From 9b315112fe71914ba551e4bc2d38252e2628b6ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Robert=20J=C3=A4schke?= Date: Fri, 26 Jul 2024 10:38:49 +0200 Subject: [PATCH] cleaned up --- src/Cod.ipynb | 1653 ++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 1584 insertions(+), 69 deletions(-) diff --git a/src/Cod.ipynb b/src/Cod.ipynb index 0b8d687..4352f06 100644 --- a/src/Cod.ipynb +++ b/src/Cod.ipynb @@ -18,10 +18,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "a3d5bae1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from dsat import Dsat\n", "import pandas as pd\n", @@ -41,7 +52,7 @@ { "cell_type": "code", "execution_count": null, - "id": "4cec8299", + "id": "be708209", "metadata": {}, "outputs": [], "source": [ @@ -54,10 +65,359 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "90b9e2dd", + "execution_count": 111, + "id": "6a0983f5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
0123456789...30313233343536373839
024100111115...33000002020
124100111115...33000002020
224100111115...33000002020
324100111115...33000002020
424100111115...126200002020
..................................................................
16424100111115...1054300001020
16524100111115...1347800001020
16624100111115...39600002020
16724100111115...59300002020
16824100111115...33000002020
\n", + "

169 rows × 40 columns

\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 ... 30 31 32 33 34 35 36 \\\n", + "0 24 1 0 0 1 1 1 1 1 5 ... 33 0 0 0 0 0 2 \n", + "1 24 1 0 0 1 1 1 1 1 5 ... 33 0 0 0 0 0 2 \n", + "2 24 1 0 0 1 1 1 1 1 5 ... 33 0 0 0 0 0 2 \n", + "3 24 1 0 0 1 1 1 1 1 5 ... 33 0 0 0 0 0 2 \n", + "4 24 1 0 0 1 1 1 1 1 5 ... 126 2 0 0 0 0 2 \n", + ".. .. .. .. .. .. .. .. .. .. .. ... ... .. .. .. .. .. .. \n", + "164 24 1 0 0 1 1 1 1 1 5 ... 105 43 0 0 0 0 1 \n", + "165 24 1 0 0 1 1 1 1 1 5 ... 134 78 0 0 0 0 1 \n", + "166 24 1 0 0 1 1 1 1 1 5 ... 39 6 0 0 0 0 2 \n", + "167 24 1 0 0 1 1 1 1 1 5 ... 59 3 0 0 0 0 2 \n", + "168 24 1 0 0 1 1 1 1 1 5 ... 33 0 0 0 0 0 2 \n", + "\n", + " 37 38 39 \n", + "0 0 2 0 \n", + "1 0 2 0 \n", + "2 0 2 0 \n", + "3 0 2 0 \n", + "4 0 2 0 \n", + ".. .. .. .. \n", + "164 0 2 0 \n", + "165 0 2 0 \n", + "166 0 2 0 \n", + "167 0 2 0 \n", + "168 0 2 0 \n", + "\n", + "[169 rows x 40 columns]" + ] + }, + "execution_count": 111, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df" ] @@ -80,10 +440,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "b826d142", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[(1338, 61620, 30, 651),\n", + " (1001, 63941, 16, 507),\n", + " (1177, 63825, 16, 500),\n", + " (1012, 64637, 12, 447),\n", + " (708, 64679, 12, 1578),\n", + " (806, 64524, 12, 1554),\n", + " (536, 64979, 10, 1498),\n", + " (513, 64926, 10, 4112),\n", + " (522, 64769, 10, 3879),\n", + " (442, 65223, 8, 2502),\n", + " (301, 65075, 8, 3249),\n", + " (245, 65260, 8, 3057),\n", + " (200, 65356, 4, 