如何使用matplotlib在同一张图上绘制具有不同采样率的两个时间序列

Question

I have two sets of data that I would like to plot on the same graph. Both sets of data have 200 seconds worth of data. DatasetA (BLUE) is sampled at 25 Hz and DatasetB (Red) is sampled at 40Hz. Hence DatasetA has 25*200 = 5000 (time,value) samples... and DatasetB has 40*200 = 8000 (time,value) samples.

datasets with different sample rates

As you can see above, I have managed to plot these in matplotlib using the 'plot_date' function. As far as I can tell, the 'plot' function will not work because the number of (x,y) pairs are different in each sample. The issue I have is the format of the xaxis. I would like the time to be a duration in seconds, rather than an exact time of the format hh:mm:ss. Currently, the seconds value resets back to zero when it hits each minute (as seen in the zoomed out image below).

zoomed out full time scale

How can I make the plot show the time increasing from 0-200 seconds rather than showing hours:min:sec ?

Is there a matplotlib.dates.DateFormatter that can do this (I have tried, but can't figure it out...)? Or do I somehow need to manipulate the datetime x-axis values to be a duration, rather than an exact time? (how to do this)?

FYI: The code below is how I am converting the original csv list of float values (in seconds) into datetime objects, and again into matplotlib date-time objects -- to be used with the axes.plot_date() function.

from matplotlib import dates        
import datetime 

## arbitrary start date... we're dealing with milliseconds here.. so only showing time on the graph.
base_datetime = datetime.datetime(2018,1,1)
csvDateTime = map(lambda x: base_datetime + datetime.timedelta(seconds=x), csvTime)
csvMatTime = map(lambda x: dates.date2num(x), csvDateTime)

Thanks for your help/suggestions!

Answer 1

Well, thanks to ImportanceOfBeingErnst for pointing out that I was vastly over-complicating things...

It turns out that I really only need the ax.plot(x,y) function rather than the ax.plot_date(mdatetime, y) function. Plot can actually plot varied lengths of data as long as each individual trace has the same number of x and y values. Since the data is all given in seconds I can easily plot using 0 as my "reference time".

For anyone else struggling with plotting duration rather than exact times, you can simply manipulate the "time" (x) data by using python's map() function, or better yet a list comprehension to "time shift" the data or convert to a single unit of time (eg simply turn minutes into seconds by dividing by 60).

"Time Shifting" might look like:

# build some sample 25 Hz time data
time = range(0,1000,1)
time = [x*.04 for x in time]
# "time shift it by 5 seconds, since this data is recorded 5 seconds after the other signal
time = [x+5 for x in time]

Here is my plotting code for any other matplotlib beginners like me :) (this will not run, since I have not converted my variables to generic data... but nevertheless it is a simple example of using matplotlib.)

fig,ax = plt.subplots()
ax.grid()
ax.set_title(plotTitle)
ax.set_xlabel("time (s)")
ax.set_ylabel("value")

# begin looping over the different sets of data.
tup = 0
while (tup < len(alldata)):
    outTime = alldata[tup][1].get("time")
    # each signal is time shifted 5 seconds later.
    # in addition each signal has different sampling frequency, 
    # so len(outTime) is different for almost every signal.
    outTime = [x +(5*tup) for x in outTime]
    for key in alldata[tup][1]:
        if(key not in channelSelection):
            ## if we dont want to plot that data then skip it.
            continue
        else:
            data = alldata[tup][1].get(key)
            ## using list comprehension to scale y values.
            data = [100*x for x in data]
        ax.plot(outTime,data,linestyle='solid', linewidth='1', marker='')
    tup+=1
plt.show()