# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (c) 2016-2021 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
"""
Utilities to compute mean and quantile curves
"""
import numpy
import pandas
from scipy.stats import norm
from scipy.special import ndtr
from openquake.baselib.general import agg_probs
def _truncnorm_sf(truncation_level, values):
"""
Survival function for truncated normal distribution.
Assumes zero mean, standard deviation equal to one and symmetric
truncation.
:param truncation_level:
Positive float number representing the truncation on both sides
around the mean, in units of sigma, or None, for non-truncation
:param values:
Numpy array of values as input to a survival function for the given
distribution.
:returns:
Numpy array of survival function results in a range between 0 and 1.
>>> from scipy.stats import truncnorm
>>> truncnorm(-3, 3).sf(0.12345) == _truncnorm_sf(3, 0.12345)
True
>>> from scipy.stats import norm
>>> norm.sf(0.12345) == _truncnorm_sf(None, 0.12345)
True
"""
if truncation_level == 0:
return values
if truncation_level is None:
return ndtr(- values)
# notation from http://en.wikipedia.org/wiki/Truncated_normal_distribution.
# given that mu = 0 and sigma = 1, we have alpha = a and beta = b.
# "CDF" in comments refers to cumulative distribution function
# of non-truncated distribution with that mu and sigma values.
# assume symmetric truncation, that is ``a = - truncation_level``
# and ``b = + truncation_level``.
# calculate CDF of b
phi_b = ndtr(truncation_level)
# calculate Z as ``Z = CDF(b) - CDF(a)``, here we assume that
# ``CDF(a) == CDF(- truncation_level) == 1 - CDF(b)``
z = phi_b * 2 - 1
# calculate the result of survival function of ``values``,
# and restrict it to the interval where probability is defined --
# 0..1. here we use some transformations of the original formula
# that is ``SF(x) = 1 - (CDF(x) - CDF(a)) / Z`` in order to minimize
# number of arithmetic operations and function calls:
# ``SF(x) = (Z - CDF(x) + CDF(a)) / Z``,
# ``SF(x) = (CDF(b) - CDF(a) - CDF(x) + CDF(a)) / Z``,
# ``SF(x) = (CDF(b) - CDF(x)) / Z``.
return ((phi_b - ndtr(values)) / z).clip(0.0, 1.0)
[docs]def norm_cdf(x, a, s):
"""
Gaussian cumulative distribution function; if s=0, returns an
Heaviside function instead. NB: for x=a, 0.5 is returned for all s.
>>> norm_cdf(1.2, 1, .1)
0.9772498680518208
>>> norm_cdf(1.2, 1, 0)
1.0
>>> norm_cdf(.8, 1, .1)
0.022750131948179216
>>> norm_cdf(.8, 1, 0)
0.0
>>> norm_cdf(1, 1, .1)
0.5
>>> norm_cdf(1, 1, 0)
0.5
"""
if s == 0:
return numpy.heaviside(x - a, .5)
else:
return norm.cdf(x, loc=a, scale=s)
[docs]def calc_momenta(array, weights):
"""
:param array: an array of shape E, ...
:param weights: an array of length E
:returns: an array of shape (2, ...) with the first two statistical moments
"""
momenta = numpy.zeros((2,) + array.shape[1:])
momenta[0] = numpy.einsum('i,i...', weights, array)
momenta[1] = numpy.einsum('i,i...', weights, array**2)
return momenta
[docs]def calc_avg_std(momenta, totweight):
"""
:param momenta: an array of shape (2, ...) obtained via calc_momenta
:param totweight: total weight to divide for
:returns: an array of shape (2, ...) with average and standard deviation
>>> arr = numpy.array([[2, 4, 6], [3, 5, 7]])
>>> weights = numpy.ones(2)
>>> calc_avg_std(calc_momenta(arr, weights), weights.sum())
array([[2.5, 4.5, 6.5],
[0.5, 0.5, 0.5]])
"""
avgstd = numpy.zeros_like(momenta)
avgstd[0] = avg = momenta[0] / totweight
avgstd[1] = numpy.sqrt(numpy.maximum(momenta[1] / totweight - avg ** 2, 0))
return avgstd
[docs]def avg_std(array, weights=None):
"""
:param array: an array of shape E, ...
