seacharts 2.0.0

Electronic navigational charts (ENCs) processing/display project forked from https://github.com/simbli/seacharts

SeaCharts

Python-based API for Electronic Navigational Charts (ENC)

Features

• 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.

Roadmap

• 1: Add better compatibility for all operating systems (Windows, Linux++). Right now, gdal and cartopy are problematic to install on most platforms. Consider finding other packages for map loading and chart projections.
• 2: Add support for multiple map data formats (.gis, .gdb, .json, ) from any region in the world, in all UTM zones or lat/lon coordinates. To do this, the current loading functionality should be separated from the simplification and geometry classification, such that one can load from any map data file first.
• 3: Separate backend (Environment object) from frontend (Display object).
• 4: Use another plotting framework that has higher refresh rate/ is feasible for real-time (Qt?, React?)
• 5: Create clearly defined interface between Environment/ENC backend and frontend Display
• 6: Add option for plotting trajectories, ships, traffic information/AIS data etc. on the frontend display.
• 7: Add support for reading and loading in weather data (wind and current maps++) in a separate module

Prerequisites

Linux (Virtual Environment)

First, ensure that you have gdal and geos libraries installed, as these are required in order to successfully install gdal and cartopy:

sudo apt-get install libgeos-dev libgdal-dev

Then, from the root folder you can install an editable version of the package as follows:

pip install -e .

This should preferably be done inside a virtual environment in order to prevent python packaging conflicts.

Anaconda

Install an edition of the Anaconda package manager, and then create a new conda environment with Python 3.10 or higher 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) DEPRECATED at the moment, needs revision.

First, ensure that Python 3.10 (or another compatible version) and the required C++ build tools are installed. In order to ensure that the correct version of Numpy+mkl linked to the Intel® Math Kernel Library is acquired, 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 3.10 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.

Installation

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

or locally inside the seacharts root folder as an editable package with pip install -e .

Usage

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.

The config.yaml file specifies what ENC data to load and how it will be processed and displayed. The corresponding config_schema.yaml specifies the required parameters that must be provided for the software to function properly.

The minimal example below imports the ENC class from seacharts.enc with the default configuration under seacharts/config.yaml, and shows the interactive seachart display.

if __name__ == '__main__':

    from seacharts.enc import ENC

    enc = ENC()
    enc.start_display()

You can also specify settings at run-time, such as the window size, coordinate (easting, northing) origin and file to load shapefile data from, and whether or not to load new data. Here, 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__':

    from seacharts.enc import ENC

    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 = ENC(size=size, center=center, files=files, new_data=True)

Note that all ENC settings parameters may be set and modified directly in seacharts/config.yaml, which are validated by the seacharts/config_schema.yaml using cerberus. 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__':
    from seacharts.enc import ENC

    size = 9000, 5062
    center = 44300, 6956450
    enc = ENC(new_data=True, size=size, center=center)
    enc.start_display()

    print(enc.seabed[10])
    print(enc.shore)
    print(enc.land)

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 (Parts of this is DEPRECATED)

The ENC.start_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 shapely.geometry as geo
    from seacharts.enc import ENC

    size = 9000, 5062
    center = 44300, 6956450
    enc = ENC(new_data=True, size=size, center=center)
    enc.start_display()

    # (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"),
    ]

    enc.add_vessels(*ships)

    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, edge_style="--", alpha=0.5)
    enc.draw_rectangle(center, (600, 1200), "blue", rotation=20, alpha=0.5)
    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", alpha=0.5)
    enc.draw_polygon(enc.shore.geometry[56], "highlight", alpha=0.5)
    for area in areas[3:8] + [areas[14], areas[17]] + areas[18:21]:
        enc.draw_polygon(area, "red", alpha=0.5)
    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 (DEPRECATED)

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.

License

This project uses the MIT license.

MIT License

Copyright (c) 2021 Simon Blindheim

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.