Merge pull request #332 from pep-dortmund/326-matplotlib-object-orien…

…ted-approach

326 matplotlib object oriented approach

exercises-latex/12-python/loesung.py

@@ -34,19 +34,21 @@ def f(t, a, b, c, d):
 
 t_plot = np.linspace(-0.5, 2 * np.pi + 0.5, 1000) * 1e-3
 
-plt.errorbar(
+fig, ax = plt.subplots(1, 1, layout="constrained")
+
+ax.errorbar(
     t * 1e3,
     noms(U) * 1e-3,
     yerr=stds(U) * 1e-3,
     fmt="k_",
     label="Daten",
 )
-plt.plot(t_plot * 1e3, f(t_plot, *noms(params)) * 1e-3, "-", label="Fit")
-plt.xlim(t_plot[0] * 1e3, t_plot[-1] * 1e3)
-plt.xlabel(r"$t \mathbin{/} \unit{\milli\second}$")
-plt.ylabel(r"$U \mathbin{/} \unit{\kilo\volt}$")
-plt.legend()
-plt.savefig("build/loesung-plot.pdf")
+ax.plot(t_plot * 1e3, f(t_plot, *noms(params)) * 1e-3, "-", label="Fit")
+ax.set_xlim(t_plot[0] * 1e3, t_plot[-1] * 1e3)
+ax.set_xlabel(r"$t \mathbin{/} \unit{\milli\second}$")
+ax.set_ylabel(r"$U \mathbin{/} \unit{\kilo\volt}$")
+ax.legend(loc="best")
+fig.savefig("build/loesung-plot.pdf")
 
 t1, t2 = np.array_split(t * 1e3, 2)
 U1, U2 = np.array_split(U * 1e-3, 2)

exercises-toolbox/2-numpy/2-indexing/loesung.py

@@ -22,8 +22,9 @@
 print("Spielfeld zu Beginn des Spiels:")
 print(field)
 
-mat = plt.matshow(field, cmap="Set1", vmax=5, vmin=-1)
-plt.savefig("Spielfeld_Beginn.pdf")
+fig, ax = plt.subplots(1, 1, layout="constrained")
+mat = ax.matshow(field, cmap="Set1", vmax=5, vmin=-1)
+fig.savefig("Spielfeld_Beginn.pdf")
 
 # Lösung für Aufgabe 1:
 
@@ -54,5 +55,5 @@
 print("Spielfeld zum Ende des Spiels:")
 print(field)
 
-mat = plt.matshow(field, cmap="Set1", vmax=5, vmin=-1)
-plt.savefig("Spielfeld_Ende.pdf")
+mat.set_array(field)
+fig.savefig("Spielfeld_Ende.pdf")

exercises-toolbox/2-numpy/2-indexing/vorlage.py

@@ -24,8 +24,9 @@
 
 # Was das hier ist lernen wir noch, kann getrost ignoriert werden.
 # In der Zwischenzeit, die Kurzfassung: Das 'macht' die Bilder. ;-)
-plt.matshow(field, cmap="Set1", vmax=5, vmin=-1)
-plt.savefig("Spielfeld_Beginn.pdf")
+fig, ax = plt.subplots(1, 1, layout="constrained")
+mat = ax.matshow(field, cmap="Set1", vmax=5, vmin=-1)
+fig.savefig("Spielfeld_Beginn.pdf")
 
 # Hier die Lösung für Aufgabe 1 und 2 eintragen (ein Beispiel ist angegeben):
 # Durch Ausführen dieser Datei kann Schritt für Schritt der Fortschritt
@@ -38,5 +39,5 @@
 
 # Was das hier ist lernen wir noch, kann getrost ignoriert werden.
 # In der Zwischenzeit, die Kurzfassung: Das 'macht' die Bilder. ;-)
-plt.matshow(field, cmap="Set1", vmax=5, vmin=-1)
-plt.savefig("Spielfeld_Ende.pdf")
+mat.set_array(field)
+fig.savefig("Spielfeld_Ende.pdf")

exercises-toolbox/3-matplotlib/1/loesung.py

@@ -3,11 +3,11 @@
 
 x = np.linspace(0, 1)
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
-plt.plot(x, x**2)
+ax.plot(x, x**2)
 
-plt.xlabel(r"$x$")
-plt.ylabel(r"$x^2$")
+ax.set_xlabel(r"$x$")
+ax.set_ylabel(r"$x^2$")
 
-plt.savefig("loesung.pdf")
+fig.savefig("loesung.pdf")

exercises-toolbox/3-matplotlib/2/loesung.py

@@ -3,12 +3,12 @@
 
 x = np.linspace(0, 1)
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
-plt.plot(x, x**2, label=r"$x^2$")
-plt.plot(x, x**5, "x", label=r"$x^5$")
+ax.plot(x, x**2, label=r"$x^2$")
+ax.plot(x, x**5, "x", label=r"$x^5$")
 
-plt.legend(loc="best")
-plt.xlabel(r"$x$")
+ax.legend(loc="best")
+ax.set_xlabel(r"$x$")
 
-plt.savefig("loesung.pdf")
+fig.savefig("loesung.pdf")

exercises-toolbox/3-matplotlib/3/loesung.py

@@ -8,17 +8,15 @@
 
 x, y = np.genfromtxt("3.txt", unpack=True)
 
-plt.figure(layout="constrained")
+fig, (ax1, ax2) = plt.subplots(1, 2, layout="constrained")
 
-plt.subplot(1, 2, 1)
-plt.plot(x, y, ".")
-plt.xlabel(r"$x$")
-plt.ylabel(r"$y$")
+ax1.plot(x, y, ".")
+ax1.set_xlabel(r"$x$")
+ax1.set_ylabel(r"$y$")
 
-plt.subplot(1, 2, 2)
-plt.plot(x, y, ".")
-plt.xlabel(r"$x$")
-plt.ylabel(r"$y$")
-plt.yscale("log")
+ax2.plot(x, y, ".")
+ax2.set_xlabel(r"$x$")
+ax2.set_ylabel(r"$y$")
+ax2.set_yscale("log")
 
-plt.savefig("loesung.pdf")
+fig.savefig("loesung.pdf")

exercises-toolbox/3-matplotlib/4/loesung.py

@@ -18,14 +18,14 @@
 
 t = np.linspace(-0.5, 2 * np.pi + 0.5)
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
-plt.errorbar(x, y, xerr=e_x, yerr=e_y, fmt="k.", label="Daten")
-plt.plot(t, np.sin(t), label=r"$\sin(t)$")
+ax.errorbar(x, y, xerr=e_x, yerr=e_y, fmt="k.", label="Daten")
+ax.plot(t, np.sin(t), label=r"$\sin(t)$")
 
-plt.xlim(t[0], t[-1])
-plt.xlabel(r"$t$")
-plt.ylabel(r"$f(t)$")
+ax.set_xlim(t[0], t[-1])
+ax.set_xlabel(r"$t$")
+ax.set_ylabel(r"$f(t)$")
 
-plt.legend()
-plt.savefig("loesung.pdf")
+ax.legend()
+fig.savefig("loesung.pdf")

exercises-toolbox/3-matplotlib/5/loesung.py

@@ -3,16 +3,16 @@
 
 x = np.linspace(0, 2 * np.pi)
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
-plt.plot(x, np.sin(x), label=r"$\sin(x)$")
-plt.plot(x, np.cos(x), label=r"$\cos(x)$")
+ax.plot(x, np.sin(x), label=r"$\sin(x)$")
+ax.plot(x, np.cos(x), label=r"$\cos(x)$")
 
-plt.xlim(0, 2 * np.pi)
-plt.ylim(-1.2, 1.2)
-plt.xlabel(r"$x$")
+ax.set_xlim(0, 2 * np.pi)
+ax.set_ylim(-1.2, 1.2)
+ax.set_xlabel(r"$x$")
 
-plt.xticks(
+ax.set_xticks(
     np.arange(0, 2.1 * np.pi, np.pi / 2),
     [
         r"$0$",
@@ -23,5 +23,5 @@
     ],
 )
 
-plt.legend()
-plt.savefig("loesung.pdf")
+ax.legend()
+fig.savefig("loesung.pdf")

exercises-toolbox/3-matplotlib/6/loesung.py

@@ -3,11 +3,11 @@
 
 x = np.linspace(0, 1, 100)
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
 for n in range(1, 11):
-    plt.plot(x, x**n, label=f"$x^{{{n}}}$")
+    ax.plot(x, x**n, label=f"$x^{{{n}}}$")
 
-plt.legend(loc="upper left")
-plt.xlabel("$x$")
-plt.savefig("loesung.pdf")
+ax.legend(loc="upper left")
+ax.set_xlabel("$x$")
+fig.savefig("loesung.pdf")

exercises-toolbox/3-matplotlib/7/loesung.py

@@ -3,13 +3,13 @@
 
 x = np.linspace(0, 2 * np.pi, 100)
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
 for A in (-1, -0.5, 0.5, 1):
-    plt.plot(x, A * np.cos(x), label=f"$A = {A}$")
+    ax.plot(x, A * np.cos(x), label=f"$A = {A}$")
 
-plt.legend()
-plt.xlim(0, 2 * np.pi)
-plt.xlabel("$x$")
-plt.ylabel(r"$A \cos(x)$")
-plt.savefig("loesung.pdf")
+ax.legend()
+ax.set_xlim(0, 2 * np.pi)
+ax.set_xlabel("$x$")
+ax.set_ylabel(r"$A \cos(x)$")
+fig.savefig("loesung.pdf")

exercises-toolbox/4-scipy/3-curve_fit/loesung.py

@@ -21,17 +21,17 @@ def f(x, a, b, c, d):
 parameters, pcov = curve_fit(f, x, y, sigma=e_y)
 print(parameters, np.sqrt(np.diag(pcov)), sep="\n")
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
-plt.errorbar(x, y, yerr=e_y, fmt="k.", label="Daten")
+ax.errorbar(x, y, yerr=e_y, fmt="k.", label="Daten")
 
 t = np.linspace(-0.5, 2 * np.pi + 0.5, 500)
-plt.plot(t, f(t, *parameters), label="Fit")
-plt.plot(t, np.sin(t), "--", label="Original")
+ax.plot(t, f(t, *parameters), label="Fit")
+ax.plot(t, np.sin(t), "--", label="Original")
 
-plt.xlim(t[0], t[-1])
-plt.xlabel(r"$t$")
-plt.ylabel(r"$f(t)$")
-plt.legend()
+ax.set_xlim(t[0], t[-1])
+ax.set_xlabel(r"$t$")
+ax.set_ylabel(r"$f(t)$")
+ax.legend()
 
-plt.savefig("loesung.pdf")
+fig.savefig("loesung.pdf")

exercises-toolbox/4-scipy/3-curve_fit/vorlage.py

@@ -9,13 +9,15 @@ def f(x, a, b, c, d):
 parameters = ?
 
 # Dieser Code erstellt einen Plot mithilfe von f und parameters
-plt.figure(layout="constrained")
-plt.errorbar(x, y, yerr=e_y, fmt='rx', label='Daten')
+fig, ax = plt.subplots(1, 1, layout="constrained")
+ax.errorbar(x, y, yerr=e_y, fmt="rx", label="Daten")
+
 t = np.linspace(-0.5, 2 * np.pi + 0.5)
-plt.plot(t, f(t, *parameters), 'b-', label='Fit')
-plt.plot(t, np.sin(t), 'g--', label='Original')
-plt.xlim(t[0], t[-1])
-plt.xlabel(r'$t$')
-plt.ylabel(r'$f(t)$')
-plt.legend()
-plt.savefig('loesung.pdf')
+
+ax.plot(t, f(t, *parameters), "b-", label="Fit")
+ax.plot(t, np.sin(t), "g--", label="Original")
+ax.set_xlim(t[0], t[-1])
+ax.set_xlabel(r"$t$")
+ax.set_ylabel(r"$f(t)$")
+ax.legend(loc="best")
+fig.savefig("loesung.pdf")

exercises-toolbox/4-scipy/4-peakdetect/loesung.py

@@ -14,26 +14,26 @@ def e(x, a, b, c):
 maxs, properties = find_peaks(U, prominence=1, distance=100)
 mins, properties = find_peaks(-U, prominence=1, distance=100)
 
-x = np.linspace(0, plt.xlim()[1], 500)
-
 parameters_max, pcov_max = curve_fit(e, t[maxs], U[maxs])
 print(parameters_max, np.sqrt(np.diag(pcov_max)), sep="\n")
 
 parameters_min, pcov_min = curve_fit(e, t[mins], U[mins])
 print(parameters_min, np.sqrt(np.diag(pcov_min)), sep="\n")
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
+
+x = np.linspace(0, ax.get_xlim()[1], 500)
 
-plt.plot(t, U, "k-", label="Gedämpfte Schwingung")
+ax.plot(t, U, "k-", label="Gedämpfte Schwingung")
 
-plt.plot(x, e(x, *parameters_max), label="Obere Einhüllende")
-plt.plot(t[maxs], U[maxs], "o", color="C0", label="Maxima")
+ax.plot(x, e(x, *parameters_max), label="Obere Einhüllende")
+ax.plot(t[maxs], U[maxs], "o", color="C0", label="Maxima")
 
