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BG_functions.py
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BG_functions.py
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##############################################################
# Date: 01/01/16
# Name: Alek Petty
# Description: Functions/classes used by the BG_drift Python scripts
import matplotlib
matplotlib.use("AGG")
# basemap import
from mpl_toolkits.basemap import Basemap, shiftgrid
# Numpy import
import numpy as np
from pylab import *
from scipy.io import netcdf
import numpy.ma as ma
import string
from matplotlib.patches import Polygon
from mpl_toolkits.axes_grid.inset_locator import zoomed_inset_axes
from mpl_toolkits.axes_grid.inset_locator import mark_inset
from mpl_toolkits.axes_grid.anchored_artists import AnchoredSizeBar
from scipy import stats
from math import log10, floor
from netCDF4 import Dataset
import os
from glob import glob
import time
from scipy.interpolate import griddata
def calc_uv(xvel, yvel, lons):
# Script to convert vectors from xy to uv
u_z = ma.masked_all((xvel.shape[0],xvel.shape[1]))
v_m = ma.masked_all((xvel.shape[0],xvel.shape[1]))
mask = ma.getmask(xvel)
#index = np.where(mask==False)
for i in xrange(xvel.shape[0]):
for j in xrange(xvel.shape[1]):
#TO TRANSPOSE OR NOT?..
alpha = (lons[i, j])*pi/180.
if (mask[i, j]==False):
u_z[i, j] = yvel[i, j]*sin(alpha) + xvel[i, j]*cos(alpha)
v_m[i, j] = yvel[ i, j]*cos(alpha) - xvel[ i, j]*sin(alpha)
return u_z, v_m
def calc_cersat_drift_UV(m, init_path, save_path, start_year, end_year, years, months, month_str, num_days_lag, plot=1):
# Script to read in CERSAT drift vectors to calculate a monthly drift estimate
files = glob(init_path+'2007/200701*.nc')
num_years = end_year - start_year +1
file_path = files[0]
f = Dataset(file_path, 'r')
lon = f.variables['longitude'][:]
lat = f.variables['latitude'][:]
xpts, ypts = m(lon, lat)
#xpts_t = loadtxt(datapath+'xpts_wind_trend'+grid_str+'.txt')
#ypts_t = loadtxt(datapath+'ypts_wind_trend'+grid_str+'.txt')
nx = xpts.shape[0]
ny = xpts.shape[1]
#months = [9, 10, 11, 12, 1, 2, 3, 4]
num_months = size(months)
drift_months = ma.masked_all((num_years, num_months, 2, nx, ny))
drift_years = ma.masked_all((num_years, 2, nx, ny))
str_num_days_lag='('+str(num_days_lag)+'day lag)'
for y in xrange (num_years):
print (y)
year1 = str(y+start_year)
year2 = str(y+start_year+years[-1])
for mon in xrange(size(months)):
month = '%02d' % (months[mon]+1)
year = str(start_year + y + years[mon])
files = glob(init_path+year+'/'+year+month+'*.nc')
num_days = size(files) #int(size(files)/int(num_days_lag)+1)
print size(files), num_days_lag, num_days
if size(files)>0:
drift_days = ma.masked_all((num_days,2, xpts.shape[0], xpts.shape[1]))
#curl_days = ma.zeros((size(files), xpts.shape[0], xpts.shape[1]))
#DO WE WAANT TO PREVENT THE LAG CALCULATIONS FROM OVERLAPPING? IF NOT THEN JUST DO FOR ALL DAILY LAG FIELDS.
i=0
#for x in xrange(0, size(files), int(num_days_lag)):
for x in xrange(0, size(files)):
print x
day = '%02d' % x
f = Dataset(files[x], 'r')
u = f.variables['zonal_motion'][0]/(60.*60.*24.*num_days_lag)
v = f.variables['meridional_motion'][0]/(60.*60.*24.*num_days_lag)
#less than 0 are flags meaning the drift hasnt passed certain tests.
#q = f.variables['quality_flag'][0]
#u = ma.masked_where((q<=0), u)
#v = ma.masked_where((q<=0), v)
#ROTATE VECOTRS TO X/Y GRID
drift_days[i, 0] = u #ma.masked_where(sqrt((drift_u**2) + (drift_v**2))<0.001, drift_u)
drift_days[i, 1] = v #ma.masked_where(sqrt((drift_u**2) + (drift_v**2))<0.001, drift_v)
i+=1
drift_mask_count = ma.count_masked(drift_days, axis=0)
drift_months[y, mon, 0] = ma.mean(drift_days[:, 0], axis=0)
drift_months[y, mon, 1] = ma.mean(drift_days[:, 1], axis=0)
#MASK WHERE MORE THAN 15 DAYS HAVE NO DATA (I.E. AT LEAST 15 DAYS OF DATA IN THAT MONTH)
#days_in_month=15
#drift_months[y, mon] = ma.masked_where(drift_mask_count>days_in_month, drift_months[y, mon])
#drift_days.dump(init_path+'OUTPUT/'+str(start_year)+'-'+year2+'-'+month_str+'-drift_data_daily.txt')
drift_months.dump(save_path+'/'+str(start_year)+'-'+year2+'-'+month_str+'-drift_data_months_uv.txt')
def calc_cersat_drift_XY(m, init_path, save_path, start_year, end_year, years, months, month_str, num_days_lag, plot=1):
#Same as clac_cersat_drift_UV but rotates the vetors back on to a an xy direction, for plotting and comparison with Fowler drifts.
files = glob(init_path+'2007/*.nc')
plot==0
num_years = end_year - start_year +1
file_path = files[0]
f = Dataset(file_path, 'r')
lon = f.variables['longitude'][:]
lat = f.variables['latitude'][:]
xpts, ypts = m(lon, lat)
#xpts_t = loadtxt(datapath+'xpts_wind_trend'+grid_str+'.txt')
#ypts_t = loadtxt(datapath+'ypts_wind_trend'+grid_str+'.txt')
nx = xpts.shape[0]
ny = xpts.shape[1]
#months = [9, 10, 11, 12, 1, 2, 3, 4]
num_months = size(months)
drift_months = ma.masked_all((num_years, num_months, 2, nx, ny))
drift_years = ma.masked_all((num_years, 2, nx, ny))
str_num_days_lag='('+str(num_days_lag)+'day lag)'
for y in xrange (num_years):
print (y)
year1 = str(y+start_year)
year2 = str(y+start_year+years[-1])
for mon in xrange(size(months)):
month = '%02d' % (months[mon]+1)
year = str(start_year + y + years[mon])
files = glob(init_path+year+'/'+year+month+'*.nc')
num_days = size(files) #int(size(files)/int(num_days_lag)+1)
print size(files), num_days_lag, num_days
if size(files)>0:
drift_days = ma.masked_all((num_days,2, xpts.shape[0], xpts.shape[1]))
#curl_days = ma.zeros((size(files), xpts.shape[0], xpts.shape[1]))
#DO WE WAANT TO PREVENT THE LAG CALCULATIONS FROM OVERLAPPING? IF NOT THEN JUST DO FOR ALL DAILY LAG FIELDS.
i=0
#for x in xrange(0, size(files), int(num_days_lag)):
for x in xrange(0, size(files)):
print x
day = '%02d' % x
f = Dataset(files[x], 'r')
u = f.variables['zonal_motion'][0]/(60.*60.*24.*num_days_lag)
v = f.variables['meridional_motion'][0]/(60.*60.*24.*num_days_lag)
#less than 0 are flags meaning the drift hasnt passed certain tests.
