def generate_Total_berg_lon_lat(Total_berg_lon, Total_berg_lat, input_file,
constraints, m):
all_bergs_lat = get_valid_data_from_traj_file(input_file, 'lat')
all_bergs_lon = get_valid_data_from_traj_file(input_file, 'lon')
#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)
print 'Lendth of field after constraints: ', len(all_bergs_lat)
x, y = m(all_bergs_lon, all_bergs_lat)
#m.scatter(x,y,3,marker='o',color=color_vec[k])
Total_berg_lon = np.concatenate((Total_berg_lon, x), axis=0)
Total_berg_lat = np.concatenate((Total_berg_lat, y), axis=0)
return (Total_berg_lon, Total_berg_lat)
def get_Total_berg_field(input_file, Total_berg_field, field, constraints):
print 'Loading ', field
get_original = False
if field[-1] == '0':
get_original = True
field = field[:-1]
all_bergs_field = get_valid_data_from_traj_file(
input_file,
field,
subtract_orig=False,
get_original_values=get_original) # Getting iceberg mass too.
all_bergs_field = add_constraint_to_data(input_file, all_bergs_field,
constraints)
Total_berg_field = np.concatenate((Total_berg_field, all_bergs_field),
axis=0)
return Total_berg_field
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():
#Clear screen
#os.system('clear')
#Defining possible paths
all_paths = define_paths_array()
#Flags
plot_full_time_series = 0
plot_scatter_plot = False
plot_mean_fields = True
plot_occurance_matrix = False
find_expected_values = True
fixed_upper_bound = True
use_log_axis = True
save_the_data = True
load_the_data = True
#Parameters and variables
#start_year=1980
#end_year0=2010
end_year0 = 2057
#end_year0=2030
Number_of_years = 12
#Number_of_years=0
#2199
#Which fields do you want to compare?
#field_name1='area'
field_name1 = 'age'
#field_name1='mass'
#field_name1='width'
#field_name1='length'
#field_name1='width'
#field_name1='thickness'
#field_name1='LW_max'
#field_name1='distance_from_calving'
field_name2 = 'mass'
#field_name2='area'
#field_name2='age'
#field_name2='thickness'
#field_name2='length'
#field_name2='width'
initial_mass_vec = np.array([
8.8e7, 4.1e8, 3.3e9, 1.8e10, 3.8e10, 7.5e10, 1.2e11, 2.2e11, 3.9e11,
7.4e11
])
#Include contraints to use to filter the data
constraint2 = {
'Constraint_field_name': 'lon',
'lower_bound': -150,
'upper_bound': -65,
'original_values': True
} #Longitude of AB
constraint_SH = {
'Constraint_field_name': 'lat',
'lower_bound': -150,
'upper_bound': -5,
'original_values': True
} #Longitude of AB
constraint_NH = {
'Constraint_field_name': 'lat',
'lower_bound': 5,
'upper_bound': 150,
'original_values': True
} #Longitude of AB
constraint_depth = {
'Constraint_field_name': 'depth',
'lower_bound': 500,
'upper_bound': 8000,
'original_values': False
}
constraint_dist_from_calving = {
'Constraint_field_name': 'distance_from_calving',
'lower_bound': 1000,
'upper_bound': 10000000000000,
'original_values': False
}
#constraint_mass = {'Constraint_field_name': 'mass', 'lower_bound': -10**16 , 'upper_bound': -0.5*(10**8), 'original_values': False, 'subtract_original': True}
constraint_mass = {
'Constraint_field_name': 'mass',
'lower_bound': 3.8 * (10**11),
'upper_bound': 4.0 * (10**11),
'original_values': True,
'subtract_original': False
}
#constraint_mass0 = {'Constraint_field_name': 'mass', 'lower_bound': 7.3*(10**11) , 'upper_bound': 7.6*(10**11), 'original_values': True, 'subtract_original': False}
constraint_mass0 = {
'Constraint_field_name': 'mass',
'lower_bound': 3.2 * (10**9),
'upper_bound': 3.4 * (10**9),
'original_values': True,
'subtract_original': False
}
constraint_day = {
'Constraint_field_name': 'day',
'lower_bound': 92,
'upper_bound': 94.,
'original_values': True,
'subtract_original': False
}
constraint_vvel = {
'Constraint_field_name': 'vvel',
'lower_bound': 0.0001,
'upper_bound': 100,
'original_values': False,
'subtract_original': False
}
constraint_uvel = {
'Constraint_field_name': 'uvel',
'lower_bound': -100,
'upper_bound': -0.00100,
'original_values': False,
'subtract_original': False
}
constraint_sst = {
'Constraint_field_name': 'sst',
'lower_bound': -10,
'upper_bound': -1.5,
'original_values': False,
'subtract_original': False
}
constraint_age = {
'Constraint_field_name': 'age',
'lower_bound': 0,
'upper_bound': 5,
'original_values': False
}
constraint_lon_Sermalik = {
'Constraint_field_name': 'lon',
'lower_bound': -38,
'upper_bound': -36,
'original_values': True
} #Longitude of Rink
constraint_lat_Sermalik = {
'Constraint_field_name': 'lat',
'lower_bound': 65.0,
'upper_bound': 66.0,
'original_values': True
} #Longitude of Rink
constraint_lon_WestGreenland = {
'Constraint_field_name': 'lon',
'lower_bound': -45,
'upper_bound': -20,
'original_values': True
}
constraint_lat_SouthGreenland = {
'Constraint_field_name': 'lat',
'lower_bound': 50.0,
'upper_bound': 66.0,
'original_values': True
}
#constraints.append(constraint2)
#if field_name1=='age' or field_name2=='age':
# print 'applying age constraint'
# day_constraint = {'Constraint_field_name': 'day', 'lower_bound': 0 , 'upper_bound': 258, 'original_values': True} #Longitude of AB
constraints = []
#constraints.append(constraint_dist_from_calving)
#constraints.append(constraint_depth)
#constraints.append(constraint_mass0)
#constraints.append(constraint_day)
#constraints.append(constraint_age)
#constraints.append(constraint_SH)
#constraints.append(constraint_sst)
#constraints.append(constraint_NH)
#constraints.append(constraint_vvel)
#constraints.append(constraint_uvel)
#constraints.append(constraint_lon_Sermalik)
#constraints.append(constraint_lat_Sermalik)
#constraints.append(day_constraint)
#constraints.append(constraint_lon_WestGreenland)
#constraints.append(constraint_lat_SouthGreenland)
rho_ice = 850.0
rho_sw = 1025.0
number_of_bins = 100
Number_of_classes = 10
mass_scaling = np.array([2000, 200, 50, 20, 10, 5, 2, 1, 1, 1])
initial_mass = np.array([
8.8e7, 4.1e8, 3.3e9, 1.8e10, 3.8e10, 7.5e10, 1.2e11, 2.2e11, 3.9e11,
7.4e11
])
initial_thickness = np.array(
[40, 67, 133, 175, 250, 250, 250, 250, 250, 250])
initial_area = initial_mass / (initial_thickness * rho_ice)
initial_W = sqrt(initial_area / 1.5)
initial_L = 1.5 * initial_W
B = np.sqrt(6. * (rho_ice / rho_sw) * (1 - (rho_ice / rho_sw)))
print B
W_roll = B * initial_W
L_roll = 1.5 * W_roll
#L_roll = initial_L-(initial_W-W_roll)
Mass_roll = initial_thickness * W_roll * L_roll * rho_ice
mass_ind_list = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
mass_num = 1
#mass_ind_list=np.array([mass_num])
#Initializing occurance matrix
number_of_age_bins = 200
#field_name='area'; x_min=1;x_max=1.e9 # Good for area including all Deltas
field_name = 'mass'
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'
])
root_path = '/ptmp/aas/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg_bergs_'
#Delta_list=np.array(['Delta9', 'rolling_tournadre_WM', 'rolling_Delta9_Burton_new'])
#Delta_list=np.array(['rolling_Delta1_Burton_new','rolling_Delta5_Burton_new','rolling_Delta9_Burton_new'])
#Delta_list=np.array(['rolling_tournadre_Burton_breaking_aspect_fixed','rolling_tournadre_Burton_breaking_D_fixed','rolling_tournadre_Burton_new'])
Delta_list = np.array(
['tournadre', 'rolling_tournadre_WM', 'rolling_tournadre_Burton_new'])
Delta_list = np.array(
['rolling_tournadre_Burton_new', 'rolling_tournadre_Rolling_off'])
Delta_list = np.array([
'rolling_tournadre_Burton_new_passive',
'rolling_tournadre_Rolling_off_passive'
])
#Delta_list=np.array(['rolling_tournadre_Burton_new/trajectories/','rolling_tournadre_Rolling_off/trajectories/'])
#Delta_list=np.array(['Delta1','Delta9'])
#Delta_list=np.array(['Delta2','Delta3', 'Delta4','Delta5','Delta6'])
#Name_list=np.array(['Martin and Adcroft','Weeks and Mellor','Burton'])
Name_list = np.array(['Rolling', 'No Rolling'])
#load_filename_list=np.array(['processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1950_to_2000_expected_values.mat',\
# 'processed_data/rolling_tournadre_Burton_new_age_vs_mass_1950_to_2000_expected_values.mat'])
#load_filename_list=np.array(['processed_data/rolling_tournadre_Burton_new_age_vs_mass_1950_to_2000_expected_values_logaxis_fixed_upperbound.mat',\
# 'processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1950_to_2000_expected_values_logaxis_fixed_upperbound.mat'])
#load_filename_list=np.array(['processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1950_to_2000_expected_values_logaxis.mat',\
# 'processed_data/rolling_tournadre_Burton_new_age_vs_mass_1950_to_2000_expected_values_logaxis.mat'])
#load_filename_list=np.array(['processed_data/rolling_tournadre_Burton_new_age_vs_mass_1948_to_2198_expected_values_logaxis_Numbins50.mat',\
# 'processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1943_to_2043_expected_values_logaxis_Numbins100.mat'])
#load_filename_list=np.array(['processed_data/rolling_tournadre_Burton_new_age_vs_mass_1946_to_2046_expected_values_logaxis__fixed_upperboundNumbins100.mat',\
# 'processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1943_to_2043_expected_values_logaxis__fixed_upperboundNumbins100.mat'])
#load_filename_list=np.array(['processed_data/rolling_tournadre_Burton_new_age_vs_mass_1950_to_2000_expected_values_logaxis__fixed_upperbound_moreageNumbins100.mat',\
#'processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1950_to_2000_expected_values_logaxis__fixed_upperbound_moreageNumbins100.mat'])
#load_filename_list=np.array(['processed_data/rolling_tournadre_Burton_new_age_vs_mass_1950_to_2000_expected_values_logaxis__fixed_upperboundNumbins100.mat',\
#'processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1950_to_2000_expected_values_logaxis__fixed_upperboundNumbins100.mat'])
#Southern_Hemisphere
No_Rolling_file = 'processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1980_to_2030_expected_values_constraints_lat_50.0_to_75.0_logaxis__fixed_upperboundNumbins100.mat'
Rolling_file = 'processed_data/rolling_tournadre_Burton_new_age_vs_mass_1980_to_2030_expected_values_constraints_lat_50.0_to_75.0_logaxis__fixed_upperboundNumbins100.mat'
#No_Rolling_file='processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1980_to_2030_expected_values_constraints_lat_150_to_5_logaxis__fixed_upperboundNumbins100.mat'
#Rolling_file='processed_data/rolling_tournadre_Burton_new_age_vs_mass_1980_to_2030_expected_values_constraints_lat_150_to_5_logaxis__fixed_upperboundNumbins100.mat'
#Norther_Hemisphere
#No_Rolling_file='processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1980_to_2030_expected_values_constraints_lat_5_to_150_logaxis__fixed_upperboundNumbins100.mat'
#Rolling_file='processed_data/rolling_tournadre_Burton_new_age_vs_mass_1980_to_2030_expected_values_constraints_lat_5_to_150_logaxis__fixed_upperboundNumbins100.mat'
load_filename_list = np.array([Rolling_file, No_Rolling_file])
fig = plt.figure(1)
ax = fig.add_subplot(1, 1, 1)
if load_the_data == True:
count1 = 0
for k in np.array([0, 1]):
#for k in np.array([1]):
count1 = count1 + 1
#input_folder=all_paths[k]
#Delta_name='Delta'+str(k)
