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In [1]:



    


from mdt import *
#import seaborn
%matplotlib inline
%load_ext autoreload
%autoreload

Load Input





In [2]:



    


mp.P_T1 = r"^(.*):$"
mp.C_TITLE = ":"
cfg = yaml.load(open("mdt_config.yaml","r").read())
auth_token = cfg['auth_token']
mu.import_from_dropbox(auth_token, "tmp")
dt = mp.parse_md_files("./tmp/"+"*.taskpaper")
del dt["T2"];del dt["T3"];del dt["ID"]

Task Stats





In [3]:



    


# Filter tasks only
df = dt[dt.TXT.str.contains("^\s*- ")]
dfs = ms.add_stat(df, "done,p")
dfs.rename(columns={"Filename": "Week", "T1": "Project", "done": "Date", "p":"Time"}, inplace=True)

Daily Stats





In [4]:



    


daily = dfs.groupby(['Week',"Date"]).agg({'Time': ['sum'], 'TXT':'count', 'Project':'nunique'})
daily.columns = daily.columns.map(mu.flattenCols)
daily.rename(columns={"TXT_count": "Task_done"}, inplace=True)
daily['Ratio_Time'] = daily.Time_sum / 8
daily['Ratio_Task'] = daily.Task_done / 4
daily['Ratio_Project'] = daily.Project_nunique / 3
dstat = daily.fillna(0).sort_index(0, ascending=False).round(2)
dstat.to_csv("rpt/daily_stats.tsv",sep="\t")
dstat[:10]



    


















    
Out[4]:











  
    

      
      
      Project_nunique
      Task_done
      Time_sum
      Ratio_Time
      Ratio_Task
      Ratio_Project
    

    

      Week
      Date
      
      
      
      
      
      
    

  
  
    

      20172026
      2017-02-26
      1
      5
      3.0
      0.38
      1.25
      0.33
    

    

      20172019
      2017-02-25
      2
      3
      2.0
      0.25
      0.75
      0.67
    

    

      2017-02-23
      1
      7
      0.0
      0.00
      1.75
      0.33
    

    

      2017-02-22
      1
      1
      0.0
      0.00
      0.25
      0.33
    

    

      2017-02-21
      1
      1
      0.0
      0.00
      0.25
      0.33
    

    

      2017-02-20
      1
      1
      0.0
      0.00
      0.25
      0.33
    

    

      20170416
      2017-04-17
      1
      1
      0.0
      0.00
      0.25
      0.33
    

    

      2017-04-16
      1
      2
      0.0
      0.00
      0.50
      0.33
    

    

      20170409 Back in LA
      2017-04-17
      1
      2
      0.0
      0.00
      0.50
      0.33
    

    

      2017-04-15
      2
      2
      0.0
      0.00
      0.50
      0.67
    

  




















In [9]:



    


plt = daily.iloc[-10:,3:].plot(kind="bar", figsize=[15,3])
plt.axhline(y=1, linewidth=1, color = 'red', linestyle = 'dotted');

Weekly Stats





In [6]:



    


weekly = dfs.groupby(["Week"]).agg({'Time': ['sum'], 'TXT': 'count', 'Date': 'count'})
weekly.columns = weekly.columns.map(mu.flattenCols)
weekly.rename(columns={"TXT_count": "Task_total", "Date_count": "Task_done"}, inplace=True)
weekly.fillna(0).sort_index(0, ascending=False)
weekly['Ratio_Time'] = weekly.Time_sum / 40
weekly['Ratio_Task'] = weekly.Task_done / weekly.Task_total
wstat = weekly.fillna(0).sort_index(0, ascending=False).round(2)
wstat.to_csv("rpt/weekly_stats.tsv",sep="\t")
wstat



    


















    
Out[6]:











  
    

      
      Task_done
      Task_total
      Time_sum
      Ratio_Time
      Ratio_Task
    

    

      Week
      
      
      
      
      
    

  
  
    

      20172026
      5
      22
      5.0
      0.12
      0.23
    

    

      20172019
      13
      19
      2.0
      0.05
      0.68
    

    

