"""Perform DNA Tracing"""
from collections import OrderedDict
import logging
from pathlib import Path
import math
import os
from typing import Union, Tuple
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from scipy import ndimage, spatial, interpolate as interp
from skimage import morphology
from skimage.filters import gaussian
from topostats.logs.logs import LOGGER_NAME
from topostats.tracing.tracingfuncs import genTracingFuncs, getSkeleton, reorderTrace
LOGGER = logging.getLogger(LOGGER_NAME)
[docs]
class dnaTrace:
"""
This class gets all the useful functions from the old tracing code and staples
them together to create an object that contains the traces for each DNA molecule
in an image and functions to calculate stats from those traces.
The traces are stored in dictionaries labelled by their gwyddion defined grain
number and are represented as numpy arrays.
The object also keeps track of the skeletonised plots and other intermediates
in case these are useful for other things in the future.
"""
def __init__(
self,
full_image_data: np.ndarray,
grains,
filename: str,
pixel_size: float,
min_skeleton_size: int = 10,
convert_nm_to_m: bool = True,
):
self.full_image_data = full_image_data * 1e-9 if convert_nm_to_m else full_image_data
# self.grains_orig = [x for row in grains for x in row]
self.grains_orig = grains
self.filename = filename
self.pixel_size = pixel_size * 1e-9 if convert_nm_to_m else pixel_size
self.min_skeleton_size = min_skeleton_size
# self.number_of_columns = number_of_columns
# self.number_of_rows = number_of_rows
self.number_of_rows = self.full_image_data.shape[0]
self.number_of_columns = self.full_image_data.shape[1]
self.sigma = 0.7 / (self.pixel_size * 1e9)
self.gauss_image = []
self.grains = {}
self.dna_masks = {}
self.skeletons = {}
self.disordered_trace = {}
self.ordered_traces = {}
self.fitted_traces = {}
self.splined_traces = {}
self.contour_lengths = {}
self.end_to_end_distance = {}
self.mol_is_circular = {}
self.curvature = {}
self.number_of_traces = 0
self.num_circular = 0
self.num_linear = 0
self.unprocessed_grains = 0
self.neighbours = 5 # The number of neighbours used for the curvature measurement
# supresses scipy splining warnings
warnings.filterwarnings("ignore")
LOGGER.debug(f"[{self.filename}] Performing DNA Tracing")
[docs]
def trace_dna(self):
"""Perform DNA tracing."""
self.get_numpy_arrays()
self.gaussian_filter()
self.get_disordered_trace()
# self.isMolLooped()
self.purge_obvious_crap()
self.linear_or_circular(self.disordered_trace)
self.get_ordered_traces()
self.linear_or_circular(self.ordered_traces)
self.get_fitted_traces()
self.get_splined_traces()
# self.find_curvature()
# self.saveCurvature()
self.measure_contour_length()
self.measure_end_to_end_distance()
self.report_basic_stats()
[docs]
def get_numpy_arrays(self):
"""Function to get each grain as a numpy array which is stored in a
dictionary
Currently the grains are unnecessarily large (the full image) as I don't
know how to handle the cropped versions
I find using the gwyddion objects clunky and not helpful once the
grains have been found
There is some kind of discrepency between the ordering of arrays from
gwyddion and how they're usually handled in np arrays meaning you need
to be careful when indexing from gwyddion derived numpy arrays"""
for grain_num in set(self.grains_orig.flatten()):
# Skip the background
if grain_num != 0:
# Saves each grain as a multidim numpy array
self.grains[int(grain_num) - 1] = self._get_grain_array(grain_num)
[docs]
def _get_grain_array(self, grain_number: int) -> np.ndarray:
"""Extract a single grains."""
return np.where(self.grains_orig == grain_number, 1, 0)
# FIXME : It is straight-forward to get bounding boxes for grains, need to then have a dictionary of original image
# and label for each grain to then be processed.
[docs]
def _get_bounding_box(array: np.ndarray) -> np.ndarray:
"""Calculate bounding box for each grain."""
rows = grain_array.any(axis=1)
LOGGER.info(f"[{self.filename}] : Cropping grain")
if rows.any():
m, n = grain_array.shape
cols = grain_array.any(0)
return (rows.argmax(), m - rows[::-1].argmax(), cols.argmax(), n - cols[::-1].argmax())
return grain_array[rows.argmax() : m - rows[::-1].argmax(), cols.argmax() : n - cols[::-1].argmax()]
return np.empty((0, 0), dtype=bool)
[docs]
def _crop_array(array: np.ndarray, bounding_box: Tuple) -> np.ndarray:
"""Crop an array.
