def load_one_data(input_path):
output_file = Path(str(file).replace("_result.txt", "_evaluated.csv"))
output_config_file = Path(
str(file).replace("_result.txt", "_evaluated_config.txt"))
# load the data and the config
data = getData(file)
config = getConfig(file)
config["channel_width_m"] = 0.00019001261833616293
""" evaluating data"""
getVelocity(data, config)
# take the mean of all values of each cell
#data = data.groupby(['cell_id'], as_index=False).mean()
if 0:
tt_file = Path(str(file).replace("_result.txt", "._tt.csv"))
if tt_file.exists():
data.set_index("cell_id", inplace=True)
data_tt = pd.read_csv(tt_file)
data["omega"] = np.zeros(len(data)) * np.nan
for i, d in data_tt.iterrows():
if d.tt_r2 > 0.2: # and d.id in data.index:
data.at[d.id, "omega"] = d.tt * 2 * np.pi
data.reset_index(inplace=True)
else:
print("WARNING: tank treading has not been evaluated yet")
correctCenter(data, config)
#data = filterCells(data, config, solidity_threshold, irregularity_threshold)
# 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["pressure"] = config["pressure_pa"] * 1e-5
data, p = apply_velocity_fit(data)
omega, mu1, eta1, k_cell, alpha_cell, epsilon = get_cell_properties(data)
#fitStiffness(data, config)
return data, config
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)
ids = pd.unique(data["cell_id"])
image_reader = imageio.get_reader(video)
def dataWithGradient(filename):
# %%
config = getConfig(filename)
config["channel_width_m"] = 0.00019001261833616293
data = getData(filename)
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)
getStressStrain(data, config)
def getVelGrad(r):
p0, p1, p2 = config["vel_fit"]
r = r
p0 = p0 * 1e3
r0 = config["channel_width_m"] * 0.5 * 1e6 # 100e-6
return - (p1 * p0 * (np.abs(r) / r0) ** p1) / r
vel = fit_func_velocity(config)
import scipy.optimize
def curve(x, x0, delta1, a1, delta2, a2, w):
x = np.abs(x)
delta1 = 2
return x0 * ((a1+a2) - a1 * (x) ** delta1 - (a2 * x) ** delta2)
def derivative(x, x0, d1, a1, d2, a2):
d1 = 2
return -((d1 * (a1 * np.abs(x)) ** d1 + d2 * (a2 * np.abs(x)) ** d2) * x0) / x
# def curve(x, x0, delta, delta2):
# return x0 * ((2) - (x)**delta - (x)**delta2)
return data
x = data.rp * 1e-6
y = data.velocity * 1e-3
x, y = removeNan(x, y)
p, popt = scipy.optimize.curve_fit(curve, x, y, [1.00512197e-02, 1.00000000e+00, 9.15342978e+03, 4.85959006e+00,
1.15235584e+04])#[25e-3, 1, 1 / 95e-6, 2, 1 / 95e-6])
getVelGrad = lambda x: derivative(x*1e-6, *p)
vel = lambda x: curve(x*1e-6, *p) * 1e3
data["grad"] = getVelGrad(data.rp)#/(2*np.pi)
dx = data.short_axis/2
#data["grad"] = (vel(data.rp+dx)-vel(data.rp))*1e-3/(dx*1e-6)
if 1:
print("vel fit", p)
plt.figure(10)
plt.subplot(131)
plt.plot(data.rp * 1e-6, data.velocity * 1e-3, "o")
dx = 1
x = np.arange(-100, 100, dx) * 1e-6
v = vel(x * 1e6) * 1e-3
plt.plot(x, v, "r+")
plt.axhline(0, color="k", lw=0.8)
plt.subplot(132)
grad = np.diff(v) / np.diff(x) # * 1e3
plt.plot(data.rp * 1e-6, data.velocity_gradient, "o")
plt.plot(data.rp * 1e-6, getVelGrad(data.rp), "s")
plt.plot(x[:-1] + 0.5 * np.diff(x), grad, "-+")
plt.show()
return data