Merge pull request #91 from QuantEcon/modify_pattern

np.ones * alpha -->> np.full(, alpha)

Author: jstac

lectures/amss.md

@@ -779,8 +779,8 @@ x_grid = UCGrid((x_min, x_max, x_num))
 crra_pref = CRRAutility(β=β, σ=σ, γ=γ)
 
 S = len(Π)
-bounds_v = np.vstack([np.hstack([np.ones(S) * -10., np.zeros(S)]),
-                      np.hstack([np.ones(S) - g, np.ones(S) * 10])]).T
+bounds_v = np.vstack([np.hstack([np.full(S, -10.), np.zeros(S)]),
+                      np.hstack([np.ones(S) - g, np.full(S, 10.)])]).T
 
 amss_model = AMSS(crra_pref, β, Π, g, x_grid, bounds_v)
 ```
@@ -906,7 +906,7 @@ state-contingent debt (circles) and the economy with only a risk-free bond
 ```{code-cell} python3
 # WARNING: DO NOT EXPECT THE CODE TO WORK IF YOU CHANGE PARAMETERS
 ψ = 0.69
-Π = 0.5 * np.ones((2, 2))
+Π = np.full((2, 2), 0.5)
 β = 0.9
 g = np.array([0.1, 0.2])
 
@@ -923,11 +923,11 @@ bounds_v = np.vstack([np.zeros(2 * S), np.hstack([1 - g, np.ones(S)]) ]).T
 V = np.zeros((len(Π), x_num))
 V[:] = -(nodes(x_grid).T + x_max) ** 2 / 14
 
-σ_v_star = 1 - np.ones((S, x_num, S * 2)) * 0.55
+σ_v_star = 1 - np.full((S, x_num, S * 2), 0.55)
 
 W = np.empty(len(Π))
 b_0 = 0.5
-σ_w_star = 1 - np.ones((S, 2)) * 0.55
+σ_w_star = 1 - np.full((S, 2), 0.55)
 
 amss_model = AMSS(log_pref, β, Π, g, x_grid, bounds_v)
 ```

lectures/amss3.md

@@ -179,7 +179,7 @@ Here is a graph of a long simulation of 102000 periods.
 :tags: ["scroll-output"]
 μ_grid = np.linspace(-0.09, 0.1, 100)
 
-log_example = CRRAutility(π=(1 / 3) * np.ones((3, 3)),
+log_example = CRRAutility(π=np.full((3, 3), 1 / 3),
                           G=np.array([0.1, 0.2, .3]),
                           Θ=np.ones(3))
 
@@ -539,7 +539,7 @@ Now let's move on to compute things step by step.
 #### Step 1
 
 ```{code-cell} python3
-u = CRRAutility(π=(1 / 3) * np.ones((3, 3)),
+u = CRRAutility(π=np.full((3, 3), 1 / 3),
                 G=np.array([0.1, 0.2, .3]),
                 Θ=np.ones(3))
 

lectures/arellano.md

@@ -480,7 +480,7 @@ def solve(model, tol=1e-8, maxiter=10_000):
     iBstar = np.zeros((ny, nB), int64)
     default_prob = np.zeros((ny, nB))
     default_states = np.zeros((ny, nB))
-    q = np.ones((ny, nB)) * 0.95
+    q = np.full((ny, nB), 0.95)
     Vd = np.zeros(ny)
     Vc, V, Vupd = np.zeros((ny, nB)), np.zeros((ny, nB)), np.zeros((ny, nB))
 

lectures/calvo.md

@@ -1168,7 +1168,7 @@ beginning, i.e., $\theta^A_{t+10} =\theta^R_t \ \ \forall t \geq 0$.
 def abreu_plan(clq, T=1000, T_A=10, μ_bar=0.1, T_Plot=20):
 
     # Append Ramsey μ series to stick μ series
-    clq.μ_A = np.append(np.ones(T_A) * μ_bar, clq.μ_series[:-T_A])
+    clq.μ_A = np.append(np.full(T_A, μ_bar), clq.μ_series[:-T_A])
 
