I've been following this tutorial and I'm currently on Project 2 chapter of it:
https://www.youtube.com/watch?v=WQeoO7MI0Bs&list=WL&index=90&t=9713s.
However when I start implementing the lines of code meant to read values off the image it gives out the following error:
ValueError: cannot reshape array of size 0 into shape (4,2)
From what I can tell, the error seems to be coming from this function:
def reorder(myPoints):
myPoints = myPoints.reshape((4,2))
myPointsNew = np.zeros((4,1,2),np.int32)
add = myPoints.sum(1)
print("add", add)
myPointsNew[0] = myPoints[np.argmin(add)]
myPointsNew[3] = myPoints[np.argmax(add)]
diff = np.diff(myPoints,axis=1)
myPointsNew[1] = myPoints[np.argmin(diff)]
myPointsNew[2] = myPoints[np.argmax(diff)]
print("New Points", myPointsNew)
However the problem is that I followed exactly how I saw on the tutorial and I couldn't come up with a solution. By the way the approximate time frame of this code in the video is at 2:45:00.
Related
So I've been tring to use .index with 2D arrays just so I can do some tests on my code, but it just comes up with an error saying that the value which is in the list actually isn't.
For example, from one project (where i was trying to revise network layering whilst practicing some coding), I tried doing this but didnt work:
answers = [['Application Layer','HTTP','HTTPS','SMTP','IMAP','FTP'],['Transport Layer','TCP','UDP'],['Network Layer','ARP','IP','ICMP'],['Data Link layer']]
correct = 0
incorrect = 0
qs = answers[randint(0,3)][0]
print(answers.index(qs))
print(qs)
Example from code
As you can see, I'm trying to get back the value of 'qs' by using index but no luck.
I've seen a few other posts saying to use numpy, but how would I do this without using numpy?
You can do it like this.
answers = [['Application Layer','HTTP','HTTPS','SMTP','IMAP','FTP'],['Transport Layer','TCP','UDP'],['Network Layer','ARP','IP','ICMP'],['Data Link layer']]
correct = 0
incorrect = 0
qs = answers[randint(0,3)][0]
for i, answer in enumerate(answers):
if qs in answer:
print(i)
break
print(qs)
I'm currently trying to determine the frequency of this plot of position vs time:
where the time and position datasets are lists of floats. I've tried using scipy.signal.find_peaks imported as fp but when I run this code:
peaks,_ = fp(pos)
peak_times = []
for i in range(len(peaks)):
peak_times.append(t[i])
peak_dists = [current-next for (current,next) in zip(peak_times,peak_times[1:])]
approx_freq = sum(peak_dists)/len(peak_dists)
return approx_freq
I get a type error: typeError: only integer scalar arrays can be converted to a scalar index
What is going wrong? And how can I fix it?
I spotted a few errors in your code. Here are the suggested corrections:
peaks,_ = fp(pos)
peak_times = []
for i in peaks: # notice correction here! No range or len needed.
peak_times.append(t[i])
peak_dists = [next-current for (current,next) in zip(peak_times,peak_times[1:])] # swapped next with current for positive result
approx_freq = 1/(sum(peak_dists)/len(peak_dists)) # take the inverse of what you did before
Good luck!
I am working on a problem which involves a batch of 19 tokens each with 400 features. I get the shape (19,1,400) when concatenating two vectors of size (1, 200) into the final feature vector. If I squeeze the 1 out I am left with (19,) but I am trying to get (19,400). I have tried converting to list, squeezing and raveling but nothing has worked.
Is there a way to convert this array to the correct shape?
def attn_output_concat(sample):
out_h, state_h = get_output_and_state_history(agent.model, sample)
attns = get_attentions(state_h)
inner_outputs = get_inner_outputs(state_h)
if len(attns) != len(inner_outputs):
print 'Length err'
else:
tokens = [np.zeros((400))] * largest
print(tokens.shape)
for j, (attns_token, inner_token) in enumerate(zip(attns, inner_outputs)):
tokens[j] = np.concatenate([attns_token, inner_token], axis=1)
print(np.array(tokens).shape)
return tokens
The easiest way would be to declare tokens to be a numpy.shape=(19,400) array to start with. That's also more memory/time efficient. Here's the relevant portion of your code revised...
import numpy as np
attns_token = np.zeros(shape=(1,200))
inner_token = np.zeros(shape=(1,200))
largest = 19
tokens = np.zeros(shape=(largest,400))
for j in range(largest):
tokens[j] = np.concatenate([attns_token, inner_token], axis=1)
print(tokens.shape)
BTW... It makes it difficult for people to help you if you don't include a self-contained and runnable segment of code (which is probably why you haven't gotten a response on this yet). Something like the above snippet is preferred and will help you get better answers because there's less guessing at what your trying to accomplish.
