ESIO/notebooks/plot_model_forecasts.py at master · NicWayand/ESIO · GitHub

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# coding: utf-8

# In[1]:


'''
Plot modeled sea ice variables forecasts
'''


import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt, mpld3
from collections import OrderedDict
import itertools
import numpy as np
import numpy.ma as ma
import pandas as pd
import struct
import os
import xarray as xr
import glob
import datetime
import cartopy.crs as ccrs
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
import seaborn as sns


from esio import EsioData as ed

# General plotting settings
sns.set_style('whitegrid')
sns.set_context("talk", font_scale=1.5, rc={"lines.linewidth": 2.5})


# In[9]:


# Define model
models_all = ['yopp','gfdlsipn']
runType='forecast'

for model in models_all:
    #############################################################
    # Load in Data
    #############################################################
    E = ed.EsioData.load()
    # Directories
    model_label = E.model[model]['model_label']
    model_grid_file = E.model[model]['grid']
    stero_grid_file = E.obs['NSIDC_0051']['grid']

    fig_dir = os.path.join(E.fig_dir, 'model', model, 'standard')
    if not os.path.exists(fig_dir):
        os.makedirs(fig_dir)

    # Load in Model
    model_forecast = os.path.join(E.model[model][runType]['sipn_nc'], '*.nc')
    ds_model = xr.open_mfdataset(model_forecast)
    ds_model.rename({'nj':'x', 'ni':'y'}, inplace=True)

    # Load in regional data
    # Note minor -0.000004 degree differences in latitude
    ds_region = xr.open_dataset(os.path.join(E.grid_dir, 'sio_2016_mask.nc'))
    ds_region.set_coords(['lat','lon'], inplace=True);
    ds_region.rename({'nx':'x', 'ny':'y'}, inplace=True);

    #############################################################

    # Get regional averages
    mod_reg = esio.agg_by_domain(da_grid=ds_model.sic, ds_region=ds_region)

    # Aggregate over entire domain
    mod_avg = (ds_model.sic * ds_region.area).sum(dim='x').sum(dim='y')/(10**6)

    # Plot pan-Arctic sea ice extent
    f = plt.figure(figsize=(10,5))
    ax1 = plt.subplot(1, 1, 1) # Observations
    ice_plot.plot_reforecast(ds=mod_avg, axin=ax1, labelin=model_label, color='cycle_ensemble', marker=None)
    ax1.set_ylabel('Millions of square km')
    plt.legend(loc='lower right') #bbox_to_anchor=(1.03, 1.05)
    f.autofmt_xdate()
    f.savefig(os.path.join(fig_dir,'panArcticSIC_LongRange_'+model+'_'+runType+'.png'),bbox_inches='tight',dpi=200)
    mpld3.save_html(f, os.path.join(fig_dir,'panArcticSIC_LongRange_'+model+'_'+runType+'.html'))


    # Plot regional sea ice extents
    cmap_reg = itertools.cycle(sns.color_palette("Set2", mod_reg.nregions.size))
    linecycler = itertools.cycle(["-","--","-.",":"])
    f = plt.figure(figsize=(10,5))
    ax1 = plt.subplot(1, 1, 1)
    for cd in mod_reg.nregions:
        print(cd.region_names.values)
        ice_plot.plot_reforecast(ds= mod_reg.sel(nregions=cd),
                             linestyle=next(linecycler),
                             color=next(cmap_reg),
                             axin=ax1,
                             labelin=mod_reg.region_names.sel(nregions=cd).values,
                             marker=None)
    ax1.set_title('Regional sea ice extents')
    ax1.set_ylabel('Millions of square km')
    # plt.legend(bbox_to_anchor=(1.03, 1.05))
    handles, labels = plt.gca().get_legend_handles_labels()
    by_label = OrderedDict(zip(labels, handles))
    plt.legend(by_label.values(), by_label.keys(), bbox_to_anchor=(1.03, 1.05))
    f.autofmt_xdate()
    f.savefig(os.path.join(fig_dir,'panArcticSIC_LongRange_Regional_'+model+'_'+runType+'.png'),bbox_inches='tight',dpi=200)
    mpld3.save_html(f, os.path.join(fig_dir,'panArcticSIC_LongRange_Regional_'+model+'_'+runType+'.html'))

    # Set up plotting info
    cmap_sic = matplotlib.colors.ListedColormap(sns.color_palette("Blues_r", 10))
    cmap_sic.set_bad(color = 'green')

    # Plot sea ice concentration MAPS
    # Most recent initialization time
    # From ensemble mean (if ensemble availble)
    # ALL forecast times

    c_da = ds_model.sic.isel(init_time=ds_model.init_time.size-1).mean(dim='ensemble')

    for (k, FT) in enumerate(ds_model.fore_time):

        (f, ax1) = ice_plot.polar_axis()
        f.set_size_inches(10, 5)

        init_time =  pd.to_datetime( c_da.init_time.values).strftime('%Y-%m-%d-%H:%M')
        valid_time = pd.to_datetime( c_da.init_time.values + c_da.sel(fore_time=FT).fore_time.values ).strftime('%Y-%m-%d-%H:%M')

        c_da.sel(fore_time=FT).plot.pcolormesh(ax=ax1, x='lon', y='lat',
                                             transform=ccrs.PlateCarree(),
                                             cmap=cmap_sic,
                              vmin=0, vmax=1, cbar_kwargs={'label':'Sea Ice Concentration (-)'})
        ax1.set_title(model+'\n Initialization Time: '+init_time+'\n Valid Time: '+valid_time)
        plt.tight_layout()
        f.savefig(os.path.join(fig_dir,'panArctic_SIC_EnsembleMean_'+model+'_'+ runType+'_'+init_time+'_'+valid_time+'.png'),bbox_inches='tight',dpi=200)
        plt.close(f)