Merge pull request #3 from cosbidev/v2.0.4

Update v2.0.4

docs/source/conf.py

@@ -24,7 +24,7 @@
 author = 'Matteo Tortora'
 
 # The full version, including alpha/beta/rc tags
-release = '2.0.3'
+release = '2.0.4'
 
 # -- General configuration ---------------------------------------------------
 

pytrack/analytics/visualization.py

@@ -211,11 +211,10 @@ def draw_candidates(self, candidates, radius):
                 popup = f"coord: {cand} \n edge_osmid: {label}"
                 if cand_type:
                     folium.Circle(location=cand, popup=popup, radius=2, color="yellow", fill=True,
-                                  fill_opacity=1).add_to(
-                        fg_cands)
+                                  fill_opacity=1).add_to(fg_cands)
                 else:
-                    folium.Circle(location=cand, popup=popup, radius=1, color="red", fill=True, fill_opacity=1).add_to(
-                        fg_cands)
+                    folium.Circle(location=cand, popup=popup, radius=1, color="orange", fill=True,
+                                  fill_opacity=1).add_to(fg_cands)
 
         del self._children[next(k for k in self._children.keys() if k.startswith('layer_control'))]
         self.add_child(folium.LayerControl())

pytrack/graph/download.py

@@ -20,27 +20,44 @@ def get_filters(network_type='drive'):
                             'elevator|escalator|footway|path|pedestrian|planned|platform|proposed|raceway|steps|track"]'
                             '["service"!~"emergency_access|parking|parking_aisle|private"]')
 
+    osm_filters['bicycle'] = ('["highway"]["area"!~"yes"]["access"!~"private"]'
+                              '["highway"!~"abandoned|bridleway|footway|bus_guideway|construction|corridor|elevator|'
+                              'escalator|planned|platform|proposed|raceway|steps|footway"]'
+                              '["service"!~"private"]["bicycle"!~"no"])')
+
+    osm_filters['service'] = ('["highway"]["area"!~"yes"]["access"!~"private"]["highway"!~"abandoned|bridleway|'
+                              'construction|corridor|platform|cycleway|elevator|escalator|footway|path|planned|'
+                              'proposed|raceway|steps|track"]["service"!~"emergency_access|parking|'
+                              'parking_aisle|private"]["psv"!~"no"]["footway"!~"yes"]')
+
     return osm_filters[network_type]
 
 
-def osm_download(bbox, network_type=None):
+def osm_download(bbox, network_type='drive', custom_filter=None):
     """ Get the OpenStreetMap response.
 
     Parameters
     ----------
     bbox: tuple
         bounding box within N, S, E, W coordinates.
-    network_type: str or None, optional, default: None
+    network_type: str, optional, default: 'drive'
         Type of street network to obtain.
+    custom_filter: str or None, optional, default: None
+        Custom filter to be used instead of the predefined ones to query the Overpass API.
+        An example of a custom filter is the following '[highway][!"footway"]'.
+        For more information visit https://overpass-turbo.eu and https://taginfo.openstreetmap.org.
+
     Returns
     -------
     response: json
         Response of the OpenStreetMao API service.
     """
-    # TODO: add network_type statement
     north, south, west, east = bbox
 
-    osm_filters = get_filters(network_type='drive')
+    if custom_filter is not None:
+        osm_filter = custom_filter
+    else:
+        osm_filter = get_filters(network_type=network_type)
 
     url_endpoint = 'https://maps.mail.ru/osm/tools/overpass/api/interpreter'
 
@@ -49,7 +66,7 @@ def osm_download(bbox, network_type=None):
 
     overpass_settings = f'[out:{out_resp}][timeout:{timeout}]'
 
-    query_str = f'{overpass_settings};(way{osm_filters}({south}, {west}, {north}, {east});>;);out;'
+    query_str = f'{overpass_settings};(way{osm_filter}({south}, {west}, {north}, {east});>;);out;'
 
     response_json = requests.post(url_endpoint, data={'data': query_str})
 

pytrack/graph/graph.py

@@ -102,7 +102,7 @@ def _simplification(G, response_json):
 
         junction = [False, False]
         if is_oneway:
-            junction[1:1] = [True if G.degree[node] > 3 else False for node in nodes[1:-1]]
+            junction[1:1] = [True if G.degree[node] > 2 else False for node in nodes[1:-1]]  # Changed >3 to >2 V2.0.4
         else:
             junction[1:1] = [True if G.degree[node] > 4 else False for node in nodes[1:-1]]
 
