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Copy path06_Tstar_seasonal.py
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06_Tstar_seasonal.py
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# -*- coding: utf-8 -*-
# ---
# jupyter:
# jupytext:
# formats: ipynb,py:light
# text_representation:
# extension: .py
# format_name: light
# format_version: '1.5'
# jupytext_version: 1.3.0
# kernelspec:
# display_name: Python 3
# language: python
# name: python3
# ---
# +
##################################################################################################################
# IMPORT MODULES
##################################################################################################################
import matplotlib as mpl
import matplotlib.pyplot as plt
#from mpl_toolkits.basemap import Basemap
from matplotlib.ticker import NullFormatter
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
from matplotlib.colors import LogNorm
import matplotlib.ticker as mticker
import xarray as xr
import numpy as np
import glob
import pandas as pd
from scipy.stats import norm
import scipy
import seaborn as sns
from pyhdf.SD import SD, SDC, SDAttr, HDF4Error
from pyhdf import HDF, VS, V
from pyhdf.HDF import *
from pyhdf.VS import *
import pprint
#from pyproj import Proj, transform
import os
import os.path
import sys
import matplotlib as mpl
import cartopy.crs as ccrs
#import pyresample
from basepath import data_path_base
import netCDF4
import datetime
label_size=14
mpl.rcParams['xtick.labelsize'] = label_size
mpl.rcParams['ytick.labelsize'] = label_size
##########################################
def save_pixel_data_monthly(month, longitude, latitude, data_lo_pixel, data_mpc_pixel, data_ltmpc_pixel, data_io_pixel):
data_lo_pixel.to_netcdf('output_files/pixel_data/monthly_total/pixel_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc')
data_mpc_pixel.to_netcdf('output_files/pixel_data/monthly_total/pixel_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc')
data_ltmpc_pixel.to_netcdf('output_files/pixel_data/monthly_total/pixel_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc')
data_io_pixel.to_netcdf('output_files/pixel_data/monthly_total/pixel_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc')
##########################################
def save_pixel_data(season, longitude, latitude, data_lo_pixel, data_mpc_pixel, data_ltmpc_pixel, data_io_pixel):
data_lo_pixel.to_netcdf('output_files/pixel_data/seasonal_total/pixel_'+season+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc')
data_mpc_pixel.to_netcdf('output_files/pixel_data/seasonal_total/pixel_'+season+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc')
data_ltmpc_pixel.to_netcdf('output_files/pixel_data/seasonal_total/pixel_'+season+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc')
data_io_pixel.to_netcdf('output_files/pixel_data/seasonal_total/pixel_'+season+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc')
##########################################
def plot_pixel_histogram_monthly(month, longitude, latitude, freq_lo, freq_mpc, freq_ltmpc, freq_io, n_lo, n_mpc, n_ltmpc, n_io):
fig, axs = plt.subplots(nrows=1,ncols=3,figsize =[18,5])
bins = np.arange(235,271,2)
if (n_lo > 0) or (n_mpc > 0) or (n_ltmpc > 0) or (n_io > 0):
axs[0].bar(bins[:-1],freq_lo,width=2, align="edge", ec="k", label = 'lo: '+str(n_lo))
axs[1].bar(bins[:-1],freq_mpc,width=2, align="edge", ec="k", color = 'steelblue', alpha = 0.6, label = 'mpc: '+str(n_mpc))
axs[0].bar(bins[:-1],freq_ltmpc,width=2, align="edge", ec="k", color = 'teal', alpha = 0.6, label = 'ltmpc: '+str(n_ltmpc))
axs[1].bar(bins[:-1],freq_io,width=2, align="edge", ec="k", color = 'r', alpha = 0.6, label = 'io: '+str(n_io))
axs[2].bar(bins[:-1],freq_mpc,width=2, align="edge", ec="k", color = 'steelblue', alpha = 0.6, label = 'mpc: '+str(n_mpc))
axs[2].bar(bins[:-1],freq_ltmpc,width=2, align="edge", ec="k", color = 'teal', alpha = 0.6, label = 'ltmpc: '+str(n_ltmpc))
if np.isnan(np.nanmax([freq_lo,freq_mpc,freq_ltmpc,freq_io])) is True:
for k in np.arange(3):
axs[k].set_xlim(275,235)
axs[k].set_ylim(0,np.nanmax([freq_lo,freq_mpc,freq_ltmpc,freq_io])+0.01)
axs[k].set_title(str(month).zfill(2)+' lat: '+str(latitude)+' lon: '+str(longitude))
axs[k].legend()
else:
for k in np.arange(3):
axs[k].set_xlim(275,235)
axs[k].set_title(str(month).zfill(2)+' lat: '+str(latitude)+' lon: '+str(longitude))
axs[k].legend()
plt.savefig('Figures/pixel_histograms/monthly/pixel_histograms_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'.png',bbox_inches='tight')
plt.close()
##########################################
def plot_pixel_histogram(season, longitude, latitude, freq_lo, freq_mpc, freq_ltmpc, freq_io, n_lo, n_mpc, n_ltmpc, n_io):
fig, axs = plt.subplots(nrows=1,ncols=3,figsize =[18,5])
bins = np.arange(235,271,2)
if (n_lo > 0) or (n_mpc > 0) or (n_ltmpc > 0) or (n_io > 0):
axs[0].bar(bins[:-1],freq_lo,width=2, align="edge", ec="k", label = 'lo: '+str(n_lo))
axs[1].bar(bins[:-1],freq_mpc,width=2, align="edge", ec="k", color = 'steelblue', alpha = 0.6, label = 'mpc: '+str(n_mpc))
axs[0].bar(bins[:-1],freq_ltmpc,width=2, align="edge", ec="k", color = 'teal', alpha = 0.6, label = 'ltmpc: '+str(n_ltmpc))
axs[1].bar(bins[:-1],freq_io,width=2, align="edge", ec="k", color = 'r', alpha = 0.6, label = 'io: '+str(n_io))
axs[2].bar(bins[:-1],freq_mpc,width=2, align="edge", ec="k", color = 'steelblue', alpha = 0.6, label = 'mpc: '+str(n_mpc))
axs[2].bar(bins[:-1],freq_ltmpc,width=2, align="edge", ec="k", color = 'teal', alpha = 0.6, label = 'ltmpc: '+str(n_ltmpc))
if np.isnan(np.nanmax([freq_lo,freq_mpc,freq_ltmpc,freq_io])) is True:
for k in np.arange(3):
axs[k].set_xlim(275,235)
axs[k].set_ylim(0,np.nanmax([freq_lo,freq_mpc,freq_ltmpc,freq_io])+0.01)
axs[k].set_title(season+' lat: '+str(latitude)+' lon: '+str(longitude))
axs[k].legend()
else:
for k in np.arange(3):
axs[k].set_xlim(275,235)
axs[k].set_title(season+' lat: '+str(latitude)+' lon: '+str(longitude))
axs[k].legend()
#plt.savefig('Figures/pixel_histograms/monthly/pixel_histograms_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'.png',bbox_inches='tight')
#plt.close()
##########################################
def calc_tstar_cloud_stats_monthly(month, latitude, longitude):
files_pixel_lo = sorted(glob.glob('output_files/pixel_data/monthly/pixel_*_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc'))
files_pixel_mpc = sorted(glob.glob('output_files/pixel_data/monthly/pixel_*_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc'))
files_pixel_ltmpc = sorted(glob.glob('output_files/pixel_data/monthly/pixel_*_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc'))
files_pixel_io = sorted(glob.glob('output_files/pixel_data/monthly/pixel_*_'+str(month).zfill(2)+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc'))
# read data
data_lo = xr.open_mfdataset(files_pixel_lo, combine = 'by_coords')
data_mpc = xr.open_mfdataset(files_pixel_mpc, combine = 'by_coords')
data_ltmpc = xr.open_mfdataset(files_pixel_ltmpc, combine = 'by_coords')
data_io = xr.open_mfdataset(files_pixel_io, combine = 'by_coords')
# get histograms
bins = np.arange(235,271,2)
hist_lo, edges_lo = np.histogram(data_lo.CTT, bins);
hist_mpc, edges_mpc = np.histogram(data_mpc.CTT, bins);
hist_ltmpc, edges_lo = np.histogram(data_ltmpc.CTT, bins);
hist_io, edges_io = np.histogram(data_io.CTT, bins);
freq_lo_total = hist_lo/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_mpc_total = hist_mpc/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_ltmpc_total = hist_ltmpc/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_io_total = hist_io/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
# plotting histograms
plot_pixel_histogram_monthly(month, longitude, latitude, freq_lo_total, freq_mpc_total, freq_ltmpc_total, freq_io_total, len(data_lo.CTT), len(data_mpc.CTT), len(data_ltmpc.CTT), len(data_io.CTT))
save_pixel_data_monthly(month, longitude, latitude, data_lo, data_mpc, data_ltmpc, data_io)
# Calculate T* again from data
#index = np.array(np.where(freq_lo_total-freq_ltmpc_total < 0)).flatten();
index = np.array(np.where((freq_lo_total-freq_ltmpc_total < 0) & (hist_ltmpc > 10))).flatten(); ####### NEW !!! ###########
if len(index) == 0:
return([np.nan, len(data_lo.CTT), len(data_ltmpc.CTT), len(data_mpc.CTT), len(data_io.CTT), np.nanmean(data_lo.CTH), np.nanmean(data_ltmpc.CTH), np.nanmean(data_lo.CTT), np.nanmean(data_ltmpc.CTT)])
else:
return([np.max(bins[index]), len(data_lo.CTT), len(data_ltmpc.CTT), len(data_mpc.CTT), len(data_io.CTT), np.nanmean(data_lo.CTH), np.nanmean(data_ltmpc.CTH), np.nanmean(data_lo.CTT), np.nanmean(data_ltmpc.CTT)])
##########################################
def calc_tstar_cloud_stats_seasonal(season, latitude, longitude):
if season == 'DJF':
files_pixel_lo = ['output_files/pixel_data/monthly_total/pixel_12_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_01_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_02_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc']
files_pixel_mpc = ['output_files/pixel_data/monthly_total/pixel_12_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc', 'output_files/pixel_data/monthly_total/pixel_01_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc', 'output_files/pixel_data/monthly_total/pixel_02_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc']
files_pixel_ltmpc = ['output_files/pixel_data/monthly_total/pixel_12_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc', 'output_files/pixel_data/monthly_total/pixel_01_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc', 'output_files/pixel_data/monthly_total/pixel_02_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc']
files_pixel_io = ['output_files/pixel_data/monthly_total/pixel_12_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc', 'output_files/pixel_data/monthly_total/pixel_01_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc', 'output_files/pixel_data/monthly_total/pixel_02_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc']
if season == 'MAM':
files_pixel_lo = ['output_files/pixel_data/monthly_total/pixel_03_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_04_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_05_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc']
files_pixel_mpc = ['output_files/pixel_data/monthly_total/pixel_03_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc', 'output_files/pixel_data/monthly_total/pixel_04_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc', 'output_files/pixel_data/monthly_total/pixel_05_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc']
