diff --git a/.gitignore b/.gitignore index 9477d07..247f05d 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,4 @@ __pycache__ *private* docs/apidoc +notebooks/outputs diff --git a/notebooks/config_compare.ipynb b/notebooks/config_compare.ipynb index 621a5dc..3691357 100644 --- a/notebooks/config_compare.ipynb +++ b/notebooks/config_compare.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -59,9 +59,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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BaseLiftDown
reception_cutup8.4523068.452306
cutup_processing6.9686976.968697
processing_microtomy28.08925628.089256
microtomy_staining3.6785113.678511
staining_labelling3.6785113.678511
labelling_scanning86.898479155.862945
scanning_qc86.898479155.862945
extra_loading15.00000015.000000
extra_unloading15.00000015.000000
\n", + "
" + ], + "text/plain": [ + " Base LiftDown\n", + "reception_cutup 8.452306 8.452306\n", + "cutup_processing 6.968697 6.968697\n", + "processing_microtomy 28.089256 28.089256\n", + "microtomy_staining 3.678511 3.678511\n", + "staining_labelling 3.678511 3.678511\n", + "labelling_scanning 86.898479 155.862945\n", + "scanning_qc 86.898479 155.862945\n", + "extra_loading 15.000000 15.000000\n", + "extra_unloading 15.000000 15.000000" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df = pd.DataFrame([{x[0]: x[1] for x in config.runner_times}, {x[0]: x[1] for x in config2.runner_times}]).T\n", "df.columns = ['Base', 'LiftDown']\n", @@ -77,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -95,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -104,9 +197,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00000000e+00, 0.00000000e+00, 4.44205889e-04, 6.93278477e-03,\n", + " 4.11710113e-02, 6.84473682e-02, 1.07719928e-01, 2.66269702e-01,\n", + " 4.90474692e-01, 6.39463463e-01, 7.23891667e-01, 7.91011705e-01,\n", + " 8.50815567e-01, 8.96240908e-01, 9.35331026e-01, 9.62221347e-01,\n", + " 9.74624739e-01, 9.83014413e-01, 9.89547941e-01, 9.93902745e-01,\n", + " 9.97078289e-01, 9.99077215e-01, 9.99751456e-01, 9.99891593e-01,\n", + " 9.99941830e-01, 9.99978847e-01, 9.99994712e-01, 9.99997356e-01])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lab_tat = lab_tat/24.\n", "np.array([np.mean(lab_tat < n) for n in range(28)])" @@ -121,7 +231,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -130,7 +240,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -148,9 +258,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.00000000e+00, 0.00000000e+00, 2.72461004e-04, 6.48729651e-03,\n", + " 3.48641101e-02, 5.90858933e-02, 9.52850623e-02, 2.16960698e-01,\n", + " 3.92847899e-01, 5.35906273e-01, 6.27646618e-01, 7.07237927e-01,\n", + " 7.74026292e-01, 8.30324910e-01, 8.82542061e-01, 9.27628908e-01,\n", + " 9.55237382e-01, 9.71991009e-01, 9.84450651e-01, 9.91123220e-01,\n", + " 9.95975751e-01, 9.98713984e-01, 9.99629453e-01, 9.99806553e-01,\n", + " 9.99871943e-01, 9.99926436e-01, 9.99961855e-01, 9.99994551e-01])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lab_tat2 = np.concatenate(lab_tats2)/24.\n", "np.array([np.mean(lab_tat2 < n) for n in range(28)])" @@ -165,7 +292,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -175,18 +302,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.27767681, 0.31861004, 0.32030816, 0.17365079, 0.37452107,\n", + " 0.18592682, 0.24519193, 0.21921569, 0.30903316, 0.25232653,\n", + " 0.3133584 , 0.35086336, 0.20480936, 0.21739479, 0.45862828,\n", + " 0.23167435, 0.20903909, 0.30490272, 0.16639584, 0.38222706,\n", + " 0.29450363, 0.21780006, 0.24786325, 0.16263112, 0.17369405,\n", + " 0.30917345, 0.35299589, 0.34414331, 0.2057995 , 0.13292876])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "mat1[:,0]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.26524291, 0.72278014, 0.93498745, 0.99906045])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "means1 = np.mean(mat1, axis=0)\n", "means1" @@ -194,9 +348,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.21616197, 0.62647169, 0.88206174, 0.99870332])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "means2 = np.mean(mat2, axis=0)\n", "means2" @@ -204,9 +369,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.02927592, 0.02518444, 0.00809137, 0.00042047])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ci1 = [\n", " stats.t.interval(0.95, loc=np.mean(x:=mat1[:,col]), scale=stats.sem(x), df=len(x))\n", @@ -218,9 +394,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.02550822, 0.02835163, 0.01067065, 0.00056114])" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ci2 = [\n", " stats.t.interval(0.95, loc=np.mean(x:=mat2[:,col]), scale=stats.sem(x), df=len(x))\n", @@ -232,9 +419,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "fig = plt.figure(figsize=(5,10))\n", "ax1, ax2 = fig.subplots(2,1)\n", @@ -264,22 +472,170 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "8.728869201205312" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.mean(lab_tat)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "9.456790067921988" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.mean(lab_tat2)" ] }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "with open('outputs/config_compare_base.tats.json', 'w', encoding='utf-8') as file:\n", + " json.dump(lab_tats, file)\n", + "\n", + "with open('outputs/config_compare_lift_down.tats.json', 'w', encoding='utf-8') as file:\n", + " json.dump(lab_tats2, file)\n", + "\n", + "with open('outputs/config_compare_base.specimens.json', 'w', encoding='utf-8') as file:\n", + " json.dump(specimen_datas, file)\n", + "\n", + "with open('outputs/config_compare_lift_down.specimens.json', 'w', encoding='utf-8') as file:\n", + " json.dump(specimen_datas2, file)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14644" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(specimen_datas[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'source': 'Internal',\n", + " 'priority': 'ROUTINE',\n", + " 'reception_start': 1111.1797935985203,\n", + " 'reception_end': 1111.566197289564,\n", + " 'cutup_start': 1111.6048799233783,\n", + " 'cutup_type': 'Large specimens',\n", + " 'num_blocks': 4,\n", + " 'cutup_end': 1113.777448285854,\n", + " 'processing_start': 1186.6196833013535,\n", + " 'processing_end': 1210.938175018628,\n", + " 'microtomy_start': 1213.1354427997985,\n", + " 'total_slides': 12,\n", + " 'microtomy_end': 1215.2238445373514,\n", + " 'staining_start': 1216.1988229602682,\n", + " 'staining_end': 1263.777355903756,\n", + " 'labelling_start': 1264.6402221273631,\n", + " 'labelling_end': 1264.7300336722797,\n", + " 'scanning_start': 1265.1209646205587,\n", + " 'scanning_end': 1281.0749646064507,\n", + " 'qc_start': 1281.169468589424,\n", + " 'qc_end': 1286.1559761118367,\n", + " 'report_start': 1287.1286549861468,\n", + " 'report_end': 1357.69563609433}" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "specimen_datas[0]['specimen.10000']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Compress files:**\n", + "\n", + "```bash\n", + "tar -cvzf config_compare.tar.gz config_compare_*.json\n", + "tar -cvjf config_compare.tar.bz2 config_compare_*.json\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "outputs/config_compare_base.specimens.json\n", + "outputs/config_compare_base.tats.json\n", + "outputs/config_compare_lift_down.specimens.json\n", + "outputs/config_compare_lift_down.tats.json\n" + ] + }, + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from os import system\n", + "system(\"tar -cvjf outputs/config_compare.tar.bz2 outputs/config_compare_*.json\")" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/notebooks/config_compare.png b/notebooks/config_compare.png new file mode 100644 index 0000000..2a7cbde Binary files /dev/null and b/notebooks/config_compare.png differ