def fitGC(self):
""" Fits great circle to observations. """
self.cartesian_points = []
self.ra_array = np.array(self.ra_array)
self.dec_array = np.array(self.dec_array)
for ra, dec in zip(self.ra_array, self.dec_array):
vect = vectNorm(raDec2Vector(ra, dec))
self.cartesian_points.append(vect)
self.cartesian_points = np.array(self.cartesian_points)
# Set begin and end pointing vectors
self.beg_vect = self.cartesian_points[0]
self.end_vect = self.cartesian_points[-1]
# Compute alt of the begining and the last point
self.beg_azim, self.beg_alt = raDec2AltAz(self.ra_array[0], self.dec_array[0], self.jd_array[0], \
self.lat, self.lon)
self.end_azim, self.end_alt = raDec2AltAz(self.ra_array[-1], self.dec_array[-1], self.jd_array[-1], \
self.lat, self.lon)
# Fit a great circle through observations
x_arr, y_arr, z_arr = self.cartesian_points.T
coeffs, self.theta0, self.phi0 = fitGreatCircle(x_arr, y_arr, z_arr)
# Calculate the plane normal
self.normal = np.array([coeffs[0], coeffs[1], -1.0])
# Norm the normal vector to unit length
self.normal = vectNorm(self.normal)
# Compute RA/Dec of the normal direction
self.normal_ra, self.normal_dec = vector2RaDec(self.normal)
# Take pointing directions of the beginning and the end of the meteor
self.meteor_begin_cartesian = vectNorm(self.cartesian_points[0])
self.meteor_end_cartesian = vectNorm(self.cartesian_points[-1])
# Compute angular distance between begin and end (radians)
self.ang_be = angularSeparationVect(self.beg_vect, self.end_vect)
# Compute meteor duration in seconds
self.duration = (self.jd_array[-1] - self.jd_array[0])*86400.0
# Set the reference JD as the JD of the beginning
self.jdt_ref = self.jd_array[0]
# Compute the solar longitude of the beginning (degrees)
self.lasun = np.degrees(jd2SolLonSteyaert(self.jdt_ref))
def computeApparentRadiant(self,
latitude,
longitude,
jdt_ref,
meteor_fixed_ht=100000):
""" Compute the apparent radiant of the shower at the given location and time.
Arguments:
latitude: [float] Latitude of the observer (deg).
longitude: [float] Longitude of the observer (deg).
jdt_ref: [float] Julian date.
Keyword arguments:
meteor_fixed_ht: [float] Assumed height of the meteor (m). 100 km by default.
Return;
ra, dec, v_init: [tuple of floats] Apparent radiant (deg and m/s).
"""
# Compute the location of the radiant due to radiant drift
if not np.any(np.isnan([self.dra, self.ddec])):
# Solar longitude difference form the peak
lasun_diff = (np.degrees(jd2SolLonSteyaert(jdt_ref)) -
self.lasun_max + 180) % 360 - 180
ra_g = self.ra_g + lasun_diff * self.dra
dec_g = self.dec_g + lasun_diff * self.ddec
# Compute the apparent radiant - assume that the meteor is directly above the station
self.ra, self.dec, self.v_init = geocentricToApparentRadiantAndVelocity(ra_g, \
dec_g, 1000*self.vg, latitude, longitude, meteor_fixed_ht, \
jdt_ref, include_rotation=True)
return self.ra, self.dec, self.v_init
def computeFlux(config, dir_path, ftpdetectinfo_path, shower_code, dt_beg, dt_end, timebin, mass_index, \
timebin_intdt=0.25, ht_std_percent=5.0, mask=None):
""" Compute flux using measurements in the given FTPdetectinfo file.
Arguments:
config: [Config instance]
dir_path: [str] Path to the working directory.
ftpdetectinfo_path: [str] Path to a FTPdetectinfo file.
shower_code: [str] IAU shower code (e.g. ETA, PER, SDA).
dt_beg: [Datetime] Datetime object of the observation beginning.
dt_end: [Datetime] Datetime object of the observation end.
timebin: [float] Time bin in hours.
mass_index: [float] Cumulative mass index of the shower.
Keyword arguments:
timebin_intdt: [float] Time step for computing the integrated collection area in hours. 15 minutes by
default. If smaller than that, only one collection are will be computed.
ht_std_percent: [float] Meteor height standard deviation in percent.
mask: [Mask object] Mask object, None by default.
"""
# Get a list of files in the night folder
file_list = sorted(os.listdir(dir_path))
# Find and load the platepar file
if config.platepar_name in file_list:
# Load the platepar
platepar = Platepar.Platepar()
platepar.read(os.path.join(dir_path, config.platepar_name), use_flat=config.use_flat)
else:
print("Cannot find the platepar file in the night directory: ", config.platepar_name)
return None
# # Load FTPdetectinfos
# meteor_data = []
# for ftpdetectinfo_path in ftpdetectinfo_list:
# if not os.path.isfile(ftpdetectinfo_path):
# print('No such file:', ftpdetectinfo_path)
# continue
# meteor_data += readFTPdetectinfo(*os.path.split(ftpdetectinfo_path))
# Load meteor data from the FTPdetectinfo file
meteor_data = readFTPdetectinfo(*os.path.split(ftpdetectinfo_path))
if not len(meteor_data):
print("No meteors in the FTPdetectinfo file!")
return None
# Find and load recalibrated platepars
if config.platepars_recalibrated_name in file_list:
with open(os.path.join(dir_path, config.platepars_recalibrated_name)) as f:
recalibrated_platepars_dict = json.load(f)
print("Recalibrated platepars loaded!")
# If the file is not available, apply the recalibration procedure
else:
recalibrated_platepars_dict = applyRecalibrate(ftpdetectinfo_path, config)
print("Recalibrated platepar file not available!")
print("Recalibrating...")
# Convert the dictionary of recalibrated platepars to a dictionary of Platepar objects
recalibrated_platepars = {}
for ff_name in recalibrated_platepars_dict:
pp = Platepar.Platepar()
pp.loadFromDict(recalibrated_platepars_dict[ff_name], use_flat=config.use_flat)
recalibrated_platepars[ff_name] = pp
# Compute nighly mean of the photometric zero point
mag_lev_nightly_mean = np.mean([recalibrated_platepars[ff_name].mag_lev \
for ff_name in recalibrated_platepars])
# Locate and load the mask file
if config.mask_file in file_list:
mask_path = os.path.join(dir_path, config.mask_file)
mask = loadMask(mask_path)
print("Using mask:", mask_path)
else:
print("No mask used!")
mask = None
# Compute the population index using the classical equation
population_index = 10**((mass_index - 1)/2.5)
### SENSOR CHARACTERIZATION ###
# Computes FWHM of stars and noise profile of the sensor
# File which stores the sensor characterization profile
sensor_characterization_file = "flux_sensor_characterization.json"
sensor_characterization_path = os.path.join(dir_path, sensor_characterization_file)
# Load sensor characterization file if present, so the procedure can be skipped
if os.path.isfile(sensor_characterization_path):
# Load the JSON file
with open(sensor_characterization_path) as f:
data = " ".join(f.readlines())
sensor_data = json.loads(data)
# Remove the info entry
if '-1' in sensor_data:
del sensor_data['-1']
else:
# Run sensor characterization
sensor_data = sensorCharacterization(config, dir_path)
# Save to file for posterior use
with open(sensor_characterization_path, 'w') as f:
# Add an explanation what each entry means
sensor_data_save = dict(sensor_data)
sensor_data_save['-1'] = {"FF file name": ['median star FWHM', 'median background noise stddev']}
# Convert collection areas to JSON
out_str = json.dumps(sensor_data_save, indent=4, sort_keys=True)
# Save to disk
f.write(out_str)
# Compute the nighly mean FWHM and noise stddev
fwhm_nightly_mean = np.mean([sensor_data[key][0] for key in sensor_data])
stddev_nightly_mean = np.mean([sensor_data[key][1] for key in sensor_data])
### ###
# Perform shower association
associations, shower_counts = showerAssociation(config, [ftpdetectinfo_path], shower_code=shower_code, \
show_plot=False, save_plot=False, plot_activity=False)
# If there are no shower association, return nothing
if not associations:
print("No meteors associated with the shower!")
