Add samples

These files have been used for a presentation
They will be place in a tutorial or default like function to faciliate running model on world

samples/Untitled.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import networkx as nx\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graph_nodes = range(25)\n",
+    "        \n",
+    "graph_edges = list()\n",
+    "for snode, enode in zip(graph_nodes[:11],\n",
+    "                        np.roll(graph_nodes[:11], 1)):\n",
+    "    graph_edges.append((snode, enode))\n",
+    "for snode, enode in zip(graph_nodes[11:],\n",
+    "                        np.roll(graph_nodes[11:], 1)):\n",
+    "    graph_edges.append((snode, enode))\n",
+    "\n",
+    "pos = dict()\n",
+    "x = np.linspace(-1,1,5)\n",
+    "y = np.linspace(-1,1,5)\n",
+    "[mx,my]=np.meshgrid(x,y)\n",
+    "mx = mx.flatten()\n",
+    "my = my.flatten()\n",
+    "i = 0\n",
+    "for i in graph_nodes:\n",
+    "    pos[i]=[mx[i], my[i]]\n",
+    "    \n",
+    "graph = nx.DiGraph()\n",
+    "graph.add_nodes_from(graph_nodes)\n",
+    "#graph.add_edges_from(graph_edges)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAeEAAAFCCAYAAADGwmVOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQoQBADBChAEAMEKEAQAw8m/R51aHlL+rZgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f0277b39198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%matplotlib inline\n",
+    "import string\n",
+    "labels = dict()\n",
+    "for i,s in enumerate(string.ascii_uppercase[:len(graph_nodes)]):\n",
+    "    labels[i]=s\n",
+    "nx.draw(graph,labels=labels,node_color='white',pos=pos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: [-1.0, -1.0],\n",
+       " 1: [-0.5, -1.0],\n",
+       " 2: [0.0, -1.0],\n",
+       " 3: [0.5, -1.0],\n",
+       " 4: [1.0, -1.0],\n",
+       " 5: [-1.0, -0.5],\n",
+       " 6: [-0.5, -0.5],\n",
+       " 7: [0.0, -0.5],\n",
+       " 8: [0.5, -0.5],\n",
+       " 9: [1.0, -0.5],\n",
+       " 10: [-1.0, 0.0],\n",
+       " 11: [-0.5, 0.0],\n",
+       " 12: [0.0, 0.0],\n",
+       " 13: [0.5, 0.0],\n",
+       " 14: [1.0, 0.0],\n",
+       " 15: [-1.0, 0.5],\n",
+       " 16: [-0.5, 0.5],\n",
+       " 17: [0.0, 0.5],\n",
+       " 18: [0.5, 0.5],\n",
+       " 19: [1.0, 0.5],\n",
+       " 20: [-1.0, 1.0],\n",
+       " 21: [-0.5, 1.0],\n",
+       " 22: [0.0, 1.0],\n",
+       " 23: [0.5, 1.0],\n",
+       " 24: [1.0, 1.0]}"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: array([ 0.67713888, -0.54708367]),\n",
+       " 1: array([ 0.80286012, -0.21521721]),\n",
+       " 2: array([ 0.82999897,  0.10611472]),\n",
+       " 3: array([ 0.6961206 ,  0.29154782]),\n",
+       " 4: array([ 0.73494093,  0.19213574]),\n",
+       " 5: array([ 0.65684106,  0.06183021]),\n",
+       " 6: array([ 0.64464326, -0.09805064]),\n",
+       " 7: array([ 0.57452293, -0.27745012]),\n",
+       " 8: array([ 0.47607059, -0.45805932]),\n",
+       " 9: array([ 0.39641685, -0.6544649 ]),\n",
+       " 10: array([ 0.49295776, -0.8057698 ]),\n",
+       " 11: array([-0.58315298,  0.5926934 ]),\n",
+       " 12: array([-0.40911029,  0.46421966]),\n",
+       " 13: array([-0.40376733,  0.18834137]),\n",
+       " 14: array([-0.48770361, -0.06679829]),\n",
+       " 15: array([-0.62684662, -0.27585655]),\n",
+       " 16: array([-0.45329037, -0.26329985]),\n",
+       " 17: array([-0.27263802, -0.07977146]),\n",
+       " 18: array([-0.11743287,  0.13464326]),\n",
+       " 19: array([-0.04559407,  0.37108798]),\n",
+       " 20: array([-0.33623618,  0.32683446]),\n",
+       " 21: array([-0.59719043,  0.19261458]),\n",
+       " 22: array([-0.81371306,  0.12269118]),\n",
+       " 23: array([-1.       ,  0.2219939]),\n",
+       " 24: array([-0.83583613,  0.47507351])}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

