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 ##
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 Rebin(self, reBin):
if isinstance(reBin, (int, long)):
# merge bins together by some integer factor 'reBin'
if reBin <= 0 or self.content.size % reBin:
print " WARNING : Cannot re-bin with {0}".format(reBin)
print " : Not re-binning the histogram"
return
else:
reBin = np.array(
[self.bins[i] for i in range(0, self.bins.size, reBin)])
elif not all((i in self.bins) for i in reBin):
print " WARNING : Cannot re-bin with {0}".format(reBin)
print " : Not re-binning the histogram"
return
# re-bin the histogram (by making a new histogram)
bin_contents, bin_edges = np.histogram(self.center,
bins=reBin,
weights=self.content)
self.content = bin_contents
self.bins = bin_edges
self.center = tools.midpoints(bin_edges)
self.width = tools.widths(bin_edges)
self.error = self.sumw2_1D(
xdata=self.center) # use data for re-binning, not bin content
return
def hist2data2D(self, histo, reBin=None, normed=False):
"""Convert ROOT histogram for internal use."""
bin_contents, (xbin_edges, ybin_edges) = histo.numpy()
bin_contents = bin_contents.T
if len(histo.allvariances) > 0:
bin_errors = histo.allvariances[
1:-1, 1:
-1] # variances() doesn't produce correct values in 2D right now
else:
bin_errors = np.sqrt(bin_contents)
xbin_centers, ybin_centers = tools.dummy_bins2D(
tools.midpoints(xbin_edges), tools.midpoints(ybin_edges))
xbin_widths, ybin_widths = tools.dummy_bins2D(tools.widths(xbin_edges),
tools.widths(ybin_edges))
results = Hist()
results.content = bin_contents.flatten()
results.error = bin_errors.flatten()
results.bins = {'x': xbin_edges, 'y': ybin_edges}
results.center = {'x': xbin_centers, 'y': ybin_centers}
results.width = {'x': xbin_widths, 'y': ybin_widths}
if reBin is not None:
results.Rebin2D(reBin)
if normed: results.normalize()
# Set extra attributes (placeholders for the moment)
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 ##
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
def Rebin2D(self, reBin):
"""Re-bin 2D histogram"""
xbins = self.bins['x']
ybins = self.bins['y']
nbinsx = len(xbins) - 1
nbinsy = len(ybins) - 1
xrebin = xbins.copy() # default to original binning
yrebin = ybins.copy() # default to original binning
if isinstance(reBin, (int, long)):
# new bins should be merged bin edges
if reBin <= 0 or nbinsx % reBin or nbinsy % reBin:
print " WARNING : Cannot re-bin with {0}".format(reBin)
print " : Not re-binning the histogram"
return
else:
xrebin = np.array(
[xbins[i] for i in range(0, nbinsx + 1, reBin)])
yrebin = np.array(
[ybins[i] for i in range(0, nbinsy + 1, reBin)])
else:
# new bins should be new array bin edges that must match previous binning:
# can't re-bin [0,1,4,6] into [0,3,6]
try:
xrebin = reBin['x']
yrebin = reBin['y']
except TypeError:
xrebin = reBin[0]
yrebin = reBin[1]
if (not all((i in xbins) for i in xrebin)) or (not all(
(i in ybins) for i in yrebin)):
print " WARNING : Cannot re-bin 2D histogram using {0}".format(
reBin)
print " : Not re-binning histogram"
return
# - generate dummy values of the bin centers and pass data as weights
xbins, ybins = tools.dummy_bins2D(tools.midpoints(xbins),
tools.midpoints(ybins))
rb_hist2d = np.histogram2d(xbins,
ybins,
bins=[xrebin, yrebin],
weights=self.content.tolist())
bin_contents, xbin_edges, ybin_edges = rb_hist2d
self.content = bin_contents.flatten()
self.bins = {'x': xbin_edges, 'y': ybin_edges}
xbin_center, ybin_center = tools.dummy_bins2D(
tools.midpoints(xbin_edges), tools.midpoints(ybin_edges))
xbin_width, ybin_width = tools.dummy_bins2D(tools.widths(xbin_edges),
tools.widths(ybin_edges))
self.center = {'x': xbin_center, 'y': ybin_center}
self.width = {'x': xbin_width, 'y': ybin_width}
if _scipy_available:
self.error = self.sumw2_2D(xdata=xbins, ydata=ybins)
else:
self.error = np.sqrt(bin_contents.flatten())
return
## THE END ##