def create_an_empty_map(plot_four_maps=False,ncols=None): start_year=1947 input_folder='/ptmp/Alon.Stern/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg_bergs_Delta2/' letter_labels=np.array(['(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)']) field='CN' months_str='all' file_type='ice_month' pole='south' (All_data, lat, lon,z) =generate_data_file(start_year, start_year,input_folder,field, months_str,file_type,section_axes='xy',lat_lon_bounds=None) projection = 'splaea' boundinglat=-45 #boundinglat=-57.5 data_mean=None #Plot blank map. if plot_four_maps==True: for k in range(ncols): subplot(1,ncols,k+1) plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title='',\ p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat) ax=gca() text(1,1,letter_labels[k], ha='right', va='bottom',transform=ax.transAxes,fontsize=15) else: plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title='',\ p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat) m = Basemap(projection=projection, boundinglat=boundinglat, lon_0=180) #x, y = m(lon, lat) #x, y = m(0, -70) #m.scatter(x,y,3,marker='o',color='black') return [lat,lon,m]
def produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list,datapath, file_extension,cscale, row_num,difference_on,pole,field_name,boundinglat):
letter_labels=np.array(['(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)'])
title=None
for k in range(ncols):
print 'Plotting ' + field_name + ' fields for ' + Delta_list[k]
subplot(nrows,ncols,(row_num-1)*ncols+(k+1))
filename=datapath + Delta_list[k] + file_extension
(lat ,lon, data, p_values,field)=load_data_from_mat_file(filename)
if field=='melt':
data=data*(60*60*24*356.25) #Multiply to get units /year rather than /s
data=data/850 # Multiply to get units of m/year
(cscale,datamap)=plot_polar_field(lat,lon,data,pole,difference_on,title,p_values,cscale,field,colorbar_on=False,return_data_map=True,\
plot_lat_lon_lines=True,boundinglat=boundinglat)
if k==0:
plt.ylabel(y_title_list[row_num-1], fontsize=20, labelpad=25, multialignment='center')
ax=gca()
text(1,1,letter_labels[(row_num-1)*ncols+(k)], ha='right', va='bottom',transform=ax.transAxes,fontsize=15)
#Creating colorbar
fig.subplots_adjust(right=0.85)
cbar_ax = fig.add_axes([0.85,0.11 , 0.03, 0.22])
cbar=fig.colorbar(datamap, cax=cbar_ax)
cbar.set_label(colorbar_unit_list[row_num-1], rotation=90,fontsize=20)
cbar.ax.tick_params(labelsize=20)
if field!='melt':
tick_locator = ticker.MaxNLocator(nbins=5)
cbar.locator = tick_locator
cbar.update_ticks()
def produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list,datapath, file_extension,cscale, row_num,\ difference_on,pole,field_name,significant_only,y_title_list2): title=None letter_labels=np.array(['(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)']) for k in range(ncols): print 'Plotting ' + field_name + ' fields for ' + Delta_list[k] ax=subplot(nrows,ncols,(row_num-1)*ncols+(k+1)) filename=datapath + Delta_list[k] + file_extension (lat ,lon, data, p_values,field)=load_data_from_mat_file(filename) #print min(p_values) if significant_only==False: p_values=None if field=='melt': data=data*(60*60*24*356.25) #Multiply to get units /year rather than /s data=data/850 # Multiply to get units of m/year (cscale,datamap)=plot_polar_field(lat,lon,data,pole,difference_on,title,p_values,cscale,field,colorbar_on=False,return_data_map=True) if k==0: plt.ylabel(y_title_list[row_num-1], fontsize=20, labelpad=25, multialignment='center') #ax.annotate(y_title_list[row_num-1], xy=(-0.4, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center') #ax.annotate(y_title_list2[row_num-1], xy=(-0.25, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center') if row_num==1: ax.set_title(title_list[Delta_list[k]], fontsize=20,y=1.13) text(1,1,letter_labels[(row_num-1)*ncols+(k)], ha='right', va='bottom',transform=ax.transAxes,fontsize=15) #Creating colorbar fig.subplots_adjust(right=0.8) #cbar_ax = fig.add_axes([0.85, 0.103+((nrows-row_num)*0.165), 0.03, 0.13]) cbar_ax = fig.add_axes([0.825, 0.115+((nrows-row_num)*0.435), 0.03, 0.33]) if row_num==1: ticks=np.array([-0.04 ,-0.02 ,0 ,0.02,0.04]) if row_num==2: ticks=np.array([-0.1 ,-0.05 ,0 ,0.05,0.1]) cbar=fig.colorbar(datamap, cax=cbar_ax,ticks=ticks) cbar.set_label(colorbar_unit_list[row_num-1], rotation=90,fontsize=20) cbar.ax.tick_params(labelsize=20)
def produce_figure_panel(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list,datapath, file_extension,cscale, panel_num, \
difference_on,pole,field_name,field_type_list,axes_direction='xy',significant_only=False,boundinglat=-45.):
title = None
letter_labels = np.array(
['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)'])
print 'Plotting ' + field_name + ' fields for ' + Delta_list[0]
ax = subplot(nrows, ncols, panel_num)
filename = datapath + Delta_list[0] + file_extension
#print filename
(lat, lon, data, p_values, field, layer_interface,
lat_lon_bounds) = load_data_from_mat_file(filename)
if significant_only == False:
p_values = None
if field == 'melt':
data = data * (60 * 60 * 24 * 356.25
) #Multiply to get units /year rather than /s
data = data / 850 # Multiply to get units of m/year
if axes_direction == 'xy':
(cscale,datamap)=plot_polar_field(lat,lon,data,pole,difference_on,title,p_values,cscale,field,colorbar_on=False,return_data_map=True\
,plot_lat_lon_lines=True,boundinglat=boundinglat)
else:
file_type = 'ocean_month_z'
datamap=plot_vertical_section(lon,lat,data,difference_on,title,p_values,cscale,field,layer_interface,axes_direction,lat_lon_bounds,file_type,\
colorbar_on=False,return_data_map=True)
#if panel_num==1:
# ax.set_title(title_list[Delta_list[k]], fontsize=18, y=1.13)
#Creating colorbar
if panel_num > 1:
#cbar_ax=fig.add_axes([0.41+((panel_num-2)*0.28),0.15,0.2,0.05])
#cbar=fig.colorbar(datamap, cax=cbar_ax, orientation="horizontal")
if panel_num == 2:
ticks = np.array([10**-6, 10**-4, 10**-2, 10**0])
if panel_num == 3:
ticks = np.array([-0.04, -0.02, 0, 0.02, 0.04])
cbar = fig.colorbar(datamap,
orientation="horizontal",
pad=0.05,
ticks=ticks)
cbar.set_label(colorbar_unit_list[panel_num - 1], fontsize=20)
cbar.ax.tick_params(labelsize=20)
#if panel_num!=2:
# tick_locator = ticker.MaxNLocator(nbins=5)
# cbar.locator = tick_locator
# cbar.update_ticks()
text(1,
1,
letter_labels[panel_num - 1],
ha='right',
va='bottom',
transform=ax.transAxes,
fontsize=15)
def produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list,datapath, file_extension,cscale, plot_num, \
difference_on,pole,field_name,axes_direction='xy', significant_only='True'):
title=None
letter_labels=np.array(['(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)'])
ncols=1
nrows= 3
for k in range(ncols):
print 'Plotting ' + field_name + ' fields for ' + Delta_list[k]
ax=subplot(ncols,nrows,plot_num)
filename=datapath + Delta_list[k] + file_extension
#print filename
(lat ,lon, data, p_values,field,layer_interface,lat_lon_bounds)=load_data_from_mat_file(filename)
if significant_only==False:
p_values=None
if axes_direction=='xy':
(cscale,datamap)=plot_polar_field(lat,lon,data,pole,difference_on,title,p_values,cscale,field,colorbar_on=False,return_data_map=True)
else:
file_type='ocean_month_z'
datamap=plot_vertical_section(lon,lat,data,difference_on,title,p_values,cscale,field,layer_interface,axes_direction,lat_lon_bounds,file_type,\
colorbar_on=False,return_data_map=True)
#print y_title_list
#ax.set_title(y_title_list[plot_num-1], fontsize=18, y=1.13)
ax.set_title(y_title_list[plot_num-1], fontsize=20)
if k>0:
ax.set_yticks([])
#plt.title(
#ax.annotate(y_title_list[plot_num-1], xy=(-0.6, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
if plot_num==1:
plt.ylabel('depth (m)', fontsize=20)
#ax.annotate(y_title_list[plot_num-1], xy=(-0.6, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
if plot_num!=1:
ax.set_yticks([])
plt.xlabel('latitude (deg)', fontsize=20)
ax.set_xticks([-40 ,-60, -80])
text(1,1,letter_labels[(plot_num-1)*ncols+(k)], ha='right', va='bottom',transform=ax.transAxes,fontsize=15)
#Creating colorbar
fig.subplots_adjust(right=0.8)
if plot_num==3:
cbar_ax = fig.add_axes([0.85, 0.1, 0.033, 0.8])
cbar=fig.colorbar(datamap, cax=cbar_ax)
cbar.set_label(colorbar_unit_list[plot_num-1], rotation=90,fontsize=20)
cbar.ax.tick_params(labelsize=20)
tick_locator = ticker.MaxNLocator(nbins=5)
cbar.locator = tick_locator
cbar.update_ticks()
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] + ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(20)
def produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list,datapath, file_extension,cscale, row_num, \
difference_on,pole,field_name,axes_direction='xy', significant_only='True'):
title=None
letter_labels=np.array(['(a)','(b)','(c)','(d)','(e)','(f)','(g)','(h)','(i)'])
for k in range(ncols):
print 'Plotting ' + field_name + ' fields for ' + Delta_list[k]
ax=subplot(nrows,ncols,(row_num-1)*ncols+(k+1))
filename=datapath + Delta_list[k] + file_extension
#print filename
(lat ,lon, data, p_values,field,layer_interface,lat_lon_bounds)=load_data_from_mat_file(filename)
if significant_only==False:
p_values=None
if axes_direction=='xy':
(cscale,datamap)=plot_polar_field(lat,lon,data,pole,difference_on,title,p_values,cscale,field,colorbar_on=False,return_data_map=True)
else:
file_type='ocean_month_z'
datamap=plot_vertical_section(lon,lat,data,difference_on,title,p_values,cscale,field,layer_interface,axes_direction,lat_lon_bounds,file_type,\
colorbar_on=False,return_data_map=True)
if row_num==1:
#ax.set_title(title_list[k], fontsize=18, y=1.13)
ax.set_title(title_list[Delta_list[k]], fontsize=18, y=1.13)
#if k==0:
# plt.ylabel(y_title_list[row_num-1], fontsize=18, labelpad=25)
if k>0:
ax.set_yticks([])
if k==0 and row_num<4:
ax.annotate(y_title_list[row_num-1], xy=(-0.6, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
