<commit_before>import numpy
import scipy
import scipy.ndimage

import advanced_debugging
import advanced_iterators


# Get the logger
logger = advanced_debugging.logging.getLogger(__name__)






class MappingDiscreteUniformDistributionGenerator(object):
    """
        Given a bunch of arguments. This will create a random element generator that returns one or many
    """
    
    def __init__(self, *args):
        """
            Builds a random element generator
        
            Args:
                *args:  Anything that needs to be drawn from.
        """
        
        self.args = args
    
    def __call__(self, size = 1):
        """
            Draws a certain number of elements with equal likelihood and returns them in a list.
        
            Args:
                size(int):       Number of elements to draw (one if unspecified).
            
            Returns:
                results(list):   a list of arguments drawn (None if no arguments)
        """
        
        results = []
        
        if len(self.args) > 0:
            indices = numpy.random.randint(0, len(self.args), size)
            
            results = [self.args[_] for _ in indices]
        else:
            results = [None for _ in xrange(size)]
        
        return(results)

class NumpyRandomArrayDiscreteUniformDistributionGenerator(object):
    """
        Creates a random numpy array (with type bool) generator that will set
        a certain number of random positions in the array to True (like Bernoulli random distribution)
    """
    
    def __init__(self, shape):
        """
            Creates a random numpy array generator that returns arrays of a certain shape.
            
            Args:
                shape(tuple):   A tuple permissible for setting the size of a numpy array.
        """
        
        self.shape = shape
    
    def __call__(self, size = 1):
        """
            Generates a numpy array.
            
            Args:
                size(int):                Number of Trues to have in the numpy array.
            
            Returns:
                results(numpy.ndarray):   a boolean numpy array with a fixed number of randomly placed Trues
        """
        
        # A completely empty numpy array
        results = numpy.zeros(self.shape, dtype = bool)
        
        # Gets a set of random indices that need to be non-zero
        indices = tuple([numpy.random.randint(0, each_dim, size) for each_dim in self.shape])
        
        # Makes them non-zero
        results[indices] = True        
        
        return(results)


class MappingDiscreteGeometricDistributionGenerator(object):
    """
        A random generator of groups. Each group has a size that is geometrically distributed.
        However, the individuals chosen for the group are all equally likely.
    """
    
    def __init__(self, *args):
        """
            Sets the arguments for use to compose the groups.
            
            Args:
                *args:  Any variety of useful items for drawing.
        """
        
        self.args = args
    
    def __call__(self, p, size = 1):
        """
            Generates a number of groups equal to size with each group size being distributed geometrically by p.
            
            Args:
                p(float)         the probability of success for a geometric distribution (starts with 1 so has mean 1/p).
                size(int)        the number of groups to make
            
            Returns:
                results(list):   a list of groups of arguments drawn (None if no arguments)
        """
        
        # Get a uniform distribution over the elements to fill each group.
        uni_gen = MappingDiscreteUniformDistributionGenerator(*self.args)        
        
        # Draw the sizes for each group
        group_sizes = numpy.random.geometric(p, size)
        
        # Using the sizes draw element to fill groups up to the right size
        results = [uni_gen(group_sizes[i]) for i in xrange(size)]
        
        return(results)





class DictionaryLearningRandomDataSample(object):
    """
        Essentially a struct with its values set at runtime by DictionaryLearningRandomDataGenerator calls.
    """
    pass


class DictionaryLearningRandomDataGenerator(object):
    """
        A Random Generator that build pseudo-data similar in nature to that which the ADINA algorithm is run.
    """
    
    def __init__(self, frame_shape, num_objects, num_groups, num_frames, mean_group_size, object_spread, object_max_intensity, object_min_intensity, background_noise_intensity):
        """
            Builds a DictionaryLearningRandomDataGenerator for draws.
            
