Fixed ibox caption and label.
Fixed python pathes.
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fc8308110b
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9525d2995d
@ -238,11 +238,10 @@
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\newenvironment{ibox}[2][tp]
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{\SetupFloatingEnvironment{iboxf}{placement=#1}%
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\begin{iboxf}%
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\stepcounter{iboxf}%
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\captionof{iboxf}{#2}%
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\captionsetup{singlelinecheck=off,labelfont={large,sf,it,bf},font={large,sf,it,bf}}
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\begin{mdframed}[linecolor=yellow!40,linewidth=1ex,%
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backgroundcolor=yellow!15,font={\sffamily},%
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frametitle={Info Box \theiboxf: #2},frametitlefont={\large\sffamily\itshape\bfseries},%
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frametitle={\caption{#2}},frametitleaboveskip=-1ex,%
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frametitlebackgroundcolor=yellow!40]}%
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{\end{mdframed}%
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\end{iboxf}}
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@ -6,10 +6,11 @@ fig = plt.figure()
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ax = fig.add_subplot(111)
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y = np.asarray([-10, 10]) * 2.**16/20.
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ax.plot([-10, 10], y, color='dodgerblue', lw=1.5)
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ax.set_xlabel('measured voltage [V]')
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ax.set_ylabel('digitized value')
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ax.set_xlabel('Measured voltage [V]')
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ax.set_ylabel('Digitized value')
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ax.set_xlim([-10.05, 10.05])
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ax.set_ylim([-2**15, 2**15 + 1])
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ax.set_ylim([-2**15, 2**15-1])
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ax.set_yticks([-2**15, -2**14, 0, 2**14, 2**15-1])
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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@ -20,8 +21,8 @@ ax.tick_params(direction="out", width=1.25)
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ax.tick_params(direction="out", width=1.25)
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ax.grid()
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fig.set_facecolor("white")
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fig.set_size_inches(5., 5.)
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fig.set_size_inches(4., 3.6)
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fig.tight_layout()
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fig.savefig("images/data_acquisition.pdf")
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fig.savefig("data_acquisition.pdf")
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plt.close()
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@ -27,6 +27,6 @@ ax.set_xlabel("time [s]")
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ax.set_ylabel("intensity")
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ax.legend(fontsize=8)
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fig.tight_layout()
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fig.savefig("images/logicalIndexingTime.pdf")
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fig.savefig("logicalIndexingTime.pdf")
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@ -131,9 +131,9 @@ interpretiert werden. Die wichtigsten Datentpyen sind folgende:
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Unter den numerischen Datentypen gibt es verschiedene Arten mit
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unterschiedlichem Speicherbedarf und Wertebreich.
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\begin{table}[]
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\begin{table}[!h]
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\centering
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\titlecaption{Grundlegende Datentypen und ihr Wertebereich.}
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\titlecaption{Grundlegende Datentypen und ihr Wertebereich.}{}
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\label{dtypestab}
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\begin{tabular}{llcl}\hline
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Datentyp & Speicherbedarf & Wertebereich & Beispiel \rule{0pt}{2.5ex} \\ \hline
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@ -147,7 +147,7 @@ unterschiedlichem Speicherbedarf und Wertebreich.
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Daten gespeichert werden. Dennoch lohnt es sich, sich ein wenig mit
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den Datentypen auseinanderzusetzen (Box \ref{daqbox}).
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\begin{ibox}[tp]{Digitalisierung von Messdaten}\label{daqbox}
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\begin{ibox}[tp]{\label{daqbox}Digitalisierung von Messdaten}
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Szenario: Die elektrische Aktivit\"at (z.B. die Membranspannung)
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einer Nervenzelle wird gemessen. Die gemessene Spannung wird mittels
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Messkarte digitalisiert und auf dem Computer f\"ur weitere Analysen
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@ -159,14 +159,16 @@ den Datentypen auseinanderzusetzen (Box \ref{daqbox}).
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Werte abgebildet.\vspace{0.25cm}
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\begin{minipage}{0.5\textwidth}
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\includegraphics[width=0.9\columnwidth]{images/data_acquisition}
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\includegraphics[width=0.9\columnwidth]{data_acquisition}
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\end{minipage}
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\begin{minipage}{0.5\textwidth}
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Um die Spannung auf den \code{int16} Datentyp abzubilden:
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\[ y = x \cdot 2^{16}/20\] mit $x$ der gemessenen Spannung und $y$
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dem digitalisierten Wert bei einem Spannungsbereich von
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$\pm10$\,V.\\
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Durch Umgekehrung kann der digitalisierte Wert wieder
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$\pm10$\,V. Das ergibt ganzzahlige Werte zwischen $-2^{15}=-32768$
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und $2^{15}-1 = 32767$.
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Durch Umkehrung kann der digitalisierte Wert wieder
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in eine Spannung zur\"uckgewandelt werden:
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\[ x = y \cdot 20/2^{16} \]
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\end{minipage}\vspace{0.25cm}
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@ -174,8 +176,8 @@ den Datentypen auseinanderzusetzen (Box \ref{daqbox}).
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Um Speicherplatz zu sparen ist es sinnvoll, die gemessenen Daten als
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``int16'' anstelle der ``double'' Werte im Rechner abzulegen. Die
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Daten als ``echte'' Spannungen, also als Flie{\ss}kommawerte,
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abzulegen ben\"otigt den 4-fachen Speicherplatz und bietet keine
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zus\"atzliche Information.
