# current arrows cdx = 7 cdy = 11 # in indices plotdates = netCDF.num2date(ts, units) if year == 2014: monthdates = [datetime(year, month, 1, 0, 0, 0) for month in np.arange(1, 10)] else: monthdates = [datetime(year, month, 1, 0, 0, 0) for month in np.arange(1, 13)] # if not os.path.exists('figures/' + str(year)): # os.makedirs('figures/' + str(year)) # Colormap for model output levels = (37 - np.exp(np.linspace(0, np.log(36.0), 10)))[::-1] - 1 # log for salinity cmap = cmPong.salinity(cmocean.cm.salt, levels) # cmap = cmPong.salinity('YlGnBu_r', levels) ilevels = [0, 1, 2, 3, 4, 5, 8] # which levels to label ticks = [int(tick) for tick in levels[ilevels]] # plot ticks ## ## Wind forcing ## # There are multiple file locations if year <= 2012: w = netCDF.Dataset("/atch/raid1/zhangxq/Projects/narr_txla/txla_blk_narr_" + str(year) + ".nc") elif year == 2013: w = netCDF.Dataset("/rho/raid/home/kthyng/txla/txla_wind_narr_2013.nc") elif year == 2014: w = netCDF.Dataset("/rho/raid/home/kthyng/txla/txla_wind_narr_2014.nc")
## Model output ## m = netCDF.Dataset(loc) # Model time period to use units = m.variables['ocean_time'].units year = 2008 starttime = netCDF.date2num(datetime(year, 5, 1, 12, 0, 0), units) endtime = netCDF.date2num(datetime(year, 10, 1, 12, 0, 0), units) dt = m.variables['ocean_time'][1] - m.variables['ocean_time'][0] # 4 hours in seconds ts = np.arange(starttime, endtime, dt) itshift = find(starttime==m.variables['ocean_time'][:]) # shift to get to the right place in model output dates = netCDF.num2date(m.variables['ocean_time'][:], units) # Colormap for model output levels = (37-np.exp(np.linspace(0,np.log(36.), 10)))[::-1]-1 # log for salinity cmap = cmPong.salinity('YlGnBu_r', levels) ilevels = [0,1,2,3,4,5,8] # which levels to label ticks = [int(tick) for tick in levels[ilevels]] # plot ticks ## ## Wind forcing ## # # Change axis and label color # ax.spines['bottom'].set_color('0.2') # ax.spines['top'].set_color('0.2') # ax.spines['left'].set_color('0.2') # ax.spines['right'].set_color('0.2') # ax.xaxis.label.set_color('0.2')