{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "
\n",
"\n",
"# Preparing the Focal Length Data\n",
"\n",
"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",
"\n",
" {trip_name: {\n",
" lens_name: [focal_length, ...]}}\n",
" \n",
"For example:\n",
" \n",
" {2008: {\n",
" '100-400': [100, 100, 100, 115, 130, ... 400, 400, 400, 400, 400, 400]}}\n",
" \n",
"For each of my photos I considered two variations of focal length:\n",
"1. The actual focal length setting of the lens when the photo was taken (as recorded in the EXIF data).\n",
"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",
"\n",
"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"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"actual = { # Actual focal lengths \n",
" 2008: {\n",
" '17-40': (*[17]*9, *[40]*5),\n",
" '24-105': (24, 24, 105),\n",
" '100-400': (*[100]*3, 115, 130, 135, *[150]*3, 210, 220, 260, 300, 330, \n",
" 350, 350, 360, 365, 365, 380, 380, *[400]*36)},\n",
" 2014: {\n",
" '16-35': (*[16]*7, 17, 26, *[35]*4),\n",
" '24-70': (*[24]*3, 28, 28, 45, 47, 58, *[70]*3),\n",
" '70-300': (70, 70, 116, 170, 170, 176, 200, 221, 244, 260, 277, 277, *[300]*24),\n",
" '150-600': (150, 165, 173, 200, 250, 256, 300, 309, 309, 309, 309, 329, 350, \n",
" 350, 350, 400, 400, 428, 450, 483, 483, 483, 483, 483, 483, \n",
" 500, 500, 500, 500, 500, 552, 552, *[600]*25)},\n",
" 2019: {\n",
" '16-35': (*[35]*5, *[16]*3, 23, 24),\n",
" '70-200': (*[200]*9, *[280]*5, 168, 170, 142, 168, 185, *[180]*5, 105, 190, 145, 155),\n",
" '100-400': (*[400]*28, 278, 286, 300, 286, 371, 300, 300, 300, 349, 330, 371, 200, 164, \n",
" 349, 397, 100),\n",
" '400': (*[400]*6, 560, *[800]*3)}}"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(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"
]
}
],
"source": [
"print(tuple(sorted((150, 150, 165, 173, 173, 182, 200, 250, 250, 256, 256, 300, 300, \n",
" *[309]*9, 329, 329, *[350]*5, 375, *[400]*4, 428, 428, 450, 450, \n",
" *[483]*11, *[500]*11, *[552]*4))[0::2]))"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"equiv = { # 35mm equivalent focal lengths (after cropping)\n",
" 2008: {\n",
" '17-40': (75, 55, 27, 62, 17, 17, 17, 17, 17, 24, 17, 17, 17, 17),\n",
" '24-105': (58, 105, 116),\n",
" '100-400': (668, 400, 766, 549, 509, 130, 150, 548, 428, 588, 405, 247, 656, 858,\n",
" 400, 159, 559, 398, 295, 427, 513, 554, 694, 317, 666, 562, 507, 491,\n",
" 552, 861, 528, 252, 304, 100, 554, 400, 248, 400, 545, 542, 145, 533,\n",
" 234, 538, 577, 667, 648, 400, 188, 407, 400, 400, 400, 400, 400, 400, 200)},\n",
" 2014: {\n",
" '16-35': (16, 26, 24, 45, 36, 25, 16, 35, 18, 16, 47, 16),\n",
" '24-70': (24, 47, 49, 24),\n",
" '70-300': (262, 270, 208, 1037, 675, 425, 382, 738, 428, 673, 70, 309, 215, 416,\n",
" 221, 503, 462, 300, 551, 512, 471, 296, 423, 947, 758, 232, 373, 476,\n",
" 656, 532, 664, 1004, 664, 537, 439),\n",
" '150-600': (1406, 331, 254, 552, 393, 758, 934, 725, 935, 875, 727, 991, 751, 949,\n",
" 804, 1172, 1304, 1037, 197, 472, 422, 468, 413, 892, 797, 965, 1191,\n",
" 1061, 885, 167, 395, 1059, 946, 868, 1227, 453, 444, 592, 1468, 372,\n",
" 1469, 970, 240, 1103, 689, 592)},\n",
" 2019: {\n",
" '16-35': (35, 26, 35, 35, 34, 16, 24, 16, 16),\n",
" '70-200': (200, 200, 168, 170, 142, 207, 475, 168, 185, 180, 141, 280, 280, 385,\n",
" 425, 254, 358, 262, 200, 190, 206, 187, 221, 212, 336, 242, 209, 477),\n",
" '100-400': (438, 724, 583, 703, 278, 300, 286, 286, 400, 431, 371, 335, 481, 400, 400,\n",
" 621, 525, 308, 400, 406, 427, 371, 606, 400, 400, 261, 476, 188, 588, 397),\n",
" '400': (1414, 1381, 800, 836, 668, 491, 668, 491, 400, 400)}}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting Histograms\n",
"\n",
"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. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"\n",
"def shot_plots(table, label='35mm equivalent', hidden=None):\n",
" \"\"\"Display and save to a file a chart of focal lengths.\"\"\"\n",
" for name, trip in table.items():\n",
" filename = f'shots-{name}-{label[:2]}.png'.lower()\n",
" N = sum(len(trip[lens]) for lens in trip)\n",
" plt.figure(figsize=(6.5, 4.5))\n",
" plt.rcParams.update({'font.size': 12})\n",
" plt.xlabel(f'Focal length ({label})')\n",
" plt.ylabel('Cumulative number of shots')\n",
" plt.title(f'{name} Best {N} shots ({label} focal length)')\n",
" plt.grid(b=True, which='major', color='grey', linestyle=':')\n",
" if hidden: plt.plot(*hidden, color='white', alpha=0)\n",
" for lens, mark in zip(trip, '_x.+'*9):\n",
" X = sorted(trip[lens])\n",
" Y = range(1, len(X) + 1)\n",
" plt.plot(X, Y, '_', ms=9, mew=1.5, label=lens + f'mm: {len(X)}', marker=mark)\n",
" plt.legend(loc=2, fontsize='small')\n",
" plt.savefig(filename)\n",
" plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Actual Focal Length Plots\n",
"\n",
"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:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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