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main_forward_model_v7.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jul 29 12:08:11 2021
v5: Implemented adjusted surface transport, permanent fouling
v6: remove suface transport for now, remove unnecessary parameters
@author: kaandorp
"""
import numpy as np
import os
from glob import glob
import matplotlib.pyplot as plt
import xarray as xr
import pandas as pd
from datetime import datetime, timedelta
from h3.unstable import vect
from h3 import h3_to_geo, geo_to_h3
from scipy.sparse import coo_matrix
from sklearn.preprocessing import normalize
import cartopy.crs as ccrs
from tools import from_pickle, to_pickle, pcolorhex
from utils import get_settling_velocity
# from tools import pcolorhex
from cartopy import feature
import matplotlib.gridspec as gridspec
from scipy import special
from time import time
import cmocean
from scipy import sparse
import gc
import socket
import multiprocessing
from multiprocessing import set_start_method
from scipy.interpolate import interp1d
from argparse import ArgumentParser
from tools import get_colors_tableau
def Gaussian(x,mu,sigma):
return np.exp(-.5*((x-mu)/sigma)**2)
def draw_coastlines(ax,h3_indices,dict_coastlines,colors='r',**kwargs):
for i1,index_ in enumerate(h3_indices):
list_lon = dict_coastlines[index_]['lon_coast']
list_lat = dict_coastlines[index_]['lat_coast']
for lon_,lat_ in zip(list_lon,list_lat):
if isinstance(colors,np.ndarray):
ax.plot(lon_,lat_,'-',color=colors[i1],**kwargs)
else:
ax.plot(lon_,lat_,'-',color=colors,**kwargs)
def get_colors(inp, colormap, vmin=None, vmax=None):
norm = plt.Normalize(vmin, vmax)
return colormap(norm(inp))
def NB_model(k_arr,i_f,p,d_N=3):
'''
The fragmentation model by Charalambous (2015)
k_arr: the size classes
i_f: the fragmentation 'index' (denoted by f in the paper)
p: fragmentation probability
d_N: spatial dimensionality
'''
pmf_m = (special.gamma(k_arr+i_f) / (special.gamma(k_arr+1)*special.gamma(i_f)))*(p**k_arr)*(1-p)**i_f
pmf_N = 2**(d_N*k_arr) * pmf_m
return pmf_m,pmf_N
def geo_to_h3_depth(lat,lon,depth,res):
h3_index = vect.geo_to_h3(lat,lon,res)
if len(depth_layers) > 2:
for i1,(d_upper,d_lower,index_remove) in enumerate(zip(depth_layers[:-1],depth_layers[1:],index_remove_depth)):
mask = (depth >= d_upper) & (depth < d_lower)
if i1 == 0: #surface: don't change the h3 index
pass
elif i1 == len(depth_layers) - 2: #below the last depth layer: move particles to index 1 (removal due to sinking too deep)
h3_index[mask] = 1
print('%i particles removed below depth %f meters' % (mask.sum(),d_upper))
else:
h3_index[mask] -= index_remove #at other depth layers: remove the appropriate depth index
print('n particles in depth layer: %i' % mask.sum())
return h3_index
def get_fragmentation_fraction(lambda_f_week = 2e-4,units='m'):
l_0 = 160
k_arr = np.arange(10)
l_arr = l_0 / (2**k_arr)
pmf_m_0,pmf_N_0 = NB_model(k_arr,1,.4,d_N=2.5)
lambda_f = lambda_f_week*4 #f/week to f/month
m_next, N_next = NB_model(k_arr, lambda_f, .4, d_N=2.5)
macro_to_macro = 0
macro_to_micro = 0
for i1, (m_, l_) in enumerate(zip(pmf_m_0, l_arr)):
if i1 == 0:
i_start = None
i_end = None
else:
i_start = i1
i_end = -i1
m_arr_ = m_next[:i_end]
l_arr_ = l_arr[i_start:]
mask_macro = l_arr_ > 5
macro_to_macro += m_*(m_arr_[mask_macro].sum())
macro_to_micro += m_*(m_arr_[~mask_macro].sum())
return macro_to_macro,macro_to_micro
def h3_cell_volume():
h3_index_upper = h3_index_base + np.uint64(1e17)
dict_h3_normalize = pd.Series(index=map_h3_to_mat.index,dtype=np.float32)
for i1 in range(len(depth_layers)-1):
mask_select = (map_h3_to_mat.index < (h3_index_upper - index_remove_depth[i1])) & (map_h3_to_mat.index >= (h3_index_upper - index_remove_depth[i1+1]))
h3_orig = map_h3_to_mat[mask_select].index + index_remove_depth[i1]
areas = np.array([dict_coastlines[h3_orig[i1]]['area'] for i1 in range(len(h3_orig))]) *1e6 #m2
if i1 == len(depth_layers)-2: #in the lowest layer, calculate difference between local bathymetry and upper depth level (otherwise this results in np.inf)
bath_ = df_bath[h3_orig].values
height = bath_ - depth_layers[i1]
else:
height = depth_layers[i1+1] - depth_layers[i1]
# dict_h3_area[h3_orig] = areas
# dict_h3_volume[h3_orig] = areas*height
dict_h3_normalize[map_h3_to_mat[mask_select].index] = areas*height
#coastlines
mask_select = map_h3_to_mat.index > h3_index_upper
h3_orig = map_h3_to_mat[mask_select].index - np.uint64(1e17)
lengths = np.array([dict_coastlines[h3_orig[i1]]['length_coast_D1.27'] for i1 in range(len(h3_orig))]) *1e3
dict_h3_normalize[map_h3_to_mat[mask_select].index] = lengths
return dict_h3_normalize
def h3_cell_location():
h3_index_upper = h3_index_base + np.uint64(1e17)
df_h3_location = pd.DataFrame(index=map_h3_to_mat.index,columns=('lon','lat'),dtype=np.float32)
for i1 in range(len(depth_layers)-1):
mask_select = (map_h3_to_mat.index < (h3_index_upper - index_remove_depth[i1])) & (map_h3_to_mat.index >= (h3_index_upper - index_remove_depth[i1+1]))
h3_orig = map_h3_to_mat[mask_select].index + index_remove_depth[i1]
lons_ = []
lats_ = []
for i2 in range(len(h3_orig)):
(lat,lon) = h3_to_geo(hex(h3_orig[i2]))
lats_.append(lat)
lons_.append(lon)
df_h3_location.loc[map_h3_to_mat[mask_select].index,'lon'] = np.array(lons_)
df_h3_location.loc[map_h3_to_mat[mask_select].index,'lat'] = np.array(lats_)
#coastlines
mask_select = map_h3_to_mat.index > h3_index_upper
h3_orig = map_h3_to_mat[mask_select].index - np.uint64(1e17)
lons_ = []
lats_ = []
for i2 in range(len(h3_orig)):
(lat,lon) = h3_to_geo(hex(h3_orig[i2]))
lats_.append(lat)
lons_.append(lon)
df_h3_location.loc[map_h3_to_mat[mask_select].index,'lon'] = np.array(lons_)
df_h3_location.loc[map_h3_to_mat[mask_select].index,'lat'] = np.array(lats_)
return df_h3_location
# def plot_dist(ax,vmax=1.,vmin=1e-16,cmap=plt.cm.tab10):
# for i1,index_ in enumerate(mat_index_plot_dist.values):
# dist = tracer_matrix_mass[index_,:]
# # n_points = (dist > 0).sum()
# dist_valid = dist[dist>0]
# # k_valid = k_arr[dist>0]
# # l_arr = l0 / (2**k_arr)
# l_valid = l_arr[dist>0]
# if vmax==None:
# vmax = dist.max()*2
# if vmin==None:
# vmin = max(1e-12,dist.min())
# for l_, n_ in zip(l_valid,dist_valid):
# ax.loglog(l_,n_,'o',color=cmap(i1) )
# ax.loglog([l_,l_],[vmin,n_],'-',color=cmap(i1) )
# ax.set_xlim(0.8*l_arr.min(),1.2*l_arr.max())
# ax.set_ylim(vmin,vmax)
def create_release_vector(type_='r',mu_log10_release=-.7,sigma_log10_release=.15):
weights = Gaussian(np.log10(l_arr),mu_log10_release,sigma_log10_release)
weights = weights / weights.sum()
if type_ == 'r':
file_release = os.path.join(data_folder,'00_data_files/input_h3_rivers.nc')
data_release = xr.open_dataset(file_release)
i_valid_h3 = np.in1d(data_release['h3_index'].values, map_mat_to_h3.values) #release locations present in transition matrix
h3_release = data_release['h3_index'].values[i_valid_h3]
tracer_release = (data_release['midpoint'].values[0,i_valid_h3] / 12) * 1e6 #tonnes/year to grams/month
release_per_month = {}
for i1 in range(12):
release_per_month[i1] = np.zeros([n_total,len(l_arr)])
for i2 in range(len(l_arr)):
np.add.at(release_per_month[i1][:,i2], map_h3_to_mat[h3_release].values, tracer_release*weights[i2])
elif type_ == 'f':
file_release = os.path.join(data_folder,'00_data_files/input_h3_fisheries_monthly.nc')
data_release = xr.open_dataset(file_release)
i_valid_h3 = np.in1d(data_release['h3_index'].values, map_mat_to_h3.values) #release locations present in transition matrix
h3_release = data_release['h3_index'].values[i_valid_h3]
release_per_month = {}
total_release = 0
# release_per_month = np.zeros([12,n_total])
for i1 in range(12):
release_per_month[i1] = np.zeros([n_total,len(l_arr)])
tracer_release = data_release['fishing_hours'].values[i1,i_valid_h3]
for i2 in range(len(l_arr)):
np.add.at(release_per_month[i1][:,i2], map_h3_to_mat[h3_release].values, tracer_release*weights[i2])
total_release += release_per_month[i1].sum()
# np.add.at(release_per_month[i1,:], map_h3_to_mat[h3_release].values, tracer_release)
for i1 in range(12):
#normalize to have a mean monthly input of 1
release_per_month[i1] /= (total_release / 12)
elif type_ == 'p':
file_release = os.path.join(data_folder,'00_data_files/input_h3_coastal_pop.nc')
data_release = xr.open_dataset(file_release)
i_valid_h3 = np.in1d(data_release['h3_index'].values, map_mat_to_h3.values) #release locations present in transition matrix
h3_release = data_release['h3_index'].values[i_valid_h3]
tracer_release = data_release['MPW_input'].values[3,i_valid_h3] #choose 2015?
release_per_month = {}
total_release = 0
for i1 in range(12):
release_per_month[i1] = np.zeros([n_total,len(l_arr)])
for i2 in range(len(l_arr)):
np.add.at(release_per_month[i1][:,i2], map_h3_to_mat[h3_release].values, tracer_release*weights[i2])
total_release += release_per_month[i1].sum()
for i1 in range(12):
#normalize to have a mean monthly input of 1
release_per_month[i1] /= (total_release / 12)
return release_per_month
def calculate_rise_velocity_Dietrich(l,rho_p=1010,rho_sw=1025):
w_b_arr = []
# d_star_arr = []
for r_tot in l:
# rho_sw = 1029
g = 9.81
sw_kin_visc = 1e-6
dn = r_tot # equivalent spherical diameter [m], calculated from Dietrich (1982) from A = pi/4 * dn**2
delta_rho = (rho_p - rho_sw) / rho_sw # normalised difference in density between total plastic+bf and seawater[-]
dstar = (abs(rho_p - rho_sw) * g * dn ** 3.) / (rho_sw * sw_kin_visc ** 2.) # [-]
if dstar > 5e9:
w_star = 265000
elif dstar < 0.05:
w_star = (dstar ** 2.) * 1.71E-4
else:
w_star = 10. ** (-3.76715 + (1.92944 * np.log10(dstar)) - (0.09815 * np.log10(dstar) ** 2.) - (
0.00575 * np.log10(dstar) ** 3.) + (0.00056 * np.log10(dstar) ** 4.))
if delta_rho > 0: # sinks
w_b = -(g * sw_kin_visc * w_star * delta_rho) ** (1. / 3.)
else: # rises
a_del_rho = delta_rho * -1.