     # Calculate implied stick θ series
     clq.θ_A = np.zeros(T)

lectures/rosen_schooling_model.md

@@ -188,7 +188,7 @@ k = 4  # Number of periods of schooling required to become an engineer
 α_d = np.array([[0.1]])
 α_s = 1
 ε_1 = 1e-7
-λ_1 = np.ones((1, k)) * ε_1
+λ_1 = np.full((1, k), ε_1)
 # Use of ε_1 is trick to aquire detectability, see HS2013 p. 228 footnote 4
 l_λ = np.hstack((α_d, λ_1))
 π_h = np.array([[0]])
@@ -244,7 +244,7 @@ econ2 = DLE(info1, tech1, pref2)
 α_d = np.array([[0.1]])
 
 k = 7
-λ_1 = np.ones((1, k)) * ε_1
+λ_1 = np.full((1, k), ε_1)
 l_λ = np.hstack((α_d, λ_1))
 d1 = np.vstack((δ_n, np.zeros((k - 1, 1))))
 d2 = np.hstack((d1, np.eye(k)))
@@ -256,7 +256,7 @@ Pref3 = Preferences(β, l_λ, π_h, δ_h, θ_h)
 econ3 = DLE(info1, tech1, Pref3)
 
 k = 10
-λ_1 = np.ones((1, k)) * ε_1
+λ_1 = np.full((1, k), ε_1)
 l_λ = np.hstack((α_d, λ_1))
 d1 = np.vstack((δ_n, np.zeros((k - 1, 1))))
 d2 = np.hstack((d1, np.eye(k)))

lectures/smoothing.md

@@ -299,7 +299,7 @@ def complete_ss(β, b0, x0, A, C, S_y, T=12):
 
     # Constant level of consumption
     cbar = (1 - β) * (S_y @ rm @ x0 - b0)
-    c_hist = np.ones(T) * cbar
+    c_hist = np.full(T, cbar)
 
     # Debt
     x_hist, y_hist = lss.simulate(T)
@@ -743,7 +743,7 @@ def consumption_incomplete(cp, s_path):
     db = ((1 - β) * v - y) / β
 
     for i, s in enumerate(s_path):
-        c_path[i] = (1 - β) * (v - b_path[i] * np.ones((n, 1)))[s, 0]
+        c_path[i] = (1 - β) * (v - np.full((n, 1), b_path[i]))[s, 0]
         b_path[i + 1] = b_path[i] + db[s, 0]
 
     return c_path, b_path[:-1], y[s_path]
@@ -922,7 +922,7 @@ fig, ax = plt.subplots(1, 2, figsize=(14, 4))
 
 ax[0].set_title('Consumption paths')
 ax[0].plot(np.arange(N_simul), c_path, label='incomplete market')
-ax[0].plot(np.arange(N_simul), c_bar * np.ones(N_simul),
+ax[0].plot(np.arange(N_simul), np.full(N_simul, c_bar),
                     label='complete market')
 ax[0].plot(np.arange(N_simul), y_path, label='income', alpha=.6, ls='--')
 ax[0].legend()

lectures/smoothing_tax.md

@@ -227,7 +227,7 @@ def consumption_incomplete(cp, s_path):
     db = ((1 - β) * v - y) / β
 
     for i, s in enumerate(s_path):
-        c_path[i] = (1 - β) * (v - b_path[i] * np.ones((n, 1)))[s, 0]
+        c_path[i] = (1 - β) * (v - np.full((n, 1), b_path[i]))[s, 0]
         b_path[i + 1] = b_path[i] + db[s, 0]
 
     return c_path, b_path[:-1], y[s_path]
@@ -255,7 +255,7 @@ fig, ax = plt.subplots(1, 2, figsize=(14, 4))
 
 ax[0].set_title('Consumption paths')
 ax[0].plot(np.arange(N_simul), c_path, label='incomplete market')
-ax[0].plot(np.arange(N_simul), c_bar * np.ones(N_simul), label='complete market')
+ax[0].plot(np.arange(N_simul), np.full(N_simul, c_bar), label='complete market')
 ax[0].plot(np.arange(N_simul), y_path, label='income', alpha=.6, ls='--')
 ax[0].legend()
 ax[0].set_xlabel('Periods')
@@ -289,7 +289,7 @@ fig, ax = plt.subplots(1, 2, figsize=(14, 4))
 
 ax[0].set_title('Tax collection paths')
 ax[0].plot(np.arange(N_simul), c_path, label='incomplete market')
-ax[0].plot(np.arange(N_simul), c_bar * np.ones(N_simul), label='complete market')
+ax[0].plot(np.arange(N_simul), np.full(N_simul, c_bar), label='complete market')
 ax[0].plot(np.arange(N_simul), y_path, label='govt expenditures', alpha=.6, ls='--')
 ax[0].legend()
 ax[0].set_xlabel('Periods')
@@ -584,7 +584,7 @@ class TaxSmoothingExample:
         plt.figure()
         plt.title('Tax collection paths')
         plt.plot(np.arange(N), self.T_path, label='incomplete market')
-        plt.plot(np.arange(N), self.T_bar * np.ones(N), label='complete market')
+        plt.plot(np.arange(N), np.full(N, self.T_bar), label='complete market')
         plt.plot(np.arange(N), self.g_path, label='govt expenditures', alpha=.6, ls='--')
         plt.legend()
         plt.xlabel('Periods')