EMBERS: Home ༶ Table of Contents ༶ API Reference
Quick links to the sections below:
Contains rf_data, align_data, colormaps modules
rf_data.read_data([rf_file]) |
Convert rf binary data into ndarray of power and time. |
rf_data.tile_names() |
List of MWA and reference antenna names |
rf_data.tile_pairs(tiles) |
Create a list of all possible AUT ref antenna pairs from tile_names(). |
rf_data.time_tree(start_date, stop_date) |
Split a date interval into 30 min observation chunks. |
rf_data.plt_waterfall(power, times, name) |
Create waterfall plot() object from rf data. |
rf_data.single_waterfall(rf_file, out_dir) |
Save a waterfall plot from rf data file. |
rf_data.batch_waterfall(tile, time_stamp, …) |
Save a waterfall plot for a batch of rf data files. |
rf_data.waterfall_batch(start_date, …) |
Save a series of waterfall plots in parallel. |
align_data.savgol_interp(ref, tile[, …]) |
Interpolate a power array followed by savgol smoothing. |
align_data.plot_savgol_interp([ref, tile, …]) |
Plot single channel of power arrays to visualise savgol_interp(). |
align_data.save_aligned(tile_pair, …) |
Save an aligned set of rf data with savez_compressed() to an npz file. |
align_data.align_batch([start_date, …]) |
Temporally align all RF files within a date interval using save_aligned(). |
colormaps.spectral() |
Beautiful non-linear spectral colormap |
colormaps.jade() |
Beautiful perceptually uniform jade green colormap. |
colormaps.waves_2d() |
Create 2d sine wave. |
colormaps.plt_colormaps(spec, spec_r, jade, …) |
Plot 2x2 grid of sample colormaps. |
sat_list.norad_ids() |
Dictionary of NORAD Satellite Catalogue IDs. |
sat_list.download_tle(start_date, stop_date, …) |
Download TLEs from space-track.org. |
sat_ephemeris.load_tle(tle_file) |
Extract orbital parameters from a TLE file. |
sat_ephemeris.epoch_ranges(epochs) |
Optimise time intervals to make the most of different epochs of TLE pairs |
sat_ephemeris.epoch_time_array(epoch_range) |
Create a Skyfield Timescale object at which to evaluate satellite positions. |
sat_ephemeris.sat_pass(sats, t_arr, index_epoch) |
Find when a satellite passes above the horizon at a gps location. |
sat_ephemeris.ephem_data(t_arr, pass_index, …) |
Satellite Ephemeris data (time, alt, az arrays ) for a single satellite pass. |
sat_ephemeris.sat_plot(sat_id, alt, az[, alpha]) |
Plots satellite passes |
sat_ephemeris.save_ephem(sat, tle_dir, …) |
Save ephemeris of all satellite passes and plot sky coverage. |
sat_ephemeris.ephem_batch(tle_dir, cadence, …) |
Process ephemeris for multiple satellites in parallel. |
chrono_ephem.obs_times(time_zone, …) |
Time conversion tools for 30 minute observations |
chrono_ephem.interp_ephem(t_array, s_alt, …) |
Interpolates satellite ephemeris from sat_ephemeris |
chrono_ephem.write_json(data[, filename, …]) |
writes data to json file in output dir |
chrono_ephem.save_chrono_ephem(time_zone, …) |
Save 30 minute ephem from all satellites to file. |
sat_channels.read_aligned([ali_file]) |
Read aligned data from save_aligned() npz file |
sat_channels.noise_floor(sat_thresh, …) |
Computes the noise floor of a rf power array |
sat_channels.time_filter(s_rise, s_set, times) |
Determine indices of time array when a satellite is above the horizon. |
sat_channels.plt_window_chans(power, sat_id, …) |
Waterfall plot with sat window and occupied channels highlighted. |
sat_channels.plt_channel(times, …) |
Plot power in channel, with various thresholds |
sat_channels.plt_sats(ids, chrono_file, …) |
Polar plot of satellite passes in a 30 minute observation |
sat_channels.good_chans(ali_file, …[, plots]) |
Determine the channels a satellite could occupy, in a 30 minute observation |
sat_channels.window_chan_map(ali_dir, …) |
Find all satellite channels in a 30 minute rf observation |
sat_channels.batch_window_map(start_date, …) |
Find satellite channels for all rfobservations in a date interval |
mwa_pointings.download_meta(start, stop, …) |
Download MWA metadata from mwatelescope.org |
mwa_pointings.clean_meta_json(out_dir) |
Organize json files. |
mwa_pointings.combine_pointings(start_gps, …) |
Combine successive observations with same pointing and save to file. |
mwa_pointings.point_integration(out_dir) |
Calculate total integration at each pointing |
mwa_pointings.pointing_hist(pointings, …) |
Plot a histogram of pointing integration |
mwa_pointings.rf_obs_times(start_date, …) |
Generate start & end times of 30 minuts rf observations in local, unix, gps formats |
mwa_pointings.obs_pointings(start, stop, …) |
Classify the pointing of each rf_obs |
mwa_pointings.tile_integration(out_dir, rf_dir) |
Calculate total integration at multiple pointings for all tiles |
mwa_pointings.plt_hist_array(tile_ints, out_dir) |
A massive grid of histograms with a subplot for pointing integration of each tile. |
mwa_pointings.mwa_point_meta(start, stop, …) |
Download mwa pointing metadata, sort and parse it, and create diagonistic plots, |
mwa_dipoles.download_metafits(num_files, …) |
Download metafits files from mwatelescope.org |
mwa_dipoles.find_flags(out_dir) |
Read metafits files and determine which dipoles are flagged |
mwa_dipoles.mwa_flagged_dipoles(num_files, …) |
Download metafits and find flagged dipoles |
mwa_fee.local_beam(za, az, freq[, delays, …]) |
Code pulled from mwapy that generates the MWA beam response. |
mwa_fee.mwa_fee_model(out_dir, nside[, …]) |
Create MWA FEE beam models at multiple pointings, with dipoles flagged. |
beam_utils.rotate_map(nside[, angle, …]) |
Rotates healpix array by the desired angle, and saves it. |
beam_utils.healpix_cardinal_indices(nside[, …]) |
Cardinal slices of healpix maps, upto an zenith angle threshold. |
beam_utils.healpix_cardinal_slices(nside, …) |
Slice healpix map along NS, EW axes, assuming it has been rotated by + 𝛑/4. |
beam_utils.nan_mad(good_ref_map) |
Compute MAD of values in pixel of healpix map while ignoring nans. |
beam_utils.map_slices(nside, good_map, za_max) |
Slice healpix map along NS & EW axes returning Median and MAD arrays of the cardinal slices. |
beam_utils.poly_fit(x, y, data, order) |
Fit polynominal of any order to data |
beam_utils.chisq_fit_gain([data, model]) |
Chisqaured fit the data and model. |
beam_utils.chisq_fit_test([data, model, offset]) |
chi-squared test for goodness of fit betweet model and data |
beam_utils.plt_slice([fig, sub, zen_angle, …]) |
Plot a slice of measured beam map with errorbars fit the fee beam model. |
beam_utils.plot_healpix([data_map, fig, …]) |
Yeesh do some healpix magic to plot the thing |
ref_fee_healpix.create_model(nside[, file_name]) |
Takes feko .ffe reference model, converts into healpix and smooths the response |
ref_fee_healpix.ref_healpix_save(nside, out_dir) |
Save and plot reference healix maps |
tile_maps.check_pointing(timestamp, …) |
Check if timestamp is at MWA sweet-pointing 0, 2, 4, 41. |
tile_maps.plt_channel(out_dir, times, ref, …) |
Plot power in a frequency channel of raw rf data, with various thresholds |
tile_maps.plt_fee_fit(times, mwa_fee_pass, …) |
Plot data and model with goodness of fit p-value to visualize the degree of fit |
tile_maps.rf_apply_thresholds(ali_file, …) |
Apply power, noise thresholds to rf data arrays. |
tile_maps.rfe_calibration(start_date, …) |
