diff --git a/docs/examples_to_rst.sh b/docs/examples_to_rst.sh index 272b0f2c..9af18044 100644 --- a/docs/examples_to_rst.sh +++ b/docs/examples_to_rst.sh @@ -9,6 +9,8 @@ rm docs/source/load_nwb.rst rm -rf docs/source/load_nwb_files rm docs/source/extrafeats_example.rst rm docs/source/multiprocessing_example.rst +rm docs/source/voltage_clamp.rst +rm -rf docs/source/voltage_clamp_files # convert jupyter nbconvert --to rst examples/sonata-network/sonata-network.ipynb @@ -16,6 +18,7 @@ jupyter nbconvert --to rst examples/nmc-portal/L5TTPC2.ipynb jupyter nbconvert --to rst examples/neo/load_nwb.ipynb jupyter nbconvert --to rst examples/extracellular/extrafeats_example.ipynb jupyter nbconvert --to rst examples/parallel/multiprocessing_example.ipynb +jupyter nbconvert --to rst examples/voltage_clamp/voltage_clamp.ipynb # move mv examples/sonata-network/sonata-network.rst docs/source/ @@ -25,4 +28,6 @@ mv examples/nmc-portal/L5TTPC2_files docs/source/ mv examples/neo/load_nwb.rst docs/source/ mv examples/neo/load_nwb_files docs/source/ mv examples/extracellular/extrafeats_example.rst docs/source/ -mv examples/parallel/multiprocessing_example.rst docs/source/ \ No newline at end of file +mv examples/parallel/multiprocessing_example.rst docs/source/ +mv examples/voltage_clamp/voltage_clamp.rst docs/source/ +mv examples/voltage_clamp/voltage_clamp_files docs/source/ \ No newline at end of file diff --git a/docs/source/eFeatures.rst b/docs/source/eFeatures.rst index 395f7821..12d483fd 100644 --- a/docs/source/eFeatures.rst +++ b/docs/source/eFeatures.rst @@ -1710,7 +1710,7 @@ time_constant The extraction of the time constant requires a voltage trace of a cell in a hyper- polarized state. Starting at stim start find the beginning of the exponential decay where the first derivative of V(t) is smaller than -0.005 V/s in 5 subsequent points. The flat subsequent to the exponential decay is defined as the point where the first derivative of the voltage trace is bigger than -0.005 -and the mean of the follwowing 70 points as well. +and the mean of the follwowing 70 ms as well. If the voltage trace between the beginning of the decay and the flat includes more than 9 points, fit an exponential decay. Yield the time constant of that decay. @@ -1802,16 +1802,19 @@ decay_time_constant_after_stim - **Units**: ms - **Pseudocode**: :: - time_interval = t[numpy.where(t => decay_start_after_stim & - t < decay_end_after_stim)] - t[numpy.where(t == stim_end)] - voltage_interval = abs(voltages[numpy.where(t => decay_start_after_stim & - t < decay_end_after_stim)] - - voltages[numpy.where(t == decay_start_after_stim)]) + interval_indices = numpy.where( + (time >= interval_start) & (time < interval_end)) + stim_start_index = get_index(time, stim_start) + interval_time = time[interval_indices] - stim_end + interval_voltage = abs( + voltage[interval_indices] - + voltage[stim_start_index]) - log_voltage_interval = numpy.log(voltage_interval) - slope, _ = numpy.polyfit(time_interval, log_voltage_interval, 1) + # fit + log_interval_voltage = numpy.log(interval_voltage) + slope, _ = numpy.polyfit(interval_time, log_interval_voltage, 1) - decay_time_constant_after_stim = -1. / slope + tau = -1. / slope multiple_decay_time_constant_after_stim ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -2085,6 +2088,126 @@ with impedance_max_freq being a setting with 50.0 as a default value. else: return None +activation_time_constant +~~~~~~~~~~~~~~~~~~~~~~~~ + +`Python efeature`_ : Time constant for an ion channel activation trace. +Fits for stim_start to trace maximum interval as A - B * exp(-t/tau). + +**Attention!** For *voltage clamp* data, user should pass the current response as voltage to efel. +See voltage clamp example for more details. + +- **Required features**: time, voltage, stim_start, stim_end +- **Units**: ms +- **Pseudocode**: :: + + def exp_fit(t, tau, A0, A1) -> np.ndarray | float: + return A0 + A1 * np.exp(-t / tau) + + # isolate stimulus interval + stim_start_idx = np.flatnonzero(time >= stim_start)[0] + stim_end_idx = np.flatnonzero(time >= stim_end)[0] + time_interval = time[stim_start_idx:stim_end_idx] + voltage_interval = voltage[stim_start_idx:stim_end_idx] + + # keep trace going from stim_start to voltage max + max_idx = np.argmax(voltage_interval) + time_interval = time_interval[:max_idx + 1] + voltage_interval = voltage_interval[:max_idx + 1] + + # correct time so that it starts from 0 + time_interval -= time_interval[0] + + # fit + popt, _ = curve_fit( + exp_fit, + time_interval, + voltage_interval, + p0=(1., voltage_interval[-1], voltage_interval[0] - voltage_interval[-1]), + bounds=((0, -np.inf, -np.inf), (np.inf, np.inf, 0)), # positive tau, negative A1 + nan_policy="omit", + ) + time_constant = np.array([abs(popt[0])]) + +deactivation_time_constant +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +`Python efeature`_ : Time constant for an ion channel deactivation trace. +Fits for stim_start to stim_end as A + B * exp(-t/tau). + +**Attention!** For *voltage clamp* data, user should pass the current response as voltage to efel. +See voltage clamp example for more details. + +- **Required features**: time, voltage, stim_start, stim_end +- **Units**: ms +- **Pseudocode**: :: + + def exp_fit(t, tau, A0, A1) -> np.ndarray | float: + return A0 + A1 * np.exp(-t / tau) + + # isolate stimulus interval + interval_indices = np.where((time >= stim_start) & (time < stim_end)) + time_interval = time[interval_indices] + voltage_interval = voltage[interval_indices] + + # correct time so that it starts from 0 + time_interval -= time_interval[0] + + # fit + popt, _ = curve_fit( + exp_fit, + time_interval, + voltage_interval, + p0=(1., voltage_interval[-1], max(0, voltage_interval[0] - voltage_interval[-1])), + bounds=((0, -np.inf, 0), np.inf), # positive tau, positive A1 + nan_policy="omit", + ) + time_constant = np.array([abs(popt[0])]) + +inactivation_time_constant +~~~~~~~~~~~~~~~~~~~~~~~~~~ + +`Python efeature`_ : Time constant for an ion channel inactivation trace. +Fits for trace maximum to stim end interval as A + B * exp(-t/tau). +Depends on inactivation_tc_end_skip setting, which removes a given number of data points at the end of the trace, +right before stim_end. This is useful to remove artifacts that would bias the fit. Default is 10 data points. + +**Attention!** For *voltage clamp* data, user should pass the current response as voltage to efel. +See voltage clamp example for more details. + +- **Required features**: time, voltage, stim_start, stim_end, inactivation_tc_end_skip (default = 10) +- **Units**: ms +- **Pseudocode**: :: + + def exp_fit(t, tau, A0, A1) -> np.ndarray | float: + return A0 + A1 * np.exp(-t / tau) + + # isolate stimulus interval + stim_start_idx = np.flatnonzero(time >= stim_start)[0] + stim_end_idx = np.flatnonzero(time >= stim_end)[0] + time_interval = time[stim_start_idx:stim_end_idx - end_skip] + voltage_interval = voltage[stim_start_idx:stim_end_idx - end_skip] + + # keep trace going from voltage max to stim end + # remove end of trace to remove artifacts due to stimulus change + max_idx = np.argmax(voltage_interval) + time_interval = time_interval[max_idx:] + voltage_interval = voltage_interval[max_idx:] + + # correct time so that it starts from 0 + time_interval -= time_interval[0] + + # fit + popt, _ = curve_fit( + exp_fit, + time_interval, + voltage_interval, + p0=(1., voltage_interval[-1], voltage_interval[0] - voltage_interval[-1]), + bounds=((0, -np.inf, 0), np.inf), # positive tau, positive A1 + nan_policy="omit", + ) + time_constant = np.array([abs(popt[0])]) + Extracellular features ---------------------- diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 6f510529..a5b3e53a 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -10,4 +10,5 @@ Examples load_nwb nmc-portal sonata-network - extrafeats_example \ No newline at end of file + extrafeats_example + voltage_clamp \ No newline at end of file diff --git a/efel/pyfeatures/pyfeatures.py b/efel/pyfeatures/pyfeatures.py index b7cb7b19..4f842b41 100644 --- a/efel/pyfeatures/pyfeatures.py +++ b/efel/pyfeatures/pyfeatures.py @@ -71,7 +71,10 @@ 'inv_third_ISI', 'inv_fourth_ISI', 'inv_fifth_ISI', - 'inv_last_ISI' + 'inv_last_ISI', + 'activation_time_constant', + 'deactivation_time_constant', + 'inactivation_time_constant', ] @@ -352,3 +355,148 @@ def phaseslope_max() -> np.ndarray | None: return np.array([np.max(phaseslope)]) except ValueError: return None + + +def exp_fit(t, tau, A0, A1) -> np.ndarray | float: + """Exponential function used in exponential fitting. + + Args: + t (ndarray or float): time series + tau (float): time constant + A0 (float): constant added to the exponential + A1 (float): constant multiplying the exponential + """ + return A0 + A1 * np.exp(-t / tau) + + +def activation_time_constant() -> np.ndarray | None: + """Time constant for an ion channel activation trace. + Fits for stim_start to trace maximum interval as A - B * exp(-t/tau).""" + from scipy.optimize import curve_fit + + stim_start = _get_cpp_data("stim_start") + stim_end = _get_cpp_data("stim_end") + voltage = get_cpp_feature("voltage") + time = get_cpp_feature("time") + + if voltage is None or time is None: + return None + + # isolate stimulus interval + stim_start_idx = np.flatnonzero(time >= stim_start)[0] + stim_end_idx = np.flatnonzero(time >= stim_end)[0] + time_interval = time[stim_start_idx:stim_end_idx] + voltage_interval = voltage[stim_start_idx:stim_end_idx] + + # keep trace going from stim_start to voltage max + max_idx = np.argmax(voltage_interval) + time_interval = time_interval[:max_idx + 1] + voltage_interval = voltage_interval[:max_idx + 1] + + # correct time so that it starts from 0 + time_interval -= time_interval[0] + + # fit + try: + popt, _ = curve_fit( + exp_fit, + time_interval, + voltage_interval, + p0=(1., voltage_interval[-1], voltage_interval[0] - voltage_interval[-1]), + # positive tau, negative A1 + bounds=((0, -np.inf, -np.inf), (np.inf, np.inf, 0)), + nan_policy="omit", + ) + except (ValueError, RuntimeError): + return None + + return np.array([abs(popt[0])]) + + +def deactivation_time_constant() -> np.ndarray | None: + """Time constant for an ion channel deactivation trace. + Fits for stim_start to stim_end as A + B * exp(-t/tau).""" + from scipy.optimize import curve_fit + + stim_start = _get_cpp_data("stim_start") + stim_end = _get_cpp_data("stim_end") + voltage = get_cpp_feature("voltage") + time = get_cpp_feature("time") + + if voltage is None or time is None: + return None + + # isolate stimulus interval + interval_indices = np.where((time >= stim_start) & (time < stim_end)) + time_interval = time[interval_indices] + voltage_interval = voltage[interval_indices] + + # correct time so that it starts from 0 + time_interval -= time_interval[0] + + # fit + try: + popt, _ = curve_fit( + exp_fit, + time_interval, + voltage_interval, + p0=( + 1., voltage_interval[-1], max( + 0, voltage_interval[0] - voltage_interval[-1] + ) + ), + bounds=((0, -np.inf, 0), np.inf), # positive tau, positive A1 + nan_policy="omit", + ) + except (ValueError, RuntimeError): + return None + + return np.array([abs(popt[0])]) + + +def inactivation_time_constant() -> np.ndarray | None: + """Time constant for an ion channel inactivation trace. + Fits for trace maximum to stim end interval as A + B * exp(-t/tau).""" + from scipy.optimize import curve_fit + + stim_start = _get_cpp_data("stim_start") + stim_end = _get_cpp_data("stim_end") + voltage = get_cpp_feature("voltage") + time = get_cpp_feature("time") + # used to remove end of trace to remove artifacts due to stimulus change + end_skip = _get_cpp_data("inactivation_tc_end_skip") + + if voltage is None or time is None: + return None + + # isolate stimulus interval + stim_start_idx = np.flatnonzero(time >= stim_start)[0] + stim_end_idx = np.flatnonzero(time >= stim_end)[0] + time_interval = time[stim_start_idx:stim_end_idx - end_skip] + voltage_interval = voltage[stim_start_idx:stim_end_idx - end_skip] + + # keep trace going from voltage max to stim end + # remove end of trace to remove artifacts due to stimulus change + max_idx = np.argmax(voltage_interval) + time_interval = time_interval[max_idx:] + voltage_interval = voltage_interval[max_idx:] + + # correct time so that it starts from 0 + if time_interval.size < 1: + return None + time_interval -= time_interval[0] + + # fit + try: + popt, _ = curve_fit( + exp_fit, + time_interval, + voltage_interval, + p0=(1., voltage_interval[-1], voltage_interval[0] - voltage_interval[-1]), + bounds=((0, -np.inf, 0), np.inf), # positive tau, positive A1 + nan_policy="omit", + ) + except (ValueError, RuntimeError): + return None + + return np.array([abs(popt[0])]) diff --git a/efel/settings.py b/efel/settings.py index 7751f347..64a10be6 100644 --- a/efel/settings.py +++ b/efel/settings.py @@ -65,6 +65,8 @@ class Settings: sahp_start (float): SAHP start (default: 5.0). ignore_first_ISI (bool): Ignore first ISI (default: True). impedance_max_freq (float): Impedance maximum frequency (default: 50.0). + inactivation_tc_end_skip (int): number of data points to skip before + stim end for inactivation_time_constant feature """ Threshold: float = -20.0 @@ -99,6 +101,7 @@ class Settings: ignore_first_ISI: bool = True impedance_max_freq: float = 50.0 AP_phaseslope_range: int = 2 + inactivation_tc_end_skip: int = 10 def set_setting(self, setting_name: str, diff --git a/efel/units/units.json b/efel/units/units.json index ba48c410..e2cc0a89 100644 --- a/efel/units/units.json +++ b/efel/units/units.json @@ -156,5 +156,8 @@ "neg_peak_diff": "s", "pos_peak_diff": "s", "neg_image": "constant", - "pos_image": "constant" + "pos_image": "constant", + "activation_time_constant": "ms", + "deactivation_time_constant": "ms", + "inactivation_time_constant": "ms" } diff --git a/examples/voltage_clamp/voltage_clamp.ipynb b/examples/voltage_clamp/voltage_clamp.ipynb new file mode 100644 index 00000000..cf46b35e --- /dev/null +++ b/examples/voltage_clamp/voltage_clamp.ipynb @@ -0,0 +1,1029 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Voltage clamp trace and eFEL" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This notebook explains how to use traces from voltage clamp in eFEL. First, you need to load your modules and your trace data. Here, we will use experimental data from Channelpedia: https://channelpedia.epfl.ch/expdata/details/9430\n", + "\n", + " Ranjan R, Logette E, Marani M, Herzog M, Tache V, Scantamburlo E, Buchillier V and Markram H (2019) A Kinetic Map of the Homomeric Voltage-Gated Potassium Channel (Kv) Family. Front. Cell. Neurosci. 13:358. doi: 10.3389/fncel.2019.00358\n", + "\n", + " Blue Brain Project Portal (https://portal.bluebrain.epfl.ch) and Channelpedia (https://channelpedia.epfl.ch)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "import numpy as np\n", + "\n", + "from matplotlib import pyplot as plt\n", + "from scipy.optimize import curve_fit\n", + "\n", + "import efel" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activation traces\n", + "\n", + "We will start to load the data form the nwb file. In this notebook, we will use the traces from three experiments: Activation, Deactivation and Inactivation." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "data_path = \"../../tests/testdata/basic/rCell9430.nwb\"\n", + "\n", + "data = {}\n", + "exp_types = [\"Activation\", \"Deactivation\", \"Inactivation\"]\n", + "with h5py.File(data_path, \"r\") as content:\n", + " for exp_type in exp_types:\n", + " data[exp_type] = {}\n", + " reps = content[\"acquisition\"][\"timeseries\"][exp_type][\"repetitions\"]\n", + " for rep_name, rep in reps.items():\n", + " data[exp_type][rep_name] = {\n", + " \"dt\": np.asarray(rep[\"x_interval\"], dtype=np.float32),\n", + " \"current\": np.asarray(rep[\"data\"], dtype=np.float32),\n", + " }" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will turn this data into the traces list expected by eFel. Here, we will start with the traces from the activation experiment. For this example, we will only use the 1st repetition. You can change the repetition by renaming the rep_name variable into 'repetition2' or 'repetition3'. Notice that we have to give the time in ms. \n", + "\n", + "**Attention!** eFEL was designed mainly to extract features from voltage timeseries. The voltage clamp data are currents. To use the eFEL features with voltage clamp data, we have to give the current recording to the \"V\" key in the trace dict. eFEL will treat the current trace as if it were a voltage trace." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "traces = []\n", + "rep_name = \"repetition1\"\n", + "exp_type = \"Activation\"\n", + "for idx in range(len(data[exp_type][rep_name][\"dt\"])):\n", + " trace = {}\n", + " i = data[exp_type][rep_name][\"current\"][:,idx]\n", + " t = np.arange(i.size) * data[exp_type][rep_name][\"dt\"][idx]\n", + " # efel expects ms: s -> ms\n", + " t = t * 1000.0\n", + " trace[\"T\"] = t\n", + " trace[\"V\"] = i # trick: input current as if it was voltage\n", + " trace[\"stim_start\"] = [99]\n", + " trace[\"stim_end\"] = [600]\n", + " traces.append(trace)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the traces to see how they look:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Activation traces')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for t in traces:\n", + " plt.plot(t[\"T\"], t[\"V\"], c=\"red\")\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Response Current (nA)\")\n", + "plt.title(\"Activation traces\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now extract the desired features from eFel. Even if there is 'voltage' in the feature names, since we gave the current trace instead of the voltage trace, it will still compute these features for the current trace. So here, 'maximum_voltage' will actually compute the maximum of the current, 'voltage_base' will actually compute the current base and 'steady_state_voltage_stimend' will actually compute the current steady state at the end of the stimulus. While those three features are usually used on voltage traces, the last one, 'activation_time_constant' has been written specifically for this kind of voltage clamp activation trace." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ajaquier/Documents/eFEL/efel/pyfeatures/pyfeatures.py:398: OptimizeWarning: Covariance of the parameters could not be estimated\n", + " popt, _ = curve_fit(\n" + ] + } + ], + "source": [ + "feature_names = [\"maximum_voltage\", \"voltage_base\", \"steady_state_voltage_stimend\", \"activation_time_constant\"]\n", + "feats = efel.get_feature_values(traces, feature_names)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these features, we can get several information. We can, for example, extract the 'activation voltage', defined as the voltage at which the maximum current of the trace is at least 10% of the maximum current among all of the traces." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Activation voltage is at -20 mV\n" + ] + } + ], + "source": [ + "# Stimulus Voltage used in the experiment - used later for plotting\n", + "stim_v = list(range(-90, 90, 10))\n", + "\n", + "max_i = np.array([feat_dict[\"maximum_voltage\"][0] for feat_dict in feats])\n", + "max_i = max_i / np.max(max_i)\n", + "\n", + "act_v_idx = np.argwhere(max_i >= 0.1)[0][0]\n", + "act_v = stim_v[act_v_idx]\n", + "act_i = max_i[act_v_idx]\n", + "ylim = [np.min(max_i) - 0.05, 1.05]\n", + "print(f\"Activation voltage is at {act_v} mV\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we plot the I-V curve for the 1st repetition, and we mark the activation voltage with a red line" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(stim_v, max_i, '.')\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Peak current / Max current\")\n", + "plt.plot([act_v, act_v], [ylim[0], act_i], color=\"red\")\n", + "plt.ylim(ylim)\n", + "plt.title(\"Activation voltage\")\n", + "\n", + "# sigmoid fit\n", + "def sigmoid(v, delta, tau):\n", + " return 1 / (1 + np.exp(-(v - delta) / tau))\n", + "\n", + "popt, _ = curve_fit(sigmoid, stim_v, max_i)\n", + "v = np.linspace(stim_v[0], stim_v[-1], 200)\n", + "i = sigmoid(v, *popt)\n", + "plt.plot(v, i, \"--\", c=\"black\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also show the evolution of the steady state current at the end of the stimulus across input voltage:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ssis = np.array([feat_dict[\"steady_state_voltage_stimend\"][0] for feat_dict in feats])\n", + "plt.plot(stim_v, ssis, '.')\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Steady State Current (nA)\")\n", + "plt.title(\"Activation steady state\")\n", + "\n", + "# sigmoid fit\n", + "def sigmoid_scaled(v, delta, tau, A):\n", + " return A / (1 + np.exp(-(v - delta) / tau))\n", + "\n", + "popt, _ = curve_fit(sigmoid_scaled, stim_v, ssis)\n", + "ss = sigmoid_scaled(v, *popt)\n", + "plt.plot(v, ss, \"--\", c=\"black\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also plot the current base. Since this feature is computed on the trace before the stimulus is applied, it should be constant across the traces." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Activation current base')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "i_base = np.array([feat_dict[\"voltage_base\"][0] for feat_dict in feats])\n", + "plt.plot(stim_v, i_base, '.')\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Current Base (nA)\")\n", + "plt.title(\"Activation current base\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the current base is the same for all stimulus voltages, which is expected since it is computed before the stimulus is applied." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can get the time constant of the activation curve (from stim_start to the maximum of the trace):" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Activation time constant')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "act_tau_efel = np.array([feat_dict[\"activation_time_constant\"][0] for feat_dict in feats])\n", + "plt.plot(stim_v, act_tau_efel, '.')\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Activation time constant (ms)\")\n", + "plt.xlim((-15, 85))\n", + "plt.ylim((0, 10))\n", + "plt.title(\"Activation time constant\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a visual verification, we can do an exponential fit to the traces, use our time constant as a parameter of the exponential and plot the exponential on top of the trace." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(90.0, 170.0)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def exp_fit(t, tau_1, A1, A0):\n", + " return A0 - A1 * np.exp(-t / tau_1)\n", + "\n", + "for i, trace in enumerate(traces):\n", + " stim_start_idx = np.argwhere(trace[\"T\"] >= trace[\"stim_start\"][0])[0][0]\n", + " stim_end_idx = np.argwhere(trace[\"V\"] >= np.max(trace[\"V\"]))[0][0]\n", + " # trace[\"stim_end\"] - 1 to account for artefact\n", + " if stim_end_idx < stim_start_idx or trace[\"T\"][stim_end_idx] > trace[\"stim_end\"][0] - 1:\n", + " continue\n", + " t_interval = trace[\"T\"][stim_start_idx:stim_end_idx + 1]\n", + " v_interval = trace[\"V\"][stim_start_idx:stim_end_idx + 1]\n", + " t0 = t_interval[0]\n", + " t_interval_corrected = t_interval - t0\n", + " \n", + " popt, _ = curve_fit(exp_fit, t_interval_corrected, v_interval)\n", + "\n", + " v_fit = exp_fit(t_interval_corrected, act_tau_efel[i], popt[1], popt[2])\n", + " plt.plot(trace[\"T\"], trace[\"V\"], c=\"red\")\n", + " plt.plot(t_interval, v_fit, \"--\", c=\"black\", lw=3)\n", + " t_tau = popt[0] + t0\n", + " plt.plot(t_tau, exp_fit(popt[0], *popt), 'o', color='black', markersize=12)\n", + "plt.xlim(90, 170)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deactivation traces\n", + "\n", + "Now we will also perform efeature extraction on traces from deactivation experiment. First, we load the data from the file. Once again, we will put the current data under the 'V' key representing the voltage, so that eFEL treats our trace like any voltage trace. Notice that here, we are setting the stimulus start at 401 and stimulus end at 599 because from 400 to 600 is the interval of interest for our time constant fit, and we start slightly after and end slightly before 'real' stimulus in order to prevent artifacts that show when there is a stimulus change that affect our computation" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "traces = []\n", + "rep_name = \"repetition1\"\n", + "exp_type = \"Deactivation\"\n", + "for idx in range(len(data[exp_type][rep_name][\"dt\"])):\n", + " trace = {}\n", + " i = data[exp_type][rep_name][\"current\"][:,idx]\n", + " t = np.arange(i.size) * data[exp_type][rep_name][\"dt\"][idx]\n", + " # efel expects ms: s -> ms\n", + " t = t * 1000.0\n", + " trace[\"T\"] = t\n", + " trace[\"V\"] = i # trick: input current as if it was voltage\n", + " trace[\"stim_start\"] = [401]\n", + " trace[\"stim_end\"] = [599]\n", + " traces.append(trace)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, we can start by plotting the traces:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Deactivation traces')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for t in traces:\n", + " plt.plot(t[\"T\"], t[\"V\"], c=\"red\")\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Response Current (nA)\")\n", + "plt.title(\"Deactivation traces\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now extract the deactivation time constant using eFEL:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-10.0, 200.0)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "efel.reset()\n", + "stim_v = list(range(-80, 40, 10))\n", + "feature_names = [\"deactivation_time_constant\"]\n", + "feats = efel.get_feature_values(traces, feature_names)\n", + "\n", + "deact_tau_efel = np.array([feat_dict[\"deactivation_time_constant\"][0] for feat_dict in feats])\n", + "plt.plot(stim_v, deact_tau_efel, '.')\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Deactivation time constant (ms)\")\n", + "plt.title(\"Deactivation time constant\")\n", + "plt.xlim((-65, 15))\n", + "plt.ylim((-10, 200))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a visual verification, we can do an exponential fit to the traces, use our time constant as a parameter of the exponential and plot the exponential on top of the trace." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9j/1kf4nxwd62v5vnvhcnf87h616469rk/T/ipykernel_12656/1772092254.py:2: RuntimeWarning: overflow encountered in exp\n", + " return A0 - A1 * np.exp(-t / tau_1)\n" + ] + }, + { + "data": { + "text/plain": [ + "(390.0, 610.0)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i, trace in enumerate(traces):\n", + " interval_indices = np.where((trace[\"T\"] >= trace[\"stim_start\"][0]) & (trace[\"T\"] < trace[\"stim_end\"][0]))\n", + " t_interval = trace[\"T\"][interval_indices]\n", + " v_interval = trace[\"V\"][interval_indices]\n", + " t0 = t_interval[0]\n", + " t_interval_corrected = t_interval - t0\n", + " \n", + " popt, _ = curve_fit(exp_fit, t_interval_corrected, v_interval)\n", + "\n", + " v_fit = exp_fit(t_interval_corrected, deact_tau_efel[i], popt[1], popt[2])\n", + " plt.plot(trace[\"T\"], trace[\"V\"], c=\"red\")\n", + " plt.plot(t_interval, v_fit, \"--\", c=\"black\", lw=3)\n", + " t_tau = popt[0] + t0\n", + " plt.plot(t_tau, exp_fit(popt[0], *popt), 'o', color='black', markersize=12)\n", + "plt.xlim(390, 610)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Inactivation traces\n", + "\n", + "Now we will also perform efeature extraction on traces from inactivation experiment. First, we load the data from the file. Once again, we will put the current data under the 'V' key representing the voltage, so that eFEL treats our trace like any voltage trace. For this one, we will have to extract the features in two steps, because they depend on which stimulus interval we are interested in. We will first look into the features we can extract from the stimulus interval between 1600 and 1700 ms." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "traces = []\n", + "rep_name = \"repetition1\"\n", + "exp_type = \"Inactivation\"\n", + "for idx in range(len(data[exp_type][rep_name][\"dt\"])):\n", + " trace = {}\n", + " i = data[exp_type][rep_name][\"current\"][:,idx]\n", + " t = np.arange(i.size) * data[exp_type][rep_name][\"dt\"][idx]\n", + " # efel expects ms: s -> ms\n", + " t = t * 1000.0\n", + " trace[\"T\"] = t\n", + " trace[\"V\"] = i # trick: input current as if it was voltage\n", + " trace[\"stim_start\"] = [1600]\n", + " trace[\"stim_end\"] = [1700]\n", + " traces.append(trace)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As usual, we will start by plotting the traces:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Inactivation traces')" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for t in traces:\n", + " plt.plot(t[\"T\"], t[\"V\"], c=\"red\")\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Response Current (nA)\")\n", + "plt.title(\"Inactivation traces\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now plot the I-V curve showing the maximum current in function of the input voltage. Remember that even though we are using the 'maximum_voltage' feature, since the trace we have given as input under 'V' is actually current, we will actually get the maximum current by using this feature:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(-0.05, 1.05)" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "efel.reset()\n", + "stim_v = list(range(-40, 80, 10))\n", + "feature_names = [\"maximum_voltage\"]\n", + "feats = efel.get_feature_values(traces, feature_names)\n", + "\n", + "max_i = np.array([feat_dict[\"maximum_voltage\"][0] for feat_dict in feats])\n", + "max_i = max_i / np.max(max_i)\n", + "\n", + "plt.clf()\n", + "plt.plot(stim_v, max_i, '.')\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Peak current / Overall max current\")\n", + "plt.title(\"Inactivation I-V curve\")\n", + "plt.ylim((-0.05, 1.05))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now compute the inactivation time constant. But in order to do that, we need to change the stimulus start and stimulus end to 100 and 1600 respectively:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "traces = []\n", + "rep_name = \"repetition1\"\n", + "exp_type = \"Inactivation\"\n", + "for idx in range(len(data[exp_type][rep_name][\"dt\"])):\n", + " trace = {}\n", + " i = data[exp_type][rep_name][\"current\"][:,idx]\n", + " t = np.arange(i.size) * data[exp_type][rep_name][\"dt\"][idx]\n", + " # efel expects ms: s -> ms\n", + " t = t * 1000.0\n", + " trace[\"T\"] = t\n", + " trace[\"V\"] = i # trick: input current as if it was voltage\n", + " trace[\"stim_start\"] = [100]\n", + " trace[\"stim_end\"] = [1600]\n", + " traces.append(trace)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And now we can compute the inactivation time constant. It will select the trace from the trace maximum up to the end of the stimulus (minus a few data points to avoid artifacts)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Time constant (ms)')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "feature_names = [\"inactivation_time_constant\"]\n", + "feats = efel.get_feature_values(traces, feature_names)\n", + "inact_tau_efel = np.array([feat_dict[\"inactivation_time_constant\"][0] for feat_dict in feats])\n", + "plt.plot(stim_v, inact_tau_efel, '.')\n", + "plt.xlim((-25, 75))\n", + "plt.ylim((-5, 450))\n", + "plt.title(\"Inactivation time constant\")\n", + "plt.xlabel(\"Stimulus voltage (mV)\")\n", + "plt.ylabel(\"Time constant (ms)\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that if you want to change the number of data points to remove from the end of the trace, you can change it by changing the setting of inactivation_tc_end_skip. Setting it to 0, for example, will keep the artifacts in and can bias the computation, depending on the trace." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/ajaquier/Documents/eFEL/efel/pyfeatures/pyfeatures.py:477: OptimizeWarning: Covariance of the parameters could not be estimated\n", + " popt, _ = curve_fit(\n" + ] + }, + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Time constant (ms)')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "efel.set_setting(\"inactivation_tc_end_skip\", 0)\n", + "feature_names = [\"inactivation_time_constant\", \"steady_state_voltage_stimend\"]\n", + "feats = efel.get_feature_values(traces, feature_names)\n", + "inact_tau_efel = np.array([feat_dict[\"inactivation_time_constant\"][0] for feat_dict in feats])\n", + "plt.plot(stim_v, inact_tau_efel, '.')\n", + "plt.xlim((-25, 75))\n", + "plt.title(\"Inactivation time constant\")\n", + "plt.xlabel(\"Stimulus voltage (mV)\")\n", + "plt.ylabel(\"Time constant (ms)\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a visual verification, we can do an exponential fit to the traces, use our time constant as a parameter of the exponential and plot the exponential on top of the trace." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/9j/1kf4nxwd62v5vnvhcnf87h616469rk/T/ipykernel_12656/1772092254.py:2: RuntimeWarning: overflow encountered in exp\n", + " return A0 - A1 * np.exp(-t / tau_1)\n", + "/var/folders/9j/1kf4nxwd62v5vnvhcnf87h616469rk/T/ipykernel_12656/870918093.py:12: OptimizeWarning: Covariance of the parameters could not be estimated\n", + " popt, _ = curve_fit(exp_fit, t_interval_corrected, v_interval)\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "for i, trace in enumerate(traces):\n", + " # - 1 to account for artifacts\n", + " interval_indices = np.where((trace[\"T\"] >= trace[\"stim_start\"][0]) & (trace[\"T\"] < trace[\"stim_end\"][0] - 1))\n", + " t_interval = trace[\"T\"][interval_indices]\n", + " v_interval = trace[\"V\"][interval_indices]\n", + " stim_start_idx = np.argwhere(v_interval >= np.max(v_interval))[0][0]\n", + " t_interval = t_interval[stim_start_idx:]\n", + " v_interval = v_interval[stim_start_idx:]\n", + " t0 = t_interval[0] # for testing\n", + " t_interval_corrected = t_interval - t_interval[0]\n", + " \n", + " popt, _ = curve_fit(exp_fit, t_interval_corrected, v_interval)\n", + "\n", + " v_fit = exp_fit(t_interval_corrected, inact_tau_efel[i], popt[1], popt[2])\n", + " plt.plot(trace[\"T\"], trace[\"V\"], c=\"red\")\n", + " plt.plot(t_interval, v_fit, \"--\", c=\"black\", lw=3)\n", + " t_tau = popt[0] + t0\n", + " plt.plot(t_tau, exp_fit(popt[0], *popt), 'o', color='black', markersize=12)\n", + "# plt.xlim(390, 610)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, the steady state of the Inactivation curve can be plotted as follow:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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AODk5Zfn5Tp06JerVqycMDQ2Fra2tGDlypDhw4ECm7b19+1Z07dpVFCtWTABQXmqc1aXgQghx+PBh0bBhQ2FoaChMTU2Fl5eXuHXrVpbH6+XLlyrtH455eHh4lpmFEOLhw4eid+/eoly5csLAwEAUL15cNG3aVBw+fFilX1hYmPjqq6+EoaGhAKByWXh0dLQYOHCgsLOzE7q6uqJkyZKiefPmYtmyZco+CoVCTJ06VZQpU0bo6+sLFxcXsXv3buHr65vpcutXr16J7t27C1NTU2FmZia6d++uvGQ9q0u8IyMjhVwuFxUqVMj2c/5bSkqK+OWXX4Szs7MoWrSoMDY2Fs7OzmLx4sUq/bL7XanzWU6fPi1q164t9PT0Ml0W/uDBA9GjRw9RsmRJoaurK0qVKiXatm0rtmzZkqPPQaQpMiE+MTOPiIjyVExMDGxsbDB+/HiMGzdO6jhEBQ7n3BAR5TOrV69GRkZGvn3oKFF+xzk3RET5xJEjR3Dr1i1MmTIF7dq1y/Q4CiLKGZ6WIiLKJ5o0aaK8bH3dunUffZYUEWWPxQ0RERFpFc65ISIiIq3C4oaIiIi0SqGbUKxQKPD8+XMULVr0s56qTERERHlPCIGEhATY2tpCR+cTYzMS3mNHHDt2TLRt21bY2NgIAGLbtm05XvfkyZNCLpcLZ2dntfb55MkTAYAvvvjiiy+++CqArydPnnzyu17SkZvExEQ4Ozujd+/e8Pb2zvF6b968QY8ePdC8eXNER0ertc+iRYsCAJ48eQJTU1O11iUiIiJpxMfHw87OTvk9/jGSFjetWrVSPq1WHf3790fXrl0hl8szPWjwUz6cijI1NWVxQ0REVMDkZEpJgZtQvGrVKjx8+BD+/v456p+SkoL4+HiVFxEREWmvAlXc3Lt3D7/++ivWrVuHIkVyNugUEBAAMzMz5cvOzi6XUxIREZGUCkxxk5GRga5du2LixImoUKFCjtcbPXo04uLilK8nT57kYkoiIiKSWoG5FDwhIQEXL17ElStXMGjQIADvL+sWQqBIkSI4ePAgmjVrlmk9fX196Ovr53VcIiIikkiBKW5MTU1x48YNlbbFixfjyJEj2LJlCxwdHSVKRkRERPmJpMXN27dvcf/+feX78PBwXL16FcWLF4e9vT1Gjx6NZ8+eYe3atdDR0UG1atVU1reysoKBgUGmdiIiIiq8JC1uLl68iKZNmyrf+/n5AQB8fX2xevVqREZGIiIiQqp4REREVAAVuqeCx8fHw8zMDHFxcbzPDRERUQGhzvd3gblaioiIiCgnWNwQERGRVmFxQ0RERFqFxQ0RERFpTGRcMk4/iEFkXLJkGQrMfW6IiIgof9t0IQKjg29AIQAdGRDgXR0+dezzPAdHboiIiOiLRcYlKwsbAFAIYExwqCQjOCxuiIiICoj8cMonKxkZGdh75CRiT21CwrWD/2sXAo9ikvI8D09LERERFQD55ZTPf61ZswZ+fn54/fo1AECvZHkUdW4JAJDLZHCwMMrzTBy5ISIiyufywymfpKQk7NmzB0OGDMHZs2eV7VZWVnj9+jVMTU1Rp7EnTJ09IYSAXCbDVO9qsDEzzLOMH3DkhoiIKJ8Lj0lUFjYffDjlk5vFw+PHj7Fnzx7s2bMHR44cwbt37wAAhoaGqFevHgCgcePGOHnyJOrWrQtdXV1ExiXjUUwSHCyMJClsABY3RERE+Z6jhTF0ZFApcHLzlE9kZCRatmyJ0NBQlXZ7e3u0atUKLVu2VLYZGRmhYcOGyvc2ZoaSFTUfsLghIiLK52zMDBHgXR1jgkORoeFTPgkJCdi3bx/evHmDH374AQBgbW2Nly9fQkdHBw0aNECbNm3Qtm1bVK1aFTKZ7Iv3mdv44EwiIqICQlOnfGJiYrBz505s27YNhw4dQkpKCqysrBAZGQkdnffTcc+ePYsKFSqgePHimor/RdT5/ubIDRERUQHxpad81q9fj+XLl+PEiRNQKBTK9vLly6N9+/ZITk6GsbExACjn1BRELG6IiIi0VHh4OEqXLg1dXV0AwLVr13Ds2DEAgIuLC9q3bw9vb29UqVKlQJxuyikWN0RERFrk6dOn+PvvvxEUFITz589j//798PT0BAB0794dtra2aNeuHRwcHKQNmotY3BARERVwUVFR2LJlCzZt2oSTJ08q22UyGa5evaosbqpXr47q1atLFTPPsLghIiIqwB48eIAKFSqozKFp1KgRfHx80LFjR5QsWVLCdNJgcUNERFRApKen49ChQ3jy5Inysu2yZcuiQoUKMDU1hY+PDzp16gQ7OzuJk0qLxQ0REVE+d+3aNaxduxbr169HdHQ0TExM8P3338PIyAgymQwXLlyAiYmJ1DHzDRY3RERE+VBUVBTWrVuHtWvX4saNG8p2CwsLdOnSBUlJSTAyen+HYhY2qljcEBER5UOLFy/G5MmTAQB6enr45ptv0KNHD3z99dfKS7spayxuiIiIJPbo0SOsXLkS7u7uaNGiBQCgd+/eCAkJQY8ePdC5c2eYm5tLnLLgYHFDREQkgdTUVOzcuRPLly/HoUOHIIRA27ZtlcWNg4MDTp06JXHKgonFDRERUR569OgRAgMDsXLlSrx8+VLZ3rx5c3Tv3l3CZNqDxQ0REVEe6tChAy5fvgwAsLGxQa9evdC7d2+UK1dO4mTaQ0fqAERERNrqzZs3mD9/PhISEpRtAwYMgIeHB7Zt24aIiAhMmTKFhY2GceSGiIhIw65fv45FixZh3bp1SEpKQpEiRfDTTz8BAPr06YO+fftKnFC7sbghIiLSAIVCgb1792L27Nk4evSosr1q1aqwsrJSvtemp2/nVyxuiIiIvlBycjJcXV1x69YtAIBcLkf79u0xcOBANG7cmAVNHmNxQ0RE9BkSEhJQtGhRAIChoSHKly+PZ8+e4YcffsDgwYML/fOdpMTihoiISA23bt3CnDlzsHHjRoSGhsLR0REAsGDBAhQrVkxZ8JB0eLUUERFRDpw+fRpt27ZF1apV8eeffyIpKQlbt25VLrezs2Nhk09w5IaIiCgbQggcOnQIU6dOxbFjxwC8nxDcvn17+Pn5oUGDBhInpKywuCEiIspGfHw8OnbsiISEBOjq6sLX1xcjR45E+fLlpY5GH8HihoiI6P+lp6dj3759aNu2LWQyGczMzDBixAjExsZi+PDhKF26tNQRKQdkQgghdYi8FB8fDzMzM8TFxcHU1FTqOERElA+kpqZi1apVmDZtGh49eoQjR46gadOmUseif1Hn+5sjN0REVGilpaVh7dq1mDx5Mh4/fgwAsLS0xIsXLyRORl+CxQ0RERU6GRkZWL9+PSZNmoQHDx4AAEqWLIlff/0V/fr1g5GRkcQJ6UtIein48ePH4eXlBVtbW8hkMmzfvv2j/YODg9GiRQtYWlrC1NQU9evXx4EDB/ImLBERaQ0hBH7//Xc8ePAAlpaWmD17Nh48eIAhQ4awsNECkhY3iYmJcHZ2xqJFi3LU//jx42jRogX27t2LS5cuoWnTpvDy8sKVK1dyOSkRERVkCoUCwcHBSElJAQAUKVIE06ZNw/Tp0xEeHg4/Pz8WNVok30wolslk2LZtG9q1a6fWelWrVoWPjw/Gjx+fo/6cUExEVHgIIbB//378+uuvyid1f3g6939FxiUjPCYRjhbGsDEzzOOk9CmFZkKxQqFAQkICihcvnm2flJQUZaUOvD84RESk/c6fP49Ro0Ypn9BtZmYGhUKRZd9NFyIwOvgGFALQkQEB3tXhU8c+D9OSJhXoxy/MmjULb9++RefOnbPtExAQADMzM+WLDzIjItJud+/eRadOneDm5oajR49CX18fI0aMwMOHDzFo0KBM/SPjkpWFDQAoBDAmOBSRccl5nJw0pcAWNxs2bMDEiROxefNmWFlZZdtv9OjRiIuLU76ePHmShymJiCivDR06FFu2bIFMJkPPnj1x9+5dzJw5M9tR/vCYRGVh80GGEHgUk5QHaSk3FMjTUkFBQejbty/+/vtveHh4fLSvvr4+9PX18ygZERHltbi4OCgUCpibmwMApkyZArlcjoCAAFSrVu2T6ztaGENHBpUCRy6TwcGCE4wLqgI3crNx40b06tULGzduRJs2baSOQ0REEklPT0dgYCCcnJzg7++vbHdxccGuXbtyVNgAgI2ZIQK8q0MukwF4X9hM9a7GScUFmKQjN2/fvsX9+/eV78PDw3H16lUUL14c9vb2GD16NJ49e4a1a9cCeH8qytfXF3/88Qfc3NwQFRUFADA0NISZmZkkn4GIiPLe4cOHMWzYMISGhgIAjh49irS0NOjq6n7W9nzq2OOrCpZ4FJMEBwsjFjYFnKQjNxcvXoSLiwtcXFwAAH5+fnBxcVFe1h0ZGYmIiAhl/2XLliE9PR0DBw6EjY2N8jVkyBBJ8hMRUd66d+8evvnmG7Ro0QKhoaEwNzfH/PnzcenSpc8ubD6wMTNE/XIlWNhogXxzn5u8wvvcEBEVTFu2bEHXrl2RlpYGuVyOgQMHwt/f/6O3AyHtUWjuc0NERIWHu7s7DAwM0KJFC8yaNQuVK1eWOhLlUyxuiIgoXzp+/Di2bduGuXPnAgCsra0RGhoKe3veXI8+rsBdLUVERNotKioK3bt3R+PGjTFv3jyVBySzsKGcYHFDRET5Qnp6Ov744w9UrFgR69atg0wmQ//+/VGnTh2po1EBw9NSREQkuZMnT2LgwIG4fv06AKBOnTpYvHgxXF1dJU5GBRFHboiISFLp6enw9fXF9evXUbx4cSxduhRnz55lYUOfjSM3RESU5zIyMiCTyaCjo4MiRYpg3rx52LVrF6ZOnQoLCwup41EBx5EbIiLKUzdu3EDDhg2xbNkyZZuXlxeWLVvGwoY0gsUNERHlieTkZIwZMwa1atXCuXPnMHXqVKSlpUkdi7QQixsiIsp1R44cQY0aNRAQEID09HR4e3vjzJkzX/zIBKKssLghIqJc8+rVK/Tu3RvNmzfH/fv3YWtri23btmHr1q0oVaqU1PFIS7G4ISKiXBMeHo41a9ZAJpPhp59+wq1bt9CuXTupY5GW49VSRESkUcnJyTA0fP9kbVdXV8yaNQtubm5o0KCBxMmosODIDRERaYQQAsuXL0eZMmVw69YtZfuwYcNY2FCeYnFDRERfLCIiAp6envjhhx/w8uVLLFy4UGV5ZFwyTj+IQWRcskQJqTDhaSkiIvpsQgisWLECw4cPR0JCAgwMDDBlyhQMGTJE2WfThQiMDr4BhQB0ZECAd3X41OEDMCn3sLghIqLPEhERgb59++LQoUMAgAYNGmDlypWoWLGisk9kXLKysAEAhQDGBIfiqwqWsDEzlCI2FQI8LUVERJ8lKCgIhw4dgoGBAebMmYPjx4+rFDYAEB6TqCxsPsgQAo9ikvIwKRU2HLkhIqIcE0JAJpMBAPz8/BAeHo5hw4ahQoUKWfZ3tDCGjgwqBY5cJoODhVFexKVCiiM3RESUI0FBQWjSpAnevXsHAChSpAiWLFmSbWEDADZmhgjwrg75/xdEcpkMU72r8ZQU5SqO3BAR0UfFxsZi0KBB2LBhAwBgyZIlGDZsWI7X96ljj68qWOJRTBIcLIxY2FCuY3FDRETZCgkJQc+ePfH06VPI5XL89ttvGDRokNrbsTEzZFFDeYbFDRERZfLu3TuMGTMGc+fOBQA4OTlh3bp1cHNzkzgZ0adxzg0REWUyePBgZWHz448/4urVqyxsqMBgcUNERJn89ttvKF++PHbv3o3AwEAYGxtLHYkox1jcEBERnj9/juXLlyvflylTBrdv30abNm0kTEX0eTjnhoiokNu9ezd69uyJV69eoXTp0mjVqhUAQC6XS5yM6PNw5IaIqJBKSUnB0KFD4eXlhVevXqFmzZooW7as1LGIvhiLGyKiQujOnTuoV68e/vjjDwDAkCFDcPbs2UyPTyAqiHhaioiokNmwYQN++OEHJCYmwsLCAqtWrULbtm2ljkWkMSxuiIgKGZlMhsTERDRr1gx//fUXbG1tpY5EpFEsboiICoF3797BwMAAANClSxcULVoUrVq14qRh0kqcc0NEpMWEEFi4cCEqV66M6OhoZXvbtm1Z2JDWYnFDRKSl4uPj4ePjg8GDB+PRo0cq97Eh0mY8LUVEpIWuXbuGjh074v79+yhSpAhmzpyJIUOGSB2LKE+wuCEi0iJCCPz5558YPHgw3r17Bzs7O2zevBn16tWTOhpRnlGruFEoFDh27BhOnDiBx48fIykpCZaWlnBxcYGHhwfs7OxyKycREeXA0qVLMWDAAABA69atsXbtWpQoUULiVER5K0dzbpKTk/H777/Dzs4OrVu3xr59+/DmzRvI5XLcv38f/v7+cHR0ROvWrXH27NnczkxERNno1q0bqlSpgmnTpmHXrl0sbKhQytHITYUKFVC/fn0sX74cLVq0gK6ubqY+jx8/xoYNG/Ddd99h7Nix6Nevn8bDEhFRZidOnECjRo0gk8lQtGhRXL58Gfr6+lLHIpKMTAghPtXp9u3bqFy5co42mJaWhoiICJQrV+6Lw+WG+Ph4mJmZIS4uDqamplLHISL6bGlpaRgxYgTmz5+PuXPnYujQoVJHIso16nx/5+i0VE4Lm9DQUOjq6ua4sDl+/Di8vLxga2sLmUyG7du3f3Kdo0ePolatWtDX14eTkxNWr16do30REWmTyMhINGvWDPPnzwcAxMbGSpyIKP/44vvcJCQkYNmyZahbty6cnZ3VWjcxMRHOzs5YtGhRjvqHh4ejTZs2aNq0Ka5evYqhQ4eib9++OHDgwOdEJyIqkE6dOoXatWvj5MmTMDU1xfbt2zFx4kSpYxHlG599Kfjx48fx559/YuvWrbC1tYW3t3eOi5QPWrVqhVatWuW4f2BgIBwdHTF79mwA70eUTp48iblz58LT01OtfRMRFTQf7jbs5+eH9PR0VK1aFcHBwahQoYLU0YjyFbWKm6ioKKxevRp//vkn4uPj0blzZ6SkpGD79u2oUqVKbmVUOnPmDDw8PFTaPD09P3qeOSUlBSkpKcr38fHxuRWPiChXhYWFYdiwYcjIyICPjw9WrFgBExMTqWMR5Ts5Pi3l5eWFihUr4vr165g3bx6eP3+OBQsW5Ga2TKKiomBtba3SZm1tjfj4eCQnJ2e5TkBAAMzMzJQv3ouHiAqqypUrY+7cuZgzZw42btyoLGwi45Jx+kEMIuOy/neQqLDJ8cjNvn378PPPP2PAgAEoX758bmbSqNGjR8PPz0/5Pj4+ngUOERUY+/fvh729vXJ0fPDgwSrLN12IwOjgG1AIQEcGBHhXh08deymiEuUbOR65OXnyJBISElC7dm24ublh4cKFiImJyc1smZQsWVLlqbYAEB0dDVNTUxgaGma5jr6+PkxNTVVeRET5nRACM2fOROvWrdG+ffssT6lHxiUrCxsAUAhgTHAoR3Co0MtxcVOvXj0sX74ckZGR+PHHHxEUFARbW1soFAocOnQICQkJuZkTAFC/fn2EhISotB06dAj169fP9X0TEeWV5ORkdO/eHSNHjoQQAl999VWWN+ULj0lUFjYfZAiBRzFJeZSUKH9S+1JwY2Nj9O7dGydPnsSNGzcwfPhwTJs2DVZWVvjmm2/U2tbbt29x9epVXL16FcD7S72vXr2KiIgIAO9PKfXo0UPZv3///nj48CFGjhyJsLAwLF68GJs3b8awYcPU/RhERPnS06dP4e7ujvXr10Mul2PhwoVYtmxZlsWNo4UxdGSqbXKZDA4WRnmUlih/+qL73FSsWBEzZszA06dPsXHjRrXXv3jxIlxcXODi4gIA8PPzg4uLC8aPHw/g/U2qPhQ6AODo6Ig9e/bg0KFDcHZ2xuzZs7FixQpeBk5EWuH06dNwdXXFpUuXUKJECRw+fBgDBw6ETCbLsr+NmSECvKtD/v/L5TIZpnpXg41Z1qfpiQqLHD1+QZvw8QtElB8JIdCyZUscPnwYNWrUwI4dO+Dg4JCjdSPjkvEoJgkOFkYsbEhrqfP9rfZN/BITEzFt2jSEhITgxYsXUCgUymUymQwPHjxQPzERUSEnk8mwfv16TJo0CdOmTVPr/jU2ZoYsaoj+Re3ipm/fvjh27Bi6d+8OGxubbIdLiYjo4169eoWtW7fihx9+AABYWVlh4cKFEqciKvjULm727duHPXv2oGHDhrmRh4ioULhx4wa+/fZbhIeHw8DAQOXiCSL6MmpPKDY3N0fx4sVzIwsRUaGwbds21K9fH+Hh4Shbtixq1aoldSQiraJ2cTN58mSMHz8eSUm8jwIRkTqEEJg2bRq8vb2RmJgIDw8PXLhwAdWqVZM6GpFWUfu01OzZs/HgwQNYW1vDwcEBurq6KssvX76ssXBERNoiJSUFP/74I9asWQMAGDRoEObOnYsiRdT+Z5iIPkHtv1Xt2rXLhRhERNrt2LFjWLNmDeRyOf744w8MHDhQ6khEWov3uSEiyiOzZs1C9erVeeNRos+g8fvcCCF4yTcRkZqOHDmCihUrolSpUgCAESNGSJyIqHDI0YTiqlWrIigoCKmpqR/td+/ePQwYMADTpk3TSDgiooIqMDAQLVu2xDfffIPExESp4xAVKjkauVmwYAFGjRqFn376CS1atICrqytsbW1hYGCA2NhY3Lp1CydPnsTNmzcxaNAgDBgwILdzExHlSxkZGRg+fDj++OMPAO//c8hJw0R5S605NydPnsSmTZtw4sQJPH78GMnJybCwsICLiws8PT3RrVs3mJub52beL8Y5N0SUW+Lj49GlSxfs3bsXADBlyhSMHj2ap/WJNECd729OKCYi0oDHjx+jbdu2CA0NhaGhIdauXYuOHTtKHYtIa+TqgzOJiCiz3r17IzQ0FDY2Nti5cydcXV2ljkRUaKl9h2IiIsps5cqV+Prrr3H+/HkWNkQSY3FDRPSZrl27pvy5TJky2LdvH0qXLi1hIiICWNwQEaktIyMDQ4YMgYuLC7Zv3y51HCL6D865ISJSQ1JSErp27YodO3YAAMLDwyVORET/pfbIjVwux4sXLzK1v3r1CnK5XCOhiIjyo+joaDRp0gQ7duyAvr4+Nm3ahGHDhkkdi4j+Q+2Rm+yuHE9JSYGent4XByIiyk8i45IRHpOI9FdP4OvjjUePHqFEiRLYsWMHGjZsKHU8IspCjoub+fPnAwBkMhlWrFgBExMT5bKMjAwcP34clSpV0nxCIiKJbLoQgdHBN5AaH4PIPwdCkZKIcuXKYd++fShfvrzU8YgoGzkububOnQvg/chNYGCgyikoPT09ODg4IDAwUPMJiYgkEBmXjNHBN6AQQJGiFjCu1gypUfew/cABlC9nL3U8IvqIHBc3HybNNW3aFMHBwfn+MQtERF/i4cu3SE9NgY6uPgDAvFlfiIx0JMBY4mRE9ClqTyj+559/WNgQkVZLT0/H4skj8XLrRIiMNACATEcOXT0DOFgYSZyOiD5F7QnFGRkZWL16NUJCQvDixQsoFAqV5UeOHNFYOCKivJaYmAgfHx/s2bMHMh0dpD29Bb0yzpDLZJjqXQ02ZoZSRySiT1C7uBkyZAhWr16NNm3aoFq1anzaLRFpjZcvX6Jt27Y4f/48DAwMsHHjRrg19cSjmCQ4WBixsCEqINQuboKCgrB582a0bt06N/IQEUni4cOH+Prrr3Hv3j0UL14cu3btQoMGDQCARQ1RAaN2caOnpwcnJ6fcyEJEJInLly+jVatWePHiBcqUKYP9+/fz1hZEBZjaE4qHDx+OP/74I9ub+RERFTRFihTBu3fvULNmTZw5c4aFDVEBp/bIzcmTJ/HPP/9g3759qFq1KnR1dVWWBwcHaywcEVFeqFGjBkJCQlChQgWYmppKHYeIvpDaxU2xYsXQvn373MhCRJQnhBCYPn06GjZsCHd3dwCAq6urxKmISFPULm5WrVqVGzmIiPJERkYGBg8ejCVLlqBYsWIICwuDtbW11LGISIPUnnMDvL/B1eHDh7F06VIkJCQAAJ4/f463b99qNBwRkSYlJyejY8eOWLJkCWQyGSZOnMjChkgLqT1y8/jxY3z99deIiIhASkoKWrRogaJFi2L69OlISUnh86WIKF969eoVvvnmG5w+fRr6+vpYt24dOnbsKHUsIsoFao/cDBkyBK6uroiNjYWh4f/u/dC+fXuEhIRoNBwRkSY8fvwYjRo1wunTp1GsWDEcPHiQhQ2RFlN75ObEiRM4ffo09PT0VNodHBzw7NkzjQUjItKUadOmISwsDKVLl8b+/ftRtWpVqSMRUS5Su7hRKBTIyMjI1P706VMULVpUI6GIiDRp3rx5kMlkGDNmDEqXLi11HCLKZWqflmrZsiXmzZunfC+TyfD27Vv4+/vzkQxElG9cunRJ+WBffX19LF68mIUNUSGhdnEza9YsnDp1ClWqVMG7d+/QtWtX5Smp6dOn50ZGIiK1rF69Gm5ubhg1apTUUYhIAmoXN3Z2drh27RrGjh2LYcOGwcXFBdOmTcOVK1dgZWWldoBFixbBwcEBBgYGcHNzw/nz5z/af968eahYsSIMDQ1hZ2eHYcOG4d27d2rvl4i008yZM9GrVy9kZGTg5cuXytEbIipEhBpSU1NF2bJlxa1bt9RZLVtBQUFCT09PrFy5Uty8eVP069dPFCtWTERHR2fZf/369UJfX1+sX79ehIeHiwMHDggbGxsxbNiwHO8zLi5OABBxcXEa+QxElD8oFAoxYsQIAUAAEL/88otQKBRSxyIiDVHn+1utkRtdXV2NjpLMmTMH/fr1Q69evVClShUEBgbCyMgIK1euzLL/6dOn0bBhQ+WpsJYtW6JLly6fHO0hIu2Wnp6OXr16YdasWQCAGTNmYMaMGZDJZBInIyIpqH1aauDAgZg+fTrS09O/aMepqam4dOkSPDw8/hdGRwceHh44c+ZMlus0aNAAly5dUhYzDx8+xN69ezmRmagQE0Lgu+++w5o1ayCXy7Fq1Sr88ssvUsciIgmpfSn4hQsXEBISgoMHD6J69eowNjZWWZ7Tp4LHxMQgIyMj063Pra2tERYWluU6Xbt2RUxMDBo1agQhBNLT09G/f3+MGTMm2/2kpKQgJSVF+T4+Pj5H+YioYJDJZOjcuTP27duHoKAgeHl5SR2JiCT2WU8F79ChQ25k+aSjR49i6tSpWLx4Mdzc3HD//n0MGTIEkydPxrhx47JcJyAgABMnTszjpESUlzp37owmTZp81kUNRKR9ZEIIkdPO6enp2LBhA1q2bImSJUt+0Y5TU1NhZGSELVu2oF27dsp2X19fvHnzBjt27Mi0jru7O+rVq4eZM2cq29atW4cffvgBb9++hY5O5rNsWY3c2NnZIS4uDqampl/0GYhIGvfu3UO/fv2wbt063ruGqJCIj4+HmZlZjr6/1ZpzU6RIEfTv31+lWPhcenp6qF27tsrzqBQKBUJCQlC/fv0s10lKSspUwMjlcgDvz7tnRV9fH6ampiovIiq4Ll++jIYNG+LYsWMYNGiQ1HGIKB9S+7RU3bp1ceXKFZQpU+aLd+7n5wdfX1+4urqibt26mDdvHhITE9GrVy8AQI8ePVCqVCkEBAQAALy8vDBnzhy4uLgoT0uNGzcOXl5eyiKHiLTXkSNH0K5dOyQkJMDFxQVLly6VOhIR5UNqFzc//fQThg8fjqdPn6J27dqZJhTXqFEjx9vy8fHBy5cvMX78eERFRaFmzZrYv3+/cpJxRESEykjNb7/9