Python matplotlib.pyplot 模块，minorticks_on() 实例源码

def save_image(real_data, fake_data, filename):
    assert real_data.shape == fake_data.shape

    import warnings
    warnings.filterwarnings("ignore", category=FutureWarning)
    import numpy as np
    import matplotlib
    matplotlib.use('Agg')
    import matplotlib.pyplot as plt

    fig, ax = plt.subplots()
    plt.scatter(fake_data[:,0], fake_data[:,1], color='red', label='noise (fake, sampled)')
    plt.scatter(real_data[:,0], real_data[:,1], color='blue', label='hidden (real, inferred)')
    #plt.axis('equal')
    plt.legend(loc='upper right', fancybox=True, shadow=True, fontsize=11)
    plt.grid(True)
    plt.xlim(-5, 5)
    plt.ylim(-5, 5)
    plt.minorticks_on()
    plt.xlabel('x', fontsize=14, color='black')
    plt.ylabel('y', fontsize=14, color='black')
    plt.title('z samples (of first two dimensions)')
    plt.savefig(filename)
    plt.close()

def save_image_fake(fake_data, filename):
    #import warnings
    #warnings.filterwarnings("ignore", category=FutureWarning)
    #import numpy as np
    #import matplotlib
    #matplotlib.use('Agg')
    #import matplotlib.pyplot as plt

    fig, ax = plt.subplots()
    #plt.scatter(real_data[:,0], real_data[:,1], color='blue', label='real')
    plt.scatter(fake_data[:,0], fake_data[:,1], color='red', label='fake')
    plt.axis('equal')
    #plt.legend(loc='upper right', fancybox=True, shadow=True, fontsize=11)
    plt.grid(True)
    plt.xlim(-25, 25)
    plt.ylim(-25, 25)
    plt.minorticks_on()
    plt.xlabel('x', fontsize=14, color='black')
    plt.ylabel('y', fontsize=14, color='black')
    #plt.title('Toy dataset')
    plt.savefig(filename)
    plt.close()

def save_image_real(real_data, filename):
    #import warnings
    #warnings.filterwarnings("ignore", category=FutureWarning)
    #import numpy as np
    #import matplotlib
    #matplotlib.use('Agg')
    #import matplotlib.pyplot as plt

    fig, ax = plt.subplots()
    plt.scatter(real_data[:,0], real_data[:,1], color='blue', label='real')
    #plt.scatter(fake_data[:,0], fake_data[:,1], color='red', label='fake')
    plt.axis('equal')
    #plt.legend(loc='upper right', fancybox=True, shadow=True, fontsize=11)
    plt.grid(True)
    plt.xlim(-25, 25)
    plt.ylim(-25, 25)
    plt.minorticks_on()
    plt.xlabel('x', fontsize=14, color='black')
    plt.ylabel('y', fontsize=14, color='black')
    #plt.title('Toy dataset')
    plt.savefig(filename)
    plt.close()

def save_image(real_data, fake_data, filename):
    #import warnings
    #warnings.filterwarnings("ignore", category=FutureWarning)
    #import numpy as np
    #import matplotlib
    #matplotlib.use('Agg')
    #import matplotlib.pyplot as plt

    fig, ax = plt.subplots()
    plt.scatter(real_data[:,0], real_data[:,1], color='blue', label='real')
    plt.scatter(fake_data[:,0], fake_data[:,1], color='red', label='fake')
    #plt.axis('equal')
    plt.legend(loc='upper right', fancybox=True, shadow=True, fontsize=11)
    plt.grid(True)
    plt.xlim(-25, 25)
    plt.ylim(-25, 25)
    plt.minorticks_on()
    plt.xlabel('x', fontsize=14, color='black')
    plt.ylabel('y', fontsize=14, color='black')
    plt.title('Toy dataset')
    plt.savefig(filename)
    plt.close()

def paper_single_mult_ax(nrows=1, ncols=1, **kwargs):
    #import matplotlib as mpl
    paper_single(FF=max(nrows,ncols))
    f, ax = plt.subplots(nrows=nrows, ncols=ncols, **kwargs)
    plt.minorticks_on()
    ylocator6 = plt.MaxNLocator(5)
    xlocator6 = plt.MaxNLocator(6)
    if len(ax.shape) > 1:
        for axrow in ax:
            for axcol in axrow:
                axcol.xaxis.set_major_locator(xlocator6)
                axcol.yaxis.set_major_locator(ylocator6)
    else:
        for axcol in ax:
            axcol.xaxis.set_major_locator(xlocator6)
            axcol.yaxis.set_major_locator(ylocator6)
    return f, ax

def show_figure(self):
        self.maxx *= 1.2
        self.maxy *= 1.2
        plt.xlim(0,self.maxx)
        plt.ylim(0,self.maxy)
        plt.xticks(np.linspace(0,self.maxx,8))
        plt.yticks(np.linspace(0,self.maxy,10))
        plt.title(r"Trajectory of a cannon shell,T = %sK" %self.t)
        plt.xlabel('x(km)')
        plt.ylabel('y(km)')
        plt.legend(loc = 'upper right',fontsize = 'x-small')
        plt.minorticks_on()
        plt.grid()