w_b = (g * sw_kin_visc * w_star * a_del_rho) ** (1. / 3.) # m s-1
w_b_arr.append(w_b)
# d_star_arr.append(dstar)
return np.array(w_b_arr)
def print_time(t1,str_,div_by=1.):
units = ''
if div_by == 1:
units = 'seconds'
elif div_by == 60:
units = 'minutes'
print('time %s: %f %s' % (str_,((time()-t1)/div_by),units))
return time()
#%%
gc.enable()
# def plot_h3_cells(h3_cells):
# pcolorhex(ax,hexagons,colors
def get_available_data(filename,map_h3_to_mat,plot_missing=False,index_add=0):
data = pd.read_csv(filename,index_col=0,parse_dates=['eventDate'])
date_cols = [col for col in data.columns if 'Date' in col]
for col in date_cols:
data[col] = pd.to_datetime(data[col])
mask_present_in_mat = ( np.isin(data['h%i'%h3_grid_res]+index_add,map_h3_to_mat.index) & ~(data['h%i'%h3_grid_res] == 0) )
data_return = data.loc[mask_present_in_mat,:].copy()
print('%f percent of data is located, n = %i' % ((mask_present_in_mat.sum()/len(mask_present_in_mat))*100,mask_present_in_mat.sum()) )
if plot_missing:
from cartopy.feature import LAND
fig = plt.figure(figsize=(13, 6))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.add_feature(LAND, zorder=0,edgecolor='black')
h3_plot = np.unique(data.loc[~mask_present_in_mat,'h%i'%h3_grid_res])
colors = [plt.cm.tab10(1)]*len(h3_plot)
pcolorhex(ax,h3_plot,colors,alpha=.7)
ax.plot(data.loc[~mask_present_in_mat,'decimalLongitude'],data.loc[~mask_present_in_mat,'decimalLatitude'],'ko')
if 'l_min' in data_return.keys():
data_return.loc[np.isnan(data_return.loc[:,'l_min']),'l_min'] = 0
if 'l_max' in data_return.keys():
data_return.loc[np.isnan(data_return.loc[:,'l_max']),'l_max'] = np.inf
return data_return, mask_present_in_mat
if 'mbp13' in socket.gethostname(): # desktop
home_folder = '.'
data_folder = '.'
n_sizes = 2
elif 'node0' in socket.gethostname() or 'lorenz' in socket.gethostname(): #Lorenz
home_folder = '/nethome/kaand004/Git_repositories/Global_Analysis_Mikael'
data_folder = '/nethome/kaand004/Git_repositories/Global_Analysis_Mikael'
n_sizes = 15
elif 'science-bs' in socket.gethostname(): #gemini
home_folder = '/nethome/kaand004/Git_repositories/Global_Analysis_Mikael'
data_folder = '/scratch/kaand004'
n_sizes = 15
folder_figure = os.path.join(home_folder,'00_figures/17_inversion_01/')
if not os.path.exists(folder_figure):
os.mkdir(folder_figure)
#3: ~40,000 cells, 60km
#2: ~6,000 cells, 160km
h3_grid_res = 3
coastline_res = '50m'
use_Hinata = True
use_Stokes = False
depth_layers = np.array([0,5,50,500,np.inf])
particles_l = [0.0001,0.0004,0.0016,0.0064,0.0256,0.1024]
months = [1,2,3,4,5,6,7,8,9,10,11]
years = [2019]
year_start = 1980
dt_days = 30
l0 = 1.6384
# l0 = 0.2048
release = 'mix' #simple, 'r','f','p', or 'mix'
mass_count_ratio = 500. #grams/unit, 0.4096 meter objects
k_arr = np.arange(0,n_sizes) #11: 0.4mm to 0.4m
l_arr = l0 / (2**k_arr)
str_info = 'Year_' + '-'.join('%2.2i'%s_ for s_ in years) + '_Stokes%s_'%use_Stokes
#-----------------parameters------------------------
h3_index_base = np.uint64(5e17)
index_add_coast = np.uint64(1e17) #indices for coastal cells are the ocean surface cells + index_add_coast
index_remove_depth = np.array([int(0),int(1e17),int(2e17),int(3e17),int(4e17)])
# the dict in which all coastline data is stored
dict_coastlines = from_pickle(os.path.join(data_folder,'00_data_files/coastal_properties_h3_res%i_%s_Dc.pickle' % (h3_grid_res,coastline_res)))
h3_index_coastline_data = np.array([hex_ for hex_ in dict_coastlines.keys() if 'length_coast' in dict_coastlines[hex_].keys() ],dtype=np.uint64)
#TODO: monthly oceanic transitions below
filename_dict_transition = os.path.join(data_folder,'00_transition_files/transitions_h%i_%s_monthly_' % (h3_grid_res,coastline_res) + 'year_' + '-'.join('%2.2i'%s_ for s_ in years) + '_merge_months_False.pickle')
P = from_pickle(filename_dict_transition)
map_mat_to_h3 = P['map_mat_to_h3']
map_h3_to_mat = P['map_h3_to_mat']
# a map from the matrix entry to removal (-1), surface (0), and deeper layers (1,2,3,4) to make computations easier
# TODO: can be made variable for different depth layers
# map_mat_to_domain = pd.Series(index=map_mat_to_h3.index,dtype=int)
# map_mat_to_domain[map_mat_to_h3.values < 1e17] = -1
# map_mat_to_domain[map_mat_to_h3.values > 6e17] = 0
# map_mat_to_domain[(map_mat_to_h3.values < 6e17) & (map_mat_to_h3.values > 5e17)] = 1
# map_mat_to_domain[(map_mat_to_h3.values < 5e17) & (map_mat_to_h3.values > 4e17)] = 2
# map_mat_to_domain[(map_mat_to_h3.values < 4e17) & (map_mat_to_h3.values > 3e17)] = 3
# map_mat_to_domain[(map_mat_to_h3.values < 3e17) & (map_mat_to_h3.values > 2e17)] = 4
# not all coastline sections are present in the transition matrix: take the intersection
h3_index_coast = np.intersect1d(h3_index_coastline_data, map_mat_to_h3)
length_coast = np.array([dict_coastlines[hex_]['length_coast_D1.27'] for hex_ in h3_index_coast])
map_h3_to_coast_length = pd.Series(index=h3_index_coast, data=length_coast )
dict_variance = {'n_surf':0.28,'m_surf':0.40,'n_beach':0.2,'m_beach':0.14,'fish_frac':0.008,
'MDMAP':0.21,'OSPAR_100m':0.29,'OSPAR_1km':0.32,
'surf_nm-3_Egger':0.08,'surf_gm-3_Egger':0.09,
'depth_nm-3_Egger':0.04,'depth_gm-3_Egger':0.04,}
data_trawl_m, _ = get_available_data(os.path.join(data_folder,'00_data_files/plastic_data/converted_surface_m.csv'),map_h3_to_mat)
data_trawl_n, _ = get_available_data(os.path.join(data_folder,'00_data_files/plastic_data/converted_surface_n.csv'),map_h3_to_mat)
data_macro_n, _ = get_available_data(os.path.join(data_folder,'00_data_files/plastic_data/converted_surface_macro.csv'),map_h3_to_mat)
data_beach_inst, _ = get_available_data(os.path.join(data_folder,'00_data_files/plastic_data/converted_beach_inst.csv'),map_h3_to_mat,index_add=index_add_coast)
data_beach_OSPAR, _ = get_available_data(os.path.join(data_folder,'00_data_files/plastic_data/converted_agg_MDMAP_OSPAR.csv'),map_h3_to_mat,index_add=index_add_coast)
data_beach_mean, _ = get_available_data(os.path.join(data_folder,'00_data_files/plastic_data/converted_beach_mean.csv'),map_h3_to_mat,index_add=index_add_coast)
data_beach_mean['modelled_n'] = 0
data_beach_mean['modelled_sum'] = 0
data_beach_mean['modelled_sum2'] = 0
# mask_present_in_mat = np.isin(data_vanSebille2015['h%i'%h3_grid_res],map_h3_to_mat.index)
# data_vanSebille2015 = data_vanSebille2015.loc[mask_present_in_mat,:]
# data_vanSebille2015 = pd.read_csv(os.path.join(data_folder,'00_data_files/plastic_data/converted_vanSebille2015.csv'),index_col=0)
# # remove measurements for which the h3 cell is not present in transition matrix
# # TODO: find closest existing cell?
# mask_present_in_mat = np.isin(data_vanSebille2015['h%i'%h3_grid_res],map_h3_to_mat.index)
# data_vanSebille2015 = data_vanSebille2015.loc[mask_present_in_mat,:]
# data_AtlantECO = pd.read_csv(os.path.join(data_folder,'00_data_files/plastic_data/converted_AtlantECO.csv'),index_col=0)
# mask_present_in_mat = np.isin(data_AtlantECO['h%i'%h3_grid_res],map_h3_to_mat.index)
# data_AtlantECO = data_AtlantECO.loc[mask_present_in_mat,:]
# data_BBCT = pd.read_csv(os.path.join(data_folder,'00_data_files/plastic_data/converted_beach_BBCT.csv'),index_col=0)
# mask_present_in_mat = np.isin(data_BBCT['h%i'%h3_grid_res]+index_add_coast,map_h3_to_mat.index)
# data_BBCT = data_BBCT.loc[mask_present_in_mat,:]
# data_OSPAR = pd.read_csv(os.path.join(data_folder,'00_data_files/plastic_data/converted_beach_ospar.csv'),index_col=0,parse_dates=['eventDate','eventDate_end'])
# mask_present_in_mat = np.isin(data_OSPAR['h%i'%h3_grid_res]+index_add_coast,map_h3_to_mat.index)
# data_OSPAR = data_OSPAR.loc[mask_present_in_mat,:]
# data_OSPAR['modelled_n'] = 0
# data_OSPAR['modelled_sum'] = 0
# data_OSPAR['modelled_sum2'] = 0
data_Egger_surf = pd.read_csv(os.path.join(data_folder,'00_data_files/plastic_data/converted_Egger2020_ERL_surface.csv'),index_col=0)
data_Egger_surf = pd.concat([data_Egger_surf,
pd.read_csv(os.path.join(data_folder,'00_data_files/plastic_data/converted_Egger2020_SR_surface.csv'),index_col=0)])
mask_present_in_mat = np.isin(data_Egger_surf['h%i'%h3_grid_res],map_h3_to_mat.index)
data_Egger_surf = data_Egger_surf.loc[mask_present_in_mat,:]
data_Egger_depth = pd.read_csv(os.path.join(data_folder,'00_data_files/plastic_data/converted_Egger2020SR_depth_frag.csv'),index_col=0)
data_Egger_depth['h%i'%h3_grid_res] = data_Egger_depth['h%i'%h3_grid_res] - index_remove_depth[np.digitize(data_Egger_depth['Depth'],depth_layers)-1]
mask_present_in_mat = np.isin(data_Egger_depth['h%i'%h3_grid_res],map_h3_to_mat.index)
data_Egger_depth = data_Egger_depth.loc[mask_present_in_mat,:]
data_Zhao_depth = pd.read_csv(os.path.join(data_folder,'00_data_files/plastic_data/converted_Zhao2022_depth.csv'),index_col=0)
data_Zhao_depth['h%i'%h3_grid_res] = data_Zhao_depth['h%i'%h3_grid_res] - index_remove_depth[np.digitize(data_Zhao_depth['Depth'],depth_layers)-1]
mask_present_in_mat = np.isin(data_Zhao_depth['h%i'%h3_grid_res],map_h3_to_mat.index)
data_Zhao_depth = data_Zhao_depth.loc[mask_present_in_mat,:]
df_bath = from_pickle(os.path.join(data_folder,'00_data_files/bathymetry_h%i.pickle' % h3_grid_res))
df_pp = from_pickle(os.path.join(data_folder,'00_data_files/mean_pp_h%i_2019.pickle' % h3_grid_res)).astype(float)
df_pp = df_pp / np.quantile(df_pp.values[~np.isnan(np.array(df_pp.values,dtype=float))],.99) #normalize for outliers
df_pp[df_pp>1] = 1.
df_pp[df_pp<0] = 0.
dict_h3_normalize = h3_cell_volume()
dict_h3_location = h3_cell_location()
n_total = len(map_h3_to_mat)
lon_plot_dist = np.array([-140.,-112.,-140.,-100.])
lat_plot_dist = np.array([20.,23.,50.,15.])
