notebook.community

Edit and run 





In [2]:



    


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



    











In [ ]:



    


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



    











In [3]:



    


mgui.show_experiments()



    


















    






[{'creation_time': '2015-09-19T21:33:26',
 'description': u'20141211_Actinorhodin 20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_9.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_9.h5',
 'head_id': u'06f898a682ea45779b1a309570d19bfa',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_9.mzML',
 'name': u'20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_9.mzML',
 'prev_uid': u'd79a27dfec814c25a12a49d90e80ab88',
 'sample': None,
 'unique_id': u'06f898a682ea45779b1a309570d19bfa',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:26',
 'description': u'20141211_Actinorhodin 20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_60.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_60.h5',
 'head_id': u'0be76bd5150c4d4d92537821c3d3c3c4',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_60.mzML',
 'name': u'20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_60.mzML',
 'prev_uid': u'2e95c84e81644f8ca3409cc4a90c488e',
 'sample': None,
 'unique_id': u'0be76bd5150c4d4d92537821c3d3c3c4',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:26',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_2F11_1to10dil_C18_POS_MSMS_ACN_7.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F11_1to10dil_C18_POS_MSMS_ACN_7.h5',
 'head_id': u'2e445c35601248b3bae4023a5ce7a8d6',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F11_1to10dil_C18_POS_MSMS_ACN_7.mzML',
 'name': u'20141211_actinorhodin_agar_2F11_1to10dil_C18_POS_MSMS_ACN_7.mzML',
 'prev_uid': u'7f7e661a34984c06a74d66229be78662',
 'sample': None,
 'unique_id': u'2e445c35601248b3bae4023a5ce7a8d6',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:26',
 'description': u'20141211_Actinorhodin 20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_6.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_6.h5',
 'head_id': u'4fddbd7fab7f437bb2d8c2e30244c637',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_6.mzML',
 'name': u'20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_6.mzML',
 'prev_uid': u'86ebf29816fe44258e7f79c22e2d6820',
 'sample': None,
 'unique_id': u'4fddbd7fab7f437bb2d8c2e30244c637',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:28',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_II_IV_C18_NEG_MSMS_ACN_20.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_C18_NEG_MSMS_ACN_20.h5',
 'head_id': u'502b6e4bc60a487588600fc622ca1cf1',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_C18_NEG_MSMS_ACN_20.mzML',
 'name': u'20141211_actinorhodin_agar_II_IV_C18_NEG_MSMS_ACN_20.mzML',
 'prev_uid': u'4fdde71e16464937b072ea91cddb86ef',
 'sample': None,
 'unique_id': u'502b6e4bc60a487588600fc622ca1cf1',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:26',
 'description': u'20141211_Actinorhodin 20141211_MeOHBlank_C18_POS_MSMS_ACN_pre3.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_pre3.h5',
 'head_id': u'513bc89d567c4f11a790cd2c37c81bd7',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_pre3.mzML',
 'name': u'20141211_MeOHBlank_C18_POS_MSMS_ACN_pre3.mzML',
 'prev_uid': u'58b10694968d426893c879e437e0e3aa',
 'sample': None,
 'unique_id': u'513bc89d567c4f11a790cd2c37c81bd7',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:28',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_II_IV_C18_POS_MSMS_ACN_10.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_C18_POS_MSMS_ACN_10.h5',
 'head_id': u'56471a27392b41c1985f5a8994fa776c',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_C18_POS_MSMS_ACN_10.mzML',
 'name': u'20141211_actinorhodin_agar_II_IV_C18_POS_MSMS_ACN_10.mzML',
 'prev_uid': u'feb4632b23a64a00a83a4525d8f7138d',
 'sample': None,
 'unique_id': u'56471a27392b41c1985f5a8994fa776c',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:27',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_2F4_C18_NEG_MSMS_ACN_50.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F4_C18_NEG_MSMS_ACN_50.h5',
 'head_id': u'6138e7242a9a496c8705e7e0c89fab9f',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F4_C18_NEG_MSMS_ACN_50.mzML',
 'name': u'20141211_actinorhodin_agar_2F4_C18_NEG_MSMS_ACN_50.mzML',
 'prev_uid': u'64d5b57190ee4348ac08a37c41376517',
 'sample': None,
 'unique_id': u'6138e7242a9a496c8705e7e0c89fab9f',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:26',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_2F11_1to10dil_C18_NEG_MSMS_ACN_8.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F11_1to10dil_C18_NEG_MSMS_ACN_8.h5',
