adjust demos

demos/bernoulli_demo.py

@@ -88,6 +88,7 @@ if __name__ == '__main__':
 
     reconstruction = sky.at(H.position).value
 
-    ift.plot(reconstruction, title='reconstruction', name='reconstruction.png')
-    ift.plot(GR.adjoint_times(data), title='data', name='data.png')
-    ift.plot(sky.at(mock_position).value, title='truth', name='truth.png')
+    ift.plot(reconstruction, title='reconstruction')
+    ift.plot(GR.adjoint_times(data), title='data')
+    ift.plot(sky.at(mock_position).value, title='truth')
+    ift.plot_finish(nx=3, xsize=16, ysize=5, title="results", name="bernoulli.png")

demos/getting_started_1.py

@@ -46,7 +46,7 @@ if __name__ == '__main__':
     # FIXME description of the tutorial
 
     # Choose problem geometry and masking
-    mode = 0
+    mode = 1
     if mode == 0:
         # One dimensional regular grid
         position_space = ift.RGSpace([1024])
@@ -108,10 +108,12 @@ if __name__ == '__main__':
                  label=['Mock signal', 'Data', 'Reconstruction'],
                  alpha=[1, .3, 1])
         ift.plot(mask_to_nan(mask, HT(m-MOCK_SIGNAL)))
-        ift.plot_finish(1, 2, xsize=10, ysize=4, title="getting_started_1")
+        ift.plot_finish(nx=2, ny=1, xsize=10, ysize=4,
+                        title="getting_started_1")
     else:
         ift.plot(HT(MOCK_SIGNAL), title='Mock Signal')
         ift.plot(mask_to_nan(mask, (GR*Mask).adjoint(data)), title='Data')
         ift.plot(HT(m), title='Reconstruction')
         ift.plot(mask_to_nan(mask, HT(m-MOCK_SIGNAL)))
-        ift.plot_finish(2, 2, xsize=10, ysize=8, title="getting_started_1")
+        ift.plot_finish(nx=4, ny=1, xsize=20, ysize=4,
+                        title="getting_started_1")

demos/getting_started_3.py

@@ -83,13 +83,14 @@ if __name__ == '__main__':
     INITIAL_POSITION = ift.from_random('normal', H.position.domain)
     position = INITIAL_POSITION
 
-    ift.plot(signal.at(MOCK_POSITION).value, name='truth.png')
-    ift.plot(R.adjoint_times(data), name='data.png')
-    ift.plot([A.at(MOCK_POSITION).value], name='power.png')
+    ift.plot(signal.at(MOCK_POSITION).value, title='ground truth')
+    ift.plot(R.adjoint_times(data), title='data')
+    ift.plot([A.at(MOCK_POSITION).value], title='power')
+    ift.plot_finish(nx=3, xsize=16, ysize=5, title="setup", name="setup.png")
 
     # number of samples used to estimate the KL
     N_samples = 20
-    for i in range(5):
+    for i in range(2):
         H = H.at(position)
         samples = [H.metric.draw_sample(from_inverse=True)
                    for _ in range(N_samples)]
@@ -99,17 +100,19 @@ if __name__ == '__main__':
         KL, convergence = minimizer(KL)
         position = KL.position
 
-        ift.plot(signal.at(position).value, name='reconstruction.png')
+        ift.plot(signal.at(position).value, title="reconstruction")
 
         ift.plot([A.at(position).value, A.at(MOCK_POSITION).value],
-                 name='power.png')
+                 title="power")
+        ift.plot_finish(nx=2, xsize=12, ysize=6, title="loop", name="loop.png")
 
     sc = ift.StatCalculator()
     for sample in samples:
         sc.add(signal.at(sample+position).value)
-    ift.plot(sc.mean, name='avrg.png')
-    ift.plot(ift.sqrt(sc.var), name='std.png')
+    ift.plot(sc.mean, title="mean")
+    ift.plot(ift.sqrt(sc.var), title="std deviation")
 
     powers = [A.at(s+position).value for s in samples]
     ift.plot([A.at(position).value, A.at(MOCK_POSITION).value]+powers,
-             name='power.png')
+             title="power")
+    ift.plot_finish(nx=3, xsize=16, ysize=5, title="results", name="results.png")

nifty5/plotting/plot.py

@@ -86,16 +86,6 @@ def _makeplot(name):
     elif extension == ".png":
         plt.savefig(name)
         plt.close()
-    # elif extension==".html":
-        # import mpld3
-        # mpld3.save_html(plt.gcf(),fileobj=name,no_extras=True)
-        # import plotly.offline as py
-        # import plotly.tools as tls
-        # plotly_fig = tls.mpl_to_plotly(plt.gcf())
-        # py.plot(plotly_fig,filename=name)
-        # py.plot_mpl(plt.gcf(),filename=name)
-        # import bokeh
-        # bokeh.mpl.to_bokeh(plt.gcf())
     else:
         raise ValueError("file format not understood")
 
@@ -306,18 +296,20 @@ def plot(f, **kwargs):
     _plots.append(f)
     _kwargs.append(kwargs)
 
-def plot_finish(nx, ny, **kwargs):
+def plot_finish(**kwargs):
     global _plots, _kwargs
     import matplotlib.pyplot as plt
     nplot = len(_plots)
     fig = plt.figure()
     if "title" in kwargs:
         plt.suptitle(kwargs.pop("title"))
+    nx = kwargs.pop("nx", 1)
+    ny = kwargs.pop("ny", 1)
     xsize = kwargs.pop("xsize", 6)
     ysize = kwargs.pop("ysize", 6)
     fig.set_size_inches(xsize, ysize)
     for i in range(nplot):
-        ax = fig.add_subplot(nx,ny,i+1)
+        ax = fig.add_subplot(ny,nx,i+1)
         _plot(_plots[i], ax, **_kwargs[i])
     _makeplot(kwargs.pop("name", None))
     _plots = []