def get_data(frame):
# Load Data Files
normalized_density = (fromfile("gasdens%d.dat" % frame).reshape(num_rad, num_theta)) / surface_density
averagedDensity = np.average(normalized_density, axis = 1)
vrad = (fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
# Find Peak in Radial Profile (in Outer Disk)
peak_rad, peak_density = find_peak(averagedDensity)
min_rad, min_density = find_min(averagedDensity, peak_rad)
# Gather Azimuthal Profiles
num_profiles = 5
spread = 2.0 * scale_height # half-width
azimuthal_radii = np.linspace(peak_rad - spread, peak_rad + spread, num_profiles)
azimuthal_indices = [np.searchsorted(rad, this_radius) for this_radius in azimuthal_radii]
azimuthal_profiles = [vortensity[azimuthal_index, :] for azimuthal_index in azimuthal_indices]
return azimuthal_radii, azimuthal_profiles
def get_data(frame):
# Load Data Files
normalized_density = (fromfile("gasdens%d.dat" % frame).reshape(
num_rad, num_theta)) / surface_density
averagedDensity = np.average(normalized_density, axis=1)
vrad = (fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame=ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
# Find Peak in Radial Profile (in Outer Disk)
peak_rad, peak_density = find_peak(averagedDensity)
min_rad, min_density = find_min(averagedDensity, peak_rad)
# Gather Azimuthal Profiles
num_profiles = 5
spread = 2.0 * scale_height # half-width
azimuthal_radii = np.linspace(peak_rad - spread, peak_rad + spread,
num_profiles)
azimuthal_indices = [
np.searchsorted(rad, this_radius) for this_radius in azimuthal_radii
]
azimuthal_profiles = [
vortensity[azimuthal_index, :] for azimuthal_index in azimuthal_indices
]
return azimuthal_radii, azimuthal_profiles
def get_data(frame_i, frame, modes = default_modes):
""" frame_i is ith frame, frame is frame number """
# Load Data Files
normalized_density = (fromfile("gasdens%d.dat" % frame).reshape(num_rad, num_theta)) / surface_density
averagedDensity = np.average(normalized_density, axis = 1)
vrad = (fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
# Find Peak in Radial Profile (in Outer Disk)
peak_rad, peak_density = find_peak(averagedDensity)
min_rad, min_density = find_min(averagedDensity, peak_rad)
# Gather Azimuthal Profiles
num_profiles = 5
spread = 1.0 * scale_height # half-width
azimuthal_radii = np.linspace(peak_rad - spread, peak_rad + spread, num_profiles)
azimuthal_indices = [np.searchsorted(rad, this_radius) for this_radius in azimuthal_radii]
azimuthal_profiles = np.array([np.fft.fft(vortensity[azimuthal_index, :]) for azimuthal_index in azimuthal_indices])
# Normalize by m = 0 mode (integral of density), Take Absolute Value
azimuthal_profiles = np.array([np.abs(azimuthal_profile / azimuthal_profile[0]) for azimuthal_profile in azimuthal_profiles])
for m, mode in enumerate(modes):
modes_over_time[m, frame_i] = np.max(azimuthal_profiles[:, mode])
print "%d: %.4f, %.4f, %.4f, %.4f, %.4f" % (frame, np.max(azimuthal_profiles[:, 1]), np.max(azimuthal_profiles[:, 2]), np.max(azimuthal_profiles[:, 3]), np.max(azimuthal_profiles[:, 4]), np.max(azimuthal_profiles[:, 5]))
def sum_vorticity(args):
i, frame = args
# Get Data
density = (fromfile("gasdens%d.dat" % frame).reshape(num_rad, num_theta)) / surface_density_zero
vrad = np.array(fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = np.array(fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
# Get Background Data
vtheta_keplerian = np.array(fromfile("gasvtheta0.dat").reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
vtheta -= (vtheta_keplerian)
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = 0)
#vorticity = np.abs(vorticity) # should be all negative
# Add up vorticity
dr = rad[1] - rad[0] # assumes arithmetic grid
d_phi = theta[1] - theta[0]
radial_vorticity = np.average(vorticity, axis = 1)
total_vorticity = np.sum((dr * d_phi) * rad[:-1, None] * radial_vorticity)
# Print Update
print "%d: %.4f" % (frame, total_vorticity)
# Store Data
vorticity_over_time[i] = total_vorticity
def find_extrema(args):
# Unwrap Args
i, frame = args
# Data
density = (fromfile("gasdens%d.dat" % frame).reshape(num_rad, num_theta)) / surface_density
averagedDensity = np.average(density, axis = 1)
vrad = (fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = ref_frame)
vortensity = vorticity / density[1:, 1:]
