Welcome to pymove’s documentation!

PyMove is a Python library for processing and visualization of trajectories and other spatial-temporal data.

We will also release wrappers to some useful Java libraries frequently used in the mobility domain.

Main Features

PyMove proposes:

• A familiar and similar syntax to Pandas;
• Clear documentation;
• Extensibility, since you can implement your main data structure by manipulating other data structures such as Dask DataFrame, numpy arrays, etc., in addition to adding new modules;
• Flexibility, as the user can switch between different data structures;
• Operations for data preprocessing, pattern mining and data visualization.

Installation

Conda instalation

1. conda install -c conda-forge pymove

Pip installation

1. pip install pymove

Indices and tables

• Index
• Module Index
• Search Page

References

• pymove package
  • Subpackages
    • pymove.core package
      • Submodules
      • pymove.core.dask module
      • pymove.core.dataframe module
      • pymove.core.grid module
      • pymove.core.interface module
      • pymove.core.pandas module
      • pymove.core.pandas_discrete module
      • Module contents
    • pymove.models package
      • Subpackages
      • Submodules
      • pymove.models.anomaly_detection module
      • pymove.models.classification module
      • Module contents
    • pymove.preprocessing package
      • Submodules
      • pymove.preprocessing.compression module
      • pymove.preprocessing.filters module
      • pymove.preprocessing.segmentation module
      • pymove.preprocessing.stay_point_detection module
      • Module contents
    • pymove.query package
      • Submodules
      • pymove.query.query module
      • Module contents
    • pymove.semantic package
      • Submodules
      • pymove.semantic.semantic module
      • Module contents
    • pymove.uncertainty package
      • Submodules
      • pymove.uncertainty.privacy module
      • pymove.uncertainty.reducing module
      • Module contents
    • pymove.utils package
      • Submodules
      • pymove.utils.constants module
      • pymove.utils.conversions module
      • pymove.utils.data_augmentation module
      • pymove.utils.datetime module
      • pymove.utils.distances module
      • pymove.utils.geoutils module
      • pymove.utils.integration module
      • pymove.utils.log module
      • pymove.utils.math module
      • pymove.utils.mem module
      • pymove.utils.trajectories module
      • pymove.utils.visual module
      • Module contents
    • pymove.visualization package
      • Submodules
      • pymove.visualization.folium module
      • pymove.visualization.matplotlib module
      • Module contents
  • Module contents
• example notebooks
  • Notebooks
    • 00 - What is PyMove?
      • Enviroment settings
      • Using PyMove
      • What can you do with PyMove?
    • 01 - Exploring MoveDataFrame
      • MoveDataFrame
      • Creating a MoveDataFrame
      • What’s in MoveDataFrame?
    • 02 - Exploring Preprocessing
      • Load data from file
      • Filtering
      • Segmentation
      • Stay point detection
      • Compression
    • 03 - Exploring Visualization
      • 1. Imports
      • 2. Load Data
      • 3. Exploring visualization module
    • 04 - Exploring Grid
      • Imports
      • Load data
      • Create virtual Grid
      • Create one polygon to point on grid
      • Create or update index grid feature
      • Create all polygons to all point on grid
      • Create all polygons on grid
      • Get point to index grid
      • Save grid to .pkl
      • Read .pkl to grid
      • Show a grid polygons
    • 05 - Exploring Utils
      • Imports
      • Conversions
      • Datetime
      • Distances
      • Math
      • Trajectories
      • Log
      • Mem
    • 06 - Exploring Integrations
      • 0. Required library installations
      • 1. Imports
      • 2. Load Data
      • 3. Loading points of interest
      • 4. Integrating Points of Interest into the DataSet
      • 5. Integrating Points of Interest into the DataSet (Using join_with_pois_optimizer)
      • 6. Integrating Points of Interest into the Category-Based DataSet
      • 7. Integrating events (points of interest with timestamp) to the DataSet
      • 8. Integration with Point of Interest HOME
      • 9. Merge of HOME with DataSet already integrated with POIs
      • 11. Union functions
      • 12. Integração entre trajetórias e áreas coletivas
    • 07 - Exploring Query
      • 1. Imports
      • 2. Load Data
      • 2. Range Query
      • 3. KNN (K-Nearest-Neighbor)
    • 08 - Exploring Semantic
      • 1. Imports
      • 2. Load Data