diff --git a/README.rst b/README.rst index 25c7dae..ab15cbb 100644 --- a/README.rst +++ b/README.rst @@ -18,7 +18,9 @@ OpenBadge Analysis :alt: Updates -Python libraries for analysing OpenBadge data +Python libraries for analysing OpenBadge data. See examples in the `examples repository`_ + +.. _examples repository: https://github.com/HumanDynamics/openbadge-analysis-examples * Free software: MIT license diff --git a/examples/misc/999.0-ol-check_gaps2-old.ipynb b/examples/misc/999.0-ol-check_gaps2-old.ipynb deleted file mode 100644 index fcf53e5..0000000 --- a/examples/misc/999.0-ol-check_gaps2-old.ipynb +++ /dev/null @@ -1,2590 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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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", - " };\n", - "\n", - " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " Bokeh.$(\"#de134258-0634-40fe-82e5-9a2158ab41fe\").text(\"BokehJS successfully loaded\");\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\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));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from data_loader2 import *\n", - "from __future__ import division\n", - "from __future__ import print_function\n", - "import pandas as pd\n", - "import bokeh\n", - "import numpy as np\n", - "\n", - "import bokeh.plotting as bp\n", - "from bokeh.plotting import figure, output_file, show,gridplot\n", - "from bokeh.models.formatters import DatetimeTickFormatter\n", - "from bokeh.models.axes import DatetimeAxis\n", - "\n", - "from datetime import datetime\n", - "from datetime import timedelta\n", - "\n", - "import pandas as pd\n", - "\n", - "bp.output_notebook()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#data = DataLoader('bat_data/E6_AA_91_B9_BF_48.scn.3') #\n", - "data = DataLoader('bat_data/D2_3C_F6_B9_87_24.scn.3') #" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "freq = data.data[0]['Frequency'] #assuming same freq. for all samples" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# take only samples\n", - "signals = map(lambda x: x['samples'], data.data)\n", - "\n", - "# convert \n", - "con_signals = [map(lambda x: {\"datetime\": x[\"time\"], \"signal\": x['signal']}, i) for i in signals]\n", - "#con_signals[0]\n", - "\n", - "# Append all samples into an array\n", - "singals_all = []\n", - "for s in con_signals:\n", - " if len(s) == 114: #only load full chunks\n", - " singals_all.extend(s)\n", - " \n", - "# sort\n", - "sdata = sorted(singals_all, key=lambda x: x['datetime'])" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#signals[2]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Check for gaps in data (before loading into pandas. It's faster)" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def check_raw_gaps(arr, max_delta_sec = 250):\n", - " #max_diff = np.timedelta64(max_delta_sec, 's')\n", - " max_diff = timedelta(milliseconds=max_delta_sec);\n", - " prev = None\n", - " for i in range (0,len(arr)): # iterate over all lines\n", - " #print df.iloc[i].datetime\n", - " curr = arr[i]['datetime']\n", - " if (prev):\n", - " diff = curr-prev\n", - " if diff > max_diff:\n", - " print (\"{},{},{}\".format(curr,prev,diff))\n", - " prev = curr\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-06-02 20:34:52.943000,2016-06-02 20:34:52.828000,0:00:00.115000\n", - "2016-06-03 00:32:23.628000,2016-06-03 00:32:23.506000,0:00:00.122000\n", - "2016-06-03 01:27:12.685000,2016-06-03 01:27:12.570000,0:00:00.115000\n", - "2016-06-03 02:45:41.114000,2016-06-03 02:45:40.992000,0:00:00.122000\n", - "2016-06-03 03:01:04.559000,2016-06-03 03:01:04.440000,0:00:00.119000\n", - "2016-06-03 14:49:20.132000,2016-06-03 14:49:20.012000,0:00:00.120000\n", - "2016-06-03 14:52:45.341000,2016-06-03 14:52:45.220000,0:00:00.121000\n" - ] - } - ], - "source": [ - "check_raw_gaps(sdata,70)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# look at chunk dates" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#data.data[3]" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# take only chunk dates\n", - "dates = map(lambda x: x['Intial_dtime'], data.data)\n", - "sdates = sorted(dates)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.datetime(2016, 6, 2, 18, 45, 31, 920000)" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sdates[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def check_raw_gaps_chunks(arr, max_delta_sec = 250):\n", - " max_diff = timedelta(milliseconds=max_delta_sec);\n", - " prev = None\n", - " for i in range (0,len(arr)): # iterate over all lines\n", - " #print df.iloc[i].datetime\n", - " curr = arr[i]\n", - " if (prev):\n", - " diff = curr-prev\n", - " if diff > max_diff:\n", - " print (\"{},{},{}\".format(curr,prev,diff))\n", - " #break\n", - " prev = curr\n", - " \n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2016-06-02 18:45:54.728000,2016-06-02 18:45:49.020000,0:00:05.708000\n", - "2016-06-02 18:46:17.529000,2016-06-02 18:46:11.828000,0:00:05.701000\n", - 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"source": [ - "ms_between = 50*114\n", - "check_raw_gaps_chunks(sdates,ms_between)" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "datetime.timedelta(0, 5, 700000)" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "timedelta(milliseconds=ms_between)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/misc/show_sample_meeting.ipynb b/examples/misc/show_sample_meeting.ipynb deleted file mode 100644 index 0255337..0000000 --- a/examples/misc/show_sample_meeting.ipynb +++ /dev/null @@ -1,496 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import datetime\n", - "import ast\n", - "import pandas\n", - "\n", - "from bokeh.io import output_notebook\n", - "from bokeh.charts import show\n", - "\n", - "# add the 'src' directory as one where we can import modules\n", - "src_dir = os.path.join(os.getcwd(), os.pardir, os.pardir)\n", - "sys.path.append(src_dir)\n", - "\n", - "# import data analysis modules\n", - "import openbadge_analysis as ob\n", - "import openbadge_analysis.visualization.contribution as contribution\n", - "import openbadge_analysis.visualization.participation as participation\n", - "import openbadge_analysis.visualization.participation_interactive as participation_interactive" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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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", - " };\n", - "\n", - " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.12.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.12.1.min.js'];\n", - "\n", - " var inline_js = [\n", - " function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - " \n", - " function(Bokeh) {\n", - " \n", - " Bokeh.$(\"#d6414074-739d-4172-8388-b5dffe2cd344\").text(\"BokehJS successfully loaded\");\n", - " var kernel = Jupyter.notebook.kernel\n", - " if (kernel.execute !== undefined) {\n", - " kernel.execute(\"import bokeh.io; bokeh.io._nb_loaded = True\");\n", - " }\n", - " },\n", - " function(Bokeh) {\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.1.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.1.min.css\");\n", - " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.1.min.css\");\n", - " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.1.min.css\");\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " for (var i = 0; i < inline_js.length; i++) {\n", - " inline_js[i](window.Bokeh);\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));" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "output_notebook()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the \"autoreload\" extension\n", - "%load_ext autoreload\n", - "# always reload modules marked with \"%aimport\"\n", - "%autoreload 1\n", - "# list of modules to autoload\n", - "%aimport openbadge_analysis\n", - "%aimport openbadge_analysis.visualization.contribution\n", - "%aimport openbadge_analysis.visualization.participation\n", - "%aimport openbadge_analysis.visualization.participation_interactive" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "log_file_path = '../sample_data/sample_meeting.txt'\n", - "member_names = {\n", - " 'B7H6NPMKQT': 'Brittany',\n", - " '8JB8GZYFVW': 'Shirley',\n", - " 'TYCN86EZ5F': 'Don',\n", - " 'TZPQ1AKVD2': 'Aaron',\n", - " '9LLE6DZUPU': 'Alexander',\n", - " 'QNT3BBQ0X1': 'Otto'\n", - "}\n", - "\n", - "meeting_name = \"Sample\"" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Read data\n", - "df_meeting = ob.sample2data(log_file_path)\n", - "\n", - "# Create speaking intervals\n", - "df_stitched = ob.make_df_stitched(df_meeting) # Data for speaking intervals" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot = participation.participation_plot(df_stitched_all=[[df_stitched]], labels=[meeting_name],\n", - " member_names=member_names)\n", - "show(plot)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/notebooks/check_gaps_in_audio_data.ipynb b/examples/notebooks/check_gaps_in_audio_data.ipynb deleted file mode 100644 index 27c5c9e..0000000 --- a/examples/notebooks/check_gaps_in_audio_data.ipynb +++ /dev/null @@ -1,745 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, we are looking for gaps in the audio data collected by two badges - one with an old firmware, and one with the new firmware (2.0)\n", - "\n", - "New badge (firmware 2.0) - C7:75:13:05:10:EE\n", - "\n", - "Old badge - F9:4A:86:6E:F4:0C" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from __future__ import absolute_import, division, print_function\n", - "\n", - "import os\n", - "import sys\n", - "import datetime\n", - "import pandas as pd\n", - "import numpy as np\n", - "import pytz\n", - "import dateutil\n", - "import gzip\n", - "\n", - "from ggplot import *\n", - "%matplotlib inline\n", - "\n", - "# add the 'src' directory as one where we can import modules\n", - "src_dir = os.path.join(os.getcwd(), os.pardir, os.pardir)\n", - "sys.path.append(src_dir)\n", - "\n", - "# import data analysis modules\n", - "import openbadge_analysis as ob" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the \"autoreload\" extension\n", - "%load_ext autoreload\n", - "# always reload modules marked with \"%aimport\"\n", - "%autoreload 1\n", - "# list of modules to autoload\n", - "%aimport openbadge_analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# config\n", - "filename = '/home/orenled/temp/logs/log_audio.txt.gz'\n", - "log_filename = '/home/orenled/temp/logs/server.log.gz'\n", - "\n", - "eastern = pytz.timezone('US/Eastern')\n", - "utc = pytz.timezone('UTC')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Find badge first reset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to get a real sense of how many gaps we have, we need to discard data after the the badges stared to reset. We need to look at the actualy logfile to find that time. This isn't going to be pretty, but it will work." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "with gzip.open(log_filename, 'r') as f:\n", - " unsync_raw = [ line for line in f if 'unsync' in line]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import re\n", - "# use regex to get date and badge\n", - "unsync_split = [ re.match('(.*) - INFO - \\[(.*)\\]',line).group(1,2) for line in unsync_raw]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime badge_address\n", - "0 2017-01-10 16:03:36,444 C7:75:13:05:10:EE\n", - "1 2017-01-10 16:03:39,620 C7:59:BE:8B:9C:E0\n", - "2 2017-01-10 16:03:42,805 F2:37:E3:BA:70:9D\n", - "3 2017-01-10 16:03:46,686 D4:E4:47:A5:F0:B3\n", - "4 2017-01-10 16:03:49,766 D8:A1:84:25:FF:B7\n", - "0 2017-01-10 16:03:36,444\n", - "1 2017-01-10 16:03:39,620\n", - "2 2017-01-10 16:03:42,805\n", - "3 2017-01-10 16:03:46,686\n", - "4 2017-01-10 16:03:49,766\n", - "Name: datetime, dtype: object\n" - ] - } - ], - "source": [ - "# load into a dataframe and convert the date\n", - "df_log = pd.DataFrame(unsync_split,columns=['datetime','badge_address'])\n", - "print(df_log.head())\n", - "print(df_log['datetime'].head())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " badge_address\n", - "datetime \n", - "2017-01-10 16:03:36.444000-05:00 C7:75:13:05:10:EE\n", - "2017-01-10 16:03:39.620000-05:00 C7:59:BE:8B:9C:E0\n", - "2017-01-10 16:03:42.805000-05:00 F2:37:E3:BA:70:9D\n", - "2017-01-10 16:03:46.686000-05:00 D4:E4:47:A5:F0:B3\n", - "2017-01-10 16:03:49.766000-05:00 D8:A1:84:25:FF:B7\n", - "datetime64[ns, US/Eastern]\n" - ] - } - ], - "source": [ - "df_log['datetime'] = pd.to_datetime(df_log['datetime'], utc=True)\n", - "df_log.sort_values('datetime')\n", - "df_log.index = pd.to_datetime(df_log.pop('datetime'), utc=True)\n", - "\n", - "# this log is in EST\n", - "df_log = df_log.tz_localize(eastern)\n", - "print(df_log.head())\n", - "print(df_log.index.dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime badge_address\n", - "0 2017-01-10 16:03:36.444000-05:00 C7:75:13:05:10:EE\n", - "1 2017-01-10 16:03:39.620000-05:00 C7:59:BE:8B:9C:E0\n", - "2 2017-01-10 16:03:42.805000-05:00 F2:37:E3:BA:70:9D\n", - "3 2017-01-10 16:03:46.686000-05:00 D4:E4:47:A5:F0:B3\n", - "4 2017-01-10 16:03:49.766000-05:00 D8:A1:84:25:FF:B7\n", - "datetime64[ns, US/Eastern]\n" - ] - } - ], - "source": [ - "# reseting index, now that we converted the timezone (i'm not sure how to do this more efficiently...)\n", - "df_log = df_log.reset_index()\n", - "print(df_log.head())\n", - "print(df_log.datetime.dtype)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first reset (for every badge) is the first time we set the clock, so it's not really a reset" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Start time\n", - " datetime\n", - "badge_address \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.620000-05:00\n", - "C7:75:13:05:10:EE 2017-01-10 16:03:36.444000-05:00\n", - "D4:E4:47:A5:F0:B3 2017-01-10 16:03:46.686000-05:00\n", - "D8:A1:84:25:FF:B7 2017-01-10 16:03:49.766000-05:00\n", - "F2:37:E3:BA:70:9D 2017-01-10 16:03:42.805000-05:00 \n", - "\n", - "\n", - "First reset\n", - " datetime\n", - "badge_address \n", - "C7:59:BE:8B:9C:E0 2017-01-17 08:43:16.419000-05:00\n", - "C7:75:13:05:10:EE 2017-01-16 00:06:19.142000-05:00\n", - "D4:E4:47:A5:F0:B3 2017-01-16 08:37:29.222000-05:00\n", - "D8:A1:84:25:FF:B7 2017-01-17 01:29:20.823000-05:00\n", - "F2:37:E3:BA:70:9D 2017-01-16 03:27:32.448000-05:00 \n", - "\n", - "\n", - "First reset, restructured\n", - " badge_address first_reset_timestamp\n", - "0 C7:59:BE:8B:9C:E0 1.484661e+09\n", - "1 C7:75:13:05:10:EE 1.484543e+09\n", - "2 D4:E4:47:A5:F0:B3 1.484574e+09\n", - "3 D8:A1:84:25:FF:B7 1.484635e+09\n", - "4 F2:37:E3:BA:70:9D 1.484555e+09 \n", - "\n", - "\n" - ] - } - ], - "source": [ - "df_log_grp = df_log.groupby('badge_address')\n", - "start_time = df_log_grp.nth(0)\n", - "print(\"Start time\\n\",start_time,\"\\n\\n\")\n", - "\n", - "first_reset = df_log_grp.nth(1)\n", - "print(\"First reset\\n\",first_reset,\"\\n\\n\")\n", - "\n", - "# change to a more convinient format\n", - "first_reset = first_reset.reset_index()\n", - "first_reset.rename(columns={'datetime': 'first_reset_datetime'}, inplace=True)\n", - "first_reset['first_reset_timestamp'] = first_reset.pop('first_reset_datetime').astype(np.int64)//1e9\n", - "print(\"First reset, restructured\\n\", first_reset,\"\\n\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Look for gaps" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "with gzip.open(filename, 'r') as f:\n", - " audio_chunks = ob.load_audio_chunks_as_json_objects(f, log_version='2.0')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime badge_address timestamp\n", - "0 2017-01-10 16:03:36.405000-05:00 C7:75:13:05:10:EE 1.484082e+09\n", - "1 2017-01-10 16:03:39.582000-05:00 C7:59:BE:8B:9C:E0 1.484082e+09\n", - "2 2017-01-10 16:03:42.765000-05:00 F2:37:E3:BA:70:9D 1.484082e+09\n", - "3 2017-01-10 16:03:46.648000-05:00 D4:E4:47:A5:F0:B3 1.484082e+09\n", - "4 2017-01-10 16:03:49.728000-05:00 D8:A1:84:25:FF:B7 1.484082e+09\n", - "5 2017-01-10 16:03:36.405000-05:00 C7:75:13:05:10:EE 1.484082e+09\n", - "6 2017-01-10 16:03:42.106000-05:00 C7:75:13:05:10:EE 1.484082e+09\n", - "7 2017-01-10 16:03:47.805000-05:00 C7:75:13:05:10:EE 1.484082e+09\n", - "8 2017-01-10 16:03:53.503000-05:00 C7:75:13:05:10:EE 1.484082e+09\n", - "9 2017-01-10 16:03:39.582000-05:00 C7:59:BE:8B:9C:E0 1.484082e+09\n", - "datetime datetime64[ns, US/Eastern]\n", - "badge_address object\n", - "timestamp float64\n", - "dtype: object\n" - ] - } - ], - "source": [ - "# load data into a data frame\n", - "df = pd.DataFrame(audio_chunks,columns=['badge_address','timestamp'])\n", - "\n", - "# Converting timestamp (epoch) to UTC datetime\n", - "df['datetime'] = pd.to_datetime(df['timestamp'],unit='s', utc=True)\n", - "\n", - "# Setting datetime as index\n", - "df.sort_values('datetime')\n", - "df.index = pd.to_datetime(df.pop('datetime'), utc=True)\n", - "df = df.tz_localize(utc)\n", - "df = df.tz_convert(eastern)\n", - "df = df.reset_index() # make it a column again\n", - "\n", - "print(df.head(10))\n", - "print(df.dtypes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## filter based on first reset time" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Records before filtering: 587629, after: 557583\n" - ] - } - ], - "source": [ - "if (len(first_reset)) > 0:\n", - " df_fil = pd.merge(df,first_reset,how='left',on='badge_address')\n", - " df_fil = df_fil[df_fil['timestamp'] < df_fil['first_reset_timestamp']]\n", - " print(\"Records before filtering: {}, after: {}\".format(len(df),len(df_fil)))\n", - "\n", - " #df_fil.index = df_fil['datetime']\n", - " del df_fil['first_reset_timestamp']\n", - "else:\n", - " print(\"No reset found, using entire dataset\")\n", - " df_fil = df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check for gaps in data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "calculations are per-badge, so we must create a multi index. Then, per-group calculations can be done with group by" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 2017-01-10 16:03:39.582000-05:00 \n", - " 2017-01-10 16:03:39.582000-05:00 2017-01-10 16:03:39.582000-05:00 \n", - " 2017-01-10 16:03:45.281000-05:00 2017-01-10 16:03:45.281000-05:00 \n", - " 2017-01-10 16:03:50.980000-05:00 2017-01-10 16:03:50.980000-05:00 \n", - " 2017-01-10 16:03:56.678000-05:00 2017-01-10 16:03:56.678000-05:00 \n", - "\n", - " badge_address \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 C7:59:BE:8B:9C:E0 \n", - " 2017-01-10 16:03:39.582000-05:00 C7:59:BE:8B:9C:E0 \n", - " 2017-01-10 16:03:45.281000-05:00 C7:59:BE:8B:9C:E0 \n", - " 2017-01-10 16:03:50.980000-05:00 C7:59:BE:8B:9C:E0 \n", - " 2017-01-10 16:03:56.678000-05:00 C7:59:BE:8B:9C:E0 \n", - "\n", - " timestamp \n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 1.484082e+09 \n", - " 2017-01-10 16:03:39.582000-05:00 1.484082e+09 \n", - " 2017-01-10 16:03:45.281000-05:00 1.484082e+09 \n", - " 2017-01-10 16:03:50.980000-05:00 1.484082e+09 \n", - " 2017-01-10 16:03:56.678000-05:00 1.484082e+09 \n" - ] - } - ], - "source": [ - "# http://pandas.pydata.org/pandas-docs/stable/advanced.html\n", - "# set multi-index\n", - "df_fil.set_index(['badge_address', 'datetime'], inplace=True, drop=False)\n", - "\n", - "# sort is important for shift to work properly\n", - "df_fil.sort_index(inplace=True)\n", - "print(df_fil.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index dtype: object\n", - "Timediff type: float64\n", - " datetime \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 2017-01-10 16:03:39.582000-05:00 \n", - " 2017-01-10 16:03:39.582000-05:00 2017-01-10 16:03:39.582000-05:00 \n", - " 2017-01-10 16:03:45.281000-05:00 2017-01-10 16:03:45.281000-05:00 \n", - " 2017-01-10 16:03:50.980000-05:00 2017-01-10 16:03:50.980000-05:00 \n", - " 2017-01-10 16:03:56.678000-05:00 2017-01-10 16:03:56.678000-05:00 \n", - "\n", - " badge_address \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 C7:59:BE:8B:9C:E0 \n", - " 2017-01-10 16:03:39.582000-05:00 C7:59:BE:8B:9C:E0 \n", - " 2017-01-10 16:03:45.281000-05:00 C7:59:BE:8B:9C:E0 \n", - " 2017-01-10 16:03:50.980000-05:00 C7:59:BE:8B:9C:E0 \n", - " 2017-01-10 16:03:56.678000-05:00 C7:59:BE:8B:9C:E0 \n", - "\n", - " timestamp \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 1.484082e+09 \n", - " 2017-01-10 16:03:39.582000-05:00 1.484082e+09 \n", - " 2017-01-10 16:03:45.281000-05:00 1.484082e+09 \n", - " 2017-01-10 16:03:50.980000-05:00 1.484082e+09 \n", - " 2017-01-10 16:03:56.678000-05:00 1.484082e+09 \n", - "\n", - " timestamp_lag time_diff \n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 NaN NaN \n", - " 2017-01-10 16:03:39.582000-05:00 1.484082e+09 0.000 \n", - " 2017-01-10 16:03:45.281000-05:00 1.484082e+09 5.699 \n", - " 2017-01-10 16:03:50.980000-05:00 1.484082e+09 5.699 \n", - " 2017-01-10 16:03:56.678000-05:00 1.484082e+09 5.698 \n" - ] - } - ], - "source": [ - "# http://pandas.pydata.org/pandas-docs/stable/groupby.html\n", - "# group by badges, and shift ones so we can get the time of the previous row\n", - "grp_shift = df_fil.groupby(level=[0]).shift(1)\n", - "df_with_lag = df_fil.copy()\n", - "df_with_lag['timestamp_lag']= grp_shift['timestamp']\n", - "df_with_lag['time_diff']=df_with_lag['timestamp']-df_with_lag['timestamp_lag']\n", - "print(\"index dtype: \",df_with_lag.index.dtype)\n", - "print(\"Timediff type:\", df_with_lag.time_diff.dtype)\n", - "print(df_with_lag.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "5\n" - ] - } - ], - "source": [ - "print(len(df_with_lag.groupby(level=0)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There is a sample for every 50 ms, and room for 114 smaples per chunk. Therefore, time between chunks should be AT MOST ~ 5.7 seconds. We'll add 0.2 seconds to get rid of some noise" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "seconds between chunks: 5.7\n", - "seconds between chunks: 5.9\n", - "Empty DataFrame\n", - "Columns: [badge_address, datetime, time_diff]\n", - "Index: []\n" - ] - } - ], - "source": [ - "seconds_between_chunks = 50*114/1000\n", - "print(\"seconds between chunks: {}\".format(seconds_between_chunks))\n", - "\n", - "# time between chunks might vary slightly since we update the clock on the badges)\n", - "seconds_between_chunks = seconds_between_chunks + 0.2\n", - "print(\"seconds between chunks: {}\".format(seconds_between_chunks))\n", - "\n", - "gaps = df_with_lag[df_with_lag['time_diff']>seconds_between_chunks]\n", - "print(gaps[['badge_address','datetime','time_diff']].head(70))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How many gaps are ther then? If you look below, you'll see that the number of gaps is very small. However, you need to remmeber that this include ALL data collected from the badges, even when they started to reset." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "587629\n", - "0\n", - "0.0\n" - ] - } - ], - "source": [ - "print(len(df))\n", - "print(len(gaps))\n", - "print(len(gaps)/len(df))" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p = ggplot(aes(x='time_diff', fill='badge_address'), data=gaps)\n", - "p + geom_histogram(binwidth=1)\n", - "p + geom_density(alpha=0.25) \n", - "p + ggtitle(\"Gap (sec)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/notebooks/check_gaps_in_proximity_data.ipynb b/examples/notebooks/check_gaps_in_proximity_data.ipynb deleted file mode 100644 index 4d08890..0000000 --- a/examples/notebooks/check_gaps_in_proximity_data.ipynb +++ /dev/null @@ -1,1020 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, we are looking for gaps in the proximity data collected by two badges - one with an old firmware, and one with the new firmware (2.0)\n", - "\n", - "New badge (firmware 2.0) - C7:75:13:05:10:EE\n", - "\n", - "Old badge - F9:4A:86:6E:F4:0C" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from __future__ import absolute_import, division, print_function\n", - "\n", - "import os\n", - "import sys\n", - "import datetime\n", - "import pandas as pd\n", - "import numpy as np\n", - "import pytz\n", - "import dateutil\n", - "import gzip\n", - "\n", - "from ggplot import *\n", - "%matplotlib inline\n", - "\n", - "# add the 'src' directory as one where we can import modules\n", - "src_dir = os.path.join(os.getcwd(), os.pardir, os.pardir)\n", - "sys.path.append(src_dir)\n", - "\n", - "# import data analysis modules\n", - "import openbadge_analysis as ob" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the \"autoreload\" extension\n", - "%load_ext autoreload\n", - "# always reload modules marked with \"%aimport\"\n", - "%autoreload 1\n", - "# list of modules to autoload\n", - "%aimport openbadge_analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# config\n", - "filename = '/home/orenled/temp/logs/log_proximity.txt.gz'\n", - "log_filename = '/home/orenled/temp/logs/server.log.gz'\n", - "\n", - "eastern = pytz.timezone('US/Eastern')\n", - "utc = pytz.timezone('UTC')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Find badge first reset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If we want to get a real sense of how many gaps we have, we need to discard data after the the badges stared to reset. We need to look at the actualy logfile to find that time. This isn't pretty, but it works." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "with gzip.open(log_filename, 'r') as f:\n", - " unsync_raw = [ line for line in f if 'unsync' in line]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import re\n", - "# use regex to get date and badge\n", - "unsync_split = [ re.match('(.*) - INFO - \\[(.