Skip to main content

API for Electronic Navigational Charts (ENC)

Project description


Python-based API for Electronic Navigational Charts (ENC)

platform platform python version license


  • Read and process spatial depth data from FileGDB files into shapefiles.
  • Access and manipulate standard geometric shapes such as points and polygon collections.
  • Visualize colorful seacharts features and vessels using multiprocessing.

Code style

This module follows the PEP8 convention for Python code.



The simplest way to acquire the necessary dependencies for the SeaCharts package is to install an edition of the Anaconda package manager, and then create a new conda environment with Python 3.9 using e.g. the graphical user interface of PyCharm Professional as detailed here.

The required data processing libraries for spatial calculations and visualization may subsequently be installed simply by running the following command in the terminal of your chosen environment:

conda install -c conda-forge fiona cartopy matplotlib

Pipwin (Windows)

Alternatively, one may manually install the required packages manually by following these steps:

First, ensure that Python 3.9 (or another compatible version) and the required C++ build tools are installed. Next, the required Python packages must be installed (ideally in a fresh virtual environment). In order to ensure that the correct version of Numpy+mkl linked to the Intel® Math Kernel Library is installed, download the wheel according to your Python version and Windows platform from here. Place the downloaded wheel file e.g. in the same directory the terminal is run from, and install it. The below snippet corresponds to Python 3.9 on Windows 64-bit:

pip install --upgrade pip
pip install wheel
pip install numpy-1.20.3+mkl-cp39-cp39-win_amd64.whl

The remaining required packages may be installed by the following:

pip install pipwin
pipwin install gdal
pipwin install scipy
pipwin install fiona
pipwin install shapely
pipwin install cartopy
pipwin install matplotlib

Simply copy and paste the entire block above (including the empty line) into the terminal of your virtual environment, and go get a cup of coffee while it does its thing.


After the necessary dependencies have been correctly installed, the SeaCharts package may be installed directly through the Python Package Index (PyPI) by running the following command in the terminal:

pip install seacharts


This module supports reading and processing FGDB files for sea depth data such as the Norwegian coastal data set used for demonstration purposes, found here.

Downloading regional datasets

Follow the above link to download the Depth data (Sjøkart - Dybdedata) dataset from the Norwegian Mapping Authority, by adding it to the Download queue and navigating to the separate download page. Choose one or more county areas (e.g. Møre og Romsdal), and select the EUREF89 UTM sone 33, 2d (UTM zone 33N) projection and FGDB 10.0 format. Finally, select your appropriate user group and purpose, and click Download to obtain the ZIP file(s).

Processing ENC data into shapefiles

Unpack the downloaded file(s) and place the extracted .gdb in the data/external/ directory, where the top-level folder data is located in the same directory as the executing script.

Import the module, and initialize an instance of seacharts.ENC with optional settings. The new_data keyword argument must be set to True during the initial setup run, and/or for any subsequent desired re-parsing in order to unpack and store ENC features from the downloaded FGDB files as shapefiles:

if __name__ == '__main__':

    import seacharts

    size = 9000, 5062                # w, h (east, north) distance in meters
    center = 44300, 6956450          # easting/northing (UTM zone 33N)
    files = ['More_og_Romsdal.gdb']  # Norwegian county database name

    enc = seacharts.ENC(size=size, center=center, files=files, new_data=True)

Note that all ENC settings parameters may be set and modified directly in data/config.ini, wherein user settings are saved alongside the project defaults. Parameters passed to ENC overrides the defaults of the configuration file. See the documentation of the ENC input parameters for descriptions of all available configuration settings.

API usage and accessing geometric shapes

After the spatial data is parsed into shapefiles as shown above, geometric shapes based on the Shapely library may be accessed and manipulated through various ENC attributes. The seacharts feature layers are stored in seabed, shore and land.

if __name__ == '__main__':

    import seacharts

    enc = seacharts.ENC(new_data=False)


Note how the new_data argument may be set to False (or omitted) if the desired regional spatial data has already been unpacked and processed into shapefiles in a previous call. Additionally, the size, center or origin parameters may be different from the one used to extract the external ENC data, allowing for loading of more specific (smaller) areas of interest into memory during runtime.

See the documentation for each top-level ENC method for all API usage and visualization possibilities currently available to the SeaCharts package.

Interactive environment visualization

The ENC.show_display method is used to show a Matplotlib figure plot of the loaded seacharts features. Zoom and pan the environment view using the mouse scroll button, and holding and dragging the plot with left click, respectively.

