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core_nopd.py
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core_nopd.py
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"""
.. module:: core
:platform: Unix
:synopsis: Core functionality and classes
.. moduleauthor:: Matthias Flor <matthias.c.flor@gmail.com>
"""
import sys, os
import numpy as np
from matplotlib.cbook import flatten
from itertools import izip
import utilities_nopd as utils
extend = utils.extend
myfloat = utils.myfloat
sum_along_axes = utils.sum_along_axes
import pdb
def linear_labels(nestedlabels):
def reclabels(labels):
lsh = utils.list_shape(labels)
if len(lsh) == 1 or len(lsh) == 0:
return labels # list of strings
first,rest = labels[0], labels[1:]
return [[x] + reclabels(rest) for x in first]
nested = reclabels(nestedlabels)
return list(flatten(nested))
def labeled_array(a, nestedlabels=None):
"""
Input:
a : numpy array
nestedlabels : nested list of strings
Output:
out : string
"""
if a is None:
return 'no array'
if nestedlabels is None:
nestedlabels = default_labels(np.shape(a))
lshape = utils.list_shape(nestedlabels)
n = len(lshape)
#~ print 'array (shape {0}):'.format(np.shape(a))
#~ print a
#~ print 'labels (shape {0}):'.format(lshape)
#~ print nestedlabels
#~ assert np.shape(a) == lshape
toplabels = nestedlabels[-1]
labels = linear_labels(nestedlabels[:-1]) # linear list of labels
labels = [''] + [' '+label+' ' for label in labels]
astring = str(a)
c = 0 # count
s = ''
indentation = []
for char in astring:
if char == '[':
name = labels[c]
s += labels[c]+'['
indentation.append(len(name))
c += 1
elif char == ']':
s += ' ]'
elif char == '\n':
indentation.pop()
s += '\n'+np.sum(indentation)*' '
else:
s += char
lines = s.split('\n')
cols = []
i = 0
first = lines[0]
for char in first:
if char == '.':
cols.append(i)
i += 1
t = ' '*len(first)
for c,lab in izip(cols,toplabels):
t = t[:c-1]+lab+t[c-1:]
ret = t+'\n'+s
ret = os.linesep.join([s for s in ret.splitlines() if s.strip()])
return ret+'\n'
def default_labels(sh):
"""If no labels are provided then create default labels.
E.g.: If _data = np.array([[1.,2.],[3.,4.],[5.,6.]] then
labels = {'axes': ['a0', 'a1'],
'elements': [['e00','e01','e02'],['e10','e11']]}.
print a would then yield:
[[1., 2.], | [['e00-e10', 'e00...
[3., 4.], |
[5., 6.]] |
"""
n = len(sh)
labels = []
for i in range(n):
temp_list = []
for j in range(sh[i]):
temp_list.append("el_%d%d" % (i,j))
labels.append(temp_list)
return labels
class Weight(object):
"""
Weight base class.
Not usable on its own because no panda respresentation is created.
"""
def __init__(self, name, axes, labels=None, arr=None, **parameters):
self.name = name
self.axes = axes
self.array = arr
if not labels:
if arr is not None:
labels = default_labels(np.shape(arr))
else:
labels = 'no labels'
self.labels = labels
self.parameters = parameters
self.__dict__.update(parameters)
def configure_extension(self, dim, pos):
self.extdim = dim
self.extpos = pos
def set(self, arr):
"""
Set weight array to `arr` and update panda representation.
"""
assert np.shape(arr) == self.shape
self.array = arr
def set_to_ones(self):
self.array = np.ones(self.shape,float)
def extended(self):
return extend(self.array, self.extdim, self.extpos)
def __str__(self):
"""
Nicely formatted string output of the reproduction weight. We
just use the panda Series output.
"""
s = self.name+':\n'+labeled_array(self.array, self.labels)
pars = ''
for k,v in sorted(self.parameters.items()):
pars += '{0}: {1}\n'.format(k,v)
return s
class MigrationWeight(Weight):
def __init__(self, \
name='migration', \
axes=['target', 'source'], \
config=None, \
arr=None, \
**parameters):
labels = utils.get_alleles(['population','population'], config=config)
sh = utils.list_shape(labels)
if arr == None:
arr = np.zeros( sh, float )
Weight.__init__(self, name, axes, labels, arr, **parameters)
self.shape = sh
self.configure_extension( dim=1+config['N_LOCI'], pos=[0,1] )
class ViabilityWeight(Weight):
def __init__(self, \
name='viability selection', \
axes=['population', 'trait'], \
config=None, \
arr=None, \
**parameters):
labels = utils.get_alleles(axes, config=config)
sh = utils.list_shape(labels)
if arr == None:
arr = np.zeros( sh, float )
Weight.__init__(self, name, axes, labels, arr, **parameters)
self.shape = sh
pos = [config['LOCI'].index(a) for a in axes]
self.configure_extension( dim=config['N_LOCI'], pos=pos )
class ReproductionWeight(Weight):
"""
Weights to be used in the reproduction step of next generation
production.
Mainly, this class takes care of
- printing a nice panda version of the weight array and
- enabling autmatic extension to the correct shape needed in the
reproduction step.
Constant weights can be instantiated directly as instances of this
class whereas dynamic weights should be defined in the scenario
files as custom classes inheriting from this class.
The class method `dynamic` should be used to achieve a dynamic
update of the weight array.
"""
def __init__(self, name, axes, config, arr=None, **parameters):
"""
Args:
name: str
name of the weight
axes: list of strings
list of axes names
config: dict
scenario configuration
arr: ndarray
if `arr` is not provided on initialization, it will be
populated with zeros and **must** be set afterwords with
the `set` method
parameters: dict
dictionary of parameter names (keys) and values (values)
"""
labels = utils.make_reproduction_allele_names(axes, config)
sh = utils.list_shape(labels)
#~ print 'init repro weight'
#~ print 'labels (shape {0}):'.format(sh)
#~ print labels
if arr == None:
arr = np.zeros( sh, float )
Weight.__init__(self, name=name, axes=axes, labels=labels, arr=arr, **parameters)
self.shape = sh
dim = config['REPRO_DIM']
repro_axes = config['REPRO_AXES']
pos = [repro_axes.index(ax) for ax in axes]
self.configure_extension( dim=dim, pos=pos )
def hms_generator((locus1, allele1), (locus2, allele2), config, h=1.):
"""
Usage: hms_generator(('A',1), ('B',0)) generates a weight for HMS
due to incompatibilities between the 'Alocus' and the 'Blocus'
with hybrid males carrying the allele combination 'A1-B0'
being sterile
"""
HMS_weight = ReproductionWeight(name='hybrid male sterility {0}/{1}'.format(locus1,locus2), \
axes=['male_{0}locus'.format(locus1), 'male_{0}locus'.format(locus2)], \
config=config, \
h=h
)
alleles = config['ALLELES']
loci = config['LOCI']
n1 = len(alleles[loci.index('{0}locus'.format(locus1))])
n2 = len(alleles[loci.index('{0}locus'.format(locus2))])
ary = np.ones((n1,n2), float)
ary[allele1,allele2] = 1-h
HMS_weight.set( ary )
HMS_weight_ = HMS_weight.extended()
return HMS_weight, HMS_weight_
class PreferenceWeight(ReproductionWeight):
def __init__(self, name, axes, pref_desc, config, **parameters):
"""
Args:
name, axes, config, and parameters: see parent class
pref_desc: dict describing preferences
e.g.: {'S1': {'pop1': ('A1-B1', 0.9), \
'pop2': ('A1-B1', 0.9)},
'S2': {'pop1': ('A2-B2', 0.9), \
'pop2': ('A2-B2', 0.9)}}
This description will be translated into a list that is
easier to use in indexing the array.
