Source code for dclab.rtdc_dataset.filter

# -*- coding: utf-8 -*-
"""RT-DC dataset core classes and methods"""
from __future__ import division, print_function, unicode_literals

import warnings

import numpy as np

from dclab import definitions as dfn

from .. import downsampling
from ..polygon_filter import PolygonFilter

[docs]class Filter(object): def __init__(self, rtdc_ds): """Boolean filter arrays for RT-DC measurements Parameters ---------- rtdc_ds: instance of RTDCBase The RT-DC dataset the filter applies to """ #: Instance of RTDCBase the filter applies to self.rtdc_ds = rtdc_ds self._filters = {} #: All filters combined (see :func:`Filter.update`) self.all = np.ones(len(rtdc_ds), dtype=bool) #: Invalid (nan/inf) events self.invalid = np.ones(len(rtdc_ds), dtype=bool) #: Reserved for manual filtering self.manual = np.ones(len(rtdc_ds), dtype=bool) #: Polygon filters self.polygon = np.ones(len(rtdc_ds), dtype=bool) # old filter configuration of `rtdc_ds` self._old_config = {} def __getitem__(self, key): """Return the filter for a feature of `self.rtdc_ds`""" if key in self.rtdc_ds: if (key not in self._filters and key in dfn.scalar_feature_names): # Generate filters on-the-fly self._filters[key] = np.ones(len(self.rtdc_ds), dtype=bool) return self._filters[key]
[docs] def update(self, force=[]): """Update the filters according to `self.rtdc_ds.config["filtering"]` Parameters ---------- force : list A list of feature names that must be refiltered with min/max values. """ # These lists may help us become very fast in the future newkeys = [] oldvals = [] newvals = [] cfg_cur = self.rtdc_ds.config["filtering"] cfg_old = self._old_config ## Determine which data was updated for skey in list(cfg_cur.keys()): if not skey in cfg_old: cfg_old[skey] = None if cfg_cur[skey] != cfg_old[skey]: newkeys.append(skey) oldvals.append(cfg_old[skey]) newvals.append(cfg_cur[skey]) # 1. Filter all feature min/max values. # This line gets the feature names that must be filtered. col2filter = [] for k in newkeys: # k[:-4] because we want to crop " min" and " max" if k[:-4] in dfn.scalar_feature_names: col2filter.append(k[:-4]) for f in force: # Manually add forced features if f in dfn.scalar_feature_names: col2filter.append(f) else: # Make sure the feature name is valid. raise ValueError("Unknown feature name {}".format(f)) col2filter = np.unique(col2filter) for col in col2filter: if col in self.rtdc_ds: fstart = col + " min" fend = col + " max" # Get the current feature filter col_filt = self[col] # If min and max exist and if they are not identical: if (fstart in cfg_cur and fend in cfg_cur and cfg_cur[fstart] != cfg_cur[fend]): # TODO: speedup # Here one could check for smaller values in the # lists oldvals/newvals that we defined above. # Be sure to check against force in that case! ivalstart = cfg_cur[fstart] ivalend = cfg_cur[fend] if ivalstart > ivalend: msg = "inverting filter: {} > {}".format(fstart, fend) warnings.warn(msg) ivalstart, ivalend = ivalend, ivalstart data = self.rtdc_ds[col] col_filt[:] = (ivalstart <= data)*(data <= ivalend) else: col_filt[:] = True # 2. Filter with polygon filters # check if something has changed pf_id = "polygon filters" if ( (pf_id in cfg_cur and not pf_id in cfg_old) or (pf_id in cfg_cur and pf_id in cfg_old and cfg_cur[pf_id] != cfg_old[pf_id])): self.polygon[:] = True # perform polygon filtering for p in PolygonFilter.instances: if p.unique_id in cfg_cur[pf_id]: # update self.polygon # iterate through axes datax = self.rtdc_ds[p.axes[0]] datay = self.rtdc_ds[p.axes[1]] self.polygon *= p.filter(datax, datay) # 3. Invalid filters self.invalid[:] = True if cfg_cur["remove invalid events"]: for col in dfn.scalar_feature_names: if col in self.rtdc_ds: data = self.rtdc_ds[col] invalid = np.isinf(data)+np.isnan(data) self.invalid *= ~invalid # 4. Finally combine all filters # get a list of all filters self.all[:] = True if cfg_cur["enable filters"]: for col in self._filters: self.all[:] *= self._filters[col] self.all[:] *= self.invalid self.all[:] *= self.manual self.all[:] *= self.polygon # Filter with configuration keyword argument "Limit Events". # This additional step limits the total number of events in # self.all. if cfg_cur["limit events"] > 0: limit = cfg_cur["limit events"] sub = self.all[self.all] _f, idx = downsampling.downsample_rand(sub, samples=limit, retidx=True) sub[~idx] = False self.all[self.all] = sub # Actual filtering is then done during plotting self._old_config = self.rtdc_ds.config.copy()["filtering"]