{ "cells": [ { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "# Mosquito Regressions " ] }, { "cell_type": "markdown", "metadata": { "hide_input": false }, "source": [ "The idea behind this notebook is to combine data from the Mosquito Habitat Mapper with data from another GLOBE protocol, such as air temperature or precipitation. The goal is to provide tools for examining the relationship between the two protocols using Weighted Least Squares Regression. " ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hide_input": true }, "source": [ "### Importing Required Modules" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "A few Python modules and tools are required to run this script." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "code_folding": [ 0 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for designation: designating hidden code blocks)\n" ] } ], "source": [ "# subroutine for designating a code block\n", "def designate(title, section='main'):\n", " \"\"\"Designate a code block with a title so that the code may be hidden and reopened.\n", " \n", " Arguments:\n", " title: str, title for code block\n", " section='main': str, section title\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # begin designation\n", " designation = ' ' * 20\n", " \n", " # if marked for user parameters\n", " if section == 'settings':\n", " \n", " # begin designation with indicator\n", " designation = '*** settings -----> '\n", " \n", " # add code designator\n", " designation += '^ [code] (for {}: {})'.format(section, title)\n", " \n", " # print\n", " print(designation)\n", " \n", " return None\n", "\n", "# apply to itself\n", "designate('designating hidden code blocks', 'designation')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "code_folding": [], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: importing Python system tools)\n" ] } ], "source": [ "designate('importing Python system tools')\n", "\n", "# import os and sys modules for system controls\n", "import os\n", "import sys\n", "\n", "# set runtime warnings to ignore\n", "import warnings\n", "\n", "# import requests and json modules for making API requests\n", "import requests\n", "import json\n", "\n", "# import fuzzywuzzy for fuzzy name matching\n", "from fuzzywuzzy import fuzz\n", "\n", "# import datetime module for manipulating date and time formats\n", "from datetime import datetime, timedelta\n", "\n", "# import pandas for dataframe manipulation\n", "import pandas" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: importing Python mathematical modules)\n" ] } ], "source": [ "designate('importing Python mathematical modules')\n", "\n", "# import numpy for math\n", "from numpy import array, isnan\n", "from numpy import exp, sqrt, log, log10, sign, abs\n", "from numpy import arcsin, arcsinh, sin, cos, pi\n", "from numpy import average, std, histogram, percentile\n", "from numpy.random import random, choice\n", "\n", "# import scipy for scientific computing\n", "from scipy import stats\n", "from scipy.optimize import curve_fit\n", "\n", "# import sci-kit for linear regressions\n", "from sklearn.neighbors import BallTree\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.linear_model import LinearRegression, PoissonRegressor" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "code_folding": [], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: importing Python visualization modules)\n" ] } ], "source": [ "designate('importing Python visualization modules')\n", "\n", "# import bokeh for plotting graphs\n", "from bokeh.plotting import figure\n", "from bokeh.io import output_notebook, show\n", "from bokeh.layouts import row as Row\n", "from bokeh.models import HoverTool, ColumnDataSource\n", "from bokeh.models import Circle, LinearAxis, Range1d\n", "\n", "# import ipyleaflet and branca for plotting maps\n", "from ipyleaflet import Map, Marker, basemaps, CircleMarker, LayerGroup\n", "from ipyleaflet import WidgetControl, ScaleControl, FullScreenControl, LayersControl\n", "from branca.colormap import linear as Linear, StepColormap\n", "\n", "# import iPython for javascript based notebook controls\n", "from IPython.display import Javascript, display, FileLink\n", "\n", "# import ipywidgets for additional widgets\n", "from ipywidgets import Label, HTML, Button, Output, Box, VBox, HBox" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for tools: inferring fuzzy match)\n" ] } ], "source": [ "designate('inferring fuzzy match', 'tools')\n", "\n", "# subroutine for fuzzy matching\n", "def fuzzy_match(text, options):\n", " \"\"\"Infer closest match of text from a list of options.\n", " \n", " Arguments:\n", " text: str, entered text\n", " options: list of str, the options\n", " \n", " Returns:\n", " str, the closest match\n", " \"\"\"\n", " \n", " # perform fuzzy search to get closest match\n", " fuzzies = [(option, fuzz.ratio(text, option)) for option in options]\n", " fuzzies.sort(key=lambda pair: pair[1], reverse=True)\n", " inference = fuzzies[0][0]\n", " \n", " return inference" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for tools: truncating field names)\n" ] } ], "source": [ "designate('truncating field names', 'tools')\n", "\n", "# truncate field names to first capital\n", "def truncate_field_name(name, size=5, minimum=4, maximum=15):\n", " \"\"\"Truncate a name to the first captial letter past the minimum.\n", " \n", " Arguments:\n", " name: str, the name for truncation\n", " size: the final size of the truncation\n", " minimum=4: int, minimum length of name\n", " maximum=15: int, maximum length of name\n", " \n", " Returns\n", " str, truncated name\n", " \"\"\"\n", " \n", " # chop name at maximum and capitalize\n", " name = name[-maximum:]\n", " name = name[0].capitalize() + name[1:]\n", " \n", " # make stub starting at minimum length\n", " length = minimum\n", " stub = name[-length:]\n", " while not stub[0].isupper():\n", " \n", " # add to length\n", " length += 1\n", " stub = name[-length:]\n", " \n", " # only pass size\n", " stub = stub[:size]\n", " \n", " return stub" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for tools: entitling a name by capitalizing)\n" ] } ], "source": [ "designate('entitling a name by capitalizing', 'tools')\n", "\n", "# entitle function to capitalize a word for a title\n", "def make_title(word):\n", " \"\"\"Entitle a word by capitalizing the first letter.\n", " \n", " Arguments:\n", " word: str\n", " \n", " Returns:\n", " str\n", " \"\"\"\n", " \n", " # capitalize first letter\n", " word = word[0].upper() + word[1:]\n", " \n", " return word" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for tools: resolving country name and code)\n" ] } ], "source": [ "designate('resolving country name and code', 'tools')\n", "\n", "# resolving country name and codes\n", "def resolve_country_code(country, code):\n", " \"\"\"Resolve the country code from given information.\n", " \n", " Arguments:\n", " country: str, country name as input\n", " code: str, country code as input\n", " \n", " Returns:\n", " (str, str) tuple, the country name and country code\n", " \"\"\"\n", " \n", " # check for code\n", " if code:\n", " \n", " # find closest matching code\n", " code = fuzzy_match(code, [member for member in codes.values()])\n", " country = countries[code]\n", " \n", " # if no code, but a country is given\n", " if not code and country:\n", " \n", " # find closest matching country\n", " country = fuzzy_match(country, [member for member in codes.keys()])\n", " code = codes[country]\n", " \n", " # if there's no code, check the country\n", " if not code and not country:\n", " \n", " # default to all countries\n", " country = 'All countries'\n", " code = ''\n", " \n", " return country, code" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for introspection: scanning notebook for cells)\n" ] } ], "source": [ "designate('scanning notebook for cells', 'introspection')\n", "\n", "# scan notebook for cell information\n", "def get_cell_info():\n", " \"\"\"Scan the notebook and collect cell information.\n", "\n", " Arguments:\n", " None\n", "\n", " Returns:\n", " list of dicts\n", " \"\"\"\n", "\n", " # open the notebook file \n", " with open('regressions_ksenia_2.ipynb', 'r', encoding='utf-8') as pointer:\n", " \n", " # and read its contents\n", " contents = json.loads(pointer.read())\n", "\n", " # get all cells\n", " cells = contents['cells']\n", "\n", " return cells" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: defining global variables)\n" ] } ], "source": [ "designate('defining global variables')\n", "\n", "# ignore runtime warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "# set pandas optinos\n", "pandas.set_option(\"display.max_rows\", None)\n", "pandas.set_option(\"display.max_columns\", None)\n", "\n", "# begin optimizations list for previous optimizations\n", "optimizations = []\n", "\n", "# establish genera and colors\n", "classification = ['Unknown', 'Other', 'Aedes', 'Anopheles', 'Culex']\n", "colors = ['gray', 'green', 'crimson', 'orange', 'magenta']\n", "\n", "# create indicator colors to be used on plots\n", "indicators = {genus: color for genus, color in zip(classification, colors)}\n", "indicators.update({'All': 'blue'})\n", "\n", "# initiate regression modes\n", "regressions = {mode: {} for mode in ('linear', 'quadratic', 'exponential', 'power', 'gaussian')}\n", "\n", "# define cancellation message\n", "cancellation = 'no fit achieved'\n", "\n", "# initialize memory dictionary for latitude, longitude measurements\n", "memory = {}\n", "\n", "# define template for units\n", "template = {'distance': '(km)', 'interval': '(d)', 'lag': '(d)', 'confidence': '', 'cutoff': '', 'inclusion': ''}\n", "template.update({'mode': '', 'genus': '', 'records': '', 'pairs': '', 'coverage': ''})\n", "template.update({'s.e.': '(larvae)', 'correlation': '', 'R^2': '', 'pvalue': '', 'equation': ''})\n", "template.update({'slope': '(larvae/feature)', 'center': '(feature)', 'onset': '(feature)'})\n", "template.update({'curvature': '(larvae/feature^2)', 'height': '(larvae)', 'rate': '(/feature)'})\n", "template.update({'power': '', 'spread': '(feature^2)'})\n", "\n", "# define units\n", "making = lambda unit: lambda feature: unit.replace('feature', truncate_field_name(feature))\n", "units = {field: making(unit) for field, unit in template.items()}\n", "\n", "# make doppelganger for navigation\n", "doppelganger = get_cell_info()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "code_folding": [], "hidden": true, "hide_input": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: import status)\n", "modules imported.\n" ] } ], "source": [ "designate('import status')\n", "\n", "# print status\n", "print('modules imported.')" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hide_input": true }, "source": [ "### Notes on Navigation" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": false }, "source": [ "#### General Organization:\n", "\n", "\n", "- The notebook is organized in two main sections, with documentation near the top and user settings and plots in the second half. Relevant subroutines are generally presented in the documentation sections, or at the end of the preceding section.\n", "\n", "\n", "- There are several sections that require the input of user selected parameters. Click Apply to see the affect of changing those parameters on that section only, then Propagate to propagate the changes down the notebook. Clicking Both will do both these actions.\n", "\n", "#### Running Cells:\n", "\n", "\n", "- Upon loading the notebook, most plots will not be visible. It is necessary to run all the code by selecting \"Restart & Run All\" from the \"Kernel\" menu and clicking \"Restart and Run All Cells\" to confirm.\n", "\n", "\n", "- This action may be performed at any time, for instance after altering the parameters or changing the code in other ways.\n", "\n", "\n", "- Alternatively, any single block of code may be rerun by highlighting the block and pressing Shift-Return.\n", "\n", "\n", "- Also, under the \"Cell\" menu is the option to \"Run All Below\" the currently selected cell, or to simply \"Run Cells\" that have been selected.\n", "\n", "\n", "#### Processing Indicator:\n", "\n", "- In the upper righthand corner it says \"Python 3\" with a circle. If this circle is black, it means the program is still processing. A hollow circle indicates all processing is done.\n", "\n", "\n", "#### Collapsible Headings and Code Blocks:\n", "\n", "- The Jupyter notebook format features collapsible code sections and headings. An entire section may be collapsed by clicking on the downward pointing triangle at the left of the heading. \n", "\n", "\n", "- Likewise, blocks of code are loaded hidden from view, and designated with '[code] ^'. Click on the '[code] ^' text and select '^' from the toolbar next to \"Download\" to expand the code. Blocks with user parameters to enter are marked with *** settings ---->.\n", "\n", "\n", "- All code blocks may be hidden or exposed by toggling the eye icon in the toolbar.\n", "\n", "\n", "- Large output boxes may be collapsed to scrollable window by clicking to the left, and may also be collapsed completely by double-clicking in the same area. Clicking on the \"...\" will reopen the area.\n", "\n", "\n", "#### Hosting by myBinder:\n", "\n", "\n", "- This notebook is hosted by myBinder.org in order to maintain its interactivity within a browser without the user needing an established Python environment. Unfortunately, connection with myBinder.org will break after 10 minutes of inactivity. In order to reconnect you may use the link under \"Browser Link\" to reload.\n", "\n", "\n", "- The state of the notebook may be saved by clicking the leftmost cloud icon in the toolbar to the right of the Download button. This saves the notebook to the browser. The rightmost cloud icon can then retrieve this saved state in a newly opened copy. Often reopening a saved version comes with all code blocks visible, so toggle this using the eye icon in the toolbar.\n", "\n", "\n", "- The following browser link will reload the notebook in case the connection is lost:\n", "https://mybinder.org/v2/git/https%3A%2F%2Fmattbandel%40bitbucket.org%2Fmattbandel%2Fglobe-mosquitoes-regressions.git/master?filepath=regressions.ipynb" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for navigation: looking for particular cell)\n" ] } ], "source": [ "designate('looking for particular cell', 'navigation')\n", "\n", "# function to look for cells with a particular text snippet\n", "def seek_text_in_cell(text):\n", " \"\"\"Look for a particular text amongst the cells.\n", " \n", " Arguments:\n", " text: str, the text to search for\n", " \n", " Returns:\n", " list of int, the cell indices.\n", " \"\"\"\n", " \n", " # search for cells \n", " indices = []\n", " for index, cell in enumerate(doppelganger):\n", " \n", " # search for text in source\n", " if any([text in line.replace(\"'{}'\".format(text), '') for line in cell['source']]):\n", " \n", " # add to list\n", " indices.append(index)\n", " \n", " return indices" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for navigation: jumping to a particular cell)\n" ] } ], "source": [ "designate('jumping to a particular cell', 'navigation')\n", "\n", "# jump to a particular cell\n", "def jump_to_cell(identifier):\n", " \"\"\"Jump to a particular cell.\n", " \n", " Arguments:\n", " identifier: int or str\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # try to look for a string\n", " try:\n", " \n", " # assuming string, take first index with string\n", " index = seek_text_in_cell(identifier)\n", " \n", " # otherwise assume int\n", " except (TypeError, IndexError):\n", " \n", " # index is identifier\n", " index = identifier \n", " \n", " # scroll to cell\n", " command = 'IPython.notebook.scroll_to_cell({})'.format(index)\n", " display(Javascript(command))\n", " \n", " return" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for navigation: executing cell range by text)\n" ] } ], "source": [ "designate('executing cell range by text', 'navigation')\n", "\n", "# execute cell range command\n", "def execute_cell_range(start, finish):\n", " \"\"\"Execute a cell range based on text snippets.\n", " \n", " Arguments:\n", " start: str, text from beginning cell of range\n", " finish: str, text from ending cell of range\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # find start and finish indices, adding 1 to be inclusive\n", " opening = seek_text_in_cell(start)[0] \n", " closing = seek_text_in_cell(finish)[0]\n", " bracket = (opening, closing)\n", " \n", " # make command\n", " command = 'IPython.notebook.execute_cell_range' + str(bracket)\n", " \n", " # perform execution\n", " display(Javascript(command))\n", " \n", " return None" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for navigation: refreshing cells by relative position)\n" ] } ], "source": [ "designate('refreshing cells by relative position', 'navigation')\n", "\n", "# execute cell range command\n", "def refresh_cells_by_position(start, finish=None):\n", " \"\"\"Refresh a particular cell relative to current cell.\n", " \n", " Arguments:\n", " start: int, the first cell offset\n", " finish=None: int, the second cell offset\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # make offset into a string\n", " stringify = lambda offset: str(offset) if offset < 0 else '+' + str(offset)\n", " \n", " # default finish to start\n", " finish = finish or start\n", " \n", " # make command\n", " command = 'IPython.notebook.execute_cell_range('\n", " command += 'IPython.notebook.get_selected_index()' + stringify(start) + ','\n", " command += 'IPython.notebook.get_selected_index()' + stringify(finish + 1) + ')'\n", " \n", " # perform execution\n", " display(Javascript(command))\n", " \n", " return None" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for navigation: revealing open cells)\n" ] } ], "source": [ "designate('revealing open cells', 'navigation')\n", "\n", "# outline headers\n", "def reveal_open_cells(cells):\n", " \"\"\"Outline the headers and collapsed or uncollapsed state.\n", " \n", " Arguments:\n", " cells: dict\n", " \n", " Returns:\n", " list of int, the indices of visible cells\n", " \"\"\"\n", " \n", " # search through all cells for headers\n", " indices = []\n", " visible = True\n", " for index, cell in enumerate(cells):\n", " \n", " # check for text\n", " header = False\n", " if any(['###' in text for text in cell['source']]):\n", " \n", " # check for header and visible state\n", " header = True\n", " visible = True\n", " if 'heading_collapsed' in cell['metadata'].keys(): \n", "\n", " # set visible flag\n", " visible = not cell['metadata']['heading_collapsed']\n", "\n", " # if either header or visible\n", " if header or visible: \n", "\n", " # add to indices\n", " indices.append(index)\n", " \n", " return indices" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for navigation: gauging cell size)\n" ] } ], "source": [ "designate('gauging cell size', 'navigation')\n", "\n", "# measure a cell's line count and graphics\n", "def measure_cell_info(cell):\n", " \"\"\"Gauge a cell's line count and graphic size.\n", " \n", " Arguments:\n", " cell: cell dict\n", " \n", " Returns:\n", " (int, boolean) tuple, line count and graphic boolean\n", " \"\"\"\n", " \n", " # check for display data\n", " graphic = False\n", " displays = [entry for entry in cell.setdefault('outputs', []) if entry['output_type'] == 'display_data']\n", " if len(displays) > 0:\n", " \n", " # check for many displays or one long one\n", " if len(displays) > 2 or '…' in displays[0]['data']['text/plain'][0]:\n", "\n", " # switch graphic to true\n", " graphic = True\n", "\n", " # determine total lines of text in source, 2 by default\n", " length = 2\n", " \n", " # determine executions\n", " executions = [entry for entry in cell.setdefault('outputs', []) if entry['output_type'] == 'execute_result']\n", " for execution in executions:\n", " \n", " # add to length\n", " length += execution['execution_count']\n", " \n", " # check hide-input state\n", " if not cell['metadata'].setdefault('hide_input', False):\n", " \n", " # add lines to source\n", " source = cell['source']\n", " for line in source:\n", "\n", " # split on newlines\n", " length += sum([int(len(line) / 100) + 1 for line in line.split('\\n')])\n", "\n", " return length, graphic" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for navigation: bookmarking cells for screenshotting)\n" ] } ], "source": [ "designate('bookmarking cells for screenshotting', 'navigation')\n", "\n", "# bookmark which cells to scroll to\n", "def bookmark_cells(cells):\n", " \"\"\"Bookmark which cells to scroll to.\n", "\n", " Arguments:\n", " cells: list of dicts\n", " visibles: list of ints\n", "\n", " Returns:\n", " list of ints\n", " \"\"\"\n", "\n", " # set page length criterion and initialize counters\n", " criterion = 15\n", " accumulation = criterion + 1\n", "\n", " # determine scroll indices\n", " bookmarks = []\n", " visibles = reveal_open_cells(cells)\n", " for index in visibles:\n", "\n", " # measure cell and add to total\n", " cell = cells[index]\n", " length, graphic = measure_cell_info(cell)\n", " accumulation += length\n", " \n", " # compare to criterion\n", " if accumulation > criterion or graphic:\n", "\n", " # add to scrolls and reset\n", " bookmarks.append(index)\n", " accumulation = length\n", "\n", " # for a graphic, make sure accumulation is already maxed\n", " if graphic:\n", " \n", " # add to accumulation\n", " accumulation = criterion + 1\n", "\n", " return bookmarks" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for buttons: propagating setting changes across cells)\n" ] } ], "source": [ "designate('propagating setting changes across cells', 'buttons')\n", "\n", "# def propagate\n", "def propagate_setting_changes(start, finish, finishii, descriptions=['Apply', 'Propagate', 'Both']):\n", " \"\"\"Propagate changes across all code cells given by the headings.\n", " \n", " Arguments:\n", " start: str, top header\n", " finish: str, update stopping point\n", " finishii: str, propagate stopping point\n", " descriptions: list of str\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # define jump points\n", " cues = [(start, finish), (finish, finishii), (start, finishii)]\n", " \n", " # make buttons\n", " buttons = []\n", " buttoning = lambda start, finish: lambda _: execute_cell_range(start, finish)\n", " for description, cue in zip(descriptions, cues):\n", "\n", " # make button\n", " button = Button(description=description)\n", " button.on_click(buttoning(*cue))\n", " buttons.append(button)\n", "\n", " # display\n", " display(HBox(buttons))\n", " \n", " return None" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for buttons: navigating to main sections)\n" ] } ], "source": [ "designate('navigating to main sections', 'buttons')\n", "\n", "# present buttons to jump to particular parts of the notebook\n", "def navigate_notebook():\n", " \"\"\"Guide the user towards regression sections with buttons.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", "\n", " # define jump points\n", " descriptions = ['Top', 'Settings', 'Filter', 'Weights', 'Data', 'Map']\n", " cues = ['# Mosquitoe Larvae Regressions', '### Setting the Parameters', '### Filtering Records']\n", " cues += ['### Defining the Weighting Scheme', '### Viewing the Data Table', '### Visualizing on a Map', ]\n", " \n", " # make buttons\n", " buttons = []\n", " buttoning = lambda cue: lambda _: jump_to_cell(cue)\n", " for description, cue in zip(descriptions, cues):\n", "\n", " # make button\n", " button = Button(description=description)\n", " button.on_click(buttoning(cue))\n", " buttons.append(button)\n", "\n", " # display\n", " display(HBox(buttons))\n", " \n", " return None" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for buttons: guiding to regression modes)\n" ] } ], "source": [ "designate('guiding to regression modes', 'buttons')\n", "\n", "# present buttons to choose the regression part of the notebook\n", "def jump_to_regression():\n", " \"\"\"Guide the user towards regression sections with buttons.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", "\n", " # make buttons\n", " buttons = []\n", " buttoning = lambda mode: lambda _: jump_to_cell('### ' + mode.capitalize() + ' Regression')\n", " for mode in regressions.keys():\n", "\n", " # make button\n", " button = Button(description=mode.capitalize())\n", " button.on_click(buttoning(mode))\n", " buttons.append(button)\n", "\n", " # display\n", " display(HBox(buttons))\n", " \n", " return None" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Getting Started" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "- Select \"Restart & Run All\" from the Kernel menu, and confirm by clicking on \"Restart and Run All Cells\" and wait for the processing to stop (the black circle in the upper right corner next to \"Python 3\" will turn hollow).\n", "\n", "\n", "- Use a Settings button from a navigation menu like the one below to navigate to the Settings section.\n", "\n", "\n", "- Find the ^ [code] block marked with *** setings ----->, and open it using the \"^\" button in the toolbar at the top of the page. Begin inputting your settings and apply the changes with the buttons. Then move on to the next section and apply those settings as well." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fde83cde28cd4fa096a519dd6f425d4a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2e01dc09c2f9476db2c9e6da1a665fac", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hide_input": true }, "source": [ "### Background: A Quick Statistics Rundown" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "#### Definition of a linear regression\n", "\n", "A straight line can be represented geometrically in the following way:\n", "\n", "\\begin{align*}\n", "y=\\beta_0+\\beta_1 X + \\varepsilon\n", "\\end{align*}\n", "\n", "where $X$ is the independent variable, $y$ is the dependent variable, $\\beta_1$ is the slope of the line, $\\beta_0$ is the intercept, and $\\varepsilon$ is the error term. Any particular line has a single value for $\\beta_0$ and for $\\beta_1$. Thus the above equation describes a family of lines, each with a unique value for $\\beta_0$ and for $\\beta_1$. \n", "\n", "If $X$ represents air temperature at a sampling site, for instance, and $y$ represents the number of mosquito larvae found there, then $\\beta_0$ describes the average number of larvae at zero degrees, and $\\beta_1$ describes the average change in the number of larvae for every single degree increase. The equation serves as a model for the relationship between air temperature and mosquito counts.\n", "\n", "Given a set of observations represented as points along an X and y axis, regression can estimate the values for $\\beta_0$ and $\\beta_1$ and give insight into a relationship between the dependent and independent variables.\n", "\n", "#### Some notes\n", "\n", "There are several points to make about this process:\n", "\n", "- The family of lines must be specified beforehand. $y=\\beta_0+\\beta_1 X$ for instance, only describes a family of straight lines. The best fitting straight line may be a poor description of data with a curving relationship. To this end, six different families (called \"modes\" here) are used in this notebook, each with particular characteristics. \n", "\n", "\n", "- For some modes, the best fitting parameters may be estimated simply. In these cases, the Ordinary Least Squares equation is sufficient to find the best fit with one calculation. In other cases, however, the fit must be found with Weighted Least Squares, needing an initial starting guess, and several subsequent iterations to find the best fit.\n", "\n", "\n", "- The initial guess must already be somewhat close to the best fitting parameters, or there is a chance the nonlinear algorithm will find only a local best and not a global best. There may be several \"basins of attraction,\" and an initial guess in the wrong basin will lead to only a local best. \n", "\n", "\n", "- In some cases, the nonlinear algorithm fails to find a fit at all, generally because the data is not distributed in a way that suggests the proposed relationship strongly enough.\n", "\n", "\n", "- In other cases, nonlinear or linear regression can produce seemingly absurd results. For example, an entire Gaussian curve has a bell shape, but just one of the tails is a good approximation to an exponential curve. If the data distribution does not clearly suggest a Gaussian curve, the regression may find that fitting a huge Gaussian with only its tail immersed amongst the data points gives a closer fit to the data than a more reasonably sized complete Gaussian.\n", "\n", "#### Statistical tools\n", "\n", "- Standard error is an excellent tool for judging how well a model fits the data.\n", " 1) The [standard error of estimate](https://en.wikipedia.org/wiki/Standard_error) ($s_e$) is the typical difference found between the estimates from the regression and the actual observations. In particular, it is the standard deviation of the sampling distribution.\n", "\n", "- The best fitting line may still be a poor description of the relationship. There are a few summary statistics given here to indicate the quality of the fit:\n", " \n", " 1) [Pearson's correlation coefficient](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) ($\\rho$) is a unitless statistic between -1 and 1 that represents the linear correlation between X and y. A value of 1 represents a positive linear relationship between the dependent and independent variable. A value of 0 represents no linear correlation, and a value of -1 indictes a negative linear relationship.\n", " \n", " 2) The [coefficient of determination](https://en.wikipedia.org/wiki/Coefficient_of_determination) ($R^2$) quantifies how much of the variabiation in y can be explained by the model. It is a unitless statistic between 0 and 1. An $R^2$ of 1 can indicate that the model fits the observations perfectly whereas a value of 0 can indicate that the model fits the data poorly. **Note** that $R^2$ will always increase with more predictors but that *does not mean* that the model is better.\n", " \n", " \n", "- Correlation and $R^2$ are excellent for understanding how well a model fits the data if we have linear data. Standard error is useful for nonlinear models. In general, these measures are mostly used for linear relationships, and are less frequently used for nonlinear modes. The goal is to fit the data as closely as possible, but not at the expense of its ability to generalize to the population as a whole.\n", "\n", "#### Caution\n", "\n", "- Note also that the statistics above are succeptible to a number of problems. First, if the dataset is small, then correlation and $R^2$ may not be quite accurate. Second, there is a chance that the observations are not representative of the population, and the correlation hinted at by the model is due to sampling bias. A fun explantion of this is the [Datasaurus](https://www.autodeskresearch.com/publications/samestats). All of these plots have the same summary statistics!\n", "![Datasaurus](https://d2f99xq7vri1nk.cloudfront.net/DinoSequentialSmaller.gif \"datasaurus\")\n", "\n", "\n", "#### Hypothesis testing\n", "\n", "- Additionally, a probability (p-value) is calculated to assess the statistical significance of the relationship between X and y. Remember that a probability ranges from 0 to 1. This [hypothesis test](http://www.biostathandbook.com/hypothesistesting.html) is structured as follows:\n", "\n", " 1) Assert the null hypothesis ($H_0$). $H_0$ is that there is no linear relationship between X and y.\n", " \n", " 2) Choose a critical value ($\\alpha$ -- typically set to 0.05 but is sometimes set to a value as high at 0.1 and as low as 0.001) which we will use to conduct a Hypothesis Test.\n", " \n", " 3) Next we calculate a [test statistic](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient#Testing_using_Student's_t-distribution) ($t* = r\\sqrt{\\frac{n-2}{1-r^2}}$ where $r$ is the sample correlation coefficient and $n$ is the number of samples). Use the Test Statistic to find the corresponding p-value (use a [table](http://www.ttable.org/) or the [calculator](http://www.ttable.org/student-t-value-calculator.html)). Note that the Test Statistic will differ between hypotheses.\n", " \n", " 4) Compare the p-value found in step 3 to the $\\alpha$ value selected in step 2. A p-value less than $\\alpha$ means that we reject the null hypothesis which means that there is not no linear relationship between X and y which often means that there is a linear relationship between X and y. A p-value greater than $\\alpha$ means that we fail to reject the null hypothesis meaning that there is likely no linear relationship between X and y. It is important to choose a critical value before performing the study, because it is very easy to grant your study significance by calculating the p-value first and then choosing a critical value higher than the one you calculated (called [\"p-hacking\"](https://en.wikipedia.org/wiki/Data_dredging)).\n", "\n", "#### Considerations\n", "\n", "- Domain knowledge is key for translating the regression models to real situations. The model can only explain the given observations. In particular, a regression model is an interpolation model. We should be wary of [extrapolation](https://online.stat.psu.edu/stat501/lesson/12/12.8) beyond the range of the data unless it is properly justifiable -- remember to ask yourself, is this model logical?\n", "\n", "\n", "- Considering confounding variables while conducting your analysis. A statistically significant model that reasonably models a relationship may still be misleading due to confounding variables. For instance, rainier weather is usually associated with cooler temperatures. An observation of a large numbers of larvae on a day with cooler temperatures may really reflect the relationship between larvae and rainfall. In this example, precipitation would be a confounding variable.\n", "\n", "\n", "- Remember! Correlation is not causation. A representative model that models the data well and is free from confounding variables can only describe a correlation between the dependent and independent variables. We can not answer the question of causation with regression.\n", "\n", "\n", "#### Checking your model\n", "\n", "First, note that the true representation of linear data is $y=\\beta_0+\\beta_1 X + \\varepsilon$ and $\\hat{y}=\\hat{\\beta}_0+\\hat{\\beta}_1 X$ is our model. We can find an estimation of the error, $\\varepsilon$, by: $y-\\hat{y} = \\hat{\\varepsilon}$ where $\\hat{\\varepsilon}$ is the estimated error.\n", "\n", "\n", "An excellent [diagnostic](http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/R/R5_Correlation-Regression/R5_Correlation-Regression7.html) for linear regression is a plot of the residuals versus fitted values plot. The residuals are an estimation of the error as seen above and the fitted values are our estimated response. In the plot below, we see the residuals on the y-axis and the fitted values on the x-axis. We can learn a lot from the following figure:\n", "![ConstantVar](https://www.researchgate.net/profile/John_Hodsoll/publication/318911883/figure/fig1/AS:535566758277120@1504700473370/Diagnostic-plots-of-the-residuals-plotted-against-fitted-values-of-regression-model-to.png \"constantvar\")\n", "\n", " A) Suggests that a linear regression could be a very good model.\n", " \n", " B) Suggests that we should use Weighted Least Squares to account for non constant variance\n", " \n", " C) Suggests that our data is not independent. This could suggest that some of our predictors are correlated and we should consider removing some of them. It could also suggest that our data is correlated in time or space and should be processed using time series or spatial statistics methods.\n", " \n", " D) Suggests we do not have a linear relationship and should consider non linear methodology.\n", "\n", "\n", "Note that Ordinary Least Squares assumes a constant variance among the errors meaning that the errors around each observation have the same standard deviation. Weighted Least Squares allows for non constant variance.\n", "\n", "\n", "For this particular study, there is an additional level of complexity because data from two protocols are being combined, and are therefore not necessarily concurrent. To this end, observations from one protocol are paired with observations from another protocol and weighted according to their closeness in space and times.\n", "\n", "#### To be continued + Summary\n", "\n", "Details of the Linear and Non-Linear Weighted Least Squares algorithms used in this notebook are found in the following sections, as well as the statistical methods for measuring model quality, and specific descriptions of each regression mode.\n", "\n", "In summary, the process will be performed as follows:\n", "\n", "- Retrieve records from the GLOBE API, process the data, and prune off outliers.\n", "- Assemble associations between the two protocols, weighing them according to the chosen weighting parameters.\n", "- Perform Linear Weighted Least Squares on an approximate problem to get reasonable starting parameter values.\n", "- Evaluate the model according to standard error, Pearson's correlation coefficient, coefficient of determination, and p-value." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What the code below is doing:\n", "\n", "#### Summary\n", "\n", "In this notebook, we are looking at the relationship between data from the MHM and other GLOBE data. In order to do so, we perform semi-complex regressions to fit a handful of different curves. See more info on which curves we use further down.\n", "\n", "#### Under the hood\n", "\n", "In particular, we use a non linear method to fit the curves called Non-Linear Weighted Least Square. This method requires initial guesses for the parameters of each function. These parameters are estimated by getting the coefficients from a [Poisson Regression](http://www.adasis-events.com/statistics-blog/2012/11/26/what-is-the-difference-between-linear-logistic-and-poisson-r.html) (implented using scikit-learn's [sklearn.linear_model.PoissonRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html#sklearn.linear_model.PoissonRegressor)). These parameters are then used in the Levenberg-Marquardt algorithm (an algorithm for Non-Linear Weighted Least Squares) which was impleneted using the scipy function [scipy.optimize.curve_fit](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9e240c17119e474c8bcead31eedf4e2a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "986e8f7939b4436b84fb1af46684286b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for graphing: notching ticks in a range)\n" ] } ], "source": [ "designate('notching ticks in a range', 'graphing')\n", "\n", "# notch function to get evenly spaced points in a range\n", "def set_ticks(left, right, number=100):\n", " \"\"\"Notch a number of ticks along a span\n", " \n", " Arguments:\n", " left: float, left axis boundary\n", " right: float, right axis boundary\n", " number=100: number ticks\n", " \n", " Returns:\n", " list of floats\n", " \"\"\"\n", " \n", " # get chunk length\n", " chunk = (right - left) / number\n", " ticks = [left + index * chunk for index in range(number + 1)]\n", " \n", " return ticks" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for graphing: sketching functions)\n" ] } ], "source": [ "designate('sketching functions', 'graphing')\n", "\n", "# sketch a function\n", "def sketch_plot(*functions, legend=None, span=(-5, 5), title=\"[TEMP TITLE]\", xlab=[\"TEMP X LABEL\"], ylab=\"[TEMP Y LABEL]\"):\n", " \"\"\"Sketch a function.\n", " \n", " Arguments:\n", " *functions: unpacked list of function objects\n", " legend=None: list of str\n", " span=(-5, 5): tuple of floats, the x-axis range\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # begin curve\n", " curve = figure(x_range=span, plot_width=300, plot_height=300, title=title)\n", " curve.xaxis.axis_label = xlab\n", " curve.yaxis.axis_label = ylab\n", " \n", " # set colors\n", " colors = ['red', 'green', 'blue', 'violet', 'cyan', 'orange']\n", " \n", " # set default legend\n", " if not legend:\n", " \n", " # set up legend\n", " legend = [str(index + 1) for index, _ in enumerate(functions)]\n", " \n", " # get points\n", " xs = set_ticks(*span)\n", " \n", " # plot functions\n", " for function, color, name in zip(functions, colors, legend):\n", " if function == arcsinh:\n", " # graph line\n", " points = [{'x': x, 'y': function(x/2)} for x in xs]\n", " else:\n", " # graph line\n", " points = [{'x': x, 'y': function(x)} for x in xs]\n", " table = ColumnDataSource(pandas.DataFrame(points))\n", " curve.line(source=table, x='x', y='y', color=color, line_width=1, legend_label=name)\n", " \n", " # add hover annotations and legend\n", " annotations = [('x', '@x'), ('y', '@y')]\n", " hover = HoverTool(tooltips=annotations)\n", " curve.add_tools(hover)\n", " curve.legend.location='top_left'\n", " \n", " # show the results\n", " output_notebook()\n", " show(curve)\n", " \n", " return None" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for graphing: annotating graphs)\n" ] } ], "source": [ "designate('annotating graphs', 'graphing')\n", "\n", "# annotate graphs subroutine\n", "def annotate_plot(graph, annotations):\n", " \"\"\"Annotate the graph with annotations.\n", " \n", " Arguments:\n", " graph: bokeh graph object\n", " annotations: list of (str, str) tuples\n", " \n", " Returns:\n", " graph object\n", " \"\"\"\n", " \n", " # set up hover summary\n", " summary = \"\"\"\n", " \n", " \n", " \"\"\"\n", " \n", " # add annotations\n", " for field, value in annotations:\n", " \n", " # add to summary\n", " summary += '{}: {}
'.format(field, value)\n", "\n", " # setup hovertool\n", " hover = HoverTool()\n", " hover.tooltips = summary\n", " graph.add_tools(hover)\n", " \n", " # set up graph legend\n", " graph.legend.location='top_left'\n", " graph.legend.click_policy='hide'\n", " \n", " return graph" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for regressions: issuing a ticket)\n" ] } ], "source": [ "designate('issuing a ticket', 'regressions')\n", "\n", "# function to issue a default ticket based on settings\n", "def issue_ticket(settings, genus, mode):\n", " \"\"\"Issue a blank ticket with default settings.\n", " \n", " Arguments:\n", " settings: dict\n", " genus: str\n", " mode: str\n", " \n", " Returns:\n", " dict\n", " \"\"\"\n", " \n", " # begin ticket with all values set to zero\n", " ticket = {parameter: 0 for parameter, _ in units.items()}\n", " \n", " # update with settings\n", " ticket.update(settings)\n", " \n", " # add other default settings\n", " ticket.update({'genus': genus, 'mode': mode, 'pvalue': 1.0, 'equation': cancellation})\n", " ticket.update({'coefficients': [0] * (regressions[mode]['polynomial'] + 2)})\n", " ticket.update({'curve': [0] * regressions[mode]['requirement']})\n", " \n", " return ticket" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for regressions: running a regression on samples)\n" ] } ], "source": [ "designate('running a regression on samples', 'regressions')\n", "\n", "# generalized regression function\n", "def regress(samples, ticket, spy=False, calculate=True):\n", " \"\"\"Perform regression on the samples, based on the submission ticket.\n", " \n", " Arguments:\n", " samples: list of dicts, the samples\n", " ticket: dict, the settings\n", " approximation: float, approximate value for zero\n", " spy: boolean, verify initial regression fits with plots?\n", " calculate: boolean, calculate jacobian directly?\n", " \n", " Returns:\n", " dict, the report\n", " \"\"\" \n", " \n", " # resolve regression styles\n", " mode = ticket['mode']\n", " size = ticket['records']\n", " \n", " # try to run regression\n", " try:\n", " \n", " # get coefficients from linear model\n", " coefficients = get_coefficients_linear_regression(samples, mode)\n", "\n", " # fit non linear model\n", " curve = perform_nonlinear_regression(samples, mode, coefficients, calculate)\n", "\n", " # check the two fits against each other\n", " if spy:\n", "\n", " # verify\n", " check_optimized_params_with_initial(samples, mode, coefficients, curve)\n", "\n", " # assess the model\n", " assessment = check_model_fit(samples, mode, curve, size)\n", " ticket.update(assessment)\n", " \n", " # but skip for math errors\n", " except (ZeroDivisionError, TypeError, ValueError, RuntimeError):\n", " \n", " # skip\n", " pass\n", " \n", " return ticket" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for regressions: assessing a model)\n" ] } ], "source": [ "designate('assessing a model', 'regressions')\n", "\n", "# assess model fit\n", "def check_model_fit(samples, mode, curve, size):\n", " \"\"\"Assess the model by comparing predictions to targets\n", " \n", " Arguments:\n", " samples: list of dicts\n", " mode: str, the regression mode\n", " curve: list of floats, the regression parameters\n", " size: int, number of records\n", " \n", " Returns:\n", " dict\n", " \"\"\"\n", " \n", " # make predictions using model\n", " matrix = [sample['x'] for sample in samples]\n", " weights = [sample['weight'] for sample in samples]\n", " truths = [sample['y'] for sample in samples]\n", " predictions = [regressions[mode]['function'](entry, *curve) for entry in matrix]\n", " \n", " # get validation scores\n", " validation = calculate_corr_coef(truths, predictions, weights, size)\n", " \n", " # create equation\n", " equation = regressions[mode]['equation']\n", " for parameter, place in zip(curve, ('β0', 'β1','β2')):\n", " \n", " # replace in equation\n", " equation = equation.replace(place, '{}'.format(round(float(parameter), 2)))\n", "\n", " # get critical points\n", " criticals = {name: round(float(quantity), 4) for name, quantity in zip(regressions[mode]['names'], curve)}\n", "\n", " # make assessment\n", " assessment = {'curve': curve, 'equation': equation}\n", " assessment.update(validation)\n", " assessment.update(criticals)\n", " \n", " return assessment" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "code_folding": [], "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for performing: performing regression study)\n" ] } ], "source": [ "designate('performing regression study', 'performing')\n", "\n", "# perform regression mode on mosquitoes data\n", "def perform(mode, associations, spy=False, calculate=True):\n", " \"\"\"Perform a mode of regression on a set of associations.\n", " \n", " Arguments:\n", " mode: str, the mode of regression\n", " associations: list of dicts\n", " spy=True: boolean, observe initial parameter fits?\n", " calculate: boolean, calculate Jacobian directly?\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # make graph\n", " graph, panda = plot_scatter_plot(associations, mode, spy, calculate)\n", "\n", " # show the results\n", " output_notebook()\n", " show(graph)\n", "\n", " # get columns and add units\n", " columns = ['genus', 'records', 'pairs', 'coverage', 'pvalue', 'correlation', 'R^2', 's.e.', 'equation']\n", " columns += regressions[mode]['names']\n", " panda = panda[columns]\n", " panda.columns = [column + units[column](feature) for column in columns]\n", "\n", " # show panda\n", " display(panda)\n", " \n", " return None" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "code_folding": [], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for regressions: studying all genera)\n" ] } ], "source": [ "designate('studying all genera', 'regressions')\n", "\n", "# function to run regression on the associations and return reports per genus\n", "def study(associations, mode, spy=True, calculate=True):\n", " \"\"\"Study the data under a regression mode.\n", " \n", " Arguments:\n", " associations: list of dicts\n", " mode: str, the regression mode\n", " spy: boolean, plot initial linear fits?\n", " calculate: boolean, calculate jacobian directly?\n", " \n", " Returns:\n", " list of dicts\n", " \"\"\"\n", " \n", " # go through each genus, running regression\n", " reports = []\n", " for genus in ['All', 'Aedes', 'Anopheles', 'Culex', 'Other', 'Unknown']:\n", " \n", " # begin ticket\n", " ticket = issue_ticket(settings, genus, mode)\n", " \n", " # perform subsampling by genus and make the samples\n", " subset = get_subset_of_data(associations, genus)\n", " samples = assemble_samples(subset)\n", " coverage = round(len(subset) / len(data), 2)\n", " ticket.update({'records': len(subset), 'pairs': len(samples), 'coverage': coverage})\n", " \n", " # fit the regressor\n", " report = regress(samples, ticket, spy, calculate)\n", " reports.append(report)\n", " \n", " return reports" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for debugging: interpolating between parameters)\n" ] } ], "source": [ "designate('interpolating between parameters', 'debugging')\n", "\n", "# define interpolation function\n", "def interpolate(start, finish, number=5):\n", " \"\"\"Interpolate between start and finish parameters.\n", " \n", " Arguments:\n", " start: list of floats\n", " finish: list of floats\n", " number=5: int, number of total curves\n", " \n", " Returns:\n", " list of lists of floats, the curves\n", " \"\"\"\n", " \n", " # find points for each pair\n", " pairs = zip(start, finish)\n", " tuplets = []\n", " for first, last in pairs:\n", " \n", " # get chunk\n", " chunk = (last - first) / (number - 1)\n", " tuplet = [first + chunk * index for index in range(number)]\n", " tuplets.append(tuplet)\n", " \n", " # zip together sets\n", " curves = [curve for curve in zip(*tuplets)]\n", " \n", " return curves" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for debugging: verifying optimized model)\n" ] } ], "source": [ "designate('verifying optimized model', 'debugging')\n", "\n", "# verify with a plot the optimized model\n", "def check_optimized_params_with_initial(samples, mode, coefficients, curve):\n", " \"\"\"Verify the fit of the optimized model compared to the initial estimates.\n", " \n", " Arguments:\n", " samples: list of dicts\n", " mode: str, regression mode\n", " coefficients: list of floats, coefficients of linear model\n", " curve: list of floats, parameters of nonlinear model\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # define points\n", " independents = [sample['x'] for sample in samples]\n", " dependents = [sample['y'] for sample in samples]\n", " sizes = [sample['size'] for sample in samples]\n", " \n", " # get ticks\n", " ticks = set_ticks(min(independents), max(independents), 100)\n", " \n", " # make predictions\n", " initials = regressions[mode]['initial'](*coefficients)\n", " approximation = [regressions[mode]['function'](tick, *initials) for tick in ticks]\n", " regression = [regressions[mode]['function'](tick, *curve) for tick in ticks]\n", "\n", " # print comparison\n", " print('{} samples'.format(len(samples)))\n", " print('{} (coefficients)'.format([round(float(entry), 8) for entry in coefficients]))\n", " print('{} (initials)'.format([round(float(entry), 8) for entry in initials]))\n", " print('{} (curve)'.format([round(float(entry), 8) for entry in curve]))\n", " \n", " # make figure\n", " graph = figure()\n", " graph.circle(x=independents, y=dependents, size=sizes, color='gray', fill_alpha=0.05)\n", " \n", " # add lines\n", " graph.line(x=ticks, y=approximation, color='blue', line_width=3, legend_label='linear')\n", " graph.line(x=ticks, y=regression, color='green', line_width=3, legend_label='nonlinear')\n", " \n", " # show the results\n", " output_notebook()\n", " show(graph)\n", "\n", " return None" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hide_input": true }, "source": [ "### Data Preparation" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The process begins with **calling the GLOBE API**:" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "https://www.globe.gov/en/globe-data/globe-api" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for api: calling the api)\n" ] } ], "source": [ "designate('calling the api', 'api')\n", "\n", "# call the api with protocol and country code\n", "def query_api(protocol, code, beginning, ending, sample=False):\n", " \"\"\"Call the api:\n", " \n", " Arguments:\n", " protocol: str, the protocol\n", " code: str, the country code\n", " beginning: str, the beginning date\n", " ending: str, the ending date\n", " sample=False: boolean, only get small sampling?\n", " \n", " Returns:\n", " list of dicts, the records\n", " \"\"\"\n", " \n", " # default to all countries unless a code is specified\n", " extension = 'country/' if code else ''\n", " extensionii = '&countrycode=' + code if code else ''\n", " \n", " # assemble the url for the API call \n", " url = 'https://api.globe.gov/search/v1/measurement/protocol/measureddate/' + extension\n", " url += '?protocols=' + protocol\n", " url += '&startdate=' + beginning \n", " url += '&enddate=' + ending\n", " url += extensionii\n", "\n", " # geojson parameter toggles between formats\n", " url += '&geojson=FALSE'\n", " \n", " # sample parameter returns small sample set if true\n", " url += '&sample=' + str(sample).upper()\n", "\n", " # make the API call and return the raw results\n", " request = requests.get(url)\n", " raw = json.loads(request.text)\n", " \n", " return raw" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "After retrieving the data, several steps are taken to prepare the data. Initially, the data is returned in a nested structure. It is useful to **flatten this nesting** so that all fields are readily accessible. " ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: flattening records)\n" ] } ], "source": [ "designate('flattening records', 'processing')\n", "\n", "# function to flatten a nested list into a single-level structure\n", "def flatten_dict_list(record, label=None):\n", " \"\"\"Flatten each record into a single level.\n", "\n", " Arguments:\n", " record: dict, a record\n", " label: str, key from last nesting\n", "\n", " Returns:\n", " dict\n", " \"\"\"\n", "\n", " # initiate dictionary\n", " flattened = {}\n", "\n", " # try to flatten the record\n", " try:\n", "\n", " # go through each field\n", " for field, info in record.items():\n", "\n", " # and flatten the smaller records found there\n", " flattened.update(flatten_dict_list(info, field))\n", "\n", " # otherwise record is a terminal entry\n", " except AttributeError:\n", "\n", " # so update the dictionary with the record\n", " flattened.update({label: record})\n", "\n", " return flattened" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "Additionally, it can be useful to **abbreviate the field names** as the initial field names are often quite long." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: abbreviating records)\n" ] } ], "source": [ "designate('abbreviating records', 'processing')\n", "\n", "# function to abbreviate the fields of a record\n", "def abbreviate_field_name(record, primary=True, removal=True):\n", " \"\"\"Abbreviate certain fields in the record for easier manipulation later.\n", " \n", " Arguments:\n", " record: dict\n", " primary=True: boolean, primary record?\n", " removal=True: boolean, remove original fields?\n", " \n", " Returns:\n", " dict\n", " \"\"\"\n", " \n", " # define abbreviations dictionary for primary records\n", " abbreviations = {}\n", " abbreviations['count'] = larvae\n", " abbreviations['genus'] = 'mosquitohabitatmapperGenus'\n", " abbreviations['source'] = 'mosquitohabitatmapperWaterSource'\n", " abbreviations['stage'] = 'mosquitohabitatmapperLastIdentifyStage'\n", " abbreviations['type'] = 'mosquitohabitatmapperWaterSourceType'\n", " abbreviations['measured'] = 'mosquitohabitatmapperMeasuredAt'\n", " abbreviations['habitat'] = 'mosquitohabitatmapperWaterSourcePhotoUrls'\n", " abbreviations['body'] = 'mosquitohabitatmapperLarvaFullBodyPhotoUrls'\n", " abbreviations['abdomen'] = 'mosquitohabitatmapperAbdomenCloseupPhotoUrls'\n", " \n", " # if a secondary record\n", " if not primary:\n", "\n", " # define abbreviations dictionary for secondary protocol\n", " abbreviations = {}\n", " abbreviations['feature'] = feature\n", " abbreviations['measured'] = measured\n", "\n", " # and each abbreviation\n", " for abbreviation, field in abbreviations.items():\n", "\n", " # copy new field from old, or None if nonexistent\n", " record[abbreviation] = record.setdefault(field, None)\n", " \n", " # remove original field if desired\n", " if removal:\n", " \n", " # remove field\n", " del(record[field])\n", " \n", " return record" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "As all measurements are recorded in reference to UTC time, it is helpful to **convert the measurements to local times**. This is accomplished by adjusting the hour according to the longitude. Though this may not accurately reflect the local time in a political sense as it ignores daylight savings time and time zone boundaries, it is perhaps a more accurate measure in the astronomical sense." ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: synchronizing times with longitudes)\n" ] } ], "source": [ "designate('synchronizing times with longitudes', 'processing')\n", "\n", "# synchronize the time of measurement with the longitude for local time\n", "def sync_time_with_long(record):\n", " \"\"\"Synchronize the measured times with longitudes.\n", " \n", " Arguments:\n", " record: dict\n", " \n", " Returns:\n", " dict\n", " \"\"\"\n", "\n", " # convert the date string to date object and normalize based on partitioning\n", " record['time'] = datetime.strptime(record['measured'], \"%Y-%m-%dT%H:%M:%S\")\n", " record['date'] = record['time'].date()\n", " \n", " # convert the date string to date object and correct for longitude\n", " zone = int(round(record['longitude'] * 24 / 360, 0))\n", " record['hour'] = record['time'] + timedelta(hours=zone)\n", "\n", " return record" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": false }, "source": [ "The larvae count data are initially returned as strings. In order to analyze the data, we need to **convert the numerical strings into number data types**. Additionally, some of the data is entered as a range (e.g., '1-25'), or as a more complicated string ('more than 100'). These strings will be converted to floats using the following rules:\n", "- a string such as '50' is converted to its floating point equivalent (50)\n", "- a range such as '1-25'is converted to its average (13)\n", "- a more complicated string, such as 'more than 100' is converted to its nearest number (100)" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: converting strings to numbers)\n" ] } ], "source": [ "designate('converting strings to numbers', 'processing')\n", "\n", "# function to convert a string into a floating point number\n", "def convert_str_to_float(record, field, name):\n", " \"\"\"Translate info given as a string or range of numbers into a numerical type.\n", " \n", " Arguments:\n", " record: dict\n", " field: str, the field to get converted\n", " name: str, the name of the new field\n", " \n", " Returns:\n", " float\n", " \"\"\"\n", " \n", " # try to convert directly\n", " info = record[field]\n", " try:\n", " \n", " # translate to float\n", " conversion = float(info)\n", " \n", " # otherwise\n", " except ValueError:\n", " \n", " # try to convert a range of values to their average\n", " try:\n", " \n", " # take the average, assuming a range separated by a hyphen\n", " first, last = info.split('-')\n", " first = float(first.strip())\n", " last = float(last.strip())\n", " conversion = float(first + last) / 2\n", " \n", " # otherwise\n", " except ValueError:\n", " \n", " # scan for digits\n", " digits = [character for character in info if character.isdigit()]\n", " conversion = ''.join(digits)\n", " conversion = float(conversion)\n", " \n", " \n", " # add new field\n", " record[name] = conversion\n", " \n", " return record" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "Also, some steps have been taken towards **mosquito genus identification**. The three noteworthy genera in terms of potentially carrying diseases are Aedes, Anopheles, and Culex. If the identification process did not lead to one of these three genera, the genus is regarded as \"Other.\" If the identification process was not fully carried out, the genus is regarded as \"Unknown.\"" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: identifying mosquito genera)\n" ] } ], "source": [ "designate('identifying mosquito genera', 'processing')\n", "\n", "# function to identify the mosquito genera based on last stage of identification\n", "def identify_mosquito_genera(record):\n", " \"\"\"Identify the genera from a record.\n", " \n", " Arguments:\n", " record: dict\n", " \n", " Returns:\n", " dict\n", " \"\"\"\n", "\n", " # check genus\n", " if record['genus'] is None:\n", "\n", " # check last stage\n", " if record['stage'] in (None, 'identify'):\n", "\n", " # correct genus to 'Unidentified'\n", " record['genus'] = 'Unknown'\n", "\n", " # otherwise\n", " else:\n", "\n", " # correct genus to 'Other'\n", " record['genus'] = 'Other'\n", " \n", " return record" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "Also, many of the records contain photo urls. The **photo urls will be parsed** and the file names formatted according to a naming convention." ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: localizing latitude and longitude)\n" ] } ], "source": [ "designate('localizing latitude and longitude', 'processing')\n", " \n", "# specify the location code for the photo based on its geo coordinates\n", "def location_code_for_photo_naming(latitude, longitude):\n", " \"\"\"Specify the location code for the photo naming convention.\n", " \n", " Arguments:\n", " latitude: float, the latitude\n", " longitude: float, the longitude\n", " \n", " Returns:\n", " str, the latlon code\n", " \"\"\"\n", " \n", " # get latlon codes based on < 0 query\n", " latitudes = {True: 'S', False: 'N'}\n", " longitudes = {True: 'W', False: 'E'}\n", " \n", " # make latlon code from letter and rounded geocoordinate with 3 places\n", " latlon = latitudes[latitude < 0] + ('000' + str(abs(int(latitude))))[-3:]\n", " latlon += longitudes[longitude < 0] + ('000' + str(abs(int(longitude))))[-3:]\n", " \n", " return latlon" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: applying photo naming convention)\n" ] } ], "source": [ "designate('applying photo naming convention', 'processing')\n", "\n", "# apply the naming convention to a photo url to make a file name\n", "def photo_naming(urls, code, latitude, longitude, time):\n", " \"\"\"Apply the naming convention to a group of urls\n", " \n", " Arguments:\n", " urls: list of str, the photo urls\n", " code: str, the photo sector code\n", " latitude: float, the latitude\n", " longitude: float, the longitude\n", " time: datetime object, the measurement time\n", " \n", " Returns:\n", " list of str, the filenames\n", " \"\"\"\n", " \n", " # begin file name with protocol and latlon\n", " base = 'GLOBEMHM_' + location_code_for_photo_naming(latitude, longitude) + '_'\n", " \n", " # add the measurement time and sector code\n", " base += time.strftime('%Y%m%dT%H%MZ') + '_' + code\n", " \n", " # add index and unique id\n", " names = []\n", " for index, url in enumerate(urls):\n", " \n", " # add index, starting with 1\n", " name = base + str(index + 1)\n", " \n", " # add unique id and extension\n", " unique = url.split('/')[-2]\n", " name += '_' + unique + '.jpg'\n", " names.append(name)\n", " \n", " return names" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: parsing photo urls)\n" ] } ], "source": [ "designate('parsing photo urls', 'processing')\n", "\n", "# function for parsing photo urls\n", "def parse_photo_info(record):\n", " \"\"\"Parse photo url information.\n", " \n", " Arguments:\n", " record: dict\n", " \n", " Returns:\n", " dict\n", " \"\"\"\n", "\n", " # dictionary of photo sector codes\n", " sectors = {'habitat': 'WS', 'body': 'FB', 'abdomen': 'AB'}\n", "\n", " # initialize fields for each sector and parse urls\n", " record['originals'] = []\n", " record['thumbs'] = []\n", " record['photos'] = []\n", " for field, stub in sectors.items():\n", " \n", " # split on semicolon, and keep all fragments with 'original'\n", " datum = record[field] or ''\n", " originals = [url.strip() for url in datum.split(';') if 'original' in url]\n", " \n", " # sort by the unique identifier as the number before the last slash\n", " originals.sort(key=lambda url: url.split('/')[-2])\n", " record['originals'] += originals\n", " \n", " # get the thumbnail versions\n", " thumbs = [url.split('original')[0] + 'small.jpg' for url in originals]\n", " record['thumbs'] += thumbs\n", " \n", " # apply the naming convention\n", " photos = photo_naming(originals, code, record['latitude'], record['longitude'], record['time'])\n", " record['photos'] += photos\n", " \n", " return record" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for processing: processing records)\n" ] } ], "source": [ "designate('processing records', 'processing')\n", "\n", "# function for processing records\n", "def process_records(records, primary=True):\n", " \"\"\"Process all records.\n", " \n", " Arguments:\n", " records: list of dicts\n", " primary=True: boolean, primary record?\n", " \n", " Returns:\n", " list of dicts\n", " \"\"\"\n", " \n", " # flatten and abbreviate all records\n", " records = [flatten_dict_list(record) for record in records]\n", " records = [abbreviate_field_name(record, primary) for record in records]\n", " records = [sync_time_with_long(record) for record in records]\n", " \n", " # process primary records\n", " if primary:\n", " \n", " # process\n", " records = [convert_str_to_float(record, 'count', 'larvae') for record in records]\n", " records = [identify_mosquito_genera(record) for record in records]\n", " records = [parse_photo_info(record) for record in records]\n", " \n", " # process secondary records\n", " if not primary:\n", " \n", " # process\n", " records = [convert_str_to_float(record, 'feature', feature) for record in records]\n", " \n", " return records" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": false }, "source": [ "Finally, it is sometimes the case that records contain **potential outliers**. For instance, an entry of '1000000' for larvae counts is suspicous because likely no one counted one million larvae. These data can skew analysis and dwarf the rest of the data in graphs. While there are [numerous ways](https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/) to detect and remove these outliers, the method implemented in this notebook uses the [interquartile range](https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/).\n", "\n", "We can remove these outliers by setting a threshold for the upper quartile boundary. We then calculate the interquartile range by finding the $x$th percentile and the $100-x$th percentile and subtract the two.\n", "\n", "We then calculate a cutoff value at 1.5 multiplied by the interquartile range and subract this value from the lower quartile and add it to the upper quartile.\n", "\n", "Any value between the two values is considered valid and any value outside of that range is considered an outlier and is removed from the dataset." ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5acaca56d2c64dc0a025ffbee7ad94b7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f2022a5064d24ab9b672ae363bf506c2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "code_folding": [], "hidden": true, "hide_input": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for pruning: outlier pruning)\n" ] } ], "source": [ "designate('outlier pruning', 'pruning')\n", "\n", "# function to prune away outlying observations\n", "def remove_outliers(records, field, threshold=75):\n", " \"\"\"Prune away outlying observations based on the interquartile range.\n", " \n", " Arguments:\n", " records: list of dicts, the records\n", " field: str, field under inspection\n", " threshold: float, upper percentile; default = 75\n", " \n", " Returns:\n", " tuple of two lists of dicts, (pruned records, outliers)\n", " \"\"\"\n", "\n", " # continually attempt pruning until there are no more records removed, but at least two remain\n", " outliers = []\n", " \n", " # reset number of records\n", " number = len(records)\n", "\n", " # calculate the mean and standard deviation\n", " values = [record[field] for record in records]\n", " q_l, q_u = percentile(values, 100-threshold), percentile(values, threshold)\n", " iqr = q_u - q_l\n", " cut_off = iqr * 1.5\n", " lower, upper = q_l - cut_off, q_u + cut_off\n", " \n", " # for each record\n", " for record in records:\n", "\n", " # set the quartiles\n", " record['lq'] = lower\n", " record['uq'] = upper\n", "\n", " # if the threshold is exceeded\n", " if record[field] < lower or record[field] > upper:\n", "\n", " # append to outliers\n", " outliers.append(record)\n", " \n", " records = [record for record in records if record[field] > lower and record[field] < upper]\n", " \n", " return records, outliers" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for filtering: sifting data through filter)\n" ] } ], "source": [ "designate('sifting data through filter', 'filtering')\n", "\n", "# function to sift data through filters\n", "def filter_data_by_field(records, parameters, fields, functions, symbols):\n", " \"\"\"Sift records according to parameters.\n", " \n", " Arguments:\n", " records: list of dicts\n", " parameters: list of settings\n", " fields: list of str\n", " functions: list of function objects\n", " symbols: list of str\n", " \n", " Returns:\n", " list of dicts, str\n", " \"\"\"\n", " \n", " # begin criteria string\n", " criteria = ''\n", "\n", " # filter primaries based on parameters\n", " for parameter, field, function, symbol in zip(parameters, fields, functions, symbols):\n", "\n", " # check for None\n", " if parameter is not None:\n", "\n", " # filter\n", " if field in records[0].keys():\n", " \n", " # filter\n", " records = [record for record in records if function(record[field], parameter)]\n", "\n", " # add to criteria string\n", " criteria += '{} {} {}\\n'.format(field, symbol, parameter)\n", " \n", " # sort data by date and add an index\n", " records.sort(key=lambda record: record['date'])\n", " [record.update({'index': index}) for index, record in enumerate(records)]\n", "\n", " return records, criteria" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for histograms: chopping data up into bins)\n" ] } ], "source": [ "designate('chopping data up into bins', 'histograms')\n", "\n", "# chopping data into histogram bars\n", "def get_bins_width_min_max_for_hist(observations, width=1, limit=1000):\n", " \"\"\"Chop the observations from the records up into bars\n", " \n", " Arguments:\n", " observations: list of floats\n", " width=1: float, minimum width of each bar\n", " limit: int, maximum number of bins\n", " \n", " Returns:\n", " (float, float) tuple, the number of bins and the width\n", " \"\"\"\n", "\n", " # adjust width until number of bins is less than a limit\n", " bins = limit + 1\n", " width = width / 10\n", " while bins > limit:\n", " \n", " # multiply width by 10\n", " width *= 10\n", " \n", " # calculate the number of histogram bins\n", " minimum = (int(min(observations) / width) * width) - (width * 0.5)\n", " maximum = (int(max(observations) / width) * (width + 1)) + (width * 0.5)\n", " bins = int((max(observations) - min(observations)) / width) + 1\n", "\n", " # readjust maximum to cover an even number of bins\n", " maximum = minimum + bins * width\n", "\n", " return bins, width, minimum, maximum" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for histograms: zooming in on best view)\n" ] } ], "source": [ "designate('zooming in on best view', 'histograms')\n", "\n", "# function to define horizontal and vertical ranges of the graph\n", "def zoom_to_best_x_y_ranges(observations, counts, width, percent=1):\n", " \"\"\"Zoom in on the best horizontal and vertical view ranges.\n", " \n", " Arguments:\n", " observations: list of float\n", " counts: list of counts per bin\n", " width: width of each bin\n", " percent: float, the percentile margin\n", " \n", " Returns:\n", " tuple of tuples of floats, the view boundaries\n", " \"\"\"\n", " \n", " # make left and right boudaries as a width past the percentiles\n", " left = percentile(observations, percent) - width\n", " right = percentile(observations, 100 - percent) + width\n", " horizontal = (left, right)\n", " \n", " # make up and down boudaries based on counts\n", " down = 0\n", " up = max(counts) * 1.1\n", " vertical = (down, up)\n", " \n", " return horizontal, vertical" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "code_folding": [], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for histograms: begin drafting a histogram)\n" ] } ], "source": [ "designate('begin drafting a histogram', 'histograms')\n", "\n", "# function for drafting a histogram\n", "def draft_histogram(field, horizontal, vertical, mean, deviation):\n", " \"\"\"Draft a histogram with beginning boundary information.\n", " \n", " Arguments:\n", " field: str\n", " horizontal: (float, float) tuple, the horizontal extent\n", " vertical: (float, float) tuple, the vertical extent\n", " mean: float, the mean of the observations\n", " deviation: standard deviation of the observations\n", " \n", " Returns:\n", " bokeh figure object\n", " \"\"\"\n", " \n", " # create parameters dictionary for histogram labels\n", " parameters = {}\n", " parameters['title'] = 'Histogram for {}'.format(make_title(field))\n", " parameters['x_axis_label'] = '{}'.format(field)\n", " parameters['y_axis_label'] = 'observations'\n", " parameters['x_range'] = horizontal\n", " parameters['y_range'] = vertical\n", " parameters['plot_height'] = 400\n", " parameters['plot_width'] = 450\n", " \n", " # set extra axis\n", " starting = (horizontal[0] - mean) / deviation\n", " ending = (horizontal[1] - mean) / deviation\n", " parameters['extra_x_ranges'] = {'z-score': Range1d(start=starting, end=ending)}\n", " \n", " # initialize the bokeh graph with the parameters\n", " gram = figure(**parameters)\n", " \n", " # label the histogram\n", " formats = round(mean, 2), round(deviation, 2)\n", " label = 'Overlay of normal distribution (mean={}, std={})'.format(*formats)\n", " gram.add_layout(LinearAxis(x_range_name='z-score', axis_label=label), 'above')\n", " \n", " # add annotations\n", " annotations = [('{}:'.format(truncate_field_name(field, 6)), '@left to @right')]\n", " annotations += [('Observations:', '@ys')]\n", " annotations += [('Z-score:', '@scores')]\n", " \n", " # activate the hover tool\n", " hover = HoverTool(tooltips=annotations)\n", " gram.add_tools(hover)\n", " \n", " return gram" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "code_folding": [], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for histograms: blocking in bars on the histogram)\n" ] } ], "source": [ "designate('blocking in bars on the histogram', 'histograms')\n", "\n", "# function to block in bars on the histogram\n", "def draw_bars(gram, counts, edges, mean, deviation):\n", " \"\"\"Block in bars on the histogram.\n", " \n", " Arguments:\n", " gram: bokeh figure\n", " counts: list of floats, the bin counts\n", " edges: list of floats, the bin edges\n", " mean: float\n", " deviation: float\n", " \n", " Returns:\n", " bokeh figure\n", " \"\"\"\n", " \n", " # get middle points\n", " middles = [(right + left) / 2 for left, right in zip(edges[:-1], edges[1:])]\n", " \n", " # calculate z-scores for all middles\n", " scores = [(middle - mean) / deviation for middle in middles]\n", " \n", " # accumulate the info into a table\n", " table = {'ys': counts, 'left': edges[:-1], 'right': edges[1:]}\n", " table.update({'scores': scores, 'xs': middles})\n", " table = ColumnDataSource(table)\n", " \n", " # set parameters for drawing the bars, indicating the source of the data\n", " bars = {'source': table}\n", " bars.update({'left': 'left', 'right': 'right', 'bottom': 0, 'top': 'ys'})\n", " bars.update({'line_color': 'white', 'fill_color': 'lightgreen'})\n", " \n", " # add to histogram\n", " gram.quad(**bars)\n", " \n", " return gram" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "code_folding": [], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for histograms: normalizing observations to a normal distribution)\n" ] } ], "source": [ "designate('normalizing observations to a normal distribution', 'histograms')\n", "\n", "# function to produce the normalization curve\n", "def draw_gaussian_on_bargraph(gram, counts, edges, mean, deviation):\n", " \"\"\"Normalize the observations by drawing the normal distribution.\n", " \n", " Arguments:\n", " gram: bokeh figure\n", " counts: list of floats, the bin counts\n", " edges: list of floats, the bin edges\n", " mean: float\n", " deviation: float\n", " \n", " Returns:\n", " bokeh figure\n", " \"\"\"\n", " \n", " # create line from z-score of -4 to 4\n", " scores = [tick * 0.01 - 4.0 for tick in range(801)]\n", " xs = [(score * deviation) + mean for score in scores]\n", " \n", " # create gaussian fucntion\n", " area = sum([count * (right - left) for count, left, right in zip(counts, edges[:-1], edges[1:])])\n", " height = area / (deviation * sqrt(2 * pi))\n", " normalizing = lambda x: height * exp(-(x - mean) ** 2 / (2 * deviation ** 2))\n", " ys = [normalizing(x) for x in xs]\n", " ys = [round(y, 3) for y in ys]\n", "\n", " # make column object\n", " table = ColumnDataSource({'xs': xs, 'ys': ys, 'scores': scores, 'left': xs, 'right': xs})\n", " summary = 'Normal Distribution'\n", " gram.line(source=table, x='xs', y='ys', color='blue')\n", " \n", " # draw standard lines\n", " for score in (-3, -2, -1, 0, 1, 2, 3):\n", " \n", " # draw std lines\n", " xs = [(deviation * score) + mean] * 2\n", " ys = [0, normalizing(xs[0])]\n", " table = ColumnDataSource({'xs': xs, 'ys': ys, 'scores': [score, score], 'left': xs, 'right': xs})\n", " gram.line(source=table, x='xs', y='ys', color='blue')\n", " \n", " return gram" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "code_folding": [], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for histograms: constructing a bar graph)\n" ] } ], "source": [ "designate('constructing a bar graph', 'histograms')\n", "\n", "# function for constructing a histogram\n", "def construct_bargraph(records, field, width=1):\n", " \"\"\"Make a histogram from the dataset.\n", " \n", " Arguments:\n", " record: list of dicts, the records\n", " field: str, the field of interest\n", " width=1: int, the width of each histogram bar\n", " \n", " Returns:\n", " bokeh figure object\n", " \"\"\"\n", " \n", " # gather up observations\n", " observations = [record[field] for record in records]\n", " \n", " # separate into bins\n", " bins, width, minimum, maximum = get_bins_width_min_max_for_hist(observations, width)\n", " \n", " # get the counts and edges of each bin\n", " counts, edges = histogram(observations, bins=bins, range=(minimum, maximum))\n", " \n", " # get the zoom coordinates\n", " horizontal, vertical = zoom_to_best_x_y_ranges(observations, counts, width)\n", "\n", " # get the normal distribution, defaulting to a small deviation in case of zero\n", " mean = average(observations)\n", " deviation = max([0.000001, std(observations)])\n", " \n", " # begin histogram\n", " gram = draft_histogram(field, horizontal, vertical, mean, deviation)\n", " \n", " # block in bars on the histogram\n", " gram = draw_bars(gram, counts, edges, mean, deviation)\n", " \n", " # draw in equivalent normal distribution\n", " gram = draw_gaussian_on_bargraph(gram, counts, edges, mean, deviation)\n", " \n", " return gram" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### Assembling Associations" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Because the two sets of measurements were not taken concurrently, there must be some criteria to determine when measurements from one protocol correspond to measurements from the other protocol. The method implemented here is a weighing function that determines how strongly to weigh the association between the two data sets, based on the following parameters:\n", " \n", "- distance: the distance in kilometers between measurements that will be granted full weight.\n", " \n", "- interval: the time interval in days between measurements that will be granted full weight.\n", " \n", "- lag: the time in days to anticipate an effect on mosquitoes from a secondary measurement some \n", " days before.\n", " \n", "- confidence: the weight to grant a measurement twice the distance or interval. This determines how steeply the weighting shrinks as the intervals are surpassed. A high confidence will grant higher weights to data outside the intervals. A confidence of zero will have no tolerance for data slightly passed the interval.\n", " \n", "- cutoff: the minimum weight to consider in the dataset. A cutoff of 0.1, for instance, will only retain data if the weight is at least 0.1.\n", " \n", "- inclusion: the maximum number of nearest secondary measurements to include for each mosquitoes measurement.\n", "\n", "The sketch below shows several examples differing in their confidence parameter." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "code_folding": [], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for weighting: weighing function)\n" ] } ], "source": [ "designate('weighing function', 'weighting')\n", "\n", "# weigh a pair of records according to the space and time between them\n", "def weigh_record_pairs_by_space_and_time(space, time, settings):\n", " \"\"\"Weigh the significance of a correlation based on the space and time between them\n", " \n", " Arguments:\n", " space: float, space in distance\n", " time: float, the time time in interval\n", " settings: dict\n", " \n", " Returns:\n", " float, the weight\n", " \"\"\"\n", " \n", " # unpack settings\n", " distance, interval, lag = settings['distance'], settings['interval'], settings['lag']\n", " confidence, cutoff = settings['confidence'], settings['cutoff']\n", " \n", " # set default weight and factors to 1.0\n", " weight = 1.0\n", " factor = 1.0\n", " factorii = 1.0\n", " \n", " # if beyond the space, calculate the gaussian factor\n", " if abs(space) > distance:\n", " \n", " # default factor to 0, but calculate gaussian\n", " factor = 0.0\n", " if confidence > 0:\n", " \n", " # calculate the gaussian factor (e ^ -a d^2 = c), a = -ln (c) / d^2\n", " alpha = -log(confidence) / distance ** 2\n", " factor = exp(-alpha * (abs(space) - distance) ** 2)\n", " \n", " # if beyond time time, calculate gaussian factor\n", " if abs(time - lag) > interval:\n", " \n", " # default factor to 0, but calculate gaussian\n", " factorii = 0.0\n", " if confidence > 0:\n", " \n", " # calculate the gaussian factor (e ^ -a d^2 = c), a = -ln (c) / d^2\n", " beta = -log(confidence) / interval ** 2\n", " factorii = exp(-beta * (abs(time - lag) - interval) ** 2)\n", " \n", " # multiply by factors and apply cutoff\n", " weight *= (factor * factorii)\n", " weight *= int(weight >= cutoff)\n", " \n", " return weight" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: sketching weight function)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? */\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"1083\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };var element = document.getElementById(\"1083\");\n", " if (element == null) {\n", " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1083' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " \n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", " var css_urls = [];\n", " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " \n", " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if (root.Bokeh !== undefined || force === true) {\n", " \n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"1083\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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Application\",\"version\":\"1.4.0\"}};\n", " var render_items = [{\"docid\":\"e77de9b8-f9de-4bce-acaa-7e2b62435f4b\",\"roots\":{\"1001\":\"5907cac2-8275-4f69-a7b8-d0fbe0894992\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "1001" } }, "output_type": "display_data" } ], "source": [ "designate('sketching weight function')\n", "\n", "# set up faux settings\n", "faux = {'distance': 10, 'interval': 1, 'lag': 0, 'confidence': 0.0, 'cutoff': 0.1}\n", "fauxii = {'distance': 10, 'interval': 1, 'lag': 0, 'confidence': 0.5, 'cutoff': 0.1}\n", "fauxiii = {'distance': 10, 'interval': 1, 'lag': 0, 'confidence': 0.8, 'cutoff': 0.1}\n", "\n", "# make functions\n", "weighting = lambda x: weigh_record_pairs_by_space_and_time(10, x, faux)\n", "weightingii = lambda x: weigh_record_pairs_by_space_and_time(10, x, fauxii)\n", "weightingiii = lambda x: weigh_record_pairs_by_space_and_time(10, x, fauxiii)\n", "\n", "# make legend\n", "legend = ['{}'.format(scheme['confidence']) for scheme in [faux, fauxii, fauxiii]]\n", "title = \"Weighting Scheme Example\"\n", "xlab = \"Parameter\"\n", "ylab = \"Weight\"\n", "# sketch\n", "sketch_plot(weighting, weightingii, weightingiii, legend=legend, title=title, xlab=xlab, ylab=ylab)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "All the secondary measurements are accumulated in a Balltree structure that automatically places measurements close together in the tree that are close together spatially and timewise. The tree can then be queried with the time and geolocation information from a mosquito record to find the closest secondary measurements. The weight is calculated for each secondary measurement, and if the weight is greater than the cutoff it is included in the dataset." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Note that potentially several secondary measurements may be associated with a single mosquitoes measurement. This has the effect of creating more regression points than mosquito records used. Because statistical significance tends to increase with increasing number of samples, the p-value calculation of the potential for sampling bias is performed with reference to only the number of mosquito records used." ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5ae351020c0d4f588b1e63dbf36a2266", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9317035891844d648c5707aca1f3679d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for weighting: plotting weighting scheme)\n" ] } ], "source": [ "designate('plotting weighting scheme', 'weighting')\n", "\n", "# function to plot weighting scheme\n", "def plot_plateaued_weighting_scheme(field, settings):\n", " \"\"\"Plot an example of the weighting function.\n", " \n", " Arguments:\n", " field: str\n", " settings: dict\n", " \n", " Returns:\n", " bokeh object\n", " \"\"\"\n", " \n", " # set offset parameter if an interval plot\n", " offset = settings['lag']\n", "\n", " # create functions\n", " functions = {'distance': lambda x: weigh_record_pairs_by_space_and_time(x, offset, settings), 'interval': lambda x: weigh_record_pairs_by_space_and_time(0, x, settings)}\n", " \n", " # calculate point range\n", " start = (settings[field] * -5) + offset\n", " finish = (settings[field] * 5) + offset\n", " total = finish - start\n", " chunk = total / 1000\n", " \n", " # calculate points\n", " xs = [start + (number * chunk) for number in range(1001)]\n", " ys = [functions[field](x) for x in xs]\n", " table = ColumnDataSource({'xs': xs, 'ys': ys})\n", " \n", " # create annotations\n", " annotations = [('{}'.format(field + units[field](feature)), '@xs')]\n", " annotations += [('weight', '@ys')]\n", "\n", " # create title\n", " title = 'Weighting Scheme visualization based on {} of {} {}'.format(field, settings[field], units[field](feature))\n", " if field == interval:\n", " \n", " # add delay\n", " title += ' with a {} day lag'.format(offset)\n", " \n", " # create parameters dictionary for guassian\n", " parameters = {}\n", " parameters['title'] = title\n", " parameters['x_axis_label'] = field\n", " parameters['y_axis_label'] = 'weight'\n", " parameters['plot_height'] = 300\n", " parameters['plot_width'] = 450\n", "\n", " # make graph\n", " plateau = figure(**parameters)\n", " plateau.line(source=table, x='xs', y='ys', line_width=1)\n", "\n", " # activate the hover tool\n", " hover = HoverTool(tooltips=annotations)\n", " plateau.add_tools(hover)\n", " \n", " return plateau" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for weighting: haversine)\n" ] } ], "source": [ "designate('haversine', 'weighting')\n", "\n", "# haversine to calculate distance in kilometers from latitude longitudes\n", "def haversine_get_distance_from_lat_lon(latitude, longitude, latitudeii, longitudeii):\n", " \"\"\"Use the haversine function to calculate a distance from latitudes and longitudes.\n", " \n", " Arguments:\n", " latitude: float, latitude of first location\n", " longitude: float, longitude of first location\n", " latitudeii: float, latitude of second location\n", " longitudeii: float, longitude of second location\n", " \n", " Returns:\n", " float, the distance\n", " \"\"\"\n", " \n", " # radius of the earth at the equator in kilometers\n", " radius = 6378.137\n", " \n", " # get distance from memory\n", " reference = (latitude, longitude, latitudeii, longitudeii)\n", " distance = memory.setdefault(reference, None)\n", " if distance is None:\n", " \n", " # convert to radians\n", " latitude = latitude * pi / 180\n", " latitudeii = latitudeii * pi / 180\n", " longitude = longitude * pi / 180\n", " longitudeii = longitudeii * pi / 180\n", "\n", " # calculate distance with haversine formula\n", " # d = 2r arcsin (sqrt (sin^2((lat2 - lat1) /2) + cos(lat1)cos(lat2)sin^2((lon2 - lon1) / 2)))\n", " radicand = sin((latitudeii - latitude) / 2) ** 2\n", " radicand += cos(latitude) * cos(latitudeii) * sin((longitudeii - longitude) / 2) ** 2\n", " distance = 2 * radius * arcsin(sqrt(radicand))\n", " \n", " # add to memory\n", " memory[reference] = distance\n", " \n", " return distance" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for weighting: get day difference)\n" ] } ], "source": [ "designate('get day difference', 'weighting')\n", "\n", "# function to measure number of days between datetimes\n", "def get_day_difference(time, timeii):\n", " \"\"\"Measure number of days between two datetimes.\n", " \n", " Arguments:\n", " time: float, first timestamp\n", " timeii: float, second timestamp\n", " \n", " Returns:\n", " float, number of days\n", " \"\"\"\n", " \n", " # find time delta\n", " delta = timeii - time\n", " \n", " # convert to days\n", " conversion = delta / (24 * 60 * 60)\n", " \n", " return conversion" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for weighting: measuring between records)\n" ] } ], "source": [ "designate('measuring between records', 'weighting')\n", "\n", "# tuple of distance and interval\n", "def measure_dist_time_bw_records(record, recordii):\n", " \"\"\"Measure the distance and time interval between two records.\n", " \n", " Arguments:\n", " record: record dict\n", " recordii: record dict\n", " \n", " Returns:\n", " (float, float) tuple, the distance and interval\n", " \"\"\"\n", " \n", " # unpack first record\n", " latitude = record['latitude']\n", " longitude = record['longitude']\n", " time = record['time'].timestamp()\n", " \n", " # unpack second record\n", " latitudeii = recordii['latitude']\n", " longitudeii = recordii['longitude']\n", " timeii = recordii['time'].timestamp()\n", " \n", " # calculate distance and interval\n", " distance = haversine_get_distance_from_lat_lon(latitude, longitude, latitudeii, longitudeii)\n", " interval = get_day_difference(time, timeii)\n", " \n", " return distance, interval" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for assembling: adjusting to euclidean points)\n" ] } ], "source": [ "designate('adjusting to euclidean points', 'assembling')\n", "\n", "# function for adjusting a lat long vector to euclidean points\n", "def convert_euclidean_point(record, settings, lag=None):\n", " \"\"\"Adjust a geotime vector to a euclidean point.\n", " \n", " Arguments:\n", " record: dict\n", " settings: dict\n", " lag=None: float, lag in days (overrides settings)\n", " \n", " Returns:\n", " euclidean vector\n", " \"\"\"\n", " \n", " # unpack settings\n", " distance, interval = settings['distance'], settings['interval']\n", " \n", " # get lag value\n", " if not lag:\n", " \n", " # get from settings\n", " lag = settings['lag']\n", " \n", " # determine number of latitude chunks based on distance sensitivity\n", " latitude = haversine_get_distance_from_lat_lon(record['latitude'], record['longitude'], 0, record['longitude']) / distance\n", " \n", " # determine number of longitude chunks based on distance sensitivity\n", " longitude = haversine_get_distance_from_lat_lon(record['latitude'], record['longitude'], record['latitude'], 0) / distance\n", " \n", " # determine number of time chunks based on time sensitivity\n", " time = lag + get_day_difference(0, record['time'].timestamp()) / interval\n", " \n", " # form euclidean vector\n", " euclidean = [latitude, longitude, time]\n", " \n", " return euclidean" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for assembling: planting the tree)\n" ] } ], "source": [ "designate('planting the tree', 'assembling')\n", "\n", "# plant a Balltree for closest sample retrieval\n", "def organize_records_by_space_time(records, settings):\n", " \"\"\"Plant a Balltree to organize records by space and time.\n", " \n", " Arguments:\n", " records: list of dicts\n", " settings: dict\n", " \n", " Returns:\n", " sklearn Balltree object\n", " \"\"\"\n", " \n", " # form matrix from secondary records\n", " matrix = array([convert_euclidean_point(record, settings) for record in records])\n", "\n", " # construct the balltree\n", " tree = BallTree(matrix, leaf_size=2, metric='euclidean') \n", " \n", " return tree" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for assembling: querying tree)\n" ] } ], "source": [ "designate('querying tree', 'assembling')\n", "\n", "# querying tree function to retrieve record indices of the closet records\n", "def find_closest_neighbor(tree, records, settings):\n", " \"\"\"Query the balltree for closest neighbors to each primary record.\n", " \n", " Arguments:\n", " tree: sklearn balltree, the tree to query\n", " records: list of records dict\n", " settings: dict\n", " \n", " Returns:\n", " list of list of dicts\n", " \"\"\"\n", " \n", " # set number to not exceed number of samples\n", " number = min([settings['inclusion'], len(dataii)])\n", " \n", " # query the tree\n", " matrix = [convert_euclidean_point(record, settings, 0) for record in records]\n", " results = tree.query(matrix, k=number, return_distance=False)\n", " \n", " # get associated records and sort by feature size\n", " neighbors = [[dataii[int(entry)] for entry in indices] for indices in results]\n", " for cluster in neighbors:\n", " \n", " # sort by feature size\n", " cluster.sort(key=lambda record: record[feature], reverse=True)\n", " \n", " return neighbors" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for assembling: webbing together associations)\n" ] } ], "source": [ "designate('webbing together associations', 'assembling')\n", "\n", "# form associations between records\n", "def merge_datasets(settings):\n", " \"\"\"Web together samples in the second dataset with those of the first based on parameters.\n", " \n", " Arguments:\n", " settings: dict\n", " \n", " Returns:\n", " list of tuples, the associations\n", " \"\"\"\n", " \n", " # plant a Balltree based on euclidean distances\n", " tree = organize_records_by_space_time(dataii, settings)\n", " \n", " # get all the nearest neighbors for each primary record\n", " neighbors = find_closest_neighbor(tree, data, settings)\n", "\n", " # assemble associations\n", " associations = []\n", " for record, cluster in zip(data, neighbors):\n", "\n", " # begin association\n", " location = (record['latitude'], record['longitude'])\n", " association = {'record': record, 'associates': [], 'location': location}\n", " for neighbor in cluster:\n", "\n", " # weigh the records, only keeping those with a 1% confidence\n", " space, time = measure_dist_time_bw_records(record, neighbor) \n", " weight = weigh_record_pairs_by_space_and_time(space, time, settings)\n", " if weight > 0.0:\n", "\n", " # add to association\n", " association['associates'].append({'record': neighbor, 'weight': weight})\n", "\n", " # append if nonzero\n", " if len(association['associates']) > 0:\n", " \n", " # sort associates by weight\n", " association['associates'].sort(key=lambda associate: associate['weight'], reverse=True)\n", " associations.append(association)\n", "\n", " # sort by highest weight\n", " associations.sort(key=lambda association: association['associates'][0]['weight'], reverse=True)\n", " \n", " # add reference number\n", " [association.update({'reference': index}) for index, association in enumerate(associations)]\n", " \n", " return associations" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for assembling: summarizing associations in a table)\n" ] } ], "source": [ "designate('summarizing associations in a table', 'assembling')\n", "\n", "# function to summarize associations into a data table\n", "def summary_df(associations):\n", " \"\"\"Summarize association information into a data table.\n", " \n", " Arguments:\n", " associations: list of dicts\n", " \n", " Returns:\n", " panda data frame\n", " \"\"\"\n", "\n", " # make a table from associations\n", " summary = []\n", "\n", " # begin headers\n", " base = ['pair', 'point', 'weight', 'distance', 'days']\n", " headers = ['larvae', 'genus', 'date', 'latitude', 'longitude']\n", " headersii = [feature, 'date', 'latitude', 'longitude']\n", " parameters = [setting + '_setting' + units[setting](feature) for setting in settings.keys()]\n", "\n", " # get remaining headers\n", " remainder = [key for key in associations[0]['record'].keys() if key not in headers]\n", " remainderii = [key for key in associations[0]['associates'][0]['record'].keys() if key not in headersii]\n", " remainder.sort(key=lambda field: len(field))\n", " remainderii.sort(key=lambda field: len(field))\n", "\n", " # go through each association\n", " for index, association in enumerate(associations):\n", "\n", " # go through each associate\n", " record = association['record']\n", " for number, pair in enumerate(association['associates']):\n", "\n", " # begin row\n", " associate = pair['record']\n", " weight = pair['weight']\n", " row = [(association['reference'], number), (round(associate[feature], 2), round(record['larvae']))]\n", " row += [round(weight, 2)]\n", " row += [haversine_get_distance_from_lat_lon(record['latitude'], record['longitude'], associate['latitude'], associate['longitude'])]\n", " row += [get_day_difference(record['time'].timestamp(), associate['time'].timestamp())]\n", "\n", " # add primary record fields, secondary record fields, and parameters\n", " row += [record[field] for field in headers]\n", " row += [associate[field] for field in headersii]\n", " row += [value for value in settings.values()]\n", "\n", " # add all remaining primary and secondary record fields\n", " row += [record[field] for field in remainder]\n", " row += [associate[field] for field in remainderii]\n", "\n", " # add row to table\n", " summary.append(row)\n", "\n", " # construct labels, adding prefix in the case of duplicates\n", " labels = base + headers + headersii + parameters + remainder + remainderii\n", " duplicating = lambda index, label: '2nd_' + label if label in labels[:index] else label\n", " labels = [duplicating(index, label) for index, label in enumerate(labels)]\n", " \n", " # create dataframe from data\n", " summary = pandas.DataFrame.from_records(summary, columns=labels)\n", " \n", " return summary" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for assembling: subsampling by genus)\n" ] } ], "source": [ "designate('subsampling by genus', 'assembling')\n", "\n", "# subset the associations for a particular genus\n", "def get_subset_of_data(associations, genus):\n", " \"\"\"Sub sample the associations based on a particular genus.\n", " \n", " Arguments:\n", " associations: list of dicts\n", " genus: str, the genus name\n", " \n", " Returns:\n", " list of dicts\n", " \"\"\"\n", " \n", " # take subsample\n", " subsample = []\n", " for association in associations:\n", " \n", " # check the genus\n", " if genus == 'All' or genus == association['record']['genus']:\n", " \n", " # add to subsample\n", " subsample.append(association)\n", " \n", " return subsample" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "code_folding": [], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for assembling: assembling samples)\n" ] } ], "source": [ "designate('assembling samples', 'assembling')\n", "\n", "# assemble samples from association routine\n", "def assemble_samples(associations):\n", " \"\"\"Assemble samples from associations.\n", " \n", " Arguments:\n", " associations: list of dicts\n", " \n", " Returns:\n", " list of dicts\n", " \"\"\"\n", "\n", " # form samples\n", " samples = []\n", " for association in associations:\n", "\n", " # go through each match\n", " record = association['record']\n", " for index, associate in enumerate(association['associates']):\n", " \n", " # make sample\n", " larvae = record['larvae']\n", " if larvae != larvae:\n", " continue\n", " measurement = associate['record'][feature]\n", " if measurement != measurement:\n", " continue\n", " weight = associate['weight']\n", " genus = record['genus']\n", " sample = {'y': larvae, 'x': measurement, 'weight': weight, 'genus': genus}\n", " \n", " # add other record info\n", " sample.update({'latitude': record['latitude'], 'longitude': record['longitude']})\n", " sample.update({'site': record['siteName'], 'organization': record['organizationName']})\n", " sample.update({'time': str(record['time'])})\n", " \n", " # add color and size components\n", " sample['color'] = indicators[genus]\n", " sample['size'] = int(1 + 20 * weight)\n", " \n", " # add indices to list\n", " sample['pair'] = (association['reference'], index)\n", " \n", " # add to list\n", " samples.append(sample)\n", " \n", " # sort\n", " samples.sort(key=lambda sample: sample['x'])\n", " \n", " return samples" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### Ordinary Least Squares and Weighted Least Squares" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "The goal is to find a relationship between the secondary measurements and the larvae counts. The approach taken here is to use the method of Weighted Least Squares to find the relationship that best fits the data. In the simplest case, this relationship can take the form of a straight line:\n", "\n", "\\begin{align*}\n", "y_i=\\beta_0 + \\beta_1 x_i\n", "\\end{align*}\n", "\n", "A quadratic relationship may also be tried:\n", "\n", "\\begin{align*}\n", "y_i=\\beta_0 + \\beta_1 x_i + \\beta_2 x_i^2\n", "\\end{align*}\n", "\n", "The goal is to find the best $\\beta$'s that minimize the distance between the estimated line and the data points for all data points (i=1,...,n).\n", "\n", "The vector form for all of the data points is more compact:\n", "\n", "\\begin{align*}\n", "\\begin{bmatrix}\n", "y_1\\\\\n", "y_2\\\\\n", "\\vdots \\\\\n", "y_n\n", "\\end{bmatrix} &= \\begin{bmatrix}\n", "1 & x_{1,1} & x_{1,2}\\\\\n", "1 & x_{2,1} & x_{2,2} \\\\\n", "\\vdots & \\vdots & \\vdots \\\\\n", "1 & x_{n,1} & x_{n,2}\n", "\\end{bmatrix} \\begin{bmatrix}\n", "\\beta_0\\\\\n", "\\beta_1 \\\\\n", "\\beta_2\n", "\\end{bmatrix} + \\begin{bmatrix}\n", "\\varepsilon_1\\\\\n", "\\varepsilon_2 \\\\\n", "\\vdots \\\\\n", "\\varepsilon_n\n", "\\end{bmatrix}\n", "\\\\\n", "\\mathbf{y}&=\\mathbf{X}\\hat{\\beta} + \\mathbf{\\varepsilon}\n", "\\end{align*}\n", "\n", "Each actual observation, $y_i$, will differ somewhat from that estimated by the $\\hat{\\beta}$'s and the particular dependent observation $x_i$. As mentioned earlier, the difference between the actual and predicted value is called a residual, $\\epsilon_i$ (the estimated error):\n", "\n", "\\begin{align*}\n", "\\mathbf{y}-\\mathbf{X}\\hat{\\beta}=\\hat{\\mathbf{\\varepsilon}}\n", "\\end{align*}\n", "\n", "The goal is to find the values for $\\hat{\\beta}$ that minimize the sum of all squared residuals, $\\frac{1}{n}\\sum_{i=1}^n{\\hat{\\varepsilon}_i^{2}}$, hence \"ordinary least squares\" ([OLS](https://en.wikipedia.org/wiki/Ordinary_least_squares)). We can derive the OLS solution as follows:\n", "\n", "\\begin{align*}\n", "\\mathbf{y}&=\\mathbf{X}\\hat{\\beta} + \\mathbf{\\varepsilon} \\\\\n", "\\mathbf{\\varepsilon}&=\\mathbf{X}\\hat{\\beta} - \\mathbf{y} \\\\\n", "\\Rightarrow \\mathbf{\\varepsilon}^T\\mathbf{\\varepsilon} &= (\\mathbf{X}\\hat{\\beta} - \\mathbf{y})^T(\\mathbf{X}\\hat{\\beta} - \\mathbf{y}) \\tag{Least squares} \\\\\n", "&= \\mathbf{y}^T\\mathbf{y}-2\\hat{\\beta}^T\\mathbf{X}^T\\mathbf{y}+ \\hat{\\beta}^T\\mathbf{X}^T\\mathbf{X}\\hat{\\beta} \\\\\n", "\\Rightarrow \\frac{\\delta}{\\delta\\hat{\\beta}} &= -2\\mathbf{X}^T\\mathbf{y}+ 2\\mathbf{X}^T\\mathbf{X}\\hat{\\beta} = 0 \\tag{Minimize} \\\\\n", "\\mathbf{X}^T\\mathbf{y} &= \\mathbf{X}^T\\mathbf{X}\\hat{\\beta} \\tag{Reorder} \\\\\n", "\\Rightarrow \\hat{\\beta} &= (\\mathbf{X}^T\\mathbf{X})^{-1}\\mathbf{X}^T\\mathbf{y} \\tag{Solution}\n", "\\end{align*}\n", "\n", "In \"weighted least squares\",the errors do not have the same variance and therefore the [variance covatiance matrix](https://en.wikipedia.org/wiki/Covariance_matrix) are weighted differently, therefore the weighted sum of squares is minimized, $\\frac{1}{n}\\sum_{i=1}^n{w_i\\hat{\\epsilon_i^{2}}}$. The $\\hat{\\beta}$ for weighted least squares would be found as follows:\n", "\n", "\n", "\\begin{align*}\n", "\\mathbf{\\varepsilon}^T\\mathbf{W}\\mathbf{\\varepsilon} &= (\\mathbf{X}\\hat{\\beta} - \\mathbf{y})^T\\mathbf{W}(\\mathbf{X}\\hat{\\beta} - \\mathbf{y}) \\tag{Least squares} \\\\\n", "&= \\mathbf{y}^T\\mathbf{W}\\mathbf{y}-\\mathbf{y}^T\\mathbf{W}\\mathbf{X}\\hat{\\beta} - \\hat{\\beta}^T\\mathbf{X}^T\\mathbf{W}\\mathbf{y}+\\hat{\\beta}^T\\mathbf{X}^T\\mathbf{W}\\mathbf{X}\\hat{\\beta} \\\\\n", "\\Rightarrow \\frac{\\delta}{\\delta\\hat{\\beta}} &= -2\\mathbf{X}^T\\mathbf{W}\\mathbf{y} + 2 \\mathbf{X}^T\\mathbf{W}\\mathbf{X}\\hat{\\beta} = 0 \\tag{Minimize}\\\\\n", "\\mathbf{X}^T\\mathbf{W}\\mathbf{y} &= \\mathbf{X}^T\\mathbf{W}\\mathbf{X}\\hat{\\beta} \\tag{Reorder} \\\\\n", "\\Rightarrow \\hat{\\beta} &= (\\mathbf{X}^T\\mathbf{W}\\mathbf{X})^{-1}\\mathbf{X}^T\\mathbf{W}\\mathbf{y} \\tag{Solution}\n", "\\end{align*}" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for regressions: approximating fit with linear least squares)\n" ] } ], "source": [ "designate('approximating fit with linear least squares', 'regressions')\n", "\n", "def get_coefficients_linear_regression(samples, mode):\n", " \"\"\"Approximate the regression fit to use for initial starting points.