Merge pull request #543 from nengo/no-disposable

Got rid of disposable_js

nengo_gui/components/netgraph.py

@@ -8,7 +8,7 @@
 import json
 
 from nengo_gui.components.component import Component
-from nengo_gui.disposable_js import infomodal
+from nengo_gui.modal_js import infomodal
 import nengo_gui.user_action
 import nengo_gui.layout
 

nengo_gui/disposable_js.py → nengo_gui/modal_js.py

@@ -5,8 +5,9 @@
 import numpy as np
 
 import nengo
-from nengo.utils.ensemble import response_curves, tuning_curves
 
+from static_plots import tuning_curve_plot, response_curve_plot
+from static_plots import node_output_plot
 
 def infomodal(ng, uid, **args):
     obj = ng.uids[uid]
@@ -28,28 +29,6 @@ def add_modal_footer_js(footer_text):
 def show_modal_js():
     return 'Nengo.modal.show();'
 
-class PlotInfo(object):
-    def __init__(self, title, plot="none"):
-        self.title = title
-        self.plot = plot
-        self.warnings = []
-        self.x = None
-        self.y = None
-
-    def to_dict(self):
-        x, y = self.x, self.y
-        if self.plot == "multiline":
-            assert self.x.shape[0] == self.y.shape[1]
-            x = self.x.tolist()
-            y = [yy.tolist() for yy in self.y]
-        return {
-            'plot': self.plot,
-            'title': self.title,
-            'warnings': self.warnings,
-            'x': x,
-            'y': y,
-        }
-
 def ensemble_infomodal(ng, uid, conn_in_uids, conn_out_uids):
     ens = ng.uids[uid]
 
@@ -63,28 +42,8 @@ def ensemble_infomodal(ng, uid, conn_in_uids, conn_out_uids):
                  "Start a simulation to see plots.")
     else:
         plots = []
-        rc = PlotInfo("Response curves", plot="multiline")
-        rc.x, rc.y = response_curves(ens, ng.page.sim)
-        rc.y = rc.y.T
-        if len(rc.y.shape) == 1:
-            rc.y.shape = 1, rc.y.shape[0]
-        if ens.n_neurons > 200:
-            rc.warnings.append("Only showing the first 200 neurons.")
-            rc.y = rc.y[:200]
-        plots.append(rc.to_dict())
-
-        tc = PlotInfo("Tuning curves")
-        if ens.dimensions == 1:
-            tc.plot = "multiline"
-            tc.x, tc.y = tuning_curves(ens, ng.page.sim)
-            tc.y = tc.y.T
-            if ens.n_neurons > 200:
-                tc.warnings.append("Only showing the first 200 neurons.")
-                tc.y = tc.y[:200]
-        else:
-            tc.warnings.append("Tuning curves only shown for "
-                               "one-dimensional ensembles.")
-        plots.append(tc.to_dict())
+        plots.append(response_curve_plot(ens, ng.page.sim))
+        plots.append(tuning_curve_plot(ens, ng.page.sim))
 
     conninfo = conn_infomodal(ng, uid, conn_in_uids, conn_out_uids)
 
@@ -101,24 +60,7 @@ def node_infomodal(ng, uid, conn_in_uids, conn_out_uids):
     params = [(attr, str(getattr(node, attr))) for attr in (
         'output', 'size_in', 'size_out') if getattr(node, attr) is not None]
 
-    f_out = PlotInfo("Node output")
-    if node.size_in > 0:
-        f_out.warnings.append("Node output only shown when 'size_in' is 0.")
-    else:
-        f_out.plot = "multiline"
-        if callable(node.output):
-            dt = 0.001
-            f_out.x = np.arange(dt, 1.0, dt)
-            f_out.y = np.asarray([node.output(x) for x in f_out.x])
-        else:
-            # Don't bother with all the copies if it's static
-            f_out.x = np.asarray([0, 1.0])
-            f_out.y = np.hstack((node.output, node.output))
-        if f_out.y.ndim == 1:
-            f_out.y = f_out.y[:, np.newaxis]
-        f_out.y = f_out.y.T
-
-    plots = [f_out.to_dict()]
+    plots = [node_output_plot(node)]
 
     conninfo = conn_infomodal(ng, uid, conn_in_uids, conn_out_uids)
 

nengo_gui/static_plots.py

@@ -0,0 +1,71 @@
+import numpy as np
+
+import nengo
+from nengo.utils.ensemble import response_curves, tuning_curves
+
+
+class PlotInfo(object):
+    def __init__(self, title, plot="none"):
+        self.title = title
+        self.plot = plot
+        self.warnings = []
+        self.x = None
+        self.y = None
+
+    def to_dict(self):
+        x, y = self.x, self.y
+        if self.plot == "multiline":
+            assert self.x.shape[0] == self.y.shape[1]
+            x = self.x.tolist()
+            y = [yy.tolist() for yy in self.y]
+        return {
+            'plot': self.plot,
+            'title': self.title,
+            'warnings': self.warnings,
+            'x': x,
+            'y': y,
+        }
+
+def response_curve_plot(ens, sim):
+    rc = PlotInfo("Response curves", plot="multiline")
+    rc.x, rc.y = response_curves(ens, sim)
+    rc.y = rc.y.T
+    if len(rc.y.shape) == 1:
+        rc.y.shape = 1, rc.y.shape[0]
+    if ens.n_neurons > 200:
+        rc.warnings.append("Only showing the first 200 neurons.")
+        rc.y = rc.y[:200]
+    return rc.to_dict()
+
+def tuning_curve_plot(ens, sim):
+    tc = PlotInfo("Tuning curves")
+    if ens.dimensions == 1:
+        tc.plot = "multiline"
+        tc.x, tc.y = tuning_curves(ens, sim)
+        tc.y = tc.y.T
+        if ens.n_neurons > 200:
+            tc.warnings.append("Only showing the first 200 neurons.")
+            tc.y = tc.y[:200]
+    else:
+        tc.warnings.append("Tuning curves only shown for "
+                           "one-dimensional ensembles.")
+    return tc.to_dict()
+
+def node_output_plot(node):
+    f_out = PlotInfo("Node output")
+    if node.size_in > 0:
+        f_out.warnings.append("Node output only shown when 'size_in' is 0.")
+    else:
+        f_out.plot = "multiline"
+        if callable(node.output):
+            dt = 0.001
+            f_out.x = np.arange(dt, 1.0, dt)
+            f_out.y = np.asarray([node.output(x) for x in f_out.x])
+        else:
+            # Don't bother with all the copies if it's static
+            f_out.x = np.asarray([0, 1.0])
+            f_out.y = np.hstack((node.output, node.output))
+        if f_out.y.ndim == 1:
+            f_out.y = f_out.y[:, np.newaxis]
+        f_out.y = f_out.y.T
+    return f_out.to_dict()