Delete PhotoFocalLengths.ipynb
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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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"<div align=\"right\" style=\"text-align: right\"><i>Peter Norvig<br>Aug 2019</i></div>\n",
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"\n",
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"# Photography Focal Length Charts\n",
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"\n",
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"Photographers with interchangable-lens cameras often obsess about which lenses they use. This is especially true when planning a trip, and trying to balance how much to pack versus what might be needed. One big factor is the **focal lengths** to be covered. This notebook will help understand what range of focal lengths was used in a collection of photos, to help you plan the next trip. Charts from this notebook appear in my [Galapagos Photography Trip Report](https://norvig.com/galapagos-photography.html).\n",
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"\n",
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"<p><a href=\"https://www.the-digital-picture.com/Photography-Tips/packing-for-landscape-photography-trip.aspx\"><img src=\"https://media.the-digital-picture.com/Images/Other/Lowepro-Photo-Trekker-AW-II-Backpack-Loaded.jpg\" width=\"300\"></a>\n",
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"\n",
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"# Preparing the Focal Length Data\n",
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"\n",
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"In your photo editing system, export (or copy down manually) a table of focal lengths of photos. (I did it only for my **best** photos.) Arrange the data in the following Python format:\n",
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"\n",
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" {trip_name: {\n",
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" lens_name: [focal_length, ...]}}\n",
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" \n",
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"For example:\n",
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" \n",
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" {2008: {\n",
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" '100-400': [100, 100, 100, 115, 130, ... 400, 400, 400, 400, 400, 400]}}\n",
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" \n",
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"For each of my photos I considered two variations of focal length:\n",
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"1. The actual focal length setting of the lens when the photo was taken (as recorded in the EXIF data).\n",
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"2. The [35mm equivalent focal length](https://en.wikipedia.org/wiki/35_mm_equivalent_focal_length), taking account both the crop factor of the sensor (if any), and any cropping done in photo editing. For example, if the focal length of the lens/photo was 200mm, but I was using a camera with a 1.5x crop, and then I further cropped the image down to half the size in both width and height, then the equivalent focal length would be 200mm x 1.5 x 2 = 600mm. \n",
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"\n",
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"I collected these two data sets at different times, and over time my definition of **best** changed, so they are not for the exact same photos.\n"
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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": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"actual = { # Actual focal lengths \n",
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" 2008: {\n",
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" '17-40': (*[17]*9, *[40]*5),\n",
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" '24-105': (24, 24, 105),\n",
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" '100-400': (*[100]*3, 115, 130, 135, *[150]*3, 210, 220, 260, 300, 330, \n",
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" 350, 350, 360, 365, 365, 380, 380, *[400]*36)},\n",
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" 2014: {\n",
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" '16-35': (*[16]*7, 17, 26, *[35]*4),\n",
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" '24-70': (*[24]*3, 28, 28, 45, 47, 58, *[70]*3),\n",
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" '70-300': (70, 70, 116, 170, 170, 176, 200, 221, 244, 260, 277, 277, *[300]*24),\n",
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" '150-600': (150, 165, 173, 200, 250, 256, 300, 309, 309, 309, 309, 329, 350, \n",
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" 350, 350, 400, 400, 428, 450, 483, 483, 483, 483, 483, 483, \n",
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" 500, 500, 500, 500, 500, 552, 552, *[600]*25)},\n",
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" 2019: {\n",
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" '16-35': (*[35]*5, *[16]*3, 23, 24),\n",
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" '70-200': (*[200]*9, *[280]*5, 168, 170, 142, 168, 185, *[180]*5, 105, 190, 145, 155),\n",
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" '100-400': (*[400]*28, 278, 286, 300, 286, 371, 300, 300, 300, 349, 330, 371, 200, 164, \n",
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" 349, 397, 100),\n",
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" '400': (*[400]*6, 560, *[800]*3)}}"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(150, 165, 173, 200, 250, 256, 300, 309, 309, 309, 309, 329, 350, 350, 350, 400, 400, 428, 450, 483, 483, 483, 483, 483, 483, 500, 500, 500, 500, 500, 552, 552)\n"
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]
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}
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],
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"source": [
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"print(tuple(sorted((150, 150, 165, 173, 173, 182, 200, 250, 250, 256, 256, 300, 300, \n",
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" *[309]*9, 329, 329, *[350]*5, 375, *[400]*4, 428, 428, 450, 450, \n",
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" *[483]*11, *[500]*11, *[552]*4))[0::2]))"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"equiv = { # 35mm equivalent focal lengths (after cropping)\n",
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" 2008: {\n",
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" '17-40': (75, 55, 27, 62, 17, 17, 17, 17, 17, 24, 17, 17, 17, 17),\n",
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" '24-105': (58, 105, 116),\n",
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" '100-400': (668, 400, 766, 549, 509, 130, 150, 548, 428, 588, 405, 247, 656, 858,\n",
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" 400, 159, 559, 398, 295, 427, 513, 554, 694, 317, 666, 562, 507, 491,\n",
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" 552, 861, 528, 252, 304, 100, 554, 400, 248, 400, 545, 542, 145, 533,\n",
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" 234, 538, 577, 667, 648, 400, 188, 407, 400, 400, 400, 400, 400, 400, 200)},\n",
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" 2014: {\n",
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" '16-35': (16, 26, 24, 45, 36, 25, 16, 35, 18, 16, 47, 16),\n",
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" '24-70': (24, 47, 49, 24),\n",
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" '70-300': (262, 270, 208, 1037, 675, 425, 382, 738, 428, 673, 70, 309, 215, 416,\n",
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" 221, 503, 462, 300, 551, 512, 471, 296, 423, 947, 758, 232, 373, 476,\n",
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" 656, 532, 664, 1004, 664, 537, 439),\n",
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" '150-600': (1406, 331, 254, 552, 393, 758, 934, 725, 935, 875, 727, 991, 751, 949,\n",
