def apply_image_processing(config, ion_datacube):
"""
Function to apply pre-defined image processing methods to ion_datacube
#todo: expose parameters in config
:param ion_datacube:
object from pyImagingMSpec.ion_datacube already containing images
:return:
ion_datacube is updated in place.
None returned
"""
from pyImagingMSpec import smoothing
#todo hot_spot_removal shouldn't be separately coded - should be within smooth_methods of config and iterated over
# every method in smoothing should accept (im,**args)
q = config['image_generation']['q']
if q > 0:
for xic in ion_datacube.xic:
smoothing.hot_spot_removal(xic, q) #updated in place
smooth_method = config['image_generation']['smooth']
smooth_params = config['image_generation']['smooth_params']
if not smooth_method == '':
for ii in range(ion_datacube.n_im):
im = ion_datacube.xic_to_image(ii)
#todo: for method in smoothing_methods:
methodToCall = getattr(smoothing,smooth_method)
im_s = methodToCall(im,**smooth_params)
ion_datacube.xic[ii] = ion_datacube.image_to_xic(im_s)
return None
def compute(iso_images_sparse, sf_ints):
np.seterr(invalid='ignore') # to ignore division by zero warnings
diff = len(sf_ints) - len(iso_images_sparse)
iso_imgs = [
empty_matrix if img is None else img.toarray()
for img in iso_images_sparse + [None] * diff
]
iso_imgs_flat = [img.flat[:][sample_area_mask] for img in iso_imgs]
if img_gen_conf['do_preprocessing']:
for img in iso_imgs_flat:
smoothing.hot_spot_removal(img)
m = ImgMetrics(metrics)
if len(iso_imgs) > 0:
m.map['spectral'] = isotope_pattern_match(iso_imgs_flat, sf_ints)
m.map['spatial'] = isotope_image_correlation(iso_imgs_flat,
weights=sf_ints[1:])
moc = measure_of_chaos(iso_imgs[0], img_gen_conf['nlevels'])
m.map['chaos'] = 0 if np.isclose(moc, 1.0) else moc
m.map['total_iso_ints'] = [img.sum() for img in iso_imgs]
m.map['min_iso_ints'] = [img.min() for img in iso_imgs]
m.map['max_iso_ints'] = [img.max() for img in iso_imgs]
return m.to_tuple()
def compute(iso_images_sparse, sf_ints):
diff = len(sf_ints) - len(iso_images_sparse)
iso_imgs = [empty_matrix if img is None else img.toarray()
for img in iso_images_sparse + [None] * diff]
iso_imgs_flat = [img.flat[:][sample_area_mask] for img in iso_imgs]
if img_gen_conf['do_preprocessing']:
for img in iso_imgs_flat:
smoothing.hot_spot_removal(img)
measures = ImgMeasures(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0)
if len(iso_imgs) > 0:
measures.pattern_match = isotope_pattern_match(iso_imgs_flat, sf_ints)
measures.image_corr = isotope_image_correlation(iso_imgs_flat, weights=sf_ints[1:])
moc = measure_of_chaos(iso_imgs[0], img_gen_conf['nlevels'])
measures.chaos = 0 if np.isclose(moc, 1.0) else moc
measures.image_corr_01, measures.image_corr_02, measures.image_corr_03, measures.image_corr_12, \
measures.image_corr_13, measures.image_corr_23 = isotope_image_correlation_sd(iso_imgs_flat)
measures.snr = snr_img(iso_imgs[0])
measures.percent_0s = percent_zero(iso_imgs[0])
measures.peak_int_diff_0, measures.peak_int_diff_1, measures.peak_int_diff_2, measures.peak_int_diff_3 = spectra_int_diff(iso_imgs_flat, sf_ints)
measures.quart_1, measures.quart_2, measures.quart_3 = quartile_pxl(iso_imgs[0])
measures.ratio_peak_01, measures.ratio_peak_02, measures.ratio_peak_03, measures.ratio_peak_12, \
measures.ratio_peak_13, measures.ratio_peak_23 = ratio_peaks(iso_imgs_flat)
measures.percentile_10, measures.percentile_20, measures.percentile_30, measures.percentile_40, \
measures.percentile_50, measures.percentile_60, measures.percentile_70, measures.percentile_80, \
measures.percentile_90 = decile_pxl(iso_imgs[0])
return measures.to_tuple()
def compute(iso_images_sparse, sf_ints):
diff = len(sf_ints) - len(iso_images_sparse)
iso_imgs = [
empty_matrix if img is None else img.toarray()
for img in iso_images_sparse + [None] * diff
]
