Python DataSet.props['xlabel']方法代码示例

# 需要导入模块: from dataset import DataSet [as 别名]
# 或者: from dataset.DataSet import props['xlabel'] [as 别名]
def ResultsToXY(sets,x,y,foreach=[]):
    """ combines observable x and y to build a list of DataSet with y vs x
 
    this function is used to collect data from a hierarchy of DataSet objects, to prepare plots or evaluation.
    the inner-most list has to contain one DataSet with props['observable'] = x and one props['observable'] = y,
    this will be the pair x-y used in the collection.

    The parameters are:
      sets:    hierarchy of datasets where the inner-most list must contain to pair x-y
      x:       the name of the observable to be used as x-value of the collected results 
      y:       the name of the observable to be used as y-value of the collected results 
      foreach: an optional list of properties used for grouping the results. A separate DataSet object is created for each unique set of values of the specified parameers.

    The function returns a list of DataSet objects.
    """
    
    dd = depth(sets)
    if dd < 2:
        raise Exception('The input hierarchy does not provide a unique pair x-y. The input structure has to be a list of lists as minimum. pyalps.groupSets might help you.')
    
    hgroups = flatten(sets, fdepth=-1)
    
    foreach_sets = {}
    for gg in hgroups:
        xset = None
        yset = None
        for d in gg:
            if d.props['observable'] == x:
                xset = d
            if d.props['observable'] == y:
                yset = d
        if xset is None or yset is None:
            continue
        
        common_props = dict_intersect([d.props for d in gg])
        fe_par_set = tuple((common_props[m] for m in foreach))
        
        if not fe_par_set in foreach_sets:
            foreach_sets[fe_par_set] = DataSet()
            foreach_sets[fe_par_set].props = common_props
            foreach_sets[fe_par_set].props['xlabel'] = x
            foreach_sets[fe_par_set].props['ylabel'] = y
        
        if len(xset.y) == len(yset.y):
            foreach_sets[fe_par_set].x = np.concatenate((foreach_sets[fe_par_set].x, xset.y))
            foreach_sets[fe_par_set].y = np.concatenate((foreach_sets[fe_par_set].y, yset.y))
        elif len(xset.y) == 1:
            foreach_sets[fe_par_set].x = np.concatenate((foreach_sets[fe_par_set].x, np.array( [xset.y[0]]*len(yset.y) )))
            foreach_sets[fe_par_set].y = np.concatenate((foreach_sets[fe_par_set].y, yset.y))
    
    for k, res in foreach_sets.items():
        order = np.argsort(res.x, kind = 'mergesort')
        res.x = res.x[order]
        res.y = res.y[order]
        res.props['label'] = ''
        for p in foreach:
            res.props['label'] += '%s = %s ' % (p, res.props[p])
        
    return foreach_sets.values()

# 需要导入模块: from dataset import DataSet [as 别名]
# 或者: from dataset.DataSet import props['xlabel'] [as 别名]
def collectXY(sets,x,y,foreach=[],ignoreProperties=False):
      """ collects specified data from a list of DataSet objects
         
          this function is used to collect data from a list of DataSet objects, to prepare plots or evaluation. The parameters are:
    
            sets:    the list of datasets
            x:       the name of the property or measurement to be used as x-value of the collected results 
            y:       the name of the property or measurement to be used as y-value of the collected results 
            foreach: an optional list of properties used for grouping the results. A separate DataSet object is created for each unique set of values of the specified parameers.
            ignoreProperties: setting ignoreProperties=True prevents collectXY() from collecting properties.
            
          The function returns a list of DataSet objects.
      """
      foreach_sets = {}
      for iset in flatten(sets):
          if iset.props['observable'] != y and not y in iset.props:
              continue
          
          fe_par_set = tuple((iset.props[m] for m in foreach))
          if fe_par_set in foreach_sets:
              foreach_sets[fe_par_set].append(iset)
          else:
              foreach_sets[fe_par_set] = [iset]
      for k,v in foreach_sets.items():
          common_props = dict_intersect([q.props for q in v])
          res = DataSet()
          res.props = common_props
          for im in range(0,len(foreach)):
              m = foreach[im]
              res.props[m] = k[im]
          res.props['xlabel'] = x
          res.props['ylabel'] = y
          
          for data in v:
              if data.props['observable'] == y:
                  if len(data.y)>1:
                      res.props['line'] = '.'
                  xvalue = np.array([data.props[x] for i in range(len(data.y))])
                  if len(res.x) > 0 and len(res.y) > 0:
                      res.x = np.concatenate((res.x, xvalue ))
                      res.y = np.concatenate((res.y, data.y))
                  else:
                      res.x = xvalue
                      res.y = data.y
              elif not ignoreProperties:
                  res.props['line'] = '.'
                  xvalue = np.array([ data.props[x] ])
                  if len(res.x) > 0 and len(res.y) > 0:
                      res.x = np.concatenate((res.x, xvalue ))
                      res.y = np.concatenate((res.y, np.array([ data.props[y] ])))
                  else:
                      res.x = xvalue
                      res.y = np.array([ data.props[y] ])
          
          order = np.argsort(res.x, kind = 'mergesort')
          res.x = res.x[order]
          res.y = res.y[order]
          res.props['label'] = ''
          for im in range(0,len(foreach)):
              res.props['label'] += '%s = %s ' % (foreach[im], k[im])
          
          foreach_sets[k] = res
      return foreach_sets.values()