From 6e79424255476ff981c1d363eff15da4532751d8 Mon Sep 17 00:00:00 2001 From: Michael Deistler Date: Thu, 21 Nov 2024 17:17:13 +0100 Subject: [PATCH] nseg->ncomp for swc reader; set assign_groups=True (#509) * nseg->ncomp for swc reader; set assign_groups=True * replace nseg with ncomp throughout the codebase --- docs/tutorials/00_jaxley_api.ipynb | 1231 +++++------------ docs/tutorials/01_morph_neurons.ipynb | 142 +- docs/tutorials/02_small_network.ipynb | 156 +-- docs/tutorials/04_jit_and_vmap.ipynb | 72 +- .../05_channel_and_synapse_models.ipynb | 46 +- docs/tutorials/06_groups.ipynb | 995 +------------ docs/tutorials/07_gradient_descent.ipynb | 132 +- .../tutorials/08_importing_morphologies.ipynb | 228 ++- .../10_advanced_parameter_sharing.ipynb | 108 +- jaxley/connect.py | 2 +- jaxley/io/swc.py | 22 +- jaxley/modules/base.py | 74 +- jaxley/modules/branch.py | 46 +- jaxley/modules/cell.py | 57 +- jaxley/modules/compartment.py | 8 +- jaxley/modules/network.py | 52 +- jaxley/solver_voltage.py | 22 +- jaxley/utils/cell_utils.py | 50 +- jaxley/utils/debug_solver.py | 34 +- jaxley/utils/misc_utils.py | 15 +- jaxley/utils/plot_utils.py | 2 +- jaxley/utils/solver_utils.py | 24 +- tests/conftest.py | 39 +- tests/jaxley_identical/test_basic_modules.py | 10 +- .../test_radius_and_length.py | 4 +- tests/jaxley_identical/test_swc.py | 8 +- tests/jaxley_vs_neuron/test_branch.py | 28 +- tests/jaxley_vs_neuron/test_cell.py | 24 +- tests/test_api_equivalence.py | 12 +- tests/test_channels.py | 10 +- tests/test_composability_of_modules.py | 14 +- tests/test_connection.py | 2 +- tests/test_distance.py | 14 +- tests/test_make_trainable.py | 2 +- tests/test_moving.py | 18 +- tests/test_plotting_api.py | 12 +- tests/test_set_ncomp.py | 8 +- tests/test_swc.py | 16 +- tests/test_viewing.py | 20 +- 39 files changed, 1308 insertions(+), 2451 deletions(-) diff --git a/docs/tutorials/00_jaxley_api.ipynb b/docs/tutorials/00_jaxley_api.ipynb index 43fc4277..cbe6399a 100644 --- a/docs/tutorials/00_jaxley_api.ipynb +++ b/docs/tutorials/00_jaxley_api.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f5b443ef", + "id": "89896082", "metadata": {}, "source": [ "# Key concepts in Jaxley" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "297089d7", + "id": "a0404fbc", "metadata": {}, "source": [ "In this tutorial, we will introduce you to the basic concepts of Jaxley.\n", @@ -36,7 +36,7 @@ "\n", "# Assembling different Modules into a Network\n", "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=1)\n", + "branch = jx.Branch(comp, ncomp=1)\n", "cell = jx.Cell(branch, parents=[-1, 0, 0])\n", "net = jx.Network([cell]*3)\n", "\n", @@ -62,7 +62,7 @@ }, { "cell_type": "markdown", - "id": "01a7754b", + "id": "371479f9", "metadata": {}, "source": [ "First, we import the relevant libraries:" @@ -71,7 +71,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "b9a591c9", + "id": "08ded085", "metadata": {}, "outputs": [], "source": [ @@ -89,7 +89,7 @@ }, { "cell_type": "markdown", - "id": "90c5cfa9", + "id": "1676c025", "metadata": {}, "source": [ "## Modules\n", @@ -106,7 +106,7 @@ }, { "cell_type": "markdown", - "id": "56696d08", + "id": "a4f282da", "metadata": {}, "source": [ "`Compartment`s are the atoms of biophysical models in Jaxley. All mechanisms and synaptic connections live on the level of `Compartment`s and can already be simulated using `jx.integrate` on their own. Everything you do in Jaxley starts with a `Compartment`." @@ -115,7 +115,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "a8ea03d8", + "id": "e971f15c", "metadata": {}, "outputs": [], "source": [ @@ -124,26 +124,26 @@ }, { "cell_type": "markdown", - "id": "4a9a9910", + "id": "da4eac1d", "metadata": {}, "source": [ - "Mutliple `Compartments` can be connected together to form longer, linear segments / cables, which we call `Branch`es and are equivalent to sections in `NEURON`." + "Mutliple `Compartments` can be connected together to form longer, linear cables, which we call `Branch`es and are equivalent to sections in `NEURON`." ] }, { "cell_type": "code", - "execution_count": 61, - "id": "87df12ec", + "execution_count": 3, + "id": "ec10bf01", "metadata": {}, "outputs": [], "source": [ - "nseg = 4\n", - "branch = jx.Branch([comp] * nseg)" + "ncomp = 4\n", + "branch = jx.Branch([comp] * ncomp)" ] }, { "cell_type": "markdown", - "id": "30fd1ffe", + "id": "9b299579", "metadata": {}, "source": [ "In order to construct cell morphologies in Jaxley, multiple `Branches` can to be connected together as a `Cell`:" @@ -151,8 +151,8 @@ }, { "cell_type": "code", - "execution_count": 62, - "id": "134c22cf", + "execution_count": 4, + "id": "ded94f2d", "metadata": {}, "outputs": [], "source": [ @@ -164,7 +164,7 @@ }, { "cell_type": "markdown", - "id": "aa8b5214", + "id": "717fee25", "metadata": {}, "source": [ "Finally, several `Cell`s can be grouped together to form a `Network`, which can than be connected together using `Synpase`s." @@ -172,8 +172,8 @@ }, { "cell_type": "code", - "execution_count": 63, - "id": "fad830ee", + "execution_count": 5, + "id": "1944ddc9", "metadata": {}, "outputs": [ { @@ -182,7 +182,7 @@ "(2, 6, 24)" ] }, - "execution_count": 63, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "aa0b4976", + "id": "a4cdb4c1", "metadata": {}, "source": [ "Every module tracks information about its current state and parameters in two Dataframes called `nodes` and `edges`.\n", @@ -207,8 +207,8 @@ }, { "cell_type": "code", - "execution_count": 64, - "id": "bda4784f", + "execution_count": 6, + "id": "f5a13fb0", "metadata": {}, "outputs": [ { @@ -240,15 +240,6 @@ " axial_resistivity\n", " capacitance\n", " v\n", - " x\n", - " y\n", - " ...\n", - " Na_m\n", - " Na_h\n", - " K\n", - " K_gK\n", - " eK\n", - " K_n\n", " global_cell_index\n", " global_branch_index\n", " global_comp_index\n", @@ -262,19 +253,10 @@ " 0\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 0\n", " 0\n", @@ -286,19 +268,10 @@ " 0\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 0\n", " 1\n", @@ -310,19 +283,10 @@ " 0\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 0\n", " 2\n", @@ -334,19 +298,10 @@ " 0\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 0\n", " 3\n", @@ -358,19 +313,10 @@ " 1\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 1\n", " 4\n", @@ -382,19 +328,10 @@ " 1\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 1\n", " 5\n", @@ -406,19 +343,10 @@ " 1\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 1\n", " 6\n", @@ -430,19 +358,10 @@ " 1\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 1\n", " 7\n", @@ -454,19 +373,10 @@ " 2\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 2\n", " 8\n", @@ -478,19 +388,10 @@ " 2\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 2\n", " 9\n", @@ -502,19 +403,10 @@ " 2\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 2\n", " 10\n", @@ -526,19 +418,10 @@ " 2\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 2\n", " 11\n", @@ -550,19 +433,10 @@ " 0\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 3\n", " 12\n", @@ -574,19 +448,10 @@ " 0\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 3\n", " 13\n", @@ -598,19 +463,10 @@ " 0\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 3\n", " 14\n", @@ -622,19 +478,10 @@ " 0\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 3\n", " 15\n", @@ -646,19 +493,10 @@ " 1\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 4\n", " 16\n", @@ -670,19 +508,10 @@ " 1\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 4\n", " 17\n", @@ -694,19 +523,10 @@ " 1\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 4\n", " 18\n", @@ -718,19 +538,10 @@ " 1\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 4\n", " 19\n", @@ -742,19 +553,10 @@ " 2\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 5\n", " 20\n", @@ -766,19 +568,10 @@ " 2\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 5\n", " 21\n", @@ -790,19 +583,10 @@ " 2\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 5\n", " 22\n", @@ -814,19 +598,10 @@ " 2\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 1\n", " 5\n", " 23\n", @@ -834,118 +609,89 @@ " \n", " \n", "\n", - "

24 rows × 28 columns

\n", "" ], "text/plain": [ - " local_cell_index local_branch_index local_comp_index length radius \\\n", - "0 0 0 0 10.0 0.020703 \n", - "1 0 0 1 10.0 0.020703 \n", - "2 0 0 2 10.0 0.020703 \n", - "3 0 0 3 10.0 0.020703 \n", - "4 0 1 0 10.0 0.020703 \n", - "5 0 1 1 10.0 0.020703 \n", - "6 0 1 2 10.0 0.020703 \n", - "7 0 1 3 10.0 0.020703 \n", - "8 0 2 0 10.0 0.020703 \n", - "9 0 2 1 10.0 0.020703 \n", - "10 0 2 2 10.0 0.020703 \n", - "11 0 2 3 10.0 0.020703 \n", - "12 1 0 0 10.0 0.020703 \n", - "13 1 0 1 10.0 0.020703 \n", - "14 1 0 2 10.0 0.020703 \n", - "15 1 0 3 10.0 0.020703 \n", - "16 1 1 0 10.0 0.020703 \n", - "17 1 1 1 10.0 0.020703 \n", - "18 1 1 2 10.0 0.020703 \n", - "19 1 1 3 10.0 0.020703 \n", - "20 1 2 0 10.0 0.020703 \n", - "21 1 2 1 10.0 0.020703 \n", - "22 1 2 2 10.0 0.020703 \n", - "23 1 2 3 10.0 0.020703 \n", + " local_cell_index local_branch_index local_comp_index length radius \\\n", + "0 0 0 0 10.0 1.0 \n", + "1 0 0 1 10.0 1.0 \n", + "2 0 0 2 10.0 1.0 \n", + "3 0 0 3 10.0 1.0 \n", + "4 0 1 0 10.0 1.0 \n", + "5 0 1 1 10.0 1.0 \n", + "6 0 1 2 10.0 1.0 \n", + "7 0 1 3 10.0 1.0 \n", + "8 0 2 0 10.0 1.0 \n", + "9 0 2 1 10.0 1.0 \n", + "10 0 2 2 10.0 1.0 \n", + "11 0 2 3 10.0 1.0 \n", + "12 1 0 0 10.0 1.0 \n", + "13 1 0 1 10.0 1.0 \n", + "14 1 0 2 10.0 1.0 \n", + "15 1 0 3 10.0 1.0 \n", + "16 1 1 0 10.0 1.0 \n", + "17 1 1 1 10.0 1.0 \n", + "18 1 1 2 10.0 1.0 \n", + "19 1 1 3 10.0 1.0 \n", + "20 1 2 0 10.0 1.0 \n", + "21 1 2 1 10.0 1.0 \n", + "22 1 2 2 10.0 1.0 \n", + "23 1 2 3 10.0 1.0 \n", "\n", - " axial_resistivity capacitance v x y ... Na_m Na_h K \\\n", - "0 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "1 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "2 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "3 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "4 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "5 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "6 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "7 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "8 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "9 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "10 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "11 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "12 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "13 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "14 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "15 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "16 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "17 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "18 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "19 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "20 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "21 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "22 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "23 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", + " axial_resistivity capacitance v global_cell_index \\\n", + "0 5000.0 1.0 -70.0 0 \n", + "1 5000.0 1.0 -70.0 0 \n", + "2 5000.0 1.0 -70.0 0 \n", + "3 5000.0 1.0 -70.0 0 \n", + "4 5000.0 1.0 -70.0 0 \n", + "5 5000.0 1.0 -70.0 0 \n", + "6 5000.0 1.0 -70.0 0 \n", + "7 5000.0 1.0 -70.0 0 \n", + "8 5000.0 1.0 -70.0 0 \n", + "9 5000.0 1.0 -70.0 0 \n", + "10 5000.0 1.0 -70.0 0 \n", + "11 5000.0 1.0 -70.0 0 \n", + "12 5000.0 1.0 -70.0 1 \n", + "13 5000.0 1.0 -70.0 1 \n", + "14 5000.0 1.0 -70.0 1 \n", + "15 5000.0 1.0 -70.0 1 \n", + "16 5000.0 1.0 -70.0 1 \n", + "17 5000.0 1.0 -70.0 1 \n", + "18 5000.0 1.0 -70.0 1 \n", + "19 5000.0 1.0 -70.0 1 \n", + "20 5000.0 1.0 -70.0 1 \n", + "21 5000.0 1.0 -70.0 1 \n", + "22 5000.0 1.0 -70.0 1 \n", + "23 5000.0 1.0 -70.0 1 \n", "\n", - " K_gK eK K_n global_cell_index global_branch_index global_comp_index \\\n", - "0 NaN NaN NaN 0 0 0 \n", - "1 NaN NaN NaN 0 0 1 \n", - "2 NaN NaN NaN 0 0 2 \n", - "3 NaN NaN NaN 0 0 3 \n", - "4 NaN NaN NaN 0 1 4 \n", - "5 NaN NaN NaN 0 1 5 \n", - "6 NaN NaN NaN 0 1 6 \n", - "7 NaN NaN NaN 0 1 7 \n", - "8 NaN NaN NaN 0 2 8 \n", - "9 NaN NaN NaN 0 2 9 \n", - "10 NaN NaN NaN 0 2 10 \n", - "11 NaN NaN NaN 0 2 11 \n", - "12 NaN NaN NaN 1 3 12 \n", - "13 NaN NaN NaN 1 3 13 \n", - "14 NaN NaN NaN 1 3 14 \n", - "15 NaN NaN NaN 1 3 15 \n", - "16 NaN NaN NaN 1 4 16 \n", - "17 NaN NaN NaN 1 4 17 \n", - "18 NaN NaN NaN 1 4 18 \n", - "19 NaN NaN NaN 1 4 19 \n", - "20 NaN NaN NaN 1 5 20 \n", - "21 NaN NaN NaN 1 5 21 \n", - "22 NaN NaN NaN 1 5 22 \n", - "23 NaN NaN NaN 1 5 23 \n", - "\n", - " controlled_by_param \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "5 0 \n", - "6 0 \n", - "7 0 \n", - "8 0 \n", - "9 0 \n", - "10 0 \n", - "11 0 \n", - "12 0 \n", - "13 0 \n", - "14 0 \n", - "15 0 \n", - "16 0 \n", - "17 0 \n", - "18 0 \n", - "19 0 \n", - "20 0 \n", - "21 0 \n", - "22 0 \n", - "23 0 \n", - "\n", - "[24 rows x 28 columns]" + " global_branch_index global_comp_index controlled_by_param \n", + "0 0 0 0 \n", + "1 0 1 0 \n", + "2 0 2 0 \n", + "3 0 3 0 \n", + "4 1 4 0 \n", + "5 1 5 0 \n", + "6 1 6 0 \n", + "7 1 7 0 \n", + "8 2 8 0 \n", + "9 2 9 0 \n", + "10 2 10 0 \n", + "11 2 11 0 \n", + "12 3 12 0 \n", + "13 3 13 0 \n", + "14 3 14 0 \n", + "15 3 15 0 \n", + "16 4 16 0 \n", + "17 4 17 0 \n", + "18 4 18 0 \n", + "19 4 19 0 \n", + "20 5 20 0 \n", + "21 5 21 0 \n", + "22 5 22 0 \n", + "23 5 23 0 " ] }, - "execution_count": 64, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -956,8 +702,8 @@ }, { "cell_type": "code", - "execution_count": 65, - "id": "ef958b1d", + "execution_count": 7, + "id": "fa4e353c", "metadata": {}, "outputs": [ { @@ -1001,7 +747,7 @@ "Index: []" ] }, - "execution_count": 65, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1012,7 +758,7 @@ }, { "cell_type": "markdown", - "id": "ac53212f", + "id": "43c42d43", "metadata": {}, "source": [ "## Views" @@ -1020,7 +766,7 @@ }, { "cell_type": "markdown", - "id": "5cdea4c8", + "id": "942ecf64", "metadata": {}, "source": [ "Since these `Module`s can become very complex, Jaxley utilizes so called `View`s to make working with `Module`s easy and intuitive. \n", @@ -1030,17 +776,17 @@ }, { "cell_type": "code", - "execution_count": 66, - "id": "8740b8f4", + "execution_count": 8, + "id": "3885678c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "View with 1 different channels. Use `.nodes` for details." + "View with 0 different channels. Use `.nodes` for details." ] }, - "execution_count": 66, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1051,7 +797,7 @@ }, { "cell_type": "markdown", - "id": "2ee1d85a", + "id": "82357af7", "metadata": {}, "source": [ "Views behave very similarly to `Module`s, i.e. the `cell(0)` (the 0th cell of the network) behaves like the `cell` we instantiated earlier. As such, `cell(0)` also has a `nodes` attribute, which keeps track of it's part of the network:" @@ -1059,8 +805,8 @@ }, { "cell_type": "code", - "execution_count": 67, - "id": "854e6897", + "execution_count": 9, + "id": "c272cecb", "metadata": {}, "outputs": [ { @@ -1092,15 +838,6 @@ " axial_resistivity\n", " capacitance\n", " v\n", - " x\n", - " y\n", - " ...\n", - " Na_m\n", - " Na_h\n", - " K\n", - " K_gK\n", - " eK\n", - " K_n\n", " global_cell_index\n", " global_branch_index\n", " global_comp_index\n", @@ -1114,19 +851,10 @@ " 0\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 0\n", " 0\n", @@ -1138,19 +866,10 @@ " 0\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 0\n", " 1\n", @@ -1162,19 +881,10 @@ " 0\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 0\n", " 2\n", @@ -1186,19 +896,10 @@ " 0\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 0\n", " 3\n", @@ -1210,19 +911,10 @@ " 1\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 1\n", " 4\n", @@ -1234,19 +926,10 @@ " 1\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 1\n", " 5\n", @@ -1258,19 +941,10 @@ " 1\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 1\n", " 6\n", @@ -1282,19 +956,10 @@ " 1\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 1\n", " 7\n", @@ -1306,19 +971,10 @@ " 2\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 2\n", " 8\n", @@ -1330,19 +986,10 @@ " 2\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 2\n", " 9\n", @@ -1354,19 +1001,10 @@ " 2\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 2\n", " 10\n", @@ -1378,19 +1016,10 @@ " 2\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " NaN\n", - " NaN\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", " 0\n", " 2\n", " 11\n", @@ -1398,70 +1027,53 @@ " \n", " \n", "\n", - "

12 rows × 28 columns

\n", "" ], "text/plain": [ - " local_cell_index local_branch_index local_comp_index length radius \\\n", - "0 0 0 0 10.0 0.020703 \n", - "1 0 0 1 10.0 0.020703 \n", - "2 0 0 2 10.0 0.020703 \n", - "3 0 0 3 10.0 0.020703 \n", - "4 0 1 0 10.0 0.020703 \n", - "5 0 1 1 10.0 0.020703 \n", - "6 0 1 2 10.0 0.020703 \n", - "7 0 1 3 10.0 0.020703 \n", - "8 0 2 0 10.0 0.020703 \n", - "9 0 2 1 10.0 0.020703 \n", - "10 0 2 2 10.0 0.020703 \n", - "11 0 2 3 10.0 0.020703 \n", - "\n", - " axial_resistivity capacitance v x y ... Na_m Na_h K \\\n", - "0 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "1 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "2 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "3 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "4 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "5 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "6 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "7 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "8 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "9 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "10 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "11 5000.0 1.0 -70.0 NaN NaN ... NaN NaN False \n", - "\n", - " K_gK eK K_n global_cell_index global_branch_index global_comp_index \\\n", - "0 NaN NaN NaN 0 0 0 \n", - "1 NaN NaN NaN 0 0 1 \n", - "2 NaN NaN NaN 0 0 2 \n", - "3 NaN NaN NaN 0 0 3 \n", - "4 NaN NaN NaN 0 1 4 \n", - "5 NaN NaN NaN 0 1 5 \n", - "6 NaN NaN NaN 0 1 6 \n", - "7 NaN NaN NaN 0 1 7 \n", - "8 NaN NaN NaN 0 2 8 \n", - "9 NaN NaN NaN 0 2 9 \n", - "10 NaN NaN NaN 0 2 10 \n", - "11 NaN NaN NaN 0 2 11 \n", + " local_cell_index local_branch_index local_comp_index length radius \\\n", + "0 0 0 0 10.0 1.0 \n", + "1 0 0 1 10.0 1.0 \n", + "2 0 0 2 10.0 1.0 \n", + "3 0 0 3 10.0 1.0 \n", + "4 0 1 0 10.0 1.0 \n", + "5 0 1 1 10.0 1.0 \n", + "6 0 1 2 10.0 1.0 \n", + "7 0 1 3 10.0 1.0 \n", + "8 0 2 0 10.0 1.0 \n", + "9 0 2 1 10.0 1.0 \n", + "10 0 2 2 10.0 1.0 \n", + "11 0 2 3 10.0 1.0 \n", "\n", - " controlled_by_param \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "5 0 \n", - "6 0 \n", - "7 0 \n", - "8 0 \n", - "9 0 \n", - "10 0 \n", - "11 0 \n", + " axial_resistivity capacitance v global_cell_index \\\n", + "0 5000.0 1.0 -70.0 0 \n", + "1 5000.0 1.0 -70.0 0 \n", + "2 5000.0 1.0 -70.0 0 \n", + "3 5000.0 1.0 -70.0 0 \n", + "4 5000.0 1.0 -70.0 0 \n", + "5 5000.0 1.0 -70.0 0 \n", + "6 5000.0 1.0 -70.0 0 \n", + "7 5000.0 1.0 -70.0 0 \n", + "8 5000.0 1.0 -70.0 0 \n", + "9 5000.0 1.0 -70.0 0 \n", + "10 5000.0 1.0 -70.0 0 \n", + "11 5000.0 1.0 -70.0 0 \n", "\n", - "[12 rows x 28 columns]" + " global_branch_index global_comp_index controlled_by_param \n", + "0 0 0 0 \n", + "1 0 1 0 \n", + "2 0 2 0 \n", + "3 0 3 0 \n", + "4 1 4 0 \n", + "5 1 5 0 \n", + "6 1 6 0 \n", + "7 1 7 0 \n", + "8 2 8 0 \n", + "9 2 9 0 \n", + "10 2 10 0 \n", + "11 2 11 0 " ] }, - "execution_count": 67, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1472,7 +1084,7 @@ }, { "cell_type": "markdown", - "id": "c3126389", + "id": "083f8351", "metadata": {}, "source": [ "Let's use `View`s to visualize only parts of the `Network`. Before we do that, we create x, y, and z coordinates for the `Network`:" @@ -1480,8 +1092,8 @@ }, { "cell_type": "code", - "execution_count": 68, - "id": "5c964d06", + "execution_count": 10, + "id": "268e253a", "metadata": {}, "outputs": [], "source": [ @@ -1494,7 +1106,7 @@ }, { "cell_type": "markdown", - "id": "9235aed3", + "id": "7fda5d83", "metadata": {}, "source": [ "We can now visualize the entire `net` (i.e., the entire `Module`) with the `.vis()` method..." @@ -1502,8 +1114,8 @@ }, { "cell_type": "code", - "execution_count": 69, - "id": "54a10467", + "execution_count": 11, + "id": "632192d3", "metadata": {}, "outputs": [ { @@ -1512,7 +1124,7 @@ "" ] }, - "execution_count": 69, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, @@ -1529,13 +1141,13 @@ ], "source": [ "# We can use the vis function to visualize Modules.\n", - "fig, ax = plt.subplots(1,1, figsize=(3,3))\n", + "fig, ax = plt.subplots(1, 1, figsize=(3,3))\n", "net.vis(ax=ax)" ] }, { "cell_type": "markdown", - "id": "cc0494b2", + "id": "37fafc71", "metadata": {}, "source": [ "...but we can also create a `View` to visualize only parts of the `net`:" @@ -1543,8 +1155,8 @@ }, { "cell_type": "code", - "execution_count": 70, - "id": "2c371279", + "execution_count": 12, + "id": "14a4e51a", "metadata": {}, "outputs": [ { @@ -1553,13 +1165,13 @@ "" ] }, - "execution_count": 70, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1580,7 +1192,7 @@ }, { "cell_type": "markdown", - "id": "9d7e9eef", + "id": "1d20882d", "metadata": {}, "source": [ "### How to create `View`s" @@ -1588,7 +1200,7 @@ }, { "cell_type": "markdown", - "id": "4a548bd2", + "id": "857c2def", "metadata": {}, "source": [ "Above, we used `net.cell(0)` to generate a `View` of the 0-eth cell. `Jaxley` supports many ways of performing such indexing:" @@ -1596,17 +1208,17 @@ }, { "cell_type": "code", - "execution_count": 71, - "id": "a0edfdf7", + "execution_count": 13, + "id": "728f6eb0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "View with 1 different channels. Use `.nodes` for details." + "View with 0 different channels. Use `.nodes` for details." ] }, - "execution_count": 71, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1625,8 +1237,8 @@ }, { "cell_type": "code", - "execution_count": 72, - "id": "aee9ee92", + "execution_count": 14, + "id": "fe4dda8e", "metadata": {}, "outputs": [ { @@ -1660,13 +1272,7 @@ " v\n", " x\n", " y\n", - " ...\n", - " Na_m\n", - " Na_h\n", - " K\n", - " K_gK\n", - " eK\n", - " K_n\n", + " z\n", " global_cell_index\n", " global_branch_index\n", " global_comp_index\n", @@ -1680,19 +1286,13 @@ " 0\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 5.000000\n", " 30.000000\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 0\n", " 0\n", @@ -1704,19 +1304,13 @@ " 0\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 15.000000\n", " 30.000000\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 0\n", " 1\n", @@ -1728,19 +1322,13 @@ " 0\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 25.000000\n", " 30.000000\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 0\n", " 2\n", @@ -1752,19 +1340,13 @@ " 0\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 35.000000\n", " 30.000000\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 0\n", " 3\n", @@ -1776,19 +1358,13 @@ " 1\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 44.850713\n", " 28.787322\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 1\n", " 4\n", @@ -1800,19 +1376,13 @@ " 1\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 54.552138\n", " 26.361966\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 1\n", " 5\n", @@ -1824,19 +1394,13 @@ " 1\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 64.253563\n", " 23.936609\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 1\n", " 6\n", @@ -1848,19 +1412,13 @@ " 1\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 73.954988\n", " 21.511253\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 1\n", " 7\n", @@ -1872,19 +1430,13 @@ " 2\n", " 0\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 44.850713\n", " 31.212678\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 2\n", " 8\n", @@ -1896,19 +1448,13 @@ " 2\n", " 1\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 54.552138\n", " 33.638034\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 2\n", " 9\n", @@ -1920,19 +1466,13 @@ " 2\n", " 2\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 64.253563\n", " 36.063391\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 2\n", " 10\n", @@ -1944,19 +1484,13 @@ " 2\n", " 3\n", " 10.0\n", - " 0.020703\n", + " 1.0\n", " 5000.0\n", " 1.0\n", " -70.0\n", " 73.954988\n", " 38.488747\n", - " ...\n", - " NaN\n", - " NaN\n", - " False\n", - " NaN\n", - " NaN\n", - " NaN\n", + " 0.0\n", " 0\n", " 2\n", " 11\n", @@ -1964,70 +1498,67 @@ " \n", " \n", "\n", - "

12 rows × 28 columns

\n", "" ], "text/plain": [ - " local_cell_index local_branch_index local_comp_index length radius \\\n", - "0 0 0 0 10.0 0.020703 \n", - "1 0 0 1 10.0 0.020703 \n", - "2 0 0 2 10.0 0.020703 \n", - "3 0 0 3 10.0 0.020703 \n", - "4 0 1 0 10.0 0.020703 \n", - "5 0 1 1 10.0 0.020703 \n", - "6 0 1 2 10.0 0.020703 \n", - "7 0 1 3 10.0 0.020703 \n", - "8 0 2 0 10.0 0.020703 \n", - "9 0 2 1 10.0 0.020703 \n", - "10 0 2 2 10.0 0.020703 \n", - "11 0 2 3 10.0 0.020703 \n", + " local_cell_index local_branch_index local_comp_index length radius \\\n", + "0 0 0 0 10.0 1.0 \n", + "1 0 0 1 10.0 1.0 \n", + "2 0 0 2 10.0 1.0 \n", + "3 0 0 3 10.0 1.0 \n", + "4 0 1 0 10.0 1.0 \n", + "5 0 1 1 10.0 1.0 \n", + "6 0 1 2 10.0 1.0 \n", + "7 0 1 3 10.0 1.0 \n", + "8 0 2 0 10.0 1.0 \n", + "9 0 2 1 10.0 1.0 \n", + "10 0 2 2 10.0 1.0 \n", + "11 0 2 3 10.0 1.0 \n", "\n", - " axial_resistivity capacitance v x y ... Na_m \\\n", - "0 5000.0 1.0 -70.0 5.000000 30.000000 ... NaN \n", - "1 5000.0 1.0 -70.0 15.000000 30.000000 ... NaN \n", - "2 5000.0 1.0 -70.0 25.000000 30.000000 ... NaN \n", - "3 5000.0 1.0 -70.0 35.000000 30.000000 ... NaN \n", - "4 5000.0 1.0 -70.0 44.850713 28.787322 ... NaN \n", - "5 5000.0 1.0 -70.0 54.552138 26.361966 ... NaN \n", - "6 5000.0 1.0 -70.0 64.253563 23.936609 ... NaN \n", - "7 5000.0 1.0 -70.0 73.954988 21.511253 ... NaN \n", - "8 5000.0 1.0 -70.0 44.850713 31.212678 ... NaN \n", - "9 5000.0 1.0 -70.0 54.552138 33.638034 ... NaN \n", - "10 5000.0 1.0 -70.0 64.253563 36.063391 ... NaN \n", - "11 5000.0 1.0 -70.0 73.954988 38.488747 ... NaN \n", + " axial_resistivity capacitance v x y z \\\n", + "0 5000.0 1.0 -70.0 5.000000 30.000000 0.0 \n", + "1 5000.0 1.0 -70.0 15.000000 30.000000 0.0 \n", + "2 5000.0 1.0 -70.0 25.000000 30.000000 0.0 \n", + "3 5000.0 1.0 -70.0 35.000000 30.000000 0.0 \n", + "4 5000.0 1.0 -70.0 44.850713 28.787322 0.0 \n", + "5 5000.0 1.0 -70.0 54.552138 26.361966 0.0 \n", + "6 5000.0 1.0 -70.0 64.253563 23.936609 0.0 \n", + "7 5000.0 1.0 -70.0 73.954988 21.511253 0.0 \n", + "8 5000.0 1.0 -70.0 44.850713 31.212678 0.0 \n", + "9 5000.0 1.0 -70.0 54.552138 33.638034 0.0 \n", + "10 5000.0 1.0 -70.0 64.253563 36.063391 0.0 \n", + "11 5000.0 1.0 -70.0 73.954988 38.488747 0.0 \n", "\n", - " Na_h K K_gK eK K_n global_cell_index global_branch_index \\\n", - "0 NaN False NaN NaN NaN 0 0 \n", - "1 NaN False NaN NaN NaN 0 0 \n", - "2 NaN False NaN NaN NaN 0 0 \n", - "3 NaN False NaN NaN NaN 0 0 \n", - "4 NaN False NaN NaN NaN 0 1 \n", - "5 NaN False NaN NaN NaN 0 1 \n", - "6 NaN False NaN NaN NaN 0 1 \n", - "7 NaN False NaN NaN NaN 0 1 \n", - "8 NaN False NaN NaN NaN 0 2 \n", - "9 NaN False NaN NaN NaN 0 2 \n", - "10 NaN False NaN NaN NaN 0 2 \n", - "11 NaN False NaN NaN NaN 0 2 \n", + " global_cell_index global_branch_index global_comp_index \\\n", + "0 0 0 0 \n", + "1 0 0 1 \n", + "2 0 0 2 \n", + "3 0 0 3 \n", + "4 0 1 4 \n", + "5 0 1 5 \n", + "6 0 1 6 \n", + "7 0 1 7 \n", + "8 0 2 8 \n", + "9 0 2 9 \n", + "10 0 2 10 \n", + "11 0 2 11 \n", "\n", - " global_comp_index controlled_by_param \n", - "0 0 0 \n", - "1 1 0 \n", - "2 2 0 \n", - "3 3 0 \n", - "4 4 0 \n", - "5 5 0 \n", - "6 6 0 \n", - "7 7 0 \n", - "8 8 0 \n", - "9 9 0 \n", - "10 10 0 \n", - "11 11 0 \n", - "\n", - "[12 rows x 28 columns]" + " controlled_by_param \n", + "0 0 \n", + "1 0 \n", + "2 0 \n", + "3 0 \n", + "4 0 \n", + "5 0 \n", + "6 0 \n", + "7 0 \n", + "8 0 \n", + "9 0 \n", + "10 0 \n", + "11 0 " ] }, - "execution_count": 72, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -2038,8 +1569,8 @@ }, { "cell_type": "code", - "execution_count": 73, - "id": "5f855fa7", + "execution_count": 15, + "id": "012b9612", "metadata": {}, "outputs": [ { @@ -2048,7 +1579,7 @@ "(2, 6, 24)" ] }, - "execution_count": 73, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -2059,7 +1590,7 @@ }, { "cell_type": "markdown", - "id": "166a1ce4", + "id": "42d8ffdd", "metadata": {}, "source": [ "_Note: In case you need even more flexibility in how you select parts of a Module, Jaxley provides a `select` method, to give full control over the exact parts of the `nodes` and `edges` that are part of a `View`. On examples of how this can be used, see the [tutorial on advanced indexing](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)._" @@ -2067,7 +1598,7 @@ }, { "cell_type": "markdown", - "id": "27f4507c", + "id": "cf68baf6", "metadata": {}, "source": [ "You can also iterate over networks, cells, and branches:" @@ -2075,8 +1606,8 @@ }, { "cell_type": "code", - "execution_count": 79, - "id": "39322d10", + "execution_count": 16, + "id": "a78d2a6c", "metadata": {}, "outputs": [ { @@ -2107,28 +1638,28 @@ " \n", " \n", " 0\n", - " 0.988127\n", + " 0.763057\n", " 100.0\n", " \n", " \n", " 1\n", - " 0.568548\n", - " 100.0\n", + " 0.334882\n", + " 10.0\n", " \n", " \n", " 2\n", - " 0.064304\n", - " 2.5\n", + " 0.805696\n", + " 100.0\n", " \n", " \n", " 3\n", - " 0.859943\n", + " 0.717921\n", " 100.0\n", " \n", " \n", " 4\n", - " 0.879433\n", - " 100.0\n", + " 0.079569\n", + " 10.0\n", " \n", " \n", "\n", @@ -2136,14 +1667,14 @@ ], "text/plain": [ " radius length\n", - "0 0.988127 100.0\n", - "1 0.568548 100.0\n", - "2 0.064304 2.5\n", - "3 0.859943 100.0\n", - "4 0.879433 100.0" + "0 0.763057 100.0\n", + "1 0.334882 10.0\n", + "2 0.805696 100.0\n", + "3 0.717921 100.0\n", + "4 0.079569 10.0" ] }, - "execution_count": 79, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -2166,7 +1697,7 @@ }, { "cell_type": "markdown", - "id": "3a6eed75", + "id": "96cb79f6", "metadata": {}, "source": [ "Finally, you can also use `View`s in a context manager:" @@ -2174,8 +1705,8 @@ }, { "cell_type": "code", - "execution_count": 80, - "id": "ff30731a", + "execution_count": 17, + "id": "859e1f6a", "metadata": {}, "outputs": [ { @@ -2226,8 +1757,8 @@ " \n", " \n", " 4\n", - " 0.879433\n", - " 100.0\n", + " 0.079569\n", + " 10.0\n", " \n", " \n", "\n", @@ -2239,10 +1770,10 @@ "1 2.000000 2.5\n", "2 2.000000 2.5\n", "3 2.000000 2.5\n", - "4 0.879433 100.0" + "4 0.079569 10.0" ] }, - "execution_count": 80, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -2258,7 +1789,7 @@ }, { "cell_type": "markdown", - "id": "3eae86ce", + "id": "90151ce8", "metadata": {}, "source": [ "## Channels" @@ -2266,7 +1797,7 @@ }, { "cell_type": "markdown", - "id": "517f8f8f", + "id": "44a31d9f", "metadata": {}, "source": [ "The `Module`s that we have created above will not do anything interesting, since by default Jaxley initializes them without any mechanisms in the membrane. To change this, we have to insert channels into the membrane. For this purpose `Jaxley` implements `Channel`s that can be inserted into any compartment using the `insert` method of a `Module` or a `View`:" @@ -2274,8 +1805,8 @@ }, { "cell_type": "code", - "execution_count": 54, - "id": "1a7f2555", + "execution_count": 18, + "id": "0d26c451", "metadata": {}, "outputs": [ { @@ -2307,13 +1838,13 @@ " axial_resistivity\n", " capacitance\n", " v\n", - " x\n", - " y\n", - " z\n", " global_cell_index\n", " global_branch_index\n", " global_comp_index\n", " controlled_by_param\n", + " x\n", + " y\n", + " z\n", " Leak\n", " Leak_gLeak\n", " Leak_eLeak\n", @@ -2325,18 +1856,18 @@ " 0\n", " 0\n", " 0\n", - " 100.0\n", - " 0.924252\n", + " 2.5\n", + " 2.000000\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " 5.000000\n", - " 30.000000\n", - " 0.0\n", " 0\n", " 0\n", " 0\n", " 0\n", + " 5.000000\n", + " 30.000000\n", + " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -2346,18 +1877,18 @@ " 0\n", " 0\n", " 1\n", - " 100.0\n", - " 0.566347\n", + " 2.5\n", + " 2.000000\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " 15.000000\n", - " 30.000000\n", - " 0.0\n", " 0\n", " 0\n", " 1\n", " 0\n", + " 15.000000\n", + " 30.000000\n", + " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -2367,18 +1898,18 @@ " 0\n", " 0\n", " 2\n", - " 10.0\n", - " 0.208471\n", + " 2.5\n", + " 2.000000\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " 25.000000\n", - " 30.000000\n", - " 0.0\n", " 0\n", " 0\n", " 2\n", " 0\n", + " 25.000000\n", + " 30.000000\n", + " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -2388,18 +1919,18 @@ " 0\n", " 0\n", " 3\n", - " 100.0\n", - " 0.596002\n", + " 2.5\n", + " 2.000000\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " 35.000000\n", - " 30.000000\n", - " 0.0\n", " 0\n", " 0\n", " 3\n", " 0\n", + " 35.000000\n", + " 30.000000\n", + " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -2410,17 +1941,17 @@ " 1\n", " 0\n", " 10.0\n", - " 0.027419\n", + " 0.079569\n", " 5000.0\n", " 1.0\n", " -70.0\n", - " 44.850713\n", - " 28.787322\n", - " 0.0\n", " 0\n", " 1\n", " 4\n", " 0\n", + " 44.850713\n", + " 28.787322\n", + " 0.0\n", " True\n", " 0.0001\n", " -70.0\n", @@ -2431,35 +1962,35 @@ ], "text/plain": [ " local_cell_index local_branch_index local_comp_index length radius \\\n", - "0 0 0 0 100.0 0.924252 \n", - "1 0 0 1 100.0 0.566347 \n", - "2 0 0 2 10.0 0.208471 \n", - "3 0 0 3 100.0 0.596002 \n", - "4 0 1 0 10.0 0.027419 \n", + "0 0 0 0 2.5 2.000000 \n", + "1 0 0 1 2.5 2.000000 \n", + "2 0 0 2 2.5 2.000000 \n", + "3 0 0 3 2.5 2.000000 \n", + "4 0 1 0 10.0 0.079569 \n", "\n", - " axial_resistivity capacitance v x y z \\\n", - "0 5000.0 1.0 -70.0 5.000000 30.000000 0.0 \n", - "1 5000.0 1.0 -70.0 15.000000 30.000000 0.0 \n", - "2 5000.0 1.0 -70.0 25.000000 30.000000 0.0 \n", - "3 5000.0 1.0 -70.0 35.000000 30.000000 0.0 \n", - "4 5000.0 1.0 -70.0 44.850713 28.787322 0.0 \n", + " axial_resistivity capacitance v global_cell_index \\\n", + "0 5000.0 1.0 -70.0 0 \n", + "1 5000.0 1.0 -70.0 0 \n", + "2 5000.0 1.0 -70.0 0 \n", + "3 5000.0 1.0 -70.0 0 \n", + "4 5000.0 1.0 -70.0 0 \n", "\n", - " global_cell_index global_branch_index global_comp_index \\\n", - "0 0 0 0 \n", - "1 0 0 1 \n", - "2 0 0 2 \n", - "3 0 0 3 \n", - "4 0 1 4 \n", + " global_branch_index global_comp_index controlled_by_param x \\\n", + "0 0 0 0 5.000000 \n", + "1 0 1 0 15.000000 \n", + "2 0 2 0 25.000000 \n", + "3 0 3 0 35.000000 \n", + "4 1 4 0 44.850713 \n", "\n", - " controlled_by_param Leak Leak_gLeak Leak_eLeak \n", - "0 0 True 0.0001 -70.0 \n", - "1 0 True 0.0001 -70.0 \n", - "2 0 True 0.0001 -70.0 \n", - "3 0 True 0.0001 -70.0 \n", - "4 0 True 0.0001 -70.0 " + " y z Leak Leak_gLeak Leak_eLeak \n", + "0 30.000000 0.0 True 0.0001 -70.0 \n", + "1 30.000000 0.0 True 0.0001 -70.0 \n", + "2 30.000000 0.0 True 0.0001 -70.0 \n", + "3 30.000000 0.0 True 0.0001 -70.0 \n", + "4 28.787322 0.0 True 0.0001 -70.0 " ] }, - "execution_count": 54, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -2472,7 +2003,7 @@ }, { "cell_type": "markdown", - "id": "9475c217", + "id": "ab5acd51", "metadata": {}, "source": [ "This is also were `View`s come in handy, as it allows to easily target the insertion of channels to specific compartments." @@ -2480,8 +2011,8 @@ }, { "cell_type": "code", - "execution_count": 77, - "id": "4cacf613", + "execution_count": 19, + "id": "e2a1b17f", "metadata": {}, "outputs": [ { @@ -2536,7 +2067,7 @@ "12 1 False False True" ] }, - "execution_count": 77, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -2557,7 +2088,7 @@ }, { "cell_type": "markdown", - "id": "40eda5e9", + "id": "24ec120a", "metadata": {}, "source": [ "## Synapses" @@ -2565,7 +2096,7 @@ }, { "cell_type": "markdown", - "id": "cf7ab81e", + "id": "d947ba43", "metadata": {}, "source": [ "To connect different cells together, Jaxley implements a `connect` method, that can be used to couple 2 compartments together using a `Synapse`. Synapses in Jaxley work only on the compartment level, that means to be able to connect two cells, you need to specify the exact compartments on a given cell to make the connections between. Below is an example of this:" @@ -2573,8 +2104,8 @@ }, { "cell_type": "code", - "execution_count": 78, - "id": "bc3e4c02", + "execution_count": 20, + "id": "a1eed847", "metadata": {}, "outputs": [ { @@ -2646,7 +2177,7 @@ "0 0 " ] }, - "execution_count": 78, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -2663,7 +2194,7 @@ }, { "cell_type": "markdown", - "id": "e0108618", + "id": "1c603a54", "metadata": {}, "source": [ "As you can see above, now the `edges` dataframe is also updated with the information of the newly added synapse. " @@ -2671,7 +2202,7 @@ }, { "cell_type": "markdown", - "id": "9772e192", + "id": "749de44c", "metadata": {}, "source": [ "Congrats! You should now have an intuitive understand of how to use Jaxley's API to construct, navigate and manipulate neuron models." diff --git a/docs/tutorials/01_morph_neurons.ipynb b/docs/tutorials/01_morph_neurons.ipynb index c7f7bfe5..e029e767 100644 --- a/docs/tutorials/01_morph_neurons.ipynb +++ b/docs/tutorials/01_morph_neurons.