[英]Matplotlib - fixing x axis scale and autoscale y axis
I would like to plot only part of the array, fixing x part, but letting y part autoscale.我只想绘制数组的一部分,固定 x 部分,但让 y 部分自动缩放。 I tried as shown below, but it does not work.我尝试如下所示,但它不起作用。
Any suggestions?有什么建议?
import numpy as np
import matplotlib.pyplot as plt
data=[np.arange(0,101,1),300-0.1*np.arange(0,101,1)]
plt.figure()
plt.scatter(data[0], data[1])
plt.xlim([50,100])
plt.autoscale(enable=True, axis='y')
plt.show()
While Joe Kington certainly proposes the most sensible answer when he recommends that only the necessary data be plotted, there are situations where it would be best to plot all of the data and just zoom to a certain section.虽然Joe Kington在建议只绘制必要数据时提出了最明智的答案,但在某些情况下,最好绘制所有数据并仅缩放到某个部分。 Additionally, it would be nice to have an "autoscale_y" function that only requires the axes object (ie, unlike the answer here , which requires direct use of the data.)此外,拥有一个只需要轴对象的“autoscale_y”函数会很好(即,不像这里的答案,它需要直接使用数据。)
Here is a function that just rescales the y-axis based on the data that is in the visible x-region:这是一个仅根据可见 x 区域中的数据重新调整 y 轴的函数:
def autoscale_y(ax,margin=0.1):
"""This function rescales the y-axis based on the data that is visible given the current xlim of the axis.
ax -- a matplotlib axes object
margin -- the fraction of the total height of the y-data to pad the upper and lower ylims"""
import numpy as np
def get_bottom_top(line):
xd = line.get_xdata()
yd = line.get_ydata()
lo,hi = ax.get_xlim()
y_displayed = yd[((xd>lo) & (xd<hi))]
h = np.max(y_displayed) - np.min(y_displayed)
bot = np.min(y_displayed)-margin*h
top = np.max(y_displayed)+margin*h
return bot,top
lines = ax.get_lines()
bot,top = np.inf, -np.inf
for line in lines:
new_bot, new_top = get_bottom_top(line)
if new_bot < bot: bot = new_bot
if new_top > top: top = new_top
ax.set_ylim(bot,top)
This is something of a hack, and will probably not work in many situations, but for a simple plot, it works well.这有点像黑客,可能在许多情况下都不起作用,但对于简单的情节,它运行良好。
Here is a simple example using this function:这是一个使用此函数的简单示例:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-100,100,1000)
y = x**2 + np.cos(x)*100
fig,axs = plt.subplots(1,2,figsize=(8,5))
for ax in axs:
ax.plot(x,y)
ax.plot(x,y*2)
ax.plot(x,y*10)
ax.set_xlim(-10,10)
autoscale_y(axs[1])
axs[0].set_title('Rescaled x-axis')
axs[1].set_title('Rescaled x-axis\nand used "autoscale_y"')
plt.show()
Autoscaling always uses the full range of the data, so the y-axis is scaled by full extent of the y-data, not just what's within the x-limits.自动缩放始终使用完整范围的数据,因此 y 轴按 y 数据的全部范围进行缩放,而不仅仅是 x 范围内的内容。
If you'd like to display a subset of the data, then it's probably easiest to plot only that subset:如果您想显示数据的一个子集,那么只绘制该子集可能是最简单的:
import numpy as np
import matplotlib.pyplot as plt
x, y = np.arange(0,101,1) ,300 - 0.1*np.arange(0,101,1)
mask = (x >= 50) & (x <= 100)
fig, ax = plt.subplots()
ax.scatter(x[mask], y[mask])
plt.show()
I've built upon @DanHickstein's answer to cover cases of plot, scatter and axhline/axvline for scaling either the x or y axis.我已经建立在@DanHickstein's answer 的基础上,涵盖了用于缩放 x 或 y 轴的 plot、scatter 和 axhline/axvline 的情况。 It can be called as simple as autoscale()
to work on the most recent axes.它可以像autoscale()
一样简单地调用以在最近的轴上工作。 If you wish to edit it, please fork it on gist .如果你想编辑它,请在 gist 上 fork 。
def autoscale(ax=None, axis='y', margin=0.1):
'''Autoscales the x or y axis of a given matplotlib ax object
to fit the margins set by manually limits of the other axis,
with margins in fraction of the width of the plot
Defaults to current axes object if not specified.
'''
import matplotlib.pyplot as plt
import numpy as np
if ax is None:
ax = plt.gca()
newlow, newhigh = np.inf, -np.inf
for artist in ax.collections + ax.lines:
x,y = get_xy(artist)
if axis == 'y':
setlim = ax.set_ylim
lim = ax.get_xlim()
fixed, dependent = x, y
else:
setlim = ax.set_xlim
lim = ax.get_ylim()
fixed, dependent = y, x
low, high = calculate_new_limit(fixed, dependent, lim)
newlow = low if low < newlow else newlow
newhigh = high if high > newhigh else newhigh
margin = margin*(newhigh - newlow)
setlim(newlow-margin, newhigh+margin)
def calculate_new_limit(fixed, dependent, limit):
'''Calculates the min/max of the dependent axis given
a fixed axis with limits
'''
if len(fixed) > 2:
mask = (fixed>limit[0]) & (fixed < limit[1])
window = dependent[mask]
low, high = window.min(), window.max()
else:
low = dependent[0]
high = dependent[-1]
if low == 0.0 and high == 1.0:
# This is a axhline in the autoscale direction
low = np.inf
high = -np.inf
return low, high
def get_xy(artist):
'''Gets the xy coordinates of a given artist
'''
if "Collection" in str(artist):
x, y = artist.get_offsets().T
elif "Line" in str(artist):
x, y = artist.get_xdata(), artist.get_ydata()
else:
raise ValueError("This type of object isn't implemented yet")
return x, y
It, like its predecessor, is a bit hacky, but that is necessary because collections and lines have different methods for returning the xy coordinates, and because axhline/axvline is tricky to work with since it only has two datapoints.与其前身一样,它有点老套,但这是必要的,因为集合和线具有不同的返回 xy 坐标的方法,并且因为 axhline/axvline 很难使用,因为它只有两个数据点。
Here it is in action:这是在行动:
fig, axes = plt.subplots(ncols = 4, figsize=(12,3))
(ax1, ax2, ax3, ax4) = axes
x = np.linspace(0,100,300)
noise = np.random.normal(scale=0.1, size=x.shape)
y = 2*x + 3 + noise
for ax in axes:
ax.plot(x, y)
ax.scatter(x,y, color='red')
ax.axhline(50., ls='--', color='green')
for ax in axes[1:]:
ax.set_xlim(20,21)
ax.set_ylim(40,45)
autoscale(ax3, 'y', margin=0.1)
autoscale(ax4, 'x', margin=0.1)
ax1.set_title('Raw data')
ax2.set_title('Specificed limits')
ax3.set_title('Autoscale y')
ax4.set_title('Autoscale x')
plt.tight_layout()
pandas
, which makes selecting data with Boolean indexing really easy.最简单的方法可能是使用pandas
,这使得使用布尔索引选择数据非常容易。x
and y
into the DataFrame, use Boolean selection with pandas.Series.between(left, right, inclusive=True)
, and directly plot with pandas.DataFrame.plot
, which uses matplotlib
.将x
和y
加载到 DataFrame 中,使用带有pandas.Series.between(left, right, inclusive=True)
布尔选择,并使用使用matplotlib
pandas.DataFrame.plot
直接绘图。import numpy as np # for the test data
import pandas as pd
# load the data into the dataframe; there are many ways to do this
df = pd.DataFrame({'x': np.arange(0,101,1), 'y': 300-0.1*np.arange(0,101,1)})
# select and plot the data
ax = df[df.x.between(50, 100)].plot(x='x', y='y', kind='scatter', figsize=(5, 4))
I would like to add to the great answer (which saved me a lot of time) of @TomNorway to handle cases in which some artists are composed partially of totally of NaNs.我想补充@TomNorway 的好答案(这为我节省了很多时间)来处理一些艺术家完全由 NaN 组成的情况。
All changes I made are inside the我所做的所有更改都在
if len(fixed) > 2:
Cheers!干杯!
def autoscale(ax=None, axis='y', margin=0.1):
'''Autoscales the x or y axis of a given matplotlib ax object
to fit the margins set by manually limits of the other axis,
with margins in fraction of the width of the plot
Defaults to current axes object if not specified.
'''
if ax is None:
ax = plt.gca()
newlow, newhigh = np.inf, -np.inf
for artist in ax.collections + ax.lines:
x,y = get_xy(artist)
if axis == 'y':
setlim = ax.set_ylim
lim = ax.get_xlim()
fixed, dependent = x, y
else:
setlim = ax.set_xlim
lim = ax.get_ylim()
fixed, dependent = y, x
low, high = calculate_new_limit(fixed, dependent, lim)
newlow = low if low < newlow else newlow
newhigh = high if high > newhigh else newhigh
margin = margin*(newhigh - newlow)
setlim(newlow-margin, newhigh+margin)
def calculate_new_limit(fixed, dependent, limit):
'''Calculates the min/max of the dependent axis given
a fixed axis with limits
'''
if len(fixed) > 2:
mask = (fixed>limit[0]) & (fixed < limit[1]) & (~np.isnan(dependent)) & (~np.isnan(fixed))
window = dependent[mask]
try:
low, high = window.min(), window.max()
except ValueError: # Will throw ValueError if `window` has zero elements
low, high = np.inf, -np.inf
else:
low = dependent[0]
high = dependent[-1]
if low == 0.0 and high == 1.0:
# This is a axhline in the autoscale direction
low = np.inf
high = -np.inf
return low, high
def get_xy(artist):
'''Gets the xy coordinates of a given artist
'''
if "Collection" in str(artist):
x, y = artist.get_offsets().T
elif "Line" in str(artist):
x, y = artist.get_xdata(), artist.get_ydata()
else:
raise ValueError("This type of object isn't implemented yet")
return x, y
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