update lecture for today

lectures/lecture-04/1 - For-loops-dicts-files.ipynb

@@ -5,8 +5,147 @@
    "id": "a795ba59-d48d-4f8d-934e-2cbc558071e4",
    "metadata": {},
    "source": [
+    "# News: \n",
+    "\n",
+    "I enabled a `.ipynb` viewer at https://strawlab-rp2.zoologie.uni-freiburg.de/straw/pm21-dragon.\n",
+    "\n",
     "# `exercise-04` review\n",
     "\n",
+    "## Flow control Q3\n",
+    "\n",
+    "Now create a function called `simulate_generation` which takes two arguments, `a` and `Rt`. This function should return `None`. The first argument `a` will be a list with the number of infected individuals and `Rt` is the effective reproduction number, as before. The function should compute the number of newly infected individuals after one generation and append this to the list passed as the first argument.\n",
+    "\n",
+    "Your function signature should look like this:\n",
+    "\n",
+    "```python\n",
+    "    def simulate_generation(a,Rt):\n",
+    "```\n",
+    "\n",
+    "Here is an example that works:\n",
+    "\n",
+    "```python\n",
+    "b = [1]\n",
+    "simulate_generation(b,3)\n",
+    "simulate_generation(b,3)\n",
+    "simulate_generation(b,3)\n",
+    "b\n",
+    "```\n",
+    "\n",
+    "In this above example, `b` would be equal to `[1, 3, 9, 27]` at the end."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5dbf80df-9df2-479b-bc33-fa5ee7465faa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def simulate_generation(a,Rt):\n",
+    "    previous = a[-1]\n",
+    "    new_infected = previous*Rt\n",
+    "    a.append(new_infected)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "13f871cc-57bd-466b-ab2c-335097d45871",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = [1]\n",
+    "simulate_generation(b,3)\n",
+    "simulate_generation(b,3)\n",
+    "simulate_generation(b,3)\n",
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d4cbed4-d429-423c-9730-8eec2e7be271",
+   "metadata": {},
+   "source": [
+    "## Flow control Q4\n",
+    "\n",
+    "Now create a function called `calculate_timeseries_to_1000` which takes one argument, `Rt`. This function should return a list containing the number of infected individuals after each cycle up to and including the first value over 1000 individuals.\n",
+    "\n",
+    "Your function signature should look like this:\n",
+    "\n",
+    "```python\n",
+    "    def calculate_timeseries_to_1000(Rt):\n",
+    "```\n",
+    "\n",
+    "Your code should work so that `calculate_timeseries_to_1000(2)` would return `[1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024]` and that `calculate_timeseries_to_1000(3)` would return `[1, 3, 9, 27, 81, 243, 729, 2187]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "73d2d5a4-7697-4e91-9e0f-95f6ba36511b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Here is a wrong answer from someone:\n",
+    "\n",
+    "def calculate_timeseries_to_1000(Rt):\n",
+    "    infected_series = [1]\n",
+    "    while infected_series[-1] <1000:\n",
+    "        print(infected_series)\n",
+    "        infected_series = infected_series * Rt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "eba13625-66b6-4910-8cdc-67b8cba4e6e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Let's try to run it:\n",
+    "\n",
+    "calculate_timeseries_to_1000(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fa76fadc-1ab5-42b3-9884-7542d5df0cac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def calculate_timeseries_to_1000(Rt):\n",
+    "    a = [1]\n",
+    "    while a[-1] < 1000:\n",
+    "        simulate_generation(a,Rt)\n",
+    "    return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "56ea8eec-1128-4415-94ae-a1060cc2443c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calculate_timeseries_to_1000(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b32e617-dc56-4f32-8953-ce4c4a12dce8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calculate_timeseries_to_1000(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8ef5d53f-d007-4727-8852-5d430eb7fef7",
+   "metadata": {},
+   "source": [
     "* I check your answers based on file name. Please keep the files names exactly as specified, i.e. `my_name.py`.\n",
     "\n",
     "* Example answers:\n",