Cookbook passes PEP8

cookbook/datasets_crust2.0_tesseroid.py

@@ -26,13 +26,17 @@
 lons, lats, heights = gridder.regular(area, shape, z=250000)
 
 # Divide the model into nproc slices and calculate them in parallel
+
+
 def calculate(chunk):
     return tesseroid.gz(lons, lats, heights, chunk)
+
+
 def split(model, nproc):
-    chunksize = len(model)/nproc
+    chunksize = len(model) / nproc
     for i in xrange(nproc - 1):
-        yield model[i*chunksize : (i + 1)*chunksize]
-    yield model[(nproc - 1)*chunksize : ]
+        yield model[i * chunksize: (i + 1) * chunksize]
+    yield model[(nproc - 1) * chunksize:]
 start = time.time()
 nproc = 8
 pool = Pool(processes=nproc)

cookbook/geothermal_climsig_abrupt.py

@@ -13,7 +13,7 @@
 # along a well at these depths
 zp = numpy.arange(0, 100, 1)
 temp, error = utils.contaminate(abrupt(amp, age, zp), 0.02,
-    percent=True, return_stddev=True)
+                                percent=True, return_stddev=True)
 
 # Preparing for the inversion
 data = SingleChange(temp, zp, mode='abrupt').config('levmarq', initial=[1, 1])
@@ -23,7 +23,7 @@
 print "  true:      amp=%.3f age=%.3f" % (amp, age)
 print "  estimated: amp=%.3f age=%.3f" % (amp_, age_)
 
-mpl.figure(figsize=(4,5))
+mpl.figure(figsize=(4, 5))
 mpl.title("Residual well temperature")
 mpl.plot(temp, zp, 'ok', label='Observed')
 mpl.plot(data.predicted(), zp, '--r', linewidth=3, label='Predicted')

cookbook/geothermal_climsig_linear.py

@@ -13,7 +13,7 @@
 # along a well at these depths
 zp = numpy.arange(0, 100, 1)
 temp, error = utils.contaminate(linear(amp, age, zp), 0.02,
-    percent=True, return_stddev=True)
+                                percent=True, return_stddev=True)
 
 # Preparing for the inversion
 data = SingleChange(temp, zp, mode='linear').config('levmarq', initial=[1, 1])

cookbook/geothermal_climsig_wrong.py

@@ -13,7 +13,7 @@
 # along a well at these depths
 zp = numpy.arange(0, 100, 1)
 temp, error = utils.contaminate(abrupt(amp, age, zp), 0.02,
-    percent=True, return_stddev=True)
+                                percent=True, return_stddev=True)
 
 # Preparing for the inversion
 data = SingleChange(temp, zp, mode='linear').config('levmarq', initial=[1, 1])

cookbook/gravmag_2d_polygon_picker.py

@@ -14,19 +14,19 @@
 mpl.ylabel("Z")
 mpl.axis('scaled')
 polygons = [mesher.Polygon(mpl.draw_polygon(area, axes),
-                              {'density':500})]
+                           {'density': 500})]
 xp = numpy.arange(-4500, 4500, 100)
 zp = numpy.zeros_like(xp)
 gz = talwani.gz(xp, zp, polygons)
 
 mpl.figure()
 mpl.axis('scaled')
-mpl.subplot(2,1,1)
+mpl.subplot(2, 1, 1)
 mpl.title(r"Gravity anomaly produced by the model")
 mpl.plot(xp, gz, '-k', linewidth=2)
 mpl.ylabel("mGal")
 mpl.xlim(-5000, 5000)
-mpl.subplot(2,1,2)
+mpl.subplot(2, 1, 2)
 mpl.polygon(polygons[0], 'o-k', linewidth=2, fill='k', alpha=0.5)
 mpl.xlabel("X")
 mpl.ylabel("Z")

cookbook/gravmag_basin2d_trapezoidal.py

@@ -8,13 +8,13 @@
 from fatiando.vis import mpl
 
 verts = [(10000, 1.), (90000, 1.), (90000, 7000), (10000, 3330)]
-model = Polygon(verts, {'density':-100})
+model = Polygon(verts, {'density': -100})
 x = numpy.arange(0., 100000., 1000.)
 z = numpy.zeros_like(x)
 gz = utils.contaminate(talwani.gz(x, z, [model]), 0.5)
 
 solver = basin2d.Trapezoidal(x, z, gz, verts[0:2], density=-100).config(
-        'levmarq', initial=[9000, 500]).fit()
+    'levmarq', initial=[9000, 500]).fit()
 estimate = solver.estimate_
 