files_pixel_ltmpc = ['output_files/pixel_data/monthly_total/pixel_03_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc', 'output_files/pixel_data/monthly_total/pixel_04_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc', 'output_files/pixel_data/monthly_total/pixel_05_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc']
files_pixel_io = ['output_files/pixel_data/monthly_total/pixel_03_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc', 'output_files/pixel_data/monthly_total/pixel_04_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc', 'output_files/pixel_data/monthly_total/pixel_05_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc']
if season == 'JJA':
files_pixel_lo = ['output_files/pixel_data/monthly_total/pixel_06_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_07_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_08_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc']
files_pixel_mpc = ['output_files/pixel_data/monthly_total/pixel_06_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc', 'output_files/pixel_data/monthly_total/pixel_07_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc', 'output_files/pixel_data/monthly_total/pixel_08_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc']
files_pixel_ltmpc = ['output_files/pixel_data/monthly_total/pixel_06_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc', 'output_files/pixel_data/monthly_total/pixel_07_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc', 'output_files/pixel_data/monthly_total/pixel_08_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc']
files_pixel_io = ['output_files/pixel_data/monthly_total/pixel_06_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc', 'output_files/pixel_data/monthly_total/pixel_07_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc', 'output_files/pixel_data/monthly_total/pixel_08_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc']
if season == 'SON':
files_pixel_lo = ['output_files/pixel_data/monthly_total/pixel_09_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_10_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_11_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc']
files_pixel_mpc = ['output_files/pixel_data/monthly_total/pixel_09_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc', 'output_files/pixel_data/monthly_total/pixel_10_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc', 'output_files/pixel_data/monthly_total/pixel_11_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc']
files_pixel_ltmpc = ['output_files/pixel_data/monthly_total/pixel_09_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc', 'output_files/pixel_data/monthly_total/pixel_10_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc', 'output_files/pixel_data/monthly_total/pixel_11_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc']
files_pixel_io = ['output_files/pixel_data/monthly_total/pixel_09_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc', 'output_files/pixel_data/monthly_total/pixel_10_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc', 'output_files/pixel_data/monthly_total/pixel_11_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc']
# read data
data_lo = xr.open_mfdataset(files_pixel_lo, combine = 'nested', concat_dim = 'time')
data_mpc = xr.open_mfdataset(files_pixel_mpc, combine = 'nested', concat_dim = 'time')
data_ltmpc = xr.open_mfdataset(files_pixel_ltmpc, combine = 'nested', concat_dim = 'time')
data_io = xr.open_mfdataset(files_pixel_io, combine = 'nested', concat_dim = 'time')
# get histograms
bins = np.arange(235,271,2)