Delta_name = Delta_list[k]
input_folder = root_path + Delta_name + '/trajectories/'
#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)
#Full mean matricies
mean_matrix = np.zeros(
(Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
std_matrix = np.zeros(
(Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
x_matrix = np.zeros((Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
sigma_matrix = np.zeros(
(Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
scale_matrix = np.zeros(
(Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
loc_matrix = np.zeros(
(Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
mu_matrix = np.zeros((Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
median_matrix = np.zeros(
(Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
mode_matrix = np.zeros(
(Number_of_classes, end_year - start_year + 1,
number_of_bins - 1))
#Time mean matricies
Total_mean = np.zeros((Number_of_classes, number_of_bins - 1))
Total_std = np.zeros((Number_of_classes, number_of_bins - 1))
Total_x = np.zeros((Number_of_classes, number_of_bins - 1))
Total_sigma = np.zeros((Number_of_classes, number_of_bins - 1))
Total_scale = np.zeros((Number_of_classes, number_of_bins - 1))
Total_loc = np.zeros((Number_of_classes, number_of_bins - 1))
Total_mu = np.zeros((Number_of_classes, number_of_bins - 1))
Total_median = np.zeros((Number_of_classes, number_of_bins - 1))
Total_mode = np.zeros((Number_of_classes, number_of_bins - 1))
occurance_y = np.logspace(np.log10(10**5),
np.log10(10**11),
num=number_of_bins,
endpoint=True)
occurance_x = np.linspace(0,
40,
num=number_of_age_bins,
endpoint=True)
occurance_matrix = np.zeros(
(Number_of_classes, len(occurance_x), len(occurance_y)))
year_count = -1
for year in range(start_year, end_year + 1):
year_count = year_count + 1
#year=1945
filename = '/' + str(year) + '0101.iceberg_trajectories.nc'
input_file = input_folder + filename
print input_file
field1_full = get_valid_data_from_traj_file(
input_file, field_name1, subtract_orig=False)
field2_full = get_valid_data_from_traj_file(
input_file, field_name2, subtract_orig=False)
print 'Lendth of field: ', len(field1_full)
#Getting the distributions from the data
#Handling all the different mass classes
for mass_ind in mass_ind_list:
mass = initial_mass[mass_ind]
constraint_m = {
'Constraint_field_name': 'mass',
'lower_bound': mass - 100,
'upper_bound': mass + 100,
'original_values': True
}
mass_constraints = []
mass_constraints = constraints
mass_constraints.append(constraint_m)
#Handeling constraints
field1 = 0.
field2 = 0.
field1 = add_constraint_to_data(input_file, field1_full,
mass_constraints)
field2 = add_constraint_to_data(input_file, field2_full,
mass_constraints)
print 'Lendth of field after constraints using mass0= ', mass, ' is ', len(
field1)
mass_constraints.pop()
if find_expected_values == True:
#Finding statistics
(x_bins,y_ave,y_std,sigma,scale,loc,mu,y_median,y_mode)=expected_values(field2,field1,number_of_bins,min_bin_value=True,\
use_log_axis=use_log_axis, fixed_upper_bound=fixed_upper_bound)
mean_matrix[mass_ind, year_count, :] = y_ave
std_matrix[mass_ind, year_count, :] = y_std
x_matrix[mass_ind, year_count, :] = x_bins
sigma_matrix[mass_ind, year_count, :] = sigma
scale_matrix[mass_ind, year_count, :] = scale
loc_matrix[mass_ind, year_count, :] = loc
mu_matrix[mass_ind, year_count, :] = mu
median_matrix[mass_ind, year_count, :] = y_median
mode_matrix[mass_ind, year_count, :] = y_mode
#Dealing with occurance (not quite read - needs to take all masses)
occurance_matrix[
mass_ind, :, :] = construct_occurance_matrix(
field1, field2,
occurance_matrix[mass_ind, :, :], occurance_x,
occurance_y)
if plot_scatter_plot == True:
label = Name_list[k]
plt.plot(field1,
field2,
'o',
color=color_vec[k],
label=label)
plt.xlabel(field_name1)
plt.ylabel(field_name2)
plt.title(Name_list[k])
#Taking the time mean
if find_expected_values == True:
Total_median = find_average_over_time_without_nan(
median_matrix, 'median')
Total_mean = find_average_over_time_without_nan(
mean_matrix, 'mean')
Total_std = find_average_over_time_without_nan(
np.sqrt(std_matrix**2), 'mean')
Total_x = find_average_over_time_without_nan(x_matrix, 'mean')
Total_sigma = find_average_over_time_without_nan(
sigma_matrix, 'mean')
Total_scale = find_average_over_time_without_nan(
scale_matrix, 'mean')
Total_loc = find_average_over_time_without_nan(
loc_matrix, 'mean')
Total_mu = find_average_over_time_without_nan(
mu_matrix, 'mean')
#Normalizing occurance
for mass_ind in range(len(mass_ind_list)):
occurance_matrix[
mass_ind, :, :] = occurance_matrix[mass_ind, :, :] / (
np.sum(occurance_matrix[mass_ind, :, :]))
print 'Sum of occurance_matrix is: ', np.sum(
occurance_matrix[mass_ind, :, :])
if save_the_data == True:
save_mat_file(mean_matrix, median_matrix, std_matrix, x_matrix, Total_mean, Total_std, Total_x, start_year,end_year,Delta_name,field_name1,field_name2,\
sigma_matrix, scale_matrix, loc_matrix, mu_matrix, Total_sigma, Total_loc, Total_scale, Total_mu,Total_median,\
constraints,use_log_axis,fixed_upper_bound,str(number_of_bins),occurance_x,occurance_y,occurance_matrix)
if plot_mean_fields == True:
first_time = True
for mass_ind in mass_ind_list:
if first_time is True:
label = Name_list[k]
plt.plot(np.squeeze(Total_median[mass_ind, :]),
Total_x[mass_ind, :],
color=color_vec[k],
label=label,
linewidth=4)
plt.plot(np.squeeze(Total_median[mass_ind, :]),
Total_x[mass_ind, :],
'o',
color=color_vec[k],