samples/asv_catchment_area.py

+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import pkg_resources
+from navipy.database import DataBaseLoad
+from navipy.processing.pcode import apcv
+from navipy.moving.agent import GraphAgent
+from navipy import Brain
+
+
+# 0) Define a class heriting from Brain
+class ASVBrain(Brain):
+    def __init__(self, renderer=None, channel=0):
+        Brain.__init__(self,renderer=renderer)
+        # Init memory
+        locid = mydb.posorients[(mydb.posorients.x==0) & (mydb.posorients.y==0)].index[0]
+        posorient = mydb.posorients.loc[locid, :]
+        self.update(posorient)
+        self.channel = channel
+        self.memory = self.asv()
+        
+
+    def asv(self):
+        if self.channel >3:
+            return apcv(1/self.vision.scene,
+                      self.vision.viewing_directions)[..., 3,:]
+        else:
+            return apcv(self.vision.scene,
+                      self.vision.viewing_directions)[..., self.channel,:]
+        
+
+    def velocity(self):
+        homing_vector = self.memory - self.asv()
+        homing_vector = np.squeeze(homing_vector)
+        velocity = pd.Series(data=0,
+                         index=['dx', 'dy', 'dz',
+                                'dalpha_0', 'dalpha_1', 'dalpha_2'])
+        velocity[['dx', 'dy', 'dz']] = homing_vector
+        return velocity
+    
+# 1) Connect to the database
+mydb_filename = '/home/bolirev/database.db'
+mydb = DataBaseLoad(mydb_filename)
+for channel  in range(5):
+    mybrain = ASVBrain(renderer=mydb, channel=channel)
+    # Create a grid agent
+    my_agent = GraphAgent(mybrain)
+    attractors = my_agent.find_attractors()
+    print(attractors)
+
+    f, axarr = plt.subplots(1, 1, figsize=(10, 10))
+    axarr.plot(trajectory.x, trajectory.y)
+    gridtoplot = mydb.posorients[(mydb.posorients.x>-1) &
+                                 (mydb.posorients.y>-1) &
+                                 (mydb.posorients.x<1) &
+                                 (mydb.posorients.y<1)]
+    axarr.plot([0],[0],'ro')
+    axarr.plot(gridtoplot.x,gridtoplot.y,'.',color='gray')
+    axarr.set_xlim([-10,10])
+    axarr.set_ylim([-10,10])
+    f.show()
+    f.savefig('plots/asv_homing_grid_channel_{}.svg'.format(channel))

samples/asv_homing_grid.py

+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import pkg_resources
+from navipy.database import DataBaseLoad
+from navipy.processing.pcode import apcv
+from navipy.moving.agent import GridAgent
+from navipy import Brain
+
+
+# 0) Define a class heriting from Brain
+class ASVBrain(Brain):
+    def __init__(self, renderer=None, channel=0):
+        Brain.__init__(self,renderer=renderer)
+        # Init memory
+        locid = mydb.posorients[(mydb.posorients.x==0) & (mydb.posorients.y==0)].index[0]
+        posorient = mydb.posorients.loc[locid, :]
+        self.update(posorient)
+        self.channel = channel
+        self.memory = self.asv()
+        
+
+    def asv(self):
+        if self.channel >3:
+            return apcv(1/self.vision.scene,
+                      self.vision.viewing_directions)[..., 3,:]
+        else:
+            return apcv(self.vision.scene,
+                      self.vision.viewing_directions)[..., self.channel,:]
+        
+
+    def velocity(self):
+        homing_vector = self.memory - self.asv()
+        homing_vector = np.squeeze(homing_vector)
+        velocity = pd.Series(data=0,
+                         index=['dx', 'dy', 'dz',
+                                'dalpha_0', 'dalpha_1', 'dalpha_2'])
+        velocity[['dx', 'dy', 'dz']] = homing_vector
+        return velocity
+    
+# 1) Connect to the database
+mydb_filename = '/home/bolirev/database.db'
+mydb = DataBaseLoad(mydb_filename)
+for channel  in range(5):
+    mybrain = ASVBrain(renderer=mydb, channel=channel)
+    # Create a grid agent
+    my_agent = GridAgent(mybrain)
+
+    # init position
+    rowid = mydb.posorients[(mydb.posorients.x==-5) & (mydb.posorients.y==-5)].index[0]
+    initpos = mybrain.posorients.loc[rowid]
+    my_agent.posorient = initpos
+
+    # Mode of motion
+    mode_of_motion = dict()
+    mode_of_motion['mode'] = 'on_cubic_grid'
+    mode_of_motion['param'] = dict()
+    mode_of_motion['param']['grid_spacing'] = 1
+    my_agent.mode_of_motion = mode_of_motion
+
+    # Run
+    max_nstep = 100
+    trajectory = my_agent.fly(max_nstep, return_tra=True)
+
+    f, axarr = plt.subplots(1, 1, figsize=(10, 10))
+    axarr.plot(trajectory.x, trajectory.y)
+    gridtoplot = mydb.posorients[(mydb.posorients.x>-1) &
+                                 (mydb.posorients.y>-1) &
+                                 (mydb.posorients.x<1) &
+                                 (mydb.posorients.y<1)]
+    axarr.plot([0],[0],'ro')
+    axarr.plot(gridtoplot.x,gridtoplot.y,'.',color='gray')
+    axarr.set_xlim([-10,10])
+    axarr.set_ylim([-10,10])
+    f.show()
+    f.savefig('plots/asv_homing_grid_channel_{}.svg'.format(channel))