def to_operation(self, **kwargs):
proc = AccumulatorProc(self.values['text'])
node = gn.Accumulator(name=self.name() + "_accumulated",
**kwargs,
res_factor=proc.res_factory,
reduction=proc)
return node
def to_operation(self, inputs, conditions={}):
outputs = self.output_vars()
map_outputs = [
self.name() + "_xbins",
self.name() + "_ybins",
self.name() + "_counts"
]
nxbins = self.values['x bins']
nybins = self.values['y bins']
xmin = self.values['range x min']
xmax = self.values['range x max']
ymin = self.values['range y min']
ymax = self.values['range y max']
density = self.values['density']
if self.x_type == float and self.y_type == float:
def bin(x, y):
counts, xbins, ybins = np.histogram2d([x], [y],
bins=[nxbins, nybins],
range=[[xmin, xmax],
[ymin, ymax]],
density=density)
return xbins, ybins, counts
else:
def bin(x, y):
counts, xbins, ybins = np.histogram2d(x,
y,
bins=[nxbins, nybins],
range=[[xmin, xmax],
[ymin, ymax]],
density=density)
return xbins, ybins, counts
def reduction(res, *rest):
res[0] = rest[0]
res[1] = rest[1]
res[2] = res[2] + rest[2]
return res
node = [
gn.Map(name=self.name() + "_map",
condition_needs=conditions,
inputs=inputs,
outputs=map_outputs,
func=bin,
parent=self.name()),
gn.Accumulator(name=self.name() + "_accumulated",
inputs=map_outputs,
outputs=outputs,
res_factory=lambda: [None, None, 0],
reduction=reduction,
parent=self.name())
]
return node
def to_operation(self, inputs, conditions={}):
outputs = self.output_vars()
axis = self.values['axis']
accumulated_outputs = [self.name() + '_accumulated_events']
if self.values['infinite']:
def reduction(res, *rest):
if type(rest[0]) is list:
res[0] = np.sum([res[0], rest[0][0]], axis=axis)
res[1] += rest[0][1]
else:
res[0] = np.sum([res[0], rest[0]], axis=axis)
res[1] = res[1] + 1
return res
def avg(*args, **kwargs):
return args[0][0] / args[0][1]
nodes = [
gn.Accumulator(name=self.name() + "_accumulated",
inputs=inputs,
outputs=accumulated_outputs,
condition_needs=conditions,
res_factory=lambda: [0, 0],
reduction=reduction,
parent=self.name()),
gn.Map(name=self.name() + "_map",
inputs=accumulated_outputs,
outputs=outputs,
func=avg,
parent=self.name())
]
else:
def func(arrs):
return np.sum(arrs, axis=axis) / len(arrs)
nodes = [
gn.PickN(name=self.name() + "_accumulated",
inputs=inputs,
outputs=accumulated_outputs,
condition_needs=conditions,
N=self.values['N'],
parent=self.name()),
gn.Map(name=self.name() + "_map",
inputs=accumulated_outputs,
outputs=outputs,
func=func,
parent=self.name())
]
return nodes
def to_operation(self, inputs, outputs, **kwargs):
accumulated_outputs = [self.name() + '_accumulated_events']
if self.values['infinite']:
def reduction(res, *rest):
if type(rest[0]) is list:
res[0] += rest[0][0]
res[1] += rest[0][1]
else:
res[0] = res[0] + rest[0]
res[1] = res[1] + 1
return res
def avg(*args, **kwargs):
return args[0][0] / args[0][1]
nodes = [
gn.Accumulator(name=self.name() + "_accumulated",
inputs=inputs,
outputs=accumulated_outputs,
res_factory=lambda: [0, 0],
reduction=reduction,
**kwargs),
gn.Map(name=self.name() + "_map",
inputs=accumulated_outputs,
outputs=outputs,
func=avg,
**kwargs)
]
else:
def func(arrs):
return np.average(arrs)
nodes = [
gn.PickN(name=self.name() + "_accumulated",
inputs=inputs,
outputs=accumulated_outputs,
N=self.values['N'],
**kwargs),
gn.Map(name=self.name() + "_map",
inputs=accumulated_outputs,
outputs=outputs,
func=func,
**kwargs)
]
return nodes
def to_operation(self, inputs, conditions={}):
outputs = self.output_vars()
map_outputs = [self.name() + "_bins", self.name() + "_counts"]
nbins = self.values['bins']
density = self.values['density']
range = None
if not self.values['auto range']:
range = (self.values['range min'], self.values['range max'])
def bin(arr, weights=None):
counts, bins = np.histogram(arr,
bins=nbins,
range=range,
density=density,
weights=weights)
return bins, counts
def reduction(res, *rest):
res[0] = rest[0]
res[1] = res[1] + rest[1]
return res
node = [
gn.Map(name=self.name() + "_map",
condition_needs=conditions,
inputs=inputs,
outputs=map_outputs,
func=bin,
parent=self.name()),
gn.Accumulator(name=self.name() + "_accumulated",
inputs=map_outputs,
outputs=outputs,
res_factory=lambda: [None, 0],
reduction=reduction,
parent=self.name())
]
return node