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" 804, 1172, 1304, 1037, 197, 472, 422, 468, 413, 892, 797, 965, 1191,\n",
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" 1061, 885, 167, 395, 1059, 946, 868, 1227, 453, 444, 592, 1468, 372,\n",
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" 1469, 970, 240, 1103, 689, 592)},\n",
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" 2019: {\n",
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" '16-35': (35, 26, 35, 35, 34, 16, 24, 16, 16),\n",
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" '70-200': (200, 200, 168, 170, 142, 207, 475, 168, 185, 180, 141, 280, 280, 385,\n",
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" 425, 254, 358, 262, 200, 190, 206, 187, 221, 212, 336, 242, 209, 477),\n",
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" '100-400': (438, 724, 583, 703, 278, 300, 286, 286, 400, 431, 371, 335, 481, 400, 400,\n",
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" 621, 525, 308, 400, 406, 427, 371, 606, 400, 400, 261, 476, 188, 588, 397),\n",
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" '400': (1414, 1381, 800, 836, 668, 491, 668, 491, 400, 400)}}"
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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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"# Plotting Histograms\n",
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"\n",
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"I want to see the range of shots made with each lens, and I also want to see the overall number and range of shots. So I got the idea to make a plot where every shot gets a hashmark, where the x-axis of the hashmark is the focal length, and the y-axis is the cumulative number of shots for that lens, sorted in ascending focal-length order. In other words, a kind of cumulative step histogram. This would get messy if the lenses overlapped in focal length a lot, but for me they didn't. The function `shot_plots` does this. "
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"def shot_plots(table, label='35mm equivalent', hidden=None):\n",
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" \"\"\"Display and save to a file a chart of focal lengths.\"\"\"\n",
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" for name, trip in table.items():\n",
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" filename = f'shots-{name}-{label[:2]}.png'.lower()\n",
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" N = sum(len(trip[lens]) for lens in trip)\n",
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" plt.figure(figsize=(6.5, 4.5))\n",
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" plt.rcParams.update({'font.size': 12})\n",
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" plt.xlabel(f'Focal length ({label})')\n",
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" plt.ylabel('Cumulative number of shots')\n",
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" plt.title(f'{name} Best {N} shots ({label} focal length)')\n",
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" plt.grid(b=True, which='major', color='grey', linestyle=':')\n",
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" if hidden: plt.plot(*hidden, color='white', alpha=0)\n",
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" for lens, mark in zip(trip, '_x.+'*9):\n",
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" X = sorted(trip[lens])\n",
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" Y = range(1, len(X) + 1)\n",
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" plt.plot(X, Y, '_', ms=9, mew=1.5, label=lens + f'mm: {len(X)}', marker=mark)\n",
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" plt.legend(loc=2, fontsize='small')\n",
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" plt.savefig(filename)\n",
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" plt.show()"
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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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"# Actual Focal Length Plots\n",
