python + matplotlib:如何在极坐标图中的轴和刻度标签之间插入更多空格？
otm*_*ger 8 python charts matplotlib
我正在尝试用matplotlib和python 2.7创建一个极坐标图,但我正在努力如何增加同一轴的X轴和刻度线标签之间的空间.正如你在图片上看到的那样,12点和6点的标签看起来很好,我希望所有其他标签都有相同的空间.
我试过了
ax.xaxis.LABELPAD = 10
但它没有任何影响.
这是我的代码(抱歉这个烂摊子......):
import numpy as np
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
import matplotlib.dates
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
import matplotlib.cm as cm
import matplotlib.ticker as tkr
import pdb
def plot_clock(data,filename,path,**kwargs): # (x,y,colors,lab_x,lab_y,xTicks,filename,legend,**kwargs):
bins = [0,0.5,1.5,2.5,3.5,4.5,5.5,6.5,7.5,8.5,9.5,10.5,11.5,12,12.5,13.5,14.5,15.5,16.5,17.5,18.5,19.5,20.5,21.5,22.5,23.5,23.999999];
data = np.array(data)/(60*60)
DATA_ = np.histogram(data,bins)[0]
def hour_formatAM(x, p):
#pdb.set_trace()
if x > 0:
return str(format(x*6/np.pi, "01.0f") + ':00')
else:
return '12:00'
def hour_formatPM(x, p):
#pdb.set_trace()
if x > 0:
return str(format(x*6/np.pi+12, "01.0f") + ':00')
else:
return '24:00'
'''font = {'family' : 'normal',
'weight' : 'bold',
'size' : 12}
mpl.rc('font', **font)'''
mpl.rcParams.update({'font.size': 8})
#sub plot AM
theta = np.array(bins[1:13]) * np.pi / 6
radii = DATA_[1:13]
radii[-1] += DATA_[0]
width = 1 * np.pi / 6
fig = plt.figure(figsize=(5.5,3),dpi=600)
ax = fig.add_subplot(121, polar=True)
bars = ax.bar(theta, radii, width=width, bottom=0)
ax.set_theta_offset(np.pi/2)
ax.set_theta_direction(-1)
ax.xaxis.set_ticks(np.arange(0, np.pi*2, np.pi/6))
ax.get_xaxis().set_major_formatter(tkr.FuncFormatter(hour_formatAM))
ax.yaxis.set_ticks(np.arange(1,max(DATA_),1))
for t, bar in zip(theta, bars):
bar.set_facecolor(plt.cm.jet(t / 12.))
bar.set_alpha(0.5)
#sub plot PM
theta = np.array(bins[14:26]) * np.pi / 6
radii = DATA_[14:26]
radii[-1] += DATA_[13]
width = 1 * np.pi / 6
ax = fig.add_subplot(122, polar=True)
bars = ax.bar(theta, radii, width=width, bottom=0)
ax.set_theta_offset(np.pi/2)
ax.set_theta_direction(-1)
pdb.set_trace()
ax.xaxis.set_ticks(np.arange(0, np.pi*2, np.pi/6))
ax.get_xaxis().set_major_formatter(tkr.FuncFormatter(hour_formatPM))
ax.yaxis.set_ticks(np.arange(1,max(DATA_),1))
for t, bar in zip(theta, bars):
bar.set_facecolor(plt.cm.jet(t / 12.))
bar.set_alpha(0.5)
#pdb.set_trace()
#fig.tight_layout()
#xlabels = [item.get_text() for item in ax.get_xticklabels()]
ax.xaxis.LABELPAD = 10
#[item.set_fontsize(12) for item in ax.xaxis.get_major_ticks()]
fig.subplots_adjust(wspace = 0.4) # http://matplotlib.org/faq/howto_faq.html
fig.savefig(path + filename,format='pdf')
data = [ 10.49531611, 22.49511583, 10.90891806, 18.99525417,
21.57165972, 6.687755 , 6.52137028, 15.86534639,
18.53823556, 6.32563583, 12.99365833, 11.06817056,
17.29261306, 15.31288556, 19.16236667, 10.38483333,
14.51442222, 17.01413611, 6.96102278, 15.98508611,
16.5287 , 15.26533889, 20.83520278, 17.21952056,
7.3225775 , 16.42534361, 14.38649722, 21.63573111, 16.19249444]
data = np.array(data)*60*60
plot_clock(data,'figure2_StartTime.pdf','./')
@dabillox已经提到过使用frackwarg来ax.set_thetagrids.
但是,正如您已经注意到的那样,您真正想要改变的是勾选标签的对齐方式,而不是刻度标签的整体径向位移.
在一个侧面说明,其原因labelpad是没有效果的是,它控制之间填充轴标签(例如plt.xlabel,plt.ylabel)和轴,没有刻度标记.
