def mzImages(filename_in_list,save_dir):
import sys
sys.path.append('C:\\Users\\Luca Rappez\\Desktop\\python_codebase\\')
from pyMS.centroid_detection import gradient
from pyIMS.hdf5.inMemoryIMS_hdf5 import inMemoryIMS_hdf5
from pyIMS.image_measures import level_sets_measure
import matplotlib.pyplot as plt
import numpy as np
#%matplotlib inline
import bokeh as bk
from bokeh.plotting import output_notebook
output_notebook()
ppm = 0.75
if len(list)>2:
raise ValueError('list should only have two entries')
c_thresh=0.05
mz_list_all=[]
for filename_in in filename_in_list:
# Parse data
IMS_dataset = inMemoryIMS_hdf5(filename_in)
hist_axis,freq_spec = IMS_dataset.generate_summary_spectrum(summary_type='freq',ppm=ppm/2)
# Centroid detection of frequency spectrum
mz_list,count_list,idx_list = gradient(np.asarray(hist_axis),np.asarray(freq_spec),weighted_bins=2)
mz_list=[m for m,c in zip(mz_list,count_list) if c>c_thresh]
mz_list_all.append(mz_list)
im_list = [m for m in mz_list_all[0] if any([1e6*(m-mm)/m<ppm for mm in mz_list_all[1]]) ]
"""# Calcualte MoC for images of all peaks
nlevels=30
im_list={}
for ii, c in enumerate(count_list):
ion_image = IMS_dataset.get_ion_image(np.asarray([mz_list[ii],]),ppm)
im = ion_image.xic_to_image(0)
m,im_moc,levels,nobjs = level_sets_measure.measure_of_chaos(im,nlevels,interp='median') #just output measure value
m=1-m
im_list[mz_list[ii]]={'image':im,'moc':m,'freq':c}
"""
from pySpatialMetabolomics.tools import colourmaps
c_map = colourmaps.get_colormap('grey')#if black images: open->save->rerun
c_pal=[[int(255*cc) for cc in c_map(c)] for c in range(0,254)]
# Export all images
import png as pypng
for filename_in in filename_in_list:
# Parse data
IMS_dataset = inMemoryIMS_hdf5(filename_in)
for mz in im_list:
with open('{}/{}_{}.png'.format(save_dir,mz,filename_in),'wb') as f_out:
im_out = IMS_dataset.get_ion_image([mz,],[ppm,]).xic_to_image(0)
im_out = 254*im_out/np.max(im_out)
w,h = np.shape(im_out)
w = pypng.Writer(h, w, palette=c_pal, bitdepth=8)
w.write(f_out,im_out)
def load_data(self):
from pyIMS.hdf5.inMemoryIMS_hdf5 import inMemoryIMS_hdf5
self.IMS_dataset = inMemoryIMS_hdf5(self.data_file)
self.coords = self.IMS_dataset.coords - 1
self._calculate_dimensions()
self.pixel_indices = self.IMS_dataset.cube_pixel_indices
def run_search(config):
### Runs the main pipeline
# Get sum formula and predicted m/z peaks for molecules in database
sum_formulae,adducts,mz_list = generate_isotope_patterns(config)
# Parse dataset
from pyIMS.hdf5.inMemoryIMS_hdf5 import inMemoryIMS_hdf5
from pyIMS.image_measures import level_sets_measure,isotope_image_correlation,isotope_pattern_match
IMS_dataset=inMemoryIMS_hdf5(config['file_inputs']['data_file'])
ppm = config['image_generation']['ppm'] #parts per million - a measure of how accuracte the mass spectrometer is
nlevels = config['image_generation']['nlevels'] # parameter for measure of chaos
q=config['image_generation']['q']
measure_value_score={}
iso_correlation_score = {}
iso_ratio_score = {}
for sum_formula in sum_formulae:
for adduct in adducts:
# 1. Geneate ion images
ion_datacube = IMS_dataset.get_ion_image(mz_list[sum_formula][adduct][0],ppm) #for each spectrum, sum the intensity of all peaks within tol of mz_list
ion_datacube.xic=hot_spot_removal(ion_datacube.xic,q)
# 2. Spatial Chaos
if not sum_formula in measure_value_score:
measure_value_score[sum_formula] = {}
measure_value_score[sum_formula][adduct] = 1-level_sets_measure.measure_of_chaos(ion_datacube.xic_to_image(0),nlevels,interp=False)[0]
if measure_value_score[sum_formula][adduct] == 1:
measure_value_score[sum_formula][adduct] = 0
# only compare pixels with values in the monoisotopic (otherwise high correlation for large empty areas)
notnull_monoiso = ion_datacube.xic[0] > 0
#for xic in ion_datacube.xic:
# xic = xic[notnull_monoiso]
# 3. Score correlation with monoiso
if not sum_formula in iso_correlation_score:
iso_correlation_score[sum_formula] = {}
if len(mz_list[sum_formula][adduct][1]) > 1:
iso_correlation_score[sum_formula][adduct] = isotope_image_correlation.isotope_image_correlation(ion_datacube.xic,weights=mz_list[sum_formula][adduct][1][1:])
else: # only one isotope peak, so correlation doesn't make sense
iso_correlation_score[sum_formula][adduct] = 1
# 4. Score isotope ratio
if not sum_formula in iso_ratio_score:
iso_ratio_score[sum_formula] = {}
iso_ratio_score[sum_formula][adduct] = isotope_pattern_match.isotope_pattern_match(ion_datacube.xic,mz_list[sum_formula][adduct][1])
return measure_value_score,iso_correlation_score,iso_ratio_score
def mzImages(filename_in,save_dir):
import sys
sys.path.append('C:\\Users\\Luca Rappez\\Desktop\\python_codebase\\')
from pyMS.centroid_detection import gradient
from pyIMS.hdf5.inMemoryIMS_hdf5 import inMemoryIMS_hdf5
from pyIMS.image_measures import level_sets_measure
import matplotlib.pyplot as plt
import numpy as np
#%matplotlib inline
import bokeh as bk
from bokeh.plotting import output_notebook
output_notebook()
print 'step1'
#filename_in = '//psi.embl.de/alexandr/shared/Luca/20150825_CoCulture_Candida_Ecoli/20150821_ADP_LR_colony250K12_DHBsub_260x280_50um.hdf5' #using a temporary hdf5 based format
#save_dir= '//psi.embl.de/alexandr/shared/Luca/20150825_CoCulture_Candida_Ecoli/20150821_ADP_LR_colony250K12_DHBsub_260x280_50um_figures'
# Parse data
IMS_dataset = inMemoryIMS_hdf5(filename_in)
print 'In memory'
ppm = 1.5
# Generate mean spectrum
#hist_axis,mean_spec =IMS_dataset.generate_summary_spectrum(summary_type='mean')
hist_axis,freq_spec = IMS_dataset.generate_summary_spectrum(summary_type='freq',ppm=ppm/2)
#p1 = bk.plotting.figure()
#p1.line(hist_axis,mean_spec/np.max(mean_spec),color='red')
#p1.line(hist_axis,freq_spec/np.max(freq_spec),color='orange')
#bk.plotting.show(p1)
print len(hist_axis)
#plt.figure(figsize=(20,10))
#plt.plot(hist_axis,freq_spec)
#plt.show()
# Centroid detection of frequency spectrum
mz_list,count_list,idx_list = gradient(np.asarray(hist_axis),np.asarray(freq_spec),weighted_bins=2)
c_thresh=0.05
moc_thresh=0.99
print np.sum(count_list>c_thresh)
# Calcualte MoC for images of all peaks
nlevels=30
im_list={}
for ii, c in enumerate(count_list):
#print ii
#print c
if c>c_thresh:
ion_image = IMS_dataset.get_ion_image(np.asarray([mz_list[ii],]),ppm)
im = ion_image.xic_to_image(0)
m,im_moc,levels,nobjs = level_sets_measure.measure_of_chaos(im,nlevels,interp='median') #just output measure value
m=1-m
im_list[mz_list[ii]]={'image':im,'moc':m,'freq':c}
from pySpatialMetabolomics.tools import colourmaps
c_map = colourmaps.get_colormap('grey')#if black images: open->save->rerun
c_pal=[[int(255*cc) for cc in c_map(c)] for c in range(0,254)]
# Export all images
import png as pypng
for mz in im_list:
if im_list[mz]['moc']>moc_thresh:
with open('{}/{}_{}.png'.format(save_dir,mz,im_list[mz]['moc']),'wb') as f_out:
im_out = im_list[mz]['image']
im_out = 254*im_out/np.max(im_out)
w,h = np.shape(im_out)
w = pypng.Writer(h, w, palette=c_pal, bitdepth=8)
w.write(f_out,im_out)
#im_out = im_list[mz]['image']
mz=333.334188269
ion_image = IMS_dataset.get_ion_image(np.asarray([mz,]),ppm)
im_out=ion_image.xic_to_image(0)
m,im_moc,levels,nobjs = level_sets_measure.measure_of_chaos(im,nlevels,interp='') #just output measure value
print 1-m
im_out = 254.*im_out/np.max(im_out)
print mz
#print im_list[mz]['moc']
#plt.figure()
#plt.imshow(im_moc)
#plt.show()
def load_data(self):
self.IMS_dataset = inMemoryIMS_hdf5(self.data_file)
self.coords = self.IMS_dataset.coords
self._calculate_dimensions()