#q = f.variables['quality_flag'][0]
#u = ma.masked_where((q<=0), u)
#v = ma.masked_where((q<=0), v)
#ROTATE VECOTRS TO X/Y GRID
drift_u,drift_v = m.rotate_vector(u,v,lon,lat)
#drift_u = interp(drift_u, xpts, ypts, xpt_t, ypts_t)
#drift_v = interp(drift_u, xpts, ypts, xpt_t, ypts_t)
drift_days[i, 0] = drift_u #ma.masked_where(sqrt((drift_u**2) + (drift_v**2))<0.001, drift_u)
drift_days[i, 1] = drift_v #ma.masked_where(sqrt((drift_u**2) + (drift_v**2))<0.001, drift_v)
date = year+'-'+month+'-'+day
i+=1
#if (plot==1):
# plot_var_xy(m, xpts , ypts, drift_days[x, 0], drift_days[x, 1], sqrt((drift_days[x, 0]**2) + (drift_days[x, 1]**2)), out=init_path+'/FIGS/days/'+date, units_lab=r'm s$^{-1}$', units_vec=r'm s$^{-1}$',
# minval=0., maxval=0.2, base_mask=1,res=1, scale_vec=1, vector_val=0.2, year_string=year, month_string=month, cbar_type='max', cmap_1=plt.cm.jet)
drift_mask_count = ma.count_masked(drift_days, axis=0)
drift_months[y, mon, 0] = ma.mean(drift_days[:, 0], axis=0)
drift_months[y, mon, 1] = ma.mean(drift_days[:, 1], axis=0)
#MASK WHERE MORE THAN 15 DAYS HAVE NO DATA (I.E. AT LEAST 15 DAYS OF DATA IN THAT MONTH)
days_in_month=15
drift_months[y, mon] = ma.masked_where(drift_mask_count>days_in_month, drift_months[y, mon])
#drift_months[y, mon, 0] = ma.masked_where(sqrt((drift_u_months_temp**2) + (drift_v_months_temp**2))<0.05, drift_u_months_temp)
#drift_months[y, mon, 1] = ma.masked_where(sqrt((drift_u_months_temp**2) + (drift_v_months_temp**2))<0.05, drift_v_months_temp)
if (plot==1):
plot_var_xy(m, xpts , ypts, drift_months[y, mon, 0], drift_months[y, mon, 1], sqrt((drift_months[y, mon, 0]**2) + (drift_months[y, mon, 1]**2)), out=init_path+'/FIGS/months/'+year+'-'+month, units_lab=r'm s$^{-1}$', units_vec=r'm s$^{-1}$',
minval=0., maxval=0.3, base_mask=1,res=2, scale_vec=0.5, vector_val=0.2, year_string=year, month_string=month,extra=str_num_days_lag, cbar_type='max', cmap_1=plt.cm.YlOrRd)
drift_years[y, 0] = ma.mean(drift_months[y, :, 0], axis=0)
drift_years[y, 1] = ma.mean(drift_months[y, :, 1], axis=0)
if (plot==1):
plot_var_xy(m, xpts , ypts, drift_years[y, 0], drift_years[y, 1], sqrt((drift_years[y, 0]**2) + (drift_years[y, 1]**2)), out=init_path+'/FIGS/years/'+year1+'-'+year2+'-'+month_str, units_lab=r'm s$^{-1}$', units_vec=r'm s$^{-1}$',
minval=0., maxval=0.2, base_mask=1,res=2, scale_vec=0.5, vector_val=0.2, year_string=year1+'-'+year2, month_string=month_str,extra=str_num_days_lag, cbar_type='max', cmap_1=plt.cm.YlOrRd)
drift_months.dump(save_path+str(start_year)+'-'+year2+'-'+month_str+'-drift_data_months.txt')
xpts.dump(save_path+'/xpts.txt')
ypts.dump(save_path+'ypts.txt')
def plot_var_xy(m, xpts , ypts, var_x, var_y, var_mag, out='./figure', units_lab='units', units_vec=r'm s$^{-1}$',
minval=1., maxval=1., base_mask=1,res=1, scale_vec=1, vector_val=1, year_string='year', month_string='months', extra='',cbar_type='both', cmap_1=plt.cm.RdBu_r):
#PLOT SCALAR FIELD WITH OVERLYING VECTORS.
#VAR MAG MAY NOT NECCESARRILY BE THE MAGNITUDE OF THE VECTORS (E.G. IN THE CASE OF WIND CURL)
fig = figure(figsize=(4.25,5))
ax1 = fig.add_axes([0.0, 0.15, 1.0, 0.85])
#if etopo==1:
# im_etopo = m.pcolormesh(xpts_etopo, ypts_etopo , etopo_var, cmap=plt.cm.Greens_r, vmin=0, vmax=1000, zorder=1)
if (maxval-minval)<1e-10:
minval = -round_to_1(np.amax(var_mag))
maxval = round_to_1(np.amax(var_mag))
#var_mag=ma.masked_where(var_mag<1e8, var_mag)
im1 = m.pcolormesh(xpts , ypts, var_mag, cmap=cmap_1,vmin=minval, vmax=maxval,shading='flat', zorder=4)
# LOWER THE SCALE THE LARGER THE ARROW
Q = m.quiver(xpts[::res, ::res], ypts[::res, ::res], var_x[::res, ::res], var_y[::res, ::res], units='inches',scale=scale_vec, zorder=5)
#ASSIGN A LEGEND OF THE VECTOR
#m.plot(xpts[191, 100], ypts[191, 100], 'x', zorder=10)
m.drawparallels(np.arange(90,-90,-10), linewidth = 0.25, zorder=10)
m.drawmeridians(np.arange(-180.,180.,30.), linewidth = 0.25, zorder=10)
#m.drawmapboundary(fill_color='0.3')
#m.drawmapboundary(fill_color='0.4' , zorder=1)
if base_mask==1:
#m.drawmapboundary(fill_color='0.4' , zorder=1)
m.fillcontinents(color='grey',lake_color='grey', zorder=6)
m.drawcoastlines(linewidth=0.5)
cax = fig.add_axes([0.1, 0.1, 0.8, 0.04])
cbar = colorbar(im1,cax=cax, orientation='horizontal', extend=cbar_type, use_gridspec=True)
cbar.set_label(units_lab)
xticks = np.linspace(minval, maxval, 5)
cbar.set_ticks(xticks)
cbar.formatter.set_powerlimits((-3, 4))
cbar.formatter.set_scientific(True)
cbar.update_ticks()
xS, yS = m(120, 55)
qk = quiverkey(Q, xS, yS, vector_val, str(vector_val)+' '+units_vec, fontproperties={'size': 'medium'}, coordinates='data', zorder = 11)
xS, yS = m(235, 48)
ax1.text(xS, yS, year_string+'\n'+month_string+'\n'+extra,fontsize=12, zorder = 11)
subplots_adjust(bottom=0.0, left=0.0, top = 1.0, right=1.0)
savefig(out+'.png', dpi=300)
close(fig)
#plot vector map (with vectors in x/y directions)
def interp_uv(ut, vt, q, xpts, ypts, xpts2m, ypts2m):
#TURN UT/VT INTO MASKED ARRAYS
#IF Q NEGATIVE THEN REMOVE AS IT IS BY COAST
#IF Q GREATER THAN 0.5 THEN REMOVE AS THIS MEANS NO POINTS NEARBY IN MAKING THE VECTOR
#IF 0 THEN MASK AS NO DATA BUT THINK THAT"S ALREADY THE CASE
#negative q is coastal, 0 is no data, greater than 1 means no data closeby
#create mask where 0 is mask and 1 is data
mask = where((q<=0) | (q>1), 0, 1)
ut = ma.masked_where(mask<0.5, ut)
vt = ma.masked_where(mask<0.5, vt)
u_int=interp_data(ut, xpts, ypts, xpts2m, ypts2m)
v_int=interp_data(vt, xpts, ypts, xpts2m, ypts2m)
mask_int=interp_data_nomask(mask, xpts, ypts, xpts2m, ypts2m)
#DO 0.5 NOT 1 AS THIS MEANS MIGHT BE INFECTED WITH OTHER DATA ON THE INTERP GRID?
u_int = ma.masked_where(mask_int<0.25, u_int)
v_int = ma.masked_where(mask_int<0.25, v_int)
return u_int, v_int
#Script to interpolate vectors onto a new grid
#needs interp_data and interp_data_nomask
def interp_uvCSAT(ut, vt, q, xpts, ypts, xpts2m, ypts2m):
q = q.filled(0)
mask = where(q==0, 0, 1)
u_int=interp_data(ut, xpts, ypts, xpts2m, ypts2m)
v_int=interp_data(vt, xpts, ypts, xpts2m, ypts2m)
#q_ma = ma.masked_where(q<0, q)
mask_int=interp_data_nomask(mask, xpts, ypts, xpts2m, ypts2m)
#LESS THAN A QUARTER OF DATA
u_int = ma.masked_where(mask_int<0.25, u_int)
v_int = ma.masked_where(mask_int<0.25, v_int)
return u_int, v_int, mask_int
#Script to interpolate vectors onto a new grid for the CSAT drift vectors
#needs interp_data and interp_data_nomask
def interp_data(var, xpts, ypts, xpts2m, ypts2m, int_meth='cubic'):
#interpoalte data onto a regular 2d grid. Used by interp_uv
data = ma.getdata(var)
mask = ma.getmask(var)
index = np.where(mask==False)
data_masked = data[index]
xpoints = xpts[index]
ypoints = ypts[index]
points = (xpoints, ypoints)
var_int = griddata(points,data_masked,(xpts2m,ypts2m),method=int_meth)
var_int = ma.masked_array(var_int,np.isnan(var_int))
return var_int
def interp_data_nomask(var, xpts, ypts, xpts2m, ypts2m, int_meth='cubic'):
#interpoalte mask of data onto a regular 2d grid. Used by interp_uv
index = np.where(var>-1)
xpoints = xpts[index]
ypoints = ypts[index]
points = (xpoints, ypoints)
data_ma = var[index]
#tck = interpolate.bisplrep(points, data_masked, , s=0)
#var_int = interpolate.bisplev(xnew[:,0], ynew[0,:], tck)
var_int = griddata(points,data_ma,(xpts2m,ypts2m),method=int_meth)
return var_int
def calc_curl_sq_2d_xy_gradient(x_vel, y_vel, dx_res):
# calculate the curl (squared) of a field given some input vector grid
#CALCULATE THE CURL OF AN X/Y VEXTOR FIELD. DX_RES IF THE GRID RESOLUTION OF THIS REGULARLY SPACED GRID.
#MULTIPLY BY MAGNITUDE TO GET THIS IN SQUARED UNITS ANALAGOUS TO THE WIND STRESS CURL.
#USE GRADIENT FUNCTION WHICH USES CENTRAL DIFFERENCES IN MIDDLE CELLS AND FIRST DIFFERENCES AT THE BOUNDARIES (GIVES SAME SHAPE AS INPUT)
mag = sqrt((x_vel**2) + (y_vel**2))
x_vel_mag = x_vel*mag
y_vel_mag = y_vel*mag
#gradient [0] returns row divs (y direction) then [1] gives column divs (x direction)
dvelydx = np.gradient(y_vel_mag, dx_res)[1]
dvelxdy = np.gradient(x_vel_mag, dx_res)[0]
zeta = dvelydx - dvelxdy
#MASK ARRAY WHERE VALUES ARE NAN
zeta = ma.array(zeta,mask=np.isnan(zeta))
return zeta
def plot_drift(drift1, zi, x, y, out, xpts2, ypts2):
fig = figure(figsize=(4.25,5))
ax1 = fig.add_axes([0.0, 0.15, 1.0, 0.85])
#if etopo==1:
# im_etopo = m.pcolormesh(xpts_etopo, ypts_etopo , etopo_var, cmap=plt.cm.Greens_r, vmin=0, vmax=1000, zorder=1)
minval = -0.2
maxval = 0.1
im1 = m.pcolormesh(xpts , ypts, drift1, cmap=cm.jet,shading='flat', vmin=minval, vmax=maxval, zorder=4)
im2 = m.scatter(xpts2 , ypts2, c=zi, edgecolor='none', vmin=minval, vmax=maxval, zorder=5)
#m2 = m.hexbin(xpts2 , ypts2, c=zi, edgecolor='k', vmin=minval, vmax=maxval, zorder=5)
#bar = colorbar(im1,orientation='horizontal', extend='both')
m.drawparallels(np.arange(90,-90,-10), linewidth = 0.25, zorder=10)
m.drawmeridians(np.arange(-180.,180.,30.), linewidth = 0.25, zorder=10)
#m.drawmapboundary(fill_color='0.3')
base_mask=1
if base_mask==1:
#m.drawmapboundary(fill_color='0.4' , zorder=1)
m.fillcontinents(color='grey',lake_color='grey', zorder=5)
m.drawcoastlines(linewidth=0.5)
cax = fig.add_axes([0.1, 0.1, 0.8, 0.04])
cbar = colorbar(im1,cax=cax, orientation='horizontal', extend='both', use_gridspec=True)
cbar.set_label('drift')
xticks = np.linspace(minval, maxval, 5)
cbar.set_ticks(xticks)
subplots_adjust(bottom=0.0, left=0.0, top = 1.0, right=1.0)
month = y+10
if (month>12):
month=month-12
x=x+1
savefig(init_path+'/AREA_FLUX/area_flux'+str(2008+x)+'_'+str(month)+out+'.png', dpi=300)
close(fig)
#plot drift vector and interpolated scatter of drift, to check the flux gate interpoaltion worked
def plot_gates_map(lon1, lon2, lon3, lat1, lat2, lat3):
#plot a map of flux gates used in flux gate analysis
fig = figure(figsize=(4,4))
gate1_lons = np.linspace(lon1, lon2, 10)
gate1_lats = np.linspace(lat3, lat3, 10)
gate2_lons = np.linspace(lon2, lon3, 10)
gate2_lats = np.linspace(lat3, lat3, 10)
gate3_lons = np.linspace(lon3, lon3, 10)
gate3_lats = np.linspace(lat1, lat2, 10)
gate4_lons = np.linspace(lon3, lon3, 10)
gate4_lats = np.linspace(lat2, lat3, 10)
xpts_g1, ypts_g1 = m(gate1_lons, gate1_lats)
xpts_g2, ypts_g2 = m(gate2_lons, gate2_lats)
xpts_g3, ypts_g3 = m(gate3_lons, gate3_lats)
xpts_g4, ypts_g4 = m(gate4_lons, gate4_lats)
m.plot(xpts_g1, ypts_g1, '-', color='r')
m.plot(xpts_g2, ypts_g2, '-', color='b')
m.plot(xpts_g3, ypts_g3, '-', color='g')
m.plot(xpts_g4, ypts_g4, '-', color='m')
m.drawparallels(np.arange(90,-90,-10), linewidth = 0.25, zorder=10)
m.drawmeridians(np.arange(-180.,180.,30.), linewidth = 0.25, zorder=10)
m.fillcontinents(color='grey',lake_color='grey', zorder=5)
m.drawcoastlines(linewidth=0.5)
savefig(outpath+'/area_flux_gates_quads_map.pdf', dpi=300)
close(fig)
def return_xpts_ypts_fowler(path, m):
lon_lat = loadtxt(path+'north_x_y_lat_lon.txt')
lons = np.zeros((361, 361))
lats = np.zeros((361, 361))
lons = np.reshape(lon_lat[:, 3], (361, 361))
lats = np.reshape(lon_lat[:, 2], (361, 361))
xpts, ypts = m(lons, lats)
return xpts, ypts
def return_xpts_ypts(path, m):
#return cersat xpts/ypts on given projection
files = glob(path+'2008/*.nc')
f = Dataset(files[0], 'r')
lon = f.variables['longitude'][:]
lat = f.variables['latitude'][:]
xpts, ypts = m(lon, lat)
return xpts, ypts
def calc_era_wind_curl_NEW(m, start_year, end_year, years, months, months_str, date_str, grid_str, box_str, dx_res, arr_res, nx, ny, figpath, datapath, outpath, plot=0):
# ERA WIND CURL CALC
num_years = end_year-start_year+1 #INITIAL YEAR
years_str= str(start_year)+'-'+str(end_year)
time_index = [0, 31*4, 59*4, 90*4, 120*4, 151*4, 181*4, 212*4, 243*4, 273*4, 304*4, 334*4, 365*4]
#time_index = [0, 31*4, 59*4, 90*4, 120*4, 151*4, 181*4, 212*4, 243*4, 263*4, 294*4, 334*4, 365*4]
ave_x_vel_years = ma.masked_all((num_years, nx, ny))
ave_y_vel_years = ma.masked_all((num_years, nx, ny))
ave_wind_curl_years = ma.masked_all((num_years, nx, ny))
wind_curl_month_ave = ma.masked_all((num_years, size(months), nx, ny))
x_vel_month_ave = ma.masked_all((num_years, size(months), nx, ny))
y_vel_month_ave = ma.masked_all((num_years, size(months), nx, ny))
#CALC LONS/LATS ETC ONCE
filepath = datapath+'/ERAI_WINDS_6HOURLY_'+str(2000)+'.nc'
read_winds_obj = read_era_vector(filepath, 'u10', 'v10')
xpts, ypts, lons1d, lons2d, lats1d, lats2d = read_winds_obj.x_y_lons_lats_era(m)
u10_temp, v10_temp = read_winds_obj.vars(0)
x_vel_temp, y_vel_temp, xpts_t, ypts_t = m.transform_vector(u10_temp,v10_temp, lons1d,lats1d,nx,ny,returnxy=True)
maskpath = datapath+'/ERA_MASK.nc'
read_mask_obj = read_era_1var(maskpath, 'lsm')
lsm = read_mask_obj.var()
lsm_t = m.transform_scalar(lsm, lons1d,lats1d,nx,ny,returnxy=False)
lons2d_t = m.transform_scalar(lons2d, lons1d,lats1d,nx,ny,returnxy=False)
lats2d_t = m.transform_scalar(lats2d, lons1d,lats1d,nx,ny,returnxy=False)
savetxt(outpath+'lsm_t'+grid_str+'.txt', lsm_t)
savetxt(outpath+'lons2d_t'+grid_str+'.txt', lons2d_t)
savetxt(outpath+'lats2d_t'+grid_str+'.txt', lats2d_t)
savetxt(outpath+'xpts_wind_trend'+grid_str+'.txt', xpts_t)
savetxt(outpath+'ypts_wind_trend'+grid_str+'.txt', ypts_t)
for x in xrange(num_years):
print 'Year:', x
#x_vel_sum = np.zeros((nx, ny))
#y_vel_sum = np.zeros((nx, ny))
#wind_curl_sum = np.zeros((nx, ny))
#num_periods=1
year1 = str(x+start_year)
year2 = str(x+start_year+years[-1])
for i in xrange(size(months)):
print i
#ADD YEAR IF STRADDLE OVER TWO YEARS
year = start_year + x + years[i]
filepath = datapath+'/ERAI_WINDS_6HOURLY_'+str(year)+'.nc'
read_winds_obj = read_era_vector(filepath, 'u10', 'v10')
wind_curl_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
x_vel_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
y_vel_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
#start = time.time()
#print start
for j in xrange(time_index[months[i]], time_index[months[i]+1], 1):
u10_temp, v10_temp = read_winds_obj.vars(j)
x_vel_month[j-time_index[months[i]]], y_vel_month[j-time_index[months[i]]] = m.transform_vector(u10_temp,v10_temp, lons1d,lats1d,nx,ny,returnxy=False)
wind_curl_month[j-time_index[months[i]]] = calc_curl_sq_2d_xy_gradient(x_vel_month[j-time_index[months[i]]], y_vel_month[j-time_index[months[i]]], dx_res)
#plot_var_xy(m, xpts_t, ypts_t,x_vel_month[j-time_index[months[i]]], y_vel_month[j-time_index[months[i]]], wind_curl_month[j-time_index[months[i]]],out=figpath+'wind_curl_'+grid_str+str(j)+'new',\
#units_lab=r'm s$^{-2}$',minval=-2e-4, maxval=2e-4, vector_val=10, base_mask=1, res=arr_res, scale_vec=50, year_string=year1+'-'+year2, month_string=str(j))
#end = time.time()
#print end - start
wind_curl_month_ave[x, i] = ma.mean(wind_curl_month, axis=0)
x_vel_month_ave[x, i] = ma.mean(x_vel_month, axis=0)
y_vel_month_ave[x, i] = ma.mean(y_vel_month, axis=0)
ave_wind_curl_years[x] = ma.mean(wind_curl_month_ave[x], axis=0)
ave_x_vel_years[x] = ma.mean(x_vel_month_ave[x], axis=0)
ave_y_vel_years[x] = ma.mean(y_vel_month_ave[x], axis=0)
date = year1+'-'+year2+months_str
#REMOVE BOX_STR AS NOT ACT CALCULATING WIHTIN THAT REGION
wind_curl_month_ave.dump(outpath+date_str+'/wind_curl_month_ave'+date_str+box_str+'.txt')
x_vel_month_ave.dump(outpath+date_str+'/x_vel_month_ave'+date_str+box_str+'.txt')
y_vel_month_ave.dump(outpath+date_str+'/y_vel_month_ave'+date_str+box_str+'.txt')
ave_wind_curl_years.dump(outpath+date_str+'/ave_wind_curl_years'+date_str+box_str+'.txt')
ave_x_vel_years.dump(outpath+date_str+'/ave_xvel_years'+date_str+box_str+'.txt')
ave_y_vel_years.dump(outpath+date_str+'/ave_yvel_years'+date_str+box_str+'.txt')
def calc_ncep_wind_curl_NEW(m, start_year, end_year, years, months, months_str, date_str, grid_str, dx_res, arr_res, nx, ny, figpath, datapath, plot=0):
# READ IN REANLYSIS 1 WIND DATA (995 SIGMA) AND CALCULATE THE CURL
num_years = end_year-start_year+1 #INITIAL YEAR
years_str= str(start_year)+'-'+str(end_year)
time_index = [0, 31*4, 59*4, 90*4, 120*4, 151*4, 181*4, 212*4, 243*4, 273*4, 304*4, 334*4, 365*4]
#time_index = [0, 31*4, 59*4, 90*4, 120*4, 151*4, 181*4, 212*4, 243*4, 263*4, 294*4, 334*4, 365*4]
ave_x_vel_years = np.zeros((num_years, nx, ny))
ave_y_vel_years = np.zeros((num_years, nx, ny))
ave_wind_curl_years = np.zeros((num_years, nx, ny))
#CALC LONS/LATS ETC ONCE
read_winds_obj = read_ncep_vector(2000)
xpts, ypts, lons1d, lons2d, lats1d, lats2d = read_winds_obj.x_y_lons_lats(m)
u10_temp, v10_temp = read_winds_obj.vars(0)
x_vel_temp, y_vel_temp, xpts_t, ypts_t = m.transform_vector(u10_temp,v10_temp, lons1d,lats1d,nx,ny,returnxy=True)
lsm = read_ncep_mask()
lsm_t = m.transform_scalar(lsm, lons1d,lats1d,nx,ny,returnxy=False)
lons2d_t = m.transform_scalar(lons2d, lons1d,lats1d,nx,ny,returnxy=False)
lats2d_t = m.transform_scalar(lats2d, lons1d,lats1d,nx,ny,returnxy=False)
savetxt(datapath+'lsm_t'+grid_str+'.txt', lsm_t)
savetxt(datapath+'lons2d_t'+grid_str+'.txt', lons2d_t)
savetxt(datapath+'lats2d_t'+grid_str+'.txt', lats2d_t)
savetxt(datapath+'xpts_wind_trend'+grid_str+'.txt', xpts_t)
savetxt(datapath+'ypts_wind_trend'+grid_str+'.txt', ypts_t)
for x in xrange(num_years):
print 'Year:', x
x_vel_sum = np.zeros((nx, ny))
y_vel_sum = np.zeros((nx, ny))
wind_curl_sum = np.zeros((nx, ny))
num_periods=0
year1 = str(x+start_year)
year2 = str(x+start_year+years[-1])
for i in xrange(size(months)):
#ADD YEAR IF STRADDLE OVER TWO YEARS
year = start_year + x + years[i]
read_winds_obj = read_ncep_vector(year)
for j in xrange(time_index[months[i]-1], time_index[months[i]], 1):
num_periods+=1
#print 'Time period:', i
u10_temp, v10_temp = read_winds_obj.vars(j)
x_vel_temp, y_vel_temp = m.transform_vector(u10_temp,v10_temp, lons1d,lats1d,nx,ny,returnxy=False)
wind_curl_temp = calc_curl_sq_2d_xy_gradient(x_vel_temp, y_vel_temp, dx_res)
x_vel_sum = x_vel_sum + x_vel_temp
y_vel_sum = y_vel_sum + y_vel_temp
wind_curl_sum = wind_curl_sum + wind_curl_temp
ave_x_vel_years[x]= ma.masked_where(x_vel_sum==0, x_vel_sum)/num_periods
ave_y_vel_years[x]= ma.masked_where(y_vel_sum==0, y_vel_sum)/num_periods
ave_wind_curl_years[x] = ma.masked_where(wind_curl_sum==0, wind_curl_sum)/num_periods
#ave_x_vel_years[x]= ma.masked_where(x_vel_sum==0, x_vel_sum)/num_periods
date = year1+'-'+year2+months_str
ave_wind_curl_years.dump(datapath+date_str+'/ave_wind_curl_years'+date_str+'.txt')
ave_x_vel_years.dump(datapath+date_str+'/ave_xvel_years'+date_str+'.txt')
ave_y_vel_years.dump(datapath+date_str+'/ave_yvel_years'+date_str+'.txt')
#xpts_t.dump('/Users/apetty/NOAA/DATA/WINDS/PCA/xpts'+grid_str+'.txt')
#ypts_t.dump('/Users/apetty/NOAA/DATA/WINDS/PCA/ypts'+grid_str+'.txt')
def calc_ncep2_wind_curl_NEW(m, start_year, end_year, years, months, months_str, date_str, grid_str, box_str, dx_res, arr_res, nx, ny, figpath, datapath, outpath, plot=0):
# READ IN REANLYSIS 2 WIND DATA AND CALCULATE THE CURL
num_years = end_year-start_year+1 #INITIAL YEAR
years_str= str(start_year)+'-'+str(end_year)
time_index = [0, 31*4, 59*4, 90*4, 120*4, 151*4, 181*4, 212*4, 243*4, 273*4, 304*4, 334*4, 365*4]
#time_index = [0, 31*4, 59*4, 90*4, 120*4, 151*4, 181*4, 212*4, 243*4, 263*4, 294*4, 334*4, 365*4]
ave_x_vel_years = ma.masked_all((num_years, nx, ny))
ave_y_vel_years = ma.masked_all((num_years, nx, ny))
ave_wind_curl_years = ma.masked_all((num_years, nx, ny))
wind_curl_month_ave = ma.masked_all((num_years, size(months), nx, ny))
x_vel_month_ave = ma.masked_all((num_years, size(months), nx, ny))
y_vel_month_ave = ma.masked_all((num_years, size(months), nx, ny))
#CALC LONS/LATS ETC ONCE FOR A RANDOM YEAR
filepath = datapath+'/REANAL2'
read_winds_obj = read_ncep2_vector(2000, filepath)
xpts, ypts, lons1d, lons2d, lats1d, lats2d = read_winds_obj.x_y_lons_lats(m)
u10_temp, v10_temp = read_winds_obj.vars(0)
x_vel_temp, y_vel_temp, xpts_t, ypts_t = m.transform_vector(u10_temp,v10_temp, lons1d,lats1d,nx,ny,returnxy=True)
lsm = read_ncep2_mask(filepath)
lsm_t = m.transform_scalar(lsm, lons1d,lats1d,nx,ny,returnxy=False)
lons2d_t = m.transform_scalar(lons2d, lons1d,lats1d,nx,ny,returnxy=False)
lats2d_t = m.transform_scalar(lats2d, lons1d,lats1d,nx,ny,returnxy=False)
savetxt(outpath+'lsm_t'+grid_str+'.txt', lsm_t)
savetxt(outpath+'lons2d_t'+grid_str+'.txt', lons2d_t)
savetxt(outpath+'lats2d_t'+grid_str+'.txt', lats2d_t)
savetxt(outpath+'xpts_wind_trend'+grid_str+'.txt', xpts_t)
savetxt(outpath+'ypts_wind_trend'+grid_str+'.txt', ypts_t)
for x in xrange(num_years):
year1 = str(x+start_year)
year2 = str(x+start_year+years[-1])
for i in xrange(size(months)):
#print i
#ADD YEAR IF STRADDLE OVER TWO YEARS
year = start_year + x + years[i]
print year
filepath = datapath+'/REANAL2'
read_winds_obj = read_ncep2_vector(year, filepath)
wind_curl_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
x_vel_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
y_vel_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
for j in xrange(time_index[months[i]], time_index[months[i]+1], 1):
u10_temp, v10_temp = read_winds_obj.vars(j)
x_vel_month[j-time_index[months[i]]], y_vel_month[j-time_index[months[i]]] = m.transform_vector(u10_temp,v10_temp, lons1d,lats1d,nx,ny,returnxy=False)
wind_curl_month[j-time_index[months[i]]] = calc_curl_sq_2d_xy_gradient(x_vel_month[j-time_index[months[i]]], y_vel_month[j-time_index[months[i]]], dx_res)
wind_curl_month_ave[x, i] = ma.mean(wind_curl_month, axis=0)
x_vel_month_ave[x, i] = ma.mean(x_vel_month, axis=0)
y_vel_month_ave[x, i] = ma.mean(y_vel_month, axis=0)
ave_wind_curl_years[x] = ma.mean(wind_curl_month_ave[x], axis=0)
ave_x_vel_years[x] = ma.mean(x_vel_month_ave[x], axis=0)
ave_y_vel_years[x] = ma.mean(y_vel_month_ave[x], axis=0)
#ave_x_vel_years[x]= ma.masked_where(x_vel_sum==0, x_vel_sum)/num_periods
date = year1+'-'+year2+months_str
wind_curl_month_ave.dump(outpath+date_str+'/wind_curl_month_ave'+date_str+box_str+'.txt')
x_vel_month_ave.dump(outpath+date_str+'/x_vel_month_ave'+date_str+box_str+'.txt')
y_vel_month_ave.dump(outpath+date_str+'/y_vel_month_ave'+date_str+box_str+'.txt')
ave_wind_curl_years.dump(outpath+date_str+'/ave_wind_curl_years'+date_str+box_str+'.txt')
ave_x_vel_years.dump(outpath+date_str+'/ave_xvel_years'+date_str+box_str+'.txt')
ave_y_vel_years.dump(outpath+date_str+'/ave_yvel_years'+date_str+box_str+'.txt')
#xpts_t.dump('/Users/apetty/NOAA/DATA/WINDS/PCA/xpts'+grid_str+'.txt')
#ypts_t.dump('/Users/apetty/NOAA/DATA/WINDS/PCA/ypts'+grid_str+'.txt')
def calc_jra_wind_curl_NEW(m, start_year, end_year, years, months, months_str, date_str, grid_str, box_str, dx_res, arr_res, nx, ny, figpath, datapath, outpath, plot=0):
# READ IN REANLYSIS 2 WIND DATA AND CALCULATE THE CURL
num_years = end_year-start_year+1 #INITIAL YEAR
years_str= str(start_year)+'-'+str(end_year)
time_index = [0, 31*4, 59*4, 90*4, 120*4, 151*4, 181*4, 212*4, 243*4, 273*4, 304*4, 334*4, 365*4]
#time_index = [0, 31*4, 59*4, 90*4, 120*4, 151*4, 181*4, 212*4, 243*4, 263*4, 294*4, 334*4, 365*4]
ave_x_vel_years = ma.masked_all((num_years, nx, ny))
ave_y_vel_years = ma.masked_all((num_years, nx, ny))
ave_wind_curl_years = ma.masked_all((num_years, nx, ny))
wind_curl_month_ave = ma.masked_all((num_years, size(months), nx, ny))
x_vel_month_ave = ma.masked_all((num_years, size(months), nx, ny))
y_vel_month_ave = ma.masked_all((num_years, size(months), nx, ny))
#CALC LONS/LATS ETC ONCE
read_winds_obj = read_jra_vector(2000, datapath)
xpts, ypts, lons1d, lons2d, lats1d, lats2d = read_winds_obj.x_y_lons_lats(m)
u10_temp, v10_temp = read_winds_obj.vars(0)
x_vel_temp, y_vel_temp, xpts_t, ypts_t = m.transform_vector(u10_temp,v10_temp, lons1d,lats1d,nx,ny,returnxy=True)
#lsm = read_era_mask()
#lsm_t = m.transform_scalar(lsm, lons1d,lats1d,nx,ny,returnxy=False)
lons2d_t = m.transform_scalar(lons2d, lons1d,lats1d,nx,ny,returnxy=False)
lats2d_t = m.transform_scalar(lats2d, lons1d,lats1d,nx,ny,returnxy=False)
#savetxt(datapath+'lsm_t'+grid_str+'.txt', lsm_t)
savetxt(outpath+'lons2d_t'+grid_str+'.txt', lons2d_t)
savetxt(outpath+'lats2d_t'+grid_str+'.txt', lats2d_t)
savetxt(outpath+'xpts_wind_trend'+grid_str+'.txt', xpts_t)
savetxt(outpath+'ypts_wind_trend'+grid_str+'.txt', ypts_t)
for x in xrange(num_years):
#print 'Year:', x
year1 = str(x+start_year)
year2 = str(x+start_year+years[-1])
for i in xrange(size(months)):
#print i
#ADD YEAR IF STRADDLE OVER TWO YEARS
year = start_year + x + years[i]
read_winds_obj = read_jra_vector(year, datapath)
wind_curl_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
x_vel_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
y_vel_month = ma.masked_all((time_index[months[i]+1] - time_index[months[i]], nx, ny))
for j in xrange(time_index[months[i]], time_index[months[i]+1], 1):
u10_temp, v10_temp = read_winds_obj.vars(j)
x_vel_month[j-time_index[months[i]]], y_vel_month[j-time_index[months[i]]] = m.transform_vector(u10_temp,v10_temp, lons1d,lats1d,nx,ny,returnxy=False)
wind_curl_month[j-time_index[months[i]]] = calc_curl_sq_2d_xy_gradient(x_vel_month[j-time_index[months[i]]], y_vel_month[j-time_index[months[i]]], dx_res)
wind_curl_month_ave[x, i] = ma.mean(wind_curl_month, axis=0)
x_vel_month_ave[x, i] = ma.mean(x_vel_month, axis=0)
y_vel_month_ave[x, i] = ma.mean(y_vel_month, axis=0)
ave_wind_curl_years[x] = ma.mean(wind_curl_month_ave[x], axis=0)
ave_x_vel_years[x] = ma.mean(x_vel_month_ave[x], axis=0)
ave_y_vel_years[x] = ma.mean(y_vel_month_ave[x], axis=0)
date = year1+'-'+year2+months_str
wind_curl_month_ave.dump(outpath+date_str+'/wind_curl_month_ave'+date_str+box_str+'.txt')
x_vel_month_ave.dump(outpath+date_str+'/x_vel_month_ave'+date_str+box_str+'.txt')
y_vel_month_ave.dump(outpath+date_str+'/y_vel_month_ave'+date_str+box_str+'.txt')
ave_wind_curl_years.dump(outpath+date_str+'/ave_wind_curl_years'+date_str+box_str+'.txt')
ave_x_vel_years.dump(outpath+date_str+'/ave_xvel_years'+date_str+box_str+'.txt')
ave_y_vel_years.dump(outpath+date_str+'/ave_yvel_years'+date_str+box_str+'.txt')
class read_era_vector:
#READ IN U/V VECTOR COMPONENETS FORM ERA-I
#SHIFT LATS FROM SOUTH TO NORTH
#SHIFT LONS FROM 0-360 to -180-180 DEG.
def __init__(self, filepath, varname1, varname2):
self.f = Dataset(filepath, 'r')
self.varname1 = varname1
self.varname2 = varname2
self.lats = self.f.variables['latitude'][::-1]
self.lons0 = self.f.variables['longitude'][:]
var_array_t = self.f.variables[varname1][0, ::-1, ::]
var_temp, self.lons_s = shiftgrid(180.,var_array_t,self.lons0,start=False)
def vars(self, index):
var_array1 = self.f.variables[self.varname1][index, ::-1, ::]
var_array2 = self.f.variables[self.varname2][index, ::-1, ::]
var_temp1, lons_temp = shiftgrid(180.,var_array1,self.lons0,start=False)
var_temp2, lons_temp = shiftgrid(180.,var_array2,self.lons0,start=False)
return var_temp1, var_temp2
def x_y_lons_lats_era(self, m):
lons1d = self.lons_s
lats1d = self.lats
lons2d, lats2d = np.meshgrid(self.lons_s, self.lats)
xpts, ypts = m(lons2d, lats2d)
return xpts, ypts, lons1d, lons2d, lats1d, lats2d
class read_era_1var:
#CLASS TO READ IN ERA-I VARIABLE
def __init__(self, filepath, varname):
self.f = Dataset(filepath, 'r')
self.varname = varname
self.var_array = self.f.variables[varname][0,::-1, ::]
self.lats = self.f.variables['latitude'][::-1]
self.lons0 = self.f.variables['longitude'][:]
var_temp, self.lons_s = shiftgrid(180.,self.var_array,self.lons0,start=False)
def var(self):
#SHIFT GRID FROM 0-360 to -180-180 DEG.
var_temp, lons_temp = shiftgrid(180.,self.var_array,self.lons0,start=False)
return var_temp
def x_y_lons_lats_era(self, m):
lons1d = self.lons_s
lats1d = self.lats
lons2d, lats2d = np.meshgrid(self.lons_s, self.lats)
xpts, ypts = m(lons2d, lats2d)
return xpts, ypts, lons1d, lons2d, lats1d, lats2d
class read_ncep_vector:
#SAME AS ABOVE BUT FOR NCEP DATA
def __init__(self, year):
filepath_u = '/Users/apetty/NOAA/DATA/WINDS/NCEP/DATA/REANAL1/uwnd.sig995.'+str(year)+'.nc'
filepath_v = '/Users/apetty/NOAA/DATA/WINDS/NCEP/DATA/REANAL1/vwnd.sig995.'+str(year)+'.nc'
self.f_u = Dataset(filepath_u, 'r')
self.f_v = Dataset(filepath_v, 'r')
self.varname1 = 'uwnd'
self.varname2 = 'vwnd'
self.lats = self.f_u.variables['lat'][::-1]
self.lons0 = self.f_u.variables['lon'][:]
var_array_t = self.f_u.variables['uwnd'][0, ::-1, ::]
var_temp, self.lons_s = shiftgrid(180.,var_array_t,self.lons0,start=False)
def vars(self, index):
var_array1 = self.f_u.variables[self.varname1][index, ::-1, ::]
var_array2 = self.f_v.variables[self.varname2][index, ::-1, ::]
var_temp1, lons_temp = shiftgrid(180.,var_array1,self.lons0,start=False)
var_temp2, lons_temp = shiftgrid(180.,var_array2,self.lons0,start=False)
return var_temp1, var_temp2
def x_y_lons_lats(self, m):
lons1d = self.lons_s
lats1d = self.lats
lons2d, lats2d = np.meshgrid(self.lons_s, self.lats)
xpts, ypts = m(lons2d, lats2d)
return xpts, ypts, lons1d, lons2d, lats1d, lats2d
class read_ncep2_vector:
#SAME AS ABOVE BUT FOR NCEP REANALYSIS 2 DATA
def __init__(self, year, filepath):
filepath_u = filepath+'/uwnd.10m.gauss.'+str(year)+'.nc'
filepath_v = filepath+'/vwnd.10m.gauss.'+str(year)+'.nc'
self.f_u = Dataset(filepath_u, 'r')
self.f_v = Dataset(filepath_v, 'r')
self.varname1 = 'uwnd'
self.varname2 = 'vwnd'
#AS ARRAYS ARE QUITE BIG - ONLY USE DATA FROM AROUND 25 DEG NORTH
self.lats = self.f_u.variables['lat'][::-1][60:-1]
self.lons0 = self.f_u.variables['lon'][:]
var_array_t = self.f_u.variables['uwnd'][0,0, ::-1, ::][60:-1, :]
var_temp, self.lons_s = shiftgrid(180.,var_array_t,self.lons0,start=False)
def vars(self, index):
var_array1 = self.f_u.variables[self.varname1][index,0, ::-1, ::][60:-1, :]
var_array2 = self.f_v.variables[self.varname2][index,0, ::-1, ::][60:-1, :]
var_temp1, lons_temp = shiftgrid(180.,var_array1,self.lons0,start=False)
var_temp2, lons_temp = shiftgrid(180.,var_array2,self.lons0,start=False)
return var_temp1, var_temp2
def x_y_lons_lats(self, m):
lons1d = self.lons_s
lats1d = self.lats
lons2d, lats2d = np.meshgrid(self.lons_s, self.lats)
xpts, ypts = m(lons2d, lats2d)
return xpts, ypts, lons1d, lons2d, lats1d, lats2d
class read_jra_vector:
#SAME AS ABOVE BUT FOR NCEP REANALYSIS 2 DATA
def __init__(self, year, filepath):
filepath_u = glob(filepath+'/anl_surf125.033_ugrd.'+str(year)+'*.nc')
#print filepath_u
filepath_v = glob(filepath+'/anl_surf125.034_vgrd.'+str(year)+'*.nc')
self.f_u = Dataset(filepath_u[0], 'r')
self.f_v = Dataset(filepath_v[0], 'r')
self.varname1 = 'UGRD_GDS0_HTGL'
self.varname2 = 'VGRD_GDS0_HTGL'
#AS ARRAYS ARE QUITE BIG - ONLY USE DATA FROM AROUND 25 DEG NORTH
self.lats = self.f_u.variables['g0_lat_1'][::-1]
self.lons0 = self.f_u.variables['g0_lon_2'][:]
var_array_t = self.f_u.variables['UGRD_GDS0_HTGL'][0, ::-1, ::]
var_temp, self.lons_s = shiftgrid(180.,var_array_t,self.lons0,start=False)
def vars(self, index):
var_array1 = self.f_u.variables[self.varname1][index,::-1, ::]
var_array2 = self.f_v.variables[self.varname2][index,::-1, ::]
var_temp1, lons_temp = shiftgrid(180.,var_array1,self.lons0,start=False)
var_temp2, lons_temp = shiftgrid(180.,var_array2,self.lons0,start=False)
return var_temp1, var_temp2
def x_y_lons_lats(self, m):
lons1d = self.lons_s
lats1d = self.lats
lons2d, lats2d = np.meshgrid(self.lons_s, self.lats)
xpts, ypts = m(lons2d, lats2d)
return xpts, ypts, lons1d, lons2d, lats1d, lats2d
def read_ncep2_mask(filepath):
#READ IN NCEP 2 LAND MASK
#SHIFT GRID FROM 0-360 to -180-180
filepath_m = filepath+'/land_sfc_gauss.nc'
f = Dataset(filepath_m, 'r')
mask_array = f.variables['land'][0,::-1, ::][60:-1, :]
lats = f.variables['lat'][::-1][60:-1]
lons0 = f.variables['lon'][:]
mask_shift, lons_s = shiftgrid(180.,mask_array,lons0,start=False)
return mask_shift
def calc_wind_curl_trend_bc(REANAL, grid_str, date_str, figpath, datapath, years_str, months_str, box_str, arr_res, start_year, end_year, nx, ny,dx_res, m, beau_lonlat, cent_lonlat):
ave_wind_curl_years= load(datapath+date_str+'/ave_wind_curl_years'+date_str+box_str+'.txt')
ave_x_vel_years= load(datapath+date_str+'/ave_xvel_years'+date_str+box_str+'.txt')
ave_y_vel_years= load(datapath+date_str+'/ave_yvel_years'+date_str+box_str+'.txt')
#NOT SURE WHAT THE JRA ONE IS, SO USE NCEP/ERA lsm_t100KM!
lsm_t = loadtxt(datapath+'/lsm_t'+grid_str+'.txt')
lons2d_t = loadtxt(datapath+'lons2d_t'+grid_str+'.txt')
lats2d_t = loadtxt(datapath+'lats2d_t'+grid_str+'.txt')
nx = ave_x_vel_years.shape[1]
ny = ave_x_vel_years.shape[1]
num_years = ave_x_vel_years.shape[0]
wind_x_trend, wind_x_sig, r_x, int_x = var_trend(ave_x_vel_years, num_years)
wind_y_trend, wind_y_sig, r_y, int_y = var_trend(ave_y_vel_years, num_years)
wind_curl_trend, wind_curl_sig, r_a, int_a = var_trend(ave_wind_curl_years, num_years)
beau_region = np.ones((nx, ny))
central_region = np.ones((nx, ny))
beau_region = where((lsm_t>0.5) | (lons2d_t<beau_lonlat[0]) | (lons2d_t>beau_lonlat[1]) | (lats2d_t>beau_lonlat[2]) | (lats2d_t<beau_lonlat[3]), 0, beau_region)
central_region = where((lsm_t>0.5) | (lons2d_t<cent_lonlat[0]) | (lons2d_t>cent_lonlat[1]) | (lats2d_t>cent_lonlat[2]) | (lats2d_t<cent_lonlat[3]), 0, central_region)
wind_curl_trend_central = ma.masked_where(central_region<0.5, wind_curl_trend)
wind_curl_trend_beau = ma.masked_where(beau_region<0.5, wind_curl_trend)
ave_wind_curl_years_b = np.zeros((num_years))
ave_wind_curl_years_c = np.zeros((num_years))
for x in xrange(num_years):
ave_wind_curl_years_b[x] = np.mean(ma.masked_where(beau_region<0.5, ave_wind_curl_years[x]))
ave_wind_curl_years_c[x] = np.mean(ma.masked_where(central_region<0.5, ave_wind_curl_years[x]))
#plot_wc_trend(m, xpts_t, ypts_t,wind_x_trend,wind_y_trend, wind_curl_trend, \
# out=outpath+'WINDCURL/wind_curl_trend_'+str(dx_res/1000)+'km_'+years_str+months_str,units_lab=r'm s$^{-2}$yr$^{-1}$',\
# units_vec=r'm s$^{-1}$yr$^{-1}$',minval=-1e-6, maxval=1e-6, vector_val=0.1, base_mask=1, res=arr_res, scale_vec=1, year_string=years_str, month_string=months_str)
savetxt(datapath+date_str+'/wind_curl_trend'+box_str+'.txt', wind_curl_trend)
savetxt(datapath+date_str+'/wind_curl_sig'+box_str+'.txt', wind_curl_sig)
savetxt(datapath+date_str+'/wind_x_trend'+box_str+'.txt', wind_x_trend)
savetxt(datapath+date_str+'/wind_x_sig'+box_str+'.txt', wind_x_sig)
savetxt(datapath+date_str+'/wind_y_trend'+box_str+'.txt', wind_y_trend)
savetxt(datapath+date_str+'/wind_y_sig'+box_str+'.txt', wind_y_sig)
savetxt(datapath+date_str+'/ave_wind_curl_years_b'+box_str+'.txt', ave_wind_curl_years_b)
savetxt(datapath+date_str+'/ave_wind_curl_years_c'+box_str+'.txt', ave_wind_curl_years_c)
#PLOT LINEPLOT
#plot_wind_curl_lines(REANAL, grid_str, date_str, figpath, datapath, years_str, months_str, arr_res, start_year, end_year, nx, ny,dx_res, m, beau_lonlat, cent_lonlat)
def var_trend(var, num_years):
#LINEAR REGRESSION OF VAR AGAINST NUMBER OF YEARS FOR EACH GRID CELL (ASSUMED TO BE 2D)
#SIG IS SIGNIFICANCE OF TREND AS A PERCENTAGE
num_x = var.shape[1]
num_y = var.shape[2]
years = np.arange(num_years)
trend = ma.masked_all((num_x, num_y))
sig_a = ma.masked_all((num_x, num_y))
r_a = ma.masked_all((num_x, num_y))