Use np.interp instead of interpolation.interp

Author: kp992

lectures/_static/lecture_specific/optgrowth/bellman_operator.py

@@ -1,5 +1,4 @@
 import numpy as np
-from interpolation import interp
 from numba import njit, prange
 from quantecon.optimize.scalar_maximization import brent_max
 
@@ -21,7 +20,7 @@ def objective(c, v, y):
         The right-hand side of the Bellman equation
         """
         # First turn v into a function via interpolation
-        v_func = lambda x: interp(grid, v, x)
+        v_func = lambda x: np.interp(x, grid, v)
         return u(c) + β * np.mean(v_func(f(y - c) * shocks))
 
     @njit(parallel=parallel_flag)

lectures/jv.md

@@ -27,15 +27,6 @@ kernelspec:
 :depth: 2
 ```
 
-In addition to what's in Anaconda, this lecture will need the following libraries:
-
-```{code-cell} ipython
----
-tags: [hide-output]
----
-!pip install interpolation
-```
-
 ## Overview
 
 In this section, we solve a simple on-the-job search model
@@ -46,10 +37,8 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 import scipy.stats as stats
-from interpolation import interp
 from numba import njit, prange
 ```
 
@@ -269,7 +258,7 @@ def operator_factory(jv, parallel_flag=True):
     @njit
     def state_action_values(z, x, v):
         s, ϕ = z
-        v_func = lambda x: interp(x_grid, v, x)
+        v_func = lambda x: np.interp(x, x_grid, v)
 
         integral = 0
         for m in range(mc_size):
@@ -460,8 +449,8 @@ v_star = solve_model(jv, verbose=False)
 s_policy, ϕ_policy = get_greedy(v_star)
 
 # Turn the policy function arrays into actual functions
-s = lambda y: interp(x_grid, s_policy, y)
-ϕ = lambda y: interp(x_grid, ϕ_policy, y)
+s = lambda y: np.interp(y, x_grid, s_policy)
+ϕ = lambda y: np.interp(y, x_grid, ϕ_policy)
 
 def h(x, b, u):
     return (1 - b) * g(x, ϕ(x)) + b * max(g(x, ϕ(x)), u)

lectures/mccall_correlated.md

@@ -30,7 +30,6 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 tags: [hide-output]
 ---
 !pip install quantecon
-!pip install interpolation
 ```
 
 ## Overview
@@ -48,10 +47,8 @@ We will use the following imports:
 
 ```{code-cell} ipython3
 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 numpy.random import randn
 from numba import njit, prange, float64
 from numba.experimental import jitclass
@@ -245,7 +242,7 @@ def Q(js, f_in, f_out):
         for m in range(M):
             e1, e2 = js.e_draws[:, m]
             z_next = d + ρ * z + σ * e1
-            go_val = interp(js.z_grid, f_in, z_next)     # f(z')
+            go_val = np.interp(z_next, js.z_grid, f_in)  # f(z')
             y_next = np.exp(μ + s * e2)                  # y' draw
             w_next = np.exp(z_next) + y_next             # w' draw
             stop_val = np.log(w_next) / (1 - β)
@@ -353,7 +350,7 @@ def compute_unemployment_duration(js, seed=1234):
 
     @njit
     def f_star_function(z):
-        return interp(z_grid, f_star, z)
+        return np.interp(z, z_grid, f_star)
 
     @njit
     def draw_tau(t_max=10_000):

lectures/mccall_fitted_vfi.md

@@ -23,14 +23,6 @@ kernelspec:
 :depth: 2
 ```
 
-In addition to what's in Anaconda, this lecture will need the following libraries:
-
-```{code-cell} ipython
----
-tags: [hide-output]
----
-!pip install interpolation
-```
 
 ## Overview
 
@@ -58,9 +50,7 @@ We will use the following imports:
 
 ```{code-cell} ipython3
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
-from interpolation import interp
 from numba import njit, float64
 from numba.experimental import jitclass
 ```
@@ -155,7 +145,7 @@ This method
    {cite}`gordon1995stable` or {cite}`stachurski2008continuous`) and
 1. preserves useful shape properties such as monotonicity and concavity/convexity.
 
-Linear interpolation will be implemented using a JIT-aware Python interpolation library called [interpolation.py](https://github.com/EconForge/interpolation.py).
+Linear interpolation will be implemented using [numpy.interp](https://numpy.org/doc/stable/reference/generated/numpy.interp.html).
 
 The next figure illustrates piecewise linear interpolation of an arbitrary
 function on grid points $0, 0.2, 0.4, 0.6, 0.8, 1$.
@@ -169,7 +159,7 @@ c_grid = np.linspace(0, 1, 6)
 f_grid = np.linspace(0, 1, 150)
 
 def Af(x):
-    return interp(c_grid, f(c_grid), x)
+    return np.interp(x, c_grid, f(c_grid))
 
 fig, ax = plt.subplots()
 
@@ -238,7 +228,7 @@ class McCallModelContinuous:
         u = lambda x: np.log(x)
 
         # Interpolate array represented value function
-        vf = lambda x: interp(w, v, x)
+        vf = lambda x: np.interp(x, w, v)
 
         # Update d using Monte Carlo to evaluate integral
         d_new = np.mean(np.maximum(vf(self.w_draws), u(c) + β * d))

lectures/navy_captain.md

@@ -24,22 +24,12 @@ kernelspec:
 :depth: 2
 ```
 
