In [1]:

    

%matplotlib notebook
# %matplotlib inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import pylab as plt
import sys
import glob, os

# import glob
# metob.load_lcms_files(glob.glob('/project/projectdirs/metatlas/data_for_metatlas_2/20150504_LPSilva_Actino_HILIC_POS_51isolates/*.*’))


curr_ld_lib_path = ''
os.environ['LD_LIBRARY_PATH'] = curr_ld_lib_path + ':/project/projectdirs/openmsi/jupyterhub_libs/boost_1_55_0/lib' + ':/project/projectdirs/openmsi/jupyterhub_libs/lib'
import sys
# sys.path.remove('/anaconda/lib/python2.7/site-packages')
sys.path.append('/global/project/projectdirs/openmsi/jupyterhub_libs/anaconda/lib/python2.7/site-packages')
sys.path.insert(0,'/project/projectdirs/openmsi/projects/meta-iq/pactolus/pactolus' )

from generate_frag_dag import *

import score_frag_dag

sys.path.insert(0,'/global/project/projectdirs/metatlas/anaconda/lib/python2.7/site-packages' )

from metatlas import metatlas_objects as metob
from metatlas import h5_query as h5q
from metatlas import mzml_to_hdf


import tables

import pickle


    

In [2]:

    

pos_mode_neutralizations = [-1.00727646677, -(1.00727646677+1.00782504), +5.4857990946e-4,]
neg_mode_neutralizations = [-el for el in pos_mode_neutralizations]

# make lookup table
path_to_trees = '/project/projectdirs/openmsi/projects/pactolus_trees/'
all_my_h5_files = glob.glob('/project/projectdirs/openmsi/projects/pactolus_trees/*_hdf5_5_*.h5')
my_tree_filename = 'metacyc_max_depth_5'

if not os.path.isfile(os.path.join(path_to_trees, my_tree_filename + '.npy')):
    score_frag_dag.make_file_lookup_table_by_MS1_mass(all_my_h5_files, 
                                                      path=path_to_trees, 
                                                      save_result='metacyc_max_depth_5')

maxdepth_5_table = os.path.join(path_to_trees, my_tree_filename + '.npy')

params = {'file_lookup_table': maxdepth_5_table,
          'ms1_mass_tol': 0.02,
          'ms2_mass_tol': 0.01,
          'neutralizations': pos_mode_neutralizations,
          'max_depth': 5,
              }


    

In [ ]:

    

with open('Actino_C18_Pos.pickle', 'rb') as handle:
    MSMS_data = pickle.load(handle)


    

In [3]:

    

with open('Actino_C18_Pos_Scores_Sorted.pickle', 'rb') as handle:
        score_results = pickle.load(handle)


    

    




---------------------------------------------------------------------------
EOFError                                  Traceback (most recent call last)
<ipython-input-3-08c3664235dd> in <module>()
      1 with open('Actino_C18_Pos_Scores_Sorted.pickle', 'rb') as handle:
----> 2         score_results = pickle.load(handle)

//anaconda/lib/python2.7/pickle.pyc in load(file)
   1376 
   1377 def load(file):
-> 1378     return Unpickler(file).load()
   1379 
   1380 def loads(str):

//anaconda/lib/python2.7/pickle.pyc in load(self)
    856             while 1:
    857                 key = read(1)
--> 858                 dispatch[key](self)
    859         except _Stop, stopinst:
    860             return stopinst.value

//anaconda/lib/python2.7/pickle.pyc in load_eof(self)
    878 
    879     def load_eof(self):
--> 880         raise EOFError
    881     dispatch[''] = load_eof
    882 

EOFError: 

In [4]:

    

foo = []
for s in score_results:
    for i in s:
        foo.append(i)
foo = np.array(foo)
print foo.shape


    

    




(17000, 8172)

In [5]:

    

s = score_frag_dag.make_pactolus_hit_table(foo,maxdepth_5_table,'/global/homes/b/bpb/notebooks/meta-iq_old/midas_lbl/MetaCyc.mdb')


    

In [6]:

    

s[0]


    

    
Out[6]:





array([ (0.010704557411372662, 'MetaCyC_2062', 'methylarsonate', 139.94546508789062, 0, 0),
       (7.020389602985233e-05, 'MetaCyC_4251', 'coenzyme M', 141.975830078125, 0, 0)], 
      dtype=[('score', '<f4'), ('id', 'S100'), ('name', 'S100'), ('mass', '<f4'), ('n_peaks', '<i4'), ('n_match', '<i4')])

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