#ax.annotate('Sector', xy=(-0.45, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
# plt.ylabel(y_title_list[i], fontsize=18, labelpad=25)
if k==0 and row_num>0:
plt.ylabel('depth (m)', fontsize=14)
ax.annotate(y_title_list[row_num-1], xy=(-0.6, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
if row_num!=nrows:
ax.set_xticks([])
else:
plt.xlabel('latitude (deg)', fontsize=14)
ax.set_xticks([-40 ,-60, -80])
text(1,1,letter_labels[(row_num-1)*ncols+(k)], ha='right', va='bottom',transform=ax.transAxes)
#Creating colorbar
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.12+((nrows-row_num)*0.28), 0.033, 0.2])
cbar=fig.colorbar(datamap, cax=cbar_ax)
cbar.set_label(colorbar_unit_list[row_num-1], rotation=90)
tick_locator = ticker.MaxNLocator(nbins=5)
cbar.locator = tick_locator
cbar.update_ticks()
def produce_figure_panel(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list,datapath, file_extension,cscale, panel_num, \
difference_on,pole,field_name,field_type_list,axes_direction='xy'):
title = None
print 'Plotting ' + field_name + ' fields for ' + Delta_list[0]
ax = subplot(nrows, ncols, panel_num)
filename = datapath + Delta_list[0] + file_extension
#print filename
(lat, lon, data, p_values, field, layer_interface,
lat_lon_bounds) = load_data_from_mat_file(filename)
if field == 'melt':
data = data * (60 * 60 * 24 * 356.25
) #Multiply to get units /year rather than /s
data = data / 850 # Multiply to get units of m/year
data = data * (0.00069)
field = 'iron'
if axes_direction == 'xy':
(cscale, datamap) = plot_polar_field(lat,
lon,
data,
pole,
difference_on,
title,
p_values,
cscale,
field,
colorbar_on=False,
return_data_map=True)
else:
file_type = 'ocean_month_z'
datamap=plot_vertical_section(lon,lat,data,difference_on,title,p_values,cscale,field,layer_interface,axes_direction,lat_lon_bounds,file_type,\
colorbar_on=False,return_data_map=True)
#if panel_num==1:
# ax.set_title(title_list[Delta_list[k]], fontsize=18, y=1.13)
#Creating colorbar
if panel_num > 0:
#cbar_ax=fig.add_axes([0.41+((panel_num-2)*0.28),0.15,0.2,0.05])
#cbar=fig.colorbar(datamap, cax=cbar_ax, orientation="horizontal")
cbar = fig.colorbar(datamap, orientation="horizontal")
cbar.set_label(colorbar_unit_list[panel_num - 1])
def main():
parser = argparse.ArgumentParser()
args = parser.parse_args()
#Flags
save_figure = True
significant_vals_only = False
plot_traj_fields = False
plot_melt_fields = True
plot_traj_AB_fields = False
plot_sea_ice_conc_fields = False
plot_sea_ice_thickness_fields = False
plot_all_panels_without_data = False #False makes the real figure
#Parameters;
pole = 'north'
title = None
cscale = None
Delta_list = np.array(['Delta1', 'Delta3', 'Delta9'])
#Title_list=np.array(['Delta1 (Area=0.0026km^2)', 'Delta6 (Area=0.029km^2)', 'Delta9 (Area=1.8km^2)'])
#Title_list=np.array(['Area=0.0026km^2', 'Area=0.35km^2', 'Area=1.8km^2'])
#title_list = {'Delta1': 'Area=0.0026km^2', 'Delta3':'Area=0.029km^2', 'Delta4':'Area=0.12km^2' , 'Delta6': 'Area=0.35km^2' , 'Delta9': 'Area=1.8km^2'}
title_list = {
'Delta1': 'Length=60m',
'Delta3': 'Length=200m',
'Delta4': 'Length=350m$',
'Delta6': 'Length=700m$',
'Delta9': 'Length=1600m'
}
color_vec = np.array([
'blue', 'red', 'green', 'grey', 'purple', 'cyan', 'magenta', 'black',
'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral',
'yellow', 'orchid'
])
letter_labels = np.array(
['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)'])
y_title_list = np.array([
'Iceberg\n Trajectories', 'AB+Ross\nIcebergs Trajectories',
'Iceberg Melt'
])
#colorbar_unit_list=np.array(['', 'melt (kg/m^2/s)', 'Conc (non-dim)', 'Thickness (m)',''])
colorbar_unit_list = np.array(
['', '', 'melt (m/year)', 'Conc (non-dim)', 'Thickness (m)', ''])
nrows = 1
ncols = len(Delta_list) #3
boundinglat = 45
projection = 'nplaea'
datapath = '/home/Alon.Stern/Iceberg_Project/iceberg_scripts/python_scripts/size_matters_paper/processed_data/'
#Setting up the figure
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
#This is used to plot the figure without the data for playing this anotations.
if plot_all_panels_without_data == True:
save_figure = False
else:
#Plotting Trajectory Fields
if plot_traj_fields == True:
row_num = 1
for k in range(ncols):
print 'Plotting Traj Fields for ' + Delta_list[k]
ax = subplot(nrows, ncols, (row_num - 1) * ncols + (k + 1))
filename = datapath + Delta_list[
k] + '_traj_1994_to_1995_all_south.mat'
(lat, lon, Total_berg_lon,
Total_berg_lat) = load_traj_data_from_mat_file(filename)
m = Basemap(projection=projection,
boundinglat=boundinglat,
lon_0=180)
plot_polar_field(lat,lon,None,pole,difference_on=0.,title=None,\
p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat)
m.scatter(Total_berg_lon,
Total_berg_lat,
1,
marker='o',
color=color_vec[k])
ax.set_title(title_list[Delta_list[k]], fontsize=20, y=1.13)
text(1,
1,
letter_labels[k],
ha='right',
va='bottom',
transform=ax.transAxes,
fontsize=15)
if k == 0:
plt.ylabel(y_title_list[k],
fontsize=20,
labelpad=25,
multialignment='center')
fig.subplots_adjust(right=0.8)
#Plotting Melt Fields
if plot_melt_fields == True:
cscale = None
row_num = 1
difference_on = 0
field_name = 'melt'
file_extension = '_melt_1959_to_2019_all_south_xy.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, \
file_extension,cscale, row_num,difference_on, pole,field_name,boundinglat)
#Plotting Sea Ice Conc Fields
if plot_sea_ice_conc_fields == True:
cscale = 0.04
row_num = 1
difference_on = 1
field_name = 'Sea Ice Conc'
file_extension = '_CN_1959_to_2019_all_south_xy_anomaly.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath,\
file_extension,cscale, row_num,difference_on, pole,field_name,boundinglat)
#Plotting Sea Ice Thickness Fields
if plot_sea_ice_thickness_fields == True:
cscale = 0.1
row_num = 2
difference_on = 1
field_name = 'Sea Ice Thickness'
file_extension = '_HI_1959_to_2019_all_south_xy_anomaly.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list,\
datapath, file_extension,cscale, row_num,difference_on, pole,field_name, boundinglat)
#Plotting Trajectory Fields
if plot_traj_AB_fields == True:
boundinglat = -45
projection = 'splaea'
row_num = 2
for k in range(ncols):
print 'Plotting Traj Fields for ' + Delta_list[k]
ax = subplot(nrows, ncols, (row_num - 1) * ncols + (k + 1))
#filename=datapath + Delta_list[k] + '_traj_1994_to_1995_all_south_AB_plus_Ross.mat'
filename = datapath + Delta_list[
k] + '_traj_1994_to_1995_all_south_AB.mat'
(lat, lon, Total_berg_lon,
Total_berg_lat) = load_traj_data_from_mat_file(filename)
m = Basemap(projection=projection,
boundinglat=boundinglat,
lon_0=180)
plot_polar_field(lat,lon,None,pole,difference_on=0.,title=None,\
p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat)
m.scatter(Total_berg_lon,
Total_berg_lat,
1,
marker='o',
color=color_vec[k])
text(1,
1,
letter_labels[3 + k],
ha='right',
va='bottom',
transform=ax.transAxes,
fontsize=15)
if k == 0:
plt.ylabel(y_title_list[row_num - 1],
fontsize=20,
labelpad=25,
multialignment='center')
fig.subplots_adjust(right=0.8)
subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=None,
hspace=None)
fig.set_size_inches(21.0, 8.5, forward=True)
if save_figure == True:
plt.savefig('figures/Northern_melt_D1_D3_D9_trajAB.png',
dpi=300,
bbox_inches='tight')
plt.show()
def main():
#Clear screen
#os.system('clear')
start_year=1915
end_year0=1992
Number_of_years=0
input_folder='/ptmp/Alon.Stern/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg_bergs_Delta9/'
second_folder=None
field='CN'
months_str='all'
constraint_name=None
months=range(0,11)
file_type='ice_month'
pole='north'
pole=None
save_traj_data=False
Title_list={'Delta1':'$L_{0}=60m$','Delta2':'$L_{0}=100m$','Delta3':'$L_{0}=200m$','Delta4':'$L_{0}=350m$','Delta5':'$L_{0}=500m$',\
'Delta6':'$L_{0}=700m$','Delta7':'$L_{0}=900m$','Delta8':'$L_{0}=1200m$','Delta9':'$L_{0}=1600m$','Delta10':'$L_{0}=2200m$'}
color_vec=np.array(['blue', 'red', 'green', 'grey','purple', 'cyan', 'magenta', 'black', 'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral', 'yellow', 'orchid' ])
cNorm = mpl.colors.LogNorm(vmin=8.8e7, vmax=7.4e11)
plot_each_time=False
# The constraint has the form [field_name, lower_bonud, upper_bound]
#constraint1=np.array(['lat',-65.,-10.]) # Latituds north of -60 in the southern hemisphere
constraint_depth = {'Constraint_field_name': 'depth', 'lower_bound': 3000 , 'upper_bound': 8000, 'original_values': True}
constraint_dist_from_calving = {'Constraint_field_name': 'distance_from_calving', 'lower_bound': 1000000 , 'upper_bound': 10000000000000, 'original_values': False}
constraint_lon_Rink = {'Constraint_field_name': 'lon', 'lower_bound': -55 , 'upper_bound': -45, 'original_values': True} #Longitude of Rink
constraint_lat_Rink = {'Constraint_field_name': 'lat', 'lower_bound': 70.75 , 'upper_bound': 72.75, 'original_values': True} #Longitude of Rink
constraints=[]
#mass=3.9e11 ;constraint_name='massD9'
#mass=8.8e7 ;constraint_name='massD1'
#constraint_m = {'Constraint_field_name': 'mass', 'lower_bound': mass-100 , 'upper_bound': mass+100, 'original_values': True}
#constraints.append(constraint2) ; constraint_name='AB'
#constraints.append(constraint3) ; constraint_name='AB_plus_Ross'
#constraints.append(constraint4)
#constraints.append(constraint_m)
#constraints.append(constraint_depth)
#constraints.append(constraint_dist_from_calving)
constraints.append(constraint_lon_Rink)
constraints.append(constraint_lat_Rink)
#constraints.append(constraint_SH)
#plot_multiple_panels==1:
constraint2 = {'Constraint_field_name': 'lon', 'lower_bound': -150 , 'upper_bound': -65, 'original_values': True} #Longitude of AB
root_path='/ptmp/aas/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg'
(All_data, lat, lon,z) =generate_data_file(start_year, start_year, input_folder, field, months_str, file_type)
data_mean=np.squeeze(np.mean(All_data,axis=0))
data_mean=None
#Deciding on the projection.
if pole=='south':
projection = 'splaea'
boundinglat=-45
if pole=='north':
projection = 'nplaea'
boundinglat=45
if pole==None:
projection = 'lcc'
lat_1=80
lat_2=60
lat_0=63.5
lon_0=-59.
run_names=np.array(['tournadre']) ; naming_flag='Delta'
#run_names=np.array(['Delta1', 'Delta3', 'Delta9']) ; naming_flag='Delta'
#run_names=np.array(['Delta1', 'Delta2','Delta3','Delta6', 'Delta9','Delta10']) ; naming_flag='Delta'
run_names=np.array(['Delta1' ,'Delta3', 'Delta9']) ; naming_flag='Delta'
nrows=1 ;ncols=3
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
for k in range(ncols*nrows):
input_folder=root_path + '_bergs_' + run_names[k]
subplot(nrows,ncols,k+1)
print input_folder
#Making sure the years work
(min_year, max_year)=find_max_min_years(input_folder,'.iceberg_trajectories.nc')
end_year=min(max_year,end_year0)
start_year=max(end_year-Number_of_years,min_year)
title1= run_names[k] + ' (' + str(start_year) + ' to ' + str(end_year) + ')'
#plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=title1\
# ,p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat)
if pole==None:
m = Basemap(width=1750000,height=4300000, rsphere=(6378137.00,6356752.3142),resolution='l',area_thresh=1000.,\
projection=projection,lat_1=lat_1,lat_2=lat_2,lat_0=lat_0,lon_0=lon_0)
else:
m = Basemap(projection=projection, boundinglat=boundinglat, lon_0=180)
m.drawcoastlines()
m.fillcontinents(color='grey',lake_color='white')
count=0
Total_berg_lat=np.array([])
Total_berg_lon=np.array([])
Total_berg_mass0=np.array([])
for year in range(start_year, end_year+1):
count=count+1
filename ='/' + str(year) + '0101.iceberg_trajectories.nc'
input_file=input_folder + filename
print input_file
all_bergs_lat = get_valid_data_from_traj_file(input_file,'lat')
all_bergs_lon = get_valid_data_from_traj_file(input_file,'lon')
all_bergs_mass0 = get_valid_data_from_traj_file(input_file,'mass',subtract_orig=False,get_original_values=True) # Getting iceberg mass too.
#Adding constraints to the data:
print 'Lendth of original field: ' , len(all_bergs_lat)
all_bergs_lat=add_constraint_to_data(input_file,all_bergs_lat,constraints)
all_bergs_lon=add_constraint_to_data(input_file,all_bergs_lon,constraints)
all_bergs_mass0=add_constraint_to_data(input_file,all_bergs_mass0,constraints)
print 'Lendth of field after constraints: ' , len(all_bergs_lat)
x, y = m(all_bergs_lon, all_bergs_lat)
if plot_each_time==True:
plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=title1\
,p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat)
m.scatter(x,y,3,marker='o',color=color_vec[k])
#plt.plot(all_bergs_lon,all_bergs_lat,'o')
Total_berg_lon=np.concatenate((Total_berg_lon,x),axis=0)
Total_berg_lat=np.concatenate((Total_berg_lat,y),axis=0)
Total_berg_mass0=np.concatenate((Total_berg_mass0,all_bergs_mass0),axis=0)
datamap=m.scatter(Total_berg_lon,Total_berg_lat, c=Total_berg_mass0, marker='o',cmap='jet',norm=cNorm)
if run_names[k]=='tournadre':
cbar=fig.colorbar(datamap)
cbar.set_label('Calving Mass (kg)')
else:
plt.title(Title_list[run_names[k]])
if save_traj_data==True:
save_traj_mat_file(lat,lon, Total_berg_lon, Total_berg_lat, Total_berg_mass0, pole,input_folder,start_year,end_year,months_str,constraint_name)
#plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=title1)
#m.scatter(Total_berg_lon,Total_berg_lat,1,marker='o',color=color_vec[k])
output_file= 'figures/Leigh_Rink_figure2.png'
#output_file= 'figures/Northern_Trajectories_tournadre.png'
#output_file= 'figures/Northern_Trajectories_Deltas.png'
fig.set_size_inches(21.0, 8.5,forward=True)
plt.savefig(output_file, dpi=150, bbox_inches='tight', pad_inches=0.4)
plt.show()
print 'Script complete'
def main():
parser = argparse.ArgumentParser()
args = parser.parse_args()
#Flags
save_figure = True
significant_vals_only = False
plot_traj_fields = False
plot_melt_fields = True
plot_CN_fields = False
plot_all_panels_without_data = False #False makes the real figure
#Parameters;
pole = 'south'
title = None
cscale = None
Delta_list = np.array(['tournadre'])
#yitle_list=np.array(['Delta1 (Area=0.0026km^2)', 'Delta6 (Area=0.029km^2)', 'Delta9 (Area=1.8km^2)'])
#title_list = {'Delta1': 'Area=0.0026km^2', 'Delta3':'Area=0.029km^2', 'Delta4':'Area=0.12km^2' , 'Delta6': 'Area=0.35km^2' , 'Delta9': 'Area=1.8km^2'}
title_list = {
'Delta1': 'Length=60m',
'Delta3': 'Length=200m',
'Delta4': 'Length=350m$',
'Delta6': 'Length=700m$',
'Delta9': 'Length=1600m'
}
color_vec = np.array([
'blue', 'red', 'green', 'grey', 'purple', 'cyan', 'magenta', 'black',
'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral',
'yellow', 'orchid'
])
#y_title_list=np.array(['Sea Surface Density', 'AB+Ross', 'East Antarctia', 'Weddell','Depth Integrated Density'])
y_title_list = np.array(['Sea Surface', 'Sea Surface', 'Sea Surface'])
field_type_list = np.array(['Temperature', 'Salinity', 'Density'])
#y_title_list=np.array(['SSD','Column Density', 'AB+Ross', 'East Antarctia', 'Weddell'])
colorbar_unit_list = np.array([
'Iron Concentration ($mol$/$m^2$)', 'melt (m/year)',
'Sea Ice Conc anomaly ', 'density (kg/$m^{3}$)', 'density (kg/$m^{3}$)'
])
nrows = 1
ncols = 1
temperature_scale = 0.2
salinity_scale = 0.1
density_scale = 0.04
datapath = '/home/Alon.Stern/Iceberg_Project/iceberg_scripts/python_scripts/size_matters_paper/processed_data/'
#Setting up the figure
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
#This is used to plot the figure without the data for playing this anotations.
if plot_all_panels_without_data == True:
save_figure = False
else:
#Plotting Panels
#Plotting Trajectory Fields
if plot_traj_fields == True:
boundinglat = -45
projection = 'splaea'
panel_num = 1
print 'Plotting Traj Fields for ' + Delta_list[0]
#ax=subplot(nrows,ncols,(row_num-1)*ncols+(k+1))
filename = datapath + Delta_list[
0] + '_traj_1994_to_1995_all_south.mat'
(lat, lon, Total_berg_lon, Total_berg_lat,
mass0) = load_traj_data_from_mat_file(filename)
m = Basemap(projection=projection,
boundinglat=boundinglat,
lon_0=180)
ax = subplot(nrows, ncols, panel_num)
plot_polar_field(lat,
lon,
None,
pole,
difference_on=0.,
title=None)
#m.scatter(Total_berg_lon,Total_berg_lat,1,marker='o',color='black')
cNorm = mpl.colors.LogNorm(vmin=8.8e7, vmax=7.4e11)
datamap = m.scatter(Total_berg_lon,
Total_berg_lat,
c=mass0,
marker='o',
cmap='jet',
norm=cNorm)
cbar = fig.colorbar(datamap, orientation="horizontal")
cbar.set_label('Calving Mass (kg)')
#ax.set_title(title_list[Delta_list[k]], fontsize=18,y=1.13)
#plt.ylabel(y_title_list[k], fontsize=18, labelpad=25)
if plot_melt_fields == True:
cscale = None
panel_num = 1
difference_on = 0
axes_direction = 'xy'
field_name = 'melt'
file_extension = '_melt_1959_to_2019_all_south_xy.mat'
produce_figure_panel(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
panel_num,difference_on, pole,field_name,field_type_list,axes_direction)
if plot_CN_fields == True:
cscale = None
panel_num = 3
difference_on = 1
axes_direction = 'xy'
field_name = 'CN'
file_extension = '_CN_1959_to_2019_all_south_xy_anomaly.mat' #Remember to change this!!!