\n", " \n", " Arguments:\n", " samples: list of dicts\n", " mode: str, regression mode\n", " \n", " Returns:\n", " list of floats, the coefficients\n", " \"\"\"\n", " \n", " # calculate weighted averages\n", " weights = [sample['weight'] for sample in samples]\n", " mean = average([sample['x'] for sample in samples], weights=weights)\n", " meanii = average([sample['y'] for sample in samples], weights=weights)\n", " \n", " # create matrix from samples\n", " transforming = regressions[mode]['independent']\n", " matrix = array([transforming(sample['x'], mean) for sample in samples]).reshape(-1, 1)\n", " \n", " # create targets from samples\n", " transforming = regressions[mode]['dependent']\n", " targets = array([transforming(sample['y'], meanii) for sample in samples]).reshape(-1, 1)\n", " \n", " # create list of weights\n", " weights = array([sample['weight'] for sample in samples])\n", " \n", " # create polynomial to fit to higher order polynomials\n", " fitter = PolynomialFeatures(degree=regressions[mode]['polynomial'])\n", " polynomial = fitter.fit_transform(matrix)\n", " \n", " # fit regression model, keeping intercept in coefficients vector\n", " model = PoissonRegressor(fit_intercept=False).fit(polynomial, targets, weights)#PoissonRegressor(fit_intercept=False).fit(polynomial, targets, weights)\n", " coeffs = model.coef_\n", " coefficients = [float(entry) for entry in coeffs]\n", " \n", " # add weighted means to coefficients\n", " coefficients += [mean, meanii]\n", " \n", " return coefficients" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6bf2f0d08b504438adf96c9a53842146", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5da171a4081343e6ada726b5e44a657e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### Nonlinear Least Squares" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "As hinted at above, the linear method can fit a variety of shapes beyond straight lines. In fact any curve with the general form:\n", "\n", "\\begin{align*}\n", "y=\\beta_0+\\beta_1f_1(x)+\\beta_2f_2(x)...\n", "\\end{align*}\n", "\n", "can be solved through linear least squares, with the requirement that each $\\beta$ is a constant parameter. The various functions $f(x)$ can be anything, as long as the $\\beta$'s are outside the functions themselves. Consider, however, an exponential relationship:\n", "\n", "\\begin{align*}\n", "y=\\beta_0 + \\beta_1e^{\\beta_2x}\n", "\\end{align*}\n", "\n", "In this case, $\\beta_0$ and $\\beta_1$ is outside the exponential function, but $\\beta_2$ is not. Finding the best $\\beta_2$ is not solvable via linear least squares. The [nonlinear alternative](https://www.itl.nist.gov/div898/handbook/pmd/section1/pmd142.htm) is similar with these key differences:\n", "- the nonlinear problem must begin with an initial estimate of the parameters that is fairly good (otherwise it may only find local solutions).\n", "- the nonlinear problem cannot be solved exactly, but must be approached asymptotically better estimates.\n", "\n", "#### Less interested in the math:\n", "Please see this website if you are less interested in the math: [link](https://physics.nyu.edu/pine/pymanual/html/chap8/chap8_fitting.html#nonlinear-fitting)\n", "\n", "#### More interested in the math:\n", "\n", "The goal is the same as in the linear case. Consider a function $y = f(x; \\hat{\\beta})$ that might be a good model for the relationship between mosquito larvae counts and the secondary data:\n", "\n", "In the linear least squares case, this model took the strictly linear form:\n", "\n", "\\begin{align*}\n", "\\mathbf{y}=\\mathbf{X}\\hat{\\beta}\n", "\\end{align*}\n", "\n", "but that restriction is lifted here. Consider a model with two unknown parameter, $\\beta_0$ and $\\beta_1$. Just as in the linear case, there will be some residual $\\epsilon_i$ between the actual observations $y_i$ and the predictions of the model:\n", "\n", "\\begin{align*}\n", "\\mathbf{y}-f(X; \\hat{\\beta})=\\hat{\\varepsilon}\n", "\\end{align*}\n", "\n", "As before, the goal is to find the set of $\\beta$'s that minimize $S = \\sum_{i=1}^{n}\\varepsilon^2$. The minimum value of S occurs when the gradient is zero:\n", "\n", "\\begin{align*}\n", "\\frac{\\delta S}{\\delta \\beta_j} = 2 \\sum_i \\varepsilon_i \\frac{\\delta \\varepsilon_i}{\\delta \\beta_j} = 0\n", "\\end{align*}\n", "\n", "Note that in a non linear system, the gradient equations do not have a closed solution which is why an initial guess is necessary. The parameters are then refined iteratively as follows:\n", "\n", "\\begin{align*}\n", "\\beta_j \\approx \\beta_j^{k+1} = \\beta_j^k + \\delta \\beta_j\n", "\\end{align*}\n", "where $k$ is the number of iterations and $\\delta \\beta$ is known as the shift vector.\n", "\n", "At each iteration, the model is approximately linearized using a [Taylor Polynomial](https://tutorial.math.lamar.edu/classes/calcii/taylorseries.aspx#:~:text=the%20n%20th%20degree%20Taylor%20polynomial%20is%20just%20the%20partial,polynomial%20for%20a%20given%20n%20.) expansion around $\\beta^k$:\n", "\\begin{align*}\n", "f(x_i;\\beta)\\approx f(x_i;\\beta^k)+\\sum_j J_{ij}\\Delta \\beta_j\n", "\\end{align*}\n", "where $J_{ij} = -\\frac{\\delta \\varepsilon_i}{\\delta\\beta_j}$ is the $i,j$th element of the [Jacobian](https://mathworld.wolfram.com/Jacobian.html).\n", "\n", "Therefore the residuals are:\n", "\\begin{align*}\n", "\\Delta y_i = y_i - f(x_i, \\beta^k)\n", "\\varepsilon_i = \\Delta y_i - \\sum_j J_{ij}\\Delta \\beta_j\n", "\\end{align*}\n", "\n", "Plugging everything into the gradient equation we get:\n", "\\begin{align*}\n", "-2 \\sum_i J_{ij}(\\Delta y_i - \\sum_\\ell J_{i\\ell}\\Delta \\beta_\\ell) = 0\n", "\\end{align*}\n", "\n", "Which gives us a new set of [normal equations](https://mathworld.wolfram.com/NormalEquation.html):\n", "\\begin{align*}\n", "(\\mathbf{J}^T\\mathbf{J})\\Delta\\beta = \\mathbf{J}^T\\Delta\\mathbf{y}\n", "\\end{align*}\n", "which we can then use to find:\n", "\\begin{align*}\n", "\\Delta\\beta\\approx(\\mathbf{J}^T\\mathbf{J})^{-1}\\mathbf{J}^T\\Delta\\mathbf{y}\n", "\\end{align*}\n", "\n", "Note, that for [**Weighted Nonlinear least squares**](http://www2.imm.dtu.dk/pubdb/edoc/imm2804.pdf), the normal equatios are:\n", "\\begin{align*}\n", "(\\mathbf{J}^TW\\mathbf{J})\\Delta\\beta = \\mathbf{J}^TW\\Delta\\mathbf{y}\n", "\\end{align*}\n", "\n", "There are multiple algorithms for solving the nonlinear least squares problem. The best known algorithm is the [Gauss-Newton algorithm](http://fourier.eng.hmc.edu/e176/lectures/NM/node36.html). In this notebook, the [Levenberg-Marquardt algorithm](https://en.wikipedia.org/wiki/Levenberg%E2%80%93Marquardt_algorithm) is implemented." ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for regressions: tighening with nonlinear least squares)\n" ] } ], "source": [ "designate('tighening with nonlinear least squares', 'regressions')\n", "\n", "# perform nonlinear regression\n", "def perform_nonlinear_regression(samples, mode, coefficients, calculate=True, direct=False, limit=1000):\n", " \"\"\"Tighten a regression model to a nonlinear function.\n", " \n", " Arguments:\n", " samples: list of dicts, the samples\n", " mode: dict\n", " coefficients: list of floats, the coefficients from linear approximation\n", " calculate: boolean, calculate jacobian directly?\n", " direct: boolean, use coefficients directly as initials?\n", " limit: int, max number of iteractions\n", " \n", " Returns:\n", " list of float, the parameters\n", " \"\"\"\n", " \n", " # assemble the matrix of secondary measurements\n", " matrix = array([sample['x'] for sample in samples])\n", " \n", " # get list of targets, taking logarithm if appropriate\n", " targets = array([sample['y'] for sample in samples])\n", " \n", " # create list of weights as if they are the sqrt of standard deviations\n", " sigmas = array([1 / sqrt(sample['weight']) for sample in samples])\n", " \n", " # set up the curve\n", " function = regressions[mode]['function']\n", " \n", " # get initial values\n", " initials = coefficients\n", " if not direct:\n", " \n", " # get initial values from coefficients\n", " initials = regressions[mode]['initial'](*coefficients)\n", "\n", " # calculate jacobian\n", " jacobian = None\n", "# if calculate:\n", " \n", "# # get jacobian from function\n", "# jacobian = regressions[mode]['jacobian']\n", "\n", " # fit the curve\n", " curve, _ = curve_fit(function, matrix, targets, p0=initials, sigma=sigmas, maxfev=limit, jac=jacobian)\n", " \n", " # raise value error if nans are there\n", " if any([isnan(entry) for entry in curve]):\n", " \n", " # raise value error\n", " raise ValueError\n", " \n", " return curve" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "023ab1bfcaca494c8380ad97cfdea7bb", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "6bd3ee466dac4115b2f30f57d8e8e57b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### Measuring Significance" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "We can check how well our model fits the data by checking the correlation between the actual observations and the fitted model. While the correlation will ideally be close to 1 indicating a perfect positive relationship between the variables, we also want the slope of the line to be 1. So, the equation that we would like to see is: $y_{observed} = y_{predicted}$ (i.e. a 1 to 1 relationship). [Note this also applies to $R^2$](https://en.wikipedia.org/wiki/Coefficient_of_determination).\n", "\n", "\n", "\n", "\n", "It is important to determine the strength of the relationships found through weighted regression. The strategy here will be to use Pearson's weighted correlation coefficient to measure the strength of the relationship, and to calculate a p-value as a measure of its statistical significance.\n", "\n", "Caluclate the weighed mean, $\\overline{y}$ for the observations $y_i$ with regard to the weights $w_i$:\n", "\n", "\\begin{align*}\n", "\\overline{y}=\\dfrac{\\sum{w_iy_i}}{\\sum{w_i}}\n", "\\end{align*}\n", "\n", "Do the same for the predicted values $z_i$ found from the regression model:\n", "\n", "\\begin{align*}\n", "z_i &=f(x_i,\\vec{\\beta}) \\\\\n", "\\overline{z} &= \\dfrac{\\sum{w_iz_i}}{\\sum{w_i}}\n", "\\end{align*}" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "Calculate the weighted variances for both the observations and predictions:\n", "\n", "\\begin{align*}\n", "s_{y} &= \\dfrac{\\sum{w_i(y_i-\\overline{y})^2}}{\\sum{w_i}} \\\\\n", "s_{z} &= \\dfrac{\\sum{w_i(z_i-\\overline{z})^2}}{\\sum{w_i}}\n", "\\end{align*}\n", "\n", "We can then find the weighted correlation coefficient as follows:\n", "\\begin{align*}\n", "\\rho_{yz} = \\frac{s_{yz}}{\\sqrt{s_ys_z}}\n", "\\end{align*}\n", "\n", "Calculate the standard error of the estimate, which shows what the standard deviation of our model is. The squared version is the very sum of squares minimized during least squares. Roughly two-thirds of observations should fall within one standard error from the regression line:\n", "\n", "\\begin{align*}\n", "s_{e}=\\sqrt{\\dfrac{\\sum{w_i(y_i-z_i)^2}}{\\sum{w_i}}}\n", "\\end{align*}" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "code_folding": [], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for regressions: calculating variance)\n" ] } ], "source": [ "designate('calculating variance', 'regressions')\n", "\n", "# calculate the variance\n", "def find_variance(targets, predictions, weights, formula, formulaii):\n", " \"\"\"Calculate the variance among weighted samples:\n", " \n", " Arguments:\n", " targets: list of float\n", " predictions: list of float\n", " weights: list of float\n", " formula: function object\n", " formulaii: function object\n", " \n", " Returns:\n", " float\n", " \"\"\"\n", " \n", " # calculate weighted means\n", " mean = average(targets, weights=weights)\n", " meanii = average(predictions, weights=weights)\n", " \n", " # create zipper\n", " zipper = zip(weights, targets, predictions)\n", " \n", " # calculate variance\n", " variance = 0.0\n", " for weight, target, prediction in zipper:\n", " \n", " # add term to variance\n", " term = weight * formula(mean, meanii, target, prediction) * formulaii(mean, meanii, target, prediction)\n", " variance += term\n", " \n", " # divide by sum of weights and convert to float\n", " variance = variance / len(targets)#sum(weights)\n", " variance = float(variance)\n", "\n", " return variance" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "The coefficient of determination ($R^2$) is the proportion of variance in the dependent variable that is predictable from the independent variable:\n", "\n", "The Pearson correlation coefficient ($\\rho_{yz}$) is calculated as the ratio of covariance to the variances:\n", "\n", "\\begin{align*}\n", "\\rho_{yz}=\\frac{s_{y_{ob}y_{pr}}}{\\sqrt{s_{y_{ob}}s_{y_{pr}}}}\n", "\\end{align*}\n", "\n", "\n", "It is always possible that a correlation found in a study is the result of sampling bias. The likelihood of the correlation being due to chance sampling is described by Student's t-distribution:\n", "\n", "\\begin{align*}\n", "T(x)=\\dfrac{\\Gamma(\\frac{n-1}{2})}{\\sqrt{(n-2)\\pi}\\Gamma({\\frac{n-2}{2}})}(1+\\frac{x^2}{n-2})^{-\\frac{n-1}{2}}\n", "\\end{align*}\n", "\n", "where $n$ is the number of samples in the study, and $\\Gamma$ is the extension of the factorial function to nonintegers:\n", "\n", "\\begin{align*}\n", "\\Gamma(n)=(n-1)!=\\int_{0}^{\\infty}x^{n-1}e^{-x}dx\n", "\\end{align*}\n", "\n", "The Student's t-distribution is similar in shape to a Gaussian normal distribution. In fact, as the number of samples increases, it makes a better and better approximation to the normal distribution:\n", "\n", "\\begin{align*}\n", "\\lim_{n\\to\\infty}T(x)=\\dfrac{1}{\\sqrt{2\\pi}}e^{-\\frac{1}{2}x^2}\n", "\\end{align*}" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "code_folding": [], "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: sketching t-distribution)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"1250\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };var element = document.getElementById(\"1250\");\n", " if (element == null) {\n", " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1250' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " \n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", " var css_urls = [];\n", " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " \n", " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if (root.Bokeh !== undefined || force === true) {\n", " \n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"1250\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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distribution\n", "normal = lambda x: (1 / sqrt(2 * pi)) * exp(-(x ** 2 / 2))\n", "\n", "# sketch t-distribution\n", "legend = ['t, n=3', 't, n=8', 'normal']\n", "title = \"Probability Distribution Function Example\"\n", "xlab = \"Support (x)\"\n", "ylab = \"Density\"\n", "\n", "sketch_plot(lambda x: stats.t(1).pdf(x), lambda x:stats.t(5).pdf(x), normal,\n", " legend=legend, title=title, xlab=xlab, ylab=ylab\n", " )" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "Just as the z-score measures the distance from the center of a normal distribution, a t-value measures the distance from the center of a t-distribution. In both cases, the relative likelihood of a result decreases away from the center. The t-value is calculated from the Pearson correlation and number of samples as follows:\n", "\n", "\\begin{align*}\n", "t=\\dfrac{\\rho\\sqrt{n-2}}{\\sqrt{1-r^2}}\n", "\\end{align*}\n", "\n", "Finally, the p-value represents the probability of getting a particular t-value through sampling bias alone. It is defined as the area under the t-distribution curve with magnitude greater than the t-value, and can be calculated with reference to the area under the curve beween -t and t:\n", "\n", "center. The t-value is calculated from the Pearson correlation and number of samples as follows:\n", "\n", "\\begin{align*}\n", "p=1-\\int_{-t}^{t}T(x)dx\n", "\\end{align*}\n", "\n", "Generally, a critical p-value is chosen prior to a study, and a correlation with a p-value less than this critical value is regarded as statistically significant, because it is unlikely to have gotten such a correlation through chance sampling alone.\n", "\n", "Note: In the case of this study, the number of samples used for the p-value calculation is based only on the number of mosquito records used, even though the regression model, weighted variances, and correlation are based on potentially several secondary measurements per mosquito record. This is because the p-value is sensitive to the number of unique measurements, and it is debatable whether one mosquito record paired with two secondary records constitutes one unique measurement or two. Underestimating the number of unique samples will tend to overestimate the p-value, and hence underestimate the statistical significance of the regression model. This is considered preferable to overestimating the number of unique measurements, thereby inflating the statistical significance." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "References: \n", "\n", "https://en.wikipedia.org/wiki/Pearson_correlation_coefficient\n", "\n", "https://en.wikipedia.org/wiki/Coefficient_of_determination\n", "\n", "https://en.wikipedia.org/wiki/Student%27s_t-distribution" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "code_folding": [], "hidden": true, "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for regressions: validating model)\n" ] } ], "source": [ "designate('validating model', 'regressions')\n", "\n", "# function for calculating pearson correlation coefficient\n", "def calculate_corr_coef(truths, predictions, weights, size):\n", " \"\"\"Calculate the correlation coefficient and pvalue significance from truths and predictions\n", " \n", " Arguments:\n", " truths: list of floats\n", " predictions: list of floats\n", " weights: list of floats\n", " size: int, number of records involved\n", " \n", " Returns:\n", " (float, float) tuple, correlation and bias\n", " \"\"\"\n", " \n", " # create variance formulas\n", " formula = lambda mean, meanii, target, prediction: target - mean\n", " formulaii = lambda mean, meanii, target, prediction: prediction - meanii\n", " formulaiii = lambda mean, meanii, target, prediction: target - prediction\n", " \n", " # calculate variances\n", " variance = find_variance(truths, predictions, weights, formula, formula)\n", " varianceii = find_variance(truths, predictions, weights, formulaii, formulaii)\n", " covariance = find_variance(truths, predictions, weights, formula, formulaii)\n", " error = find_variance(truths, predictions, weights, formulaiii, formulaiii)\n", " \n", " # calculate correlation\n", " pearson = covariance / sqrt(variance * varianceii)\n", "\n", " # calculate ttest value, using the cumulative distribution function of Student's t\n", " test = pearson * sqrt(size - 2) / sqrt(1 - pearson ** 2)\n", " distribution = stats.t(size - 2)\n", " bias = 2 * (1 - distribution.cdf(abs(test))) # or 1 - 2 * (distribution.cdf(abs(test)) - 0.5)\n", " \n", " # remove nans\n", " removing = lambda x, y: y if isnan(x) else x\n", " pearson = removing(pearson, 0.0)\n", " bias = removing(bias, 1.0)\n", " \n", " # make validation report\n", " validation = {'correlation': round(pearson, 4), 'R^2': round((1 - (error / variance)), 4)}\n", " validation.update({'pvalue': round(bias, 4), 's.e.': round(sqrt(error), 2)})\n", " \n", " return validation" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ed448fe3acac42f1b864d591db690e80", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "412d8992651a4b9e8fcac8be8b3d0cde", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hide_input": true }, "source": [ "### Linear Mode Description" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "The simplest relationship to look for is a linear one, where the goal is to fit the data to the closest straight line given by:\n", "\n", "$y=\\beta_0+\\beta_1x$\n", "\n", "where:\n", "\n", "- $y$ is the predicted mosquito larvae count\n", "- $x$ is the secondary measurement\n", "- $\\beta_0$ is the y-intercept\n", "- $\\beta_1$ is the slope of the line" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "hidden": true, "hide_input": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: defining linear regression)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"1429\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };var element = document.getElementById(\"1429\");\n", " if (element == null) {\n", " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1429' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " \n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", " var css_urls = [];\n", " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " \n", " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if (root.Bokeh !== undefined || force === true) {\n", " \n", " for (var i = 0; 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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1429\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"1429\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1429' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1429\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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regression\n", "linear = regressions['linear']\n", "linear['requirement'] = 2\n", "linear['polynomial'] = 1\n", "linear['independent'] = lambda x, b: x\n", "linear['dependent'] = lambda y, d: y\n", "linear['initial'] = lambda b, m, c, d: (m / b, m)\n", "linear['function'] = lambda x, b, m: m * x + b\n", "linear['equation'] = 'y = β0+β1 x'\n", "linear['names'] = ['onset', 'slope']\n", "\n", "# define jacobian\n", "# linear['dydc'] = lambda x, c, m: -m\n", "# linear['dydm'] = lambda x, c, m: x - c\n", "# linear['gradient'] = lambda x, c, m: [linear[slope](x, c, m) for slope in ('dydc', 'dydm')]\n", "# linear['jacobian'] = lambda xs, c, m: array([linear['gradient'](x, c, m) for x in xs], dtype=float)\n", "\n", "# make versions with different parameters\n", "versions = [(1, 1), (-1, 0.5), (2, -2)]\n", "legend = ['y = {}x+{}'.format(*version) if version[1] > 0 else 'y = {}x{}'.format(*version) for version in versions]\n", "\n", "# make functions\n", "making = lambda version: lambda x: linear['function'](x, *version)\n", "linears = [making(version) for version in versions]\n", "title = \"Line Plot Example\"\n", "xlab = \"x\"\n", "ylab = \"y\"\n", "sketch_plot(*linears, legend=legend, title=title, xlab=xlab, ylab=ylab)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Note that if $m$ is positive, the line grows towards the right, whereas if $m$ is negative, the line grows towards the left." ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": false }, "source": [ "" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "87e44022747746de949de479e09872ed", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "4951ab4ca1c64972b0d7a4af752d0d26", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hide_input": true }, "source": [ "### Quadratic Mode Description" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "However, it may be that a curved relationship is more appropriate. The simplest curve to try is a parabola:\n", "\n", "\\begin{align*}\n", "y &= k(x-c)^2+h = kx^2-2kcx+kc^2+h \\\\\n", "y &= \\beta_0 + \\beta_1 x + \\beta_2 x^2 \\tag{standard statistics form}\n", "\\end{align*}\n", "\n", "where:\n", " \n", "- $y$ is the predicted mosquito larvae count\n", "- $x$ is the secondary measurement\n", "- $c$ is the center of the parabola\n", "- $h$ is the height of the parabola at the center\n", "- $k$ is the curvature of the parabola" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: defining quadratic regressions)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"1620\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };var element = document.getElementById(\"1620\");\n", " if (element == null) {\n", " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1620' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " \n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", " var css_urls = [];\n", " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " \n", " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if (root.Bokeh !== undefined || force === true) {\n", " \n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"1620\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1620\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"1620\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '1620' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1620\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", " \n", " var docs_json = {\"02ba0b7f-5a39-41d0-a582-b3f0ee2a31b0\":{\"roots\":{\"references\":[{\"attributes\":{\"below\":[{\"id\":\"1549\",\"type\":\"LinearAxis\"}],\"center\":[{\"id\":\"1553\",\"type\":\"Grid\"},{\"id\":\"1558\",\"type\":\"Grid\"},{\"id\":\"1584\",\"type\":\"Legend\"}],\"left\":[{\"id\":\"1554\",\"type\":\"LinearAxis\"}],\"plot_height\":300,\"plot_width\":300,\"renderers\":[{\"id\":\"1576\",\"type\":\"GlyphRenderer\"},{\"id\":\"1590\",\"type\":\"GlyphRenderer\"},{\"id\":\"1605\",\"type\":\"GlyphRenderer\"}],\"title\":{\"id\":\"1539\",\"type\":\"Title\"},\"toolbar\":{\"id\":\"1565\",\"type\":\"Toolbar\"},\"x_range\":{\"id\":\"1541\",\"type\":\"Range1d\"},\"x_scale\":{\"id\":\"1545\",\"type\":\"LinearScale\"},\"y_range\":{\"id\":\"1543\",\"type\":\"DataRange1d\"},\"y_scale\":{\"id\":\"1547\",\"type\":\"LinearScale\"}},\"id\":\"1538\",\"subtype\":\"Figure\",\"type\":\"Plot\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"1559\",\"type\":\"PanTool\"},{\"id\":\"1560\",\"type\":\"WheelZoomTool\"},{\"id\":\"1561\",\"type\":\"BoxZoomTool\"},{\"id\":\"1562\",\"type\":\"SaveTool\"},{\"id\":\"1563\",\"type\":\"ResetTool\"},{\"id\":\"1564\",\"type\":\"HelpTool\"},{\"id\":\"1618\",\"type\":\"HoverTool\"}]},\"id\":\"1565\",\"type\":\"Toolbar\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1575\",\"type\":\"Line\"},{\"attributes\":{\"source\":{\"id\":\"1572\",\"type\":\"ColumnDataSource\"}},\"id\":\"1577\",\"type\":\"CDSView\"},{\"attributes\":{},\"id\":\"1598\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"text\":\"Quadratic Plot Example\"},\"id\":\"1539\",\"type\":\"Title\"},{\"attributes\":{},\"id\":\"1597\",\"type\":\"Selection\"},{\"attributes\":{\"callback\":null,\"end\":5,\"start\":-5},\"id\":\"1541\",\"type\":\"Range1d\"},{\"attributes\":{\"source\":{\"id\":\"1601\",\"type\":\"ColumnDataSource\"}},\"id\":\"1606\",\"type\":\"CDSView\"},{\"attributes\":{\"line_color\":\"green\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1588\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"1545\",\"type\":\"LinearScale\"},{\"attributes\":{\"label\":{\"value\":\"\\u03b20=-1, \\u03b21=0.5, \\u03b22=0.3\"},\"renderers\":[{\"id\":\"1590\",\"type\":\"GlyphRenderer\"}]},\"id\":\"1600\",\"type\":\"LegendItem\"},{\"attributes\":{\"callback\":null},\"id\":\"1543\",\"type\":\"DataRange1d\"},{\"attributes\":{},\"id\":\"1580\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"label\":{\"value\":\"\\u03b20=2, \\u03b21=1, 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regression\n", "quadratic = regressions['quadratic']\n", "quadratic['requirement'] = 3\n", "quadratic['polynomial'] = 2\n", "quadratic['independent'] = lambda x, c: x\n", "quadratic['dependent'] = lambda y, d: y\n", "quadratic['initial'] = lambda b, m, k, c, d: (-m / (2 * k), b - m ** 2 / (4 * k), k)\n", "quadratic['function'] = lambda x, a,b,c: a+b*x+c*x**2#k*x**2 - 2*k*x*c + k*c**2 + h\n", "quadratic['equation'] = 'y = β0+β1 x+β2 x^2'\n", "quadratic['names'] = ['center', 'height', 'curvature']\n", "\n", "# define jacobian\n", "# quadratic['dydc'] = lambda x, c, h, k: -2 * k * (x - c)\n", "# quadratic['dydh'] = lambda x, c, h, k: 1\n", "# quadratic['dydk'] = lambda x, c, h, k: (x - c) ** 2\n", "# quadratic['gradient'] = lambda x, c, h, k: [quadratic[slope](x, c, h, k) for slope in ('dydc', 'dydh', 'dydk')]\n", "# quadratic['jacobian'] = lambda xs, c, h, k: array([quadratic['gradient'](x, c, h, k) for x in xs], dtype=float)\n", "\n", "# make versions with different parameters\n", "versions = [(1, 3, 0.1), (-1, 0.5, 0.3), (2, 1, -0.2)]\n", "legend = ['β0={}, β1={}, β2={}'.format(*version) for version in versions]\n", "\n", "# make functions\n", "making = lambda version: lambda x: quadratic['function'](x, *version)\n", "quadratics = [making(version) for version in versions]\n", "title = \"Quadratic Plot Example\"\n", "xlab = \"x\"\n", "ylab = \"y\"\n", "sketch_plot(*quadratics, legend=legend, title=title, xlab=xlab, ylab=ylab)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Note that if the curvature is positive, the parabola has a minimum, whereas if the curvature is negative, the parabola has a maximum." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f95691ac609d4e1a8f68cd264900dfbd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5abfebf04dd3421ebd57dc1801838c3c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### Exponential Mode Description" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": false }, "source": [ "On the other hand, quadratic models tend to predict negative larvae outside the region near the center. An exponential relationship solves this problem, as it makes no predictions below zero. It is described like this:\n", "\n", "\\begin{align*}\n", "y=e^{\\beta_0+\\beta_1 x}\n", "\\end{align*}" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: defining exponential regression)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
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i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"1823\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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"exponential['requirement'] = 2\n", "exponential['polynomial'] = 1\n", "exponential['independent'] = lambda x, c: x\n", "exponential['dependent'] = lambda y, d: y#arcsinh(y)\n", "exponential['initial'] = lambda b, m, c, d: (m / b, m)\n", "exponential['function'] = lambda x, m, b: exp(m*x + b)\n", "exponential['equation'] = 'y = e^(β0 + β1* x)'\n", "exponential['names'] = ['onset', 'rate']\n", "\n", "# define jacobian\n", "# exponential['dydc'] = lambda x, c, r: -r * exp(r * (x - c))\n", "# exponential['dydr'] = lambda x, c, r: (x - c) * exp(r * (x - c))\n", "# exponential['gradient'] = lambda x, c, r: [exponential[slope](x, c, r) for slope in ('dydc', 'dydr')]\n", "# exponential['jacobian'] = lambda xs, c, r: array([exponential['gradient'](x, c, r) for x in xs], dtype=float)\n", "\n", "# make versions with different parameters\n", "versions = [(2, 1), (-1, 0.2), (-2, -0.5)]\n", "legend = ['β0={}, β1={}'.format(*version) for version in versions]\n", "\n", "# make functions\n", "making = lambda version: lambda x: exponential['function'](x, *version)\n", "exponentials = [making(version) for version in versions]\n", "title = \"Exponential Plot Example\"\n", "xlab = \"x\"\n", "ylab = \"y\"\n", "sketch_plot(*exponentials, legend=legend, title=title, xlab=xlab, ylab=ylab, span=(-5, 5))" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Note that a positive $r$ means a curve that grows to the right, and a negative $r$ means a curve that grows to the left. Also note that no value on either curve is below zero." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Note that the equation:\n", "\n", "\\begin{align*}\n", "y=e^{\\beta_0+\\beta_1 x}\n", "\\end{align*}\n", "\n", "can be transformed to a linear function by taking the natural log:\n", "\n", "\\begin{align*}\n", "\\ln(y)=\\beta_0+\\beta_1 x\n", "\\end{align*}" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: sketching logarithmic approximation)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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sketch functions\n", "legend = ['natural log', 'hyperbolic']\n", "title = \"Log and Hyperbolic Plot Example\"\n", "xlab = \"x\"\n", "ylab = \"y\"\n", "sketch_plot(log, arcsinh, legend=legend, title=title, xlab=xlab, ylab=ylab)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": true }, "source": [ "Note that the natural log has no value at zero, but gets increasingly negative. The inverse hyperbolic sine approximation sets the value at zero to zero, and is therefore a poor approximation to the natural log near zero. However, the inverse hyperbolic sine becomes an increasingly better approximation to the natural log with increasing values. Also, the inverse hyperbolic sine is conveniently defined for negative values as well, whereas the natural log is undefined for negative values." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ee573214af0c4e86bebc8ae0b98adc4b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fb93b9c4a12b4e65a71808476ccc34f8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### Power Mode Description" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Exponential relationships are however limited to sharply increasing or sharply decreasing curves. A power law relationship can model less steep curves:\n", "\n", "\\begin{align*}\n", "y=\\beta_0(x-c)^{\\beta_1}\n", "\\end{align*}\n", "\n", "where:\n", " \n", "- $y$ is the predicted mosquito count\n", "- $x$ is the secondary observation\n", "- $c$ is the onset at zero larvae\n", "- $\\beta_0$ is the height in larvae at one unit passed the onset\n", "- $\\beta_1$ is the power to which the secondary measurement is raised" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: defining power law regression)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
