diff --git a/README.md b/README.md index 002c62c..35f4a1c 100644 --- a/README.md +++ b/README.md @@ -16,9 +16,6 @@ Two exercises to activate the body and the mind Common goal of both exercises is to sort a deck of tarot cards by value - -Before starting, make yourself acquainted with the meanining and the power of the tarot cards. Carefully read [this booklet](https://aspp.school/wiki/_media/tarot-runic.pdf) - ### First experiment: human sorting Setup: - 1 volunteer to keep the time spent sorting diff --git a/architecture/README.md b/architecture/README.md index 699660a..de9596b 100644 --- a/architecture/README.md +++ b/architecture/README.md @@ -11,7 +11,7 @@ - `bin(14)` ➔ `'0b1110'` - `np.iinfo(np.int32)` ➔ `iinfo(min=-2147483648, max=2147483647, dtype=int32)` - Python integers, as opposed to numpy integer types, are represented with a flexible number of bits: `sys.int_info` ➔ `bits_per_digit=30, sizeof_digit=4, default_max_str_digits=4300, str_digits_check_threshold=640` - - they are called "long" or "sperlong" integers, because they can have arbitrary size. Low level implementation explained: + - they are called "long" or "superlong" integers, because they can have arbitrary size. Low level implementation explained: - [Arpit Bhayani's blog](https://arpitbhayani.me/blogs/long-integers-python/) - [Artem Golubin's blog](https://rushter.com/blog/python-integer-implementation/) @@ -37,7 +37,6 @@ - UTF8 encoded, flexible width from 1B (byte) to 4B (bytes): 1,112,064 Unicode characters (code points) - ASCII: 7 bits (fits in one byte), 127 characters ➔ [ASCII table](https://upload.wikimedia.org/wikipedia/commons/2/26/ASCII_Table_%28suitable_for_printing%29.svg) - [visualization](https://sonarsource.github.io/utf8-visualizer/) - - actually in Python strings (more precisely: unicode objects) are stored in different formats depending on which characters are stored for memory efficiency. Look at the gory details [here](https://docs.python.org/3.14/c-api/unicode.html) ➔ not for the faint-hearted! - **hexadecimal notation**: - base16 ➔ '0, 1, 2, 3, 4, 5, 6, 7, 8, 9, a, b, c, d, e, f' diff --git a/exercise-my-solution.ipynb b/exercise-my-solution.ipynb deleted file mode 100644 index bb98c7e..0000000 --- a/exercise-my-solution.ipynb +++ /dev/null @@ -1,172 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-04T09:40:28.904Z", - "iopub.status.busy": "2024-03-04T09:40:28.896Z", - "iopub.status.idle": "2024-03-04T09:40:28.978Z", - "shell.execute_reply": "2024-03-04T09:40:28.967Z" - } - }, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-04T10:02:39.062Z", - "iopub.status.busy": "2024-03-04T10:02:39.057Z", - "iopub.status.idle": "2024-03-04T10:02:39.068Z", - "shell.execute_reply": "2024-03-04T10:02:39.071Z" - } - }, - "outputs": [], - "source": [ - "n_series = 32\n", - "len_one_series = 5*2**20\n", - "time_series = np.random.rand(n_series, len_one_series)\n", - "gap = 16*2**10" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-04T10:02:41.027Z", - "iopub.status.busy": "2024-03-04T10:02:41.020Z", - "iopub.status.idle": "2024-03-04T10:02:41.036Z", - "shell.execute_reply": "2024-03-04T10:02:41.040Z" - }, - "scrolled": true - }, - "outputs": [], - "source": [ - "print(f'Size of one time series: {int(time_series[0].nbytes/2**20)} M')\n", - "print(f'Size of collection: {int(time_series.nbytes/2**20)} M')\n", - "print(f'Gap size: {int(gap*8/2**10)} K')\n", - "print(f'Gapped series size: {int(time_series[0, ::gap].nbytes/2**10)} K')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-04T10:06:08.461Z", - "iopub.status.busy": "2024-03-04T10:06:08.459Z", - "iopub.status.idle": "2024-03-04T10:06:08.466Z", - "shell.execute_reply": "2024-03-04T10:06:08.468Z" - } - }, - "outputs": [], - "source": [ - "# compute a Taylor-like series\n", - "def taylor(time_series, mean, gap):\n", - " for row, ts in enumerate(time_series):\n", - " for pwr in range(1,20):\n", - " mean[row] += (ts[::gap]**pwr).sum()\n", - " return mean\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-04T10:06:08.461Z", - "iopub.status.busy": "2024-03-04T10:06:08.459Z", - "iopub.status.idle": "2024-03-04T10:06:08.466Z", - "shell.execute_reply": "2024-03-04T10:06:08.468Z" - } - }, - "outputs": [], - "source": [ - "def taylor_improved(time_series, mean, gap):\n", - " y = time_series[:,::gap].copy()\n", - " for row, ts in enumerate(y):\n", - " for pwr in range(1,20):\n", - " mean[row] += (ts**pwr).sum()\n", - " return mean" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# verify that they yield the same results\n", - "out1 = taylor(time_series, np.zeros(n_series), gap)\n", - "out2 = taylor_improved(time_series, np.zeros(n_series), gap)\n", - "np.testing.assert_allclose(out1, out2)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-04T10:06:14.959Z", - "iopub.status.busy": "2024-03-04T10:06:14.956Z", - "iopub.status.idle": "2024-03-04T10:06:17.437Z", - "shell.execute_reply": "2024-03-04T10:06:17.443Z" - } - }, - "outputs": [], - "source": [ - "mean = np.zeros(n_series, dtype='float64')\n", - "%timeit taylor(time_series, mean, gap)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": { - "iopub.execute_input": "2024-03-04T10:06:20.056Z", - "iopub.status.busy": "2024-03-04T10:06:20.053Z", - "iopub.status.idle": "2024-03-04T10:06:21.695Z", - "shell.execute_reply": "2024-03-04T10:06:21.700Z" - } - }, - "outputs": [], - "source": [ - "mean = np.zeros(n_series, dtype='float64')\n", - "%timeit taylor_improved(time_series, mean, gap)" - ] - } - ], - "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": "3.12.5" - }, - "nteract": { - "version": "0.28.0" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -}