def get_height_histogram(n_t=None, d_delta=7):
"""
Look at the height histograms.
"""
md = mr.mmaria_data(c.data_directory)
b = md.get_bounds()
if n_t == None:
n_t = int(n.floor((b[1] - b[0]) / (d_delta * 24 * 3600)))
print(n_t)
t0 = 24 * 3600 * n.floor(b[0] / (24 * 3600))
n_ranges = 30
rgs = n.linspace(70, 110, num=n_ranges + 1)
S = n.zeros([n_t, n_ranges])
for di in range(n_t):
d = md.read_data(t0 + di * d_delta * 24 * 3600,
t0 + (di + 1) * d_delta * 24 * 3600)
hist, bins = n.histogram(d["heights"] / 1e3, bins=rgs)
ranges = 0.5 * (bins[0:len(hist)] + bins[1:(len(hist) + 1)])
S[di, :] = hist
tvec = d_delta * n.arange(n_t)
plt.pcolormesh(tvec, ranges, n.transpose(S))
plt.plot(tvec, ranges[n.argmax(S, axis=1)])
plt.show()
def get_decay_histogram(n_t=None,
d_delta=1,
height=[70, 110],
logalpha=[0, 2.0],
N=50):
"""
Look at the height histograms.
"""
md = mr.mmaria_data(c.data_directory)
b = md.get_bounds()
if n_t == None:
n_t = int(n.floor((b[1] - b[0]) / (d_delta * 24 * 3600)))
hbins = n.linspace(height[0], height[1], num=N)
abins = n.linspace(logalpha[0], logalpha[1], num=N)
S = n.zeros([N, N])
t0 = 24 * 3600 * n.floor(b[0] / (24 * 3600))
for di in range(n_t):
d = md.read_data(t0 + di * d_delta * 24 * 3600,
t0 + (di + 1) * d_delta * 24 * 3600)
bragg_scale = n.sqrt(d["braggs"][:, 0]**2.0 + d["braggs"][:, 1]**2.0 +
d["braggs"][:, 2]**2.0)
print(bragg_scale)
H, x, y = n.histogram2d(n.log10(d["alpha_norm"] * bragg_scale**2.0),
d["heights"] / 1e3,
bins=[abins, hbins])
plt.pcolormesh(abins, hbins, n.transpose(H))
plt.show()
def get_horizontal_histogram(n_t=None,d_delta=1,lat_min=65,lat_max=74,lon_min=5,lon_max=32,N=50):
"""
Look at the height histograms.
"""
md=mr.mmaria_data(c.data_directory)
b=md.get_bounds()
if n_t == None:
n_t=int(n.floor((b[1]-b[0])/(d_delta*24*3600)))
lons=n.linspace(lon_min,lon_max,num=N)
lats=n.linspace(lat_min,lat_max,num=N)
S=n.zeros([N,N])
t0=24*3600*n.floor(b[0]/(24*3600))
for di in range(n_t):
d=md.read_data(t0+di*d_delta*24*3600,t0+(di+1)*d_delta*24*3600)
H,x,y=n.histogram2d(d["lons"],d["lats"],bins=[lons,lats])
plt.pcolormesh(lons,lats,n.transpose(H))
plt.show()
pairs = []
latdeg2km = 111.321
londeg2km = 65.122785
meridional_distance = True
zonal_distance = False
horizontal_distance = False
#h=h5py.File("res/simone_nov2018_multilink_juha_30min_1000m.h5","r")# For a single file
#lats=h["lats"].value
#lons=h["lons"].value
#heights=h["heights"].value #heights in km
#t=h["t"].value
#works for mmaria_data
md = mr.mmaria_data(c.data_directory) #for many files in a directory
b = md.get_bounds()
d = md.read_data(1514774804, 1515974804)
lats = d["lats"]
lons = d["lons"]
heights = d["heights"] #hights in meters
heights = heights / 1000
t = d["t"]
# In[]
print("total number of measurements %d" % (len(t)))
# define a histogram
n_m = len(lats)
zonb = n.real(n.fft.ifft(Z))
return (zonb)
def lpf_mag(mag, N=60):
return (n.convolve(n.abs(mag)**2.0, n.repeat(1.0 / N, N), mode="same"))
d = smr.supermag_data(fname="tro_2018_2019.csv", plot=False)
b = d.get_bounds()
print(b)
# get all data from "TRO"
m = d.get_meas(b[0], b[0] + 365 * 24 * 3600, station="TRO")
# directory with all mmaria network data
md = mmr.mmaria_data(c.data_directory)
# what is the data bounds (first and last time stamp)
print(md.get_bounds())
# read all meteor radar data between these two timestamps
d = md.read_data(b[0], b[0] + 365 * 24 * 3600, read_all_detections=False)
zon = d["v"][0, :, :]
mer = d["v"][1, :, :]
#zonb=remove_baseline(zon[:,20])
zonb = remove_baseline(zon[:, 25])
merb = remove_baseline(mer[:, 25])
wken = zonb**2.0 + merb**2.0
plt.plot(d["times"], zonb)