hist_lo, edges_lo = np.histogram(data_lo.CTT, bins);
hist_mpc, edges_mpc = np.histogram(data_mpc.CTT, bins);
hist_ltmpc, edges_lo = np.histogram(data_ltmpc.CTT, bins);
hist_io, edges_io = np.histogram(data_io.CTT, bins);
freq_lo_total = hist_lo/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_mpc_total = hist_mpc/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_ltmpc_total = hist_ltmpc/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_io_total = hist_io/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
save_pixel_data(season, longitude, latitude, data_lo, data_mpc, data_ltmpc, data_io)
# Calculate T* again from data
#index = np.array(np.where(freq_lo_total-freq_ltmpc_total < 0)).flatten();
index = np.array(np.where((freq_lo_total-freq_ltmpc_total < 0) & (hist_ltmpc > 10))).flatten(); ####### NEW !!! ###########
if len(index) == 0:
return([np.nan, len(data_lo.CTT), len(data_ltmpc.CTT), len(data_mpc.CTT), len(data_io.CTT), np.nanmean(data_lo.CTH), np.nanmean(data_ltmpc.CTH), np.nanmean(data_lo.CTT), np.nanmean(data_ltmpc.CTT)])
else:
return([np.max(bins[index]), len(data_lo.CTT), len(data_ltmpc.CTT), len(data_mpc.CTT), len(data_io.CTT), np.nanmean(data_lo.CTH), np.nanmean(data_ltmpc.CTH), np.nanmean(data_lo.CTT), np.nanmean(data_ltmpc.CTT)])
##########################################
def check_tstar(season, latitude, longitude):
print(season)
print('longitude: ', longitude)
print('latitude: ', latitude)
print('===========================================')
file_pixel_lo = 'output_files/pixel_data/seasonal_total/pixel_'+season+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc'
file_pixel_mpc = 'output_files/pixel_data/seasonal_total/pixel_'+season+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_mpc.nc'
file_pixel_ltmpc = 'output_files/pixel_data/seasonal_total/pixel_'+season+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_ltmpc.nc'
file_pixel_io = 'output_files/pixel_data/seasonal_total/pixel_'+season+'_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_io.nc'
# read data
data_lo = xr.open_dataset(file_pixel_lo)
data_mpc = xr.open_dataset(file_pixel_mpc)
data_ltmpc = xr.open_dataset(file_pixel_ltmpc)
data_io = xr.open_dataset(file_pixel_io)
# read T*
file_tstar = data_path_base+'/tcarlsen/05_INP_SO/output_files/T_star/seasonal/Tstar_5x5_total_'+season+'.nc'
ds = xr.open_dataset(file_tstar)
tstar_file = ds.Tstar
# get T* from data file
x = np.where((tstar_file.lon == longitude) & (tstar_file.lat == latitude))
print('T*: ', tstar_file.isel(lon = x[0][0], lat = x[1][0]))
print('T*: ', tstar_file.sel(lon = longitude, lat = latitude))
print('===========================================')
# get histograms
bins = np.arange(235,271,2)
hist_lo, edges_lo = np.histogram(data_lo.CTT, bins);
hist_mpc, edges_mpc = np.histogram(data_mpc.CTT, bins);
hist_ltmpc, edges_lo = np.histogram(data_ltmpc.CTT, bins);
hist_io, edges_io = np.histogram(data_io.CTT, bins);
freq_lo_total = hist_lo/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_mpc_total = hist_mpc/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_ltmpc_total = hist_ltmpc/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
freq_io_total = hist_io/float(hist_lo.sum() + hist_mpc.sum() + hist_io.sum())
# plotting histograms
plot_pixel_histogram(season, longitude, latitude, freq_lo_total, freq_mpc_total, freq_ltmpc_total, freq_io_total, len(data_lo.CTT), len(data_mpc.CTT), len(data_ltmpc.CTT), len(data_io.CTT))
# Calculate T* again from data
#index = np.array(np.where(freq_lo_total-freq_ltmpc_total < 0)).flatten();
