I misidentified where the error was coming from. Here is my function in its entirety (sorry if some of the lines are obscure and confusing...)
def removeLines(input,CRVAL1,CDELT1): #Masks out the Balmer lines from the spectrum
#Numbers 4060, 4150, 4300, 4375, 4800, and 4950 obtained from fit_RVs.pro.
#Other numbers obtained from the Balmer absorption series lines
for i in range(0,len(lineWindows),2):
left = toIndex(lineWindows[i],CRVAL1,CDELT1)
right = toIndex(lineWindows[i+1],CRVAL1,CDELT1)
print "left = ", left
print "right = ", right
print "20 from right =\n", input[right:right+20]
print "mean of 20 = ", numpy.mean(input[right:right+20])
#Find the averages on the left and right sides
left_avg = numpy.mean(input[left-20:left])
right_avg = numpy.mean(input[right:right+20]) #<--- NOT here
print "right_avg = ", right_avg
#Find the slope between the averages
slope = (left_avg - right_avg)/(left - right)
#Find the y-intercept of the line conjoining the averages
bval = ((left_avg - slope*left) + (right_avg - slope*right)) / 2
for j in range(left,right): #Redefine the data to follow the line conjoining
input[j] = slope*j + bval #the sides of the peaks
left = int(input[0])
left_avg = int(input[0])
right = toIndex(lineWindows[0],CRVAL1,CDELT1)
right_avg = numpy.mean(input[right:right+20]) #<---- THIS IS WHERE IT IS!
slope = (left_avg - right_avg)/(left - right)
bval = ((left_avg - slope*left) + (right_avg - slope*right)) / 2
for i in range(left, right):
input[i] = slope*i + bval
return input
I have investigated the issue and found the answer, which is posted below (not in this post).
#left = An index in the data (on the 'left' side)
#right = An index in the data (on the 'right' side)
#input = The data array
print "left = ", left
print "right = ", right
print "20 from right =\n", input[right:right+20]
print "mean of 20 = ", numpy.mean(input[right:right+20])
#Find the averages on the left and right sides
left_avg = numpy.mean(input[left-20:left])
right_avg = numpy.mean(input[right:right+20])
produced the output
left = 1333
right = 1490
20 from right =
[ 0.14138737 0.14085886 0.14038289 0.14045525 0.14078836 0.14083192
0.14072289 0.14082283 0.14058594 0.13977806 0.13955595 0.13998236
0.1400764 0.1399636 0.14025062 0.14074247 0.14094831 0.14078569
0.14001536 0.13895717]
mean of 20 = 0.140395
Traceback (most recent call last):
...
File "getRVs.py", line 201, in removeLines
right_avg = numpy.mean(input[right:right+20])
File "C:\Users\MyName\Anaconda\lib\site-packages\numpy\core\fromnumeric.py", line 2735, in mean
out=out, keepdims=keepdims)
File "C:\Users\MyName\Anaconda\lib\site-packages\numpy\core\_methods.py", line 59, in _mean
warnings.warn("Mean of empty slice.", RuntimeWarning)
RuntimeWarning: Mean of empty slice.
It would appear that numpy.mean runs correctly when I print it, but differently when I assign it to a value. Any feedback would be very appreciated. Thank you for taking the time to read my question.
In short, I am writing a code to handle scientific data and part of the code involves taking the mean of about 20 values.
#left = An index in the data (on the 'left' side)
#right = An index in the data (on the 'right' side)
#input = The data array
#Find the averages on the left and right sides
left_avg = numpy.mean(input[left-20:left])
right_avg = numpy.mean(input[right:right+20])
This code returns a numpy "Mean of empty slice." warning and annoyingly prints it in my precious output! I decided to try and track down the source of the warning as seen here, for example, so I placed
import warnings
warnings.simplefilter("error")
at the top of my code, which then returned the following snipped Traceback:
File "getRVs.py", line 201, in removeLines
right_avg = numpy.mean(input[right:right+20])
File "C:\Users\MyName\Anaconda\lib\site-packages\numpy\core\fromnumeric.py", line 2735, in mean
out=out, keepdims=keepdims)
File "C:\Users\MyName\Anaconda\lib\site-packages\numpy\core\_methods.py", line 59, in _mean
warnings.warn("Mean of empty slice.", RuntimeWarning)
RuntimeWarning: Mean of empty slice.
I omitted about 2/3 of the Traceback because it moves through about 5 difficult-to-explain functions that do not affect the readability or size of the data.
So I decided to print out the whole operation to see if right_avg really was attempting a numpy.mean of an empty slice... And that's when things got really weird.
I misidentified which line of code the error sat on. What I needed to do is write in code for the specific case in which the window (left and right sides) around the center-point being considered in the data is too close to the edge of the data array.
def removeLines(input,CRVAL1,CDELT1): #Masks out the Balmer lines from the spectrum
for i in range(0,len(lineWindows),2):
left = toIndex(lineWindows[i],CRVAL1,CDELT1)
right = toIndex(lineWindows[i+1],CRVAL1,CDELT1)
#Find the averages on the left and right sides
left_avg = numpy.mean(input[left-20:left])
right_avg = numpy.mean(input[right:right+20])
#Find the slope between the averages
slope = (left_avg - right_avg)/(left - right)
#Find the y-intercept of the line conjoining the averages
bval = ((left_avg - slope*left) + (right_avg - slope*right)) / 2
for j in range(left,right): #Redefine the data to follow the line conjoining
input[j] = slope*j + bval #the sides of the peaks
left = 0
left_avg = int(input[0])
if toIndex(lineWindows[0],CRVAL1,CDELT1) < 0: right = 0
else: right = toIndex(lineWindows[0],CRVAL1,CDELT1)
right_avg = numpy.mean(input[right:right+20])
slope = (left_avg - right_avg)/(left - right)
bval = ((left_avg - slope*left) + (right_avg - slope*right)) / 2
for i in range(left, right):
input[i] = slope*i + bval
return input
Simply change this
right = toIndex(lineWindows[0],CRVAL1,CDELT1) #Error occurs where right = -10
right_avg = numpy.mean(input[right:right+20]) #Index of -10? Yeah, right.
to this
if toIndex(lineWindows[0],CRVAL1,CDELT1) < 0: right = 0 #Index 0, much better!
else: right = toIndex(lineWindows[0],CRVAL1,CDELT1) #Leave it alone if it isn't a problem.
right_avg = numpy.mean(input[right:right+20])
Also, I was wrong about left = int(input[0]), so I changed it to left = 0.