 mpl.figure()
@@ -27,7 +27,7 @@
 mpl.xlim(0, 100000)
 mpl.subplot(2, 1, 2)
 mpl.polygon(estimate, 'o-r', linewidth=2, fill='r',
-    alpha=0.3, label='Estimated')
+            alpha=0.3, label='Estimated')
 mpl.polygon(model, '--k', linewidth=2, label='True')
 mpl.legend(loc='lower left', numpoints=1)
 mpl.xlabel("X")

cookbook/gravmag_basin2d_triangular.py

@@ -8,13 +8,13 @@
 from fatiando.vis import mpl
 
 verts = [(10000, 1.), (90000, 1.), (80000, 5000)]
-model = Polygon(verts, {'density':-100})
+model = Polygon(verts, {'density': -100})
 x = numpy.arange(0., 100000., 1000.)
 z = numpy.zeros_like(x)
 gz = utils.contaminate(talwani.gz(x, z, [model]), 1)
 
 solver = basin2d.Triangular(x, z, gz, verts[0:2], density=-100).config(
-        'levmarq', initial=[10000, 1000]).fit()
+    'levmarq', initial=[10000, 1000]).fit()
 estimate = solver.estimate_
 
 mpl.figure()
@@ -27,7 +27,7 @@
 mpl.xlim(0, 100000)
 mpl.subplot(2, 1, 2)
 mpl.polygon(estimate, 'o-r', linewidth=2, fill='r',
-    alpha=0.3, label='Estimated')
+            alpha=0.3, label='Estimated')
 mpl.polygon(model, '--k', linewidth=2, label='True')
 mpl.legend(loc='lower left', numpoints=1)
 mpl.xlabel("X")

cookbook/gravmag_eqlayer_joint.py

@@ -9,7 +9,7 @@
 from fatiando.vis import mpl
 
 # Make synthetic data
-props = {'density':1000}
+props = {'density': 1000}
 model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
 area = [-5000, 5000, -5000, 5000]
 x1, y1, z1 = gridder.scatter(area, 80, z=0, seed=0)
@@ -21,12 +21,13 @@
 # and the inversion
 # Apply a scaling factor to make both portions of the misfit the same order of
 # magnitude
-scale = np.linalg.norm(gz)**2/np.linalg.norm(gzz)**2
+scale = np.linalg.norm(gz) ** 2 / np.linalg.norm(gzz) ** 2
 misfit = (EQLGravity(x1, y1, z1, gz, layer)
-          + scale*EQLGravity(x2, y2, z2, gzz, layer, field='gzz'))
+          + scale * EQLGravity(x2, y2, z2, gzz, layer, field='gzz'))
 regul = Smoothness2D(layer.shape)
 # Use an L-curve analysis to find the best regularization parameter
-solver = LCurve(misfit, regul, [10**i for i in range(-30, -20)], jobs=8).fit()
+solver = LCurve(
+    misfit, regul, [10 ** i for i in range(-30, -20)], jobs=8).fit()
 layer.addprop('density', solver.estimate_)
 
 # Now I can forward model gz using my layer to produce an integrated map in a

cookbook/gravmag_eqlayer_pel_polereduc.py

@@ -10,7 +10,7 @@
 
 # Make synthetic data
 inc, dec = -60, 23
-props = {'magnetization':10}
+props = {'magnetization': 10}
 model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
 shape = (50, 50)
 x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=-150)
@@ -24,7 +24,7 @@
 misfit = PELTotalField(x, y, z, tf, inc, dec, layer, windows, degree)
 regul = PELSmoothness(layer, windows, degree)
 # Use an L-curve analysis to find the best regularization parameter
-solver = LCurve(misfit, regul, [10**i for i in range(-20, -10)]).fit()
+solver = LCurve(misfit, regul, [10 ** i for i in range(-20, -10)]).fit()
 layer.addprop('magnetization', solver.estimate_)
 residuals = solver.residuals()
 print "Residuals:"

cookbook/gravmag_eqlayer_pel_upcontinue.py

@@ -8,7 +8,7 @@
 from fatiando.vis import mpl
 
 # Make synthetic data
-props = {'density':1000}
+props = {'density': 1000}
 model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
 shape = (50, 50)
 x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=0)
@@ -22,7 +22,7 @@
 misfit = PELGravity(x, y, z, gz, layer, windows, degree)
 regul = PELSmoothness(layer, windows, degree)
 # Use an L-curve analysis to find the best regularization parameter
-solver = LCurve(misfit, regul, [10**i for i in range(-30, -18)]).fit()
+solver = LCurve(misfit, regul, [10 ** i for i in range(-30, -18)]).fit()
 layer.addprop('density', solver.estimate_)
 residuals = solver.residuals()
 print "Residuals:"

cookbook/gravmag_eqlayer_polereduc.py

@@ -1,6 +1,6 @@
 """
-GravMag: Use an equivalent layer to reduce a magnetic total field anomaly to the
-pole
+GravMag: Use an equivalent layer to reduce a magnetic total field anomaly to
+the pole
 """
 from fatiando.gravmag import prism, sphere
 from fatiando.gravmag.eqlayer import EQLTotalField
@@ -10,7 +10,7 @@
 
