class TairMosaic():
'''
A class for containing the full mosaic of arrays
'''
def __init__(self,
if __name__ == '__main__':
#Example code for calculating annual trends for a TopoWx tile and outputting them as a GeoTiff
workspace = 'E:\\TOPOWX\\h06v02\\topowx_tile_output\\'
directories = ['h06v02','h06v03','h06v04','h07v02','h07v03','h07v04','h08v02'] #file structure for the NetCDF4 files
variables = ['tmin']#, 'tmax']
mosaic_tair = [] # create an empty mosaic to store the outputs and string arrays together
for i in range(1):
for v in variables:
filename = '%s%s\\%s_%s.nc' %(workspace, directories[i],directories[i], v)
nc_ds = Dataset(filename)
days = utld.get_days_metadata_dates(num2date(nc_ds.variables['time'][:], units=nc_ds.variables['time'].units))
tair_trend = twxs.TairTrend(days,1948,2012)
#tair_agg = twxs.TairAggregate(days)
#Doing this in one shot will take ~10GB of memory
#For less memory usage, process in chunks
tair = nc_ds.variables[v][:]
tair_ann = tair_trend.get_ann_trend(tair)
mosaic_tair.append(tair_ann)
#tair_mon_agg = tair_agg.daily_to_mthly(tair)
#tair_ann_agg = tair_agg.daily_to_ann(tair)
#figure out the non-spatial average
#Output results
#twx_tile_to_gtiff(nc_ds, tair_ann, 'E:\\TOPOWX\\topowx_tile_output\\h06v02\\h06v02_tmin_trend.tiff')
if __name__ == '__main__':
startyear = 1948
endyear = 2012
workspace = 'E:\\TOPOWX\\annual\\'
var = ['tmin'] #,'tmax']
i = 0
dataset = []
for year in range(startyear, endyear + 1):
filename = '%s%s\\%s_%s.nc' % (workspace, var[i], var[i], str(year))
nc_ds = Dataset(filename)
dataset.append(nc_ds)
days = utld.get_days_metadata_dates(
num2date(nc_ds.variables['time'][:],
units=nc_ds.variables['time'].units))
tair_trend = twxs.TairTrend(days, 2012)
#tair_agg = twxs.TairAggregate(days)
#Doing this in one shot will take ~10GB of memory
#For less memory usage, process in chunks
tair = nc_ds.variables[v][:]
tair_ann = tair_trend.get_ann_trend(tair)
ymin = nc_ds.variables['lat'][-1]
ymax = nc_ds.variables['lat'][0]
xmin = nc_ds.variables['lon'][0]
xmax = nc_ds.variables['lon'][-1]
bbox = [xmin, xmax, ymin, ymax]
mosaic_tair.append([tair_ann, bbox])
#tair_mon_agg = tair_agg.daily_to_mthly(tair)
#tair_ann_agg = tair_agg.daily_to_ann(tair)
ens_trend.append((topowx_trend[i]+prism_trend[i])/2) mask.append(np.abs(diff_mask[i])) threshold.append(np.std(diff_mask[i])*2) #check for values twice the standard deviation binary_mask.append(np.where(mask[0]> threshold[0], 0,np.nan)) for i in range(2): plt.subplot(2,2,i+1) plt.imshow(topowx_trend[i]) #look at the elevation trend n = np.shape(elevation)[0]*np.shape(elevation)[1] trend_series = [np.reshape(ens_trend[0],n),np.reshape(ens_trend[1],n)] elev_series = np.reshape(elevation,n) days = utld.get_days_metadata_dates(num2date(nc_ds.variables['time'][:], units=nc_ds.variables['time'].units)) tair_trend = twxs.TairTrend(days,2012) #tair_agg = twxs.TairAggregate(days) #Doing this in one shot will take ~10GB of memory #For less memory usage, process in chunks tair = nc_ds.variables[var[0]][:] tair_ann = tair_trend.get_ann_trend(tair) ymin = nc_ds.variables['lat'][-1] ymax = nc_ds.variables['lat'][0] xmin = nc_ds.variables['lon'][0] xmax = nc_ds.variables['lon'][-1] bbox = [xmin, xmax, ymin, ymax] mosaic_tair.append([tair_ann, bbox]) #tair_mon_agg = tair_agg.daily_to_mthly(tair) #tair_ann_agg = tair_agg.daily_to_ann(tair) #figure out the non-spatial average