# Find Peak in Radial Profile (in Outer Disk)
peak_rad, peak_rad_index, peak_density = find_radial_peak(averagedDensity)
# Find Peaks in Azimuthal Profiles (and center around that peak)
density_peak_theta, density_theta_index, max_density = find_azimuthal_extrema(density[peak_rad_index], maximum = True) # Max
vortensity_peak_theta, vortensity_peak_theta_index, min_vortensity = find_azimuthal_extrema(vortensity[peak_rad_index], maximum = False) # Min
print "%d, %.2f, %.3f" % (frame, max_density, min_vortensity)
#return max_density, min_vortensity
maximum_densities[i] = max_density
minimum_vortensities[i] = min_vortensity
def sum_vorticity(args):
i, frame = args
# Get Data
density = (fromfile("gasdens%d.dat" % frame).reshape(
num_rad, num_theta)) / surface_density_zero
vrad = np.array(
fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = np.array(
fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
# Get Background Data
vtheta_keplerian = np.array(
fromfile("gasvtheta0.dat").reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
vtheta -= (vtheta_keplerian)
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame=0)
#vorticity = np.abs(vorticity) # should be all negative
# Add up vorticity
dr = rad[1] - rad[0] # assumes arithmetic grid
d_phi = theta[1] - theta[0]
radial_vorticity = np.average(vorticity, axis=1)
total_vorticity = np.sum((dr * d_phi) * rad[:-1, None] * radial_vorticity)
# Print Update
print "%d: %.4f" % (frame, total_vorticity)
# Store Data
vorticity_over_time[i] = total_vorticity
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize=(700 / my_dpi, 600 / my_dpi), dpi=my_dpi)
ax = fig.add_subplot(111)
# Axis
angles = np.linspace(0, 2 * np.pi, 7)
degree_angles = ["%d" % d_a for d_a in np.linspace(0, 360, 7)]
plot.ylim(0, 2 * np.pi)
plot.yticks(angles, degree_angles)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
density = (fromfile("gasdens%d.dat" % i).reshape(num_rad, num_theta))
normalized_density = density / surface_density_zero
vrad = (fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
vortensity = util.velocity_curl(
vrad, vtheta, rad, theta, frame=ref_frame) / normalized_density[1:,
1:]
### Plot ###
result = ax.pcolormesh(x, theta, np.transpose(vortensity), cmap=cmap)
fig.colorbar(result)
result.set_clim(clim[0], clim[1])
# Annotate
plot.xlabel(xlabel, fontsize=fontsize)
plot.ylabel(r"$\phi$", fontsize=fontsize)
plot.title("Vortensity Map at Orbit %d" % orbit, fontsize=fontsize + 1)
# Save and Close
plot.savefig("%s/%svortensityMap_%03d.png" %
(save_directory, prefix, i),
bbox_inches='tight',
dpi=my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize = (700 / my_dpi, 600 / my_dpi), dpi = my_dpi)
ax = fig.add_subplot(111)
# Axis
angles = np.linspace(0, 2 * np.pi, 7)
degree_angles = ["%d" % d_a for d_a in np.linspace(0, 360, 7)]
plot.ylim(0, 2 * np.pi)
plot.yticks(angles, degree_angles)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
vrad = np.array(fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = np.array(fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
vtheta_keplerian = np.array(fromfile("gasvtheta0.dat").reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
vtheta -= (vtheta_keplerian)
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = 0)
# Divide out Angular Frequency (for Rossby Number)
vorticity = vorticity / (np.array([r**(-1.5) for r in rad[:-1]]))[:, None]
### Plot ###
result = ax.pcolormesh(x, theta, np.transpose(vorticity), cmap = cmap)
fig.colorbar(result)
result.set_clim(clim[0], clim[1])
# Annotate
plot.xlabel(xlabel, fontsize = fontsize)
plot.ylabel(r"$\phi$", fontsize = fontsize)
plot.title("Vorticity (v - vK) Map at Orbit %d" % orbit, fontsize = fontsize + 1)
# Save and Close
plot.savefig("%s/%sexcessVorticityMap_%03d.png" % (save_directory, prefix, i), bbox_inches = 'tight', dpi = my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize = (700 / my_dpi, 600 / my_dpi), dpi = my_dpi)
ax = fig.add_subplot(111)
# Axis
angles = np.linspace(0, 2 * np.pi, 7)
degree_angles = ["%d" % d_a for d_a in np.linspace(0, 360, 7)]
plot.ylim(0, 2 * np.pi)
plot.yticks(angles, degree_angles)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
density = (fromfile("gasdens%d.dat" % i).reshape(num_rad, num_theta))
normalized_density = density / surface_density_zero
vrad = (fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
vortensity = util.velocity_curl(vrad, vtheta, rad, theta, frame = ref_frame) / normalized_density[1:, 1:]
avg_vortensity = np.average(vortensity, axis = 1)
excess_vortensity = avg_vortensity[:, None] - vortensity #### Vortex is Positive ####
### Plot ###
result = ax.pcolormesh(x, theta, np.transpose(excess_vortensity), cmap = cmap)
fig.colorbar(result)
result.set_clim(clim[0], clim[1])
# Annotate
plot.xlabel(xlabel, fontsize = fontsize)
plot.ylabel(r"$\phi$", fontsize = fontsize)
plot.title("Excess Vortensity Map at Orbit %d" % orbit, fontsize = fontsize + 1)
# Save and Close
plot.savefig("%s/%sexcessVortensityMap_%03d.png" % (save_directory, prefix, i), bbox_inches = 'tight', dpi = my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize=(700 / my_dpi, 600 / my_dpi), dpi=my_dpi)
# Axis
plot.ylim(0, 1.3)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
#plot.ylim(0, 1.3)
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
density = (fromfile("gasdens%d.dat" % i).reshape(num_rad, num_theta))
normalized_density = density / surface_density_zero
vrad = (fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad,
vtheta,
rad,
theta,
average=True,
frame=ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
averaged_w = np.average(vortensity, axis=1)
### Plot ###
plot.plot(x[1:], averaged_w, linewidth=linewidth)
# Annotate
this_title = readTitle()
plot.xlabel(xlabel, fontsize=fontsize)
plot.ylabel("Azimuthally Averaged Vortensity", fontsize=fontsize)
plot.title("Orbit %d: %s" % (orbit, this_title), fontsize=fontsize + 1)
# Save and Close
plot.savefig("%s/%saveragedVortensity_%03d.png" %
(save_directory, prefix, i),
bbox_inches='tight',
dpi=my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def get_data(frame):
# Load Data Files
normalized_density = (fromfile("gasdens%d.dat" % frame).reshape(
num_rad, num_theta)) / surface_density
averagedDensity = np.average(normalized_density, axis=1)
vrad = (fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame=ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
# Find Peak in Radial Profile (in Outer Disk)
peak_rad, peak_density = find_peak(averagedDensity)
min_rad, min_density = find_min(averagedDensity, peak_rad)
# Gather Azimuthal Profiles
num_profiles = 5
spread = 1.0 * scale_height # half-width
azimuthal_radii = np.linspace(peak_rad - spread, peak_rad + spread,
num_profiles)
azimuthal_indices = [
np.searchsorted(rad, this_radius) for this_radius in azimuthal_radii
]
azimuthal_profiles = [
np.fft.fft(vortensity[azimuthal_index, :])
for azimuthal_index in azimuthal_indices
]
# Normalize by m = 0 mode (integral of vortensity), Take Absolute Value
azimuthal_profiles = [
np.abs(azimuthal_profile / azimuthal_profile[0])
for azimuthal_profile in azimuthal_profiles
]
### Gather Averaged Profiles ###
start_half = azimuthal_indices[1]
end_half = azimuthal_indices[-2]
avg_half_profile = np.average(vortensity[start_half:end_half, :], axis=0)
avg_half = np.fft.fft(avg_half_profile)
start_full = azimuthal_indices[0]
end_full = azimuthal_indices[-1]
avg_full_profile = np.average(vortensity[start_full:end_full, :], axis=0)
avg_full = np.fft.fft(avg_full_profile)
# Normalize
avg_half = np.abs(avg_half / avg_half[0])
avg_full = np.abs(avg_full / avg_full[0])
return azimuthal_radii, azimuthal_profiles, avg_half, avg_full
def sum_vorticity(args):
i, frame = args
# Get Data
density = (fromfile("gasdens%d.dat" % frame).reshape(num_rad, num_theta)) / surface_density_zero
vrad = np.array(fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = np.array(fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
# Get Background Data
vtheta_keplerian = np.array(fromfile("gasvtheta0.dat").reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
vtheta -= (vtheta_keplerian)
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = 0)
# Mask Non-Vortex Regions
min_vorticity = -0.65
vorticity[density[:-1, :-1] < 0.45] = 0 # if density is too low, it's not in the vortex
vorticity[vorticity > 0] = 0 # if vorticity is positive, it's not in the vortex
vorticity[vorticity < min_vorticity] = min_vorticity # if vorticity is too low, it's not in the vortex
vorticity = np.abs(vorticity) # everything that remains should be negative. switch to positive.
