def obs_pp(cdat, alp, lw, gn):
"""replace obs type params"""
# Get parameter sums
r = make_psums(cdat, alp, lw, gn)
phat = 100
# Get wealth ratios
exptot = cdat['exptot']
w = lw / exptot
# Fit spline
gp = run_est.make_grid(cdat) #should move this to save on calculation
#spline = fit_spline.fit(gp, alp)
w_pts, r_pts, op_pts = fit_spline.fit(gp, alp)
# Get gammas
#gam = spline.ev(w,r)
gam = np.nan_to_num(griddata((w_pts, r_pts), op_pts, (w,r)))
op = gam * phat
op[op <= 1e-10] = 1e3 #eliminate negative and exactly zero values (approximation error)
# Read into consumption data
cdat['op'] = op
for i in range(gn):
pname = 'p' + str(i + 1)
addme = (cdat['ot'] == i + 1) * op
cdat[pname] = addme.add((cdat['ot'] != i + 1) * cdat[pname])
return cdat
# Remove weird values
x = [k[0] for k in gp]
y = [k[1] for k in gp]
df = pd.DataFrame(np.array([x, y, sols]).T)
clean = df.groupby(0).apply(lambda row: elim_weirds(row))
return clean[0], clean[1], clean[2]
def elim_weirds(group):
"""even a zero param will have some consumption of the obs good
which means that some consumptions are impossible, and confuse my
solver. This script eliminates those weird values"""
minind = group[2].idxmin()
group[group.index < minind] = 1e-12
return group
if __name__ == "__main__":
cdat = pd.read_pickle("cdat.pickle")
# get grid points in form of (w, r)
gp = run_est.make_grid(cdat)
alp = 0.3
spline = fit(gp, alp)
