Metadata-Version: 2.1
Name: aurora-asi-lib
Version: 0.7.0
Summary: Easily download, plot, animate, and analyze auroral all sky imager (ASI) data.
Home-page: https://github.com/mshumko/aurora-asi-lib
Author: Mykhaylo Shumko
Author-email: msshumko@gmail.com
License: GNU GENERAL PUBLIC LICENSE v3.0
Description: ![Test python package](https://github.com/mshumko/aurora-asi-lib/workflows/Test%20python%20package/badge.svg) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4746447.svg)](https://doi.org/10.5281/zenodo.4746446)
        
        # aurora-asi-lib
        Easily download, plot, animate, and analyze aurora all sky imager (ASI) data. Currently the two supported camera systems (missions) are: 
        * Red-line Emission Geospace Observatory (REGO)
        * Time History of Events and Macroscale Interactions during Substorms (THEMIS).
        
        [API Documentation](https://aurora-asi-lib.readthedocs.io/) / [Code on GitHub](https://github.com/mshumko/aurora-asi-lib)
        
        
        Easily make ASI plots (example 1)!
        
        ![Aurora plot from example 1.](https://github.com/mshumko/aurora-asi-lib/blob/main/docs/_static/example_1.png?raw=true)
        
        And movies! (example4; the track and mean ASI intensity plot is a little bit more work.)
        ![Aurora movie from example 4.](https://github.com/mshumko/aurora-asi-lib/blob/main/docs/_static/20170915_023400_023557_themis_rank.gif?raw=true)
        
        Feel free to contact me and request that I add other ASI missions to `asilib`.
        
        ## Examples
        Before you can run these examples, make sure that `asilib` is configured with the installation steps below. These examples, and more, are in the `asilib/examples/` folder.
        
        ### Example 1
        This example uses asilib to plot one frame of a bright auroral arc.
        ```python
        from datetime import datetime
        
        import matplotlib.pyplot as plt
        
        import asilib
        
        # A bright auroral arc that was analyzed by Imajo et al., 2021 "Active 
        # auroral arc powered by accelerated electrons from very high altitudes"
        frame_time, frame, ax, im = asilib.plot_frame(datetime(2017, 9, 15, 2, 34, 0), 'THEMIS', 'RANK', 
                            color_norm='log', force_download=False)
        plt.colorbar(im)
        ax.axis('off')
        plt.show()
        ```
        
        ### Example 2
        This example uses asilib to plot a 5 minute movie of an auroral arc brightening right as a meteor burns up at zenith!
        
        ```python
        from datetime import datetime
        
        import asilib
        
        time_range = (datetime(2015, 3, 26, 6, 7), datetime(2015, 3, 26, 6, 12))
        asilib.plot_movie(time_range, 'THEMIS', 'FSMI')
        print(f'Movie saved in {asilib.config.ASI_DATA_DIR / "movies"}')
        ```
        
        ### Example 3
        This example is longer and it shows how to superpose a hypothetical satellite path through the THEMIS camera located at Rankin Inlet.
        
        ```python
        from datetime import datetime
        
        import numpy as np
        import matplotlib.pyplot as plt
        
        import asilib
        
        
        # ASI parameters
        mission = 'THEMIS'
        station = 'RANK'
        time_range = (datetime(2017, 9, 15, 2, 32, 0), datetime(2017, 9, 15, 2, 35, 0))
        
        fig, ax = plt.subplots(2, 1, figsize=(7, 10), gridspec_kw={'height_ratios':[4, 1]}, 
                                constrained_layout=True)
        
        # Load the calibration data. This is only necessary to create a fake satellite track.
        skymap_dict = asilib.load_skymap(mission, station, time_range[0])
        
        # Create the fake satellite track coordinates: latitude, longitude, altitude (LLA).
        # This is a north-south satellite track oriented to the east of the THEMIS/RANK 
        # station.
        n = int((time_range[1] - time_range[0]).total_seconds() / 3)  # 3 second cadence.
        lats = np.linspace(skymap_dict["SITE_MAP_LATITUDE"] + 5, skymap_dict["SITE_MAP_LATITUDE"] - 5, n)
        lons = (skymap_dict["SITE_MAP_LONGITUDE"]-0.5) * np.ones(n)
        alts = 110 * np.ones(n)
        lla = np.array([lats, lons, alts]).T
        