depth_plot_dist = np.array([0,0,0,0])
mask_neuston_net = (l_arr > 3e-4) & (l_arr < 0.2)
mask_beach_cleanup = l_arr > 2.5e-2
# mask_Egger = [(l_arr>=4e-4)&(l_arr<1.5e-3),(l_arr>1.5e-3)&(l_arr<5.0e-3),
# (l_arr>5e-3)&(l_arr<1.5e-2),(l_arr>1.5e-2)&(l_arr<5.2e-2)]
mask_Egger = [(l_arr>=5.6e-4)&(l_arr<1.13e-3),(l_arr>1.13e-3)&(l_arr<4.5e-3),
(l_arr>4.5e-3)&(l_arr<18e-3),(l_arr>18e-3)&(l_arr<36e-3)]
mask_Egger_sizes = [[5e-4,1.5e-3],[1.5e-3,5e-3],[5e-3,1.5e-2],[1.5e-2,5e-2]]
Egger_corr_n = [1.5029822975240785,
0.6564421139154354,
0.7997358204039169,
1.7005002991087623]
Egger_corr_m = [1.6803211760254935,
0.9670585583407109,
0.7636187765408871,
1.8350271441960349]
mask_Zhao = (l_arr >= 1e-4) & (l_arr < 5e-3)
parameter_names = [r'$S_{riv}$',r'$S_{pop}$',r'$S_{fis}$',
r'$\tau_{beach}$',r'$l_{coast,beach,max}$',r'$a_{resus.}$',r'$b_{resus.}$',
r'$f_{fouled}$',r'$f_{nb}$',r'$f_{pf}$',
r'$p_{land,remove}$',
r'$\lambda_{f}$',r'$p_f$',r'$d_N$',
r'$a_{L_{eff}}$',r'$b_{L_{eff}}$',
r'$\mu_{release,log10}$','PW_factor']
log_parameters = np.array([True,True,True,
True,False,False,False,
False,False,True,
True,
True,False,False,
False,False,
False,False])
# mu_parameters = np.array([-1.0,0.7,0.0,
# np.log10(100),125,260,7.1,
# .2,.2,.2,
# np.log10(0.01),
# -3.0,0.4,2.18,
# .64,-1.,
# -.4,0.05])
# sigma_parameters = np.array([0.16,0.08,0.22,
# .25,30,1.,0.1,
# .1,.1,.1,
# .3,
# 0.16,.001,.1,
# 0.06,0.15,
# .13,0.01]) #sigma is given for the log-transformed parameters
# mu_parameters = np.array([-9.89405350e-01, 6.98970004e-01, 3.57155523e-02,
# 2.00000000e+00, 4.70465152e+02, 2.60000000e+02, 7.10000000e+00,
# 2.50000000e-01, 2.50000000e-01, -8.92197672e-01,
# -2.69929574e+00,
# -2.66549661e+00, 4.00000000e-01, 2.18718327e+00,
# 6.37325580e-01, -9.48615760e-01,
# -3.92022000e-01, 5.00000000e-02])
# sigma_parameters = np.array([4.10814466e-01, 7.04270806e-02, 1.77824465e-01,
# 2.00686664e-01, 1.24409219e+02, 2.60000000e+00, 7.10000000e-02,
# 8.10000000e-02, 8.10000000e-02, 2.93321072e-01,
# 3.33333333e-01,
# 1.43804468e-01, 1.00000000e-03, 4.60050728e-02,
# 8.74484991e-02, 2.26095201e-01,
# 1.50947667e-01, 1.66666667e-02])
#2 sigma
mu_parameters = np.array([-9.89405350e-01, 6.98970004e-01, 3.57155523e-02,
2.00000000e+00, 4.70465152e+02, 2.60000000e+02, 7.10000000e+00,
2.50000000e-01, 2.50000000e-01, -8.92197672e-01,
-2.69929574e+00,
-2.66549661e+00, 4.20000000e-01, 2.18718327e+00,
6.37325580e-01, -9.48615760e-01,
-3.92022000e-01, 2.50000000e-02])
sigma_parameters = np.array([4.10814466e-01, 7.04270806e-02, 1.77824465e-01, 2.00686664e-01,
1.24409219e+02, 2.60000000e+00, 7.10000000e-02, 8.10000000e-02,
8.10000000e-02, 2.93321072e-01, 3.33333333e-01, 1.43804468e-01,
1.00000000e-02, 4.60050728e-02, 8.74484991e-02, 2.26095201e-01,
1.50947667e-01, 8.33333333e-03])
cap_0_1 = np.array([False,False,False,
False,False,False,False,
True,True,True,
True,
False,False,False,
False,False,
False,False])
def forward_model(parameters,final_run=False):
def draw_map(vec_,extent,gs,col_,base_title='k0, %3.3f-%3.3f m depth',stop_earlier=0,log_scale=True):
h3_index_upper = h3_index_base + np.uint64(1e17)
for i1 in range(len(depth_layers)-1-stop_earlier):
ax_ = plt.subplot(gs[i1,col_], projection=ccrs.PlateCarree())
mask_select = (map_h3_to_mat.index < (h3_index_upper - index_remove_depth[i1])) & (map_h3_to_mat.index >= (h3_index_upper - index_remove_depth[i1+1]))
h3_orig = map_h3_to_mat[mask_select].index + index_remove_depth[i1]
vec_select = vec_[mask_select]
if not log_scale:
colors = get_colors(vec_select,cmap,
vmin=vmin,vmax=vmax)
h3_plot = h3_orig
else:
colors = get_colors(np.log10(vec_select[vec_select>0]),cmap,
vmin=np.log10(vmin),vmax=np.log10(vmax))
h3_plot = h3_orig[vec_select > 0]
mask = (dict_h3_location.loc[h3_plot,'lon']>extent[0]) & (dict_h3_location.loc[h3_plot,'lon']<extent[1]) & (dict_h3_location.loc[h3_plot,'lat']>extent[2]) & (dict_h3_location.loc[h3_plot,'lat']<extent[3])
h3_plot = h3_plot[mask]
colors = colors[mask,:]
pcolorhex(ax_,h3_plot,colors,draw_edges=False,alpha=1.)
ax_.add_feature(feature.NaturalEarthFeature('physical','land',coastline_res),facecolor='grey',zorder=1000)
if i1 == 0:
mask_select = map_h3_to_mat.index > h3_index_upper # coastlines
h3_orig = map_h3_to_mat[mask_select].index - np.uint64(1e17)
vec_select = vec_[mask_select]
if not log_scale:
colors = get_colors(vec_select,cmap2,
vmin=vmin2,vmax=vmax2)
h3_plot = h3_orig
else:
colors = get_colors(np.log10(vec_select[vec_select>0]),cmap2,
vmin=np.log10(vmin2),vmax=np.log10(vmax2))
h3_plot = h3_orig[vec_select > 0]
mask = (dict_h3_location.loc[h3_plot,'lon']>extent[0]) & (dict_h3_location.loc[h3_plot,'lon']<extent[1]) & (dict_h3_location.loc[h3_plot,'lat']>extent[2]) & (dict_h3_location.loc[h3_plot,'lat']<extent[3])
h3_plot = h3_plot[mask]
colors = colors[mask,:]
# colors = get_colors(np.log10(vec_select[vec_select>0]),cmap2,
# vmin=np.log10(vmin2),vmax=np.log10(vmax2))
# h3_plot = h3_orig[vec_select > 0]
if vec_select.sum() > 0:
draw_coastlines(ax_,h3_plot,dict_coastlines,transform=ccrs.PlateCarree(),linewidth=7)
draw_coastlines(ax_,h3_plot,dict_coastlines,colors=colors,transform=ccrs.PlateCarree(),linewidth=6)
ax_.set_extent((extent[0]+1,extent[1]-1,extent[2]+1,extent[3]-1),ccrs.PlateCarree()) #reduce the margins to hide white cells
ax_.set_title(base_title % (depth_layers[i1],depth_layers[i1+1]))
t1 = time()
def to_scaled(p_unscaled,mu_params_,sigma_params_):
p_scaled = (p_unscaled - mu_params_) / sigma_params_
return p_scaled
def to_unscaled(p_scaled,mu_params_,sigma_params_):
p_unscaled = (p_scaled*sigma_params_) + mu_params_
return p_unscaled
def lin_max(length,l_max,max_val=1.):
if length.size > 1:
p = (1/l_max)*length
p[length > l_max] = max_val
elif length.size == 1:
p = min((1/l_max)*length,max_val)
else:
p = None
return p
#-------------get back the parameters
parameters_unscaled = to_unscaled(parameters,mu_parameters,sigma_parameters)
parameters_unscaled[log_parameters] =10**(parameters_unscaled[log_parameters])
parameters_unscaled[cap_0_1 & (parameters_unscaled < 0)] = 0.01
parameters_unscaled[cap_0_1 & (parameters_unscaled > 1)] = .99
for name_,p_ in zip(parameter_names,parameters_unscaled):
print('%s: %4.4e' % (name_,p_))
(r_riv,r_pop,r_fis,
tau_b_,l_max,a_resus,b_resus,
frac_fouled,frac_nb,frac_pf,
p_land_remove,
frag_if_month,p_f,d_N,
a_L_Leff,b_L_Leff,
mu_log10_release,PW_factor) = parameters_unscaled
sigma_L_eff = .13
pf_mode = 'permanent_fouled' # permanent_fouled_removal500
# unrealisic scenario: per time step more than the available tracer is removed (shouldnt happen)
# if 1-p_ocean_max_remove <= 0:
# print('max removal ocean outside of bounds (%f), setting to .99' % (p_ocean_max_remove))
# p_ocean_max_remove = .99
if 1-p_land_remove <= 0:
print('max removal land outside of bounds (%f), setting to .95' % (p_land_remove))
p_land_remove = .95
if b_resus < 0:
b_resus = 0
if l_max < 0:
l_max = .001
# fractions of fouled and neutrally bouyant particles shouldn't make up more than 100%
if frac_fouled + frac_nb + frac_pf >= 1:
print('adjusting fouled/nb/pf fractions to 98% in total')
sum_ = frac_fouled + frac_nb + frac_pf
frac_fouled = (frac_fouled / sum_) - 0.01
frac_nb = (frac_nb / sum_) - 0.01
frac_pf = (frac_pf / sum_) - 0.01
#t1 = print_time(t1,'init')
# p_ocean_min_remain = (1-p_ocean_max_remove)
p_land_remain = (1-p_land_remove)
release_vector = np.array([r_riv,r_pop,r_fis])
def calc_mass_per_item(l_arr,d_N):
l_calib = 0.0064 #from calculate_priors.py
m_calib = 0.0161
mass_per_item = np.zeros(len(l_arr))
mask_small = l_arr <= l_calib
mask_large = l_arr > l_calib
mass_per_item[mask_small] = ((l_arr[mask_small] / l_calib)**2.187)*m_calib
mass_per_item[mask_large] = ((l_arr[mask_large] / l_calib)**d_N)*m_calib
return mass_per_item
# mass_per_item = ((l_arr / 0.0064)**d_N)*0.02039 #from test_count_mass_ratio.py
mass_per_item = calc_mass_per_item(l_arr,d_N)
tau_b = tau_b_ * np.ones(len(l_arr)) #days
if not use_Hinata:
tau_r = 25 * np.ones(len(l_arr)) #days
else:
w_b = calculate_rise_velocity_Dietrich(l_arr)
tau_r = a_resus*w_b + b_resus
p_frag_m,p_frag_N = NB_model(k_arr,frag_if_month,p_f,d_N=d_N)
p_beach = 1-np.exp(-dt_days/tau_b)
p_resus = 1-np.exp(-dt_days/tau_r)
# sigma_log10_wb = 0.2
if release == 'rivers' or release == 'r':
vec_start = create_release_vector(type_='r')
elif release == 'coastlines' or release == 'p':
vec_start_r = create_release_vector(type_='r')
mass_calibrate = vec_start_r.sum() / 12
vec_start = create_release_vector(type_='p') #1/month
vec_start *= release_vector[1]*mass_calibrate
elif release == 'fisheries' or release == 'f':
vec_start_r = create_release_vector(type_='r')
mass_calibrate = vec_start_r.sum() / 12
vec_start = create_release_vector(type_='f') #1/month
vec_start *= release_vector[2]*mass_calibrate
elif release == 'mix': #mixed scenario
vec_start_r = create_release_vector(type_='r',mu_log10_release=mu_log10_release,sigma_log10_release=.3) #grams/month
vec_start_p = create_release_vector(type_='p',mu_log10_release=mu_log10_release,sigma_log10_release=.3)
vec_start_f = create_release_vector(type_='f',mu_log10_release=mu_log10_release,sigma_log10_release=.3)
mass_calibrate = vec_start_r[0].sum() #grams/month
vec_start = {}
vec_start_fN = {}
vec_start_N = {}
for i2 in range(12):
vec_start_r[i2] *= release_vector[0]
vec_start_p[i2] *= (release_vector[1]*release_vector[0]*mass_calibrate)
vec_start_f[i2] *= (release_vector[2]*release_vector[0]*mass_calibrate)
vec_start[i2] = vec_start_r[i2] + vec_start_p[i2] + vec_start_f[i2] #each column: in grams per month
vec_start_fN[i2] = vec_start_f[i2] / mass_per_item
vec_start_N[i2] = vec_start[i2] / mass_per_item
print('Releasing %3.3f kilotons of plastics from rivers per year' % (np.array([vec_start_r[i].sum() for i in range(12)]).sum()/1e9))
print('Compare to: 1000 kilotons (Meijer), 6 kilotons (Weiss)')
print('Total: %3.3f kilotons per year from rivers, pop, fisheries' % (np.array([vec_start[i].sum() for i in range(12)]).sum()/1e9))
#t1 = print_time(t1,'sources init.')
