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import tables as tb
import numpy as np
import map_utils
from pylab import csv2rec, rec2csv
import os
import sys
from dufvax import covariate_names
# TODO: draw these straight from /Volumes/data
data_in = csv2rec(sys.argv[1])
covariate_path = sys.argv[2]
data_box = data_in
cols = dict([(key,data_box[key]) for key in data_box.dtype.names])
for k in ['urban','rural','africa']:
def mode(a):
vals = list(set(a))
counts = [(a==v).sum() for v in vals]
return np.argmin(counts)
def nan_callback(lon_old, lat_old, data, lon_new, lat_new, order):
lon_ind = np.argmin(np.abs(np.subtract.outer(lon_old, lon_new)), axis=0)
lat_ind = np.argmin(np.abs(np.subtract.outer(lat_old, lat_new)), axis=0)
out = lat_new*0
for i in xrange(len(lon_new)):
lai, loi = lat_ind[i], lon_ind[i]
if data.mask[lai, loi]:
for d in xrange(10):
if True-np.all(data.mask[lai-d:lai+d,loi-d:loi+d]):
out[i] = mode([lai-d:lai+d,loi-d:loi+d][np.where(True-data.mask[lai-d:lai+d,loi-d:loi+d])])
out[i] = data[lai,loi]
if np.any(np.isnan(out)):
raise ValueError
return out
for fname in map(lambda n: n+'.hdf5', covariate_names):
print 'Evaluating %s'%fname
colname = os.path.splitext(fname)[0]
hf = tb.openFile(os.path.join(covariate_path,fname))
cols[colname] = map_utils.interp_geodata(hf.root.lon[:],[:],[:],cols['lon'],cols['lat'],hf.root.mask[:],order=0,nan_handler=nan_callback)
if np.any(np.isnan(cols[colname])):
raise ValueError
keys = cols.keys()
data_out = np.rec.fromarrays([cols[k] for k in keys], names=keys)
rec2csv(data_out, os.path.splitext(os.path.basename(sys.argv[1]))[0]+'_with_covariates.csv')
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