make examples consistent and contained to a single episode; fix #3

_episodes/02-writing-classes.md

@@ -24,8 +24,8 @@ If we wanted to plot a variety of quadratic functions, with a
 consistent set of styles, we could define a class that does this:
 
 ~~~
-import matplotlib.pyplot
-import numpy
+from matplotlib.pyplot import subplots
+from numpy import linspace
 
 class QuadraticPlotter:
     color = 'red'
@@ -37,11 +37,10 @@ class QuadraticPlotter:
         The line is plotted in the colour specified by color, and with width
         linewidth.'''
 
-        fig, ax = matplotlib.pyplot.subplots()
-        x = numpy.linspace(-10, 10, 1000)
+        fig, ax = subplots()
+        x = linspace(-10, 10, 1000)
         ax.plot(x, a * x ** 2 + b * x + c,
                 color=self.color, linewidth=self.linewidth)
-        fig.show()
 ~~~
 {: .language-python}
 
@@ -165,11 +164,10 @@ Line width of blue plotter is 5
 >>        The line is plotted in the colour specified by color, and with width
 >>        linewidth.'''
 >>
->>        fig, ax = matplotlib.pyplot.subplots()
->>        x = numpy.linspace(self.x_min, self.x_max, 1000)
+>>        fig, ax = subplots()
+>>        x = linspace(self.x_min, self.x_max, 1000)
 >>        ax.plot(x, a * x ** 2 + b * x + c,
 >>                color=self.color, linewidth=self.linewidth)
->>        fig.show()
 >>
 >> narrow_plot = QuadraticPlotter()
 >> wide_plot = QuadraticPlotter()
@@ -192,8 +190,7 @@ Line width of blue plotter is 5
 >
 > ~~~
 > from scipy.odr import ODR, Model, RealData
-> from matplotlib.pyplot import subplots, show
-> from numpy import linspace
+> from matplotlib.pyplot import show
 >
 > def linear(params, x):
 >     return params[0] * x + params[1]
@@ -290,11 +287,10 @@ class QuadraticPlotter:
         The line is plotted in the colour specified by color, and with width
         linewidth.'''
 
-        fig, ax = matplotlib.pyplot.subplots()
-        x = numpy.linspace(-10, 10, 1000)
+        fig, ax = subplots()
+        x = linspace(-10, 10, 1000)
         ax.plot(x, a * x ** 2 + b * x + c,
                 color=self.color, linewidth=self.linewidth)
-        fig.show()
 
 pink_plotter = QuadraticPlotter(color='magenta', linewidth=3)
 pink_plotter.plot(0, 1, 0)
@@ -327,11 +323,10 @@ usable, rather than deferring these errors to a long way down the line.
 >>        The line is plotted in the colour specified by color, and with width
 >>        linewidth.'''
 >>
->>        fig, ax = matplotlib.pyplot.subplots()
->>        x = numpy.linspace(self.x_min, self.x_max, 1000)
+>>        fig, ax = subplots()
+>>        x = linspace(self.x_min, self.x_max, 1000)
 >>        ax.plot(x, a * x ** 2 + b * x + c,
 >>                color=self.color, linewidth=self.linewidth)
->>        fig.show()
 >>
 >> narrow_plot = QuadraticPlotter()
 >> wide_plot = QuadraticPlotter(x_min=-5, x_max=50)

_episodes/03-inheritance.md

@@ -328,6 +328,10 @@ increasingly complex and build up functionality in layers.
 >> ## Solution
 >>
 >> ~~~
+>> from numpy import linspace
+>> from matplotlib.pyplot import subplots
+>> from matplotlib.colors import is_color_like
+>>
 >> class PolynomialPlotter:
 >>     def __init__(self, color='red', linewidth=1, x_min=-10, x_max=10):
 >>         assert is_color_like(color)
@@ -349,11 +353,10 @@ increasingly complex and build up functionality in layers.
 >>         plot the polynomial f(x) = ax^n + bx^(n-1) + cx^(n-2) + ... .
 >>         The line is plotted in the colour specified by color, and with width
 >>         linewidth.'''
->>         fig, ax = matplotlib.pyplot.subplots()
->>         x = numpy.linspace(self.x_min, self.x_max, 1000)
+>>         fig, ax = subplots()
+>>         x = linspace(self.x_min, self.x_max, 1000)
 >>         ax.plot(x, self.polynomial(x, coefficients),
 >>                 color=self.color, linewidth=self.linewidth)
->>         fig.show()
 >>
 >> class QuadraticPlotter(PolynomialPlotter):
 >>     def plot(self, a, b, c):
@@ -383,10 +386,9 @@ increasingly complex and build up functionality in layers.
 >>         '''Plot a function of a single argument.
 >>         The line is plotted in the colour specified by color, and with width
 >>         linewidth.'''
->>         fig, ax = matplotlib.pyplot.subplots()
->>         x = numpy.linspace(self.x_min, self.x_max, 1000)
+>>         fig, ax = subplots()
+>>         x = linspace(self.x_min, self.x_max, 1000)
 >>         ax.plot(x, function(x), color=self.color, linewidth=self.linewidth)
->>         fig.show()
 >>
 >>
 >> class PolynomialPlotter(FunctionPlotter):

_episodes/04-decorators.md

@@ -517,6 +517,10 @@ Modified perimeter: 21
 >> ## Solution
 >>
 >> ~~~
+>> from numpy import linspace
+>> from matplotlib.pyplot import subplots
+>> from matplotlib.colors import is_color_like
+>>
 >> class FunctionPlotter:
 >>     def __init__(self, color='red', linewidth=1, x_min=-10, x_max=10):
 >>         self.color = color
@@ -537,10 +541,9 @@ Modified perimeter: 21
 >>         '''Plot a function of a single argument.
 >>         The line is plotted in the colour specified by color, and with width
 >>         linewidth.'''
->>         fig, ax = matplotlib.pyplot.subplots()
->>         x = numpy.linspace(self.x_min, self.x_max, 1000)
+>>         fig, ax = subplots()
+>>         x = linspace(self.x_min, self.x_max, 1000)
 >>         ax.plot(x, function(x), color=self._color, linewidth=self.linewidth)
->>         fig.show()
 >> ~~~
 >> {: .language-python}
 > {: .solution}

_episodes/05-dunder.md

@@ -292,6 +292,10 @@ class to be called like functions. For example, returning to the
 `FunctionPlotter` example:
 
 ~~~
+from numpy import linspace, sin
+from matplotlib.colors import is_color_like
+from matplotlib.pyplot import subplots
+
 class FunctionPlotter:
     def __init__(self, color='red', linewidth=1, x_min=-10, x_max=10):
         self.color = color
@@ -312,16 +316,15 @@ class FunctionPlotter:
         '''Plot a function of a single argument.
         The line is plotted in the colour specified by color, and with width
         linewidth.'''
-        fig, ax = matplotlib.pyplot.subplots()
-        x = numpy.linspace(self.x_min, self.x_max, 1000)
+        fig, ax = subplots()
+        x = linspace(self.x_min, self.x_max, 1000)
         ax.plot(x, function(x), color=self._color, linewidth=self.linewidth)
-        fig.show()
 
     def __call__(self, *args, **kwargs):
         return self.plot(*args, **kwargs)
 
 plotter = FunctionPlotter()
-plotter(numpy.sin)
+plotter(sin)
 ~~~
 {: .language-python}
 

_episodes/06-duck.md

@@ -91,7 +91,7 @@ fractal.
 
 ~~~
 %matplotlib inline
-from numpy import linspace, newaxis, pi
+from numpy import angle, linspace, newaxis, pi
 from matplotlib.pyplot import colorbar, subplots
 
 def complex_linspace(lower, upper, num_real, num_imag):
@@ -113,14 +113,13 @@ initial_z = complex_linspace(z_min, z_max, 1000, 1000)
 results = newton(test_polynomial, test_derivative, initial_z, 20)
 
 fig, ax = subplots()
-image = ax.imshow(numpy.angle(results), vmin=-3, vmax=3,
+image = ax.imshow(angle(results), vmin=-3, vmax=3,
                   extent=(z_min.real, z_max.real, z_min.imag, z_max.imag))
 cbar = colorbar(image, ax=ax, ticks=(-2*pi/3, 0, 2*pi/3))
 cbar.set_label(r'$\arg(z_n)$')
 cbar.ax.set_yticklabels((r'$-\frac{2\pi}{3}$', '0', r'$\frac{2\pi}{3}$'))
 ax.set_xlabel(r'$\operatorname{Re}(z_0)$')
 ax.set_ylabel(r'$\operatorname{Im}(z_0)$')
-fig.show()
 ~~~
 {: .language-python}