In [1]:

    

import sys


# os.environ['R_LIBS_USER'] = '/project/projectdirs/metatlas/r_pkgs/'

# sys.path.insert(0, '/project/projectdirs/metatlas/python_pkgs/')
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 gui as mgui

import qgrid
import metatlas_get_data_helper_fun as ma_data
import pandas as pd
import os
import tables
import pickle

%matplotlib notebook


    

    




:0: FutureWarning: IPython widgets are experimental and may change in the future.

In [ ]:

    

# lcms_files = metob.retrieve('Lcmsrun', mzml_file = '%_SJ_%BEMC%',username='*')
# print len(lcms_files)
# metob.edit_objects(lcms_files)


    

In [2]:

    

mgui.show_experiments()


    

    




/global/project/projectdirs/metatlas/anaconda/lib/python2.7/site-packages/pandas/core/internals.py:1031: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  return self._try_coerce_result(func(values, other))

In [16]:

    

# f = '/global/project/projectdirs/metatlas/raw_data/tls/20150920_TS_BSCporewater_neg_6550/150920_TS_neg_19B_49h.h5'
# with tables.open_file(f) as fid:
#     data = h5q.get_data(fid,ms_level=1,polarity = 0)


    

In [17]:

    

data


    

    
Out[17]:





array([(60.002628326416016, 231.50694274902344, 36.82743453979492),
       (60.003421783447266, 329.058837890625, 27.451549530029297),
       (60.003604888916016, 242.08688354492188, 37.627567291259766), ...,
       (1199.703369140625, 266.8292541503906, 27.14313316345215),
       (1199.703857421875, 268.0277099609375, 20.5174503326416),
       (1199.703857421875, 236.2022705078125, 18.933950424194336)], 
      dtype=[('mz', '<f4'), ('i', '<f4'), ('rt', '<f4')])

In [20]:

    

#Select the group
# group = metob.retrieve('Groups', name = '%LS_Archetypes%hilic%neg%')
temp_group = metob.retrieve('Groups', name = '%20160203_KBL-BC_Root-Exudate_Hilic_QExactive_Trial-Run_POS%',username='*')
# temp_group = metob.retrieve('Groups', name = '%Replicate_Analysis_R2A_HILIC_neg%')
print temp_group
group = []
for i,g in enumerate(temp_group):
    if len(g.items) > 0:
        group.append(g)

# group = metob.retrieve('Groups',name='%Avena_Hopland%RootCass%_neg')
# group = metob.retrieve('Groups',name='%Avena_Hopland%RootCass%_pos')
# group = metob.retrieve('Groups',name='%Avena_Hopland%RootExu%')
# group = metob.retrieve('Groups',name='%Avena_Hopland%RootExu%_pos')
# group = metob.retrieve('Groups',name='%Avena_Hopland%RootExu%_neg')

mgui.edit_objects(group)


    

    




[{'creation_time': '2016-02-03T19:25:20',
 'description': u'nan',
 'head_id': u'b1898c973f7b4a7fa726fd7cb19fe587',
 'items': [{'creation_time': '2016-02-03T18:17:36',
 'description': u'20160203_KBL-BC_Root-Exudate_Hilic_QExactive_Trial-Run 20160119_pHILIC___POS_MSMS_KBL_Ben_rootexudate_______Run1.mzML',
 'experiment': u'20160203_KBL-BC_Root-Exudate_Hilic_QExactive_Trial-Run',
 'hdf5_file': u'/global/project/projectdirs/metatlas/raw_data/bpb/20160203_KBL-BC_Root-Exudate_Hilic_QExactive_Trial-Run/20160119_pHILIC___POS_MSMS_KBL_Ben_rootexudate_______Run1.h5',
 'head_id': u'd9ff92d4c4874e4c9d333d822a6501e0',
 'last_modified': '2016-02-03T19:25:20',
 'method': None,
 'mzml_file': u'/global/project/projectdirs/metatlas/raw_data/bpb/20160203_KBL-BC_Root-Exudate_Hilic_QExactive_Trial-Run/20160119_pHILIC___POS_MSMS_KBL_Ben_rootexudate_______Run1.mzML',
 'name': u'20160119_pHILIC___POS_MSMS_KBL_Ben_rootexudate_______Run1.mzML',
 'prev_uid': u'origin',
 'sample': None,
 'unique_id': u'd9ff92d4c4874e4c9d333d822a6501e0',
 'username': u'bpb'}],
 'last_modified': '2016-02-03T19:25:20',
 'name': u'20160203_KBL-BC_Root-Exudate_Hilic_QExactive_Trial-Run_POS',
 'prev_uid': u'origin',
 'unique_id': u'b1898c973f7b4a7fa726fd7cb19fe587',
 'username': u'bpb'}]






    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-20-a1b41cbb52c1> in <module>()
     15 # group = metob.retrieve('Groups',name='%Avena_Hopland%RootExu%_neg')
     16 
---> 17 mgui.edit_objects(group)

/global/project/projectdirs/metatlas/anaconda/lib/python2.7/site-packages/metatlas/gui.pyc in edit_objects(objects)
    323         return _edit_object(objects)
    324     if len(objects) == 1:
--> 325         return _edit_object(objects[0])
    326 
    327     grid = create_qgrid(objects)

/global/project/projectdirs/metatlas/anaconda/lib/python2.7/site-packages/metatlas/gui.pyc in _edit_object(obj)
    276                 value is None):
    277             if isinstance(trait, traitlets.Instance):
--> 278                 value = value.unique_id
    279             elif isinstance(trait, traitlets.List):
    280                 value = [o.unique_id for o in value]

AttributeError: 'NotifyList' object has no attribute 'unique_id'

In [2]:

    

#Select the atlas
# atlas = metob.retrieve('Atlas',name = '%_Hilic_QExactive_AvenaExudates_2%')
# atlas = metob.retrieve('Atlas',name = '%_Positive%_Hilic_QExactive_AvenaExudates_2%')
# atlas = metob.retrieve('Atlas',name = '%_Negative%_Hilic_QExactive_AvenaExudates_2%')
# atlas = metob.retrieve('Atlas',name = '%internal standards%')
atlas = metob.retrieve('Atlas',name = '%_KZ_%',username='*')#_Archetypes_ISTDs%')
# 20151130_LS_Positive_Hilic_QExactive_Archetypes_ISTDs
# 20151130_LS_Negative_Hilic_QExactive_Archetypes_ISTDs
# mgui.edit_objects(atlas)
len(atlas)
# atlas


    

    
Out[2]:





12

In [3]:

    

print atlas[11]


    

    




{'compound_identifications': [{'compound': [{'creation_time': '2015-10-08T17:37:14',
 'description': u'',
 'formula': u'C7H6O3',
 'functional_sets': [],
 'head_id': u'75423646be08414a8a6337ca6a7120ce',
 'inchi': u'InChI=1S/C7H6O3/c8-6-4-2-1-3-5(6)7(9)10/h1-4,8H,(H,9,10)/p-1',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:12',
 'mono_isotopic_molecular_weight': 138.0316925048828,
 'name': u'salicylate',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'75423646be08414a8a6337ca6a7120ce',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'cd052496a1b342b6a89e0f1b231dc531',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'c78f3a26b1bf4bf78b1779058f55fa59',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 137.0244181,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'c78f3a26b1bf4bf78b1779058f55fa59',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'5f1bc95c00494ef19a070590c6ceea96',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 2.5,
 'rt_min': 2.1,
 'rt_peak': 2.4,
 'rt_units': 'min',
 'unique_id': u'5f1bc95c00494ef19a070590c6ceea96',
 'username': u'bpb'}],
 'unique_id': u'cd052496a1b342b6a89e0f1b231dc531',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:37:14',
 'description': u'',
 'formula': u'C5H5N5',
 'functional_sets': [],
 'head_id': u'2070f5dbf1744165966d6f4e7042df4e',
 'inchi': u'InChI=1S/C5H5N5/c6-4-3-5(9-1-7-3)10-2-8-4/h1-2H,(H3,6,7,8,9,10)',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:12',
 'mono_isotopic_molecular_weight': 135.0544891357422,
 'name': u'adenine',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'2070f5dbf1744165966d6f4e7042df4e',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'a1d98cb440fc41c8b21b1c2e2a5a8cfe',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'31fab53d7ac94d749dd72b5762e31bc6',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 134.0472192,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'31fab53d7ac94d749dd72b5762e31bc6',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'0866f77693f643f58f78991a07bb7dd9',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 4.7,
 'rt_min': 4.1,
 'rt_peak': 4.3,
 'rt_units': 'min',
 'unique_id': u'0866f77693f643f58f78991a07bb7dd9',
 'username': u'bpb'}],
 'unique_id': u'a1d98cb440fc41c8b21b1c2e2a5a8cfe',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:37:12',
 'description': u'',
 'formula': u'C10H10O4',
 'functional_sets': [],
 'head_id': u'192c5aee40904dc98f16ba73e2798f27',
 'inchi': u'InChI=1S/C10H10O4/c1-14-9-6-7(2-4-8(9)11)3-5-10(12)13/h2-6,11H,1H3,(H,12,13)/p-1/b5-3+',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:12',
 'mono_isotopic_molecular_weight': 194.0579071044922,
 'name': u'ferulate',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'192c5aee40904dc98f16ba73e2798f27',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'9ca605a89f5742fca80b31a8ced55ffb',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'48e288060aaf4d0389b017887ef5c2b5',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 193.0506328,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'48e288060aaf4d0389b017887ef5c2b5',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'61de20a90d5741ae91c4f09712878897',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 4.7,
 'rt_min': 3.4,
 'rt_peak': 3.8,
 'rt_units': 'min',
 'unique_id': u'61de20a90d5741ae91c4f09712878897',
 'username': u'bpb'}],
 'unique_id': u'9ca605a89f5742fca80b31a8ced55ffb',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:37:14',
 'description': u'',
 'formula': u'C9H12N2O6',
 'functional_sets': [],
 'head_id': u'8ca7b13e23b4474dbc2650da74aed01b',
 'inchi': u'InChI=1S/C9H12N2O6/c12-3-4-6(14)7(15)8(17-4)11-2-1-5(13)10-9(11)16/h1-2,4,6-8,12,14-15H,3H2,(H,10,13,16)/t4-,6-,7-,8-/m1/s1',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:12',
 'mono_isotopic_molecular_weight': 244.0695343017578,
 'name': u'uridine',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'8ca7b13e23b4474dbc2650da74aed01b',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'3400dcf9276f4ad887022a8a9ee36a5e',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'7a1af45f4f6e47bf98a192ff2e9aac92',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 243.0622601,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'7a1af45f4f6e47bf98a192ff2e9aac92',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'ae40cc0e9e694ca093734efce1e7cc01',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 5.2,
 'rt_min': 4.7,
 'rt_peak': 4.9,
 'rt_units': 'min',
 'unique_id': u'ae40cc0e9e694ca093734efce1e7cc01',
 'username': u'bpb'}],
 'unique_id': u'3400dcf9276f4ad887022a8a9ee36a5e',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:37:10',
 'description': u'',
 'formula': u'C6H5NO2',
 'functional_sets': [],
 'head_id': u'4ca085bae0a8495fa9bd0aa026ca4f8e',
 'inchi': u'InChI=1S/C6H5NO2/c8-6(9)5-2-1-3-7-4-5/h1-4H,(H,8,9)/p-1',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:12',
 'mono_isotopic_molecular_weight': 123.03202819824219,
 'name': u'nicotinate',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'4ca085bae0a8495fa9bd0aa026ca4f8e',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'f451ad65ffd548c786cc534e86b8628f',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'320e1d6cd2794a29b8e32b2b469bab9b',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 122.0247524,
 'mz_tolerance': 20.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'320e1d6cd2794a29b8e32b2b469bab9b',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'139a2cf615994c82a3533c81de0e5628',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 6.7,
 'rt_min': 5.8,
 'rt_peak': 6.1,
 'rt_units': 'min',
 'unique_id': u'139a2cf615994c82a3533c81de0e5628',
 'username': u'bpb'}],
 'unique_id': u'f451ad65ffd548c786cc534e86b8628f',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:36:26',
 'description': u'',
 'formula': u'C5H10O5',
 'functional_sets': [],
 'head_id': u'ad17dd6a67ce4e93a92d59e17955f819',
 'inchi': u'InChI=1S/C5H10O5/c6-2-1-10-5(9)4(8)3(2)7/h2-9H,1H2/t2-,3+,4-,5+/m1/s1',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:11',
 'mono_isotopic_molecular_weight': 150.05282592773438,
