"""
Created on Wed Apr 22 10:02:45 2020
@author: josmarti
"""
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import rms_plots as rpl
import nc as nc
import numpy as np
import re
#import cdo; c=cdo.Cdo()
syear=1980; eyear=1995
obsin=nc.getvar('/home/josmarti/Data/Observations/NSIDC_1979_2010_nh_siea.nc', 'sia').squeeze()
start=(1990-1979)*12; end=start+12
mean_obs=np.mean(np.reshape(obsin[((1980-1979)*12):((1995-1979)*12)],(15,12)).transpose(), axis=1)
gridpoint=nc.getvar('/home/josmarti/Data/areacella_fx_CanCM4_decadal2001_r0i0p0.nc','areacella')
PIOMAS=np.loadtxt('/home/josmarti/Data/Observations/PIOMAS_SIV_monthly_1979-2020.txt',usecols=range(1,13))
mean_obs=np.mean(PIOMAS[(syear-1979):(eyear-1979)], axis=0)
years=[2312, 2435, 2413, 2356, 2387, 2332]
ensembles=["%03d" % i for i in range(1,13)]
#ensembles=["001"]
run_length=1 #years
daily_matrix=np.zeros((6,12,1095))
days=365*3
for e in ensembles:
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 3 13:56:25 2020
@author: josmarti
"""
import sys
sys.path.append('/HOME/rms/PMODS')
import os as os
import nc as nc
import numpy as np
import matplotlib.pyplot as plt
from scipy import signal
os.system('cdo selmon,3 ~/Data/Observations/had2cis_1x1_198001_202004_sicn.nc ./tmp1.nc')
os.system('cdo remapbil,1x1_reg_mask.nc tmp1.nc tmp2.nc') #regrid
os.system('cdo -gtc,0.15 tmp2.nc tmp3.nc') # SIE mask: 1's where sic>0.15, 0 elsewhere
os.system('cdo -setctomiss,0 -eqc,1 1x1_reg_mask.nc 1centralarctic.nc') # create central arctic mask
os.system('cdo mul 1centralarctic.nc tmp3.nc tmp4.nc') # mask out everything outside central Arctic
os.system('cdo -fldmean -sellonlatbox,0,360,0,90 tmp4.nc tmp5.nc') # Average fraction per km^2 in NH
SIE_im03_ca=nc.getvar('tmp5.nc','SICN').squeeze()*2*np.pi*6371.*6371./1e6
plt.plot(signal.detrend(SIE_im03_ca))
print np.std(signal.detrend(SIE_im03_ca))
import cartopy.crs as ccrs
import rms_plots as rpl
import numpy as np
import numpy.ma as ma
import nc as nc
import matplotlib as mpl
#from netCDF4 import Dataset
clevslab = np.arange(16) - 1 #from rms_plots.py
r = 0
jet3 = colors.ListedColormap(
loadmat('/home/josmarti/Downloads/cmap_jet3.mat')['cmap'], name='jet3')
####### read data
infile = '/home/josmarti/Data/reg_grid_iceregions.nc'
lsmask = nc.getvar('/home/josmarti/Data/lsmask_cansipsv2_sea.nc', 'LSMASK')
#infile ='/home/josmarti/Data/Observations/had2cis_1x1_198001_202004_sicn.nc'
#infile ='/home/josmarti/Data/sic_monthly_CCCma-CanCM4_NEW_1x1_grid_i20171201.nc'
#infile = '/home/josmarti/Data/Observations/had2cis_1x1_198001_202004_sicn.nc'
#data=Dataset(infile)
#data.set_auto_mask(False)
lon = nc.getvar(infile, 'lon').squeeze()
lat = nc.getvar(infile, 'lat').squeeze()
region = nc.getvar(infile, 'region').squeeze()
#skill=np.load('/home/josmarti/Data/GeoACC_NEW.npy', allow_pickle=True).astype(float)
#mask=nc.getvar('/home/josmarti/Data/1x1_reg_mask.nc', 'region').squeeze()
#mask[mask != ]=0
#region=nc.getvar('/home/josmarti/Data/lsmask_cansipsv2_sea.nc', 'LSMASK') #landsea mask troubleshooting
regionlabs = [
'0land', '1ARC', '2GIN', '3BAR', '4KAR', '5LAP', '6ESI', '7CHU', '8BER',
import calendar
import rms_utils_boot as bt
import CCMA_plot
from scipy.io import loadmat
import math as math
years = range(1979, 2011)
monthly_matrix = np.zeros(((10 * (max(years) + 1 - min(years))), 12))
model = np.zeros(((10 * (max(years) + 1 - min(years))), 12))
control = np.zeros((12, max(years) + 1 - min(years)))
