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@Maarrk
Created November 11, 2021 16:12
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ZiDKL Python
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "Hn7ko-sVa9ID"
},
"source": [
"# Zmęczenie i Diagnostyka Konstrukcji Lotniczych\n",
"\n",
"Nowa wersja programu która nie wymaga samodzielnego programowania grafiki - to faktycznie warto polecać. Inne wersje wymagały umiejętności grafików komputerowych, ta jest skierowana do inżynierów\n",
"\n",
"## Ustawienie środowiska\n",
"\n",
"Importowane są potrzebne biblioteki, rozmiar wykresów jest ustawiony na szerokość 13 i wysokość 7.\n",
"\n",
"Funkcje `round_numbers`, `tex` oraz `md` ułatwiają wyświetlanie równań."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "nZMqI_Kia9IL"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.rc('figure', figsize=(13, 7))\n",
"from IPython.display import display, Markdown, Latex # Display cells with dynamic content\n",
"from string import Template # Replace text with $ instead of {}, much better for LaTeX equations\n",
"\n",
"def round_numbers(data: dict, places: int) -> dict:\n",
" \"\"\"Setup floating point variables for string substitution\n",
"\n",
" Parameters\n",
" ----------\n",
" data : dict\n",
" A dictionary of values with string keys\n",
" places : int\n",
" Number of decimal places in string representation\n",
"\n",
" Returns\n",
" -------\n",
" dict\n",
" A deep copy of data with floating points converted to str\n",
" \"\"\"\n",
" format_string = '{:.' + str(places) + 'f}'\n",
" result = dict()\n",
" for k, v in data.items():\n",
" if isinstance(v, float):\n",
" result[k] = format_string.format(v)\n",
" else:\n",
" result[k] = v\n",
" return result\n",
"\n",
"tex = lambda text, data, places = 3: display(Latex(Template(text).substitute(round_numbers(data, places))))\n",
"md = lambda text, data, places = 3: display(Markdown(Template(text).substitute(round_numbers(data, places))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Filtrowanie danych\n",
"W zmiennej `data` podana jest lista punktów, funkcja `only_extremes` usuwa punkty które nie są ekstremami. Zmienna `numlevels` będzie potem potrzebna do wyświetlania tabel."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"id": "B06QCSqUa9IO",
"outputId": "faf6b3c3-f2ac-443e-a898-9144a39c7d0f",
"tags": [
"hide-input"
]
},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 936x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"numlevels = 12\n",
"data = [8, 9, 10, 9, 10, 3, 4, 3, 4, 5, 2, 3, 2, 6, 10, 12, 2, 4, 7, 6, 7, 1, 3, 2, 5]\n",
"\n",
"def only_extremes(l: list) -> list:\n",
" extremes = [l[0]]\n",
" indices = [0]\n",
" for i, n in enumerate(l[:-1]):\n",
" back = l[i -1] - n\n",
" front = l[i+1] - n\n",
" if back == 0:\n",
" continue\n",
" if back * front < 0:\n",
" continue\n",
" extremes.append(n)\n",
" indices.append(i)\n",
"\n",
" extremes.append(l[-1])\n",
" indices.append(len(l) - 1)\n",
"\n",
" return indices, extremes\n",
"\n",
"i, e = only_extremes(data)\n",
"plt.scatter(np.arange(len(data)), data, marker='x', s=20, c='red')\n",
"plt.plot(i, e)\n",
"plt.scatter(i, e)\n",
"plt.ylabel('wartość')\n",
"plt.xlabel('numer punktu')\n",
