matplotlib：如何使用标记大小/颜色作为绘图中的额外尺寸？ [英] matplotlib: How to use marker size / color as an extra dimension in plots?
问题描述
我正在绘制一个时间序列，其中 x 是一系列 datetime.datetime 对象，而 y 是一系列双打。
我想将标记大小映射到三分之一系列 z （也可能将标记颜色映射到第四系列 w ），这在大多数情况下可以通过：
散点图（x，y，s = z，c = w）
除了 scatter（） 之外，不允许 x 是一系列 datetime.datetime 对象。
plot（x，y，marker ='o'，linestyle ='None'）
在 x 为 datetime.datetime （带有正确的刻度标签）的情况下有效，但是markersize / color只能一次为所有点设置，即无法将它们映射到额外的序列。
看到分散和 plot 都可以做一半
UPDATE 之后是
步骤A ：来自 datetime 到 matplotlib 约定-兼容 float 用于日期/时间
步骤B ：添加 3D | 4D | 5D 功能（使用其他{ color | size | alpha }-信息的编码维）
像往常一样，魔鬼被详细隐藏。
matplotlib 日期等于 几乎 ，但不等于：
＃mPlotDATEs.date2num .__ doc__
＃
＃* d *是类datetime实例或日期时间序列。
＃
＃返回值是一个浮点数（或浮点数序列）
＃给出自此以来的天数（小数部分表示小时，
＃分钟，秒） 0001-01-01 00:00:00 UTC，*加**一个*。
＃这里加一个是历史文物。另外，注意
＃假定使用公历；这不是通用的
＃练习。有关详细信息，请参见模块文档字符串。
因此，强烈建议重新使用其自有工具：
来自matplotlib的导入日期为mPlotDATEs的b
＃辅助函数num2date（）
＃＃和date2num（）
＃＃进行转换。
管理轴标签&格式化和规模（最小/最大）是一个单独的问题
不过，matplotlib也为您带来了这一方面的武器：
<$来自matplotlib.dates的p $ p>
import DateFormatter，\
AutoDateLocator，\
HourLocator，\
MinuteLocator，\
epoch2num $ b来自matplotlib.ticker的$ b导入ScalarFormatter，FuncFormatter
例如，可以这样做：
aPlotAX.set_xlim（x_min，x_MAX）＃X轴限制------------------ -------------------------------------------------- ----------- X-LIMITs
＃lt.gca（）。xaxis.set_major_locator（matplotlib.ticker.FixedLocator（secs））
＃lt.gca （）.xaxis.set_major_formatter（matplotlib.ticker.FuncFormatter（lambda pos，_：time.strftime（％d-％m-％Y％H：％M：％S，time.localtime（pos）））））
aPlotAX.xaxis.set_major_locator（AutoDateLocator（））
aPlotAX.xaxis.set_major_formatter（DateFormatter（'％Y-％m-％d％H：％M'））＃------ -------------------------------------------------- --------------------------------- X-FORMAT
＃----- ----------------------------------------＃90度x-tick-LABELs
plt.setp（plt.gca（）。get_xticklabels（），旋转= 90，
horizontalalignment ='right'
）
＃- -------------------------------------------------- --------------
添加{ 3D | 4D | 5D }转码
仅是想像这种方法，请查看此示例$ b使用不同的工具将$ b的附加信息维编码为{ color | 大小 | alpha }。而{ size | alpha }与散点相关，对于颜色， matplotlib中还有其他工具包括一组针对各种特定领域或人眼视觉/感知适应的色标进行缩放的着色。在
您可能已经注意到，该 4D 示例仍具有常量 alpha （在真正的 5D 维度可视化中未用于第五个DOF）
I am plotting a time series where x is a series of datetime.datetime objects and y is a series of doubles.
I'd like to map the marker size to a third series z (and possibly also map marker color to a fourth series w), which in most cases could be accomplished with:
scatter(x, y, s=z, c=w)
except scatter() does not permit x being a series of datetime.datetime objects.
plot(x, y, marker='o', linestyle='None')
on the other hand works with x being datetime.datetime (with properly tick label), but markersize/color can only be set for all points at once, namely no way to map them to extra series.
Seeing that scatter and plot each can do half of what I need, is there a way to do both?
