def graph_pred_truth(predictions, forMonth, year, resolution):
"""
Purpose: Graph prediction and truth grids
"""
#Graph predictions
m = Basemap(projection='npstere',boundinglat=65,lon_0=0, resolution='l')
dx_res = resolution * 1000
nx = int((m.xmax-m.xmin)/dx_res)+1; ny = int((m.ymax-m.ymin)/dx_res)+1
#grid_str=str(int(dx_res/1000))+'km'
lonsG, latsG, xptsG, yptsG = m.makegrid(nx, ny, returnxy=True)
# Get lon/lats pf the ice concentration data on polar sterographic grid
datapath = '../../../Data/'
lats, lons = ff.get_psnlatslons(datapath)
xpts, ypts =m(lons, lats)
monthStr = get_month_str(forMonth)
yearStr = str(year)
fig_title = 'Comparison for ' + monthStr + ' ' + yearStr
fig = figure(figsize=(6,6))
fig.suptitle(fig_title, fontsize=18)
ax = fig.add_subplot(221)
ax.set_title('Predictions for ' + monthStr + ' ' + yearStr)
im1 = m.pcolormesh(xptsG , yptsG, predictions, cmap=cm.Blues_r, vmin=0, vmax=1,shading='flat', zorder=2)
truthGrid = get_conc_grid(forMonth, year, resolution)
ax = fig.add_subplot(223)
ax.set_title('Truth for ' + monthStr + ' ' + yearStr)
im2 = m.pcolormesh(xptsG , yptsG, truthGrid, cmap=cm.Blues_r, vmin=0, vmax=1,shading='flat', zorder=2)
#Calculate ice extent and ice areas
areaStr = "Ice Area: \n\nIce Extent: "
ax = fig.add_subplot(222)
ax.axis('off')
ax.text(0.05, 0.95, areaStr, fontsize=12,
verticalalignment='top')
ax = fig.add_subplot(224)
ax.axis('off')
ax.text(0.05, 0.95, areaStr, fontsize=12,
verticalalignment='top')
plt.show()
def get_thick_grid(month, year, resolution):
"""
Purpose: Retrieve the concentration grids using given month, year, and resolution
"""
# File paths
datapath = '../../../Data/'
# Get map projection and create regularly spaced grid from this projection
m = Basemap(projection='npstere',boundinglat=65,lon_0=0, resolution='l')
dx_res = resolution*1000. # 100 km
nx = int((m.xmax-m.xmin)/dx_res)+1; ny = int((m.ymax-m.ymin)/dx_res)+1
lonsG, latsG, xptsG, yptsG = m.makegrid(nx, ny, returnxy=True)
# Get lon/lats on polar sterographic grid
lats, lons = ff.get_psnlatslons(datapath)
xpts, ypts =m(lons, lats)
xptsP, yptsP, thickness=ff.get_pmas_month(m, datapath, year, month)
iceThicknessG = griddata((xptsP, yptsP),thickness, (xptsG, yptsG), method='linear')
return iceThicknessG
def main(year, month, alg=0, poleStr='A', outputGrid=0):
m = Basemap(projection='npstere', boundinglat=65, lon_0=0, resolution='l')
#datapath='/Users/aapetty/GitRepos/GitHub/SeaIcePrediction/Data/'
#dataoutpath='/Users/aapetty/GitRepos/GitHub/SeaIcePrediction/DataOutput/IceConcA/'
#figpath='/Users/aapetty/GitRepos/GitHub/SeaIcePrediction/Figures/Arctic/IceConc/'
datapath = '../../Data'
dataoutpath = '../../DataOutput/IceConcA/'
figpath = '../../Figures/Arctic/IceConc'
dx_res = 100000.
nx = int((m.xmax - m.xmin) / dx_res) + 1
ny = int((m.ymax - m.ymin) / dx_res) + 1
grid_str = str(int(dx_res / 1000)) + 'km'
lonsG, latsG, xptsG, yptsG = m.makegrid(nx, ny, returnxy=True)
if (outputGrid == 1):
xptsG.dump(dataoutpath + 'xpts' + grid_str + poleStr)
yptsG.dump(dataoutpath + 'ypts' + grid_str + poleStr)
lats, lons = ff.get_psnlatslons(datapath)
xpts, ypts = m(lons, lats)
f = Dataset(datapath + '/OTHER/NIC_valid_ice_mask.N25km.01.1972-2007.nc',
'r')
ice_flag = f.variables['valid_ice_flag'][:]
region_mask = ff.get_region_mask_sect(datapath, m, xypts_return=0)
if (year > 2015):
ice_conc = ff.get_month_concSN_NRT(datapath,
year,
month,
alg=alg,
pole=poleStr,
lowerConc=1,
maxConc=1,
mask=1,
monthMean=1)
#ice_conc=ma.masked_where(ice_conc<=0.15, ice_conc)
else:
ice_conc = ff.get_month_concSN(datapath,
year,
month,
alg=alg,
pole=poleStr,
lowerConc=1,
maxConc=1,
mask=1)
#ice_conc = ice_conc.filled(0)
#ice_conc = ma.masked_where(ice_conc>1., ice_conc)
#ice_conc = ma.where(ice_conc>1.,0, ice_conc)
#ice_conc = ma.where(ice_conc<0.15,0, ice_conc)
ice_conc = ma.where((ice_flag >= 1.5), 0, ice_conc)
# get mean conc around the pole hole (time varying)
pmask = ff.get_pmask(year, month)
concHole = ma.mean(ice_conc[(lats > pmask - 0.5) & (lats < pmask)])
ice_conc = where((lats >= pmask - 0.5), concHole, ice_conc)
#ice_conc[where(region_mask>18)]=0
ice_concG = griddata((xpts.flatten(), ypts.flatten()),
ice_conc.flatten(), (xptsG, yptsG),
method='linear')
ice_conc_ma = ma.masked_where(np.isnan(ice_concG), ice_concG)
plot_conc(figpath,
m,
xptsG,
yptsG,
ice_conc_ma,
year,
month,
grid_str,
poleStr='A')
ice_conc_ma.dump(dataoutpath + 'ice_conc' + grid_str + str(month) +
str(year) + poleStr)
import numpy as np from pylab import * import numpy.ma as ma from mpl_toolkits.basemap import Basemap, shiftgrid from glob import glob from netCDF4 import Dataset import forecast_funcs as ff dataPath = '/Users/aapetty/GitRepos/GitHub/SeaIcePrediction/Data/' outPath='/Users/aapetty/GitRepos/GitHub/SeaIcePrediction/DataOutput/Extent/' figPath='/Users/aapetty/GitRepos/GitHub/SeaIcePrediction/Figures/' m = Basemap(projection='npstere',boundinglat=66,lon_0=0, resolution='l' ) lats, lons = ff.get_psnlatslons(dataPath) areaF=reshape(fromfile(file=open(dataPath+'/OTHER/psn25area_v3.dat', 'rb'), dtype='<i4')/1000., [448, 304])/1e6 region_mask, xpts, ypts = ff.get_region_mask_sect(dataPath, m, xypts_return=1) start_year=1979 end_year=2017 month = 9 # 9 = Sept alg=0 #0=NASA TEAM, 1=BOOTSTRAP ice_ext_mean=[] ice_area_mean=[] regions=[12, 13]
def get_conc_grid(month, year, resolution):
"""
Purpose: Retrieve the concentration grids using given month, year, and resolution
"""
poleStr='A'# 'A: Arctic, AA: Antarctic
alg=0 #0=Nasa team
# File paths
datapath = '../../../Data'
# Get map projection and create regularly spaced grid from this projection
m = Basemap(projection='npstere',boundinglat=65,lon_0=0, resolution='l')
dx_res = resolution * 1000
nx = int((m.xmax-m.xmin)/dx_res)+1; ny = int((m.ymax-m.ymin)/dx_res)+1
lonsG, latsG, xptsG, yptsG = m.makegrid(nx, ny, returnxy=True)
# Get lon/lats pf the ice concentration data on polar sterographic grid
lats, lons = ff.get_psnlatslons(datapath)
xpts, ypts =m(lons, lats)
f = Dataset(datapath+'/OTHER/NIC_valid_ice_mask.N25km.01.1972-2007.nc', 'r')
ice_flag = f.variables['valid_ice_flag'][:]
if (year>2015):
ice_conc = ff.get_month_concSN_NRT(datapath, year, month, alg=alg, pole=poleStr, monthMean=1)
# Mask values below 0.15
ice_conc=ma.masked_where(ice_conc<=0.15, ice_conc) #I don't get this...
else:
ice_conc = ff.get_month_concSN(datapath, year, month, alg=alg, pole=poleStr)
# fill ice concentration data with zeroes
ice_conc = ice_conc.filled(0)
ice_conc = where((ice_flag >=1.5), 0, ice_conc)
# Note the pole hole due to the incomplete satellite orbit
if (year<1987):
pmask=84.
elif((year==1987)&(month<=5)):
pmask=84.
elif ((year==1987)&(month>5)):
pmask=86.5
elif ((year>1987)&(year<2008)):
pmask=86.5
else:
pmask=88.5
# Grid data
ice_concG = griddata((xpts.flatten(), ypts.flatten()),ice_conc.flatten(), (xptsG, yptsG), method='linear')
ice_conc_ma=ma.masked_where(np.isnan(ice_concG), ice_concG)
#ice_conc_ma=ma.masked_where((latsG>pmask), ice_conc_ma)
test = np.where(latsG>pmask)
gridData = ice_conc_ma.data
gridData[ice_conc_ma.mask == True] = 0
#WARNING: Super ghetto solution to not just set the hole to 1
arr1 = np.arange(np.min(test[0]), np.max(test[0]))
arr2 = np.ones((1, np.size(arr1)), dtype=np.int ) * (np.min(test[0])-1)
tup = (arr1, arr2)
gridMean = sum(gridData[tup])/np.size(arr1)
gridData[test] = gridMean #fix
return gridData