# Extract Near Vortex
averagedDensity = np.average(density, axis = 1)
peak_rad, peak_density = find_peak(averagedDensity)
vortex_start = np.max([1.0, peak_rad - 5.0 * scale_height])
vortex_end = peak_rad + 5.0 * scale_height
vortex_start_i = np.searchsorted(rad, vortex_start)
vortex_end_i = np.searchsorted(rad, vortex_end)
vortex_rad = rad[vortex_start_i : vortex_end_i]
vortex_vorticity_grid = vorticity[vortex_start_i : vortex_end_i]
vortex_vorticity = np.average(vortex_vorticity_grid, axis = 1)
# Add up vorticity
dr = rad[1] - rad[0] # assumes arithmetic grid
d_phi = theta[1] - theta[0]
total_vorticity = np.sum((dr * d_phi) * vortex_rad[:, None] * vortex_vorticity)
##### Instead: take max vorticity (90th percentile???) #####
# Print Update
print "%d: %.4f" % (frame, total_vorticity)
# Store Data
vorticity_over_time[i] = total_vorticity
def get_data(frame_i, frame, modes=default_modes):
""" frame_i is ith frame, frame is frame number """
# Load Data Files
normalized_density = (fromfile("gasdens%d.dat" % frame).reshape(
num_rad, num_theta)) / surface_density
averagedDensity = np.average(normalized_density, axis=1)
vrad = np.array(
fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = np.array(
fromfile("gasvtheta%d.dat" % frame).reshape(
num_rad, num_theta)) - vtheta_keplerian
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame=ref_frame)
combo = normalized_density[:-1, :-1] * vorticity
# Find Peak in Radial Profile (in Outer Disk)
peak_rad, peak_density = find_peak(averagedDensity)
min_rad, min_density = find_min(averagedDensity, peak_rad)
# Gather Azimuthal Profiles
num_profiles = 5
spread = 1.0 * scale_height # half-width
azimuthal_radii = np.linspace(peak_rad - spread, peak_rad + spread,
num_profiles)
azimuthal_indices = [
np.searchsorted(rad, this_radius) for this_radius in azimuthal_radii
]
azimuthal_profiles = np.array([
np.fft.fft(combo[azimuthal_index, :])
for azimuthal_index in azimuthal_indices
])
# Normalize by m = 0 mode (integral of density), Take Absolute Value
azimuthal_profiles = np.array([
np.abs(azimuthal_profile / azimuthal_profile[0])
for azimuthal_profile in azimuthal_profiles
])
for m, mode in enumerate(modes):
modes_over_time[m, frame_i] = np.max(azimuthal_profiles[:, mode])
single_mode_strength[frame_i] = modes_over_time[0, frame_i] / np.max(
modes_over_time[1:, frame_i]) # m = 1 / Max of Higher Number Modes
print "%d: %.4f, %.4f, %.4f, %.4f, %.4f - [%.4f]" % (
frame, np.max(azimuthal_profiles[:, 1]),
np.max(azimuthal_profiles[:, 2]), np.max(azimuthal_profiles[:, 3]),
np.max(azimuthal_profiles[:, 4]), np.max(
azimuthal_profiles[:, 5]), single_mode_strength[frame_i])
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize = (700 / my_dpi, 600 / my_dpi), dpi = my_dpi)
# Axis
plot.ylim(0, 1.3)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
#plot.ylim(0, 1.3)
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
density = (fromfile("gasdens%d.dat" % i).reshape(num_rad, num_theta))
normalized_density = density / surface_density_zero
vrad = (fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, average = True, frame = ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
averaged_w = np.average(vortensity, axis = 1)
### Plot ###
plot.plot(x[1:], averaged_w, linewidth = linewidth)
# Annotate
this_title = readTitle()
plot.xlabel(xlabel, fontsize = fontsize)
plot.ylabel("Azimuthally Averaged Vortensity", fontsize = fontsize)
plot.title("Orbit %d: %s" % (orbit, this_title), fontsize = fontsize + 1)
# Save and Close
plot.savefig("%s/%saveragedVortensity_%03d.png" % (save_directory, prefix, i), bbox_inches = 'tight', dpi = my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize = (700 / my_dpi, 600 / my_dpi), dpi = my_dpi)
ax = fig.add_subplot(111, polar = True)
# Axis
if axis == "zoom":
