# from input_parameters import rhoat, rhooc, fnot
import utils as utils
datapath = '/Users/rk2014/Documents/q-gcm/src/outdata/'
# Files that describe oceanic and atmospheric state
atpa = nc.Dataset(datapath + 'atpa.nc')
ocpo = nc.Dataset(datapath + 'ocpo.nc')
q_oc = ocpo.variables['q'][:, :, :, :]
q_at = atpa.variables['q'][:, :, :, :]
time = q_oc.shape[0] # total time
for i in range(2):
for j in range(3):
if i == 0:
q = utils.ContourData(q_oc[:, j, :, :])
else:
q = utils.ContourData(q_at[:, j, :, :])
if i == 0:
[fig, ax] = q.init_frame('Potential Vorticity ($s^{-1}$)', True)
anim = ani.FuncAnimation(fig, q.frame_i, frames=time)
anim.save('/Users/rk2014/Documents/q-gcm/post-process/pv' +
str(j + 1) + '_oc.mp4')
else:
[fig, ax] = q.init_frame('Potential Vorticity ($s^{-1}$)', False)
anim = ani.FuncAnimation(fig, q.frame_i, frames=time)
anim.save('/Users/rk2014/Documents/q-gcm/post-process/pv' +
str(j + 1) + '_at.mp4')
import matplotlib.pyplot as plt
import matplotlib.animation as ani
from matplotlib import cm
# from input_parameters import rhoat, rhooc, fnot
import utils as utils
datapath = '/rds/general/user/rk2014/home/WORK/q-gcm/outdata_toptest/1/'
# Files that describe oceanic and atmospheric state
atpa = nc.Dataset(datapath + 'atpa.nc')
ocpo = nc.Dataset(datapath + 'ocpo.nc')
steps = [0, 36, 73, 109, 146, 182, 219, 255, 292, 328, 365]
q_oc = ocpo.variables['q'][steps, :, :, :]
q_at = atpa.variables['q'][steps, :, :, :]
savepath = './'
for i in range(2):
for j in range(3):
if i == 0:
name = 'PV_oc' + '_' + str(j + 1) + 'b_'
q = utils.ContourData(q_oc[:, j, :, :], name=name)
[fig, ax] = q.init_frame('Potential Vorticity ($s^{-1}$)', True)
q.take_snapshots(1, 365, savepath, True)
else:
name = 'PV_at' + '_' + str(j + 1) + 'b_'
q = utils.ContourData(q_at[:, j, :, :], name=name)
[fig, ax] = q.init_frame('Potential Vorticity ($s^{-1}$)', False)
q.take_snapshots(1, 365, savepath, False)
import matplotlib.animation as ani
from matplotlib import cm
# from input_parameters import rhoat, rhooc, fnot
import utils as utils
datapath = '/rds/general/user/rk2014/home/WORK/q-gcm/outdata_210_a/'
# Files that describe oceanic and atmospheric state
atast = nc.Dataset(datapath + 'atast.nc')
ocsst = nc.Dataset(datapath + 'ocsst.nc')
steps = [0, 36, 73, 109, 146, 182, 219, 255, 292, 328, 365]
ast = atast.variables['ast'][steps, :, :]
sst = ocsst.variables['sst'][steps, :, :]
time = ast.shape[0] # total time
savepath = './'
for i in range(2):
if i == 0:
name = 'sst_a_'
sst = utils.ContourData(sst[:, :, :], name=name)
[fig, ax] = sst.init_frame('SST ($K$)', True)
sst.take_snapshots(1, 365, savepath, True)
else:
name = 'ast_a_'
ast = utils.ContourData(ast[:, :, :], name=name)
[fig, ax] = ast.init_frame('AST ($K$)', False)
ast.take_snapshots(1, 365, savepath, False)
import netCDF4 as nc
import matplotlib.pyplot as plt
import matplotlib.animation as ani
from matplotlib import cm