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i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };var element = document.getElementById(\"2234\");\n", " if (element == null) {\n", " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2234' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " \n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", " var css_urls = [];\n", " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " \n", " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if (root.Bokeh !== undefined || force === true) {\n", " \n", " for (var i = 0; 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\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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regression\n", "power = regressions['power']\n", "power['requirement'] = 3\n", "power['polynomial'] = 1\n", "power['independent'] = lambda x, c: arcsinh(x - c)\n", "power['dependent'] = lambda y, d: arcsinh(y)\n", "power['initial'] = lambda b, m, c, d: (c, exp(b), m)\n", "power['function'] = lambda x, c, h, p: sign(p * (x -c)) * h * abs(x - c) ** abs(p)\n", "power['equation'] = 'y = β0 * (x - c)^β1'\n", "power['names'] = ['onset', 'height', 'power']\n", "\n", "# define jacobian\n", "# power['dydc'] = lambda x, c, h, p: -c * h * p * abs(x - c) ** (abs(p) - 1)\n", "# power['dydh'] = lambda x, c, h, p: sign(p * (x -c)) * abs(x - c) ** abs(p)\n", "# power['dydp'] = lambda x, c, h, p: sign(x - c) * log(abs(x - c)) * h * abs(x - c) ** abs(p)\n", "# power['gradient'] = lambda x, c, h, p: [power[slope](x, c, h, p) for slope in ('dydc', 'dydh', 'dydp')]\n", "# power['jacobian'] = lambda xs, c, h, p: array([power['gradient'](x, c, h, p) for x in xs], dtype=float)\n", "\n", "# make versions with different parameters\n", "versions = [(1, 1, 0.5), (0, 0.5, 0.2), (-1, 1, -0.3)]\n", "legend = ['c={}, β0 ={}, β1={}'.format(*version) for version in versions]\n", "\n", "# make functions\n", "making = lambda version: lambda x: power['function'](x, *version)\n", "powers = [making(version) for version in versions]\n", "title = \"Power Law Plot Example\"\n", "xlab = \"x\"\n", "ylab = \"y\"\n", "sketch_plot(*powers, legend=legend, title=title, xlab=xlab, ylab=ylab)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Note that a negative value of $\\beta_1$ produces a curve growing in the opposite direction. This behavior requires a modification described below." ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": false }, "source": [ "As with exponential regression, taking the natural log of both sides yields a linear equation:\n", "\n", "\\begin{align*}\n", "\\ln(y)=\\ln(\\beta_0)+\\beta_1\\ln(x-c)\n", "\\end{align*}\n", "\n", "This describes a line with slope $p$ and intercept $\\ln(\\beta_0)$. However, a value for $c$ is also necessary. As an approximation, this will be the weighted mean of all secondary measurements:\n", "\n", "\\begin{align*}\n", "c=\\frac{\\sum{w_ix_i}}{\\sum{w_i}}\n", "\\end{align*}\n", "\n", "As mentioned in the description for exponential regression, the natural log is not defined for either zero or negative values. As there are plenty of zero larvae observations, as well as potentially zero or negative secondary measurements, the natural logs on both sides of the equation must be approximated. The hyperbolic approximation will be used, because it produces values for both zero and negative measurements:\n", "\n", "\\begin{align*}\n", "\\ln(y)\\approx\\ln\\biggl(\\dfrac{y}{2}+\\sqrt{1+\\dfrac{y^2}{4}}\\biggr)\n", "\\end{align*}\n", "\n", "With these two estimates in place, the regression can be solved exactly using linear least squares to get intial values for $h$ and $p$. These initial values begin the process of nonlinear least squares to tighten the fit of all three parameters.\n", "\n", "It may happen that the slope $p$ solved for with linear regression is negative, because larvae counts may increase for smaller values of the secondary measurement. This poses a problem for the original power law, because a negative power is radically different in form than a positive power. In fact, taking a noninteger negative $(x-c)$ value to any kind of power is undefined. Both of these problems can be solved with the following adjustment:\n", "\n", "\\begin{align*}\n", "y=sgn(p(x-c))\\cdot h\\lvert x-c\\rvert^{\\lvert p\\rvert}\n", "\\end{align*}\n", "\n", "The absolute values and sign function create the symmetric form seen in the diagram above, allowing the regression to fit a curve pointing in either direction. From the initial conditions, a tighter fit can be found with the help of the Jacobian equations:\n", "\n", "\\begin{align*}\n", "j_c &=\\dfrac{\\delta y}{\\delta c}=-chp\\lvert x-c\\rvert^{\\lvert p\\rvert-1} \\\\\n", "j_h &=\\dfrac{\\delta y}{\\delta h}=sgn(p(x-c))\\cdot \\lvert x-c\\rvert^{\\lvert p\\rvert} \\\\\n", "j_p &=\\dfrac{\\delta y}{\\delta p}=sgn(x-c)\\cdot h \\cdot\\ln\\lvert x-c\\rvert\\lvert x-c\\rvert^{\\lvert p\\rvert} \\\\\n", "\\end{align*}" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8e6eb73601c446b3997104aa39eeeeaf", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e41be03a29774bc5a7bef2963e5aec01", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hide_input": false }, "source": [ "### Gaussian Mode Description" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": false }, "source": [ "Alternatively, it may be appropriate to search for a peak value of larvae at a secondary value with a relationship like this:\n", "\n", "\\begin{align*}\n", "y=\\beta_0e^{-(x-\\beta_1)^2/2\\beta_2}\n", "\\end{align*}\n", "\n", "where:\n", "\n", "- $y$ is the predicted mosquito count\n", "- $x$ is the secondary observation\n", "- $c$ is the center, equivalent to the mean in a normal distribution\n", "- $h$ is the height at the center\n", "- $s$ is the spread, equivalent to the variance in a normal distribution" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "hidden": true, "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: defining gaussian regression)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"2471\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; 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i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"2471\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"2471\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"2471\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2471' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"2471\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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" if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "2389" } }, "output_type": "display_data" } ], "source": [ "designate('defining gaussian regression')\n", "\n", "# gaussian law regression\n", "gaussian = regressions['gaussian']\n", "gaussian['requirement'] = 3\n", "gaussian['polynomial'] = 2\n", "gaussian['independent'] = lambda x, c: x\n", "gaussian['dependent'] = lambda y, d: y # arcsinh(y)\n", "gaussian['initial'] = lambda b, m, k, c, d: (-m / (2 * k), exp(b - m ** 2 / (4 * k)), -1 / (2 * k))\n", "gaussian['function'] = lambda x, c, h, s: h * exp(-(x - c) ** 2 / (2 * s))\n", "gaussian['equation'] = 'y = β0 * e^(-(x - β1)^2 / 2 * c)'\n", "gaussian['names'] = ['center', 'height', 'spread']\n", "\n", "# define jacobian\n", "# gaussian['dydc'] = lambda x, c, h, s: (h * (x - c) / s) * exp(-(x - c) ** 2 / (2 * s))\n", "# gaussian['dydh'] = lambda x, c, h, s: exp(-(x - c) ** 2 / (2 * s))\n", "# gaussian['dyds'] = lambda x, c, h, s: (h * (x - c) ** 2 / (2 * s ** 2)) * exp(-(x - c) ** 2 / (2 * s))\n", "# gaussian['gradient'] = lambda x, c, h, s: [gaussian[slope](x, c, h, s) for slope in ('dydc', 'dydh', 'dyds')]\n", "# gaussian['jacobian'] = lambda xs, c, h, s: array([gaussian['gradient'](x, c, h, s) for x in xs], dtype=float)\n", "\n", "# make versions with different parameters\n", "versions = [(1, 1, 1), (-1, 0.5, 4), (2, 2, -40)]\n", "legend = ['β2={}, β0={}, β2={}'.format(*version) for version in versions] # c={}, h={}, s={}\n", "\n", "# make functions\n", "making = lambda version: lambda x: gaussian['function'](x, *version)\n", "gaussians = [making(version) for version in versions]\n", "title = \"Gaussian (Normal) Plot Example\"\n", "xlab = \"x\"\n", "ylab = \"y\"\n", "sketch_plot(*gaussians, legend=legend, title=title, xlab=xlab, ylab=ylab)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "Note that this family of curves also includes those that curve upwards, marked by a negative value for the spread. An upward curving \"Gaussian\" indicates that a curve with a minimum that grows to either side is a better fit for the data set." ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "With the following transformations:\n", "\n", "\\begin{align*}\n", "\\beta_2 &=\\dfrac{-m}{2k} \\\\\n", "\\beta_0 &=e^{b-m^2/4k} \\\\\n", "\\beta_1 &=\\dfrac{-1}{2k}\n", "\\end{align*}\n", "\n", "the equation takes on a new form:\n", "\n", "\\begin{align*}\n", "y=e^{kx^2+mx+b}\n", "\\end{align*}\n", "\n", "Taking the natural logarithm of both sides:\n", "\n", "\\begin{align*}\n", "\\ln{(y)}=kx^2+mx+b\n", "\\end{align*}\n", "\n", "creates a polynomial, and would be solvable through linear least squares if not for observations of zero larvae that render the logarithm undefined. The strategy here is to use the inverse hyperbolic sine approximation and solve for the initial parameters:\n", "\n", "\\begin{align*}\n", "\\ln(y)\\approx\\ln\\biggl(\\dfrac{y}{2}+\\sqrt{1+\\dfrac{y^2}{4}}\\biggr)\n", "\\end{align*}\n", "\n", "These parameters are then fed into nonlinear least squares for a tighter fit using the transformation equations above. With the associated Jacobian equations, the nonlinear problem can be solved:\n", "\n", "\\begin{align*}\n", "y &=he^{-(x-\\beta_2)^2/2\\beta_1} \\\\\n", "j_{\\beta_2} &=\\dfrac{\\delta y}{\\delta \\beta_2}=\\dfrac{h(x-\\beta_2)}{\\beta_1}e^{-(x-\\beta_2)^2/2\\beta_1} \\\\\n", "j_{\\beta_0} &=\\dfrac{\\delta y}{\\delta \\beta_0}=e^{-(x-\\beta_2)^2/2\\beta_1} \\\\\n", "j_{\\beta_1} &=\\dfrac{\\delta y}{\\delta \\beta_1}=\\dfrac{h(x-\\beta_2)^2}{2\\beta_1^2}e^{-(x-\\beta_2)^2/2\\beta_2}\n", "\\end{align*}" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e6ec75fa7ab84ccd91ffb3fff18e20b1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "30a62fc9086b40babc2cc3900b2aba1c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for scatters: preparing scatter graph)\n" ] } ], "source": [ "designate('preparing scatter graph', 'scatters')\n", "\n", "# preparing scatter graph\n", "def prepare_scatter_plot(associations, mode):\n", " \"\"\"Prepare the scatter graph.\n", " \n", " Arguments:\n", " associations: list of dicts\n", " mode: str, the regression mode\n", " \n", " Returns:\n", " bokeh graph object\n", " \"\"\"\n", " \n", " # get samples\n", " samples = assemble_samples(associations)\n", " \n", " # get sample collections\n", " primaries = [sample['y'] for sample in samples]\n", " secondaries = [sample['x'] for sample in samples]\n", " \n", " # begin the plot\n", " entitling = lambda word: word[0].upper() + word[1:]\n", " title = '{} Regression of Larvae Counts Predicted by {} in {}'.format(entitling(mode), entitling(feature), country)\n", " parameters = {'title': title, 'x_axis_label': feature, 'y_axis_label': 'larvae counts'}\n", " parameters.update({'plot_width': 900, 'plot_height': 400})\n", "\n", " # set y range based on range of primary data\n", " extent = max(primaries) - min(primaries)\n", " parameters['y_range'] = [min(primaries) - 0.05 * extent, max(primaries) + 0.05 * extent]\n", " \n", " # set x range based on range of secondary data extent = max(primaries) - min(primaries)\n", " extent = max(secondaries) - min(secondaries)\n", " parameters['x_range'] = [min(secondaries) - 0.05 * extent, max(secondaries) + 0.05 * extent]\n", " \n", " # begin graph\n", " graph = figure(**parameters)\n", " \n", " return graph" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for scatters: tracing lines on the graph)\n" ] } ], "source": [ "designate('tracing lines on the graph', 'scatters')\n", "\n", "# tracing lines on the graph\n", "def draw_function(function, graph, ticks, genus, width=2, style='solid', offset=0):\n", " \"\"\"Trace a function on the graph at particular tickmarks and other characteristics.\n", " \n", " Arguments:\n", " function: function object\n", " graph: bokeh graph object\n", " ticks: list of floats\n", " genus: str\n", " width=2: int\n", " style='solid': str\n", " offset=0: float, offset of line\n", " \n", " Returns:\n", " bokeh graph object\n", " \"\"\"\n", " \n", " # apply function to get the ticks and filter for large values\n", " points = [{'x': tick, 'y': function(tick) + offset, 'genus': genus, 'weight': 'NA'} for tick in ticks]\n", " [point.update({'latitude': 'NA', 'longitude': 'NA', 'time': 'NA'}) for point in points]\n", " [point.update({'pair': 'NA', 'site': 'NA', 'organization': 'NA'}) for point in points]\n", " points = [point for point in points if abs(point['y']) < 1000]\n", " table = ColumnDataSource(pandas.DataFrame(points))\n", " \n", " # check for len\n", " if len(points) > 0 and any([point['y'] != 0.0 for point in points]):\n", " \n", " # trace line on graph\n", " color = indicators[genus]\n", " line = {'source': table, 'x': 'x', 'y': 'y', 'color': color, 'line_width': width}\n", " line.update({'legend_label': genus, 'line_dash': style})\n", " graph.line(**line)\n", " \n", " return graph" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for scatters: splotching markers on the graph)\n" ] } ], "source": [ "designate('splotching markers on the graph', 'scatters')\n", "\n", "# splotching markers on the graph\n", "def add_points(samples, graph, genus):\n", " \"\"\"Splotch samples onto the graph as markers, based on a genus for color.\n", " \n", " Arguments:\n", " samples: list of dicts\n", " graph: bokeh graph obeject\n", " genus: str, the genus\n", " \n", " Returns:\n", " bokeh graph object\n", " \"\"\"\n", " \n", " # add circle marker\n", " table = ColumnDataSource(pandas.DataFrame(samples))\n", " parameters = {'source': table, 'x': 'x', 'y': 'y', 'size': 'size'}\n", " parameters.update({'fill_color': 'color', 'line_width': 1, 'line_color': 'color'})\n", " parameters.update({'fill_alpha': 0.2, 'legend_label': genus})\n", " graph.circle(**parameters)\n", " \n", " return graph" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for scatters: presenting a genus report on the graph)\n" ] } ], "source": [ "designate('presenting a genus report on the graph', 'scatters')\n", "\n", "# presenting a report on the graph\n", "def add_report(report, graph, associations, ticks):\n", " \"\"\"Present a report on the graph about the associations at certain tick marks.\n", " \n", " Arguments:\n", " report: dict\n", " associations: list of dicts\n", " ticks: list of floate\n", " graph: bokeh graph object\n", " \n", " Returns:\n", " bokeh graph object\n", " \"\"\"\n", " \n", " # get samples\n", " genus = report['genus']\n", " mode = report['mode']\n", " subset = get_subset_of_data(associations, genus)\n", " samples = assemble_samples(subset)\n", " \n", " # add markers\n", " if len(samples) > 0:\n", " \n", " # add markers\n", " graph = add_points(samples, graph, genus)\n", " \n", " # add lines\n", " if report['equation'] != cancellation :\n", " \n", " # add the regression line\n", " width = 1 + int((1 - report['pvalue']) * 3)\n", " function = lambda x: regressions[mode]['function'](x, *report['curve'])\n", " graph = draw_function(function, graph, ticks, genus, width=width)\n", "\n", " # add the error lines\n", " error = report['s.e.']\n", " graph = draw_function(function, graph, ticks, genus, width=width, style='dashed', offset=error)\n", " graph = draw_function(function, graph, ticks, genus, width=width, style='dashed', offset=-error)\n", "\n", " # add the extrapolation lines\n", " extent = max(ticks) - min(ticks)\n", " extrapolation = [tick - extent for tick in ticks]\n", " extrapolationii = [tick + extent for tick in ticks]\n", " graph = draw_function(function, graph, extrapolation, genus, style='dotted')\n", " graph = draw_function(function, graph, extrapolationii, genus, style='dotted')\n", "\n", " return graph" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "code_folding": [ 3 ], "hidden": true, "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for scatters: plotting scatter graphs)\n" ] } ], "source": [ "designate('plotting scatter graphs', 'scatters')\n", "\n", "# function for plotting a scatter graph\n", "def plot_scatter_plot(associations, mode, spy=False, calculate=True):\n", " \"\"\"Plot a scatter graph based on a regression style.\n", " \n", " Arguments:\n", " associations: list of dicts\n", " mode: str ('linear, 'quadratic', 'exponential', 'power', 'logistic', 'gaussian')\n", " spy=True: boolean, observe initial linear regression fit?\n", " calculate: boolean, calculate jacobian directly?\n", " \n", " Returns\n", " bokeh graph object\n", " \"\"\"\n", " \n", " # get regression results\n", " reports = study(associations, mode, spy, calculate)\n", " \n", " # begin graph\n", " graph = prepare_scatter_plot(associations, mode)\n", " \n", " # get ticks for regression lines\n", " ticks = set_ticks(graph.x_range.start, graph.x_range.end, 1000)\n", "\n", " # add each report to the graph\n", " for report in reports:\n", " \n", " # add to graph\n", " graph = add_report(report, graph, associations, ticks)\n", " \n", " # add annotations\n", " annotations = [('Genus', '@genus'), ('Larvae', '@y')]\n", " annotations += [(truncate_field_name(feature), '@x'), ('Weight', '@weight'), ('Pair', '@pair')]\n", " annotations += [('Latitude', '@latitude'), ('Longitude', '@longitude'), ('Time', '@time')]\n", " graph = annotate_plot(graph, annotations)\n", " \n", " # make the panda\n", " pandas.set_option('max_colwidth', 60)\n", " panda = pandas.DataFrame(reports)\n", "\n", " return graph, panda" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hide_input": true }, "source": [ "### Table of GLOBE Countries" ] }, { "cell_type": "markdown", "metadata": { "hidden": true, "hide_input": false }, "source": [ "The follow is a list of all GLOBE supporting countries and their country codes." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "code_folding": [], "hidden": true, "hide_input": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: extracting all GLOBE countries)\n", "123 GLOBE countries:\n" ] }, { "data": { "text/plain": [ "[('Argentina', 'ARG'),\n", " ('Australia', 'AUS'),\n", " ('Austria', 'AUT'),\n", " ('Bahamas', 'BHS'),\n", " ('Bahrain', 'BHR'),\n", " ('Bangladesh', 'BGD'),\n", " ('Belgium', 'BEL'),\n", " ('Benin', 'BEN'),\n", " ('Bermuda', 'BMU'),\n", " ('Bolivia', 'BOL'),\n", " ('Botswana', 'BWA'),\n", " ('Brazil', 'BRA'),\n", " ('Bulgaria', 'BGR'),\n", " ('Burkina Faso', 'BFA'),\n", " ('Cameroon', 'CMR'),\n", " ('Canada', 'CAN'),\n", " ('Cape Verde', 'CPV'),\n", " ('Chad', 'TCD'),\n", " ('Chile', 'CHL'),\n", " ('Colombia', 'COL'),\n", " ('Congo, The Republic of', 'COG'),\n", " ('Costa Rica', 'CRI'),\n", " ('Croatia', 'HRV'),\n", " ('Cyprus', 'CYP'),\n", " ('Czech Republic', 'CZE'),\n", " ('Denmark', 'DNK'),\n", " ('Dominican Republic', 'DOM'),\n", " ('Ecuador', 'ECU'),\n", " ('Egypt', 'EGY'),\n", " ('El Salvador', 'SLV'),\n", " ('Estonia', 'EST'),\n", " ('Ethiopia', 'ETH'),\n", " ('Fiji', 'FJI'),\n", " ('Finland', 'FIN'),\n", " ('France', 'FRA'),\n", " ('Gabon', 'GAB'),\n", " ('Gambia', 'GMB'),\n", " ('Georgia', 'GEO'),\n", " ('Germany', 'DEU'),\n", " ('Ghana', 'GHA'),\n", " ('Greece', 'GRC'),\n", " ('Guatemala', 'GTM'),\n", " ('Guinea', 'GIN'),\n", " ('Honduras', 'HND'),\n", " ('Hungary', 'HUN'),\n", " ('Iceland', 'ISL'),\n", " ('India', 'IND'),\n", " ('Ireland', 'IRL'),\n", " ('Israel', 'ISR'),\n", " ('Italy', 'ITA'),\n", " ('Japan', 'JPN'),\n", " ('Jordan', 'JOR'),\n", " ('Kazakhstan', 'KAZ'),\n", " ('Kenya', 'KEN'),\n", " ('Kuwait', 'KWT'),\n", " ('Kyrgyz Republic', 'KGZ'),\n", " ('Latvia', 'LVA'),\n", " ('Lebanon', 'LBN'),\n", " ('Liberia', 'LBR'),\n", " ('Liechtenstein', 'LIE'),\n", " ('Lithuania', 'LTU'),\n", " ('Luxembourg', 'LUX'),\n", " ('Madagascar', 'MDG'),\n", " ('Maldives', 'MDV'),\n", " ('Mali', 'MLI'),\n", " ('Malta', 'MLT'),\n", " ('Marshall Islands', 'MHL'),\n", " ('Mauritania', 'MRT'),\n", " ('Mauritius', 'MUS'),\n", " ('Mexico', 'MEX'),\n", " ('Micronesia', 'FSM'),\n", " ('Moldova', 'MDA'),\n", " ('Monaco', 'MCO'),\n", " ('Mongolia', 'MNG'),\n", " ('Montenegro', 'MNE'),\n", " ('Morocco', 'MAR'),\n", " ('Namibia', 'NAM'),\n", " ('Nepal', 'NPL'),\n", " ('Netherlands', 'NLD'),\n", " ('New Zealand', 'NZL'),\n", " ('Niger', 'NER'),\n", " ('Nigeria', 'NGA'),\n", " ('North Macedonia', 'MKD'),\n", " ('Norway', 'NOR'),\n", " ('Oman', 'OMN'),\n", " ('Pakistan', 'PAK'),\n", " ('Palau', 'PLW'),\n", " ('Panama', 'PAN'),\n", " ('Paraguay', 'PRY'),\n", " ('Peru', 'PER'),\n", " ('Philippines', 'PHL'),\n", " ('Poland', 'POL'),\n", " ('Portugal', 'PRT'),\n", " ('Qatar', 'QAT'),\n", " ('Republic of Korea', 'KOR'),\n", " ('Romania', 'ROU'),\n", " ('Russia', 'RUS'),\n", " ('Rwanda', 'RWA'),\n", " ('Saudi Arabia', 'SAU'),\n", " ('Senegal', 'SEN'),\n", " ('Seychelles', 'SYC'),\n", " ('Slovak Republic', 'SVK'),\n", " ('South Africa', 'ZAF'),\n", " ('Spain', 'ESP'),\n", " ('Sri Lanka', 'LKA'),\n", " ('Suriname', 'SUR'),\n", " ('Sweden', 'SWE'),\n", " ('Switzerland', 'CHE'),\n", " ('Taiwan Partnership', 'TWN'),\n", " ('Tanzania', 'TZA'),\n", " ('Thailand', 'THA'),\n", " ('The Republic of Serbia', 'SRB'),\n", " ('Togo', 'TGO'),\n", " ('Trinidad and Tobago', 'TTO'),\n", " ('Tunisia', 'TUN'),\n", " ('Turkey', 'TUR'),\n", " ('Uganda', 'UGA'),\n", " ('Ukraine', 'UKR'),\n", " ('United Arab Emirates', 'ARE'),\n", " ('United Kingdom', 'GBR'),\n", " ('United States', 'USA'),\n", " ('Uruguay', 'URY'),\n", " ('Vietnam', 'VNM')]" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "designate('extracting all GLOBE countries')\n", "\n", "# retrieve list of GLOBE countries and country codes from the API\n", "url = 'https://api.globe.gov/search/dev/country/all/'\n", "request = requests.get(url)\n", "raw = json.loads(request.text)\n", "countries = [record for record in raw['results']]\n", "\n", "# create codes reference for later\n", "codes = {record['countryName']: record['id'] for record in countries}\n", "countries = {record['id']: record['countryName'] for record in countries}\n", "\n", "# make table\n", "table = [item for item in codes.items()]\n", "table.sort(key=lambda item: item[0])\n", "\n", "# print table\n", "print('{} GLOBE countries:'.format(len(table)))\n", "table" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setting the Parameters" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "Set the main geographic and time parameters in the box below. You may specify a country name or a country code, with the code overriding the name when both are given. Refer to the Table of GLOBE Countries above to see all countries involved in the GLOBE program. Misspelled names and codes will be matched to the closest options available. If both code and country name are left blank, it will default to all countries. Also, the beginning date will default to Earth Day 1995 if left blank, and the ending date will default to the current date if left blank. \n", "\n", "Set the name of the secondary protocol to use as the independent variable of the study. A list of the protocols in the mosquito habitat mapper bundle are listed for reference. A close match to the name is generally sufficient.\n", "\n", "Set the name of the particular feature of interest. Currently this notebook only supports numerical features, so choose a feature with numerical values as opposed to categorical names. (For example, a field like 'mosquitohabitatmapperWaterSource' is not appropriate for the regression analyses in this notebook because the values are discrete source names instead of a continuum of values). It may be difficult to know the name of this feature offhand. In this case, you may use the Peruse button to see some sample records. A close match to the name should be sufficient.\n", "\n", "Also set the name of the time of measurement field, as this may vary depending on the protocol. Usually 'measured' is sufficient to find the right one. \n", "\n", "You may click the Apply button to verify your choices. Clicking the Propagate button will propagate the changes to the rest of the notebook. Clicking Both will both apply the new parameters and propagate the changes throughout the notebook." ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "code_folding": [], "hide_input": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: table of secondary protocols)\n", "21 mosquito bundle protocols:\n" ] }, { "data": { "text/plain": [ "['air_temp_dailies',\n", " 'air_temp_monthlies',\n", " 'air_temp_noons',\n", " 'air_temps',\n", " 'conductivities',\n", " 'dissolved_oxygens',\n", " 'freshwater_macroinvertebrates',\n", " 'humidities',\n", " 'humidity_monthlies',\n", " 'humidity_noons',\n", " 'hydrology_alkalinities',\n", " 'hydrology_phs',\n", " 'land_covers',\n", " 'precipitation_monthlies',\n", " 'precipitations',\n", " 'salinities',\n", " 'surface_temperature_noons',\n", " 'surface_temperatures',\n", " 'transparencies',\n", " 'vegatation_covers',\n", " 'water_temperatures']" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "designate('table of secondary protocols')\n", "\n", "# get from protocols file\n", "with open('protocols.txt', 'r') as pointer:\n", " \n", " # get all protocols\n", " protocols = [protocol.strip('\\n') for protocol in pointer.readlines()]\n", " protocols = [protocol for protocol in protocols if 'X' in protocol]\n", " protocols = [protocol.strip('X').strip() for protocol in protocols]\n", "\n", "# print as list\n", "print('{} mosquito bundle protocols:'.format(len(protocols)))\n", "protocols" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "code_folding": [], "hide_input": true, "run_control": { "marked": true } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** settings -----> ^ [code] (for settings: setting the country, date range, and secondary protocol)\n" ] } ], "source": [ "designate('setting the country, date range, and secondary protocol', 'settings')\n", "\n", "# set the country name, defaulting to All countries if blank\n", "country = 'United States'\n", "\n", "# set the country code, defaulting to country name if left blank and overriding otherwise\n", "code = ''\n", "\n", "# set beginning date in 'YYYY-mm-dd' format, defaulting to 1995-04-22\n", "beginning = '2016-01-01'\n", "\n", "# set ending date in 'YYYY-mm-dd' format, defaulting to today's date\n", "ending = ''\n", "\n", "# set secondary protocol\n", "secondary = 'dissolvedoxygen'\n", "\n", "# set secondary protocol feature\n", "feature = 'dissolvedoxygensDissolvedOxygenViaKitMgl'\n", "\n", "# set secondary protocal measured time field ('measured' is usually close enough)\n", "measured = 'measured'" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: applying setting changes or propagating throughout notebook)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bf8c248a7c254dd6af712da5742949d7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Apply', style=ButtonStyle()), Button(description='Propagate', style=ButtonS…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('applying setting changes or propagating throughout notebook')\n", "\n", "# propagate changes\n", "propagate_setting_changes('### Setting the Parameters', '### Retrieving Records from the API', '### Optimizing Parameters')" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: resolving user settings)\n", "\n", "primary protocol: mosquito_habitat_mapper\n", "checking number of records...\n", "3464 mosquito_habitat_mapper records from United States (USA), from 2016-01-01 to 2020-06-18\n", "\n", "secondary protocol: dissolved_oxygens\n", "checking number of records...\n", "2858 records for dissolved_oxygens from United States (USA), 2016-01-01 to 2020-06-18\n", "\n", "secondary feature: dissolvedoxygensDissolvedOxygenViaKitMgl\n", "secondary time field: dissolvedoxygensMeasuredAt\n" ] } ], "source": [ "designate('resolving user settings')\n", "\n", "# define primary protocol name and larvae field\n", "mosquitoes = 'mosquito_habitat_mapper'\n", "larvae = 'mosquitohabitatmapperLarvaeCount'\n", "\n", "# resolve country and code to default values\n", "country, code = resolve_country_code(country, code)\n", "\n", "# default beginning to first day of GLOBE and ending to current date if unspecified\n", "beginning = beginning or '1995-04-22'\n", "ending = ending or str(datetime.now().date())\n", "\n", "# make api call to get number of records\n", "print('\\nprimary protocol: {}'.format(mosquitoes))\n", "print('checking number of records...')\n", "raw = query_api(mosquitoes, code, beginning, ending, sample=True)\n", "count = raw['count']\n", "print('{} {} records from {} ({}), from {} to {}'.format(count, mosquitoes, country, code, beginning, ending))\n", "\n", "# infer closest match\n", "perusal = [0]\n", "secondary = fuzzy_match(secondary, protocols)\n", "print('\\nsecondary protocol: {}'.format(secondary))\n", "\n", "# make the API call and get the examples and record count\n", "print('checking number of records...')\n", "raw = query_api(secondary, code, beginning, ending, sample=True)\n", "examples = [record for record in raw['results']]\n", "count = raw['count']\n", "print('{} records for {} from {} ({}), {} to {}'.format(count, secondary, country, code, beginning, ending))\n", "\n", "# assert there must be more records\n", "assert count > 0, '* Error * Unfortunately no records were returned for that protocol.'\n", "\n", "# infer best matches\n", "fields = [key for key in examples[0]['data'].keys()]\n", "feature = fuzzy_match(feature, fields)\n", "measured = fuzzy_match(measured, fields)\n", "\n", "# print for inspection\n", "print('\\nsecondary feature: {}'.format(feature))\n", "print('secondary time field: {}'.format(measured))" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "code_folding": [], "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: perusing secondary records)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b96ac77e256a4a659096d7f9e5bbaa48", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='Peruse', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('perusing secondary records')\n", "\n", "# peruse through the ten sample records\n", "def view_ten_records(_):\n", " \"\"\"Peruse through example records.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # refresh this cell\n", " refresh_cells_by_position(0)\n", " \n", " # get last number\n", " last = perusal[-1]\n", " \n", " # advance\n", " after = last + 1\n", " if after > 9:\n", " \n", " # return to beginning\n", " after = 0\n", " \n", " # append to numberings\n", " perusal.append(after)\n", "\n", " # print record\n", " print('record {} of 10:'.format(last))\n", " print(json.dumps(examples[last]['data'], indent=1))\n", " \n", " return None\n", "\n", "# create button\n", "button = Button(description=\"Peruse\")\n", "display(button)\n", "button.on_click(view_ten_records)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "16cf156ef8e54d1aadfeb54a02788ac3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "415faf261eb844b78a7e45501f6c65a2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Retrieving Records from the API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Click Retrieve to retrieve the data from the GLOBE API. Clicking Propagate afterward will recalculate the notebook with the new dataset." ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: applying setting changes or propagating throughout notebook)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e891f07ff82d40d2972107b4ba469806", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Retrieve', style=ButtonStyle()), Button(description='Propagate', style=Butt…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('applying setting changes or propagating throughout notebook')\n", "\n", "# propagate button\n", "labels = ['Retrieve', 'Propagate', 'Both']\n", "propagate_setting_changes('### Retrieving Records from the API', '### Pruning Outliers', '### Optimizing Parameters', labels)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: retrieving and processing records from the api)\n", "\n", "making api request...\n", "1268 valid mosquito_habitat_mapper records (of 3464 total) from United States, 2016-01-01 to 2020-06-18\n", "\n", "making api request...\n", "1769 valid dissolved_oxygens records (of 2858 total) from United States, 2016-01-01 to 2020-06-18\n" ] } ], "source": [ "designate('retrieving and processing records from the api')\n", "\n", "# get the mosquitoes records from the 'results' field, and prune off nulls\n", "print('\\nmaking api request...')\n", "raw = query_api(mosquitoes, code, beginning, ending)\n", "count = len(raw['results'])\n", "results = [record for record in raw['results'] if record['data'][larvae] is not None]\n", "results = process_records(results, primary=True)\n", "formats = (len(results), mosquitoes, count, country, beginning, ending)\n", "print('{} valid {} records (of {} total) from {}, {} to {}'.format(*formats))\n", "\n", "# get the secondary protocol records and prune off nulls\n", "print('\\nmaking api request...')\n", "raw = query_api(secondary, code, beginning, ending)\n", "count = len(raw['results'])\n", "resultsii = [record for record in raw['results'] if record['data'][feature] is not None]\n", "\n", "# try to process data, may encounter error\n", "try:\n", "\n", " # process data\n", " resultsii = process_records(resultsii, primary=False)\n", " formats = (len(resultsii), secondary, count, country, beginning, ending)\n", " print('{} valid {} records (of {} total) from {}, {} to {}'.format(*formats))\n", " \n", "# but if trouble\n", "except ValueError:\n", " \n", " # raise error\n", " message = '* Error! * Having trouble processing the data. Is the requested field a numberical one?'\n", " raise Exception(message)\n", "\n", " \n", "# raise assertion error if zero records\n", "assert len(results) > 0, '* Error! * No valid {} records in the specified range'.format(mosquitoes)\n", "assert len(resultsii) > 0, '* Error! * No valid {} records in the specified range'.format(secondary)" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3151e297e9b14b71bdbca596edb1f456", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b03140d812de4e5f8a9d78f7fc1df44e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pruning Outliers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may wish to remove datapoints that seem suspiciously like outliers. Observations will be removed from the study if they fall outside the interquartile range." ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "hide_input": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** settings -----> ^ [code] (for settings: setting upper quartile)\n" ] } ], "source": [ "designate('setting upper quartile', 'settings')\n", "\n", "# set upper quartile (max 100, min 0)\n", "threshold = 85\n", "thresholdii = 85" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: applying setting changes or propagating throughout notebook)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "36d5ab22a09c45b897b38d1d814acbb3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Apply', style=ButtonStyle()), Button(description='Propagate', style=ButtonS…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('applying setting changes or propagating throughout notebook')\n", "\n", "# propagate button\n", "propagate_setting_changes('### Pruning Outliers', '### Filtering Records', '### Optimizing Parameters')" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: pruning away outliers)\n", "\n", "\n", "larvae where lower quartile and upper quartile \n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "75.5 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "38.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "75.5 -19.5 32.5\n", "38.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "38.0 -19.5 32.5\n", "75.5 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "75.5 -19.5 32.5\n", "75.5 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "38.0 -19.5 32.5\n", "40.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "500.0 -19.5 32.5\n", "99.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "63.0 -19.5 32.5\n", "88.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "53.0 -19.5 32.5\n", "42.0 -19.5 32.5\n", "54.0 -19.5 32.5\n", "56.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "54.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "45.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "200.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "43.0 -19.5 32.5\n", "40.0 -19.5 32.5\n", "58.0 -19.5 32.5\n", "105.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "200.0 -19.5 32.5\n", "60.0 -19.5 32.5\n", "40.0 -19.5 32.5\n", "36.0 -19.5 32.5\n", "200.0 -19.5 32.5\n", "200.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "60.0 -19.5 32.5\n", "60.0 -19.5 32.5\n", "60.0 -19.5 32.5\n", "75.0 -19.5 32.5\n", "816.0 -19.5 32.5\n", "272.0 -19.5 32.5\n", "200.0 -19.5 32.5\n", "36.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "50.0 -19.5 32.5\n", "35.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "77.0 -19.5 32.5\n", "150.0 -19.5 32.5\n", "56.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "200.0 -19.5 32.5\n", "38.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "200.0 -19.5 32.5\n", "200.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "63.0 -19.5 32.5\n", "70.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "40.0 -19.5 32.5\n", "47.0 -19.5 32.5\n", "100.0 -19.5 32.5\n", "47.0 -19.5 32.5\n", "42.0 -19.5 32.5\n", "\n", "99 observations removed\n", "1169 mosquito_habitat_mapper records after removing outliers\n", "\n", "\n", "dissolvedoxygensDissolvedOxygenViaKitMgl where lower quartile and upper quartile \n", "\n", "0 observations removed\n", "1769 dissolved_oxygens records after removing outliers\n" ] } ], "source": [ "designate('pruning away outliers')\n", "\n", "# add default quartile field\n", "for record in results + resultsii:\n", " \n", " # add default score field\n", " record['lq'] = -999.0\n", " record['uq'] = -999.0\n", "\n", "# prune data\n", "authentics, outliers = remove_outliers(results, 'larvae', threshold)\n", "authenticsii, outliersii = remove_outliers(resultsii, feature, thresholdii)\n", "\n", "# report\n", "zipper = zip((authentics, authenticsii), (outliers, outliersii), (mosquitoes, secondary), ('larvae', feature))\n", "for records, prunes, protocol, field in zipper:\n", " \n", " # report each outlier\n", " print('\\n\\n' + field + ' where lower quartile and upper quartile ')\n", " for outlier in prunes:\n", "\n", " # print\n", " print(outlier[field], outlier['lq'], outlier['uq'])\n", "\n", " # report total\n", " print('\\n{} observations removed'.format(len(prunes)))\n", " print('{} {} records after removing outliers'.format(len(records), protocol))" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: drawing histograms)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

