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Repository: jagodki/Offline-MapMatching
Branch: master
Commit: 0e345da5e241
Files: 37
Total size: 888.3 KB
Directory structure:
gitextract_54u4k5gb/
├── .gitignore
├── LICENSE
├── README.md
├── src/
│ └── offlinemapmatching/
│ ├── __init__.py
│ ├── help_docs/
│ │ ├── help.html
│ │ ├── help_processing_clipping_network.html
│ │ ├── help_processing_match_trajectory.html
│ │ └── help_processing_reduce_density.html
│ ├── i18n/
│ │ └── af.ts
│ ├── metadata.txt
│ ├── mm/
│ │ ├── __init__.py
│ │ ├── helper/
│ │ │ ├── __init__.py
│ │ │ └── measurement_statistics.py
│ │ ├── hidden_states/
│ │ │ ├── __init__.py
│ │ │ ├── candidate.py
│ │ │ ├── hidden_model.py
│ │ │ └── transition.py
│ │ ├── map_matcher.py
│ │ └── observation/
│ │ ├── __init__.py
│ │ ├── intersection.py
│ │ ├── network.py
│ │ ├── observation.py
│ │ └── trajectory.py
│ ├── mm_processing/
│ │ ├── clip_network_algorithm.py
│ │ ├── offline_map_matching_algorithm.py
│ │ ├── offline_map_matching_provider.py
│ │ └── reduce_trajectory_density.py
│ ├── offline_map_matching.py
│ ├── offline_map_matching_dialog.py
│ ├── offline_map_matching_dialog_base.ui
│ ├── pb_tool.cfg
│ ├── resources.py
│ ├── resources.qrc
│ └── style.qml
└── testdata/
├── network.geojson
├── trajectory1.geojson
└── trajectory2.geojson
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
# Build and Release Folders
bin-debug/
bin-release/
[Oo]bj/
[Bb]in/
# Other files and folders
.settings/
# Executables
*.swf
*.air
*.ipa
*.apk
# Project files, i.e. `.project`, `.actionScriptProperties` and `.flexProperties`
# should NOT be excluded as they contain compiler settings and other important
# information for Eclipse / Flash Builder.
.DS_Store
================================================
FILE: LICENSE
================================================
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================================================
FILE: README.md
================================================
# Offline-MapMatching
A <a href="https://github.com/qgis/QGIS">QGIS</a>-plugin for matching a trajectory with a network using a Hidden Markov Model and Viterbi algorithm.
## Goal of the project
Matching a trajectory with a network, e.g. a road network, is a classical task in geoinformatics.
It can be differentiated into online, i.e. during the measurement of the trajectory like in car navigation,
and offline, i.e. after the measurement of the trajectory, map matching. This plugin addresses only the offline map mathing.
<br>
Because of inaccuracies of the GNSS signal and/or a low data quality of the network, the positions of the trajectory
and the network are differing. In many cases a simple snapping of the trajectory on the network will not work. Reasons for that
can be e.g. outliers in the trajectory or crossings of edges in the network.
<br>
This plugin provides a statistical approach to solve the problem of offline map matching using the principles of
Hidden Markov Models (HMM) and the Viterbi algorithm.
## Installation
<img src="screenshots/download_1.png" height="150">
<img src="screenshots/download_2.png" height="250">
<img src="screenshots/download_3.png" height="350">
## Usage
### Match Trajectory
<img src="screenshots/mt_call.png"><br>
- just fill in all entries in the plugin and click the start button
- the tool provides an explanation for each entry directly in the dialog
- the plugin will not run correctly, if at least one entry is not filled
- the CRS of the input layers have to be the same, otherwise the calculations will not start
- information about the current processing step will be written to the log section of the plugin window
- further information, e.g. detailed information about the algorithm, can be found under the next captions
- the gui of the plugin is based on the processing framework, i.e. it will run in the background without freezing QGIS
- the plugin can be started via the processing framework directly in python too
- example for the python command:
```python
processing.run('omm:match_trajectory', {'NETWORK': 'network_layer',
'TRAJECTORY': 'trajectory_layer',
'TRAJECTORY_ID': 'id',
'MAX_SEARCH_DISTANCE': 20.0,
'OUTPUT': 'destination_in_filesystem'})
```
- the processing plugin returns a python dictionary, e.g.:
```python
{'OUTPUT': 'destination_in_filesystem',
'ERROR_CODE': 0,
'COMPUTATION_TIME': 123.45}
```
- the ERROR_CODE will be a negative number if an error occured during processing, otherwise it is equal 0
- the plugin writes all messages, also errors, to the log of the plugin window
### Clip Network
<img src="screenshots/cn_call.png"><br>
- this function can be used to preprocess your network
- the network will be clipped with a buffer around the trajectory (trajectory will be converted to a linestring first)
- the tool provides an explanation for each entry directly in the dialog
- the plugin will not run correctly, if at least one entry is not filled
- the plugin can be started via the toolbox too
- the plugin can be started via the processing framework directly in python too
- example for the python command:
```python
processing.run('omm:clip_network', {'NETWORK': 'network_layer',
'TRAJECTORY': 'trajectory_layer',
'ORDER_FIELD': 'field_name',
'BUFFER_RADIUS': 20.0,
'OUTPUT': 'destination_in_filesystem'})
```
### Reduce Trajectory Density
<img src="screenshots/rd_call.png"><br>
- this function can be used to preprocess your trajectory
- the distance between two consecutive points of the trajectory will be measured
- if the distance is less than a value inserted by the user, the second, i.e. the following, point will be removed
- the parameter KEEP_LAST_FEATURE can be used to keep the last point/feature of the trajectory, regardless of the distance between the previous keeped feature (the parameter is optional, default is <i>False</i>)
- the trajectory layer will not be changed, the reduced trajectory will be stored in a new layer
- example for the python command without optional parameters:
```python
processing.run('omm:reduce_trajectory_density', {'TRAJECTORY': 'trajectory_layer',
'DISTANCE': 100.0,
'OUTPUT': ':memory'})
```
- example for the python command with optional parameters:
```python
processing.run('omm:reduce_trajectory_density', {'TRAJECTORY': 'trajectory_layer',
'DISTANCE': 100.0,
'KEEP_LAST_FEATURE': True,
'OUTPUT': ':memory'})
```
## Hints for usage of the map matching
- the progress of the computation will be displayed with a progressbar (starts from zero for every computation step)
and written to the log of the plugin window
- it is recommended to use a low segmented network layer, because candidate points will be searched for
every linestring, i.e. the more linestrings can be found in the maximum search distance, the more candidates will be found
and the more computation time is needed
- use a metric CRS, because distances will be calculated and compared
- the quality of the trajectory is very important for a good result, i.e. big outliers should be removed before running the plugin
(the algorithm does not know, which observation is an outlier, and searches the most likely path for the whole trajectory)
- if positions where measured time-controlled, standing times should be removed before running the plugin
## Hints for developement
- the plugin was created using the QGIS-plugin "plugin builder"
- additional python files related to the Hidden Markov Model and the Viterbi algorithm can be found in the
<a href="https://github.com/jagodki/Offline-MapMatching/tree/master/src/offlinemapmatching/mm">mm</a>-folder
- additional python files related to the processing framework can be found in the
<a href="https://github.com/jagodki/Offline-MapMatching/tree/master/src/offlinemapmatching/mm_processing">mm_processing</a>-folder
## Description of the computation of the map matching
First, the plugin extracts all intersections from the network, that have a distance to at least one trajectory point less or equal the maximum search distance.
Then, the plugin calculates possible candidate points for each point of the trajectory (observations) (Budig 2012: 10).
Candidates, that are in a range less or equal the maximum search distance of the extracted intersection points, will be ignored. Therefore these extracted intersections will be used as candidates.
The candidates will be arranged in a candidate graph with n layers, n = count of observation points (Budig 2012: 13).
After calculating different posible paths and their probabilities, the path with the highest probability will be found using the Viterbi algorithm.
Below the different parts of the computation in a nutshell, followed by explicit descriptions of each step:
- read the input layers and initialise the internal data structure and objects
- calculate candidate points for each trajectory point using the maximum search distance and store them in a graph
- calculate the probability for each candidate point to be emitted by the correpsonding trajectory point
- calculate the similarity of the transitions between the observations and their candidates
- find the best sequence of candidates using Viterbi algorithm
- create a layer of linestrings using this candidates
### Read the input layers
The network and the trajectory layers will be imported into the plugin and the internal structur of objects will be initialised.
The trajectory cannot be imported, if the layer has no attribute field.
Each position of the trajectory is called observation and one part of the five tupel of a Hidden Markov Model (HMM).
### Calculate candidate points/create candidate graph
A candidate point is a possible position of an observation on the network.
The distance to each linestring of the network will be calculated for every observation.
If the distance is less or equal the maximum search distance, a candidate as the nearest point
on the linestring to the observation will be extracted and added to the candidate graph (Budig 2012: 14):
<img src="screenshots/candidates.png" />
<br>
"A candidate graph is a directed acyclic graph (DAG) which represents all paths that are
considered possible final matching results for the given GPS trajectory" (Budig 2012: 13). The graph will be used as a stochastic matrix
for the HMM. The graph has n layers with n as the count of observations.
Each column contains all candidates of the same/corresponding observation (Budig 2012: 13):
<img src="screenshots/graph.png" />
<br>
The calculation of candidates has a huge impact of the computation time. A higher search distance can result in more candidates per
observation and more possible paths, which will be calculated in the next steps. Also a high segmentation of the network can cause
a lot of candidates per observation with the same problem for the computation time.
### Calculate emission probabilities
A GNSS-measured position is not the real position because of the inaccuracy of the GNSS signal. The GNSS-errors are following
almost, but not strictly, a normal distribution (Newson, Krumm 2009: 4). Also the quality of the network geometry
can be very low. Therefore the candidates are more or less away from the observations.
To calculate the probability, that an observation was emitted by a candidate, a normal distribution is postulated, i.e.
the probability is pending on the distance between observation and candidate, the standard deviation of the GNSS-error and
the expected value of the difference (Newson, Krumm 2009: 4).
The emission probabilities for each candidate are one part of the five tupel of a Hidden Markov Model (Haenelt 2007: 4-5) and the emission probabilities
of the candidates of the first observation will be used as initial state
probabilities in the HMM and represent another part of the five tupel (Raymond et al. 2012: 2244).
The expected value and the standard deviation can be set by the user. The expected value represents the best possible or expected distance between
a candidate and an observer. In most cases, this should be a value of zero (Newson, Krumm 2009: 3).
If the standard deviation of the GNSS-receiver is unknown, it has to be estimated e.g. with this equation (Newson, Krumm 2009: 6):
<br>
<img src="screenshots/estimator_1.png" />
<br>
The term (z - x) means the distance between an observation and its candidate. I reached good results to set the standard deviation to around 2 * (maximum search distance). A larger value represents less trust in the measurement.
A too high or too low standard deviation can overemphasise or underemphasise the emission probability (compared to the transition
probability) and maybe results in an incorrect map matching.
### Calculate transition probabilities
Another and the last tupel of the HMM are the transition probabilities whereat transitions are connections between two candidates in the graph,
i.e. the edges of the graph. Two characteristics of the transition between two candidates and the transition between the two corresponding observations will be compared:
1. the distances
2. the directions
<br>
<br>
The difference between the euklidean distance of the two observations and the distance on the network of two correpsonding candidates can be described by
an exponential probability distribution (Newson, Krumm 2009: 4):
<br>
<img src="screenshots/epd_chart.png" />
<br>
The distance on the network will be calculated using the analysis framework of QGIS and the Dijkstra algorithm with the distance between nodes as
edge weight. The difference of the two distances will be used in the following equation, in which the parameter d is the difference of the distances (Newson, Krumm 2009: 4):
<br>
<img src="screenshots/epd.png" />
<br>
The parameter beta (named "Transition weight" in the GUI of the plugin) can be estimated as around 1.5 * (median of all distances) (Newson, Krumm 2009: 6). I get good results setting beta = 1.5 * (maximum search distance).
A larger value of beta represents more tolerance of non-direct routes and vice-versa.
<br>
<br>
The directions will be calculated using the slope of the line through both observations and the product of the
slops of each subline, i.e. each line between two following vertices, of the shortest way from one candidate
to the other candidate on the network. The angle is the arcustanges of the slope and will have a value between 0 and 180 (the python function atan is used and the
result will be summed up by 90 to avoid negative values). The difference of this two angles will be normalised to receive
a value between 0 and 1 (Budig 2012: 17):
<img src="screenshots/direction.png" />
### Viterbi algorithm
All probabilities (initial, emission and transition) along each path will be multiplied in an efficient way by using the Viterbi algorithm.
The Viterbi algorithm uses results of former calculations again to avoid multiple calculations of same sequences.
Also just the highest probabilities to reach a candidate will be stored and reused (Haenelt 2007: 12).
The most likely path through the HMM, i.e. the path through the candidate graph with the highest probability, is the result of this algorithm.
### Store the path in a new layer
The founded Viterbi path represents the vertices of the resulting line. A routing between following points of the Viterbi path
returns linestrings, which will be written to the output.
### Parameters of plugin-versions less 3.0.0
Starting with version 3.0.0, the former parameters standard deviation and transistion weight will be calculated using the formulas of Newson & Krumm (2009).
This increases the usability of the plugin. The maximum search distance and the quality of the network and trajectory (e.g. density of the trajectory) will now influence the result of the map matching.
## Attribute Table of the resulting layer
#### id
an integer value as counter of the linestrings
#### observation_id_start
the id of the trajectory feature, which corresponds to the start vertex
#### observation_id_end
the id of the trajectory feature, which corresponds to the end vertex
#### emission_probability_start
the emission probability of the start vertex
#### emission_probability_end
the emission probability of the end vertex
#### transition_probability
the transition probability of the current linestring
#### total_probability_start
the total probability of the start vertex of the current linestring, i.e. the product
of the emission and transition probability of the start vertex and the total probability of the parent vertex/candidate
#### total_probability_end
the total probability of the end vertex of the current linestring, i.e. the product of the emission
and transition probability of the end vertex and the total probability of the parent vertex/candidate
(should be the start vertex of the current linestring)
## Screenshots of matched and unmatched trajectories
<table style="width:100%">
<tr>
<th>not successful matched</th>
<th>successful matched</th>
<th>successful matched</th>
<th>legend</th>
</tr>
<tr>
<td><img src="screenshots/not_correct.png" /></td>
<td><img src="screenshots/correct.png" /></td>
<td><img src="screenshots/correct_2.png" /></td>
<td><img src="screenshots/legend.png" /></td>
</tr>
<tr>
<td>st. deviation = 50, max. search dist. = 20, transition weight = 30</td>
<td>st. deviation = 50, max. search dist. = 20, transition weight = 35</td>
<td>st. deviation = 60, max. search dist. = 50, transition weight = 30</td>
<td></td>
</tr>
</table>
## Further Description
More information about the calculations can be found in the following paper (in german language with an english abstract):<br>
<a href="https://gispoint.de/fileadmin/user_upload/paper_gis_open/AGIT_2019/537669014.pdf">Jung, C. (2019): Offline-MapMatching – A QGIS Plugin for Matching a Trajectory with a Network. In: <i>AGIT ‒ Journal für Angewandte Geoinformatik 5-2019</i> (pp. 156-163). Berlin/Offenbach: Wichmann Verlag. doi: 10.14627/537669014</a>
## Sources
Newson, P., & Krumm, J. (2009). <i>Hidden Markov map matching through noise and sparseness.</i>
In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems (pp. 336-343). ACM.
Retrieved Oct 10, 2018 from
<a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.5145&rep=rep1&type=pdf">http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.5145&rep=rep1&type=pdf</a>
<br><br>
Budig, B. (2012). <i>An algorithm for map matching on incomplete road databases</i>.
Retrieved Jul 31, 2018 from
<a href="http://www1.pub.informatik.uni-wuerzburg.de/pub/theses/2012-budig-bachelor.pdf">http://www1.pub.informatik.uni-wuerzburg.de/pub/theses/2012-budig-bachelor.pdf</a>
<br><br>
Raymond, R., Morimura, T., Osogami, T., Hirosue, N. (2012). <i>Map Matching with Hidden Markov Model on Sampled Road Network</i>.
21st International Conference on Pattern Recognition (ICPR 2012).
Retrieved Jul 31, 2018 from
<a href="https://www.computer.org/csdl/proceedings/icpr/2012/2216/00/06460610.pdf">https://www.computer.org/csdl/proceedings/icpr/2012/2216/00/06460610.pdf</a>
<br><br>
Haenelt, K. (2007). <i>Der Viterbi-Algorithmus. Eine Erl\ufffduterung der formalen Spezifikation am Beispiel des Part-of-Speech Tagging</i>.
Retrieved Jul 31, 2018 from
<a href="http://kontext.fraunhofer.de/haenelt/kurs/folien/Haenelt_Viterbi-Tutor.pdf">http://kontext.fraunhofer.de/haenelt/kurs/folien/Haenelt_Viterbi-Tutor.pdf</a>
<br>
## Other
The icons are based on products from <a href="https://icons8.com">icons8.com</a>
================================================
FILE: src/offlinemapmatching/__init__.py
================================================
# -*- coding: utf-8 -*-
"""
/***************************************************************************
OfflineMapMatching
A QGIS plugin
a QGIS-plugin for matching a trajectory with a network using a Hidden Markov Model and Viterbi algorithm
Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
-------------------
begin : 2018-06-14
copyright : (C) 2018 by Christoph Junh
email : jagodki.cj@gmail.com
git sha : $Format:%H$
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
This script initializes the plugin, making it known to QGIS.
"""
# noinspection PyPep8Naming
def classFactory(iface): # pylint: disable=invalid-name
"""Load OfflineMapMatching class from file OfflineMapMatching.
:param iface: A QGIS interface instance.
:type iface: QgsInterface
"""
#
from .offline_map_matching import OfflineMapMatching
return OfflineMapMatching(iface)
================================================
FILE: src/offlinemapmatching/help_docs/help.html
================================================
<html>
<body>
<h2><font face="helvetica, arial">Offline-MapMatching</font></h2>
<p>This plugin matches a trajectory as a point layer with a network as a linestring layer based on a Hidden-Markov-Model and the Viterbi algorithm. There are also routines for preprocessing the data to improve the computation time for the map matching.</p>
<h4><font face="helvetica, arial">Network-Layer:</font></h4>
<p><font size=3 face="helvetica, arial">This combobox shows all visible layers of the
current QGIS project with the geometry type LINESTRING. The network, needed to match the trajectory on it, shall be selected.
It is recommended, that
this layer should be a clean graph following a node-edge-model, i.e. different lines
should only touch other lines, never cross them. You can get souch a clean graph e.g.
by running the PostGIS function "CreateTopology".</font></p>
<h4><font face="helvetica, arial">Trajectory-Layer:</font></h4>
<p><font size=3 face="helvetica, arial">The combobox shows all visible layers of the
current QGIS project with the geometry type POINT.
The trajectory, which will be matched with the network, shall be selected.</font></p>
<h4><font face="helvetica, arial">Trajectory-ID:</font></h4>
<p><font size=3 face="helvetica, arial">The combobox shows all fields of the chosen trajectory layer.
The selected field will be added to the result features, so that the vertices of the result layer
can be referred to the points of the trajectory.</font></p>
<h4><font face="helvetica, arial">CRS of output layer (EPSG):</font></h4>
<p><font size=3 face="helvetica, arial">Insert the EPSG code, which should be used for the result layer.
It is recommended to insert the CRS of the network and trajectory layer (both layer should have the same CRS).
It is also recommended to use a metric CRS.</font></p>
<h4><font face="helvetica, arial">Maximum Search Distance [m]:</font></h4>
<p><font size=3 face="helvetica, arial">The maximum distance to find points on the network for each trajectory point.
It is mandatory, that this value is greater oder equal than the highest shortest distance between all trajectory points
and the whole network. This setting has a big influence on the computing time. Go to the
<a href="https://github.com/jagodki/Offline-MapMatching">repository on GitHub</a> for further information
about this setting.</font></p>
<h4><font face="helvetica, arial">Standard Deviation:</font></h4>
<p><font size=3 face="helvetica, arial">The standard deviation of the distance between the positions from the
trajectory and their possible positions on the network. It will be assumed, that the points of the trajectory
and their possible positions are normal distributed. A larger value represents less trust in the measurement.
Go to the <a href="https://github.com/jagodki/Offline-MapMatching">repository on GitHub</a> for
additonal information about this setting.</font></p>
<h4><font face="helvetica, arial">Expected Value:</font></h4>
<p><font size=3 face="helvetica, arial">The expected distance between the positions from the
trajectory and their possible positions on the network. It is recommended to choose the value 0, i.e.
ideally, the trajectory points should be on the network, if the accuracy of the coordinates would be 100%.</font></p>
<h4><font face="helvetica, arial">Transition Weight:</font></h4>
<p><font size=3 face="helvetica, arial">A weight for the calculation of the difference between the distances of two following trajectory points
and their corresponding points on the network. A larger value represents more tolerance of non-direct routes.
Go to the <a href="https://github.com/jagodki/Offline-MapMatching">repository on GitHub</a> for
additonal information about this setting.</font></p>
<hr>
<p><font size=3 face="helvetica, arial"><h4>The plugin runs the following tasks:</h4>
read the input layers<br><br>
calculate candidate points for each trajectory point using the maximum search distance and create a graph of candidates<br><br>
calculate the probability for each candidate point to be emitted by the correpsonding trajectory point<br><br>
calculate the similarity of the transitions compared to the trajectory points<br><br>
find the best candidates using Viterbi algorithm<br><br>
create a layer of linestrings using this candidates</font></p>
<hr>
<p><font size=3 face="helvetica, arial">A detailed description about the algorithm is provided
in the <a href="https://github.com/jagodki/Offline-MapMatching">README document</a> of the project on GitHub.</font></p>
</body>
</html>
================================================
FILE: src/offlinemapmatching/help_docs/help_processing_clipping_network.html
================================================
<html>
<body>
<p><font face="helvetica, arial">This algorithm gives the possibility to clip a network layer by using a trajectory. The trajectory will be transformed to a linestring and buffered after this. Thereafter the buffer will be used as a masking layer for clipping the network.</font></p>
<h4><font face="helvetica, arial">Network-Layer:</font></h4>
<p><font size=3 face="helvetica, arial">This combobox shows all layers of the current QGIS project with the geometry type LINESTRING. The features of this layer will be reduced and stored in a memory layer.</font></p>
<h4><font face="helvetica, arial">Trajectory-Layer:</font></h4>
<p><font size=3 face="helvetica, arial">This combobox shows all layers of the current QGIS project with the geometry type POINT and will be used for clipping/reducing the network layer.</font></p>
<h4><font face="helvetica, arial">Order Trajectory by:</font></h4>
<p><font size=3 face="helvetica, arial">This combobox shows all fields of the trajectory layer. The selected field will be used to order the features of the tractory layer (e.g. order by timestamps or way) and to transform the trajectory into linestrings.</font></p>
<h4><font face="helvetica, arial">Buffer radius around the Trajectory:</font></h4>
<p><font size=3 face="helvetica, arial">The radius or distance of the buffer around the trajectory linestring. The resulting polygon will be used to clip the network.</font></p>
<h4><font face="helvetica, arial">Output:</font></h4>
<p><font size=3 face="helvetica, arial">The destination where the clipped network should be saved.</font></p>
</body>
</html>
================================================
FILE: src/offlinemapmatching/help_docs/help_processing_match_trajectory.html
================================================
<html>
<body>
<p><font face="helvetica, arial">This algorithm matches a trajectory on a network using a Hidden Markov Model and the Viterbi algorithm.</font></p>
<h4><font face="helvetica, arial">Network-Layer:</font></h4>
<p><font size=3 face="helvetica, arial">This combobox shows all layers of the current QGIS project with the geometry type LINESTRING/MULTILINESTRING. The network, needed to match the trajectory on it, shall be selected. It is recommended, that this layer should be a clean graph following a node-edge-model, i.e. different lines should only touch other lines, never cross them.</font></p>
<h4><font face="helvetica, arial">Trajectory-Layer:</font></h4>
<p><font size=3 face="helvetica, arial">The combobox shows all layers of the current QGIS project with the geometry type POINT/MULTIPOINT. The trajectory, which will be matched with the network, shall be selected.</font></p>
<h4><font face="helvetica, arial">Trajectory-ID:</font></h4>
<p><font size=3 face="helvetica, arial">The combobox shows all fields of the chosen trajectory layer. The selected field will be added to the result features, so that the vertices of the result layer can be referred to the points of the trajectory.</font></p>
<h4><font face="helvetica, arial">Maximum Search Distance [m]:</font></h4>
<p><font size=3 face="helvetica, arial">The maximum distance to find points on the network for each trajectory point. It is mandatory, that this value is greater or equal than the highest shortest distance between all trajectory points and the whole network. This setting has a big influence on the computing time. Go to the <a href="https://github.com/jagodki/Offline-MapMatching">repository on GitHub</a> for further information about it.</font></p>
<h4><font face="helvetica, arial">Matching Type:</font></h4>
<p><font size=3 face="helvetica, arial">Two matching types are available. The slower type uses a routing between candidate points to calculate the distance of the shortes route between them. The faster type just calculates the euklidean distance of the beeline between the candidate points. The slower type is less vulnerable to outliers of the trajectory. Go to the <a href="https://github.com/jagodki/Offline-MapMatching">repository on GitHub</a> for additonal information about this setting.</font></p>
<hr>
<p><font size=3 face="helvetica, arial">A detailed description about the algorithm is provided in the <a href="https://github.com/jagodki/Offline-MapMatching">README document</a> of the project on GitHub.</font></p>
</body>
</html>
================================================
FILE: src/offlinemapmatching/help_docs/help_processing_reduce_density.html
================================================
<html>
<body>
<p><font face="helvetica, arial">This algorithm reduces the density of a given trajectory, i.e. the count of features will be reduced. The original trajectory will not be changed, but the results will be stored in a new layer.</font></p>
<h4><font face="helvetica, arial">Trajectory Layer:</font></h4>
<p><font size=3 face="helvetica, arial">This combobox shows all layers of the current QGIS project with the geometry type POINT. A layer with trajectory data should be selected.</font></p>
<h4><font face="helvetica, arial">Keep the last feature of the trajectory:</font></h4>
<p><font size=3 face="helvetica, arial">A checkbox to indicate, whether the algorithm keeps the last feature of the trajectory, regardless of the distance to the last feature.</font></p>
<h4><font face="helvetica, arial">Minimum distance between two consecutive points:</font></h4>
<p><font size=3 face="helvetica, arial">The minimum distance between consecutive trajectory points. If the distance to the following point is shorter than the inserted value, the following point will be removed.</font></p>
<h4><font face="helvetica, arial">Output:</font></h4>
<p><font size=3 face="helvetica, arial">The destination where the reduced trajectory should be saved.</font></p>
</body>
</html>
================================================
FILE: src/offlinemapmatching/i18n/af.ts
================================================
<?xml version="1.0" encoding="utf-8"?>
<!DOCTYPE TS><TS version="2.0" language="af" sourcelanguage="en">
<context>
<name>@default</name>
<message>
<location filename="test_translations.py" line="48"/>
<source>Good morning</source>
<translation>Goeie more</translation>
</message>
</context>
</TS>
================================================
FILE: src/offlinemapmatching/metadata.txt
================================================
# This file contains metadata for your plugin. Since
# version 2.0 of QGIS this is the proper way to supply
# information about a plugin. The old method of
# embedding metadata in __init__.py will
# is no longer supported since version 2.0.
# This file should be included when you package your plugin.# Mandatory items:
[general]
name=Offline-MapMatching
qgisMinimumVersion=3.0
description=a QGIS-plugin for matching a trajectory with a network using a Hidden Markov Model and Viterbi algorithm
version=3.0.0
author=Christoph Jung
email=jagodki.cj@gmail.com
about=Because of inaccuracies of the GNSS signal and/or a low data quality of the network, the positions of the trajectory and the network are differing. In many cases a simple snapping of the trajectory on the network will not work to reproduce the real track on the network. Reasons for that can be e.g. outliers in the trajectory or crossings of edges in the network. This plugin provides a statistical approach to solve the problem of offline map matching using the principles of Hidden Markov Models (HMM) and the Viterbi algorithm.