iso_imgs_flat = [img.flat[:][sample_area_mask] for img in iso_imgs]
if img_gen_conf['do_preprocessing']:
for img in iso_imgs_flat:
smoothing.hot_spot_removal(img)
measures = ImgMeasures(0, 0, 0)
if len(iso_imgs) > 0:
measures.pattern_match = isotope_pattern_match(
iso_imgs_flat, sf_ints)
measures.image_corr = isotope_image_correlation(
iso_imgs_flat, weights=sf_ints[1:])
moc = measure_of_chaos(iso_imgs[0], img_gen_conf['nlevels'])
measures.chaos = 0 if np.isclose(moc, 1.0) else moc
return measures.to_tuple()
def compute(iso_images_sparse, sf_ints):
np.seterr(invalid='ignore') # to ignore division by zero warnings
diff = len(sf_ints) - len(iso_images_sparse)
iso_imgs = [empty_matrix if img is None else img.toarray()
for img in iso_images_sparse + [None] * diff]
iso_imgs_flat = [img.flat[:][sample_area_mask] for img in iso_imgs]
if img_gen_conf['do_preprocessing']:
for img in iso_imgs_flat:
smoothing.hot_spot_removal(img)
m = ImgMetrics(metrics)
if len(iso_imgs) > 0:
m.map['spectral'] = isotope_pattern_match(iso_imgs_flat, sf_ints)
m.map['spatial'] = isotope_image_correlation(iso_imgs_flat, weights=sf_ints[1:])
moc = measure_of_chaos(iso_imgs[0], img_gen_conf['nlevels'])
m.map['chaos'] = 0 if np.isclose(moc, 1.0) else moc
m.map['total_iso_ints'] = [img.sum() for img in iso_imgs]
m.map['min_iso_ints'] = [img.min() for img in iso_imgs]
m.map['max_iso_ints'] = [img.max() for img in iso_imgs]
return m.to_tuple()
def compute(iso_images_sparse, formula_ints):
np.seterr(invalid='ignore') # to ignore division by zero warnings
m = METRICS.copy()
if len(iso_images_sparse) > 0:
iso_imgs = [
img.toarray() if img is not None else empty_matrix
for img in iso_images_sparse
]
iso_imgs_flat = [
img.flatten()[sample_area_mask_flat] for img in iso_imgs
]
iso_imgs_flat = iso_imgs_flat[:len(formula_ints)]
if img_gen_config.get('do_preprocessing', False):
for img in iso_imgs_flat:
smoothing.hot_spot_removal(img)
m['spectral'] = isotope_pattern_match(iso_imgs_flat, formula_ints)
if m['spectral'] > 0:
m['spatial'] = isotope_image_correlation(
iso_imgs_flat, weights=formula_ints[1:])
if m['spatial'] > 0:
moc = measure_of_chaos(iso_imgs[0],
img_gen_config.get('nlevels', 30))
m['chaos'] = 0 if np.isclose(moc, 1.0) else moc
if m['chaos'] > 0:
m['msm'] = m['chaos'] * m['spatial'] * m['spectral']
m['total_iso_ints'] = [img.sum() for img in iso_imgs]
m['min_iso_ints'] = [img.min() for img in iso_imgs]
m['max_iso_ints'] = [img.max() for img in iso_imgs]
metrics = OrderedDict((k, replace_nan(v)) for k, v in m.items())
return metrics
def plot_images(ion_datacube,iso_spect,iso_max,q_val=99,c_map='hot'):
import numpy as np
import matplotlib.pyplot as plt
from pyImagingMSpec.image_measures import measure_of_chaos, isotope_image_correlation, isotope_pattern_match
from pyImagingMSpec import smoothing as im_smoothing
for ii in range(0, iso_max):
# hot-spot removal
xic = ion_datacube.xic[ii]
im_smoothing.hot_spot_removal(xic, q_val) # updated in place
im = ion_datacube.xic_to_image(ii)
#im = im_smoothing.median(im, size=3)
ion_datacube.xic[ii] = ion_datacube.image_to_xic(im)
measure_value_score = measure_of_chaos(
ion_datacube.xic_to_image(0), 30)
# 3. Score correlation with monoiso
if len(iso_spect[1]) > 1:
iso_correlation_score = isotope_image_correlation(
ion_datacube.xic, weights=iso_spect[1][1:])
else: # only one isotope peak, so correlation doesn't make sense
iso_correlation_score = 1
iso_ratio_score = isotope_pattern_match(ion_datacube.xic,iso_spect[1])
msm_score = measure_value_score*iso_correlation_score*iso_ratio_score
ax = [ plt.subplot2grid((2, 4), (0, 0)),
plt.subplot2grid((2, 4), (0, 1)),
plt.subplot2grid((2, 4), (0, 2)),
plt.subplot2grid((2, 4), (0, 3)),
plt.subplot2grid((2, 4), (1, 0), colspan=4, rowspan=1)
]
for a in ax:
a.cla()
# plot images
for ii in range(0,iso_max):
im = ion_datacube.xic_to_image(ii)
ax[ii].imshow(im,cmap=c_map,interpolation='nearest')
ax[ii].set_title('m/z: {:3.4f}'.format(iso_spect[0][ii]))
ax[ii].set_xticks([],[])
ax[ii].set_yticks([],[])
# plot spectrum
notnull=ion_datacube.xic_to_image(0)>0
data_spect = [np.sum(ion_datacube.xic_to_image(ii)[notnull]) for ii in range(0,iso_max)]
data_spect = data_spect / np.linalg.norm(data_spect)
iso_spect[1] = iso_spect[1]/np.linalg.norm(iso_spect[1])
markerline, stemlines, baseline = ax[4].stem(iso_spect[0][0:iso_max],iso_spect[1][0:iso_max],'g')
plt.title("moc: {:3.5f} spat: {:3.5f} spec: {:3.5f} msm: {:3.5f}".format(measure_value_score,iso_correlation_score,iso_ratio_score,msm_score))
plt.setp(stemlines, linewidth=2, color='g') # set stems colors
plt.setp(markerline, 'markerfacecolor', 'g','markeredgecolor','g') # make points
markerline, stemlines, baseline = ax[4].stem(iso_spect[0][0:iso_max],data_spect,'r')
plt.setp(stemlines, linewidth=2, color='r') # set stems colors
plt.setp(markerline, 'markerfacecolor', 'r','markeredgecolor','r') # make points
#plot proxy artist
proxies=[]
h, = plt.plot(iso_spect[0][0],[0],'-g')
proxies.append(h)
h, = plt.plot(iso_spect[0][0],[0],'-r')
proxies.append(h)
ax[4].legend(proxies,('predicted pattern','data pattern'), numpoints=1)
return ax
def plot_images(ion_datacube, iso_spect, iso_max, q_val=99, c_map='hot'):
import numpy as np
import matplotlib.pyplot as plt
from pyImagingMSpec.image_measures import measure_of_chaos, isotope_image_correlation, isotope_pattern_match
from pyImagingMSpec import smoothing as im_smoothing
for ii in range(0, iso_max):
# hot-spot removal
xic = ion_datacube.xic[ii]
im_smoothing.hot_spot_removal(xic, q_val) # updated in place
im = ion_datacube.xic_to_image(ii)
#im = im_smoothing.median(im, size=3)
ion_datacube.xic[ii] = ion_datacube.image_to_xic(im)
measure_value_score = measure_of_chaos(ion_datacube.xic_to_image(0), 30)
# 3. Score correlation with monoiso
if len(iso_spect[1]) > 1:
iso_correlation_score = isotope_image_correlation(
ion_datacube.xic, weights=iso_spect[1][1:])
else: # only one isotope peak, so correlation doesn't make sense
iso_correlation_score = 1
iso_ratio_score = isotope_pattern_match(ion_datacube.xic, iso_spect[1])
msm_score = measure_value_score * iso_correlation_score * iso_ratio_score
ax = [
plt.subplot2grid((2, 4), (0, 0)),
plt.subplot2grid((2, 4), (0, 1)),
plt.subplot2grid((2, 4), (0, 2)),
plt.subplot2grid((2, 4), (0, 3)),
plt.subplot2grid((2, 4), (1, 0), colspan=4, rowspan=1)
]
for a in ax:
a.cla()
# plot images
for ii in range(0, iso_max):
im = ion_datacube.xic_to_image(ii)
ax[ii].imshow(im, cmap=c_map, interpolation='nearest')
ax[ii].set_title('m/z: {:3.4f}'.format(iso_spect[0][ii]))
ax[ii].set_xticks([], [])
ax[ii].set_yticks([], [])
# plot spectrum
notnull = ion_datacube.xic_to_image(0) > 0
data_spect = [
np.sum(ion_datacube.xic_to_image(ii)[notnull])
for ii in range(0, iso_max)
]
data_spect = data_spect / np.linalg.norm(data_spect)
iso_spect[1] = iso_spect[1] / np.linalg.norm(iso_spect[1])
markerline, stemlines, baseline = ax[4].stem(iso_spect[0][0:iso_max],
iso_spect[1][0:iso_max], 'g')
plt.title("moc: {:3.5f} spat: {:3.5f} spec: {:3.5f} msm: {:3.5f}".format(
measure_value_score, iso_correlation_score, iso_ratio_score,
msm_score))
plt.setp(stemlines, linewidth=2, color='g') # set stems colors
plt.setp(markerline, 'markerfacecolor', 'g', 'markeredgecolor',
'g') # make points
markerline, stemlines, baseline = ax[4].stem(iso_spect[0][0:iso_max],
data_spect, 'r')
plt.setp(stemlines, linewidth=2, color='r') # set stems colors
plt.setp(markerline, 'markerfacecolor', 'r', 'markeredgecolor',
'r') # make points
#plot proxy artist
proxies = []
h, = plt.plot(iso_spect[0][0], [0], '-g')
proxies.append(h)
h, = plt.plot(iso_spect[0][0], [0], '-r')
proxies.append(h)
ax[4].legend(proxies, ('predicted pattern', 'data pattern'), numpoints=1)
return ax