561)]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pandas as pd\n", "from cis import Cis\n", @@ -113,10 +496,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "820189f7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "39 17\n", + "42 2\n", + "48 1\n", + "dtype: int64\n", + "1\n", + "36 162\n", + "39 7\n", + "dtype: int64\n", + "2\n", + "36 2238\n", + "37 1\n", + "39 1\n", + "dtype: int64\n", + "3\n", + "13 24667\n", + "14 33\n", + "dtype: int64\n" + ] + } + ], "source": [ "from dsat import Dsat\n", "import pandas as pd\n", @@ -139,53 +547,376 @@ }, { "cell_type": "markdown", - "id": "63836464", + "id": "5123e3f1", "metadata": {}, "source": [ - "### Looking at the some header parts" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b471bdb8", - "metadata": {}, - "outputs": [], - "source": [ - "from dsat import Dsat\n", - "import pandas as pd\n", + "### Looking at the some header parts\n", "\n", - "d = Dsat.from_file(\"../dsatnord.mp\")\n", - "\n", - "def count_cod(b):\n", - " \"\"\"Statistics about \"cod\" parts in byte array b.\"\"\"\n", - " pos = []\n", - " for i in range(0, len(b) - 2):\n", - " if chr(b[i]) == 'c' and chr(b[i+1]) == 'o' and chr(b[i+2]) == 'd':\n", - " pos.append(i)\n", - " return len(pos)\n", - "\n", - "df = pd.DataFrame(\n", - " [\n", - " [count_cod(t.data.planes)] + \n", - " [int(i) for i in t.header.unknown5] for t in d.tiles_zoom3.tiles]\n", - ")\n", - "df" + "There are still some parts in the header whose purpose is not clear, yet. Thus, we here look at the bytes in the part named `unknown5`:" ] }, { "cell_type": "code", - "execution_count": null, - "id": "3d55e682", + "execution_count": 15, + "id": "a0d1e228", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namewidthheightbandsh0h1h2h3h4h5...h30h31h32h33h34h35h36h37h38h39
0fox640480302410011...128000002.01.02.01.0
1wolf768512332410011...49000001.01.02.01.0
2zoom0250250482410011...882200000.00.00.00.0
3zoom0250250392410011...1803200001.00.02.00.0
4zoom0250250392410011...1065400001.00.02.00.0
..................................................................
27126zoom31000100013800011...000010NaNNaNNaNNaN
27127zoom31000100013800011...000010NaNNaNNaNNaN
27128zoom31000100013800011...000010NaNNaNNaNNaN
27129zoom31000100013800011...000010NaNNaNNaNNaN
27130zoom31000100013800011...000010NaNNaNNaNNaN
\n", + "

27131 rows × 44 columns

\n", + "
" + ], + "text/plain": [ + " name width height bands h0 h1 h2 h3 h4 h5 ... h30 h31 \\\n", + "0 fox 640 480 30 24 1 0 0 1 1 ... 128 0 \n", + "1 wolf 768 512 33 24 1 0 0 1 1 ... 49 0 \n", + "2 zoom0 250 250 48 24 1 0 0 1 1 ... 88 22 \n", + "3 zoom0 250 250 39 24 1 0 0 1 1 ... 180 32 \n", + "4 zoom0 250 250 39 24 1 0 0 1 1 ... 106 54 \n", + "... ... ... ... ... .. .. .. .. .. .. ... ... ... \n", + "27126 zoom3 1000 1000 13 8 0 0 0 1 1 ... 0 0 \n", + "27127 zoom3 1000 1000 13 8 0 0 0 1 1 ... 0 0 \n", + "27128 zoom3 1000 1000 13 8 0 0 0 1 1 ... 