path = sys.path[0] + '/'
fileNameOnly = generalUtility.getFileNameOnly(fName)
# calculate the Intensity data using Praat
dataT, dataI = praatUtil.calculateIntensity(path + fName)
# normalize the dB data, since it's not calibrated
dataI -= dataI.max()
# generate the graph
graph = matplotlibUtil.CGraph(width = 8, height = 3)
graph.createFigure()
ax = graph.getArrAx()[0]
ax.plot(dataT, dataI, linewidth = 2)
ax.set_xlabel("Time [s]")
ax.set_ylabel("SPL [dB]")
ax.set_title(fileNameOnly)
graph.padding = 0.1
graph.adjustPadding(bottom = 2, right = 0.5)
ax.grid()
# It is not aesthetically pleasing when graph data goes to all the way to the
# upper and lower edges of the graph. I prefer to have a little space.
matplotlibUtil.setLimit(ax, dataI, 'y', rangeMultiplier = 0.1)
# every doubling of sound pressure level (SPL) results in an increase of SPL by
# 6 dB. Therefore, we need to change the y-axis ticks
matplotlibUtil.formatAxisTicks(ax, 'y', 6, '%d')
# finally, save the graph to a file
plt.savefig(fileNameOnly + "_intensity.png")
##############################
# ----- PANELS 2 and 3 ----- #
##############################
# extract parts of the data and plot in next row
n = len(arrData)
for i in range(2):
idx1 = (n / 2) * i
idx2 = idx1 + n / 4
ax = arrAx[i + 1]
ax.plot(arrT[idx1:idx2], arrData[idx1:idx2], linewidth = graph.lineWidth)
ax.grid()
ax.set_xlabel("Time [s]")
ax.set_ylabel("Amplitude")
matplotlibUtil.setLimit(ax, arrData[idx1:idx2], 'y', 0.1)
#######################
# ----- PANEL 4 ----- #
#######################
# create a noisy data distribution and plot in bottom left panel. fit a
# polinomial to the data
n = 100
arrX = range(n)
arrY = numpy.zeros(n)
for i in arrX:
arrY[i] = numpy.sqrt(i) + numpy.random.random() * 3
ax = arrAx[-2]