tracker=https://github.com/jagodki/Offline-MapMatching/issues
repository=https://github.com/jagodki/Offline-MapMatching
# End of mandatory metadata
# Recommended items:
# Uncomment the following line and add your changelog:
changelog=
3.0.0 - new algorithm to improve calculation time
2.5.2 - show icon in plugin manager correctly
2.5.1 - make the new parameter from v2.5.0 optional to keep the plugin backward compatible
2.5.0 - the algorithm "reduce trajectory density" has a new parameter for keeping the last vertex/feature after processing
2.4.0 - add a new preprocessing algorithm (reduce trajectory density)
2.3.2 - avoid a rare error with the crs selector
2.3.1 - remove an error while unloading the plugin
2.3.0 - provide a processing routine to reduce the network
2.2.2 - remove of an error in the calculation of the transition probabilities
2.2.1 - remove of an error in the implementation of the Viterbi-algorithm, that could cause an empty result layer
2.2.0 - log the id of the trajectory point for which the search distance is too low
2.1.1 - allow the processing of trajectory positions with the same coordinates
2.1.0 - reuse routing calculations for improvement of computation time
2.0.0 - introduce a new parameter to avoid u-turns
1.1.3 - experimental version to avoid u-turns (preparation for the new major version)
1.1.2 - the plugin will not crash anymore, if a raster layer is loaded
1.1.1 - bugs according to the case of different directions of the network and the trajectory have been solved
1.1.0 - make the plugin accessible via the processing framework
1.0.3 - enable the usage of layers with geometries containing Z- and/or M-value
1.0.2 - edit metadata and improve exception handling
1.0.1 - adjust field names of result layer and some minor changes
1.0.0 - first release
# Tags are comma separated with spaces allowed
tags=trajectory, map matching, viterbi, hmm
homepage=https://github.com/jagodki/Offline-MapMatching
category=Vector
icon=icons/icon.png
# experimental flag
experimental=False
# deprecated flag (applies to the whole plugin, not just a single version)
deprecated=False
================================================
FILE: src/offlinemapmatching/mm/__init__.py
================================================
================================================
FILE: src/offlinemapmatching/mm/helper/__init__.py
================================================
================================================
FILE: src/offlinemapmatching/mm/helper/measurement_statistics.py
================================================
import statistics
class MeasurementStatistics:
def __init__(self):
self.measurments = []
def addMeasurement(self, value):
self.measurments.append(value)
def getMeanValue(self):
return statistics.mean(self.measurments)
def getStandardDeviation(self):
return statistics.stdev(self.measurments)
================================================
FILE: src/offlinemapmatching/mm/hidden_states/__init__.py
================================================
================================================
FILE: src/offlinemapmatching/mm/hidden_states/candidate.py
================================================
import math
from ..observation.observation import *
class Candidate:
def __init__(self, point, distance, observation_id):
self.point = point
self.distance_to_observation = distance
self.observation_id = observation_id
def getEmissionProbability(self, sigma, my):
return (1 / math.sqrt(2 * math.pi * sigma)) * math.pow(math.e, -1 * math.pow(self.distance_to_observation - my, 2) / (2 * math.pow(sigma, 2)))
================================================
FILE: src/offlinemapmatching/mm/hidden_states/hidden_model.py
================================================
from ..observation.network import *
from ..observation.trajectory import *
from ..observation.observation import *
from .candidate import *
from .transition import *
from ..helper.measurement_statistics import *
from qgis.core import *
import os, math
from PyQt5.QtWidgets import QProgressBar, QApplication
from PyQt5.QtCore import QVariant, QDir
class HiddenModel:
def __init__(self, trajectory, network):
self.trajectory = trajectory
self.network = network
self.counter_candidates = 0
self.candidate_graph = []
self.candidates = {}
self.candidates_backtracking = {}
self.observation_measurements = MeasurementStatistics()
self.transition_measurements = MeasurementStatistics()
self.pb = None
self.feedback = None
self.feedback_total = 100.0
def createGraph(self, maximum_distance):
#init progressbar
self.initFeedback(len(self.trajectory.observations))
#init data structur
self.candidate_graph = []
self.candidates = {}
self.counter_candidates = 0
#iterate over all observations from our trajectory
for observation in self.trajectory.observations:
#extract all candidates of the current observation
candidates = observation.getCandidates(self.network, maximum_distance)
#candidates = observation.getAllCandidates(self.network, maximum_distance)
if len(candidates) == 0:
QgsMessageLog.logMessage('could not find any candidates for trajectory point ' + str(observation.id), level=Qgis.Info)
return -5
else:
#create the current level of the graph and the candidate measurements
current_graph_level = {}
for candidate in candidates:
#store the observation measurements for a later usage of their statistics
#e.g. for calculations of the emission probabilities
self.observation_measurements.addMeasurement(candidate.distance_to_observation)
#candidate.calculateEmissionProbability(observation, sigma, my)
# current_graph_level[self.counter_candidates] = {
# #'observation_id' : observation.id,
# #'emitted_probability' : candidate.emission_probability,
# 'candidate': candidate,
# 'transition_probabilities' : {},
# 'transition_probability' : 0.0,
# 'curvature_probability' : 0.0,
# 'total_probability' : 0.0
# }
current_graph_level.update({self.counter_candidates: {
#'observation_id' : observation.id,
#'emitted_probability' : candidate.emission_probability,
'candidate': candidate,
'transition_probabilities' : {},
'transition_probability' : 0.0,
'curvature_probability' : 0.0,
'total_probability' : 0.0
}})
#store the candidate in a dictionary (key can be used to find the candidate in the graph and vice versa)
self.counter_candidates += 1
#add the current graph level to the graph
self.candidate_graph.append(current_graph_level)
#update progressbar
self.updateFeedback()
return 0
def createBacktracking(self):
#init progressbar
self.initFeedback(len(self.candidate_graph))
self.candidates_backtracking = {}
for i, graph_level in enumerate(self.candidate_graph):
#the candidates of the first observation have no parent
if i != 0:
for id, entry in graph_level.items():
#get all transition probabilities of the current entry and iterate over them to find the highest total probability
transition_probabilities = entry['transition_probabilities']
for previous_id, transition in transition_probabilities.items():
#calculate the probabilities of the current transition
start_observation = self.trajectory.observations[i - 1]
end_observation = self.trajectory.observations[i]
distance_between_observations = start_observation.point.distance(end_observation.point)
transition.setDirectionProbability(start_observation, end_observation)
transition.setRoutingProbability(distance_between_observations, self.transition_measurements.getStandardDeviation())
transition.setTransitionProbability()
#calculate the total probability and compare it
current_total_probability = transition.transition_probability * entry['candidate'].getEmissionProbability(self.observation_measurements.getStandardDeviation(), 0.0)# * self.candidate_graph[i - 1][key]['total_probability']
# #calculate the emission probability for the current entry/candidate in the graph level
# current_total_probability = value * entry["candidate"].getEmissionProbability(self.observation_measurements.getStandardDeviation(), 0.0) * self.candidate_graph[i - 1][key]['total_probability']
if current_total_probability >= entry['total_probability']:
entry['total_probability'] = current_total_probability
entry['transition_probability'] = transition.transition_probability
self.candidates_backtracking[id] = previous_id
#update progressbar
self.updateFeedback()
return 0
def findViterbiPath(self):
#init an array to store all candidates of the most likely path
viterbi_path = []
#find the highest total probability in the last graph level
highest_prob = 0.0
id = None
graph_counter = len(self.candidate_graph) - 1
last_graph_level = self.candidate_graph[graph_counter]
for candidate_id, entry in last_graph_level.items():
if entry['total_probability'] >= highest_prob:
highest_prob = entry['total_probability']
id = candidate_id
#add the last vertex of the path
last_graph_level_entry = self.candidate_graph[graph_counter][id]
viterbi_path.insert(0, {'vertex': last_graph_level_entry["candidate"],
'total_probability': highest_prob,
'emitted_probability': last_graph_level_entry["candidate"].getEmissionProbability(self.observation_measurements.getStandardDeviation(), 0.0),
'transition_probability': last_graph_level_entry['transition_probability'],
'observation_id': last_graph_level_entry["candidate"].observation_id
})
#now find all parents of this vertex/candidate
graph_counter -= 1
current_id = self.candidates_backtracking[id]
while(current_id is not None and graph_counter >= 0):
searched_graph_level_entry = self.candidate_graph[graph_counter][current_id]
viterbi_path.insert(0, {'vertex': searched_graph_level_entry["candidate"],
'total_probability': searched_graph_level_entry['total_probability'],
'emitted_probability': searched_graph_level_entry["candidate"].getEmissionProbability(self.observation_measurements.getStandardDeviation(), 0.0),
'transition_probability': searched_graph_level_entry['transition_probability'],
'observation_id': searched_graph_level_entry["candidate"].observation_id
})
#preparation for the next iteration
if current_id in self.candidates_backtracking:
current_id = self.candidates_backtracking[current_id]
graph_counter -= 1
return viterbi_path
def setTransitions(self, fast_map_matching=False):
#init progressbar
self.initFeedback(len(self.trajectory.observations))
for i, observation in enumerate(self.trajectory.observations):
#skip the first observation, because first observation has no parent
if i != 0:
#get the current and previous graph level
previous_graph_level = self.candidate_graph[i - 1]
current_graph_level = self.candidate_graph[i]
for previous_id, previous_entry in previous_graph_level.items():
for current_id, current_entry in current_graph_level.items():
#get the candidates
current_candidate = current_entry["candidate"]
previous_candidate = previous_entry["candidate"]
#create a new transition
transition = None
if fast_map_matching is True:
transition = Transition(previous_candidate, current_candidate, self.network, False, True)
else:
transition = Transition(previous_candidate, current_candidate, self.network, self.candidatesHaveDifferentPositions(current_candidate, previous_candidate))
#calculate the probabilities of the transition
# transition.setDirectionProbability(self.trajectory.observations[i - 1], observation)
# transition.setRoutingProbability(observation.point.distance(self.trajectory.observations[i - 1].point), beta)
# transition.setTransitionProbability()
#calculate the difference of the distances between candidates and observations
transition_length = transition.getLengthOfTransition()
observation_distance = math.sqrt(math.pow(observation.point.asPoint().x() - self.trajectory.observations[i - 1].point.asPoint().x(), 2) + math.pow(observation.point.asPoint().y() - self.trajectory.observations[i - 1].point.asPoint().y(), 2))
self.transition_measurements.addMeasurement(abs(transition_length - observation_distance))
#insert the transition into the graph
# current_entry['transition_probabilities'] = {previous_id : transition.transition_probability}
current_entry['transition_probabilities'].update({previous_id : transition})
self.updateFeedback()
return 0
def candidatesHaveDifferentPositions(self, candidate_1, candidate_2):
#get coordinates of the previous entry and the current candidate
x_candidate_1 = candidate_1.point.asPoint().x()
y_candidate_1 = candidate_1.point.asPoint().y()
x_candidate_2 = candidate_2.point.asPoint().x()
y_candidate_2 = candidate_2.point.asPoint().y()
#if points are not equal, return True, otherwise False
if x_candidate_1 != x_candidate_2 or y_candidate_1 != y_candidate_2:
return True
else:
return False
def setStartingProbabilities(self):
first_graph_level = self.candidate_graph[0]
#init progressbar
self.initFeedback(len(first_graph_level))
for id, entry in first_graph_level.items():
entry['total_probability'] = entry['candidate'].getEmissionProbability(self.observation_measurements.getStandardDeviation(), 0.0)
self.candidates_backtracking[id] = None
self.updateFeedback()
return 0
def addFeaturesToLayer(self, features, attributes, crs):
#create a new layer
layer = QgsVectorLayer('LineString?crs=' + crs + '&index=yes', 'matched trajectory', 'memory')
#load the layer style
dir = os.path.dirname(__file__)
filename = os.path.abspath(os.path.join(dir, '..', '..', 'style.qml'))
layer.loadNamedStyle(filename, loadFromLocalDb=False)
#add the layer to the project
layer.startEditing()
layer_data = layer.dataProvider()
layer_data.addAttributes(attributes)
layer.updateFields()
#add features to the layer
layer.addFeatures(features)
layer.commitChanges()
#add the layer to the map
QgsProject.instance().addMapLayer(layer)
return layer
def getPathOnNetwork(self, vertices, field_array):
#init progressbar
self.initFeedback(len(vertices))
#create an array to store all features
features = []
#iterate over the vertices
for i, vertex in enumerate(vertices):
#if we are in the first loop, we skip them because we have no previous point to create a routing with start and end
if i != 0:
#check, whether the two current candidates share the same position or not
if self.candidatesHaveDifferentPositions(vertices[i - 1]['vertex'], vertex['vertex']) == False:
self.updateFeedback()
continue
#get all edges of the graph/network along the shortest way from the previous to the current vertex
points = self.network.routing(vertices[i - 1]['vertex'].point.asPoint(), vertex['vertex'].point.asPoint())
if points == -1:
return points
#now create a new line feature and add all needed fields
fields = QgsFields()
for field in field_array:
fields.append(field)
feature = QgsFeature(fields)
#create the geometry of the new feature
feature.setGeometry(QgsGeometry.fromPolylineXY(points))
#insert the attributes and add the feature to the layer
feature.setAttribute('id', i)
feature.setAttribute('total_probability_start', vertices[i - 1]['total_probability'])
feature.setAttribute('total_probability_end', vertex['total_probability'])
feature.setAttribute('emission_probability_start', vertices[i - 1]['emitted_probability'])
feature.setAttribute('emission_probability_end', vertex['emitted_probability'])
feature.setAttribute('transition_probability', vertices[i - 1]['transition_probability'])
feature.setAttribute('observation_id_start', vertices[i - 1]['observation_id'])
feature.setAttribute('observation_id_end', vertex['observation_id'])
features.append(feature)
self.updateFeedback()
return features
def initProgressbar(self, maximum):
if self.pb is not None:
self.pb.setValue(0)
self.pb.setMaximum(maximum)
QApplication.processEvents()
def updateProgressbar(self):
if self.pb is not None:
self.pb.setValue(self.pb.value() + 1)
QApplication.processEvents()
def initFeedback(self, maximum):
if self.feedback is not None:
print(self.feedback.progress())
self.feedback.setProgress(0)
self.feedback_total = 100.0 / maximum
else:
print("feedback is None")
def updateFeedback(self):
if self.feedback is not None:
self.feedback.setProgress(int(self.feedback_total + self.feedback.progress()) + 1)
================================================
FILE: src/offlinemapmatching/mm/hidden_states/transition.py
================================================
from .candidate import *
import math
class Transition:
def __init__(self, start_candidate, end_candidate, network, different_positions, use_beeline=False):
self.start_candidate = start_candidate
self.end_candidate = end_candidate
self.network = network
self.direction_probability = 0.0
self.routing_probability = 0.0
self.transition_probability = 0.0
self.points_on_network = self.getAllpoints_on_network(network) if different_positions else 0
self.use_beeline = use_beeline
def setDirectionProbability(self, start_observation, end_observation):
#variables to store the slops
m_observation = 0.0
m_candidate = 0.0
#variable to store the intermediate results of the probability
p_intermediate = 1.0
#calculate the slope of the observations using arctan (we get results between 0 and 180 degrees)
if (end_observation.point.asPoint().x() - start_observation.point.asPoint().x()) != 0:
m_observation = math.degrees(math.atan((end_observation.point.asPoint().y() -
start_observation.point.asPoint().y()) /
(end_observation.point.asPoint().x() -
start_observation.point.asPoint().x()))) + 90
elif end_observation.point.asPoint().y() != start_observation.point.asPoint().y():
m_observation = 90.0
#iterate over all points and use their precursor to calculate directions
if self.points_on_network != -1 and self.points_on_network != 0:
for i, current_point in enumerate(self.points_on_network):
if i != 0:
#get the precursor of the current point
precursor = self.points_on_network[i - 1]
#calculate the slope of the points using arctan (we get results between 0 and 180 degrees)
if (current_point.x() - precursor.x()) != 0:
m_candidate = math.degrees(math.atan((current_point.y() - precursor.y()) /
(current_point.x() - precursor.x()))) + 90
elif current_point.y() - precursor.y():
m_candidate = 90.0
#calculate the difference of the slopes
difference = abs(m_observation - m_candidate)
#normalisation of the difference
p_intermediate *= (180 - difference) / 180
#clear the slope
m_candidate = 0.0
#store the result
if self.points_on_network != -1:
self.direction_probability = p_intermediate
def setRoutingProbability(self, distance_between_observations, beta):
#get the distance of the shortest path between the two candidates of the current transition
distance_on_network = 0
if self.use_beeline is True:
distance_on_network = self.getDistanceOfBeeline()
else:
distance_on_network = self.getLengthOfTransition()
#calculate the difference between the distances of the observations and the candidates
difference = abs(distance_on_network - distance_between_observations)
#calculate the exponential probability distribution
if distance_on_network != -1:
self.routing_probability = 1 / beta * math.pow(math.e, -1 * difference / beta)
def setTransitionProbability(self):
self.transition_probability = self.direction_probability * self.routing_probability
#if self.direction_probability != None and self.routing_probability != None:
#self.transition_probability = self.direction_probability * self.routing_probability
#return True
#else:
#return False
def getAllpoints_on_network(self, network):
#get all points of the shortest path on the network from the start to the end of the transistion and store them
return network.routing(self.start_candidate.point.asPoint(), self.end_candidate.point.asPoint())
def getLengthOfTransition(self):
#points == -1, if routing was not possible; points == 0, if canidates share the same position
if self.points_on_network == -1 or self.points_on_network == 0:
return self.points_on_network
else:
distance = 0
for i, vertex in enumerate(self.points_on_network):
#get the distance between the current vertice and the next vertice
if len(self.points_on_network) > (i + 1):
distance = distance + vertex.distance(self.points_on_network[i + 1].x(), self.points_on_network[i + 1].y())
else:
return distance
return distance
def getDistanceOfBeeline(self):
return self.start_candidate.point.distance(self.end_candidate.point)
================================================
FILE: src/offlinemapmatching/mm/map_matcher.py
================================================
from PyQt5.QtWidgets import QProgressBar, QComboBox, QLabel, QApplication
from qgis.core import *
from .hidden_states.hidden_model import *
from .observation.network import *
from .observation.trajectory import *
class MapMatcher:
def __init__(self):
self.layers = []
self.attributes = []
self.hidden_model = None
self.network = None
self.trajectory = None
def startViterbiMatchingGui(self, pb, trajectory_name, network_name, attribute_name, sigma, my, beta, max_dist, label, crs):
check_results = 0
label.setText('initialise data structur...')
QgsMessageLog.logMessage('initialise data structur...', level=Qgis.Info)
self.setUp(network_name, trajectory_name, attribute_name, pb)
label.setText('create candidate graph...')
QgsMessageLog.logMessage('create candidate graph...', level=Qgis.Info)
#check_results = self.hidden_model.createGraph(sigma, my, max_dist)
check_results = self.hidden_model.createGraph(max_dist)
if check_results != 0:
label.setText('error during calculation of candidates...')