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "9657a992", + "id": "9f7be2a4", "metadata": {}, "source": [ "# Basics of Jaxley" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "d7bceb7e", + "id": "2db89a9f", "metadata": {}, "source": [ "In this tutorial, you will learn how to:\n", @@ -30,7 +30,7 @@ "\n", "# Build the cell.\n", "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=2)\n", + "branch = jx.Branch(comp, ncomp=2)\n", "cell = jx.Cell(branch, parents=[-1, 0, 0, 1, 1])\n", "\n", "# Insert channels.\n", @@ -61,7 +61,7 @@ }, { "cell_type": "markdown", - "id": "bfc8a92d", + "id": "6c8a0eb9", "metadata": {}, "source": [ "First, we import the relevant libraries:" @@ -70,7 +70,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "355ccba4", + "id": "f8cb454b", "metadata": {}, "outputs": [], "source": [ @@ -91,7 +91,7 @@ }, { "cell_type": "markdown", - "id": "13e0565f", + "id": "d717ef05", "metadata": {}, "source": [ "We will now build our first cell in `Jaxley`. You have two options to do this: you can either build a cell bottom-up by defining the morphology yourselve, or you can [load cells from SWC files](https://jaxley.readthedocs.io/en/latest/tutorials/08_importing_morphologies.html).\n" @@ -99,7 +99,7 @@ }, { "cell_type": "markdown", - "id": "a4b5077e", + "id": "3883d5aa", "metadata": {}, "source": [ "### Define the cell from scratch\n", @@ -109,18 +109,18 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "48a5b24a", + "execution_count": 6, + "id": "1eba83a8", "metadata": {}, "outputs": [], "source": [ "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=2)" + "branch = jx.Branch(comp, ncomp=2)" ] }, { "cell_type": "markdown", - "id": "1a491c98", + "id": "acfbf1ab", "metadata": {}, "source": [ "Next, we can assemble branches into a cell. To do so, we have to define for each branch what its parent branch is. A `-1` entry means that this branch does not have a parent." @@ -128,8 +128,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "8a079a56", + "execution_count": 7, + "id": "4c26d47d", "metadata": {}, "outputs": [], "source": [ @@ -139,7 +139,7 @@ }, { "cell_type": "markdown", - "id": "1f0a3bc4", + "id": "efc170cc", "metadata": {}, "source": [ "To learn more about `Compartment`s, `Branch`es, and `Cell`s, see [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/00_jaxley_api.html)." @@ -147,21 +147,21 @@ }, { "cell_type": "markdown", - "id": "34f29844", + "id": "60d62a97", "metadata": {}, "source": [ "### Read the cell from an SWC file\n", "\n", "Alternatively, you could also load cells from SWC with \n", "\n", - "```cell = jx.read_swc(fname, nseg=4)```\n", + "```cell = jx.read_swc(fname, ncomp=4)```\n", "\n", "Details on handling SWC files can be found in [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/08_importing_morphologies.html)." ] }, { "cell_type": "markdown", - "id": "5773c5d7", + "id": "c8afc7cf", "metadata": {}, "source": [ "### Visualize the cells" @@ -169,7 +169,7 @@ }, { "cell_type": "markdown", - "id": "d702da32", + "id": "a3fbe809", "metadata": {}, "source": [ "Cells can be visualized as follows:" @@ -177,8 +177,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "21ea592c", + "execution_count": 9, + "id": "447c99bd", "metadata": {}, "outputs": [ { @@ -201,7 +201,7 @@ }, { "cell_type": "markdown", - "id": "d6079203", + "id": "fe86583b", "metadata": {}, "source": [ "### Insert mechanisms\n", @@ -211,8 +211,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "7f585bbf", + "execution_count": 10, + "id": "bdddba0e", "metadata": {}, "outputs": [], "source": [ @@ -223,7 +223,7 @@ }, { "cell_type": "markdown", - "id": "152741b7", + "id": "dbc08017", "metadata": {}, "source": [ "Once the cell is created, we can inspect its `.nodes` attribute which lists all properties of the cell:" @@ -231,8 +231,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "bf260e5a", + "execution_count": 11, + "id": "eae355bd", "metadata": {}, "outputs": [ { @@ -577,7 +577,7 @@ "[10 rows x 25 columns]" ] }, - "execution_count": 6, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -588,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "df13d383", + "id": "a9506866", "metadata": {}, "source": [ "_Note that `Jaxley` uses the same units as the `NEURON` simulator, which are listed [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html)._\n", @@ -598,8 +598,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "d8cc87df", + "execution_count": 12, + "id": "6312e227", "metadata": {}, "outputs": [ { @@ -720,7 +720,7 @@ "[2 rows x 25 columns]" ] }, - "execution_count": 7, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -731,7 +731,7 @@ }, { "cell_type": "markdown", - "id": "db2dbe05", + "id": "e9425ae3", "metadata": {}, "source": [ "The easiest way to know which branch is the 1st branch (or, e.g., the zero-eth compartment of the 1st branch) is to plot it in a different color:" @@ -739,13 +739,13 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "d935b6e8", + "execution_count": 14, + "id": "9eefce4d", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -758,12 +758,12 @@ "fig, ax = plt.subplots(1, 1, figsize=(4, 2))\n", "_ = cell.vis(ax=ax, col=\"k\")\n", "_ = cell.branch(1).vis(ax=ax, col=\"r\")\n", - "_ = cell.branch(1).comp(1).vis(ax=ax, col=\"b\", type=\"scatter\")" + "_ = cell.branch(1).comp(1).vis(ax=ax, col=\"b\")" ] }, { "cell_type": "markdown", - "id": "002eb2b3", + "id": "8b0459c4", "metadata": {}, "source": [ "More background and features on indexing as `cell.branch(0)` is in [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/00_jaxley_api.html)." @@ -771,7 +771,7 @@ }, { "cell_type": "markdown", - "id": "b19839ca", + "id": "611aa6fb", "metadata": {}, "source": [ "### Change parameters of the cell\n", @@ -781,8 +781,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "40099c6a", + "execution_count": 15, + "id": "d8b8e544", "metadata": {}, "outputs": [], "source": [ @@ -791,7 +791,7 @@ }, { "cell_type": "markdown", - "id": "8ee9cb57", + "id": "08892ab8", "metadata": {}, "source": [ "And we can again inspect the `.nodes` to make sure that the axial resistivity indeed changed:" @@ -799,8 +799,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "d9ba00c0", + "execution_count": 16, + "id": "6d3f14aa", "metadata": {}, "outputs": [ { @@ -921,7 +921,7 @@ "[2 rows x 25 columns]" ] }, - "execution_count": 10, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -932,7 +932,7 @@ }, { "cell_type": "markdown", - "id": "b201a433", + "id": "005f1e20", "metadata": {}, "source": [ "In a similar way, you can modify channel properties or initial states (units are again [here](https://www.neuron.yale.edu/neuron/static/docs/units/unitchart.html)):" @@ -940,8 +940,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "a2bf8e00", + "execution_count": 17, + "id": "a098f360", "metadata": {}, "outputs": [], "source": [ @@ -951,7 +951,7 @@ }, { "cell_type": "markdown", - "id": "f80e3f7c", + "id": "a08da8da", "metadata": {}, "source": [ "### Stimulate the cell\n", @@ -961,8 +961,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "d932bf8a", + "execution_count": 18, + "id": "90d876b4", "metadata": {}, "outputs": [ { @@ -988,7 +988,7 @@ }, { "cell_type": "markdown", - "id": "07be7011", + "id": "76534f64", "metadata": {}, "source": [ "We then stimulate one of the compartments of the cell with this step current:" @@ -996,8 +996,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "17ca9168", + "execution_count": 19, + "id": "472309b3", "metadata": {}, "outputs": [ { @@ -1015,7 +1015,7 @@ }, { "cell_type": "markdown", - "id": "00879bc5", + "id": "bdbd193f", "metadata": {}, "source": [ "### Define recordings" @@ -1023,7 +1023,7 @@ }, { "cell_type": "markdown", - "id": "5e8b5d2d", + "id": "16881662", "metadata": {}, "source": [ "Next, you have to define where to record the voltage. In this case, we will record the voltage at two locations:" @@ -1031,8 +1031,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "89fa9aa9", + "execution_count": 20, + "id": "46107eb1", "metadata": {}, "outputs": [ { @@ -1052,7 +1052,7 @@ }, { "cell_type": "markdown", - "id": "be0b8320", + "id": "1cd6625b", "metadata": {}, "source": [ "We can again visualize these locations to understand where we inserted recordings:" @@ -1060,13 +1060,13 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "473b933f", + "execution_count": 21, + "id": "74cb63b9", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1078,13 +1078,13 @@ "source": [ "fig, ax = plt.subplots(1, 1, figsize=(4, 2))\n", "_ = cell.vis(ax=ax)\n", - "_ = cell.branch(0).loc(0.0).vis(ax=ax, col=\"b\", type=\"scatter\")\n", - "_ = cell.branch(3).loc(1.0).vis(ax=ax, col=\"g\", type=\"scatter\")" + "_ = cell.branch(0).loc(0.0).vis(ax=ax, col=\"b\")\n", + "_ = cell.branch(3).loc(1.0).vis(ax=ax, col=\"g\")" ] }, { "cell_type": "markdown", - "id": "1c9fef15", + "id": "38f1cf41", "metadata": {}, "source": [ "### Simulate the cell response\n", @@ -1094,8 +1094,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "9e480661", + "execution_count": 22, + "id": "19e7805b", "metadata": {}, "outputs": [ { @@ -1113,7 +1113,7 @@ }, { "cell_type": "markdown", - "id": "cc4af7d6", + "id": "bb99315b", "metadata": {}, "source": [ "The `jx.integrate` function returns an array of shape `(num_recordings, num_timepoints)`. In our case, we inserted `2` recordings and we simulated for 10ms at a 0.025 time step, which leads to 402 time steps.\n", @@ -1123,8 +1123,8 @@ }, { "cell_type": "code", - "execution_count": 17, - "id": "f643f430", + "execution_count": 23, + "id": "721ad2ef", "metadata": {}, "outputs": [ { @@ -1146,7 +1146,7 @@ }, { "cell_type": "markdown", - "id": "16df27fb", + "id": "e8997a9b", "metadata": {}, "source": [ "At the location of the first recording (in blue) the cell spiked, whereas at the second recording, it did not. This makes sense because we only inserted sodium and potassium channels into the first branch, but not in the entire cell." @@ -1154,7 +1154,7 @@ }, { "cell_type": "markdown", - "id": "7382b636", + "id": "dfed7c10", "metadata": {}, "source": [ "Congrats! You have just run your first morphologically detailed neuron simulation in `Jaxley`. We suggest to continue by learning how to [build networks](https://jaxley.readthedocs.io/en/latest/tutorials/02_small_network.html). If you are only interested in single cell simulations, you can directly jump to learning how to [speed up simulations](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html). If you want to simulate detailed morphologies from SWC files, checkout our tutorial on [working with detailed morphologies](https://jaxley.readthedocs.io/en/latest/tutorials/08_importing_morphologies.html)." diff --git a/docs/tutorials/02_small_network.ipynb b/docs/tutorials/02_small_network.ipynb index d7107d8f..84b3807e 100644 --- a/docs/tutorials/02_small_network.ipynb +++ b/docs/tutorials/02_small_network.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "48401559", + "id": "10cb8b05", "metadata": {}, "source": [ "# Network simulations in Jaxley" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "0acd0aaa", + "id": "3149c330", "metadata": {}, "source": [ "In this tutorial, you will learn how to:\n", @@ -48,7 +48,7 @@ }, { "cell_type": "markdown", - "id": "813f0cb4", + "id": "7dd2ee98", "metadata": {}, "source": [ "In the previous tutorial, you learned how to build single cells with morphological detail, how to insert stimuli and recordings, and how to run a first simulation. In this tutorial, we will define networks of multiple cells and connect them with synapses. Let's get started:" @@ -57,7 +57,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "7a1d3a77", + "id": "c08d10cb", "metadata": {}, "outputs": [], "source": [ @@ -78,7 +78,7 @@ }, { "cell_type": "markdown", - "id": "4c823295", + "id": "9c39dfef", "metadata": {}, "source": [ "### Define the network\n", @@ -89,18 +89,18 @@ { "cell_type": "code", "execution_count": 2, - "id": "1b756b73", + "id": "3858f198", "metadata": {}, "outputs": [], "source": [ "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=4)\n", + "branch = jx.Branch(comp, ncomp=4)\n", "cell = jx.Cell(branch, parents=[-1, 0, 0, 1, 1, 2, 2])" ] }, { "cell_type": "markdown", - "id": "a7c03145", + "id": "9d3e84bc", "metadata": {}, "source": [ "We can assemble multiple cells into a network by using `jx.Network`, which takes a list of `jx.Cell`s. Here, we assemble 11 cells into a network:" @@ -109,7 +109,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "0a7b7bd2", + "id": "a214b164", "metadata": {}, "outputs": [], "source": [ @@ -119,7 +119,7 @@ }, { "cell_type": "markdown", - "id": "cb9fdc89", + "id": "d8e091d5", "metadata": {}, "source": [ "At this point, we can already visualize this network:" @@ -128,7 +128,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "fe08e4a2", + "id": "d184c739", "metadata": {}, "outputs": [ { @@ -151,7 +151,7 @@ }, { "cell_type": "markdown", - "id": "aa1f48ef", + "id": "c7b39541", "metadata": {}, "source": [ "_Note: you can use `move_to` to have more control over the location of cells, e.g.: `network.cell(i).move_to(x=0, y=200)`._" @@ -159,7 +159,7 @@ }, { "cell_type": "markdown", - "id": "f12c95ae", + "id": "1e1e5d74", "metadata": {}, "source": [ "As you can see, the neurons are not connected yet. Let's fix this by connecting neurons with synapses. We will build a network consisting of two layers: 10 neurons in the input layer and 1 neuron in the output layer.\n", @@ -170,7 +170,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "d65c7953", + "id": "e4b94afc", "metadata": {}, "outputs": [], "source": [ @@ -181,7 +181,7 @@ }, { "cell_type": "markdown", - "id": "ec569fe8", + "id": "1d629fbe", "metadata": {}, "source": [ "Let's visualize this again:" @@ -190,12 +190,12 @@ { "cell_type": "code", "execution_count": 6, - "id": "577f14a0", + "id": "39d172dc", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -211,7 +211,7 @@ }, { "cell_type": "markdown", - "id": "25f3fc02", + "id": "7886a6a9", "metadata": {}, "source": [ "As you can see, the `full_connect` method inserted one synapse (in blue) from every neuron in the first layer to the output neuron. The `fully_connect` method builds this synapse from the zero-eth compartment and zero-eth branch of the presynaptic neuron onto a random branch of the postsynaptic neuron. If you want more control over the pre- and post-synaptic branches, you can use the `connect` method:" @@ -220,7 +220,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "8a34cb5f", + "id": "f78efb05", "metadata": {}, "outputs": [], "source": [ @@ -232,12 +232,12 @@ { "cell_type": "code", "execution_count": 8, - "id": "3524a008", + "id": "10cc3baa", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -253,7 +253,7 @@ }, { "cell_type": "markdown", - "id": "528c4ba5", + "id": "96d8182e", "metadata": {}, "source": [ "### Inspecting and changing synaptic parameters" @@ -261,7 +261,7 @@ }, { "cell_type": "markdown", - "id": "b4c5b2e7", + "id": "66a544f8", "metadata": {}, "source": [ "You can inspect synaptic parameters via the `.edges` attribute:" @@ -270,7 +270,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "5c600370", + "id": "50f8a206", "metadata": {}, "outputs": [ { @@ -313,11 +313,11 @@ " 0\n", " 0\n", " 0\n", - " 307\n", + " 286\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.875\n", + " 0.625\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -328,11 +328,11 @@ " 1\n", " 1\n", " 28\n", - " 303\n", + " 298\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.875\n", + " 0.625\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -343,11 +343,11 @@ " 2\n", " 2\n", " 56\n", - " 280\n", + " 286\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.125\n", + " 0.625\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -358,11 +358,11 @@ " 3\n", " 3\n", " 84\n", - " 281\n", + " 295\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.375\n", + " 0.875\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -373,7 +373,7 @@ " 4\n", " 4\n", " 112\n", - " 306\n", + " 302\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", @@ -388,11 +388,11 @@ " 5\n", " 5\n", " 140\n", - " 298\n", + " 288\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.625\n", + " 0.125\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -403,11 +403,11 @@ " 6\n", " 6\n", " 168\n", - " 301\n", + " 287\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.375\n", + " 0.875\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -418,7 +418,7 @@ " 7\n", " 7\n", " 196\n", - " 293\n", + " 305\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", @@ -433,11 +433,11 @@ " 8\n", " 8\n", " 224\n", - " 300\n", + " 299\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.125\n", + " 0.875\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -448,11 +448,11 @@ " 9\n", " 9\n", " 252\n", - " 303\n", + " 284\n", " IonotropicSynapse\n", " 0\n", " 0.125\n", - " 0.875\n", + " 0.125\n", " 0.0001\n", " 0.0\n", " 0.025\n", @@ -480,29 +480,29 @@ ], "text/plain": [ " global_edge_index global_pre_comp_index global_post_comp_index \\\n", - "0 0 0 307 \n", - "1 1 28 303 \n", - "2 2 56 280 \n", - "3 3 84 281 \n", - "4 4 112 306 \n", - "5 5 140 298 \n", - "6 6 168 301 \n", - "7 7 196 293 \n", - "8 8 224 300 \n", - "9 9 252 303 \n", + "0 0 0 286 \n", + "1 1 28 298 \n", + "2 2 56 286 \n", + "3 3 84 295 \n", + "4 4 112 302 \n", + "5 5 140 288 \n", + "6 6 168 287 \n", + "7 7 196 305 \n", + "8 8 224 299 \n", + "9 9 252 284 \n", "10 10 23 280 \n", "\n", " type type_ind pre_locs post_locs IonotropicSynapse_gS \\\n", - "0 IonotropicSynapse 0 0.125 0.875 0.0001 \n", - "1 IonotropicSynapse 0 0.125 0.875 0.0001 \n", - "2 IonotropicSynapse 0 0.125 0.125 0.0001 \n", - "3 IonotropicSynapse 0 0.125 0.375 0.0001 \n", + "0 IonotropicSynapse 0 0.125 0.625 0.0001 \n", + "1 IonotropicSynapse 0 0.125 0.625 0.0001 \n", + "2 IonotropicSynapse 0 0.125 0.625 0.0001 \n", + "3 IonotropicSynapse 0 0.125 0.875 0.0001 \n", "4 IonotropicSynapse 0 0.125 0.625 0.0001 \n", - "5 IonotropicSynapse 0 0.125 0.625 0.0001 \n", - "6 IonotropicSynapse 0 0.125 0.375 0.0001 \n", + "5 IonotropicSynapse 0 0.125 0.125 0.0001 \n", + "6 IonotropicSynapse 0 0.125 0.875 0.0001 \n", "7 IonotropicSynapse 0 0.125 0.375 0.0001 \n", - "8 IonotropicSynapse 0 0.125 0.125 0.0001 \n", - "9 IonotropicSynapse 0 0.125 0.875 0.0001 \n", + "8 IonotropicSynapse 0 0.125 0.875 0.0001 \n", + "9 IonotropicSynapse 0 0.125 0.125 0.0001 \n", "10 IonotropicSynapse 0 0.875 0.125 0.0001 \n", "\n", " IonotropicSynapse_e_syn IonotropicSynapse_k_minus IonotropicSynapse_s \\\n", @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "586ac140", + "id": "9590bd7b", "metadata": {}, "source": [ "To modify a parameter of all synapses you can again use `.set()`:" @@ -552,7 +552,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "2d4c1ad4", + "id": "a4578607", "metadata": {}, "outputs": [], "source": [ @@ -561,7 +561,7 @@ }, { "cell_type": "markdown", - "id": "755f58f4", + "id": "1f63ec83", "metadata": {}, "source": [ "To modify individual syanptic parameters, use the `.select()` method. Below, we change the values of the first two synapses:" @@ -570,7 +570,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "f86ee73d", + "id": "b36c9d54", "metadata": {}, "outputs": [], "source": [ @@ -579,7 +579,7 @@ }, { "cell_type": "markdown", - "id": "d004460d", + "id": "22f89733", "metadata": {}, "source": [ "For more details on how to flexibly set synaptic parameters (e.g., by cell type, or by pre-synaptic cell index,...), see [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)." @@ -587,7 +587,7 @@ }, { "cell_type": "markdown", - "id": "c25e2f35", + "id": "85713b1f", "metadata": {}, "source": [ "### Stimulating, recording, and simulating the network" @@ -595,7 +595,7 @@ }, { "cell_type": "markdown", - "id": "e1bfec97", + "id": "42fcf594", "metadata": {}, "source": [ "We will now set up a simulation of the network. This works exactly as it does for single neurons:" @@ -604,7 +604,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "6c240b83", + "id": "1899674f", "metadata": {}, "outputs": [], "source": [ @@ -621,7 +621,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "a2026b98", + "id": "c8613e12", "metadata": {}, "outputs": [], "source": [ @@ -630,7 +630,7 @@ }, { "cell_type": "markdown", - "id": "8d804be0", + "id": "35d1a94b", "metadata": {}, "source": [ "As a simple example, we insert sodium, potassium, and leak into every compartment of every cell of the network." @@ -639,7 +639,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "32eb2952", + "id": "08b9e276", "metadata": {}, "outputs": [], "source": [ @@ -650,7 +650,7 @@ }, { "cell_type": "markdown", - "id": "29eba81d", + "id": "75991e3f", "metadata": {}, "source": [ "We stimulate every neuron in the input layer and record the voltage from the output neuron:" @@ -659,7 +659,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "91b8a24a", + "id": "399c0a74", "metadata": {}, "outputs": [ { @@ -692,7 +692,7 @@ }, { "cell_type": "markdown", - "id": "ad274bb4", + "id": "0199e07f", "metadata": {}, "source": [ "Finally, we can again run the network simulation and plot the result:" @@ -701,7 +701,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "014b4b61", + "id": "821e6863", "metadata": {}, "outputs": [], "source": [ @@ -711,12 +711,12 @@ { "cell_type": "code", "execution_count": 17, - "id": "6a796161", + "id": "021edd8c", "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -732,7 +732,7 @@ }, { "cell_type": "markdown", - "id": "e38bd614", + "id": "66e0c675", "metadata": {}, "source": [ "That's it! You now know how to simulate networks of morphologically detailed neurons. We recommend that you now have a look at how you can [speed up your simulation](https://jaxley.readthedocs.io/en/latest/tutorials/04_jit_and_vmap.html). To learn more about handling synaptic parameters, we recommend to check out [this tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/09_advanced_indexing.html)." diff --git a/docs/tutorials/04_jit_and_vmap.ipynb b/docs/tutorials/04_jit_and_vmap.ipynb index 2d29f625..c090c78e 100644 --- a/docs/tutorials/04_jit_and_vmap.ipynb +++ b/docs/tutorials/04_jit_and_vmap.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "99e0512a", + "id": "cfd523b5", "metadata": {}, "source": [ "# Speeding up simulations" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "007bf12f", + "id": "adfd37cf", "metadata": {}, "source": [ "In this tutorial, you will learn how to:\n", @@ -47,7 +47,7 @@ }, { "cell_type": "markdown", - "id": "b596f6ba", + "id": "757dcad9", "metadata": {}, "source": [ "In the previous tutorials, you learned how to build single cells or networks and how to change their parameters. In this tutorial, you will learn how to speed up such simulations by many orders of magnitude. This can be achieved in to ways:\n", @@ -60,7 +60,7 @@ }, { "cell_type": "markdown", - "id": "7b1628bf", + "id": "c813d313", "metadata": {}, "source": [ "### Using GPU or CPU" @@ -68,7 +68,7 @@ }, { "cell_type": "markdown", - "id": "163b1c49", + "id": "f69b53c7", "metadata": {}, "source": [ "In `Jaxley` you can set whether you want to use `gpu` or `cpu` with the following lines at the beginning of your script:" @@ -77,7 +77,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "b9cc56b7", + "id": "2f080339", "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ }, { "cell_type": "markdown", - "id": "611633d6", + "id": "c38c665a", "metadata": {}, "source": [ "`JAX` (and `Jaxley`) also allow to choose between `float32` and `float64`. Especially on GPUs, `float32` will be faster, but we have experienced stability issues when simulating morphologically detailed neurons with `float32`." @@ -96,7 +96,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "b7093b86", + "id": "86d4a917", "metadata": {}, "outputs": [], "source": [ @@ -105,7 +105,7 @@ }, { "cell_type": "markdown", - "id": "3c3bd156", + "id": "dc16b92d", "metadata": {}, "source": [ "Next, we will import relevant libraries:" @@ -114,7 +114,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "ad756d90", + "id": "bd054087", "metadata": {}, "outputs": [], "source": [ @@ -129,7 +129,7 @@ }, { "cell_type": "markdown", - "id": "54be0755", + "id": "9d2ae1fa", "metadata": {}, "source": [ "### Building the cell or network\n", @@ -140,7 +140,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "bc8c6f17", + "id": "a869e670", "metadata": {}, "outputs": [ { @@ -157,7 +157,7 @@ "t_max = 10.0\n", "\n", "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=4)\n", + "branch = jx.Branch(comp, ncomp=4)\n", "cell = jx.Cell(branch, parents=[-1, 0, 0, 1, 1, 2, 2])\n", "\n", "cell.insert(Na())\n", @@ -174,7 +174,7 @@ }, { "cell_type": "markdown", - "id": "39165d99", + "id": "d9193627", "metadata": {}, "source": [ "### Parameter sweeps\n", @@ -185,7 +185,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "d5088abb", + "id": "79a01358", "metadata": {}, "outputs": [], "source": [ @@ -198,7 +198,7 @@ }, { "cell_type": "markdown", - "id": "f815d90f", + "id": "2f8e301a", "metadata": {}, "source": [ "The `.data_set()` method takes three arguments: \n", @@ -210,7 +210,7 @@ }, { "cell_type": "markdown", - "id": "7e319037", + "id": "a343e454", "metadata": {}, "source": [ "Having done this, the simplest (but least efficient) way to perform the parameter sweep is to run a for-loop over many parameter sets:" @@ -219,7 +219,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "436d00a1", + "id": "4806598a", "metadata": {}, "outputs": [ { @@ -240,7 +240,7 @@ }, { "cell_type": "markdown", - "id": "a24cc7bd", + "id": "e0f1becb", "metadata": {}, "source": [ "The resulting voltages have shape `(num_simulations, num_recordings, num_timesteps)`." @@ -248,7 +248,7 @@ }, { "cell_type": "markdown", - "id": "cbb72c8b", + "id": "c4345c02", "metadata": {}, "source": [ "### Stimulus sweeps\n", @@ -266,7 +266,7 @@ }, { "cell_type": "markdown", - "id": "fdb15f95", + "id": "5dd3c975", "metadata": {}, "source": [ "### Speeding up for loops via `jit` compilation\n", @@ -277,7 +277,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "83c96a95", + "id": "017e98d9", "metadata": {}, "outputs": [], "source": [ @@ -287,7 +287,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "0419f5ec", + "id": "d9aa805a", "metadata": {}, "outputs": [], "source": [ @@ -298,7 +298,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "d197f6de", + "id": "27c12fe3", "metadata": {}, "outputs": [ { @@ -317,7 +317,7 @@ }, { "cell_type": "markdown", - "id": "10946557", + "id": "401d1f52", "metadata": {}, "source": [ "`jit` compilation can be up to 10k times faster, especially for small simulations with few compartments. For very large models, the gain obtained with `jit` will be much smaller (`jit` may even provide no speed up at all)." @@ -325,7 +325,7 @@ }, { "cell_type": "markdown", - "id": "9417a2c3", + "id": "d29ff570", "metadata": {}, "source": [ "### Speeding up with GPU parallelization via `vmap`\n", @@ -336,7 +336,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "af123748", + "id": "fefffaf7", "metadata": {}, "outputs": [], "source": [ @@ -346,7 +346,7 @@ }, { "cell_type": "markdown", - "id": "86df1175", + "id": "fd03669d", "metadata": {}, "source": [ "We can then run this method on __all__ parameter sets (`all_params.shape == (100, 2)`), and `Jaxley` will automatically parallelize across them. Of course, you will only get a speed-up if you have a GPU available and you specified `gpu` as device in the beginning of this tutorial." @@ -355,7 +355,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "dfb1977e", + "id": "c2a22648", "metadata": {}, "outputs": [], "source": [ @@ -364,7 +364,7 @@ }, { "cell_type": "markdown", - "id": "96788177", + "id": "a4464e06", "metadata": {}, "source": [ "GPU parallelization with `vmap` can give a large speed-up, which can easily be 2-3 orders of magnitude." @@ -372,7 +372,7 @@ }, { "cell_type": "markdown", - "id": "fd4a4d1c", + "id": "0df64cc1", "metadata": {}, "source": [ "### Combining `jit` and `vmap`" @@ -380,7 +380,7 @@ }, { "cell_type": "markdown", - "id": "13ea4636", + "id": "8125f061", "metadata": {}, "source": [ "Finally, you can also combine using `jit` and `vmap`. For example, you can run multiple batches of many parallel simulations. Each batch can be parallelized with `vmap` and simulating each batch can be compiled with `jit`:" @@ -389,7 +389,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "156c4333", + "id": "db1eced1", "metadata": {}, "outputs": [], "source": [ @@ -399,7 +399,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "5ef62fd1", + "id": "82f34a7d", "metadata": {}, "outputs": [], "source": [ @@ -410,7 +410,7 @@ }, { "cell_type": "markdown", - "id": "1c1e2a1d", + "id": "a5cca5a0", "metadata": {}, "source": [ "That's all you have to know about `jit` and `vmap`! If you have worked through this and the previous tutorials, you should be ready to set up your first network simulations." @@ -418,7 +418,7 @@ }, { "cell_type": "markdown", - "id": "31f43b9b", + "id": "37fc2f3c", "metadata": {}, "source": [ "### Next steps\n", diff --git a/docs/tutorials/05_channel_and_synapse_models.ipynb b/docs/tutorials/05_channel_and_synapse_models.ipynb index 1ce91d40..96412184 100644 --- a/docs/tutorials/05_channel_and_synapse_models.ipynb +++ b/docs/tutorials/05_channel_and_synapse_models.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "4ce0e4e0", + "id": "c1157b43", "metadata": {}, "source": [ "# Building ion channel models\n", @@ -17,7 +17,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "ed56e35e", + "id": "56c05124", "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "markdown", - "id": "3c2b7ae6", + "id": "470b4f8f", "metadata": {}, "source": [ "First, we define a cell as you saw in the [previous tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html):" @@ -45,18 +45,18 @@ { "cell_type": "code", "execution_count": 2, - "id": "7ab6b367", + "id": "3f6c47d2", "metadata": {}, "outputs": [], "source": [ "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=4)\n", + "branch = jx.Branch(comp, ncomp=4)\n", "cell = jx.Cell(branch, parents=[-1, 0, 0, 1, 1, 2, 2])" ] }, { "cell_type": "markdown", - "id": "f43fd8a6", + "id": "3450d0d6", "metadata": {}, "source": [ "You have also already learned how to insert preconfigured channels into `Jaxley` models:\n", @@ -71,7 +71,7 @@ }, { "cell_type": "markdown", - "id": "84c00849", + "id": "934fd9fa", "metadata": {}, "source": [ "### Your own channel\n", @@ -81,7 +81,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "25b24a7f", + "id": "e5a5f4f8", "metadata": {}, "outputs": [], "source": [ @@ -127,7 +127,7 @@ }, { "cell_type": "markdown", - "id": "f91e4492", + "id": "6682c9fc", "metadata": {}, "source": [ "Let's look at each part of this in detail. \n", @@ -187,7 +187,7 @@ }, { "cell_type": "markdown", - "id": "22aa1164", + "id": "07cffb1d", "metadata": {}, "source": [ "Alright, done! We can now insert this channel into any `jx.Module` such as our cell:" @@ -196,7 +196,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "e1661c2b", + "id": "72046028", "metadata": {}, "outputs": [], "source": [ @@ -206,7 +206,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "9e2e9f35", + "id": "8943b07b", "metadata": {}, "outputs": [ { @@ -230,7 +230,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "8ed8274c", + "id": "388dee2d", "metadata": {}, "outputs": [], "source": [ @@ -240,7 +240,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "666d2898", + "id": "e2a4bb2d", "metadata": {}, "outputs": [ { @@ -264,7 +264,7 @@ }, { "cell_type": "markdown", - "id": "aae612b2", + "id": "63056871", "metadata": {}, "source": [ "### Your own synapse\n", @@ -279,7 +279,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "52e39f0a", + "id": "5c6e7e9a", "metadata": {}, "outputs": [], "source": [ @@ -310,7 +310,7 @@ }, { "cell_type": "markdown", - "id": "2fa83b21", + "id": "eb80aa94", "metadata": {}, "source": [ "As you can see above, synapses follow closely how channels are defined. The main difference is that the `compute_current` method takes two voltages: the pre-synaptic voltage (a `jnp.ndarray` of shape `()`) and the post-synaptic voltage (a `jnp.ndarray` of shape `()`)." @@ -319,7 +319,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "df10f6e0", + "id": "ee961d5d", "metadata": {}, "outputs": [], "source": [ @@ -329,7 +329,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "aa1ef410", + "id": "2db6ac96", "metadata": {}, "outputs": [], "source": [ @@ -343,7 +343,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "d6fe1eef", + "id": "522ce876", "metadata": {}, "outputs": [ { @@ -366,7 +366,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "3d06f3ea", + "id": "d94c2440", "metadata": {}, "outputs": [], "source": [ @@ -376,7 +376,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "34273223", + "id": "14ea80f5", "metadata": {}, "outputs": [ { @@ -400,7 +400,7 @@ }, { "cell_type": "markdown", - "id": "b527efa6", + "id": "658b032d", "metadata": {}, "source": [ "That's it! You are now ready to build your own custom simulations and equip them with channel and synapse models!\n", diff --git a/docs/tutorials/06_groups.ipynb b/docs/tutorials/06_groups.ipynb index cc6ff71f..362f6525 100644 --- a/docs/tutorials/06_groups.ipynb +++ b/docs/tutorials/06_groups.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d2391d78", + "id": "51419bb0", "metadata": {}, "source": [ "# Defining groups\n", @@ -40,8 +40,8 @@ }, { "cell_type": "code", - "execution_count": 40, - "id": "512857ee", + "execution_count": 1, + "id": "d703515b", "metadata": {}, "outputs": [], "source": [ @@ -64,7 +64,7 @@ }, { "cell_type": "markdown", - "id": "f947b991", + "id": "94f247bc", "metadata": {}, "source": [ "First, we define a network as you saw in the [previous tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/02_small_network.html):" @@ -72,22 +72,13 @@ }, { "cell_type": "code", - "execution_count": 41, - "id": "d1af07da", + "execution_count": 2, + "id": "10c4f776", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/michaeldeistler/Documents/phd/jaxley/jaxley/modules/base.py:1533: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n", - " self.pointer.edges = pd.concat(\n" - ] - } - ], + "outputs": [], "source": [ "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=2)\n", + "branch = jx.Branch(comp, ncomp=2)\n", "cell = jx.Cell(branch, parents=[-1, 0, 0, 1])\n", "network = jx.Network([cell for _ in range(3)])\n", "\n", @@ -102,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "28fa2342", + "id": "465fc6fa", "metadata": {}, "source": [ "### Group: apical dendrites\n", @@ -111,8 +102,8 @@ }, { "cell_type": "code", - "execution_count": 42, - "id": "09ac3d79", + "execution_count": 3, + "id": "3f23fceb", "metadata": {}, "outputs": [], "source": [ @@ -123,7 +114,7 @@ }, { "cell_type": "markdown", - "id": "e13e0f5f", + "id": "ee58e3e9", "metadata": {}, "source": [ "After this, we can access `network.apical` as we previously accesses anything else:" @@ -131,8 +122,8 @@ }, { "cell_type": "code", - "execution_count": 43, - "id": "61cd784b", + "execution_count": 4, + "id": "5b2c9ee1", "metadata": {}, "outputs": [], "source": [ @@ -141,409 +132,17 @@ }, { "cell_type": "code", - "execution_count": 44, - "id": "9b114506", + "execution_count": 5, + "id": "1e6efa3e", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
