def imu_hist_plot_multiple(x_vals, y_vals, z_vals):
fig, (ax1, ax2, ax3) = plt.subplots(1,
3,
figsize=set_size(width, subplot=[1,
3]))
ax1.hist(np.array(x_vals), bins=280, edgecolor='black', color='#2ca02c')
ax1.set_xlabel(r'Acceleration $(mm/{s^2})$')
ax1.set_ylabel(r'Frequency')
ax1.set_title(r'${a_x}_i$')
ax1.set_xlim(-250.0, 250.0)
ax2.hist(np.array(y_vals), bins=280, edgecolor='black', color='#ff7f0e')
ax2.set_xlabel(r'Acceleration $(mm/{s^2})$')
ax2.set_title(r'${a_y}_i$')
ax2.set_xlim(-250.0, 250.0)
# ax2.set_ylabel(r'Frequency')
ax3.hist(np.array(z_vals), bins=800, edgecolor='black', color='#d62728')
ax3.set_xlabel(r'Orientation $(deg)$')
ax3.set_title(r'$\theta_i$')
ax3.set_xlim(335, 385)
# ax3.set_ylabel(r'Frequency')
plt.subplots_adjust(wspace=0.5)
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/imu_hist_multiple.pdf',
format='pdf',
bbox_inches='tight')
def lidar_plots(data_scan, title):
fig, (ax1, ax2) = plt.subplots(2,
1,
figsize=set_size(width, subplot=[2, 1]))
ax1.plot(data_scan, color='#1f77b4')
ax1.set_xlabel(r'Angle $(deg)$')
ax1.set_ylabel(r'Distance $(mm)$')
ax1.set_title(r'Lidar Scan - Distance vs. Angle')
x_pos = []
y_pos = []
x_lim = []
y_lim = []
max_range = 1792
for i, dist in enumerate(data_scan):
radians = (i * np.pi / 180)
x_pos.append(-1 * dist * np.sin(radians))
y_pos.append(-1 * dist * np.cos(radians))
ax2.scatter(x_pos, y_pos, c='#1f77b4')
for i in range(360):
radians = (i * np.pi / 180)
x_lim.append(max_range * np.cos(radians))
y_lim.append(max_range * np.sin(radians))
ax2.scatter(x_lim, y_lim, c='#ff7f0e')
ax2.set_xlabel(r'X (mm)')
ax2.set_ylabel(r'Y (mm)')
ax2.axis('equal')
ax2.set_xlim(-2000, 2000)
ax2.set_xlim(-2000, 2000)
ax2.set_title(r'Lidar Scan - Real World Coordinates')
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/lidar_plot_{}.pdf'
.format(title),
format='pdf',
bbox_inches='tight')
def position_plot(x_vals, y_vals, title):
fig, ax1 = plt.subplots(1, 1, figsize=set_size(width, subplot=[1, 1]))
ax1.scatter(x_vals, y_vals)
ax1.set_title(r'Position Plot')
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/position_{}.pdf'.
format(title),
format='pdf',
bbox_inches='tight')
def lidar_plot_raw_data(data_scan):
fig, ax1 = plt.subplots(1, 1, figsize=set_size(width, subplot=[1, 1]))
ax1.plot(data_scan, color='#1f77b4')
ax1.set_xlabel(r'Angle $(deg)$')
ax1.set_ylabel(r'Distance $(mm)$')
ax1.set_title(r'Lidar Scan - Distance vs. Angle')
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/lidar_plot_raw_data.pdf',
format='pdf',
bbox_inches='tight')
def imu_time_plot(time_vals, data_vals, title, color='#d62728'):
fig, ax1 = plt.subplots(1, 1, figsize=set_size(width, subplot=[1, 1]))
ax1.plot(time_vals, data_vals, color=color)
ax1.set_xlabel(r'Time $(s)$')
ax1.set_ylabel(r'Orientation $(deg)$')
ax1.set_title(title)
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/imu_plot_{}.pdf'.
format(title),
format='pdf',
bbox_inches='tight')
def encoder_plot_lr(l_vals, r_vals):
fig, ax1 = plt.subplots(1, 1, figsize=set_size(width, subplot=[1, 1]))
ax1.plot(l_vals, label='Left Encoder')
ax1.plot(r_vals, label='Right Encoder')
ax1.set_xlabel(r'Samples')
ax1.set_ylabel(r'Encoder Counts')
ax1.legend()
ax1.set_title(r'Quadrature Encoder Sample Data')
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/encoder_plot_lr.pdf',
format='pdf',
bbox_inches='tight')
def compare_plots(title, *args):
fig, ax1 = plt.subplots(1, 1, figsize=set_size(width, subplot=[1, 1]))
for i, data in enumerate(args):
ax1.plot(data, label=i)
ax1.legend()
ax1.set_title(r'Kalman Filter - Comparison')
ax1.set_xlabel(r'Samples')
ax1.set_ylabel(r'Displacement (mm)')
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/compare_{}.pdf'.