QgsMessageLog.logMessage('the maximum search distance is too low for one trajectory point to find at least one candidate', level=Qgis.Info)
return -1
label.setText('calculate starting probabilities...')
QgsMessageLog.logMessage('calculate starting probabilities...', level=Qgis.Info)
check_results = self.hidden_model.setStartingProbabilities()
if check_results != 0:
label.setText('error during calculation of starting probabilities...')
QgsMessageLog.logMessage('calculation of starting probabilities was not successfull, maybe an exception was thrown', level=Qgis.Info)
return -2
label.setText('calculate transition probabilities...')
QgsMessageLog.logMessage('calculate transition probabilities...', level=Qgis.Info)
#check_results = self.hidden_model.setTransitionProbabilities(beta)
check_results = self.hidden_model.setTransitions()
if check_results != 0:
label.setText('error during calculation of transition probabilities...')
QgsMessageLog.logMessage('calculation of transition probabilities was not succesfull, have a look on the used parameters and the check the datasets', level=Qgis.Info)
return -3
label.setText('create backtracking...')
QgsMessageLog.logMessage('create backtracking...', level=Qgis.Info)
check_results = self.hidden_model.createBacktracking()
if check_results != 0:
label.setText('error during calculation of backtracking...')
QgsMessageLog.logMessage('it was not able to create a complete backtracking, maybe the calculated probabilities are corrupt', level=Qgis.Info)
return -4
label.setText('get most likely path...')
QgsMessageLog.logMessage('get most likely path...', level=Qgis.Info)
vertices = self.hidden_model.findViterbiPath()
if len(vertices) <= 1:
label.setText('error during calculating the most likely path...')
QgsMessageLog.logMessage('cannot calculate the most likely path, maybe backtracking went wrong, check your parameters', level=Qgis.Critical)
return -5
label.setText('get network path...')
QgsMessageLog.logMessage('get network path...', level=Qgis.Info)
features = self.hidden_model.getPathOnNetwork(vertices, self.defineAttributes())
if features == -1:
label.setText('error during calculating the path on network...')
QgsMessageLog.logMessage('routing between the points of the most likely path does not work', level=Qgis.Critical)
return -6
layer = self.hidden_model.addFeaturesToLayer(features, self.defineAttributes(), crs)
layer.select([])
QgsProject.instance().addMapLayer(layer)
label.setText('finished ^o^')
QgsMessageLog.logMessage('finished ^o^', level=Qgis.Info)
return 0
def startViterbiMatchingProcessing(self, trajectory_name, network_name, attribute_name, max_dist, feature_sink, feedback, fast_map_matching=False):
check_results = 0
#total = 100.0 / 8
#current = 1
QgsMessageLog.logMessage('initialise data structur...', level=Qgis.Info)
feedback.pushInfo('initialise data structur...')
self.setUp(network_name, trajectory_name, attribute_name, feedback)
#feedback.setProgress(int(current * total))
#current += 1
if feedback.isCanceled():
return -99
QgsMessageLog.logMessage('create candidate graph...', level=Qgis.Info)
feedback.pushInfo('create candidate graph...')
#check_results = self.hidden_model.createGraph(sigma, my, max_dist)
check_results = self.hidden_model.createGraph(max_dist)
#feedback.setProgress(int(current * total))
#current += 1
if feedback.isCanceled():
return -99
if check_results != 0:
error_text = 'the maximum search distance is too low for one trajectory point point to find at least one candidate'
feedback.pushInfo(error_text)
QgsMessageLog.logMessage(error_text, level=Qgis.Info)
return -1
QgsMessageLog.logMessage('calculate starting probabilities...', level=Qgis.Info)
feedback.pushInfo('calculate starting probabilities...')
check_results = self.hidden_model.setStartingProbabilities()
#feedback.setProgress(int(current * total))
#current += 1
if feedback.isCanceled():
return -99
if check_results != 0:
error_text = 'calculation of starting probabilities was not successfull, maybe an exception was thrown'
feedback.pushInfo(error_text)
QgsMessageLog.logMessage(error_text, level=Qgis.Info)
return -2
QgsMessageLog.logMessage('calculate transition probabilities...', level=Qgis.Info)
feedback.pushInfo('calculate transition probabilities...')
#check_results = self.hidden_model.setTransitionProbabilities(beta)
check_results = self.hidden_model.setTransitions(fast_map_matching)
#feedback.setProgress(int(current * total))
#current += 1
if feedback.isCanceled():
return -99
if check_results != 0:
error_text = 'calculation of transition probabilities was not succesfull, have a look on the used parameters and the check the datasets'
feedback.pushInfo(error_text)
QgsMessageLog.logMessage(error_text, level=Qgis.Info)
return -3
QgsMessageLog.logMessage('create backtracking...', level=Qgis.Info)
feedback.pushInfo('create backtracking...')
check_results = self.hidden_model.createBacktracking()
#feedback.setProgress(int(current * total))
#current += 1
if feedback.isCanceled():
return -99
if check_results != 0:
error_text = 'it was not able to create a complete backtracking, maybe the calculated probabilities are corrupt'
feedback.pushInfo(error_text)
QgsMessageLog.logMessage(error_text, level=Qgis.Info)
return -4
QgsMessageLog.logMessage('get most likely path...', level=Qgis.Info)
feedback.pushInfo('get most likely path...')
vertices = self.hidden_model.findViterbiPath()
#feedback.setProgress(int(current * total))
#current += 1
if feedback.isCanceled():
return -99
if len(vertices) == 0:
error_text = 'cannot calculate the most likely path, maybe backtracking went wrong, check your parameters'
feedback.pushInfo(error_text)
QgsMessageLog.logMessage(error_text, level=Qgis.Critical)
return -5
QgsMessageLog.logMessage('get network path...', level=Qgis.Info)
feedback.pushInfo('get network path...')
features = self.hidden_model.getPathOnNetwork(vertices, self.defineAttributes())
#feedback.setProgress(int(current * total))
#current += 1
if feedback.isCanceled():
return -99
if features == -1:
error_text = 'routing between the points of the most likely path does not work'
feedback.pushInfo(error_text)
QgsMessageLog.logMessage(error_text, level=Qgis.Critical)
return -6
feature_sink.addFeatures(features)
#feedback.setProgress(int(current * total))
#current += 1
return 0
def fillLayerComboBox(self, iface, combobox, geom_type):
#first clear the combobox
combobox.clear()
#get all layers in the current QGIS project
self.layers = []
self.layers = iface.mapCanvas().layers()
#populate the combobox
for layer in self.layers:
#ignore raster layer, because just vector layers have a wkbType
if layer.type() == 0:
if (QgsWkbTypes.flatType(layer.wkbType()) == QgsWkbTypes.Point and geom_type == 'POINT') or (QgsWkbTypes.flatType(layer.wkbType()) == QgsWkbTypes.LineString and geom_type == 'LINESTRING'):
combobox.addItem(layer.name())
def fillAttributeComboBox(self, combobox, layername):
#first clear the combobox
combobox.clear()
#extract all attributes
layer = self.getLayer(layername)
if layer is not None:
self.attributes = layer.fields()
#populate the combobox
for attr in self.attributes:
combobox.addItem(attr.name())
def getLayer(self, layername):
for layer in self.layers:
if layer.name() == layername:
return layer
return None
def setUp(self, line_layer, point_layer, point_attr, feedback):
if type(line_layer) is str:
self.network = Network(self.getLayer(line_layer))
else:
self.network = Network(line_layer)
if type(line_layer) is str:
self.trajectory = Trajectory(self.getLayer(point_layer), point_attr)
else:
self.trajectory = Trajectory(point_layer, point_attr)
self.hidden_model = HiddenModel(self.trajectory, self.network)
self.hidden_model.feedback = feedback
def defineAttributes(self):
attributes = [QgsField('id', QVariant.Int),
QgsField('observation_id_start', QVariant.Int),
QgsField('observation_id_end', QVariant.Int),
QgsField('emission_probability_start', QVariant.Double),
QgsField('emission_probability_end', QVariant.Double),
QgsField('transition_probability', QVariant.Double),
QgsField('total_probability_start', QVariant.Double),
QgsField('total_probability_end', QVariant.Double)]
return attributes
================================================
FILE: src/offlinemapmatching/mm/observation/__init__.py
================================================
================================================
FILE: src/offlinemapmatching/mm/observation/intersection.py
================================================
from qgis.core import *
class Intersection:
def __init__(self, geometry, edge_ids):
self.geometry = geometry
self.edge_ids = edge_ids
================================================
FILE: src/offlinemapmatching/mm/observation/network.py
================================================
from qgis.analysis import *
from qgis.core import *
from .intersection import *
import processing
class Network:
# def __init__(self, linestring_layer, precalculated_network):
def __init__(self, linestring_layer):
self.vector_layer = linestring_layer
self.intersections = []
self.extractAllIntersections()
#self.precalculated_network = precalculated_network
def extractAllIntersections(self):
#dissolve the network
result_dissolve = processing.run("native:dissolve", {
'INPUT': self.vector_layer,
'OUTPUT': 'memory:dissolve'
})
#create single geometries from the dissolved network
result_single = processing.run("native:multiparttosingleparts", {
'INPUT': result_dissolve["OUTPUT"],
'OUTPUT': 'memory:single'
})
#get the line intersections
result_intersections = processing.run("native:lineintersections", {
'INPUT': result_single["OUTPUT"],
'INTERSECT': result_single["OUTPUT"],
'OUTPUT': 'memory:intersections'
})
#remove duplicate geometries
result_cleaned = processing.run("qgis:deleteduplicategeometries", {
'INPUT': result_intersections["OUTPUT"],
'OUTPUT': 'memory:cleaned'
})
#create intersection objects
for feature in result_cleaned["OUTPUT"].getFeatures():
intersecting_edges = []
#iterate over alle edges of the network
for edge in self.vector_layer.getFeatures():
if edge.geometry().intersects(feature.geometry()):
intersecting_edges.append(edge.id())
self.intersections.append(Intersection(feature.geometry(), intersecting_edges))
def routing(self, start, end):
#create director and strategy
director = QgsVectorLayerDirector(self.vector_layer, -1, '', '', '', 2)
strategy = QgsNetworkDistanceStrategy()
director.addStrategy(strategy)
#buildiung the graph
builder = QgsGraphBuilder(self.vector_layer.sourceCrs())
tied_points = director.makeGraph(builder, [start, end])
graph = builder.graph()
start_id = graph.findVertex(tied_points[0])
end_id = graph.findVertex(tied_points[1])
#start routing
(tree, cost) = QgsGraphAnalyzer.dijkstra(graph, start_id, 0)
if tree[end_id] == -1:
return -1
else:
points = []
cur_pos = end_id
#get the first vertex
if cur_pos != start_id:
if cur_pos == graph.edge(tree[cur_pos]).toVertex():
#insert the vertices to the result list
points.insert(0, graph.vertex(graph.edge(tree[cur_pos]).toVertex()).point())
points.insert(0, graph.vertex(graph.edge(tree[cur_pos]).fromVertex()).point())
#set cur_pos to the next vertex
cur_pos = graph.edge(tree[cur_pos]).fromVertex()
else:
#insert the vertices to the result list
points.insert(0, graph.vertex(graph.edge(tree[cur_pos]).fromVertex()).point())
points.insert(0, graph.vertex(graph.edge(tree[cur_pos]).toVertex()).point())
#set cur_pos to the next vertex
cur_pos = graph.edge(tree[cur_pos]).toVertex()
while cur_pos != start_id:
#just insert the vertex of the current edge, which does not already exist in the result list
if cur_pos == graph.edge(tree[cur_pos]).toVertex():
points.insert(0, graph.vertex(graph.edge(tree[cur_pos]).fromVertex()).point())
#set cur_pos to the next vertex
cur_pos = graph.edge(tree[cur_pos]).fromVertex()
else:
points.insert(0, graph.vertex(graph.edge(tree[cur_pos]).toVertex()).point())
#set cur_pos to the next vertex
cur_pos = graph.edge(tree[cur_pos]).toVertex()
return points
================================================
FILE: src/offlinemapmatching/mm/observation/observation.py
================================================
from .network import *
from .intersection import *
from ..hidden_states.candidate import *
from qgis.core import *
class Observation:
def __init__(self, point, id):
self.point = point
self.id = id
def getAllCandidates(self, network, max_distance):
candidates = []
#iterate over all lines of the network to check for candidates on this lines
for feature in network.vector_layer.getFeatures():
#init some vars
linestring = feature.geometry()
distance = self.point.distance(linestring)
#check whether the distance is equal or less the search distance
if distance <= max_distance:
candidates.append(Candidate(linestring.nearestPoint(self.point), distance, self.id))
return candidates
def getCandidates(self, network, max_distance):
candidates = []
intersections_within_distance = []
#get all intersection points within the search distance
for intersection in network.intersections:
if intersection.geometry.distance(self.point) <= max_distance:
intersections_within_distance.append(intersection)
# #iterate over all lines of the network to check for candidates on this lines
# for feature in network.vector_layer.getFeatures():
# #check if the current edge intersects an intersection within search distance
# skip_iteration_step = False
# for intersection in intersections_within_distance:
# if feature.id() in intersection.edge_ids:
# skip_iteration_step = True
# break
# #skip the iteration step if necessary
# if skip_iteration_step is True:
# continue
# #init some vars
# linestring = feature.geometry()
# distance = self.point.distance(linestring)
# #check whether the distance is equal or less the search distance
# if distance <= max_distance:
# candidates.append(Candidate(linestring.nearestPoint(self.point), distance, self.id))
#iterate over all lines of the network to check for candidates on this lines
candidate_points = []
for feature in network.vector_layer.getFeatures():
#init some vars
linestring = feature.geometry()
distance = self.point.distance(linestring)
#check whether the distance is equal or less the search distance
if distance <= max_distance:
nearest_point = linestring.nearestPoint(self.point)
candidate_is_not_near_to_intersection = True
#check, whether the possible candidate is within the range to an intersection
for intersection in intersections_within_distance:
if intersection.geometry.distance(nearest_point) <= max_distance:
candidate_is_not_near_to_intersection = False
break
if candidate_is_not_near_to_intersection is True:
candidates.append(Candidate(nearest_point, distance, self.id))
#now add the intersections to the candidates
for intersection in intersections_within_distance:
candidates.append(Candidate(intersection.geometry, intersection.geometry.distance(self.point), self.id))
return candidates
def isIntersectionInSearchDistance(self, edges, candidates):
for index, edge in edges.enumerate():
intersection_count = 0
intersected_edges = []
for other_index, other_edge in edges.enumerate():
if other_index != index:
if edge.geometry().intersects(other_edge.geometry()):
intersection_count += 1
intersected_edges.append(other_index)
#init a variable to count the intersections
counter = 0
#iterate over all network features
for feature in network.getFeatures():
#increase the counter if the geometries intersect each other
if feature.geometry().insersects(self.point):
counter += 1
#break up the iteration when the counter reached a specific number
if counter == 3:
return True
return False
================================================
FILE: src/offlinemapmatching/mm/observation/trajectory.py
================================================
from .observation import *
from qgis.core import *
class Trajectory:
def __init__(self, point_layer, id_field):
self.observations = []
self.extractObservations(point_layer, id_field)
def extractObservations(self, point_layer, id_field):
#extract all points from the QGIS layer
features = point_layer.getFeatures()
for feature in features:
point = feature.geometry()
id = feature[id_field]
#create a new observation object and add them to our observation list
obs = Observation(point, id)
self.observations.append(obs)
================================================
FILE: src/offlinemapmatching/mm_processing/clip_network_algorithm.py
================================================
# -*- coding: utf-8 -*-
'''
/***************************************************************************
Offline-MapMatching
A QGIS plugin
desciption of the plugin
Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
-------------------
begin : 2018-08-08
copyright : (C) 2018 by Christoph Jung
email : jagodki.cj@gmail.com
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
'''
__author__ = 'Christoph Jung'
__date__ = '2018-08-08'
__copyright__ = '(C) 2018 by Christoph Jung'
# This will get replaced with a git SHA1 when you do a git archive
__revision__ = '$Format:%H$'
from PyQt5.QtCore import QCoreApplication, QUrl
from PyQt5.QtGui import QIcon
from qgis.core import (QgsProcessing,
QgsProcessingAlgorithm,
QgsProcessingParameterFeatureSource,
QgsProcessingParameterVectorLayer,
QgsProcessingParameterField,
QgsProcessingParameterString,
QgsProcessingParameterNumber,
QgsCoordinateReferenceSystem,
QgsProject,
QgsProcessingParameterFeatureSink,
QgsWkbTypes)
import processing
import time, os.path
class ClipNetworkAlgorithm(QgsProcessingAlgorithm):
'''
This is an example algorithm that takes a vector layer and
creates a new identical one.
It is meant to be used as an example of how to create your own
algorithms and explain methods and variables used to do it. An
algorithm like this will be available in all elements, and there
is not need for additional work.
All Processing algorithms should extend the QgsProcessingAlgorithm
class.
'''
# Constants used to refer to parameters and outputs. They will be
# used when calling the algorithm from another algorithm, or when
# calling from the QGIS console.
NETWORK = 'NETWORK'
TRAJECTORY = 'TRAJECTORY'
ORDER_FIELD = 'ORDER_FIELD'
BUFFER_RADIUS = 'BUFFER_RADIUS'
OUTPUT = 'OUTPUT'
def initAlgorithm(self, config):
'''
Here we define the inputs and output of the algorithm, along
with some other properties.
'''
self.addParameter(
QgsProcessingParameterVectorLayer(
self.NETWORK,
self.tr('Network layer'),
[QgsProcessing.TypeVectorLine]
)
)
self.addParameter(
QgsProcessingParameterVectorLayer(
self.TRAJECTORY,
self.tr('Trajectory layer'),
[QgsProcessing.TypeVectorPoint]
)
)
self.addParameter(
QgsProcessingParameterField(
self.ORDER_FIELD,
self.tr('Order Trajectory by'),
parentLayerParameterName=self.TRAJECTORY,
type=QgsProcessingParameterField.Any
)
)
self.addParameter(
QgsProcessingParameterNumber(
self.BUFFER_RADIUS,
self.tr('Buffer radius around the Trajectory'),
type=QgsProcessingParameterNumber.Double,
defaultValue=0,
minValue=0.0
)
)
self.addParameter(
QgsProcessingParameterFeatureSink(
self.OUTPUT,
self.tr('Clipped Network')
)
)
def processAlgorithm(self, parameters, context, feedback):
'''
Here is where the processing itself takes place.
'''
start_time = time.time()
#extract all parameters
network_layer = self.parameterAsVectorLayer(
parameters,
self.NETWORK,
context
)
trajectory_layer = self.parameterAsVectorLayer(
parameters,
self.TRAJECTORY,
context
)
trajectory_order_field = self.parameterAsString(
parameters,
self.ORDER_FIELD,
context
)
buffer = self.parameterAsDouble(
parameters,
self.BUFFER_RADIUS,
context
)
(sink, dest_id) = self.parameterAsSink(
parameters,
self.OUTPUT,
context,
network_layer.fields(),
QgsWkbTypes.LineString,
network_layer.crs()
)
#init the progressbar
max_count = 4
counter = 0
feedback.setProgress(int((counter / max_count) * max_count))
#trajectory to path
points_to_path = processing.run("qgis:pointstopath", {
'INPUT':trajectory_layer,
'ORDER_FIELD':trajectory_order_field,
'GROUP_FIELD':None,
'DATE_FORMAT':'',
'OUTPUT':'memory:path'
})
counter += 1
feedback.setProgress(int((counter / max_count) * max_count))
if feedback.isCanceled():
return {'canceled °o°': str(round(time.time() - start_time, 2))}
#buffer path
buffer = processing.run("native:buffer", {
'INPUT':points_to_path['OUTPUT'],
'DISTANCE':buffer,
'SEGMENTS':5,
'END_CAP_STYLE':0,
'JOIN_STYLE':0,
'MITER_LIMIT':2,
'DISSOLVE':False,
'OUTPUT':'memory:buffer'})
counter += 1
feedback.setProgress(int((counter / max_count) * max_count))
feedback.pushInfo('created buffer successfully')
if feedback.isCanceled():
return {'canceled °o°': str(round(time.time() - start_time, 2))}
#clip network
clipped_network = processing.run("native:clip", {
'INPUT':network_layer,
'OVERLAY':buffer['OUTPUT'],
'OUTPUT':'memory:clip'})
counter += 1
feedback.setProgress(int((counter / max_count) * max_count))
feedback.pushInfo('clipped buffer successfully')
if feedback.isCanceled():
return {'canceled °o°': str(round(time.time() - start_time, 2))}
#multipart to single part
single_part_network = processing.run("native:multiparttosingleparts", {
'INPUT':clipped_network['OUTPUT'],
'OUTPUT':'memory:omm_clipped_network'})
counter += 1
feedback.setProgress(int((counter / max_count) * max_count))
feedback.pushInfo('created single parts successfully')
if feedback.isCanceled():
return {'canceled °o°': str(round(time.time() - start_time, 2))}
#add the result to the QGIS project
sink.addFeatures(single_part_network['OUTPUT'].getFeatures())
return {'OUTPUT': dest_id}
def name(self):
'''
Returns the algorithm name, used for identifying the algorithm. This
string should be fixed for the algorithm, and must not be localised.
The name should be unique within each provider. Names should contain
lowercase alphanumeric characters only and no spaces or other
formatting characters.
'''
return 'clip_network'
def helpUrl(self):
'''
Returns the URL for the help document, if a help document does exist.
'''
dir = os.path.dirname(__file__)
file = os.path.abspath(os.path.join(dir, '..', 'help_docs', 'help.html'))
if not os.path.exists(file):
return ''
return QUrl.fromLocalFile(file).toString(QUrl.FullyEncoded)
def shortHelpString(self):
'''Returns the text for the help widget, if a help document does exist.'''
dir = os.path.dirname(__file__)
file = os.path.abspath(os.path.join(dir, '..', 'help_docs', 'help_processing_clipping_network.html'))
if not os.path.exists(file):
return ''
with open(file) as helpf:
help=helpf.read()
return help
def displayName(self):
'''
Returns the translated algorithm name, which should be used for any
user-visible display of the algorithm name.
'''
return self.tr('Clip Network')
def group(self):
'''
Returns the name of the group this algorithm belongs to. This string
should be localised.
'''
return self.tr(self.groupId())
def groupId(self):
'''
Returns the unique ID of the group this algorithm belongs to. This
string should be fixed for the algorithm, and must not be localised.
The group id should be unique within each provider. Group id should
contain lowercase alphanumeric characters only and no spaces or other
formatting characters.
'''
return 'Preprocessing'
def tr(self, string):
return QCoreApplication.translate('Processing', string)
def createInstance(self):
return ClipNetworkAlgorithm()
def icon(self):
return QIcon(':/plugins/offline_map_matching/icons/clipping_icon.png')
================================================
FILE: src/offlinemapmatching/mm_processing/offline_map_matching_algorithm.py
================================================
# -*- coding: utf-8 -*-
'''
/***************************************************************************
Offline-MapMatching
A QGIS plugin
desciption of the plugin
Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
-------------------
begin : 2018-08-08
copyright : (C) 2018 by Christoph Jung
email : jagodki.cj@gmail.com
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
'''
__author__ = 'Christoph Jung'
__date__ = '2018-08-08'
__copyright__ = '(C) 2018 by Christoph Jung'
# This will get replaced with a git SHA1 when you do a git archive
__revision__ = '$Format:%H$'
from PyQt5.QtCore import QCoreApplication, QUrl
from PyQt5.QtGui import QIcon
from qgis.core import (QgsProcessing,
QgsFeatureSink,
QgsProcessingAlgorithm,
QgsProcessingParameterFeatureSink,
QgsProcessingParameterVectorLayer,
QgsProcessingParameterField,
QgsProcessingParameterNumber,
QgsWkbTypes,
QgsCoordinateReferenceSystem,
QgsFields,
QgsProcessingParameterEnum)
from ..mm.map_matcher import MapMatcher
import time, os.path
class OfflineMapMatchingAlgorithm(QgsProcessingAlgorithm):
'''
This is an example algorithm that takes a vector layer and
creates a new identical one.
It is meant to be used as an example of how to create your own
algorithms and explain methods and variables used to do it. An
algorithm like this will be available in all elements, and there
is not need for additional work.
All Processing algorithms should extend the QgsProcessingAlgorithm
class.
'''
# Constants used to refer to parameters and outputs. They will be
# used when calling the algorithm from another algorithm, or when
# calling from the QGIS console.
NETWORK = 'NETWORK'
TRAJECTORY = 'TRAJECTORY'
TRAJECTORY_ID = 'TRAJECTORY_ID'
MAX_SEARCH_DISTANCE = 'MAX_SEARCH_DISTANCE'
TYPE = 'TYPE'
OUTPUT = 'OUTPUT'
def initAlgorithm(self, config):
'''
Here we define the inputs and output of the algorithm, along
with some other properties.
'''
self.addParameter(
QgsProcessingParameterVectorLayer(
self.NETWORK,
self.tr('Network layer'),
[QgsProcessing.TypeVectorLine]
)
)
self.addParameter(
QgsProcessingParameterVectorLayer(
self.TRAJECTORY,
self.tr('Trajectory layer'),
[QgsProcessing.TypeVectorPoint]
)
)
self.addParameter(
QgsProcessingParameterField(
self.TRAJECTORY_ID,
self.tr('Trajectory ID'),
parentLayerParameterName=self.TRAJECTORY,
type=QgsProcessingParameterField.Any
)
)
self.addParameter(
QgsProcessingParameterNumber(
self.MAX_SEARCH_DISTANCE,
self.tr('Maximum Search Distance [m]'),
type=QgsProcessingParameterNumber.Double,
defaultValue=20.0,
minValue=0.0
)
)
self.addParameter(
QgsProcessingParameterEnum(
self.TYPE,
self.tr('Matching type'),
options=["Based on network routing (slow)", "Based on euklidean distances (fast)"]
)
)
self.addParameter(
QgsProcessingParameterFeatureSink(
self.OUTPUT,
self.tr('Matched Trajectory')
)
)
def processAlgorithm(self, parameters, context, feedback):
'''
Here is where the processing itself takes place.
'''
start_time = time.time()
mm = MapMatcher()
#create a QgsFields-object
attrs = mm.defineAttributes()
fields = QgsFields()
for field in attrs:
fields.append(field)
#extract all parameters
network_layer = self.parameterAsVectorLayer(
parameters,
self.NETWORK,
context
)
trajectory_layer = self.parameterAsVectorLayer(
parameters,
self.TRAJECTORY,
context
)
trajectory_id = self.parameterAsString(
parameters,
self.TRAJECTORY_ID,
context
)
max_search_distance = self.parameterAsDouble(
parameters,
self.MAX_SEARCH_DISTANCE,
context
)
matching_type = self.parameterAsEnum(
parameters,
self.TYPE,
context
)
#check the CRS of the input layers
network_crs = network_layer.sourceCrs().authid()
trajectory_crs = trajectory_layer.sourceCrs().authid()
if network_crs != trajectory_crs:
raise ValueError("The CRS of the both input layers are different (" + network_crs + " and " + trajectory_crs + ").")
(sink, dest_id) = self.parameterAsSink(
parameters, self.OUTPUT,
context,
fields,
QgsWkbTypes.LineString,
network_layer.sourceCrs()
)
#check the inputs
if network_layer is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.NETWORK))
if trajectory_layer is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.TRAJECTORY))
if trajectory_id is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.TRAJECTORY_ID))
if matching_type is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.TYPE))
error_code = mm.startViterbiMatchingProcessing(trajectory_layer,
network_layer,
trajectory_id,
max_search_distance,
sink,
feedback,
matching_type == 1)
return {'OUTPUT': dest_id,
'ERROR_CODE': error_code,
'COMPUTATION_TIME': str(round(time.time() - start_time, 2))}
def name(self):
'''
Returns the algorithm name, used for identifying the algorithm. This
string should be fixed for the algorithm, and must not be localised.
The name should be unique within each provider. Names should contain
lowercase alphanumeric characters only and no spaces or other
formatting characters.
'''
return 'match_trajectory'
def helpUrl(self):
'''
Returns the URL for the help document, if a help document does exist.
'''
dir = os.path.dirname(__file__)
file = os.path.abspath(os.path.join(dir, '..', 'help_docs', 'help.html'))
if not os.path.exists(file):
return ''
return QUrl.fromLocalFile(file).toString(QUrl.FullyEncoded)
def shortHelpString(self):
'''Returns the text for the help widget, if a help document does exist.'''
dir = os.path.dirname(__file__)
file = os.path.abspath(os.path.join(dir, '..', 'help_docs', 'help_processing_match_trajectory.html'))
if not os.path.exists(file):
return ''
with open(file) as helpf:
help=helpf.read()
return help
def displayName(self):
'''
Returns the translated algorithm name, which should be used for any
user-visible display of the algorithm name.
'''
return self.tr('Match Trajectory')
def group(self):
'''
Returns the name of the group this algorithm belongs to. This string
should be localised.
'''
return self.tr(self.groupId())
def groupId(self):
'''
Returns the unique ID of the group this algorithm belongs to. This
string should be fixed for the algorithm, and must not be localised.
The group id should be unique within each provider. Group id should
contain lowercase alphanumeric characters only and no spaces or other
formatting characters.
'''
return 'Matching'
def tr(self, string):
return QCoreApplication.translate('Processing', string)
def createInstance(self):
return OfflineMapMatchingAlgorithm()
def icon(self):
return QIcon(':/plugins/offline_map_matching/icons/icon.png')
================================================
FILE: src/offlinemapmatching/mm_processing/offline_map_matching_provider.py
================================================
# -*- coding: utf-8 -*-
'''
/***************************************************************************
OfflineMapMatcher
A QGIS plugin
desciption of the plugin
Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
-------------------
begin : 2018-08-08
copyright : (C) 2018 by Christoph Jung
email : jagodki.cj@gmail.com
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
'''
__author__ = 'Christoph Jung'
__date__ = '2018-08-08'
__copyright__ = '(C) 2018 by Christoph Jung'
# This will get replaced with a git SHA1 when you do a git archive
__revision__ = '$Format:%H$'
from qgis.core import QgsProcessingProvider
from PyQt5.QtGui import QIcon
from .offline_map_matching_algorithm import OfflineMapMatchingAlgorithm
from .clip_network_algorithm import ClipNetworkAlgorithm
from .reduce_trajectory_density import ReduceTrajectoryDensity
# from .fast_trajectory_matching import FastTrajectoryMatching
class OfflineMapMatchingProvider(QgsProcessingProvider):
def __init__(self):
QgsProcessingProvider.__init__(self)
# Load algorithms
self.alglist = [OfflineMapMatchingAlgorithm(),
#FastTrajectoryMatching(),
ClipNetworkAlgorithm(),
ReduceTrajectoryDensity()]
def unload(self):
'''
Unloads the provider. Any tear-down steps required by the provider
should be implemented here.
'''
pass
def loadAlgorithms(self):
'''
Loads all algorithms belonging to this provider.
'''
for alg in self.alglist:
self.addAlgorithm( alg )
def id(self):
'''
Returns the unique provider id, used for identifying the provider. This
string should be a unique, short, character only string, eg 'qgis' or
'gdal'. This string should not be localised.
'''
return 'omm'
def icon(self):
return QIcon(':/plugins/offline_map_matching/icons/icon.png')
def name(self):
'''
Returns the provider name, which is used to describe the provider
within the GUI.
This string should be short (e.g. 'Lastools') and localised.
