multivariate bugfixes (0.3.0 release) (#7)

* MVSamples: fix to pass samples.T to stats.kde_gaussian
* MVSamples.to_mvhistogram: use samples directly if N>=nsamples
* temporary fix: accept unit/as_quantity kwargs to MV*.sample

Author: kecnry

distl/distl.py

@@ -45,7 +45,7 @@
 else:
     _has_dill = True
 
-__version__ = '0.2.0'
+__version__ = '0.3.0'
 version = __version__
 
 _math_symbols = {'__mul__': '*', '__add__': '+', '__sub__': '-',
@@ -3255,7 +3255,8 @@ def uncertainties(self, sigma=1, tex=False, dimension=None, samples=None):
         else:
             return qs_per_dim
 
-    def sample(self, size=None, dimension=None, seed=None, cache_sample=True):
+    def sample(self, size=None, dimension=None, seed=None, cache_sample=True,
+               unit=None, as_quantity=False):
         """
         Sample from the distribution.
 
@@ -3270,13 +3271,18 @@ def sample(self, size=None, dimension=None, seed=None, cache_sample=True):
             prior to sampling.
         * `cache_sample` (bool, optional, default=True): whether to override the
             existing <<class>.cached_sample>.
+        * `unit` (None): NOT YET IMPLEMENTED will raise error if not None
+        * `as_quantity` (False): NOT YET IMPLEMENTED will raise error if not False
 
         Returns
         ---------
         * float or array: float if `size=None`, otherwise a numpy array with
             shape defined by `size`.
         """
 
+        if unit is not None or as_quantity:
+            raise NotImplementedError("unit and quantities not yet supported for multivariate distributions")
+
         # TODO: add support for per-dimension unit, wrap_at, as_quantity (and pass in to_mvhistogram)
         # TODO: add support for seed
         if isinstance(seed, dict):
@@ -3714,14 +3720,16 @@ def uncertainties(self, sigma=1, tex=False):
 
     ### SAMPLING & PLOTTING
 
-    def sample(self, size=None, wrap_at=None, seed=None, cache_sample=True):
+    def sample(self, size=None, wrap_at=None, seed=None, cache_sample=True,
+               unit=None, as_quantity=False):
         """
         Sample the underlying <<class>.multivariate> distribution in the dimension
         defined in <<class>.dimension>.
         """
 
         # TODO: support unit, wrap_at, as_quantity
-        return self.multivariate.sample(size=size, seed=seed, dimension=self.dimension, cache_sample=cache_sample)
+        return self.multivariate.sample(size=size, seed=seed, dimension=self.dimension, cache_sample=cache_sample,
+                                        unit=unit, as_quantity=as_quantity)
 
     def plot_sample(self, *args, **kwargs):
         if hasattr(self, 'bins'):
@@ -3958,7 +3966,7 @@ def get_distributions_with_values(self, values=None, as_univariates=False):
             if not as_univariates and isinstance(dist_orig, BaseMultivariateSliceDistribution):
                 d = dist_orig.multivariate
             else:
-                d = dist_orig
+                d = dist_orig  #.to_univariate()?
 
             # if as_univariates then we want MVSlices with the same parent MV to be treated separately
             take_dimensions = not as_univariates and isinstance(dist_orig, BaseMultivariateSliceDistribution)
@@ -4044,7 +4052,7 @@ def logpdf(self, values=None, as_univariates=False):
             samples are available, a ValueError will be raised.
         * `as_univariates` (bool, optional, default=False): whether `values` corresponds
             to the passed distributions (<DistributionCollection.distributions>)
-            or the underlying unpacked distributions (<DistributionCollection.distributions_unpacked>).
+            or the underlying unpacked distributions (<DistributionCollection.dists_unpacked>).
             If the former (`as_univariates=False`), covariances will be respected
             from any underlying multivariate distributions.  If the latter
             (`as_univariates=True`) covariances will be ignored.
@@ -7245,7 +7253,8 @@ def take_dimensions(self, dimensions):
                            labels=[self.labels[d] for d in dimensions] if self.labels is not None else None,
                            wrap_ats=[self.wrap_ats[d] for d in dimensions] if self.wrap_ats is not None else None)
 
-    def sample(self, size=None, dimension=None, seed=None, cache_sample=True):
+    def sample(self, size=None, dimension=None, seed=None, cache_sample=True,
+               unit=None, as_quantity=False):
         """
 
         Arguments
@@ -7256,6 +7265,8 @@ def sample(self, size=None, dimension=None, seed=None, cache_sample=True):
             prior to sampling.
         * `cache_sample` (bool, optional, default=True): whether to override the
             existing <<class>.cached_sample>.
+        * `unit` (None): NOT YET IMPLEMENTED will raise error if not None
+        * `as_quantity` (False): NOT YET IMPLEMENTED will raise error if not False
 
         """
         # if dimension is not None:
@@ -7266,6 +7277,10 @@ def sample(self, size=None, dimension=None, seed=None, cache_sample=True):
         #     bins = self.bins
         #     density = self.density
 
+        if unit is not None or as_quantity:
+            raise NotImplementedError("unit and quantities not yet supported for multivariate distributions")
+
+
         if isinstance(seed, dict):
             seed = seed.get(self.uniqueid, None)
 
@@ -7535,7 +7550,7 @@ def __init__(self, samples, weights=None, bw_method=None, units=None,
 
         Arguments
         --------------
-        * `samples` (np.array object with shape (nsamples, <MVSamples.ndimensions>)):
+        * `samples` (np.array object with shape (<MVSamples.nsamples>, <MVSamples.ndimensions>)):
             the samples.
         * `weights` (np.array object with shape (nsamples) or None, optional, default=None):
             weights for each entry in `samples`.  NOTE: only supported with scipy
@@ -7560,6 +7575,8 @@ def __init__(self, samples, weights=None, bw_method=None, units=None,
         --------
         * an <MVSamples> object
         """
+        # NOTE: the passed samples need to be transposed, so see the override
+        # in dist_constructor_args
         super(MVSamples, self).__init__(units, labels, labels_latex, wrap_ats,
                                         _stats.gaussian_kde, ('samples', 'bw_method') if StrictVersion(_scipy_version) < StrictVersion("1.2.0") else ('samples', 'bw_method', 'weights'),
                                         samples=samples, weights=weights, bw_method=bw_method,
@@ -7580,7 +7597,7 @@ def samples(self, value):
     @property
     def weights(self):
         """
-        weights for each entry in <Samples.samples>
+        weights for each sample in <Samples.samples> (nsamples)
         """
         return self._weights
 
@@ -7608,6 +7625,25 @@ def bw_method(self, value):
         self._bw_method = is_float(value)
         self._dist_constructor_object_clear_cache()
 
+    @property
+    def dist_constructor_args(self):
+        """
+        Return the arguments to pass to the the underlying distribution
+        constructor (often the scipy.stats random variable generator function)
+
+        <MVSamples.samples> is transposed before passing on to gaussian_kde
+
+        See also:
+
+        * <<class>.dist_constructor_func>
+        * <<class>.dist_constructor_object>
+
+        Returns
+        -------
+        * tuple
+        """
+        return [getattr(self, a).T if a=='samples' else getattr(self,a) for a in self.dist_constructor_argnames]
+
     @property
     def ndimensions(self):
         """
@@ -7733,7 +7769,8 @@ def interval(self, *args, **kwargs):
         # TODO: manual implementation
         raise NotImplementedError()
 
-    def sample(self, size=None, dimension=None, seed=None, cache_sample=True):
+    def sample(self, size=None, dimension=None, seed=None, cache_sample=True,
+               unit=None, as_quantity=False):
         """
         Sample from the  samples (<MVSamples.samples> if <MVSamples.weights>
         is not provided, otherwise <MVSamples.samples_weighted>)
@@ -7746,9 +7783,16 @@ def sample(self, size=None, dimension=None, seed=None, cache_sample=True):
             prior to sampling.
         * `cache_sample` (bool, optional, default=True): whether to override the
             existing <<class>.cached_sample>.
+        * `unit` (None): NOT YET IMPLEMENTED will raise error if not None
+        * `as_quantity` (False): NOT YET IMPLEMENTED will raise error if not False
+
 
         """
 
+        if unit is not None or as_quantity:
+            raise NotImplementedError("unit and quantities not yet supported for multivariate distributions")
+
+
         if isinstance(seed, dict):
             seed = seed.get(self.uniqueid, None)
 
@@ -7928,7 +7972,8 @@ def to_mvhistogram(self, N=1e6, bins=15, range=None):
         Arguments
         -----------
         * `N` (int, optional, default=1e6): number of samples to use for
-            the histogram.
+            the histogram.  If N>=<MVSamples.nsamples>, <MVSamples.samples>
+            will be passed directly.
         * `bins` (int, optional, default=15): number of bins to use for the
             histogram.
         * `range` (tuple or None): range to use for the histogram.
@@ -7938,7 +7983,7 @@ def to_mvhistogram(self, N=1e6, bins=15, range=None):
         * an <MVHistogram> object
         """
         # TODO: if sample is updated to take wrap_at/wrap_ats... pass wrap_at=False here
-        return MVHistogram.from_data(self.sample(size=int(N), cache_sample=False),
+        return MVHistogram.from_data(self.samples if N >= self.nsamples else self.sample(size=int(N), cache_sample=False),
                                      bins=bins, range=range,
                                      units=self.units,
                                      labels=self.labels, labels_latex=self._labels_latex,
@@ -8015,6 +8060,18 @@ def ppf(self, q, unit=None, as_quantity=False, wrap_at=None):
         """
         return Samples(samples=self.samples, weights=self.weights, bw_method=self.bw_method, unit=self.unit).ppf(q, unit=unit, as_quantity=as_quantity, wrap_at=wrap_at)
 
+    # def pdf(self, x, unit=None, as_quantity=False, wrap_at=None):
+    #     """
+    #     See <Samples.pdf>
+    #     """
+    #     return Samples(samples=self.samples, weights=self.weights, bw_method=self.bw_method, unit=self.unit).pdf(x, unit=unit, as_quantity=as_quantity, wrap_at=wrap_at)
+    #
+    # def logpdf(self, x, unit=None, as_quantity=False, wrap_at=None):
+    #     """
+    #     See <Samples.logpdf>
+    #     """
+    #     return Samples(samples=self.samples, weights=self.weights, bw_method=self.bw_method, unit=self.unit).logpdf(x, unit=unit, as_quantity=as_quantity, wrap_at=wrap_at)
+
     def interval(self, alpha, unit=None, as_quantity=False, wrap_at=None):
         """
         See <Samples.interval>

setup.py

@@ -6,7 +6,7 @@
     long_description = fh.read()
 
 setup(name='distl',
-      version='0.2.0',
+      version='0.3.0',
       description='Simple Distributions: math operations, serializing, covariances',
       long_description=long_description,
       long_description_content_type="text/markdown",