bins data into a preset number of classes. Returns a 3-tuple (#positive
bins, #negative bins, list of bin boundaries) derived from the data in
data. Bin boundaries are spaced either evenly (scale == "linear") or
logarithmically (scale == "log") across the span of the data. If
pretty is set to one, bin boundaries are rounded to the next order
of magnitude.
If no negative/positive data are given, an empty tuple is returned. The
list of bin boundaries begins with the most negative and ends with the
most positive value.
returns an array of shape (product(shapeT),len(shapeT)) containing
the indices of an array of shape shapeT.
minIndices and maxIndices are optional sequences providing minimum/
maximum indices for each dimension.
Note that the indices traverse the dimensions given in shapeT
"depth-first", i.e., in a 2d array of indices, column indices are repeated
while row indices are increased steadily (in the manner a cathode ray tube
monitor image is created...).
frequency distribution (= histogram) for an array. Calculates the values
for a frequency distribution of an arbitrary array of observed values
obs. Each frequency class has the width iwidth and includes the
lower/excludes the upper value of its interval. omin and omax provide
optional (global) lower and upper boundaries. If counts is set to 1, the
raw counts are returned instead of the frequencies.