def test_heatmap():
data = get_heatmap(fid, 1000)
assert np.allclose(data['arr'].mean(), 3790.08673939)
assert np.allclose(data['mz_bins'][0], 30.004)
assert np.allclose(data['rt_bins'][-1], 19.2667)
data = get_heatmap(fid, 1000, min_mz=50)
assert np.allclose(data['mz_bins'][0], 50.0002)
plot_heatmap(data['arr'], data['rt_bins'], data['mz_bins'])
def test_heatmap():
data = get_heatmap(fid, 1000)
assert np.allclose(data['arr'].mean(), 3790.08673939)
assert np.allclose(data['mz_bins'][0], 30.004)
assert np.allclose(data['rt_bins'][-1], 19.2667)
data = get_heatmap(fid, 1000, min_mz=50)
assert np.allclose(data['mz_bins'][0], 50.0002)
plot_heatmap(data['arr'], data['rt_bins'], data['mz_bins'])
def get_data_for_a_compound(mz_ref, rt_ref, what_to_get, h5file, extra_time):
"""
A helper function to query the various metatlas data selection
commands for a compound defined in an experimental atlas.
Parameters
----------
MZref : a MetAtlas Object for a m/z reference Class
this contains the m/z, m/z tolerance, and tolerance units to slice the m/z dimension
RTref : a MetAtlas Object for a retention time reference Class
this contains the rt min, max, peak, and units to slice the retention time dimension
what_to_get : a list of strings
this contains one or more of [ 'ms1_summary', 'eic', '2dhist', 'msms' ]
h5_file : str
Path to input_file
polarity : int
[0 or 1] for negative or positive ionzation
Returns
-------
"""
#TODO : polarity should be handled in the experiment and not a loose parameter
import numpy as np
from metatlas import h5_query as h5q
import tables
#get a pointer to the hdf5 file
fid = tables.open_file(h5file) #TODO: should be a "with open:"
if mz_ref.detected_polarity == 'positive':
polarity = 1
else:
polarity = 0
mz_theor = mz_ref.mz
if mz_ref.mz_tolerance_units == 'ppm': #convert to ppm
ppm_uncertainty = mz_ref.mz_tolerance
else:
ppm_uncertainty = mz_ref.mz_tolerance / mz_ref.mz * 1e6
# if 'min' in rt_ref.rt_units: #convert to seconds
# rt_min = rt_ref.rt_min / 60
# rt_max = rt_ref.rt_max / 60
# else:
rt_min = rt_ref.rt_min
rt_max = rt_ref.rt_max
mz_min = mz_theor - mz_theor * ppm_uncertainty / 1e6
mz_max = mz_theor + mz_theor * ppm_uncertainty / 1e6
return_data = {}
if 'ms1_summary' in what_to_get:
#Get Summary Data
#First get MS1 Raw Data
ms_level = 1
return_data['ms1_summary'] = {}
try:
ms1_data = h5q.get_data(fid,
ms_level=1,
polarity=polarity,
min_mz=mz_min,
max_mz=mz_max,
min_rt=rt_min,
max_rt=rt_max)
return_data['ms1_summary']['polarity'] = polarity
return_data['ms1_summary']['mz_centroid'] = np.sum(
np.multiply(ms1_data['i'], ms1_data['mz'])) / np.sum(
ms1_data['i'])
return_data['ms1_summary']['rt_centroid'] = np.sum(
np.multiply(ms1_data['i'], ms1_data['rt'])) / np.sum(
ms1_data['i'])
idx = np.argmax(ms1_data['i'])
return_data['ms1_summary']['mz_peak'] = ms1_data['mz'][idx]
return_data['ms1_summary']['rt_peak'] = ms1_data['rt'][idx]
return_data['ms1_summary']['peak_height'] = ms1_data['i'][idx]
return_data['ms1_summary']['peak_area'] = np.sum(ms1_data['i'])
except:
return_data['ms1_summary']['polarity'] = []
return_data['ms1_summary']['mz_centroid'] = []
return_data['ms1_summary']['rt_centroid'] = []
return_data['ms1_summary']['mz_peak'] = []
return_data['ms1_summary']['rt_peak'] = []
return_data['ms1_summary']['peak_height'] = []
return_data['ms1_summary']['peak_area'] = []
if 'eic' in what_to_get:
#Get Extracted Ion Chromatogram
# TODO : If a person calls for summary, then they will already have the MS1 raw data
return_data['eic'] = {}
try:
rt, intensity = h5q.get_chromatogram(fid,
mz_min,
mz_max,
ms_level=ms_level,
