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size_distribution.py
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size_distribution.py
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# %%
import numpy as np
import xarray as xr
import itertools
import matplotlib.pyplot as plt
from cartopy import geodesic
import cartopy.crs as ccrs
import shapely
import pandas as pd
from tqdm import tqdm
from datetime import datetime
import analysis_functions as funk
import cartopy.feature as cfeature
from cycler import cycler
# %%
initial_depth = -5100 # int(sys.argv[1]) # 5 # 60 # 5179
lon_sample = 6.287 # 6.25
lat_sample = -32.171 # -32.171
origin = (lon_sample, lat_sample)
start_time = datetime.strptime('2019-01-20 12:00:00', '%Y-%m-%d %H:%M:%S')
sim_time = 4484
# create the date range in reverse from sampling time to the past
datelist = pd.date_range(end=start_time, periods=sim_time)[::-1]
end_time = datelist[0]
# simulations = [10] + [i for i in range(100, 501, 100)]
simulations = [100, 1000, 10000, 23000]
# %% Extracting the data from simulation outputs
surface_events = {}
for ft in tqdm(simulations):
# print('Computing fragmentation timescale: ', ft)
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
# loading fields as np arrays to do some nasty indexing
radiuses = sim['radius'].values
depths = sim['z'].values
latitudes = sim['lat'].values
longitudes = sim['lon'].values
df_sim = {} # dictiorany to store data per sim. Could be a pandas DF
# Detecting the index and the particles that reach the surface
k1, k2 = np.where(depths < 10)
idx_particles, idx_k2 = np.unique(k1, return_index=True)
surface_time = k2[idx_k2]
df_sim['particle_index'] = idx_particles
df_sim['surface_time'] = surface_time # Days. equivalent to index in simus
df_sim['radius'] = radiuses[idx_particles, surface_time]
df_sim['depths'] = depths[idx_particles, surface_time]
latz = latitudes[idx_particles, surface_time]
lonz = longitudes[idx_particles, surface_time]
xy_pos = (lonz, latz)
df_sim['displacement'] = funk.haversine(origin, xy_pos)
df_sim['lat'] = latz
df_sim['lon'] = lonz
surface_events[ft] = df_sim # Storing the dictionaris in another dict
np.save('../data/surface_events.npy', surface_events)
# %% greate a pandas dataframe with the mean and std of the size distribution
# at the surface
df = pd.DataFrame(columns=['Particles', 'L median', 'L min', 'L 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'])
for ft in simulations:
df.loc[ft] = [len(surface_events[ft]['radius']),
np.median(surface_events[ft]['radius']),
surface_events[ft]['radius'].min(),
surface_events[ft]['radius'].max(),
surface_events[ft]['surface_time'].mean(),
surface_events[ft]['surface_time'].std(),
np.median(surface_events[ft]['surface_time']),
surface_events[ft]['surface_time'].min(),
surface_events[ft]['surface_time'].max(),
surface_events[ft]['displacement'].mean(),
surface_events[ft]['displacement'].std(),
np.median(surface_events[ft]['displacement']),
surface_events[ft]['displacement'].min(),
surface_events[ft]['displacement'].max()]
df.to_csv('../data/size_distribution_surface.csv')
# df.to_latex('../article_figs/surface_events_numbers.tex') # to print in latex format and save in a file
# %%
# Load frag_into_NPs
frag_into_NPs = np.load('../data/frag_into_NPs.npy', allow_pickle=True).item()
# %% ecdf surfacetime and size distribution of particles at the surface
def forward(x):
return x**(1/2)
def inverse(x):
return x**2
fig, ax = plt.subplots(1, 3, figsize=(12, 3.5), tight_layout=True)
ax[0].axvline(1e-6, ls=':', color='black')
ax[0].axvline(1e-4, ls=':', label=r"Fragmentation limit", color='red')
ax[1].axvline(sim_time, ls=':', label=r"Simulation time limit", color='black')
ax[1].text(4300, 0.19, r'Time Limit', fontsize=6, color='k', rotation=-90)
ax[0].text(1e-6, 0.19, r"1 $\mu m$ Limit", fontsize=6, color='k', rotation=-90)
