diff --git a/analysis/Map_origin_particles_surface.ipynb b/analysis/Map_origin_particles_surface.ipynb
index 8e9d5fd..3c3bedb 100644
--- a/analysis/Map_origin_particles_surface.ipynb
+++ b/analysis/Map_origin_particles_surface.ipynb
@@ -68,7 +68,7 @@
"metadata": {},
"outputs": [],
"source": [
- "mask = xr.open_dataset('/storage/shared/oceanparcels/input_data/MOi/domain_ORCA0083-N006/PSY4V3R1_mask.nc')\n",
+ "mask = xr.open_dataset('../data/PSY4V3R1_mask.nc')\n",
"indices = {'lat': range(400, 1600), 'lon': range(2000, 4300)}\n",
"landmask = mask['tmask'][0, 0, indices['lat'], indices['lon']]\n",
"xlon = mask['nav_lon'][indices['lat'], indices['lon']].values\n",
diff --git a/analysis/size_distribution.py b/analysis/size_distribution.py
index fe078ba..b74ae6f 100644
--- a/analysis/size_distribution.py
+++ b/analysis/size_distribution.py
@@ -34,7 +34,7 @@
for ft in tqdm(simulations):
# print('Computing fragmentation timescale: ', ft)
- local_path = f'/storage/shared/oceanparcels/output_data/data_Claudio/hc13_3/hc13_{ft}.zarr'
+ local_path = f'../data/simulations/hc13_{ft}.zarr'
sim = xr.open_zarr(local_path)
sim = sim.where(sim.time >= np.datetime64('2007-01-01'), drop=True) # analysis stops at 2007-01-01
diff --git a/analysis/supplementary_material_plots.ipynb b/analysis/supplementary_material_plots.ipynb
index b8ff2be..c9a7a93 100644
--- a/analysis/supplementary_material_plots.ipynb
+++ b/analysis/supplementary_material_plots.ipynb
@@ -48,7 +48,7 @@
"\n",
"depth_bins = np.linspace(-5500, 0, 56) # creates a 100m bins\n",
"\n",
- "simulations = ['_k2', '', '_k4']\n",
+ "simulations = ['_k2', '_k3', '_k4']\n",
"sim_names = ['k2', 'k3', 'k4']"
]
},
@@ -113,7 +113,7 @@
"\n",
"for k, ft in enumerate(tqdm(simulations)):\n",
" # print('Computing fragmentation timescale: ', ft)\n",
- " local_path = f'/storage/shared/oceanparcels/output_data/data_Claudio/hc13_2/hc13_1000{ft}.zarr'\n",
+ " local_path = f'../data/simulations/hc13_1000{ft}.zarr'\n",
" sim = xr.open_zarr(local_path)\n",
" nano = sim.where(sim.radius < 1e-6/2, drop=False)\n",
" sim_dict = {}\n",
@@ -196,7 +196,7 @@
"\n",
"for k, ft in enumerate(tqdm(simulations)):\n",
" # print('Computing fragmentation timescale: ', ft)\n",
- " local_path = f'/storage/shared/oceanparcels/output_data/data_Claudio/hc13_2/hc13_1000{ft}.zarr'\n",
+ " local_path = f'../data/simulations/hc13_1000{ft}.zarr'\n",
" sim = xr.open_zarr(local_path)\n",
"\n",
" # loading fields as np arrays to do some nasty indexing\n",
diff --git a/analysis/surface_and_near_shore_particles_stats.ipynb b/analysis/surface_and_near_shore_particles_stats.ipynb
index 47ee03e..040a3cd 100644
--- a/analysis/surface_and_near_shore_particles_stats.ipynb
+++ b/analysis/surface_and_near_shore_particles_stats.ipynb
@@ -85,7 +85,7 @@
"\n",
"for k, ft in enumerate(tqdm(simulations)):\n",
" # print('Computing fragmentation timescale: ', ft)\n",
- " local_path = f'/storage/shared/oceanparcels/output_data/data_Claudio/hc13_3/hc13_{ft}.zarr'\n",
+ " local_path = f'../data/simulations/hc13_{ft}.zarr'\n",