        plt.show()

def paper_single_ax(TW = 6.64, AR = 0.74, FF = 1.):
    #import matplotlib as mpl
    paper_single(TW=TW, AR=AR, FF=FF)
    f = plt.figure()
    ax = plt.subplot(111)
    plt.minorticks_on()
    ylocator6 = plt.MaxNLocator(5)
    xlocator6 = plt.MaxNLocator(6)
    ax.xaxis.set_major_locator(xlocator6)
    ax.yaxis.set_major_locator(ylocator6)
    return f, ax

def paper_double_ax():
    #import matplotlib as mpl
    paper_single(TW = 12)
    f = plt.figure()
    ax = plt.subplot(111)
    plt.minorticks_on()
    ylocator6 = plt.MaxNLocator(5)
    xlocator6 = plt.MaxNLocator(6)
    ax.xaxis.set_major_locator(xlocator6)
    ax.yaxis.set_major_locator(ylocator6)
    return f, ax

def paper_double_mult_ax(nrows=1, ncols=1, setticks=True, **kwargs):
    #import matplotlib as mpl
    paper_single()


    TW = 6.97*2
    AR = 0.74
    FF = 1.
    mpl.rc('figure', figsize=(FF*TW, FF*TW*AR), dpi=200)
    mpl.rc('figure.subplot', left=0.1, right=0.97, bottom=0.1, top=0.97)
    mpl.rc('font', size=24.0, family="serif", serif="CM")

    f, ax = plt.subplots(nrows=nrows, ncols=ncols, **kwargs)
    plt.minorticks_on()
    if setticks:
        ylocator6 = plt.MaxNLocator(5)
        xlocator6 = plt.MaxNLocator(6)
        if len(ax.shape) > 1:
            for axrow in ax:
                for axcol in axrow:
                    axcol.xaxis.set_major_locator(xlocator6)
                    axcol.yaxis.set_major_locator(ylocator6)
        else:
            for axcol in ax:
                axcol.xaxis.set_major_locator(xlocator6)
                axcol.yaxis.set_major_locator(ylocator6)
    return f, ax

def make_ax3():
    paper_single(TW=8, AR=0.9)
    f = plt.figure()

    from matplotlib.ticker import NullFormatter, MaxNLocator

    nullfmt   = NullFormatter()         # no labels

    # definitions for the axes
    left, width = 0.1, 0.65
    bottom, height = 0.1, 0.6
    bottom_h = bottom+height+0.02
    left_h = left+width+0.02

    rect_scatter = [left, bottom, width, height]
    rect_histx = [left, bottom_h, width, 0.2]
    rect_histy = [left_h, bottom, 0.2, height]

    ax = plt.axes(rect_scatter)
    plt.minorticks_on()
    axx = plt.axes(rect_histx)
    plt.minorticks_on()
    axy = plt.axes(rect_histy)
    plt.minorticks_on()

    # no labels
    axx.xaxis.set_major_formatter(nullfmt)
    axy.yaxis.set_major_formatter(nullfmt)


    axy.xaxis.set_major_locator(MaxNLocator(3))
    axx.yaxis.set_major_locator(MaxNLocator(3))

    return f,ax,axx,axy

def iplt(fdic,
        key,
        cut=None,
        ax=None,
        cbar=None,
        smth=None,
        nolabels=None,
        lblsz='small',
        **kwargs):
    """
    A wrapper function for imshow to do most 
    tedious stuff for my simulations
    """
    old_ax = plt.gca() # Get Current Axis
    if ax is None: 
        ax = old_ax
    else:
        plt.sca(ax)    # Set Current Axis

    if type(key) is str: plt_val = fdic[key]
    else               : plt_val = key

    if cut is None:
      if len(plt_val.shape) == 1:
         IDX=np.s_[:]
      elif len(plt_val.shape) == 2:
         IDX=np.s_[:,0]
      else:
         IDX=np.s_[:,0,0]
    else:
      if len(plt_val.shape) == 1:
         IDX=compute1didx([fdic['xx']],cut)
      elif len(plt_val.shape) == 2:
         IDX=compute1didx([fdic['xx'],fdic['yy']],cut)
      else:
         IDX=compute1didx([fdic['xx'],fdic['yy'],fdic['zz']],cut)

    im = ax.plot(fdic['xx'],plt_val[IDX], **kwargs)

    ax.autoscale(False)

    if nolabels is None:
      ax.set_xlabel(r'$X (d_i)$',size=lblsz)
      ax.set_ylabel(key,size=lblsz)

    ax.xaxis.set_tick_params(which='both',labelsize=lblsz)
    ax.yaxis.set_tick_params(which='both',labelsize=lblsz)

    plt.minorticks_on()
    plt.sca(old_ax)

    return im