Quick MAJ before code sprint

Notebooks/Attribute Profiles Classifier.ipynb

@@ -332,6 +332,16 @@
     "show(cv_labels)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.imsave('../Res/labels.png', labels)\n",
+    "plt.imsave('../Res/prediction.png', cv_labels)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

Notebooks/DFC2018 Ground truth split train and validation.ipynb

@@ -101,7 +101,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "train_part = .7\n",
+    "train_part = .9\n",
     "\n",
     "split = np.zeros_like(gt)\n",
     "\n",
@@ -118,6 +118,32 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.animation\n",
+    "import numpy as np\n",
+    "\n",
+    "t = np.linspace(0,2*np.pi)\n",
+    "x = np.sin(t)\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "ax.axis([0,2*np.pi,-1,1])\n",
+    "l, = ax.plot([],[])\n",
+    "\n",
+    "def animate(i):\n",
+    "    l.set_data(t[:i], x[:i])\n",
+    "\n",
+    "ani = matplotlib.animation.FuncAnimation(fig, animate, frames=len(t))\n",
+    "\n",
+    "from IPython.display import HTML\n",
+    "HTML(ani.to_jshtml())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},

Notebooks/Node Streaming Benchmark.ipynb

@@ -41,17 +41,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "l = ld2dap.LoadTIFF(layers_files[5])\n",
+    "l = ld2dap.LoadTIFF(layers_files)\n",
     "t = ld2dap.Treshold(1e4)\n",
-    "a = ld2dap.AttributeProfiles(area = [1e3, 1e6])\n",
+    "a = ld2dap.SelfDualAttributeProfiles(area = [1e3, 1e6])\n",
     "f = ld2dap.Differential()\n",
-    "d = ld2dap.ShowFig('all', fname='../Res/aps.png', pad_inches=0, symb=True, vmax=255)\n",
+    "d_ap_3 = ld2dap.ShowFig(stack_id=3, fname='../Res/daps_3.png', pad_inches=0, symb=True, vmax=255, transparent=True)\n",
+    "d_dap_3 = ld2dap.ShowFig(stack_id=3, fname='../Res/aps_3.png', pad_inches=0, symb=True, vmax=255, transparent=True)\n",
+    "d_ap_6 = ld2dap.ShowFig(stack_id=6, fname='../Res/daps_6.png', pad_inches=0, symb=True, vmax=255, transparent=True)\n",
+    "d_dap_6 = ld2dap.ShowFig(stack_id=6, fname='../Res/aps_6.png', pad_inches=0, symb=True, vmax=255, transparent=True)\n",
+    "\n",
+    "\n",
     "\n",
     "t.input = l\n",
     "a.input = t\n",
     "f.input = a\n",
-    "d.input = f\n",
+    "d_dap_3.input = f\n",
-    "d.run()"
+    "d_dap_6.input = f\n",
+    "d_ap_3.input = a\n",
+    "d_ap_6.input = a\n",
+    "l.run()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$\\rho^{area}_{1000}$"
    ]
   },
   {

ld2dap/SelfDualAttributeProfiles.py

@@ -42,7 +42,7 @@ class SelfDualAttributeProfiles(Filter):
             if self.__getattribute__(att) is not None:
                 att_symb[att].append(None)
                 att_symb[att].extend(
-                  ['\rho^{{{}}}_{{{}}}'.format(att, x) for x in self.__getattribute__(att)])
+                  ['\\rho^{{{}}}_{{{}}}'.format(att, x) for x in self.__getattribute__(att)])
 
         return att_symb
 

ld2dap/ShowFig.py

@@ -13,7 +13,7 @@ from ld2dap.core import Output
 import matplotlib.pyplot as plt
 
 class ShowFig(Output):
-    def __init__(self, stack_id=0, symb=False, fname=None, bbox_inches='tight', pad_inches=1, vmax=None):
+    def __init__(self, stack_id=0, symb=False, fname=None, bbox_inches='tight', pad_inches=1, vmax=None, transparent=False):
         super().__init__(self.__class__.__name__)
         self.bbox_inches = bbox_inches
         self.pad_inches  = pad_inches
@@ -21,6 +21,7 @@ class ShowFig(Output):
         self.symb        = symb
         self.fname       = fname
         self.vmax        = vmax
+        self.transparent = transparent
 
     def _process(self, data, metadata):
         if self.stack_id == 'all':
@@ -49,6 +50,6 @@ class ShowFig(Output):
                 f1.set_title(' > '.join(filter(None, stack.desc[i])))
 
         if self.fname is not None:
-            fig.savefig(self.fname, bbox_inches=self.bbox_inches, pad_inches=self.pad_inches)
+            fig.savefig(self.fname, bbox_inches=self.bbox_inches, pad_inches=self.pad_inches, transparent=self.transparent)
         plt.show()
         plt.close(fig)