In [13]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [27]:

    

import pandas
import numpy


    

In [3]:

    

file = '/Users/schriste/Desktop/bamsringsdata.csv'


    

In [6]:

    

data = pandas.read_csv(file, index_col=2, parse_dates=True)


    

In [7]:

    

data


    

    
Out[7]:




<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2345 entries, 2013-09-21 12:44:27 to 2013-09-22 14:26:57
Data columns (total 12 columns):
Unnamed: 0                      2345  non-null values
time                            2345  non-null values
aft x movement arcmin           389  non-null values
aft y movement arcmin           389  non-null values
fwd ring 2 x movement arcmin    528  non-null values
fwd ring 2 y movement arcmin    528  non-null values
fwd ring 3 x movement arcmin    604  non-null values
fwd ring 3 y movement arcmin    604  non-null values
fwd ring 7 x movement arcmin    317  non-null values
fwd ring 7 y movement arcmin    317  non-null values
fwd ring F x movement arcmin    507  non-null values
fwd ring F y movement arcmin    507  non-null values
dtypes: float64(10), object(2)

In [8]:

    

data.describe()


    

    
Out[8]:






  

    

      

      
aft x movement arcmin
      
aft y movement arcmin
      
fwd ring 2 x movement arcmin
      
fwd ring 2 y movement arcmin
      
fwd ring 3 x movement arcmin
      
fwd ring 3 y movement arcmin
      
fwd ring 7 x movement arcmin
      
fwd ring 7 y movement arcmin
      
fwd ring F x movement arcmin
      
fwd ring F y movement arcmin
    
  
  

    

      
count
      
 389.000000
      
 389.000000
      
 528.000000
      
 528.000000
      
 604.000000
      
 604.000000
      
 317.000000
      
 317.000000
      
 507.000000
      
 507.000000
    
    

      
mean
      
  -0.067532
      
   0.771003
      
  -0.202121
      
  -0.982083
      
  -0.077235
      
  -0.825960
      
  -0.030379
      
  -0.965868
      
  -0.133018
      
  -1.039704
    
    

      
std
      
   0.772922
      
   1.170815
      
   0.152193
      
   0.559925
      
   0.198987
      
   0.577304
      
   0.176882
      
   0.575705
      
   0.210340
      
   0.519244
    
    

      
min
      
  -2.720000
      
  -0.370000
      
  -0.530000
      
  -2.670000
      
  -0.480000
      
  -2.660000
      
  -0.570000
      
  -2.800000
      
  -0.590000
      
  -2.570000
    
    

      
25%
      
  -0.460000
      
   0.100000
      
  -0.310000
      
  -1.320000
      
  -0.240000
      
  -1.002500
      
  -0.150000
      
  -1.140000
      
  -0.310000
      
  -1.245000
    
    

      
50%
      
  -0.110000
      
   0.420000
      
  -0.220000
      
  -0.880000
      
  -0.120000
      
  -0.720000
      
  -0.080000
      
  -0.830000
      
  -0.140000
      
  -0.980000
    
    

      
75%
      
   0.190000
      
   0.770000
      
  -0.060000
      
  -0.710000
      
   0.110000
      
  -0.580000
      
   0.140000
      
  -0.680000
      
   0.040000
      
  -0.720000
    
    

      
max
      
   2.180000
      
   4.410000
      
   0.080000
      
   0.150000
      
   0.300000
      
   0.130000
      
   0.270000
      
   0.050000
      
   0.240000
      
   0.090000
    
  

In [28]:

    

ndata = data.resample('600s', how=np.mean)


    

In [29]:

    

ndata


    

    
Out[29]:




<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 155 entries, 2013-09-21 12:40:00 to 2013-09-22 14:20:00
Freq: 600S
Data columns (total 10 columns):
aft x movement arcmin           155  non-null values
aft y movement arcmin           155  non-null values
fwd ring 2 x movement arcmin    155  non-null values
fwd ring 2 y movement arcmin    155  non-null values
fwd ring 3 x movement arcmin    155  non-null values
fwd ring 3 y movement arcmin    155  non-null values
fwd ring 7 x movement arcmin    155  non-null values
fwd ring 7 y movement arcmin    155  non-null values
fwd ring F x movement arcmin    155  non-null values
fwd ring F y movement arcmin    155  non-null values
dtypes: float64(10)

In [41]:

    

ndata['fwd ring F y movement arcmin'].plot()
ndata['aft y movement arcmin'].plot()


    

    
Out[41]:





<matplotlib.axes.AxesSubplot at 0x10a8bb410>






    

In [43]:

    

ndata['fwd ring F x movement arcmin'].plot()
ndata['aft x movement arcmin'].plot()


    

    
Out[43]:





<matplotlib.axes.AxesSubplot at 0x10ae5aad0>






    

In [44]:

    

ydiff = ndata['fwd ring F y movement arcmin']+ndata['aft y movement arcmin']


    

In [45]:

    

xdiff = ndata['fwd ring F x movement arcmin']+ndata['aft x movement arcmin']


    

In [46]:

    

ydiff.plot()


    

    
Out[46]:





<matplotlib.axes.AxesSubplot at 0x10ae76250>






    

In [47]:

    

xdiff.plot()


    

    
Out[47]:





<matplotlib.axes.AxesSubplot at 0x10aecd1d0>






    

In [35]:

    

import Pysolar
sunel = [Pysolar.GetAltitude(34.4731, -104.2422, t) for t in ndata['aft x movement arcmin'].index]


    

In [37]:

    

sunel = pandas.Series(sunel, index=ndata['aft x movement arcmin'].index)


    

In [40]:

    

ax = sunel.plot()
ax.set_ylabel('Sun Elevation [degrees]')


    

    
Out[40]:





<matplotlib.text.Text at 0x10a0ac690>






    

In [ ]: