Python绘图Matplotlib之坐标轴及刻度总结

Python  /  管理员 发布于 7年前   348

学习https://matplotlib.org/gallery/index.html 记录，描述不一定准确，具体请参考官网

Matplotlib使用总结图

import matplotlib.pyplot as pltplt.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 pdimport numpy as np

新建隐藏坐标轴

from mpl_toolkits.axisartist.axislines import SubplotZeroimport numpy as npfig = 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_axisax.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')

from mpl_toolkits.axes_grid1 import host_subplotimport mpl_toolkits.axisartist as AAimport matplotlib.pyplot as plthost = host_subplot(111, axes_class=AA.Axes)plt.subplots_adjust(right=0.75)par1 = host.twinx()par2 = host.twinx()offset = 100new_fixed_axis = par2.get_grid_helper().new_fixed_axispar2.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()

刻度及标记

import mpl_toolkits.axisartist.axislines as axislinesfig = plt.figure(1, figsize=(10, 6))fig.subplots_adjust(bottom=0.2)# 子图1ax1 = 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2ax2 = 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3ax3 = 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 reproducibilitynp.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 axisartistdef 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 axfig = 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

# 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 sharexax2 = plt.subplot(312, sharex=ax1)plt.plot(t, s2)# make these tick labels invisibleplt.setp(ax2.get_xticklabels(), visible=False)plt.title('我共享了X轴')# 共享X轴和Y轴 sharex、shareyax3 = 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０f.subplots_adjust(hspace=0)# 设置全部标签在外部for ax in axarr: ax.label_outer()

放大缩小

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 fitax1.plot(t1, f(t1), 'k')ax2 = plt.subplot(221)ax2.margins(2, 2)   # Values >0.0 zoom outax2.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 centerax3.plot(t1, f(t1), 'g')ax3.set_title('Zoomed in')plt.show()

from matplotlib.transforms import Bbox, TransformedBbox, \ blended_transform_factoryfrom mpl_toolkits.axes_grid1.inset_locator import BboxPatch, BboxConnector,\ BboxConnectorPatchdef 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, pdef 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, pdef 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, pimport matplotlib.pyplot as pltplt.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()

嵌入式标轴轴

#　相同随机数np.random.seed(19680801)# create some data to use for the plotdt = 0.001t = np.arange(0.0, 10.0, dt)r = np.exp(-t[:1000] / 0.05) # impulse responsex = np.random.randn(len(t))s = np.convolve(x, r)[:len(x)] * dt # colored noise# the main axes is subplot(111) by defaultplt.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 axesa = 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 axesa = 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()

非常规坐标轴

# 30 points between [0, 0.2) originally made using np.random.rand(30)*.2pts = 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 dataf, (ax, ax2) = plt.subplots(2, 1, sharex=True)# plot the same data on both axesax.plot(pts)ax2.plot(pts)# zoom-in / limit the view to different portions of the dataax.set_ylim(.78, 1.) # outliers onlyax2.set_ylim(0, .22) # most of the data# hide the spines between ax and ax2ax.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 topax2.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 themkwargs = dict(transform=ax.transAxes, color='k', clip_on=False)ax.plot((-d, +d), (-d, +d), **kwargs)  # top-left diagonalax.plot((1 - d, 1 + d), (-d, +d), **kwargs) # top-right diagonalkwargs.update(transform=ax2.transAxes) # switch to the bottom axesax2.plot((-d, +d), (1 - d, 1 + d), **kwargs) # bottom-left diagonalax2.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 Affine2Dimport mpl_toolkits.axisartist.floating_axes as floating_axesimport numpy as npimport mpl_toolkits.axisartist.angle_helper as angle_helperfrom matplotlib.projections import PolarAxesfrom mpl_toolkits.axisartist.grid_finder import (FixedLocator, MaxNLocator, DictFormatter)import matplotlib.pyplot as plt# Fixing random state for reproducibilitynp.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_axdef 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_axdef 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_axfig = 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.piradius = 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 degreesradius = np.random.rand(10)*14000.aux_ax3.scatter(theta, radius)plt.show()

import numpy as npimport matplotlib.pyplot as pltimport mpl_toolkits.axisartist.angle_helper as angle_helperfrom matplotlib.projections import PolarAxesfrom matplotlib.transforms import Affine2Dfrom mpl_toolkits.axisartist import SubplotHostfrom mpl_toolkits.axisartist import GridHelperCurveLineardef 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()

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