def _plot_tracer_thalweg(ax, plot_data, bathy, mesh_mask, cmap, clevels):
cbar = vis.contour_thalweg(
ax, plot_data.tracer_hr, bathy, mesh_mask, clevels=clevels, cmap=cmap,
thalweg_file='/results/nowcast-sys/tools/bathymetry/thalweg_working'
'.txt',
cbar_args={'fraction': 0.030, 'pad': 0.04, 'aspect': 45}
)
return cbar
def _plot_tracer_thalweg(ax, plot_data, bathy, mesh_mask, cmap, clevels):
cbar = vis.contour_thalweg(
ax,
plot_data.tracer_hr,
bathy,
mesh_mask,
clevels=clevels,
cmap=cmap,
## TODO: Can this path be moved into nowcast.yaml config file?
thalweg_file="/SalishSeaCast/tools/bathymetry/thalweg_working"
".txt",
cbar_args={
"fraction": 0.030,
"pad": 0.04,
"aspect": 45
},
)
return cbar
def thalweg_animator(plotdat, tstart, tend, vmin, vmax, stepsize, tit, tit2,
figtit, dirstr, indexer, t_cmap, clabel):
"TESTED"
from salishsea_tools import visualisations as vis
import cmocean as cm
import matplotlib.pyplot as plt
import netCDF4 as nc
import numpy as np
bathy = nc.Dataset('/data/tjarniko/MEOPAR/grid/bathymetry_201702.nc')
mesh = nc.Dataset('/data/tjarniko/MEOPAR/grid/mesh_mask201702.nc')
for i in range(tstart, tend):
td = plotdat[i, :, :, :]
cmap = t_cmap
fig, ax = plt.subplots(1, 1, figsize=(15, 5))
cbar = vis.contour_thalweg(ax,
td,
bathy,
mesh,
np.arange(vmin, vmax, stepsize),
cmap=t_cmap)
cbar.set_label(clabel, fontsize=20)
t_i = indexer + i
si = str(t_i)
#print(len(si))
if len(si) == 1:
lsi = '00' + si
if len(si) == 2:
lsi = '0' + si
if len(si) == 3:
lsi = si
print(lsi)
fig.suptitle(tit + str(lsi) + tit2, fontsize=18)
fig.savefig(dirstr + figtit + str(lsi))
plt.close(fig)
def make_avgthwg_plot_OmA(start,end,sdir_PI, sdir_BR, figstr):
#extract months, days, years from initial date string
#(I can see Doug wouldn't like this) - hacky but multi-functional
yr_s = int(start[0:4])
mon_s = int(start[5:7])
day_s = int(start[8:10])
yr_e = int(end[0:4])
mon_e = int(end[5:7])
day_e = int(end[8:10])
st = dt.datetime(yr_s,mon_s,day_s)
en = dt.datetime(yr_e,mon_e,day_e)
y_st = st.timetuple().tm_yday
print(y_st)
y_en = en.timetuple().tm_yday
print(y_en)
ts_BR = np.arange(y_st,y_en+1,1)
days_in = len(ts_BR)
#extract files for carbon and temp/sal model results
dates_preind_carp, files_preind_carp, doy_preind = make_nclen(start,end,'carp', sdir_PI)
dates_br_carp, files_br_carp, doy_br = make_nclen(start,end,'carp', sdir_BR)
dates_preind_grid, files_preind_grid, doy_preind = make_nclen(start,end,'grid_T', sdir_PI)
dates_br_grid, files_br_grid, doy_br = make_nclen(start,end,'grid_T', sdir_BR)
#create array of appropriate depths
dfile = nc.Dataset('/data/tjarniko/results/BASERUN_EXP/PI_3rd_2015/ncs/SKOG_1d_20150101_20150301_carp_T_20150101-20150101.nc')
depths = dfile['deptht'][:]
depth_broad = np.zeros([1,40,898,398])
depth_broad2 = np.zeros([1,898,398])
for i in range(0,40):
depth_broad2[:] = depths[i]
depth_broad[:,i,:,:] = depth_broad2
#initialize empty arrays for relevant variables
mon3_dic_BR = np.zeros([days_in,40,898,398])
mon3_dic_PI = np.zeros([days_in,40,898,398])
mon3_ta_BR = np.zeros([days_in,40,898,398])
mon3_ta_PI = np.zeros([days_in,40,898,398])
mon3_temp_BR = np.zeros([days_in,40,898,398])
mon3_temp_PI = np.zeros([days_in,40,898,398])
mon3_sal_BR = np.zeros([days_in,40,898,398])
mon3_sal_PI = np.zeros([days_in,40,898,398])
mon3_OmA_BR = np.zeros([days_in,40,898,398])
mon3_OmA_PI = np.zeros([days_in,40,898,398])
#sequentially cycle through netcdfs, open them, calculate mocsy parameters
for i in range (0,days_in):
if i%5 ==0:
print(i)
test_br_carp = nc.Dataset(files_br_carp[i])
test_pi_carp = nc.Dataset(files_preind_carp[i])
test_br_grid = nc.Dataset(files_br_grid[i])
test_pi_grid = nc.Dataset(files_preind_grid[i])
t_dic_br = np.squeeze(test_br_carp['dissolved_inorganic_carbon'][:])
t_dic_pi = np.squeeze(test_pi_carp['dissolved_inorganic_carbon'][:])
t_ta_br = np.squeeze(test_br_carp['total_alkalinity'][:])
t_ta_pi = np.squeeze(test_pi_carp['total_alkalinity'][:])
t_sal_br = np.squeeze(test_br_grid['vosaline'][:])
t_sal_pi = np.squeeze(test_pi_grid['vosaline'][:])
t_temp_br = np.squeeze(test_br_grid['votemper'][:])
t_temp_pi = np.squeeze(test_pi_grid['votemper'][:])
#this assignment is no longer necessary - I would
mon3_dic_BR[i,:,:,:] = t_dic_br
mon3_dic_PI[i,:,:,:] = t_dic_pi
mon3_ta_BR[i,:,:,:] = t_ta_br
mon3_ta_PI[i,:,:,:] = t_ta_pi
mon3_sal_BR[i,:,:,:] = t_sal_br
mon3_sal_PI[i,:,:,:] = t_sal_pi
mon3_temp_BR[i,:,:,:] = t_temp_br
mon3_temp_PI[i,:,:,:] = t_temp_pi
#calculate mocsy for each day for each field
pHr_pi, OmAr_pi, pco2r_pi = oned_moxy(t_sal_pi, t_temp_pi, t_dic_pi, t_ta_pi, 1, depth_broad)
pHr_br, OmAr_br, pco2r_br = oned_moxy(t_sal_br, t_temp_br, t_dic_br, t_ta_br, 1, depth_broad)
#
mon3_OmA_BR[i,:,:,:] = OmAr_br
mon3_OmA_PI[i,:,:,:] = OmAr_pi
mon3_OmA_BR_m = np.ma.masked_where(mon3_OmA_BR >= 1e10, mon3_OmA_BR)
mon3_OmA_PI_m = np.ma.masked_where(mon3_OmA_PI >= 1e10, mon3_OmA_PI)
OmAr_pi_av = np.mean(mon3_OmA_PI_m,axis = 0)
OmAr_br_av = np.mean(mon3_OmA_BR_m,axis = 0)
bathy = nc.Dataset('/data/tjarniko/MEOPAR/grid/bathymetry_201702.nc')
mesh = nc.Dataset('/data/tjarniko/MEOPAR/grid/mesh_mask201702.nc')
t_cmap = cm.cm.balance
t_vmin = 0
t_vmax = 2
stepsize = 0.01
fig, (ax1, ax2, ax3) = plt.subplots(3,1,figsize=(10,10))
vis.contour_thalweg(ax1, OmAr_br_av, bathy, mesh, np.arange(t_vmin, t_vmax, stepsize), cmap = t_cmap)
ax1.set_title('BASE RUN 2015, averaged OmA: '+start+' - '+end , fontsize = 16)
t_cmap = cm.cm.balance
t_vmin = 0
t_vmax = 2
stepsize = 0.01
vis.contour_thalweg(ax2, OmAr_pi_av, bathy, mesh, np.arange(t_vmin, t_vmax, stepsize), cmap = t_cmap)
ax2.set_title('PREINDUSTRIAL RUN 2015, averaged OmA: '+start+' - '+end , fontsize = 16)
t_cmap = cm.cm.balance
t_vmin = -0.3
t_vmax = 0.3
stepsize = 0.005
vis.contour_thalweg(ax3, OmAr_br_av - OmAr_pi_av, bathy, mesh, np.arange(t_vmin, t_vmax, stepsize), cmap = t_cmap)
ax3.set_title('BASE - PREINDUSTRIAL RUN 2015, averaged OmA: '+start+' - '+end, fontsize = 16)
fig.tight_layout()
fig.savefig(figstr)
return mon3_OmA_BR, mon3_OmA_PI, days_in
def make_avgthwg_plot_OmA(start, end, sdir_PI, sdir_BR, figstr):
yr_s = int(start[0:4])
mon_s = int(start[5:7])
day_s = int(start[8:10])
yr_e = int(end[0:4])
mon_e = int(end[5:7])
day_e = int(end[8:10])
st = dt.datetime(yr_s, mon_s, day_s)
en = dt.datetime(yr_e, mon_e, day_e)
y_st = st.timetuple().tm_yday
print(y_st)
y_en = en.timetuple().tm_yday
print(y_en)
ts_BR = np.arange(y_st, y_en + 1, 1)
days_in = len(ts_BR)
dates_preind_carp, files_preind_carp, doy_preind = make_nclen(
start, end, 'carp', sdir_PI)
dates_br_carp, files_br_carp, doy_br = make_nclen(start, end, 'carp',
sdir_BR)
dates_preind_grid, files_preind_grid, doy_preind = make_nclen(
start, end, 'grid_T', sdir_PI)
dates_br_grid, files_br_grid, doy_br = make_nclen(start, end, 'grid_T',
sdir_BR)
dfile = nc.Dataset(
'/data/tjarniko/results/BASERUN_EXP/PI_3rd_2015/ncs/SKOG_1d_20150101_20150301_carp_T_20150101-20150101.nc'
)
depths = dfile['deptht'][:]
depth_broad = np.zeros([1, 40, 898, 398])
depth_broad2 = np.zeros([1, 898, 398])
#expand_dims
for i in range(0, 40):
depth_broad2[:] = depths[i]
depth_broad[:, i, :, :] = depth_broad2
mon3_dic_BR = np.zeros([days_in, 40, 898, 398])
mon3_dic_PI = np.zeros([days_in, 40, 898, 398])
mon3_ta_BR = np.zeros([days_in, 40, 898, 398])
mon3_ta_PI = np.zeros([days_in, 40, 898, 398])
mon3_temp_BR = np.zeros([days_in, 40, 898, 398])
mon3_temp_PI = np.zeros([days_in, 40, 898, 398])
mon3_sal_BR = np.zeros([days_in, 40, 898, 398])
mon3_sal_PI = np.zeros([days_in, 40, 898, 398])
mon3_OmA_BR = np.zeros([days_in, 40, 898, 398])
mon3_OmA_PI = np.zeros([days_in, 40, 898, 398])
for i in range(0, days_in):
if i % 5 == 0:
print(i)
test_br_carp = nc.Dataset(files_br_carp[i])
test_pi_carp = nc.Dataset(files_preind_carp[i])
test_br_grid = nc.Dataset(files_br_grid[i])
test_pi_grid = nc.Dataset(files_preind_grid[i])
t_dic_br = np.squeeze(test_br_carp['dissolved_inorganic_carbon'][:])
t_dic_pi = np.squeeze(test_pi_carp['dissolved_inorganic_carbon'][:])
t_ta_br = np.squeeze(test_br_carp['total_alkalinity'][:])
t_ta_pi = np.squeeze(test_pi_carp['total_alkalinity'][:])
t_sal_br = np.squeeze(test_br_grid['vosaline'][:])
t_sal_pi = np.squeeze(test_pi_grid['vosaline'][:])
t_temp_br = np.squeeze(test_br_grid['votemper'][:])
t_temp_pi = np.squeeze(test_pi_grid['votemper'][:])
mon3_dic_BR[i, :, :, :] = t_dic_br
mon3_dic_PI[i, :, :, :] = t_dic_pi
mon3_ta_BR[i, :, :, :] = t_ta_br
mon3_ta_PI[i, :, :, :] = t_ta_pi
mon3_sal_BR[i, :, :, :] = t_sal_br
mon3_sal_PI[i, :, :, :] = t_sal_pi
mon3_temp_BR[i, :, :, :] = t_temp_br
mon3_temp_PI[i, :, :, :] = t_temp_pi
pHr_pi, OmAr_pi, pco2r_pi = oned_moxy(t_sal_pi, t_temp_pi, t_dic_pi,
t_ta_pi, 1, depth_broad)
pHr_br, OmAr_br, pco2r_br = oned_moxy(t_sal_br, t_temp_br, t_dic_br,
t_ta_br, 1, depth_broad)
mon3_OmA_BR[i, :, :, :] = OmAr_br
mon3_OmA_PI[i, :, :, :] = OmAr_pi
mon3_OmA_BR_m = np.ma.masked_where(mon3_OmA_BR >= 1e10, mon3_OmA_BR)
mon3_OmA_PI_m = np.ma.masked_where(mon3_OmA_PI >= 1e10, mon3_OmA_PI)
OmAr_pi_av = np.mean(mon3_OmA_PI_m, axis=0)
OmAr_br_av = np.mean(mon3_OmA_BR_m, axis=0)
bathy = nc.Dataset('/data/tjarniko/MEOPAR/grid/bathymetry_201702.nc')
mesh = nc.Dataset('/data/tjarniko/MEOPAR/grid/mesh_mask201702.nc')
t_cmap = cm.cm.balance
t_vmin = 0
t_vmax = 2
stepsize = 0.01
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(10, 10))
vis.contour_thalweg(ax1,
OmAr_br_av,
bathy,
mesh,
np.arange(t_vmin, t_vmax, stepsize),
cmap=t_cmap)
ax1.set_title('BASE RUN 2015, averaged OmA: ' + start + ' - ' + end,
fontsize=16)
t_cmap = cm.cm.balance
t_vmin = 0
t_vmax = 2
stepsize = 0.01
vis.contour_thalweg(ax2,
OmAr_pi_av,
bathy,
mesh,
np.arange(t_vmin, t_vmax, stepsize),
cmap=t_cmap)
ax2.set_title('PREINDUSTRIAL RUN 2015, averaged OmA: ' + start + ' - ' +
end,
fontsize=16)
t_cmap = cm.cm.balance
t_vmin = -0.3
t_vmax = 0.3
stepsize = 0.005
vis.contour_thalweg(ax3,
OmAr_br_av - OmAr_pi_av,
bathy,
mesh,
np.arange(t_vmin, t_vmax, stepsize),
cmap=t_cmap)
ax3.set_title('BASE - PREINDUSTRIAL RUN 2015, averaged OmA: ' + start +
' - ' + end,
fontsize=16)
fig.tight_layout()
plt.show()
fig.savefig(figstr)