" sim = xr.open_zarr(local_path)\n",
"\n",
" # loading fields as np arrays to do some nasty indexing\n",
diff --git a/analysis/vertical_Kz_profiles.py b/analysis/vertical_Kz_profiles.py
deleted file mode 100644
index ed9bb0f..0000000
--- a/analysis/vertical_Kz_profiles.py
+++ /dev/null
@@ -1,79 +0,0 @@
-import numpy as np
-import xarray as xr
-import matplotlib.pyplot as plt
-from glob import glob
-from datetime import datetime
-from tqdm import tqdm
-
-location = 'HC11'
-if location == 'HC13':
- start_time = datetime.strptime('2019-01-20 12:00:00', '%Y-%m-%d %H:%M:%S')
- lat_obs = -32.171
- lon_obs = 6.287
-
-elif location == 'HC11':
- start_time = datetime.strptime('2019-01-16 12:00:00', '%Y-%m-%d %H:%M:%S')
- lon_obs = -3.822
- lat_obs = -29.992
-
-path_flow = '/storage/shared/oceanparcels/input_data/MOi/psy4v3r1/'
-files = sorted(glob(path_flow + 'psy4v3r1-daily_KZ_*.nc'))
-
-end_time = datetime.strptime('2006-10-11 12:00:00', '%Y-%m-%d %H:%M:%S')
-start_index = 0
-end_index = 0
-
-for file in files:
- if file[-13:-3] == start_time.strftime('%Y-%m-%d'):
- end_index = files.index(file)
-
- if file[-13:-3] == end_time.strftime('%Y-%m-%d'):
- start_index = files.index(file)
-
-files = files[start_index:end_index+1]
-
-mesh_mask = xr.open_dataset(files[0])
-z_depths = mesh_mask['depthw'].values
-
-lon_rest = abs(mesh_mask['nav_lon'][0,:] - lon_obs)
-lat_rest = abs(mesh_mask['nav_lat'][:,0] - lat_obs)
-lon_index = np.where(lon_rest == lon_rest.min())[0][0]
-lat_index = np.where(lat_rest == lat_rest.min())[0][0]
-
-flow = xr.open_dataset(files[10])
-
-d_deg = 5
-
-indices = {'lat': range(lat_index-d_deg, lat_index+d_deg),
- 'lon': range(lon_index-d_deg, lon_index+d_deg)}
-flow['votkeavt'][44, indices['lat'], indices['lon']].plot()
-
-Kz = np.zeros((len(files), 50, len(indices['lat']), len(indices['lon'])))
-# Kz_std = np.zeros((len(files), 50))
-
-time = np.zeros(len(files),dtype='datetime64[ns]')
-
-for i, filename in enumerate(tqdm(files)):
- T = xr.open_dataset(filename)
- Kz[i] = T['votkeavt'][:, indices['lat'], indices['lon']].values
-
- time[i] = T['time_counter'].values
-
-Kz_mean = np.nanmean(Kz, axis=(0,2,3))
-Kz_std = np.nanstd(Kz, axis=(0,2,3))
-Kz_median = np.nanmedian(Kz, axis=(0,2,3))
-
-np.save(f'../data/Kz_profile_{location}.npy', Kz)
-
-fig, ax = plt.subplots(figsize=(3,4))
-ax.plot(Kz_mean, -z_depths, color='k', label='mean')
-ax.plot(Kz_std, -z_depths, label='std', ls=':', color='k')
-ax.plot(Kz_median, -z_depths, label='median', ls='--', color='k')
-ax.semilogx()
-ax.legend(shadow=True, fancybox=True, fontsize=8)
-ax.set_xlabel('$K_z$ [m$^2$/s]')
-ax.set_ylabel('Depth [m]')
-ax.set_title('HC13')
-ax.set_xticks([1e-7,1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1])
-fig.savefig(f'../article_figs/Kz_profile_{location}.png', dpi=300,
- facecolor=(1, 0, 0, 0))
\ No newline at end of file
diff --git a/analysis/vertical_histograms.py b/analysis/vertical_histograms.py
index 19f07dc..7fdcf51 100644
--- a/analysis/vertical_histograms.py