 # Make synthetic data
 inc, dec = -60, 23
-props = {'magnetization':10}
+props = {'magnetization': 10}
 model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
 shape = (25, 25)
 x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=0)
@@ -22,7 +22,7 @@
 misfit = EQLTotalField(x, y, z, tf, inc, dec, layer)
 regul = Damping(layer.size)
 # Use an L-curve analysis to find the best regularization parameter
-solver = LCurve(misfit, regul, [10**i for i in range(-30, -15)]).fit()
+solver = LCurve(misfit, regul, [10 ** i for i in range(-30, -15)]).fit()
 residuals = solver.residuals()
 layer.addprop('magnetization', solver.estimate_)
 print "Residuals:"

cookbook/gravmag_eqlayer_upcontinue.py

@@ -8,7 +8,7 @@
 from fatiando.vis import mpl
 
 # Make synthetic data
-props = {'density':1000}
+props = {'density': 1000}
 model = [mesher.Prism(-500, 500, -1000, 1000, 500, 4000, props)]
 shape = (25, 25)
 x, y, z = gridder.regular([-5000, 5000, -5000, 5000], shape, z=0)
@@ -20,7 +20,7 @@
 misfit = EQLGravity(x, y, z, gz, layer)
 regul = Damping(layer.size)
 # Use an L-curve analysis to find the best regularization parameter
-solver = LCurve(misfit, regul, [10**i for i in range(-30, -20)]).fit()
+solver = LCurve(misfit, regul, [10 ** i for i in range(-30, -20)]).fit()
 layer.addprop('density', solver.estimate_)
 residuals = solver.residuals()
 print "Residuals:"

cookbook/gravmag_euler_classic.py

@@ -12,7 +12,7 @@
 # Make a model
 bounds = [-5000, 5000, -5000, 5000, 0, 5000]
 model = [
-    Prism(-1500, -500, -500, 500, 1000, 2000, {'magnetization':2})]
+    Prism(-1500, -500, -500, 500, 1000, 2000, {'magnetization': 2})]
 # Generate some data from the model
 shape = (200, 200)
 area = bounds[0:4]

cookbook/gravmag_euler_classic_expanding_window.py

@@ -14,8 +14,8 @@
 # Make a model
 bounds = [-5000, 5000, -5000, 5000, 0, 5000]
 model = [
-    Prism(-1500, -500, -1500, -500, 1000, 2000, {'magnetization':2}),
-    Prism(500, 1500, 1000, 2000, 1000, 2000, {'magnetization':2})]
+    Prism(-1500, -500, -1500, -500, 1000, 2000, {'magnetization': 2}),
+    Prism(500, 1500, 1000, 2000, 1000, 2000, {'magnetization': 2})]
 # Generate some data from the model
 shape = (100, 100)
 area = bounds[0:4]
@@ -66,7 +66,7 @@
 myv.figure()
 myv.points([r.estimate_ for r in results], size=100.)
 myv.prisms(model, opacity=0.5)
-axes = myv.axes(myv.outline(bounds), ranges=[b*0.001 for b in bounds])
+axes = myv.axes(myv.outline(bounds), ranges=[b * 0.001 for b in bounds])
 myv.wall_bottom(bounds)
 myv.wall_north(bounds)
 myv.show()

cookbook/gravmag_euler_classic_moving_window.py

@@ -11,8 +11,8 @@
 # Make a model
 bounds = [-5000, 5000, -5000, 5000, 0, 5000]
 model = [
-    Prism(-1500, -500, -1500, -500, 500, 1500, {'density':1000}),
-    Prism(500, 1500, 1000, 2000, 500, 1500, {'density':1000})]
+    Prism(-1500, -500, -1500, -500, 500, 1500, {'density': 1000}),
+    Prism(500, 1500, 1000, 2000, 500, 1500, {'density': 1000})]
 # Generate some data from the model
 shape = (100, 100)
 area = bounds[0:4]
@@ -51,7 +51,7 @@
 myv.figure()
 myv.points(solver.estimate_, size=100.)
 myv.prisms(model, opacity=0.5)
-axes = myv.axes(myv.outline(bounds), ranges=[b*0.001 for b in bounds])
+axes = myv.axes(myv.outline(bounds), ranges=[b * 0.001 for b in bounds])
 myv.wall_bottom(bounds)
 myv.wall_north(bounds)
 myv.title('Euler solutions')

cookbook/gravmag_fourier_ansig.py

@@ -5,7 +5,7 @@
 from fatiando.gravmag import prism, fourier
 from fatiando.vis import mpl
 