'''
return self.tr('Offline-MapMatching')
def longName(self):
'''
Returns the a longer version of the provider name, which can include
extra details such as version numbers. E.g. 'Lastools LIDAR tools
(version 2.2.1)'. This string should be localised. The default
implementation returns the same string as name().
'''
return self.name()
================================================
FILE: src/offlinemapmatching/mm_processing/reduce_trajectory_density.py
================================================
# -*- coding: utf-8 -*-
'''
/***************************************************************************
Offline-MapMatching
A QGIS plugin
desciption of the plugin
Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
-------------------
begin : 2019-11-04
copyright : (C) 2019 by Christoph Jung
email : jagodki.cj@gmail.com
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
'''
__author__ = 'Christoph Jung'
__date__ = '2019-11-04'
__copyright__ = '(C) 2019 by Christoph Jung'
# This will get replaced with a git SHA1 when you do a git archive
__revision__ = '$Format:%H$'
from PyQt5.QtCore import QCoreApplication, QUrl
from PyQt5.QtGui import QIcon
from qgis.core import (QgsProcessing,
QgsProcessingAlgorithm,
QgsProcessingParameterFeatureSource,
QgsProcessingParameterVectorLayer,
QgsProcessingParameterField,
QgsProcessingParameterString,
QgsProcessingParameterNumber,
QgsCoordinateReferenceSystem,
QgsProject,
QgsProcessingParameterFeatureSink,
QgsWkbTypes,
QgsProcessingParameterBoolean)
import processing
import time, os.path
class ReduceTrajectoryDensity(QgsProcessingAlgorithm):
'''
This is an example algorithm that takes a vector layer and
creates a new identical one.
It is meant to be used as an example of how to create your own
algorithms and explain methods and variables used to do it. An
algorithm like this will be available in all elements, and there
is not need for additional work.
All Processing algorithms should extend the QgsProcessingAlgorithm
class.
'''
# Constants used to refer to parameters and outputs. They will be
# used when calling the algorithm from another algorithm, or when
# calling from the QGIS console.
TRAJECTORY = 'TRAJECTORY'
KEEP_LAST_FEATURE = 'KEEP_LAST_FEATURE'
DISTANCE = 'DISTANCE'
OUTPUT = 'OUTPUT'
def initAlgorithm(self, config):
'''
Here we define the inputs and output of the algorithm, along
with some other properties.
'''
self.addParameter(
QgsProcessingParameterVectorLayer(
self.TRAJECTORY,
self.tr('Trajectory Layer'),
[QgsProcessing.TypeVectorPoint]
)
)
self.addParameter(
QgsProcessingParameterBoolean(
self.KEEP_LAST_FEATURE,
self.tr('Keep the last feature of the trajectory'),
optional=True
)
)
self.addParameter(
QgsProcessingParameterNumber(
self.DISTANCE,
self.tr('Minimum distance between two consecutive points'),
type=QgsProcessingParameterNumber.Double,
defaultValue=100,
minValue=0.0
)
)
self.addParameter(
QgsProcessingParameterFeatureSink(
self.OUTPUT,
self.tr('Reduced Trajectory')
)
)
def processAlgorithm(self, parameters, context, feedback):
'''
Here is where the processing itself takes place.
'''
start_time = time.time()
#extract all parameters
trajectory_layer = self.parameterAsVectorLayer(
parameters,
self.TRAJECTORY,
context
)
distance = self.parameterAsDouble(
parameters,
self.DISTANCE,
context
)
keep_last_feature = self.parameterAsBool(
parameters,
self.KEEP_LAST_FEATURE,
context
)
(output, dest_id) = self.parameterAsSink(
parameters,
self.OUTPUT,
context,
trajectory_layer.fields(),
QgsWkbTypes.Point,
trajectory_layer.crs()
)
#check the inputs
if trajectory_layer is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.TRAJECTORY))
if distance is None:
raise QgsProcessingException(self.invalidSourceError(parameters, self.DISTANCE))
if keep_last_feature is None:
keep_last_feature = False
#present some information to the user
feedback.pushInfo('CRS of the trajectory is {}'.format(trajectory_layer.sourceCrs().authid()))
#init the progressbar
feedback.setProgress(0)
#reduce the trajectory density
result = self.reduceDensity(0, 1, trajectory_layer, distance, output, feedback, trajectory_layer.featureCount(), keep_last_feature)
return {'OUTPUT': dest_id}
def name(self):
'''
Returns the algorithm name, used for identifying the algorithm. This
string should be fixed for the algorithm, and must not be localised.
The name should be unique within each provider. Names should contain
lowercase alphanumeric characters only and no spaces or other
formatting characters.
'''
return 'reduce_trajectory_density'
def helpUrl(self):
'''
Returns the URL for the help document, if a help document does exist.
'''
dir = os.path.dirname(__file__)
file = os.path.abspath(os.path.join(dir, '..', 'help_docs', 'help.html'))
if not os.path.exists(file):
return ''
return QUrl.fromLocalFile(file).toString(QUrl.FullyEncoded)
def shortHelpString(self):
'''Returns the text for the help widget, if a help document does exist.'''
dir = os.path.dirname(__file__)
file = os.path.abspath(os.path.join(dir, '..', 'help_docs', 'help_processing_reduce_density.html'))
if not os.path.exists(file):
return ''
with open(file) as helpf:
help=helpf.read()
return help
def displayName(self):
'''
Returns the translated algorithm name, which should be used for any
user-visible display of the algorithm name.
'''
return self.tr('Reduce Trajectory Density')
def group(self):
'''
Returns the name of the group this algorithm belongs to. This string
should be localised.
'''
return self.tr(self.groupId())
def groupId(self):
'''
Returns the unique ID of the group this algorithm belongs to. This
string should be fixed for the algorithm, and must not be localised.
The group id should be unique within each provider. Group id should
contain lowercase alphanumeric characters only and no spaces or other
formatting characters.
'''
return 'Preprocessing'
def tr(self, string):
return QCoreApplication.translate('Processing', string)
def createInstance(self):
return ReduceTrajectoryDensity()
def icon(self):
return QIcon(':/plugins/offline_map_matching/icons/reduce_density_icon.png')
def reduceDensity(self, startIndex, nextIndex, layer, distance, output, feedback, feature_count, keep_last_feature):
#handle a pressed cancel button and the progressbar
feedback.setProgress(int(startIndex / feature_count) * 100)
if feedback.isCanceled():
return -99
#init some variables for the following iteration
start_feature = None
trajectory_features = layer.getFeatures()
for index, feature in enumerate(trajectory_features):
#set the starting feature if none and skip to the next loop
if start_feature is None:
start_feature = feature
output.addFeature(feature)
continue
#calculate distance, adjust the starting feature after it if distance is big enough
if start_feature.geometry().distance(feature.geometry()) >= distance:
output.addFeature(feature)
start_feature = feature
#check, whether we reached the last feature and keep the last vertex if necessary
elif keep_last_feature and index == (layer.featureCount() - 1):
output.addFeature(feature)
return -99
================================================
FILE: src/offlinemapmatching/offline_map_matching.py
================================================
# -*- coding: utf-8 -*-
'''
/***************************************************************************
OfflineMapMatching
A QGIS plugin
a QGIS-plugin for matching a trajectory with a network using a Hidden Markov Model and Viterbi algorithm
Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
-------------------
begin : 2018-06-14
git sha : $Format:%H$
copyright : (C) 2018 by Christoph Jung
email : jagodki.cj@gmail.com
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
'''
from PyQt5.QtCore import QSettings, QTranslator, qVersion, QCoreApplication
from PyQt5.QtGui import QIcon, QTextCursor
from PyQt5.QtWidgets import QAction, QMenu
from qgis.gui import QgsMessageBar
from qgis.core import *
import time, traceback, sys, inspect, processing
# Initialize Qt resources from file resources.py
from .resources import *
# Import the code for the dialog
from .offline_map_matching_dialog import OfflineMapMatchingDialog
import os.path
#import own classes
from .mm.map_matcher import *
from .mm_processing.offline_map_matching_provider import *
'''
cmd_folder = os.path.split(inspect.getfile(inspect.currentframe()))[0]
if cmd_folder not in sys.path:
sys.path.insert(0, cmd_folder)
'''
class OfflineMapMatching:
'''QGIS Plugin Implementation.'''
def __init__(self, iface):
'''Constructor.
:param iface: An interface instance that will be passed to this class
which provides the hook by which you can manipulate the QGIS
application at run time.
:type iface: QgsInterface
'''
# Save reference to the QGIS interface
self.iface = iface
# initialize plugin directory
self.plugin_dir = os.path.dirname(__file__)
# initialize locale
locale = QSettings().value('locale/userLocale')[0:2]
locale_path = os.path.join(
self.plugin_dir,
'i18n',
'OfflineMapMatching_{}.qm'.format(locale))
if os.path.exists(locale_path):
self.translator = QTranslator()
self.translator.load(locale_path)
if qVersion() > '4.3.3':
QCoreApplication.installTranslator(self.translator)
# Create the dialog (after translation) and keep reference
self.dlg = OfflineMapMatchingDialog()
# Declare instance attributes
self.actions = []
self.menu = self.tr(u'&Offline-MapMatching')
# TODO: We are going to let the user set this up in a future iteration
self.toolbar = self.iface.addToolBar(u'OfflineMapMatching')
self.toolbar.setObjectName(u'OfflineMapMatching')
#add help-document to the GUI
dir = os.path.dirname(__file__)
file = os.path.abspath(os.path.join(dir, 'help_docs', 'help.html'))
if os.path.exists(file):
with open(file) as helpf:
help = helpf.read()
self.dlg.textBrowser_help.insertHtml(help)
self.dlg.textBrowser_help.moveCursor(QTextCursor.Start)
#declare additional instance vars
self.map_matcher = MapMatcher()
self.provider = OfflineMapMatchingProvider()
#connect slots and signals
self.dlg.comboBox_trajectory.currentIndexChanged.connect(self.startPopulateFieldsComboBox)
self.dlg.pushButton_start.clicked.connect(self.startMapMatching)
#set a default crs to avoid problems in QGIS 3.4
self.dlg.mQgsProjectionSelectionWidget.setCrs(QgsCoordinateReferenceSystem('EPSG:4326'))
# noinspection PyMethodMayBeStatic
def tr(self, message):
'''Get the translation for a string using Qt translation API.
We implement this ourselves since we do not inherit QObject.
:param message: String for translation.
:type message: str, QString
:returns: Translated version of message.
:rtype: QString
'''
# noinspection PyTypeChecker,PyArgumentList,PyCallByClass
return QCoreApplication.translate('OfflineMapMatching', message)
def add_action(
self,
icon_path,
text,
callback=None,
enabled_flag=True,
add_to_menu=True,
add_to_toolbar=True,
status_tip=None,
whats_this=None,
parent=None,
action=None):
'''Add a toolbar icon to the toolbar.
:param icon_path: Path to the icon for this action. Can be a resource
path (e.g. ':/plugins/foo/bar.png') or a normal file system path.
:type icon_path: str
:param text: Text that should be shown in menu items for this action.
:type text: str
:param callback: Function to be called when the action is triggered.
:type callback: function
:param enabled_flag: A flag indicating if the action should be enabled
by default. Defaults to True.
:type enabled_flag: bool
:param add_to_menu: Flag indicating whether the action should also
be added to the menu. Defaults to True.
:type add_to_menu: bool
:param add_to_toolbar: Flag indicating whether the action should also
be added to the toolbar. Defaults to True.
:type add_to_toolbar: bool
:param status_tip: Optional text to show in a popup when mouse pointer
hovers over the action.
:type status_tip: str
:param parent: Parent widget for the new action. Defaults None.
:type parent: QWidget
:param whats_this: Optional text to show in the status bar when the
mouse pointer hovers over the action.
:returns: The action that was created. Note that the action is also
added to self.actions list.
:rtype: QAction
'''
icon = QIcon(icon_path)
if action is None:
action = QAction(icon, text, parent)
action.triggered.connect(callback)
action.setEnabled(enabled_flag)
if status_tip is not None:
action.setStatusTip(status_tip)
if whats_this is not None:
action.setWhatsThis(whats_this)
if add_to_toolbar:
self.toolbar.addAction(action)
if add_to_menu:
self.iface.addPluginToVectorMenu(
self.menu,
action)
# self.iface.vectorMenu().addAction(action)
self.actions.append(action)
return action
def initGui(self):
'''Create the menu entries, toolbar icons inside the QGIS GUI and add a new processing provider.'''
icon_path = ':/plugins/offline_map_matching/icons/icon.png'
#set up entries for the gui
self.add_action(
icon_path,
text=self.tr(u'Match Trajectory'),
callback=self.matchTrajectory,
parent=self.iface.mainWindow())
#init the preprocessing group with their entries
menu = QMenu()
#add icons
icon_clip = QIcon(':/plugins/offline_map_matching/icons/clipping_icon.png')
icon_density = QIcon(':/plugins/offline_map_matching/icons/reduce_density_icon.png')
icon_pp = QIcon(':/plugins/offline_map_matching/icons/preprocessing_icon.png')
#add actions
action_clip = menu.addAction(icon_clip, 'Clip Network', self.clipNetwork)
action_clip.setObjectName('clip_network')
action_reduce = menu.addAction(icon_density, 'Reduce Trajectory Density', self.reduceDensity)
action_reduce.setObjectName('reduce_density')
#add main entry
preprocessing_action = QAction(icon_pp, 'Preprocessing', self.iface.mainWindow())
preprocessing_action.setMenu(menu)
self.add_action(
'',
text=self.tr(u'Preprocessing'),
action=preprocessing_action,
parent=self.iface.mainWindow(),
add_to_toolbar=False)
# preprocessing_action.setMenu(menu)
# self.actions.append(preprocessing_action)
# self.iface.addPluginToVectorMenu(self.menu, preprocessing_action)
#add the processing provider
QgsApplication.processingRegistry().addProvider(self.provider)
def clipNetwork(self):
processing.execAlgorithmDialog('omm:clip_network', {})
def reduceDensity(self):
processing.execAlgorithmDialog('omm:reduce_trajectory_density', {})
def fastTrajectoryMatching(self):
processing.execAlgorithmDialog('omm:fast_trajectory_matching', {})
def matchTrajectory(self):
processing.execAlgorithmDialog('omm:match_trajectory', {})
def unload(self):
'''Removes the plugin menu item and icon from QGIS GUI. Remove the processing provider.'''
for action in self.actions:
self.iface.removePluginVectorMenu(
self.tr(u'&Offline-MapMatching'),
action)
#self.iface.vectorMenu().removeAction(action)
self.iface.removeToolBarIcon(action)
# remove the toolbar
del self.toolbar
#remove the processing provider
QgsApplication.processingRegistry().removeProvider(self.provider)
def run(self):
'''Run method that performs all the real work'''
#populate the comboboxes with the available layers
self.populateComboBox('network')
self.populateComboBox('trajectory')
self.populateComboBox('fields')
#clear all other gui elements
self.dlg.progressBar.setValue(0)
self.dlg.doubleSpinBox_sigma.setValue(50.0)
self.dlg.doubleSpinBox_my.setValue(0.0)
self.dlg.doubleSpinBox_beta.setValue(30.0)
self.dlg.doubleSpinBox_max.setValue(0.0)
self.dlg.label_info.setText('')
#self.dlg.lineEdit_crs.setText('')
# show the dialog
self.dlg.show()
# Run the dialog event loop
#result = self.dlg.exec_()
# See if OK was pressed
#if result:
# Do something useful here - delete the line containing pass and
# substitute with your code.
#pass
def populateComboBox(self, type):
'''Populate the given combobox.'''
