Source code for dclab.rtdc_dataset.fmt_dict

# -*- coding: utf-8 -*-
"""RT-DC dictionary format"""
from __future__ import division, print_function, unicode_literals

import time

import numpy as np

from .. import definitions as dfn
from ..util import hashobj

from .config import Configuration
from .core import RTDCBase

[docs]class RTDC_Dict(RTDCBase): def __init__(self, ddict, *args, **kwargs): """Dictionary-based RT-DC dataset Parameters ---------- ddict: dict Dictionary with features as keys (valid features like "area_cvx", "deform", "image" are defined by `dclab.definitions.feature_exists`) with which the class will be instantiated. The configuration is set to the default configuration of dclab. .. versionchanged:: 0.27.0 Scalar features are automatically converted to arrays. *args: Arguments for `RTDCBase` **kwargs: Keyword arguments for `RTDCBase` """ assert ddict super(RTDC_Dict, self).__init__(*args, **kwargs) t = time.localtime() # Get an identifying string keys = list(ddict.keys()) keys.sort() ids = hashobj(ddict[keys[0]]) self._ids = ids self.path = "none" self.title = "{}_{:02d}_{:02d}/{}.dict".format(t[0], t[1], t[2], ids) # Populate events self._events = {} for key in ddict: if dfn.feature_exists(key): if dfn.scalar_feature_exists(key): data = np.array(ddict[key]) else: data = ddict[key] else: raise ValueError("Invalid feature name '{}'".format(key)) self._events[key] = data event_count = len(ddict[list(ddict.keys())[0]]) self.config = Configuration() self.config["experiment"]["event count"] = event_count # Set up filtering self._init_filters() @property def hash(self): return self._ids