Hi everyone,

I hope this finds you well. I am finding it a challenge to plot empirical green’s function data (derived from cross-correlation of two stations or more…) with azimuth on a rose diagram. I am trying to evaluate the source noise directivity. Please see an attached example of what i would like to reproduce with my data and a snippet of the code am trying to implement.

I have 20 stations that i would like to work on.

Below is what i had between 1 station pair:

#full_stack is an mseed data stream from cross-correlating the 2 stations (i.e my green’s function with both the positive and negative lags), then converted to numpy array data from

full_stack = read("./NAKA_MBAR_stack_ 8050.mseed")[0].data

azimuths = 221 #for the pair i am looking at

lengths = len(full_stack) * [0.001] # all columns the same length #??? here is the challenge

fig.rose(

azimuth=azimuths,

length=lengths,

scale=“u”,

sector=2,

# specify the “region” of interest in the (r,azimuth) space

# [r0, r1, az0, az1], here, r0 is 0 and r1 is 1000, for azimuth, az0 is 0

# and az1 is 360 which means we plot a full circle between 0 and 360 degrees

region=[0, 1, 0, 360],

# set the diameter of the rose diagram to 7.5 cm

diameter=“7.5c”,

# use red3 as color fill for the sectors

color=“red3”,

# define the frame with ticks and gridlines every 0.2

# length unit in radial direction and every 30 degrees

# in azimuthal direction, set background color to white

frame=[“x0.2g0.2”, “y30g30”, “+gwhite”],

# use a pen size of 1p to draw the outlines

pen=“1p”,

)

fig.show()

Thank you all in advance.

Image source: https://agupubs.onlinelibrary.wiley.com/cms/asset/622d1936-63a4-4649-b96c-bf8b6a56e748/jgrb53501-fig-0002-m.jpg

That is the idea of what i would like to do