index = np.array(np.where((freq_lo_total-freq_ltmpc_total < 0) & (hist_ltmpc > 10))).flatten(); ####### NEW !!! ###########
if len(index) == 0:
print('T* (re-calculated): nan')
else:
print('T* (re-calculated): ', np.max(bins[index]))
fig, axs = plt.subplots(nrows=1,ncols=1,figsize =[18,5])
axs.scatter(bins[:-1], freq_lo_total-freq_ltmpc_total)
axs.plot([0,300],[0,0],'k')
axs.set_xlim(234,270)
# -
def preprocess(ds):
print(ds.encoding["source"])
time = np.ones([len(ds.CTT)],dtype=np.float64)
ds.assign_coords({'time': time})
start = 1
end = 3
time = pd.date_range(start,end,3) # create one time step
time
#ds = ds.assign_coords({‘time’: time})
latitude = 80
longitude = 0
files_pixel_lo = ['output_files/pixel_data/monthly_total/pixel_12_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_01_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_02_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc']
data_lo = xr.open_mfdataset(files_pixel_lo, combine = 'nested', concat_dim = 'time')
data_lo
print(len(data_lo.CTT))
latitude = 80
longitude = 0
files_pixel_lo = ['output_files/pixel_data/monthly_total/pixel_12_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_01_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc', 'output_files/pixel_data/monthly_total/pixel_02_'+str(latitude).zfill(3)+'_'+str(longitude).zfill(3)+'_data_lo.nc']
data_lo = xr.open_dataset(files_pixel_lo[0])
data_lo
# +
time = np.ones([len(data_lo.CTT)],dtype=np.float64)
data_lo.assign_coords({'time': time})
# -
time.flatten()
print(np.nanmean(data_lo.CTT))
data_lo
data_lo
data_lo = xr.open_mfdataset(files_pixel_lo[1], combine = 'by_coords')
data_lo
data_lo.encoding["source"]
# +
season = 'MAM'
lat_grid = np.arange(-90,91,5)
lon_grid = np.arange(-180,181,5)
lon2d, lat2d = np.meshgrid(lon_grid,lat_grid,indexing='ij')
cld_lo = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
cld_lo[:,:] = np.nan
cld_ltmpc = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
cld_ltmpc[:,:] = np.nan
cld_mpc = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
cld_mpc[:,:] = np.nan
cld_io = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
cld_io[:,:] = np.nan
cth_lo = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
cth_lo[:,:] = np.nan
cth_ltmpc = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
cth_ltmpc[:,:] = np.nan
ctt_lo = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
ctt_lo[:,:] = np.nan
ctt_ltmpc = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
ctt_ltmpc[:,:] = np.nan
T_star = np.ones([len(lon_grid), len(lat_grid)],dtype=np.float64)
T_star[:,:] = np.nan
count = 0
count_longi = 0
count_lati = 0
for longi in np.arange(-180,181,5):
print(longi)
for lati in np.arange(-90,91,5):
try:
result = calc_tstar_cloud_stats_seasonal(season, lati, longi)
T_star[count_longi, count_lati] = result[0]
# statistics
cld_lo[count_longi, count_lati] = result[1]
cld_ltmpc[count_longi, count_lati] = result[2]
cld_mpc[count_longi, count_lati] = result[3]
cld_io[count_longi, count_lati] = result[4]
cth_lo[count_longi, count_lati] = result[5]
cth_ltmpc[count_longi, count_lati] = result[6]
ctt_lo[count_longi, count_lati] = result[7]
ctt_ltmpc[count_longi, count_lati] = result[8]
except:
count_lati += 1
continue
count_lati += 1
count_longi += 1
count_lati = 0
# limit T* (exclude results too close to phase algorithm limits)
T_star[T_star > 269] = np.nan
T_star[T_star < 244] = np.nan
# save data as xarray in netcdf files
T_star_xr = xr.DataArray(data = T_star, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'Tstar')
cld_lo_xr = xr.DataArray(data = cld_lo, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'n_lo')
cld_ltmpc_xr = xr.DataArray(data = cld_ltmpc, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'n_ltmpc')
cld_mpc_xr = xr.DataArray(data = cld_mpc, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'n_mpc')
cld_io_xr = xr.DataArray(data = cld_io, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'n_io')
cth_lo_xr = xr.DataArray(data = cth_lo, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'cth_lo')
cth_ltmpc_xr = xr.DataArray(data = cth_ltmpc, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'cth_ltmpc')
ctt_lo_xr = xr.DataArray(data = ctt_lo, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'ctt_lo')
ctt_ltmpc_xr = xr.DataArray(data = ctt_ltmpc, coords = [lon_grid,lat_grid], dims = ['lon','lat'], name = 'ctt_ltmpc')
T_star_xr.to_netcdf('output_files/T_star/seasonal/Tstar_5x5_total_'+season+'.nc')
cld_lo_xr.to_netcdf('output_files/T_star/seasonal/n_obs_lo_5x5_total_'+season+'.nc')
cld_ltmpc_xr.to_netcdf('output_files/T_star/seasonal/n_obs_ltmpc_5x5_total_'+season+'.nc')
cld_mpc_xr.to_netcdf('output_files/T_star/seasonal/n_obs_mpc_5x5_total_'+season+'.nc')
cld_io_xr.to_netcdf('output_files/T_star/seasonal/n_obs_io_5x5_total_'+season+'.nc')
cth_lo_xr.to_netcdf('output_files/T_star/seasonal/cth_lo_5x5_total_'+season+'.nc')
cth_ltmpc_xr.to_netcdf('output_files/T_star/seasonal/cth_ltmpc_5x5_total_'+season+'.nc')
ctt_lo_xr.to_netcdf('output_files/T_star/seasonal/ctt_lo_5x5_total_'+season+'.nc')
ctt_ltmpc_xr.to_netcdf('output_files/T_star/seasonal/ctt_ltmpc_5x5_total_'+season+'.nc')
sys.exit()
# +
lat_grid = np.arange(-90,91,5)
lon_grid = np.arange(-180,181,5)
lon2d, lat2d = np.meshgrid(lon_grid,lat_grid,indexing='ij')
season_1 = 'DJF'
file = data_path_base+'/tcarlsen/05_INP_SO/output_files/T_star/seasonal/Tstar_5x5_total_'+season_1+'.nc'
ds = xr.open_dataset(file)
T_star_1 = ds.Tstar
file = data_path_base+'/tcarlsen/05_INP_SO/output_files/T_star/seasonal/n_obs_lo_5x5_total_'+season_1+'.nc'
ds = xr.open_dataset(file)
cld_lo_1 = ds.n_lo
file = data_path_base+'/tcarlsen/05_INP_SO/output_files/T_star/seasonal/n_obs_ltmpc_5x5_total_'+season_1+'.nc'
ds = xr.open_dataset(file)
cld_ltmpc_1 = ds.n_ltmpc
########################################
season_2 = 'JJA'
file = data_path_base+'/tcarlsen/05_INP_SO/output_files/T_star/seasonal/Tstar_5x5_total_'+season_2+'.nc'
ds = xr.open_dataset(file)
T_star_2 = ds.Tstar
file = data_path_base+'/tcarlsen/05_INP_SO/output_files/T_star/seasonal/n_obs_lo_5x5_total_'+season_2+'.nc'
ds = xr.open_dataset(file)
cld_lo_2 = ds.n_lo
file = data_path_base+'/tcarlsen/05_INP_SO/output_files/T_star/seasonal/n_obs_ltmpc_5x5_total_'+season_2+'.nc'
ds = xr.open_dataset(file)
cld_ltmpc_2 = ds.n_ltmpc
# -
print(np.nanmin(T_star_1), np.nanmax(T_star_1), np.nanmean(T_star_1))
print(np.nanmin(cld_lo_1), np.nanmax(cld_lo_1), np.nanmean(cld_lo_1))
print(np.nanmin(cld_ltmpc_1), np.nanmax(cld_ltmpc_1), np.nanmean(cld_ltmpc_1))
print('####################################')
print(np.nanmin(T_star_2), np.nanmax(T_star_2), np.nanmean(T_star_2))
print(np.nanmin(cld_lo_2), np.nanmax(cld_lo_2), np.nanmean(cld_lo_2))
print(np.nanmin(cld_ltmpc_2), np.nanmax(cld_ltmpc_2), np.nanmean(cld_ltmpc_2))
# +
# Plotting
fig, axs = plt.subplots(nrows=3,ncols=2,figsize =[16,16], subplot_kw={'projection': ccrs.PlateCarree()})
custom_cmap = cm.get_cmap('Spectral_r', 40)
cs = axs[0,0].pcolormesh(lon2d, lat2d, cld_lo_1, cmap=custom_cmap, vmin = 0, vmax = 50000)
gl = axs[0,0].gridlines(crs = ccrs.PlateCarree(), draw_labels=True, linewidth = 1, color = 'k', linestyle = ':')
gl.xlocator = mticker.FixedLocator([-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180])
gl.ylocator = mticker.FixedLocator([-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90])
cbar = fig.colorbar(cs, ax=axs[0,0], orientation='horizontal',pad=0.05)
cbar.set_label('Liquid-only '+season_1,fontsize=16)
extent = [-180, 180, -90, 90]
axs[0,0].set_extent(extent)
axs[0,0].coastlines()
cs = axs[1,0].pcolormesh(lon2d, lat2d, cld_ltmpc_1, cmap=custom_cmap, vmin = 0, vmax = 50000)
gl = axs[1,0].gridlines(crs = ccrs.PlateCarree(), draw_labels=True, linewidth = 1, color = 'k', linestyle = ':')
gl.xlocator = mticker.FixedLocator([-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180])
gl.ylocator = mticker.FixedLocator([-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90])
cbar = fig.colorbar(cs, ax=axs[1,0], orientation='horizontal',pad=0.05)
cbar.set_label('LTMPC '+season_1,fontsize=16)
extent = [-180, 180, -90, 90]
axs[1,0].set_extent(extent)
axs[1,0].coastlines()
cs = axs[2,0].pcolormesh(lon2d, lat2d, T_star_1, cmap=custom_cmap, vmin = 240, vmax = 270)
gl = axs[2,0].gridlines(crs = ccrs.PlateCarree(), draw_labels=True, linewidth = 1, color = 'k', linestyle = ':')
gl.xlocator = mticker.FixedLocator([-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180])
gl.ylocator = mticker.FixedLocator([-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90])
cbar = fig.colorbar(cs, ax=axs[2,0], orientation='horizontal',pad=0.05)
cbar.set_label('Tstar (K) ' +season_1,fontsize=16)
extent = [-180, 180, -90, 90]
axs[2,0].set_extent(extent)
axs[2,0].coastlines()
###############################################################
custom_cmap = cm.get_cmap('Spectral_r', 40)
cs = axs[0,1].pcolormesh(lon2d, lat2d, cld_lo_2, cmap=custom_cmap, vmin = 0, vmax = 50000)
gl = axs[0,1].gridlines(crs = ccrs.PlateCarree(), draw_labels=True, linewidth = 1, color = 'k', linestyle = ':')
gl.xlocator = mticker.FixedLocator([-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180])
gl.ylocator = mticker.FixedLocator([-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90])
cbar = fig.colorbar(cs, ax=axs[0,1], orientation='horizontal',pad=0.05)
cbar.set_label('Liquid-only '+season_2,fontsize=16)
extent = [-180, 180, -90, 90]
axs[0,1].set_extent(extent)
axs[0,1].coastlines()
cs = axs[1,1].pcolormesh(lon2d, lat2d, cld_ltmpc_2, cmap=custom_cmap, vmin = 0, vmax = 50000)
gl = axs[1,1].gridlines(crs = ccrs.PlateCarree(), draw_labels=True, linewidth = 1, color = 'k', linestyle = ':')
gl.xlocator = mticker.FixedLocator([-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180])
gl.ylocator = mticker.FixedLocator([-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90])
cbar = fig.colorbar(cs, ax=axs[1,1], orientation='horizontal',pad=0.05)
cbar.set_label('LTMPC '+season_2,fontsize=16)
extent = [-180, 180, -90, 90]
axs[1,1].set_extent(extent)
axs[1,1].coastlines()
cs = axs[2,1].pcolormesh(lon2d, lat2d, T_star_2, cmap=custom_cmap, vmin = 240, vmax = 270)
gl = axs[2,1].gridlines(crs = ccrs.PlateCarree(), draw_labels=True, linewidth = 1, color = 'k', linestyle = ':')
gl.xlocator = mticker.FixedLocator([-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150, 180])
gl.ylocator = mticker.FixedLocator([-90, -75, -60, -45, -30, -15, 0, 15, 30, 45, 60, 75, 90])
cbar = fig.colorbar(cs, ax=axs[2,1], orientation='horizontal',pad=0.05)
cbar.set_label('Tstar (K) '+season_2,fontsize=16)
extent = [-180, 180, -90, 90]
axs[2,1].set_extent(extent)
axs[2,1].coastlines()
# -
check_tstar(9, 80, 0)
check_tstar(3, 80, 0)