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", 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" ] @@ -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", 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kZSXyAgJQW14udSyiNljgRB0Y++abKPjgA9QCGHX9Oi7ddx9qLlyQOhaREQuc6A7uX7IEZdu2oVqhQFh9Pa4OH47KrCypYxEBYIET/arQefNQvWcPNEolhjY1oXHsWPycliZ1LCIWOFFnBD3yCPSHD6PU2hr+zc2wionBha+/ljoW9XEscKJO8ouNhU16OopUKngZDFD/9rfI++c/pY5FfRgLnKgLvMaMgcuPPyLXwQFuQsD76afx48aNUseiPooFTtRFA4KD4VNQgCy1GmoAQxcvxg9/+pPUsagPMluBv/POO3jggQfg4OAAZ2fndrcpLS3F9OnT4eDggIEDB+LVV19FMy9dJhlw8vFBcHExTru7wwHAyFWrkL5smdSxqI8xW4Hr9XrMnj0bizq4x3JLSwumT58OvV6P77//Htu3b8e2bduwevVqc0Ui6lb2rq4ILy7G976+sAUw7v33cXz+fKljUV8izOyTTz4RarXaZP0333wjlEql0Gg0xnWbN28WTk5OoqmpqdOfr9VqBQCh1Wq7Iy5RlzU3NYnUkBAhACEAkTJzptSRSOY622uSHQNPT0/HiBEj4OHhYVyXkJAAnU6HnJycDt/X1NQEnU7XZiGSkpWtLSbm5CBlzBgAQMzevUiJjeXtaMnsJCtwjUbTprwBGH/WaDQdvi8pKQlqtdq4+Pr6mjUnUWcolErEnDyJlLg4AEBsaiqOjRnD29GSWXWpwFesWAGFQnHHJT8/31xZAQArV66EVqs1LmVlZWb9PqLOUiiViE1ORurjjwMAYs6exYnQULTo9RInI0tl3ZWNX3nlFTz77LN33CYwMLBTn+Xp6YlTp061WVdZWWl8rSMqlQoqlapT30EkhZjdu5G2cCGiP/4YEwsLkR4YiNG5uVA5OUkdjSxMlwrc3d0d7u7u3fLF0dHReOedd1BVVYWBAwcCAJKTk+Hk5ITQ0NBu+Q4iqUzYuhUZLi4YtX49oi9fxg+BgQjNz4eDm5vU0aiHaLVa5OTk4Pr163j44YfN8h1dKvCuKC0tRXV1NUpLS9HS0oKsf9/BLSgoCI6OjoiPj0doaCieeeYZrFu3DhqNBm+88QYSExO5h00WIWrdOmS6uiJk5UqMuXYNPw4ZgsHnz0Pt5yd1NOpGDQ0NyMvLQ3Z2tnHJyckxHt4dNGgQfv75Z7N8t0IIIczxwc8++yy2b99usv7o0aOIjY0FAFy6dAmLFi1CSkoK+vXrh3nz5mHt2rWwtu78vys6nQ5qtRparRZO/F9U6oXOb9kCvxdegBpAnr093DMz4TZsmNSxqIv0ej0KCgqMBd1a1sXFxeioRn18fBAWFoZ9+/bB1ta209/V2V4zW4H3FBY4yUHB55/D9Xe/g7sQuGBrC4e0NHiNHSt1LGpHc3MzLly40Kaks7OzUVhY2OGV4gMHDkRYWBiGDx+OsLAwhIWFITQ0tMOr0H9NZ3vNbIdQiOg/gufMQbGTE/QzZmCIXo+fo6Nx6bvv4D9litTR+iyDwYDS0tI2hz2ys7ORl5eHpqamdt+jVquNBd1a2MOHDzeex+tp3AMn6kGX09Ohj4lBwM2bqFQqof3iC9z372mHZB5CCFRUVJgc+sjJyUF9fX2773FwcDCW862F7e3tDYVCYfbM3AMn6oUGRUfjypkzKBg7FsGNjbCdPRvZW7ci7LnnpI5mEa5evWpS0tnZ2aipqWl3e1tbW4SEhLTZow4LC8PgwYOhVPb+m7WywIl6mHtYGGxyc3F+5EiMqKuDzR/+gLNaLUbxboad1nrLjdv3qluvJbmdlZUVhg4danKcOigoqEuTJnob+SYnkjHngABYFxbiTGgoRtfUYNgrr+DktWuIfOcdqaP1Kq1T9G4/oXinK7ADAgJMjlMHBwfDzs6uB5P3DBY4kUQcPT0RWlyMjNBQRFVUIOJ//xcnrl/H+E2bpI7W4/R6PX766SeTQx8XLlzocIreoEGDTPaohw0bBkdHxx5OLx0WOJGE7JydMaa4GGnDh2NCcTGiP/wQx6qrMWnHDqmjmUVLSwsuXLhgcujjp59+6nCKnpubW5s96tYpei4uLj2cvvdhgRNJzNrODg8UFCB19GjEnD+PSTt3IqWmBrEHDkgd7a61TtG7/YRiXl4eGhsb232Pk5OTycnEsLAwyaboyQGnERL1EsJgQOqkSYg9cQIAkBIVhZgTJ6DoxbMhhBDQaDQmhz5ycnJQV1fX7nvs7e0RGhpqcpzax8enR6boyQGvxCSSqZTp0xH7zTcAgGPDh2NCVhaUvWCmxLVr10xOJubk5KC6urrd7W1sbDqcomdlZdXD6eWFBU4kY8eefhoT/vlPKAGcGDwY43JyYOPg0CPfrdPpkJuba7JX3dGDVpRKJYYOHWpy0UtQUBBsbGx6JLOl4YU8RDI26bPP8L2LC8Zu3IjxFy/i5JAhCM/Lg91d3lujPTdu3DDeRe/WPevS0tIO3zN48GCTPeqQkBCLnKInByxwol7qgb/9DadcXDDy7bcRqdHgTGAg7svNheMdHnjSHr1ej8LCQpNDHxcuXIChg+d2ent7m+xRh4aG9qkpenLAAifqxcb9z/8gy9UVQ15+GaNrapAdFIRBP/4IlyFDTLZtaWlBcXGxyR51QUFBh1P0BgwY0O7NmVxdXc09NOoGLHCiXu7+pUuRo1bD67nnEFZfj4LQUFzcuxcVQrTZq77TFL3+/fubTM9rnaLHmR/yxQInkoHh8+ej0NkZ+lmzEKzXo/Dhh/EigEu3bWdnZ9dmil5rYfv6+rKoLVDvnWBKRG0MffRRlH72GUoA2AJoaWcbOzs79OvXz2RheVsmTiMkkpn03bvRcPUqqgcMaHMIpaioqMOTkp6enu0+MYb/zfROnAdO1Mc0NjYiPz/f5CTmxYsXO3yPn5+fyeGWYcOGwd7evueCkwkWOBEBAGpra9t9anp5eXm72ysUCgwZMsRkdsp9993XpQfz0t1jgRPRHVVXVyMnJ8fk8vhr1661u721tTWCg4NNDsUEBgby0vhuxgInoi4TQqCqqspkbz07Oxu1tbXtvsfOzg7Dhg0zORTj5+fHk6d3iQVORN1GCIGysjKTvfXc3Nw7zj1v78HAHh4eLPZfwQInIrNraWlBSUmJyd56fn5+h1d/urq6mhxfDwsL49Wft2CBE5FkWu+/cvsDHe401dHLy6vd+6/079+/h9NLjwVORL3OjRs32p3qeOnS7deU/oe/v3+7d0C05KmOLHAiko3a2tp270FeUVHR7vZKpbLDqY6WcA9yyQv84sWLePvtt3HkyBFoNBp4e3vj6aefxuuvv95mLum5c+eQmJiI06dPw93dHYsXL8by5cs7/T0scCLL1foUoNtPnt7pKUDtTXUMCAiQ1VRHyR/okJ+fD4PBgC1btiAoKAjZ2dlYuHAh6uvr8d577xlDxsfHIy4uDh999BHOnz+PBQsWwNnZGc8//7y5ohGRTAwYMACTJk3CpEmTjOuEEKisrDTZW8/OzkZdXZ3xz7ey1Jt89eghlPXr12Pz5s0oLi4GAGzevBmvv/46NBqNca98xYoV2Lt3L/Lz89v9jKamJjQ1NRl/1ul08PX15R44UR8nhEBpaanJidPOTHW8/VCM1FMdJd8Db49Wq20zVSg9PR2TJk1qc0glISEB7777LmpqauDi4mLyGUlJSXjrrbd6JC8RyYdCoYC/vz/8/f3x8MMPG9ff6UEXtbW1yMjIQEZGRpvPuvVBF60F3xsfdNFje+BFRUWIiIjAe++9h4ULFwIA4uPjERAQgC1bthi3y83NxfDhw5Gbm4thw4aZfA73wImoO3T0qLmioiJ0VIu3P2pu+PDhZpnqaLY98BUrVuDdd9+94zZ5eXkICQkx/nz58mVMmzYNs2fPNpb33VKpVFCpVPf0GUREtra2xitF58yZY1zf0NCA/Px8kxOnpaWlKC8vR3l5OZKTk9t8VuvDnm8t95542HOX98CvXLnS4c1uWgUGBhoPi5SXlyM2NhZRUVHYtm0blMr/PEPi97//PXQ6Hfbu3Wtcd/ToUUyePBnV1dXtHkK5HWehEFFP0Ol0xqmOt5a7RqNpd3ulUomgoCCEhYVhx44dXbqTo9n2wN3d3eHu7t6pbS9fvowHH3wQERER+OSTT9qUNwBER0fj9ddfx82bN41zN5OTkxEcHNyp8iYi6ilOTk6IiopCVFRUm/WtUx1v3VvPzs5GTU0NfvrpJ9TW1prtNrxmOwZ++fJlxMbGwt/fH9u3b28zB9PT0xPALyc1g4ODER8fj9deew3Z2dlYsGAB3n///U5PI+QeOBH1NkIIaDQaZGdnQ6vVYtasWV16v+SzUJKTk1FUVISioiL4+Pi0ea313wy1Wo2DBw8iMTERERERcHNzw+rVqzkHnIhkTaFQwMvLC15eXub9Hl5KT0TUu3S21/hUeiIimWKBExHJVI9eiWkOrUeAdDqdxEmIiLpHa5/92hFu2Rd463P6fH19JU5CRNS9amtroVarO3xd9icxDQYDysvL0b9//y7dfKb1EvyysjKLO/nJsckTxyZP5hibEAK1tbXw9vY2uX7mVrLfA1cqlSbTFLvCycnJ4v5CteLY5Iljk6fuHtud9rxb8SQmEZFMscCJiGSqzxa4SqXCmjVrLPLOhhybPHFs8iTl2GR/EpOIqK/qs3vgRERyxwInIpIpFjgRkUyxwImIZIoFTkQkU322wDdt2oTBgwfDzs4OkZGROHXqlNSRuuzYsWOYMWMGvL29oVAo2jxbFPjlctzVq1fDy8sL9vb2iIuLQ2FhoTRhuyApKQljx45F//79MXDgQMycORMFBQVttmlsbERiYiIGDBgAR0dHPP7446isrJQocdds3rwZI0eONF65Fx0djW+//db4upzHdqu1a9dCoVBg6dKlxnVyHtubb74JhULRZrn14e1SjK1PFvjnn3+OZcuWYc2aNThz5gzCw8ORkJCAqqoqqaN1SX19PcLDw7Fp06Z2X1+3bh02bNiAjz76CCdPnkS/fv2QkJCAxsbGHk7aNampqUhMTERGRgaSk5Nx8+ZNxMfHo76+3rjNyy+/jK+++gq7du1CamoqysvL8dhjj0mYuvN8fHywdu1aZGZm4ocffsDkyZPxyCOPICcnB4C8x9bq9OnT2LJlC0aOHNlmvdzHNnz4cFRUVBiXtLQ042uSjE30QePGjROJiYnGn1taWoS3t7dISkqSMNW9ASD27Nlj/NlgMAhPT0+xfv1647rr168LlUolduzYIUHCu1dVVSUAiNTUVCHEL+OwsbERu3btMm6Tl5cnAIj09HSpYt4TFxcX8fHHH1vE2Gpra8XQoUNFcnKyiImJEUuWLBFCyP/3tmbNGhEeHt7ua1KNrc/tgev1emRmZiIuLs64TqlUIi4uDunp6RIm614lJSXQaDRtxqlWqxEZGSm7cWq1WgCAq6srACAzMxM3b95sM7aQkBD4+fnJbmwtLS3YuXMn6uvrER0dbRFjS0xMxPTp09uMAbCM31thYSG8vb0RGBiIuXPnorS0FIB0Y5P93Qi76urVq2hpaYGHh0eb9R4eHsjPz5coVffTaDQA0O44W1+TA4PBgKVLl2L8+PEICwsD8MvYbG1t4ezs3GZbOY3t/PnziI6ORmNjIxwdHbFnzx6EhoYiKytL1mPbuXMnzpw5g9OnT5u8JvffW2RkJLZt24bg4GBUVFTgrbfewsSJE5GdnS3Z2PpcgZO8JCYmIjs7u82xRksQHByMrKwsaLVa7N69G/PmzUNqaqrUse5JWVkZlixZguTkZNjZ2Ukdp9s99NBDxj+PHDkSkZGR8Pf3xxdffAF7e3tJMvW5Qyhubm6wsrIyOTtcWVkJT09PiVJ1v9axyHmcL730Er7++mscPXq0zT3fPT09odfrcf369Tbby2lstra2CAoKQkREBJKSkhAeHo6//vWvsh5bZmYmqqqqMHr0aFhbW8Pa2hqpqanYsGEDrK2t4eHhIduxtcfZ2Rn33XcfioqKJPu99bkCt7W1RUREBA4fPmxcZzAYcPjwYURHR0uYrHsFBATA09OzzTh1Oh1OnjzZ68cphMBLL72EPXv24MiRIwgICGjzekREBGxsbNqMraCgAKWlpb1+bB0xGAxoamqS9dimTJmC8+fPIysry7iMGTMGc+fONf5ZrmNrT11dHS5cuAAvLy/pfm9mOz3ai+3cuVOoVCqxbds2kZubK55//nnh7OwsNBqN1NG6pLa2Vpw9e1acPXtWABB/+ctfxNmzZ8WlS5eEEEKsXbtWODs7i3379olz586JRx55RAQEBIgbN25InPzOFi1aJNRqtUhJSREVFRXGpaGhwbjNCy+8IPz8/MSRI0fEDz/8IKKjo0V0dLSEqTtvxYoVIjU1VZSUlIhz586JFStWCIVCIQ4ePCiEkPfYbnfrLBQh5D22V155RaSkpIiSkhJx4sQJERcXJ9zc3ERVVZUQQpqx9ckCF0KIv/3tb8LPz0/Y2tqKcePGiYyMDKkjddnRo0cFAJNl3rx5QohfphKuWrVKeHh4CJVKJaZMmSIKCgqkDd0J7Y0JgPjkk0+M29y4cUO8+OKLwsXFRTg4OIhHH31UVFRUSBe6CxYsWCD8/f2Fra2tcHd3F1OmTDGWtxDyHtvtbi9wOY9tzpw5wsvLS9ja2opBgwaJOXPmiKKiIuPrUoyN9wMnIpKpPncMnIjIUrDAiYhkigVORCRTLHAiIpligRMRyRQLnIhIpljgREQyxQInIpIpFjgRkUyxwImIZIoFTkQkU/8PPwmNBt4zGKsAAAAASUVORK5CYII=\n", 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" ] @@ -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", 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scKJGhIWF4fDhw7C2tkZ8fDyCg4NRVFQkdSwiPRY40QMMGjQIx44dQ6dOnXDmzBkMGjQI+fn5UsciAsACJ/pDTz75JE6cOAFnZ2ekpKQgKCgI165dkzoWEQucqCl69+6NpKQkeHp64sqVKxgwYADS0tKkjkUdHAucqIm6deuGxMRE9OrVC9evX8fAgQNx/vx5qWNRB8YCJ2qGLl26ICEhAU888QRu3ryJwYMHIzExUepY1EGxwImayd7eHnFxcRg4cCB0Oh1CQ0Nx+PBhqWNRB9RqBb5y5Ur85S9/gaWlJWxtbRvcJicnB2FhYbC0tETnzp3x1ltvoaqqqrUiEbUYlUqFw4cPY+TIkbhz5w5GjRqF6OhoqWNRB9NqBV5ZWYkJEyZg9uzZDb5eXV2NsLAwVFZW4scff8S2bduwdetWLF26tLUiEbUoCwsLxMTEYOLEibh79y4mTZqE//znP1LHoo5EtLItW7YItVpdb/13330njIyMhEaj0a/buHGjUKlUoqKiosmfr9VqBQCh1WpbIi5Rs1VVVYmZM2cKAAKA+PDDD6WORDLX1F6T7Bj4yZMn0adPHzg6OurXhYaGQqfTITU1tdH3VVRUQKfT1VmIpGRsbIzNmzfjzTffBAC8/vrrWL58OW9HS61OsgLXaDR1yhuA/meNRtPo+6KioqBWq/WLm5tbq+YkagqFQoE1a9bg/fffBwCsWLECCxYs4O1oqVU1q8AjIyOhUCgeuKSnp7dWVgDAokWLoNVq9Utubm6rfh9RUykUCixevBjr1q0DAHz00Ud45ZVXUF1dLXEyMlQmzdn4jTfewIsvvvjAbbp169akz3JycsKZM2fqrCsoKNC/1hilUgmlUtmk7yCSwty5c6FSqTBjxgxs2bIFxcXF2LFjB//eUotrVoE7ODjAwcGhRb44MDAQK1euRGFhITp37gwAiI2NhUqlQq9evVrkO4ikMn36dNjY2GDy5MnYs2cPiouL8c0338DS0lLqaNRGtFotUlNTcfv2bYwcObJVvqNZBd4cOTk5KCoqQk5ODqqrq5GcnAwA8PLygrW1NUJCQtCrVy9MnToVa9asgUajwbvvvouIiAjuqZBBePbZZ3HgwAGMGTMG33//PUJDQ3HgwAGo1Wqpo1ELKisrQ1paGlJSUvRLamqq/vBuly5d8Ouvv7bOl7fWNJjp06frp1Xdu8THx+u3uXbtmhgxYoSwsLAQ9vb24o033hB3795t1vdwGiG1dz/88INQq9UCgOjXr58oLCyUOhL9CRUVFeLixYviq6++EosXLxajR48Wjz76qFAoFA12HQDh6uoqhg8f3qyp0UI0vdcUQsh7rpNOp4NarYZWq4VKpZI6DlGDkpOTERISghs3bsDHxwexsbFwdXWVOhY1oKqqCleuXEFqamqdverMzMxGrxTv3LkzfH190bt3b/j6+sLX1xe9evVq9Cr0P9LUXmu1QyhE9H8ee+wxJCYmYtiwYUhPT8eAAQNw9OhReHl5SR2tw6qpqUFOTk6dwx4pKSlIS0tDRUVFg+9Rq9X6gq4t7N69e+vP47U1FjhRG/H29kZSUhKCg4ORmZmJoKAgHDlyBH369JE6mkETQiA/P79OSdf+ubS0tMH3WFpa6sv53sJ2cXGBQqFo4xE0jgVO1Ibc3d2RmJiIkJAQXLx4EYMGDcKhQ4cQEBAgdTSDcPPmzXolnZKSglu3bjW4vZmZGXx8fOrsUfv6+qJr164wMmr/N2vlMXAiCdy6dQsjR47EqVOnYGVlhf3792Pw4MFSx5KN2ltu3L9XXXstyf2MjY3RvXv3esepvby8YGLS/vZjm9prLHAiiZSUlGDMmDGIi4uDUqnE7t27MWrUKKljtSu1U/TuP6H4oCuwPT096x2n9vb2hrm5eRsmfzgscCIZKC8vx+TJk7F3714YGxtj+/bteP7556WO1eYqKyvx888/1zv0ceXKlUZvCtalS5d6e9Q9e/aEtbV1G6dveZyFQiQD5ubmiI6OxowZM/Dll1/ihRdegFarbfQ++nJXXV2NK1eu1Dv08fPPPzc6Rc/e3r7OHnXtFD07O7s2Tt/+sMCJJGZiYoKtW7dCpVJhw4YNmDNnDrRaLSIjI6WO9qfVTtG7/4RiWloaysvLG3yPSqWqdzLR19dXsil6csACJ2oHjIyM8Mknn8DW1hYrV67U33XzH//4R7uatnY/IQQ0Gk29Qx+pqakoKSlp8D0WFhbo1atXvePUrq6u7Xqs7RELnKidUCgUeP/996FWq/H2229j1apV0Gq1WL9+fbuY0vbbb7/VO5mYmpqKoqKiBrc3NTVtdIqesbFxG6c3TCxwonbmrbfeglqtxqxZs7Bx40bodDps2bIFpqambfL9Op0Oly9frrdX3diDVoyMjNC9e/d6F714eXm1WeaOigVO1A69+uqrsLGxwbRp0/Df//4XxcXF+Prrr1t0KtydO3f0d9G7d886Jyen0fd07dq13h61j4+PrKboGRIWOFE7NXnyZNjY2GD8+PH49ttvERYWhn379jV7mlxlZSUyMzPrHfq4cuVKo498c3FxqbdH3atXL4OYomdIOA+cqJ07fvw4wsPDUVJSgoCAAHz33Xfo1KlTve2qq6tx9erVenvUGRkZjU7Re+SRRxq8OVNDn09thxfyEBmQM2fOYMSIESgqKoK3tze2b9+Omzdv1tmrftAUPRsbm3rT82qn6HHmR/vDAicyMCkpKRgyZAhu3LjR6Dbm5uZ1pujVFrabmxuLWkaa2mvSz00ioibx9fXFnj17HriNubk5rKys6i0sb8PEPXAimUlISIBWq0VFRUWdQyhZWVmNnpR0cnJq8Ikx/DfTPvEQClEHU15ejvT09HonMa9du9boe9zd3esdbunZsycsLCzaLjjVwwInIgBAcXFxg09Nz8vLa3B7hUKBRx99tN7slB49esDMzKyN03dMLHAieqCioiKkpqbWuzz+t99+a3B7ExMTeHt71zsU061bN14a38JY4ETUbEIIFBYW1ttbT0lJQXFxcYPvMTc3R8+ePesdinF3d+fJ0z+JBU5ELUYIgdzc3Hp765cvX37g3POGHgzs6OjIYv8DLHAianXV1dXIzs6ut7eenp7e6NWfnTp1qnd83dfXl1d/3oMFTkSSqb3/yv0PdHjQVEdnZ+cG779iY2PTxumlxwInonbnzp07DU51/OWXXxp9j4eHR4N3QDTkqY4scCKSjeLi4gbvQZ6fn9/g9kZGRo1OdTSEe5BLXuDXrl3De++9h2PHjkGj0cDFxQUvvPACFi9eXGcu6cWLFxEREYGzZ8/CwcEBc+fOxdtvv93k72GBExmu2qcA3X/y9EFPAWpoqqOnp6espjpK/lT69PR01NTUYPPmzfDy8kJKSgpmzpyJ0tJSfPDBB/qQISEhCA4OxqZNm3Dp0iXMmDEDtra2ePXVV1srGhHJxCOPPIKBAwdi4MCB+nVCCBQUFNTbW09JSUFJSYn+z/cy1Jt8tekhlLVr12Ljxo24evUqAGDjxo1YvHgxNBqNfq88MjISe/fuRXp6eoOfUVFRgYqKCv3POp0Obm5u3AMn6uCEEMjJyal34rQpUx3vPxQj9VRHyffAG6LVautMFTp58iQGDhxY55BKaGgoVq9ejVu3bsHOzq7eZ0RFRWHFihVtkpeI5EOhUMDDwwMeHh4YOXKkfv2DHnRRXFyMU6dO4dSpU3U+694HXdQWfHt80EWb7YFnZWXB398fH3zwAWbOnAkACAkJgaenJzZv3qzf7vLly+jduzcuX76Mnj171vsc7oETUUto7FFzWVlZaKwW73/UXO/evVtlqmOr7YFHRkZi9erVD9wmLS0NPj4++p+vX7+O4cOHY8KECfry/rOUSiWUSuVDfQYRkZmZmf5K0YkTJ+rXl5WVIT09vd6J05ycHOTl5SEvLw+xsbF1Pqv2Yc/3lntbPOy52XvgN27caPRmN7W6deumPyySl5eHZ555Bv3798fWrVthZPR/z5CYNm0adDod9u7dq18XHx+PIUOGoKioqMFDKPfjLBQiags6nU4/1fHectdoNA1ub2RkBC8vL/j6+mLnzp3NupNjq+2BOzg4wMHBoUnbXr9+HYMHD4a/vz+2bNlSp7wBIDAwEIsXL8bdu3f1czdjY2Ph7e3dpPImImorKpUK/fv3R//+/eusr53qeO/eekpKCm7duoWff/4ZxcXFrXYb3lY7Bn79+nU888wz8PDwwLZt2+rMwXRycgLw+0lNb29vhISEYOHChUhJScGMGTPw4YcfNnkaIffAiai9EUJAo9EgJSUFWq0W48ePb9b7JZ+FEhsbi6ysLGRlZcHV1bXOa7X/Z6jVahw5cgQRERHw9/eHvb09li5dyjngRCRrCoUCzs7OcHZ2bt3v4aX0RETtC59KT0Rk4FjgREQy1aZXYraG2iNAOp1O4iRERC2jts/+6Ai37Au89jl9bm5uEichImpZxcXFUKvVjb4u+5OYNTU1yMvLg42NTbNuPlN7CX5ubq7Bnfzk2OSJY5On1hibEALFxcVwcXGpd/3MvWS/B25kZFRvmmJzqFQqg/sLVYtjkyeOTZ5aemwP2vOuxZOYREQyxQInIpKpDlvgSqUSy5YtM8g7G3Js8sSxyZOUY5P9SUwioo6qw+6BExHJHQuciEimWOBERDLFAicikikWOBGRTHXYAt+wYQO6du0Kc3NzBAQE4MyZM1JHarYTJ04gPDwcLi4uUCgUdZ4tCvx+Oe7SpUvh7OwMCwsLBAcHIzMzU5qwzRAVFYUnn3wSNjY26Ny5M8aMGYOMjIw625SXlyMiIgKPPPIIrK2tMW7cOBQUFEiUuHk2btyIvn376q/cCwwMxKFDh/Svy3ls91q1ahUUCgXmz5+vXyfnsS1fvhwKhaLOcu/D26UYW4cs8K+//hoLFizAsmXLcP78efj5+SE0NBSFhYVSR2uW0tJS+Pn5YcOGDQ2+vmbNGqxbtw6bNm3C6dOnYWVlhdDQUJSXl7dx0uZJSEhAREQETp06hdjYWNy9exchISEoLS3Vb/P6669j//79iI6ORkJCAvLy8vDss89KmLrpXF1dsWrVKpw7dw4//fQThgwZgtGjRyM1NRWAvMdW6+zZs9i8eTP69u1bZ73cx9a7d2/k5+frl6SkJP1rkoxNdEBPPfWUiIiI0P9cXV0tXFxcRFRUlISpHg4AERMTo/+5pqZGODk5ibVr1+rX3b59WyiVSrFz504JEv55hYWFAoBISEgQQvw+DlNTUxEdHa3fJi0tTQAQJ0+elCrmQ7GzsxNffPGFQYytuLhYdO/eXcTGxopBgwaJefPmCSHk/3tbtmyZ8PPza/A1qcbW4fbAKysrce7cOQQHB+vXGRkZITg4GCdPnpQwWcvKzs6GRqOpM061Wo2AgADZjVOr1QIAOnXqBAA4d+4c7t69W2dsPj4+cHd3l93YqqursWvXLpSWliIwMNAgxhYREYGwsLA6YwAM4/eWmZkJFxcXdOvWDVOmTEFOTg4A6cYm+7sRNtfNmzdRXV0NR0fHOusdHR2Rnp4uUaqWp9FoAKDBcda+Jgc1NTWYP38+nn76afj6+gL4fWxmZmawtbWts62cxnbp0iUEBgaivLwc1tbWiImJQa9evZCcnCzrse3atQvnz5/H2bNn670m999bQEAAtm7dCm9vb+Tn52PFihUICgpCSkqKZGPrcAVO8hIREYGUlJQ6xxoNgbe3N5KTk6HVarFnzx5Mnz4dCQkJUsd6KLm5uZg3bx5iY2Nhbm4udZwWN2LECP2f+/bti4CAAHh4eGD37t2wsLCQJFOHO4Rib28PY2PjemeHCwoK4OTkJFGqllc7FjmP87XXXsOBAwcQHx9f557vTk5OqKysxO3bt+tsL6exmZmZwcvLC/7+/oiKioKfnx8+/vhjWY/t3LlzKCwsxOOPPw4TExOYmJggISEB69atg4mJCRwdHWU7tobY2tqiR48eyMrKkuz31uEK3MzMDP7+/oiLi9Ovq6mpQVxcHAIDAyVM1rI8PT3h5ORUZ5w6nQ6nT59u9+MUQuC1115DTEwMjh07Bk9Pzzqv+/v7w9TUtM7YMjIykJOT0+7H1piamhpUVFTIemxDhw7FpUuXkJycrF+eeOIJTJkyRf9nuY6tISUlJbhy5QqcnZ2l+7212unRdmzXrl1CqVSKrVu3isuXL4tXX31V2NraCo1GI3W0ZikuLhYXLlwQFy5cEADEv/71L3HhwgXxyy+/CCGEWLVqlbC1tRX79u0TFy9eFKNHjxaenp7izp07Eid/sNmzZwu1Wi2OHz8u8vPz9UtZWZl+m1mzZgl3d3dx7Ngx8dNPP4nAwEARGBgoYeqmi4yMFAkJCSI7O1tcvHhRREZGCoVCIY4cOSKEkPfY7nfvLBQh5D22N954Qxw/flxkZ2eLH374QQQHBwt7e3tRWFgohJBmbB2ywIUQ4pNPPhHu7u7CzMxMPPXUU+LUqVNSR2q2+Ph4AaDeMn36dCHE71MJlyxZIhwdHYVSqRRDhw4VGRkZ0oZugobGBEBs2bJFv82dO3fEnDlzhJ2dnbC0tBRjx44V+fn50oVuhhkzZggPDw9hZmYmHBwcxNChQ/XlLYS8x3a/+wtczmObOHGicHZ2FmZmZqJLly5i4sSJIisrS/+6FGPj/cCJiGSqwx0DJyIyFCxwIiKZYoETEckUC5yISKZY4EREMsUCJyKSKRY4EZFMscCJiGSKBU5EJFMscCIimWKBExHJ1P8D6Jzl1+lHxSkAAAAASUVORK5CYII=\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", 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" ] @@ -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", 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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", 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" ] @@ -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": [ - "
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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
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" - ], "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 ad841bd6..672e78a7 100644 --- a/docs/tutorials/08_importing_morphologies.ipynb +++ b/docs/tutorials/08_importing_morphologies.ipynb @@ -650,10 +650,6 @@ } ], "source": [ - "# We noticed a bug in visualizations when the number of compartments is not the same\n", - "# in all branches. We are working on fixing it.\n", - "cell = jx.read_swc(fname, ncomp=2)\n", - "\n", "# visualize the cell\n", "cell.vis()\n", "plt.axis(\"off\")\n", @@ -713,7 +709,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] 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 8f2ca15f..3d6b39da 100644 --- a/jaxley/modules/cell.py +++ b/jaxley/modules/cell.py @@ -19,7 +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, @@ -96,18 +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() @@ -149,7 +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"] @@ -160,29 +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, @@ -210,14 +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 @@ -225,15 +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 [