<jupyter_start><jupyter_text>Imports<jupyter_code>import pynncml as pnc 
from matplotlib import pyplot as plt
import numpy as np<jupyter_output><empty_output><jupyter_text>Load OpenCML Dataset<jupyter_code>rsl_array=np.asarray([-40 ,-40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -41, -46, -52, -51, -48, -51, -50, -47, -44, -44, -43, -43, -42, -41, -41, -41, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -40, -41, -41])
rain_gauge=np.asarray([ 0. ,   0. ,   0.   , 0.    ,0.   , 0.   , 0.   , 0.   , 0. ,   0.  ,  0.  ,  0.,  0.  ,  0.  ,  0.  ,  0.   , 0. ,   0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.  ,  0.,  0.  ,  0.  ,  0.  ,  0. ,   0.  ,  0.    ,0. ,   0.  ,  0.  ,  0.  ,  0.   , 0.,  0.  ,  0.   , 0. ,   0. ,   0. ,   0. ,   0.  ,  0.  ,  0.,    0.   , 0.,    3.72,  7.44, 11.16 ,14.88 ,18.6,  18.6,  18.6 , 18.6  ,18.6  ,18.6 , 15.36 ,12.12,  8.88,  5.64,  2.4 ,  2.4   ,2.4   ,2.4   ,2.4 ,  2.4  , 2.04 , 1.68,  1.32,  0.96,  0.6  ,0.6  , 0.6 ,  0.6  , 0.6  , 0.6   ,0.48  ,0.36 , 0.24  ,0.12,  0.    ,0. ,   0.,  0. ,   0.  ,  0.  ,  0.  ,  0.  ,  0.   , 0.  ,  0.   , 0.   , 0.  ,  0.   , 0. , 0.    ,0.   , 0.   , 0.  ])
time_array=np.arange(len(rsl_array))*300
link_data=pnc.Link(rsl_array,rain_gauge,time_array,pnc.MetaData(26,25,25,5.6,0))<jupyter_output><empty_output><jupyter_text>Select Link and Plot link data<jupyter_code>link_min_max=link_data.create_min_max_link(300)
link_data.plot() # plot link data
plt.show()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.2645234066553864, 'fraction_numerical': 0.21150592216582065, 'mean_word_length': 1.6596701649175412, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 10, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '315203', 'license': 'MIT', 'n_tokens_mistral': 1153, 'n_tokens_neox': 989, 'n_words': 228, 'path': 'examples/example_using_different_dataset.ipynb', 'repo_name': 'haihabi/PyNNcml', 'size': '1773'}