Parameters
----------
array: np.ndarray
2D Numpy array to be cropped.
bounding_box: Tuple
Tuple of co-ordinates to crop, should be of form (max_x, min_x, max_y, min_y) as returned by
_get_bounding_box().
Returns
-------
np.ndarray()
Cropped array
"""
return array[bounding_box[0] : bounding_box[1], bounding_box[2] : bounding_box[3]]
[docs]
def gaussian_filter(self, **kwargs) -> np.array:
"""Apply Gaussian filter"""
self.gauss_image = gaussian(self.full_image_data, sigma=self.sigma, **kwargs)
LOGGER.info(f"[{self.filename}] : Gaussian filter applied.")
[docs]
def get_disordered_trace(self):
"""Create a skeleton for each of the grains in the image.
Uses my own skeletonisation function from tracingfuncs module. I will
eventually get round to editing this function to try to reduce the branching
and to try to better trace from looped molecules"""
for grain_num in sorted(self.grains.keys()):
# What purpose does binary dilation serve here? Name suggests some sort of smoothing around the edges and
# the resulting array is used as a mask during the skeletonising process.
smoothed_grain = ndimage.binary_dilation(self.grains[grain_num], iterations=1).astype(
self.grains[grain_num].dtype
)
sigma = 0.01 / (self.pixel_size * 1e9)
very_smoothed_grain = ndimage.gaussian_filter(smoothed_grain, sigma)
try:
dna_skeleton = getSkeleton(
self.gauss_image, smoothed_grain, self.number_of_columns, self.number_of_rows, self.pixel_size
)
self.disordered_trace[grain_num] = dna_skeleton.output_skeleton
except IndexError:
# Some gwyddion grains touch image border causing IndexError
# These grains are deleted
self.grains.pop(grain_num)
# skel = morphology.skeletonize(self.grains[grain_num])
# self.skeletons[grain_num] = np.argwhere(skel == 1)
[docs]
def purge_obvious_crap(self):
all_grains = len(self.disordered_trace)
for dna_num in sorted(self.disordered_trace.keys()):
if len(self.disordered_trace[dna_num]) < self.min_skeleton_size:
self.disordered_trace.pop(dna_num, None)
self.unprocessed_grains = all_grains - len(self.disordered_trace)
LOGGER.info(f"[{self.filename}] : Crap grains removed : {self.unprocessed_grains}")
[docs]
def linear_or_circular(self, traces):
"""Determines whether each molecule is circular or linear based on the local environment of each pixel from the trace
This function is sensitive to branches from the skeleton so might need to implement a function to remove them"""
self.num_circular = 0
self.num_linear = 0
for dna_num in sorted(traces.keys()):
points_with_one_neighbour = 0
fitted_trace_list = traces[dna_num].tolist()
# For loop determines how many neighbours a point has - if only one it is an end
for x, y in fitted_trace_list:
if genTracingFuncs.countNeighbours(x, y, fitted_trace_list) == 1:
points_with_one_neighbour += 1
else:
pass
if points_with_one_neighbour == 0:
self.mol_is_circular[dna_num] = True
self.num_circular += 1
else:
self.mol_is_circular[dna_num] = False
self.num_linear += 1
[docs]
def get_ordered_traces(self):
for dna_num in sorted(self.disordered_trace.keys()):
circle_tracing = True
if self.mol_is_circular[dna_num]:
self.ordered_traces[dna_num], trace_completed = reorderTrace.circularTrace(
self.disordered_trace[dna_num]
)
if not trace_completed:
self.mol_is_circular[dna_num] = False
try:
self.ordered_traces[dna_num] = reorderTrace.linearTrace(self.ordered_traces[dna_num].tolist())
except UnboundLocalError:
self.mol_is_circular.pop(dna_num)
self.disordered_trace.pop(dna_num)
self.grains.pop(dna_num)
self.ordered_traces.pop(dna_num)
elif not self.mol_is_circular[dna_num]:
self.ordered_traces[dna_num] = reorderTrace.linearTrace(self.disordered_trace[dna_num].tolist())
[docs]
def report_basic_stats(self):
"""Report number of circular and linear DNA molecules detected."""
LOGGER.info(
f"There are {self.num_circular} circular and {self.num_linear} linear DNA molecules found in the image"
)
[docs]
def get_fitted_traces(self):
"""Create trace coordinates (for each identified molecule) that are adjusted to lie
along the highest points of each traced molecule
"""
for dna_num in sorted(self.ordered_traces.keys()):
individual_skeleton = self.ordered_traces[dna_num]
# This indexes a 3 nm height profile perpendicular to DNA backbone
# note that this is a hard coded parameter
index_width = int(3e-9 / (self.pixel_size))
if index_width < 2:
index_width = 2
for coord_num, trace_coordinate in enumerate(individual_skeleton):
height_values = None
# Block of code to prevent indexing outside image limits
# e.g. indexing self.gauss_image[130, 130] for 128x128 image
if trace_coordinate[0] < 0:
# prevents negative number indexing
# i.e. stops (trace_coordinate - index_width) < 0
trace_coordinate[0] = index_width
elif trace_coordinate[0] >= (self.number_of_rows - index_width):
# prevents indexing above image range causing IndexError
trace_coordinate[0] = self.number_of_rows - index_width
# do same for y coordinate
elif trace_coordinate[1] < 0:
trace_coordinate[1] = index_width
elif trace_coordinate[1] >= (self.number_of_columns - index_width):
trace_coordinate[1] = self.number_of_columns - index_width
# calculate vector to n - 2 coordinate in trace
if self.mol_is_circular[dna_num]:
nearest_point = individual_skeleton[coord_num - 2]
vector = np.subtract(nearest_point, trace_coordinate)
vector_angle = math.degrees(math.atan2(vector[1], vector[0]))
else:
try:
nearest_point = individual_skeleton[coord_num + 2]
except IndexError:
nearest_point = individual_skeleton[coord_num - 2]
vector = np.subtract(nearest_point, trace_coordinate)
vector_angle = math.degrees(math.atan2(vector[1], vector[0]))
if vector_angle < 0:
vector_angle += 180
# if angle is closest to 45 degrees
if 67.5 > vector_angle >= 22.5:
perp_direction = "negative diaganol"
# positive diagonal (change in x and y)
# Take height values at the inverse of the positive diaganol
# (i.e. the negative diaganol)
y_coords = np.arange(trace_coordinate[1] - index_width, trace_coordinate[1] + index_width)[::-1]
x_coords = np.arange(trace_coordinate[0] - index_width, trace_coordinate[0] + index_width)
# if angle is closest to 135 degrees
elif 157.5 >= vector_angle >= 112.5:
perp_direction = "positive diaganol"
y_coords = np.arange(trace_coordinate[1] - index_width, trace_coordinate[1] + index_width)
x_coords = np.arange(trace_coordinate[0] - index_width, trace_coordinate[0] + index_width)
# if angle is closest to 90 degrees
if 112.5 > vector_angle >= 67.5:
perp_direction = "horizontal"
x_coords = np.arange(trace_coordinate[0] - index_width, trace_coordinate[0] + index_width)
y_coords = np.full(len(x_coords), trace_coordinate[1])
elif 22.5 > vector_angle: # if angle is closest to 0 degrees
perp_direction = "vertical"
y_coords = np.arange(trace_coordinate[1] - index_width, trace_coordinate[1] + index_width)