*)\\]',line).group(1,2) for line in unsync_raw]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime badge_address\n", - "0 2017-01-10 16:03:36,444 C7:75:13:05:10:EE\n", - "1 2017-01-10 16:03:39,620 C7:59:BE:8B:9C:E0\n", - "2 2017-01-10 16:03:42,805 F2:37:E3:BA:70:9D\n", - "3 2017-01-10 16:03:46,686 D4:E4:47:A5:F0:B3\n", - "4 2017-01-10 16:03:49,766 D8:A1:84:25:FF:B7\n", - "0 2017-01-10 16:03:36,444\n", - "1 2017-01-10 16:03:39,620\n", - "2 2017-01-10 16:03:42,805\n", - "3 2017-01-10 16:03:46,686\n", - "4 2017-01-10 16:03:49,766\n", - "Name: datetime, dtype: object\n" - ] - } - ], - "source": [ - "# load into a dataframe and convert the date\n", - "df_log = pd.DataFrame(unsync_split,columns=['datetime','badge_address'])\n", - "print(df_log.head())\n", - "print(df_log['datetime'].head())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " badge_address\n", - "datetime \n", - "2017-01-10 16:03:36.444000-05:00 C7:75:13:05:10:EE\n", - "2017-01-10 16:03:39.620000-05:00 C7:59:BE:8B:9C:E0\n", - "2017-01-10 16:03:42.805000-05:00 F2:37:E3:BA:70:9D\n", - "2017-01-10 16:03:46.686000-05:00 D4:E4:47:A5:F0:B3\n", - "2017-01-10 16:03:49.766000-05:00 D8:A1:84:25:FF:B7\n", - "datetime64[ns, US/Eastern]\n" - ] - } - ], - "source": [ - "df_log['datetime'] = pd.to_datetime(df_log['datetime'], utc=True)\n", - "df_log.sort_values('datetime')\n", - "df_log.index = pd.to_datetime(df_log.pop('datetime'), utc=True)\n", - "\n", - "# this log is in EST\n", - "df_log = df_log.tz_localize(eastern)\n", - "print(df_log.head())\n", - "print(df_log.index.dtype)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime badge_address\n", - "0 2017-01-10 16:03:36.444000-05:00 C7:75:13:05:10:EE\n", - "1 2017-01-10 16:03:39.620000-05:00 C7:59:BE:8B:9C:E0\n", - "2 2017-01-10 16:03:42.805000-05:00 F2:37:E3:BA:70:9D\n", - "3 2017-01-10 16:03:46.686000-05:00 D4:E4:47:A5:F0:B3\n", - "4 2017-01-10 16:03:49.766000-05:00 D8:A1:84:25:FF:B7\n", - "datetime64[ns, US/Eastern]\n" - ] - } - ], - "source": [ - "# reseting index, now that we converted the timezone (i'm not sure how to do this more efficiently...)\n", - "df_log = df_log.reset_index()\n", - "print(df_log.head())\n", - "print(df_log.datetime.dtype)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first reset (for every badge) is the first time we set the clock, so it's not really a reset" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Start time\n", - " datetime\n", - "badge_address \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.620000-05:00\n", - "C7:75:13:05:10:EE 2017-01-10 16:03:36.444000-05:00\n", - "D4:E4:47:A5:F0:B3 2017-01-10 16:03:46.686000-05:00\n", - "D8:A1:84:25:FF:B7 2017-01-10 16:03:49.766000-05:00\n", - "F2:37:E3:BA:70:9D 2017-01-10 16:03:42.805000-05:00 \n", - "\n", - "\n", - "First reset\n", - " datetime\n", - "badge_address \n", - "C7:59:BE:8B:9C:E0 2017-01-17 08:43:16.419000-05:00\n", - "C7:75:13:05:10:EE 2017-01-16 00:06:19.142000-05:00\n", - "D4:E4:47:A5:F0:B3 2017-01-16 08:37:29.222000-05:00\n", - "D8:A1:84:25:FF:B7 2017-01-17 01:29:20.823000-05:00\n", - "F2:37:E3:BA:70:9D 2017-01-16 03:27:32.448000-05:00 \n", - "\n", - "\n", - "First reset, restructured\n", - " badge_address first_reset_timestamp\n", - "0 C7:59:BE:8B:9C:E0 1.484661e+09\n", - "1 C7:75:13:05:10:EE 1.484543e+09\n", - "2 D4:E4:47:A5:F0:B3 1.484574e+09\n", - "3 D8:A1:84:25:FF:B7 1.484635e+09\n", - "4 F2:37:E3:BA:70:9D 1.484555e+09 \n", - "\n", - "\n" - ] - } - ], - "source": [ - "df_log_grp = df_log.groupby('badge_address')\n", - "start_time = df_log_grp.nth(0)\n", - "print(\"Start time\\n\",start_time,\"\\n\\n\")\n", - "\n", - "first_reset = df_log_grp.nth(1)\n", - "print(\"First reset\\n\",first_reset,\"\\n\\n\")\n", - "\n", - "# change to a more convinient format\n", - "first_reset = first_reset.reset_index()\n", - "first_reset.rename(columns={'datetime': 'first_reset_datetime'}, inplace=True)\n", - "first_reset['first_reset_timestamp'] = first_reset.pop('first_reset_datetime').astype(np.int64)//1e9\n", - "print(\"First reset, restructured\\n\", first_reset,\"\\n\\n\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Look for gaps" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "with gzip.open(filename, 'r') as f:\n", - " proximity_chunks = ob.load_proximity_chunks_as_json_objects(f, log_version='2.0')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime badge_address timestamp\n", - "0 2017-01-10 16:04:36-05:00 C7:75:13:05:10:EE 1.484082e+09\n", - "1 2017-01-10 16:04:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09\n", - "2 2017-01-10 16:04:42-05:00 F2:37:E3:BA:70:9D 1.484082e+09\n", - "3 2017-01-10 16:04:46-05:00 D4:E4:47:A5:F0:B3 1.484082e+09\n", - "4 2017-01-10 16:04:49-05:00 D8:A1:84:25:FF:B7 1.484082e+09\n", - "5 2017-01-10 16:05:36-05:00 C7:75:13:05:10:EE 1.484082e+09\n", - "6 2017-01-10 16:05:42-05:00 F2:37:E3:BA:70:9D 1.484082e+09\n", - "7 2017-01-10 16:05:46-05:00 D4:E4:47:A5:F0:B3 1.484082e+09\n", - "8 2017-01-10 16:05:49-05:00 D8:A1:84:25:FF:B7 1.484082e+09\n", - "9 2017-01-10 16:05:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09\n", - "datetime datetime64[ns, US/Eastern]\n", - "badge_address object\n", - "timestamp float64\n", - "dtype: object\n" - ] - } - ], - "source": [ - "# load data into a data frame\n", - "df = pd.DataFrame(proximity_chunks,columns=['badge_address','timestamp'])\n", - "\n", - "# Converting timestamp (epoch) to UTC datetime\n", - "df['datetime'] = pd.to_datetime(df['timestamp'],unit='s', utc=True)\n", - "\n", - "# Setting datetime as index\n", - "df.sort_values('datetime')\n", - "df.index = pd.to_datetime(df.pop('datetime'), utc=True)\n", - "df = df.tz_localize(utc)\n", - "df = df.tz_convert(eastern)\n", - "df = df.reset_index() # make it a column again\n", - "\n", - "print(df.head(10))\n", - "print(df.dtypes)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Filter based on first reset time" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Records before filtering: 42910, after: 42649\n" - ] - } - ], - "source": [ - "if (len(first_reset)) > 0:\n", - " df_fil = pd.merge(df,first_reset,how='left',on='badge_address')\n", - " df_fil = df_fil[df_fil['timestamp'] < df_fil['first_reset_timestamp']]\n", - " print(\"Records before filtering: {}, after: {}\".format(len(df),len(df_fil)))\n", - "\n", - " #df_fil.index = df_fil['datetime']\n", - " del df_fil['first_reset_timestamp']\n", - "else:\n", - " df_fil = df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Check for gaps in data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "calculations are per-badge, so we must create a multi index. Then, per-group calculations can be done with group by" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " datetime \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:04:39-05:00 2017-01-10 16:04:39-05:00 \n", - " 2017-01-10 16:05:39-05:00 2017-01-10 16:05:39-05:00 \n", - " 2017-01-10 16:06:39-05:00 2017-01-10 16:06:39-05:00 \n", - " 2017-01-10 16:07:39-05:00 2017-01-10 16:07:39-05:00 \n", - " 2017-01-10 16:08:39-05:00 2017-01-10 16:08:39-05:00 \n", - "\n", - " badge_address timestamp \n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:04:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09 \n", - " 2017-01-10 16:05:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09 \n", - " 2017-01-10 16:06:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09 \n", - " 2017-01-10 16:07:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09 \n", - " 2017-01-10 16:08:39-05:00 C7:59:BE:8B:9C:E0 1.484083e+09 \n" - ] - } - ], - "source": [ - "# http://pandas.pydata.org/pandas-docs/stable/advanced.html\n", - "# set multi-index\n", - "df_fil.set_index(['badge_address', 'datetime'], inplace=True, drop=False)\n", - "\n", - "# sort is important for shift to work properly\n", - "df_fil.sort_index(inplace=True)\n", - "print(df_fil.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "index dtype: object\n", - "Timediff type: float64\n", - " datetime \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:04:39-05:00 2017-01-10 16:04:39-05:00 \n", - " 2017-01-10 16:05:39-05:00 2017-01-10 16:05:39-05:00 \n", - " 2017-01-10 16:06:39-05:00 2017-01-10 16:06:39-05:00 \n", - " 2017-01-10 16:07:39-05:00 2017-01-10 16:07:39-05:00 \n", - " 2017-01-10 16:08:39-05:00 2017-01-10 16:08:39-05:00 \n", - "\n", - " badge_address timestamp \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:04:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09 \n", - " 2017-01-10 16:05:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09 \n", - " 2017-01-10 16:06:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09 \n", - " 2017-01-10 16:07:39-05:00 C7:59:BE:8B:9C:E0 1.484082e+09 \n", - " 2017-01-10 16:08:39-05:00 C7:59:BE:8B:9C:E0 1.484083e+09 \n", - "\n", - " timestamp_lag time_diff \n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:04:39-05:00 NaN NaN \n", - " 2017-01-10 16:05:39-05:00 1.484082e+09 60.0 \n", - " 2017-01-10 16:06:39-05:00 1.484082e+09 60.0 \n", - " 2017-01-10 16:07:39-05:00 1.484082e+09 60.0 \n", - " 2017-01-10 16:08:39-05:00 1.484082e+09 60.0 \n" - ] - } - ], - "source": [ - "# http://pandas.pydata.org/pandas-docs/stable/groupby.html\n", - "# group by badges, and shift ones so we can get the time of the previous row\n", - "grp_shift = df_fil.groupby(level=[0]).shift(1)\n", - "df_with_lag = df_fil.copy()\n", - "df_with_lag['timestamp_lag']= grp_shift['timestamp']\n", - "df_with_lag['time_diff']=df_with_lag['timestamp']-df_with_lag['timestamp_lag']\n", - "print(\"index dtype: \",df_with_lag.index.dtype)\n", - "print(\"Timediff type:\", df_with_lag.time_diff.dtype)\n", - "print(df_with_lag.head())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Scans are performed every 60 seconds" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "seconds between chunks: 60\n", - "seconds between chunks: 62\n", - " time_diff timestamp\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-17 02:00:59-05:00 120.0 1.484636e+09\n", - " 2017-01-17 03:20:59-05:00 1020.0 1.484641e+09\n", - " 2017-01-17 03:25:59-05:00 300.0 1.484642e+09\n", - " 2017-01-17 03:28:59-05:00 180.0 1.484642e+09\n", - " 2017-01-17 03:34:59-05:00 180.0 1.484642e+09\n", - " 2017-01-17 03:39:59-05:00 240.0 1.484642e+09\n", - " 2017-01-17 03:42:59-05:00 180.0 1.484643e+09\n", - " 2017-01-17 03:45:59-05:00 120.0 1.484643e+09\n", - " 2017-01-17 03:49:59-05:00 120.0 1.484643e+09\n", - " 2017-01-17 03:52:59-05:00 120.0 1.484643e+09\n", - " 2017-01-17 04:14:59-05:00 1320.0 1.484644e+09\n", - " 2017-01-17 04:38:59-05:00 1440.0 1.484646e+09\n", - " 2017-01-17 04:45:59-05:00 420.0 1.484646e+09\n", - " 2017-01-17 04:55:59-05:00 540.0 1.484647e+09\n", - "C7:75:13:05:10:EE 2017-01-15 22:12:54-05:00 120.0 1.484536e+09\n", - "D8:A1:84:25:FF:B7 2017-01-17 00:05:11-05:00 360.0 1.484630e+09\n", - " 2017-01-17 00:12:11-05:00 300.0 1.484630e+09\n", - " 2017-01-17 00:14:11-05:00 120.0 1.484630e+09\n", - " 2017-01-17 00:20:11-05:00 300.0 1.484630e+09\n", - " 2017-01-17 00:22:11-05:00 120.0 1.484631e+09\n", - " 2017-01-17 00:31:11-05:00 480.0 1.484631e+09\n", - " 2017-01-17 00:36:11-05:00 300.0 1.484631e+09\n", - " 2017-01-17 00:55:11-05:00 1140.0 1.484633e+09\n", - " 2017-01-17 01:10:11-05:00 900.0 1.484633e+09\n", - " 2017-01-17 01:13:11-05:00 180.0 1.484634e+09\n" - ] - } - ], - "source": [ - "seconds_between_chunks = 60\n", - "print(\"seconds between chunks: {}\".format(seconds_between_chunks))\n", - "\n", - "# time between chunks might vary slightly\n", - "seconds_between_chunks = seconds_between_chunks + 2\n", - "print(\"seconds between chunks: {}\".format(seconds_between_chunks))\n", - "\n", - "gaps = df_with_lag[df_with_lag['time_diff']>seconds_between_chunks]\n", - "print(gaps[['time_diff','timestamp']].head(70))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "How many gaps are ther then? If you look below, you'll see that the number of gaps is very small. However, you need to remmeber that this include ALL data collected from the badges, even when they started to reset." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "42910\n", - "25\n", - "0.000582614775111\n" - ] - } - ], - "source": [ - "print(len(df))\n", - "print(len(gaps))\n", - "print(len(gaps)/len(df))" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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datetimebadge_addresstimestamptimestamp_lagtime_diff
badge_addressdatetime
C7:59:BE:8B:9C:E02017-01-17 02:00:59-05:002017-01-17 02:00:59-05:00C7:59:BE:8B:9C:E01.484636e+091.484636e+09120.0
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2017-01-17 03:39:59-05:002017-01-17 03:39:59-05:00C7:59:BE:8B:9C:E01.484642e+091.484642e+09240.0
2017-01-17 03:42:59-05:002017-01-17 03:42:59-05:00C7:59:BE:8B:9C:E01.484643e+091.484642e+09180.0
2017-01-17 03:45:59-05:002017-01-17 03:45:59-05:00C7:59:BE:8B:9C:E01.484643e+091.484643e+09120.0
2017-01-17 03:49:59-05:002017-01-17 03:49:59-05:00C7:59:BE:8B:9C:E01.484643e+091.484643e+09120.0
2017-01-17 03:52:59-05:002017-01-17 03:52:59-05:00C7:59:BE:8B:9C:E01.484643e+091.484643e+09120.0
2017-01-17 04:14:59-05:002017-01-17 04:14:59-05:00C7:59:BE:8B:9C:E01.484644e+091.484643e+091320.0
2017-01-17 04:38:59-05:002017-01-17 04:38:59-05:00C7:59:BE:8B:9C:E01.484646e+091.484644e+091440.0
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2017-01-17 00:55:11-05:002017-01-17 00:55:11-05:00D8:A1:84:25:FF:B71.484633e+091.484631e+091140.0
2017-01-17 01:10:11-05:002017-01-17 01:10:11-05:00D8:A1:84:25:FF:B71.484633e+091.484633e+09900.0
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" - ], - "text/plain": [ - " datetime \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-17 02:00:59-05:00 2017-01-17 02:00:59-05:00 \n", - " 2017-01-17 03:20:59-05:00 2017-01-17 03:20:59-05:00 \n", - " 2017-01-17 03:25:59-05:00 2017-01-17 03:25:59-05:00 \n", - " 2017-01-17 03:28:59-05:00 2017-01-17 03:28:59-05:00 \n", - " 2017-01-17 03:34:59-05:00 2017-01-17 03:34:59-05:00 \n", - " 2017-01-17 03:39:59-05:00 2017-01-17 03:39:59-05:00 \n", - " 2017-01-17 03:42:59-05:00 2017-01-17 03:42:59-05:00 \n", - " 2017-01-17 03:45:59-05:00 2017-01-17 03:45:59-05:00 \n", - " 2017-01-17 03:49:59-05:00 2017-01-17 03:49:59-05:00 \n", - " 2017-01-17 03:52:59-05:00 2017-01-17 03:52:59-05:00 \n", - " 2017-01-17 04:14:59-05:00 2017-01-17 04:14:59-05:00 \n", - " 2017-01-17 04:38:59-05:00 2017-01-17 04:38:59-05:00 \n", - " 2017-01-17 04:45:59-05:00 2017-01-17 04:45:59-05:00 \n", - " 2017-01-17 04:55:59-05:00 2017-01-17 04:55:59-05:00 \n", - "C7:75:13:05:10:EE 2017-01-15 22:12:54-05:00 2017-01-15 22:12:54-05:00 \n", - "D8:A1:84:25:FF:B7 2017-01-17 00:05:11-05:00 2017-01-17 00:05:11-05:00 \n", - " 2017-01-17 00:12:11-05:00 2017-01-17 00:12:11-05:00 \n", - " 2017-01-17 00:14:11-05:00 2017-01-17 00:14:11-05:00 \n", - " 2017-01-17 00:20:11-05:00 2017-01-17 00:20:11-05:00 \n", - " 2017-01-17 00:22:11-05:00 2017-01-17 00:22:11-05:00 \n", - " 2017-01-17 00:31:11-05:00 2017-01-17 00:31:11-05:00 \n", - " 2017-01-17 00:36:11-05:00 2017-01-17 00:36:11-05:00 \n", - " 2017-01-17 00:55:11-05:00 2017-01-17 00:55:11-05:00 \n", - " 2017-01-17 01:10:11-05:00 2017-01-17 01:10:11-05:00 \n", - " 2017-01-17 01:13:11-05:00 2017-01-17 01:13:11-05:00 \n", - "\n", - " badge_address timestamp \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-17 02:00:59-05:00 C7:59:BE:8B:9C:E0 1.484636e+09 \n", - " 2017-01-17 03:20:59-05:00 C7:59:BE:8B:9C:E0 1.484641e+09 \n", - " 2017-01-17 03:25:59-05:00 C7:59:BE:8B:9C:E0 1.484642e+09 \n", - " 2017-01-17 03:28:59-05:00 C7:59:BE:8B:9C:E0 1.484642e+09 \n", - " 2017-01-17 03:34:59-05:00 C7:59:BE:8B:9C:E0 1.484642e+09 \n", - " 2017-01-17 03:39:59-05:00 C7:59:BE:8B:9C:E0 1.484642e+09 \n", - " 2017-01-17 03:42:59-05:00 C7:59:BE:8B:9C:E0 1.484643e+09 \n", - " 2017-01-17 03:45:59-05:00 C7:59:BE:8B:9C:E0 1.484643e+09 \n", - " 2017-01-17 03:49:59-05:00 C7:59:BE:8B:9C:E0 1.484643e+09 \n", - " 2017-01-17 03:52:59-05:00 C7:59:BE:8B:9C:E0 1.484643e+09 \n", - " 2017-01-17 04:14:59-05:00 C7:59:BE:8B:9C:E0 1.484644e+09 \n", - " 2017-01-17 04:38:59-05:00 C7:59:BE:8B:9C:E0 1.484646e+09 \n", - " 2017-01-17 04:45:59-05:00 C7:59:BE:8B:9C:E0 1.484646e+09 \n", - " 2017-01-17 04:55:59-05:00 C7:59:BE:8B:9C:E0 1.484647e+09 \n", - "C7:75:13:05:10:EE 2017-01-15 22:12:54-05:00 C7:75:13:05:10:EE 1.484536e+09 \n", - "D8:A1:84:25:FF:B7 2017-01-17 00:05:11-05:00 D8:A1:84:25:FF:B7 1.484630e+09 \n", - " 2017-01-17 00:12:11-05:00 D8:A1:84:25:FF:B7 1.484630e+09 \n", - " 2017-01-17 00:14:11-05:00 D8:A1:84:25:FF:B7 1.484630e+09 \n", - " 2017-01-17 00:20:11-05:00 D8:A1:84:25:FF:B7 1.484630e+09 \n", - " 2017-01-17 00:22:11-05:00 D8:A1:84:25:FF:B7 1.484631e+09 \n", - " 2017-01-17 00:31:11-05:00 D8:A1:84:25:FF:B7 1.484631e+09 \n", - " 2017-01-17 00:36:11-05:00 D8:A1:84:25:FF:B7 1.484631e+09 \n", - " 2017-01-17 00:55:11-05:00 D8:A1:84:25:FF:B7 1.484633e+09 \n", - " 2017-01-17 01:10:11-05:00 D8:A1:84:25:FF:B7 1.484633e+09 \n", - " 2017-01-17 01:13:11-05:00 D8:A1:84:25:FF:B7 1.484634e+09 \n", - "\n", - " timestamp_lag time_diff \n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-17 02:00:59-05:00 1.484636e+09 120.0 \n", - " 2017-01-17 03:20:59-05:00 1.484640e+09 1020.0 \n", - " 2017-01-17 03:25:59-05:00 1.484641e+09 300.0 \n", - " 2017-01-17 03:28:59-05:00 1.484642e+09 180.0 \n", - " 2017-01-17 03:34:59-05:00 1.484642e+09 180.0 \n", - " 2017-01-17 03:39:59-05:00 1.484642e+09 240.0 \n", - " 2017-01-17 03:42:59-05:00 1.484642e+09 180.0 \n", - " 2017-01-17 03:45:59-05:00 1.484643e+09 120.0 \n", - " 2017-01-17 03:49:59-05:00 1.484643e+09 120.0 \n", - " 2017-01-17 03:52:59-05:00 1.484643e+09 120.0 \n", - " 2017-01-17 04:14:59-05:00 1.484643e+09 1320.0 \n", - " 2017-01-17 04:38:59-05:00 1.484644e+09 1440.0 \n", - " 2017-01-17 04:45:59-05:00 1.484646e+09 420.0 \n", - " 2017-01-17 04:55:59-05:00 1.484646e+09 540.0 \n", - "C7:75:13:05:10:EE 2017-01-15 22:12:54-05:00 1.484536e+09 120.0 \n", - "D8:A1:84:25:FF:B7 2017-01-17 00:05:11-05:00 1.484629e+09 360.0 \n", - " 2017-01-17 00:12:11-05:00 1.484630e+09 300.0 \n", - " 2017-01-17 00:14:11-05:00 1.484630e+09 120.0 \n", - " 2017-01-17 00:20:11-05:00 1.484630e+09 300.0 \n", - " 2017-01-17 00:22:11-05:00 1.484630e+09 120.0 \n", - " 2017-01-17 00:31:11-05:00 1.484631e+09 480.0 \n", - " 2017-01-17 00:36:11-05:00 1.484631e+09 300.0 \n", - " 2017-01-17 00:55:11-05:00 1.484631e+09 1140.0 \n", - " 2017-01-17 01:10:11-05:00 1.484633e+09 900.0 \n", - " 2017-01-17 01:13:11-05:00 1.484633e+09 180.0 " - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": 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79+9X8+bNw46rXnKyd+9ebd++XQcOHJB0cvnKunXrtG7dOl177bU1jnnllVc0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\nKu3evVvx8fGaMmWKJIX9fRk3bpxiYmL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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "p = ggplot(aes(x='time_diff', fill='badge_address'), data=gaps)\n", - "p + geom_histogram(binwidth=1)\n", - "p + geom_density(alpha=0.25) \n", - "p + ggtitle(\"Gap (sec)\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/notebooks/plot_audio_voltage.ipynb b/examples/notebooks/plot_audio_voltage.ipynb deleted file mode 100644 index 59637a3..0000000 --- a/examples/notebooks/plot_audio_voltage.ipynb +++ /dev/null @@ -1,621 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot audio voltage\n", - "\n", - "This notebook plots the voltage of a badge based on the voltage stored in an audio samples file. Can be used for measuring badge battery usage and total run time. Here, we use it for comparing a badge with an old firmware, and a badge with the new firmware (2.0)\n", - "\n", - "New badge (firmware 2.0) - F2:37:E3:BA:70:9D\n", - "\n", - "old badge - CE:05:A0:D6:8C:80" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from __future__ import absolute_import, division, print_function\n", - "\n", - "import os\n", - "import sys\n", - "import datetime\n", - "import pandas as pd\n", - "import numpy as np\n", - "import pytz\n", - "import dateutil\n", - "#import seaborn as sns\n", - "#sns.set(style=\"whitegrid\")\n", - "from ggplot import *\n", - "import gzip\n", - "\n", - "%matplotlib inline\n", - "\n", - "# add the 'src' directory as one where we can import modules\n", - "src_dir = os.path.join(os.getcwd(), os.pardir, os.pardir)\n", - "sys.path.append(src_dir)\n", - "\n", - "# import data analysis modules\n", - "import openbadge_analysis as ob" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Load the \"autoreload\" extension\n", - "%load_ext autoreload\n", - "# always reload modules marked with \"%aimport\"\n", - "%autoreload 1\n", - "# list of modules to autoload\n", - "%aimport openbadge_analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "eastern = pytz.timezone('US/Eastern')\n", - "utc = pytz.timezone('UTC')\n", - "\n", - "filename = '/home/orenled/temp/logs/log_audio.txt.gz'\n", - "with gzip.open(filename, 'r') as f:\n", - " audio_chunks = ob.load_audio_chunks_as_json_objects(f, log_version='2.0')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " badge_address timestamp voltage num_samples\n", - "0 C7:75:13:05:10:EE 1.484082e+09 3.170 1\n", - "1 C7:59:BE:8B:9C:E0 1.484082e+09 3.179 1\n", - "2 F2:37:E3:BA:70:9D 1.484082e+09 3.192 1\n", - "3 D4:E4:47:A5:F0:B3 1.484082e+09 3.169 1\n", - "4 D8:A1:84:25:FF:B7 1.484082e+09 3.202 1\n" - ] - } - ], - "source": [ - "# load data into a data frame\n", - "df = pd.DataFrame(audio_chunks,columns=['badge_address','timestamp','voltage','num_samples'])\n", - "print(df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#print(df_audio_voltage[df_audio_voltage['badge_address']=='D4:E4:47:A5:F0:B3'])\n", - "#df_audio_voltage=df_audio_voltage[df_audio_voltage['badge_address']=='D4:E4:47:A5:F0:B3']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sainty check" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Max:\n", - " index timestamp voltage num_samples\n", - "badge_address \n", - "C7:59:BE:8B:9C:E0 587624 1.484667e+09 3.181 114\n", - "C7:75:13:05:10:EE 547498 1.484592e+09 3.191 114\n", - "D4:E4:47:A5:F0:B3 537151 1.484579e+09 3.169 114\n", - "D8:A1:84:25:FF:B7 587628 1.484667e+09 3.204 114\n", - "F2:37:E3:BA:70:9D 556276 1.484605e+09 3.204 114 \n", - "\n", - "Min:\n", - " index timestamp voltage num_samples\n", - "badge_address \n", - "C7:59:BE:8B:9C:E0 1 1.484082e+09 1.912 1\n", - "C7:75:13:05:10:EE 0 1.484082e+09 1.920 1\n", - "D4:E4:47:A5:F0:B3 3 1.484082e+09 2.213 0\n", - "D8:A1:84:25:FF:B7 4 1.484082e+09 1.814 1\n", - "F2:37:E3:BA:70:9D 2 1.484082e+09 1.916 0\n" - ] - } - ], - "source": [ - "# min/max values\n", - "df_group_by_badge = df.reset_index().groupby('badge_address')\n", - "print(\"Max:\\n\",df_group_by_badge.max(),\"\\n\")\n", - "print(\"Min:\\n\",df_group_by_badge.min())" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "587629\n", - "Empty DataFrame\n", - "Columns: [badge_address, timestamp, voltage, num_samples]\n", - "Index: []\n", - "Empty DataFrame\n", - "Columns: [badge_address, timestamp, voltage, num_samples]\n", - "Index: []\n", - "Empty DataFrame\n", - "Columns: [badge_address, timestamp, voltage, num_samples]\n", - "Index: []\n" - ] - } - ], - "source": [ - "# Nans?\n", - "print(len(df))\n", - "print(df[np.isnan(df['voltage'])])\n", - "print(df[np.isnan(df['timestamp'])])\n", - "print(df[df['badge_address'].isnull()])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# show voltage graphs" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "datetime64[ns, US/Eastern]\n", - " timestamp voltage \\\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 1.484082e+09 3.179 \n", - " 2017-01-10 16:03:39.582000-05:00 1.484082e+09 3.179 \n", - " 2017-01-10 16:03:45.281000-05:00 1.484082e+09 3.181 \n", - " 2017-01-10 16:03:50.980000-05:00 1.484082e+09 3.181 \n", - " 2017-01-10 16:03:56.678000-05:00 1.484082e+09 3.181 \n", - "\n", - " num_samples \n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:39.582000-05:00 1 \n", - " 2017-01-10 16:03:39.582000-05:00 114 \n", - " 2017-01-10 16:03:45.281000-05:00 114 \n", - " 2017-01-10 16:03:50.980000-05:00 114 \n", - " 2017-01-10 16:03:56.678000-05:00 92 \n" - ] - } - ], - "source": [ - "# Converting timestamp (epoch) to UTC datetime\n", - "df['datetime'] = pd.to_datetime(df['timestamp'],unit='s', utc=True)\n", - "\n", - "# Setting datetime as index so I can convert the timezone\n", - "df.sort_values('datetime')\n", - "df.index = pd.to_datetime(df.pop('datetime'), utc=True)\n", - "df = df.tz_localize(utc)\n", - "df = df.tz_convert(eastern)\n", - "\n", - "# make it a column again\n", - "df = df.reset_index() \n", - "print(df['datetime'].dtype)\n", - "\n", - "# set index\n", - "df.set_index(['badge_address','datetime'], inplace=True, drop=True)\n", - "\n", - "# Sort\n", - "df.sort_index(inplace=True)\n", - "print(df.head())" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "df_grouped\n", - " \n", - "badge_address\n", - "C7:59:BE:8B:9C:E0 129229\n", - "C7:75:13:05:10:EE 109297\n", - "D4:E4:47:A5:F0:B3 107509\n", - "D8:A1:84:25:FF:B7 129685\n", - "F2:37:E3:BA:70:9D 111909\n", - "dtype: int64 \n", - "\n", - "\n", - "df_resampled:\n", - " voltage num_samples\n", - "badge_address datetime \n", - "C7:59:BE:8B:9C:E0 2017-01-10 16:03:35-05:00 3.179 114.0\n", - " 2017-01-10 16:03:40-05:00 NaN NaN\n", - " 2017-01-10 16:03:45-05:00 3.181 114.0\n", - " 2017-01-10 16:03:50-05:00 3.181 114.0\n", - " 2017-01-10 16:03:55-05:00 3.181 114.0 \n", - "\n", - "\n", - "df_flat:\n", - " badge_address datetime voltage num_samples\n", - "0 C7:59:BE:8B:9C:E0 2017-01-10 16:03:35-05:00 3.179 114.0\n", - "1 C7:59:BE:8B:9C:E0 2017-01-10 16:03:40-05:00 NaN NaN\n", - "2 C7:59:BE:8B:9C:E0 2017-01-10 16:03:45-05:00 3.181 114.0\n", - "3 C7:59:BE:8B:9C:E0 2017-01-10 16:03:50-05:00 3.181 114.0\n", - "4 C7:59:BE:8B:9C:E0 2017-01-10 16:03:55-05:00 3.181 114.0 \n", - "\n", - "\n" - ] - } - ], - "source": [ - "# http://pandas.pydata.org/pandas-docs/stable/cookbook.html\n", - "# http://pandas.pydata.org/pandas-docs/stable/timeseries.html#aggregation\n", - "# reample doesn't always like multi index\n", - "df_by_datetime = df.reset_index('badge_address', drop=False)\n", - "\n", - "\n", - "df_grouped = df_by_datetime.groupby('badge_address')\n", - "print(\"df_grouped\\n\",df_grouped)\n", - "print(df_grouped.size(),\"\\n\\n\")\n", - "\n", - "df_resampled = df_grouped.resample('5S').agg({'voltage': np.mean, 'num_samples': np.max})\n", - "print(\"df_resampled:\\n\",df_resampled.head(),\"\\n\\n\")\n", - "\n", - "df_flat = df_resampled.reset_index()\n", - "print(\"df_flat:\\n\",df_flat.head(),\"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " badge_address datetime voltage num_samples\n", - "0 C7:59:BE:8B:9C:E0 2017-01-10 16:03:35-05:00 3.179 114.0\n", - "1 C7:59:BE:8B:9C:E0 2017-01-10 16:03:40-05:00 NaN NaN\n", - "2 C7:59:BE:8B:9C:E0 2017-01-10 16:03:45-05:00 3.181 114.0\n", - "3 C7:59:BE:8B:9C:E0 2017-01-10 16:03:50-05:00 3.181 114.0\n", - "4 C7:59:BE:8B:9C:E0 2017-01-10 16:03:55-05:00 3.181 114.0\n" - ] - } - ], - "source": [ - "print(df_flat[:5])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot using ggplot" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# for ggplot, you need to use dateutil timezones\n", - "tz_dateutil_est = dateutil.tz.gettz('US/Eastern')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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lzNdeomZy/HiDzWmJzQiutjvsNwc2Jexr9/2ZFUDsBryjiAMWjKjbabE6XrfB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BAAAg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\nDg3D0PLlyzV58mTT+u1MOZQCf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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ggplot(aes(x='datetime', y='voltage', color='badge_address'), data=df_flat) +\\\n", - " geom_line()+\\\n", - " scale_x_date(labels = date_format(\"%m/%d %H\", tz = tz_dateutil_est)) +\\\n", - " ggtitle(\"Voltage\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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fn68FCxbonnvu0YkTJxQYGKilS5cqOTlZY8aMkZ+fn6666iq9/fbbuuuuu3Tk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\nWlpaaJlRo0bFpEgAAAAAiRXxfRxuueUWzZs3Tz/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badge_addressfirst_datetime
0C7:59:BE:8B:9C:E02017-01-10 16:03:35-05:00
1C7:75:13:05:10:EE2017-01-10 16:03:35-05:00
2D4:E4:47:A5:F0:B32017-01-10 16:03:45-05:00
3D8:A1:84:25:FF:B72017-01-10 16:03:45-05:00
4F2:37:E3:BA:70:9D2017-01-10 16:03:40-05:00
\n", - "
" - ], - "text/plain": [ - " badge_address first_datetime\n", - "0 C7:59:BE:8B:9C:E0 2017-01-10 16:03:35-05:00\n", - "1 C7:75:13:05:10:EE 2017-01-10 16:03:35-05:00\n", - "2 D4:E4:47:A5:F0:B3 2017-01-10 16:03:45-05:00\n", - "3 D8:A1:84:25:FF:B7 2017-01-10 16:03:45-05:00\n", - "4 F2:37:E3:BA:70:9D 2017-01-10 16:03:40-05:00" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "first_data = df_flat.groupby('badge_address').first().rename(columns={'datetime':'first_datetime'})\n", - "first_data.reset_index(inplace=True)\n", - "del first_data['voltage']\n", - "del first_data['num_samples']\n", - "first_data" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "df_flat_realtive_time = pd.merge(df_flat, first_data, how='left',on='badge_address')\n", - "df_flat_realtive_time['relative'] = df_flat_realtive_time['datetime'] - df_flat_realtive_time['first_datetime']\n", - "df_flat_realtive_time['relative_days'] = df_flat_realtive_time['relative']/pd.Timedelta(days=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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9qe56T3XXe+11J9IX1FUpBTHDIqrB0SIiIiJyEFNwSEHMsIgpOIiIiIjIQUzBIQWmYRGL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\ngHUszZY2p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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ggplot(aes(x='relative_days', y='voltage', color='badge_address'), data=df_flat_realtive_time) +\\\n", - " geom_line() +\\\n", - " xlab(\"days\") +\\\n", - " ggtitle(\"Voltage\")" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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9996rV199VVu2bJEkvf3222rXrp1Dn2+88YY2bdqkzMxMzZ8/X7feeqsOHjyo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\nUTn6/BDnndQP9EZaX44VAID01I9fxfNFeFL43IYuyE/r72u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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ggplot(aes(x='relative_days', y='num_samples', color='badge_address'), data=df_flat_realtive_time) +\\\n", - " geom_line() +\\\n", - " xlab(\"days\") +\\\n", - " ggtitle(\"Number of samples\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/notebooks/show_sample_meeting.ipynb b/examples/notebooks/show_sample_meeting.ipynb deleted file mode 100644 index 6bcbfd3..0000000 --- a/examples/notebooks/show_sample_meeting.ipynb +++ /dev/null @@ -1,537 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import os\n", - "import sys\n", - "import datetime\n", - "import ast\n", - "import pandas\n", - "\n", - "from bokeh.io import output_notebook\n", - "from bokeh.charts import show\n", - "\n", - "# add the 'src' directory as one where we can import modules\n", - "src_dir = os.path.join(os.getcwd(), os.pardir, os.pardir)\n", - "sys.path.append(src_dir)\n", - "\n", - "# import data analysis modules\n", - "import openbadge_analysis as ob\n", - "import openbadge_analysis.visualization.contribution as contribution\n", - "import openbadge_analysis.visualization.participation as participation\n", - "import openbadge_analysis.visualization.participation_interactive as participation_interactive" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "
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" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "plot = participation.participation_plot(df_stitched_all=[[df_stitched]], labels=[meeting_name],\n", - " member_names=member_names)\n", - "show(plot)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/notebooks/test_threshold.py b/examples/notebooks/test_threshold.py deleted file mode 100644 index 8be7d7e..0000000 --- a/examples/notebooks/test_threshold.py +++ /dev/null @@ -1,160 +0,0 @@ -import pandas as pd -import numpy as np - -def test_threshold(df_meeting,sampleDelay = 50,avg_speech_power_threshold = 42,custom_power_threshold=None): - frame_size = 1000 #milliseconds - median_window = 2*60*1000 #milliseconds - median_window = int(median_window/sampleDelay) - power_window = int(frame_size/sampleDelay) - clipping_value = 120 #Maximum value of volume above which the signal is assumed to have non-speech external noise - df_meeting = df_meeting.clip(upper=clipping_value) - #Calculate the rolling median and subtract this value from the volume to remove the envelope - df_median = df_meeting.apply(lambda x:x.rolling(min_periods=1,window=median_window,center=False).median()) - #df_median.plot(kind='area',subplots=True);plt.show() - df_normalized = df_meeting - df_median - #df_normalized[df_normalized>10]['PW5UJDRM35'].plot(subplots=True,kind='kde') - #Calculate power and apply avg speech power threshold - df_energy = df_normalized.apply(np.square) - df_power = df_energy.apply(lambda x:x.rolling(window=power_window, min_periods=1,center=False).mean()) - power_mean = df_power.mean() - power_pooled_mean = df_power.mean().mean() - power_variance = df_power.var() - - power_threshold_single = power_mean + 2*np.sqrt(power_variance) # Outlier threshold mean +2*std - - power_pooled_variance = (power_variance+np.square(power_mean)).mean()-np.square(power_pooled_mean) - power_threshold_pooled = power_pooled_mean + 2*np.sqrt(power_pooled_variance) - - df_is_speech = df_power > avg_speech_power_threshold - #df_is_speech.plot(kind='area',subplots=True);plt.show() - df_max_power = df_power.apply(np.max,axis=1) - df_is_winner = df_power.apply(lambda x:x==df_max_power) #Find the badge with the highest power reading at every sample interval and declare it to be the main speaker - - #The assumption here is that there is only one speaker at any given time - df_is_speech = df_is_speech & df_is_winner - - #fig, ax = plt.subplots() - #df_meeting.plot(subplots=True,sharey=True) - #df_power.plot(subplots=True) - if(custom_power_threshold is None): - #(df_power>power_threshold_single).plot(subplots=True) - print 'power_variance : ',power_variance - print 'power_mean : ',power_mean - print 'power_threshold = (power_mean + 2*(power_variance)^0.5): ',power_threshold_single - return power_threshold_single - else: - #(df_power>custom_power_threshold).plot(subplots=True) - df_custom_power = df_power[df_power>custom_power_threshold] - custom_power_mean = df_custom_power.mean() - custom_power_variance = df_custom_power.var() - custom_power_threshold_single = custom_power_mean + 2*np.sqrt(custom_power_variance) - - custom_power_pooled_mean = custom_power_mean.mean() - custom_power_pooled_variance = (custom_power_variance+np.square(custom_power_mean)).mean()-np.square(custom_power_pooled_mean) - custom_power_threshold_pooled = custom_power_pooled_mean + 2*np.sqrt(custom_power_pooled_variance) - print 'For all power samples greater than custom_power_threshold = ', custom_power_threshold - print 'power_mean : ', custom_power_mean - print 'power_variance : ', custom_power_variance - print 'power_threshold = (power_mean + 2*(power_variance)^0.5): ', custom_power_threshold_single - return custom_power_threshold_single - #df_power.hist(bins = 40) - #df_power_mean.plot(subplots=True,sharey=True) - #(df_power_mean+df_power_std).plot(subplots=True,sharey=True) - -def is_speaking(df_meeting,avg_speech_power_threshold = 42,sampleDelay = 50): - frame_size = 1000 #milliseconds - median_window = 2*60*1000 #milliseconds - median_window = int(median_window/sampleDelay) - power_window = int(frame_size/sampleDelay) - clipping_value = 120 #Maximum value of volume above which the signal is assumed to have non-speech external noise - df_meeting = df_meeting.clip(upper=clipping_value) - #Calculate the rolling median and subtract this value from the volume to remove the envelope - df_median = df_meeting.apply(lambda x:x.rolling(min_periods=1,window=median_window,center=False).median()) - #df_median.plot(kind='area',subplots=True);plt.show() - df_normalized = df_meeting - df_median - #Calculate power and apply avg speech power threshold - df_energy = df_normalized.apply(np.square) - df_power = df_energy.apply(lambda x:x.rolling(window=power_window, min_periods=1,center=False).mean()) - - df_is_speech = df_power > avg_speech_power_threshold - #df_is_speech.plot(kind='area',subplots=True);plt.show() - df_max_power = df_power.apply(np.max,axis=1) - df_is_winner = df_power.apply(lambda x:x==df_max_power) #Find the badge with the highest power reading at every sample interval and declare it to be the main speaker - #The assumption here is that there is only one speaker at any given time - - df_is_speech = df_is_speech & df_is_winner - return df_is_speech - -def fill_boolean_segments(x_series,min_length,value): - #Given a boolean series fill in all value (True or False) sequences less than length min_length with their inverse - total_samples = len(x_series) - not_value = not value - i=0 - length=0 - start=0 - while(i0 else datetime.timedelta(0) - #current_member['total_speaking_time'] = np.sum(all_segments['length'])*sampleDelay - all_stats.append(current_member) - return all_stats - -def get_speaking_series(df_meeting,sampleDelay = 50): - def custom_resampler(array_like): - return len([ sum( 1 for _ in group ) for key, group in itertools.groupby(array_like) if key ]) - - df_is_speech = is_speaking(df_meeting) - #df_is_speech.plot(kind='area',subplots=True);plt.show() - df_stitched = get_stitched(df_is_speech) - #df_stitched.plot(kind='area',subplots=True);plt.show() - df_stitched = df_stitched.resample('1T').apply(custom_resampler) - - return df_stitched \ No newline at end of file diff --git a/examples/notebooks/voice-activity-detection-for-sociometric-badge-data.ipynb b/examples/notebooks/voice-activity-detection-for-sociometric-badge-data.ipynb deleted file mode 100644 index f4d5b17..0000000 --- a/examples/notebooks/voice-activity-detection-for-sociometric-badge-data.ipynb +++ /dev/null @@ -1,738 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Task**: Use the sociometric badge's mic output to detect if the badge wearer is speaking/silent (voice activity detection or VAD)\n", - "\n", - "**Input Data**: The sociometric badges sample the output of a bandpass filter (100Hz to ~340Hz), and output the mean of the absolute values for the first 20 ms out of every 50 millisecond frame. The data we will be working with will therefore be highly undersampled." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/orenled/.virtualenvs/analysis_env_new/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", - " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline\n", - "import pandas as pd\n", - "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "import os,sys" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "sns.set_context(\"notebook\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# add the 'src' directory as one where we can import modules\n", - "src_dir = os.path.join(os.getcwd(), os.pardir, os.pardir, 'openbadge_analysis')\n", - "sys.path.append(src_dir)\n", - "\n", - "# import my method from the source code\n", - "from analysis import sample2data\n", - "from test_threshold import *" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Import a sample data file (Dataset 3 or **D3**) using the sample2data function. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The sample2data function returns the volume sample time-series grouped by Member ID. This can be pivoted to get a pandas dataframe with a datetime index and columns corresponding to the participant's badge mic outputs. We can plot the audio volume time-series for each badge using the dataframe's plot function. All timestamps are in UTC by default." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "input_file_name = '../sample_data/two_speakers.txt' #two speakers\n", - "df_meeting = sample2data(input_file_name)\n", - "metadata = {}\n", - "metadata.update(df_meeting.metadata)\n", - "df_meeting=pd.pivot_table(df_meeting.reset_index(),index='datetime',columns='member',values='signal').fillna(value=0)\n", - "df_meeting.metadata = metadata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The meeting metadata contains information such as the list of participants, meeting start time, and a description of the experimental setup. Always useful to check out." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{u'group': u'NVGWO', u'uuid': u'NVGWO_1467322252396', u'showVisualization': True, u'moderator': u'none', u'location': u'office', u'startTime': u'2016-06-30T21:30:52.396Z', u'members': [u'PW5UJDRM35', u'LPH58QI85F', u'2J63OSU3ZC', u'BUAULG5KFB'], u'type': u'other', u'description': u\"Experiment 12. I'm wearing badge 1 . all badges are arranges in a square formation 3 ft away from each other. I'm speaking at regular intervals. b2 with fman 4 ft away in front , b4 on my left and b3 in behind. Speak at regular intervals\"}\n" - ] - } - ], - "source": [ - "print df_meeting.metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([,\n", - " ,\n", - " ,\n", - " ], dtype=object)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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S+vV7itjYBfTq1ZeWLc89RXjixNH8+ecGwsPvduzvUrm5Oaxbt4pPP/0Co9HIU0/1pkOH\nKB56qCMPPdQRgOPHkxk9eiTPP/8CBoOB6dMn8tpr86lWrTqKojB58ljWrl1Ft249rjrOF/PYRGHx\n4sWsXbsWf39/ABISEhg8eDCDBg1ylMnMzJQhpoUQQjh06NCRBx+MAkBR7Gi1xX8GTSYT2dln2bRp\nI4sWvUeDBg0ZNmwEKpWKN998H4C0tFTHgINr167i6aefITg4BAC1Ws3w4S8zePBT9Ov3FCEhIXz/\n/Tf4+vrSoEEjZsyYg0ajIT09zWU9y5UL4tNPv0CtVpOVlYm3t7fTcoPBwMSJo3n11fFUq1adH374\nloiIFlSrVh04l/BMmjSjxOO7Wh6bKISFhbFgwQLGjBkDwP79+0lOTmb9+vXUrFmT8ePHs2/fPhli\nWgghbkFPdKh72bP/klzvkxl9fHwAKCwsYPLkcTz//AvFyuTn5/HPP0nExIzl+edfYM6c//HDD9/y\nyCOPoVar+fDDhaxcuZyXXx4NQGpqCo8++rjTNvz8/DEajQC8+OLLrFmzktjYBRw7dpT77rufmJgx\nLut4vpVBrVazatVXfPJJLL169XUqM2PGZB555DFHK0VmZiZVq1Yr8Vivl8dezNixY0c0Go1jukmT\nJowZM4alS5dSo0YN3n//fQwGgwwxLYQQwsnp0+mMGDGMzp0f5aGH/uv0u6BSQUBAIH5+fjRt2hyA\n++5ry6FDBxxlnn/+Bdas+ZEvvlhCamoKoaEVSUtLddpHQYEBne7cuEJ79+6md+++vP/+h6xe/T2+\nvr589tnHeHt7YzabnNYrKirCy+tC60GPHr1Zs+ZH9u7dw969uwFYvPgD/Pz86N//aUe5ypUrc/r0\naadtpaWlEh+/93pCBXhwi8KloqKiHElBVFQUM2fOpGXLllc9xDRAcLAfWq2m1DKlPRdbXD+Jr3tJ\nfN1L4ute1xPfzMxMxowZ6Rh52GAw0LfvAH799VdOnjxCpUoVqV69ArVr1+bEicNERERw+HACjRs3\n5Nix/fz8889MmTIFi8UHX18fKlQI4Jlnnmby5Mk88EBrKlSogMVi4a23ZjNgwJOEhgYQG/selSoF\nExkZCQTQoEE9srOzqVcvDLPZRF7eGerUqYPNZiM+fhdDhw7FYMjkjTfe4P3330dRFPR6X0JC9MTH\nb2fnzq0sW7bMqTuia9fO9Ou3BKMxhxo1amCxWJg+/T3atGlDaGg71wG5ArdNovDss88yadIkGjdu\nzNatW2nUqNE1DTENMsx0WZP4upfE170kvu51vfF95533ycnJ5Z133uPtt99FpVLRtu2DPP54j39H\nGB5DRkY+o0ZNYM6cudjtdqpUqcqgQdGo1WoKCr6hV68nUBSFrl174OUVSGhoIEOGRPPii8NRFAWr\n1coDDzxI165PkJGRz9Sps3jrrdcxGPLRarVUrVqdV18dT0ZGPmPHTmb06LGo1WqsVitt2z5ArVoN\nAAgLq0OPHr1Qq1W0atWGsLC7GTu2B15eOgYNGuw4pmrVajB27ETGjZvC2LHjURSFwsJC7r+/HVFR\nj15RvEpLvjx69MiUlBRGjRrFl19+yaFDh5g+fTo6nY7Q0FBmzJiBv78/X331FcuXL0dRFIYNG0ZU\nVNRltyujR5Ytia97SXzdS+LrXhJf97htEwV3kUShbEl83Uvi614SX/eS+LqHDDMthBBCiGsiiYIQ\nQgghXJJEQQghhBAuSaIghBBCCJckURBCCCGES5IoCCGEEMIlSRSEEEII4ZIkCkIIIYRwyaMThfj4\neAYMGADAiRMn6N+/P0899RTTp093lFmxYgU9e/akb9++bNiwoYxqKoQQQngmj00UFi9ezKRJk7BY\nLADMnj2bmJgYli5dit1uZ/369WRmZhIXF8fy5ctZvHgxb7zxhqO8EEIIIS7PYxOFsLAwFixY4Jje\nv3//vyNzQbt27diyZQv79u0jIiICrVaLXq+nZs2aJCYmllWVhRBCCI/jsaNHduzYkZSUFMf0xUNW\n+Pv7YzAYKCgocAw9DeDn5+c07rgrMsx02ZP4upfE170kvu4l8b25PDZRuJRafaFxpKCggMDAQPR6\nPQaDodj8y5FhpsuWxNe9JL7uJfF1L4mve9wRg0I1bNiQnTt3ArBx40YiIiJo3Lgxu3fvxmw2k5+f\nT1JSEuHh4WVcUyGEEMJz3DYtCmPHjmXy5MlYLBbq1KlDp06dUKlUDBgwgP79+6MoCjExMXh5eZV1\nVYUQQgiPoVIu7twXAJdt1pKmL/eS+LqXxNe9JL7uJfF1jzui60EIIYQQN54kCkIIIYRwSRIFIYQQ\nQrgkiYIQQgghXJJEQQghhBAuSaIghBBCCJckURBCCCGES7fNA5fO69GjB3q9HoDq1asTHR3NuHHj\nUKvVhIeHM3Xq1DKuoRBCCOE5bqtEwWw2A7BkyRLHvGHDhhETE0NkZCRTp05l/fr1REVFlVUVhRBC\nCI9yW3U9HDp0iMLCQoYMGcKgQYOIj4/nwIEDTsNPb926tYxrKYQQQniO26pFwcfHhyFDhtC7d2+S\nk5N57rnnig0/LcNMewaJr3tJfN1L4uteEt+b67ZKFGrWrElYWJjjdVBQEAcOHHAsl2GmPYPE170k\nvu4l8XUvia973DFjPaxatYo5c+YAcPr0aQwGA23atGHHjh3AheGnhRBCCHFlbqsWhV69ejFhwgSe\nfPJJVCoVc+bMISgoiEmTJjkNPy2EEEJcjaxcI+X0Xmg1t9X59RW5rRIFrVbLvHnzis2Pi4srg9oI\nIYS4HZzJKWLcB1u5p1YIMX2alnV1bro7LzUSQgghrsKZf69bS/jnbBnXpGxIoiCEEEKUQneDuhu2\nJqTz4Tf7ne7G8wSSKAghhBCl0GpvzE/lR98eYNv+0+QVmG/I9m4WSRTELU1RFCxWe1lXQ4jrsm7T\nP+xOzCjrarjVqQwDx9Nvz9sWNWqV4/Xpy9w+f6nk9DxSMwtudJVuKkkUxC0lx2DCbj/XLPfzzpMM\nmfs7Q+dvwGqTZEF4JpvdzppN/7Bg9d9lXRW3mvLxDqZ/tpOMnKKyrsoVsVjtbNibQoHRctmyeQUX\nyoyP3XZV+5nx2S4mLd7OP2l5jnlbEtJ5c/lf/Lzz5FVtq6xIoiBuCYYiC7sTzxDz/mbeWb4XgC9/\nPeJYXmi0llXVPJLZYiMty/VZjMli48Rp95z9HT6Z47j461J7D2ew89AZTpzOx+5h/bQAa/5M4sNv\n9l/VOmbLhST3z/hU5n2xhxOn8x3J7+nsQopMV/f5NpltpJ+9ujPbm2XsB7fuY/J/2H6c91f9jcVq\nZ+j8DSz5KZGPvz3osnxmThGD5/zG21/Fl7j8y1+PMHjOb+QVmLHZ7by3ch9r/kwi12ACziWJ5/3v\n812O119tOEbCP2f58tcjrN916ycLt9XtkTeTXVFQASqV6rJl3UVRlMvu/3w9DxzPpm7Vcuh0atRl\nVGe7orB6YxKNa5enbrVyqC9qzhvxzp+O17/tOkn/h+o6rVtktqL31ZFXaGb20t088WA4zetV4HR2\nEXa7QtUK/je8vucvOCrL9/hqnf9MLFqTQPyxLGYMbknVCv6OWJ//cX73630cPJ7N1EEtCKscQHa+\niSkfb2fAw3fTskElp21abXYOHs/mnlohpcbCblfYl5TFu1/vA2D+C/cREuiDoiiczi7CZld4b5Xz\nWbVWo6J1o8rUvyuYBjWD+eyHQ5w8Y2D+C/c57etA8lmSUvN49L6awLnb1cxmG9Ur6i/sX1FIPJ5N\nraqB+Hi556tt3eZkAPYezsRksfHGi20IDvDmyKkcVKhYtDaBMQMiqRTo7Vjn4q6zT384BMC0T3cC\nUKW8H2lZhYRVDqBvh7rM/WIv1Sr4k/JvU/WInveSeDKbng/Ucbp//80Vf3HkVC5zoltTMcjX8b4m\nJGVxKqOAR1qFOdXbZLHx3dZkvt1yHIAFr7TD19s5Rna7gkoFChT7jji/zNX7v/nvNKdpm93OgeRs\nGtUKQa1S8eP2E1QP9SfbYGLdpmRe6tGYaqH+PP/6BgA+HN2elX8co+29Vd3yvwyw/5+zfPX7MQC+\n/O3CSchfRzPJyjVSvpwPAMvWH6Gc3ot2TaoyxkXSY7LY+HX3KUeLwMvvbWJo10bsPZLJ3iOZrNuc\njLeXBpPZdtl6fbH+CF+sP8Ls51tRKcTvqo7pn7Q8gvTeBAd4X77wdVApnnb55TVQFIVp06aRmJiI\nl5cXs2bNokaNGi7LX+7xoBUq6Ok78XsKTVb6dKhLgdFCs/DQm/YDbLXb+ftYluNL6zy9r47e7euQ\nYzBhstix2e38tKN4ttqyQUU6/ecuVNy4+iaezKF8oA8V/v1nu5jNrmC3K8z5vz3FziLvqR1C9VA9\nP24/cV37nzqoxXWtf6kTZ/L59PtzX+oR9ULJyCnixBkDKhXcU6s8tasGUqtKAG9/de5HsWWDijzc\n8q7r+gyczTcS4Od1VVdYFxot+PpoMZpszFu2t9Syd1XSc+K0wWlelfJ+PP9YI95btY+zeSbH/IpB\nvmTlGbHZL7xfFcr5oPfVUTHYlx0Hz+DjpWF0v2b8fSyLv5OyOJaaR4VyPmTmGh3rvNz7XkeMrkXH\nyBr8ctEZl0oFEwdEMnPJLqdynf9zFz9c9BmqHurPqYwCwquXo2HNEAL9vahd5cLj2602Oza7grfu\n3JgudkXBZLZhKLKQbTBRr3pQsbqYLDbm/N+eaz6WW1GNinrCKgWw6ZIf+vO8dRpMFucfu4rBvpzJ\nvvLuhTpVAzmWmlds/oCH7ybup8QS14np04QAX68SlwUH+132MfsXO3g8mxW/H71suX4PhbPsolbM\nsjC8R2OsdoW8AjN1q5UrtazVbmfWkt1A6d9/el+dIwkqTWmPcL4jEoVffvmF3377jdmzZxMfH09s\nbCwLFy50Wf5yiYKvvzf9Jv9wo6sphBBC3HCznvsPVcqX3lJTWqJwR3Q97N69m7Zt2wLQpEkTEhIS\nSi3/3srLnAFddNbo662lyGSlad0KhAb5Xnddr8T+5LOoVSpOZRiKLatXvRyHT+UC567U1ahVmEu4\na+CeWiGX/eBcjZ2HTlOjYgCVS2g6O39GqFGrsNkV/H20FFx0zUFUZHU2xqc69eUCPBRRnV93n7rs\nvhvVCqHqDTwWOHeGefG+K4X4cfqiPmGVCto1qcoff6U61fdaWxROnM4n8WQO1UP9aRAWckXrmCxW\n/k46S42KeioF+zmdeZekQVgwB49nA1C7aiBJqXkE+nvxnwaVKDRa2JyQ7nJdtUrlaA3y0qkd79WD\nzarx575U7qlVntPZhTSqFcL6XRfidmmLwHnl9F7kGpxvEYu8O5RdV3BnQMfIGmzdn46h6PIXoZ0X\nHOBN5N0VHdMJ/2Sh06q5u0YwAIdOZGNXFCoG+XLkVC6tG1UucTtbEtKcPrsXU3Gu2b40bRpXZvPf\nxeN8d40gUrMKCA3yJenfs++mdStgtto4kJxdrHyNinpOnin+/38talUJdLrQ7lqFVQrAbLWRlnXh\n/yQk0NuppepirRtVYuv+0yUuq1e9HGGVSx7Az9dPR1Hhlb/3OQYTOw+duWw5lQouPm2uGOyLl1bN\nqYwCmoVXwGy1U62Cv9MFiM3CK3D3XcFO11MBNK5dnr+TshzTtaoEkpyeh6JA3erlsFjsaLUqFAXH\n+w3Qvlk1cvJNHEvNpVXDkj+DFzv/v9UxsuQW8pNn8jl0IodPvjtIoH/JLTTnzYhu43LZHdGiMGnS\nJB5++GFHstChQwfWr1+PWi3XcgohhBCluSN+KfV6PQUFF64At9vtkiQIIYQQV+CO+LVs3rw5f/zx\nBwB//fUX9erVK+MaCSGEEJ7hjuh6uPiuB4DZs2dTq1atMq6VEEIIceu7IxIFIYQQQlybO6LrQQgh\nhBDXRhIFIYQQQrgkiYIQQgghXJJEQQghhBAueeyTGa1WKxMmTCAlJQWLxUJ0dDR169Zl3LhxqNVq\nwsPDmTp1KgArVqxg+fLl6HQ6oqOjad++fdlWXgghhPAQHpsorFu3juDgYObNm0deXh7dunWjfv36\nxMTEEBkZydSpU1m/fj1NmzYlLi6O1atXYzQa6devH23atEGn05X1IQghhBC3PI9NFDp37kynTp0A\nsNlsaDTnPUnfAAAgAElEQVQaDhw4QGRkJADt2rVj8+bNqNVqIiIi0Gq16PV6atasSWJiIvfcc09Z\nVl8IIYTwCB57jYKvry9+fn4YDAZGjhzJK6+8wsWPhPD398dgMFBQUEBAwIVRsfz8/MjPL310SKv1\n8mOICyGEEHcCj21RAEhLS+Oll17iqaeeokuXLrz++uuOZQUFBQQGBqLX6zEYDMXml+ZyY52HhgZc\ndihqce0kvu4l8XUvia97SXzdo7Rhpj22RSEzM5MhQ4YwevRounfvDkCDBg3YuXMnABs3biQiIoLG\njRuze/duzGYz+fn5JCUlER4eXpZVF0IIITyGx7YoxMbGkpeXx8KFC1mwYAEqlYqJEycyc+ZMLBYL\nderUoVOnTqhUKgYMGED//v1RFIWYmBi8vEofl1sIIYQQ58hYDyW4XLOWNH25l8TXvSS+7iXxdS+J\nr3vcll0PQgghhHA/SRSEEEII4ZIkCkIIIYRwSRIFIYQQQrgkiYIQQgghXPLY2yOFEEKIy9m7dzdT\npoynVq3a2O12rFYro0aN5auvviQq6mFatmzlKNut28OsXfuTY3rs2FcAmDv3Lce83r278sUXKx3j\nBZ04kczrr8/mvfdiGT58KKNHT+Cuu8Kc6pCamsLChe+SmZmBt7c33t4+DBs2nFq1apOXl0f//j2o\nXbsuAO3atadXr75O+8nKymTUqBH07/80997bhIED+3L33Q1QFAWVSkXz5pF06tTFMd9ut2MyGXn+\n+Rdp0eI/1x1DSRSEEELc1iIiWjBt2iwAdu7czkcffUBQUFAJJVWOV6dPp2M0GrHZbKSlpVKlStVi\nZRxrqYrPO89kMjJuXAzjxk2mYcNzYwwdOnSAt96ax7vvfsDhw4eIiurEyy+/WmJdMjLO8OqrI3nu\nuWHcf3870tPTqFWrDu+++4FT6Uvnnzx5gokTR7NkyfJSInNlPDpRiI+PZ/78+cTFxXHw4EGGDh1K\nzZo1AejXrx+dO3eWIaaFEOIWserot+w98/d1bUOjVmGzX3j8T7OKjelR99FS17n4cUF5ebmEhIRg\nt9tLXee779bRtu0DeHv7sGrVV7z44shrqu+mTX8SEdHSkSQA1K/f0PGDnph4kMTEA7z00vOEhJTn\n5ZdfJSSkPHDux3/8+FHExIylefPIEo/H9XHmERxc/prqfCmPTRQWL17M2rVr8ff3ByAhIYHBgwcz\naNAgR5nMzEwZYloIIe5we/bsYsSIaMxmM8eOHeG1117nl19+clleURR++eVHPvzwc9RqFQMG9OG5\n54a5fKpvaS0KaWkpVK9e3TE9fvwoDAYDWVmZvPvuB4SF1aJ+/YZERLTg559/5M035zFz5lxAYfLk\ncfj4+JKVlem0zeTkJEaMiHZ0PUyZMtNpvtVq5ciRw7zyyuiriJJrHpsohIWFsWDBAsaMGQPA/v37\nSU5OZv369dSsWZPx48ezb98+GWJaCCFuET3qPnrZs//LuZYnM17c9XDy5AmGDn2Gtm0fwGw2O5U7\n38qwbdsWioqKmD59IudP0n/55Ue6dOmKt7cXFovZccJZWFiEt7e3y31XrFiZxMQDjunZs98AYOjQ\nZ7BabTRvHomPjw8ADzzQnk8+iXWUnTBhCkFBwQwbNoS7727guPbhSroesrPPMmhQfyIiWlCpUuWr\nitelPDZR6NixIykpKY7pJk2a8MQTT9CwYUNiY2N5//33adCgwVUPMQ0QHOyHVqsptUxpj7sU10/i\n614SX/eS+LrX1cQ3KMgPb2+tYx1v7xqo1SoiIpqyffufdO/eBYBdu3YRHl6X0NAA1q//ntmzX6Nd\nu3YA7Nmzh5kzZzJo0JPce29jdu3aTM+ePQH46qtdREQ0IzQ0AJ1OQ3Cwn1P9unfvQt++caSl/cO9\n994LwPHjxzl7NpMKFfS8/vpcOnbsSOfOnfntt53ce29jQkMDUKtVtGjRBC8vLyZOnMC0aeNZuXIl\nZrM/Wq26WAzM5jyn+eXKeePv70e5cj7X/Xn02EThUlFRUY6kICoqipkzZ9KyZcurHmIaZJjpsibx\ndS+Jr3tJfN3rauObk1PI1q3b6NfvSVQqNUVFhbz00is89FAnEhIO8thjXfH19UOn0/Hqq+M5fPg4\ne/f+xcSJ/3Psp0aNcAoLi/j99y0MHjyMefNmERf3f2i1GqpWrc7o0RPIyMjHYrHx0kvDHV0UzZpF\n8MILI5k16w0WLXqXs2ezsFqtaDQaXnrpFbRaPc88M4zZs6cTF/d/+Pj4MHbsJDIy8rHbITPTgE6n\no3nz+7j77t8ZN24izzzzHFarvVgMzp4t4OjRo/8epwqj0UiXLt3w8Qm6oniVlkx49KBQKSkpjBo1\nii+//JI+ffowadIkGjduzNKlS0lPT2fQoEEMHjyYr7/+GpPJRJ8+fVizZs1lR4+UQaHKlsTXvSS+\n7iXxdS+Jr3uUlijcNi0K06dPZ/r06eh0OkJDQ5kxYwb+/v4yxLQQQghxHTy6RcFdpEWhbEl83Uvi\n614SX/eS+LqHDDMthBBCiGsiiYIQQgghXJJEQQghhBAuSaIghBBCCJckURBCCCGES5IoCCGEEMIl\nSRSEEEII4ZJHJwrx8fEMGDAAgBMnTtC/f3+eeuoppk+f7iizYsUKevbsSd++fdmwYUMZ1VQIIYTw\nTB6bKCxevJhJkyZhsVgAmD17NjExMSxduhS73c769esdw0wvX76cxYsX88YbbzjKCyGEEOLyPDZR\nOD/M9Hn79+8nMjISgHbt2rFlyxaXw0wLIYQQ4sp47FgPlw4zffGTqP39/TEYDBQUFMgw0x5K4ute\nEl/3kvi6l8T35vLYROFSavWFxpHzw0nr9XoZZtoDSXzdS+LrXhJf95L4uscdMdZDw4YN2blzJwAb\nN24kIiKCxo0bs3v3bsxmM/n5+SQlJREeHl7GNRVCCCE8x23TojB27FgmT56MxWKhTp06dOrUCZVK\nJcNMCyGEENdBhpkugQwzXbYkvu4l8XUvia97SXzd447oehBCCCHEjSeJghBCCCFckkRBCCGEEC5J\noiCEEEIIlyRREEIIIYRLkigIIYQQwiVJFIQQQgjh0m3zwKXzevTogV6vB6B69epER0czbtw41Go1\n4eHhTJ06tYxrKIQQQniO2ypRMJvNACxZssQxb9iwYcTExBAZGcnUqVNZv349UVFRZVVFIYQQwqPc\nVl0Phw4dorCwkCFDhjBo0CDi4+M5cOCA0/DTW7duLeNaCiGEEJ7jtmpR8PHxYciQIfTu3Zvk5GSe\ne+65YsNPyzDTnkHi614SX/eS+LqXxPfmuq0ShZo1axIWFuZ4HRQUxIEDBxzLZZhpzyDxdS+Jr3tJ\nfN1L4used8xYD6tWrWLOnDkAnD59GoPBQJs2bdixYwdwYfhp4RnyzPkkZB4s62oIIe5wiqLw9ZF1\nHMhKLOuqlInbqkWhV69eTJgwgSeffBKVSsWcOXMICgpi0qRJTsNPC88wftP/ABgTOZywwBplXBsh\nxJ0qoyiT309u4veTm1jQYV5ZV+emu60SBa1Wy7x5xd/EuLi4MqiNuFFyTLmEIYmCEKJsXHyt2/U4\ndPYIWUVnaVPtPyiKgkqluiHbdbfbKlEQtyebYi/rKggh7mBq1YWL2212Gxp16Re7u/LeXx8B0KpK\nJCM2jAfwiBaK2+oaBeG5sorOsjD+E84UZhRbVt4nuAxqJIS43ZltZgospV+8DrDnTLzj9dStcx2v\nFUUhx5SLoigoisKSA8vZlb7XaV2b3camlG3kmw2OeYXWohtQezDZzBReQf2vlyQK4pbw9ZFv2J91\niKUHvy62zC4tClfNbLPwXdLPZBtzyqwO+WYDKw6vYfrWeey/Qy8Cu5PkmHL5YN+n/Hz897KuyhUb\nt2kGY/6cVmqZg2cPsy7pR8d0tunC/9Sm1G1M3DyLP1O2kWPKZXv6bj49sMxp/W3pu1iWuIoP//7c\nMc9qt96Q+o/9cxqjL1P/G0ESBXFLsCrn/nEsdkuxZfYb1D94J9mYsoXvk9c7fTndbF8mruKPU1s4\nU5TJwviPr3i9G/Uleis5kX+K0wVnAPhg36e8sXuBY9m3ST/ftLt7ThdmcCL/lFu2PXHzLP7OPMja\nYz+4Zfs3wsWfLavdislmLrV8Um4yR7KTXC7/MnE1AMsPr8am2BzzD2cfw2g1oigKXxxa+e+2jjuW\nX/w9t/t0/DUnV5ab9L8iiYK46QoshcU+4OdvO7IpNn468gerj37nWPb23g+w2W2IK5dnOnefeXoJ\nXTnu9ldGAukFp8koyrrqdT9OWMrIDRMotJTeNGuxWzFajddaRbew2K1sTt1Ogbl4U/Dcne8yY/t8\nFEXh78yDJOUeZ2f6XgotRfyQvJ5F+z4FwGg1YrFbSTGk8eJvY8gx5Za4rwNZiXycsLTY/4XFZnEZ\nF7tiZ8a215m7893rPNKrt+bo92w4tbnY/D1n9nH6Jn1Gd6bvZeSGCRw8exir3crIDRNKrE/cwRUo\nioLNbuON3Qv56fhvl922Tq1z6k54Z28sozZO4aXfx5ZYfl3ST47Xn+z/P9Ye+4G/Mw+UWPZWIInC\nbcCu2PntxEaXXyq3krSC04z5cxqzd7ztmHdx10KKIY2P93zJ+hN/OC3/7KLmPLti5+OEpWxN3Xlz\nKu2Bfj25ETjXB1sSRVFYf+IPUgxpwLl+1DVHv+dw9rFr2p/FbuV0wRmKrEV89PcS/rf9jWJlTuan\nYLAUMGv7m7z42xgyCi8kEtnGHAoshew5sw84dzsawJnCDAotRRzOPsq2tF2O8i9vmMCojVMw2cyO\nY7iZ9pzZx8TNs5z6t+fseJsvDq3kmdWjHPNsdhsf7rvQqjNu0wzH688OLOO1HW85pnNN+YzaOIWX\nN0xwzJ+4eVaJ+18Q/zF7zuwjMfsoB88eZt7O9zia8w/zdy9g1MYpWGyWYj/Ap/JTHa9HbpjAisNr\nr+hYrXYrPyX/dl194b+c2MBXh9eSZ87nSPYxbHYbOaZcPk5Yyoxtr1/zdq/UN0k/Ob5D/jy1laJL\nkqnz1w98nLCUbWm7yDJmU2S78kTUYrfwY/LlE4rz9v77Ob/YB/s+u+L1L+XuEym56+E2sOd0PCuP\nfsvvpzbzv/vOXUlrtVtZd+xHqgdUxa7YiTu4gn5390CFihaVm+Ol0d30eh7LSebNPQsBOF14xjH/\nSr6w9pzZx6GNU5n0n1EsS1zF35kH2HNmH62rtnBLXRVF4bt/fqFh+XrULlfTZbmk3GQq+1XCT+fr\nlnpcjc2p21GhRqNyzv9PF2aAolDJv6LjNq+lB79iW/ouVvMdCzrM44+ULfxyYgO/nNjAs/cMIMWQ\nRpdaHVGpVKQXnOGdvbE813iAy1gsOfAle87s48n6vR3zLv0Bn7PzHafpadvm0qVWR2qXq8l7f32E\nt8bLsWzerveo4FuezEtaJVpWbo71oi/FmD8mAfBys2g+P/Al2aYcOtRoS8/wx64walfnjd0LScpN\ndkyP+XMa01qNpYJvCOkXfaZf/G0MAMPufYb4zP2O+QZLgdP2Lu7vnrD5fyXu88XfxtC84r0oisKz\njQc4LVtwUZfOW3sWOV4vTogjIesQtQLv4skGvTFajcy/qLvDarfyx6nN1AuuQ745n6yibJJyj3Ms\n9x+q+Feigm8IAxr0IcWQyod/L6HIauSPU5uJqNSUR2s/jNVu5UhOEkezk3isTieWHVrlVK9xm2aQ\nbzZQ3ieEphXvccw//2yUiIpNeKz2hWfarDzyDb+d/JPJ/xlFZf9KJcbhevyY/KvjdXzmfp5QHnda\nnmJIo35IuGN66tY5tKzc3OX2Nqdu574qLZ3mxWck3KDaXpmLb9k8ZUh167NmVMqNukH0FqYoCtOm\nTSMxMREvLy9mzZpFjRqug3q5x4PeCo8QtdltzNrxZrGzhmca9adR+bv56O84ErOPlrhuFf9KjI0c\nge4mJwtv7l7Esdx/nOZ1q9P5uvs0n6zfm/tucMKQakhn1o43AWhTtSWFViN7z+wjyLscFpuFgY36\nEqDTM3fXhWbciS1jSC1Ip0FIPfx1fje0PpdjspkdP5qu+Gv9KLAWPyvsX7+nox/1Wgxv+pzjti93\nG9nseYK8g5i+7fK3lN0dXJcRzZ6/Yfu+khjfDEHe5Tyi9fByNCqNU7/+xe6v+h/61e9Z4rKr/f7d\nlraLuIMrLlvuwRr38/vJTY7p8KDaHMlxfX2CO0z6zygq+IRgsVsve/JhtBoZtXGKY/p6b7Ms7RHO\nd0Si8Msvv/Dbb78xe/Zs4uPjiY2NZeHChS7LX0uicLow46Y0oQkhxJ0kxCeYs8bssq7Gbef9B+c6\nPfCptEThjuh62L17N23btgWgSZMmJCSU3kR0cV9oSQIMPuTnO/dfbU/fc32VFEIIUcytfnt07XJh\nTnc0eIotqTucHhz1WOiDLsveEYmCwWAgIOBCtqTVarHb7ajVJV/LeSXNVK48HNaB/1SJwGQ1YbSZ\nqOhXATjXB+an9cNLo0Oj0mC2mfHWeGGwFFJkLaKKfyUKrUXo1DpMNhMGSyEVfEMwWo2YbCZ8tX7s\nOXPuNppArwCi7x2El8aLAC89atTYFBtatRZvjRcZRVn4ac81W6lQ4a31xma34aP1ZlH8pyRkHaRD\njbbcV7UlRdYivDXepBrSqaqvTFbRWayKjSr+ldCqtBTZisg3F6DX+eGt8SbHlIvRZiLQKwB/rS8m\nm5lA7wCyjTloVBoUFLKM2VTzr0ym8SzV9dUosBRQaC1CUexU9KuIyWZCr/PHbDdjtlkotBYS7B18\nrm9dBbWqVubQiRMYbUZCfILIM+Xjq/PFbLM44rY9fTff/fML95Svz5B7BpBecBqtWkuAl56zxmyK\nrEYCvPQUWY0Eeumx2m3YFDteGh06tZZUQ7rjOEO8gzhlSMNf50uOKY/K/hUptBRhsVuoEVAN679X\nofvr/Aj2DsJst2Cz28goyiLAS0+eOQ8fjQ8mm4kQnxByTDn4an0psBSiYAdU5JryCPQKwEfrTZHV\nSKGlkFC/ChRZjeSYcinvE4xGpSHXnEeAlx4fjQ9F1iIUFEJ8gimyFHHSkEqITzA6tZYg73KOC7AW\nJ8RxujCD7nW7cF+VlpjtZoqsRir5haL+93oFo9VItimXYO9ylAv24eCpZHw0PnhpvDDbzGjVGny1\nvmjVGgzmQny1PhRZjfjpfPHX+WEwF6CgEOClJ8eUi5/WFy+NFwZLARmFWYT4BKNSnduPv84ff50f\nx/NOUt43BKvdiq/WF6PVRK45lyp+lZy6vfLNBvQ6f04XZmBTbFT0reDoIvHT+mK0mTDbLOjUOqx2\nK14aHWabmQAvPXDuyZ2KopBtyqGyX0XUKjVnijJRFDvZplxQIMinHCpUGG1GDOZz1wl4a7zx0ujI\nMmZTL7gORRYjRpsRb403ob7lySjKcsRPp9ZhsBjw0fgQ7FMOo9WEj9Ybu2Kn0FqExWbFqlip6FsB\n/yAtKaezyDPnE+ITjJ/Wl0JrEUarCZPNRJB3OXy1PqhVaoqsRVgVG9nGHKrrq2KymbFjx1/rh8lm\nxqpY0ajUGK0msozZVPWvjJdGx5nCTDQqNb46X0xWMwFe/tgVBS+NjjxzPipUmGxmKviGUGgtQoUK\nnVqHWqVGQUFR7FjtNhQUso052BQbYYE1HMcLOOb7af2wY0eNCj+dH5lFZ/HReuOj8UatUlNoKcJf\n50eh9dzf88fppdGhQk2OKYdgn2CyjTkEeZfDW+NFrjmPU/mpVPGvhEatIdDr3PdzZlEWFXzLO7pW\nNCot5bwDMNvMGCwFaNVaqlUM4Xj6GXRq3b91tBPoFYDVbqHQaiTIO5BArwCMNhMAXhovdOoLP3WF\nliJSC9LR6/yp7F8Rg6WAVEM6VfwrYbFbCPn3AW92xU6R1YgK8Pn3/Tq/vk2xoVGpsSsK/jo/pzPz\nQkshfiV0PRosBee+92wWCiwFBHmXc1rv/PeWoiho1VryzHn/vkdQwTcERVHQqDSU8w4kz5yHzW7D\naDMR6luB1IL0fz+nWvQ6PQaLwfG5VVA4XZiBRqXmroDqfHV4LfGZ+/ki0bm78bF7XScKd0TXw5w5\nc2jatKljQKj27duzYcOGsq2UEEII4QHuiNsjmzdvzh9/nLvd7q+//qJevXplXCMhhBDCM9wRLQoX\n3/UAMHv2bGrVqlXGtRJCCCFufXdEoiCEEEKIa3NHdD0IIYQQ4tpIoiCEEEIIlyRREEIIIYRLkigI\nIYQQwiWPfeCS1WplwoQJpKSkYLFYiI6Opm7duowbNw61Wk14eDhTp04FYMWKFSxfvhydTkd0dDTt\n27cv28oLIYQQHsJjE4V169YRHBzMvHnzyMvLo1u3btSvX5+YmBgiIyOZOnUq69evp2nTpsTFxbF6\n9WqMRiP9+vWjTZs26HQ3f/REIYQQwtN4bKLQuXNnx5MWbTYbGo2GAwcOEBkZCUC7du3YvHkzarWa\niIgItFoter2emjVrkpiYyD333FPa5oUQQgiBB1+j4Ovri5+fHwaDgZEjR/LKK684jc/t7++PwWCg\noKDAaZwHPz8/8vPLdohoIYQQwlN4bKIAkJaWxsCBA+nevTtdunRxGuSpoKCAwMBA9Ho9BoOh2PzS\nWK0lj5EuhBBC3Gk8tushMzOTIUOGMGXKFFq1agVAgwYN2LlzJy1atGDjxo20atWKxo0b89Zbb2E2\nmzGZTCQlJREeHl7qtrOzC0tdHhoaQEaGtEq4i8TXvSS+7iXxda+yiq+iKE6jPd5uQkMDXC7z2EQh\nNjaWvLw8Fi5cyIIFC1CpVEycOJGZM2disVioU6cOnTp1QqVSMWDAAPr374+iKMTExODl5VXW1RdX\nQFEUsNlQaT32YyqEuA3YDAaOvfwSFXr0IuSRR8u6OjedjPVQgstlq3LG4F7n43ty/lyKDh0k/MNP\nUKk9upfsliKfX/eS+LpXWcQ3f89u0ha+B0C9xZ/d1H3fLKW1KMi3r7hlFR06CIBiNpdxTYQQ4s4l\niYK45dktkigIIcrObXxpwhWRREHc+mz2sq6BEELcsSRRELc8xS6JghCiLN3ZTQqSKIhbn12eayGE\nEGVFEgVxy1PscmOOEEKUFblBXdz6pOvB4+zdu5s1a1YyffprjnnDhw/FZDLh6+uLoihotVomTpyG\nxWJh6tQJxMZ+6ii7Zs1KsrPP8swzz/H22/NJSNiHn58fAHPmvIFGo2HatInk5+ej0+mYMuV/BAeH\nYLVa+fzzj9m2bQu+vr5otVqefTaahg3Pje3SrdvDrF37E9nZZ5k+fRJWq5XAwHJMnToTb29vVqz4\ngm++WUNwcAgAo0dPQKfTMXBgX+6+u4HjoTvNm0cyaNCzNzGiokzd4VczenSiEB8fz/z584mLi+Pg\nwYMMHTqUmjVrAtCvXz86d+4sQ0zfBuQahWuX8dWX5O/a6TTvuEaN7TouEA2IbEFo776XLVfSU+wm\nT55BjRp3AbBmzdcsW7aUXr36lPrEu8OHD/Hmm+8RGFjOMW/NmpXcdVdNhg0bzjffrOGLL+J48cWR\nLFr0Hlqtlo8++hyA9PR0xowZybx5b1O5chXO9zUvX/4F7dt34PHHe/Hhhwv59tu19Oz5BImJB5k8\neQb16tV37Cs9PY1aterw7rsfXFF8hLjdeGyisHjxYtauXYu/vz8ACQkJDB48mEGDBjnKZGZmyhDT\ntwXperhdKMqFBCUvLw9fX99/55f8HiuKwqlTJ5k3bxZZWVk8+mg3unTpire3N3l5eQAYDAa0Wi1W\nq5Xff1/P119/41i/cuXK9OzZh++//4bBg593zPfy8nKsX1hYQKVKlQFITDxEXNxnZGVl0rr1/QwY\nMKjU+glxJ/DYRCEsLIwFCxYwZswYAPbv309ycjLr16+nZs2ajB8/nn379skQ07cD+Y6+ZqG9+xY7\n+y/LJwfOnDkNHx8fVCo1d90VxgsvjCA3N4fk5CRGjIgGzv0oZ2Vl0rFjJ4qKiujVqw99+jyJzWZj\nxIhoGjRoyAMPPMjSpZ/x1FNPkJ+fx8KFi8nNzaFcuXJOg8MBVK5chf37//536tyHqUuXrgwd+gy/\n/PIjFouVwYOHAhAV9TA9evTGz8+fCRNeZevWTdSqVcdRv/NdD1OmzKRChQo3KWpClC2PTRQ6duxI\nSkqKY7pJkyY88cQTNGzYkNjYWN5//30aNGggQ0zfFiRTuF1c3PVwXm5uTrGm/fPXKPj6+tKrV1+8\nvb0BaN48kqNHj/D118vp0+dJunbtzrFjR5kwYTQffxxHbm4udrvdKVk4efI45cuf/1E/1/Uwd+5M\nJk6cRosW/2Hr1k3MnDmFefPepnfvvvj76wFo3fp+Dh9OpFatOtL1IO5oHpsoXCoqKsqRFERFRTFz\n5kxatmx51UNMAwQH+6HVakotU9pzscX1Cw0N4PC/r4OD/PCXeN9Q7v78BgX54e2tddqPTqchONiv\n2L7N5jy0WrXT/IAAH0wmL/LzM4iJiWHNmjVYrVYOHUrgqaf6snfvDqpUqfDvOtUxmYqoUiWYRx/t\nwtKli4mJiWHJkiWcPHmSDRs2EBsbS2hoAGq1itDQAKxWM9WrVyQ0NIC6dcMwGgvx9VXxxBP9+f77\n7/Hx8SEhYS+9evUiJMS/WP0uR74f3Otmx1dTzpfUMtr3reC2SRSeffZZJk2aROPGjdm6dSuNGjW6\npiGmQYaZLmuXxjf7bAGF/hLvG+VmfH5zcgrZtGkTjz/eHQBFgaysTM6eLUCvd9732bMFWK12pzrl\n5xspLDQTGFiRjh070717T3Q6HZ06dSEwsCIDBz7P3Lkz+eyzJdjtNkaPnkhGRj4DBw7ls88W06NH\nL7RaLSqViqCgEP766wCBgRWx2xUyMvIZPnwUM2e+hlqtRlEUXn55NEVFCs8//yL9+vXHy8ubiIgW\nNGjQjPT0tGL1K418P7hXWcTXkGd0vL5d39vSEiCPHj0yJSWFUaNG8eWXX3Lo0CGmT5+OTqcjNDSU\nGaosZ8sAACAASURBVDNm4O/vz1dffcXy5ctRFIVhw4YRFRV12e3K6JFl63x8Dz87CIC7pkzH566w\nsq3UbeRO+/xaLJb/Z+/OA5so88ePv5Om900LFChQjnIjIocIiqzHCq6rK8IqqF9dUZF1f+6KrojL\nirjugueuLh4cXhyKKCqyInLJfV+lFEpbSulB7ztJc878/ihNU5r0zDRJ+7z+oSSTzJNPJjOfeU4y\nMtKJjx/YJvvraPFta66Kb01/k6bQJpzi8n//A3TM1SO9OlFQikgU3OvqRAHa74/THcTxqywRX2W5\nIr6yxULqU48TNn4CMY890ej2HT1REDMzCoIgCB2KVVfdd63iwP4mbW8pKbH9XZWerkiZPJlIFARB\nEATBCePlHArWrrL9P+tfr7qxNO4hEgXBK4jZGQVBcJlmNLgbs7OUK4eXEImC4B1EoiAIghuoxUy+\nIlEQvINsFUtNN4fx8mWKN20UNTEeQJZlsv/9FsWbNrq7KEJLqMRlst3MoyAIQq3MV19Gtljw79mL\nkGtHurs4HZsso086gz7pDFG/vcfdpRGaq4OvHAmiRkEQ2iXZYgFA0jc8eZjQBsQIdA/Udt+J9tRJ\nDJmX2mx/SvDqRCEhIYGHH34YgMzMTGbOnMlDDz3EokWLbNusX7+e++67jwceeIBdu3a5qaRCQySz\nCUPmJSzlZVx6dSFlpxPrbyROti1iv1qj4Cbi2PVauqQztvkTWkKWZS4vfZfMVxe6sFRtz2sThZUr\nV7JgwQLMZjMAixcvZu7cuaxZswZJkti+fbttmemvvvqKlStX8vbbb9u2FzxH3oplZL66kMsfLMWY\neYmzr/7TwVbiZNsSom+H6xkyL2HKvdz4hoJHkUwmtKdPIVssTc7dcv79Vqv2WXn0cKte7ym8NlGo\nWWa6RlJSEqNHjwZg4sSJHDhwwOky04J7SGYTlz9civ58cp3HtSeOA2C4kAaASl3/sBQ3ZS0j6UTT\ng6tlvrqQjL+/hKWioknbi1odz1C4fh2X3/sPpdu3ttk+q1JTbH9fnP/XOs/pEk97TZOE1yYKt99+\nOz4+tSs82s9EHRwcjFarRafTiWWmPYj2xAm0x4+R/eaShjcUnYdcRjIZxThwhaTPfQZDxsXGNxRJ\nrkeounKDYriYXufOw1xcpOBea89l5sLCOjV8Oe++4zVNEu1m1IP9+vM1y0mHhISIZaY9iBziR96V\nv+3jl+rri2zXJKRSqeosMw0QHRWMJjgYU0kpWeu/oecD0/GLiGibgnuhmtiVbNpIyaaNDP3HK0Rc\nM9z2vFLHryxJlCWcJmzwIHwCApxuV3Y6keA+cfiGet/vyP64VGdfpPOYawCwGo1c+nw1wZPvoHOv\nnrZtrEYjaVf+FucN12hJHLN9fTAB/n4+REWFUJPiXZz3PBM2brBtJ1utoFajUqnqfNct2X9lkB/l\ndv+PigzEx98fqD2OvOGYaDeJwpAhQzh69Chjxoxhz549jBs3Tiwz7WEqtUbb3/bxU2k0dRIFWZbr\nxbeoqBIfvcTlDz5Ee+I4eT9toducpwkdNUb5grcD+aeSMHeLA5Q9fisOHyRvxTJUfn7Ef7Dc4TaG\nzEtkvvoKft17EOewP4r30GoNtliW7dlFwY8/Ubh7L33/s9S2jWRo/0sUt6WWHr8Wa3UtgsFgpri4\n7utr3k+WJFKffIygYdcQ+5e5Tt/L0f5lWaZg9ecEDR1qOy9VVZnqbHP+kzV0nvb7Rt/LHTrEolDz\n5s3jvffe44EHHsBisTB58mSio6N5+OGHmTlzJo8++ihz587Fz8/P3UXtsFROJi6Rqqoaf/GVmkKr\nXQ1R7ofvO9lYqK9tmnOMWdXNHLKp+gQpyzLGnJw6Ez+VbvsZANPlHNtj5uJirDpdm5SxNbSnTjp9\nruYzW+yO0SvPKFgioalU6iu/gQY6PNV8h/ozp5v0nrLFQtZbr1N57CiWsjLK9+xq8LxUumVzvccy\nl/yzznnNE3l1jUKPHj1Yt24dAHFxcaxevbreNtOnT2f69OltXTTBEXXTLlaSwUDBurUOn1P5ePUh\n6z6q6jv5ysMHiZ79mGK7kU21tUayLFN55DB5Kz4i8teT6fz7BwCoPHig3usuznsOVCoGrPhUsbK5\nwuWl79Z9wP6a42QWTNER10NcuVEx5eU6zd2s2ubd3evPJ1OVfI6q5HP0eeMdRzt1+Dr7PnWGtFTK\ndu0k6q67m7XvttRuahQEb9D0u9qy7dvq/F+WrEiGKvBpuO+I4IRKRearCyn9eQslx04othvJUJso\nIMtUnT8HVDdJQCNL9HrlFbW2zLK1NlGQTCYk85VqZ6/8XO3QlX5s5ry8ek/V1HhdfPGv9Z5rSJ0R\nWs3phH3VMaFPPtes/bY1cXsmtB0nvyOfkNBGM/mL855HNpkIGja8we2Exlmr9C5viLCUlVYPc7Wv\nNZIkzIWFQO18Do6W6JXbyYW0Kq2261vaH5+srSFpJ5/P26nq3GRc9Z1Iki2RaBa797TPE3SJpwka\nOgxTfv2kxJEqD08URI2C4Hb+cXGNblPbduhg1kahUSpV3Qu4q2UsXEDBF2tsQ9AACtZ9gf7c2er/\nOJn4qWzndq+5kNZMi22vaMPXtr91CaeueoGMpbLCaz5fe6dqoDbS0eJpTUlgjVmZ9nuw/ZXz7juU\nbt2CPumM4xe66DdoystrcjLSGiJRENqMs04+KrGMa6tJBgO5K5Y1MIFL7UlMiRUlpSsdEe07ZVUc\n3F/7vJMOqwVfrHF5WZTSkrgVfrm2aZ11BcXV6d90dRLgKFFoYBbfisOHACje+L3dDupuoz1xrFnl\nq0pNbdb2ABkLXiTjby82+3XNJRIFwS3Kftlp+1utES1grVW+ZxeVhw+S9fq/MOXn19/A7iTWVtM6\nq5r6vbaDpbDNxcW1/7Grvak8crjejHyCexhznE885jAJbKBGoeib9QCofO2P8bqZgqGB/jiOaitM\n+blOt3c3kSgIblGwdpXtb5VG1Ci0llQzHNFoJONv8+pvYHfxsu90p6irzoXmkhLHm3lL1byTclr1\nuupRGzUa6NTmqPlCUJ4h4yJW+ym3r/oqTZdzmlVjZCktQZblOs0ZltLSphfI4bHkuTPSikRBcL9G\nZsFsSMnm/1G+d7cLC+OlGu1xbZcotNHFStJfNS+Cs5oMb08Url7zoYELjn2fBqFtmEuKyXxtUYPb\nZL2xGEtZWbPeN/WJP2CxS35NBc3oK+DoWPLcPKH9jXqYOnUqISEhAMTGxvLUU0/x4osvolariY+P\nZ+FC75hbW2iaom+/ASD8ppvdXBL3UjWWKNj3ZbySKBR+/RXqwEDXjt9u4KIvGQ2On/CWRMHpxElN\nP8NXHj9K5/tnuKY4QpM4utM3FRbUfcBBcmfVN3NBtSYex6bcy+SvrT/njydnCu0qUTBdqX5dtaq2\nWnvOnDnMnTuX0aNHs3DhQrZv385tt93mriIKjnjLdcKbqerXKJT+/BOASxOFhs6Vl175e/Nf5EFk\nyQXl9JLP2q44SKKLvv7KwYZ1v5vy3b80bz9NbLrI+PtLDh8v/fknwifc2Lx9tpF21fSQnJyMXq9n\n1qxZPProoyQkJHD27Nk6y08fPHjQzaUUapiLi9ElnUFkCm3A7gLlae3kdYeYeTAXXOS9pj9GC8iy\nTMEXq2uHxHoKBzF3eMxdPRDi6qazxnbTyk65pss5dTvFNmN/Sh9X7apGISAggFmzZjF9+nQyMjJ4\n4okn6i0/LZaZ9hw1HcCChg5zc0nagUaaHgq/XFs9OYzVimy1elSyUPzj/9xdhKZxxcm4/eYJmC7n\nULZzB2U7dzBg5WfuLo5N00f51P1yynbuaNZ+TNnZzdreYQkszodk1mOfmEiSorPWtqtEIS4ujt69\ne9v+joiI4OzZ2uxWLDPtXs6WbHU6KUkzdPTvwxjsT1Ej26jUamSrlexvviUkoXbiKlfErua7Vama\nfy20X4DHk79Hs7/MBQePGw/va/J7qJE9+jO2ht4YRs0sHkp/xua8/8E/vt2k7SKCWnc5rFnsrDU6\nRYUQ2MTPJplM1My8EN0pCLWCCx62q0Th22+/5fz58yxcuJD8/Hy0Wi0TJkzgyJEjjB071rb8dGPE\nMtPep6N/HzqdqdFt7CeQ0dpN7uLK2EkNTFLTFJ78PVoqHJctd1PTa0SsJlODn1Eym1FpNI13TvVA\npvK2WU67ueffmqHDjTn5//7S0iK5TEmJHkovYC0rIzB+QIPbSsbadVUKckvxCQpq1b4bSr7aVaIw\nbdo0XnrpJR588EFUKhVLliwhIiKCBQsWYDab6devH5MnT3Z3MQXBo5jy89F0ikTt64I7kjaazMkt\n5NbPP9HQbH+y1UranCcIHDCQni/Mb/W+2pz35TYeKWP+CwDEL/+k7qJTV7Hvo6A7c5qwsY3fBLdU\nu0oUNBoNb7zxRr3HHS0/LQjtSitO0hl/m4cmshN933S0TK5g44ouCg30DalJIqpSzrd+R+7gilEh\nHZ3977ixharsOzO2siavMe1q1IMgCC1jKXU8a6JQS/ERC17Y3GCv8tgRdxfB69kvqnb1KAp98rm6\nk0LZPe8bFa1oudpVjYIgdFjt5GZOlmXPbZ93QdNDg2+v8F2h0oq/2+DuIni9og3f1P7HLhEwl5aS\n/dbrqPz9iX9/GVA3kVD7+ytaLlGjIAjtgcIXsbZSb6lmT+LiGgVzcREWu+mftSeP2/6+9NoiLOXN\nm1JY8H72HS+rLqTVPn5lVVbZrgOj/W++dNtWRcslEoUOxJPGzguu5Yqloz3h+LCvem1I7vKPuPSP\nV+o8lvXW6+S8q2A/CxcnChfnPU/63Gdq395S2xHUmHGRki0/IUuSw+/WqtWSv2YV1iuTAtV8d5by\nMvQN9HGw6nSUbt+GubjhwbT2x4IsSRSsW4vhovPVEAXXkO2mOdeftRs2rq5fy2Z/XFQeOdTkfWhP\nniBl9iyMOTlNfo1oeuggKg4dIG/lcmKfe4GgwUPcXRyhEZayUkq3byNo4CCCh1/T+AtccBFLfepx\n4l5bgl9MTKvfq6VKt/2MX49YQq4diSxLaEIdz3vi6MRYlXwOqB42pkRVrKv6KFx6bRHGjIu2/1ce\nO4ImPIKKQwfqbFe27WfKtv2MOiiY/u+9X+e5C3/5EwDGzEt0mz2Hi/OeJ/T6G9CdOY2k09HnzX+j\niYig8KsvCRo0mJBrR1a/7s9PA1C4bm29SZEkswntsaP4Rnch6/V/EnXvfWjCw5HNZsq2b6Ns+zZi\nX5iPSq0msH98ndcWb9pY5//WykrUQUF1V1esrEATGlb9XHBw9bweFkv1kFC1mrxPVhB5+x3IZjPq\nwCAC4uKaH9x2xD5xdNhbuTmrXV6JPUD+qs/AaqVs5za6Pvxok14vEoV2QJYkynbuIGTUaHwjI+s9\nLxmN5K1cDkDZrp0iUXAhWZIw5eTg16NHg0OZmivrzSWY8/Mp3bKZ+BWfNtpu3/TZ5xqWv/ozev71\nRWSLharUFAyZl/Dr0gVTXj4Rt92O2lf5JcHzP/uY/Ct/95y/gMB+/es8b71SDQtgyMhAExmJJjzc\n9lja07OVmRnQRb367ZMEgNyPPmh4tw1MJWxIv8DFec8DUHm4dnp6w4U09MnnKN+1k7LtW+nz+ltc\n/vCqZOO5v9Br/t9QBwbhExxM0YZvKNteW4XtqM9B9huLARiw8jPMxUX4BIdguJRB8cbv6r73s/8P\nTadOyGYLUfdOpWDVZ3WeDx17PTGPzyb1qccBCBwwkKqU82iPH7Nt40mzO7pDnRo++7VaJKk6ybrq\neJTMJodDnCuPHSH3ow/oPPMhIm+5rTbnaMbhLBIFN5CtVgq/WU/YDeMJ6NW75e9jsaBPPkvBl2sx\n5+dTuG4tsX99kaCBg5AliarUFLTHj9aZitSQno65uBjfqChXfBSP0dJOcLLVWueup4ZkNpO56GX8\ne/Wm25NPAWDKy0Plq7H1MDYVFtjGPEdPnUanO+/CkHERbcIpon57T6sSB3N+vu1v2WJG1dgcBy5o\neoDqqv/KE8fJ/eC/9Z6TrRbbAlK5yz+k8shhou65l6jf3uOSfTuStfg1Yl+Yj6TX49etG35dYzCX\n1M6Hn/naK0D9i0rK44/SbfYfCR0z1oWlcV+P0ZTHH0UdHEz3P/4/AgcMbHT73I/qJgV5K5fXS1Cs\n5WVcfPGvAMTMeqJOktCYyx99gLaRUQ41SzBfnSQAVB45TKjduH+vHRKqINlc21/h8vvv2v4u3bKZ\nTnfeVe83X/jlWrr+3x9s/9eePIGpIB9DevV8ouU7d1xJFKrPk3Iz+jV1iERBlmVeeeUVzp8/j5+f\nH//85z/p2bOn28qjTThlq1bs8uDDRPzq1ma/hzE7y+FqfNlvLmnwdZbSEi7Oe44+r7+l+JCaNmW1\ngqbph7NVr6f4h+8o276NmMeeIGz8hDrPm3JyMOXlYsrLpfLIIcIn3UL5rp11trFvEij69huMOTm2\nuzq/mG6EXe+aCVBkkxkaSRRcVaMAOEwSAIq//5bi77+t+9jG79A4qMVypZq72IakPj273mO5yz5w\nbaLg5gWdJJ2u0d+3M1WpziZQr5b38YpmvV9jSUJTXF76buMbdWD2My/a3zgUffsNne68q17fFWNO\nDla93jZD4+X33wMg+Eqzk62G4srrVKqm38io5Pa8nNkV27ZtY+fOnSxevJiEhASWLVvGBx84r+6r\nmR608vhRcj98n5BRo+tUiTXGN7ozktmEtby81WUHqifduPLlaqKjCbl2JGXbt7X6bVW+vvj3jsOQ\nVj2drzooGL/u3bFWVhDQpy+VR48QPuEmyvfsAiBo2HBb+7W5oADDxXRCx1xP2c7tBMYPwFxUhLWy\nAtliIWjoMKpSziObzUTcejtlO1pf3ob49YjFlHNlURa1mohbbkWfnIwpOwuAsJsmUrF3j9PXqzQa\nxTrzRdx2O5WHD2OtrO7hHjR4KH49ulO2YzvIMhG33V6dmOTn4d8jFl3i6QbfTx0YSOjYcc1fBtfD\n+Mf1qXeXqwSfkFBCx41DtlhtyV74pFuoOLgf/9iemAvyCb2S1FWlpdnKpA4MRDIY3J4gCLUC4wcg\nVZRhKi6x/V7DJ06ynaNqKPF71kRHYylqbEUVBVxZzK05mnrNqrkBCp84iWHP/T+n23WIRGHJkiVc\nc8013HnnnUD1ctN79ji/aORdqs7eLjzzxzYpnyB0RF3/8Dj5n650dzGEDkwdGIhUVVXvcU2nKCx2\nTVyd759ByKjRFH2znsojhxt8zy4P/R/hN04k+z9v2zrYNiZy8p2UbtncvMK72ISNzufB6BBND1qt\nltDQ2gUvNBoNkiShdtKGfHWC0P3p/8fl92urYyPHjKL0aPWYZ3VAAJLBQKc776puE8q9TPR9vwe1\nCt2pk1gqKjDn56EOCiJk5Cgq9u8l7KaJSDod2hO146aDhgxFfzYJgNAxY5FMJtuY8vCJN1O+fx9Y\nrURPnUbQsOGYCwso37WLsPHj0Z1JRB0QiH+v3lTs24NKo6EqNQVNZCQhI6/DN6YbwUOHYczKpCo1\nFXNxEVG/vQfdmUQ0YWHkf/4pwSOvI2jwEHwCA5GtEpqICIzZWQT260/53t1Yq6oIH38jmit9G8wF\n+VgrtQT07UvB2lVE/OpWrHo91spKrBXlBA+/BsPFi9Wvu/Emcpd/iH/PXkT95rdoT53EXFCAf+/e\nhFw3moyXXiDsxokE9utPyMiRRHcJ59IvB6g8egS/mK74x/YioE9fJL0elcYHn5BQDJcuUvT9d3Se\n9ntUPhos5aW2TmFBQ4YSfe99FP/wPbrE0wRfO5LI2+8gb+UyLKWltpgHXzsS3amTdP3D41i1lZiL\nipCNRjQREaBWETzsGiRDFVUXLlC2YxthN0wgcOAgfCMjkS1WVP7VzQHFP3yP7tRJfLt0Jfre+yjd\n9jPGrExCrh2JZDYTdffvqDqfjKWset8Bffvj27kzlQcPIBmNhE/6FabcXCSjAZ+gIEq3bSWgbz8k\nvR7tiWNIVVUEDhp85fNrCB09hvID+221JQB+3XvQ9ZE/4BsdjWQwUvy/jUTfcy/ahFME9o/HkJ6O\nT0gIqNVEdg6nICmVog3rba8PGTWa8ImT0J1OqFf7Ez39fsxFhUhaLQF9+mHIzMBaWUlA7zgC+vSl\n6Ltv8O0aQ+jIUVjKywm/aSK60wkEDRmKuaQEY0Z69d1YcTEFa1fT5eFHCLn2OjTh4YSOGo0pLw//\nnj3JWDAfc2EBYTfehGyx4BsVjU9wMIXr11X/bqOi8O0UhSkvD9lipusjf6DiwH50pxNsZe296J8Y\nLqRRvn8vhgtpBA0eQvC1IwmMH4BsNqM9eQKVjw/B14ygKjUFtX8AIBNwpcOkMSsTQ0YGhrQUQq4b\njWQwoE8+h9rPj6Chw6g4sJ+QUaMJu34choyLmPLzUfv54RMeTsW+PXR7cg6WhGPotAZksxlTXi6y\n0UTo9dej6RSF9tgR/GN74hMaRsWRQ4ReNxpd4mlUfr749+yF4WI6lrIyqpLPEXbTRCylpQT2jydw\nwEC0J47ZaqH8esQS0KcPksGALvE0YeNuIGjwEMyFhVSlXyDkmhGoNL4Yc7IJ6NMHv5huyGYzmshI\nJIMR3elT6M4kEjLiWqrS0pAMVYSMGoM+KRFTfn71963TEXnHFKyVlWg6RVYfowYD+qQzhI4dR+j1\n47BqtegSEwgbPwFNWAT+sbGYiwqRrVJ1J0yVCu2pkxgvZYCPhqi770Ht54cxM5OCL9cAEDb+RgL7\n9cc3JobMVxfSbfYfKfpuA34xMfh171F9XlSr8Y2KInT0WAJVZoqSzttqCHvOX0DeimVIBgNWbXVt\ncKe77kZ/7ixWnRafwCA63XU3/j17oonsRN7yDzHm5oLVSu9X/4mlrIzKg/tR+fqiO51AzKwnAZnc\nZR8SPe33+MV0qx7BoVLR7ck5dP2/P2ApL0MdEED+Z5/QecZDaMJCMVy6hGyxEDx0WHW5np+H9uQJ\nNJGR+PfshVWrxScsDKmqCmtFOVVpaQQNHlzdmTQoCE1YGIXr1xF6w3hUah8kowG1n391HHrEUnnk\ncHXTplqNSq3GJzSMgL59q3+3iafRhIdTcfAA/j17ETLiWvSp5ynf9Qs+oaEExg9Ae/wYYRNuIvia\nEZjz8wjo0xeVnx/6s0kEDRnq8FpYo8PUKFx77bW2BaEmTZrErl273FsoQRAEQfACHWLCpeuuu47d\nu3cDcOrUKQYMaHj5TkEQBEEQqnWIGgX7UQ8Aixcvpk+fPm4ulSAIgiB4vg6RKAiCIAiC0DIdoulB\nEARBEISWEYmCIAiCIAhOiURBEARBEASnRKIgCIIgCIJTXjvhksVi4aWXXiInJwez2cxTTz1F//79\nefHFF1Gr1cTHx7Nw4UIA1q9fz1dffYWvry9PPfUUkyZNcm/hBUEQBMFLeG2i8MMPPxAZGckbb7xB\nRUUF99xzD4MGDWLu3LmMHj2ahQsXsn37dq699lpWr17Nd999h8FgYMaMGUyYMAHfNlguVxAEQRC8\nndcmClOmTLHNtGi1WvHx8eHs2bOMHj0aqF7PYf/+/ajVakaNGoVGoyEkJIS4uDjOnz/PsGHD3Fl8\nQRAEQfAKXttHITAwkKCgILRaLX/+85959tlnsZ8SIjg4GK1Wi06nq7POQ1BQEJWVle4osiAIgiB4\nHa9NFAByc3N55JFHuPfee/nNb35TZ5EnnU5HWFgYISEhaLXaeo83xGJp3pKegiAIgtBeeW3TQ1FR\nEbNmzeLll19m3LjqteQHDx7M0aNHGTNmDHv27GHcuHEMHz6cf//735hMJoxGI+np6cTHxzf43qWl\n+gaf79w5lMJCUSuhFBFfZYn4KkvEV1kivsro3DnU6XNemygsW7aMiooKPvjgA95//31UKhV/+9vf\neO211zCbzfTr14/JkyejUql4+OGHmTlzJrIsM3fuXPz8/NxdfEEQBEHwCmKtBwcay1ZFRqssEV9l\nifgqS8RXWSK+ymioRsGr+ygIgiAIgqAskSgIgiAIguCUSBQEQRAEQXBKJAqCIAiCIDglEgVBEARB\nEJwSiYIgCIIgCE557TwKgiAIgtCQkyeP8/LL8+nTpy8AJpOJcePGs337VtasWQ/Atm1beO21hWzc\n+DMRERHk5eUyf/5zfPrpF7z44lwqKirQaDT4+/vz5pvv8skny4mKiuaee6ba9jN79h9YtGgxMTEx\ntsemT7+bL77YwOrVn7Jt2xY6d+6C1WrF3z+AOXP+RHz8QH766X+sXPkRPXrEIssyOp2W4cNH8Oyz\nL3Dy5HGeeeYpXnnlX9x66+22933kkQcYOHAwL720kF27drB27eeoVGpuv30y06c/AMBjjz1ESEgI\nAN26dWf+/JdbFUevThQSEhJ46623WL16NefOnWP27NnExcUBMGPGDKZMmSKWmBYEQXCzAzsvkJ5c\n4JL3UvuokawSfQd1Yfwt/RrdftSoMbzyyj8BsFgszJgxFX//ACoqygkLC+fQoQNMmnQrhw7tZ/Lk\n33DixDHGjZsAQHZ2ti2haIhKpXL0qO2vBx54yJZYZGZmMH/+83z22ZcA/PrXU5g9+2nbtnPmzOL8\n+WQAeveOY8eOrbZEIT09DYPBAIAkSSxb9j4ff7yGgIAAHnpoOnfcMYWAgEAA3nvvo0bL3VRemyis\nXLmSjRs3EhwcDMCZM2d47LHHePTRR23bFBUViSWmBUEQOjD7OQW1Wi0+Pj4MHTqM06dPMWHCRNLT\n05g37+988cUqJk/+DSdPHuc3v7mb0tIStNpK5s17Fq1Wy4MPPsL48Tc2uo/G9OoVx8CBgzl9+pTD\n8ul0WkJCQtDrdfTrF09WViZ6vY6goGB+/vknfv3rKeTn56FWq1m79hvUajWlpSVIkoxG40taWgoG\nQxVz5/4Jq1XiySf/yNChrVst2WsThd69e/P+++/zwgsvAJCUlERGRgbbt28nLi6O+fPnc/r0QLtD\n2gAAIABJREFUabHEtCAIgpuNv6Vfk+7+m6K5MzOeOHGMZ555CpVKhUbjy1/+8lesVisnTx4nKiqa\nQYOGMGjQYNLSUpBlmZSUZF588e8UFxcxY8ZDTJ8+g/LycubMmcWQIc6vHQ4rFZyIjIykrKwMqG76\nSEpKpKiokODgEB55ZBY9esRSUJAPwKRJt7B79y9MmXIX584l8dBDj5KfnweAWq1m9+5feOed1xk/\n/iYCAwMJCAhg5syHueuu35GVlcnzzz/Dl19+W2fRxOby2kTh9ttvJycnx/b/ESNG8Pvf/54hQ4aw\nbNkyli5dyuDBg8US04IgCB2YfdNDDb1ex5o1nxEcHGxrZhg6dDj79++lR4+e+Pj40KlTFPfccx9q\ntZrIyEgGDBhIZmYG/v7+mM2mOu9XVaXH3z8ArVZr6xsAzpokIC8vl0mTbiU7O8vW9JCbe5nnn3+G\n2NhedV5/++2TefPNxXTr1p0RI0bWq724+eZfcfPNv+K11xayZcuP3HbbHfTo0ROAnj17ERYWTnFx\nEZ07d2lxDL02UbjabbfdZksKbrvtNl577TXGjh3b7CWmASIjg9BofBrcpqF5sYXWE/FVloivskR8\nldXU+EZEBBEQ4Otg+1CCgwNJSDjO00/PJjg4mNtvv4W1a9dyzz330LlzKHv37mX16tUsX74cnU5H\nVlYGo0YNJzDQhxUrVvDkk4/h4+NDZmYmkmQlPr4nU6dO5b///S9RUVGo1dCtWyTBwf6EhPjbypCa\nmkp2diaTJo1n48aNBAX50blzKJ07D2TRoldYsOBFNm/ebCv7NdcMxGo1sWnTBp577jkyMzMJCPAl\nMFDFU089xSeffIKfnx+RkWGEhQWyZ89Wzp8/z8KFC8nPz8dkMjBoUJ+OWaNwtccff5wFCxYwfPhw\nDh48yNChQ1u0xDSIZabdTcRXWSK+yhLxVVZz4ltWpsdgMDvcfujQEZw8eRy9XkKvr2TQoBGcOvVX\n5s9fRGFhJYMGXUv37ruZOnUaPj4+PP74HzGbfYiPH87AgUO5++7fERISgizLvPTSKxQWVvLww48x\nZ87TyLLMgw8+SmFhJTqdkY0bP2Hjxk2oVGp8fTUsWrSE4mIdlZUG9HqTrXz9+w/juuvGsGTJW9xw\nwwRb2SdOvIWff/6JoKBOlJefx2AwU1Ulc8std3D//TPw9fWlX7/+jB9/C1arlQMHDvP73z+ASqXi\nr39dQHGxrklxdcarV4/MycnhueeeY926dSQnJ7No0SJ8fX3p3Lkzr776KsHBwXz99dd89dVXyLLM\nnDlzuO222xp9X7F6pHuJ+CpLxFdZIr7KEvFVRrtNFJQiEgX3EvFVloivskR8lSXiqwyxzLQgCIIg\nCC0iEgVBEARBEJwSiYIgCIIgCE6JREEQBEEQBKdEoiAIgiAIglMiURAEQRAEwSmRKAiCIAiC4JRX\nJwoJCQk8/PDDAGRmZjJz5kweeughFi1aZNtm/fr13HfffTzwwAPs2rXLTSUVBEFo/0qLdZhNFncX\nQ3Axr00UVq5cyYIFCzCbzQAsXryYuXPnsmbNGiRJYvv27bZlpr/66itWrlzJ22+/bdteEATBW+Vf\nruDAzrRmLW+sNJ3WyLoVR9mw6oS7i+KxftmczJcrjri7GM3mtYlCzTLTNZKSkhg9ejQAEydO5MCB\nA06XmRYEQfBm3646QcKRbC5nlrm7KDbGquqahNKihtfK6ciST+dRVux98fHaROH222/Hx6d2hUf7\nzDo4OBitVotOpxPLTAuC0G5ZLJK7i2CjUjteUlnwfu1m9Uj7JTRrlpMOCQkRy0x7KRFfZYn4Kqut\n4hseHugx36Wa2kRB6TJ5ymdOOJqFLMO1Y3s263XR0SGoVN6TWLWbRGHIkCEcPXqUMWPGsGfPHsaN\nGyeWmfZSIr7Kag/xlSQJi1nCz9/zTmFtGd/y8iqP+S73bk2x/a1kmTzp+N247hQAPfpENOt1hYWV\nHpcoNJR8ed6vrIXmzZvH3//+d8xmM/369WPy5MmoVCoefvhhZs6ciSzLzJ07Fz8/P3cXVRCEVlr/\nyTFKi/TMfuFm1KLK2yOcOXHZ3UXwCHqdCZUKAoOcX2tkWfa4RKEhXp0o9OjRg3Xr1gEQFxfH6tWr\n620zffp0pk+f3tZFEwRBQTUd5iSrhFrdcDOhILhCdkYJhXlaRo7r5eC5UmLjIgH4/L8HAJjz4iSn\n7yXLUJhXSXCoP0HBnn/z6rWdGQVBEAShrWxad5pDu9IxGurPE9Hc0Se6SiPffHbcllR4OpEoCIKA\nJEkkHM1CW2l0d1HatfLSKhKPZzud/0BbYcBssrZxqYTmcPTdSVL90SfWBkakrP3osEvLpDSRKAiC\nwPnEfA7suMD/vkpwd1GaxXOmG2qarz89xr5taeRcqn8Hqq0wsPqDQ6x8Z6+iZSgu0KLTujYhrCw3\nuPT9PFlTJ7m6dKFY4ZK0HZEoCIKA/sqFwxsmy5EkuxO1l2UKNbUFhiozZpO1zkVn9QeHmv+Gzfz8\nsiyz/pNjrFp6sPn7asCaD1tQ9nbEYq6uPbCvDTqwI42Ksirb/8tKPP+35YxIFASPZTZbXX7n0xyV\n5QaHVYqCcjJSixpt761bNV97pZQkmcRj2V7RfHJgRxor39nL5q8THT6/YdVxtmw44/IpmlvyftpK\nIz98eYqifG3jGzdT8ulctm8661FTUTfGUVETj+dgMVvr1AZVVhjrNDFoKxzXunjDZxeJguCx1n54\niFVLD9a9g2wjxQVa1nx4iK3fn23zfbuFhwzV+mnDGTZ+carBbeyLan+OTTubz77tafxvnec3n+i0\nJgAy00scPl9wuZKLqUVkXXT8fEvJdnlvZnrTqsaP7csg51IZP393xqVlAfhl83lSkwowVHnuGjyS\nJHNkz0Xb/08ezHS4XXlplcPHZVkmL6fc6Syax/ZltLqMShOJguCxqvTVJw/ZDYlCYV71hC4XU4ra\nfN9C0xUV1N7lVlxpJy/1srn0zWar07vKtLMFjb5ekiQuJBdiNlk4fuAS58/kOd22uLA2Xj+ud1yb\nUf/95Tr/uor9Z/bUm2pZlvl86QGOH7hke+z0sWyH267/5JjDxxOOZPPd6pPs2ux4naFj+y85fNyT\nePU8CkLHIMkybT5S3kPusNuKN31c+7KWl1TRvWf1rHhH92a4p0CttPLtvfQd2NnhczGx4Y2+/uyp\nXPZuTaX/4M6knSsEYOCwGGRZRlthJDQ8AACrVWLD542v7FhZbiAkzL9VEwLptUb8A3zx0dS9F826\nWIJaraJH78g67ffuuBlojMlo4cShTAz61tV2HPzlAlB74+ON2l2iMHXqVEJCQgCIjY3lqaee4sUX\nX0StVhMfH8/ChQvdXEKhudxxEmnqKbIwr5KIToH4+rW7n5JX2LctlayLJfz6d0PdXZRWST9f6PDx\nPT+nkJNZxu13D3H4/KX0YpKuzIhYkyTUOH7gEkf3ZnD7PUPo2j3MaYdDi9lKSZGOLt3CuJhSyJZv\nkwiPDGTm7Otb/Hk+X3qQ8MhA+g/uwoBhXYnoFATA/746DVRPRmTf/cdqlTi6L4OhI7t7zAREB3+5\nwNlTuW2yL6tVwsfHcyv4PbdkLWAyVbf7rVq1ilWrVvGvf/2LxYsXM3fuXNasWYMkSWzfvt3NpWx7\nkiRxMaXIo9sBG+KWzj52mYIkyVitUr2OjcUFWr757Dibrpz8vJn93WP+5QqyM0rbvAz2x2dNxy+j\nwUxGapHTamqLpbravb2S5ermB1mWHY7LT2pg2uSa5xKPZ5NwJMvpdls3nmXD5yfIzijl0oXqPhHO\n2tsd/Q6cKS+t4viBS3y/9iRQtzNf9RTGtdsmHsvh2L4Mj5qAqC1HAO3fkdZm+2qJdpUoJCcno9fr\nmTVrFo8++igJCQmcPXuW0aNHAzBx4kQOHnTtsCBvcPZkLlu+PcOn7+532nnKk1mt7q1RyLlUyuf/\nPcDn/6177NS0hefnVLRhyZSRm1Vu+/vbVSfYtC6B0iJdnW2O7cvg+P6MNinD5q8TSUnKZ/M3ify0\n4UyjY9KLC1zfI9+TbPziFMvf2oPV2vRROHpd9Y1TXnZFgyNBLqVVxzY3q4xzCbV30KcOVycX9r+F\nT/6zr9kX8yqdmQ+X7Koz/PPY/kt1klN3jm5ypiC37X7XGamePedCu6ovDQgIYNasWUyfPp2MjAye\neOKJOnciwcHBVFY2vuqYNywznX+5gu/WnmDqQ9fRpVvDS2fv3ZZq+zsjpYhR1/dWunitZh9fjdqn\nzeN9ObR2iF5wsL9t2lb7cuTZXdj8fTWERQS2XQFb6ep4OroQr1t5lN9MG87pY9k88sfxHL3SO3vy\n74YrUqaS/NrEpLhQx45N52z/txglW5kd1Yzt+qluRzGVrCI4xK/BhXmU5OrjtSaJCgsNbFLV/NX7\nd9YpNzIyyPb31Z3qDv5ygZFje2EyVg9H1VZUX8wtZqnO+5vNzZ9J8ti+DG6Y2M/2f/taoabEri3O\nB33iO5OW7Lgzqav37++vcfs1pSHtKlGIi4ujd+/etr8jIiI4e7Z2eJtOpyMsrOGLKnjHMtMbvzpJ\nQV4lm75O4K77R3BgRxr9h3Qhpkdt5yerRapXpZV06jITJw9o6+I2y9XxLSqqxC+wbbszVpTXVr2W\n23W6ysstt3XQst9m7YpDTHt0dNsVsBWac/z++E11z/iU8/m2x75edYy0cwX0H9yFSVMGuqxc5eWO\nq7sBtFqDrcxGQ/1E4eqq8g9e/wVoeGEepSh5figqqiRQ33iiUFDQtLvhf83b3ODzNXG8Wn5+OXu3\npjJ4RDeSE52PsmhIarLj1xUUVDTYkbKtzr8BQb5On9u7M5VBw2Nctq+SIp3brykNJSrtqunh22+/\nZcmSJQDk5+ej1WqZMGECR44cAWDPnj2MGjXKnUV0oeofkixX9yROPJ7Dd6tP1tki9VwBSSe9f+lX\nd/eItq+VOrqvdjy1fbHKSpxf5NqD9R/XDv06l5CL2WStU02ttOLC2tqGmjtce2ofzx62cXh3OglH\nnfcTaKrNXydy+qjj4Xn2Pnp9d6v31ZDPlx7k7KlcNnx+osF+Eg3Z8m2Sw8c/en03Jw85nqugLUVG\nBzl97pcfkxschtoSnjwNdruqUZg2bRovvfQSDz74ICqViiVLlhAREcGCBQswm83069ePyZMnt2of\nyYl5bE5O5I6pQ93aS9X+tGg/u1t2RinhkYGEhgc4XZTEaDDjH+A8W/Y07ujLaL9P+0QhOTEPXz8N\nI8f1qpPAuDuZcZdtP5wltnckg0d0U3Q/KWfysVokfv27oSQ6GMeuVnv2Pc8JJ5P0NFdBbiUFue69\n8wRaPWSwMYd2pTtcztmT7Pxfskvfb82Hh9xSA9YU7SpR0Gg0vPHGG/UeX716tcv28cuP1QdHcYG2\n0b4BirqSKeiu6qS06cqsdHNenIRa7fguKyUpn+GjYhUtnis1tAqbUmS7qYHtZ2es0pk5suciZcV6\nuvao/f4tFomKsiqv6qdQo0pvavFr084WkHa2gIHDY5web65S047tqE1c6X0L7Yskyaz+4CD9B3Vh\nwm39HW/UMXN/hzw7Dfdg7p5JrKYNz9kwppSkfKedjBytXOfJNn+T2Kze3i7RyPebkpTP0b0X6zy2\n9qPDfPzvvRzek65gwZpGW2kk5Uxek4aWumLGvbYawirLMpIbRsEI7YPZZMFitmI0mNFrTU5nWYS6\nNwsdnUgUWsjTF/LYsekc+7c7Hpt7MaWIC05687qTLMucPJxJyVXD8gD02rp3vRVlVQ47tbmuLI1v\nY6iy1HvMZLRy4kCm2+es+G71CXb8L7nRBZaAVs3AV6M5yYbVKnHi4KX6bbJNKMb/vjrtsPOcJ7fv\nevq5oqMoyq9k5Tv7WPF23WW87WeIrEN8bTYiUWgpt9cotO717ljsyGK2sur9g/U6KpmMlurJXtKK\nOfRLOh+/u7feayvKqmzjmiVJYu1Hh/nsPeUmZ2ntyf3Td/e7qCQtUzOULf9yBXu3ptqGdzriiumb\nr24Cqy2HgY//vZeUpOpRE+fP5LFx7SkO777Img8Pcb6ZPeabOxFUW67+Kcuyw4TJHYuaCfX98mPt\nEFr7n/fajw47bN70hm+ttEjXJklyu+qj0JbcfZegdGciJZQU6dBVGut1VNr4Rd0lbKv05nqjNX74\nsrrvxR33DuXn76p7Syt5Aj5zIkex925Lh3dXN4/4aNSMv6VfI1u33JfLjzjsiJV6tgCT0cqOTecY\nMLRrvQ5gO39MZqALh5ldzWqVcWU/x7OnLlNWUsX1E/vUW8dg5Tt7sZglHnvmRtS+Ki4kF9InPhqr\npfnzDAg1sze6ru+J/QJiV5+/LRYrPho1kiRjNlnwD/B1+zm+KdatPAooPwxYJAot5O5jyGTyrJNP\nWYme0LCAeidPe/bPbf46kV//bggaXx+H69zv+TnF4XvUJAk1DuxIIzDYz+U9pNty+ta2YKwyo9ca\nuXC+kF/9elCd51x5LEuSzIbPjjN8dA8GDIupc3LOznA+K+ihXRfIaUIzSbPLY5Upq2z82Gyq3Vuq\nj8uEI1n0H9KFCbf0IyjEn4qyKizm6rvST97bZ9ve8SwEQlNYrVKjE9+11NWjlC5dKGHA0K788OUp\ncrPKmTJtmMdXKdgnMpIkK9qhVyQKLeTubLNbbLjHtMuWFOn4auVRYuMi+e0DI+o8Z38A26+7fulC\nMes/PYa5lQlPwpUx5Z44lMposOAf0DY/MaPBwtF9Fxk6sgdJTmpDNqw6gbbCyL5taTz89A3kZpVh\nNlvp3S/KJWU4sucifv4+FBVo+WXzec6cuGxbrhtg0zrHa2Ic2pXOyUOtn2PAkeICLRu/OEXPPpHc\ndf+Ies8bDWZKivR0a8IqjSZj3eabmhEfk6YMrDczpNB6xioLmlDXJApX11AWF9TtB7Vj0zn6D+5s\nmwHzp2/OKFoD50zNdeXInotkpBXz+8dGO6xVOXU4s84IK7PJqui5RiQKTuzYdI5LF4r5w58nOPyi\nlM4TrBYJVDidqyEsIkDZAjRDzZoAV7cfnz6azf4daTzwxFhyLpWSfr7uNLLlbpikSJIkJElW7E7F\nXvV8FW3zEzu2P4PEYzkkHnOcJFzdAXD1+7XrVsx4cqxLynD8QN0pgO2ThIYoOblO/uXqfi1ZFx33\nbfhuzUlKi/TcP2sMnToHN/heW793PEGQSBKUsW97GnfcW39VUL3OREpSPvFDujSpaSLp5OV6NZSb\nv0mst93V/bbKStq+VtFitrLjf8m2KbeNBgtff3oMXz8fHni8+ndqtUoc/KXuyCqTUdmbkg7RmVGW\nZRYuXMgDDzzA//3f/5GV1fjdS0pSPkaDxdYpzJWsVonDu9MpLtQ67R2//K09fP1p7Wx4FWVVdTpm\nubvpw5maFeISj2fbpo9et+IIe7emNvQyl6koq6p3Udz6fRIfLtlFdkYJy97Yw4q39rZJjVBr53/Q\nVhiwNLF9++oFnJrjy+VHWvxaT1fVSF+emiamrz4+2ugoGmfJhqAMZwt9bVh9nB2bzpF6tv7Ircpy\nQ72h1M6aMa929XoYbbXE9NX7tC+HxWxFW2GktEjPd2tOYjRYHA5v3/mjayd/uppKdncdehvYtm0b\nO3fuZPHixSQkJLBs2TI++OADp9tv/i6xTjX50JHd6dYznCqdud7aCUNGdqd7z8arLaF6/oLiAm29\nmdW6dAt1OtvaoOEx+AdobFXs/QZ1JjDIlzMtnDbVXtceYXSJCcVikejeK6JO7/eCy5WcP5PHNWNi\nCY+sP4lQzqUy1GoVvn4+lJdUcTG19uAOCvGrN5xRaYNHdKOsRG+rOgwO8WPszX3x8VGx/Ydz9ba/\nefIAju7NoEpv4trre+If6EtRnpbuvcLx89c4fE1L9O4XhY9GZatNGTWhN5FRtVPDyjJkXigmNCIA\nPz8NqKpPWPYrUsbEhpGfU0FoeAAqlYprxsTiH6DBZLRyYGca3XtGeOWqoG2tZ59Isi6W0m9QZ4JC\n/LBaJIcXg06dgxk6sjvFBVqqdGY0vmoqyw3ktYNVQr3NNaNjMRotBAb5EhoeUO+G45rRsWSkFVFR\nVrcZNi4+isioIM6cuNzq5s0av31ghG1Cu7bSd2B0vZpYZ2LjIikt1qGrNBEbF0lAoC9p5wro3S8K\nja8ajUZNdkYpVqtMt9hwfP196D+4i63psaG1HjpEorBkyRKuueYa7rzzTqB6uek9e/Y43f7V5za1\nVdEEQRDaDV8/H5ddmD1JXP8opkwbTvr5wnodqjt1DqaksH6NXkRUEGVXlqL3D9A4HKIcHOpH/JCu\ndO8Z4bA5pC2EhPkTFhHI43++yek2HaKPglarJTTUbtlijQZJkpzOD//y279tq6IJgiAIXqJz51Cu\nv7GvIu89+oY4Rd7XFTpEH4WQkBB0utqMr6EkQRAEQRCEWh3ianndddexe3f1squnTp1iwIABbi6R\nIAiCIHiHDtFHQZZlXnnlFc6frx7GtHjxYvr06ePmUgmCIAiC5+sQiYIgCIIgCC3TIZoeBEEQBEFo\nGZEoCIIgCILglEgUBEEQBEFwSiQKgiAIgiA4JRKFJrBfY0FwPRFfZYn4KkvEV1kivu4nRj04sHLl\nSkpLSxk8eDB33XWXu4vT7oj4KkvEV1kivsoS8fU8okbhClmWsVgsLF++nOTkZO644w5WrVrFhg0b\n3F20dkHEV1kivsoS8VWWiK9nE4kCoNPpUKlUaDQaMjMz+fWvf80111zD888/zw8//EB2dra7i+jV\nRHyVJeKrLBFfZYn4ej6fV1555RV3F8JdzGYzb731Fps2bSI7O5vevXuj1WopLS1l4MCBxMXFcfr0\nadLT0xk3bhySJKGyX4tZaJCIr7JEfJUl4qssEV/v0aFrFH744Qd0Oh0vvPACly5d4rvvvqOsrAyj\n0UhiYvWSn08++SRbt25Fr9eLhaSaScRXWSK+yhLxVZaIr/focJHPzMy0/X348GEmTpxITEwMM2fO\nRKVSYTQa0Wg07Nixg5KSEgoLCxk3bhz+/v5uLLX3EPFVloivskR8lSXi6506TNNDaWkpb7zxBl9+\n+SVZWVn4+PjQt29fPv74Y6ZOnUp0dDRFRUVotVrGjx9PaWkpa9asYevWrdx7773069fP3R/Bo4n4\nKkvEV1kivsoS8fVycgfx7bffyi+99JIsy7J85MgR+Te/+Y2ckZEh//Wvf5W/+uorWZZlOTMzU549\ne7ZcWVkpy7Isp6enu6283kbEV1kivsoS8VWWiK93a9dNDwkJCeTk5ABgMpkYNGgQsiwzZswYbrnl\nFt577z1mz57N8uXLycvLIyUlBV9fX3Q6HYBYiroRIr7KEvFVloivskR82w+NuwughJKSEubPn48k\nSUiSxIwZM/Dz8+P8+fNUVFQQHh7OM888w8yZMwkPD2fWrFl8/PHHJCcnM2/ePLp27eruj+DRRHyV\nJeKrLBFfZYn4tj/tMlE4deoUMTExLFq0iBMnTvDll18ydepUNm/ezJEjR7j55pvx8/Nj9OjRVFRU\nMGPGDMxmM76+vu4uulcQ8VWWiK+yRHyVJeLb/rSbRGHTpk2EhITwq1/9ik6dOpGRkYHRaOS6667j\n0KFDpKWlcffdd7Nz505yc3NRqVQcPXqURx55BEAcpI0Q8VWWiK+yRHyVJeLbvnn9qIeioiIef/xx\nqqqqSE1NJSUlhaCgIIKDgykqKmLAgAH07NmTpUuXMmvWLIYNG0ZSUhJ5eXnMnz+fbt26ufsjeDQR\nX2WJ+CpLxFdZIr4dg9cnCtnZ2ZSXl7NgwQIGDhxIfn4+R48eZdCgQZw7d45+/foRExPDiRMniIiI\nYOTIkYwdO5abb76ZsLAwdxff44n4KkvEV1kivsoS8e0YvK7pQa/Xs3TpUvr06cO1116L1Wrl4MGD\nAPTs2ZPrrruOjIwMLBYLERERLFy4kH79+pGens7AgQPdXHrPJ+KrLBFfZYn4KkvEt2PyquGRBQUF\nPPfcc2g0GoKDg3n22Wfp3bs3nTp14vPPPwega9euREREEBwczJNPPsnUqVPp0aMHn3zyCTExMW7+\nBJ5NxFdZIr7KEvFVlohvx+VVNQpqtRo/Pz/+9Kc/4efnR2JiIkuWLGHhwoU8/PDD3HfffXTq1Ini\n4mLbTF533HGHm0vtPUR8lSXiqywRX2WJ+HZcXlWjUFZWRpcuXUhOTgZg3rx5HDhwAIvFwv3338/r\nr7/OnDlzyM7Opn///m4urfcR8VWWiK+yRHyVJeLbcXlkZ0az2cy3336LXq+nS5cu+Pj4ABAREcHe\nvXuRJImYmBiCgoJQq9Xs37+fuXPnMnjwYCIiInj22Wfp1KmTmz+F5xLxVZaIr7JEfJUl4itczeMS\nhfT0dJ588kn8/Pw4ffo0GRkZ9OrVi+DgYFQqFQEBARw5cgS9Xs+QIUPYt28f/fv3Z+DAgYSGhopM\nthEivsoS8VWWiK+yRHwFRzwuUTh//jwhISHMnTuX3r17k5KSQlJSEmPHjkWWZWJjY4HqJUpXr15N\nfn4+999/P+Hh4W4uuXcQ8VWWiK+yRHyVJeIrOOL2RKGwsJB33nkHnU5HYGAgubm5bNmyhXvuuYew\nsDACAgI4dOgQPXv2JDo6Gq1WS3x8POPHj2fQoEHMmjVLHKQNEPFVloivskR8lSXiKzSFWzszXrhw\ngRdeeIEuXbqg1+t55plnuPXWWyksLGTHjh34+vrSrVs3OnXqRElJCbIss3jxYtLT0/H392fIkCHu\nLL7HE/FVloivskR8lSXiKzSVWxIFSZJs/3bq1InZs2czbdo0YmNjWbFiBS+//DLvvPMOADExMeTl\n5eHr64tKpeLll1+2Db0RHBPxVZaIr7JEfJVltVoBEV+h6dySKKjV1bvVarV07tyZlJQUABYuXMia\nNWsYNGgQY8eO5bXXXuOxxx7DarXa2sb8/f3dUWSvIuKrLBFfZYn4KqOyshLANopBxFdLeOz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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_meeting.plot(subplots=True,sharey=True,title=\"Volume Signal for each badge\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The volume signal at each badge consists of the following components \n", - "1. environmental noise (assumed to be stationary and gaussian)\n", - "2. attenuated speech from other speakers \n", - "3. badge wearer's speech\n", - "4. badge specific constant offset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A threshold on time-domain signal power will be used for the purpose of speech. We actually calculate 2 thresholds for the following:\n", - " 1. Environmental noise power (when there are no speakers)\n", - " 2. Attenuated speech power (when there is a speaker other than the badge wearer)\n", - "\n", - "In our current approach we assume that there is a single speaker in the meeting at any point in time and the rest of the participants are silent. This assumption is used to remove false positives for speech detection that arise due to attenuated speech of a nearby speaker or some spikes in environmental noise. \n", - "\n", - "Some assumptions:\n", - " 1. The minimum distance between any 2 speakers in a group is assumed to be 3ft.\n", - " 2. The smallest time interval for which we would like to be able to a decision on whether speech is present is taken as 1 second.\n", - "\n", - "Some badge wearers may have a very low speaking volume close to either environment noise or attenuated speech levels. This will result in false negatives in speech detection. How low is too low of a speaking volume for a meeting is debatable." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "frame_size = 1000 #milliseconds\n", - "sampleDelay = 50 # sampling period in milliseconds\n", - "median_window_time = 2*60*1000 #milliseconds\n", - "median_window = int(median_window_time/sampleDelay) #number of samples\n", - "power_window = int(frame_size/sampleDelay) #number of samples\n", - "clipping_value = 120" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The clipping value is the maximum value of volume above which the signal is assumed to have non-speech external noise. Clip signal values above this" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "df_meeting = df_meeting.clip(upper=clipping_value)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each badge's mic output has a constant offset associated with it. The mode or median over a suitably large window (in this case the median window is 2 minutes) gives a good estimate of this offset. We chose to subtract the median over a rolling window for this." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "df_median = df_meeting.apply(lambda x:x.rolling(min_periods=1,window=median_window,center=False).median())\n", - "df_normalized = df_meeting - df_median" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The thresholds mentioned earlier are all based on short-time signal power calculated over a rolling window of 1 second. Power is given by the mean-squared signal value over a given window size. \n", - "$$Power[n] = \\frac{1}{power\\_window}\\sum_{i=n-power\\_window+1}^{n}(Signal[n])^{2}$$\n", - "The following code calculates the signal power for each of the badges and plots it." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([,\n", - " ,\n", - " ,\n", - " ], dtype=object)" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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0cjarAKUULRrXxd/HXGr58i28ug6F0jakysxUFWzLy8ukHb+qsteV2YmrLXA4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\nPmFhYdp5Qkep89VSmbFmsF28CID3bbd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NNjpGec+WmelzzV1VlTXFlYCyTRsVI7Ly+DEM\nySnKe56aIpkvvej7ffXlcAUAQWEoLF68WFlWGhCVGYOUOoujVMf0lNCDV5U+tbFujYTokbcor3Vt\nYgEw794V1AV7/IV3OMe8dw9Z816meO0azn20iIPPziLz5bmNfPrCcDvsmPf9AoCmerqjq8K3iIyn\nyI1Kr/fJoyhe8w0nHn4Q+7mzLd6ulsQzgjQkdUAdYpK3VT/4a3tTAM6+W3cBIVFHoXXxGHgAaDQ+\noQdHYYHPvi5rtUfBoEdtMJD62ptK/ZbC/6z0KeLkrqzEZa6ZtWDs1MknFKHW69G3SwJAF59A8do1\ndcpDu4ShcOEkJyfz3nvvKX8fPnxYVGYMQmobClmvzZe3V3saVJqa3Fq1wVjn896zG7xHBcEU474U\nVOzcUScOCmDZv4+Kn7cB4MjPq/P+xZL1yjwKli8DakbZngJZxtS0OvtHjxqtvC5c9TkAef/6pMXb\n1ZK4PZnxOp0y26Fix3ag+UmZ4n699Hg/gPWJbZXXKo1G6Wuc5WU4CuSQgC4+AYCqdNkjoPaakZBw\n/0PKa59y5Tabz4O/bPMmpU9rM/b3AET9ZqT8pttN4ec1pZw1UVFyO83nNz1Scrs5+/57PiWn/UFQ\nGAo333wzGq/RpqjMGJzU7iAdublILpcywlL5eBQaNxTUoaFexw2erPlLwblFf693u8urqqVnKldL\n4bJYsHnFVz0eBXt2FgAh3br77G89dBBdTBsMHZNRGQxoqst0e2K3gYrnwaDS6XzuV+vRI8rsh8g+\njc/CCaZZHpcLhas+U157r26qUqvRJ8hGgbOkBEeB7FHQJyYCNfknGq/+JnzAQOV17QRE8y++1Rlz\n3nq9+jt11f/L90ztMGzcHfJsPfd5Pp/Kf96KefcuKo8ewVnqv7B6wE+PrA+115Q5UZkxeDCXGTgD\nmJI7Yj0jW94lyz7BFB9HMRAdG0FEta5RCdF4xrwere06N55HUXx8BCXXD6Vwy1ZiIgzoo8X1ADj9\n0T8afM/brWow6lr0Hi444huLD2/fzqcT1TttdJhwN1nLPwVkQzAuLpz8NtHYMs8oxr+rrJQ2UUbU\nAVobo9Ahh8tiE6PRhoVyqnp7mNaNXS8nrCWOGUXc8BvJXfsd5hN11wCICNURI/qPi+J8791yg5Yy\nIHH0LRj1Nc8Pg8lAfNtoTun1aFxO9JJsxOkNOiyAs7gIgPg+3Yn2+s7slGSsGWcUD1pThEeFEhcX\njhQdRh5gdPsmTcZ3TyUX0Lts53Vu5uIawzrSAKF+uq+C0lDo2bOnqMwYhFQWyvFqQ7eeiqFQ+ONP\nSoJQabkNW0EFcXHhmL1+Rx6tXZU2n22eQmmFuSXonEF5K7cobruds19906x9q8zWFr2Hi9KzfP52\nGEN9/g65YYRPoljH2XMoKKjAZZTj/G6vbPPcUzno2rRpsba1JJUl8j1c5tSgKrdj6tEL65HDlJvt\n2IvkCpRqgwGpax/UvxyAegyF0qJyXKL/uGAupP81FxQDYLr5VkrWf69stzslCgoqUOkN2C0WrKXy\ncXVde8Ku3YoBW25x4PT6Toel4VwCU6/eWA8f8tlmqXJSUFBB0Sm538v4xz+V95KemEGFJBvGFflF\n53Vu5qKaaqCF2QVYQy/8d9OYgRIUoYfazJw5k3fffZfx48fjdDoZPXo0sbGxSmXGBx54wKcy44kT\nJ5g4cSKfffYZ06ZNa+3mX7EoGfEGA3ET7lG2O0vkH7HKy4ujCY+o83lNSAhtH5lKcnUZ6GAs2ONP\nPImEzcHdwppJtbLFvd27ALqERJ/aA544cX0hpuYurtQauO12VHq9kp8QVj3wqDx5gsL/VLu3qx8u\n3nkgsXf+QVkUSNyvlx57bi4qvR5NRCQR19WsrOoJH6mMBtxVVVgOHgRQQmGeaca1q796chk8aCJq\n+iuVTkfiw4/6vO9JzvYsQKU2mUClwti5C6G9r0ITJj+kncXF53Ve3qvpuv00kwmCyKOQlJTEihUr\nAEhJSRGVGYMQzywHtU5HxHVD67jtvGO+2urkntqED7rW6wOyqzfj+b/QeeGigK5sdimoPHaszrZ2\nj03HWVpC/jLf34urvBzrsaOUrv+e+En3+SzlfSHUNjy8DQVjaioqtdqnM/UQdlVfyjZu8NlW3+yB\nQMFlrlCmfkJN3kfJ2jXKNo9B3O5/n+DU9D8BEDXyJizVUyWDqW7E5YDkcmHPyUaXkIhKpVKWlQaU\nyq9qrc5n+WddbKzPMWobvt6E9euPLj5BuQdUWq2PARzWfwDhA2UDwZCUhCYyUll62nNczz1Veezo\neZ2by6tOib+mPEOQehQEwYnH+lWHhqExmUh9/S2lHgL4GgqN/TA9VKXXuHXLq7P5r2Qc1YmAnd/7\ngLS3F9DptbcIu6ZfTaZ1NYb4eOxnc8h+bT7mvXs489Kci/7u2kv0eo/Aon5zk7xNrSbuDxNIfOhh\n5T1T7z51j+Ww19kWKDgLC1GparrN0KuurrNPzCA52U1jMinbVDodmojqhM2zgT0F9HLDswy4xwvk\n/RBXqeU+x9tIADC074DKUDPTofYgxGNstJv2OO3+9L8+76u0WnReUzDb/O5/fPozbWSU177VSY5e\nBZmaqkbrjbcXwW31nwEqDAXBJcMzkvJkEGujookYPKRmBy9Doak1HgDi7p6ovK7YLgwFR2E+mshI\n1AYDmrAwdF4jp7g/jK95PfwGn8+5SkuV+eIXimSv61GInzgJlV6PyWstjuhbRhExxMv1W8/Kny0d\nFmkpJEmS70uve9NYPWvDG2+DV9mmUqFPkDPpbVmXb5Ewy+FDFH/3bUBNAfUYCiFduwK+91x9MwXC\nBgyUZ0O0bads08XF++zT4c9/Ie4PEwi9ui+AUlsBQBsRqUyvBOqUfg69qmZqt7FjR+W1p6CcLds3\n36cxvA0F4VG4SCRJYs6cOYwfP5777ruPrKzmXwhBy1GVUXeqkfePxmd6ZLWFbuxUt0KjB1P3HnRe\nuAgAR1FRi7Y12JBcLhwFBXXr0VfjHZeNu2FYnffzl9YN5Z0P7lpeALVWR9SIm+iycBHaekIO3nSc\n9YLcxiHXAwEcw3e5wO3GkJTkszn5hbkYO6USfu1gkmY84/Neu8f+l8TJjwDI4R2NBmdZGZcjkiRx\n9m9vU/jZp5x8fFrAFELzFP3yznvyTNe15WQDkPL/XlXeM3XtJr+obr+pR6+6OTdxcUTfMkoxOnyM\nioRE1AYDId26Y0zrjLqWIendjpjf3q689ngavKs7Nnlu3h6FSmEoXBTr16/HbrezYsUKnnrqKebN\nm9faTboicZbKGbreMUJtTE2Wbu3QQ+cF79PhudmNHlOt1xN6TT+cxUU4igpbuMXBg+LObqAMsiY8\nnKQnZtBu+hOEtK950Jl6yItvVezcjvU846Pe1K6Rfz5zuo2dUum6+BMMKSnysQLUUPC4hGt7EPQJ\niXSc9QJtH36U0OrFzDyEXdOfiOtqvGb6tu2U+HR9SG43zvLygBqRNxdHfp7SbslWpYzkWxt7dVKp\nt8Ea2lseoHjaqI+Pp+PsOUQMu4HwwdfJO3l+S+q6Xq/ahHSuCaF68n3aP/VnOjxbd+VjTx2G2Dv/\n4GNEeMJYtuwsqjLPKEZMQ0huN+5KqzJrzFVe3uj+F0PQJDNeDHv27GHYMHkUdfXVV3Po0KFG93eZ\nzfICIQ4nbocDl7kCd1UVmtBQtBGROENUNT+C6ptJQpJL1ysddfXfSF6bJK/tnn2k6k2SsknZz/N3\n9X6Sz+fw2sdzQ6tRqatrl0tuuZiR04XkciI5HDhLilHp5FXQ5IIxalBr5HrnanX1D6LpH8WF4olj\n66pdsODrBvSuzAj1Z8TXh6F9Byx7f6EqI6OmoqMkya5iH80lJLfv37gl32un7O/RW0Kl06HWyR4O\nye1S4pry8eWFYSSXW9ZRq0GlqV4pTq2+KDkllwt3VRWSwylfH5Wq5jqp1Kiq/0etwlEo5yfULmzk\njadzVKlUpL71LrglXOVlnJkrj+izX5tP/L0PYOzYEU1kJJLdgbuqsjq5UNZBGxUtjxSl6uV4VWpU\nWi2O6mztNv9zB0VfriLcy4PRXNTV8dqir74EtRq1Xo/kdqEJDZMrPWrUssYaLa7yciyHDqKNikSf\n1B5cLtw2G/b8PBz5+YR07oIhqb3sEq4nvFEvTezm8QRcTNKsJiwMu9NJ7icfYc/JIaRzF7TR0Wgi\nIlCbTBSsXIEjNxeQq1lqY2KIuPY6VFot2qiomsW0JLk0t+3cWbSRkejbJckJo+ERcv9lt6ONjpb7\nALtNyQuy5eRg3rsHl9mMsUNHwgZeK0/ndMkrJ7rKSpFcbhyFBagNBgzJKUh2O9Yjv+KyWAhJS0Mb\nHYMmLEyZ0qoJC8eWcbpOoa/ybVvQt0tCGxmJ5HLjLC1BrdchOV2oDQbUJpN8j1utqHQ63FVVOIuL\nMXTsiFqvR2UwogkJqfl9AkgSdq0TZ5nZpw+Uave9yn8Slv1yEqn3oCTqNyOoPHWC6JE3K9uMKZ1I\nTOmk/G3o0AFbVqZv8mMDqI0htJ3yJ8q2/Iipu/wbVDUQPtVGRtF18Sd1tnvKO5euX0fp+nUAJP7x\nEUK6dkdyOak8cgSX1YI2OlreZqsCSUIXG4uzpJiKnTtoM/YO+X5XVfetnn+o5P4C+T1U6up9qtuo\nVgENT4+8IgwFs9nsU2hJq9Xidrt9Cjd5c+qJxqdQBub6XsGBOiysTly60/zXsOflKdX8zhdt9bU9\n9/cFF92+YEfbjIJi8n7y6EobGUmnV17n7MIF2M5kkL/kkwv+bpXBQJvbfkeb2353QZ83dOgAgP1s\nDucW/u2C2wHgz6Xf6isv3lyMHTpSefQI5Vt+Amoq/9VHVfopSD+FefeuC/6+xqg8eoSS7787r8+U\nbdrQ5D7hgwZTsXO7T4nilqRhxRrH24hWG40kTXu80f3j/jABTVi4T3igMcIHDlJmN1wImpAQ9O3a\n+SS75i5e1OTndPEJuG02bJlnyJg184K/P/G//2nwPZUUDEu2XSTz58+nb9++jB4t15YfPny4srqk\nQCAQCASChrkichT69evH5s2bAdi3bx9dq7NfBQKBQCAQNM4V4VGQJIkXX3yRY9UFaebNm0enTp2a\n+JRAIBAIBIIrwlAQCAQCgUBwYVwRoQeBQCAQCAQXhjAUBAKBQCAQNIgwFAQCgUAgEDSIMBQEAoFA\nIBA0SFAUXFq0aBEbNmzA6XQyadIk+vXrx7PPPotaraZLly7MmSOvfrdy5Uo+/fRTdDodjz76KMOH\nD8dms/HMM89QVFREWFgY8+fPJ7oZlbYEAoFAIBAEgUdh586d7N27lxUrVvCvf/2LzMxM5s2bx4wZ\nM1i6dClut5v169dTWFjIkiVL+PTTT1m8eDFvvPEGDoeD5cuX07VrV5YtW8bYsWNZuHBha5+SQCAQ\nCARBQ8AbClu2bKFr16786U9/YurUqYwYMYJff/2VAQMGAHDDDTewbds2Dhw4QP/+/dFqtYSFhZGS\nksLRo0fZs2cPN9xwg7Lvzz//3JqnIxAIBAJBUBHwoYeSkhLOnj3LBx98QFZWFlOnTsXttXxpaGgo\nZrMZi8Xis56DyWRStodVryHg2VcgEAgEAkHzCHhDISoqirS0NLRaLZ06dcJgMJCXl6e8b7FYiIiI\nICwszMcI8N5usViUbeHNWDTH6XSh1Wqa3E8gEAgEgsudgDcU+vfvz5IlS3jggQfIy8ujsrKSwYMH\ns3PnTgYNGsSPP/7I4MGD6dOnD2+99RZ2ux2bzUZ6ejpdunThmmuuYfPmzfTp04fNmzcrIYvGKCmx\nNvp+XFw4BQUVLXWKgloIff2L0Ne/CH39i9DXP8TFBfEy08OHD2f37t3cddddypoNSUlJzJ49G4fD\nQVpaGqNHj0alUnHvvfcyceJEJElixowZ6PV6JkyYwMyZM5k4cSJ6vZ433nijtU9JIBAIBIKgQaz1\nUA9NWavCovUvQl//IvT1L0Jf/yL09Q+NeRQCftaDQCAQCASC1kMYCgKBQCAQCBok4HMUAO644w5l\nimP79u159NFHRWVGgUAgEAguAQFvKNjtdgD+9a9/KdumTp3KjBkzGDBgAHPmzGH9+vX07duXJUuW\n8MUXX1BVVcWECRMYOnSoUplx2rRprFmzhoULFzJr1qzWOh2BQCAQCIKKgA89HD16FKvVyuTJk3ng\ngQfYv3+/qMwoEAgEAsElIuA9CkajkcmTJzNu3DgyMjJ4+OGH8Z6oISozCgQCgUDgPwLeUEhJSSE5\nOVl5HRUVxa+//qq874/KjNHRpiYrMzY2lURw8Qh9/YvQ178Iff1Lc/XduXMnTzzxBJ07dwbkUPYN\nN9zA6tWrWb16NQDffPMNM2fO5KeffiImJoacnBwee+wxvvzyS6ZOnUpZWRlarRaj0ciiRYtYsGAB\ncXFx3H333cr33H333bz11lu0a9dO2TZixAjWrl3LokWL+Prrr0lISMDlcmE0Gnn66afp0aMHX3zx\nBe+++y4dOnRAkiTMZjP9+vXj+eefZ+fOndx33328+eab3Hrrrcpxb7/9dnr37s28efP47rvv+PDD\nD1Gr1dx2223cd999QN28vv/3//7fRekd8IbCqlWrOHbsGHPmzCEvLw+z2czQoUNFZcbLGKGvfxH6\n+hehb122bThF+tH8FjmWWqPG7XKT2j2eISPSGt23tNTKNdcM4MUX/w8Ap9PJhAl3YDAYOXUqm4iI\nSNat+4EbbxzB6tXrGD36t6xfv5kBAwZTUFBBenoGS5euVI5XUFCBxWLDaKzyucZOp5uiIgs6Xc02\ntxsKC81YLDbGjZvI2LF3AJCZmcHjjz/BJ58sp6KiipEjRzFlymPK56ZOncyWLbuwWq0kJ6fwxRdf\nMXDgMADS009isVipqnKQl1fGa6+9zkcfLcVoNDJp0jiGDh2B0RiCw+HijTfe82l3UwR1HYW77roL\ni8XCPffcw1NPPcX8+fOZNWsWf/vb3xg/fjxOp5PRo0cTGxurVGZ84IEHfCoznjhxgokTJ/LZZ58x\nbdq01j4lgUAgEFwivEPVZrMZjUZDr169OXBgH5IkkZ5+kgkT7mXbti0A7N27h2uvvY6SkmLM5gpm\nznySxx57WHm/qe9oio4dU+jWrQcHDuyrt30Wi1nxBqSldSE39xxWq+wV/+67b7nlljEAqNVqli37\nHJPJRFlZKW63hFar4+TJ41RVVTJjxjQef/xPHD58qNlta4iA9yhotVpeffXVOtuXLFlSZ9u4ceMY\nN26czzaj0cg777zjt/YJBAKBoHGGjEhrcvTfXM7XY/PLL7uZPv1RVCoVWq2OJ554BpfLxd69e2jT\nJpbu3XvSvXsPTp48jiRJHD9+lGeffZ6iokImTJjEuHETKCsrY+rUyfTs2bvB71Gpmn8O0dHRlJaW\nAvD992s5fPgghYUFhIaGcf/9k0lKak9+vrz44fDhI9i8eSNjxtzGkSOHmTTpAfLycgHZWNi8eSNv\nvvkKQ4YMIyQkBKPRyMSJ93Lbbf9DVlYmTz89neXLV6FWX7hfIOANBYFAIBAILpT+/QcqoQcPVquF\npUs/ITQ0lMGDhwLQq1cftm79iaSkDmg0GmJi2jB27J2o1Wqio6Pp2rUbmZkZGAwGHA67z/EqK60Y\nDEbM5hpvAICqAeshN/ccw4ePJDs7i1tuGcOUKY9x7txZnn56Ou3bd/T5/M03j+a11+bRtm07rr76\nmjreixtv/A033vgbXn55DmvXruamm0aRlNQBgA4dOhIREUlRUSFxcfEXrGHAhx4EAoFAIGhJTKZQ\n9Ho9u3fvZODAawG49trr+Pe//8W1114HwJ49u3j++ZkAWK1WTp8+RUpKJ7p27c6WLT/icrkAyMnJ\nxuFwEBUVxeOPTyU39xw2WxWS5Earlcfi3g/39PRTnDmTQa9efXza1LZtO5588s/Mnj0Tm83ms72q\nqpLPP/+UUaNqkhqtVgvTpj2Cw+EAwGgMQaVSsXr1VyxY8DYAhYUFVFZaadMm9qL0ChqPQlFREXfe\neSf/+Mc/0Gg0ojKjQCAQCC6Yvn37sXfvHkwmEwCDBg3mpZde4IUXXgZkw2H37p1MmfIgGo2GRx/9\nXyIiIhk48FoOHNjH5Mn3EhYWhiRJPP/8XwF48ME/MmvWn5Ekifvue0j5rpUr/82GDd+jUqnR6bS8\n/PKr9YYCBgwYxMCBg/joow+47rqhyvaRI2/mu+++pX37DuTkZAOysTNq1K089tjD6HQ60tI6M2rU\nrbhcLubN+yuPPfYwKpWKZ5994aLCDhAkq0c6nU6eeOIJTp48yd///ndeffVVJk+erFRmHDZsGH37\n9uXBBx/0qcy4atUqli1bhtlsVioz7t27t8nKjGL1yNZF6OtfhL7+RejrX4S+/iGoZz0AvPLKK0yY\nMIH4+HgkSRKVGQUCgUAguEQEvKGwatUq2rRpw9ChQ5U4j9vtVt4XlRkFAoFAIPAfAZ+jsGrVKlQq\nFVu3buXYsWPMnDmTkpIS5X1RmfHyROjrX4S+/kXo61+EvpeWgDcUli5dqry+7777mDt3Lq++gEsR\nOwAAIABJREFU+iq7du1i4MCBojLjZYjQ178Iff2L0Ne/CH39Q2PGV8AbCvUxc+ZMnn/+eRwOB2lp\naYwePRqVSqVUZpQkyacy48yZM5k4cSJ6vZ433nijtZsvEAgEAkHQEBSzHi41YtZD6yL09S9CX/9y\nJevrcsn5YxqN/9LfrmR9/UnQz3oQCAQCQeDz1fL9/PuDHee19oEg8AnK0INAIBAIAo/c7DIALGY7\nYeGGVm6NoKUIeEPB7XYze/ZsTp8+jVqtZu7cuej1elGZUSAQCAIUe5UTrhBDwe2WqLTaCQ27fM83\n4EMPGzZsQKVSsXz5ch5//HHefPNN5s2bx4wZM1i6dClut5v169dTWFjIkiVL+PTTT1m8eDFvvPEG\nDoeD5cuX07VrV5YtW8bYsWNZuHBha5+SQCAQXNZ4chWuBDauPsq/FvxMeWllazfFbwS8oXDTTTfx\n0ksvAXD27FkiIyNFZUaBQCAIYK4kQ+H4YXk5aGEotDJqtZrnnnuOl19+mdtuu80nUUZUZhQIBILW\nx7tfdruuvGRG92VsGwV8joKHefPm8fTTT3PXXXf5LMEpKjNengh9/YvQ179cifp6exHCw41+1SAQ\n9TWF6AOyXS1BwBsKX375JXl5eUyZMgWDwYBaraZ3797s3LmTQYMGicqMlyFCX/8i9PUvV6q+DrtL\neV1cZCG8wOiX7wlUfYuLzOfdrtMnClGrVSSntfFTq5pPUFdmHD16NM8++yyTJk3C6XQye/ZsUlNT\nmT17tqjMKBAIBAGC92J9V0qOgne4xeE4/3Ne+59DAEx9dnhLNckvBLyhYDQaefvtt+tsX7JkSZ1t\n48aNY9y4cXU+/8477/itfQKBQCAAl1dewpViKDi9jAOnw9XInnVxB1FSQ1AkMwoEAoEgsHF7GQdX\nSjKjw8s4OF9DwTtUE+gIQ0EgEAgEF82JX/OV164gGi1fDN7GgXfoITujhE3fHqu3lHVRvpniAguf\nf7JH2RboHpiADz04nU7+8pe/kJOTg8Ph4NFHH6Vz586iMqNAIBAEENs3pSuvzeW2Rva8fHA6ax7w\nVotdef31iv0AJCVH0aVngtf+LlZ+vBuNVo3L6Ru28OdCWhdL4Lasmq+++oro6GiWLVvG4sWLeeml\nl0RlRoFAIAhg9mw909pNuCT4eBTsTsA3wdFc4WswFeTKU/i9jQT5s4Edhgh4Q2HMmDE8/vjjALhc\nLjQajajMKBAIBIJWxzuZ0fPw967QuH1jus/+Bbn1T58UhsJFEhISolRZfPzxx3nyySdFZUaBQHBZ\n43K52bL+BHlny1v82NkZJeTmlLX4cWtzJSw17ZPMWG0o2G0NP/QrSquaPE4gEvA5CgDnzp1j2rRp\nTJo0id/+9re89tprynuiMuPlidDXvwh9/cvF6rt/VxYHd+dw8td8nnlpdAu1Subv8zcB8MIbt7fo\ncWsTEx2KVtd4P3qhtNb9++k/dmEx23jof68HoOBsjYdArVIRFxeOtcLu8xnvtuoa0MMUYgjo32TA\nGwqFhYVMnjyZF154gcGDBwPQo0cPdu3axcCBA0VlxssQoa9/Efr6l5bQt6hIHvRUWh0teq285+7n\n55ejUqla7Ni1ycsrR29o+UdMa9y/xQUWKsqrOHYoF4Bz50rRajUUF1mUfSor5WtVWFAzYNVoVD5t\nraio36NQUFBBWFTrLlMd1JUZP/jgA8rLy1m4cCHvvfceKpWKWbNm8fLLL4vKjAKB4LLEXw/wwrya\nh5jT4UKn998jwO2+fEIPXy3fR6XVofxtq3KiDdPgdNaEDDxTHL0THE1hvg9/ZwPVGwM9RyHgDYVZ\ns2Yxa9asOttFZUaBQCBJkl9Hxf6ipMhKZLQRtbr+NDHvrHi7zdliI/N1X/6qvM5MLyate3yLHNeb\nhKQI8nLKA742wPngbSSAbCiEhhlw2OsmM3obChVlVbicbjRadfU+9RsE5vL6PQ2BQsAnMwoEgiuL\nonyzz5z0+nA4XHy9Yj///mDHeVfEa22yM0pY8eFOtq4/1eA+3iPM9GMFDe7ncrnPK2lQp6+Jka/7\n8tcWSzj0eA/adYwiMjpE3naZVGesTyNblTwV0tuj4ElmPLL/nM++m749hsvlpry00qfugjc7Np9u\nqeb6BWEoCASCgMFqsbPy493882/bGt0v40Qh2RkllJdWYTHbfLbv35nl72ZeFFvWnwDg0C85De7j\nnQVvsznr3acgt4JFr/3I4b1nm/W9lgobxQUWn22VTRhkzcUzmtZq1ajVsocnmNYyaIz6ZjHYqmQP\ng7OeEs5ns3xnlBw/nMe/P9jBsvd3UF5r1kO7DpEt3Vy/IAwFgUAQMHi7YHPOlDS4n62y5uHpXQXw\n2/8cYtuGUw3OVw8EGopTe0g/VsDZzNIm9z9+KA+Anzc27JnwpqrSUXdbVf1GyPny5bK9AFSUVykV\nBl31eBQkSWLH5vRGr22g4TEKfLdVexSqr40xRIvd5qpTSMmD5x6tKPM1FMKjQpTPBzJBYyjs37+f\ne++9F4DMzEwmTpzIpEmTmDt3rrLPypUrufPOOxk/fjybNm0CwGazMX36dO655x6mTJlCSUnw3KAt\nicvpZuv6kz7JTAJBbSqt9ibd/v7E+2FmMTfcDru95gHnGaV5x8RLixufudSaqDU1ORXvv7KJNZ8d\nVFz3Lpeb7744TP65GkOnoTn2nu36ZiYk1nechpLozOVV2BvwZNSHp1+xVTqV83PXk6NQUmTll58z\n+Wr5/mYfu7XZtSWjzjaPoeDRNKqNCfAttnTn/f2aPLZOpyEqJiTg82yCwlBYvHgxs2fPxuGQOxFR\nwvn8OXU0nwO7s1m98kBrN0UQoFgtdj55d1uTbn9/4j3iaqz2vfdDzzOKs3iVyw3kEsLe7mpJgjOn\nihQD6bN/7K67fyMPc5DzDo4dzOXjt7dw5lQRf5+/ibX/OVRnf8+Dv9c17ZRt9RkKDruLJQu388XS\nvc0+p5BQHQDjHx6oJGjW51Hw9pQEA263W/Hc9O6XxPAx3YCakI3HoxBZ7RmwWuyoVNC2fSTxbSNo\n277x0IJOr8EQosNW5QzoAlVBYSgkJyfz3nvvKX8fPnxYlHA+TzyjxNYcLQYju346TWZ6UWs345Jw\n6mh+0zv5mZLCGk+Aw97wiNb7AeeJ4du83OglRYHrUfDOlPeQl1OOJEk+5++hvgS48tJKsk7L3lG1\nRsWG1UexVTkVA+H0iUJAdvXv3Z5JQW6FEmuPiQ1lyIi06rZ46Vjl4Pv//krW6WIAJZ/B7ZZY9+Vh\ndmz2LUfsjSRBVEwIBqMOjcejUM/0yPrOz8M//7aNv8/fxLls/1eNbC7e+kTHmohLlKv8/rpPzgvx\nGH2hEfI0yNycciQJwiLlv40mXaPHV6nAaNThdksBPUUysAMj1dx8883k5NQk/vi7hPPlWJlRq6k5\nn0qzg46dYlqxNU0TCPqWl1Wyu3pk6u8qdpea+vTV62q6g9jYsHrdoS6Xm8I8MwntIvzSLsnrmbhx\nzTE6dY6jfXLd1V616pr7WatRExcXjrnMdwGe1ryHGvpuh8NVr0t/7apD/GX+rfV+RqNW1+mTcr0S\n5rwfvt4P5+hoEyd+zWP7pnTUGhW33tEHgDZxYcoDzu2SlLZuWHOEk0fyOXmkxmA0hegxl9s4dVSe\nedF3YMc610OSJGxVTmLjw4iLCyc83AhAeLixjg7e3hRrhZ3k1DbyMdySMogpLbRy1TXt69XCw6W6\ntueyazwgbWLD6NYjEYDQMLmSouc3kthW9hzs/FGevZCQGEFcXDinjxc2enxTqAFndPVN71YFRL9X\nH0FhKNTGe+6xP0o4X46VGQvya9q7ce1Rbr2rTyu2pnFq6+tyuVtlCVbPqAwIuuvdGA3dv973yOlT\nhYRHGuvs89O6Exz6JYdbx/UhOa1Ni7etrNT3t/fVp/sY92DdaqoVFTWx4NISKwUFFeSe9R2J+rvy\nYEM01j8U5Tc8UDl5vH6PzqG9ORzam8MfJg+gTZw86PG+Vg2RdaaYtV8eBmSDoLCwOo/A5iAyRo6p\n79mWQVqPOADKyirrHGPrxpMkJNUYhR+/u4Wpzw5HkiR+WneCNvFhdO4Rh+SW0GjVFBRUUFmd/Pfz\n5lOERuiVayBJEsWFNbMuigrNmML1OJ0un5kF+bnljf7eLmX/u21zTaJoWamVomILIaE6nC43BQUV\nWK121GoVrlozPDp1i6WgoIJuvRM4Vh26qI/E9hEc3icPgj965yemPjvcL+fRHBozUoIi9FCbnj17\nsmvXLgB+/PFH+vfvT58+fdizZw92u52Kioo6JZyBZpdwvhzxjt8Gy7xzSZLYuPooi9/4ifxzLb84\nTlOUeHVqjS3aEsixxfPBe6qcZ+6+3eakvLSSSqsdl8utTOlrbG7/xWCrlYXfUPKt3ctN++u+c5w8\nkl+naE3tYwUCnsI99dkv3gmMAHGJvh13+rEaw7Wqsulzyz9XoeR8RMWEUFYsGwLhkUaiq5Pvigos\nSojn4O660zV1ek29HpDCPDOH957lx++OK6ECY4jsZvf8Hk7+ms++HfJU1eyMEt5/ZbPPOZ7LKiPn\nTAkfvv6TT16M9yyW1ib7dE3yuydh0Riio8paMz1Sq1P7XKu07nGERchG9tCbOjd6/LjEcGXfQCYo\nDYWZM2fy7rvvMn78eJxOJ6NHjyY2NlYp4fzAAw/4lHA+ceIEEydO5LPPPmPatGmt3Xy/cOLXPI4e\nOFfvVB6nw6XEMwFyzpQGRdW0E4fzOHowF7db4lzWpY9bWr2y7n/67ni9+1jMNj5+e4vSIQYz3g+i\nyurkuo/e2sKy93fwybvb2Lj6qPL+0QO5PhneLYWtyokpTO+zrb7vqR3P/f6/v1KQKxsVngSyE4cb\nHsm1FqXVuRPdr2qLwahVag4AdWocdL8q0efv3VsylKTH5sSzvX8zDodLKQQUVe1N8FCUb2lwOqmt\n0lGnjsCerRlkZ9T0J4d/keP1nil+3n2Lx9D7ekXdWQ57tp1RQhremCsCw1CQJImKauPzjvv6kZgk\n31fG6uRDt1vC6XCj1WkIi6gp1exdRdNgbDxHAWD073sBEBEVuAZD0IQekpKSWLFiBQApKSmihHM1\n2Rkl2KocrP/qCABbfzjF5Cev99mnqLoD0mjVqFRypu65rDLap9SN/QYS3qPJ1qgbX+b1gHK5Jb75\ndD/lZVXceV9/yksriYkL5czJIuw2Fz9vPEWf/klKqdZgw1bl8NG4tNCqrDLo4cSvvq7xZe/v4De3\ndqP7VW1brB1VlQ5MYXrGTuzL8kU7le+JjjVx9+SBihvbYZdHct41Bk4eyUelqkks27L+JH0GNB7r\nvtR4DPnUbrEMH9ON0mKrcp4Hdmf77BsTG1rn82s+P8hNt/do1tRF7+N5G72ee9TjFv9i6V5CGki6\n27PtTJ17eudPGT7z/jPT5eRHj0chtWscv2zLBORrMuyWLj6f79Q1Vond13cexQUWSoqsitejtbDb\nnLhdEsmd2/jk5HjO01blkD0KWrVPiEtv8M1vm/DIICS3xJrPD/oUXPIYF1qdhrjEcApyKwK2JHlw\n9moCQJ5K9vWK/T712+02p0/RD6fDpVjtg4alMOD6FEDucAIdb/eyvZEMeH/gdLrIPl2ixOlP/ppP\n1ukSyoor+fbzg3z+yR62b0znp3UnlM98+rEcDquqdARdOMLTgUXHyp2zd35Gbbr2TlBeb1xzjDWf\nHWyR8/UkxRmMOqJiTIyd2Fd5r6TQSsaJmtknhXlmn3LEHowmHdd7uXsDzXPmqDZsPLUPap8nwA2j\nunD7+Ktp1zGqzufzcspZ9v6OOmWCm8JzeTzXF6BNfJjy2hMSSU7zTXJ2uaR6KxPWF/rwPEDjEsN5\n5JkblO3/eGerz37eUwaLanlRtDr5kbTiw52tXpTJ472pbUR5zrOq0oGj2qPgTe11OaJiTETHhirX\nvGNqDAOHpfhcd49xUdurFCgIQyGIOXOq/ml7i17/kb/P38SebWf48I2flJK2pjADnbrEAjRYQSyQ\n8J4e56ins/IntkrZtZjQLqLOFCdPTPbA7myfUXhZcSV7t2fyj3e2suS9n4Nq9Ty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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_energy = df_normalized.apply(np.square)\n", - "df_power = df_energy.apply(lambda x:x.rolling(window=power_window, min_periods=1,center=False).mean())\n", - "df_power.plot(sharey=True,subplots=True,title=\"Volume Signal Power for each badge\") # Add title here" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Assuming a gaussian distribution for the power signals we can assign an power outlier threshold of $\\mu_{power}\\pm2\\sigma_{power}$ (where $\\mu_{power}$ is the mean power and $\\sigma_{power}$ is the standard deviation) for each of the badge volumes signals." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "power_mean : member\n", - "2J63OSU3ZC 3.638414\n", - "BUAULG5KFB 11.561216\n", - "LPH58QI85F 358.039606\n", - "PW5UJDRM35 281.816434\n", - "dtype: float64\n", - "power_std : member\n", - "2J63OSU3ZC 9.654779\n", - "BUAULG5KFB 26.701476\n", - "LPH58QI85F 748.998225\n", - "PW5UJDRM35 472.835565\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "power_mean = df_power.mean()\n", - "power_std = df_power.std()\n", - "print 'power_mean : ',power_mean\n", - "print 'power_std : ',power_std" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "power_threshold : member\n", - "2J63OSU3ZC 22.947973\n", - "BUAULG5KFB 64.964167\n", - "LPH58QI85F 1856.036055\n", - "PW5UJDRM35 1227.487564\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "print 'power_threshold : ',power_mean + 2*power_std" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the test_threshold module we have a function test_threshold which takes df_meeting as the input, goes throught the above steps and returns the power threshold (average) for each badge." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "power_variance : member\n", - "2J63OSU3ZC 93.214766\n", - "BUAULG5KFB 712.968794\n", - "LPH58QI85F 560998.340984\n", - "PW5UJDRM35 223573.471685\n", - "dtype: float64\n", - "power_mean : member\n", - "2J63OSU3ZC 3.638414\n", - "BUAULG5KFB 11.561216\n", - "LPH58QI85F 358.039606\n", - "PW5UJDRM35 281.816434\n", - "dtype: float64\n", - "power_threshold = (power_mean + 2*(power_variance)^0.5): member\n", - "2J63OSU3ZC 22.947973\n", - "BUAULG5KFB 64.964167\n", - "LPH58QI85F 1856.036055\n", - "PW5UJDRM35 1227.487564\n", - "dtype: float64\n" - ] - } - ], - "source": [ - "power_threshold = test_threshold(df_meeting)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The power threshold when the operating environment has no speakers will give us an estimate for the **environmental noise threshold $thr_{env}$ **. Dataset 1 **D1** (NVGWO_1467320376817.txt) where all the badgewearers are silent can be used to obtain this.\n", - "\n", - "Applying this threshold to the volume signal power time series\n", - "\n", - "$$signal[n] = \\begin{cases}\n", - " noise, & \\text{if } power[n]>thr_{env}, \\\\\n", - " not\\ noise, & \\text{otherwise}.