produce_figure_panel(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
panel_num,difference_on, pole,field_name,field_type_list,axes_direction)
subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=None,
hspace=None)
fig.set_size_inches(10.5, 10.5, forward=True)
if save_figure == True:
plt.savefig('iron_melt_figure.png')
plt.show()
def main():
parser = argparse.ArgumentParser()
args = parser.parse_args()
#Flags
save_figure=True
significant_only=False
plot_SST_fields=True
plot_AB_plus_Ross_Temp_Section=True
plot_EastA_plus_Adele_Temp_Section=True
plot_Weddell_Temp_Section=True
plot_depth_int_temp_fields=True
plot_all_panels_without_data=False #False makes the real figure
#Parameters;
pole='south'
title=None
cscale=None
Delta_list=np.array(['Delta1', 'Delta3', 'Delta9'])
#yitle_list=np.array(['Delta1 (Area=0.0026km^2)', 'Delta6 (Area=0.029km^2)', 'Delta9 (Area=1.8km^2)'])
#title_list = {'Delta1': 'Area=0.0026km^2', 'Delta3':'Area=0.029km^2', 'Delta4':'Area=0.12km^2' , 'Delta6': 'Area=0.35km^2' , 'Delta9': 'Area=1.8km^2'}
title_list = {'Delta1': 'Length=60m', 'Delta3':'Length=200m', 'Delta4':'Length=350m$' , 'Delta6': 'Length=700m$' , 'Delta9': 'Length=1600m'}
color_vec=np.array(['blue', 'red', 'green', 'grey','purple', 'cyan', 'magenta', 'black', 'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral', 'yellow', 'orchid' ])
#y_title_list=np.array(['Sea Surface Temperature', 'AB+Ross', 'East Antarctia', 'Weddell','Depth Integrated Temperature'])
y_title_list=np.array(['Sea Surface','Column', 'AB+Ross', 'East Antarctia', 'Weddell'])
#y_title_list=np.array(['SST','Column Temperature', 'AB+Ross', 'East Antarctia', 'Weddell'])
colorbar_unit_list=np.array(['SST (C)', '(C*m)','temperature (C)', 'temperature (C)','temperature (C)'])
nrows=5
ncols=len(Delta_list)#3
temperature_scale=0.2
datapath='/home/Alon.Stern/Iceberg_Project/iceberg_scripts/python_scripts/size_matters_paper/processed_data/'
#Setting up the figure
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
#This is used to plot the figure without the data for playing this anotations.
if plot_all_panels_without_data==True:
save_figure=False
count=0
cscale=None
difference_on=0
boundinglat=-45
projection = 'splaea'
m = Basemap(projection=projection, boundinglat=boundinglat, lon_0=180)
#filename=datapath + 'Delta9_melt_1959_to_2019_all_south_xy.mat'
filename=datapath + 'Delta9_temp_1959_to_2019_all_south_yz_anomaly_EastA_plus_AdeleLand.mat'
(lat ,lon, data, p_values,field ,layer_interface,lat_lon_bounds )=load_data_from_mat_file(filename)
datamap=plot_vertical_section(lon,lat,data,difference_on,title,p_values,cscale,field,layer_interface,'yz',lat_lon_bounds,'ocean_month_z',\
colorbar_on=False,return_data_map=True)
for i in range(nrows):
for j in range(ncols):
count=count+1
ax=subplot(nrows,ncols,count)
print 'Working on subplot ' ,str(count)
if i!=0 and i!=1:
datamap=plot_vertical_section(lon,lat,data,difference_on,title,p_values,cscale,field,layer_interface,'yz',lat_lon_bounds,'ocean_month_z',\
colorbar_on=False,return_data_map=True)
else:
plot_polar_field(lat,lon,None,pole,difference_on=0.,title=None)
if i==0:
ax.set_title(title_list[Delta_list[j]], fontsize=18, y=1.13)
if j==0 and i<2:
ax.annotate(y_title_list[i], xy=(-0.6, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
ax.annotate('Temperature', xy=(-0.45, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
# plt.ylabel(y_title_list[i], fontsize=18, labelpad=25)
if j==0 and i>1:
plt.ylabel('depth (m)', fontsize=14)
ax.annotate(y_title_list[i], xy=(-0.6, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
if i!=nrows-1:
ax.set_xticks([])
else:
plt.xlabel('latitude (deg)', fontsize=14)
ax.set_xticks([-40 ,-60, -80])
if j>0:
ax.set_yticks([])
if j==ncols-1 :
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.11+((nrows-i-1)*0.165), 0.03, 0.13])
cbar=fig.colorbar(datamap, cax=cbar_ax)
cbar.set_label(colorbar_unit_list[i], rotation=90)
else:
#Plotting Melt
if plot_SST_fields==True:
cscale=temperature_scale
row_num=1
difference_on=1
axes_direction='xy'
field_name='Sea Surface Temperature'
file_extension='_SST_1959_to_2019_all_south_xy_anomaly.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
row_num,difference_on, pole,field_name,axes_direction)
#Plotting Depth Integrated Temp
if plot_depth_int_temp_fields==True:
cscale=200
row_num=2
difference_on=1
axes_direction='xy'
field_name='Depth Integrated Temperature'
file_extension='_temp_1959_to_2019_all_south_xy_anomaly.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
row_num,difference_on, pole,field_name)
#Plotting AB_plus_Ross Temperature
if plot_AB_plus_Ross_Temp_Section==True:
cscale=temperature_scale
row_num=3
difference_on=1
axes_direction='yz'
field_name='AB_plus_Ross Temperature Section'
file_extension='_temp_1959_to_2019_all_south_yz_anomaly_AB_plus_Ross.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
row_num,difference_on, pole,field_name,axes_direction)
#Plotting EastA_plus_Adele Temperature
if plot_EastA_plus_Adele_Temp_Section==True:
cscale=temperature_scale
row_num=4
difference_on=1
axes_direction='yz'
field_name='EastA_plus_Adele Temperature Section'
file_extension='_temp_1959_to_2019_all_south_yz_anomaly_EastA_plus_AdeleLand.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
row_num,difference_on, pole,field_name,axes_direction)
#Plotting Weddell Temperature
if plot_Weddell_Temp_Section==True:
cscale=temperature_scale
row_num=5
difference_on=1
axes_direction='yz'
field_name='Weddell Temperature Section'
file_extension='_temp_1959_to_2019_all_south_yz_anomaly_Weddell.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
row_num,difference_on, pole,field_name,axes_direction)
subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
fig.set_size_inches(13.5, 20.5,forward=True)
if save_figure==True:
plt.savefig('paper_figures/Fig_SST_sections_temp_D1_D3_D9.png')
plt.show()
def main():
parser = argparse.ArgumentParser()
args = parser.parse_args()
#Flags
save_figure = True
significant_vals_only = False
plot_melt_fields = True
plot_all_panels_without_data = False #False makes the real figure
#Parameters;
pole = 'south'
title = None
cscale = None
Delta_list = np.array(['Delta1', 'Delta9'])
#Title_list=np.array(['Delta1 (Area=0.0026km^2)', 'Delta6 (Area=0.029km^2)', 'Delta9 (Area=1.8km^2)'])
#Title_list=np.array(['Area=0.0026km^2', 'Area=1.8km^2'])
Title_list = np.array(['DELTA1 - DELTA9', 'DELTA9 -DELTA1'])
color_vec = np.array([
'blue', 'red', 'green', 'grey', 'purple', 'cyan', 'magenta', 'black',
'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral',
'yellow', 'orchid'
])
letter_labels = np.array(
['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)'])
y_title_list = np.array(['Iceberg Melt'])
colorbar_unit_list = np.array(['melt (m/year)'])
nrows = 1
ncols = 2
datapath = '/home/Alon.Stern/Iceberg_Project/iceberg_scripts/python_scripts/size_matters_paper/processed_data/'
#Setting up the figure
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
#Plotting Trajectory Fields
#Plotting Melt Fields
if plot_melt_fields == True:
cscale = None
row_num = 1
difference_on = 0
field_name = 'melt'
file_extension = '_melt_1959_to_2019_all_south_xy.mat'
title = None
filename1 = datapath + Delta_list[0] + file_extension
filename2 = datapath + Delta_list[1] + file_extension
(lat, lon, data1, p_values, field) = load_data_from_mat_file(filename1)
(lat, lon, data2, p_values, field) = load_data_from_mat_file(filename2)
#Converting melt rates to m/yr
data1 = data1 * (60 * 60 * 24 * 356.25
) #Multiply to get units /year rather than /s
data1 = data1 / 850 # Multiply to get units of m/year
data2 = data2 * (60 * 60 * 24 * 356.25
) #Multiply to get units /year rather than /s
data2 = data2 / 850 # Multiply to get units of m/year
#Panel 1
print 'Plotting ' + field_name + ' fields for ' + Delta_list[
0] + ' minus ' + Delta_list[1]
ax = subplot(nrows, ncols, 1)
(cscale, datamap) = plot_polar_field(lat,
lon,
data1 - data2,
pole,
difference_on,
title,
p_values,
cscale,
field,
colorbar_on=False,
return_data_map=True)
plt.ylabel(y_title_list[row_num - 1], fontsize=18, labelpad=25)
ax.set_title(Title_list[0], fontsize=18, y=1.13)
text(1,
1,
letter_labels[0],
ha='right',
va='bottom',
transform=ax.transAxes)
#Panel 2
print 'Plotting ' + field_name + ' fields for ' + Delta_list[
1] + ' minus ' + Delta_list[0]
ax = subplot(nrows, ncols, 2)
(cscale, datamap) = plot_polar_field(lat,
lon,
data2 - data1,
pole,
difference_on,
title,
p_values,
cscale,
field,
colorbar_on=False,
return_data_map=True)
ax.set_title(Title_list[1], fontsize=18, y=1.13)
text(1,
1,
letter_labels[1],
ha='right',
va='bottom',
transform=ax.transAxes)
#Creating colorbar
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.3, 0.05, 0.4])
cbar = fig.colorbar(datamap, cax=cbar_ax)
cbar.set_label(colorbar_unit_list[row_num - 1], rotation=90)
if field != 'melt':
tick_locator = ticker.MaxNLocator(nbins=5)
cbar.locator = tick_locator
cbar.update_ticks()
subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=None,
hspace=None)
#fig.set_size_inches(10.5, 20.5,forward=True)
if save_figure == True:
plt.savefig('paper_figures/Fig_melt_D9_subtract_D1.png')
plt.show()
def main():
#Clear screen
#os.system('clear')
start_year=1915
end_year0=2019
Number_of_years=0
input_folder='/ptmp/Alon.Stern/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg_bergs_Delta9/'
second_folder=None
field='CN'
months_str='all'
constraint_name=None
months=range(0,11)
file_type='ice_month'
pole='north'
save_traj_data=False
color_vec=np.array(['blue', 'red', 'green', 'grey','purple', 'cyan', 'magenta', 'black', 'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral', 'yellow', 'orchid' ])
# The constraint has the form [field_name, lower_bonud, upper_bound]
#constraint1=np.array(['lat',-65.,-10.]) # Latituds north of -60 in the southern hemisphere
constraint_depth = {'Constraint_field_name': 'depth', 'lower_bound': 3000 , 'upper_bound': 8000, 'original_values': True}
constraint_dist_from_calving = {'Constraint_field_name': 'distance_from_calving', 'lower_bound': 1000000 , 'upper_bound': 10000000000000, 'original_values': False}
constraint2 = {'Constraint_field_name': 'lon', 'lower_bound': -150 , 'upper_bound': -65, 'original_values': True} #Longitude of AB
constraint3 = {'Constraint_field_name': 'lon', 'lower_bound': -210 , 'upper_bound': -65, 'original_values': True} #Longitude of AB_plus_Ross
constraint_SH = {'Constraint_field_name': 'lat', 'lower_bound': -90 , 'upper_bound': 0, 'original_values': True} #age
constraint_age = {'Constraint_field_name': 'age', 'lower_bound': 30 , 'upper_bound': 300, 'original_values': True} #age
constraints=[]
#mass=3.9e11 ;constraint_name='massD9'
#mass=8.8e7 ;constraint_name='massD1'
#constraint_m = {'Constraint_field_name': 'mass', 'lower_bound': mass-100 , 'upper_bound': mass+100, 'original_values': True}
#constraints.append(constraint2) ; constraint_name='AB'
#constraints.append(constraint3) ; constraint_name='AB_plus_Ross'
#constraints.append(constraint4)
#constraints.append(constraint_m)
#constraints.append(constraint_depth)
#constraints.append(constraint_dist_from_calving)
#constraints.append(constraint_age)
#constraints.append(constraint_SH)
#plot_multiple_panels==1:
constraint2 = {'Constraint_field_name': 'lon', 'lower_bound': -150 , 'upper_bound': -65, 'original_values': True} #Longitude of AB
root_path='/ptmp/aas/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg'
(All_data, lat, lon,z) =generate_data_file(start_year, start_year, input_folder, field, months_str, file_type)
data_mean=np.squeeze(np.mean(All_data,axis=0))
data_mean=None
#run_names=np.array(['freq','all_big', 'mass', 'all_small']) ; naming_flag='groups'
#run_names=np.array(['Delta1', 'Delta2', 'Delta3', 'Delta6', 'Delta9','Delta10']) ; naming_flag='Delta'
#run_names=np.array(['Delta1' ,'Delta10','Delta1_thick', 'Delta10_thick', 'Delta1b_thick', 'Delta10b_thick']) ; naming_flag='Thick'
#run_names=np.array(['Delta6', 'Delta6','Delta9']) ; naming_flag='Delta'
run_names=np.array(['tournadre']) ; naming_flag='Delta'
#run_names=np.array(['Delta1','Delta3','Delta4','Delta5', 'Delta6','Delta9']) ; naming_flag='Delta'
for k in range(1):
input_folder=root_path + '_bergs_' + run_names[k]
subplot(1,1,k)
print input_folder
#Making sure the years work
(min_year, max_year)=find_max_min_years(input_folder,'.iceberg_trajectories.nc')
end_year=min(max_year,end_year0)
start_year=max(end_year-Number_of_years,min_year)
title1= run_names[k] + ' (' + str(start_year) + ' to ' + str(end_year) + ')'
#Plot blank map.
if pole=='south':
projection = 'splaea'
boundinglat=-45
if pole=='north':
projection = 'nplaea'
boundinglat=45
projection = 'nplaea'
plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=title1\
,p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat)
m = Basemap(projection=projection, boundinglat=boundinglat, lon_0=180)
print 'You are here'
print projection
count=0
Total_berg_lat=np.array([])
Total_berg_lon=np.array([])
Total_berg_mass0=np.array([])
for year in range(start_year, end_year+1):
count=count+1
filename ='/' + str(year) + '0101.iceberg_trajectories.nc'
input_file=input_folder + filename
print input_file
all_bergs_lat = get_valid_data_from_traj_file(input_file,'lat')
all_bergs_lon = get_valid_data_from_traj_file(input_file,'lon')
all_bergs_mass0 = get_valid_data_from_traj_file(input_file,'mass',subtract_orig=False,get_original_values=True) # Getting iceberg mass too.
#Adding constraints to the data:
print 'Lendth of original field: ' , len(all_bergs_lat)
all_bergs_lat=add_constraint_to_data(input_file,all_bergs_lat,constraints)
all_bergs_lon=add_constraint_to_data(input_file,all_bergs_lon,constraints)
all_bergs_mass0=add_constraint_to_data(input_file,all_bergs_mass0,constraints)
print 'Lendth of field after constraints: ' , len(all_bergs_lat)
x, y = m(all_bergs_lon, all_bergs_lat)
plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=title1\
,p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat)
m.scatter(x,y,3,marker='o',color=color_vec[k])
#plt.plot(all_bergs_lon,all_bergs_lat,'o')
Total_berg_lon=np.concatenate((Total_berg_lon,x),axis=0)
Total_berg_lat=np.concatenate((Total_berg_lat,y),axis=0)
Total_berg_mass0=np.concatenate((Total_berg_mass0,all_bergs_mass0),axis=0)
if save_traj_data==True:
save_traj_mat_file(lat,lon, Total_berg_lon, Total_berg_lat, Total_berg_mass0, pole,input_folder,start_year,end_year,months_str,constraint_name)
#plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=title1)
#m.scatter(Total_berg_lon,Total_berg_lat,1,marker='o',color=color_vec[k])
output_file= 'figures/traj_plot_' + str(start_year)+ '_to_' + str(end_year) + '_with_' + run_names[k] + '.png'
#plt.savefig(output_file, dpi=150, bbox_inches='tight', pad_inches=0.4)
plt.show()
print 'Script complete'
def main():
#Clear screen
#os.system('clear')
start_year = 1995
end_year0 = 1995
Number_of_years = 0
input_folder = '/ptmp/Alon.Stern/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg_bergs_Delta9/'
second_folder = None
#field0='CN'
months_str = 'all'
constraint_name = None
months = range(0, 11)
file_type = 'ice_month'
pole = 'south'
save_Force_data = False
load_data_from_mat = False
#load_data_from_mat=True
file_path = 'processed_data/'
#mat_filename='processed_data/open_ocean_iceberg_forces.mat'
mat_filename = 'Forces_on_berg_Tournadre_1995_1995_constraints_depth_8000_to_1000.mat'
mat_filename = 'Forces_on_berg_Delta9_1995_1995_constraints_depth_8000_to_1000.mat'
mat_filename = 'Forces_on_berg_Delta9_1995_1995_constraints_lon_55_to_65_lat_82_to_78_mass_1000000000000000_to_10000000000.mat'
mat_filename = 'Forces_on_berg_Delta9_1995_1995_constraints_depth_8000_to_1000_mass_1000000000000000_to_10000000000.mat'
mat_filename = 'Forces_on_berg_Delta9_1990_1995_constraints_lat_0_to_90_depth_8000_to_1000_mass_1000000000000000_to_10000000000.mat'
color_vec = np.array([
'blue', 'red', 'green', 'grey', 'purple', 'cyan', 'magenta', 'black',
'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral',
'yellow', 'orchid'
])
Force_list = np.array(['Fa', 'Fo', 'Fi', 'Fr', 'Fc', 'Fp', 'F_total'])
# The constraint has the form [field_name, lower_bonud, upper_bound]
constraint_lon_petermann = {
'Constraint_field_name': 'lon',
'lower_bound': -65,
'upper_bound': -55,
'original_values': True
} #Longitude of AB_plus_Ross
constraint_lat_petermann = {
'Constraint_field_name': 'lat',
'lower_bound': 78,
'upper_bound': 82,
'original_values': True
} #Longitude of AB_plus_Ross
constraint_depth = {
'Constraint_field_name': 'depth',
'lower_bound': 1000,
'upper_bound': 8000,
'original_values': False
}
constraints = []
constraint_mass = {
'Constraint_field_name': 'mass',
'lower_bound': 10**10,
'upper_bound': 10**15,
'original_values': False
}
constraint_lat_baffin_coast = {
'Constraint_field_name': 'lat',
'lower_bound': 30,
'upper_bound': 80,
'original_values': False
}
constraint_lat_southern_hemisphere = {
'Constraint_field_name': 'lat',
'lower_bound': -90,
'upper_bound': 0,
'original_values': False
}
#constraint_m = {'Constraint_field_name': 'mass', 'lower_bound': mass-100 , 'upper_bound': mass+100, 'original_values': True}
#constraints.append(constraint_m)
#constraints.append(constraint_lon_petermann)
#constraints.append(constraint_lat_petermann)
#constraints.append(constraint_lat_baffin_coast)
constraints.append(constraint_lat_southern_hemisphere)
constraints.append(constraint_depth)
#constraints.append(constraint_mass)
#Definging fields to be used.
field_list = np.array([
'width', 'length', 'mass', 'thickness', 'uvel', 'vvel', 'uo', 'vo',
'ui', 'vi', 'ua', 'va', 'ssh_x', 'ssh_y', 'hi'
])
root_path = '/ptmp/aas/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg'
(All_data, lat, lon, z) = generate_data_file(start_year, start_year,
input_folder, 'CN',
months_str, file_type)
data_mean = np.squeeze(np.mean(All_data, axis=0))
data_mean = None
#Plot blank map.
#plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=title1)
boundinglat = -45
projection = 'splaea'
m = Basemap(projection=projection, boundinglat=boundinglat, lon_0=180)
#run_names=np.array(['tournadre']) ; naming_flag='Tournadre'
run_names = np.array(['Delta1'])
naming_flag = 'Delta1'
if load_data_from_mat == False:
for k in range(1):
input_folder = root_path + '_bergs_' + run_names[k]
#subplot(2,4,k+1)
print input_folder
#Making sure the years work
(min_year,
max_year) = find_max_min_years(input_folder,
'.iceberg_trajectories.nc')
end_year = min(max_year, end_year0)
start_year = max(end_year - Number_of_years, min_year)
title1 = run_names[k] + ' (' + str(start_year) + ' to ' + str(
end_year) + ')'
Total_berg_lat = np.array([])
Total_berg_lon = np.array([])
#Total_berg_mass0=np.array([])
#initializing the Total_berg_list
Total_berg_list = {}
for k in range(len(field_list)):
Total_berg_list['Total_berg_' + field_list[k]] = np.array([])
Total_berg_list[field_list[k]] = np.array([])
#Total_berg_list['Total_berg_'+field_list[k]]=[]
count = 0
for year in range(start_year, end_year + 1):
count = count + 1
filename = '/' + str(year) + '0101.iceberg_trajectories.nc'
input_file = input_folder + filename
print input_file
#Handling lon and lat first
[Total_berg_lon, Total_berg_lat
] = generate_Total_berg_lon_lat(Total_berg_lon,
Total_berg_lat, input_file,
constraints, m)
for k in range(len(field_list)):
Total_berg_list[field_list[k]] = get_Total_berg_field(
input_file, Total_berg_list[field_list[k]],
field_list[k], constraints)
[Force_x, Force_y, Force_mod, Force_list,
mass] = calculate_forces(Total_berg_list, Total_berg_lat)
if save_Force_data == True:
mat_filename = 'Forces_on_berg_' + naming_flag + '_' + str(
start_year) + '_' + str(end_year)
if constraints != None:
if len(constraints) > 0:
mat_filename = mat_filename + '_constraints'
for k in range(len(constraints)):
current_constraint = constraints[k]
constraint_name = current_constraint[
'Constraint_field_name']
lower_bound = current_constraint['lower_bound']
upper_bound = current_constraint['upper_bound']
original_values = current_constraint['original_values']
mat_filename = mat_filename + '_' + constraint_name + '_' + str(
abs(upper_bound)) + '_to_' + str(abs(lower_bound))
mat_filename = mat_filename + '.mat'
print 'Saving file: ', (file_path + mat_filename)
sc.savemat(file_path+mat_filename, {'Force_x':Force_x , 'Force_y':Force_y , 'Force_mod':Force_mod, 'mass':mass, 'Force_list':Force_list,'Total_berg_lat':Total_berg_lat,\
'Total_berg_lon':Total_berg_lon})
if load_data_from_mat == True:
print 'Loading file: ', mat_filename
mat_contents = sc.loadmat(file_path + mat_filename)
Force_x = mat_contents['Force_x'][0][0]
Force_y = mat_contents['Force_y'][0][0]
Force_mod = mat_contents['Force_mod'][0][0]
#Force_list=mat_contents['Force_list'][:]
Total_berg_lon = mat_contents['Total_berg_lon'][:]
Total_berg_lat = mat_contents['Total_berg_lat'][:]
mass = mat_contents['mass'][:]
#Converting force to acceleration
Accel_x = {}
Accel_y = {}
Accel_mod = {}
for k in range(7):
Accel_x[Force_list[k]] = Force_x[Force_list[k]] / mass
Accel_y[Force_list[k]] = Force_y[Force_list[k]] / mass
Accel_mod[Force_list[k]] = Force_mod[Force_list[k]] / mass
#Calculating the mean and std of each force
ind_list = np.arange(7)
Force_x_mean = ind_list * 0.
Force_y_mean = ind_list * 0.
Force_mod_mean = ind_list * 0.
Accel_x_mean = ind_list * 0.
Accel_y_mean = ind_list * 0.
Accel_mod_mean = ind_list * 0.
Force_x_std = ind_list * 0.
Force_y_std = ind_list * 0.
Force_mod_std = ind_list * 0.
Accel_x_std = ind_list * 0.
Accel_y_std = ind_list * 0.
Accel_mod_std = ind_list * 0.
for k in range(7):
Force_x_mean[k] = np.mean(Force_x[Force_list[k]])
Force_y_mean[k] = np.mean(Force_y[Force_list[k]])
Force_mod_mean[k] = np.mean(Force_mod[Force_list[k]])
Accel_x_mean[k] = np.mean(Accel_x[Force_list[k]])
Accel_y_mean[k] = np.mean(Accel_y[Force_list[k]])
Accel_mod_mean[k] = np.mean(Accel_mod[Force_list[k]])
Force_x_std[k] = np.std(Force_x[Force_list[k]])
Force_y_std[k] = np.std(Force_y[Force_list[k]])
Force_mod_std[k] = np.std(Force_mod[Force_list[k]])
Accel_x_std[k] = np.std(Accel_x[Force_list[k]])
Accel_y_std[k] = np.std(Accel_y[Force_list[k]])
Accel_mod_std[k] = np.std(Accel_mod[Force_list[k]])
plot_map_traj = True
if plot_map_traj == True:
#plt.figure(3)
ax1 = subplot(1, 1, 1)
plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=''\
,p_values=None,cscale=None,field=None,colorbar_on=True,return_data_map=False,plot_lat_lon_lines=True,boundinglat=boundinglat )
m.scatter(Total_berg_lon,
Total_berg_lat,
1,
marker='o',
color=color_vec[k])
#F_max_lim=10**12
F_max_lim = 10**-4
F_min_lim = -10**-4
#F_min_lim=10**(-10)
do_scatter_plot = False
if do_scatter_plot == True:
plt.figure(2)
for k in range(7):
#for k in np.array([4,5]):
#A=Force_y[Force_list[k]]
#print Force_list[k],'max', np.max(A,axis=1) ,'min',np.min(A,axis=1)
ax = subplot(3, 3, k + 1)
ax.scatter(mass[:],
Accel_y[Force_list[k]][:],
color=color_vec[k],
label=Force_list[k])
#plt.plot(mass[:],Accel_y[Force_list[k]][:])
plt.ylim([F_min_lim, F_max_lim])
#ax.set_xscale('log')
#ax.set_yscale('log')
#plt.legend(loc='upper left', frameon=True)
plt.xlabel('Mass (kg)')
plt.ylabel('Force (N)')
plot_bar_graph = False
if plot_bar_graph == True:
plt.figure(3)
label_list = np.array(['Force_x', 'Force_y', 'Accel_x', 'Accel_y'])
Mean_Force_and_Accel = {
'Force_x': Force_x_mean,
'Force_y': Force_y_mean,
'Accel_x': Accel_x_mean,
'Accel_y': Accel_y_mean
}
for k in range(4):
ax = subplot(2, 2, k + 1)
width = 3
#ax.bar(ind+((bar_width/2)*j), mean_list[:,j],width=width/2,color=color_vec[j],label=sector_title_list[j])
#ax.bar(ind_list,Force_x_mean)
ax.bar(ind_list,
Mean_Force_and_Accel[label_list[k]],
width=width / 3,
label=label_list[k])
plt.title(label_list[k])
ax.set_xticks(ind_list + 0.5)
ax.set_xticklabels(Force_list)
#output_file= 'figures/traj_plot_' + str(start_year)+ '_to_' + str(end_year) + '_with_' + run_names[k] + '.png'
#plt.savefig(output_file, dpi=150, bbox_inches='tight', pad_inches=0.4)
plt.show()
print 'Script complete'
def main():
#Clear screen
#os.system('clear')
start_year=1980
end_year0=1980
field='melt'
months_str='all'
months=range(0,11)
file_type='icebergs'
pole='south'
Number_of_years=20
color_vec=np.array(['blue', 'red', 'green', 'grey','purple', 'cyan', 'magenta', 'black', 'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral', 'yellow', 'orchid' ])
second_folder=None
distribution=np.array([0.24, 0.12, 0.15, 0.18, 0.12, 0.07, 0.03, 0.03, 0.03, 0.02])
initial_mass=np.array([8.8e7, 4.1e8, 3.3e9, 1.8e10, 3.8e10, 7.5e10, 1.2e11, 2.2e11, 3.9e11, 7.4e11])
Total_mass=np.sum(distribution*initial_mass)
#plot_multiple_panels==1:
root_path='/ptmp/aas/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg'
#run_names=np.array(['freq','all_big', 'mass', 'all_small']) ; naming_flag='groups'
#run_names=np.array(['Delta1', 'Delta2', 'Delta3', 'Delta6', 'Delta9','Delta10']) ; naming_flag='Delta'
run_names=np.array(['Delta1', 'Delta2', 'Delta3','Delta4','Delta5','Delta6','Delta7', 'Delta8', 'Delta9','Delta10']) ; naming_flag='Delta'
#run_names=np.array(['Delta1', 'Delta9']) ; naming_flag='Delta'
Number_of_files=10
multi_berg_run_name='freq'
if multi_berg_run_name=='freq':
weights=np.zeros((10,1))
for i in range(10):
weights[i]=distribution[i]*initial_mass[i]/Total_mass
print sum(weights)
count=-1
for k in range(Number_of_files):
count=count+1
input_folder=root_path + '_bergs_' + run_names[k]
(min_year, max_year)=find_max_min_years(input_folder,'.' + file_type + '_month.nc')
end_year=max_year
start_year=max(end_year-Number_of_years+1,min_year)
(All_data, lat, lon) =generate_data_file(start_year, end_year, input_folder, field,months,file_type)
data_mean=np.squeeze(np.mean(All_data,axis=0))
#Set up the matrix on the first time around
if count==0:
M=data_mean.shape
Big_data_matrix=np.zeros((Number_of_files,M[0],M[1]))
Big_data_matrix[count,:,:]=data_mean
linear_sum_data=construct_linear_sum(Big_data_matrix,weights)
#Getting the data for the multi_berg_run
input_folder=root_path + '_bergs_' + multi_berg_run_name
(min_year, max_year)=find_max_min_years(input_folder,'.' + file_type + '_month.nc')
end_year=max_year
start_year=max(end_year-Number_of_years+1,min_year)
(All_data, lat, lon) =generate_data_file(start_year, end_year, input_folder, field,months,file_type)
data_mean=np.squeeze(np.mean(All_data,axis=0))
relative_error=(data_mean-linear_sum_data)#/data_mean
#ralative_error=relative_error*np.where(data_mean!=0)
#Plotting the linear sum
subplot(1,3,1)
plot_polar_field(lat,lon,linear_sum_data,pole,difference_on=0.,title='linear combination',p_values=None,cscale=None,field=field)
subplot(1,3,2)
plot_polar_field(lat,lon,data_mean,pole,difference_on=0.,title=multi_berg_run_name,p_values=None,cscale=None,field=field)
subplot(1,3,3)
plot_polar_field(lat,lon,-relative_error,pole,difference_on=0.,title='relative_error',p_values=None,cscale=None,field=field)
output_file='linearity_test_neg' + multi_berg_run_name + '.png'
plt.savefig(output_file, dpi=150, bbox_inches='tight', pad_inches=0.4)
plt.show()
print 'Script complete'
def main():
#Clear screen
#os.system('clear')
#Defining possible paths
all_paths=define_paths_array()
#Flags
plot_average_dist=1
plot_full_time_series=0
#Parameters and variables
#start_year=1980
end_year0=1985
number_of_bins=100
Number_of_years=25
#Include contraints to use to filter the data
# The constraint has the form [field_name, lower_bonud, upper_bound]
constraint1=np.array(['lat',-85.,-10.]) # Latituds north of -60 in the southern hemisphere
#constraint2=np.array(['lon',-60.,0.]) #Longitude of Weddel Sea , lon appears to go from -270 to 90.