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i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"2822\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"2822\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"2822\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '2822' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"2822\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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Application\",\"version\":\"1.4.0\"}};\n", " var render_items = [{\"docid\":\"e91bd7ba-4651-41f9-8727-6dc9e3b7c7e8\",\"roots\":{\"2823\":\"564e98d6-33e4-4406-9d18-b7b296286220\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "2823" } }, "output_type": "display_data" } ], "source": [ "designate('drawing histograms')\n", "\n", "# construct histograms\n", "gram = construct_bargraph(authentics, 'larvae', width=5)\n", "gramii = construct_bargraph(authenticsii, feature, width=1)\n", "\n", "# display plots\n", "output_notebook()\n", "show(Row(gram, gramii))" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8b23b71c37ed40588624d98141a3af37", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "33bd553a40534783985e9444bd8e4165", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filtering Records" ] }, { "cell_type": "markdown", "metadata": { "hide_input": false }, "source": [ "You may further filter the data if desired. Smaller datasets render more quickly, for instance. Set the criteria and click the Apply button to perform the search. Clicking Propagate will propagate the changes down the notebook. You may set a parameter to None to ignore any filtering." ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** settings -----> ^ [code] (for settings: setting filter parameters)\n" ] } ], "source": [ "designate('setting filter parameters', 'settings')\n", "\n", "# set the specific genera of interest ['Anopheles', 'Aedes', 'Culex', 'Unknown', 'Other']\n", "# None defaults to all genera\n", "genera = ['Anopheles', 'Aedes', 'Culex', 'Unknown', 'Other']\n", "\n", "# set fewest and most larvae counts or leave as None\n", "fewest = None\n", "most = None\n", "\n", "# set the inital or final date ranges (in 'YYYY-mm-dd' format), or leave as None\n", "initial = None\n", "final = None\n", "\n", "# set the latitude boundaries, or leave as None\n", "south = None\n", "north = None\n", "\n", "# set the longitude boundaries, or leave as None\n", "west = None\n", "east = None" ] }, { "cell_type": "code", "execution_count": 107, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: applying setting changes or propagating throughout notebook)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5ef486dcfd3348b6ac3d0056e008a1fd", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Apply', style=ButtonStyle()), Button(description='Propagate', style=ButtonS…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('applying setting changes or propagating throughout notebook')\n", "\n", "# propagate changes\n", "propagate_setting_changes('### Filtering Records', '### Defining the Weighting Scheme', '### Optimizing Parameters')" ] }, { "cell_type": "code", "execution_count": 108, "metadata": { "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: filtering records)\n", "\n", "1169 of 1169 mosquito_habitat_mapper records meeting criteria:\n", "\n", "genus in ['Anopheles', 'Aedes', 'Culex', 'Unknown', 'Other']\n", "\n", "\n", "1769 of 1769 dissolved_oxygens records meeting criteria:\n", "\n", "\n" ] } ], "source": [ "designate('filtering records')\n", "\n", "# set records to data\n", "records = [record for record in authentics]\n", "recordsii = [record for record in authenticsii]\n", "\n", "# make parameters and fields list\n", "parameters = [genera, fewest, most, initial, final, south, north, west, east]\n", "fields = ['genus', 'larvae', 'larvae', 'date', 'date', 'latitude', 'latitude', 'longitude', 'longitude']\n", "\n", "# make comparison functions list\n", "within = lambda value, setting: value in setting\n", "after = lambda value, setting: value >= datetime.strptime(str(setting), \"%Y-%m-%d\").date()\n", "before = lambda value, setting: value <= datetime.strptime(str(setting), \"%Y-%m-%d\").date()\n", "greater = lambda value, setting: value >= setting\n", "lesser = lambda value, setting: value <= setting\n", "\n", "# make associated functions\n", "functions = [within, greater, lesser, after, before, greater, lesser, greater, lesser]\n", "symbols = ['in', '>', '<', '>', '<', '>', '<', '>', '<']\n", "\n", "# filter primaries\n", "data, criteria = filter_data_by_field(authentics, parameters, fields, functions, symbols)\n", "formats = (len(data), len(authentics), mosquitoes, criteria)\n", "print('\\n{} of {} {} records meeting criteria:\\n\\n{}'.format(*formats))\n", "\n", "# filter secondaries\n", "dataii, criteria = filter_data_by_field(authenticsii, parameters, fields, functions, symbols)\n", "formats = (len(dataii), len(authenticsii), secondary, criteria)\n", "print('\\n{} of {} {} records meeting criteria:\\n\\n{}'.format(*formats))\n", "\n", "# set genera to classification by default\n", "genera = genera or classification" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "47a779997f134c52bbe479a1e02d25e4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2330140028a34482b19abe839fa39567", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining the Weighting Scheme" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because the two sets of measurements were not taken concurrently, there must be some criteria to determine when measurements from one protocol correspond to measurements from the other protocol. The strategy implemented here is using a weighting function that determines how strongly to weigh the association, based on the following parameters:\n", " \n", "- distance: the distance in kilometers between measurements that will be granted full weight.\n", " \n", "- interval: the time interval in days between measurements that will be granted full weight.\n", " \n", "- lag: the time in days to anticipate an effect on mosquitoes from a secondary measurement some \n", " days before.\n", " \n", "- confidence: the weight to grant a measurement twice the distance or interval. This determines how steeply the weighting shrinks as the intervals are surpassed. A high confidence will grant higher weights to data outside the intervals. A confidence of zero will have no tolerance for data slightly passed the interval.\n", " \n", "- cutoff: the minimum weight to consider in the dataset. A cutoff of 0.1, for instance, will only retain data if the weight is at least 0.1.\n", " \n", "- inclusion: the maximum number of nearest secondary measurements to include for each mosquitos measurement.\n", "\n", "If you have tried the optimizer at the bottom of this notebook, you may use the Remember button to bring up a list of those results." ] }, { "cell_type": "code", "execution_count": 110, "metadata": { "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** settings -----> ^ [code] (for settings: setting the weighting scheme)\n" ] } ], "source": [ "designate('setting the weighting scheme', 'settings')\n", "\n", "# set the distance sensitivity in kilometers\n", "distance = 50\n", "\n", "# set time interval sensitivity in days\n", "interval = 1\n", "\n", "# set the expected time lag in days\n", "lag = 0\n", "\n", "# weight for twice the interval\n", "confidence = 0.8\n", "\n", "# minimum weight to include\n", "cutoff = 0.1\n", "\n", "# number of nearest neighboring points to include\n", "inclusion = 5" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "code_folding": [], "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: remembering optimization paramters button)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "60826e8ab119458780b47065436b2a62", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='Remember', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('remembering optimization paramters button')\n", "\n", "# reset parameters function\n", "def remember(_):\n", " \"\"\"Remeber previous obtimization results.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # refresh\n", " refresh_cells_by_position(0)\n", " \n", " # view last panda\n", " try:\n", " \n", " # view last panda\n", " display(optimizations[-1])\n", " \n", " # otherwise\n", " except IndexError:\n", " \n", " # message\n", " print('no optimizations generated yet')\n", " \n", " return None\n", "\n", "# create button\n", "button = Button(description=\"Remember\")\n", "display(button)\n", "button.on_click(remember)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: applying setting changes or propagating throughout notebook)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "9699a323772146c39af25d100cd3e268", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Apply', style=ButtonStyle()), Button(description='Propagate', style=ButtonS…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('applying setting changes or propagating throughout notebook')\n", "\n", "# propagate changes\n", "propagate_setting_changes('### Defining the Weighting Scheme', '### Linear Regression', '### Optimizing Parameters')" ] }, { "cell_type": "code", "execution_count": 113, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: plotting weighting scheme)\n" ] }, { "data": { "text/html": [ "\n", "
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\\n\"+\n \"

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dictionary\n", "settings = {'distance': distance, 'interval': interval, 'lag': lag, 'confidence': confidence}\n", "settings.update({'cutoff': cutoff, 'inclusion': inclusion})\n", "\n", "# create plateaus\n", "plateau = plot_plateaued_weighting_scheme('distance', settings)\n", "plateauii = plot_plateaued_weighting_scheme('interval', settings)\n", "\n", "# display plots\n", "output_notebook()\n", "show(Row(plateau, plateauii))" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "The histograms below show the distribution of weights amongst the data set, and the distribution of secondary-measurement pairs found." ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: assembling associations)\n", "\n", "assembling associations...\n", "112 mosquito_habitat_mapper records used, 0.1 of 1169 records pulled\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? */\n", " safe: true,\n", " /* Index of renderer in `output_area.display_order` */\n", " index: 0\n", " });\n", " }\n", "\n", " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", " if (root.Jupyter !== undefined) {\n", " var events = require('base/js/events');\n", " var OutputArea = require('notebook/js/outputarea').OutputArea;\n", "\n", " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", " register_renderer(events, OutputArea);\n", " }\n", " }\n", "\n", " \n", " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", " root._bokeh_timeout = Date.now() + 5000;\n", " root._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"
\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"3810\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };var element = document.getElementById(\"3810\");\n", " if (element == null) {\n", " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '3810' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " \n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", " var css_urls = [];\n", " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " \n", " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if (root.Bokeh !== undefined || force === true) {\n", " \n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"3810\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"3810\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };var element = document.getElementById(\"3810\");\n if (element == null) {\n console.error(\"Bokeh: ERROR: autoload.js configured with elementid '3810' but no matching script tag was found. \")\n return false;\n }\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n \n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n var css_urls = [];\n \n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n function(Bokeh) {\n \n \n }\n ];\n\n function run_inline_js() {\n \n if (root.Bokeh !== undefined || force === true) {\n \n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"3810\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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Application\",\"version\":\"1.4.0\"}};\n", " var render_items = [{\"docid\":\"0e259721-4793-4908-a7b2-78c6640d7b87\",\"roots\":{\"3811\":\"088db88a-58a6-4e8f-afe1-939b88f005cd\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " clearInterval(timer);\n", " embed_document(root);\n", " } else {\n", " attempts++;\n", " if (attempts > 100) {\n", " clearInterval(timer);\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", " }\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "3811" } }, "output_type": "display_data" } ], "source": [ "designate('assembling associations')\n", "\n", "# make associations\n", "print('\\nassembling associations...')\n", "associations = merge_datasets(settings)\n", "coverage = round(len(associations) / len(data), 2)\n", "print('{} {} records used, {} of {} records pulled'.format(len(associations), mosquitoes, coverage, len(data)))\n", "\n", "# raise assertion error\n", "assert len(associations) > 1, '* Error! * Not enough records retrieved for regression' \n", "\n", "# make summary table\n", "summary = summary_df(associations)\n", "\n", "# construct histograms\n", "records = [{'weight': association['associates'][0]['weight']} for association in associations]\n", "gram = construct_bargraph(records, 'weight', width=0.1)\n", "\n", "# construct histogram\n", "records = [{'pairs': len(association['associates'])} for association in associations]\n", "gramii = construct_bargraph(records, 'pairs', width=1)\n", "\n", "# display\n", "output_notebook()\n", "show(Row([gram, gramii]))" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "86e1670a007c47318e39cd334d64a6be", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8268ac6fbbe347a3bc28202fa30afe4e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Curve Fitting and Table Legend\n", "\n", "Once the weighting characteristics have been chosen, propagating the changes will perform each mode of regression on each genus of larvae from the protocol. The graph indicates the results of the regression in the following way:\n", "- The solid lines represents the best fitting lines for each genus.\n", "- The dashed lines above and below represent one standared error away from the best fit.\n", "- A thicker line indicates a lower probability of sampling bias (lower p-value).\n", "- The dotted lines represent exptrapolation of the model beyond the data range.\n", "- The genus is colored as indicated in the legend. Clicking in the legend will hide the regression for that genus.\n", "- Each marker is colored according to genus and sized according to its weight.\n", "\n", "The validation results of each study are presented in a table with the following fields:\n", "- genus: the mosquito genus of the study.\n", "- records: the number of mosquito records involved in the model.\n", "- pairs: the number of mosquito-secondary paired records.\n", "- coverage: the fraction of mosquito records involved compared to pulled records.\n", "- pvalue: testing the hypothesis $H_0: \\rho=0$ (correlation is 0) vs $H_1: \\rho \\neq 0$ (correlation is not zero).\n", "- correlation: the correlation between the true observations and the model [why?](https://analyse-it.com/docs/user-guide/fit-model/linear/predicted-actual-plot#:~:text=Predicted%20against%20actual%20Y%20plot,line%2C%20with%20narrow%20confidence%20bands.&text=Points%20that%20are%20vertically%20distant%20from%20the%20line%20represent%20possible%20outliers.)\n", "- $R^2$: the coefficient of determination which is the proportion of variance in the dependent variable that is predictable from the independent variable\n", "- s.e: the standard error of estimate (residuals) between truths and predictions, in units of larvae.\n", "- equation: the regression equation for the model.\n", "- mode specific parameters that have been fit by the regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Linear Curve Fit" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: performing linear regression)\n" ] }, { "data": { "text/html": [ "\n", "
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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJwrQGuad5yioytA0ETY8PSqK0A17zhFR7IrQJqZmZmZuStA/kP67evAK0Bj7lpCPsgrQMiYu5aQzytALUMc6+LWK0CR7Xw/Nd4rQPaX3ZOH5StAW0I+6NnsK0DA7J48LPQrQCSX/5B++ytAiUFg5dACLEDu68A5IwosQFOWIY51ESxAt0CC4scYLEAc6+I2GiAsQIGVQ4tsJyxA5j+k374uLEBK6gQ0ETYsQK+UZYhjPSxAFD/G3LVELEB56SYxCEwsQN2Th4VaUyxAQj7o2axaLECn6Egu/2EsQAyTqYJRaSxAcT0K16NwLEDV52or9ncsQDqSy39IfyxAnzws1JqGLEAE54wo7Y0sQGiR7Xw/lSxAzTtO0ZGcLEAy5q4l5KMsQJeQD3o2qyxA+zpwzoiyLEBg5dAi27ksQMWPMXctwSxAKjqSy3/ILECO5PIf0s8sQPOOU3Qk1yxAWDm0yHbeLEC94xQdyeUsQCGOdXEb7SxAhjjWxW30LEDr4jYawPssQFCNl24SAy1AtTf4wmQKLUAZ4lgXtxEtQH6MuWsJGS1A4zYawFsgLUBI4XoUrictQKyL22gALy1AETY8vVI2LUB24JwRpT0tQNuK/WX3RC1APzVeuklMLUCk374OnFMtQAmKH2PuWi1AbjSAt0BiLUDS3uALk2ktQDeJQWDlcC1AnDOitDd4LUAB3gIJin8tQGWIY13chi1AyjLEsS6OLUAv3SQGgZUtQJSHhVrTnC1A+THmriWkLUBd3EYDeKstQMKGp1fKsi1AJzEIrBy6LUCM22gAb8EtQPCFyVTByC1AVTAqqRPQLUC62or9ZdctQB+F61G43i1Agy9MpgrmLUDo2az6XO0tQE2EDU+v9C1Asi5uowH8LUAW2c73UwMuQHuDL0ymCi5A4C2QoPgRLkBF2PD0ShkuQKmCUUmdIC5ADi2yne8nLkBz1xLyQS8uQNiBc0aUNi5APSzUmuY9LkCh1jTvOEUuQAaBlUOLTC5Aayv2l91TLkDQ1VbsL1suQDSAt0CCYi5AmSoYldRpLkD+1HjpJnEuQGN/2T15eC5Axyk6kst/LkAs1JrmHYcuQJF++zpwji5A9ihcj8KVLkBa07zjFJ0uQL99HThnpC5AJCh+jLmrLkCJ0t7gC7MuQO18PzVeui5AUiegibDBLkC30QDeAskuQBx8YTJV0C5AgSbChqfXLkDl0CLb+d4uQEp7gy9M5i5AryXkg57tLkAU0ETY8PQuQHh6pSxD/C5A3SQGgZUDL0BCz2bV5wovQKd5xyk6Ei9ACyQofowZL0BwzojS3iAvQNV46SYxKC9AOiNKe4MvL0CezarP1TYvQAN4CyQoPi9AaCJseHpFL0DNzMzMzEwvQA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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jgQwGB2Tx4WKhDAmCGOdXEbEMDOzMzMzAwQwAjwFkhQ/A/AdEaU9gbfD8DknBGlvcEPwFDzjlN0pA/AvEkMAiuHD8AooImw4WkPwJj2Bl+YTA/ABE2EDU8vD8BwowG8BRIPwNz5fmq89A7ATFD8GHPXDsC4pnnHKboOwCT99nXgnA7AkFN0JJd/DsD8qfHSTWIOwGwAb4EERQ7A2FbsL7snDsBErWnecQoOwLAD54wo7Q3AIFpkO9/PDcCMsOHplbINwPgGX5hMlQ3AZF3cRgN4DcDUs1n1uVoNwEAK16NwPQ3ArGBUUicgDcAYt9EA3gINwIgNT6+U5QzA9GPMXUvIDMBgukkMAqsMwMwQx7q4jQzAPGdEaW9wDMCovcEXJlMMwBQUP8bcNQzAgGq8dJMYDMDwwDkjSvsLwFwXt9EA3gvAyG00gLfAC8A0xLEubqMLwKAaL90khgvAEHGsi9toC8B8xyk6kksLwOgdp+hILgvAVHQkl/8QC8DEyqFFtvMKwDAhH/Rs1grAnHecoiO5CsAIzhlR2psKwHgkl/+QfgrA5HoUrkdhCsBQ0ZFc/kMKwLwnDwu1JgrALH6MuWsJCsCY1AloIuwJwAQrhxbZzgnAcIEExY+xCcDg14FzRpQJwEwu/yH9dgnAuIR80LNZCcAk2/l+ajwJwJAxdy0hHwnAAIj029cBCcBs3nGKjuQIwNg07zhFxwjARIts5/upCMC04emVsowIwCA4Z0RpbwjAjI7k8h9SCMD45GGh1jQIwGg730+NFwjA1JFc/kP6B8BA6Nms+twHwKw+V1uxvwfAHJXUCWiiB8CI61G4HoUHwPRBz2bVZwfAYJhMFYxKB8DQ7snDQi0HwDxFR3L5DwfAqJvEILDyBsAU8kHPZtUGwIBIv30duAbA8J48LNSaBsBc9bnain0GwMhLN4lBYAbANKK0N/hCBsCk+DHmriUGwBBPr5RlCAbAfKUsQxzrBcDo+6nx0s0FwFhSJ6CJsAXAxKikTkCTBcAw/yH99nUFwJxVn6utWAXADKwcWmQ7BcB4ApoIGx4FwORYF7fRAAXAUK+UZYjjBMDABRIUP8YEwCxcj8L1qATAmLIMcayLBMAECYofY24EwHBfB84ZUQTA4LWEfNAzBMBMDAIrhxYEwLhif9k9+QPAJLn8h/TbA8CUD3o2q74DwABm9+RhoQPAbLx0kxiEA8DYEvJBz2YDwEhpb/CFSQPAtL/snjwsA8AgFmpN8w4DwIxs5/up8QLA/MJkqmDUAsBoGeJYF7cCwNRvXwfOmQLAQMbctYR8AsCwHFpkO18CwBxz1xLyQQLAiMlUwagkAsD0H9JvXwcCwGB2Tx4W6gHA0MzMzMzMAcA8I0p7g68BwKh5xyk6kgHAFNBE2PB0AcCEJsKGp1cBwPB8PzVeOgHAXNO84xQdAcDIKTqSy/8AwDiAt0CC4gDApNY07zjFAMAQLbKd76cAwHyDL0ymigDA7Nms+lxtAMBYMCqpE1AAwMSGp1fKMgDAMN0kBoEVAMBAZ0Rpb/D/vxgUP8bctf+/8MA5I0p7/7/IbTSAt0D/v6AaL90kBv+/gMcpOpLL/r9YdCSX/5D+vzAhH/RsVv6/CM4ZUdob/r/oehSuR+H9v8AnDwu1pv2/mNQJaCJs/b9wgQTFjzH9v1Au/yH99vy/KNv5fmq8/L8AiPTb14H8v9g07zhFR/y/uOHplbIM/L+QjuTyH9L7v2g730+Nl/u/QOjZrPpc+78gldQJaCL7v/hBz2bV5/q/0O7Jw0Kt+r+om8QgsHL6v4BIv30dOPq/YPW52or9+b84orQ3+ML5vxBPr5RliPm/6Pup8dJN+b/IqKROQBP5v6BVn6ut2Pi/eAKaCBue+L9Qr5RliGP4vzBcj8L1KPi/CAmKH2Pu97/gtYR80LP3v7hif9k9efe/mA96Nqs+979wvHSTGAT3v0hpb/CFyfa/IBZqTfOO9r8Aw2SqYFT2v9hvXwfOGfa/sBxaZDvf9b+IyVTBqKT1v2B2Tx4WavW/QCNKe4Mv9b8Y0ETY8PT0v/B8PzVeuvS/yCk6kst/9L+o1jTvOEX0v4CDL0ymCvS/WDAqqRPQ878w3SQGgZXzvxCKH2PuWvO/6DYawFsg87/A4xQdyeXyv5iQD3o2q/K/eD0K16Nw8r9Q6gQ0ETbyvyiX/5B++/G/AET67evA8b/g8PRKWYbxv7id76fGS/G/kErqBDQR8b9o9+Rhodbwv0Ck374OnPC/IFHaG3xh8L/4/dR46Sbwv6BVn6ut2O+/UK+UZYhj778QCYofY+7uv8Bif9k9ee6/cLx0kxgE7r8gFmpN847tv+BvXwfOGe2/kMlUwaik7L9AI0p7gy/sv/B8PzVeuuu/sNY07zhF679gMCqpE9DqvxCKH2PuWuq/wOMUHcnl6b+APQrXo3DpvzCX/5B+++i/4PD0SlmG6L+QSuoENBHov0Ck374OnOe/AP7UeOkm57+wV8oyxLHmv2Cxv+yePOa/EAu1pnnH5b/QZKpgVFLlv4C+nxov3eS/MBiV1Alo5L/gcYqO5PLjv6DLf0i/feO/UCV1ApoI478Af2q8dJPiv7DYX3ZPHuK/cDJVMCqp4b8gjErqBDThv9DlP6TfvuC/gD81XrpJ4L+AMlUwKqnfv+DlP6Tfvt6/QJkqGJXU3b+gTBWMSurcvwAAAAAAANy/gLPqc7UV27/gZtXnaivav0AawFsgQdm/oM2qz9VW2L8ggZVDi2zXv4A0gLdAgta/4OdqK/aX1b9Am1Wfq63Uv8BOQBNhw9O/IAIrhxbZ0r+AtRX7y+7Rv+BoAG+BBNG/YBzr4jYa0L+An6ut2F/Ov0AGgZVDi8y/AG1Wfa62yr8A1CtlGeLIv8A6AU2EDce/gKHWNO84xb9ACKwcWmTDv0BvgQTFj8G/AKyt2F92v7+AeVioNc27vwBHA3gLJLi/gBSuR+F6tL+A4lgXt9GwvwBgB84ZUaq/APtcbcX+or8ALGUZ4liXvwDIILByaIG/AJgRpb3Bdz8AMJkqGJWUPwD99nXgnKE/AGGh1jTvqD8A46WbxCCwP4AV+8vuybM/AEhQ/Bhztz8AeqUsQxy7P4Cs+lxtxb4/gO+nxks3wT/AiNLe4AvDP8Ah/fZ14MQ/ALsnDwu1xj9AVFInoInIP4DtfD81Xso/wIanV8oyzD/AH9JvXwfOPwC5/If0288/IKkT0ETY0D/A9Shcj8LRP0BCPujZrNI/4I5TdCSX0z+A22gAb4HUPyAofoy5a9U/oHSTGARW1j9AwaikTkDXP+ANvjCZKtg/gFrTvOMU2T8Ap+hILv/ZP6Dz/dR46do/QEATYcPT2z/gjCjtDb7cP2DZPXlYqN0/ACZTBaOS3j+gcmiR7XzfP6Dfvg6cM+A/8IXJVMGo4D8wLNSa5h3hP4DS3uALk+E/0HjpJjEI4j8gH/RsVn3iP2DF/rJ78uI/sGsJ+aBn4z8AEhQ/xtzjP1C4HoXrUeQ/kF4pyxDH5D/gBDQRNjzlPzCrPldbseU/gFFJnYAm5j/A91PjpZvmPxCeXinLEOc/YERpb/CF5z+w6nO1FfvnP/CQfvs6cOg/QDeJQWDl6D+Q3ZOHhVrpP+CDns2qz+k/MCqpE9BE6j9w0LNZ9bnqP8B2vp8aL+s/EB3J5T+k6z9gw9MrZRnsP6Bp3nGKjuw/8A/pt68D7T9AtvP91HjtP5Bc/kP67e0/0AIJih9j7j8gqRPQRNjuP3BPHhZqTe8/wPUoXI/C7z8AzhlR2hvwPyghH/RsVvA/UHQkl/+Q8D94xyk6ksvwP5gaL90kBvE/wG00gLdA8T/owDkjSnvxPxAUP8bctfE/OGdEaW/w8T9YukkMAivyP4ANT6+UZfI/qGBUUieg8j/Qs1n1udryP/AGX5hMFfM/GFpkO99P8z9ArWnecYrzP2gAb4EExfM/iFN0JJf/8z+wpnnHKTr0P9j5fmq8dPQ/AE2EDU+v9D8goImw4en0P0jzjlN0JPU/cEaU9gZf9T+YmZmZmZn1Pw==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"CCtuelpoKEDsl22pVmYoQNEEbdhSZChAtXFsB09iKECa3ms2S2AoQH5La2VHXihAYrhqlENcKEBHJWrDP1ooQCySafI7WChAEP9oIThWKED0a2hQNFQoQNjYZ38wUihAvUVnrixQKECismbdKE4oQIYfZgwlTChAaoxlOyFKKEBP+WRqHUgoQDNmZJkZRihAGNNjyBVEKED8P2P3EUIoQOGsYiYOQChAxRliVQo+KECqhmGEBjwoQI7zYLMCOihAcmBg4v43KEBXzV8R+zUoQDs6X0D3MyhAIKdeb/MxKEAEFF6e7y8oQOiAXc3rLShAze1c/OcrKECxWlwr5CkoQJbHW1rgJyhAejRbidwlKEBfoVq42CMoQEMOWufUIShAKHtZFtEfKEAM6FhFzR0oQPBUWHTJGyhA1cFXo8UZKEC5LlfSwRcoQJ6bVgG+FShAgghWMLoTKEBmdVVfthEoQEviVI6yDyhAL09Uva4NKEAUvFPsqgsoQPgoUxunCShA3ZVSSqMHKEDBAlJ5nwUoQKZvUaibAyhAitxQ15cBKEBuSVAGlP8nQFO2TzWQ/SdAOCNPZIz7J0AckE6TiPknQAD9TcKE9ydA5GlN8YD1J0DJ1kwgffMnQK5DTE958SdAkrBLfnXvJ0B2HUutce0nQFuKStxt6ydAP/dJC2rpJ0AkZEk6ZucnQAjRSGli5SdA7D1ImF7jJ0DRqkfHWuEnQLYXR/ZW3ydAmoRGJVPdJ0B+8UVUT9snQGJeRYNL2SdAR8tEskfXJ0AsOEThQ9UnQBClQxBA0ydA9BFDPzzRJ0DZfkJuOM8nQL3rQZ00zSdAolhBzDDLJ0CGxUD7LMknQGsyQCopxydAT58/WSXFJ0AzDD+IIcMnQBh5PrcdwSdA/OU95hm/J0DhUj0VFr0nQMW/PEQSuydAqiw8cw65J0COmTuiCrcnQHIGO9EGtSdAV3M6AAOzJ0A84Dkv/7AnQCBNOV77ridABLo4jfesJ0DoJji886onQM2TN+vvqCdAsgA3GuymJ0CWbTZJ6KQnQHraNXjkoidAX0c1p+CgJ0BDtDTW3J4nQCghNAXZnCdADI4zNNWaJ0Dw+jJj0ZgnQNVnMpLNlidAutQxwcmUJ0CeQTHwxZInQIKuMB/CkCdAZhswTr6OJ0BLiC99uownQDD1Lqy2iidAFGIu27KIJ0D4zi0Kr4YnQN07LTmrhCdAwagsaKeCJ0CmFSyXo4AnQIqCK8affidAb+8q9Zt8J0BTXCokmHonQDjJKVOUeCdAHDYpgpB2J0AAoyixjHQnQOUPKOCIcidAyXwnD4VwJ0Cu6SY+gW4nQJJWJm19bCdAdsMlnHlqJ0BbMCXLdWgnQD+dJPpxZidAJAokKW5kJ0AIdyNYamInQO3jIodmYCdA0VAitmJeJ0C2vSHlXlwnQJoqIRRbWidAfpcgQ1dYJ0BjBCByU1YnQEdxH6FPVCdALN4e0EtSJ0AQSx7/R1AnQPS3HS5ETidA2SQdXUBMJ0C9kRyMPEonQKL+G7s4SCdAhmsb6jRGJ0Br2BoZMUQnQE9FGkgtQidANLIZdylAJ0AYHxmmJT4nQPyLGNUhPCdA4fgXBB46J0DGZRczGjgnQKrSFmIWNidAjj8WkRI0J0BzrBXADjInQFcZFe8KMCdAPIYUHgcuJ0Ag8xNNAywnQARgE3z/KSdA6cwSq/snJ0DNORLa9yUnQLKmEQn0IydAlhMROPAhJ0B6gBBn7B8nQF/tD5boHSdARFoPxeQbJ0Aoxw704BknQAw0DiPdFydA8aANUtkVJ0DVDQ2B1RMnQLp6DLDRESdAnucL380PJ0CCVAsOyg0nQGfBCj3GCydASy4KbMIJJ0AwmwmbvgcnQBQICcq6BSdA+XQI+bYDJ0Dd4QcoswEnQMFOB1ev/yZAprsGhqv9JkCKKAa1p/smQG+VBeSj+SZAUwIFE6D3JkA4bwRCnPUmQBzcA3GY8yZAAEkDoJTxJkDltQLPkO8mQMoiAv6M7SZAro8BLYnrJkCS/ABchekmQHZpAIuB5yZAW9b/uX3lJkBAQ//oeeMmQCSw/hd24SZACB3+RnLfJkDtif11bt0mQNH2/KRq2yZAtmP802bZJkCa0PsCY9cmQH49+zFf1SZAY6r6YFvTJkBIF/qPV9EmQCyE+b5TzyZAEPH47U/NJkD0XfgcTMsmQNnK90tIySZAvjf3ekTHJkCipPapQMUmQIYR9tg8wyZAa371BznBJkBP6/Q2Nb8mQDRY9GUxvSZAGMXzlC27JkD9MfPDKbkmQOGe8vIltyZAxgvyISK1JkCqePFQHrMmQI7l8H8asSZAc1LwrhavJkBXv+/dEq0mQDws7wwPqyZAIJnuOwupJkAEBu5qB6cmQOly7ZkDpSZAzd/syP+iJkCyTOz3+6AmQJa56yb4niZAeybrVfScJkBfk+qE8JomQEQA6rPsmCZAKG3p4uiWJkAM2ugR5ZQmQPFG6EDhkiZA1bPnb92QJkC6IOee2Y4mQJ6N5s3VjCZAgvrl/NGKJkBnZ+UrzogmQEzU5FrKhiZAMEHkicaEJkAUruO4woImQPka4+e+gCZA3YfiFrt+JkDC9OFFt3wmQKZh4XSzeiZAis7go694JkBvO+DSq3YmQFSo3wGodCZAOBXfMKRyJkAcgt5foHAmQAHv3Y6cbiZA5VvdvZhsJkDKyNzslGomQK413BuRaCZAkqLbSo1mJkB3D9t5iWQmQFt82qiFYiZAQOnZ14FgJkAkVtkGfl4mQAnD2DV6XCZA7S/YZHZaJkDSnNeTclgmQLYJ18JuViZAmnbW8WpUJkB/49UgZ1ImQGNQ1U9jUCZASL3Ufl9OJkAsKtStW0wmQBCX09xXSiZA9QPTC1RIJkDZcNI6UEYmQL7d0WlMRCZAokrRmEhCJkCHt9DHREAmQGsk0PZAPiZAT5HPJT08JkA0/s5UOTomQBhrzoM1OCZA/dfNsjE2JkDhRM3hLTQmQMaxzBAqMiZAqh7MPyYwJkCOi8tuIi4mQHP4yp0eLCZAWGXKzBoqJkA80sn7FigmQCA/ySoTJiZABKzIWQ8kJkDpGMiICyImQM6Fx7cHICZAsvLG5gMeJkCWX8YVABwmQHvMxUT8GSZAXznFc/gXJkBEpsSi9BUmQCgTxNHwEyZADYDDAO0RJkDx7MIv6Q8mQNZZwl7lDSZAusbBjeELJkCeM8G83QkmQIOgwOvZByZAZw3AGtYFJkBMer9J0gMmQDDnvnjOASZAFFS+p8r/JUD5wL3Wxv0lQN0tvQXD+yVAwpq8NL/5JUCmB7xju/clQIt0u5K39SVAb+G6wbPzJUBUTrrwr/ElQDi7uR+s7yVAHCi5TqjtJUABlbh9pOslQOUBuKyg6SVAym6325znJUCu27YKmeUlQJJItjmV4yVAd7W1aJHhJUBbIrWXjd8lQECPtMaJ3SVAJPyz9YXbJUAJabMkgtklQO3VslN+1yVA0kKygnrVJUC2r7GxdtMlQJocseBy0SVAf4mwD2/PJUBk9q8+a80lQEhjr21nyyVALNCunGPJJUAQPa7LX8clQPWprfpbxSVA2hatKVjDJUC+g6xYVMElQKLwq4dQvyVAh12rtky9JUBryqrlSLslQFA3qhRFuSVANKSpQ0G3JUAYEalyPbUlQP19qKE5syVA4uqn0DWxJUDGV6f/Ma8lQKrEpi4urSVAjzGmXSqrJUBznqWMJqklQFgLpbsipyVAPHik6h6lJUAg5aMZG6MlQAVSo0gXoSVA6b6idxOfJUDOK6KmD50lQLKYodULmyVAlwWhBAiZJUB7cqAzBJclQGDfn2IAlSVAREyfkfySJUAouZ7A+JAlQA0mnu/0jiVA8ZKdHvGMJUDW/5xN7YolQLpsnHzpiCVAntmbq+WGJUCDRpva4YQlQGezmgnegiVATCCaONqAJUAwjZln1n4lQBX6mJbSfCVA+WaYxc56JUDe05f0ynglQMJAlyPHdiVApq2WUsN0JUCLGpaBv3IlQHCHlbC7cCVAVPSU37duJUA4YZQOtGwlQBzOkz2waiVAATuTbKxoJUDmp5KbqGYlQMoUksqkZCVAroGR+aBiJUCS7pAonWAlQHdbkFeZXiVAXMiPhpVcJUBANY+1kVolQCSijuSNWCVACQ+OE4pWJUDte41ChlQlQNLojHGCUiVAtlWMoH5QJUCbwovPek4lQH8vi/52TCVAZJyKLXNKJUBICYpcb0glQCx2iYtrRiVAEeOIumdEJUD1T4jpY0IlQNq8hxhgQCVAvimHR1w+JUCjloZ2WDwlQIcDhqVUOiVAa3CF1FA4JUBQ3YQDTTYlQDRKhDJJNCVAGbeDYUUyJUD9I4OQQTAlQOKQgr89LiVAxv2B7jksJUCqaoEdNiolQI/XgEwyKCVAc0SAey4mJUBYsX+qKiQlQDwef9kmIiVAIYt+CCMgJUAF+H03Hx4lQOpkfWYbHCVAztF8lRcaJUCyPnzEExglQJere/MPFiVAexh7IgwUJUBghXpRCBIlQETyeYAEECVAKF95rwAOJUANzHje/AslQPI4eA35CSVA1qV3PPUHJUC6Endr8QUlQJ5/dprtAyVAg+x1yekBJUBoWXX45f8kQEzGdCfi/SRAMDN0Vt77JEAVoHOF2vkkQPkMc7TW9yRA3nly49L1JEDC5nESz/MkQKdTcUHL8SRAi8BwcMfvJEBvLXCfw+0kQFSab86/6yRAOAdv/bvpJEAddG4suOckQAHhbVu05SRA5k1tirDjJEDKumy5rOEkQK4nbOio3yRAk5RrF6XdJEB3AWtGodskQFxuanWd2SRAQNtppJnXJEAlSGnTldUkQAm1aAKS0yRA7SFoMY7RJEDSjmdgis8kQLb7Zo+GzSRAm2hmvoLLJEB/1WXtfskkQGRCZRx7xyRASK9kS3fFJEAsHGR6c8MkQBGJY6lvwSRA9vVi2Gu/JEDaYmIHaL0kQL7PYTZkuyRAozxhZWC5JECHqWCUXLckQGwWYMNYtSRAUINf8lSzJEA08F4hUbEkQBldXlBNryRA/cldf0mtJEDiNl2uRaskQMajXN1BqSRAqhBcDD6nJECPfVs7OqUkQHTqWmo2oyRAWFdamTKhJEA8xFnILp8kQCExWfcqnSRABZ5YJiebJEDqClhVI5kkQM53V4QflyRAs+RWsxuVJECXUVbiF5MkQHu+VREUkSRAYCtVQBCPJEBEmFRvDI0kQCkFVJ4IiyRADXJTzQSJJEDy3lL8AIckQNZLUiv9hCRAurhRWvmCJECfJVGJ9YAkQIOSULjxfiRAaP9P5+18JEBMbE8W6nokQDHZTkXmeCRAFUZOdOJ2JED5sk2j3nQkQN4fTdLaciRAwoxMAddwJECn+Usw024kQItmS1/PbCRAcNNKjstqJEBUQEq9x2gkQDitSezDZiRAHRpJG8BkJEACh0hKvGIkQObzR3m4YCRAymBHqLReJECvzUbXsFwkQJM6RgatWiRAeKdFNalYJEBcFEVkpVYkQECBRJOhVCRAJe5Dwp1SJEAJW0PxmVAkQO7HQiCWTiRA0jRCT5JMJEC2oUF+jkokQJsOQa2KSCRAgHtA3IZGJEBk6D8Lg0QkQEhVPzp/QiRALcI+aXtAJEARLz6Ydz4kQPabPcdzPCRA2gg99m86JEC/dTwlbDgkQKPiO1RoNiRAh087g2Q0JEBsvDqyYDIkQFApOuFcMCRANZY5EFkuJEAZAzk/VSwkQP1vOG5RKiRA4tw3nU0oJEDGSTfMSSYkQKu2NvtFJCRAjyM2KkIiJEB0kDVZPiAkQFj9NIg6HiRAPGo0tzYcJEAh1zPmMhokQAVEMxUvGCRA6rAyRCsWJEDOHTJzJxQkQLOKMaIjEiRAl/cw0R8QJEB8ZDAAHA4kQGDRLy8YDCRARD4vXhQKJEApqy6NEAgkQA0YLrwMBiRA8oQt6wgEJEDW8SwaBQIkQLpeLEkBACRAn8sreP39I0CEOCun+fsjQGilKtb1+SNATBIqBfL3I0Axfyk07vUjQBXsKGPq8yNA+lgokubxI0DexSfB4u8jQMMyJ/De7SNAp58mH9vrI0CLDCZO1+kjQHB5JX3T5yNAVOYkrM/lI0A5UyTby+MjQB3AIwrI4SNAAi0jOcTfI0DmmSJowN0jQMoGIpe82yNAr3MhxrjZI0CT4CD1tNcjQHhNICSx1SNAXLofU63TI0BBJx+CqdEjQCWUHrGlzyNACQEe4KHNI0DubR0PnssjQNLaHD6aySNAt0ccbZbHI0CbtBucksUjQIAhG8uOwyNAZI4a+orBI0BI+xkph78jQC1oGViDvSNAEtUYh3+7I0D2QRi2e7kjQNquF+V3tyNAvxsXFHS1I0CjiBZDcLMjQIj1FXJssSNAbGIVoWivI0BQzxTQZK0jQDU8FP9gqyNAGakTLl2pI0D+FRNdWacjQOKCEoxVpSNAxu8Ru1GjI0CrXBHqTaEjQJDJEBlKnyNAdDYQSEadI0BYow93QpsjQD0QD6Y+mSNAIX0O1TqXI0AG6g0EN5UjQOpWDTMzkyNAz8MMYi+RI0CzMAyRK48jQJedC8AnjSNAfAoL7yOLI0BgdwoeIIkjQEXkCU0chyNAKVEJfBiFI0AOvgirFIMjQPIqCNoQgSNA1pcHCQ1/I0C7BAc4CX0jQJ9xBmcFeyNAhN4FlgF5I0BoSwXF/XYjQE24BPT5dCNAMSUEI/ZyI0AVkgNS8nAjQPr+AoHubiNA3msCsOpsI0DD2AHf5mojQKdFAQ7jaCNAjLIAPd9mI0BwHwBs22QjQFSM/5rXYiNAOfn+ydNgI0AdZv74z14jQALT/SfMXCNA5j/9VshaI0DKrPyFxFgjQK8Z/LTAViNAlIb747xUI0B48/oSuVIjQFxg+kG1UCNAQc35cLFOI0AlOvmfrUwjQAqn+M6pSiNA7hP4/aVII0DSgPcsokYjQLft9lueRCNAm1r2ippCI0CAx/W5lkAjQGQ09eiSPiNASaH0F488I0AtDvRGizojQBJ783WHOCNA9ufypIM2I0DbVPLTfzQjQL/B8QJ8MiNAoy7xMXgwI0CIm/BgdC4jQGwI8I9wLCNAUXXvvmwqI0A14u7taCgjQBlP7hxlJiNA/rvtS2EkI0DiKO16XSIjQMeV7KlZICNAqwLs2FUeI0CQb+sHUhwjQ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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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genusrecordspairscoveragepvaluecorrelationR^2s.e.(larvae)equationonset(KitMg)slope(larvae/KitMg)
0All1122510.100.19230.12410.01543.80y = 1.27+0.27 x1.27330.2725
1Aedes3100.000.57630.61750.38135.90y = -14.11+3.59 x-14.10973.5894
2Anopheles000.001.00000.00000.00000.00no fit achieved0.00000.0000
3Culex5150.000.90200.07710.00591.31y = 8.64+-0.28 x8.6400-0.2752
4Other23510.020.09200.35950.12934.48y = 1.49+0.91 x1.48870.9078
5Unknown811750.070.00300.32530.10581.73y = 3.63+-0.34 x3.6265-0.3398
\n", "
" ], "text/plain": [ " genus records pairs coverage pvalue correlation R^2 \\\n", "0 All 112 251 0.10 0.1923 0.1241 0.0154 \n", "1 Aedes 3 10 0.00 0.5763 0.6175 0.3813 \n", "2 Anopheles 0 0 0.00 1.0000 0.0000 0.0000 \n", "3 Culex 5 15 0.00 0.9020 0.0771 0.0059 \n", "4 Other 23 51 0.02 0.0920 0.3595 0.1293 \n", "5 Unknown 81 175 0.07 0.0030 0.3253 0.1058 \n", "\n", " s.e.(larvae) equation onset(KitMg) slope(larvae/KitMg) \n", "0 3.80 y = 1.27+0.27 x 1.2733 0.2725 \n", "1 5.90 y = -14.11+3.59 x -14.1097 3.5894 \n", "2 0.00 no fit achieved 0.0000 0.0000 \n", "3 1.31 y = 8.64+-0.28 x 8.6400 -0.2752 \n", "4 4.48 y = 1.49+0.91 x 1.4887 0.9078 \n", "5 1.73 y = 3.63+-0.34 x 3.6265 -0.3398 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('performing linear regression')\n", "\n", "perform('linear', associations)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2a2069e8e315459480fb99c14a4e86c4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "898dd269238249e78c2b00b308bc7195", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Quadratic Curve Fit" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: performing quadratic curve fit)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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PH0DKw0KtaV4fQJQYBFYObR9AXm3F/rJ7H0AmwoanV4ofQPAWSFD8mB9AumsJ+aCnH0CEwMqhRbYfQEwVjErqxB9AFmpN847TH0Dgvg6cM+IfQKoT0ETY8B9AcmiR7Xz/H0CeXinLEAcgQAMJih9jDiBAaLPqc7UVIEDMXUvIBx0gQDEIrBxaJCBAlrIMcawrIED7XG3F/jIgQGAHzhlROiBAxLEubqNBIEApXI/C9UggQI4G8BZIUCBA87BQa5pXIEBXW7G/7F4gQLwFEhQ/ZiBAIbByaJFtIECGWtO843QgQOoENBE2fCBAT6+UZYiDIEC0WfW52oogQBkEVg4tkiBAfa62Yn+ZIEDiWBe30aAgQEcDeAskqCBArK3YX3avIEAQWDm0yLYgQHUCmggbviBA2qz6XG3FIEA/V1uxv8wgQKQBvAUS1CBACKwcWmTbIEBtVn2utuIgQNIA3gIJ6iBAN6s+V1vxIECbVZ+rrfggQAAAAAAAACFAZapgVFIHIUDKVMGopA4hQC7/If32FSFAk6mCUUkdIUD4U+OlmyQhQF3+Q/rtKyFAwaikTkAzIUAmUwWjkjohQIv9ZffkQSFA8KfGSzdJIUBUUiegiVAhQLn8h/TbVyFAHqfoSC5fIUCDUUmdgGYhQOj7qfHSbSFATKYKRiV1IUCxUGuad3whQBb7y+7JgyFAe6UsQxyLIUDfT42XbpIhQET67evAmSFAqaROQBOhIUAOT6+UZaghQHL5D+m3ryFA16NwPQq3IUA8TtGRXL4hQKH4MeauxSFABaOSOgHNIUBqTfOOU9QhQM/3U+Ol2yFANKK0N/jiIUCYTBWMSuohQP32deCc8SFAYqHWNO/4IUDHSzeJQQAiQCz2l92TByJAkKD4MeYOIkD1SlmGOBYiQFr1udqKHSJAv58aL90kIkAjSnuDLywiQIj029eBMyJA7Z48LNQ6IkBSSZ2AJkIiQLbz/dR4SSJAG55eKctQIkCASL99HVgiQOXyH9JvXyJASZ2AJsJmIkCuR+F6FG4iQBPyQc9mdSJAeJyiI7l8IkDcRgN4C4QiQEHxY8xdiyJAppvEILCSIkALRiV1ApoiQHDwhclUoSJA1JrmHaeoIkA5RUdy+a8iQJ7vp8ZLtyJAApoIG56+IkBnRGlv8MUiQMzuycNCzSJAMZkqGJXUIkCVQ4ts59siQPrt68A54yJAX5hMFYzqIkDEQq1p3vEiQCjtDb4w+SJAjZduEoMAI0DyQc9m1QcjQFfsL7snDyNAu5aQD3oWI0AgQfFjzB0jQIXrUbgeJSNA6pWyDHEsI0BPQBNhwzMjQLPqc7UVOyNAGJXUCWhCI0B9PzVeukkjQOLplbIMUSNARpT2Bl9YI0CrPldbsV8jQBDpt68DZyNAdZMYBFZuI0DZPXlYqHUjQD7o2az6fCNAo5I6AU2EI0AIPZtVn4sjQGzn+6nxkiNA0ZFc/kOaI0A2PL1SlqEjQJvmHafoqCNA/5B++zqwI0BkO99PjbcjQMnlP6TfviNALpCg+DHGI0CTOgFNhM0jQPfkYaHW1CNAXI/C9SjcI0DBOSNKe+MjQCbkg57N6iNAio7k8h/yI0DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJwrQGuad5yioytA0ETY8PSqK0A17zhFR7IrQJqZmZmZuStA/kP67evAK0Bj7lpCPsgrQMiYu5aQzytALUMc6+LWK0CR7Xw/Nd4rQPaX3ZOH5StAW0I+6NnsK0DA7J48LPQrQCSX/5B++ytAiUFg5dACLEDu68A5IwosQFOWIY51ESxAt0CC4scYLEAc6+I2GiAsQIGVQ4tsJyxA5j+k374uLEBK6gQ0ETYsQK+UZYhjPSxAFD/G3LVELEB56SYxCEwsQN2Th4VaUyxAQj7o2axaLECn6Egu/2EsQAyTqYJRaSxAcT0K16NwLEDV52or9ncsQDqSy39IfyxAnzws1JqGLEAE54wo7Y0sQGiR7Xw/lSxAzTtO0ZGcLEAy5q4l5KMsQJeQD3o2qyxA+zpwzoiyLEBg5dAi27ksQMWPMXctwSxAKjqSy3/ILECO5PIf0s8sQPOOU3Qk1yxAWDm0yHbeLEC94xQdyeUsQCGOdXEb7SxAhjjWxW30LEDr4jYawPssQFCNl24SAy1AtTf4wmQKLUAZ4lgXtxEtQH6MuWsJGS1A4zYawFsgLUBI4XoUrictQKyL22gALy1AETY8vVI2LUB24JwRpT0tQNuK/WX3RC1APzVeuklMLUCk374OnFMtQAmKH2PuWi1AbjSAt0BiLUDS3uALk2ktQDeJQWDlcC1AnDOitDd4LUAB3gIJin8tQGWIY13chi1AyjLEsS6OLUAv3SQGgZUtQJSHhVrTnC1A+THmriWkLUBd3EYDeKstQMKGp1fKsi1AJzEIrBy6LUCM22gAb8EtQPCFyVTByC1AVTAqqRPQLUC62or9ZdctQB+F61G43i1Agy9MpgrmLUDo2az6XO0tQE2EDU+v9C1Asi5uowH8LUAW2c73UwMuQHuDL0ymCi5A4C2QoPgRLkBF2PD0ShkuQKmCUUmdIC5ADi2yne8nLkBz1xLyQS8uQNiBc0aUNi5APSzUmuY9LkCh1jTvOEUuQAaBlUOLTC5Aayv2l91TLkDQ1VbsL1suQDSAt0CCYi5AmSoYldRpLkD+1HjpJnEuQGN/2T15eC5Axyk6kst/LkAs1JrmHYcuQJF++zpwji5A9ihcj8KVLkBa07zjFJ0uQL99HThnpC5AJCh+jLmrLkCJ0t7gC7MuQO18PzVeui5AUiegibDBLkC30QDeAskuQBx8YTJV0C5AgSbChqfXLkDl0CLb+d4uQEp7gy9M5i5AryXkg57tLkAU0ETY8PQuQHh6pSxD/C5A3SQGgZUDL0BCz2bV5wovQKd5xyk6Ei9ACyQofowZL0BwzojS3iAvQNV46SYxKC9AOiNKe4MvL0CezarP1TYvQAN4CyQoPi9AaCJseHpFL0DNzMzMzEwvQA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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4KzVAkDF3LSEvNUDChqdXyjI1QPXb14FzNjVAJzEIrBw6NUBahjjWxT01QIzbaABvQTVAvjCZKhhFNUDwhclUwUg1QCPb+X5qTDVAVjAqqRNQNUCIhVrTvFM1QLraiv1lVzVA7C+7Jw9bNUAfhetRuF41QFLaG3xhYjVAhC9MpgpmNUC2hHzQs2k1QOjZrPpcbTVAGy/dJAZxNUBOhA1Pr3Q1QIDZPXlYeDVAsi5uowF8NUDkg57Nqn81QBfZzvdTgzVASS7/If2GNUB8gy9Mpoo1QK7YX3ZPjjVA4C2QoPiRNUASg8DKoZU1QEXY8PRKmTVAeC0hH/ScNUCqglFJnaA1QNzXgXNGpDVADi2yne+nNUBBguLHmKs1QHTXEvJBrzVApixDHOuyNUDYgXNGlLY1QArXo3A9ujVAPSzUmua9NUBwgQTFj8E1QKLWNO84xTVA1CtlGeLINUAGgZVDi8w1QDnWxW000DVAayv2l93TNUCegCbChtc1QNDVVuwv2zVAAiuHFtneNUA0gLdAguI1QGfV52or5jVAmioYldTpNUDMf0i/fe01QP7UeOkm8TVAMCqpE9D0NUBjf9k9efg1QJbUCWgi/DVAyCk6ksv/NUD6fmq8dAM2QCzUmuYdBzZAXynLEMcKNkCRfvs6cA42QMTTK2UZEjZA9ihcj8IVNkAofoy5axk2QFvTvOMUHTZAjSjtDb4gNkDAfR04ZyQ2QPLSTWIQKDZAJCh+jLkrNkBWfa62Yi82QInS3uALMzZAvCcPC7U2NkDufD81Xjo2QCDSb18HPjZAUiegibBBNkCFfNCzWUU2QLjRAN4CSTZA6iYxCKxMNkAcfGEyVVA2QE7RkVz+UzZAgSbChqdXNkCze/KwUFs2QObQItv5XjZAGCZTBaNiNkBKe4MvTGY2QH3Qs1n1aTZAryXkg55tNkDiehSuR3E2QBTQRNjwdDZARiV1App4NkB4eqUsQ3w2QKvP1VbsfzZA3iQGgZWDNkAQejarPoc2QELPZtXnijZAdCSX/5CONkCneccpOpI2QNrO91PjlTZADCQofoyZNkA+eVioNZ02QHDOiNLeoDZAoyO5/IekNkDVeOkmMag2QAjOGVHaqzZAOiNKe4OvNkBseHqlLLM2QJ/Nqs/VtjZA0SLb+X66NkAEeAskKL42QDbNO07RwTZAaCJseHrFNkCad5yiI8k2QM3MzMzMzDZAACL99nXQNkAydy0hH9Q2QGTMXUvI1zZAliGOdXHbNkDJdr6fGt82QPzL7snD4jZALiEf9GzmNkBgdk8eFuo2QJLLf0i/7TZAxSCwcmjxNkD3deCcEfU2QCrLEMe6+DZAXCBB8WP8NkCOdXEbDQA3QMHKoUW2AzdA8x/Sb18HN0AmdQKaCAs3QFjKMsSxDjdAih9j7loSN0C8dJMYBBY3QO/Jw0KtGTdAIh/0bFYdN0BUdCSX/yA3QIbJVMGoJDdAuB6F61EoN0Drc7UV+ys3QB7J5T+kLzdAUB4Wak0zN0CCc0aU9jY3QLTIdr6fOjdA5x2n6Eg+N0AZc9cS8kE3QEzIBz2bRTdAfh04Z0RJN0CwcmiR7Uw3QOPHmLuWUDdAFR3J5T9UN0BIcvkP6Vc3QHrHKTqSWzdArBxaZDtfN0DecYqO5GI3QBHHuriNZjdARBzr4jZqN0B2cRsN4G03QKjGSzeJcTdA2ht8YTJ1N0ANcayL23g3QEDG3LWEfDdAchsN4C2AN0CkcD0K14M3QNbFbTSAhzdACRueXimLN0A7cM6I0o43QG7F/rJ7kjdAoBov3SSWN0DSb18Hzpk3QAXFjzF3nTdANxrAWyChN0Bqb/CFyaQ3QJzEILByqDdAzhlR2husN0AAb4EExa83QDPEsS5uszdAZhniWBe3N0CYbhKDwLo3QMrDQq1pvjdA/Bhz1xLCN0AvbqMBvMU3QGHD0ytlyTdAlBgEVg7NN0DGbTSAt9A3QPjCZKpg1DdAKhiV1AnYN0BdbcX+sts3QJDC9Shc3zdAwhcmUwXjN0D0bFZ9ruY3QCbChqdX6jdAWRe30QDuN0CMbOf7qfE3QL7BFyZT9TdA8BZIUPz4N0AibHh6pfw3QFXBqKROADhAiBbZzvcDOEC6awn5oAc4QOzAOSNKCzhAHhZqTfMOOEBRa5p3nBI4QIPAyqFFFjhAthX7y+4ZOEDoaiv2lx04QBrAWyBBIThATBWMSuokOEB/arx0kyg4QLK/7J48LDhA5BQdyeUvOEAWak3zjjM4QEi/fR04NzhAexSuR+E6OECuad5xij44QOC+DpwzQjhAEhQ/xtxFOEBEaW/whUk4QHe+nxovTThAqhPQRNhQOEDcaABvgVQ4QA6+MJkqWDhAQBNhw9NbOEBzaJHtfF84QKW9wRcmYzhA2BLyQc9mOEAKaCJseGo4QDy9UpYhbjhAbhKDwMpxOEChZ7Pqc3U4QNS84xQdeThABhIUP8Z8OEA4Z0Rpb4A4QGq8dJMYhDhAnRGlvcGHOEDQZtXnaos4QAK8BRIUjzhANBE2PL2SOEBmZmZmZpY4QJm7lpAPmjhAzBDHuridOED+ZffkYaE4QDC7Jw8LpThAYhBYObSoOECVZYhjXaw4QMe6uI0GsDhA+g/pt6+zOEAsZRniWLc4QF66SQwCuzhAkA96Nqu+OEDDZKpgVMI4QPa52or9xThAKA8LtabJOEBaZDvfT804QIy5awn50DhAvw6cM6LUOEDyY8xdS9g4QCS5/If02zhAVg4tsp3fOECIY13cRuM4QLu4jQbw5jhA7g2+MJnqOEAgY+5aQu44QFK4HoXr8ThAhA1Pr5T1OEC3Yn/ZPfk4QOm3rwPn/DhAHA3gLZAAOUBOYhBYOQQ5QIC3QILiBzlAsgxxrIsLOUDlYaHWNA85QBi30QDeEjlASgwCK4cWOUB8YTJVMBo5QK62Yn/ZHTlA4QuTqYIhOUAUYcPTKyU5QEa28/3UKDlAeAskKH4sOUCqYFRSJzA5QN21hHzQMzlAEAu1pnk3OUBCYOXQIjs5QHS1FfvLPjlApgpGJXVCOUDZX3ZPHkY5QAu1pnnHSTlAPgrXo3BNOUBwXwfOGVE5QKK0N/jCVDlA1AloImxYOUAHX5hMFVw5QDq0yHa+XzlAbAn5oGdjOUCeXinLEGc5QNCzWfW5ajlAAwmKH2NuOUA2XrpJDHI5QGiz6nO1dTlAmggbnl55OUDMXUvIB305QP+ye/KwgDlAMgisHFqEOUBkXdxGA4g5QJayDHGsizlAyAc9m1WPOUD7XG3F/pI5QC2yne+nljlAYAfOGVGaOUCSXP5D+p05QMSxLm6joTlA9gZfmEylOUApXI/C9ag5QFyxv+yerDlAjgbwFkiwOUDAWyBB8bM5QPKwUGuatzlAJQaBlUO7OUBYW7G/7L45QIqw4emVwjlAvAUSFD/GOUDuWkI+6Mk5QCGwcmiRzTlAVAWjkjrROUCGWtO849Q5QLivA+eM2DlA6gQ0ETbcOUAdWmQ73985QE+vlGWI4zlAggTFjzHnOUC0WfW52uo5QOauJeSD7jlAGARWDi3yOUBLWYY41vU5QH6utmJ/+TlAsAPnjCj9OUDiWBe30QA6QBSuR+F6BDpARwN4CyQIOkB6WKg1zQs6QKyt2F92DzpA3gIJih8TOkAQWDm0yBY6QEOtad5xGjpAdgKaCBseOkCoV8oyxCE6QNqs+lxtJTpADAIrhxYpOkA/V1uxvyw6QHGsi9toMDpApAG8BRI0OkDWVuwvuzc6QAisHFpkOzpAOgFNhA0/OkBtVn2utkI6QKCrrdhfRjpA0gDeAglKOkAEVg4tsk06QDarPldbUTpAaQBvgQRVOkCcVZ+rrVg6QM6qz9VWXDpAAAAAAABgOkAyVTAqqWM6QGWqYFRSZzpAmP+QfvtqOkDKVMGopG46QPyp8dJNcjpALv8h/fZ1OkBhVFInoHk6QJOpglFJfTpAxv6ye/KAOkD4U+Olm4Q6QCqpE9BEiDpAXP5D+u2LOkCPU3Qkl486QMKopE5AkzpA9P3UeOmWOkAmUwWjkpo6QFioNc07njpAi/1l9+ShOkC+UpYhjqU6QPCnxks3qTpAIv32deCsOkBUUiegibA6QIenV8oytDpAuvyH9Nu3OkDsUbgehbs6QB6n6EguvzpAUPwYc9fCOkCDUUmdgMY6QLWmeccpyjpA6Pup8dLNOkAaUdobfNE6QEymCkYl1TpAfvs6cM7YOkCxUGuad9w6QOSlm8Qg4DpAFvvL7snjOkBIUPwYc+c6QHqlLEMc6zpArfpcbcXuOkDgT42XbvI6QBKlvcEX9jpARPrt68D5OkB2Tx4Wav06QKmkTkATATtA3Pl+arwEO0AOT6+UZQg7QECk374ODDtAcvkP6bcPO0ClTkATYRM7QNejcD0KFztACvmgZ7MaO0A8TtGRXB47QG6jAbwFIjtAoPgx5q4lO0DTTWIQWCk7QAajkjoBLTtAOPjCZKowO0BqTfOOUzQ7QJyiI7n8NztAz/dT46U7O0ACTYQNTz87QDSitDf4QjtAZvfkYaFGO0CYTBWMSko7QMuhRbbzTTtA/vZ14JxRO0AwTKYKRlU7QGKh1jTvWDtAlPYGX5hcO0DHSzeJQWA7QPmgZ7PqYztALPaX3ZNnO0BeS8gHPWs7QJCg+DHmbjtAwvUoXI9yO0D1SlmGOHY7QCigibDheTtAWvW52op9O0CMSuoENIE7QL6fGi/dhDtA8fRKWYaIO0AkSnuDL4w7QFafq63YjztAiPTb14GTO0C6SQwCK5c7QO2ePCzUmjtAIPRsVn2eO0BSSZ2AJqI7QISezarPpTtAtvP91HipO0DpSC7/Ia07QBueXinLsDtATvOOU3S0O0CASL99Hbg7QLKd76fGuztA5PIf0m+/O0AXSFD8GMM7QEqdgCbCxjtAfPKwUGvKO0CuR+F6FM47QOCcEaW90TtAE/JBz2bVO0BGR3L5D9k7QHicoiO53DtAqvHSTWLgO0DcRgN4C+Q7QA+cM6K05ztAQvFjzF3rO0B0RpT2Bu87QKabxCCw8jtA2PD0Sln2O0ALRiV1Avo7QD2bVZ+r/TtAcPCFyVQBPECiRbbz/QQ8QNSa5h2nCDxABvAWSFAMPEA5RUdy+Q88QGyad5yiEzxAnu+nxksXPEDQRNjw9Bo8QAKaCBueHjxANe84RUciPEBoRGlv8CU8QJqZmZmZKTxAzO7Jw0ItPED+Q/rt6zA8QDGZKhiVNDxAZO5aQj44PECWQ4ts5zs8QMiYu5aQPzxA+u3rwDlDPEAtQxzr4kY8QF+YTBWMSjxAku18PzVOPEDEQq1p3lE8QPaX3ZOHVTxAKO0NvjBZPEBbQj7o2Vw8QI6XbhKDYDxAwOyePCxkPEDyQc9m1Wc8QCSX/5B+azxAV+wvuydvPECKQWDl0HI8QLyWkA96djxA7uvAOSN6PEAgQfFjzH08QFOWIY51gTxAhutRuB6FPEC4QILix4g8QOqVsgxxjDxAHOviNhqQPEBPQBNhw5M8QIGVQ4tslzxAtOpztRWbPEDmP6Tfvp48QBiV1AloojxASuoENBGmPEB9PzVeuqk8QLCUZYhjrTxA4umVsgyxPEAUP8bctbQ8QEaU9gZfuDxAeekmMQi8PECsPldbsb88QN6Th4VawzxAEOm3rwPHPEBCPujZrMo8QHWTGARWzjxAqOhILv/RPEDaPXlYqNU8QAyTqYJR2TxAPujZrPrcPEBxPQrXo+A8QKOSOgFN5DxA1udqK/bnPEAIPZtVn+s8QDqSy39I7zxAbOf7qfHyPECfPCzUmvY8QNKRXP5D+jxABOeMKO39PEA2PL1SlgE9QGiR7Xw/BT1Am+Ydp+gIPUDOO07RkQw9QACRfvs6ED1AMuauJeQTPUBkO99PjRc9QJeQD3o2Gz1AyuU/pN8ePUD8OnDOiCI9QC6QoPgxJj1AYOXQItspPUCTOgFNhC09QMWPMXctMT1A+ORhodY0PUAqOpLLfzg9QFyPwvUoPD1AjuTyH9I/PUDBOSNKe0M9QPSOU3QkRz1AJuSDns1KPUBYObTIdk49QIqO5PIfUj1AveMUHclVPUDwOEVHclk9QCKOdXEbXT1AVOOlm8RgPUCGONbFbWQ9QLmNBvAWaD1A7OI2GsBrPUAeOGdEaW89QFCNl24Scz1AguLHmLt2PUC1N/jCZHo9QOeMKO0Nfj1AGuJYF7eBPUBMN4lBYIU9QH6MuWsJiT1AsOHplbKMPUDjNhrAW5A9QBaMSuoElD1ASOF6FK6XPUB6Nqs+V5s9QKyL22gAnz1A3+ALk6miPUASNjy9UqY9QESLbOf7qT1AduCcEaWtPUCoNc07TrE9QNuK/WX3tD1ADuAtkKC4PUBANV66Sbw9QHKKjuTyvz1ApN++DpzDPUDXNO84Rcc9QAmKH2Puyj1APN9PjZfOPUBuNIC3QNI9QKCJsOHp1T1A0t7gC5PZPUAFNBE2PN09QDiJQWDl4D1Aat5xio7kPUCcM6K0N+g9QM6I0t7g6z1AAd4CCYrvPUA0MzMzM/M9QA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"Mues7Ob1A0DcR8G96d4DQKCxkMD0xwNAgCQb9QexA0B8oGBbI5oDQJYlYfNGgwNAyLMcvXJsA0AWS5O4plUDQIDrxOXiPgNABpWxRCcoA0CoR1nVcxEDQGQDvJfI+gJAPsjZiyXkAkA0lrKxis0CQEJtRgn4tgJAbk2Vkm2gAkC0Np9N64kCQBgpZDpxcwJAmCTkWP9cAkAwKR+plUYCQOY2FSs0MAJAuE3G3toZAkCmbTLEiQMCQK6WWdtA7QFA1Mg7JADXAUASBNmex8ABQG5IMUuXqgFA5pVEKW+UAUB47BI5T34BQChMnHo3aAFA8LTg7SdSAUDWJuCSIDwBQNihmmkhJgFA9iUQcioQAUAus0CsO/oAQIJJLBhV5ABA8ujStXbOAEB+kTSFoLgAQCZDUYbSogBA6P0ouQyNAEDGwbsdT3cAQMCOCbSZYQBA1mQSfOxLAEAIRNZ1RzYAQFQsVaGqIABAvh2P/hULAECDMAgbE+v/P8M3aJwKwP8/OVE+gRKV/z/pfIrJKmr/P826THVTP/8/6wqFhIwU/z87bTP31en+P8nhV80vv/4/i2jyBpqU/j+FAQOkFGr+P7WsiaSfP/4/H2qGCDsV/j+9OfnP5ur9P5Mb4vqiwP0/ow9BiW+W/T/nFRZ7TGz9P2MuYdA5Qv0/F1kiiTcY/T8DllmlRe78PyXlBiVkxPw/f0YqCJOa/D8PusNO0nD8P9c/0/ghR/w/19dYBoId/D8NglR38vP7P3s+xktzyvs/IQ2ugwSh+z/77Qsfpnf7PxHh3x1YTvs/WeYpgBol+z/f/elF7fv6P5cnIG/Q0vo/iWPM+8Op+j+vse7rx4D6Pw0Shz/cV/o/pYSV9gAv+j9zCRoRNgb6P3egFI973fk/s0mFcNG0+T8nBWy1N4z5P9HSyF2uY/k/tbKbaTU7+T/NpOTYzBL5Px2po6t06vg/o7/Y4SzC+D9j6IN79Zn4P1kjpXjOcfg/h3A82bdJ+D/rz0mdsSH4P4dBzcS7+fc/W8XGT9bR9z9nWzY+Aar3P6UDHJA8gvc/Ib53RYha9z/Rikle5DL3P7lpkdpQC/c/11pPus3j9j8rXoP9Wrz2P7lzLaT4lPY/f5tNrqZt9j971eMbZUb2P60h8OwzH/Y/F4ByIRP49T+58Gq5AtH1P5Nz2bQCqvU/oQi+ExOD9T/rrxjWM1z1P2Vp6ftkNfU/HTUwhaYO9T8LE+1x+Of0Py8DIMJawfQ/iQXJdc2a9D8dGuiMUHT0P+dAfQfkTfQ/63mI5Ycn9D8fxQknPAH0P5MiAcwA2/M/N5Ju1NW08z8XFFJAu47zPy2oqw+xaPM/eU57QrdC8z/9BsHYzRzzP7fRfNL09vI/q66uLyzR8j/VnVbwc6vyPzWfdBTMhfI/z7IInDRg8j+d2BKHrTryP6MQk9U2FfI/5VqJh9Dv8T9Xt/WcesrxPwcm2BU1pfE/6aYw8v9/8T8FOv8x21rxP1ffQ9XGNfE/35b+28IQ8T+hYC9Gz+vwP5s81hPsxvA/ySrzRBmi8D8xK4bZVn3wP809j9GkWPA/o2IOLQM08D+xmQPscQ/wP+bF3Rzi1e8/3nygKAGN7z8+WE/7QETvPxZY6pSh++4/Vnxx9SKz7j8GxeQcxWruPyYyRAuIIu4/ssOPwGva7T+uecc8cJLtPxpU63+VSu0/7lL7idsC7T82dvdaQrvsP+693/LJc+w/Diq0UXIs7D+munR3O+XrP6JvIWQlnus/Fkm6FzBX6z/2Rj+SWxDrPz5psNOnyeo/+q8N3BSD6j8iG1erojzqP76qjEFR9uk/xl6uniCw6T82N7zCEGrpPx40tq0hJOk/blWcX1Pe6D8um27YpZjoP2IFLRgZU+g//pPXHq0N6D8KR27sYcjnP4Ie8YA3g+c/bhpg3C0+5z/GOrv+RPnmP4p/Auh8tOY/vug1mNVv5j9edlUPTyvmP3YoYU3p5uU/9v5YUqSi5T/e+TwegF7lP0IZDbF8GuU/Dl3JCprW5D9GxXEr2JLkP+5RBhM3T+Q/BgOHwbYL5D+S2PM2V8jjP4LSTHMYheM/5vCRdvpB4z+6M8NA/f7iP/qa4NEgvOI/pibqKWV54j/C1t9IyjbiP06rwS5Q9OE/TqSP2/ax4T+ywUlPvm/hP4YD8ImmLeE/zmmCi6/r4D+C9ABU2angP6aja+MjaOA/NnfCOY8m4D9k3gquNsrfP0wXaXaQR98/BJmfzCvF3j+cY66wCEPePwR3lSInwd0/XNNUIoc/3T+MeOyvKL7cP5RmXMsLPdw/fJ2kdDC82z88HcWrljvbP+zlvXA+u9o/ZPeOwyc72j+0UTikUrvZP/T0uRK/O9k/FOETD2282D/0FUaZXD3YP8STULGNvtc/bFozVwBA1z/8ae6KtMHWP2zCgUyqQ9Y/tGPtm+HF1T/ETTF5WkjVP8yATeQUy9Q/rPxB3RBO1D9cwQ5kTtHTP/TOs3jNVNM/XCUxG47Y0j+0xIZLkFzSP+CstAnU4NE/5N26VVll0T/UV5kvIOrQP5AaUJcob9A/WEy+GeXozz9Y9Ywg/PPOPxgwDEOW/80/mPw7gbMLzT+gWhzbUxjMP3hKrVB3Jcs/GMzu4R0zyj943+COR0HJP5iEg1f0T8g/SLvWOyRfxz/Ig9o7127GPxjejlcNf8U/IMrzjsaPxD/IRwniAqHDPxhXz1DCssI/UPhF2wTFwT9IK22BytfAP7DfiYYm1r8/UIyaQb79vT9QXAw0XCa8PxBQ310AULo/EGcTv6p6uD9woahXW6a2P5D/nicS07Q/0ID2Ls8Asz+gJa9tki+xP8Dbkce3vq4/QLOHIlcgqz/g0T/sAoSnP+A2uiS76aM/AOP2y39RoD9ArOvDoXaZP8Agbs1cTpI/gEbqaGFUhj8AxwLkdShwPwCGeSfnjmi/AAq1ynxThL+AS0EIhz2Rv4CEo802TZi/ADCBtc1Yn79AJ+3fJTCjv4BuV3bYsaa/YG//nX4xqr+AKeVWGK+tv1BOhNBSlbC/YOQ0PhNSsr8QV4R0zQ20v0CmcnOByLW/ANL/Oi+Ct79A2ivL1jq5vyC/9iN48rq/gIBgRROpvL9wHmkvqF6+v1BMCHGbCcC/+Herrl/jwL/AEZ7QoLzBv9AZ4NZelcK/AJBxwZltw7+gdFKQUUXEv4DHgkOGHMW/yIgC2zfzxb84uNFWZsnGv8BV8LYRn8e/wGFe+zl0yL8A3Bsk30jJv6DEKDEBHcq/SBuFIqDwyr9Y4DD4u8PLv7ATLLJUlsy/SLV2UGpozb8wxRDT/DnOv1hD+jkMC8+/yC8zhZjbz788xV3a0FXQv6ypSeSTvdC/XEVdYBUl0b8cmJhOVYzRvwSi+65T89G//GKGgRBa0r8o2zjGi8DSv4QKE33FJtO//PAUpr2M07+Qjj5BdPLTvzTjj07pV9S/EO8Izhy91L8gsqm/DiLVvzwsciO/htW/cF1i+S3r1b/YRXpBW0/Wv2TluftGs9a/EDwhKPEW17/gSbDGWXrXv9AOZ9eA3de/6IpFWmZA2L8gvktPCqPYv3yoebZsBdm//EnPj41n2b+cokzbbMnZv2Cy8ZgKK9q/NHm+yGaM2r9Q97Jqge3av3wsz35aTtu/0BgTBfKu279EvH79Rw/cv8wWEmhcb9y/kCjNRC/P3L9w8a+TwC7dv3BxulQQjt2/gKjshx7t3b/IlkYt60vevzA8yER2qt6/yJhxzr8I379krELKx2bfvzh3OziOxN+/mPwtjAkR4L8iGVI1qz/gv0ARihcsbuC/7uTVMoyc4L8ulDWHy8rgvwAfqRTq+OC/YoUw2+cm4b9Wx8vaxFThv97kehOBguG/9N09hRyw4b+eshQwl93hv9Zi/xPxCuK/pO79MCo44r8AVhCHQmXiv/CYNhY6kuK/ardw3hC/4r94sb7fxuvivyaHIBpcGOO/WjiWjdBE478axR86JHHjv3AtvR9XneO/WHFuPmnJ47/ckDOWWvXjv+CLDCcrIeS/fmL58NpM5L+uFPrzaXjkv26iDjDYo+S/wAs3pSXP5L+kUHNTUvrkvxpxwzpeJeW/Im0nW0lQ5b+4RJ+0E3vlv+L3Kke9peW/oIbKEkbQ5b/q8H0Xrvrlv8g2RVX1JOa/MFggzBtP5r84VQ98IXnmv8wtEmUGo+a/8uEoh8rM5r+ecVPibfbmv+bckXbwH+e/xiPkQ1JJ578wRkpKk3LnvyZExImzm+e/sB1SArPE57/Q0vOzke3nv35jqZ5PFui/ws9ywuw+6L+UF1AfaWfov/g6QbXEj+i/8DlGhP+36L92FF+MGeDov5DKi80SCOm/OlzMR+sv6b92ySD7olfpv0ISiec5f+m/ojYFDbCm6b+SNpVrBc7pvxISOQM69em/Jsnw000c6r/GW7zdQEPqvwLKmyATauq/yhOPnMSQ6r8iOZZRVbfqvwY6sT/F3eq/hhbgZhQE67+SziLHQirrvzZieWBQUOu/YtHjMj12678iHGI+CZzrv3ZC9IK0weu/WkSaAD/n67/aIVS3qAzsv97aIafxMey/dm8D0BlX7L+e3/gxIXzsv14rAs0Hoey/qlIfoc3F7L+OVVCucursv/ozlfT2Du2//u3tc1oz7b+Sg1osnVftv7r02h2/e+2/ckFvSMCf7b+6aResoMPtv45t00hg5+2/9kyjHv8K7r/+B4ctfS7uv4qefnXaUe6/ohCK9hZ17r9WXqmwMpjuv5aH3KMtu+6/cowj0Afe7r/SbH41wQDvv8oo7dNZI++/UsBvq9FF779qMwa8KGjvvxqCsAVfiu+/VqxuiHSs778mskBEac7vv4aTJjk98O+/OyiQM/gI8L999BZnwRnwv4euJzd6KvC/W1bCoyI78L/36+asukvwv1dvlVJCXPC/ieDNlLls8L9/P5BzIH3wvz+M3O52jfC/w8ayBr2d8L8T7xK78q3wvy8F/QsYvvC/EQlx+SzO8L+5+m6DMd7wvyva9qkl7vC/Z6cIbQn+8L9rYqTM3A3xvz8LysifHfG/06F5YVIt8b8vJrOW9Dzxv1eYdmiGTPG/SfjD1gdc8b8BRpvheGvxv4WB/IjZevG/z6rnzCmK8b/jwVytaZnxv7/GWyqZqPG/ZbnkQ7i38b/Tmff5xsb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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"iKI8DmFGMUD0eSqGS0AxQH5wWUE3OjFAJYbJPyQ0MUDrunqBEi4xQM4ObQYCKDFAzoGgzvIhMUDsExXa5BsxQCnFyijYFTFAhJXBuswPMUD7hPmPwgkxQJGTcqi5AzFARsEsBLL9MEAXDiijq/cwQAZ6ZIWm8TBAEgXiqqLrMEA9r6AToOUwQIZ4oL+e3zBA7GDhrp7ZMEBwaGPhn9MwQBOPJleizTBA0tQqEKbHMECwOXAMq8EwQKu99kuxuzBAxGC+zri1MED7IseUwa8wQFAEEZ7LqTBAwgSc6tajMEBTJGh6450wQAFjdU3xlzBAzcDDYwCSMEC2PVO9EIwwQL7ZI1oihjBA5JQ1OjWAMEAnb4hdSXowQIhoHMRedDBABoHxbXVuMECiuAdbjWgwQF0PX4umYjBANYX3/sBcMEArGtG13FYwQD/O66/5UDBAcKFH7RdLMEDAk+RtN0UwQCylwjFYPzBAt9XhOHo5MEBgJUKDnTMwQCaU4xDCLTBACyLG4ecnMEAOz+n1DiIwQC2bTk03HDBAaob052AWMEDGkNvFixAwQD+6A+e3CjBA1gJtS+UEMEAW1S7mJ/4vQLriBbyH8i9Amy5fGOrmL0C4uDr7TtsvQBGBmGS2zy9ApId4VCDEL0BzzNrKjLgvQH5Pv8f7rC9AxRAmS22hL0BGEA9V4ZUvQAROeuVXii9A/Mln/NB+L0AzhNeZTHMvQKJ8yb3KZy9AT7M9aEtcL0A3KDSZzlAvQFjbrFBURS9AuMynjtw5L0BS/CRTZy4vQChqJJ70Ii9AOhamb4QXL0CHAKrHFgwvQBApMKarAC9A1o84C0P1LkDUNMP23OkuQBAY0Gh53i5AhjlfYRjTLkA4mXDguccuQCg3BOZdvC5AUhMacgSxLkC3LbKEraUuQFiGzB1Zmi5ANB1pPQePLkBO8ofjt4MuQKEFKRBreC5AMFdMwyBtLkD85vH82GEuQAG1Gb2TVi5AQ8HDA1FLLkDAC/DQEEAuQHuUniTTNC5AcVvP/pcpLkCiYIJfXx4uQA6kt0YpEy5AtiVvtPUHLkCY5aioxPwtQLfjZCOW8S1AECCjJGrmLUCmmmOsQNstQHpTproZ0C1AhkprT/XELUDQf7Jq07ktQFTzewy0ri1AFKXHNJejLUAQlZXjfJgtQEfD5RhljS1AuS+41E+CLUBq2gwXPXctQFDD498sbC1AeOo8Lx9hLUDZTxgFFFYtQHbzdWELSy1AUNVVRAVALUBj9betATUtQLJTnJ0AKi1APvACFAIfLUADy+sQBhQtQAfkVpQMCS1ARDtEnhX+LEC+0LMuIfMsQHSkpUUv6CxAZLYZ4z/dLECQBhAHU9IsQPiUiLFoxyxAm2GD4oC8LEB7bACam7EsQJa1/9e4pixA7DyBnNibLECAAoXn+pAsQEoGC7kfhixAVkgTEUd7LECayJ3vcHAsQBqHqlSdZSxA1oM5QMxaLEDMvkqy/U8sQP833qoxRSxAbu/zKWg6LEAY5YsvoS8sQAAZprvcJCxAIItCzhoaLEB+O2FnWw8sQBYqAoeeBCxA6lYlLeT5K0D6wcpZLO8rQERr8gx35CtAy1KcRsTZK0CPeMgGFM8rQI7cdk1mxCtAyH6nGru5K0A9X1puEq8rQO59j0hspCtA2tpGqciZK0ABdoCQJ48rQGRPPP6IhCtABmd68ux5K0DgvDptU28rQPZQfW68ZCtASCNC9idaK0DWM4kElk8rQKCCUpkGRStApA+etHk6K0Dk2mtW7y8rQGHku35nJStAGSyOLeIaK0AMsuJiXxArQDp2uR7fBStApHgSYWH7KkBMue0p5vAqQC04S3lt5ipASvUqT/fbKkCj8Iyrg9EqQDcqcY4SxypABqLX96O8KkASWMDnN7IqQFpMK17OpypA3n4YW2edKkCc74feApMqQJWeeeigiCpAyovteEF+KkA7t+OP5HMqQOggXC2KaSpA0MhWUTJfKkD0rtP73FQqQFTT0iyKSipA8DVU5DlAKkDG1lci7DUqQNi13eagKypAJtPlMVghKkCwLnADEhcqQHTIfFvODCpAdKALOo0CKkCythyfTvgpQCoLsIoS7ilA3J3F/NjjKUDMbl31odkpQPZ9d3RtzylAXssTejvFKUD/VjIGDLspQNwg0xjfsClA9Cj2sbSmKUBIb5vRjJwpQNrzwndnkilAprZspESIKUCst5hXJH4pQPD2RpEGdClAb3R3UetpKUAoMCqY0l8pQB4qX2W8VSlAT2IWuahLKUC82E+Tl0EpQGWNC/SINylASYBJ23wtKUBqsQlJcyMpQMQgTD1sGSlAW84QuGcPKUAuule5ZQUpQDvkIEFm+yhAhkxsT2nxKEAL8znkbucoQMvXif923ShAyPpboYHTKED/W7DJjskoQHT7hnievyhAItnfrbC1KEAN9bppxasoQDVPGKzcoShAluf3dPaXKEA0vlnEEo4oQAzTPZoxhChAICak9lJ6KEByt4zZdnAoQP6G90KdZihAxpTkMsZcKEDK4FOp8VIoQAhrRaYfSShAgjO5KVA/KEA4Oq8zgzUoQCh/J8S4KyhAVgIi2/AhKEC/w554KxgoQGPDnZxoDihAQwEfR6gEKEBefSJ46vonQLY3qC8v8SdASDCwbXbnJ0AWZzoywN0nQCLcRn0M1CdAZo/VTlvKJ0DngOamrMAnQKSweYUAtydAmx6P6latJ0DQyibWr6MnQD+1QEgLmidA6t3cQGmQJ0DSRPu/yYYnQPPpm8UsfSdAUc2+UZJzJ0Dq7mNk+mknQL5Oi/1kYCdA0Ow0HdJWJ0AcyWDDQU0nQKPjDvCzQydAZzw/oyg6J0Bl0/HcnzAnQKGoJp0ZJydAFrzd45UdJ0DIDRexFBQnQLad0gSWCidA32sQ3xkBJ0BDeNA/oPcmQOLCEicp7iZAvkvXlLTkJkDWEh6JQtsmQCkY5wPT0SZAt1syBWbIJkCA3f+M+74mQIedT5uTtSZAyJshMC6sJkBF2HVLy6ImQP1STO1qmSZA8gulFQ2QJkAhA4DEsYYmQIw43flYfSZAMqy8tQJ0JkAWXh74rmomQDNOAsFdYSZAjXxoEA9YJkAh6VDmwk4mQPSTu0J5RSZAAH2oJTI8JkBHpBeP7TImQMoJCX+rKSZAiq189WsgJkCFj3LyLhcmQLuv6nX0DSZALA7lf7wEJkDaqmEQh/slQMSFYCdU8iVA6J7hxCPpJUBI9uTo9d8lQOSLapPK1iVAvF9yxKHNJUDPcfx7e8QlQB7CCLpXuyVAp1CXfjayJUBuHajJF6klQG8oO5v7nyVArHFQ8+GWJUAk+efRyo0lQNm+ATe2hCVAycKdIqR7JUD0BLyUlHIlQFuFXI2HaSVA/kN/DH1gJUDcQCQSdVclQPV7S55vTiVASvX0sGxFJUDcrCBKbDwlQKiizmluMyVAsNb+D3MqJUD0SLE8eiElQHL55e+DGCVALuicKZAPJUAkFdbpngYlQFaAkTCw/SRAxCnP/cP0JEBuEY9R2uskQFM30Svz4iRAc5uVjA7aJEDOPdxzLNEkQGgepeFMyCRAOj3w1W+/JEBJmr1QlbYkQJM1DVK9rSRAGg/f2eekJEDbJjPoFJwkQNh8CX1EkyRAEBFimHaKJECE4zw6q4EkQDb0mWLieCRAIUN5ERxwJEBI0NpGWGckQKqbvgKXXiRASaUkRdhVJEAk7QwOHE0kQDhzd11iRCRAijdkM6s7JEAXOtOP9jIkQN96xHJEKiRA4/k33JQhJEAity3M5xgkQJ6ypUI9ECRAVOyfP5UHJEBGZBzD7/4jQHQaG81M9iNA3g6cXaztI0CCQZ90DuUjQGOyJBJz3CNAf2EsNtrTI0DXTrbgQ8sjQGt6whGwwiNAOuRQyR66I0BEjGEHkLEjQIpy9MsDqSNADJcJF3qgI0DK+aDo8pcjQMKaukBujyNA93lWH+yGI0Bol3SEbH4jQBTzFHDvdSNA+ow34nRtI0AdZdza/GQjQH17A1qHXCNAFtCsXxRUI0DsYtjro0sjQP0zhv41QyNAS0O2l8o6I0DTkGi3YTIjQJccnV37KSNAluZTipchI0DR7ow9NhkjQEk1SHfXECNA+7mFN3sII0DpfEV+IQAjQBJ+h0vK9yJAeb1Ln3XvIkAaO5J5I+ciQPb2WtrT3iJADfGlwYbWIkBiKXMvPM4iQPGfwiP0xSJAu1SUnq69IkDBR+ifa7UiQAR5vicrrSJAgugWNu2kIkA6lvHKsZwiQC+CTuZ4lCJAYKwtiEKMIkDMFI+wDoQiQHO7cl/deyJAVqDYlK5zIkB0w8BQgmsiQM8kK5NYYyJAZcQXXDFbIkA2ooarDFMiQEO+d4HqSiJAjRjr3cpCIkAQseDArToiQNCHWCqTMiJAzJxSGnsqIkAE8M6QZSIiQHaBzY1SGiJAJFFOEUISIkANX1EbNAoiQDSr1qsoAiJAlDXewh/6IUAx/mdgGfIhQAgFdIQV6iFAHkoCLxTiIUBszRJgFdohQPeOpRcZ0iFAvo66VR/KIUC/zFEaKMIhQP5Ia2UzuiFAeAMHN0GyIUAs/CSPUaohQBwzxW1koiFASqjn0nmaIUCxW4y+kZIhQFRNszCsiiFAMn1cKcmCIUBO64eo6HohQKSXNa4KcyFANYJlOi9rIUACqxdNVmMhQAwSTOZ/WyFAULcCBqxTIUDQmjus2kshQIy89tgLRCFAgxw0jD88IUC2uvPFdTQhQCSXNYauLCFAzrH5zOkkIUC0CkCaJx0hQNWhCO5nFSFAMndTyKoNIUDKiiAp8AUhQJ7cbxA4/iBArmxBfoL2IED5OpVyz+4gQIBHa+0e5yBAQpLD7nDfIEBAG552xdcgQHri+oQc0CBA7ufZGXbIIECfKzs10sAgQIytHtcwuSBAtG2E/5GxIEAXbGyu9akgQLao1uNboiBAkiPDn8SaIECo3DHiL5MgQPnTIqudiyBAhgmW+g2EIEBRfYvQgHwgQFUvAy32dCBAlR/9D25tIEAQTnl56GUgQMi6d2llXiBAvGX43+RWIEDqTvvcZk8gQFR2gGDrRyBA+duHanJAIEDcfxH7+zggQPlhHRKIMSBAUYKrrxYqIEDl4LvTpyIgQLV9Tn47GyBAwFhjr9ETIEAHcvpmagwgQIrJE6UFBSBAkL5e00b7H0CEZppph+wfQO6K2gzN3R9A0CsfvRfPH0ApSWh6Z8AfQPnitUS8sR9AQfkHHBajH0D/i14AdZQfQDWbufHYhR9A4iYZ8EF3H0AGL337r2gfQKKz5RMjWh9As7RSOZtLH0A+MsRrGD0fQD4sOquaLh9AtqK09yEgH0CllTNRrhEfQA0Ft7c/Ax9A6vA+K9b0HkA+WcurceYeQAo+XDkS2B5AUJ/x07fJHkAJfYt7YrseQDrXKTASrR5A4q3M8caeHkAEAXTAgJAeQJvQH5w/gh5AqBzQhAN0HkAt5YR6zGUeQCkqPn2aVx5AoOv7jG1JHkCKKb6pRTseQOvjhNMiLR5AxBpQCgUfHkAXzh9O7BAeQN79857YAh5AHKrM/Mn0HUDS0qlnwOYdQAF4i9+72B1ApplxZLzKHUDBN1z2wbwdQFNSS5XMrh1AXuk+QdygHUDf/Db68JIdQNeMM8AKhR1AR5k0kyl3HUAuIjpzTWkdQIwnRGB2Wx1AYalSWqRNHUCsp2Vh1z8dQHAifXUPMh1ArBmZlkwkHUBejbnEjhYdQIZ93v/VCB1AJuoHSCL7HEA/0zWdc+0cQM04aP/J3xxA0hqfbiXSHEBPedrqhcQcQEVUGnTrthxAsKteClapHECSf6etxZscQOvP9F06jhxAvpxGG7SAHEAG5pzlMnMcQMSr97y2ZRxA+u1WoT9YHECqrLqSzUocQM7nIpFgPRxAap+PnPgvHEB80wC1lSIcQAaEdto3FRxACrHwDN8HHECDWm9Mi/obQHKA8pg87RtA2iJ68vLfG0C5QQZZrtIbQA7dlsxuxRtA2vQrTTS4G0AgicXa/qobQNqZY3XOnRtADScGHaOQG0C2MK3RfIMbQNa2WJNbdhtAb7kIYj9pG0B9OL09KFwbQAQ0diYWTxtAAqwzHAlCG0B2oPUeATUbQGIRvC7+JxtAxP6GSwAbG0CeaFZ1Bw4bQPBOKqwTARtAuLEC8CT0GkD4kN9AO+caQK7swJ5W2hpA3sSmCXfNGkCCGZGBnMAaQJ7qfwbHsxpAMjhzmPamGkA+Ams3K5oaQMBIZ+NkjRpAuAtonKOAGkAoS21i53MaQBIHdzUwZxpAcD+FFX5aGkBG9JcC0U0aQJIlr/woQRpAWNPKA4Y0GkCU/eoX6CcaQEakDzlPGxp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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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Nk9eVgoFEBJLv8h/TYUQBKDwMqhRRRA3NeBc0ZUFECmLEMc62IUQG+BBMWPcRRAONbFbTSAFEACK4cW2Y4UQMx/SL99nRRAltQJaCKsFEBfKcsQx7oUQCh+jLlryRRA8tJNYhDYFEC8Jw8LteYUQIV80LNZ9RRATtGRXP4DFUAYJlMFoxIVQOJ6FK5HIRVAq8/VVuwvFUB0JJf/kD4VQD55WKg1TRVACM4ZUdpbFUDSItv5fmoVQJp3nKIjeRVAZMxdS8iHFUAuIR/0bJYVQPh14JwRpRVAwMqhRbazFUCKH2PuWsIVQFR0JJf/0BVAHsnlP6TfFUDmHafoSO4VQLByaJHt/BVAescpOpILFkBEHOviNhoWQAxxrIvbKBZA1sVtNIA3FkCgGi/dJEYWQGpv8IXJVBZAMsSxLm5jFkD8GHPXEnIWQMZtNIC3gBZAkML1KFyPFkBaF7fRAJ4WQCJseHqlrBZA7MA5I0q7FkC2FfvL7skWQIBqvHST2BZASL99HTjnFkASFD/G3PUWQNxoAG+BBBdApr3BFyYTF0BuEoPAyiEXQDhnRGlvMBdAArwFEhQ/F0DMEMe6uE0XQJRliGNdXBdAXrpJDAJrF0AoDwu1pnkXQPJjzF1LiBdAuriNBvCWF0CEDU+vlKUXQE5iEFg5tBdAGLfRAN7CF0DiC5OpgtEXQKpgVFIn4BdAdLUV+8vuF0A+CtejcP0XQAhfmEwVDBhA0LNZ9bkaGECaCBueXikYQGRd3EYDOBhALrKd76dGGED2Bl+YTFUYQMBbIEHxYxhAirDh6ZVyGEBUBaOSOoEYQBxaZDvfjxhA5q4l5IOeGECwA+eMKK0YQHpYqDXNuxhAQq1p3nHKGEAMAiuHFtkYQNZW7C+75xhAoKut2F/2GEBqAG+BBAUZQDJVMCqpExlA/Knx0k0iGUDG/rJ78jAZQJBTdCSXPxlAWKg1zTtOGUAi/fZ14FwZQOxRuB6FaxlAtqZ5xyl6GUB++zpwzogZQEhQ/BhzlxlAEqW9wRemGUDc+X5qvLQZQKROQBNhwxlAbqMBvAXSGUA4+MJkquAZQAJNhA1P7xlAyqFFtvP9GUCU9gZfmAwaQF5LyAc9GxpAKKCJsOEpGkDy9EpZhjgaQLpJDAIrRxpAhJ7Nqs9VGkBO845TdGQaQBhIUPwYcxpA4JwRpb2BGkCq8dJNYpAaQHRGlPYGnxpAPptVn6utGkAG8BZIULwaQNBE2PD0yhpAmpmZmZnZGkBk7lpCPugaQCxDHOvi9hpA9pfdk4cFG0DA7J48LBQbQIpBYOXQIhtAUpYhjnUxG0Ac6+I2GkAbQOY/pN++ThtAsJRliGNdG0B66SYxCGwbQEI+6NmsehtADJOpglGJG0DW52or9pcbQKA8LNSaphtAaJHtfD+1G0Ay5q4l5MMbQPw6cM6I0htAxo8xdy3hG0CO5PIf0u8bQFg5tMh2/htAIo51cRsNHEDs4jYawBscQLQ3+MJkKhxAfoy5awk5HEBI4XoUrkccQBI2PL1SVhxA2or9ZfdkHECk374OnHMcQG40gLdAghxAOIlBYOWQHEAC3gIJip8cQMoyxLEurhxAlIeFWtO8HEBe3EYDeMscQCgxCKwc2hxA8IXJVMHoHEC62or9ZfccQIQvTKYKBh1AToQNT68UHUAW2c73UyMdQOAtkKD4MR1AqoJRSZ1AHUB01xLyQU8dQDws1JrmXR1ABoGVQ4tsHUDQ1VbsL3sdQJoqGJXUiR1AYn/ZPXmYHUAs1JrmHacdQPYoXI/CtR1AwH0dOGfEHUCK0t7gC9MdQFInoImw4R1AHHxhMlXwHUDm0CLb+f4dQLAl5IOeDR5AeHqlLEMcHkBCz2bV5yoeQAwkKH6MOR5A1njpJjFIHkCezarP1VYeQGgibHh6ZR5AMnctIR90HkD8y+7Jw4IeQMQgsHJokR5AjnVxGw2gHkBYyjLEsa4eQCIf9GxWvR5A6nO1FfvLHkC0yHa+n9oeQH4dOGdE6R5ASHL5D+n3HkAQx7q4jQYfQNobfGEyFR9ApHA9CtcjH0Buxf6yezIfQDgawFsgQR9AAG+BBMVPH0DKw0KtaV4fQJQYBFYObR9AXm3F/rJ7H0AmwoanV4ofQPAWSFD8mB9AumsJ+aCnH0CEwMqhRbYfQEwVjErqxB9AFmpN847TH0Dgvg6cM+IfQKoT0ETY8B9AcmiR7Xz/H0CeXinLEAcgQAMJih9jDiBAaLPqc7UVIEDMXUvIBx0gQDEIrBxaJCBAlrIMcawrIED7XG3F/jIgQGAHzhlROiBAxLEubqNBIEApXI/C9UggQI4G8BZIUCBA87BQa5pXIEBXW7G/7F4gQLwFEhQ/ZiBAIbByaJFtIECGWtO843QgQOoENBE2fCBAT6+UZYiDIEC0WfW52oogQBkEVg4tkiBAfa62Yn+ZIEDiWBe30aAgQEcDeAskqCBArK3YX3avIEAQWDm0yLYgQHUCmggbviBA2qz6XG3FIEA/V1uxv8wgQKQBvAUS1CBACKwcWmTbIEBtVn2utuIgQNIA3gIJ6iBAN6s+V1vxIECbVZ+rrfggQAAAAAAAACFAZapgVFIHIUDKVMGopA4hQC7/If32FSFAk6mCUUkdIUD4U+OlmyQhQF3+Q/rtKyFAwaikTkAzIUAmUwWjkjohQIv9ZffkQSFA8KfGSzdJIUBUUiegiVAhQLn8h/TbVyFAHqfoSC5fIUCDUUmdgGYhQOj7qfHSbSFATKYKRiV1IUCxUGuad3whQBb7y+7JgyFAe6UsQxyLIUDfT42XbpIhQET67evAmSFAqaROQBOhIUAOT6+UZaghQHL5D+m3ryFA16NwPQq3IUA8TtGRXL4hQKH4MeauxSFABaOSOgHNIUBqTfOOU9QhQM/3U+Ol2yFANKK0N/jiIUCYTBWMSuohQP32deCc8SFAYqHWNO/4IUDHSzeJQQAiQCz2l92TByJAkKD4MeYOIkD1SlmGOBYiQFr1udqKHSJAv58aL90kIkAjSnuDLywiQIj029eBMyJA7Z48LNQ6IkBSSZ2AJkIiQLbz/dR4SSJAG55eKctQIkCASL99HVgiQOXyH9JvXyJASZ2AJsJmIkCuR+F6FG4iQBPyQc9mdSJAeJyiI7l8IkDcRgN4C4QiQEHxY8xdiyJAppvEILCSIkALRiV1ApoiQHDwhclUoSJA1JrmHaeoIkA5RUdy+a8iQJ7vp8ZLtyJAApoIG56+IkBnRGlv8MUiQMzuycNCzSJAMZkqGJXUIkCVQ4ts59siQPrt68A54yJAX5hMFYzqIkDEQq1p3vEiQCjtDb4w+SJAjZduEoMAI0DyQc9m1QcjQFfsL7snDyNAu5aQD3oWI0AgQfFjzB0jQIXrUbgeJSNA6pWyDHEsI0BPQBNhwzMjQLPqc7UVOyNAGJXUCWhCI0B9PzVeukkjQOLplbIMUSNARpT2Bl9YI0CrPldbsV8jQBDpt68DZyNAdZMYBFZuI0DZPXlYqHUjQD7o2az6fCNAo5I6AU2EI0AIPZtVn4sjQGzn+6nxkiNA0ZFc/kOaI0A2PL1SlqEjQJvmHafoqCNA/5B++zqwI0BkO99PjbcjQMnlP6TfviNALpCg+DHGI0CTOgFNhM0jQPfkYaHW1CNAXI/C9SjcI0DBOSNKe+MjQCbkg57N6iNAio7k8h/yI0DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJwrQGuad5yioytA0ETY8PSqK0A17zhFR7IrQJqZmZmZuStA/kP67evAK0Bj7lpCPsgrQMiYu5aQzytALUMc6+LWK0CR7Xw/Nd4rQPaX3ZOH5StAW0I+6NnsK0DA7J48LPQrQCSX/5B++ytAiUFg5dACLEDu68A5IwosQFOWIY51ESxAt0CC4scYLEAc6+I2GiAsQIGVQ4tsJyxA5j+k374uLEBK6gQ0ETYsQK+UZYhjPSxAFD/G3LVELEB56SYxCEwsQN2Th4VaUyxAQj7o2axaLECn6Egu/2EsQAyTqYJRaSxAcT0K16NwLEDV52or9ncsQDqSy39IfyxAnzws1JqGLEAE54wo7Y0sQGiR7Xw/lSxAzTtO0ZGcLEAy5q4l5KMsQJeQD3o2qyxA+zpwzoiyLEBg5dAi27ksQMWPMXctwSxAKjqSy3/ILECO5PIf0s8sQPOOU3Qk1yxAWDm0yHbeLEC94xQdyeUsQCGOdXEb7SxAhjjWxW30LEDr4jYawPssQFCNl24SAy1AtTf4wmQKLUAZ4lgXtxEtQH6MuWsJGS1A4zYawFsgLUBI4XoUrictQKyL22gALy1AETY8vVI2LUB24JwRpT0tQNuK/WX3RC1APzVeuklMLUCk374OnFMtQAmKH2PuWi1AbjSAt0BiLUDS3uALk2ktQDeJQWDlcC1AnDOitDd4LUAB3gIJin8tQGWIY13chi1AyjLEsS6OLUAv3SQGgZUtQJSHhVrTnC1A+THmriWkLUBd3EYDeKstQMKGp1fKsi1AJzEIrBy6LUCM22gAb8EtQPCFyVTByC1AVTAqqRPQLUC62or9ZdctQB+F61G43i1Agy9MpgrmLUDo2az6XO0tQE2EDU+v9C1Asi5uowH8LUAW2c73UwMuQHuDL0ymCi5A4C2QoPgRLkBF2PD0ShkuQKmCUUmdIC5ADi2yne8nLkBz1xLyQS8uQNiBc0aUNi5APSzUmuY9LkCh1jTvOEUuQAaBlUOLTC5Aayv2l91TLkDQ1VbsL1suQDSAt0CCYi5AmSoYldRpLkD+1HjpJnEuQGN/2T15eC5Axyk6kst/LkAs1JrmHYcuQJF++zpwji5A9ihcj8KVLkBa07zjFJ0uQL99HThnpC5AJCh+jLmrLkCJ0t7gC7MuQO18PzVeui5AUiegibDBLkC30QDeAskuQBx8YTJV0C5AgSbChqfXLkDl0CLb+d4uQEp7gy9M5i5AryXkg57tLkAU0ETY8PQuQHh6pSxD/C5A3SQGgZUDL0BCz2bV5wovQKd5xyk6Ei9ACyQofowZL0BwzojS3iAvQNV46SYxKC9AOiNKe4MvL0CezarP1TYvQAN4CyQoPi9AaCJseHpFL0DNzMzMzEwvQA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"4mwcFVMQCUBki5eHHAcJQNOiGxTw/QhAM7Oous30CEB/vD57tesIQLq+3VWn4ghA5LmFSqPZCED8rTZZqdAIQAKb8IG5xwhA9oCzxNO+CEDZX38h+LUIQKs3VJgmrQhAaggyKV+kCEAZ0hjUoZsIQLWUCJnukghAQFABeEWKCEC5BANxpoEIQCCyDYQReQhAd1ghsYZwCEC79z34BWgIQO6PY1mPXwhADyGS1CJXCEAfq8lpwE4IQBwuChloRghACapT4hk+CEDjHqbF1TUIQKyMAcObLQhAY/Nl2mslCEAJU9MLRh0IQJ2rSVcqFQhAH/3IvBgNCECRR1E8EQUIQO+K4tUT/QdAPcd8iSD1B0B5/B9XN+0HQKMqzD5Y5QdAvFGBQIPdB0DCcT9cuNUHQLmKBpL3zQdAnJzW4UDGB0Bup69LlL4HQC+rkc/xtgdA3qd8bVmvB0B7nXAly6cHQAeMbfdGoAdAgXNz48yYB0DoU4LpXJEHQD8tmgn3iQdAhf+6Q5uCB0C4yuSXSXsHQNqOFwYCdAdA6ktTjsRsB0DoAZgwkWUHQNWw5exnXgdAsVg8w0hXB0B7+ZuzM1AHQDOTBL4oSQdA2iV24idCB0BusfAgMTsHQPI1dHlENAdAYrMA7GEtB0DDKZZ4iSYHQBGZNB+7HwdATgHc3/YYB0B5Yoy6PBIHQJO8Ra+MCwdAmg8IvuYEB0CQW9PmSv4GQHWgpym59wZAR96EhjHxBkAJFWv9s+oGQLlEWo5A5AZAV21SOdfdBkDkjlP+d9cGQF2pXd0i0QZAx7xw1tfKBkAeyYzplsQGQGTOsRZgvgZAmMzfXTO4BkC6wxa/ELIGQMyzVjr4qwZAy5yfz+mlBkC6fvF+5Z8GQJVZTEjrmQZAYC2wK/uTBkAZ+hwpFY4GQMC/kkA5iAZAVn4RcmeCBkDZNZm9n3wGQEzmKSPidgZArI/Doi5xBkD7MWY8hWsGQDnNEfDlZQZAZWHGvVBgBkB/7oOlxVoGQIh0SqdEVQZAfvMZw81PBkBja/L4YEoGQDfc00j+RAZA+UW+sqU/BkCpqLE2VzoGQEgErtQSNQZA1VizjNgvBkBSpsFeqCoGQLrs2EqCJQZAEyz5UGYgBkBaZCJxVBsGQI+VVKtMFgZAsr+P/04RBkDD4tNtWwwGQMT+IPZxBwZAshN3mJICBkCQIdZUvf0FQFsoPivy+AVAFCivGzH0BUC8ICkmeu8FQFMSrErN6gVA2Pw3iSrmBUBK4MzhkeEFQKu8alQD3QVA+5ER4X7YBUA6YMGHBNQFQGYnekiUzwVAgOc7Iy7LBUCLoAYY0sYFQIJS2iaAwgVAaP22Tzi+BUA8oZyS+rkFQP89i+/GtQVAsdOCZp2xBUBPYoP3fa0FQN7pjKJoqQVAWmqfZ12lBUDF47pGXKEFQB5W3z9lnQVAZcEMU3iZBUCbJUOAlZUFQL+Cgse8kQVA0tjKKO6NBUDSJxykKYoFQMJvdjlvhgVAn7DZ6L6CBUBr6kWyGH8FQCYdu5V8ewVAzkg5k+p3BUBmbcCqYnQFQOqKUNzkcAVAYKHpJ3FtBUDBsIuNB2oFQBK5Ng2oZgVAULrqplJjBUB+tKdaB2AFQJqnbSjGXAVAo5M8EI9ZBUCceBQSYlYFQIJW9S0/UwVAWC3fYyZQBUAc/dGzF00FQM7FzR0TSgVAbofSoRhHBUD8QeA/KEQFQHn19vdBQQVA5aEWymU+BUA+Rz+2kzsFQIflcLzLOAVAvHyr3A02BUDiDO8WWjMFQPaVO2uwMAVA9xeR2RAuBUDnku9heysFQMUGVwTwKAVAknPHwG4mBUBN2UCX9yMFQPc3w4eKIQVAjo9OkicfBUAV4OK2zhwFQIopgPV/GgVA7GsmTjsYBUA+p9XAABYFQH3bjU3QEwVArAhP9KkRBUDILhm1jQ8FQNNN7I97DQVAzGXIhHMLBUCzdq2TdQkFQIqAm7yBBwVAToOS/5cFBUAAf5JcuAMFQKFzm9PiAQVAMWGtZBcABUCvR8gPVv4EQBsn7NSe/ARAdf8YtPH6BEC+0E6tTvkEQPaajcC19wRAG17V7Sb2BEAvGiY1ovQEQDLPf5Yn8wRAIn3iEbfxBEACJE6nUPAEQM/Dwlb07gRAi1xAIKLtBEA17sYDWuwEQM54VgEc6wRAVfzuGOjpBEDKeJBKvugEQC7uOpae5wRAgFzu+4jmBEDAw6p7feUEQO8jcBV85ARADH0+yYTjBEAYzxWXl+IEQBIa9n604QRA+l3fgNvgBEDRmtGcDOAEQJbQzNJH3wRASf/QIo3eBEDrJt6M3N0EQHxH9BA23QRA+mATr5ncBEBncztnB9wEQMJ+bDl/2wRADIOmJQHbBEBEgOkrjdoEQGp2NUwj2gRAf2WKhsPZBECCTejabdkEQHQuT0ki2QRAVAi/0eDYBEAi2zd0qdgEQN6muTB82ARAiWtEB1nYBEAiKdj3P9gEQKrfdAIx2ARAII8aJyzYBECGN8llMdgEQNjYgL5A2ARAGHNBMVrYBEBJBgu+fdgEQGaS3WSr2ARAdBe5JePYBEBulZ0AJdkEQFYMi/Vw2QRAL3yBBMfZBED05IAtJ9oEQKhGiXCR2gRAS6GazQXbBEDc9LREhNsEQFxB2NUM3ARAyIYEgZ/cBEAkxTlGPN0EQHD8dyXj3QRAqCy/HpTeBEDQVQ8yT98EQOR3aF8U4ARA6JLKpuPgBEDcpjUIveEEQLuzqYOg4gRAi7kmGY7jBEBIuKzIheQEQPSvO5KH5QRAjqDTdZPmBEAWinRzqecEQIxsHovJ6ARA80fRvPPpBEBGHI0IKOsEQIjpUW5m7ARAuK8f7q7tBEDXbvaHAe8EQOQm1jte8ARA4Ne+CcXxBEDKgbDxNfMEQKIkq/Ow9ARAaMCuDzb2BEAeVbtFxfcEQMDi0JVe+QRAUmnv/wH7BEDS6BaEr/wEQEBhRyJn/gRAntKA2igABUDpPMOs9AEFQCKgDpnKAwVASvxin6oFBUBgUcC/lAcFQGafJvqICQVAWOaVTocLBUA6Jg69jw0FQApfj0WiDwVAyJAZ6L4RBUB0u6yk5RMFQA/fSHsWFgVAmPvta1EYBUAQEZx2lhoFQHYfU5vlHAVAyiYT2j4fBUAOJ9wyoiEFQD4grqUPJAVAXRKJMocmBUBs/WzZCCkFQGfhWZqUKwVAUr5PdSouBUArlE5qyjAFQPJiVnl0MwVAqCpnoig2BUBM64Dl5jgFQOCko0KvOwVAYFfPuYE+BUDOAgRLXkEFQCynQfZERAVAeESIuzVHBUCy2teaMEoFQNxpMJQ1TQVA8vGRp0RQBUD4cvzUXVMFQOvsbxyBVgVAzV/sfa5ZBUCey3H55VwFQFwwAI8nYAVACo6XPnNjBUCm5DcIyWYFQDA04esoagVAqHyT6ZJtBUAPvk4BB3EFQGT4EjOFdAVAqCvgfg14BUDZV7bkn3sFQPp8lWQ8fwVACJt9/uKCBUAFsm6yk4YFQPHBaIBOigVAyspraBOOBUCSzHdq4pEFQErHjIa7lQVA7rqqvJ6ZBUCCp9EMjJ0FQAKNAXeDoQVAcms6+4SlBUDRQnyZkKkFQB4Tx1GmrQVAWdwaJMaxBUCCnncQ8LUFQJpZ3RYkugVAoA1MN2K+BUCUusNxqsIFQHZgRMb8xgVASv/NNFnLBUAIl2C9v88FQLYn/F8w1AVAU7GgHKvYBUDdM07zL90FQFevBOS+4QVAviPE7lfmBUAVkYwT++oFQFj3XVKo7wVAilY4q1/0BUCsrhseIfkFQLv/B6vs/QVAuEn9UcICBkCljPsSogcGQIDIAu6LDAZASf0S438RBkAAKyzyfRYGQKVRThuGGwZAOnF5XpggBkC8ia27tCUGQC2b6jLbKgZAi6UwxAswBkDZqH9vRjUGQBWl1zSLOgZAP5o4FNo/BkBYiKINM0UGQF9vFSGWSgZAVE+RTgNQBkA4KBaWelUGQAr6o/f7WgZAysQ6c4dgBkB5iNoIHWYGQBZFg7i8awZAovo0gmZxBkAcqe9lGncGQIRQs2PYfAZA2/B/e6CCBkAfilWtcogGQFMcNPlOjgZAdacbXzWUBkCFKwzfJZoGQISoBXkgoAZAcB4ILSWmBkBMjRP7M6wGQBb1J+NMsgZAzVVF5W+4BkB0r2sBnb4GQAgCmzfUxAZAi03ThxXLBkD9kRTyYNEGQFzPXna21wZAqwWyFBbeBkDnNA7Nf+QGQBFdc5/z6gZALH7hi3HxBkAzmFiS+fcGQCqr2LKL/gZADbdh7ScFB0Dgu/NBzgsHQKK5jrB+EgdAUbAyOTkZB0Dvn9/b/R8HQHuIlZjMJgdA9mlUb6UtB0BgRBxgiDQHQLcX7Wp1OwdA/OPGj2xCB0AwqanObUkHQFNnlSd5UAdAZR6Kmo5XB0Bjzocnrl4HQFB3js7XZQdALhmejwttB0D3s7ZqSXQHQK9H2F+RewdAV9QCb+OCB0DsWTaYP4oHQHHYctulkQdA4k+4OBaZB0BDwAawkKAHQJIpXkEVqAdA0Iu+7KOvB0D85ieyPLcHQBU7mpHfvgdAHogVi4zGB0AVzpmeQ84HQPoMJ8wE1gdAzUS9E9DdB0CPdVx1peUHQECfBPGE7QdA38G1hm71B0Br3W82Yv0HQObxMgBgBQhAUf/+42cNCECoBdTheRUIQO4EsvmVHQhAJP2YK7wlCEBH7oh37C0IQFrYgd0mNghAWbuDXWs+CEBHl473uUYIQCRsoqsSTwhA8Dm/eXVXCECoAOVh4l8IQFHAE2RZaAhA53hLgNpwCEBsKoy2ZXkIQN/U1Qb7gQhAP3gocZqKCECQFIT1Q5MIQM6p6JP3mwhA+zdWTLWkCEAVv8wefa0IQB4/TAtPtghAFrjUESu/CED8KWYyEcgIQM+UAG0B0QhAk/ijwfvZCEBDVVAwAOMIQOSqBbkO7AhAcPnDWyf1CEDsQIsYSv4IQFiBW+92BwlAsbo04K0QCUD27Bbr7hkJQC4YAhA6IwlAUjz2To8sCUBlWfOn7jUJQGVv+RpYPwlAUn4IqMtICUAyhiBPSVIJQP6GQRDRWwlAuIBr62JlCUBgc57g/m4JQPZe2u+keAlAfUMfGVWCCUDwIG1cD4wJQFP3w7nTlQlAo8YjMaKfCUDhjozCeqkJQA9Q/m1dswlAKgp5M0q9CUA0vfwSQccJQC1piQxC0QlAEw4fIE3bCUDoq71NYuUJQKxCZZWB7wlAXdIV96r5CUD/Ws9y3gMKQI7ckQgcDgpACVdduGMYCkB1yjGCtSIKQM42D2YRLQpAF5z1Y3c3CkBM+uR750EKQHJR3a1hTApAhqHe+eVWCkCH6uhfdGEKQHcs/N8MbApAVWcYeq92CkAhmz0uXIEKQN7Ha/wSjApAhu2i5NOWCkAeDOPmnqEKQKUjLAN0rApAGDR+OVO3CkB7PdmJPMIKQMw/PfQvzQpADDuqeC3YCkA7LyAXNeMKQFYcn89G7gpAYAInomL5CkBZ4beOiAQLQEG5UZW4DwtAGYr0tfIaC0DbU6DwNiYLQI4WVUWFMQtAL9IStN08C0C/htk8QEgLQDw0qd+sUwtAp9qBnCNfC0AEemNzpGoLQEwSTmQvdgtAgqNBb8SBC0CoLT6UY40LQLywQ9MMmQtAvyxSLMCkC0CvoWmffbALQI0PiixFvAtAXXaz0xbIC0AY1uWU8tMLQMIuIXDY3wtAWoBlZcjrC0DiyrJ0wvcLQFYOCZ7GAwxAvEpo4dQPDEAMgNA+7RsMQE2uQbYPKAxAe9W7Rzw0DECY9T7zckAMQKIOy7izTAxAnyBgmP5YDECGK/6RU2UMQFwvpaWycQxAICxV0xt+DEDUIQ4bj4oMQHgQ0HwMlwxACPia+JOjDECE2G6OJbAMQPKxSz7BvAxATIQxCGfJDECYTyDsFtYMQNATGOrQ4gxA9NAYApXvDEAKhyI0Y/wMQAw2NYA7CQ1A/N1Q5h0WDUDefnVmCiMNQKwYowABMA1AaKvZtAE9DUASNxmDDEoNQKq7YWshVw1ANDmzbUBkDUCqrw2KaXENQA4fccCcfg1AYIfdENqLDUCi6FJ7IZkNQNJC0f9ypg1A8JVYns6zDUD64ehWNMENQPYmgimkzg1A4GQkFh7cDUC4m88coukNQHzLgz0w9w1AMvRAeMgEDkDUFQfNahIOQGQw1jsXIA5A4EOuxM0tDkBSUI9njjsOQKxVeSRZSQ5A+FNs+y1XDkAuS2jsDGUOQFY7bff1cg5AbCR7HOmADkByBpJb5o4OQGDhsbTtnA5AQrXaJ/+qDkAQggy1GrkOQNBHR1xAxw5AegaLHXDVDkAUvtf4qeMOQJxuLe7t8Q5AFBiM/TsAD0B6uvMmlA4PQM5VZGr2HA9AEOrdx2IrD0A+d2A/2TkPQF7969BZSA9AbHyAfORWD0Bm9B1CeWUPQFBlxCEYdA9AKM9zG8GCD0DsMSwvdJEPQKKN7VwxoA9ARuK3pPiuD0DYL4sGyr0PQFh2Z4KlzA9AxrVMGIvbD0Ai7jrIeuoPQG4fMpJ0+Q9A0yQZOzwEEEBoth06wwsQQHPEJkZPExBA9U40X+AaEEDwVUaFdiIQQF/ZXLgRKhBAR9l3+LExEECmVZdFVzkQQHxOu58BQRBAysPjBrFIEECPtRB7ZVAQQMojQvweWBBAfQ54it1fEECmdbIloWcQQElZ8c1pbxBAYbk0gzd3EEDxlXxFCn8QQPfuyBTihhBAdcQZ8b6OEEBrFm/aoJYQQNjkyNCHnhBAui8n1HOmEEAV94nkZK4QQOc68QFbthBAMPtcLFa+EEDyN81jVsYQQCnxQahbzhBA2Ca7+WXWEED92DhYdd4QQJsHu8OJ5hBAr7JBPKPuEEA62szBwfYQQD1+XFTl/hBAt57w8w0HEUCnO4mgOw8RQBFVJlpuFxFA7+rHIKYfEUBG/W304icRQBOMGNUkMBFAWJfHwms4EUAWH3u9t0ARQEgjM8UISRFA8qPv2V5REUAUobD7uVkRQK0adioaYhFAvhBAZn9qEUBEgw6v6XIRQENy4QRZexFAud24Z82DEUCmxZTXRowRQAsqdVTFlBFA5Apa3kidEUA3aEN10aURQAFCMRlfrhFAQpgjyvG2EUD5ahqIib8RQCm6FVMmyBFAz4UVK8jQEUDszRkQb9kRQICSIgIb4hFAjNMvAczqEUAQkUENgvMRQArLVyY9/BFAe4FyTP0EEkBktJF/wg0SQMRjtb+MFhJAnI/dDFwfEkDpNwpnMCgSQK5cO84JMRJA6/1wQug5EkCeG6vDy0ISQMq16VG0SxJAa8ws7aFUEkCFX3SVlF0SQBVvwEqMZhJAHfsQDYlvEkCbA2bcingSQJKIv7iRgRJAAIodop2KEkDkB4CYrpMSQD4C55vEnBJAEXlSrN+lEkBdbMLJ/64SQB3cNvQkuBJAVcivK0/BEkAFMS1wfsoSQCsWr8Gy0xJAyXc1IOzcEkDeVcCLKuYSQGqwTwRu7xJAbofjibb4EkDo2nscBAITQNyqGLxWCxNAQ/e5aK4UE0AjwF8iCx4TQHoFCulsJxNASce4vNMwE0CNBWydPzoTQEzAI4uwQxNAgfffhSZNE0Arq6CNoVYTQE3bZaIhYBNA5ocvxKZpE0D4sP3yMHMTQIBW0C7AfBNAf3ind1SGE0D1FoPN7Y8TQOMxYzCMmRNASclHoC+jE0Ak3TAd2KwTQHdtHqeFthNAQXoQPjjAE0CDAwfi78kTQDwJApOs0xNAbIsBUW7dE0ASigUcNecTQDEFDvQA8RNAx/wa2dH6E0DScCzLpwQUQFdhQsqCDhRAUs5c1mIYFEDEt3vvRyIUQK0dnxUyLBRADQDHSCE2FEDmXvOIFUAUQDQ6JNYOShRA+ZFZMA1UFEA3ZpOXEF4UQOu20QsZaBRAGIQUjSZyFEC6zVsbOXwUQNSTp7ZQhhRAZtb3Xm2QFEBulUwUj5oUQPDQpda1pBRA5YgDpuGuFEBSvWWCErkUQDhuzGtIwxRAlZs3YoPNFEBnRadlw9cUQLNrG3YI4hRAdA6Uk1LsFECtLRG+ofYUQF3JkvX1ABVAheEYOk8LFUAkdqOLrRUVQDqHMuoQIBVAxhTGVXkqFUDKHl7O5jQVQEWl+lNZPxVAOaib5tBJFUChJ0GGTVQVQIMj6zLPXhVA25uZ7FVpFUCqkEyz4XMVQPIBBIdyfhVAru+/ZwiJFUDkWYBVo5MVQJBARVBDnhVAs6MOWOioFUBNg9xskrMVQF/fro5BvhVA57eFvfXIFUDoDGH5rtMVQF7eQEJt3hVATSwlmDDpFUCy9g37+PMVQJA9+2rG/hVA4gDt55gJFkCuQONxcBQWQPH83QhNHxZAqjXdrC4qFkDZ6uBdFTUWQIIc6RsBQBZAocr15vFKFkA49Qa/51UWQEecHKTiYBZAyb82luJrFkDFX1WV53YWQ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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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OOU9QhQM/3U+Ol2yFANKK0N/jiIUCYTBWMSuohQP32deCc8SFAYqHWNO/4IUDHSzeJQQAiQCz2l92TByJAkKD4MeYOIkD1SlmGOBYiQFr1udqKHSJAv58aL90kIkAjSnuDLywiQIj029eBMyJA7Z48LNQ6IkBSSZ2AJkIiQLbz/dR4SSJAG55eKctQIkCASL99HVgiQOXyH9JvXyJASZ2AJsJmIkCuR+F6FG4iQBPyQc9mdSJAeJyiI7l8IkDcRgN4C4QiQEHxY8xdiyJAppvEILCSIkALRiV1ApoiQHDwhclUoSJA1JrmHaeoIkA5RUdy+a8iQJ7vp8ZLtyJAApoIG56+IkBnRGlv8MUiQMzuycNCzSJAMZkqGJXUIkCVQ4ts59siQPrt68A54yJAX5hMFYzqIkDEQq1p3vEiQCjtDb4w+SJAjZduEoMAI0DyQc9m1QcjQFfsL7snDyNAu5aQD3oWI0AgQfFjzB0jQIXrUbgeJSNA6pWyDHEsI0BPQBNhwzMjQLPqc7UVOyNAGJXUCWhCI0B9PzVeukkjQOLplbIMUSNARpT2Bl9YI0CrPldbsV8jQBDpt68DZyNAdZMYBFZuI0DZPXlYqHUjQD7o2az6fCNAo5I6AU2EI0AIPZtVn4sjQGzn+6nxkiNA0ZFc/kOaI0A2PL1SlqEjQJvmHafoqCNA/5B++zqwI0BkO99PjbcjQMnlP6TfviNALpCg+DHGI0CTOgFNhM0jQPfkYaHW1CNAXI/C9SjcI0DBOSNKe+MjQCbkg57N6iNAio7k8h/yI0DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJwrQGuad5yioytA0ETY8PSqK0A17zhFR7IrQJqZmZmZuStA/kP67evAK0Bj7lpCPsgrQMiYu5aQzytALUMc6+LWK0CR7Xw/Nd4rQPaX3ZOH5StAW0I+6NnsK0DA7J48LPQrQCSX/5B++ytAiUFg5dACLEDu68A5IwosQFOWIY51ESxAt0CC4scYLEAc6+I2GiAsQIGVQ4tsJyxA5j+k374uLEBK6gQ0ETYsQK+UZYhjPSxAFD/G3LVELEB56SYxCEwsQN2Th4VaUyxAQj7o2axaLECn6Egu/2EsQAyTqYJRaSxAcT0K16NwLEDV52or9ncsQDqSy39IfyxAnzws1JqGLEAE54wo7Y0sQGiR7Xw/lSxAzTtO0ZGcLEAy5q4l5KMsQJeQD3o2qyxA+zpwzoiyLEBg5dAi27ksQMWPMXctwSxAKjqSy3/ILECO5PIf0s8sQPOOU3Qk1yxAWDm0yHbeLEC94xQdyeUsQCGOdXEb7SxAhjjWxW30LEDr4jYawPssQFCNl24SAy1AtTf4wmQKLUAZ4lgXtxEtQH6MuWsJGS1A4zYawFsgLUBI4XoUrictQKyL22gALy1AETY8vVI2LUB24JwRpT0tQNuK/WX3RC1APzVeuklMLUCk374OnFMtQAmKH2PuWi1AbjSAt0BiLUDS3uALk2ktQDeJQWDlcC1AnDOitDd4LUAB3gIJin8tQGWIY13chi1AyjLEsS6OLUAv3SQGgZUtQJSHhVrTnC1A+THmriWkLUBd3EYDeKstQMKGp1fKsi1AJzEIrBy6LUCM22gAb8EtQPCFyVTByC1AVTAqqRPQLUC62or9ZdctQB+F61G43i1Agy9MpgrmLUDo2az6XO0tQE2EDU+v9C1Asi5uowH8LUAW2c73UwMuQHuDL0ymCi5A4C2QoPgRLkBF2PD0ShkuQKmCUUmdIC5ADi2yne8nLkBz1xLyQS8uQNiBc0aUNi5APSzUmuY9LkCh1jTvOEUuQAaBlUOLTC5Aayv2l91TLkDQ1VbsL1suQDSAt0CCYi5AmSoYldRpLkD+1HjpJnEuQGN/2T15eC5Axyk6kst/LkAs1JrmHYcuQJF++zpwji5A9ihcj8KVLkBa07zjFJ0uQL99HThnpC5AJCh+jLmrLkCJ0t7gC7MuQO18PzVeui5AUiegibDBLkC30QDeAskuQBx8YTJV0C5AgSbChqfXLkDl0CLb+d4uQEp7gy9M5i5AryXkg57tLkAU0ETY8PQuQHh6pSxD/C5A3SQGgZUDL0BCz2bV5wovQKd5xyk6Ei9ACyQofowZL0BwzojS3iAvQNV46SYxKC9AOiNKe4MvL0CezarP1TYvQA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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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genusrecordspairscoveragepvaluecorrelationR^2s.e.(larvae)equationcenter(KitMg)height(larvae)curvature(larvae/KitMg^2)
