some mods

Training_IC_on_Demand.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "2863fcc5",
+   "id": "7a9ce3d8",
    "metadata": {},
    "source": [
     "# Create ICs on Demand"
@@ -11,7 +11,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "26d5bda1",
+   "id": "3b5cc730",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c90efc94",
+   "id": "3b4e1ada",
    "metadata": {},
    "source": [
     "# Setup the model"
@@ -35,7 +35,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "603fb2f7",
+   "id": "b521b99e",
    "metadata": {},
    "outputs": [
     {
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6801dea6",
+   "id": "066fb79e",
    "metadata": {},
    "source": [
     "# test training with white noise"
@@ -68,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "43ae8f71",
+   "id": "8001a82b",
    "metadata": {},
    "outputs": [
     {
@@ -100,7 +100,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "a9716f58",
+   "id": "46589304",
    "metadata": {},
    "outputs": [
     {
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f82355d9",
+   "id": "623c9205",
    "metadata": {},
    "source": [
     "# load BORG data"
@@ -143,7 +143,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "733a7793",
+   "id": "725f2c38",
    "metadata": {},
    "outputs": [
     {
@@ -168,7 +168,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "8542d959",
+   "id": "ae7e24a8",
    "metadata": {},
    "outputs": [
     {
@@ -196,7 +196,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dc019fd6",
+   "id": "1abeba05",
    "metadata": {},
    "source": [
     "## generate sample"
@@ -205,7 +205,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "b7ae1741",
+   "id": "8d7b6bf2",
    "metadata": {},
    "outputs": [
     {
@@ -238,41 +238,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9e7daea8",
+   "id": "7b286278",
    "metadata": {},
    "source": [
     "## get an ensemble"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "36fe2f0b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def incremental_variance(xs):\n",
-    "    w_sum = w_sum2 = mean = S = 0\n",
-    "\n",
-    "    for x in xs:\n",
-    "        w_sum = w_sum + w\n",
-    "        w_sum2 = w_sum2 + w**2\n",
-    "        mean_old = mean\n",
-    "        mean = mean_old + (w / w_sum) * (x - mean_old)\n",
-    "        S = S + w * (x - mean_old) * (x - mean)\n",
-    "\n",
-    "    population_variance = S / w_sum\n",
-    "    # Bessel's correction for weighted samples\n",
-    "    # Frequency weights\n",
-    "    sample_frequency_variance = S / (w_sum - 1)\n",
-    "    # Reliability weights\n",
-    "    sample_reliability_variance = S / (w_sum - w_sum2 / w_sum)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 29,
-   "id": "80e8b13e",
+   "id": "99a8d21a",
    "metadata": {},
    "outputs": [
     {
@@ -308,7 +283,7 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "id": "80c14c44",
+   "id": "2169349e",
    "metadata": {},
    "outputs": [
     {
@@ -348,7 +323,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "754e543e",
+   "id": "7dbea77a",
    "metadata": {},
    "outputs": [],
    "source": []