 'head_id': u'9330fe391a54475dbdb48a09230272c5',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F11_1to10dil_C18_NEG_MSMS_ACN_8.mzML',
 'name': u'20141211_actinorhodin_agar_2F11_1to10dil_C18_NEG_MSMS_ACN_8.mzML',
 'prev_uid': u'8867c0f11cf1486eaa30cb72de151cfb',
 'sample': None,
 'unique_id': u'9330fe391a54475dbdb48a09230272c5',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:26',
 'description': u'20141211_Actinorhodin 20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_30.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_30.h5',
 'head_id': u'94fd1228622c4efabd351b0ea0d060a9',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_30.mzML',
 'name': u'20141211_MeOHBlank_C18_POS_MSMS_ACN_mid_30.mzML',
 'prev_uid': u'b0a25a8e580949cda8feb0674722d244',
 'sample': None,
 'unique_id': u'94fd1228622c4efabd351b0ea0d060a9',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:27',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_2F11_C18_POS_MSMS_ACN_70.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F11_C18_POS_MSMS_ACN_70.h5',
 'head_id': u'9a98b54784214c278e42a748315d249c',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F11_C18_POS_MSMS_ACN_70.mzML',
 'name': u'20141211_actinorhodin_agar_2F11_C18_POS_MSMS_ACN_70.mzML',
 'prev_uid': u'3731f542ac90462daccfbfdb60746cc8',
 'sample': None,
 'unique_id': u'9a98b54784214c278e42a748315d249c',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:25',
 'description': u'20141211_Actinorhodin 20141211_MeOHBlank_C18_POS_MSMS_ACN_pre1_141211150851.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_pre1_141211150851.h5',
 'head_id': u'9db02bd901064dd680e19eb9d43b6fec',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_pre1_141211150851.mzML',
 'name': u'20141211_MeOHBlank_C18_POS_MSMS_ACN_pre1_141211150851.mzML',
 'prev_uid': u'6351ee8209c14ae2919b386378b93e88',
 'sample': None,
 'unique_id': u'9db02bd901064dd680e19eb9d43b6fec',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:25',
 'description': u'20141211_Actinorhodin 20141211_MeOHBlank_C18_POS_MSMS_ACN_pre2.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_pre2.h5',
 'head_id': u'a5e0880732a34926ba01b42f7301b57d',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_MeOHBlank_C18_POS_MSMS_ACN_pre2.mzML',
 'name': u'20141211_MeOHBlank_C18_POS_MSMS_ACN_pre2.mzML',
 'prev_uid': u'd658a46c26a84e639759579dbbfd7072',
 'sample': None,
 'unique_id': u'a5e0880732a34926ba01b42f7301b57d',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:27',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_2F11_C18_NEG_MSMS_ACN_80.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F11_C18_NEG_MSMS_ACN_80.h5',
 'head_id': u'c2a6f0df42614f1ebec53b2f39a714cb',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:20',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F11_C18_NEG_MSMS_ACN_80.mzML',
 'name': u'20141211_actinorhodin_agar_2F11_C18_NEG_MSMS_ACN_80.mzML',
 'prev_uid': u'560db2eee3a44efe91967348a407d5c3',
 'sample': None,
 'unique_id': u'c2a6f0df42614f1ebec53b2f39a714cb',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:25',
 'description': u'20141211_Actinorhodin 20141211_ITSD_C18_FPS_MSMS_ACN_mid_100.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_ITSD_C18_FPS_MSMS_ACN_mid_100.h5',
 'head_id': u'd1d59fd36f9b465096e8c5ef5dd81043',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:21',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_ITSD_C18_FPS_MSMS_ACN_mid_100.mzML',
 'name': u'20141211_ITSD_C18_FPS_MSMS_ACN_mid_100.mzML',
 'prev_uid': u'ca7bff2918ec40c5a17b23dd2f27b129',
 'sample': None,
 'unique_id': u'd1d59fd36f9b465096e8c5ef5dd81043',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:28',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_II_IV_1to10dil_C18_NEG_MSMS_ACN_2.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_1to10dil_C18_NEG_MSMS_ACN_2.h5',
 'head_id': u'd3e77b46e0c74cf78282be8b3347c75d',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:21',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_1to10dil_C18_NEG_MSMS_ACN_2.mzML',
 'name': u'20141211_actinorhodin_agar_II_IV_1to10dil_C18_NEG_MSMS_ACN_2.mzML',
 'prev_uid': u'41eaa637e8af4035898ddfdbeb040f20',
 'sample': None,
 'unique_id': u'd3e77b46e0c74cf78282be8b3347c75d',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:27',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_2F4_1to10dil_C18_POS_MSMS_ACN_4.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F4_1to10dil_C18_POS_MSMS_ACN_4.h5',
