Python绘图Matplotlib之坐标轴及刻度总结

 import matplotlib.pyplot as plt plt.rcParams['font.sans-serif']=['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus']=False # 用来正常显示负号 import pandas as pd import numpy as np

 from mpl_toolkits.axisartist.axislines import SubplotZero import numpy as np fig = plt.figure(1, (10, 6)) ax = SubplotZero(fig, 1, 1, 1) fig.add_subplot(ax) """新建坐标轴""" ax.axis["xzero"].set_visible(True) ax.axis["xzero"].label.set_text("新建y=0坐标") ax.axis["xzero"].label.set_color('green') # ax.axis['yzero'].set_visible(True) # ax.axis["yzero"].label.set_text("新建x=0坐标") # 新建一条y=2横坐标轴 ax.axis["新建1"] = ax.new_floating_axis(nth_coord=0, value=2,axis_direction="bottom") ax.axis["新建1"].toggle(all=True) ax.axis["新建1"].label.set_text("y = 2横坐标") ax.axis["新建1"].label.set_color('blue') """坐标箭头""" ax.axis["xzero"].set_axisline_style("-|>") """隐藏坐标轴""" # 方法一：隐藏上边及右边 # ax.axis["right"].set_visible(False) # ax.axis["top"].set_visible(False) #方法二：可以一起写 ax.axis["top",'right'].set_visible(False) # 方法三：利用 for in # for n in ["bottom", "top", "right"]: #  ax.axis[n].set_visible(False) """设置刻度""" ax.set_ylim(-3, 3) ax.set_yticks([-1,-0.5,0,0.5,1]) ax.set_xlim([-5, 8]) # ax.set_xticks([-5,5,1]) #设置网格样式 ax.grid(True, linestyle='-.') xx = np.arange(-4, 2*np.pi, 0.01) ax.plot(xx, np.sin(xx)) # 于 offset 处新建一条纵坐标 offset = (40, 0) new_axisline = ax.get_grid_helper().new_fixed_axis ax.axis["新建2"] = new_axisline(loc="right", offset=offset, axes=ax) ax.axis["新建2"].label.set_text("新建纵坐标") ax.axis["新建2"].label.set_color('red') plt.show() # 存为图像 # fig.savefig('test.png-600')

 from mpl_toolkits.axes_grid1 import host_subplot import mpl_toolkits.axisartist as AA import matplotlib.pyplot as plt host = host_subplot(111, axes_class=AA.Axes) plt.subplots_adjust(right=0.75) par1 = host.twinx() par2 = host.twinx() offset = 100 new_fixed_axis = par2.get_grid_helper().new_fixed_axis par2.axis["right"] = new_fixed_axis(loc="right", axes=par2, offset=(offset, 0)) par1.axis["right"].toggle(all=True) par2.axis["right"].toggle(all=True) host.set_xlim(0, 2) host.set_ylim(0, 2) host.set_xlabel("Distance") host.set_ylabel("Density") par1.set_ylabel("Temperature") par2.set_ylabel("Velocity") p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density") p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature") p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity") par1.set_ylim(0, 4) par2.set_ylim(1, 65) host.legend() host.axis["left"].label.set_color(p1.get_color()) par1.axis["right"].label.set_color(p2.get_color()) par2.axis["right"].label.set_color(p3.get_color()) plt.draw() plt.show()

 #　第二坐标 fig, ax_f = plt.subplots() # 这步是关键 ax_c = ax_f.twinx() ax_d = ax_f.twiny() # automatically update ylim of ax2 when ylim of ax1 changes. # ax_f.callbacks.connect("ylim_changed", convert_ax_c_to_celsius) ax_f.plot(np.linspace(-40, 120, 100)) ax_f.set_xlim(0, 100) # ax_f.set_title('第二坐标', size=14) ax_f.set_ylabel('Y轴',color='r') ax_f.set_xlabel('X轴',color='c') ax_c.set_ylabel('第二Ｙ轴', color='b') ax_c.set_yticklabels(["$0$", r"$\frac{1}{2}\pi$", r"$\pi$", r"$\frac{3}{2}\pi$", r"$2\pi$"]) # ax_c.set_ylim(1,5) ax_d.set_xlabel('第二X轴', color='g') ax_d.set_xlim(-1,1) plt.show()

