restarted a new git repository

removed private information before public release

.gitignore

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+*.ipynb_*
+*.swp
+*.vscode
+*.h5
+*.dat
+*.pyc
+*w11/swe/output/frames/*
+*_old

README.md

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+# Mathematische Modellierung in der Klimaforschung
+
+
+
+
+----
+
+## .gitignore
+
+`.gitignore` contains files which are to be ignored by git.
+

w0/hello_world.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "57178893-d689-4d3d-b699-e49917a47a7f",
+   "metadata": {},
+   "source": [
+    "# Tutorial 1\n",
+    "## Exercise 1: Hello world!\n",
+    "This exercise is to help us get started with *git* and our Python setup.\n",
+    "\n",
+    "1. Pull this notebook and open it with jupyter lab.\n",
+    "2. Run the cell below. Does it print \"Hello world!\"?\n",
+    "3. Familiarise yourself with the jupyer lab interface."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c70e0481-afe1-4c8e-b8b3-7fae045e2a01",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World!\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello World!\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.9.5 64-bit",
+   "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": "3.9.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

w1/functions_and_lists.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a8d5e426-5dff-42d2-b819-b68fdbf63f9f",
+   "metadata": {},
+   "source": [
+    "# Introduction to Python - 1\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## Functions\n",
+    "\n",
+    "Functions are self-contained reusable code that does a specific task. Functions may take one or more arguments, and functions may return a result. The code snippet below demonstrates the function of a function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5139ac13-2bc3-42c8-b048-95ca3cf15884",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a36cf1cf-83b6-4559-b5e9-56b90dcba828",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15\n",
+      "-5\n",
+      "(6+1j)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# In Python, comments start with a hashtag\n",
+    "# And the keyword ``def`` introduces a function definition.\n",
+    "\n",
+    "# Let's make a function called `sum_func` that takes two values `a` and `b` as inputs, i.e. arguments:\n",
+    "def sum_func(a,b):\n",
+    "    # All code belonging to a function must be indented\n",
+    "    \n",
+    "    # Let's calculate the sum of `a` and `b`, and store it in `c`:\n",
+    "    c = a + b\n",
+    "    \n",
+    "    # finally, we want to return the result of sum:\n",
+    "    return c\n",
+    "\n",
+    "# Then now we can reuse this code snippet by calling the function with any values of `a` and `b`:\n",
+    "print(sum_func(5,10))\n",
+    "print(sum_func(-8,3))\n",
+    "\n",
+    "# Because the Python `+` operator supports addition of complex numbers, the function also works with complex inputs\n",
+    "print(sum_func(5+3j,1-2j))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8b6d4766-f28f-4465-8d24-3d7a889163f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "sum_func() missing 1 required positional argument: 'b'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-0ddc42518b76>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Finally, recall that positional arguments are mandatory.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# Let's see what happens if we only input `a` but not `b`:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msum_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: sum_func() missing 1 required positional argument: 'b'"
+     ]
+    }
+   ],
+   "source": [
+    "# Finally, recall that positional arguments are mandatory.\n",
+    "# Let's see what happens if we only input `a` but not `b`:\n",
+    "print(sum_func(5,))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3f45138c-cf4b-4bc1-9055-a63747a10d54",
+   "metadata": {},
+   "source": [
+    "### Questions:\n",
+    "1. Can you create a function that takes both positional and keyword arguments?\n",
+    "2. What happens if you change the order of the keyword arguments?\n",
+    "\n",
+    "----"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "61a037f2-6e54-4b6f-a648-598200f85a6f",
+   "metadata": {},
+   "source": [
+    "## Lists\n",
+    "\n",
+    "Lists in Python store a collection of items in a single variable, for example<br>\n",
+    "``a=[1,2,3,4,5]``<br>\n",
+    "stores the values 1 to 5 in the variable `a`.\n",
+    "\n",
+    "Lists are pretty flexible in Python, see the code snippet below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "3847780c-1b0b-42f2-971d-5145453aa3b2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1, 2, 3, 'a', 'dog', 'item', 10.0, 55.5, 1e-06]\n",
+      "[1, 2, 3, 'a', 'dog', 'item', 10.0, 55.5, 1e-06, <function sum_func at 0x7f045c3fa200>]\n",
+      "[1, 2, 3, 'a', 'dog', 'item', 10.0, 55.5, 1e-06, <function sum_func at 0x7f045c3fa200>, array([[ 0,  1,  2,  3,  4],\n",
+      "       [ 5,  6,  7,  8,  9],\n",
+      "       [10, 11, 12, 13, 14],\n",
+      "       [15, 16, 17, 18, 19],\n",
+      "       [20, 21, 22, 23, 24]])]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# A list can store a collection (floating point) numbers, integers, and strings, etc.\n",
+    "lst = [1,2,3,'a','dog','item',10.0,55.5,1e-6]\n",
+    "\n",
+    "# Now, when we print `lst`, we get the collection of items we defined above.\n",
+    "print(lst)\n",
+    "\n",
+    "# We can even store a function (and other objects) in a list.\n",
+    "# Let's add the `sum_func` function into the list:\n",
+    "lst_func = lst + [sum_func]\n",
+    "print(lst_func)\n",
+    "\n",
+    "# Or a 2D array:\n",
+    "array = np.arange(25).reshape(5,5)\n",
+    "lst_func += [array]\n",
+    "print(lst_func)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c206297a-03fd-4d41-be33-157bd12c5267",
+   "metadata": {},
+   "source": [
+    "----\n",
+    "\n",
+    "## Additional reading:\n",
+    "There are many ways to store data in Python, and `list` is just one them. Here are some other examples of [*data structures*](https://en.wikipedia.org/wiki/Data_structure): \n",
+    "\n",
+    "* In pure Python, i.e. Python without any additional libraries, bells, and whistles, we have *dictionaries* and *lists*. \n",
+    "* In the `numpy` library, we have [*numpy arrays*](https://numpy.org/doc/stable/reference/generated/numpy.array.html),\n",
+    "* while for data analysis, there are [*pandas dataframes*](https://pandas.pydata.org/docs/user_guide/dsintro.html#dataframe) and [*xarrays*](http://xarray.pydata.org/en/stable/).\n",
+    "* For larger-than-memory arrays, Python has the dask library with its [*dask arrays*](https://docs.dask.org/en/latest/array.html).\n",
+    "\n",
+    "These are a just a few more-popular examples of the types of data structures that Python and its libraries offers. In this course, we will stick to the [KISS principle](https://en.wikipedia.org/wiki/KISS_principle), and we will only use the standard Python lists and dictionaries. We will also be using [classes](https://docs.python.org/3/tutorial/classes.html) to create our own data containers. The most 'advanced' arrays that we will be using will be numpy arrays.\n",
+    "\n",
+    "Numpy array, dictionaries and classes will be introduced in later tutorial sessions.\n",
+    "\n",
+    "### Question: \n",
+    "1. What is a *list*, an *array*, a *matrix*, and a *tuple*? What are their similarities and differences?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87d65e32-2724-4451-bc03-493fb7c03e3b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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
+}