"""
ReproductionWeight.__init__(self, name=name, axes=axes, config=config, **parameters)
# use config for determining preference allele indexes
self.pref_desc = pref_desc
preferences = []
for pref_allele,pop_prefs in sorted(pref_desc.items()):
prefidx = config['ADICT'][pref_allele][1] # retrieve allele index
for pop,(cues,pr) in sorted(pop_prefs.items()):
if pop == 'all pops': # same preference in all populations
popidx = slice(None,None,None)
else:
popidx = config['ADICT'][pop][1]
cues = cues.split('-')
cueidx = tuple( [config['ADICT'][c][1] for c in cues] ) # get cue allele indexes
preferences.append( ((popidx,prefidx)+cueidx, pr) ) # tuple of all indexes together (as a tuple) and the rejection probability
self.preferences = preferences
def calculate(self, x):
"""
Args:
x: ndarray
frequency array of preferred traits in the appropriate shape
pt: float
transition probability
"""
self.set_to_ones()
for idx,pr in self.preferences: # idx: complete indexes
#~ pdb.set_trace()
idx2 = idx[:1] + idx[2:] # idx2: preference allele index removed
tmp = 1./(1-pr*self.pt*(1-x[idx2]))
tmp_ = extend(tmp, dim=len(idx2), pos=0) # tmp[:,np.newaxis,np.newaxis]
self.array[idx[:2]] *= (1-pr)*tmp_ # idx[:2]: preferred trait indexes removed
self.array[idx] = tmp
self.array = np.nan_to_num(self.array)
class GeneralizedPreferenceWeight(ReproductionWeight):
def __init__(self, name, axes, pref_desc, config, **parameters):
"""
Args:
name, axes, config, and parameters: see parent class
pref_desc: dict describing preferences
e.g.: {'P0': {'baseline': 0.}, # 0. is the default baseline!
'P1': {'baseline': 0.9, 'T3': 0.}, # all traits not explicitely mentioned will be rejected with the baseline probability
'P2': {'baseline': 0.8, 'T4': 0.}
}
This description will be translated into an array containing
the rejection probabilities that can be accessed by the
preference allele index and cue indexes.
"""
ReproductionWeight.__init__(self, name=name, axes=axes, config=config, **parameters)
self.cue_axes = []
split_axes = [a.split('_') for a in axes]
for a in split_axes:
if a[0]=='female':
self.pref_locus = a[1]
elif a[0]=='male':
self.cue_axes.append(a[1])
fshape = config['FSHAPE']
loci = config['LOCI']
alleles = config['ALLELES']
adict = config['ADICT']
n_prefs = fshape[loci.index(self.pref_locus)]
self.cshape = tuple( [fshape[loci.index(a)] for a in self.cue_axes] ) # cue_shape
rprobs = np.zeros( (n_prefs,)+self.cshape, float ) # rejection probabilities array with default baseline of 0.
self.pref_desc = pref_desc
for pref_allele,prefs in sorted(pref_desc.items()):
prefidx = adict[pref_allele][1] # retrieve allele index
keys = sorted(prefs.keys())
if 'baseline' in keys:
pr = prefs['baseline']
rprobs[prefidx] = pr
for cue,pr in sorted(prefs.items()):
if cue == 'baseline':
break
cues = cue.split('-')
cueidx = tuple( [adict[c][1] for c in cues] ) # get cue allele indexes
rprobs[(prefidx,)+cueidx] = pr
#~ preferences.append( ((popidx,prefidx)+cueidx, pr) ) # tuple of all indexes together (as a tuple) and the rejection probability
self.rejection_probabilities = self.rprobs = rprobs # shape: (pref, cue1, cue2, ...)
names = [self.pref_locus] + self.cue_axes
self.rlabels = utils.get_alleles(names, config=config)
def calculate(self, x):
"""
Args:
x: ndarray
frequency array of preferred traits in the appropriate shape
pt: float
transition probability
"""
#~ self.set_to_ones()
rej = self.rprobs[np.newaxis,...] # bring rejection probabilities to correct shape
cues = x[:,np.newaxis,...] # same for preference cues
norm = 1. - self.pt * utils.sum_along_axes(rej*cues, [0,1]) # sum along population and preference a.k.a. sum over all cues
self.array = np.nan_to_num( (1.-rej)/norm[...,np.newaxis] )
def __str__(self):
s = Weight.__str__(self).rstrip() + '\nrejection probabilities:\n'
s += labeled_array(self.rprobs, self.rlabels)
return s
class DummyProgressBar(object):
def update(self, val):
pass
def finish(self):
pass
def generate_progressbar():
return DummyProgressBar()
class MetaPopulation(object):
def __init__(self, frequencies, config, generation=0, name='metapopulation', eq='undetermined'):
self.loci = config['LOCI']
self.n_loci = len(self.loci)
self.alleles = self.labels = config['ALLELES']
#~ self.repro_axes = config['REPRO_AXES'] # reproduction_axes(loci)
#~ self.repro_dim = config['REPRO_DIM'] #len(self.repro_axes)
assert np.shape(frequencies) == utils.list_shape(self.alleles)
self.freqs = frequencies
self.ndim = self.freqs.ndim
self.shape = self.freqs.shape
self.size = self.freqs.size
self.normalize()
self.allele_idxs = config['ADICT']
self.populations = self.alleles[0]
self.n_pops = len(self.populations)
self.generation = generation
self.name = name
self.eq = eq
r_axes = config['REPRO_AXES']
self.repro_axes = {'all': r_axes}
#~ self.repro_shape = 3 * self.shape
self.repro_dim = config['REPRO_DIM']
self.repro_idxs = {}
for who in ['female', 'male', 'offspring']:
w_axes = utils.reproduction_axes(self.loci, who)
self.repro_axes[who] = w_axes
self.repro_idxs[who] = [r_axes.index(a) for a in w_axes]
def __str__(self):
"""
Returns nicely formatted string representation of metapopulation
as unstacked panda series.