This question may be a little specialist, but hopefully someone might be able to help. I normally use IDL, but for developing a pipeline I'm looking to use python to improve running times.
My fits file handling setup is as follows:
import numpy as numpy
from astropy.io import fits
#Directory: /Users/UCL_Astronomy/Documents/UCL/PHASG199/M33_UVOT_sum/UVOTIMSUM/M33_sum_epoch1_um2_norm.img
with fits.open('...') as ima_norm_um2:
#Open UVOTIMSUM file once and close it after extracting the relevant values:
ima_norm_um2_hdr = ima_norm_um2[0].header
ima_norm_um2_data = ima_norm_um2[0].data
#Individual dimensions for number of x pixels and number of y pixels:
nxpix_um2_ext1 = ima_norm_um2_hdr['NAXIS1']
nypix_um2_ext1 = ima_norm_um2_hdr['NAXIS2']
#Compute the size of the images (you can also do this manually rather than calling these keywords from the header):
#Call the header and data from the UVOTIMSUM file with the relevant keyword extensions:
corrfact_um2_ext1 = numpy.zeros((ima_norm_um2_hdr['NAXIS2'], ima_norm_um2_hdr['NAXIS1']))
coincorr_um2_ext1 = numpy.zeros((ima_norm_um2_hdr['NAXIS2'], ima_norm_um2_hdr['NAXIS1']))
#Check that the dimensions are all the same:
print(corrfact_um2_ext1.shape)
print(coincorr_um2_ext1.shape)
print(ima_norm_um2_data.shape)
# Make a new image file to save the correction factors:
hdu_corrfact = fits.PrimaryHDU(corrfact_um2_ext1, header=ima_norm_um2_hdr)
fits.HDUList([hdu_corrfact]).writeto('.../M33_sum_epoch1_um2_corrfact.img')
# Make a new image file to save the corrected image to:
hdu_coincorr = fits.PrimaryHDU(coincorr_um2_ext1, header=ima_norm_um2_hdr)
fits.HDUList([hdu_coincorr]).writeto('.../M33_sum_epoch1_um2_coincorr.img')
I'm looking to then apply the following corrections:
# Define the variables from Poole et al. (2008) "Photometric calibration of the Swift ultraviolet/optical telescope":
alpha = 0.9842000
ft = 0.0110329
a1 = 0.0658568
a2 = -0.0907142
a3 = 0.0285951
a4 = 0.0308063
for i in range(nxpix_um2_ext1 - 1): #do begin
for j in range(nypix_um2_ext1 - 1): #do begin
if (numpy.less_equal(i, 4) | numpy.greater_equal(i, nxpix_um2_ext1-4) | numpy.less_equal(j, 4) | numpy.greater_equal(j, nxpix_um2_ext1-4)): #then begin
#UVM2
corrfact_um2_ext1[i,j] == 0
coincorr_um2_ext1[i,j] == 0
else:
xpixmin = i-4
xpixmax = i+4
ypixmin = j-4
ypixmax = j+4
#UVM2
ima_UVM2sum = total(ima_norm_um2[xpixmin:xpixmax,ypixmin:ypixmax])
xvec_UVM2 = ft*ima_UVM2sum
fxvec_UVM2 = 1 + (a1*xvec_UVM2) + (a2*xvec_UVM2*xvec_UVM2) + (a3*xvec_UVM2*xvec_UVM2*xvec_UVM2) + (a4*xvec_UVM2*xvec_UVM2*xvec_UVM2*xvec_UVM2)
Ctheory_UVM2 = - alog(1-(alpha*ima_UVM2sum*ft))/(alpha*ft)
corrfact_um2_ext1[i,j] = Ctheory_UVM2*(fxvec_UVM2/ima_UVM2sum)
coincorr_um2_ext1[i,j] = corrfact_um2_ext1[i,j]*ima_sk_um2[i,j]
The above snippet is where it is messing up, as I have a mixture of IDL syntax and python syntax. I'm just not sure how to convert certain aspects of IDL to python. For example, the ima_UVM2sum = total(ima_norm_um2[xpixmin:xpixmax,ypixmin:ypixmax]) I'm not quite sure how to handle.
I'm also missing the part where it will update the correction factor and coincidence correction image files, I would say. If anyone could have the patience to go over it with a fine tooth comb and suggest the neccessary changes I need that would be excellent.