 'name': u'α-D-xylopyranose',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'ad17dd6a67ce4e93a92d59e17955f819',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'b03c5d8d056642b897aca6ad717d78fa',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'f1f13066e70e40eca92eba50b451fb83',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 149.0455474,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'f1f13066e70e40eca92eba50b451fb83',
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 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'47922d008a574d94be0125c117803d5f',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'c2a1b8653ae24f53afbdee16f8ad0824',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'8be6ccf02ee24facba6ac9e0c858a1af',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 209.0819329,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'8be6ccf02ee24facba6ac9e0c858a1af',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'754cd16531ba4160a00562199ac88d68',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 1.6,
 'rt_min': 1.4,
 'rt_peak': 1.5,
 'rt_units': 'min',
 'unique_id': u'754cd16531ba4160a00562199ac88d68',
 'username': u'bpb'}],
 'unique_id': u'c2a1b8653ae24f53afbdee16f8ad0824',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:37:12',
 'description': u'',
 'formula': u'C10H8O3',
 'functional_sets': [],
 'head_id': u'e9e85c40246342df896dbfd950bc165f',
 'inchi': u'InChI=1S/C10H8O3/c1-6-4-10(12)13-9-5-7(11)2-3-8(6)9/h2-5,11H,1H3',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:12',
 'mono_isotopic_molecular_weight': 176.04734802246094,
 'name': u'4-methylumbelliferone',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'e9e85c40246342df896dbfd950bc165f',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'a27cd239f0834a2c86af70acc42521d9',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'edc48a33a0dc49ada9d16305af72ce69',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 175.0400681,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'edc48a33a0dc49ada9d16305af72ce69',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'be31c24b28a24524962bb4c3f26e1c54',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 1.6,
 'rt_min': 1.4,
 'rt_peak': 1.5,
 'rt_units': 'min',
 'unique_id': u'be31c24b28a24524962bb4c3f26e1c54',
 'username': u'bpb'}],
 'unique_id': u'a27cd239f0834a2c86af70acc42521d9',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:37:24',
 'description': u'',
 'formula': u'C18H36O2',
 'functional_sets': [],
 'head_id': u'34a43a0870c749298c58fcaf42517706',
 'inchi': u'InChI=1S/C18H36O2/c1-2-3-4-5-6-7-8-9-10-11-12-13-14-15-16-17-18(19)20/h2-17H2,1H3,(H,19,20)/p-1',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:12',
 'mono_isotopic_molecular_weight': 284.27154541015625,
 'name': u'stearate',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'34a43a0870c749298c58fcaf42517706',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'312e04f7437a4852a6fe674db5353978',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'2d7e06deaead4fa2b41a08cc06692b5a',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 283.2642544,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'2d7e06deaead4fa2b41a08cc06692b5a',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'680559383462426c8dc399be6b6f7a92',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 1.1,
 'rt_min': 1.4,
 'rt_peak': 1.2,
 'rt_units': 'min',
 'unique_id': u'680559383462426c8dc399be6b6f7a92',
 'username': u'bpb'}],
 'unique_id': u'312e04f7437a4852a6fe674db5353978',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:37:24',
 'description': u'',
 'formula': u'C6H13NO2',
 'functional_sets': [],
 'head_id': u'870ba5a8c9d84f0681f5fe52a7843b49',
 'inchi': u'InChI=1S/C6H13NO2/c1-3-4(2)5(7)6(8)9/h4-5H,3,7H2,1-2H3,(H,8,9)/t4-,5-/m0/s1',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:13',
 'mono_isotopic_molecular_weight': 131.09463500976562,
 'name': u'|L-isoleucine|',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'870ba5a8c9d84f0681f5fe52a7843b49',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'06515277378445eea79e0b00acbd72e9',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'597b09e005924ac48e4d5241a3bc33d6',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 130.0873527,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'597b09e005924ac48e4d5241a3bc33d6',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'03f677c6f7c04f50b56d4b2d01dbf1c6',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 8.0,
 'rt_min': 7.5,
 'rt_peak': 7.8,
 'rt_units': 'min',
 'unique_id': u'03f677c6f7c04f50b56d4b2d01dbf1c6',
 'username': u'bpb'}],
 'unique_id': u'06515277378445eea79e0b00acbd72e9',
 'username': u'bpb'},
                              {'compound': [{'creation_time': '2015-10-08T17:37:21',
 'description': u'',
 'formula': u'C6H12O6',
 'functional_sets': [],
 'head_id': u'04b1af49dd9548018fb438cad88f92a4',
 'inchi': u'InChI=1S/C6H12O6/c7-1-2(8)4(10)6(12)5(11)3(1)9/h1-12H/t1-,2-,3-,4+,5-,6-',
 'inchi_key': None,
 'last_modified': '2015-10-08T17:38:12',
 'mono_isotopic_molecular_weight': 180.06338500976562,
 'name': u'<I>myo</I>-inositol',
 'neutralized_2d_inchi': None,
 'neutralized_2d_inchi_key': None,
 'neutralized_inchi': None,
 'neutralized_inchi_key': None,
 'number_components': None,
 'permanent_charge': None,
 'prev_uid': u'origin',
 'reference_xrefs': [],
 'synonyms': u'',
 'unique_id': u'04b1af49dd9548018fb438cad88f92a4',
 'url': u'',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'frag_references': [],
 'head_id': u'c4520ac972604b65a90c410c87c859f3',
 'identification_grade': None,
 'last_modified': '2015-11-23T19:40:21',
 'mz_references': [{'adduct': u'[M-H]-',
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'detected_polarity': 'negative',
 'enabled': True,
 'head_id': u'29be5d9b39594ad3a411a8bbad250786',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'modification': u'',
 'mz': 179.0561121,
 'mz_tolerance': 10.0,
 'mz_tolerance_units': 'ppm',
 'name': u'Untitled',
 'observed_formula': u'',
 'prev_uid': u'origin',
 'ref_type': u'',
 'unique_id': u'29be5d9b39594ad3a411a8bbad250786',
 'username': u'bpb'}],
 'name': u'Untitled',
 'prev_uid': u'origin',
 'rt_references': [{'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'enabled': True,
 'head_id': u'1a511e793bdf4e76b9255c7a859ee1cb',
 'last_modified': '2015-11-23T19:40:21',
 'lcms_run': None,
 'name': u'Untitled',
 'prev_uid': u'origin',
 'ref_type': u'',
 'rt_max': 13.2,
 'rt_min': 12.8,
 'rt_peak': 13.0,
 'rt_units': 'min',
 'unique_id': u'1a511e793bdf4e76b9255c7a859ee1cb',
 'username': u'bpb'}],
 'unique_id': u'c4520ac972604b65a90c410c87c859f3',
 'username': u'bpb'}],
 'creation_time': '2015-11-23T19:40:21',
 'description': u'No description',
 'head_id': u'3b7b4c6aa842431baa6ec6e2b009f21a',
 'last_modified': '2015-11-23T19:40:21',
 'name': u'20151023_KZ_Negative_Hilic_QExactive_AvenaExudates_2',
 'prev_uid': u'origin',
 'unique_id': u'3b7b4c6aa842431baa6ec6e2b009f21a',
 'username': u'bpb'}

In [9]:

    

# myAtlases = [atlas[0],atlas[1]] #concatenate the atlases you want to use
# myAtlases = [atlas[0]]
myAtlas = [atlas[0]]
compound_list = []
for i in range(len(myAtlas[0].compound_identifications)):
    compound_list.append(myAtlas[0].compound_identifications[i].compound[0].name)

cols = ['inchi',
 'mono_isotopic_molecular_weight',
 'creation_time',
 'description',
 'formula',
 'functional_sets',
 'last_modified',
 'reference_xrefs',
 'synonyms',
 'unique_id',
 'url',
 'username']
    
    # print myAtlas[0].compound_identifications[0].compound
atlas_export = pd.DataFrame( index=compound_list, columns=cols)

atlas_export['name'] = compound_list
atlas_export.set_index('name',drop=True)
for i in range(len(myAtlas[0].compound_identifications)):
    n = myAtlas[0].compound_identifications[i].compound[0].name
    for c in cols:
        g = getattr(myAtlas[0].compound_identifications[i].compound[0],c)
        if g:
            atlas_export.ix[n,c] = getattr(myAtlas[0].compound_identifications[i].compound[0],c)
    atlas_export.ix[n,'rt_min'] = myAtlas[0].compound_identifications[i].rt_references[0].rt_min
    atlas_export.ix[n,'rt_max'] = myAtlas[0].compound_identifications[i].rt_references[0].rt_max
    atlas_export.ix[n,'rt_peak'] = myAtlas[0].compound_identifications[i].rt_references[0].rt_peak
    atlas_export.ix[n,'mz'] = myAtlas[0].compound_identifications[i].mz_references[0].mz
    atlas_export.ix[n,'mz_tolerance'] = myAtlas[0].compound_identifications[i].mz_references[0].mz_tolerance
atlas_export.to_csv('arkin.csv')
# for d in data:
#     c = d[2]
#     f = d[4]
#     if len(d[1])>0:
#         v = max(d[1])
#     else:
#         v = 0
#     peak_height.ix[c,f] = v


    

    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-9-56f92846f9d1> in <module>()
      4 compound_list = []
      5 for i in range(len(myAtlas[0].compound_identifications)):
----> 6     compound_list.append(myAtlas[0].compound_identifications[i].compound[0].name)
      7 
      8 cols = ['inchi',

AttributeError: 'Stub' object has no attribute 'compound'

In [11]:

    

myAtlas[0].compound_identifications[i].rt_references[0].rt_min
myAtlas[0].compound_identifications[i].rt_references[0].rt_max
myAtlas[0].compound_identifications[i].rt_references[0].rt_peak
myAtlas[0].compound_identifications[i].mz_references[0]
myAtlas[0].compound_identifications[i].mz_references[0].mz
myAtlas[0].compound_identifications[i].mz_references[0].mz_tolerance


    

    
Out[11]:





5.0

In [23]:

    

myAtlases = [atlas[-2]]


    

In [24]:

    

print myAtlases[0].name
print myAtlases[0].compound_identifications[0].compound[0].name
myAtlases[0].compound_identifications[0].mz_references
# print myAtlases[0].compound_identifications[0].references[0].mz
# print myAtlases[0].compound_identifications[0].references[0].mz_tolerance
# print myAtlases[0].compound_identifications[0].rt_references[0].rt_min
# print myAtlases[0].compound_identifications[0].rt_references[0].rt_max
# print myAtlases[0].compound_identifications[0].rt_references[0].rt_peak
# print myAtlases[0].compound_identifications[0].rt_references[0].rt_units