+++ b/analysis/vertical_histograms.py
@@ -46,7 +46,7 @@
sim_dict = {}
# Load the data from the simulation
- local_path = f'/storage/shared/oceanparcels/output_data/data_Claudio/hc13_3/hc13_{ft}.zarr'
+ local_path = f'../data/simulations/hc13_{ft}.zarr'
sim = xr.open_zarr(local_path)
sim = sim.where(sim.time >= np.datetime64('2007-01-01'), drop=True) # analysis stops at 2007-01-01
nano = sim.where(sim.radius < 1e-6/2, drop=False)
diff --git a/article_figs/Figure3_legend_patch.png b/article_figs/Figure3_legend_patch.png
deleted file mode 100644
index 9536ed4..0000000
Binary files a/article_figs/Figure3_legend_patch.png and /dev/null differ
diff --git a/article_figs/stats_frag_into_NPs.csv b/article_figs/stats_frag_into_NPs.csv
deleted file mode 100644
index c7cc5ee..0000000
--- a/article_figs/stats_frag_into_NPs.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-,Particles,z median,z min,z max,T_s mean,T_s std,T_s median,T_s min,T_s max,X mean,X std,X median,X min,X max
-100,8192.0,-4950.0222879551675,-5492.897725732681,-3115.6953769159486,119.045166015625,116.4921805779782,85.0,0.0,1105.0,212247.33552554937,156129.74960796727,187006.27768894134,154.62232183589518,1195266.8880830365
-1000,8192.0,-4634.582599658053,-5495.58294032956,-1140.9295256698006,1141.559814453125,1053.1294018066226,807.5,0.0,4402.0,545775.3617649877,375534.1180765641,468681.3736023853,649.6496115458041,3872416.2886222294
-10000,8192.0,-4123.204920216056,-5484.762455489719,-9.0,3674.337890625,1287.3911723347012,4402.0,3.0,4402.0,956081.8747460541,682707.0896657215,789449.2975281788,14693.517185845663,7052479.159699347
-23000,8192.0,-4040.5838458904573,-5495.211747527972,-303.27109784392906,4059.7950439453125,947.4400747148248,4402.0,4.0,4402.0,1029578.3526320494,699086.7660447955,860531.9169256894,2219.465999773064,7486441.350030665
diff --git a/notebooks/julia/about_fragmentation.jl b/notebooks/julia/about_fragmentation.jl
deleted file mode 100644
index a4bd451..0000000
--- a/notebooks/julia/about_fragmentation.jl
+++ /dev/null
@@ -1,81 +0,0 @@
-# using Plots
-
-sim_time = 3 # days
-dt = 24 #hours
-fragmentation_timescale = 5*24. #hours
-fragmentation_prob = 0.5# exp(-dt/fragmentation_timescale)
-
-N = 10
-diameters = zeros(sim_time, N)
-diameters[1,:] .= 5e-8
-fragmenting_state = rand(sim_time, N) .> fragmentation_prob
-#
-# for i = 1:sim_time
-#
-
-#
-# end
-
-function number_fragments(k, p)
- N = 0
- n_fragments = zeros(Float32, k)
-
- for i = 1:k
- n = (1 - p)*(p*2^3)^i
- n_fragments[i] = n
- N += n
- end
- p_fragments = n_fragments./N
-
- return n_fragments, p_fragments
-
-end
-
-a, b =number_fragments(3, 1/2)
-println(a, b)
-
-
-Π(x) = (3*x^4 - 8*x^3 + 4*x^2 + 2)x^4
-
-function p_n(p::Float64)
- a = Π(p)
- return a
-end
-
-p_n(0.5)
-
-k = 3
-p = 0.5
-
-n_dist, p_fragmenting = number_fragments(10, 0.5)
-
-## ---
-
-
-
-
-a = L_is_4_lenght(5e-8, 10)
-
-
-function Fragmentation (particle, fieldset, time)
-
- if ParcelsRandom.random(0., 1.) > fragmentation_prob:
- nummer = ParcelsRandom.random(0., 1.)
