diff --git a/.github/workflows/build-wheels.yml b/.github/workflows/build-wheels.yml index 6fce2b8b..ebf2775c 100644 --- a/.github/workflows/build-wheels.yml +++ b/.github/workflows/build-wheels.yml @@ -10,11 +10,11 @@ jobs: strategy: fail-fast: false matrix: - os: [ubuntu-22.04, macos-latest, windows-latest] + os: [ubuntu-22.04, macos-12, windows-latest] python: [cp39, cp310, cp311, cp312] arch: [x86_64, amd64] exclude: - - os: macos-latest + - os: macos-12 arch: amd64 - os: ubuntu-22.04 arch: amd64 diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 407224e3..1f6f13e5 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -84,6 +84,10 @@ jobs: run: | python -m pip install --upgrade pip setuptools pip install tox tox-gh-actions + - name: Install pandoc # needed by nbconvert + uses: pandoc/actions/setup@main + with: + version: 2.19 - name: Run tox run: tox -e docs diff --git a/.gitignore b/.gitignore index 2244d766..536bdc90 100644 --- a/.gitignore +++ b/.gitignore @@ -16,6 +16,12 @@ cpp_coverage.xml docs/bin docs/lib docs/source/lib +docs/source/sonata-network.rst +docs/source/sonata-network_files/ +docs/source/nmc-portal.rst +docs/source/L5TTPC2_files/ +docs/source/load_nwb.rst +docs/source/load_nwb_files/ bin *.tox-info.json *.ipynb_checkpoints @@ -23,3 +29,4 @@ lib fllog.txt *.DS_Store pyvenv.cfg +x86_64 diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 2953340d..90cc98f0 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -10,6 +10,9 @@ build: os: ubuntu-22.04 tools: python: "3.10" + jobs: + pre_build: + - sh docs/examples_to_rst.sh python: # Install our python package before building the docs diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 876a23e2..f68b78d1 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -5,6 +5,11 @@ All notable changes to this project will be documented in this file. The format is based on `Keep a Changelog `_, and this project adheres to `Semantic Versioning `_. +5.6.6 - 2024-04 +--------------- + +- Adding AP_height to documentation + 5.6.0 - 2024-02 ---------------- diff --git a/README.md b/README.md index 66dd2ec9..e2bf6a7d 100644 --- a/README.md +++ b/README.md @@ -156,10 +156,10 @@ import efel To get a list with all the available feature names ```python -efel.getFeatureNames() +efel.get_feature_names() ``` -The python function to extract features is getFeatureValues(...). +The python function to extract features is get_feature_values(...). Below is a short example on how to use this function. The code and example trace are available [here](https://github.com/BlueBrain/eFEL/blob/master/examples/basic/basic_example1.py) @@ -207,7 +207,7 @@ def main(): # Now we pass 'traces' to the efel and ask it to calculate the feature # values - traces_results = efel.getFeatureValues(traces, + traces_results = efel.get_feature_values(traces, ['AP_amplitude', 'voltage_base']) # The return value is a list of trace_results, every trace_results diff --git a/docs/examples_to_rst.sh b/docs/examples_to_rst.sh new file mode 100644 index 00000000..6b68c73b --- /dev/null +++ b/docs/examples_to_rst.sh @@ -0,0 +1,22 @@ +# to call from root + +# clean up +rm docs/source/sonata-network.rst +rm -rf docs/source/sonata-network_files +rm docs/source/nmc-portal.rst +rm -rf docs/source/L5TTPC2_files +rm docs/source/load_nwb.rst +rm -rf docs/source/load_nwb_files + +# convert +jupyter nbconvert --to rst examples/sonata-network/sonata-network.ipynb +jupyter nbconvert --to rst examples/nmc-portal/L5TTPC2.ipynb +jupyter nbconvert --to rst examples/neo/load_nwb.ipynb + +# move +mv examples/sonata-network/sonata-network.rst docs/source/ +mv examples/sonata-network/sonata-network_files docs/source/ +mv examples/nmc-portal/L5TTPC2.rst docs/source/nmc-portal.rst +mv examples/nmc-portal/L5TTPC2_files docs/source/ +mv examples/neo/load_nwb.rst docs/source/ +mv examples/neo/load_nwb_files docs/source/ \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index ec3662aa..e0fe7638 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -55,7 +55,8 @@ # General information about the project. project = u'eFEL' -copyright = u'2015-2024, BBP, EPFL' +# do not define copyright here, in order to use the default Blue Brain project copyright +# copyright = u'2015-2024, BBP, EPFL' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the @@ -108,7 +109,8 @@ # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'sphinx_rtd_theme' +html_theme = "sphinx-bluebrain-theme" +# html_theme = 'sphinx_rtd_theme' # html_theme = 'haiku-bbp' # Theme options are theme-specific and customize the look and feel of a theme diff --git a/docs/source/deap_optimisation.rst b/docs/source/deap_optimisation.rst index 528013f4..436fb149 100644 --- a/docs/source/deap_optimisation.rst +++ b/docs/source/deap_optimisation.rst @@ -98,7 +98,7 @@ This translates into the following file (let's call it 'deap_efel_eval1.py'):: trace['stim_end'] = [stim_end] traces = [trace] - features = efel.getFeatureValues(traces, ["voltage_base", + features = efel.get_feature_values(traces, ["voltage_base", "steady_state_voltage"]) voltage_base = features[0]["voltage_base"][0] steady_state_voltage = features[0]["steady_state_voltage"][0] diff --git a/docs/source/eFeatures.rst b/docs/source/eFeatures.rst index 6ed4057a..67bac2fc 100644 --- a/docs/source/eFeatures.rst +++ b/docs/source/eFeatures.rst @@ -134,8 +134,8 @@ The interspike intervals, i.e., the time intervals between adjacent peaks. else: inv_last_ISI = 0 -`Python efeature`: inv_ISI_values -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +`Python efeature`_ : inv_ISI_values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Computes all inverse spike interval values. @@ -534,7 +534,7 @@ The burst detection can be fine-tuned by changing the setting strict_burst_facto - **Units**: mV - **Pseudocode**: :: - interburst_min = [ + postburst_min = [ numpy.min( v[peak_indices[i]:peak_indices[i + 1]] ) for i in burst_end_indices if i + 1 < len(peak_indices) @@ -551,6 +551,36 @@ The burst detection can be fine-tuned by changing the setting strict_burst_facto v[peak_indices[burst_end_indices[-1]]:] )) +`LibV5`_ : postburst_slow_ahp_values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The slow AHP voltage after the end of a burst. + +The number of ms to skip after the spike to skip fast AHP and look for slow AHP can be set with sahp_start. +Default is 5. + +This implementation does not assume that every spike belongs to a burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: peak_indices, burst_end_indices +- **Units**: mV +- **Pseudocode**: :: + + postburst_slow_ahp = [] + for i in burst_end_indices: + i_start = numpy.where(t >= t[peak_indices[i]] + sahp_start)[0][0] + if i + 1 < len(peak_indices): + postburst_slow_ahp.append(numpy.min(v[i_start:peak_indices[i + 1]])) + else: + if t[burst_end_indices[-1]] < stim_end: + end_idx = numpy.where(t >= stim_end)[0][0] + postburst_slow_ahp.append(numpy.min(v[i_start:end_idx])) + else: + postburst_slow_ahp.append(numpy.min(v[i_start:])) + `LibV5`_ : time_to_interburst_min ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -573,6 +603,181 @@ The burst detection can be fine-tuned by changing the setting strict_burst_facto for i in burst_end_indices if i + 1 < len(peak_indices) ] +`LibV5`_ : time_to_postburst_slow_ahp +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The time between the last spike of a burst and the slow ahp afterwards. + +The number of ms to skip after the spike to skip fast AHP and look for slow AHP can be set with sahp_start. +Default is 5. + +This implementation does not assume that every spike belongs to a burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: postburst_slow_ahp_indices, burst_end_indices, peak_time +- **Units**: ms +- **Pseudocode**: :: + + time_to_postburst_slow_ahp_py = t[postburst_slow_ahp_indices] - peak_time[burst_end_indices] + +`LibV5`_ : postburst_fast_ahp_values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The fast AHP voltage after the end of a burst. + +This implementation does not assume that every spike belongs to a burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: peak_indices, burst_end_indices +- **Units**: mV +- **Pseudocode**: :: + + postburst_fahp = [] + for i in burst_end_indices: + if i + 1 < len(peak_indices): + stop_i = peak_indices[i + 1] + elif i + 1 < stim_end_index: + stop_i = stim_end_index + else: + stop_i = len(v) - 1 + + v_crop = v[peak_indices[i]:stop_i] + # get where the voltage is going up + crop_args = numpy.argwhere(numpy.diff(v_crop) >= 0)[:,0] + # the voltage should go up for at least two consecutive points + crop_arg_arg = numpy.argwhere(numpy.diff(crop_args) == 1)[0][0] + crop_arg = crop_args[crop_arg_arg] + end_i = peak_indices[i] + crop_arg + 1 + # the fast ahp is between last peak of burst and the point where voltage is going back up + postburst_fahp.append(numpy.min(v[peak_indices[i]:end_i])) + + return postburst_fahp + +`LibV5`_ : postburst_adp_peak_values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +The small ADP peak after the fast AHP after the end of a burst. + +This implementation does not assume that every spike belongs to a burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: postburst_fast_ahp_indices, postburst_slow_ahp_indices +- **Units**: mV +- **Pseudocode**: :: + + adp_peak_values = [] + for i, sahpi in enumerate(postburst_sahpi): + if sahpi < postburst_fahpi[i]: + continue + adppeaki = numpy.argmax(v[postburst_fahpi[i]:sahpi]) + postburst_fahpi[i] + if adppeaki != sahpi - 1: + adp_peak_values.append(v[adppeaki]) + + if len(adp_peak_values) == 0: + return None + return adp_peak_values + +`LibV5`_ : time_to_postburst_fast_ahp +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Time to the fast AHP after the end of a burst. + +This implementation does not assume that every spike belongs to a burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: postburst_fast_ahp_indices, burst_end_indices, peak_time +- **Units**: ms +- **Pseudocode**: :: + + [t[fahpi] - peak_time[burst_endi[i]] for i, fahpi in enumerate(postburst_fahpi)] + +`LibV5`_ : time_to_postburst_adp_peak +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Time to the small ADP peak after the fast AHP after the end of a burst. + +This implementation does not assume that every spike belongs to a burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: postburst_adp_peak_indices, burst_end_indices, peak_time +- **Units**: ms +- **Pseudocode**: :: + + time_to_postburst_adp_peaks = [] + n_peaks = len(peak_time) + for i, adppeaki in enumerate(postburst_adppeaki): + # there are not always an adp peak after each burst + # so make sure that the burst and adp peak indices are consistent + k = 0 + while ( + burst_endi[i] + k + 1 < n_peaks and peak_time[burst_endi[i] + k + 1] < t[adppeaki] + ): + k += 1 + + time_to_postburst_adp_peaks.append(t[adppeaki] - peak_time[burst_endi[i] + k]) + + return time_to_postburst_adp_peaks + + +`LibV5`_ : interburst_15percent_values, interburst_20percent_values, interburst_25percent_values, interburst_30percent_values, interburst_40percent_values, interburst_60percent_values +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Voltage value after a given percentage (15%, 20%, 25%, 30%, 40% or 60%) of the interburst duration after the fast AHP. + +This implementation does not assume that every spike belongs to a burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: postburst_fast_ahp_indices, burst_end_indices, peak_indices +- **Units**: mV +- **Pseudocode**: :: + + interburst_XXpercent_values = [] + for i, postburst_fahp_i in enumerate(postburst_fahpi): + if i < len(burst_endi) and burst_endi[i] + 1 < len(peaki): + time_interval = t[peaki[burst_endi[i] + 1]] - t[postburst_fahp_i] + time_at_XXpercent = t[postburst_fahp_i] + time_interval * percentage / 100. + index_at_XXpercent = numpy.argwhere(t >= time_at_XXpercent)[0][0] + interburst_XXpercent_values.append(v[index_at_XXpercent]) + +`LibV5`_ : interburst_duration +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Duration between the last spike of each burst and the next spike. + +This implementation does not assume that every spike belongs to a burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: burst_end_indices, peak_time +- **Units**: ms +- **Pseudocode**: :: + + interburst_duration = [ + peak_time[idx + 1] - peak_time[idx] + for idx in burst_end_indices + if idx + 1 < len(peak_time) + ] + `Python efeature`_ : single_burst_ratio ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -589,6 +794,67 @@ the length of the first isi over the median of the rest of the isis. single_burst_ratio = ISI_values[0] / numpy.mean(ISI_values) +`Python efeature`_ : spikes_per_burst +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Number of spikes in each burst. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: LibV5: burst_begin_indices, LibV5: burst_end_indices +- **Units**: constant +- **Pseudocode**: :: + + spike_per_bursts = [] + for idx_begin, idx_end in zip(burst_begin_indices, burst_end_indices): + spike_per_bursts.append(idx_end - idx_begin + 1) + +`Python efeature`_ : spikes_per_burst_diff +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Difference of number of spikes between each burst and the next one. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: spikes_per_burst +- **Units**: constant +- **Pseudocode**: :: + + spikes_per_burst[:-1] - spikes_per_burst[1:] + +`Python efeature`_ : spikes_in_burst1_burst2_diff +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Difference of number of spikes between the first burst and the second one. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: spikes_per_burst_diff +- **Units**: constant +- **Pseudocode**: :: + + numpy.array([spikes_per_burst_diff[0]]) + +`Python efeature`_ : spikes_in_burst1_burstlast_diff +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Difference of number of spikes between the first burst and the last one. + +The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. + +The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. + +- **Required features**: spikes_per_burst +- **Units**: constant +- **Pseudocode**: :: + + numpy.array([spikes_per_burst[0] - spikes_per_burst[-1]]) Spike shape features -------------------- @@ -618,6 +884,16 @@ The voltages at the maxima of the peaks peak_voltage = voltage[peak_indices] +`LibV1`_ : AP_height +~~~~~~~~~~~~~~~~~~~~ + +Same as peak_voltage: The voltages at the maxima of the peaks + +- **Required features**: LibV1:peak_voltage +- **Units**: mV +- **Pseudocode**: :: + + AP_height = peak_voltage `LibV1`_ : AP_amplitude, AP1_amp, AP2_amp, APlast_amp ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -1284,8 +1560,56 @@ Slope of the V, dVdt phasespace plot at the beginning of every spike range_min_idxs = AP_begin_indices - AP_phseslope_range AP_phaseslope = (dvdt[range_max_idxs] - dvdt[range_min_idxs]) / (v[range_max_idxs] - v[range_min_idxs]) -Voltage features ----------------- +`Python efeature`_ : phaseslope_max +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Computes the maximum of the phase slope. +Attention, this feature is sensitive to interpolation timestep. + +- **Required features**: time, voltage +- **Units**: V/s +- **Pseudocode**: :: + + phaseslope = numpy.diff(voltage) / numpy.diff(time) + phaseslope_max = numpy.array([numpy.max(phaseslope)]) + +`Python efeature`_ : initburst_sahp +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Slow AHP voltage after initial burst + +The end of the initial burst is detected when the ISIs frequency gets lower than initburst_freq_threshold, in Hz. +Then the sahp is searched for the interval between initburst_sahp_start (in ms) after the last spike of the burst, +and initburst_sahp_end (in ms) after the last spike of the burst. + +- **Required features**: LibV1: peak_time +- **Parameters**: initburst_freq_threshold (default=50), initburst_sahp_start (default=5), initburst_sahp_end (default=100) +- **Units**: mV + +`Python efeature`_ : initburst_sahp_ssse +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Slow AHP voltage from steady_state_voltage_stimend after initial burst + +- **Required features**: LibV5: steady_state_voltage_stimend, initburst_sahp +- **Units**: mV +- **Pseudocode**: :: + + numpy.array([initburst_sahp_value[0] - ssse[0]]) + +`Python efeature`_ : initburst_sahp_vb +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Slow AHP voltage from voltage base after initial burst + +- **Required features**: LibV5: voltage_base, initburst_sahp +- **Units**: mV +- **Pseudocode**: :: + + numpy.array([initburst_sahp_value[0] - voltage_base[0]]) + +Subthreshold features +--------------------- .. image:: _static/figures/voltage_features.png @@ -1796,40 +2120,6 @@ Difference between minimum and steady state during stimulation Python features --------------- -`Python efeature`_ : initburst_sahp -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Slow AHP voltage after initial burst - -The end of the initial burst is detected when the ISIs frequency gets lower than initburst_freq_threshold, in Hz. -Then the sahp is searched for the interval between initburst_sahp_start (in ms) after the last spike of the burst, -and initburst_sahp_end (in ms) after the last spike of the burst. - -- **Required features**: LibV1: peak_time -- **Parameters**: initburst_freq_threshold (default=50), initburst_sahp_start (default=5), initburst_sahp_end (default=100) -- **Units**: mV - -`Python efeature`_ : initburst_sahp_ssse -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Slow AHP voltage from steady_state_voltage_stimend after initial burst - -- **Required features**: LibV5: steady_state_voltage_stimend, initburst_sahp -- **Units**: mV -- **Pseudocode**: :: - - numpy.array([initburst_sahp_value[0] - ssse[0]]) - -`Python efeature`_ : initburst_sahp_vb -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Slow AHP voltage from voltage base after initial burst - -- **Required features**: LibV5: voltage_base, initburst_sahp -- **Units**: mV -- **Pseudocode**: :: - - numpy.array([initburst_sahp_value[0] - voltage_base[0]]) `Python efeature`_ : depol_block_bool ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -1843,68 +2133,6 @@ A hyperpolarization block is detected when, after stimulus start, the voltage st - **Required features**: LibV5: AP_begin_voltage - **Units**: constant -`Python efeature`_ : spikes_per_burst -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Number of spikes in each burst. - -The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. - -The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. - -- **Required features**: LibV5: burst_begin_indices, LibV5: burst_end_indices -- **Units**: constant -- **Pseudocode**: :: - - spike_per_bursts = [] - for idx_begin, idx_end in zip(burst_begin_indices, burst_end_indices): - spike_per_bursts.append(idx_end - idx_begin + 1) - -`Python efeature`_ : spikes_per_burst_diff -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Difference of number of spikes between each burst and the next one. - -The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. - -The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. - -- **Required features**: spikes_per_burst -- **Units**: constant -- **Pseudocode**: :: - - spikes_per_burst[:-1] - spikes_per_burst[1:] - -`Python efeature`_ : spikes_in_burst1_burst2_diff -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Difference of number of spikes between the first burst and the second one. - -The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. - -The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. - -- **Required features**: spikes_per_burst_diff -- **Units**: constant -- **Pseudocode**: :: - - numpy.array([spikes_per_burst_diff[0]]) - -`Python efeature`_ : spikes_in_burst1_burstlast_diff -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Difference of number of spikes between the first burst and the last one. - -The first spike is ignored by default. This can be changed by setting ignore_first_ISI to 0. - -The burst detection can be fine-tuned by changing the setting strict_burst_factor. Defalt value is 2.0. - -- **Required features**: spikes_per_burst -- **Units**: constant -- **Pseudocode**: :: - - numpy.array([spikes_per_burst[0] - spikes_per_burst[-1]]) - `Python efeature`_ : impedance ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -1934,19 +2162,6 @@ with impedance_max_freq being a setting with 50.0 as a default value. else: return None -`Python efeature`_ : phaseslope_max -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -Computes the maximum of the phase slope. -Attention, this feature is sensitive to interpolation timestep. - -- **Required features**: time, voltage -- **Units**: V/s -- **Pseudocode**: :: - - phaseslope = numpy.diff(voltage) / numpy.diff(time) - phaseslope_max = numpy.array([numpy.max(phaseslope)]) - .. _LibV1: https://github.com/BlueBrain/eFEL/blob/master/efel/cppcore/LibV1.cpp diff --git a/docs/source/neoIO_example.rst b/docs/source/neoIO_example.rst index b2b29bed..479c30dc 100644 --- a/docs/source/neoIO_example.rst +++ b/docs/source/neoIO_example.rst @@ -27,7 +27,7 @@ Since our file contains only a single recording episode, our list of traces is:: which we pass to eFEL as follows:: - features = efel.getFeatureValues(traces, ['AP_amplitude', 'voltage_base']) + features = efel.get_feature_values(traces, ['AP_amplitude', 'voltage_base']) Stimulus information within the file ------------------------------------ diff --git a/docs/source/python_example1.rst b/docs/source/python_example1.rst index 70598a9c..f0355655 100644 --- a/docs/source/python_example1.rst +++ b/docs/source/python_example1.rst @@ -7,9 +7,9 @@ First you need to import the module:: To get a list with all the available eFeature names:: - efel.getFeatureNames() + efel.get_feature_names() -The python function to extract eFeatures is getFeatureValues(...). +The python function to extract eFeatures is get_feature_values(...). Below is a short example on how to use this function. The code and example trace are available @@ -57,7 +57,7 @@ The code and example trace are available # Now we pass 'traces' to the efel and ask it to calculate the feature # values - traces_results = efel.getFeatureValues(traces, + traces_results = efel.get_feature_values(traces, ['AP_amplitude', 'voltage_base']) # The return value is a list of trace_results, every trace_results diff --git a/docs/source/python_examples.rst b/docs/source/python_examples.rst index 444adde7..59c0fc32 100644 --- a/docs/source/python_examples.rst +++ b/docs/source/python_examples.rst @@ -6,4 +6,7 @@ Python python_example1 deap_optimisation neoIO_example + load_nwb + nmc-portal + sonata-network diff --git a/efel/DependencyV5.txt b/efel/DependencyV5.txt index 12a4c4cb..fe43da29 100644 --- a/efel/DependencyV5.txt +++ b/efel/DependencyV5.txt @@ -114,5 +114,27 @@ LibV5:ADP_peak_amplitude #LibV5:ADP_peak_values #LibV5:min_AHP_values #LibV1:in LibV5:interburst_min_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV1:interpolate LibV5:interburst_min_values #LibV5:interburst_min_indices #LibV1:interpolate LibV5:postburst_min_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV1:interpolate -LibV5:postburst_min_values #LibV5:postburst_min_indices #LibV1:interpolate +LibV5:postburst_min_values #LibV5:postburst_min_indices #LibV1: interpolate +LibV5:postburst_slow_ahp_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV1:interpolate +LibV5:postburst_slow_ahp_values #LibV5:postburst_slow_ahp_indices #LibV1:interpolate LibV5:time_to_interburst_min #LibV5:interburst_min_indices #LibV1:peak_time #LibV5:burst_end_indices #LibV1:interpolate +LibV5:time_to_postburst_slow_ahp #LibV5:postburst_slow_ahp_indices #LibV1:peak_time #LibV5:burst_end_indices #LibV1:interpolate +LibV5:postburst_fast_ahp_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV1:interpolate +LibV5:postburst_fast_ahp_values #LibV5:postburst_fast_ahp_indices #LibV1:interpolate +LibV5:postburst_adp_peak_indices #LibV5:postburst_fast_ahp_indices #LibV5:postburst_slow_ahp_indices #LibV1:interpolate +LibV5:postburst_adp_peak_values #LibV5:postburst_adp_peak_indices #LibV1:interpolate +LibV5:time_to_postburst_fast_ahp #LibV5:postburst_fast_ahp_indices #LibV1:peak_time #LibV5:burst_end_indices #LibV1:interpolate +LibV5:time_to_postburst_adp_peak #LibV5:postburst_adp_peak_indices #LibV1:peak_time #LibV5:burst_end_indices #LibV1:interpolate +LibV5:interburst_15percent_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV5:postburst_fast_ahp_indices #LibV1:interpolate +LibV5:interburst_15percent_values #LibV5:interburst_15percent_indices #LibV1:interpolate +LibV5:interburst_20percent_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV5:postburst_fast_ahp_indices #LibV1:interpolate +LibV5:interburst_20percent_values #LibV5:interburst_20percent_indices #LibV1:interpolate +LibV5:interburst_25percent_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV5:postburst_fast_ahp_indices #LibV1:interpolate +LibV5:interburst_25percent_values #LibV5:interburst_25percent_indices #LibV1:interpolate +LibV5:interburst_30percent_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV5:postburst_fast_ahp_indices #LibV1:interpolate +LibV5:interburst_30percent_values #LibV5:interburst_30percent_indices #LibV1:interpolate +LibV5:interburst_40percent_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV5:postburst_fast_ahp_indices #LibV1:interpolate +LibV5:interburst_40percent_values #LibV5:interburst_40percent_indices #LibV1:interpolate +LibV5:interburst_60percent_indices #LibV5:peak_indices #LibV5:burst_end_indices #LibV5:postburst_fast_ahp_indices #LibV1:interpolate +LibV5:interburst_60percent_values #LibV5:interburst_60percent_indices #LibV1:interpolate +LibV5:interburst_duration #LibV1:peak_time #LibV5:burst_end_indices #LibV1:interpolate diff --git a/efel/api.py b/efel/api.py index 2a130358..74d77a52 100644 --- a/efel/api.py +++ b/efel/api.py @@ -26,7 +26,7 @@ from __future__ import annotations from pathlib import Path -from typing import Callable +from typing import Callable, Iterator, Literal, overload from typing_extensions import deprecated import numpy as np @@ -89,13 +89,13 @@ def reset(): _initialise() -@deprecated("Changing the dependency file will not be supported in the future.") -def setDependencyFileLocation(location: str | Path) -> None: +def set_dependency_file_location(location: str | Path) -> None: """Sets the location of the Dependency file. eFEL uses 'Dependency' files to let the user define versions of features to use. The installation directory of eFEL contains a default 'DependencyV5.txt' file. Unless users want to change this file, it is not necessary to call this function. + Modifying the Dependency file can be useful in debugging. Args: location: Path to the location of a Dependency file. @@ -109,8 +109,7 @@ def setDependencyFileLocation(location: str | Path) -> None: _settings.dependencyfile_path = str(location) -@deprecated("Changing the dependency file will not be supported in the future.") -def getDependencyFileLocation() -> str: +def get_dependency_file_location() -> str: """Gets the location of the Dependency file. Returns: @@ -130,11 +129,6 @@ def set_threshold(new_threshold: float) -> None: set_double_setting('Threshold', _settings.threshold) -@deprecated("Use set_threshold instead") -def setThreshold(newThreshold: float) -> None: - set_threshold(newThreshold) - - def set_derivative_threshold(new_derivative_threshold: float) -> None: """Set the threshold for the derivative for detecting the spike onset. @@ -149,11 +143,6 @@ def set_derivative_threshold(new_derivative_threshold: float) -> None: set_double_setting('DerivativeThreshold', _settings.derivative_threshold) -@deprecated("Use set_derivative_threshold instead") -def setDerivativeThreshold(newDerivativeThreshold: float) -> None: - set_derivative_threshold(newDerivativeThreshold) - - def get_feature_names() -> list[str]: """Return a list with the name of all the available features @@ -171,21 +160,11 @@ def get_feature_names() -> list[str]: return feature_names -@deprecated("Use get_feature_names instead") -def getFeatureNames() -> list[str]: - return get_feature_names() - - def feature_name_exists(feature_name: str) -> bool: """Returns True if the feature name exists in eFEL, False otherwise.""" return feature_name in get_feature_names() -@deprecated("Use feature_name_exists instead") -def FeatureNameExists(feature_name: str) -> bool: - return feature_name_exists(feature_name) - - def _get_feature(feature_name: str, raise_warnings=False) -> np.ndarray | None: """Get feature value, decide to use python or cpp""" if feature_name in pyfeatures.all_pyfeatures: @@ -254,17 +233,6 @@ def get_distance( return distance -@deprecated("Use get_distance instead") -def getDistance( - trace, - featureName, - mean, - std, - trace_check=True, - error_dist=250) -> float: - return get_distance(trace, featureName, mean, std, trace_check, error_dist) - - def _initialise() -> None: """Set cppcore initial values.""" cppcore.Initialize(_settings.dependencyfile_path, "log") @@ -291,37 +259,45 @@ def set_int_setting(setting_name: str, new_value: int) -> None: _int_settings[setting_name] = new_value -@deprecated("Use set_int_setting instead") -def setIntSetting(setting_name: str, new_value: int) -> None: - set_int_setting(setting_name, new_value) - - def set_double_setting(setting_name: str, new_value: float) -> None: """Set a certain double setting to a new value""" _double_settings[setting_name] = new_value -@deprecated("Use set_double_setting instead") -def setDoubleSetting(setting_name: str, new_value: float) -> None: - set_double_setting(setting_name, new_value) - - def set_str_setting(setting_name: str, new_value: str) -> None: """Set a certain string setting to a new value""" _string_settings[setting_name] = new_value -@deprecated("Use set_str_setting instead") -def setStrSetting(setting_name: str, new_value: str) -> None: - set_str_setting(setting_name, new_value) +@overload +def get_feature_values( + traces: list[dict], + feature_names: list[str], + parallel_map: Callable | None, + return_list: Literal[True], + raise_warnings: bool = True, +) -> list: + ... +@overload def get_feature_values( - traces: list[dict], - feature_names: list[str], - parallel_map: Callable | None = None, - return_list: bool = True, - raise_warnings: bool = True) -> list | map: + traces: list[dict], + feature_names: list[str], + parallel_map: Callable | None, + return_list: Literal[False], + raise_warnings: bool = True, +) -> Iterator: + ... + + +def get_feature_values( + traces: list[dict], + feature_names: list[str], + parallel_map: Callable | None = None, + return_list: bool = True, + raise_warnings: bool = True, +) -> list | Iterator: """Calculate feature values for a list of traces. This function is the core of eFEL API. A list of traces (in the form @@ -359,9 +335,7 @@ def get_feature_values( if parallel_map is None: parallel_map = map - traces_featurenames = ( - (trace, feature_names, raise_warnings) - for trace in traces) + traces_featurenames = ((trace, feature_names, raise_warnings) for trace in traces) map_result = parallel_map(_get_feature_values_serial, traces_featurenames) if return_list: @@ -370,17 +344,6 @@ def get_feature_values( return map_result -@deprecated("Use get_feature_values instead") -def getFeatureValues( - traces, - featureNames, - parallel_map=None, - return_list=True, - raise_warnings=True): - return get_feature_values( - traces, featureNames, parallel_map, return_list, raise_warnings) - - def get_py_feature(feature_name: str) -> np.ndarray | None: """Return values of the given feature name.""" return getattr(pyfeatures, feature_name)() @@ -457,6 +420,8 @@ def get_mean_feature_values( featureDicts = get_feature_values( traces, feature_names, + parallel_map=None, + return_list=True, raise_warnings=raise_warnings) for featureDict in featureDicts: # type: ignore for (key, values) in list(featureDict.items()): @@ -468,6 +433,67 @@ def get_mean_feature_values( return featureDicts # type: ignore +reset() + + +# Deprecated functions +@deprecated("Use set_threshold instead") +def setThreshold(newThreshold: float) -> None: + set_threshold(newThreshold) + + +@deprecated("Use set_derivative_threshold instead") +def setDerivativeThreshold(newDerivativeThreshold: float) -> None: + set_derivative_threshold(newDerivativeThreshold) + + +@deprecated("Use get_feature_names instead") +def getFeatureNames() -> list[str]: + return get_feature_names() + + +@deprecated("Use feature_name_exists instead") +def FeatureNameExists(feature_name: str) -> bool: + return feature_name_exists(feature_name) + + +@deprecated("Use get_distance instead") +def getDistance( + trace, + featureName, + mean, + std, + trace_check=True, + error_dist=250) -> float: + return get_distance(trace, featureName, mean, std, trace_check, error_dist) + + +@deprecated("Use set_int_setting instead") +def setIntSetting(setting_name: str, new_value: int) -> None: + set_int_setting(setting_name, new_value) + + +@deprecated("Use set_double_setting instead") +def setDoubleSetting(setting_name: str, new_value: float) -> None: + set_double_setting(setting_name, new_value) + + +@deprecated("Use set_str_setting instead") +def setStrSetting(setting_name: str, new_value: str) -> None: + set_str_setting(setting_name, new_value) + + +@deprecated("Use get_feature_values instead") +def getFeatureValues( + traces, + featureNames, + parallel_map=None, + return_list=True, + raise_warnings=True): + return get_feature_values( + traces, featureNames, parallel_map, return_list, raise_warnings) + + @deprecated("Use get_mean_feature_values instead") def getMeanFeatureValues( traces, @@ -476,4 +502,11 @@ def getMeanFeatureValues( return get_mean_feature_values(traces, featureNames, raise_warnings) -reset() +@deprecated("Use get_dependency_file_location instead") +def getDependencyFileLocation() -> str: + return get_dependency_file_location() + + +@deprecated("Use set_dependency_file_location instead") +def setDependencyFileLocation(location: str | Path) -> None: + return set_dependency_file_location(location) diff --git a/efel/cppcore/FillFptrTable.cpp b/efel/cppcore/FillFptrTable.cpp index 6c60463a..333b4713 100644 --- a/efel/cppcore/FillFptrTable.cpp +++ b/efel/cppcore/FillFptrTable.cpp @@ -181,7 +181,77 @@ int FillFptrTable() { FptrTableV5["interburst_min_values"] = &LibV5::interburst_min_values; FptrTableV5["postburst_min_indices"] = &LibV5::postburst_min_indices; FptrTableV5["postburst_min_values"] = &LibV5::postburst_min_values; + FptrTableV5["postburst_slow_ahp_indices"] = &LibV5::postburst_slow_ahp_indices; + FptrTableV5["postburst_slow_ahp_values"] = &LibV5::postburst_slow_ahp_values; FptrTableV5["time_to_interburst_min"] = &LibV5::time_to_interburst_min; + FptrTableV5["time_to_postburst_slow_ahp"] = &LibV5::time_to_postburst_slow_ahp; + FptrTableV5["postburst_fast_ahp_indices"] = &LibV5::postburst_fast_ahp_indices; + FptrTableV5["postburst_fast_ahp_values"] = &LibV5::postburst_fast_ahp_values; + FptrTableV5["postburst_adp_peak_indices"] = &LibV5::postburst_adp_peak_indices; + FptrTableV5["postburst_adp_peak_values"] = &LibV5::postburst_adp_peak_values; + FptrTableV5["time_to_postburst_fast_ahp"] = &LibV5::time_to_postburst_fast_ahp; + FptrTableV5["time_to_postburst_adp_peak"] = &LibV5::time_to_postburst_adp_peak; + FptrTableV5["interburst_15percent_indices"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 15); + }; + FptrTableV5["interburst_15percent_values"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 15); + }; + FptrTableV5["interburst_20percent_indices"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 20); + }; + FptrTableV5["interburst_20percent_values"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 20); + }; + FptrTableV5["interburst_25percent_indices"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 25); + }; + FptrTableV5["interburst_25percent_values"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 25); + }; + FptrTableV5["interburst_30percent_indices"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 30); + }; + FptrTableV5["interburst_30percent_values"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 30); + }; + FptrTableV5["interburst_40percent_indices"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 40); + }; + FptrTableV5["interburst_40percent_values"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 40); + }; + FptrTableV5["interburst_60percent_indices"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 60); + }; + FptrTableV5["interburst_60percent_values"] = [](mapStr2intVec& intData, + mapStr2doubleVec& doubleData, + mapStr2Str& strData) { + return LibV5::interburst_XXpercent_indices(intData, doubleData, strData, 60); + }; + FptrTableV5["interburst_duration"] = &LibV5::interburst_duration; //****************** end of FptrTableV5 ***************************** diff --git a/efel/cppcore/LibV2.cpp b/efel/cppcore/LibV2.cpp index e8d6383b..ee5f1e9a 100644 --- a/efel/cppcore/LibV2.cpp +++ b/efel/cppcore/LibV2.cpp @@ -76,6 +76,9 @@ static int __AP_fall_indices(const vector& v, const vector& apbi, vector& apfi) { apfi.resize(std::min(apbi.size(), pi.size())); for (size_t i = 0; i < apfi.size(); i++) { + if (pi[i] >= v.size() || apbi[i] >= v.size() || apei[i] >= v.size() || pi[i] > apei[i]) { + continue; + } double halfheight = (v[pi[i]] + v[apbi[i]]) / 2.; vector vpeak(&v[pi[i]], &v[apei[i]]); transform(vpeak.begin(), vpeak.end(), vpeak.begin(), diff --git a/efel/cppcore/LibV3.cpp b/efel/cppcore/LibV3.cpp index 2a8ec0fd..9af5a602 100644 --- a/efel/cppcore/LibV3.cpp +++ b/efel/cppcore/LibV3.cpp @@ -36,26 +36,25 @@ static int __depolarized_base(const vector& t, const vector& v, int i, n, k, startIndex, endIndex, nPt; double baseValue; // to make sure it access minimum index of both length - if (apendi.size() < apbi.size()) - n = apendi.size(); - else - n = apbi.size(); - - if (apendi.size() == apbi.size()) n = apendi.size() - 1; + n = std::min(apendi.size(), apbi.size()); if (n > 2) { dep_base.clear(); - for (i = 0; i < n; i++) { + for (i = 0; i < n - 1; i++) { nPt = 0; baseValue = 0; startIndex = apendi[i]; endIndex = apbi[i + 1]; for (k = startIndex; k < endIndex; k++) { - baseValue += v[k]; - nPt++; + if (k >= 0 && k < v.size()) { + baseValue += v[k]; + ++nPt; + } + } + if (nPt > 0) { + baseValue /= nPt; + dep_base.push_back(baseValue); } - baseValue = baseValue / nPt; - dep_base.push_back(baseValue); } return dep_base.size(); } diff --git a/efel/cppcore/LibV5.cpp b/efel/cppcore/LibV5.cpp index 28a7f01c..55a80a51 100644 --- a/efel/cppcore/LibV5.cpp +++ b/efel/cppcore/LibV5.cpp @@ -26,6 +26,7 @@ #include #include #include +#include #include "EfelExceptions.h" @@ -402,15 +403,22 @@ static int __AP_end_indices(const vector& t, const vector& v, peak_indices.push_back(v.size() - 1); for (size_t i = 0; i < peak_indices.size() - 1; i++) { - max_slope = - distance(dvdt.begin(), std::min_element(dvdt.begin() + peak_indices[i] + 1, - dvdt.begin() + peak_indices[i + 1])); + size_t start_index = peak_indices[i] + 1; + size_t end_index = peak_indices[i + 1]; + + if (start_index >= end_index || start_index >= dvdt.size() || end_index >= dvdt.size()) { + continue; + } + + auto min_element_it = std::min_element(dvdt.begin() + start_index, dvdt.begin() + end_index); + auto max_slope = std::distance(dvdt.begin(), min_element_it); // assure that the width of the slope is bigger than 4 - apei.push_back(distance(dvdt.begin(), find_if(dvdt.begin() + max_slope, - dvdt.begin() + peak_indices[i + 1], - [derivativethreshold](double x) { - return x >= derivativethreshold; - }))); + auto threshold_it = std::find_if(dvdt.begin() + max_slope, dvdt.begin() + end_index, + [derivativethreshold](double x) { return x >= derivativethreshold; }); + + if (threshold_it != dvdt.begin() + end_index) { + apei.push_back(std::distance(dvdt.begin(), threshold_it)); + } } return apei.size(); } @@ -1343,7 +1351,7 @@ double __decay_time_constant_after_stim(const vector& times, if (decayTimes.size() < 1 || decayValues.size() < 1) { throw FeatureComputationError("No data points to calculate decay_time_constant_after_stim"); - } + } linear_fit_result fit; fit = slope_straight_line_fit(decayTimes, decayValues); @@ -2257,21 +2265,27 @@ static int __postburst_min_indices(const vector& t, [stim_end](double x) { return x >= stim_end; })); end_index = distance(t.begin(), t.end()); for (size_t i = 0; i < burst_end_indices.size(); i++) { - if (burst_end_indices[i] + 1 < peak_indices.size()) { - postburst_min_index = - min_element(v.begin() + peak_indices[burst_end_indices[i]], - v.begin() + peak_indices[burst_end_indices[i] + 1]) - - v.begin(); - } else if (peak_indices[burst_end_indices[i]] < stim_end_index) { - postburst_min_index = - min_element(v.begin() + peak_indices[burst_end_indices[i]], - v.begin() + stim_end_index) - - v.begin(); + if (burst_end_indices[i] + 1 < peak_indices.size()){ + postburst_min_index = min_element( + v.begin() + peak_indices[burst_end_indices[i]], + v.begin() + peak_indices[burst_end_indices[i] + 1] + ) - v.begin(); + } else if (peak_indices[burst_end_indices[i]] < stim_end_index){ + postburst_min_index = min_element( + v.begin() + peak_indices[burst_end_indices[i]], + v.begin() + stim_end_index + ) - v.begin(); + if (postburst_min_index == stim_end_index){ + continue; + } } else { - postburst_min_index = - min_element(v.begin() + peak_indices[burst_end_indices[i]], - v.begin() + end_index) - - v.begin(); + postburst_min_index = min_element( + v.begin() + peak_indices[burst_end_indices[i]], + v.begin() + end_index + ) - v.begin(); + if (postburst_min_index == end_index){ + continue; + } } postburst_min_indices.push_back(postburst_min_index); @@ -2310,6 +2324,106 @@ int LibV5::postburst_min_values(mapStr2intVec& IntFeatureData, return 1; } +static int __postburst_slow_ahp_indices(const vector& t, + const vector& v, + const vector& peak_indices, + const vector& burst_end_indices, + vector& postburst_slow_ahp_indices, + vector& postburst_slow_ahp_values, + const double stim_end, + const double sahp_start) { + unsigned postburst_slow_ahp_index, stim_end_index, end_index, t_start_index; + stim_end_index = + distance(t.begin(), + find_if(t.begin(), t.end(), + [stim_end](double x) { return x >= stim_end; })); + end_index = distance(t.begin(), t.end()); + for (size_t i = 0; i < burst_end_indices.size(); i++) { + double t_start = t[peak_indices[burst_end_indices[i]]] + sahp_start; + + if (burst_end_indices[i] + 1 < peak_indices.size()){ + t_start_index = find_if( + t.begin() + peak_indices[burst_end_indices[i]], + t.begin() + peak_indices[burst_end_indices[i] + 1], + [t_start](double x) { return x >= t_start; } + ) - t.begin(); + postburst_slow_ahp_index = min_element( + v.begin() + t_start_index, + v.begin() + peak_indices[burst_end_indices[i] + 1] + ) - v.begin(); + } else if (peak_indices[burst_end_indices[i]] < stim_end_index){ + t_start_index = find_if( + t.begin() + peak_indices[burst_end_indices[i]], + t.begin() + stim_end_index, + [t_start](double x) { return x >= t_start; } + ) - t.begin(); + if (t_start_index < stim_end_index){ + postburst_slow_ahp_index = min_element( + v.begin() + t_start_index, v.begin() + stim_end_index + ) - v.begin(); + } else { + // edge case: stim_end_index is 1 index after stim_end + continue; + } + } else { + t_start_index = find_if( + t.begin() + peak_indices[burst_end_indices[i]], + t.begin() + end_index, + [t_start](double x) { return x >= t_start; } + ) - t.begin(); + if (t_start_index < end_index){ + postburst_slow_ahp_index = min_element( + v.begin() + t_start_index, v.begin() + end_index + ) - v.begin(); + } else{ + // edge case: end_index is 1 index after end + continue; + } + } + + postburst_slow_ahp_indices.push_back(postburst_slow_ahp_index); + postburst_slow_ahp_values.push_back(v[postburst_slow_ahp_index]); + } + + return postburst_slow_ahp_indices.size(); +} + +int LibV5::postburst_slow_ahp_indices(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + const auto& doubleFeatures = + getFeatures(DoubleFeatureData, {"T", "V", "stim_end", "sahp_start"}); + const auto& intFeatures = + getFeatures(IntFeatureData, {"peak_indices", "burst_end_indices"}); + + vector postburst_slow_ahp_values; + vector postburst_slow_ahp_indices; + int retVal = __postburst_slow_ahp_indices( + doubleFeatures.at("T"), + doubleFeatures.at("V"), + intFeatures.at("peak_indices"), + intFeatures.at("burst_end_indices"), + postburst_slow_ahp_indices, + postburst_slow_ahp_values, + doubleFeatures.at("stim_end").front(), + // time after the spike in ms after which to start searching for minimum + doubleFeatures.at("sahp_start").empty() ? 5.0 : doubleFeatures.at("sahp_start").front() + ); + if (retVal >= 0) { + setVec(IntFeatureData, StringData, "postburst_slow_ahp_indices", + postburst_slow_ahp_indices); + setVec(DoubleFeatureData, StringData, "postburst_slow_ahp_values", + postburst_slow_ahp_values); + } + return retVal; +} + +int LibV5::postburst_slow_ahp_values(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + return 1; +} + int LibV5::time_to_interburst_min(mapStr2intVec& IntFeatureData, mapStr2doubleVec& DoubleFeatureData, mapStr2Str& StringData) { @@ -2339,3 +2453,346 @@ int LibV5::time_to_interburst_min(mapStr2intVec& IntFeatureData, time_to_interburst_min); return time_to_interburst_min.size(); } + +int LibV5::time_to_postburst_slow_ahp(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + const auto& doubleFeatures = + getFeatures(DoubleFeatureData, {"T", "peak_time"}); + const auto& intFeatures = + getFeatures(IntFeatureData, {"postburst_slow_ahp_indices", "burst_end_indices"}); + + vector time_to_postburst_slow_ahp; + const vector& time = doubleFeatures.at("T"); + const vector& peak_time = doubleFeatures.at("peak_time"); + const vector& burst_end_indices = intFeatures.at("burst_end_indices"); + const vector& postburst_slow_ahp_indices = intFeatures.at("postburst_slow_ahp_indices"); + + if (burst_end_indices.size() < postburst_slow_ahp_indices.size()){ + GErrorStr += + "\nburst_end_indices should not have less elements than postburst_slow_ahp_indices\n"; + return -1; + } + + for (size_t i = 0; i < postburst_slow_ahp_indices.size(); i++) { + time_to_postburst_slow_ahp.push_back(time[postburst_slow_ahp_indices[i]] - + peak_time[burst_end_indices[i]]); + } + setVec(DoubleFeatureData, StringData, "time_to_postburst_slow_ahp", + time_to_postburst_slow_ahp); + return (time_to_postburst_slow_ahp.size()); +} + +static int __postburst_fast_ahp_indices(const vector& t, const vector& v, + const vector& peak_indices, + const vector& burst_end_indices, + const double stim_end, + vector& postburst_fast_ahp_indices, + vector& postburst_fast_ahp_values) { + vector start_indices, end_indices; + for (size_t i = 0; i < burst_end_indices.size(); i++) { + start_indices.push_back(peak_indices[burst_end_indices[i]]); + if (burst_end_indices[i] + 1 < peak_indices.size()){ + end_indices.push_back(peak_indices[burst_end_indices[i] + 1]); + } + } + + unsigned end_index = 0; + if (t[start_indices.back()] < stim_end) { + end_index = + distance(t.begin(), + find_if(t.begin(), t.end(), + [stim_end](double x) { return x >= stim_end; })); + } else { + end_index = distance(t.begin(), t.end()); + } + + if (end_indices.size() < start_indices.size()){ + end_indices.push_back(end_index); + } + + size_t fahpindex = 0; + for (size_t i = 0; i < start_indices.size(); i++) { + // can use first_min_element because dv/dt is very steep before fash ahp + // and noise is very unlikely to make voltage go up before reaching fast ahp + fahpindex = distance( + v.begin(), first_min_element(v.begin() + start_indices[i], + v.begin() + end_indices[i])); + + if (fahpindex != end_index - 1) { + postburst_fast_ahp_indices.push_back(fahpindex); + + EFEL_ASSERT(fahpindex < v.size(), + "fast AHP index falls outside of voltage array"); + postburst_fast_ahp_values.push_back(v[fahpindex]); + } + } + + return postburst_fast_ahp_indices.size(); +} + +int LibV5::postburst_fast_ahp_indices(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + const auto& doubleFeatures = + getFeatures(DoubleFeatureData, {"T", "V", "stim_end"}); + const auto& intFeatures = + getFeatures(IntFeatureData, {"peak_indices", "burst_end_indices"}); + + vector postburst_fast_ahp_indices; + vector postburst_fast_ahp_values; + int retVal = + __postburst_fast_ahp_indices( + doubleFeatures.at("T"), + doubleFeatures.at("V"), + intFeatures.at("peak_indices"), + intFeatures.at("burst_end_indices"), + doubleFeatures.at("stim_end").front(), + postburst_fast_ahp_indices, + postburst_fast_ahp_values + ); + + if (retVal > 0) { + setVec(IntFeatureData, StringData, "postburst_fast_ahp_indices", postburst_fast_ahp_indices); + setVec(DoubleFeatureData, StringData, "postburst_fast_ahp_values", + postburst_fast_ahp_values); + return postburst_fast_ahp_indices.size(); + } + return -1; +} + +int LibV5::postburst_fast_ahp_values(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + return 1; +} + +static int __postburst_adp_peak_indices(const vector& t, const vector& v, + const vector& postburst_fast_ahp_indices, + const vector& postburst_slow_ahp_indices, + vector& postburst_adp_peak_indices, + vector& postburst_adp_peak_values) { + + if (postburst_slow_ahp_indices.size() > postburst_fast_ahp_indices.size()){ + GErrorStr += + "\n postburst_slow_ahp should not have more elements than " + "postburst_fast_ahp for postburst_adp_peak_indices calculation.\n"; + return -1; + } + size_t adppeakindex = 0; + for (size_t i = 0; i < postburst_slow_ahp_indices.size(); i++) { + if (postburst_slow_ahp_indices[i] < postburst_fast_ahp_indices[i]){ + continue; + } + adppeakindex = distance( + v.begin(), max_element(v.begin() + postburst_fast_ahp_indices[i], + v.begin() + postburst_slow_ahp_indices[i])); + + if (adppeakindex < postburst_slow_ahp_indices[i] - 1) { + postburst_adp_peak_indices.push_back(adppeakindex); + + EFEL_ASSERT(adppeakindex < v.size(), + "ADP peak index falls outside of voltage array"); + postburst_adp_peak_values.push_back(v[adppeakindex]); + } + } + + return postburst_adp_peak_indices.size(); +} + +int LibV5::postburst_adp_peak_indices(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + const auto& doubleFeatures = + getFeatures(DoubleFeatureData, {"T", "V"}); + const auto& intFeatures = + getFeatures(IntFeatureData, {"postburst_fast_ahp_indices", "postburst_slow_ahp_indices"}); + vector postburst_adp_peak_indices; + vector postburst_adp_peak_values; + int retVal = + __postburst_adp_peak_indices( + doubleFeatures.at("T"), + doubleFeatures.at("V"), + intFeatures.at("postburst_fast_ahp_indices"), + intFeatures.at("postburst_slow_ahp_indices"), + postburst_adp_peak_indices, + postburst_adp_peak_values + ); + + if (retVal > 0) { + setVec(IntFeatureData, StringData, "postburst_adp_peak_indices", postburst_adp_peak_indices); + setVec(DoubleFeatureData, StringData, "postburst_adp_peak_values", + postburst_adp_peak_values); + return retVal; + } + return -1; +} + +int LibV5::postburst_adp_peak_values(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + return 1; +} + +int LibV5::time_to_postburst_fast_ahp(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + const auto& doubleFeatures = + getFeatures(DoubleFeatureData, {"T", "peak_time"}); + const auto& intFeatures = + getFeatures(IntFeatureData, {"postburst_fast_ahp_indices", "burst_end_indices"}); + + vector time_to_postburst_fast_ahp; + const vector& time = doubleFeatures.at("T"); + const vector& peak_time = doubleFeatures.at("peak_time"); + const vector& burst_end_indices = intFeatures.at("burst_end_indices"); + const vector& postburst_fast_ahp_indices = intFeatures.at("postburst_fast_ahp_indices"); + + if (burst_end_indices.size() < postburst_fast_ahp_indices.size()){ + GErrorStr += + "\nburst_end_indices should not have less elements than postburst_fast_ahp_indices\n"; + return -1; + } + + for (size_t i = 0; i < postburst_fast_ahp_indices.size(); i++) { + time_to_postburst_fast_ahp.push_back(time[postburst_fast_ahp_indices[i]] - + peak_time[burst_end_indices[i]]); + } + setVec(DoubleFeatureData, StringData, "time_to_postburst_fast_ahp", + time_to_postburst_fast_ahp); + return (time_to_postburst_fast_ahp.size()); +} + +int LibV5::time_to_postburst_adp_peak(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + const auto& doubleFeatures = + getFeatures(DoubleFeatureData, {"T", "peak_time"}); + const auto& intFeatures = + getFeatures(IntFeatureData, {"postburst_adp_peak_indices", "burst_end_indices"}); + + vector time_to_postburst_adp_peak; + const vector& time = doubleFeatures.at("T"); + const vector& peak_time = doubleFeatures.at("peak_time"); + const vector& burst_end_indices = intFeatures.at("burst_end_indices"); + const vector& postburst_adp_peak_indices = intFeatures.at("postburst_adp_peak_indices"); + + if (burst_end_indices.size() < postburst_adp_peak_indices.size()){ + GErrorStr += + "\nburst_end_indices should not have less elements than postburst_adp_peak_indices\n"; + return -1; + } + + for (size_t i = 0; i < postburst_adp_peak_indices.size(); i++) { + // there are not always an adp peak after each burst + // so make sure that the burst and adp peak indices are consistent + size_t k = 0; + while (burst_end_indices[i] + k + 1 < peak_time.size() && + peak_time[burst_end_indices[i] + k + 1] < time[postburst_adp_peak_indices[i]]){ + k++; + } + time_to_postburst_adp_peak.push_back(time[postburst_adp_peak_indices[i]] - + peak_time[burst_end_indices[i] + k]); + } + setVec(DoubleFeatureData, StringData, "time_to_postburst_adp_peak", + time_to_postburst_adp_peak); + return (time_to_postburst_adp_peak.size()); +} + +// index and voltage value at a given percentage of the duration of the interburst after fast AHP +int __interburst_percent_indices(const vector& t, const vector& v, + const vector& postburst_fast_ahp_indices, + const vector& peak_indices, + const vector& burst_end_indices, + vector& interburst_percent_indices, + vector& interburst_percent_values, + // percentage should be a value between 0 and 1 + double fraction) { + + double time_interval, time_at_fraction; + size_t index_at_fraction; + for (size_t i = 0; i < postburst_fast_ahp_indices.size(); i++) { + if (i < burst_end_indices.size()){ + if (burst_end_indices[i] + 1 < peak_indices.size()){ + time_interval = t[peak_indices[burst_end_indices[i] + 1]] - t[postburst_fast_ahp_indices[i]]; + time_at_fraction = t[postburst_fast_ahp_indices[i]] + time_interval * fraction; + index_at_fraction = + distance(t.begin(), + find_if(t.begin(), t.end(), + [time_at_fraction](double x){ return x >= time_at_fraction; })); + interburst_percent_indices.push_back(index_at_fraction); + interburst_percent_values.push_back(v[index_at_fraction]); + } + } + } + return interburst_percent_indices.size(); +} + +int LibV5::interburst_XXpercent_indices(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData, int percent) { + const auto& doubleFeatures = + getFeatures(DoubleFeatureData, {"T", "V"}); + const auto& intFeatures = + getFeatures(IntFeatureData, {"peak_indices", "burst_end_indices", "postburst_fast_ahp_indices"}); + + vector interburst_XXpercent_indices; + vector interburst_XXpercent_values; + char featureNameIndices[30], featureNameValues[30]; + sprintf(featureNameIndices, "interburst_%dpercent_indices", percent); + sprintf(featureNameValues, "interburst_%dpercent_values", percent); + + int retVal = + __interburst_percent_indices( + doubleFeatures.at("T"), + doubleFeatures.at("V"), + intFeatures.at("postburst_fast_ahp_indices"), + intFeatures.at("peak_indices"), + intFeatures.at("burst_end_indices"), + interburst_XXpercent_indices, + interburst_XXpercent_values, + percent / 100. + ); + + if (retVal > 0) { + setVec(IntFeatureData, StringData, featureNameIndices, interburst_XXpercent_indices); + setVec(DoubleFeatureData, StringData, featureNameValues, + interburst_XXpercent_values); + return interburst_XXpercent_indices.size(); + } + return -1; +} + +static int __interburst_duration(const vector& peak_time, + const vector& burst_end_indices, + vector& interburst_duration) { + + double duration; + for (size_t i = 0; i < burst_end_indices.size(); i++) { + if (burst_end_indices[i] + 1 < peak_time.size()){ + duration = peak_time[burst_end_indices[i] + 1] - peak_time[burst_end_indices[i]]; + interburst_duration.push_back(duration); + } + } + return interburst_duration.size(); +} + +int LibV5::interburst_duration(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData) { + const auto& doubleFeatures = + getFeatures(DoubleFeatureData, {"peak_time"}); + const auto& intFeatures = + getFeatures(IntFeatureData, {"burst_end_indices"}); + + vector interburst_duration; + int retVal = + __interburst_duration( + doubleFeatures.at("peak_time"), intFeatures.at("burst_end_indices"), interburst_duration); + + if (retVal > 0) { + setVec(DoubleFeatureData, StringData, "interburst_duration", interburst_duration); + return interburst_duration.size(); + } + return -1; +} diff --git a/efel/cppcore/LibV5.h b/efel/cppcore/LibV5.h index 95c4d019..f4868045 100644 --- a/efel/cppcore/LibV5.h +++ b/efel/cppcore/LibV5.h @@ -259,8 +259,42 @@ int postburst_min_indices(mapStr2intVec& IntFeatureData, int postburst_min_values(mapStr2intVec& IntFeatureData, mapStr2doubleVec& DoubleFeatureData, mapStr2Str& StringData); +int postburst_slow_ahp_indices(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); +int postburst_slow_ahp_values(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); int time_to_interburst_min(mapStr2intVec& IntFeatureData, mapStr2doubleVec& DoubleFeatureData, mapStr2Str& StringData); +int time_to_postburst_slow_ahp(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); +int postburst_fast_ahp_indices(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); +int postburst_fast_ahp_values(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); +int postburst_adp_peak_indices(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); +int postburst_adp_peak_values(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); +int time_to_postburst_fast_ahp(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); +int time_to_postburst_adp_peak(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); +int interburst_XXpercent_indices(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData, + int percent); +int interburst_duration(mapStr2intVec& IntFeatureData, + mapStr2doubleVec& DoubleFeatureData, + mapStr2Str& StringData); } #endif diff --git a/efel/cppcore/cfeature.cpp b/efel/cppcore/cfeature.cpp index f2b2c956..e2d9b125 100644 --- a/efel/cppcore/cfeature.cpp +++ b/efel/cppcore/cfeature.cpp @@ -204,7 +204,29 @@ void cFeature::fillfeaturetypes() { featuretypes["interburst_min_values"] = "double"; featuretypes["postburst_min_indices"] = "int"; featuretypes["postburst_min_values"] = "double"; + featuretypes["postburst_slow_ahp_indices"] = "int"; + featuretypes["postburst_slow_ahp_values"] = "double"; featuretypes["time_to_interburst_min"] = "double"; + featuretypes["time_to_postburst_slow_ahp"] = "double"; + featuretypes["postburst_fast_ahp_indices"] = "int"; + featuretypes["postburst_fast_ahp_values"] = "double"; + featuretypes["postburst_adp_peak_indices"] = "int"; + featuretypes["postburst_adp_peak_values"] = "double"; + featuretypes["time_to_postburst_fast_ahp"] = "double"; + featuretypes["time_to_postburst_adp_peak"] = "double"; + featuretypes["interburst_15percent_indices"] = "int"; + featuretypes["interburst_15percent_values"] = "double"; + featuretypes["interburst_20percent_indices"] = "int"; + featuretypes["interburst_20percent_values"] = "double"; + featuretypes["interburst_25percent_indices"] = "int"; + featuretypes["interburst_25percent_values"] = "double"; + featuretypes["interburst_30percent_indices"] = "int"; + featuretypes["interburst_30percent_values"] = "double"; + featuretypes["interburst_40percent_indices"] = "int"; + featuretypes["interburst_40percent_values"] = "double"; + featuretypes["interburst_60percent_indices"] = "int"; + featuretypes["interburst_60percent_values"] = "double"; + featuretypes["interburst_duration"] = "double"; // end of feature types } diff --git a/efel/io.py b/efel/io.py index 8753e702..6aae477a 100644 --- a/efel/io.py +++ b/efel/io.py @@ -195,13 +195,14 @@ def load_neo_file(file_name: str, stim_start=None, stim_end=None, **kwargs) -> l for seg in bl.segments: traces = [] for sig in seg.analogsignals: - trace = { - 'T': sig.times.rescale('ms').magnitude, - 'V': sig.rescale('mV').magnitude, - 'stim_start': [stim_start], - 'stim_end': [stim_end] - } - traces.append(trace) + if 'V' in sig.units.dimensionality.string: + trace = { + 'T': (sig.times - sig.times[0]).rescale('ms').magnitude, + 'V': sig.rescale('mV').magnitude, + 'stim_start': [stim_start], + 'stim_end': [stim_end] + } + traces.append(trace) efel_segments.append(traces) efel_blocks.append(efel_segments) diff --git a/efel/pyfeatures/isi.py b/efel/pyfeatures/isi.py index f90f0b42..e5ce5f5e 100644 --- a/efel/pyfeatures/isi.py +++ b/efel/pyfeatures/isi.py @@ -58,6 +58,8 @@ def ISI_CV() -> np.ndarray | None: isi_values = isi_values[1:] result = __ISI_CV(isi_values) + if result is None: + return None return np.array([result]) diff --git a/efel/pyfeatures/multitrace.py b/efel/pyfeatures/multitrace.py index 72919285..d919561d 100644 --- a/efel/pyfeatures/multitrace.py +++ b/efel/pyfeatures/multitrace.py @@ -15,7 +15,9 @@ def bpap_attenuation(soma_trace: dict, dendrite_trace: dict) -> float: The attenuation is computed by first subtracting the resting potential from the voltage traces. """ - f_values = efel.getFeatureValues([soma_trace, dendrite_trace], ["voltage_base"]) + f_values = efel.get_feature_values( + [soma_trace, dendrite_trace], ["voltage_base"], + parallel_map=None, return_list=True, raise_warnings=True) vb_soma = f_values[0]["voltage_base"][0] vb_dend = f_values[1]["voltage_base"][0] v_soma = soma_trace["V"] diff --git a/efel/pyfeatures/validation.py b/efel/pyfeatures/validation.py index 467b74b8..1e07c729 100644 --- a/efel/pyfeatures/validation.py +++ b/efel/pyfeatures/validation.py @@ -4,7 +4,10 @@ def check_ais_initiation(soma_trace: dict, ais_trace: dict) -> bool: """Checks the initiation of action potential in AIS with respect to soma.""" - f_values = efel.getFeatureValues([soma_trace, ais_trace], ["AP_begin_time"]) + f_values = efel.get_feature_values( + traces=[soma_trace, ais_trace], feature_names=["AP_begin_time"], + parallel_map=None, return_list=True, raise_warnings=True + ) soma_ap_begin_time = f_values[0]["AP_begin_time"] ais_ap_begin_time = f_values[1]["AP_begin_time"] diff --git a/efel/settings.py b/efel/settings.py index 5f22bd81..d5e0512e 100644 --- a/efel/settings.py +++ b/efel/settings.py @@ -19,26 +19,16 @@ 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. """ -import inspect -import os +from pathlib import Path -def _get_script_path(): - """Get directory path of current script""" - script_filename = inspect.getframeinfo(inspect.currentframe()).filename - script_path = os.path.dirname(os.path.abspath(script_filename)) - - return script_path - - -class Settings(object): - +class Settings: """FEL settings class""" def __init__(self): self.threshold = -20.0 self.derivative_threshold = 10.0 self.down_derivative_threshold = -12.0 - self.dependencyfile_path = os.path.join( - _get_script_path(), - 'DependencyV5.txt') + self.dependencyfile_path = str( + Path(__file__).parent.absolute() / "DependencyV5.txt" + ) diff --git a/efel/units/units.json b/efel/units/units.json index c80ca33d..bf8d74b7 100644 --- a/efel/units/units.json +++ b/efel/units/units.json @@ -138,5 +138,18 @@ "initburst_sahp_vb": "mV", "depol_block_bool": "constant", "impedance": "Hz", + "postburst_slow_ahp_values": "mV", + "time_to_postburst_slow_ahp": "ms", + "postburst_fast_ahp_values": "mV", + "postburst_adp_peak_values": "mV", + "time_to_postburst_fast_ahp": "ms", + "time_to_postburst_adp_peak": "ms", + "interburst_15percent_values": "mV", + "interburst_20percent_values": "mV", + "interburst_25percent_values": "mV", + "interburst_30percent_values": "mV", + "interburst_40percent_values": "mV", + "interburst_60percent_values": "mV", + "interburst_duration": "ms", "phaseslope_max": "V/s" } diff --git a/examples/basic/basic_example1.py b/examples/basic/basic_example1.py index 37cab7f6..f8e888de 100644 --- a/examples/basic/basic_example1.py +++ b/examples/basic/basic_example1.py @@ -43,7 +43,7 @@ def main(): # Now we pass 'traces' to the efel and ask it to calculate the feature # values - traces_results = efel.getFeatureValues(traces, + traces_results = efel.get_feature_values(traces, ['AP_amplitude', 'voltage_base']) # The return value is a list of trace_results, every trace_results diff --git a/examples/basic/basic_notebook1.ipynb b/examples/basic/basic_notebook1.ipynb index 650e0d35..ba6218eb 100644 --- a/examples/basic/basic_notebook1.ipynb +++ b/examples/basic/basic_notebook1.ipynb @@ -24,17 +24,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "!pip install efel json2html\n", "import efel\n", "\n", - "import numpy, IPython\n", - "from json2html import *\n", + "import numpy\n", + "import json\n", "\n", "%matplotlib notebook\n", "%matplotlib inline\n", @@ -87,7 +86,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -122,7 +121,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -156,7 +155,7 @@ "source": [ "Now that we have the data, we can use the eFEL to extract eFeature values from the trace.\n", "\n", - "We will use the getFeatureValues() function, which accepts as input a dictionary that represents the trace, and the request eFeature names. The trace dictionary consists of the time and voltage trace, and two floats stim_start and stim_end that specify when the stimulus starts and stops." + "We will use the get_feature_values() function, which accepts as input a dictionary that represents the trace, and the request eFeature names. The trace dictionary consists of the time and voltage trace, and two floats stim_start and stim_end that specify when the stimulus starts and stops." ] }, { @@ -177,7 +176,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now we call the getFeatureValues() function and print the eFeature values. The eFeatures that are calculated on single action potentials will return a list with the values for the individual spikes. " + "Now we call the get_feature_values() function and print the eFeature values. The eFeatures that are calculated on single action potentials will return a list with the values for the individual spikes. " ] }, { @@ -188,24 +187,70 @@ }, "outputs": [ { - "data": { - "text/html": [ - "
AP_amplitude
  • 72.57824412616736
  • 46.36725526181844
  • 41.154667915823914
  • 39.76317509528857
  • 36.161465303116316
  • 37.848929573728334
peak_time
  • 708.0000000000924
  • 911.3000000001387
  • 1405.9999999998167
  • 1711.9999999995384
  • 2387.499999998924
  • 2637.7999999986964
peak_voltage
  • 18.74902009950373
  • 9.262291412641208
  • 5.71846652014266
  • 5.843234642606939
  • 3.562320229253816
  • 4.475069908715679
AP_begin_time
  • 706.8000000000922
  • 909.8000000001383
  • 1404.2999999998183
  • 1710.29999999954
  • 2385.6999999989257
  • 2635.999999998698
AP_begin_width
  • 4.700000000001069
  • 4.4000000000010004
  • 4.699999999995725
  • 4.599999999995816
  • 4.799999999995634
  • 4.9999999999954525
AP_begin_voltage
  • -53.82922402666364
  • -37.10496384917723
  • -35.43620139568125
  • -33.91994045268163
  • -32.5991450738625
  • -33.37385966501265
mean_frequency
  • 3.096294767263926
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "{\n", + " \"AP_amplitude\": [\n", + " 72.57824412616736,\n", + " 46.36725526181844,\n", + " 41.154667915823914,\n", + " 39.76317509528857,\n", + " 36.161465303116316,\n", + " 37.848929573728334\n", + " ],\n", + " \"peak_time\": [\n", + " 708.0000000000924,\n", + " 911.3000000001387,\n", + " 1405.9999999998167,\n", + " 1711.9999999995384,\n", + " 2387.499999998924,\n", + " 2637.7999999986964\n", + " ],\n", + " \"peak_voltage\": [\n", + " 18.74902009950373,\n", + " 9.262291412641208,\n", + " 5.71846652014266,\n", + " 5.843234642606939,\n", + " 3.562320229253816,\n", + " 4.475069908715679\n", + " ],\n", + " \"AP_begin_time\": [\n", + " 706.8000000000922,\n", + " 909.8000000001383,\n", + " 1404.2999999998183,\n", + " 1710.29999999954,\n", + " 2385.6999999989257,\n", + " 2635.999999998698\n", + " ],\n", + " \"AP_begin_width\": [\n", + " 4.700000000001069,\n", + " 4.4000000000010004,\n", + " 4.699999999995725,\n", + " 4.599999999995816,\n", + " 4.799999999995634,\n", + " 4.9999999999954525\n", + " ],\n", + " \"AP_begin_voltage\": [\n", + " -53.82922402666364,\n", + " -37.10496384917723,\n", + " -35.43620139568125,\n", + " -33.91994045268163,\n", + " -32.5991450738625,\n", + " -33.37385966501265\n", + " ],\n", + " \"mean_frequency\": [\n", + " 3.096294767263926\n", + " ]\n", + "}\n" + ] } ], "source": [ - "feature_values = efel.getFeatureValues([trace], ['AP_amplitude', 'peak_time', 'peak_voltage', 'AP_begin_time', 'AP_begin_width', \n", + "feature_values = efel.get_feature_values([trace], ['AP_amplitude', 'peak_time', 'peak_voltage', 'AP_begin_time', 'AP_begin_width', \n", " 'AP_begin_voltage', 'mean_frequency'])[0]\n", "feature_values = {feature_name: list(values) for feature_name, values in feature_values.items()}\n", - "IPython.display.HTML(json2html.convert(json=feature_values))" + "print(json.dumps(feature_values, indent=4))" ] }, { @@ -225,7 +270,7 @@ "outputs": [ { "data": { - "image/png": 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y2P4nMVYdAIBkEaoAwCb8I0aqZ5I5Vr2VShUAAFNCqAIAm/AHMz9SXZLqSmOVKvZUAQAwNYQqALAJf9isVGW2/a+h3NxTRagCAGAqCFUAYBODVrf/9RGqAACYCkIVANiEP5T5kerSiPa/HvZUAQAwFYQqALCJAYsqVXXxSlXHQFBDkWhGXxsAgFxAqAIAm7Cq/a+q2C2HQzIMqcsfzuhrAwCQCwhVAGATZvtfpgdVFBY4VVXsliS199MCCADAZBGqAMAmrKpUSVJ1SSxUdfSHMv7aAABkO0IVANiEefhvpgdVSFJNSWxfFZUqAAAmj1AFADbhD1lzTpUkVROqAACYMkIVANiEuafKZ0mlytxTRfsfAACTlZOh6qc//almz54tr9erxYsX69VXX7V6SQCwX3Zo/+ugUgUAwKTlXKh67LHHdPXVV+vmm2/WG2+8ocMPP1zLli3T7t27rV4aAIwrEjUUHIqdEWVF+59ZqeoYoFIFAMBk5Vyouuuuu3TppZfq4osv1iGHHKJ7771XxcXFuv/++61eGgCMy2z9kyya/udjTxUAAFOVU6EqFArp9ddf19KlSxO3OZ1OLV26VKtWrRrzc4LBoHp7e0d9AECmmePUnQ7JU5j5b801pWb7H5UqAAAmK6dCVXt7uyKRiOrr60fdXl9fr5aWljE/5/bbb1d5eXnio7GxMRNLBYBRRk7+czgcGX/9al+s/a+tPyjDMDL++gAAZLOcClVTccMNN6inpyfxsW3bNquXBCAPWTmkQhoeVBEaiqo/OLSfRwMAgJEyvxs6jWpqalRQUKDW1tZRt7e2tqqhoWHMz/F4PPJ4PJlYHgCMy9xTZcV+KikW5nzuAg2EImrvD6nU67JkHQAAZKOcqlS53W4dffTReuaZZxK3RaNRPfPMM1qyZImFKwOAfUtUqlzWhCpp+ABgxqoDADA5OVWpkqSrr75aX/jCF3TMMcfo2GOP1Y9+9CMNDAzo4osvtnppADAuM1T5PNZ9W64pcau5088EQAAAJinnQtVnPvMZtbW16aabblJLS4uOOOIIPfnkk3sNrwAAOxkMW9v+Jw1XqtqZAAgAwKTkXKiSpK985Sv6yle+YvUyAGDC7ND+V1PCWHUAAKYip/ZUAUC28gfNkepWhqrYWHXa/wAAmBxCFQDYQOKcKkv3VMUrVQOEKgAAJoNQBQA24Df3VFk6/S9eqeqj/Q8AgMkgVAGADQyG7ND+Fx9UQaUKAIBJIVQBgA0MxPdUFbmtHakuSe19hCoAACaDUAUANmCOVPd5LGz/88UqVb2BIYWGopatAwCAbEOoAgAbsMNI9fIilwqdDkkMqwAAYDIIVQBgA4npfxa2/zmdDlX5Yi2AnFUFAMDEEaoAwAb8ofj0PwsHVUjDwyraOKsKAIAJI1QBgA0k2v8sDlXmWHUqVQAATByhCgBswByp7rOw/U+Sas0DgKlUAQAwYYQqALABu1Wq2glVAABMGKEKAGzADof/SsN7qmj/AwBg4ghVAGCxcCSqUCR2LpTVoaqaQRUAAEwaoQoALGa2/knWjlSXpBoGVQAAMGmEKgCwmNn6V+h0yF1o7bdls/2PPVUAAEwcoQoALGaeUWX1kAppeFBF50BI0ahh8WoAAMgOhCoAsJjfJkMqJKnaF6tUDUUN9QyGLV4NAADZgVAFABYbDlXW7qeSJHehU2Xe2Do6BmgBBABgIghVAGAxs/3PDpUqSaopNfdVMawCAICJIFQBgMXsckaVqcbHsAoAACaDUAUAFhuIh6oiG7T/SVJNKWPVAQCYDEIVAFhs0Gz/c9mjUlVNpQoAgEkhVAGAxRKDKjz2CFXDZ1VRqQIAYCIIVQBgMTuNVJeGz6qiUgUAwMQQqgDAYoNh+4xUl4YrVR2EKgAAJoRQBQAWGwjG9lQV2WRPVU2iUkX7HwAAE0GoAgCLmSPVfTbZU1VNpQoAgEkhVAGAxfx2G6ker1QNhCKJwAcAAMZHqAIAi/nNPVU2af8r8RTKXRi7PDCsAgCA/SNUAYDF/PE9VXaZ/udwOFRrtgAOsK8KAID9IVQBgMWG2//sEaqkEWPV+6hUAQCwP4QqALCYOVLd57HHnippxFj1AUIVAAD7Q6gCAIv5Q/YaqS5J1T7GqgMAMFGEKgCwmD9oHv5rn1BVUxqrVDGoAgCA/SNUAYCFDMMYnv5nk5HqEpUqAAAmg1AFABYKRaKKRA1JUrFNDv+VpNpSDgAGAGCiCFUAYKGRh+va5ZwqSar20f4HAMBEEaoAwELmOHV3gVOFBfb5lmyOVO+g/Q8AgP2yzxUcAPJQYvKfjYZUSMMj1Tv9oUR7IgAAGBuhCgAsZFaq7DT5T5Iqi11yOCTDkDoHqFYBALAvhCoAsJBdQ1VhgVNVxfEWQA4ABgBgnwhVAGChwZD9xqmbzH1V7X1UqgAA2BdCFQBYaMCme6qk4X1VVKoAANg3QhUAWMiu7X+SVB0PVW19hCoAAPaFUAUAFjLb/3w2bP+rMceqM6gCAIB9IlQBgIXMSpWd2//aqVQBALBPhCoAsJB5TpUd2/+oVAEAMDGEKgCwkJ0rVdW++KCKfipVAADsC6EKACzkt/OeqtJ4+18/lSoAAPaFUAUAFhq0cftftY/DfwEAmAhCFQBYyM7tf5XxUBUIRxNTCgEAwN4IVQBgITufU+VzF8hdELtMdPppAQQAYDyEKgCwkDn9r8hlvz1VDodDVfFqVSf7qgAAGBehCgAslBhU4bFfpUoabgGkUgUAwPgIVQBgocGwfdv/pOFhFZ0MqwAAYFyEKgCw0EAwPqjChu1/0ohK1UDY4pUAAGBfhCoAsJCdR6pLUlWxS5LUNUD7HwAA4yFUAYBFDMOQ32z/s+meqipf7ADgDkIVAADjIlQBgEWCQ1EZRuzXxW57tv9V+ahUAQCwP4QqALDIQHAo8esilz0rVUz/AwBg/whVAGARc5y6p9CpAqfD4tWMLXFOFZUqAADGRagCAIvYfZy6NByqaP8DAGB8hCoAsIhZqbLrfipJqiqOhyp/SNGoYfFqAACwJ0IVAFjEb/Nx6tLwnqqoIfUMclYVAABjIVQBgEX8Qfu3/7kKnCr1xippDKsAAGBshCoAsIh5RlWRjUOVJFUzrAIAgH0iVAGARQbj7X8+G++pkkaMVSdUAQAwJkIVAFjEHFRh90pVYlgFoQoAgDERqgDAIsPT/2wequKVqg5CFQAAYyJUAYBFhqf/2bv9j7OqAADYN0IVAFgkWypV7KkCAGDfCFUAYJHBLAlVZqWKkeoAAIyNUAUAFhlIDKqwefsfgyoAANgnQhUAWGQwsafK5pWqEgZVAACwL4QqALBItuypolIFAMC+EaoAwCLDocre7X/moIqBUESBcMTi1QAAYD+EKgCwSLYMqijzFqrQ6ZAkdTGsAgCAvRCqAMAiA/E9VUU2D1UOh4Ox6gAA7AOhCgAski2VKml4XxWhCgCAvRGqAMAi5p4qn833VEkjzqoiVAEAsBdCFQBYIBo1NBg2z6nKgkqVjwmAAACMh1AFABYYHDFFLxva/yp9LklUqgAAGAuhCgAsYLb+SZK30P6hqsrnkSR1Mv0PAIC9EKoAwALmkIoiV4Gc8XHldlZVHKtUdQ2ELV4JAAD2Q6gCAAv4w7Fx6j6P/atUklRVEqtUdQwELV4JAAD2Q6gCAAsMBLNnSIU0PFKdShUAAHsjVAGABRJnVLnsP05dGh5U0cGgCgAA9kKoAgAL+EOx9r9sqVRVxwdVdPlDMgzD4tUAAGAvhCoAsIA5Uj1b9lRVxAdVRKKGegNDFq8GAAB7IVQBgAX8iel/2dH+53UVyBevqnFWFQAAoxGqAMACA8FYtScbDv41VZXEhlUQqgAAGI1QBQAWSAyqyKZQVUyoAgBgLIQqALCAP2yGquxo/5OkSp85Vp1QBQDASIQqALBAVlaq4qGq00+oAgBgJEIVAFjA3FOVLSPVJdr/AAAYD6EKACwQHIpKik3VyxYMqgAAYGyEKgCwQHAo1v7ndWXPt2GzUsWeKgAARsueqzkA5JBAOFap8hRmT6XKHFTRQagCAGAUQhUAWMCsVHkKs+fbcLU5/Y9BFQAAjJI9V3MAyCHmnqpsClVmpaqzn1AFAMBI2XM1B4AcEjTb/7JpUEV8T1VfcEiheCgEAACEKgCwRMAcVJFFlaryIpecjtivu2kBBAAgIXuu5gCQQ7KxUuV0OlRZzLAKAAD2RKgCAAtk454qSaryMVYdAIA9ZdfVHAByRDZO/5NGDKug/Q8AgITsupoDQI4w2/+8WdT+Jw0Pq+ikUgUAQAKhCgAyLBo1FIpkaftfCaEKAIA9ZdfVHABygBmopOwaVCFRqQIAYCyEKgDIMLP1T8q+SlViTxWhCgCAhOy6mgNADjCHVBQ4HXIVZNe34Wpz+h+DKgAASMiuqzkA5IBAODv3U0nDlaqOfkIVAACm7LuiA0CWy9Zx6tLwnioqVQAADMu+KzoAZLnhg3+za0iFNDz9r2sgLMMwLF4NAAD2QKgCgAwzK1VeV/Z9CzYrVaFIVP3BIYtXAwCAPWTfFR0Astzwnqrsq1QVuQtUFB8D3zUQtng1AADYA6EKADIssacqCytVklRlDqsYCFq8EgAA7CE7r+gAkMWCWTz9T5IqfS5JDKsAAMCUnVd0AMhi2TyoQpKqfB5JUiftfwAASMqiUHXbbbfpuOOOU3FxsSoqKsZ8THNzsz7+8Y+ruLhYdXV1uvbaazU0xEZqAPYSCGfvoApJqiqOVao6af8DAECSVGj1AiYqFArpvPPO05IlS/SLX/xir/sjkYg+/vGPq6GhQS+//LJ27dqlCy+8UC6XS9/5zncsWDEAjC3bK1XmAcBUqgAAiMmaH5N+61vf0lVXXaXDDjtszPufeuoprVu3Tg8//LCOOOIInXHGGbr11lv105/+VKEQff8A7CObD/+VpGqfeVYV31sBAJCyKFTtz6pVq3TYYYepvr4+cduyZcvU29urd955Z9zPCwaD6u3tHfUBAOmUGFSRpe1/lYnpf4QqAACkHApVLS0towKVpMTvW1paxv2822+/XeXl5YmPxsbGtK4TALK9/c88AJjpfwAAxFgaqq6//no5HI59frz33ntpXcMNN9ygnp6exMe2bdvS+noAYA6qyNZKVVViTxWhCgAAyeJBFddcc40uuuiifT5m7ty5E3quhoYGvfrqq6Nua21tTdw3Ho/HI4/HM6HXAIBUyPpKlY9KFQAAI1kaqmpra1VbW5uS51qyZIluu+027d69W3V1dZKkFStWqKysTIccckhKXgMAUiHbB1WYe6p6BsMaikRVWJCdXwcAAKmSNSPVm5ub1dnZqebmZkUiEa1evVqSNG/ePJWUlOj000/XIYccon/6p3/SnXfeqZaWFn3jG9/QFVdcQSUKgK2YlSqvKzsrVRVFsXOqDCMWrKpL+B4LAMhvWROqbrrpJj300EOJ3x955JGSpOeee04nn3yyCgoK9MQTT+hLX/qSlixZIp/Ppy984Qv69re/bdWSAWBMiT1VWVqpKixwqrzIpZ7BsLr8IUIVACDvZU2oevDBB/Xggw/u8zFNTU36y1/+kpkFAcAUDe+pys5QJcX2VfUMhtXRH9K8OqtXAwCAtbL3ig4AWWr4nKrsbP+TpMriWAsgwyoAACBUAUDGZfugCmnkWPWwxSsBAMB62XtFB4AsFQhn96AKSarkAGAAABIIVQCQYblVqSJUAQCQvVd0AMhSuTCowjyrqotQBQAAoQoAMm04VGVv+19VvP2vk/Y/AAAIVQCQaeY5VV5X9n4LrqJSBQBAQvZe0QEgSyUqVdk8qMJHpQoAABOhCgAyyDAMhXJgT9VwpYqR6gAAZO8VHQCykFmlkrI8VMX3VPUHhxLTDAEAyFfZe0UHgCw0MlRl8zlVpd5CFTgdkqRuP9UqAEB+I1QBQAYF40MqnA6pMB5KspHT6VBlsUsSZ1UBAECoAoAMGjlO3eHI3lAlSZXFTAAEAEAiVAFARpn7jzxZPE7dxARAAABisv+qDgBZJBCOVaq8WXzwr6mKShUAAJIIVQCQUTlZqWKsOgAgz2X/VR0AskgwnP1nVJmqfLFBFV20/wEA8lz2X9UBIIuMHFSR7cxBFUz/AwDkO0IVAGRQov0vJypVhCoAACRCFQBkVGJQRRYf/GuqJFQBACCJUAUAGZVTlSpz+h97qgAAeS77r+oAkEUSe6pyYPrfyPY/wzAsXg0AANbJ/qs6AGSR4el/udP+FxyKajAcsXg1AABYh1AFABlktv95c6BS5XMXyF0Q+zrYVwUAyGfZf1UHgCwSyKFKlcPhUKV5VhUHAAMA8hihCgAyKJcGVUhSlc8jSepkWAUAII/lxlUdALLE8OG/ufHttypRqSJUAQDyV25c1QEgSyQGVeTAOVWSVFnMWVUAABCqACCDAjnX/sdZVQAA5MZVHQCyBJUqAAByD6EKADIo9wZVUKkCACA3ruoAkCVybVCFeQAwlSoAQD7Ljas6AGSJQNg8/Dc32v+q4u1/nFMFAMhnhCoAyKDcq1TFRqpzThUAIJ/lxlUdALLEcKjKkUqVuadqICTDMCxeDQAA1iBUAUAGJQZVuHLj2685/W8oaqgvOGTxagAAsEZuXNUBIEuYI9W9OVKp8roKVOyOfS1dDKsAAOQpQhUAZJA5qCJXKlUSZ1UBAJA7V3UAyAK5NqhCGt5XRagCAOSr3LmqA4DNGYaRc4MqJM6qAgCAUAUAGRKKRBO/9uZQ+19VcWysehdj1QEAeSp3ruoAYHOB8HCoys1KFQcAAwDyE6EKADLEHKfucEiuAofFq0mdquLhs6oAAMhHhCoAyBBznLqn0CmHI3dCVaJSRfsfACBPEaoAIENycUiFNDz9j0oVACBfEaoAIEPMM6pyaUiFNGKkOpUqAECeKpzKJ4XDYbW0tMjv96u2tlZVVVWpXhcA5BwqVQAA5KYJ/7i0r69PP/vZz3TSSSeprKxMs2fP1sEHH6za2lo1NTXp0ksv1WuvvZbOtQJAVjMHVeTSwb+SVBkfVNE9GFYkali8GgAAMm9CV/a77rpLs2fP1gMPPKClS5fq8ccf1+rVq/X+++9r1apVuvnmmzU0NKTTTz9dy5cv14YNG9K9bgDIOolKVY61/1XEz6kyDKlnkLHqAID8M6H2v9dee00vvviiFi5cOOb9xx57rL74xS/q3nvv1QMPPKCVK1dq/vz5KV0oAGS7oLmnKsfa/1wFTpV5C9UbGFLnQCjRDggAQL6YUKj6zW9+M6En83g8uvzyy5NaEADkqlytVEmxfVW9gSF1MawCAJCHJnxlP/fcc/Xkk0/KMOiXB4CpGD6nKrcqVdKIs6oYVgEAyEMTDlVdXV36+Mc/rlmzZummm27Spk2b0rkuAMg5uTqoQpKqipkACADIXxO+sj/zzDPatGmTLrnkEj388MOaP3++Tj31VP36179WMBhM5xoBICeY7X9eVw5Xqmj/AwDkoUn9uLSpqUm33HKLNm3apBUrVmj69Om69NJLNW3aNF1xxRV6/fXX07VOAMh65uG/OVmp4qwqAEAem/KV/dRTT9XDDz+slpYW3X777Xr00Ue1ePHiVK4NAHLK8OG/uReqzLOqOgcYqQ4AyD8Tmv43ns2bN+vBBx/Ugw8+qJ6eHi1dujRV6wKAnDM8/S/32v+qfLGzqpj+BwDIR5P+cWkgENDDDz+sU089VfPnz9cvf/lLXXLJJdq8ebOefPLJdKwRAHLC8DlVuVup6qD9DwCQhyZcqXr11Vd1//3367HHHlMgENA555yjJ598UqeddpocDkc61wgAOSEQzuVKFXuqAAD5a8Kh6sMf/rAOP/xw3XrrrbrgggtUWVmZznUBQM7J5ZHqlYQqAEAem3Co+sc//