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Merge pull request #7 from xmichaelx/dirty_hdf5_support 

Dirty hdf5 support
merge-requests/2/head
Cees Bassa 2022-06-23 09:24:31 +02:00 committed by GitHub
parent 7f2e37d22c e98fd37ce7
commit 945d17a599
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GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 50 additions and 12 deletions
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62
rfplot.py
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@ -13,14 +13,36 @@ from matplotlib.widgets import RectangleSelector
import matplotlib as mpl
from skyfield.api import EarthSatellite
from skyfield.api import load, wgs84, utc
from datetime import datetime, timedelta
import h5py
import json
from modest import imshow
class Artifact:
def __init__(self, filename):
hdf5_file = h5py.File(filename, 'r')
if hdf5_file.attrs['artifact_version'] != 2:
raise Exception(hdf5_file.attrs['artifact_version'])
wf = hdf5_file.get('waterfall')
metadata = json.loads(hdf5_file.attrs['metadata'])
start_time = datetime.strptime(wf.attrs['start_time'].decode('ascii'), '%Y-%m-%dT%H:%M:%S.%fZ')
print(metadata)
self.z = np.transpose((np.array(wf['data']) * np.array(wf['scale']) + np.array(wf['offset'])))
self.fcen = float(metadata['frequency'])
self.freq = np.array(hdf5_file.get('waterfall').get("frequency")) + self.fcen
self.t = [ start_time + timedelta(seconds=x) for x in np.array(hdf5_file.get('waterfall').get("relative_time"))]
self.location = metadata["location"]
self.tle = [ x for x in metadata["tle"].split("\n") if x.strip() != "" ]
def main():
plt.style.use('dark_background')
mpl.rcParams['keymap.save'].remove('s')
mpl.rcParams['keymap.fullscreen'].remove('f')
mpl.rcParams['backend'] = "TkAgg"
ts = load.timescale()
parser = argparse.ArgumentParser(description='rfplot: plot RF observations', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-p', help='Input path to parent directory /a/b/', required=True)
@ -29,6 +51,7 @@ def main():
parser.add_argument('-l', type=int, default=3600, help='Number of subintegrations to plot')
parser.add_argument('-C', type=int, help='Site ID', default=4171)
parser.add_argument('-F', help='List with frequencies')
parser.add_argument('-a', help='Input path to artifact')
args = parser.parse_args()
@ -46,7 +69,6 @@ def main():
print(f"Site with no: {args.C} does not exist")
sys.exit(1)
site_location = wgs84.latlon(site["lat"], site["lon"], site["height"])
if args.F is not None:
freq_fname = args.F
@ -60,7 +82,20 @@ def main():
tle_fname = os.path.join(os.environ["ST_TLEDIR"], "bulk.tle")
# Read spectrogram
s = Spectrogram(args.p, args.P, args.s, args.l, args.C)
if args.a is None:
s = Spectrogram(args.p, args.P, args.s, args.l, args.C)
timestamps = [ x.replace(tzinfo=utc) for x in s.t]
range_rate_base = [ 0 for x in timestamps]
site_location = wgs84.latlon(site["lat"], site["lon"], site["height"])
else:
s = Artifact(args.a)
site = {"lat" : s.location["latitude"],"lon" : s.location["longitude"],"height" : s.location["altitude"]}
site_location = wgs84.latlon(site["lat"], site["lon"], site["height"])
satellite = EarthSatellite(s.tle[-2], s.tle[-1])
timestamps = [ x.replace(tzinfo=utc) for x in s.t]
pos = (satellite - site_location).at(ts.utc(timestamps))
_, _, _, _, _, range_rate_base = pos.frame_latlon_and_rates(site_location)
range_rate_base = range_rate_base.km_per_s
# Create plot
vmin, vmax = np.percentile(s.z, (5, 99.95))
@ -69,9 +104,8 @@ def main():
tmin, tmax = mdates.date2num(s.t[0]), mdates.date2num(s.t[-1])
# Frequency limits
fcen = np.mean(s.freq)
fcen = s.fcen
fmin, fmax = (s.freq[0] - fcen) * 1e-6, (s.freq[-1] - fcen) * 1e-6
ts = load.timescale()
frequencies = []
satellite_info = []
@ -93,7 +127,6 @@ def main():
mark = ax.scatter([], [],c="white",s=5)
line_fitting = ax.scatter([], [], edgecolors="yellow",s=10, facecolors='none')
# imshow(ax, s.z, vmin=vmin, vmax=vmax)
timestamps = [ x.replace(tzinfo=utc) for x in s.t]
for sat_info in satellite_info:
satellite = EarthSatellite(sat_info["tle"][-2], sat_info["tle"][-1])
t, events = satellite.find_events(site_location, t0, t1, altitude_degrees=0.0)
@ -110,20 +143,25 @@ def main():
sat_info["timeslot"] = [ (pairs[i][0].utc_datetime(), pairs[i+1][0].utc_datetime()) for i in range(0, len(pairs), 2)]
for timeslot in sat_info["timeslot"]:
selected_timestamps = [ x for x in timestamps if x >= timeslot[0] and x <= timeslot[1]]
selected_pairs = [ x for x in zip(timestamps,range_rate_base) if x[0] >= timeslot[0] and x[0] <= timeslot[1]]
selected_timestamps = [x[0] for x in selected_pairs]
selected_range_rate_base = np.array([x[1] for x in selected_pairs])
pos = (satellite - site_location).at(ts.utc(selected_timestamps))
_, _, _, _, _, range_rate = pos.frame_latlon_and_rates(site_location)
range_rate_sat = range_rate.km_per_s
C = 299792.458 # km/s
for freq in sat_info["frequencies"]:
freq1 = (freq - fcen * 1e-6)
dfreq = freq1 - range_rate.km_per_s / C * freq # MHz
ax.plot([mdates.date2num(x) for x in selected_timestamps], dfreq,c="lime")
for fsat in sat_info["frequencies"]:
fbase = fcen * 1e-6
dfreq = (1 - range_rate_sat / C) * fsat - (1 - selected_range_rate_base / C) * fbase
tt = [mdates.date2num(x) for x in selected_timestamps]
ax.plot(tt, dfreq,c="orange")
ax.text(tt[0], dfreq[0], sat_info["noradid"],c="orange")
image = imshow(ax, s.z, origin="lower", aspect="auto", interpolation="None",
vmin=vmin, vmax=vmax,
extent=[tmin, tmax, fmin, fmax])
mode = {
"current_mode" : None,
"vmin" : vmin,