0 0 \n", + "27129 zoom3 1000 1000 13 8 0 0 0 1 1 ... 0 0 \n", + "27130 zoom3 1000 1000 13 8 0 0 0 1 1 ... 0 0 \n", + "\n", + " h32 h33 h34 h35 h36 h37 h38 h39 \n", + "0 0 0 0 0 2.0 1.0 2.0 1.0 \n", + "1 0 0 0 0 1.0 1.0 2.0 1.0 \n", + "2 0 0 0 0 0.0 0.0 0.0 0.0 \n", + "3 0 0 0 0 1.0 0.0 2.0 0.0 \n", + "4 0 0 0 0 1.0 0.0 2.0 0.0 \n", + "... ... ... ... ... ... ... ... ... \n", + "27126 0 0 1 0 NaN NaN NaN NaN \n", + "27127 0 0 1 0 NaN NaN NaN NaN \n", + "27128 0 0 1 0 NaN NaN NaN NaN \n", + "27129 0 0 1 0 NaN NaN NaN NaN \n", + "27130 0 0 1 0 NaN NaN NaN NaN \n", + "\n", + "[27131 rows x 44 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from dsat import Dsat\n", "from cis import Cis\n", "import pandas as pd\n", "\n", "\n", - "def count_cod_parts(b):\n", + "def count_cod(b):\n", " \"\"\"Statistics about \"cod\" parts in byte array b.\"\"\"\n", " pos = []\n", " for i in range(0, len(b) - 2):\n", @@ -198,7 +929,7 @@ " return [\n", " img.header.width, \n", " img.header.height, \n", - " count_cod(img.data.planes)\n", + " count_cod(img.data.planes),\n", " ] + [int(i) for i in img.header.unknown5]\n", "\n", "\n", @@ -229,18 +960,377 @@ }, { "cell_type": "markdown", - "id": "1e682e49", + "id": "4de94db4", "metadata": {}, "source": [ - "Only few parts of the header actually change from image to image: mainly h10 to h33 which might represent six 32 bit numbers, given their value distribution in the first plot of this notebook. Thus, we focus on the parts which are the same for most images and try to understand their meaning: " + "Only few bytes of `unknown5` change from image to image: mainly `h10` to `h33` which might represent six 32 bit numbers, given their value distribution in the first plot of this notebook. Thus, we focus on the parts which are the same for most images and try to understand their meaning: " ] }, { "cell_type": "code", - "execution_count": null, - "id": "3026a626", + "execution_count": 10, + "id": "df358efa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namewidthheightbandsh0h1h2h3h4h5h6h7h8h9h34h35h36h37h38h39
2431zoom31000100013800011111510NaNNaNNaNNaN
4410zoom31000100014800011111510NaNNaNNaNNaN
0fox6404803024100111115002.01.02.01.0
1wolf7685123324100111115001.01.02.01.0
22zoom15005003624100111115002.00.02.00.0
191zoom25005003624100111115002.00.02.00.0
1003zoom25005003724100111115002.00.02.00.0
3zoom02502503924100111115001.00.02.00.0
131zoom15005003924100111115001.00.02.00.0
504zoom25005003924100111115001.00.02.00.0
6zoom02502504224100111115001.00.01.00.0
2zoom02502504824100111115000.00.00.00.0
\n", + "
" + ], + "text/plain": [ + " name width height bands h0 h1 h2 h3 h4 h5 h6 h7 h8 h9 \\\n", + "2431 zoom3 1000 1000 13 8 0 0 0 1 1 1 1 1 5 \n", + "4410 zoom3 1000 1000 14 8 0 0 0 1 1 1 1 1 5 \n", + "0 fox 640 480 30 24 1 0 0 1 1 1 1 1 5 \n", + "1 wolf 768 512 33 24 1 0 0 1 1 1 1 1 5 \n", + "22 zoom1 500 500 36 24 1 0 0 1 1 1 1 1 5 \n", + "191 zoom2 500 500 36 24 1 0 0 1 1 1 1 1 5 \n", + "1003 zoom2 500 500 37 24 1 0 0 1 1 1 1 1 5 \n", + "3 zoom0 250 250 39 24 1 0 0 1 1 1 1 1 5 \n", + "131 zoom1 500 500 39 24 1 0 0 1 1 1 1 1 5 \n", + "504 zoom2 500 500 39 24 1 0 0 1 1 1 1 1 5 \n", + "6 zoom0 250 250 42 24 1 0 0 1 1 1 1 1 5 \n", + "2 zoom0 250 250 48 24 1 0 0 1 1 1 1 1 5 \n", + "\n", + " h34 h35 h36 h37 h38 h39 \n", + "2431 1 0 NaN NaN NaN NaN \n", + "4410 1 0 NaN NaN NaN NaN \n", + "0 0 0 2.0 1.0 2.0 1.0 \n", + "1 0 0 1.0 1.0 2.0 1.0 \n", + "22 0 0 2.0 0.0 2.0 0.0 \n", + "191 0 0 2.0 0.0 2.0 0.0 \n", + "1003 0 0 2.0 0.0 2.0 0.0 \n", + "3 0 0 1.0 0.0 2.0 0.0 \n", + "131 0 0 1.0 0.0 2.0 0.0 \n", + "504 0 0 1.0 0.0 2.0 0.0 \n", + "6 0 0 1.0 0.0 1.0 0.0 \n", + "2 0 0 0.0 0.0 0.0 0.0 " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dfa = df.drop(columns = [\n", " \"h10\", \"h11\", \"h12\", \"h13\",\n", @@ -255,18 +1345,18 @@ }, { "cell_type": "markdown", - "id": "a46abbe4", + "id": "b8b62231", "metadata": {}, "source": [ "Given that the zoom3 images are greyscale and all others colour, we could guess the following purposes:\n", "\n", "| byte | purpose |\n", "|------|:--------|\n", - "| h0 | number of bits for each pixel (8 for greyscale, 24 for colour)? |\n", - "| h1 | greyscale vs. colour? |\n", - "| h9 | default depth of wavelet decomposition? |\n", - "| h10 | relative depth (to default) used for greyscale images, i.e., actual depth = h9 - h10? |\n", - "| h36 to h39 | relative depths for the individual colour components (although we would expect only three values not four)? |\n", + "| `h0` | number of bits for each pixel (8 for greyscale, 24 for colour)? |\n", + "| `h1` | greyscale vs. colour? |\n", + "| `h9` | default depth of wavelet decomposition? |\n", + "| `h10` | relative depth (to default) used for greyscale images, i.e., actual depth = `h9` - `h10`? |\n", + "| `h36` to `h39` | relative depths for the individual colour components (although we would expect only three values not four)? |\n", "\n", "Given the depth *d*, the number of bands for one component (e.g., colour) is computed as *3d + 1*. So for the greyscale images, a depth of *5 - 1 = 4* would result in *3 * 4 + 1 = 13* bands which fits nicely to almost all of the greyscale images (24667 out of 24700).\n", "\n", @@ -275,8 +1365,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "598bb975", + "execution_count": 11, + "id": "dbccc5e2", "metadata": {}, "outputs": [], "source": [ @@ -286,7 +1376,7 @@ }, { "cell_type": "markdown", - "id": "41921faa", + "id": "194c6258", "metadata": {}, "source": [ "Let's take the values h36, h37, and h38 as relative depths for y, b, and r, respectively:" @@ -294,21 +1384,397 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "b29e0f01", + "execution_count": 14, + "id": "250c120e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namewidthheightbandsh0h1h2h3h4h5...h8h9h34h35h36h37h38h39bands'b == b'?
