notebook.community

Edit and run



In [1]:

    
%pylab inline
%config InlineBackend.figure_format = 'retina'









    



Populating the interactive namespace from numpy and matplotlib



In [17]:

    
import pandas as pd
import seaborn as sns



In [99]:

    
k35_df = pd.read_csv('mmetsp/Asterionellopsis_glacialis/k35/decision_nodes.csv', skipinitialspace=True)
k27_df = pd.read_csv('mmetsp/Asterionellopsis_glacialis/k27/decision_nodes.csv', skipinitialspace=True)



In [100]:

    
k35_df.head()









    Out[100]:







  
    
      
      read_n
      l_degree
      r_degree
      position
      hash
    
  
  
    
      0
      414
      2
      1
      9
      4552537295491192989
    
    
      1
      555
      2
      1
      3
      10400599629126302337
    
    
      2
      917
      2
      1
      5
      8559623946996443022
    
    
      3
      1056
      1
      2
      12
      9867647372000252236
    
    
      4
      1929
      2
      1
      6
      10446294276324287310

We can find the number of decision nodes in the dBG by counting unique hashes...



In [101]:

    
k27_df.hash.nunique(), k35_df.hash.nunique()









    Out[101]:





(1465148, 966286)

We'll make a new column for total degree, for convenience.



In [102]:

    
k35_df['degree'] = k35_df['l_degree'] + k35_df['r_degree']
k27_df['degree'] = k27_df['l_degree'] + k27_df['r_degree']

Let's start with the overal degree distribution during the entire construction process.



In [103]:

    
figsize(18,10)
fig, ax_mat = subplots(ncols=3, nrows=2)
top = ax_mat[0]
sns.distplot(k35_df.degree, kde=False, ax=top[0], bins=8)
sns.distplot(k35_df.l_degree, kde=False, ax=top[1], bins=5)
sns.distplot(k35_df.r_degree, kde=False, ax=top[2], bins=5)

bottom = ax_mat[1]
sns.distplot(k27_df.degree, kde=False, ax=bottom[0], bins=8)
sns.distplot(k27_df.l_degree, kde=False, ax=bottom[1], bins=5)
sns.distplot(k27_df.r_degree, kde=False, ax=bottom[2], bins=5)









    



/home/camille/miniconda/envs/goetia/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "






    Out[103]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f5f175dccc0>

So most decision nodes in this dataset have degree 3. Note that a few have degree 2; these forks without handles.



In [109]:

    
figsize(12,8)
sns.distplot(k35_df.position, kde=False, label='K=35', bins=15)
sns.distplot(k27_df.position, kde=False, label='K=27', bins=15)
legend()









    



/home/camille/miniconda/envs/goetia/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.
  warnings.warn("The 'normed' kwarg is deprecated, and has been "






    Out[109]:





<matplotlib.legend.Legend at 0x7f5f16f6b4a8>



In [36]:

    
melted_df = k35_df.melt(id_vars=['hash', 'position'], value_vars=['l_degree', 'r_degree'], )
melted_df.head()









    Out[36]:







  
    
      
      hash
      position
      variable
      value
    
  
  
    
      0
      4552537295491192989
      9
      l_degree
      2
    
    
      1
      10400599629126302337
      3
      l_degree
      2
    
    
      2
      8559623946996443022
      5
      l_degree
      2
    
    
      3
      9867647372000252236
      12
      l_degree
      1
    
    
      4
      10446294276324287310
      6
      l_degree
      2



In [60]:

    
figsize(18,8)
sns.violinplot('position', 'value', 'variable', melted_df)









    Out[60]:





<matplotlib.axes._subplots.AxesSubplot at 0x7f5f18c3fa58>



In [113]:

    
k35_dnodes_per_read = k35_df.groupby('read_n').count().\
    reset_index()[['read_n', 'hash']].rename({'hash': 'n_dnodes'}, axis='columns')
    
k27_dnodes_per_read = k27_df.groupby('read_n').count().\
    reset_index()[['read_n', 'hash']].rename({'hash': 'n_dnodes'}, axis='columns')



In [117]:

    
ax = k35_dnodes_per_read.rolling(1000, min_periods=10, on='read_n').mean().plot(x='read_n', 
                                                                               y='n_dnodes', 
                                                                               label='k = 35')

ax = k27_dnodes_per_read.rolling(1000, min_periods=10, on='read_n').mean().plot(x='read_n', 
                                                                               y='n_dnodes', 
                                                                               label='k = 27', 
                                                                               ax=ax)

ax.xaxis.set_major_formatter(mpl.ticker.StrMethodFormatter("{x:,}"))



In [ ]:

	read_n	l_degree	r_degree	position	hash
0	414	2	1	9	4552537295491192989
1	555	2	1	3	10400599629126302337
2	917	2	1	5	8559623946996443022
3	1056	1	2	12	9867647372000252236
4	1929	2	1	6	10446294276324287310

	hash	position	variable	value
0	4552537295491192989	9	l_degree	2
1	10400599629126302337	3	l_degree	2
2	8559623946996443022	5	l_degree	2
3	9867647372000252236	12	l_degree	1
4	10446294276324287310	6	l_degree	2