def test_interp_fill(self):
result_lin = masked_array(self.data).astype(float)
result_lin[0] = masked
test = interp_masked1d(self.test_array.astype(float), kind='linear')
assert_almost_equal(test, result_lin)
def test_interp_fill(self):
result_lin = masked_array(self.data).astype(float)
result_lin[0] = masked
test = interp_masked1d(self.test_array.astype(float), kind='linear')
assert_almost_equal(test, result_lin)
else: valo2[i1] = float(elem)
valo = numpy.array(valo2)
dato = [x['date'] for x in resu['observations']['observation']]
if freqf == 'D':
tso = ts.time_series(valo,dates=dato,freq='D')
tso2 = ts.convert(tso,'M',func=numpy.ma.mean)
elif freqf == 'W':
tso = ts.time_series(valo,dates=dato,freq='D')
tso2 = ts.convert(tso,'M',func=numpy.ma.mean)
elif freqf == 'M':
tso = ts.time_series(valo,dates=dato,freq='M')
tso2 = copy.deepcopy(tso)
elif freqf == 'Q':
tso = ts.time_series(valo,dates=dato,freq='Q')
tso2 = ts.convert(tso,'M')
tso2 = interp_masked1d(tso2)
valor = tso2['1985-01-01':'2011-09-01']
dator = valor.dates
valor = valor.data
valor = numpy.reshape(valor,(len(valor),1))
matd = numpy.hstack((matd,valor))
tmpstr = tmpstr + r"\bottomrule"+"\n"
tmpstr = tmpstr + r"\multicolumn{7}{l}{{\bf Notes:} Transformation codes are: 1=levels, 2=first seasonal difference, 3=second seasonal difference, 4=log level, 5=log first seasonal difference, 6=log second seasonal difference,}\\"+"\n"
tmpstr = tmpstr + r"\multicolumn{7}{l}{7=log hp-filtered monthly data. Note that investment data were intrapolated in order to obtain monthly from quarterly data. The choice of fast/slow variables in this table reflects the benchmark ordering.}"+"\n"
tmpstr = tmpstr + r"\end{tabular}"+"\n"
tmpstr = tmpstr + r"\end{sidewaystable}"+"\n"
'''
if 'table1.tex' in os.listdir('../tex_papers/bloom_favar/'):
os.remove('../tex_papers/bloom_favar/table1.tex')
else: valo2[i1] = float(elem)
valo = numpy.array(valo2)
dato = [x['date'] for x in resu['observations']['observation']]
if freqf == 'D':
tso = ts.time_series(valo, dates=dato, freq='D')
tso2 = ts.convert(tso, 'M', func=numpy.ma.mean)
elif freqf == 'W':
tso = ts.time_series(valo, dates=dato, freq='D')
tso2 = ts.convert(tso, 'M', func=numpy.ma.mean)
elif freqf == 'M':
tso = ts.time_series(valo, dates=dato, freq='M')
tso2 = copy.deepcopy(tso)
elif freqf == 'Q':
tso = ts.time_series(valo, dates=dato, freq='Q')
tso2 = ts.convert(tso, 'M')
tso2 = interp_masked1d(tso2)
valor = tso2['1985-01-01':'2011-09-01']
dator = valor.dates
valor = valor.data
valor = numpy.reshape(valor, (len(valor), 1))
matd = numpy.hstack((matd, valor))
tmpstr = tmpstr + r"\bottomrule" + "\n"
tmpstr = tmpstr + r"\multicolumn{7}{l}{{\bf Notes:} Transformation codes are: 1=levels, 2=first seasonal difference, 3=second seasonal difference, 4=log level, 5=log first seasonal difference, 6=log second seasonal difference,}\\" + "\n"
tmpstr = tmpstr + r"\multicolumn{7}{l}{7=log hp-filtered monthly data. Note that investment data were intrapolated in order to obtain monthly from quarterly data. The choice of fast/slow variables in this table reflects the benchmark ordering.}" + "\n"
tmpstr = tmpstr + r"\end{tabular}" + "\n"
tmpstr = tmpstr + r"\end{sidewaystable}" + "\n"
'''
if 'table1.tex' in os.listdir('../tex_papers/bloom_favar/'):
os.remove('../tex_papers/bloom_favar/table1.tex')
f1 = open('../tex_papers/bloom_favar/table1.tex','w')