prefix = "zoom_"
rmax = 2.4 # to match ApJL paper
else:
prefix = ""
rmax = float(fargo_par["Rmax"])
plot.xticks([], []) # Angles
plot.yticks([rmax], ["%.1f" % rmax]) # Max Radius
plot.ylim(0, rmax)
# Data
density = (fromfile("gasdens%d.dat" % i).reshape(num_rad, num_theta))
normalized_density = density / surface_density_zero
vrad = (fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta) / normalized_density[1:, 1:]
### Plot ###
result = ax.pcolormesh(theta, rad, vorticity, cmap = cmap)
fig.colorbar(result)
result.set_clim(clim[0], clim[1])
# Annotate
this_title = readTitle()
plot.title("Vortensity Map at Orbit %d\n%s" % (orbit, this_title), fontsize = fontsize + 1)
# Save and Close
plot.savefig("%s/%svortensityMap_%03d.png" % (save_directory, prefix, i), bbox_inches = 'tight', dpi = my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def get_data(frame):
# Load Data Files
normalized_density = (fromfile("gasdens%d.dat" % frame).reshape(num_rad, num_theta)) / surface_density
averagedDensity = np.average(normalized_density, axis = 1)
vrad = (fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
# Find Peak in Radial Profile (in Outer Disk)
peak_rad, peak_density = find_peak(averagedDensity)
min_rad, min_density = find_min(averagedDensity, peak_rad)
# Gather Azimuthal Profiles
num_profiles = 5
spread = 1.0 * scale_height # half-width
azimuthal_radii = np.linspace(peak_rad - spread, peak_rad + spread, num_profiles)
azimuthal_indices = [np.searchsorted(rad, this_radius) for this_radius in azimuthal_radii]
azimuthal_profiles = [np.fft.fft(vortensity[azimuthal_index, :]) for azimuthal_index in azimuthal_indices]
# Normalize by m = 0 mode (integral of vortensity), Take Absolute Value
azimuthal_profiles = [np.abs(azimuthal_profile / azimuthal_profile[0]) for azimuthal_profile in azimuthal_profiles]
### Gather Averaged Profiles ###
start_half = azimuthal_indices[1]
end_half = azimuthal_indices[-2]
avg_half_profile = np.average(vortensity[start_half : end_half, :], axis = 0)
avg_half = np.fft.fft(avg_half_profile)
start_full = azimuthal_indices[0]
end_full = azimuthal_indices[-1]
avg_full_profile = np.average(vortensity[start_full : end_full, :], axis = 0)
avg_full = np.fft.fft(avg_full_profile)
# Normalize
avg_half = np.abs(avg_half / avg_half[0])
avg_full = np.abs(avg_full / avg_full[0])
return azimuthal_radii, azimuthal_profiles, avg_half, avg_full
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Axis
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
#plot.ylim(0, 1.2)
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]) - 0.05, float(fargo_par["Rmax"]) + 0.05)
#plot.ylim(0, 5.0)
xlabel = "Radius"
# Data
density = (fromfile("gasdens%d.dat" % i).reshape(num_rad, num_theta))
normalized_density = density / surface_density_zero
vrad = (fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, average = True, frame = ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
averaged_w = np.average(vortensity, axis = 1)
### Plot ###
plot.plot(x, averaged_w, linewidth = linewidth, label = "%d" % frame)
# Annotate
this_title = readTitle()
plot.xlabel(xlabel, fontsize = fontsize)
plot.ylabel(r"Azimuthally Averaged Vortensity", fontsize = fontsize)
plot.title("%s" % this_title, fontsize = fontsize + 1)
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize=(1400 / my_dpi, 600 / my_dpi), dpi=my_dpi)
#gs = gridspec.GridSpec(1, 2)
#ax1 = fig.add_subplot(gs[0])
#ax2 = fig.add_subplot(gs[1])
###### Density ######
# Select Plot
plot.subplot(1, 2, 1)
# Axis
angles = np.linspace(0, 2 * np.pi, 7)
degree_angles = ["%d" % d_a for d_a in np.linspace(0, 360, 7)]
plot.ylim(0, 2 * np.pi)
plot.yticks(angles, degree_angles)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
density = (fromfile("gasdens%d.dat" % i).reshape(
num_rad, num_theta)) / surface_density_zero
background_density = (fromfile("gasdens%d.dat" % (i - 1)).reshape(
num_rad, num_theta)) / surface_density_zero
diff_density = density - background_density
### Plot ###
result = plot.pcolormesh(x,
theta,
np.transpose(diff_density),
cmap=d_cmap)
fig.colorbar(result)
result.set_clim(d_clim[0], d_clim[1])
# Annotate
this_title = readTitle()
plot.xlabel(xlabel, fontsize=fontsize)
plot.ylabel(r"$\phi$", fontsize=fontsize)
plot.title("Gas Density Difference Map at Orbit %d\n%s" %
(orbit, this_title),
fontsize=fontsize + 1)
###### Excess Vortensity ######
# Select Plot
plot.subplot(1, 2, 2)
# Axis
angles = np.linspace(0, 2 * np.pi, 7)
degree_angles = ["%d" % d_a for d_a in np.linspace(0, 360, 7)]
plot.ylim(0, 2 * np.pi)
plot.yticks(angles, degree_angles)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
vtheta_keplerian = np.array(
fromfile("gasvtheta0.dat").reshape(num_rad, num_theta))
### FRAME 1 ###
vrad = np.array(
fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = np.array(
fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
vtheta -= (vtheta_keplerian)
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame=0)
# Divide out Angular Frequency (for Rossby Number)
vorticity = vorticity / (np.array([r**(-1.5)
for r in rad[:-1]]))[:, None]
### FRAME 2 ###
background_vrad = np.array(
fromfile("gasvrad%d.dat" % (i - 1)).reshape(num_rad, num_theta))
background_vtheta = np.array(
fromfile("gasvtheta%d.dat" % (i - 1)).reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
background_vtheta -= (vtheta_keplerian)
background_vorticity = util.velocity_curl(background_vrad,
background_vtheta,
rad,
theta,
frame=0)
# Divide out Angular Frequency (for Rossby Number)
background_vorticity = background_vorticity / (np.array(
[r**(-1.5) for r in rad[:-1]]))[:, None]
# Remove (vorticity > 0) from background
background_vorticity[background_vorticity > 0] = 0
### TAKE DIFFERENCE
diff_vorticity = vorticity - background_vorticity
### Plot ###
result = plot.pcolormesh(x,
theta,
np.transpose(diff_vorticity),
cmap=v_cmap)
fig.colorbar(result)
result.set_clim(v_clim[0], v_clim[1])
# Annotate
plot.xlabel(xlabel, fontsize=fontsize)
plot.ylabel(r"$\phi$", fontsize=fontsize)
plot.title("Vorticity (v - vK) Map at Orbit %d\n%s" %
(orbit, this_title),
fontsize=fontsize + 1)
# Save and Close
plot.savefig("%s/%sdiffComboMap_%03d.png" %
(save_directory, prefix, i),
bbox_inches='tight',
dpi=my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize=(700 / my_dpi, 600 / my_dpi), dpi=my_dpi)
ax = fig.add_subplot(111)
# Axis
angles = np.linspace(0, 2 * np.pi, 7)
degree_angles = ["%d" % d_a for d_a in np.linspace(0, 360, 7)]
plot.ylim(0, 2 * np.pi)
plot.yticks(angles, degree_angles)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
vrad = np.array(
fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = np.array(
fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
vtheta_keplerian = np.array(
fromfile("gasvtheta0.dat").reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
vtheta -= (vtheta_keplerian)
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame=0)
# Divide out Angular Frequency (for Rossby Number)
vorticity = vorticity / (np.array([r**(-1.5)
for r in rad[:-1]]))[:, None]
### Plot ###
result = ax.pcolormesh(x, theta, np.transpose(vorticity), cmap=cmap)
fig.colorbar(result)
result.set_clim(clim[0], clim[1])
# Annotate
plot.xlabel(xlabel, fontsize=fontsize)
plot.ylabel(r"$\phi$", fontsize=fontsize)
plot.title("Vorticity (v - vK) Map at Orbit %d" % orbit,
fontsize=fontsize + 1)
# Save and Close
plot.savefig("%s/%sexcessVorticityMap_%03d.png" %
(save_directory, prefix, i),
bbox_inches='tight',
dpi=my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def choose_axis(i, axis):