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abzulegen ben\"otigt den 4-fachen Speicherplatz (8 statt 2 Bytes)
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und bietet keine zus\"atzliche Information.
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\end{ibox}
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@ -1316,7 +1318,7 @@ gemacht den Rahmen zu bilden und den Ablauf zu koordinieren (Abbildung
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nimmt R\"uckgabewerte entgegen.}\label{programlayoutfig}
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\end{figure}
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\begin{ibox}[tp]{Python}
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The cooler programming language.
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\end{ibox}
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@ -1,11 +1,11 @@
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import matplotlib.pyplot as plt
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import numpy as np
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from IPython import embed
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from matplotlib.transforms import Bbox
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def create_data():
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m = 0.75
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n= -30
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x = np.arange(0.,101., 2.5)
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n= -40
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x = np.arange(5.,115., 2.5)
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y = m * x + n;
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noise = np.random.randn(len(x))*15
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y += noise
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@ -13,79 +13,85 @@ def create_data():
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def plot_data(x, y):
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plt.xkcd()
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plt.scatter(x, y, marker='o', color='dodgerblue', s=40)
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plt.xlabel("Input x")
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plt.ylabel("Output y")
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plt.xlim([-2.5, 102.5])
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ax = plt.gca()
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.xaxis.linewidth=1.5
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ax.yaxis.linewidth=1.5
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ax.tick_params(direction="out", width=1.25)
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ax.tick_params(direction="out", width=1.25)
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ax.set_xlabel('Input x')
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ax.set_ylabel('Output y')
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ax.set_xlim(0, 120)
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ax.set_ylim(-80, 80)
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ax.set_xticks(np.arange(0,121, 40))
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ax.set_yticks(np.arange(-80,81, 40))
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fig = plt.gcf()
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fig.set_facecolor("white")
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fig.set_size_inches(3., 3.)
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fig.savefig("figures/lin_regress.pdf")
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plt.tight_layout()
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fig.savefig("lin_regress.pdf")
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plt.close()
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def plot_data_slopes(x, y, m, n):
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plt.xkcd()
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plt.scatter(x, y, marker='o', color='dodgerblue', s=40)
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for i in np.linspace(m/4, m*1.5, 5):
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plt.plot(x, i*x+n, color="r", lw=2)
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plt.xlabel("Input x")
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plt.ylabel("Output y")
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plt.xlim([-2.5, 102.5])
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ax = plt.gca()
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.xaxis.linewidth=1.5
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ax.yaxis.linewidth=1.5
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ax.tick_params(direction="out", width=1.25)
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ax.tick_params(direction="out", width=1.25)
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ax.set_xlabel('Input x')
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ax.set_ylabel('Output y')
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ax.set_xlim(0, 120)
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ax.set_ylim(-80, 80)
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ax.set_xticks(np.arange(0,121, 40))
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ax.set_yticks(np.arange(-80,81, 40))
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fig = plt.gcf()
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fig.set_facecolor("white")
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fig.set_size_inches(3., 3.)
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fig.savefig("figures/lin_regress_slope.pdf")
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plt.tight_layout()
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fig.savefig("lin_regress_slope.pdf")
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plt.close()
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def plot_data_intercepts(x, y, m, n):
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plt.xkcd()
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plt.scatter(x, y, marker='o', color='dodgerblue', s=40)
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for i in np.linspace(n-n/2, n+n/2, 5):
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plt.plot(x, m * x + i, color="r", lw=2)
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plt.xlabel("Input x")
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plt.ylabel("Output y")
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plt.xlim([-2.5, 102.5])
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ax = plt.gca()
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.xaxis.linewidth=1.5
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ax.yaxis.linewidth=1.5
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ax.tick_params(direction="out", width=1.25)
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ax.tick_params(direction="out", width=1.25)
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ax.set_xlabel('Input x')
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ax.set_ylabel('Output y')
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ax.set_xlim(0, 120)
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ax.set_ylim(-80, 80)
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ax.set_xticks(np.arange(0,121, 40))
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ax.set_yticks(np.arange(-80,81, 40))
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fig = plt.gcf()
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fig.set_facecolor("white")
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fig.set_size_inches(3., 3.)
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fig.savefig("figures/lin_regress_intercept.pdf")
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plt.tight_layout()
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fig.savefig("lin_regress_intercept.pdf")
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plt.close()
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if __name__ == "__main__":
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x, y, m, n = create_data()
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plt.xkcd()
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plot_data(x,y)
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plot_data_slopes(x,y,m,n)
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plot_data_intercepts(x,y,m,n)
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@ -10,9 +10,9 @@ ein Optimierungsproblem, der besser als Kurvenfit bekannt ist
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(\enterm{curve fitting}).
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\begin{figure}[tp]
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\includegraphics[width=0.32\columnwidth]{lin_regress}\hfill
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\includegraphics[width=0.32\columnwidth]{lin_regress_slope}\hfill
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\includegraphics[width=0.32\columnwidth]{lin_regress_intercept}
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\includegraphics[width=0.33\columnwidth]{lin_regress}\hfill
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\includegraphics[width=0.33\columnwidth]{lin_regress_slope}\hfill
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\includegraphics[width=0.33\columnwidth]{lin_regress_intercept}
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\titlecaption{.}{F\"ur eine Reihe von Eingangswerten $x$,
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z.B. Stimulusintensit\"aten, wurden die Antworten $y$ eines
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Systems gemessen (links). Der postulierte lineare Zusammenhang hat
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