BplMht9++w3Pnj2DpaUlvLy8MGXKFHU/BhEVMFu3bkXXrl2RmpqKpk2bYvv27RyJJaIsqTXnBkCW81pkMhmEEJDJZFk+VDM/UeecHRHlD0uXLsWAAQMghECHDh2wbt06GBgYSB2LiPKQOt/fao/chIeHf3YwIiJ1paSkYOHChRBC4Mcff8SiRYt4GpqIPkrt4kYTc22IiHJKX18f+/fvR1BQEPz8/HjXYSL6JLVPS61du/ajy3v06PFFgXIbT0sR5X+pqan4559/4OnpKXUUIson1Pn+Vru4MTc3V3mflpaGpKQk6OnpwcjICK9fv1Y/cR5icUOUv719+xYdOnTAwYMHsWHDBnTp0kXqSESUD+TafW4AIDY2VuX19u1b3LlzB40aNcLGjRs/OzQRUUxMDJo3b46DBw/CyMgIxYsXlzoSERVAahc3WSlfvjymTZuGIUOGaGJzRFQIRUREwN3dHefPn0fx4sVx5MgRnpYios+i9oTibDdUpAieP3+uqc0RUSFy+/ZttGzZEk+fPkXp0qVx8OBBVK5cWepYRFRAqV3c7Ny5U+W9EAKRkZFYuHAhGjZsqLFgRFQ4REVFoVGjRnj9+jUqVaqEAwcOwN7eXupYRFSAqV3c/Pshl8D7G/hZWlqiWbNmmD17tqZyEVEhUbJkSfTt2xdHjx7Fnj17YGFhIXUkIirg1L5aqqDj1VJE+UNGRobKg2/fvXsHQ0NDiVMRUX6Va1dLxcfHQ6FQZGpXKBSIj49XLyURFVrz589Hy5Yt8e7dOwDvR4BZ2BCRpuS4uNm2bRtcXV2V/xj9W3JyMurUqYNdu3ZpNBwRaRchBMaNG4chQ4bgyJEjWLxiNU4/iEFkXLLU0YhIi+S4uFmyZAlGjhwJIyOjTMuMjY0xatQoLFy4UKPhiEh7ZGRkYMCAAfj9998BAJ1/HI4FT0uj6/JzaDjtCDZdiJA4IRFpixwXN6GhoWjSpEm2y7/66ivcuHFDE5mISMukpKTAx8cHS5cuhUwmw/S583HBvCkE3j8nSiGAMcGhHMEhIo3IcXETGxuL9PT0bJenpaUhNjZWI6GISHvEx8ejdevW2Lp1K/T09LB582Y08uoCxX8uZcgQAo9ikqQJSURaJcfFjYODAy5evJjt8osXL/KJ4USUyfPnz3HlyhWYmJhg37596NixIxwtjKHzn4d7y2UyOFhkPu1NRKSuHBc33t7eGDt2LKKjozMti4qKwm+//YYOHTpoNBwRFXyVKlXCnj17cPToUTRr1gwAYGNmiADv6pDL3lc4cpkMU72rwcaMV0wR0ZfL8X1uEhISUL9+fUREROD7779HxYoVAQBhYWFYv3497OzscPbsWRQtWjRXA38p3ueGKPfduHEDsbGx+Oqrrz7aLzIuGY9ikuBgYcTChog+Sp3v7xzfobho0aI4deoURo8ejU2bNinn1xQrVgzff/89pkyZku8LGyLKfSdPnoSXlxcyMjJw8uRJ1KhRI9u+NmaGLGqISOM+6w7FQgjExMRACAFLS0vIZLJPr5RPcOSGKPfs2rULnTt3xrt379CwYUPs2rUL5ubmUsciIi2QKyM3//bheVJERB+sWbMGffr0QUZGBtq0aYPNmzdneV8sIqLcptbjF4iIsjJz5kz07NkTGRkZ6NGjB7Zt28bChogkw+KGiL7Ihg0bMHLkSADAiBEjsGrVKujq6kqciogKs886LUVE9EHHjh2xdu1aeHh4YMSIEVLHISL6suLm3bt3MDAw0FQWIiogkpOToa+vDx0dHejp6WHPnj2Qy+VSxyIiAvAZp6UUCgUmT56MUqVKwcTEBA8fPgQAjBs3Dn/++afGAxJR/vLq1Ss0a9YMQ4cOxYeLLVnYEFF+onZx8/vvv2P16tWYMWMG9PT0lO3VqlXDihUrNBqOiPKXJ0+ewN3dHWfPnsW6devw9OlTqSMREWWidnGzdu1aLFu2DN26dVP535qzszPCwsI0Go6I8o/bt2+jQYMGuH37NkqVKoUTJ07Azs5O6lhERJmoXdw8e/YMTk5OmdoVCgXS0tI0EoqI8pezZ8+iUaNGePr0KSpVqoTTp0+jatWqUsciIsqS2sVNlSpVcOLEiUztW7ZsgYuLi0ZCEVH+sW/fPjRv3hyvX7+Gm5sbTpw4AXt7e6ljERFlS+2rpcaPHw9fX188e/YMCoUCwcHBuHPnDtauXYvdu3fnRkYiktC7d+/w7t07eHp6YuvWrTA2NpY6EhHRR33Ws6VOnDiBSZMm4dq1a3j79i1q1aqF8ePHo2XLlrmRUaP4bCki9YWEhMDd3V3lIgIiorykzvf3ZxU3BRmLG6KPE0IgICAA3bp1Q5kyZaSOQ0QEQL3vb7Xn3JQtWxavXr3K1P7mzRuULVtW3c0RUT6Snp6OPn36YOzYsfD09MS7d++kjkREpDa159w8evQIGRkZmdpTUlLw7NkzjYQioryXlJQEHx8f7N69G3K5HKNGjeIdyImoQMpxcbNz507lzwcOHICZmZnyfUZGBkJCQuDg4KDRcESUN2JjY+Hl5YVTp07BwMAAmzdvhpeXl9SxiIg+S46Lm3bt2gEAZDIZfH19VZbp6urCwcEBs2fP1mg4Isp9z549w9dff43Q0FAUK1YMu3btQqNGjaSORUT02XJc3CgUCgCAo6MjLly4AAsLi1wLRUR5Z+DAgQgNDYWtrS3279+P6tWrSx2JiOiLqD3nJjw8PDdyEJFEAgMDkZKSgiVLlvDUMhFpBbWLGwBITEzEsWPHEBERgdTUVJVlP//8s0aCEVHuuXfvHsqXLw8AKFmyJPbt2ydxIiIizVH7UvArV67AyckJXbp0waBBg/D7779j6NChGDNmDObNm6d2gEWLFsHBwQEGBgZwc3PD+fPnP9r/zZs3GDhwIGxsbKCvr48KFSpg7969au+XqLBatGgRKlWqhL/++kvqKEREuULt4mbYsGHw8vJCbGwsDA0NcfbsWTx+/Bi1a9fGrFmz1NrWpk2b4OfnB39/f1y+fBnOzs7w9PTEixcvsuyfmpqKFi1a4NGjR9iyZQvu3LmD5cuXo1SpUup+DCKtFxmXjNMPYhAZlwzg/c35Ro8ejUGDBkGhUHzyPxJERAWV2ncoLlasGM6dO4eKFSuiWLFiOHPmDCpXroxz587B19cXYWFhOd6Wm5sb6tSpg4ULFwJ4P2nZzs4OgwcPxq+//pqpf2BgIGbOnImwsDDo6uqqE1uJdyimwmDThQiMDr4BhQB0ZMBkr4o4EDhJOVozefJkjB07FjKZTOKkREQ5k6t3KNbV1YWOzvvVrKysEBERAQAwMzPDkydPcryd1NRUXLp0CR4eHv8Lo6MDDw8PnDlzJst1du7cifr162PgwIGwtrZGtWrVMHXq1CxvKvhBSkoK4uPjVV5E2iwyLllZ2ABA+rsk9PveB3/99RfkcjlWrlyJ3377jYUNEWkttScUu7i44MKFCyhfvjwaN26M8ePHIyYmBn/99ReqVauW4+3ExMQgIyMD1tbWKu3W1tbZjv48fPgQR44cQbdu3bB3717cv38fP/30E9LS0uDv75/lOgEBAZg4cWLOPyBRARcek6gsbER6KqI3jkZq9AMYGBoheOsWtGrVStqARES5TO2Rm6lTp8LGxgYAMGXKFJibm2PAgAF4+fIlli5dqvGA/6ZQKGBlZYVly5ahdu3a8PHxwdixYxEYGJjtOqNHj0ZcXJzypc7oElFB5GhhDJ3/H5SRFdGDYVlX6BiZIXjPfhY2RFQoqD1y4+rqqvzZysoK+/fv/6wdW1hYQC6XIzo6WqU9OjoaJUuWzHIdGxsb6OrqQi6XK9sqV66MqKgopKamQk9PL9M6+vr60NfX/6yMRAWRjZkhpravhrHbbiJDCBT/qjumjxuBVk1dP70yEZEWUHvkplmzZnjz5k2m9vj4eDRr1izH29HT00Pt2rUREhKibFMoFAgJCUH9+vWzXKdhw4a4f/++8m7JAHD37l3Y2NhkWdgQFUY7d+7EijF9cPBnN2zsVw+nRjfDj61Y2BBR4aF2cXP06NFMN+4DgHfv3uHEiRNqbcvPzw/Lly/HmjVrcPv2bQwYMACJiYno1asXAKBHjx4YPXq0sv+AAQPw+vVrDBkyBHfv3sWePXswdepUDBw4UN2PQaSVli1bhvbt2yMkJASbVy9D/XIlYGNmKHUsIqI8lePTUtevX1f+fOvWLURFRSnfZ2RkYP/+/Wrfb8bHxwcvX77E+PHjERUVhZo1a2L//v3KScYRERHKK7MAwM7ODgcOHMCwYcNQo0YNlCpVCkOGDMGoUaPU2i+RthFCYNy4cZgyZQoAoHfv3vjll18kTkVEJI0c3+dGR0dHeeloVqsYGhpiwYIF6N27t2YTahjvc0PaJiUlBb1798aGDRsAAOPHj8eECRN4qTcRaRV1vr9zPHITHh4OIQTKli2L8+fPw9LSUrlMT08PVlZWKhN9iSj3vX79Gu3bt8fx48dRpEgRLFu2THlal4iosMpxcVOmTBkAUJnMS0TSio2Nxa1bt2BqaoqtW7eq3BSTiKiwyvGE4rt372Z6Fk1ISAiaNm2KunXrYurUqRoPR0QfV65cOezevRsnT55kYUNE9P9yXNyMGjUKu3fvVr4PDw+Hl5cX9PT0UL9+fQQEBHzWU8GJSD3bt2/Hvn37lO/d3NxQvXp1CRMREeUvOS5uLl68qHJ30/Xr16NChQo4cOAA/vjjD8ybNw+rV6/OjYxE9P/++OMPeHt7o1OnTrhz547UcYiI8qUcFzcxMTEoXbq08v0///wDLy8v5fsmTZrg0aNHGg1HRO9lZGRg6NChGDp0KIQQ+P7771GuXDmpYxER5Us5Lm6KFy+OyMhIAO8nFV+8eBH16tVTLk9NTc3yEnEi+jJJSUno2LEj/vjjDwDA9OnTsWTJEhQpovbTU4iICoUcFzdNmjTB5MmT8eTJE8ybNw8KhQJNmjRRLr916xYcHBxyISJR4RUdHY2mTZti+/bt0NfXR1BQEEaOHMl72BARfUSO/+s3ZcoUtGjRAmXKlIFcLsf8+fNhbGysXP7XX3+p9WwpIvq0RYsW4fz58yhevDh27NiBRo0aSR2JiCjfy/EdigEgPT0dN2/ehKWlJWxtbVWWXbt2DaVLl0aJEiU0HlKTeIdiKkjS0tIwaNAgDB8+HBUqVJA6DhGRZNT5/laruNEGLG4oPxNCYNu2bfDy8oKurq7UcYiI8g11vr/Vfio4EeWO9PR0DBo0CB06dMDPP//MCfpERJ+Jl1sQ5QNv3ryBj48PDh48CJlMhrJly0odiYiowGJxQySxhw8fom3btrh9+zaMjIywfv16tGvXTupYREQFFosbos8QGZeM8JhEOFoYw8bM8LO3c/LkSbRv3x4xMTEoVaoUdu3aBRcXFw0mJSIqfNQubhwcHNC7d2/07NkT9vb2uZGJKF/bdCECo4NvQCEAHRkQ4F0dPnXU/7vw9u1btGvXDq9evYKrqyt27NiR6SpEIiJSn9oTiocOHYrg4GCULVsWLVq0QFBQEFJSUnIjG1G+ExmXrCxsAEAhgDHBoYiMS1Z7WyYmJlizZg06d+6MY8eOsbAhItKQzypurl69ivPnz6Ny5coYPHgwbGxsMGjQIFy+fDk3MhLlG+ExicrC5oMMIfAoJilH6ycmJuLatWvK923atMGmTZtgZGSkyZhERIXaZ18KXqtWLcyfPx/Pnz+Hv78/VqxYgTp16qBmzZpYuXIlL2MlreRoYQyd/zz5QC6TwcHi08XJ48eP0bBhQ3h4ePAhs0REueizi5u0tDRs3rwZ33zzDYYPHw5XV1esWLECHTp0wJgxY9CtWzdN5iTKF2zMDBHgXR3y/3+2k1wmw1Tvap+cVHz8+HG4urri2rVr0NHRwcuXL/MiLhFRoaT2HYovX76MVatWYePGjdDR0UGPHj3Qt29fVKpUSdknNDQUderUQXKy+vMQchvvUEyaEBmXjEcxSXCwMPpkYRMYGIjBgwcjPT0dtWrVwvbt22FnZ5dHSYmItIM6399qXy1Vp04dtGjRAkuWLEG7du2yvEW8o6MjvvvuO3U3TVRg2JgZfrKoSU1Nxc8//4ylS5cCALp06YIVK1Zwfg0RUS5Tu7h5+PAhypQp89E+xsbGWLVq1WeHItIGs2bNwtKlSyGTyRAQEICRI0dCJpN9ekUiIvoifHAmUS5JTk6Gl5cXhg0bhjZt2kgdh4ioQNP4aSlzc/Mc/4/z9evXOepHpI2OHz+ORo0aQUdHB4aGhjh06BBHa4iI8liOipt58+Ypf3716hV+//13eHp6on79+gCAM2fO4MCBAxg3blyuhCTK7xQKBcaPH48pU6bA398fEyZMAAAWNkREEshRcePr66v8uUOHDpg0aRIGDRqkbPv555+xcOFCHD58GMOGDdN8SqJ8LD4+Ht27d8fOnTsBvD8dJYRgYUNEJBG159yYmJjg6tWrcHJyUmm/f/8+atasibdv32o0oKZxzg1p0s2bN+Ht7Y27d+9CX18fK1aswPfffy91LCIiraPO97faN/ErUaIEduzYkal9x44dKFGihLqbIyqwNm/eDDc3N9y9exd2dnY4ceIECxsionxA7UvBJ06ciL59++Lo0aNwc3MDAJw7dw779+/H8uXLNR6QKD96/vw5fH198e7dOzRr1gxBQUGwtLSUOhYREeEzipuePXuicuXKmD9/PoKDgwEAlStXxsmTJ5XFDpG2s7W1xeLFixEWFoYpU6agSBG1/yoREVEu4X1uiHLo3Llz0NXVRa1ataSOQkRU6OTqnJt/e/fuHeLj41VeRNpGCIHAwEC4u7ujffv2iImJkToSERF9hNrFTVJSEgYNGgQrKysYGxvD3Nxc5UWkTZKTk9GnTx8MGDAAaWlpcHV1hZ6entSxiIjoI9Qubn755RccOXIES5YsUV76OnHiRNja2mLt2rW5kZFIEvfu3UP9+vWxatUq6OjoYNq0adiyZQtPZxIR5XNqz4LctWsX1q5diyZNmqBXr15wd3eHk5MTypQpg/Xr16Nbt265kZMoT/3999/o06cPEhISYGlpiY0bN6J58+ZSxyIiohxQe+Tm9evXKFu2LADA1NRU+SypRo0a4fjx45pNRyQBIQTWrVuHhIQEuLu74+rVqyxsiIgKELWLm7JlyyI8PBwAUKlSJWzevBnA+xGdYsWKaTQckRRkMhlWr16NqVOn4siRI7C1tZU6EhERqUHt4qZXr164du0aAODXX3/FokWLYGBggGHDhuGXX37ReECivLBr1y4MGjQIH+6MYG5ujtGjR/P+NUREBdAX3+fm8ePHuHTpEpycnFCjRg1N5co1vM8N/VtaWhrGjh2LmTNnAnj/SIVOnTpJnIqIiP4rT+9zU6ZMGXh7e39RYbNo0SI4ODjAwMAAbm5uOH/+fI7WCwoKgkwmQ7t27T5735T/RMYl4/SDGETGJefqfp4+fYpmzZopC5uhQ4fi22+/zdV9EhFR7lO7uMnIyMDkyZNRqlQpmJiY4OHDhwCAcePG4c8//1Q7wKZNm+Dn5wd/f39cvnwZzs7O8PT0xIsXLz663qNHjzBixAi4u7urvU/KvzZdiEDDaUfQdfk5NJx2BJsuROTKfnbs2AFnZ2ecPHkSpqam2LJlC+bOnct72BARaQG1i5spU6Zg9erVmDFjhsoXQbVq1bBixQq1A8yZMwf9+vVDr169UKVKFQQGBsLIyAgrV67Mdp2MjAx069YNEydOVF65RQVfZFwyRgffgOL/T5QqBDAmOFTjIzgTJ05Eu3bt8Pr1a7i6uuLSpUvo0KGDRvdBRETSUbu4Wbt2LZYtW4Zu3bpBLpcr252dnREWFqbWtlJTU3Hp0iV4eHj8L5CODjw8PHDmzJls15s0aRKsrKzQp0+fT+4jJSWFj4goIMJjEpWFzQcZQuBRTJJG9+Pu7g4dHR2MGDECp06dgpOTk0a3T0RE0lL7UpBnz55l+WWgUCiQlpam1rZiYmKQkZEBa2trlXZra+tsC6WTJ0/izz//xNWrV3O0j4CAAEycOFGtXCQNRwtj6MigUuDIZTI4WBh90XaFEHjw4IHyz22zZs1w9+5dlCtX7ou2S0RE+ZPaIzdVqlTBiRMnMrVv2bIFLi4uGgmVnYSEBHTv3h3Lly+HhYVFjtYZPXo04uLilK8nT57kakb6fDZmhgjwrg65TAbgfWEz1bsabMwMP3ubb968gY+PD2rWrIl79+4p21nYEBFpL7VHbsaPHw9fX188e/YMCoUCwcHBuHPnDtauXYvdu3ertS0LCwvI5XJER0ertEdHR6NkyZKZ+j948ACPHj2Cl5eXsk2hULz/IEWK4M6dO5m+tPT19aGvr69WLpKOTx17fFXBEo9ikuBgYfRFhc2ZM2fQpUsXPH78GEWKFMG5c+dQvnx5DaYlIqL8SO2Rm2+//Ra7du3C4cOHYWxsjPHjx+P27dvYtWsXWrRooda29PT0ULt2bYSEhCjbFAoFQkJCUL9+/Uz9K1WqhBs3buDq1avK1zfffIOmTZvi6tWrsLOzU/fjUD5kY2aI+uVKfHZhk56ejsmTJ8Pd3R2PHz9G2bJlcerUKXz//fcaTkpERPnRZ91+1d3dHYcOHdJIAD8/P/j6+sLV1RV169bFvHnzkJiYiF69egEAevTogVKlSiEgIAAGBgaoVq2ayvofHvnw33YqnO7du4fu3bvj3LlzAICuXbtiyZIlvGEjEVEhonZxU7ZsWVy4cAElSpRQaX/z5g1q1aqlvO9NTvn4+ODly5cYP348oqKiULNmTezfv185yTgiIgI6Ol90r0EqRP766y+cO3cOZmZmWLx4Mbp27Sp1JCIiymNqP35BR0cHUVFRsLKyUmmPjo6Gvb09UlJSNBpQ0/j4Be2WmpoKPz8/jBw5Evb29lLHISIiDVHn+zvHIzc7d+5U/nzgwAGYmZkp32dkZCAkJAQODg7qpyX6Art27cKSJUuwfft26OnpQU9PDwsXLpQ6FhERSSjHxc2H5zfJZDL4+vqqLNPV1YWDgwNmz56t0XBE2Xn79i38/PywfPlyAMCSJUswZMgQiVMREVF+kOPi5sMl146Ojrhw4UKO7zNDpGmnT59Gjx498ODBA8hkMgwfPhz9+/eXOhYREeUTak8oDg8Pz40cRJ+UlJSEcePGYe7cuRBCwM7ODmvXrkWTJk2kjkZERPlIji9DOnPmTKab9K1duxaOjo6wsrLCDz/8kO8nE1PB1r9/f8yZMwdCCPTs2RPXr19nYUNERJnkuLiZNGkSbt68qXx/48YN9OnTBx4eHvj111+xa9cuBAQE5EpIIgAYN24cnJycsHv3bqxatUp5jyMiIqJ/y3Fxc/XqVTRv3lz5PigoCG5ubli+fDn8/Pwwf/58bN68OVdCUuF0+vRplUnq5cuXR1hYGNq0aSNhKiIiyu9yXNzExsaqPL372LFjaNWqlfJ9nTp1+FBK0ojk5GQMHz4cjRo1wi+//IJTp04pl8nlcgmTERFRQZDj4sba2lo5mTg1NRWXL19GvXr1lMsTEhKgq6ur+YRUqJw6dQo1a9ZUzq3p0aMHqlSpInUsIiIqQHJc3LRu3Rq//vorTpw4gdGjR8PIyAju7u7K5devX8/0RG6inIqLi8OAAQPQqFEj3L17F7a2tti9ezdWr14Nc3NzqeMREVEBkuNLwSdPngxvb280btwYJiYmWLNmDfT09JTLV65ciZYtW+ZKSNJuCoUCjRo1QmhoKACgd+/emDVrFosaIiL6LGo/WyouLg4mJiaZ5j68fv0aJiYmKgVPfsRnS+VPK1euxPTp07F06VJe3k1ERJmo8/2tdnFT0LG4kV5GRgYCAwPh4OCgvPJJCIGUlBQYGBhInI6IiPKjXHlwJpEmhIaGol+/fjh79ixKlSqF27dvo2jRopDJZCxsiIhII3I8oZjoSyQmJmLMmDFwcXHB2bNnUbRoUeXEdCIiIk3iyA3lKiEEtm/fjqFDhyIiIgIA8M0332DRokUoXbq0xOmIiEgbsbihXHX58mV4e3sDAOzt7TFv3jy0a9cOMplM4mRERKStWNyQxgkhlMVL7dq18f3338Pe3h5jxoyBsbGxxOmIiEjbcc4NadTu3btRs2ZNPHv2TNm2du1aTJkyhYUNERHlCRY3pBEPHz7EN998Ay8vL1y/fh1TpkxRLuMpKCIiykssbuiLxMfHY/To0ahcuTJ27dqFIkWKYNSoUZgxY4bU0YiIqJDinBv6bGvXrsXIkSMRHR0NAGjevDkWLFiAypUrS5yMiIgKM47c0Ge7desWoqOjUb58eezcuROHDh1iYUNERJLjyA3l2MOHD/Hu3TtUqVIFADBmzBjY2tqif//++f6ZYkREVHhw5IY+KSEhQTmvpm/fvvjwODJTU1P8/PPPLGyIiChf4cgNZSs1NRXLli3DpEmT8PLlSwCAsbEx4uLiUKxYMWnDERERZYMjN5SJQqFAUFAQKleujMGDB+Ply5fKeTUHDx5kYUNERPkaR24okx07dqBLly4AAGtra0yYMAF9+vSBrq6uxMmIiIg+jcVNIRMZl4zwmEQ4WhjDxsxQ2R4fHw9TU1MA7x9s2bBhQ7Rq1QpDhw7lnYWJiKhAYXFTiGy6EIHRwTegEICODAjwro46JdLx22+/4eTJk7hz5w4MDQ0hl8tx4sQJ3lmYiIgKJBY3hURkXLKysAGA1Dcv0K/fD0i+GYL09HQAwKFDh/DNN98A4CMTiIio4GJxU0iExyRCIYD0+BjEnd2Mt9cOAor3RY2npycCAgLg4uIicUoiIqIvx+KmkHC0MIbi7Ss8X/4DRHoqAMCwjDM2Lp2Dbz2bSZyOiIhIc3gpuJZLTk4GANiYGWKmbxMYOtaGvl012HSdhlV/72RhQ0REWocjN1oqKioKc+bMwYoVK3D16lXY29vDp449XI/uRHQS4GiperUUERGRtuDIjZZ5/PgxBg4cCAcHB8ycOROxsbFYt26dcnk5Wws0cLJgYUNERFqLIzdaIiwsDNOmTcP69euVVz/Vr18fY8eORevWrSVOR0RElHdY3GiBpKQkuLm5IT4+HgDg4eGBsWPHonHjxrykm4iICh0WNwWQEAKXL19GrVq1IJPJYGRkhP79++POnTsYM2YM6tatK3VEIiIiyeSLOTeLFi2Cg4MDDAwM4ObmhvPnz2fbd/ny5XB3d4e5uTnMzc3h4eHx0f7aJD09HZs2bYKbmxtcXV1x/Phx5bKAgABs376dhQ0RERV6khc3mzZtgp+fH/z9/XH58mU4OzvD09MTL168yLL/0aNH0aVLF/zzzz84c+YM7Ozs0LJlSzx79iyPk+ed+Ph4zJ07F05OTvjuu+9w4cIF6Ovr48aNG8o+OjqS/yqJiIjyBZkQQkgZwM3NDXXq1MHChQsBAAqFAnZ2dhg8eDB+/fXXT66fkZEBc3NzLFy4ED169Phk//j4eJiZmSEuLk75oMj8KikpCePHj8fy5cuV82ksLCwwcOBA/PTTT7CyspI4IRERUd5Q5/tb0jk3qampuHTpEkaPHq1s09HRgYeHB86cOZOjbSQlJSEtLQ3FixfPrZiSMTAwwM6dOxEfH4+KFSvCz88P3bt3h6EhL+MmIiLKjqTFTUxMDDIyMmBtba3Sbm1tjbCwsBxtY9SoUbC1tYWHh0eWy1NSUpCSkqJ8/2EEJL9JSUnBli1bsG7dOgQHB8PQ0BA6OjqYM2cOAKB169Y89URERJQDBfpqqWnTpiEoKAhHjx6FgYFBln0CAgIwceLEPE6Wc0+ePMHSpUuxfPly5TyjoKAg9OrVCwDQtm1bKeMREREVOJIWNxYWFpDL5YiOjlZpj46ORsmSJT+67qxZszBt2jQcPnwYNWrUyLbf6NGj4efnp3wfHx8POzu7Lwv+hYQQ+Oeff7Bo0SLs2LEDGRkZAIBSpUqhf//+vOkeERHRF5C0uNHT00Pt2rUREhKCdu3aAXg/oTgkJASDBg3Kdr0ZM2ZgypQpOHDgAFxdXT+6D319fejr62sy9hd78OABmjdvrnzfpEkTDBo0CN9++y2KFCnQg2lERESSk/yb1M/PD76+vnB1dUXdunUxb948JCYmKk/L9OjRA6VKlUJAQAAAYPr06Rg/fjw2bNgABwcHREVFAQBMTExgYmIi2efIjhACp06dwuXLl/Hzzz8DAJycnNC+fXuULFkSAwcORNWqVSVOSUREpD0kL258fHzw8uVLjB8/HlFRUahZsyb279+vnGQcERGhMpF2yZIlSE1NRceOHVW24+/vjwkTJuRl9I96+fIl1q5dixUrViAsLAxyuRydO3dWnm7bunUrH41ARESUCyS/z01ey8373Hw4pbZ8+XJs374daWlpAAAjIyN899138Pf3h729vUb3SUREVBgUmPvcaJuVK1eiX79+yvd16tRB37598d133+X7GwYSERFpC47caFBsbCyqVq2KDh06oG/fvnB2dtbo9omIiAorjtxIxNzcHE+ePIFcLpc6ChERUaHFW95qGAsbIiIiabG4ISIiIq3C4oaIiIi0CosbIiIi0iosboiIiEirsLghIiIircLihoiIiLQKixsiIiLSKixuiIiISKuwuCEiIiKtwuKGiIiItAqLGyIiItIqLG6IiIhIq7C4ISIiIq3C4oaIiIi0CosbIiIi0iosboiIiEirsLghIiIircLihoiIiLQKixsiIiLSKixuiIiISKuwuCEiIiKtwuKGiIiItAqLGyIiItIqLG6IiIhIq7C4ISIiIq3C4oaIiIi0CosbIiIi0iosboiIiEirsLghIiIircLihoiIiLQKixsiIiLSKixuiIiISKuwuNGgyLhknH4Qg8i4ZKmjEBERFVpFpA6gLTZdiMDo4BtQCEBHBgR4V4dPHXupYxERERU6HLnRgMi4ZGVhAwAKAYwJDuUIDhERkQRY3GhAeEyisrD5IEMIPIpJkiYQERFRIZYviptFixbBwcEBBgYGcHNzw/nz5z/a/++//0alSpVgYGCA6tWrY+/evXmUNGuOFsbQkam2yWUyOFgYSROIiIioEJO8uNm0aRP8/Pzg7++Py5cvw9nZGZ6ennjx4kWW/U+fPo0uXbqgT58+uHLlCtq1a4d27dohNDQ0j5P/j42ZIQK8q0Mue1/hyGUyTPWuBhszQ8kyERERFVYyIYT4dLfc4+bmhjp16mDhwoUAAIVCATs7OwwePBi//vprpv4+Pj5ITEzE7t27lW316tVDzZo1ERgY+Mn9xcfHw8zMDHFxcTA1NdXcB8H7uTePYpLgYGHEwoaIiEiD1Pn+lnTkJjU1FZcuXYKHh4eyTUdHBx4eHjhz5kyW65w5c0alPwB4enpm2z8lJQXx8fEqr9xiY2aI+uVKsLAhIiKSkKTFTUxMDDIyMmBtba3Sbm1tjaioqCzXiYqKUqt/QEAAzMzMlC87OzvNhCciIqJ8SfI5N7lt9OjRiIuLU76ePHkidSQiIiLKRZLexM/CwgJyuRzR0dEq7dHR0ShZsmSW65QsWVKt/vr6+tDX19dMYCIiIsr3JB250dPTQ+3atRESEqJsUygUCAkJQf369bNcp379+ir9AeDQoUPZ9iciIqLCRfLHL/j5+cHX1xeurq6oW7cu5s2bh8TERPTq1QsA0KNHD5QqVQoBAQEAgCFDhqBx48aYPXs22rRpg6CgIFy8eBHLli2T8mMQERFRPiF5cePj44OXL19i/PjxiIqKQs2aNbF//37lpOGIiAjo6PxvgKlBgwbYsGEDfvvtN4wZMwbly5fH9u3bUa1aNak+AhEREeUjkt/nJq/l5n1uiIiIKHcUmPvcEBEREWkaixsiIiLSKixuiIiISKuwuCEiIiKtIvnVUnntw/zp3HzGFBEREWnWh+/tnFwHVeiKm4SEBADgM6aIiIgKoISEBJiZmX20T6G7FFyhUOD58+coWrQoZDKZRrcdHx8POzs7PHnyhJeZfwEeR83gcdQMHkfN4HHUjMJ8HIUQSEhIgK2trcr977JS6EZudHR0ULp06Vzdh6mpaaH7Q5cbeBw1g8dRM3gcNYPHUTMK63H81IjNB5xQTERERFqFxQ0RERFpFRY3GqSvrw9/f3/o6+tLHaVA43HUDB5HzeBx1AweR83gccyZQjehmIiIiLQbR26IiIhIq7C4ISIiIq3C4oaIiIi0CosbIiIi0iosbjQsJSUFNWvWhEwmw9WrV1WWXb9+He7u7jAwMICdnR1mzJghTch86tGjR+jTpw8cHR1haGiIcuXKwd/fH6mpqSr9eBw/bdGiRXBwcICBgQHc3Nxw/vx5qSPlawEBAahTpw6KFi0KKysrtGvXDnfu3FHp8+7dOwwcOBAlSpSAiYkJOnTogOjoaIkSFwzTpk2DTCbD0KFDlW08jjnz7NkzfP/99yhRogQMDQ1RvXp1XLx4UblcCIHx48fDxsYGhoaG8PDwwL179yRMnL+wuNGwkSNHwtbWNlN7fHw8WrZsiTJlyuDSpUuYOXMmJkyYgGXLlkmQMn8KCwuDQqHA0qVLcfPmTcydOxeBgYEYM2aMsg+P46dt2rQJfn5+8Pf3x+XLl+Hs7AxPT0+8ePFC6mj51rFjxzBw4ECcPXsWhw4dQlpaGlq2bInExERln2HDhmHXrl34+++/cezYMTx//hze3t4Sps7fLly4gKVLl6JGjRoq7TyOnxYbG4uGDRtCV1cX+/btw61btzB79myYm5sr+8yYMQPz589HYGAgzp07B2NjY3h6euLdu3cSJs9HBGnM3r17RaVKlcTNmzcFAHHlyhXlssWLFwtzc3ORkpKibBs1apSoWLGiBEkLjhkzZghHR0flex7HT6tbt64YOHCg8n1GRoawtbUVAQEBEqYqWF68eCEAiGPHjgkhhHjz5o3Q1dUVf//9t7LP7du3BQBx5swZqWLmWwkJCaJ8+fLi0KFDonHjxmLIkCFCCB7HnBo1apRo1KhRtssVCoUoWbKkmDlzprLtzZs3Ql9fX2zcuDEvIuZ7HLnRkOjoaPTr1w9//fUXjIyMMi0/c+YMvvrqK+jp6SnbPD09cefOHcTGxuZl1AIlLi4OxYsXV77ncfy41NRUXLp0CR4eHso2HR0deHh44MyZMxImK1ji4uIAQPln79KlS0hLS1M5rpUqVYK9vT2PaxYGDhyINm3aqBwvgMcxp3bu3AlXV1d06tQJVlZWcHFxwfLly5XLw8PDERUVpXIczczM4ObmxuP4/1jcaIAQAj179kT//v3h6uqaZZ+oqChYW1urtH14HxUVlesZC6L79+9jwYIF+PHHH5VtPI4fFxMTg4yMjCyPEY9PzigUCgwdOhQNGzZEtWrVALz/s6Wnp4dixYqp9OVxzSwoKAiXL19GQEBApmU8jjnz8OFDLFmyBOXLl8eBAwcwYMAA/Pzzz1izZg2A//1bx7/n2WNx8xG//vorZDLZR19hYWFYsGABEhISMHr0aKkj50s5PY7/9uzZM3z99dfo1KkT+vXrJ1FyKowGDhyI0NBQBAUFSR2lwHny5AmGDBmC9evXw8DAQOo4BZZCoUCtWrUwdepUuLi44IcffkC/fv0QGBgodbQCo4jUAfKz4cOHo2fPnh/tU7ZsWRw5cgRnzpzJ9KwPV1dXdOvWDWvWrEHJkiUzXRHw4X3JkiU1mju/yelx/OD58+do2rQpGjRokGmicGE+jjlhYWEBuVye5THi8fm0QYMGYffu3Th+/DhKly6tbC9ZsiRSU1Px5s0blVEHHldVly5dwosXL1CrVi1lW0ZGBo4fP46FCxfiwIEDPI45YGNjgypVqqi0Va5cGVu3bgXwv3/roqOjYWNjo+wTHR2NmjVr5lnO/IzFzUdYWlrC0tLyk/3mz5+P33//Xfn++fPn8PT0xKZNm+Dm5gYAqF+/PsaOHYu0tDTo6uoCAA4dOoSKFSuqzIDXRjk9jsD7EZumTZuidu3aWLVqFXR0VAcXC/NxzAk9PT3Url0bISEhaNeuHYD3/wsMCQnBoEGDpA2XjwkhMHjwYGzbtg1Hjx6Fo6OjyvLatWtDV1cXISEh6NChAwDgzp07iIiIQP369aWInC81b94cN27cUGnr1asXKlWqhFGjRsHOzo7HMQcaNmyY6VYEd+/eRZkyZQAAjo6OKFmyJEJCQpTFTHx8PM6dO4cBAwbkddz8SeoZzdooPDw809VSb968EdbW1qJ79+4iNDRUBAUFCSMjI7F06VLpguYzT58+FU5OTqJ58+bi6dOnIjIyUvn6gMfx04KCgoS+vr5YvXq1uHXrlvjhhx9EsWLFRFRUlNTR8q0BAwYIMzMzcfToUZU/d0lJSco+/fv3F/b29uLIkSPi4sWLon79+qJ+/foSpi4Y/n21lBA8jjlx/vx5UaRIETFlyhRx7949sX79emFkZCTWrVun7DNt2jRRrFgxsWPHDnH9+nXx7bffCkdHR5GcnCxh8vyDxU0uyKq4EUKIa9euiUaNGgl9fX1RqlQpMW3aNGkC5lOrVq0SALJ8/RuP46ctWLBA2NvbCz09PVG3bl1x9uxZqSPla9n9uVu1apWyT3Jysvjpp5+Eubm5MDIyEu3bt1cpvClr/y1ueBxzZteuXaJatWpCX19fVKpUSSxbtkxluUKhEOPGjRPW1tZCX19fNG/eXNy5c0eitPmPTAghpBgxIiIiIsoNvFqKiIiItAqLGyIiItIqLG6IiIhIq7C4ISIiIq3C4oaIiIi0CosbIiIi0iosboiIiEirsLgh0lITJkzIk+fMrF69OtNTnvObgpAxNTUVTk5OOH36dK7vKyYmBlZWVnj69Gmu74tICixuiPK5ly9fYsCAAbC3t4e+vj5KliwJT09PnDp1StlHJpNh+/btKuuNGDECISEheZxWsy5dugSZTIazZ89mubx58+bw9vZWe7t5VfipIzAwEI6OjmjQoMFnb2Pw4MGoXLlylssiIiIgl8uxc+dOWFhYoEePHvD39//sfRHlZyxuiPK5Dh064MqVK1izZg3u3r2LnTt3okmTJnj16tVH1zMxMUGJEiXyKGXuqF27NpydnbFy5cpMyx49eoR//vkHffr0kSCZZgkhsHDhwi/+LH369EFYWFiWoz+rV6+GlZUVWrduDeD9Ay3Xr1+P169ff9E+ifIliR//QEQfERsbKwCIo0ePZtunTJkyKs9DKlOmjBBCCH9/f+Hs7Kzs5+vrK7799lsxZcoUYWVlJczMzMTEiRNFWlqaGDFihDA3NxelSpUSK1euVK7zzz//CAAiNjZW2XblyhUBQISHhwsh3j8TzMzMLNN+/m3IkCGicePGyvd///23qFatmjAwMBDFixcXzZs3F2/fvs3y882fP1+YmpqKxMRElXZ/f39ha2sr0tPTxevXr0X37t1FsWLFhKGhofj666/F3bt3lX3/nTGrZ5h9eIbU7NmzRbVq1YSRkZEoXbq0GDBggEhISFDZ77Jly0Tp0qWFoaGhaNeunZg9e7bK5xdCiO3btwsXFxehr68vHB0dxYQJE0RaWlqWn08IIS5cuCB0dHREfHy8su3DM+o2bdokGjVqJAwMDISrq6u4c+eOOH/+vKhdu7YwNjYWX3/9tXjx4oVyvVq1aok+ffqobF+hUAhHR0cxatQolXZHR0exYsWKbHMRFVQcuSHKx0xMTGBiYoLt27cjJSUlyz4XLlwAAKxatQqRkZHK91k5cuQInj9/juPHj2POnDnw9/dH27ZtYW5ujnPnzqF///748ccfc3UuRmRkJLp06YLevXvj9u3bOHr0KLy9vSGyecxdt27dkJKSgi1btijbhBBYs2YNevbsCblcjp49e+LixYvYuXMnzpw5AyEEWrdujbS0tEzb8/HxwfDhw1G1alVERkYiMjISPj4+AAAdHR3Mnz8fN2/exJo1a3DkyBGMHDlSue6pU6fQv39/DBkyBFevXkWLFi0wZcoUle2fOHECPXr0wJAhQ3Dr1i0sXboUq1evztTvv+tUqFABRYsWzbTM398fv/32Gy5fvowiRYqga9euGDlyJP744w+cOHEC9+/fx/jx45X9+/Tpg82bNyMxMVHZdvToUYSHh6N3794q265bty5OnDiRbS6iAkvi4oqIPmHLli3C3NxcGBgYiAYNGojRo0eLa9euqfQBILZt26bSltXITZkyZURGRoayrWLFisLd3V35Pj09XRgbG4uNGzcKIXJn5ObSpUsCgHj06FGOj8F3332nMvITEhIiAIh79+6Ju3fvCgDi1KlTyuUxMTHC0NBQbN68OcuM/z022fn7779FiRIllO99fHxEmzZtVPp069ZNZdvNmzcXU6dOVenz119/CRsbm2z3M2TIENGsWTOVtg8jN/8eWdm4caMAIEJCQpRtAQEBomLFisr3sbGxwsDAQOWJ5t27dxeNGjXKtN9hw4aJJk2aZJuLqKDiyA1RPtehQwc8f/4cO3fuxNdff42jR4+iVq1aWL16tdrbqlq1KnR0/vfX3traGtWrV1e+l8vlKFGiBF68eKGJ6FlydnZG8+bNUb16dXTq1AnLly9HbGzsR9fp3bs3jh8/jgcPHgAAVq5cicaNG8PJyQm3b99GkSJF4ObmpuxfokQJVKxYEbdv31Yr2+HDh9G8eXOUKlUKRYsWRffu3fHq1SskJSUBAO7cuYO6deuqrPPf99euXcOkSZOUo24mJibo168fIiMjldv5r+TkZBgYGGS5rEaNGsqfra2tAUDld2Ztba3y+ypWrBi8vb2V85Ti4+OxdevWLOfzGBoaZpuJqCBjcUNUABgYGKBFixYYN24cTp8+jZ49e37WlS66uroq72UyWZZtCoUCAJSFkPjXKaOsTvX8m46OTqZTTP9eRy6X49ChQ9i3bx+qVKmCBQsWoGLFiggPD892m82bN4e9vT1Wr16N+Ph4BAcHa3wi8aNHj9C2bVvUqFEDW7duxaVLl7Bo0SIA7y/Tzqm3b99i4sSJuHr1qvJ148YN3Lt3L9sCxsLCItsC79+/H5lMlmXbh9/XB3369FGestq0aRPkcjk6deqUaduvX7+GpaVljj8bUUHB4oaoAKpSpYrKnApdXV1kZGRofD8fvvgiIyOVbVevXv3kOv/un9U6MpkMDRs2xMSJE3HlyhXo6elh27Zt2W5TR0cHvXr1wpo1a7Bhwwbo6emhY8eOAIDKlSsjPT0d586dU/Z/9eoV7ty5gypVqmS5PT09vUzH69KlS1AoFJg9ezbq1auHChUq4Pnz5yp9KlasmGlO03/f16pVC3fu3IGTk1Om179Hzf7NxcUFYWFh2c47UlfTpk3h6OiIVatWYdWqVfjuu+9gbGycqV9oaChcXFw0sk+i/ITFDVE+9urVKzRr1gzr1q3D9evXER4ejr///hszZszAt99+q+zn4OCAkJAQREVFffIUjzqcnJxgZ2eHCRMm4N69e9izZw9mz5790XWaNWuGixcvYu3atbh37x78/f0RGhqqXH7u3DlMnToVFy9eREREBIKDg/Hy5cts78/yQa9evfDs2TOMGTMGXbp0gaGhIQCgfPny+Pbbb9GvXz+cPHkS165dw/fff49SpUqpHKN/c3BwQHh4OK5evYqYmBikpKTAyckJaWlpWLBgAR4+fIi//voLgYGBKusNHjwYe/fuxZw5c3Dv3j0sXboU+/btU46oAMD48eOxdu1aTJw4ETdv3sTt27cRFBSE3377LdvP1rRpU7x9+xY3b9786DHIKZlMht69e2PJkiU4c+ZMlqNcSUlJuHTpElq2bKmRfRLlJyxuiPIxExMTuLm5Ye7cufjqq69QrVo1jBs3Dv369cPChQuV/WbPno1Dhw7Bzs5Oo/8T19XVxcaNGxEWFoYaNWpg+vTp+P333z+6jqenJ8aNG4eRI0eiTp06SEhIQI8ePZTLTU1Ncfz4cbRu3RoVKlTAb7/9htmzZ6NVq1Yf3a69vT08PDwQGxub6aqfVatWoXbt2mjbti3q168PIQT27t2b6ZTbBx06dMDXX3+Npk2bwtLSEhs3boSzszPmzJmD6dOno1q1ali/fj0CAgJU1mvYsCECAwMxZ84cODs7Y//+/Rg2bJjK6SZPT0/s3r0bBw8eRJ06dVCvXj3MnTsXZcqUyfazlShRAu3bt8f69es/egzU0bNnT8TFxaFq1aoq85E+2LFjB+zt7eHu7q6xfRLlFzKhqXFQIqJCqF+/fggLC/viS6qvX7+OFi1a4MGDBzAxMdFQuuzVq1cPP//8M7p27Zrr+yLKaxy5ISJSw6xZs3Dt2jXcv38fCxYswJo1a+Dr6/vF2/0wMvaxidWaEhMTA29vb3Tp0iXX90UkBY7cEBGpoXPnzjh69CgSEhJQtmxZDB48GP3795c6FhH9C4sbIiIi0io8LUVERERahcUNERERaRUWN0RERKRVWNwQERGRVmFxQ0RERFqFxQ0RERFpFRY3REREpFVY3BAREZFWYXFDREREWuX/ALU+9YhumFGKAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ssis = np.array([feat_dict[\"steady_state_voltage_stimend\"][0] for feat_dict in feats])\n", + "plt.plot(stim_v, ssis, '.')\n", + "plt.xlabel(\"Stimulus Voltage (mV)\")\n", + "plt.ylabel(\"Steady State Current (nA)\")\n", + "plt.title(\"Inactivation steady state\")\n", + "\n", + "popt, _ = curve_fit(sigmoid_scaled, stim_v, ssis)\n", + "v = np.linspace(stim_v[0], stim_v[-1], 200)\n", + "ss = sigmoid_scaled(v, *popt)\n", + "plt.plot(v, ss, \"--\", c=\"black\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "myenv-py310", + "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": "3.10.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/tests/featurenames.json b/tests/featurenames.json index d69c648c..33449830 100644 --- a/tests/featurenames.json +++ b/tests/featurenames.json @@ -173,5 +173,8 @@ "interburst_60percent_values", "interburst_duration", "AHP_depth_slow", - "phaseslope_max" + "phaseslope_max", + "activation_time_constant", + "deactivation_time_constant", + "inactivation_time_constant" ] \ No newline at end of file diff --git a/tests/test_allfeatures.py b/tests/test_allfeatures.py index bb61b2de..164da769 100644 --- a/tests/test_allfeatures.py +++ b/tests/test_allfeatures.py @@ -129,6 +129,10 @@ def test_allfeatures(): # drop Spikecount and Spikecount_stimint deprecated features feature_values.pop('Spikecount') feature_values.pop('Spikecount_stimint') + # remove features that expects voltage clamp trace + feature_values.pop('activation_time_constant') + feature_values.pop('deactivation_time_constant') + feature_values.pop('inactivation_time_constant') test_data_path = os.path.join(testdata_dir, 'expectedresults.json') with open(test_data_path, 'r') as expected_json: expected_results = json.load(expected_json) @@ -177,7 +181,9 @@ def test_allfeatures_on_constant_voltage(): "voltage_deflection", "voltage_deflection_begin", "voltage_deflection_vb_ssse", "depol_block", "depol_block_bool", "voltage_base", "Spikecount", "Spikecount_stimint", "burst_number", "strict_burst_number", "trace_check", - "spike_count", "spike_count_stimint", "phaseslope_max" + "spike_count", "spike_count_stimint", "phaseslope_max", + "activation_time_constant", "deactivation_time_constant", + "inactivation_time_constant" ] for field in array_fields: diff --git a/tests/test_pyfeatures.py b/tests/test_pyfeatures.py index 943b55df..307b9d3a 100644 --- a/tests/test_pyfeatures.py +++ b/tests/test_pyfeatures.py @@ -29,6 +29,7 @@ """ +import h5py from pathlib import Path import numpy @@ -87,7 +88,16 @@ 'v_col': 2, 'i_col': 3, 'stim_start': 100.0, - 'stim_end': 5100.0} + 'stim_end': 5100.0}, + 'voltage_clamp': { + 'url': testdata_dir / 'basic' / 'rCell9430.nwb', + 'act_stim_start': 99.5, + 'act_stim_end': 600.0, + 'deact_stim_start': 401.0, + 'deact_stim_end': 599.0, + 'inact_stim_start': 100.0, + 'inact_stim_end': 1600.0 + } } @@ -120,6 +130,53 @@ def _load_trace(trace_name): return trace +def _load_voltage_clamp_traces(exp_type, repetition_name="repetition1"): + """Load the voltage clamp traces from nwb file. + + Args: + exp_type (str): type of the experiment. Can be + 'Activation', 'Deactivation' or 'Inactivation'. + """ + trace_data = traces_data["voltage_clamp"] + url = trace_data['url'] + if exp_type == "Activation": + stim_start = trace_data["act_stim_start"] + stim_end = trace_data["act_stim_end"] + elif exp_type == "Deactivation": + stim_start = trace_data["deact_stim_start"] + stim_end = trace_data["deact_stim_end"] + elif exp_type == "Inactivation": + stim_start = trace_data["inact_stim_start"] + stim_end = trace_data["inact_stim_end"] + else: + raise ValueError( + "exp_type should be 'Activation', 'Deactivation' or 'Inactivation'" + f", got {exp_type}" + ) + data = {} + with h5py.File(url, "r") as content: + reps = content["acquisition"]["timeseries"][exp_type]["repetitions"] + for rep_name, rep in reps.items(): + data[rep_name] = { + "dt": numpy.array(rep["x_interval"], dtype="float32"), + "current": numpy.array(rep["data"], dtype="float32"), + } + traces = [] + for idx in range(len(data[repetition_name]["dt"])): + trace = {} + i = data[repetition_name]["current"][:, idx] + t = numpy.arange(i.size) * data[repetition_name]["dt"][idx] + # efel expects ms: s -> ms + t = t * 1000.0 + trace["T"] = t + trace["V"] = i # trick: input current as if it was voltage + trace["stim_start"] = [stim_start] + trace["stim_end"] = [stim_end] + traces.append(trace) + + return traces + + def _test_expected_value(feature_name, expected_values): """Test expected values for feature""" for trace_name, expected_value in expected_values.items(): @@ -336,3 +393,50 @@ def test_phaseslope_max(): "depol_block_db": 180.7325033, } _test_expected_value("phaseslope_max", expected_values) + + +def test_activation_time_constant(): + """Unit test for activation_time_constant.""" + efel.reset() + + traces = _load_voltage_clamp_traces("Activation") + feats = efel.get_feature_values(traces, ["activation_time_constant"]) + act_tau = [feat_dict["activation_time_constant"][0] for feat_dict in feats] + act_tau_ref = [ + 4.42464151e-02, 6.36560480e-02, 1.35239568e+00, 6.788478e-08, + 2.00926636e-06, 1.08786116e+02, 3.51324930e+01, 1.01711405e+01, + 4.61677565e+00, 2.83639400e+00, 1.97298305e+00, 1.59010996e+00, + 1.21969111e+00, 1.07930254e+00, 8.55734307e-01, 7.39074539e-01, + 6.58848184e-01, 5.96009883e-01 + ] + numpy.testing.assert_allclose(act_tau, act_tau_ref, rtol=1e-6, atol=1e-10) + + +def test_deactivation_time_constant(): + """Unit test for deactivation_time_constant.""" + efel.reset() + + traces = _load_voltage_clamp_traces("Deactivation") + feats = efel.get_feature_values(traces, ["deactivation_time_constant"]) + deact_tau = [feat_dict["deactivation_time_constant"][0] for feat_dict in feats] + deact_tau_ref = [ + 1., 18.428159, 17.22834973, 27.27256786, 39.47847711, + 49.2782552, 65.81509027, 71.9253926, 81.54669955, 107.68102719, + 134.8814237, 120.34484955 + ] + numpy.testing.assert_allclose(deact_tau, deact_tau_ref) + + +def test_inactivation_time_constant(): + """Unit test for inactivation_time_constant.""" + efel.reset() + + traces = _load_voltage_clamp_traces("Inactivation") + feats = efel.get_feature_values(traces, ["inactivation_time_constant"]) + inact_tau = [feat_dict["inactivation_time_constant"][0] for feat_dict in feats] + inact_tau_ref = [ + 88.05764617, 397.85146878, 431.22382104, 239.30375528, 175.7584476, + 168.54772458, 143.78127593, 146.63424961, 145.0907385, 167.04060933, + 142.24530676, 137.62498461 + ] + numpy.testing.assert_allclose(inact_tau, inact_tau_ref, rtol=1e-6) diff --git a/tests/test_settings.py b/tests/test_settings.py index 8c8626ab..2196c793 100644 --- a/tests/test_settings.py +++ b/tests/test_settings.py @@ -162,6 +162,7 @@ def test_str_method(): "sahp_start: 5.0\n" "ignore_first_ISI: True\n" "impedance_max_freq: 50.0\n" - "AP_phaseslope_range: 2" + "AP_phaseslope_range: 2\n" + "inactivation_tc_end_skip: 10" ) assert str(settings) == expected_output diff --git a/tests/testdata/basic/rCell9430.nwb b/tests/testdata/basic/rCell9430.nwb new file mode 100644 index 00000000..d7730a8a Binary files /dev/null and b/tests/testdata/basic/rCell9430.nwb differ diff --git a/tox.ini b/tox.ini index 12d5aa83..887183f2 100644 --- a/tox.ini +++ b/tox.ini @@ -16,6 +16,7 @@ deps = pytest-xdist>=3.3.1 extras = neo + h5py usedevelop=True commands = pytest -sx -n auto tests @@ -36,6 +37,7 @@ allowlist_externals = rm extras = neo + h5py usedevelop=True commands = make clean