Hi All,
I just posted this on the SciPy forum but realised it might be more appropriate here?

I have a separate text file for daily rainfall data that covers the whole country. I would like to calculate the monthly mean, min, max and the mean of the sum for one state.

The mean, max and min are just the mean, max and min for all data in that month however the sum data needs to work out the total for the month across the array and then sum that value.

I use gdal tools to mask out the rest of the country and I use numpy tools for the summary stats.

I can get the max, min and mean for the state, but the mean of the sum keeps giving me a result for the whole country rather than just the state, even though I am performing the analysis on the state only data. I am not sure if this is a masking issue or a numpy calculation issue. The mask works fine for the other summary statistics.

Any feedback will be greatly appreciated!

import numpy as np
import matplotlib.pyplot as plt
from numpy import ma as MA
from mpl_toolkits.basemap import Basemap
from datetime import datetime as dt
from datetime import timedelta
import os
from StringIO import StringIO
from osgeo import gdal, gdalnumeric, ogr, osr
import glob
import matplotlib.dates as mdates
import sys

shapefile=r"/Users/state.shp"

## Create masked array from shapefile
xmin,ymin,xmax,ymax=[111.975,-

9.975, 156.275,-44.525]
ncols,nrows=[886, 691] #Your rows/cols
maskvalue = 1

xres=(xmax-xmin)/float(ncols)
yres=(ymax-ymin)/float(nrows)
geotransform=(xmin,xres,0,ymax,0, -yres)
0
src_ds = ogr.Open(shapefile)
src_lyr=src_ds.GetLayer()

dst_ds = gdal.GetDriverByName('MEM').Create('',ncols, nrows, 1 ,gdal.GDT_Byte)
dst_rb = dst_ds.GetRasterBand(1)
dst_rb.Fill(0) #initialise raster with zeros
dst_rb.SetNoDataValue(0)
dst_ds.SetGeoTransform(geotransform)

err = gdal.RasterizeLayer(dst_ds, [maskvalue], src_lyr)
dst_ds.FlushCache()

mask_arr=dst_ds.GetRasterBand(1).ReadAsArray()
np.set_printoptions(threshold='nan')
mask_arr[mask_arr == 255] = 1

newmask=MA.masked_equal(mask_arr,0)

### calculate monthly summary stats for state Only

rainmax=[]
rainmin=[]
rainmean=[]
rainsum=[]

yearmonthlist=[]
yearmonth_int=[]
errors=[]

OutputFolder=r"/outputfolder"
GLOBTEMPLATE = r"/daily-rainfall/combined/rainfall-{year}/r{year}{month:02}??.txt"

def accumulate_month(year, month):
    files = glob.glob(GLOBTEMPLATE.format(year=year, month=month))
    monthlyrain=[]
     for ifile in files:
        try:
            f=np.genfromtxt(ifile,skip_header=6)
        except:
            print "ERROR with file:", ifile
            errors.append(ifile)
        f=np.flipud(f)

        stateonly_f=np.ma.masked_array(f, mask=newmask.mask) # this masks data to state

        print "stateonly_f:", stateonly_f.max(), stateonly_f.mean(), stateonly_f.sum()

        monthlyrain.append(stateonly_f)

    yearmonth=dt(year,month,1)
    yearmonthlist.append(yearmonth)
    yearmonthint=str(year)+str(month)
    d=dt.strptime(yearmonthint, '%Y%m')
    print d
    date_string=dt.strftime(d,'%Y%m')
    yearmonthint=int(date_string)
    yearmonth_int.append(yearmonthint)

    r_sum=np.sum(monthlyrain, axis=0)
    r_mean_of_sum=MA.mean(r_sum)

    r_max, r_mean, r_min=MA.max(monthlyrain), MA.mean(monthlyrain), MA.min(monthlyrain)
    rainmax.append(r_max)
    rainmean.append(r_mean)
    rainmin.append(r_min)
    rainsum.append(r_mean_of_sum)

    print " state only:", yearmonthint,r_max, r_mean, r_min, r_mean_of_sum