ex-6: improve plots

ex-6/plot.py

@@ -2,15 +2,14 @@
 
 from pylab import *
 import sys
-import matplotlib.pyplot as plt
 
-plt.rcParams['font.size'] = 20
+rcParams['font.size'] = 12
 
 a, b, f = loadtxt(sys.stdin, unpack=True)
-suptitle('Fraunhofer diffraction')
 title(sys.argv[1] if len(sys.argv) > 1 else "", loc='right')
-hist(a, np.insert(b, 0, a[0]), weights=f/sum(f),
-     color='#dbbf0d', edgecolor='#595856', linewidth=0.5)
+hist(a, np.insert(b, 0, a[0]), weights=100*f/sum(f),
+     histtype='stepfilled', color='#e3c5ca', edgecolor='#92182b')
 xlabel(r'$\theta$ (radians)')
 ylabel(r'$I(\theta)$ (a.u.)')
+tight_layout()
 show()

notes/sections/6.md

@@ -1,6 +1,6 @@
 # Exercise 6
 
-## Generating points according to Fraunhofer diffraction
+## Generating points according to Fraunhöfer diffraction
 
 The diffraction of a plane wave thorough a round slit must be simulated by
 generating $N =$ 50'000 points according to the intensity distribution
@@ -16,7 +16,7 @@ where:
 - $E$ is the electric field amplitude, default set $E = \SI{1e4}{V/m}$;
 - $a$ is the radius of the slit aperture, default set $a = \SI{0.01}{m}$;
 - $\theta$ is the angle specified in @fig:slit;
-- $J_1$ is the Bessel function of first order;
+- $J_1$ is a Bessel function of first kind;
 - $k$ is the wavenumber, default set $k = \SI{1e-4}{m^{-1}}$;
 - $L$ default set $L = \SI{1}{m}$.
 
@@ -46,7 +46,7 @@ where:
   \node [cyclamen] at (5.5,-0.4) {$\theta$};
   \node [rotate=-90] at (10.2,0) {screen};
 \end{tikzpicture}
-\caption{Fraunhofer diffraction.}\label{fig:slit}
+\caption{Fraunhöfer diffraction.}\label{fig:slit}
 }
 \end{figure}
 
@@ -88,8 +88,7 @@ omitted:
 The sample was binned and stored in a histogram with a customizable number $n$
 of bins default set $n = 150$. In @fig:original an example is shown.
 
-![Example of sorted points according to
-  $I(\theta)$.](images/6_original.pdf){#fig:original}
+![Example of an intensity histogram.](images/fraun-original.pdf){#fig:original}
 
 
 ## Gaussian noise convolution {#sec:convolution}
@@ -417,41 +416,41 @@ On the other hand, the Richardson-Lucy routine is less affected by this further
 complication being already inaccurate in itself.
 
 <div id="fig:results1">
-![Convolved signal.](images/noise-0.05.pdf){width=12cm}
+![Convolved signal.](images/fraun-conv-0.05.pdf){width=12cm}
 
-![Deconvolved signal with FFT.](images/deco-fft-0.05.pdf){width=12cm}
+![Deconvolved signal with FFT.](images/fraun-fft-0.05.pdf){width=12cm}
 
-![Deconvolved signal with RL.](images/deco-rl-0.05.pdf){width=12cm}
+![Deconvolved signal with RL.](images/fraun-rl-0.05.pdf){width=12cm}
 
 Results for $\sigma = 0.05 \Delta \theta$, where $\Delta \theta$ is the bin
 width.
 </div>
 
 <div id="fig:results2">
-![Convolved signal.](images/noise-0.5.pdf){width=12cm}
+![Convolved signal.](images/fraun-conv-0.5.pdf){width=12cm}
 
-![Deconvolved signal with FFT.](images/deco-fft-0.5.pdf){width=12cm}
+![Deconvolved signal with FFT.](images/fraun-fft-0.5.pdf){width=12cm}
 
-![Deconvolved signal with RL.](images/deco-rl-0.5.pdf){width=12cm}
+![Deconvolved signal with RL.](images/fraun-rl-0.5.pdf){width=12cm}
 
 Results for $\sigma = 0.5 \Delta \theta$, where $\Delta \theta$ is the bin
 width.
 </div>
 
 <div id="fig:results3">
-![Convolved signal.](images/noise-1.pdf){width=12cm}
+![Convolved signal.](images/fraun-conv-1.pdf){width=12cm}
 
-![Deconvolved signal with FFT.](images/deco-fft-1.pdf){width=12cm}
+![Deconvolved signal with FFT.](images/fraun-fft-1.pdf){width=12cm}
 
-![Deconvolved signal with RL.](images/deco-rl-1.pdf){width=12cm}
+![Deconvolved signal with RL.](images/fraun-rl-1.pdf){width=12cm}
 
 Results for $\sigma = \Delta \theta$, where $\Delta \theta$ is the bin width.
 </div>
 
 <div id="fig:poisson">
-![Deconvolved signal with FFT.](images/poisson-fft.pdf){width=12cm}
+![Deconvolved signal with FFT.](images/fraun-noise-fft.pdf){width=12cm}
 
-![Deconvolved signal withh RL.](images/poisson-rl.pdf){width=12cm}
+![Deconvolved signal withh RL.](images/fraun-noise-rl.pdf){width=12cm}
 
-Results for $\sigma = \Delta \theta$, poissoned data.
+Results for $\sigma = \Delta \theta$, with Poisson noise.
 </div>