Python C2_8_mystyle.printout方法代码示例

# 需要导入模块: import C2_8_mystyle [as 别名]
# 或者: from C2_8_mystyle import printout [as 别名]
def smSolution(M1, M2, M3):
    '''Solution with the tools from statsmodels'''
    
    import statsmodels.api as sm
    import C2_8_mystyle
    
    Res1 = sm.OLS(y, M1).fit()
    Res2 = sm.OLS(y, M2).fit()
    Res3 = sm.OLS(y, M3).fit()
    
    print(Res1.summary2())
    print(Res2.summary2())
    print(Res3.summary2())
    
    # Plot the data
    plt.plot(x,y, '.', label='Data')
    plt.plot(x, Res1.fittedvalues, 'r--', label='Linear Fit')
    plt.plot(x, Res2.fittedvalues, 'g', label='Quadratic Fit')
    plt.plot(x, Res3.fittedvalues, 'y', label='Cubic Fit')
    plt.legend(loc='upper left', shadow=True)
    
    C2_8_mystyle.printout('linearModel.png', xlabel='x', ylabel='y')

# 需要导入模块: import C2_8_mystyle [as 别名]
# 或者: from C2_8_mystyle import printout [as 别名]
def main():
    '''Demonstrate the generation of different statistical standard plots'''
    
    # Univariate data -------------------------
    # Generate data that are normally distributed
    x = np.random.randn(500)
    
    # Set the fonts the way I like them
    sns.set_context('poster')
    sns.set_style('ticks')
    C2_8_mystyle.set(fs=32)
    
    # Scatter plot
    plt.scatter(np.arange(len(x)), x)
    plt.xlim([0, len(x)])
    
    # Save and show the data, in a systematic format
    C2_8_mystyle.printout('scatterPlot.png', xlabel='x', ylabel='y', title='Scatter')
    
    # Histogram
    plt.hist(x)
    C2_8_mystyle.printout('histogram_plain.png', xlabel='Data Values', ylabel='Frequency', title='Histogram, default settings')
    
    plt.hist(x,25)
    C2_8_mystyle.printout('histogram.png', xlabel='Data Values', ylabel='Frequency', title='Histogram, 25 bins')
    
    # Cumulative probability density
    numbins = 20
    plt.plot(stats.cumfreq(x,numbins)[0])
    C2_8_mystyle.printout('CumulativeFrequencyFunction.png', xlabel='Data Values', ylabel='CumFreq', title='Cumulative Frequncy')

    # KDE-plot
    sns.kdeplot(x)
    C2_8_mystyle.printout('kde.png', xlabel='Data Values', ylabel='Density',
            title='KDE_plot')
    
    # Boxplot
    # The ox consists of the first, second (middle) and third quartile
    plt.boxplot(x, sym='*')
    C2_8_mystyle.printout('boxplot.png', xlabel='Values', title='Boxplot')
    
    plt.boxplot(x, sym='*', vert=False)
    plt.title('Boxplot, horizontal')
    plt.xlabel('Values')
    plt.show()
    
    # Errorbars
    x = np.arange(5)
    y = x**2
    errorBar = x/2
    plt.errorbar(x,y, yerr=errorBar, fmt='o', capsize=5, capthick=3)
    plt.xlim([-0.2, 4.2])
    plt.ylim([-0.2, 19])
    C2_8_mystyle.printout('Errorbars.png', xlabel='Data Values', ylabel='Measurements', title='Errorbars')
    
    # Violinplot
    nd = stats.norm
    data = nd.rvs(size=(100))
    
    nd2 = stats.norm(loc = 3, scale = 1.5)
    data2 = nd2.rvs(size=(100))
    
    # Use pandas and the seaborn package for the violin plot
    df = pd.DataFrame({'Girls':data, 'Boys':data2})
    sns.violinplot(df)
    
    C2_8_mystyle.printout('violinplot.png', title='Violinplot')
    
    # Barplot
    df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
    df.plot(kind='bar', grid=False)
    C2_8_mystyle.printout('barplot.png', title='Barplot')

    # Grouped Boxplot
    sns.set_style('whitegrid')
    sns.boxplot(df)
    C2_8_mystyle.set(fs=28)
    C2_8_mystyle.printout('groupedBoxplot.png', title='sns.boxplot')

    # Bivariate Plots
    df2 = pd.DataFrame(np.random.rand(50, 4), columns=['a', 'b', 'c', 'd'])
    df2.plot(kind='scatter', x='a', y='b', s=df['c']*300);
    C2_8_mystyle.printout('bivariate.png')

