also the add methods

Author: jpivarski

uproot_methods/classes/TH1.py

@@ -140,76 +140,6 @@ def index(self, data):
         elif not math.isnan(data):
             return int(math.floor(self._fXaxis._fNbins * (data - low) / (high - low))) + 1
 
-    def fill(self, datum):
-        numbins = self._fXaxis._fNbins
-        low = self._fXaxis._fXmin
-        high = self._fXaxis._fXmax
-        if datum < low:
-            self[0] += 1
-        elif datum >= high:
-            self[-1] += 1
-        else:
-            self[int(math.floor(numbins * (datum - low) / (high - low))) + 1] += 1
-
-    def fillw(self, datum, weight):
-        numbins = self._fXaxis._fNbins
-        low = self._fXaxis._fXmin
-        high = self._fXaxis._fXmax
-        if datum < low:
-            self[0] += weight
-        elif datum >= high:
-            self[-1] += weight
-        else:
-            self[int(math.floor(numbins * (datum - low) / (high - low))) + 1] += weight
-
-    def fillall(self, data):
-        numbins = self._fXaxis._fNbins
-        low = self._fXaxis._fXmin
-        high = self._fXaxis._fXmax
-
-        if not isinstance(data, numpy.ndarray):
-            data = numpy.array(data)
-
-        freq, edges = numpy.histogram(data, bins=numbins, range=(low, high), density=False)
-        for i, x in enumerate(freq):
-            self[i + 1] += x
-
-        self[0] += (data < low).sum()
-        self[-1] += (data >= high).sum()
-
-    def fillallw(self, data, weights):
-        numbins = self._fXaxis._fNbins
-        low = self._fXaxis._fXmin
-        high = self._fXaxis._fXmax
-
-        if not isinstance(data, numpy.ndarray):
-            data = numpy.array(data)
-
-        if isinstance(weights, numbers.Real):
-            weights = numpy.full(data.shape, weights)
-
-        freq, edges = numpy.histogram(data, bins=numbins, range=(low, high), weights=weights, density=False)
-        for i, x in enumerate(freq):
-            self[i + 1] += x
-
-        self[0] += weights[data < low].sum()
-        self[-1] += weights[data >= high].sum()
-
-    def __add__(self, other):
-        if not isinstance(other, TH1Methods) or self.numbins != other.numbins or self.low != other.low or self.high != other.high:
-            raise TypeError("TH1 histograms can only be combined with other TH1 histograms with the same binning")
-        return hist(self.numbins, self.low, self.high, name=(self.name if self.name is not None else other.name), title=(self.title if self.title is not None else other.title), allvalues=(numpy.array(self) + numpy.array(other)))
-
-    def __radd__(self, other):
-        return self.__add__(other)
-
-    def __iadd__(self, other):
-        if not isinstance(other, TH1Methods) or self.numbins != other.numbins or self.low != other.low or self.high != other.high:
-            raise TypeError("TH1 histograms can only be combined with other TH1 histograms with the same binning")
-        for i in range(len(self)):
-            self[i] = other[i]
-        return self
-
     @property
     def xlabels(self):
         if self._fXaxis._fLabels is None:

uproot_methods/classes/TH2.py

@@ -199,101 +199,6 @@ def xindex(self, data):
     def yindex(self, data):
         return self.index(data, "y")
 
-    def fill(self, datumx, datumy):
-        self.fillw(datumx,datumy, 1.0)
-
-    def fillw(self, datumx, datumy, weight):
-        xnumbins = self.xnumbins
-        xlow     = self.xlow
-        xhigh    = self.xhigh
-        ynumbins = self.ynumbins
-        ylow     = self.ylow
-        yhigh    = self.yhigh
-
-        binx = self.index(datumx, "x")    # get the x bin
-        biny = self.index(datumy, "y")    # get the y bin
-
-        # translate biny,binx into index for self[index]
-        bin = biny*(xnumbins + 2) + binx
-        self[bin] += weight
-
-    def fillall(self, datax, datay):
-        self.fillallw(datax, datay, weights=None)
-
-    def fillallw(self, datax, datay, weights):
-        xbins    = self._fXaxis._fXbins
-        xlow     = self.xlow
-        xhigh    = self.xhigh
-        ybins    = self._fYaxis._fXbins
-        ylow     = self.ylow
-        yhigh    = self.yhigh
-
-        if not xbins: xbins = self.xnumbins
-        if not ybins: ybins = self.ynumbins
-
-        if not isinstance(datax, numpy.ndarray):
-            datax = numpy.array(datax)
-        if not isinstance(datay, numpy.ndarray):
-            datay = numpy.array(datay)
-
-        if isinstance(weights, numbers.Real):
-            weights = numpy.full(datax.shape, weights)
-
-        freq, xedges, yedges = numpy.histogram2d(datax,
-                                                 datay,
-                                                 bins=[xbins, ybins],
-                                                 range=[[xlow, xhigh], [ylow, yhigh]],
-                                                 weights=weights,
-                                                 normed=False)
-        freq = freq.flatten().tolist()
-        for i, x in enumerate(freq):
-            self[i+1] += x
-
-        # get all values for underflow
-        data_xlow = numpy.where(datax<xlow)
-        data_ylow = numpy.where(datay<ylow)
-        data_low  = numpy.unique(numpy.concatenate((data_xlow,data_ylow),0)) # indices for underflow
-
-        # get all values for overflow
-        data_xhigh = numpy.where(datax>xhigh)
-        data_yhigh = numpy.where(datax>yhigh)
-        data_high  = numpy.unique(numpy.concatenate((data_xhigh,data_yhigh),0)) # indices for overflow
-
-        # put all (unique) underflow and overflow values into an area 
-        data_uo    = numpy.unique(numpy.concatenate((data_low,data_high),0)) # indices for under/overflow
-
-        if weights is None:
-            for d in data_uo:
-                binx = self.index(datax[d],'x')    # get the x bin
-                biny = self.index(datay[d],'y')    # get the y bin
-
-                # translate biny,binx into index for self[index]
-                bin = biny*(xnumbins+2) + binx
-                self[bin] += 1
-        else:
-            for d in data_uo:
-                binx = self.index(datax[d],'x')    # get the x bin
-                biny = self.index(datay[d],'y')    # get the y bin
-
-                # translate biny,binx into index for self[index]
-                bin = biny*(xnumbins+2) + binx
-                self[bin] += weights[d]
-
-    def __add__(self, other):
-        if not isinstance(other, TH2Methods) or self.numbins != other.numbins or self.low != other.low or self.high != other.high:
-            raise TypeError("TH2 histograms can only be combined with other TH2 histograms with the same binning")
-        return hist2d(self.xnumbins, self.xlow, self.xhigh, self.ynumbins, self.ylow, self.yhigh, name=(self.name if self.name is not None else other.name), title=(self.title if self.title is not None else other.title), allvalues=(numpy.array(self) + numpy.array(other)))
-
-    def __radd__(self, other):
-        return self.__add__(other)
-
-    def __iadd__(self, other):
-        if not isinstance(other, TH2Methods) or self.numbins != other.numbins or self.low != other.low or self.high != other.high:
-            raise TypeError("TH2 histograms can only be combined with other TH2 histograms with the same binning")
-        for i in range(len(self)):
-            self[i] = other[i]
-        return self
-
     @property
     def ylabels(self):
         if self._fYaxis._fLabels is None: