<jupyter_start><jupyter_text>OK, we need to read in some data. I wanted to get some data that look human-understandable, and crushed out a lot of the amplitude variation we see in the ISM. I am just going to use my "promotional" ISM images from GALFA-HI DR2 to start with.<jupyter_code>#Let's test reading a PNG
whereami ='/Users/jegpeek/'
path = 'Dropbox/Documents/batch/GALFA/DR2/testmovie/allsky/'
file = 'allsky988.png'
#read in all the data
# I am going to break things down into 32x32s, but do an offset so we don't get exact match ups of the same features...
nacross = np.floor(10800/32.0)
f1 = 970
f2 = 1077
ntall = np.floor(1180/32.0)
nfiles = (1077-970+1)
filenums = np.linspace(f1,f2, nfiles)
filenames = np.array(nfiles, dtype=str)
for i in range(nfiles):
    filename[i] = 'allsky%d.png'%(filenums[i],)

#image = misc.imread(whereami + path + file)
filenums[0]
image.shape
plt.imshow(image)
misc.imsave('test.png', image[600:640, 2000:2040,  :])
print(10800/32)
for i in range(10):
    filename = 'data_%d.dat'%(i,)
    print(filename)
filenames = whereami + path + 'allsky%d.png'%(i for [i in filenums])


filenames
result = whereami + path + 'allsky'.join("&markers=%s" % ','.join(map(str, x)) for x in markers)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13853503184713375, 'fraction_numerical': 0.05334394904458599, 'mean_word_length': 4.63677130044843, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '529298', 'license': 'MIT', 'n_tokens_mistral': 535, 'n_tokens_neox': 479, 'n_words': 145, 'path': '.ipynb_checkpoints/Parse GALFA-HI DR2 for CAEs-checkpoint.ipynb', 'repo_name': 'jegpeek/caediffuse', 'size': '1256'}