- plim0 = 8./14.5
- plim1 = 12./14.5
- plim2 = 14./14.5
-
- if nummer <= plim0:
- frag_mode = 8
-
- elif (plim0 < nummer) and (nummer <= plim1):
- frag_mode = 4
-
- elif (plim1 < nummer) and (nummer <= plim2):
- frag_mode = 2
-
- else:
- frag_mode = 1
-
- particle.diameter = particle.diameter*frag_mode # division for reverse
-end
diff --git a/notebooks/julia/density_profile.npy b/notebooks/julia/density_profile.npy
deleted file mode 100644
index 60994f7..0000000
Binary files a/notebooks/julia/density_profile.npy and /dev/null differ
diff --git a/notebooks/julia/depth_profile.npy b/notebooks/julia/depth_profile.npy
deleted file mode 100644
index 882c7cc..0000000
Binary files a/notebooks/julia/depth_profile.npy and /dev/null differ
diff --git a/notebooks/julia/reverse-fragmentation-analysis.ipynb b/notebooks/julia/reverse-fragmentation-analysis.ipynb
deleted file mode 100644
index 0102586..0000000
--- a/notebooks/julia/reverse-fragmentation-analysis.ipynb
+++ /dev/null
@@ -1,2781 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": 1,
- "id": "2c229044",
- "metadata": {},
- "outputs": [],
- "source": [
- "using Plots\n",
- "using NPZ\n",
- "using Interpolations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "id": "aca441bc",
- "metadata": {},
- "outputs": [],
- "source": [
- "using StatsBase\n",
- "using Interpolations"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "id": "7016fa7d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "frag_vec (generic function with 1 method)"
- ]
- },
- "execution_count": 3,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "function frag_vec(n, dt, fragmentation_timescale, mode)\n",
- " \n",
- " fragmentation_mode = ones(n)\n",
- " frag_prob = exp(-abs(dt)/(fragmentation_timescale))\n",
- " frag_prob = ones(n)*frag_prob\n",
- " rnd_num = rand(n)\n",
- " \n",
- " truth = rnd_num .> frag_prob\n",
- " \n",
- " frag_weights = aweights([8/14.5, 4/14.5, 2/14.5, 0.5/14.5])\n",
- " \n",
- " if mode == \"rand\"\n",
- " fragmentation_mode = rand(n).*truth + .!truth\n",
- " \n",
- " elseif mode == \"Mika\"\n",
- " \n",
- " raw_mode = sample([8, 4, 2, 1], frag_weights, n)\n",
- " fragmentation_mode = 1 ./raw_mode.*truth + .!truth\n",
- " \n",
- " end\n",
- " \n",
- " return fragmentation_mode\n",
- "end"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "id": "49127bc9",
- "metadata": {},
- "outputs": [],
- "source": [
- "depth = npzread(\"depth_profile.npy\")\n",
- "density = npzread(\"density_profile.npy\")\n",
- "\n",
- "water_density = LinearInterpolation(depth, density);"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "id": "e70d6fdf",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n"
- ]
- },
- "execution_count": 5,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "plot(density, -depth)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "id": "78378929",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "sinkingVelocity (generic function with 1 method)"
- ]
- },
- "execution_count": 19,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "function sinkingVelocity(diameter, depth, ρ_p, ν)\n",
- " alpha = diameter/2\n",
- " g = 9.81\n",
- " \n",
- " ρ_f = water_density(depth)\n",
- " β = 3 .* ρ_f ./(2*ρ_p .+ ρ_f)\n",
- " \n",
- " tau_p = alpha.^2 ./ (3 .*β*ν)\n",
- " \n",
- " v_s = (1 .- β)*g .* tau_p\n",
- " \n",
- " return v_s\n",
- " \n",
- "end"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "1a652923",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "stokes_law (generic function with 1 method)"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "function stokes_law(diameter, depth, ρ_p, ν)\n",
- " ρ_f = water_density(depth)\n",
- " g = 9.81\n",
- " \n",
- " v_s = (ρ_p - ρ_f)*g*diameter.^2/(18*ν*ρ_f)\n",
- " \n",
- " return v_s\n",
- "end"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 32,
- "id": "f0342c6d",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1000-element LinRange{Float64, Int64}:\n",
- " 1.0e-6,1.1009e-5,2.1018e-5,3.1027e-5,…,0.00996997,0.00997998,0.00998999,0.01"
- ]
- },
- "execution_count": 32,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "sizes = LinRange(1e-6, 1e-2, 1000)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "98157e91",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "18.67367685564726"
- ]
- },
- "execution_count": 11,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "500/d/3600"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "ccbc5fb9",
- "metadata": {