 import mpl_toolkits.axisartist.axislines as axislines fig = plt.figure(1, figsize=(10, 6)) fig.subplots_adjust(bottom=0.2) # 子图1 ax1 = axislines.Subplot(fig, 131) fig.add_subplot(ax1) # for axis in ax.axis.values(): #  axis.major_ticks.set_tick_out(True) # 标签全部在外部 ax1.axis[:].major_ticks.set_tick_out(True) # 这句和上面的for循环功能相同 ax1.axis["left"].label.set_text("子图1 left标签") # 显示在左边 # 设置刻度 ax1.set_yticks([2,4,6,8]) ax1.set_xticks([0.2,0.4,0.6,0.8]) # 子图2 ax2 = axislines.Subplot(fig, 132) fig.add_subplot(ax2) ax2.set_yticks([1,3,5,7]) ax2.set_yticklabels(('one','two','three', 'four', 'five')) # 不显示‘five' ax2.set_xlim(5, 0) # X轴刻度 ax2.axis["left"].set_axis_direction("right") ax2.axis["left"].label.set_text("子图2 left标签") # 显示在右边 ax2.axis["bottom"].set_axis_direction("top") ax2.axis["right"].set_axis_direction("left") ax2.axis["top"].set_axis_direction("bottom") # 子图3 ax3 = axislines.Subplot(fig, 133) fig.add_subplot(ax3) # 前两位表示X轴范围，后两位表示Y轴范围 ax3.axis([40, 160, 0, 0.03]) ax3.axis["left"].set_axis_direction("right") ax3.axis[:].major_ticks.set_tick_out(True) ax3.axis["left"].label.set_text("Long Label Left") ax3.axis["bottom"].label.set_text("Label Bottom") ax3.axis["right"].label.set_text("Long Label Right") ax3.axis["right"].label.set_visible(True) ax3.axis["left"].label.set_pad(0) ax3.axis["bottom"].label.set_pad(20) plt.show()

 import matplotlib.ticker as ticker # Fixing random state for reproducibility np.random.seed(19680801) fig, ax = plt.subplots() ax.plot(100*np.random.rand(20)) # 设置 y坐标轴刻度 formatter = ticker.FormatStrFormatter('$%1.2f') ax.yaxis.set_major_formatter(formatter) # 刻度 for tick in ax.yaxis.get_major_ticks(): tick.label1On = True # label1On 左边纵坐标 tick.label2On = True # label2On 右边纵坐标 tick.label1.set_color('red') tick.label2.set_color('green') # 刻度线 for line in ax.yaxis.get_ticklines(): # line is a Line2D instance line.set_color('green') line.set_markersize(25) line.set_markeredgewidth(3) # 刻度 文字 for label in ax.xaxis.get_ticklabels(): # label is a Text instance label.set_color('red') label.set_rotation(45) label.set_fontsize(16) plt.show()

 import mpl_toolkits.axisartist as axisartist def setup_axes(fig, rect): ax = axisartist.Subplot(fig, rect) fig.add_subplot(ax) ax.set_yticks([0.2, 0.8]) # 设置刻度标记 ax.set_yticklabels(["short", "loooong"]) ax.set_xticks([0.2, 0.8]) ax.set_xticklabels([r"$\frac{1}{2}\pi$", r"$\pi$"]) return ax fig = plt.figure(1, figsize=(3, 5)) fig.subplots_adjust(left=0.5, hspace=0.7) ax = setup_axes(fig, 311) ax.set_ylabel("ha=right") ax.set_xlabel("va=baseline") ax = setup_axes(fig, 312) # 刻度标签对齐方式 ax.axis["left"].major_ticklabels.set_ha("center") # 居中 ax.axis["bottom"].major_ticklabels.set_va("top") # 项部 ax.set_ylabel("ha=center") ax.set_xlabel("va=top") ax = setup_axes(fig, 313) ax.axis["left"].major_ticklabels.set_ha("left")  # 左边 ax.axis["bottom"].major_ticklabels.set_va("bottom") # 底部 ax.set_ylabel("ha=left") ax.set_xlabel("va=bottom") plt.show()