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"\n",
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"The actual lens focal length data. This makes it clear how often I was at the very far end of my 400mm, 300mm, and 600mm zooms:"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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truncated
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"metadata": {
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"needs_background": "light"
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},
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{
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"data": {
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||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 468x324 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 468x324 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"shot_plots(actual, label='actual', hidden=([800], [60]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# 35mm Equivalent Focal Length Plots"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"scrolled": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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|
||||
"text/plain": [
|
||||
"<Figure size 468x324 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
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|
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||||
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||||
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||||
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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 468x324 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
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|
||||
},
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||||
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||||
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||||
{
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"data": {
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||||
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAaQAAAE5CAYAAADftFSoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXl8VdW5//9+kElkKIiiGGQKBQvBOABaKzKIDN6qVetYrQO3om2t3jq2ikOvYv229vrTKt7iVcGBVgWkiAECxipKGDTVILOQQqQRAgQIhCSc5/fH2udk53BOcrJzTvbZZr1fr/1Kztprr/3Z+6yz137W8DyiqlgsFovF4jct/BZgsVgsFgvYBslisVgsaYJtkCwWi8WSFtgGyWKxWCxpgW2QLBaLxZIW2AbJYrFYLGmBbZAsKUNEHhaRjX7rcCMi7UWkWESG+K0lyIjICBFREclIcrlbROSBZJbpQcMvRWSbiIRE5OEmPK+KyE/q2J+Se94YEvmNi8jZIvIvETm6vvIC1SCJyN0i8omI7BaRPSLykYiMi5FvmIh8LCIVIrJdRKaIyFGu/W1F5CUR+UxEKuPdUBE5V0TynHPtEpHpInJsPRpvcCpNeDsoImtF5NeNvwO1zvMDp/xe9eTrFaXHvf3ZlS9enneTqdsLIjJNRPKSVNy9wEpVXeEq/1UR2eR8V6UiskhEzo7SkBfj3mxLkqYg8jFwIvC130ISQUQ2JtK4iEh34H+AKcBJwB9SLC0QiEiGU+dHNPRYVf0EKATqfQYGqkECRgH/B4wEhmJ+FPNE5JxwBhHpASwC1gFnALcCtwCPuco5CqgE/heYGetEIjLIKWe5c67xQD9gjohIPToPY36sJwLfA/4EPCEi1zXgWpPFVpeW8PZjZ5/72qPznB0jT6ARkbaY+vBC1K5lwA3AKZi6tQ1YJCInReV7ndr36LRU6k1nVLVSVf+tqiG/tSSZPpjn4lxV3a6q+/0W9C1hGvBzEWlVZy5VDfQGfA780fX5ccwDpYUr7edAOXBMjOMfBjbGSP9vYG1U2mmAAiPr0HMDUB0jfRXwdFTaL4G1QAWwAfgt0NK1/2LgM+AAsAfTOJ4G9HJ0uLe8Btyz14DV9eR5DCgF2taT7zfAV8AhYAewADjafW+d61jrfAd5QL+oMiY49+cQ8A3wXPi7csqIvtYbnH0TgTXO/dsF/APIqEPrJY6GlvVcUyfnPBe70vKAafUctwX4HfC88319A/wCaAM8A+wGioFfRB2nTl34q6PvX8Dljo7XgH3OPb4sge92DLAUOOic6yXgWNf+Fo7Gb4D9zjnvdNdZYvwmgB84Ons5n0c4nzOcMv8F/CbqmDbONU90actzvqsy4ANgaIx7+IDrcytHz2bne14N3BLj/t0GzHDu1Tbg/qjvLroO9YrzLIiZD/gp8CXmRXYb5vnQMur4nzt5wvX4bde+a4B857p3Au8C341xHT+p47uN3HNXWibwNqa+7QYWAlnRzyPgHOBTzLNkFTAkquzRwBfOPf4cOM+tJ8Z92dLA33hb576Mq7P+JvoQS8fN+SFsAR51pX0A/F9Uvr7OTfxBnEoYq0H6A1AQlTbAKeehOjTdQO0ftzgV6QBwZdR5i4AfAb0xD+V/Ab9z9p/gVP57nP2nOJU6C2PhXeRoGeLk7ZLgPevqVLrb68jTCtgOPFVPWZcCe4EfAicD2cAd1G6QyoEcjLV6qvNj+NBVxmDnB/Mn5/6Od+7DDGd/e8xD+WPnOk8AjnbKqwauB3o692UidTdIfwI+quea2gL3Yx5s3V3peZgGegewHngZODnq2C2YB8N/YR4UDzjf0XxX2v1ACPie6zgF/o156GViGuSDwHtOfcrENGjluBqXGNpHOfXslxhrfgjwPuY3IU6eXznl/BT4rlO/9tCIBsn5/DiwJuqYK5zr6OR8/pGT1h8YiHlr3kXtBnMLtRuklzEPyAswv4MrHb03R92/EuA/Mb/1nztpo539XTAN2h9cdeioGPevPaZOK+bF7wTMb+1CTK/H/c49uxLz8P+d69hHMA38L5w8pwO/de2/EfM76euUPRfzEto66joSbpCAbk69eR5T//s79aQUOM71PAphXtbOxfzG3nPuR0snz0mYejMN06MzGtN4uRuk8Mv4pc59CZf/MPX8xl36lwO/r/P3l8hDLF03zA9+D7XfGNYDj0flO8a5mT+OUcbDxG6QRjvH3IJ5QB8LzHbSXqhD0w1Onv3OVuVUiCdcedo5FWBc1LHXA3uiKkCvOOf5QV3769B3l3Pu79SR53Kn7AH1lHWnc79bxdn/MKbROM6VdqVzP9o6n2cAy6OOu9jJ09P5PI0oCxDzcCsDOjbg2ucAf42z7zbn+wphujmj39xvwbw0DMI8oPIxb8EnuPJsAea4PrfANNh/j0rbjctKcu71/7g+H+ekPeNK6+yk/Ucd15fnrmdO2snOcdnO523AY1F53qLxDVL4ZW2I65h5wBt16A3fi2uj7uEDzv+9ne9jQNRxk3G9LDrn/f+i8qwBprg+bwQeTqCO1LouJ+1D4G9R+X6FaWxbY54vB4G7GlAXuzjnOSfqOhrSID0MLIvKI8Am4A7n8w3OMae78gxz0vo7nx9z7vtRrjzjqN0gZTifRzT0N+5KnwW8Wdd9CdoYUgQRuQ3TXXS5qiZ9cFlVF2PeNKdQ0/2xDvMmVl+/+WGMtZCNaVgmAr8UkVud/QMxb/lvi8j+8IYZ2+gkIsdh3goXAIUiMltEfuWMj3nGGfv6GebHtaeOrLcAH6jq2nqK/BumsS4SkZdF5DoR6RCV52tV3eH+jPnRHO98Hoh5e3PzgZPne3WcexGmG2uziMwUkZ+JSNd69B6NsQ5j8Rrm+/oBsBh4S0RODu9U1RdUdb6qFqrqu5gfbGvgpqhy/uk6JoSxqD6PSvuGmuuPddwOTB1yH7cbYzFHH+dmCHBHVJ360tnXT0Q6Yt6GP4467qM6ykwIp64sB64DEJHjgbHA9HAeEektIjOcCQZ7MY11J4yFG4szMfVgZdQ1/QZjAbopiPr8NcaCSAbx6mhbjMUz0Pl/YbwCRCTb+R1vFpF9mF4AiH/tiTAEOCPq3uzDdOm774/iql/UTEQJ35/vAStU9bArzycN0FHfbzxMBeY3GJeWDThp2iAid2FM5ItUNTdq93aMSemmm2tfwqjqs85MtBMwPx4B7sa8gdR3rHvmXqGIDMWMET1PzWSSH2MsjGh2qephERmPqXTnA5dhJkb8WFXnNeQ6XIzCVNTr42UQkUyMdXhNfYWparGIDMBMBBgFPAj8XkSGqepWJ1tl9GHO30a9DKnqfhE5E9M3fj4wCXhSREar6qo4h+3AvJnGKq8MY3FtBD4WkbUYq+m+OPl3i8gazI/fTVV01jhp0dcfnSdeWXXdtxbA7zFWZzT/rudYNyFMXXdT92C0YTrwkDOj9BrMWIn7IT3PSfs5xgqtxDSGreOUF9b7fYxV70ajPseqZ2nxwi0i7TD34SNM112Js2s18a89EVpgXp5+EWNfmev/UFRjE+s3GH0/G0Kiv/Eu1PMMTosvrCGIyKPAQ8CEGI0RmAHdMSLivrZxmAr9WUPPp4btqloOXOUkz2loOZg33vDbwWrM20IfVd0YYzvsOvdyVX1cVYdj3spudMoIV4KjSJxbgM9VdVkdeX6GeWjMSqRAVT2kqjmqeg+mH7sdZvJAoqwGhkelnYep1Kudz5XEuE5VPayq/1DVyZj+6+3U3ZB+inmbTYQWmLfemIhIe8xYwdZ4eXxgJTAwTp3ar6p7MZb+96OOOyfq8zfA8e6lEpgxkfp4A2PxjMO89LwWrsvOconvYboUF6jql5jfQF0WX/jF4uQY11PvS2EUMetQgsSrowcxL6fha7kgzvGnYLphf6uqeaq6BtMFW99s3fpYianP22Lcnx31HeziS2BI1Pd9VlQeL8+baLIwmuMSKAtJRP4H81C9GlgnImFL6KDzhgvGAvkF8BcReQpjUv8O0x9f7irre5i3kxOA1iKS7ez6UlUrnTx3Y95sDmG6H57AjE/Vu9jTpa0Nps/2OkwXV/jt/nHgcRFRIBfzXWQBp6nqvSLyfYylshDzoO2HmQDwolNuEeZNdoKI/BU45LoHsfR0wzQUd9SRpzWmz/nl8D2o5xpvxjy4l2PG8kYDHajpJkqE/wd8KiJ/wnRZ9sIMzL6mquFujc3Aj0VkIObtch/modcH05WyA9Mg9ajn3O8BfxSRHmELzpnePx5Y4pTTDTM1vDemGw8R6Yt5wL7rnL8npu9cMLPY0oXJwEKn3k/H3Kd+GEv8F6p6EPgj8DvHAlyGmRxzflQ572NeLB4Vkf/DNEY/r+/kqrrLWbf2KKb786eu3bsx9/c/RWQTZkz2ScxDPV55G53z/0VE7sF0Ix2D+a6PU9Xf16fJxWbgHKcb9gCmFyLRKetTgL+LyH2YF7VszPf/R+d3UikifwQeFpGDmO7kozEvzVMwv9VDmG77P2Lq+BM0zioBeBa4GXhHRP4b83KUganP76pqdNdsPJ7DjAc/7/wOT6BmmUxY407MGOsFIrIa87zZnahQEemHWSrxXp0Z6xpgSreNI6cehreXo/Kdheknr8B0VUwhalYNZhAvVlm9XHkWYn5IhzD9+f+ZgMYboso7hHmLegJoF5V3Iqbvu8I5Tz5wq7NvIGZ21r+dMoowD2/3rJx7MG+8h6ln2jem62k/dUwCwFiAISAzwe/jUuc+78b8yAupPfvpYeoZHHfS3NO+d2BeKo5x7e/i3Isy59gbMG+s4UYkPG3+vgQ0v49rejJmBlsOpqGpdO7nHOAsV54eznE7nDxFmMYqM6rsLbhmiDlpRwymY6bH/ndUvf5JVJ5qnOntrrQKnCnUdVzfuZgXnH2Y2U9rMAs9wzOqWmBmxO109r9F1LRvJ99NmDG68Gy/q9zfGzEG/530i530z2JoOw8Line truncated
|
||||
"text/plain": [
|
||||
"<Figure size 468x324 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"shot_plots(equiv, hidden=([1400], [60]))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Totals (Actual and Equivalent)\n",
|
||||
"\n",
|
||||
"Here I aggregate all the shots (all lens and all trips) together. Again, the two data sets (actual and equivalent) were collected at different times with different definitions of **best** photos, so they have different totals."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
"text/plain": [
|
||||
"<Figure size 468x324 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 468x324 with 1 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {
|
||||
"needs_background": "light"
|
||||
},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"def all_shots(table): return [m for y in table for lens in table[y] \n",
|
||||
" for m in table[y][lens]]\n",
|
||||
"\n",
|
||||
"shot_plots({'All': {'actual ': all_shots(actual)}}, 'actual') \n",
|
||||
"shot_plots({'All': {'equiv ': all_shots(equiv)}})"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"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": "3.7.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Reference in new issue
Block a user