sum of array for masked area
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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
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On 28 November 2013 09:06, questions anon <questions.anon@gmail.com> wrote:
r_sum=np.sum(monthlyrain, axis=0)
^^^^^^^^^^^ Passing a list of masked arrays to np.sum returns an np.ndarray object (*not* a masked array)
r_mean_of_sum=MA.mean(r_sum)
Therefore this call to MA.mean returns the mean of all values in the ndarray r_sum. To fix: convert your monthlyrain list to a 3D maksed array before calling np.sum(monthlyrain, axis=0). In this case np.sum will call the masked array's .sum() method which knows about the mask. monthlyrain = np.ma.asarray(monthlyrain) r_sum=np.sum(monthlyrain, axis=0) Consider the following simplified example: alist = [] for k in range(2): a = np.arange(4).reshape((2,2)) alist.append(np.ma.masked_array(a, mask=[[0,1],[0,0]])) print(alist) print(type(alist)) alist = np.ma.asarray(alist) print(alist) print(type(alist)) asum = np.sum(alist, axis=0) print(asum) print(type(asum)) print(asum.mean()) Cheers, Scott
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On 28 November 2013 09:06, questions anon <questions.anon@gmail.com> wrote:
r_sum=np.sum(monthlyrain, axis=0)
^^^^^^^^^^^ Passing a list of masked arrays to np.sum returns an np.ndarray object (*not* a masked array)
r_mean_of_sum=MA.mean(r_sum)
Therefore this call to MA.mean returns the mean of all values in the ndarray r_sum. To fix: convert your monthlyrain list to a 3D maksed array before calling np.sum(monthlyrain, axis=0). In this case np.sum will call the masked array's .sum() method which knows about the mask. monthlyrain = np.ma.asarray(monthlyrain) r_sum=np.sum(monthlyrain, axis=0) Consider the following simplified example: alist = [] for k in range(2): a = np.arange(4).reshape((2,2)) alist.append(np.ma.masked_array(a, mask=[[0,1],[0,0]])) print(alist) print(type(alist)) alist = np.ma.asarray(alist) print(alist) print(type(alist)) asum = np.sum(alist, axis=0) print(asum) print(type(asum)) print(asum.mean()) Cheers, Scott
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questions anon
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Scott Sinclair