"plt.show()\n",
"\n",
"data = e"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 279
},
"id": "PGDkXRyia9IS",
"outputId": "fe826729-acfa-4f8e-be4f-0205bd4747a9"
},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 936x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data)\n",
"plt.ylabel('wartości ekstremów')\n",
"plt.xlabel('numer punktu')\n",
"plt.xticks([0, 5, 10, 15, 20])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Tablica przejść\n",
"\n",
"Zliczanie ile razy wystąpiło przejście pomiędzy konkretnymi dwoma stanami."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "jKRmauUna9IT"
},
"outputs": [],
"source": [
"transfers = []\n",
"for i, d in enumerate(data[:-1]):\n",
" transfers.append((d, data[i+1]))\n",
"\n",
"transfer_counts = {}\n",
"for t in transfers:\n",
" if t in transfer_counts:\n",
" transfer_counts[t] += 1\n",
" else:\n",
" transfer_counts[t] = 1"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 297
},
"id": "41QHDSkIa9IU",
"outputId": "64051de5-7eea-48b9-fb06-6bc4b4607fa8"
},
"outputs": [
{
"data": {
"text/markdown": [
"| Z \\ Do | &nbsp;&nbsp;&nbsp;&nbsp;1 | &nbsp;&nbsp;&nbsp;&nbsp;2 | &nbsp;&nbsp;&nbsp;&nbsp;3 | &nbsp;&nbsp;&nbsp;&nbsp;4 | &nbsp;&nbsp;&nbsp;&nbsp;5 | &nbsp;&nbsp;&nbsp;&nbsp;6 | &nbsp;&nbsp;&nbsp;&nbsp;7 | &nbsp;&nbsp;&nbsp;&nbsp;8 | &nbsp;&nbsp;&nbsp;&nbsp;9 | &nbsp;&nbsp;&nbsp;&nbsp;10 | &nbsp;&nbsp;&nbsp;&nbsp;11 | &nbsp;&nbsp;&nbsp;&nbsp;12\n",
"| --: | --: | --: | --: | --: | --: | --: | --: | --: | --: | --: | --: | --: |\n",
"| **12** | | 1| | | | | | | | | | X |\n",
"| **11** | | | | | | | | | | | X | |\n",
"| **10** | | | 1| | | | | | 1| X | | |\n",
"| **9** | | | | | | | | | X | 1| | |\n",
"| **8** | | | | | | | | X | | 1| | |\n",
"| **7** | 1| | | | | 1| X | | | | | |\n",
"| **6** | | | | | | X | 1| | | | | |\n",
"| **5** | | 1| | | X | | | | | | | |\n",
"| **4** | | | 1| X | | | | | | | | |\n",
"| **3** | | 2| X | 1| 1| | | | | | | |\n",
"| **2** | | X | 1| | 1| | 1| | | | | 1|\n",
"| **1** | X | | 1| | | | | | | | | |"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"table_rows = [ '| Z \\\\ Do | &nbsp;&nbsp;&nbsp;&nbsp;' + ' | &nbsp;&nbsp;&nbsp;&nbsp;'.join([str(n + 1) for n in range(numlevels)]),\n",
"'| --: '*(numlevels + 1) + '|' ]\n",
"for m in reversed(range(numlevels)):\n",
" row = '| **' + str(m+1) + '** |'\n",
" for n in range(numlevels):\n",
" if m == n:\n",
" row += ' X |'\n",
" elif (m+1, n+1) in transfer_counts:\n",
" row += ' ' + str(transfer_counts[(m+1, n+1)]) + '|'\n",
" else:\n",
" row += ' |'\n",
" table_rows.append(row)\n",
"\n",
"table = '\\n'.join(table_rows)\n",
"display(Markdown(table))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Rysowanie tablic\n",
"\n",
"| Kolumna 1 | Kolumna 2 | Kolumna 3 |\n",
"| :-- | :-: | --: |\n",
"| Rząd 1 | środek | do prawej |\n",
"| Rząd 2 | **pogrubiony tekst** | *kursywa* |\n",
"\n",
"Definicja tabeli jest w formie tekstowej, w drugiej linijce ustalone jest wyrównanie tekstu w kolumnach\n",
"\n",
"```\n",
"| Kolumna 1 | Kolumna 2 | Kolumna 3 |\n",
"| :-- | :-: | --: |\n",
"| Rząd 1 | środek | do prawej |\n",
"| Rząd 2 | **pogrubiony** | *kursywa* |\n",
"```\n",
"\n",
"## Zliczanie półcykli\n",
