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Commit e305448b authored by Martin Reinecke's avatar Martin Reinecke
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nifty/spaces/power_space/power_space.py
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......@@ -39,25 +39,19 @@ class PowerSpace(Space):
The distribution strategy used for the distributed_data_objects
derived from this PowerSpace, e.g. the pindex.
(default : 'not')
logarithmic : bool *optional*
True if logarithmic binning should be used (default : None).
nbin : {int, None} *optional*
The number of bins that should be used for power spectrum binning
(default : None).
if nbin == None, then nbin is set to the length of kindex.
binbounds : {list, array-like} *optional*
Boundaries between the power spectrum bins.
(If binbounds has n entries, there will be n+1 bins, the first bin
starting at -inf, the last bin ending at +inf.)
binbounds: None, or tuple/array/list of float
if None:
There will be as many bins as there are distinct k-vector lengths
in the harmonic partner space.
The "binbounds" property of the PowerSpace will also be None.
else:
the bin bounds requested for this PowerSpace. The array
must be sorted and strictly ascending. The first entry is the right
boundary of the first bin, and the last entry is the left boundary
of the last bin, i.e. thee will be len(binbounds)+1 bins in total,
with the first and last bins reaching to -+infinity, respectively.
(default : None)
if binbounds == None:
Calculates the bounds from the kindex while applying the
logarithmic and nbin keywords.
Note: if "bindounds" is not None, both "logarithmic" and "nbin" must be
None!
Note: if "binbounds", "logarithmic", and "nbin" are all None, then
"natural" binning is performed, i.e. there will be one bin for every
distinct k-vector length.
Attributes
----------
......@@ -72,13 +66,14 @@ class PowerSpace(Space):
dim : np.int
Total number of dimensionality, i.e. the number of pixels.
harmonic : bool
Specifies whether the space is a signal or harmonic space.
Always True for this space.
total_volume : np.float
The total volume of the space.
shape : tuple of np.ints
The shape of the space's data array.
binbounds : tuple or None
Boundaries between the power spectrum bins
binbounds : tuple or None
Boundaries between the power spectrum bins; None is used to indicate
natural binning
Notes
-----
......@@ -91,8 +86,46 @@ class PowerSpace(Space):
# ---Overwritten properties and methods---
@staticmethod
def linear_binbounds (nbin, first_bound, last_bound):
"""
nbin: integer
the number of bins
first_bound, last_bound: float
the k values for the right boundary of the first bin and the left
boundary of the last bin, respectively. They are given in length
units of the harmonic partner space.
This will produce a binbounds array with nbin-1 entries with
binbounds[0]=first_bound and binbounds[-1]=last_bound and the remaining
values equidistantly spaced (in linear scale) between these two.
"""
nbin = int(nbin)
assert nbin>=3, "nbin must be at least 3"
first_bound = float(first_bound)
last_bound = float(last_bound)
binbounds = np.arange(nbin-1,dtype=np.float64)/(nbin-2)
binbounds*=last_bound-first_bound
binbounds+=first_bound
return binbounds
@staticmethod
def logarithmic_binbounds (nbin, first_bound, last_bound):
"""
nbin: integer
the number of bins
first_bound, last_bound: float
the k values for the right boundary of the first bin and the left
boundary of the last bin, respectively. They are given in length
units of the harmonic partner space.
This will produce a binbounds array with nbin-1 entries with
binbounds[0]=first_bound and binbounds[-1]=last_bound and the remaining
values equidistantly spaced (in natural logarithmic scale)
between these two.
"""
return np.exp(linear_binbounds(np.log(first_bound),np.log(last_bound)))
def __init__(self, harmonic_partner, distribution_strategy=None,
logarithmic=None, nbin=None, binbounds=None):
binbounds=None):
super(PowerSpace, self).__init__()
self._ignore_for_hash += ['_pindex', '_kindex', '_rho']
......@@ -108,35 +141,24 @@ class PowerSpace(Space):
raise ValueError("harmonic_partner must be a harmonic space.")
self._harmonic_partner = harmonic_partner
# sanity check
if binbounds is not None and not(nbin is None and logarithmic is None):
raise ValueError(
"if binbounds is defined, nbin and logarithmic must be None")
if binbounds is not None:
binbounds = tuple(binbounds)
key = (harmonic_partner, distribution_strategy, logarithmic, nbin,
binbounds)
key = (harmonic_partner, distribution_strategy, binbounds)
if self._powerIndexCache.get(key) is None:
distance_array = \
self.harmonic_partner.get_distance_array(distribution_strategy)
temp_binbounds = self._compute_binbounds(
harmonic_partner=self.harmonic_partner,
distribution_strategy=distribution_strategy,
logarithmic=logarithmic,
nbin=nbin,
binbounds=binbounds)
temp_pindex = self._compute_pindex(
harmonic_partner=self.harmonic_partner,
distance_array=distance_array,
binbounds=temp_binbounds,
binbounds=binbounds,
distribution_strategy=distribution_strategy)
temp_rho = temp_pindex.bincount().get_full_data()
assert not np.any(temp_rho==0), "empty bins detected"
temp_kindex = \
(temp_pindex.bincount(weights=distance_array).get_full_data() /
temp_rho)
self._powerIndexCache[key] = (temp_binbounds,
self._powerIndexCache[key] = (binbounds,
temp_pindex,
temp_kindex,
temp_rho)
......@@ -144,43 +166,6 @@ class PowerSpace(Space):
(self._binbounds, self._pindex, self._kindex, self._rho) = \
self._powerIndexCache[key]
@staticmethod
def _compute_binbounds(harmonic_partner, distribution_strategy,
logarithmic, nbin, binbounds):
if logarithmic is None and nbin is None and binbounds is None:
result = None
else:
if binbounds is not None:
bb = np.sort(np.array(binbounds))
else:
if logarithmic is not None:
logarithmic = bool(logarithmic)
if nbin is not None:
nbin = int(nbin)
# equidistant binning (linear or log)
# MR FIXME: this needs to improve
kindex = harmonic_partner.get_unique_distances()
if (logarithmic):
k = np.r_[0, np.log(kindex[1:])]
else:
k = kindex
dk = np.max(k[2:] - k[1:-1]) # minimum dk to avoid empty bins
if(nbin is None):
nbin = int((k[-1] - 0.5 * (k[2] + k[1])) /
dk - 0.5) # maximal nbin
else:
nbin = min(int(nbin), int(
(k[-1] - 0.5 * (k[2] + k[1])) / dk + 2.5))
dk = (k[-1] - 0.5 * (k[2] + k[1])) / (nbin - 2.5)
bb = np.r_[0.5 * (3 * k[1] - k[2]),
0.5 * (k[1] + k[2]) + dk * np.arange(nbin-2)]
if(logarithmic):
bb = np.exp(bb)
result = tuple(bb)
return result
@staticmethod
def _compute_pindex(harmonic_partner, distance_array, binbounds,
distribution_strategy):
......
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