for j, ft in enumerate(simulations[::-1]):
x, y = funk.ecdf(surface_events[ft]['radius'], normalized=True)
ax[0].plot(x, y, drawstyle='steps-post')
x, y = funk.ecdf(surface_events[ft]['surface_time'], normalized=True)
ax[1].plot(x, y, drawstyle='steps-post')
x, y = funk.ecdf(surface_events[ft]['displacement']/1e3, normalized=True)
ax[2].plot(x, y, drawstyle='steps-post', label=f'$\lambda_f$ = {ft} days')
ax[2].set_xscale('function', functions=(forward, inverse))
handles, labels = ax[2].get_legend_handles_labels()
handles = handles[::-1]
labels = labels[::-1]
ax[0].legend(fontsize=7, shadow=True)
ax[2].legend(handles, labels, fontsize=7, shadow=True, loc='center right')
ax[0].semilogx()
ax[0].set_xlabel('Surface Particle Radius, $R$ [m]')
ax[0].set_ylabel(r'ECDF: $P(x \leq R)$')
# ax[0].set_title('Particle Radius from Surface')
ax[1].set_xlabel(r'Surface Drift Time, $T_s$ [days]')
ax[1].set_ylabel(r'ECDF: $P(x \leq T_s)$')
# ax[1].set_title('Drift Time from Surface')
ax[2].set_xlabel(r'Displacement from Surface, $X$ [km]')
ax[2].set_ylabel(r'ECDF: $P(x \leq X)$')
# ax[2].set_title('Displacement from Surface')
gridy = np.linspace(0, 1, 11)
gridx = [500, 1000] + [i for i in range(2000, 10000, 2000)]
ax[0].set_yticks(gridy)
ax[1].set_yticks(gridy)
ax[2].set_yticks(gridy)
ax[2].set_xticks(gridx)
ax[0].grid()
ax[1].grid()
ax[2].grid()
ax[0].text(1e-3, 0, r'A', fontsize=12,
ha='right')
ax[1].text(4400, 0, r'B', fontsize=12,
ha='right')
ax[2].text(6800, 0, r'C', fontsize=12,
ha='right')
fig.savefig('../article_figs/ECDF_surface', dpi=300,
facecolor=(1, 0, 0, 0))
# %% FIGURE 6 -
fig, ax = plt.subplots(1, 1, figsize=(4, 3.5), tight_layout=True)
ax.axvline(initial_depth, ls='-', color='k', lw=0.5)
ax.text(initial_depth - 200, 0.65, r'Sampling Depth', fontsize=6, color='k', rotation=-90)
ax.axvline(0, ls='-', color='k', lw=0.5)
ax.text(0-220, 0.7, r'Surface', fontsize=6, color='k', rotation=-90)
colors = plt.get_cmap('tab10').colors
line_styles = ['--', '-', ':', '-.']
ax.set_prop_cycle(cycler(color=colors[:4]) + cycler(linestyle=line_styles))
for j, ft in enumerate(simulations[::-1]):
x, y = funk.ecdf(frag_into_NPs[ft]['depths'], normalized=True,
invert=False)
ax.plot(x, y, drawstyle='steps-post', label=f'$\lambda_f$ = {ft} days',
zorder=5-j, lw=2)
handles, labels = ax.get_legend_handles_labels()
handles = handles[::-1]
labels = labels[::-1]
ax.legend(handles, labels, fontsize=7, shadow=True, ncol=1,
loc='best')
ax.set_xlabel('$R < 1\ \mu m$ Fragmentation Depth, $z$ [m]')
ax.set_ylabel(r'ECDF: $P(x \leq z)$')
gridy = np.linspace(0, 1, 11)
ax.set_yticks(gridy)
ax.grid(linestyle=':')
fig.savefig('../article_figs/Figure6.png', dpi=300,
facecolor=(1, 0, 0, 0))
# %% FIGURE 4 -
fig, ax = plt.subplots(1, 1, figsize=(4, 3.5), tight_layout=True)
ax.axvline(1e-6, ls='-', lw=0.5, color='black')
ax.axvline(1e-4, ls='-', lw=0.5, color='black')
ax.text(1e-6, 0.08, r"Colloid Limit", fontsize=6, color='k', rotation=-90)
ax.text(1.e-4, 0.01, r"Fragmentation Limit", fontsize=6, color='black', rotation=-90)
colors = plt.get_cmap('tab10').colors
line_styles = ['--', '-', ':', '-.']
ax.set_prop_cycle(cycler(color=colors[:4]) + cycler(linestyle=line_styles))
for j, ft in enumerate(simulations[::-1]):
x, y = funk.ecdf(surface_events[ft]['radius'], normalized=True)
ax.plot(x, y, drawstyle='steps-post', label=f'$\lambda_f$ = {ft} days',
lw=2)
handles, labels = ax.get_legend_handles_labels()
handles = handles[::-1]
labels = labels[::-1]
ax.legend(handles, labels, fontsize=7, shadow=True, ncol=1,
loc='best')
ax.semilogx()
ax.set_xlabel('Surface Particles Radius, $R$ [m]')
ax.set_ylabel(r'ECDF: $P(x \leq R)$')
gridy = np.linspace(0, 1, 11)
ax.set_yticks(gridy)
ax.grid(linestyle=':')
fig.savefig('../article_figs/Figure4.png', dpi=300,
facecolor=(1, 0, 0, 0))
# %%