From 572d01b74c43fb218ecd4db8e2dcecce3c09c913 Mon Sep 17 00:00:00 2001
From: "Egli Adrian (IT-SCI-API-PFI)" <adrian.egli@sbb.ch>
Date: Wed, 24 Jul 2019 06:51:08 +0200
Subject: [PATCH] #119 render_env refactored

---
 flatland/utils/editor.py                      |  7 +-
 flatland/utils/rendertools.py                 | 78 ++++++++++---------
 notebooks/Scene_Editor.ipynb                  | 12 +--
 .../simple_example1_env_from_tuple.ipynb      |  6 +-
 4 files changed, 55 insertions(+), 48 deletions(-)

diff --git a/flatland/utils/editor.py b/flatland/utils/editor.py
index 5247cc1..69be59a 100644
--- a/flatland/utils/editor.py
+++ b/flatland/utils/editor.py
@@ -54,7 +54,7 @@ class View(object):
     def init_canvas(self):
         # update the rendertool with the env
         self.new_env()
-        self.oRT.render_env(spacing=False, arrows=False, rail_color="gray", show=False)
+        self.oRT.render_env(show=False)
         img = self.oRT.get_image()
         self.wImage = jpy_canvas.Canvas(img)
         self.yxSize = self.wImage.data.shape[:2]
@@ -154,8 +154,9 @@ class View(object):
                 if hasattr(a, 'old_direction') is False:
                     a.old_direction = a.direction
 
-            self.oRT.render_env(rail_color="gray", agents=True,
-                                show=False, selected_agent=self.model.selected_agent,
+            self.oRT.render_env(agents=True,
+                                show=False,
+                                selected_agent=self.model.selected_agent,
                                 show_observations=False)
             img = self.oRT.get_image()
 
diff --git a/flatland/utils/rendertools.py b/flatland/utils/rendertools.py
index bc24a21..4aca5c3 100644
--- a/flatland/utils/rendertools.py
+++ b/flatland/utils/rendertools.py
@@ -293,7 +293,7 @@ class RenderTool(object):
             for visited_cell in prediction_dict[agent]:
                 cell_coord = array(visited_cell[:2])
                 cell_coord_trans = np.matmul(cell_coord, rt.row_col_to_xy) + rt.x_y_half
-                self._draw_square(cell_coord_trans, 1 / (agent + 1.1), color, layer=1, opacity=100)
+                self._draw_square(cell_coord_trans, 1 / (agent + 1.1), color, layer=1, opacity=100)  # TODO : Track highlighting (Adrian)
 
     def render_rail(self, spacing=False, rail_color="gray", curves=True, arrows=False):
 
@@ -393,31 +393,54 @@ class RenderTool(object):
 
     def render_env(self,
                    show=False,  # whether to call matplotlib show() or equivalent after completion
-                   # use false when calling from Jupyter.  (and matplotlib no longer supported!)
-                   curves=True,  # draw turns as curves instead of straight diagonal lines
-                   spacing=False,  # defunct - size of spacing between rails
-                   arrows=False,  # defunct - draw arrows on rail lines
                    agents=True,  # whether to include agents
                    show_observations=True,  # whether to include observations
                    show_predictions=False,  # whether to include predictions
-                   rail_color="gray",  # color to use in drawing rails (not used with SVG)
                    frames=False,  # frame counter to show (intended since invocation)
                    episode=None,  # int episode number to show
                    step=None,  # int step number to show in image
-                   selected_agent=None,  # indicate which agent is "selected" in the editor
-                   action_dict=None):  # defunct - was used to indicate agent intention to turn
+                   selected_agent=None):  # indicate which agent is "selected" in the editor
         """ Draw the environment using the GraphicsLayer this RenderTool was created with.
             (Use show=False from a Jupyter notebook with %matplotlib inline)
         """
-
         if not self.gl.is_raster():
-            self.render_env_2(show=show, curves=curves, spacing=spacing,
-                              arrows=arrows, agents=agents, show_observations=show_observations,
-                              show_predictions=show_predictions,
-                              rail_color=rail_color,
-                              frames=frames, episode=episode, step=step,
-                              selected_agent=selected_agent, action_dict=action_dict)
-            return
+            self.render_env_svg(show=show,
+                                show_observations=show_observations,
+                                show_predictions=show_predictions,
+                                selected_agent=selected_agent
+                                )
+        else:
+            self.render_env_pil(show=show,
+                                agents=agents,
+                                show_observations=show_observations,
+                                show_predictions=show_predictions,
+                                frames=frames,
+                                episode=episode,
+                                step=step,
+                                selected_agent=selected_agent
+                                )
+
+    def _draw_square(self, center, size, color, opacity=255, layer=0):
+        x0 = center[0] - size / 2
+        x1 = center[0] + size / 2
+        y0 = center[1] - size / 2
+        y1 = center[1] + size / 2
+        self.gl.plot([x0, x1, x1, x0, x0], [y0, y0, y1, y1, y0], color=color, layer=layer, opacity=opacity)
+
+    def get_image(self):
+        return self.gl.get_image()
+
+    def render_env_pil(self,
+                       show=False,  # whether to call matplotlib show() or equivalent after completion
+                       # use false when calling from Jupyter.  (and matplotlib no longer supported!)
+                       agents=True,  # whether to include agents
+                       show_observations=True,  # whether to include observations
+                       show_predictions=False,  # whether to include predictions
+                       frames=False,  # frame counter to show (intended since invocation)
+                       episode=None,  # int episode number to show
+                       step=None,  # int step number to show in image
+                       selected_agent=None  # indicate which agent is "selected" in the editor
+                       ):
 