    # Pieplot
    series = pd.Series(3 * np.random.rand(4), index=['a', 'b', 'c', 'd'], name='series')
    oldPalette = sns.color_palette()
    sns.set_palette("husl")
    series.plot(kind='pie', figsize=(6, 6))
    C2_8_mystyle.printout('piePlot.png', title='pie-plot')
    sns.set_palette(oldPalette)

# 需要导入模块: import C2_8_mystyle [as 别名]
# 或者: from C2_8_mystyle import printout [as 别名]
def simplePlots():
    '''Demonstrate the generation of different statistical standard plots'''
    
    # Univariate data -------------------------
    
    # Make sure that always the same random numbers are generated
    np.random.seed(1234)
    
    # Generate data that are normally distributed
    x = np.random.randn(500)
    
    # Other graphics settings
    sns.set(context='poster', style='ticks', palette=sns.color_palette('muted'))
    
    # Set the fonts the way I like them
    C2_8_mystyle.set(fs=32)
    
    # Scatter plot
    plt.scatter(np.arange(len(x)), x)
    plt.xlim([0, len(x)])
    
    # Save and show the data, in a systematic format
    C2_8_mystyle.printout('scatterPlot.png', xlabel='Datapoints', ylabel='Values', title='Scatter')
    
    # Histogram
    plt.hist(x)
    C2_8_mystyle.printout('histogram_plain.png', xlabel='Data Values',
                          ylabel='Frequency', title='Histogram, default settings')
    
    plt.hist(x,25)
    C2_8_mystyle.printout('histogram.png', xlabel='Data Values', ylabel='Frequency',
                          title='Histogram, 25 bins')
    
    # Cumulative probability density
    numbins = 20
    plt.plot(stats.cumfreq(x,numbins)[0])
    C2_8_mystyle.printout('CumulativeFrequencyFunction.png', xlabel='Data Values',
                          ylabel='CumFreq', title='Cumulative Frequency')

    # KDE-plot
    sns.kdeplot(x)
    C2_8_mystyle.printout('kde.png', xlabel='Data Values', ylabel='Density',
            title='KDE_plot')
    
    # Boxplot
    # The ox consists of the first, second (middle) and third quartile
    plt.boxplot(x, sym='*')
    C2_8_mystyle.printout('boxplot.png', xlabel='Values', title='Boxplot')
    
    plt.boxplot(x, sym='*', vert=False)
    plt.title('Boxplot, horizontal')
    plt.xlabel('Values')
    plt.show()
    
    # Errorbars
    x = np.arange(5)
    y = x**2
    errorBar = x/2
    plt.errorbar(x,y, yerr=errorBar, fmt='o', capsize=5, capthick=3)
    plt.xlim([-0.2, 4.2])
    plt.ylim([-0.2, 19])
    C2_8_mystyle.printout('Errorbars.png', xlabel='Data Values', ylabel='Measurements', title='Errorbars')
    
    # Violinplot
    nd = stats.norm
    data = nd.rvs(size=(100))
    
    nd2 = stats.norm(loc = 3, scale = 1.5)
    data2 = nd2.rvs(size=(100))
    
    # Use pandas and the seaborn package for the violin plot
    df = pd.DataFrame({'Girls':data, 'Boys':data2})
    sns.violinplot(df)
    
    C2_8_mystyle.printout('violinplot.png', title='Violinplot')
    
    # Barplot
    # The font-size is set such that the legend does not overlap with the data
    np.random.seed(1234)
    C2_8_mystyle.set(20)
    
    df = pd.DataFrame(np.random.rand(10, 4), columns=['a', 'b', 'c', 'd'])
    df.plot(kind='bar', grid=False, color=sns.color_palette('muted'))
    
    C2_8_mystyle.printout_plain('barplot.png')
    C2_8_mystyle.set(28)

    # Bivariate Plots
    df2 = pd.DataFrame(np.random.rand(50, 3), columns=['a', 'b', 'c'])
    df2.plot(kind='scatter', x='a', y='b', s=df2['c']*500);
    plt.axhline(0, ls='--', color='#999999')
    plt.axvline(0, ls='--', color='#999999')
    C2_8_mystyle.printout('bivariate.png')
    
    # Grouped Boxplot
    sns.set_style('whitegrid')
    sns.boxplot(df)
    C2_8_mystyle.set(fs=28)
    C2_8_mystyle.printout('groupedBoxplot.png', title='sns.boxplot')

#.........这里部分代码省略.........