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
]
pos = "inlet"
axes = []
for j in range(3):
data_list = []
config_list = []
for pressure in [1, 2, 3]:
data1, config1 = load_all_data(files[j], pressure=pressure)
data_list.append(data1)
config_list.append(config1)
data = pd.concat(data_list)
config = config_list[0]
p = fitStiffness(data, config)
if 0:
fits = get_bootstrap_fit(data, config, 100)
np.save(__file__[:-3] + f"_fits{j}.npy", fits)
else:
fits = np.load(__file__[:-3] + f"_fits{j}.npy")
print("fits", fits.shape)
p2 = np.std(fits, axis=0)
for i in config_list:
config1["fit"] = config["fit"]
for i in range(3):
axes.append(plt.subplot(3, 4, 4 * j + 1 + i))
if i == 0:
plt.text(
0.5, 0.5,
import numpy as np
import matplotlib.pyplot as plt
import pylustrator
pylustrator.start()
data3, config3 = load_all_data([
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\september_2020\2020_09_30_alginate2%_NIH3T3_blebbistatin\inlet\[0-9]\*_result.txt"
], pressure=3)
data3b, config3b = load_all_data([
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope_1\september_2020\2020_09_30_alginate2%_NIH3T3_DMSO\inlet\[0-9]\*_result.txt",
], pressure=3)
fitStiffness(data3, config3)
fitStiffness(data3b, config3b)
plt.subplot(121)
plotStressStrain(data3b, config3b)
plotStressStrainFit(data3b, config3b)
plt.title("DMSO")
plt.text(0.5, 0.5, f"k = {config3b['fit']['p'][0]:3.0f} Pa\n$\\alpha$ = {config3b['fit']['p'][1]:2.2f}")
plt.subplot(122)
plotStressStrain(data3, config3)
plotStressStrainFit(data3, config3)
plt.title("blebbistation")
plt.text(0.5, 0.5, f"k = {config3['fit']['p'][0]:3.0f} Pa\n$\\alpha$ = {config3['fit']['p'][1]:2.2f}")
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
(np.pi * 2 * config1["imaging_pos_mm"])))**alpha) + p2
#return np.log(x/k + 1) / ((np.abs(w2) + (v2/(np.pi*2*config1["imaging_pos_mm"]))) ** alpha) + p2
def fitfunc3(x, p0, p1, p2): # for stress versus strain
k = p0 #(p0 + x)
alpha = p1
#return x / (k * (np.abs(w)) ** alpha)
return x / (k * (np.abs(w3) +
(v3 /
(np.pi * 2 * config1["imaging_pos_mm"])))**alpha) + p2
#return np.log(x/k + 1) / ((np.abs(w3) + (v3/(np.pi*2*config1["imaging_pos_mm"]))) ** alpha) + p2
indices = data.stress < 9999999
p = fitStiffness(data, config)
p1 = p
p2 = p
p3 = p
if 0:
ps = []
ii = np.arange(10, 300, 50)
for i in ii:
indices = data.stress < i
p = fitStiffness(data[data.stress < i], config)
ps.append(p)
print(i, p)
p1 = p
p2 = p
p3 = p
data1, config1 = load_all_data([
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_july\2020_07_29_aslginate2%_NIH_diff_x_position_2\inlet\[0-9]\*_result.txt",
r"\\131.188.117.96\biophysDS\emirzahossein\microfluidic cell rhemeter data\microscope4\2020_july\2020_07_29_aslginate2%_NIH_diff_x_position_3\inlet\[0-9]\*_result.txt",
], pressure=1)
data = data1#pd.concat([data1, data2, data3])
config = config1
fits = []
frac = np.arange(0.1, 1.1, 0.1)
for i in frac:
d = data[data.stress < i]
ps = []
for j in range(100):
d0 = d.sample(frac=i, replace=True)
p = fitStiffness(d0, config)
#p = np.random.rand(3)
ps.append(p)
fits.append(ps)
fits = np.array(fits)
#np.save(__file__[:-3], fits)
print(fits)
print(fits.shape)
fit_means = np.mean(fits, axis=-2)
print(fit_means)
fit_stds = np.std(fits, axis=-2)
print(fit_stds)
for i in range(3):
plt.subplot(1, 3, i+1)
print(fit_means.shape)
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
