def get_fit_parms(fields=GOOD_FITS, fit_style='stretched exponential',
xmin='custom', return_oil_field=True):
"""Return a hash with the keys being the fields (names) and value the
relevant fit parameters given the fit style.
"""
fit_parms = {}
if fit_style == 'exponential':
fit = fit_exponential
elif fit_style == 'stretched exponential':
fit = fit_stretched_exponential
elif fit_style == 'power law':
fit = fit_power_law
oil_fields = get_oil_fields()
if xmin == 'custom':
def get_xmin(field, MAXES):
return MAXES[field] if field in MAXES.keys() else 'max'
for field in fields:
fit_parms[field] = fit(oil_fields[field], xmin=get_xmin(field, MAXES))
if return_oil_field:
return (fit_parms, oil_fields)
else:
return fit_parms
def rate_of_discoveries(bins=[1e4, 1e6, 1e7, 1e8], fields='all'):
"""Plots the number of fields discovered up to time t. This informs us about
the underlying discovery mechanism. This mechanism can depend on size.
"""
oil_fields = get_oil_fields()
if fields == 'all':
urrs = Series(URRS)[oil_fields.columns].dropna() * 1e6 * M3_TO_BARRELS
binned_urrs = [urrs[(urrs > bins[i]) & (urrs < bins[i+1])]
for i in range(len(bins)-1)]
res = []
plt.ion()
fig = plt.figure(figsize=(28, 10))
for i, bin in enumerate(binned_urrs):
starts = []
for field in bin.index:
starts.append(oil_fields[field].dropna().index[0])
starts = array(starts)
dates = date_range(START, END)
number_of_discoveries = [(starts <= date).sum() for date in dates]
res.append((dates, number_of_discoveries))
fig.add_subplot(1, len(bins)-1, i+1)
ax = plt.gca()
ax.plot(dates, number_of_discoveries, 'ok', markersize=4.)
plt.xlabel('Date of discovery')
plt.ylabel('Number of fields discovered')
plt.title('bin=%s-%s' % (log10(bins[i]), log10(bins[i+1])))
return res
