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, filters
import math
import warnings
import os
from topostats.tracingfuncs import genTracingFuncs, getSkeleton, reorderTrace
[docs]class dnaTrace(object):
'''
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, gwyddion_grains, afm_image_name, pixel_size,
number_of_columns, number_of_rows):
self.full_image_data = full_image_data
self.gwyddion_grains = gwyddion_grains
self.afm_image_name = afm_image_name
self.pixel_size = pixel_size
self.number_of_columns = number_of_columns
self.number_of_rows = number_of_rows
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.neighbours = 5 # The number of neighbours used for the curvature measurement
# supresses scipy splining warnings
warnings.filterwarnings('ignore')
self.getNumpyArraysfromGwyddion()
self.getDisorderedTrace()
# self.isMolLooped()
self.purgeObviousCrap()
self.determineLinearOrCircular(self.disordered_trace)
self.getOrderedTraces()
self.determineLinearOrCircular(self.ordered_traces)
self.getFittedTraces()
self.getSplinedTraces()
# self.findCurvature()
# self.saveCurvature()
self.measureContourLength()
self.measureEndtoEndDistance()
self.reportBasicStats()
[docs] def getNumpyArraysfromGwyddion(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.gwyddion_grains):
# Skip the background
if grain_num == 0:
continue
# Saves each grain as a multidim numpy array
single_grain_1d = np.array([1 if i == grain_num else 0 for i in self.gwyddion_grains])
self.grains[int(grain_num)] = np.reshape(single_grain_1d, (self.number_of_columns, self.number_of_rows))
# Get a 7 A gauss filtered version of the original image
# used in refining the pixel positions in getFittedTraces()
sigma = 0.7 / (self.pixel_size * 1e9)
self.gauss_image = filters.gaussian(self.full_image_data, sigma)
[docs] def getDisorderedTrace(self):
'''Function to make 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()):
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)
def purgeObviousCrap(self):
for dna_num in sorted(self.disordered_trace.keys()):
if len(self.disordered_trace[dna_num]) < 10:
self.disordered_trace.pop(dna_num, None)
[docs] def determineLinearOrCircular(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
def getOrderedTraces(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())
def reportBasicStats(self):
# self.determineLinearOrCircular()
print('There are %i circular and %i linear DNA molecules found in the image' % (
self.num_circular, self.num_linear))
[docs] def getFittedTraces(self):
'''
Creates self.fitted_traces dictonary which contains trace
coordinates (for each identified molecule) that are adjusted to lie
along the highest points of each traced molecule
:param self.ordered_traces: the unadjusted skeleton traces
:param self.gauss_image: gaussian filtered AFM image of the original molecules
:param index_width: 1/2th the width of the height profile indexed from self.gauss_image at each coordinate (e.g. 2*index_width pixels are indexed)
:return: no direct output but instance variable self.fitted_traces
is populated with adjusted x,y coordinates
'''
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 getSplinedTraces(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)
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
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)
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]
def showTraces(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()
def saveTraceFigures(self, filename_with_ext, channel_name, vmaxval, vminval, directory_name=None):
if directory_name:
filename_with_ext = self._checkForSaveDirectory(filename_with_ext, directory_name)
save_file = filename_with_ext[:-4]
# vmaxval = 20e-9
# vminval = -10e-9
plt.pcolormesh(self.full_image_data, vmax=vmaxval, vmin=vminval)
plt.colorbar()
plt.savefig('%s_%s_originalImage.png' % (save_file, channel_name))
plt.close()
# plt.pcolormesh(self.full_image_data, vmax=vmaxval, vmin=vminval)
# plt.colorbar()
# for dna_num in sorted(self.splined_traces.keys()):
# # disordered_trace_list = self.ordered_traces[dna_num].tolist()
# # less_dense_trace = np.array([disordered_trace_list[i] for i in range(0,len(disordered_trace_list),5)])
# plt.plot(self.splined_traces[dna_num][:, 0], self.splined_traces[dna_num][:, 1], color='c', linewidth=1.0)
# if self.mol_is_circular[dna_num]:
# starting_point = 0
# else:
# starting_point = self.neighbours
# length = len(self.curvature[dna_num])
# plt.plot(self.splined_traces[dna_num][starting_point, 0],
# self.splined_traces[dna_num][starting_point, 1],
# color='#D55E00', markersize=3.0, marker=5)
# plt.plot(self.splined_traces[dna_num][starting_point + int(length / 6), 0],
# self.splined_traces[dna_num][starting_point + int(length / 6), 1],
# color='#E69F00', markersize=3.0, marker=5)
# plt.plot(self.splined_traces[dna_num][starting_point + int(length / 6 * 2), 0],
# self.splined_traces[dna_num][starting_point + int(length / 6 * 2), 1],
# color='#F0E442', markersize=3.0, marker=5)
# plt.plot(self.splined_traces[dna_num][starting_point + int(length / 6 * 3), 0],
# self.splined_traces[dna_num][starting_point + int(length / 6 * 3), 1],
# color='#009E74', markersize=3.0, marker=5)
# plt.plot(self.splined_traces[dna_num][starting_point + int(length / 6 * 4), 0],
# self.splined_traces[dna_num][starting_point + int(length / 6 * 4), 1],
# color='#0071B2', markersize=3.0, marker=5)
# plt.plot(self.splined_traces[dna_num][starting_point + int(length / 6 * 5), 0],
# self.splined_traces[dna_num][starting_point + int(length / 6 * 5), 1],
# color='#CC79A7', markersize=3.0, marker=5)
# plt.savefig('%s_%s_splinedtrace_with_markers.png' % (save_file, channel_name))
# plt.close()
plt.pcolormesh(self.full_image_data, vmax=vmaxval, vmin=vminval)
plt.colorbar()
for dna_num in sorted(self.splined_traces.keys()):
plt.plot(self.splined_traces[dna_num][:, 0], self.splined_traces[dna_num][:, 1], color='c', linewidth=1.0)
plt.savefig('%s_%s_splinedtrace.png' % (save_file, channel_name))
plt.close()
'''
plt.pcolormesh(self.full_image_data)
plt.colorbar()
for dna_num in sorted(self.ordered_traces.keys()):
#disordered_trace_list = self.ordered_traces[dna_num].tolist()
#less_dense_trace = np.array([disordered_trace_list[i] for i in range(0,len(disordered_trace_list),5)])
plt.plot(self.ordered_traces[dna_num][:,0], self.ordered_traces[dna_num][:,1])
plt.savefig('%s_%s_splinedtrace.png' % (save_file, channel_name))
plt.close()
'''
plt.pcolormesh(self.full_image_data, vmax=vmaxval, vmin=vminval)
plt.colorbar()
for dna_num in sorted(self.disordered_trace.keys()):
# disordered_trace_list = self.disordered_trace[dna_num].tolist()
# less_dense_trace = np.array([disordered_trace_list[i] for i in range(0,len(disordered_trace_list),5)])
plt.plot(self.disordered_trace[dna_num][:, 0], self.disordered_trace[dna_num][:, 1], 'o', markersize=0.5,
color='c')
plt.savefig('%s_%s_disorderedtrace.png' % (save_file, channel_name))
plt.close()
plt.pcolormesh(self.full_image_data, vmax=vmaxval, vmin=vminval)
plt.colorbar()
for dna_num in sorted(self.grains.keys()):
grain_plt = np.argwhere(self.grains[dna_num] == 1)
plt.plot(grain_plt[:, 0], grain_plt[:, 1], 'o', markersize=2, color='c')
plt.savefig('%s_%s_grains.png' % (save_file, channel_name))
plt.close()
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
def findWrithe(self):
pass
def findCurvature(self):
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
def saveCurvature(self):
# 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.afm_image_name), "Curvature")):
os.mkdir(os.path.join(os.path.dirname(self.afm_image_name), "Curvature"))
directory = os.path.join(os.path.dirname(self.afm_image_name), "Curvature")
savename = os.path.join(directory, os.path.basename(self.afm_image_name)[:-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)
if not os.path.exists(os.path.join(os.path.dirname(self.afm_image_name), "Curvature")):
os.mkdir(os.path.join(os.path.dirname(self.afm_image_name), "Curvature"))
directory = os.path.join(os.path.dirname(self.afm_image_name), "Curvature")
savename = os.path.join(directory, os.path.basename(self.afm_image_name)[:-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('%s_%s_curvature.png' % (savename, dna_num))
plt.close()
[docs] def measureContourLength(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 * 1e9
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 * 1e9
del hypotenuse_array
break
def writeContourLengths(self, filename, channel_name):