linewidth=4)
first_time = False
else:
plt.plot(np.squeeze(Total_median[mass_ind, :]),
Total_x[mass_ind, :],
color=color_vec[k],
linewidth=4)
plt.plot(np.squeeze(Total_median[mass_ind, :]),
Total_x[mass_ind, :],
'o',
color=color_vec[k],
linewidth=4,
linestyle='*')
plt.xlabel(field_name1)
plt.ylabel(field_name2)
ax.set_yscale('log')
plt.ylim([10**5, 10**12])
#ax.set_xscale('log')
#Normalizing
if plot_occurance_matrix is True:
#ax = fig.add_subplot(2,1,k+1)
vmax = np.max(occurance_matrix[mass_num, :, :])
#cNorm = MidpointNormalize(vmin=0, vmax=5,midpoint=0)
cNorm = mpl.colors.Normalize(vmin=0, vmax=vmax)
cNorm = mpl.colors.LogNorm(vmin=10**-5, vmax=0.1)
print len(occurance_x), len(occurance_y)
plt.pcolor(occurance_x,
occurance_y, (np.squeeze(
occurance_matrix[mass_num, :, :])).transpose(),
cmap='jet',
norm=cNorm)
plt.colorbar()
else: # Loading data from mat file
if plot_mean_fields == True:
count1 = 0
#for k in np.array([1]):
for k in np.array([0, 1]):
count1 = count1 + 1
filename = load_filename_list[k]
print filename
(Total_median, median_matrix, Total_mean, mean_matrix, Total_x,
x_matrix) = load_mass_vs_age_from_mat_file(filename,
load_type='median')
first_time = True
#for mass_ind in mass_ind_list:
#for mass_ind in np.array([1,2,3,4,5,6,7,8,9]):
for mass_ind in np.array([1, 2, 3, 4, 5, 6, 7, 8, 9]):
#for mass_ind in np.array([9]):
#These should be uncommented
#plt.plot(Total_mean,Total_x,color='black',label=label,linewidth=4)
#plt.plot(Total_median,Total_x,color='green',label=label,linewidth=4)
#plt.plot(Total_mean+Total_std,Total_x,color='magenta',linewidth=4)
#plt.plot(Total_mean-Total_std,Total_x,color='magenta',linewidth=4)
#(fit_age,fit_mass)=fit_curve(Total_median[mass_ind,:], Total_x[mass_ind,:])
#if np.max(np.isnan(Total_median[mass_ind,:]))==1:
# print 'STOP!'
# print Total_median[mass_ind,:]
#(filtered_age,filtered_mass)=filter_curve(Total_median[mass_ind,:], Total_x[mass_ind,:],k,mass_ind)
if first_time is True:
label = Name_list[k]
plt.plot(np.squeeze(Total_median[mass_ind, :]),
Total_x[mass_ind, :],
'o',
color=color_vec[k],
linewidth=1)
#plt.plot(filtered_age, filtered_mass,'o' ,color=color_vec[k], linewidth=2,label=label)
plt.plot(np.squeeze(Total_median[mass_ind, :]),
Total_x[mass_ind, :],
color=color_vec[k],
label=label,
linewidth=4)
first_time = False
else:
plt.plot(np.squeeze(Total_median[mass_ind, :]),
Total_x[mass_ind, :],
color=color_vec[k],
linewidth=4)
plt.plot(np.squeeze(Total_median[mass_ind, :]),
Total_x[mass_ind, :],
'o',
color=color_vec[k],
linewidth=1)
#plt.plot(filtered_age, filtered_mass,'o' ,color=color_vec[k], linewidth=2)
#plt.plot(np.squeeze(Total_median[mass_ind,:]),Total_x[mass_ind,:],color=color_vec[k], linewidth=1)
#plt.plot(np.squeeze(Total_median[mass_ind,:]),Total_x[mass_ind,:],color=color_vec[k], linewidth=4,linestyle=':')
#plt.plot(filtered_age, filtered_mass ,color=color_vec[k], linewidth=2)
#plt.plot(fit_age, fit_mass ,color=color_vec[k], linewidth=3)
#print Total_x[mass_ind,:]
#plt.plot(5,Mass_roll[mass_ind],'o',color='k')
plt.xlabel(field_name1)
plt.ylabel(field_name2)
ax.set_yscale('log')
plt.ylim([10**5, 10**12])
plt.xlim([0, 25.])
#ax.set_xscale('log')
if plot_occurance_matrix is True:
for k in np.array([0, 1]):
filename = load_filename_list[k]
(occurance_x, occurance_y,
occurance_matrix) = load_mass_vs_age_from_mat_file(
filename, load_type='occurance')
dp = occurance_x[1] - occurance_x[0]
#for mass_num in np.array([7]):
#for mass_num in np.array([1,2,3,4,5,6,7,8,9]):
max_occurance = np.zeros((len(occurance_y)))
mode_occurance = np.zeros((len(occurance_y)))
for j in range(len(occurance_y)):
#max_occurance[j]=np.sum( occurance_matrix[mass_num,:,j]*occurance_x*dp /np.sum(occurance_matrix[mass_num,:,j]) )
max_occurance[j] = np.sum(
occurance_matrix[mass_num, :, j] * occurance_x /
np.sum(occurance_matrix[mass_num, :, j]))
#values=occurance_matrix[mass_num,:,j]
#mode_occurance[j]=occurance_x[np.argmax(values)]
#plt.subplot(2,1,k+1)
vmax = np.max(occurance_matrix[mass_num, :, :])
#cNorm = MidpointNormalize(vmin=0, vmax=5,midpoint=0)
#cNorm = mpl.colors.Normalize(vmin=-vmax, vmax=vmax)
cNorm = mpl.colors.LogNorm(vmin=10**-5, vmax=0.1)
#if k==0:
# max_occurance0=max_occurance
if k == 1:
plt.pcolor(occurance_x,
occurance_y, (np.squeeze(
occurance_matrix[mass_num, :, :])).transpose(),
cmap='jet',
norm=cNorm)
plt.colorbar()
plt.title(Name_list[k])
plt.plot(max_occurance,
occurance_y,
color=color_vec[k],
linewidth=2)
#plt.plot(mode_occurance,occurance_y,color=color_vec[k],linewidth=2)
#title=Name_list[k]
plt.plot(max_occurance,
occurance_y,
color=color_vec[k],
linewidth=1,
label=Name_list[k])
#plt.plot(mode_occurance,occurance_y,color=color_vec[k],linewidth=1, label=Name_list[k])
ax.set_yscale('log')
#plt.legend(loc='upper right', frameon=True,prop={'size':12})
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():
#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=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():
#Clear screen
#os.system('clear')
#Defining possible paths
all_paths = define_paths_array()
#Flags
plot_full_time_series = 0
plot_scatter_plot = False
plot_mean_fields_again = True
plot_occurance_matrix = False
plot_derivatives_and_fits = False
plot_rolling_equation = False
find_expected_values = True
save_the_data = False
load_the_data = True
#Parameters and variables
#start_year=1980
end_year0 = 2010
#end_year0=1999
Number_of_years = 3
#Number_of_years=0
#Which fields do you want to compare?
#field_name1='area'
#field_name1='age'
#field_name1='side_decay'
#field_name1='Me'
#field_name1='sst'
#field_name1='Ss'
field_name1 = 'cn'
#field_name1='speed'
#field_name1='Mb'
#field_name1='mass'
#field_name1='length'
#field_name1='width'
#field_name1='thickness'
#field_name1='LW_max'
#field_name1='distance_from_calving'
field_name2 = 'mass'
#field_name2='area'
#field_name2='age'
#field_name2='thickness'
#field_name2='length'
#field_name2='width'
initial_mass_vec = np.array([
8.8e7, 4.1e8, 3.3e9, 1.8e10, 3.8e10, 7.5e10, 1.2e11, 2.2e11, 3.9e11,
7.4e11
])
#Include contraints to use to filter the data
constraint2 = {
'Constraint_field_name': 'lon',
'lower_bound': -150,
'upper_bound': -65,
'original_values': True
} #Longitude of AB
constraint_SH = {
'Constraint_field_name': 'lat',
'lower_bound': -150,
'upper_bound': -5,
'original_values': True
} #Longitude of AB
constraint_NH = {
'Constraint_field_name': 'lat',
'lower_bound': 5,
'upper_bound': 100,
'original_values': True
} #Longitude of AB
constraint_birth_year = {
'Constraint_field_name': 'year',
'lower_bound': 1899.9,
'upper_bound': 1900.1,
'original_values': True
}
constraint_depth = {
'Constraint_field_name': 'depth',
'lower_bound': 500,
'upper_bound': 8000,
'original_values': False
}
constraint_dist_from_calving = {
'Constraint_field_name': 'distance_from_calving',
'lower_bound': 1000,
'upper_bound': 10000000000000,
'original_values': False
}
#constraint_mass = {'Constraint_field_name': 'mass', 'lower_bound': -10**16 , 'upper_bound': -0.5*(10**8), 'original_values': False, 'subtract_original': True}
constraint_mass = {
'Constraint_field_name': 'mass',
'lower_bound': 1.0 * (10**10),
'upper_bound': 4.0 * (10**15),
'original_values': False,
'subtract_original': False
}
constraint_mass0 = {
'Constraint_field_name': 'mass',
'lower_bound': 7.3 * (10**11),
'upper_bound': 7.6 * (10**11),
'original_values': True,
'subtract_original': False
}
#constraint_mass0 = {'Constraint_field_name': 'mass', 'lower_bound': 3.2*(10**9) , 'upper_bound': 3.4*(10**9), 'original_values': True, 'subtract_original': False}
constraint_day = {
'Constraint_field_name': 'day',
'lower_bound': 92,
'upper_bound': 94.,
'original_values': True,
'subtract_original': False
}
constraint_vvel = {
'Constraint_field_name': 'vvel',
'lower_bound': 0.0001,
'upper_bound': 100,
'original_values': False,
'subtract_original': False
}
constraint_uvel = {
'Constraint_field_name': 'uvel',
'lower_bound': -100,
'upper_bound': -0.00100,
'original_values': False,
'subtract_original': False
}
constraint_age = {
'Constraint_field_name': 'age',
'lower_bound': 0,
'upper_bound': 5,
'original_values': False
}
constraint_lon_WestGreenland = {
'Constraint_field_name': 'lon',
'lower_bound': -45,
'upper_bound': -20,
'original_values': True
}
constraint_lat_SouthGreenland = {
'Constraint_field_name': 'lat',
'lower_bound': 50.0,
'upper_bound': 66.0,
'original_values': True
}
#constraints.append(constraint2)
#if field_name1=='age' or field_name2=='age':
# print 'applying age constraint'
# day_constraint = {'Constraint_field_name': 'day', 'lower_bound': 0 , 'upper_bound': 258, 'original_values': True} #Longitude of AB
constraints = []
#constraints.append(constraint_dist_from_calving)
#constraints.append(constraint_depth)
constraints.append(constraint_mass0)
#constraints.append(constraint_day)
#constraints.append(constraint_age)
#constraints.append(constraint_SH)
#constraints.append(constraint_birth_year)
#constraints.append(constraint_NH)
#constraints.append(constraint_vvel)
#constraints.append(constraint_uvel)
# constraints.append(day_constraint)
#constraints.append(constraint_lon_WestGreenland)
constraints.append(constraint_lat_SouthGreenland)
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 = 'mass'
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'
])
root_path = '/ptmp/aas/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg_bergs_'
#Delta_list=np.array(['Delta9', 'rolling_tournadre_WM', 'rolling_Delta9_Burton_new'])
#Delta_list=np.array(['rolling_Delta1_Burton_new','rolling_Delta5_Burton_new','rolling_Delta9_Burton_new'])
#Delta_list=np.array(['rolling_tournadre_Burton_breaking_aspect_fixed','rolling_tournadre_Burton_breaking_D_fixed','rolling_tournadre_Burton_new'])
Delta_list = np.array(
['tournadre', 'rolling_tournadre_WM', 'rolling_tournadre_Burton_new'])
#Delta_list=np.array(['rolling_tournadre_Burton_new/trajectories/','rolling_tournadre_Rolling_off/trajectories/'])
Delta_list = np.array(
['rolling_tournadre_Burton_new', 'rolling_tournadre_Rolling_off'])
#Delta_list=np.array(['Delta1','Delta9'])
#Delta_list=np.array(['Delta2','Delta3', 'Delta4','Delta5','Delta6'])
#Rolling_file='processed_data/rolling_tournadre_Burton_new_Me_vs_mass_1960_to_2010_expected_values_constraints_mass_7.3e+11_to_7.6e+11_lat_50.0_to_75.0_scatter.mat'
