def array2data(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
"""
data, bins = np.histogram(data,
bins=binning,
weights=weights,
normed=normed)
results = Hist()
results.content = data
results.bins = bins
results.center = tools.midpoints(bins)
results.width = tools.widths(bins)
results.error = np.sqrt(data)
if weights is not None:
# numpy digitize to get sumw2
results.error = results.sumw2_1D(xdata=data, values=weights)
if reBin is not None:
results.Rebin(reBin)
if normed: results.normalize() # normalize after re-binning
return results
def hist2data(self, histo, reBin=None, normed=False):
"""Convert ROOT histogram for internal use."""
bin_contents, bin_edges = histo.numpy()
results = Hist()
results.content = bin_contents
results.bins = bin_edges
results.center = tools.midpoints(bin_edges)
results.width = tools.widths(bin_edges)
if len(histo.variances) > 0:
results.error = histo.variances
else:
results.error = np.sqrt(bin_contents)
if reBin is not None:
results.Rebin(reBin)
if normed: results.normalize()
return results
