Ejemplo n.º 1
0

# XXX: not sure the best thing to do
@simple_block
def array(value):
    """ Converts the value to a Numpy array. """
    return np.array(value)


@simple_block
def list2array(value):
    """ Converts a list of uniform elements to a Numpy array. """
    return np.array(value, dtype=value[0].dtype)


register_simple_block(np.mean, "mean", params={"axis": 0}, doc="Wrapper around :np:data:`np.mean`.")

register_simple_block(np.square, "square", doc="Wrapper around :np:data:`np.square`.")

register_simple_block(np.log, "log", doc="Wrapper around :np:data:`np.log`.")

register_simple_block(np.abs, "abs", doc="Wrapper around :np:data:`np.absolute`.")

register_simple_block(np.sign, "sign", doc="Wrapper around :np:data:`np.sign`.")

register_simple_block(np.arctan, "arctan", doc="Wrapper around :np:data:`np.arctan`.")

register_simple_block(np.real, "real", doc="Wrapper around :np:data:`np.real`.")


register_simple_block(
Ejemplo n.º 2
0
def norm2(value):  # @ReservedAssignment
    ''' Returns the norm of the vector. '''
    return np.linalg.norm(value)

# XXX: not sure the best thing to do
@simple_block
def array(value):
    ''' Converts the value to a Numpy array. '''
    return np.array(value)

@simple_block
def list2array(value):
    """ Converts a list of uniform elements to a Numpy array. """
    return np.array(value, dtype=value[0].dtype)

register_simple_block(np.mean, 'mean', params={'axis': 0},
      doc='Wrapper around :np:data:`np.mean`.')

register_simple_block(np.square, 'square',
      doc='Wrapper around :np:data:`np.square`.')

register_simple_block(np.log, 'log',
      doc='Wrapper around :np:data:`np.log`.')

register_simple_block(np.abs, 'abs',
      doc='Wrapper around :np:data:`np.absolute`.')

register_simple_block(np.sign, 'sign',
      doc='Wrapper around :np:data:`np.sign`.')

register_simple_block(np.arctan, 'arctan',
      doc='Wrapper around :np:data:`np.arctan`.')
Ejemplo n.º 3
0
from procgraph import register_simple_block
from procgraph_images.copied_from_reprep import skim_top_and_bottom


def skim(a, percent=5):

    return skim_top_and_bottom(a, percent)

register_simple_block(skim, doc=skim_top_and_bottom.__doc__)

#doc='Skims the top and bottom percentile from the data.')
Ejemplo n.º 4
0
# XXX: not sure the best thing to do
@simple_block
def array(value):
    ''' Converts the value to a Numpy array. '''
    return np.array(value)


@simple_block
def list2array(value):
    """ Converts a list of uniform elements to a Numpy array. """
    return np.array(value, dtype=value[0].dtype)


register_simple_block(np.mean,
                      'mean',
                      params={'axis': 0},
                      doc='Wrapper around :np:data:`np.mean`.')

register_simple_block(np.square,
                      'square',
                      doc='Wrapper around :np:data:`np.square`.')

register_simple_block(np.log, 'log', doc='Wrapper around :np:data:`np.log`.')

register_simple_block(np.abs,
                      'abs',
                      doc='Wrapper around :np:data:`np.absolute`.')

register_simple_block(np.sign,
                      'sign',
                      doc='Wrapper around :np:data:`np.sign`.')
Ejemplo n.º 5
0
from procgraph import register_simple_block
from procgraph_images.copied_from_reprep import skim_top_and_bottom


def skim(a, percent=5):

    return skim_top_and_bottom(a, percent)


register_simple_block(skim, doc=skim_top_and_bottom.__doc__)

#doc='Skims the top and bottom percentile from the data.')
Ejemplo n.º 6
0
from numpy import sign

from procgraph import Block, register_simple_block 

from .utils import my_pickle_load

register_simple_block(lambda x: 1.0 / x, 'one_over')

def count_less_than_zero(values):
    ''' Returns the fraction of zero elements in the array. '''
    ok = (values <= 0).mean()
    return ok

register_simple_block(count_less_than_zero)


class BGDSPredictor(Block):
    
    Block.alias('bgds_predictor')
    
    Block.config('G', 'Data produced by camera_bgds_boot_display')
   
    Block.input('gx', 'Gradient of image along direction x.')
    Block.input('gy', 'Gradient of image along direction y.')
    Block.input('y_dot', 'Derivative of y.')
    Block.input('commands', 'Commands (``[vx,vy,omega]``).')
    
    Block.output('y_dot_pred', 'Predicted y_dot')
    Block.output('error', 'Disagreement between actual and predicted y_dot.')
    
    def init(self):