def correlation_matrix():
	
	def plotCorrelationMatrix(df, graphWidth):
	    filename = df.dataframeName
	    df = df.dropna('columns') 
	    df = df[[col for col in df if df[col].nunique()  1]] 
	    if df.shape[1]  2:
	        print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')
	        return
	    corr = df.corr()
	    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')
	    corrMat = plt.matshow(corr, fignum = 1)
	    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)
	    plt.yticks(range(len(corr.columns)), corr.columns)
	    plt.gca().xaxis.tick_bottom()
	    plt.colorbar(corrMat)
	    plt.title(f'Correlation Matrix for {filename}', fontsize=15)
	    plt.show()
	

def correlation_matrix():
	
	def plotCorrelationMatrix(df, graphWidth):
	    filename = df.dataframeName
	    df = df.dropna('columns') 
	    df = df[[col for col in df if df[col].nunique()  1]] 
	    if df.shape[1]  2:
	        print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')
	        return
	    corr = df.corr()
	    plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')
	    corrMat = plt.matshow(corr, fignum = 1)
	    plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)
	    plt.yticks(range(len(corr.columns)), corr.columns)
	    plt.gca().xaxis.tick_bottom()
	    plt.colorbar(corrMat)
	    plt.title(f'Correlation Matrix for {filename}', fontsize=15)
	    plt.show()
	
