Python pylab 模块，axes() 实例源码

def add_colorbar(ax, im, side='right', size='5%', pad=0.1, **kwds):
    """
    Add colorbar to the axes *ax* with colors corresponding to the
    color mappable object *im*. Place the colorbar at the *side* of
    *ax* (options are `'right'`, `'left'`, `'top'`, or
    `'bottom'`). The width (or height) of the colorbar is specified by
    *size* and is relative to *ax*. Add space *pad* between *ax* and
    the colorbar. The remaining keyword arguments *kwds* are passed to
    the call to :func:`colorbar`. Return the colorbar instance.

    Reference: http://matplotlib.org/mpl_toolkits/axes_grid/users/overview.html
    """
    divider = make_axes_locatable(ax)
    cax = divider.append_axes(side, size=size, pad=pad)
    cb = PL.colorbar(im, cax=cax, **kwds)
    PL.axes(ax)
    return cb

def mybut(text, dummy, xl, yb, xw=0, yh=0, axisbg=None, color=0.85, fun=None, bspace=0.005):
    """ create axes and populate button with text, automatically adjusting
    xw if not given.  Has a side effect on xl. (button_layout_cursor)
    dummy is for if and when I can place these on an obect rather than using pylab
    """
    if axisbg==None: axisbg='lightgoldenrodyellow'

    global button_layout_cursor
    if xw==0: xw=0.015*(len(text)+1)
    if yh==0: yh=0.05
##    thisax=fig.add_axes([xl, yb, xw, yh], axisbg=axisbg) fundamentally wrong
    thisax=pl.axes([xl, yb, xw, yh], axisbg=axisbg)
    thisbut=Button(thisax, text)
    thisbut.on_clicked(fun)
    button_layout_cursor += xw+bspace
    return(thisbut)

def generateImages(picklefile, pickledir, filehash, imagedir, pietype):

    leaf_file = open(os.path.join(pickledir, picklefile), 'rb')
    (piedata, pielabels) = cPickle.load(leaf_file)
    leaf_file.close()

    pylab.figure(1, figsize=(6.5,6.5))
    ax = pylab.axes([0.2, 0.15, 0.6, 0.6])

    pylab.pie(piedata, labels=pielabels)

    pylab.savefig(os.path.join(imagedir, '%s-%s.png' % (filehash, pietype)))
    pylab.gcf().clear()
    os.unlink(os.path.join(pickledir, picklefile))

def class_distributions():
    # Create the Class Distributions Diagram
    labels = ['Diamond', 'Platinum', 'Gold', 'Silver', 'Bronze']
    fracs = [1.89, 8.05, 23.51, 38.96, 27.59]
    figure(1, figsize=(6,6))
    ax = axes([0.1, 0.1, 0.8, 0.8])
    pie(fracs, labels=labels, autopct='%1.1f%%')
    title('Tier Population Distribution', bbox={'facecolor': '0.8', 'pad': 5})
    savefig('images/pie.png')

def plot_map(ax=None, alpha=0.3, zorder=0):
    """
    Add map features (coastlines, national boundaries, etc.) to *a*
    with transparency level *alpha* and *zorder*. Return *ax*.
    """
    if ax is None:
        ax = PL.axes(projection=ccrs.PlateCarree())
    # national boundaries
    boundaries_50m = cartopy.feature.NaturalEarthFeature(category='cultural',
                                                         name='admin_0_boundary_lines_land',
                                                         scale='50m',
                                                         edgecolor='k',
                                                         facecolor='none')
    ax.add_feature(boundaries_50m,
                   alpha=alpha,
                   zorder=zorder)
    # states
    states_50m = cartopy.feature.NaturalEarthFeature(category='cultural',
                                                     name='admin_1_states_provinces_lines',
                                                     scale='50m',
                                                     edgecolor='k',
                                                     facecolor='none')
    ax.add_feature(states_50m,
                   alpha=alpha,
                   zorder=zorder)
    # coastlines
    coastline_50m = cartopy.feature.NaturalEarthFeature('physical',
                                                        'coastline',
                                                        '50m',
                                                        edgecolor='k',
                                                        facecolor='none')
    ax.add_feature(coastline_50m,
                   alpha=alpha,
                   zorder=zorder)
    # lakes
    lakes_110m = cartopy.feature.NaturalEarthFeature('physical',
                                                     'lakes',
                                                     '110m',
                                                     edgecolor='k',
                                                     facecolor='none')
    # add all shape objects
    ax.add_feature(lakes_110m,
                   alpha=alpha,
                   zorder=zorder)

    return ax

def plotPrecisionRecallDiagram(title="title", points=None, labels=None, loc="best",xy_ranges = [0.6, 1.0, 0.6, 1.0], save_file = None):
    """Plot (precision,recall) values with 10 f-Measure equipotential lines.

    Plots into the current canvas.
    Points is a list of (precision,recall) pairs.
    Optionally you can also provide labels (list of strings), which will be
    used to create a legend, which is located at loc.
    """
    if labels:
        ax = pl.axes([0.1, 0.1, 0.7, 0.8])  # llc_x, llc_y, width, height
    else:
        ax = pl.gca()
    pl.title(title)
    pl.xlabel("Precision")
    pl.ylabel("Recall")
    _plotFMeasures(start = min(xy_ranges[0],xy_ranges[2]), end = max(xy_ranges[1],xy_ranges[3]))

    if points:
        getColor = it.cycle(colors).next
        getMarker = it.cycle(markers).next

        scps = []  # scatter points
        for i, (x, y) in enumerate(points):
            label = None
            if labels:
                label = labels[i]
            print i, x, y, label
            scp = ax.scatter(x, y, label=label, s=50, linewidths=0.75,
                             facecolor=getColor(), alpha=0.75, marker=getMarker())
            scps.append(scp)
            # pl.plot(x,y, label=label, marker=getMarker(), markeredgewidth=0.75, markerfacecolor=getColor())
            # if labels: pl.text(x, y, label, fontsize="x-small")
        if labels:
            # pl.legend(scps, labels, loc=loc, scatterpoints=1, numpoints=1, fancybox=True) # passing scps & labels explicitly to work around a bug with legend seeming to miss out the 2nd scatterplot
            #pl.legend(scps, labels, loc=(1.01, 0), scatterpoints=1, numpoints=1, fancybox=True)  # passing scps & labels explicitly to work around a bug with legend seeming to miss out the 2nd scatterplot
            pl.legend(scps, labels, loc= loc, scatterpoints=1, numpoints=1, fancybox=True,fontsize = 10)  # passing scps & labels explicitly to work around a bug with legend seeming to miss out the 2nd scatterplot
    pl.axis(xy_ranges)  # xmin, xmax, ymin, ymax
    if save_file:
        pl.savefig(save_file)

    pl.show()
    pl.close()