def plotPathList(paths):
global ax
paths = list(paths)
print(paths)
fit_data = []
data_list = []
for index, file in enumerate(paths):#[repetition:repetition+1]):
output_file = Path(str(file).replace("_result.txt", "_evaluated.csv"))
# load the data and the config
data = getData(file)
config = getConfig(file)
""" evaluating data"""
if not output_file.exists():
#refetchTimestamps(data, config)
getVelocity(data, config)
# take the mean of all values of each cell
data = data.groupby(['cell_id']).mean()
correctCenter(data, config)
data = filterCells(data, config)
# reset the indices
data.reset_index(drop=True, inplace=True)
getStressStrain(data, config)
#data = data[(data.stress < 50)]
data.reset_index(drop=True, inplace=True)
data["area"] = data.long_axis * data.short_axis * np.pi
data.to_csv(output_file, index=False)
data = pd.read_csv(output_file)
#data = data[(data.area > 0) * (data.area < 2000) * (data.stress < 250)]
#data.reset_index(drop=True, inplace=True)
data_list.append(data)
data = pd.concat(data_list)
data.reset_index(drop=True, inplace=True)
fitStiffness(data, config)
#plotDensityScatter(data.stress, data.strain)
#plotStressStrainFit(data, config)
#plotBinnedData(data.stress, data.strain, [0, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250])
#plt.title(f'{config["fit"]["p"][0] * config["fit"]["p"][1]:.2f}')
fit_data.append([config["fit"]["p"][0], config["fit"]["p"][1], config["fit"]["p"][0] * config["fit"]["p"][1]])
return fit_data
flatfield = output_path + r'/' + filename_base + '.npy'
configfile = output_path + r'/' + filename_base + '_config.txt'
#%% Setup model
# shallow model (faster)
#unet = UNet().create_model((720, 540, 1), 1, d=8)
unet = UNet().create_model((540, 720, 1), 1, d=8)
# change path for weights
unet.load_weights(
str(
Path(__file__).parent /
"weights/Unet_0-0-5_fl_RAdam_20200610-141144.h5"))
#%%
config = getConfig(configfile)
batch_size = 100
print(video)
vidcap = imageio.get_reader(video)
vidcap2 = getRawVideo(video)
progressbar = tqdm.tqdm(vidcap)
cells = []
im = vidcap.get_data(0)
batch_images = np.zeros([batch_size, im.shape[0], im.shape[1]],
dtype=np.float32)
batch_image_indices = []
ips = 0
for image_index, im in enumerate(progressbar):
# 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 scripts.helper_functions import getInputFile, getConfig, getData
from scripts.helper_functions import refetchTimestamps, getVelocity, filterCells, correctCenter, getStressStrain, fitStiffness
from scripts.helper_functions import initPlotSettings, plotVelocityProfile, plotStressStrain, plotMessurementStatus
from scripts.helper_functions import storeEvaluationResults
""" 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 = getData(datafile)
config = getConfig(datafile)
""" evaluating data"""
#refetchTimestamps(data, config)
getVelocity(data, config)
# take the mean of all values of each cell
data = data.groupby(['cell_id']).mean()
correctCenter(data, config)
data = filterCells(data, config)
# reset the indices
data.reset_index(drop=True, inplace=True)
rows = 1
cols = 3
#row_index = 0
data_index = -1
dataset = datasets[0]
datafiles = dataset["datafiles"]
for data_index, datafile in enumerate(datafiles):
data_index += 1
paths = []
pressures = []
ax = None
datafiles = dataset["datafiles"]
#
for index, file in enumerate(Path(datafile).glob("**/*_result.txt")):
config = getConfig(file)
paths.append(file)
pressures.append(config['pressure_pa'] / 100_000)
paths = np.array(paths)
pressures = np.array(pressures)
unique_pressures = np.unique(pressures)
unique_pressures = [1, 2, 3] #unique_pressures[unique_pressures > 0.5]
print(unique_pressures)
fit_data = []
index = 1
plt.subplot(1, 2, 1)
f = plotPathList(paths[pressures == 1])
def plotPathList(paths):
global ax
paths = list(paths)
print(paths)
fit_data = []
data_list = []
for index, file in enumerate(paths):
output_file = Path(str(file).replace("_result.txt", "_evaluated.csv"))
# load the data and the config
data = getData(file)
config = getConfig(file)
""" evaluating data"""
if not output_file.exists():
#refetchTimestamps(data, config)
getVelocity(data, config)
# take the mean of all values of each cell
data = data.groupby(['cell_id']).mean()
correctCenter(data, config)
data = filterCells(data, config)
# reset the indices
data.reset_index(drop=True, inplace=True)
getStressStrain(data, config)
#data = data[(data.stress < 50)]
data.reset_index(drop=True, inplace=True)
data["area"] = data.long_axis * data.short_axis * np.pi
data.to_csv(output_file, index=False)
data = pd.read_csv(output_file)
if 0:
plt.plot(data.rp, data.angle, "o")
plt.axhline(0)
plt.axvline(0)
plt.axhline(45)
plt.axhline(-45)
print(data.angle)
plt.show()
#data = data[(data.area > 0) * (data.area < 2000) * (data.stress < 250)]
#data.reset_index(drop=True, inplace=True)
data_list.append(data)
data = pd.concat(data_list)
data.reset_index(drop=True, inplace=True)
getStressStrain(data, config)
if 0:
if 1:
data.strain[(data.angle > 0) & (data.rp > 0)] *= -1
data.strain[(data.angle < 0) & (data.rp < 0)] *= -1
else:
data.strain[(data.angle > 45)] *= -1
data.strain[(data.angle < -45)] *= -1
fits = []
errors = []
for i in np.arange(30, 250, 10):
data2 = data[data.stress < i].reset_index(drop=True)
print(i, len(data2))
fitStiffness(data2, config)
fits.append(config["fit"]["p"])
errors.append(config["fit"]["err"])
print("err", config["fit"]["err"], errors)
plotDensityScatter(data.stress, data.strain)
plotStressStrainFit(data, config)
plotBinnedData(data.stress, data.strain,
[0, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250])
#plt.title(f'{config["fit"]["p"][0] * config["fit"]["p"][1]:.2f}')
fit_data.append(config["fit"]["p"][0] * config["fit"]["p"][1])
return fits, errors #fit_data
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
video = getInputFile()
#%%
config = getConfig(video)
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)]
#data = data[(data.solidity > 0.98) & (data.irregularity < 1.02)]
data.reset_index(drop=True, inplace=True)
def plotPathList(paths, cmap=None, alpha=None):
global ax, global_im
paths = list(paths)
print(paths)
fit_data = []
data_list = []
for index, file in enumerate(paths):
output_file = Path(str(file).replace("_result.txt", "_evaluated.csv"))
# load the data and the config
data = getData(file)
config = getConfig(file)
""" evaluating data"""
if not output_file.exists():
#refetchTimestamps(data, config)
getVelocity(data, config)
# take the mean of all values of each cell
data = data.groupby(['cell_id']).mean()
correctCenter(data, config)
data = filterCells(data, config)
# reset the indices
data.reset_index(drop=True, inplace=True)
getStressStrain(data, config)
#data = data[(data.stress < 50)]
data.reset_index(drop=True, inplace=True)
data["area"] = data.long_axis * data.short_axis * np.pi
data.to_csv(output_file, index=False)
data = pd.read_csv(output_file)
#data = data[(data.area > 0) * (data.area < 2000) * (data.stress < 250)]
#data.reset_index(drop=True, inplace=True)
data_list.append(data)
data = pd.concat(data_list)
data.reset_index(drop=True, inplace=True)
fitStiffness(data, config)
#plotDensityScatter(data.stress, data.strain, cmap=cmap, alpha=0.5)
def densityPlot(x, y, cmap, alpha=0.5):
global global_im, global_index
from scipy.stats import kde
ax = plt.gca()
# Thus we can cut the plotting window in several hexbins
nbins = np.max(x) / 10
ybins = 20
# Evaluate a gaussian kde on a regular grid of nbins x nbins over data extents
k = kde.gaussian_kde(np.vstack([x, y]))
if 0:
xi, yi = np.mgrid[x.min():x.max():nbins * 1j,
y.min():y.max():ybins * 1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
# plot a density
ax.set_title('Calculate Gaussian KDE')
ax.pcolormesh(xi,
yi,
zi.reshape(xi.shape),
shading='gouraud',
alpha=alpha,
cmap=cmap)
else:
xi, yi = np.meshgrid(
np.linspace(-10, 250, 200), np.linspace(0, 1, 80)
) #np.mgrid[x.min():x.max():nbins * 1j, y.min():y.max():ybins * 1j]
zi = k(np.vstack([xi.flatten(), yi.flatten()]))
im = zi.reshape(xi.shape)
if 0:
if global_im is None:
global_im = np.zeros((im.shape[0], im.shape[1], 3),
dtype="uint8")
if 1: #global_index == 1:
print("_____", im.min(), im.max())
im -= np.percentile(im, 10)
global_im[:, :, global_index] = im / im.max() * 255
print("_____", global_im[:, :, global_index].min(),
global_im[:, :, global_index].max())
print("COLOR", global_index)
global_index += 1
if global_index == 3:
print(global_im.shape, global_im.dtype)
plt.imshow(global_im[::-1],
extent=[
np.min(xi),
np.max(xi),
np.min(yi),
np.max(yi)
],
aspect="auto")
else:
if global_im is None:
global_im = []
im -= im.min()
im /= im.max()
global_im.append(plt.get_cmap(cmap)(im**0.5))
global_im[-1][:, :, 3] = im
plt.imshow(
global_im[-1][::-1],
vmin=0,
vmax=1,
extent=[np.min(xi),
np.max(xi),
np.min(yi),
np.max(yi)],
aspect="auto")
global_index += 1
if global_index == 3:
print("COLOR", global_im[0].shape, global_im[0].min(),
global_im[0].max())
im = global_im[0] + global_im[1] + global_im[2] - 2
#im[im<0] = 0
#im[im>255] = 255
print("COLOR", im.shape, im.min(), im.max())
#plt.imshow(im[::-1], vmin=0, vmax=1, extent=[np.min(xi), np.max(xi), np.min(yi), np.max(yi)], aspect="auto")
densityPlot(data.stress, data.strain, cmap=cmap, alpha=alpha)
plotStressStrainFit(data, config)
#plotStressStrainFit(data, config)
#plotBinnedData(data.stress, data.strain, [0, 10, 20, 30, 40, 50, 75, 100, 125, 150, 200, 250])
#plt.title(f'{config["fit"]["p"][0] * config["fit"]["p"][1]:.2f}')
fit_data.append(config["fit"]["p"][0] * config["fit"]["p"][1])
return fit_data