x_coords = np.full(len(y_coords), trace_coordinate[0])
elif vector_angle >= 157.5: # if angle is closest to 180 degrees
perp_direction = "vertical"
y_coords = np.arange(trace_coordinate[1] - index_width, trace_coordinate[1] + index_width)
x_coords = np.full(len(y_coords), trace_coordinate[0])
# Use the perp array to index the guassian filtered image
perp_array = np.column_stack((x_coords, y_coords))
height_values = self.gauss_image[perp_array[:, 1], perp_array[:, 0]]
"""
# Old code that interpolated the height profile for "sub-pixel
# accuracy" - probably slow and not necessary, can delete
#Use interpolation to get "sub pixel" accuracy for heighest position
if perp_direction == 'negative diaganol':
int_func = interp.interp1d(perp_array[:,0], np.ndarray.flatten(height_values), kind = 'cubic')
interp_heights = int_func(np.arange(perp_array[0,0], perp_array[-1,0], 0.1))
elif perp_direction == 'positive diaganol':
int_func = interp.interp1d(perp_array[:,0], np.ndarray.flatten(height_values), kind = 'cubic')
interp_heights = int_func(np.arange(perp_array[0,0], perp_array[-1,0], 0.1))
elif perp_direction == 'vertical':
int_func = interp.interp1d(perp_array[:,1], np.ndarray.flatten(height_values), kind = 'cubic')
interp_heights = int_func(np.arange(perp_array[0,1], perp_array[-1,1], 0.1))
elif perp_direction == 'horizontal':
#print(perp_array[:,0])
#print(np.ndarray.flatten(height_values))
int_func = interp.interp1d(perp_array[:,0], np.ndarray.flatten(height_values), kind = 'cubic')
interp_heights = int_func(np.arange(perp_array[0,0], perp_array[-1,0], 0.1))
else:
quit('A fatal error occured in the CorrectHeightPositions function, this was likely caused by miscalculating vector angles')
#Make "fine" coordinates which have the same number of coordinates as the interpolated height values
if perp_direction == 'negative diaganol':
fine_x_coords = np.arange(perp_array[0,0], perp_array[-1,0], 0.1)
fine_y_coords = np.arange(perp_array[-1,1], perp_array[0,1], 0.1)[::-1]
elif perp_direction == 'positive diaganol':
fine_x_coords = np.arange(perp_array[0,0], perp_array[-1,0], 0.1)
fine_y_coords = np.arange(perp_array[0,1], perp_array[-1,1], 0.1)
elif perp_direction == 'vertical':
fine_y_coords = np.arange(perp_array[0,1], perp_array[-1,1], 0.1)
fine_x_coords = np.full(len(fine_y_coords), trace_coordinate[0], dtype = 'float')
elif perp_direction == 'horizontal':
fine_x_coords = np.arange(perp_array[0,0], perp_array[-1,0], 0.1)
fine_y_coords = np.full(len(fine_x_coords), trace_coordinate[1], dtype = 'float')
"""
# Grab x,y coordinates for highest point
# fine_coords = np.column_stack((fine_x_coords, fine_y_coords))
sorted_array = perp_array[np.argsort(height_values)]
highest_point = sorted_array[-1]
try:
# could use np.append() here
fitted_coordinate_array = np.vstack((fitted_coordinate_array, highest_point))
except UnboundLocalError:
fitted_coordinate_array = highest_point
self.fitted_traces[dna_num] = fitted_coordinate_array
del fitted_coordinate_array # cleaned up by python anyway?
