def get_minmax(sample, min_samples = 8):
"""
We consider that a ratio of connections is typical if it falls within the 99.99 % percentile of the Normal distribution N(m, v) modelling ratios. This ensures that the expected rate of false alarms is about 1/10000, and therefore only a handful a week (given the large number of jurisdictions). Similarly, we infer the range of the rate of usage from each jurisdiction (given Ci j) to be the 99.99 % percentile range of a Poisson distribution with parameter Ci j. This full range must be within the typical range of ratios to avoid raising an alarm.
Args:
sample (pandas.core.series.Series): A series containing the relative change values for a set of countries.
"""
log.debug("Getting min and max for a sample on {0}'s data: {1}".format(sample.name, sample))
initial_sample_len = len(sample)
if initial_sample_len > min_samples:
sample = drop_outliers(sample)
num_outliers = initial_sample_len - len(sample)
log.debug("Sample had {0} outliers removed. Current sample: {1}".format(num_outliers, sample))
if len(sample) > min_samples:
mu, signma = norm.fit(sample)
sample_max = norm.ppf(0.9999, mu, signma)
sample_min = norm.ppf(1 - 0.9999, mu, signma)
log.debug("Sample min == {0}, Sample max == {1}".format(sample_min, sample_max))
return pd.Series({"max":sample_max, "min":sample_min})
else:
log.debug("After removing outliers the sample was a length of {0}. This is shorter than acceptable minimum length of {1}.".format(len(sample), min_samples))
return pd.Series({"max":None, "min":None})
else:
log.debug("Sample with length of {0} is shorter than acceptable minimum length of {1}.".format(initial_sample_len, min_samples))
return pd.Series({"max":None, "min":None})
def make_tendencies_minmax(l, INTERVAL = 1):
lminus1 = dict([(ccode, n_day_rel(l[ccode], INTERVAL)) for ccode in l])
c = lminus1[lminus1.keys()[0]]
dists = []
minx = []
maxx = []
for i in range(len(c)):
vals = [lminus1[ccode][i] for ccode in lminus1.keys() if lminus1[ccode][i] != None]
if len(vals) < 8:
dists += [None]
minx += [None]
maxx += [None]
else:
vals.sort()
median = vals[len(vals)/2]
q1 = vals[len(vals)/4]
q2 = vals[(3*len(vals))/4]
qd = q2 - q1
vals = [v for v in vals if median - qd*4 < v and v < median + qd*4]
if len(vals) < 8:
dists += [None]
minx += [None]
maxx += [None]
continue
mu, signma = norm.fit(vals)
dists += [(mu, signma)]
maxx += [norm.ppf(0.9999, mu, signma)]
minx += [norm.ppf(1 - 0.9999, mu, signma)]
## print minx[-1], maxx[-1]
return minx, maxx
def make_tendencies_minmax(l, INTERVAL=1):
lminus1 = dict([(ccode, n_day_rel(l[ccode], INTERVAL)) for ccode in l])
c = lminus1[lminus1.keys()[0]]
dists = []
minx = []
maxx = []
for i in range(len(c)):
vals = [
lminus1[ccode][i] for ccode in lminus1.keys()
if lminus1[ccode][i] != None
]
if len(vals) < 8:
dists += [None]
minx += [None]
maxx += [None]
else:
vals.sort()
median = vals[len(vals) / 2]
q1 = vals[len(vals) / 4]
q2 = vals[(3 * len(vals)) / 4]
qd = q2 - q1
vals = [
v for v in vals if median - qd * 4 < v and v < median + qd * 4
]
if len(vals) < 8:
dists += [None]
minx += [None]
maxx += [None]
continue
mu, signma = norm.fit(vals)
dists += [(mu, signma)]
maxx += [norm.ppf(0.9999, mu, signma)]
minx += [norm.ppf(1 - 0.9999, mu, signma)]
## print minx[-1], maxx[-1]
return minx, maxx
def norm_fit():
# this is used to define AN and BN thresholds
# https://www.hko.gov.hk/en/wxinfo/season/intpret.htm
# loop through two fields and four seasons
return [
[
[
norm.fit(
[seasonal_avg_dict_ls_ls[f][s][year] for year in range(*yrs)]
) for yrs in climat_yrs
] for s, season in enumerate(season_names)
] for f, field in enumerate(fields)
]
def uncertainty_histplot(self,
xcumprod: bool = True,
color_style: str = 'oil',
hidey: bool = True,
figsize=(12, 8)):
""" Plot EUR uncertainty based on specified Arps model.
Parameters
---
rate_eur : float
Minimum rate based on commercial or technical limit.
sample : int
Sample size to estimate the uncertainty
seed : int
Random seed
xcumprod : bool
True for cumulative production, False for time
color_style : str
set 'oil' theme or 'gas' theme
hidey : bool
hide probability density axis
figsize : tuple
Figure size
"""
plt.style.use('ggplot')
if color_style == 'gas':
regclr = 'brown'
sampleclr = 'coral'
else:
regclr = 'darkgreen'
sampleclr = 'limegreen'
_, ax = plt.subplots(figsize=figsize)
if xcumprod:
loc, scale = norm.fit(self._cumprod_mc[:, -1])
ax.hist(self._cumprod_mc[:, -1],
bins='auto',
density=True,
color=sampleclr,
alpha=0.5)
else:
loc, scale = norm.fit(self._time_mc[:, -1])
ax.hist(self._time_mc[:, -1],
bins='auto',
density=True,
color=sampleclr,
alpha=0.5)
dist = norm(loc=loc, scale=scale)
xaxis = np.linspace(dist.ppf(0.0005), dist.ppf(0.9995))
normal = ax.plot(xaxis, dist.pdf(xaxis), color=regclr)
if xcumprod:
P90 = np.percentile(self._cumprod_mc[:, -1], 10)
P50 = self.eur(self._rate_eur)
P10 = np.percentile(self._cumprod_mc[:, -1], 90)
else:
P90 = np.percentile(self._time_mc[:, -1], 10)
P50 = self.time(self._rate_eur)
P10 = np.percentile(self._time_mc[:, -1], 90)
low = ax.axvline(P90, color=regclr, linestyle='--', alpha=0.5)
mid = ax.axvline(P50, color=regclr, linestyle='--', alpha=0.5)
hgh = ax.axvline(P10, color=regclr, linestyle='--', alpha=0.5)
ax.set_xlim(xaxis[0], xaxis[-1])
ax.get_xaxis().set_major_formatter(
FuncFormatter(lambda x, p: format(int(x), ',')))
if xcumprod:
ax.set_title((f'EUR Distribution based on Arps Model\n'
f'Selected Method: {self._MODEL_NAME[self._model]}'))
ax.set_xlabel('Estimated Ultimate Recovery')
else:
ax.set_title((f'Time Distribution at EUR based on Arps Model\n'
f'Selected Method: {self._MODEL_NAME[self._model]}'))
ax.set_xlabel('Time')
ax.set_ylabel('Probability Density')
if hidey:
ax.yaxis.set_ticklabels([])
ax.legend([normal, low, mid, hgh],
('PDF', f'P90: {round(P90, 2)}', f'P50: {round(P50, 2)}',
f'P10: {round(P10, 2)}'))
plt.show()