-In addition to what's in Anaconda, this lecture will need the following libraries:
-
-```{code-cell} ipython
----
-tags: [hide-output]
----
-!pip install interpolation
-```
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
 from numba import njit, prange, float64, int64
 from numba.experimental import jitclass
-from interpolation import interp
 from math import gamma
 from scipy.optimize import minimize
 ```
@@ -496,7 +486,7 @@ def Q(h, wf):
     κ = wf.κ
 
     h_new = np.empty_like(π_grid)
-    h_func = lambda p: interp(π_grid, h, p)
+    h_func = lambda p: np.interp(p, π_grid, h)
 
     for i in prange(len(π_grid)):
         π = π_grid[i]
@@ -679,7 +669,7 @@ def V_q(wf, flag):
 
             for j in prange(len(z_arr)):
                 π_next = wf.κ(z_arr[j], π)
-                V[i] += wf.c + interp(wf.π_grid, V_old, π_next)
+                V[i] += wf.c + np.interp(π_next, wf.π_grid, V_old)
 
             V[i] /= wf.mc_size
 

lectures/odu.md

@@ -58,9 +58,8 @@ Let’s start with some imports
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 from numba import njit, prange, vectorize
-from interpolation import mlinterp, interp
+from interpolation import mlinterp
 from math import gamma
 import numpy as np
 from matplotlib import cm
@@ -633,7 +632,7 @@ def Q_factory(sp, parallel_flag=True):
 
     @njit
     def ω_func(p, ω):
-        return interp(π_grid, ω, p)
+        return np.interp(p, π_grid, ω)
 
     @njit
     def κ(w, π):
@@ -783,7 +782,7 @@ w_bar = solve_wbar(sp, verbose=False)
 
 # Interpolate reservation wage function
 π_grid = sp.π_grid
-w_func = njit(lambda x: interp(π_grid, w_bar, x))
+w_func = njit(lambda x: np.interp(x, π_grid, w_bar))
 
 @njit
 def update(a, b, e, π):
@@ -907,7 +906,7 @@ def empirical_dist(F_a, F_b, G_a, G_b, w_bar, π_grid,
                 π = π * lw / (π * lw + 1 - π)
 
                 # move to next agent if accepts
-                if w >= interp(π_grid, w_bar, π):
+                if w >= np.interp(π, π_grid, w_bar):
                     break
 
             # record the unemployment duration

lectures/optgrowth_fast.md

@@ -31,7 +31,6 @@ In addition to what's in Anaconda, this lecture will need the following librarie
 tags: [hide-output]
 ---
 !pip install quantecon
-!pip install interpolation
 ```
 
 ## Overview
@@ -61,17 +60,11 @@ Let's start with some imports:
 
 ```{code-cell} ipython
 import matplotlib.pyplot as plt
-plt.rcParams["figure.figsize"] = (11, 5)  #set default figure size
 import numpy as np
-from interpolation import interp
 from numba import jit, njit
 from quantecon.optimize.scalar_maximization import brent_max
 ```
 
-We are using an interpolation function from
-[interpolation.py](https://github.com/EconForge/interpolation.py) because it
-helps us JIT-compile our code.
-
 The function `brent_max` is also designed for embedding in JIT-compiled code.
 
 These are alternatives to similar functions in SciPy (which, unfortunately, are not JIT-aware).
@@ -152,7 +145,7 @@ def state_action_value(c, y, v_array, og):
 
     u, f, β, shocks = og.u, og.f, og.β, og.shocks
 
-    v = lambda x: interp(og.grid, v_array, x)
+    v = lambda x: np.interp(x, og.grid, v_array)
 
     return u(c) + β * np.mean(v(f(y - c) * shocks))
 ```
@@ -398,7 +391,7 @@ for β in (0.8, 0.9, 0.98):
     v_greedy, v_solution = solve_model(og, verbose=False)
 
     # Define an optimal policy function
-    σ_func = lambda x: interp(og.grid, v_greedy, x)
+    σ_func = lambda x: np.interp(x, og.grid, v_greedy)
     y = simulate_og(σ_func, og)
     ax.plot(y, lw=2, alpha=0.6, label=rf'$\beta = {β}$')
 

lectures/wald_friedman.md

@@ -31,14 +31,6 @@ kernelspec:
 :depth: 2
 ```
 
-In addition to what's in Anaconda, this lecture will need the following libraries:
-
-```{code-cell} ipython3
-:tags: [hide-output]
-
-!pip install interpolation
-```
-
 ## Overview
 
 This lecture describes a statistical decision problem presented to Milton
@@ -69,7 +61,6 @@ import numpy as np
 import matplotlib.pyplot as plt
 from numba import jit, prange, float64, int64
 from numba.experimental import jitclass
-from interpolation import interp
 from math import gamma
 ```
 
@@ -487,7 +478,7 @@ def Q(h, wf):
     κ = wf.κ
 
     h_new = np.empty_like(π_grid)
-    h_func = lambda p: interp(π_grid, h, p)
+    h_func = lambda p: np.interp(p, π_grid, h)
 
     for i in prange(len(π_grid)):
         π = π_grid[i]