plot xy data with missing values

Hello:

I want to draw a multiple line plot using the data from the bottom of the email, but I get a error message: <type ‘exceptions.AssertionError’>: Dimensions of x and y are incompatible, which is normal, because the number of values on x are not equal with the number of values on y. The empty spaces from the table are missing values (which were not measured for a certain moment in time) and I can’t replace them with any other values

Is it possible to interpolate the missing values or to the draw the plot as: on x the time values and on y the f1,f2,f3, represented as a continuous line

The plot should look like the image attached, but with a line crossing all the points for f1, f2, f3

time
f1
f2
f3
30
3.39

13.55
33

16.94
35

6.78

36

10.16

37
10.16
13.55

38
13.55

39
16.94
16.94

40

20.33
41
20.33
20.33

42
23.71

44

23.71

plot.jpg

···

Hi Sandric,

Hello:
I want to draw a multiple line plot using the data from the bottom of the
email, but I get a error message: <type 'exceptions.AssertionError'>:
Dimensions of x and y are incompatible, which is normal, because the number
of values on x are not equal with the number of values on y. The empty
spaces from the table are missing values (which were not measured for a
certain moment in time) and I can't replace them with any other values

It is more robust to use a NaN to represent a missed data point. Then when you
load your data, your arrays will be the same length. There is already some
support for plotting data with NaNs in it, but the preferred solution is to
take a further step and use a masked array to mask your missing values. For
example, at the ipython -pylab prompt:

time=array([1,2,3,4,5])
f1=array([1,2,NaN,4,5])
f1_m=ma.masked_where(isnan(f1),f1)
plot(f1_m)

That will give you a line with a break in it at the missed observation.

Is it possible to interpolate the missing values or to the draw the plot as:
on x the time values and on y the f1,f2,f3, represented as a continuous
line

If you do not want breaks in your line due to the missed observation, you
could interpolate the missing values yourself, but if you just want a
continuous line through whatever data you have, you could use numpy's flexible
indexing and do:

plot(time[isfinite(f1)], f1[isfinite(f1)])

The plot should look like the image attached, but with a line crossing
all the points for f1, f2, f3

time f1 f2 f3
30 3.39 13.55
33 16.94
35 6.78
36 10.16
37 10.16 13.55
38 13.55
39 16.94 16.94
40 20.33
41 20.33 20.33
42 23.71
44 23.71

All of my suggestions are based on the assumption that you have recorded
missing values as NaNs. If you can't reformat your data, or insert NaN's for
missing values when you load it, I'm not sure how to help.

Darren

···

On Saturday 28 June 2008 04:31:19 sandric ionut wrote: