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jhitesma/qav-r-HQ-5x4x3-AT.bin

Created March 25, 2017 06:08
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qav-r-HQ-5x4x3-AT.bin
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"text": [
"Autotune data: pts = 1024, aux_data_len = 0, timestep = 0.62ms\n"
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}
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"source": [
"%matplotlib inline\n",
"\n",
"# Import all the numerical computing stuff we need...\n",
"\n",
"from dronin import autotune\n",
"import numpy as np\n",
"import pandas\n",
"import math\n",
"from scipy import fftpack, signal\n",
"from bokeh.plotting import figure, show\n",
"from bokeh.io import output_notebook\n",
"from bokeh.models import LinearAxis, Range1d, Span\n",
"\n",
"output_notebook() # Tell bokeh to send plots to notebook\n",
"\n",
"# And load up a datafile of autotune data. In this file, the average\n",
"# motor commands and gyro responses from all 90-120 wobbles during the\n",
"# autotune sequence are stored.\n",
"df, time_step = autotune.read_autotune_lump('/Users/jhitesma/Downloads/qav-r-HQ-5x4x3-AT.bin', filter_freq=55)#, filter_order=6)\n"
]
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"source": [
"# next let's plot what the roll gyro does vs. the flight controller's roll command\n",
"\n",
"p = figure(title=\"roll gyro vs actuators\")\n",
"p.line(df.time, df.gyroX)\n",
"p.extra_y_ranges = { 'actuation' : Range1d(min(df.desiredX), max(df.desiredX)) }\n",
"p.line(df.time, df.desiredX, y_range_name='actuation', color=\"red\")\n",
"p.add_layout(LinearAxis(y_range_name=\"actuation\"), 'right')\n",
"\n",
"show(p)\n",
"\n",
"# Looks really nasty, huh? This is the gyro data vs. the actuator data. They're clearly related\n",
"# but phase is off and shape is a fair bit different."
]
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{
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" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" document.getElementById(\"a5503e09-b4c3-43e5-adfa-6a387e0dc175\").textContent = \"BokehJS successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"a5503e09-b4c3-43e5-adfa-6a387e0dc175\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'a5503e09-b4c3-43e5-adfa-6a387e0dc175' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" (function() {\n",
" var fn = function() {\n",
" var docs_json = 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"p = figure(title=\"roll gyrodiff vs actuators\")\n",
"p.line(df.time, df.derivgyroX)\n",
"p.extra_y_ranges = { 'actuation' : Range1d(min(df.desiredX), max(df.desiredX)) }\n",
"p.line(df.time, df.desiredX, y_range_name='actuation', color=\"red\")\n",
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"\n",
"show(p)\n",
"\n",
"# If we differentiate the gyro, the shape is more clearly related, but boy is\n",
"# there a lot of noise."
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\",\"dtype\":\"float64\",\"shape\":[1024]}}},\"id\":\"b4d34426-eff7-42d0-ad50-05882399de88\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"line_alph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"source": [
"p = figure(title=\"filtered roll gyrodiff vs actuators\")\n",
"p.line(df.time, df.derivfilteredX)\n",
"p.extra_y_ranges = { 'actuation' : Range1d(min(df.desiredX), max(df.desiredX)) }\n",
"p.line(df.time, df.desiredX, y_range_name='actuation', color=\"red\")\n",
"p.add_layout(LinearAxis(y_range_name=\"actuation\"), 'right')\n",
"\n",
"show(p)\n",
"\n",
"# Low pass filtering clearly helps. Now let's get down to business of finding how the input\n",
"# relates to the output."
]
},
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\",\"dtype\":\"float64\",\"shape\":[1024]}}},\"id\":\"c35f55a9-ef53-4cac-a9c7-ea0fb8bca4a8\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"plot\":{\"id\":\"f88cac25-c814-4b5e-844d-de1112ae50b0\",\"subtype\":\"Figure\",\"type\":\"Plot\"}},\"id\":\"841a493f-17d3-415e-817f-eef5ef0f9de5\",\"type\":\"SaveTool\"}],\"root_ids\":[\"f88cac25-c814-4b5e-844d-de1112ae50b0\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
" var render_items = [{\"docid\":\"a5ee10f6-18b5-4e02-9d80-081277dde520\",\"elementid\":\"679404fb-4111-40b5-bb55-db8e3d7f327c\",\"modelid\":\"f88cac25-c814-4b5e-844d-de1112ae50b0\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"679404fb-4111-40b5-bb55-db8e3d7f327c\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
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},
{
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"\n",
"\n",
" <div class=\"bk-root\">\n",
" <div class=\"bk-plotdiv\" id=\"f9de3dee-8db5-41f7-81a7-e3bc793b8e35\"></div>\n",
" </div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" (function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
" \n",
" var force = false;\n",
" \n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
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" }\n",
" \n",
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" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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" if (window._bokeh_is_loading === 0) {\n",
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" s.onerror = function() {\n",
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" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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" return false;\n",
" }\n",
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"Axis Y: tau=25.6 samples (16.01 ms / -4.134)\n",
"\tgain=22.7 (beta=10.502), bias=50.1\n"
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"<script type=\"text/javascript\">\n",
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" \n",
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" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
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" \n",
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" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
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" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
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" s.src = url;\n",
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" if (window._bokeh_is_loading === 0) {\n",
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" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
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" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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" }\n",
" };var element = document.getElementById(\"fdbaedab-a5ef-4449-b19f-fd51602cc897\");\n",
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" console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'fdbaedab-a5ef-4449-b19f-fd51602cc897' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
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nAoGk8JfZFMMDQJsur6QhJwCDUDR8OwVHAoEptcIDZSMCAbxzCEm5CwADh1qT+ozbA0OJ64pXSQ8CA8HFQuVlJwMBVDsRbojzAAPxlTwwESsD4KixbtudOwFDPTk9QCTvAwI9QWpE3OMAgW5N/L5Q2wAB8WoDYiQxAYK35+RDUOMDs1lL8FUlQwKDzyzUemkLAaIhcr4c/RsByT/Hi6W5RwJDOoSSJFS3A0OZewafvKUBQdwd9CucpwJALQB8eVTHAsAGUZKjKPsB2nM5ImzhPwE4zDmFN10XAmf2xntsIOMDkRJ4fCOZGwMD1bDgAE/M/7bYcQFMmQkAoPDpN8045wNu+AlVw80fATKBWhSZwOsBk8TmqoSE/wKjZYw67HTvA4LVpId5bFkDAqI33eLYkQKDRUqPmsh7AcKwWzlsJLsBIE4u67iNCwDho7BsthEXA4Ko0DaROJ8BYLKaOqhc1QDB946cCfypAMDYgW2y1MMDgzmWcGbobwIAcbQebi/y/0AP4QAVOPMCwz9s8gXY6wMBHlBGrBDNA0HjQl8J/RkDIGID8efAzQIChT/YFCg1AID3Mnr/4HsD4mP11ZbMxwHAZOsoeniXA4KrRYjhsE0BQffIlCtI1QCB0OArFIjZAWJjASRDxOEBAmRLV1ecSQCjSyNKz4kLAIEJAkuiMQ8DArY0bizQgQJCm/j0VUkFAGGQ+mkB9MEDQHEsT5pA5QPCcGAN95j1AkHwPxlRaJcCwqOhuUwZCwHCKFFkMKi7AYCC066o1KUCModyISn9BQBpRKWcECE9Aq6fXSNR3Q0AsF1nYJh8owCAKHyI1fiLAYK0GJehPB0AIQ+uUPIkmwJhlzM4+wSxAusEM7TRiSUBwdPHSN3JEQPxV/brgojZAAD17qlC17L+OQJLfP7hBwGlf8KZ2hDPAvhiqyv7eQEDKazSednBAQKh4fJka+TlA3JiVF/hNP0AA4I+rXr6Pv0DDo85HrCrAwJJ4pnlB8D8AH01zouXvv/zuQfQSeThAJ8FkflruUEA48wcJmfE/QJARNhGZnyLA4EnUZf0XEMAIPy9hvqgzwADQRJ96Eh7AbEKBRXtmS0Asl1mEN2ZJQPCDQxy1KjVA8EeqdfiEQUBAvB6pGwIkwABf5acGDknAQJjJwSlLF8Bg9+jEi9goQABMLYQ0ozRA+NYiDS6PUkCQNHnQkXNIQPAruTQxrzjAECRdyx2xOcBQ8z/ybBg7wMCRNYCFCjXAcF+9CMNjQECAlZf/QtJEQFBRCKcKXDhAuMm2PFCcQkAgMOpR11UjwFAFchLiXU3AYF4EaMbNM8DAL6ESa5UiQNC5pDGHGTFAeCLngJSNREAw0ThFb342QMDV7WNJ4DvAQI99BMtULMAQqVKHLz0wwBiM5OB6SUjAgJqn+diyAUBgMmK2p8VDQCCb8kSioS9AQIbrhh3eIUCAtm7kCLgmwPAldOhplkXA4M70qHmcIcBQHLbQ2Kw3QMBfddNFcyZAsHxIA1crO0AgabZBA7gwQKCuyo1EXDjA4DLI81IPNsCQHXx2uxQ2wLC5J6eJBDfA0LqK6qnkMkDwLscCaKI+QAASLNqUH+m/IB1rCDUvIMAw/4lcMpZDwBDdrlyFYkzAwBEWII3yJsCggfl9WrUsQGDDUlHK7zVAAMiFTAP6QUCAEyxvUYMiQHCug4y+VjzA8NEppgEjPcAAaDLNRl44wACLHqisxhvAgKOhEX5JNkCgZ3fUKFhAQIBIUx5aSDdAAEb+p2ED5L+grc18SIM8wOA1g4WtEjzAIIcwTZYzMsCghYA1dNkpwIAhanYZjh5AUFQqGkiKNUAAZ2UihID9P4Cmw+18/zDAUDGmEXQRMcCgCuYDK10wwACcQHa5FwhAALXbaUS9G0AAZpd1LQEjQAA+i1gVyDtAABNl2aUYCUBQi5emYU00wAD4qRsChPY/AEB1qtGokb9Ay7OiCL8pwJDU4roVRjdAgEPCup9OMECQYpUeQsI0wICN7OpvMw7AgD7aezuDJcAs/9Qzn0tDwGDhjRk7sjDAsO79MFxvIECAFGSktyEJQMBzYL4CsShAwNt68QqdGkBgTOw8RI46wOAXU4PGuTXAAGBZiAhu/b8AiabVBPYOwKAzbdMEoCtAaAXnvc2KPUAAmK7GkNUjQAA/KO3PCPI/AO+CezOE7D841XFODXA4wLB6RVKeby3A8KpAhAkVM0CwRD6AIc4sQBCtzZ8vRSpA4DfYJGAwMkCACqCTw3UiwCBzkWA0DzrAUDVvwk7iIsCQHCMYcWQywBCQLnaWICvASMnruI0yNEBA4ShV3bUoQFDPz7YgCCTAYLus/SoGLsDYzXEtuPU6wDgwpleg1DzAgOKeG1eOAsDg/sjP6fIyQLhz/m/StDdAMCQcLQmRMkCAmgDMP4sKQAi2XUq+dzXACPBOMzXyPMBAqWYaRDwnwEBZsJ+yfg1AMMDS6OO9NEDgixoGzz5BQLh63zLA7DdAEMDgmJ40McA0mchJNipBwJDaCNLc7jDAcJAi2+68M8Bw4ShMMA0hwNQ6b0q/4EBAMDwkyqAjPUAAVh6DKzjbP2AWB0LHQxNAeHIC6bmmN8D0WDmX8ilJwOCasaH7yR1AVPH4mfN9RkDA7M3O1TALQAA2TSjKGvm/8LmILP2yIkB89Zb3VuJCwHDFIFScCELAAB9HFB9CKkBgsCIkkRQeQFB3kvnW5itAdFInuMsgT0BAV2aQGpooQCh8xe9MPU7A0H/sRyhwJcBI/fbTp1I0QOBdgauqNhXA2DZQc/jjQEDag/EA/FtQQIAAqTeihyVAYOJqvqPCJ8AAw+Zfg4guwE4qKm3nQ1DAaAuhL97aNcB6dfWDqkJTQPSvCRPUIUxAcLawIvt7MECwUpsOFB4yQLBRAUpkbz7A9CbMRB/cTMBwgDAfCQ4mwJBewGxveTFA6CIsn28EQkC0w7TkcgZUQADwSfabakNAKCJWq3KIRcAw/xOu0RRDwEC8J2T9qy7AADrmgdDI+L+oyI8sDKxCQDD167ZFfUpAcOLD4no+PEAAv4TImMf7P2AHxu4Y8z3AED8tmhITSMDw0x2KMFUwwCDrxOe4SjtAqFJhssFtS0D49AWxzDZDQKCTO4F6jyLAkJRE1nh9N8CQU8GC5u04wDCszJqnaTHA0KuCXPVaMUCwa0qnECpAQPj0yVP2wEJAUGhNkvIYQEAw9j9n79EwwFDD60/6OknA0J+XTp8xOcDAbNwtWOEVQADzdyJG4jhAGHJYUck7REDgpCqjNpctQBDscd7MmTbAIKa+O6eEQcCY3VwNa9ZEwODPRc+j8EDAgOv/GuPKIkDAwIOpBpVJQFCdbHJ+zENAAJzpJAcK6D/QUU6s/904wPDVJ0XW4UXA2FZPJT3SQsAAsJorMuMOQFD8siZ700JAME9cNThkQ0AQ1J4jRdlBQAA+Aet73xRAmHYev0fMSMAwYHRgCw9KwODqNo4E3C/AIAJiLGAeKEAwPqPK268+QNAR64FGrj5AgPL0i/stBUBQq8jKq/w6wCAg0NzQKkbAKFrPcUniQ8DgL8S9WBghwPC0iKbDsjlA2KAFl5lzQEBg5Kndw7kjQLB4VXrVLDXAEFqeE1NmScCwPKGASx1JwMDh5z/yXinA4MhDo6OpKkBQlyk6eIkzQOBUIRWB5DhAAEhpPTRa+T94JaKyQp9BwExPT74GpULAoFNbnJruOcCAuqUC+MLxP8ja91W/dz5A6DRT4l9rMkCAt0Kr60kLwLh229QRDTDASCuAWjl0SMDY79lYgYpLwCBePwycQBDAuHPSJikFNkCQ9/c3jV82QFDHuv999zhAoGTELxkLIMCM/uXlZMtIwAYfFPvpa0bAwF0UYGsrLcBA7KEbFhEQQFCWcaaMtzdAFLwDDiHiPEAAMYYEdlIwQAC8v2+Pcy7AtNkl7e8ATsAGXBko1LlHwAAkmGZ8F6e/1JmGNWS1IkBIeZsTriYeQMXKl3jtaUNAaHCv/wFiK0CLCm0ZQexIwLq3LEUgIUDAAi//Vi8LJ8D0v/iojEs5wI6V3Hva0DBASo6Jl59ETUCgV0KnK5EZQMgjzqBBBzjAcNxPF1pzLEAUbdcVGdEtwEZNszYE0jfA3K82JXNbSUCUPxBy541DQDipTF97qSvAqADW6ydLJ8BYnaadzasywF5rdZ2gJT7AeC/1Q9uPEcBUC/Ok5CgkQJzdhU0HrilA2lsNZeH4RECghrHEwE4oQKwORRYvMkvA0kgqauf9R8CWRF4FfW4xwEyyskvGrAdA3lQIPt9AQ0BekgOtwZRCQLDUlkSkZCzAtKSF5CgOMMCgK0EG5Wj5v5JlmfNYq0jAVBsc+OWIQcB3/asPtidFQMWUmJAzsUNAAC/6EzSn3L+gxFvoIZsBwA56bDOQvSfAsC0Tq2siRMCi9oiaarhAwDijUGAM5ynAfFHfG2NmJUDY9aCvSbU7QHi+yDTyvyfAxSBdR9bQQMAYOC9cOXcgwFxbdmKm/THAmMw62TTfPMAcgUUco+E1QJL0zm0nuEVAcE+YQnhaHED43Ss6L8kgwKQiu4MkgTDAjDJMMYpIO8CwZcuZNWIowPiJCc0gKCdAKEZBMfl0MkBIfud9TbQxQIApIMMtDPS/2pLlCmZpQcB8sF5hsfZDwLA+FXXcmS/AgORcHPVCEcBA0IHAlTEpQEhEh5IlOUZANJvzPkg0PEDQdEXLKXc8wPKCnxIyh0LAQKLrlPmoBcDAugUggtIdQEh7uAKA6TNAoJQuFRwNRkCX4vMznlxGQGz233jHHzJAL71R0+hlQMAagTw5McFEwDDf98l88jRA8GlitwjiPUDQmSA11RcWQK75AGqT+kJAXaBRaetlNkDIopb4Lj4/wOskgKbusTbAMHRceqyYB8Co8/EVjjMawCpsd/kebTFAsw3WmryeRUAElYUwkJQkQJPSruUp1zLAgI8JJeahLMAmBUuBS60IwA91lyPXCiFAUAUAg98cOEC2pOy6lWlHQL6oFMq15kFAII2xi9vWIcBYg+CPRFQwwCC4O14arhlA8JikB2eKFECwPxdlw1c9QDiSGEsETkxA4D8/GuixMEDwzkS5wXMuwEDFDgS5OhTAUBvK6AmENMBI8/1atBkzwPiSm6RrjTxA7KBt3v7gSUBYirGdd1pBQACID2N73O2/qJSFA870NcCQV8c4OVk1wKBTN2fQgTPAMHHuXItiIkBkABMekDNHQERlWyvFNkhA4BTgs+VeM0DAmoGiDu4SwGARoUJETzPAQDTexmK2PMCAPdWl90UYQGCpvaYuDkRAuOoSorsKQ0DgQC7OPz8zQICxm6W1zQVAgHBP87J0JsAAiMgKHCM9wCD+S+CQTyvAILEYgNwsOkDAhn3kon9EQJB77zxeTTxAgHpRuzIDFUBQ9Jisqxs3wAhHm0GzLETAEAaK4PGbNcDAOriiw4wtQGijgcX8+UZAsBfqABu1TkCA3XSow+05QCCepRnk5SfAIO2XYLhCK8AADMUc7o/nv5Dqjzn0REdAgCXleLiIUUCgZHjUOHxEQBB0/lCom0hAAL7eS88dUEAA/qd3kIgqQIA/VVkFchPA8D2dQ5AXRkCom4xCzgRTQIDiPFgJYlVA4PIJyi+jVEDgbJo7aOpLQMDO6TrcwjRAANv7mp6uKUCgogQDODxHQLDTo5kRY1VAIHOzIutuWECQiwoGyL1XQACb7rtPb1ZAoHnm8lsPTkBA+o9+PEM4QMBAmsbCiUhAUCcqCZEdVkCAiWtcbMJXQMAwzS8MQF5AYFXljTO8YEBAImJRRV9TQIBJM6aknDxAwP3l5S2cSkCA7boArOlUQID8KVBxPlVA4NMyLO7WW0AwT9Ylz0JgQOA9juenrVVAQPkbc0zeQ0AAtTPjHV1BQID3Y6y1wEhAgBOqkZJYUUDAv4nTv89ZQKDS6N8cP2BAwBnt67nMV0CARosUHYZDQIBdMrL1xkVAgDschGDRTUCAp03mhmhIQGBoh9hze1VAcFwyuwrfYEBArqeJhcFYQAAacBS5t0xAQP+nEMMZTEAAkvOEQiQ4QACzGOthtTpAYLROgnIXVkDAos7o8VtYQMDLZ9SzilNAAMGKufg0T0AArw/vDq41QADIQjqIfhdAAHagF9aIKUAABIq4Dp4nQACekbAmlE9AAIVXLY5RW0AAlOLNPRdLQAB6GN30gTFAAClcHtajP0AAdkazP0wiwADswRsKDS3AAC9GNpfMT0DAO5aWkL5SQIAHDQb8wUNAgHR5ROIFSEAA3v0qxr47QABmrtiKjynAALxPyI7sF8AAP1QyLcMyQAA35QNaBkNAgKzRKO5mRUAAL0hWze82QAAgRbh8q/E/AP8Yeze7NsAAuhya2FRFwADG/Ede9SHAgIVdhn2LQ0AATMWr05UtQABQREIxNQjAAEWJGbL7NUAAYNj6g+I8wACt0HqNvFLAAMBfh90rMsAAWreZXDssQACI+ZrknQTAAMqU+knlPkAANEMth/k+QIA8dZASzUPAgKlidi6RTMAAXXkkQxJAwABSA5v97T/AADJhiIPDMcAAblRq0DI3QAAB9fBJQjJAAILpw38KMcCAKJHRuQFDwIApXU8G9kzAgFGvX/YHTMAA99NzBos6wAAANPF8heS/APDXvsIS+r8AkKJXAnkywABBfrIuM0LAAD8ZVTLqRcAA9wPz1GxHwADan62yBUrAAKUZuRVPOcAAILkTHaD4PwAkiygh5yHAgBFkR1ibQsAAbh8I+mZNwMCoA5zZPVDAgBv5o8mYScAASEiqiiUywADC/7aFMyPAABTjasH4N8DABJliJKRDwOC16XjW01TAQO3UwItAXcAAo3lXv5RZwICya5KUxlHAAIG069emScAA8Rb6as1IwIBnC+qqEkrA4K0/ys+OUsAg4Jm4RMlYwKAic2Go9VzA4HH7fNfiYMCAtTd0gqFZwMBg7J2+XkHAgItekZ4mRMCgUiHZJM1UwOCDPDW7R1jAwJNwF6spX8CgFjONx6BfwKDfilNiClnAQPAEs/4jXcBgS0vGZsVZwAAJZnatw0jAgKWZw8KaV8AwB8tHwVJiwGCYpDTC913AWC8JyCGNYMBgea2v7PhfwLClxCNyzVHAALjnzHeVVcCQsPL9iX5ewAC73+QtZ1nAyLFDkbWhXcD8HKGzrdxiwABNRljSgFzASGX4MyJhU8B4cbhWfJRSwKyZXAtB2lbA/FkR6W/uXcCUKK1aOSpewNjjfjinKF/AvuVUVMBjXsB63GUIPpJSwDxKXeqtxk3A/qoV5MZ+VsD888G7vshVwPiKBREGC1nAZm39ivppYsDQRV7nfqddwChOt9YOvlDAEBIm9PnBRMDAN3ZBLGc0wNDTu8MTXUnAgORVnqQLVcAwwlJfd5xYwGD0mIcSf17AICikZ7XvV8BAPBHzg+5BwEBLRw6p5jvAoBKuQ2BCQMCgzxBEfhdBwLCpZfUc7VjAwNIkEgcXYMDgEttirblPwEBDgRZIWkjAgMfv+WMBSMAAAP5kDUYIwADyE1pjKzbAMEWAhtSuUcCgNYV7jxtQwAA3nutAwEzAwAtIEU4YSsAAWGYp8NLwP4AG0Vk3mjRAALvGzG32JMAADusz24A4wAClN3NgoEXAwPqdcx5GS8AAcB+16JRAwADOasifTCHAgDLkgqdTM0CALzcSMgA4QADokd5GtSLAgDe67bSLPsBAmQyav+VBwEB53Z47WEHAAL7HOW3zFcBA71SipclAQIAgGTYmtTtAAK/d+BmfI0AAhGLFNW8BQMDm5eYJRUHAAB/clF3NQsAAEy85LjInQIA0/SzSoUBAgLNHIy1AQkBAAAZRVb9NQABLb5uX8D5AALmdwyr7PsCA0AH1nTkzwIBtSDPP4TFAANWgfBqcKkBACasxKCBIQPA4K7ZCG1VAwDY6pqG+N0AAA+PmRmEYwEDTmxy6IzNAADs1VaLTJsAA0lOqofkPwNCGDrNp0VJAEJbmIuSRUEBAVsKB5zUzQIDxYtcYOEdAQGgU0fu0MkBgV0/lXCdBwCCbDPgDU0BA0Fe/T9UFU0DAf6opQ4VCQEBYFreOaUxAoFYQYpMWT0AArNGyhe4vwAAFGAM+JCzAgBoWiQI+RkBA0mnf81w6QAC+8SnQokFAsNHEShoWWEDANK1BCjVMQAABlwuj7yPAAHT2ieS+9j8ANg+E7n0wwACC1ZkaCynAELgQsE9yUkDA63KsqR5PQIDdrJsAcTnAAB8Q6UVTLsAA4KWifcACwGDtEEBj1UfAQAoKWpNWNsDAg3cycIEwQIB4OMDRfyLAABDcd+jM1z8A0ZdcTh0kQOCNq6m110zA0LCmKOVPUsBAPuP17BQ8wMBXNFCfajvAgHzOy/ZoL8AA3b+2PZ8eQIDdaW252j3AkD1qE0qMUsCQ+rgwkgZQwLDof1AvvlDAYA+zaDDjUcBAFOZzMMs+wABqXE17WD/AYGxaypVTUMAwkgLek6pRwLDcIXZjpFbAIHj59GnOXsBALqf90PVVwEBlSy2Ow0HAgFSXjOzVRsDA7SR6OANPwOBti6J4/FDAoFuIjf9RWcDguRB472ZdwECKGIq1N1TAgHnD02RGUMCAy/GwJZVOwADF+DZjID3AwGwfEU1bRcAgDSlxxs5ZwACWwrwwsljAoDpM2dVKUsAA4efjk2VXwIBzidpd6E7AAPpZIPHlPcDgIXPEhHxQwCAtFAB4+VPAYP6MxFJ8VMDgTkPWhARdwECMkeUsNlfAQHFwfbglQ8AA0BwPRhFLwCBFyUE8H1LAABu9AJjbTcDAv6Edj7xTwADdnUdGiVfAANavPDIrT8AAqjT9Q3dFwIAkt26Qy0LAALIe+fFFOsAAuxfCnLFDwMApLSQ6/lLAABqjQWMtVMAAN/iPIDhNwICjtqZ5xEfAAI6064KeP8AAOndFfEAgwACWWJZL9yDAAFquOikRR8CA7YepjYtSwIDvN70WulHAAEsAubS9TMAAlkaDMpw/wADI5HeGohbAAEodc6cBKMAADgCJPyA+wABDz9rDiDLAgCbc8s3bQsCABN5cOU5QwAB73XNbADXAAMhdDNfXLkAAag+XqlQjwADaEC+TBTPAAJCe6fRk+z8AKfBlwrY2wABd/TrUvjLAAG6xF2wiLEAA2hPd3i8rwAC4qw6i+wHAAELXGirpQkAA+Bkwll8awACeLUlT0knAANzVWXwXE8AAbKmUChgkQAAogNe3oQfAAF2TGDzIRUAALLtfsTE9QADKewS9OzLAAHhsKDUhDsAA6DbvF7wkwADFn0i8pznAAO4KuiywM0AAxbRFN29IQADYC7+/Vj9AACc4t57LP0AA3x/vZP44QADEJ0UWHBTAAIweS/5bF8AAujPw4GwhQAAfna0S1jZAAK+zRSt6S0CA9bQ1ggBMQAATlcivpTtAALWQA4OnNUAANeBZBwczQAC6wf5IiyxAAPcjeuxjOUA=\",\"dtype\":\"float64\",\"shape\":[1024]}}},\"id\":\"13a85d2e-76b4-4464-89f9-67efffba9d13\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"7dda9e01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" var render_items = [{\"docid\":\"0a402d95-9317-411f-a413-241096f88665\",\"elementid\":\"fdbaedab-a5ef-4449-b19f-fd51602cc897\",\"modelid\":\"1a8e73e7-53a9-4438-9ca3-b5b06c2cb537\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
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" } else if (!window._bokeh_failed_load) {\n",
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" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
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\",\"dtype\":\"float64\",\"shape\":[1024]}}},\"id\":\"13cd7542-24e2-4cae-9c13-f4650ca5d0dd\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"ad7a80bc-044c-4c32-8459-4076bdfb6ae1\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"6b036d83-a8af-499a-8549-fcae468da91e\",\"type\":\"BasicTicker\"}},\"id\":\"f75517d8-f094-4d24-b68a-307ef2703835\",\"type\":\"Grid\"},{\"attributes\":{\"callback\":null},\"id\":\"585bf605-eff2-48a3-8f3e-4d1349ffe0c4\",\"type\":\"DataRange1d\"},{\"attributes\":{\"callback\":null},\"id\":\"33d65c03-a5c6-4dc4-a760-a7d870a4a661\",\"type\":\"DataRange1d\"},{\"attributes\":{\"data_source\":{\"id\":\"13cd7542-24e2-4cae-9c13-f4650ca5d0dd\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"0f690e62-256f-465f-8cf9-76e69cdea12e\",\"type\":\"Line\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"dc984aa7-1d35-4eed-834c-2c5c60d3bad1\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"2618bbcb-3456-40dd-a54c-c0559712ab94\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_alpha\":{\"value\":0.1},\"line_color\":{\"value\":\"#1f77b4\"},\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"i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" var render_items = [{\"docid\":\"d9ab0339-382a-4659-ab87-cd3ce40eb1cb\",\"elementid\":\"bddfac45-1f75-44e3-bd66-84d7b1a2ac68\",\"modelid\":\"ad7a80bc-044c-4c32-8459-4076bdfb6ae1\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"bddfac45-1f75-44e3-bd66-84d7b1a2ac68\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
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" </div>\n",
"<script type=\"text/javascript\">\n",
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" function now() {\n",
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" }\n",
" \n",
" var force = false;\n",
" \n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
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" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
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" s.onerror = function() {\n",
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" return false;\n",
" }\n",
" \n",
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" var render_items = [{\"docid\":\"50db758d-f83b-42f4-a02d-27cd3bb78ed2\",\"elementid\":\"74de83f5-daff-472e-b09b-e726dc556861\",\"modelid\":\"0348aab6-8ba5-41b7-befb-7ade51a52a11\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
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\",\"dtype\":\"float64\",\"shape\":[1024]}}},\"id\":\"f800c9cc-eb99-4c49-9190-64099e78982e\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"dimension\":1,\"plot\":{\"id\":\"ced67d78-74b8-4f76-8544-e9fe8a9b9102\",\"subtype\":\"Figure\",\"type\":\"Plot\"},\"ticker\":{\"id\":\"cbd6ed0c-cc7a-462f-878e-70a117943be1\",\"type\":\"BasicTicker\"}},\"id\":\"4a0e94e3-4df5-442c-be92-3440cd4e0c59\",\"type\":\"Grid\"},{\"attributes\":{\"data_source\":{\"id\":\"fe764975-dc1f-45c5-a25a-86d163c291b8\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"cba2091c-35b9-4ada-a080-c40c8c2ab165\",\"type\":\"Line\"},\"hover_glyph\":null,\"nonselection_glyph\":{\"id\":\"a18d5a58-ab83-4077-8a74-63b84da1fdfd\",\"type\":\"Line\"},\"selection_glyph\":null},\"id\":\"1afc4b19-f4e9-425e-9c25-c360a70dc967\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"9677e4ed-dd8e-4c74-8624-ed2fb190bb58\",\"type\":\"PanTool\"},{\"id\":\"b4b73421-e78d-4cd5-9d5d-f06294dff593\",\"type\":\"WheelZoomTool\"},{\"id\":\"57c6977f-7993-4552-9182-d99b31f00845\",\"type\":\"BoxZoomTool\"},{\"id\":\"38b9b882-4a0b-46e8-bbf1-a6a4e6e8ed36\",\"type\":\"SaveTool\"},{\"id\":\"2ce75623-6fdb-4c73-9f9a-4eb06688f287\",\"type\":\"ResetTool\"},{\"id\":\"74755fb0-9988-492a-af18-536ae6bdebe6\",\"type\":\"HelpTool\"}]},\"id\":\"e647c23e-b27d-4bec-b5d2-a12e3c436615\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"3d38e682-05e7-44f1-8116-d9da7c5ae50f\",\"type\":\"ToolEvents\"}],\"root_ids\":[\"ced67d78-74b8-4f76-8544-e9fe8a9b9102\"]},\"title\":\"Bokeh Application\",\"version\":\"0.12.4\"}};\n",
" var render_items = [{\"docid\":\"79cab5a1-841f-4b22-9d01-20c83129aafe\",\"elementid\":\"3d9b86e8-4f25-4c1c-b0d2-8387e82e2b5a\",\"modelid\":\"ced67d78-74b8-4f76-8544-e9fe8a9b9102\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"3d9b86e8-4f25-4c1c-b0d2-8387e82e2b5a\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"\n",
"\n",
" <div class=\"bk-root\">\n",
" <div class=\"bk-plotdiv\" id=\"fb5cb9c2-8ece-48e2-b2cc-b65d63d75956\"></div>\n",
" </div>\n",
"<script type=\"text/javascript\">\n",
" \n",
" (function(global) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
" \n",
" var force = false;\n",
" \n",
" if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" window._bokeh_onload_callbacks = [];\n",
" window._bokeh_is_loading = undefined;\n",
" }\n",
" \n",
" \n",
" \n",
" if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
" window._bokeh_timeout = Date.now() + 0;\n",
" window._bokeh_failed_load = false;\n",
" }\n",
" \n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
" \n",
" function display_loaded() {\n",
" if (window.Bokeh !== undefined) {\n",
" document.getElementById(\"fb5cb9c2-8ece-48e2-b2cc-b65d63d75956\").textContent = \"BokehJS successfully loaded.\";\n",
" } else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
" \n",
" function run_callbacks() {\n",
" window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" delete window._bokeh_onload_callbacks\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
" \n",
" function load_libs(js_urls, callback) {\n",
" window._bokeh_onload_callbacks.push(callback);\n",
" if (window._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" window._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" window._bokeh_is_loading--;\n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"fb5cb9c2-8ece-48e2-b2cc-b65d63d75956\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'fb5cb9c2-8ece-48e2-b2cc-b65d63d75956' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
" \n",
" var js_urls = [];\n",
" \n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" (function() {\n",
" var fn = function() {\n",
" var docs_json = 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" \n",
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" };\n",
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" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
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" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
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" }} else if (Date.now() < window._bokeh_timeout) {\n",
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"text": [
"[ 3258596.07724545]\n",
"[ 452109.84347166]\n",
"[ 530916.74643985]\n",
"[ 2522727.97224676]\n",
"[ 512406.34436268]\n",
"[ 1497730.56685358]\n"
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"source": [
"def find_real_peak(series):\n",
" p = np.argmax(series)\n",
" \n",
" min_idx = p - 3\n",
" \n",
" if (min_idx < 0):\n",
" min_idx = 0\n",
" \n",
" max_idx = p + 3\n",
" \n",
" if (max_idx >= len(series)):\n",
" max_idx = len(series) - 1\n",
" \n",
" fit = np.polyfit(range(min_idx, max_idx+1), series[min_idx:max_idx+1], 2)\n",
" \n",
" peak = fit[1] / -fit[0] / 2\n",
"\n",
" return fit[1] / -fit[0] / 2\n",
"\n",
"\n",
"\n",
"# For now, we just want to figure the parameters that existing autotune\n",
"# uses, but in a more accurate and more robust way. In the future we'll\n",
"# do much fancier things.\n",
"\n",
"# So, for each of the three axes, let's perform fourier analysis in order to\n",
"# correlate the differentiated, filtered gyros vs. the actuator command.\n",
"# This will tell us the delay, or tau. From \n",
"\n",
"def compute_axis(desired_series, diff_gyro_series, filter_order=3,\n",
" percentile_cutoff=2, plot_tau=True, series_cutoff=2,\n",
" axis_name=\"\"):\n",
" desired_fft = fftpack.fft(desired_series)\n",
" diff_fft = fftpack.fft(diff_gyro_series)\n",
"\n",
" desired_fft_r = -desired_fft.conjugate()\n",
"\n",
" correl = fftpack.ifft(desired_fft_r * diff_fft) \n",
"\n",
" tau_offset = find_real_peak(np.abs(correl[0:len(correl)/series_cutoff]))\n",
" \n",
" if plot_tau:\n",
" p = figure(title=\"%s delay time vs. correlation\"%(axis_name), y_axis_type = \"log\")\n",
" p.line(np.linspace(0,len(correl) / 2 * time_step, len(correl) / 2), np.abs(correl[0:len(correl)/2]))\n",
" tauspan = Span(location=tau_offset * time_step, dimension='height', line_color='red', line_width=1)\n",
" #bogospan = Span(location=.0168, dimension='height', line_color='green', line_width=1)\n",
" #p.renderers.extend([bogospan])\n",
" p.renderers.extend([tauspan])\n",
" show(p)\n",
"\n",
" # half cycles per sample.e\n",
" filter_edge = filter_order / tau_offset / 3.14159 / 1.22\n",
"\n",
" # A nth order filter doesn't result in n* the delay with bessel\n",
" # design technique. 1.22 is a fudge factor for order 3. Not sure where\n",
" # 1.22 comes from :D\n",
" db, da = signal.iirfilter(filter_order, [filter_edge], btype='lowpass', ftype='bessel')\n",
"\n",
" #w, gd = signal.group_delay((db, da))\n",
" #p = figure()\n",
" #p.line(w, gd)\n",
" #show(p)\n",
" \n",
" desired_series = pandas.concat([desired_series, desired_series], ignore_index = True)\n",
" \n",
" delayedact = signal.lfilter(db, da, desired_series)\n",
" \n",
" delayedact = delayedact[len(delayedact)/2:]\n",
" \n",
" # Now compare the response magnitude of the filtered waveform vs. the\n",
" # differentiated gyro series, to compute beta and then the bias.\n",
" span_desired = np.percentile(delayedact, [percentile_cutoff, 100-percentile_cutoff])\n",
"\n",
" span_gyrodiff = np.percentile(diff_gyro_series, [percentile_cutoff, 100-percentile_cutoff])\n",
"\n",
" lin_gain = (span_gyrodiff[1]-span_gyrodiff[0]) / (span_desired[1]-span_desired[0])\n",
" \n",
" bias = delayedact.mean() * lin_gain - diff_gyro_series.mean()\n",
"\n",
" return (tau_offset, lin_gain, bias, delayedact * lin_gain - bias)\n",
" \n",
"def compute_axis_by_name(series_name, do_plots=True, series_cutoff=2):\n",
" desired = df['desired'+series_name]\n",
" deriv_filtered = df['derivfiltered'+series_name]\n",
" \n",
" tau_offset, lin_gain, bias, adjact = compute_axis(desired, deriv_filtered, plot_tau=do_plots,\n",
" series_cutoff=series_cutoff,\n",
" axis_name=series_name)\n",
" \n",
" df['adjdesired' + series_name] = pandas.Series(adjact)\n",
" \n",
" print \"Axis %s: tau=%.1f samples (%0.2f ms / %0.3f)\"%(series_name, tau_offset, tau_offset*time_step*1000, math.log(tau_offset*time_step))\n",
" print \"\\tgain=%0.1f (beta=%0.3f), bias=%0.1f\"%(lin_gain, math.log(lin_gain / time_step), bias)\n",
" \n",
" if (do_plots):\n",
" p = figure(title=\"%s shifted to line up\"%(series_name))\n",
" \n",
" p.line(df.time, deriv_filtered)\n",
" p.line(df.time, adjact, color=\"green\")\n",
"\n",
" show(p)\n",
" \n",
" p = figure(title=\"residual %s\"%(series_name))\n",
" \n",
" p.line(df.time, deriv_filtered - adjact)\n",
" show(p)\n",
"\n",
"compute_axis_by_name(\"X\", series_cutoff=5)\n",
"compute_axis_by_name(\"Y\", series_cutoff=5)\n",
"compute_axis_by_name(\"Z\", series_cutoff=5)\n",
"\n",
"print np.correlate(df.derivfilteredX, df.derivfilteredX)\n",
"print np.correlate(df.derivfilteredX, df.derivfilteredY)\n",
"print np.correlate(df.derivfilteredX, df.derivfilteredZ)\n",
"print np.correlate(df.derivfilteredY, df.derivfilteredY)\n",
"print np.correlate(df.derivfilteredY, df.derivfilteredZ)\n",
"print np.correlate(df.derivfilteredZ, df.derivfilteredZ)"
]
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