def HistogramWeighted(data,bins,weights,sidx=None,no_copy = False):
bins = np.ascontiguousarray(bins,dtype = data.dtype)
bincounts = np.ascontiguousarray(np.zeros(bins.shape[0] -1,dtype = np.float64))
if no_copy:
data = np.ravel(data)
weights = np.ravel(weights)
else:
data = np.ravel(data.copy())
weights = np.ravel(weights)
if sidx is None:
sidx = argsort(data,no_copy=True)
data = np.ascontiguousarray(data[sidx])
weights = np.ascontiguousarray(weights[sidx],dtype=data.dtype)
codefile = open(loompath+"/csrc/inline_histogram_weighted.cc","r")
code = codefile.read().format(FLOAT = c_spec.num_to_c_types[data.dtype.char])
codefile.close()
auxcodefile = open(loompath+"/csrc/weighted_histogram_aux.cc","r")
auxcode = auxcodefile.read().format(FLOAT = c_spec.num_to_c_types[data.dtype.char])
auxcodefile.close()
Configuration.weave(code,
["bins","data","weights","bincounts"],
locals(),
support_code=auxcode)
return bincounts, bins,sidx
def Histogram(data,bins,no_copy=False):
bins = np.ascontiguousarray(bins,dtype = data.dtype)
bincounts = np.ascontiguousarray(np.zeros(bins.shape[0] -1,dtype = np.float64))
if no_copy:
data = np.ascontiguousarray(np.ravel(data))
else:
data = np.ascontiguousarray(np.ravel(np.copy(data)))
codefile = open(loompath+"/csrc/inline_histogram.cc","r")
code = codefile.read().format(FLOAT = c_spec.num_to_c_types[data.dtype.char])
codefile.close()
Configuration.weave(code,
["bins","data","bincounts"],
locals())
return bincounts, bins
def argsort(data,no_copy=False):
if not no_copy:
data = data.copy()
codefile = open(loompath+"/csrc/argsort.cc","r")
code = codefile.read().format(FLOAT = c_spec.num_to_c_types[data.dtype.char])
codefile.close()
auxcodefile = open(loompath+"/csrc/weighted_histogram_aux.cc","r")
auxcode = auxcodefile.read().format(FLOAT = c_spec.num_to_c_types[data.dtype.char])
auxcodefile.close()
idx = np.ascontiguousarray(np.arange(data.size,dtype = np.uint64))
Configuration.weave(code,
["data","idx"],
locals(),
support_code=auxcode)
return idx
def Bin(positions,gridN1d,boxsize = 1.0,dt = np.float64(),method = "tsc",rfft=False):
assert(method == "tsc","Other binning methods have not been added. Please request them or check back later")
positions = positions[:,0:3]
positions = np.ascontiguousarray(positions,dtype=dt)
N = positions.shape[0]
#we pad the array to make in place rfft based convolutions easier
density = np.ascontiguousarray(np.zeros((gridN1d,gridN1d,2*(gridN1d/2+1)),dtype = dt))
codefile = open(Configuration.loompath +"/csrc/inline_tsc.cc","rb")
tsc_code = codefile.read().format(FLOAT=c_spec.num_to_c_types[dt.dtype.char],gridN1D = gridN1d)
codefile.close()
Configuration.weave(tsc_code,
["positions","N","density","boxsize"],
locals())
if rfft:
return density
else:
return density[:gridN1d,:gridN1d,gridN1d]