:param weights: an array of length E (or None for equal weights)
:returns: an array of shape (2, ...) with average and standard deviation
>>> avg_std(numpy.array([[2, 4, 6], [3, 5, 7]]))
array([[2.5, 4.5, 6.5],
[0.5, 0.5, 0.5]])
"""
if weights is None:
weights = numpy.ones(len(array))
return calc_avg_std(calc_momenta(array, weights), weights.sum())
[docs]def geom_avg_std(array, weights=None):
"""
:returns: geometric mean and geometric stddev (see
https://en.wikipedia.org/wiki/Log-normal_distribution)
"""
return numpy.exp(avg_std(numpy.log(array), weights))
[docs]def mean_curve(values, weights=None):
"""
Compute the mean by using numpy.average on the first axis.
"""
if weights is None:
weights = [1. / len(values)] * len(values)
if not isinstance(values, numpy.ndarray):
values = numpy.array(values)
return numpy.average(values, axis=0, weights=weights)
[docs]def std_curve(values, weights=None):
if weights is None:
weights = [1. / len(values)] * len(values)
m = mean_curve(values, weights)
res = numpy.sqrt(numpy.einsum('i,i...', weights, (m - values) ** 2))
return res
# NB: for equal weights and sorted values the quantile is computed as
# numpy.interp(q, [1/N, 2/N, ..., N/N], values)
[docs]def quantile_curve(quantile, curves, weights=None):
"""
Compute the weighted quantile aggregate of an array or list of arrays
:param quantile:
Quantile value to calculate. Should be in the range [0.0, 1.0].
:param curves:
R arrays
:param weights:
R weights with sum 1, or None
:returns:
A numpy array representing the quantile of the underlying arrays
>>> arr = numpy.array([.15, .25, .3, .4, .5, .6, .75, .8, .9])
>>> quantile_curve(.8, arr)
array(0.76)
"""
if not isinstance(curves, numpy.ndarray):
curves = numpy.array(curves)
R = len(curves)
if weights is None:
weights = numpy.ones(R) / R
else:
weights = numpy.array(weights)
assert len(weights) == R, (len(weights), R)
result = numpy.zeros(curves.shape[1:])
for idx, _ in numpy.ndenumerate(result):
data = numpy.array([a[idx] for a in curves])
sorted_idxs = numpy.argsort(data)
cum_weights = numpy.cumsum(weights[sorted_idxs])
# get the quantile from the interpolated CDF
result[idx] = numpy.interp(quantile, cum_weights, data[sorted_idxs])
return result
[docs]def max_curve(values, weights=None):
"""
Compute the maximum curve by taking the upper limits of the values;
the weights are ignored and present only for API compatibility.
The values can be arrays and then the maximum is taken pointwise:
>>> max_curve([numpy.array([.3, .2]), numpy.array([.1, .4])])
array([0.3, 0.4])
"""
return numpy.max(values, axis=0)
[docs]def compute_pmap_stats(pmaps, stats, weights, imtls):
"""
:param pmaps:
a list of R probability maps
:param stats:
a sequence of S statistic functions
:param weights:
a list of ImtWeights
:param imtls:
a DictArray of intensity measure types
:returns:
a probability map with S internal values
"""
sids = set()
p0 = next(iter(pmaps))
L = p0.shape_y
for pmap in pmaps:
sids.update(pmap)
assert pmap.shape_y == L, (pmap.shape_y, L)
if len(sids) == 0:
raise ValueError('All empty probability maps!')
sids = numpy.array(sorted(sids), numpy.uint32)
nstats = len(stats)
curves = numpy.zeros((len(pmaps), len(sids), L), numpy.float64)
for i, pmap in enumerate(pmaps):
for j, sid in enumerate(sids):
if sid in pmap:
curves[i, j] = pmap[sid].array[:, 0]
out = p0.__class__.build(L, nstats, sids)
for imt in imtls:
slc = imtls(imt)
w = [weight[imt] if hasattr(weight, 'dic') else weight
for weight in weights]
if sum(w) == 0: # expect no data for this IMT
continue
for i, array in enumerate(compute_stats(curves[:, :, slc], stats, w)):
for j, sid in numpy.ndenumerate(sids):
out[sid].array[slc, i] = array[j]
return out
# NB: this is a function linear in the array argument
[docs]def compute_stats(array, stats, weights):
"""
:param array:
an array of R elements (which can be arrays)
:param stats:
a sequence of S statistic functions
:param weights:
a list of R weights
:returns:
an array of S elements (which can be arrays)
"""
result = numpy.zeros((len(stats),) + array.shape[1:], array.dtype)
for i, func in enumerate(stats):
result[i] = apply_stat(func, array, weights)
return result
# like compute_stats, but on a matrix of shape (N, R)
[docs]def compute_stats2(arrayNR, stats, weights):
"""
:param arrayNR:
an array of (N, R) elements
:param stats:
a sequence of S statistic functions
:param weights:
a list of R weights
:returns:
an array of (N, S) elements
"""
newshape = list(arrayNR.shape)
if newshape[1] != len(weights):
raise ValueError('Got %d weights but %d values!' %
(len(weights), newshape[1]))
newshape[1] = len(stats) # number of statistical outputs
newarray = numpy.zeros(newshape, arrayNR.dtype)
data = [arrayNR[:, i] for i in range(len(weights))]
for i, func in enumerate(stats):
newarray[:, i] = apply_stat(func, data, weights)
return newarray
[docs]def apply_stat(f, arraylist, *extra, **kw):
"""
:param f: a callable arraylist -> array (of the same shape and dtype)
:param arraylist: a list of arrays of the same shape and dtype
:param extra: additional positional arguments
:param kw: keyword arguments
:returns: an array of the same shape and dtype
Broadcast statistical functions to composite arrays. Here is an example:
>>> dt = numpy.dtype([('a', (float, 2)), ('b', float)])
>>> a1 = numpy.array([([1, 2], 3)], dt)
>>> a2 = numpy.array([([4, 5], 6)], dt)
>>> apply_stat(mean_curve, [a1, a2])
array([([2.5, 3.5], 4.5)], dtype=[('a', '<f8', (2,)), ('b', '<f8')])
"""
dtype = arraylist[0].dtype
shape = arraylist[0].shape
if dtype.names: # composite array
new = numpy.zeros(shape, dtype)
for name in dtype.names:
new[name] = f([arr[name] for arr in arraylist], *extra, **kw)
return new
else: # simple array
return f(arraylist, *extra, **kw)
[docs]def set_rlzs_stats(dstore, prefix, **attrs):
"""
:param dstore: a DataStore object
:param prefix: dataset prefix, assume <prefix>-rlzs is already stored
"""
arrayNR = dstore[prefix + '-rlzs'][()]
R = arrayNR.shape[1]
pairs = list(attrs.items())
pairs.insert(1, ('rlz', numpy.arange(R)))
dstore.set_shape_descr(prefix + '-rlzs', **dict(pairs))
if R > 1:
stats = dstore['oqparam'].hazard_stats()
if not stats:
return
statnames, statfuncs = zip(*stats.items())
weights = dstore['weights'][()]
name = prefix + '-stats'
dstore[name] = compute_stats2(arrayNR, statfuncs, weights)
pairs = list(attrs.items())
pairs.insert(1, ('stat', statnames))
dstore.set_shape_descr(name, **dict(pairs))
[docs]def combine_probs(values_by_grp, cmakers, rlz):
"""
:param values_by_grp: C arrays of shape (D1, D2..., G)
:param cmakers: C ContextMakers with G gsims each
:param rlz: a realization index
:returns: array of shape (D1, D2, ...)
"""
probs = []
for values, cmaker in zip(values_by_grp, cmakers):
assert values.shape[-1] == len(cmaker.gsims)
for g, rlzs in enumerate(cmaker.gsims.values()):
if rlz in rlzs:
probs.append(values[..., g])
return agg_probs(*probs)
[docs]def average_df(dframes, weights=None):
"""
Compute weighted average of DataFrames with the same index and columns.
>>> df1 = pandas.DataFrame(dict(value=[1, 1, 1]), [1, 2, 3])
>>> df2 = pandas.DataFrame(dict(value=[2, 2, 2]), [1, 2, 3])
>>> average_df([df1, df2], [.4, .6])
value
1 1.6
2 1.6
3 1.6
"""
d0 = dframes[0]
n = len(dframes)
if n == 1:
return d0
elif weights is None:
weights = numpy.ones(n)
elif len(weights) != n:
raise ValueError('There are %d weights for %d dataframes!' %
(len(weights), n))
data = numpy.average([df.to_numpy() for df in dframes],
weights=weights, axis=0) # shape (E, C)
return pandas.DataFrame({
col: data[:, c] for c, col in enumerate(d0.columns)}, d0.index)