elevation_spec = [0, 1,
0] # grab time index 0, quantity code 1, radial index 0
elevation_scale_factor = 1.5 # scale_factor (used in tandem with stddev -- see scale_data above)
color_spec = [0, 3, 0] #grab time index 0, quantity code 3, radial index 0
color_scale_factor = 2.5
if (vis_option == 2):
# Radial component of velocity gives the elevation and the color
hillsize = 0.003
elevation_spec = [0, 1, 0]
elevation_scale_factor = 1.5
color_spec = [0, 1, 0]
color_scale_factor = 1.5
shell1 = ShellSlice(shellfile, path='./', slice_spec=elevation_spec)
elevation_data = shell1.vals[:, :].reshape(shell1.nphi, shell1.ntheta)
elevation_data = np.transpose(elevation_data)
shell2 = ShellSlice(shellfile, path='./', slice_spec=color_spec)
color_data = shell2.vals[:, :].reshape(shell1.nphi, shell1.ntheta)
color_data = np.transpose(color_data)
nphi = shell1.nphi
ntheta = shell2.ntheta
# Scale both the elevation and the color data as desired
# If rendering something with a spherical mean, set no_mean = True
# in the call to scale_data (as with temperature/entropy)
scale_data(elevation_data, scale_factor=elevation_scale_factor)
scale_data(color_data, scale_factor=color_scale_factor)
rec_spec = [0, 3, 1] # grab time index 0, quantity code 3, and radial index 1
save_figure = False # Set to true to save to a figure_file (below)
figure_file = 'shell_slice_basemap.png'
#The resolution of the png file or the view window in pixels
xpixels = 1024
ypixels = 1024
# Initialize the projection.
# lon_0, lat_0 are the center point of the projection.
# resolution = 'l' means use low resolution coastlines.
m = Basemap(projection='ortho', lon_0=-20, lat_0=35, resolution='l')
#Read in the data
a = ShellSlice(shellfile, path='./', slice_spec=rec_spec)
data = a.vals[:, :, 0, 0, 0].reshape(a.nphi, a.ntheta)
data = np.transpose(data)
maxabs = 3 * np.std(data) # saturate data at +/- 3 sigma
dlon = 360.0 / (a.nphi)
dlat = 180.0 / a.ntheta
#Scale the data
for i in range(a.ntheta):
for j in range(a.nphi):
if (data[i, j] > maxabs):
data[i, j] = maxabs
if (data[i, j] < -maxabs):
data[i, j] = -maxabs
data[i, j] = data[i, j] / maxabs
# Create 'datadir' if it doesn't exist already
if (not os.path.isdir(datadir)):
os.makedirs(datadir)
slicedir = dirname + '/Shell_Slices/'
files = os.listdir(slicedir)
files.sort()
rr, tt, cost, sint, rr_depth, ri, ro, d = np.load(datadir + 'grid_info.npy')
nr = len(rr)
nt = len(tt)
nph = 2 * nt
sint_2d = sint.reshape((1, nt))
print('Reading ' + slicedir + files[-1] + ' ...')
a = ShellSlice(slicedir + files[-1], '')
vr = a.vals[:, :, :, a.lut[1], 0]
nrad = len(a.radius)
fplus = np.zeros(nrad)
fminus = np.zeros(nrad)
for i in range(nrad):
vr_loc = vr[:, :, i]
where_p = np.where(vr_loc > 0)
where_m = np.where(vr_loc < 0)
ind_p = np.zeros((nph, nt))
ind_p[where_p] = 1
ind_m = np.zeros((nph, nt))
ind_m[where_m] = 1
# self.lut : Lookup table for the different diagnostics output
# -------------------------------------
import pylab as p
import numpy as np
from diagnostic_reading import ShellSlice
import matplotlib.pyplot as plt
from matplotlib import ticker
# Set saveplot to True to save to a .png file.
# Set to False to view plots interactively on-screen.
saveplot = False
savefile = 'shell_slice.png'
#Read in our shell slice
a = ShellSlice(filename='03280000', path='Shell_Slices/')
#Identify the variables indices for vr,vphi, and entropy
var_inds = [a.lut[1], a.lut[3], a.lut[64]]
rad_inds = [0, 3, 6] # pick 3 depth to plot at
tind = 0 # grab time index 0 (the first record of the file)
#Tex can be enclosed in dollar signs within a string. The r in front of the string is necessary for strings enclosing Tex
units = [r'm s$^{-1}$', r'm s$^{-1}$', r'erg g$^{-1}$ K$^{-1}$']
vnames = [r'v$_r$', r'v$_\phi$', "S'"]
ncol = len(var_inds)
nrow = len(rad_inds)
nplots = ncol * nrow
#Create the plots. This first portion only handles the projection and colorbars.