 # 共享坐标轴 方法一 t = np.arange(0.01, 5.0, 0.01) s1 = np.sin(2 * np.pi * t) s2 = np.exp(-t) s3 = np.sin(4 * np.pi * t) plt.subplots_adjust(top=2) #位置调整 ax1 = plt.subplot(311) plt.plot(t, s1) plt.setp(ax1.get_xticklabels(), fontsize=6) plt.title('我是原坐标') # 只共享X轴 sharex ax2 = plt.subplot(312, sharex=ax1) plt.plot(t, s2) # make these tick labels invisible plt.setp(ax2.get_xticklabels(), visible=False) plt.title('我共享了X轴') # 共享X轴和Y轴 sharex、sharey ax3 = plt.subplot(313, sharex=ax1, sharey=ax1) plt.plot(t, s3) plt.xlim(0.01, 5.0) #不起作用 plt.title('我共享了X轴和Y轴') plt.show()

 # 共享坐标轴 方法二 x = np.linspace(0, 2 * np.pi, 400) y = np.sin(x ** 2) f, axarr = plt.subplots(2, sharex=True) f.suptitle('共享X轴') axarr[0].plot(x, y) axarr[1].scatter(x, y, color='r') f, (ax1, ax2) = plt.subplots(1, 2, sharey=True) f.suptitle('共享Y轴') ax1.plot(x, y) ax2.scatter(x, y) f, axarr = plt.subplots(3, sharex=True, sharey=True) f.suptitle('同时共享X轴和Ｙ轴') axarr[0].plot(x, y) axarr[1].scatter(x, y) axarr[2].scatter(x, 2 * y ** 2 - 1, color='g') # 间距调整为０ f.subplots_adjust(hspace=0) # 设置全部标签在外部 for ax in axarr: ax.label_outer()

 def f(t): return np.exp(-t) * np.cos(2*np.pi*t) t1 = np.arange(0.0, 3.0, 0.01) ax1 = plt.subplot(212) ax1.margins(0.05)   # Default margin is 0.05, value 0 means fit ax1.plot(t1, f(t1), 'k') ax2 = plt.subplot(221) ax2.margins(2, 2)   # Values >0.0 zoom out ax2.plot(t1, f(t1), 'r') ax2.set_title('Zoomed out') ax3 = plt.subplot(222) ax3.margins(x=0, y=-0.25) # Values in (-0.5, 0.0) zooms in to center ax3.plot(t1, f(t1), 'g') ax3.set_title('Zoomed in') plt.show()

 from matplotlib.transforms import Bbox, TransformedBbox, \ blended_transform_factory from mpl_toolkits.axes_grid1.inset_locator import BboxPatch, BboxConnector,\ BboxConnectorPatch def connect_bbox(bbox1, bbox2, loc1a, loc2a, loc1b, loc2b, prop_lines, prop_patches=None): if prop_patches is None: prop_patches = prop_lines.copy() prop_patches["alpha"] = prop_patches.get("alpha", 1) * 0.2 c1 = BboxConnector(bbox1, bbox2, loc1=loc1a, loc2=loc2a, **prop_lines) c1.set_clip_on(False) c2 = BboxConnector(bbox1, bbox2, loc1=loc1b, loc2=loc2b, **prop_lines) c2.set_clip_on(False) bbox_patch1 = BboxPatch(bbox1, **prop_patches) bbox_patch2 = BboxPatch(bbox2, **prop_patches) p = BboxConnectorPatch(bbox1, bbox2, # loc1a=3, loc2a=2, loc1b=4, loc2b=1, loc1a=loc1a, loc2a=loc2a, loc1b=loc1b, loc2b=loc2b, **prop_patches) p.set_clip_on(False) return c1, c2, bbox_patch1, bbox_patch2, p def zoom_effect01(ax1, ax2, xmin, xmax, **kwargs): """ ax1 : the main axes ax1 : the zoomed axes (xmin,xmax) : the limits of the colored area in both plot axes. connect ax1 & ax2. The x-range of (xmin, xmax) in both axes will be marked. The keywords parameters will be used ti create patches. """ trans1 = blended_transform_factory(ax1.transData, ax1.transAxes) trans2 = blended_transform_factory(ax2.transData, ax2.transAxes) bbox = Bbox.from_extents(xmin, 0, xmax, 1) mybbox1 = TransformedBbox(bbox, trans1) mybbox2 = TransformedBbox(bbox, trans2) prop_patches = kwargs.copy() prop_patches["ec"] = "none" prop_patches["alpha"] = 0.2 c1, c2, bbox_patch1, bbox_patch2, p = \ connect_bbox(mybbox1, mybbox2, loc1a=3, loc2a=2, loc1b=4, loc2b=1, prop_lines=kwargs, prop_patches=prop_patches) ax1.add_patch(bbox_patch1) ax2.add_patch(bbox_patch2) ax2.add_patch(c1) ax2.add_patch(c2) ax2.add_patch(p) return c1, c2, bbox_patch1, bbox_patch2, p def zoom_effect02(ax1, ax2, **kwargs): """ ax1 : the main axes ax1 : the zoomed axes Similar to zoom_effect01. The xmin & xmax will be taken from the ax1.viewLim. """ tt = ax1.transScale + (ax1.transLimits + ax2.transAxes) trans = blended_transform_factory(ax2.transData, tt) mybbox1 = ax1.bbox mybbox2 = TransformedBbox(ax1.viewLim, trans) prop_patches = kwargs.copy() prop_patches["ec"] = "none" prop_patches["alpha"] = 0.2 c1, c2, bbox_patch1, bbox_patch2, p = \ connect_bbox(mybbox1, mybbox2, loc1a=3, loc2a=2, loc1b=4, loc2b=1, prop_lines=kwargs, prop_patches=prop_patches) ax1.add_patch(bbox_patch1) ax2.add_patch(bbox_patch2) ax2.add_patch(c1) ax2.add_patch(c2) ax2.add_patch(p) return c1, c2, bbox_patch1, bbox_patch2, p import matplotlib.pyplot as plt plt.figure(1, figsize=(5, 5)) ax1 = plt.subplot(221) ax2 = plt.subplot(212) ax2.set_xlim(0, 1) ax2.set_xlim(0, 5) zoom_effect01(ax1, ax2, 0.2, 0.8) ax1 = plt.subplot(222) ax1.set_xlim(2, 3) ax2.set_xlim(0, 5) zoom_effect02(ax1, ax2) plt.show()

 #　相同随机数 np.random.seed(19680801) # create some data to use for the plot dt = 0.001 t = np.arange(0.0, 10.0, dt) r = np.exp(-t[:1000] / 0.05) # impulse response x = np.random.randn(len(t)) s = np.convolve(x, r)[:len(x)] * dt # colored noise # the main axes is subplot(111) by default plt.plot(t, s) #坐标轴 plt.axis([0, 1, 1.1 * np.min(s), 2 * np.max(s)]) plt.xlabel('time (s)') plt.ylabel('current (nA)') plt.title('Gaussian colored noise') # this is an inset axes over the main axes a = plt.axes([.65, .6, .2, .2], facecolor='k') n, bins, patches = plt.hist(s, 400, density=True, orientation='horizontal') plt.title('Probability') plt.xticks([]) plt.yticks([]) # # this is another inset axes over the main axes a = plt.axes([0.2, 0.6, .2, .2], facecolor='k') plt.plot(t[:len(r)], r) plt.title('Impulse response') plt.xlim(0, 0.2) plt.xticks([]) plt.yticks([]) plt.show()

 # 30 points between [0, 0.2) originally made using np.random.rand(30)*.2 pts = np.array([ 0.015, 0.166, 0.133, 0.159, 0.041, 0.024, 0.195, 0.039, 0.161, 0.018, 0.143, 0.056, 0.125, 0.096, 0.094, 0.051, 0.043, 0.021, 0.138, 0.075, 0.109, 0.195, 0.050, 0.074, 0.079, 0.155, 0.020, 0.010, 0.061, 0.008]) # Now let's make two outlier points which are far away from everything. pts[[3, 14]] += .8 # If we were to simply plot pts, we'd lose most of the interesting # details due to the outliers. So let's 'break' or 'cut-out' the y-axis # into two portions - use the top (ax) for the outliers, and the bottom # (ax2) for the details of the majority of our data f, (ax, ax2) = plt.subplots(2, 1, sharex=True) # plot the same data on both axes ax.plot(pts) ax2.plot(pts) # zoom-in / limit the view to different portions of the data ax.set_ylim(.78, 1.) # outliers only ax2.set_ylim(0, .22) # most of the data # hide the spines between ax and ax2 ax.spines['bottom'].set_visible(False) ax2.spines['top'].set_visible(False) ax.xaxis.tick_top() ax.tick_params(labeltop=False) # don't put tick labels at the top ax2.xaxis.tick_bottom() # This looks pretty good, and was fairly painless, but you can get that # cut-out diagonal lines look with just a bit more work. The important # thing to know here is that in axes coordinates, which are always # between 0-1, spine endpoints are at these locations (0,0), (0,1), # (1,0), and (1,1). Thus, we just need to put the diagonals in the # appropriate corners of each of our axes, and so long as we use the # right transform and disable clipping. d = .015 # how big to make the diagonal lines in axes coordinates # arguments to pass to plot, just so we don't keep repeating them kwargs = dict(transform=ax.transAxes, color='k', clip_on=False) ax.plot((-d, +d), (-d, +d), **kwargs)  # top-left diagonal ax.plot((1 - d, 1 + d), (-d, +d), **kwargs) # top-right diagonal kwargs.update(transform=ax2.transAxes) # switch to the bottom axes ax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonal ax2.plot((1 - d, 1 + d), (1 - d, 1 + d), **kwargs) # bottom-right diagonal # What's cool about this is that now if we vary the distance between # ax and ax2 via f.subplots_adjust(hspace=...) or plt.subplot_tool(), # the diagonal lines will move accordingly, and stay right at the tips # of the spines they are 'breaking' plt.show()

