2
from scipy_base import *
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def abut (source, *args):
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"""\nLike the |Stat abut command. It concatenates two arrays column-wise
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and returns the result. CAUTION: If one array is shorter, it will be
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repeated until it is as long as the other.
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Format: abut (source, args) where args=any # of arrays
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Returns: an array as long as the LONGEST array past, source appearing on the
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'left', arrays in <args> attached on the 'right'.\n"""
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source = asarray(source)
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if len(source.shape)==1:
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source = N.resize(source,[source.shape[0],width])
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width = source.shape[1]
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if len(addon.shape)==1:
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addon = N.resize(addon,[source.shape[0],width])
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width = source.shape[1]
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if len(addon) < len(source):
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addon = N.resize(addon,[source.shape[0],addon.shape[1]])
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elif len(source) < len(addon):
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source = N.resize(source,[addon.shape[0],source.shape[1]])
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source = N.concatenate((source,addon),1)
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"""Returns unique items in the FIRST dimension of the passed array. Only
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works on arrays NOT including string items (e.g., type 'O' or 'c').
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inarray = asarray(inarray)
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uniques = N.array([inarray[0]])
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if len(uniques.shape) == 1: # IF IT'S A 1D ARRAY
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for item in inarray[1:]:
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if N.add.reduce(N.equal(uniques,item).flat) == 0:
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uniques = N.concatenate([uniques,N.array[N.NewAxis,:]])
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uniques = N.concatenate([uniques,N.array([item])])
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else: # IT MUST BE A 2+D ARRAY
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if inarray.typecode() != 'O': # not an Object array
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for item in inarray[1:]:
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if not N.sum(N.alltrue(N.equal(uniques,item),1)):
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uniques = N.concatenate( [uniques,item[N.NewAxis,:]] )
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except TypeError: # the item to add isn't a list
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uniques = N.concatenate([uniques,N.array([item])])
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pass # this item is already in the uniques array
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else: # must be an Object array, alltrue/equal functions don't work
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for item in inarray[1:]:
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for unq in uniques: # NOTE: cmp --> 0=same, -1=<, 1=>
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test = N.sum(abs(N.array(map(cmp,item,unq))))
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if test == 0: # if item identical to any 1 row in uniques
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newflag = 0 # then not a novel item to add
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uniques = N.concatenate( [uniques,item[N.NewAxis,:]] )
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except TypeError: # the item to add isn't a list
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uniques = N.concatenate([uniques,N.array([item])])
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def colex (a,indices,axis=1):
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"""\nExtracts specified indices (a list) from passed array, along passed
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axis (column extraction is default). BEWARE: A 1D array is presumed to be a
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column-array (and that the whole array will be returned as a column).
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Returns: the columns of a specified by indices\n"""
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if type(indices) not in [ListType,TupleType,N.ArrayType]:
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if len(N.shape(a)) == 1:
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cols = N.resize(a,[a.shape[0],1])
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cols = N.take(a,indices,axis)
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def printcc (lst,extra=2):
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"""\nPrints a list of lists in columns, customized by the max size of items
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within the columns (max size of items in col, plus 'extra' number of spaces).
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Use 'dashes' or '\n' in the list(oflists) to print dashes or blank lines,
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Format: printcc (lst,extra=2)
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if type(x) <> StringType:
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if type(lst[0]) not in [ListType,TupleType]:
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list2print = copy.deepcopy(lst)
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for i in range(len(lst)):
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if lst[i] == ['\n'] or lst[i]=='\n' or lst[i]=='dashes':
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rowstokill = rowstokill + [i]
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rowstokill.reverse() # delete blank rows from the end
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for row in rowstokill:
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maxsize = [0]*len(list2print[0])
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for col in range(len(list2print[0])):
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items = colex(list2print,col)
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items = map(makestr,items)
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maxsize[col] = max(map(len,items)) + extra
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if row == ['\n'] or row == '\n':
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elif row == ['dashes'] or row == 'dashes':
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dashes = [0]*len(maxsize)
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for j in range(len(maxsize)):
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dashes[j] = '-'*(maxsize[j]-2)
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print lineincustcols(dashes,maxsize)
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print lineincustcols(row,maxsize)
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def linexand (a,columnlist,valuelist):
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"""Returns the rows of an array where col (from columnlist) = val
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(from valuelist). One value is required for each column in columnlist.
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Returns: the rows of a where columnlist[i]=valuelist[i] for ALL i\n"""
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if type(columnlist) not in [ListType,TupleType,N.ArrayType]:
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columnlist = [columnlist]
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if type(valuelist) not in [ListType,TupleType,N.ArrayType]:
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valuelist = [valuelist]
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for i in range(len(columnlist)):
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if type(valuelist[i])==StringType:
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critval = '\'' + valuelist[i] + '\''
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critval = str(valuelist[i])
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criterion = criterion + ' x['+str(columnlist[i])+']=='+critval+' and'
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criterion = criterion[0:-3] # remove the "and" after the last crit
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return adm(a,criterion)
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def collapse (a,keepcols,collapsecols,stderr=0,ns=0,cfcn=None):
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"""Averages data in collapsecol, keeping all unique items in keepcols
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(using unique, which keeps unique LISTS of column numbers), retaining
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the unique sets of values in keepcols, the mean for each. If the sterr or
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N of the mean are desired, set either or both parameters to 1.
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Returns: unique 'conditions' specified by the contents of columns specified
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by keepcols, abutted with the mean(s) of column(s) specified by
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avgcol = colex(a,collapsecols)
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if type(keepcols) not in [ListType,TupleType,N.ArrayType]:
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keepcols = [keepcols]
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values = colex(a,keepcols) # so that "item" can be appended (below)
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uniques = unique(values) # get a LIST, so .sort keeps rows intact
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if type(item) not in [ListType,TupleType,N.ArrayType]:
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tmprows = linexand(a,keepcols,item)
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for col in collapsecols:
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avgcol = colex(tmprows,col)
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item.append(cfcn(avgcol))
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item.append(stats.sterr(avgcol))
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item.append(len(avgcol))
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new_a = N.array(newlist)
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new_a = N.array(newlist,'O')
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if type(item) <> StringType:
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def lineincustcols (inlist,colsizes):
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"""\nReturns a string composed of elements in inlist, with each element
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right-aligned in a column of width specified by a sequence colsizes. The
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length of colsizes must be greater than or equal to the number of columns in
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Format: lineincustcols (inlist,colsizes)
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Returns: formatted string created from inlist\n"""
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for i in range(len(inlist)):
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if type(inlist[i]) <> StringType:
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item = str(inlist[i])
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if size <= colsizes[i]:
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for j in range(colsizes[i]-size):
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outstr = outstr + ' '
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outstr = outstr + item
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outstr = outstr + item[0:colsizes[i]+1]
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def list2string (inlist):
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"""\nConverts a 1D list to a single long string for file output, using
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the string.join function.
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Format: list2string (inlist)
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Returns: the string created from inlist\n"""
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stringlist = map(makestr,inlist)
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return string.join(stringlist)
added
removed
Lib/stats/_support.py
