def arr_set(self, arr):
"""
DESC
Given array input <arr>, overwrite internal
data with elements as defined in <arr>
ARGS
arr in array to process; can
be 'ndarray' or 'list'
PRECONDITIONS
o self.ml_keys must be valid.
POSTCONDITIONS
o If unable to set array, return False. Make no change
to internal data.
"""
b_setOK = False
if type(arr).__name__ == 'ndarray':
self.mdict_spectrum = misc.dict_init(self.ml_keys, arr.tolist())
b_setOK = True
if type(arr) is types.ListType:
self.mdict_spectrum = misc.dict_init(self.ml_keys, arr)
b_setOK = True
if b_setOK: self.keys_index()
return b_setOK
def arr_set(self, arr): """ DESC Given array input <arr>, overwrite internal data with elements as defined in <arr> ARGS arr in array to process; can be 'ndarray' or 'list' PRECONDITIONS o self.ml_keys must be valid. POSTCONDITIONS o If unable to set array, return False. Make no change to internal data. """ b_setOK = False if type(arr).__name__ == 'ndarray': self.mdict_spectrum = misc.dict_init(self.ml_keys, arr.tolist()) b_setOK = True if type(arr) is types.ListType: self.mdict_spectrum = misc.dict_init(self.ml_keys, arr) b_setOK = True if b_setOK: self.keys_index() return b_setOK
def initialize(self):
"""
(Re)builds internal dictionary structures. Typically called
once the number of rotations has been set and/or a new cloud
has been read.
"""
if self._numRotations < 1:
self.fatal('Rotations')
self._M_Crows, self._M_Ccols = self._M_C.shape
self._M_Cdimensionality = self._M_Ccols
# The numRotations defines the number of rotations between
# 0 and 90 degrees (i.e. the first quadrant). This is used
# to create a dictionary of rotationKeys and rotationVals
self._rotationKeys = range(0, self._numRotations)
v_keys = np.array(self._rotationKeys)
v_vals = v_keys * 90.0/self._numRotations
for rotation in self._rotationKeys:
self._d_rotatedCloudStats[rotation] = self._dict_stats.copy()
self._dict_rotationVal = misc.dict_init(self._rotationKeys,
list(v_vals))
self._dict_projection = misc.dict_init(self._rotationKeys,
np.zeros( (self._M_Ccols, self._M_Ccols),
dtype = 'object'))
# 2D Planar quadrant projections
for quadrant in range(1,5):
self._dict_Q[quadrant] = misc.dict_init(self._rotationKeys,
np.zeros( (1,2) ))
def __init__(self, *args):
self.__name__ = 'C_spectrum'
self.mstr_obj = 'C_spectrum'; # name of object class
self.mstr_name = 'unnamed'; # name of object variable
self.mstr_def = 'void'; # name of function being processed
self.m_id = -1; # int id
self.m_iter = 0; # current iteration in an
#+ arbitrary processing
#+ scheme
self.m_verbosity = 0; # debug related value for
#+ object
self.m_warnings = 0; # show warnings
#+ (and warnings level)
self.mdict_keyIndex = {}; # lookup of keys to indices
self.mdict_spectrum = {}; # the actual spectrum
self.mNumKeys = 0;
self.mb_printHist = False; # If true, print an actual
#+ histogram representation
self.mb_printAsRow = False; # If true, print spectrum as a
# row, else print as column
self.mb_printConcise = False; # If true, print concise
#+ version of spectrum
self.m_cellWidth = 12 # For row printing, the width
#+ of a column
self.mf_totalPower = 0.0;
c = args[0]
if type(c) is types.ListType:
self.ml_keys = c
if type(c).__name__ == 'ndarray':
ilist = range(1, np.size(c)+1)
self.ml_keys = misc.list_i2str(ilist)
if len(args) >= 2:
if type(args[1] is types.ListType):
if len(args[1]) == np.size(c):
self.ml_keys = args[1]
self.core_construct()
self.mdict_keyIndex = misc.dict_init(self.ml_keys, 0)
self.mdict_spectrum = misc.dict_init(self.ml_keys, 0)
if type(c).__name__ == 'ndarray':
self.mdict_spectrum = misc.dict_init(self.ml_keys,
c.tolist())
self.keys_index()
if isinstance(c, int):
self.component_add(c)
if type(c) is types.TupleType:
for component in c:
self.component_add(component)
self.mNumKeys = len(self.ml_keys)
def __init__(self, *args):
self.__name__ = 'C_spectrum'
self.mstr_obj = 'C_spectrum'
# name of object class
self.mstr_name = 'unnamed'
# name of object variable
self.mstr_def = 'void'
# name of function being processed
self.m_id = -1
# int id
self.m_iter = 0
# current iteration in an
#+ arbitrary processing
#+ scheme
self.m_verbosity = 0
# debug related value for
#+ object
self.m_warnings = 0
# show warnings
#+ (and warnings level)
self.mdict_keyIndex = {}
# lookup of keys to indices
self.mdict_spectrum = {}
# the actual spectrum
self.mNumKeys = 0
self.mb_printHist = False
# If true, print an actual
#+ histogram representation
self.mb_printAsRow = False
# If true, print spectrum as a
# row, else print as column
self.mb_printConcise = False
# If true, print concise
#+ version of spectrum
self.m_cellWidth = 12 # For row printing, the width
#+ of a column
self.mf_totalPower = 0.0
c = args[0]
if type(c) is types.ListType:
self.ml_keys = c
if type(c).__name__ == 'ndarray':
ilist = range(1, np.size(c) + 1)
self.ml_keys = misc.list_i2str(ilist)
if len(args) >= 2:
if type(args[1] is types.ListType):
if len(args[1]) == np.size(c):
self.ml_keys = args[1]
self.core_construct()
self.mdict_keyIndex = misc.dict_init(self.ml_keys, 0)
self.mdict_spectrum = misc.dict_init(self.ml_keys, 0)
if type(c).__name__ == 'ndarray':
self.mdict_spectrum = misc.dict_init(self.ml_keys, c.tolist())
self.keys_index()
if isinstance(c, int):
self.component_add(c)
if type(c) is types.TupleType:
for component in c:
self.component_add(component)
self.mNumKeys = len(self.ml_keys)