if not self.contour_lengths:
self.measureContourLength()
with open('%s_%s_contours.txt' % (filename, channel_name), '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])
def writeCoordinates(self, dna_num):
if not os.path.exists(os.path.join(os.path.dirname(self.afm_image_name), "Coordinates")):
os.mkdir(os.path.join(os.path.dirname(self.afm_image_name), "Coordinates"))
directory = os.path.join(os.path.dirname(self.afm_image_name), "Coordinates")
savename = os.path.join(directory, os.path.basename(self.afm_image_name)[:-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('%s_%s.csv' % (savename, dna_num))
plt.plot(coordinates_array[:, 0], coordinates_array[:, 1], 'ko')
plt.savefig('%s_%s_coordinates.png' % (savename, dna_num))
def measureEndtoEndDistance(self):
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 * 1e9
[docs]class traceStats(object):
''' Class used to report on the stats for all the traced molecules in the
given directory '''
def __init__(self, trace_object):
self.trace_object = trace_object
self.pd_dataframe = []
self.createTraceStatsObject()
[docs] def createTraceStatsObject(self):
'''Creates a pandas dataframe with the shape:
dna_num directory ImageName contourLength Circular
1 exp_dir img1_name 200 True
2 exp_dir img2_name 210 False
3 exp_dir2 img3_name 100 True
'''
data_dict = {}
trace_directory_file = self.trace_object.afm_image_name
trace_directory = os.path.dirname(trace_directory_file)
basename = os.path.basename(trace_directory)
img_name = os.path.basename(trace_directory_file)
for mol_num, dna_num in enumerate(sorted(self.trace_object.ordered_traces.keys())):
try:
data_dict['Molecule number'].append(mol_num)
data_dict['Image Name'].append(img_name)
data_dict['Experiment Directory'].append(trace_directory)
data_dict['Basename'].append(basename)
data_dict['Contour Lengths'].append(self.trace_object.contour_lengths[dna_num])
data_dict['Circular'].append(self.trace_object.mol_is_circular[dna_num])
data_dict['End to End Distance'].append(self.trace_object.end_to_end_distance[dna_num])
except KeyError:
data_dict['Molecule number'] = [mol_num]
data_dict['Image Name'] = [img_name]
data_dict['Experiment Directory'] = [trace_directory]
data_dict['Basename'] = [basename]
data_dict['Contour Lengths'] = [self.trace_object.contour_lengths[dna_num]]
data_dict['Circular'] = [self.trace_object.mol_is_circular[dna_num]]
data_dict['End to End Distance'] = [self.trace_object.end_to_end_distance[dna_num]]
self.pd_dataframe = pd.DataFrame(data=data_dict)
def updateTraceStats(self, new_traces):
data_dict = {}
trace_directory_file = new_traces.afm_image_name
trace_directory = os.path.dirname(trace_directory_file)
basename = os.path.basename(trace_directory)
img_name = os.path.basename(trace_directory_file)
for mol_num, dna_num in enumerate(sorted(new_traces.contour_lengths.keys())):
try:
data_dict['Molecule number'].append(mol_num)
data_dict['Image Name'].append(img_name)
data_dict['Experiment Directory'].append(trace_directory)
data_dict['Basename'].append(basename)
data_dict['Contour Lengths'].append(new_traces.contour_lengths[dna_num])
data_dict['Circular'].append(new_traces.mol_is_circular[dna_num])
data_dict['End to End Distance'].append(new_traces.end_to_end_distance[dna_num])
except KeyError:
data_dict['Molecule number'] = [mol_num]
data_dict['Image Name'] = [img_name]
data_dict['Experiment Directory'] = [trace_directory]
data_dict['Basename'] = [basename]
data_dict['Contour Lengths'] = [new_traces.contour_lengths[dna_num]]
data_dict['Circular'] = [new_traces.mol_is_circular[dna_num]]
data_dict['End to End Distance'] = [new_traces.end_to_end_distance[dna_num]]
pd_new_traces_dframe = pd.DataFrame(data=data_dict)
self.pd_dataframe = self.pd_dataframe.append(pd_new_traces_dframe, ignore_index=True)
def saveTraceStats(self, save_path):
save_file_name = ''
if save_path[-1] == '/':
pass
else:
save_path = save_path + '/'
for i in self.trace_object.afm_image_name.split('/')[:-1]:
save_file_name = save_file_name + i + '/'
print(save_file_name)
self.pd_dataframe.to_json('%stracestats.json' % save_path)
self.pd_dataframe.to_csv('%stracestats.csv' % save_path)
print('Saved trace info for all analysed images into: %stracestats.json' % save_path)