# from input_parameters import rhoat, rhooc, fnot
import utils as utils
datapath = '/rds/general/user/rk2014/home/WORK/q-gcm/outdata_toptest/2/'
writer = ani.ImageMagickWriter()
# Files that describe oceanic and atmospheric state
atast = nc.Dataset(datapath + 'atast.nc')
ocsst = nc.Dataset(datapath + 'ocsst.nc')
ast = atast.variables['ast']
sst = ocsst.variables['sst']
time = ast.shape[0] # total time
for i in range(2):
if i == 0:
ast = utils.ContourData(ast[:, :, :])
[fig, ax] = ast.init_frame('AST ($K$)', False)
anim = ani.FuncAnimation(fig, ast.frame_i, frames=time)
anim.save('./ast_b.gif', writer=writer)
else:
sst = utils.ContourData(sst[:, :, :])
[fig, ax] = sst.init_frame('SST ($K$)', True)
anim = ani.FuncAnimation(fig, sst.frame_i, frames=time)
anim.save('./sst_b.gif', writer=writer)
# Files that describe oceanic and atmospheric state
atpa = nc.Dataset(datapath + 'atpa.nc')
ocpo = nc.Dataset(datapath + 'ocpo.nc')
steps = [0, 36, 73, 109, 146, 182, 219, 255, 292, 328, 365]
p_oc = ocpo.variables['p'][steps, :, :, :]
p_at = atpa.variables['p'][steps, :, :, :]
H_at = [2000, 3000, 4000]
H_oc = [350, 750, 2900]
f0 = fnot
savepath = './'
for i in range(2):
for j in range(3):
if i == 0:
name = 'Sv_oc' + '_a_' + str(j + 1)
psi = utils.ContourData(p_oc[:, j, :, :] * H_oc[j] / (f0 * 10**6),
name=name)
[fig, ax] = psi.init_frame('Layer Transport (Sv)', True)
psi.take_snapshots(1, 365, savepath, True)
else:
name = 'Sv_at' + '_a_' + str(j + 1)
psi = utils.ContourData(p_at[:, j, :, :] * H_at[j] / (f0 * 10**6),
name=name)
[fig, ax] = psi.init_frame('Layer Transport (Sv)', False)
psi.take_snapshots(1, 365, savepath, False)
#variable = 'zonal'
#variable = 'meridional'
variable = 'speed'
for i in range(1, 2):
for j in range(3):
if i == 0:
dx = dxo
# Dimensionalise alpha
alpha = bccooc / dx
p_oc = np.array(avges.variables['po'][j, :, :])
p_oc = utils.Pressure(p_oc[np.newaxis, :, :])
if variable == 'zonal':
name = 'u_oc_avge' + '_' + str(j + 1) + '_a'
u = utils.ContourData(p_oc.zonal(0, alpha, dx, fnot).data,
name=name)
[fig, ax] = u.init_frame('Zonal Velocity ($ms^{-1}$)', True)
u.take_snapshots(1, 1, savepath, True)
elif variable == 'meridional':
name = 'v_oc_avge' + '_' + str(j + 1) + '_a'
v = utils.ContourData(p_oc.meridional(0, alpha, dx, fnot).data,
name=name)
[fig, ax] = v.init_frame('Meridional Velocity ($ms^{-1}$)',
True)
v.take_snapshots(1, 1, savepath, True)
elif variable == 'speed':
name = 's_oc_avge' + '_' + str(j + 1) + '_a'
s = utils.ContourData(p_oc.speed(0, alpha, dx, fnot).data,
name=name)
[fig, ax] = s.init_frame('Speed ($ms^{-1}$)', True)
s.take_snapshots(1, 1, savepath, True)
import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
import matplotlib.animation as ani
from matplotlib import cm
# from input_parameters import rhoat, rhooc, fnot
import utils as utils
datapath = '/rds/general/user/rk2014/home/WORK/q-gcm/outdata_toptest/test/apalachee_coupled/'
savepath = './'
ocsst = nc.Dataset(datapath + 'ocsst.nc')