# print myAtlases[0].compound_identifications[0].references[1].RTmax
# print myAtlases[0].compound_identifications[0].references[1].RTpeak
# a = atlas.compound_identifications[0]
# a.description


    

    




20151023_KZ_Positive_Hilic_QExactive_AvenaExudates_2
2-aminoisobutyrate






    
Out[24]:





[{'adduct': u'[M+H]+',
  'creation_time': '2015-11-23T19:36:35',
  'description': u'No description',
  'detected_polarity': 'positive',
  'enabled': True,
  'head_id': u'80388a8b05214d51ab3247ff9fac1c4a',
  'last_modified': '2015-11-23T19:36:35',
  'lcms_run': None,
  'modification': u'',
  'mz': 104.0706045,
  'mz_tolerance': 10.0,
  'mz_tolerance_units': 'ppm',
  'name': u'Untitled',
  'observed_formula': u'',
  'prev_uid': u'origin',
  'ref_type': u'',
  'unique_id': u'80388a8b05214d51ab3247ff9fac1c4a',
  'username': u'bpb'}]

In [6]:

    

# print len(allcompounds)
# print allcompounds[0]
# print allcompounds[1]
# compound_list = []
# for c in allcompounds:
#     compound_list.append(c['name'])
# dataCols = ['mzmin','mzmax','rtmin','rtmax']
# myAtlas = pd.DataFrame( index=compound_list, columns=dataCols, dtype=float)

# for c in allcompounds:
#     for d in dataCols:
#         myAtlas.ix[c['name'],d] = c[d]
# qgrid.show_grid(myAtlas)


    

In [9]:

    

# print myreferences[0].compound[0].name
# print myreferences[0].references[0].mz
# print myreferences[0].references[1].RTpeak


    

In [6]:

    




    

In [ ]:

    

# get and pickle everything This is MSMS, raw MS1 datapoints, compound, group info, and file info
# combine positive and negative mode atlas, by join atlases together vs two compound references
# typically file-groups are NOT split by polarity, files with un-matched polarity will be discarded for analysis of an identification
from metatlas import h5_query as h5q
import os
import tables
import dill
ma_data = reload(ma_data)
data = []
for i,treatment_groups in enumerate(group):
    for j in range(len(treatment_groups.items)):
        myFile = treatment_groups.items[j].hdf5_file
        print i,myFile
        row = []
        for atlas in myAtlases:
            for compound in atlas.compound_identifications:
                result = {}
                result['lcmsrun'] = treatment_groups.items[j] #.unique_id
                result['group'] = treatment_groups #.unique_id
#                 result['atlas'] = atlas.unique_id
                result['identification'] = compound #.unique_id
                result['data'] = ma_data.get_data_for_a_compound(compound.mz_references[0],
                                        compound.rt_references[0],
                                        [ 'ms1_summary', 'eic', 'msms' ],
                                        myFile,0.3)
#                 print result['data']['ms1_summary']
                row.append(result)
        data.append(row)
with open('20160203_KBL-BC_Root-Exudate_Hilic_QExactive_Trial-Run_POS.pkl','w') as f:
    dill.dump(data,f)


    

In [7]:

    

data[0][0]['data']


    

    
Out[7]:





{'eic': {'intensity': array([  1.29519736e+04,   2.42093789e+04,   2.49738125e+04,
           3.11992812e+04,   3.15344688e+04,   6.35300391e+04,
           8.90617969e+04,   1.10729250e+05,   1.17598562e+05,
           6.68327500e+04,   9.95365625e+04,   8.34206250e+04,
           8.91792500e+04,   6.28883125e+04,   8.48820625e+04,
           8.49846250e+04,   8.58570000e+04,   8.59568750e+04,
           1.07759625e+05,   1.08285750e+05,   1.19434250e+05,
           1.40532250e+05,   1.44119500e+05,   1.54220750e+05,
           1.50184250e+05,   1.44659000e+05,   1.26636750e+05,
           1.25226000e+05,   1.16083250e+05,   1.07779000e+05,
           1.04105250e+05,   7.25500000e+04,   5.49607500e+04,
           2.73907500e+04,   2.12835000e+04,   1.70222500e+04,
           1.94332500e+04,   1.48000000e+04,   1.06090000e+04,
           1.05970000e+04,   6.95600000e+03,   9.98125000e+03,
           6.26800000e+03,   2.88975000e+03,   7.12400000e+03,
           2.86575000e+03,   2.53775000e+03,   5.09125000e+03,
           5.46725000e+03,   6.41050000e+03,   3.30350000e+03,
           5.03875000e+03,   7.13500000e+03,   8.82375000e+03,
           1.26395000e+04,   1.23217500e+04,   1.72695000e+04,
           2.08132500e+04,   2.76935000e+04,   3.75457500e+04,
           5.04075000e+04,   7.88115000e+04,   9.68902500e+04,
           1.79129750e+05,   2.83346000e+05,   4.19647500e+05,
           6.21539500e+05,   8.34228000e+05,   1.19976750e+06,
           1.57344700e+06,   1.94058400e+06,   2.47854600e+06,
           3.75742500e+06,   4.33471400e+06,   4.75906600e+06,
           4.90027800e+06,   6.98421000e+06,   6.95590800e+06,
           8.16382400e+06,   7.58091200e+06,   7.01474400e+06,
           6.55264000e+06,   7.62947200e+06,   6.46512000e+06,
           7.48177600e+06,   5.48298400e+06,   5.10733600e+06,
           4.02980000e+06,   4.33424800e+06,   3.98467200e+06,
           3.06313600e+06,   2.18289600e+06,   2.42668800e+06,
           2.03624800e+06,   1.97527200e+06,   1.75364000e+06,
           1.79571200e+06,   1.63038400e+06,   1.38254400e+06,
           1.64148800e+06,   1.14422400e+06,   1.25272000e+06,
           9.56992000e+05,   9.42352000e+05,   9.49648000e+05,
           6.83472000e+05,   6.36736000e+05,   5.74848000e+05,
           5.83088000e+05,   4.71248000e+05,   4.44752000e+05,
           4.05248000e+05,   3.49120000e+05,   3.19824000e+05,
           2.82448000e+05,   2.64720000e+05,   2.03920000e+05,
           2.42144000e+05,   1.83792000e+05,   1.48480000e+05,
           1.41712000e+05,   1.44192000e+05,   1.30144000e+05,
           1.16464000e+05,   1.12128000e+05,   1.07168000e+05,
           1.06976000e+05,   8.69760000e+04,   7.90720000e+04,
           5.99520000e+04,   6.32320000e+04,   7.09920000e+04,
           6.92480000e+04,   5.00000000e+04,   5.59680000e+04,
           5.29280000e+04,   5.18720000e+04,   5.83040000e+04,
           4.70080000e+04,   6.11040000e+04,   5.34560000e+04,
           4.59040000e+04,   5.04800000e+04,   5.47680000e+04,
           6.53600000e+04,   5.98080000e+04,   4.36000000e+04,
           4.96480000e+04,   5.62720000e+04,   6.84640000e+04,
           7.23680000e+04,   5.63200000e+04,   5.56320000e+04,
           5.46240000e+04,   5.98400000e+04,   4.53440000e+04,
           4.42080000e+04,   3.89760000e+04,   4.10560000e+04,
           3.68160000e+04,   4.39200000e+04,   3.59360000e+04,
           2.99040000e+04,   2.97600000e+04,   3.13120000e+04,
           3.57280000e+04], dtype=float32),
  'polarity': 1,
  'rt': array([ 10.22797775,  10.2377739 ,  10.24757004,  10.25713253,
          10.26685905,  10.27564144,  10.28439331,  10.29304123,
          10.3018074 ,  10.31011772,  10.31887436,  10.32770538,
          10.33646965,  10.34541798,  10.35425758,  10.36333179,
          10.3722887 ,  10.38121605,  10.39028931,  10.39933014,
          10.4082756 ,  10.41723156,  10.42623901,  10.435256  ,
          10.44428539,  10.45335579,  10.46234894,  10.47144508,
          10.4805851 ,  10.48933125,  10.49817944,  10.50747395,
          10.51665592,  10.52622604,  10.53606224,  10.54584694,
          10.55564499,  10.56541824,  10.57519531,  10.58498096,
          10.59477234,  10.60455132,  10.61434078,  10.62412643,
          10.63393116,  10.64372635,  10.65350628,  10.66328716,
          10.67307854,  10.68286419,  10.69264507,  10.70242691,
          10.71219921,  10.72202206,  10.73180199,  10.74158859,
          10.75138092,  10.7611618 ,  10.77097034,  10.7807579 ,
          10.79054737,  10.80035114,  10.81013489,  10.81991577,
          10.82972431,  10.83950806,  10.84928894,  10.85909462,
          10.86889935,  10.87870789,  10.88852882,  10.89830399,
          10.90813255,  10.9179287 ,  10.92772007,  10.9375124 ,
          10.94729519,  10.95708656,  10.96688461,  10.97666836,
          10.98647594,  10.99626637,  11.00605392,  11.01584339,
          11.02563858,  11.03542042,  11.0452137 ,  11.05500984,
          11.06481838,  11.07460785,  11.08439541,  11.09417439,
          11.10397911,  11.11376953,  11.12355804,  11.13335228,
          11.14314842,  11.15294456,  11.16272736,  11.1725502 ,
          11.18233681,  11.19213676,  11.20192528,  11.21170616,
          11.22149086,  11.23130226,  11.24111843,  11.25092125,
          11.26071358,  11.27050591,  11.2802887 ,  11.2900753 ,
          11.29987907,  11.30965614,  11.3191061 ,  11.32891846,
          11.3387022 ,  11.34849644,  11.35830212,  11.36809254,
          11.37794971,  11.38772869,  11.39750957,  11.4073019 ,
          11.41711426,  11.42690563,  11.43670845,  11.44650841,
          11.45630836,  11.46609116,  11.4758997 ,  11.48568916,
          11.49547482,  11.50526428,  11.51504993,  11.52484226,
          11.53465557,  11.54446411,  11.55427456,  11.56410599,
          11.57389832,  11.58368111,  11.59348106,  11.60325623,
          11.61306667,  11.62285614,  11.6326437 ,  11.64244175,
          11.65224648,  11.66204643,  11.67183876,  11.68162727,
          11.69141006,  11.70119953,  11.71098137,  11.72077465,
          11.73056889,  11.74035645,  11.75016022,  11.75996017,
          11.76975441,  11.77955437,  11.78935432,  11.79914761,
          11.80895805,  11.81876183], dtype=float32)},
 'ms1_summary': {'mz_centroid': 90.055763,
  'mz_peak': 90.055786,
  'peak_area': 887352.75,
  'peak_height': 236646.86,
  'polarity': 1,
  'rt_centroid': 10.853844,
  'rt_peak': 10.795056},
 'msms': {'data': array([ (59.97118377685547, 1140.446044921875, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (61.72518539428711, 1347.5426025390625, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (64.1128921508789, 1239.113525390625, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (65.59719848632812, 1312.1641845703125, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (70.06611633300781, 6754.47021484375, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (72.08172607421875, 2713.16552734375, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (80.2898941040039, 1130.07080078125, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (88.0765380859375, 21636.6484375, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (88.28744506835938, 1261.2904052734375, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (89.07991790771484, 1576.0416259765625, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (90.05532836914062, 1811.094482421875, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (91.05504608154297, 1939.3316650390625, 10.835905075073242, 90.05999755859375, 283345.90625, 20.0),
         (68.93399047851562, 2695.657958984375, 11.020352363586426, 90.05999755859375, 6465119.5, 20.0),
         (70.06603240966797, 4331.9111328125, 11.020352363586426, 90.05999755859375, 6465119.5, 20.0),
         (88.07646942138672, 18082.388671875, 11.020352363586426, 90.05999755859375, 6465119.5, 20.0),
         (90.05575561523438, 59360.35546875, 11.020352363586426, 90.05999755859375, 6465119.5, 20.0),
         (57.43141174316406, 1115.161376953125, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (70.06611633300781, 5520.91845703125, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (72.08191680908203, 2627.969970703125, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (73.0849380493164, 1137.9666748046875, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (73.85841369628906, 1133.70556640625, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (77.85479736328125, 1697.6953125, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (88.07647705078125, 21345.900390625, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (88.2959976196289, 1226.486083984375, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (90.05577087402344, 9590.4541015625, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (91.0550765991211, 2583.353271484375, 11.196645736694336, 90.05999755859375, 1252725.875, 20.0),
         (53.599952697753906, 929.1982421875, 11.372602462768555, 90.05553436279297, 148479.359375, 20.0),
         (55.83987045288086, 1052.127197265625, 11.372602462768555, 90.05553436279297, 148479.359375, 20.0),
         (70.0660629272461, 5205.19189453125, 11.372602462768555, 90.05553436279297, 148479.359375, 20.0),
         (88.02259063720703, 1694.0731201171875, 11.372602462768555, 90.05553436279297, 148479.359375, 20.0),
         (88.07649993896484, 16998.85546875, 11.372602462768555, 90.05553436279297, 148479.359375, 20.0),
         (91.05525970458984, 1914.97412109375, 11.372602462768555, 90.05553436279297, 148479.359375, 20.0),
         (92.0393295288086, 1450.9754638671875, 11.372602462768555, 90.05553436279297, 148479.359375, 20.0)], 
        dtype=[('mz', '<f4'), ('i', '<f4'), ('rt', '<f4'), ('precursor_MZ', '<f4'), ('precursor_intensity', '<f4'), ('collision_energy', '<f4')]),
  'most_intense_precursor': {'precursor_intensity': array([ 6465119.5     ,  1252725.875   ,   283345.90625 ,   148479.359375], dtype=float32),
   'precursor_mz': array([ 90.05999756,  90.05999756,  90.05999756,  90.05553436], dtype=float32),
   'spectra': array([[    68.93399048,   2695.65795898],
          [    70.06603241,   4331.91113281],
          [    88.07646942,  18082.38867188],
          [    90.05575562,  59360.35546875]], dtype=float32)},
  'polarity': 1}}