The original normalised image can be downloaded here: Replace ... in above code with this file
One very important thing about numpy is that it does every mathematical or comparison function on an element-basis. So you probably don't need to loop through the arrays.
So maybe start where you convolve your image with a sum-filter. This can be done for 2D images by astropy.convolution.convolve or scipy.ndimage.filters.uniform_filter
I'm not sure what you want but I think you want a 9x9 sum-filter that would be realized by
from scipy.ndimage.filters import uniform_filter
ima_UVM2sum = uniform_filter(ima_norm_um2_data, size=9)
since you want to discard any pixel that are at the borders (4 pixel) you can simply slice them away:
ima_UVM2sum_valid = ima_UVM2sum[4:-4,4:-4]
This ignores the first and last 4 rows and the first and last 4 columns (last is realized by making the stop value negative)
now you want to calculate the corrections:
xvec_UVM2 = ft*ima_UVM2sum_valid
fxvec_UVM2 = 1 + (a1*xvec_UVM2) + (a2*xvec_UVM2**2) + (a3*xvec_UVM2**3) + (a4*xvec_UVM2**4)
Ctheory_UVM2 = - np.alog(1-(alpha*ima_UVM2sum_valid*ft))/(alpha*ft)
these are all arrays so you still do not need to loop.
But then you want to fill your two images. Be careful because the correction is smaller (we inored the first and last rows/columns) so you have to take the same region in the correction images:
corrfact_um2_ext1[4:-4,4:-4] = Ctheory_UVM2*(fxvec_UVM2/ima_UVM2sum_valid)
coincorr_um2_ext1[4:-4,4:-4] = corrfact_um2_ext1[4:-4,4:-4] *ima_sk_um2
still no loop just using numpys mathematical functions. This means it is much faster (MUCH FASTER!) and does the same.
Maybe I have forgotten some slicing and that would yield a Not broadcastable error if so please report back.
Just a note about your loop: Python's first axis is the second axis in FITS and the second axis is the first FITS axis. So if you need to loop over the axis bear that in mind so you don't end up with IndexErrors or unexpected results.
Hey so I am just working on some coding homework for my Python class using JES. Our assignment is to take a sound, add some white noise to the background and to add an echo as well. There is a bit more exacts but I believe I am fine with that. There are four different functions that we are making: a main, an echo equation based on a user defined length of time and amount of echos, a white noise generation function, and a function to merge the noises.
Here is what I have so far, haven't started the merging or the main yet.
#put the following line at the top of your file. This will let
#you access the random module functions
import random
#White noise Generation functiton, requires a sound to match sound length
def whiteNoiseGenerator(baseSound) :
noise = makeEmptySound(getLength(baseSound))
index = 0
for index in range(0, getLength(baseSound)) :
sample = random.randint(-500, 500)
setSampleValueAt(noise, index, sample)
return noise
def multipleEchoesGenerator(sound, delay, number) :
endSound = getLength(sound)
newEndSound = endSound +(delay * number)
len = 1 + int(newEndSound/getSamplingRate(sound))
newSound = makeEmptySound(len)
echoAmplitude = 1.0
for echoCount in range (1, number) :
echoAmplitude = echoAmplitude * 0.60
for posns1 in range (0, endSound):
posns2 = posns1 + (delay * echoCount)
values1 = getSampleValueAt(sound, posns1) * echoAmplitude
values2 = getSampleValueAt(newSound, posns2)
setSampleValueAt (newSound, posns2, values1 + values2)
return newSound
I receive this error whenever I try to load it in.
The error was:
Inappropriate argument value (of correct type).
An error occurred attempting to pass an argument to a function.
Please check line 38 of C:\Users\insanity180\Desktop\Work\Winter Sophomore\CS 140\homework3\homework_3.py
That line of code is:
setSampleValueAt (newSound, posns2, values1 + values2)
Anyone have an idea what might be happening here? Any assistance would be great since I am hoping to give myself plenty of time to finish coding this assignment. I have gotten a similar error before and it was usually a syntax error however I don't see any such errors here.
The sound is made before I run this program and I defined delay and number as values 1 and 3 respectively.
Check the arguments to setSampleValueAt; your sample value must be out of bounds (should be within -32768 - 32767). You need to do some kind of output clamping for your algorithm.
Another possibility (which indeed was the error, according to further input) is that your echo will be out of the range of the sample - that is, if your sample was 5 seconds long, and echo was 0.5 seconds long; or the posns1 + delay is beyond the length of the sample; the length of the new sound is not calculated correctly.