typo

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

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'

exercise-my-solution.ipynb

@@ -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
-}