#No_Rolling_file='processed_data/rolling_tournadre_Rolling_off_Me_vs_mass_1960_to_2010_expected_values_constraints_mass_7.3e+11_to_7.6e+11_lat_50.0_to_75.0_scatter.mat'
No_Rolling_file = 'processed_data/rolling_tournadre_Rolling_off_Me_vs_mass_1960_to_2010_expected_values_constraints_mass_7.3e+11_to_7.6e+11_lon_45_to_20_lat_50.0_to_75.0_scatter.mat'
Rolling_file = 'processed_data/rolling_tournadre_Burton_new_Me_vs_mass_1960_to_2010_expected_values_constraints_mass_7.3e+11_to_7.6e+11_lon_45_to_20_lat_50.0_to_75.0_scatter.mat'
load_filename_list = np.array([Rolling_file, No_Rolling_file])
#Name_list=np.array(['Martin and Adcroft','Weeks and Mellor','Burton'])
Name_list = np.array(['Rolling', 'No Rolling'])
fig = plt.figure(1)
#for k in range(13):#for k in np.array([3]):
count1 = 0
print 'field1 = ', field_name1, ', field2 = ', field_name2
if load_the_data is True:
for k in np.array([0, 1]):
#for k in np.array([0,1,2,3,4]):
#for k in np.array([8,5,3,1]):
#for k in np.array([9,5,1]):
#for k in np.array([4,5,6,9]):
#for k in np.array([7,8,10]):
#for k in np.array([10,9,8,7,6,5,4,3,2,1]):
#for k in range(1,9):
#ax = fig.add_subplot(2,1,k+1)
ax = fig.add_subplot(1, 1, 1)
count1 = count1 + 1
#input_folder=all_paths[k]
#Delta_name='Delta'+str(k)
Delta_name = Delta_list[k]
input_folder = root_path + Delta_name
input_folder = input_folder + '/trajectories/'
print input_folder
#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)
number_of_bins = 100
mean_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
std_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
x_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
sigma_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
scale_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
loc_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
mu_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
median_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
mode_matrix = np.zeros(
(end_year - start_year + 1, number_of_bins - 1))
number_of_age_bins = 40
occurance_y = np.logspace(np.log10(10**5),
np.log10(10**11),
num=number_of_bins,
endpoint=True)
occurance_x = np.linspace(0,
3,
num=number_of_age_bins,
endpoint=True)
#occurance_x = np.linspace(0, 40, num=number_of_age_bins, endpoint=True)
occurance_matrix = np.zeros((len(occurance_x), len(occurance_y)))
year_count = -1
for year in range(start_year, end_year + 1):
year_count = year_count + 1
#year=1945
filename = '/' + str(year) + '0101.iceberg_trajectories.nc'
input_file = input_folder + filename
print input_file
field1 = get_valid_data_from_traj_file(input_file,
field_name1,
subtract_orig=False)
field2 = get_valid_data_from_traj_file(input_file,
field_name2,
subtract_orig=False)
print 'Lendth of field: ', len(field1)
#Getting the distributions from the data
#Handeling constraints
field1 = add_constraint_to_data(input_file, field1,
constraints)
field2 = add_constraint_to_data(input_file, field2,
constraints)
print 'Lendth of field after constraints: ', len(field1)
#field2=field2*0.81
#if create_2D_histogram==True:
if find_expected_values == True:
(x_bins, y_ave, y_std, sigma, scale, loc, mu, y_median,
y_mode) = expected_values(field2,
field1,
number_of_bins,
min_bin_value=True)
mean_matrix[year_count, :] = y_ave
std_matrix[year_count, :] = y_std
x_matrix[year_count, :] = x_bins
sigma_matrix[year_count, :] = sigma
scale_matrix[year_count, :] = scale
loc_matrix[year_count, :] = loc
mu_matrix[year_count, :] = mu
median_matrix[year_count, :] = y_median
mode_matrix[year_count, :] = y_mode
#Dealing with occurance
Occurance_matrix = construct_occurance_matrix(
field1, field2, occurance_matrix, occurance_x,
occurance_y)
if plot_scatter_plot == True:
#Plotting the distributions
#ax = fig.add_subplot(3,1,k+1)
#ax = fig.add_subplot(2,1,count1)
label = Name_list[k]
#label='Delta' + str(k)
plt.plot(field1,
field2,
'.',
color=color_vec[k],
label=label)
plt.xlabel(field_name1)
plt.ylabel(field_name2)
#plt.ylim([-75., -40]) #For mass
#plt.xlim([0, 30]) #For mass
#ax.set_yscale('log')
#ax.set_xscale('log')
plt.title(Name_list[k])
if plot_derivatives_and_fits == True:
#dy_dx=derivative(x_bins,y_ave)
#Note: x_bins is mass, y_ave is age
(dy_dx, x_new, y_new,
x_fit) = fit_derivative(x_bins, y_ave)
ax = fig.add_subplot(1, 1, 1)
label = 'Delta' + str(k + 1)
plt.plot(y_ave,
x_bins,
color=color_vec[k],
label=label,
linewidth=2.)
plt.plot(y_ave, x_bins, '*', color=color_vec[k])
plt.plot(y_new,
x_new,
color='red',
label=label,
linewidth=4.)
plt.plot(y_new,
x_fit,
color='magenta',
label=label,
linewidth=2.)
plt.plot(y_ave + y_std, x_bins, color='green')
plt.plot(y_ave - y_std, x_bins, color='green')
plt.xlabel(field_name1)
plt.ylabel(field_name2)
#ax.set_yscale('log')
#ax.set_xscale('log')
ax = fig.add_subplot(1, 2, 2)
plt.plot(x_bins,
dy_dx,
color=color_vec[k],
label=label,
linewidth=4.)
plt.plot(x_bins, dy_dx, '*')
plt.plot(x_new, dy_dx)
plt.ylabel('dy/dx')
plt.xlabel('Mass')
#ax.set_xscale('log')
#ax.set_yscale('log')
if plot_rolling_equation == True:
D = np.linspace(0.1, 300, 100)
L = sqrt(0.92 * (D**2) + (58.32 * D))
plt.plot(L, D, linestyle='--')
#plt.plot(L,L,linestyle=':')
plt.plot(L, 0.81 * L, linestyle=':')
#plt.plot(L,L/1.2,linestyle=':')
#plt.plot(L,L-(,linestyle=':')
#Normalizing
print np.sum(occurance_matrix)
occurance_matrix = occurance_matrix / (np.sum(occurance_matrix))
if plot_occurance_matrix is True:
vmax = np.max(occurance_matrix)
#cNorm = MidpointNormalize(vmin=0, vmax=5,midpoint=0)
cNorm = mpl.colors.Normalize(vmin=0, vmax=vmax)
cNorm = mpl.colors.LogNorm(vmin=10**-5, vmax=0.1)
print len(occurance_x), len(occurance_y)
plt.pcolor(occurance_x,
occurance_y,
occurance_matrix.transpose(),
cmap='jet',
norm=cNorm)
plt.colorbar()
plt.xlabel(field_name1)
plt.ylabel(field_name2)
ax.set_yscale('log')
ax.set_xscale('log')
if plot_mean_fields_again == True:
Total_median = find_average_over_time_without_nan(
median_matrix, 'median')
Total_mean = find_average_over_time_without_nan(
mean_matrix, 'mean')
Total_std = find_average_over_time_without_nan(
np.sqrt(std_matrix**2), 'mean')
Total_x = find_average_over_time_without_nan(x_matrix, 'mean')
Total_sigma = find_average_over_time_without_nan(
sigma_matrix, 'mean')
Total_scale = find_average_over_time_without_nan(
scale_matrix, 'mean')
Total_loc = find_average_over_time_without_nan(
loc_matrix, 'mean')
Total_mu = find_average_over_time_without_nan(
mu_matrix, 'mean')
if save_the_data == True:
save_mat_file(mean_matrix, std_matrix, x_matrix, Total_mean, Total_std, Total_x, start_year,end_year,Delta_name,field_name1,field_name2,\
sigma_matrix, scale_matrix, loc_matrix, mu_matrix, Total_sigma, Total_loc, Total_scale, Total_mu,Total_median,constraints,\
occurance_x,occurance_y,occurance_matrix)
#ax = fig.add_subplot(1,1,1)
#label='Delta' + str(k+1)
label = Delta_list[k]
#These should be uncommented
#plt.plot(Total_mean,Total_x,color='black',label=label,linewidth=4)
#plt.plot(Total_median,Total_x,color='green',label=label,linewidth=4)
#plt.plot(Total_mean+Total_std,Total_x,color='magenta',linewidth=4)
#plt.plot(Total_mean-Total_std,Total_x,color='magenta',linewidth=4)
#plt.plot(Total_median,Total_x,color=color_vec[k],label=label,linewidth=4)
print Total_median
plt.title(Name_list[k])
M = median_matrix.shape
#for loop1 in range(M[0]):
# plt.plot(np.squeeze(median_matrix[loop1,:]),Total_x,color=color_vec[loop1+3],label=label,linewidth=1)
plt.plot(Total_median,
Total_x,
color=color_vec[k],
label=label,
linewidth=4)
plt.xlabel(field_name1)
plt.ylabel(field_name2)
ax.set_yscale('log')
#ax.set_xscale('log')
else:
if plot_mean_fields_again == True:
ax = fig.add_subplot(1, 1, 1)
for k in range(2):
print k
filename = load_filename_list[k]
(Total_median, Total_mean,
Total_x) = load_mass_vs_age_from_mat_file(filename,
load_type='median')
plt.title(Name_list[k])
print Total_median.shape, Total_x.shape
plt.plot(Total_median,
Total_x,
color=color_vec[k],
linewidth=4)
plt.xlabel(field_name1)
plt.ylabel(field_name2)
ax.set_yscale('log')
if plot_occurance_matrix is True:
for k in range(2):
ax = fig.add_subplot(1, 1, 1)
filename = load_filename_list[k]
(occurance_x, occurance_y,
occurance_matrix) = load_mass_vs_age_from_mat_file(
filename, load_type='occurance')
dp = occurance_x[1] - occurance_x[0]
max_occurance = np.zeros((len(occurance_y)))
mode_occurance = np.zeros((len(occurance_y)))
for j in range(len(occurance_y)):
max_occurance[j] = np.sum(occurance_matrix[:, j] *
occurance_x /
np.sum(occurance_matrix[:, j]))
#plt.subplot(2,1,k+1)
vmax = np.max(occurance_matrix[:, :])
#cNorm = MidpointNormalize(vmin=0, vmax=5,midpoint=0)
#cNorm = mpl.colors.Normalize(vmin=-vmax, vmax=vmax)
cNorm = mpl.colors.LogNorm(vmin=10**-5, vmax=0.1)
#if k==0:
# max_occurance0=max_occurance
if k == 1:
plt.pcolor(occurance_x,
occurance_y, (np.squeeze(
occurance_matrix[:, :])).transpose(),
cmap='jet',
norm=cNorm)
plt.colorbar()
plt.title(Name_list[k])
plt.plot(max_occurance,
occurance_y,
color=color_vec[k],
linewidth=2)
#plt.plot(mode_occurance,occurance_y,color=color_vec[k],linewidth=2)
#title=Name_list[k]
plt.plot(max_occurance,
occurance_y,
color=color_vec[k],
linewidth=1,
label=Name_list[k])
#plt.plot(mode_occurance,occurance_y,color=color_vec[k],linewidth=1, label=Name_list[k])
ax.set_yscale('log')
plt.legend(loc='upper right', frameon=True)
#plt.legend(loc='lower right', frameon=True,prop={'size':12})
#plt.legend(loc='lower right', frameon=True,prop={'size':12})
#plt.legend(loc='upper right', frameon=True)
#fig = matplotlib.pyplot.gcf()
fig.set_size_inches(9, 4.5)
print 'field1 = ', field_name1, ', field2 = ', field_name2
plt.show()
print 'Script complete'
def main():
#Clear screen
#os.system('clear')
#Defining possible paths
all_paths = define_paths_array()
#Flags
save_the_data = True
load_the_data = True
#Parameters and variables
#start_year=1980
#end_year0=2010
end_year0 = 2000
Number_of_years = 50
#Number_of_years=0
#Which fields do you want to compare?
