From 644617a5b58ee2db1d30b1033e96227a3862e8d6 Mon Sep 17 00:00:00 2001
From: mariuzka <64357369+mariuzka@users.noreply.github.com>
Date: Fri, 15 Dec 2023 17:42:31 +0100
Subject: [PATCH] Update introduction_static_networks.ipynb

---
 .../introduction_static_networks.ipynb        | 232 +++++++++---------
 1 file changed, 114 insertions(+), 118 deletions(-)

diff --git a/docs/examples/introduction_static_networks.ipynb b/docs/examples/introduction_static_networks.ipynb
index 7b5d835..1702666 100644
--- a/docs/examples/introduction_static_networks.ipynb
+++ b/docs/examples/introduction_static_networks.ipynb
@@ -2,15 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
     "import popy.utils as utils\n",
     "import popy\n",
     "from popy.pop_maker import PopMaker\n",
-    "from bokehgraph import BokehGraph\n",
-    "import networkx as nx\n",
     "import pandas as pd"
    ]
   },
@@ -29,18 +27,16 @@
     "As viruses are transmitted via contacts between people, which can be represented as network graphs, network data on the contacts within a school would be a perfect base for building the simulation.\n",
     "However, in most situations, network data is not available.\n",
     "In those cases, Popy can be a solution for missing network data.\n",
-    "Popy enables you to create artificial networks by defining who meets whom.\n",
+    "Popy enables you to create artificial networks by defining different locations where specific people meet.\n",
     "\n",
     "In the following, we want to recreate the contact network of a school.\n",
-    "This school consists of multiple classes.\n",
-    "Each class consists of multiple students and at least one teacher.\n",
-    "\n",
-    "Asume we collected the following sample of individual data:"
+    "Assume we have access to the following sample of individual data collected in a school.\n",
+    "We use this data as a starting point for our network."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -125,7 +121,7 @@
        "4  pupil      m    2.0      4             3"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -137,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
@@ -222,7 +218,7 @@
        "41  social_worker      w    NaN      4             0"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -247,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -260,14 +256,14 @@
    "metadata": {},
    "source": [
     "Using the `PopMaker`, we can create both the population of agents and the location objects.\n",
-    "If we are only interested in the network, only the agents are relevant for us, as they contain all the relevant network information.\n",
-    "If we want to build a simulatio model, the locations become relevant, too.\n",
-    "For now, we only focus on the creation of static networks and, thus, ignore the created locations."
+    "If we are only interested in the network, only the agents are relevant to us, as they contain all the relevant network information.\n",
+    "If we want to build a simulation model, the locations become relevant, too.\n",
+    "For now, we only focus on creating static networks and, thus, ignore the created locations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
@@ -286,7 +282,7 @@
        "LocationList (21 objects)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -304,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
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@@ -319,7 +315,7 @@
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       "application/vnd.bokehjs_exec.v0+json": ""
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@@ -346,17 +342,17 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The network graph shows that if we do not specify the location further, for each pair of random agents one location of this type is created.\n",
+    "The network graph shows that if we do not specify the location further, one location of this type is created for each pair of random agents.\n",
     "\n",
     "## Setting the size\n",
     "\n",
-    "In the next step, we specify the number of people that belong to one classroom.\n",
-    "In this example we assume very small classrooms of four agents."
+    "In the next step, we specify the number of people in one classroom.\n",
+    "In this example, we assume tiny classrooms of four agents."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -371,7 +367,7 @@
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@@ -405,13 +401,13 @@
    "cell_type": "markdown",
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    "source": [
-    "As you can see, some classrooms appear to have more than 4 members.\n",
+    "As you can see, some classrooms have more than four members.\n",
     "If you want to make sure that no classroom has more than four members, set the location attribute `allow_overcrowding` to `False`:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
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    "cell_type": "markdown",
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-    "## Building seperated locations\n",
+    "## Building separated locations\n",
     "\n",
-    "If we color the nodes by the grade of the agents, we can see that the classrooms are not seperated by grade."
+    "If we color the nodes by the grade of the agents, we can see that the classrooms are not separated by grade."
    ]
   },
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-   "execution_count": 12,
+   "execution_count": 39,
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    "cell_type": "markdown",
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-    "As we want to make sure that classes are seperated by grade, we use the location method `split()` to implement seperated classrooms for each grade."
+    "To ensure that classes are separated by grade, we use the location method `split()` to implement separated classrooms for each grade."
    ]
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   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
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-    "## Keep agents together\n",
+    "## Keeping agents together\n",
     "\n",
     "The following plot shows that the members of one friend group are distributed over different classrooms."
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-    "Although this is a very realistic situation, in this example we assume that all members of a friend group are always in the same class.\n",
+    "Although this is a very realistic situation, in this example, we assume that all friend group members are always in the same class.\n",
     "To implement that, we use the location method `stick_together()`:"
    ]
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+   "execution_count": 42,
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@@ -749,12 +745,12 @@
     "\n",
     "Until now, all edges between nodes have a weight of `1`.\n",
     "The location method `weight()` can be used to set different weights.\n",
-    "In the following we set the weight of all edges generated by a classroom to `5`."
+    "In the following, we set the weight of all edges generated by a classroom to `5`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -769,7 +765,7 @@
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-    "As the value which is returned by `location.weight()` refers to the weight between the agent and the location, all the weights between the agents and the locations must be somehow combined when determining the weight between agents (aka graph projection).\n",
-    "How those weights are combined is defined in the location method `project_weights()`.\n",
-    "By default, `project_weights()` returns the smallest weight of the two weights which are to be combined.\n",
+    "As the value returned by `location.weight()` refers to the weight between the agent and the location, all the weights between the agents and the locations must be somehow combined when determining the weight between agents (aka graph projection).\n",
+    "The location method `project_weights()` defines how those weights are combined.\n",
+    "By default, `project_weights()` returns the smallest weight of the two to be combined.\n",
     "The code cell below shows how `project_weights()` combines the two weights by default.\n",
-    "In this example we keep this way of combining the weights, but this method could be easily rewritten."
+    "In this example, we keep this way of combining the weights, but this method could be easily rewritten."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -906,7 +902,7 @@
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     "## More than one location\n",
     "\n",
-    "In the next step we introduce a school as a second type of locations.\n",
+    "In the next step, we introduce a school as a second type of location.\n",
     "Each school has about `20` members."
    ]
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-    "## Nesting of locations\n",
+    "## Nesting locations\n",
     "\n",
     "The plot above shows two big clusters.\n",
     "Each of those clusters represents one school.\n",
     "The plot above also shows something unrealistic: The schools are connected because members of one class are not always in the same school.\n",
-    "To fix this issue we can use the `School`-method `stick_together()`.\n",
-    "This works because each agent gets a new attribute named after the location class and set to a location identifier, when getting assigned to a location.\n",
-    "This attribute can be used afterwards to define the structure of other locations."
+    "We can use the `School`-method `stick_together()` to fix this issue.\n",
+    "This works because each agent gets a new attribute named after the location class and is set to a location identifier when assigned to a location.\n",
+    "This attribute can be used afterward to define the structure of other locations."
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-    "However, it is very important that the agents are assigned to the classrooms before getting assigned to the schools.\n",
+    "However, the agents must be assigned to the classrooms before getting assigned to the schools.\n",
     "If we build schools before classrooms, the above method of sticking together classrooms does not work anymore:"
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     "However, this approach is limited because we can only specify one location class as the return value of `stick_together()`.\n",
     "\n",
     "Another way to nest locations into other locations is to use the location method `nest()`.\n",
-    "The method `nest()` can either return `None` or a location class.\n",
+    "The method `nest()` can return `None` or a location class.\n",
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-    "For instance, in order to nest classrooms within schools, we have to define the method `nest()` for the location `ClassRoom` and let this method return `School`.\n",
-    "Again, the order of location creation plays a crucial role: The level-1 location has to be always created after the level-2 location."
+    "For instance, to nest classrooms within schools, we must define the method `nest()` for the location `ClassRoom` and let this method return `School`.\n",
+    "Again, the order of location creation plays a crucial role: The level-1 location must always be created after the level-2 location."
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@@ -1327,7 +1323,7 @@
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+   "execution_count": 51,
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