首先,您可以更清楚地编写示例代码.这里或多或少是我接近你正在做的事情(注意这仍然会与刻度标签定位有同样的问题):
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
def main():
data = [ 10.49531611, 22.49511583, 10.90891806, 18.99525417,
21.57165972, 6.687755 , 6.52137028, 15.86534639,
18.53823556, 6.32563583, 12.99365833, 11.06817056,
17.29261306, 15.31288556, 19.16236667, 10.38483333,
14.51442222, 17.01413611, 6.96102278, 15.98508611,
16.5287 , 15.26533889, 20.83520278, 17.21952056,
7.3225775 , 16.42534361, 14.38649722, 21.63573111, 16.19249444]
data = np.array(data)*60*60
plot_clock(data)
plt.show()
def plot_clock(data):
def hour_formatAM(x, p):
hour = x * 6 / np.pi
return '{:0.0f}:00'.format(hour) if x > 0 else '12:00'
def hour_formatPM(x, p):
hour = x * 6 / np.pi
return '{:0.0f}:00'.format(hour + 12) if x > 0 else '24:00'
def plot(ax, theta, counts, formatter):
colors = plt.cm.jet(theta / 12.0)
ax.bar(theta, counts, width=np.pi/6, color=colors, alpha=0.5)
ax.xaxis.set_major_formatter(tkr.FuncFormatter(formatter))
plt.rcParams['font.size'] = 8
bins = np.r_[0, 0.5:12, 12, 12.5:24, 23.99999]
data = np.array(data) / (60*60)
counts = np.histogram(data,bins)[0]
counts[13] += counts[0]
counts[-1] += counts[13]
fig, axes = plt.subplots(ncols=2, figsize=(5.5, 3), dpi=200,
subplot_kw=dict(projection='polar'))
fig.subplots_adjust(wspace=0.4)
for ax in axes:
ax.set(theta_offset=np.pi/2, theta_direction=-1,
xticks=np.arange(0, np.pi*2, np.pi/6),
yticks=np.arange(1, counts.max()))
plot(axes[0], bins[1:13] * np.pi / 6, counts[1:13], hour_formatAM)
plot(axes[1], bins[14:26] * np.pi / 6, counts[14:26], hour_formatPM)
main()
如果我们想避免错误对齐的刻度标签,我们可以根据它们的位置设置水平对齐:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.ticker as tkr
def main():
data = [ 10.49531611, 22.49511583, 10.90891806, 18.99525417,
21.57165972, 6.687755 , 6.52137028, 15.86534639,
18.53823556, 6.32563583, 12.99365833, 11.06817056,
17.29261306, 15.31288556, 19.16236667, 10.38483333,
14.51442222, 17.01413611, 6.96102278, 15.98508611,
16.5287 , 15.26533889, 20.83520278, 17.21952056,
7.3225775 , 16.42534361, 14.38649722, 21.63573111, 16.19249444]
data = np.array(data)*60*60
axes = plot_clock(data)
for ax in axes:
realign_polar_xticks(ax)
plt.show()
def realign_polar_xticks(ax):
for x, label in zip(ax.get_xticks(), ax.get_xticklabels()):
if np.sin(x) > 0.1:
label.set_horizontalalignment('left')
if np.sin(x) < -0.1:
label.set_horizontalalignment('right')
def plot_clock(data):
def hour_formatAM(x, p):
hour = x * 6 / np.pi
return '{:0.0f}:00'.format(hour) if x > 0 else '12:00'
def hour_formatPM(x, p):
hour = x * 6 / np.pi
return '{:0.0f}:00'.format(hour + 12) if x > 0 else '24:00'
def plot(ax, theta, counts, formatter):
colors = plt.cm.jet(theta / 12.0)
ax.bar(theta, counts, width=np.pi/6, color=colors, alpha=0.5)
ax.xaxis.set_major_formatter(tkr.FuncFormatter(formatter))
plt.rcParams['font.size'] = 8
bins = np.r_[0, 0.5:12, 12, 12.5:24, 23.99999]
data = np.array(data) / (60*60)
counts = np.histogram(data,bins)[0]
counts[13] += counts[0]
counts[-1] += counts[13]
fig, axes = plt.subplots(ncols=2, figsize=(5.5, 3), dpi=200,
subplot_kw=dict(projection='polar'))
fig.subplots_adjust(wspace=0.5)
for ax in axes:
ax.set(theta_offset=np.pi/2, theta_direction=-1,
xticks=np.arange(0, np.pi*2, np.pi/6),
yticks=np.arange(1, counts.max()))
plot(axes[0], bins[1:13] * np.pi / 6, counts[1:13], hour_formatAM)
plot(axes[1], bins[14:26] * np.pi / 6, counts[14:26], hour_formatPM)
return axes
main()
最后,如果您想"正确"执行此操作,无论θ方向和偏移量如何,请执行以下操作:
def realign_polar_xticks(ax):
for theta, label in zip(ax.get_xticks(), ax.get_xticklabels()):
theta = theta * ax.get_theta_direction() + ax.get_theta_offset()
theta = np.pi/2 - theta
y, x = np.cos(theta), np.sin(theta)
if x >= 0.1:
label.set_horizontalalignment('left')
if x <= -0.1:
label.set_horizontalalignment('right')
if y >= 0.5:
label.set_verticalalignment('bottom')
if y <= -0.5:
label.set_verticalalignment('top')
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