-plt.plot(x, e(x, *parameters_min), label="Untere Einhüllende")
-plt.plot(t[mins], U[mins], "o", color="C1", label="Minima")
+ax.plot(x, e(x, *parameters_min), label="Untere Einhüllende")
+ax.plot(t[mins], U[mins], "o", color="C1", label="Minima")
 
-plt.xlabel(r"$t \ / \ \mathrm{ms}$")
-plt.ylabel(r"$U \ / \ \mathrm{V}$")
-plt.legend()
-plt.xlim(0, 0.3)
-plt.savefig("loesung.pdf")
+ax.set_xlabel(r"$t \ / \ \mathrm{ms}$")
+ax.set_ylabel(r"$U \ / \ \mathrm{V}$")
+ax.set_xlim(0, 0.3)
+ax.legend(loc="best")
+fig.savefig("loesung.pdf")

exercises-toolbox/4-scipy/4-peakdetect/vorlage.py

@@ -8,18 +8,18 @@
 parameters_min = #
 
 # Dieser Code erzeugt den Plot
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
-plt.plot(t, U, 'b-', label='Gedämpfte Schwingung')
+ax.plot(t, U, "b-", label="Gedämpfte Schwingung")
 
-plt.plot(t[maxs], U[maxs], 'rx', label='Extrema')
-plt.plot(x, e(x, *parameters_max), 'g-', label='Obere Einhüllende')
+ax.plot(t[maxs], U[maxs], "rx", label="Extrema")
+ax.plot(x, e(x, *parameters_max), "g-", label="Obere Einhüllende")
 
-plt.plot(t[mins], U[mins], 'rx')
-plt.plot(x, e(x, *parameters_min), 'y-', label='Untere Einhüllende')
+ax.plot(t[mins], U[mins], "rx")
+ax.plot(x, e(x, *parameters_min), "y-", label="Untere Einhüllende")
 
-plt.xlabel(r'$t \ / \ \mathrm{ms}$')
-plt.ylabel(r'$U \ / \ \mathrm{V}$')
-plt.legend()
-plt.xlim(0, 0.3)
-plt.savefig('loesung.pdf')
+ax.set_xlabel(r"$t \ / \ \mathrm{ms}$")
+ax.set_ylabel(r"$U \ / \ \mathrm{V}$")
+ax.set_xlim(0, 0.3)
+ax.legend(loc="best")
+fig.savefig("loesung.pdf")

exercises-toolbox/4-scipy/5-beugung/loesung.py

@@ -26,13 +26,13 @@ def theory(phi, A0, b):
 
 x = np.linspace(-0.03, 0.03, 100)
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
-plt.plot(x, theory(x, *parameters), "-", label="Fit")
-plt.plot(phi, I, "k.", label="Daten")
+ax.plot(x, theory(x, *parameters), "-", label="Fit")
+ax.plot(phi, I, "k.", label="Daten")
 
-plt.xlabel(r"$\varphi \ / \ \mathrm{rad}$")
-plt.ylabel(r"$I \ / \ \mathrm{A}$")
-plt.legend()
+ax.set_xlabel(r"$\varphi \ / \ \mathrm{rad}$")
+ax.set_ylabel(r"$I \ / \ \mathrm{A}$")
+ax.legend(loc="best")
 
-plt.savefig("loesung.pdf")
+fig.savefig("loesung.pdf")

exercises-toolbox/4-scipy/5-beugung/vorlage.py

@@ -4,7 +4,7 @@
 
 
 def theory(phi, A0, b):
-    '''Hier Funktion ergänzen'''
+    """Hier Funktion ergänzen"""
 
 
 # Hier Fit ergänzen
@@ -13,13 +13,13 @@ def theory(phi, A0, b):
 # Hier wird der Plot erstellt
 x = np.linspace(-0.03, 0.03, 100)
 
-plt.figure(layout="constrained")
+fig, ax = plt.subplots(1, 1, layout="constrained")
 
-plt.plot(x, theory(x, *parameters), '-', label='Fit')
-plt.plot(phi, I, 'k.', label='Daten')
+ax.plot(x, theory(x, *parameters), "-", label="Fit")
+ax.plot(phi, I, "k.", label="Daten")
 
-plt.xlabel(r'$\varphi \ / \ \mathrm{rad}$')
-plt.ylabel(r'$I \ / \ \mathrm{A}$')
-plt.legend()
+ax.set_xlabel(r"$\varphi \ / \ \mathrm{rad}$")
+ax.set_ylabel(r"$I \ / \ \mathrm{A}$")
+ax.legend(loc="best")
 
-plt.savefig('loesung.pdf')
+fig.savefig("loesung.pdf")