Added simple scripts to make masks and calculate percentage of missin…

…g data.

makemask.py

@@ -0,0 +1,30 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+"""
+@author: Tammas Loughran
+"""
+import netCDF4 as nc
+import numpy as np
+# Load file
+ncfile = nc.Dataset('HadGHCND_TXTN_1950-2014.nc','r')
+data = ncfile.variables['tmax'][:]
+# Make mask where data exists at any point in time
+mask = np.array(data.any(axis=0).astype(int))
+# Save to mask.nc
+maskfile = nc.Dataset('mask.nc','w')
+maskfile.createDimension('latitude', size=mask.shape[0])
+maskfile.createDimension('longitude', size=mask.shape[1])
+latsvar = maskfile.createVariable('latitude', float, dimensions=('latitude'))
+lonsvar = maskfile.createVariable('longitude', float, dimensions=('longitude'))
+maskvar = maskfile.createVariable('mask', int, dimensions=('latitude','longitude'))
+setattr(latsvar, 'standard_name', 'latitude')
+setattr(latsvar, 'units', 'degrees_north')
+setattr(latsvar, 'axis', 'Y')
+setattr(lonsvar, 'standard_name', 'longitude')
+setattr(lonsvar, 'units', 'degrees_east')
+setattr(lonsvar, 'axis', 'X')
+latsvar[:] = ncfile.variables['latitude'][:]
+lonsvar[:] = ncfile.variables['longitude'][:]
+maskvar[:] = mask
+ncfile.close()
+maskfile.close()

percent_summer.py

@@ -0,0 +1,43 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Jun  1 16:32:54 2018
+
+@author: Tammas Loughran
+"""
+import numpy as np
+import netCDF4 as nc
+import pandas as pd
+import datetime as dt
+ncfile = nc.Dataset('HadGHCND_TXTN_1950-2014.nc','r')
+data = ncfile.variables['tmax'][:]
+if type(data)!=np.ma.core.MaskedArray: data = np.ma.array(data, mask=np.isnan(data))
+data = np.logical_not(data.mask)
+time = ncfile.variables['time'][:]
+dayone = dt.datetime(int(str(time[0])[:4]),int(str(time[0])[4:6]),int(str(time[0])[6:8]))
+daylast = dt.datetime(int(str(time[-1])[:4]),int(str(time[-1])[4:6]),int(str(time[-1])[6:8]))
+dates = pd.date_range(dayone,daylast)
+percent = np.ones((daylast.year-dayone.year+1, data.shape[1], data.shape[2]))*np.nan
+for i,year in enumerate(range(dayone.year,daylast.year+1)):
+    year_index = (dates.year==year)&((dates.month==11)|(dates.month==12)|(dates.month==1)|(dates.month==2)|(dates.month==13))
+    percent[i,...] = 100*data[year_index,...].sum(axis=0)/year_index.sum()
+outfile = nc.Dataset('percent.nc','w')
+outfile.createDimension('time', size=percent.shape[0])
+outfile.createDimension('latitude',size=ncfile.dimensions['latitude'].size)
+outfile.createDimension('longitude',size=ncfile.dimensions['longitude'].size)
+timevar = outfile.createVariable('time', float, dimensions=('time'))
+latsvar = outfile.createVariable('latitude', float, dimensions=('latitude'))
+lonsvar = outfile.createVariable('longitude', float, dimensions=('longitude'))
+percentvar = outfile.createVariable('percent', float, dimensions=('time','latitude','longitude'))
+setattr(timevar, 'units', 'years since 1949-01-01')
+setattr(latsvar, 'standard_name', 'latitude')
+setattr(latsvar, 'units', 'degrees_north')
+setattr(latsvar, 'axis', 'Y')
+setattr(lonsvar, 'standard_name', 'longitude')
+setattr(lonsvar, 'units', 'degrees_east')
+setattr(lonsvar, 'axis', 'X')
+timevar[:] = np.array(range(dayone.year, daylast.year+1)) - dayone.year
+latsvar[:] = ncfile.variables['latitude'][:]
+lonsvar[:] = ncfile.variables['longitude'][:]
+percentvar[:] = percent
+outfile.close()

version

@@ -1 +1 @@
-1.1.4
+1.1.5