Estimated Phytoplankton Size

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import warnings
warnings.filterwarnings("ignore")
import os
import os.path as op
import sys

import pandas as pd
import numpy as np
import xarray as xr
import geopandas as gpd
import cartopy.crs as ccrs
import matplotlib.pyplot as plt

sys.path.append("../../../../indicators_setup")
from ind_setup.plotting_int import plot_timeseries_interactive
from ind_setup.plotting import plot_base_map, plot_map_subplots, plot_bar_probs
from ind_setup.core import fontsize

sys.path.append("../../../functions")
from data_downloaders import download_ERDDAP_data

Define area of interest

#Area of interest
lon_range  = [129.4088, 137.0541]
lat_range = [1.5214, 11.6587]

EEZ shapefile

shp_f = op.join(os.getcwd(), '..', '..','..', 'data/Palau_EEZ/pw_eez_pol_april2022.shp')
shp_eez = gpd.read_file(shp_f)

Download Data

DATASET: https://oceanwatch.pifsc.noaa.gov/erddap/info/md50_exp/index.html

update_data = False
path_data = "../../../data"
path_figs = "../../../matrix_cc/figures"

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base_url = 'https://oceanwatch.pifsc.noaa.gov/erddap/griddap/md50_exp.csv'
dataset_id = 'MD50'

if update_data:
    date_ini = '1998-01-01T00:00:00Z'
    date_end = '2023-12-01T00:00:00Z'
    data = download_ERDDAP_data(base_url, dataset_id, date_ini, date_end, lon_range, lat_range)
    data_xr = data.set_index(['latitude', 'longitude', 'time']).to_xarray()
    data_xr['time'] = pd.to_datetime(data_xr.time)
    data_xr = data_xr.coarsen(longitude=2, latitude=2, boundary = 'pad').mean()
    data_xr.to_netcdf(op.join(path_data, f'griddap_{dataset_id}.nc'))
else:
    data_xr = xr.open_dataset(op.join(path_data, f'griddap_{dataset_id}.nc'))

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
im = ax.pcolor(data_xr.longitude, data_xr.latitude, data_xr.mean(dim='time')[dataset_id], transform=ccrs.PlateCarree(), 
                cmap = 'YlGnBu', vmin = 0.8, vmax = 1.3)
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
plt.colorbar(im, ax=ax, label='Phytoplankton (µm)')
plt.savefig(op.join(path_figs, 'F16_phytoplankton_mean_map.png'), dpi=300, bbox_inches='tight')

data_y = data_xr.resample(time='1YE').mean()
plot_map_subplots(data_y, 'MD50', shp_eez = shp_eez, cmap = 'YlGnBu', vmin = 0.4, vmax = 1.6, cbar = 1)

data_an = data_y - data_xr.mean(dim='time')
plot_map_subplots(data_an, dataset_id, shp_eez = shp_eez, cmap='RdBu_r', vmin=-.3, vmax=.3, cbar = 1)

Mean Area

dict_plot = [{'data' : data_xr.mean(dim = ['longitude', 'latitude']).to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : 'Median Phytoplankton Size - MEAN AREA'},]

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12));
fig.write_html(op.join(path_figs, 'F16_phytoplankton_mean_trend.html'), include_plotlyjs="cdn")

Given point

loc = [7.35, 134.48]
dict_plot = [{'data' : data_xr.sel(longitude=loc[1], latitude=loc[0], method='nearest').to_dataframe(), 
              'var' : dataset_id, 'ax' : 1, 'label' : f'Median Phytoplankton Size at [{loc[0]}, {loc[1]}]'},]

ax = plot_base_map(shp_eez = shp_eez, figsize = [10, 6])
ax.set_extent([lon_range[0], lon_range[1], lat_range[0], lat_range[1]], crs=ccrs.PlateCarree())
ax.plot(loc[1], loc[0], '*', markersize = 12, color = 'royalblue', transform=ccrs.PlateCarree(), label = 'Location Analysis')
ax.legend()

<matplotlib.legend.Legend at 0x185e7c590>

fig = plot_timeseries_interactive(dict_plot, trendline=True, scatter_dict = None, figsize = (25, 12));