Merge pull request #552 from AllenInstitute/release-1.0.7

Release 1.0.7

CHANGELOG.md

@@ -7,6 +7,15 @@ All notable changes to this project will be documented in this file.
 
 ### Changed
 
+## [1.0.7] = 2022-12-5
+Changed:
+- Added StimulusType and STIMULUS_TYPE_NAME_MAPPING to stimulus ontology, replacing definitions in EphysDataset
+- Updated data_set_features to use correct sweep feature extractor detection parameters based on StimulusType
+
+## [1.0.6] = 2022-6-29
+Changed: 
+- Stop IPFX from caching its NWB Schemas when writing/modifying NWB files
+
 ## [1.0.5] = 2021-12-13
 Bug fixes: 
 - Converts nwb_version attribute to string if it is in utf-8 encoded bytes.

ipfx/data_set_features.py

@@ -35,26 +35,28 @@
 #
 import functools
 import numpy as np
+from collections import defaultdict
 import logging
 from .feature_extractor import SpikeFeatureExtractor,SpikeTrainFeatureExtractor
-from ipfx.dataset.ephys_data_set import EphysDataSet
+from ipfx.stimulus import StimulusOntology, StimulusType, get_stimulus_type
 from . import spike_features as spkf
 from . import stimulus_protocol_analysis as spa
 from . import stim_features as stf
 from . import feature_record as fr
 from . import error as er
 from . import logging_utils as lu
 
-DEFAULT_DETECTION_PARAMETERS = { 'dv_cutoff': 20.0, 'thresh_frac': 0.05 }
-
-DETECTION_PARAMETERS = {
-    EphysDataSet.SHORT_SQUARE: {'thresh_frac_floor': 0.1 },
-    EphysDataSet.RAMP: { },
-    EphysDataSet.LONG_SQUARE: { }
-}
-
-
 
+DETECTION_PARAMETERS = defaultdict(
+    lambda: {},
+    {
+        # To override detection parameters for specific StimulusType, add it here.
+        # If not explicitly listed, default detection parameters will be used (see extractors_for_sweeps()).
+        # See ipfx.stimulus for stimulus types
+        StimulusType.SHORT_SQUARE: {'thresh_frac_floor': 0.1 },
+        StimulusType.CHIRP: {"filter_frequency": None}
+    }
+)
 
 
 SUBTHRESHOLD_LONG_SQUARE_MIN_AMPS = {
@@ -65,8 +67,18 @@
 TEST_PULSE_DURATION_SEC = 0.4
 
 
-def detection_parameters(stimulus_name):
-    return DETECTION_PARAMETERS.get(stimulus_name, {})
+def detection_parameters(stimulus_type):
+    return DETECTION_PARAMETERS[stimulus_type]
+
+
+def detection_parameters_from_stimulus_name(stimulus_name):
+    try:
+        stimulus_type = get_stimulus_type(stimulus_name)
+        dp = detection_parameters(stimulus_type)
+    except ValueError as e:
+        logging.warning(f"Warning: {e}\nUsing default detection parameters")
+        dp = detection_parameters(None)
+    return dp
 
 
 def record_errors(fn):
@@ -102,6 +114,7 @@ def extractors_for_sweeps(sweep_set,
                           dv_cutoff=20., thresh_frac=0.05,
                           reject_at_stim_start_interval=0,
                           min_peak=-30,
+                          filter_frequency=10.,
                           thresh_frac_floor=None,
                           est_window=None,
                           start=None, end=None):
@@ -139,6 +152,7 @@ def extractors_for_sweeps(sweep_set,
         start=start,
         end=end,
         min_peak=min_peak,
+        filter=filter_frequency,
         reject_at_stim_start_interval=reject_at_stim_start_interval)
 
     stfx = SpikeTrainFeatureExtractor(start, end)
@@ -162,7 +176,7 @@ def extract_sweep_features(data_set, sweep_table):
         sweep_set = data_set.sweep_set(sweep_numbers)
         sweep_set.align_to_start_of_epoch("experiment")
 
-        dp = detection_parameters(stimulus_name).copy()
+        dp = detection_parameters_from_stimulus_name(stimulus_name).copy()
         for k in [ "start", "end" ]:
             if k in dp:
                 dp.pop(k)
@@ -246,7 +260,7 @@ def extract_cell_long_square_features(data_set, subthresh_min_amp=None):
     lsq_spx, lsq_spfx = extractors_for_sweeps(lsq_sweeps,
                                               start=lsq_start,
                                               end=lsq_start+lsq_dur,
-                                              **detection_parameters(data_set.LONG_SQUARE))
+                                              **detection_parameters(StimulusType.LONG_SQUARE))
 
     lsq_an = spa.LongSquareAnalysis(lsq_spx, lsq_spfx,
                                     subthresh_min_amp=subthresh_min_amp)
@@ -285,7 +299,7 @@ def extract_cell_short_square_features(data_set):
     SSQ_WINDOW = 0.001
     ssq_spx, ssq_spfx = extractors_for_sweeps(ssq_sweeps,
                                               est_window=[ssq_start, ssq_start+SSQ_WINDOW],
-                                              **detection_parameters(data_set.SHORT_SQUARE))
+                                              **detection_parameters(StimulusType.SHORT_SQUARE))
 
     ssq_an = spa.ShortSquareAnalysis(ssq_spx, ssq_spfx)
 
@@ -315,7 +329,7 @@ def extract_cell_ramp_features(data_set):
 
     ramp_spx, ramp_spfx = extractors_for_sweeps(ramp_sweeps,
                                                 start=ramp_start,
-                                                **detection_parameters(data_set.RAMP))
+                                                **detection_parameters(StimulusType.RAMP))
     ramp_an = spa.RampAnalysis(ramp_spx, ramp_spfx)
     ramp_features = ramp_an.analyze(ramp_sweeps)
     return ramp_an.as_dict(

ipfx/dataset/ephys_data_set.py

@@ -32,12 +32,6 @@ class EphysDataSet(object):
                     SWEEP_NUMBER,
                     ]
 
-    LONG_SQUARE = 'long_square'
-    COARSE_LONG_SQUARE = 'coarse_long_square'
-    SHORT_SQUARE_TRIPLE = 'short_square_triple'
-    SHORT_SQUARE = 'short_square'
-    RAMP = 'ramp'
-
     VOLTAGE_CLAMP = "VoltageClamp"
     CURRENT_CLAMP = "CurrentClamp"
 

ipfx/defaults/stimulus_ontology.json

@@ -2043,5 +2043,20 @@
       "core",
       "Core 1"
     ]
+  ],
+  [
+    [
+      "code",
+      "C2CHIRPA",
+      "C2CHIRPA180503"
+    ],
+    [
+      "core",
+      "Core 2"
+    ],
+    [
+      "name",
+      "Chirp A Threshold"
+    ]
   ]
 ]

ipfx/script_utils.py

@@ -15,6 +15,7 @@
 import ipfx.data_set_features as dsf
 import ipfx.time_series_utils as tsu
 import ipfx.error as er
+from ipfx.stimulus import StimulusType
 from ipfx.sweep import SweepSet
 from ipfx.dataset.create import create_ephys_data_set
 
@@ -221,7 +222,7 @@ def preprocess_long_square_sweeps(data_set, sweep_numbers, extra_dur=0.2, subthr
         start=lsq_start,
         end=lsq_end,
         min_peak=-25,
-        **dsf.detection_parameters(data_set.LONG_SQUARE)
+        **dsf.detection_parameters(StimulusType.LONG_SQUARE)
     )
     lsq_an = spa.LongSquareAnalysis(lsq_spx, lsq_spfx,
         subthresh_min_amp=subthresh_min_amp)
@@ -243,7 +244,7 @@ def preprocess_short_square_sweeps(data_set, sweep_numbers, extra_dur=0.2, spike
                                                   start=ssq_start,
                                                   end=ssq_end + spike_window,
                                                   reject_at_stim_start_interval=0.0002,
-                                                  **dsf.detection_parameters(data_set.SHORT_SQUARE))
+                                                  **dsf.detection_parameters(StimulusType.SHORT_SQUARE))
     ssq_an = spa.ShortSquareAnalysis(ssq_spx, ssq_spfx)
     ssq_features = ssq_an.analyze(ssq_sweeps)
 
@@ -259,7 +260,7 @@ def preprocess_ramp_sweeps(data_set, sweep_numbers):
     ramp_start, ramp_dur, _, _, _ = stf.get_stim_characteristics(ramp_sweeps.sweeps[0].i, ramp_sweeps.sweeps[0].t)
     ramp_spx, ramp_spfx = dsf.extractors_for_sweeps(ramp_sweeps,
                                                 start = ramp_start,
-                                                **dsf.detection_parameters(data_set.RAMP))
+                                                **dsf.detection_parameters(StimulusType.RAMP))
     ramp_an = spa.RampAnalysis(ramp_spx, ramp_spfx)
     ramp_features = ramp_an.analyze(ramp_sweeps)
 

ipfx/stimulus.py

@@ -4,6 +4,70 @@
 
 import allensdk.core.json_utilities as ju
 
+from enum import Enum
+
+
+class StimulusType(Enum):
+    RAMP = "ramp"
+    LONG_SQUARE = "long_square"
+    COARSE_LONG_SQUARE = "coarse_long_square"
+    SHORT_SQUARE_TRIPLE = "short_square_triple"
+    SHORT_SQUARE = "short_square"
+    CHIRP = "chirp"
+    SEARCH = "search"
+    TEST = "test"
+    BLOWOUT = "blowout"
+    BATH = "bath"
+    SEAL = "seal"
+    BREAKIN = "breakin"
+    EXTP = "extp"
+
+
+STIMULUS_TYPE_NAME_MAPPING = {
+    # Maps stimulus type to set of names
+    StimulusType.RAMP: {"Ramp"},
+    StimulusType.LONG_SQUARE: {
+        "Long Square",
+        "Long Square Threshold",
+        "Long Square SupraThreshold",
+        "Long Square SubThreshold",
+    },
+    StimulusType.COARSE_LONG_SQUARE: {
+        "C1LSCOARSE",
+    },
+    StimulusType.SHORT_SQUARE_TRIPLE: {
+        "Short Square - Triple",
+    },
+    StimulusType.SHORT_SQUARE: {
+        "Short Square",
+        "Short Square Threshold",
+        "Short Square - Hold -60mV",
+        "Short Square - Hold -70mV",
+        "Short Square - Hold -80mV",
+    },
+    StimulusType.CHIRP: {
+        "Chirp",
+        "Chirp A Threshold",
+        "Chirp B - Hold -65mV",
+        "Chirp C - Hold -60mV",
+        "Chirp D - Hold -55mV",
+    },
+    StimulusType.SEARCH: {"Search"},
+    StimulusType.TEST: {"Test"},
+    StimulusType.BLOWOUT: {"EXTPBLWOUT"},
+    StimulusType.BATH: {"EXTPINBATH"},
+    StimulusType.SEAL: {"EXTPCllATT"},
+    StimulusType.BREAKIN: {"EXTPBREAKN"},
+    StimulusType.EXTP: {"EXTP"}
+}
+
+
+def get_stimulus_type(stimulus_name):
+    for stim_type, stim_names in STIMULUS_TYPE_NAME_MAPPING.items():
+        if stimulus_name in stim_names:
+            return stim_type
+    else:
+        raise ValueError(f"stimulus_name {stimulus_name} not found.\nSTIMULUS_TYPE_NAME_MAPPING: {STIMULUS_TYPE_NAME_MAPPING}")
 
 
 class Stimulus(object):
@@ -45,29 +109,23 @@ def __init__(self, stim_ontology_tags=None):
 
         self.stimuli = list(Stimulus(s) for s in stim_ontology_tags)
 
-        self.ramp_names = ( "Ramp",)
-
-        self.long_square_names = ( "Long Square",
-                                   "Long Square Threshold",
-                                   "Long Square SupraThreshold",
-                                   "Long Square SubThreshold" )
-
-        self.coarse_long_square_names = ( "C1LSCOARSE",)
-        self.short_square_triple_names = ( "Short Square - Triple", )
-
-        self.short_square_names = ( "Short Square",
-                                    "Short Square Threshold",
-                                    "Short Square - Hold -60mV",
-                                    "Short Square - Hold -70mV",
-                                    "Short Square - Hold -80mV" )
-
-        self.search_names = ("Search",)
-        self.test_names = ("Test",)
-        self.blowout_names = ( 'EXTPBLWOUT', )
-        self.bath_names = ( 'EXTPINBATH', )
-        self.seal_names = ( 'EXTPCllATT', )
-        self.breakin_names = ( 'EXTPBREAKN', )
-        self.extp_names = ( 'EXTP', )
+        # Must match Stimulus Type Name Mapping, e.g 
+        # for stimulus_type, names in _STIMULUS_TYPE_NAME_MAPPING.items():
+        #     setattr(self, f"{stimulus_type.upper()}_NAMES", names)
+        self.ramp_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.RAMP]
+        self.long_square_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.LONG_SQUARE]
+        self.coarse_long_square_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.COARSE_LONG_SQUARE]
+        self.short_square_triple_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.SHORT_SQUARE_TRIPLE]
+        self.short_square_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.SHORT_SQUARE]
+        self.chirp_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.CHIRP]
+        self.search_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.SEARCH]
+        self.test_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.TEST]
+        self.blowout_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.BLOWOUT]
+        self.bath_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.BATH]
+        self.seal_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.SEAL]
+        self.breakin_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.BREAKIN]
+        self.extp_names = STIMULUS_TYPE_NAME_MAPPING[StimulusType.EXTP]
+
 
     def find(self, tag, tag_type=None):
         """

ipfx/version.txt

@@ -1 +1 @@
-1.0.6
+1.0.7