         if type(self.gl) is PILGL:
             self.gl.begin_frame()
@@ -466,28 +489,11 @@ class RenderTool(object):
 
         return
 
-    def _draw_square(self, center, size, color, opacity=255, layer=0):
-        x0 = center[0] - size / 2
-        x1 = center[0] + size / 2
-        y0 = center[1] - size / 2
-        y1 = center[1] + size / 2
-        self.gl.plot([x0, x1, x1, x0, x0], [y0, y0, y1, y1, y0], color=color, layer=layer, opacity=opacity)
-
-    def get_image(self):
-        return self.gl.get_image()
-
-    def render_env_2(
-        self, show=False, curves=True, spacing=False, arrows=False, agents=True,
-        show_observations=True, show_predictions=False, rail_color="gray",
-        frames=False, episode=None, step=None, selected_agent=None,
-        action_dict=dict()
+    def render_env_svg(
+        self, show=False, show_observations=True, show_predictions=False, selected_agent=None
     ):
         """
-        Draw the environment using matplotlib.
-        Draw into the figure if provided.
-
-        Call pyplot.show() if show==True.
-        (Use show=False from a Jupyter notebook with %matplotlib inline)
+        Renders the environment with SVG support (nice image)
         """
 
         env = self.env
diff --git a/notebooks/Scene_Editor.ipynb b/notebooks/Scene_Editor.ipynb
index 1f653d0..3a95729 100644
--- a/notebooks/Scene_Editor.ipynb
+++ b/notebooks/Scene_Editor.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -32,12 +32,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
     "from flatland.utils.editor import EditorMVC\n",
-    "mvc = EditorMVC(sGL=\"PILSVG\" ) "
+    "mvc = EditorMVC(sGL=\"PIL\" ) "
    ]
   },
   {
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 6,
    "metadata": {
     "scrolled": false
    },
@@ -70,7 +70,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "68a566e6228d40398ea8432582880311",
+       "model_id": "2e206d6a7c254d7e816cd6e3694ba147",
        "version_major": 2,
        "version_minor": 0
       },
@@ -111,7 +111,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.8"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
diff --git a/notebooks/simple_example1_env_from_tuple.ipynb b/notebooks/simple_example1_env_from_tuple.ipynb
index a24dbca..3fd55bc 100644
--- a/notebooks/simple_example1_env_from_tuple.ipynb
+++ b/notebooks/simple_example1_env_from_tuple.ipynb
@@ -51,9 +51,9 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAs4AAAHgCAYAAAC1ouv3AAAMeUlEQVR4nO3dwXEbRxqA0cEWr6hSCpuC8/CG4BOdgBKgGYcYhZWHU/DJdxUmAOxhRS9BweRHEuQAPe9V6TAQWGrWT4gfuhrAZrfb7ScAAOBJ/1p6AQAAcAmEMwAABFcPL7bb7VLr4MTmeT64NttxmO24zHZcZjsusx3XsdnacQYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAIJwBACAQzgAAEAhnAAAIhDMAAATCGQAAAuEMAACBcAYAgEA4AwBAsNntdvulFwEAAOfOjjMAAATCGQAAAuEMAADB1cOL7Xa71Do4sXmeD67NdhxmOy6zHZfZjstsx3VstnacAQAgEM4AABAIZwAACIQzAAAEwhkAAALhDAAAgXAGAIBAOAMAQCCcAQAgEM4AABAIZwAACIQzAAAEwhkAAALhDAAAgXAGAIBAOAMAQCCcAQAgEM4AABAIZwAACIQzAAAEwhkAAALhDAAAgXAGAIBAOAMAQCCcAQAgEM4AABAIZwAACIQzAAAEwhkAAALhDAAAgXAGAIBAOAMAQCCcAQAgEM4AABAIZwAACIQzAAAEwhkAAALhDAAAgXAGAIBAOAMAQCCcAQAgEM4AABAIZwAACIQzAAAEwhkAAALhDAAAgXAGAIBAOAMAQCCcAQAgEM4AABAIZwAACIQzAAAEwhkAAALhDAAAgXAGAIDgaukFAABjuL29/eG2m5ubBVYC78OOMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAACCq4cX8zwvtQ7emdmOy2zHZbbjWtNs1/S9TtP6vt81mefZjjMAABTCGQA4ic+fPy+9BHhXwhkAAILNbrfb319st9tpmqbp293dYgvidT5dXx9cPz5jZbaXy2zHZbbjWutsf//69eD6Pz//vNBK3k+dLZfv2GztOAMAQPBDOI/27HctytzM9jKZ7bjMdlxmOy5zWzc7zgAAEFw9d4fHZ3k4H2991mu258tsx2W241r7bG9vb6fpp5/+vr65uVlwNadll5l7dpwBACAQzgAAEAhnAAAIhDMAcFIjnW+Gh4QzAPAmt7e3Sy8BPoRwBgCAQDgDAEAgnAEAIBDOAMCrPT7f7IWBjEw4AwBAIJwBACAQzgAAEAhnAOBVnG9mbYQzAAAEwhkAeDGfFsgaCWcAAAiEMwDwIsd2m51vZg2EMwAABMIZAMjsNrNmwhkASLwgkLUTzgDAq9ltZk2EMwDwLEc0QDgDAEAinAGAJ9lthv8RzgDAP/KCQPg/4QwAvIjdZtZKOAMARzmiAYeEMwAABMIZAPiB3Wb40dXSCwAAzosXBA5ks1l6Bc/b75deQWbHGQB4lt1mEM4AwAOOaAzkEnabp+ly1jkJZwDgO0c04GnOOAPAyj0VzHabB3JOZ4kvaJf5ITvOALBidpmhs+MMACtUgtluMxwSzgCwIs8Fs1iGf+aoBgCshGiGt7HjDACDE8xwGsIZAAYlmOG0HNUAgAGJZjg9O85wTu7f1/LLl+O3T9O0PfZ1T9z/rN63E3h3ghnej3AGgAEIZnh/jmoAwIUTzfAx7DgDwIUSzPCxhDMAXBCf+AfLEc4AcKZKJD8mmuH9CGcAOBOvCeV7ghnen3AGgAX8/vXrwfWff/2Vv1YkwzJWFc5veSZ/jv79xx8H18/9p/vS+1+Sm99+W3oJJ/X4F+ovb7j/aD/3l87jdlwvnu0b/i2P6491qY/Dx0+vTvVzs+Ynbt6ODgAAAuEMAACBcAYAgGBVZ5wZ1+0gZ5xHO6sNACPZ7Ha7/f3Fdrudvt3dHdzh0/X1hy+K5qlZzfN88HdmeyE2m2mapunbly8HN3/69dcnv+zJ++/3E+fD43ZcZjuul872bHz/nfK3c/p9cM5r++7YbB3VAACAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QznZL8//l6W97fv99O82x38ee7+AMBpCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAILhaegEAAHyAzWbpFVw8O84AABAIZwCAEV3Kp8deyjon4QwAAIkzzgAAo7qg3dxLYMcZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAILh67g7f7u4+Yh0swGzHZbbjMttxmS2cPzvOAAAQ/BDOn66vl1gHb1TmZraXyWzHZbbjMttxmdu62XEGAIBgs9vt9vcX2+12ybVwQvM8H1yb7TjMdlxmOy6zHZfZjuvYbO04AwBAcLDjDAAAHGfHGQAAAuEMAACBcAYAgODgkwO9EnQcXuU7LrMdl9mOy2zHZbbj8q4aAADwSsIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAAAC4QwAAIFwBgCAQDgDAEAgnAEAIBDOAAAQCGcAAAiEMwAABMIZAACCzW632y+9CAAAOHd2nAEAIBDOAAAQCGcAAAiuHl5st9ul1sGJzfN8cG224zDbcZntuMx2XGY7rmOz/S9WE9qvdTmG7wAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<PIL.Image.Image image mode=RGBA size=574x384 at 0x1B694FABB38>"
+       "<PIL.Image.Image image mode=RGBA size=718x480 at 0x14DD8FD52E8>"
       ]
      },
      "execution_count": 3,
@@ -83,7 +83,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.8"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
-- 
GitLab