format(title),
format='pdf',
bbox_inches='tight')
def imu_hist_plot(data_vals, title, x_axis_limits=None, color='#1f77b4'):
fig, ax1 = plt.subplots(1, 1, figsize=set_size(width, subplot=[1, 1]))
ax1.hist(np.array(data_vals), bins=700, edgecolor='black', color=color)
if x_axis_limits:
ax1.set_xlim(x_axis_limits[0], x_axis_limits[1])
ax1.set_xlabel(r'Orientation $(deg)$')
ax1.set_ylabel(r'Frequency')
ax1.set_title(r'\theta_i')
#plt.subplots_adjust(wspace = 0.4)
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/imu_hist_{}.pdf'.
format(title),
format='pdf',
bbox_inches='tight')
def lr_odometry_pos_plot(l_vals, r_vals, x_vals, y_vals):
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=set_size(width, subplot=[1, 2]))
ax1.plot(l_vals, label='Left Encoder')
ax1.plot(r_vals, label='Right Encoder')
ax1.set_xlabel(r'Samples')
ax1.set_ylabel(r'Encoder Counts')
ax1.legend()
ax1.set_title(r'Quadrature Encoder Data')
ax2.scatter(x_vals, y_vals)
ax2.set_title(r'Odometry Model')
ax2.set_xlabel(r'x-axis $(mm)$')
ax2.set_ylabel(r'y-axis $(mm)$')
plt.subplots_adjust(wspace=0.4)
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/lr_odometry_pos_plot.pdf',
format='pdf',
bbox_inches='tight')
def imu_time_plot_multiple(time_vals, x_vals, y_vals, z_vals):
fig, (ax1, ax2, ax3) = plt.subplots(1,
3,
figsize=set_size(width, subplot=[1,
3]))
ax1.plot(time_vals, x_vals, color='#1f77b4')
ax1.set_xlabel(r'Time $(s)$')
ax1.set_ylabel(r'Acceleration $(m/{s^2})$')
ax1.set_title(r'x-axis')
ax2.plot(time_vals, y_vals, color='#ff7f0e')
ax2.set_xlabel(r'Time $(s)$')
ax2.set_title(r'y-axis')
ax3.plot(time_vals, z_vals, color='#2ca02c')
ax3.set_xlabel(r'Time $(s)$')
ax3.set_title(r'z-axis')
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/imu_plot_multiple.pdf',
format='pdf',
bbox_inches='tight')
def lidar_plot_coords(x, y):
fig, ax1 = plt.subplots(1, 1, figsize=set_size(width, subplot=[1, 1]))
ax1.scatter(x, y, c='#1f77b4')
ax1.scatter(0, 0, c='#2ca02c')
ax1.arrow(0, 0, 100, 0)
max_range = 1792
x_lim = []
y_lim = []
for i in range(360):
radians = (i * np.pi / 180)
x_lim.append(max_range * np.cos(radians))
y_lim.append(max_range * np.sin(radians))
ax1.scatter(x_lim, y_lim, c='#ff7f0e')
ax1.set_xlabel(r'X (mm)')
ax1.set_ylabel(r'Y (mm)')
ax1.axis('equal')
ax1.set_xlim(-2000, 2000)
ax1.set_xlim(-2000, 2000)
ax1.set_title(r'Lidar Scan - Real World Coordinates')
plt.savefig(
'/home/pi/BEng_Project/report_stuff/graph_formatting/lidar_coords_plot.pdf',
format='pdf',
bbox_inches='tight')
}
mpl.rcParams.update(nice_fonts)
###############################
print('Settings updated')
#Figure Plotting######################
#Wake_bck_multiple#
q = np.array(map(int,np.linspace(0,len(Tgrid)-1,3)))
qreal=Tgrid[q]/(2*np.pi)
eks=Xgrid/(2*np.pi)
fig, axs = plt.subplots(3,1, figsize=set_size(subplots=(1.,1.)))