 'head_id': u'dabd70fde99247049e7674779917b3da',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:21',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F4_1to10dil_C18_POS_MSMS_ACN_4.mzML',
 'name': u'20141211_actinorhodin_agar_2F4_1to10dil_C18_POS_MSMS_ACN_4.mzML',
 'prev_uid': u'54fe141ea40b4a35bcc26fffdbf5b40f',
 'sample': None,
 'unique_id': u'dabd70fde99247049e7674779917b3da',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:27',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_2F4_1to10dil_C18_NEG_MSMS_ACN_5.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F4_1to10dil_C18_NEG_MSMS_ACN_5.h5',
 'head_id': u'de8d88c81490424283fc844bd92e299e',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-13T09:50:21',
 'method': None,
 'mzml_file': u'/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F4_1to10dil_C18_NEG_MSMS_ACN_5.mzML',
 'name': u'20141211_actinorhodin_agar_2F4_1to10dil_C18_NEG_MSMS_ACN_5.mzML',
 'prev_uid': u'329f424aa81c4f65b6f5ee58a5137d52',
 'sample': None,
 'unique_id': u'de8d88c81490424283fc844bd92e299e',
 'username': u'kblouie'}, {'creation_time': '2015-09-19T21:33:25',
 'description': u'20141211_Actinorhodin 20141211_MeOHBlank_C18_FPS_MSMS_ACN_mid_90.mzML',
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 'sample': None,
 'unique_id': u'ea509f86341d4167b52043ae18bf7c92',
 'username': u'pasteur'}, {'creation_time': '2015-09-19T21:33:27',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_2F4_C18_POS_MSMS_ACN_40.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F4_C18_POS_MSMS_ACN_40.h5',
 'head_id': u'87ac62b2e5b546cbb4bfba061d6effad',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-27T03:41:30',
 'method': None,
 'mzml_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_2F4_C18_POS_MSMS_ACN_40.mzML',
 'name': u'20141211_actinorhodin_agar_2F4_C18_POS_MSMS_ACN_40.mzML',
 'prev_uid': u'origin',
 'sample': None,
 'unique_id': u'87ac62b2e5b546cbb4bfba061d6effad',
 'username': u'pasteur'}, {'creation_time': '2015-09-19T21:33:28',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_II_IV_1to10dil_C18_NEG_MSMS_ACN_2.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_1to10dil_C18_NEG_MSMS_ACN_2.h5',
 'head_id': u'91e44c2076c544a7807fa276266f606d',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-27T03:41:43',
 'method': None,
 'mzml_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_1to10dil_C18_NEG_MSMS_ACN_2.mzML',
 'name': u'20141211_actinorhodin_agar_II_IV_1to10dil_C18_NEG_MSMS_ACN_2.mzML',
 'prev_uid': u'origin',
 'sample': None,
 'unique_id': u'91e44c2076c544a7807fa276266f606d',
 'username': u'pasteur'}, {'creation_time': '2015-09-19T21:33:28',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_II_IV_C18_NEG_MSMS_ACN_20.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_C18_NEG_MSMS_ACN_20.h5',
 'head_id': u'cb37a5f999014d3a9f462f6ba20c469a',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-27T03:41:57',
 'method': None,
 'mzml_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_C18_NEG_MSMS_ACN_20.mzML',
 'name': u'20141211_actinorhodin_agar_II_IV_C18_NEG_MSMS_ACN_20.mzML',
 'prev_uid': u'origin',
 'sample': None,
 'unique_id': u'cb37a5f999014d3a9f462f6ba20c469a',
 'username': u'pasteur'}, {'creation_time': '2015-09-19T21:33:28',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_II_IV_1to10dil_C18_POS_MSMS_ACN_1.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_1to10dil_C18_POS_MSMS_ACN_1.h5',
 'head_id': u'06d7674208dc434793a44295417f8775',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-27T03:42:15',
 'method': None,
 'mzml_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_1to10dil_C18_POS_MSMS_ACN_1.mzML',
 'name': u'20141211_actinorhodin_agar_II_IV_1to10dil_C18_POS_MSMS_ACN_1.mzML',
 'prev_uid': u'origin',
 'sample': None,
 'unique_id': u'06d7674208dc434793a44295417f8775',
 'username': u'pasteur'}, {'creation_time': '2015-09-19T21:33:28',
 'description': u'20141211_Actinorhodin 20141211_actinorhodin_agar_II_IV_C18_POS_MSMS_ACN_10.mzML',
 'experiment': u'20141211_Actinorhodin',
 'hdf5_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_C18_POS_MSMS_ACN_10.h5',
 'head_id': u'5c0ac00eb7e2468b8e079d27c7332956',
 'injection_volume': 0.0,
 'injection_volume_units': 'xx',
 'last_modified': '2015-12-27T03:42:35',
 'method': None,
 'mzml_file': u'/global/project/projectdirs/metatlas/raw_data/kblouie/20141211_Actinorhodin/20141211_actinorhodin_agar_II_IV_C18_POS_MSMS_ACN_10.mzML',
 'name': u'20141211_actinorhodin_agar_II_IV_C18_POS_MSMS_ACN_10.mzML',
 'prev_uid': u'origin',
 'sample': None,
 'unique_id': u'5c0ac00eb7e2468b8e079d27c7332956',
 'username': u'pasteur'}]

















