# and fits a stress stiffening equation to the data
# The results such as maximum flow speed, cell mechanical parameters, etc. are stored in
# the file 'all_data.txt' located at the same directory as this script
"""
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from deformationcytometer.includes.includes import getInputFile, getConfig, getData
#refetchTimestamps,
from deformationcytometer.evaluation.helper_functions import getVelocity, filterCells, correctCenter, getStressStrain, fitStiffness
from deformationcytometer.evaluation.helper_functions import initPlotSettings, plotVelocityProfile, plotStressStrain, plotMessurementStatus
from deformationcytometer.evaluation.helper_functions import storeEvaluationResults
from deformationcytometer.evaluation.helper_functions import load_all_data
""" loading data """
# get the results file (by config parameter or user input dialog)
datafile = getInputFile(filetype=[("txt file", '*_result.txt')])
# load the data and the config
data, config = load_all_data(datafile)
fitStiffness(data, config)
""" plotting data """
initPlotSettings()
# add multipage plotting
pp = PdfPages(datafile[:-11] + '.pdf')
# generate the velocity profile plot
plotVelocityProfile(data, config)
# update the count of the progressbar with the current batch
progressbar.update(len(batch_image_indices))
# save the results
save_cells_to_file(Path(video[:-3] + '_result.txt'), cells)
if __name__ == "__main__":
from multiprocessing import Process, Queue
from deformationcytometer.includes.includes import getInputFile, read_args_detect_cells
# reading commandline arguments if executed from terminal
file, network_weight = read_args_detect_cells()
video = getInputFile(settings_name="detect_cells.py", video=file)
print(video)
# initialize the queues
image_batch_queue = Queue(2)
mask_queue = Queue(2)
# initialize the processes
processes = [
Process(target=process_load_images, args=(video, image_batch_queue)),
Process(target=process_detect_masks,
args=(video, image_batch_queue, mask_queue)),
Process(target=process_find_cells, args=(video, mask_queue)),
]
# start the processes
for p in processes:
def getArcLength(points, major_axis, minor_axis, ellipse_angle, center):
p = points - np.array(center)#[None, None]
alpha = np.deg2rad(ellipse_angle)
p = p @ np.array([[np.cos(alpha), -np.sin(alpha)], [np.sin(alpha), np.cos(alpha)]])
distance_from_center = np.linalg.norm(p, axis=-1)
angle = np.arctan2(p[..., 0], p[..., 1])
angle = np.arctan2(np.sin(angle) / (major_axis / 2), np.cos(angle) / (minor_axis / 2))
angle = np.unwrap(angle)
r = np.linalg.norm([major_axis / 2 * np.sin(angle), minor_axis / 2 * np.cos(angle)], axis=0)
length = getEllipseArcSegment(angle, minor_axis/2, major_axis/2)
return length, distance_from_center/r
video = getInputFile()
#video = r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\september_2020\2020_09_15_alginate2%_NIH_tanktreading_1\2\2020_09_15_10_30_43.tif"#getInputFile()
id = 8953
id = 5084
id = 4115
config = getConfig(video)
target_folder = Path(video[:-4])
data = pd.read_csv(target_folder / "output.csv")
print(data)
getStressStrain(data, config)
from deformationcytometer.includes.includes import getInputFile, read_args_evaluate
import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
from deformationcytometer.evaluation.helper_functions import plotDensityScatter, load_all_data_new, get_cell_properties
from deformationcytometer.evaluation.helper_functions import plot_velocity_fit, plot_density_hist, \
plotDensityLevels, plotBinnedData
settings_name = "strain_vs_stress_clean"
""" loading data """
# reading commandline arguments if executed from terminal
file, irregularity_threshold, solidity_threshold = read_args_evaluate()
# get the results file (by command line parameter or user input dialog)
datafile = getInputFile(filetype="csv file (*_evaluated_new.csv)", settings_name=settings_name, video=file)
print("evaluate file", datafile)
# load the data and the config
data, config = load_all_data_new(datafile)
plt.figure(0, (10, 8))
plt.subplot(2, 3, 1)
plt.cla()
plot_velocity_fit(data)
plt.text(0.9, 0.9, f"$\\eta_0$ {data.eta0[0]:.2f}\n$\\delta$ {data.delta[0]:.2f}\n$\\tau$ {data.tau[0]:.2f}", transform=plt.gca().transAxes, va="top", ha="right")
plt.subplot(2, 3, 2)
plt.cla()
plotDensityScatter(data.stress, data.epsilon)
plotBinnedData(data.stress, data.epsilon, bins=np.arange(0, 300, 10))
num = 20
num = np.round(sp.special.ellipeinc(2.0 * np.pi, e))
angles = 2 * np.pi * np.arange(num) / num
if a != b:
tot_size = sp.special.ellipeinc(2.0 * np.pi, e)
arc_size = tot_size / num
arcs = np.arange(num) * arc_size
res = sp.optimize.root(lambda x: (sp.special.ellipeinc(x, e) - arcs),
angles)
angles = res.x
return angles
r_min = 5 #cells smaller than r_min (in um) will not be analyzed
file = read_args_tank_treading()
video = getInputFile(settings_name=settings_name, video=file)
#%%
config = getConfig(video)
config["channel_width_m"] = 0.00019001261833616293
data = getData(video)
getVelocity(data, config)
# take the mean of all values of each cell
#data = data.groupby(['cell_id']).mean()
correctCenter(data, config)
#exit()
data = data[(data.solidity > 0.96) & (data.irregularity < 1.06)]
from pathlib import Path import tqdm import pandas as pd from scipy.ndimage import shift import skimage.registration from deformationcytometer.includes.includes import getInputFile, read_args_tank_treading from deformationcytometer.evaluation.helper_functions import getConfig, getData, getVelocity, correctCenter from deformationcytometer.evaluation.helper_functions import fit_func_velocity import scipy as sp import scipy.optimize import tifffile from deformationcytometer.tanktreading.helpers import getCroppedImages, doTracking, CachedImageReader file = read_args_tank_treading() video = getInputFile(settings_name="extract_cell_snippets.py", video=file) print(video) config = getConfig(video) config["channel_width_m"] = 0.00019001261833616293 data = getData(video) getVelocity(data, config) correctCenter(data, config) data = data[(data.solidity > 0.96) & (data.irregularity < 1.06)] data.reset_index(drop=True, inplace=True) ids = pd.unique(data["cell_id"])