#-------------------------set-up matrix-------------------
i_r_ll,i_c_ll,_ = sparse.find(P['land_land'])
cols_land = np.unique(i_c_ll)
P_land_removed = coo_matrix( (np.ones(len(cols_land)),(2*np.ones(len(cols_land)),cols_land)),
shape=(n_total,n_total))# * (1-p_land_remain)
i_r_o,i_c_o,_ = sparse.find(P['water_water']+P['coast_water'])
# cols_ocean = np.unique(i_c_o)
i_r_ww,i_c_ww,_ = sparse.find(P['water_water'])
i_r_cw,i_c_cw,_ = sparse.find(P['coast_water'])
i_r_cl,i_c_cl,_ = sparse.find(P['coast_land'])
#t1 = print_time(t1,'mat init')
# -------------------sinking parameterization-------------------------
# pp_water_water = P['pp_water_water']
# pp_coast_water = P['pp_coast_water']
# pp_coast_land = P['pp_coast_land']
# P_ocean_removed = P['P_ocean_removed']
# -------------------beaching parameterization-------------------
p_coast_cl = lin_max(map_h3_to_coast_length[map_mat_to_h3[i_c_cl]],l_max)
p_coast_cw = lin_max(map_h3_to_coast_length[map_mat_to_h3[i_c_cw]],l_max)
P_cascade_m = {}
P_cascade_m[0] = P['cascade_water'] + P['cascade_land']*p_frag_m[0]
for i3 in range(1,len(k_arr)):
P_cascade_m[i3] = P['cascade_land']*p_frag_m[i3]
P_cascade_N = {}
P_cascade_N[0] = P['cascade_water'] + P['cascade_land']*p_frag_N[0]
for i3 in range(1,len(k_arr)):
P_cascade_N[i3] = P['cascade_land']*p_frag_N[i3]
#t1 = print_time(t1,'casc')
P_total_l = {}
for month_ in np.arange(12):
P_ocean_l = {}
P_total_l[month_] = {}
available_lengths = np.sort(np.array(list(P['ocean'][month_]['nonfouled'].keys())))
def l_to_leff(l_,n_pad=5):
# for the rise velocity, take a linear combination for different particle sizes (i.e. w_b's)
# as to try to solve the problem of too little mixing. A Gaussian function is used to weigh the samples
n_pad = 5
assert(available_lengths[1]/available_lengths[0] == 4.)
left_ = np.sort(np.array([available_lengths[0] / (4**n) for n in range(1,n_pad+1)]))
right_ = np.sort(np.array([available_lengths[-1] * (4**n) for n in range(1,n_pad+1)]))
l_arr_padded = np.append(left_,np.append(available_lengths,right_))
mu_L_eff = a_L_Leff*np.log10(l_) + b_L_Leff
weights_Leff_padded = Gaussian(np.log10(l_arr_padded),mu_L_eff,sigma_L_eff)
weights_Leff = weights_Leff_padded[n_pad:-n_pad].copy()
weights_Leff[0] += weights_Leff_padded[:n_pad].sum()
weights_Leff[-1] += weights_Leff_padded[-n_pad:].sum()
weights_Leff /= weights_Leff.sum()
l_use = available_lengths[weights_Leff > 0]
weight_use = weights_Leff[weights_Leff > 0]
if not len(weight_use)>0:
#exception where the the spread gets too small -> no weights
l_use = np.array([l_])
weight_use = np.array([1])
return l_use, weight_use
P_ocean_nb = P['ocean'][month_]['neutral'][1e-5] #the 1e-5 is the arbitrary small size denotation that was used in this case
for i1,l_ in enumerate(l_arr):
l_use,weight_use = l_to_leff(l_)
#------------------------------------------------------------------------
# non-fouled bouyant particles: create a combination of rise velocities
P_ocean_nonfouled = P['ocean'][month_]['nonfouled'][l_use[0]]*weight_use[0]
if len(weight_use) > 1:
for i2 in range(1,len(weight_use)):
P_ocean_nonfouled += P['ocean'][month_]['nonfouled'][l_use[i2]]*weight_use[i2]
#t1 = print_time(t1,'non-fouled')
#------------------------------------------------------------------------
# fouled bouyant particles: take the closest fouled case for transport
i_right = np.searchsorted(available_lengths,l_)
if i_right == 0:
i_right = 1
if i_right == len(available_lengths): #above max avail. length: assume same behavior for macroplastics
l_max = available_lengths.max()
P_ocean_pf = P['ocean'][month_][pf_mode][l_max]
P_ocean_fouled = P['ocean'][month_]['fouled'][l_max]
else:
l_left = available_lengths[i_right-1]
l_right = available_lengths[i_right]
w_2 = (np.log10(l_) - np.log10(l_left)) / (np.log10(l_right) - np.log10(l_left)) #0 at 0.0004, 1 at 0.102
assert(0<=w_2<=1)
P_ocean_fouled = P['ocean'][month_]['fouled'][l_left]*(1-w_2) + P['ocean'][month_]['fouled'][l_right]*(w_2)
P_ocean_pf = P['ocean'][month_][pf_mode][l_left]*(1-w_2) + P['ocean'][month_][pf_mode][l_right]*(w_2)
#t1 = print_time(t1,'fouled')
#------------------------------------------------------------------------
# A linear combination is made of fouled (oscill.), non-fouled, neutrally bouyant, and permanently fouled particles
P_ocean_l[l_] = P_ocean_nonfouled*(1-frac_fouled-frac_nb-frac_pf) + P_ocean_fouled*frac_fouled + P_ocean_nb*frac_nb + P_ocean_pf*frac_pf
#t1 = print_time(t1,'combin.')
#------------------------------------------------------------------------
# Surface transport can be adjusted, using bi-linear interpolation with wind and Stokes
# frac_Stokes = lin_max(l_,l_max_Stokes,max_val=.99)
# frac_wind = lin_max(l_,l_max_wind,max_val=.99)
# # frac_Stokes = .99
# # frac_wind = .99
# P_ocean_l[l_] = (P_ocean_l[l_]*(1-frac_Stokes) + P['ocean'][month_]['2D_Stokes'][1.0]*(frac_Stokes) )*(1 - frac_wind) + \
# (P['ocean'][month_]['2D_wind'][1.0]*(1-frac_Stokes) + P['ocean'][month_]['2D_Stokes_wind'][1.0]*(frac_Stokes) )*(frac_wind)
# # needs to be normalized again to retain mass balance in the deeper water layer matrix columns
# P_ocean_l[l_] = normalize(P_ocean_l[l_], norm='l1', axis=0)
#t1 = print_time(t1,'surf+norm')
p_beach_per_cell_cl = p_beach[i1]*p_coast_cl
p_beach_per_cell_cw = 1-(p_beach[i1]*p_coast_cw)
P_coast_land_beach = coo_matrix( (p_beach_per_cell_cl,(i_r_cl,i_c_cl)), shape=(n_total,n_total))
P_coast_water_beach = coo_matrix( (p_beach_per_cell_cw,(i_r_cw,i_c_cw)), shape=(n_total,n_total))
# P_coast_water_beach = P['coast_water']*(1-p_beach[i1])
# P_coast_land_beach = P['coast_land']*p_beach[i1]
P_land_coast_beach = P['land_coast']*p_resus[i1]
P_land_land_beach = P['land_land']*(1-p_resus[i1])
#t1 = print_time(t1,'beach')
#cells on land have permanent sinks
P_land_coast_sinks = P_land_coast_beach.multiply(p_land_remain)
P_land_land_sinks = P_land_land_beach.multiply(p_land_remain)
# sinking due to primary productivity
# A linear map is made from min pp (0 -> 1), to max pp (1 -> p_ocean_min_remain), such that no particles sink when there's no pp, and the prescribed max amount at pp=1
# P_water_water_remain = (p_ocean_min_remain - 1)*pp_water_water[month_] + P['water_water']
# P_coast_water_remain = (p_ocean_min_remain - 1)*pp_coast_water[month_] + P['coast_water']
# P_coast_land_remain = (p_ocean_min_remain - 1)*pp_coast_land[month_] + P['coast_land']
P_water_water_remain = P['water_water']
P_coast_water_remain = P['coast_water']
P_coast_land_remain = P['coast_land']
#t1 = print_time(t1,'pp')
#TO DO: size-dependent bottom hitting can be added here as well for fouled/non-fouled particles
P_water_water = P_water_water_remain
P_coast_water = P_coast_water_remain.multiply(P_coast_water_beach) #probabilities after fouling and beaching
P_coast_land = P_coast_land_remain.multiply(P_coast_land_beach)
# P_total_l[month_][l_] = (P_ocean_l[l_].multiply(P_water_water + P_coast_water)) + P_coast_land + \
# P_land_coast_sinks + P_land_land_sinks + P_land_removed*(1-p_land_remain) + P_ocean_removed[month_]*(1-p_ocean_min_remain) + P['removal']
P_total_l[month_][l_] = (P_ocean_l[l_].multiply(P_water_water + P_coast_water)) + P_coast_land + \
P_land_coast_sinks + P_land_land_sinks + P_land_removed*(1-p_land_remain) + P['removal']
#t1 = print_time(t1,'total')
colsum = np.array(np.sum(P_total_l[month_][l_],axis=0)).ravel()
leakiness = 1 - (colsum.sum()/len(colsum))
if leakiness > 0.0001: #check that mass conservation is not violated too much
print('leakiness bigger than 0.01 percent (m:%i l:%4.4f): %f' % (month_,l_,leakiness))
#-------------------------Iterate-------------------
# df_aggregated = pd.DataFrame(columns=['modelled_n','modelled_sum','modelled_sum2'])
df_results = pd.DataFrame(columns=['Year','Month',
'h3_cell_id','Modelled','Measured',
'Units','var_log10','l_min','l_max',
'ParentEventID','index_parent','Depth','detection_limit'])
extent = (-179,179,-80,80)
plot_map = False
do_plot_dist = False
plot_tracer_amount = False
plot_per = 120#120*2#12*10
i_start = 0
n_rows = 1
n_cols = 1
n_steps = (2020-1980)*12#12*11
vmin = 1e-9
vmax = 1e-3
cmap = plt.cm.viridis
vmin2 = 1e-5 #beaches
vmax2 = 1e1
cmap2 = cmocean.cm.turbid_r
tracer_matrix_mass = np.zeros([n_total,len(k_arr)],dtype=np.float32)
tracer_matrix_num = np.zeros([n_total,len(k_arr)],dtype=np.float32)
tracer_matrix_num_f = np.zeros([n_total,len(k_arr)],dtype=np.float32)
if plot_map:
fig = plt.figure(figsize=(20,9))
gs = gridspec.GridSpec(1, 4, bottom=.05, top=.95, wspace=.25, width_ratios=[30,30,1,1])
ax_1 = plt.subplot(gs[0,0])
ax_3 = plt.subplot(gs[0,2])
cplt1 = ax_1.scatter(np.random.random(1000),np.random.random(1000),c=np.linspace(np.log10(vmin),np.log10(vmax),1000),cmap=cmap)
cbar1 = plt.colorbar(cplt1,cax=ax_3)
cbar1.set_label('Tracer concentration [log$_{10}$(g m$^{-3}$)]')
ax_2 = plt.subplot(gs[0,1])
ax_4 = plt.subplot(gs[0,3])
cplt2 = ax_2.scatter(np.random.random(1000),np.random.random(1000),c=np.linspace(np.log10(vmin2),np.log10(vmax2),1000),cmap=cmap2)
cbar2 = plt.colorbar(cplt2,cax=ax_4)