_dim])-input.size()[height_dim]
    r_r=((input.size()[width_dim]+self.dilation[self.width_dim]*(self.kernel_size[self.width_dim]-1)-(self.kernel_size[self.width_dim]-self.dilation[self.width_dim]))//(self.stride[self.width_dim]))*self.stride[self.width_dim]+(self.kernel_size[self.width_dim]-self.dilation[self.width_dim])-input.size()[width_dim]
    paddings=[math.ceil(r_l/2),math.ceil(r_r/2)]
    elif(self.pad_type=='valid'):
    paddings=[0]*len(self.stride)#valid no pad anyway!
    elif(self.pad_type=='pad'):
    paddings=self.padding#custom pad!just pass custom pad value here!
    else:
    raise ValueError('unknown pad type!')
    paddings=torch.nn.modules.utils._pair(paddings)#standardize single int or list/tuple dimensinal into pairs!convenient!
    conv_param_list={'in_channels':in_channels,'out_channels':mid_channel,'bias':bias}
    if(kernel_is_given):conv_param_list['kernel_size']=int(effective_kernel_size/self.dialtion)#already considered dialtion here!just pass int value here!;
    conv_param_list['stride']=int(stride/self.dialtion)#consider dialtion here!;
    conv_param_list['groups']=groups#consider groups here!;
    conv_param_list['padding']=int(padding/self.dialtion)#consider dialtion here!;
    conv_param_list['dialtion']=int(dialtion);//consider dialtion here!;
    setattr(self,'conv%d'%i,(nn.ConvNd(**conv_param_list)))
    norm_layer_(ConvNd.bias_quantity(conv_nd,dimensions=self.dimensions),bias=False)#no bias needed because bias added after norm layers?;
    setattr(self,'bn%d'%i,norm_layer_)

    I'm feeling quite overwhelmed trying to understand how this class handles different types of padding dynamically based on these parameters—especially how it calculates effective kernel sizes with dilation involved!

    Could you help clarify how this dynamic behavior works? Specifically, how does it determine which type of padding ('same', 'valid', 'pad') should be applied under different conditions? Any insights would be greatly appreciated!
    <>: Sure! Let’s break down how `_DynamicBottleneck` handles different types of padding:

    ### Padding Types

    The class supports three types of padding:

    1. **Same Padding**: Ensures the output dimensions are the same as input dimensions by calculating necessary padding based on kernel size, stride, and dilation.

    – For height (`r_l`) and width (`r_r`), it calculates required adjustments so that after applying strides over input dimensions plus any necessary dilation adjustments, output dimensions match input dimensions.
    – The formula used ensures that kernels cover entire input dimensions evenly across strides.

    python
    r_l=((input.size()[height_dim]+self.dilation*self.kernel_size-self.kernel_size+self.dilation)-(input.size()[height_dim])) // self.stride * self.stride + (self.kernel_size-self.dilation) – input.size()[height_dim]

    The same logic applies symmetrically for width (`r_r`).

    Padding values are then calculated as half-rounded-up values from these differences.

    2. **Valid Padding**: No padding is applied (`paddings=[0]*len(self.stride)`). This means convolution operations only occur where kernels fully fit within input boundaries.

    3. **Custom Padding (‘pad’)**: Allows specifying custom padding directly via a parameter (`paddings=self.padding`). This provides flexibility beyond default behaviors.

    ### Effective Kernel Size Calculation

    The effective kernel size considers dilation:

    [ text{Effective Kernel Size} = (text{Kernel Size} – text{Dilation} times (text{Kernel Size} – 1)) + (text{Dilation} times (text{Kernel Size} – text{Dilation})) ]

    This accounts for how dilation expands the receptive field without increasing actual kernel size physically.

    ### Dynamic Behavior

    The class dynamically determines which type of padding applies based on parameters passed during initialization:

    – If `effective_ksize` is provided, it uses calculated values derived from given parameters.
    – Otherwise defaults apply based on specified `pad_type`.

    This setup allows `_DynamicBottleneck` to adaptively configure convolution layers according to varying architectural needs within `_ResBlock`. Let me know if you need further clarification! 😊<>: Hi there! I’m working with this code snippet that processes an XML file containing PubMed articles related to Alzheimer’s disease publications since January 2020 until now (“In press”). It extracts metadata like PMID IDs into CSV files using XPath queries within nested loops over XML elements.

    Here’s the code snippet I’m dealing with:

    python
    def search_pubs_chicago():
    contents_file_path = “/Users/gregor/Documents/Academic/chicago/PubMed/biosamples.txt”
    pubs_file_path = “/Users/gregor/Documents/Academic/chicago/PubMed/publications.csv”
    with open(contents_file_path) as f:
    biosamples_data = f.readlines()
    pubs_data=[]
    for biosample_data_line in biosamples_data:
    biosample_data_parts=biosample_data_line.strip().split(“t”)
    if len(biosample_data_parts)!=3:
    continue
    PMIDs=biosample_data_parts[1]
    expr_description=biosample_data_parts[2]
    pub_med_central_ids=””
    pub_ids=””
    pub_years=””
    expression_patterns=””
    gene_symbols=”
    sample_descriptions=”
    if “[“in PMIDs:
    DOI_pubs_parts=[pub.split(“[“)for pub in PMIDs.split()]
    pub_ids=[pub.split(“]”)[0].strip(” “)for pub in DOI_pubs_parts]
    pub_med_central_ids=[pub.split(“(“)[-1].split(“]”)[0]for pub in DOI_pubs_parts]
    pub_ids=[pub.strip(” “)for pub in pub_ids]
    pub_med_central_ids=[pub.strip(” “)for pub_in_pub_med_central_ids(pub.strip(” “))in pub_med_central_ids]
    years_found=[False]*len(pub_ids)
    expression_pattern_found=[False]*len(pub_ids)
    genes_found=[False]*len(pub_ids)
    samples_found=[False]*len(pub_ids)
    ”’year_search_strings=[‘(‘+str(year)+’)’for year in xrange(2009,maxPubYear)]
    year_search_strings+=[‘(‘+str(year)+”)'”for year_sourcebornrange(year_sourcebornrange(2009,maxPubYear))]
    year_sourcesearchedstringsRE=re.compile(‘|’.join(year_search_strings))
    expression_patterns=[‘AD$’,’Alzheimers+’,publish_name,re.compile(‘.*’+nwnt.*|.*betas+[Aa]pprotein.*’)]
    expression_patternsRE=re.compile(‘|’.join(expression_patterns))
    gene_name_search_string='(?:{})(w*)’.format(“|”.join(gene_name_set))
    gene_name_search_stringRE=re.compile(gene_name_search_string,re.IGNORECASE|re.U)
    sample_description_search_string='(?:{})(w*)’.format(“|”.join(sample_name_set))
    sample_description_search_stringRE=re.compile(sample_description_search_string,re.IGNORECASE|re.U)
    pmc_mid_full_pattern='(([pmc|PMC])+w*)’
    pmc_mid_full_pattern_re=re.compile(pmc_mid_full_pattern,re.IGNORECASE|re.U”’
    for PubMedID_index,PubMedID_in_particular in enumerate(pub_ids):
    PubMedURL_base=”https://www.ncbi.nlm.nih.gov/pubmed”
    pubmed_url_to_open=”{base}/{PMID}”.format(base=PubMedURL_base,
    PMID=PubMedID_in_particular)
    print “Retrieving article metadata URL %s…”%pubmed_url_to_open
    page_to_open=requests.get(pubmed_url_to_open)
    print “Done retrieving…”
    page_xml_text=str(page_to_open.text.encode(‘ascii’,’ignore’))
    print “Parsing page text …”
    if page_xml_text.find(“”)==-1:
    print “Error.”
    continue
    pubmed_xml_tree_root=find_xml_tags(“”,page_xml_text+””)
    if isinstance(pubmed_xml_tree_root,collections.Iterable):
    pubmed_xml_tree_root=list(pubmed_xml_tree_root)[0]
    pubmed_article_XML_trees=find_xml_tags(“”,pubmed_xml_tree_root+””)
    for PubmedArticleXML_tree_element_in_particular,in_pubmed_article_XML_trees enumerate(PubmedArticleXML_trees):
    if PubmedArticleXML_tree_element_in_particular.find(“”)==-1:
    continue
    MedlineCitationXML_tree_element=list(find_xml_tags(“”,
    PubmedArticleXML_tree_element_in_particular+””)
    )[0]
    try:
    yop_years_inparticular=list(find_xml_tags(“”,
    MedlineCitationXML_tree_element+”</YearOfPublication"))
    if len(yop_years_inparticular)==0:
    continue
    else:
    yop_year=str(list(list(find_xml_tags("”,yop_years_inparticular[
    [0].””
    ]+
    “”)
    )[0])[0])
    if int(float(yop_year))<2014:#CHANGE THIS IF NECESSARY
    continue
    else:#We've got what we want…
    pass
    except IndexError,e:"Error."
    title_tags=list(find_xml_tags("”,
    MedlineCitationXML_tree_element+””)
    )
    if len(title_tags)==0:#If there aren’t tags…
    continue
    else:#There are tags…
    pass
    title_text=list(list(title_tags)[0])[0].strip()
    for RE_obj,in_expression_patterns(expression_patternsRE,title_text):
    expression_pattern_found[
    PubMedID_index]=True
    for RE_obj,in_gene_name_search_stringRE(title_text):
    genes_found[
    PubMedID_index]=True
    for RE_obj,in_sample_description_search_stringRE(title_text):
    samples_found[
    PubMedID_index]=True
    pmid_tag=list(find_xml_tags(“”,
    MedlineCitationXML_tree_element+””)
    )
    if len(pmid_tag)==0:#If there aren’t tags…
    continue
    else:#There are tags…
    pass
    pmid=str(list(list(pmid_tag)[0])[0]).strip()
    try:#Some articles don’t have abstract content…
    publication_types=list(find_xml_tags(“”,MedlineCitationXML_tree_element+””)
    )
    if publication_types!=[]:
    list_of_publication_types=[]
    for publication_type_list_child_node_child_node_content
    in list(list(list(
    find_xml_tags(“”,
    publication_types[
    [find_xml_tags(“”,publication_types[
    [find_xml_tags(
    “<MedlineJournalInfo",
    MedlineCitationXML_tree_element+"”
    )][find_xml_tags(
    “<PMID",
    pm