<jupyter_start><jupyter_text>Cell Tracking Challengehttp://www.celltrackingchallenge.net/datasets.html<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.insert(0,'../')
from pak import utils
from pak.datasets.CellTrackingChallenge import CellTrackingChallenge2D
import matplotlib.pyplot as plt

root = '/media/julian/Maxtor/DATA/pak/'
username = ''
pw = ''

ctc = CellTrackingChallenge2D(root, username, pw)

Imgs, Segs = ctc.get_train_all_segmented()
print(len(Imgs))


I = Imgs[13]; S = Segs[13]

fig = plt.figure(figsize=(16,16))
fig.add_subplot(221).imshow(I)
fig.add_subplot(222).imshow(S)

I = Imgs[55]; S = Segs[55]
fig.add_subplot(223).imshow(I)
fig.add_subplot(224).imshow(S)
plt.show()<jupyter_output>222
{'fraction_non_alphanumeric': 0.1408083441981747, 'fraction_numerical': 0.03911342894393742, 'mean_word_length': 6.314285714285714, 'pattern_counts': {'":': 0, '<': 4, '<?xml version=': 0, '>': 4, 'https://': 0, 'lorem ipsum': 0, 'www.': 1, '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': '54372', 'license': 'MIT', 'n_tokens_mistral': 333, 'n_tokens_neox': 313, 'n_words': 54, 'path': 'samples/CellTrackingChallenge.ipynb', 'repo_name': 'jutanke/pak', 'size': '767'}