In [12]:

    

# result['identification']


    

In [25]:

    

# file_column_name = []
# group_column_name = []
# for i,treatment_groups in enumerate(group):
#     for j in range(len(treatment_groups.items)):
#         myFile = treatment_groups.items[j].hdf5_file
#         file_column_name.append(os.path.basename(myFile))
#         group_column_name.append(treatment_groups.name)
# # print file_column_name
# # print group_column_name
# compound_list = []
# for compound in atlas.compound_identifications[:3]:
#     compound_list.append(compound.compound[0].name)


    

In [24]:

    

# # column name list
# # row name list
# import pandas as pd
# import os
# peak_height = pd.DataFrame( index=compound_list, columns=file_column_name, dtype=float)
# # peak_height['compound'] = compound_list
# # peak_height.set_index('compound',drop=True)
# for d in data:
#     c = d[2]
#     f = d[4]
#     if len(d[1])>0:
#         v = max(d[1])
#     else:
#         v = 0
#     peak_height.ix[c,f] = v
    
# peak_area = pd.DataFrame( index=compound_list, columns=file_column_name, dtype=float)
# # peak_height['compound'] = compound_list
# # peak_height.set_index('compound',drop=True)
# for d in data:
#     c = d[2]
#     f = d[4]
#     if len(d[1])>0:
#         v = sum(d[1])
#     else:
#         v = 0
#     peak_area.ix[c,f] = v


    

In [13]:

    

# qgrid.show_grid(peak_height)


    

    




//anaconda/lib/python2.7/site-packages/pandas/core/internals.py:956: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  return self._try_coerce_result(func(values, other))

In [19]:

    

# columns = []
# for i,f in enumerate(file_column_name):
#     columns.append((group_column_name[i],f))
# peak_height.columns = pd.MultiIndex.from_tuples(columns,names=['group', 'file'])
# peak_area.columns = pd.MultiIndex.from_tuples(columns,names=['group', 'file'])


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-19-455c690ba0b0> in <module>()
      1 columns = []
----> 2 for i,f in enumerate(file_column_name):
      3     columns.append((group_column_name[i],f))
      4 peak_height.columns = pd.MultiIndex.from_tuples(columns,names=['group', 'file'])
      5 peak_area.columns = pd.MultiIndex.from_tuples(columns,names=['group', 'file'])

NameError: name 'file_column_name' is not defined

In [18]:

    

# m = peak_height.groupby(axis=1,level='group').mean()
# qgrid.show_grid(m)


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-18-563ccb1f649b> in <module>()
----> 1 m = peak_height.groupby(axis=1,level='group').mean()
      2 qgrid.show_grid(m)

NameError: name 'peak_height' is not defined

In [26]:

    

# from matplotlib import pyplot as plt

# m = peak_height.ix['adenine'].groupby(level='group').mean()
# e = peak_height.ix['adenine'].groupby(level='group').std()
# c = peak_height.ix['adenine'].groupby(level='group').count()

