From 70c99a844ee7fa74e51cdd8cb6ec815774bfd9e9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <amuoz@ed.ac.uk>
Date: Fri, 18 Mar 2022 16:49:24 +0000
Subject: [PATCH] specify submodules called
---
extraction/core/functions/custom/localisation.py | 14 +++++++-------
1 file changed, 7 insertions(+), 7 deletions(-)
diff --git a/extraction/core/functions/custom/localisation.py b/extraction/core/functions/custom/localisation.py
index 09f9094a..6393d492 100644
--- a/extraction/core/functions/custom/localisation.py
+++ b/extraction/core/functions/custom/localisation.py
@@ -6,7 +6,7 @@ Especially lines 342 to 399.
This part only replicates the method to get the nuc_est_conv values
"""
import numpy as np
-import scipy
+from scipy import signal, stats
import skimage
@@ -59,11 +59,11 @@ def small_peaks_conv(cell_mask, trap_image):
# expect the size of the nucleus to be.
# TODO directly get a disk of that size?
# new_shape = tuple(x * ratio_overlap / 5 for x in conv_matrix.shape)
- # conv_matrix = scipy.misc.imresize(conv_matrix, new_shape)
+ # conv_matrix = misc.imresize(conv_matrix, new_shape)
conv_matrix = skimage.morphology.disk(3 * ratio_overlap / 5)
# Apply convolution to the image
# TODO maybe rename 'conv_matrix' to 'kernel'
- fluo_peaks = scipy.signal.convolve(trap_image, conv_matrix, "same")
+ fluo_peaks = signal.convolve(trap_image, conv_matrix, "same")
fluo_peaks = fluo_peaks[cell_mask]
small_peak_conv = np.max(fluo_peaks)
return small_peak_conv
@@ -82,7 +82,7 @@ def nuc_est_conv(cell_mask, trap_image):
# Nuc Est Conv
alpha = 0.95
approx_nuc_radius = np.sqrt(0.085 * num_cell_fluo / np.pi)
- chi2inv = scipy.stats.distributions.chi2.ppf(alpha, df=2)
+ chi2inv = stats.distributions.chi2.ppf(alpha, df=2)
sd_est = approx_nuc_radius / np.sqrt(chi2inv)
nuc_filt_hw = np.ceil(2 * approx_nuc_radius)
@@ -91,7 +91,7 @@ def nuc_est_conv(cell_mask, trap_image):
cell_image = trap_image - np.median(cell_fluo)
cell_image[~cell_loc] = 0
- nuc_conv = scipy.signal.convolve(cell_image, nuc_filter, "same")
+ nuc_conv = signal.convolve(cell_image, nuc_filter, "same")
nuc_est_conv = np.max(nuc_conv)
nuc_est_conv /= np.sum(nuc_filter ** 2) * alpha * np.pi * chi2inv * sd_est ** 2
return nuc_est_conv
@@ -105,13 +105,13 @@ def nuc_conv_3d(cell_mask, trap_image, pixel_size=0.23, spacing=0.6):
# Nuc Est Conv
alpha = 0.95
approx_nuc_radius = np.sqrt(0.085 * num_cell_fluo / np.pi)
- chi2inv = scipy.stats.distributions.chi2.ppf(alpha, df=2)
+ chi2inv = stats.distributions.chi2.ppf(alpha, df=2)
sd_est = approx_nuc_radius / np.sqrt(chi2inv)
nuc_filt_hw = np.ceil(2 * approx_nuc_radius)
nuc_filter = gauss3D((2 * nuc_filt_hw + 1,) * 3, (sd_est, sd_est, sd_est * ratio))
cell_image = trap_image - np.median(cell_fluo)
cell_image[~cell_mask] = 0
- nuc_conv = scipy.signal.convolve(cell_image, nuc_filter, "same")
+ nuc_conv = signal.convolve(cell_image, nuc_filter, "same")
nuc_est_conv = np.max(nuc_conv)
nuc_est_conv /= np.sum(nuc_filter ** 2) * alpha * np.pi * chi2inv * sd_est ** 2
return nuc_est_conv
--
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