@@ -259,7 +259,7 @@ def _oneway_path_values(path):
     ret: dict
         Indicates whether an OSM path is oneway.
     """
-    return {path[key] for key in path.keys() if key.startswith("oneway") and path[key] == "no"}
+    return {path[key] for key in path.keys() if key.startswith("oneway")}  # Removed 'and path[key] == "no"' v2.0.4
 
 
 def _is_oneway(path, bidirectional):

pytrack/matching/__init__.py

@@ -0,0 +1,5 @@
+# This lets you use package.module.Class as package.Class in your code.
+from .candidate import Candidate
+
+# This lets Sphinx know you want to document package.module.Class as package.Class.
+__all__ = ['Candidate']

pytrack/matching/candidate.py

@@ -105,6 +105,12 @@ def get_candidates(G, points, interp_dist=1, closest=True, radius=10):
                        "candidate_type": np.full(len(nodes.index[idx]), False),
                        "dists": list(dist)} for i, (point, idx, dist) in enumerate(zip(points, idxs, dists))}
 
+    no_cands = [node_id for node_id, cand in results.items() if not cand["candidates"]]
+
+    if no_cands:
+        for cand in no_cands:
+            del results[cand]
+        print(f"A total of {len(no_cands)} points has no candidates: {*no_cands,}")
     return G, results
 
 

pytrack/matching/mpmatching.py

@@ -1,4 +1,5 @@
 from collections import deque
+import math
 
 from . import mpmatching_utils
 
@@ -40,12 +41,12 @@ def viterbi_search(G, trellis, start="start", target="target", beta=mpmatching_u
     # Initialize joint probability for each node
     joint_prob = {}
     for u_name in trellis.nodes():
-        joint_prob[u_name] = 0
+        joint_prob[u_name] = -float('inf')
     predecessor = {}
     queue = deque()
 
     queue.append(start)
-    joint_prob[start] = mpmatching_utils.emission_prob(trellis.nodes[start]["candidate"], sigma)
+    joint_prob[start] = math.log10(mpmatching_utils.emission_prob(trellis.nodes[start]["candidate"], sigma))
     predecessor[start] = None
 
     while queue:
@@ -58,8 +59,11 @@ def viterbi_search(G, trellis, start="start", target="target", beta=mpmatching_u
         for v_name in trellis.successors(u_name):
             v = trellis.nodes[v_name]["candidate"]
 
-            new_prob = joint_prob[u_name] * mpmatching_utils.transition_prob(G, u, v, beta) * \
-                mpmatching_utils.emission_prob(v, sigma)
+            try:
+                new_prob = joint_prob[u_name] + math.log10(mpmatching_utils.transition_prob(G, u, v, beta)) + \
+                           math.log10(mpmatching_utils.emission_prob(v, sigma))
+            except Exception as e:
+                print(e)
 
             if joint_prob[v_name] < new_prob:
                 joint_prob[v_name] = new_prob

pytrack/matching/mpmatching_utils.py

@@ -8,7 +8,27 @@
 from pytrack.matching import candidate
 
 SIGMA_Z = 4.07
-BETA = 20
+BETA = 3
+
+
+def _emission_prob(dist, sigma=SIGMA_Z):
+    """ Compute emission probability of a node
+
+    Parameters
+    ----------
+    dist: float
+        Distance between a real GPS point and a candidate node.
+    sigma: float, optional, default: SIGMA_Z
+        It is an estimate of the magnitude of the GPS error. See https://www.ismll.uni-hildesheim.de/lehre/semSpatial-10s/script/6.pdf
+        for a more detailed description of its calculation.
+
+    Returns
+    -------
+    ret: float
+        Emission probability of a node.
+    """
+    c = 1 / (sigma * math.sqrt(2 * math.pi))
+    return c * math.exp(-(dist / sigma) ** 2)
 
 
 # A gaussian distribution
@@ -17,9 +37,9 @@ def emission_prob(u, sigma=SIGMA_Z):
 
     Parameters
     ----------
-    u: dict
+    u: pytrack.matching.Candidate
         Node of the graph.
-    sigma: float
+    sigma: float, optional, default: SIGMA_Z
         It is an estimate of the magnitude of the GPS error. See https://www.ismll.uni-hildesheim.de/lehre/semSpatial-10s/script/6.pdf
         for a more detailed description of its calculation.
 
@@ -56,13 +76,13 @@ def transition_prob(G, u, v, beta=BETA):
     ret: float
         Transition probability between node u and v.
     """
-    c = 1 / BETA
+    c = 1 / beta
 
     if u.great_dist and v.great_dist:
         delta = abs(
             nx.shortest_path_length(G, u.node_id, v.node_id, weight="length", method='dijkstra')
             - distance.haversine_dist(*u.coord, *v.coord))
-        return c * math.exp(-delta / BETA) * 1e5
+        return c * math.exp(-delta / beta)
     else:
         return 1
 

setup.py

@@ -16,7 +16,7 @@ def reqs(*f):
 
 setuptools.setup(
     name='PyTrack-lib',
-    version='2.0.3',
+    version='2.0.4',
     packages=setuptools.find_packages(),
     # namespace_packages=['pytrack'],
     url='https://github.com/cosbidev/PyTrack',