        # Map the satellite track to the station's azimuth and elevation coordinates and
        # image pixels. NOTE: the mapping is not along the magnetic field lines! You need
        # to install IRBEM and then use asilib.lla2footprint() before 
        # lla2azel() is called.
        sat_azel, sat_azel_pixels = asilib.lla2azel(mission, station, time_range[0], lla)
        
        # Initiate the movie generator function. Any errors with the data will be raised here.
        movie_generator = asilib.plot_movie_generator(
            time_range, mission, station, azel_contours=True, overwrite=True,
            ax=ax[0]
        )
        
        # Use the generator to get the frames and time stamps to estimate mean the ASI
        # brightness along the satellite path and in a (10x10 km) box.
        frame_data = movie_generator.send('data')
        
        # Calculate what pixels are in a box_km around the satellite, and convolve it
        # with the frames to pick out the ASI intensity in that box.
        area_box_mask = asilib.equal_area(mission, station, time_range[0], lla, box_km=(20, 20))
        asi_brightness = np.nanmean(frame_data.frames*area_box_mask, axis=(1,2))
        area_box_mask[np.isnan(area_box_mask)] = 0  # To play nice with plt.contour()
        
        for i, (time, frame, _, im) in enumerate(movie_generator):
            # Note that because we are drawing moving data: ASI image in ax[0] and 
            # the ASI time series + a vertical bar at the frame time in ax[1], we need
            # to redraw everything at every iteration.
             
            # Clear ax[1] (ax[0] cleared by asilib.plot_movie_generator())
            ax[1].clear()
            # Plot the entire satellite track
            ax[0].plot(sat_azel_pixels[:, 0], sat_azel_pixels[:, 1], 'red')
            ax[0].contour(area_box_mask[i, :, :], levels=[0.99], colors=['yellow'])
            # Plot the current satellite position.
            ax[0].scatter(sat_azel_pixels[i, 0], sat_azel_pixels[i, 1], c='red', marker='o', s=50)
        
            # Plot the time series of the mean ASI intensity along the satellite path
            ax[1].plot(frame_data.time, asi_brightness)
            ax[1].axvline(time, c='k') # At the current frame time.
        
            # Annotate the station and satellite info in the top-left corner.
            station_str = (
                f'{mission}/{station} '
                f'LLA=({skymap_dict["SITE_MAP_LATITUDE"]:.2f}, '
                f'{skymap_dict["SITE_MAP_LONGITUDE"]:.2f}, {skymap_dict["SITE_MAP_ALTITUDE"]:.2f})'
            )
            satellite_str = f'Satellite LLA=({lla[i, 0]:.2f}, {lla[i, 1]:.2f}, {lla[i, 2]:.2f})'
            ax[0].text(0, 1, station_str + '\n' + satellite_str, va='top', 
                    transform=ax[0].transAxes, color='red')
            ax[1].set(xlabel='Time', ylabel='Mean ASI intensity [counts]')
        
        print(f'Movie saved in {asilib.config["ASI_DATA_DIR"] / "movies"}')
        ```
        
        ## Installation
        To install this package as a user, run:
        
        ```shell
        python3 -m pip install aurora-asi-lib
        ```
        
        To install this package as a developer, run:
        
        ```shell
        git clone git@github.com:mshumko/aurora-asi-lib.git
        cd aurora-asi-lib
        python3 -m pip install -r requirements.txt # or
        python3 -m pip install -e .
        ```
        
        
        In either case, you'll need to configure your system paths to tell `asilib` (the import name) where to save the ASI data and movies. Run ```python3 -m asilib config``` to set up the data directory where the image, skymap, and movie files will be saved. Your settings will be stored in `config.py`. If you configure `asilib`, but don't specify a data directory, a default directory in `~/asilib-data` will be created if it doesn't exist.
        
        ### ffmpeg dependency
        To make  movies you'll also need to install the ffmpeg library.
         - **Ubuntu**: ```apt install ffmpeg```
         - **Mac**: ```brew install ffmpeg```
        
        __NOTES__
        - If you get the "ERROR: Could not build wheels for pymap3d which use PEP 517 and cannot be installed directly" error when installing, you need to upgrade your pip, setuptools, and wheel libaries via ```python3 -m pip install --upgrade pip setuptools wheel```.
        
Keywords: aurora, all sky imager, Red-line Emission Geospace Observatory, REGO,,Time History of Events and Macroscale Interactions during Substorms, THEMIS
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Description-Content-Type: text/markdown