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 [4]:



    


data



    


















    






---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-4-6137cde4893c> in <module>()
----> 1 data

NameError: name 'data' is not defined
















In [5]:



    


#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)



    


















    






[]

















In [6]:



    


#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[6]:








12

















In [9]:



    


print atlas[11]



    


















    






{'compound_identifications': [Compoundidentifications cd052496a1b342b6a89e0f1b231dc531,
                              Compoundidentifications a1d98cb440fc41c8b21b1c2e2a5a8cfe,
                              Compoundidentifications 9ca605a89f5742fca80b31a8ced55ffb,
                              Compoundidentifications 3400dcf9276f4ad887022a8a9ee36a5e,
                              Compoundidentifications f451ad65ffd548c786cc534e86b8628f,
                              Compoundidentifications b03c5d8d056642b897aca6ad717d78fa,
                              Compoundidentifications e57b99686b084969ac9329c6800f53d4,
                              Compoundidentifications 23c6b97708a748868df17d2593c72c1a,
                              Compoundidentifications 790cb02229dd4166b9e525838d418486,
                              Compoundidentifications aa09bfca2312463a9da914b198d0683a,
                              Compoundidentifications 2a9b167811014dae82e8e2b8e56449ca,
                              Compoundidentifications 207d593a65e449b7a6884fc4f1a140c7,
                              Compoundidentifications a33e45c3784140d690841a5fa05c9623,
                              Compoundidentifications 36827e864e12486c8d1d6f08165385b8,
                              Compoundidentifications 707760ae3b794e058b70b1afbf1ea8c8,
                              Compoundidentifications 1c9f22921b414646b96a6f6ab11e947f,
                              Compoundidentifications 6775367d0e73417cbc3a3aa175ed7102,
                              Compoundidentifications 2894b9c281e34b34b7e80c54f8b83cc1,
                              Compoundidentifications c1b7da4514444921876cccf0f276aaa0,
                              Compoundidentifications b94b4451f1094247ad9d7af816f68d50,
                              Compoundidentifications 75fd8a18fcdc4753958ffa58c43cf70a,
                              Compoundidentifications 14fcf40f8ab14bd1b4cd0c97c7fe1ba8,
                              Compoundidentifications b9ed4b391a9d485fbd2cfa43a3186ed5,
                              Compoundidentifications ce131347993e4f738703c54fc9857cb0,
                              Compoundidentifications 4bf122cc4c024708a0e402928a804258,
                              Compoundidentifications 3577b8c5957140dc8499167d972bc4e7,
                              Compoundidentifications 2c6d9cd3a8054e4191fde612c96cc9d8,
                              Compoundidentifications 5989af7b06ab4c15821e1d58598912ab,
                              Compoundidentifications 579595a55c8843679e2a7c176ed4d9c1,
                              Compoundidentifications d31ca68c710f440892b3f584ad3b130e,
                              Compoundidentifications 377d40e6307a4dc8807bb46783cfad06,
                              Compoundidentifications dcd793fdfd78410c88c7af12a20ba95e,
                              Compoundidentifications f31423c14de84d1d8ee4df80cc313166,
                              Compoundidentifications 24809eb30fd8477583d34f7f12762e83,
                              Compoundidentifications 99694f7ee2074029a5b5b17304bf9be6,
                              Compoundidentifications 959a8c8cd0cf4677aae965fb02e3c51d,
                              Compoundidentifications d2aa8038079448afa39dd2f33ab3da54,
                              Compoundidentifications 0612fe37cd734aac90ebcd32e39f1b54,
                              Compoundidentifications 92230c64f7fa433da6ce51d9d4e45566,
                              Compoundidentifications 0f0c9f9d9977414c8096390f3a8c0010,
                              Compoundidentifications 0b3c31cb7bbb49e0b2ab0b05bbabaad3,
                              Compoundidentifications 6caf73679a404ffcbb126b864c10c937,
                              Compoundidentifications 4bc39709ae524959a40bd835eff552e8,
                              Compoundidentifications c8e4090b8cdb41afbd507c6377eea33d,
                              Compoundidentifications e5be642452464d8eb9bac464d2609a08,
                              Compoundidentifications 30d4eb1a0fa14646b62d9d192e7cdb25,
                              Compoundidentifications 29bbaa0222904bf695b993640238ac8e,
                              Compoundidentifications cb4aa7af5f0d4d6483c2925f06119eb1,
                              Compoundidentifications 707f772c8ed44afead3795bc6099f799,
                              Compoundidentifications c73921f1093848eeb6a5b1b03a1cbf98,
                              Compoundidentifications cf824bde19cf4804b9c7c8309d9c8a6b,
                              Compoundidentifications d1e79a428fd14b4ab57db9afae3b85e1,
                              Compoundidentifications 41d25704303b41b586b04f4471f3379c,
                              Compoundidentifications bd6e4a8b40104445ac85d477fc54a5f0,
                              Compoundidentifications a02bf11754e840d793d82e0e79ae9065,
                              Compoundidentifications 59f036b7207942ea963f115cc0aa9ef9,
                              Compoundidentifications 75ceea3690664cd69a3a768d77c8c8b2,
                              Compoundidentifications 3918ae81204942eabf9e81e84b929537,
                              Compoundidentifications af2945f7f76e4d0685ca64c18a8756f1,
                              Compoundidentifications 044b95a6053f46d5a5df3740f046fea3,
                              Compoundidentifications 1a98b84ec43b4b40b4b61d86c07a1cff,
                              Compoundidentifications 38a0a1f88f334bfab73448576c9413c5,
                              Compoundidentifications 16ce2a99f0cd4280ae94edeb312360a3,
                              Compoundidentifications 539a513700204ae4a44e86e47744d0ae,
                              Compoundidentifications f1b9c261991444d4a9c5a3381e6a6e9f,
                              Compoundidentifications c98664fc8ede45859bb783fa30f85955,
                              Compoundidentifications 1c009ade39b84580b244a422e478d725,
                              Compoundidentifications e027cfdcae0041ca982485049c05d562,
                              Compoundidentifications c8100619016c415ba34899ee479d9e0e,
                              Compoundidentifications 4e92455780364f3cad38aeae26701c50,
                              Compoundidentifications f79ca989ef054c51b57f498010894234,
                              Compoundidentifications 69d55977c9bd476a971dda76d8031c7f,
                              Compoundidentifications cd9bd0adf09f41999511b1f0c96a3d64,
                              Compoundidentifications 91880ec0f7f8429aa3ae089b1cce4fec,
                              Compoundidentifications 30479d313aa044d784b1a7c76d7141c9,
                              Compoundidentifications 92d10c2f89804c9c9cdcbd4e501b7cf0,
                              Compoundidentifications 62f5ef5135d44fbeb96257577d1229e1,
                              Compoundidentifications c2a1b8653ae24f53afbdee16f8ad0824,
                              Compoundidentifications a27cd239f0834a2c86af70acc42521d9,
                              Compoundidentifications 312e04f7437a4852a6fe674db5353978,
                              Compoundidentifications 06515277378445eea79e0b00acbd72e9,
                              Compoundidentifications c4520ac972604b65a90c410c87c859f3],
 'creation_time': '2015-11-23T13:40:21',
 'description': u'No description',
 'head_id': u'3b7b4c6aa842431baa6ec6e2b009f21a',
 'last_modified': '2015-11-23T13:40:21',
 '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]

Content source: aitatanit/metatlas

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