def logistic_extrap(country, field, until, start, cutoff, k_frac):
"""Extrapolates the the logistic fit of a field from start, only taking
data from cutoff into account, until the fit reaches k_frac * k.
"""
prod = get_production(country, until)[field].dropna()[cutoff:]
x, y = prod.index, prod.values
nx = len(x)
fname = os.path.join(DPATH,
'%s_logistic_production_%s.pkl' % (country, until[:4]))
if os.path.exists(fname):
f = open(fname)
rkp_dict = load(f)
f.close()
r, k, p = rkp_dict[field]
else:
r, k, p = fit_logistic(x, y, Timestamp(start))
if y[-1] > k_frac * k:
return (x, y)
while y[-1] < k_frac * k:
x = list(x)
last_date = x[-1]
future = date_range(last_date, periods=120, freq='MS')[1:]
x.extend(future)
y = compute_logistic(x, (r, k, p), Timestamp(start))
_y = y[nx:]
_y = _y[_y < k_frac * k]
_x = x[nx:]
_x = _x[:len(_y)]
return (_x, _y)
def rate_of_discoveries(country, until='2013-02-01', fit_style='logistic',
confid=None, show_plot=False, style='urr'):
"""Fits a logistic curve to the number of fields discovered up to time t.
This informs us about the underlying discovery mechanism. This mechanism can
depend on size.
Args:
country -> str:
The string representing the name of the country.
until -> str:
datelike string useful for backtesting (left cutoff)
Return:
discoveries -> DataFrame:
DataFrame containing all the discoveries.
"""
production = get_production(country, until)
fields = production.columns
start_of_prod = dict((field, production[field].dropna().index[0]) for
field in fields)
start_of_prod = Series(start_of_prod)
categories = classify_fields_according_to_urr(country, until, style)
activity = date_range(START[country], until, freq='MS')
n_vs_ts= []
for category in categories:
starts = array(sorted(start_of_prod[category]))
n = [(starts <= start).sum() for start in activity]
t = activity
n_vs_ts.append((t, n))
#return n_vs_ts
fit_params = []
fname = os.path.join(DPATH, '%s_%s_rate.pkl' % (country, until[:4]))
if os.path.exists(fname):
f = open(fname)
rkp_dict = load(f)
f.close()
for i, n_vs_t in enumerate(n_vs_ts):
x, y = n_vs_t[0], n_vs_t[1]
if os.path.exists(fname):
(r, k, p) = rkp_dict[i]
else:
#res = fit_logistic(x, y, START[country], confid, show_plot)
(r, k, p) = fit_logistic(x, y, START[country], confid, show_plot)
fit_params.append((r, k, p))
return fit_params