ACC = np.zeros(12, dtype=np.ndarray)
for y in years:
for i in range(1, 11):
monthly_matrix[((y - min(years)) * 10 + (i - 1)), :] = nc.getvar((
'/home/josmarti/Data/APPOSITE/pred/Jul/SIE/SIE_Omon_pred_%iJul%i.nc'
% (y, i)), 'sic').squeeze() * 2 * math.pi * 6.371**2
for k in years:
control[:, k - min(years)] = nc.getvar(
('/home/josmarti/Data/APPOSITE/ctrl/sie_Omon_ctrl_%i.nc' % k),
'sic').squeeze() * 2 * math.pi * 6.371**2
clim_mean = np.mean(control, axis=1)
#%%
for y in range(len(years)):
for e in range(10):
model[(y * 10 + e), :] = np.mean(np.delete(
monthly_matrix[y * 10:y * 10 + 10, :], e, axis=0),
axis=0)
@author: Joseph Martin, University of Victoria (edited by Michael Sigmond)
"""
from netCDF4 import Dataset
import math as math
import nc as nc
import numpy as np
import array as ar
import matplotlib.colors
import matplotlib.pyplot as plt
import pandas as pd
import calendar
basedir = '/home/josmarti/Data/SIE'
obsin = nc.getvar(
'/home/josmarti/Data/Observations/NSIDC_1979_2010_nh_siea.nc',
'sie').squeeze()
sim = np.zeros((12, 12, 31))
#Shape observations
obs = np.reshape(obsin, (32, 12)).transpose()
#Build model array
for months in range(1, 13):
if months < 10:
m = "0%s" % months
else:
m = str(months)
for years in range(1979, 2009 + 1):
y = str(years)
"""
import nc as nc
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
#regionlabs=['0land','1ARC','2GIN','3BAR','4KAR','5LAP','6ESI','7CHU','8BER','9OKH','10BEA','11CAN','12HUD','13BAF','14LAB','15OTHER']
region = "3BAR"
if region[1].isdigit():
r = int(region[0:2])
else:
r = int(region[0])
mask = nc.getvar('/home/josmarti/Data/iceregions_128_64.nc', 'REG').squeeze()
mask[mask != float(r)] = 0
mask[mask == float(r)] = 1
years = range(2300, 2449)
siv = pd.DataFrame(0, index=range(12), columns=years)
gridpoint = nc.getvar(
'/home/josmarti/Data/areacella_fx_CanCM4_decadal2001_r0i0p0.nc',
'areacella')
for year in years:
var = nc.getvar((
'/home/josmarti/Data/CanCM4_control/sc_dhfp1e_e001_%s_m01_%s_m12_sic.nc'
% (year, year)), 'sic').squeeze()
sit = var / 913.0 #SIT=SIC/913 kg/m3
obsin[obsin == -9999] = np.nan
if metric == 'SIA':
for m in ["%02d" % i for i in range(1, 13)]:
obsin[int(m) - 1, :] = np.loadtxt(
'/home/josmarti/Data/Observations/NSIDC/north/N_%s_extent_v3.0.csv'
% m,
usecols=5,
skiprows=1,
delimiter=',',
max_rows=40)
obsin[obsin == -9999] = np.nan
mean_obs = np.nanmean(obsin[:, (syear - 1979):(eyear - 1979)], axis=1) * 1e12
annual_mean_obs = np.nanmean(obsin[:, (syear - 1979):(eyear - 1979)],
axis=0) * 1e12
gridpoint = nc.getvar(
'/home/josmarti/Data/areacella_fx_CanCM4_decadal2001_r0i0p0.nc',
'areacella')
PIOMAS = np.loadtxt(
'/home/josmarti/Data/Observations/PIOMAS_SIV_monthly_1979-2020.txt',
usecols=range(1, 13))
if metric == 'SIV':
mean_obs = np.mean(PIOMAS[(syear - 1979):(eyear - 1979)], axis=0) * 1e12
years = [2312, 2435, 2413, 2356, 2387, 2332]
ensembles = ["%03d" % i for i in range(1, 13)]
#ensembles=["001"]
run_length = 1 #years
daily_matrix = np.zeros((6, 12, 1095))
days = 365 * 3
'Pan-Arctic', 'Central Arctic', 'GIN Seas', 'Barents Sea', 'Kara Sea',
'Laptev Sea', 'East Siberian Sea', 'Chukchi Sea', 'Bering Sea',
'Sea of Okhotsk', 'Beaufort Sea', 'Canadian Archipelago', 'Hudson Bay',
'Baffin Bay', 'Labrador Sea'
]
#regionlabs=['0land','1ARC','2GIN','3BAR','4KAR','5LAP','6ESI','7CHU','8BER','9OKH','10BEA','11CAN','12HUD','13BAF','14LAB','15OTHER']
region = "0NONE"
if region[1].isdigit():
r = int(region[0:2])
else:
r = int(region[0])
std_mask = True
if std_mask:
mask = nc.getvar('/home/josmarti/Data/1x1_reg_mask.nc', 'region').squeeze()
gridpoint = nc.getvar('/home/josmarti/Data/gridpoint_1x1.nc', 'areacella')
mask[mask != r] = 0
mask[mask == r] = 1
area = np.sum(np.multiply(gridpoint, mask))
#%%
cyears = range(2300, 2449)
control = np.zeros((12, len(cyears)))
if r != 0:
for year in cyears:
control[:, cyears.index(year)] = nc.getvar((
'/home/josmarti/Data/CanCM4_control/SIE/%s/sc_dhfp1e_e001_%s_%i_SIE.nc'
%
(region, region, year)), 'sicn').squeeze() * 2 * math.pi * 6.371**2
else:
##
## AUTHOR: Michael Sigmond (CCCma)
##
## INPUT
#####################################################
import numpy as np
import cdo#; cdo = cdo.Cdo()
import matplotlib.pyplot as plt # for basic plotting
import rms_plots as rpl
import nc as nc
###############################################################################
#######READ########## #########################################################
###############################################################################
infile = '/home/josmarti/Data/iceregions_128_64.nc'
lon=nc.getvar(infile,'lon')
lat=nc.getvar(infile,'lat')
region=nc.getvar(infile,'REG').squeeze()
regionlabs=['-1ocean','0land','1ARC','2BER','3STL','4BAF','5GRE','6BAR','7KAR','8LAP','9ESI','10CHU','11BEA','12CAN','13HUD','14OKH']
###############################################################################
#######PLOT####################################################################
###############################################################################
###colors####################################################################
clevs=np.arange(17)-1.5
clevslab=np.arange(17)-1
full_cmap = plt.get_cmap('jet')
colors=full_cmap([0,20,40,60,80,100,110,120,130,140,160,180,200,220,240,255])
####open figs####################################################################
ncol=1; nrow=1; fig, axs = plt.subplots(nrow,ncol); fig.set_size_inches(ncol*2,nrow*2.75)
if r != 0:
"""obsingeo=nc.getvar('/home/josmarti/Data/Observations/had2cis_1x1_198001_202004_sicn.nc', 'SICN').squeeze()
#obsingeo[obsingeo.mask==True]=1 #CHECK WITH MICHAEL!!!!!!
if metric=="SIE":
obsingeo[obsingeo <= 0.15] = 0
obsingeo[obsingeo > 0.15] = 1
mask=nc.getvar('/home/josmarti/Data/1x1_reg_mask.nc', 'region').squeeze()
gridpoint=nc.getvar('/home/josmarti/Data/gridpoint_1x1.nc','areacella')
mask[mask != r]=0
mask[mask == r]=1
obs1=np.multiply(obsingeo.astype(float),mask.astype(float)) #changes to dtype=float64 to avoid run time error
#obs2=obs1 #Select Northern Hemisphere
obsnh=np.multiply(obs1,gridpoint.astype(float))
obstemp=np.sum(np.sum((obsnh/(1e6*2.8**2)), axis=1), axis=1) #CHANGED FROM MEAN"""
obsin = nc.getvar(
('/home/josmarti/Data/Observations/Observed_SIE_%i.nc' % r),
'SICN').squeeze() * 2 * math.pi * 6.371**2
#obsin=mitch['regionalSIE'][0:456,(r-1)]
obs2mask = obsin[((min(years) - 1980) * 12):((max(years) + 2 - 1980) * 12)]
obs = np.reshape(obs2mask, ((period + 2), 12)).transpose()
else:
obsingeo = np.delete(
nc.getvar(
'/home/josmarti/Data/Observations/had2cis_128_64_195901_202004_sic.nc',
'SICN').squeeze(), 128, 2)
gridpoint = nc.getvar(
'/home/josmarti/Data/areacella_fx_CanCM4_decadal2001_r0i0p0.nc',
'areacella')
obs1 = np.multiply(obsingeo, gridpoint)
obsnh = np.delete(obs1, range(32), axis=1)
obstemp = np.mean(np.mean(obsnh, axis=1), axis=1)