return None
# Print the list of used meteors
peak_mags = []
for key in associations:
meteor, shower = associations[key]
if shower is not None:
# Compute peak magnitude
peak_mag = np.min(meteor.mag_array)
peak_mags.append(peak_mag)
print("{:.6f}, {:3s}, {:+.2f}".format(meteor.jdt_ref, shower.name, peak_mag))
print()
# Init the flux configuration
flux_config = FluxConfig()
### COMPUTE COLLECTION AREAS ###
# Make a file name to save the raw collection areas
col_areas_file_name = generateColAreaJSONFileName(platepar.station_code, flux_config.side_points, \
flux_config.ht_min, flux_config.ht_max, flux_config.dht, flux_config.elev_limit)
# Check if the collection area file exists. If yes, load the data. If not, generate collection areas
if col_areas_file_name in os.listdir(dir_path):
col_areas_ht = loadRawCollectionAreas(dir_path, col_areas_file_name)
print("Loaded collection areas from:", col_areas_file_name)
else:
# Compute the collecting areas segments per height
col_areas_ht = collectingArea(platepar, mask=mask, side_points=flux_config.side_points, \
ht_min=flux_config.ht_min, ht_max=flux_config.ht_max, dht=flux_config.dht, \
elev_limit=flux_config.elev_limit)
# Save the collection areas to file
saveRawCollectionAreas(dir_path, col_areas_file_name, col_areas_ht)
print("Saved raw collection areas to:", col_areas_file_name)
### ###
# Compute the pointing of the middle of the FOV
_, ra_mid, dec_mid, _ = xyToRaDecPP([jd2Date(J2000_JD.days)], [platepar.X_res/2], [platepar.Y_res/2], \
[1], platepar, extinction_correction=False)
azim_mid, elev_mid = raDec2AltAz(ra_mid[0], dec_mid[0], J2000_JD.days, platepar.lat, platepar.lon)
# Compute the range to the middle point
ref_ht = 100000
r_mid, _, _, _ = xyHt2Geo(platepar, platepar.X_res/2, platepar.Y_res/2, ref_ht, indicate_limit=True, \
elev_limit=flux_config.elev_limit)
### Compute the average angular velocity to which the flux variation throught the night will be normalized
# The ang vel is of the middle of the FOV in the middle of observations
# Middle Julian date of the night
jd_night_mid = (datetime2JD(dt_beg) + datetime2JD(dt_end))/2
# Compute the apparent radiant
ra, dec, v_init = shower.computeApparentRadiant(platepar.lat, platepar.lon, jd_night_mid)
# Compute the radiant elevation
radiant_azim, radiant_elev = raDec2AltAz(ra, dec, jd_night_mid, platepar.lat, platepar.lon)
# Compute the angular velocity in the middle of the FOV
rad_dist_night_mid = angularSeparation(np.radians(radiant_azim), np.radians(radiant_elev),
np.radians(azim_mid), np.radians(elev_mid))
ang_vel_night_mid = v_init*np.sin(rad_dist_night_mid)/r_mid
###
# Compute the average limiting magnitude to which all flux will be normalized
# Standard deviation of star PSF, nightly mean (px)
star_stddev = fwhm_nightly_mean/2.355
# Compute the theoretical stellar limiting magnitude (nightly average)
star_sum = 2*np.pi*(config.k1_det*stddev_nightly_mean + config.j1_det)*star_stddev**2
lm_s_nightly_mean = -2.5*np.log10(star_sum) + mag_lev_nightly_mean
# A meteor needs to be visible on at least 4 frames, thus it needs to have at least 4x the mass to produce
# that amount of light. 1 magnitude difference scales as -0.4 of log of mass, thus:
frame_min_loss = np.log10(config.line_minimum_frame_range_det)/(-0.4)
lm_s_nightly_mean += frame_min_loss
# Compute apparent meteor magnitude
lm_m_nightly_mean = lm_s_nightly_mean - 5*np.log10(r_mid/1e5) - 2.5*np.log10( \
np.degrees(platepar.F_scale*v_init*np.sin(rad_dist_night_mid)/(config.fps*r_mid*fwhm_nightly_mean)) \
)
#
print("Stellar lim mag using detection thresholds:", lm_s_nightly_mean)
print("Apparent meteor limiting magnitude:", lm_m_nightly_mean)
### Apply time-dependent corrections ###
sol_data = []
flux_lm_6_5_data = []
# Go through all time bins within the observation period
total_time_hrs = (dt_end - dt_beg).total_seconds()/3600
nbins = int(np.ceil(total_time_hrs/timebin))
for t_bin in range(nbins):
# Compute bin start and end time
bin_dt_beg = dt_beg + datetime.timedelta(hours=timebin*t_bin)
bin_dt_end = bin_dt_beg + datetime.timedelta(hours=timebin)
if bin_dt_end > dt_end:
bin_dt_end = dt_end
# Compute bin duration in hours
bin_hours = (bin_dt_end - bin_dt_beg).total_seconds()/3600
# Convert to Julian date
bin_jd_beg = datetime2JD(bin_dt_beg)
bin_jd_end = datetime2JD(bin_dt_end)
# Only select meteors in this bin
bin_meteors = []
bin_ffs = []
for key in associations:
meteor, shower = associations[key]
if shower is not None:
if (shower.name == shower_code) and (meteor.jdt_ref > bin_jd_beg) \
and (meteor.jdt_ref <= bin_jd_end):
bin_meteors.append([meteor, shower])
bin_ffs.append(meteor.ff_name)
if len(bin_meteors) > 0:
### Compute the radiant elevation at the middle of the time bin ###
jd_mean = (bin_jd_beg + bin_jd_end)/2
# Compute the mean solar longitude
sol_mean = np.degrees(jd2SolLonSteyaert(jd_mean))
print()
print()
print("-- Bin information ---")
print("Bin beg:", bin_dt_beg)
print("Bin end:", bin_dt_end)
print("Sol mid: {:.5f}".format(sol_mean))
print("Meteors:", len(bin_meteors))
# Compute the apparent radiant
ra, dec, v_init = shower.computeApparentRadiant(platepar.lat, platepar.lon, jd_mean)
# Compute the mean meteor height
meteor_ht_beg = heightModel(v_init, ht_type='beg')
meteor_ht_end = heightModel(v_init, ht_type='end')
meteor_ht = (meteor_ht_beg + meteor_ht_end)/2
# Compute the standard deviation of the height
meteor_ht_std = meteor_ht*ht_std_percent/100.0
# Init the Gaussian height distribution
meteor_ht_gauss = scipy.stats.norm(meteor_ht, meteor_ht_std)
# Compute the radiant elevation
radiant_azim, radiant_elev = raDec2AltAz(ra, dec, jd_mean, platepar.lat, platepar.lon)
### ###
### Weight collection area by meteor height distribution ###
# Determine weights for each height
weight_sum = 0
weights = {}
for ht in col_areas_ht:
wt = meteor_ht_gauss.pdf(float(ht))
weight_sum += wt
weights[ht] = wt
# Normalize the weights so that the sum is 1
for ht in weights:
weights[ht] /= weight_sum
### ###
# Compute the angular velocity in the middle of the FOV
rad_dist_mid = angularSeparation(np.radians(radiant_azim), np.radians(radiant_elev),
np.radians(azim_mid), np.radians(elev_mid))
ang_vel_mid = v_init*np.sin(rad_dist_mid)/r_mid
### Compute the limiting magnitude ###
# Compute the mean star FWHM in the given bin
fwhm_bin_mean = np.mean([sensor_data[ff_name][0] for ff_name in bin_ffs])
# Compute the mean background stddev in the given bin
stddev_bin_mean = np.mean([sensor_data[ff_name][1] for ff_name in bin_ffs])
# Compute the mean photometric zero point in the given bin
mag_lev_bin_mean = np.mean([recalibrated_platepars[ff_name].mag_lev for ff_name in bin_ffs if ff_name in recalibrated_platepars])
# Standard deviation of star PSF, nightly mean (px)
star_stddev = fwhm_bin_mean/2.355
# Compute the theoretical stellar limiting magnitude (nightly average)
star_sum = 2*np.pi*(config.k1_det*stddev_bin_mean + config.j1_det)*star_stddev**2
lm_s = -2.5*np.log10(star_sum) + mag_lev_bin_mean
lm_s += frame_min_loss
# Compute apparent meteor magnitude
lm_m = lm_s - 5*np.log10(r_mid/1e5) - 2.5*np.log10( \
np.degrees(platepar.F_scale*v_init*np.sin(rad_dist_mid)/(config.fps*r_mid*fwhm_bin_mean))\
)
### ###
# Final correction area value (height-weightned)
collection_area = 0
# Go through all heights and segment blocks
for ht in col_areas_ht:
for img_coords in col_areas_ht[ht]:
x_mean, y_mean = img_coords
# Unpack precomputed values
area, azim, elev, sensitivity_ratio, r = col_areas_ht[ht][img_coords]
# Compute the angular velocity (rad/s) in the middle of this block
rad_dist = angularSeparation(np.radians(radiant_azim), np.radians(radiant_elev),
np.radians(azim), np.radians(elev))
ang_vel = v_init*np.sin(rad_dist)/r
# Compute the range correction
range_correction = (1e5/r)**2
#ang_vel_correction = ang_vel/ang_vel_mid
# Compute angular velocity correction relative to the nightly mean
ang_vel_correction = ang_vel/ang_vel_night_mid
### Apply corrections
correction_ratio = 1.0
# Correct the area for vignetting and extinction
correction_ratio *= sensitivity_ratio
# Correct for the range
correction_ratio *= range_correction
# Correct for the radiant elevation
correction_ratio *= np.sin(np.radians(radiant_elev))
# Correct for angular velocity
correction_ratio *= ang_vel_correction
# Add the collection area to the final estimate with the height weight
# Raise the correction to the mass index power
collection_area += weights[ht]*area*correction_ratio**(mass_index - 1)
# Compute the flux at the bin LM (meteors/1000km^2/h)
flux = 1e9*len(bin_meteors)/collection_area/bin_hours
# Compute the flux scaled to the nightly mean LM
flux_lm_nightly_mean = flux*population_index**(lm_m_nightly_mean - lm_m)
# Compute the flux scaled to +6.5M
flux_lm_6_5 = flux*population_index**(6.5 - lm_m)
print("-- Sensor information ---")
print("Star FWHM: {:5.2f} px".format(fwhm_bin_mean))
print("Bkg stddev: {:4.1f} ADU".format(stddev_bin_mean))
print("Photom ZP: {:+6.2f} mag".format(mag_lev_bin_mean))
print("Stellar LM: {:+.2f} mag".format(lm_s))
print("-- Flux ---")
print("Col area: {:d} km^2".format(int(collection_area/1e6)))
print("Ang vel: {:.2f} deg/s".format(np.degrees(ang_vel_mid)))
print("LM app: {:+.2f} mag".format(lm_m))
print("Flux: {:.2f} meteors/1000km^2/h".format(flux))
print("to {:+.2f}: {:.2f} meteors/1000km^2/h".format(lm_m_nightly_mean, flux_lm_nightly_mean))
print("to +6.50: {:.2f} meteors/1000km^2/h".format(flux_lm_6_5))
sol_data.append(sol_mean)
flux_lm_6_5_data.append(flux_lm_6_5)
# Print the results
print("Solar longitude, Flux at LM +6.5:")
for sol, flux_lm_6_5 in zip(sol_data, flux_lm_6_5_data):
print("{:9.5f}, {:8.4f}".format(sol, flux_lm_6_5))
# Plot a histogram of peak magnitudes
plt.hist(peak_mags, cumulative=True)
plt.show()
def computeFlux(config, dir_path, ftpdetectinfo_path, shower_code, dt_beg, dt_end, timebin, mass_index, \
timebin_intdt=0.25, ht_std_percent=5.0, mask=None, show_plots=True):
""" Compute flux using measurements in the given FTPdetectinfo file.
