giving up
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# slide number 1
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we will do it in jupyter notebook as it is better for this purpose
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## what we kinda learn in slide 1
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computer friendly calculation methods. its different to just math.
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it is an approximation method for some of the harder problems that would be hard with math alone
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it gets the job done for some people
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doesnt for others
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it idk
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+171
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# dont i hate this shit\n",
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"\n",
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"im manually translating the slides\n",
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"\n",
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"### why numpy and not pure python\n",
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"\n",
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"[../0/tutorial2.ipynb#numpy](../0/tutorial2.ipynb#numpy)\n",
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"\n",
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"### honestly\n",
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"\n",
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"[numpy beginners click here](https://numpy.org/doc/stable/user/absolute_beginners.html)\n",
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"\n",
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"## import steps. use for import\n",
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"\n",
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"you may need to:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
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"pip install numpy matplotlib"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"%matplotlib inline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## usage\n",
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"\n",
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"make the list in python and then you np.array it and then feed the numpy array to numpy functions\n",
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"\n",
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"notice: make it\n",
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"\n",
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"1. with same size: `all(len(i) == len(j) for i in pyarray for j in pyarray) == True`\n",
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"2. all elements have same type `all(type(i) == type(r[0]) for i in r for r in pyarray) == True`\n",
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"\n",
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"then you will be able to harness the peak of numpy. Or else goodbye"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pyarray = [\n",
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" [1, 2, 3],\n",
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" [4, 5, 6],\n",
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"]\n",
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"\n",
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"\n",
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"numpy_array = np.array(pyarray)\n",
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"\n",
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"pyarray, numpy_array"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"the 3:11:2 notation in python is called a slice object. numpy (probably) modified its `array.__index__` to yk accommodate to dimensions\n",
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"\n",
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"so normally you wouldnt fit more than one thing in that bracket: now you can! pretty great"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"newarr = numpy_array[:,1:3]\n",
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"newarr"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"kinda cool. normally it would be:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"[i[1:3] for i in pyarray[:]] #uh why so complicated"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"remember this: Python lists count from zero. Everything related to computer memory counts from 0. only Maniacs count from 1.\n",
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"\n",
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"also a tip for me actually, for i struggle with matrix rows and columns: `m2d[0,:]` is first Row. `m2d[:, 0]` is first Column. \n",
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"row goes across. row number x is counted top down. Column goes top down. column number x is counted left to right "
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### numpy array (matrix) operations\n",
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"\n",
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"the slides (and numpy also) have all the shit: the slice which we just covered, np.transpose `'`, np.matmul `*`, np.multiply `.*`, matrix divide `/` with np.dot and np.linarg.inv, np.divide `./`, concat arrays with np.concatenate np.stack or np.block etc.\n",
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"\n",
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"### math functions\n",
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"\n",
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"infinitely easier than matlab with python def\n",
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"\n",
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"you know exactly what this means:\n",
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"\n",
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"```python\n",
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"def function(x):\n",
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" return 3*x + 2\n",
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"```\n",
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"this WILL work with ndarrays as they do have `__mul__` `__rmul__` and `__add__`\n",
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"\n",
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"as opposed to matlab with its cumbersome ahh syms\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.12.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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