"\n",
"Następujący fragment kodu symuluje \"kropelki\" spływające od lewej po wykresie. Dla każdej zapisywane są punkty przez które przepływa, oraz dodawane do listy `taken_peaks`. Dzięki temu w kolejnych iteracjach można wykryć które z ekstremów już były oznaczone i przerwać wskutek \"kolizji\" z jednym z poprzedzających półcykli."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"from math import copysign\n",
"sign = lambda x: copysign(1, x)\n",
"\n",
"def make_droplets(data: list, direction: int) -> list:\n",
" assert direction != 0\n",
" direction = sign(direction)\n",
" droplets = []\n",
" taken_peaks = []\n",
" for i, start in enumerate(data[:-1]):\n",
" if sign(data[i + 1] - start) == direction:\n",
" drop = { 'from': start, 'points': [(i, start)]}\n",
" current_d = start\n",
" for j in range(i+1, len(data)):\n",
" if (data[j] - current_d) * direction >= 0: # Fall on the next line\n",
" taken_index = next((i for i, x in enumerate(taken_peaks) if x['index'] == j), -1)\n",
" old_intercept = taken_peaks[taken_index]['intercept'] if taken_index != -1 else np.NaN\n",
"\n",
" if len(drop['points']) > 1:\n",
" # Move the last point to intercept this roof nicely\n",
" intercept_delta_x = (current_d - data[j-1]) / (data[j] - data[j-1])\n",
" drop['points'][-1] = (drop['points'][-1][0] + intercept_delta_x, drop['points'][-1][1])\n",
" # Store all interceptions to allow checking for collisons\n",
" if taken_index == -1:\n",
" taken_peaks.append({'index': j, 'intercept': current_d})\n",
" else:\n",
" taken_peaks[taken_index] = { 'index': j, 'intercept': current_d}\n",
"\n",
" if taken_index != -1:\n",
" lerp_arg = ((old_intercept - direction * 0.2) - data[j-1]) / (data[j] - data[j-1])\n",
" \n",
" drop['points'].append((j-1+ lerp_arg, data[j-1] + lerp_arg * (data[j] - data[j-1])))\n",
" drop['to'] = old_intercept\n",
" break\n",
"\n",
" drop['points'].append((j, data[j]))\n",
" current_d = data[j]\n",
"\n",
" else:\n",
" drop['points'].append((j, current_d)) # Fall down straight\n",
"\n",
" if(data[j] < drop['from']): # Termination condition\n",
" drop['to'] = current_d\n",
" break\n",
" \n",
" if j == len(data) - 1: # Extend beyond last point\n",
" drop['points'].append((j + 1, current_d))\n",
" drop['to'] = current_d\n",
"\n",
" droplets.append(drop)\n",
" return droplets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Żeby poprawić czytelność wykresu, obliczone półcykle są rysowane nieco przesunięte względem oryginalnych danych."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 936x504 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data, c='black')\n",
"\n",
"drops_top = make_droplets(data, 1)\n",
"for drop in drops_top:\n",
" points = np.array(drop['points'])\n",
" # Offset diagonally by a small value to display next to original data\n",
" points = points + np.tile(np.array([-0.15, 0.15]), (points.shape[0], 1))\n",
" plt.plot(points[:,0], points[:,1], c='red')\n",
"\n",
"drops_bottom = make_droplets(data, -1)\n",
"for drop in drops_bottom:\n",
" points = np.array(drop['points'])\n",
" points = points + np.tile(np.array([-0.15, -0.15]), (points.shape[0], 1))\n",
" plt.plot(points[:,0], points[:,1], c='green')\n",
"\n",
