DOC: update plots to new interfaces

matthew-brett · matthew-brett · commit f0a8ced731a9 · 2012-07-21T15:05:59.000-07:00
Update to changes in use of symbolic HRFs.
Turn of 'usetex' for matplotlib; matplotlib handles the TeX in the plots
fine with its own renderer.
Add comments and change imports to more canonical form.
diff --git a/doc/users/plots/event_amplitude.py b/doc/users/plots/event_amplitude.py
@@ -3,18 +3,32 @@
 import numpy as np
 import pylab
 
-from nipy.modalities.fmri.utils import events, Symbol, Vectorize
-from nipy.modalities.fmri.hrf import glover_sympy
+from nipy.modalities.fmri.utils import events, Symbol, lambdify_t
+from nipy.modalities.fmri.hrf import glover
 
+# Symbol for amplitude
 a = Symbol('a')
-b = np.linspace(0,50,6)
-ba = b*([-1,1]*3)
-d = events(b, amplitudes=ba, g=a+0.5*a**2, f=glover_sympy)
-dt = Vectorize(d)
-tt = np.linspace(0,60,601)
-
-pylab.plot(tt, dt(tt), c='r')
-for bb, aa in zip(b,ba):
-    pylab.plot([bb,bb],[0,25*aa], c='b')
+
+# Some event onsets regularly spaced
+onsets = np.linspace(0,50,6)
+
+# Make amplitudes from onset times (greater as function of time)
+amplitudes = onsets[:]
+
+# Flip even numbered amplitudes
+amplitudes = amplitudes * ([-1, 1] * 3)
+
+# Make event functions
+evs = events(onsets, amplitudes=amplitudes, g=a + 0.5 * a**2, f=glover)
+
+# Real valued function for symbolic events
+real_evs = lambdify_t(evs)
+
+# Time points at which to sample
+t_samples = np.linspace(0,60,601)
+
+pylab.plot(t_samples, real_evs(t_samples), c='r')
+for onset, amplitude in zip(onsets, amplitudes):
+    pylab.plot([onset, onset],[0, 25 * amplitude], c='b')
 
 pylab.show()
diff --git a/doc/users/plots/hrf.py b/doc/users/plots/hrf.py
@@ -6,18 +6,15 @@
 
 import numpy as np
 
-from nipy.modalities.fmri import hrf
+from nipy.modalities.fmri import hrf, utils
 
-import pylab
+import matplotlib.pyplot as plt
 
-
-from matplotlib import rc
-rc('text', usetex=True)
+# hrf.glover is a symbolic function; get a function of time to work on arrays
+hrf_func = utils.lambdify_t(hrf.glover(utils.T))
 
 t = np.linspace(0,25,200)
-pylab.plot(t, hrf.glover(t))
-a=pylab.gca()
+plt.plot(t, hrf_func(t))
+a=plt.gca()
 a.set_xlabel(r'$t$')
 a.set_ylabel(r'$h_{can}(t)$')
-
-
diff --git a/doc/users/plots/hrf_delta.py b/doc/users/plots/hrf_delta.py
@@ -5,21 +5,21 @@
 of delta functions times coefficient values
 """
 
-import pylab
-from matplotlib import rc
-rc('text', usetex=True)
+import matplotlib.pyplot as plt
 
+# Coefficients for a and b
 ba = 1
 bb = -2
 
-ta = [0,4,8,12,16]; tb = [2,6,10,14,18]
+# Times for a and b
+ta = [0,4,8,12,16]
+tb = [2,6,10,14,18]
 
 for t in ta:
-    pylab.plot([t,t],[0,ba],c='r')
+    plt.plot([t,t],[0,ba],c='r')
 for t in tb:
-    pylab.plot([t,t],[0,bb],c='b')
+    plt.plot([t,t],[0,bb],c='b')
 
-a = pylab.gca()
+a = plt.gca()
 a.set_xlabel(r'$t$')
 a.set_ylabel(r'$n(t)$')
-
diff --git a/doc/users/plots/hrf_different.py b/doc/users/plots/hrf_different.py
@@ -6,33 +6,34 @@
 
 import numpy as np
 
-import pylab
-
-from sympy import lambdify
+import matplotlib.pyplot as plt
 
 from nipy.modalities.fmri import hrf
+from nipy.modalities.fmri.utils import T, lambdify_t
+
 
-glover = hrf.glover_sympy
-afni = hrf.afni_sympy
+# HRFs as functions of (symbolic) time
+glover = hrf.glover(T)
+afni = hrf.afni(T)
 
 ta = [0,4,8,12,16]; tb = [2,6,10,14,18]
 ba = 1; bb = -2
-na = ba * sum([glover.subs(hrf.t, hrf.t - t) for t in ta])
-nb = bb * sum([afni.subs(hrf.t, hrf.t - t) for t in tb])
+na = ba * sum([glover.subs(T, T - t) for t in ta])
+nb = bb * sum([afni.subs(T, T - t) for t in tb])
 
-nav = lambdify(hrf.vector_t, na.subs(hrf.t, hrf.vector_t), 'numpy')
-nbv = lambdify(hrf.vector_t, nb.subs(hrf.t, hrf.vector_t), 'numpy')
+nav = lambdify_t(na)
+nbv = lambdify_t(nb)
 
 t = np.linspace(0,30,200)
-pylab.plot(t, nav(t), c='r', label='Face')
-pylab.plot(t, nbv(t), c='b', label='Object')
-pylab.plot(t, nbv(t)+nav(t), c='g', label='Neuronal')
+plt.plot(t, nav(t), c='r', label='Face')
+plt.plot(t, nbv(t), c='b', label='Object')
+plt.plot(t, nbv(t)+nav(t), c='g', label='Combined')
 
 for t in ta:
-    pylab.plot([t,t],[0,ba*0.5],c='r')
+    plt.plot([t,t],[0,ba*0.5],c='r')
 for t in tb:
-    pylab.plot([t,t],[0,bb*0.5],c='b')
-pylab.plot([0,30], [0,0],c='#000000')
-pylab.legend()
+    plt.plot([t,t],[0,bb*0.5],c='b')
+plt.plot([0,30], [0,0],c='#000000')
+plt.legend()
 
-pylab.show()
+plt.show()