-model = [mesher.Prism(-100,100,-100,100,0,2000,{'magnetization':10})]
+model = [mesher.Prism(-100, 100, -100, 100, 0, 2000, {'magnetization': 10})]
 area = (-5000, 5000, -5000, 5000)
 shape = (100, 100)
 z0 = -500
@@ -14,7 +14,7 @@
 tf = utils.contaminate(prism.tf(xp, yp, zp, model, inc, dec), 0.001,
                        percent=True)
 
-    # Need to convert gz to SI units so that the result is also in SI
+# Need to convert gz to SI units so that the result is also in SI
 ansig = fourier.ansig(xp, yp, utils.nt2si(tf), shape)
 
 mpl.figure()

cookbook/gravmag_fourier_deriv.py

@@ -5,7 +5,7 @@
 from fatiando.gravmag import prism, fourier
 from fatiando.vis import mpl
 
-model = [mesher.Prism(-1000,1000,-1000,1000,0,2000,{'density':100})]
+model = [mesher.Prism(-1000, 1000, -1000, 1000, 0, 2000, {'density': 100})]
 area = (-5000, 5000, -5000, 5000)
 shape = (51, 51)
 z0 = -500
@@ -28,7 +28,7 @@
 mpl.colorbar(shrink=0.7)
 mpl.m2km()
 
-mpl.figure(figsize=(14,10))
+mpl.figure(figsize=(14, 10))
 mpl.subplots_adjust(top=0.95, left=0.05, right=0.95)
 mpl.subplot(2, 3, 1)
 mpl.title("x deriv (contour) + true (color map)")

cookbook/gravmag_grav_polyprism.py

@@ -17,7 +17,7 @@
     model.append(
         mesher.PolygonalPrism(
             mpl.draw_polygon(area, axes, xy2ne=True),
-            depths[i - 1], depths[i], {'density':500}))
+            depths[i - 1], depths[i], {'density': 500}))
 # Calculate the effect
 shape = (100, 100)
 xp, yp, zp = gridder.regular(area, shape, z=-1)
@@ -35,5 +35,5 @@
 # Show the model
 myv.figure()
 myv.polyprisms(model, 'density')
-myv.axes(myv.outline(bounds), ranges=[i*0.001 for i in bounds])
+myv.axes(myv.outline(bounds), ranges=[i * 0.001 for i in bounds])
 myv.show()

cookbook/gravmag_grav_prism.py

@@ -6,10 +6,10 @@
 from fatiando.gravmag import prism
 from fatiando.vis import mpl, myv
 
-model = [mesher.Prism(-4000,-3000,-4000,-3000,0,2000,{'density':1000}),
-          mesher.Prism(-1000,1000,-1000,1000,0,2000,{'density':-900}),
-          mesher.Prism(2000,4000,3000,4000,0,2000,{'density':1300})]
-shape = (100,100)
+model = [mesher.Prism(-4000, -3000, -4000, -3000, 0, 2000, {'density': 1000}),
+         mesher.Prism(-1000, 1000, -1000, 1000, 0, 2000, {'density': -900}),
+         mesher.Prism(2000, 4000, 3000, 4000, 0, 2000, {'density': 1300})]
+shape = (100, 100)
 xp, yp, zp = gridder.regular((-5000, 5000, -5000, 5000), shape, z=-150)
 fields = [prism.potential(xp, yp, zp, model),
           prism.gx(xp, yp, zp, model),
@@ -30,9 +30,10 @@
     mpl.subplot(4, 3, i + 3)
     mpl.axis('scaled')
     mpl.title(titles[i])
-    levels = mpl.contourf(yp*0.001, xp*0.001, field, shape, 15)
+    levels = mpl.contourf(yp * 0.001, xp * 0.001, field, shape, 15)
     cb = mpl.colorbar()
-    mpl.contour(yp*0.001, xp*0.001, field, shape, levels, clabel=False, linewidth=0.1)
+    mpl.contour(yp * 0.001, xp * 0.001, field, shape,
+                levels, clabel=False, linewidth=0.1)
 mpl.show()
 
 myv.figure()

cookbook/gravmag_grav_sphere.py

@@ -6,7 +6,7 @@
 from fatiando.gravmag import sphere
 from fatiando.vis import mpl
 
-model = [mesher.Sphere(0, 0, 2000, 1000, {'density':1000})]
+model = [mesher.Sphere(0, 0, 2000, 1000, {'density': 1000})]
 area = (-5000, 5000, -5000, 5000)
 shape = (100, 100)
 x, y, z = gridder.regular(area, shape, z=-100)