Fullscreen mode may be toggled using the f key, and dark mode may be toggled using the d key. An optional colorbar showing the various depth legends may be toggled using the c key. Moreover, the visibility of each individual layer may be toggled on and off using the t, g,h, b, and l keys. Press and hold the left Alt key and press any of the arrow keys to move the anchor of the figure window.

A controllable ownship with a sector horizon of hazards and arrows pointing to the closest point on a polygon within it may be added and toggled through the o, z and a keys, respectively. Steer the ship with the arrows keys.

The filter depths of the displayed hazardous obstacles may be toggled using the n and m keys, and the size of the horizon may be altered using ,, ., [, and ] (on a US keyboard layout). Furthermore, vessels may be added and shown by passing appropriately formatted vessel poses to the ENC.add_vessels method, or manually storing the vessel details in data/vessels.csv and pressing the u key to update the display. Toggle their visibility through the v key.

Shift left-click on the environment to add yellow path waypoints, move them around by pressing Shift and holding down the mouse button, and Shift right- click to remove them. One may also Shift right-click on a path edge to create an intermediate waypoint between two existing waypoints. Additionally, a second path of magenta color may be added and manipulated by replacing Shift with the Control key.

Images of the currently shown display may be saved in various resolutions by pressing Control + s, Shift + s or s. The below snippet produces the example usage visualization images shown at the top of this page, assuming default settings and that a More_og_Romsdal.gdb directory is correctly extracted and placed as discussed in the shapefile processing section:

if __name__ == '__main__':

    import seacharts

    size = 9000, 5062
    center = 44300, 6956450
    enc = seacharts.ENC(border=True)

    # (id, easting, northing, heading, color)
    ships = [
        (1, 46100, 6957000, 132, 'orange'),
        (2, 45000, 6956000, 57, 'yellow'),
        (3, 44100, 6957500, 178, 'red'),
        (4, 42000, 6955200, 86, 'green'),
        (5, 44000, 6955500, 68, 'pink'),


    import shapely.geometry as geo

    x, y = center
    width, height = 1900, 1900
    box = geo.Polygon((
        (x - width, y - height),
        (x + width, y - height),
        (x + width, y + height),
        (x - width, y + height),
    areas = list(box.difference(enc.seabed[10].geometry))
    enc.draw_circle(center, 1000, 'yellow', thickness=2,
    enc.draw_rectangle(center, (600, 1200), 'blue', rotation=20)
    enc.draw_circle(center, 700, 'green', edge_style=(0, (5, 8)),
                    thickness=3, fill=False)
    enc.draw_line([(center[0], center[1] + 800), center,
                   (center[0] - 300, center[1] - 400)], 'white')
    enc.draw_line([(center[0] - 300, center[1] + 400), center,
                   (center[0] + 200, center[1] - 600)],
                  'magenta', width=0.0, thickness=5.0,
                  edge_style=(0, (1, 4)))
    enc.draw_arrow(center, (center[0] + 700, center[1] + 600), 'orange',
                   head_size=300, width=50, thickness=5)
    enc.draw_polygon(enc.seabed[100].geometry[-3], 'cyan')
    enc.draw_polygon(enc.shore.geometry[56], 'highlight')
    for area in areas[3:8] + [areas[14], areas[17]] + areas[18:21]:
        enc.draw_polygon(area, 'red')
    enc.draw_rectangle(center, (width, height), 'pink', fill=False,
                       edge_style=(0, (10, 10)), thickness=1.5)

    enc.save_image('example1', extension='svg')


The id values of the vessel details should be unique identifiers, used as references to the feature artists added to the Matplotlib plot. The color values may be strings of one of the custom ship colors of this package, or any named Matplotlib CSS4 color.

Visualization using multiprocessing

Initializing an ENC instance with the multiprocessing parameter set to True spawns a Process thread from the Python standard library multiprocessing module, creating an independent environment display running an infinite visualization loop, based on the current user (or default) settings stored in the data/config.ini file. The visualization loop continuously reads the data/vessels.csv file, and updates the plot with any present vessels. Repeated updating of the vessels file by any arbitrary alternative method is thus reflected in the plot in near real-time. As such, this feature may be utilized for parallel or concurrent visualization of vessels in an environment, e.g. based on vessel trajectories produced by a separate and independent simulation or optimization algorithm.



This project uses the MIT license.

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

seacharts-1.0.1.tar.gz (31.9 kB view hashes)

Uploaded source

Built Distribution

seacharts-1.0.1-py3-none-any.whl (32.3 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page