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Merge pull request #3 from pythonhealthdatascience/img
chore(img): moved /img -> content/img. +update nb
2 parents 2be65c1 + 34b8559 commit 8acf855

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content/02_basic_simpy.ipynb

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"\n",
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"In this first example, let's assume (unrealistically) that prescriptions arrive **exactly** 5 minutes apart.\n",
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"![Pharmacy with prescriptions every 5 minutes](../img/pharmacy.png)\n",
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"![Pharmacy with prescriptions every 5 minutes](./img/pharmacy.png)\n",
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"\n",
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"## 4. The model building blocks\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.1.undefined"
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"version": "3.12.6"
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}
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"nbformat": 4,

content/04_111_model.ipynb

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"Call operators in an 111 (urgent care) service receive calls at a rate of 100 per hour. Call length can be represented by a triangular distribution. Calls last between 5 minutes and 15 minutes. Most calls last 7 minutes. There are 13 call operators.\n",
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"![Call Centre Diagram](../img/callcentre.png)"
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"![Call Centre Diagram](./img/callcentre.png)"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.10"
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"version": "3.12.6"
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}
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"nbformat": 4,

content/05_basic_results.ipynb

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"* 😊 An advantage of this strategy is that it is very **simple**, captures all data, and has minimal computational overhead during a model run!\n",
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"* 😢 A potential disadvantage is that for complex simulation you may end up storing a **large amount of data in memory**. In these circumstances, it may be worth exploring event driven strategies to reduce memory requirements.\n",
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"![](../img/callcentre_waittime.png)\n",
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"![](./img/callcentre_waittime.png)\n",
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"In our example, we will:\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.6"
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}
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},
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"nbformat": 4,
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