Calibrate the gain variations of a RF Explorers at high powers. |
tile_maps.rfe_collate_cali(start_gain, …) |
Collate RF Explorer gain calibration data from all MWA tile pairs, and plot a gain solution. |
tile_maps.rfe_batch_cali(start_date, …[, …]) |
Batch gain calibrate all pairs of RF explorers and compute a global solution. |
tile_maps.project_tile_healpix(start_date, …) |
There be magic here. |
tile_maps.mwa_clean_maps(nside, …) |
Extract data from 18 good satellites and make the best possible MWA beam maps. |
tile_maps.plt_sat_maps(sat, out_dir) |
Create healpix plots of the sky coverage of a satellite |
tile_maps.plt_clean_maps(clean_map, out_dir) |
Plot healpix clean beam, error and count maps at all pointings. |
tile_maps.tile_maps_batch(start_date, …[, …]) |
Batch process satellite RF data to create clean beam maps and all intermediate data products. |
null_test.good_ref_maps(nside, map_dir, …) |
Collates reference data from 18 good satellites into a good_ref_map |
null_test.plt_null_test([fig, sub, …]) |
Plot null test between two corresponding slices of reference beam maps |
null_test.null_test(nside, za_max, …) |
Plot all null tests for reference beam maps |
compare_beams.beam_slice(nside, tile_map, …) |
Compare slices of measured beam maps and FEE models. |
compare_beams.batch_compare_beam(nside, …) |
Batch compare multiple beam maps |
embers.kindle contains a set of command-line (cli) tools or executable script to process data in various ways.
Check out Embers by Example for comprehensive real world examples of the following cli-tools.
waterfall_single.main() |
Saves a single waterfall plot with a raw RF data file using the single_waterfall() function. |
waterfall_batch.main() |
Create a set of waterfall plots for all rf_files within a date interval using the waterfall_batch() function. |
colormaps.main() |
Preview EMBERS two beautiful custom colormaps - spectral() & jade(). |
align_single.main() |
Temporally align reference and tile data using the plot_savgol_interp() function. |
align_batch.main() |
Temporally align all RF files within a date interval using the align_batch() function. |
download_tle.main() |
Download tle files with the download_tle() from space-tracks.org |
ephem_single.main() |
Analyse TLE data file and create a sky coverage plot created with the save_ephem() function. |
ephem_batch.main() |
Analyse a batch of TLE files is with the ephem_batch() function. |
ephem_chrono.main() |
Collate ephemeris data generated above by ephem_batch for multiple satellites and determine all satellites present in each 30 minute observation and what their trajectories at the geographic location. |
sat_channels.main() |
Determine satellite transmission channels using the batch_window_map() function. |
mwa_pointings.main() |
Download MWA pointing metadata using the mwa_point_meta() function. |
mwa_dipoles.main() |
Check MWA antenna dipole flagging using the mwa_flagged_dipoles() function. |
mwa_fee.main() |
Create MWA Fully Embedded Element (FEE) beam models healpix maps at the given nside using the mwa_fee_model() function. |
ref_models.main() |
Convert FEKO models on the reference antennas into healpix maps using the ref_healpix_save() function. |
rfe_calibration.main() |
Determine the RF Explorer gain calibration solution using the rfe_batch_cali() function. |
tile_maps.main() |
Create tile beam maps using the tile_maps_batch(). |
null_test.main() |
Perform a null test of the reference antennas using the null_test() function. |
compare_beams.main() |
Compare measured MWA beam maps with MWA FEE models using the batch_compare_beam() function. |