subtxt=['(a)','(b)','(c)']
# Plot
for i in range(len(q)):
if i == range(len(q))[-1]:
axs[i].set_xlabel('$x/\lambda_d$')
axs[i].set_ylabel('$E_s/E^0_s$')
axs[i].plot(eks,Eout[q[i]])
axs[i].set_xlim(50,160)
axs[i].plot(eks,10*np.interp(Xgrid-vphi*Tgrid[q[i]]-push,ksigrid,Profile),'r',label='$n_e/\gamma_e$')
axs[i].set_title('$\omega_d t = %.2f$' %qreal[i])
axs[i].legend(loc='lower right')
axs[i].text(0.03,0.8,subtxt[i],transform=axs[i].transAxes)
fig.tight_layout()
plt.show()
pp = kastar.KinematicAStar(extents)
#start = (randint(0, 10), randint(0, 10), np.radians(randint(0, 20)))
#goal = (randint(40, 50), randint(40, 50), np.radians(randint(30, 50)))
#path, moves, visited, obstacles_array = pp.plan_path(start, goal, obstacles)
path, moves, visited, obstacles_array = pp.plan_path(start, goal, obstacles)
print(path)
print(moves)
x_vals = []
y_vals = []
for node in path:
x_vals.append(node[0])
y_vals.append(node[1])
fig, ax1 = plt.subplots(1, 1, figsize=set_size(width, subplot=[1, 1]))
ax1.scatter(x_vals, y_vals, c='#ff7f0e')
ax1.scatter(x_vals[0], y_vals[0], c='#2ca02c')
ax1.scatter(x_vals[-1], y_vals[-1], c='#d62728')
scalar = 3
dx_start = scalar * np.cos(start[2])
dy_start = scalar * np.sin(start[2])
dx_goal = scalar * np.cos(goal[2])
dy_goal = scalar * np.sin(goal[2])
ax1.arrow(x_vals[0], y_vals[0], dx_start, dy_start)
ax1.arrow(x_vals[-1], y_vals[-1], dx_goal, dy_goal)
ax1.set_xlabel(r'x-axis')
ax1.set_xlim(0, extents[0])
ax1.set_ylim(0, extents[1])
from env_GR import kappa
import IO
# Parameters
M, RNS, y_inner, comp, save, img = IO.load_params()
# Available solutions
Rphotkms = IO.get_phot_list()
# Plotting
import matplotlib.pyplot as plt
from my_plot import set_size, plot_settings
plot_settings()
fig, ax = plt.subplots(1, 1, figsize=set_size('mnras2col'))
save = 1
for i, R in enumerate(Rphotkms):
r, rho, T, P, Linf = IO.read_from_file(R)
Kap = kappa(rho, T)
taustar = Kap * rho * r * 1e5
ax.loglog(r, taustar, 'r', linewidth=0.7)
ax.set_xlabel('r (km)')
ax.set_ylabel(r'$\tau^* = \kappa\rho r$')
ax.axhline(3, color='k', linewidth=0.7, linestyle='--')
ax.set_ylim([0.6, 1e3])
"legend.fontsize": 6,
"xtick.labelsize": 6,
"ytick.labelsize": 6,
}
mpl.rcParams.update(nice_fonts)
###############################
print('Settings updated')
#Figure Plotting######################
#Vacuum propagation#
q = np.array(map(int, np.linspace(0, len(Tgrid) - 1, 3)))
qreal = Tgrid[q] / (2 * np.pi)
eks = Xgrid / (2 * np.pi)
wy = Eout
fig, axs = plt.subplots(1, 3, figsize=set_size(subplots=(1., 1.)))
subtxt = ['(a)', '(b)', '(c)']
# Plot
for i in range(len(q)):
axs[i].axes.yaxis.set_visible(False)
if i == 0:
axs[i].set_ylabel('$E_s/E^0_s$')
axs[i].axes.yaxis.set_visible(True)
axs[i].plot(eks, wy[q[i]])
axs[i].set_xlabel('$x/ \lambda_d$')
axs[i].set_title('$\omega_d t = %.2f$' % qreal[i])
axs[i].text(0.03, 0.95, subtxt[i], transform=axs[i].transAxes)
plt.show()
#fig.savefig('/home/christoffer/Thesis_MCs_figures/vacuumprop.pdf', format='pdf', bbox_inches='tight')
# Use 10pt font in plots, to match 10pt font in document
"axes.labelsize": 8,
"axes.titlesize": 8,
"font.size": 8,
# Make the legend/label fonts a little smaller
"legend.fontsize": 6,
"xtick.labelsize": 6,
"ytick.labelsize": 6,
}
mpl.rcParams.update(nice_fonts)
###############################
print('Settings updated')
#Plot time series of Electron beam#
eks = Xgrid / (2 * np.pi)
fig, axs = plt.subplots(1, 4, figsize=set_size(subplots=(1., 1.)))
subtxt = ['(a)', '(b)', '(c)', '(d)']
q = np.array(map(int, np.linspace(0, len(Tgrid) - 1, 4)))
qreal = Tgrid[q] / (2 * np.pi)
for i in range(len(q)):
axs[i].set_xlabel('$x/\lambda_d$')
axs[i].set_ylabel('$E_s/E^0_s$')
axs[i].plot(eks, Eout[q[i]])
axs[i].plot(eks,
20 * neProf.gauss(Xgrid,
Tgrid,
vphi,
Tgrid[q[i]],
tgrow[q[i]],
twid[q[i]],
