def addMoleculesWithXYCatF(self, x, y, cat,f): i3data = i3dtype.createDefaultI3Data(x.size) i3dtype.posSet(i3data, 'x', x) i3dtype.posSet(i3data, 'y', y) i3dtype.setI3Field(i3data, 'c', cat) i3dtype.setI3Field(i3data, 'fr', f) self.addMolecules(i3data)
def addMoleculesWithXYAFrame(self, x, y, pa, frame): i3data = i3dtype.createDefaultI3Data(x.size) i3dtype.posSet(i3data, 'x', x) i3dtype.posSet(i3data, 'y', y) i3dtype.setI3Field(i3data, 'a', pa) i3dtype.setI3Field(i3data, 'fr', frame) self.addMolecules(i3data)
def addMoleculesWithXYZCat(self, x, y, z, cat): i3data = i3dtype.createDefaultI3Data(x.size) i3dtype.posSet(i3data, 'x', x) i3dtype.posSet(i3data, 'y', y) i3dtype.posSet(i3data, 'z', z) i3dtype.setI3Field(i3data, 'c', cat) self.addMolecules(i3data)
def findClusters(mlist_name, clist_name, eps, mc, ignore_z = True, ignore_category = True): # Load the data. pix_to_nm = 160.0 i3_data_in = readinsight3.loadI3GoodOnly(mlist_name) c = i3_data_in['c'] x = i3_data_in['xc']*pix_to_nm y = i3_data_in['yc']*pix_to_nm if ignore_z: print("Warning! Clustering without using localization z value!") z = numpy.zeros(x.size) else: z = i3_data_in['zc'] # Perform analysis without regard to category. if ignore_category: print("Warning! Clustering without regard to category!") c = numpy.zeros(c.size) # Cluster the data. labels = dbscanC.dbscan(x, y, z, c, eps, mc, z_factor=1.0) # Save the data. i3_data_out = writeinsight3.I3Writer(clist_name) i3dtype.setI3Field(i3_data_in, 'lk', labels) i3_data_out.addMolecules(i3_data_in) i3_data_out.close()
def addMoleculesWithXYCatF(self, x, y, cat, f): i3data = i3dtype.createDefaultI3Data(x.size) i3dtype.posSet(i3data, 'x', x) i3dtype.posSet(i3data, 'y', y) i3dtype.setI3Field(i3data, 'c', cat) i3dtype.setI3Field(i3data, 'fr', f) self.addMolecules(i3data)
def addMoleculesWithXYZIFrame(self, x, y, z, pi, f): i3data = i3dtype.createDefaultI3Data(x.size) i3dtype.posSet(i3data, 'x', x) i3dtype.posSet(i3data, 'y', y) i3dtype.posSet(i3data, 'z', z) i3dtype.setI3Field(i3data, 'i', pi) i3dtype.setI3Field(i3data, 'fr', f) self.addMolecules(i3data)
def test_i3dtype_1(): """ Test conversion to and from the fitter format. """ x_size = 100 y_size = 100 frame = 10 nm_per_pixel = 100.0 data_in = i3dtype.createDefaultI3Data(10) i3dtype.posSet(data_in, 'x', numpy.arange(10)) i3dtype.posSet(data_in, 'y', numpy.arange(10) + 30.0) i3dtype.posSet(data_in, 'z', numpy.arange(10) + 60.0) i3dtype.setI3Field(data_in, 'fr', frame) peaks = i3dtype.convertToMultiFit(data_in, frame, nm_per_pixel) data_out = i3dtype.createFromMultiFit(peaks, frame, nm_per_pixel) fields = ['x', 'ax', 'w'] for i in range(10): for field in fields: assert(abs(data_in[field][i] - data_out[field][i]) < 1.0e-6)
def addMoleculesWithXYIWFrame(self, x, y, pi, width, frame): i3data = i3dtype.createDefaultI3Data(x.size) i3dtype.posSet(i3data, 'x', x) i3dtype.posSet(i3data, 'y', y) i3dtype.setI3Field(i3data, 'i', pi) i3dtype.setI3Field(i3data, 'w', width) i3dtype.setI3Field(i3data, 'fr', frame) self.addMolecules(i3data)