# for i in range(len(e)):
#     if c[i]>0:
#         e[i] = e[i] / c[i]**0.5

# fig, ax = plt.subplots()
# m.plot(yerr=e, kind='bar')
# plt.tight_layout()
# a = plt.gca()
# a.set_yscale('log')
# plt.show()


    

In [27]:

    

# peak_height.to_csv('compounds neg peak height.tab',sep='\t')
# peak_area.to_csv('compounds neg peak area.tab',sep='\t')


    

In [17]:

    

# df=pd.DataFrame({'a':[1,2,3],'b':[4,5,6]})

# columns=[('c','a'),('c','b')]

# df.columns=pd.MultiIndex.from_tuples(columns)
# df


    

    
Out[17]:






  

    

      

      
c
    
    

      

      
a
      
b
    
  
  

    

      
0
      
1
      
4
    
    

      
1
      
2
      
5
    
    

      
2
      
3
      
6
    
  

In [73]:

    

%%javascript
var nb = IPython.notebook;
var kernel = IPython.notebook.kernel;
var command = "NOTEBOOK_FULL_PATH = '" + nb.base_url + nb.notebook_path + "'";
kernel.execute(command);


    

In [74]:

    

import os
filename = os.path.basename(NOTEBOOK_FULL_PATH)
%system cp $filename /project/projectdirs/openmsi/www/
temp = '%s/%s'%('/project/projectdirs/openmsi/www',filename)
%system chmod 775 $temp
print 'http://nbviewer.ipython.org/url/portal.nersc.gov/project/openmsi/%s?flush_cache=true'%filename


    

    




http://nbviewer.ipython.org/url/portal.nersc.gov/project/openmsi/Get a Group of Files and Atlas and Get Data.ipynb?flush_cache=true

In [ ]:

    

# helpful pandas hints for commands
# df
# print df.keys()
# df.loc[0,'name']
# df.filter(regex = 'name')
# df.iloc[0]
# for x in df.index:
#     print df.name[x]
#     if not metob.retrieve('Compounds',name=df.name[x]):
#         print df.name[x], "is not in database"
#     else:
#         print df.name[x], "Success"

#         
#     df = df.append(data)
#     print df.iloc[x,0]
#     print df.iloc[x]
#     print df.name[x]
# df.ix[(df['name']=='Adenine') == True]['mz']
# df.ix[(df['name']=='Adenine') == True]
# df.name.str.contains('Ad')
# df.name.str.contains('Ad').tolist()
# df


    

In [15]:

    

# files = metob.retrieve('lcmsrun',name='%Actino%')
import numpy as np
my_run = metob.retrieve('lcmsrun', hdf5_file='%20150510_C18_POS_MSMS_HE08-3%', username='*')
my_run
# for f in files:
#     print f.hdf5_file
with tables.open_file(my_run[0].hdf5_file) as fid:
    data = h5q.get_data(fid,2,1,min_rt = 2.1)#,max_rt = 17.9,min_precursor_MZ=633.12,max_precursor_MZ = 633.2)
print data['rt']
prt,pmz = get_unique_scan_data(data)
rt_cutoff = 0.23
mz_cutoff = 0.05
list_of_prt,list_of_pmz = get_non_redundant_precursor_list(prt,pmz,rt_cutoff,mz_cutoff)
#setup data format for searching
pactolus_input = {}
pactolus_input['spectra'] = []
pactolus_input['precursor_mz'] = []
for i,(prt,pmz) in enumerate(zip(list_of_prt,list_of_pmz)):
    idx = np.argwhere((data['precursor_MZ'] == pmz) & (data['rt'] == prt )).flatten()
    arr = np.array([data['mz'][idx], data['i'][idx]]).T
    pactolus_input['spectra'].append(arr)
    pactolus_input['precursor_mz'].append(pmz)


    

    




[  2.104074    2.104074    2.104074  ...,  11.5003891  11.5003891
  11.5003891]

In [20]:

    

import glob

# 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,'/global/project/projectdirs/metatlas/anaconda/lib/python2.7/site-packages' )


sys.path.append('/project/projectdirs/openmsi/projects/meta-iq/pactolus/pactolus')

import score_frag_dag


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')

path_to_trees = '/project/projectdirs/openmsi/projects/ben_trees/'
all_my_h5_files = glob.glob('/project/projectdirs/openmsi/projects/ben_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': 0.05,
          'ms2_mass_tol': 0.05,
          'neutralizations': pos_mode_neutralizations,
          'max_depth': 5,
              }


    

In [21]:

    

print len(pactolus_input['spectra'])


    

    




580

In [39]:

    

import time

for i in range(10):
    start = time.time()
    foo = score_frag_dag.score_scan_list_against_trees([pactolus_input['spectra'][i]], [pactolus_input['precursor_mz'][i]], params)
    stop = time.time()
    print stop - start, np.sum(foo>0)
# np.argmax(foo)


    

    




0.00915908813477 0
0.225470066071 31
13.8581519127 24
0.199229955673 29
0.205362796783 13
0.293752908707 37
1.79723000526 133
0.200758218765 28
0.278684139252 48
0.00717997550964 0

In [27]:

    

metatlas_molecules = np.load(maxdepth_5_table)


    

In [33]:

    

metatlas_molecules.shape


    

    
Out[33]:





(13190,)

In [25]:

    

foo.shape


    

    
Out[25]:





(5, 13190)

In [ ]:

    

my_db = '/project/projectdirs/openmsi/projects/meta-iq/pactolus/data/' + 'MetaCyc.mdb'
pactolus_results = score_frag_dag.make_pactolus_hit_table(foo, maxdepth_5_table, original_db=my_db)


    

In [ ]:

    

for r in pactolus_results:
    if len(r)>0:
        print r[0]