Remove unused imports (#357)

* Remove unused imports

* Fix import errors

lectures/_static/lecture_specific/optgrowth_fast/ogm.py

@@ -1,3 +1,5 @@
+from numba import float64
+from numba.experimental import jitclass
 
 opt_growth_data = [
     ('α', float64),          # Production parameter
@@ -12,8 +14,8 @@
 class OptimalGrowthModel:
 
     def __init__(self,
-                α=0.4, 
-                β=0.96, 
+                α=0.4,
+                β=0.96,
                 μ=0,
                 s=0.1,
                 grid_max=4,
@@ -29,12 +31,12 @@ def __init__(self,
         # Store shocks (with a seed, so results are reproducible)
         np.random.seed(seed)
         self.shocks = np.exp(μ + s * np.random.randn(shock_size))
-       
+
 
     def f(self, k):
         "The production function"
         return k**self.α
-       
+
 
     def u(self, c):
         "The utility function"
@@ -52,5 +54,3 @@ def u_prime(self, c):
     def u_prime_inv(self, c):
         "Inverse of u'"
         return 1/c
-
-

lectures/_static/lecture_specific/optgrowth_fast/ogm_crra.py

@@ -1,4 +1,5 @@
-
+from numba import float64
+from numba.experimental import jitclass
 
 opt_growth_data = [
     ('α', float64),          # Production parameter
@@ -14,11 +15,11 @@
 class OptimalGrowthModel_CRRA:
 
     def __init__(self,
-                α=0.4, 
-                β=0.96, 
+                α=0.4,
+                β=0.96,
                 μ=0,
                 s=0.1,
-                γ=1.5, 
+                γ=1.5,
                 grid_max=4,
                 grid_size=120,
                 shock_size=250,
@@ -32,7 +33,7 @@ def __init__(self,
         # Store shocks (with a seed, so results are reproducible)
         np.random.seed(seed)
         self.shocks = np.exp(μ + s * np.random.randn(shock_size))
-       
+
 
     def f(self, k):
         "The production function."
@@ -52,4 +53,3 @@ def u_prime(self, c):
 
     def u_prime_inv(c):
         return c**(-1 / self.γ)
-

lectures/aiyagari.md

@@ -59,7 +59,6 @@ Let's start with some imports:
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
-import quantecon as qe
 from quantecon.markov import DiscreteDP
 from numba import jit
 ```
@@ -441,4 +440,3 @@ ax.legend(loc='upper right')
 
 plt.show()
 ```
-

lectures/coleman_policy_iter.md

@@ -65,11 +65,9 @@ Let's start with some imports:
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
-import quantecon as qe
 from interpolation import interp
 from quantecon.optimize import brentq
-from numba import njit, float64
-from numba.experimental import jitclass
+from numba import njit
 ```
 
 ## The Euler Equation

lectures/egm_policy_iter.md

@@ -29,7 +29,6 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 ---
 tags: [hide-output]
 ---
-!pip install quantecon
 !pip install interpolation
 ```
 
@@ -55,11 +54,8 @@ Let's start with some standard imports:
 import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
-import quantecon as qe
 from interpolation import interp
-from numba import njit, float64
-from numba.experimental import jitclass
-from quantecon.optimize import brentq
+from numba import njit
 ```
 
 ## Key Idea
@@ -266,4 +262,3 @@ has managed to shave off still more run time without compromising accuracy.
 This is due to the lack of a numerical root-finding step.
 
 We can now solve the optimal growth model at given parameters extremely fast.
-

lectures/geom_series.md

@@ -54,7 +54,7 @@ import matplotlib.pyplot as plt
 plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import sympy as sym
-from sympy import init_printing, latex
+from sympy import init_printing
 from matplotlib import cm
 from mpl_toolkits.mplot3d import Axes3D
 ```