            Args:
                frame_shape(tuple)                  a tuple of ints for constructing a numpy array<commit_msg>random_dictionary_learning_data: Created a constructor for DictionaryLearningRandomDataSample that clearly sets its values to None.<commit_after>import numpy
import scipy
import scipy.ndimage

import advanced_debugging
import advanced_iterators


# Get the logger
logger = advanced_debugging.logging.getLogger(__name__)






class MappingDiscreteUniformDistributionGenerator(object):
    """
        Given a bunch of arguments. This will create a random element generator that returns one or many
    """
    
    def __init__(self, *args):
        """
            Builds a random element generator
        
            Args:
                *args:  Anything that needs to be drawn from.
        """
        
        self.args = args
    
    def __call__(self, size = 1):
        """
            Draws a certain number of elements with equal likelihood and returns them in a list.
        
            Args:
                size(int):       Number of elements to draw (one if unspecified).
            
            Returns:
                results(list):   a list of arguments drawn (None if no arguments)
        """
        
        results = []
        
        if len(self.args) > 0:
            indices = numpy.random.randint(0, len(self.args), size)
            
            results = [self.args[_] for _ in indices]
        else:
            results = [None for _ in xrange(size)]
        
        return(results)

class NumpyRandomArrayDiscreteUniformDistributionGenerator(object):
    """
        Creates a random numpy array (with type bool) generator that will set
        a certain number of random positions in the array to True (like Bernoulli random distribution)
    """
    
    def __init__(self, shape):
        """
            Creates a random numpy array generator that returns arrays of a certain shape.
            
            Args:
                shape(tuple):   A tuple permissible for setting the size of a numpy array.
        """
        
        self.shape = shape
    
    def __call__(self, size = 1):
        """
            Generates a numpy array.
            
            Args:
                size(int):                Number of Trues to have in the numpy array.
            
            Returns:
                results(numpy.ndarray):   a boolean numpy array with a fixed number of randomly placed Trues
        """
        
        # A completely empty numpy array
        results = numpy.zeros(self.shape, dtype = bool)
        
        # Gets a set of random indices that need to be non-zero
        indices = tuple([numpy.random.randint(0, each_dim, size) for each_dim in self.shape])
        
        # Makes them non-zero
        results[indices] = True        
        
        return(results)


class MappingDiscreteGeometricDistributionGenerator(object):
    """
        A random generator of groups. Each group has a size that is geometrically distributed.
        However, the individuals chosen for the group are all equally likely.
    """
    
    def __init__(self, *args):
        """
            Sets the arguments for use to compose the groups.
            
            Args:
                *args:  Any variety of useful items for drawing.
        """
        
        self.args = args
    
    def __call__(self, p, size = 1):
        """
            Generates a number of groups equal to size with each group size being distributed geometrically by p.
            
            Args:
                p(float)         the probability of success for a geometric distribution (starts with 1 so has mean 1/p).
                size(int)        the number of groups to make
            
            Returns:
                results(list):   a list of groups of arguments drawn (None if no arguments)
        """
        
        # Get a uniform distribution over the elements to fill each group.
        uni_gen = MappingDiscreteUniformDistributionGenerator(*self.args)        
        
        # Draw the sizes for each group
        group_sizes = numpy.random.geometric(p, size)
        
        # Using the sizes draw element to fill groups up to the right size
        results = [uni_gen(group_sizes[i]) for i in xrange(size)]
        
        return(results)





class DictionaryLearningRandomDataSample(object):
    """
        Essentially a struct with its values set at runtime by DictionaryLearningRandomDataGenerator calls.
    """
    def __init__(self):
        self.points = None
        self.centroid_activation_frames = None
        self.noiseless_frames = None
        self.frames = None


class DictionaryLearningRandomDataGenerator(object):
    """
        A Random Generator that build pseudo-data similar in nature to that which the ADINA algorithm is run.
    """
    
    def __init__(self, frame_shape, num_objects, num_groups, num_frames, mean_group_size, object_spread, object_max_intensity, object_min_intensity, background_noise_intensity):
        """
            Builds a DictionaryLearningRandomDataGenerator for draws.
            
            Args:
                frame_shape(tuple)                  a tuple of ints for constructing a numpy array
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