change np.ones patterns to np.full

lectures/career.md

@@ -288,7 +288,7 @@ def solve_model(cw,
     T, _ = operator_factory(cw, parallel_flag=use_parallel)
 
     # Set up loop
-    v = np.ones((cw.grid_size, cw.grid_size)) * 100  # Initial guess
+    v = np.full((cw.grid_size, cw.grid_size), 100.)  # Initial guess
     i = 0
     error = tol + 1
 
@@ -519,4 +519,3 @@ In the new figure, you see that the region for which the worker
 stays put has grown because the distribution for $\epsilon$
 has become more concentrated around the mean, making high-paying jobs
 less realistic.
-

lectures/geom_series.md

@@ -720,7 +720,7 @@ T = np.arange(0, T_max+1)
 fig, ax = plt.subplots()
 ax.set_title('Infinite and Finite Lease Present Value $T$ Periods Ahead')
 f_1 = finite_lease_pv_true(T, g, r, x_0)
-f_2 = np.ones(T_max+1)*infinite_lease(g, r, x_0)
+f_2 = np.full(T_max+1, infinite_lease(g, r, x_0))
 ax.plot(T, f_1, label='T-period lease PV')
 ax.plot(T, f_2, '--', label='Infinite lease PV')
 ax.set_xlabel('$T$ Periods Ahead')

lectures/inventory_dynamics.md

@@ -352,7 +352,7 @@ first_diffs = np.diff(sample_dates)
 
 fig, ax = plt.subplots()
 
-X = np.ones(num_firms) * x_init
+X = np.full(num_firms, x_init)
 
 current_date = 0
 for d in first_diffs:

lectures/lqcontrol.md

@@ -1470,7 +1470,7 @@ c = up.flatten() + c_bar           # Consumption
 
 time = np.arange(1, K+1)
 income_w = σ * wp_w[0, 1:K+1] + m1 * time + m2 * time**2  # Income
-income_r = np.ones(T-K) * s
+income_r = np.full(T-K, s)
 income = np.concatenate((income_w, income_r))
 
 # Plot results

lectures/markov_asset.md

@@ -922,7 +922,7 @@ Consider the following primitives
 
 ```{code-cell} python3
 n = 5
-P = 0.0125 * np.ones((n, n))
+P = np.full((n, n), 0.0125)
 P += np.diag(0.95 - 0.0125 * np.ones(5))
 # State values of the Markov chain
 s = np.array([0.95, 0.975, 1.0, 1.025, 1.05])
@@ -1010,7 +1010,7 @@ First, let's enter the parameters:
 
 ```{code-cell} python3
 n = 5
-P = 0.0125 * np.ones((n, n))
+P = np.full((n, n), 0.0125)
 P += np.diag(0.95 - 0.0125 * np.ones(5))
 s = np.array([0.95, 0.975, 1.0, 1.025, 1.05])  # State values
 mc = qe.MarkovChain(P, state_values=s)

lectures/mccall_correlated.md

@@ -265,7 +265,7 @@ def compute_fixed_point(js,
                         verbose=True,
                         print_skip=25):
 
-    f_init = np.log(js.c) * np.ones(len(js.z_grid))
+    f_init = np.full(len(js.z_grid), np.log(js.c))
     f_out = np.empty_like(f_init)
 
     # Set up loop

lectures/multi_hyper.md

@@ -218,7 +218,7 @@ class Urn:
         μ = n * K_arr / N
 
         # variance-covariance matrix
-        Σ = np.ones((c, c)) * n * (N - n) / (N - 1) / N ** 2
+        Σ = np.full((c, c), n * (N - n) / (N - 1) / N ** 2)
         for i in range(c-1):
             Σ[i, i] *= K_arr[i] * (N - K_arr[i])
             for j in range(i+1, c):

lectures/odu.md

@@ -783,7 +783,7 @@ def simulate_path(F_a=F_a,
     """Simulates path of employment for N number of works over T periods"""
 
     e = np.ones((N, T+1))
-    π = np.ones((N, T+1)) * 1e-3
+    π = np.full((N, T+1), 1e-3)
 
     a, b = G_a, G_b   # Initial distribution parameters
 

lectures/short_path.md

@@ -389,8 +389,7 @@ destination_node = 99
 def map_graph_to_distance_matrix(in_file):
 
     # First let's set of the distance matrix Q with inf everywhere
-    Q = np.ones((num_nodes, num_nodes))
-    Q = Q * np.inf
+    Q = np.full((num_nodes, num_nodes), np.inf)
 
     # Now we read in the data and modify Q
     infile = open(in_file)

lectures/time_series_with_matrices.md

@@ -157,7 +157,7 @@ for i in range(T):
     if i-2 >= 0:
         A[i, i-2] = -𝛼2
 
-b = np.ones(T) * 𝛼0
+b = np.full(T, 𝛼0)
 b[0] = 𝛼0 + 𝛼1 * y0 + 𝛼2 * y_1
 b[1] = 𝛼0 + 𝛼2 * y0
 ```
@@ -200,7 +200,7 @@ then $y_{t}$ will be constant
 y_1_steady = 𝛼0 / (1 - 𝛼1 - 𝛼2) # y_{-1}
 y0_steady = 𝛼0 / (1 - 𝛼1 - 𝛼2)
 
-b_steady = np.ones(T) * 𝛼0
+b_steady = np.full(T, 𝛼0)
 b_steady[0] = 𝛼0 + 𝛼1 * y0_steady + 𝛼2 * y_1_steady
 b_steady[1] = 𝛼0 + 𝛼2 * y0_steady
 ```

lectures/wealth_dynamics.md

@@ -441,7 +441,7 @@ def generate_lorenz_and_gini(wdy, num_households=100_000, T=500):
     Generate the Lorenz curve data and gini coefficient corresponding to a
     WealthDynamics mode by simulating num_households forward to time T.
     """
-    ψ_0 = np.ones(num_households) * wdy.y_mean
+    ψ_0 = np.full(num_households, wdy.y_mean)
     z_0 = wdy.z_mean
 
     ψ_star = update_cross_section(wdy, ψ_0, shift_length=T)
@@ -576,7 +576,7 @@ For sample size and initial conditions, use
 ```{code-cell} ipython3
 num_households = 250_000
 T = 500                                      # shift forward T periods
-ψ_0 = np.ones(num_households) * wdy.y_mean   # initial distribution
+ψ_0 = np.full(num_households, wdy.y_mean)   # initial distribution
 z_0 = wdy.z_mean
 ```
 
@@ -616,7 +616,7 @@ First let's generate the distribution:
 ```{code-cell} ipython3
 num_households = 250_000
 T = 500  # how far to shift forward in time
-ψ_0 = np.ones(num_households) * wdy.y_mean
+ψ_0 = np.full(num_households, wdy.y_mean)
 z_0 = wdy.z_mean
 
 ψ_star = update_cross_section(wdy, ψ_0, shift_length=T)