UPDATE following @tcaswell's question, I realized scatter raised an KeyError deep in the default_units() in matplotlib/dates.py on the line:
x = x[0]
and sure enough my x and y are both Series taken from a pandas DataFrame which has no '0' in index. I then tried two things (both feel somewhat hacky):
First, I tried modify the DataFrame index to 0..len(x), which led to a different error inside matplotlib/axes/_axes.py at:
offsets = np.dstack((x,y))
dstack doesn't play nice with pandas Series. So I then tried convert x and y to numpy.array:
scatter(numpy.array(x), numpy.array(y), s=numpy.array(z))
This almost worked except scatter seemed to have trouble auto-scaling x axis and collapsed everything into a straight line, so I have to reset xlim explicitly to see the plot.
All of this is to say that scatter could do the job albeit with a bit of convolution. I had always thought matplotlib can take any array-like inputs but apparently that's not quite true if the data is not simple numbers that require some internal gymnastics.
UPDATE2 I also tried to follow @user3666197's suggestion (thanks for the editing tips btw). If I understood correctly, I first converted x into a series of 'matplotlib style days':
mx = mPlotDATEs.date2num(list(x))
which then allows me to directly call:
scatter(mx, y, s=z)
then to label axis properly, I call:
gca().xaxis.set_major_formatter( DateFormatter('%Y-%m-%d %H:%M'))
(call show() to update the axis label if interactive mode)
It worked quite nicely and feels to me a more 'proper' way of doing things, so I'm going to accept that as the best answer.
Is there a way to do both? Yes.
However, lets work by example:
step A: from a datetime to a matplotlib convention-compatible float for dates/times
step B: adding 3D | 4D | 5D capabilities ( using additional { color | size | alpha } --coded dimensionality of information )
As usual, devil is hidden in detail.
matplotlib dates are almost equal, but not equal:
# mPlotDATEs.date2num.__doc__
#
# *d* is either a class `datetime` instance or a sequence of datetimes.
#
# Return value is a floating point number (or sequence of floats)
# which gives the number of days (fraction part represents hours,
# minutes, seconds) since 0001-01-01 00:00:00 UTC, *plus* *one*.
# The addition of one here is a historical artifact. Also, note
# that the Gregorian calendar is assumed; this is not universal
# practice. For details, see the module docstring.
So, highly recommended to re-use their "own" tool:
from matplotlib import dates as mPlotDATEs # helper functions num2date()
# # and date2num()
# # to convert to/from.
Managing axis-labels & formatting & scale (min/max) is a separate issue
Nevertheless, matplotlib brings you arms for this part too:
from matplotlib.dates import DateFormatter, \
AutoDateLocator, \
HourLocator, \
MinuteLocator, \
epoch2num
from matplotlib.ticker import ScalarFormatter, FuncFormatter
and may for example do:
aPlotAX.set_xlim( x_min, x_MAX ) # X-AXIS LIMITs ------------------------------------------------------------------------------- X-LIMITs
#lt.gca().xaxis.set_major_locator( matplotlib.ticker.FixedLocator( secs ) )
#lt.gca().xaxis.set_major_formatter( matplotlib.ticker.FuncFormatter( lambda pos, _: time.strftime( "%d-%m-%Y %H:%M:%S", time.localtime( pos ) ) ) )
aPlotAX.xaxis.set_major_locator( AutoDateLocator() )
aPlotAX.xaxis.set_major_formatter( DateFormatter( '%Y-%m-%d %H:%M' ) ) # ----------------------------------------------------------------------------------------- X-FORMAT
#--------------------------------------------- # 90-deg x-tick-LABELs
plt.setp( plt.gca().get_xticklabels(), rotation = 90,
horizontalalignment = 'right'
)
#------------------------------------------------------------------
Adding { 3D | 4D | 5D } transcoding
Just to imagine the approach, check this example,
additional dimensionality of information was coded using different tools into { color | size | alpha }. Whereas { size | alpha } are scatter-point related, for color there are additional tools in matplotlib included a set of colouring scaled for various domain-specific or human-eye vision / perception adapted colour-scales. A nice explanation of color-scale / normalisation scaler is presented here
You may have noticed, that this 4D example still has a constant alpha ( unused for 5th DOF in true 5D dimensionality visualisation )
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