Arguments:
config: [Config instance]
dir_path: [str] Path to the working directory.
ftpdetectinfo_path: [str] Path to a FTPdetectinfo file.
shower_code: [str] IAU shower code (e.g. ETA, PER, SDA).
dt_beg: [Datetime] Datetime object of the observation beginning.
dt_end: [Datetime] Datetime object of the observation end.
timebin: [float] Time bin in hours.
mass_index: [float] Cumulative mass index of the shower.
Keyword arguments:
timebin_intdt: [float] Time step for computing the integrated collection area in hours. 15 minutes by
default. If smaller than that, only one collection are will be computed.
ht_std_percent: [float] Meteor height standard deviation in percent.
mask: [Mask object] Mask object, None by default.
show_plots: [bool] Show flux plots. True by default.
Return:
[tuple] sol_data, flux_lm_6_5_data
- sol_data: [list] Array of solar longitudes (in degrees) of time bins.
- flux_lm6_5_data: [list] Array of meteoroid flux at the limiting magnitude of +6.5 in
meteors/1000km^2/h.
"""
# Get a list of files in the night folder
file_list = sorted(os.listdir(dir_path))
# Find and load the platepar file
if config.platepar_name in file_list:
# Load the platepar
platepar = Platepar.Platepar()
platepar.read(os.path.join(dir_path, config.platepar_name), use_flat=config.use_flat)
else:
print("Cannot find the platepar file in the night directory: ", config.platepar_name)
return None
# # Load FTPdetectinfos
# meteor_data = []
# for ftpdetectinfo_path in ftpdetectinfo_list:
# if not os.path.isfile(ftpdetectinfo_path):
# print('No such file:', ftpdetectinfo_path)
# continue
# meteor_data += readFTPdetectinfo(*os.path.split(ftpdetectinfo_path))
# Load meteor data from the FTPdetectinfo file
meteor_data = readFTPdetectinfo(*os.path.split(ftpdetectinfo_path))
if not len(meteor_data):
print("No meteors in the FTPdetectinfo file!")
return None
# Find and load recalibrated platepars
if config.platepars_recalibrated_name in file_list:
with open(os.path.join(dir_path, config.platepars_recalibrated_name)) as f:
recalibrated_platepars_dict = json.load(f)
print("Recalibrated platepars loaded!")
# If the file is not available, apply the recalibration procedure
else:
recalibrated_platepars_dict = applyRecalibrate(ftpdetectinfo_path, config)
print("Recalibrated platepar file not available!")
print("Recalibrating...")
# Convert the dictionary of recalibrated platepars to a dictionary of Platepar objects
recalibrated_platepars = {}
for ff_name in recalibrated_platepars_dict:
pp = Platepar.Platepar()
pp.loadFromDict(recalibrated_platepars_dict[ff_name], use_flat=config.use_flat)
recalibrated_platepars[ff_name] = pp
# Compute nighly mean of the photometric zero point
mag_lev_nightly_mean = np.mean([recalibrated_platepars[ff_name].mag_lev \
for ff_name in recalibrated_platepars])
# Locate and load the mask file
if config.mask_file in file_list:
mask_path = os.path.join(dir_path, config.mask_file)
mask = loadMask(mask_path)
print("Using mask:", mask_path)
else:
print("No mask used!")
mask = None
# Compute the population index using the classical equation
population_index = 10**((mass_index - 1)/2.5) # Found to be more consistent when comparing fluxes
#population_index = 10**((mass_index - 1)/2.3) # TEST !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!1
### SENSOR CHARACTERIZATION ###
# Computes FWHM of stars and noise profile of the sensor
# File which stores the sensor characterization profile
sensor_characterization_file = "flux_sensor_characterization.json"
sensor_characterization_path = os.path.join(dir_path, sensor_characterization_file)
# Load sensor characterization file if present, so the procedure can be skipped
if os.path.isfile(sensor_characterization_path):
# Load the JSON file
with open(sensor_characterization_path) as f:
data = " ".join(f.readlines())
sensor_data = json.loads(data)
# Remove the info entry
if '-1' in sensor_data:
del sensor_data['-1']
else:
# Run sensor characterization
sensor_data = sensorCharacterization(config, dir_path)
# Save to file for posterior use
with open(sensor_characterization_path, 'w') as f:
# Add an explanation what each entry means
sensor_data_save = dict(sensor_data)
sensor_data_save['-1'] = {"FF file name": ['median star FWHM', 'median background noise stddev']}
# Convert collection areas to JSON
out_str = json.dumps(sensor_data_save, indent=4, sort_keys=True)
# Save to disk
f.write(out_str)
# Compute the nighly mean FWHM and noise stddev
fwhm_nightly_mean = np.mean([sensor_data[key][0] for key in sensor_data])
stddev_nightly_mean = np.mean([sensor_data[key][1] for key in sensor_data])
### ###
# Perform shower association
associations, _ = showerAssociation(config, [ftpdetectinfo_path], shower_code=shower_code, \
show_plot=False, save_plot=False, plot_activity=False)
# Init the flux configuration
flux_config = FluxConfig()
# Remove all meteors which begin below the limit height
filtered_associations = {}
for key in associations:
meteor, shower = associations[key]
if meteor.beg_alt > flux_config.elev_limit:
print("Rejecting:", meteor.jdt_ref)
filtered_associations[key] = [meteor, shower]
associations = filtered_associations
# If there are no shower association, return nothing
if not associations:
print("No meteors associated with the shower!")
return None
# Print the list of used meteors
peak_mags = []
for key in associations:
meteor, shower = associations[key]
if shower is not None:
# Compute peak magnitude
peak_mag = np.min(meteor.mag_array)
peak_mags.append(peak_mag)
print("{:.6f}, {:3s}, {:+.2f}".format(meteor.jdt_ref, shower.name, peak_mag))
print()
### COMPUTE COLLECTION AREAS ###
# Make a file name to save the raw collection areas
col_areas_file_name = generateColAreaJSONFileName(platepar.station_code, flux_config.side_points, \
flux_config.ht_min, flux_config.ht_max, flux_config.dht, flux_config.elev_limit)
# Check if the collection area file exists. If yes, load the data. If not, generate collection areas
if col_areas_file_name in os.listdir(dir_path):
col_areas_ht = loadRawCollectionAreas(dir_path, col_areas_file_name)
print("Loaded collection areas from:", col_areas_file_name)
else:
# Compute the collecting areas segments per height
col_areas_ht = collectingArea(platepar, mask=mask, side_points=flux_config.side_points, \
ht_min=flux_config.ht_min, ht_max=flux_config.ht_max, dht=flux_config.dht, \
elev_limit=flux_config.elev_limit)
# Save the collection areas to file
saveRawCollectionAreas(dir_path, col_areas_file_name, col_areas_ht)
print("Saved raw collection areas to:", col_areas_file_name)
### ###
# Compute the raw collection area at the height of 100 km
col_area_100km_raw = 0
col_areas_100km_blocks = col_areas_ht[100000.0]
for block in col_areas_100km_blocks:
col_area_100km_raw += col_areas_100km_blocks[block][0]
print("Raw collection area at height of 100 km: {:.2f} km^2".format(col_area_100km_raw/1e6))
# Compute the pointing of the middle of the FOV
_, ra_mid, dec_mid, _ = xyToRaDecPP([jd2Date(J2000_JD.days)], [platepar.X_res/2], [platepar.Y_res/2], \
[1], platepar, extinction_correction=False)
azim_mid, elev_mid = raDec2AltAz(ra_mid[0], dec_mid[0], J2000_JD.days, platepar.lat, platepar.lon)
# Compute the range to the middle point
ref_ht = 100000
r_mid, _, _, _ = xyHt2Geo(platepar, platepar.X_res/2, platepar.Y_res/2, ref_ht, indicate_limit=True, \
elev_limit=flux_config.elev_limit)
print("Range at 100 km in the middle of the image: {:.2f} km".format(r_mid/1000))
### Compute the average angular velocity to which the flux variation throught the night will be normalized
# The ang vel is of the middle of the FOV in the middle of observations
# Middle Julian date of the night
jd_night_mid = (datetime2JD(dt_beg) + datetime2JD(dt_end))/2
# Compute the apparent radiant
ra, dec, v_init = shower.computeApparentRadiant(platepar.lat, platepar.lon, jd_night_mid)
# Compute the radiant elevation
radiant_azim, radiant_elev = raDec2AltAz(ra, dec, jd_night_mid, platepar.lat, platepar.lon)
# Compute the angular velocity in the middle of the FOV
rad_dist_night_mid = angularSeparation(np.radians(radiant_azim), np.radians(radiant_elev),
np.radians(azim_mid), np.radians(elev_mid))
ang_vel_night_mid = v_init*np.sin(rad_dist_night_mid)/r_mid
###
# Compute the average limiting magnitude to which all flux will be normalized
# Standard deviation of star PSF, nightly mean (px)
star_stddev = fwhm_nightly_mean/2.355
# # Compute the theoretical stellar limiting magnitude (nightly average)
# star_sum = 2*np.pi*(config.k1_det*stddev_nightly_mean + config.j1_det)*star_stddev**2
# lm_s_nightly_mean = -2.5*np.log10(star_sum) + mag_lev_nightly_mean
# Compute the theoretical stellar limiting magnitude using an empirical model (nightly average)
lm_s_nightly_mean = stellarLMModel(mag_lev_nightly_mean)
# A meteor needs to be visible on at least 4 frames, thus it needs to have at least 4x the mass to produce
# that amount of light. 1 magnitude difference scales as -0.4 of log of mass, thus:
# frame_min_loss = np.log10(config.line_minimum_frame_range_det)/(-0.4)
frame_min_loss = 0.0 # TEST !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!11
print("Frame min loss: {:.2} mag".format(frame_min_loss))
lm_s_nightly_mean += frame_min_loss
# Compute apparent meteor magnitude
lm_m_nightly_mean = lm_s_nightly_mean - 5*np.log10(r_mid/1e5) - 2.5*np.log10( \
np.degrees(platepar.F_scale*v_init*np.sin(rad_dist_night_mid)/(config.fps*r_mid*fwhm_nightly_mean)) \
)
#
print("Stellar lim mag using detection thresholds:", lm_s_nightly_mean)
print("Apparent meteor limiting magnitude:", lm_m_nightly_mean)
### Apply time-dependent corrections ###
# Track values used for flux
sol_data = []
flux_lm_6_5_data = []
meteor_num_data = []
effective_collection_area_data = []
radiant_elev_data = []
radiant_dist_mid_data = []
ang_vel_mid_data = []
lm_s_data = []
lm_m_data = []
sensitivity_corr_data = []
range_corr_data = []
radiant_elev_corr_data = []
ang_vel_corr_data = []
total_corr_data = []
# Go through all time bins within the observation period
total_time_hrs = (dt_end - dt_beg).total_seconds()/3600
nbins = int(np.ceil(total_time_hrs/timebin))
for t_bin in range(nbins):
for subbin in range(flux_config.sub_time_bins):
# Compute bin start and end time
bin_dt_beg = dt_beg + datetime.timedelta(hours=(timebin*t_bin + timebin*subbin/flux_config.sub_time_bins))
bin_dt_end = bin_dt_beg + datetime.timedelta(hours=timebin)
if bin_dt_end > dt_end:
bin_dt_end = dt_end
# Compute bin duration in hours
bin_hours = (bin_dt_end - bin_dt_beg).total_seconds()/3600
# Convert to Julian date
bin_jd_beg = datetime2JD(bin_dt_beg)
bin_jd_end = datetime2JD(bin_dt_end)
jd_mean = (bin_jd_beg + bin_jd_end)/2
# Compute the mean solar longitude
sol_mean = np.degrees(jd2SolLonSteyaert(jd_mean))
### Compute the radiant elevation at the middle of the time bin ###
# Compute the apparent radiant
ra, dec, v_init = shower.computeApparentRadiant(platepar.lat, platepar.lon, jd_mean)
# Compute the mean meteor height
meteor_ht_beg = heightModel(v_init, ht_type='beg')
meteor_ht_end = heightModel(v_init, ht_type='end')
meteor_ht = (meteor_ht_beg + meteor_ht_end)/2
# Compute the standard deviation of the height
meteor_ht_std = meteor_ht*ht_std_percent/100.0
# Init the Gaussian height distribution
meteor_ht_gauss = scipy.stats.norm(meteor_ht, meteor_ht_std)
# Compute the radiant elevation
radiant_azim, radiant_elev = raDec2AltAz(ra, dec, jd_mean, platepar.lat, platepar.lon)
# Only select meteors in this bin and not too close to the radiant
bin_meteors = []
bin_ffs = []
for key in associations:
meteor, shower = associations[key]
if shower is not None:
if (shower.name == shower_code) and (meteor.jdt_ref > bin_jd_beg) \
and (meteor.jdt_ref <= bin_jd_end):
# Filter out meteors ending too close to the radiant
if np.degrees(angularSeparation(np.radians(radiant_azim), np.radians(radiant_elev), \
np.radians(meteor.end_azim), np.radians(meteor.end_alt))) >= flux_config.rad_dist_min:
bin_meteors.append([meteor, shower])
bin_ffs.append(meteor.ff_name)
### ###
print()
print()
print("-- Bin information ---")
print("Bin beg:", bin_dt_beg)
print("Bin end:", bin_dt_end)
print("Sol mid: {:.5f}".format(sol_mean))
print("Radiant elevation: {:.2f} deg".format(radiant_elev))
print("Apparent speed: {:.2f} km/s".format(v_init/1000))
# If the elevation of the radiant is below the limit, skip this bin
if radiant_elev < flux_config.rad_elev_limit:
print("!!! Mean radiant elevation below {:.2f} deg threshold, skipping time bin!".format(flux_config.rad_elev_limit))
continue
# The minimum duration of the time bin should be larger than 50% of the given dt
if bin_hours < 0.5*timebin:
print("!!! Time bin duration of {:.2f} h is shorter than 0.5x of the time bin!".format(bin_hours))
continue
if len(bin_meteors) >= flux_config.meteros_min:
print("Meteors:", len(bin_meteors))
### Weight collection area by meteor height distribution ###
# Determine weights for each height
weight_sum = 0
weights = {}
for ht in col_areas_ht:
wt = meteor_ht_gauss.pdf(float(ht))
weight_sum += wt
weights[ht] = wt
# Normalize the weights so that the sum is 1
for ht in weights:
weights[ht] /= weight_sum
### ###
col_area_meteor_ht_raw = 0
for ht in col_areas_ht:
for block in col_areas_ht[ht]:
col_area_meteor_ht_raw += weights[ht]*col_areas_ht[ht][block][0]
print("Raw collection area at meteor heights: {:.2f} km^2".format(col_area_meteor_ht_raw/1e6))
# Compute the angular velocity in the middle of the FOV
rad_dist_mid = angularSeparation(np.radians(radiant_azim), np.radians(radiant_elev),
np.radians(azim_mid), np.radians(elev_mid))
ang_vel_mid = v_init*np.sin(rad_dist_mid)/r_mid
### Compute the limiting magnitude ###
# Compute the mean star FWHM in the given bin
fwhm_bin_mean = np.mean([sensor_data[ff_name][0] for ff_name in bin_ffs])
# Compute the mean background stddev in the given bin
stddev_bin_mean = np.mean([sensor_data[ff_name][1] for ff_name in bin_ffs])
# Compute the mean photometric zero point in the given bin
mag_lev_bin_mean = np.mean([recalibrated_platepars[ff_name].mag_lev for ff_name in bin_ffs if ff_name in recalibrated_platepars])
# # Standard deviation of star PSF, nightly mean (px)
# star_stddev = fwhm_bin_mean/2.355
# Compute the theoretical stellar limiting magnitude (bin average)
# star_sum = 2*np.pi*(config.k1_det*stddev_bin_mean + config.j1_det)*star_stddev**2
# lm_s = -2.5*np.log10(star_sum) + mag_lev_bin_mean
# Use empirical LM calculation
lm_s = stellarLMModel(mag_lev_bin_mean)
lm_s += frame_min_loss
# ### TEST !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!11
# # Artificialy increase limiting magnitude
# lm_s += 1.2
# #####
# Compute apparent meteor magnitude
lm_m = lm_s - 5*np.log10(r_mid/1e5) - 2.5*np.log10( \
np.degrees(platepar.F_scale*v_init*np.sin(rad_dist_mid)/(config.fps*r_mid*fwhm_bin_mean)))
### ###
# Final correction area value (height-weightned)
collection_area = 0
# Keep track of the corrections
sensitivity_corr_arr = []
range_corr_arr = []
radiant_elev_corr_arr = []
ang_vel_corr_arr = []
total_corr_arr = []
col_area_raw_arr = []
col_area_eff_arr = []
col_area_eff_block_dict = {}
# Go through all heights and segment blocks
for ht in col_areas_ht:
for img_coords in col_areas_ht[ht]:
x_mean, y_mean = img_coords
# Unpack precomputed values
area, azim, elev, sensitivity_ratio, r = col_areas_ht[ht][img_coords]
# Compute the angular velocity (rad/s) in the middle of this block
rad_dist = angularSeparation(np.radians(radiant_azim), np.radians(radiant_elev),
np.radians(azim), np.radians(elev))
ang_vel = v_init*np.sin(rad_dist)/r
# If the angular distance from the radiant is less than 15 deg, don't use the block
# in the effective collection area
if np.degrees(rad_dist) < flux_config.rad_dist_min:
area = 0.0
# Compute the range correction
range_correction = (1e5/r)**2
#ang_vel_correction = ang_vel/ang_vel_mid
# Compute angular velocity correction relative to the nightly mean
ang_vel_correction = ang_vel/ang_vel_night_mid
### Apply corrections
correction_ratio = 1.0
# Correct the area for vignetting and extinction
sensitivity_corr_arr.append(sensitivity_ratio)
correction_ratio *= sensitivity_ratio
# Correct for the range (cap to an order of magnitude correction)
range_correction = max(range_correction, 0.1)
range_corr_arr.append(range_correction)
correction_ratio *= range_correction
# Correct for the radiant elevation (cap to an order of magnitude correction)
radiant_elev_correction = np.sin(np.radians(radiant_elev))
radiant_elev_correction = max(radiant_elev_correction, 0.1)
radiant_elev_corr_arr.append(radiant_elev_correction)
correction_ratio *= radiant_elev_correction
# Correct for angular velocity (cap to an order of magnitude correction)
ang_vel_correction = min(max(ang_vel_correction, 0.1), 10)
correction_ratio *= ang_vel_correction
ang_vel_corr_arr.append(ang_vel_correction)
# Add the collection area to the final estimate with the height weight
# Raise the correction to the mass index power
total_correction = correction_ratio**(mass_index - 1)
total_correction = min(max(total_correction, 0.1), 10)
collection_area += weights[ht]*area*total_correction
total_corr_arr.append(total_correction)
col_area_raw_arr.append(weights[ht]*area)
col_area_eff_arr.append(weights[ht]*area*total_correction)
if img_coords not in col_area_eff_block_dict:
col_area_eff_block_dict[img_coords] = []
col_area_eff_block_dict[img_coords].append(weights[ht]*area*total_correction)
# Compute mean corrections
sensitivity_corr_avg = np.mean(sensitivity_corr_arr)
range_corr_avg = np.mean(range_corr_arr)
radiant_elev_corr_avg = np.mean(radiant_elev_corr_arr)
ang_vel_corr_avg = np.mean(ang_vel_corr_arr)
total_corr_avg = np.median(total_corr_arr)
col_area_raw_sum = np.sum(col_area_raw_arr)
col_area_eff_sum = np.sum(col_area_eff_arr)
print("Raw collection area at meteor heights (CHECK): {:.2f} km^2".format(col_area_raw_sum/1e6))
print("Eff collection area at meteor heights (CHECK): {:.2f} km^2".format(col_area_eff_sum/1e6))
# ### PLOT HOW THE CORRECTION VARIES ACROSS THE FOV
# x_arr = []
# y_arr = []
# col_area_eff_block_arr = []
# for img_coords in col_area_eff_block_dict:
# x_mean, y_mean = img_coords
# #if x_mean not in x_arr:
# x_arr.append(x_mean)
# #if y_mean not in y_arr:
# y_arr.append(y_mean)
# col_area_eff_block_arr.append(np.sum(col_area_eff_block_dict[img_coords]))
# x_unique = np.unique(x_arr)
# y_unique = np.unique(y_arr)
# # plt.pcolormesh(x_arr, y_arr, np.array(col_area_eff_block_arr).reshape(len(x_unique), len(y_unique)).T, shading='auto')
# plt.title("TOTAL = " + str(np.sum(col_area_eff_block_arr)/1e6))
# plt.scatter(x_arr, y_arr, c=np.array(col_area_eff_block_arr)/1e6)
# #plt.pcolor(np.array(x_arr).reshape(len(x_unique), len(y_unique)), np.array(y_arr).reshape(len(x_unique), len(y_unique)), np.array(col_area_eff_block_arr).reshape(len(x_unique), len(y_unique))/1e6)
# plt.colorbar(label="km^2")
# plt.gca().invert_yaxis()
# plt.show()
# ###
# Compute the flux at the bin LM (meteors/1000km^2/h)
flux = 1e9*len(bin_meteors)/collection_area/bin_hours
# Compute the flux scaled to the nightly mean LM
flux_lm_nightly_mean = flux*population_index**(lm_m_nightly_mean - lm_m)
# Compute the flux scaled to +6.5M
flux_lm_6_5 = flux*population_index**(6.5 - lm_m)
print("-- Sensor information ---")
print("Star FWHM: {:5.2f} px".format(fwhm_bin_mean))
print("Bkg stddev: {:4.1f} ADU".format(stddev_bin_mean))
print("Photom ZP: {:+6.2f} mag".format(mag_lev_bin_mean))
print("Stellar LM: {:+.2f} mag".format(lm_s))
print("-- Flux ---")
print("Meteors: {:d}".format(len(bin_meteors)))
print("Col area: {:d} km^2".format(int(collection_area/1e6)))
print("Ang vel: {:.2f} deg/s".format(np.degrees(ang_vel_mid)))
print("LM app: {:+.2f} mag".format(lm_m))
print("Flux: {:.2f} meteors/1000km^2/h".format(flux))
print("to {:+.2f}: {:.2f} meteors/1000km^2/h".format(lm_m_nightly_mean, flux_lm_nightly_mean))
print("to +6.50: {:.2f} meteors/1000km^2/h".format(flux_lm_6_5))
sol_data.append(sol_mean)
flux_lm_6_5_data.append(flux_lm_6_5)
meteor_num_data.append(len(bin_meteors))
effective_collection_area_data.append(collection_area)
radiant_elev_data.append(radiant_elev)
radiant_dist_mid_data.append(np.degrees(rad_dist_mid))
ang_vel_mid_data.append(np.degrees(ang_vel_mid))
lm_s_data.append(lm_s)
lm_m_data.append(lm_m)
sensitivity_corr_data.append(sensitivity_corr_avg)
range_corr_data.append(range_corr_avg)
radiant_elev_corr_data.append(radiant_elev_corr_avg)
ang_vel_corr_data.append(ang_vel_corr_avg)
total_corr_data.append(total_corr_avg)
# Print the results
print("Solar longitude, Flux at LM +6.5:")
for sol, flux_lm_6_5 in zip(sol_data, flux_lm_6_5_data):
print("{:9.5f}, {:8.4f}".format(sol, flux_lm_6_5))
if show_plots and len(sol_data):
# Plot a histogram of peak magnitudes
plt.hist(peak_mags, cumulative=True, log=True, bins=len(peak_mags), density=True)
# Plot population index
r_intercept = -0.7
x_arr = np.linspace(np.min(peak_mags), np.percentile(peak_mags, 60))
plt.plot(x_arr, 10**(np.log10(population_index)*x_arr + r_intercept))
plt.title("r = {:.2f}".format(population_index))
plt.show()
# Plot how the derived values change throughout the night
fig, axes \
= plt.subplots(nrows=4, ncols=2, sharex=True, figsize=(10, 8))
((ax_met, ax_lm),
(ax_rad_elev, ax_corrs),
(ax_rad_dist, ax_col_area),
(ax_ang_vel, ax_flux)) = axes
fig.suptitle("{:s}, s = {:.2f}, r = {:.2f}".format(shower_code, mass_index, population_index))
ax_met.scatter(sol_data, meteor_num_data)
ax_met.set_ylabel("Meteors")
ax_rad_elev.plot(sol_data, radiant_elev_data)
ax_rad_elev.set_ylabel("Radiant elev (deg)")
ax_rad_dist.plot(sol_data, radiant_dist_mid_data)
ax_rad_dist.set_ylabel("Radiant dist (deg)")
ax_ang_vel.plot(sol_data, ang_vel_mid_data)
ax_ang_vel.set_ylabel("Ang vel (deg/s)")
ax_ang_vel.set_xlabel("La Sun (deg)")
ax_lm.plot(sol_data, lm_s_data, label="Stellar")
ax_lm.plot(sol_data, lm_m_data, label="Meteor")
ax_lm.set_ylabel("LM")
ax_lm.legend()
ax_corrs.plot(sol_data, sensitivity_corr_data, label="Sensitivity")
ax_corrs.plot(sol_data, range_corr_data, label="Range")
ax_corrs.plot(sol_data, radiant_elev_corr_data, label="Rad elev")
ax_corrs.plot(sol_data, ang_vel_corr_data, label="Ang vel")
ax_corrs.plot(sol_data, total_corr_data, label="Total (median)")
ax_corrs.set_ylabel("Corrections")
ax_corrs.legend()
ax_col_area.plot(sol_data, np.array(effective_collection_area_data)/1e6)
ax_col_area.plot(sol_data, len(sol_data)*[col_area_100km_raw/1e6], color='k', \
label="Raw col area at 100 km")
ax_col_area.plot(sol_data, len(sol_data)*[col_area_meteor_ht_raw/1e6], color='k', linestyle='dashed', \
label="Raw col area at met ht")
ax_col_area.set_ylabel("Eff. col. area (km^2)")
ax_col_area.legend()
ax_flux.scatter(sol_data, flux_lm_6_5_data)
ax_flux.set_ylabel("Flux@+6.5M (met/1000km^2/h)")
ax_flux.set_xlabel("La Sun (deg)")
plt.tight_layout()
plt.show()
return sol_data, flux_lm_6_5_data
def generateActivityDiagram(config,
shower_data,
ax_handle=None,
sol_marker=None,
colors=None):
""" Generates a plot of shower activity across all solar longitudes. """
shower_data = np.array(shower_data)
# Fill in min/max solar longitudes if they are not present
for shower in shower_data:
sol_min, sol_peak, sol_max = list(shower)[3:6]
if np.isnan(sol_min):
shower[3] = (sol_peak - config.shower_lasun_threshold) % 360
if np.isnan(sol_max):
shower[5] = (sol_peak + config.shower_lasun_threshold) % 360
# Sort showers by duration
durations = [(shower[5] - shower[3] + 180) % 360 - 180
for shower in shower_data]
shower_data = shower_data[np.argsort(durations)][::-1]
# Generate an array of shower activity per 1 deg of solar longitude
code_name_dict = {}
activity_stack = np.zeros((20, 360), dtype=np.uint16)
if not colors: colors = makeShowerColors(shower_data)
shower_index = 0
for i in range(20):
for sol_plot in range(0, 360):
# If the cell is unassigned, check to see if a shower is active
if activity_stack[i, sol_plot] > 0:
continue
for shower in shower_data:
code = int(shower[0])
name = shower[1]
# Skip assigned showers
if code in code_name_dict:
continue
sol_min, sol_peak, sol_max = list(shower)[3:6]
sol_min = int(np.floor(sol_min)) % 360
sol_max = int(np.ceil(sol_max)) % 360
# If the shower is active at the given solar longitude and there aren't any other showers
# in the same activity period, assign shower code to this solar longitude
shower_active = False
if (sol_max - sol_min) < 180:
# Check if the shower is active
if (sol_plot >= sol_min) and (sol_plot <= sol_max):
# Leave a buffer of +/- 3 deg around the shower
sol_min_check = sol_min - 3
if sol_min_check < 0:
sol_min_check = 0
sol_max_check = sol_max + 3
if sol_max_check > 360:
sol_max_check = 360
# Check if the solar longitue range is free of other showers
if not np.any(
activity_stack[i,
sol_min_check:sol_max_check]):
# Assign the shower code to activity stack
activity_stack[i, sol_min:sol_max] = code
code_name_dict[code] = [name, sol_peak]
shower_active = True
else:
if (sol_plot >= sol_min) or (sol_plot <= sol_max):
# Check if the solar longitue range is free of other showers
if (not np.any(activity_stack[i, 0:sol_max])) and \
(not np.any(activity_stack[i, sol_min:])):
# Assign shower code to activity stack
activity_stack[i, 0:sol_max] = code
activity_stack[i, sol_min:] = code
shower_active = True
if shower_active:
# Get shower color
color = colors[name]
shower_index += 1
# Assign shower params
code_name_dict[code] = [
name, sol_min, sol_peak, sol_max, color
]
# If no axis was given, crate one
if ax_handle is None:
ax_handle = plt.subplot(111, facecolor='black')
# Set background color
plt.gcf().patch.set_facecolor('black')
# Change axis color
ax_handle.spines['bottom'].set_color('w')
ax_handle.spines['top'].set_color('w')
ax_handle.spines['right'].set_color('w')
ax_handle.spines['left'].set_color('w')
# Change tick color
ax_handle.tick_params(axis='x', colors='w')
ax_handle.tick_params(axis='y', colors='w')
# Change axis label color
ax_handle.yaxis.label.set_color('w')
ax_handle.xaxis.label.set_color('w')
# Plot the activity graph
active_shower = 0
vertical_scale_line = 0.5
vertical_shift_text = 0.01
text_size = 8
for i, line in enumerate(activity_stack):
for shower_block in line:
# If a new shower was found, plot it
if (shower_block != active_shower) and (shower_block > 0):
# Get shower parameters
name, sol_min, sol_peak, sol_max, color = code_name_dict[
shower_block]
# Plot the shower activity period
if (sol_max - sol_min) < 180:
x_arr = np.arange(sol_min, sol_max + 1, 1)
ax_handle.plot(x_arr, np.zeros_like(x_arr) + i*vertical_scale_line, linewidth=3, \
color=color, zorder=3)
ax_handle.text(round((sol_max + sol_min)/2), i*vertical_scale_line + vertical_shift_text, \
name, ha='center', va='bottom', color='w', size=text_size, zorder=3)
else:
x_arr = np.arange(0, sol_max + 1, 1)
ax_handle.plot(x_arr, np.zeros_like(x_arr) + i*vertical_scale_line, linewidth=3, \
color=color, zorder=3)
x_arr = np.arange(sol_min, 361, 1)
ax_handle.plot(x_arr, np.zeros_like(x_arr) + i*vertical_scale_line, linewidth=3, \
color=color, zorder=3)
ax_handle.text(0, i*vertical_scale_line + vertical_shift_text, name, ha='center', \
va='bottom', color='w', size=text_size, zorder=2)
# Plot peak location
ax_handle.scatter(sol_peak,
i * vertical_scale_line,
marker='+',
c="w",
zorder=4)
active_shower = shower_block
# Hide y axis
ax_handle.get_yaxis().set_visible(False)
ax_handle.set_xlabel('Solar longitude (deg)')
# Get the plot Y limits
y_min, y_max = ax_handle.get_ylim()
# Plot a line with given solver longitude
if sol_marker is not None:
# Plot the solar longitude line behind everything else
y_arr = np.linspace(y_min, y_max, 5)
if not isinstance(sol_marker, list):
sol_marker = [sol_marker]
for sol_value in sol_marker:
ax_handle.plot(np.zeros_like(y_arr) + sol_value, y_arr, color='r', linestyle='dashed', zorder=2, \
linewidth=1)
# Plot month names at the 1st of that month (start in April of this year)
for month_no, year_modifier in [[4, 0], [5, 0], [6, 0], [7, 0], [8, 0],
[9, 0], [10, 0], [11, 0], [12, 0], [1, 1],
[2, 1], [3, 1]]:
# Get the solar longitude of the 15th date of the month
curr_year = datetime.datetime.now().year
dt = datetime.datetime(curr_year + year_modifier, month_no, 15, 0, 0,
0)
sol = np.degrees(jd2SolLonSteyaert(datetime2JD(dt))) % 360
# Plot the month name in the background of the plot
plt.text(sol, y_max, dt.strftime("%b").upper(), alpha=0.3, rotation=90, size=20, \
zorder=1, color='w', va='top', ha='center')
# Get the solar longitude of the 1st date of the month
curr_year = datetime.datetime.now().year
dt = datetime.datetime(curr_year + year_modifier, month_no, 1, 0, 0, 0)
sol = np.degrees(jd2SolLonSteyaert(datetime2JD(dt))) % 360
# Plot the manth beginning line
y_arr = np.linspace(y_min, y_max, 5)
plt.plot(np.zeros_like(y_arr) + sol,
y_arr,
linestyle='dotted',
alpha=0.3,
zorder=3,
color='w')
# Force Y limits
ax_handle.set_ylim([y_min, y_max])
ax_handle.set_xlim([0, 360])
# Get the solar longitude of the 1st date of the month
curr_year = datetime.datetime.now().year
dt = datetime.datetime(curr_year + year_modifier, month_no, 1, 0, 0, 0)
sol = np.degrees(jd2SolLonSteyaert(datetime2JD(dt))) % 360
# Plot the manth beginning line
y_arr = np.linspace(y_min, y_max, 5)
plt.plot(np.zeros_like(y_arr) + sol,
y_arr,
linestyle='dotted',
alpha=0.3,
zorder=3,
color='w')
# Force Y limits
ax_handle.set_ylim([y_min, y_max])
ax_handle.set_xlim([0, 360])
if __name__ == "__main__":
import RMS.ConfigReader as cr
shower_data = loadShowers("share", "established_showers.csv")
# Generate activity diagram
config = cr.parse('.config')
generateActivityDiagram(config, shower_data, \
sol_marker=np.degrees(jd2SolLonSteyaert(datetime2JD(datetime.datetime.now()))))
plt.show()
def generateActivityDiagram(config,
shower_data,
ax_handle=None,
sol_marker=None,
color_map='viridis'):
""" Generates a plot of shower activity across all solar longitudes. """
shower_data = np.array(shower_data)
# Fill in min/max solar longitudes if they are not present
for shower in shower_data:
sol_min, sol_peak, sol_max = shower.lasun_beg, shower.lasun_max, shower.lasun_end
if np.isnan(sol_min):
shower.lasun_beg = (sol_peak - config.shower_lasun_threshold) % 360
if np.isnan(sol_max):
shower.lasun_end = (sol_peak + config.shower_lasun_threshold) % 360
# Sort showers by duration
durations = [(shower.lasun_end - shower.lasun_beg + 180) % 360 - 180
for shower in shower_data]
shower_data = shower_data[np.argsort(durations)][::-1]
# Sort showers into rows so that they do no overlap on the graph. This will also generate colors
# for every shower, sorted per row
activity_stack, code_name_dict = sortShowersIntoRows(shower_data,
color_map=color_map)
# If no axis was given, crate one
if ax_handle is None:
ax_handle = plt.subplot(111, facecolor='black')
# Set background color
plt.gcf().patch.set_facecolor('black')
# Change axis color
ax_handle.spines['bottom'].set_color('w')
ax_handle.spines['top'].set_color('w')
ax_handle.spines['right'].set_color('w')
ax_handle.spines['left'].set_color('w')
# Change tick color
ax_handle.tick_params(axis='x', colors='w')
ax_handle.tick_params(axis='y', colors='w')
# Change axis label color
ax_handle.yaxis.label.set_color('w')
ax_handle.xaxis.label.set_color('w')
# Plot the activity graph
active_shower = 0
vertical_scale_line = 0.5
vertical_shift_text = 0.02
text_size = 8
for i, line in enumerate(activity_stack):
for shower_block in line:
# If a new shower was found, plot it
if (shower_block != active_shower) and (shower_block > 0):
# Get shower parameters
name, sol_min, sol_peak, sol_max, color = code_name_dict[
shower_block]
# Plot the shower activity period
if (sol_max - sol_min) < 180:
x_arr = np.arange(sol_min, sol_max + 1, 1)
ax_handle.plot(x_arr, np.zeros_like(x_arr) + i*vertical_scale_line, linewidth=3, \
color=color, zorder=3)
ax_handle.text(round((sol_max + sol_min)/2), i*vertical_scale_line + vertical_shift_text, \
name, ha='center', va='bottom', color='w', size=text_size, zorder=3)
else:
x_arr = np.arange(0, sol_max + 1, 1)
ax_handle.plot(x_arr, np.zeros_like(x_arr) + i*vertical_scale_line, linewidth=3, \
color=color, zorder=3)
x_arr = np.arange(sol_min, 361, 1)
ax_handle.plot(x_arr, np.zeros_like(x_arr) + i*vertical_scale_line, linewidth=3, \
color=color, zorder=3)
ax_handle.text(0, i*vertical_scale_line + vertical_shift_text, name, ha='center', \
va='bottom', color='w', size=text_size, zorder=2)
# Plot peak location
ax_handle.scatter(sol_peak,
i * vertical_scale_line,
marker='+',
c="w",
zorder=4)
active_shower = shower_block
# Hide y axis
ax_handle.get_yaxis().set_visible(False)
ax_handle.set_xlabel('Solar longitude (deg)')
# Get the plot Y limits
y_min, y_max = ax_handle.get_ylim()
# Shift the plot maximum to accomodate the upper text
y_max *= 1.25
# Plot a line with given solver longitude
if sol_marker is not None:
# Plot the solar longitude line behind everything else
y_arr = np.linspace(y_min, y_max, 5)
if not isinstance(sol_marker, list):
sol_marker = [sol_marker]
for sol_value in sol_marker:
ax_handle.plot(np.zeros_like(y_arr) + sol_value, y_arr, color='r', linestyle='dashed', zorder=2, \
linewidth=1)
# Plot month names at the 1st of that month (start in April of this year)
for month_no, year_modifier in [[4, 0], [5, 0], [6, 0], [7, 0], [8, 0],
[9, 0], [10, 0], [11, 0], [12, 0], [1, 1],
[2, 1], [3, 1]]:
# Get the solar longitude of the 15th date of the month
curr_year = datetime.datetime.now().year
dt = datetime.datetime(curr_year + year_modifier, month_no, 15, 0, 0,
0)
sol = np.degrees(jd2SolLonSteyaert(datetime2JD(dt))) % 360
# Plot the month name in the background of the plot
plt.text(sol, y_max, dt.strftime("%b").upper(), alpha=0.3, rotation=90, size=20, \
zorder=1, color='w', va='top', ha='center')
# Get the solar longitude of the 1st date of the month
curr_year = datetime.datetime.now().year
dt = datetime.datetime(curr_year + year_modifier, month_no, 1, 0, 0, 0)
sol = np.degrees(jd2SolLonSteyaert(datetime2JD(dt))) % 360
# Plot the month begin line
y_arr = np.linspace(y_min, y_max, 5)
plt.plot(np.zeros_like(y_arr) + sol,
y_arr,
linestyle='dotted',
alpha=0.3,
zorder=3,
color='w')
# Force Y limits
ax_handle.set_ylim([y_min, y_max])
ax_handle.set_xlim([0, 360])
def activeShowers(self, dt_beg, dt_end, min_zhr=1):
""" Return a list of active showers given a range of solar longitudes.
Arguments:
dt_beg: [float] Starting datetime.
dt_end: [float] End datetime.
Keyword arguments:
min_zhr: [float] Minimum ZHR for the shower to be considered active.
Return:
[list] A list of Shower objects with the modified activity period according to the ZHR threshold.
"""
# Convert dates to solar longitudes
la_sun_beg = np.degrees(jd2SolLonSteyaert(datetime2JD(dt_beg)))
la_sun_end = np.degrees(jd2SolLonSteyaert(datetime2JD(dt_end)))
# Sample the range with a 0.02 deg delta in sol (~30 minutes)
if la_sun_beg > la_sun_end:
la_sun_beg -= 360
sol_array = np.arange(la_sun_beg, la_sun_end, 0.02)
# Compute ZHR values for every shower, accunting for the peak and base component
shower_codes = np.unique([shower.name for shower in self.showers])
shower_zhrs = {
shower_code: np.zeros_like(sol_array)
for shower_code in shower_codes
}
for shower in self.showers:
# Only take the shower if it's annual, or the year the correct
if not shower.flux_year == "annual":
if not (int(shower.flux_year) == dt_beg.year) and not (int(
shower.flux_year) == dt_end.year):
continue
# Compute the ZHR profile
zhr_arr = shower.computeZHR(sol_array)
# Add the profile to the shower dictionary
shower_zhrs[shower.name] += zhr_arr
# List of showers with a total ZHR above the threshold
active_showers = []
# Go through all showers and deterine if they were active or not
for shower in self.showers:
# Don't add already added showers
if shower.name not in [shower.name for shower in active_showers]:
# Determine the activity period
activity_above_threshold = shower_zhrs[shower.name] > min_zhr
if np.any(activity_above_threshold):
# Determine the activity period within the given range of solar longitudes
la_sun_min = np.min(
sol_array[activity_above_threshold]) % 360
la_sun_max = np.max(
sol_array[activity_above_threshold]) % 360
# Create a copy of the shower object with the modified activity period
shower_active = copy.deepcopy(shower)
shower_active.lasun_beg = la_sun_min
shower_active.lasun_end = la_sun_max
active_showers.append(shower_active)
return active_showers
def showerAssociation(config, ftpdetectinfo_list, shower_code=None, show_plot=False, save_plot=False, \
plot_activity=False):
""" Do single station shower association based on radiant direction and height.
Arguments:
config: [Config instance]
ftpdetectinfo_list: [list] A list of paths to FTPdetectinfo files.
Keyword arguments:
shower_code: [str] Only use this one shower for association (e.g. ETA, PER, SDA). None by default,
in which case all active showers will be associated.
show_plot: [bool] Show the plot on the screen. False by default.
save_plot: [bool] Save the plot in the folder with FTPdetectinfos. False by default.
plot_activity: [bool] Whether to plot the shower activity plot of not. False by default.
Return:
associations, shower_counts: [tuple]
- associations: [dict] A dictionary where the FF name and the meteor ordinal number on the FF
file are keys, and the associated Shower object are values.
- shower_counts: [list] A list of shower code and shower count pairs.
"""
# Load the list of meteor showers
shower_list = loadShowers(config.shower_path, config.shower_file_name)
# Load FTPdetectinfos
meteor_data = []
for ftpdetectinfo_path in ftpdetectinfo_list:
if not os.path.isfile(ftpdetectinfo_path):
print('No such file:', ftpdetectinfo_path)
continue
meteor_data += readFTPdetectinfo(*os.path.split(ftpdetectinfo_path))
if not len(meteor_data):
return {}, []
# Dictionary which holds FF names as keys and meteor measurements + associated showers as values
associations = {}
for meteor in meteor_data:
ff_name, cam_code, meteor_No, n_segments, fps, hnr, mle, binn, px_fm, rho, phi, meteor_meas = meteor
# Skip very short meteors
if len(meteor_meas) < 4:
continue
# Check if the data is calibrated
if not meteor_meas[0][0]:
print(
'Data is not calibrated! Meteors cannot be associated to showers!'
)
break
# Init container for meteor observation
meteor_obj = MeteorSingleStation(cam_code, config.latitude,
config.longitude, ff_name)
# Infill the meteor structure
for entry in meteor_meas:
calib_status, frame_n, x, y, ra, dec, azim, elev, inten, mag = entry
# Compute the Julian data of every point
jd = datetime2JD(
filenameToDatetime(ff_name) +
datetime.timedelta(seconds=float(frame_n) / fps))
meteor_obj.addPoint(jd, ra, dec, mag)
# Fit the great circle and compute the geometrical parameters
meteor_obj.fitGC()
# Skip all meteors with beginning heights below 15 deg
if meteor_obj.beg_alt < 15:
continue
# Go through all showers in the list and find the best match
best_match_shower = None
best_match_dist = np.inf
for shower_entry in shower_list:
# Extract shower parameters
shower = Shower(shower_entry)
# If the shower code was given, only check this one shower
if shower_code is not None:
if shower.name.lower() != shower_code.lower():
continue
### Solar longitude filter
# If the shower doesn't have a stated beginning or end, check if the meteor is within a preset
# threshold solar longitude difference
if np.any(np.isnan([shower.lasun_beg, shower.lasun_end])):
shower.lasun_beg = (shower.lasun_max -
config.shower_lasun_threshold) % 360
shower.lasun_end = (shower.lasun_max +
config.shower_lasun_threshold) % 360
# Filter out all showers which are not active
if not isAngleBetween(np.radians(shower.lasun_beg),
np.radians(meteor_obj.lasun),
np.radians(shower.lasun_end)):
continue
### ###
### Radiant filter ###
# Assume a fixed meteor height for an approximate apparent radiant
meteor_fixed_ht = 100000 # 100 km
shower.computeApparentRadiant(config.latitude, config.longitude, meteor_obj.jdt_ref, \
meteor_fixed_ht=meteor_fixed_ht)
# Compute the angle between the meteor radiant and the great circle normal
radiant_separation = meteor_obj.angularSeparationFromGC(
shower.ra, shower.dec)
# Make sure the meteor is within the radiant distance threshold
if radiant_separation > config.shower_max_radiant_separation:
continue
# Compute angle between the meteor's beginning and end, and the shower radiant
shower.radiant_vector = vectNorm(
raDec2Vector(shower.ra, shower.dec))
begin_separation = np.degrees(angularSeparationVect(shower.radiant_vector, \
meteor_obj.meteor_begin_cartesian))
end_separation = np.degrees(angularSeparationVect(shower.radiant_vector, \
meteor_obj.meteor_end_cartesian))
# Make sure the beginning of the meteor is closer to the radiant than it's end
if begin_separation > end_separation:
continue
### ###
### Height filter ###
# Estimate the limiting meteor height from the velocity (meters)
filter_beg_ht = heightModel(shower.v_init, ht_type='beg')
filter_end_ht = heightModel(shower.v_init, ht_type='end')
### Estimate the meteor beginning height with +/- 1 frame, otherwise some short meteor may get
### rejected
meteor_obj_orig = copy.deepcopy(meteor_obj)
# Shorter
meteor_obj_m1 = copy.deepcopy(meteor_obj_orig)
meteor_obj_m1.duration -= 1.0 / config.fps
meteor_beg_ht_m1 = estimateMeteorHeight(config, meteor_obj_m1,
shower)
# Nominal
meteor_beg_ht = estimateMeteorHeight(config, meteor_obj_orig,
shower)
# Longer
meteor_obj_p1 = copy.deepcopy(meteor_obj_orig)
meteor_obj_p1.duration += 1.0 / config.fps
meteor_beg_ht_p1 = estimateMeteorHeight(config, meteor_obj_p1,
shower)
meteor_obj = meteor_obj_orig
### ###
# If all heights (even those with +/- 1 frame) are outside the height range, reject the meteor
if ((meteor_beg_ht_p1 < filter_end_ht) or (meteor_beg_ht_p1 > filter_beg_ht)) and \
((meteor_beg_ht < filter_end_ht) or (meteor_beg_ht > filter_beg_ht)) and \
((meteor_beg_ht_m1 < filter_end_ht) or (meteor_beg_ht_m1 > filter_beg_ht)):
continue
### ###
# Compute the radiant elevation above the horizon
shower.azim, shower.elev = raDec2AltAz(shower.ra, shower.dec, meteor_obj.jdt_ref, \
config.latitude, config.longitude)
# Take the shower that's closest to the great circle if there are multiple candidates
if radiant_separation < best_match_dist:
best_match_dist = radiant_separation
best_match_shower = copy.deepcopy(shower)
# If a shower is given and the match is not this shower, skip adding the meteor to the list
# If no specific shower is give for association, add all meteors
if ((shower_code is not None) and
(best_match_shower is not None)) or (shower_code is None):
# Store the associated shower
associations[(ff_name,
meteor_No)] = [meteor_obj, best_match_shower]
# Find shower frequency and sort by count
shower_name_list_temp = []
shower_list_temp = []
for key in associations:
_, shower = associations[key]
if shower is None:
shower_name = '...'