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 79.965172 seconds (4.97 M allocations: 87.591 GiB, 16.08% gc time)\n"
- ]
- }
- ],
- "source": [
- "# parameters\n",
- "r = 5e-7 # 100nm\n",
- "k = 0\n",
- "sim_time = 1000 #365*10\n",
- "N = sim_time + 1\n",
- "V = 4/3 *π*r^3\n",
- "\n",
- "frag_timescale = 5000#*24*3600\n",
- "plastic_density = 1380 #kg/m3\n",
- "\n",
- "particles = 10000\n",
- "initial_depth = 4500\n",
- "depth_lim = 500\n",
- "diameter_lim = 1e-5\n",
- "β = 0.8\n",
- "ν = 1e-6\n",
- "\n",
- "dt = 1 #60*60 #seconds\n",
- "\n",
- "lengthxx = zeros(particles)\n",
- "depthxx = ones(particles)*initial_depth\n",
- "agexx = zeros(particles)\n",
- "\n",
- "lengthxx[:] .= r\n",
- "online = sum(depthxx .> depth_lim)\n",
- "\n",
- "i = 0\n",
- "@time while online != 0\n",
- " truth = depthxx .> depth_lim\n",
- " length_lim = lengthxx .< diameter_lim\n",
- " vs = sinkingVelocity(lengthxx, depthxx, plastic_density, ν)\n",
- " depthxx = depthxx - vs*dt.*truth*24*3600\n",
- " modexx = frag_vec(particles, dt, frag_timescale, \"Mika\")\n",
- " lengthxx = lengthxx.*(1 .+ (1 ./modexx .- 1).*length_lim)\n",
- " agexx = agexx + 1 .*truth\n",
- " online = sum(truth)\n",
- " i += 1\n",
- " \n",
- "end"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "id": "3f8aed1b",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n"
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "histogram(agexx)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 37,
- "id": "087160eb",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "histogram(agexx)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 24,
- "id": "aa574271",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "10000-element Vector{Float64}:\n",
- " 4674.0\n",
- " 4282.0\n",
- " 5239.0\n",
- " 4635.0\n",
- " 421.0\n",
- " 3923.0\n",
- " 2142.0\n",
- " 1098.0\n",
- " 1982.0\n",
- " 1699.0\n",
- " 4551.0\n",
- " 1717.0\n",
- " 2998.0\n",
- " ⋮\n",
- " 364.0\n",
- " 1412.0\n",
- " 984.0\n",
- " 1666.0\n",
- " 2385.0\n",
- " 3537.0\n",
- " 310.0\n",
- " 2739.0\n",
- " 1383.0\n",
- " 4601.0\n",
- " 1111.0\n",
- " 1631.0"
- ]
- },
- "execution_count": 24,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "agexx"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "debbcbe2",
- "metadata": {},
- "source": [
- "# Several fragmentation timescales"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "id": "b5ca55e0",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "86400000"
- ]
- },
- "execution_count": 8,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "frag_timescale"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "id": "d4ca0c8a",
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- " 10.426724 seconds (663.98 k allocations: 11.156 GiB, 15.78% gc time)\n"
- ]
- }
- ],
- "source": [
- "# parameters\n",
- "r = 5e-5 # 100nm\n",
- "k = 0\n",
- "sim_time = 1000 #365*10\n",
- "N = sim_time + 1\n",
- "V = 4/3 *π*r^3\n",
- "\n",
- "N = 10\n",
- "frag_timescale = LinRange(1000, 5000, 10) \n",
- "plastic_density = 1380 #kg/m3\n",
- "\n",
- "particles = 10000\n",
- "initial_depth = 5000\n",
- "depth_lim = 500\n",
- "diameter_lim = 1e-3\n",
- "β = 0.8\n",
- "ν = 1e-6\n",
- "dt = 60*60\n",
- "\n",
- "age = zeros(particles, N)\n",
- "L_particles = zeros(particles, N)\n",
- "\n",
- "\n",
- "@time for (index,frag_t) in enumerate(frag_timescale)\n",
- " lengthxx = zeros(particles)\n",
- " depthxx = ones(particles)*initial_depth\n",
- " agexx = zeros(particles)\n",
- " lengthxx[:] .= r\n",
- " online = sum(depthxx .> depth_lim)\n",
- " i = 0\n",
- " FT = frag_t*24*60^2\n",
- " \n",
- " while online != 0\n",
- " truth = depthxx .> depth_lim\n",
- " length_lim = lengthxx .< diameter_lim\n",
- " vs = sinkingVelocity(lengthxx, depthxx, plastic_density, ν)\n",
- " depthxx = depthxx - vs*dt.*truth\n",
- " modexx = frag_vec(particles, dt, FT, \"Mika\")\n",
- " lengthxx = lengthxx.*(1 .+ (1 ./modexx .- 1).*length_lim)\n",
- " agexx = agexx + 1 .*truth\n",
- " online = sum(truth)\n",
- " i += 1\n",
- "\n",
- " end\n",
- " age[:, index] = agexx\n",
- " L_particles[:, index] = lengthxx\n",
- " \n",
- "end"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "id": "fab3d1c7",
- "metadata": {},
- "outputs": [],
- "source": [
