def simple_plot(means_filename, stdev_filename):
#graph 1
baseAngle, armAngle, intensitiesMean = csvinterface.read_csv(
means_filename)
baseAngle, armAngle, intensitiesStdev = csvinterface.read_csv(
means_filename)
intensityMean0 = intensitiesMean[0]
intensityMean90 = intensitiesMean[1]
intensityStdev0 = intensitiesStdev[0]
intensityStdev90 = intensitiesStdev[1]
directivity_mean_0_x = []
directivity_mean_0_y = []
directivity_mean_90_x = []
directivity_mean_90_y = []
for angle, intensity in zip(armAngle, intensityMean0):
x, y = translatecoords(angle, intensity)
directivity_mean_0_x.append(x)
directivity_mean_0_y.append(y)
for angle, intensity in zip(armAngle, intensityMean90):
x, y = translatecoords(angle, intensity)
directivity_mean_90_x.append(x)
directivity_mean_90_y.append(y)
snr0 = []
snr90 = []
for val, err in zip(intensityMean0, intensityStdev0):
snr0.append(20 * math.log10(val / err))
for val, err in zip(intensityMean90, intensityStdev90):
snr90.append(20 * math.log10(val / err))
plt.subplot(1, 2, 1)
plt.plot(directivity_mean_0_x, directivity_mean_0_y, '.-')
plt.plot(directivity_mean_90_x, directivity_mean_90_y, '.-')
plt.title('Mean Directivity')
plt.legend(['Base: 0 Deg', 'Base: 90 Deg'])
#plt.xlim([-0.1,0.1])
#plt.ylim([0,0.2])
plt.subplot(1, 2, 2)
plt.plot(armAngle, snr0, '.-')
plt.plot(armAngle, snr90, '.-')
plt.title('Signal to Noise Ratio')
plt.xlabel('Arm Angle')
plt.ylabel('SNR (dB, approx from σ)')
plt.legend(['Base: 0 Deg', 'Base: 90 Deg'])
plt.show()
def plot_surface_file(ax, filename):
xData, yData, zData = csvinterface.read_csv(means_filename)
corrected_x_data = []
corrected_y_data = []
corrected_z_data = []
for phiindex in range(len(zdata)):
phirow = zdata[thetaindex]
for thetaindex in range(len(phirow)):
intensity = thetarow[thetaindex]
phi = xData[phiindex]
theta = yData[thetaindex]
x, y, z = transform_val(phi, theta, intensity)
corrected_x_data.append(x)
corrected_y_data.append(y)
corrected_z_data.append(z)
# Plot the surface.
ax1 = fig.gca(projection='3d')
surf = ax1.plot_trisurf(xData, yData, zData, cmap=cm.viridis,
linewidth=0, antialiased=False)
def one_axis_plot(means_filename, stdev_filename):
baseAngle, armAngle, intensityMean = csvinterface.read_csv(means_filename)
baseAngle, armAngle, intensityStdev = csvinterface.read_csv(stdev_filename)
intensityMean = intensityMean[0]
intensityStdev = intensityStdev[0]
directivity_mean_x = []
directivity_mean_y = []
for angle, intensity in zip(armAngle, intensityMean):
x, y = translatecoords(angle, intensity)
directivity_mean_x.append(x)
directivity_mean_y.append(y)
snr = []
for val, err in zip(intensityMean, intensityStdev):
if val == 0 or err == 0:
snr.append(0)
else:
snr.append(20 * math.log10(val / err))
plt.subplot(1, 2, 1)
plt.plot(directivity_mean_x, directivity_mean_y, '.-')
plt.title('Mean Directivity')
plt.legend(['Base: 0 Deg'])
plt.subplot(1, 2, 2)
plt.plot(armAngle, snr, '.-')
plt.title('Signal to Noise Ratio')
plt.xlabel('Arm Angle')
plt.ylabel('SNR (dB, approx from σ)')
plt.legend(['Base: 0 Deg'])
plt.show()
def plot_surface_file(ax, filename, scatter=False):
xData, yData, zData = csvinterface.read_csv(filename)
# assume constant baseline and linear scaling - subtract off mininum value
minval = 10 # max of 5v on ADC, so this is absurdly high
for row in zData:
for el in row:
if el < minval: minval = el
corrected_x_data = []
corrected_y_data = []
corrected_z_data = []
for phiindex in range(len(zData)):
phirow = zData[phiindex]