      20170416
      3
      7
      0.0
      0.00
      0.43
    

    

      20170409 Back in LA
      5
      7
      0.0
      0.00
      0.71
    

    

      20170402 @ SF
      8
      16
      0.0
      0.00
      0.50
    

    

      20170326
      18
      31
      0.0
      0.00
      0.58
    

    

      20170319
      7
      16
      0.0
      0.00
      0.44
    

    

      20170312
      1
      4
      0.0
      0.00
      0.25
    

    

      20170305
      13
      22
      2.0
      0.05
      0.59
    

    

      20170226
      21
      40
      5.0
      0.12
      0.52
    

    

      20170219
      13
      19
      2.0
      0.05
      0.68
    

    

      20170212
      12
      20
      0.0
      0.00
      0.60
    

    

      20170205
      22
      26
      34.0
      0.85
      0.85
    

    

      20170129
      6
      19
      12.0
      0.30
      0.32
    

    

      20170122
      13
      26
      19.0
      0.48
      0.50
    

    

      20170115
      11
      23
      17.0
      0.42
      0.48
    

    

      20170108
      20
      33
      17.0
      0.42
      0.61
    

    

      20170101
      14
      24
      18.0
      0.45
      0.58
    

  




















In [10]:



    


plt = weekly.iloc[:,3:].plot(kind="bar", figsize=[10,3])
plt.axhline(y=1, linewidth=1, color = 'red', linestyle = 'dotted');

Journal Stats





In [8]:



    


# Filter journals only
dj = dt[dt.T1.str.contains("^20")]
djs = ms.add_stat(dj, "h")
djs.rename(columns={"Filename": "Week", "T1": "Date", "h":"Happiness"}, inplace=True)



    


















    






---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-8-93e71e9e4396> in <module>()
      1 # Filter journals only
----> 2 dj = dt[dt.T1.str.contains("^20")]
      3 djs = ms.add_stat(dj, "h")
      4 djs.rename(columns={"Filename": "Week", "T1": "Date", "h":"Happiness"}, inplace=True)

/home/jink/bin/conda3/envs/py27/lib/python2.7/site-packages/pandas/core/frame.pyc in __getitem__(self, key)
   1956         if isinstance(key, (Series, np.ndarray, Index, list)):
   1957             # either boolean or fancy integer index
-> 1958             return self._getitem_array(key)
   1959         elif isinstance(key, DataFrame):
   1960             return self._getitem_frame(key)

/home/jink/bin/conda3/envs/py27/lib/python2.7/site-packages/pandas/core/frame.pyc in _getitem_array(self, key)
   1983     def _getitem_array(self, key):
   1984         # also raises Exception if object array with NA values
-> 1985         if com.is_bool_indexer(key):
   1986             # warning here just in case -- previously __setitem__ was
   1987             # reindexing but __getitem__ was not; it seems more reasonable to

/home/jink/bin/conda3/envs/py27/lib/python2.7/site-packages/pandas/core/common.pyc in is_bool_indexer(key)
    187             if not lib.is_bool_array(key):
    188                 if isnull(key).any():
--> 189                     raise ValueError('cannot index with vector containing '
    190                                      'NA / NaN values')
    191                 return False

ValueError: cannot index with vector containing NA / NaN values

Writing Amount





In [ ]:



    


djsa = djs.groupby(["Date"]).agg({'TXT': ['count']})
plt = djsa[-10:].plot(kind="bar", figsize=[15,3])
plt.legend_.remove()
plt.axhline(y=5, linewidth=1, color = 'red', linestyle = 'dotted');

Happiness





In [ ]:



    


djs[~djs.Happiness.isnull()]



    











In [ ]:



    


djsa = djs[~djs.Happiness.isnull()].groupby(["Date"]).agg({'Happiness': ['mean']})
plt = djsa.plot(kind="bar", figsize=[10,3])
plt.legend_.remove()
plt.axhline(y=3.5, linewidth=1, color = 'red', linestyle = 'dotted');

Content source: jykim/mdt

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