if False: z = i3_data_in['zc'] else: print "Warning! Clustering without using localization z value!" z = numpy.zeros(x.size) # Perform analysis without regard to category. if True: print "Warning! Clustering without regard to category!" c = numpy.zeros(c.size) # Cluster the data. if (len(sys.argv) == 4): print "Using eps =", sys.argv[2], "mc =", sys.argv[3] labels = dbscanC.dbscan(x, y, z, c, float(sys.argv[2]), int(sys.argv[3]), z_factor=1.0) else: print "Using eps = 80, mc = 5" labels = dbscanC.dbscan(x, y, z, c, 80.0, 5, z_factor=1.0) # Save the data. i3_data_out = writeinsight3.I3Writer(sys.argv[1][:-8] + "clusters_list.bin") i3dtype.setI3Field(i3_data_in, 'lk', labels) i3_data_out.addMolecules(i3_data_in) i3_data_out.close()
def addMoleculesWithXYI(self, x, y, pi): i3data = i3dtype.createDefaultI3Data(x.size) i3dtype.posSet(i3data, 'x', x) i3dtype.posSet(i3data, 'y', y) i3dtype.setI3Field(i3data, 'i', pi) self.addMolecules(i3data)
def addDAOSTORMMolecules(self, frame, xc, yc, br, be, msky, niter, sharp, chi, err): # # DAOSTORM -> Insight3 format mapping. # # xc - xcenter # yc - ycenter # br - brightness -> peak height # be - brightness error (?) -> peak area # msky - background -> peak background # niter - fit iterations # sharp - sharpness (?) -> peak angle # chi - fit quality -> peak width # err - error flag -> link # i3data = i3dtype.createDefaultI3Data(xc.size) i3dtype.posSet(i3data, 'x', xc) i3dtype.posSet(i3data, 'y', yc) i3dtype.setI3Field(i3data, 'h', br) i3dtype.setI3Field(i3data, 'a', be) i3dtype.setI3Field(i3data, 'bg', msky) i3dtype.setI3Field(i3data, 'fi', niter) i3dtype.setI3Field(i3data, 'phi', sharp) i3dtype.setI3Field(i3data, 'w', chi) i3dtype.setI3Field(i3data, 'lk', err) self.addMolecules(i3data)
def addDAOSTORMMolecules(self, frame, xc, yc, br, be, msky, niter, sharp, chi, err): """ This is for localizations identified by the original DAOSTORM algorithm, not the 3D-DAOSTORM algorithm. DAOSTORM -> Insight3 format mapping. xc - xcenter yc - ycenter br - brightness -> peak height be - brightness error (?) -> peak area msky - background -> peak background niter - fit iterations sharp - sharpness (?) -> peak angle chi - fit quality -> peak width err - error flag -> link """ i3data = i3dtype.createDefaultI3Data(xc.size) i3dtype.posSet(i3data, 'x', xc) i3dtype.posSet(i3data, 'y', yc) i3dtype.setI3Field(i3data, 'h', br) i3dtype.setI3Field(i3data, 'a', be) i3dtype.setI3Field(i3data, 'bg', msky) i3dtype.setI3Field(i3data, 'fi', niter) i3dtype.setI3Field(i3data, 'phi', sharp) i3dtype.setI3Field(i3data, 'w', chi) i3dtype.setI3Field(i3data, 'lk', err) self.addMolecules(i3data)
cx = numpy.random.uniform(low = 20.0, high = 236.0, size = clusters) cy = numpy.random.uniform(low = 20.0, high = 236.0, size = clusters) cz = numpy.random.uniform(low = -300.0, high = 300.0, size = clusters) with writeinsight3.I3Writer("../data/test_clustering_list.bin") as i3_fp: for i in range(length): if((i % 500) == 0): print("Creating frame", i) on = (numpy.random.uniform(size = clusters) < p_on) number_on = numpy.count_nonzero(on) x = cx + numpy.random.normal(scale = 0.5, size = clusters) y = cy + numpy.random.normal(scale = 0.5, size = clusters) z = cz + numpy.random.normal(scale = 50.0, size = clusters) # Add background x = numpy.concatenate((x[on], numpy.random.uniform(high = 256.0, size = background))) y = numpy.concatenate((y[on], numpy.random.uniform(high = 256.0, size = background))) z = numpy.concatenate((z[on], numpy.random.uniform(low = -500.0, high = 500.0, size = background))) i3d = i3dtype.createDefaultI3Data(number_on + background) i3dtype.posSet(i3d, "x", x) i3dtype.posSet(i3d, "y", y) i3dtype.posSet(i3d, "z", z) i3dtype.setI3Field(i3d, "fr", i+1) i3_fp.addMolecules(i3d)
#z_planes = [-250.0, 250.0] #z_planes = [-250.0, 250.0] #z_planes = [-750.0, -250.0, 250.0, 750.0] z_value = -500.0 # Load emitter locations. i3_locs = readinsight3.loadI3File("emitters.bin") if False: i3_locs = i3_locs[0] # Make a bin file with emitter locations for each frame. with writeinsight3.I3Writer("test_olist.bin") as i3w: for i in range(frames): i3_temp = i3_locs.copy() i3dtype.setI3Field(i3_temp, "fr", i + 1) i3dtype.posSet(i3_temp, "z", z_value) i3w.addMolecules(i3_temp) # Load channel to channel mapping file. with open("map.map", 'rb') as fp: mappings = pickle.load(fp) # Create bin files for each plane. for i, z_plane in enumerate(z_planes): cx = mappings["0_" + str(i) + "_x"] cy = mappings["0_" + str(i) + "_y"] i3_temp = i3_locs.copy() xi = i3_temp["x"] yi = i3_temp["y"] xf = cx[0] + cx[1] * xi + cx[2] * yi
# Save the molecule locations. a_vals = PSF.PSFIntegral(z_vals, h_vals) ax = numpy.ones(num_objects) ww = 2.0 * 160.0 * numpy.ones(num_objects) if (PSF.psf_type == "astigmatic"): sx_vals = objects[:,3] sy_vals = objects[:,4] ax = sy_vals/sx_vals ww = 2.0*160.0*numpy.sqrt(sx_vals*sy_vals) mols = i3dtype.createDefaultI3Data(num_objects) i3dtype.posSet(mols, 'x', x_vals + 1.0) i3dtype.posSet(mols, 'y', y_vals + 1.0) i3dtype.posSet(mols, 'z', z_vals) i3dtype.setI3Field(mols, 'a', a_vals) i3dtype.setI3Field(mols, 'h', h_vals) i3dtype.setI3Field(mols, 'w', ww) i3dtype.setI3Field(mols, 'ax', ax) i3dtype.setI3Field(mols, 'fr', i+1) i3_data.addMolecules(mols) dax_data.close() i3_data.close() # # The MIT License # # Copyright (c) 2016 Zhuang Lab, Harvard University #
def addMultiFitMolecules(self, molecules, x_size, y_size, frame, nm_per_pixel, inverted=False): n_molecules = molecules.shape[0] h = molecules[:, 0] if inverted: xc = y_size - molecules[:, 1] yc = x_size - molecules[:, 3] wx = 2.0 * molecules[:, 2] * nm_per_pixel wy = 2.0 * molecules[:, 4] * nm_per_pixel else: xc = molecules[:, 3] + 1 yc = molecules[:, 1] + 1 wx = 2.0 * molecules[:, 4] * nm_per_pixel wy = 2.0 * molecules[:, 2] * nm_per_pixel bg = molecules[:, 5] zc = molecules[:, 6] * 1000.0 # fitting is done in um, insight works in nm st = numpy.round(molecules[:, 7]) err = molecules[:, 8] # calculate peak area, which is saved in the "a" field. parea = 2.0 * 3.14159 * h * molecules[:, 2] * molecules[:, 4] ax = wy / wx ww = numpy.sqrt(wx * wy) i3data = i3dtype.createDefaultI3Data(xc.size) i3dtype.posSet(i3data, 'x', xc) i3dtype.posSet(i3data, 'y', yc) i3dtype.posSet(i3data, 'z', zc) i3dtype.setI3Field(i3data, 'h', h) i3dtype.setI3Field(i3data, 'bg', bg) i3dtype.setI3Field(i3data, 'fi', st) i3dtype.setI3Field(i3data, 'a', parea) i3dtype.setI3Field(i3data, 'w', ww) i3dtype.setI3Field(i3data, 'ax', ax) i3dtype.setI3Field(i3data, 'fr', frame) i3dtype.setI3Field(i3data, 'i', err) self.addMolecules(i3data)