- "using Statistics"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "id": "0e62b86e",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "10000×10 Matrix{Float64}:\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 52.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 618.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " ⋮ ⋮ \n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 208.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 22.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 463.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0\n",
- " 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0 703.0"
- ]
- },
- "execution_count": 14,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "age"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "21126266",
- "metadata": {},
- "outputs": [],
- "source": [
- "histogram(age[:,1], label=frag_timescale[1])\n",
- "histogram!(age[:,3], label=frag_timescale[3])\n",
- "histogram!(age[:,4], label=frag_timescale[4])\n",
- "histogram!(age[:,7], label=frag_timescale[7])\n",
- "histogram!(age[:,8], label=frag_timescale[8])\n",
- "histogram!(age[:,10], label=frag_timescale[10])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 257,
- "id": "9b6f5527",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n"
- ]
- },
- "execution_count": 257,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "histogram(age[:,10], label=frag_timescale[10])\n",
- "xlabel!(\"Age\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 183,
- "id": "c8813302",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n"
- ]
- },
- "execution_count": 183,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "histogram(agexx)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 256,
- "id": "6ffd1bc7",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "image/svg+xml": [
- "\n",
- "\n"
- ]
- },
- "execution_count": 256,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "histogram(L_particles[:,10])\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 55,
- "id": "9bb40f46",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "14870.0"
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "maximum(agexx)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 56,
- "id": "29df0931",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "249.0"
- ]
- },
- "execution_count": 56,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "minimum(agexx)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 185,
- "id": "7697ee39",
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "1000-element LinRange{Float64, Int64}:\n",
- " 1.0,6.004,11.008,16.012,21.016,26.02,…,4979.98,4984.99,4989.99,4995.0,5000.0"
- ]
- },
- "execution_count": 185,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "LinRange(1, 5000, 1000) "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "id": "c5e17c82",
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Julia 1.7.0",
- "language": "julia",
- "name": "julia-1.7"
- },
- "language_info": {
- "file_extension": ".jl",
- "mimetype": "application/julia",
- "name": "julia",
- "version": "1.7.0"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/simulation/submit-abyssal-backtrack_copy_1.sh b/simulation/submit-abyssal-backtrack_copy_1.sh
deleted file mode 100644
index 6cc5b74..0000000
--- a/simulation/submit-abyssal-backtrack_copy_1.sh
+++ /dev/null
@@ -1,27 +0,0 @@
-#!/bin/bash -l
-#
-#SBATCH -J aby25 # the name of your job
-#SBATCH -p normal # request normal partition, job takes > 1 hour (this line can also be left out because 'normal' is the default)
-#SBATCH -t 120:00:00 # time in hh:mm:ss you want to reserve for the job
-#SBATCH -n 1 # the number of cores you want to use for the job, SLURM automatically determines how many nodes are needed
-#SBATCH -o logs/hc13.%j.o # the name of the file where the standard output will be written to. %j will be the jobid determined by SLURM
-#SBATCH -e logs/hc13.%j.e # the name of the file where potential errors will be written to. %j will be the jobid determined by SLURM
-#SBATCH --mail-user=c.m.pierard@uu.nl
-#SBATCH --mail-type=ALL
-
-conda activate abyssal-nps
-
-echo 'Running Backtracking Abyssal Nanoplastics simulation'
-cd ${HOME}/3DModelling_SouthAtlantic/simulation
-
-# first agument is the fragmentation timescale (int) second argument is the boolean for the fragmentation kernel
-
-python3 backtrack_from_sampling_locations.py -ft 10000 -bm 1 -s 77 &
-sleep 20
-python3 backtrack_from_sampling_locations.py -ft 10000 -bm 1 -s 29 &
-sleep 20
-python3 backtrack_from_sampling_locations.py -ft 10000 -bm 1 -s 61 &
-sleep 20
-python3 backtrack_from_sampling_locations.py -ft 10000 -bm 1 -s 33
-
-echo 'Finished computation.'
