def __init__(self, config):
self.config = config
self.q = config['image_generation']['q']
self.ppm = config['image_generation']['ppm'] #parts per million - a measure of how accurate the mass spectrometer is
self.nlevels = config['image_generation']['nlevels'] # parameter for measure of chaos
self.data_file = config['file_inputs']['data_file']
self.measure_value_score = {}
self.iso_correlation_score = {}
self.iso_ratio_score = {}
self.monotone_pattern_score = {}
self.intensity = {} # total image intensity
self.chunk_size = max(1, 600 / len(config['isotope_generation']['adducts']))
from colourmaps import viridis_colormap
self.cmap = viridis_colormap()
self.measure_tol = config['results_thresholds']['measure_tol']
self.iso_corr_tol = config['results_thresholds']['iso_corr_tol']
self.iso_ratio_tol = config['results_thresholds']['iso_ratio_tol']
return get_datacube(self.paths[dataset], mzs, tol)
app = ImageWebserver()
@app.route('/', method='GET')
def show_form():
return bottle.template('show_images', hs_removal=True, selected_dataset=app.paths.iterkeys().next(),
isotope_patterns={}, formula="", selected_adduct='H', pretty_formula="", tol=5,
resolution=100000, npeaks=4, datasets=app.paths.keys())
import io
import os
import numpy as np
from matplotlib.colors import Normalize
cmap = viridis_colormap()
@app.route("/show_image/<dataset>/<mz>/<tol>")
def generate_image(dataset, mz, tol):
mz, tol = float(mz), float(tol)
img = app.get_ion_image(dataset, mz, tol)
if img.shape[0] > img.shape[1]:
img = img.T
buf = io.BytesIO()
mask = img >= 0
if bottle.request.query.remove_hotspots:
pc = np.percentile(img[mask], 99)
img[img > pc] = pc
values = img[mask]
norm = Normalize(vmin=np.min(values), vmax=np.max(values))
colorized_img = np.zeros((img.shape[0], img.shape[1], 4))
import matplotlib
matplotlib.use('Agg')
import h5py
import numpy as np
import logging
import matplotlib.pyplot as plt
from matplotlib import gridspec
from colourmaps import viridis_colormap
viridis = viridis_colormap()
import os
from pyMS.pyisocalc import pyisocalc
from pyIMS.inMemoryIMS import inMemoryIMS
from pyIMS.image_measures.isotope_image_correlation import *
from pyimzml.ImzMLParser import ImzMLParser
def read_mean_spectrum(raw_data):
if isinstance(raw_data, h5py.File):
return read_mean_spectrum_h5(raw_data)
elif isinstance(raw_data, str) and raw_data.endswith(".RAW"):
return read_mean_spectrum_raw(raw_data)
else:
return NotImplemented
def read_mean_spectrum_h5(raw_h5):
for k in raw_h5['Regions'].keys():
try:
mzs = raw_h5['Regions'][k]['SamplePositions/SamplePositions'][:]
total_intensities = raw_h5['Regions'][k]['Intensities'][:]