[docs]
def get_splined_traces(self):
"""Gets a splined version of the fitted trace - useful for finding the radius of gyration etc
This function actually calculates the average of several splines which is important for getting a good fit on
the lower res data"""
step_size = int(7e-9 / (self.pixel_size)) # 3 nm step size
interp_step = int(1e-10 / self.pixel_size)
# Lets see if we just got with the pixel_to_nm_scaling
# step_size = self.pixel_size
# interp_step = self.pixel_size
# FIXME : Iterate over self.fitted_traces directly use either self.fitted_traces.values() or self.fitted_trace.items()
for dna_num in sorted(self.fitted_traces.keys()):
self.splining_success = True
nbr = len(self.fitted_traces[dna_num][:, 0])
# Hard to believe but some traces have less than 4 coordinates in total
if len(self.fitted_traces[dna_num][:, 1]) < 4:
self.splined_traces[dna_num] = self.fitted_traces[dna_num]
continue
# The degree of spline fit used is 3 so there cannot be less than 3 points in the splined trace
# LOGGER.info(f"DNA Number : {dna_num}")
# LOGGER.info(f"nbr : {nbr}")
# LOGGER.info(f"step_size : {step_size}")
# LOGGER.info(f"self.pixel_size : {self.pixel_size}")
while nbr / step_size < 4:
if step_size <= 1:
step_size = 1
break
step_size = -1
if self.mol_is_circular[dna_num]:
# if nbr/step_size > 4: #the degree of spline fit is 3 so there cannot be less than 3 points in splined trace
# ev_array = np.linspace(0, 1, nbr * step_size)
ev_array = np.linspace(0, 1, int(nbr * step_size))
for i in range(step_size):
x_sampled = np.array(
[
self.fitted_traces[dna_num][:, 0][j]
for j in range(i, len(self.fitted_traces[dna_num][:, 0]), step_size)
]
)
y_sampled = np.array(
[
self.fitted_traces[dna_num][:, 1][j]
for j in range(i, len(self.fitted_traces[dna_num][:, 1]), step_size)
]
)
try:
tck, u = interp.splprep([x_sampled, y_sampled], s=0, per=2, quiet=1, k=3)
out = interp.splev(ev_array, tck)
splined_trace = np.column_stack((out[0], out[1]))
except ValueError:
# Value error occurs when the "trace fitting" really messes up the traces
x = np.array(
[
self.ordered_traces[dna_num][:, 0][j]
for j in range(i, len(self.ordered_traces[dna_num][:, 0]), step_size)
]
)
y = np.array(
[
self.ordered_traces[dna_num][:, 1][j]
for j in range(i, len(self.ordered_traces[dna_num][:, 1]), step_size)
]
)
try:
tck, u = interp.splprep([x, y], s=0, per=2, quiet=1)
out = interp.splev(np.linspace(0, 1, nbr * step_size), tck)
splined_trace = np.column_stack((out[0], out[1]))
except (
ValueError
): # sometimes even the ordered_traces are too bugged out so just delete these traces
self.mol_is_circular.pop(dna_num)
self.disordered_trace.pop(dna_num)
self.grains.pop(dna_num)
self.ordered_traces.pop(dna_num)
self.splining_success = False
try:
del spline_running_total
except UnboundLocalError: # happens if splining fails immediately
break
break
try:
spline_running_total = np.add(spline_running_total, splined_trace)
except NameError:
spline_running_total = np.array(splined_trace)
if not self.splining_success:
continue
spline_average = np.divide(spline_running_total, [step_size, step_size])
del spline_running_total
self.splined_traces[dna_num] = spline_average
# else:
# x = self.fitted_traces[dna_num][:,0]
# y = self.fitted_traces[dna_num][:,1]
# try:
# tck, u = interp.splprep([x, y], s=0, per = 2, quiet = 1, k = 3)
# out = interp.splev(np.linspace(0,1,nbr*step_size), tck)
# splined_trace = np.column_stack((out[0], out[1]))
# self.splined_traces[dna_num] = splined_trace
# except ValueError: #if the trace is really messed up just delete it
# self.mol_is_circular.pop(dna_num)
# self.disordered_trace.pop(dna_num)
# self.grains.pop(dna_num)
# self.ordered_traces.pop(dna_num)
else:
"""
start_x = self.fitted_traces[dna_num][0, 0]
end_x = self.fitted_traces[dna_num][-1, 0]
for i in range(step_size):
x_sampled = np.array([self.fitted_traces[dna_num][:, 0][j] for j in
range(i, len(self.fitted_traces[dna_num][:, 0]), step_size)])
y_sampled = np.array([self.fitted_traces[dna_num][:, 1][j] for j in
range(i, len(self.fitted_traces[dna_num][:, 1]), step_size)])
interp_f = interp.interp1d(x_sampled, y_sampled, kind='cubic', assume_sorted=False)
x_new = np.linspace(start_x, end_x, interp_step)
y_new = interp_f(x_new)
print(y_new)
# tck = interp.splrep(x_sampled, y_sampled, quiet = 0)
# out = interp.splev(np.linspace(start_x,end_x, nbr*step_size), tck)
splined_trace = np.column_stack((x_new, y_new))
try:
np.add(spline_running_total, splined_trace)
except NameError:
spline_running_total = np.array(splined_trace)
spline_average = spline_running_total
self.splined_traces[dna_num] = spline_average
"""
# can't get splining of linear molecules to work yet
self.splined_traces[dna_num] = self.fitted_traces[dna_num]
[docs]
def show_traces(self):
plt.pcolormesh(self.gauss_image, vmax=-3e-9, vmin=3e-9)
plt.colorbar()
for dna_num in sorted(self.disordered_trace.keys()):
plt.plot(self.ordered_traces[dna_num][:, 0], self.ordered_traces[dna_num][:, 1], markersize=1)
plt.plot(self.fitted_traces[dna_num][:, 0], self.fitted_traces[dna_num][:, 1], markersize=1)
plt.plot(self.splined_traces[dna_num][:, 0], self.splined_traces[dna_num][:, 1], markersize=1)
# print(len(self.skeletons[dna_num]), len(self.disordered_trace[dna_num]))
# plt.plot(self.skeletons[dna_num][:,0], self.skeletons[dna_num][:,1], 'o', markersize = 0.8)
plt.show()
plt.close()
# FIXME : Replace with Path() (.mkdir(parent=True, exists=True) negate need to handle errors.)
[docs]
def _checkForSaveDirectory(self, filename, new_directory_name):
split_directory_path = os.path.split(filename)
try:
os.mkdir(os.path.join(split_directory_path[0], new_directory_name))
except OSError: # OSError happens if the directory already exists
pass
updated_filename = os.path.join(split_directory_path[0], new_directory_name, split_directory_path[1])
return updated_filename
[docs]
def findWrithe(self):
pass
[docs]
def find_curvature(self):
# FIXME : Iterate directly over self.splined_traces.values() or self.splined_traces.items()
for dna_num in sorted(self.splined_traces.keys()): # the number of molecules identified
# splined_traces is a dictionary, where the keys are the number of the molecule, and the values are a
# list of coordinates, in a numpy.ndarray
# if self.mol_is_circular[dna_num]:
curve = []
contour = 0
coordinates = np.zeros([2, self.neighbours * 2 + 1])
for i, (x, y) in enumerate(self.splined_traces[dna_num]):
# Extracts the coordinates for the required number of points and puts them in an array
if self.mol_is_circular[dna_num] or (
self.neighbours < i < len(self.splined_traces[dna_num]) - self.neighbours
):
for j in range(self.neighbours * 2 + 1):
coordinates[0][j] = self.splined_traces[dna_num][i - j][0]
coordinates[1][j] = self.splined_traces[dna_num][i - j][1]
# Calculates the angles for the tangent lines to the left and the right of the point
theta1 = math.atan(
(coordinates[1][self.neighbours] - coordinates[1][0])
/ (coordinates[0][self.neighbours] - coordinates[0][0])
)
theta2 = math.atan(
(coordinates[1][-1] - coordinates[1][self.neighbours])
/ (coordinates[0][-1] - coordinates[0][self.neighbours])
)
left = coordinates[:, : self.neighbours + 1]
right = coordinates[:, -(self.neighbours + 1) :]
xa = np.mean(left[0])
ya = np.mean(left[1])
xb = np.mean(right[0])
yb = np.mean(right[1])
# Calculates the curvature using the change in angle divided by the distance
dist = math.hypot((xb - xa), (yb - ya))
dist_real = dist * self.pixel_size
curve.append([i, contour, (theta2 - theta1) / dist_real])
contour = contour + math.hypot(
(coordinates[0][self.neighbours] - coordinates[0][self.neighbours - 1]),
(coordinates[1][self.neighbours] - coordinates[1][self.neighbours - 1]),
)
self.curvature[dna_num] = curve
[docs]
def saveCurvature(self):
# FIXME : Iterate directly over self.splined_traces.values() or self.splined_traces.items()
# roc_array = np.zeros(shape=(1, 3))
for dna_num in sorted(self.curvature.keys()):
for i, [n, contour, c] in enumerate(self.curvature[dna_num]):
try:
roc_array = np.append(roc_array, np.array([[dna_num, i, contour, c]]), axis=0)
# oc_array.append([dna_num, i, contour, c])
except NameError:
roc_array = np.array([[dna_num, i, contour, c]])
# roc_array = np.vstack((roc_array, np.array([dna_num, i, c])))
# roc_array = np.delete(roc_array, 0, 0)
roc_stats = pd.DataFrame(roc_array)
if not os.path.exists(os.path.join(os.path.dirname(self.filename), "Curvature")):
os.mkdir(os.path.join(os.path.dirname(self.filename), "Curvature"))
directory = os.path.join(os.path.dirname(self.filename), "Curvature")
savename = os.path.join(directory, os.path.basename(self.filename)[:-4])
roc_stats.to_json(savename + ".json")
roc_stats.to_csv(savename + ".csv")
[docs]
def plotCurvature(self, dna_num):
"""Plot the curvature of the chosen molecule as a function of the contour length (in metres)"""
curvature = np.array(self.curvature[dna_num])
length = len(curvature)
# FIXME : Replace with Path()
if not os.path.exists(os.path.join(os.path.dirname(self.filename), "Curvature")):
os.mkdir(os.path.join(os.path.dirname(self.filename), "Curvature"))
directory = os.path.join(os.path.dirname(self.filename), "Curvature")
savename = os.path.join(directory, os.path.basename(self.filename)[:-4])
plt.figure()
sns.lineplot(curvature[:, 1] * self.pixel_size, curvature[:, 2], color="k")
plt.ylim(-1e9, 1e9)
plt.ticklabel_format(axis="both", style="sci", scilimits=(0, 0))
plt.axvline(curvature[0][1], color="#D55E00")
plt.axvline(curvature[int(length / 6)][1] * self.pixel_size, color="#E69F00")
plt.axvline(curvature[int(length / 6 * 2)][1] * self.pixel_size, color="#F0E442")
plt.axvline(curvature[int(length / 6 * 3)][1] * self.pixel_size, color="#009E74")
plt.axvline(curvature[int(length / 6 * 4)][1] * self.pixel_size, color="#0071B2")
plt.axvline(curvature[int(length / 6 * 5)][1] * self.pixel_size, color="#CC79A7")
plt.savefig(f"{savename}_{dna_num}_curvature.png")
plt.close()
[docs]
def measure_contour_length(self):
"""Measures the contour length for each of the splined traces taking into
account whether the molecule is circular or linear
Contour length units are nm"""
for dna_num in sorted(self.splined_traces.keys()):
if self.mol_is_circular[dna_num]:
for num, i in enumerate(self.splined_traces[dna_num]):
x1 = self.splined_traces[dna_num][num - 1, 0]
y1 = self.splined_traces[dna_num][num - 1, 1]
x2 = self.splined_traces[dna_num][num, 0]
y2 = self.splined_traces[dna_num][num, 1]
try:
hypotenuse_array.append(math.hypot((x1 - x2), (y1 - y2)))
except NameError:
hypotenuse_array = [math.hypot((x1 - x2), (y1 - y2))]
self.contour_lengths[dna_num] = np.sum(np.array(hypotenuse_array)) * self.pixel_size
del hypotenuse_array
else:
for num, i in enumerate(self.splined_traces[dna_num]):
try:
x1 = self.splined_traces[dna_num][num, 0]
y1 = self.splined_traces[dna_num][num, 1]
x2 = self.splined_traces[dna_num][num + 1, 0]
y2 = self.splined_traces[dna_num][num + 1, 1]
try:
hypotenuse_array.append(math.hypot((x1 - x2), (y1 - y2)))
except NameError:
hypotenuse_array = [math.hypot((x1 - x2), (y1 - y2))]
except IndexError: # IndexError happens at last point in array
self.contour_lengths[dna_num] = np.sum(np.array(hypotenuse_array)) * self.pixel_size
del hypotenuse_array
break
[docs]
def writeContourLengths(self, filename, channel_name):
if not self.contour_lengths:
self.measure_contour_length()
with open(f"{filename}_{channel_name}_contours.txt", "w") as writing_file:
writing_file.write("#units: nm\n")
for dna_num in sorted(self.contour_lengths.keys()):
writing_file.write("%f \n" % self.contour_lengths[dna_num])