else:
shower_name = shower.name
shower_name_list_temp.append(shower_name)
shower_list_temp.append(shower)
_, unique_showers_indices = np.unique(shower_name_list_temp,
return_index=True)
unique_shower_names = np.array(
shower_name_list_temp)[unique_showers_indices]
unique_showers = np.array(shower_list_temp)[unique_showers_indices]
shower_counts = [[shower_obj, shower_name_list_temp.count(shower_name)] for shower_obj, \
shower_name in zip(unique_showers, unique_shower_names)]
shower_counts = sorted(shower_counts, key=lambda x: x[1], reverse=True)
# Create a plot of showers
if show_plot or save_plot:
# Generate consistent colours
colors_by_name = makeShowerColors(shower_list)
def get_shower_color(shower):
try:
return colors_by_name[shower.name] if shower else "0.4"
except KeyError:
return 'gray'
# Init the figure
plt.figure()
# Init subplots depending on if the activity plot is done as well
if plot_activity:
gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1])
ax_allsky = plt.subplot(gs[0], facecolor='black')
ax_activity = plt.subplot(gs[1], facecolor='black')
else:
ax_allsky = plt.subplot(111, facecolor='black')
# Init the all-sky plot
allsky_plot = AllSkyPlot(ax_handle=ax_allsky)
# Plot all meteors
for key in associations:
meteor_obj, shower = associations[key]
### Plot the observed meteor points ###
color = get_shower_color(shower)
allsky_plot.plot(meteor_obj.ra_array,
meteor_obj.dec_array,
color=color,
linewidth=1,
zorder=4)
# Plot the peak of shower meteors a different color
peak_color = 'blue'
if shower is not None:
peak_color = 'tomato'
allsky_plot.scatter(meteor_obj.ra_array[-1], meteor_obj.dec_array[-1], c=peak_color, marker='+', \
s=5, zorder=5)
### ###
### Plot fitted great circle points ###
# Find the GC phase angle of the beginning of the meteor
gc_beg_phase = meteor_obj.findGCPhase(
meteor_obj.ra_array[0], meteor_obj.dec_array[0])[0] % 360
# If the meteor belongs to a shower, find the GC phase which ends at the shower
if shower is not None:
gc_end_phase = meteor_obj.findGCPhase(shower.ra,
shower.dec)[0] % 360
# Fix 0/360 wrap
if abs(gc_end_phase - gc_beg_phase) > 180:
if gc_end_phase > gc_beg_phase:
gc_end_phase -= 360
else:
gc_beg_phase -= 360
gc_alpha = 1.0
else:
# If it's a sporadic, find the direction to which the meteor should extend
gc_end_phase = meteor_obj.findGCPhase(meteor_obj.ra_array[-1], \
meteor_obj.dec_array[-1])[0]%360
# Find the correct direction
if (gc_beg_phase - gc_end_phase) % 360 > (gc_end_phase -
gc_beg_phase) % 360:
gc_end_phase = gc_beg_phase - 170
else:
gc_end_phase = gc_beg_phase + 170
gc_alpha = 0.7
# Store great circle beginning and end phase
meteor_obj.gc_beg_phase = gc_beg_phase
meteor_obj.gc_end_phase = gc_end_phase
# Get phases 180 deg before the meteor
phase_angles = np.linspace(gc_end_phase, gc_beg_phase, 100) % 360
# Compute RA/Dec of points on the great circle
ra_gc, dec_gc = meteor_obj.sampleGC(phase_angles)
# Cull all points below the horizon
azim_gc, elev_gc = raDec2AltAz(ra_gc, dec_gc, meteor_obj.jdt_ref, config.latitude, \
config.longitude)
temp_arr = np.c_[ra_gc, dec_gc]
temp_arr = temp_arr[elev_gc > 0]
ra_gc, dec_gc = temp_arr.T
# Plot the great circle fitted on the radiant
gc_color = get_shower_color(shower)
allsky_plot.plot(ra_gc,
dec_gc,
linestyle='dotted',
color=gc_color,
alpha=gc_alpha,
linewidth=1)
# Plot the point closest to the shower radiant
if shower is not None:
allsky_plot.plot(ra_gc[0],
dec_gc[0],
color='r',
marker='+',
ms=5,
mew=1)
# Store shower radiant point
meteor_obj.radiant_ra = ra_gc[0]
meteor_obj.radiant_dec = dec_gc[0]
### ###
### Plot all showers ###
# Find unique showers and their apparent radiants computed at highest radiant elevation
# (otherwise the apparent radiants can be quite off)
shower_dict = {}
for key in associations:
meteor_obj, shower = associations[key]
if shower is None:
continue
# If the shower name is in dict, find the shower with the highest radiant elevation
if shower.name in shower_dict:
if shower.elev > shower_dict[shower.name].elev:
shower_dict[shower.name] = shower
else:
shower_dict[shower.name] = shower
# Plot the location of shower radiants
for shower_name in shower_dict:
shower = shower_dict[shower_name]
heading_arr = np.linspace(0, 360, 50)
# Compute coordinates on a circle around the given RA, Dec
ra_circle, dec_circle = sphericalPointFromHeadingAndDistance(shower.ra, shower.dec, \
heading_arr, config.shower_max_radiant_separation)
# Plot the shower circle
allsky_plot.plot(ra_circle,
dec_circle,
color=colors_by_name[shower_name])
# Plot the shower name
x_text, y_text = allsky_plot.raDec2XY(shower.ra, shower.dec)
allsky_plot.ax.text(x_text, y_text, shower.name, color='w', size=8, va='center', \
ha='center', zorder=6)
# Plot station name and solar longiutde range
allsky_plot.ax.text(-180,
89,
"{:s}".format(cam_code),
color='w',
family='monospace')
# Get a list of JDs of meteors
jd_list = [associations[key][0].jdt_ref for key in associations]
if len(jd_list):
# Get the range of solar longitudes
jd_min = min(jd_list)
sol_min = np.degrees(jd2SolLonSteyaert(jd_min))
jd_max = max(jd_list)
sol_max = np.degrees(jd2SolLonSteyaert(jd_max))
# Plot the date and solar longitude range
date_sol_beg = u"Beg: {:s} (sol = {:.2f}\u00b0)".format(
jd2Date(jd_min, dt_obj=True).strftime("%Y%m%d %H:%M:%S"),
sol_min)
date_sol_end = u"End: {:s} (sol = {:.2f}\u00b0)".format(
jd2Date(jd_max, dt_obj=True).strftime("%Y%m%d %H:%M:%S"),
sol_max)
allsky_plot.ax.text(-180,
85,
date_sol_beg,
color='w',
family='monospace')
allsky_plot.ax.text(-180,
81,
date_sol_end,
color='w',
family='monospace')
allsky_plot.ax.text(-180,
77,
"-" * len(date_sol_end),
color='w',
family='monospace')
# Plot shower counts
for i, (shower, count) in enumerate(shower_counts):
if shower is not None:
shower_name = shower.name
else:
shower_name = "..."
allsky_plot.ax.text(-180, 73 - i*4, "{:s}: {:d}".format(shower_name, count), color='w', \
family='monospace')
### ###
# Plot yearly meteor shower activity
if plot_activity:
# Plot the activity diagram
generateActivityDiagram(config, shower_list, ax_handle=ax_activity, \
sol_marker=[sol_min, sol_max], colors=colors_by_name)
# Save plot and text file
if save_plot:
dir_path, ftpdetectinfo_name = os.path.split(ftpdetectinfo_path)
ftpdetectinfo_base_name = ftpdetectinfo_name.replace(
'FTPdetectinfo_', '').replace('.txt', '')
plot_name = ftpdetectinfo_base_name + '_radiants.png'
# Increase figure size
allsky_plot.fig.set_size_inches(18, 9, forward=True)
allsky_plot.beautify()
plt.savefig(os.path.join(dir_path, plot_name),
dpi=100,
facecolor='k')
# Save the text file with shower info
if len(jd_list):
with open(
os.path.join(dir_path, ftpdetectinfo_base_name +
"_radiants.txt"), 'w') as f:
# Print station code
f.write("# RMS single station association\n")
f.write("# \n")
f.write("# Station: {:s}\n".format(cam_code))
# Print date range
f.write(
"# Beg | End \n"
)
f.write(
"# -----------------------------------------------------\n"
)
f.write("# Date | {:24s} | {:24s} \n".format(jd2Date(jd_min, \
dt_obj=True).strftime("%Y%m%d %H:%M:%S.%f"), jd2Date(jd_max, \
dt_obj=True).strftime("%Y%m%d %H:%M:%S.%f")))
f.write("# Sol | {:>24.2f} | {:>24.2f} \n".format(
sol_min, sol_max))
# Write shower counts
f.write("# \n")
f.write("# Shower counts:\n")
f.write("# --------------\n")
f.write("# Code, Count, IAU link\n")
for i, (shower, count) in enumerate(shower_counts):
if shower is not None:
shower_name = shower.name
# Create link to the IAU database of showers
iau_link = "https://www.ta3.sk/IAUC22DB/MDC2007/Roje/pojedynczy_obiekt.php?kodstrumienia={:05d}".format(
shower.iau_code)
else:
shower_name = "..."
iau_link = "None"
f.write("# {:>4s}, {:>5d}, {:s}\n".format(
shower_name, count, iau_link))
f.write("# \n")
f.write("# Meteor parameters:\n")
f.write("# ------------------\n")
f.write(
"# Date And Time, Beg Julian date, La Sun, Shower, RA beg, Dec beg, RA end, Dec end, RA rad, Dec rad, GC theta0, GC phi0, GC beg phase, GC end phase, Mag\n"
)
# Create a sorted list of meteor associations by time
associations_list = [
associations[key] for key in associations
]
associations_list = sorted(associations_list,
key=lambda x: x[0].jdt_ref)
# Write out meteor parameters
for meteor_obj, shower in associations_list:
# Find peak magnitude
if np.any(meteor_obj.mag_array):
peak_mag = "{:+.1f}".format(
np.min(meteor_obj.mag_array))
else:
peak_mag = "None"
if shower is not None:
f.write("{:24s}, {:20.12f}, {:>10.6f}, {:>6s}, {:6.2f}, {:+7.2f}, {:6.2f}, {:+7.2f}, {:6.2f}, {:+7.2f}, {:9.3f}, {:8.3f}, {:12.3f}, {:12.3f}, {:4s}\n".format(jd2Date(meteor_obj.jdt_ref, dt_obj=True).strftime("%Y%m%d %H:%M:%S.%f"), \
meteor_obj.jdt_ref, meteor_obj.lasun, shower.name, \
meteor_obj.ra_array[0]%360, meteor_obj.dec_array[0], \
meteor_obj.ra_array[-1]%360, meteor_obj.dec_array[-1], \
meteor_obj.radiant_ra%360, meteor_obj.radiant_dec, \
np.degrees(meteor_obj.theta0), np.degrees(meteor_obj.phi0), \
meteor_obj.gc_beg_phase, meteor_obj.gc_end_phase, peak_mag))
else:
f.write("{:24s}, {:20.12f}, {:>10.6f}, {:>6s}, {:6.2f}, {:+7.2f}, {:6.2f}, {:+7.2f}, {:>6s}, {:>7s}, {:9.3f}, {:8.3f}, {:12.3f}, {:12.3f}, {:4s}\n".format(jd2Date(meteor_obj.jdt_ref, dt_obj=True).strftime("%Y%m%d %H:%M:%S.%f"), \
meteor_obj.jdt_ref, meteor_obj.lasun, '...', meteor_obj.ra_array[0]%360, \
meteor_obj.dec_array[0], meteor_obj.ra_array[-1]%360, \
meteor_obj.dec_array[-1], "None", "None", np.degrees(meteor_obj.theta0), \
np.degrees(meteor_obj.phi0), meteor_obj.gc_beg_phase, \
meteor_obj.gc_end_phase, peak_mag))
if show_plot:
allsky_plot.show()
else:
plt.clf()
plt.close()
return associations, shower_counts
