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In [1]:

    
# Import all libraries needed for the tutorial

# General syntax to import specific functions in a library:
##from (library) import (specific library function)
from pandas import DataFrame, read_csv

# General syntax to import a library but no functions:
##import (library) as (give the library a nickname/alias)
import matplotlib.pyplot as plt
import pandas as pd #only needed to determine version number

import glob
from scipy import randn
from pandas import *

# Enable inline plotting
%matplotlib inline



In [2]:

    
path =r'/analysis'
allFiles = glob.glob("*.txt")
frame = pd.DataFrame()
list = []
for files in allFiles:
    df = pd.read_csv(files,index_col=0, header=0, delimiter="\t")
    list.append(df)
frame = pd.concat(list)
glued = pd.concat(list, axis=1)

#print(frame)

#print(glued)
#print(glued.groupby(level=0, axis=1).std())
stdtab = glued.groupby(level=0, axis=1).std()
meantab = glued.groupby(level=0, axis=1).mean()
#glued.groupby(level=0, axis=1).mean().to_csv("glued.txt", sep="\t")

#plt.figure(figsize=(10, 8), dpi=120);
#meantab.plot(yerr=stdtab)

#dff = pd.concat([meantab, stdtab], axis=1)

#plt.figure(figsize=(10, 8), dpi=120);
#dff.plot()

#plt.figure(figsize=(10, 8), dpi=120);
#bp = frame.boxplot(column=['# Clock'])
#plt.figure(figsize=(10, 8), dpi=120);
#bp = frame.boxplot(column=['In Chain'])
#plt.figure(figsize=(10, 8), dpi=120);
#bp = frame.boxplot(column=['On Target'])



In [3]:

    
meantab









    Out[3]:






  
    
      
      In Chain
      On Target
    
    
      # Clock
      
      
    
  
  
    
      1 
       0.00
       0
    
    
      2 
       0.00
       0
    
    
      3 
       0.00
       0
    
    
      4 
       0.00
       0
    
    
      5 
       0.00
       0
    
    
      6 
       0.00
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      7 
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      8 
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      12
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      13
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      14
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      15
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      16
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      17
       0.02
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      18
       0.02
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      19
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       0
    
    
      20
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      21
       0.03
       0
    
    
      22
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      23
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      24
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      25
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      26
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      27
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      28
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      29
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      30
       0.13
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      31
       0.14
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      32
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      33
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      34
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      35
       0.20
       0
    
    
      36
       0.22
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      37
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      38
       0.25
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      39
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      40
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      41
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      42
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      43
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      44
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      45
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      46
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      47
       0.63
       0
    
    
      48
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       0
    
    
      49
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       0
    
    
      50
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       0
    
    
      51
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       0
    
    
      52
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       0
    
    
      53
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      54
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      55
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      56
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      57
       1.01
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      58
       1.03
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      59
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       0
    
    
      60
       1.15
       0
    
    
      
      ...
      ...
    
  

8659 rows × 2 columns



In [4]:

    
stdtab









    Out[4]:






  
    
      
      In Chain
      On Target
    
    
      # Clock
      
      
    
  
  
    
      1 
       0.000000
       0
    
    
      2 
       0.000000
       0
    
    
      3 
       0.000000
       0
    
    
      4 
       0.000000
       0
    
    
      5 
       0.000000
       0
    
    
      6 
       0.000000
       0
    
    
      7 
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       0
    
    
      8 
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       0
    
    
      9 
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       0
    
    
      10
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      11
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       0
    
    
      12
       0.000000
       0
    
    
      13
       0.000000
       0
    
    
      14
       0.100000
       0
    
    
      15
       0.100000
       0
    
    
      16
       0.140705
       0
    
    
      17
       0.140705
       0
    
    
      18
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      19
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      20
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      21
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      22
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      23
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      24
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      25
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      26
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      27
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      28
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      29
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      30
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      31
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      32
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      33
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      34
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      35
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      36
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      37
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      38
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      39
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      40
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      41
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      42
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      43
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      44
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      45
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      46
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      47
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      48
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      49
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      50
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      51
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      52
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      53
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      54
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      55
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      56
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      57
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      58
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      59
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      60
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      ...
      ...
    