"""
return self.name+':\n'+labeled_array(self.freqs, self.labels)
def overview(self, *args):
"""
Return nicely formatted string representation of locus sums.
If arguments are passed then each argument must be a locus name
or a list of locus names.
"""
s = 'overview:\n'
if not args:
args = self.loci[1:]
for a in args:
if isinstance(a, list):
s += ', '.join(a) + ':\n'
axes = [self.loci[0]] + a
else:
s += a+':\n'
axes = [self.loci[0], a]
labels = [self.alleles[self.loci.index(locus)] for locus in axes]
s += labeled_array(self.get_sums(a), labels)+'\n'
return s
def normalize(self):
"""
Normalize frequencies so that they sum up to one in each
population.
"""
s = sum_along_axes(self.freqs, 0) # first axis are `populations`
self.freqs /= extend(s, self.ndim, 0) # in-place, no copy
def load_freqs_from_file(self, g, filename, snum, rnum):
self.freqs = storage.get_frequencies(g, filename, snum, rnum)
def get_sum(self, allele, pop):
"""
Return the summed frequency of `allele` in `pop`.
Args:
allele: string
allele name
pop: int or string
population index or name
Returns:
out: float
"""
if not isinstance(pop,int): pop = self.allele_idxs(pop)[1]
l,a = self.allele_idxs[allele]
return sum_along_axes(self.freqs, [0,l])[pop,a]
def get_sums(self, locus, pop=None):
"""
Return the summed frequency at `locus` (in `pop` if given, or
in all populations).
Args:
locus: int or string or list of these
locus indexes or names
pop: int or string
population index or name
Returns:
out: ndarray
"""
level = [0]
if not isinstance(locus, list):
locus = [locus]
for loc in locus:
if isinstance(loc, int): level.append(loc)
else: level.append( self.loci.index(loc) )
if pop or pop==0:
if not isinstance(pop,int):
popname, pop = pop, self.allele_idxs(pop)[1]
else:
popname = self.populations[pop]
return sum_along_axes(self.freqs, level)[pop]
return sum_along_axes(self.freqs, level)
def all_sums(self):
"""
Returns:
out: list of ndarrays
list of loci sums (each locus sum is an ndarray)
"""
sums = []
for locus in self.loci[1:]:
sums.append( self.get_sums(locus) )
return sums
#~ def get_sums_pd(self, locus, pop=None):
#~ """
#~ Return the summed frequency at `locus` (in `pop` if given, or
#~ in all populations) as a panda series for nice print output.
#~
#~ Args:
#~ locus: int or string or list of these
#~ locus indexes or names
#~ pop: int or string
#~ population index or name
#~
#~ Returns:
#~ out: ndarray
#~ """
#~ if not self.isuptodate():
#~ self.update()
#~ level = [0]
#~ if not isinstance(locus, list):
#~ locus = [locus]
#~ for loc in locus:
#~ if isinstance(loc, int): level.append(loc)
#~ else: level.append( self.loci.index(loc) )
#~ p = self.panda.sum(level=level)
#~ if pop or pop==0:
#~ if isinstance(pop,int):
#~ pop = self.populations[pop]
#~ return p[pop]
#~ return p
def introduce_allele(self, pop, allele, intro_freq, advance_generation_count=True):
"""
Introduce `allele` into `pop` with frequency `intro_freq`.
The introduction is a way such that the summed frequencies of
at all other loci are unaffected by the new allele. The allele
must not be present in the population already.
