added all files to ws1

WS1/intro_to_ws1.md

@@ -21,5 +21,3 @@ with examples of upstreams and downstream process modelling. This course was pro
 |  Module 4    |   design of reactors, combine mass and energy balances |
 |  Module 5    |   Fed-batch model for E.coli (model A)                 |
 |  Module 6&7  |   diafiltration and cell lysis (downstream)            |
-
-

WS1/module_2/0_intro_module_2.ipynb

@@ -0,0 +1,25 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Module 2\n",
+    "\n",
+    "in this modules, we will explore the following concepts\n",
+    "\n",
+    "1. Dictionaries & Sets\n",
+    "2. Dataframes\n",
+    "3. Numpy\n",
+    "4. Visualizations"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

WS1/module_2/5_exercise_1.ipynb

@@ -0,0 +1,300 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1ab533bc-63d7-4c28-9482-176cb447ac54",
+   "metadata": {},
+   "source": [
+    "# Exercises about dictionaries, sets, dataframes, arrays and visualizations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "39ef4c7d-6ddc-4119-b2b8-54088051d957",
+   "metadata": {},
+   "source": [
+    "## Dictionaries and sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7149dfaa-0417-4c7d-95c3-8e7d3e12e5ff",
+   "metadata": {},
+   "source": [
+    "**Exercise**: Take the list of reactors below and create a dictionary where the keys are the elements in the list (the reactors), and the values are the indexes of the elements in the list.\n",
+    "You can do this by using both a for loop or a list comprehension.\n",
+    "\n",
+    "Your result should look like this:\n",
+    "- dict_reactors = {'batch': 0, 'continuous stirred tank': 1, 'plug flow': 2}\n",
+    "\n",
+    "\n",
+    "Level: <span style=\"color:green\">Easy</span>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ca455779-70dd-44da-a827-a85eeef8c594",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_reactors = ['batch', 'continuous stirred tank', 'plug flow']\n",
+    "\n",
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "048c509f-df22-4781-9cf7-6638b2d036ee",
+   "metadata": {},
+   "source": [
+    "Now do the same exercise as above, but instead of the index, the values should be properties of the various reactors!\n",
+    "\n",
+    "\n",
+    "Level: <span style=\"color:green\">Easy</span>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ebfcbc9-50c3-44c1-b972-19dc01e5c59d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea036b2d-2573-4ea9-8350-f17de29f212a",
+   "metadata": {},
+   "source": [
+    "**Exercise**: find the set of the list below.\n",
+    "\n",
+    "Level: <span style=\"color:green\">Easy</span>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c7f1f9d-6f4a-481b-950e-b3bd91dd6f0a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list_reactors = ['batch', 'continuous stirred tank', 'plug flow', 'batch']\n",
+    "\n",
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "609c2fa8-9c9c-4f8b-9cea-27570dba301e",
+   "metadata": {},
+   "source": [
+    "**Exercise**: Find the (i) intersection and (ii) union of the lists given below.\n",
+    "\n",
+    "Level: <span style=\"color:green\">Easy</span>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "961477f9-d4a8-4994-985e-a69b315635f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['batch']"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reactors1 = ['batch', 'continuous stirred tank']\n",
+    "reactors2 = ['plug flow', 'batch']\n",
+    "\n",
+    "# Your code here\n",
+    "\n",
+    "list(set(reactors1).intersection(set(reactors2)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "703372f3-0f81-4403-be43-e730d5b17205",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['batch']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(set(reactors1) & set(reactors2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a41492a0-37a4-4227-922f-c05a5901571c",
+   "metadata": {},
+   "source": [
+    "## DataFrames, arrays and plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f926889e-b7f0-4d5f-af6c-a6261dc4b7dc",
+   "metadata": {},
+   "source": [
+    "In the folder, you can load the .csv file 'packed_column_2.csv'.\n",
+    "\n",
+    "**Exercise**: load the data in `pandas`, visualize the top 7 rows of the DataFrame, calculate mean, sum, median of the columns and describe the DataFrame.\n",
+    "\n",
+    "Level: <span style=\"color:green\">Easy</span>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7fc4a022-6567-4ddb-83e0-05b70cbe3a0b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beed64aa-6e1f-431d-84ac-1f0271162cb6",
+   "metadata": {},
+   "source": [
+    "**Exercise**: plot the data, remembering to add a title, axis labels and the legend. Try to make your plot as informative (and pretty) as possible.\n",
+    "\n",
+    "Level: <span style=\"color:green\">Easy</span>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3a9bf1c8-224b-4514-b272-ecc0705d2486",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "56197752-e6da-4ba8-b8fa-fdf80c11872a",
+   "metadata": {},
+   "source": [
+    "**Exercise**: import the data we worked with in the theory ('week02/htory/packed_column.csv') and compare the two DataFrames using `pandas` functions. Create a plot (plot can be done with matplotlib) for each water flow in the two dataframes (e.g., one plot with 0 kg/h water flow in column 1 and in column 2).\n",
+    "\n",
+    "Level: <span style=\"color:orange\">Medium</span>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f58b607e-c50f-4335-acfd-482360c8691e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8bf0f902-c4cd-4538-bfb9-eabff06fc482",
+   "metadata": {},
+   "source": [
+    "**Exercise**: convert the two DataFrames to numpy arrays and compare them (e.g., calculate the mean, sum, etc).\n",
+    "\n",
+    "Level: <span style=\"color:green\">Easy</span>."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "af3d38a5-fd34-487a-9ac5-3ba714bd14a1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1769e5b4-d43a-4c10-b2cc-29402006e5cc",
+   "metadata": {},
+   "source": [
+    "**Exercise**: what can you conclude from comparing the flow in the two packed columns? Write some text in markdown."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab7a6a1c-949b-422d-8e0d-4057269f6360",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# write here\n",
+    "# remember to make this a markdown cell"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18a4fd51-3734-4e54-bd40-0052ed1d001a",
+   "metadata": {},
+   "source": [
+    "**Exercise**: explore the data as you want, plot it, investigate it, have fun with it!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c4d4eba8-896a-4120-a8ef-a4276981a8c2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Your code here"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

WS1/module_2/packed_column_2.csv

@@ -0,0 +1,11 @@
+flow type;water flow / air flow;0 kg/h;100 kg/h;200 kg/h;300 kg/h
+small;15;0,1;1;1;1
+small;30;2;1;1;2
+small;50;2;1;2;5
+small;80;3;3;5;8
+small;100;4;6;8;13
+big;40;5;10;14;27
+big;50;7;16;21;48
+big;60;9;21;31;117
+big;70;13;31;47;300
+big;80;17;37;93;

WS1/module_3/0_intro_module_3.ipynb

@@ -0,0 +1,25 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Module 3\n",
+    "\n",
+    "in this modules, we will explore the following concepts\n",
+    "\n",
+    "1. Operations\n",
+    "2. Linear Regression\n",
+    "3. Stoichiometry (1)\n",
+    "4. Stoichiometry (2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}