steps = [0, 36, 73, 109, 146, 182, 219, 255, 292, 328, 365]
ek_vel = ocsst['wekt'][steps, :, :]
time = ek_vel.shape[0]
ek_av = np.einsum('ijk->jk', np.array(ocsst['wekt'])) / time
name = 'ek_av_oc'
ek_av = utils.ContourData(ek_av[np.newaxis, :, :], name=name)
[fig, ax] = ek_av.init_frame('Ekman Velocity ($ms^{-1}$)', True)
ek_av.take_snapshots(1, 1, savepath, True)
name = 'ek_oc'
ek = utils.ContourData(ek_vel[:, :, :], name=name)
[fig, ax] = ek.init_frame('Ekman Velocity ($ms^{-1}$)', True)
ek.take_snapshots(1, 365, savepath, True)
from matplotlib import cm
from input_parameters import fnot
import utils as utils
datapath = '/rds/general/user/rk2014/home/WORK/q-gcm/outdata_210_b/'
# Files that describe oceanic and atmospheric state
atpa = nc.Dataset(datapath + 'atpa.nc')
ocpo = nc.Dataset(datapath + 'ocpo.nc')
steps = [0, 36, 73, 109, 146, 182, 219, 255, 292, 328, 365]
p_oc = ocpo.variables['p'][steps, :, :, :]
p_at = atpa.variables['p'][steps, :, :, :]
f0 = fnot
savepath = './'
for i in range(2):
for j in range(3):
if i == 0:
name = 'P_oc' + '_b_' + str(j + 1)
p = utils.ContourData(p_oc[:, j, :, :], name=name)
[fig, ax] = p.init_frame('Pressure ($m^2s^{-2}$)', True)
p.take_snapshots(1, 365, savepath, True)
else:
name = 'P_at' + '_b_' + str(j + 1)
p = utils.ContourData(p_at[:, j, :, :], name=name)
[fig, ax] = p.init_frame('Pressure ($m^2s^{-2}$)', False)
p.take_snapshots(1, 365, savepath, False)
import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
import matplotlib.animation as ani
from matplotlib import cm
# from input_parameters import rhoat, rhooc, fnot
import utils as utils
datapath = '/rds/general/user/rk2014/home/WORK/q-gcm/outdata_210_b/'
ocsst = nc.Dataset(datapath + 'ocsst.nc')
steps = [0, 36, 73, 109, 146, 182, 219, 255, 292, 328, 365]
ek_vel = ocsst['wekt'][steps, :, :]
time = ek_vel.shape[0]
ek_av = np.einsum('ijk->jk',np.array(ocsst['wekt'])/time
name = 'ek_av_oc'
ek_av = utils.ContourData(ek_av[np.newaxis, :, :], name=name)
[fig, ax] = ek_av.init_frame('Ekman Velocity ($ms^{-1}$)', True)
ek_av.take_snapshots(1, 1, savepath, True)
name = 'ek_oc'
ek = utils.ContourData(ek_vel[:, :, :], name=name)
[fig, ax] = ek.init_frame('Ekman Velocity ($ms^{-1}$)', True)
ek.take_snapshots(1, 365, savepath, True)
import numpy as np
import netCDF4 as nc
import matplotlib.pyplot as plt
from matplotlib import rc
from matplotlib import cm
import utils as utils
# plt.rcParams.update({'font.size': 14})
# rc('text', usetex=True)
datapath = '/rds/general/user/rk2014/home/WORK/q-gcm/outdata_toptest/test/NA_atmos_only/'
savepath = './'
topog = nc.Dataset(datapath + 'topog.nc')['dtopat']
# plt.figure()
# plt.contourf(topog[:],100, cmap=cm.jet)
# plt.colorbar()
# plt.show()
top = np.array(topog)
print(top.shape)
name = 'atmos_topog'
top = utils.ContourData(top[np.newaxis, :, :], name=name)
[fig, ax] = top.init_frame('Height ($m$)', False)
top.take_snapshots(1, 1, savepath, False)