diff --git a/simulation/submit-abyssal-backtrack_copy_2.sh b/simulation/submit-abyssal-backtrack_copy_2.sh
deleted file mode 100644
index 7088ec7..0000000
--- a/simulation/submit-abyssal-backtrack_copy_2.sh
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/bin/bash -l
-#
-#SBATCH -J aby25 # the name of your job
-#SBATCH -p normal # request normal partition, job takes > 1 hour (this line can also be left out because 'normal' is the default)
-#SBATCH -t 120:00:00 # time in hh:mm:ss you want to reserve for the job
-#SBATCH -n 1 # the number of cores you want to use for the job, SLURM automatically determines how many nodes are needed
-#SBATCH -o logs/hc13.%j.o # the name of the file where the standard output will be written to. %j will be the jobid determined by SLURM
-#SBATCH -e logs/hc13.%j.e # the name of the file where potential errors will be written to. %j will be the jobid determined by SLURM
-#SBATCH --mail-user=c.m.pierard@uu.nl
-#SBATCH --mail-type=ALL
-
-conda activate abyssal-nps
-
-echo 'Running Backtracking Abyssal Nanoplastics simulation'
-cd ${HOME}/3DModelling_SouthAtlantic/simulation
-
-# first agument is the fragmentation timescale (int) second argument is the boolean for the fragmentation kernel
-
-python3 backtrack_from_sampling_locations.py -ft 1000 -bm 1 -s 14 &
-sleep 20
-python3 backtrack_from_sampling_locations.py -ft 1000 -bm 1 -s 78 &
-sleep 20
-python3 backtrack_from_sampling_locations.py -ft 1000 -bm 1 -s 27 &
-sleep 20
-python3 backtrack_from_sampling_locations.py -ft 1000 -bm 1 -s 62 &
-sleep 20
-python3 backtrack_from_sampling_locations.py -ft 1000 -bm 1 -s 34
-
-echo 'Finished computation.'
diff --git a/simulation/submit-part2.sh b/simulation/submit-part2.sh
deleted file mode 100644
index e7a1582..0000000
--- a/simulation/submit-part2.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-#!/bin/bash -l
-#
-#SBATCH -J ANPsP2 # the name of your job
-#SBATCH -p normal # request normal partition, job takes > 1 hour (this line can also be left out because 'normal' is the default)
-#SBATCH -t 120:00:00 # time in hh:mm:ss you want to reserve for the job
-#SBATCH -n 1 # the number of cores you want to use for the job, SLURM automatically determines how many nodes are needed
-#SBATCH -o logs/hc13.%j.o # the name of the file where the standard output will be written to. %j will be the jobid determined by SLURM
-#SBATCH -e logs/hc13.%j.e # the name of the file where potential errors will be written to. %j will be the jobid determined by SLURM
-#SBATCH --mail-user=c.m.pierard@uu.nl
-#SBATCH --mail-type=ALL
-
-conda activate abyssal-nps
-
-echo 'Running Backtracking Abyssal Nanoplastics simulation'
-cd ${HOME}/3DModelling_SouthAtlantic/simulation
-
-# first agument is the fragmentation timescale (int) second argument is the boolean for the fragmentation kernel
-
-python3 backtrack_from_existing_file.py -ft 23000 -bm True
-
-echo 'Finished computation.'