\n", - "\\end{cases}$$\n", - "\n", - "When the signal is not noise it can either be attenuated speech or non-attenuated badge wearer speech. If we consider the case when there is only one speaker and we ignore the speaker's badge data then the rest of the badges record attenuated speech (assuming the minimum distance between the badge wearers for speech power attenuation is **3 feet**). After ignoring the power samples below $thr_{env}$ we can calculate a power threshold which will give us an estimate for the **attenuated speech threshold $thr_{speech}$**. Dataset 2 **D2** where a single speaker (badge PW5UJDRM35) is present can be used to obtain $thr_{speech}$\n", - "\n", - "Applying this threshold to the volume signal power time series\n", - "\n", - "$$signal[n] = \\begin{cases}\n", - " badge\\ wearer\\ speech, & \\text{if } power[n]>thr_{speech}, \\\\\n", - " not\\ speech, & \\text{otherwise}.\n", - "\\end{cases}$$\n", - "\n", - "We therefore set the speech detection threshold in 2 stages. \n", - "* Using a dataset similar to **D1** collected in the operating environment where the badgewearers are silent to set $thr_{env}$\n", - "* Using a dataset similar to **D2** collected in the operating environment where there is a single speaker (badge B1) and ignore B1's data to set $thr_{speech}$\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Dataset 1**:\n", - "There are 4 badges in this test case. PW5UJDRM35 (who will be the speaker in the next stage) will not be considered when setting the thresholds since only the other 3 badges register silence or attenuated speech. PW5UJDRM35 also has some unknown noise associated with it in the absence of input for this dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "input_file_name = '../sample_data/mostly_silence.txt' \n", - "df_meeting = sample2data(input_file_name)\n", - "metadata = {}\n", - "metadata.update(df_meeting.metadata)\n", - "df_meeting=pd.pivot_table(df_meeting.reset_index(),index='datetime',columns='member',values='signal').fillna(value=0)\n", - "df_meeting.metadata = metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "power_variance : member\n", - "2J63OSU3ZC 14.083815\n", - "BUAULG5KFB 10.672603\n", - "LPH58QI85F 1.666880\n", - "PW5UJDRM35 1773.499894\n", - "dtype: float64\n", - "power_mean : member\n", - "2J63OSU3ZC 0.660883\n", - "BUAULG5KFB 0.959447\n", - "LPH58QI85F 0.273754\n", - "PW5UJDRM35 6.621968\n", - "dtype: float64\n", - "power_threshold = (power_mean + 2*(power_variance)^0.5): member\n", - "2J63OSU3ZC 8.166565\n", - "BUAULG5KFB 7.493237\n", - "LPH58QI85F 2.855908\n", - "PW5UJDRM35 90.847852\n", - "dtype: float64\n" - ] - }, - { - "data": { - "image/png": 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mJoY1a9ZUu3xNb5S4pAze+/IIE4b5VfqQXa3GcNPHgRM5vLo5Cago6n9U1DsJ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\nvaR2i8USZ8Jp9+ByeUlKtRBjNlJcUEtcQjRanYYDuyqC02c1EBcfjdf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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_meeting.plot(subplots=True,sharey=True,title=\"Volume Signal for each badge\")\n", - "power_threshold = test_threshold(df_meeting)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Ignoring PW5UJDRM35 we see that test_threshold gives a maximum power threshold of 8.166 for $thr_{env}$ (Badge PW5UJDRM35 was noisy during the experiment)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Dataset 2**:\n", - "Use the power threshold from dataset 1 as the environmental noise threshold. The test_threshold function takes an argument custom_power_threshold which when set to a non-null value calculates the mean and std for power values above custom_power_threshold to calculate the output power threshold. Set custom_power_threshold to $thr_{env}$ from the previous stage. The output power threshold obtained for the badges with only attenuated speech recorded gives us the speech detection threshold $thr_{speech}$ to be used for the environment." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "input_file_name = '../sample_data/one_speaker.txt' #Single speaker\n", - "df_meeting = sample2data(input_file_name)\n", - "metadata = {}\n", - "metadata.update(df_meeting.metadata)\n", - "df_meeting=pd.pivot_table(df_meeting.reset_index(),index='datetime',columns='member',values='signal').fillna(value=0)\n", - "df_meeting.metadata = metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "For all power samples greater than custom_power_threshold = 8.166565\n", - "power_mean : member\n", - "2J63OSU3ZC 19.975629\n", - "BUAULG5KFB 16.952946\n", - "LPH58QI85F 15.661860\n", - "PW5UJDRM35 145.956466\n", - "dtype: float64\n", - "power_variance : member\n", - "2J63OSU3ZC 47.371670\n", - "BUAULG5KFB 17.374326\n", - "LPH58QI85F 5.906498\n", - "PW5UJDRM35 21083.362065\n", - "dtype: float64\n", - "power_threshold = (power_mean + 2*(power_variance)^0.5): member\n", - "2J63OSU3ZC 33.741046\n", - "BUAULG5KFB 25.289450\n", - "LPH58QI85F 20.522517\n", - "PW5UJDRM35 436.358684\n", - "dtype: float64\n" - ] - }, - { - "data": { - "image/png": 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a+HDNAQY/2MKu0oRVVVmy7hD3tqxV7vEoCuQVWFj81X76dG7CPc19SE7LZtlP\nR3nlibZ4uRlAAUr5Gbbn2AViDqYwqn/bIiUGe6mo/OeHP2nRwIMH72vAL7vPkXIxh+cfaYWqQlJa\nNst+PsoD7euzeV8Srz11z7VfwaXEdCopk282n2TUk+1wu4VfzHFHzhP7Zyqj+rdFe12jw/KupeqW\nfDGH8I93AfDOmG4s//koXm4GElKyCO7fDg8Xh2Lr/H7sAh+sPcjAHs2K3c/ddyKNzfuSCHmyLQ5X\n7pUrSmHJYOGqfdT1dmb80+355PvDOOi13H2XOwO6NyUn38yYd7bS9Z56vNjHD4Bdf6by7+8OM6p/\nW/xb17H7mKyqyqIv93Ek4RLhL/jT5LpWv4oCFqvK26v3FymtvD++BwYH7bXLtoTrJCffzAdrDvBE\n16a0blS8hHcmNYsvfz3OsXOFtQALQu4v/CxQ9JrteW8DLFYrc1b8zpmULMY/3Z62Tb2xWlVeWbAJ\ngI9DHyz1Wr1KReWTH47QrL47D97XoNj8JNtnsg37TqQRn5LFi4/5onBDLZRy7Y9adBKqev2bogut\n23aa9Mw8LlzK5ZleLbm7XtHEsH7XGb7ZfIqX+/rRuU3x75TvdsSTk2emc9u6vPH5bgDeHtONZf87\nwt7jaXi5GXjliTa0vPFc3xDH9U4lZ/L1ppOEPNmO9Mt5rN54nOD+7cgrMBfmhMf9ipXcK8o7X+/n\n4KmLzB75f9S9xfv6cUfOs+TbQ7ZbbBXhlkrEv/zyCxs3bmTu3Lns37+fJUuW8MEHH5S6/MDQ72hY\n25XwF/z/UrBCCCFEddp77ALvRR/gmV4teLRT4zKXrdQS8Z49e+jevTsA7du35+DBg2UubzJb0Wul\nCbsQQoi/N92V9ilHrrTXKZUCQ/u0sW+btxKI0WjEze1aptfpdFit1iJ9jK/33Vv9b2U3QgghxG2l\nV203ev3f3RW6zVvqle/q6kp29rXxX8tKwkIIIYQo3S1lzw4dOrB582YA9u3bR6tWrSo0KCGEEKKm\nuKXGWte3mobCJzI1bdq0woMTQggh7nRV0o9YCCGEECWTG7tCCCFENZJELIQQQlQjScRCCCFENZJE\nLIQQQlSjcgf0WLNmDdHR0SiKQn5+PkeOHOG///0vc+bMQaPR0LJlSyIiIqoiViGEEOKOc1OtpmfN\nmoWfnx8bN25kxIgR+Pv7ExERQffu3QkMDKzMOIUQQog7kt1V0wcOHODEiRMMHjyYQ4cO4e9f+ACH\nHj16EBNFK+inAAAgAElEQVQTU2kBCiGEEHcyuxPx0qVLGTNmTLHpLi4uZGVllbDGNdJVWQghhCiZ\nXQ99yMrKIj4+noCAwucJXz+udHZ2Nu7uZT9LV1EULlwoO1kLIYQQdxJ7H4NoV4k4Li6Ozp072977\n+fkRFxcHwJYtW+jYseMthCiEEEIIu0rEp0+fplGjRrb3oaGhhIeHYzKZaN68Ob179660AIUQQog7\nWZWNNS1V00IIIWoSe6um7SoRL126lI0bN2I2mwkKCqJDhw6EhYVJP2IhhBDiLyr3HnFsbCx79+5l\n1apVLFu2jISEBObOncuECRNYsWIFVquVDRs2VEWsQgghxB2n3ES8bds2WrVqxejRowkJCaFXr14c\nPnxY+hELIYQQFaDcqumMjAySkpJYsmQJZ8+eJSQkBKvVaptvTz9iIYQQQpSs3ETs6elJ8+bN0el0\nNG3aFIPBQGpqqm2+Pf2Iwf6b1kIIIURNUm4i7tixI8uXL2f48OGkpqaSm5tL586diY2NpVOnTmzZ\nsqVIH+PSSKtpIYS4M+zdu4cZM6bQtGkzrFYrZrOZiRND+eqrVQQGPkqnTtdyQv/+j7Ju3U+296Gh\n4wGYN+9t27TBg/vxxRffoNfrAUhIiGfBgrm8994SxowJZvLkqTRu3KRIDElJiXzwwbukpV3AYDBg\nMDgSEjKGpk2bkZmZydChA2nWrAUAPXr0ZNCgIUX2k56exsSJYxk6dBj/+Ed7XnhhCK1b+6GqKoqi\n0KGDP717P26bbrVayc/P45VXXiUg4P/sOk8V1mq6Z8+e7N69m0GDBqGqKjNnzqRBgwZMnz5d+hEL\nIUQN1bFjADNnzgYgLm4X//73R3h6epawpGJ7lZqaQl5eHhaLheTkJO66q36xZWxrKcWnXZWfn0dY\n2ATCwsJp06YdAEeOHObtt+fz7rsfcezYEQIDe/PPf04qMZYLF84zadI4Ro4MoVu3HqSkJNO0aXPe\nffejIkvfOP3s2QSmTZvMsmVflnFmbp5d3ZcmTbrxYGD58uUVGogQQoibE33ie/aeP1Ch27yvzj0M\nbNG33OWuH4IiM/My3t7eRdoPleSHH76le/cHMBgciY7+ildfHXdLMW7btpWOHTvZkjCAr28bW8I8\nevRPjh49zGuvvYK3tw///OckvL19gMLkOmXKRCZMCKVDB/8Sj6f048zEy8vnlmIui12JWAghhLje\n77/vZuzYURQUFHDy5HHmzFnAL7/8VOryqqryyy//Y+nSz9FoFJ5//hlGjgzBwcGhxOXLKhEnJyfS\nsGFD2/spUyZiNBpJT0/j3Xc/okmTpvj6tqFjxwB+/vl/LFo0n6ioeYBKeHgYjo5OpKenFdlmfPwp\nxo4dZauanjEjqsh0s9nM8ePHGD9+8k2cJfvYlYgHDhyIq6srAA0bNmTUqFEyoIcQQlSzgS362lV6\nrQzXV02fPZtAcPCLdO/+AAUFBUWWu1pK3rlzB7m5uURGTuNqIfOXX/7H44/3w2BwwGQqsN0jzsnJ\nxWAwlLrvOnXqcfToYdv7uXPfAiA4+EXMZgsdOvjj6OgIwAMP9OSTT5bYlp06dQaenl6EhIygdWs/\n271ne6qmMzIuMnz4UDp2DKBu3Xo3d8LKUG4/4qsnddmyZSxbtow5c+bIgB5CCFHDXV9l6+nphaJA\nq1a+bN680TZ9//693H13UwB++GEdYWHhLFz4Lm+99S6RkXP45pvVALRu7ctvv/1qW2/nzu34+ra5\nfm9F9t29+wPs3h3H4cMHbdPOnTvLhQvnURSYNy+KTZsK44iLi6V1a1/bck2bNqdOnbqMGTOe8PBQ\nW46zp2ra1dUNR0dHLBaLXefIXuWWiI8cOUJOTg4jRozAYrEwfvz4YgN67Nixg8DAwAoNTAghxO1r\n7949jB07CkXRkJubw5gxE3j44d4sXnyal156DicnZ/R6PaGh08nIuMjhw4eYNetN2/r33NMek6mA\ngwcPEBIylvnzZ7N27TfodFrq12/I5MlTbcuGh4fZqrDvu68jo0ePY968t/nww3e5eDEds9mMVqtl\n3LiJ1K1bj5CQscydG8m6dd/g6OhIaOj0K1u6Vt3ds+dD7Nq1k7feepMXXxxZalX4mTOnrxynQl5e\nHv36DaB+/QYVei7LfejDsWPH2L9/P4MHDyY+Pp6RI0eSn5/Pli1bANi5cyfR0dHMnz+/zB1J9yUh\nhBA1SYV1X7r77rtp0qSJ7bWnpyeHD1+rm5cBPYQQQohbV24ijo6O5ujRo0RERJCamorRaKRr164y\noIcQQghRBnsLoOVWTZvNZqZOnUpiYiKKojB58mQ8PT2LDOgRFRVVZlNzkEQshBCiZqmwRFxRJBEL\nIYSoSexNxOV2XxJCCCFE5bErEaenp9OzZ09Onz5NQkICQ4cOJSgoiMjIyMqOTwghhLijlZuIzWYz\nERERtlFKZDAPIYQQouKUm4jnzZvHs88+S506dVBVtdhgHjExMZUepBBCCHGnKrP7UnR0ND4+PnTt\n2pWPPioca/P6p2u4uLiQlWVfIyzpRyyEEEIUV24iVhSF7du3c/ToUUJDQ8nIyLDNt3cwD5BW00II\nIWqWChlZa8WKFbbXw4YNIzIykvnz5xMXF0dAQIDdg3kIIYQQomQ3/Tzi0NBQwsPDbYN59O7duzLi\nEkIIIWoEGdBDCCGEqAQV9tAHq9XK9OnTOX36NBqNhsjISBwcHAgLC0Oj0dCyZUsiIiL+csBCCCFE\nTVRuIt64cSOKorBy5UpiY2NZtGgRqqoyYcIE/P39iYiIYMOGDfI8YiGEEOIWlNuPODAwkDfeeAOA\npKQkPDw8pC+xEEIIUUHsGuJSo9EwZcoUoqKi6Nu3L9ffVr6ZvsRCCCGEKMruVtNz585l0qRJDBo0\niPz8fNt0e/sSy4AeQgghRHHlJuK1a9eSmppKcHAwBoMBjUZDu3btiI2NpVOnTnb3JZZW00IIIWqS\nCnsecV5eHmFhYaSlpWE2mwkODqZZs2ZMnz7d1pc4KioKRVHK3JEkYiGEEDVJhSXiiiKJWAghRE1i\nbyK2q7GWEEIIISpHmfeIzWYzU6dOJTExEZPJxKhRo2jRooUM5iGEEEJUkDIT8bfffouXlxfz588n\nMzOT/v374+vrK4N5CCGEEBWkzKrpPn36MG7cOAAsFgtarVYG8xBCCCEqUJmJ2MnJCWdnZ4xGI+PG\njWP8+PEymIcQQghRgcrtR5ycnMxrr71GUFAQjz/+OAsWLLDNs3cwD5ABPYQQQoiSlJmI09LSGDFi\nBDNmzLAN2uHn50dcXBwBAQF2D+YB0n1JCFG+jLxLrDnxA/2bP4aPk1d1hyPEX1Ihj0FcsmQJmZmZ\nfPDBB7z//vsoisK0adOIioqyDebRu3fvCglYCCG+Ov4t+y8cJMecy2v3vlzd4QhRJWRADyHEbeO9\nvf/mSMZxmro3YWLH0WSbc3DVu1R3WOI6RlO2/E/sVKEDeuzfv5/nn38egISEBIYOHUpQUBCRkZG3\nHqEQQpTiQm4aP53ZSOjWSP68eMw2PSHrHMcyTmK2mqsxuuqTkXeJ9NyMatv/xrNbCd0ayf4LB6st\nhjtRuYn4448/to0rDYVPYZowYQIrVqzAarWyYcOGSg9S/H0duXicr46tq+4wxN+M0ZTNb2e3AbD/\nwiEAzmUlMS/uXRbvXVIl19TZrEROXY6v9P3cjOk75jAjZi4FFhNxKXspsJiqdP9bzxV2V/344Ioq\n3e+drtxE3KRJE95//33b+0OHDkk/YmG39/b9m03ntpOSfb66QxGlOH35DFsTS/4cJxqT+TVhC1V0\nB4sjGcdtr42mbACsqgWAlOxU27xtSbsqPZY34xbz1p4PKn0/5Tl1+QxbE3cWmfbD6Z/57PBK3ti1\nsEpjOZ+bBoBVtVbpfu905XZfevjhh0lMTLS9l37Et7+DaX9yNOMErnoXPAzudL7Lv7pDIqsgi3ou\ndao7DFGChXsKf2h3qNMeF70zUJgEo49/z66UPQC08mpOI7cG1RKf9cp3TtX8FLj9vHXl/6PTXPu6\nPpeVBMDFvAzyzPlk5F/ilzObGNyqP1sTY3DVu3B//U6VGteWczvo0fD+St1HTVFuIr6RRnOtEC39\niG9PH278tMj7J/7xIKqqoiiK7S9Q5HVlSyw4x/o/NjCp6yt4ONp3zVS0A6lH+PrQD0zuNgpXB2ls\ncqPXt85k2cC3MegM/LDvf7YkDIWlw9XPfFgtccUkx9GxcVtc3QxFpteq5Vpp1+/Zy0m211naizTz\nblKln5eSrPhzte319TUHO9Ji2HQ6hvTcDO6uVZ91J9cD0L/9Q5Uaz5fH1jLw3kcAin2nnLx4hs9+\nX824+0dQy9m7UuO4Xd1MLdJNJ+I2bdpIP+K/mae/DAGgvks9krJTuP+uAFRgV8oe3n4gqsgv7cry\n1aEfAPg09muG+g7CZDXjoNUDUGApwEHrYLvfdXV6RXtj02IAfji4mV6NulfKPm6G2WpGVVX0lXS8\nt+K7A5tYc+J7zFeqg68377ePeNZ3IE46pyqP61+7Pis27ZnVo1nQPRJnfcXHMzfmWpV02C9v2l6/\n1v5llhz4nGdaD+C3s1tp4dmUfs364KgzlLSZKvHN4R9tr788+J3tdVV85z6zejQAz7YeiFbRsuLI\nV0Xm/2fXal5q91ylx3G7+fjgCvae/8PuH683/Q0cGhpKeHi49CP+G0rKTgFgR3KcbdqKP7/ikSYP\nUt+1XpXEsCM5jmxzLvsvHKTLXQFoFA3bk3ahUTS2+07jO4TQwrNppcWgUPjr/XzOBf68eJyOddqT\nZ8nH3cGNS/mXqONcu9L2fVWBxcT4zdMAePuBKLSKln0XDtLGpzVZBVlVEsPVOK731fHSG0HtOb+f\nPef3M8zvGf7vro6VEEvBTa+zNTGGR+/uVeGx5JnzSpz+r/0fA9dKp4nGZDaf28G7Peei1WgrPI6/\ni5VHo0ucfvzSqSqO5Paw9/wfN7X8bdOPOCPvEuE75qJW4J2gkH+8SLtafnYvbzRlE7q19C5ZM/5v\nEnVvuM+pqirL/vyS2JTfS1zHx9Gb9LyLdsdwO3HVu9gazNQkCkqFXoeiODe9K1kmY3WHccdzc3Al\nq+D2Oc86jY4WHk1x0jmy98KBcpe/Wot3M26nz6+9JWK7+hFXhek75lT4yfvwj0/LX+g67+5dWub8\nWSW0UIxJjis1CQN/2yQM1MgkDNw2H+I7mSThqnE7JWEovB1zJOO4XUkYuOkkDH/Pz+8t3RxUVZWZ\nM2dy9OhRHBwcmD17No0aNaro2Kpcjin3ptcxFtTMZCWEEKJi3FIi3rBhAwUFBaxatYr9+/czd+5c\nPvjgr/W3u7d2O/ZdOMjzfk/j4+hNniWPAosJb0dPLuSmA9DYrSFGUzZ6jQ69Rs/FvAy0Gi2qqmKy\nmvAwuKPX6NEqGt7Y9RaOWsebiqGNT2u2J+2iV6PuNHStT645Dx8nL1z1Lizc8z5N3ZsUW8fDUNgC\n2M+7FR3r3ouXwQMFBYtqwVHniLuDG4nGZACMJiOueleSs1P47tRPALx278soKDjpHDFZzahX7pNe\nzs8k15KHi94FVVUxmrKp41QLrUaL0ZSNxWrBy9GTHFMOZtWCm96FgivraxQNjdwaoCgKTlpH8i0F\npOddxKJa8HBwJ9GYjIPWASedI1bVislqxmjKxqBxQKvRotfoMVtNeDt6c86YiJPOCb1GT74ln5Sc\n89zlXBeNokGr0aKgoFEUjKYc8i35NHC9izOZZ6ntVIu03HRc9S62+2qDWvajrU9rsgqy0Wm0+Dh5\no0HBaMom15yHQetAVkE2bg6uXMzLoLZTLc7nppFZkIWT1oCD1gEFhQKrCVW14m641hL/Ym4GZ41J\nbEjYDBTeltBpdFhUC656F7JNObjonSmwFKAoGowFRtwN7igoLNzzLwCe8x2Em4Mrjd0akZpznhxz\nLl4GD7IKjDjqHMksyEJVrbg5uGGymnDSOdn+1yarCXeHwnisqhUHjZ70vAxUwGI14+rgglbRotPo\nUFDIMedi0DpgVa3kmvPwMnhgNOVQx7kWKdmpOOmc0CgazKqZPHMeeo0DAFqNBovVQqIxGW9HL6xX\nzkNmfhbOeiesqoqDRk9G/mWcdU6cz7mAXqvH0+BBVkEW7g5uWFW18NrSORfGqnXAzcEVdwc3UnPO\ncyn/MnWca+Og0aPX6rmcn4lO0WJWLXgZPLmUfxmLakWraEjPu4iTzgmtosVsNaNRNBRYCsi+Upvi\n7eSNVtFwKT8TJ50jueZcdBod9V3qkZCVCKhoFA0mqxkPB3csqgVPgwdW1YJW0aEooFW0JGenYrKa\naeB6FzqNlqwCI7nmPIymbJx1TqioXMzLQEHBWe+Mg0aPgoIVK7Ucvckx52Gymrh05bzkWfLxcHBH\no2iuXL/ZWK98dhy1BiyqlWYeTbCoFhQ0nLh0CkedI2//XljV+Mb9U8gsyKLAUkCuOR8XvTNGUzb5\n5vzCz5nBHTe9a+E1bcq2nQO9RodOo8NF78zFvAxyTLl4GNxRAWedE/mWfLwcPck25aC/0ojSWJCN\nt5MXLjrnwmsQFa2iobZTLXLNeaTknMfb0RMHjQPJ2al4GjxIzk5Bq9FRy8mbzPxMtBodOaYcHLR6\nDFoDJqsZq2q1/c9UVcVR50hWQRZ1XeqQnnsRraLFipUCSwH5lgJc9C44aHTUcvIh15xHHedaGE3Z\nZORdxlnnyKX8TDwM7uRZ8jBoDdRy8uFM5llyzblk5F3C29GLOs61bMeakXcJnUZHviUfs9VMZoER\nR52BfEsBrnoXnHROuDu4ciE3nXxLAQatAwatgbNZiTRxb4iCwqX8y+RZ8jFoHci3FKDX6KjjXNtW\nE2BVLeg1DleOIR9XBxesqopeoyOzIAu9Ro+DVo+zzomsgmwu5V+mrnNttBotJquJbFMuVtVSeO25\n1ENRNORZ8sg15dm2Bwomi4lccy6KosHdwZXLBVmYb2KwlVtKxHv27KF798JWp+3bt+fgwbKHO7uc\nl0l67qUyl8m50jji3trtcNQVTaBNPYonQKDMBkYNXetzPjftpoaDyzHlAPBAw67Ucira5F6v0VNg\nLSi2vcsFmQAE1L2v1AYsN27L6boWln7ereyO71Y5a5xw1l/rA+rl6Gn3ujc+AcfXu2W569S90tCo\nqUfjItO9HT2p41y7WEMk5yt9VwHquVz9W3gvvrazj11xNvO4G48MD1sivpm2AVdd3+/Sw/DXu9vd\n2J6g3OWv/HVzcC132WKfiRvCbUxDAFp6NbupGBq5NSjWX/jGcYWvb6VsbyO/kj7BN3Md3risp8HD\n7nUrwo3Xk7ejF96O9j0dqm4p02+lX/aN3e5cHVxo4XCtYWNLh8L/9/Wfm7q30PCvvHWuHrmnwcP2\nvyjpei+r0aU91zlcK+xc1citvu11adffrRxzvWrs0XhLidhoNOLmdu2Tr9PpsFqtRfoYX2/kulD7\nA6qgrjQOWj0FlgJmxMy96XX1JcTgoNWTaEwudXs30wVFr7l9uqtUJYO2crt4VFa3JyGEqEy31Gr6\nzTff5N5777V1XerZsyebNm2q6NiEEEKIO94ttZru0KEDmzcXVgHu27ePVq0qv3pVCCGEuBPdUon4\n+lbTUPhEpqZNK28ABiGEEOJOVWUDegghhBCiuNtmQA8hhBCiJpJELIQQQlQjScRCCCFENZJELIQQ\nQlSjckfPWLNmDdHR0SiKQn5+PkeOHOG///0vc+bMQaPR0LJlSyIiIqoiViGEEOKOc1OtpmfNmoWf\nnx8bN25kxIgR+Pv7ExERQffu3QkMDKzMOIUQQog7kt1V0wcOHODEiRMMHjyYQ4cO4e/vD0CPHj2I\niYmptACFEEKIO5ndiXjp0qWMGTOm2HQXFxeysrIqNCghhBCiprArEWdlZREfH09AQEDhStc93CE7\nOxt3d/fSVgUKR+ISQgghRHF2PeooLi6Ozp072977+fkRFxdHQEAAW7ZsKTKvJIqicOGClJqFEELU\nHLVr2/c4VbsS8enTp2nUqJHtfWhoKOHh4ZhMJpo3b257CpMQQgghbk6VjTUtJWIhhBA1ib0lYhnQ\nQwghhKhGdlVNL126lI0bN2I2mwkKCqJDhw6EhYXJgB5CCCHEX1RuiTg2Npa9e/eyatUqli1bRkJC\nAnPnzmXChAmsWLECq9XKhg0bqiJWIYQQ4o5TbiLetm0brVq1YvTo0YSEhNCrVy8OHz4sA3oIIYQQ\nFaDcqumMjAySkpJYsmQJZ8+eJSQkBKvVaptv74Ae9t60FkIIIWqSchOxp6cnzZs3R6fT0bRpUwwG\nA6mpqbb59gzoAdJqWgghRM1SYf2IO3bsyPLlyxk+fDipqank5ubSuXNnYmNj6dSpk10DegghhKgZ\n9u7dw9q13xAZOcc2bcyYYPLz83FyckJVVXQ6HdOmzcRkMhERMZUlSz61Lbt27TdkZFzkxRdH8s47\nCzl48A+cnZ0BePPNt9BqtcycOY2srCz0ej0zZryBl5c3ZrOZzz//Dzt37sDJyQmdTsfLL4+iTZt2\nAPTv/yjr1v1ERsZFIiOnYzabcXf3ICIiCoPBwOrVX/Ddd2vx8vIGYPLkqej1el54YQitW/uhqiqK\notChgz/Dh79coees3ETcs2dPdu/ezaBBg1BVlZkzZ9KgQQOmT58uA3oIIcRt7MJXq8jaHVeh23Tz\nD6D24CFlLqMoSrFp4eGzaNSoMQBr137NypUrGDTomRKXverYsSMsWvQe7u4etmlr135D48Z3ExIy\nhu++W8sXXyzn1VfH8eGH76HT6fj3vz8HICUlhddfH8f8+e9Qr95dQOF+vvzyC3r27MWTTw5i6dIP\n+P77dTz11NMcPfon4eGzaNXK17avlJRkmjZtzrvvfmT3+bkVdnVfmjRpUrFpy5cvr/BghBBC3JlU\n9VrboszMTJycnK5ML3lMKVVVOXfuLPPnzyY9PZ2+ffvz+OP9MBgMZGZmAmA0GtHpdJjNZn77bQNf\nf/2dbf169erx1FPP8OOP3/HSS6/Ypjs4ONjWz8nJpm7degAcPXqE5cs/Iz09jS5duvH888PLjK8i\n2ZWIBw4ciKurKwANGzZk1KhR0o9YCCFuc7UHDym39FpVoqJm4ujoiKJoaNy4CaNHj+Xy5UvEx59i\n7NhRQGHSS09P4+GHe5Obm8ugQc/wzDPPYbFYGDt2FH5+bXjggQdZseIzgoKeJisrkw8++JjLly/h\n4eFR5IFEAPXq3cWhQweuvCtMqI8/3o/g4Bf55Zf/YTKZeemlYAACAx9l4MDBODu7MHXqJGJittG0\naXNbfFerpmfMiKJWrVoVem7KTcQFBQUALFu2zDYtJCSECRMm4O/vT0REBBs2bCAwMLBCAxNCCHHn\nuL5q+qrLly8Vq/q9eo/YycmJQYOGYDAYAOjQwZ8TJ47z9ddf8swzz9Gv3wBOnjzB1KmT+c9/lnP5\n8mWsVmuRZHz27Bl8fK4mzcKq6Xnzopg2bSYBAf9HTMw2oqJmMH/+OwwePAQXl8ICZ5cu3Th27ChN\nmzavkqrpcvsRHzlyhJycHEaMGMHw4cPZv3+/9CMWQghRqpKqc8uqgi5JQsIZRo9+GVVVMZvNHDiw\nn1atfMnLy7MlTE9PT3JyctDpdPTq9TBLl36AqqqsXr2Sd95ZyOrVK+nd+/Ei283NzcXFxQUAH59a\nZGVlkZ1tZNiwIeTl5aGqKnv2xNG6tV+Z8VWkckvEjo6OjBgxgsGDBxMfH8/IkSOLBGZvP2IhhBA1\nw+7duxg5chgAqgrp6WmlLltaY60mTe6mT5/HeeWV4ej1evr06cvddzfllVdGM29eFNHRq7FaLYSF\nTQcgJGQMn332McHBL6LT6VAUBR+fWpw5E0+TJnfbtjthwuu8885CNBoNqqoycWIYLi6uhISMYcyY\nV3BwMNCxYwCdO99PSkpymY3JKkq5T18qKChAVVVb9cDgwYM5fPgwhw4dAuDXX38lJiaG6dOnV3qw\nQgghhL0KCgo4efIkfn5+1R1KmcotEUdHR3P06FEiIiJITU3FaDTStWvXm+5HLAN6CCGEqGq1ajWs\ntvxj74Ae5ZaIzWYzU6dOJTExEUVRmDx5Mp6enkX6EUdFRZVbfJdELIQQoiapsERcUSQRCyGEqEns\nTcTltpoWQgghROWxKxGnp6fTs2dPTp8+TUJCAkOHDiUoKIjIyMjKjk8IIYS4o5WbiM1mMxERETg6\nOgIwd+5cJkyYwIoVK7BarWzYsKHSgxRCCCHuVOUm4nnz5vHss89Sp04dVFWVwTyEEEKIClRmIo6O\njsbHx4euXbvaBvGwWq8N3C2DeQghhBB/TZn9iKOjo1EUhe3bt3P06FFCQ0PJyMiwzc/Ozsbd3d2u\nHdnbekwIIYSoScpMxCtWrLC9HjZsGJGRkcyfP5+4uDgCAgLsHswDpPuSEEKImsXeAqhdj0G8Xmho\nKOHh4bbBPHr37n3TwQkhhBCikAzoIYQQQlSCCisRW61Wpk+fzunTp9FoNERGRuLg4EBYWBgajYaW\nLVsSERHxlwMWQgghaqJyE/HGjRtRFIWVK1cSGxvLokWLUFWVCRMm4O/vT0REBBs2bCAwMLAq4hVC\nCCHuKOX2Iw4MDOSNN94AICkpCQ8PD+lLLIQQQlQQu4a41Gg0TJkyhaioKPr27cv1t5WlL7EQQghx\n6+xuNT137lwmTZrEoEGDyM/Pt023ty+x9CMWQgghiis3Ea9du5bU1FSCg4MxGAxoNBratWtHbGws\nnTp1srsvsbSaFkIIUZNU2POI8/LyCAsLIy0tDbPZTHBwMM2aNWP69Om2vsRRUVEoilLmjiQRCyGE\nqEkqLBFXFEnEQgghahJ7E7FdjbWEEEIIUTnKvEdsNpuZOnUqiYmJmEwmRo0aRYsWLWQwDyGEEKKC\nlJmIv/32W7y8vJg/fz6ZmZn0798fX19fGcxDCCGEqCBlVk336dOHcePGAWCxWNBqtTKYhxBCCFGB\nygHjzw0AACAASURBVEzETk5OODs7YzQaGTduHOPHj5fBPIQQQogKVG4/4uTkZF577TWCgoJ4/PHH\nWbBggW2evYN5gAzoIYQQQpSkzESclpbGiBEjmDFjhm3QDj8/P+Li4ggICLB7MA+Q7ktCCCFqlgrp\nRzx79mzWr19Ps2bNUFUVRVGYNm0aUVFRNzWYB0giFkKIvzvVaiXjp/W4dvDHoW7d6g7nticDeggh\n/nYsRiMXVq/C+/En5Iv+NmTcu4ek99/7//bOOzyqKn38nymZdJIAoSWhhN5EUYqwKIsFkFV/urhf\nFGygAuriguyCSBUU1LWsyi5YkKUIsoCuK9IiAtIMCUWKSUgISUjvPZly7++PyUwyySQzCZlCcj7P\nw8Pkzp173vue8p7ynveg9PGh10f/dLU4bo8I6CEQCG46cr//jqITx0hft9bVogisUHL+HABSWZmL\nJWlZ2GWIz58/z5NPPglAcnIyTzzxBNOmTWPFihUOFU5w8yNLElKN07oE7oc75ZGpgTeUllj9Pv/g\nfuKeewZdXq4zxXJLpIpyp6dZdOxn82cnTaa2Cmwa4s8//9x8wAMYj0OcN28eW7ZsQZIkIiIiHC6k\n4Obl+vvvEv/STCSt1tWiCOohacVS4l+aWe/3lampyHq9U2QxGRd9Xl6d7wwlJWR/vQ2A4tORDpVD\nlmUqr6cgGwwOTccuWfR6KtNSLa4VR0US//Jsik6ecJFUkLLmzTrXDOXlaLOyXCDNzY1NQ9ytWzfW\nrq2eJrp06ZII6OHm6HJzKfvtsqvFAKA85jcASi9ecLEkgvrQpl4HjCPj2pTFxZK07HXSP1/vFFnK\nr1wxf9bl5lJ8JtrcCdBmZpi/y/nP1w6Vo+R0JEnLl5DyzmqHpmMP6Z/+i6Slr1voprBqZJqx4TNX\niUVFQnyda9cWv8a1RX+j5NxZSs5Gu0VH5mbA5j7i++67j9TU6t5YUwN6iH3EzuP4c88AMGzj5+gK\nCymOi6fT/a4JQxpX9X/6Pz8m/L+7XCKDoGFMedSujQaVt7fFd6kn0gAoiTrtlDocV1xk/pyz4VOK\nY2PpOvVxwv40meJ8P1Jq3OtIeQoTjUavIiEe3S8/03nSRLt2hziCuDPRAFRGn6TrqKEAxF+pyjVZ\ndmrbGlfr79ppxxUWAJD2yT8A6PHcdLo8OMkZot3U2DTEtVEqqwfRjQnoIbymnc+Zv8zHUGCsGFJY\nOB7tg10qT+rlBDTBHVyWvjYzg/wD+2k/+U91DI4AUs/H4N2zFwBSZSXZO7ZTHhdr/j7xwBH8bhvq\nNHmKY41p516KwSu7mIoCSwchR7Yp5eXVSymJn30B4f3wCHZt/cmKOITcvjMqX1+LNf2rew/hf8cw\nAEovX6I85jfaPzrZKTJd+ngdRcd+pseav1vMWJjIvRSLx8i7nCKLO2JvJ6nRhnjAgAFNCughcD4m\nIwyQ9MYyAsbchcrPH6WPL1mbNwLgd8cwvHqE03b8RIfLc+21v9Fr7XqUnp4OT8saqR99gC4zE3Vg\nIO0efNglMrgbNWe4UlavoucHH6Py96fwyE8UHvnJ4t60tR/R5/ONTpGlJqXnzpJ/cL95fdgZ6Gs5\ng6W89zbdl68ke8d2Co8eoduylXiGhaHNSCdr+zY6zXgOtb99g5IbIXv71jrX0tetJTck1LzEAOA/\nbASeYWEOl6cg4iAACX952er3YmraPhptiBcsWMCSJUvMAT0mTJjQLILIkgSyDDWnfyTJ+Lcso1Cr\nqzO1doVVKo33GQzIeh2ojK9VcypJ0lai1HgaA5Oo1Y2eZpL1emRJQqFUGtOrJVtjMZSXo/TwML63\nJIFKiUKhRDYYjO+q1xufrdEYv8c4SlF6ehrTx1jIze+hUhn1Uk9jJpWVkb9/X53rJVGnKYk6TdB9\n483pAKA0yoIkoVCpjNcUCuM/STI3mubvTHlnZZ2xJvEvzaTX2vXIej15P/yP/P37CJo4CY+gILJ3\n7qD7ytWoAwLM95v0YX4v04yMJBnf2VQmFApj/phkNN2vUBifoVSiy8w0/rS83KxPWadD6eVlfjfZ\noAcUgGzMD1lGoVIhG/TIekN1/gOyVotCozGXJ1mSqsqoSTfqap3IMqhUyHq9UWeyTPqn/0Lp6UXw\n41NRenoi63QoNJqqNVEZUKBQKpG0WuNvqvLaouzWeqY5L6rKqEl+s85MOpQkUCrR51oanIS5f24w\n/xLmziHklXl4hoYay4hOhyxJxrIsS2aZLcqCqSzVlL1mPlZ91mXX7+RjzQjHPfcMPd5+z1xeZFOd\nlCQUHh7VddSkE4UCWTKgUCiN7YGnF8iyOViRZTti6Zymz8khP+IghUePAJC69h90W7KC6x++hz4n\nh5xd/6Hjk89UP0+pNOe5uYyqVOb6JOv11Tow1R1T/a36bH6OHdQ0wgBJK5bQe93n1enXfGZVeTDV\nC2TZWIY0muo8qdKnrNMZ659CYbcsNTGUlVa3cabyYjCg1GiQZQmFUlVdb02YdCfLxnIjy8b8rCrf\nxnzGWHer2nRzO2wqT7Xy3iKPTToHY5nQ6YzPhya9Y3PgFgE9pMrKBr02AQLuGkvh0cPNIotCrTYW\nUjvJP7CP7B3b6/2+y0tzGjVll7llE4WHD9l9v6D58QrvScXVhGZ7Xvs//omcXTua7XmClos6qC36\n/Lpe4QLX0/vTDc1qjB0a0EOWZZYtW8aUKVN46qmnSElJsf2jBii7fNHmPc1lhIFGb8VoyAgDpDUy\n+IAwwq6nOY0wIIywwG6EEXZfdC7aetUkQxwREYFWq2X79u28+uqrrF59Yy7+klZXLZC3N55du9WZ\n7m076cF6f+875Fba3Dna7vQaO5XsP2y4xd9KH1+Lv/1uubVRz/OqcoipjSogsFHPsZDhDksZVTWm\nd28I07RmFeqgIIu/NaFhqPyMvT7vPn3r/Nzatcagbt/e8oJSiVd4OAD+d45q8nPb/uEhy8fegPNW\nm1Gjafvgwyh9fCy/UKkIuPv35j/VbdsZLzfjWqJflZOOCdMUW03HIoVGY/X9apdD89RkM6P08qp7\nrUYd8hkwsFE6afO7u6w6Hio8PPDu3Qd1u3ZNqkveffuhCQ0DhQKPjp0a/Xtr1NSxR1XIzqD7J9S4\nZkxHoVZX17UadU7h6Ynf7cbtoqbyUxNb5bZ2WwWgCQm1eq93v/5W06iNV49wm/cA1XnUyBHmjbSD\nAJpOnev9Tt22rUXZqV02DS6KGNb4xU0gOjqaMWPGADBkyBAuXrQ9om0IWWf0UOz47AwCRo+p9772\nj/yxwed0mvG8zbQS5s1B6e1j876aSFXBTHp++AkqPz/zdUN5OQl/no0sNdIhQTaub/ZZ/0XjfmeT\nF5v5ee5P5xkvNPm37f/fo80oCbR/+BGr1zs++bRdv9cX5COVl6Pp3MXiuizLlF+Jw7tXb5etYQma\nl+A/TXG1CIIa5Hy7i7zv/2e2Rc6mSYa4pKQEf//quW+1Wo0kSRZbm2ry64JFaEvrD8dmKDGGs1N6\nOKZHXhOFRoMuJ5ukFUvt/o3JiaT2iEFZNfooi4lp1PO0GekoHTT6ENy8qAODIDCoznWFQoHPDc4s\nCASC+lFU2Z7MjV+g9GqmrY0KBcEfv2/frU1x1lqzZg233nqr2WN67NixHD58uLGPEQgEAoGg1dOk\nea6hQ4dy5IjRjf/cuXP06dOnWYUSCAQCgaC10KQRsSzLLF++nNiqyDerV6+mR48ezS6cQCAQCAQt\nHaftIxYIBAKBQFAX4YIpEAgEAoELEYZYIBAIBAIXIgyxQCAQCAQuRBhigUAgEAhciM2AHt988w27\nd+9GoVBQWVlJTEwMW7du5a233kKpVNK7d2+WLVvmDFkFAoFAIGhxNMpr+o033qB///4cOnSIGTNm\ncMcdd7Bs2TLGjBnDvffe60g5BQKBQCBokdg9NX3hwgXi4+N57LHHuHTpEnfcYQxEftddd3Hy5EmH\nCSgQCAQCQUvGbkP86aef8uc/1z003NfXl+Li+s8aFggEAoFAUD92GeLi4mKuXbvGsGHGI9dqHu5Q\nWlpKmzYNH2EmYoYIBAKBQGAdu05fOn36NCNHjjT/3b9/f06fPs2wYcM4evSoxXfWUCgUZGeLUbNA\nIBDczMiyTGWFHi9vD1eLclMQHOxv+ybsNMSJiYmEhYWZ/16wYAFLlixBp9PRs2dP8ylMAoFAIGi5\nRB5N5MzJZB5+4la6dA10tTgtBqfFmhYjYoFAILi5Wf/uESSDzG0jwxg5tqerxXF77B0Ri4AeAoFA\nIBC4ELumpj/99FMOHTqEXq9n2rRpDB06lIULF4qAHgKBQCAQ3CA2R8SRkZGcPXuW7du3s2nTJpKT\nk1m9ejXz5s1jy5YtSJJERESEM2QVCAQCgaDFYdMQHzt2jD59+vDiiy8ye/Zsxo0bx+XLl0VAD4FA\nIGhlKFwtQAvF5tR0fn4+aWlprF+/npSUFGbPno0kSebvRUAPgUAgaF2I0BDNi01DHBgYSM+ePVGr\n1fTo0QNPT08yMzPN39sT0APs9x4TCADiY7Lo2LkN/gFerhZFIBBUoVAoABkfH41o05sRm4b49ttv\nZ/PmzTzzzDNkZmZSXl7OyJEjiYyMZPjw4XYF9AD33L509mw0S5e+Ro8e4QBotVpGjhxFRMQBtmzZ\nAcDBg/tYtWoZ//3vfgIDA8nISOe1117lyy+/YuHCeRQVFaFWq/H09OTdd//Bhg2f0q5dex5++FFz\nOjNnPsuKFavp1KmT+dpjjz3EV1/tYvPmLzl4cB/BwR0wGAx4enoxe/bL9O7dl717v+fzz9cREhKK\nLMuUlpYwePAQ5s79G2fPRjNnziyWL3+Le+65z/zcp5+eQt++/Vm0aBmHD//I1q3/RqFQct99E3js\nsSkATJ8+DT8/PwA6d+7Ca68tdbiuG0NBXhnbPovEQ6PiuXljXC2OQCCowjQQLivTumWb7m40W0CP\nsWPHEhUVxeTJk5FlmeXLlxMSEsLixYtbRECP228fxvLlbwKg1+t5/PFH8fT0oqiokDZtAjh16gRj\nx97DqVPHmTBhEmfORDFy5GgArl+/bjbYDWHsRda5av40Zco0s+FOTr7Ga6/NZ+PGbQDcf/9EZs58\nyXzv7NkziI2NAaBbt+78+OMBsyG+ejWeiooKACRJYv36tXzxxRa8vLyYNu0xxo+fiJeXNwAffbSu\nMWpyKuVlOgB0WoOLJREIBALHY9f2pfnz59e5tnnz5mYT4sShBK7GZDXb8wDC+3Vg1DjbG85rxjMp\nKSlBpVIxcOAgfv31HKNH38XVq/EsWLCEr77axIQJkzh7NppJkx4iPz+PkpJiFiyYS0lJCVOnPs2o\nUb+zmYYtunbtTt++/fn113NW5SstLcHPz4+yslJ69uxNSkoyZWWl+Pj4sn//Xu6/fyKZmRkolUq2\nbt2JUqkkPz8PSZJRqz2Ij4+joqKcefNexmCQeOGFFxk4cJDd8jkD4RDSeinML+fw3ljuGt+HoHY+\nrhZHUAuDXrJ9k6DR2GWIWzJnzkQxZ84sFAoFarUHf/nLXzEYDJw9G027du3p128A/fr1Jz4+DlmW\niYuLYeHCJeTm5vD449N47LHHKSwsZPbsGQwYUL9BszooroegoCAKCgoA49T4pUsXyMnJxtfXj6ef\nnkFISChZWcZ1+rFjx3HkyE9MnPgHfvvtEtOmPUNmZgZgPJzjyJGfeP/9txk1agze3t54eXnxxBNP\n8oc//D9SUpKZP38O27bttjjIw+UIS9xq+fngFdKSCzi8N5ZHpt3manEEAqdglyF+9NFHzWuKoaGh\nzJo1q1kDeowa19Ou0asjqDk1baKsrJQtWzbi6+trnoYeOHAwx4//TEhIGCqVirZt2/Hww39EqVQS\nFBREnz59SU6+hqenJzqd1uJ55eVleHp6UVJSYtYj1DdlDRkZ6Ywdew/Xr6eYp6bT09OYP38OoaFd\nLX5/330TePfd1XTu3IUhQ26rM/q+++7fc/fdv2fVqmXs27eHe+8dT0iIMW54WFhX2rQJIDc3h+Dg\nDk1XokDQTMiSsfyKE9sErQmbwyCt1mhUNm3axKZNm3jrrbdafEAPHx9fNBoNUVGRDBs2AoARI+7k\nq682MWLEnQBER59myZIFAJSVlZGYmED37j3o06cfx44dxWAwrm+mpl5Hp9MRGBjIK6/MJiMjncrK\nCmRZQq029oNqNjpXryaQlHSNgQMHW8jUuXMX5s79G4sXL6CystLiekVFOTt3fs348Q+Yr5eVlfLy\nyy+g0xnXW728vFEoFOzZ8x2ffPIhADk52ZSXl9GuXftm1Z9AIBAI7MfmiDgmJoaysjJmzJiBwWBg\n7ty5dQJ6nDhxgnvvvdfhwjqTW28dytmz0fj4GNephg8fycqVS1m6dBVgNMxRUZHMnPksKpWKWbP+\nTJs2AQwbNoJffz3HjBlP4ufnhyzLLFnyBgDPPvscr7/+N2RZ5qmnppvT2rHjKw4dOohCocTDQ82q\nVe9YnSq+447hDBs2nC++WM+dd442X7/nnvvYv38voaFhpKZeB4ydifHjH+Cll57Hw8ODnj17MX78\nAxgMBlavfoOXXnoehULBwoVL3WtamvpnCgQCgZGKch2xFzMYeGsX1B4qp6cvJiyaF5unL8XFxXH+\n/Hkee+wxrl27xvPPP09lZSVHjx4F4NSpU+zevZt33nnHKQILWj7Xk/LZ8NExAJa+96CLpRE4ky3r\nT3I1LofQbkFMn2Pd+VEA27+IJO5yJmPu683vJ/RzWrpvvPo/AO4c25P7HhzgtHRbOjZHxN27d6db\nt27mz4GBgVy+fNn8vb0BPcSeM4G9FBSUmT+LctO60FZtWdPpDSLvGyAjrRCAzLQil+iprKxS5I8d\nNNsxiLt372bNmjUAZGZmUlJSwujRo4mMjATg6NGj3H777TcgqkAgEBiRqpy1MlOL6nxXWaFj48fH\nuXwuzdliuS0yYo64JWBzRDx58mQWLVrE1KlTUSgUrFmzhsDAwBYT0EMgELgPRQXl9X6XkphPeamO\nI/viGHBrFydK5XpKiivx8lajVjt/PVjgeGwaYrVabXX9tzkDeggENRG+Wq0Xe7NeluVW49RXWaFn\n89qTBLXzYcrzw10tjsAB2OUum5uby9ixY0lMTCQ5OZknnniCadOmsWLFCkfLJxAIBADoddUhT1MS\n810oiXMpLzNuIc3PLbNxp/NIis91tQgtCpuGWK/Xs2zZMry8jKfgtPQ9xAKBwIU0MMqNOp5k/pyf\nW+oMadyCBgf+LloiLsirfwlB0HhsGuK3336bxx9/nA4dOiDLcp09xCdPnnS4kIKbl4SYLH76IaZR\nkZJay5SjoGEK8y0b++LCCqelra3Us2/3RbLS6zqNOZ+69UHUkZZFg4Z49+7dtGvXjtGjR5sbUkmq\nDvrt6+tLcbFwYRfUz4FvLxPzawYlRZW2bxa4JSZPZmfT4AEDDhbp4plUEuNy2PXvM6QlFzg2sUaw\nb9dFju6PEyFAWxgNOmvt3r0bhULB8ePHiY2NZcGCBeTnV6/N2LuHGOzfTyVomQQF+dp9mo6+snot\nUJQbx6PXG6is0OPr51nnu9zsEv71zmHGPdCP393T2+GyqFTVY4PAIJ9689/Xz9OhZSPht2zz58N7\nY3llsesiB546dNX8OfFKDoC5Lnl6erisjoi62Xw0aIi3bNli/vzUU0+xYsUK3nnnHU6fPs2wYcM4\nevQoI0eOtCshsfm7dZObW4xesu984fx8EdDDmXz16S8U5pXz/PwxdbbHnPslBYBDP8TQ95ZODpfF\nYKgeBefllqJUW5+CLS1xbECJnKwSC5lcWQ7PRibXuWaapais1LlMNlE3bdNsAT1qs2DBAj766COm\nTJmCXq8Xe4gFdtGYmTSx/OVcCqscb7SV9nWUnIVeZ+C38+loK/VOT7trz7ZOT7MlUV6mJebXdJct\na9xs2H0e8aZNm8yfxR5i9ybjeiEZqUXcOiLM1aKYkUWFFDSSb7acBYxhHMdO7GvxnaOXSDWa6pkB\n4d/QePbuumiMjqZQ0G+w42dSbnbc69gdQbPwzZaznPwpgdLi+huQ5Kt5/LSncd7MN0LjkhFDYpdQ\nI5PKy7Qc+PYSBXmu37uam11i5aro2AFcuZzlsrQbGu2aQpQ2FClNUI3NEbEkSSxevJjExESUSiUr\nVqxAo9GwcOFClEolvXv3ZtmyZc6QVdBIaq631WbPjl8B6H9rZzqFBDhclsZtX3KgIIJ6qZlDp49d\nIyEmu957HYW1vHe3KXN3oObWLlmW+WHnBTqHBjD0zm5OkyEtuYDQ7kEN3yT6S3Zhc0R86NAhFAoF\n27Zt45VXXuH9998XQT1uEuwxflnpznG42LEhyinpCBqHRWetRnEx6BrYOuRAdNq6Rrcgt6zOViZn\nt+/uvF1IliE5IY9fjiQ6OV331cnNhk1DfO+997Jy5UoA0tLSCAgIEEE9bhIkO9rS386nO16QKiR7\nBBI4lbISrfmzxRSwi2Ylyst0Vq9v/PiExd+SwdFGwFIB7m10XCNbQyoxzWyI06Hsw641YqVSyWuv\nvcaqVav4wx/+YFEoRVAP9yX5qu14sM70aow6lmT7JoHL2LPjAmWlWts3uoDantORR507+jPVE4NB\nIu5ihks8ueujpkF0dYehIK+MpIRcc+Qvt+6/uBF2e02vXr2a+fPnM3nyZCorq52A7A3qITZ/O5+i\nvAryskrR6yQG1nNsnALn5U30iSR69+9InwEdG7xP1lfXXlFuHIuHynLfsJfGGCDC20tT5153ywtH\nyuPpadk0tg3yw9NLzbEfr3DohxhQwODbQhh8eyi9+nVwmBz20K6dn/lzfnYZfQc6x0u5TRuvOnnw\nrzWHLf728da4XblxR2wa4m+//ZbMzExmzpyJp6cnSqWSQYMGERkZyfDhw+0O6iE2fzufigotX284\nDUCHEOuVQaczODVvtn8RyeyFY61+dzU2m8y0IvoMqjbUotw4ltrxm/PySlB6KKiorDtFfCU2k+jj\nSYy6pyfePnUNtbNxZNmofajE5QuphHZvy/WkqsiCMlw4k8qFM6n1lmdnkZVZHQ87NSWfth18nZJu\nYUG5zTwoK3Ns4BV3x95OiE1DPGHCBBYuXMi0adPQ6/UsXryY8PBwFi9ejE6no2fPniKoh5sSXyNM\nn+n81trnuDp+rc1+9n9zCYDwvsEulqT1UHsqs6GpxIjvLpOdUYJao+Lu8X0cLJlrkWop4tfTRkMc\ndynTRRLVT908dM5ZzQaDxL/WHCaovQ9TnhtudVpcTE3bh01D7OXlxYcffljneksK6lFZoUPjqW7R\nJ5qse/sI7Tv6UVpcyTNzRpuvu6MzhavXuVozDenetI3IXdZH83NKCWrvmNGfslZbkJTgvufv1vTz\nOHYwnhM/JvDcvDGo1I4NE2HqOOfnGPeab1pb12m3wYM7BGYaNMR6vZ5FixaRmpqKTqdj1qxZ9OrV\nq0XtIc7LLuXrL04zaGgXxtzfsnv5OZlGr9iK8hrTji6weTV77JIkk5tVQvuOfjZ+dePodAaKCspp\nF+z4tG5WTHbYWp/U7AnrJlHSYi9mMnJsuEOerVDePJ3yinLLjpEkyZSWVNIm0NtpMsiybOGBb6K4\nyHlHV97MNNhl+u677wgKCmLr1q18/vnnrFy5ssXtITYdcXbxTJqLJXEe/66xFaS8TOf06EnnI1PM\nn8+eTGLnxmguRKWarzlqZmLP17+y44socrOsRWoSQMNG1mSc3CV+cMrVPOeml+jc9Oxl+2eRrhbB\nqdsgWyINGuKJEyfyyiuvAGAwGFCpVGIPcQugdkO67VPnVuQL0amcPZWMJMmcOWU8WSb6hOO3NqVf\nLwSqZwYEddfwaq+N1sQ0Beku5GSVUFrivDjQpw5ftX2Tm+Ds5Z3r1/KtXneX2RN3p0FD7O3tjY+P\nDyUlJbzyyivMnTtX7CEW3DAlRZWcOnyV+MuZ6KsiOFlMlzuYX6OuOy0t96eWo48kU1mh4/K5BkY4\nbtS2aiscs15tzY7V14H771fnHCLDjRBzIcOp6dVn+A1u5Azqzth01kpPT+fll19m2rRpTJo0iXff\nfdf8nb17iMH99iCa8KtxGLq7yugMXPHuV2NzrF4PDPQxf3aEXKXFlW6f199tP0fHLm0YcZdj1kBN\nqBSWffHLZ9MJ7tSwbjQaldvoLzDI1yGyeHiobN9URVpygdvow4Skl50qk0Zj3ZQolQq304070qAh\nzsnJYcaMGSxdutS8V7h///6cPn2aYcOG2b2HGNx3P2hJjaktd5XRGbji3ROvWDfE+fnVezgdIZfB\nILt9Xp87bVxHD+/v2K1chfmW083xMVnExzR8ok9lpd5t9JeXW4LCfptpNzpd4w6acBd9mKgo1zlV\npvpG4KHdg9xON86kWfYRr1+/nqKiIv75z3+ydu1aFAoFr7/+OqtWrRJ7iFsYcZcy6TOw4YhXrqCi\nXIeXt0ezPtNdtkcVF1ag8VTj6WVZDZ0pX1OSchdnLTCeQtSugwO84BupmPzcUoLaOSeQhj24SxlX\nN2JmwR0wGCSKCsqdnpcNGuLXX3+d119/vc71lrSHuCXioVFZPcWmIS5EX3cbQ5yVVt2D/vIfx10e\nucgRyLLMln+dAmDWgrvJyy5FlqF9Rz+3MnTWKHSjM2ZrHgfYnJQ2MuZ2SmK+WxliR+mlsbhLh8Be\nDv73MolxOTzy5G1OOR7WhGN3fAtuGhpruB1JZlqR7ZtugPoah6KCci5EXXdK46GvMfWZkpjPjg1R\n/OfLKAx6ycLTdP07R/j5wBVkWaaiXMf5yJRmzaumvGphnmMa+aY47Dlq/3lj31HfyKlsR5OR6tg6\nZC83myFOjDMul+VkOHdnhV2G+Pz58zz55JMAJCcn88QTTzBt2jRWrFjhUOEETaMphd+dthk4ulHr\nHGq9p7tr0xmORcRz8L+XHZo+YJFGbI31tV9rdQQkSebimVTSrxdyZF8sJw4lEHX8msPlcwVNXWGa\nXgAAFLtJREFUidjlLiFa9S46v9ndkd1ELV9+dJwfv/+NnMwSjh28YnkOtxtg0xB//vnn5rjSQIsL\n6CEw4k7ToY6uJMGdqx0oCvLK+Neaw5w9lUxF1Vm4CTHZ9f202UhKqA4OEf9btXNUYX651XOkS4oq\nyc81OlYVudHUcHNyM69Xu1vD7i64Q2jQygodFWU64i5msnNjFBeiU0n4rWGHRGdj0xB369aNtWvX\nmv++dOmSCOjRAnGn/X6149NmVAXiuFGUVZGhTFO7BXll5mAmtYM1fPnRcRJseA87gt/Op/Pvj4/X\nuZ5yNc9BZ7y6T743RRZ3mfp0pxkld6K+QB+uwlRcbHnF5zg5+p5NQ3zfffehqnFmaWsN6CFJcosd\niYBxb627ULtTcPRAXLM81zR6uhCVyuG9sQ1GFKso03HgW8dPUVvDWqco7lImednGbV2mdazaVFbo\nKWukk5E72eGbeUTsLnIImgdnh+y0GdCjNkplte1uTQE9Nq87SeKVHJ5+aRTdwts5QzQLKsp1pCYX\nEN6nvc1YzE2N1eyueVRUUNHsstlb0dxVJ8HB/hgMEolXcujeqx1qtYo3Xv0fAEvfe9D+BzVxOd4R\nelHS+HLr5+fpFnmk0ajdQo6auIs8rpajvKxu59Tf38umXM6Uu9GGeMCAATdNQI+czGIK88vp2a9D\nvffYG9DDFHzitwvp+Pg7/1D0b7acJeN6IQ9OuYXQ7m0bvLep03XusvG+osLSe1YySC6TzV10Et43\nmKux1WvX2dnFnDmZxC9HEhkyLJRR9/Sy+M5ecvOaNgXnCL005fCRwkLbh9M7g9LSSreQoyZpaQWN\nihBmi5uhXSkrqeRqXA4Dbu1sHjRaM8TFxRU25WoOue015o3evrRgwQI++ugjpkyZgl6vd+uAHv/5\nMpoD315uVi9cVzllmNZJTQ47DXGze3DW3jrS2PpvMEicOBRvnsptCSgUoPaorq5nf0k2b1E5f9oy\ndnajGkw3mlFtSjvvLmuz7jg1HXXsms17fjufzpXLmY4Xxkns3X2Rnw9c4VhEPCd/SkCW5QbLlSRJ\nnDiU4PIT2ewyxCEhIWzfvh2A7t27s3nzZrZv386bb77psCPrbFFZoWPvzgtkpdveL9ecjkiNPei6\nrFTLDzsvNFtG1wx2YQ13ObS9OWlMI6et1PPpu0c5H3mdXZuiHSiVJVd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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_meeting.plot(subplots=True,sharey=True,title=\"Volume Signal for each badge\")\n", - "power_threshold = test_threshold(df_meeting,custom_power_threshold=8.166565)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the plot showing volume for each badge we can clearly see that PW5UJDRM35 is the speaker and there is attenuated speech at the other badges at every instant that PW5UJDRM35 is speaking. The maximum power threshold calculated for power samples above $thr_{env}=8.166$ on the non-speaker badges is $thr_{speech} = 33.741$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When attenuated speech power exceeds our speech detection threshold $thr_{speech}$ we will still detect false positives. As mentioned previously, we assume that there is a **single speaker** in the meeting. In case we detect 2 or more badge wearers to be speaking at a certain time interval we choose the badge wearer with the higher volume power value as the speaker for that interval." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The test_threshold module has a function is_speaking. This calculates the volume signal power time series, applies the single speaker assumption and returns a boolean time series for each badge indicating when the power value for a badge exceeds avg_speech_power_threshold (argument passed to the function). When avg_speech_power_threshold = $thr_{speech}$ we get a boolean time series which is True when a badge is speaking." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([,\n", - " ,\n", - " ,\n", - " ], dtype=object)" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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IyEhcunQJYWFhuH37NrKyslCnTh2TxfA6aiIi64m8fAhn7/xm1XW293gWI5sN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\nS02may0fPU5R2iefy8UtX18qr5vbvJaqCtujxt1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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_is_speech = is_speaking(df_meeting,avg_speech_power_threshold=33.741046)\n", - "df_is_speech.plot(kind='area',subplots=True,title=\"Boolean 'Is Speaking' Time Series\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see that only badge PW5UJDRM35 is detected as having speech at regular intervals. df_is_speaking is the boolean time series we will require for the next stage i.e. \"stitching\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to having a single speaker we also assume the following:\n", - "* A speaker is said to have had a turn (A custom definition for the minimum speaking duration that indicates a contribution to the conversation by the badge wearer) if he/she speaks for a minimum of 2 seconds $(min\\_talk\\_length)$\n", - "* A pause in speech can be a maximum of 0.5 seconds $(min\\_gap\\_size)$.\n", - "\n", - "Given a boolean value time series for each badge indicating speech/not speech we \"stitch\" the speaking intervals by:\n", - "* Filling in/Ignoring pauses of duration < $min\\_gap\\_size$ (Equivalent to correcting false negatives)\n", - "* Discarding speaking intervals of duration < $min\\_talk\\_length$" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "The test_threshold module has a function get_stitched which takes df_is_speaking (a dataframe with each column being a boolean time series associated with a single badge) and returns another set of boolean time series which are \"stitched\". It takes the arguments $min\\_gap\\_size$ and $min\\_talk\\_length$ whose roles have been explained above." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([,\n", - " ,\n", - " ,\n", - " ], dtype=object)" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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FY7dR33///ezYsYPu3buzf/9+WrZsaZvWokULTpw4wcWLF/Hx8WHPnj1ERETg5eXFmjVr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\nkDAuXLjAqFERNGzYgqysPHx8cktst6C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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df_stitched = get_stitched(df_is_speech)\n", - "df_stitched.plot(kind='area',subplots=True,title=\"Stitched 'Is Speaking' Time Series\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An issue that we experience with badge wearers having a low speaking volume is patchy detection of speech which may lead to removal of detected speech segments in the stitching phase. Tuning the minimum pause length or speaking duration helps deal with this." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "df_stitched is the dataframe that is used to calculate the speech based metrics that we report: airtime, turntaking, ..." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/sample_data/mostly_silence.txt b/examples/sample_data/mostly_silence.txt deleted file mode 100644 index 5c60f7e..0000000 --- a/examples/sample_data/mostly_silence.txt +++ /dev/null @@ -1,223 +0,0 @@ -{"uuid":"NVGWO_1467320376817","group":"NVGWO","members":["PW5UJDRM35","LPH58QI85F","2J63OSU3ZC","BUAULG5KFB"],"startTime":"2016-06-30T20:59:36.817Z","moderator":"PW5UJDRM35","location":"office","type":"other","description":"Experiment 8. I'm wearing badge 1 . all badges are arranges in a square formation 3 ft away from each other. 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@@ -{"uuid":"NVGWO_1467320792533","group":"NVGWO","members":["PW5UJDRM35","LPH58QI85F","2J63OSU3ZC","BUAULG5KFB"],"startTime":"2016-06-30T21:06:32.533Z","moderator":"PW5UJDRM35","location":"office","type":"other","description":"Experiment 9. 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__author__ = 'Oren Lederman' __email__ = 'orenled@mit.edu' -__version__ = '0.1.0' +__version__ = '0.3.0' + +__all__ = ["core", "data", "preprocessing", "visualization", "analysis"] -from .core import * diff --git a/openbadge_analysis/analysis/__init__.py b/openbadge_analysis/analysis/__init__.py new file mode 100644 index 0000000..03c210b --- /dev/null +++ b/openbadge_analysis/analysis/__init__.py @@ -0,0 +1,4 @@ +# -*- coding: utf-8 -*- + +from .gatherings_cores import extract_groups, gather_groups, extract_cores + diff --git a/openbadge_analysis/analysis/gatherings_cores.py b/openbadge_analysis/analysis/gatherings_cores.py new file mode 100644 index 0000000..7a12383 --- /dev/null +++ b/openbadge_analysis/analysis/gatherings_cores.py @@ -0,0 +1,227 @@ +import pandas as pd +import numpy as np +import networkx as nx + + +"""This module is based on the paper: Sekara, Vedran, Arkadiusz Stopczynski, and Sune Lehmann. "Fundamental structures of dynamic social networks." Proceedings of the national academy of sciences 113.36 (2016): 9977-9982. +""" + + +def extract_groups(m2m): + """Extracts a list of groups from a social network varying through time. + + Groups are defined as connected components of the social graph at a given + time bin. + + Parameters + ---------- + m2m : pd.DataFrame + The social network, for instance, member-to-member bluetooth proximity + data. It must have the following columns: 'datetime', 'member1', and + 'member2'. + + Returns + ------- + pd.DataFrame : + The groups, as a sets of members with datetime. + """ + groups = m2m.groupby('datetime').apply( + lambda df: + pd.Series([frozenset(c) for c in nx.connected_components(nx.from_pandas_dataframe(df.reset_index(), 'member1', 'member2'))]) + ) + groups.name = 'members' + + return groups.reset_index()[['datetime', 'members']] + + +def _set_similarity(s, t): + """Computes the similarity between two sets, namely, the ratio of intersection + size to union size.""" + return float(len(s.intersection(t)))/len(s.union(t)) + + +def _group_distance(g, h, gamma): + """Computes a distance metric between two groups, between 0 and 1. + + This metric depends on the set similarity of the two groups, and the number of + time bins between the two groups. + + Parameters + ---------- + g : tuple + h : tuple + Groups, where the first element is a datetime, and the second is a set. + + gamma : float + Exponential decay, for time differences between groups. + """ + dt = np.abs(g[0] - h[0]).total_seconds()/60 + return 1. - _set_similarity(g[1], h[1]) * np.exp(-gamma * (dt - 1.)) + + +def gather_groups(groups, distance_threshold=.49, gamma=.08): + """Gather groups into gatherings. + + A gathering is defined as series of groups with similar members, existing + within a somewhat continuous timeframe. + + Parameters + ---------- + groups : pd.DataFrame + A list of groups, as returned by `extract_groups`. + + distance_threshold : float + The minimum distance between two groups for them to be joined into a + single gathering. Default (recommended) value is 0.49. + + gamma : float + The `gamma` parameter of the group distance. + + Returns + ------- + list(pd.DataFrame) : + A list of gatherings, where each gathering is a DataFrame containing + the members of the gathering present at each time bin. + """ + n = len(groups) + + # Convert the DataFrame to an array for fast indexing + ga = groups.as_matrix() + + # `dist` is the distance matrix of gatherings, "indexed by groups" + # In other words, `dist` has a column/row for each group. For each group, + # it contains the distance from the gathering of this group, to the gathering + # of every other group. + # The distance between two gatherings is the minimum distance between every + # pair of groups. + # We start with having as many gatherings as groups, and so `dist` begins as + # the matrix of distances between groups + dist = np.zeros((n, n)) + for i in range(n): + for j in range(i+1, n): + dist[i, j] = _group_distance(ga[i,:], ga[j,:], gamma=gamma) + dist += dist.T + + # We set the invariant on `dist` that two groups in the same gathering have a + # distance of 1.0, so they won't be chosen by `np.argmin` + np.fill_diagonal(dist, 1.0) + + # Mapping group index -> gathering index + # It's initialized as `i -> i` for all i + grp2gth = dict(zip(range(n), range(n))) + # Mapping gathering index -> groups indices + # It's initialized as `i -> [i]` for all i + gth2grp = dict(zip(range(n), [[i] for i in range(n)])) + + # As long as there are at least two gatherings + while len(gth2grp) > 1: + # Pick the minimum distance between two gatherings + i, j = np.unravel_index(dist.argmin(), dist.shape) + + # If the minimum distance is above the threshold, stop the algorithm + if dist[i,j] > distance_threshold: + break + + # Index of the first gathering + gth0 = min(grp2gth[i], grp2gth[j]) + # Index of the second gathering + gth1 = max(grp2gth[i], grp2gth[j]) + + # Change the gathering index of the groups in the second gathering + for g in gth2grp[gth1]: + grp2gth[g] = gth0 + + # Add the groups of the second gathering to the first + gth2grp[gth0].extend(gth2grp[gth1]) + # Remove the second gathering + del gth2grp[gth1] + + # The new distance vector of elements of this new gathering is set as + # the minimum of the vectors of the two original gatherings + newdist = np.minimum(dist[i,:], dist[j,:]) + + # Set the within-gathering distance to 1.0 (enforce the invariant) + for g in gth2grp[gth0]: + newdist[g] = 1.0 + + # Update the distance of each element of the new gathering + for g in gth2grp[gth0]: + dist[g,:] = newdist # Column + dist[:,g] = newdist # Row + + # Store each gathering in a DataFrame + gatherings = [groups.iloc[gs].copy().set_index('datetime').sort_index()['members'] + for gs in gth2grp.itervalues()] + + return gatherings + + +def _participation_threshold(n): + """Computes the participation threshold for the gap significancy test. + + The threshold is defined as one standard deviation away from the mean gap in + a vector of `n` uniformly distributed random variables, taking values between + 0 and 1. This is equivalent to a beta distribution with parameters 1 and `n`. + """ + mean = 1./(n + 1) + std = np.sqrt(1.*n/(n+1)**2/(n+2)) + + return mean + std + + +def _extract_core(gathering): + """Extracts a core from a given gathering, using the gap significancy test in + the participation profile.""" + + # Extract the list of participants to the gathering for each time bin + # The result is a DataFrame listing each member participating at each time bin + df = gathering.apply(lambda x: pd.Series(sorted(list(x)))).stack().rename('member').to_frame() + + # Pivot the dataframe, giving a matrix of member vs time bin, with 1 when + # a member participated to the gathering at that moment, and 0 otherwise + df['participates'] = 1. + df = df.reset_index().set_index(['datetime', 'member'])['participates'].unstack().fillna(0.0) + + # Sum the participations and normalize, to get the percentage of participation + # for each member + participations = (df.sum()/len(df.index)).sort_values(ascending=False) + + # Compute the participation gap threshold + threshold = _participation_threshold(len(df.columns)) + + # Compute the gaps in the sorted participation profiles + gaps = participations - participations.shift(-1) + + # Return the participations index, truncated after the first significant + # gap in participations + return list(participations.loc[:(gaps > threshold).idxmax()].index) + + +def extract_cores(gatherings): + """Extract the core from each gathering. + + Parameters + ---------- + gatherings : list(pd.DataFrame) + The gatherings, as returned by `gather_groups`. + + Returns + ------- + pd.DataFrame : + A list of cores, with columns `start`, `end`, and `members`, + containing for each core the time it started, the time it ended, and its + members. + """ + # Extract core from each gathering of + cores = [(gathering.index.min(), + gathering.index.max(), + frozenset(_extract_core(gathering))) for gathering in gatherings if len(gathering) > 2] + + # Filter out cores with a single individual + cores = filter(lambda x: len(x[2]) > 1, cores) + + # Store the cores in a DataFrame + cores = pd.DataFrame(cores, columns=['start', 'end', 'members']) + + return cores + diff --git a/openbadge_analysis/core.py b/openbadge_analysis/core.py index 051f795..439ead2 100644 --- a/openbadge_analysis/core.py +++ b/openbadge_analysis/core.py @@ -3,6 +3,7 @@ import numpy as np import itertools import datetime +import crc16 def is_meeting_metadata(json_record): @@ -53,9 +54,12 @@ def load_audio_chunks_as_json_objects(file_object, log_version=None): elif log_version == '2.0': batched_sample_data = [] for row in raw_data[first_data_row:]: - data = json.loads(row) - if data['type'] == 'audio received': - batched_sample_data.append(data['data']) + try: + data = json.loads(row) + if data['type'] == 'audio received': + batched_sample_data.append(data['data']) + except ValueError: + continue else: raise Exception('file log version was not set and cannot be identified') @@ -84,9 +88,12 @@ def load_proximity_chunks_as_json_objects(file_object, log_version=None): elif log_version == '2.0': batched_sample_data = [] for row in raw_data[first_data_row:]: - data = json.loads(row) - if data['type'] == 'proximity received': - batched_sample_data.append(data['data']) + try: + data = json.loads(row) + if data['type'] == 'proximity received': + batched_sample_data.append(data['data']) + except ValueError: + continue else: raise Exception('file log version was not set and cannot be identified') @@ -275,3 +282,91 @@ def total_turns(df_stitched): df_turns.duration = duration #columns: member, totalSpeakingTime, totalTurns for each member return df_turns + + +def mac_address_to_id(mac): + """Converts a MAC address to an id used by the badges for the proximity pings. + """ + # convert hex to bytes and reverse + macstr = mac.replace(':', '').decode('hex')[::-1] + crc = crc16.crc16xmodem(macstr,0xFFFF) + return crc + + +def load_member_badges_from_logs(logs, log_version=None, log_kind='audio', time_bins_size='1min', tz='US/Eastern'): + """Extracts the badge address and id of every badge used by each member, at each moment, + for a given time bins size. + + Parameters + ---------- + logs : list of str + Paths to (audio or proximity) logs used to extract the addresses. + + log_version : str or None + The version of the logs, in case the files are missing a header. + + log_kind : 'audio' or 'proximity' + Whether the logs are audio logs or proximity logs. + + time_bins_size : str + The size, in units of time, of the time bins used for the resampling. + Defaults to '1min', the resolution of the badges + + Returns + ------- + DataFrame : + The id, MAC address and owner of each badge that appeared in `logs`. + """ + + if log_kind == 'audio': + load_chunks = load_audio_chunks_as_json_objects + elif log_kind == 'proximity': + load_chunks = load_proximity_chunks_as_json_objects + else: + raise ValueError("Log kind {} not recoginzed".format(kind)) + + fulldf = pd.DataFrame(columns=( + 'datetime', 'member', 'badge_address', 'id' + )) + + fulldf['datetime'] = pd.to_datetime(fulldf['datetime'], unit='s', utc=True) \ + .dt.tz_localize('UTC').dt.tz_convert(tz) + + # Load chunks + # A chunk contains a set of observations by a given badge at a given timestamp + for filename in logs: + chunks = [] + with open(filename, 'r') as f: + chunks.extend(load_chunks(f, log_version=log_version)) + + # Extract relevant information from chunks, i.e. member and id (from address) + data = [( + chunk['member'], + chunk['badge_address'], + mac_address_to_id(chunk['badge_address']), + chunk['timestamp'] + ) for chunk in chunks] + + # Encapsulate the data in a DataFrame + df = pd.DataFrame(data, columns=( + 'member', 'badge_address', 'id', 'timestamp' + )).drop_duplicates() + + del data + + # Convert the timestamp to a datetime, localized in UTC + df['datetime'] = pd.to_datetime(df['timestamp'], unit='s', utc=True) \ + .dt.tz_localize('UTC').dt.tz_convert(tz) + del df['timestamp'] + + fulldf = fulldf.append(df) + del df + + # Group by id and resample + fulldf = fulldf.groupby([ + pd.TimeGrouper(time_bins_size, key='datetime'), + 'id' + ]).first() + + return fulldf + diff --git a/openbadge_analysis/datasets/sloan_data.pyc b/openbadge_analysis/datasets/sloan_data.pyc deleted file mode 100644 index b44b727..0000000 Binary files a/openbadge_analysis/datasets/sloan_data.pyc and /dev/null differ diff --git a/openbadge_analysis/preprocessing/__init__.py b/openbadge_analysis/preprocessing/__init__.py new file mode 100644 index 0000000..dcdd781 --- /dev/null +++ b/openbadge_analysis/preprocessing/__init__.py @@ -0,0 +1,12 @@ + + +from .raw import split_raw_data_by_day + + +from .metadata import id_to_member_mapping +from .metadata import voltages + + +from .proximity import member_to_badge_proximity +from .proximity import member_to_member_proximity +from .proximity import member_to_beacon_proximity diff --git a/openbadge_analysis/preprocessing/audio.py b/openbadge_analysis/preprocessing/audio.py new file mode 100644 index 0000000..e69de29 diff --git a/openbadge_analysis/preprocessing/metadata.py b/openbadge_analysis/preprocessing/metadata.py new file mode 100644 index 0000000..f501e0d --- /dev/null +++ b/openbadge_analysis/preprocessing/metadata.py @@ -0,0 +1,96 @@ +import pandas as pd +import json +from ..core import mac_address_to_id + + +def id_to_member_mapping(fileobject, time_bins_size='1min', tz='US/Eastern'): + """Creates a mapping from badge id to member, for each time bin, from a cleaned-up proximity data file. + + Parameters + ---------- + fileobject : file or iterable list of str + The cleaned-up proximity data, as an iterable of JSON strings. + + time_bins_size : str + The size of the time bins used for resampling. Defaults to '1min'. + + tz : str + The time zone used for localization of dates. Defaults to 'US/Eastern'. + + Returns + ------- + pd.Series : + A mapping from badge id to member, indexed by datetime and id. + """ + + def readfile(fileobject): + for line in fileobject: + data = json.loads(line) + + yield (data['timestamp'], + mac_address_to_id(data['badge_address']), + str(data['member'])) + + df = pd.DataFrame(readfile(fileobject), columns=['timestamp', 'id', 'member']) + + # Convert the timestamp to a datetime, localized in UTC + df['datetime'] = pd.to_datetime(df['timestamp'], unit='s', utc=True) \ + .dt.tz_localize('UTC').dt.tz_convert(tz) + del df['timestamp'] + + # Group by id and resample + df = df.groupby([ + pd.TimeGrouper(time_bins_size, key='datetime'), + 'id' + ]).first() + + df.sort_index(inplace=True) + + return df['member'] + + +def voltages(fileobject, time_bins_size='1min', tz='US/Eastern'): + """Creates a DataFrame of voltages, for each member and time bin, from a cleaned-up proximity data file. + + Parameters + ---------- + fileobject : file or iterable list of str + The cleaned-up proximity data, as an iterable of JSON strings. + + time_bins_size : str + The size of the time bins used for resampling. Defaults to '1min'. + + tz : str + The time zone used for localization of dates. Defaults to 'US/Eastern'. + + Returns + ------- + pd.Series : + Voltages, indexed by datetime and member. + """ + + def readfile(fileobject): + for line in fileobject: + data = json.loads(line) + + yield (data['timestamp'], + str(data['member']), + float(data['voltage'])) + + df = pd.DataFrame(readfile(fileobject), columns=['timestamp', 'member', 'voltage']) + + # Convert the timestamp to a datetime, localized in UTC + df['datetime'] = pd.to_datetime(df['timestamp'], unit='s', utc=True) \ + .dt.tz_localize('UTC').dt.tz_convert(tz) + del df['timestamp'] + + # Group by id and resample + df = df.groupby([ + pd.TimeGrouper(time_bins_size, key='datetime'), + 'member' + ]).mean() + + df.sort_index(inplace=True) + + return df['voltage'] + diff --git a/openbadge_analysis/preprocessing/proximity.py b/openbadge_analysis/preprocessing/proximity.py new file mode 100644 index 0000000..cff2fbc --- /dev/null +++ b/openbadge_analysis/preprocessing/proximity.py @@ -0,0 +1,178 @@ +import pandas as pd +import json + + +def member_to_badge_proximity(fileobject, time_bins_size='1min', tz='US/Eastern'): + """Creates a member-to-badge proximity DataFrame from a cleaned-up proximity data file. + + Parameters + ---------- + fileobject : file or iterable list of str + The cleaned-up proximity data, as an iterable of JSON strings. + + time_bins_size : str + The size of the time bins used for resampling. Defaults to '1min'. + + tz : str + The time zone used for localization of dates. Defaults to 'US/Eastern'. + + Returns + ------- + pd.DataFrame : + The member-to-badge proximity data. + """ + + def readfile(fileobject): + for line in fileobject: + data = json.loads(line) + + for (observed_id, distance) in data['rssi_distances'].items(): + yield ( + data['timestamp'], + str(data['member']), + int(observed_id), + float(distance['rssi']), + float(distance['count']), + ) + + df = pd.DataFrame( + readfile(fileobject), + columns=('timestamp', 'member', 'observed_id', 'rssi', 'count') + ) + + # Convert timestamp to datetime for convenience, and localize to UTC + df['datetime'] = pd.to_datetime(df['timestamp'], unit='s', utc=True) \ + .dt.tz_localize('UTC').dt.tz_convert(tz) + del df['timestamp'] + + # Group per time bins, member and observed_id, + # and take the first value, arbitrarily + df = df.groupby([ + pd.TimeGrouper(time_bins_size, key='datetime'), + 'member', + 'observed_id' + ]).first() + + # Sort the data + df.sort_index(inplace=True) + + return df + + +def member_to_member_proximity(m2badge, id2m): + """Creates a member-to-member proximity DataFrame from member-to-badge proximity data. + + Parameters + ---------- + m2badge : pd.DataFrame + The member-to-badge proximity data, as returned by `member_to_badge_proximity`. + + id2m : pd.Series + The badge IDs used by each member, indexed by datetime and badge id, as returned by + `id_to_member_mapping`. + + Returns + ------- + pd.DataFrame : + The member-to-member proximity data. + """ + + df = m2badge.copy().reset_index() + + # Join the member names using their badge ids + df = df.join(id2m, on=['datetime', 'observed_id'], lsuffix='1', rsuffix='2') + + # Filter out the beacons (i.e. those ids that did not have a mapping) + df.dropna(axis=0, subset=['member2'], inplace=True) + + # Reset the members type to their original type + # This is done because pandas likes to convert ints to floats when there are + # missing values + df['member2'] = df['member2'].astype(id2m.dtype) + + # Set the index and sort it + df.set_index(['datetime', 'member1', 'member2'], inplace=True) + df.sort_index(inplace=True) + + # Remove duplicate indexes, keeping the first (arbitrarily) + df = df[~df.index.duplicated(keep='first')] + + # Reorder the index such that 'member1' is always lexicographically smaller than 'member2' + df.index = df.index.map(lambda ix: (ix[0], min(ix[1], ix[2]), max(ix[1], ix[2]))) + df.index.names = ['datetime', 'member1', 'member2'] + + # For cases where we had proximity data coming from both sides, + # we take the average RSSI weighted by the counts, and the sum of the counts + df['rssi'] *= df['count'] + df = df.groupby(level=df.index.names).sum() + df['rssi'] /= df['count'] + + # Select only the fields 'rssi' and 'count' + return df[['rssi', 'count']] + + +def _member_to_beacon_proximity(m2badge, beacons): + """Creates a member-to-beacon proximity DataFrame from member-to-badge proximity data. + + Parameters + ---------- + m2badge : pd.DataFrame + The member-to-badge proximity data, as returned by `member_to_badge_proximity`. + + beacons : list of str + A list of beacon ids. + + Returns + ------- + pd.DataFrame : + The member-to-member proximity data. + """ + + df = m2badge.copy() + + # Rename 'observed_id' to 'beacon' + df = df.rename_axis(['datetime', 'member', 'beacon']) + + # Filter out ids that are not in `beacons` + return df.loc[pd.IndexSlice[:, :, beacons],:] + + +def member_to_beacon_proximity(m2badge, id2b): + """Creates a member-to-beacon proximity DataFrame from member-to-badge proximity data. + + Parameters + ---------- + m2badge : pd.DataFrame + The member-to-badge proximity data, as returned by `member_to_badge_proximity`. + + id2b : pd.Series + A mapping from badge ID to beacon name. Index must be ID, and series name must be 'beacon'. + + Returns + ------- + pd.DataFrame : + The member-to-member proximity data. + """ + + df = m2badge.copy().reset_index() + + # Join the beacon names using their badge ids + df = df.join(id2b, on='observed_id') + + # Filter out the members (i.e. those ids that did not have a mapping) + df.dropna(axis=0, subset=['beacon'], inplace=True) + + # Reset the beacons type to their original type + # This is done because pandas likes to convert ints to floats when there are + # missing values + df['beacon'] = df['beacon'].astype(id2b.dtype) + + # Set the index and sort it + df.set_index(['datetime', 'member', 'beacon'], inplace=True) + df.sort_index(inplace=True) + + # Remove duplicate indexes, keeping the first (arbitrarily) + df = df[~df.index.duplicated(keep='first')] + + return df[['rssi', 'count']] + diff --git a/openbadge_analysis/preprocessing/raw.py b/openbadge_analysis/preprocessing/raw.py new file mode 100644 index 0000000..c73fb41 --- /dev/null +++ b/openbadge_analysis/preprocessing/raw.py @@ -0,0 +1,108 @@ +import json +import os +import datetime + + +def is_meeting_metadata(json_record): + """ + returns true if given record is a header + :param json_record: + :return: + """ + if 'startTime' in json_record: + return True + elif "type" in json_record and json_record["type"] == "meeting started": + return True + else: + return False + + +def meeting_log_version(meeting_metadata): + """ + returns version number for a given meeting metadata. This is done for + backward compatibility with meetings that had no version number + :param meeting_metadata: + :return: + """ + log_version = '1.0' + if 'data' in meeting_metadata and 'log_version' in meeting_metadata['data']: + log_version = meeting_metadata['data']['log_version'] + return log_version + + +def peek_line(f): + """Returns the content of the next line of a fileobject, without advancing the + cursor.""" + pos = f.tell() + line = f.readline() + f.seek(pos) + return line + + +def extract_log_version(fileobject): + """Extracts the metadata from a fileobject, if present.""" + # Check if first line contains metadata + metadata = json.loads(peek_line(fileobject)) + + if is_meeting_metadata(metadata): + # If it does, extract the log version from there + fileobject.readline() # Skip first line + return meeting_log_version(metadata) + + else: + return None + + +def split_raw_data_by_day(fileobject, target, kind, log_version=None): + """Splits the data from a raw data file into a single file for each day. + + Parameters + ---------- + fileobject : object, supporting tell, readline, seek, and iteration. + The raw data to be split, for instance, a file object open in read mode. + + target : str + The directory into which the files will be written. This directory must + already exist. + + kind : str + The kind of data being extracted, either 'audio' or 'proximity'. + + log_version : str + The log version, in case no metadata is present. + """ + # The days fileobjects + # It's a mapping from iso dates (e.g. '2017-07-29') to fileobjects + days = {} + + # Extract log version from metadata, if present + log_version = extract_log_version(fileobject) or log_version + if log_version not in ('1.0', '2.0'): + raise Exception('file log version was not set and cannot be identified') + + # Read each line + for line in fileobject: + data = json.loads(line) + + # Remove the v2.0 metadata + if log_version == '2.0': + if data['type'] == kind + ' received': + data = data['data'] + else: + continue + + # Extract the day from the timestamp + day = datetime.date.fromtimestamp(data['timestamp']).isoformat() + + # If no fileobject exists for that day, create one + if day not in days: + days[day] = open(os.path.join(target, day), 'a') + + # Write the data to the corresponding day file + json.dump(data, days[day]) + days[day].write('\n') + + # Free the memory + for f in days.values(): + f.close() + diff --git a/openbadge_analysis/visualization/__init__.py b/openbadge_analysis/visualization/__init__.py index e69de29..b1d0e8a 100644 --- a/openbadge_analysis/visualization/__init__.py +++ b/openbadge_analysis/visualization/__init__.py @@ -0,0 +1,5 @@ +# -*- coding: utf-8 -*- + + +from .embedding import rssi_to_distance, members_2d_embedding, members_2d_embedding_with_beacons + diff --git a/openbadge_analysis/visualization/embedding.py b/openbadge_analysis/visualization/embedding.py new file mode 100644 index 0000000..8e18a29 --- /dev/null +++ b/openbadge_analysis/visualization/embedding.py @@ -0,0 +1,285 @@ +import numpy as np +import pandas as pd +from sklearn.manifold import MDS +from .mds_with_anchors import smacof + + +def rssi_to_distance(rssi, P=-61.5, n=2.6): + """Computes a distance in meters from an RSSI value. + + Parameters + ---------- + rssi : number or array + The RSSI value, or an array of RSSI values. + + P : float + The RSSI value for 1 meter. Defaults to -61.5. + + n : float + The "scaling factor" used when converting RSSI to meters. Defaults to 2.6. + + Returns + ------- + number or array : + The distance, or an array of distances, measured in meters. + """ + # NOTE for the paper I used -60 and 2.4 + return np.power(10, (P - rssi)/(10*n)) + + +def coords_to_distance(coords): + """Transforms a list of coordinates into a list of distances between each + point of these coordinates. + + Parameters + ---------- + coords : pd.DataFrame + The coordinates, for each point. Must have a single index, and columns + 'x' and 'y'. + + Returns + ------- + pd.Series : + The distances, indexed by the index name of `coords` suffixed with '1' + and '2'. + """ + + index_name = coords.index.names[0] + + coords = coords.copy() + + # Make the scalar product of `coords` with itself + coords['_tmpkey'] = 1 + distances = pd.merge(coords, coords, on='_tmpkey', suffixes=('1', '2')).drop('_tmpkey', axis=1) + distances.index = pd.MultiIndex.from_product((coords.index, coords.index)) + distances.index.names = [index_name + '1', index_name + '2'] + coords.drop('_tmpkey', axis=1, inplace=True) + + del coords + + # Compute the Euclidean distance for each pair of points + distances['distance'] = distances.apply( + lambda df: np.sqrt((df['x1'] - df['x2'])**2 + (df['y1'] - df['y2'])**2), + axis=1 + ) + + return distances['distance'] + + +def members_2d_embedding(m2m, prev=None, P=-61.5, n=2.6, method='weighted', random_state=0): + """Computes a 2D embedding of the members through multidimensional scaling. + + Parameters + ---------- + m2m : pandas.DataFrame + Member-to-member proximity data, indexed by 'datetime', 'member1', and 'member2'. + + prev : None or pd.DataFrame + If not None, then the positions of the members, as returned by this function. This + can be used to initialized the MDS with the values from a previously computed MDS + (e.g. to create a time series). Defaults to None. + + P : float + n : float + The parameters for `rssi_to_distance`. + + method : str + The method used to compute the embedding. Either 'weighted' or 'sklearn'. Defaults to 'weighted'. + + random_state : None, int or np.random.RandomState + The seed/RNG used by sklearn when computing the embedding. Defaults to None. + + Returns + ------- + DataFrame : + The (x, y) coordinates of each member. + """ + + # Initialize the RNG + if type(random_state) != np.random.RandomState: + random_state = np.random.RandomState(random_state) + + # The list of members + members = sorted(list(set(list(m2m.reset_index()['member1']) + list(m2m.reset_index()['member2'])))) + + # Member-to-member + df = rssi_to_distance(m2m['rssi'], P=P, n=n).groupby(['member1', 'member2']).mean() + df = df.append(df.reorder_levels([1, 0])) + distances = df + + # Diagonal + distances = distances.append(pd.Series(np.zeros(len(members)), + index=pd.MultiIndex.from_tuples(zip(members, members)))) + + # Remove duplicates, in case there are any (there shouldn't be) + distances = distances[~distances.index.duplicated(keep='first')] + + # Distance matrix + dist_matrix = distances.unstack() + + # This is to deal with a "bug/feature" in pandas + # Using .loc on a DF that has non-int indexes will call .iloc internally, which is not what we want + # Converting the indexes to a string fixes the problem + dist_matrix.columns = dist_matrix.columns.astype(str) + dist_matrix.index = dist_matrix.index.astype(str) + + # Convert into a numpy matrix, with the rows/columns containing + # the members first and the beacons second + D = dist_matrix.loc[map(str, members), map(str, members)].as_matrix() + + if method == 'weighted': + # The weight matrix is set to 0. for missing values, and 1. everywhere else + W = 1 - np.isnan(D) + + # Missing values in `D` + D[np.isnan(D)] = 0.0 # Arbitrary value; those distances are ignored anyway + + init = None + # If previous values were given for the members positions + if prev is not None: + # Select those that match our list of members + prev = prev.loc[members] + # Fill the NA's with random values + prev = prev.fillna(pd.DataFrame(random_state.randn(*np.isnan(previous).shape), + index=members, columns=['x', 'y'])) + init = prev.as_matrix() + + # Compute the embedding using the SMACOF algorithm + positions = smacof(D, weights=W, init=init, random_state=random_state) + + elif method == 'sklearn': + D[np.isnan(D)] = D[~np.isnan(D)].max().max() + positions = MDS(dissimilarity='precomputed', random_state=random_state).fit_transform(D) + + else: + raise ValueError("method not recognized") + + # Return a dataframe of 2d coordinates associated to each member + return pd.DataFrame( + positions, + index=members, + columns=['x', 'y'] + ).rename_axis('member') + + +def members_2d_embedding_with_beacons(m2m, m2b, beacons_position, prev=None, m2m_weight=1., m2b_weight=1., + P=-61.5, n=2.6, random_state=None): + """Computes a 2D embedding of the members using aggregated distances and the beacons + data. + + Parameters + ---------- + m2m : pd.DataFrame + Member-to-member proximity data, indexed by 'datetime', 'member1', and 'member2', + with column 'rssi'. + + m2b : pd.DataFrame + Member-to-beacon proximity data, indexed by 'datetime', 'member', and 'beacon', + with column 'rssi'. + + beacons_position : pd.DataFrame + The position of each beacon, indexed by 'beacon', with columns 'x' and 'y'. + + prev : None or pd.DataFrame + If not None, then the positions of the members, as returned by this function. This + can be used to initialized the MDS with the values from a previously computed MDS + (e.g. to create a time series). Defaults to None. + + m2m_weight : float + m2b_weight : float + The weight to give to member-to-member (resp. member-to-beacon) distances in the + SMACOF algorithm. A higher weight means that the algorithm will try to have those + distances respected better. Defaults to 1. for both. + + P : float + n : float + The parameters for `rssi_to_distance`. Defaults to their default value in + `rssi_to_distance`. + + random_state : None, int or np.random.RandomState + The seed/RNG used by sklearn when computing the embedding. Defaults to None. + + Returns + ------- + DataFrame : + The (x, y) coordinates of each member. + """ + + # Initialize the RNG + if type(random_state) != np.random.RandomState: + random_state = np.random.RandomState(random_state) + + # The list of members + members = sorted(list(set(list(m2m.reset_index()['member1']) + list(m2m.reset_index()['member2'])))) + # The list of beacons + beacons = sorted(list(set(m2b.reset_index()['beacon']))) + + # Beacon-to-beacon + distances = coords_to_distance(beacons_position) + + # Member-to-member + df = rssi_to_distance(m2m['rssi'], P=P, n=n).groupby(['member1', 'member2']).mean() + df = df.append(df.reorder_levels([1, 0])) + distances = distances.append(df) + + # Member-to-beacon + df = rssi_to_distance(m2b['rssi'], P=P, n=n).groupby(['member', 'beacon']).mean() + df = df.append(df.reorder_levels([1, 0])) + distances = distances.append(df) + + # Diagonal + distances = distances.append(pd.Series(np.zeros(len(members)), + index=pd.MultiIndex.from_tuples(zip(members, members)))) + + # Remove duplicates, in case there are any (there shouldn't be) + distances = distances[~distances.index.duplicated(keep='first')] + + # Distance matrix + dist_matrix = distances.unstack() + + # This is to deal with a "bug/feature" in pd + # Using .loc on a DF that has non-int indexes will call .iloc internally, which is not what we want + # Converting the indexes to a string fixes the problem + dist_matrix.columns = dist_matrix.columns.astype(str) + dist_matrix.index = dist_matrix.index.astype(str) + + # Convert into a numpy matrix, with the rows/columns containing + # the members first and the beacons second + D = dist_matrix.loc[map(str, members + beacons), map(str, members + beacons)].as_matrix() + + # The weight matrix is set to 0. for missing values, and 1. everywhere else + W = 1 - np.isnan(D) + + m = len(members) + # Custom weight for member-to-member + W[m:,m:] = m2m_weight * W[m:,m:] + # Custom weight for member-to-beacon + W[m:,:m] = m2b_weight * W[m:,:m] + W[:m,m:] = m2b_weight * W[:m,m:] + + # Missing values in `D` + D[np.isnan(D)] = 0.0 # Arbitrary value; those distances are ignored anyway + + # The position of the anchors + A = beacons_position.loc[beacons].as_matrix() + + init = None + # If previous values were given for the members positions + if prev is not None: + # Select those that match our list of members + prev = prev.loc[members] + # Fill the NA's with random values + prev = prev.fillna(pd.DataFrame(random_state.randn(*np.isnan(previous).shape), + index=members, columns=['x', 'y'])) + init = prev.as_matrix() + + # Compute the embedding using the SMACOF algorithm + positions = smacof(D, weights=W, init=init, anchors=A, random_state=random_state) + + # Return a dataframe of 2d coordinates associated to each member + return pd.DataFrame( + positions, + index=members, + columns=['x', 'y'] + ).rename_axis('member') + diff --git a/openbadge_analysis/visualization/mds_with_anchors.py b/openbadge_analysis/visualization/mds_with_anchors.py new file mode 100644 index 0000000..04ee453 --- /dev/null +++ b/openbadge_analysis/visualization/mds_with_anchors.py @@ -0,0 +1,149 @@ +import numpy as np + +from scipy.spatial import distance + + +def _gt_weights(W): + """Computes the weights V for a Guttman transform V X = B(X) Z.""" + V = -W + V[np.diag_indices(V.shape[0])] = W.sum(axis=1) - W.diagonal() + + return V + + +def _gt_mapping(D, W, Z): + """Computes the mapping B(X) for a Guttman transform V X = B(X) Z.""" + # Compute the Euclidean distances between all pairs of points + Dz = distance.cdist(Z, Z) + # Fill the diagonal of Dz, because *we don't want a division by zero* + np.fill_diagonal(Dz, 1e-5) + + B = - W * D / Dz + np.fill_diagonal(B, 0.0) + B[np.diag_indices(B.shape[0])] = -np.sum(B, axis=1) + + return B + + +def _guttman_transform(D, W, Zu, Xa, V): + """Applies the Guttman transform with anchors. + + See Di Franco, Carmelo, et al. "Multidimensional scaling localization with anchors." + Autonomous Robot Systems and Competitions (ICARSC), 2017 IEEE International Conference on. IEEE, 2017. + """ + n, m = Zu.shape[0], Xa.shape[0] + Z = np.vstack([Zu, Xa]) + V11, V12 = V[0:n, 0:n], V[0:n, n:] + B = _gt_mapping(D, W, Z) + B11, B12 = B[0:n, 0:n], B[0:n, n:] + + #return np.linalg.solve( + # V11, + # np.dot(B11, Zu) + np.dot(B12, Za) - np.dot(V12, Xa) + # ) + + return np.dot( + np.linalg.pinv(V11), + np.dot(B11, Zu) + np.dot(B12, Xa) - np.dot(V12, Xa) + ) + + +def _stress(D, W, X): + """Computes the value of the weighted stress function of the MDS.""" + Dx = distance.cdist(X, X) + S = W * (D - Dx)**2 + return np.triu(S, 1).sum() + + +def _smacof_single(dissimilarities, weights, init=None, anchors=None, n_components=2, maxitr=300, eps=1e-6, random_state=None): + # Pre-compute the weights of the Guttman transform + V = _gt_weights(weights) + + if random_state is None: + random_state = np.random.RandomState() + + # Initial positions are random by default + if init is None: + init = random_state.randn(dissimilarities.shape[0]-anchors.shape[0], n_components) + X = init + + Sprev = _stress(dissimilarities, weights, np.vstack([X, anchors])) # Stress at previous iteration + for itr in range(maxitr): + X = _guttman_transform(dissimilarities, weights, X, anchors, V) + + S = _stress(dissimilarities, weights, np.vstack([X, anchors])) + if np.abs(S - Sprev) < eps: + break + Sprev = S + + return X, Sprev + + +def smacof(dissimilarities, weights=None, init=None, anchors=None, n_components=2, n_init=8, maxitr=300, eps=1e-6, random_state=None): + """Executes the SMACOF with anchors algorithm to find a Euclidean embedding of dissimilarities between n samples in a d-dimensional space. + + Parameters + ---------- + dissimilarities : n-by-n matrix + The distances/dissimilarities between each pair sample, as a two-dimensional square matrix. + + weights : None or n-by-n matrix + The weight of each distance. The greater the weight on a distance, the harder SMACOF will try to respect this distance in its solutions. If None, a matrix of ones is assumed. + + init : None or n-by-d matrix + A starting position for the algorithm. If None, `n_init` different random positions will be tried, and the best fitting solution will be kept. + + anchors : None or m-by-d matrix + The positions of the m anchors. If None, it is assumed that there are no anchors. + + n_components : int + The size (i.e. dimensions) of the embedding space. + + n_init : int + The number of initial random positions to try. + + maxitr : int + The maximum number of iterations to run. + + eps : float + The threshold on the stress change between iterations below which convergence is attained. + + random_state : None, int or np.RandomState + The state for the random numbers generator. + + Returns + ------- + n-by-d array : + The positions of the n samples in the d-dimensional Euclidean space. + """ + # Default weights are 1's + if weights is None: + weights = np.ones(dissimilarities.shape) + + if anchors is None: + anchors = np.zeros((0, n_components)) + + if random_state is None: + random_state = np.random.RandomState() + elif type(random_state) == int: + random_state = np.random.RandomState(random_state) + + # Pre-compute the weights of the Guttman transform + V = _gt_weights(weights) + + # Only run SMACOF once if an initial position is passed + if init is not None: + n_init = 1 + + Xbest = None + Sbest = np.inf + for itr in range(n_init): + X, S = _smacof_single(dissimilarities, weights, init, anchors, n_components, maxitr, eps, random_state) + + if S < Sbest: + Xbest, Sbest = X, S + + return Xbest + + + diff --git a/openbadge_analysis/visualization/participation.py b/openbadge_analysis/visualization/participation.py index fc6df1a..44350b3 100644 --- a/openbadge_analysis/visualization/participation.py +++ b/openbadge_analysis/visualization/participation.py @@ -1,6 +1,7 @@ import ast import pandas as pd import openbadge_analysis as ob +import openbadge_analysis.core from bokeh.plotting import * from bokeh.charts import Bar, output_file, show @@ -102,7 +103,7 @@ def percentage_participation(df_stitched_all, labels, member_names=None): continue df = pd.DataFrame() for df_stitched in df_stitched_list: - df_turns = ob.total_turns(df_stitched) + df_turns = ob.core.total_turns(df_stitched) df_turns.set_index('member', inplace=True) df = df.add(df_turns, fill_value=0) @@ -191,4 +192,4 @@ def add_member(member): add_member(key) plot = stack_bar(data_dict) - return plot \ No newline at end of file + return plot diff --git a/requirements.txt b/requirements.txt index eb2a1a2..45fb5b9 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,3 +6,6 @@ pandas seaborn bokeh==0.12.1 ggplot +networkx +crc16 +sklearn diff --git a/setup.cfg b/setup.cfg index c502f23..745be9e 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,5 +1,5 @@ [bumpversion] -current_version = 0.1.0 +current_version = 0.3.0 commit = True tag = True @@ -16,3 +16,4 @@ universal = 1 [flake8] exclude = docs + diff --git a/setup.py b/setup.py index 6b9492e..9681d71 100644 --- a/setup.py +++ b/setup.py @@ -1,7 +1,7 @@ #!/usr/bin/env python # -*- coding: utf-8 -*- -from setuptools import setup +from setuptools import setup, find_packages with open('README.rst') as readme_file: readme = readme_file.read() @@ -19,15 +19,13 @@ setup( name='openbadge_analysis', - version='0.1.0', + version='0.3.0', description="Python libraries for analysing OpenBadge data", long_description=readme + '\n\n' + history, author="Oren Lederman", author_email='orenled@mit.edu', url='https://github.com/orenlederman/openbadge_analysis', - packages=[ - 'openbadge_analysis', - ], + packages=find_packages(), package_dir={'openbadge_analysis': 'openbadge_analysis'}, include_package_data=True,