<jupyter_start><jupyter_text>Parser Markdown<jupyter_code>dta = mp.parse_md_files("../mdt/mdt_demo.md")
dta.head()
#dta<jupyter_output><empty_output><jupyter_text>TaskPaper<jupyter_code>mp.P_T1 = r"^(.*):$"
mp.C_TITLE = ":"
dta = mp.parse_md_files("../mdt/mdt_demo*.taskpaper")
dta<jupyter_output><empty_output><jupyter_text>Stats<jupyter_code>reload(ms)
dta.apply(ms.stat_journal,axis=1)
reload(ms)
ms.add_stat(dta, "done,p")<jupyter_output><empty_output><jupyter_text>Visualization  Bokeh<jupyter_code>from bokeh.io import push_notebook, show, output_notebook
from bokeh.layouts import row
from bokeh.plotting import figure
output_notebook()<jupyter_output><empty_output><jupyter_text>ggolpt<jupyter_code># from ggplot import *
# diamonds.head()
# p = ggplot(aes(x='date', y='beef'), data=meat)
# p + geom_point() + geom_line() + stat_smooth(color='blue')<jupyter_output><empty_output><jupyter_text>Dropbox I/O<jupyter_code>auth_token = "<KEY>"
dbx = dropbox.Dropbox(auth_token)
dbx.users_get_current_account()
dbx.files_list_folder("")
#dbx.files_upload('!!!', "/Hello2", )
root = ''
for entry in dbx.files_list_folder(root).entries:
    print(entry.name)
    md, res = dbx.files_download('/'+entry.name)
res.content.decode().split("\n")
md.name
reload(mp)
auth_token = "<KEY>"
mp.import_from_dropbox(auth_token, 'tmp')
dt = mp.parse_md_files("./tmp/"+"*.taskpaper")<jupyter_output><empty_output><jupyter_text>Test<jupyter_code>"a b c".split(" ")
reload(ms)
print(ms.P_TAG2)
text = ["abc @tag def", "a @tag(value!) c @tag2", "@done(2015-01-01)", "@p(1)"]
for t in text:
    print("## ", t)
    print(re.findall(ms.P_TAG, t))
    print(re.findall(ms.P_TAG2, t))

import yaml
mc = open("mdt_config.yaml","r").read()
yaml.load(mc)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.18579545454545454, 'fraction_numerical': 0.008522727272727272, 'mean_word_length': 7.805, 'pattern_counts': {'":': 1, '<': 31, '<?xml version=': 0, '>': 31, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '7', 'dir': 'jupyter-structured-clean-dedup', 'id': '43111', 'license': 'Apache-2.0', 'n_tokens_mistral': 764, 'n_tokens_neox': 748, 'n_words': 96, 'path': 'notebooks/MDT_dev.ipynb', 'repo_name': 'jykim/mdt', 'size': '1878'}