polarity=polarity,
min_rt=rt_min - extra_time,
max_rt=rt_max + extra_time)
return_data['eic']['rt'] = rt
return_data['eic']['intensity'] = intensity
return_data['eic']['polarity'] = polarity
except:
return_data['eic']['rt'] = []
return_data['eic']['intensity'] = []
return_data['eic']['polarity'] = []
if '2dhist' in what_to_get:
#Get 2D histogram of intensity values in m/z and retention time
mzEdges = np.logspace(np.log10(100), np.log10(1000), 10000)
# mzEdges = np.linspace(mz_theor - 3, mz_theor + 30,100) #TODO : number of mz bins should be an optional parameter
rtEdges = np.linspace(
rt_min, rt_max, 100
) #TODO : number of rt bins should be an optional parameter. When not provided, it shoulddefauly to unique bins
ms_level = 1 #TODO : ms_level should be a parameter
return_data['2dhist'] = {}
return_data['2dhist'] = h5q.get_heatmap(fid, mzEdges, rtEdges,
ms_level, polarity)
return_data['2dhist']['polarity'] = polarity
if 'msms' in what_to_get:
#Get Fragmentation Data
ms_level = 2
return_data['msms'] = {}
try:
fragmentation_data = h5q.get_data(
fid,
ms_level=ms_level,
polarity=polarity,
min_mz=0,
max_mz=mz_theor + 2, #TODO : this needs to be a parameter
min_rt=rt_min,
max_rt=rt_max,
min_precursor_MZ=mz_min - 0.015,
max_precursor_MZ=mz_max + 0.015
) #Add the 0.01 because Thermo doesn't store accurate precursor m/z
# min_precursor_intensity=0, #TODO : this needs to be a parameter
# max_precursor_intensity=0,#TODO : this needs to be a parameter
# min_collision_energy=0,#TODO : this needs to be a parameter
# max_collision_energy=0)#TODO : this needs to be a parameter
# prt,pmz = get_unique_scan_data(fragmentation_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)
# return_data['msms']['data'] = organize_msms_scan_data(fragmentation_data,list_of_prt,list_or_pmz)
return_data['msms'][
'most_intense_precursor'] = retrieve_most_intense_msms_scan(
fragmentation_data)
return_data['msms']['data'] = fragmentation_data
return_data['msms']['polarity'] = polarity
except:
return_data['msms']['most_intense_precursor'] = []
return_data['msms']['data'] = []
return_data['msms']['polarity'] = []
fid.close() #close the file
return return_data
def get_data_for_a_compound(mz_ref,rt_ref,what_to_get,h5file,extra_time):
"""
A helper function to query the various metatlas data selection
commands for a compound defined in an experimental atlas.
Parameters
----------
MZref : a MetAtlas Object for a m/z reference Class
this contains the m/z, m/z tolerance, and tolerance units to slice the m/z dimension
RTref : a MetAtlas Object for a retention time reference Class
this contains the rt min, max, peak, and units to slice the retention time dimension
what_to_get : a list of strings
this contains one or more of [ 'ms1_summary', 'eic', '2dhist', 'msms' ]
h5_file : str
Path to input_file
polarity : int
[0 or 1] for negative or positive ionzation
Returns
-------
"""
#TODO : polarity should be handled in the experiment and not a loose parameter
import numpy as np
from metatlas import h5_query as h5q
import tables
#get a pointer to the hdf5 file
fid = tables.open_file(h5file) #TODO: should be a "with open:"
if mz_ref.detected_polarity == 'positive':
polarity = 1
else:
polarity = 0
mz_theor = mz_ref.mz
if mz_ref.mz_tolerance_units == 'ppm': #convert to ppm
ppm_uncertainty = mz_ref.mz_tolerance
else:
ppm_uncertainty = mz_ref.mz_tolerance / mz_ref.mz * 1e6
# if 'min' in rt_ref.rt_units: #convert to seconds
# rt_min = rt_ref.rt_min / 60
# rt_max = rt_ref.rt_max / 60
# else:
rt_min = rt_ref.rt_min
rt_max = rt_ref.rt_max
mz_min = mz_theor - mz_theor * ppm_uncertainty / 1e6