comp_indexbranch_indexcell_indexlengthradiusaxial_resistivitycapacitancevNaNa_gNa...K_gKeKK_nLeakLeak_gLeakLeak_eLeakglobal_comp_indexglobal_branch_indexglobal_cell_indexcontrolled_by_param
221010.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.02100
331010.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.03100
663010.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.06300
773010.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.07300
10105110.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.010510
11115110.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.011510
14147110.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.014710
15157110.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.015710
18189210.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.018920
19199210.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.019920
222211210.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.0221120
232311210.00.35000.01.0-70.0True0.05...0.005-90.00.2True0.0001-70.0231120
\n", - "

12 rows × 25 columns

\n", - "
" - ], "text/plain": [ - " comp_index branch_index cell_index length radius axial_resistivity \\\n", - "2 2 1 0 10.0 0.3 5000.0 \n", - "3 3 1 0 10.0 0.3 5000.0 \n", - "6 6 3 0 10.0 0.3 5000.0 \n", - "7 7 3 0 10.0 0.3 5000.0 \n", - "10 10 5 1 10.0 0.3 5000.0 \n", - "11 11 5 1 10.0 0.3 5000.0 \n", - "14 14 7 1 10.0 0.3 5000.0 \n", - "15 15 7 1 10.0 0.3 5000.0 \n", - "18 18 9 2 10.0 0.3 5000.0 \n", - "19 19 9 2 10.0 0.3 5000.0 \n", - "22 22 11 2 10.0 0.3 5000.0 \n", - "23 23 11 2 10.0 0.3 5000.0 \n", - "\n", - " capacitance v Na Na_gNa ... K_gK eK K_n Leak Leak_gLeak \\\n", - "2 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "3 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "6 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "7 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "10 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "11 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "14 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "15 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "18 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "19 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "22 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "23 1.0 -70.0 True 0.05 ... 0.005 -90.0 0.2 True 0.0001 \n", - "\n", - " Leak_eLeak global_comp_index global_branch_index global_cell_index \\\n", - "2 -70.0 2 1 0 \n", - "3 -70.0 3 1 0 \n", - "6 -70.0 6 3 0 \n", - "7 -70.0 7 3 0 \n", - "10 -70.0 10 5 1 \n", - "11 -70.0 11 5 1 \n", - "14 -70.0 14 7 1 \n", - "15 -70.0 15 7 1 \n", - "18 -70.0 18 9 2 \n", - "19 -70.0 19 9 2 \n", - "22 -70.0 22 11 2 \n", - "23 -70.0 23 11 2 \n", - "\n", - " controlled_by_param \n", - "2 0 \n", - "3 0 \n", - "6 0 \n", - "7 0 \n", - "10 0 \n", - "11 0 \n", - "14 0 \n", - "15 0 \n", - "18 0 \n", - "19 0 \n", - "22 0 \n", - "23 0 \n", - "\n", - "[12 rows x 25 columns]" + "View with 3 different channels. Use `.nodes` for details." ] }, - "execution_count": 44, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -554,7 +153,7 @@ }, { "cell_type": "markdown", - "id": "a22bfbc6", + "id": "ac885848", "metadata": {}, "source": [ "### Group: fast spiking\n", @@ -563,8 +162,8 @@ }, { "cell_type": "code", - "execution_count": 45, - "id": "a1d820d0", + "execution_count": 6, + "id": "0b8e9b38", "metadata": {}, "outputs": [], "source": [ @@ -574,8 +173,8 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "88800571", + "execution_count": 7, + "id": "25322ebf", "metadata": {}, "outputs": [], "source": [ @@ -584,521 +183,17 @@ }, { "cell_type": "code", - "execution_count": 47, - "id": "654e33ff", + "execution_count": 8, + "id": "f98f4e74", "metadata": {}, "outputs": [ { "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
comp_indexbranch_indexcell_indexlengthradiusaxial_resistivitycapacitancevNaNa_gNa...K_gKeKK_nLeakLeak_gLeakLeak_eLeakglobal_comp_indexglobal_branch_indexglobal_cell_indexcontrolled_by_param
000010.01.05000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.00000
110010.01.05000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.01000
221010.00.35000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.02100
331010.00.35000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.03100
442010.01.05000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.04200
552010.01.05000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.05200
663010.00.35000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.06300
773010.00.35000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.07300
884110.01.05000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.08410
994110.01.05000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.09410
10105110.00.35000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.010510
11115110.00.35000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.011510
12126110.01.05000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.012610
13136110.01.05000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.013610
14147110.00.35000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.014710
15157110.00.35000.01.0-70.0True0.4...0.005-90.00.2True0.0001-70.015710
\n", - "

16 rows × 25 columns

\n", - "
" - ], "text/plain": [ - " comp_index branch_index cell_index length radius axial_resistivity \\\n", - "0 0 0 0 10.0 1.0 5000.0 \n", - "1 1 0 0 10.0 1.0 5000.0 \n", - "2 2 1 0 10.0 0.3 5000.0 \n", - "3 3 1 0 10.0 0.3 5000.0 \n", - "4 4 2 0 10.0 1.0 5000.0 \n", - "5 5 2 0 10.0 1.0 5000.0 \n", - "6 6 3 0 10.0 0.3 5000.0 \n", - "7 7 3 0 10.0 0.3 5000.0 \n", - "8 8 4 1 10.0 1.0 5000.0 \n", - "9 9 4 1 10.0 1.0 5000.0 \n", - "10 10 5 1 10.0 0.3 5000.0 \n", - "11 11 5 1 10.0 0.3 5000.0 \n", - "12 12 6 1 10.0 1.0 5000.0 \n", - "13 13 6 1 10.0 1.0 5000.0 \n", - "14 14 7 1 10.0 0.3 5000.0 \n", - "15 15 7 1 10.0 0.3 5000.0 \n", - "\n", - " capacitance v Na Na_gNa ... K_gK eK K_n Leak Leak_gLeak \\\n", - "0 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "1 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "2 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "3 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "4 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "5 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "6 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "7 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "8 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "9 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "10 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "11 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "12 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "13 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "14 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "15 1.0 -70.0 True 0.4 ... 0.005 -90.0 0.2 True 0.0001 \n", - "\n", - " Leak_eLeak global_comp_index global_branch_index global_cell_index \\\n", - "0 -70.0 0 0 0 \n", - "1 -70.0 1 0 0 \n", - "2 -70.0 2 1 0 \n", - "3 -70.0 3 1 0 \n", - "4 -70.0 4 2 0 \n", - "5 -70.0 5 2 0 \n", - "6 -70.0 6 3 0 \n", - "7 -70.0 7 3 0 \n", - "8 -70.0 8 4 1 \n", - "9 -70.0 9 4 1 \n", - "10 -70.0 10 5 1 \n", - "11 -70.0 11 5 1 \n", - "12 -70.0 12 6 1 \n", - "13 -70.0 13 6 1 \n", - "14 -70.0 14 7 1 \n", - "15 -70.0 15 7 1 \n", - "\n", - " controlled_by_param \n", - "0 0 \n", - "1 0 \n", - "2 0 \n", - "3 0 \n", - "4 0 \n", - "5 0 \n", - "6 0 \n", - "7 0 \n", - "8 0 \n", - "9 0 \n", - "10 0 \n", - "11 0 \n", - "12 0 \n", - "13 0 \n", - "14 0 \n", - "15 0 \n", - "\n", - "[16 rows x 25 columns]" + "View with 3 different channels. Use `.nodes` for details." ] }, - "execution_count": 47, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1109,7 +204,7 @@ }, { "cell_type": "markdown", - "id": "4721b618", + "id": "c8ad35a5", "metadata": {}, "source": [ "### Groups from SWC files" @@ -1117,7 +212,7 @@ }, { "cell_type": "markdown", - "id": "7a0fa060", + "id": "72de2fb6", "metadata": {}, "source": [ "If you are reading `.swc` morphologigies, you can automatically assign groups with \n", @@ -1129,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "025e96e1", + "id": "e08a5b66", "metadata": {}, "source": [ "### How groups are interpreted by `.make_trainable()`\n", @@ -1138,8 +233,8 @@ }, { "cell_type": "code", - "execution_count": 48, - "id": "305e64fb", + "execution_count": 9, + "id": "a5d4f8ca", "metadata": {}, "outputs": [ { @@ -1156,7 +251,7 @@ }, { "cell_type": "markdown", - "id": "8881edc1", + "id": "99082cca", "metadata": {}, "source": [ "As such, `get_parameters()` returns only a single trainable parameter, which will be the sodium conductance for every compartment of every fast-spiking neuron:" @@ -1164,8 +259,8 @@ }, { "cell_type": "code", - "execution_count": 49, - "id": "db131033", + "execution_count": 10, + "id": "62b0dc0c", "metadata": {}, "outputs": [ { @@ -1174,7 +269,7 @@ "[{'Na_gNa': Array([0.4], dtype=float64)}]" ] }, - "execution_count": 49, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1185,7 +280,7 @@ }, { "cell_type": "markdown", - "id": "1bdcc8d1", + "id": "4941d565", "metadata": {}, "source": [ "If, instead, you would want a separate parameter for every fast-spiking cell, you should not use the group, but instead do the following (remember that fast-spiking neurons had indices [0,1]):" @@ -1193,8 +288,8 @@ }, { "cell_type": "code", - "execution_count": 50, - "id": "a023cced", + "execution_count": 11, + "id": "4e6108e9", "metadata": {}, "outputs": [ { @@ -1211,8 +306,8 @@ }, { "cell_type": "code", - "execution_count": 51, - "id": "e45969d2", + "execution_count": 12, + "id": "13db06ab", "metadata": {}, "outputs": [ { @@ -1222,7 +317,7 @@ " {'axial_resistivity': Array([5000., 5000.], dtype=float64)}]" ] }, - "execution_count": 51, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -1233,7 +328,7 @@ }, { "cell_type": "markdown", - "id": "49dc611c", + "id": "3d6a4dee", "metadata": {}, "source": [ "This generated two parameters for the axial resistivitiy, each corresponding to one cell." @@ -1241,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "38f08471", + "id": "3ed0a8d6", "metadata": {}, "source": [ "### Summary" @@ -1249,7 +344,7 @@ }, { "cell_type": "markdown", - "id": "9ddfb1d9", + "id": "4476ff6b", "metadata": {}, "source": [ "Groups allow you to organize your simulation in a more intuitive way, and they allow to perform parameter sharing with `make_trainable()`." diff --git a/docs/tutorials/07_gradient_descent.ipynb b/docs/tutorials/07_gradient_descent.ipynb index d2180968..baad3c6f 100644 --- a/docs/tutorials/07_gradient_descent.ipynb +++ b/docs/tutorials/07_gradient_descent.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "7782586e", + "id": "7b2b1351", "metadata": {}, "source": [ "# Training biophysical models\n", @@ -77,7 +77,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "c1534d1e", + "id": "b414dd72", "metadata": {}, "outputs": [], "source": [ @@ -99,7 +99,7 @@ }, { "cell_type": "markdown", - "id": "1b419c93", + "id": "b41aa1e5", "metadata": {}, "source": [ "First, we define a network as you saw in the [previous tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/01_morph_neurons.html):" @@ -108,14 +108,14 @@ { "cell_type": "code", "execution_count": 2, - "id": "3d6408f6", + "id": "4ca62f3b", "metadata": {}, "outputs": [], "source": [ "_ = np.random.seed(0) # For synaptic locations.\n", "\n", "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=2)\n", + "branch = jx.Branch(comp, ncomp=2)\n", "cell = jx.Cell(branch, parents=[-1, 0, 0])\n", "net = jx.Network([cell for _ in range(3)])\n", "\n", @@ -133,7 +133,7 @@ }, { "cell_type": "markdown", - "id": "c0457276", + "id": "d7a10185", "metadata": {}, "source": [ "This network consists of three neurons arranged in two layers:" @@ -142,7 +142,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "b405f30e", + "id": "886cea53", "metadata": {}, "outputs": [ { @@ -165,7 +165,7 @@ }, { "cell_type": "markdown", - "id": "98b7fb1f", + "id": "8048a833", "metadata": {}, "source": [ "We consider the last neuron as the output neuron and record the voltage from there:" @@ -174,7 +174,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "cd181c84", + "id": "f4e23c03", "metadata": {}, "outputs": [ { @@ -196,7 +196,7 @@ }, { "cell_type": "markdown", - "id": "4c077c35", + "id": "c21f1595", "metadata": {}, "source": [ "### Defining a dataset" @@ -204,7 +204,7 @@ }, { "cell_type": "markdown", - "id": "18d7ec29", + "id": "673697b7", "metadata": {}, "source": [ "We will train this biophysical network on a classification task. The inputs will be values and the label is binary:" @@ -213,7 +213,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "59f911b7", + "id": "8f032363", "metadata": {}, "outputs": [], "source": [ @@ -224,7 +224,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "aa6a7309", + "id": "b1583465", "metadata": {}, "outputs": [ { @@ -247,7 +247,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "4dcd8906", + "id": "4f648cd4", "metadata": {}, "outputs": [], "source": [ @@ -256,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "652ffd7a", + "id": "209a3098", "metadata": {}, "source": [ "### Defining trainable parameters" @@ -265,7 +265,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "8f4b11a7", + "id": "8892c796", "metadata": {}, "outputs": [], "source": [ @@ -274,7 +274,7 @@ }, { "cell_type": "markdown", - "id": "bb82d199", + "id": "28471b94", "metadata": {}, "source": [ "This follows the same API as `.set()` seen in the previous tutorial. If you want to use a single parameter for all `radius`es in the entire network, do:" @@ -283,7 +283,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "8c41f214", + "id": "8ca68b36", "metadata": {}, "outputs": [ { @@ -300,7 +300,7 @@ }, { "cell_type": "markdown", - "id": "4cf2fde5", + "id": "abfc4125", "metadata": {}, "source": [ "We can also define parameters for individual compartments. To do this, use the `\"all\"` key. The following defines a separate parameter the sodium conductance for every compartment in the entire network:" @@ -309,7 +309,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ee4988e8", + "id": "a846bce2", "metadata": {}, "outputs": [ { @@ -326,7 +326,7 @@ }, { "cell_type": "markdown", - "id": "75f442dc", + "id": "1e0a9ed6", "metadata": {}, "source": [ "### Making synaptic parameters trainable" @@ -334,7 +334,7 @@ }, { "cell_type": "markdown", - "id": "0659c2f5", + "id": "fff33fb7", "metadata": {}, "source": [ "Synaptic parameters can be made trainable in the exact same way. To use a single parameter for all syanptic conductances in the entire network, do\n", @@ -345,7 +345,7 @@ }, { "cell_type": "markdown", - "id": "b4d37d63", + "id": "096e37e2", "metadata": {}, "source": [ "Here, we use a different syanptic conductance for all syanpses. This can be done as follows:" @@ -354,7 +354,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "c669ba77", + "id": "22074636", "metadata": {}, "outputs": [ { @@ -371,7 +371,7 @@ }, { "cell_type": "markdown", - "id": "67d7f440", + "id": "601bab3c", "metadata": {}, "source": [ "### Running the simulation" @@ -379,7 +379,7 @@ }, { "cell_type": "markdown", - "id": "88621f0d", + "id": "89c9e348", "metadata": {}, "source": [ "Once all parameters are defined, you have to use `.get_parameters()` to obtain all trainable parameters. This is also the time to check how many trainable parameters your network has:" @@ -388,7 +388,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "c653c4dc", + "id": "f6ca6114", "metadata": {}, "outputs": [], "source": [ @@ -397,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "104e3fa4", + "id": "fb887688", "metadata": {}, "source": [ "You can now run the simulation with the trainable parameters by passing them to the `jx.integrate` function." @@ -406,7 +406,7 @@ { "cell_type": "code", "execution_count": 13, - "id": "5d21d576", + "id": "1f8b4afe", "metadata": {}, "outputs": [], "source": [ @@ -415,7 +415,7 @@ }, { "cell_type": "markdown", - "id": "300306d8", + "id": "3aba8d4c", "metadata": {}, "source": [ "### Stimulating the network\n", @@ -426,7 +426,7 @@ { "cell_type": "code", "execution_count": 14, - "id": "5c0a0f17", + "id": "38037ad4", "metadata": {}, "outputs": [], "source": [ @@ -444,7 +444,7 @@ }, { "cell_type": "markdown", - "id": "f7870998", + "id": "2e4e0970", "metadata": {}, "source": [ "We can also inspect some traces:" @@ -453,7 +453,7 @@ { "cell_type": "code", "execution_count": 15, - "id": "0fe3b501", + "id": "76e63570", "metadata": {}, "outputs": [], "source": [ @@ -463,7 +463,7 @@ { "cell_type": "code", "execution_count": 16, - "id": "a5ba1732", + "id": "da8d329f", "metadata": {}, "outputs": [ { @@ -484,7 +484,7 @@ }, { "cell_type": "markdown", - "id": "46608479", + "id": "cc7b2fa6", "metadata": {}, "source": [ "### Defining a loss function" @@ -492,7 +492,7 @@ }, { "cell_type": "markdown", - "id": "a4432a42", + "id": "e774b36f", "metadata": {}, "source": [ "Let us define a loss function to be optimized:" @@ -501,7 +501,7 @@ { "cell_type": "code", "execution_count": 17, - "id": "c224ff75", + "id": "f7ff757f", "metadata": {}, "outputs": [], "source": [ @@ -515,7 +515,7 @@ }, { "cell_type": "markdown", - "id": "04b1d69b", + "id": "e85619c9", "metadata": {}, "source": [ "And we can use `JAX`'s inbuilt functions to take the gradient through the entire ODE:" @@ -524,7 +524,7 @@ { "cell_type": "code", "execution_count": 18, - "id": "08a2df7e", + "id": "70ee2cda", "metadata": {}, "outputs": [], "source": [ @@ -534,7 +534,7 @@ { "cell_type": "code", "execution_count": 19, - "id": "3e210537", + "id": "6698502f", "metadata": {}, "outputs": [], "source": [ @@ -543,7 +543,7 @@ }, { "cell_type": "markdown", - "id": "2abeaf3d", + "id": "66888350", "metadata": {}, "source": [ "### Defining parameter transformations" @@ -551,7 +551,7 @@ }, { "cell_type": "markdown", - "id": "55cd1a06", + "id": "f1c5e0ef", "metadata": {}, "source": [ "Before training, however, we will enforce for all parameters to be within a prespecified range (such that, e.g., conductances can not become negative)" @@ -560,7 +560,7 @@ { "cell_type": "code", "execution_count": 20, - "id": "cbac82e9", + "id": "964a4cc3", "metadata": {}, "outputs": [], "source": [ @@ -570,7 +570,7 @@ { "cell_type": "code", "execution_count": 21, - "id": "50fc9e9b", + "id": "6762e2af", "metadata": {}, "outputs": [], "source": [ @@ -594,7 +594,7 @@ { "cell_type": "code", "execution_count": 22, - "id": "f75ae136", + "id": "ed6d271f", "metadata": {}, "outputs": [], "source": [ @@ -605,7 +605,7 @@ }, { "cell_type": "markdown", - "id": "d1e97163", + "id": "69df4690", "metadata": {}, "source": [ "With these modify the loss function acocrdingly:" @@ -614,7 +614,7 @@ { "cell_type": "code", "execution_count": 23, - "id": "b3df9d75", + "id": "1791e84f", "metadata": {}, "outputs": [], "source": [ @@ -630,7 +630,7 @@ }, { "cell_type": "markdown", - "id": "b722c4de", + "id": "fcddd13b", "metadata": {}, "source": [ "### Using checkpointing" @@ -638,7 +638,7 @@ }, { "cell_type": "markdown", - "id": "d6396629", + "id": "3ca350ca", "metadata": {}, "source": [ "Checkpointing allows to vastly reduce the memory requirements of training biophysical models (see also [JAX's full tutorial on checkpointing](https://jax.readthedocs.io/en/latest/gradient-checkpointing.html))." @@ -647,7 +647,7 @@ { "cell_type": "code", "execution_count": 24, - "id": "006c6875", + "id": "825e988a", "metadata": {}, "outputs": [], "source": [ @@ -661,7 +661,7 @@ }, { "cell_type": "markdown", - "id": "f3e53ae3", + "id": "907090cb", "metadata": {}, "source": [ "To enable checkpointing, we have to modify the `simulate` function appropriately and use\n", @@ -674,7 +674,7 @@ { "cell_type": "code", "execution_count": 25, - "id": "4a508dd7", + "id": "855ea0ce", "metadata": {}, "outputs": [], "source": [ @@ -710,7 +710,7 @@ }, { "cell_type": "markdown", - "id": "424ed676", + "id": "7ba885ee", "metadata": {}, "source": [ "### Training\n", @@ -721,7 +721,7 @@ { "cell_type": "code", "execution_count": 26, - "id": "325d6e44", + "id": "9957d8de", "metadata": {}, "outputs": [], "source": [ @@ -731,7 +731,7 @@ { "cell_type": "code", "execution_count": 27, - "id": "b8d1af16", + "id": "c8c080ce", "metadata": {}, "outputs": [], "source": [ @@ -742,7 +742,7 @@ }, { "cell_type": "markdown", - "id": "289e4494", + "id": "418e2e24", "metadata": {}, "source": [ "### Writing a dataloader" @@ -750,7 +750,7 @@ }, { "cell_type": "markdown", - "id": "b238a543", + "id": "114f07c8", "metadata": {}, "source": [ "Below, we just write our own (very simple) dataloader. Alternatively, you could use the dataloader from any deep learning library such as pytorch or tensorflow:" @@ -759,7 +759,7 @@ { "cell_type": "code", "execution_count": 28, - "id": "500f117f", + "id": "73486cbc", "metadata": {}, "outputs": [], "source": [ @@ -802,7 +802,7 @@ }, { "cell_type": "markdown", - "id": "2ebb6e36", + "id": "863daf96", "metadata": {}, "source": [ "### Training loop" @@ -811,7 +811,7 @@ { "cell_type": "code", "execution_count": 29, - "id": "895f8d7f", + "id": "a1c04203", "metadata": {}, "outputs": [ { @@ -854,7 +854,7 @@ { "cell_type": "code", "execution_count": 30, - "id": "262cbb1d", + "id": "983dbd4f", "metadata": {}, "outputs": [], "source": [ @@ -865,7 +865,7 @@ { "cell_type": "code", "execution_count": 31, - "id": "a8fa241c", + "id": "3091698e", "metadata": {}, "outputs": [ { @@ -888,7 +888,7 @@ }, { "cell_type": "markdown", - "id": "a71d0466", + "id": "6e8a104d", "metadata": {}, "source": [ "Indeed, the loss goes down and the network successfully classifies the patterns." @@ -896,7 +896,7 @@ }, { "cell_type": "markdown", - "id": "22624ced", + "id": "cd9e7cc4", "metadata": {}, "source": [ "### Summary" @@ -904,7 +904,7 @@ }, { "cell_type": "markdown", - "id": "edba3463", + "id": "b6fc5e6d", "metadata": {}, "source": [ "Puh, this was a pretty dense tutorial with a lot of material. You should have learned how to:\n", @@ -918,7 +918,7 @@ }, { "cell_type": "markdown", - "id": "55a83657", + "id": "7cef661e", "metadata": {}, "source": [ "This was the last \"basic\" tutorial of the `Jaxley` toolbox. If you want to learn more, check out our [Advanced Tutorials](https://jaxley.readthedocs.io/en/latest/advanced_tutorials.html). If anything is still unclear please create a [discussion](https://github.com/jaxleyverse/jaxley/discussions). If you find any bugs, please open an [issue](https://github.com/jaxleyverse/jaxley/issues). Happy coding!" diff --git a/docs/tutorials/08_importing_morphologies.ipynb b/docs/tutorials/08_importing_morphologies.ipynb index 14306623..672e78a7 100644 --- a/docs/tutorials/08_importing_morphologies.ipynb +++ b/docs/tutorials/08_importing_morphologies.ipynb @@ -16,7 +16,8 @@ "```python\n", "import jaxley as jx\n", "\n", - "cell = jx.read_swc(\"my_cell.swc\", nseg=4, assign_groups=True)\n", + "cell = jx.read_swc(\"my_cell.swc\", ncomp=4)\n", + "cell.branch(2).set_ncomp(2) # Modify the number of compartments of a branch.\n", "```\n", "\n", "To work with more complicated morphologies, `Jaxley` supports importing morphological reconstructions via `.swc` files.\n", @@ -227,7 +228,7 @@ "source": [ "# import swc file into jx.Cell object\n", "fname = \"data/morph.swc\"\n", - "cell = jx.read_swc(fname, nseg=8, max_branch_len=2000.0, assign_groups=True)\n", + "cell = jx.read_swc(fname, ncomp=8) # Use four compartments per branch.\n", "\n", "# print shape (num_branches, num_comps)\n", "print(cell.shape)\n", @@ -424,7 +425,7 @@ } ], "source": [ - "cell = jx.read_swc(fname, nseg=2, max_branch_len=2000.0, assign_groups=True)\n", + "cell = jx.read_swc(fname, ncomp=2)\n", "\n", "# print shape (num_branches, num_comps)\n", "print(cell.shape)\n", @@ -436,17 +437,210 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Once imported the compartmentalized morphology can be viewed using `vis`. " + "The above assigns the same number of compartments to every branch. To use a different number of compartments in individual branches, you can use `.set_ncomp()`:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, + "outputs": [], + "source": [ + "cell.branch(1).set_ncomp(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see below, branch `0` has two compartments (because this is what was passed to `jx.read_swc(..., ncomp=2)`), but branch `1` has four compartments:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
local_cell_indexlocal_branch_indexlocal_comp_indexlengthradiusaxial_resistivitycapacitancevglobal_cell_indexglobal_branch_indexglobal_comp_indexcontrolled_by_param
00000.0500008.1190005000.01.0-70.00000
10010.0500008.1190005000.01.0-70.00010
20103.1207797.8061725000.01.0-70.00121
30113.1207797.1112315000.01.0-70.00131
40123.1207795.6523945000.01.0-70.00141
50133.1207793.8692475000.01.0-70.00151
\n", + "
" + ], + "text/plain": [ + " local_cell_index local_branch_index local_comp_index length radius \\\n", + "0 0 0 0 0.050000 8.119000 \n", + "1 0 0 1 0.050000 8.119000 \n", + "2 0 1 0 3.120779 7.806172 \n", + "3 0 1 1 3.120779 7.111231 \n", + "4 0 1 2 3.120779 5.652394 \n", + "5 0 1 3 3.120779 3.869247 \n", + "\n", + " axial_resistivity capacitance v global_cell_index \\\n", + "0 5000.0 1.0 -70.0 0 \n", + "1 5000.0 1.0 -70.0 0 \n", + "2 5000.0 1.0 -70.0 0 \n", + "3 5000.0 1.0 -70.0 0 \n", + "4 5000.0 1.0 -70.0 0 \n", + "5 5000.0 1.0 -70.0 0 \n", + "\n", + " global_branch_index global_comp_index controlled_by_param \n", + "0 0 0 0 \n", + "1 0 1 0 \n", + "2 1 2 1 \n", + "3 1 3 1 \n", + "4 1 4 1 \n", + "5 1 5 1 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cell.branch([0, 1]).nodes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once imported the compartmentalized morphology can be viewed using `vis`. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPwAAAERCAYAAABfBsqPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABZaklEQVR4nO2dd1hUx9fHv0vvHRQBCyooKqhgCfbeggZ7VKyx95JoiCUaY9SY2GOsWGKJvSv2hh1FVFBRMEgH6Z3de94/eHd+XHdBysKCez/PM4/uLTNnLvfcmTlz5oyIiAgCAgIqgZqyBRAQEKg4BIUXEFAhBIUXEFAhBIUXEFAhBIUXEFAhBIUXEFAhBIUXEFAhBIUXEFAhBIUXEFAhBIUXEFAhBIWvQuzevRsikQiPHz8u9BqRSCQ3rVy5knfd6NGjeeeNjIzg4uKCP/74Azk5OTL5BgQEYMSIEbCzs4O2tjbMzMzQtWtX+Pj4QCKRKLyuAuWDhrIFEFA83bp1w8iRI3nHmjVrJnOdtrY2duzYAQBITk7GsWPHMG/ePDx69AiHDh1i1+3YsQOTJk1CtWrV4OXlhfr16yMtLQ1Xr17FuHHjEB0dDW9v7/KtlIBiIIEqg4+PDwGgR48eFXoNAJo6depn8xo1ahTp6+vzjkkkEnJzcyMAFBkZSURE9+7dI3V1dWrbti2lpqbK5PPo0SPy8fEpWUUElIbQpf9CycrKQnZ2donuUVNTQ8eOHQEA79+/BwAsXboUIpEI+/fvh6Ghocw9bm5uGD16dBmlFagoBIX/Atm9ezf09fWhq6sLJycnHDhwoNj3vnv3DgBgbm6OzMxMXL16Fe3bt0fNmjXLS1yBCkQYw39huLu7Y/DgwahTpw6ioqKwefNmDB8+HCkpKZg8ebLM9QkJCQCAlJQUHD58GCdPnoSzszMcHR0RGBiIvLw8NGnSpKKrIVBOCAr/heHn58f7PXbsWLi6usLb2xujR4+Grq4uO5eRkQFLS0ve9e7u7ti3bx8AIDU1FQDkduUFqiaCwn/haGlpYdq0aZg0aRL8/f3Rtm1bdk5HRwdnzpwBkG+xr1OnDmxtbdl5IyMjAEBaWlrFCi1QbggKrwLY2dkBABITE3nH1dXV0bVr10Lvq1evHjQ0NPD8+fNylU+g4hCMdipAaGgoAMh03z+Hnp4eOnfujFu3buHDhw/lIZpABSMo/BdEfHy8zLG0tDSsW7cOFhYWcHV1LXGeS5YsARHBy8sL6enpMuf9/f2xZ8+eUskrUPEIXfoqyK5du3Dx4kWZ40lJSbh69So8PDxQs2ZNREdHY9euXQgPD8e+ffugpaVV4rLc3d2xefNmTJkyBQ0aNOB52t24cQOnT5/G8uXLFVEtgYpA2Z4/AsVH6mlXWNq7dy9169aNqlevTpqammRiYkLdu3enq1evyuQlz9OuKPz9/WnYsGFUo0YN0tTUJFNTU+rSpQvt2bOHJBKJIqspUI6IiIS49AICqoIwhhcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEhRcQUCEEha/kTJ48GevXr0dMTIyyRRH4AhAi3lRiIiMjWZx4dXV19OjRAyNHjkS/fv2go6OjZOkEqiJCC1+J0dXVxZ9//onWrVtDIpHg/PnzGDp0KKytrXH06NFyKTMrKwv37t1DeHh4ueQvoGSUG1JPoLi8fv2afvrpJ7K2tmZBK+vUqUMXL15UaDmrV69m+bu4uNDixYvZ1tECVR+hS1/FyMjIQPfu3XH37l0AwPTp07FhwwaF5X///n306tULqamp4DgOAGBjYwNfX180atRIYeUIKAehS1/F0NfXh5+fH7766isA+cqoSFq1aoXExETExsZi9+7dUFdXR2RkJHr37o28vDyFliVQ8ai8whMRsrKylC1GienevTsAKHzfN5FIBJFIBAsLC7i7u7PNKywtLaGhIexbUtWpsn/BhQsX4tdff0VeXl6ZXsSePXvi0qVLAIA2bdrA3t4ednZ2MDc3h5mZGczMzCASiWBkZARbW1vY2NhUCgv5mzdvAJR8v7ji8urVK7Rv3x5ZWVlo2LAhzp07B5FIVC5lCVQgSrYhlIqcnBzS0tIiANSmTRviOK7UeQ0cOLDI3Vw+Terq6jRo0CAKDQ1VYI1KTrNmzQgAzZ8/X+F5BwcHU61atQgAOTo6UkxMjMLLEFAOVdJo9/jxY7Ro0YL9fv/+PWrVqlWqvIgIjx8/hpqaGt69e4d3794hOjoaHz9+xMePH5GUlAQiQnJyMiIjI5GZmQkAMDMzw8GDB1nXuqLZsmULpkyZAnV1dVy/fh3t2rUrc55v377FwoULceTIEXAcB3t7e9y/f7/cehGFERQUBCcnpwotU2VQ7vemeGRkZNDhw4dp7Nix1LhxY9baqqmp0bp168rUwpcEjuPo2bNn1LJlS1b+lStXKqRsebIMGzaMAFD16tXpzp07lJmZWaY8XV1d2bPt2bMnvX37VkHSfp6wsDDavHkzmZiYEAD6888/K6xsVaLSK3x6errcrvXQoUMpLi5OKTJlZWVRv379CAANGDBAKTIQEcXExPCeiZqaGjk5OdHx48dLlE9mZiYNHTqU5bNmzZpykliW58+fU5s2bWT+vrNnz64wGVSJSq/wZ86c4b0I3333XaVwBDl16hQBIFdXV3r69CkNHTqUNm7cWKEyvH//nj0Xc3Nz9v/evXsX6/7MzEw6cuQI2dvbs3sHDhxIGRkZ5Sw5UW5uLi1fvpw0NTWZbcTY2JgAUMuWLSkxMbHcZVBFKr3CZ2VlUaNGjdgLuXLlSmWLREREAQEBBIB0dHRo1KhRTL7//vuvQspPTEwkBwcHAkANGjQgsVhMM2bMIAA0atSoQu8Ti8U0f/58qlOnDolEIia3mZlZiXsGpSEyMpLGjRtHNjY2rGwPDw+6f/8++/3+/ftyl0NVqfQKT0S0b98+9jKUh1W6NEgkEurUqRMBYDMGAEhXV5eWLFlSbi3UixcvKCIigs6ePcvKfP36NRERLViwgADQhAkT5N7LcRxNnTqV12MyMzOjBQsWUFJSUrnIS0QUGxtLZ8+epSVLllDdunV5Zf/zzz/EcRytX7+eAFDjxo3LTQ6BKqLwRES//fYbe1FOnTqlbHGIKL9L/M0338i1Mejp6dGkSZMU2uKvXr2aRCIRTZo0iTIzM8nU1JQA0LFjxygyMpIsLCwIAPn4+Mi9f8eOHQSARCIRLVu2jGJjY8vN4Hnx4kUaPHgw1a5dW+bZ2Nra0sWLFykzM5M4jqMpU6awc7Vr1y4XeQTyqTIKLxaLqV69euzFaNWqFW3dupVyc3OVJpOfnx/t37+fNmzYwDM8qamp8f7ft29funDhAkkkkjKV5+vrSwDIwsKCbt++zcp4+PAh+yCqq6tTVlaWzL1paWmko6PDxunlBcdxdOzYMd4zEIlE1KBBA/Ly8qINGzbw5vULLtZp164dnTt3rtxkE6hCCk9ElJqaSgsWLGCGHgC0ZMkSpckjbd27d+9OREQhISHUvn17AkA2NjbUsWNHXstWu3ZtWrJkCYWFhRWaJ8dxdPbsWbkfh2PHjhEA0tfXZ44348aNIyKikydPsp7FixcvZO5NS0tjY34A9ODBA8U8BMo3wP3111/Uv39/qlatGitj0KBBdOXKFUpOTpa5JzMzkyZPnsyuXb16tcLkESicKqXwUqKjo3mKlJeXpxQ5GjZsSACoS5cu7FhiYiLVqFGDANCMGTMoODiYZs6cySzQ0tS5c2favn07hYaGEsdxrGs9ZMgQ9sGYMmUKHTp0iKKjo4mIyNPTkwDQ6NGjmcHtzp07RJTfA2rQoAEBIGtra7ny7t27l7W4ihxq/Pvvv7y6aWpq0pgxY+T+XcLCwmjy5MlkaGjIrre3ty+zD4FA8VCowsfGxtK1a9fo48ePisxWLmvWrGEvjLOzM50/f77CHHCkSI1kY8eO5R0/d+4ck23q1KmUm5tLGRkZtH//furSpYvMmFZdXZ2MjIxo4MCBtHTpUp4RUJocHR3Jzc2NfQw0NDSY4UssFhMRMSs9ABkF4jiOLC0t2XnpPYpg7dq1LN9bt24VqryhoaG8OllZWdFff/1V5LDs5cuXtGbNGmrTpg3VqlWL6tWrR61bt6a+ffvStGnTaMGCBbRo0SJaunQpff/99wqt15eIQhV+9uzZ7CXctWtXuSugt7c37wUaP358ifOQGo5Kg3T2QF9fn7XCUlatWsV7sceNG0fXrl0jjuMoLCyMli1bRu7u7kxxCyaRSEQGBgbUrl07cnFx4U2ffZr69+9PRESXL18mdXV1AkCWlpZy5ZV+MAAw63hOTk6p6k5E9OzZM15rPXHixCKfU8E0aNAgmR5AQkICLV++nAYMGEAtWrQgOzu7QutdWEpISCh1fVQBhSr8sWPHeC9whw4d6NWrV4osgojyx6NSw09CQgJNnz6d9yLduXPns0qcnp5O3t7epKWlRS1atCiV115gYCAr19/fX+b8iRMnyMzMjPdCtm/fnvz8/Hh1CQ0NpXv37rEue8HUpUsXioqKopMnT1L//v1lznft2pXevHnDO3b58mUZWTZs2MDO16pVi96+fUsLFy6kzp07l8jwmZubS3v27KGvvvqKV2a9evXo4cOHvGszMjJo69atvOs6depEmzZt4il7YmIizZ49m3R1dWXqp6WlRT179qS1a9fSvXv36Nq1a9S6dWvy8vIib29vmjVrFk2dOpUmTJhAc+bMqZDeZVVG4WP43NxcWr16NfvjaWtr02+//aawcXZoaCjZ2tqShoYGHTt2jB0v6Pwi7eYX1Xr17t2bXevu7l4q+ZYuXUpAvt95YeTk5NDly5dp/PjxpK2tzcosbK48KSmJoqOj6ffff2fXOjg40KtXrygvL09GKaZOnUrjx49nv+vUqSM334KtZVJSEv3333/s94ULF4pVX47jeB8lDQ0NZpiTGhklEgnduXOHpkyZwqYNpWnv3r28/MLDw2nYsGGkp6fHG7qsXbuWTpw4QXfv3pXxD5A6PAGgo0ePFktugf9Rbka7sLAw6tGjB/vjNG/enN69e1fmfNu1a8fy/HSJ6tOnT2nMmDHsfO3atWn//v1yLd5Sw9rMmTMpPj6+RDIEBwfT7NmzeQpcHHff8PBw+u6779g9t2/fLvTa58+f85RF6tBTcPEQkO/3XnCKDoDc3oqPjw87/+DBA8rJyWG/x4wZU6TcHz58IB8fHxo0aBBrdX/99VeZYYxYLOYNG6QfoD/++IOnuCEhITR69GgyMjJi1zVu3JiOHz/+2anLvLw86ty5MwEgQ0NDevnyZZHXl5W5c+fS1atXy7WMiqRcrfQcx9Hu3bvZl97Kykpu17ckFFRoBwcHCggIkLlm+/btPN9yNzc3un//PjsfGRnJ5okDAwOLLO/69es8i3ZSUhLp6+vLdD3fvHlTLPnT0tLYPbt37y7y2gsXLtCKFStkLPwF0759+2S85zZs2MDy4DhOpvezadMmWrhwIa83JA+O4+jPP/9ktoGCHxkpCQkJ9MMPP1Dv3r3ZSjcANHLkSLpw4QLPiCYWi+nixYu8lr9ly5Z0//79EtlR0tPTydHRkYDync6TejOamJiUydZRmaiQabmIiAhycXFh3UB7e3tq27YtDRkyhJYuXUo7d+6kc+fOkb+/P0VFRVFWVlahLwDHcXTw4EHWQuvo6NDWrVtlrs/IyKBff/2VDAwMCMg3hE2fPp1SU1PpyJEj7IUravyamJhIlpaWpKurSxMnTqSgoCDy8/OTUbqCQ4vPIVW0OnXqUHZ2drHuiY6OpuXLl1OrVq1kyp4zZw7vd7Vq1Sg2Npbde/36dXbO1dWVFi9eTBkZGWxK0cHBQa7venZ2No0dO5b30VywYAHduHGDXcNxnFyZOnfuTET5w7vAwEDat28fTZkyhTdLYG1tTb6+vqW2qnfo0IEAkLGxsVybhSIouFJTnm9DVaTC5uGTk5OpW7duhbZUnyZNTU0yNzcnBwcHGjduHB0/fpxSU1NZfgkJCbxx+KBBgyg4OFimSxgTE8Nr4aysrOjo0aNs3Ni/f/9CPy5hYWG8IQTwv1Vp0hbGwMCg2M/gxIkTzOJ++PDhEj/D4cOH82QpOKSQpr/++ot3T8HZgoLPpuA96enpvHs4jmPddzU1Nfrzzz/lPqOwsDCWx7p16+jy5cv08uVLOnz4MA0ePJg3NpcmExMTGjJkCD19+pSI8rvoEyZMKLG7dEBAAPsb6Ojo0IcPH0p0f3GRfhgPHjxYLvlXNBXqeMNxHIWGhtLt27fp0KFDtGrVKhozZgz16tWLmjVrRtbW1jyXzE+TlpYWc1NNSEggiURCv//+O29mwNDQkDp27Ejz5s1j02BE+dNW0mvU1NTYFCKQ76br4+NDaWlpcmW+efOmXAu6tIUsTnc0Ly+PTV+NGjWqVFOB06ZN45UtdZUt+Hw+dauV+s8D+RZ9juPozp077JiNjQ1lZGRQamoqHTt2jK5evUqbNm1iH93CXF0jIyPZeF1dXZ3S09Np06ZNMlNphoaG1K5dO5o2bRqdPn2a9ahSUlLo4MGDTGk1NDSK9ECUR0JCAiunPLr2Uu9F4MsJyFHpPO0kEgmlpKTQhw8f6OXLl3T+/HmaMWMGb5WVNLVp04bOnDlD9+/fpw4dOsid1mnatCkdOXKExGIxOTs7s5cwLS2N1qxZIzM+PXPmTKFGvIIOJgXTzz///FkrP8dxzEh169atUj2bgt3zgklaB3Nzc5l7xGIx1alTh4B8/4i4uDg2VdiwYUNKSkqiCxcukJWVFetqS12X165dK5NfXFwcrVu3jqpXr84+CkOGDOHZTKpVq0Zz586lhw8fsl4Fx3F0+fJl8vb2pq+++or33M3Nzemff/4p0bM4duwYkxlQ/LLpc+fOMRk7depUrqsJK5JKp/CFwXEcvXjxgiZPniyzAsvGxoZ69+5NixYtopMnT9LOnTtp+PDhvA+ANCgjkD+WlXZjw8PD6ddff5VxgJkzZw5vLCyl4LrtgsnIyIj69OkjM8dcUH5p7+Xbb78tcf1jYmJkhhfy6iavlyLtnUycOJFZ+fX09OjatWsUFhbGjIJGRkZFDnUCAwOZogP5lvWNGzeyD4S9vT1t2rSJ18vIycmh3bt3U5MmTWTkrlu3Li1atEiur31RfPjwgbeeYvPmzQp1r3779i0zzI4YMUKpC7QUTZVR+E+Jioqi77//nhnlCrZ2c+bModTUVPr48SMtWrSI57ctTaampvTbb78xg9XFixdluqNqampyyy4YDkpbW1tmrCrP+s5xHLm7uzPFKAmHDh2SmdOW18IDkBuHTurOK31Wenp6FBQURM+ePWNbV9WoUYPq16/PhikpKSns/rS0NNq7dy+zwjs6OtK6devIx8eHfcQGDRpEubm5JBaL6cyZM/Trr7/SmDFjeIEu9PX1ycvLi3bs2FHi7ruUN2/eUNOmTQn4vK9FacjOzmaGUCMjoy/GOi+lyiq8lJSUFLp27Rpt3ryZevbsyV6uGjVq0KFDh4jjOHr69ClPQQpOHwH5U0jSF/xTf+/Zs2dTy5Yt6d9//2VlPnv2rFDlkyZ5Rrlt27YR8L/VdcXhzz///GxZBbvGBVvlO3fu8Ob9C/ZGrl69yoYYderUYR8UW1tbCg4Opvj4eNq5cyd9/fXXPOOgu7s7JSYmUmZmJvtYDBo0iBISEujy5ctsNqZgsra2ppUrV5Y5KMjr16/Zx9vc3JyCgoLKlF9Bzp49S/379+dNuerq6pZ5SXNlo8or/KecP3+eF6Otc+fOJJFIePHnHR0daebMmTIvprTrFhISIneRi9RwI50SAkALFy6kmjVrylVALy8v3rBg1qxZTKaCMw7yePnyJQ0YMEBuvoUZNqXj2JiYmEJ7BJMnT6ZFixaxBTpOTk6se+zm5kZRUVH033//yfSc6tatS8uWLWNDoZ9//pmA/PF6wW4+kD9V5uXlRUuXLqV///232NOPn0Mapbd58+YKtcpLjZQFG4uJEyfSo0ePFFZGZaHKKTzHcXT37t0ir8nMzGTdU2mL3rx5c94fVSQSkY2NDU95+vXrx/OYkzq0FFSeH3/8kSlIy5YtmUzx8fGFtry7du0iIv4Kv6JWihUMTvlp6tq1K/3zzz8yx3v37k0pKSn04MEDmXNS+8SIESN4Tjpubm7M0t+vXz8WvPLvv/8mIN/It2zZMnr+/Dmv51DQeahg0tHRoenTp5fYc7EoUlNTWcBQaSqr81ZBCir7d999R48fP67wVZcVSZVSeLFYTG3btiUAdPPmzSKvTU5OpnHjxskdvxc0+ACgXr168VonqcU4Pj5eZvELAN70mHQ9OsdxvGs+vU96zYkTJ3gfI0dHRzp58iRxHEe5ubk0btw4mZkDaRc2JCSEiIgOHDhQ6AdBXk+goAurvKSlpUUaGhp08uRJnn1i6NChcv8Gn/ZoWrdurdBwWdLn1KtXL5lnMWzYMIWUQUR09epVlu8PP/zwRSu6lCql8BzHsdaquKGYc3Nz6fHjx7Rx40amADo6OhQTE0PLli1jPYDr16/zPMHGjx9PmZmZ9Ndff8m0YpcuXWK/C3qeSUNQfZqmT58uI9OmTZtYDDogP6iF9GMmLz179ozdHxISUqQCGxgY8JySipukH0KRSESTJ0+WOxVVcM29mZkZz2VZUVy7dk1GNisrK4X6tCclJTEj7dixY1VC2YmqmMJLx8DA/9aBlwRpZFQAtGjRIsrOzmbTRXXr1pWZZzc2NqaVK1fyjtWrV49N97m7u/NcQ1++fMlrWaWpRYsWcuVJSUkhb29v9hGTtzYekJ1jLrjS7dN0+PBh4jhOrvEMAG+qslOnTuTn58ezV7Ro0UKmy3znzh0aM2YMb94bgMIt2CEhITzDK/C/iLyKIisri06ePMnCj9WtW1fuVOaXSpVR+Li4ONYCLV++vNQv2/fff8/rih44cIDN6+vp6dEPP/wgs9a7YJw2aTI0NORZ7on+F0LKxMRExhD0xx9/FCqTdMz8aRoyZAitX79exugl7WHIM94lJCRQXFyc3Pyk01nSD1doaChzylFXV6dly5bJzGdv3bpV7hBj+fLlpXr+RVFwma+Wlhbt2bNHIflmZmbS8ePH6dtvv+UZIzU0NHixCVSBKqPwBeOwe3p6ftbKTZQ/BIiNjaW4uDheS3zw4EHeuLZHjx6spTc0NKQLFy4Uuxs8dOhQZolPTk4mJycnAvI3h/j666/Zderq6jRjxgzeqrqkpCQaPXq03HznzZtXaL1ev34tY0UH8qPZFnQZliZzc3P66aefmL9AzZo16dy5cywGXp06dXhBLR8+fEjTp0/nGTq7d+/O/l/clYHFRSKR8KYPNTQ0SuyMI4+IiAhasGCBzIyFra0tzZo1S+5Kyy+dKqPwOTk5NHnyZNba9OvXj4VDcnBwoAYNGlCHDh1o0KBB1K1bN3JwcJDxNTcxMWEx0Xr16sX8uKVJurBFR0eHN7X3uWRtbc3GgOHh4czZpLButYeHB2VlZfH83D9Nw4cP59Wf4zjasWOH3DjvRSUrKyvKyMhgsd87d+5M8fHxrLW3s7Njy39PnTrFMyhKn8nSpUt5fuufLtApC0+ePJGxN5w9e7ZMeaalpdHkyZN5Q6SaNWvS3Llz6d69e1/c3HpJqDIKL+XGjRtFLrD5NBUVD+5zSVNTs1Cl/TS1aNGCdYf379/PWnnpeR8fH57X2YMHDygtLU1GwaSpffv2vHp/++237Nyn9ReJRHKNdDNmzGBTbVIF37RpE3OYMTMzo1evXtG9e/dowoQJMs9KQ0ODmjZtSq1bt2a9A+ly1PDwcPLz86MDBw7Q4MGDycjIiL7++usS/S0L2mQ0NTVp586dZXo3oqKiaPXq1byPdfv27enEiRNCcMv/p8opPBHRli1byNXVlYYPH05r1qyhkydP0qZNm2jp0qU0YsQIatGihYw33acvsoaGBqmpqZXpg/BpsrGxofv379POnTsJyB8zSw1iY8eO5U2ntWnThl6/fl2kZV7q/cdxHPN3d3NzYzHppalHjx68+HrSj43UbbjgLrMFWz0tLS3evn0F6yFvOvNzqbg76X78+JG328yAAQPKtN48Ly+PNmzYwJPZzs6OLl26VOo8v1SqpMIT5U/dyFsP/mmys7OjHj16UN++fcnBwUGuAaq0adCgQbRy5UqeO6ampiadP3+e55wDgNq2bUsSiYTWrl3LLOU6Ojq8D07BVhzId7JJSUmhkSNHFinHggULeFOKBRMRf5uuwpKXlxddu3aN8vLyKDIykvdRsbKyol69etHq1atp4MCBZGxsTBoaGlSrVi1q27YtzZgxg27fvl3kJpAfP36khQsXykTqLSzSbXGIiIigefPm8eru5uZGGzduVIgN4Eukyir8p55zNjY2VKdOHXJxcSFvb2/y8/OTa9jLycmh9+/f0/v37yk8PJwiIiIoKiqKYmJiKC4ujuLj4+njx4+UlJREYWFhhU6VAfkumBEREZSamkpBQUFs2qqgEtva2rL/SyPtFowgK006OjqUmJjI7pV+FD7nNAOAt1a/4EIeExMTIiLavHlzofdqaWmxljA3N5d8fX3Z/LSlpSWdPXtWZo5aLBYXu4uclJREe/fuZYEkCvZAShKphuM4un79Oo0bN47atm0r84EzMzOjjRs3Cl33z1BlFf7JkydkZWVF+/fvL9dY5IsXLyYg35nl0660VFHv3btHHMfRw4cPeS+ipqYmjRgxgv3u1q0bEZHcSDAFldXBwYEePnzIu+7T4YfUHlCvXj1eyK6CgUNr165N9+7dk3FNlaaFCxfSmzdv6Pjx4zRy5EieNdvR0bFMQUcfPnxIffr04Xk1mpmZ0fbt20u0Uu7du3e0dOlSufEQgPyp1VOnTn1xq9rKCxEREQQK5fTp0+jXrx8AwN3dHWZmZrhy5Qqys7NlrrW1tYWfnx+GDBmCwMBAZGZmAgCaN2+OJ0+eAAC6du2KK1eu8O6zt7dHaGgo+z19+nRs2LABw4YNw8GDBwEAV65cwfz58+Hv74+WLVsiPT0dQUFBaN++Pc6cOYMffvgBW7duLXa92rVrBz09Pdy6dQtZWVnsuIWFBTw8PDBr1iyIRCIkJyfj7du3CAgIQEBAABITE1G9enVYW1ujUaNGcHZ2Rk5ODqKiohAVFQUDAwMYGxtjypQpLM+GDRti4MCB6NChAzIyMqCmpgZ1dXWYmZnB0tISlpaWAIC3b98iKCiIpZcvXyIkJITlo6+vj2HDhqFTp05wdHSEg4MDDAwMil1nAaDKtvAVhbxVdZ9LPXr0oFWrVvEcXQozgjk6OtL27dt5xwwMDOjmzZv0yy+/sGO+vr6stdyzZw9dvXqV9QA8PT2JKD/Om7QXUKtWLZlhT2FJQ0OD9PX15UYMKkuSuvg2a9asWPYWeUkkElHXrl1p7969MrH3BEqO0MIXARFh27ZtmDRpEu+4gYEB1NXVYWBggJSUFKSnp382Lw0NDXTt2hXXrl1Dbm4uDAwMkJ6ejm3btmH69OnIycnB0KFDcfjwYXAcBx0dHejp6SExMREAYGdnhw8fPmDgwIE4fPgwRCIRfvnlFyxevBgtW7bEgwcPAAA//vgjVq5cWaQsDg4OaN26Ne7fv483b97IvUZLSwvGxsYwMTGBjY0NmjVrhqZNm6J69eqIjY1FREQEnj17hhcvXrBWPTo6Gu/evWM9m0/R1dVFo0aNoKamhtzcXCQlJSE+Pp5db2FhAScnJzg5OaFhw4ZwcnKCs7MzrKysPvt8BYqJkj84lRaJRMJzzClsvhzIj7zi6upaqik+6XSbg4MDBQcHFxnZpnbt2jxDpNQffNasWXT06FHq1q1bkTKoqalR7969qXv37rzVfO3bt6cnT55QSEgIxcTEFGsnV47jKDo6mi5fvkz9+vXjlauurk7Ozs40YsQIWrFiBR0/fpwCAwMLDRWVnp6uUv7sykRlW/j4+Hh8/PgRAJCVlYWrV6/izJkziI2NhY6ODlJSUvD+/XsAgJqaGoyNjdGyZUvExcVBJBKxMfmn6OrqQiQSFdrKlYVly5Zh1qxZ+PjxI5o3b46UlBRwHFfq/HR0dPDdd9/h559/hrm5ebHu4TgOW7ZswZ9//smzOwBAt27dMHXqVHTr1g16enqllkug/Kh0Cp+RkYE3b94gNjYW2dnZLGlra6N+/fowNjZmRh9NTU1Ur14d6urqMvkQEZKSkhATE8PSu3fv4O/vD39/f0RERCihdnyqVauGhIQESCQSdkwkEqGoP4mGhgbEYnGx8jcwMEDr1q1haGgIIyMjmJqawtjYGNbW1nB1dUWTJk2gra1dbHkDAwPx22+/4dChQ0xWMzMzDB06FNOmTUODBg2KnZeAclCawt+9exdnz55FbGwssrKyEB8fj9evX+PDhw8lykdHRweOjo6wt7dHZmYm4uPjWZJnSZciEolgYmICkUgEkUiEpk2b4ptvvkHjxo2Rk5OD5ORkZGRkQCwWIzExEa9evYKrqyskEgk4jkNubi7y8vKgp6fHrNknTpxAQkICDAwMYGNjA0NDQ5w7d65IBR49ejRiY2NRq1Yt7Ny5E3l5eSWq/+fQ09NDXl4e8vLy2IdSW1sbBgYGMDIywu+//46UlBQYGBigXbt2MDMzQ15eHtLS0pCSkoKUlBTcv38fu3btgr+/P8vX29sbP/30k9CSVzHKXeFjY2Mxd+5cjBo1irXG+/btw+rVqwvtjlpYWMDW1ha6urrQ1dWFjo4OUlNT8fbtW2RmZoLjOHAch5ycHF7rKA9TU1NYW1ujevXqsLW1RbNmzeDq6oqmTZvC0NCw1PUKDw/HypUrERkZidDQUISFhSEjI6PQ6zU0NEBEn5VXW1sbeXl5Zeqqlxeampro27cvJk6ciG7duilbHIFSUO4K7+7ujnv37sk917VrV3Ts2BF6enowNTVF/fr14ejoCAsLi2LlLZFI8P79ewQFBSE0NBTGxsZsXtfS0hLW1tbQ0dFRZHUYQUFBaNSoEe+YSCSCk5MTunbtCi0tLeTk5KBu3boYPXo09PX1oa6ujsOHD+PgwYM4efIkAKBLly5wcXHBhw8fcPr0aeTk5LC8+vbti4yMDLx9+xbx8fFFflCKg7q6OtTV1SEWi4v1QdHU1ISxsTGsrKzQrFkz9OvXDz179izTh1JAuZS7wu/YsQPjx48HAFhZWSEvLw+1atXC4sWL4enpWZ5FlyvJyclYu3YtqlWrBnt7e9jb26NWrVolGhPLy3P79u1YsGABOI7D6NGjMWfOHERERCAyMhIvX77E9evX8ezZMwBAzZo1MWTIEAQFBeHatWs8B5o2bdrAy8sL9erVg42NDWxtbXlOKidOnMCGDRugo6ODnJwcJCQksFTUsEJTUxMdO3aEg4MDXFxcULduXbi7u5fbh1VAsVQ6o50AsG3bNkycOLHIa0aPHo3ff/+d9YZ8fHwwduxY3jUikQjm5uawtLREw4YN2Vy6g4MDOI6DiYkJqlWrBpFIxO4hIsTGxuLMmTO4fv06Tp06BYlEAnd3d7x//x5hYWFy5Rk5ciRGjhzJDIR169aFpqZmGZ+EgKIRFL4SwnEcbGxsEBMTAyDfmu/q6gpbW1vY2NigR48eaNWqlcx9oaGhbLpOW1ubDQ+KQldXF1lZWbC3t8eyZcswfPhw3vmkpCRm4ASA58+f48GDB/D398eLFy9w586dQvO1tLSEkZERMxK6urqiX79+aNGiBbS1taGmplbCJyNQVgSFr6RkZGRg5syZ2LlzJ6ytrfH06VNUq1at2Pfn5uYiISEBiYmJiI6ORmBgIJ49e4anT58iLCwMmpqaSE1N5Y3lR44ciT179pRIzpiYGHh7e+PVq1dITU1FWloa4uPjecMLeWhra8POzg6WlpawsrKClZUVbG1tYW9vDzc3N2GKr5wQFL4SEx8fD2tra0gkEqipqWHdunWYPn26wvLPyclBREQE0tPTsWPHDvz444+oUaNGmfPNy8tDWFgYm9bLyspCbGwsbt++jbNnzzJ34aJ4+fIlnJycyiyLAB9B4Ss5+/fvx6hRoyCRSFCzZk0EBQVBX19f2WKVGolEgvT0dHz8+BFRUVGIi4tDXFwcYmNj8eLFCxw9ehQGBgZ4//59sb3/BIqPoPBVgIJjcy8vL+zZs4dnaPvSiIqKUkhPQ0AWwWpSBbC3t8fp06eZ09KYMWPKPCdfmRGUvfwQFL6K0L59e2zcuBFqamrYs2cPWrZsiUePHilbLIEqhtClr2LcuHED3377LWJiYqCmpoYxY8Zg9uzZMl5/AgLyEFr4KkbHjh0RGBiIYcOGgeM47Ny5E40bN0bHjh1x+/ZtZYsnUMkRWvgqjJ+fH9atW4fjx4+D4zgYGRnh0aNHcHBwULZoApUUQeG/AN69e8fcZV1cXODv7y83RoCAgNCl/wKoW7cuIiIiYGpqimfPnpXYW05AdRAU/gvB2toaP/30EwBg8eLF5RJiS6DqIyj8F8TUqVNhZ2eHyMhInDp1StniCFRCBIX/gtDR0cGIESMA5K93FxD4FEHhvzCaNGkCACziroBAQQSF/4KIj4/H999/DwBo2bKlkqURqIwICv8FsX//fkRGRqJ+/fpYunSpssURqIQICv8FcfnyZQDA+PHjS7209N27d4iLi1OkWAKVCEHhvxBOnz6NixcvAgB69OhR4vv9/f0xZMgQODg4YM2aNYoWT6CSoKFsAQQUw9ixY8FxHDQ1NeHs7Fyse4gIly9fxqpVq3Dt2jV2/L///isvMQWUjNDCfyH0798fQH54qaVLlxa52w0AXL9+Hc2aNUOPHj1w7do1qKurY8SIEXj27Bn+/fffihBZQBmU61aVAhWGRCKhH3/8ke3gumbNmiKvr1evHgEgfX19mjVrFr1//76CJBVQJsLimS+M5cuXY9GiRTAzM8P79+/l7hKTmJjIjHqRkZFChBkVQujSf2H8+OOPsLCwQGJiIrZv3y5z/vr162jXrh0AoH79+oKyqxiC0e4Lgohw4cIFJCUlAQD7V0pSUhK6d+8OsVgMc3NzbNy4URliCigRQeG/EIKDgzF79mz4+voCyG+9pV53Ut68eQOxWAwzMzOEhITA1NRUGaIKKBGhS1/FkUgkmDlzJpo0aQJfX19oaWlh/vz5ePz4MYyMjAAAqampWLBgAbp06QIgPwquoOwqipKNhgJl5PTp08wy37dvXwoJCeGdv3LlCtWsWZNd06xZM/Lz81OStALKRujSV3F2794NAPj66695a+A5jsOcOXOwfv16APmt+tq1a+Hh4fFFb2IhUDTCtFwVJiEhATVq1EBeXh4CAwPZ0lgAePbsGZo2bQoAmDhxItasWcPbH15ANRHG8FWUyMhIDBkyBHl5eXB1deUpOwA0aNAAxsbGAIBhw4YJyi4AQFD4KsuWLVuY/7s0ll1BtLW14enpCQA4dOhQhcomUHkRFL6K0rt3b/b/NWvW4PXr1zLXWFtbAwCuXLlSYXIJVG4Eha+iWFpasv/fvXsXbm5u+Pfff5GXl4cnT55g7Nix+O233wAAtWrVwr1795QlqkAlQjDaVVEWLVqE5cuXo0GDBrCyssKtW7c+e09ERARsbGwqQDqByorQwldRpFNwixYtwtWrV/HTTz/JTLfVq1eP91tPT6/C5BOonAgKX0XJyckBANja2kJDQwPLly/H69evYWZmBgDo2rWrzOaSgnedgKDwlZTExETmFy8PExMTdp0Uc3NziMViAEBUVJTM7jMPHz5UvKACVQpB4Ssh58+fh7m5Ob755htkZ2fLvUaq8AkJCeyYmZkZrl+/jho1aiAoKAitWrXCX3/9xc4PHz4csbGx5Sq7QOVGUPhKyOnTpwEA2dnZzDW2IESEwMBAAEDDhg1555o3b46HDx+iefPmSEhIwKxZs3Dw4EHY2tri7du3mDhxIjiOK/9KCFROlOrJLyCX1atXs8UuACggIIB3PjY2lp3LyMiQm0d6ejr16dOHLZh58uQJu+fnn3+uiGoIVEKEFr4SMmfOHDx58oT9fvXqFe+8qakp1NTy/3QpKSly89DX14ePjw+MjIzw9OlTPHv2DPXr1wcA/Pzzz9i2bZvQ0qsggsJXQtTV1dGsWTNMnDgRALBjxw5IJBJ2XiQSQVdXFwB/DP8plpaWWLhwIQDA29sbe/fuRaNGjQDkL6jp2bOnsOmEqqHsLoZA4dy8eZN1w7/55ht2/OrVqwSALCwsKC8vr8g8srOzyd7engDQjBkzKCcnh9auXcvy1dPTK+9qCFQihBa+EkMFnCCjo6PZ/48dOwYA6NevHzQ0ig5poK2tjT/++AMAsGHDBsyePRszZszAd999BwDIzMxka+oFVABlf3EE8jlz5oyMAS44OJh0dHQIALm5uZFEIiEiIhsbGwJA586dK3b+mzdvJpFIRABoyJAhlJmZSWPGjGEt/dq1axVZHYFKiqDwlQBfX19SU1MjDw8PmXM3btzgWeyzs7PJysqKANCNGzdKVM6hQ4dIU1OTAFDz5s1p6NCh5ODgwPKuWbMmBQcHK6paApUQQeErAcOGDSMA1K5dO7nnO3bsyJTyhx9+YC31vXv3SlyWr68v6evr8z4iBZOmpiaNHDmSHjx4UNZqCVRChJh2SoaImPdb9+7d5V7Tvn173LhxAwCwevVqAPkRbVq1alXi8rp3745nz57h0qVLyM7ORk5ODpKTk7FlyxYQEdLS0rB3717s3bsXW7duxYQJE0pXMYHKibK/OKrOkSNHWOvasWNHunbtGhHl7xV3+vRpat26NWlpacm0xF5eXsRxnEJl4TiO7t27R7Vq1SIAZG5uTqmpqQotQ0C5CAqvZPbt20dqamo8ZbazsyM9Pb1Cu93SVL9+fVqyZAklJSUpVKaCdgMrKyv6+++/Pzv9J1A1EBReSWRkZFDnzp0/q9TSJB23m5iY0NSpU0lXV5enlLt372ZWfEVw4sQJtsMsAHJyciIfHx8KCwtTeM9CoOIQFF4J5OTkUJcuXQo1mpmamso916dPHxKLxURElJqaSvv27SNHR0d23s3NjdasWUOJiYkKk3P9+vVkZmbGk8PW1paGDRtGW7ZsoejoaIWUJVAxCApfgWRmZpKbm1uhrXjBFlXaohf8nZycLJNnTk4OrVy5kmd579Kli0LlTkpKoiVLllCrVq1IQ0ODJ5epqSmdOXNGoeV9Csdx9OrVK/rnn3/KtRxVQIhpV4FcvXoVXbt2BQBoaGigevXq0NTUxH///QcDAwOkpqZCXV0d+vr6SE1NBQDMmDEDGzZsgI2NDSIiIgrNOzo6mm39LBKJym1hTEZGBh48eICbN2/ixIkTeP78OQDghx9+wPLly6GpqVmqfIkIAQEBuHXrFjIyMpCbm4vk5GQEBgYiICAASUlJ0NDQwMuXL+Hg4KDIKqkWSv7gqBTff/89ASBPT0+SSCTEcRxduXKFqlevLrdVd3Nzo5kzZxIA6tat22fzv3v3LjMArlixolwMbRKJhA4cOED169envXv30vTp03lDjpKO72/evEnTpk3j7X8nL2lqalLXrl3p5cuXCq+TKqESCh8REUERERFKK5/jOFq6dCl7eXfu3EmnTp2i5s2by7zYampq1KBBA1q3bh29evWKecZdunSpWGXNmDGD5fXHH38oRP6MjAxavHgxeXp68mwG7u7uRETk5eVFAMja2vqzCh8aGkpnz56ltWvXUteuXXl119XVpa+//prGjRtHU6ZMoblz59LOnTvpyZMnlJ2drZC6qDoqofCTJk1ic9dpaWkVWjbHcTR37lz2Ui9atIjWrVsno+i9evWigIAA3os9f/58AkCdO3cudnlPnz5lee7atavM8r98+ZKaNGnCk9XY2Jh++eUXNkcvPe/p6SlX4V+/fk2//PKLTD7S5OHhQadOnSo0mIeA4lAJhR87dix7uQwNDalv3760fv16evHiRblPMc2ePZuVvW7dOuI4jlnhpV341q1by8ghkUjIzs6OANCRI0eKXV6jRo14PYmoqKgSyZudnU27du2iZcuW0aBBg9gQoVq1arRu3To6dOgQffz4kXfP4cOHWV1mzpxJp0+fpo0bN9L8+fOpadOmMl1zFxcXGjhwIP344490/vx5YZqvAlEJhSfKdyapXbu2TOtSrVo1+uabb+jnn3+mkydP0ocPHxT2Ar548YKVs23bNpljQP46908VSCovADIyMqKsrKxilzlu3DiZOpqamlKTJk1o+PDhdPr0acrJyZF7b0REBPXv31/m/r59+xY5/SaRSGjFihWFjr81NDSoZ8+e5OPjo7ApQ4HSoTIKT0QkFovp0aNHtHLlSurWrRvPeaVgMjQ0JDc3N+rVqxcNHTqUVq1aRb6+vvTu3btiO7eEhYWxKThnZ2caNWoUNWnShLWYhoaGtHPnzkI/LtIFNd99912J6vj8+XNWRvXq1WUMgQDIzMyMJk6cyBbIcBzH4t8VTB06dKAnT54QUf68/82bN2nz5s00efJk8vT0pAULFlC/fv0K9Rto1KgRbdq0iRISEkpUB4HyQ6UU/lOys7Pp1q1btHbtWho1ahQ1btyY1NXVi7QW6+np0dy5cwtVVIlEQn/99VeRK9L69u1L//33X6FyJSQkMP/5R48elbhemzdvJgDUsmVLSk1NpRcvXtC5c+do5syZbEZAmoyMjHgfhfr169PXX39NU6dOpQULFtDw4cOpcePGMu6/hT0bFxcXsra2ZsemT59eYvkFyg9hHv4TcnNz8fbtW7x58wZJSUmIiorCo0ePEBISgrdv3yI3NxcAMHDgQPTs2RPt2rVDYmIiPDw8YGFhAS0tLRZCWkqPHj3QunVrNG/eHM2bN4etrW2RMuzfvx8jRoyAi4sLAgICSlyHwMBAuLi4QE1NDX/99ReLjQcAYrEYU6ZMwc6dO0s8V29nZwcXFxc0atQIVlZWePXqFerXr4+OHTvCxcUFWlpaAACO47Bx40bMmjUL+vr6CA8PZzviCCgXQeFLgFgsxvbt2zFlypRi3zNp0iRs2bKlROXMnz8fq1evxuTJk7FixQr4+fnhxo0buHXrFtLT02FoaAgjIyMYGhrC0NAQampqiI6ORlRUFKKiomQCW/bv3x96enp48+YN3rx5g+TkZACArq4usrKy5MpgZGQEZ2dndO7cGc2bN0fLli3Z9tPFgYjg7OyMFy9ewN3dHRcuXICRkVGJnoNAOaDU/kUV5caNGzR79mzq1KkTaWhoyO3u6uvr0+LFi0tlACw4l14eSVNTk1auXEkcxxHHcXT+/HnmMmtgYMAb1mhoaNDIkSPp9evXJa6Hv78/mZiYEABq2rQpvX37tsR5CCgWoYUvA2KxGPb29vjw4QM71qdPHwwbNgweHh4wNDQsVb6+vr7o2bMn+12vXj107NgRHTt2hLW1NdLS0lhKTU2FRCKBtbU1atSogRo1asDGxgY6Ojo4ffo0NDU1ERYWBrFYDAcHBzg4OKBevXrQ0dGRKXPAgAHIyMgAkL+Vlb29PYuPr6amBg8PD4wZMwa9e/cutgvtkydP0LNnT8THx8PY2Bi7d+/GN998U6rnIqAAlP3FqcocP36ctYTW1tZ069YtheQbFBTEWtuUlBSF5FkcYmJiaNOmTSysda9evcjPz0/Ggm9hYUFTp04lf3//YuX74cMHcnd3Z/fPnTuXcnNzy7k2AvIQFL4MDB06lL3EilyL/uHDB5bv48ePFZZvcQkMDGTRcl1cXOjx48f04sULmjdvHlWrVo2n/MOGDaNr165RbGxskXnm5ubSnDlz2H2WlpalmoEQKBuCwpeBffv2kY2NTalDPGdlZZGfnx+dPn2a9uzZQ2vXrqX58+eTl5cXmZubM+X48OGDYgUvBqdOnWIyqKurk6+vLxER5eXl0cWLF2nw4MEytoFOnTrR9u3bi3SRPXbsGBkZGREA8vPzq6jqCPw/gsIriZ07d5KhoWGxjGyTJk2Suxa+vCm4aWXTpk3p3bt3vPP+/v7Uvn17GXmtrKzo999/L1Tm+Ph4unLlilLqpOoIRrsKIjc3F3fv3sWlS5dw6dIl+Pv7A8hfF+/o6Ah1dXWkp6cjNDRU7v2mpqb4+++/MXjw4IoUG46Ojnjz5g2A/Gk8f39/mS2qHz9+jD59+iAuLg4aGhoQi8UA8tflOzk5wc3NDa6urnB0dESLFi1gampaoXUQ+B+CwlcAYrEYTk5OCAkJ4R3v3bs31q1bhwYNGvCcYExMTJCcnIxq1arh+++/x86dOxEcHAwAGDNmDDZs2AADA4MKkf3OnTs4cOAAbt68iaCgILi7u+P8+fMwNjbmXRcfH482bdogJCQE1tbW0NbWxvv372XyE4lEsLa2hqmpKUxMTGBiYgJDQ0Pk5eXB0NAQzZo1Q61atSAWi5GXlwd9fX3Ur18f9vb2zLFHoPQICl9OEBFevnyJ06dPY9u2bfjvv/8AAMOHD0ePHj3QrVs3VK9eHatWrcKCBQsA5E/pBQcHIzQ0FLVq1cLTp09hamqKvLw8LF26FCtWrAARwdHREQ8fPqxQR5bw8HA0atQI6enpMDExwdatW2V6G2FhYWjTpg2io6Oho6ODn376Cc7Oznjy5An8/f0RHByMd+/elap8NTU11K9fHy4uLnBxcUG/fv3YTrhScnNz8eTJE7x79w55eXnsoyEWi8FxHCwtLWFra4smTZqobC9DUHgFEx8fj7///hs7duxAeHg4O66uro558+Zh5cqVvOtNTEyQkpICDQ0NcBzHWvp79+6hdevWvGsLzs+/evUKjo6O5VwbPteuXUOXLl3Y74iICNjY2PCuiYyMxNixY3Hp0iUAQMuWLTFr1iw4ODggKSkJqampsLGxQXp6OpKTk5GcnIz09HRoamoiLi4OT548QXx8PDQ1NaGpqYmkpCSEhIQgPT1dRp4aNWrAwsICFhYWyM3NxePHj5Gdnf3Zehw9ehQDBgwo49OooijLePClcfDgQerbty/PeKWjo0O9e/em7du3U3x8PO/6Xbt2Ud26deUa6ezs7OSWkZubyzzXZs2apZS57NzcXBb1ZtCgQXKv4TiONm/eXOhqxGrVqtHcuXMpMDCwWGVyHEdRUVHk6+tLq1atkmsolCZzc3Pq2LEj9ezZkzw8PMjT05MGDx5MgwcPpo4dO1L9+vVVejpQUHgFcPfuXZkXb+fOnYVOT326QaSuri7beKJ3794sFLU8li1bxu4bMWJEeVWpSL766ismQ1EEBwfTpEmTqFWrVmRtbU2NGzcmS0tLXt0NDQ2pffv2dPDgQXr79m2xP2Lh4eH0+PFjunjxIu3fv5/27t1Lr169EoJpfAZB4RXAvHnzCMjfKiogIOCzL93Dhw/ZC6+mpkanTp1ivz8XpNHb25tdW9K18oqiYKjtkSNHlshPIDc3l06fPk39+/dn8foKJjU1NapduzZ16dKF5s6dK6ylVzCCwpeRiIgIFgDic6Go7ty5Q506deK94GvXrqW8vDwWmqpGjRqFvuRPnjxhC1u2bNmitNYsPDycBejA/3vNlSYeXUZGBj1//pwWLVpEjRo1kjsEqF69Oh09elTh22mpKoLClwGO46hHjx4EgFxdXQvtjiYkJPD87qVp2LBhREQUFRVFLVq0YMfv3r0rN5/evXsTAOrXr195ValEFKxLYWGzSoJ0rH779m3y8fGhhg0b8sqwtbWlsWPHFtuHX0AWQeHLQFxcHDVu3Jh0dHQoKChI7jWPHz9mrqSfjl0TEhIoJiaGGe+MjIxox44dhZYn3fJpwoQJ5VWlErF48WIC8nfMKQ8yMzPp+++/J1tbW5nn99VXX9G///4rbHJZQgSFLyNZWVmFrpKLiYlhkWcLKr23tzdbbLJy5UoCQLVq1Spyvfjq1avZ/W5ubuVSl5ISGhpKAEhbW7vchxfJycl06dIlGjZsGG/sX7NmTfLy8mLbbAsUjaDw5URBS3zNmjVpw4YN1LFjRzp+/Djvuh9++IEA0Jw5c4rMb8iQIQSAWrVqVapgFOVBdnY2i4dXkd3sqKgoWrx4MVlYWPBa/bFjx9LJkycpLi6uwmSpaggKr0AyMzPp9OnTNGbMGF7UmAMHDhTaAv7yyy8EgHr27Flk3r/++itv3F9Z8PT0JADUrFkzhYzjS0JmZiYdOXKERo0aJdPlt7GxoUGDBtHatWspJCSkQuWqzAgKX0bEYjHt3r2bPD092Vy6NPXr14/u379f5P1v3rxh01GFbRrx9u1bmjx5MgH5+7RXJqKiotgsxfz585Umx7Vr1+i7774jJycnuQ45/fv3ZyG3VRlB4cvIggULZLzkpk2bRleuXCnWuDYzM5MFm5Aa/oKDgyk1NZXEYjENHz6cl3/Dhg3Lu0olRjoDIRKJFBb1pywkJyfTjRs3aMWKFdS1a1deGG4vLy/6559/igwT/iUjKHwZKBiZpkuXLvTkyZNS7Z4qnX8PDAykCRMmEJAfBLNgdJk2bdrQ6tWrKTIyspxqUzbGjBlDQP5a+FevXilbHB4vXrzgRSeSGlHl7fjzpSMofCl49+4dbdy4kaeQpQ1FdeHCBTbHLG+XGFNTU9qwYYOCa6B4UlNT2W64tra29P79e2WLJMPdu3fZ0AhQzGabVQ1B4UtATk4OeXl5sZDO0paitFNCmZmZbMzZs2dPtjBmyZIl9O+//9LVq1dLtK+csomLi2POMnXq1Km0i1T69etHAEhLS0vl5vEFhS8mt2/f5nl+6enp0ZIlS8oUa/3atWus+x4dHU3R0dEVbulWNBEREVSnTh0C8mPh/fTTTxQTE6NssXicPHmS/R0bNWpEly5dUrZIFYag8MUgKSmJxZ+zsrIqcpqtJEgX0djY2ChAyspDfHw8DRo0iLcgpkuXLrRx48ZKsaKN4zjasmULL1DokCFDilyl+KUgKHwxWb9+PX333XcKNfRs3LiRtTJfIocPH6ZWrVrJ2CXs7Oxo9OjRtGnTJqVayxMTE2nmzJnMdnLgwAGlyVJRCBFvlMjAgQNx7NgxuLq64tGjRxCJRMoWqVwIDQ3FkSNH4OvrCz8/P7YhpxQ3NzcMGDAAkyZNgomJSYXLt337dnAch3HjxkFDQ6PCy69IBIVXIocOHcK3334LADhy5AgGDhyoZInKn4yMDNy5cwe3bt3C7du3cefOHUhfQTMzMyxatAhTp04t9lZWAiVDUHgl4eHhgbNnz7LfZ8+eRZ8+fZQokXKIjY3F6dOnsW7dOgQFBQEAHBwcMGjQIPTu3RutW7eGmpqakqX8glDicEJlGTt2LBvPtmjRotJOX1UkeXl5tG3bNrYEWJqaNm1Kp06dUrqh70tBaOErmODgYDRq1Ih1Y9u0aYM7d+4oWarKQ1JSEs6ePYvz58/j7NmzLFqtq6srvL298c033wgtfllQ8gdH5Thz5oyM1frGjRuUmZmpbNEqHR8/fiRvb2/S19dnz8rR0ZF27txJ2dnZyhavSiIofAXy+PFjatCgAQH54ZSbNGnCXmQtLS1at26dskWslMTFxdHChQuZJyL+f+3B77//TqmpqcoWr0ohKHwFcfHiRdLS0iIgfy/5K1eu0I0bN6hXr1680M379+9XtqiVltTUVFqzZg3VqFGDPS9zc3Py9vamp0+fCuP8YiAofAVw8+ZNFpG1d+/eMlFpOY5je6dramrS5cuXlSRp1SA7O5t27txJDg4OvKFRvXr1aMGCBfT48WOSSCTKFrNSIih8OSKRSOjixYvshezdu3ehvvISiYQGDhzI1pVPmDBBiMn+GXJycujAgQPk6enJYgpIk5mZGXl6etL69espNDRU2aJWGgSFLydOnjzJi4Cjo6PzWcNcVlYWjRw5kvfSbtmyRSV8vMtKamoqHTp0iPr3788z8klTy5Yt6Y8//ijRphlfIoLClwMvX74ka2tr3gvXrVu3Yt9/69YtcnZ2Zve6urrSjRs3hG5qMcnNzaV79+7RihUrqFOnTqSmpsb7W7Rt25Y2btxI0dHRyha1whEUXkFwHEc3b96UiawCgH799dcS55eXl0cbNmwgY2Njlo+hoSF17tyZ/vrrL6G7XwKio6Np06ZN1K5dO7nx7g4ePFil4g6UBcHxpgzk5OTg5s2buHnzJo4dO4bXr1/LvS4gIAAuLi6lKiM8PBzjx4/H7du3kZWVxY5ramqiXbt20NbWxu+//y6zV7qAfCIiInDkyBHMmTOHd9zU1BRdunSBiYkJHB0dMXLkSFhZWSlJyvJDUPgy0K1bN1y5coX91tHRgYaGBtLT06Grq8sU1MfHB6NGjSrTajixWIzg4GD4+vpi//79CAgI4J2vXbs23NzcMGHCBHTr1q3U5agSoaGh2LNnD3x8fPDhwwfeOU1NTXTs2BENGzaEk5MTnJyc0KhRI5iZmSlJWgWh3A5G1eXRo0c8J5Dx48eTtrY2AaC6detSQEAAzZw5k13TuXNnhZb//Plzatq0qUz3VENDo9IFkazsiMViunz5Mq1bt46WLFnC2+fv01SjRg2aNm0a3b9/v0rO+wsKXwIkEgkdO3aMxowZw7Y7qlWrFptDB0AeHh5sp1OxWMzbF608Qj1JN4Jwdnam9u3bs+k/gbIREBBAf//9N82dO5d69epFNWvWlFH+evXq0c8//0wpKSnKFrfYCApfTPbt28fz8AJAX3/9Na1atYr9XrBggYwlPTo6mrcyTtGt7/v375lTz8SJE1lZa9asqZItUGUmNTWVzp8/T8OHD5fZdKR///60ZcuWMsU4rAgEhf8M2dnZ9Pz5c5oyZQrzhHN0dGQx7qRpyZIlhebh7u7OrlNXV6dJkyYpdEpo3759LP+C/uZbtmxRWBkCfNLS0mjdunVs152CqU6dOjRx4kQ6fvw4ffjwoVJ9eAWF/wTpfmVz586lr776io3Li0oLFy4sco5c2uUv+JHQ09NTqELOmDGDlSHdeNLAwEBld1ipKMRiMd2/f59++eUX6tChA29nW2mqVq0a9enTh37++Wc6e/YsRUVFKe0jICg85TtqXLlyhcaMGSPTckuVyNLSkrfIRZpu3rz52fyPHj3Kegc3b95kgR1FIhFt3bpVIX/83NxccnNzY/P+0l5FkyZNKnRnV1UnLS2Nzp07R5MmTSIXFxfepqIFk5WVFXXv3p1++OEHOnDgAL18+bJCYuSrrMKnpaXRgQMHaOjQobxuMACqXbs2TZo0iXbt2kWrVq3ihTMuaLDJyMgoVllhYWHsvt9++41yc3N54+1OnTopRCmHDRtGAGjSpEn06tUrJreamhotWbJE8NRTApmZmXT37l3asGEDjRw5kpycnGQ8/6RJR0eHWrRoQePHj6fNmzeTn5+fwvcpUDmFf/nyJU2bNo2MjIx4D9vCwoImTJhAt2/fJolEQikpKeTq6srON2/enJYuXcr+MCWxuHMcx7Pyzpgxg/Ly8mjlypW8IUOPHj3o8uXLpW7xvb29CQB17dqViIhiYmJ4nn8eHh6UnJxcqrwFFEdGRgY9fPiQtm3bRlOmTCF3d3e5/v/ShuXdu3cKK1slFD40NJT++OMPatOmDe9h1q1blxYsWEB+fn68BSpJSUnUs2dPAkDGxsa0atUqSkpKYvPe3377bYll+PDhA61YsYKVvX37diLK3wp68ODBvK++i4sL7dmzp8Rf9wcPHrC5+CNHjrDje/bsYR8WKysrWrt2rcq4klYVJBIJvXnzhg4fPkw//fQT9enTh0xMTMjFxUWhrfwXqfBRUVF09OhRmjNnDm8RitRK7unpSZcuXZLp4nIcR3v37iUrKyvWkksdLEaMGMF6AmVZcbVs2TI2nr9x4wY7HhoaSjNmzOB96a2trWnFihXF3vyC4zjy8vJi9fzzzz9Zb+HRo0dUu3Ztlnfbtm0pNze31PUQKH+SkpIUPs1XpRU+NzeXQkJCyNfXlzZu3EjDhw9n+5p9quSdOnWiDRs2UEREhNy8EhMTqX///uye+vXr0/Xr14mIaNOmTWwsXNZ9yDiOo8GDBxOQvzPs69eveec/fvxIv/32G2/OX09Pj+bNm0eJiYmfzV8sFrOtm4F8J5zY2Fgiyl8/vm3bNjaccXd3L/Y+9gJfBpVK4SUSCSUlJVFCQgIFBATQpk2baMyYMdS3b18aOHAgjRgxgkaPHk1dunShOnXqFGoBVVNTIxcXF5o8eTL9888/n11ZxnEcryfw66+/sm7UkSNH2FTLH3/8oZB6ZmZmMku9vb09PXv2TOaanJwc2rdvH8991tTUlNasWfPZAI4cx9HmzZtZN97Ozo55/xERnTp1imc7aNeuHd26dUshdROo3FQahV+1apXc/dE/l3R1dalRo0bUt29f+vnnn+ny5cslcnVMSUmh9evXs/yaNm3Kzh0/fpzJNGzYMIW2hDExMWzo4OjoWOh1HMfR+fPnqXHjxrxZhP3793/W6h4YGEi1atViH7GCREZG0vTp03mKP3r0aGHZ7RdOpVF4abdZmgwMDKh79+60ZMkS2rp1K23atInWrFlDK1asIB8fH7p16xZFRkaWSQkXL14sExpp9+7dRJQ/ppaeGz16dLlEnZGGvzIxMflsNByxWEy7du0iGxsb3lzu4MGDacuWLYXuyurj48OuX758ucw1ERERNGHCBPZhs7CwoL179wrd/C+USqPwKSkpFB0dTVlZWSQWiytkzrjgeH/dunX0/v17kkgkFBgYyLrcbdq0KTfjVkREBK/VLo4xMCMjg1asWMELjCFNTk5OMl3z7Oxs6tWrF7vm3LlzcvO9e/curxfRq1cvhe6UK1A5qDQKrwykq8v++ecfIsrvZVhYWPCUqLy3gSo4ZTZ79uxi35eTk0O3b9+mZcuWUadOnXhd88mTJ/OGNRzH0ejRowkAeXp6Fppnbm4urVixgvVs7O3t6ejRo0JMvS8IlVZ4qRL06dOH5s6dy4yAIpGIRCIRTZw4sULkOHfuHCu3sBb4cyQlJdH48eOZ0n/aM3n+/Dmbo4+MjCwyr4CAAN4UXp06dWjbtm2lkkugcqHSCr98+fLPGgX/+uuvch/PSiQSat26NSvz22+/lZmuKy5Xrlxh027ffvstb/GM1PFo9OjRn80nMTGRFi5cyHMrvnLlSqlkEqg8qLTCR0VF0ZgxY6ht27asde/bty99//33PKX38vIqtt98aUlLS6PZs2fzPO66d+9OAQEBJc6r4OyCuro6DR06lGJjY+n+/fvsWHGX52ZkZDB5CnrvCVRNVFrhQ0JC6JtvvmEvtLu7OzMWchxHv//+O/sQNGnShN68eVPuMj1+/Jg8PDyYwpqampbK2+r69evUuXNnVrdly5YREdFXX31FAKhnz57MIacoPnz4wPv4DRs2rEpFeBHgo5IKHxkZSRMnTiQNDQ02rvX29pY7NXbjxg2qVq0aASAjIyM6c+ZMhcgYGhrKlrtKN04sjqfdp0jXxnt5eRER0dmzZ9lHTFtbm6ZNm0bp6eky92VnZ9OoUaMKHer0799f7n0ClRuVU/i7d++ykFBS19Pnz58XeU9kZCRv4Y104Ut5ExkZyZs6tLCwKLHSnzx5koD8XWykK/z++ecfnvJOmDCBd09iYiJ16NCBnTcxMWEfvYJJGNNXPVRO4Xfu3Mkce4oTvEJKTk4OzxW1osjMzKQdO3bw7AklMSLm5uYy99zWrVtTp06dZBT35s2bJBaLKSkpiQICAtiW1kZGRjJrB7KysmjhwoW0Z88eYX19FUTlFD42NpZ1aUNCQkp0r52dHQGgjRs3lpN0hVPQeSY4OLhE935qhBSJRFSjRg2qVasW2djYyI3yY2trS4GBgeVUGwFloQYVw8rKCl26dAEAHDx4sET3tmzZEgCQnZ2tcLk+R8GdZQIDA4t1j0QiwYYNG7BmzRrecSJCVFQU/vvvP0RGRiItLY2d09bWRqdOnfDgwQM0adJEMcILVBpUTuEB4NtvvwWQr/BUgo13pNtFrV+/HocOHUJubm65yCePgjueDBkyBHfv3v3sPd7e3pg5cyavjjVr1oSnpyd+/fVXnD9/Ho8ePcKbN28QGxuL7OxsZGdn49q1a6hRo0a51ENAySi5h6EUkpOTSUtLiwCUqNsaFBTE6/4OHTq0HKWUJTg4mEaPHk0zZ84schz/5s0buZtaRkVFVaC0ApURld1b7ptvvsGpU6fQoUMHtGvXDmZmZrxkYWGBevXqQV1dnXdfVFQUOnTogLdv36Jx48Z4/vx5hcot/XPJ26eO4zj88ssv+OWXXyCRSAAAffr0wblz52BgYMDruguoKEr+4CiNf//997Nutebm5jR06FDy8fHh+Z9fu3aNgPy1+PHx8Uqsxf8Qi8XUu3dvnvy7d++mW7duEQCqXr26skUUqASobAvPcRz++ecfvHv3DomJibz08eNHREdHIzMzk3ePs7Mzhg8fjhEjRsDDwwNPnjzBihUr8OOPPyqpFv/j+fPncHZ2/ux1N27cQIcOHSpAIoHKiMoq/OfIy8vDgwcP4OvrC19fXzx+/Jh1p9XV1WFhYYHY2FgsXLgQv/zyi5Klzf+ADRgwACdPnizyOnV1dYjF4ooRSqDSISh8MUlISMDJkyfh4+PDs5BraGigUaNGcHV1haurKxwcHADkKyARQV9fH82aNYO+vn65y7hkyRIsW7bss9c1a9YMhw8fRr169cpdJoHKhaDwpcDZ2RnPnz+HtrY2cnJyPnu9uro6mjVrhjZt2qBly5awsLCAgYEBmjZtCj09PYXJlZiYCG9vb9y5cwdxcXGIj48HkD+H7+zszPM7MDc3x507d9CgQQOFlS9Q+REUvhTo6uoiOzsbGzZsQL9+/fDkyRP4+/vD398fHz58gJqaGkQiEdTU1BAXF4fIyEi5+dSoUQMXL14ssYNLbGwsHj16hKCgIMTHxyM+Ph6vX7/Go0ePmHUeyP/Q/Pbbb5g3bx6z6oeEhKBt27aIi4uDnZ0dHj9+DCsrq9I/DIEqhaDwpUCqPE2bNsXTp08/e314eDju3r0LPz8/PHv2DKmpqYiIiMDHjx9hYmKCs2fPok2bNnLvTU1Nhb+/Px49eoSHDx/i0aNHCA8PL7SsunXronPnzujcuTM6deqEatWqyVwTExMDa2trAMCAAQNw9OjR4lRb4AtAQ9kCVDXevHnD/j969Ohi3VOzZk3UrFkTQ4cOZccSExPh4eGBu3fvolOnTvj6669hb28PkUgEkUjEWvFXr17JeAOKRCI0bNgQTZs2RfXq1WFpaQlbW1u0a9cOtWrV+qw8FhYW0NTURF5enkKHFAKVH0HhS4i0dVdXV8f48eNLlUdeXh6ePn2KGjVqQCQSIS8vDydOnCj0+po1a6JFixZo2bIlWrZsiebNm8PIyKhUZUv5448/sH79eqxdu7ZM+QhULYQufQkhItjZ2SEyMhJXrlxhC3EKIycnBwEBAbh+/TqWLVsGjuOQm5vLa7WtrKwQFxcHANDU1IS1tTVq166NiRMnomvXruU2xpZIJDKehAJfOBXs6PNFIF2q2r9//0J92h8+fEgTJkyQ2ZZaXho9ejT5+/tTs2bNZDz9hE0hBBSJoPCl4O7duyw81sGDB2XOh4eH8/a9s7CwoB49epCRkRG1bt2aPD09WfgqIH/vN6J899iHDx/Sjh07eHvdtWvXjs6dOycEnBAoM4LClwJfX18WXfbff/9lx3Nzc+nEiRNUt25dAkAODg509erVQhWV4zgKCAigCxcuyJyTbgpRcIMJJycn2rp1a7lH0BX4chEUvoRkZGSQmZkZ4f/jvnMcRxKJhNavX0/W1ta8LvnWrVvLXF54eDjNmzdPJipNr1696MCBA0J3X6BECApfQtLS0pjC9+vXjxITE8nDw4MpopWVFc2fP1/hIa2Tk5NpwoQJbMdZaWrdujU9ePBAoWUJfLkICl8KLl26xMbwJiYmLOTzpk2b2L7y5QXHcfTs2TNavHgx6evr84JbhoWFlWvZAlUfQeGLSVBQEK1cuZK3S400GRsbK6WVjYyM5MWOV1NTo/79+9P169eFrr6AXIR5eDnExsbi2bNnePLkCR4+fIgHDx4gKiqKd42xsTGMjY2Zm+utW7fQrl07ZYiLR48e4aeffsLly5fZsSZNmsDT0xPOzs6oX78+6tWrJ3jVCaim401WVhYePnyIiIgItvgkISEBYWFhePbsGXOCKYiWlhY6d+4MDw8P9OrVC7Vr1wYA/P7771BTU8O8efMquBayvHz5Eps2bcLevXtlgncA+R57nTt3xldffYWvvvoKTk5OguONiqGSCj9gwAAcP3680PMikQj169dH06ZN0aJFC7Rq1Qqurq5VpoVMSkrCkSNHcPfuXQQHByMkJARJSUky11WrVg0bN27EoEGDlCClgDJQSYVfv349Vq9ejYYNG8LCwgKWlpawtLREjRo14OzsjMaNG1cZ5S4uHz9+xOPHj3H79m3cu3cP165dAwDUqVMHoaGhSpZOoKJQSYXPy8uDhoaG3MivqkJMTAzWrl0LPT09LFmyRNniCFQQKqnwAgKqikruPCMgoKoICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEIICi8goEL8HxlrHMgK+Rp4AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -472,12 +666,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAGrCAYAAAAM3trbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADZ+0lEQVR4nOydd3wUZf7H37N9N713SCEJLfQuoHQVBRXFevZy9lNP7zy987zz9Ken3p2eYq/gqdhBKYLSe68hIYX0nmy2t5nfHxOCkRY0kADP+/Xa1+7OTvnObjKfeZ5vkxRFURAIBAKBoAPRdLYBAoFAIDjzEOIiEAgEgg5HiItAIBAIOhwhLgKBQCDocIS4CAQCgaDDEeIiEAgEgg5HiItAIBAIOhwhLgKBQCDocIS4CAQCgaDDEeIiEJwE/vrXvyJJEnV1dZ1tikDQKQhxEXRZ3nvvPSRJavOIjY1l3LhxLFiwoLPNEwgEx0DX2QYIBMfjb3/7G2lpaSiKQnV1Ne+99x4XXngh8+bN46KLLups8wQCwREQ4iLo8lxwwQUMGTKk9f0tt9xCXFwc//vf/44qLn6/H1mWMRgMp8pMgUDwE8S0mOC0Izw8HLPZjE6n3hsVFxcjSRLPP/88//73v8nIyMBoNLJnzx68Xi9/+ctfGDx4MGFhYQQFBTFmzBh+/PHHNvv86T7eeOON1n0MHTqUjRs3HmZDbm4uM2fOJCYmBrPZTHZ2No899thh6zU1NXHjjTcSHh5OWFgYN910E06n87D1Zs+ezeDBgzGbzURGRnLVVVdRWlraZp38/HxmzJhBfHw8JpOJ5ORkrrrqKqxW66/5OgWCk4IYuQi6PFarlbq6OhRFoaamhpdffhm73c51113XZr13330Xt9vN7bffjtFoJDIykubmZt566y2uvvpqbrvtNmw2G2+//TZTpkxhw4YNDBgwoM0+PvroI2w2G3fccQeSJPHcc89x2WWXUVhYiF6vB2DHjh2MGTMGvV7P7bffTmpqKgUFBcybN49//OMfbfY3c+ZM0tLSeOaZZ9iyZQtvvfUWsbGxPPvss63r/OMf/+DPf/4zM2fO5NZbb6W2tpaXX36ZsWPHsnXrVsLDw/F6vUyZMgWPx8O9995LfHw85eXlzJ8/n6amJsLCwk7Oly8Q/FIUgaCL8u677yrAYQ+j0ai89957resVFRUpgBIaGqrU1NS02Yff71c8Hk+bZY2NjUpcXJxy8803H7aPqKgopaGhoXX5119/rQDKvHnzWpeNHTtWCQkJUQ4cONBmv7Ist75+4oknFKDNMRRFUS699FIlKiqq9X1xcbGi1WqVf/zjH23W27lzp6LT6VqXb926VQGUuXPnHvtLEwi6CGJaTNDleeWVV/j+++/5/vvvmT17NuPGjePWW2/liy++aLPejBkziImJabNMq9W2+l1kWaahoQG/38+QIUPYsmXLYce68soriYiIaH0/ZswYAAoLCwGora1lxYoV3HzzzXTr1q3NtpIkHba/3/72t23ejxkzhvr6epqbmwH44osvkGWZmTNnUldX1/qIj48nMzOzdfru4Mhk0aJFR5xWEwi6GmJaTNDlGTZsWBuH/tVXX83AgQO555572jj009LSjrj9+++/zwsvvEBubi4+n++Y6/9cMA4KTWNjI3BIZPr27dsu24+1v9DQUPLz81EUhczMzCNuf3AqLi0tjQcffJAXX3yROXPmMGbMGKZNm8Z1110npsQEXRIhLoLTDo1Gw7hx4/jPf/5Dfn4+QUFBAJjN5sPWnT17NjfeeCOXXHIJDz/8MLGxsWi1Wp555hkKCgoOW1+r1R7xmMov7AZ+vP3JsowkSSxYsOCI6wYHB7e+fuGFF7jxxhv5+uuvWbx4Mffddx/PPPMM69atIzk5+RfZJxCcLIS4CE5L/H4/AHa7vVVcjsRnn31Geno6X3zxRZtpqyeeeOIXHTc9PR2AXbt2/aLtf05GRgaKopCWlkZWVtZx18/JySEnJ4fHH3+cNWvWcM455/Daa6/x1FNPdYg9AkFHIXwugtMOn8/H4sWLMRgM9OrV65jrHhwN/HTksX79etauXfuLjh0TE8PYsWN55513KCkpafPZLxndXHbZZWi1Wp588snDtlcUhfr6egCam5tbBfUgOTk5aDQaPB7PCR9XIDjZiJGLoMuzYMECcnNzAaipqeGjjz4iPz+fP/7xj4SGhtLQ0HDUbS+66CK++OILLr30UqZOnUpRURGvvfYavXv3xm63/yJ7XnrpJUaPHs2gQYO4/fbbSUtLo7i4mG+//ZZt27ad0L4yMjJ46qmnePTRRykuLuaSSy4hJCSEoqIivvzyS26//XZ+//vf88MPP3DPPfdwxRVXkJWVhd/v58MPP0Sr1TJjxoxfdB4CwclEiIugy/OXv/yl9bXJZKJnz57MmjWLO+6447jb3njjjVRVVfH666+zaNEievfuzezZs5k7dy7Lli37Rfb079+fdevW8ec//5lZs2bhdrvp3r07M2fO/EX7++Mf/0hWVhb/+te/ePLJJwFISUlh8uTJTJs2rfWYU6ZMYd68eZSXl2OxWOjfvz8LFixgxIgRv+i4AsHJRFJ+qadSIBAIBIKjIHwuAoFAIOhwhLgIBAKBoMMR4iIQCASCDkeIi0AgEAg6HCEuAoFAIOhwhLgIBAKBoMMR4iIQCASCDkeIi0AgEAg6HCEuAoFAIOhwhLgIBAKBoMMR4iIQCASCDkeIi0AgEAg6HCEuAoFAIOhwhLgIBAKBoMMR4iIQCASCDkeIi0AgEAg6HCEuAoFAIOhwhLgIBAKBoMMR4iIQCASCDkeIi0AgEAg6HCEuAoFAIOhwhLgIBAKBoMMR4iIQCASCDkfX2QYIBEdlywdQvx96TYekQSBJnW2RQCBoJ5KiKEpnGyEQHJG3JkLZRvV1dBb0vwr6XQlhyZ1rl0AgOC5CXARdl12fQ+63kPsd+F0tCyVIGwv9r4ZeF4MxuFNNFAgER0aIi6Dr426Gvd/Atv/BgVWHluuDYOzvYcyDnWebQCA4IkJcBKcXjQdgx6ew/SNoKFSXDbsd+l0Fcb1Bb+5c+1rw+/3Mnz8fm81Gamoq2dnZxMTEIAm/keAsQYiL4PREUeDzW9Sps4MYQ+GGbyBxYOfZ9RNefPFFmpubW99HRkaSnZ1N3759SUpK6kTLBIKTjxAXwemLox7WvwYH1hyaLrv6E8g+v3PtamHnzp3YbDYKCwspKioiEAi0fjZ16lSGDh3aidYJBCcXIS6C05/i1fDehWAKg4cLQdv1Iuw9Hg8FBQVs27aNvLw8AMaNG8fYsWPFVJngjEQkUQpOf6J6qM/uZvDaOteWo2A0GunduzdXX301o0ePBuDHH38kPz+/ky0TCE4OQlwEpz8hcS0Co0DJ+s625pgEAgGqq6sB0Ol0REREdLJFAsHJQYiL4MwgMkN9rsvrXDuOgd/v55NPPiE/Px+dTsc111xDTExMZ5slEJwUut7kdBeioNaOw+OnX3L4KTum4vVS8fjjNH8zj9ALL8A8cBCGbinoU7qhT05CYzCcMltOGzw2KFqhvk49p3NtOQput5uPP/6Y4uLiVmFJT0/vbLMEgpOGcOgfhaV7q7nl/U0A7HvqfIw67Sk5rq+8nP0TJh75Q0lCFxuLNiICbXi4+ggLU59DQ1BkGRTQRUWhi49DHx+PLi4ObfAZnsW+dx58ch2EdYPf7ehyNcgCgQBz5syhsLBQCIvgrEGMXI5CXKip9fWt72/izeuHYNKffIHRJyWR/Oqr1P7nPwSNGoWvrBRvSSne0lIUpxN/dTX+ljn79mLq14/I668n9ILzkbSnRiRPKbqW38peBbX7ILZn59rzExRFYf78+RQWFqLX67nuuuvo3r17Z5slEJx0xMjlKCiKwlPf7uXtVUUA3De+Bw9Ozu5UewINDfgqKgg0NbU8rK2vZbsdtFqQZfx1dfirq/BV1yD/JIkvaNRIEl94Ad2Z5kQO+GH2perUWFQm3PYDmEI71SRFUSgoKGDFihWUlJQgSRJXX301WVlZnWqXQHCqEOJyBKqb3fz5q10s3qOOEEKMOl65dhBjs04/56u/tpbGTz+l/q23UVwu9MnJJP/3ZUw9u87dfYdgr4XXx4KtAnpNg5kfdMr0mKIo7Nq1i1WrVrVGhWk0Gi688EKGDBlyyu05GSiKInJzBMdFiAvqP0tZo4td5Va2lTXx0foSbG4/Oo3ELaPTuPO8DMItp7cj3b1vH2X33IuvtBTJZCLltVkEjRjR2WZ1LKUb4d0LQPbBxS/B4BtOvQmlpbz99tsA6PV6Bg8ezIgRIwgPDz/ltnQ0Llszaz//H8XbtnDV357DEhrW2SYJujBnrbgoisKK/DoW767ih9waKq3uNp/3Sw7j2Rn96JXQudMrHUmgqYnyBx/CsWYNuoQEeny/GEl3hrndljwJq17E1/MSVsbeQHp6OvHx8ZhMpuNv2wFs376dL7/8EoBHHnkEi8VySo57MpADAcpzd1OweQMlu7ZTe6Co9bOx197E0GkzOtE6QVfnDLuytJ/HvtrFR+tLWt8btBqy40PomxTK0NRIpvVPRKc9s9KAtOHhJL/yX/KGDcdfWYmvvBzDmeZcbmkkZmusZUXuClasUEOUw8PDSUhIYNCgQWRmZnb4Ya1WK9999x379u0DID09/bQVloDfx/ovP2Xrgnm4HfY2n+kCAXqPOpc+5x0lolEgaOGsFJeSemcbYclJCuPVaweREnl6XgxOBI3ZjD45GW9REb7KqjNPXPTqb2jSKvTu3Zvy8nKsVitNTU00NTVRWVnJ7373uw49ZH19PbNnz6axsRGAnj17MmHChA49xqmiunA/C2f9m7qSYgBMIaFkDBpK8JoNBG/bSeSw4STecBs6MSUmOA5npbiYDVoyY4PJr1HvynaWW7ntg00s/N3YTrbs1KBPiG8Rl0oAGt2NPLLiEWRF5v/G/B8xltMvcAFQy/C3VEe2GPXMnDkTAKfTyYIFC9i5c2eH+j6am5tZunQpO3bsQFEUDAYD119/PcnJp18bZr/Px7rP/8eGrz9DkWXMoWGMv+kOskacg1zfQP7Lb4KiEPf44+iiojrbXMFpwFkpLjEhRr5/8FzeXlXE3+fvASDMrO9kq04duoQEAOwrlhM27WJqnDWsq1wHgNPv7EzTfjmyDN89BFtnAxIMuan1I4vFQkpKCjt37sRs/mXNxPx+PwUFBdTW1lJXV0ddXR1VVVX4/X4AMjMzmTRpErGxsR1xNqcERVFoKC/jwM5t7FiygPoydTSfPWos42+6o9Vh3/jtt6AomPv3x5ie1pkmC04jzkpxOcjHGw5NjT1/Rf9OtOTUEjplCtYvvsS2YCHlAZkezz1LrCWWGmcN1y+4ngcGP8C0jGlopNPI57Tpbdj0jvr6klehz6VtPtZo1HM5KAYngt/vZ86cORQVFR32WWJiIhdccAEpKSknbnMnYm9s4ItnnmjjpLeEhTPxlrvIHD6qzbrN3y0AIHT6tFNqo+D05qwWl/+bkcOMWWsBuOqNdXx19znEhBg72aqTT/DYsSS9+AIVj/wB2+LFBBoamPXY33h433MUWgv58+o/M3ffXP4w7A/0i+nX2ea2D81PKg/kzDzs45qaGkDtBnkiyLLMl19+2SosvXv3JjY2lujoaKKjo4mNjW0Vrq6OoijY6mqpKshj3ecfU1tSjFavJ6lnH7rnDCBn/GTMIYdHR8pOBwD6xMRTbbLgNOasDUU+SHGdgxmz1lDv8PL7yVncM77jI4m6Ko516ym7+25kh3rx0A3qz/cXxvO+tAan3wXA6KTRXNvrWkYljuoyI5liazFv7HiDB4c8SLQ5Wl3odcK/+4KzHi57C/pd0bq+2+1m1qxZWK1WLr/8cvr27dvuYy1dupSVK1ei0Wi49tprycjI6OjTOWm4bM1UFeRTtT+PqoI8Kvfn4Wq2tn4eFB7BVX/7J+Fx8UfefucumufPp+mLL5BtNmIeepDo2247VeYLTnPO6pELQGp0EHeel8FT3+7l+cV5WAw6rh/Z/YwLQz4SQSOG0/2jj6h5/nkcq1fj37KdcVu2MzBCz2czE/k+spJV5atYVb6K1NBUbu57M9MypqHVdG59sifWPMGWmi0kBCdw78B71YUGC6QMh33fQcEPreKiKArz5s3DarUSFhZ2QmHI+fn5rFy5EoDp06efFsJStmcX25csoGp/Hk3VlYd9rtFqie6WSkKPLAZPveSowtL0+RdUPvbYoe2Cg7EMHHjS7BaceZz1IxdQy71c+9Z69rdEj/VOCOWOc9OZ0if+lBSr7Ar4amponjcf61df4WnpjljfN5lV943m67KF2H3qd5MQlMDlWZdzWeZlh0YNp5hvCr7hsVWPkRWRxWcXf6aWIilZB+9MUVe4eRF0U6sPNDU18e9//xuAm266qd1FI10uFy+//DJOp5NBgwYxbVrX9zeU7t7B50//hcBP/EoRCUnE98giPiOT+IwsYlPT0R2nbYNz0yYO3HQz+HwEjx9P+GWXEjR6NJpTlIgqODMQ4tKCLCv8b2MJzy3ch9XlAyDUpGP6gCTum5B5xvliHJur8VU7MGVHYsoIb12uKAru3Xsou+9e/BWVGHv1Inb2O3xW9BVv7XoLq0edVtFpdEzsNpGZ2TMZEjfklNaaKmgq4JKvLwFg83WbMSCpdcVq9sDA38D0/7auK8syTz31FLIsc8MNN5CW1r5op9raWl555RUATCYTEyZMYOjQoR1+Lh2BLAeo2LeXr577Ox6ng7SBQxh04XTi0zMxnWC7BdnlYv+EiQQaGgg5/3ySXnwB6TTxKQm6FkJcfka93cP7aw/w+eYyyptUv8OVQ1J49vLTxLHdTmrf3oknvwlJryHxyVFImrbi4D1wgOKrryHQ0EDU7bcT++ADuP1uFh9YzCf7PmFH7Y7WddPC0piWMY1pGdOItZz8UNxnNzzL7L2zyYnO4aOpH8G612DhH8AcCfduBktbp/3nn3/Ozp07SUlJ4eabb263EO7fv5/Fixe3BgP87ne/6xI1wnxuN5X791G+bw8V+/ZSkZeL16WGkCf17MPlj/39uKOTn6MoCk2fzqVu1iz8VVVIBgNZ69eh+YWh2wKBuCX5GVHBRh6clMXKR8a1LtNpz7wKsPq4IAAko/YwYQEwdO9O/JN/BaD+rbdw7diBSWdiWsY05lw4h08v+pQZmTMw68wUWYv4z5b/MOmzSdy15C6+P/A9voCv3bbsd7q5ensBm6wOjnev0+Ru4pN9nwBwz4B71IUlasQfI+48TFgAJk6ciF6vp7S0lMWLF7fbrh49ejB8+HAAQkNDCQkJafe2J4vm2hpev+sG5v79MdZ8Oofi7VvwupzojSYyh4/ikof/fMLC4ikspOL3D1P1xBP4q6oACL3oIiEsgl/FWe/QPxoajcRlA5P4Yms5c9aX8NtzM86o8jD6BFVc9LFHP6fQSZOwXXwxzfPmUfHHR0n7bC6alnpZvaJ68ddRf+WhIQ/x/YHv+Wr/V2yt2crK8pWsLF9JhDGCqelTGZcyjv6x/TFqjzytqCgKd+0+wA67ix8bbETrdYwID2JEeDCjwoPpGWRC85ORRrO3GZ+sCld6eEs3xxZBqWguJV6RD4tqCwsLY8SIEaxcuZK1a9cyZMgQotqRZV5WVsaCBWqOx7Bhw9B2gUZr1cUFeFqi+7JHjSUpuxeJ2b2J6ZaK5gTsk10uGj/6COv8b/Hs3du6PHTqVKLvuRtDampHmy44yxDTYsegosnFqP/7AYAgg5ZHzu/J1cO6YdCd/gM+64IibMvLCBqRQMQlPY66XqCpiYKLLyZQW4epTx+SX30FfVzcEdctthbzdcHXfLP/G2pcNa3LDRoDA2IHkBWRRb27nlhzLGOTxzIwbiB6jZ48h5u/5JezzmrHLbf9cwzXaRkRHsSoFrHpFWTilsU3s7l6M1dmX8njIx6HRY/B2v8yNySYH/tP59WJrx5mm8vl4tlnnwXg3HPPZdy4cYet81MUReGll16isbGRuLg47rjjji6Rz7Jn5Y8s+O8LdO83kMsf+/sv3k/1P/9Jw9stSac6HcHnnEPYJdMJOf/8X+U/UxSFNU12Dri9NPkC6CWJZJOeIK2WUJ2W7mYDEXpxT3s2cMrExVthx1NgxZAUjDH99Cl6t6eimQc+2ca+ahsA3SItPDQ5i4v7JaI5wnTS6ULzkgM0LylBF2Mm7v5BSMcQTOfWrZTdeReBpiZ0MTEkv/JfzP2O7oPyy37WVqxlQdEC1leubyM0PyXSFMljwx9jcupkADyyzLZmJ+uaHKxtsrOh2YEzILfZJs6gY0qYh2+33IUeD7PSr2TED88jyX6eiYwgbvxfuLnvzW22CQQCzJ49uzUR8s477yTuKAJ5EEVRmDNnDvv37wegT58+XHLJJej1nVsmaOM3n7NizrtkDhvFtIf+dMLbe4qKsC1ZQu0LL7Yuy1y75ld1J3UGZJY1NPNjg40fG5opcx97SjRYqyFMp8Wk0aDTSOglCa0ECtA32MyLPbv9YlsEXYdTJi7NP5bSvKgYAPOAGMIvSkcbfHo04ArICh9tKOE/S/Kps3sAGJYayexbh5+2oxjZ6aPqxc3Idh+hk7oTOuHY/9DesjLK7rwTT756sTX17Uvw+HGETJyI6RitexVFobi5mA2VGyi1lRJljmJ/035Wlq2k0aNWEdZr9Og0OjSSht5RvXl8xOOkh6XjkxV22pysabKzpsnOeqsDR4vY6PAyqOpD/pc3myBFZlGQhUdiovhuxiKSQpIOnacs8+mnn5KbmwuoGfr33Xdfu74jv9/Pu+++S3l5OcAJJ2B2FH6vl7z1q9mxZAHluWotvJwJU5h8+73t2t5bVk7DO+/gWLcOb2Fhm89iHniA6Dtub9d+qj0+1lsdHHB5qPD4qPH6aPD52WVzYfvJTYBZo2FEeBBReh1uWabc7cMlyzT4/NR4j11+Z0CIhYVDRCvoM4FTJi6ekmaavinAV24HBSSzjvAL0rAMiTuiQ7kr4vT6eWdVEc8vzgPgyWl9uH5k9w4Nw7XZ9tBs20l83CVoj+Kn6Cjq5+zFtbMOY49wYm7NOe76Abudysf/jG3RIrUCcQvG7Gyi77qLkMmT2v1d+GQfs7bN4p1d7xBQAm0+M2qNPDD4Aa7ueXUb/4lXlvm6polXSmrw1uYzb9vdRPmsrDVbuDsuCp8k8fjwx7my55Wt23z55Zds374dUAtY3nDDDccdtQDY7Xa++uqr1pFLZmYmV1xxBYYWZ7nb7cZgMJzUqbKGinJ2LF3I7mVLcNvVkbOk0dBjyAjGXnfzURMgf86+wUNaqzAABJ1zDiGTJhI8bjz6uONH91V7fLxQXMXsinrko6yTYjIwJTqU8yJDGRkeRNDP/T+KApKEIxCg0uOj2RfAqyj4Wx6+lunQeKOenJAzx7d5NnPKfS7eMhuNX+5XRQYwdA8l4rIerdFLXYVj9Ql/9Iud/K+l6GWvhFBuHNWd6QOSflXCpc/XSEHhi5SX/w9QSEyYSa9ez/zi/R0Pf52Lquc3gQQxt/fDmNb+qUp/XR325cuxLVmKY9UqFJ86DWLq04eYBx4g6JxR7RYZu9eO3WcnoASwe+28uPlF1lSsAeDc5HP5z7j/HFYRQAn4cb08BEtTEdtCsrkhZRhazxp8so8/j/gzM7PV2mLr1q1j4cKFAEiSxIMPPtiuiK/Kykpmz56Nw+FAp9MxefJkhg4d2npOTqeTDz74gLi4OC699NLj7O3ECPh97N+4jh1LFlCy61C4d0h0DP3GT6HvuEkERx4/GEFRFFxbttDw4WxsLd8BQI8flra7RliNx8ef8sv4rtbaKio5wWaygkwkmwzEGXRE6nV0MxsYEGJpE3hxGEv/Dm4rTHkadKfHjIXg13HKPWuG5BBi7xqAfW0FzYuL8R5opvqlrYSOSyHkvJRjzv2fCvx+P59//jnl5eVMmzaNHj0Od3Y/PCUbUPhyazl7K5v5w+c7eWZBLveM68GtY9JP+JhOZxGbNs/E52toXZaW1r4pj1+Ke596LGNa2AkJC4AuOprwGTMInzGDgNVKw/sf0PDee7h376b01luxDBtG3GN/wpSdfdx9BRuCCTYcSvR7beJrvLT1Jd7a+RbLy5ZTbi8nITgBveaQr0NCwSJ7VVvkAJhyiJRyqXZW0y1Und7zeDwsWrSodZugoCCC25lQmJeXh6PlTn/KlCmHJU9WVFRQVVVFVVUV55577gkXwzwazmYrH//lERor1Wk4SdKQNmgI/SdeQOqAQWjaUXZHdjhoXriQhtlz2kSBWYYNI+6PfziusAQUhdWNdubVNvFJZQPelnvPIaEW7u4WywUx4Sd+Yn4PrP43yH4IeGDayye+D8FpR6eEbUhaiZDRSZj7RtH0VQHu3Aaal5Tg2lVPxOWZGJI7J58gEAjw2Weftc7PBwKBI64XGWTgmcv68Yfze/LpplI+WHuAskYXT327l1qbh99PyUZ/ArXJ6uqX4fM1oNdHktP3v4SHD0GSTm7YqztP9XeYsn/dhVEbFkbMffcScd211L/+Oo0f/Q/nhg2U3HwLPRYvQhN0YiNSSZKod9W3vp/65VTCjGG8Nfktekb2bDmonsC1c2l6cxx9Hfv5OPdVrotRQAPdQ9TyLgaDgeHDh7N582Z8Ph92u52ysrJ2lcYfOXIkxcXFFBUVsWTJEnJycjD9pPRJjx49MBgMeL1enE5nh4iLoigsfetVGivLMYeG0X/SBeSMn0xo9PGnrWSvF8eKFVi//Rb7j8tQ3G4AJKORsGkXE3Hdde0S+n0ON/ftPcB2m6t12cAQC//ITGJQ2K+YWdAZof/VsPVD2PYRZE6BXhf98v0JTgs6dZigCzcRdUNvIq/KRmPR4atyUPPqNqwLilACR5vdPXls27atVVhSU1PJPs4/ZLjFwO1jM1j+8DguG6Q6kV9fUcjEF5fz9bZyZLl9M45Ky114VNRYIiKGnzRhUQIy7rxG6mfvwb1PFRfrgiKal5X+6n3rIiOJe/RRMhYtRJ+SQqC+nsaPP/5F+8oIz8CsO5TAZ/VYueP7O1hRtoIaZ41aoiaiB1vjzgOgl7OYdF8ACYm4INWfIkkSEyZMICzs0Khs9erVx03SBFWYrrnmGsLCwvB4POzcufOwdYxG1R+2fPnydu3zaPh9Psr27mLZB2+Rt341Gq2WGY8+yTkzr2uXsLh27Wb/mLGU3XMvtgULUdxuDN27E/v7h+ix7EcS/v73YwqLV5aZX9PEw/tKmbJpH9ttLkK0Gq5LiOKzARksGJL164TlIBf/R22FIPth7o2Qv+TX7/Mk46934W9wd7YZpy2dHnAuSRKWAbEYe4TT9E0Brh112JaX4atyEHltLzSGU5e4Fhsbi16vx+fzUVxczFdffcWkSZMIOs7dt1Yj8dQlfUkONzNnfQkH6p3c//E23llVxGNTezMs7dh3tn6/6n+SA54OO5ef49hcTdO8QhT34dE67n0NhJ7XMc2u9ImJWAYNwlpaimvb9l+0jxv63MANfW4A1Iz86xdeT5G1iLuX3g1ApDGSZKud8c5qAP4blUauwU+kKaJNAMDq1aupq6tDq9USCATIzc1lz5499OnT56jHbmpqIjc3l4KCgtapsdzc3NapMY/Hw5w5c7DZVAd7fn4+Doej3VNuoNYC27boOwo2raMiLxe/99DvPnLG1cSlHz3v6KcEmptpnj+fgNWKNiKCsEsuIXTqVEx9erfL5xVQFK7bUciKRnvrsnGRIfy7ZzfijB0ccq3RwiWzwGtXK1dvegcyJ3bsMToQRVZo+GQfvmoncQ8MQhcuinaeKJ0uLgfRBhuIuqYXzpw6Gj/dh3tfI7Vv7iT6xj5og05NbkFKSgr33nsvS5YsYceOHWzbto3CwkIuv/xyunU7dqiuxaDjwcnZ3HFuBm+vKuK15QVsL7My8/W1TOkTx18u7kNS+OHlNHw+K2XlHwIQEzP5F9ve3NxMUFDQUbPIbT+WqsKildDHW/CVOzD2jMSUFoplQMfVA/MUFGCdPx+AiOuu+9X7CzeFM2viLP6+9u/kN+VT76qnwdNAgwm8UjDr4u5nhWYDBk8uIfpD06k2m40tW7YAh6Y3DQYDoaGHN8M6SFVVFW+//TY+36E8jeDgYPr9JKdnx44dlJSowRzR0dEMGjTohITF43Ty7UvPUbR1U+syS1g4yb1zSB84hN5jjp7c2fT559h+/BFfeQW+sjLkFoEDNbM+7g+PtNuOKo+Pv+wvZ0WjHb0kcX1iFJOiQzk3IuTkFSHV6iCyxSdpOvrv0BWQXX789S4UTwB3biPBIxI626TTji4jLgex5ESjDTNQ/95ufKU2amdtx9wvGm2YEW2IQX0ONaAJ0p+UEObQ0FAuu+wyhg4dyldffUV9fT3vvvsuEydOZOTIkccNPQ0y6rhvQiZXDUvh30vy+XhDCYt2V7Myv47fT87mhlGpaH9it92Rh9+vXiQiI0f/IpsDgQAfffQRsiwzduxYevXqdZjIyC0jlphb+uLaXY+v3IE2WE/IuR3bnrfmuX9CIEDwhAkEDR/WIftMCk7itUmvAeDyu/gmdzHrF73IZl8/1gaGEZ0WTKA6l8Rg1Vnd1NTErFmz8HhaRgQKjPJlM+rGKQSnHH0UuXTpUnw+H7GxsfTr148ePXoQFxfX5mJb2JInMnr0aCZOPLE776aqSr7659+pLytBpzcwaua1pA8aSmRSynEv6PVvvUXN8y8ctlwbFYWhe3fCLzt+1FqJy8OX1U18V9fUxq/yf1nJXJt4/Ai0DsHXctzt/4OEATDsdugClQ9+jjZIj2VwPPYVZfiqHcffQHAYXU5cAIzdQon5bX/q3tmFv86F7Ycj+AR0GgxJwarQBOtV4QkxoGl51obokUw6JL3mF92JpaSkcPvttzNv3jx27drF999/T3FxMZdccslxp8kAYkNMPH1pDjeMTOWxL3ey6UAjf5u/h6+3V/B/l+XQK0G9cwsPG0xwUDZ2xz5KSt6kR48/nLCt9fX1WK1WXC4Xn332GaGhoQwbNozBgwdjNpvxltmQ7erduGTQ4qtV/8Fl54n3kz8W1m+/xb58Oeh0xP7+oQ7d90H0Gj2v7HqBGkMi7trz0TocRPVwUQNMSp2Ez+fjzTffbBUWvaLFJwUwRpoJTj329GRVS9HGiy+++KiO/4MVkjdv3syYMWNafS/Ho3T3Dr558RncdhvBEZFMf/jPxGe0r3GZr7y8VVgib7iBoFEj0ScloU9MbK319nMafX52213ssbvYbXe3vj7oyZSAfiFmpsaEc01Cx0S7tYvz/ggNhVD4o1rJet93cNVHYDyx1gCngoN19/zVzk625PSkS4oLqD9s7N0DcGyuJtDoJtDsbXl41AulX8Z7oPn4O9KAZNShMWnRGHVIJi36hKCWPiZhSMfITTEajcyYMYPU1FQWLlxIfn4+s2bNYsaMGe3uC5IdH8Knd4zkow0lPLsgl+2lTVz88ipuG5vO/RMyMem1ZGQ8zPYdt3Kg5A1iYy8gNPTEyvvHxsZyzz33sGHDBjZu3EhzczNLlixh+fLl5OTkMKhMvaM3ZoarkXgtDmhDYsflFjk3b6byUbUcSdTNN2Ns5/dzorj9bho9jWgO+gQUKHc2owf6RffDZrMdCiP29meNbh8+KUDs1ONHSx30oxiOUVX4vPPO4/PPP8flcjF37lyua8fUX3Xh/tYmXvEZmUz//ePtylU5iL+pCQBtdDRxj/7xqOsVOT18XdPI1zVN7HUc2RE9KjyYS+PCOT86jBjD0aeb8x1uAij0DOrgysjBsfCbL2HjW/D9X6BouRqmPP7xjj1OB6CPV8XFV+U4Zt6b4Mh0WXEB0IYYjuhoVgIyvhoX/jonss1HwOYlYPMitzwHbD5ku1ctViSD4vITcPkJoN7NeoubcaytRNJrMGaEY+oZgaln5BGddpIkMWTIEFJSUpg7dy51dXW8//77ZGVlkZycTGJiIomJiViOcgcJaoXl60Z0Z1LvOJ74ejcLd1cxa1kBC3ZW8ukdI4mJOg+TKQm3u5yNmy4lp+8rxMRMOaE/5qCgIMaNG8fo0aPZtWsX69ato66qlpB1bpDVkUpliotoRWkdxegTO+Zu0VdZSdnd96B4vYRMmkjM/e0rr/JLMGgNWHQWmhrUu37FosMtBZManERjfiOfLfkMAEkBk6LHpnGj1WpJTz92/tFPw84/+OADrrjiClJ/VhnYZrOxdevW1vftqZLscTqZ/+9nCfj9pA0cwsUPPore0P7KC4qi4C0oAGiTZX8QryzzWVUj71fUtZnqAjVrvm+wmd7BJvoGm+kbYiHFdOwERkVRmF9r5Yn95TT6/LzZN42JUR3sH5EkGHYbBMfBp7+Bta/C2Ee6XHKlv1EVaNnpR3b4TptyVV2FM7YqsiIrKL4AijuA7Akgu/0ongCyw4enyIo7t4GA1dtmG2OPcELGJGHsEYF0hB4uXq+XBQsWtLnAHCQiIoLExETi4uLo3bs30dFHbgHs8Pjp88Sh5L6PbhvOqIxo3O4Kdu66j+Zmdd8WSzoJ8TNISLgUo/H45UoOO39FoWTZXrSLDuWMfKffgpxkpE9DPN2bI4iYmkHImOQT3vfPaV6wgPIHHkQbHU2P7xef9D4gX238jr9+aaVJDkWOMKDkVPPPdB3l88uxtZRJucA7EE8fEz/kryUlJYVbbrnluPtdv359a4l9OLwt8ieffMLevXuPmLUPoCgBbLbdaLRmLOY0JEnL/P88R97alYTGxPKb/3vphDpD+uvqKH/o9zjXrwdA360bPRarfzvOgMynVQ28fKCaco96s6CVYEx4CNNaRiaRJ1B9+GDy5DOFlWy1tTQeM+pZMDiL2I6OHAN19Fz4I3zY4iu6fTkkDuj44/wCFEXB9kMpzd8fANTrQvRNfY94TRAcnS49cvk1SBoJyagDo46f319aBsSiKAq+Kifu3AbcuQ14S5rx7G/Cs78JdBL6GAuGlBCCRiZiaOl9YjAYmD59OkOGDKGkpITy8nIqKipoaGigsbGRxsZGdu/ezQ8//EBWVhYjR44kNTW1zQWorPHQ3eXLVw9kVIYqQiZTIoMHfURBwfOUln2I01lIQeE/KSh8gaiosfTs+Q9MxvbVkgJ1xJUyKpumugKcm9WQ3fG+HOZUrqBKqiLYYGLw4lrGDLgY3a9s4Wzu3x+AQGPjKclPGmAcRnfXJpqMATSNXpRd4WRGp2JyetmGDUmRyJ45jBJNLeQfPRn25wwfPpzExETefvttoO30WF1dHXtbMt6vuOKKw3Kg6uqXsX//szgcat25MaM3sGvpGvLWrkSj1XLR/X84IWFxbd9O2X3346+uRjKbibjySqJuuRlXQOblkmreK6+jwaeeV6xBx50psVwRH0m0oX3/0oqiUOTysqLRxspGG6sb7TT51f1ZtBruTInhzpRYgnUdnAogy7DvW1j5IlSo0XzoLWDs/EZsB2leVIxtWRkAQSMTCL8oXQjLL+CMFZfjIUkShoQgDAlBhI5Lwd/oxr66AsemKhR3AF+lA1+lA8eGKkw9Iwk5LxljqpqQl5SURFLSocq7LpeLyspKKioqKCgooKioiLy8PPLy8khJSWHcuHGkpaUhBXx0L5jDHbodvO6fSt+ktmVXNBoDmZl/Ii3tPmpqFlBR+RlW6ybq65eRn/80yUnXIWm0hAT3bVdRS41RS+QVWYRN7k7lMxswoWdyt3NYXrIBu8bNcnaRP6eeiZMn0b1791/eDEvb8mcUCKC4XRB8cuvEJWVH0CvYxKYMHfpcK1KDxJXf7OR/SgQ79RoCkow3UUs8qhjX19e3e878YPhyjx49SEhQw09LSkqYM2cOAHq9nsSflVApK5vNvrwnAJAkHXp9JDWFVSz/8C0Axl57MwmZx/f5yB4PTR9/TPPi73Ft2QKKgiEjg+T/vowxLQ2HP8D1OwpZ3aTmpaSYDPw2JYZrEqIwt6MihE9W+LqmkW9qmthtd7WOeA4SotVweXwkD6bGHdMf86tY8hdY01L+RWeCQTfAqHsgvGuU2bevq2wVlvDpGQSPbF8dNsHhnLHTYr8URVYINLrxVTlxbq/BtbNO9d0A+vgg9IlB6GLMGJJCMKSGHjHJs66ujvXr17N161b8fjUiq3v37kxL8xC1TI0Gm+2fgGHsA8ycPOaY9pSUvkt+/lNtlhmNCYwcsQSttv2JXU3fFGBfUwEaCb/sZ5e2lB2WErwtOR1Go5Fu3bqRlpZGamoq8fHx7a7427xwIeW/ewCAqFtvIeahh06a8/PL/C/5bv0PZG+4jBcvCkO/pgbJHUDS2umZ9QEXNE+hob6hddrqH//4BwC///3v25WPMmvWLKqrq7niiivo06cPxcXFzJkzB5/PR0pKCpdcckmbLpZudwUbNk7H52sgNvZCemY/hdchM/uP92NvbCB75Bim3v/Icb+PgN1O2V1349ywoXVZyAXnk/D3p9AGB2H1+bluRxEbmx0EazU8n53CxbHhaNvxPTf7A8yuqOetsloqfiIoekliaFgQYyOCGRsRQr8QC7qTWaG8eg+8dg4oMmRMgEtfh+CYk3e8E8Rd0ETdWztBgdCJ3Qid2P34GwmOylk7cjkakkZCF2VGF2XG3CdKDYVeUYZjczW+Kge+qp84VbUS5pxowi9MQxt6aCQRHR3N1KlTGTNmDKtWrWLz5s0cOHCAzz2x3BYUh+So5jrdUgJrfgT7FTDyHkg4coRYYsIV1Ncvx+2uwOlUHbseTyXLlvchLHQgWdl/JTTk+D1GLEPjsa+tAFlBh5bBIdmMuekiVmxYxe7du3G73eTn55Ofnw9AdnY2V199dbu+s+BzzyXk/POxLVxI/VtvE3HNNe2uvNtefAEfT294mk1r85icdyMaFAz7rOBWp3ISM76ljBLypXyiiKKhoYH6+np0Oh1+v5/GxsZ2iUtoaCjV1dU4nU7279/Pxx9/jN/vJz09nauuuqrNVJndkc+2bTfi8zVgNMbTM/vv+Fzw9fNPYW9sIDIphcm/ve+4wuJvaKD0tttx796NJiiImPvvI2TSJPQtI6dar4+rthew2+4mTKflf/3TGRR6/NFhudvLm2W1zK6ox94yXRlj0HFjYjRDwoIYEmY5vDT+ycLnhnn3qcKSPRWu/ujUHPcE8BZZQQFDtxBCjtPfSHB8xMilnQSavXiKrfhrXfhrnXiKmwk0qdFnklFL6KTuBI9MPOLcbGNjI6+++io+n49LZ1zOm/OXcYXzE87VHiqpTs5MNQcgMl2NpjkCfr+NvLy/U1n1eeuytNT7SE+/v13n4N7fRNP8QvwtAqmx6Agem4x5UCy19nqKi4vZs2cPZWXqtMBjjz3W7s6LvspK9o8bDzodPbdsRjpGOO+JUm2r4p6Fd5Lr3E+fqtGMKboCgBej3AQC6p+v1qJBF/EpfRQr/Rr6kZOTQ0VFRavAPPDAA8fNT/J6vbz44ou43W6GDh7C5i1bkBWZZH0MV156BSG9D1UyaLJuZvv22/D7rVgsGQwc8B7OBpkv/+9JmqorMVqCuPqp54lKOnqSquLzYV+xgpoXXsRbWIg2IoKUN9/E3PdQeRqrz8/5m/MocnmJMej4uH8GfYIPD5g44PKw3ebigMtDkctDscvLJqujtapxlsXEb7vFMCMuAuOpTlqszYPPbobqnWAIhrvXQ9ivDyTpSAJ2L9bvinBuqUETrCfhT8NPmz5TXRUxcmkn2lADln6HhvCKouArs9P4TQG+UhvW+YU4N1cTfmkPjN3ahm5GREQwcuRIVqxYwXeLlvCttQcrTI+z7sYoLOv/DXvnwc5P1QfAuMfh3IcPs0GnC6F37+cwGKI4UPIGAPUNK4mLu5igoHTKysqIjIw8ali0xqIj0NSS/6BRQyybFxbT/P0BjOlh9PAFkRV6Dj/6V5OvreLDDz/kiiuuaFcPFE9LUy1DavcOFZYD5flcu+BarHoXZsXATE9/wk0a9rplrmoy8F2sj0aXTMApE3DOwB3/PqA28zrI9OnTjyssSkBh24qNuN1u9JKOjZvV8iwZgXjGunshlzmht7pube0Sdu2+D1n2EBo6kAH936RyXwnfvPg0HoeD0Jg4Lv3DX44oLLLLhWP1amxLlmJftoxASw6LLiGBbm+/hfFnIdMfVNRT5PISqtPwzcBM0ixtfW3VHh/PFlXyv8oGjnSXOCIsiHu7xzEuMuTY/VZOFnvnwxe3gc8Jlmi4/O0uISyKouCvceLa24B7Tz3eUlvr9LfiO/VFc89EhLj8QiRJwpASQuyd/XFsrMK6sBhfpYPaV7cTNCye0End0YYcusiOGjWKjRs34rI3kaJpQh+SjCV1CKTOhoIfYNFjUKO2sOXHpyAkHuL6QOLAw0Yyqal3I8teyso/pLl5K5s2X8HAAR/w8ccLcLvdTJs2rU09rIM4t9WgtEwjxd4/CF+JDfv6Snxldjz5Ta3rjSSbJDmSxSU7eOmll7jooovo3xIRdjQOtj829mhf1vnxUBSFnT8u5pHtT2CNdBHi1PN/yY+RnhsDJkiZlsGPXxcyzePh/ZZtwoOq6e5Up+Pq6+tpaFB71hzLd+Te14B9fRUNhVWsljeBBnyK6icbru/J8JxBGNPDMaaqNwyq8/5JQCY6ajx9+77E7mUrWPr2q8iBAAlZPbnk949jCQtvPUaguRnbDz9gW7IEx6rVrSXxQS3fEjZ9OpE33HBYV0h3QOaNsloAnspMJs1ixCvLuAIyjoDMx1UN/LekBmfLlNeAEAs9LEZSzUbSzAaygtT8lk5L/nNb4Zt7VGFJP0/1sYS0P+LxZOHcVoN18QECP6t4rE8KxtQzkqCBsWLU0gGIabEOImD3Yl1Q3Br2Kxm1xN0/CF3kIaf7okWLWLt2LUWBCJb7enBRvwSeuLgPMSFGCPjh2e5q1difctVH0HPqEY/pcpWwa/fvaG7ejtGYxNatl1Ff1wTA1KlT2zS58pbaqHltOwQUDN1Dib3zkFh4y2x4y+xozFoa/revdXluZA2rHTtRJBgyZAgXXnjhUS/UFY89hvXzLwiZMoWkfz73q0YvtoY6vn/jv2wsXM38c9SSLFG6SF6s/AOxdeooytgjHM0FaXz76g52R6wlqNdWBhgOsGX1BGRkNKhlf4YNG8bkyZMPi4Tbt74KZ4OH2DVllARqWKbfjUc6VA5n5KDhTJl2QZttSkvfIy//7wAkJFxOz+x/sP6LuayZq0aS9TznXKb89n50Pzl3xedj/+Qp+CsrW5fpExMJnjiBkAkTsQwaiHSUqccXi6t4rqiKJKOeNcN78XBeKZ9WNR623qBQC0/2SGJoR5TG70hW/RuWPAHRWXDn2kNRhZ2IY2MVjV/kq6MUnYQpIxxTryhMvSLRhZ3ctuJnG53/a58haIMNRF6RRdDQOGpf24HiCVD39k5Cp6Ri7B6KNsxITk4Oa9euJU3XxFa/m/k7KlmRV8sDk7IY3zOWbrcsRipcBiVr1akygHWz1Mga/eGRYWZzNwYOeJ+16ybi8ZRz6SXxbN+eycaNG/n2229xu92MGaNGo1m/PwABBU2IgegberfZjyE5pLVB29P5/2TajpEkemPo2RBLdMZ5fFW+jE2bNrF3716GDx/O4MGDD5tm0oaHA2BbtIiCvXuJffBBQqZMPqG7ZlkO8M0Lz1C0dSNWg5uKlENJrvX+Bj7Uf8lDXI8+MYiQscmYkoK59KFBKK9sItm8Frc7AoAacw3xrnh69erFBRdccNhxvG4/mz/YS5ZJg0+v8INpNz78JCQkoNfrKSkpQRfUVhxLyz5sFZbU7neSnv4QWxfObxWWkZdfw8jLrz7sfP319aqwaDRE//a3hEyaiLFnz+N+L6VuL/8sUoV1UlQov88rZe5PhEUDpJmNPJwWz/TY8K5VmsTrgPItaokXgHPu73LCEjQigbAL005pS4+zDTFyORIB/6/6Z6h7d1drM66DaMMM6FNC+LR6KTW2ekyWIDbr+7Cx+tDXHx1sZGxmNI9P7UXk8sdg45vqBzE91dawKUeuMlxS8g75+/+BXh9Bj4xH2ZsbxsoVqwA455xzmDhxIq7ttTR8rI5KwqamEzIm6Yj76vd+PyRF4ony3zLM1hdNsJ7q8w0s/H5RazFIk8nEfffd18a3o/j9NH3+BbUvv0ygrg4A84ABxD7yCJZBA9v1vRWV7+Opl26jKMFBbUTb6gk9XCn8OfgB+l96XpsWDIqisPS7y5HM26irTWHv3vPYGbaHHGvvwzLzFUXBsbeB0o/3EeJVpwetkpO5xrUAPP7448ydO5d9+/YxadIkhg7tSV3dj1RUzsVqVX0w3bvfSUb6Q+SuWcF3L/0TgFEzr2XkjMMj6xRFoWnuXKr+8gTa6GiyVq1s1/cAsMfuYvzGfYctfyoziesTo9BLUtcSlIPU5MJro0FuCXk2R8KDe0B/cqs2HI82wjIygfBpGV3z+zuD6HRxOXj4LvFD1+TCkr+CJRIuefUX7+ags9+5rQZPoVUNX275lh24WWTZQYNsIyIyktDBFzN/RyU7y6x4W+bOLx2YxL+uHAD7FsA394KjFpDU8uQT/nxYNnMg4GLjpstas8NDgvvg893O99+rF8SLL76YwYMH07yslOaFxQBEXd8bc+/Diyfua9jH5fMuRy/r+LLiZbQ2hfDpGRgGR/P000+3rvfwww8f0UkuOxzUv/Mu9e+8g+JSqxGETJ5M7IMPYPhZra7WbWSZp394gk/LvkaR1C9Kg8QgYz/OKc3hnMb+xPbtTtQ1vQ7btrFpI1u2XIWiSGzaOA2vIxqTtSfekEK04T7OTb+CujIbYX6ZOKuH4BZRkRUFTVYkO8OLWb9zE6mpqVx66aW89NJLaLU2zjuvGJd7K3DIuas0DaF2awr15WV4HOr0ZWxaBlkjRqPT64nvkU1Stmqjp6CA6n/8A8caVbiCzh1Lt9dfP+L5/5ynCyp4uaSm1UFv0kiMiwxlfFQI1yVEdY3/lYM0laqBKPnfQ1MJNJcf+qz3dBhyC6Sf23n2AZ7CJmrf3CmE5RTT6WPVLvUjex2QtwAkLYx5CKIyftFuDjr7DSmqCMiegOrXKLEhrS4n2GOkQWsjMiSc35yTxk3npOH2BVieV8sdH27my63l3DI6jb7ZF8Bd62HxY2r/iw2vQ+63cPOCNhnNWq2ZoUO+YtH+z8gv/4y+9h2kdq9s7fOu06k/c8i5ybj3NeItsuI50HxEccmOzGZaxjS+KfiGLUn5DM3tgX11BfEjE8nJyWlt+Xu0ysGaoCBi7r2H8JkzqfvvyzR9/gW2xYux/fADEVddRfTdd6GLUKevFEVh1oJn+frAPCoszSBBTKORrKpw7ol4mPBGdWRkSA0l4pIjd2c0m5KRJBPgJiy8mnpXHEHecCz1/bEG9lFxoJQMo4YYveorCigKVTotUVO6kzwsmm3/Uqcfs7Oz+eijjwgEfAwavAGXW23z4Kw10lwSgr3Sgr3cDuS2OX5NUQE1RWr+UXyPLK569G/UzZpFw/sfgN+PZDAQdeutRN1267H/aFrY2uzgpZKaNst2j+576vJR2kvJOvjhKSheBUeKU7v2M8icdMrN+jmy20/Dp3mggGVgrBCWU0inj1y6FAEfvD5WjdoafKPa97uDqcmv4M3Zb+NHZmLa+Zxz4/A2n9/3v618s72CMZnRfHjLTz7LWwwfqfkdjH0ERt4F5ojWjxVFodvy7fgUOF+Zx0xPGBvWNxAXF8cdd9yBRqPBU2yl7u1dKD6Z6NtyMGWEH9HGTVWbuGnRTSQqcbyd/1fVVxNhpMxayX5NFfHjMhk/YXy7ztedl0fNCy/gWL4CUH0z3T/8AGNmJisXfMpdNaofQ1Jgumc4vz3vISI18dS/vQuAkPEphE7qfswLwpufP0x6xBf4vEZszX+mfmswgUZ1VJVh1NDbpEHSSHi7hWIZk0R8H/Xuf82aNSxevJiwsDCsVqu6fo9tJCbuRPZpyZ+XgqvWTEh0DDHdUgn4/ZTs3IaiKER3SyVz2CgcTQ04mhqxNzSQFZNA5Hff42vJEwoeN464Pz2K4Si9YX6OoihcunU/66xqHtIdyTHc2S2W+JNROPLX8vIQqFcTbkkdAzmXQ1yOGmYcFNNlGoA1zM3DubkabaSJuPsHojF2+v30WYP4pg9SmwdzbzwUDnySiM1MZOqwy9m0dAd7tnvo1+Am5CcRZQ9PyWbBrkpW5tfx3MJcHpqcrXau/EkzpfXLvuHVJRIT+ybzm2uuB9TRUpReS5U3wELpYqJ075Ec7uL8829Ao9Hg2lNPwyf7UHwypuwIjGlhh9l2kO6hatmLCqka/YhofKtrkRs9JBJJohyJL9+Acp7SrmJ+pqwsur3+Oo61a6l++mk8+fspuf0OUj/+mJ1ffo1lkBanOcDjQ/7ERb4J2BaXU1+5u3V7yaA97p3muqZL8LryqJIv4Q8XT+HDbW/hCIolyNGNAo+MM6Aw9S/DMca2zf/ZvVs9jt1uR6dzk5W9k6godWRy4Md4AvZIrn36aUKTE1k7+wO2Lf4WgN5jxjHp9ntbo8IUn4/aV1+l/vU38MkyusQE4v/yF0LOO++43w+oorLP4ebRvLJWYbFoJJ7MPLJfrFPxe6DxwCFh6SIjlCPh2l2nRm9KEHlFlhCWU0zXuL3obPwe+GA61OwGSxRc+Lz6OEn0u6AnwZFpeF1+PnlqAwVbD02DpERaeGBSFgCvLivg5vc2YnX6IGkwDPwNJAxgk6Y/y+X+/HlHFI8++wINO78HYMuovlxjVP0cc3Q30pAjY7M/T9mKr6j7YBeKJ4AxPYzIa3sdM45/WdkyAHKic4i9KJu4BwfjvTCKTTp1+kdf7MVT1HRC5xw0ciTdPvgAQ2oq/spKSu/8LWk9+9K9Sr3gb9i0ioZP9uErt4OsoAnSYeoViSXnyK0LDtLs9rGm2MY/d93O1NHTWPzaf9B7vGjMFWhQ/SuVfoWa+rY5DV6vtyUPRiE6eh/Dhs9vFZaqzdFYi0I5/64HMBDElsc/IWNfFrHm7oy55kbOv/vBVmHxFBZRfPU11M96DWSZsOnTSP/66+MKi8Mf4KUD1Vy9vYDslTs5b+M+1loPlRbKMHexsNjKHepo5alYeOVQiDsJAzrNpCOhyAqeA800zS+kYa7qgwwem3zMmynByeGMkvKfzvCd0Lxq7nywVUBIAtyxQu2WdxLRaCQu/G0Oi97cRc0BGwtf30Xv0YmMviITvVHLXef1ICnczB8+38HyvFqmvbKK928aRur0/wJwi9dPwX/f54uaeP7X2JNv5zTy0MBPufbyy0lcu4Z+sd3YkZzJm5q7cde+zRQewjA6lhjpYmLG3Xbc8MudtapfRStp8ct+9LEWagqb2aYrJi0QS5QSwuqPlzLo7glEREQcc18/RRcRQcqbb1B81dV49uwl3uVEP0j9rNneBBKETuyOZVAs2nBju37DWpsHly+AJEHzjtUUbduMVq9nwyA959aNAMBg1hKd3Lau2NatW9FRQ79+awgLV8Xd1WCkdEU8zmoLA6ZcRI+hI3nxvT9xheZ8NJKGgROm03f65NZ9NC9YQMWjf0Jxu9GEhZHw5F8JPf/849pc5fFx/Y5Cdthdh312QXQYF8WEdXyDrl9L/uJDoxW9BWJ7w8i7u0ThSUVW8JY049pRh2tXHYHmQ5GG+uRgwiaJApSdwRntc2m32HwwHQqXqSODlgv4qSDgl9kwr5Ati0tUh2OYgaEXptLrnES0Og27yq3c8eFmyptcJIWbmfvbkSSGt4R0+lxsfPdhnijqxR4lFYCe8SFcnOiiZs9aVvccxK441ek/Xv6BG6TX0eFHkgykJP+G9PTfodUeuUzM7vrd3LTwJlx+F1PTp/L06KcJ+AOsWLEC75oachxq+Q4l3ULS9f3RmNrfQ8TtsNOwaiXNj/wRvD5+zNEwa6rEY+W3c/HkmQQNPbEMbkVRuOK1tWw60EgvRx4Ta5YSm3ExTU3paGRVRC/4bQ7pAw5dBAOBAB/P+YiIsFcwhtQi+3VUboqkdmck3XoPYNglV9Ctb3/e3/0+L2x+gQuL+3FV6vX0nTIafbzqy7EtXUrZffdDIEDQqJEkPPMM+rjjN3XbY3dx3Y5CKjw+IvVaHkyNZ3ZFPbkONzPiInildxe8EDrqYdGjsOMT0Jnhscqj1r87VSiygre4GefOWly76pFthwRFMmox94rEnBONKTsSSScmaDqDM1pc2s3bU6B0nfq6x0Q4/1mIPnJ00smgNLeBHz/MxdYydRMabWLo1DQyBsXS5PVz5etrKaxzkB4dxCd3jFQz+hc9Bmv/S0CR+CgwgReUa2gKqL6bkREOslx7KM0ZysLIJGQgzudketAyBnk+JQQbJlMKvXr+g8jIc45o06ryVdy79F78ip+b+tzEg0MeBOCtN98i7AAM92ehQUIbaSJ8ahqmXlFHnWrz+3zsXLqQdV98gtPaBEBck42BJTVoFPh8jInrIl8g5oo+BA058a6bq/ZVct27W5AUmfsqlqC3TGu9mVC0Ac65MkC3PkFERo5CkrR89dVXWFbVoo1pxJO6hILVkZiVIC66/w/E91CnJFeWreTupXejoPDHYX/k2l7Xth7PsXYtpbffgeLzETZ9GgnPPIN0FAe2R5bZ1uxkg9XBpmYHKxrsuGSZTIuR2f3S6W428mV1I3fuOUCUXsfmkb0xtaM3yymjZB28M+XQe61RFRdN50Svyd4Azi3V2FdV4K87NPKTjFrMvaNUQcmMQNJ3oe/wLEWIC4CzAZY/p2YUyz4ISYQ7V6v5Lj/F71VzTiyRR04KUxRw1Knx/tYS9dnvBUuEmkxmiWz7bDg0cgj4ZHavqmDTgmJcLcN6nUFDar9oInuG89CKPEqaXfSMD+GT20cSdmARfHYTBNR1G5Vg/umfyUeBiQDMsOQSItuIPG8K//UYaTao/2w6SWEQ2zlHXsQAttAt4TJ69nwKSTr8YvFNwTc8tuoxAB4Z+gi/6f0b/vrXvwJw+ZiLidoUINCoJlZqw40EDY8naEh8m5pqAAtf/Te7ly9pfR8UHkEvc082WpcwdW0VC4alclFZNDEPPkHERccXdUVRaCgvozx3N2V7d/H1fieLzIOJ9jdxQ3MYGo36vVYFFxFvTwMgqtd8Gg2ZOBwO4vKqOC/iYiRJw6rqLyl35TPjj38ldcDg1mNM/2o6hdZCZmTO4ImRT7SKlXPrVkpuuRXF6SRk0kSS/vUvJN2RR26L66z8Ma+sTQ8VgHPCg3m7byrhLW2IPbJM9+Vqhew3+qQyLTb8uN/BKWHrbJh3P8gtZXFG3gN9LoPkwcfe7iQQsHqwr6vEsb4S2anaIxm1mPtGq4LSI1yMULoYQlx+SkMhzLkC6vdD9oUw6j6o2qE6M6u2q0mWBzOPjaFqyGVwrNpRr7lcTSjzHz6PflR0ZgiJg9Ak1d+jNyPLCvWVHuqr3LhdICtaFLR4NKHslFP5MdCN7oNSefY3g1QxayxW28WWb4Edn3JX49V8Jw9nZJKBrLrVSBJ4dSbKY9LZnZBMWcghQQtRrNzOK9w68I9ERAw6oolv7XyL/2z5D2admdVXr+aVl16hqamJiRMnMmrwCGzLS3FsqGr9h0dq6eA34lA/lx1LF/H9Gy+3vp9+wx8wrYAmvcRDiTvYkdWfD5/4EwOuv5no22845ldWV1LMwln/obowv3XZ5/HTqTAncp7dzlC/Ov21POsjYnJMTCg8j8qt6k2CPWInOs8BzivVEpt+MWWOPQRGB9F77HgiEw9FZjW6Gxn7yVgAVl21ijCj6gx25+Zy4PobkJubCRo1iuTXZqE5Qr7PN2sP8GZ5HZv0ARSzjkiLnpHhwQwNDWJoWBADQy1tKhQ/VVDBf0tqMGkkFgzOotcRSuqfUiq3w4rnYe836vte0+DS18Bw6muXKQEF27JSmpeWgNzSXiHSRMg5iViGxKMxdrH8H0ErQlx+TuV2eGti64jgMCSN2vDoqEhq5dfwbhCWoo5wXI3q6MjVcOhZ9h9jH8em0Z9IlZyJO24QCUPGETNkJFqjAYpWsOvde7jI+wwAg6IV+nn3qsmhLURqR7F7SBKLDXZcGiNRgRp+v/ALJt50Fyl9Dq+kXNJcwtQvp2LSmthw7Qa++OILdu7cSVJSErfddhuglih3bK2m6Qu1MrKpZyTRN/Zps5/qwv3MfvR3AETFdWNS2LUoPpn7z49kteLj7k/f5+qCPWR89+0RRwKKorDh689YO3cOAb8fnd5AQmY2Sb368nhBNEVWPzPtBrr71YvNtc8NJTw0BFn289nLz1O7dxi6kHxGrf4ATUMD7iHj6fviUxhjDw9I+LHkR+778T7Sw9L5+pKvAfAUFXHgut8QqK/HPGgQ3d56E83PWht4K+w0LD3AmIpKfC0jOoBwi5606CCGpUYyuHsEg7tHEBVsZK/dxRtltfyvUq3e/Frv7lwSd/QAiYCssCK/luX7aokJMXL3uA6eurWWqw299h8cZUpw7iNw7h87JW/FV+ukcW4e3hIbAIbuoYSMScLU++hTsIKuwxkVLdYhJPSHC56F+Q9CaCLE94P4HJT4HLVbZFgKksemTo/Za8BeDT4XhCWpYhKWjE+Gxopy6stLaSgvpb6iBJ/HQ0RCIlFZKUQmJhMVE4FZ60OyV0FzBdgqwe8GJFV4ZL+a1Cn7UWQ/nqoDSJXbMHoridBVEEEFNC6H7/9F08IEis75CwPOu5i+2lIe083m/wLXsqVOojxkAE9cnE7QnmL255fgqtLSr7yabI2fzecU0WfLChrKrMz9++Nc8/f/EJ+Z1ubrKLer5TwSgxORJIkJEyawe/duysvLKSoqIi0tDUmvQaM/dAdpzDg87DM2LYOIhCQaK8vp2+c8pFotik8m0w2rjVAXEYmvpARvSSnG9LTDti/dvZNV/1OL66cPGsqk2+8lOCISnzeA9+kf2qwb0PqxGNS7f41GR79z+7N0L8jWbkhNNvQpyWS/9Hd0kUe+kG+r3QbAgNgBADRvWMmyf67AZMqhR1YF0Xfe2SosssuPc3sNjk3V+MrUkjD9QiR2BusxB8Dq8tHk9LG1pImtJU0ogBxtRNcjDFfYoeRIbYGNN3fuoqRPPBN7xRFu0VNpdVHR5KaiyUVpo5Nl+2opa1RHxhkxQR0rLnIAPrwU6vapN1B9L4fRD0Bc7+Nv24HIbj/2NRW4dtbhq1RviiSjlvBLemAZECOy608jhLgciSE3w8Dr2xSvbPMnbQ5HMYUhRau9S5zNVgo3b6Bg88fUlhRhralWp6x+RvG2zW3eG4OC0Or0KIqiRrYpCooio8g/fX9oGWRjlNKIM1pJMNtIMNtINDcTrq2k39q7WLXjG3p3P5/bir9jmCaXB3x3UWhL5K65uTwQ/D33peyiKn0Umx0zsDd6MK3KRA6k4NV+iRKo5NvHX+DSqcMIv+IKNOa2UzNWjxW33014eDiDBw9m48aN/PDDD62FIQ1pYWgjTQQa3Fi/LcJbZifsglR04abWc2+sLCctvD/xRQnI+NAE64mLC4amRpqj1fDv5gXfEXP33Yd9dxEJiWi0WuRAgBGXXoPPY6RwWy3rvirA4/CBFur1MmZjGdasYgymQyHD63U1aH1BBPSRVMf1Ymu/ZIbt2sXYsWOP+POvq1SDOwbEDMBTV8+nr9XgiRoJURC97Rm8d99N5qo1WBeW4dxRB3659Y/EnBPNR+NTkCLNGAxa7B4/JfVOdpQ38Ul1I1s0fnxmLT4ARUFT7UZ3wI6myctuYHdFMy9+n3dEuwBCTTouzEkgO/74DdzaTe0++PruQ8Jy+/Kjtt0+mbhyG2j6Mp+A9dCsgTErgojLerT+HQlOH8S0WDtQFIXm2hpqDhRSW1xE7YEiAj4vBrMFe2M9FftyUX42VWYKDiEyKYWo5BSiklLQm8w0VJTR0DKasdbWHFGAThSjxsekhP1kh9a1LiuQuhNl8KMLuJhs/ysVqImIRcZr1AjS+7ZBSxJnxf4mNn4wm7LS9SQ1yeQU70AbFUXKa7Mw5+Rg89q48IsLafI0cVH6RTw9+mlKS0t55513AHjiiUPObtkboHnxAeyry0FRO19GXt0TU2YEL199OUmWTAbFTEKHHssgtc7T/xqsPLSvlLHWOp78471oY6JJ//prdJGHgilcdi+bvitmz7L3sNfvQGPohSHoUCn9VWEB1krqBckQs4CLB5t44Tw1Cfagz+i2ZdMxKcno+n6HJgZSUtTs/NGj12M0HErU3F23m6u+vQqARTMWkbj+bVZ8UM5O80xyrAuJ3joPXVQUsY++in2VWjJGF2chaGg8lv4xhwUzADgCAe7ec4CFdc0ABGk0jDWbmWgyMzA6lLToIJxeP0v31rBodxWrC+rwBxTiQk0khZtJCDeREGYmJymMCb1iMek7wM/g96h+utz5sOENdRrYEAwTnoDht//6/bcTRVbwHmjGtrIc9556QPWphI5PwdQzEm1wx3U1FZxaxMjlKCiKwtaF8/jxvTfatX5sagYZQ4aT0rsvUcndMIeGHXMI7/N6aK6pRpZlpIPl0yUJSVLrYElI6rOkaVne8tBofrKu+vmX277mm3XPMs1bQ6bHTQYHcLkNvOKfDqYwaElOt5vjCXFXQcVWiEzDYNaRmhNNt2fvRfHdifXLr2l4sx5feTl1r7xKymuzCDGE8OSoJ7n/x/uZXzifKd2nsH+x6lvJzMxsc44ag5bwi9Ix94um6Yv9+Koc1L2zC120mend7kHTEpGmjTMRMSMTSashqiViypaYjD45GV9ZGcVXX023N97A0L27WlL/vb0U76zG71az1mXvPrThEwiPjSSym5new5toXH6A3KIEvLUXEOpUp7ve3fUu/9mi1ofT3BNBetM/D0vPcNjzMEYeEpcv93/Z+jqhoQxl1b8YnaqQtn099lw3uoQcwq99FPtqVViChsUTfmmPo/7WtV4fv9lRxDabE4Mk8bvUOG5NjiFU11YgzAYtM4emMHNoCr6AjEaS1LI/HYnHrgpJ4TIo3dA2+CRzMlz0b3V69xQgu/3YV5Xj2FRNoOmQfyp4TBKhk7qLPitnAEJcjoLP424jLBqtjqjkFGJT04npnobREoTH6URnMJA2YDChMUfP6j9SMqfeYCQqudvRNjkhLht5OVPK3+QDt8L/Zd7IsE3fEdOwid/r5zJDXsGTmutZJg/kJsd9zNX8CSlvIfS5tDURTqPRglFL5FUzMfftQ/Hll2NfuZLyRx7Be8FA/mP9GACj1kh8UDzr6tRpI1mWkWX5sO6UhpQQLMPisM4vAlnBX+tCI2mx+5oosu8gol86CVq1hEigpaKuC0h5/TVKb7sd34ESiq+6muRXX6HQZqJg47f4PdtAcaoHCNKz94Jv2OcqVH1C60ExSkj6h1B80RBQfT6f538OwOik0dwz+CH27bPRZN2K1ZqLVusHJCIjR7WxfXrGdD7Z9wkABZ9dSw8lQHNpEB7PCIImTUATFIdnv+pbMWVHEHZh2lGFJc/h5todhZS6vUTqtbzXN41h4cFHXPen6E9GnovXCR9dCQdWHVpmiYbUc6D3JW3+HjoSRVaQHT789S78tS58deqz94AV2dE2pDh4ZEJr0zrB6Y+YFjsGhVs2Mu/FZ7jqb88R3a07Wl3HVKc9GT1sPtzzIc9tfI4gfRBzp35KyoH1yIsfQ2NX2y5vkXvwrv98Bmv2caPue5RBNyJd9OJhyXCKLFPxyB9onj+/ddnWdIl3r43i31Neo090HxYvXsyaNWsAiImJ4dZbb8VoPFQLq/Gr/TjWqW19JYsOxRsAv3rOJa5csu6eRHRWKgD37j3A3KpGbk2O5qnMZPy1tZT+9k7cu3cjWywszEoDRR16BUdFk5fm5MeIXHz6Q3+2sZZY4v2Xs3p7N4KNOn74/bnEhpj4w4o/8F3Rd1yQdgHPjH4GrUZLwO/ns7kXEB1XiFFzIaPPOxQifZBbv7uZ9bUbub+hieuKXdQqLyFZ1MrGkkGLZXAswSMT0cceXuFAURTynR4W1Fp5tbQGqz9AmtnAnH4ZpFtOcb0wr1ON/NrzFeQtUltoG0Jg/GOQPg5isk9apn3A7sWxqRrHuso2I5Ofoos2EzqxG+Y+UUgdMdUn6FIIcelkfnE9tJ/hl/3csugWttRsoU9UH2ZfOBud1wnLnz00pw5UKhHIikSSpgFnz8uxzHzjiNnWrp272P/sQ2g2l7AvQ2H08BpiMybDeX/AHdmLL774grw81fH8u9/9jvCWNscANbO24z2g+hdi/zSYb//zTyIqI8gIGYAkSfiNCkXJLsz9ovmNR48tIPPNwB6td/Wyw0HBxRezJ+ClILkvKG5GzpjGsOnTeD/3A/61+V8A3JpzKzf0vgE5YGHc88uwunw8cXFvbjpHjTZbWLSQh1c8DEDfqL5cnnU556Wcx9olUzAHNZK3cgCDB1zNoAuno2vpYy97PPz9lQv5LKqG3q4Af3sDgibPAjSETOhGyJikw8rdyIrC1mYnC+qsLKi1UuA6dDEdGhrEezlpRBlO0SRBwA/7voXdX6ptGnyHwtAJ66Y2wUsbc1IOrfhl3LkNOLbU4M5taM1LATXJVhdjRhdtRh9jQRdrxpgWhtSVqhEIOhQhLmcQVY4qZnwzg2ZvM0+PfpqLMy5WP7DXoGx8m+ZVrxMWUNsv+xUNBUoi24056Cf8FUtIOH0SQ0mOaHs3vm7pf+m/9wfMdT8Ailq08N7N+C2xPPPMMwQCAS699FL69+/fuo1jczWNc/NAA1tDV5O3dRV6o4nLbvsz+u0y2ytzWWHI54deQyiMSSTBqGfzyN5tEgvz/vsy81YsBEli4u2/o/8EtfKAoijc8f0drK1cy/iU8fxn/H94d3URT87bg14rsfKR8cSHmVrX/Sz/M17c9CJ2nzqVpZE09AntRU6xjGGDHQIagqOiGT79CtItodQ88VeuHXQpfZM/JvtAAnfPfJjGr1WxCL+0B8HDEwDwyjJrmuwsqLWyqK6ZKu+hLHyDJDE6IpipMeHMiItoXzkXv1cdRWh/5eh4wR9h/axD78NS1I6QfS5VK2t38EhF9gTwVdhxbq/Fub0WxXUof0ufHEzwiATM/WKED+UsRIjLGcJPo5wAIk2R/Gvcv9hZu5MfSn6g0FrI5MTz8S+3c4XvO/pqilvXdSpGNsrZrFX6cM552YwZf//hSXN1+Wq/m+pdMPy3MOVp5n37HZs3q+HVwcHBREZG0qtHL5LXa5DqvSg9DXy6QG0GNvMvT5PSpx+KrLDpm53crtRQGR6NNqDwN0sEt4xKbXO4grWr+Orf/wfA7Xf/gZCx6t12nauO6V9Np9nbzLNjnuXC9AspqXdy2aw11Nk9pEcH8eGtw0kKPxRKXeOs4av9X7G0ZCl76tV+PZnhmfw9/G5WffwB9gY1SsnkC5BU4+B3o/8AwKYbh+H9ohC52UuNUaJochJ74o1saXaww+bCETgUIRis1TAhKpQLosOYEBVKiO4oF9OAX02qrdkDVTsPPer2qblNligIjlcrP0SkqnlXCf0hOlPdzlat5kQ1l6t5VkHREBSrru93w0cz1eMMuRkGXAdJgw4TlECzB9kTUEfKGvWhMWuP2TtHdvnx1Tjx1zjxVTtbX/98yksbasAyMBbLoFj0cac+o1/QdRDichpyJJ/Nusp13Lb4tuNuK3sj8DYMZ7C/kVu8uQyjgDDJ2fq5T9GydOxYMtPOJSpyDKGh/dFoWu6md3wKX7QcI2EA9kve59tl69m3bx+yfOhCa1L0XOAdiL+vh2XfvE33fgO5/DFVZMrdXq7ZUcg+hxtzQOHy5TZSa/30HpPIuVdloWm5y/c4Hbx6w0xkjcRl515A2l1345N93LroVrbUbCEzIpO5F81F2zKlV1Tn4Lq31h+5gvRPuH7B9Wyt2dpajNPv9bL53TfZ9P13uLUSFcYEPk+8hMRgI38zBvFuso5dETqqjYdfdKM1Cufrm7lAU8tofyVGn02NyPK2PDw2cFvB3aw+e5rV5Seb9HFw/VfI3gD+Whf+2hYxqHXhLbERsB7ZB4JOQhukRxNsQBOkR2PUErB58Te4kZuPUrEC0ATrMWVGYBkUizEjXGTPCwARLXZa8lNROSg0IxJG8NQ5T/H46scPW//K7CsZGj+Ud3e9y76GfUwZHiAjfCAPbl9PuD+aGRYHEz0+mg8kEmRwolF2U1y8k+Li/6LVBhMRMZzg4J5YYlKxTLoXy+oP0FduI3j5X7jyyg9wOp3U19dTVFDE+h/WICPT7bIBLPxG9Y30GDoSgAafn4u35FPh8RFv0DM7Jw2Hq4rN3xWzZ2UFHoefSbf0RqvVUPbZXJSW0wwadx4AL2x6gS01WwjSB/HCuS+0CgtAWnQQc387koteXkV5k4tFu6tafS8/ZUetWiBSVmS8ZeXU/POfhC5axDmmIDjv93zY0qs+1Cdz63ATbl1LRB3QM8jEoNAgBoVa6F+zhp5fXY+WY5UCOgYRqRCf01IBoh/E91Vr1NmqwF6ljlDq8tRyRJXb1ZJBGr1aWujgwxQOPieKrQZ/k4zPHoIvkI7PMQ3fsxvUUcWRbh01amACMmqirqxAQAG/QsDqbZPE+FO0YQZ0sRb0sRb1Oc6CLsaCNqgLtmEWdDpCXE5zWkvLKwrv7X6vdblRa8QTUO9QC62F9I7qzZXZV6KgkBKSQlZEFncPuJtiazFLSpbw7wPfMDh6P1kWD1ogIJnQa7QEAnbq6pZSV7f00EGH6NH7IrE4lxG87kYiM64mPn4EzcUZxNT5cBo86DLMVOSrnR3TB6lhx19VN1Lh8dHNZOCLgT1INhng4nSMZh2rP9tPwZYagsON9BljYeH8z1AkiRSdidhefVhTsYY5e+cA8MzoZ0gLO1w49FoNVpfq+xiTeeQmVnf0u4NXt7/K+3veR/vyB5y/ocVXojMzJyiaz1u+s92pZnw6iXPDg7k/NZ7+IWaCfjrVtXcrIKsVtBMHqm2oDcGHng3BYAxR84xMYWAKVZ+NLa+P5lsJigb6tl2mKOooyBjSOsWlyAruvEYcG6vwlloPFQ4FOOAH1Pcaiw5drAVdhAldtBlDaiiG5JDDCj7K3gCy3Yfs8BFwqM+y2482WI82woQ+1tLuvj0CAQhxOSNYVLyIOXvnsL9pPxpJw3/H/5dgfTA3LLwBBYWNVRvZWLXxsO3u6n8Xdw64k1tzbuXWnFvJrd/DvM330F97AC1u5ADU+qAiYEIjmYjSyYRr3FgkLz69BmuYBqtzJeU7VwIaPE2pGLP6EyibzI/vvQaKQkxqGvogFx5vHQvq1MTDm5KiVWFBFcWi7bWtNnk9tXzy5Gu4JQh2e8neX0T1l3P5h/IeANf0vIZx3cYd8Xv4els5AVlhQEo4PWLb5pPITie2H35k6vztVMoyX56j4Z0JChavnnO2B7hh2gNYnaqw+NND8HcP5pLoMF7q0x3DQf+Tx65WyS74AVa+oC7reSFMfeGX/nTtQ5JUQUJNPnRsqsaxtgL/T1s36yT0CcEYEoPQJwS3jC7M7c5w1xi0aCK1ECnKrAg6BiEupzmf5X3Gk2ufbH1/V/+7GJOsOr/fnvI2c/fNpcHdgEFrQJIkVpStaF331e2v8t7u9xjfbTxB+iCizFHcce5XLCv8lL1Fr9LXYCVGDzF6Nw1+D4utetY7tOglM9E6hWcdzZizhtJgtON0FmAMLyQ2vBBn4iJ8Ng09Z/owReSzZu13OKUo1vA6IHF+tJrk6PcGWPTWbiryVdGxKPvYufAH/H4XUUkpjPZo8Owr5cUlf6VkmIYYcwz3Drz3qN/F7go1/NntC2B1+Qg1anFu2Ij1yy+wLl6MDTc1YRCWCOpEF2zN9DG00UvA7EBxGQntpWdimodRgRJmVO5DU9wMria1/lbdvrYVsSPTof81v/5HbAe+Gif2NRU4t1SjeFUbJJOWoCHxmPtFY0gMFv1MBF0K4dA/DfBVVuLcsAElICMZDWiMRiSjkZ2eYv6641n8Gjg3bQI39L+FpJAkFJ8PSa9H0uvVqC9ZVt8bDCwp+4EHlz2IJCsY/KBILdPyLc+p4Vm8e8Fswsxm3N4G9pe8y4aKNTQHwI8Ot1vLfLcJp20TEhLjUsYxofsEhkRlYKv+jLLyD45wBhKrGMMs6X4SAw7+7mlC69XQuKMJZ6MJtzsMRXbiaf4QFAdhBj89Jz9G8rnp/OXNKWztoU4FPX/u80xJnXL47su3QP5i9oeO4MpvfdQ7vPTWu3l63ZsYSw8wf6jE3NEaXCaJRLcWp0amyaCQ4fXyVmUN0bJMpS6KA74YRki5x/4xQhIhcQD0naGG956EjoyKouAtteFYW4mvxoniCbTpuqiLtRA8KhHLwFjRz0TQZRHi0gXw1dTgKytrU8hS8QdwbdmMbclS3Lt3d9zBNBokvR7F74dA4KirPfjmq6REprHT5qTI1dbBq1N83FRyI19Jh98p3xjWn957iwgvacBUKZNy3z8IH3sBly7/nI2afkx2LCZ906HotBRvEhOuupTGOY+wuSYUl7eSi5OL0OqdXJkSjSZQR3qlwkhfDlf2m4bi9hB0zjmYsrPwyjJf1zSxct23kHuA+KIaTHV2XsqcAcCj6z+gd6CQh69VcJg86P0mkgIuio0S3X0+3q+oJtwUgdYYAvogtfeOMVhtBHfQV2IMVaekIlJV30pIfMf8DkfB3+Cm4X+5eEttbT+QwNQriuBRiRgzjl23TiDoCohpsVOIoij4q6tx796Ne/ce3Lt349qzm0Bt3bE3lCRM/XLQhoWheLwoHg8Buw1/QyN+nxutXwG/H8XnO/Z+AGQZxXOUUNSfsMMdxNaaJgCCtBpSzQZ0PgcBWy56fPytuIxrDXoWBVl4K/xQ/5Zl+7Yx4vMA4XYADUYlAp0uiD91H8LXFSsYED6c2LEBGovrcRbVk2yMpHtOFKmpeQww7cSvaNBrZPL1/fDonAw4oPD7L2RgO9VfbW89Tt3AwaxO7EaPPbu4pWk/vhw/niyZvw56CIpApw8gpw8mN3ARl223UhcaINbRA03863wbV8wfV4fw/OXP8eCkC4gxdI1oJ2+FncYv8tW+MDoJS/9YtTSKUYsu2owu7BSXjxEIfgVn1MhFURT8tbV4C4vwFhXiKSrCW1SMbLMhez2tF2bF40H2esHvR5cQjyE1FX1iIpJWp5ajkDSg1SBpNKDTYUhOxpCRgTEt7bDugwCK14u/sZFAQwP+unr81VX46+rw19bhr61VX7c8FKfzcMM1GvX4P+vAaEhLI2TCeILHjUMXFdWu88fvh4P78flQ/H4knQ7F52vzQKtDGxzUsh2A2j/G5nGztq6AQEQ2pR4fUQYdU6PDWiOlZGcTRfOfIWPPa22OXaTXkauzsLQ2g5vmV9MQrCH1+ZdIOW/CkW31yVT8fS2KV8aUHYGm+Fs0/jokXQAl53xqFJnldQ2UFOxj3N4d2LUKcpaGQJCTlIoiJL2CYgJPTxlvj0N/wn/Nf4ySojgCfUL52/eFuFpqgh1E57ORnfcmz914PVvSs4jW6/hPr26MjwyhwuNjt92FApg1GsxaDWaNRKReR4xBj/4k5G8EHD5c22pwbK7GV9HSHMukJe7egeiiOrndsUDwKzjtxMWzfz/25cvxHighYLWCLBOw25CtzXgPHEB2OI6/k1+BPikJfUoKituNv6GBQEMDsv0EEuO0WowZGZj69MHUu7f63DP7iKLVWbi8Ab7dWUlhrR2nN4BeK6HXatBpJBburiKv2g4opEuVjNLsZrxmK2M1O9BJqqP5H2XnMiKuBKs5AWWohvjgqchyDl6vF5/Ph8lkok+fPmiW1uPaVntsY1ooHfQ8zuhd7Vq3vCmeoBCF+tJEErbrMO0PQnEbCLUdIMRWiucSC7ZpYZR7ArhkCQUJSdLiU9TXMprWh4IGGQkFDVpJi0mrxazVk512JyMShyN7/Lj3NaKx6DCmty+BUJEVPPubcGyqwrW7Xs0xAdBKmPtEEXJuCoak41dPFgi6MqeNuCiKQvO331H5pz+heI+eLYxGgz45GWNaGob0dAxpqWgjItCYTEgGIxqjAanlNRL4ysrxFhXhr61BCcjqtJEcUIvuyTKy14Ov+ACeggICjY1HP65WizYiAl1EBLr4eHSxMeiiY9DFxKCLjkYXE60+x8WhMXV+uGdAVqhqdlNS76S00Ulpg5OSBifF9U72VTXj9h09OTDEpGNMZjSKAt2iLPxuQhbV1eW8/+a/mKysZbPcg3v083ApBu4Z8iDXBL3Fxg3TcbtD2+ynW3wyQ2J6kxyTiL65Djl/NbI7gMPlA9mETjYgKSbsRFI14C3cMXt+4cmCIV/CtEeDbFawT5ZBC353MH5XBH53GH5nBD5XhPreFY7fHYoS0KPIOvWhSOjNTYT6mwnX+AmL70F6VBae/EaUlu9KY9GhiwtCG6xXy6oEZBSfjOwOILv9KAefvYE2yY36hCCChsRhHhArEhIFZwxdRlw8hYWU3HwLlmFDCZkwEU1QEJJOh6TTIrvcNHzwAY6VKwEw9uxJyPhxaMMjQKdFGxKCJigYQ0oy+u7d0RhOTvc6f0MD3oICvCWlaEKC0UVFoY2IRBcZgSY0VJ1GOw3481e7mLP+wE+L1h5GUriZ8T1jCTPr8QVkvAEZX0AmOcLC1cO6EWY+/CLo8vrJq7ZR2Oxi77oXsXk0GHvWMczThNdzLQaDGb1eT21tLfn5+a3VBTQaDSFJIURlRfGb0b+hqtmOxagnWK+l2dZIcWUdmxZ/y0fenjRaXQRJMm/oZ5GqLcOuc1HY14SiSaC4OYhKfxoJ3loqjTF8HjcJWdK0jkWCXfCYMQFXqUJNYYDm2hP/0++pqyQ7uG0fHr/Jj8avQeNv/+8vmXUEDYzFMjhOjFIEZyRdRlxK77kH+5Klx1xH0uuJuvO3RN96K9JJEpCzgb/P38Pbq4rQayWSIywkR5jpFmmhW6SFlEgLWXEhpEUHdUgnREVRjhjZZLfb2bVrF9u3b6eyUu39IiNz4503kh6XfsR9uX0B3ltTzMaiBtJca7iq6V32D7uf3ku34/hiEaWJsWy6/lw+73YB5ZLqrzABI3c46VXqJcp++GjMaNERHGkiOMJIcLiR4AgjQeFGLGFG9AYNGq0GrU6D3xvA7fBR/eMGAvvLcPltuAJ26tzlNHgr0aAhzBBLiD4Ck1b1ZclKAPQaYjPTiUpPJbFPL4zhwWhMOjQWPZJWRHwJzly6jLi4du2m/KEHkZusGNLTkV0uFL8PfH7QaDD3yyHqjt9iTD+87IfgxGhwePEFZKKDjR3fSvcXUFVdxf99/n80uBqIGBjBv8f9+7ihtoqiYFuwgOpnn8NfXQ1aLXF//CMR112LV1H4pLIBn6KQscnK7oUlAMgSVIdricoIY/qQJBJ7hGMKPvFpqIaKcqqL9hPw+WiqqsTeUIff58Ntt+G0NuG0NuFxOvF720blSZKGuIwexKX1wO/zUrBxHQG/n6DwCGLTMsgeNYYeQ0eonUEFgtOcLiMugrObvMY8rpx3JX7Fz1MjnmB69uUAyC4X/upqfNU1+Kur8FVV46+qwr13L66tWwHQp6SQ8NRTBA0fdth+V36Sx44fywDwSR6CbNuZNzyFL26/HJ3x5Ib2yoEAe1b+SNG2zdQU7qepuvK425iCgonuloo5NJTkXn1JyMzGEhqGJTwCvUGEIgtOH4S4CLoMb+x4g8XzXuKKlTI9PBaCnTpkq/Wo60sGA1G3307UrbccNUgi4JNZ8Wkee1ZXgAyS7Gf0mkcxGsDUvx/6uHi04eHqIyICfXISpp492xX6faI019WSv341WxfOIz4ji/6TLiA4KhprdRXF2zez68cleJxHjnbUGYx07zeQvuMm0WPI8A63TSDoaIS4CLoMATnAq89fyMR3Stosl8xm9PHx6OLi0MfFoYuPRx8fR9CYsRiSk9q1b6fTx+K/vYd+Wy6ZtvUE6uuPub4uNhZjr56YevbC1Ksn+pQUNVHV60UTFoahe3c0HTzy8Xu9VOTtxWFtwlpdRe7q5bgddlzNzciBQ1WPR8y4GlNQEHqTGYPZjMkShMFiITIxBVOwCA4QdA2EuAi6FL6aGpbOup3UmvWkGbzohl6K5uo3O7TciSLL6rTa9u3IDgeBxiYCTU0EGhrwFhXhLSlpU4rniGg06GJi1BD3IAv4A5gHDyL+sccOS4b91fYqCrt+/J7Fr790zPUkjYaIhCSMFgsGswWNRgOShEarwxIaSmRSCumDhhGZ2D5BFgh+DUJcBF2THXPhi1vV13+qAMOpa5krOxy48/Lw5Obi3rMX9549+GtqkAwGJL0ef13dURNnM75fjCEl5Yif/VqqC/dTsnsHTVUVeF0uvG4XXpcTj8OBvaEel625XftJ6tmHXqPPxRIWro5+TGbVrxMWht5kFnXLBB2CEBdB1+UfieBzwC3fQ8rhzvrOQlEUAnV1+KqqUTxuZKcT67z56CIjiHv00U6zyVpTTXNtDR6XA6/TiSLLKCgoAZmGilJqSw5QumsHinL0BFmtXo+EhFavJzQmlrDYOKK7pRKRkERYbDwR8QlYwsJP3YkJTluEuAi6LnOugPzFeI2JrNX/heHXjsaUcHJGBWcLtoY6di9bStneXXjdLnxuN16XE6fVeljo9NEYcvFlnHvdzSfZUsHpjhAXQdeluQI+upL5ey/mgGcI3SJLueip69tVv0tw4vjcbpzNanSez+Omua6G+rJSGsrLsNZUUbp7BwD9Jp7PpNvu6UxTBacBQlwEXRu/l7qV3/LZpxYCip7h09IYcqFIpO0syvbsIiIxiaDwiM42RdDFEeIiOC3Yu6aSHz7YCxJMu28AKb0iO9skgUBwDE6PSouCs55eoxLofU4CKPD9O7uxN7bPPyAQCDoHIS6C04YxV2YRnRKMy+Zj/n+30VRzhMZrAoGgSyCmxQSnFdZaJ58/txmXzYfepGXcdT3JHBLX2WYJBIKfIcRFcNphb/Sw+O1dVO5XI5v6jE1i1GUZGEwdmxkvEAh+OUJcBKclckBmw7wiNi88AEBIpInzrs2mW5+OLzgpEAhOHCEugtOa0r0N/Dg7F1u9G4CUXhH0G59C9z5RIh9GIOhEhLgITnu8bj/rvylk549lrfUmw+Ms9BuXTNawOIwW0ZdeIDjVCHERnDE017nYuayMPasq8LoDrcvHXdeT3qMTO9EygeDsQ4iL4IzD6/aTu7aKDfML8Tj8aLQSlz40iPj0sM42TSA4axDiIjhjUWSFBa/vpGh7HUHhRmb+aSiWUENnmyUQnBWIJErBGYukkZh4U2/C4yw4mjys/iy/s00SCM4ahLgIzmgMJh2Tbu4NQN6GampLbJ1skUBwdiDERXDGE9s9lMyhahb/2i/3d7I1AsHZgRAXwVnBiOnpaHQSpXsbKd3T0NnmCARnPEJcBGcFodFmcsYmA7B50YFOtkYgOPMR4iI4a+g3XhWXivwm3HZfJ1sjEJzZCHERnDWERpuJSg5GkRWKdtR1tjkCwRmNEBfBWUVav2hArUkmEAhOHkJcBGcVfq9aFsZkEeX5BYKTiRAXwVlDfYWdvWsrAYhNC+1kawSCMxshLoKzAq/Lz7yXtuNx+IlLC6XHoNjONkkgOKMR4iI4Kziwux5Hk4fgSCMX3dMfnUHb2SYJBGc0QlwEZwVlLQ78jEGxmIJEfxeB4GQjxEVwxqMoCqV7GwFI6RXZydYIBGcHQlwEZzwluxuwNbjR6CQSe4Sf9OP561w0fVOA7PGf9GMJBF0VEY8pOKOpOdDMwjd3AdD7nET0xpPna/GW2rCtKMO1qw4U0EaaCBmddNKOJxB0ZYS4CM5oFr21G78ngCTB6MszO3z/iqLgyWvEtrwMT6G1dbkpOwJDSkiHH08gOF0Q4iI4o4lMCKK51oWiwP7N1WSPSOiQ/SoBGeeOOuzLS/FVOdWFGgnLgBhCxiajjw/qkOMIBKcros2x4Iwm4JNZ+v4e8jfVADB8WjqDL+iOJEm/aH+KL4B9fRX2leUErB4AJIOGoGEJBI9ORBdu6jDbBYLTGSEugjMeRVZY+1UBWxeXANBzZDzjftMLjebEBabhszycm6oB0ATrCT4nkeDhCWgsIrxZIPgpYlpMcMYjaSRGXdaDkEgTKz7OI3dtFSm9IskaFn/C+zroVwmd1J2QsclIehFwKRAcCfGfIThryDkvmSEXpgKw6btiFPnEBu2y00egwQ1A8MgEISwCwTEQ/x2Cs4oBk7phtOhorHJSsLX2hLZ1728CQBtlEtNgAsFxEOIiOKswmnX0G6d2pNz0XXG7tgnYvDR8lkfD/3LVfaSFnSzzBIIzBuFzEZx1HCxaaW90o8gK0jEc+56SZure3oXiUfvAWAbEEHZB2imxUyA4nRHiIjhrcDZ7Wf9NIXtWVwAwakaPYwoLgHtvA4ongC7GTMTlWRi7iz4wAkF7EOIiOOMJ+GV2Litj4/wivG51BNJnbBK9Rh0/oTJg8wJgGRgrhEUgOAGEuAjOaA7sqmfV3HyaqtUs+phuIYyZmUlCOwpY+hvd+MpsAGhDDCfTTIHgjEOIi+CMpK7MxtovCyjZrfZxMYfoGXFJBr1GJhx3Kszf4Ma2rBTHpmpoCVfWxVlOus0CwZmEEBfBGYffF+Drf23D7fCh0Ur0G5/C0AtTMZiP/eeu+GWafyzF9mNpq6gYe4QTOrEbxm5iSkwgOBGEuAjOOIq21+F2+AC46s/DiGhHEUlflYOGT/fhq3AAPxGVVBF2LBD8EoS4CM44ctdUAjDkwtTjCosSULCtLKP5+wMQUNBYdIRP74Glf8ypMFUgOGMR4iI4o7A3eijdq/pZeo48du0wRVGoe28XnvwmAEy9Iom4LFM47wWCDkCIi+CMYt/6ShQFEnqEERZzfCe8t7gZgNCJ3QiZ0O0Xl+IXCARtEeVfBGcMckAmd20VQLtyWCRJwtQ7St3WHRDCIhB0IEJcBGcEBVtq+PDPa2mqdqIzaMgYFNuu7Sz9VN+Ka0ftCVdJFggER0eIi+CMYNXcfOwNHswhesZf3wuDqX0zvqbsCCSTlkCzt3WKTCAQ/HqEuAjOCLr3jUJRFGK6hZA5JK7d20k6DeY+0QA4t9ecLPMEgrMOIS6CM4KBk7szyFhLQkE+B15dj215GYpfPu52il8m0KQ2APPXuk62mQLBWYOIFhOcEZiCZKINoVh0IVDixVpShGt3HZHX9EIXbjziNoqs0PDpPjwFViSDhrALRSl9gaCjECMXwRnBvrUr2Vi3gO2u1VjGJSOZtHhLbNS8tAV3XmObdRVfAHexldq3duLaUQcSGNPDcO2qw7m9Ftnp66SzEAjOHMTIRXBGsHvZUqpcRWRddh6RU9LwD4mn/qNcfOV26t7ZRdCweJDAW2rDV+VsrR0GgALu3EbcuaoI6eIsxD8wuJPORCA4MxDiIjjtaagopyJvL5KkodeYcQDooszE/rY/Td8W4lhXiWND1RG31aeEYOweimTU4iuz4d7XiOI7vq9GIBAcGyEugtOesj07AUju3ZfgiMjW5ZJeQ8QlPTD1jMS5pRptuAlQsK8sBwVCxqUQNiUVANe+BpwbVQEyJAef6lMQCM44hLgITn9aMuv1xiM77s09IzH3jMRTZKX27V2ggGVIHKGTuwNq/5b693dDy4AldGL3U2K2QHAmIxz6gtMenUEtNOn3eo+5XuNneeCX0ScGEXFpZmu5F22IvlVYAHTR5pNmq0BwtiDERXDaYwlVe644rU3HXM/Y0trYV+Gg+fvi1nIvkl6LZcChEvu1b+5oV46MQCA4OkJcBF0Kt7uCyqqvTmibkGhVGJrrao+5Xvj0HoSMTwHAtqyMhjl7kb0BdR/ju2HMigDAW9SMc2fdCVouEAh+ihAXQZehru5HVq8Zw549D+PzNbV7u4CvfXkpkkYibHIqETOzQCvh2l1P7es7CDR70MdaiLm5L5aBasHLxs/zceU2/JLTEAgECHERdBEURWHP3kda3smcyJ9mdeF+AOLSe7Rr/aBBccTcloPGosNXbqfmlW14K+wAhF+qRpfhl2mYsxd/g/tETkMgELQgxEXQZfD77a2v162fQpN1c7u2q8jLBdovLgDG1DBi7x6ALsZMwOql7u2dyE4fGoOWqN/0wpAWiuKTafwsDyUg/C8CwYkixEXQJZAkiajI0S2vdXi9NWzdegOBgOe421YXqSOXhB5ZJ3RMXZSZ2LsGoIu1IDv8NP9Qqh5fqyHiskwkvQZPoZXGL/ef4NkIBAIhLoIuQ//+bzLuvD3k9H0FAEXx/3979x1fVXk/cPxz98292XuTkIQRRliyZIOAiCgu3LNardraaodt/VVta62to2q1at0bBREEVGTKkD0TRvbeyb03d49zfn9cCKasADeD5Hm/Xnkl95znnPNcifneZ30fFIoz/4oemy3mdpx9VmNlkJrwuX0BsG6pwtPgv4cmxoDh6IZj9h21ovUiCGdJBBehW1Eqdej1/i2KZdmD3V50xmvCYvz7t5jra8/pmfp+Eej7R4BPxryiuPW4JvH4Sv3aF3fjLDSd0/0FoTcSwUXodkJCBhETMxOA6prFZywfHBkFQMsZpiKfTticdFCCM6+xNYgYL4onfH4mSoMab62dhjf20/jJISSH95yfIwi9hQguQrcUGzsHgOrqxbjcp19zUp3vH9APj0885+dp4owYR/tbTOblRciSjEKpIHhMAvGPjMI4NgEU4NhTT+2Lu3CViS2RBeF0RHARuqWoyAmoVMF4PE1s3DiG2roVJy3ntFkp3b8XgKwx48/rmaGX9EGhV+GpsmHfdbyLTWnQEHFlJrE/G4YyWIOv2UX9K3vx1NnP63mC0JOJ4CJ0SxpNBMlJN7W+Li19/aTlindtR/J5iUxKISop5byeqTJqCJ2WCoDpy8ITNhnTpoQQPi+j9bW7VLReBOFURHARuiWLZR+lZa+1vu7X77GTljuydbP//Hm2Wo4JHp+Irl8Eskei4d1c7Hvr2pwPyo7yL7IEmr8sxHGwMSDPFYSeRgQXoVvS65PQaCKP/pyMXnfieIrH6aRk7y4AMkcHJrgo1Eqib80mKCcGfDJNnxzGuqWqzfmomweiH+hfxd/4Xh7WH6oD8mxB6ElEcBE6nc3UjCydft2IVhvFyBGfotMl4HRWsHPXAjyett1QVfmH8LpdBEdEEpvWN2D1U6iVRC7oj3FcAsj+LjLzqlJkWW49H3XzQAyj4vznlxTQ8n1lwJ4vCD2BCC5Cp/K63Xz+18f44pknkSTfacsajX0ZNXIhOl08TmclG74fQUPD2tbzoTFHFzlaLHhcgc0BplAqCJ+XQegM/xhMy+oyTEsL8VndSE4vSBB+VSbG0fGAf4aZtUxkUhaEY8ROlEKnKt6zg4ayEhrKSvC5PSj1qtOW1+sTCQ8fTW3tUkAmv+CvREdPBUAXZEClVuPzemmuriIuPeO09zpbCoWC0Bl9UBo1mJYWYttSjW3L/3SBKf0bjsmyTPWL2/EoXRhu7kOfIcNaNyMThN5ItFyETnUsRUtc3yw0en27rklPewCNxr9Q0m4vxuezI0sSX7/yPD6vl6jkVKKSUzuszsHjEom8fgBKo+bEk8c2HFMoCFIHgxcW/fUx3v7VfexauQyX3dZh9RKE7ky0XIROVbzHn+k4fdiIdl+jVgfj9ZpbX5eUvEJh3h4ccVUYYoKY+4vfoNac5A9/ABlyYjDkxPh3r/TJyD4J2Sshe2XwSvgcXhr2F9FYlI+mMYjmqgrWvvMaGz9+l+xJUxl/7U0YwsI7tI6C0J2I4CJ0GpfdRum+3QCk5Yxs93VqdQjG4MEU1FRS1pLMU0eCaLTM4ap+y0kfGEx0aloH1fhECqUClAoUmraNfjWQlJJDEjmMcVxP3oa17Pl2OY0VZexdtRKFUsn0O+/rtHoKQlcTwUXoFJLk49PHf4fT2oJSpSIhq3+7rjObnPxhUS5rS+7G5vFPAMgxrkSVsJaD1aP4yby7OrLa50QbZGDYrMvImTmH7UsX8f1H71BXUnzmCwWhB1HIx+ZXCkIHclqtvH7/HXic/jEXtUaLITyc7IlTGXfNjShV/oF9WZJpKm/m8OdbqC9ooIIUkMGHjE0JUd5adLYKPh25lvKoCjLDM3lg+ANMSZ6CSnn6yQFdofJQHp/8yb/D5qjLr2LM/OvQG4PPcJUgXPhEcBE6Tdn+XFb99yVMNRVtjhsj4skccy/megWNlVY8Ln8LRe2xElezjfKUqShpO/MqXPMWL40rwuZpASBKH8XMtJnMSZ9DTkxOt5mp5XG7WPbsU61jTfqQUMZdfQM5l8xGpe7YcSJB6EoiuAgdrq7Uwu5VZRTtqsfnkwAPsmTH59qJz+VPOqnSDkJjnAWAUgkGcxnB1koyCj/nqjlP8t+JsQQ/+wwOnw5Fv0xGPvt7vEEu3s19l4VHFmJ2HR/wH5swlkdHP0rf8MAtrDwfsixTvGcHGz54m8aKMgAiEhKZeOPtZF40rtsEQkEIJBFchA4jSzIFO+v49s3c1mNqrRKVyovLuhGnZcfRowoGTLqbtORUPP99Fk3BLpSyfwX/syMWsD1+IJ/ueQO5qpKgkSNJffstlFpt6z09kocfqn5gZfFKvin5BrfkRq1Qc0v2Lfw056cYNcbOfNunJPl87F/zLZs/+xC72QRA0oBBjJo7n6SBgwgKDunaCgpCAIngInQIWZZZ8txuqvJNrcfmPzwcn7ucr1/9F5aju0ZmT5zK5Ft/gm/HTqoe+TWS3Y4yJITg6dP5i34oK52hvHHgfZIL9qJJSSFt4aeoIyJO+dxySznPbH+GdRXrAIgNiuWhkQ8xM20mOpWuI99yu7kddrZ9uYidX32B1+NuPR6d0oekAYNIHjiIpIGDCImM7sJaCsL5EcFFCDjZI+E2O1n+zE5weNEpFOgVEjrqUPjM/FC/jNCYWC65+wGSouNo+M9rmD77DAD1qItYf+Ov+OigiaJ6G+EqiY8X+QfE079cgr5/+2aZbajYwN+2/o0Kq398J0gdRE5MDncPuZvRCaM75o2fJUtDPduXLqJs/x6aqipOOB8WG0fSgEFkjR5P5kVju6CGgnDuRHARAsa8shjrthrk02wDLMsy+5N3Mf32u2j56GMaXn4Z2eMBoGDSXB6OnIT7aOIIg1bFM6PDyHjkTpQGA/127jir8QmXz8U7B97h08OfUu/wb4GcEpLCiqtOvvFYV7KbTVQeyqPiUC6Vh3KpKy5Clo8n9xw0ZQbT77wXja59WQ26nM8LKrHSoTcTwUUICNkrUfmnzeA7+uukUqAK0aIK1aIM1qIM0VCYdwAMwQy/YyyVP38A+7ZtABjGjCH6/p8xZ72N0kY7sSE6Hp7Zj8uGJsK2LZTffQ+6rCz6Llt6TnXzSB5GvO/PCDA1ZSovTnsxIO+5I7nsdqqPHKRozw72fL0cWZaITunD3F/+7rw3ResQsgzmCqjZB9V7Yd+nMP81SBUtrt5KfLQQAsJTawefjEKvJuE3o1AEqU9oZUTOz8JntVJ+9z04du9GaTQS98c/EnblFeytMKNX7wPA4fZx3agUFAoFTeXlAGiSk8+5bhqlhp8O/Smv7XuNvfV7aXI2EamPPPc32wl0BgNpw0aSNmwkmaPGseKlf9BQXsqHj/6SS+55gIETpnRuhZpLoXQTuG3gc4PXBT4PuCxQs98fVBxtd+5k079EcOnFRHARAsJTZQVAm2REaTj5+g1XURGVv3gIV34+XkMwq27/Pdvq4yh8ajX1LS4A1EoFd0xIR6FQIEsSps8/B0A/cMB51e+nQ3/KmvI15Dfn89yO5/jzxX/u1lOAC02FlFpKmZY6jdTBQ7nl7y+y/MV/UJ67jxUv/ZOKgweYftd9KDtq4agsQ9VuOLwCDq+E2gNnvkaphpgBED8UkkZAzg0dUzfhgiC6xYTz5qm10fDWAXxmNyGTkwm7NL3NeZ/ZTPOnC2l49VVkh4MmXQj/N+4uCsOPt0ZUSgVXDkviF9OzSI0yAGD+ajlVjzyCMjiYjFXfnnaWWHvsqdvDLStvAWB47HB+N/p3ZEdln9c9A628pZxX97zK8uLlBGuC+frqrwnR+qcoS5KPTZ+8z7Yv/QH36t8/SVpO+xOAtkv1Ptj1rj+gWH60AZpCCcmjISQOVDpQaUGlAY0BYvpDwlCIGQiaC2RMSOhwouUinBdXqYWGd3KRHV7UsUEET0hqPecuK6PpvfcxLV6MbLcDsCc6k7+PuonsQWnMSgknIyaYzNhg+sYYCdEfb/HIbjf1L7wAQNRP7jrvwAIwLHYYD498mJf3vMzuut38ev2vWX7V8vO+77mSZZmtNVspMZdg9VgpNhezomgFXtk/IeKi+Iuwe+ytwUWpVMHR1pYhLJz4jH6BqYjHAU3FUJsLX94PPn8rEo0RMqdB/8ug3ywwdO+uRKF7EcGlE9hsBVRXLyYy8mIiIy/u6uoEjPNwE40fHET2SGhTQ4i6bRCqo3ueNL37LrV/fwaObmdcFp7IZ30nsm/geJ6eP5TZg+NPe2/LN9/iqahAFRNN5K23BqzOtw++nbzGPFaWrCQjPLCbi50Nm8fGE1ueYGXxyhPOXZx4MQ8Of5BB0YPaHHfZbWxfugiA8dfeiD74HHKUeV1QuAYKvoOGfGgq8g/E86MOjLghMP0xSJ8sWiLCORPBpRO0tORRWvYapWWvkZ72IGlpD6BUXtj/6R2Hmmj8IA+8Mvr+EUTeNBCl1t//b1mxgtq/PQ2AQqNh3e2P8nRdCGEGLRsenkrYKcZkfsx8dGZYxHULUBoMAav3ptpiFu8HTVgE1/S7JmD3PRtHmo/w8LqHKbGUoFKomJQ8iTBdGKHaUKalTmNk3Mm3I9Dqg4hLz6CmMJ/tSxeRedE4jOHtaNF53VC0FnK/gEPL/YPw/0sXBlEZkDgMpv4RjFHn9yaFXk+MuXQCs2UvpSWvUt+wCoCQkMHEx80jImI8wcH9USgurA1B7fvqafrkMEgy+uwoom4agELlfw+yLJM/cRK+hgY0867kjxlz+aHYBMAvZ/TjFzOyznh/b2Mj+ZMmg89Hxtcr0aalBaTesiwz6Lnl2OsVaFKrOHTvnZ2aSbnEXMKbB97kq8Kv8Mpe4gxx/HPyPxkWO6zd97CbTXz82K8x1VYT0yedBY8/jc5wkvQ2Pg8UrTsaUL4C5/Hca4QkwsDL/YEkMsMfVAxRrV1ughAIIrh0ouqaJRw58jheb0vrMY0mkoiIsUREjCMyYjxBQX269Swmd5WVupd3gwRBOTFEXtevNbAcc2TsOHwmE7+Y8QhHguMxaFX8+YrBXD2yfdOJmz74kNq//AX9kCGkf7YwoPUf+8kKavb4f+UHJYZycWY04zOiGJ0eiUEb2Nbku7nvsqRgCQa1AafPSX5zPvLR7qcpyVN48uInidCf/ViSqaaaj//v19jNJiISkrjm5/cTWv61v3urpQZaqqG5pG0LJTgeBl0Jg+b7B+aVF9YHGuHCI4JLJ3O5aqmpXUZz82ZMpu34fPY253W6BCIixhAaMoTg4GxCQgaiVnefhIaNHx3Esa8B/YBIom7N9u/M+COe2joKJk8G4Ksbfs3OhIE8NX8IfWPaPz5QfO11OPfvJ+73jwZ0vAXgoYNlfL6uCHVp273tlQqIC9WTEKYnISyI1CgDMwbGMiI14pyCvU/yMez9YSccn5w8mbuH3k1OTM65vgUAaosLWfjEo7gddoLVLq5OPUC0ru3vEsZYyL7CH1BSx0I33O9G6LlEcOlCkuTGYtlHU/MWmps3YzbvRpY9J5TT61MIDR1CSPBADIYMjMYMgoL6oFR27n4gngYHtc/uABlifz4cbaI/YMiyjLe6mpbvVlP/4otIViuo1SR89AlhQ7LP6o+z8+BBiudfBRoNWevWoo4KbN//R1WN/OpwObh8zFcFEWL2sKmgkUqT46Tlk8KDmJuTwJXDkhiYEHraex9sPMjW6q3srN3JrrpdWNzHWw7PTHqGAZEDSA9LP80dzsBSBeVbwdYAThPN5YUs+foITW4DSiRGDYll+IQxBCf2hZAEiBskAorQZURw6UZ8Pgdm8y5Mph20WPOwtuThdFWdtKxCoUani0OtCkalNqLRRBIWNoKI8IvQB6Wi1UQFtHvNcaiJ5kX5SC1utCl69On1OPPycOYdxJmXh89kai2rHzqUhCceRz9w4Fk/p/rxxzF98ikhl84m+fnnA1b/Y2RZ5p8lNTxbUosC2Dt+EDFaNfUtLqrMTqpNDqrNTvZXmvk2twab29d6bf+4EK4YnsjVI5KJC207i2pv/V5uXnFzm2NB6iCyo7KZ23fu6ScPSD7/rK26g1B/COry/FODI/pA4nD/zyUboanwhEsdPjWf1k6i0Xz0Q4lCQUr2EKbc+hNi07rHfjZC7ySCSzfn8ZhoacnFYtmPzZ6PzVaI3V6Ez2c77XUqlRGjMROjIZPQ0BwSE69BqTz7lPOSw4tpWSH2XXX+17Y67FteQrbWti2oVqPLzCT8umuJWLAAhersPzFLNhv5kyYj2WykvvM2xrEdlzpkwtaDFNhdfJqTweTIk3c7Oj0+1hyq48s9law9VI/b559WrVEpuGZkMpcOTiA0SEOfaB3P7vwrXxZ+CcAjox5hZNxIBkQOQP2/swIlH5jL/avfK3dCxU6o3gOe/+nSOhmFEuKHQEQa6EJBHwZpE/CkTeXAuu84tGkDVYfzAAiPT+DO519DIcZWhC4igssFSJZlXK5qXK5afD47Xp8Vh70Us2U3JtNOPJ4m2qxbAGJj5zB40L/Oamaa43ATpkX5+CxuUIC74DtcuV+g0CjR9e+PPnsg+uxs9NmD0PXLarOB17kwLVpE9R/+iDYtjb4rV3ToxIY79xezosHMYxmJ3J8ae8byZruHFQeq+XxHOTvLTMdPKJ2MDV1EXXA19SENvJR4KVMiBoBaD/Ymf1eWpfLoV5V/wF32nfgAdZB/pXtsNsQOgIh0aCzwJ4EMS4a0if5xk6Dw09azsaKM93/7c3xeL1f+5jEyRo45u/8wghAgF/Zii15KoVCg1yei1yee9LwkeXA4SrHa8rFaD1Ja+jp1dSs4qDKSknI7RkPGCeM1taWVeKxHiI0aibvIivNIM85DTQCoInVI5g249i9EGRJCv82bUGgCP95j3+HfZz7k0tkdPmNueKiBFQ1mnimuJlGnYX7c6WdthaiUXJLXwsQyib0Y+Aw3B1R1XBy8mDUJBQDEer3k5n5CtN1BtvvYxgEnoVT7x0OSRkLSKP/36KyAjI9EJacy/NJ57Fi2mF0rvhTBRegyouXSC1TXLCEv7+HW1wqFBqMxg+ioqRiMmTQ1fk9lxTeoNA7kHfcwoGn80YKgiXXQ8s2/8JQUgUJB3KOPEnnrLR1Sz6L5V+E6eJDkl18iZMaMk5aRJRnZ7UOhVZ0wU+1UZFlGdnjxtbjxWfxfdoeHX2FllccJwOdDM5gQ1bZ7TPbJ+ExOPA0OWtaV4y72D9ArdCrU0UGYDDt53L4cr/Ygh/Rtg20UKiaoI5gcnM6kmBHowlMhNAlCEyE4tkMH2i0Ndfz3wZ8gSxIzf/pzhkyb2WHPEoRTEcGll2hoWENZ2ZtYWg7g81lPWsbr1RB2+EbSvNORmguxb12Gp/R4NtzUd97BOLbjPgkfHjMWyWymz8cfYRg+HPAHE0+NDVeR2f9VbG7djEyhVaLQqVDq1Ee/q1q/y5LsDyQtbiSLG9kjnfA8HzBrqhGTVslvD7u40atFE2tAcnjxNjjwNjmP70+DP6hE3zkYbWpI25bV3