2431zoom31000100013800011...1510NaNNaNNaNNaNNaNFalse
4410zoom31000100014800011...1510NaNNaNNaNNaNNaNFalse
0fox640480302410011...15002.01.02.01.033.0False
1wolf768512332410011...15001.01.02.01.036.0False
22zoom1500500362410011...15002.00.02.00.036.0True
191zoom2500500362410011...15002.00.02.00.036.0True
1003zoom2500500372410011...15002.00.02.00.036.0False
3zoom0250250392410011...15001.00.02.00.039.0True
131zoom1500500392410011...15001.00.02.00.039.0True
504zoom2500500392410011...15001.00.02.00.039.0True
6zoom0250250422410011...15001.00.01.00.042.0True
2zoom0250250482410011...15000.00.00.00.048.0True
\n", + "

12 rows × 22 columns

\n", + "
" + ], + "text/plain": [ + " name width height bands h0 h1 h2 h3 h4 h5 ... h8 h9 h34 \\\n", + "2431 zoom3 1000 1000 13 8 0 0 0 1 1 ... 1 5 1 \n", + "4410 zoom3 1000 1000 14 8 0 0 0 1 1 ... 1 5 1 \n", + "0 fox 640 480 30 24 1 0 0 1 1 ... 1 5 0 \n", + "1 wolf 768 512 33 24 1 0 0 1 1 ... 1 5 0 \n", + "22 zoom1 500 500 36 24 1 0 0 1 1 ... 1 5 0 \n", + "191 zoom2 500 500 36 24 1 0 0 1 1 ... 1 5 0 \n", + "1003 zoom2 500 500 37 24 1 0 0 1 1 ... 1 5 0 \n", + "3 zoom0 250 250 39 24 1 0 0 1 1 ... 1 5 0 \n", + "131 zoom1 500 500 39 24 1 0 0 1 1 ... 1 5 0 \n", + "504 zoom2 500 500 39 24 1 0 0 1 1 ... 1 5 0 \n", + "6 zoom0 250 250 42 24 1 0 0 1 1 ... 1 5 0 \n", + "2 zoom0 250 250 48 24 1 0 0 1 1 ... 1 5 0 \n", + "\n", + " h35 h36 h37 h38 h39 bands' b == b'? \n", + "2431 0 NaN NaN NaN NaN NaN False \n", + "4410 0 NaN NaN NaN NaN NaN False \n", + "0 0 2.0 1.0 2.0 1.0 33.0 False \n", + "1 0 1.0 1.0 2.0 1.0 36.0 False \n", + "22 0 2.0 0.0 2.0 0.0 36.0 True \n", + "191 0 2.0 0.0 2.0 0.0 36.0 True \n", + "1003 0 2.0 0.0 2.0 0.0 36.0 False \n", + "3 0 1.0 0.0 2.0 0.0 39.0 True \n", + "131 0 1.0 0.0 2.0 0.0 39.0 True \n", + "504 0 1.0 0.0 2.0 0.0 39.0 True \n", + "6 0 1.0 0.0 1.0 0.0 42.0 True \n", + "2 0 0.0 0.0 0.0 0.0 48.0 True \n", + "\n", + "[12 rows x 22 columns]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dfa[\"bands'\"] = calc_bands(df[\"h9\"], df[\"h36\"], df[\"h37\"], df[\"h38\"])\n", + "dfa[\"b == b'?\"] = dfa[\"bands\"] == dfa[\"bands'\"]\n", "dfa" ] }, { "cell_type": "markdown", - "id": "9c37d28e", + "id": "78290f2e", "metadata": {}, "source": [ - "The computed number of bands fits nicely for almost all colour tiles, except only one (!) with 37 bands. The \"fox\" and \"wolf\" example images are the only ones that have a non-zero value for h39 and the number of computed bands is too large by 3 for each of them. So maybe the 1 in h39 tells us to reduce the number of bands by 3 – but why? " + "The computed number of bands fits nicely for almost all colour tiles, except only one (!) zoom2 tile with 37 bands. The \"fox\" and \"wolf\" example images are the only ones that have a non-zero value for `h39` and the number of computed bands is too large by 3 for each of them. So maybe the 1 in `h39` tells us to reduce the number of bands by 3 – but why? " ] }, { @@ -321,10 +1787,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "37eb2144", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "39 17\n", + "42 2\n", + "48 1\n", + "dtype: int64\n", + "1\n", + "36 162\n", + "39 7\n", + "dtype: int64\n", + "2\n", + "36 2238\n", + "37 1\n", + "39 1\n", + "dtype: int64\n", + "3\n", + "13 24667\n", + "14 33\n", + "dtype: int64\n" + ] + } + ], "source": [ "from dsat import Dsat\n", "import pandas as pd\n", @@ -356,10 +1847,21 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "905c9bdb", + "execution_count": 55, + "id": "7d2e4c00", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "from dsat import Dsat\n", "import pandas as pd\n", @@ -392,9 +1894,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "myenv", + "language": "python", + "name": "myenv" + }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "pygments_lexer": "ipython3" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.2" } }, "nbformat": 4,