#constraint3=np.array(['lon',-120.,-60.]) #Longitude of Weddel Sea , lon appears to go from -270 to 90.
constraints=[]
constraints.append(constraint1)
#constraints.append(constraint2)
#constraints.append(constraint3)
#contraints
mass_scaling=np.array([2000, 200, 50, 20, 10, 5, 2, 1, 1, 1])
#field_name='area'; x_min=1;x_max=1.e9 # Good for area including all Deltas
field_name='area'; x_min=1;x_max=1.e7 # Good for area
#field_name='mass' ; #x_min=100;x_max=1.e12 # Good for mass
#Defining a list of colors
color_vec=np.array(['blue', 'red','purple','green', 'coral', 'cyan', 'magenta','orange', 'black', 'grey', 'yellow', 'orchid', 'blue', 'red','purple','green', 'coral', 'cyan' ])
fig = plt.figure(1)
input_file='/ptmp/aas/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg_bergs_Delta1/19000101.icebergs_month.nc'
area=nc.Dataset(input_file).variables['area'][:, :]
lon=nc.Dataset(input_file).variables['xT'][:]
lat=nc.Dataset(input_file).variables['yT'][:]
count1=0
#for k in range(13):#for k in np.array([3]):
#for k in np.array([0 , 8]):
for k in np.array([8 ]):
#for k in np.array([0,1,2,5,8,9]):
count1=count1+1
#for k in range(0,8):
input_folder=all_paths[k]
#Make sure that data exists of the years allocated, othersize change it.
(min_year, max_year)=find_max_min_years(input_folder,'.iceberg_trajectories.nc')
end_year=min(max_year,end_year0)
start_year=max(end_year-Number_of_years,min_year)
#if max_year>=start_year:
for year in range(start_year,end_year):
#year=1945
filename ='/' + str(year) + '0101.iceberg_trajectories.nc'
input_file=input_folder + filename
print input_file
field_name1='uvel'
#field_name1='lon'
field_name2='vvel'
iceberg_lats = get_valid_data_from_traj_file(input_file,'lat',subtract_orig=False)
iceberg_lons = get_valid_data_from_traj_file(input_file,'lon',subtract_orig=False)
iceberg_mass = get_valid_data_from_traj_file(input_file,'mass',subtract_orig=False)
iceberg_field1 = get_valid_data_from_traj_file(input_file,field_name1,subtract_orig=False)
iceberg_field2 = get_valid_data_from_traj_file(input_file,field_name2,subtract_orig=False)
print 'Lendth of original field: ' , len(iceberg_field1)
#Getting the distributions from the data
#Handeling constraints
iceberg_lats=add_constraint_to_data(input_file,iceberg_lats,constraints)
iceberg_lons=add_constraint_to_data(input_file,iceberg_lons,constraints)
iceberg_mass=add_constraint_to_data(input_file,iceberg_mass,constraints)
iceberg_field1=add_constraint_to_data(input_file,iceberg_field1,constraints)
iceberg_field2=add_constraint_to_data(input_file,iceberg_field2,constraints)
print 'Lendth of field after constraints: ' , len(iceberg_field1)
(field_gridded1,mass_gridded)=interpolat_bergs_onto_map(lat,lon,area,iceberg_lats,iceberg_lons,iceberg_field1,iceberg_mass,mass_weighted=True)
(field_gridded2,mass_gridded)=interpolat_bergs_onto_map(lat,lon,area,iceberg_lats,iceberg_lons,iceberg_field2,iceberg_mass,mass_weighted=True)
if year==start_year:
Total_gridded1=field_gridded1
Total_gridded2=field_gridded2
Total_mass_gridded= mass_gridded
else:
Total_gridded1=Total_gridded1+field_gridded1
Total_gridded2=Total_gridded2+field_gridded2
Total_mass_gridded=Total_mass_gridded+ mass_gridded
M=field_gridded1.shape
norm_field1=np.zeros((M[0],M[1]))
norm_field2=np.zeros((M[0],M[1]))
for i in range(M[0]):
for j in range(M[1]):
if mass_gridded[i,j]>0:
norm_field1[i,j]=field_gridded1[i,j]/mass_gridded[i,j]
norm_field2[i,j]=field_gridded2[i,j]/mass_gridded[i,j]
subplot(2,2,1)
plot_polar_field(lat,lon,field_gridded1,pole='south',difference_on=1.,title='uvel',p_values=None,cscale=None,field=None)
subplot(2,2,2)
plot_polar_field(lat,lon,field_gridded2,pole='south',difference_on=1.,title='vvel',p_values=None,cscale=None,field=None)
subplot(2,2,3)
#plot_polar_field(lat,lon,field_gridded1/mass_gridded,pole='south',difference_on=1.,title='uvel',p_values=None,cscale=None,field=None)
plot_polar_field(lat,lon,norm_field1,pole='south',difference_on=1.,title='uvel',p_values=None,cscale=None,field=None)
subplot(2,2,4)
#plot_polar_field(lat,lon,field_gridded2/mass_gridded,pole='south',difference_on=1.,title='vvel',p_values=None,cscale=None,field=None)
plot_polar_field(lat,lon,norm_field2,pole='south',difference_on=1.,title='vvel',p_values=None,cscale=None,field=None)
#Plotting the distributions
#ax = fig.add_subplot(1,2,count1)
#ax = fig.add_subplot(1,1,1)
#plt.plot(field1,-field2,'o',color=color_vec[k])
#plt.xlabel(field_name1)
#plt.ylabel(field_name2)
#plt.ylim([-75., -40]) #For mass
#plt.xlim([10.e3, 7.4e11]) #For mass
#ax.set_yscale('log')
#plt.legend(loc='upper right', frameon=True)
#fig = matplotlib.pyplot.gcf()
#fig.set_size_inches(9,4.5)
plt.show()
print 'Script complete'
def main():
parser = argparse.ArgumentParser()
args = parser.parse_args()
#Flags
save_figure = True
significant_vals_only = False
plot_melt_fields = False
plot_split_Tournadre_dist = True
plot_split_Tournadre_dist_depth = True
plot_all_panels_without_data = False #False makes the real figure
#Parameters;
pole = 'south'
title = None
cscale = None
Delta_list = np.array(['tournadre'])
#Area_list=np.array(['Area$_{1}$=0.0026km$^2$', 'Area$_{2}$=0.0072km$^2$','Area$_{3}$=0.029km$^2$',\
# 'Area$_{4}$=0.12km$^2$','Area$_{5}$=0.18km$^2$', 'Area$_{6}$=0.35km$^2$','Area$_{7}$=0.56km$^2$', 'Area$_{8}$=1.0km$^2$','Area$_{9}$=1.8km$^2$', 'Area$_{10}$=3.5km$^2$'])
Area_list=np.array(['0.0026km$^2$', '0.0072km$^2$','0.029km$^2$',\
'0.12km$^2$','0.18km$^2$', '0.35km$^2$','0.56km$^2$', '1.0km$^2$','1.8km$^2$', '3.5km$^2$'])
Title_list = np.array(['TOURNADRE distribution'])
letter_labels = np.array(
['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)'])
color_vec = np.array([
'blue', 'red', 'green', 'grey', 'purple', 'cyan', 'magenta', 'orange',
'coral', 'yellow', 'orchid', 'orange', 'coral', 'yellow', 'orchid'
])
y_title_list = np.array(['Iceberg Melt'])
colorbar_unit_list = np.array(['melt (kg/m$^2$)'])
nrows = 1
ncols = 2
datapath = '/home/Alon.Stern/Iceberg_Project/iceberg_scripts/python_scripts/size_matters_paper/processed_data/'
#Setting up the figure
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
ax = subplot(nrows, ncols, 2)
fig.delaxes(ax)
#Plotting Trajectory Fields
#Plotting Melt Fields
if plot_melt_fields == True:
cscale = None
row_num = 1
difference_on = 0
field_name = 'melt'
file_extension = '_melt_1959_to_2019_all_south_xy.mat'
title = None
filename = datapath + Delta_list[0] + file_extension
(lat, lon, data, p_values, field) = load_data_from_mat_file(filename)
print 'Plotting ' + field_name + ' fields for ' + Delta_list[row_num -
1]
ax = subplot(nrows, ncols, row_num)
(cscale, datamap) = plot_polar_field(lat,
lon,
data,
pole,
difference_on,
title,
p_values,
cscale,
field,
colorbar_on=False,
return_data_map=True)
#Creating colorbar
cbar_ax = fig.add_axes([0.52, 0.62, 0.04, 0.3])
cbar = fig.colorbar(datamap, cax=cbar_ax)
cbar.set_label(colorbar_unit_list[row_num - 1], rotation=90)
if plot_split_Tournadre_dist == True:
position = 1
field_name = 'size distribution no constrains'
label = Delta_list[0]
file_extension = '_size_distribution_1959_to_2019.mat'