if type == 'network':
self.map_matcher.fillLayerComboBox(self.iface, self.dlg.comboBox_network, 'LINESTRING')
elif type == 'trajectory':
self.map_matcher.fillLayerComboBox(self.iface, self.dlg.comboBox_trajectory, 'POINT')
elif type == 'fields':
self.map_matcher.fillAttributeComboBox(self.dlg.comboBox_trajectoryID, self.dlg.comboBox_trajectory.currentText())
def startPopulateFieldsComboBox(self):
self.populateComboBox('fields')
def startMapMatching(self):
self.dlg.groupBox_data.setEnabled(False)
self.dlg.groupBox_settings.setEnabled(False)
self.dlg.pushButton_start.setEnabled(False)
try:
start_time = time.time()
result = self.map_matcher.startViterbiMatchingGui(
self.dlg.progressBar,
self.dlg.comboBox_trajectory.currentText(),
self.dlg.comboBox_network.currentText(),
self.dlg.comboBox_trajectoryID.currentText(),
self.dlg.doubleSpinBox_sigma.value(),
self.dlg.doubleSpinBox_my.value(),
self.dlg.doubleSpinBox_beta.value(),
self.dlg.doubleSpinBox_max.value(),
self.dlg.label_info,
self.dlg.mQgsProjectionSelectionWidget.crs().authid())
if result == 0:
self.iface.messageBar().pushMessage('map matching finished ^o^ - time: ' + str(round(time.time() - start_time, 2)) + ' sec', level=Qgis.Success, duration=60)
elif result == -1:
self.iface.messageBar().pushMessage('Error during calculation of candidates. Check the QGIS-log for further information.', level=Qgis.Warning, duration=60)
elif result == -2:
self.iface.messageBar().pushMessage('Error during calculation of starting probabilities. Check the QGIS-log for further information.', level=Qgis.Warning, duration=60)
elif result == -3:
self.iface.messageBar().pushMessage('Error during calculation of transition probabilities. Check the QGIS-log for further information.', level=Qgis.Warning, duration=60)
elif result == -4:
self.iface.messageBar().pushMessage('Error during calculation of backtracking. Check the QGIS-log for further information.', level=Qgis.Warning, duration=60)
elif result == -5:
self.iface.messageBar().pushMessage('Error during calculating the most likely path. Check the QGIS-log for further information.', level=Qgis.Warning, duration=60)
elif result == -6:
self.iface.messageBar().pushMessage('Error during calculating the path on network. Check the QGIS-log for further information.', level=Qgis.Warning, duration=60)
except:
QgsMessageLog.logMessage(traceback.format_exc(), level=Qgis.Critical)
self.iface.messageBar().pushMessage('An error occured. Please look into the log and/or Python console for further information.', level=Qgis.Critical, duration=60)
self.dlg.groupBox_data.setEnabled(True)
self.dlg.groupBox_settings.setEnabled(True)
self.dlg.pushButton_start.setEnabled(True)
================================================
FILE: src/offlinemapmatching/offline_map_matching_dialog.py
================================================
# -*- coding: utf-8 -*-
"""
/***************************************************************************
OfflineMapMatchingDialog
A QGIS plugin
a QGIS-plugin for matching a trajectory with a network using a Hidden Markov Model and Viterbi algorithm
Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
-------------------
begin : 2018-06-14
git sha : $Format:%H$
copyright : (C) 2018 by Christoph Jung
email : jagodki.cj@gmail.com
***************************************************************************/
/***************************************************************************
* *
* This program is free software; you can redistribute it and/or modify *
* it under the terms of the GNU General Public License as published by *
* the Free Software Foundation; either version 2 of the License, or *
* (at your option) any later version. *
* *
***************************************************************************/
"""
import os
from PyQt5 import uic
from PyQt5 import QtWidgets
FORM_CLASS, _ = uic.loadUiType(os.path.join(
os.path.dirname(__file__), 'offline_map_matching_dialog_base.ui'))
class OfflineMapMatchingDialog(QtWidgets.QDialog, FORM_CLASS):
def __init__(self, parent=None):
"""Constructor."""
super(OfflineMapMatchingDialog, self).__init__(parent)
# Set up the user interface from Designer.
# After setupUI you can access any designer object by doing
# self.<objectname>, and you can use autoconnect slots - see
# http://qt-project.org/doc/qt-4.8/designer-using-a-ui-file.html
# #widgets-and-dialogs-with-auto-connect
self.setupUi(self)
================================================
FILE: src/offlinemapmatching/offline_map_matching_dialog_base.ui
================================================
<?xml version="1.0" encoding="UTF-8"?>
<ui version="4.0">
<class>OfflineMapMatchingDialogBase</class>
<widget class="QDialog" name="OfflineMapMatchingDialogBase">
<property name="geometry">
<rect>
<x>0</x>
<y>0</y>
<width>679</width>
<height>472</height>
</rect>
</property>
<property name="windowTitle">
<string>Match Trajectory</string>
</property>
<layout class="QHBoxLayout" name="horizontalLayout">
<item>
<layout class="QVBoxLayout" name="verticalLayout_3">
<item>
<widget class="QGroupBox" name="groupBox_data">
<property name="sizePolicy">
<sizepolicy hsizetype="Expanding" vsizetype="Preferred">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="title">
<string>Data</string>
</property>
<layout class="QVBoxLayout" name="verticalLayout_2">
<item>
<layout class="QGridLayout" name="gridLayout">
<item row="0" column="0">
<widget class="QLabel" name="label_network">
<property name="text">
<string>Network-Layer:</string>
</property>
<property name="alignment">
<set>Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter</set>
</property>
</widget>
</item>
<item row="0" column="1">
<widget class="QComboBox" name="comboBox_network">
<property name="sizePolicy">
<sizepolicy hsizetype="Expanding" vsizetype="Fixed">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
</widget>
</item>
<item row="1" column="0">
<widget class="QLabel" name="label_trajectory">
<property name="text">
<string>Trajectory-Layer:</string>
</property>
<property name="alignment">
<set>Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter</set>
</property>
</widget>
</item>
<item row="1" column="1">
<widget class="QComboBox" name="comboBox_trajectory">
<property name="sizePolicy">
<sizepolicy hsizetype="Expanding" vsizetype="Fixed">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
</widget>
</item>
<item row="2" column="0">
<widget class="QLabel" name="label_trajectoryID">
<property name="text">
<string>Trajectory-ID:</string>
</property>
<property name="alignment">
<set>Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter</set>
</property>
</widget>
</item>
<item row="2" column="1">
<widget class="QComboBox" name="comboBox_trajectoryID">
<property name="sizePolicy">
<sizepolicy hsizetype="Expanding" vsizetype="Fixed">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
</widget>
</item>
</layout>
</item>
</layout>
</widget>
</item>
<item>
<widget class="QGroupBox" name="groupBox_settings">
<property name="sizePolicy">
<sizepolicy hsizetype="Expanding" vsizetype="Preferred">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="title">
<string>Settings</string>
</property>
<layout class="QVBoxLayout" name="verticalLayout_5">
<item>
<layout class="QGridLayout" name="gridLayout_2">
<item row="3" column="0">
<widget class="QLabel" name="label_my">
<property name="text">
<string>Expected Value:</string>
</property>
<property name="alignment">
<set>Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter</set>
</property>
</widget>
</item>
<item row="3" column="1">
<widget class="QDoubleSpinBox" name="doubleSpinBox_my">
<property name="maximum">
<double>9900000000.989999771118164</double>
</property>
</widget>
</item>
<item row="4" column="0">
<widget class="QLabel" name="label_beta">
<property name="text">
<string>Transition Weight:</string>
</property>
<property name="alignment">
<set>Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter</set>
</property>
</widget>
</item>
<item row="2" column="1">
<widget class="QDoubleSpinBox" name="doubleSpinBox_sigma">
<property name="maximum">
<double>9900000000.989999771118164</double>
</property>
<property name="value">
<double>50.000000000000000</double>
</property>
</widget>
</item>
<item row="1" column="1">
<widget class="QDoubleSpinBox" name="doubleSpinBox_max">
<property name="sizePolicy">
<sizepolicy hsizetype="Expanding" vsizetype="Fixed">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="maximum">
<double>9900000000.989999771118164</double>
</property>
<property name="value">
<double>20.000000000000000</double>
</property>
</widget>
</item>
<item row="2" column="0">
<widget class="QLabel" name="label_sigma">
<property name="text">
<string>Standard Deviation:</string>
</property>
<property name="alignment">
<set>Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter</set>
</property>
</widget>
</item>
<item row="4" column="1">
<widget class="QDoubleSpinBox" name="doubleSpinBox_beta">
<property name="maximum">
<double>9900000000.989999771118164</double>
</property>
<property name="value">
<double>30.000000000000000</double>
</property>
</widget>
</item>
<item row="1" column="0">
<widget class="QLabel" name="label_max">
<property name="sizePolicy">
<sizepolicy hsizetype="Minimum" vsizetype="Preferred">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="text">
<string>max. Search Distance [m]:</string>
</property>
<property name="alignment">
<set>Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter</set>
</property>
</widget>
</item>
<item row="0" column="0">
<widget class="QLabel" name="label_crs">
<property name="text">
<string>CRS of input layers:</string>
</property>
<property name="alignment">
<set>Qt::AlignRight|Qt::AlignTrailing|Qt::AlignVCenter</set>
</property>
</widget>
</item>
<item row="0" column="1">
<widget class="QgsProjectionSelectionWidget" name="mQgsProjectionSelectionWidget" native="true"/>
</item>
</layout>
</item>
</layout>
</widget>
</item>
<item>
<spacer name="verticalSpacer">
<property name="orientation">
<enum>Qt::Vertical</enum>
</property>
<property name="sizeHint" stdset="0">
<size>
<width>20</width>
<height>40</height>
</size>
</property>
</spacer>
</item>
<item>
<widget class="QLabel" name="label_info">
<property name="sizePolicy">
<sizepolicy hsizetype="Preferred" vsizetype="Fixed">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="text">
<string>Ready to start calculation</string>
</property>
</widget>
</item>
<item>
<widget class="QProgressBar" name="progressBar">
<property name="value">
<number>0</number>
</property>
</widget>
</item>
<item>
<widget class="QPushButton" name="pushButton_start">
<property name="sizePolicy">
<sizepolicy hsizetype="Expanding" vsizetype="Fixed">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="text">
<string>start map matching</string>
</property>
</widget>
</item>
</layout>
</item>
<item>
<layout class="QVBoxLayout" name="verticalLayout">
<item>
<widget class="QTextBrowser" name="textBrowser_help">
<property name="sizePolicy">
<sizepolicy hsizetype="Maximum" vsizetype="Expanding">
<horstretch>0</horstretch>
<verstretch>0</verstretch>
</sizepolicy>
</property>
<property name="readOnly">
<bool>true</bool>
</property>
<property name="openExternalLinks">
<bool>true</bool>
</property>
</widget>
</item>
</layout>
</item>
</layout>
</widget>
<customwidgets>
<customwidget>
<class>QgsProjectionSelectionWidget</class>
<extends>QWidget</extends>
<header>qgsprojectionselectionwidget.h</header>
</customwidget>
</customwidgets>
<tabstops>
<tabstop>comboBox_network</tabstop>
<tabstop>comboBox_trajectory</tabstop>
<tabstop>comboBox_trajectoryID</tabstop>
<tabstop>doubleSpinBox_max</tabstop>
<tabstop>doubleSpinBox_sigma</tabstop>
<tabstop>doubleSpinBox_my</tabstop>
<tabstop>doubleSpinBox_beta</tabstop>
<tabstop>pushButton_start</tabstop>
<tabstop>textBrowser_help</tabstop>
</tabstops>
<resources/>
<connections/>
</ui>
================================================
FILE: src/offlinemapmatching/pb_tool.cfg
================================================
#/***************************************************************************
# OfflineMapMatching
#
# Configuration file for plugin builder tool (pb_tool)
# Generated by Plugin Builder: http://g-sherman.github.io/Qgis-Plugin-Builder/
# -------------------
# begin : 2018-06-14
# copyright : (C) 2018 by Christoph Junh
# email : jagodki.cj@gmail.com
# ***************************************************************************/
#
#/***************************************************************************
# * *
# * This program is free software; you can redistribute it and/or modify *
# * it under the terms of the GNU General Public License as published by *
# * the Free Software Foundation; either version 2 of the License, or *
# * (at your option) any later version. *
# * *
# ***************************************************************************/
#
#
# You can install pb_tool using:
# pip install http://geoapt.net/files/pb_tool.zip
#
# Consider doing your development (and install of pb_tool) in a virtualenv.
#
# For details on setting up and using pb_tool, see:
# http://g-sherman.github.io/plugin_build_tool/
#
# Issues and pull requests here:
# https://github.com/g-sherman/plugin_build_tool:
#
# Sane defaults for your plugin generated by the Plugin Builder are
# already set below.
#
# As you add Python source files and UI files to your plugin, add
# them to the appropriate [files] section below.
[plugin]
# Name of the plugin. This is the name of the directory that will
# be created in .qgis2/python/plugins
name: Offline-MapMatching
# Full path to where you want your plugin directory copied. If empty,
# the QGIS default path will be used. Don't include the plugin name in
# the path.
plugin_path: /Users/christophjung/Library/Application Support/QGIS/QGIS3/profiles/default/python/plugins
[files]
# Python files that should be deployed with the plugin
python_files: __init__.py offline_map_matching.py offline_map_matching_dialog.py
# The main dialog file that is loaded (not compiled)
main_dialog: offline_map_matching_dialog_base.ui
# Other ui files for dialogs you create (these will be compiled)
compiled_ui_files:
# Resource file(s) that will be compiled
resource_files: resources.qrc
# Other files required for the plugin
extras: metadata.txt style.qml
# Other directories to be deployed with the plugin.
# These must be subdirectories under the plugin directory
extra_dirs: mm mm_processing help_docs icons
# ISO code(s) for any locales (translations), separated by spaces.
# Corresponding .ts files must exist in the i18n directory
locales:
[help]
# the built help directory that should be deployed with the plugin
#dir: help/build/html
# the name of the directory to target in the deployed plugin
#target: help
================================================
FILE: src/offlinemapmatching/resources.py
================================================
# -*- coding: utf-8 -*-
# Resource object code
#
# Created by: The Resource Compiler for PyQt5 (Qt v5.14.0)
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore
qt_resource_data = b"\
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gitextract_54u4k5gb/
├── .gitignore
├── LICENSE
├── README.md
├── src/
│ └── offlinemapmatching/
│ ├── __init__.py
│ ├── help_docs/
│ │ ├── help.html
│ │ ├── help_processing_clipping_network.html
│ │ ├── help_processing_match_trajectory.html
│ │ └── help_processing_reduce_density.html
│ ├── i18n/
│ │ └── af.ts
│ ├── metadata.txt
│ ├── mm/
│ │ ├── __init__.py
│ │ ├── helper/
│ │ │ ├── __init__.py
│ │ │ └── measurement_statistics.py
│ │ ├── hidden_states/
│ │ │ ├── __init__.py
│ │ │ ├── candidate.py
│ │ │ ├── hidden_model.py
│ │ │ └── transition.py
│ │ ├── map_matcher.py
│ │ └── observation/
│ │ ├── __init__.py
│ │ ├── intersection.py
│ │ ├── network.py
│ │ ├── observation.py
│ │ └── trajectory.py
│ ├── mm_processing/
│ │ ├── clip_network_algorithm.py
│ │ ├── offline_map_matching_algorithm.py
│ │ ├── offline_map_matching_provider.py
│ │ └── reduce_trajectory_density.py
│ ├── offline_map_matching.py
│ ├── offline_map_matching_dialog.py
│ ├── offline_map_matching_dialog_base.ui
│ ├── pb_tool.cfg
│ ├── resources.py
│ ├── resources.qrc
│ └── style.qml
└── testdata/
├── network.geojson
├── trajectory1.geojson
└── trajectory2.geojson
SYMBOL INDEX (117 symbols across 17 files)
FILE: src/offlinemapmatching/__init__.py
function classFactory (line 28) | def classFactory(iface): # pylint: disable=invalid-name
FILE: src/offlinemapmatching/mm/helper/measurement_statistics.py
class MeasurementStatistics (line 3) | class MeasurementStatistics:
method __init__ (line 5) | def __init__(self):
method addMeasurement (line 8) | def addMeasurement(self, value):
method getMeanValue (line 11) | def getMeanValue(self):
method getStandardDeviation (line 14) | def getStandardDeviation(self):
FILE: src/offlinemapmatching/mm/hidden_states/candidate.py
class Candidate (line 4) | class Candidate:
method __init__ (line 6) | def __init__(self, point, distance, observation_id):
method getEmissionProbability (line 11) | def getEmissionProbability(self, sigma, my):
FILE: src/offlinemapmatching/mm/hidden_states/hidden_model.py
class HiddenModel (line 12) | class HiddenModel:
method __init__ (line 14) | def __init__(self, trajectory, network):
method createGraph (line 27) | def createGraph(self, maximum_distance):
method createBacktracking (line 85) | def createBacktracking(self):
method findViterbiPath (line 125) | def findViterbiPath(self):
method setTransitions (line 168) | def setTransitions(self, fast_map_matching=False):
method candidatesHaveDifferentPositions (line 214) | def candidatesHaveDifferentPositions(self, candidate_1, candidate_2):
method setStartingProbabilities (line 227) | def setStartingProbabilities(self):
method addFeaturesToLayer (line 241) | def addFeaturesToLayer(self, features, attributes, crs):
method getPathOnNetwork (line 265) | def getPathOnNetwork(self, vertices, field_array):
method initProgressbar (line 313) | def initProgressbar(self, maximum):
method updateProgressbar (line 319) | def updateProgressbar(self):
method initFeedback (line 325) | def initFeedback(self, maximum):
method updateFeedback (line 333) | def updateFeedback(self):
FILE: src/offlinemapmatching/mm/hidden_states/transition.py
class Transition (line 4) | class Transition:
method __init__ (line 6) | def __init__(self, start_candidate, end_candidate, network, different_...
