numpy_test():
  Exercises for introduction to numpy.

  x = 
[111 222 333]
  x[1] =  222
  type(x) =  <class 'numpy.ndarray'>
  len(x) =  3
  np.ndim(x)  =  1
  np.shape(x) =  (3,)
  np.size(x)  =  3

  Random vector a: [0.20497062 0.47667047 0.00088011 0.46724502 0.25808177]
  np.all ( 0.25 <= a ) =  False
  np.any ( 0.75 <= a ) =  False
  np.all ( ( 0.05 <= a ) & ( a <= 0.95 ) ) =  False
  np.any ( ( a < 0.05 ) | ( 0.95 < a ) ) =  True

sinc_plot():
  Consider y=sin(x)/x.
  ymin =  nan imin =  50 xmin =  0.0
  ymax =  nan imax =  50 xmax =  0.0
  Graphics saved as "sinc.png"
  Replace y(0) = 0/0 by the value 1.
  ymin =  -0.2167568534954234 imin =  69 xmin =  4.559999999999999
  ymax =  1.0 imax =  50 xmax =  0.0
  Graphics saved as "sinc_marked.png"
  Plot a red dot wherever y(x) < 0.
  Graphics saved as "sinc_negative.png"
[ 20 120 220 320 420]

temperature_plot():
  T is a 7 x 8 matrix of temperatures.
  T[i,j] is Celsius temperature on i-th day at j-th time.

  T:
[[ -1  -4  -8  -9  -9  -8  -9  -8]
 [-12 -12 -12 -10  -5   0   0   0]
 [  1   2   2   4   7   8   7   6]
 [  3   3   2   2   3   5   3   1]
 [  1   1   2   6  11  12  12  11]
 [  8   6   5   5   8  11   9   7]
 [  6   5   4   6   8  10   8   7]]

  plot ( h, T[day,:] )
  Graphics saved as "temperature_daily.png"

  Using flatten() to make a single row,
  plot all data as one record.
  Graphics saved as "temperature_weekly.png"

  Compute minimum:
    Minimum each day:  np.min ( T, axis = 0 ) [-12 -12 -12 -10  -9  -8  -9  -8]
    Minimum each hour: np.min ( T, axis = 1 ) [ -9 -12   1   1   1   5   4]
    Minimum over all:  np.min ( T )           -12

contour_plot():
  Consider z = 2x^2 - 1.05x^4 + x^6/6 + xy + y^2.
  Create a contour plot by sampling z(x,y) on an x y grid.
  Use np.linspace() to create x and y vector grids.
  Use np.meshgrd() to create X and Y matrix grids.
  Graphics saved as "contours.png"