  

8659 rows × 2 columns



In [21]:

    
fig, ax = plt.subplots()
meantab.plot(kind='bar',ax=ax, yerr=glued.groupby(level=0, axis=1).std())









    



---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-21-9d1524b451ae> in <module>()
      1 fig, ax = plt.subplots()
----> 2 meantab.plot(kind='bar',ax=ax, yerr=glued.groupby(level=0, axis=1).std())

/Users/fabianeidens/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc in plot_frame(frame, x, y, subplots, sharex, sharey, use_index, figsize, grid, legend, rot, ax, style, title, xlim, ylim, logx, logy, xticks, yticks, kind, sort_columns, fontsize, secondary_y, **kwds)
   1733                              secondary_y=secondary_y, **kwds)
   1734 
-> 1735     plot_obj.generate()
   1736     plot_obj.draw()
   1737     if subplots:

/Users/fabianeidens/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc in generate(self)
    903         self._compute_plot_data()
    904         self._setup_subplots()
--> 905         self._make_plot()
    906         self._post_plot_logic()
    907         self._adorn_subplots()

/Users/fabianeidens/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc in _make_plot(self)
   1559             else:
   1560                 rect = bar_f(ax, self.ax_pos + i * 0.75 / K, y, 0.75 / K,
-> 1561                              start=start, label=label, **kwds)
   1562             rects.append(rect)
   1563             if self.mark_right:

/Users/fabianeidens/anaconda/lib/python2.7/site-packages/pandas/tools/plotting.pyc in f(ax, x, y, w, start, **kwds)
   1506         if self.kind == 'bar':
   1507             def f(ax, x, y, w, start=None, **kwds):
-> 1508                 return ax.bar(x, y, w, bottom=start,log=self.log, **kwds)
   1509         elif self.kind == 'barh':
   1510             def f(ax, x, y, w, start=None, log=self.log, **kwds):

/Users/fabianeidens/anaconda/lib/python2.7/site-packages/matplotlib/axes.pyc in bar(self, left, height, width, bottom, **kwargs)
   5073             errorbar = self.errorbar(x, y,
   5074                                      yerr=yerr, xerr=xerr,
-> 5075                                      fmt=None, **error_kw)
   5076         else:
   5077             errorbar = None

/Users/fabianeidens/anaconda/lib/python2.7/site-packages/matplotlib/axes.pyc in errorbar(self, x, y, yerr, xerr, fmt, ecolor, elinewidth, capsize, barsabove, lolims, uplims, xlolims, xuplims, errorevery, capthick, **kwargs)
   5756                 # using list comps rather than arrays to preserve units
   5757                 lower = [thisy - thiserr for (thisy, thiserr)
-> 5758                          in cbook.safezip(y, yerr)]
   5759                 upper = [thisy + thiserr for (thisy, thiserr)
   5760                          in cbook.safezip(y, yerr)]

TypeError: unsupported operand type(s) for -: 'numpy.float64' and 'str'



In [ ]:

	In Chain	On Target
# Clock
1	0.00	0
2	0.00	0
3	0.00	0
4	0.00	0
5	0.00	0
6	0.00	0
7	0.00	0
8	0.00	0
9	0.00	0
10	0.00	0
11	0.00	0
12	0.00	0
13	0.00	0
14	0.01	0
15	0.01	0
16	0.02	0
17	0.02	0
18	0.02	0
19	0.03	0
20	0.03	0
21	0.03	0
22	0.05	0
23	0.05	0
24	0.07	0
25	0.08	0
26	0.09	0
27	0.10	0
28	0.11	0
29	0.12	0
30	0.13	0
31	0.14	0
32	0.16	0
33	0.18	0
34	0.19	0
35	0.20	0
36	0.22	0
37	0.24	0
38	0.25	0
39	0.28	0
40	0.34	0
41	0.37	0
42	0.43	0
43	0.49	0
44	0.54	0
45	0.56	0
46	0.60	0
47	0.63	0
48	0.65	0
49	0.69	0
50	0.73	0
51	0.79	0
52	0.83	0
53	0.86	0
54	0.90	0
55	0.94	0
56	0.97	0
57	1.01	0
58	1.03	0
59	1.13	0
60	1.15	0
	...	...

	In Chain	On Target
# Clock
1	0.000000	0
2	0.000000	0
3	0.000000	0
4	0.000000	0
5	0.000000	0
6	0.000000	0
7	0.000000	0
8	0.000000	0
9	0.000000	0
10	0.000000	0
11	0.000000	0
12	0.000000	0
13	0.000000	0
14	0.100000	0
15	0.100000	0
16	0.140705	0
17	0.140705	0
18	0.140705	0
19	0.171447	0
20	0.171447	0
21	0.171447	0
22	0.219043	0
23	0.219043	0
24	0.256432	0
25	0.272660	0
26	0.287623	0
27	0.301511	0
28	0.314466	0
29	0.326599	0
30	0.337998	0
31	0.348735	0
32	0.368453	0
33	0.386123	0
34	0.419114	0
35	0.426401	0
36	0.439927	0
37	0.473969	0
38	0.479372	0
39	0.514045	0
40	0.554504	0
41	0.580056	0
42	0.685418	0
43	0.745288	0
44	0.809290	0
45	0.832666	0
46	0.887625	0
47	0.917341	0
48	0.946818	0
49	0.960797	0
50	0.993463	0
51	1.027845	0
52	1.054619	0
53	1.091936	0
54	1.087115	0
55	1.117537	0
56	1.114233	0
57	1.167705	0
58	1.175980	0
59	1.219828	0
60	1.242147	0
	...	...