k+oX3ofG4OC2NBnGJvdjdi9x8dQQrQhzEmfw/zM+WRHnd3suXO14cO3W1PFXHLPAwydPrvDnykIPyaCSy9zbLzGZNpBXf1KXM4a1JpBrFvbiNOWwLU79+E5mNtaXhkaSuismUT/7GdoEhI6tG5F9/+S2koHIf2GkTRuOt5mF+7yltPubHk2FEFqVKFHNzDTqfDZvDwb5uXbUPjFYRczak/yHLUSTbQedayBkMkpaJNOsl5HlmH932Hd3wDwjLiVnQNmsMF8mFWlq6ix1bQWzQzP5Jp+13BNv2vQqc5+gkV7ybLM2ndfZ/fKZQBMuvlORs2d360X6Ao9iwguvZzVamXpZ59xpLSUxMpKJn6/EaXBQPCM6YReeinBF1+M4jwH6tur7P0lKHNPXNei0KrQpYei6xuGrm846jgDstuH7PIhuX783ev/7vSBQoEqzB9Iju2IqdCc2BVl8/rQKRTITU48NTa8dQ6UBjXq6CDU0UGownTt7n5j62uw8jfHXycOR8q+kq19hrOkeAWry1bjOppxOCYohtsG3ca1/a7FoAlc7rQfk2WZ9e//l53L/bPYskaPZ9Z9vzh5uhhBCDARXHqxPWvWsnz9OjxHP82O37yFoRMmEP3gAwFJcX+2anIPUfn6DlySlexZ01HHBqOJN6JNCkGhukA+cR/5Bja/5N+1UT7aFdf/Mrj+QyyeFpYXLeetA2+1tmaig6K5MvNKrsi4grSwtIBXR5Zl9nzzFeveexPJ5yU8PoHLf/moWAMjdDgRXHopt9vNc//8J063m4imJsZ5PAz95S/R9wvQHiHnoOrIIT5+7BFCY+K4++U3u6weAWGtg7wv4Zvf+7cFnvNPGH03AB6fh2VFy3ht72tU2Y4vkh0WM4x5mfOY2WcmYbqwgFanuuAwy55/mpaGetQaLdPuupchU8VAv9BxRHDppXbt2sXSpUsJVam459JLMY4c2eX98eV5+1n4xKNEJCZz5/P/6dK6BMyWV+CbR/27N/5kFSQczynm9rlZV76OJQVL2FS1CeloS0ej1DApeRJz+85lUvIktKrAdEs6Wiys/PdzFO/eAcCgKTOYfue9aHRizxYh8ERw6aXefPNNysvLMRqN/PrXv+7q6gDQWFHOOw/fh0Kh5OanX+gZXTeyDB8tgPxv/FsCT/8/GH3PCVOR6+31LCtaxldFX5HfnN96PDk4mUdGPcK01GkBCf6yJLHty8/Z9OkHyLJEdEofRsy5guxJ01CpxbI3IXBEcOmlPvvsM3Jzc5k+fToTJ07s6uq0WvbC3zmy5XsSBw7m+j/9rctbUwFhb4KFt0LJ9/7XyRfBvJf9K/FP4nDTYZYXL2dpwVIanY0AjIgdwW2DbmOkOovQhOTz/u9SdmAvy1/8B3azCYBfvL8YdSdN3BB6BxFceilJkjCbzeh0OgwB3OmxvWRZxuez4nLV4XLV+r/cdbSYSsjduQZdOARpRjBr7is9I8BIEux6F1b9n3+fFaUGbv0S0k697bXNY+OtA2/xXu57OH1OkhpDueLgH1Arqhk8ZyLJ/aKJTQ9FF3RuLQ5rUyObFn6Apb6Oq//wJEqRQVkIIBFchA4jyxJm8y7q6r/BbivA47Xg9ZrxeMx4vRZk+cR1JZIMj217AkWoj+uCv+ahWz/qGcHlGHMlPJ/t/3nu8zDqzjNeUmOr4d3cd3EuLiLatKDNOYVSQb+L4hg1J43wuM7/kCAIpyKCi9BhcvMepqZmyWnLNMvBfG3W0SKrcMsqTDYvOo8FfdNw9jXfyGVDE7l3cl8GJQZ29lSX8brg6VTwOmHKo5A2AeKHgv70e8UA+HwSez74hKYPNmAOTsMSm41D4V/UqVBA1tEgExEv1rEIXU8EFyGgXK56mpq+p7FpA7W1/tXh0dEziImejkYTjloddvR7KFYf3PL1nVRYK064z1hLAqsqf9H6enK/GJ68YhB9oi7wP5w/brkcExwHP1kN4SntuoXthx+o+Nn9SHY73unXUDrwakoP+JOMooDEzHDi0kKJ6RNCbJ8QQqODelbrT7ggiOAinBefz4XZvIPGpg00NW3Eaj3U5nxC/NVkZz8DQIOjgeVFy2l2NtPibmF3/W7ym/NJCk7i5zn3EbT2b+gbi3BGDqDvgg9psYby2oYilu+rQpLBqFXx5ysHM3940oX7x1KWYe8n/kWWpjKozQV7A2TNghs/9TdB2sG2dRvlP/kJssdD+HXXobzzYXasKKFkX8MJZXUGNTGpIUQnBxMSpSe+bxjRycEoVWKvF6HjiOAinDNJ8rJ122zs9uIfHVUQEjKYqMgJREVNISzs+PqZP23+E4vzF7e5R7AmmPcvfZ/MLa/DttcgKBLu3QhhSa1lShps/ObzfWwr8X86n5eTyF/mDyZU37nbPHeI+sPw6sUgeeCat2HwVe2+1PLNt1Q+9BDIMpG33UbsIw9janRTXWimvrSFulILDZVWJO+J/4urdSrCYoLQG9Rog9ToDGp0QRq0Bv/PGp0KySvh88podCqiU4KJTDSiPkkKHUE4GRFchHMiSR7q6laSm/dLwN9CiYyaSGTExWi1kSe95s5v7mR7zXYmJ09mYNRAQjQhTE2ZSkrFblh4NI3/jZ9BvxNXjvskmVfWFvDC6nx8kkxyRBD/un4YI/uc/FkXlLV/g/VP+zcMu+guuPghCI5p16XNn3xCzeNPAKAfPJjEZ/6Oru/x9UE+r0RTlY26UgtN1TZMtQ5qisy4zyEZqFKpICw2CIVSgeSTkXwSkk9GoVAQEqUnPDaIsFgD4bEGIhIMhMUaULY3L5vQ44jgIrSbz+ekuXkLdfVfU1//HV6vCYDw8NGMHPHxaa/dU7eH+767D6vHyn9n/pcxCf7U/VLlQRTvzkLhNsP4n8PMP5/2PrvKmvnFJ7spb3KgUir48v6LGZx0gQ/2e13w0XVQtM7/WmOAK1+FQVe263LLihVUP/EkktmMQqcj9uFfEXHzzafc4liWZJpqbNiaXbgcXlx2Ly67B/exnx1ePC4fKpUSlVqBw+qhodyK0+Y5q7el1iiJTAomOtn/lZAZTnTySbJKn4TT5sFUZ0fyHg1iknw0oB39kiRkn4xCqcAYpsMYrsMYoUOjFS2r7kIEF+G0PB4ztbXLaGhcR3PzFiTJ2XpOo4kkJmYmfVJ/gsGQftr73P3t3RwuLCHMEcuTOX/FafFibbDR8P0PKJQy1439DOXdX4PqzF1dtRYnY55aDcCi+8b1jNaLLEPBd/DhNf7XSjX8thR07ftj7KmtpfoPf8S2cSMAhnFjSXzqqYBtkyDLMtZmF6ZaOyhApVKgUCpRqvytGEuDA1OdHXOdA3OdnaZqG173iXvoZI6MJSopGH2wBr1RQ1CwBn2wBoVSQV2phepCMzWFZpqqbOdUz+sfG03UybZFEDqdCC7CSdntpVRUvEdV9UJ8vuMbX+l08URHzyAu9lLCwy9CoTjzJ0VJlvjHG28SvCvj5AVkibseCEY/ZGy76tZkczPyL6v826j8esqFP4Psx9w2eHk0WCpg3AMw66/tvlSWZUyffELtM/9AdjhAqSR09iz02dloMzORnU68TU1IZjO6AQMInjChQ7arBpAkGUu9g/ryFhorrFQXmqnKN53VPYzhOjQ6FUqVAoVScTSgKVCqjn0pkbwSNrMbq8mF1+Xjrmcnojf2gLG4HkAEF6GVw1FBQ8NqGhrW0NS8sfV4sLE/cXGXExU9lWBj/3bP1NpYuZHPDn/G4eIS5uy4H5Wsps5YRk7fbELqqvBtXo3ObSblhkn0vfvOs5q9dMubW/k+v4Ex6ZH8+6YRRAd33MZbnS5/lb8Fo1DC3WshcdhZXe4uKaHiwQdx5RectpwqOpqwyy8nbP6VnZINu2hPPTVFZpw2D07r0S+bB4fVg9ftIzo5mPiMcBIywojvG4YhtP3paGRZxu30odWrLtyZhD2MCC4CAKVlb1BQ8PQJxwcP+hexsZed9f+w7+W+xz92/ANkuDzvAZIsWbTEVdPvJgOXVEdScd/PQJKI+/3vibz1lrOu75bCRm59aysen0x0sJZ/XJvD1P6xZ32fbslpgZcvAmsNjLkPLj3x3+VMJJcL+/YdOPPycObl4S4uRmk0oo6KRBEUhG3jJnxNTa3l9YMGEXbFFegG9EcTF4cmJeWUYzaC0B4iuAjIsszmLVNwOisIDR1ObOxsYqKnn3Ec5XRmL5qNudHOiOoZZFdPQKVRcuOfxhAaHUThrNm4S0sJv/Ya4p988pw/aS7aWcHDn+0FQKdWcvDJ2T1jdtLnd8KBRf5xl7vXQsLQgD9C9niwfv895i++oGXtOvC2nT2mSU0l8rZbCbviClTBYgxDOHsix7aAzXYEp7MCpVLHiOHvoVKdf46qq1Ouw7w2miCv/w/T6MvTCY0OAvyfqgFC515+zoGl1uLkiWW5AGhVSu6bktEzAgsc38FS8sLiu+GSP0PWJe1eYNkeCo2GkGnTCJk2DW9TE5avltOydg3eqmrcVVV4ysqo/fNfqP3zX1BFR6NNTkaTkoI2JeXo92Q0KamoY6JFC0c4KdFyESgq+hfFJS8SHT2dnKGvn/f9PC4fbz//LZ4SHSZ9Hf1mRnLNpZe0BpLy+36Gde1a1LGxpL7zdpt1Ge0hyzJ3v7eT7w7WMigxlNduGUlyRA9K2ujzwI63YN3T4DjadZU+GWY9BfGDO/zxks2G6YslNL//Pu7S0tOW1aSkkPCXv2AcM7rD6yVcWERw6eXs9mK2brscSXKQnf0sCfFXntN9nFYPe1aXUX6wmboyM0j+QNIy6wC/vuJ+VD9K5+6pq6P8rrtw5Regioykz4cfoEtvfxfc0r1V/Pzj3WhUCr56cCL940POqc7dnsME6/8OP7zif60Ogof2t3uBZSD4LBbc5eV4yitwl5fhKa/AU1GOu6wcT3U1+HygUBB5++3EPPQLlLoeNLFCOC+iW6wXkyQvuXmPIEkOIiLGER8375zuY66389nTO3DZjvXbK7BqTVjTK3jyygdP6PpSR0YSPHkyrvwCfE1NtKz6Dt09d7frWc02N48v9XeH3T81s+cGFoCgcAj7UTLLmP6g7dwWmio0lKBBgwgaNOiEcz6rjbq//x3TZ5/R9PbbWNetwzhxArqsLPRZWWgzM8V4TS8mgksvVlr6HyyWPahUwWQPfAaF4uz6zr0eH/VlVnauLGkNLO6JJSy0vYNV18yLU188IbB4Gxoo/9n9OPftAyB48mQirru23c/8oaiRJpubxDA9P5uSeVb1vSBF9Dn+82XPgbb7rOlRBRtJ+POTBE+dSvVjj+EuLsZdXNymjGHUKCJuvpmQ6dM6bE2N0D2J4NKLhYWPRKdLIKPvw+j1ie2+TpZk1n10mEM/VLdJiuhVuHnP9TJenYch0UMYETeizXWemhrKbr8Dd0kJytBQ4n7/KGFXXHFWg/opkf5P7iaHh7oWZ88aazmZ/nMg+wrI+9LfRXbTwq6u0QlCpk0laPgyrGvW4srP938VFOCtrcW+Ywf2HTtQx8URccP1hF97LeqoqK6ustAJxJhLL+fz2VEqz26/j61Li9ixogQAXbCKYn0epYZDlEbmkpM1gKuzrmZKypQ295S9XormXo67pAR1YgJ93noLbVraWdd3V1kzV72yGYBLB8fz6s0jz/oeF5zi7+HduRCVCQ/u7OratJunspLmzz7DtPCz1jU1Co2G0HmXE3X77eiysrq4hkJHEsFFOCtFe+pZ+Z/9AEy7dQCFcbt5dOPvAFhyxRIywk+e4sVdWkrhrNkotFoyvl6JJrH9LaVjnliWy9ubSlpfPzgtk4dn9j/7N3GhaSyEl0aAxgi/rwzolOTOILndtHz9NU0ffNjaHQpgnDiR4IkT0CSnEDyx41LRCF1DdIsJ7dZYZWXVW/7B9KHTkhk4PpFEl5ErM6/kun7XnTKwAEgOBwDKsNBzCiwAu8tMAEzMiubRSweSnXjmrYF7hJCjySc9NnA0g+HCStSp1GoJmzePsHnzsO/aTdM779Dy3XfYvv8e2/ffA6DPzvZvF5DZC8bRegkRXIQzcju97FxZyq5v/GseQiL1jL/a/0cgTBfGny8+fZp8+FFw0Z77VNUB8SHsKTeRkxzeewIL+GeIRaRBcwlsfglm/Kmra3TODCOGYxgxHHdZGaZFi3GXlmLbsgVnXh7FV11NxC03YxwzhqBhw1CF9qJ/4x5IBBfhlGRZpqnKxpr3D1FXYmk9PmJ2H1RnuUWuJi4OAE9VFZ6qqnNqvRwLKHnVljOU7IEueRIW3gqbXoCBcyHpwh5r0qamEvvLhwDw1NZR/Yc/YNu4kaY336LpzbdAoUCXmYl+8GB0/fphGDEc/cCBKLTtT2YpdC0x5iK0IUkyNUVmivc2ULy3HnOdv8WhM6qZcG0Wyf0jCI7Qn9O9S2+9Dfu2bUT//EFifvazs75+R0kT1/xnC/Ghen74/fRzqsMF7fO74MDn/oH9e9aBrues8ZFlmZavv8a6fgOO3btPmhlAodOhHzIY4+jRhM6de9aZHYTOJYKLgMflo+JQE8V7GyjZ34Cj5fiOg0q1gpQBkYy7KoOoxPNbEGf+8kuqfvs7NElJZHy36qxmqMmyzM7SZq75zxYAdv5xBlE9Kc1+e9ib4NXx0FINI26DeS92dY06jLexEcfu3TgPHcaZm4tj9258JlObMiGXzCD6vvvQZ2d3TSWF0xLBpZfb+XUJW78s4se/BdogNWlDokjPiSF1UCRafWB6T52HD1N8xZWg0TBgz24UqjNvNLb+SD1fH6hh3eE6qs3Hd8H87leTyYzthau/SzbCO3MBGW74BPpf2tU16hSyLOMuLsGxexctq77Dun49x35pg6dOJfbhX4nJAN2MCC69mKnWzkdPbEWWZAxhWjKGx5I+LJrErPCzHlNpj4bX36D+uecwTpxI6httE2R6fRIqpaJNa+bT7WX8dtH+1td6jZKLM6KZNyyReTmJvXdTqG/+AFteBmMM3LelU3ONdReuggIa/vMalhUrQJJArSbiuuswjL4IXVYW2j59UKjFkHJXEsGlF9u9qozNiwqISw/lmt+O6vDnld31E2ybNhH32B+JvOmm1uN2t5dLntuAVu1PnR+kUbFifzUrD9QAMCwlnF/MyGJc3yj0mjO3dno8jxPemAp1eZA8Gm75AnS9sBUHuIqLqfvns1hXr25zXJOcTN8vl6A0dp90Ob2NCO29VP72WjYv8m+DK0ud9Pni6OcYVUgIHp/Esr1VbClsZPn+auxuHwC/+Xxfm0vG9Y3i9VtHEqIXC+xaafRwzdvw1iyo2Aaf3Ag3LvQf72V06emk/PtlrN9vxPLN17jy83Hu3YenooKWtesIm3tZV1ex1xLBpZdx2jzUFJpZ8/5BAAxhWoZMTe6chx/txpLsdl5dV8hzq46cUCQnOQyFQsGwlHCuG5XSu9aznI3YAXDzInjvCiheD5/fAde9B6reGYSDJ04geOIEAGr++hTN77+P5euVIrh0IRFcegGfTyJ3QxX711VgqrW3Hk/qF868h4Z3yg6OllWrsG3aBIAuI4PJ8TE8t+oImbHB/GZWf0b2ieh9s7/OV/Io/6D+h9fA4RWw5D6Y/zr08p0htSn+bQqs360+Q0mhI4kxlx7OVGtn4VPb8bh8rcdCY4JIyAhj3PwMjGEd/wfdXVFB8bwrkOx2Im+7jbhH/bnIzA4PYUG985N2QB35xt81JnkhaybMewlC4ru6Vl3GU1lJwfQZAPT56EMMI0ac4QqhI4jg0kP5vBK7vy1lx4pSfF4JFDBkcjKjL09Hb+zcP+hNH31E7ZN/RpeVRfriRSJBYUc4sBi++Cn43KAPh8uehSHXdHWtukzxVVfjzMsDIHTOHGIfeficc9oJ56Z3t597qOpCM5/+dTtblxbj80qkZEdy85PjmHR9v04PLEDrboTqmGgRWDrK4KvgnvWQkANOEyy6Cz6+AY58Cz7PGS/vaVLeeJ2wa64GhQLLihUUzrmM+hdfQrLbz3yxEBCi5dIDHVhfwfqPjxAUomHCtVlkXRTXpWtCrBs3Uf6Tn6Dr35++Xy7psnr0Cj4PbPgnbPgHyEe7QoMiYOA8fwBKHQ/q3pOfy5mXR+1Tf8O+YwcA6rg4Ev76V4InXNzFNev5RHDpgWRJZuc3pQyelNQlLZX/1fjf/1L3z2cJyskh7dNPuro6vUNtLux8F3K/AFvd8eMaI6RdDH2nQN+pEDvwgtsf5mzJskzLN99S98wzeKqqUIaGkvHN16gjIrq6aj2aCC5Ch5I9HgpmXIK3tpaEv/6V8Kuv6uoq9S6Sz58y5sAiOLQc7A1tzwfHQfpkiMuGtImQOKLHzjaTHA5KbroJ49hxRP/svtbuWqFjiOAidCh3SQmFs/35r/p88D6GUR2fCUA4BUmCulwoWgeFa6F0M3gdbcuEJMKAy/xp/ftc3OPWzcg+X7ty2gnnTwQXoUPJskzFgw9i/W416thY0j7/DE1sbFdXSwDwuqB8K5RsgvqDULAa3Nbj54MiYMh1MOnXvTJ/mXB+RHAROpy3uZnCWbORLBZCZs4k+cV/dXWVhJPxOP2r/Q8u8y/KtDf6j2uD4eKHYNz9/l0xBaEdembnqtAtyLJMy9q1lN5wI5LFv3uk0iD+OHVbGj30mwVXvAwPH/Gnl0kY5m/NrP0LvDQCDn4F4vOo0A6i5SJ0CFdNDcWXz0NuaQFAFRVF7C8fImz+fNHnfSGRJMhdDKufAFOZ/1hEuj8IZV0CfSb0yoSZwpmJ4CJ0iN0/exGv20fQ1veJve1Gou69V8zOuZB5XbDub7Dl3/4sAMdojJBzPUx8GMKSuq5+QrcjgosQcHu/WE7ED8EoFUp8sp24u0ejzxRrCnoEVwsUrYf8byB/lX/LZQCVFkbeARN/1avzmgnHieAiBNShTetZ/uI/iNYlMzFpPlrZgEKjJP63F6EK7j0rw3sFWYaS72Ht36Bss/+YWg+j7oLxD0CoyOXVm4kBfSGgGspL/d9dFfR5fDrqmCBkj4SryNzFNRMCTqGA9Elwxwq49Uv/rpheJ/zwb3hhKHz5ADTkd3UthS4igosQUNGpaQDEpPVFpVOjz/J3hznzGpGc3i6smdBhFAp/Opm7voWbFvnzl0ke2P0+vHwRfHozVO7s6loKnUx0iwkBU7JnJ1//51/YmpsIjYnj7pffxHGggcYPDraWUUcHoU0OJnhyCtoEsb95j1W2FTa94F8vc0z6JJjwS39Osx6ez0wQwUUIAI/bxYYP3mbPN18BEJGYzGU//zVx6RnIXonmLwpwFZrwmVyt1ygNamLuy0ETI9a99Gh1B2HTi7B/oX8zM4D4of4gk30FKMW09J5KBBfhvHjdbr74+xOUHdgLwPDZlzPxxtvQ6E5c++CzuvFUWjGvKsVTYUUVqSf2ZzlioL83MJX7pzHvehc8R/dUiR3kDzL9Z4MupGvrJwScCC7COfN5PSx99imKdm1How/i8l/+jvRhI898XYubulf34mtyok0JIfruISi14hNsr2Bvgm2vw9b/gKPZf0ylg8wZ/pZM/9mgD+vaOgoBIYKLcE4kycfyf/2DIz9sRK3RctXvnyAle0i7r/fU26l/dS+S3Ys+O4qomwagUIn5Jb2Gtd4fYPKWQGPB8eMqLWRM9weaAXNEoLmAieAinJOtXyxk4yfvoVSpufI3j7WrxfK/XCVm6v+7H7wy6jgD4fMy0GeEB76yQvcly/6NzfK+9AeahiPHzyk1kHwRpE+EtAn+qc4i1cwFQwQXod1kWcbjaaJg3xrWr/wAd4mTaTc9zJBpM8/5no6DjTR/fgTJ5h/sDcqJIXxOOqowXaCqLVwoZNk/ASBviX8HzR8HGvB3nyVfBBlTYNBVEJXRFbUU2kkEF6FdjuT/lcrKj5AkZ+uxsv3DuO2BhSjPMxGlZPdg/rYU29ZqkEGhVRI6PZXgi5NQqEVXWa8ky9BU5M8AUPy9fzdNa03bMvFDYfDVMGg+RPTpmnoKpySCi3BGFst+tu+4knIpCZ3XTZhsxe0yEBs6n/HTHg3Yc9yVVkxfFuAu82dSVscE+bvKskResl5PlqGx0L/fzKHl/t00Zd/x80mjYPBV/kAj0s50CyK4CKdlsxWQd/A3rK+UeXnLT1Bo4Kfxpfz0xhuIiAj8H31ZkrHvrsO8shjJ6gFA3z+CyAX9URp61pa7wnmwNcLBL+HAYn+rhmN/xhSQOs4faLKvgGCx62lXEcFFOCm7vZTikhepqVlKLTE86f4zznX+LrEV9+eQnZLcoc+XHF4s35Vi3VTlP6BWoksNQZMUjDYpGF3fcFShYn2MALTU+CcEHFgM5T+ceH7iw/71NGItTacSwUVoJUkeGhrWUFn1MU1N/k+D9cTwlOof1HmD0a/y/6H/+qGJDIgP7ZQ6OQtNNH1yGKnF3faEAnSZ4RiGxRI0OAqlTt0p9RG6OXMF5C6BA4ugatfx4xqDv8ts+M3+lo1IP9PhRHDp5STJjcWyj4aGNVTXLMLtbmg95wm/nMcct1PuhlQ31K2tBODtOy5iav/O626QfTLeejvuihbclVbcZS14Kq2t5xUaJfrsKAzDY9FnhSMrJJRKEWx6vZoDsOwXYK0Dc9nx45EZMHSBf3MzbbC/RZM4HAyRXVfXHkgEl15IlmUqqz6mrm4FZvPuNjPAtNpoEhKu4XDQfH5V6MDs9dFHr+VGt5bnVx4GYONvp5Ic0bU5wbyNDux76rHvrsPb4ADArq8lN/s/aJQRxCQ8TFKfZOLi4tBoxFhNrybLUL4Ndr8HB74Aj+3EMuogGHK1f8OzpJGiZRMAIrj0Qk5nFZs2T2x9rdFEEhE+hri4y4mImspzpY08X1oLwPAQA28OTiMYBde+uoXDtS30iwvmo7vHEh3c9WtRZFnGU2HFvruOw0UrsA59AVmGPbvnYLVGoVQqiYuLIyUlhUmTJhEstlru3VxW/zqawjXgtIDbCpYqMJUeLxORBuF9IGYAxPSH2IH+n0XL5qyI4NIL2WwF/LB1FgA5Q98gKmoqCoUCk8fLfXmlrG3yTwW+PSmaJzIT0Sn9a02qTA6u/Pcm6lpcXBwq8/e5g0kemtZVb+MEDpudnVvvw8VG3O5k9u+bg93uaD2fnp7OrbfeikJ8KhV+TJahfCvsfMe/eNPrPHk5Y8zRgDPAv5hzwGWgEx9WTkUEl16mpmYphw4/hs9nRaOJ5OLx36NS6Tlkc3DX/hIKHS6ClAr+0T+Fa+JP/KR2pLaFT59dyq2KRKq8DYx4+ip0mu6TdNLlqmXLD5fg89kYOOAZDIbplJeXs2TJEnw+H9dddx3Z2dldXU2hu3I0+zc2a6mF+kPHv0xlJ5bVGPwBZugC/x41KjHO92MiuPQCHo8Fs3kHNbXLqK1dCkBY2CgGD3oevT6RbxvM/DS3BIckk6TT8O6QdAaHnHpMZeemPGKXNvhbO1NkBs+e1FlvpV1KS1+joPAZNJooxo39Do0mlDVr1rBhwwbCw8O5//77xTiMcHbcNqg/7P+qy4VDK6Cp8Ph5YwwMvgaGXuefHCBaxyK49FSy7MPhqKCk9BWqqxcD0tEzCtLTHiAt7QGUSjUHrQ7m7srH5pMYFmLg/aHpxGjP/Ic398mvCbMbyXfu5uKn70arD+rQ93M2JMnN1m2XYbcXER09naFD/oPH4+WFF17Abrdz+eWXM3Lk2SfaFIRWsgyVu2Dfp/5pz/bjsyyJHQRXvAQJw0HZe9MXieDSQ+3bfx/19d+2vjYY0gkPu4iEhKsJDx8FQI3Lw+W78il3urk4PJhPcjLQKNv3icuyowrL54VY3I1s5Rsm3ng7GSNHd5vxDItlPzt3XYckuemb/hDp6Q/y5ptvUl5eDsCcOXMYNWoUyl78P78QID6Pf4LAsUBzjCEa+k72d5llTIWwjl143N2I4NJD5eX9muqaxYSHjyYz4zdoDP1pdDTS6GwkN38du6sOsDD6PtyoSAvSsmJkPyI17e8z9ppd1PxtG7Iss6z8FRw+K0kDspl00x0k9hvYge+s/aqqP+fgwd8CCnKGvsGhQxrWrFmDJPlbcRMnTmT69OldW0mhZylcAz/8x5+S5n+nPEdl+YNM36n+LQT0nbMQuauI4NJDOLwOCpoLqLBWUNFSQZmlmAprFTW2WhqdjTi8/llTfRoSqQupxKFTkGW4HUf65bycnUqG4ez3yaj7z17cJRaa4htZu+M9vB7/Kvqs0eOZcMOtRCZ2/Se1Q4f/j8rKD1GrQ7lo1BIgho0bN7J582YUCgX33HMPCQkJXV1NoafxuqFyBxSuhaK1/kkCsnT8vELlDzSpYyFxRI9cxCmCywVIkiWqbdXsrdvL3vq97Knfw5GmI3hl70nLq3wyYw/JjC69GEfE9XhZyq7RW7j1kjeZnjL4nOtxLLho+4QSdF0Smz/7kNx1q5FlCYVSydDps5l8y51odF23wZMkudm560Yslt0EG/szatQiVKogFi5cSF5eHvHx8dx9992oznPbAEE4LYfJv33AsWDTVHRimfA+/iCTdDTYJORc0DtxiuBygZBlmb31e/mq6Cu+KfkGk8t0QpkofRR9QvuQHJJMckgyKSEpJHpDCLriPhoistk35F5QKBlgWsL0D58776mTpq+KsG6sRJMSQtz9wwBoKC/l+4/eoWjXdgAGXDyZOQ8+0qVjMU5XDdu2XY7H08TAgX8nMeEarFYrL7/8Mk6nk6lTpzJ58uQuq5/QC9UfgfxvoXqPf2LAj2ee/VhUpj/QxA+F+CH+L2N0p1b1XIng0s2VWkpZXrScr4q+orylvPW4WqFmQOQAcmJzyInJYVjMMOKN8fgaGqh/5RVceQdxl5TgM5uxGhPZOfxhfGo9/bJkZvxyKooADGTbdtfR/OlhtH1Cib0vp825ol3bWfKPPyNLEjmXzGHSzXd06Yyyg4d+T1XVp6Sn/Zy+fX8BwJ49e1iyZAkAgwYNYvbs2YSEiMy5QhdwmKB6L1Tt9ifcrNp98rU1ACGJED/4eLCJHwoR6d1uZpoILt2MT/Kxv2E/68rXsb5iPQWmgtZzQeogLulzCZf1vYxRcaPQqtqmnLd+v5Gq3/0OX2Pj8fspNWwd+zhObThJ/SO4/Oc5qFSB+SX01NqofX4XCq2ShD+ORalt27W0a+VS1r7zOgChMbHM+Mn9pA/rminAuXkPU1OzhMTEBQwc8BTgbw2uWbOGjRs3Issyer2eWbNmMWzYsG4z603oxWyNUL3bH2hqDkDN/lO3cDRGiBvUNuDEDgRt1+UAFMGlG/BJPjZWbmRV6Sq+r/yeJmdT6zmVQsXYxLFc3vdypqZMxaA58ZfFefgIDa+8Qss33wCg69+f6Ht/irZvX0obDHz3fj7GcB3XPzYavTFwiwdln0zV45uRPRIoFegHRvo39fpRkCnevYPv3nwFS30dAH1Hjmb8NTcS1zczYPVoj/0Hfk5d3XJiY+cwZPBLbc5VV1ezdOlSqqurARg2bBiXXXaZWGgpdD+uFqjNg5p9/mBTewBqc0+eskah9M9Q+3ErJ20iqDsnJ6AILl2owdHAF/lf8NmRz6i2VbceD9GEMCFpApNTJjMhaQJhulMP6jW8+ir1/3qx9XXEjTcQ+5vfoNT7B9GXvbSXstxGRs1JY8y8vgF/Dy0bKmj5vrJ1v5WgnBgir+/f5pO/2+lg0yfvs+vrZf7FZ0Dq4KGMmnsVacNGdkor4di05ODgbMaMXnbCeZ/Px+bNm1mzZg2yLJOUlMSCBQsIDe3Z00WFHsDn9bdoavYfDToH/N9t9SeWjcqEW5f6txvoYCK4dDJZltlZu5OFhxeyqmwVXsk/wytMF8bcvnOZljKN4XHD0SjP/KnZ8vU3VD70EAAhM2cS/cD96Pv1az1fltfIspf2ggw3PTGW8LiOaSLLsoyrwETD27kgyYTOSiN0asoJ5epKitixbDGHNm9APrrWJCo5lVFz5zNgwhTUHdhScLub+H7jGEBixPCPiYgYfdJyhYWFfP755zgcDoxGI/PmzaNfv36im0y48LTU/ijg7IeidRCdBXesBGXHz44UwaUT+CQfe+r3sLpsNWvK1lBprWw9NzRmKNf3v56ZaTPRqdrfXLV+/z0VDzyI7HIReccdxP32N23OtzQ5WfjX7ThtHrInJjL1pgEBez+nrNMP1ZiWFIACom7JJig76qTlLA317Fq5lP2rv8bt8K+/MYZHMGzWXHIuuZSgkI5pLew/8CB1dStQqQzkDP0vERFjTlquqamJTz75hLo6f1deUlISU6dOJSMjQwQZ4cJlbwKPvdMyBYjg0kHsHjvba7azpnwN68rXtRlHCVIHcVnfy1jQfwEDIs/uj76nuprap//eOr4SPHkyyf9+GYX6+LRij9vHl8/vprbYQnRKMFf/ZiTqTspc3LykANsP1Si0KmLuy0GbYDxlWZfdxv7V37Bz5VKsjf7cTGqtjkGTpzFizhUBX4Tp8znYt+9empo3olTqGTL4ZaKjp560rNvtZvXq1ezcuROv19+67NevHwsWLBBrYgShHURwCZBqazV76vewu243e+r2cKT5CD7Z13o+VBvK5OTJTE+dzvik8QSpz25aruzz0fT229T/+xVkhwOUSsLmzSP+icdR6o63eGRZZtVbeeRvr0UbpOa6319EWEznTQGWfRINbx7AVWRGFaYj9oFhqEK0p73G5/Vy5IeN7PjqC+qKj8+G6TviIkZeNp+UQUMC1mLw+Vzs338vjU0bAEhNvZuMvr9CqTx5HVtaWti0aRPbt2/H5/MxYcIEZsyYEZC6CEJPJoLLOfBIHo40HfEHkvo97KnbQ6299oRyCcYEf0DpM52RcSPbNY5y0ufV1lH18MPYd+wAIGjECOL/7zH0A05s9RxYX8H6j4+gUCqY9/Mckgd0fkoJye6h7pW9eBscaJKDibln6AnTlE9GlmUqDh5g5/IlFO7c1jr4H5uWwbhrbwxYYkxJcpGf/zcqKt8HICRkCIMHvYDBkHbKa3Jzc/nss88A/5qYyZMnExsbe951EYSeSgSXU3B6nVS0VFDeUk6F1f+9vKWcipYKKq2VeCRPm/IqhYoBkQMYFjuMYTHDGBbrX9R4vqzr11P16O/xNTWhNBiI+8PvCbvqqpP+ka0ttrD42Z1IXpnxV2UyfGbqeT//XHkbHNS9sgfJ7iVoUBSRNw1E0c6MywBNVZXsWvEluetX43W7AEgeOJhJN99BQmb/gNSxrv4bDh58FK/XjEplZED/vxAfP++U5b/77js2btzY+nrw4MFMmjRJBBlBOIleEVxkWabYXMzOup3sq99HlbWKFncLOpUOnUqHVqVFp9KhVqqps9dR0VJBnaPutPcM1Ya2CSSDogaddA3K+dY7f+w4fGYzABlfr0SblnbSshWHmljxn/14nD7Sc6K59N7AdSWdK1exmfr/7gefTNCwGMIv63vGLrL/5WixsH3ZYnat+BKfxx/Q+4+byITrbyU8/vwTTjqdVeTmPYzJtA2AUSM/Jyxs+CnL19TUsH79eg4ePNh6bPDgwUyZMoXo6AsjLYcgdIYeFVy2VW9jScESWtwttHha8EpeNEoNBaaCk+biOpMQTUhrjq5j34/9nGBMQKno2HQLPquNwtmz8TU0oNDpyFq/DlV4+AnlSvY3sPK1/UhemcSscC772VC0Qd1jy9VjKWIAdFnhxNw15JzuY2moY/PCD8ndsAZkGaVKzbCZcxh79fXnPbtMln0cyH2IuroVxMTMYuiQV854TU1NDevWrePQoUMAKJVKJkyYwMSJE8XiS0GghwWXL/K/4P82/99Jz+lVeobEDGF47HDSQtMI04Xh8Xlw+Vy4fC7cPjduyU10ULQ/gAQnE6YL6/RP/7Is48zLo/mDDzF/9RUc/bSuioqi71fLUEdEtCnfUNHCkud247J7yRgew4w7szttZlh7WVaXYVlViibeSNxDI87rXnUlRWz48G1K9+0GQGcwMvrKaxl+6eVotOe+8thqPcLWbZcCCkaP/oqQ4PbN4quurmbNmjXk5+cDEBkZyeWXX056evo510UQeoIeFVwKmgvYULmBUG0oIdoQ1Eo1Lq+LpJAksiOz0ai69ydK8/LlNLz4Eu7S0tZj6vh4QmZeQvS996KObDs47/NJfPjYD7Q0OYnvG8oVvxze7QILgKvITP3r+1DHBBH/8KiA3LNk7y42fPg29aXFAKh1OpL6Z5MyaCipg4YS1zcT5VlOGd6z5w4amzagUGjIynyU5ORbULSjdSrLMnl5eaxcuRKr1Qr4U8jMnDkTg6HrcjsJQlfqUcHlQle8YAHOvfsACJ1zKZG33UZQTs4py+9fV8GGT44QFKLhpifGojN0z+DpLm+h7t97QAnGkfGETElGHXX+06NlSeLgxnVsWvhBa+6yY7RBQSQNGETKoKFkjBzdrjUzNlsR+w/cj812BICUlDvpl/WHdtfH6XTy3XffsePorD6DwcDMmTPJycnp8vEvQehsIrh0I+X3P4B19WpiHnqI6Ht/espyPo9EZX4zK/+zH69bYtL1/Rgypet3fTwV2SPR+EEezsPNACj0auIfHnnWg/unvL8s01heSlnufspz91KRdwCnzdp6XqFUMmrufMZde+MZu85kWaK8/B3yC/6KUhnExAk/oFYHn1V9ysvLWbZsWesK/+zsbK688kq02sC8X0G4EIjg0o3U/u1pmt59l8jbbyfud79tc66p2kbR7joqDpuoKTLj8/hzc4XFBHHD42MClka/I7lKzDS+l4dk9xIyPZWwS/p0yHMkyUd9aQnlufso3r2DsgN7AQiPT2DmPQ+SMmjoaa+XZZkfts7Ebi9iwICnSEpccNZ1OJYIc+3atUiSRFxcHAsWLCAysmdtZSsIpyKCSzfS9MGH1P7lL4RcMoPkl46nhXfaPHz4fz/gtB1fWxMUoiGpfwSj56YTEX/qFCvdjX1vPU0fH0Jp1JDwu9EoNB0fFAt2bGX1f/+NtdmfgmfI9FlMufUnp928rLT0NQoKnyEsdDijRn1+zs8uLS1l4cKF2Gw2VCoVw4YNY+zYscTExJzzPQXhQtD9P+72ItoUf9eWu6wcyenE29yMp7KSLe/vbg0so0fA3ClO5kRsZHx67QUVWACCBkehCtMi2Tw0f1mA7JU6/JmZo8Zw+3OvMnTGbAD2r/6GlS8/y+k+V8XHX4VCocJs2Y3Vevicn92nTx/uuece+vTpg8/nY+fOnfz73//mww8/pKio6LR1EIQLmWi5dCOuwkKKLpvb5piMgo0XP41HE8yg3DeJq9/V5nzMQw8R9dN7LqgBY+uWKkxf+nOIqWMNRFydha5P5+ybUrJ3F0ueeRKf18vkW+5i1Nz5pyy7b//PqK//hqioKQzLefO8nivLMmVlZWzevJnDh48Hq4svvphLLrnkvO4tCN2RaLl0I9qUFDSpbVO21CaNw6MJRu1zkBTagn7IEHTZA1vP17/wAlWP/BrJZuvs6p4z49gEIm8YgNKowVtnp/4/e2lenI/XdJLd9AIsLWcEU267B4ANH77NpoUfYreYT1o2o+8jKBQaGhvX0dC47ryeq1Ao6NOnDzfccAMPPvggA47mhdu3b9953VcQuivRculmZI8Hb3MzSoMRl1fJZ8/swtrkYuyVfRk5O+14OVmm+b33qP3HP8HrRZuZQfKLL6LrG/jdJjuKz+bBvKIY+86jST+VEDQwCuO4BHQZ4R3WGpNlmW9fe4kDa78FjqX5n340zX/bHfry85+irPxNoiInMWzY2wGrw/bt21m+fDnR0dE88MADAbuvIHQXIrh0Q7Isc3hrDZs+K8Bp82AM13Hzk2NRnySzsH3nTiof+iXe+nqUBgMJTz1F6OxZXVDrc+cqMmNZXYqr8HgLQpsaQtTN2ahCO2b6rizLHN68ge3LFrdJ8z9m/gImXH9L62u7vZQtP0wDFFw8fgN6fWJAnv/ZZ5+Rm5sLwJ/+9KcLqltTENpDBJduxlRrZ/3Hh6k45F8TEpVkZMYd2UQnh5zyGm99PZW/ehj79u0ARN5xB7G/+iWKCyzHlafWhnVLNfZdtchuCU1KCLH3DEHRgVkHZFmmIm8/X7/6Lyz1tUQkJHHnC6+1KbNz142YTFsJDx/D4EEvoNOdfxbkxYsXt+kSmzVrFmPGjEGpFD3VQs8ggks3YTO52L68mLxN1ciSjEqt5KK5aQy7JLVda1hkr5e655+n6c23AAgaNZKk555DcwGmg/fU26l/dS+S3YthWAwR1/ZD0cHreCz1dbzxwJ2o1Gp+/v4ilD/aY7ypaTN7992FJLlRqYLJzPg1iYkLUJ7l/jxer5e9e/diMpmwWCzs3bv3hDLR0dFce+21xMXFnfd7EoSuJIJLF3PaPOz+tpR9ayrwHl0Y2WdwFBOuzSI87uzzUlm+/ZbqR3+PZLOhjo0l+aUXT5tCprtyFphoeGs/SKAK1WIcl4BxdAIqY8e0xiTJx79uvgrJ5+Puf79FaHTboGy1Hubgwd9hafG3NvT6ZFJSbic56eZ2BRmHw8Enn3xC6Y/yxp1OcHAwt99+u0jjL1ywRHDpQvvXVbB1aREuu3+P9vi+YYyb35fErIgzXHl6ruJiKh58EHdBIQqNhvgnniD8qlNPue2u7HvqMH1VhGQ9unhUrSR0Wgqh0zpmE7R3H7mfhvJSRl95LRNvuO2E87Lso6zsvxQUPtN6LCZmNkOH/Pu0921ububDDz+koaEBrVZLTk4OOp0OtVpNZmYmSUlJVFZW8umnn9LS0tJ63YwZM5gwYULg3qAgdCIRXLqI2+HljV/693GPTDQy9soM0oZEBW6veKuNqt/8BuuaNQD0ef89DBddFJB7dybZK2HfW491UyWeKhuKIDVJfxrXIc/K37aZpc8+hUKp5Ka/Pkdc38zWc1ZbPpWVH1NT8wVer6X1eFbWH0lNueOU96yoqODjjz/GZrMRGhrKjTfeSHz8qXcolSSJ3Nxc7HY7w4cPF/nIhAtW99hRqhfS6FWExxkw1doZPjOV9KGB7f5QBRuRff4WkSIoCPUFmm5EoVZiHBmHNiWE2ud2Iru8tHxfQfC4RBTqwI7DZI0eT7+xEzjyw0a+ee1Fbvjz0zQ2fUdl5UeYzNtby+n1ySQl3kBC4jXotKf+dzt06BCff/45Xq+X+Ph4brzxRkJDT79YVKlUMmTIuW2oJgjdiQguXUShUJB1URzbvyomf3stA8ae/5a9/8tTUQlA/B9+f8rtkS8U6kg9mqRgPJVWzMuL8ZndhM8N/JqeaXf8lOrSrWjit7BhwxhQ2gFQKFRER00jKelGwsPH4/F4cdidmJprcTqduFyu1u8ulwuz2cz2o7P3MjMzufbaa9Hpzn0zM0G40Ijg0oX6HQ0u5QebcbS4CQpQCvpjgoYMwV1YiKeqKqD37QoKtZKYuwZT/fR2ZLcPpTHwv7oej4X8kl+RMe/4FGGtNp7wsLm4PaOpqHCxdethamrW4/P52nXPkSNHMmfOHFRnuXGZIFzoRHDpQuFxBmJSQ6gva6FwVx2DJwd2T5agnKGYlyzBceBAQO/bVSxry5Hd/j/qhpzAT7Gub9hAY9MGQIHTmobZOpKiIiNerwv4/oTyKpUKnU6HXq9v8/3Yz8nJyQwePFgskBR6JRFculjWqDjqy1o4sr024MFF178/AK4j+QG9b1fx1jtaf655ZjuGUXFEXJWFQnl+f7zLysr4+uuv8Ukb6dcPmpoSyD1wbJaWhE6nIzY2lqSkJJKSkkhMTCQ0NBTNBbZIVRA6kwguXSxzVCybFxdQXWCmpclJSKQ+YPfW9esHCgXemhpqn/kHkbfcjCYh8GM7nSV8XgbW2CBcBSY8VTbsu2oxjo5Hl3p+GZXXrFlDVVUV8Qn+CRAGQxjjxo0jNjaW5ORkoqKixMp5QThLYipyN7D4nzupLjBz8TWZDJsR2DUcVX/4A+ZFi/0vVCpCpk0l9uGHL/gBfseBBgCCBp/7LDun08nmzZvZtGkTPp+P2bPV2OxvExd3OYMHvRCgmgpC7yRaLt1A1qg4qgvM5O+oC3hwSXjySYInTqL5o4+wb9tGy6rvQK0m+fnnA/qcznY+QcXj8bBt2zY2btyIw+HvaktJSSE6xoatFFTKU+9QKQhC+4i2fjeQMSIWhQLqSixYGhxnvuAsKFQqQmfPos977xJ+7TUAqKN6Z0oRWZbZvXs3L774IqtWrcLhcBAdHc11113HnXfeCbgAUKkvrN09BaE7Ei2XbsAQqiWpfwQVh5r56PGt6I1qdEYNeqMGnUHt/27U+I8bjh43qomIMxIc0f61E7Lkz13mra/vqLfSbXk8HpYvX86ePXsACAsLY8qUKQwdOrR1mrDP699wTaU6+5xugiC0JYJLNzFkSjJVR0z4vBI2sxub2d2u6yITjaRkR5KaHUliZvhJ93w5JvyaazAvWkzL6tW4KyrRJiedsmxP0tLSwieffEJlpX9R6bRp0xg/fjxqddtff6/PCoBaJVougnC+xIB+N+Jx+XBY3bhsXpw2D06bB5fd/7PL5sFp9+K0enDZPTitHppr7fCjfz2VRklSVvjRYBNFRILhhDUWZXfehW3zZsKvvZaEPz/Zye+wayxatIj9+/e3vtZoNISFhREWFkZ4eHjrzw7nv7DZ1pOZ+Uf6pJ46X5ggCGcmgssFzGnzUH6wibK8JspzG09o7YTGBDFwXDz9xya0TnG2btxE+U9+gjIkhP7bt3VFtTvd4cOH2bBhAyaTCZvNdspymVlbSEgooLQ0B1PzBCIjI5k9e/ZpE00KgnByolvsAqY3asgaFUfWqDhkWaapyuYPNHmNVOWbsdQ72Lq0mK3LikkZEMGA8QmEF/v3E9H2Te/i2nee/v37Exsby/r167HZbDQ0NNDc3HxCOcnn/99Bo3FgsViwWCwUFhaK4CII50AElx5CoVAQlRRMVFIwwy9JxePyUbi7jkObq6k8YqL8YDPlB5vRyBHEZi0ge3wmkk9C2cE7PHYXK1eu5MiRI6c4K5OYdIjEpMMA+LzHV96bTCY8Ho9YjS8IZ0l0i/UC5noHh7ZUc3B9CT/uFVJrlEQlBxObGkJMnxBiUkOJTDD0yIBTVlbGW2+9dcJxhcJHZtZW4uMLAaipyaAgfwyyfHxihFarJTs7m8suu0wEGUFoJxFceglPXR1F86+mQYqicfQCahSJeJwnZvZVaZREJRrR6NUgy8iyf30IMsgyKFUKYlJCiM8IIyEzDGPYhZNGXpZlampq8Hg8KBQKPJ5KqmuewunMBZTExv6MqMgFKJVKLBYLu3btorKysnV3yEGDBnHttdd27ZsQhAuECC69RN2zz9L4xn/R9etH2sJPUWh1mOrs1Je1UFfWQn1pC/XlLScNOKcTGq33B5q+YcSmhaI3atDoVWh0KlRqZbfKCOx01dDc/APNzVtobt6C0+mfmqxWhzJ40ItERU084RpJkti3bx9LliwBYMiQIcydO1fszSIIZyDGXHoJn9W/hsMwejRKvX/mWES8kYh4I/1G+wesZUnGXO+gocKKJEkoFIrW4KBQ+sd13E4vtUUWqovMNFZasTQ4sTQ4ObK19oRnavUqxl6ZwZApgc323B6S5MXtacBs3nU0mPyA3V7UpoxCoSY8/CIG9P8zBsPJJzgolUqGDRuG1Wpl9erV7N+/H4fDwY033iiSWQrCaYiWSy/R/PHH1DzhX9cS//jjRFy/4Lzv6XZ4qSk2U11opqbQH2w8Th9ej9Sm3Kg5aYy+PD3grRiv10ZLSy6Wlr20WA7gdFXj8TThdjfj9ZpOcoWSkJBBREaMIyJiHGFhI1GfRaqX0tJS3n//fbxeLxMmTGDGjBkBey+C0NOI4NJLSC4XhZfMxFtXh3HSRFJff73jniXJeF0+9q4pZ9uyYgAGTUxk0g39UZ7j3iuS5MFqO4zFsu/o115stgJAOs1VCozGLCIjxhMRMY7w8NFoNOeXnn/fvn0sXuzPMn3XXXeRkpJyXvcThJ5KdIv1ErVP/Q1vXR1Kg4HYhx/p0GcplQq0QWouuiydoBAt6z8+TO73VTitHmbcmY1ac/otf2VZwm4vwdLiDyIWy36s1jwkyXVCWZ0untDQoYSGDCXIkIZWE4lGE4FWG4laHY5SGdhf8WPbGysUCjFzTBBOQwSXXkCy2zF9+ikACX/7G/r+/Trt2YMnJREUrOHbtw5QXriHrz9cReYYNyr10RaHQgEoUKBAkr1YrQexWPbjO5rn68fU6lBCQ4b6g8nRL50urtPeC9C6Vmb69OlicaUgnIYILr2A0mBAk5qKp6wMhbZzP217vS3o4r5jyIIPcHn8ixQrq898nVKpIyRkUJtgEhSU1uWzz6677joOHTpEv36dF6AF4UIkgksvYRw7FlNZGfYffiBk6tQOfZYkeWlp2U9l1afU1n6FJPn3qFEotLhMGdjq45C8OkKjdCQPjEAXpEZGRoECg6EvoaE5GI1ZAe/SCgSFQsHAgQO7uhqC0O11v/97hQ5hGD0a08KF2HfvCeh9mz1eVhQeZuGWQoaGFZGh3Uq0dh9BaufxZxsySUq6noT4K1Eqwtj9bRk7VpRQ75Uo3agkJTuSyEQjUUnBxKREodWLX0tBuNCJ2WK9hH3nTkpvuhmFwUDmd6tQR0ae031qXB6W1ZnY3WJnt8VGscONstaBdk9Tm3KR+mZSwmWy4pPITk6nX1wII/qEY9D6A4ep1s66Dw9RecTU5jqdQc34qzMZOD6hy7vABEE4dyK49BKS203xvCtwl5RgHD+elDdeR6E6/aytY+w+iRX1JhbXNrO+uQXf//zGKCxuVJV2Ul1W3FYlDbaT/0oNTgrlqwePr4KXZZnqAjMNFS00VtmoPNyMuc7fhZbUL5wpNw0gPE7sCikIFyIRXHoR55EjlCy4HtnhIOYXPyf6vvvadd0Vu/LZaj6e8XJ0mJGpkSEMDzWQE2IgQtO2G8tkd5NfZ6W8yU5RvY3FuyqoMjvJTghlxS9OTLFyjOST2Lumgm1Li/B6JFRqJSMv7UPOtBS0QaKrTBAuJCK49CKyLFN+113YNm8haNgw0j75+IzX1Ls9DNmUC8Cv0uK4Oi6CDIP+rJ/t9UlUm52kRJ65JWJpcLDuo8OU5/m72rRBagZNTGTo1BSCI0ROL0G4EIjg0os0vP4G9c89B2o1fd5+C8NFF52yrFeS+a7Rwl+Lqsi3uxgcHMR3F/XvtLrKskz+jlp2LC+hucYO+BdnZo2OY9iMVKKTgzutLoIgnD0RXHoJ6/r1lN97H8gy8Y//iYjrrz9puQqnmw+rGvm4uokat6f1+JOZidyTEttZ1W0lSzKluY3s/raMqnxT6/Gw2CBGzOxD32Ex6IPFSnlB6G5EcOkliq64EtfhwyjDwui/9YcTzte5PDxZWMWi2maO/UJEalQsiI/khoQo+hnPviss0GpLLOz5royCHXXHDyogJiWEvsNiyJmegkbXvkkKgiB0LBFceom6Z5+j8Y03QKmkzwcfYBgxvPXcVpOVu3NLqHN7Abg4PJhbEqO4NCYMXTdMK99Q0cKhzTWUH2qiqer4RANjuI5x8zPod1EcinNMkCkIQmCI4NJLyD4fFQ/+HOuaNagiIkh95x10/bJ4r6qRP+RX4JWhv1HPvwakMiz0wpn+azO7KMttZMeKEiwN/oWbcemhTLyuH3Hp55cBWRCEcyeCSy8i2e2U3nIrjtxcqoYO49NHn2SZyf/J/8rYcJ4dkIKxnWtfuhuvx8fe1eXsWFmK1+XPXNx/TDxjr8wQM8wEoQuI4NIL1Ls97DTbOWJ3srPRzNaqOkxG/2wrFfC7vgk8kBrbI1bE28wuflhSyKEtNQCoNUoGTkhk6NRkwmMvnBaZIFzoRHDpgWRZ5qDNybcNZlY1WthlsfO//8gar4cRBw9wT/5+Zj3xx3NOB9Nd1ZZY2Lgwn5ois/+AAvoMimLo1GRSBkaKMRlB6GAiuPQAkixTYHexy2Jjl8XO2qYWyp3uNmUGGvX0N+rJCTFwUZiRlJVf0fTEEyDLRNx0E/GP/bGLat9xZFmm4mAze9eWU7q/sfV4eJyBIVOSGTA2Xqz8F4QOIoLLBcYjyRQ6nBy0Osm1OtjXYmdPix2Lt+12v0FKBRMjQrgkOpQZUaEk6LQn3Mt56BBNb79N/BNPoNR3/VTjjmSqtbN/fQUHN1fjcfrHZJRKBVHJwUSnBBMeZyA81v8VGqM/426ZgiCcnggu3YxPlmn2+Gj0eGnyeGl0e6l0uTlgdXDQ6uSIzYn7JP9kQUoFOSEGhocaGBMWzKTIEAyq7jeNuKu5nV4ObanhwPqK1pX/J1BASISemNQQUgdFEt83jMgEo+hKE4SzIIJLF/u0uoll9SZKHS4aPV6aPb4Txkf+l1GlJNsYxMBgPUNDDOSEBDHQGIRa/PE7K5YGB3WlLTRWWjHV2THXOTDV2VtbNj+m1auISw8lrm8Yyf0iSOwX3iMmQAhCRxHBpYv99nA571Y1nnA8Qq0iUqMmSqsmRqsm2xjEoGB/QEnRa1GKP2wdQpZlHC0eTLV2yg82UV1oorakpXV68zGxfUK49N4hBEf07O5EQThXIrh0sa0mK3tb7GQHBxGlUROtVROhVotWSDci+SQaq2zUFpmpLjJzZGstAOPmZzBiVp8urp0gdE8iuAjCWSo90MjhrTWMnN2HqCSRnVkQTkYEF0EQBCHgxHQiQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeBEcBEEQRACTgQXQRAEIeD+Hxblyup+D/dFAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -510,12 +704,12 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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\n", "text/plain": [ "
" ] @@ -537,7 +731,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -547,12 +741,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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\n", "text/plain": [ "
" ] @@ -579,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -591,7 +785,7 @@ }, { "data": { - "image/png": "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", + "image/png": "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\n", "text/plain": [ "
" ] @@ -622,12 +816,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": "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", + "image/png": "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\n", "text/plain": [ "
" ] @@ -670,7 +864,7 @@ ], "metadata": { "kernelspec": { - "display_name": "jaxley", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, diff --git a/docs/tutorials/10_advanced_parameter_sharing.ipynb b/docs/tutorials/10_advanced_parameter_sharing.ipynb index 45de7f20..db9d826e 100644 --- a/docs/tutorials/10_advanced_parameter_sharing.ipynb +++ b/docs/tutorials/10_advanced_parameter_sharing.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "f1bce5d2", + "id": "5f0bc78a", "metadata": {}, "source": [ "# Synaptic parameter sharing" @@ -10,7 +10,7 @@ }, { "cell_type": "markdown", - "id": "cdd8e5d8", + "id": "7ca7f94a", "metadata": {}, "source": [ "In this tutorial, you will learn how to:\n", @@ -37,7 +37,7 @@ }, { "cell_type": "markdown", - "id": "0bccac0f", + "id": "422006f3", "metadata": {}, "source": [ "In a [previous tutorial](https://jaxley.readthedocs.io/en/latest/tutorials/07_gradient_descent.html) about training networks, we briefly touched on parameter sharing. In this tutorial, we will show you how you can flexibly share parameters within a network." @@ -45,8 +45,8 @@ }, { "cell_type": "code", - "execution_count": 3, - "id": "bc247996", + "execution_count": 1, + "id": "4feb39c3", "metadata": {}, "outputs": [], "source": [ @@ -58,7 +58,7 @@ }, { "cell_type": "markdown", - "id": "a82d9ba3", + "id": "7c18b422", "metadata": {}, "source": [ "### Preface: Building the network\n", @@ -68,8 +68,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "70ebcb76", + "execution_count": 2, + "id": "5b3dacee", "metadata": {}, "outputs": [], "source": [ @@ -77,7 +77,7 @@ "t_max = 10.0\n", "\n", "comp = jx.Compartment()\n", - "branch = jx.Branch(comp, nseg=2)\n", + "branch = jx.Branch(comp, ncomp=2)\n", "cell = jx.Cell(branch, parents=[-1, 0])\n", "net = jx.Network([cell for _ in range(6)])\n", "fully_connect(net.cell([0, 1, 2]), net.cell([3, 4, 5]), IonotropicSynapse())" @@ -85,7 +85,7 @@ }, { "cell_type": "markdown", - "id": "aa7453c1", + "id": "7c1e73e0", "metadata": {}, "source": [ "### Sharing parameters by modifying `controlled_by_param`" @@ -93,8 +93,8 @@ }, { "cell_type": "code", - "execution_count": 8, - "id": "74b0b0d2", + "execution_count": 3, + "id": "c94aa7f7", "metadata": {}, "outputs": [ { @@ -119,7 +119,7 @@ }, { "cell_type": "markdown", - "id": "4ccb8526", + "id": "75aded8e", "metadata": {}, "source": [ "Let's look at this line by line. First, we exactly follow the previous tutorial in selecting the synapses which we are interested in training (i.e., the ones whose presynaptic neuron has index 0, 1, 2):" @@ -127,8 +127,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "cf8d0b29", + "execution_count": 4, + "id": "3d73ce97", "metadata": {}, "outputs": [], "source": [ @@ -139,7 +139,7 @@ }, { "cell_type": "markdown", - "id": "5299c76a", + "id": "0d8a9f19", "metadata": {}, "source": [ "As second step, we enable parameter sharing. This is done by setting the `controlled_by_param`. Synapses that have the same value in `controlled_by_param` will be shared. Let's inspect `controlled_by_param` _before_ we modify it:" @@ -147,8 +147,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "id": "bd8a93e7", + "execution_count": 5, + "id": "5be614a3", "metadata": {}, "outputs": [ { @@ -239,7 +239,7 @@ "8 2 8" ] }, - "execution_count": 10, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -250,7 +250,7 @@ }, { "cell_type": "markdown", - "id": "148bd79f", + "id": "f5e8b81a", "metadata": {}, "source": [ "Every synapse has a different value. Because of this, no synaptic parameters will be shared. To enable parameter sharing we override the `controlled_by_param` column with the presynaptic cell index:" @@ -258,8 +258,8 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "85c5c6e1", + "execution_count": 6, + "id": "f22af5fe", "metadata": {}, "outputs": [], "source": [ @@ -269,8 +269,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "id": "e1dcbfca", + "execution_count": 7, + "id": "7f88d535", "metadata": {}, "outputs": [ { @@ -361,7 +361,7 @@ "8 2 2" ] }, - "execution_count": 12, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -372,7 +372,7 @@ }, { "cell_type": "markdown", - "id": "e976fdca", + "id": "cef2bed9", "metadata": {}, "source": [ "Now, all we have to do is to make these synaptic parameters trainable with the `make_trainable()` method:" @@ -380,8 +380,8 @@ }, { "cell_type": "code", - "execution_count": 13, - "id": "42125f14", + "execution_count": 8, + "id": "f3d3ce72", "metadata": {}, "outputs": [ { @@ -398,7 +398,7 @@ }, { "cell_type": "markdown", - "id": "54fca2da", + "id": "4da29681", "metadata": {}, "source": [ "It correctly says that we added three parameters (because we have three cells, and we share individual synaptic parameters). We now have 6 trainable parameters in total (because we already added 3 trainable parameters above)." @@ -406,7 +406,7 @@ }, { "cell_type": "markdown", - "id": "07d9665c", + "id": "1c902a3e", "metadata": {}, "source": [ "### A more involved example: sharing by pre- and post-synaptic cell type\n", @@ -416,8 +416,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "id": "46b5c5fa", + "execution_count": 9, + "id": "af856a23", "metadata": {}, "outputs": [], "source": [ @@ -426,8 +426,8 @@ }, { "cell_type": "code", - "execution_count": 15, - "id": "98852f57", + "execution_count": 10, + "id": "642245db", "metadata": {}, "outputs": [], "source": [ @@ -441,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "da2d0f37", + "id": "b11c9625", "metadata": {}, "source": [ "We want to make all synapses that start from excitatory or inhibitory neurons trainable. In addition, we want to use the same parameter for synapses if they have the same pre- **and** post-synaptic cell type." @@ -449,7 +449,7 @@ }, { "cell_type": "markdown", - "id": "aadfce3d", + "id": "7ebcfedd", "metadata": {}, "source": [ "To achieve this, we will first want a column in `net.nodes` which indicates the cell type. " @@ -457,8 +457,8 @@ }, { "cell_type": "code", - "execution_count": 16, - "id": "57fd2f6b", + "execution_count": 11, + "id": "3e587ba0", "metadata": {}, "outputs": [], "source": [ @@ -469,7 +469,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "50a0663f", + "id": "3d0d7d8f", "metadata": {}, "outputs": [ { @@ -513,7 +513,7 @@ }, { "cell_type": "markdown", - "id": "f671d489", + "id": "c5675586", "metadata": {}, "source": [ "The `cell_type` is now part of the `net.nodes`. However, we would like to do parameter sharing of synapses based on the pre- and post-synaptic node values. To do so, we import the `cell_type` column into `net.edges`. To do this, we use the `.copy_node_property_to_edges()` which the name of the property you are copying from nodes: " @@ -521,8 +521,8 @@ }, { "cell_type": "code", - "execution_count": 18, - "id": "fcc33380", + "execution_count": 13, + "id": "a521b569", "metadata": {}, "outputs": [], "source": [ @@ -531,7 +531,7 @@ }, { "cell_type": "markdown", - "id": "ab9da3b4", + "id": "dbbf82e5", "metadata": {}, "source": [ "After this, you have columns in the **`.edges`** which indicate the pre- and post-synaptic cell type:" @@ -539,8 +539,8 @@ }, { "cell_type": "code", - "execution_count": 19, - "id": "9a674c31", + "execution_count": 14, + "id": "91bfd2ca", "metadata": {}, "outputs": [ { @@ -793,7 +793,7 @@ "35 unknown unknown" ] }, - "execution_count": 19, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -804,7 +804,7 @@ }, { "cell_type": "markdown", - "id": "4ed00d1e", + "id": "0f96f368", "metadata": {}, "source": [ "Next, we specify which parts of the network we actually want to change (in this case, all synapses which have excitatory or inhibitory presynaptic neurons):" @@ -812,8 +812,8 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "5e70b2a8", + "execution_count": 15, + "id": "d5beeeae", "metadata": {}, "outputs": [ { @@ -834,7 +834,7 @@ }, { "cell_type": "markdown", - "id": "35abe6cb", + "id": "920a141b", "metadata": {}, "source": [ "As the last step, we again have to specify parameter sharing by setting `controlled_by_param`. In this case, we want to share parameters that have the same pre- and post-synaptic neuron. We achieve this by **grouping** the synpases by their pre- and post-synaptic cell type (see [pd.DataFrame.groupby](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.groupby.html) for details):" @@ -842,8 +842,8 @@ }, { "cell_type": "code", - "execution_count": 21, - "id": "cb724510", + "execution_count": 16, + "id": "320e2938", "metadata": {}, "outputs": [ { @@ -862,7 +862,7 @@ }, { "cell_type": "markdown", - "id": "9407c986", + "id": "bffb1286", "metadata": {}, "source": [ "This created six trainable parameters, which makes sense as we have two types of pre-synaptic neurons (excitatory and inhibitory) and each has three options for the postsynaptic neuron (pre, post, unknown)." @@ -870,7 +870,7 @@ }, { "cell_type": "markdown", - "id": "22ce8839", + "id": "d9992480", "metadata": {}, "source": [ "### Summary\n", diff --git a/jaxley/connect.py b/jaxley/connect.py index 0b32186c..0d05893d 100644 --- a/jaxley/connect.py +++ b/jaxley/connect.py @@ -117,7 +117,7 @@ def sparse_connect( post_rows = post_cell_view.base.nodes.loc[global_post_indices] # Pre-synapse is at the zero-eth branch and zero-eth compartment. - global_pre_indices = pre_cell_view.base._cumsum_nseg_per_cell[pre_syn_neurons] + global_pre_indices = pre_cell_view.base._cumsum_ncomp_per_cell[pre_syn_neurons] pre_rows = pre_cell_view.base.nodes.loc[global_pre_indices] if len(pre_rows) > 0: diff --git a/jaxley/io/swc.py b/jaxley/io/swc.py index c1198451..ff4a61d3 100644 --- a/jaxley/io/swc.py +++ b/jaxley/io/swc.py @@ -17,6 +17,7 @@ _split_into_branches_and_sort, build_radiuses_from_xyzr, ) +from jaxley.utils.misc_utils import deprecated_kwargs def swc_to_jaxley( @@ -93,9 +94,11 @@ def swc_to_jaxley( return parents, pathlengths, radius_fns, types, all_coords_of_branches +@deprecated_kwargs("0.6.0", ["nseg"]) def read_swc( fname: str, - nseg: int, + ncomp: Optional[int] = None, + nseg: Optional[int] = None, max_branch_len: float = 300.0, min_radius: Optional[float] = None, assign_groups: bool = False, @@ -109,7 +112,8 @@ def read_swc( Args: fname: Path to the swc file. - nseg: The number of compartments per branch. + ncomp: The number of compartments per branch. + nseg: Deprecated. Use `ncomp` instead. max_branch_len: If a branch is longer than this value it is split into two branches. min_radius: If the radius of a reconstruction is below this value it is clipped. @@ -121,13 +125,21 @@ def read_swc( Returns: A `Cell` object. """ + # Deak with deprecation of `nseg`. + assert ncomp is not None or nseg is not None, "You must pass `ncomp`." + assert not ( + ncomp is not None and nseg is not None + ), "Cannot set `ncomp` and `nseg`. Only use `ncomp`." + if ncomp is None and nseg is not None: + ncomp = nseg + parents, pathlengths, radius_fns, types, coords_of_branches = swc_to_jaxley( fname, max_branch_len=max_branch_len, sort=True, num_lines=None ) nbranches = len(parents) comp = Compartment() - branch = Branch([comp for _ in range(nseg)]) + branch = Branch([comp for _ in range(ncomp)]) cell = Cell( [branch for _ in range(nbranches)], parents=parents, xyzr=coords_of_branches ) @@ -135,14 +147,14 @@ def read_swc( # of compartments with `.set_ncomp()`. cell._radius_generating_fns = radius_fns - lengths_each = np.repeat(pathlengths, nseg) / nseg + lengths_each = np.repeat(pathlengths, ncomp) / ncomp cell.set("length", lengths_each) radiuses_each = build_radiuses_from_xyzr( radius_fns, range(len(parents)), min_radius, - nseg, + ncomp, ) cell.set("radius", radiuses_each) diff --git a/jaxley/modules/base.py b/jaxley/modules/base.py index b40f3951..90d48f5d 100644 --- a/jaxley/modules/base.py +++ b/jaxley/modules/base.py @@ -113,7 +113,7 @@ def change_attr_in_view(self): """ def __init__(self): - self.nseg: int = None + self.ncomp: int = None self.total_nbranches: int = 0 self.nbranches_per_cell: List[int] = None @@ -335,7 +335,7 @@ def _compute_coords_of_comp_centers(self) -> np.ndarray: Note: For sake of performance, interpolation is not done for each branch individually, but only once along a concatenated (and padded) array of all branches. - This means for nsegs = [2,4] and normalized cum_branch_lens of [[0,1],[0,1]] we would + This means for ncomps = [2,4] and normalized cum_branch_lens of [[0,1],[0,1]] we would interpolate xyz at the locations comp_ends = [[0,0.5,1], [0,0.25,0.5,0.75,1]], where 0 is the start of the branch and 1 is the end point at the full branch_len. To avoid do this in one go we set comp_ends = [0,0.5,1,2,2.25,2.5,2.75,3], and @@ -344,10 +344,10 @@ def _compute_coords_of_comp_centers(self) -> np.ndarray: incrementing. """ nodes_by_branches = self.nodes.groupby("global_branch_index") - nsegs = nodes_by_branches["global_comp_index"].nunique().to_numpy() + ncomps = nodes_by_branches["global_comp_index"].nunique().to_numpy() comp_ends = [ - np.linspace(0, 1, nseg + 1) + 2 * i for i, nseg in enumerate(nsegs) + np.linspace(0, 1, ncomp + 1) + 2 * i for i, ncomp in enumerate(ncomps) ] comp_ends = np.hstack(comp_ends) @@ -365,9 +365,9 @@ def _compute_coords_of_comp_centers(self) -> np.ndarray: xyz = np.vstack(self.xyzr)[:, :3] xyz = v_interp(comp_ends, cum_branch_lens, xyz).T centers = (xyz[:-1] + xyz[1:]) / 2 # unaware of inter vs intra comp centers - cum_nsegs = np.cumsum(nsegs) + cum_ncomps = np.cumsum(ncomps) # this means centers between comps have to be removed here - between_comp_inds = (cum_nsegs + np.arange(len(cum_nsegs)))[:-1] + between_comp_inds = (cum_ncomps + np.arange(len(cum_ncomps)))[:-1] centers = np.delete(centers, between_comp_inds, axis=0) return centers @@ -558,15 +558,15 @@ def loc(self, at: Any) -> View: View of the module at the specified branch location.""" global_comp_idxs = [] for i in self._branches_in_view: - nseg = self.base.nseg_per_branch[i] - comp_locs = np.linspace(0, 1, nseg) + ncomp = self.base.ncomp_per_branch[i] + comp_locs = np.linspace(0, 1, ncomp) at = comp_locs if is_str_all(at) else self._reformat_index(at, dtype=float) - comp_edges = np.linspace(0, 1 + 1e-10, nseg + 1) - idx = np.digitize(at, comp_edges) - 1 + self.base.cumsum_nseg[i] + comp_edges = np.linspace(0, 1 + 1e-10, ncomp + 1) + idx = np.digitize(at, comp_edges) - 1 + self.base.cumsum_ncomp[i] global_comp_idxs.append(idx) global_comp_idxs = np.concatenate(global_comp_idxs) orig_scope = self._scope - # global scope needed to select correct comps, for i.e. branches w. nseg=[1,2] + # global scope needed to select correct comps, for i.e. branches w. ncomp=[1,2] # loc(0.9) will correspond to different local branches (0 vs 1). view = self.scope("global").comp(global_comp_idxs).scope(orig_scope) view._current_view = "loc" @@ -913,7 +913,7 @@ def set_ncomp( view = self.nodes.copy() all_nodes = self.base.nodes start_idx = self.nodes["global_comp_index"].to_numpy()[0] - nseg_per_branch = self.base.nseg_per_branch + ncomp_per_branch = self.base.ncomp_per_branch channel_names = [c._name for c in self.base.channels] channel_param_names = list( chain(*[c.channel_params for c in self.base.channels]) @@ -993,7 +993,7 @@ def set_ncomp( radius_fns=radius_generating_fns, branch_indices=branch_indices, min_radius=min_radius, - nseg=ncomp, + ncomp=ncomp, ) else: view["radius"] = within_branch_radiuses[0] * np.ones(ncomp) @@ -1014,15 +1014,15 @@ def set_ncomp( all_nodes["global_comp_index"] = np.arange(len(all_nodes)) # Update compartment structure arguments. - nseg_per_branch[branch_indices] = ncomp - nseg = int(np.max(nseg_per_branch)) - cumsum_nseg = cumsum_leading_zero(nseg_per_branch) - internal_node_inds = np.arange(cumsum_nseg[-1]) + ncomp_per_branch[branch_indices] = ncomp + ncomp = int(np.max(ncomp_per_branch)) + cumsum_ncomp = cumsum_leading_zero(ncomp_per_branch) + internal_node_inds = np.arange(cumsum_ncomp[-1]) self.base.nodes = all_nodes - self.base.nseg_per_branch = nseg_per_branch - self.base.nseg = nseg - self.base.cumsum_nseg = cumsum_nseg + self.base.ncomp_per_branch = ncomp_per_branch + self.base.ncomp = ncomp + self.base.cumsum_ncomp = cumsum_ncomp self.base._internal_node_inds = internal_node_inds # Update the morphology indexing (e.g., `.comp_edges`). @@ -1054,11 +1054,11 @@ def make_trainable( assert ( self.allow_make_trainable ), "network.cell('all').make_trainable() is not supported. Use a for-loop over cells." - nsegs_per_branch = ( + ncomps_per_branch = ( self.base.nodes["global_branch_index"].value_counts().to_numpy() ) assert np.all( - nsegs_per_branch == nsegs_per_branch[0] + ncomps_per_branch == ncomps_per_branch[0] ), "Parameter sharing is not allowed for modules containing branches with different numbers of compartments." data = self.nodes if key in self.nodes.columns else None @@ -1439,7 +1439,7 @@ def _init_morph_for_debugging(self): branchpoint_weights_parents[debug_states["par_inds"]], branchpoint_diags, branchpoint_solves, - debug_states["nseg"], + debug_states["ncomp"], nbranches, ) ) @@ -1449,7 +1449,7 @@ def _init_morph_for_debugging(self): ) solution = spsolve(sparse_matrix, solve) solution = solution[:start_ind_for_branchpoints] # Delete branchpoint voltages. - solves = jnp.reshape(solution, (debug_states["nseg"], nbranches)) + solves = jnp.reshape(solution, (debug_states["ncomp"], nbranches)) return solves ``` """ @@ -1459,7 +1459,7 @@ def _init_morph_for_debugging(self): self.base._child_belongs_to_branchpoint, self.base._par_inds, self.base._child_inds, - self.base.nseg, + self.base.ncomp, self.base.total_nbranches, ) @@ -1475,7 +1475,7 @@ def _init_morph_for_debugging(self): self.base.debug_states["indices"] = indices self.base.debug_states["indptr"] = indptr - self.base.debug_states["nseg"] = self.base.nseg + self.base.debug_states["ncomp"] = self.base.ncomp self.base.debug_states["child_inds"] = self.base._child_inds self.base.debug_states["par_inds"] = self.base._par_inds @@ -1859,7 +1859,7 @@ def step( "sinks": np.asarray(self._comp_edges["sink"].to_list()), "sources": np.asarray(self._comp_edges["source"].to_list()), "types": np.asarray(self._comp_edges["type"].to_list()), - "nseg_per_branch": self.nseg_per_branch, + "ncomp_per_branch": self.ncomp_per_branch, "par_inds": self._par_inds, "child_inds": self._child_inds, "nbranches": self.total_nbranches, @@ -2415,7 +2415,7 @@ def __init__( # attrs affected by view # indices need to be update first, since they are used in the following self._set_inds_in_view(pointer, nodes, edges) - self.nseg = pointer.nseg + self.ncomp = pointer.ncomp self.nodes = pointer.nodes.loc[self._nodes_in_view] ptr_edges = pointer.edges @@ -2424,14 +2424,14 @@ def __init__( ) self.xyzr = self._xyzr_in_view() - self.nseg = 1 if len(self.nodes) == 1 else pointer.nseg + self.ncomp = 1 if len(self.nodes) == 1 else pointer.ncomp self.total_nbranches = len(self._branches_in_view) self.nbranches_per_cell = self._nbranches_per_cell_in_view() self._cumsum_nbranches = jnp.cumsum(np.asarray(self.nbranches_per_cell)) self.comb_branches_in_each_level = pointer.comb_branches_in_each_level self.branch_edges = pointer.branch_edges.loc[self._branch_edges_in_view] - self.nseg_per_branch = self.base.nseg_per_branch[self._branches_in_view] - self.cumsum_nseg = cumsum_leading_zero(self.nseg_per_branch) + self.ncomp_per_branch = self.base.ncomp_per_branch[self._branches_in_view] + self.cumsum_ncomp = cumsum_leading_zero(self.ncomp_per_branch) self.synapse_names = np.unique(self.edges["type"]).tolist() self._set_synapses_in_view(pointer) @@ -2452,7 +2452,7 @@ def __init__( .item() ) - self.nseg_per_branch = pointer.base.nseg_per_branch[self._branches_in_view] + self.ncomp_per_branch = pointer.base.ncomp_per_branch[self._branches_in_view] self.comb_parents = self.base.comb_parents[self._branches_in_view] self._set_externals_in_view() self.groups = { @@ -2657,13 +2657,13 @@ def _xyzr_in_view(self) -> List[np.ndarray]: If a branch is not completely in view, the coordinates are interpolated.""" xyzr = [] - viewed_nseg_for_branch = self.nodes.groupby("global_branch_index").size() + viewed_ncomp_for_branch = self.nodes.groupby("global_branch_index").size() for i in self._branches_in_view: xyzr_i = self.base.xyzr[i] - nseg_i = self.base.nseg_per_branch[i] - global_comp_offset = self.base.cumsum_nseg[i] + ncomp_i = self.base.ncomp_per_branch[i] + global_comp_offset = self.base.cumsum_ncomp[i] global_comp_inds = self.nodes["global_comp_index"] - if viewed_nseg_for_branch.loc[i] != nseg_i: + if viewed_ncomp_for_branch.loc[i] != ncomp_i: local_inds = ( global_comp_inds.loc[ self.nodes["global_branch_index"] == i @@ -2672,7 +2672,7 @@ def _xyzr_in_view(self) -> List[np.ndarray]: ) local_ind_range = np.arange(min(local_inds), max(local_inds) + 1) inds = [i if i in local_inds else None for i in local_ind_range] - comp_ends = np.linspace(0, 1, nseg_i + 1) + comp_ends = np.linspace(0, 1, ncomp_i + 1) locs = np.hstack( [comp_ends[[i, i + 1]] if i is not None else [np.nan] for i in inds] ) diff --git a/jaxley/modules/branch.py b/jaxley/modules/branch.py index e51927f8..74ca31a4 100644 --- a/jaxley/modules/branch.py +++ b/jaxley/modules/branch.py @@ -2,6 +2,7 @@ # licensed under the Apache License Version 2.0, see from typing import Callable, Dict, List, Optional, Tuple, Union +from warnings import warn import jax.numpy as jnp import numpy as np @@ -10,7 +11,7 @@ from jaxley.modules.base import Module from jaxley.modules.compartment import Compartment from jaxley.utils.cell_utils import compute_children_and_parents -from jaxley.utils.misc_utils import cumsum_leading_zero +from jaxley.utils.misc_utils import cumsum_leading_zero, deprecated_kwargs from jaxley.utils.solver_utils import JaxleySolveIndexer, comp_edges_to_indices @@ -26,48 +27,57 @@ class Branch(Module): branch_params: Dict = {} branch_states: Dict = {} + @deprecated_kwargs("0.6.0", ["nseg"]) def __init__( self, compartments: Optional[Union[Compartment, List[Compartment]]] = None, + ncomp: Optional[int] = None, nseg: Optional[int] = None, ): """ Args: compartments: A single compartment or a list of compartments that make up the branch. - nseg: Number of segments to divide the branch into. If `compartments` is an - a single compartment, than the compartment is repeated `nseg` times to + ncomp: Number of segments to divide the branch into. If `compartments` is an + a single compartment, than the compartment is repeated `ncomp` times to create the branch. """ + # Warnings and errors that deal with the change from `nseg` to `ncomp` change + # in Jaxley v0.5.0. + if ncomp is not None and nseg is not None: + raise ValueError("You passed `ncomp` and `nseg`. Please pass only `ncomp`.") + if ncomp is None and nseg is not None: + ncomp = nseg + super().__init__() assert ( isinstance(compartments, (Compartment, List)) or compartments is None ), "Only Compartment or List[Compartment] is allowed." if isinstance(compartments, Compartment): assert ( - nseg is not None - ), "If `compartments` is not a list then you have to set `nseg`." + ncomp is not None + ), "If `compartments` is not a list then you have to set `ncomp`." compartments = Compartment() if compartments is None else compartments - nseg = 1 if nseg is None else nseg + ncomp = 1 if ncomp is None else ncomp if isinstance(compartments, Compartment): - compartment_list = [compartments] * nseg + compartment_list = [compartments] * ncomp else: compartment_list = compartments - self.nseg = len(compartment_list) - self.nseg_per_branch = np.asarray([self.nseg]) + self.ncomp = len(compartment_list) + self.ncomp_per_branch = np.asarray([self.ncomp]) self.total_nbranches = 1 self.nbranches_per_cell = [1] self._cumsum_nbranches = jnp.asarray([0, 1]) - self.cumsum_nseg = cumsum_leading_zero(self.nseg_per_branch) + self.cumsum_ncomp = cumsum_leading_zero(self.ncomp_per_branch) # Indexing. self.nodes = pd.concat([c.nodes for c in compartment_list], ignore_index=True) self._append_params_and_states(self.branch_params, self.branch_states) - self.nodes["global_comp_index"] = np.arange(self.nseg).tolist() - self.nodes["global_branch_index"] = [0] * self.nseg - self.nodes["global_cell_index"] = [0] * self.nseg + self.nodes["global_comp_index"] = np.arange(self.ncomp).tolist() + self.nodes["global_branch_index"] = [0] * self.ncomp + self.nodes["global_cell_index"] = [0] * self.ncomp self._update_local_indices() self._init_view() @@ -82,7 +92,7 @@ def __init__( self._par_inds, self._child_inds, self._child_belongs_to_branchpoint = ( compute_children_and_parents(self.branch_edges) ) - self._internal_node_inds = jnp.arange(self.nseg) + self._internal_node_inds = jnp.arange(self.ncomp) self._initialize() @@ -91,7 +101,7 @@ def __init__( def _init_morph_jaxley_spsolve(self): self._solve_indexer = JaxleySolveIndexer( - cumsum_nseg=self.cumsum_nseg, + cumsum_ncomp=self.cumsum_ncomp, branchpoint_group_inds=np.asarray([]).astype(int), remapped_node_indices=self._internal_node_inds, children_in_level=[], @@ -111,8 +121,8 @@ def _init_morph_jax_spsolve(self): """ self._comp_edges = pd.DataFrame().from_dict( { - "source": list(range(self.nseg - 1)) + list(range(1, self.nseg)), - "sink": list(range(1, self.nseg)) + list(range(self.nseg - 1)), + "source": list(range(self.ncomp - 1)) + list(range(1, self.ncomp)), + "sink": list(range(1, self.ncomp)) + list(range(self.ncomp - 1)), } ) self._comp_edges["type"] = 0 @@ -123,4 +133,4 @@ def _init_morph_jax_spsolve(self): self._indptr_jax_spsolve = indptr def __len__(self) -> int: - return self.nseg + return self.ncomp diff --git a/jaxley/modules/cell.py b/jaxley/modules/cell.py index 7db12cdd..3d6b39da 100644 --- a/jaxley/modules/cell.py +++ b/jaxley/modules/cell.py @@ -2,6 +2,7 @@ # licensed under the Apache License Version 2.0, see from typing import Callable, Dict, List, Optional, Tuple, Union +from warnings import warn import jax.numpy as jnp import numpy as np @@ -18,7 +19,7 @@ compute_morphology_indices_in_levels, compute_parents_in_level, ) -from jaxley.utils.misc_utils import cumsum_leading_zero +from jaxley.utils.misc_utils import cumsum_leading_zero, deprecated_kwargs from jaxley.utils.solver_utils import ( JaxleySolveIndexer, comp_edges_to_indices, @@ -95,18 +96,18 @@ def __init__( # Compartment structure. These arguments have to be rebuilt when `.set_ncomp()` # is run. - self.nseg_per_branch = np.asarray([branch.nseg for branch in branch_list]) - self.nseg = int(np.max(self.nseg_per_branch)) - self.cumsum_nseg = cumsum_leading_zero(self.nseg_per_branch) - self._internal_node_inds = np.arange(self.cumsum_nseg[-1]) + self.ncomp_per_branch = np.asarray([branch.ncomp for branch in branch_list]) + self.ncomp = int(np.max(self.ncomp_per_branch)) + self.cumsum_ncomp = cumsum_leading_zero(self.ncomp_per_branch) + self._internal_node_inds = np.arange(self.cumsum_ncomp[-1]) # Build nodes. Has to be changed when `.set_ncomp()` is run. self.nodes = pd.concat([c.nodes for c in branch_list], ignore_index=True) - self.nodes["global_comp_index"] = np.arange(self.cumsum_nseg[-1]) + self.nodes["global_comp_index"] = np.arange(self.cumsum_ncomp[-1]) self.nodes["global_branch_index"] = np.repeat( - np.arange(self.total_nbranches), self.nseg_per_branch + np.arange(self.total_nbranches), self.ncomp_per_branch ).tolist() - self.nodes["global_cell_index"] = np.repeat(0, self.cumsum_nseg[-1]).tolist() + self.nodes["global_cell_index"] = np.repeat(0, self.cumsum_ncomp[-1]).tolist() self._update_local_indices() self._init_view() @@ -148,7 +149,7 @@ def _init_morph_jaxley_spsolve(self): branchpoint_group_inds = build_branchpoint_group_inds( len(self._par_inds), self._child_belongs_to_branchpoint, - self.cumsum_nseg[-1], + self.cumsum_ncomp[-1], ) parents = self.comb_parents children_inds = children_and_parents["children"] @@ -159,29 +160,29 @@ def _init_morph_jaxley_spsolve(self): parents_in_level = compute_parents_in_level( levels, self._par_inds, parents_inds ) - levels_and_nseg = pd.DataFrame().from_dict( + levels_and_ncomp = pd.DataFrame().from_dict( { "levels": levels, - "nsegs": self.nseg_per_branch, + "ncomps": self.ncomp_per_branch, } ) - levels_and_nseg["max_nseg_in_level"] = levels_and_nseg.groupby("levels")[ - "nsegs" + levels_and_ncomp["max_ncomp_in_level"] = levels_and_ncomp.groupby("levels")[ + "ncomps" ].transform("max") - padded_cumsum_nseg = cumsum_leading_zero( - levels_and_nseg["max_nseg_in_level"].to_numpy() + padded_cumsum_ncomp = cumsum_leading_zero( + levels_and_ncomp["max_ncomp_in_level"].to_numpy() ) # Generate mapping to deal with the masking which allows using the custom - # sparse solver to deal with different nseg per branch. + # sparse solver to deal with different ncomp per branch. remapped_node_indices = remap_index_to_masked( self._internal_node_inds, self.nodes, - padded_cumsum_nseg, - self.nseg_per_branch, + padded_cumsum_ncomp, + self.ncomp_per_branch, ) self._solve_indexer = JaxleySolveIndexer( - cumsum_nseg=padded_cumsum_nseg, + cumsum_ncomp=padded_cumsum_ncomp, branchpoint_group_inds=branchpoint_group_inds, children_in_level=children_in_level, parents_in_level=parents_in_level, @@ -209,14 +210,14 @@ def _init_morph_jax_spsolve(self): pd.DataFrame() .from_dict( { - "source": list(range(cumsum_nseg, nseg - 1 + cumsum_nseg)) - + list(range(1 + cumsum_nseg, nseg + cumsum_nseg)), - "sink": list(range(1 + cumsum_nseg, nseg + cumsum_nseg)) - + list(range(cumsum_nseg, nseg - 1 + cumsum_nseg)), + "source": list(range(cumsum_ncomp, ncomp - 1 + cumsum_ncomp)) + + list(range(1 + cumsum_ncomp, ncomp + cumsum_ncomp)), + "sink": list(range(1 + cumsum_ncomp, ncomp + cumsum_ncomp)) + + list(range(cumsum_ncomp, ncomp - 1 + cumsum_ncomp)), } ) .astype(int) - for nseg, cumsum_nseg in zip(self.nseg_per_branch, self.cumsum_nseg) + for ncomp, cumsum_ncomp in zip(self.ncomp_per_branch, self.cumsum_ncomp) ] ) self._comp_edges["type"] = 0 @@ -224,15 +225,15 @@ def _init_morph_jax_spsolve(self): # Edges from branchpoints to compartments. branchpoint_to_parent_edges = pd.DataFrame().from_dict( { - "source": np.arange(len(self._par_inds)) + self.cumsum_nseg[-1], - "sink": self.cumsum_nseg[self._par_inds + 1] - 1, + "source": np.arange(len(self._par_inds)) + self.cumsum_ncomp[-1], + "sink": self.cumsum_ncomp[self._par_inds + 1] - 1, "type": 1, } ) branchpoint_to_child_edges = pd.DataFrame().from_dict( { - "source": self._child_belongs_to_branchpoint + self.cumsum_nseg[-1], - "sink": self.cumsum_nseg[self._child_inds], + "source": self._child_belongs_to_branchpoint + self.cumsum_ncomp[-1], + "sink": self.cumsum_ncomp[self._child_inds], "type": 2, } ) diff --git a/jaxley/modules/compartment.py b/jaxley/modules/compartment.py index 6a400ca4..d5f00beb 100644 --- a/jaxley/modules/compartment.py +++ b/jaxley/modules/compartment.py @@ -32,12 +32,12 @@ class Compartment(Module): def __init__(self): super().__init__() - self.nseg = 1 - self.nseg_per_branch = np.asarray([1]) + self.ncomp = 1 + self.ncomp_per_branch = np.asarray([1]) self.total_nbranches = 1 self.nbranches_per_cell = [1] self._cumsum_nbranches = np.asarray([0, 1]) - self.cumsum_nseg = cumsum_leading_zero(self.nseg_per_branch) + self.cumsum_ncomp = cumsum_leading_zero(self.ncomp_per_branch) # Setting up the `nodes` for indexing. self.nodes = pd.DataFrame( @@ -66,7 +66,7 @@ def __init__(self): def _init_morph_jaxley_spsolve(self): self._solve_indexer = JaxleySolveIndexer( - cumsum_nseg=self.cumsum_nseg, + cumsum_ncomp=self.cumsum_ncomp, branchpoint_group_inds=np.asarray([]).astype(int), children_in_level=[], parents_in_level=[], diff --git a/jaxley/modules/network.py b/jaxley/modules/network.py index 2966cfd7..62d74045 100644 --- a/jaxley/modules/network.py +++ b/jaxley/modules/network.py @@ -53,10 +53,12 @@ def __init__( self.xyzr += deepcopy(cell.xyzr) self._cells_list = cells - self.nseg_per_branch = np.concatenate([cell.nseg_per_branch for cell in cells]) - self.nseg = int(np.max(self.nseg_per_branch)) - self.cumsum_nseg = cumsum_leading_zero(self.nseg_per_branch) - self._internal_node_inds = np.arange(self.cumsum_nseg[-1]) + self.ncomp_per_branch = np.concatenate( + [cell.ncomp_per_branch for cell in cells] + ) + self.ncomp = int(np.max(self.ncomp_per_branch)) + self.cumsum_ncomp = cumsum_leading_zero(self.ncomp_per_branch) + self._internal_node_inds = np.arange(self.cumsum_ncomp[-1]) self._append_params_and_states(self.network_params, self.network_states) self.nbranches_per_cell = [cell.total_nbranches for cell in cells] @@ -64,13 +66,13 @@ def __init__( self._cumsum_nbranches = cumsum_leading_zero(self.nbranches_per_cell) self.nodes = pd.concat([c.nodes for c in cells], ignore_index=True) - self.nodes["global_comp_index"] = np.arange(self.cumsum_nseg[-1]) + self.nodes["global_comp_index"] = np.arange(self.cumsum_ncomp[-1]) self.nodes["global_branch_index"] = np.repeat( - np.arange(self.total_nbranches), self.nseg_per_branch + np.arange(self.total_nbranches), self.ncomp_per_branch ).tolist() self.nodes["global_cell_index"] = list( itertools.chain( - *[[i] * int(cell.cumsum_nseg[-1]) for i, cell in enumerate(cells)] + *[[i] * int(cell.cumsum_ncomp[-1]) for i, cell in enumerate(cells)] ) ) self._update_local_indices() @@ -115,7 +117,7 @@ def _init_morph_jaxley_spsolve(self): branchpoint_group_inds = build_branchpoint_group_inds( len(self._par_inds), self._child_belongs_to_branchpoint, - self.cumsum_nseg[-1], + self.cumsum_ncomp[-1], ) children_in_level = merge_cells( self._cumsum_nbranches, @@ -129,22 +131,22 @@ def _init_morph_jaxley_spsolve(self): [cell._solve_indexer.parents_in_level for cell in self._cells_list], exclude_first=False, ) - padded_cumsum_nseg = cumsum_leading_zero( + padded_cumsum_ncomp = cumsum_leading_zero( np.concatenate( - [np.diff(cell._solve_indexer.cumsum_nseg) for cell in self._cells_list] + [np.diff(cell._solve_indexer.cumsum_ncomp) for cell in self._cells_list] ) ) # Generate mapping to dealing with the masking which allows using the custom - # sparse solver to deal with different nseg per branch. + # sparse solver to deal with different ncomp per branch. remapped_node_indices = remap_index_to_masked( self._internal_node_inds, self.nodes, - padded_cumsum_nseg, - self.nseg_per_branch, + padded_cumsum_ncomp, + self.ncomp_per_branch, ) self._solve_indexer = JaxleySolveIndexer( - cumsum_nseg=padded_cumsum_nseg, + cumsum_ncomp=padded_cumsum_ncomp, branchpoint_group_inds=branchpoint_group_inds, children_in_level=children_in_level, parents_in_level=parents_in_level, @@ -158,7 +160,7 @@ def _init_morph_jax_spsolve(self): The reason that this function is a bit involved for a `Network` is that Jaxley considers branchpoint nodes to be at the very end of __all__ nodes (i.e. the branchpoints of the first cell are even after the compartments of the second - cell. The reason for this is that, otherwise, `cumsum_nseg` becomes tricky). + cell. The reason for this is that, otherwise, `cumsum_ncomp` becomes tricky). To achieve this, we first loop over all compartments and append them, and then loop over all branchpoints and append those. The code for building the indices @@ -171,13 +173,13 @@ def _init_morph_jax_spsolve(self): `type == 3`: parent-compartment --> branchpoint `type == 4`: child-compartment --> branchpoint """ - self._cumsum_nseg_per_cell = cumsum_leading_zero( - jnp.asarray([cell.cumsum_nseg[-1] for cell in self.cells]) + self._cumsum_ncomp_per_cell = cumsum_leading_zero( + jnp.asarray([cell.cumsum_ncomp[-1] for cell in self.cells]) ) self._comp_edges = pd.DataFrame() # Add all the internal nodes. - for offset, cell in zip(self._cumsum_nseg_per_cell, self._cells_list): + for offset, cell in zip(self._cumsum_ncomp_per_cell, self._cells_list): condition = cell._comp_edges["type"].to_numpy() == 0 rows = cell._comp_edges[condition] self._comp_edges = pd.concat( @@ -185,13 +187,13 @@ def _init_morph_jax_spsolve(self): ) # All branchpoint-to-compartment nodes. - start_branchpoints = self.cumsum_nseg[-1] # Index of the first branchpoint. + start_branchpoints = self.cumsum_ncomp[-1] # Index of the first branchpoint. for offset, offset_branchpoints, cell in zip( - self._cumsum_nseg_per_cell, + self._cumsum_ncomp_per_cell, self._cumsum_nbranchpoints_per_cell, self._cells_list, ): - offset_within_cell = cell.cumsum_nseg[-1] + offset_within_cell = cell.cumsum_ncomp[-1] condition = cell._comp_edges["type"].isin([1, 2]) rows = cell._comp_edges[condition] self._comp_edges = pd.concat( @@ -209,11 +211,11 @@ def _init_morph_jax_spsolve(self): # All compartment-to-branchpoint nodes. for offset, offset_branchpoints, cell in zip( - self._cumsum_nseg_per_cell, + self._cumsum_ncomp_per_cell, self._cumsum_nbranchpoints_per_cell, self._cells_list, ): - offset_within_cell = cell.cumsum_nseg[-1] + offset_within_cell = cell.cumsum_ncomp[-1] condition = cell._comp_edges["type"].isin([3, 4]) rows = cell._comp_edges[condition] self._comp_edges = pd.concat( @@ -573,12 +575,12 @@ def _append_multiple_synapses(self, pre_nodes, post_nodes, synapse_type): post_loc = loc_of_index( post_nodes["global_comp_index"].to_numpy(), post_nodes["global_branch_index"].to_numpy(), - self.nseg_per_branch, + self.ncomp_per_branch, ) pre_loc = loc_of_index( pre_nodes["global_comp_index"].to_numpy(), pre_nodes["global_branch_index"].to_numpy(), - self.nseg_per_branch, + self.ncomp_per_branch, ) # Define new synapses. Each row is one synapse. diff --git a/jaxley/solver_voltage.py b/jaxley/solver_voltage.py index 07738f6e..7895a15b 100644 --- a/jaxley/solver_voltage.py +++ b/jaxley/solver_voltage.py @@ -23,7 +23,7 @@ def step_voltage_explicit( sinks: jnp.ndarray, sources: jnp.ndarray, types: jnp.ndarray, - nseg_per_branch: jnp.ndarray, + ncomp_per_branch: jnp.ndarray, par_inds: jnp.ndarray, child_inds: jnp.ndarray, nbranches: int, @@ -66,7 +66,7 @@ def step_voltage_implicit_with_jaxley_spsolve( sinks: jnp.ndarray, sources: jnp.ndarray, types: jnp.ndarray, - nseg_per_branch: jnp.ndarray, + ncomp_per_branch: jnp.ndarray, par_inds: jnp.ndarray, child_inds: jnp.ndarray, nbranches: int, @@ -78,7 +78,7 @@ def step_voltage_implicit_with_jaxley_spsolve( """Solve one timestep of branched nerve equations with implicit (backward) Euler.""" # Build diagonals. c2c = np.isin(types, [0, 1, 2]) - total_ncomp = idx.cumsum_nseg[-1] + total_ncomp = idx.cumsum_ncomp[-1] diags = jnp.ones(total_ncomp) # if-case needed because `.at` does not allow empty inputs, but the input is @@ -179,7 +179,7 @@ def step_voltage_implicit_with_jaxley_spsolve( branchpoint_diags, branchpoint_solves, solver, - nseg_per_branch, + ncomp_per_branch, idx, debug_states, ) @@ -204,7 +204,7 @@ def step_voltage_implicit_with_jaxley_spsolve( branchpoint_diags, branchpoint_solves, solver, - nseg_per_branch, + ncomp_per_branch, idx, debug_states, ) @@ -317,7 +317,7 @@ def _triang_branched( branchpoint_diags, branchpoint_solves, tridiag_solver, - nseg_per_branch, + ncomp_per_branch, idx, debug_states, ): @@ -356,7 +356,7 @@ def _triang_branched( branchpoint_weights_parents, branchpoint_diags, branchpoint_solves, - nseg_per_branch, + ncomp_per_branch, idx, ) # At last level, we do not want to eliminate anymore. @@ -387,7 +387,7 @@ def _backsub_branched( branchpoint_diags, branchpoint_solves, tridiag_solver, - nseg_per_branch, + ncomp_per_branch, idx, debug_states, ): @@ -411,7 +411,7 @@ def _backsub_branched( solves, branchpoint_weights_parents, branchpoint_solves, - nseg_per_branch, + ncomp_per_branch, idx, ) branchpoint_conds_children, solves = _eliminate_children_upper( @@ -527,7 +527,7 @@ def _eliminate_parents_upper( branchpoint_weights_parents, branchpoint_diags, branchpoint_solves, - nseg_per_branch: jnp.ndarray, + ncomp_per_branch: jnp.ndarray, idx, ): bil = pil[:, 0] @@ -566,7 +566,7 @@ def _eliminate_parents_lower( solves, branchpoint_weights_parents, branchpoint_solves, - nseg_per_branch: jnp.ndarray, + ncomp_per_branch: jnp.ndarray, idx, ): bil = pil[:, 0] diff --git a/jaxley/utils/cell_utils.py b/jaxley/utils/cell_utils.py index ba055eab..229e5789 100644 --- a/jaxley/utils/cell_utils.py +++ b/jaxley/utils/cell_utils.py @@ -268,21 +268,21 @@ def build_radiuses_from_xyzr( radius_fns: List[Callable], branch_indices: List[int], min_radius: Optional[float], - nseg: int, + ncomp: int, ) -> jnp.ndarray: """Return the radiuses of branches given SWC file xyzr. - Returns an array of shape `(num_branches, nseg)`. + Returns an array of shape `(num_branches, ncomp)`. Args: radius_fns: Functions which, given compartment locations return the radius. branch_indices: The indices of the branches for which to return the radiuses. min_radius: If passed, the radiuses are clipped to be at least as large. - nseg: The number of compartments that every branch is discretized into. + ncomp: The number of compartments that every branch is discretized into. """ # Compartment locations are at the center of the internal nodes. - non_split = 1 / nseg - range_ = np.linspace(non_split / 2, 1 - non_split / 2, nseg) + non_split = 1 / ncomp + range_ = np.linspace(non_split / 2, 1 - non_split / 2, ncomp) # Build radiuses. radiuses = np.asarray([radius_fns[b](range_) for b in branch_indices]) @@ -297,7 +297,7 @@ def build_radiuses_from_xyzr( return radiuses_each -def equal_segments(branch_property: list, nseg_per_branch: int): +def equal_segments(branch_property: list, ncomp_per_branch: int): """Generates segments where some property is the same in each segment. Args: @@ -305,11 +305,11 @@ def equal_segments(branch_property: list, nseg_per_branch: int): `len(branch_property) == num_branches`. """ assert isinstance(branch_property, list), "branch_property must be a list." - return jnp.asarray([branch_property] * nseg_per_branch).T + return jnp.asarray([branch_property] * ncomp_per_branch).T def linear_segments( - initial_val: float, endpoint_vals: list, parents: jnp.ndarray, nseg_per_branch: int + initial_val: float, endpoint_vals: list, parents: jnp.ndarray, ncomp_per_branch: int ): """Generates segments where some property is linearly interpolated. @@ -327,11 +327,11 @@ def compute_rad(branch_ind, loc): end = endpoint_radiuses[branch_ind] return (end - start) * loc + start - branch_inds_of_each_comp = jnp.tile(jnp.arange(num_branches), nseg_per_branch) - locs_of_each_comp = jnp.linspace(1, 0, nseg_per_branch).repeat(num_branches) + branch_inds_of_each_comp = jnp.tile(jnp.arange(num_branches), ncomp_per_branch) + locs_of_each_comp = jnp.linspace(1, 0, ncomp_per_branch).repeat(num_branches) rad_of_each_comp = compute_rad(branch_inds_of_each_comp, locs_of_each_comp) - return jnp.reshape(rad_of_each_comp, (nseg_per_branch, num_branches)).T + return jnp.reshape(rad_of_each_comp, (ncomp_per_branch, num_branches)).T def merge_cells( @@ -467,21 +467,23 @@ def compute_children_indices(parents) -> List[jnp.ndarray]: def get_num_neighbours( num_children: jnp.ndarray, - nseg_per_branch: int, + ncomp_per_branch: int, num_branches: int, ): """ Number of neighbours of each compartment. """ - num_neighbours = 2 * jnp.ones((num_branches * nseg_per_branch)) - num_neighbours = num_neighbours.at[nseg_per_branch - 1].set(1.0) - num_neighbours = num_neighbours.at[jnp.arange(num_branches) * nseg_per_branch].set( + num_neighbours = 2 * jnp.ones((num_branches * ncomp_per_branch)) + num_neighbours = num_neighbours.at[ncomp_per_branch - 1].set(1.0) + num_neighbours = num_neighbours.at[jnp.arange(num_branches) * ncomp_per_branch].set( num_children + 1.0 ) return num_neighbours -def local_index_of_loc(loc: float, global_branch_ind: int, nseg_per_branch: int) -> int: +def local_index_of_loc( + loc: float, global_branch_ind: int, ncomp_per_branch: int +) -> int: """Returns the local index of a comp given a loc [0, 1] and the index of a branch. This is used because we specify locations such as synapses as a value between 0 and @@ -490,23 +492,23 @@ def local_index_of_loc(loc: float, global_branch_ind: int, nseg_per_branch: int) Args: branch_ind: Index of the branch. loc: Location (in [0, 1]) along that branch. - nseg_per_branch: Number of segments of each branch. + ncomp_per_branch: Number of segments of each branch. Returns: The local index of the compartment. """ - nseg = nseg_per_branch[global_branch_ind] # only for convenience. - possible_locs = np.linspace(0.5 / nseg, 1 - 0.5 / nseg, nseg) + ncomp = ncomp_per_branch[global_branch_ind] # only for convenience. + possible_locs = np.linspace(0.5 / ncomp, 1 - 0.5 / ncomp, ncomp) ind_along_branch = np.argmin(np.abs(possible_locs - loc)) return ind_along_branch -def loc_of_index(global_comp_index, global_branch_index, nseg_per_branch): +def loc_of_index(global_comp_index, global_branch_index, ncomp_per_branch): """Return location corresponding to global compartment index.""" - cumsum_nseg = cumsum_leading_zero(nseg_per_branch) - index = global_comp_index - cumsum_nseg[global_branch_index] - nseg = nseg_per_branch[global_branch_index] - return (0.5 + index) / nseg + cumsum_ncomp = cumsum_leading_zero(ncomp_per_branch) + index = global_comp_index - cumsum_ncomp[global_branch_index] + ncomp = ncomp_per_branch[global_branch_index] + return (0.5 + index) / ncomp def compute_coupling_cond(rad1, rad2, r_a1, r_a2, l1, l2): diff --git a/jaxley/utils/debug_solver.py b/jaxley/utils/debug_solver.py index 84743e0c..1f999222 100644 --- a/jaxley/utils/debug_solver.py +++ b/jaxley/utils/debug_solver.py @@ -12,7 +12,7 @@ def compute_morphology_indices( child_belongs_to_branchpoint, par_inds, child_inds, - nseg, + ncomp, nbranches, ): """Return (row, col) to build the sparse matrix defining the voltage eqs. @@ -32,23 +32,23 @@ def compute_morphology_indices( 7) All child branchpoint rows 8) All branchpoint diagonals """ - diag_col_inds = jnp.arange(nseg * nbranches) - diag_row_inds = jnp.arange(nseg * nbranches) + diag_col_inds = jnp.arange(ncomp * nbranches) + diag_row_inds = jnp.arange(ncomp * nbranches) - upper_col_inds = drop_nseg_th_element(diag_col_inds, nseg, nbranches, 0) - upper_row_inds = drop_nseg_th_element(diag_row_inds, nseg, nbranches, nseg - 1) + upper_col_inds = drop_ncomp_th_element(diag_col_inds, ncomp, nbranches, 0) + upper_row_inds = drop_ncomp_th_element(diag_row_inds, ncomp, nbranches, ncomp - 1) - lower_col_inds = drop_nseg_th_element(diag_col_inds, nseg, nbranches, nseg - 1) - lower_row_inds = drop_nseg_th_element(diag_row_inds, nseg, nbranches, 0) + lower_col_inds = drop_ncomp_th_element(diag_col_inds, ncomp, nbranches, ncomp - 1) + lower_row_inds = drop_ncomp_th_element(diag_row_inds, ncomp, nbranches, 0) - start_ind_for_branchpoints = nseg * nbranches + start_ind_for_branchpoints = ncomp * nbranches branchpoint_inds_parents = start_ind_for_branchpoints + jnp.arange(num_branchpoints) branchpoint_inds_children = ( start_ind_for_branchpoints + child_belongs_to_branchpoint ) - branch_inds_parents = par_inds * nseg + (nseg - 1) - branch_inds_children = child_inds * nseg + branch_inds_parents = par_inds * ncomp + (ncomp - 1) + branch_inds_children = child_inds * ncomp branchpoint_parent_columns_col_inds = branchpoint_inds_parents branchpoint_parent_columns_row_inds = branch_inds_parents @@ -107,7 +107,7 @@ def build_voltage_matrix_elements( branchpoint_weights_parents, branchpoint_diags, branchpoint_solves, - nseg, + ncomp, nbranches, ): """Return data to build the sparse matrix defining the voltage equations. @@ -123,13 +123,13 @@ def build_voltage_matrix_elements( 8) All branchpoint diagonals """ num_branchpoints = len(branchpoint_conds_parents) - num_entries = nseg * nbranches + num_branchpoints + num_entries = ncomp * nbranches + num_branchpoints diag_elements = diags.flatten() upper_elements = uppers.flatten() lower_elements = lowers.flatten() - start_ind_for_branchpoints = nseg * nbranches + start_ind_for_branchpoints = ncomp * nbranches branchpoint_parent_columns_elements = branchpoint_conds_parents branchpoint_children_columns_elements = branchpoint_conds_children branchpoint_parent_row_elements = branchpoint_weights_parents @@ -161,8 +161,8 @@ def build_voltage_matrix_elements( ) -def drop_nseg_th_element( - arr: jnp.ndarray, nseg: int, nbranches: int, start: int +def drop_ncomp_th_element( + arr: jnp.ndarray, ncomp: int, nbranches: int, start: int ) -> jnp.ndarray: """ Create an array of integers from 0 to limit, dropping every n-th element. @@ -171,7 +171,7 @@ def drop_nseg_th_element( Args: arr: The array from which to drop elements. - nseg: The interval of elements to drop (every n-th element). + ncomp: The interval of elements to drop (every n-th element). start: An offset on where to start removing. Returns: @@ -179,7 +179,7 @@ def drop_nseg_th_element( """ # Drop every n-th element result = jnp.delete( - arr, jnp.arange(start, nseg * nbranches, nseg), assume_unique_indices=True + arr, jnp.arange(start, ncomp * nbranches, ncomp), assume_unique_indices=True ) return result diff --git a/jaxley/utils/misc_utils.py b/jaxley/utils/misc_utils.py index d78b1d40..2d221904 100644 --- a/jaxley/utils/misc_utils.py +++ b/jaxley/utils/misc_utils.py @@ -56,7 +56,10 @@ def __init__(self, version: str, amend_msg: str = ""): def __call__(self, func): def wrapper(*args, **kwargs): - msg = f"{func.__name__} is deprecated and will be removed in version {self._version}." + msg = ( + f"{func.__name__} is deprecated and will be removed in version " + f"{self._version}." + ) warnings.warn(msg + self._amend_msg) return func(*args, **kwargs) @@ -64,7 +67,7 @@ def wrapper(*args, **kwargs): class deprecated_kwargs: - """Decorator to mark a keyword arguemnt of a function as deprecated. + """Decorator to mark a keyword argument of a function as deprecated. Can be used to mark kwargs that will be removed in future versions. This will also be tested in the CI pipeline to ensure that deprecated kwargs are removed. @@ -72,7 +75,8 @@ class deprecated_kwargs: Warns with: "kwarg is deprecated and will be removed in version version." Args: - version: The version in which the keyword argument will be removed, i.e. "0.1.0". + version: The version in which the keyword argument will be removed, i.e. + `0.1.0`. deprecated_kwargs: A list of keyword arguments that are deprecated. amend_msg: An optional message to append to the deprecation warning. """ @@ -86,7 +90,10 @@ def __call__(self, func): def wrapper(*args, **kwargs): for deprecated_kwarg in self._depcrecated_kwargs: if deprecated_kwarg in kwargs and kwargs[deprecated_kwarg] is not None: - msg = f"{deprecated_kwarg} is deprecated and will be removed in version {self._version}." + msg = ( + f"{deprecated_kwarg} is deprecated and will be removed in " + f"version {self._version}." + ) warnings.warn(msg + self._amend_msg) return func(*args, **kwargs) diff --git a/jaxley/utils/plot_utils.py b/jaxley/utils/plot_utils.py index 7c2066b9..e7a0b13c 100644 --- a/jaxley/utils/plot_utils.py +++ b/jaxley/utils/plot_utils.py @@ -369,7 +369,7 @@ def plot_comps( lens = np.sqrt(np.nansum(np.diff(locs, axis=0) ** 2, axis=1)) lens = np.cumsum([0] + lens.tolist()) comp_ends = v_interp( - np.linspace(0, lens[-1], module_or_view.nseg + 1), lens, locs + np.linspace(0, lens[-1], module_or_view.ncomp + 1), lens, locs ).T axes = np.diff(comp_ends, axis=0) cylinder_lens = np.sqrt(np.sum(axes**2, axis=1)) diff --git a/jaxley/utils/solver_utils.py b/jaxley/utils/solver_utils.py index 0125728f..c3b883f6 100644 --- a/jaxley/utils/solver_utils.py +++ b/jaxley/utils/solver_utils.py @@ -9,25 +9,25 @@ def remap_index_to_masked( - index, nodes: pd.DataFrame, padded_cumsum_nseg, nseg_per_branch: jnp.ndarray + index, nodes: pd.DataFrame, padded_cumsum_ncomp, ncomp_per_branch: jnp.ndarray ): """Convert actual index of the compartment to the index in the masked system. - E.g. if `nsegs = [2, 4]`, then the index `3` would be mapped to `5` because the - masked `nsegs` are `[4, 4]`. I.e.: + E.g. if `ncomps = [2, 4]`, then the index `3` would be mapped to `5` because the + masked `ncomps` are `[4, 4]`. I.e.: original: [0, 1, 2, 3, 4, 5] masked: [0, 1, (2) ,(3) ,4, 5, 6, 7] """ - cumsum_nseg_per_branch = jnp.concatenate( + cumsum_ncomp_per_branch = jnp.concatenate( [ jnp.asarray([0]), - jnp.cumsum(nseg_per_branch), + jnp.cumsum(ncomp_per_branch), ] ) branch_inds = nodes.loc[index, "global_branch_index"].to_numpy() - remainders = index - cumsum_nseg_per_branch[branch_inds] - return padded_cumsum_nseg[branch_inds] + remainders + remainders = index - cumsum_ncomp_per_branch[branch_inds] + return padded_cumsum_ncomp[branch_inds] + remainders def convert_to_csc( @@ -114,14 +114,14 @@ class JaxleySolveIndexer: def __init__( self, - cumsum_nseg: np.ndarray, + cumsum_ncomp: np.ndarray, branchpoint_group_inds: Optional[np.ndarray] = None, children_in_level: Optional[np.ndarray] = None, parents_in_level: Optional[np.ndarray] = None, root_inds: Optional[np.ndarray] = None, remapped_node_indices: Optional[np.ndarray] = None, ): - self.cumsum_nseg = np.asarray(cumsum_nseg) + self.cumsum_ncomp = np.asarray(cumsum_ncomp) # Save items for easier access. self.branchpoint_group_inds = branchpoint_group_inds @@ -132,11 +132,11 @@ def __init__( def first(self, branch_inds: np.ndarray) -> np.ndarray: """Return the indices of the first compartment of all `branch_inds`.""" - return self.cumsum_nseg[branch_inds] + return self.cumsum_ncomp[branch_inds] def last(self, branch_inds: np.ndarray) -> np.ndarray: """Return the indices of the last compartment of all `branch_inds`.""" - return self.cumsum_nseg[branch_inds + 1] - 1 + return self.cumsum_ncomp[branch_inds + 1] - 1 def branch(self, branch_inds: np.ndarray) -> np.ndarray: """Return indices of all compartments in all `branch_inds`.""" @@ -169,7 +169,7 @@ def _consecutive_indices( ) -> np.ndarray: """Return array of all indices in [start, end], for every start, end. - It also reshape the indices to `(nbranches, nseg)`. + It also reshape the indices to `(nbranches, ncomp)`. E.g.: ``` diff --git a/tests/conftest.py b/tests/conftest.py index 01a97976..dad1c4a5 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -3,6 +3,7 @@ import os from copy import deepcopy +from typing import Optional import pytest @@ -40,23 +41,23 @@ def SimpleBranch(SimpleComp): branches = {} def get_or_build_branch( - nseg: int, copy: bool = True, force_init: bool = False + ncomp: int, copy: bool = True, force_init: bool = False ) -> jx.Branch: """Create or retrieve a branch. If a branch with the same number of compartments already exists, it is returned. Args: - nseg: Number of compartments in the branch. + ncomp: Number of compartments in the branch. copy: Whether to return a copy of the branch. Default is True. force_init: Force the init from scratch. Default is False. Returns: jx.Branch().""" - if nseg not in branches or force_init: + if ncomp not in branches or force_init: comp = SimpleComp(force_init=force_init) - branches[nseg] = jx.Branch([comp] * nseg) - return deepcopy(branches[nseg]) if copy and not force_init else branches[nseg] + branches[ncomp] = jx.Branch([comp] * ncomp) + return deepcopy(branches[ncomp]) if copy and not force_init else branches[ncomp] yield get_or_build_branch branches = {} @@ -68,7 +69,7 @@ def SimpleCell(SimpleBranch): cells = {} def get_or_build_cell( - nbranches: int, nseg: int, copy: bool = True, force_init: bool = False + nbranches: int, ncomp: int, copy: bool = True, force_init: bool = False ) -> jx.Cell: """Create or retrieve a cell. @@ -77,20 +78,20 @@ def get_or_build_cell( Args: nbranches: Number of branches in the cell. - nseg: Number of compartments in each branch. + ncomp: Number of compartments in each branch. copy: Whether to return a copy of the cell. Default is True. force_init: Force the init from scratch. Default is False. Returns: jx.Cell().""" - if key := (nbranches, nseg) not in cells or force_init: + if key := (nbranches, ncomp) not in cells or force_init: parents = [-1] depth = 0 while nbranches > len(parents): parents = [-1] + [b // 2 for b in range(0, 2**depth - 2)] depth += 1 parents = parents[:nbranches] - branch = SimpleBranch(nseg=nseg, force_init=force_init) + branch = SimpleBranch(ncomp=ncomp, force_init=force_init) cells[key] = jx.Cell([branch] * nbranches, parents) return deepcopy(cells[key]) if copy and not force_init else cells[key] @@ -106,7 +107,7 @@ def SimpleNet(SimpleCell): def get_or_build_net( ncells: int, nbranches: int, - nseg: int, + ncomp: int, connect: bool = False, copy: bool = True, force_init: bool = False, @@ -119,16 +120,16 @@ def get_or_build_net( Args: ncells: Number of cells in the network. nbranches: Number of branches in each cell. - nseg: Number of compartments in each branch. + ncomp: Number of compartments in each branch. connect: Whether to connect the first two cells in the network. copy: Whether to return a copy of the network. Default is True. force_init: Force the init from scratch. Default is False. Returns: jx.Network().""" - if key := (ncells, nbranches, nseg, connect) not in nets or force_init: + if key := (ncells, nbranches, ncomp, connect) not in nets or force_init: net = jx.Network( - [SimpleCell(nbranches=nbranches, nseg=nseg, force_init=force_init)] + [SimpleCell(nbranches=nbranches, ncomp=ncomp, force_init=force_init)] * ncells ) if connect: @@ -147,8 +148,8 @@ def SimpleMorphCell(): cells = {} def get_or_build_cell( - fname: str = None, - nseg: int = 1, + fname: Optional[str] = None, + ncomp: int = 1, max_branch_len: float = 2_000.0, copy: bool = True, force_init: bool = False, @@ -160,7 +161,7 @@ def get_or_build_cell( Args: fname: Path to the SWC file. - nseg: Number of compartments in each branch. + ncomp: Number of compartments in each branch. max_branch_len: Maximum length of a branch. copy: Whether to return a copy of the cell. Default is True. force_init: Force the init from scratch. Default is False. @@ -170,8 +171,10 @@ def get_or_build_cell( dirname = os.path.dirname(__file__) default_fname = os.path.join(dirname, "swc_files", "morph.swc") fname = default_fname if fname is None else fname - if key := (fname, nseg, max_branch_len) not in cells or force_init: - cells[key] = jx.read_swc(fname, nseg, max_branch_len, assign_groups=True) + if key := (fname, ncomp, max_branch_len) not in cells or force_init: + cells[key] = jx.read_swc( + fname, ncomp=ncomp, max_branch_len=max_branch_len, assign_groups=True + ) return deepcopy(cells[key]) if copy and not force_init else cells[key] yield get_or_build_cell diff --git a/tests/jaxley_identical/test_basic_modules.py b/tests/jaxley_identical/test_basic_modules.py index 4b46a5e8..61d201f2 100644 --- a/tests/jaxley_identical/test_basic_modules.py +++ b/tests/jaxley_identical/test_basic_modules.py @@ -58,7 +58,7 @@ def test_compartment(voltage_solver, SimpleComp, SimpleBranch, SimpleCell, Simpl assert max_error <= tolerance, f"Compartment error is {max_error} > {tolerance}" # Test branch of a single compartment. - branch = SimpleBranch(nseg=1) + branch = SimpleBranch(ncomp=1) branch.insert(HH()) branch.record() branch.stimulate(current) @@ -202,10 +202,10 @@ def test_cell_unequal_compartment_number(SimpleBranch): i_delay=0.5, i_dur=1.0, i_amp=0.1, delta_t=0.025, t_max=5.0 ) - branch1 = SimpleBranch(nseg=1) - branch2 = SimpleBranch(nseg=2) - branch3 = SimpleBranch(nseg=3) - branch4 = SimpleBranch(nseg=4) + branch1 = SimpleBranch(ncomp=1) + branch2 = SimpleBranch(ncomp=2) + branch3 = SimpleBranch(ncomp=3) + branch4 = SimpleBranch(ncomp=4) cell = jx.Cell([branch1, branch2, branch3, branch4], parents=[-1, 0, 0, 1]) cell.set("axial_resistivity", 10_000.0) cell.insert(HH()) diff --git a/tests/jaxley_identical/test_radius_and_length.py b/tests/jaxley_identical/test_radius_and_length.py index cd19b020..e81aaf1e 100644 --- a/tests/jaxley_identical/test_radius_and_length.py +++ b/tests/jaxley_identical/test_radius_and_length.py @@ -70,7 +70,7 @@ def test_radius_and_length_branch(voltage_solver, SimpleBranch): i_delay=0.5, i_dur=1.0, i_amp=0.02, delta_t=0.025, t_max=5.0 ) - branch = SimpleBranch(nseg=2) + branch = SimpleBranch(ncomp=2) np.random.seed(1) branch.set("length", np.flip(5 * np.random.rand(2))) @@ -112,7 +112,7 @@ def test_radius_and_length_cell(voltage_solver, SimpleCell): ) num_branches = 3 - cell = SimpleCell(num_branches, nseg=2) + cell = SimpleCell(num_branches, ncomp=2) np.random.seed(1) rands1 = 5 * np.random.rand(2 * num_branches) diff --git a/tests/jaxley_identical/test_swc.py b/tests/jaxley_identical/test_swc.py index ea15cf94..fa50c9a6 100644 --- a/tests/jaxley_identical/test_swc.py +++ b/tests/jaxley_identical/test_swc.py @@ -32,7 +32,7 @@ def test_swc_cell(voltage_solver: str, file: str, SimpleMorphCell): dirname = os.path.dirname(__file__) fname = os.path.join(dirname, "../swc_files", file) - cell = SimpleMorphCell(fname, nseg=2, max_branch_len=300.0) + cell = SimpleMorphCell(fname, ncomp=2, max_branch_len=300.0) _ = cell.soma # Only to test whether the `soma` group was created. cell.insert(HH()) cell.branch(1).loc(0.0).record() @@ -93,8 +93,8 @@ def test_swc_net(voltage_solver: str, SimpleMorphCell): dirname = os.path.dirname(__file__) fname = os.path.join(dirname, "../swc_files/morph.swc") - cell1 = SimpleMorphCell(fname, nseg=2, max_branch_len=300.0) - cell2 = SimpleMorphCell(fname, nseg=2, max_branch_len=300.0) + cell1 = SimpleMorphCell(fname, ncomp=2, max_branch_len=300.0) + cell2 = SimpleMorphCell(fname, ncomp=2, max_branch_len=300.0) network = jx.Network([cell1, cell2]) connect( @@ -104,7 +104,7 @@ def test_swc_net(voltage_solver: str, SimpleMorphCell): ) network.insert(HH()) - # first cell, 0-eth branch, 1-st compartment because loc=0.0 -> comp = nseg-1 = 1 + # first cell, 0-eth branch, 1-st compartment because loc=0.0 -> comp = ncomp-1 = 1 radius_post = network[1, 0, 1].nodes["radius"].item() lenght_post = network[1, 0, 1].nodes["length"].item() area = 2 * pi * lenght_post * radius_post diff --git a/tests/jaxley_vs_neuron/test_branch.py b/tests/jaxley_vs_neuron/test_branch.py index fa4022ef..c829b718 100644 --- a/tests/jaxley_vs_neuron/test_branch.py +++ b/tests/jaxley_vs_neuron/test_branch.py @@ -43,13 +43,13 @@ def test_similarity(solver): def _run_jaxley(i_delay, i_dur, i_amp, dt, t_max, solver): - nseg_per_branch = 8 + ncomp_per_branch = 8 comp = jx.Compartment() - branch = jx.Branch([comp for _ in range(nseg_per_branch)]) + branch = jx.Branch([comp for _ in range(ncomp_per_branch)]) branch.insert(HH()) - radiuses = np.linspace(3.0, 15.0, nseg_per_branch) - for i, loc in enumerate(np.linspace(0, 1, nseg_per_branch)): + radiuses = np.linspace(3.0, 15.0, ncomp_per_branch) + for i, loc in enumerate(np.linspace(0, 1, ncomp_per_branch)): branch.loc(loc).set("radius", radiuses[i]) branch.set("length", 10.0) @@ -82,19 +82,19 @@ def _run_neuron(i_delay, i_dur, i_amp, dt, t_max, solver): else: raise ValueError - nseg_per_branch = 8 + ncomp_per_branch = 8 h.dt = dt for sec in h.allsec(): h.delete_section(sec=sec) branch = h.Section() - branch.nseg = nseg_per_branch + branch.nseg = ncomp_per_branch branch.Ra = 1_000.0 - branch.L = 10.0 * nseg_per_branch + branch.L = 10.0 * ncomp_per_branch branch.cm = 5.0 - radiuses = np.linspace(3.0, 15.0, nseg_per_branch) + radiuses = np.linspace(3.0, 15.0, ncomp_per_branch) for i, comp in enumerate(branch): comp.diam = 2 * radiuses[i] @@ -178,9 +178,9 @@ def test_similarity_complex(solver): def _jaxley_complex(i_delay, i_dur, i_amp, dt, t_max, diams, capacitances, solver): - nseg = 16 + ncomp = 16 comp = jx.Compartment() - branch = jx.Branch(comp, nseg) + branch = jx.Branch(comp, ncomp) branch.insert(HH()) @@ -202,12 +202,12 @@ def _jaxley_complex(i_delay, i_dur, i_amp, dt, t_max, diams, capacitances, solve branch.loc(loc).set("axial_resistivity", 800.0) counter = 0 - for loc in np.linspace(0, 1, nseg): + for loc in np.linspace(0, 1, ncomp): branch.loc(loc).set("radius", diams[counter] / 2) branch.loc(loc).set("capacitance", capacitances[counter]) counter += 1 - # 0.02 is fine here because nseg=8 for NEURON, but nseg=16 for jaxley. + # 0.02 is fine here because ncomp=8 for NEURON, but ncomp=16 for jaxley. current = jx.step_current(i_delay, i_dur, i_amp, dt, t_max) branch.loc(0.02).stimulate(current) branch.loc(0.02).record() @@ -257,13 +257,13 @@ def _neuron_complex(i_delay, i_dur, i_amp, dt, t_max, diams, capacitances, solve seg.cm = capacitances[counter] counter += 1 - # 0.05 is fine here because nseg=8, but nseg=16 for jaxley. + # 0.05 is fine here because ncomp=8, but ncomp=16 for jaxley. stim = h.IClamp(branch1(0.05)) stim.delay = i_delay stim.dur = i_dur stim.amp = i_amp - # 0.05 is fine here because nseg=8, but nseg=16 for jaxley. + # 0.05 is fine here because ncomp=8, but ncomp=16 for jaxley. voltage_recs = {} v = h.Vector() v.record(branch1(0.05)._ref_v) diff --git a/tests/jaxley_vs_neuron/test_cell.py b/tests/jaxley_vs_neuron/test_cell.py index 22c8d6ee..00f840fc 100644 --- a/tests/jaxley_vs_neuron/test_cell.py +++ b/tests/jaxley_vs_neuron/test_cell.py @@ -40,9 +40,9 @@ def test_similarity(solver): def _run_jaxley(i_delay, i_dur, i_amp, dt, t_max, solver): - nseg_per_branch = 8 + ncomp_per_branch = 8 comp = jx.Compartment() - branch = jx.Branch(comp, nseg_per_branch) + branch = jx.Branch(comp, ncomp_per_branch) cell = jx.Cell(branch, parents=[-1, 0, 0]) cell.insert(HH()) @@ -77,7 +77,7 @@ def _run_neuron(i_delay, i_dur, i_amp, dt, t_max, solver): else: raise ValueError - nseg_per_branch = 8 + ncomp_per_branch = 8 h.dt = dt for sec in h.allsec(): @@ -91,10 +91,10 @@ def _run_neuron(i_delay, i_dur, i_amp, dt, t_max, solver): branch3.connect(branch1, 1, 0) for sec in h.allsec(): - sec.nseg = nseg_per_branch + sec.nseg = ncomp_per_branch sec.Ra = 1_000.0 - sec.L = 10.0 * nseg_per_branch + sec.L = 10.0 * ncomp_per_branch sec.diam = 2 * 5.0 sec.cm = 7.0 @@ -152,10 +152,10 @@ def test_similarity_unequal_number_of_compartments(): def _run_jaxley_unequal_ncomp(i_delay, i_dur, i_amp, dt, t_max): comp = jx.Compartment() - branch1 = jx.Branch(comp, nseg=1) - branch2 = jx.Branch(comp, nseg=2) - branch3 = jx.Branch(comp, nseg=3) - branch4 = jx.Branch(comp, nseg=4) + branch1 = jx.Branch(comp, ncomp=1) + branch2 = jx.Branch(comp, ncomp=2) + branch3 = jx.Branch(comp, ncomp=3) + branch4 = jx.Branch(comp, ncomp=4) cell = jx.Cell([branch1, branch2, branch3, branch4], parents=[-1, 0, 0, 1]) cell.set("axial_resistivity", 10_000.0) cell.insert(HH()) @@ -201,12 +201,12 @@ def _run_neuron_unequal_ncomp(i_delay, i_dur, i_amp, dt, t_max): branch3.connect(branch1, 1, 0) branch4.connect(branch2, 1, 0) - nsegs = [1, 2, 3, 4] + ncomps = [1, 2, 3, 4] for i, sec in enumerate(h.allsec()): - sec.nseg = nsegs[i] + sec.nseg = ncomps[i] sec.Ra = 1_000.0 - sec.L = 20.0 * nsegs[i] + sec.L = 20.0 * ncomps[i] sec.diam = 2 * 5.0 sec.insert("hh") diff --git a/tests/test_api_equivalence.py b/tests/test_api_equivalence.py index 9fef8759..1fbbfcd7 100644 --- a/tests/test_api_equivalence.py +++ b/tests/test_api_equivalence.py @@ -19,7 +19,7 @@ def test_api_equivalence_morphology(SimpleComp): """Test the API for how one can build morphologies from scratch.""" - nseg_per_branch = 2 + ncomp_per_branch = 2 depth = 2 dt = 0.025 @@ -29,10 +29,10 @@ def test_api_equivalence_morphology(SimpleComp): comp = SimpleComp() - branch1 = jx.Branch([comp for _ in range(nseg_per_branch)]) + branch1 = jx.Branch([comp for _ in range(ncomp_per_branch)]) cell1 = jx.Cell([branch1 for _ in range(num_branches)], parents=parents) - branch2 = jx.Branch(comp, nseg=nseg_per_branch) + branch2 = jx.Branch(comp, ncomp=ncomp_per_branch) cell2 = jx.Cell(branch2, parents=parents) cell1.branch(2).loc(0.4).record() @@ -199,9 +199,9 @@ def test_api_equivalence_network_matches_cell(SimpleBranch): i_delay=0.5, i_dur=1.0, i_amp=0.1, delta_t=0.025, t_max=5.0 ) - branch1 = SimpleBranch(nseg=1) - branch2 = SimpleBranch(nseg=2) - branch3 = SimpleBranch(nseg=3) + branch1 = SimpleBranch(ncomp=1) + branch2 = SimpleBranch(ncomp=2) + branch3 = SimpleBranch(ncomp=3) cell1 = jx.Cell([branch1, branch2, branch3], parents=[-1, 0, 0]) cell2 = jx.Cell([branch1, branch2], parents=[-1, 0]) cell1.insert(HH()) diff --git a/tests/test_channels.py b/tests/test_channels.py index 7af7bb99..4063fd3e 100644 --- a/tests/test_channels.py +++ b/tests/test_channels.py @@ -152,7 +152,7 @@ def test_integration_with_renamed_channels(): standard_hh = HH() comp = jx.Compartment() - branch = jx.Branch(comp, nseg=4) + branch = jx.Branch(comp, ncomp=4) branch.loc(0.0).insert(standard_hh) branch.insert(neuron_hh) @@ -352,15 +352,15 @@ def compute_current(self, states, v, params): def test_delete_channel(SimpleBranch): # test complete removal of a channel from a module - branch1 = SimpleBranch(nseg=3) + branch1 = SimpleBranch(ncomp=3) branch1.comp(0).insert(K()) branch1.delete_channel(K()) - branch2 = SimpleBranch(nseg=3) + branch2 = SimpleBranch(ncomp=3) branch2.comp(0).insert(K()) branch2.comp(0).delete_channel(K()) - branch3 = SimpleBranch(nseg=3) + branch3 = SimpleBranch(ncomp=3) branch3.insert(K()) branch3.delete_channel(K()) @@ -393,7 +393,7 @@ def channel_present(view, channel, partial=False): assert not channel_present(branch, K()) # test correct channels are removed only in the viewed part of the module - branch4 = SimpleBranch(nseg=3) + branch4 = SimpleBranch(ncomp=3) branch4.insert(HH()) branch4.comp(0).insert(K()) branch4.comp([1, 2]).insert(Leak()) diff --git a/tests/test_composability_of_modules.py b/tests/test_composability_of_modules.py index 66f3457e..fd302731 100644 --- a/tests/test_composability_of_modules.py +++ b/tests/test_composability_of_modules.py @@ -27,7 +27,7 @@ def test_compose_branch(): branch1.loc(0.0).stimulate(current) comp = jx.Compartment() - branch2 = jx.Branch(comp, nseg=2) + branch2 = jx.Branch(comp, ncomp=2) branch2.loc(0.0).insert(HH()) branch2.loc(0.0).record() branch2.loc(0.0).stimulate(current) @@ -40,7 +40,7 @@ def test_compose_branch(): def test_compose_cell(): """Test inserting to branch and composing to cell equals inserting to cell.""" - nseg_per_branch = 4 + ncomp_per_branch = 4 dt = 0.025 current = jx.step_current( i_delay=0.5, i_dur=1.0, i_amp=0.1, delta_t=0.025, t_max=5.0 @@ -48,14 +48,14 @@ def test_compose_cell(): comp = jx.Compartment() - branch1 = jx.Branch(comp, nseg_per_branch) + branch1 = jx.Branch(comp, ncomp_per_branch) branch1.insert(HH()) - branch2 = jx.Branch(comp, nseg_per_branch) + branch2 = jx.Branch(comp, ncomp_per_branch) cell1 = jx.Cell([branch1, branch2], parents=[-1, 0]) cell1.branch(0).loc(0.0).record() cell1.branch(0).loc(0.0).stimulate(current) - branch = jx.Branch(comp, nseg_per_branch) + branch = jx.Branch(comp, ncomp_per_branch) cell2 = jx.Cell(branch, parents=[-1, 0]) cell2.branch(0).insert(HH()) cell2.branch(0).loc(0.0).record() @@ -69,14 +69,14 @@ def test_compose_cell(): def test_compose_net(): """Test inserting to cell and composing to net equals inserting to net.""" - nseg_per_branch = 4 + ncomp_per_branch = 4 dt = 0.025 current = jx.step_current( i_delay=0.5, i_dur=1.0, i_amp=0.1, delta_t=0.025, t_max=5.0 ) comp = jx.Compartment() - branch = jx.Branch(comp, nseg_per_branch) + branch = jx.Branch(comp, ncomp_per_branch) cell1 = jx.Cell(branch, parents=[-1, 0, 0]) cell1.insert(HH()) diff --git a/tests/test_connection.py b/tests/test_connection.py index bb8d1b04..d8277e5a 100644 --- a/tests/test_connection.py +++ b/tests/test_connection.py @@ -51,7 +51,7 @@ def test_connect(SimpleBranch, SimpleCell, SimpleNet): # test after all connections are made, to catch "overwritten" connections get_comps = lambda locs: [ - local_index_of_loc(loc, 0, net2.nseg_per_branch) for loc in locs + local_index_of_loc(loc, 0, net2.ncomp_per_branch) for loc in locs ] # check if all connections are made correctly diff --git a/tests/test_distance.py b/tests/test_distance.py index 03abdb01..06c58955 100644 --- a/tests/test_distance.py +++ b/tests/test_distance.py @@ -10,26 +10,26 @@ def test_direct_distance(SimpleCell): - nseg = 4 + ncomp = 4 length = 15.0 - cell = SimpleCell(5, nseg) + cell = SimpleCell(5, ncomp) cell.branch("all").loc("all").set("length", length) cell.compute_xyz() dist = cell.branch(0).loc(0.0).distance(cell.branch(0).loc(1.0)) - assert dist == (nseg - 1) * length + assert dist == (ncomp - 1) * length comp = jx.Compartment() - branch = jx.Branch(comp, nseg=nseg) + branch = jx.Branch(comp, ncomp=ncomp) cell = jx.Cell(branch, parents=[-1, 0, 1]) cell.branch("all").loc("all").set("length", length) cell.compute_xyz() dist = cell.branch(0).loc(0.0).distance(cell.branch(2).loc(1.0)) - assert dist == (3 * nseg - 1) * length + assert dist == (3 * ncomp - 1) * length move_x = 220.0 comp = jx.Compartment() - branch = jx.Branch(comp, nseg=nseg) + branch = jx.Branch(comp, ncomp=ncomp) cell = jx.Cell(branch, parents=[-1, 0, 1]) cell.branch("all").loc("all").set("length", length) net = jx.Network([cell for _ in range(2)]) @@ -45,4 +45,4 @@ def test_direct_distance(SimpleCell): assert dist == 0.0 dist = net.cell(1).branch(0).loc(0.0).distance(net.cell(1).branch(2).loc(1.0)) - assert dist == (3 * nseg - 1) * length + assert dist == (3 * ncomp - 1) * length diff --git a/tests/test_make_trainable.py b/tests/test_make_trainable.py index 79bc51a5..50ece696 100644 --- a/tests/test_make_trainable.py +++ b/tests/test_make_trainable.py @@ -336,7 +336,7 @@ def test_group_trainable_corresponds_to_set(): def build_net(): comp = jx.Compartment() - branch = jx.Branch(comp, nseg=4) + branch = jx.Branch(comp, ncomp=4) cell = jx.Cell(branch, parents=[-1, 0, 0, 1, 1]) net = jx.Network([cell for _ in range(4)]) net.cell(0).add_to_group("test") diff --git a/tests/test_moving.py b/tests/test_moving.py index e0ef0403..ca47b531 100644 --- a/tests/test_moving.py +++ b/tests/test_moving.py @@ -17,7 +17,7 @@ def test_move_cell(SimpleBranch, SimpleCell): # Test move on a cell with compute_xyz() - cell = SimpleCell(5, nseg=4) + cell = SimpleCell(5, ncomp=4) cell.compute_xyz() cell.move(20.0, 30.0, 5.0) assert cell.xyzr[0][0, 0] == 20.0 @@ -25,7 +25,7 @@ def test_move_cell(SimpleBranch, SimpleCell): assert cell.xyzr[0][0, 2] == 5.0 # Test move_to on a cell that starts with a specified xyzr - branch = SimpleBranch(nseg=4) + branch = SimpleBranch(ncomp=4) cell = jx.Cell( branch, parents=[-1], @@ -64,7 +64,7 @@ def test_move_to_cell(SimpleBranch, SimpleCell): assert cell.xyzr[0][0, 1] == 30.0 assert cell.xyzr[0][0, 2] == 5.0 - branch = SimpleBranch(nseg=4) + branch = SimpleBranch(ncomp=4) cell = jx.Cell( branch, parents=[-1], @@ -100,15 +100,15 @@ def test_move_to_network(SimpleNet): def test_move_to_arrays(SimpleNet): """Test with network""" - nseg = 4 - net = SimpleNet(3, 3, nseg) + ncomp = 4 + net = SimpleNet(3, 3, ncomp) net.compute_xyz() x_coords = np.array([10.0, 20.0, 30.0]) y_coords = np.array([5.0, 15.0, 25.0]) z_coords = np.array([1.0, 2.0, 3.0]) net.move_to(x_coords, y_coords, z_coords) assert net.xyzr[0][0, 0] == 10.0 - assert net.xyzr[0][1, 0] == nseg * 10.0 + 10.0 + assert net.xyzr[0][1, 0] == ncomp * 10.0 + 10.0 assert net.xyzr[0][0, 1] == 5.0 assert net.xyzr[0][0, 2] == 1.0 assert net.xyzr[3][0, 0] == 20.0 @@ -142,9 +142,9 @@ def test_move_to_cellview(SimpleNet): def test_move_to_swc_cell(SimpleMorphCell): dirname = os.path.dirname(__file__) fname = os.path.join(dirname, "swc_files", "morph.swc") - cell1 = SimpleMorphCell(fname, nseg=1) - cell2 = SimpleMorphCell(fname, nseg=1) - cell3 = SimpleMorphCell(fname, nseg=1) + cell1 = SimpleMorphCell(fname, ncomp=1) + cell2 = SimpleMorphCell(fname, ncomp=1) + cell3 = SimpleMorphCell(fname, ncomp=1) # Try move_to on a cell cell1.move_to(10.0, 20.0, 30.0) diff --git a/tests/test_plotting_api.py b/tests/test_plotting_api.py index c3857215..a2e3b9e8 100644 --- a/tests/test_plotting_api.py +++ b/tests/test_plotting_api.py @@ -22,7 +22,7 @@ def test_cell(SimpleMorphCell): dirname = os.path.dirname(__file__) fname = os.path.join(dirname, "swc_files", "morph.swc") - cell = SimpleMorphCell(fname, nseg=1) + cell = SimpleMorphCell(fname, ncomp=1) cell.branch(0).set_ncomp(2) # test inhomogeneous ncomp # Plot 1. @@ -40,9 +40,9 @@ def test_cell(SimpleMorphCell): def test_network(SimpleMorphCell): dirname = os.path.dirname(__file__) fname = os.path.join(dirname, "swc_files", "morph.swc") - cell1 = SimpleMorphCell(fname, nseg=1) - cell2 = SimpleMorphCell(fname, nseg=1) - cell3 = SimpleMorphCell(fname, nseg=1) + cell1 = SimpleMorphCell(fname, ncomp=1) + cell2 = SimpleMorphCell(fname, ncomp=1) + cell3 = SimpleMorphCell(fname, ncomp=1) net = jx.Network([cell1, cell2, cell3]) connect( @@ -124,7 +124,7 @@ def test_vis_networks_built_from_scratch(SimpleComp, SimpleBranch, SimpleCell): def test_mixed_network(SimpleMorphCell): dirname = os.path.dirname(__file__) fname = os.path.join(dirname, "swc_files", "morph.swc") - cell1 = SimpleMorphCell(fname, nseg=1) + cell1 = SimpleMorphCell(fname, ncomp=1) comp = jx.Compartment() branch = jx.Branch(comp, 4) @@ -171,7 +171,7 @@ def test_volume_plotting( module.compute_xyz() fname = os.path.join(os.path.dirname(__file__), "swc_files", "morph.swc") - morph_cell = SimpleMorphCell(fname, nseg=1) + morph_cell = SimpleMorphCell(fname, ncomp=1) fig, ax = plt.subplots() for module in [comp, branch, cell, net, morph_cell]: diff --git a/tests/test_set_ncomp.py b/tests/test_set_ncomp.py index 8a9222ed..e98f709a 100644 --- a/tests/test_set_ncomp.py +++ b/tests/test_set_ncomp.py @@ -146,8 +146,8 @@ def test_api_equivalence_swc_lengths_and_radiuses(SimpleMorphCell, new_ncomp, fi dirname = os.path.dirname(__file__) fname = os.path.join(dirname, "swc_files", file) - cell1 = SimpleMorphCell(fname, nseg=new_ncomp) - cell2 = SimpleMorphCell(fname, nseg=1) + cell1 = SimpleMorphCell(fname, ncomp=new_ncomp) + cell2 = SimpleMorphCell(fname, ncomp=1) for b in range(cell2.total_nbranches): cell2.branch(b).set_ncomp(new_ncomp) @@ -167,8 +167,8 @@ def test_simulation_accuracy_swc_init_vs_set_ncomp(SimpleMorphCell, new_ncomp, f dirname = os.path.dirname(__file__) fname = os.path.join(dirname, "swc_files", file) - cell1 = SimpleMorphCell(fname, nseg=new_ncomp) - cell2 = SimpleMorphCell(fname, nseg=1) + cell1 = SimpleMorphCell(fname, ncomp=new_ncomp) + cell2 = SimpleMorphCell(fname, ncomp=1) for b in range(cell2.total_nbranches): cell2.branch(b).set_ncomp(new_ncomp) diff --git a/tests/test_swc.py b/tests/test_swc.py index 53393aa8..745b2e70 100644 --- a/tests/test_swc.py +++ b/tests/test_swc.py @@ -64,10 +64,10 @@ def test_dummy_compartment_length(swc2jaxley): @pytest.mark.parametrize("file", ["morph_250_single_point_soma.swc", "morph_250.swc"]) def test_swc_radius(file, swc2jaxley): - """We expect them to match for sufficiently large nseg. See #140.""" - nseg = 64 - non_split = 1 / nseg - range_16 = np.linspace(non_split / 2, 1 - non_split / 2, nseg) + """We expect them to match for sufficiently large ncomp. See #140.""" + ncomp = 64 + non_split = 1 / ncomp + range_16 = np.linspace(non_split / 2, 1 - non_split / 2, ncomp) # Can not use full morphology because of branch sorting. dirname = os.path.dirname(__file__) @@ -88,7 +88,7 @@ def test_swc_radius(file, swc2jaxley): neuron_diams = [] for sec in h.allsec(): - sec.nseg = nseg + sec.nseg = ncomp diams_in_branch = [] for seg in sec: diams_in_branch.append(seg.diam) @@ -119,7 +119,7 @@ def test_swc_voltages(file, SimpleMorphCell, swc2jaxley): t_max = 20.0 dt = 0.025 - nseg_per_branch = 8 + ncomp_per_branch = 8 ##################### NEURON ################## h.secondorder = 0 @@ -133,13 +133,13 @@ def test_swc_voltages(file, SimpleMorphCell, swc2jaxley): i3d.instantiate(None) for sec in h.allsec(): - sec.nseg = nseg_per_branch + sec.nseg = ncomp_per_branch pathlengths_neuron = np.asarray([sec.L for sec in h.allsec()]) ####################### jaxley ################## _, pathlengths, _, _, _ = swc2jaxley(fname, max_branch_len=2_000) - cell = SimpleMorphCell(fname, nseg_per_branch, max_branch_len=2_000.0) + cell = SimpleMorphCell(fname, ncomp_per_branch, max_branch_len=2_000.0) cell.insert(HH()) trunk_inds = [1, 4, 5, 13, 15, 21, 23, 24, 29, 33] diff --git a/tests/test_viewing.py b/tests/test_viewing.py index 1e38eb8e..f4fba00f 100644 --- a/tests/test_viewing.py +++ b/tests/test_viewing.py @@ -58,16 +58,16 @@ def test_getitem(SimpleBranch, SimpleCell, SimpleNet): def test_loc_v_comp(SimpleBranch): branch = SimpleBranch(4) - nsegs = branch.nseg_per_branch + ncomps = branch.ncomp_per_branch branch_ind = 0 assert np.all(branch.comp(0).show() == branch.loc(0.0).show()) assert np.all(branch.comp(3).show() == branch.loc(1.0).show()) - inferred_loc = loc_of_index(2, branch_ind, nsegs) + inferred_loc = loc_of_index(2, branch_ind, ncomps) assert np.all(branch.loc(inferred_loc).show() == branch.comp(2).show()) - inferred_ind = local_index_of_loc(0.4, branch_ind, nsegs) + inferred_ind = local_index_of_loc(0.4, branch_ind, ncomps) assert np.all(branch.comp(inferred_ind).show() == branch.loc(0.4).show()) @@ -199,9 +199,9 @@ def test_local_indexing(SimpleNet): def test_indexing_a_compartment_of_many_branches(SimpleBranch): - branch1 = SimpleBranch(nseg=3) - branch2 = SimpleBranch(nseg=4) - branch3 = SimpleBranch(nseg=5) + branch1 = SimpleBranch(ncomp=3) + branch2 = SimpleBranch(ncomp=4) + branch3 = SimpleBranch(ncomp=5) cell1 = jx.Cell([branch1, branch2, branch3], parents=[-1, 0, 0]) cell2 = jx.Cell([branch3, branch2], parents=[-1, 0]) net = jx.Network([cell1, cell2]) @@ -227,9 +227,9 @@ def test_indexing_a_compartment_of_many_branches(SimpleBranch): def test_solve_indexer(): - nsegs = [4, 3, 4, 2, 2, 3, 3] - cumsum_nseg = cumsum_leading_zero(nsegs) - idx = JaxleySolveIndexer(cumsum_nseg) + ncomps = [4, 3, 4, 2, 2, 3, 3] + cumsum_ncomp = cumsum_leading_zero(ncomps) + idx = JaxleySolveIndexer(cumsum_ncomp) branch_inds = np.asarray([0, 2]) assert np.all(idx.first(branch_inds) == np.asarray([0, 7])) assert np.all(idx.last(branch_inds) == np.asarray([3, 10])) @@ -269,7 +269,7 @@ def test_view_attrs(SimpleComp, SimpleBranch, SimpleCell, SimpleNet): exceptions += [ "_cells_list", "_cumsum_nbranchpoints_per_cell", - "_cumsum_nseg_per_cell", + "_cumsum_ncomp_per_cell", ] # for network for module in [