qGjjjoqnWsBgJyWy4MqzHOq+oJDCg1F5c7BrxEAgPFMOFSNDFSBQEBr1qzR7t27FY1GRz3urLPOSt3qACCHDIeq3Gv/KytyyemQoobU7Q+prsxr9ZIAAMiYSU//e/LJJ3XhhReqvb19r/scDocikUhKFgYAuSZxTpUr96o4BU6HKord6hwIqZNQBQDIM5O+sv/rv/6rzjvvPO3atUvRaHTUB4EKAMaXy5UqSaosjo1V72RfFQAgz0w6VLW2turqq69WfX19OtYDADkrMagiBytV0sgJgBwADADIL5O+sp977rl6/vnn07AUAMhtwXDuDqqQhkNVJwcAAwDyzKT3VP3kJz/Reeedp5UrV+qwww6Ty+Uadf+VV16ZssUBQC4x2/+8OXhOlcRZVQCA/DXpUPWb3/xGTz31lLxer55//vlRB/86HA5CFQCMIzGoIkcrVZXxsersqQIA5JtJh6obb7xR3/rWt3T99dfL6czNfxgAQDrk+qCKRKWK9j8AQJ6ZdCoKhUL6zGc+Q6ACgElKDKqgUgUAQE6Z9JX9C1/4gh577LF0rAUAcpZhGHmzp6qjn1AFAMgvk27/i0QiuvPOO/W3v/1NixYt2mtQxV133ZWyxQFArghFojKM2K9zdaR6dQmVKgBAfpp0qFq7dq2OPPJISdLbb7896r6RQysAAMPMKpWUu+1/1SUeSVLHQFCGYXBNAADkjUmHqueeey4d6wCAnGaeUSVJ7oIcDVXx9r9wxFBfcEhlXtd+PgMAgNyQm1d2ALCZkUMqcrWC43UVyOeO7RdjXxUAIJ9MKFRdfvnl2r59+4Se8LHHHtMjjzyS1KIAINcEwrk9pMJUldhXFbR4JQAAZM6E2v9qa2u1cOFCfeQjH9GZZ56pY445RtOnT5fX61VXV5fWrVunl156SY8++qimT5+u++67L93rBoCskuvj1E3VPo+2dQ6qnUoVACCPTChU3XrrrfrKV76i//7v/9Z//ud/at26daPuLy0t1dKlS3Xfffdp+fLlaVkoAGSzxMG/OTr5z2Tuq2ICIAAgn0x4UEV9fb1uvPFG3Xjjjerq6lJzc7MGBwdVU1OjAw44IGf3CABAKpiDKjyFud3+Z45V7+in/Q8AkD8mPf1PkiorK1VZWZnqtQBAzjLb/7w5Xqmq8plj1alUAQDyR25f3QHAJgJ5UqmqSVSqCFUAgPxBqAKADMibQRVmqGL6HwAgj+T21R0AbCIxqCLHQ1Wi/Y9KFQAgj+T21R0AbMIMVbl+TpU5/Y89VQCAfDKlUDU0NKSnn35aP//5z9XX1ydJ2rlzp/r7+1O6OADIFcFwfrX/dQ2EFI0aFq8GAIDMmPT0v61bt2r58uVqbm5WMBjURz/6UZWWluqOO+5QMBjUvffem451AkBWG27/y+1KVVW8UjUUNdQbCKui2G3xigAASL9J/8j0q1/9qo455hh1dXWpqKgocfs555yjZ555JqWLA4BckahU5fhIdU9hgUo9sZ/X0QIIAMgXk65UrVy5Ui+//LLc7tE/fZw9e7Z27NiRsoUBQC7Jl0EVUqwFsC84pI7+kA6otXo1AACk36Sv7tFoVJFIZK/bt2/frtLS0pQsCgByTSBsHv6b2+1/0nALYCdj1QEAeWLSoer000/Xj370o8TvHQ6H+vv7dfPNN+tjH/tYKtcGADkjvypVsbHq7YxVBwDkiUm3//3gBz/QsmXLdMghhygQCOj888/Xhg0bVFNTo9/85jfpWCMAZL18GVQhSTUlZqWKUAUAyA+TDlUzZ87UW2+9pUcffVRr1qxRf3+/LrnkEl1wwQWjBlcAAIYFh/JjUIU03P7X0U/7HwAgP0w6VElSYWGhPv/5z6d6LQCQswLh+OG/eVCpqvbF2/+oVAEA8sSkQ9Wf/vSnMW93OBzyer2aN2+e5syZk/TCACCX5FOlyjwAuJM9VQCAPDHpUHX22WfL4XDIMIxRt5u3ORwOHX/88Xr88cdVWVmZsoUCQDYLhvNoUEW8UtXB9D8AQJ6Y9NV9xYoV+tCHPqQVK1aop6dHPT09WrFihRYvXqwnnnhCL774ojo6OvS1r30tHesFgKyUT4MqhkeqU6kCAOSHSVeqvvrVr+q+++7Tcccdl7jttNNOk9fr1WWXXaZ33nlHP/rRj/TFL34xpQsFgGyWT+1/I6f/RaOGnE6HxSsCACC9Jn11/+CDD1RWVrbX7WVlZdq0aZMkaf78+Wpvb09+dQCQIwLh/KlUVcYrVVFD6h4MW7waAADSb9Kh6uijj9a1116rtra2xG1tbW36+te/rg996EOSpA0bNqixsTF1qwSALJeoVOXBnipXgVPlRS5JjFUHAOSHSV/df/GLX2jz5s2aOXOm5s2bp3nz5mnmzJnasmWL/vu//1uS1N/fr2984xspXywAZCtzT5U3D9r/pOEJgB3sqwIA5IFJ76lasGCB1q1bp6eeekrvv/9+4raPfvSjcjpj/1g4++yzU7pIAMh2wTxq/5Okap9bm9oG1MFYdQBAHpjS4b9Op1PLly/X8uXLU70eAMg5hmEokEeDKqThseqdjFUHAOSBKYWqgYEBvfDCC2publYoNPqnkFdeeWVKFgYAuSIcMWQe7ZcvlaqqePtfO5UqAEAemHSoevPNN/Wxj31Mfr9fAwMDqqqqUnt7u4qLi1VXV0eoAoA9mEMqpPwYVCFJNT5zTxWVKgBA7pv01f2qq67SmWeeqa6uLhUVFenvf/+7tm7dqqOPPlrf//7307FGAMhq5pAKKX9CFQcAAwDyyaSv7qtXr9Y111wjp9OpgoICBYNBNTY26s4779S///u/p2ONAJDVAuHhceoOR34chFtdEttTRfsfACAfTDpUuVyuxJS/uro6NTc3S5LKy8u1bdu21K4OAHKAWanKlyqVFJv+J1GpAgDkh0nvqTryyCP12muvaf78+TrppJN00003qb29Xb/61a906KGHpmONAJDVEuPUXfkxpEIarlRx+C8AIB9M+sem3/nOdzRt2jRJ0m233abKykp96UtfUltbm37+85+nfIEAkO3MQRV5VamKT//rHgxrKBLdz6MBAMhuk65UHXPMMYlf19XV6cknn0zpggAg15jtf948qlRVFrvlcEiGIXX5w6ot9Vi9JAAA0mbSPzY99dRT1d3dvdftvb29OvXUU1OxJgDIKSMHVeSLAqdDlcXsqwIA5IdJX+Gff/75vQ78laRAIKCVK1emZFEAkEvycVCFNDxWnX1VAIBcN+H2vzVr1iR+vW7dOrW0tCR+H4lE9OSTT2rGjBmpXR0A5IDhUJU/7X9SbALgRkkdVKoAADluwqHqiCOOkMPhkMPhGLPNr6ioSD/+8Y9TujgAyAXBePuf15VflSpzWAWVKgBArptwqNq8ebMMw9DcuXP16quvqra2NnGf2+1WXV2dCgry66ewADARgbytVMXHqlOpAgDkuAmHqqamJklSNMpoXACYDLNS5cmzSlViTxWhCgCQ4yYUqv70pz9N+AnPOuusKS8GAHJRvg6qqKH9DwCQJyYUqs4+++wJPZnD4VAkEklmPQCQc/J2UEVJrP2PkeoAgFw3oVBFyx8ATF2+DqoYHqlOqAIA5LasuMJv2bJFl1xyiebMmaOioiIdcMABuvnmm/c6L2vNmjU64YQT5PV61djYqDvvvNOiFQPAsHytVCXa/6hUAQBy3JRC1QsvvKAzzzxT8+bN07x583TWWWel9eDf9957T9FoVD//+c/1zjvv6Ic//KHuvfde/fu//3viMb29vTr99NPV1NSk119/Xd/73vd0yy236L777kvbugBgIoJD8UEVebanqio+/a9nMKxwhI4HAEDumvD0P9PDDz+siy++WJ/85Cd15ZVXSpL+7//+T6eddpoefPBBnX/++Slf5PLly7V8+fLE7+fOnav169frZz/7mb7//e9Lkh555BGFQiHdf//9crvdWrhwoVavXq277rpLl112WcrXBAATFQzHK1V51v5XUeSS0yFFDalrIKS6Mq/VSwIAIC0mfYW/7bbbdOedd+qxxx7TlVdeqSuvvFKPPfaYvvvd7+rWW29NxxrH1NPTo6qqqsTvV61apRNPPFFutztx27Jly7R+/Xp1dXWN+zzBYFC9vb2jPgAglcz2P68rv9r/nE5HYl9VO/uqAAA5bNKhatOmTTrzzDP3uv2ss87S5s2bU7Ko/dm4caN+/OMf61/+5V8St7W0tKi+vn7U48zft7S0jPtct99+u8rLyxMfjY2N6Vk0gLwVCOdn+580fAAwEwABALls0lf4xsZGPfPMM3vd/vTTT086kFx//fVyOBz7/HjvvfdGfc6OHTu0fPlynXfeebr00ksnu/y93HDDDerp6Ul8bNu2LennBICR8nVQhTTyAGDOqgIA5K5J76m65pprdOWVV2r16tU67rjjJMX2VD344IO6++67J/1cF1100T4fM3fu3MSvd+7cqVNOOUXHHXfcXgMoGhoa1NraOuo28/cNDQ3jPr/H45HH45nUugFgMvJ1UIUkVZfQ/gcAyH2TDlVf+tKX1NDQoB/84Af67W9/K0k6+OCD9dhjj+kTn/jEpJ6rtrZWtbW1E3rsjh07dMopp+joo4/WAw88IKdz9D9OlixZohtvvFHhcFgul0uStGLFCi1YsECVlZWTWhcApFK+7qmSpJrEAcBUqgAAuWvSoUqSzjnnHJ1zzjmpXsu4duzYoZNPPllNTU36/ve/r7a2tsR9ZhXq/PPP17e+9S1dcskluu666/T222/r7rvv1g9/+MOMrRMAxpLPe6o4ABgAkA8mHar++Z//WZ///Od18sknp2E5Y1uxYoU2btyojRs3aubMmaPuMwxDklReXq6nnnpKV1xxhY4++mjV1NTopptuYpw6AMsl9lTl2Uh1abj9jwOAAQC5bNKhqq2tTcuXL1dtba0++9nP6oILLtARRxyRhqUNu+iii/a790qSFi1alNZDiAFgKhLnVOXhoIrqRKWK9j8AQO6a9I9N//jHP2rXrl365je/qddee01HH320Fi5cqO985zvasmVLGpYIANktvwdVMFIdAJD7pnSFr6ys1GWXXabnn39eW7du1UUXXaRf/epXmjdvXqrXBwBZLxDO30EV7KkCAOSDpH5sGg6H9Y9//EOvvPKKtmzZstfhuwCQ7wzDyOtKVU388N++4FDizwEAgFwzpSv8c889p0svvVT19fW66KKLVFZWpieeeELbt29P9foAIKsNRQ1FY/N08nJPVVlRoQqdDkm0AAIActekB1XMmDFDnZ2dWr58ue677z6deeaZHJ4LAOMwJ/9J+Tn9z+FwqMrn1u6+oDr6Q5pWXmT1kgAASLlJh6pbbrlF5513nioqKtKwHADILcHwcMtbPrb/SRoOVVSqAAA5atKh6tJLL03HOgAgJwXilSp3oVMOh8Pi1VijpsQjqY+x6gCAnJWfPzYFgAwxK1X5WqWShg8AZk8VACBX5e9VHgAywNxTlY9DKkzmWPV2xqoDAHIUoQoA0sgMVd48HFJhqkkcAEz7HwAgN+XvVR4AMiBA+5+q45Wqtj5CFQAgN+XvVR4AMoD2P6m2NFapov0PAJCrCFUAkEaJQRV53P5XV+qVRKUKAJC78vcqDwAZkNhTRaVK7f1BRaOGxasBACD1CFUAkEYBKlWJkepDUUNdfloAAQC5J3+v8gCQAcN7qvL3262rwJkYq97GAcAAgByUv1d5AMgABlXE1MbHqrOvCgCQiwhVAJBGwSFGqktSXRmhCgCQu/L7Kg8AaRYIm4f/UqmSpN2EKgBADiJUAUAamZUqd55XqswJgFSqAAC5KL+v8gCQZsF4paoo3ytVhCoAQA4jVAFAGpkj1b15PFJdIlQBAHJbfl/lASDNBhOhikqVJO3uC1i8EgAAUo9QBQBpFCBUSZLqqFQBAHIYoQoA0ojpfzG1JV5JUm9gKBE0AQDIFYQqAEijQfZUSZLKigoTExDb+6lWAQByS35f5QEgzYJmqCrM70qVw+FInFVFCyAAINcQqgAgjcz2vyJ3focqaeSwCkIVACC3EKoAII0CQ7T/mRirDgDIVVzlASCNBkOxUOXJ8/Y/iVAFAMhdhCoASCNz0h3tfxreU8WgCgBAjiFUAUAaBYYYqW6qK4vvqeolVAEAcguhCgDSJBI1FDJDVSHfbqlUAQByFVd5AEiT4NDwIbe0/w3vqWpnTxUAIMcQqgAgTcxx6hLnVEmjB1UYhmHxagAASB1CFQCkyWB8SIW7wCmn02HxaqxXE2//C0Wi6h0csng1AACkDqEKANLEnPzHGVUxXleByotckqTdfQGLVwMAQOpwpQeANBkOVbT+mTirCgCQiwhVAJAmhKq9MQEQAJCLCFUAkCbmoIoiQlUClSoAQC4iVAFAmrCnam9mqNpNqAIA5BCu9ACQJub0Pw+VqoQ6KlUAgBxEqAKANKH9b2+0/wEAchGhCgDShPa/vRGqAAC5iCs9AKQJ0//2lghVTP8DAOQQQhUApIkZqmj/G1ZX6pUkdQ6EFBqKWrwaAABSg1AFAGli7qmiUjWsosilQqdDktQxQLUKAJAbCFUAkCbD0//4VmtyOh2qKWFfFQAgt3ClB4A0of1vbAyrAADkGkIVAKQJ7X9jI1QBAHINoQoA0iQx/a+Qb7UjmQcA7yZUAQByBFd6AEiTRPufm0rVSFSqAAC5hlAFAGkSGOKcqrEQqgAAuYZQBQBpYu6p8hQSqkaqLeEAYABAbiFUAUCaDIZo/xtLbWJPVcDilQAAkBqEKgBIk0T7H4MqRqkr9UqKtf8ZhmHxagAASB5XegBIkyAj1cdUU+qWFGuP7A8OWbwaAACSR6gCgDQZZPrfmIrdhSrxFEpiWAUAIDcQqgAgTYbPqSJU7YkJgACAXEKoAoA0MAxjOFS5+Fa7p1oOAAYA5BCu9ACQBqFIVNH4DAYPe6r2QqUKAJBLCFUAkAbmGVWSVESo2gtnVQEAcgmhCgDSIBhv/XM6JFeBw+LV2A+VKgBALiFUAUAaDCb2UxXI4SBU7Yk9VQCAXEKoAoA0MNv/aP0bWx2VKgBADiFUAUAaBEZUqrA32v8AALmEUAUAaWC2/3kYpz4mM1R1DgQVMcckAgCQpbjaA0AamJUq2v/GVu3zyOmQoobUMUC1CgCQ3QhVAJAG5p4q2v/GVuB0qMoXH1bRS6gCAGQ3QhUApMHwniq+zY4nMayCs6oAAFmOqz0ApAHtf/vHsAoAQK4gVAFAGgQSgyoIVeMhVAEAcgWhCgDSYNDcU1VIqBoPoQoAkCsIVQCQBon2PzffZsfTUOaVJLX0BCxeCQAAyeFqDwBpEBiKD6qgUjWuhvJYqNrVS6gCAGQ3QhUApEEgZE7/I1SNZ1q5WakatHglAAAkh1AFAGlgnlNV5CZUjcesVO3uCyociVq8GgAApo5QBQBpYLb/eQr5NjueGp9HhU6HDINhFQCA7MbVHgDSYJD2v/1yOh2qjw+r2MWwCgBAFiNUAUAaBIbi7X+Eqn0a3ldFqAIAZC9CFQCkgTlSnUrVviUmADKsAgCQxQhVAJAGwUSo4tvsvlCpAgDkAq72AJAGg+bhv1Sq9mlaeZEkzqoCAGQ3QhUApIE5Ut1DqNonKlUAgFxAqAKANAjQ/jchDYQqAEAO4GoPAGlA+9/EmO1/rb0BRaKGxasBAGBqCFUAkAbBePsf0//2rbbUowKnQ0NRQx39HAAMAMhOhCoASLFI1FAoQqiaiAKnQ3WlHknSTloAAQBZilAFAClm7qeSaP+biOF9VZxVBQDIToQqAEixkaHKU8i32f2ZljgAmEoVACA7cbUHgBQLDMVa/9yFTjmdDotXY38NZbFhFUwABABkK0IVAKTYYIjJf5NBpQoAkO0IVQCQYpxRNTmcVQUAyHZc8QEgxYJDZqiiUjURiUpVL4MqAADZiVAFACk2GIqPUy8kVE2EWalq7QkqygHAAIAsRKgCgBRLtP+5CVUTUVfqlcMhhSJRdfpDVi8HAIBJI1QBQIoFzPY/xqlPiLvQqZqS2AHA7KsCAGQjrvgAkGLm9D/2VE0cEwABANmMUAUAKWaeU8VI9YlrKDMnADKsAgCQfQhVAJBiQUaqTxqVKgBANuOKDwApRvvf5DWUF0liTxUAIDsRqgAgxQKcUzVpVKoAANmMUAUAKRYIx8+pIlRNmHlWVUsvoQoAkH0IVQCQYoPsqZq06fH2v109gzIMDgAGAGQXrvgAkGLm4b9M/5u4urLYOVWBcFQ9g2GLVwMAwOQQqgAgxYK0/02a11Wgap9bEvuqAADZh1AFACkWoP1vShL7qghVAIAswxUfAFJseE8VlarJYAIgACBbEaoAIMUChKopGa5UDVq8EgAAJodQBQApxkj1qZmWmABIpQoAkF0IVQCQYkz/m5qGMs6qAgBkJ0IVAKQYgyqmhj1VAIBsxRUfAFIsMET731Qw/Q8AkK0IVQCQYoMh2v+mwgxV/cEh9QU4ABgAkD0IVQCQQoZhKDAUC1Ue2v8mpdhdqPIilySqVQCA7MIVHwBSKBSJyjBiv6b9b/LYVwUAyEaEKgBIoUAomvg17X+Tx74qAEA2IlQBQAqZrX8FTodcBXyLnSyzUrWTA4ABAFmEKz4ApFBinHoh316noqEsdgAwlSoAQDbJmqv+WWedpVmzZsnr9WratGn6p3/6J+3cuXPUY9asWaMTTjhBXq9XjY2NuvPOOy1aLYB8NWge/Oum9W8q2FMFAMhGWROqTjnlFP32t7/V+vXr9bvf/U4ffPCBzj333MT9vb29Ov3009XU1KTXX39d3/ve93TLLbfovvvus3DVAPJNIBzbU+UpJFRNBXuqAADZqNDqBUzUVVddlfh1U1OTrr/+ep199tkKh8NyuVx65JFHFAqFdP/998vtdmvhwoVavXq17rrrLl122WUWrhxAPkm0/zFOfUqGK1XsqQIAZI+svOp3dnbqkUce0XHHHSeXK3amyapVq3TiiSfK7XYnHrds2TKtX79eXV1dVi0VQJ6h/S85ZqWqNzCkgeCQxasBAGBisipUXXfddfL5fKqurlZzc7P++Mc/Ju5raWlRfX39qMebv29paRn3OYPBoHp7e0d9AMBUBRODKghVU1HqdanEE2uiaOmlBRAAkB0sDVXXX3+9HA7HPj/ee++9xOOvvfZavfnmm3rqqadUUFCgCy+8UIZ5yuYU3X777SovL098NDY2JvtlAchj5p4qDv6dumnsqwIAZBlL91Rdc801uuiii/b5mLlz5yZ+XVNTo5qaGh144IE6+OCD1djYqL///e9asmSJGhoa1NraOupzzd83NDSM+/w33HCDrr766sTve3t7CVYApmwwsaeKUDVVDeVebdjdzwRAAEDWsDRU1dbWqra2dkqfG43GfhocDAYlSUuWLNGNN96YGFwhSStWrNCCBQtUWVk57vN4PB55PJ4prQEA9sSgiuQlhlV0M6wCAJAdsuKq/8orr+gnP/mJVq9era1bt+rZZ5/V5z73OR1wwAFasmSJJOn888+X2+3WJZdconfeeUePPfaY7r777lFVKABIN9r/kjejoliStK3Lb/FKAACYmKwIVcXFxfr973+v0047TQsWLNAll1yiRYsW6YUXXkhUmcrLy/XUU09p8+bNOvroo3XNNdfopptuYpw6gIwapFKVtNk1sVC1tYNQBQDIDllxTtVhhx2mZ599dr+PW7RokVauXJmBFQHA2Mzpf0VUqqZsVhWhCgCQXfhRKgCkUIBBFUlrqvZJio1UN/88AQCwM0IVAKQQe6qSV1nsUmn8rKptnVSrAAD2R6gCgBRipHryHA6HZlXTAggAyB6EKgBIIUaqp0aTGaqoVAEAsgBXfQBIocBQvP2vkEpVMmZVxfZVNXcMWLwSAAD2j1AFACkUCMWn/7kJVcmYTaUKAJBFCFUAkEKBIdr/UoE9VQCAbMJVHwBSKLGniva/pJhj1bd3+RWJGhavBgCAfSNUAUAKJab/0f6XlIYyr9wFToUjhnZ2D1q9HAAA9olQBQAplDinikpVUgqcDs2sKpIkNbOvCgBgc4QqAEghRqqnTlMV+6oAANmBqz4ApJAZqpj+lzxzX9XWTsaqAwDsjVAFACkSiRoKR2JDFWj/S17iAOB2KlUAAHsjVAFAiphVKknyughVyWrirCoAQJYgVAFAigyOCFWeQr69JmtWVaz9r7ljQIbBWHUAgH1x1QeAFDErVZ5Cp5xOh8WryX6NVUVyOKSBUEQdAyGrlwMAwLgIVQCQIolx6rT+pYSnsEDTyrySmAAIALA3QhUApEhi8h+hKmVmxfdVNTMBEABgY4QqAEgRzqhKvdnxsepbmAAIALAxrvwAkCK0/6XecKWKUAUAsC9CFQCkyGCiUkWoSpWm+ATArR20/wEA7ItQBQApQvtf6jVRqQIAZAGu/ACQIgEqVSlntv+194fUHxyyeDUAAIyNUAUAKcL0v9Qr87pUWeySJDUzVh0AYFOEKgBIEQZVpEdTNfuqAAD2RqgCgBRhT1V6mPuqtrKvCgBgU1z5ASBFAkPsqUqHpqp4qKL9DwBgU4QqAEiRwRDtf+kwK97+19xJ+x8AwJ4IVQCQIolKVSGhKpUS7X9UqgAANkWoAoAUSUz/c/OtNZXM9r+d3YMKDUUtXg0AAHvjyg8AKcI5VelRW+pRkatAUUPa3kW1CgBgP4QqAEiRxEh12v9SyuFwMAEQAGBrhCoASBGzUuVhpHrKzYq3AHIAMADAjrjyA0CKDJp7qmj/SzmGVQAA7IxQBQApkmj/I1SlHGPVAQB2RqgCgBQJMqgibTgAGABgZ4QqAEgR2v/SZ+SgimjUsHg1AACMRqgCgBQZHqnOt9ZUm1FRpEKnQ6GhqFr7AlYvBwCAUbjyA0CKsKcqfQoLnJpRWSSJFkAAgP0QqgAgBQzDSLT/EarSY1ZiXxXDKgAA9kKoAoAUCA5FE7+m/S89DqgtkSRtaO23eCUAAIzGlR8AUiAYHhmqqFSlw4KGUknS+tY+i1cCAMBohCoASAGz9a/Q6ZCrgG+t6WCGqvdaCFUAAHvhyg8AKRBgP1XaHVgfC1VtfUF1DoQsXg0AAMMIVQCQAoEhxqmnW4mnUI1VsQmA77X0WrwaAACGcfUHgBQYDFGpyoQF9WWSpPdpAQQA2AihCgBSgDOqMuMghlUAAGyIUAUAKUD7X2YwrAIAYEdc/QEgBQLx9r8iKlVpZVaq3m/pUzRqWLwaAABiCFUAkALDlSpCVTrNrvHJXeDUQCiiHd2DVi8HAABJhCoASAlzT5WnkFCVTq4Cpw6oK5FECyAAwD4IVQCQAub0vyI3oSrdEsMqGKsOALAJQhUApEB/cEiSVEz7X9oxrAIAYDeEKgBIgeZOvyRpZmWRxSvJfQsSlSpCFQDAHghVAJACWzsGJElNNT6LV5L7zPa/Te0DCsYHhAAAYCVCFQCkwJaOWKVqdnWxxSvJfQ1lXpV5CxWJGvpg94DVywEAgFAFAMkaCA6prS8oSWqqplKVbg6HY7gFsJVhFQAA6xGqACBJW+NVqiqfW+VFLotXkx8YVgEAsBNCFQAkaYu5n4rWv4xZ0FAmSXqfUAUAsAFCFQAkyQxVs2n9y5iDmAAIALARQhUAJGlre6z9j0pV5hxYHwtVO3sC6hkMW7waAEC+I1QBQJKoVGVeeZFL08u9kqT3W6lWAQCsRagCgCQlQhVnVGUUwyoAAHZBqAKAJPhDQ2rtjY1T54yqzDKHVaxvYaw6AMBahCoASEJzZ2w/VXmRSxXFbotXk18YVgEAsAtCFQAkYUt8SAVVqswb2f5nGIbFqwEA5DNCFQAkYWvijCr2U2XaAbUlKnQ61BcY0q6egNXLAQDkMUIVACSBIRXWcRc6Nbc29udOCyAAwEqEKgBIAu1/1jKHVTABEABgJUIVACSB9j9rDQ+rYAIgAMA6hCoAmKJAOKKd8b08VKqssaCes6oAANYjVAHAFG2Lj1Mv9Raqysc4dSuYEwA3tQ0oHIlavBoAQL4iVAHAFG1ujw+pqPbJ4XBYvJr8NLOySD53gUKRqLbE3w8AADKNUAUAU7S1I1apaqL1zzIOhyNRrVq3i31VAABrEKoAYIoS49QZUmGpRTMrJElvbeuxdiEAgLxFqAKAKaJSZQ9HNFZIkt7c1mXtQgAAeYtQBQBTZO6pmsPBv5Y6claFJOmdHb0KDkWsXQwAIC8RqgBgCoJDEe3sGZTEGVVWm1VVrCqfW6FIVO/uYrQ6ACDzCFUAMAXbOgdlGJLPXaCaEsapW8nhcAy3ADbTAggAyDxCFQBMwdb4kIomxqnbwpGJUNVt6ToAAPmJUAUAU7AlPqRidg1DKuzgyFmVkqTV27qtXQgAIC8RqgBgCra0M07dThY1lsvhkJo7/WrvD1q9HABAniFUAcAUcEaVvZR5XZpXWyJJWk0LIAAgwxyGYRhWL8JOent7VV5erp6eHpWVlVm9HAA2c8vLt2hD9wa9s6NXoaGo5teXqMRbaM1iWt+VQv2jb3OXSPUHW7Mei23t8KuzP6SGcq+mVXgTt8+vmK9bjrvFuoUBALLWRLOBRf8SAIDstKF7g9a0rZHcUoFb2tQnyaop3k5JXs8eN4altjVWrMYWCoqltrDU1mb1SgAA+YT2PwAAAABIAqEKAAAAAJJAqAIAAACAJLCnCgAmYX7FfG3vHFRbX1B1ZR7NqCyybjGdW6Tw4OjbXEVS1WwrVmMLO7sG1dobVHWJW7OqY2eIza+Yb/GqAAC5jlAFAJNwy3G3aMv6V7V1a5v+5ZOH6XPHzrJ6SRjhb++06F9+9bpmNZTqkX860erlAADyBO1/ADBJ5sG/TfFKCOzjyMYKSdL61j71B4esXQwAIG8QqgBgEsKRqLZ3xVru5tRw8K/d1JV5NaOiSIYhrdnebfVyAAB5glAFAJOws3tQQ1FDnkKn6ku9+/8EZNwRsyokSW82d1u6DgBA/iBUAcAkbB7R+ud0OixeDcZitgASqgAAmUKoAoAJCg5FdPczGyRJCxrKLF4NxnNkvFK1elu3DMOwdjEAgLxAqAKACTAMQ998/G292dytMm+hrvnogVYvCeNYOL1crgKH2vuDif1vAACkE6EKACbgl6u26rf/2C6nQ/rJ+UdpNkMqbMvrKtDB02KVxNXbuq1dDAAgLxCqAGA/Vn3QoW8/sU6SdP0ZB+nEA2stXhH2h31VAIBMIlQBwD5s7/Lril+/oUjU0NlHTNelJ8y1ekmYgCNnVUqS3tzWZfFKAAD5gFAFAOMYDEV02S9fV+dASIfOKNN3P7VIDgcT/7LBEfFK1Ts7exUcili7GABAziNUAcAYDMPQtf/7ltbt6lW1z62f/9Mx8roKrF4WJqipuliVxS6FhqJ6Z2ev1csBAOQ4QhUA7CEciera/12jJ9bsUqHToZ99/mjNqCiyelmYBIfDocVzqiVJL6xvs3g1AIBcR6gCgBEGgkO69Jf/0P++vl0FTofu+NQiHTunyuplYQpOPbhOkvTse7stXgkAINcVWr0AALCL9v6gvvjga1qzvUdel1M/Pf8onXZwvdXLwhSdsqBODoe0dkePWnsDqi/zWr0kAECOolIFAJK2tA/oUz97WWu296jK59ZvLv0wgSrL1ZZ6dPjMCknSc1SrAABpRKgCkPfe2tatT/3sZW3t8Kuxqkj/e/mSxEhuZLfTDoq1AD5DqAIApBGhCkDeCkeiuu/FD/SZ+1apIz42/fdf+ojm1pZYvTSkiLmv6qUN7QqEGa0OAEgP9lQByEv/2NKpG//wtta39kmSTl5Qq5+cf5RKPHxbzCWHTCtTQ5lXLb0BrdrUoVMW1Fm9JABADqJSBSCvdPtDuv53a3Tuvau0vrVPlcUufe/cRXrgog8RqHKQw+EYngL4Li2AAID04F8QAHJeJGpoU1u/Xv6gQ3c/s0GdAyFJ0meOadT1ZxykSp/b4hUinU47qE6/fqVZz763W982DDkcDquXBADIMYQqADlnd19Ar2zq1Jrt3VqzvUdv7+jRQGh4P82B9SW67ZzD9KHZnD+VD447oEaeQqd2dA9qfWufDmoos3pJAIAcQ6gCkDO6/SH99LmNeujlrQpFoqPuK3IV6NAZZVq2sEEXLpktdyHdz/miyF2g4+fV6Jn3duuZd3cTqgAAKUeoApD1gkMR/fLlrfrJcxvVMxiWJB08rUxHN1Vo0cwKHT6zQvPqSlTgpO0rX516cJ2eeW+3nn1vt644ZZ7VywEA5BhCFYCsFY0a+vOanfre39Zre9egJGlBfamu/9hBOvnAWvbOIOHU+HlVbzR3qXMgpCr20QEAUohQBSDrDASH9PjqHfrVqq16ryU2Er2+zKNrPrpAnzp6JhUp7GVaeZEOmVamdbt69fz63frkUTOtXhIAIIcQqgBkjfdb+/Tw37fq92/sUH9wSJJU4inU5SfN1SXHz1WRu8DiFcLOTju4Tut29eqZ9whVAIDUIlQBsLW+QFgr1rXq0de26dXNnYnb59T4dMHiWTr36JmqKKaVC/t36kF1+vGzG/Xi+jaFI1G5ChhWAgBIDUIVANsZDEX07Hu79cSanXr2vd0KDsUm+RU4HfrowfX6/IebdNwB1XLS5odJOHxmhap9bnUMhPTa5k4dN6/G6iUBAHIEoQqALQSHIlr5frv+vGanVqxrlX/EuVJza3w664jp+syHGjWtvMjCVSKbOZ0OnXJQnf739e165r3dhCoAQMpkXe9DMBjUEUccIYfDodWrV4+6b82aNTrhhBPk9XrV2NioO++805pFApiQoUhUKze06ev/+5Y+9B9P659/+Q/9cfVO+UMRzaws0uUnHaD/d+Xxeuaak/RvSw8kUCFpSw+OTQF89r3dFq8EAJBLsq5S9fWvf13Tp0/XW2+9Ner23t5enX766Vq6dKnuvfderV27Vl/84hdVUVGhyy67zKLVAjAZhqEuf1hbOgbU3OHX61u79Je1u9QxEEo8pq7Uo48vmqazDp+uIxorGImOlDt+fq1cBQ5tbh/Qxt19mldXavWSAAA5IKtC1V//+lc99dRT+t3vfqe//vWvo+575JFHFAqFdP/998vtdmvhwoVavXq17rrrLkIVkCaGYWhrh18rN7Tpre09ikSNvR4TCEfU3OlXc4dfffGJfSNVFrv0scOm6czDp+tDs6sYh460KvEU6vh5NXpufZv+tHqnrj59gdVLAgDkgKwJVa2trbr00kv1+OOPq7i4eK/7V61apRNPPFFu9/AUsGXLlumOO+5QV1eXKisrM7lcIGUMw5A/FFH3YFg9/rC6B0Pq8YfVMxiW0+FQebFL5UUuVcT/W17kkqdw7NHiqQgsvYGwXt7YoZUb2vTihjZt6xyc1Oc3lHk1q7pY8+pKtGxhg447oJopbMios4+coefWt+nx1Tt11UcPpCIKAEhaVoQqwzB00UUX6fLLL9cxxxyjLVu27PWYlpYWzZkzZ9Rt9fX1ifvGC1XBYFDBYDDx+97e3tQtHJiE1t6A3m/t09YOv5o7/draMaCtHX5t6/RrYMTQhmR4Cp17BDC3yotcchXs/Y/KqGGoLzCknsGwuuMhrmcwnDgfyuQqcOiYpiotnlulEs/e31IKnQ7NrCxWU3WxGquK5XVxlhSs9dFD6lXsLlBzp19vbuvWUbP4oRsAIDmWhqrrr79ed9xxxz4f8+677+qpp55SX1+fbrjhhpSv4fbbb9e3vvWtlD8vsC9dAyGt2dGjNdu69db2Hq3d0a3W3uA+P8dd4FR5sUsVRcMVqahhxEJPoooVHrMFzxQcimp3X1C7+/b9Wvszt9anE+fX6sQDa7R4TrV8Y4QpwK6K3YU6/ZB6Pb56p/745g5CFQAgaQ7DMMb/F1iatbW1qaOjY5+PmTt3rj796U/rz3/+86gWjUgkooKCAl1wwQV66KGHdOGFF6q3t1ePP/544jHPPfecTj31VHV2dk6qUtXY2Kienh6VlZUl9wVmSHAooh5/rIJQW+pRqddl9ZJsIRo1tKl9QGu2d2t9a5/Ki1xqqvKpqTpWNUn1n9NgKKJ3dvbore09WrO9W2u292h7l3/Mx4Yje/+1czqk2TU+zan2aVZ1sZqqitVUHVtvQ7lXRa6C/bYpGYahgVBE4fi5TqPukzQQHEpUnMzqU/dgSNExgpjD4VCJp3BUW2FFsVuVxS4O20XWe279bl38wGuq9rn1938/jRZUAMCYent7VV5evt9sYOmPl2tra1VbW7vfx91zzz36j//4j8Tvd+7cqWXLlumxxx7T4sWLJUlLlizRjTfeqHA4LJcr9o/lFStWaMGCBfvcT+XxeOTxeJL8StKnZzCs5g6/tnbGWsGa461hXf5Q4h/Fg+HRrWFVPrdmVRVrdnWxZlX7NLOySJ7Cif+DwelwqMz8R3S8VcwMIDu7B9Xc6U9McNva4VfUMPTRQ+p1+sIGlRelL9C19AQSYeWdnT2KGEqsz/xHf4mnUJvbB7Rme4/e3tEz5mAEU5XPrcaqYtX4Yi1wib1J8fBg3jayMuR0ONTaF0i8F+b7snF3v95v7dM+ikR7mVPj06KZ5Vo0s0KLZpZr4fQyFbuT+ytpBiGN8790lc+txqReAcgNJ8yrSRwE/NLGdp2yoM7qJQEAspillaqp2rJli+bMmaM333xTRxxxhCSpp6dHCxYs0Omnn67rrrtOb7/9tr74xS/qhz/84aSm/000jabbinWt+vr/vqUuf3hCj3c6Yi0te+53SaUCp2OfrWXuAqdOWlCrMw+frqUH100pIBiGofb+kJrjYWVrh1/v7OzVmu3dU2pZ87qcWji9XIdMK1NfIKyt8Sl0I8d4T0ah06GhffwZ1JV6tGhmhQ6fWa7DZpZrXl2JCp17B1qfp4CKImCxm//4th5atVVnHzFdP/rskVYvBwBgQ1lRqUql8vJyPfXUU7riiit09NFHq6amRjfddFPWjlMv9RYmAlVNiSfWslZVrFnVxZpVVazaUo8qioarKaWeQjmdDvUFwonx1Vs6/GruHNCO7sCY7V3jGYpG1TtoDigIJYYkRKKG3AVOzawq0uxqn2ZVxdro+gJD+vNbO7Vhd79WrGvVinWtKnIV6IjGChWOMQBhLIYhdQyE1NwxMO5QBqdDOrC+VItmluuwmRXyFjr3amXrDYQ1vaJIh8crQPPrSlQ4RluP+ee0rXNQ3f5QbE9S/Hl6B8Pq8ocSz93jDycqXkNRQwVOh2ZWFsWrgbH2vNnVPh02s1z1Zd4J/zkDsNYnjpyhh1Zt1VPrWuUPDSVdKQYA5K+srFSlk10qVf7QkLa0+9VUXWz5EIDQUFS9gbDCkajqSr3jjuVe39KnP7+1U39es1NbO8beSzQRDoc0LT52u6nKp/n1JTq8sSIl7XFTNRSJqjcwpMFwRPWlnjGDGoDsYhiGTv7+89ra4dfdnz1CnzhihtVLAgDYzESzAaFqD3YJVdnMMAyt3dGjTW0Dk/q8sqJCzaqK7QFj7DaATLjrqfW659mNOmVBrR64+FirlwMAsJm8a/+DfTgcjvjwhQqrlwIA+/SJI2fonmc36sUN7eroD6q6xL6DiwAA9kUPEwAgbx1QW6LDZpQrEjX0/9busno5AIAsRagCAOS1TxwxXZL0+Js7LF4JACBbEaoAAHntrMOny+GQ3mjuVnMSQ3YAAPmLUAUAyGt1ZV4dd0C1JOmPq6lWAQAmj1AFAMh75jj1x1fvEENxAQCTRagCAOS95Yc2yFPo1AdtA1q1qcPq5QAAsgyhCgCQ98q8Ln36mEZJ0k+e3WjxagAA2YZQBQCApH85aa4KnQ69/EGHXt/aafVyAABZhFAFAICkmZXF+uRRsb1VVKsAAJNBqAIAIO7LJ8+T0yE9t75Nb+/osXo5AIAsQagCACBudo1PZx0eOwz4x89usHg1AIBsQagCAGCEK06ZJ4dD+ts7rVrf0mf1cgAAWYBQBQDACPPrS3XGoQ2SpJ8+x94qAMD+EaoAANjDFafMkyQ9sWanNrX1W7waAIDdEaoAANjDwunlOu2gOkUN6T+f/8Dq5QAAbI5QBQDAGL5yaqxa9Yc3d2hbp9/i1QBAfugaCOn59bvVMxi2eimTUmj1AgAAsKMjZ1XqhPk1WrmhXfe+8IFuO+cwq5cEADmlPziktdt7tGZ7t9bsiP13W+egJOkXXzhGpx1cb/EKJ45QBQDAOL5yyjyt3NCu3/5jmy45fo7m1pZYvSQAyFqhoajeaO7Syg1tWrmhXWt39Mgw9n7c3BqfgkPRzC8wCYQqAADGsXhutU5eUKvn17fp5j+9o19+8Vg5HA6rlwUAthUIR9QzGFa3Pxz/b0g7ugf1fxvbteqDDg2EIqMeP73cq0UzK7SosVyHz6zQoTPKVV7ksmj1U0eoAgBgH7511kJ99IcvauWGdv1lbYs+vmia1UsCAMtFo4Y2tQ/EWvfiLXzvtfTJv0do2lO1z63j59fohPm1OmF+jerLvBlacXoRqgAA2Iemap++fPIB+tHTG/TtJ97RSQtqVeLh8gkgu3T0B/XOzl4VOh2aVV2saeVFKnCOXXkfCA5pa4dfzZ1+dQ6EYhWnwZB64xWojv6Q3t3Vq77g0JifX+B0qLzIpYoil8qKXKr2uXX07EqdOL9Wh0wrk3Oc181mXBUAANiPy086QH94c4e2dvh199Pv68aPH2L1kgDkoNBQVG39wTHv6/aH1Nzh19ZOfzzwDKi50y/DkGZVFaupulhN1T41VRVrVnWxegbDiQrSW9t6tKN7cNTzuQucmllZpFnVxZpVVaz+wJC2dMSes70/NKH1el1OLZxerkUzh1v36ss8KvEU5l2rtMMwxtoelr96e3tVXl6unp4elZWVWb0cAIBNPLd+ty5+4DUVOB36y5UnaEFDqdVLAmBjPf6wtnYOqNsf1vSKIjVWFclTWDDqMYZhaHP7gFZuaNfKDW1j7jlKpbm1PknStk6/wpF9R4DKYpdmVftUW+JWeZE7VnkqdiX+e2B9qebXlaiwILdPaJpoNqBSBQDABJyyoE7LFzboyXda9M3H39Zj//LhvPtJLJDvDMNQ50BIXf6wegZDiYEM3f6wOgdC2trpV3PHgLZ2+tXtH33OksMhTS8vSlSVJOmlje3a3jW6glTodIzZHudzF2hWtU+zq4vj1SifmqqL5ZC0NV7BMl+7ucOvIneBFs0sjw2BmFmuw2aUq9QbGwARiRra1TOYqHxt6/SrxFuopqrYc86qLlaZN/uGRViJStUeqFQBAMazs3tQp/3gBQ2GI/rBeYfrU0fPtHpJAMZhGIb6g0OJKXS9g2F5XAWjKi6uEVWWaNRQX2AosX+ocyCkbV2Dau4Y0JaOWFBp7vRrMDzxSlJtqUeVxS7t6BoctwLlLnDqmNmVicENubrnKFtRqQIAIMWmVxTpytPm644n39Ptf31XSw+uV3kxP80FrBYIR/Tq5k6t3NCm/9vYoZbegHoGw4pE91078LkLVOp1aTAcUW8gPOaZSXtyOKRST6Eqiodb4sqKXLF2uapizTKrPVXF8sWH2hiGoY6BUGIv1NaOWDj78JxqLZ5bpWI3/yTPdlSq9kClCgCwL6GhqD52z0pt3N2vz394lv7j7MOsXhKQs8wpdP1jTJmLRA29vaNHL25o06ubO8c9LNZd6ExMoQsORdTtD6svMPbUOkkqchUkKlkzK4uGQ1K87W5mZbHchbm9jwjDqFQBAJAG7kKnbv3Eofrcf/1dD/+9WR+eW63/b9F0q5cFZKXhCk6semOO8d46ySl0ktRQ5tUJ82t0/PwaLWgoVUWRWxXFLnldBXs9NhI11DsYbwsMhFXsLlBZUSxI7TlMApgIQhUAAJO05IBqXXbiXN334iZd+z9rNLemRIdMp7sB9hQcimhLu1+VPpdqSzz7HLBiGIba+0Pa3RdQiadQ5UUulXpd455nNBGRqKGd3YPxYQoDseEIIwYr7G/aXZXPrYoilzTGEhori3XC/BqddGCt5tWVTHh4TIHToUqfW5U+91S+JGAvhCoAAKbguuUH6d1dvVq5oV2X/vIf+vO/Hq8q/oEGiw1Fotqwuz92NtH2Hq3d3qP3WnoT47OL3QXxfT+xCXT1ZV619QX3GXLG2kNUXuQa9WuvqyBxMGxs0EPsv219QW3v2vf47j2n4sXa7Hzxc5eKExPrADtjT9Ue2FMFAJioHn9YZ/30JW3t8GvJ3Gr98pJjR00TA5JhGIZ2dA+qtTeonsHQcGCJ/zf261AixJihZmiM4QylnkINhIa0n7kNkmIhp9rnlj8UkT9FZya5C5xqrCpSU7VvxEG1saEOY53fBNgFe6oAAEiz8mKX/uvCY3TOT/9PqzZ16Lb/965uOWuh1ctCltrdG4hXl+JVph096hyY+J4iU4mnUIfNKNeixnItmhE7o2hmZZHCkVhI29Ix3ILX2htQbaknEXKaqn2aWTkcckJD0USAGxns9gx3gXBEZd7hSXhmBavK51ZTtU8NZd6kWggBu6NStQcqVQCAyfrbOy36l1+9Lkm689xF+vQxjRavCHbVFwjr/db+xFht8/DVrR0DYw5lcBU41FDuTQxdKCtyqWJE611FkXtUiKkodqm+1Ms5R0CKUKkCACBDli1s0L8tna8fPb1B3/jD2zqg1qejm6qsXhbSKBo11NIb0NYOvwJDETVWFo/ZxjYUiWrNjh6tfL9dL25o0+pt3eOeneR0SPPqSrRoZoUOn1muRTMrdNC0UlrjgCxAqAIAIAWuPHW+1u3s1VPrWvXZ+/6uz3+4SVeeOp/pYlksOBTR9q7BeKvcQHyQQ3ygQ6dfoT3ORXI4pGll3sSghd5AWP+3sV29e5yJNK3cG2u1q/LFHhv/9dxaX+KwWADZhfa/PdD+BwCYqv7gkL7y6zf0/Po2SVKpt1BXnDJPFx03e8yzcmAP0aihLR0DWrujR29ti03L29rh186eQe3rX0mFTodmVhbJ6yrQtk7/uKPBy4tcOn5eTeIMpZmVxWn6SgCk2kSzAaFqD4QqAECyXny/Td/5y7t6r6VPkjSjokhfW3agPnH4DPa6WMgwjNj48E6/trQP6IO2Aa3d0a0123vUt0c1yWSOIDeHOJi/nl3t07Ryrwrj0x6HD7H1J/ZLFTod+si8Gi2aWcGQBiBLEaqmiFAFAEiFSNTQH97coR88tV67egKSpGNnV+mezx2phnKvxavLfYZhaMPufr34fpte29KpLe2xlr3B8NjVJE+hU4dML9PhMyu0cHqZ5tTEWvP2d1gugNxGqJoiQhUAIJUC4Yju/7/N+umzGzUQiqja59YPP3OETjyw1uql5ZzOgZBe2tiule+3aeWGdrX0BvZ6jNMhTa8oSlSbDp1RrkUzy3VgfSlnjAHYC6FqighVAIB02Nw+oCseeUPrdvXK4ZD+9ZR5+urSA2kLG4NhGOr2h7Wta+9hEJI0FDW0s3sw3moXGyLR3OnfayS5p9CpxXOrdfy8ah1YX6qmap9mVBTJXUh4AjAxhKopIlQBANIlEI7o20+s069faZYkLZlbrbs/d4TqSvOzHXAoEtXGtn6t3d6jTe3xA2nj+5HG2+O0Pwc1lOrEA2t1wvwafWh2FQNCACSFUDVFhCoAQLr9cfUO3fD7tfKHIqop8ejaZQdq+aHTVF7ksnppKWcYhgLhqLoHQ+oaCGvD7j69ta1Ha7Z3652dvePucZKk+jKPit17jxh3SKor86ipyqemmtg48qbqYs2qLlaZN/f+DAFYh1A1RYQqAEAmbNzdryseeUPrW2MTAl0FDp10YK3OPHy6lh5cb/vzinb3BbR2e4+2dw2q2x9Wz2BY3YMh9Q6G1e0Pq3swdluPP6xQZO8WPpPPXaBDZ5RrQUNpfLKeT7Ori9VYVUyVCYDlCFVTRKgCAGTKYCiiB17erD+t3pkYvy5JXpdTpx5Up6UH1+v4+TWWtwf2+MNaEx89/ta2bq3d0ZOYaDhRhU6HyotcmlVdrMNnVuiwGeU6vLFcc2tKGDMPwLYIVVNEqAIAWOH91j498dZO/XnNLm1uHxh130ENpTrpwFqdML9Wx8yunHIFJxI1tKsnNuCh2x9WqbdQFcUulRe5VFHkVqm3UIPhiN7e0RM7CHd7rE1va4d/r+dyOKR5tSWaV1eiimJ37DkSzxX7b3mxK3Gfz13AaHIAWYdQNUWEKgCAlQzD0Ds7e/WXtbv04oY2vb2jd9T9XpdTx86p1onza3TigbWaX1eyV1gZikT1fmu/1u7o1ru7+rSlIzYEYluXX+HI+Jd982nG+pfBrKpiLZpZrsNnVmjRzHIdOqPc9i2KAJAsQtUUEaoAAHbS0R/USxvb9eL77Vq5oU27+4Kj7m8o8+qE+TVa1FihTW39WrO9R+/s7FEgPPY+JleBQ41VxarxedQbCMf2QA2G5Q9FRj3nopnlOrwx1qa3aGa5Kordaf06AcCOCFVTRKgCANiVYRha39qnle+368UNbXp1c6eCY5zjJEmlnkIdOqNch84o05yaEs2OT8ebVl405tlYwaGIegbDcjocqinxpPtLAYCsQKiaIkIVACBbBMIRvbalUy++36b3Wvp0QG2JDm8s16KZFZpT7WMABAAkaaLZgGZoAACylNdVoBPmxwZYAACs47R6AQAAAACQzQhVAAAAAJAEQhUAAAAAJIFQBQAAAABJIFQBAAAAQBIIVQAAAACQBEIVAAAAACSBUAUAAAAASSBUAQAAAEASCFUAAAAAkARCFQAAAAAkgVAFAAAAAEkgVAEAAABAEghVAAAAAJAEQhUAAAAAJIFQBQAAAABJIFQBAAAAQBIIVQAAAACQBEIVAAAAACSBUAUAAAAASSBUAQAAAEASCFUAAAAAkARCFQAAAAAkgVAFAAAAAEkgVAEAAABAEghVAAAAAJAEQhUAAAAAJIFQBQAAAABJIFQBAAAAQBIIVQAAAACQBEIVAAAAACSBUAUAAAAASSi0egF2YxiGJKm3t9filQAAAACwkpkJzIwwHkLVHvr6+iRJjY2NFq8EAAAAgB309fWpvLx83Psdxv5iV56JRqPauXOnSktL5XA4LF1Lb2+vGhsbtW3bNpWVlVm6FqQO72vu4r3NTbyvuYn3NXfx3uYmq95XwzDU19en6dOny+kcf+cUlao9OJ1OzZw50+pljFJWVsY3hRzE+5q7eG9zE+9rbuJ9zV28t7nJivd1XxUqE4MqAAAAACAJhCoAAAAASAKhysY8Ho9uvvlmeTweq5eCFOJ9zV28t7mJ9zU38b7mLt7b3GT395VBFQAAAACQBCpVAAAAAJAEQhUAAAAAJIFQBQAAAABJIFQBAAAAQBIIVRnW19enf/u3f1NTU5OKiop03HHH6bXXXkvcbxiGbrrpJk2bNk1FRUVaunSpNmzYMOo5Ojs7dcEFF6isrEwVFRW65JJL1N/fn+kvBXtIxXs7e/ZsORyOUR/f/e53M/2lYIT9va+///3vdfrpp6u6uloOh0OrV6/e6zkCgYCuuOIKVVdXq6SkRJ/61KfU2tqawa8CY0nFe3vyySfv9Xf28ssvz+BXgT3t630Nh8O67rrrdNhhh8nn82n69Om68MILtXPnzlHPwXXWflLxvnKNtaf9fS++5ZZbdNBBB8nn86myslJLly7VK6+8Muo57PB3llCVYf/8z/+sFStW6Fe/+pXWrl2r008/XUuXLtWOHTskSXfeeafuuece3XvvvXrllVfk8/m0bNkyBQKBxHNccMEFeuedd7RixQo98cQTevHFF3XZZZdZ9SUhLhXvrSR9+9vf1q5duxIf//qv/2rFl4O4/b2vAwMDOv7443XHHXeM+xxXXXWV/vznP+t//ud/9MILL2jnzp365Cc/makvAeNIxXsrSZdeeumov7N33nlnJpaPcezrffX7/XrjjTf0zW9+U2+88YZ+//vfa/369TrrrLNGPQfXWftJxfsqcY21o/19Lz7wwAP1k5/8RGvXrtVLL72k2bNn6/TTT1dbW1viOWzxd9ZAxvj9fqOgoMB44oknRt1+1FFHGTfeeKMRjUaNhoYG43vf+17ivu7ubsPj8Ri/+c1vDMMwjHXr1hmSjNdeey3xmL/+9a+Gw+EwduzYkZkvBHtJxXtrGIbR1NRk/PCHP8zUsrEf+3tfR9q8ebMhyXjzzTdH3d7d3W24XC7jf/7nfxK3vfvuu4YkY9WqVWlbO/YtFe+tYRjGSSedZHz1q19N40oxGZN5X02vvvqqIcnYunWrYRhcZ+0oFe+rYXCNtaOpvLc9PT2GJOPpp582DMM+f2epVGXQ0NCQIpGIvF7vqNuLior00ksvafPmzWppadHSpUsT95WXl2vx4sVatWqVJGnVqlWqqKjQMccck3jM0qVL5XQ69yqFInNS8d6avvvd76q6ulpHHnmkvve972loaCgjXwP2tr/3dSJef/11hcPhUe/9QQcdpFmzZu313iNzUvHemh555BHV1NTo0EMP1Q033CC/35/KpWISpvK+9vT0yOFwqKKiQhLXWTtKxftq4hprL5N9b0OhkO677z6Vl5fr8MMPl2Sfv7OFGXslqLS0VEuWLNGtt96qgw8+WPX19frNb36jVatWad68eWppaZEk1dfXj/q8+vr6xH0tLS2qq6sbdX9hYaGqqqoSj0HmpeK9laQrr7xSRx11lKqqqvTyyy/rhhtu0K5du3TXXXdl9OtBzP7e14loaWmR2+3e68K+53uPzErFeytJ559/vpqamjR9+nStWbNG1113ndavX6/f//73aVw9xjPZ9zUQCOi6667T5z73OZWVlUniOmtHqXhfJa6xdjTR9/aJJ57QZz/7Wfn9fk2bNk0rVqxQTU2NJPv8naVSlWG/+tWvZBiGZsyYIY/Ho3vuuUef+9zn5HTyVmS7VLy3V199tU4++WQtWrRIl19+uX7wgx/oxz/+sYLBYBpXjn3h72zuSsV7e9lll2nZsmU67LDDdMEFF+iXv/yl/vCHP+iDDz5I48qxLxN9X8PhsD796U/LMAz97Gc/s2i1mKhUvK9cY+1pIu/tKaecotWrV+vll1/W8uXL9elPf1q7d++2cNV7418FGXbAAQfohRdeUH9/v7Zt26ZXX31V4XBYc+fOVUNDgyTtNRWstbU1cV9DQ8Ne/xMNDQ2ps7Mz8RhYI9n3diyLFy/W0NCQtmzZks6lYx/29b5ORENDg0KhkLq7u0fdvr/3HumX7Hs7lsWLF0uSNm7cmKplYpIm8r6a//DeunWrVqxYMaqawXXWnpJ9X8fCNdYeJvLe+nw+zZs3Tx/+8If1i1/8QoWFhfrFL34hyT5/ZwlVFvH5fJo2bZq6urr0t7/9TZ/4xCc0Z84cNTQ06Jlnnkk8rre3V6+88oqWLFkiSVqyZIm6u7v1+uuvJx7z7LPPKhqNJi7msNZU39uxrF69Wk6nc6+yNjJvrPd1Io4++mi5XK5R7/369evV3Ny8z/cemTPV93Ys5tj1adOmpWh1mKrx3lfzH94bNmzQ008/rerq6lGfx3XW3qb6vo6Fa6y9TOZ7cTQaTVQY7fJ31mEYhpGxV4P+9re/yTAMLViwQBs3btS1114rr9erlStXyuVy6Y477tB3v/tdPfTQQ5ozZ46++c1vas2aNVq3bl1iE98ZZ5yh1tZW3XvvvQqHw7r44ot1zDHH6Ne//rXFX11+S/a9XbVqlV555RWdcsopKi0t1apVq3TVVVfpjDPO0EMPPWT1l5e39ve+dnZ2qrm5WTt37tTHP/5xPfroo1qwYIEaGhoSPyH70pe+pL/85S968MEHVVZWlhjh+/LLL1v5peW9ZN/bDz74QL/+9a/1sY99TNXV1VqzZo2uuuoqzZw5Uy+88ILVX17e2tf7Kknnnnuu3njjDT3xxBOj9rlWVVXJ7XZL4jprR8m+r1xj7Wtf720oFNJtt92ms846S9OmTVN7e7t++tOf6te//rVef/11LVy4UJJN/s5mbM4gDMMwjMcee8yYO3eu4Xa7jYaGBuOKK64wuru7E/dHo1Hjm9/8plFfX294PB7jtNNOM9avXz/qOTo6OozPfe5zRklJiVFWVmZcfPHFRl9fX6a/FOwh2ff29ddfNxYvXmyUl5cbXq/XOPjgg43vfOc7RiAQsOLLQdz+3tcHHnjAkLTXx80335x4zODgoPHlL3/ZqKysNIqLi41zzjnH2LVrlwVfDUZK9r1tbm42TjzxRKOqqsrweDzGvHnzjGuvvdbo6emx6CuCYez7fTXH44/18dxzzyWeg+us/ST7vnKNta99vbeDg4PGOeecY0yfPt1wu93GtGnTjLPOOst49dVXRz2HHf7OUqkCAAAAgCSwpwoAAAAAkkCoAgAAAIAkEKoAAAAAIAmEKgAAAABIAqEKAAAAAJJAqAIAAACAJBCqAAAAACAJhCoAAAAASAKhCgCQFZ5//nk5HA51d3db8vrPPPOMDj74YEUikbS9xoc//GH97ne/S9vzAwDSw2EYhmH1IgAAGOnkk0/WEUccoR/96EeJ20KhkDo7O1VfXy+Hw5HxNR199NG6+uqrdcEFF6TtNZ544gldddVVWr9+vZxOfu4JANmC79gAgKzgdrvV0NBgSaB66aWX9MEHH+hTn/pUWl/njDPOUF9fn/7617+m9XUAAKlFqAIA2MpFF12kF154QXfffbccDoccDoe2bNmyV/vfgw8+qIqKCj3xxBNasGCBiouLde6558rv9+uhhx7S7NmzVVlZqSuvvHJUy14wGNTXvvY1zZgxQz6fT4sXL9bzzz+/zzU9+uij+uhHPyqv15u47ZZbbtERRxyh+++/X7NmzVJJSYm+/OUvKxKJ6M4771RDQ4Pq6up02223JT7HMAzdcsstmjVrljwej6ZPn64rr7wycX9BQYE+9rGP6dFHH03NHyYAICMKrV4AAAAj3X333Xr//fd16KGH6tvf/rYkqba2Vlu2bNnrsX6/X/fcc48effRR9fX16ZOf/KTOOeccVVRU6C9/+Ys2bdqkT33qU/rIRz6iz3zmM5Kkr3zlK1q3bp0effRRTZ8+XX/4wx+0fPlyrV27VvPnzx9zTStXrtT555+/1+0ffPCB/vrXv+rJJ5/UBx98oHPPPVebNm3SgQceqBdeeEEvv/yyvvjFL2rp0qVavHixfve73+mHP/yhHn30US1cuFAtLS166623Rj3nscceq+9+97tJ/ikCADKJUAUAsJXy8nK53W4VFxeroaFhn48Nh8P62c9+pgMOOECSdO655+pXv/qVWltbVVJSokMOOUSnnHKKnnvuOX3mM59Rc3OzHnjgATU3N2v69OmSpK997Wt68skn9cADD+g73/nOmK+zdevWxONHikajuv/++1VaWpp4rfXr1+svf/mLnE6nFixYoDvuuEPPPfecFi9erObmZjU0NGjp0qVyuVyaNWuWjj322FHPOX36dG3btk3RaJR9VQCQJfhuDQDIWsXFxYlAJUn19fWaPXu2SkpKRt22e/duSdLatWsViUR04IEHqqSkJPHxwgsv6IMPPhj3dQYHB0e1/plmz56t0tLSUa91yCGHjApDI1//vPPO0+DgoObOnatLL71Uf/jDHzQ0NDTqOYuKihSNRhUMBif5pwEAsAqVKgBA1nK5XKN+73A4xrwtGo1Kkvr7+1VQUKDXX39dBQUFox43MojtqaamRl1dXUm/fmNjo9avX6+nn35aK1as0Je//GV973vf0wsvvJD4vM7OTvl8PhUVFe3rSwcA2AihCgBgO263Oy3nQR155JGKRCLavXu3TjjhhEl93rp161KyhqKiIp155pk688wzdcUVV+iggw7S2rVrddRRR0mS3n77bR155JEpeS0AQGYQqgAAtjN79my98sor2rJli0pKSlRVVZWS5z3wwAN1wQUX6MILL9QPfvADHXnkkWpra9MzzzyjRYsW6eMf//iYn7ds2TI99NBDSb/+gw8+qEgkosWLF6u4uFgPP/ywioqK1NTUlHjMypUrdfrppyf9WgCAzGFPFQDAdr72ta+poKBAhxxyiGpra9Xc3Jyy537ggQd04YUX6pprrtGCBQt09tln67XXXtOsWbPG/ZwLLrhA77zzjtavX5/Ua1dUVOi//uu/9JGPfESLFi3S008/rT//+c+qrq6WJO3YsUMvv/yyLr744qReBwCQWQ7DMAyrFwEAgN1de+216u3t1c9//vO0vcZ1112nrq4u3XfffWl7DQBA6lGpAgBgAm688UY1NTUlhk6kQ11dnW699da0PT8AID2oVAEAAABAEqhUAQAAAEASCFUAAAAAkARCFQAAAAAkgVAFAAAAAEkgVAEAAABAEghVAAAAAJAEQhUAAAAAJIFQBQAAAABJIFQBAAAAQBL+f/P3G6rkWLRUAAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -272,7 +317,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/examples/deap/GranuleCell1/GranuleCellDeap1-scoop.py b/examples/deap/GranuleCell1/GranuleCellDeap1-scoop.py index d867a2a7..60cbab51 100644 --- a/examples/deap/GranuleCell1/GranuleCellDeap1-scoop.py +++ b/examples/deap/GranuleCell1/GranuleCellDeap1-scoop.py @@ -58,7 +58,7 @@ def get_features(conductances): trace['stim_end'] = [500] traces = [trace] # a think is a list from a dictionary - result = efel.getFeatureValues(traces, FEATURES, raise_warnings=False) + result = efel.get_feature_values(traces, FEATURES, raise_warnings=False) for feature_name, feature_list in result[0].items(): if feature_list is not None and len(feature_list) > 0: diff --git a/examples/deap/GranuleCell1/GranuleCellDeap1.ipynb b/examples/deap/GranuleCell1/GranuleCellDeap1.ipynb index 195a2525..07ad2690 100644 --- a/examples/deap/GranuleCell1/GranuleCellDeap1.ipynb +++ b/examples/deap/GranuleCell1/GranuleCellDeap1.ipynb @@ -97,7 +97,7 @@ " trace['stim_end'] = [500]\n", " traces = [trace]\n", "\n", - " result = efel.getFeatureValues(\n", + " result = efel.get_feature_values(\n", " traces, [\n", " 'voltage_base', 'spike_count', 'min_voltage_between_spikes', 'AP_height', 'steady_state_voltage_stimend'])\n", "\n", diff --git a/examples/deap/deap_efel_eval1.py b/examples/deap/deap_efel_eval1.py index 6e1b4d3e..f3a0b903 100644 --- a/examples/deap/deap_efel_eval1.py +++ b/examples/deap/deap_efel_eval1.py @@ -50,7 +50,7 @@ def evaluate(individual, target_voltage1=-80, target_voltage2=-60): trace['stim_end'] = [stim_end] traces = [trace] - features = efel.getFeatureValues(traces, ["voltage_base", + features = efel.get_feature_values(traces, ["voltage_base", "steady_state_voltage"]) voltage_base = features[0]["voltage_base"][0] steady_state_voltage = features[0]["steady_state_voltage"][0] diff --git a/examples/deap/deap_efel_eval2.py b/examples/deap/deap_efel_eval2.py index f967a18c..be35f142 100644 --- a/examples/deap/deap_efel_eval2.py +++ b/examples/deap/deap_efel_eval2.py @@ -53,7 +53,7 @@ def evaluate(individual, target_voltage1=-80, target_voltage2=-60): trace['stim_end'] = [stim_end] traces = [trace] - features = efel.getFeatureValues(traces, ["voltage_base", + features = efel.get_feature_values(traces, ["voltage_base", "steady_state_voltage"]) voltage_base = features[0]["voltage_base"][0] steady_state_voltage = features[0]["steady_state_voltage"][0] diff --git a/examples/deap/deap_efel_neuron2.ipynb b/examples/deap/deap_efel_neuron2.ipynb index a8c6352d..dc43a5e4 100644 --- a/examples/deap/deap_efel_neuron2.ipynb +++ b/examples/deap/deap_efel_neuron2.ipynb @@ -89,7 +89,7 @@ " trace['stim_end'] = [stim_end]\n", " traces = [trace]\n", "\n", - " features = efel.getFeatureValues(traces, [\"voltage_base\",\n", + " features = efel.get_feature_values(traces, [\"voltage_base\",\n", " \"steady_state_voltage_stimend\"])\n", " voltage_base = features[0][\"voltage_base\"][0]\n", " steady_state_voltage = features[0][\"steady_state_voltage_stimend\"][0]\n", diff --git a/examples/neo/load_nwb.ipynb b/examples/neo/load_nwb.ipynb new file mode 100644 index 00000000..ae998456 --- /dev/null +++ b/examples/neo/load_nwb.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Loading NWB files using Neo\n", + "\n", + "This notebook demonstrates how to load electrophysiology data using Neo for eFEL eFeatures extraction." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import efel\n", + "\n", + "import numpy\n", + "\n", + "%matplotlib notebook\n", + "%matplotlib inline\n", + "from matplotlib import pyplot as plt\n", + "plt.rcParams['figure.figsize'] = 10, 10" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use the voltage trace recordings obtained from a mouse thalamic cell classified as a bursting accommodating (bAC) etype. The data is stored in an NWB (Neurodata Without Borders) file, and we will use the [Python Neo](https://github.com/NeuralEnsemble/python-neo) library to load it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "test_data = \"../../tests/testdata/JY180308_A_1.nwb\"\n", + "stim_start = 250\n", + "stim_end = 600\n", + "blocks = efel.io.load_neo_file(test_data, stim_start, stim_end)\n", + "traces = blocks[0][0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get a trace that includes a burst" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "trace = traces[5]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "time = trace['T']\n", + "voltage = trace['V']" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = 10, 10\n", + "\n", + "fig1, ax1 = plt.subplots(1)\n", + "ax1.plot(time, voltage)\n", + "ax1.set_xlabel('Time (ms)')\n", + "ax1.set_ylabel('Membrane voltage (mV)');" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now use the eFEL to extract eFeature values from the trace shown above.\n", + "\n", + "We will use the ``get_feature_values()`` function, which accepts a list of trace and the requested eFeature names as input.\n", + "\n", + "Let's extract some burst-related features" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'spikes_per_burst': [4],\n", + " 'strict_burst_number': [1],\n", + " 'strict_burst_mean_freq': [28.964518464873937],\n", + " 'peak_time': [330.4000000000066,\n", + " 343.20000000000954,\n", + " 382.7000000000185,\n", + " 468.500000000038],\n", + " 'AP_height': [11.400000572174852,\n", + " 11.712499618465365,\n", + " 12.237500190711486,\n", + " 12.19999980918174],\n", + " 'peak_indices': [3304, 3432, 3827, 4685],\n", + " 'burst_begin_indices': [0],\n", + " 'burst_end_indices': [3]}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "efel.set_int_setting(\"ignore_first_ISI\", 0) # Don't ignore the first spike\n", + "efel.set_double_setting(\"strict_burst_factor\", 4.0) # The burst detection can be fine-tuned by changing the setting strict_burst_factor. Default value is 2.0.\n", + "feature_values = efel.get_feature_values([trace], ['spikes_per_burst', 'strict_burst_number', 'strict_burst_mean_freq', 'peak_time', 'AP_height', 'peak_indices', 'burst_begin_indices', 'burst_end_indices'])[0]\n", + "feature_values = {feature_name: list(values) for feature_name, values in feature_values.items()}\n", + "feature_values" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "burst_begin_indices = feature_values['burst_begin_indices'][0]\n", + "burst_end_indices = feature_values['burst_end_indices'][0]\n", + "peak_times = feature_values['peak_time']\n", + "ap_heights = feature_values['AP_height']\n", + "burst_mean_freq = feature_values['strict_burst_mean_freq']\n", + "\n", + "time_spike_indices = numpy.where((time > stim_start) & (time < stim_end))\n", + "time_spike = time[time_spike_indices]\n", + "voltage_spike = voltage[time_spike_indices]\n", + "\n", + "plt.figure(figsize=(10, 6))\n", + "plt.plot(time_spike, voltage_spike, label='Voltage Trace')\n", + "\n", + "burst_start = peak_times[burst_begin_indices]\n", + "burst_end = peak_times[burst_end_indices]\n", + "mean_frequency = burst_mean_freq[0]\n", + "\n", + "plt.axvspan(burst_start, burst_end, color='yellow', alpha=0.3, label='Burst Interval')\n", + "\n", + "for spike_time in peak_times[burst_begin_indices:burst_end_indices+1]:\n", + " plt.axvline(x=spike_time, color='red', linestyle='--', label='Spike' if 'Spike' not in plt.gca().get_legend_handles_labels()[1] else \"\")\n", + "\n", + "plt.text((burst_start + burst_end) / 2, max(voltage_spike), f'Burst Mean Freq: {mean_frequency:.2f} Hz', horizontalalignment='center', color='black')\n", + "\n", + "plt.legend()\n", + "plt.xlabel('Time (s)')\n", + "plt.ylabel('Voltage (mV)')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/nmc-portal/L5TTPC2.ipynb b/examples/nmc-portal/L5TTPC2.ipynb index 82d28b04..297d26ac 100644 --- a/examples/nmc-portal/L5TTPC2.ipynb +++ b/examples/nmc-portal/L5TTPC2.ipynb @@ -5,10 +5,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Example of the use of the eFEL (eFeature Extraction Library) in conjunction with models downloaded from the Neocortical Microcircuit Portal website\n", + "# Example of the use of the eFEL (eFeature Extraction Library) in conjunction with models downloaded from the Neocortical Microcircuit Portal website\n", "\n", "Requirements:\n", - "- Python 3.8+, including Pip (https://pip.readthedocs.org)\n", + "- Python 3.9+, including Pip (https://pip.readthedocs.org)\n", "- A version of Neuron (with Python support) installed on your computer (for instruction, see https://bbp.epfl.ch/nmc-portal/tools)" ] }, @@ -293,7 +293,7 @@ }, "outputs": [], "source": [ - "feature_values = efel.getFeatureValues(traces, ['mean_frequency', 'adaptation_index2', 'ISI_CV', 'doublet_ISI', 'time_to_first_spike', 'AP_height', 'AHP_depth_abs', 'AHP_depth_abs_slow', 'AHP_slow_time', 'AP_width', 'peak_time', 'AHP_time_from_peak'])" + "feature_values = efel.get_feature_values(traces, ['mean_frequency', 'adaptation_index2', 'ISI_CV', 'doublet_ISI', 'time_to_first_spike', 'AP_height', 'AHP_depth_abs', 'AHP_depth_abs_slow', 'AHP_slow_time', 'AP_width', 'peak_time', 'AHP_time_from_peak'])" ] }, { diff --git a/examples/parallel/multiprocessing_example.py b/examples/parallel/multiprocessing_example.py index 1dc5f557..874213df 100644 --- a/examples/parallel/multiprocessing_example.py +++ b/examples/parallel/multiprocessing_example.py @@ -47,7 +47,7 @@ def main(): import multiprocessing pool = multiprocessing.Pool() - traces_results = efel.getFeatureValues( + traces_results = efel.get_feature_values( traces, [ 'AP_amplitude', 'voltage_base'], parallel_map=pool.map) diff --git a/examples/parallel/scoop_example.py b/examples/parallel/scoop_example.py index ac40cb27..90a6b441 100644 --- a/examples/parallel/scoop_example.py +++ b/examples/parallel/scoop_example.py @@ -46,7 +46,7 @@ def main(): # values from scoop import futures - traces_results = efel.getFeatureValues( + traces_results = efel.get_feature_values( traces, [ 'AP_amplitude', 'voltage_base'], parallel_map=futures.map) diff --git a/examples/sonata-network/sonata-network.ipynb b/examples/sonata-network/sonata-network.ipynb index 9976c1d3..064c7b8b 100644 --- a/examples/sonata-network/sonata-network.ipynb +++ b/examples/sonata-network/sonata-network.ipynb @@ -43,7 +43,7 @@ "\n", "from matplotlib import pyplot as plt\n", "\n", - "from bluecellulab import SSim\n", + "from bluecellulab import CircuitSimulation\n", "import efel" ] }, @@ -136,7 +136,7 @@ "simulation_config = Path(\"./\") / \"simulation_config.json\"\n", "with open(simulation_config) as f:\n", " simulation_config_dict = json.load(f)\n", - "sim = SSim(simulation_config)" + "sim = CircuitSimulation(simulation_config)" ] }, { @@ -368,6 +368,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ diff --git a/requirements_docs.txt b/requirements_docs.txt index 1aeaefe1..2df0b397 100644 --- a/requirements_docs.txt +++ b/requirements_docs.txt @@ -25,7 +25,11 @@ # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. sphinx>=7.2.6 +sphinx-bluebrain-theme sphinx_rtd_theme>=2.0.0 sphinx-autobuild>=2021.3.14 sphinx-autorun>=1.1.1 sphinx-autodoc-typehints>=1.25.2 +ipython +jupyter +pandoc diff --git a/setup.py b/setup.py index 33de406b..db090cc9 100644 --- a/setup.py +++ b/setup.py @@ -79,14 +79,15 @@ version=versioneer.get_version(), cmdclass=versioneer.get_cmdclass(), install_requires=[ - 'numpy>=1.6', - 'neo>=0.5.2', - 'typing-extensions>=4.8.0', + 'numpy>=1.6', + 'neo>=0.5.2', + 'pynwb>=2.6.0', + 'typing-extensions>=4.8.0', 'scipy>=1.12.0,<2.0.0', ], packages=[ - 'efel', - 'efel.pyfeatures', + 'efel', + 'efel.pyfeatures', 'efel.units' ], author="BlueBrain Project, EPFL", diff --git a/tests/featurenames.json b/tests/featurenames.json index 47706097..f97a0514 100644 --- a/tests/featurenames.json +++ b/tests/featurenames.json @@ -147,7 +147,30 @@ "interburst_min_values", "postburst_min_indices", "postburst_min_values", + "postburst_slow_ahp_indices", + "postburst_slow_ahp_values", "time_to_interburst_min", + "time_to_postburst_slow_ahp", + "AHP_depth_slow", + "postburst_fast_ahp_indices", + "postburst_fast_ahp_values", + "postburst_adp_peak_indices", + "postburst_adp_peak_values", + "time_to_postburst_fast_ahp", + "time_to_postburst_adp_peak", + "interburst_15percent_indices", + "interburst_15percent_values", + "interburst_20percent_indices", + "interburst_20percent_values", + "interburst_25percent_indices", + "interburst_25percent_values", + "interburst_30percent_indices", + "interburst_30percent_values", + "interburst_40percent_indices", + "interburst_40percent_values", + "interburst_60percent_indices", + "interburst_60percent_values", + "interburst_duration", "AHP_depth_slow", "phaseslope_max" ] \ No newline at end of file diff --git a/tests/test_allfeatures.py b/tests/test_allfeatures.py index a7e8a91b..21c414bb 100644 --- a/tests/test_allfeatures.py +++ b/tests/test_allfeatures.py @@ -45,7 +45,7 @@ def get_allfeature_values(): """Get back all the feature names and value""" efel.reset() - all_featurenames = efel.getFeatureNames() + all_featurenames = efel.get_feature_names() def load_data(filename): data = np.loadtxt(os.path.join(testdata_dir, filename)) @@ -109,11 +109,11 @@ def load_data(filename): with warnings.catch_warnings(): warnings.simplefilter("ignore") - feature_values = efel.getFeatureValues(traces, soma_featurenames)[0] + feature_values = efel.get_feature_values(traces, soma_featurenames)[0] with warnings.catch_warnings(): warnings.simplefilter("ignore") - feature_values.update(efel.getFeatureValues([trace_db], db_featurenames)[0]) + feature_values.update(efel.get_feature_values([trace_db], db_featurenames)[0]) for feature_name in feature_values: if feature_values[feature_name] is not None: @@ -162,8 +162,8 @@ def test_allfeatures_on_constant_voltage(): efel.reset() traces = [{'T': time, 'V': voltage, 'stim_start': [100], 'stim_end': [999]}] - all_featurenames = efel.getFeatureNames() - feature_values = efel.getFeatureValues(traces, all_featurenames)[0] + all_featurenames = efel.get_feature_names() + feature_values = efel.get_feature_values(traces, all_featurenames)[0] assert all(feature_values["voltage"] == -80.0) # Assert that each element in time is greater than or equal to the previous element assert np.all(feature_values["time"][1:] >= feature_values["time"][:-1]) diff --git a/tests/test_basic.py b/tests/test_basic.py index fb190b38..2948c0b6 100644 --- a/tests/test_basic.py +++ b/tests/test_basic.py @@ -47,6 +47,7 @@ testdata_dir = Path(__file__).parent / 'testdata' meanfrequency1_url = testdata_dir / 'basic' / 'mean_frequency_1.txt' +meanfrequency2_url = testdata_dir / 'basic' / 'mean_frequency_2.txt' ahptest1_url = testdata_dir / 'basic' / 'ahptest_1.txt' tau20_0_url = testdata_dir / 'basic' / 'tau20.0.csv' spikeoutsidestim_url = testdata_dir / 'basic' / 'spike_outside_stim.txt' @@ -64,6 +65,8 @@ ) spontaneous_url = testdata_dir / 'basic' / 'spontaneous.txt' +testdata_url = testdata_dir / 'allfeatures' / 'testdata.txt' + def load_data(data_name, interp=False, interp_dt=0.1): """Load data file""" @@ -72,6 +75,10 @@ def load_data(data_name, interp=False, interp_dt=0.1): stim_start = 500.0 stim_end = 900.0 time, voltage = load_ascii_input(meanfrequency1_url) + elif data_name == 'mean_frequency2': + stim_start = 500.0 + stim_end = 900.0 + time, voltage = load_ascii_input(meanfrequency2_url) elif data_name == 'tau20.0': stim_start = 100.0 stim_end = 1000.0 @@ -1058,8 +1065,8 @@ def test_currentbase_median(): rtol=0, atol=1e-8) -def test_getDistance1(): - """basic: Test getDistance 1""" +def test_get_distance1(): + """basic: Test get_distance 1""" import efel efel.reset() @@ -1085,8 +1092,8 @@ def test_getDistance1(): 10)) -def test_getDistance_error_dist(): - """basic: Test getDistance error_dist option""" +def test_get_distance_error_dist(): + """basic: Test get_distance error_dist option""" import efel efel.reset() @@ -1119,8 +1126,8 @@ def test_getDistance_error_dist(): numpy.testing.assert_allclose(score_150, 150) -def test_getDistance_trace_check(): - """basic: Test getDistance trace_check option""" +def test_get_distance_trace_check(): + """basic: Test get_distance trace_check option""" import efel efel.reset() @@ -1713,7 +1720,7 @@ def test_min_voltage_between_spikes_single_spike(): assert feature_values[0]['min_voltage_between_spikes'] is None -def test_getFeatureNames(): +def test_get_feature_names(): """basic: Test getting all feature names""" import efel efel.reset() @@ -1724,15 +1731,15 @@ def test_getFeatureNames(): expected_featurenames = json.load(featurenames_json) # add the new names for the deprecated ones expected_featurenames += ["Spikecount", "Spikecount_stimint"] - assert set(efel.getFeatureNames()) == set(expected_featurenames) + assert set(efel.get_feature_names()) == set(expected_featurenames) -def test_getFeatureNameExists(): - """basic: Test FeatureNameExists""" +def test_feature_name_exists(): + """basic: Test feature_name_exists""" import efel efel.reset() - assert efel.FeatureNameExists('voltage_base') - assert not efel.FeatureNameExists('voltage_base_wrong') + assert efel.feature_name_exists('voltage_base') + assert not efel.feature_name_exists('voltage_base_wrong') def test_steady_state_voltage1(): @@ -1990,8 +1997,8 @@ def test_sag_time_constant(): feature_values['sag_time_constant'][0]) -def test_getmeanfeaturevalues(): - """basic: Test getMeanFeatureValues""" +def test_get_mean_feature_values(): + """basic: Test get_mean_feature_values""" import efel efel.reset() @@ -2013,7 +2020,7 @@ def test_getmeanfeaturevalues(): [trace], ['AP_amplitude'], raise_warnings=False) - mean_feature_values = efel.getMeanFeatureValues( + mean_feature_values = efel.get_mean_feature_values( [trace], [ 'AP_amplitude'], raise_warnings=False) @@ -2627,38 +2634,49 @@ def test_time_constant(): numpy.testing.assert_allclose(time_cst, py_tau, rtol=1e-3) -def test_depolarized_base(): - """basic: Test depolarized base""" +def assert_depolarized_base(trace_name, interp=True): + """ + Calculates depolarized base using given trace data and checks for correctness. + + :param trace_name: Name of the trace data to load. + :param interp: Whether to interpolate the data. + :return: None + """ import efel efel.reset() - trace, time, voltage, stim_start, stim_end = load_data( - 'mean_frequency1', interp=True) + trace, time, voltage, stim_start, stim_end = load_data(trace_name, interp=interp) features = ["depolarized_base", "AP_begin_time", "AP_duration"] - - feature_values = \ - get_feature_values( - [trace], - features, raise_warnings=False) + feature_values = get_feature_values([trace], features, raise_warnings=False) depolarized_base = feature_values[0]['depolarized_base'] AP_begin_times = feature_values[0]['AP_begin_time'] AP_durations = feature_values[0]['AP_duration'] py_dep_base = [] - for i, (AP_begin, AP_dur) in enumerate( - zip(AP_begin_times[:-1], AP_durations[:-1]) - ): + for i, (AP_begin, AP_dur) in enumerate(zip(AP_begin_times[:-1], AP_durations[:-1])): dep_start_time = AP_begin + AP_dur dep_end_time = AP_begin_times[i + 1] + if dep_end_time <= dep_start_time: + continue start_idx = numpy.argwhere(time > dep_start_time)[0][0] - 1 end_idx = numpy.argwhere(time > dep_end_time)[0][0] - 1 py_dep_base.append(numpy.mean(voltage[start_idx:end_idx])) - numpy.testing.assert_allclose(depolarized_base, py_dep_base) + numpy.testing.assert_allclose(depolarized_base, py_dep_base, rtol=1e-3) + + +def test_depolarized_base(): + """Test depolarized base with standard data.""" + assert_depolarized_base('mean_frequency1', interp=True) + + +def test_depolarized_base_outlier(): + """Test depolarized base with outlier data.""" + assert_depolarized_base('mean_frequency2', interp=False) def test_AP_duration(): @@ -3538,7 +3556,7 @@ def py_postburst_min_values(t, v, peak_indices, burst_end_indices, stim_end): def test_postburst_min_values(): - """basic: Test interburst_min_values""" + """basic: Test postburst_min_values""" urls = [burst1_url, burst2_url, burst3_url] for i, url in enumerate(urls): import efel @@ -3676,6 +3694,668 @@ def test_spikes_in_burst1_burstlast_diff(): assert list(spikes_in_burst1_burstlast_diff) == [1] +def test_postburst_slow_ahp_values(): + """basic: Test postburst_slow_ahp_values when no fast AHP is present""" + urls = [burst1_url, burst2_url, burst3_url] + for i, url in enumerate(urls): + import efel + efel.reset() + # use this to have all spikes in burst for burst3_url case + efel.setDoubleSetting('strict_burst_factor', 4.0) + + time, voltage = load_ascii_input(url) + + interp_time, interp_voltage = interpolate(time, voltage, 0.1) + + trace = {} + + trace['T'] = time + trace['V'] = voltage + if i in [0, 1]: + trace['stim_start'] = [250] + trace['stim_end'] = [1600] + elif i == 2: + trace['stim_start'] = [800] + trace['stim_end'] = [2150] + + features = [ + "postburst_slow_ahp_values", + "postburst_min_values", + ] + + feature_values = efel.getFeatureValues( + [trace], + features, + raise_warnings=False + ) + + postburst_slow_ahp_values = feature_values[0][ + "postburst_slow_ahp_values" + ] + postburst_min_values = feature_values[0]["postburst_min_values"] + + # convert to float so that None edge case get converted to nan + # and can pass in assert_allclose + postburst_slow_ahp_values = numpy.array( + postburst_slow_ahp_values, dtype=numpy.float64 + ) + postburst_min_values = numpy.array( + postburst_min_values, dtype=numpy.float64 + ) + + # are equal when there is no fast AHP + numpy.testing.assert_allclose( + postburst_slow_ahp_values, postburst_min_values + ) + + +def test_time_to_postburst_slow_ahp(): + """basic: Test time_to_postburst_slow_ahp""" + urls = [burst1_url, burst2_url, burst3_url] + for i, url in enumerate(urls): + import efel + efel.reset() + + time, voltage = load_ascii_input(url) + + interp_time, interp_voltage = interpolate(time, voltage, 0.1) + + trace = {} + + trace['T'] = time + trace['V'] = voltage + if i in [0, 1]: + trace['stim_start'] = [250] + trace['stim_end'] = [1600] + elif i == 2: + trace['stim_start'] = [800] + trace['stim_end'] = [2150] + + features = [ + "burst_end_indices", + "peak_time", + "time_to_postburst_slow_ahp", + "postburst_slow_ahp_indices", + ] + + feature_values = efel.getFeatureValues( + [trace], + features, + raise_warnings=False + ) + + peak_time = feature_values[0]["peak_time"] + burst_end_indices = feature_values[0]["burst_end_indices"] + postburst_slow_ahp_indices = feature_values[0][ + "postburst_slow_ahp_indices" + ] + time_to_postburst_slow_ahp = feature_values[0][ + "time_to_postburst_slow_ahp" + ] + + if ( + postburst_slow_ahp_indices is None or len(postburst_slow_ahp_indices) == 0 + or burst_end_indices is None or len(burst_end_indices) == 0 + ): + time_to_postburst_slow_ahp_py = None + else: + time_to_postburst_slow_ahp_py = interp_time[ + postburst_slow_ahp_indices + ] - peak_time[burst_end_indices] + + # convert to float so that None edge case get converted to nan + # and can pass in assert_allclose + time_to_postburst_slow_ahp_py = numpy.array( + time_to_postburst_slow_ahp_py, dtype=numpy.float64 + ) + time_to_postburst_slow_ahp = numpy.array( + time_to_postburst_slow_ahp, dtype=numpy.float64 + ) + + numpy.testing.assert_allclose( + time_to_postburst_slow_ahp_py, time_to_postburst_slow_ahp + ) + + +def py_postburst_fahp(v, peak_indices, burst_end_indices, stim_end_index): + """python implementation of post burst fast ahp.""" + if burst_end_indices is None: + return None, None + + postburst_fahp = [] + postburst_fahp_idxs = [] + for i in burst_end_indices: + if i + 1 < len(peak_indices): + stop_i = peak_indices[i + 1] + elif i + 1 < stim_end_index: + stop_i = stim_end_index + else: + stop_i = len(v) - 1 + + v_crop = v[peak_indices[i]:stop_i] + # get where the voltage is going up + crop_args = numpy.argwhere(numpy.diff(v_crop) >= 0)[:, 0] + # the voltage should go up for at least two consecutive points + crop_arg_arg = numpy.argwhere(numpy.diff(crop_args) == 1)[0][0] + crop_arg = crop_args[crop_arg_arg] + end_i = peak_indices[i] + crop_arg + 1 + # the fast ahp is between the last peak of the burst and + # the point where the voltage is going back up + postburst_fahp.append(numpy.min(v[peak_indices[i]:end_i])) + postburst_fahp_idxs.append( + numpy.argmin(v[peak_indices[i]:end_i]) + peak_indices[i] + ) + + return postburst_fahp_idxs, postburst_fahp + + +def test_postburst_fast_ahp_values(): + """basic: Test postburst_fast_ahp_values""" + urls = [burst1_url, burst2_url, burst3_url, testdata_url] + for i, url in enumerate(urls): + import efel + efel.reset() + + time, voltage = load_ascii_input(url) + + interp_time, interp_voltage = interpolate(time, voltage, 0.1) + + trace = {} + + trace['T'] = time + trace['V'] = voltage + if i in [0, 1]: + trace['stim_start'] = [250] + trace['stim_end'] = [1600] + elif i == 2: + trace['stim_start'] = [800] + trace['stim_end'] = [2150] + elif i == 3: + trace['stim_start'] = [700] + trace['stim_end'] = [2700] + + features = [ + "burst_end_indices", + "peak_indices", + "postburst_fast_ahp_indices", + "postburst_fast_ahp_values", + ] + + feature_values = efel.getFeatureValues( + [trace], + features, + raise_warnings=False + ) + + peak_indices = feature_values[0]["peak_indices"] + burst_end_indices = feature_values[0]["burst_end_indices"] + postburst_fahpi = feature_values[0][ + "postburst_fast_ahp_indices" + ] + postburst_fahp = feature_values[0][ + "postburst_fast_ahp_values" + ] + + stim_end_index = numpy.argwhere(trace['stim_end'] > interp_time)[0][0] + postburst_fahpi_py, postburst_fahp_py = py_postburst_fahp( + interp_voltage, peak_indices, burst_end_indices, stim_end_index + ) + + postburst_fahp = numpy.array( + postburst_fahp, dtype=numpy.float64 + ) + postburst_fahp_py = numpy.array( + postburst_fahp_py, dtype=numpy.float64 + ) + numpy.testing.assert_allclose( + postburst_fahp, postburst_fahp_py + ) + + postburst_fahpi = numpy.array( + postburst_fahpi, dtype=numpy.float64 + ) + postburst_fahpi_py = numpy.array( + postburst_fahpi_py, dtype=numpy.float64 + ) + numpy.testing.assert_allclose( + postburst_fahpi, postburst_fahpi_py + ) + + +def py_postburst_adppeak( + v, postburst_sahpi, postburst_fahpi, +): + """python implementation of adp peak after burst.""" + if postburst_sahpi is None or postburst_fahpi is None: + return None, None + + adp_peak_indices = [] + adp_peak_values = [] + for i, sahpi in enumerate(postburst_sahpi): + if sahpi < postburst_fahpi[i]: + continue + adppeaki = numpy.argmax( + v[postburst_fahpi[i]:sahpi] + ) + postburst_fahpi[i] + if adppeaki != sahpi - 1: + adp_peak_indices.append(adppeaki) + adp_peak_values.append(v[adppeaki]) + + if len(adp_peak_indices) == 0: + return None, None + return adp_peak_indices, adp_peak_values + + +def test_postburst_adp_peak_values(): + """basic: Test postburst_adp_peak_values""" + urls = [burst1_url, burst2_url, burst3_url, testdata_url] + for i, url in enumerate(urls): + import efel + efel.reset() + + time, voltage = load_ascii_input(url) + + interp_time, interp_voltage = interpolate(time, voltage, 0.1) + + trace = {} + + trace['T'] = time + trace['V'] = voltage + if i in [0, 1]: + trace['stim_start'] = [250] + trace['stim_end'] = [1600] + elif i == 2: + trace['stim_start'] = [800] + trace['stim_end'] = [2150] + elif i == 3: + trace['stim_start'] = [700] + trace['stim_end'] = [2700] + + features = [ + "postburst_fast_ahp_indices", + "postburst_slow_ahp_indices", + "postburst_adp_peak_indices", + "postburst_adp_peak_values", + ] + + feature_values = efel.getFeatureValues( + [trace], + features, + raise_warnings=False + ) + + postburst_fahpi = feature_values[0][ + "postburst_fast_ahp_indices" + ] + postburst_sahpi = feature_values[0][ + "postburst_slow_ahp_indices" + ] + postburst_adppeaki = feature_values[0][ + "postburst_adp_peak_indices" + ] + postburst_adppeak = feature_values[0][ + "postburst_adp_peak_values" + ] + + postburst_adppeaki_py, postburst_adppeak_py = py_postburst_adppeak( + interp_voltage, postburst_sahpi, postburst_fahpi, + ) + + postburst_adppeak = numpy.array( + postburst_adppeak, dtype=numpy.float64 + ) + postburst_adppeak_py = numpy.array( + postburst_adppeak_py, dtype=numpy.float64 + ) + numpy.testing.assert_allclose( + postburst_adppeak, postburst_adppeak_py + ) + + postburst_adppeaki = numpy.array( + postburst_adppeaki, dtype=numpy.float64 + ) + postburst_adppeaki_py = numpy.array( + postburst_adppeaki_py, dtype=numpy.float64 + ) + numpy.testing.assert_allclose( + postburst_adppeaki, postburst_adppeaki_py + ) + + +def py_time_to_postburst_fast_ahp(t, postburst_fahpi, burst_endi, peak_time): + """python implementation of time to post burst fast ahp.""" + if postburst_fahpi is None or burst_endi is None: + return None + return [t[fahpi] - peak_time[burst_endi[i]] for (i, fahpi) in + enumerate(postburst_fahpi)] + + +def test_time_to_postburst_fast_ahp(): + """basic: Test time_to_postburst_fast_ahp""" + urls = [burst1_url, burst2_url, burst3_url, testdata_url] + for i, url in enumerate(urls): + import efel + efel.reset() + + time, voltage = load_ascii_input(url) + + interp_time, interp_voltage = interpolate(time, voltage, 0.1) + + trace = {} + + trace['T'] = time + trace['V'] = voltage + if i in [0, 1]: + trace['stim_start'] = [250] + trace['stim_end'] = [1600] + elif i == 2: + trace['stim_start'] = [800] + trace['stim_end'] = [2150] + elif i == 3: + trace['stim_start'] = [700] + trace['stim_end'] = [2700] + + features = [ + "time_to_postburst_fast_ahp", + "postburst_fast_ahp_indices", + "burst_end_indices", + "peak_time", + ] + + feature_values = efel.getFeatureValues( + [trace], + features, + raise_warnings=False + ) + + time_to_postburst_fast_ahp = feature_values[0][ + "time_to_postburst_fast_ahp" + ] + postburst_fahpi = feature_values[0][ + "postburst_fast_ahp_indices" + ] + burst_endi = feature_values[0][ + "burst_end_indices" + ] + peak_time = feature_values[0][ + "peak_time" + ] + + time_to_postburst_fast_ahp_py = py_time_to_postburst_fast_ahp( + interp_time, postburst_fahpi, burst_endi, peak_time + ) + + time_to_postburst_fast_ahp = numpy.array( + time_to_postburst_fast_ahp, dtype=numpy.float64 + ) + time_to_postburst_fast_ahp_py = numpy.array( + time_to_postburst_fast_ahp_py, dtype=numpy.float64 + ) + numpy.testing.assert_allclose( + time_to_postburst_fast_ahp, time_to_postburst_fast_ahp_py + ) + + +def py_time_to_postburst_adp_peak( + t, postburst_adppeaki, burst_endi, peak_time +): + """python implementation of time to post burst ADP peak.""" + if postburst_adppeaki is None or burst_endi is None: + return None + + time_to_postburst_adp_peaks = [] + n_peaks = len(peak_time) + for i, adppeaki in enumerate(postburst_adppeaki): + # there are not always an adp peak after each burst + # so make sure that the burst and adp peak indices are consistent + k = 0 + while (burst_endi[i] + k + 1 < n_peaks and + peak_time[burst_endi[i] + k + 1] < t[adppeaki]): + k += 1 + + time_to_postburst_adp_peaks.append( + t[adppeaki] - peak_time[burst_endi[i] + k] + ) + + return time_to_postburst_adp_peaks + + +def test_time_to_postburst_adp_peak(): + """basic: Test time_to_postburst_adp_peak""" + urls = [burst1_url, burst2_url, burst3_url, testdata_url] + for i, url in enumerate(urls): + import efel + efel.reset() + + time, voltage = load_ascii_input(url) + + interp_time, interp_voltage = interpolate(time, voltage, 0.1) + + trace = {} + + trace['T'] = time + trace['V'] = voltage + if i in [0, 1]: + trace['stim_start'] = [250] + trace['stim_end'] = [1600] + elif i == 2: + trace['stim_start'] = [800] + trace['stim_end'] = [2150] + elif i == 3: + trace['stim_start'] = [700] + trace['stim_end'] = [2700] + + features = [ + "time_to_postburst_adp_peak", + "postburst_adp_peak_indices", + "burst_end_indices", + "peak_time", + ] + + feature_values = efel.getFeatureValues( + [trace], + features, + raise_warnings=False + ) + + time_to_postburst_adp_peak = feature_values[0][ + "time_to_postburst_adp_peak" + ] + postburst_adppeaki = feature_values[0][ + "postburst_adp_peak_indices" + ] + burst_endi = feature_values[0][ + "burst_end_indices" + ] + peak_time = feature_values[0][ + "peak_time" + ] + + time_to_postburst_adp_peak_py = py_time_to_postburst_adp_peak( + interp_time, postburst_adppeaki, burst_endi, peak_time + ) + + time_to_postburst_adp_peak = numpy.array( + time_to_postburst_adp_peak, dtype=numpy.float64 + ) + time_to_postburst_adp_peak_py = numpy.array( + time_to_postburst_adp_peak_py, dtype=numpy.float64 + ) + numpy.testing.assert_allclose( + time_to_postburst_adp_peak, time_to_postburst_adp_peak_py + ) + + +def py_XXpercent_interburst_values( + t, v, postburst_fahpi, burst_endi, peaki, percent=20. +): + """python implementation of 20% / 25% / 30% interburst values.""" + if postburst_fahpi is None or burst_endi is None: + return None, None + + XXpercent_interburst = [] + XXpercent_interburst_i = [] + for i, postburst_fahp_i in enumerate(postburst_fahpi): + if i < len(burst_endi) and burst_endi[i] + 1 < len(peaki): + time_interval = t[peaki[burst_endi[i] + 1]] - t[postburst_fahp_i] + time_at_XXpercent = (t[postburst_fahp_i] + + time_interval * percent / 100.) + index_at_XXpercent = numpy.argwhere(t >= time_at_XXpercent)[0][0] + XXpercent_interburst_i.append(index_at_XXpercent) + XXpercent_interburst.append(v[index_at_XXpercent]) + + return XXpercent_interburst_i, XXpercent_interburst + + +def interburst_XXpercent_values_compare_with_py( + feature_values, interp_time, interp_voltage, percent +): + """compare python implementation with efel output for interburst values""" + postburst_fahpi = feature_values[0]["postburst_fast_ahp_indices"] + burst_endi = feature_values[0]["burst_end_indices"] + peaki = feature_values[0]["peak_indices"] + interbursti = feature_values[0][f"interburst_{percent}percent_indices"] + interburst = feature_values[0][f"interburst_{percent}percent_values"] + + interbursti_py, interburst_py = py_XXpercent_interburst_values( + interp_time, + interp_voltage, + postburst_fahpi, + burst_endi, + peaki, + percent, + ) + + interburst = numpy.array(interburst, dtype=numpy.float64) + interburst_py = numpy.array(interburst_py, dtype=numpy.float64) + numpy.testing.assert_allclose(interburst, interburst_py) + + interbursti = numpy.array(interbursti, dtype=numpy.float64) + interbursti_py = numpy.array(interbursti_py, dtype=numpy.float64) + numpy.testing.assert_allclose(interbursti, interbursti_py) + + +def test_interburst_XXpercent_values(): + """basic: Test interburst_15percent_values, interburst_20percent_values, + interburst_25percent_values, interburst_30percent_values, + interburst_40percent_values, interburst_60percent_values + """ + urls = [burst1_url, burst2_url, burst3_url, testdata_url] + for i, url in enumerate(urls): + import efel + efel.reset() + + time, voltage = load_ascii_input(url) + + interp_time, interp_voltage = interpolate(time, voltage, 0.1) + + trace = {} + + trace['T'] = time + trace['V'] = voltage + if i in [0, 1]: + trace['stim_start'] = [250] + trace['stim_end'] = [1600] + elif i == 2: + trace['stim_start'] = [800] + trace['stim_end'] = [2150] + elif i == 3: + trace['stim_start'] = [700] + trace['stim_end'] = [2700] + + features = [ + "postburst_fast_ahp_indices", + "burst_end_indices", + "peak_indices", + "interburst_15percent_indices", + "interburst_15percent_values", + "interburst_20percent_indices", + "interburst_20percent_values", + "interburst_25percent_indices", + "interburst_25percent_values", + "interburst_30percent_indices", + "interburst_30percent_values", + "interburst_40percent_indices", + "interburst_40percent_values", + "interburst_60percent_indices", + "interburst_60percent_values", + ] + + feature_values = efel.getFeatureValues( + [trace], + features, + raise_warnings=False + ) + + interburst_XXpercent_values_compare_with_py( + feature_values, interp_time, interp_voltage, 15 + ) + interburst_XXpercent_values_compare_with_py( + feature_values, interp_time, interp_voltage, 20 + ) + interburst_XXpercent_values_compare_with_py( + feature_values, interp_time, interp_voltage, 25 + ) + interburst_XXpercent_values_compare_with_py( + feature_values, interp_time, interp_voltage, 30 + ) + interburst_XXpercent_values_compare_with_py( + feature_values, interp_time, interp_voltage, 40 + ) + interburst_XXpercent_values_compare_with_py( + feature_values, interp_time, interp_voltage, 60 + ) + + +def test_interburst_duration(): + """basic: Test interburst_duration""" + urls = [burst1_url, burst2_url, burst3_url, testdata_url] + for i, url in enumerate(urls): + import efel + efel.reset() + + time, voltage = load_ascii_input(url) + + trace = {} + trace['T'] = time + trace['V'] = voltage + if i in [0, 1]: + trace['stim_start'] = [250] + trace['stim_end'] = [1600] + elif i == 2: + trace['stim_start'] = [800] + trace['stim_end'] = [2150] + elif i == 3: + trace['stim_start'] = [700] + trace['stim_end'] = [2700] + + features = ["burst_end_indices", "peak_time", "interburst_duration"] + + feature_values = efel.getFeatureValues( + [trace], + features, + raise_warnings=False + ) + + burst_endi = feature_values[0]["burst_end_indices"] + peak_time = feature_values[0]["peak_time"] + inter_dur = feature_values[0]["interburst_duration"] + + inter_dur_py = None + if burst_endi is not None: + print(peak_time) + print(burst_endi) + inter_dur_py = [ + peak_time[idx + 1] - peak_time[idx] + for idx in burst_endi + if idx + 1 < len(peak_time) + ] + + inter_dur = numpy.array(inter_dur, dtype=numpy.float64) + inter_dur_py = numpy.array(inter_dur_py, dtype=numpy.float64) + numpy.testing.assert_allclose(inter_dur, inter_dur_py) + + def test_spontaneous_firing(): """basic: Test features with spontaneous firing""" import efel diff --git a/tests/test_cppcore.py b/tests/test_cppcore.py index d08e57c0..41711c71 100644 --- a/tests/test_cppcore.py +++ b/tests/test_cppcore.py @@ -85,7 +85,7 @@ def setup_data(self): # pylint: disable=R0201 efel.cppcore.setFeatureInt("DerivativeWindow", [3]) - def test_getFeatureNames(self): # pylint: disable=R0201 + def test_get_feature_names(self): # pylint: disable=R0201 """cppcore: Testing getting all feature names""" import efel feature_names = [] @@ -268,4 +268,4 @@ def test_efel_assertion_error(): "stim_end": [75], } with pytest.raises(AssertionError): - efel.getFeatureValues([trace], ["__test_efel_assertion__"]) + efel.get_feature_values([trace], ["__test_efel_assertion__"]) diff --git a/tests/test_io.py b/tests/test_io.py index 47b8cd76..4524216a 100644 --- a/tests/test_io.py +++ b/tests/test_io.py @@ -13,6 +13,7 @@ neo_test_files_dir = Path(__file__).parent / 'neo_test_files' meanfrequency1_filename = testdata_dir / 'basic' / 'mean_frequency_1.txt' meanfrequency1_url = str(meanfrequency1_filename) +nwb1_filename = testdata_dir / 'JY180308_A_1.nwb' def test_load_ascii_input(): @@ -269,3 +270,27 @@ def test_load_neo_file_stim_time_events_incomplete(): "neo_test_file_events_time_incomplete.mat") pytest.raises(ValueError, efel.io.load_neo_file, file_name) + + +def test_load_neo_file_nwb(): + """Test loading of data from an NWB file.""" + import efel + import neo + + efel_blocks = efel.io.load_neo_file(nwb1_filename, 250, 600) + efel_trace = efel_blocks[0][0][0] + + reader = neo.io.NWBIO(filename=nwb1_filename) + neo_blocks = reader.read() + neo_trace = neo_blocks[0].segments[0].analogsignals[0] + time_shifted = neo_trace.times[-1] - neo_trace.times[0] + rescaled_time_shifted = time_shifted.rescale('ms').magnitude + + assert neo_trace[0].rescale('mV').magnitude == efel_trace['V'][0] + assert rescaled_time_shifted == efel_trace['T'][-1] + + for efel_seg in efel_blocks: + for traces in efel_seg: + for trace in traces: + assert trace['stim_start'] == [250] + assert trace['stim_end'] == [600] diff --git a/tests/test_isi.py b/tests/test_isi.py index 6a8c1253..d9ac33e2 100644 --- a/tests/test_isi.py +++ b/tests/test_isi.py @@ -29,7 +29,7 @@ def test_ISIs_single_spike(): } features = ["peak_time", "ISIs"] - feature_values = efel.getFeatureValues([trace], features)[0] + feature_values = efel.get_feature_values([trace], features)[0] assert len(feature_values["peak_time"]) == 1 assert feature_values["ISIs"] is None @@ -149,7 +149,7 @@ def setup_and_teardown(self): def test_burst_ISI_indices(self, mock_ISI_values): mock_ISI_values.return_value = np.array([50, 100, 50, 200, 50]) - self.feature_values = efel.getFeatureValues( + self.feature_values = efel.get_feature_values( [self.trace], self.features, raise_warnings=False )[0] diff --git a/tests/test_pyfeatures.py b/tests/test_pyfeatures.py index 4e1f5190..a7f0e8cf 100644 --- a/tests/test_pyfeatures.py +++ b/tests/test_pyfeatures.py @@ -125,7 +125,7 @@ def _test_expected_value(feature_name, expected_values): for trace_name, expected_value in expected_values.items(): trace = _load_trace(trace_name) - feature_values = efel.getFeatureValues([trace], [feature_name]) + feature_values = efel.get_feature_values([trace], [feature_name]) if expected_value is None: assert feature_values[0][feature_name] is None @@ -184,14 +184,14 @@ def test_ISIs(): mf1_trace = _load_trace('mean_frequency1') - feature_values = efel.getFeatureValues([mf1_trace], ['ISI_values', 'ISIs']) + feature_values = efel.get_feature_values([mf1_trace], ['ISI_values', 'ISIs']) assert numpy.allclose( feature_values[0]['ISIs'][1:], feature_values[0]['ISI_values']) numpy.testing.assert_allclose( - efel.getDistance( + efel.get_distance( mf1_trace, 'ISIs', 1.0, @@ -234,7 +234,7 @@ def test_pydistance(): mf1_trace['stim_start'] = [0] mf1_trace['stim_end'] = [900] # with successful trace_check - numpy.testing.assert_allclose(efel.getDistance( + numpy.testing.assert_allclose(efel.get_distance( mf1_trace, feature_name, mean, @@ -245,7 +245,7 @@ def test_pydistance(): # with failed trace_check mf1_trace["stim_end"] = [600] error_value = 250.0 - res = efel.getDistance( + res = efel.get_distance( mf1_trace, feature_name, mean, @@ -256,7 +256,7 @@ def test_pydistance(): def test_pydistance_featurefail(): - """pyfeatures: Test failure of feature in getdistance""" + """pyfeatures: Test failure of feature in get_distance""" mf1_trace = _load_trace('mean_frequency1') feature_name = 'initburst_sahp' @@ -264,7 +264,7 @@ def test_pydistance_featurefail(): std = 1.0 efel.reset() - numpy.testing.assert_allclose(efel.getDistance( + numpy.testing.assert_allclose(efel.get_distance( mf1_trace, feature_name, mean, @@ -289,7 +289,7 @@ def interpolate(time, voltage, new_dt): feature_name = ['time', 'current', 'voltage'] trace = _load_trace('current') - feature_values = efel.getFeatureValues([trace], feature_name) + feature_values = efel.get_feature_values([trace], feature_name) feature_time = feature_values[0]["time"] feature_current = feature_values[0]["current"] feature_voltage = feature_values[0]["voltage"] @@ -316,15 +316,15 @@ def test_trace_check(): efel.reset() mf1_trace = _load_trace('mean_frequency1') - feature_values = efel.getFeatureValues([mf1_trace], ['trace_check']) + feature_values = efel.get_feature_values([mf1_trace], ['trace_check']) assert feature_values[0]['trace_check'][0] == 0 # failure mf1_trace["stim_end"] = [600] - feature_values = efel.getFeatureValues([mf1_trace], ['trace_check']) + feature_values = efel.get_feature_values([mf1_trace], ['trace_check']) assert feature_values[0]['trace_check'] is None # no spikes mf1_trace["V"] = [0] * len(mf1_trace["V"]) - feature_values = efel.getFeatureValues([mf1_trace], ['trace_check']) + feature_values = efel.get_feature_values([mf1_trace], ['trace_check']) assert feature_values[0]['trace_check'][0] == 0 diff --git a/tests/testdata/JY180308_A_1.nwb b/tests/testdata/JY180308_A_1.nwb new file mode 100644 index 00000000..5315045f Binary files /dev/null and b/tests/testdata/JY180308_A_1.nwb differ diff --git a/tests/testdata/allfeatures/expectedresults.json b/tests/testdata/allfeatures/expectedresults.json index 06db917d..27463db9 100644 --- a/tests/testdata/allfeatures/expectedresults.json +++ b/tests/testdata/allfeatures/expectedresults.json @@ -1,4 +1,28 @@ { + "ADP_peak_amplitude": [ + 0.0, + 2.6873666528663236, + 0.48122323161050673, + 0.06877493979540361, + 0.0, + 2.99957475153942 + ], + "ADP_peak_indices": [ + 7115, + 9317, + 14162, + 17198, + 23943, + 27000 + ], + "ADP_peak_values": [ + -47.716407064147894, + -41.60422947192351, + -42.066708105903004, + -41.97919073015028, + -41.12299346923828, + -38.12341871769886 + ], "AHP1_depth_from_peak": [ 66.46542716365163 ], @@ -42,6 +66,12 @@ 44.685313698492095, 45.59806337795396 ], + "AHP_depth_slow": [ + 28.81047723194662, + 32.02907786238346, + 32.65404519917785, + 33.435288866699345 + ], "AHP_slow_time": [ 0.14554275318387552, 0.07352941176470588, @@ -290,6 +320,14 @@ 3.2999999999969987, 3.2999999999969987 ], + "AP_width_between_threshold": [ + 1.7000000000003865, + 2.700000000000614, + 2.8999999999973625, + 2.9999999999972715, + 3.2999999999969987, + 3.2999999999969987 + ], "APlast_amp": [ 37.848929573728334 ], @@ -345,6 +383,12 @@ "burst_ISI_indices": [ 3 ], + "burst_begin_indices": [ + 4 + ], + "burst_end_indices": [ + 5 + ], "burst_mean_freq": [ 3.9840637450221186, 7.990411506199836 @@ -355,7 +399,6 @@ "decay_time_constant_after_stim": [ 13.45188514197874 ], - "multiple_decay_time_constant_after_stim": null, "depolarized_base": [ -43.027576049717304, -41.06055206163736, @@ -379,6 +422,21 @@ -2.3296693271049533, -2.3944223175083863 ], + "interburst_15percent_indices": null, + "interburst_15percent_values": null, + "interburst_20percent_indices": null, + "interburst_20percent_values": null, + "interburst_25percent_indices": null, + "interburst_25percent_values": null, + "interburst_30percent_indices": null, + "interburst_30percent_values": null, + "interburst_40percent_indices": null, + "interburst_40percent_values": null, + "interburst_60percent_indices": null, + "interburst_60percent_values": null, + "interburst_duration": null, + "interburst_min_indices": [], + "interburst_min_values": [], "interburst_voltage": [ -38.820613862535545 ], @@ -447,6 +505,22 @@ -41.12299346923828, -41.12299346923828 ], + "min_between_peaks_indices": [ + 7115, + 9833, + 14285, + 17215, + 23960, + 28300 + ], + "min_between_peaks_values": [ + -47.716407064147894, + -45.90401077270508, + -42.68541014226824, + -42.06044280547385, + -41.279199137952354, + -80.43351009404397 + ], "min_voltage_between_spikes": [ -47.716407064147894, -45.90401077270508, @@ -457,6 +531,7 @@ "minimum_voltage": [ -75.87104945779376 ], + "multiple_decay_time_constant_after_stim": null, "number_initial_spikes": [ 1 ], @@ -486,6 +561,30 @@ 3.562320229253816, 4.475069908715679 ], + "postburst_adp_peak_indices": [ + 26438 + ], + "postburst_adp_peak_values": [ + -41.12299346923828 + ], + "postburst_fast_ahp_indices": [ + 26438 + ], + "postburst_fast_ahp_values": [ + -41.12299346923828 + ], + "postburst_min_indices": [ + 26465 + ], + "postburst_min_values": [ + -41.52919675004766 + ], + "postburst_slow_ahp_indices": [ + 26465 + ], + "postburst_slow_ahp_values": [ + -41.52919675004766 + ], "sag_amplitude": null, "sag_ratio1": null, "sag_ratio2": [ @@ -510,12 +609,6 @@ 2.6602184713542556, 2.8238770245407068 ], - "spikes_per_burst": [ - 2 - ], - "spikes_per_burst_diff": [], - "spikes_in_burst1_burst2_diff": null, - "spikes_in_burst1_burstlast_diff": null, "steady_state_hyper": [ -39.7063975216648 ], @@ -525,6 +618,13 @@ "steady_state_voltage_stimend": [ -38.28601014909481 ], + "strict_burst_mean_freq": [ + 7.990411506199836 + ], + "strict_burst_number": [ + 1 + ], + "strict_interburst_voltage": [], "time": [ 0.0, 0.1, @@ -30532,9 +30632,19 @@ "time_to_first_spike": [ 8.000000000092427 ], + "time_to_interburst_min": [], "time_to_last_spike": [ 1937.7999999986964 ], + "time_to_postburst_adp_peak": [ + 5.999999999994543 + ], + "time_to_postburst_fast_ahp": [ + 5.999999999994543 + ], + "time_to_postburst_slow_ahp": [ + 8.699999999992087 + ], "time_to_second_spike": [ 211.30000000013865 ], @@ -60567,6 +60677,12 @@ "initburst_sahp": null, "initburst_sahp_vb": null, "initburst_sahp_ssse": null, + "spikes_per_burst": [ + 2 + ], + "spikes_per_burst_diff": [], + "spikes_in_burst1_burst2_diff": null, + "spikes_in_burst1_burstlast_diff": null, "depol_block": null, "depol_block_bool": [ 1 diff --git a/tests/testdata/basic/mean_frequency_2.txt b/tests/testdata/basic/mean_frequency_2.txt new file mode 100644 index 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a/tests/update_featurenames.py b/tests/update_featurenames.py index 46cb9514..aeeb319a 100755 --- a/tests/update_featurenames.py +++ b/tests/update_featurenames.py @@ -38,7 +38,7 @@ efel.cppcore.Initialize(efel.getDependencyFileLocation(), "log") with open('featurenames.json', 'w') as featurenames_json: - feature_names = efel.getFeatureNames() + feature_names = efel.get_feature_names() feature_names = sorted(set(feature_names)) json.dump( feature_names, diff --git a/tox.ini b/tox.ini index f458d4a0..12d5aa83 100644 --- a/tox.ini +++ b/tox.ini @@ -73,14 +73,14 @@ deps = -r{toxinidir}/requirements_docs.txt pytest>=7.4.4 allowlist_externals = - make -changedir = docs + sh commands = + sh docs/examples_to_rst.sh # build the docs to see if it builds fine - make html SPHINXOPTS=-W + sphinx-build -b html -W docs/source docs/_build/html # make sure the feature names and units are up-to-date - pytest test_feature_units_in_docs.py + pytest docs/test_feature_units_in_docs.py [testenv:lint] envdir = {toxworkdir}/lint