# FIXME : This method doesn't appear to be used here nor within pygwytracing, can it be removed?
[docs]
def writeCoordinates(self, dna_num):
# FIXME: Replace with Path()
if not os.path.exists(os.path.join(os.path.dirname(self.filename), "Coordinates")):
os.mkdir(os.path.join(os.path.dirname(self.filename), "Coordinates"))
directory = os.path.join(os.path.dirname(self.filename), "Coordinates")
savename = os.path.join(directory, os.path.basename(self.filename)[:-4])
for i, (x, y) in enumerate(self.splined_traces[dna_num]):
try:
coordinates_array = np.append(coordinates_array, np.array([[x, y]]), axis=0)
except NameError:
coordinates_array = np.array([[x, y]])
coordinates = pd.DataFrame(coordinates_array)
coordinates.to_csv(f"{savename}_{dna_num}.csv")
plt.plot(coordinates_array[:, 0], coordinates_array[:, 1], "ko")
plt.savefig(f"{savename}_{dna_num}_coordinates.png")
[docs]
def measure_end_to_end_distance(self):
"""Calculate the Euclidean distance between the start and end of linear molecules.
The hypotenuse is calculated between the start ([0,0], [0,1]) and end ([-1,0], [-1,1]) of linear
molecules. If the molecule is circular then the distance is set to zero (0).
"""
for dna_num in sorted(self.splined_traces.keys()):
if self.mol_is_circular[dna_num]:
self.end_to_end_distance[dna_num] = 0
else:
x1 = self.splined_traces[dna_num][0, 0]
y1 = self.splined_traces[dna_num][0, 1]
x2 = self.splined_traces[dna_num][-1, 0]
y2 = self.splined_traces[dna_num][-1, 1]
self.end_to_end_distance[dna_num] = math.hypot((x1 - x2), (y1 - y2)) * self.pixel_size
# self.end_to_end_distance = {
# dna_num: math.hypot((trace[0, 0] - trace[-1, 0]), (trace[0, 1] - trace[-1, 1]))
# if self.mol_is_circular[dna_num]
# else 0
# for dna_num, trace in self.splined_traces.items()
# }
[docs]
class traceStats:
"""Combine and save trace statistics."""
def __init__(self, trace_object: dnaTrace, image_path: Union[str, Path]) -> None:
"""Initialise the class.
Parameters
----------
trace_object: dnaTrace
Object produced from tracing.
image_path: Union[str, Path]
Path for saving images to.
Returns
-------
None
"""
self.trace_object = trace_object
self.image_path = Path(image_path)
self.df = []
self.create_trace_stats()
[docs]
def create_trace_stats(self):
"""Creates a pandas dataframe of the contour length, whether its circular and end to end distance
combined with details of the working directory, directory images were found in and the image name.
"""
stats = OrderedDict()
for mol_num, _ in self.trace_object.ordered_traces.items():
stats[mol_num] = {}
stats[mol_num]["contour_lengths"] = self.trace_object.contour_lengths[mol_num]
stats[mol_num]["circular"] = self.trace_object.mol_is_circular[mol_num]
stats[mol_num]["end_to_end_distance"] = self.trace_object.end_to_end_distance[mol_num]
self.df = pd.DataFrame.from_dict(data=stats, orient="index")
# self.df.reset_index(drop=True, inplace=True)
self.df.index.name = "molecule_number"
# self.df["Experiment Directory"] = str(Path().cwd())
self.df["image"] = self.image_path.name
[docs]
def save_trace_stats(self, save_path: Union[str, Path], json: bool = True, csv: bool = True) -> None:
"""Write trace statistics to JSON and/or CSV.
Parameters
----------
save_path: Union[str, Path]
Directory to save results to.
json: bool
Whether to save a JSON version of statistics.
csv: bool
Whether to save a CSV version of statistics.
"""
if json:
self.df.to_json(save_path / "tracestats.json")
LOGGER.info(f"Saved trace info for all analysed images to: {str(save_path / 'tracestats.json')}")
if csv:
self.df.to_csv(save_path / "tracestats.csv")
LOGGER.info(f"Saved trace info for all analysed images to: {str(save_path / 'tracestats.csv')}")