"plt.ylabel('wartość')\n",
"plt.xlabel('numer punktu')\n",
"plt.xticks([0, 5, 10, 15, 20])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Zliczanie półcykli i wyświetlanie w tabeli odbywa się tak samo jak przejść. Półcykle dla których wystąpił przynajmniej jeden cykl obciążenia są wyróżnione w tabeli kwadratowymi nawiasami: [N]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"drops_all = drops_top + drops_bottom\n",
"halfcycle_counts = {}\n",
"for d in drops_all:\n",
" t = (d['from'], d['to'])\n",
" if t in halfcycle_counts:\n",
" halfcycle_counts[t] += 1\n",
" else:\n",
" halfcycle_counts[t] = 1"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"| Z \\ Do | &nbsp;&nbsp;&nbsp;&nbsp;1 | &nbsp;&nbsp;&nbsp;&nbsp;2 | &nbsp;&nbsp;&nbsp;&nbsp;3 | &nbsp;&nbsp;&nbsp;&nbsp;4 | &nbsp;&nbsp;&nbsp;&nbsp;5 | &nbsp;&nbsp;&nbsp;&nbsp;6 | &nbsp;&nbsp;&nbsp;&nbsp;7 | &nbsp;&nbsp;&nbsp;&nbsp;8 | &nbsp;&nbsp;&nbsp;&nbsp;9 | &nbsp;&nbsp;&nbsp;&nbsp;10 | &nbsp;&nbsp;&nbsp;&nbsp;11 | &nbsp;&nbsp;&nbsp;&nbsp;12\n",
"| --: | --: | --: | --: | --: | --: | --: | --: | --: | --: | --: | --: | --: |\n",
"| **12** | | [1]| | | | | | | | | | X |\n",
"| **11** | | | | | | | | | | | X | |\n",
"| **10** | | | 1| | | | | | [1]| X | | |\n",
"| **9** | | | | | | | | | X | [1]| | |\n",
"| **8** | | | | | | | | X | | 1| | |\n",
"| **7** | | [1]| | | | [1]| X | | | | | |\n",
"| **6** | | | | | | X | [1]| | | | | |\n",
"| **5** | | | [1]| | X | | | | | | | |\n",
"| **4** | | 1| | X | | | | | | | | |\n",
"| **3** | 1| [1]| X | 1| [1]| | | | | | | |\n",
"| **2** | | X | [2]| | | | [1]| | | | | [1]|\n",
"| **1** | X | | | | 1| | | | | | | |"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"table_rows = [ '| Z \\\\ Do | &nbsp;&nbsp;&nbsp;&nbsp;' + ' | &nbsp;&nbsp;&nbsp;&nbsp;'.join([str(n + 1) for n in range(numlevels)]),\n",
"'| --: '*(numlevels + 1) + '|' ]\n",
"for m in reversed(range(numlevels)):\n",
" row = '| **' + str(m+1) + '** |'\n",
" for n in range(numlevels):\n",
" if m == n:\n",
" row += ' X |'\n",
" elif (m+1, n+1) in halfcycle_counts:\n",
" if (n+1, m+1) in halfcycle_counts:\n",
" row += ' [' + str(halfcycle_counts[(m+1, n+1)]) + ']|'\n",
" else:\n",
" row += ' ' + str(halfcycle_counts[(m+1, n+1)]) + '|'\n",
" else:\n",
" row += ' |'\n",
" table_rows.append(row)\n",
"\n",
"table = '\\n'.join(table_rows)\n",
"display(Markdown(table))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sprawdzenie liczby punktów\n",
"\n",
"Liczba przejść oraz wszystkich półcykli (w stronę większego i mniejszego obciążenia) powinny być równe.\n",
"\n",
"$$ N - 1 = \\sum{\\Delta LL_{TA}} = \\sum{\\Delta LL_{top}} + \\sum{\\Delta LL_{bottom}} $$"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/latex": [
"$$ 20 - 1 = 19 = 10 + 9 $$"
],
"text/plain": [
"<IPython.core.display.Latex object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"num = {\n",
" 'n': len(data),\n",
" 'llta': len(transfers),\n",
" 'lltop': len(drops_top),\n",
" 'llbottom': len(drops_bottom)\n",
"}\n",
"tex(r'$$$$ $n - 1 = $llta = $lltop + $llbottom $$$$', num)"
]
}
],
"metadata": {
"colab": {
"name": "zidkl.ipynb",
"provenance": []
},
"interpreter": {
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