# Orbit Number
time = float(fargo_par["Ninterm"]) * float(fargo_par["DT"])
orbit = int(round(time / (2 * np.pi), 0)) * i
# Set up figure
fig = plot.figure(figsize = (1400 / my_dpi, 600 / my_dpi), dpi = my_dpi)
#gs = gridspec.GridSpec(1, 2)
#ax1 = fig.add_subplot(gs[0])
#ax2 = fig.add_subplot(gs[1])
###### Density ######
# Select Plot
plot.subplot(1, 2, 1)
# Axis
angles = np.linspace(0, 2 * np.pi, 7)
degree_angles = ["%d" % d_a for d_a in np.linspace(0, 360, 7)]
plot.ylim(0, 2 * np.pi)
plot.yticks(angles, degree_angles)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
density = (fromfile("gasdens%d.dat" % i).reshape(num_rad, num_theta)) / surface_density_zero
background_density = (fromfile("gasdens%d.dat" % (i - 1)).reshape(num_rad, num_theta)) / surface_density_zero
diff_density = density - background_density
### Plot ###
result = plot.pcolormesh(x, theta, np.transpose(diff_density), cmap = d_cmap)
fig.colorbar(result)
result.set_clim(d_clim[0], d_clim[1])
# Annotate
this_title = readTitle()
plot.xlabel(xlabel, fontsize = fontsize)
plot.ylabel(r"$\phi$", fontsize = fontsize)
plot.title("Gas Density Difference Map at Orbit %d\n%s" % (orbit, this_title), fontsize = fontsize + 1)
###### Excess Vortensity ######
# Select Plot
plot.subplot(1, 2, 2)
# Axis
angles = np.linspace(0, 2 * np.pi, 7)
degree_angles = ["%d" % d_a for d_a in np.linspace(0, 360, 7)]
plot.ylim(0, 2 * np.pi)
plot.yticks(angles, degree_angles)
if axis == "zoom":
x = (rad - 1) / scale_height
prefix = "zoom_"
plot.xlim(0, 40) # to match the ApJL paper
xlabel = r"($r - r_p$) $/$ $h$"
else:
x = rad
prefix = ""
plot.xlim(float(fargo_par["Rmin"]), float(fargo_par["Rmax"]))
xlabel = "Radius"
# Data
vtheta_keplerian = np.array(fromfile("gasvtheta0.dat").reshape(num_rad, num_theta))
### FRAME 1 ###
vrad = np.array(fromfile("gasvrad%d.dat" % i).reshape(num_rad, num_theta))
vtheta = np.array(fromfile("gasvtheta%d.dat" % i).reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
vtheta -= (vtheta_keplerian)
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = 0)
# Divide out Angular Frequency (for Rossby Number)
vorticity = vorticity / (np.array([r**(-1.5) for r in rad[:-1]]))[:, None]
### FRAME 2 ###
background_vrad = np.array(fromfile("gasvrad%d.dat" % (i - 1)).reshape(num_rad, num_theta))
background_vtheta = np.array(fromfile("gasvtheta%d.dat" % (i - 1)).reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
background_vtheta -= (vtheta_keplerian)
background_vorticity = util.velocity_curl(background_vrad, background_vtheta, rad, theta, frame = 0)
# Divide out Angular Frequency (for Rossby Number)
background_vorticity = background_vorticity / (np.array([r**(-1.5) for r in rad[:-1]]))[:, None]
# Remove (vorticity > 0) from background
background_vorticity[background_vorticity > 0] = 0
### TAKE DIFFERENCE
diff_vorticity = vorticity - background_vorticity
### Plot ###
result = plot.pcolormesh(x, theta, np.transpose(diff_vorticity), cmap = v_cmap)
fig.colorbar(result)
result.set_clim(v_clim[0], v_clim[1])
# Annotate
plot.xlabel(xlabel, fontsize = fontsize)
plot.ylabel(r"$\phi$", fontsize = fontsize)
plot.title("Vorticity (v - vK) Map at Orbit %d\n%s" % (orbit, this_title), fontsize = fontsize + 1)
# Save and Close
plot.savefig("%s/%sdiffComboMap_%03d.png" % (save_directory, prefix, i), bbox_inches = 'tight', dpi = my_dpi)
if show:
plot.show()
plot.close(fig) # Close Figure (to avoid too many figures)
def measure_asymmetry(frame):
print frame
# Load Data Files
normalized_density = (fromfile("gasdens%d.dat" % frame).reshape(num_rad, num_theta)) / surface_density
averagedDensity = np.average(normalized_density, axis = 1)
vrad = (fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = (fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame = ref_frame)
vortensity = vorticity / normalized_density[1:, 1:]
# Find Peak in Radial Profile (in Outer Disk)
peak_rad, peak_density = find_peak(averagedDensity)
# Gather Azimuthal Profiles
num_profiles = 5
spread = 1.0 * scale_height # half-width
azimuthal_radii = np.linspace(peak_rad - spread, peak_rad + spread, num_profiles)
azimuthal_indices = [np.searchsorted(rad, this_radius) for this_radius in azimuthal_radii]
azimuthal_profiles = [vortensity[azimuthal_index, :] for azimuthal_index in azimuthal_indices]
# For each profile, measure the azimuthal extent of the vortex (vortensity < threshold = 0.25)
vortex_value = 360.0 / float(fargo_par["Nsec"])
background_value = 0.0
asymmetry_values = []
avg_densities = []
for azimuthal_profile in azimuthal_profiles:
# Get Asymmetry
copy = np.zeros(np.shape(azimuthal_profile))
copy[azimuthal_profile > threshold] = background_value
copy[azimuthal_profile <= threshold] = vortex_value
azithumal_extent = np.sum(copy) # should be the angle spanned by the vortex (in degrees)
asymmetry_values.append(azithumal_extent)
# Get Vortensity
copy = azimuthal_profile[azimuthal_profile <= threshold]
avg_vortensity = np.mean(copy)
avg_densities.append(avg_vortensity)
# Take Median of Profiles between -H and +H about peak in radial density profile
asymmetry = np.median(asymmetry_values)
# Get Mean Density of Vortex within One Scale Height (+ 25% and 75% values)
start = azimuthal_indices[0]
end = azimuthal_indices[-1]
vortex_zone = vortensity[start : end, :]
vortex_vortensities = vortex_zone[vortex_zone < threshold]
avg_vortensity = np.mean(vortex_vortensities)
# Detect Nan
if avg_vortensity != avg_vortensity:
avg_vortensity = threshold
lower_quartile = threshold
upper_quartile = threshold
else:
# If no nan, compute quartiles
lower_quartile = np.percentile(vortex_vortensities, 25) # 25%
upper_quartile = np.percentile(vortex_vortensities, 75) # 75%
return asymmetry, avg_vortensity, lower_quartile, upper_quartile
def sum_vorticity(args):
i, frame = args
# Get Data
density = (fromfile("gasdens%d.dat" % frame).reshape(
num_rad, num_theta)) / surface_density_zero
vrad = np.array(
fromfile("gasvrad%d.dat" % frame).reshape(num_rad, num_theta))
vtheta = np.array(
fromfile("gasvtheta%d.dat" % frame).reshape(num_rad, num_theta))
# Get Background Data
vtheta_keplerian = np.array(
fromfile("gasvtheta0.dat").reshape(num_rad, num_theta))
# Subtract off Keplerian velocity (and rotate back into non-rotating frame???)
vtheta -= (vtheta_keplerian)
vorticity = util.velocity_curl(vrad, vtheta, rad, theta, frame=0)
# Mask Non-Vortex Regions
min_vorticity = -0.65
vorticity[density[:-1, :-1] <
0.45] = 0 # if density is too low, it's not in the vortex
vorticity[
vorticity > 0] = 0 # if vorticity is positive, it's not in the vortex
vorticity[
vorticity <
min_vorticity] = min_vorticity # if vorticity is too low, it's not in the vortex
vorticity = np.abs(
vorticity
) # everything that remains should be negative. switch to positive.
# Extract Near Vortex
averagedDensity = np.average(density, axis=1)
peak_rad, peak_density = find_peak(averagedDensity)
vortex_start = np.max([1.0, peak_rad - 5.0 * scale_height])
vortex_end = peak_rad + 5.0 * scale_height
vortex_start_i = np.searchsorted(rad, vortex_start)
vortex_end_i = np.searchsorted(rad, vortex_end)
vortex_rad = rad[vortex_start_i:vortex_end_i]
vortex_vorticity_grid = vorticity[vortex_start_i:vortex_end_i]
vortex_vorticity = np.average(vortex_vorticity_grid, axis=1)
# Add up vorticity
dr = rad[1] - rad[0] # assumes arithmetic grid
d_phi = theta[1] - theta[0]
total_vorticity = np.sum(
(dr * d_phi) * vortex_rad[:, None] * vortex_vorticity)
##### Instead: take max vorticity (90th percentile???) #####
# Print Update
print "%d: %.4f" % (frame, total_vorticity)
# Store Data
vorticity_over_time[i] = total_vorticity