<jupyter_start><jupyter_text>What is in the dir<jupyter_code>files = os.listdir(RAW_PATH)
num_file_types = defaultdict(int)
for f in files:
    num_file_types[jview.get_extention(f)] +=1
print("Number of different files (based on extentions)")
jview.print_dir(num_file_types)<jupyter_output>Number of different files (based on extentions)
tif :  124<jupyter_text>What is in the files?<jupyter_code>ndims = []
shapes = []
dtypes = []
mins = []
maxs = []
for f in files:
    if jview.get_extention(f) != 'tif': continue
    im = skimage.io.imread(os.path.join(RAW_PATH, f))
    ndims.append(im.ndim)
    shapes.append(im.shape)
    dtypes.append(im.dtype)
    mins.append(im.min())
    maxs.append(im.max())

print("Ndims: ", ndims)
print("dtypes: ", dtypes)
print("mins: ", mins)
print("maxs: ", maxs)
print("shapes: \n", shapes)<jupyter_output>Ndims:  [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
dtypes:  [dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), dtype('uint8'), d[...]
{'fraction_non_alphanumeric': 0.24553812871822606, 'fraction_numerical': 0.0897782585181179, 'mean_word_length': 5.045751633986928, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, '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': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '520355', 'license': 'MIT', 'n_tokens_mistral': 920, 'n_tokens_neox': 792, 'n_words': 229, 'path': 'src/datasets/investigate_dataset/investigate_raw.ipynb', 'repo_name': 'AllaVinner/Augmentation-in-3D-microscopy', 'size': '1849'}