for thetaindex in range(len(phirow)):
intensity = phirow[thetaindex]
phi = xData[phiindex]
theta = yData[thetaindex]
x, y, z = transform_val(phi, theta, intensity - minval)
corrected_x_data.append(x)
corrected_y_data.append(y)
corrected_z_data.append(z)
if scatter:
surf = ax.scatter(corrected_x_data,
corrected_y_data,
corrected_z_data,
marker='.',
c=corrected_z_data,
s=20,
alpha=1)
#ax.contourf(corrected_x_data, corrected_y_data, corrected_z_data)
else:
# generating a triangular mesh
num_phipoints = len(zData[0])
# points are theta, phi 0-180
# first 91 points for theta = 0, next for theta = 1, etc
# connect points in consecutive theta
triangles = []
# there will be as many triangles as there are points - 2
for theta_index in range(len(zData) - 1):
for phi_index in range(len(zData[0]) - 1):
# indices will be two consecutive points for theta = base, then one point for theta = base + 1
first_point_index = theta_index * num_phipoints + phi_index
second_point_index = theta_index * num_phipoints + phi_index + 1
third_point_index = (theta_index +
1) * num_phipoints + phi_index
fourth_point_index = (theta_index +
1) * num_phipoints + phi_index + 1
triangles.append(
[first_point_index, second_point_index, third_point_index])
triangles.append([
fourth_point_index, second_point_index, third_point_index
])
triang = mtri.Triangulation(corrected_x_data,
corrected_y_data,
triangles=triangles)
surf = ax.plot_trisurf(triang,
np.array(corrected_z_data),
linewidths=0.1,
edgecolor='black',
cmap=cm.viridis,
antialiased=True)
return surf, zData
def plot_everything(means_filename, stdev_filename):
print("Plotting in Matplotlib")
#set up a figure
fig = plt.figure(figsize=plt.figaspect(0.3))
ax1 = fig.add_subplot(1, 2, 1, projection='3d')
ax1 = fig.gca(projection='3d')
surf, zDataAbs = plot_surface_file(ax1, means_filename)
# Title
# Placement 0, 0 would be the bottom left, 1, 1 would be the top right.
ax1.text2D(0.05,
0.95,
"Mean Directivity (Unnormalized)",
transform=ax1.transAxes)
# Tweaking display region and labels
#ax1.set_xlim(0, 180)
#ax1.set_ylim(0, 180)
#ax1.set_xlabel('Base', color='crimson')
#ax1.set_ylabel('Base', color='crimson')
#ax1.set_zlabel('Z Magnitude', color='crimson')
# Customize the z axis.
#ax1.set_zlim(0, 5)
#Second graph
ax2 = fig.add_subplot(1, 2, 2, projection='3d')
xData2, yData2, zData2 = csvinterface.read_csv(stdev_filename)
# Dividing by mag
for i in range(len(zData2)):
ilist = zData2[i]
for j in range(len(ilist)):
jval = ilist[j]
if jval == 0: zData2[i][j] == 0
elif zDataAbs[i][j] == 0: zData2[i][j] == 0
else: zData2[i][j] = 20 * math.log10(zDataAbs[i][j] / jval)
xData2, yData2 = np.meshgrid(yData2, xData2)
# Plot the surface.
ax2 = fig.gca(projection='3d')
surf = ax2.plot_surface(xData2,
yData2,
zData2,
cmap=cm.viridis,
linewidths=0.1,
edgecolor='black',
antialiased=True)
# Title
# Placement 0, 0 would be the bottom left, 1, 1 would be the top right.
ax2.text2D(0.05, 0.95, "Signal to Noise Ratio", transform=ax2.transAxes)
# Tweaking display region and labels
ax2.set_xlim(0, 180)
ax2.set_ylim(0, 180)
ax2.set_ylabel('θ (base, degrees)', color='crimson')
ax2.set_xlabel('ɸ (arm, degrees)', color='crimson')
ax2.set_zlabel('SNR (dB, approx from σ)', color='crimson')
# Customize the z axis.
#ax2.set_zlim(0, 5)
#ax2.zaxis.set_major_locator(LinearLocator(10))
#ax2.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))
# Add a color bar which maps values to colors.
fig.colorbar(surf, shrink=0.5, aspect=5, ax=ax2)
plt.show()