method setDirectionProbability (line 16) | def setDirectionProbability(self, start_observation, end_observation):
method setRoutingProbability (line 61) | def setRoutingProbability(self, distance_between_observations, beta):
method setTransitionProbability (line 76) | def setTransitionProbability(self):
method getAllpoints_on_network (line 84) | def getAllpoints_on_network(self, network):
method getLengthOfTransition (line 88) | def getLengthOfTransition(self):
method getDistanceOfBeeline (line 103) | def getDistanceOfBeeline(self):
FILE: src/offlinemapmatching/mm/map_matcher.py
class MapMatcher (line 7) | class MapMatcher:
method __init__ (line 9) | def __init__(self):
method startViterbiMatchingGui (line 16) | def startViterbiMatchingGui(self, pb, trajectory_name, network_name, a...
method startViterbiMatchingProcessing (line 84) | def startViterbiMatchingProcessing(self, trajectory_name, network_name...
method fillLayerComboBox (line 184) | def fillLayerComboBox(self, iface, combobox, geom_type):
method fillAttributeComboBox (line 199) | def fillAttributeComboBox(self, combobox, layername):
method getLayer (line 212) | def getLayer(self, layername):
method setUp (line 218) | def setUp(self, line_layer, point_layer, point_attr, feedback):
method defineAttributes (line 234) | def defineAttributes(self):
FILE: src/offlinemapmatching/mm/observation/intersection.py
class Intersection (line 3) | class Intersection:
method __init__ (line 5) | def __init__(self, geometry, edge_ids):
FILE: src/offlinemapmatching/mm/observation/network.py
class Network (line 6) | class Network:
method __init__ (line 9) | def __init__(self, linestring_layer):
method extractAllIntersections (line 15) | def extractAllIntersections(self):
method routing (line 52) | def routing(self, start, end):
FILE: src/offlinemapmatching/mm/observation/observation.py
class Observation (line 6) | class Observation:
method __init__ (line 8) | def __init__(self, point, id):
method getAllCandidates (line 12) | def getAllCandidates(self, network, max_distance):
method getCandidates (line 27) | def getCandidates(self, network, max_distance):
method isIntersectionInSearchDistance (line 85) | def isIntersectionInSearchDistance(self, edges, candidates):
FILE: src/offlinemapmatching/mm/observation/trajectory.py
class Trajectory (line 4) | class Trajectory:
method __init__ (line 6) | def __init__(self, point_layer, id_field):
method extractObservations (line 11) | def extractObservations(self, point_layer, id_field):
FILE: src/offlinemapmatching/mm_processing/clip_network_algorithm.py
class ClipNetworkAlgorithm (line 49) | class ClipNetworkAlgorithm(QgsProcessingAlgorithm):
method initAlgorithm (line 73) | def initAlgorithm(self, config):
method processAlgorithm (line 121) | def processAlgorithm(self, parameters, context, feedback):
method name (line 221) | def name(self):
method helpUrl (line 231) | def helpUrl(self):
method shortHelpString (line 241) | def shortHelpString(self):
method displayName (line 251) | def displayName(self):
method group (line 258) | def group(self):
method groupId (line 265) | def groupId(self):
method tr (line 275) | def tr(self, string):
method createInstance (line 278) | def createInstance(self):
method icon (line 281) | def icon(self):
FILE: src/offlinemapmatching/mm_processing/offline_map_matching_algorithm.py
class OfflineMapMatchingAlgorithm (line 49) | class OfflineMapMatchingAlgorithm(QgsProcessingAlgorithm):
method initAlgorithm (line 74) | def initAlgorithm(self, config):
method processAlgorithm (line 130) | def processAlgorithm(self, parameters, context, feedback):
method name (line 210) | def name(self):
method helpUrl (line 220) | def helpUrl(self):
method shortHelpString (line 230) | def shortHelpString(self):
method displayName (line 240) | def displayName(self):
method group (line 247) | def group(self):
method groupId (line 254) | def groupId(self):
method tr (line 264) | def tr(self, string):
method createInstance (line 267) | def createInstance(self):
method icon (line 270) | def icon(self):
FILE: src/offlinemapmatching/mm_processing/offline_map_matching_provider.py
class OfflineMapMatchingProvider (line 41) | class OfflineMapMatchingProvider(QgsProcessingProvider):
method __init__ (line 43) | def __init__(self):
method unload (line 52) | def unload(self):
method loadAlgorithms (line 59) | def loadAlgorithms(self):
method id (line 66) | def id(self):
method icon (line 74) | def icon(self):
method name (line 77) | def name(self):
method longName (line 86) | def longName(self):
FILE: src/offlinemapmatching/mm_processing/reduce_trajectory_density.py
class ReduceTrajectoryDensity (line 50) | class ReduceTrajectoryDensity(QgsProcessingAlgorithm):
method initAlgorithm (line 73) | def initAlgorithm(self, config):
method processAlgorithm (line 112) | def processAlgorithm(self, parameters, context, feedback):
method name (line 165) | def name(self):
method helpUrl (line 175) | def helpUrl(self):
method shortHelpString (line 185) | def shortHelpString(self):
method displayName (line 195) | def displayName(self):
method group (line 202) | def group(self):
method groupId (line 209) | def groupId(self):
method tr (line 219) | def tr(self, string):
method createInstance (line 222) | def createInstance(self):
method icon (line 225) | def icon(self):
method reduceDensity (line 228) | def reduceDensity(self, startIndex, nextIndex, layer, distance, output...
FILE: src/offlinemapmatching/offline_map_matching.py
class OfflineMapMatching (line 46) | class OfflineMapMatching:
method __init__ (line 49) | def __init__(self, iface):
method tr (line 107) | def tr(self, message):
method add_action (line 122) | def add_action(
method initGui (line 199) | def initGui(self):
method clipNetwork (line 243) | def clipNetwork(self):
method reduceDensity (line 246) | def reduceDensity(self):
method fastTrajectoryMatching (line 249) | def fastTrajectoryMatching(self):
method matchTrajectory (line 252) | def matchTrajectory(self):
method unload (line 255) | def unload(self):
method run (line 270) | def run(self):
method populateComboBox (line 296) | def populateComboBox(self, type):
method startPopulateFieldsComboBox (line 305) | def startPopulateFieldsComboBox(self):
method startMapMatching (line 308) | def startMapMatching(self):
FILE: src/offlinemapmatching/offline_map_matching_dialog.py
class OfflineMapMatchingDialog (line 34) | class OfflineMapMatchingDialog(QtWidgets.QDialog, FORM_CLASS):
method __init__ (line 35) | def __init__(self, parent=None):
FILE: src/offlinemapmatching/resources.py
function qInitResources (line 470) | def qInitResources():
function qCleanupResources (line 473) | def qCleanupResources():
Condensed preview — 37 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (973K chars).
[
{
"path": ".gitignore",
"chars": 361,
"preview": "# Build and Release Folders\nbin-debug/\nbin-release/\n[Oo]bj/\n[Bb]in/\n\n# Other files and folders\n.settings/\n\n# Executables"
},
{
"path": "LICENSE",
"chars": 35147,
"preview": " GNU GENERAL PUBLIC LICENSE\n Version 3, 29 June 2007\n\n Copyright (C) 2007 Free "
},
{
"path": "README.md",
"chars": 18443,
"preview": "# Offline-MapMatching\r\nA <a href=\"https://github.com/qgis/QGIS\">QGIS</a>-plugin for matching a trajectory with a network"
},
{
"path": "src/offlinemapmatching/__init__.py",
"chars": 1699,
"preview": "# -*- coding: utf-8 -*-\n\"\"\"\n/***************************************************************************\n OfflineMapMatc"
},
{
"path": "src/offlinemapmatching/help_docs/help.html",
"chars": 4997,
"preview": "<html>\n <body>\n <h2><font face=\"helvetica, arial\">Offline-MapMatching</font></h2>\n <p>This plugin match"
},
{
"path": "src/offlinemapmatching/help_docs/help_processing_clipping_network.html",
"chars": 1708,
"preview": "<html>\n <body>\n <p><font face=\"helvetica, arial\">This algorithm gives the possibility to clip a network layer "
},
{
"path": "src/offlinemapmatching/help_docs/help_processing_match_trajectory.html",
"chars": 2664,
"preview": "<html>\n <body>\n <p><font face=\"helvetica, arial\">This algorithm matches a trajectory on a network using a Hidd"
},
{
"path": "src/offlinemapmatching/help_docs/help_processing_reduce_density.html",
"chars": 1359,
"preview": "<html>\n <body>\n <p><font face=\"helvetica, arial\">This algorithm reduces the density of a given trajectory, i.e"
},
{
"path": "src/offlinemapmatching/i18n/af.ts",
"chars": 333,
"preview": "<?xml version=\"1.0\" encoding=\"utf-8\"?>\n<!DOCTYPE TS><TS version=\"2.0\" language=\"af\" sourcelanguage=\"en\">\n<context>\n <"
},
{
"path": "src/offlinemapmatching/metadata.txt",
"chars": 3255,
"preview": "# This file contains metadata for your plugin. Since \n# version 2.0 of QGIS this is the proper way to supply \n# informat"
},
{
"path": "src/offlinemapmatching/mm/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/offlinemapmatching/mm/helper/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/offlinemapmatching/mm/helper/measurement_statistics.py",
"chars": 386,
"preview": "import statistics\r\n\r\nclass MeasurementStatistics:\r\n \r\n def __init__(self):\r\n self.measurments = []\r\n \r\n "
},
{
"path": "src/offlinemapmatching/mm/hidden_states/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/offlinemapmatching/mm/hidden_states/candidate.py",
"chars": 475,
"preview": "import math\r\nfrom ..observation.observation import *\r\n\r\nclass Candidate:\r\n \r\n def __init__(self, point, distance, "
},
{
"path": "src/offlinemapmatching/mm/hidden_states/hidden_model.py",
"chars": 17474,
"preview": "from ..observation.network import *\r\nfrom ..observation.trajectory import *\r\nfrom ..observation.observation import *\r\nfr"
},
{
"path": "src/offlinemapmatching/mm/hidden_states/transition.py",
"chars": 5334,
"preview": "from .candidate import *\r\nimport math\r\n\r\nclass Transition:\r\n \r\n def __init__(self, start_candidate, end_candidate,"
},
{
"path": "src/offlinemapmatching/mm/map_matcher.py",
"chars": 11647,
"preview": "from PyQt5.QtWidgets import QProgressBar, QComboBox, QLabel, QApplication\r\nfrom qgis.core import *\r\nfrom .hidden_states."
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{
"path": "src/offlinemapmatching/mm/observation/__init__.py",
"chars": 0,
"preview": ""
},
{
"path": "src/offlinemapmatching/mm/observation/intersection.py",
"chars": 181,
"preview": "from qgis.core import *\r\n\r\nclass Intersection:\r\n \r\n def __init__(self, geometry, edge_ids):\r\n self.geometry"
},
{
"path": "src/offlinemapmatching/mm/observation/network.py",
"chars": 4510,
"preview": "from qgis.analysis import *\r\nfrom qgis.core import *\r\nfrom .intersection import *\r\nimport processing\r\n\r\nclass Network:\r\n"
},
{
"path": "src/offlinemapmatching/mm/observation/observation.py",
"chars": 4839,
"preview": "from .network import *\r\nfrom .intersection import *\r\nfrom ..hidden_states.candidate import *\r\nfrom qgis.core import *\r\n\r"
},
{
"path": "src/offlinemapmatching/mm/observation/trajectory.py",
"chars": 687,
"preview": "from .observation import *\r\nfrom qgis.core import *\r\n\r\nclass Trajectory:\r\n \r\n def __init__(self, point_layer, id_f"
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{
"path": "src/offlinemapmatching/mm_processing/clip_network_algorithm.py",
"chars": 10209,
"preview": "# -*- coding: utf-8 -*-\r\n\r\n'''\r\n/***************************************************************************\r\n Offline-M"
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"path": "src/offlinemapmatching/mm_processing/offline_map_matching_algorithm.py",
"chars": 10036,
"preview": "# -*- coding: utf-8 -*-\r\n\r\n'''\r\n/***************************************************************************\r\n Offline-M"
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"path": "src/offlinemapmatching/mm_processing/offline_map_matching_provider.py",
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"preview": "# -*- coding: utf-8 -*-\r\n\r\n'''\r\n/***************************************************************************\r\n OfflineMa"
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"path": "src/offlinemapmatching/mm_processing/reduce_trajectory_density.py",
"chars": 9622,
"preview": "# -*- coding: utf-8 -*-\r\n\r\n'''\r\n/***************************************************************************\r\n Offline-M"
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"chars": 14842,
"preview": "# -*- coding: utf-8 -*-\r\n'''\r\n/***************************************************************************\r\n OfflineMapM"
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"preview": "# -*- coding: utf-8 -*-\n\n# Resource object code\n#\n# Created by: The Resource Compiler for PyQt5 (Qt v5.14.0)\n#\n# WARNING"
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{
"path": "src/offlinemapmatching/resources.qrc",
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"preview": "<RCC>\n <qresource prefix=\"/plugins/offline_map_matching\" >\n <file>icons/icon.png</file>\n <file>icons/cl"
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"chars": 6403,
"preview": "<!DOCTYPE qgis PUBLIC 'http://mrcc.com/qgis.dtd' 'SYSTEM'>\n<qgis version=\"3.2.0-Bonn\" hasScaleBasedVisibilityFlag=\"0\" mi"
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"preview": "{\n\"type\": \"FeatureCollection\",\n\"name\": \"trajectory1\",\n\"crs\": { \"type\": \"name\", \"properties\": { \"name\": \"urn:ogc:def:crs:"
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"preview": "{\n\"type\": \"FeatureCollection\",\n\"name\": \"trajectory2\",\n\"crs\": { \"type\": \"name\", \"properties\": { \"name\": \"urn:ogc:def:crs:"
}
]
About this extraction
This page contains the full source code of the jagodki/Offline-MapMatching GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 37 files (888.3 KB), approximately 315.4k tokens, and a symbol index with 117 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.