If advance_generation_count is True, the generation of the
metapopulation is advanced by one.
Args:
pop: int or string
population index or name
allele: string
allele name
intro_freq: float in interval [0, 1]
introduction frequency of `allele`
"""
if not isinstance(pop,int):
pop = self.allele_idxs[pop][1]
loc,al = self.allele_idxs[allele]
lfreqs = sum_along_axes(self.freqs, [0,loc])[pop]
try:
assert lfreqs[al] == 0.
except AssertionError:
raise AssertionError, 'allele `{0}` already present in {1}'.format(allele,self.populations[pop])
locus_sums = np.sum( self.freqs, axis=loc )[pop] # freqs: (2,2,3,2) --> (2,2,2)[pop] --> (2,2)
idxs = [slice(None,None,None) for i in range(self.ndim)]
idxs[0] = pop
idxs[loc] = al
self.freqs[pop] *= 1 - intro_freq
self.freqs[idxs] = intro_freq * locus_sums
if advance_generation_count:
self.generation += 1
self.eq = 'not determined'
def run(self, n, weights, step=100, threshold=1e-4, runstore=None, progress=True):
"""
Simulate next `n` generations. Abort if average overall difference
between consecutive generations is smaller than `threshold` (i.e.
an equilibrium has been reached).
Args:
weights: dictionary of weights to be used in the calculation
of the next generation frequencies
n: int
maximum number of generations to run
step: int
frequencies are stored every `step` generations
threshold: float
the `threshold` is divided by the frequency size
(arr.ndim) to calculate `thresh`, and the simulation run
is stopped if the average difference between consecutive
generations has become smaller than `thresh`.
runstore: storage.runstore instance
if provided, simulation run is stored in datafile
progress: progressbar.ProgressBar instance
if none is provided, a new one is created
"""
MIG = weights['migration']
VIAB_SEL = weights['viability_selection']
REPRO_CONST = weights['constant_reproduction']
if 'dynamic_reproduction' in weights.keys():
dyn_repro_weights = weights['dynamic_reproduction']
#~ pt = dyn_repro_weights[0][0].pt
else:
dyn_repro_weights = []
#~ SR,TP = weights['dynamic_reproduction']
#~ pt = SR.pt
self.runstore = runstore
n += self.generation
thresh = threshold/self.size # on average, each of the frequencies should change less than `thresh` if an equilibrium has been reached
still_changing = True
progress = generate_progressbar()
while still_changing and self.generation < n:
# data storage:
if self.runstore != None:
if self.generation % step == 0:
#~ self.runstore.dump_data(self.generation, self.freqs, self.all_sums())
pass #self.runstore.dump_data(self)
#~ self.runstore.flush()
previous = np.copy(self.freqs)
### migration ##################################
self.freqs = np.sum(self.freqs[np.newaxis,...] * MIG, 1) # sum over `source` axis
self.normalize()
### viability selection ########################
self.freqs = self.freqs * VIAB_SEL
self.normalize()
### reproduction ###############################
#~ # species recognition:
#~ SR.calculate( self.get_sums(['backA','backB']) )
#~
#~ # trait preferences:
#~ TP.calculate( self.get_sums('trait') )
REPRO_DYN = 1. #np.ones( (1,)*self.repro_dim )
for DRW, target_loci in dyn_repro_weights:
DRW.calculate( self.get_sums(target_loci) )
REPRO_DYN = REPRO_DYN * DRW.extended()
# offspring production:
females = extend( self.freqs, self.repro_dim, self.repro_idxs['female'] )
males = extend( self.freqs, self.repro_dim, self.repro_idxs['male'] )
#~ self.freqs = sum_along_axes( females * males * R * SR.extended() * TP.extended(), self.repro_idxs['offspring'] )
self.freqs = sum_along_axes( females * males * \
REPRO_CONST * \
REPRO_DYN, self.repro_idxs['offspring'] )
self.normalize()
self.generation += 1
progress.update(self.generation)
still_changing = utils.diff(self.freqs, previous) > thresh
self.eq = not still_changing
#~ if self.runstore != None: # store final state
#~ self.runstore.dump_data(self)
#~ if self.eq:
#~ state_desc = 'eq'
#~ else:
#~ state_desc = 'max'
#~ self.runstore.record_special_state(self.generation, state_desc)
# return ProgressBar instance so we can reuse it for further running:
return progress