filename = datapath + Delta_list[0] + file_extension
produce_distribution_panel(filename, nrows, ncols, position,
field_name, Delta_list, Area_list,
color_vec, letter_labels)
if plot_split_Tournadre_dist_depth == True:
position = 2
field_name = 'size distribution no constrains'
file_extension = '_size_distribution_1959_to_2019_constraints_lat_10_to_70.mat'
file_extension = '_size_distribution_1959_to_2019_constraints_depth_8000_to_1000.mat'
filename = datapath + Delta_list[0] + file_extension
produce_distribution_panel(filename, nrows, ncols, position,
field_name, Delta_list, Area_list,
color_vec, letter_labels)
fig.subplots_adjust(right=0.65)
plt.legend(loc=(1.1, 0.0), frameon=True, prop={'size': 20})
subplots_adjust(left=None,
bottom=None,
right=0.8,
top=None,
wspace=None,
hspace=None)
#fig.tight_layout()
fig.set_size_inches(20.5, 10.5, forward=True)
if save_figure == True:
plt.savefig('paper_figures/Fig_distributions_only_Tournadre.png',
dpi=300,
bbox_inches='tight')
plt.show()
def produce_figure_variable_row(fig,axes,nrows,ncols,file_type_list, title_list,colorbar_unit_list,y_title_list,datapath, file_extension,cscale, row_num, \
difference_on,pole,field_name,variable_list,axes_direction='xy',letter_labels=None):
difference_on = 0
title = None
for k in range(len(variable_list)):
#for k in np.array([0,1,2]):
print 'Plotting ' + field_name + ' fields for ' + file_type_list[
variable_list[k]]
ax = subplot(nrows, ncols, (row_num - 1) * ncols + (k + 1))
filename = datapath + 'Control_' + file_type_list[
variable_list[k]] + file_extension
#print filename
(lat, lon, data, p_values, field, layer_interface,
lat_lon_bounds) = load_data_from_mat_file(filename)
if axes_direction == 'xy':
(cscale, datamap) = plot_polar_field(lat,
lon,
data,
pole,
difference_on,
title,
p_values,
cscale,
field,
colorbar_on=False,
return_data_map=True)
else:
file_type = 'ocean_month_z'
datamap=plot_vertical_section(lon,lat,data,difference_on,title,p_values,cscale,field,layer_interface,axes_direction,lat_lon_bounds,file_type,\
colorbar_on=False,return_data_map=True)
if row_num == 1:
#ax.set_title(title_list[k], fontsize=18, y=1.13)
ax.set_title(title_list[variable_list[k]], fontsize=18, y=1.13)
#if k==0:
# plt.ylabel(y_title_list[row_num-1], fontsize=18, labelpad=25)
if k > 0:
ax.set_yticks([])
#if k==0 and row_num<2:
#ax.annotate(y_title_list[row_num-1], xy=(-0.6, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
#ax.annotate('Salinity', xy=(-0.45, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
# plt.ylabel(y_title_list[i], fontsize=18, labelpad=25)
#if k==0 and row_num>1:
#plt.ylabel('depth (m)', fontsize=14)
#ax.annotate(y_title_list[row_num-1], xy=(-0.5, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
#if row_num!=nrows:
# ax.set_xticks([])
#else:
# plt.xlabel('latitude (deg)', fontsize=14)
# ax.set_xticks([-40 ,-60, -80])
#if row_num==nrows:
#cbar=fig.colorbar(datamap,shrink=0.5)
#cbar=fig.colorbar(datamap)
#Creating colorbar
#fig.subplots_adjust(bottom=0.15)
text(1,
1,
letter_labels[(row_num - 1) * ncols + (k + 1) - 1],
ha='right',
va='bottom',
transform=ax.transAxes,
fontsize=15)
#cbar_ax = fig.add_axes([0.13+(k*0.28),0.07, 0.20, 0.03])
#cbar=fig.colorbar(datamap, cax=cbar_ax, orientation="horizontal")
cbar = fig.colorbar(datamap, orientation="horizontal")
cbar.set_label(colorbar_unit_list[k])
tick_locator = ticker.MaxNLocator(nbins=5)
cbar.locator = tick_locator
cbar.update_ticks()
def main():
parser = argparse.ArgumentParser()
args = parser.parse_args()
#Flags
save_figure=True
significant_only=True
plot_traj_fields=False
plot_melt_fields=False
plot_sea_ice_conc_fields=True
plot_sea_ice_thickness_fields=True
plot_traj_AB_fields=False
plot_all_panels_without_data=False #False makes the real figure
#Parameters;
pole='south'
#pole='north'
title=None
cscale=None
Delta_list=np.array(['Delta1', 'Delta3', 'Delta9'])
#Title_list=np.array(['Delta1 (Area=0.0026km^2)', 'Delta6 (Area=0.029km^2)', 'Delta9 (Area=1.8km^2)'])
#Title_list=np.array(['Area=0.0026km^2', 'Area=0.35km^2', 'Area=1.8km^2'])
#title_list = {'Delta1': 'Area=0.0026km^2', 'Delta3':'Area=0.029km^2', 'Delta4':'Area=0.12km^2' , 'Delta6': 'Area=0.35km^2' , 'Delta9': 'Area=1.8km^2'}
#title_list = {'Delta1': 'Length=60m', 'Delta3':'Length=200m', 'Delta4':'Length=350m$' , 'Delta6': 'Length=700m$' , 'Delta9': 'Length=1600m'}
title_list = {'Delta1': 'Length=62m', 'Delta3':'Length=209m', 'Delta9': 'Length=1659m'}
color_vec=np.array(['blue', 'red', 'green', 'grey','purple', 'cyan', 'magenta', 'black', 'orange', 'coral', 'yellow', 'orchid', 'black', 'orange', 'coral', 'yellow', 'orchid' ])
y_title_list=np.array(['Sea-Ice Concentration\nAnomaly', 'Sea-Ice Thickenssn\nAnomaly'])
#y_title_list=np.array(['Sea Ice Concentration', 'Sea Ice Thickenss'])
y_title_list2=np.array(['Anomaly', 'Anomaly'])
#colorbar_unit_list=np.array(['', 'melt (kg/m^2/s)', 'Conc (fraction)', 'Thickness (m)',''])
colorbar_unit_list=np.array(['Concentration (fraction)', 'Thickness (m)'])
nrows=2
ncols=len(Delta_list)#3
datapath='/home/Alon.Stern/Iceberg_Project/iceberg_scripts/python_scripts/size_matters_paper/processed_data/'
#Setting up the figure
fig, axes = plt.subplots(nrows=nrows, ncols=ncols)
#This is used to plot the figure without the data for playing this anotations.
if plot_all_panels_without_data==True:
save_figure=False
else:
#Plotting Trajectory Fields
if plot_traj_fields==True:
boundinglat=-45
projection = 'splaea'
row_num=1
for k in range(ncols):
print 'Plotting Traj Fields for ' + Delta_list[k]
ax=subplot(nrows,ncols,(row_num-1)*ncols+(k+1))
filename=datapath + Delta_list[k] + '_traj_1994_to_1995_all_south.mat'
(lat ,lon,Total_berg_lon,Total_berg_lat)=load_traj_data_from_mat_file(filename)
m = Basemap(projection=projection, boundinglat=boundinglat, lon_0=180)
plot_polar_field(lat,lon,None,pole,difference_on=0.,title=None)
m.scatter(Total_berg_lon,Total_berg_lat,1,marker='o',color=color_vec[k])
ax.set_title(title_list[Delta_list[k]], fontsize=20,y=1.13)
if k==0:
plt.ylabel(y_title_list[k], fontsize=20, labelpad=25)
fig.subplots_adjust(right=0.8)
#Plotting Sea Ice Conc Fields
if plot_sea_ice_conc_fields==True:
cscale=0.04
row_num=1
difference_on=1
field_name='Sea Ice Concentration'
file_extension='_CN_1959_to_2019_all_south_xy_anomaly.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
row_num,difference_on, pole,field_name,significant_only,y_title_list2)
#Plotting Sea Ice Thickness Fields
if plot_sea_ice_thickness_fields==True:
cscale=0.1
row_num=2
difference_on=1
field_name='Sea Ice Thickness'
file_extension='_HI_1959_to_2019_all_south_xy_anomaly.mat'
produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list, datapath, file_extension,cscale, \
row_num,difference_on, pole,field_name,significant_only,y_title_list2)
fig.subplots_adjust(right=0.8)
subplots_adjust(left=None, bottom=None, right=None, top=None, wspace=None, hspace=None)
fig.set_size_inches(18.5, 10.5,forward=True)
if save_figure==True:
plt.savefig('paper_figures/Fig_CN_HI_D1_D3_D9.png',dpi=300,bbox_inches='tight')
#plt.savefig('paper_figures/Fig_CN_HI_D1_D3_D9.eps',bbox_inches='tight')
plt.show()
