def array2data2D(self,
data,
weights=None,
normed=False,
binning=1,
reBin=None):
"""
Convert array of data to internal format
- Designed for arrays of raw, un-binned data.
- If you pass values here from an existing histogram ('weights' is not None
and the 'data' param is just bin centers), it is possible to re-bin
this histogram using the 'reBin' keyword
"""
try:
x = data['x']
y = data['y']
except TypeError:
x = data[0]
y = data[1]
data, bins_x, bins_y = np.histogram2d(x,
y,
bins=binning,
normed=normed,
weights=weights)
results = Hist()
results.content = data.flatten() # data is a ndarray (nxbins,nybins)
results.bins = {'x': bins_x, 'y': bins_y}
xcenter, ycenter = tools.dummy_bins2D(tools.midpoints(bins_x),
tools.midpoints(bins_y))
xwidth, ywidth = tools.dummy_bins2D(tools.widths(bins_x),
tools.widths(bins_y))
results.center = {'x': xcenter, 'y': ycenter}
results.width = {'x': xwidth, 'y': ywidth}
results.error = np.sqrt(data)
if weights is not None:
# scipy.stats to get sumw2 (x,y,weights should have the same shape)
results.error = results.sumw2_2D(xdata=x, ydata=y, values=weights)
if reBin is not None:
# re-binning after making data from array, likely that the user
# passed in binned data and wants to re-bin.
results.Rebin2D(reBin)
if normed: results.normalize() # normalize after re-binning
results.xbins = results.bins['x']
results.ybins = results.bins['y']
results.xcenter = results.center['x']
results.ycenter = results.center['y']
results.xwidth = results.width['x']
results.ywidth = results.width['y']
return results
## THE END ##
