Merge pull request #110 from QuantEcon/pep8

STY: made all .py files pep8 compliant

examples/3dplot.py

@@ -3,14 +3,15 @@
 import numpy as np
 from matplotlib import cm
 
+
 def f(x, y):
     return np.cos(x**2 + y**2) / (1 + x**2 + y**2)
 
 xgrid = np.linspace(-3, 3, 50)
 ygrid = xgrid
-x, y = np.meshgrid(xgrid, ygrid)  
+x, y = np.meshgrid(xgrid, ygrid)
 
-fig = plt.figure(figsize=(8,6))
+fig = plt.figure(figsize=(8, 6))
 ax = fig.add_subplot(111, projection='3d')
 ax.plot_surface(x,
                 y,

examples/3dvec.py

@@ -59,7 +59,5 @@ def f(x, y):
 x2, y2 = np.meshgrid(xr2, yr2)
 z2 = f(x2, y2)
 ax.plot_surface(x2, y2, z2, rstride=1, cstride=1, cmap=cm.jet,
-        linewidth=0, antialiased=True, alpha=0.2)
+                linewidth=0, antialiased=True, alpha=0.2)
 plt.show()
-
-

examples/ar1_sd.py

@@ -4,6 +4,7 @@
 import numpy as np
 import matplotlib.pyplot as plt
 
+
 def ar1_sd(phi, omega):
     return 1 / (1 - 2 * phi * np.cos(omega) + phi**2)
 

examples/beta-binomial.py

@@ -7,6 +7,7 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
+
 def gen_probs(n, a, b):
     probs = np.zeros(n+1)
     for k in range(n+1):
@@ -22,4 +23,3 @@ def gen_probs(n, a, b):
     ax.plot(list(range(0, n+1)), gen_probs(n, a, b), '-o', label=ab_label)
 ax.legend()
 plt.show()
-

examples/binom_df.py

@@ -1,12 +1,11 @@
-import numpy as np
 import matplotlib.pyplot as plt
 from scipy.stats import binom
 
 fig, axes = plt.subplots(2, 2)
 plt.subplots_adjust(hspace=0.4)
 axes = axes.flatten()
 ns = [1, 2, 4, 8]
-dom = list(range(9)) 
+dom = list(range(9))
 
 for ax, n in zip(axes, ns):
     b = binom(n, 0.5)
@@ -18,4 +17,3 @@
     ax.set_title(r'$n = {}$'.format(n))
 
 fig.show()
-

examples/boxplot_example.py

@@ -11,7 +11,5 @@
 ax.boxplot([x, y, z])
 ax.set_xticks((1, 2, 3))
 ax.set_ylim(-2, 14)
-ax.set_xticklabels((r'$X$', r'$Y$', r'$Z$'), fontsize=16) 
+ax.set_xticklabels((r'$X$', r'$Y$', r'$Z$'), fontsize=16)
 plt.show()
-
-

examples/career_vf_plot.py

@@ -19,7 +19,7 @@
 v = qe.compute_fixed_point(wp.bellman_operator, v_init)
 
 # === plot value function === #
-fig = plt.figure(figsize=(8,6))
+fig = plt.figure(figsize=(8, 6))
 ax = fig.add_subplot(111, projection='3d')
 tg, eg = np.meshgrid(wp.theta, wp.epsilon)
 ax.plot_surface(tg,

examples/cauchy_samples.py

@@ -21,11 +21,9 @@
         sample_mean[i] = np.mean(data[:i])
 
     # == Plot == #
-    ax.plot(list(range(n)), sample_mean, 'r-', lw=3, alpha=0.6, label=r'$\bar X_n$')
+    ax.plot(list(range(n)), sample_mean, 'r-', lw=3, alpha=0.6,
+            label=r'$\bar X_n$')
     ax.plot(list(range(n)), [0] * n, 'k--', lw=0.5)
     ax.legend()
 
 fig.show()
-
-
-

examples/clt3d.py

@@ -8,24 +8,25 @@
 """
 
 import numpy as np
-from scipy.stats import beta, bernoulli, gaussian_kde
+from scipy.stats import beta, gaussian_kde
 from mpl_toolkits.mplot3d import Axes3D
 from matplotlib.collections import PolyCollection
 import matplotlib.pyplot as plt
 
