update imports njit to jit and usage

Author: mmcky

lectures/career.md

@@ -51,7 +51,7 @@ import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import quantecon as qe
-from numba import njit, prange
+from numba import jit, prange
 from quantecon.distributions import BetaBinomial
 from scipy.special import binom, beta
 from mpl_toolkits.mplot3d.axes3d import Axes3D

lectures/cass_koopmans_1.md

@@ -67,7 +67,7 @@ Let's start with some standard imports:
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
-from numba import njit, float64
+from numba import jit, float64
 from numba.experimental import jitclass
 import numpy as np
 ```

lectures/cass_koopmans_2.md

@@ -68,7 +68,7 @@ Let's start with some standard imports:
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
-from numba import njit, float64
+from numba import jit, float64
 from numba.experimental import jitclass
 import numpy as np
 ```

lectures/coleman_policy_iter.md

@@ -63,7 +63,7 @@ Let's start with some imports:
 import matplotlib.pyplot as plt
 import numpy as np
 from quantecon.optimize import brentq
-from numba import njit
+from numba import jit
 ```
 
 ## The Euler Equation

lectures/egm_policy_iter.md

@@ -44,7 +44,7 @@ Let's start with some standard imports:
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
 import numpy as np
-from numba import njit
+from numba import jit
 ```
 
 ## Key Idea

lectures/eig_circulant.md

@@ -31,7 +31,7 @@ We begin by importing some Python packages
 
 ```{code-cell} ipython3
 import numpy as np
-from numba import njit
+from numba import jit
 import matplotlib.pyplot as plt
 ```
 

lectures/exchangeable.md

@@ -72,7 +72,7 @@ tags: [hide-output]
 ---
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
-from numba import njit, vectorize
+from numba import jit, vectorize
 from math import gamma
 import scipy.optimize as op
 from scipy.integrate import quad
@@ -416,8 +416,8 @@ def learning_example(F_a=1, F_b=1, G_a=3, G_b=1.2):
     given the parameters which specify F and G distributions.
     """
 
-    f = njit(lambda x: p(x, F_a, F_b))
-    g = njit(lambda x: p(x, G_a, G_b))
+    f = jit(lambda x: p(x, F_a, F_b))
+    g = jit(lambda x: p(x, G_a, G_b))
 
     # l(w) = f(w) / g(w)
     l = lambda w: f(w) / g(w)
@@ -557,8 +557,8 @@ To proceed, we create some Python code.
 def function_factory(F_a=1, F_b=1, G_a=3, G_b=1.2):
 
     # define f and g
-    f = njit(lambda x: p(x, F_a, F_b))
-    g = njit(lambda x: p(x, G_a, G_b))
+    f = jit(lambda x: p(x, F_a, F_b))
+    g = jit(lambda x: p(x, G_a, G_b))
 
     @jit
     def update(a, b, π):
@@ -678,8 +678,8 @@ The following code approximates the integral above:
 def expected_ratio(F_a=1, F_b=1, G_a=3, G_b=1.2):
 
     # define f and g
-    f = njit(lambda x: p(x, F_a, F_b))
-    g = njit(lambda x: p(x, G_a, G_b))
+    f = jit(lambda x: p(x, F_a, F_b))
+    g = jit(lambda x: p(x, G_a, G_b))
 
     l = lambda w: f(w) / g(w)
     integrand_f = lambda w, π: f(w) * l(w) / (π * l(w) + 1 - π)

lectures/ifp.md

@@ -61,7 +61,7 @@ We'll need the following imports:
 import matplotlib.pyplot as plt
 import numpy as np
 from quantecon.optimize import brentq
-from numba import njit, float64
+from numba import jit, float64
 from numba.experimental import jitclass
 from quantecon import MarkovChain
 ```

lectures/ifp_advanced.md

@@ -57,7 +57,7 @@ We require the following imports:
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
 import numpy as np
-from numba import njit, float64
+from numba import jit, float64
 from numba.experimental import jitclass
 from quantecon import MarkovChain
 ```

lectures/imp_sample.md

@@ -31,7 +31,7 @@ We start by importing some Python packages.
 
 ```{code-cell} ipython3
 import numpy as np
-from numba import njit, vectorize, prange
+from numba import jit, vectorize, prange
 import matplotlib.pyplot as plt
 from math import gamma
 ```
@@ -81,8 +81,8 @@ def p(w, a, b):
     return r * w ** (a-1) * (1 - w) ** (b-1)
 
 # The two density functions.
-f = njit(lambda w: p(w, F_a, F_b))
-g = njit(lambda w: p(w, G_a, G_b))
+f = jit(lambda w: p(w, F_a, F_b))
+g = jit(lambda w: p(w, G_a, G_b))
 ```
 
 ```{code-cell} ipython3
@@ -99,7 +99,7 @@ plt.show()
 The likelihood ratio is `l(w)=f(w)/g(w)`.
 
 ```{code-cell} ipython3
-l = njit(lambda w: f(w) / g(w))
+l = jit(lambda w: f(w) / g(w))
 ```
 
 ```{code-cell} ipython3