# Source code for spynnaker.pyNN.utilities.neo_convertor

```
# Copyright (c) 2017 The University of Manchester
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import quantities
import numpy as np
# needed as dealing with quantities
# pylint: disable=c-extension-no-member
[docs]
def convert_analog_signal(signal_array, time_unit=quantities.ms):
"""
Converts part of a NEO object into told spynnaker7 format.
:param ~neo.core.AnalogSignal signal_array: Extended Quantities object
:param quantities.unitquantity.UnitTime time_unit:
Data time unit for time index
:rtype: ~numpy.ndarray
"""
ids = signal_array.annotations["channel_names"]
xs = range(len(ids))
if time_unit == quantities.ms:
times = signal_array.times.magnitude
else:
times = signal_array.times.rescale(time_unit).magnitude
all_times = np.tile(times, len(xs))
neurons = np.repeat(ids, len(times))
values = np.concatenate([signal_array.magnitude[:, x] for x in xs])
return np.column_stack((neurons, all_times, values))
[docs]
def convert_data(data, name, run=0):
"""
Converts the data into a numpy array in the format ID, time, value.
:param ~neo.core.Block data: Data as returned by a getData() call
:param str name: Name of the data to be extracted.
Same values as used in getData()
:param int run: Zero based index of the run to extract data for
:rtype: ~numpy.ndarray
"""
if len(data.segments) <= run:
raise ValueError(
f"Data only contains {len(data.segments)} so unable to run {run}. "
"Note run is the zero based index.")
if name == "all":
raise ValueError("Unable to convert all data in one go "
"as result would be comparing apples and oranges.")
if name == "spikes":
return convert_spikes(data, run)
return convert_analog_signal(
data.segments[run].filter(name=name)[0])
[docs]
def convert_data_list(data, name, runs=None):
"""
Converts the data into a list of numpy arrays in the format ID, time,
value.
:param ~neo.core.Block data: Data as returned by a getData() call
:param str name: Name of the data to be extracted.
Same values as used in getData()
:param runs: List of Zero based index of the run to extract data for.
Or `None` to extract all runs
:type runs: list(int) or None
:rtype: list(~numpy.ndarray)
"""
results = []
if runs is None:
runs = range(len(data.segments))
for run in runs:
results.append(convert_data(data, name, run=run))
return results
[docs]
def convert_v_list(data, runs=None):
"""
Converts the voltage into a list numpy array one per segment (all
runs) in the format ID, time, value.
:param ~neo.core.Block data: The data to convert; it must have V data in it
:param runs: List of Zero based index of the run to extract data for.
Or `None` to extract all runs
:type runs: list(int) or None
:rtype: list(~numpy.ndarray)
"""
return convert_data_list(data, "v", runs=runs)
[docs]
def convert_gsyn_exc_list(data, runs=None):
"""
Converts the gsyn_exc into a list numpy array one per segment (all
runs) in the format ID, time, value.
:param ~neo.core.Block data:
The data to convert; it must have Gsyn_exc data in it
:param runs: List of Zero based index of the run to extract data for.
Or `None` to extract all runs
:type runs: list(int) or None
:rtype: list(~numpy.ndarray)
"""
return convert_data_list(data, "gsyn_exc", runs=runs)
[docs]
def convert_gsyn_inh_list(data, runs=None):
"""
Converts the gsyn_inh into a list numpy array one per segment (all
runs) in the format ID, time, value.
:param ~neo.core.Block data:
The data to convert; it must have Gsyn_inh data in it
:param runs: List of Zero based index of the run to extract data for.
Or `None` to extract all runs
:type runs: list(int) or None
:rtype: list(~numpy.ndarray)
"""
return convert_data_list(data, "gsyn_inh", runs=runs)
[docs]
def convert_gsyn(gsyn_exc, gsyn_inh):
"""
Converts two neo objects into the spynnaker7 format.
.. note::
It is acceptable for both neo parameters to be the same object
:param ~neo.core.Block gsyn_exc: neo with gsyn_exc data
:param ~neo.core.Block gsyn_inh: neo with gsyn_exc data
:rtype: ~numpy.ndarray
"""
exc = gsyn_exc.segments[0].filter(name='gsyn_exc')[0]
inh = gsyn_inh.segments[0].filter(name='gsyn_inh')[0]
ids = exc.annotations["channel_names"]
ids2 = inh.annotations["channel_names"]
if len(ids) != len(ids2):
raise ValueError(
f"Found {len(ids)} neuron IDs in gsyn_exc "
f"but {len(ids2)} in gsyn_inh")
if not np.allclose(ids, ids2):
raise ValueError("IDs in gsyn_exc and gsyn_inh do not match")
times = exc.times.rescale(quantities.ms)
times2 = inh.times.rescale(quantities.ms)
if len(times) != len(times2):
raise ValueError(
f"Found {len(times)} times in gsyn_exc "
f"but {len(times2)} in gsyn_inh")
if not np.allclose(times, times2):
raise ValueError("times in gsyn_exc and gsyn_inh do not match")
all_times = np.tile(times, len(ids))
neurons = np.repeat(ids, len(times))
idlist = list(range(len(ids)))
exc_np = np.concatenate([exc[:, x] for x in idlist])
inh_np = np.concatenate([inh[:, x] for x in idlist])
return np.column_stack((neurons, all_times, exc_np, inh_np))
[docs]
def convert_spiketrains(spiketrains):
"""
Converts a list of spiketrains into spynnaker7 format.
:param list(~neo.core.SpikeTrain) spiketrains: List of SpikeTrains
:rtype: ~numpy.ndarray
"""
if len(spiketrains) == 0:
return np.empty(shape=(0, 2))
neurons = np.concatenate([
np.repeat(x.annotations['source_index'], len(x))
for x in spiketrains])
spikes = np.concatenate([x.magnitude for x in spiketrains])
return np.column_stack((neurons, spikes))
[docs]
def convert_spikes(neo, run=0):
"""
Extracts the spikes for run one from a Neo Object.
:param ~neo.core.Block neo: neo Object including Spike Data
:param int run: Zero based index of the run to extract data for
:rtype: ~numpy.ndarray
"""
if len(neo.segments) <= run:
raise ValueError(
f"Data only contains {len(neo.segments)} so unable to run {run}. "
"Note run is the zero based index.")
return convert_spiketrains(neo.segments[run].spiketrains)
[docs]
def count_spiketrains(spiketrains):
"""
Help function to count the number of spikes in a list of spiketrains.
:param list(~neo.core.SpikeTrain) spiketrains: List of SpikeTrains
:return: Total number of spikes in all the spiketrains
:rtype: int
"""
return sum(map(len, spiketrains))
[docs]
def count_spikes(neo):
"""
Help function to count the number of spikes in a list of spiketrains.
Only counts run 0
:param ~neo.core.Block neo: Neo Object which has spikes in it
:return: The number of spikes in the first segment
"""
return count_spiketrains(neo.segments[0].spiketrains)
```