-beta_dist = beta(2, 2)  
+beta_dist = beta(2, 2)
+
 
 def gen_x_draws(k):
     """
-    Returns a flat array containing k independent draws from the 
+    Returns a flat array containing k independent draws from the
     distribution of X, the underlying random variable.  This distribution is
     itself a convex combination of three beta distributions.
     """
     bdraws = beta_dist.rvs((3, k))
     # == Transform rows, so each represents a different distribution == #
-    bdraws[0,:] -= 0.5
-    bdraws[1,:] += 0.6
-    bdraws[2,:] -= 1.1
+    bdraws[0, :] -= 0.5
+    bdraws[1, :] += 0.6
+    bdraws[2, :] -= 1.1
     # == Set X[i] = bdraws[j, i], where j is a random draw from {0, 1, 2} == #
     js = np.random.random_integers(0, 2, size=k)
     X = bdraws[js, np.arange(k)]
@@ -40,7 +41,7 @@ def gen_x_draws(k):
 # == Form a matrix Z such that each column is reps independent draws of X == #
 Z = np.empty((reps, nmax))
 for i in range(nmax):
-    Z[:,i] = gen_x_draws(reps)
+    Z[:, i] = gen_x_draws(reps)
 # == Take cumulative sum across columns
 S = Z.cumsum(axis=1)
 # == Multiply j-th column by sqrt j == #
@@ -52,23 +53,23 @@ def gen_x_draws(k):
 ax = fig.gca(projection='3d')
 
 a, b = -3, 3
-gs = 100 
+gs = 100
 xs = np.linspace(a, b, gs)
 
 # == Build verts == #
 greys = np.linspace(0.3, 0.7, nmax)
 verts = []
 for n in ns:
-    density = gaussian_kde(Y[:,n-1])
+    density = gaussian_kde(Y[:, n-1])
     ys = density(xs)
     verts.append(list(zip(xs, ys)))
 
-poly = PolyCollection(verts, facecolors = [str(g) for g in greys])
+poly = PolyCollection(verts, facecolors=[str(g) for g in greys])
 poly.set_alpha(0.85)
 ax.add_collection3d(poly, zs=ns, zdir='x')
 
-#ax.text(np.mean(rhos), a-1.4, -0.02, r'$\beta$', fontsize=16)
-#ax.text(np.max(rhos)+0.016, (a+b)/2, -0.02, r'$\log(y)$', fontsize=16)
+# ax.text(np.mean(rhos), a-1.4, -0.02, r'$\beta$', fontsize=16)
+# ax.text(np.max(rhos)+0.016, (a+b)/2, -0.02, r'$\log(y)$', fontsize=16)
 ax.set_xlim3d(1, nmax)
 ax.set_xticks(ns)
 ax.set_xlabel("n")
@@ -77,5 +78,3 @@ def gen_x_draws(k):
 ax.set_zlim3d(0, 0.4)
 ax.set_zticks((0.2, 0.4))
 plt.show()
-
-

examples/dice.py

@@ -4,13 +4,13 @@
 
 import random
 
+
 class Dice:
 
     faces = (1, 2, 3, 4, 5, 6)
 
     def __init__(self):
-       self.current_face = 1
+        self.current_face = 1
 
     def roll(self):
         self.current_face = random.choice(Dice.faces)
-

examples/duopoly_lqnash.py

@@ -10,20 +10,15 @@
 
 """
 from __future__ import division
-import sys
-import numpy as np
-import scipy.linalg as la
-import matplotlib.pyplot as plt
-from numpy import sqrt, max, eye, dot, zeros, cumsum, array
-from numpy.random import randn
+from numpy import array, eye
 from quantecon.lqnash import nnash
 
 
-#---------------------------------------------------------------------#
+# ---------------------------------------------------------------------#
 # Set up parameter values and LQ matrices
 # Remember state is x_t = [1, y_{1, t}, y_{2, t}] and
 # control is u_{i, t} = [y_{i, t+1} - y_{i, t}]
-#---------------------------------------------------------------------#
+# ---------------------------------------------------------------------#
 a0 = 10.
 a1 = 1.
 beta = 1.
@@ -45,9 +40,9 @@
 q2 = array([[-.5*d]])
 
 
-#---------------------------------------------------------------------#
+# ---------------------------------------------------------------------#
 # Solve using QE's nnash function
-#---------------------------------------------------------------------#
+# ---------------------------------------------------------------------#
 
 f1, f2, p1, p2 = nnash(a, b1, b2, r1, r2, q1, q2, 0., 0., 0., 0., 0., 0.,
                        tol=1e-8, max_iter=1000)

examples/duopoly_mpe.py

@@ -39,11 +39,10 @@
 
 # == Solve using QE's nnash function == #
 F1, F2, P1, P2 = qe.nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2,
-                       beta=beta)
+                          beta=beta)
 
 # == Display policies == #
 print("Computed policies for firm 1 and firm 2:\n")
 print("F1 = {}".format(F1))
 print("F2 = {}".format(F2))
 print("\n")
-

examples/eigenvec.py

@@ -13,7 +13,7 @@
      (2, 1))
 A = np.array(A)
 evals, evecs = eig(A)
-evecs = evecs[:,0], evecs[:,1]
+evecs = evecs[:, 0], evecs[:, 1]
 
 fig, ax = plt.subplots()
 # Set the axes through the origin
@@ -22,30 +22,30 @@
 for spine in ['right', 'top']:
     ax.spines[spine].set_color('none')
 ax.grid(alpha=0.4)
-    
+
 xmin, xmax = -3, 3
 ymin, ymax = -3, 3
 ax.set_xlim(xmin, xmax)
 ax.set_ylim(ymin, ymax)
-#ax.set_xticks(())
-#ax.set_yticks(())
+# ax.set_xticks(())
+# ax.set_yticks(())
 
 # Plot each eigenvector
 for v in evecs:
-    ax.annotate('', xy=v, xytext=(0, 0), 
-                arrowprops=dict(facecolor='blue', 
-                    shrink=0, 
-                    alpha=0.6,
-                    width=0.5))
+    ax.annotate('', xy=v, xytext=(0, 0),
+                arrowprops=dict(facecolor='blue',
+                shrink=0,
+                alpha=0.6,
+                width=0.5))
 
 # Plot the image of each eigenvector
 for v in evecs:
     v = np.dot(A, v)
-    ax.annotate('', xy=v, xytext=(0, 0), 
-                arrowprops=dict(facecolor='red', 
-                    shrink=0, 
-                    alpha=0.6,
-                    width=0.5))
+    ax.annotate('', xy=v, xytext=(0, 0),
+                arrowprops=dict(facecolor='red',
+                shrink=0,
+                alpha=0.6,
+                width=0.5))
 
 # Plot the lines they run through
 x = np.linspace(xmin, xmax, 3)
@@ -55,4 +55,3 @@
 
 
 plt.show()
-

examples/evans_sargent.py

@@ -22,6 +22,7 @@
 from quantecon.matrix_eqn import solve_discrete_lyapunov
 from scipy.optimize import root
 
+
 def computeG(A0, A1, d, Q0, tau0, beta, mu):
     """
     Compute government income given mu and return tax revenues and
@@ -107,6 +108,7 @@ def computeG(A0, A1, d, Q0, tau0, beta, mu):
 Q0   = 1000.0
 tau0 = 0.0
 
+
 def gg(mu):
     """
     Computes the tax revenues for the government given Lagrangian
@@ -175,4 +177,3 @@ def gg(mu):
     print(y)
     print("-F")
     print(-F)
-

examples/evans_sargent_plot1.py

@@ -7,7 +7,7 @@
 """
 import numpy as np
 import matplotlib.pyplot as plt
-from evans_sargent import T, y 
+from evans_sargent import T, y
 
 tt = np.arange(T)  # tt is used to make the plot time index correct.
 
@@ -20,8 +20,8 @@
     ax.set_xlim(0, 15)
 
 bbox = (0., 1.02, 1., .102)
-legend_args = {'bbox_to_anchor' : bbox, 'loc' : 3, 'mode' : 'expand'}
-p_args = {'lw' : 2, 'alpha' : 0.7}
+legend_args = {'bbox_to_anchor': bbox, 'loc': 3, 'mode': 'expand'}
+p_args = {'lw': 2, 'alpha': 0.7}
 
 ax = axes[0]
 ax.plot(tt, y[1, :], 'b-', label="output", **p_args)
@@ -42,4 +42,3 @@
 ax.set_xlabel(r'time', fontsize=16)
 
 plt.show()
-

examples/evans_sargent_plot2.py

@@ -7,7 +7,7 @@
 """
 import numpy as np
 import matplotlib.pyplot as plt
-from evans_sargent import T, y, uhatdif, tauhatdif, mu, G, GPay
+from evans_sargent import T, uhatdif, tauhatdif, mu, G
 
 tt = np.arange(T)  # tt is used to make the plot time index correct.
 tt2 = np.arange(T-1)
@@ -21,18 +21,20 @@
     ax.set_xlim(-0.5, 15)
 
 bbox = (0., 1.02, 1., .102)
-legend_args = {'bbox_to_anchor' : bbox, 'loc' : 3, 'mode' : 'expand'}
-p_args = {'lw' : 2, 'alpha' : 0.7}
+legend_args = {'bbox_to_anchor': bbox, 'loc': 3, 'mode': 'expand'}
+p_args = {'lw': 2, 'alpha': 0.7}
 
 ax = axes[0]
-ax.plot(tt2, tauhatdif, label=r'time inconsistency differential for tax rate', **p_args)
+ax.plot(tt2, tauhatdif, label=r'time inconsistency differential for tax rate',
+        **p_args)
 ax.set_ylabel(r"$\Delta\tau$", fontsize=16)
 ax.set_ylim(-0.1, 1.4)
 ax.set_yticks((0.0, 0.4, 0.8, 1.2))
 ax.legend(ncol=1, **legend_args)
 
 ax = axes[1]
-ax.plot(tt, uhatdif, label=r'time inconsistency differential for $u$', **p_args)
+ax.plot(tt, uhatdif, label=r'time inconsistency differential for $u$',
+        **p_args)
 ax.set_ylabel(r"$\Delta u$", fontsize=16)
 ax.set_ylim(-3, .1)
 ax.set_yticks((-3.0, -2.0, -1.0, 0.0))
@@ -55,4 +57,4 @@
 ax.set_xlabel(r'time', fontsize=16)
 
 plt.show()
-#lines = plt.plot(tt, GPay, "o")
+# lines = plt.plot(tt, GPay, "o")