 from matplotlib.transforms import Affine2D import mpl_toolkits.axisartist.floating_axes as floating_axes import numpy as np import mpl_toolkits.axisartist.angle_helper as angle_helper from matplotlib.projections import PolarAxes from mpl_toolkits.axisartist.grid_finder import (FixedLocator, MaxNLocator, DictFormatter) import matplotlib.pyplot as plt # Fixing random state for reproducibility np.random.seed(19680801) def setup_axes1(fig, rect): """ A simple one. """ tr = Affine2D().scale(2, 1).rotate_deg(30) grid_helper = floating_axes.GridHelperCurveLinear( tr, extremes=(-0.5, 3.5, 0, 4)) ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper) fig.add_subplot(ax1) aux_ax = ax1.get_aux_axes(tr) grid_helper.grid_finder.grid_locator1._nbins = 4 grid_helper.grid_finder.grid_locator2._nbins = 4 return ax1, aux_ax def setup_axes2(fig, rect): """ With custom locator and formatter. Note that the extreme values are swapped. """ tr = PolarAxes.PolarTransform() pi = np.pi angle_ticks = [(0, r"$0$"), (.25*pi, r"$\frac{1}{4}\pi$"), (.5*pi, r"$\frac{1}{2}\pi$")] grid_locator1 = FixedLocator([v for v, s in angle_ticks]) tick_formatter1 = DictFormatter(dict(angle_ticks)) grid_locator2 = MaxNLocator(2) grid_helper = floating_axes.GridHelperCurveLinear( tr, extremes=(.5*pi, 0, 2, 1), grid_locator1=grid_locator1, grid_locator2=grid_locator2, tick_formatter1=tick_formatter1, tick_formatter2=None) ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper) fig.add_subplot(ax1) # create a parasite axes whose transData in RA, cz aux_ax = ax1.get_aux_axes(tr) aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax ax1.patch.zorder = 0.9 # but this has a side effect that the patch is # drawn twice, and possibly over some other # artists. So, we decrease the zorder a bit to # prevent this. return ax1, aux_ax def setup_axes3(fig, rect): """ Sometimes, things like axis_direction need to be adjusted. """ # rotate a bit for better orientation tr_rotate = Affine2D().translate(-95, 0) # scale degree to radians tr_scale = Affine2D().scale(np.pi/180., 1.) tr = tr_rotate + tr_scale + PolarAxes.PolarTransform() grid_locator1 = angle_helper.LocatorHMS(4) tick_formatter1 = angle_helper.FormatterHMS() grid_locator2 = MaxNLocator(3) # Specify theta limits in degrees ra0, ra1 = 8.*15, 14.*15 # Specify radial limits cz0, cz1 = 0, 14000 grid_helper = floating_axes.GridHelperCurveLinear( tr, extremes=(ra0, ra1, cz0, cz1), grid_locator1=grid_locator1, grid_locator2=grid_locator2, tick_formatter1=tick_formatter1, tick_formatter2=None) ax1 = floating_axes.FloatingSubplot(fig, rect, grid_helper=grid_helper) fig.add_subplot(ax1) # adjust axis ax1.axis["left"].set_axis_direction("bottom") ax1.axis["right"].set_axis_direction("top") ax1.axis["bottom"].set_visible(False) ax1.axis["top"].set_axis_direction("bottom") ax1.axis["top"].toggle(ticklabels=True, label=True) ax1.axis["top"].major_ticklabels.set_axis_direction("top") ax1.axis["top"].label.set_axis_direction("top") ax1.axis["left"].label.set_text(r"cz [km$^{-1}$]") ax1.axis["top"].label.set_text(r"$\alpha_{1950}$") # create a parasite axes whose transData in RA, cz aux_ax = ax1.get_aux_axes(tr) aux_ax.patch = ax1.patch # for aux_ax to have a clip path as in ax ax1.patch.zorder = 0.9 # but this has a side effect that the patch is # drawn twice, and possibly over some other # artists. So, we decrease the zorder a bit to # prevent this. return ax1, aux_ax fig = plt.figure(1, figsize=(8, 4)) fig.subplots_adjust(wspace=0.3, left=0.05, right=0.95) ax1, aux_ax1 = setup_axes1(fig, 131) aux_ax1.bar([0, 1, 2, 3], [3, 2, 1, 3]) ax2, aux_ax2 = setup_axes2(fig, 132) theta = np.random.rand(10)*.5*np.pi radius = np.random.rand(10) + 1. aux_ax2.scatter(theta, radius) ax3, aux_ax3 = setup_axes3(fig, 133) theta = (8 + np.random.rand(10)*(14 - 8))*15. # in degrees radius = np.random.rand(10)*14000. aux_ax3.scatter(theta, radius) plt.show()

 import numpy as np import matplotlib.pyplot as plt import mpl_toolkits.axisartist.angle_helper as angle_helper from matplotlib.projections import PolarAxes from matplotlib.transforms import Affine2D from mpl_toolkits.axisartist import SubplotHost from mpl_toolkits.axisartist import GridHelperCurveLinear def curvelinear_test2(fig): """Polar projection, but in a rectangular box. """ # see demo_curvelinear_grid.py for details tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform() extreme_finder = angle_helper.ExtremeFinderCycle(20, 20, lon_cycle=360, lat_cycle=None, lon_minmax=None, lat_minmax=(0, np.inf), ) grid_locator1 = angle_helper.LocatorDMS(12) tick_formatter1 = angle_helper.FormatterDMS() grid_helper = GridHelperCurveLinear(tr, extreme_finder=extreme_finder, grid_locator1=grid_locator1, tick_formatter1=tick_formatter1 ) ax1 = SubplotHost(fig, 1, 1, 1, grid_helper=grid_helper) fig.add_subplot(ax1) # Now creates floating axis # floating axis whose first coordinate (theta) is fixed at 60 ax1.axis["lat"] = axis = ax1.new_floating_axis(0, 60) axis.label.set_text(r"$\theta = 60^{\circ}$") axis.label.set_visible(True) # floating axis whose second coordinate (r) is fixed at 6 ax1.axis["lon"] = axis = ax1.new_floating_axis(1, 6) axis.label.set_text(r"$r = 6$") ax1.set_aspect(1.) ax1.set_xlim(-5, 12) ax1.set_ylim(-5, 10) ax1.grid(True) fig = plt.figure(1, figsize=(5, 5)) fig.clf() curvelinear_test2(fig) plt.show()