###################################################################### # # Space Weather Adaptive Network (SWAN) # Hyper Spectral Riometer (HSR) # # Principal Investigator: Emma Spanswick (elspansw@ucalgary.ca) # ###################################################################### Summary: ---------------------- The Python software in this directory contains a function for reading in SWAN HSR K0 txt files. This data can be found at https://data.phys.ucalgary.ca/sort_by_project/SWAN/hsr/l0/multi_freq/. The read function takes in the path of a K0 txt file (must be downloaded prior) and returns a Pandas dataframe. Requirements: ---------------------- - Python 3.8+ recommended (likely will work on older versions of Python; 3.8-3.11 tested) - The Python package "pandas" installed (v1.5.3 and v2.0.3 tested) Acknowledgements: ---------------------- SWAN Hyper Spectral Riometer data is openly available. We request that users, at a minimum, acknowledge our funding agencies when data is used in publications and/or presentations. Below is an example acknowledgement for consideration. “The Space Weather Adaptive Network riometers are a Canada Foundation for Innovation project developed by the University of Calgary with collaboration with the TREx IRIS team.” Example: ---------------------- An example program is supplied in this directory as well, found in the file "example_usage.py". This program reads the file 20230901_mean-hsr_k0_v01.txt (found in this directory too, and in the main data tree) and prints out the metadata and data. Output from this program looks like the following. {'Acknowledgement': 'The SWAN riometer network is funded by the Canada ' 'Foundation for Innovation, the Province of Alberta and ' 'the University of Calgary, with operational support from ' 'the Canadian Space Agency.', 'Band Unique IDs': 'band_00, band_01, band_02, band_03, band_04, band_05, ' 'band_06, band_07, band_08, band_09, band_10', 'Bin Sampling File': '20230221_mean-hsr_sampling_v01.conf', 'Binwidth': '12.21 kHz', 'Central Frequency (MHz)': '30.00, 20.00, 22.00, 24.00, 26.00, 28.00, 32.00, ' '34.00, 36.00, 38.00, 40.00', 'Data Generation Date': '2023-09-01', 'Date': '2023-09-01', 'Device Unique ID': 'hsr-01', 'Disclaimer': 'Data processing is semi-automated. This data has not been ' 'checked for errors.', 'FPGA Unique ID': 'V2_R01F00_20200329F', 'Instrument Contact': 'Emma Spanswick (elspansw@ucalgary.ca)', 'Instrument PI': 'Emma Spanswick, University of Calgary', 'Passband (kHz)': '36.62, 24.41, 12.21, 48.83, 61.03, 85.45, 109.86, 24.41, ' '36.62, 36.62, 85.45', 'SDDR Unique ID': '5a', 'Sensor Network': 'Space Weather Adaptive Network (SWAN) Hyper-Spectral ' 'Riometer (HSR)', 'Site Geodetic Latitude (N)': '54.62', 'Site Geodetic Longitude (W)': '-113.35', 'Site Unique ID': 'mean', 'Version': '01'} timestamp band_00 band_01 band_02 band_03 band_04 band_05 band_06 band_07 band_08 band_09 band_10 0 2023-09-01 00:00:00 38.1918 30.6760 30.7775 31.8491 32.6211 33.4281 41.5436 40.3325 40.1134 39.3758 36.9270 1 2023-09-01 00:00:01 37.9169 31.0487 32.0448 32.0174 32.2468 33.3054 41.4548 41.3237 39.8045 39.0930 37.1005 2 2023-09-01 00:00:02 38.3529 30.6085 31.5325 31.8036 32.2096 33.6362 41.7417 40.5634 39.6826 39.3247 37.0499 3 2023-09-01 00:00:03 38.3198 34.7851 31.8217 31.9774 32.3591 33.5895 41.6719 41.2055 39.4804 39.5858 37.0210 4 2023-09-01 00:00:04 38.7500 32.2389 30.9809 31.7433 32.3459 33.7713 41.5224 41.3656 39.9965 39.8543 37.3017 ... ... ... ... ... ... ... ... ... ... ... ... ... 86215 2023-09-01 23:59:55 37.6779 29.6033 27.9968 28.9454 30.1786 32.8737 41.6508 41.1957 40.2588 39.7942 37.4688 86216 2023-09-01 23:59:56 38.1180 26.9015 28.5797 29.3209 29.9843 32.9675 41.4627 40.4520 40.0852 39.2084 37.2146 86217 2023-09-01 23:59:57 37.8288 26.5549 28.0757 28.5063 29.9213 32.7370 41.5454 41.0118 40.0637 39.7429 37.1205 86218 2023-09-01 23:59:58 37.9820 26.5341 27.6253 28.4143 30.0700 33.0364 41.6041 41.2024 40.4138 39.8941 37.0734 86219 2023-09-01 23:59:59 37.8751 26.6440 27.4731 28.5440 29.9972 32.9798 41.4288 40.8790 40.5462 39.5555 37.2165 [86220 rows x 12 columns]