#field_name1='sst'
#field_name1='ua'
#field_name1='va'
#field_name1='uo'
#field_name1='vo'
#field_name1='ui'
#field_name1='vi'
#field_name1='cn'
#field_name1='speed_o'
#field_name1='speed_i'
#field_name1='speed_a'
#field_name1='Mb'
#field_name1='Me'
field_name1 = 'Mv'
initial_mass = np.array([
8.8e7, 4.1e8, 3.3e9, 1.8e10, 3.8e10, 7.5e10, 1.2e11, 2.2e11, 3.9e11,
7.4e11
])
#Include contraints to use to filter the data
constraint_SH = {
'Constraint_field_name': 'lat',
'lower_bound': -150,
'upper_bound': -5,
'original_values': True
}
constraint_NH = {
'Constraint_field_name': 'lat',
'lower_bound': 5,
'upper_bound': 150,
'original_values': True
}
#if field_name1=='age' or field_name2=='age':
# print 'applying age constraint'
# day_constraint = {'Constraint_field_name': 'day', 'lower_bound': 0 , 'upper_bound': 258, 'original_values': True} #Longitude of AB
constraints = []
#constraints.append(constraint_NH)
constraints.append(constraint_SH)
rho_ice = 850.0
rho_sw = 1025.0
mass_ind_list = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
#mass_ind_list=np.array([9])
#field_name='area'; x_min=1;x_max=1.e9 # Good for area including all Deltas
field_name = 'mass'
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'
])
root_path = '/ptmp/aas/model_output/ulm_mom6_2015.07.20_again_myIcebergTag/AM2_LM3_SIS2_MOM6i_1deg_bergs_'
#Delta_list=np.array(['Delta9', 'rolling_tournadre_WM', 'rolling_Delta9_Burton_new'])
#Delta_list=np.array(['rolling_Delta1_Burton_new','rolling_Delta5_Burton_new','rolling_Delta9_Burton_new'])
#Delta_list=np.array(['rolling_tournadre_Burton_breaking_aspect_fixed','rolling_tournadre_Burton_breaking_D_fixed','rolling_tournadre_Burton_new'])
Delta_list = np.array(
['tournadre', 'rolling_tournadre_WM', 'rolling_tournadre_Burton_new'])
Delta_list = np.array(
['rolling_tournadre_Burton_new', 'rolling_tournadre_Rolling_off'])
#Delta_list=np.array(['rolling_tournadre_Burton_new/trajectories/','rolling_tournadre_Rolling_off/trajectories/'])
#Delta_list=np.array(['Delta1','Delta9'])
#Delta_list=np.array(['Delta2','Delta3', 'Delta4','Delta5','Delta6'])
#Name_list=np.array(['Martin and Adcroft','Weeks and Mellor','Burton'])
Name_list = np.array(['Rolling', 'No Rolling'])
filename = 'processed_data/rolling_tournadre_Burton_new_Mean_forcing_' + field_name1 + '_1950_to_2000.mat'
#load_filename_list=np.array(['processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1950_to_2000_expected_values.mat',\
# 'processed_data/rolling_tournadre_Burton_new_age_vs_mass_1950_to_2000_expected_values.mat'])
load_filename_list=np.array(['processed_data/rolling_tournadre_Rolling_off_age_vs_mass_1950_to_2000_expected_values_logaxis.mat',\
'processed_data/rolling_tournadre_Burton_new_age_vs_mass_1950_to_2000_expected_values_logaxis.mat'])
fig = plt.figure(1)
ax = fig.add_subplot(1, 1, 1)
if load_the_data == True:
count1 = 0
for k in np.array([1]):
#for k in np.array([1]):
count1 = count1 + 1
#input_folder=all_paths[k]
#Delta_name='Delta'+str(k)
Delta_name = Delta_list[k]
input_folder = root_path + Delta_name + '/trajectories/'
#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)
number_of_bins = 500
Number_of_classes = 10
#Initializing matricies
field1_matrix = np.zeros(
(Number_of_classes, end_year - start_year + 1))
year_count = -1
for year in range(start_year, end_year + 1):
year_count = year_count + 1
#year=1945
filename = '/' + str(year) + '0101.iceberg_trajectories.nc'
input_file = input_folder + filename
print input_file
field1_full = get_valid_data_from_traj_file(
input_file, field_name1, subtract_orig=False)
print 'Lendth of field: ', len(field1_full)
#Getting the distributions from the data
#Handling all the different mass classes
for mass_ind in mass_ind_list:
mass = initial_mass[mass_ind]
constraint_m = {
'Constraint_field_name': 'mass',
'lower_bound': mass - 100,
'upper_bound': mass + 100,
'original_values': True
}
mass_constraints = []
mass_constraints = constraints
mass_constraints.append(constraint_m)
#Handeling constraints
field1 = 0.
field2 = 0.
field1 = add_constraint_to_data(input_file, field1_full,
mass_constraints)
print 'Lendth of field after constraints using mass0= ', mass, ' is ', len(
field1)
mass_constraints.pop()
field1_matrix[mass_ind, year_count] = np.mean(field1)
mean_field1 = np.mean(field1_matrix, axis=1)
Total_mean_field1 = np.mean(mean_field1)
if save_the_data is True:
save_mat_file(Delta_name, field1_matrix, field_name1, constraints,
mean_field1, Total_mean_field1, start_year, end_year)
else:
(field1_matrix, mean_field1,
Total_mean_field1) = load_data_from_mat_file(filename, field_name1)
#print field1_matrix[:,:]
print mean_field1
print Total_mean_field1
#plt.legend(loc='upper right', frameon=True,prop={'size':12})
#plt.legend(loc='upper right', frameon=True)
#fig = matplotlib.pyplot.gcf()
#fig.set_size_inches(9,4.5)
#plt.show()
print 'Script complete'
