Source code for csep.utils.basic_types

import collections

import numpy as np

from csep.utils.calc import bin1d_vec

def seq_iter(iterable):
    helper function to handle iterating over a dict or list

    should iterate using:

    for idx in iterable:
         value = iterable[idx]

        iterable: an iterable object

        key to access iterable

    return iterable if isinstance(iterable, dict) else range(len(iterable))

[docs]class AdaptiveHistogram: """ Allows us to work with data that need to be discretized and aggregated even though the the global min/max values are not known before hand. Data are discretized according to the dh and anchor positions and their extreme values. If necessary the range of the bin_edges are expanded to accommodate new data Using this function incurs some addition overhead, instead of simply just binning and combining. """
[docs] def __init__(self, dh=0.1, anchor=0.0): self.dh = dh self.anchor = anchor = np.array([]) self.bins = np.array([])
def add(self, data): if len(data) == 0: return # float point arithmitic can be an issue here data_min = np.min(data) data_max = np.max(data) # need to know the range of the data to be inserted on discretized grid (min, max) # this is to determine the discretization of the data eps=np.finfo(np.float).eps disc_min = np.floor((data_min+eps-self.anchor)*self.rec_dh)/self.rec_dh+self.anchor disc_max = np.ceil((data_max+eps-self.anchor)*self.rec_dh)/self.rec_dh+self.anchor # compute new bin edges from data new_bins = np.arange(disc_min, disc_max+self.dh/2, self.dh) # merge data self._merge(new_bins, data) def _merge(self, bins, data): # 1) current bins dont exist if self.bins.size == 0: self.bins = bins = np.zeros(len(self.bins)) idx = bin1d_vec(data, self.bins), idx, 1) return # 2) new bins subset of current bins if bins[0] >= self.bins[0] and bins[-1] <= self.bins[-1]: idx = bin1d_vec(data, self.bins),idx,1) return # 3) new bins are outside current bins if bins[0] < self.bins[0]: bin_min = bins[0] else: bin_min = self.bins[0] if bins[-1] > self.bins[-1]: bin_max = bins[-1] else: bin_max = self.bins[-1] # generate new bins new_bins = np.arange(bin_min, bin_max+self.dh/2, self.dh) tmp_data = np.zeros(len(new_bins)) # merge new data to new bins # get old bin locations relative to new bins idx = bin1d_vec(self.bins, new_bins) # add old data tmp_data[idx] = = tmp_data idx = bin1d_vec(data, new_bins), idx, 1) self.bins = new_bins return @property def rec_dh(self): return 1.0 / self.dh
def transpose_dict(adict): """Transposes a dict of dicts to regroup the data.""" out = collections.defaultdict(dict) for k,v in adict.items(): for ik,iv in v.items(): out[ik][k] = iv return out