mz_max = mz_theor + mz_theor * ppm_uncertainty / 1e6
return_data = {}
if 'ms1_summary' in what_to_get:
#Get Summary Data
#First get MS1 Raw Data
ms_level=1
return_data['ms1_summary'] = {}
try:
ms1_data = h5q.get_data(fid,
ms_level=1,
polarity=polarity,
min_mz=mz_min,
max_mz=mz_max,
min_rt=rt_min,
max_rt=rt_max)
return_data['ms1_summary']['polarity'] = polarity
return_data['ms1_summary']['mz_centroid'] = np.sum(np.multiply(ms1_data['i'],ms1_data['mz'])) / np.sum(ms1_data['i'])
return_data['ms1_summary']['rt_centroid'] = np.sum(np.multiply(ms1_data['i'],ms1_data['rt'])) / np.sum(ms1_data['i'])
idx = np.argmax(ms1_data['i'])
return_data['ms1_summary']['mz_peak'] = ms1_data['mz'][idx]
return_data['ms1_summary']['rt_peak'] = ms1_data['rt'][idx]
return_data['ms1_summary']['peak_height'] = ms1_data['i'][idx]
return_data['ms1_summary']['peak_area'] = np.sum(ms1_data['i'])
except:
return_data['ms1_summary']['polarity'] = []
return_data['ms1_summary']['mz_centroid'] = []
return_data['ms1_summary']['rt_centroid'] = []
return_data['ms1_summary']['mz_peak'] = []
return_data['ms1_summary']['rt_peak'] = []
return_data['ms1_summary']['peak_height'] = []
return_data['ms1_summary']['peak_area'] = []
if 'eic' in what_to_get:
#Get Extracted Ion Chromatogram
# TODO : If a person calls for summary, then they will already have the MS1 raw data
return_data['eic'] = {}
try:
rt,intensity = h5q.get_chromatogram(fid, mz_min, mz_max, ms_level=ms_level, polarity=polarity, min_rt = rt_min - extra_time, max_rt = rt_max + extra_time)
return_data['eic']['rt'] = rt
return_data['eic']['intensity'] = intensity
return_data['eic']['polarity'] = polarity
except:
return_data['eic']['rt'] = []
return_data['eic']['intensity'] = []
return_data['eic']['polarity'] = []
if '2dhist' in what_to_get:
#Get 2D histogram of intensity values in m/z and retention time
mzEdges = np.logspace(np.log10(100),np.log10(1000),10000)
# mzEdges = np.linspace(mz_theor - 3, mz_theor + 30,100) #TODO : number of mz bins should be an optional parameter
rtEdges = np.linspace(rt_min,rt_max,100) #TODO : number of rt bins should be an optional parameter. When not provided, it shoulddefauly to unique bins
ms_level = 1 #TODO : ms_level should be a parameter
return_data['2dhist'] = {}
return_data['2dhist'] = h5q.get_heatmap(fid,mzEdges,rtEdges,ms_level,polarity)
return_data['2dhist']['polarity'] = polarity
if 'msms' in what_to_get:
#Get Fragmentation Data
ms_level=2
return_data['msms'] = {}
try:
fragmentation_data = h5q.get_data(fid,
ms_level=ms_level,
polarity=polarity,
min_mz=0,
max_mz=mz_theor+2,#TODO : this needs to be a parameter
min_rt=rt_min,
max_rt=rt_max,
min_precursor_MZ=mz_min - 0.015,
max_precursor_MZ=mz_max + 0.015) #Add the 0.01 because Thermo doesn't store accurate precursor m/z
# min_precursor_intensity=0, #TODO : this needs to be a parameter
# max_precursor_intensity=0,#TODO : this needs to be a parameter
# min_collision_energy=0,#TODO : this needs to be a parameter
# max_collision_energy=0)#TODO : this needs to be a parameter
# prt,pmz = get_unique_scan_data(fragmentation_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)
# return_data['msms']['data'] = organize_msms_scan_data(fragmentation_data,list_of_prt,list_or_pmz)
return_data['msms']['most_intense_precursor'] = retrieve_most_intense_msms_scan(fragmentation_data)
return_data['msms']['data'] = fragmentation_data
return_data['msms']['polarity'] = polarity
except:
return_data['msms']['most_intense_precursor'] = []
return_data['msms']['data'] = []
return_data['msms']['polarity'] = []
fid.close() #close the file
return return_data
