Basics of Python and Data Reduction.py

# coding: utf-8

# Each gray box with 
# 
#     In []:
#     
# beside it is a bit of computer code and you run then by pressing the play button or pressing "Shift-Enter". 

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#this is a comment - the computer will remember it but not act on it.
print("this is a command or function, it tells the computer to print out the text")


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#try to display "hello world":
print("hello world")


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#these commands setup our python environment for scientific programming and plotting
get_ipython().magic('pylab')
get_ipython().magic('matplotlib inline')
import astropy.io.fits as fits

rcParams['image.cmap'] = 'viridis' 
plt.xkcd() #this gives us squiggly XKCD style figures (http://matplotlib.org/xkcd/examples/showcase/xkcd.html)


# ## Just like algebra
# 
# We can assign a number to a variable and do math

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x = 2
a = 3.5
y = x + a

print(y)




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#you can skip a step and do math inside the print function:
print(y/x)


# ### The it remembered our variables from one box to the next --- spooky?

# ## Debugging

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print(z)
z=x*a


# # we can do lots of math at once
# 
# This is good -- astronomers are lazy

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x=array([3,2,5,2,2,2,2,1])
a=array([5,5,5,5,5,5,5,5,])
SubtractedArrays=a-x
print(SubtractedArrays)


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plot(SubtractedArrays)
plt.xlabel("location of number in arrays")
plt.ylabel("a-x")


# ## Just look at the first four numbers

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plot(SubtractedArrays[:4])
plt.xlabel("location of number in arrays")
plt.ylabel("a-x")


# ## print the original array and see if it as has changed:

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# ## Let's get some real data
# Last night we collected some images of the interacting galaxies NGC 6240
# 
# (https://en.wikipedia.org/wiki/NGC_6240)

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image = fits.getdata("data/wiyn0_9/2016june18/NGC6240/c7558t0054o00.fits")


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#decrease the vmax until you can see the galaxies
figure(figsize=(10,10))
imshow(image,vmax=20000)
colorbar()


# ## more debugging -- getting help
# 

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#you can call a function with a ? to get help on it.
get_ipython().magic('pinfo fits.getdata')


# ## Removing Bias levels
# per the HDI reduction guide, it's preferable to subtract an average bias from the frame than to use
# one from earlier in the night
# 
# "calculate bias 1a.  compute mean value, M, of pixels in overscan columns 4100 - 4140" ( HDI data reduction tutorial
# Michael Richmond, July 8, 2014 ,http://stupendous.rit.edu/richmond/wiyn/hdi_oct2013/tech_7/tech_7.html)

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#calculate the average bias in the overscan pixels.
overscan=image[4100:4140,4100:4140] 
mean_bias=mean(overscan) #this is the average

image_without_bias = image - mean_bias


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#plot it
figure(figsize=(12,10))
imshow(image_without_bias,vmax=9000,vmin=5000)
colorbar()


# ## Flat field to correct the dim corners and the donuts due to dust

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flat =  fits.getdata("data/wiyn0_9/2016june18/c7558t0010f00.fits")
#Generate a flat field with a mean of one by subtracting the bias:
flat_bias=mean(flat[4100:4140,4100:4140] )
flat = flat - flat_bias
#and dividing by the average:
flat = flat/mean(flat)


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#show the flat field:
imshow(flat)
colorbar()


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#divide the image by the flat field:
image_flattened = image_without_bias/flat


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figure(figsize=(12,10))
imshow(image_flattened,vmax=9000,vmin=0)
colorbar()


# ## why is the background still so high?
# 
# What was in the sky last night?

# ## Look at just the pixels close to NGC 6240

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figure(figsize=(12,10))
#just like when we looked at the first four values in an array, look at pixels from 1800 to 2300, in both directions
imshow(image_flattened[1800:2300,1800:2300],vmax=7000,vmin=5000)
colorbar()


# ## TRY THIS AT HOME!
# 
# You can install  the Anaconda Scientific python platform on your (Windows/Mac/Linux) computer for **free** and run this  notebook in your web-browser or make your own:
# 
# https://www.continuum.io/downloads
# 
# Or you can use a webserver operated by a company to run it remotely, such as:
# https://cloud.sagemath.com/
# 
# A copy of this tutorial for refence:
# https://github.com/douglase/astronomy_camp_tutorials/blob/master/Basics%20of%20Python%20and%20Data%20Reduction.ipynb

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