def plot_variances(filename):
_, x, y, z, _, vx, vy, vz, _, _, _ = read_file(filename)
zbins, xbins, x_variances, y_variances, z_variances = twoD_binned_energy_fluctuations(
x, y, z, vx, vy, vz, min(z), 1, min(x), 1)
plt.figure()
plt.contourf(xbins, zbins, x_variances)
plt.colorbar()
plt.xlim(xbins[0], xbins[-1])
plt.ylim(zbins[0], zbins[-1])
plt.show()
xplot = plt.hexbin(x, y, C=vx, gridsize=gridsize)
yplot = plt.hexbin(x, y, C=vy, gridsize=gridsize)
plt.colorbar()
xdata = xplot.get_array()
ydata = yplot.get_array()
for i in range(len(xdata)):
factor = (xdata[i]**2 + ydata[i]**2)**0.5
xdata[i] /= factor
ydata[i] /= factor
x, y = get_centers(xplot)
plt.figure()
plt.xlim([-90, 70])
plt.ylim([70, 210])
plt.gca().set_aspect('equal')
plt.quiver(x,
y,
xdata,
ydata,
headwidth=3,
headlength=2,
headaxislength=1.5)
plt.show()
if __name__ == '__main__':
__, x, y, __, __, vx, vy, __, __, __, __ = read_file("../Data/q_hig_10")
plotVelocities(x, y, vx, vy, gridsize=50)
from file_reading import read_file
from mayavi import mlab
import matplotlib.pyplot as plt
import numpy as np
import hexbin
t,x,y,z,u,vx,vy,vz,uvx,uvy,uvz = read_file("../Data/h_med_5")
mlab.quiver3d(x,y,z, vx, vy, vz)
mlab.vectorbar(title="Velocity (metres per second)", orientation='Vertical', nb_labels=4)
mlab.show()
popt = fit(f,t,x, p0, names, u = u)
##################################################PLOTTING HERE############################################################
plt.figure()
plt.title("Angular fit")
plt.plot(t,x, 'ro', markersize = 0.5) #Plots the raw data
tmodel = np.linspace(t[0], t[-1], 10000)
plt.plot(tmodel, f(tmodel, *popt)) #Plots using the returned fit parameters
def findLinearVelocity(t,v_mag):
mean = np.mean(v_mag)
std = np.std(v_mag)
print("Mean:", mean, " m/s - Standard deviation:", std, "m/s")
for i,filename in enumerate(filenames):
print(filename)
print("______________________________________")
t,x,y,z,u,vx,vy,vz,uvx,uvy,uvz = read_file(filename)
v_mag = magnitude(vx,vy,vz)
print("______________Linear Speed______________")
findLinearVelocity(t,v_mag)
print("_______________Angular Speed_______________")
findAngularSpeed(t,x,u, angular_p0s[i])
print("______________________________________")
window = np.ones(window_size) / window_size
smoothed_y = np.convolve(y, window, mode='same')
return smoothed_y
filenames = [
"f_hig_5", "f_low_51", "f_med_5", "h_hig_5", "h_hig_10", "h_low_10",
"h_low_51", "h_med_5", "h_med_10", "q_hig_5", "q_hig_10", "q_low_10",
"q_low_51", "q_med_5_2", "q_med_10", "t_hig_5", "t_hig_10", "t_low_5",
"t_low_10", "t_med_5", "t_med_10"
]
for filename in filenames:
bead_size = 5 #Using smallest size, since even 10mm can make 5mm changes
t, x, y, z, u, vx, vy, vz, uvx, uvy, uvz = read_file("../Data/" + filename)
x = smooth_data(x, 10) # Smoothing data to make peak finding better
#Can be used to plot if wanted
# t = t[10000:30000]
# x = x[10000:30000]
# plt.figure()
# plt.plot(t,x)
indices, properties = find_peaks(
x, prominence=1, distance=30, width=5
) # Finds peaks in x, prominence main setting, distance and width settings reduce false peaks
# plt.plot(t[indices], x[indices] , 'ro')
count = 0