0All1122510.100.12140.14720.02173.78y = 3.64+-0.44 x+0.05 x^23.6444-0.44250.0471
1Aedes3100.000.57230.62240.38745.87y = -4.77+0.93 x+0.17 x^2-4.77300.92570.1719
2Anopheles000.001.00000.00000.00000.00no fit achieved0.00000.00000.0000
3Culex5150.000.78580.16910.02861.29y = 65.75+-13.5 x+0.73 x^265.7550-13.49980.7334
4Other23510.020.05990.39810.15854.41y = -4.73+2.56 x+-0.1 x^2-4.72572.5605-0.0952
5Unknown811750.070.00170.34370.11821.72y = 5.35+-0.89 x+0.04 x^25.3452-0.88870.0382
\n", "
" ], "text/plain": [ " genus records pairs coverage pvalue correlation R^2 \\\n", "0 All 112 251 0.10 0.1214 0.1472 0.0217 \n", "1 Aedes 3 10 0.00 0.5723 0.6224 0.3874 \n", "2 Anopheles 0 0 0.00 1.0000 0.0000 0.0000 \n", "3 Culex 5 15 0.00 0.7858 0.1691 0.0286 \n", "4 Other 23 51 0.02 0.0599 0.3981 0.1585 \n", "5 Unknown 81 175 0.07 0.0017 0.3437 0.1182 \n", "\n", " s.e.(larvae) equation center(KitMg) height(larvae) \\\n", "0 3.78 y = 3.64+-0.44 x+0.05 x^2 3.6444 -0.4425 \n", "1 5.87 y = -4.77+0.93 x+0.17 x^2 -4.7730 0.9257 \n", "2 0.00 no fit achieved 0.0000 0.0000 \n", "3 1.29 y = 65.75+-13.5 x+0.73 x^2 65.7550 -13.4998 \n", "4 4.41 y = -4.73+2.56 x+-0.1 x^2 -4.7257 2.5605 \n", "5 1.72 y = 5.35+-0.89 x+0.04 x^2 5.3452 -0.8887 \n", "\n", " curvature(larvae/KitMg^2) \n", "0 0.0471 \n", "1 0.1719 \n", "2 0.0000 \n", "3 0.7334 \n", "4 -0.0952 \n", "5 0.0382 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('performing quadratic curve fit')\n", "\n", "# perform\n", "perform('quadratic', associations)" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "45be03c0041b400c9f6976525fa8ca05", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a4cb3e0124f745f1afcb80aa53aae557", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Exponential Curve Fit" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "hide_input": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: performing exponential curve fit)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(\"9574\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", " if (root.Bokeh !== undefined) {\n", " if (el != null) {\n", " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", " }\n", " } else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; 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i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"9574\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"OgVMN3b/2j8VJynhQwnbPwDvUhoVE9s/U0UU5Okc2z+Firg/wibbP3CXiy6eMNs/YL3ZsX062z9Vxu/KYETbPx71GntHTts/mgWpwzFY2z/KLOilH2LbPxMZJyMRbNs/WfK0PAZ22z9AWuHz/n/bPz1s/En7ids/3b1WQPuT2z/bXkHY/p3bP1zZDRMGqNs/FzIO8hCy2z+A6JR2H7zbP+329KExxts/2dKBdUfQ2z/2bI/yYNrbP2oxchp+5Ns//Ad/7p7u2z80VAtww/jbP5z1bKDrAtw/2Ef6gBcN3D/gIgoTRxfcPy3b81d6Idw/4UEPUbEr3D/ypLT/6zXcP2XPPGUqQNw/ZwkBg2xK3D+UGFtaslTcPwZApez7Xtw/oEA6O0lp3D8mWXVHmnPcP3dGshLvfdw/tkNNnkeI3D96CqPro5LcP/bSEPwDndw/NlT00Gen3D82xKtrz7HcPyTYlc06vNw/iMQR+KnG3D9qPX/sHNHcP5N2PqyT29w/pSOwOA7m3D9aeDWTjPDcP6woML0O+9w/BWkCuJQF3T9q7g6FHhDdP7HuuCWsGt0/piBkmz0l3T9IvHTn0i/dP+Z6TwtsOt0/XpdZCAlF3T9AzvjfqU/dPwdek5NOWt0/QQeQJPdk3T/EDFaUo2/dP9EzTeRTet0/WsTdFQiF3T8ZiXAqwI/dP9DPbiN8mt0/cWlCAjyl3T9NqlXI/6/dP09qE3fHut0/FwXnD5PF3T9AWjyUYtDdP4PNfwU2290/5kYeZQ3m3T/zMoW06PDdP+eCIvXH+90/2qxkKKsG3j//q7pPkhHeP8EAlGx9HN4/ArFggGwn3j9HSJGMXzLeP+XXlpJWPd4/N/fik1FI3j/Pw+eRUFPeP5vhF45TXt4/LHvmiVpp3j/OQceGZXTeP8ptLoZ0f94/lb6QiYeK3j/yemOSnpXeP0BxHKK5oN4/jPcxutir3j/V6xrc+7bePz20Tgkjwt4/Lz9FQ07N3j+cA3eLfdjePysBXeOw494/XcBwTOju3j/aUizII/reP4NTClhjBd8/veaF/aYQ3z+Xuhq67hvfP/kGRY86J98/442Bfooy3z+Um02J3j3fP7sGJ7E2Sd8/ujCM95JU3z++Bfxd81/fPwj99eVXa98/GBn6kMB23z/V54hgLYLfP9mCI1aejd8/ho9LcxOZ3z9MP4O5jKTfP9pPTSoKsN8/Rgstx4u73z9PSKaREcffP4VqPYub0t8/fGJ3tSne3z8OrtkRvOnfP3dY6qFS9d8/Tv2Xs3YA4D+c3ZgxRgbgP4Onu8sXDOA/g3zEgusR4D9SxXdXwRfgP/YxmkqZHeA/57nwXHMj4D8anECPTyngPylfT+ItL+A/aNHiVg414D/5CMHt8DrgP/VjsKfVQOA/coh3hbxG4D+wZN2HpUzgPygvqa+QUuA/qmai/X1Y4D910pBybV7gP1eCPA9fZOA/vc5t1FJq4D/bWO3CSHDgP7kKhNtAduA/Vxf7Hjt84D/C+huON4LgPzJ6sCk2iOA/I6SC8jaO4D9w0FzpOZTgP2mgCQ8/muA/+/5TZEag4D+5IAfqT6bgPwOE7qBbrOA/IPHViWmy4D9ReomlebjgP/N71fSLvuA/mpyGeKDE4D8ozWkxt8rgP+pITCDQ0OA/tJX7RevW4D/3g0WjCN3gP+cu+Dgo4+A/hvzhB0rp4D/SndEQbu/gP88OllSU9eA/sJb+07z74D/lx9qP5wHhP0SA+ogUCOE/GuktwEMO4T9Nd0U2dRThP3DrEeyoGuE/6lFk4t4g4T8DAw4aFyfhPw2j4JNRLeE/eSKuUI4z4T/wvUhRzTnhP3X+gpYOQOE/frkvIVJG4T8PESLyl0zhP9ZzLQrgUuE/Sp0laipZ4T++ld4Sd1/hP4qyLAXGZeE/GpbkQRds4T8WMNvJanLhP3S95Z3AeOE/mcjZvhh/4T90KY0tc4XhP5wF1urPi+E/aNCK9y6S4T8RS4JUkJjhP8aEkwL0nuE/1tqVAlql4T+7+GBVwqvhP0PYzPsssuE/q8Gx9pm44T+zS+hGCb/hP8dbSe16xeE/ECau6u7L4T+ZLfA/ZdLhP2lE6e3d2OE/nItz9Vjf4T+Fc2lX1uXhP8i7pRRW7OE/dnMDLtjy4T8u+V2kXPnhPzX7kHjj/+E/l3d4q2wG4j9AvPA9+AziPxpn1jCGE+I/L2YGhRYa4j/A9107qSDiP2GqulQ+J+I/IV360dUt4j+YP/uzbzTiPxHSm/sLO+I/n+W6qapB4j8+nDe/S0jiP/Ro8TzvTuI/5A/II5VV4j92ppt0PVziP3GTTDDoYuI/FI+7V5Vp4j86o8nrRHDiP3QrWO32duI/KdVIXat94j+yn308YoTiP3jc2Isbi+I/Ei89TNeR4j9jjY1+lZjiP7U/rSNWn+I/4eB/PBmm4j9eXunJ3qziP2j4zcyms+I/IkISRnG64j+pIZs2PsHiPzrQTZ8NyOI/TtoPgd/O4j+4H8fcs9XiP8XTWbOK3OI/V32uBWTj4j8G96vUP+riPz9vOSEe8eI/X2g+7P734j/VuKI24v7iPz2LTgHIBeM/g14qTbAM4z8ABh8bmxPjP5WpFWyIGuM/z8X3QHgh4z8BLK+aaijjP2cCJnpfL+M/RMRG4FY24z/9QfzNUD3jPzyhMURNROM/EF3SQ0xL4z8HRsrNTVLjP1CCBeNRWeM/2o1whFhg4z9zOviyYWfjP+uviW9tbuM/KWwSu3t14z9UQ4CWjHzjP/FfwQKgg+M//ELEALaK4z8QxHeRzpHjP34Ry7XpmOM/dLCtbgeg4z8ZfQ+9J6fjP6qq4KFKruM/osMRHnC14z/OqZMymLzjP3SWV+DCw+M/eBpPKPDK4z9tHmwLINLjP8HioIpS2eM/2v/fpofg4z80Zhxhv+fjP4JeSbr57uM/z4laszb24z+a4UNNdv3jP/+3+Yi4BOQ/zbdwZ/0L5D+s5J3pRBPkPzubdhCPGuQ/NZHw3Nsh5D+K1QFQKynkP4LQoGp9MOQ/40PELdI35D8JS2OaKT/kPwxbdbGDRuQ/4ULyc+BN5D9zK9LiP1XkP9CXDf+hXOQ/QGWdyQZk5D9py3pDbmvkP21cn23YcuQ/FAUFSUV65D/iDKbWtIHkPzwWfRcnieQ/iR6FDJyQ5D9afrm2E5jkP3vpFReOn+Q/JW+WLgun5D8Tejf+iq7kP6vQ9YYNtuQ/HZXOyZK95D+ARb/HGsXkP/u7xYGlzOQ/4S7g+DLU5D/TMA0uw9vkP+SwSyJW4+Q/ufqa1uvq5D+qtvpLhPLkP+PpaoMf+uQ/jPbrfb0B5T/fm348XgnlP1b2I8ABEeU/xX/dCagY5T9/D60aUSDlP3jalPP8J+U/ZHOXlasv5T/eyrcBXTflP4cv+TgRP+U/KE5fPMhG5T/TMe4Mgk7lPwlEqqs+VuU/3UyYGf5d5T8Pc71XwGXlPzM8H2eFbeU/2YzDSE115T+kqLD9F33lP3Uy7YblhOU/jSyA5bWM5T+o+HAaiZTlPytYxyZfnOU/QGyLCzik5T/4tcXJE6zlP24Wf2L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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__nda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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[877]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shap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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"hHjD0vFkAMCSCbZS418AwIzKmvzSWgDASAzHz8BVAMCm4Y/LrFAAwHIfSu+WSwDAVFxKOn9GAMCy8OSrZUEAwKD2bUNKPADAxkk5AC03AMBKh5rhDTIAwLsN5ebsLADA9fxrD8onAMASNoJapSIAwEpbesd+HQDA6M+mVVYYAMAouFkELBMAwCP55NL/DQDAvjiawNEIAMCM3crMoQMAwHMdkO3f/P+/72fFe3jy/7/B6tZCDej/v8l2ZUGe3f+/Yl0RdivT/78xcHrftMj/v/wAQHw6vv+/eeEAS7yz/78cY1tKOqn/v+xW7Xi0nv+/Vg1U1SqU/7/8VSxenYn/v4V/EhIMf/+/cFei73Z0/7/lKXf13Wn/v4bBKyJBX/+/QGdadKBU/78Z4pzq+0n/vwd3jINTP/+/vOjBPac0/793d9UX9yn/v9XgXhBDH/+/pV/1JYsU/7+zqy9Xzwn/v5z5o6IP//6/nvrnBkz0/r9l3JCChOn+v+FIMxS53v6/FGZjuunT/r/c1bRzFsn+v821uj4/vv6/+p4HGmSz/r/IpS0Ehaj+v7xZvvuhnf6/TMVK/7qS/r+wbWMN0If+v6xSmCThfP6/Z+54Q+5x/r82NZRo92b+v2yVeJL8W/6/KPezv/1Q/r8rvNPu+kX+v5y/ZB70Ov6/4lXzTOkv/r9rTAt52iT+v4PpN6HHGf6/GOwDxLAO/r+Ti/nflQP+v6R3ovN2+P2/DNiH/VPt/b9zTDL8LOL9vzDsKe4B1/2/HEb20dLL/b9cYB6mn8D9vzi4KGlotf2/3kGbGS2q/b84aPu17Z79v7cMzjyqk/2/I4eXrGKI/b9lpdsDF339v16rHUHHcf2/qFLgYnNm/b9vyqVnG1v9vzi3702/T/2/sTI/FF9E/b9/yxS5+jj9vw2F8DqSLf2/VddRmCUi/b+vrrfPtBb9v6JroN8/C/2/reKJxsb//L8VXPGCSfT8v7OTUxPI6Py/wLgsdkLd/L+lbfipuNH8v8PHMa0qxvy/QU9Tfpi6/L/f/tYbAq/8v7hDNoRno/y/Fv3ptciX/L9AfGqvJYz8vz6EL29+gPy/rEmw89J0/L+IcmM7I2n8v/YVv0RvXfy/Erw4DrdR/L/AXUWW+kX8v25kWds5Ovy/6Kno23Qu/L8ceGaWqyL8v/CIRQneFvy/CAb4MgwL/L+OiO8RNv/7vwQZnaRb8/u/Di9x6Xzn+786sdvemdv7v9D0S4Oyz/u/mL0w1cbD+7+sPfjS1rf7vzoVEHviq/u/WFLly+mf+7/McOTD7JP7v9RZeWHrh/u/8GMPo+V7+7+2UhGH22/7v5JW6QvNY/u/lgwBMLpX+79AfsHxokv7v04hk0+HP/u/fNfdR2cz+79Y7gjZQif7vwYfewEaG/u/EI6av+wO+78oy8wRuwL7v/jQdvaE9vq/7gT9a0rq+r/+NsNwC976v3KhLAPI0fq/sOibIYDF+r8IG3PKM7n6v3iwE/zirPq/eIretI2g+r/G8zPzM5T6vyqgc7XVh/q/RKz8+XJ7+r9UnS2/C2/6v/5gZAOgYvq/GE3+xC9W+r90H1gCu0n6v6j9zblBPfq/0HS76cMw+r9ieXuQQST6v+xmaKy6F/q/5P/bOy8L+r9sbS89n/75vxo/u64K8vm/xmrXjnHl+b9OTNvb09j5v1ylHZQxzPm/MJ30tYq/+b9owLU/37L5v8wAti8vpvm/ELVJhHqZ+b+amMQ7wYz5v1LLeVQDgPm/ZNG7zEBz+b8Ek9yieWb5v0BcLdWtWfm/uNz+Yd1M+b94J6FHCED5v7CyY4QuM/m/gFeVFlAm+b++UYT8bBn5v8I/fjSFDPm/KCLQvJj/+L+WW8aTp/L4v4KwrLex5fi/AEfOJrfY+L+CpnXft8v4v5y37N+zvvi/0sN8Jqux+L9WdW6xnaT4v9jWCX+Ll/i/RFOWjXSK+L+ItVrbWH34v2AonWY4cPi/GDajLRNj+L9OyLEu6VX4v8AnDWi6SPi/CPz414Y7+L9uS7h8Ti74v6B6jVQRIfi/gky6Xc8T+L/q4X+WiAb4v2q5Hv08+fe/Gq/Wj+zr979W/OZMl973v4I3jjI90fe/0FMKP97D978QoZhwerb3v2DLdcURqfe/AtvdO6Sb978YNAzSMY73v2iWO4a6gPe/Kh2mVj5z97/APoVBvWX3v4DMEUU3WPe/dPKDX6xK978oNxOPHD33v2R79tGHL/e/7vljJu4h979cR5GKTxT3v8hRs/yrBve/nGD+egP59r9SFKYDVuv2vzpm3ZSj3fa/PqjWLOzP9r+ehMPJL8L2v7791GlutPa/3m07C6im9r/mhias3Jj2vyhSxUoMi/a/GjBG5TZ99r8e2NZ5XG/2v0xYpAZ9Yfa/LBXbiZhT9r92yaYBr0X2v9yFMmzAN/a/yLCox8wp9r8gBjMS1Bv2vwaX+knWDfa/nMknbdP/9b/EWOJ5y/H1v+RTUW6+4/W/pB6bSKzV9b+4cOUGlcf1v5JVVad4ufW/OCwPKFer9b/2pjaHMJ31vyLL7sIEj/W/4PBZ2dOA9b/mwpnInXL1vzQ+z45iZPW/3rEaKiJW9b/GvpuY3Ef1v2BXcdiROfW/dr+550Er9b/ii5LE7Bz1v1KiGG2SDvW/CDlo3zIA9b+Y1pwZzvH0v7BR0Rlk4/S/zNAf3vTU9L8AyqFkgMb0v7QCcKsGuPS/Zo+isIep9L9k01ByA5v0v5CAke55jPS/KJd6I+t99L9wZSEPV2/0v4aHmq+9YPS/GOf5Ah9S9L8iu1IHe0P0v7iHt7rRNPS/sh06GyMm9L96musmbxf0v8pn3Nu1CPS/ZjscOPf587/YFro5M+vzvzhHxN5p3PO/3mRIJZvN878wU1MLx77zv1JA8Y7tr/O/7KQtrg6h87/iQxNnKpLzvxwqrLdAg/O/PK4BnlF0879ccBwYXWXzv85ZBCRjVvO/2pzAv2NH8794tFfpXjjzvxZkz55UKfO/SLcs3kQa87+QAXSlLwvzvxjeqPIU/PK/dC/Ow/Ts8r9OH+YWz93yvzYe8umjzvK/WuPyOnO/8r88bOgHPbDyv3D80U4BofK/Yh2uDcCR8r8InnpCeYLyv6SSNOssc/K/elTYBdtj8r+QgWGQg1Tyv3L8yogmRfK/4usO7cM18r+Suia7Wybyv+wWC/HtFvK/yPKzjHoH8r8igxiMAfjxv+Q/L+2C6PG/huPtrf7Y8b/saknMdMnxvwoVNkblufG/pGKnGVCq8b8GFpBEtZrxv8wy4sQUi/G/jv2OmG578b+i+4a9wmvxv9DyuTERXPG/FukW81lM8b9cJIz/nDzxvzYqB1XaLPG/ir908REd8b9k6MDSQw3xv6Dn1vZv/fC/qD6hW5bt8L8srQn/tt3wv94w+d7RzfC/LgVY+ea98L/+og1M9q3wv1bAANX/nfC/OlAXkgOO8L8wgjaBAX7wvzDCQqD5bfC/Prgf7etd8L8cSLBl2E3wvySR1ge/PfC/3O1z0Z8t8L/E82jAeh3wvw5zldJPDfC/lOywCz76779QhCCw0Nnvv3SqNI5Xue+/EM+modKY77/s1C7mQXjvv7QQg1elV++/jEhY8fw2779os2GvSBbvv4D4UI2I9e6/4C7WhrzU7r+k3J+X5LPuv3j2WrsAk+6/LN+y7RBy7r/QZlEqFVHuv4DK3mwNMO6/oLMBsfkO7r8oN1/y2e3tv2jVmiyuzO2/KHlWW3ar7b88dzJ6Mortv+SNzYTiaO2/MOTEdoZH7b+ICbRLHibtvxD1NP+pBO2/3ATgjCnj7L+8/UvwnMHsv1gKDiUEoOy/xLq5Jl9+7L/cA+HwrVzsv6w+FH/wOuy/9CfizCYZ7L9o39fVUPfrvzTngJVu1eu/eCNnB4Cz67982RInhZHrv1CvCvB9b+u/JKvTXWpN67+MMvFrSivrvywK5RUeCeu/7FQvV+Xm6r9sk04roMTqv3yjv41Oouq/cL/9efB/6r+gfYLrhV3qv8DPxd0OO+q/KAI+TIsY6r+Iu18y+/XpvwD8nYte0+m/uBxqU7Ww6b80zzOF/43pv7AcaRw9a+m/tGV2FG5I6b80YcZokiXpvygcwhSqAum/+PjQE7Xf6L+grlhhs7zov2RIvfikmei/FCVh1Yl26L9Q9qTyYVPovyzA50stMOi/XNiG3OsM6L+45d2fnennv5TfRpFCxue/IA0arNqi57/cBK7rZX/nv/CrV0vkW+e/dDVqxlU4578IIjdYuhTnvxQ/DvwR8ea/KKY9rVzN5r9wvBFnmqnmv/gx1STLhea/QAHR4e5h5r9sbkyZBT7mv6wGjUYPGua/yJ/W5Av25b84V2tv+9Hlv8SRi+HdreW/3Pp1NrOJ5b+0g2dpe2Xlvwxjm3U2QeW/RBRLVuQc5b/IVq4Ghfjkv4wt+4EY1OS/RN5lw56v5L/o8CDGF4vkv/wuXYWDZuS/2KJJ/OFB5L9IlxMmMx3kv5yW5v12+OO/QGrsfq3T47/4GU2k1q7jv0TrLmnyieO/0GC2yABl47+0OQa+AUDjv9RwP0T1GuO/YDyBVtv14r/sDOnvs9DivxCNkgt/q+K/rKCXpDyG4r8YZBC27GDiv8grEzuPO+K/cIO0LiQW4r94LQeMq/Dhv0AiHE4ly+G/lI8CcJGl4b/418fs73/hvwySd79AWuG/vIcb44M04b/YtbtSuQ7hv0RLXgnh6OC/TKgHAvvC4L8UXro3B53gv+Qtd6UFd+C/hAg9RvZQ4L+IDQkV2Srgv6iK1gyuBOC/gPY9Ueq837+gDrTGXHDfv1gG+2+zI9+/YN36Qu7W3r/Y6Jc1DYrev6DSsj0QPd6/YJcoUffv3b+IhdJlwqLdv/A7hnFxVd2/cKgVagQI3b+QBk9Fe7rcv1je/PjVbNy/eALmehQf3L+wj83ANtHbv9jqcsA8g9u/wL+RbyY127/Y/+HD8+bav9DgF7OkmNq/aNvjMjlK2r+wqfI4sfvZv/hF7boMrdm/kOl4rkte2b/oCjcJbg/Zv9BcxcBzwNi/2My9ylxx2L+YgbY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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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JAv58aL90kIkAjSnuDLywiQIj029eBMyJA7Z48LNQ6IkBSSZ2AJkIiQLbz/dR4SSJAG55eKctQIkCASL99HVgiQOXyH9JvXyJASZ2AJsJmIkCuR+F6FG4iQBPyQc9mdSJAeJyiI7l8IkDcRgN4C4QiQEHxY8xdiyJAppvEILCSIkALRiV1ApoiQHDwhclUoSJA1JrmHaeoIkA5RUdy+a8iQJ7vp8ZLtyJAApoIG56+IkBnRGlv8MUiQMzuycNCzSJAMZkqGJXUIkCVQ4ts59siQPrt68A54yJAX5hMFYzqIkDEQq1p3vEiQCjtDb4w+SJAjZduEoMAI0DyQc9m1QcjQFfsL7snDyNAu5aQD3oWI0AgQfFjzB0jQIXrUbgeJSNA6pWyDHEsI0BPQBNhwzMjQLPqc7UVOyNAGJXUCWhCI0B9PzVeukkjQOLplbIMUSNARpT2Bl9YI0CrPldbsV8jQBDpt68DZyNAdZMYBFZuI0DZPXlYqHUjQD7o2az6fCNAo5I6AU2EI0AIPZtVn4sjQGzn+6nxkiNA0ZFc/kOaI0A2PL1SlqEjQJvmHafoqCNA/5B++zqwI0BkO99PjbcjQMnlP6TfviNALpCg+DHGI0CTOgFNhM0jQPfkYaHW1CNAXI/C9SjcI0DBOSNKe+MjQCbkg57N6iNAio7k8h/yI0DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJwrQGuad5yioytA0ETY8PSqK0A17zhFR7IrQJqZmZmZuStA/kP67evAK0Bj7lpCPsgrQMiYu5aQzytALUMc6+LWK0CR7Xw/Nd4rQPaX3ZOH5StAW0I+6NnsK0DA7J48LPQrQCSX/5B++ytAiUFg5dACLEDu68A5IwosQFOWIY51ESxAt0CC4scYLEAc6+I2GiAsQIGVQ4tsJyxA5j+k374uLEBK6gQ0ETYsQK+UZYhjPSxAFD/G3LVELEB56SYxCEwsQN2Th4VaUyxAQj7o2axaLECn6Egu/2EsQAyTqYJRaSxAcT0K16NwLEDV52or9ncsQDqSy39IfyxAnzws1JqGLEAE54wo7Y0sQGiR7Xw/lSxAzTtO0ZGcLEAy5q4l5KMsQJeQD3o2qyxA+zpwzoiyLEBg5dAi27ksQMWPMXctwSxAKjqSy3/ILECO5PIf0s8sQPOOU3Qk1yxAWDm0yHbeLEC94xQdyeUsQCGOdXEb7SxAhjjWxW30LEDr4jYawPssQFCNl24SAy1AtTf4wmQKLUAZ4lgXtxEtQH6MuWsJGS1A4zYawFsgLUBI4XoUrictQKyL22gALy1AETY8vVI2LUB24JwRpT0tQNuK/WX3RC1APzVeuklMLUCk374OnFMtQAmKH2PuWi1AbjSAt0BiLUDS3uALk2ktQDeJQWDlcC1AnDOitDd4LUAB3gIJin8tQGWIY13chi1AyjLEsS6OLUAv3SQGgZUtQJSHhVrTnC1A+THmriWkLUBd3EYDeKstQMKGp1fKsi1AJzEIrBy6LUCM22gAb8EtQPCFyVTByC1AVTAqqRPQLUC62or9ZdctQB+F61G43i1Agy9MpgrmLUDo2az6XO0tQE2EDU+v9C1Asi5uowH8LUAW2c73UwMuQHuDL0ymCi5A4C2QoPgRLkBF2PD0ShkuQKmCUUmdIC5ADi2yne8nLkBz1xLyQS8uQNiBc0aUNi5APSzUmuY9LkCh1jTvOEUuQAaBlUOLTC5Aayv2l91TLkDQ1VbsL1suQDSAt0CCYi5AmSoYldRpLkD+1HjpJnEuQGN/2T15eC5Axyk6kst/LkAs1JrmHYcuQJF++zpwji5A9ihcj8KVLkBa07zjFJ0uQL99HThnpC5AJCh+jLmrLkCJ0t7gC7MuQO18PzVeui5AUiegibDBLkC30QDeAskuQBx8YTJV0C5AgSbChqfXLkDl0CLb+d4uQEp7gy9M5i5AryXkg57tLkAU0ETY8PQuQHh6pSxD/C5A3SQGgZUDL0BCz2bV5wovQKd5xyk6Ei9ACyQofowZL0BwzojS3iAvQNV46SYxKC9AOiNKe4MvL0CezarP1TYvQAN4CyQoPi9AaCJseHpFL0DNzMzMzEwvQA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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UQzQGTuWkI+SDNAlkOLbOdLM0DImLuWkE8zQPrt68A5UzNALUMc6+JWM0BfmEwVjFozQJLtfD81XjNAxEKtad5hM0D2l92Th2UzQCntDb4waTNAW0I+6NlsM0COl24Sg3AzQMDsnjwsdDNA8kHPZtV3M0All/+QfnszQFfsL7snfzNAikFg5dCCM0C8lpAPeoYzQO7rwDkjijNAIEHxY8yNM0BTliGOdZEzQIbrUbgelTNAuECC4seYM0DqlbIMcZwzQBzr4jYaoDNAT0ATYcOjM0CClUOLbKczQLTqc7UVqzNA5j+k376uM0AYldQJaLIzQEvqBDQRtjNAfT81Xrq5M0CwlGWIY70zQOLplbIMwTNAFD/G3LXEM0BGlPYGX8gzQHnpJjEIzDNArD5XW7HPM0Dek4eFWtMzQBDpt68D1zNAQj7o2azaM0B1kxgEVt4zQKjoSC7/4TNA2j15WKjlM0AMk6mCUekzQD7o2az67DNAcT0K16PwM0CkkjoBTfQzQNbnaiv29zNACD2bVZ/7M0A6kst/SP8zQG3n+6nxAjRAnzws1JoGNEDSkVz+Qwo0QATnjCjtDTRANjy9UpYRNEBoke18PxU0QJvmHafoGDRAzjtO0ZEcNEAAkX77OiA0QDLmriXkIzRAZDvfT40nNECXkA96Nis0QMrlP6TfLjRA/DpwzogyNEAukKD4MTY0QGDl0CLbOTRAkzoBTYQ9NEDGjzF3LUE0QPjkYaHWRDRAKjqSy39INEBcj8L1KEw0QI/k8h/STzRAwTkjSntTNED0jlN0JFc0QCbkg57NWjRAWDm0yHZeNECKjuTyH2I0QL3jFB3JZTRA8DhFR3JpNEAijnVxG200QFTjpZvEcDRAhjjWxW10NEC5jQbwFng0QOziNhrAezRAHjhnRGl/NEBQjZduEoM0QILix5i7hjRAtTf4wmSKNEDojCjtDY40QBriWBe3kTRATDeJQWCVNEB+jLlrCZk0QLHh6ZWynDRA4zYawFugNEAWjErqBKQ0QEjhehSupzRAejarPlerNECsi9toAK80QN/gC5OpsjRAEjY8vVK2NEBEi2zn+7k0QHbgnBGlvTRAqDXNO07BNEDbiv1l98Q0QA7gLZCgyDRAQDVeuknMNEByio7k8s80QKTfvg6c0zRA1zTvOEXXNEAKih9j7to0QDzfT42X3jRAbjSAt0DiNECgibDh6eU0QNPe4AuT6TRABTQRNjztNEA4iUFg5fA0QGrecYqO9DRAnDOitDf4NEDOiNLe4Ps0QAHeAgmK/zRANDMzMzMDNUBmiGNd3AY1QJjdk4eFCjVAyjLEsS4ONUD9h/Tb1xE1QDDdJAaBFTVAYjJVMCoZNUCUh4Va0xw1QMbctYR8IDVA+THmriUkNUAshxbZzic1QF7cRgN4KzVAkDF3LSEvNUDChqdXyjI1QPXb14FzNjVAJzEIrBw6NUBahjjWxT01QIzbaABvQTVAvjCZKhhFNUDwhclUwUg1QCPb+X5qTDVAVjAqqRNQNUCIhVrTvFM1QLraiv1lVzVA7C+7Jw9bNUAfhetRuF41QFLaG3xhYjVAhC9MpgpmNUC2hHzQs2k1QOjZrPpcbTVAGy/dJAZxNUBOhA1Pr3Q1QIDZPXlYeDVAsi5uowF8NUDkg57Nqn81QBfZzvdTgzVASS7/If2GNUB8gy9Mpoo1QK7YX3ZPjjVA4C2QoPiRNUASg8DKoZU1QEXY8PRKmTVAeC0hH/ScNUCqglFJnaA1QNzXgXNGpDVADi2yne+nNUBBguLHmKs1QHTXEvJBrzVApixDHOuyNUDYgXNGlLY1QArXo3A9ujVAPSzUmua9NUBwgQTFj8E1QKLWNO84xTVA1CtlGeLINUAGgZVDi8w1QDnWxW000DVAayv2l93TNUCegCbChtc1QNDVVuwv2zVAAiuHFtneNUA0gLdAguI1QGfV52or5jVAmioYldTpNUDMf0i/fe01QP7UeOkm8TVAMCqpE9D0NUBjf9k9efg1QJbUCWgi/DVAyCk6ksv/NUD6fmq8dAM2QCzUmuYdBzZAXynLEMcKNkCRfvs6cA42QMTTK2UZEjZA9ihcj8IVNkAofoy5axk2QFvTvOMUHTZAjSjtDb4gNkDAfR04ZyQ2QPLSTWIQKDZAJCh+jLkrNkBWfa62Yi82QInS3uALMzZAvCcPC7U2NkDufD81Xjo2QCDSb18HPjZAUiegibBBNkCFfNCzWUU2QLjRAN4CSTZA6iYxCKxMNkAcfGEyVVA2QE7RkVz+UzZAgSbChqdXNkCze/KwUFs2QObQItv5XjZAGCZTBaNiNkBKe4MvTGY2QH3Qs1n1aTZAryXkg55tNkDiehSuR3E2QBTQRNjwdDZARiV1App4NkB4eqUsQ3w2QKvP1VbsfzZA3iQGgZWDNkAQejarPoc2QELPZtXnijZAdCSX/5CONkCneccpOpI2QNrO91PjlTZADCQofoyZNkA+eVioNZ02QHDOiNLeoDZAoyO5/IekNkDVeOkmMag2QAjOGVHaqzZAOiNKe4OvNkBseHqlLLM2QJ/Nqs/VtjZA0SLb+X66NkAEeAskKL42QDbNO07RwTZAaCJseHrFNkCad5yiI8k2QM3MzMzMzDZAACL99nXQNkAydy0hH9Q2QGTMXUvI1zZAliGOdXHbNkDJdr6fGt82QPzL7snD4jZALiEf9GzmNkBgdk8eFuo2QJLLf0i/7TZAxSCwcmjxNkD3deCcEfU2QCrLEMe6+DZAXCBB8WP8NkCOdXEbDQA3QMHKoUW2AzdA8x/Sb18HN0AmdQKaCAs3QFjKMsSxDjdAih9j7loSN0C8dJMYBBY3QO/Jw0KtGTdAIh/0bFYdN0BUdCSX/yA3QIbJVMGoJDdAuB6F61EoN0Drc7UV+ys3QB7J5T+kLzdAUB4Wak0zN0CCc0aU9jY3QLTIdr6fOjdA5x2n6Eg+N0AZc9cS8kE3QEzIBz2bRTdAfh04Z0RJN0CwcmiR7Uw3QOPHmLuWUDdAFR3J5T9UN0BIcvkP6Vc3QHrHKTqSWzdArBxaZDtfN0DecYqO5GI3QBHHuriNZjdARBzr4jZqN0B2cRsN4G03QKjGSzeJcTdA2ht8YTJ1N0ANcayL23g3QEDG3LWEfDdAchsN4C2AN0CkcD0K14M3QNbFbTSAhzdACRueXimLN0A7cM6I0o43QG7F/rJ7kjdAoBov3SSWN0DSb18Hzpk3QAXFjzF3nTdANxrAWyChN0Bqb/CFyaQ3QJzEILByqDdAzhlR2husN0AAb4EExa83QDPEsS5uszdAZhniWBe3N0CYbhKDwLo3QMrDQq1pvjdA/Bhz1xLCN0AvbqMBvMU3QGHD0ytlyTdAlBgEVg7NN0DGbTSAt9A3QPjCZKpg1DdAKhiV1AnYN0BdbcX+sts3QJDC9Shc3zdAwhcmUwXjN0D0bFZ9ruY3QCbChqdX6jdAWRe30QDuN0CMbOf7qfE3QL7BFyZT9TdA8BZIUPz4N0AibHh6pfw3QFXBqKROADhAiBbZzvcDOEC6awn5oAc4QOzAOSNKCzhAHhZqTfMOOEBRa5p3nBI4QIPAyqFFFjhAthX7y+4ZOEDoaiv2lx04QBrAWyBBIThATBWMSuokOEB/arx0kyg4QLK/7J48LDhA5BQdyeUvOEAWak3zjjM4QEi/fR04NzhAexSuR+E6OECuad5xij44QOC+DpwzQjhAEhQ/xtxFOEBEaW/whUk4QHe+nxovTThAqhPQRNhQOEDcaABvgVQ4QA6+MJkqWDhAQBNhw9NbOEBzaJHtfF84QKW9wRcmYzhA2BLyQc9mOEAKaCJseGo4QDy9UpYhbjhAbhKDwMpxOEChZ7Pqc3U4QNS84xQdeThABhIUP8Z8OEA4Z0Rpb4A4QGq8dJMYhDhAnRGlvcGHOEDQZtXnaos4QAK8BRIUjzhANBE2PL2SOEBmZmZmZpY4QJm7lpAPmjhAzBDHuridOED+ZffkYaE4QDC7Jw8LpThAYhBYObSoOECVZYhjXaw4QMe6uI0GsDhA+g/pt6+zOEAsZRniWLc4QF66SQwCuzhAkA96Nqu+OEDDZKpgVMI4QPa52or9xThAKA8LtabJOEBaZDvfT804QIy5awn50DhAvw6cM6LUOEDyY8xdS9g4QCS5/If02zhAVg4tsp3fOECIY13cRuM4QLu4jQbw5jhA7g2+MJnqOEAgY+5aQu44QFK4HoXr8ThAhA1Pr5T1OEC3Yn/ZPfk4QOm3rwPn/DhAHA3gLZAAOUBOYhBYOQQ5QIC3QILiBzlAsgxxrIsLOUDlYaHWNA85QBi30QDeEjlASgwCK4cWOUB8YTJVMBo5QK62Yn/ZHTlA4QuTqYIhOUAUYcPTKyU5QEa28/3UKDlAeAskKH4sOUCqYFRSJzA5QN21hHzQMzlAEAu1pnk3OUBCYOXQIjs5QHS1FfvLPjlApgpGJXVCOUDZX3ZPHkY5QAu1pnnHSTlAPgrXo3BNOUBwXwfOGVE5QKK0N/jCVDlA1AloImxYOUAHX5hMFVw5QDq0yHa+XzlAbAn5oGdjOUCeXinLEGc5QNCzWfW5ajlAAwmKH2NuOUA2XrpJDHI5QGiz6nO1dTlAmggbnl55OUDMXUvIB305QP+ye/KwgDlAMgisHFqEOUBkXdxGA4g5QJayDHGsizlAyAc9m1WPOUD7XG3F/pI5QC2yne+nljlAYAfOGVGaOUCSXP5D+p05QMSxLm6joTlA9gZfmEylOUApXI/C9ag5QFyxv+yerDlAjgbwFkiwOUDAWyBB8bM5QPKwUGuatzlAJQaBlUO7OUBYW7G/7L45QIqw4emVwjlAvAUSFD/GOUDuWkI+6Mk5QCGwcmiRzTlAVAWjkjrROUCGWtO849Q5QLivA+eM2DlA6gQ0ETbcOUAdWmQ73985QE+vlGWI4zlAggTFjzHnOUC0WfW52uo5QOauJeSD7jlAGARWDi3yOUBLWYY41vU5QH6utmJ/+TlAsAPnjCj9OUDiWBe30QA6QBSuR+F6BDpARwN4CyQIOkB6WKg1zQs6QKyt2F92DzpA3gIJih8TOkAQWDm0yBY6QEOtad5xGjpAdgKaCBseOkCoV8oyxCE6QNqs+lxtJTpADAIrhxYpOkA/V1uxvyw6QHGsi9toMDpApAG8BRI0OkDWVuwvuzc6QAisHFpkOzpAOgFNhA0/OkBtVn2utkI6QKCrrdhfRjpA0gDeAglKOkAEVg4tsk06QDarPldbUTpAaQBvgQRVOkCcVZ+rrVg6QM6qz9VWXDpAAAAAAABgOkAyVTAqqWM6QGWqYFRSZzpAmP+QfvtqOkDKVMGopG46QPyp8dJNcjpALv8h/fZ1OkBhVFInoHk6QJOpglFJfTpAxv6ye/KAOkD4U+Olm4Q6QCqpE9BEiDpAXP5D+u2LOkCPU3Qkl486QMKopE5AkzpA9P3UeOmWOkAmUwWjkpo6QFioNc07njpAi/1l9+ShOkC+UpYhjqU6QPCnxks3qTpAIv32deCsOkBUUiegibA6QIenV8oytDpAuvyH9Nu3OkDsUbgehbs6QB6n6EguvzpAUPwYc9fCOkCDUUmdgMY6QLWmeccpyjpA6Pup8dLNOkAaUdobfNE6QEymCkYl1TpAfvs6cM7YOkCxUGuad9w6QOSlm8Qg4DpAFvvL7snjOkBIUPwYc+c6QHqlLEMc6zpArfpcbcXuOkDgT42XbvI6QBKlvcEX9jpARPrt68D5OkB2Tx4Wav06QKmkTkATATtA3Pl+arwEO0AOT6+UZQg7QECk374ODDtAcvkP6bcPO0ClTkATYRM7QNejcD0KFztACvmgZ7MaO0A8TtGRXB47QG6jAbwFIjtAoPgx5q4lO0DTTWIQWCk7QAajkjoBLTtAOPjCZKowO0BqTfOOUzQ7QJyiI7n8NztAz/dT46U7O0ACTYQNTz87QDSitDf4QjtAZvfkYaFGO0CYTBWMSko7QMuhRbbzTTtA/vZ14JxRO0AwTKYKRlU7QGKh1jTvWDtAlPYGX5hcO0DHSzeJQWA7QPmgZ7PqYztALPaX3ZNnO0BeS8gHPWs7QJCg+DHmbjtAwvUoXI9yO0D1SlmGOHY7QCigibDheTtAWvW52op9O0CMSuoENIE7QL6fGi/dhDtA8fRKWYaIO0AkSnuDL4w7QFafq63YjztAiPTb14GTO0C6SQwCK5c7QO2ePCzUmjtAIPRsVn2eO0BSSZ2AJqI7QISezarPpTtAtvP91HipO0DpSC7/Ia07QBueXinLsDtATvOOU3S0O0CASL99Hbg7QLKd76fGuztA5PIf0m+/O0AXSFD8GMM7QEqdgCbCxjtAfPKwUGvKO0CuR+F6FM47QOCcEaW90TtAE/JBz2bVO0BGR3L5D9k7QHicoiO53DtAqvHSTWLgO0DcRgN4C+Q7QA+cM6K05ztAQvFjzF3rO0B0RpT2Bu87QKabxCCw8jtA2PD0Sln2O0ALRiV1Avo7QD2bVZ+r/TtAcPCFyVQBPECiRbbz/QQ8QNSa5h2nCDxABvAWSFAMPEA5RUdy+Q88QGyad5yiEzxAnu+nxksXPEDQRNjw9Bo8QAKaCBueHjxANe84RUciPEBoRGlv8CU8QJqZmZmZKTxAzO7Jw0ItPED+Q/rt6zA8QDGZKhiVNDxAZO5aQj44PECWQ4ts5zs8QMiYu5aQPzxA+u3rwDlDPEAtQxzr4kY8QF+YTBWMSjxAku18PzVOPEDEQq1p3lE8QPaX3ZOHVTxAKO0NvjBZPEBbQj7o2Vw8QI6XbhKDYDxAwOyePCxkPEDyQc9m1Wc8QCSX/5B+azxAV+wvuydvPECKQWDl0HI8QLyWkA96djxA7uvAOSN6PEAgQfFjzH08QFOWIY51gTxAhutRuB6FPEC4QILix4g8QOqVsgxxjDxAHOviNhqQPEBPQBNhw5M8QIGVQ4tslzxAtOpztRWbPEDmP6Tfvp48QBiV1AloojxASuoENBGmPEB9PzVeuqk8QLCUZYhjrTxA4umVsgyxPEAUP8bctbQ8QEaU9gZfuDxAeekmMQi8PECsPldbsb88QN6Th4VawzxAEOm3rwPHPEBCPujZrMo8QHWTGARWzjxAqOhILv/RPEDaPXlYqNU8QAyTqYJR2TxAPujZrPrcPEBxPQrXo+A8QKOSOgFN5DxA1udqK/bnPEAIPZtVn+s8QDqSy39I7zxAbOf7qfHyPECfPCzUmvY8QNKRXP5D+jxABOeMKO39PEA2PL1SlgE9QGiR7Xw/BT1Am+Ydp+gIPUDOO07RkQw9QACRfvs6ED1AMuauJeQTPUBkO99PjRc9QJeQD3o2Gz1AyuU/pN8ePUD8OnDOiCI9QC6QoPgxJj1AYOXQItspPUCTOgFNhC09QMWPMXctMT1A+ORhodY0PUAqOpLLfzg9QFyPwvUoPD1AjuTyH9I/PUDBOSNKe0M9QPSOU3QkRz1AJuSDns1KPUBYObTIdk49QIqO5PIfUj1AveMUHclVPUDwOEVHclk9QCKOdXEbXT1AVOOlm8RgPUCGONbFbWQ9QLmNBvAWaD1A7OI2GsBrPUAeOGdEaW89QFCNl24Scz1AguLHmLt2PUC1N/jCZHo9QOeMKO0Nfj1AGuJYF7eBPUBMN4lBYIU9QH6MuWsJiT1AsOHplbKMPUDjNhrAW5A9QBaMSuoElD1ASOF6FK6XPUB6Nqs+V5s9QKyL22gAnz1A3+ALk6miPUASNjy9UqY9QESLbOf7qT1AduCcEaWtPUCoNc07TrE9QNuK/WX3tD1ADuAtkKC4PUBANV66Sbw9QHKKjuTyvz1ApN++DpzDPUDXNO84Rcc9QAmKH2Puyj1APN9PjZfOPUBuNIC3QNI9QKCJsOHp1T1A0t7gC5PZPUAFNBE2PN09QDiJQWDl4D1Aat5xio7kPUCcM6K0N+g9QM6I0t7g6z1AAd4CCYrvPUA0MzMzM/M9QA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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genusrecordspairscoveragepvaluecorrelationR^2s.e.(larvae)equationonset(KitMg)rate(/KitMg)
0All1122510.100.14300.13930.01923.79y = e^(0.1 + 0.42* x)0.09910.4205
1Aedes3100.000.59190.59800.35306.03y = e^(0.21 + 0.88* x)0.21380.8800
2Anopheles000.001.00000.00000.00000.00no fit achieved0.00000.0000
3Culex5150.000.89850.07980.00641.31y = e^(-0.05 + 2.24* x)-0.04842.2447
4Other23510.020.12880.32610.10534.55y = e^(0.08 + 1.51* x)0.07901.5093
5Unknown811750.070.00200.33770.11371.72y = e^(-0.26 + 1.89* x)-0.26461.8881
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" ], "text/plain": [ " genus records pairs coverage pvalue correlation R^2 \\\n", "0 All 112 251 0.10 0.1430 0.1393 0.0192 \n", "1 Aedes 3 10 0.00 0.5919 0.5980 0.3530 \n", "2 Anopheles 0 0 0.00 1.0000 0.0000 0.0000 \n", "3 Culex 5 15 0.00 0.8985 0.0798 0.0064 \n", "4 Other 23 51 0.02 0.1288 0.3261 0.1053 \n", "5 Unknown 81 175 0.07 0.0020 0.3377 0.1137 \n", "\n", " s.e.(larvae) equation onset(KitMg) rate(/KitMg) \n", "0 3.79 y = e^(0.1 + 0.42* x) 0.0991 0.4205 \n", "1 6.03 y = e^(0.21 + 0.88* x) 0.2138 0.8800 \n", "2 0.00 no fit achieved 0.0000 0.0000 \n", "3 1.31 y = e^(-0.05 + 2.24* x) -0.0484 2.2447 \n", "4 4.55 y = e^(0.08 + 1.51* x) 0.0790 1.5093 \n", "5 1.72 y = e^(-0.26 + 1.89* x) -0.2646 1.8881 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('performing exponential curve fit')\n", "\n", "# perform\n", "perform('exponential', associations)" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "40d4fd2b9cfe4631bbc5ebddf9782e5e", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "fc7ca9150c624f308bed69251cd35f8d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Power Curve Fit" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: performing power law curve fit)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
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i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"11655\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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Ck374OnHMcQG40gLdAghxAOIlBYOWQHEAC3gIJip8cQMoyxLEurhxAlIeFWtO8HEBe3EYDeMscQCgxCKwc2hxA8IXJVMHoHEC62or9ZfccQIQvTKYKBh1AToQNT68UHUAW2c73UyMdQOAtkKD4MR1AqoJRSZ1AHUB01xLyQU8dQDws1JrmXR1ABoGVQ4tsHUDQ1VbsL3sdQJoqGJXUiR1AYn/ZPXmYHUAs1JrmHacdQPYoXI/CtR1AwH0dOGfEHUCK0t7gC9MdQFInoImw4R1AHHxhMlXwHUDm0CLb+f4dQLAl5IOeDR5AeHqlLEMcHkBCz2bV5yoeQAwkKH6MOR5A1njpJjFIHkCezarP1VYeQGgibHh6ZR5AMnctIR90HkD8y+7Jw4IeQMQgsHJokR5AjnVxGw2gHkBYyjLEsa4eQCIf9GxWvR5A6nO1FfvLHkC0yHa+n9oeQH4dOGdE6R5ASHL5D+n3HkAQx7q4jQYfQNobfGEyFR9ApHA9CtcjH0Buxf6yezIfQDgawFsgQR9AAG+BBMVPH0DKw0KtaV4fQJQYBFYObR9AXm3F/rJ7H0AmwoanV4ofQPAWSFD8mB9AumsJ+aCnH0CEwMqhRbYfQEwVjErqxB9AFmpN847TH0Dgvg6cM+IfQKoT0ETY8B9AcmiR7Xz/H0CeXinLEAcgQAMJih9jDiBAaLPqc7UVIEDMXUvIBx0gQDEIrBxaJCBAlrIMcawrIED7XG3F/jIgQGAHzhlROiBAxLEubqNBIEApXI/C9UggQI4G8BZIUCBA87BQa5pXIEBXW7G/7F4gQLwFEhQ/ZiBAIbByaJFtIECGWtO843QgQOoENBE2fCBAT6+UZYiDIEC0WfW52oogQBkEVg4tkiBAfa62Yn+ZIEDiWBe30aAgQEcDeAskqCBArK3YX3avIEAQWDm0yLYgQHUCmggbviBA2qz6XG3FIEA/V1uxv8wgQKQBvAUS1CBACKwcWmTbIEBtVn2utuIgQNIA3gIJ6iBAN6s+V1vxIECbVZ+rrfggQAAAAAAAACFAZapgVFIHIUDKVMGopA4hQC7/If32FSFAk6mCUUkdIUD4U+OlmyQhQF3+Q/rtKyFAwaikTkAzIUAmUwWjkjohQIv9ZffkQSFA8KfGSzdJIUBUUiegiVAhQLn8h/TbVyFAHqfoSC5fIUCDUUmdgGYhQOj7qfHSbSFATKYKRiV1IUCxUGuad3whQBb7y+7JgyFAe6UsQxyLIUDfT42XbpIhQET67evAmSFAqaROQBOhIUAOT6+UZaghQHL5D+m3ryFA16NwPQq3IUA8TtGRXL4hQKH4MeauxSFABaOSOgHNIUBqTfOOU9QhQM/3U+Ol2yFANKK0N/jiIUCYTBWMSuohQP32deCc8SFAYqHWNO/4IUDHSzeJQQAiQCz2l92TByJAkKD4MeYOIkD1SlmGOBYiQFr1udqKHSJAv58aL90kIkAjSnuDLywiQIj029eBMyJA7Z48LNQ6IkBSSZ2AJkIiQLbz/dR4SSJAG55eKctQIkCASL99HVgiQOXyH9JvXyJASZ2AJsJmIkCuR+F6FG4iQBPyQc9mdSJAeJyiI7l8IkDcRgN4C4QiQEHxY8xdiyJAppvEILCSIkALRiV1ApoiQHDwhclUoSJA1JrmHaeoIkA5RUdy+a8iQJ7vp8ZLtyJAApoIG56+IkBnRGlv8MUiQMzuycNCzSJAMZkqGJXUIkCVQ4ts59siQPrt68A54yJAX5hMFYzqIkDEQq1p3vEiQCjtDb4w+SJAjZduEoMAI0DyQc9m1QcjQFfsL7snDyNAu5aQD3oWI0AgQfFjzB0jQIXrUbgeJSNA6pWyDHEsI0BPQBNhwzMjQLPqc7UVOyNAGJXUCWhCI0B9PzVeukkjQOLplbIMUSNARpT2Bl9YI0CrPldbsV8jQBDpt68DZyNAdZMYBFZuI0DZPXlYqHUjQD7o2az6fCNAo5I6AU2EI0AIPZtVn4sjQGzn+6nxkiNA0ZFc/kOaI0A2PL1SlqEjQJvmHafoqCNA/5B++zqwI0BkO99PjbcjQMnlP6TfviNALpCg+DHGI0CTOgFNhM0jQPfkYaHW1CNAXI/C9SjcI0DBOSNKe+MjQCbkg57N6iNAio7k8h/yI0DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJ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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"4qxe+kURF0DTyr32iwcXQMn5dhPU/RZAJO7jUB70FkCNjV6vauoWQB/vQC+54BZAnFvl0AnXFkCWTaaUXM0WQKRx3nqxwxZAiKbogwi6FkBn/R+wYbAWQPW53/+8phZApFKDcxqdFkDUcGYLepMWQAbx5MfbiRZACeNaqT+AFkAuiiSwpXYWQHddntwNbRZAxwclL3hjFkAYaBWo5FkWQKqRzEdTUBZAMsynDsRGFkATlAT9Nj0WQIyaQBOsMxZA7cW5USMqFkDJMc64nCAWQCgv3EgYFxZAvkRCApYNFkAdL1/lFQQWQOrgkfKX+hVAD4M5KhzxFUD2dLWMoucVQLdMZRor3hVAU9eo07XUFUDjGOC4QssVQNZMa8rRwRVAIeaqCGO4FUB6j/9z9q4VQI0rygyMpRVAL9Vr0yOcFUCd30XIvZIVQLDWuetZiRVAEn8pPvh/FUB+1va/mHYVQPEThHE7bRVA6KczU+BjFUCWPGhlh1oVQB+2hKgwURVA1DLsHNxHFUBqCwLDiT4VQDPTKZs5NRVAYVjHpesrFUA4pD7jnyIVQE/781NWGRVAzd1L+A4QFUCaB6vQyQYVQK1wdt2G/RRAPU0TH0b0FED/DeeVB+sUQGpgV0LL4RRA7C7KJJHYFEAwoaU9Wc8UQFocUI0jxhRAQkMwFPC8FEC79qzSvrMUQMtVLcmPqhRA870Y+GKhFEBny9ZfOJgUQFNZzwAQjxRAH4Jq2+mFFECrnxDwxXwUQJFLKj+kcxRAcl8gyYRqFEAm9VuOZ2EUQBFnRo9MWBRAW1BJzDNPFEA3jc5FHUYUQCo7QPwIPRRATrkI8PYzFECSqJIh5yoUQAnsSJHZIRRAJqmWP84YFEALSOcsxQ8UQMhzplm+BhRApxpAxrn9E0BxbiBzt/QTQLfks2C36xNAGzdnj7niE0CYY6f/vdkTQMis4bHE0BNAMpqDps3HE0CW+Prd2L4TQCvatVjmtRNA/5YiF/asE0Asza8ZCKQTQDFhzGAcmxNAQH7n7DKSE0B8lnC+S4kTQFpj19VmgBNA4OWLM4R3E0D2Zv7Xo24TQLt3n8PFZRNAzPHf9ulcE0CY9zByEFQTQK/0AzY5SxNAEZ7KQmRCE0CC8vaYkTkTQNk6+zjBMBNAUgpKI/MnE0DlPlZYJx8TQI8Bk9hdFhNAscZzpJYNE0BbTmy80QQTQKak8CAP/BJACCJ10k7zEkCna27RkOoSQLBzUR7V4RJAsHmTuRvZEkDrCqqjZNASQK4CC92vxxJAr4osZv2+EkBiG4U/TbYSQFB8i2mfrRJAd8S25POkEkCeWn6xSpwSQLX1WdCjkxJALp3BQf+KEkBaqS0GXYISQMTDFh69eRJAlOf1iR9xEkDkYURKhGgSQCbSe1/rXxJAgSoWylRXEkAxsI2KwE4SQOP7XKEuRhJAH/r+Dp89EkCh6+7TETUSQL9lqPCGLBJAzFKnZf4jEkB88mczeBsSQEXaZlr0EhJAx/Ug23IKEkAyhxO28wESQKknvOt2+RFAqseYfPzwEUB3rydphOgRQH1/57EO4BFAuzBXV5vXEUAvFfZZKs8RQD/YQ7q7xhFAIn/AeE++EUBNaeyV5bURQOFQSBJ+rRFAFEtV7hilEUClyJQqtpwRQEKWiMdVlBFA/tyyxfeLEUDAIpYlnIMRQLJKtedCexFAsZWTDOxyEUDDorSUl2oRQIlvnIBFYhFAsVjP0PVZEUBtGtKFqFERQObQKaBdSRFAs/hbIBVBEUBQb+4GzzgRQJhzZ1SLMBFAOKZNCUooEUAwCigmCyARQEYFfqvOFxFAiGDXmZQPEUDASLzxXAcRQPdOtbMn/xBA8WhL4PT2EECs8Qd4xO4QQNypdHuW5hBAcbgb62reEEASq4fHQdYQQKZ2QxEbzhBA0XfayPbFEEB4c9ju1L0QQEyXyYO1tRBASno6iJitEEBEHbj8faUQQGfrz+FlnRBAxboPOFCVEEDfzAUAPY0QQC/PQDoshRBAtNtP5x19EEB8ecIHEnUQQDadKJwIbRBAvqkSpQFlEECscBEj/VwQQOUythb7VBBAL6GSgPtMEEDA3Dhh/kQQQNh3O7kDPRBASnYtiQs1EEAiTqLRFS0QQC7oLZMiJRBAoKBkzjEdEECeR9uDQxUQQOchJ7RXDRBAZendX24FEECkmysPD/sPQJrqyldG6w9AAPrHmoLbD0B+81HZw8sPQEzxmBQKvA9AgP/NTVWsD0BKHSOGpZwPQFA+y776jA9A6Ev6+FR9D0ByJuU1tG0PQKSmwXYYXg9A5J7GvIFOD0CY3CsJ8D4PQIopKl1jLw9ALk37udsfD0AeDtogWRAPQGYzApPbAA9A7oWwEWPxDkDe0SKe7+EOQBLolzmB0g5Aep9P5RfDDkCO1oqis7MOQL50i3JUpA5A7GuUVvqUDkDauelPpYUOQLJp0F9Vdg5AeJWOhwpnDkCKZ2vIxFcOQDAcryOESA5AGAOjmkg5DkDegJEuEioOQKIQxuDgGg5AjkWNsrQLDkB0zDSljfwNQFptC7pr7Q1AFg1h8k7eDUDyroZPN88NQER2ztIkwA1AEqiLfRexDUC8rBJRD6INQKYRuU4Mkw1A5YrVdw6EDUDv9L/NFXUNQFBW0VEiZg1AY+FjBTRXDUAO9tLpSkgNQIIjewBnOQ1A/Cm6SogqDUCM/O7JrhsNQOLCeX/aDA1AG9u7bAv+DECS2xeTQe8MQLiU8fN84AxA7xKukL3RDEBioLNqA8MMQO/GaYNOtAxABFI53J6lDECUUIx29JYMQP0WzlNPiAxA+0Brda95DECgs9HcFGsMQFKfcIt/XAxAxIG4gu9NDEAAKBvEZD8MQGewC1HfMAxAxoz+Kl8iDEBghGlT5BMMQAq2w8tuBQxAPpqFlf72C0A7BSmyk+gLQC8pKSMu2gtAWpgC6s3LC0A4RzMIc70LQLqOOn8drwtAdS6ZUM2gC0DnTtF9gpILQLKDZgg9hAtA3c3d8fx1C0Arnr07wmcLQGLXjeeMWQtAptDX9lxLC0DLVyZrMj0LQMSzBUYNLwtA+6YDie0gC0DGca810xILQM3UmU2+BAtAjBNV0q72CkDI9nTFpOgKQBjPjiig2gpAYHc5/aDMCkBsVw1Fp74KQH1mpAGzsApA6S2aNMSiCkCuy4vf2pQKQCX1FwT3hgpArPneoxh5CkBVxYLAP2sKQKLjpltsXQpAQILwdp5PCkDVcwYU1kEKQMoykTQTNApAHeQ62lUmCkA5Wq8GnhgKQOEXnLvrCgpAEFOw+j79CUDs95zFl+8JQLqrFB724QlA4s/LBVrUCUDwhHh+w8YJQKCt0okyuQlA8vGTKaerCUBMwndfIZ4JQJtaOy2hkAlAfMWdlCaDCUBs31+XsXUJQA5aRDdCaAlAa78PdthaCUA4dYhVdE0JQDTAdtcVQAlAdcek/bwyCUDil97JaSUJQJIn8j0cGAlASlmvW9QKCUDz/+ckkv0IQDLib5tV8AhA6r0cwR7jCEDnS8aX7dUIQG1DRiHCyAhA/F14X5y7CED6WjpUfK4IQHsDbAFioQhAAi7vaE2UCEBiwqeMPocIQJi9e241eghAtjVTEDJtCEDMXRh0NGAIQPuJt5s8UwhAazMfiUpGCEBr/D8+XjkIQIq0DL13LAhAvlx6B5cfCECkK4AfvBIIQLWRFwfnBQhAnj08wBf5B0CJIOxMTuwHQI9yJ6+K3wdAIrfw6MzSB0CKwUz8FMYHQGi5QutiuQdAWB/ct7asB0CI0SRkEKAHQG0QK/JvkwdAeYP/Y9WGB0DqPbW7QHoHQKLDYfuxbQdABg4dJSlhB0DykAE7plQHQLw/LD8pSAdARpK8M7I7B0ASitQaQS8HQHq3mPbVIgdA2j4wyXAWB0Du3cSUEQoHQCLxglu4/QZA+HiZH2XxBkCGHzrjF+UGQP09majQ2AZAR+LtcY/MBkCs1HFBVMAGQIidYRkftAZAJIv8+++nBkCLt4TrxpsGQHgOP+qjjwZAVVNz+oaDBkBWJ2wecHcGQJQPd1hfawZATnvkqlRfBkAqygcYUFMGQJhSN6JRRwZARmjMS1k7BkChYiMXZy8GQG6jmwZ7IwZAd52XHJUXBkBU23xbtQsGQDwGtMXb/wVA9uyoXQj0BUDWisolO+gFQOAOiyB03AVA7OJfULPQBUD6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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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IEA/V1uxv8wgQKQBvAUS1CBACKwcWmTbIEBtVn2utuIgQNIA3gIJ6iBAN6s+V1vxIECbVZ+rrfggQAAAAAAAACFAZapgVFIHIUDKVMGopA4hQC7/If32FSFAk6mCUUkdIUD4U+OlmyQhQF3+Q/rtKyFAwaikTkAzIUAmUwWjkjohQIv9ZffkQSFA8KfGSzdJIUBUUiegiVAhQLn8h/TbVyFAHqfoSC5fIUCDUUmdgGYhQOj7qfHSbSFATKYKRiV1IUCxUGuad3whQBb7y+7JgyFAe6UsQxyLIUDfT42XbpIhQET67evAmSFAqaROQBOhIUAOT6+UZaghQHL5D+m3ryFA16NwPQq3IUA8TtGRXL4hQKH4MeauxSFABaOSOgHNIUBqTfOOU9QhQM/3U+Ol2yFANKK0N/jiIUCYTBWMSuohQP32deCc8SFAYqHWNO/4IUDHSzeJQQAiQCz2l92TByJAkKD4MeYOIkD1SlmGOBYiQFr1udqKHSJAv58aL90kIkAjSnuDLywiQIj029eBMyJA7Z48LNQ6IkBSSZ2AJkIiQLbz/dR4SSJAG55eKctQIkCASL99HVgiQOXyH9JvXyJASZ2AJsJmIkCuR+F6FG4iQBPyQc9mdSJAeJyiI7l8IkDcRgN4C4QiQEHxY8xdiyJAppvEILCSIkALRiV1ApoiQHDwhclUoSJA1JrmHaeoIkA5RUdy+a8iQJ7vp8ZLtyJAApoIG56+IkBnRGlv8MUiQMzuycNCzSJAMZkqGJXUIkCVQ4ts59siQPrt68A54yJAX5hMFYzqIkDEQq1p3vEiQCjtDb4w+SJAjZduEoMAI0DyQc9m1QcjQFfsL7snDyNAu5aQD3oWI0AgQfFjzB0jQIXrUbgeJSNA6pWyDHEsI0BPQBNhwzMjQLPqc7UVOyNAGJXUCWhCI0B9PzVeukkjQOLplbIMUSNARpT2Bl9YI0CrPldbsV8jQBDpt68DZyNAdZMYBFZuI0DZPXlYqHUjQD7o2az6fCNAo5I6AU2EI0AIPZtVn4sjQGzn+6nxkiNA0ZFc/kOaI0A2PL1SlqEjQJvmHafoqCNA/5B++zqwI0BkO99PjbcjQMnlP6TfviNALpCg+DHGI0CTOgFNhM0jQPfkYaHW1CNAXI/C9SjcI0DBOSNKe+MjQCbkg57N6iNAio7k8h/yI0DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJwrQGuad5yioytA0ETY8PSqK0A17zhFR7IrQJqZmZmZuStA/kP67evAK0Bj7lpCPsgrQMiYu5aQzytALUMc6+LWK0CR7Xw/Nd4rQPaX3ZOH5StAW0I+6NnsK0DA7J48LPQrQCSX/5B++ytAiUFg5dACLEDu68A5IwosQFOWIY51ESxAt0CC4scYLEAc6+I2GiAsQIGVQ4tsJyxA5j+k374uLEBK6gQ0ETYsQK+UZYhjPSxAFD/G3LVELEB56SYxCEwsQN2Th4VaUyxAQj7o2axaLECn6Egu/2EsQAyTqYJRaSxAcT0K16NwLEDV52or9ncsQDqSy39IfyxAnzws1JqGLEAE54wo7Y0sQGiR7Xw/lSxAzTtO0ZGcLEAy5q4l5KMsQJeQD3o2qyxA+zpwzoiyLEBg5dAi27ksQMWPMXctwSxAKjqSy3/ILECO5PIf0s8sQPOOU3Qk1yxAWDm0yHbeLEC94xQdyeUsQCGOdXEb7SxAhjjWxW30LEDr4jYawPssQFCNl24SAy1AtTf4wmQKLUAZ4lgXtxEtQH6MuWsJGS1A4zYawFsgLUBI4XoUrictQKyL22gALy1AETY8vVI2LUB24JwRpT0tQNuK/WX3RC1APzVeuklMLUCk374OnFMtQAmKH2PuWi1AbjSAt0BiLUDS3uALk2ktQDeJQWDlcC1AnDOitDd4LUAB3gIJin8tQGWIY13chi1AyjLEsS6OLUAv3SQGgZUtQJSHhVrTnC1A+THmriWkLUBd3EYDeKstQMKGp1fKsi1AJzEIrBy6LUCM22gAb8EtQPCFyVTByC1AVTAqqRPQLUC62or9ZdctQB+F61G43i1Agy9MpgrmLUDo2az6XO0tQE2EDU+v9C1Asi5uowH8LUAW2c73UwMuQHuDL0ymCi5A4C2QoPgRLkBF2PD0ShkuQKmCUUmdIC5ADi2yne8nLkBz1xLyQS8uQNiBc0aUNi5APSzUmuY9LkCh1jTvOEUuQAaBlUOLTC5Aayv2l91TLkDQ1VbsL1suQDSAt0CCYi5AmSoYldRpLkD+1HjpJnEuQGN/2T15eC5Axyk6kst/LkAs1JrmHYcuQJF++zpwji5A9ihcj8KVLkBa07zjFJ0uQL99HThnpC5AJCh+jLmrLkCJ0t7gC7MuQO18PzVeui5AUiegibDBLkC30QDeAskuQBx8YTJV0C5AgSbChqfXLkDl0CLb+d4uQEp7gy9M5i5AryXkg57tLkAU0ETY8PQuQHh6pSxD/C5A3SQGgZUDL0BCz2bV5wovQKd5xyk6Ei9ACyQofowZL0BwzojS3iAvQNV46SYxKC9AOiNKe4MvL0CezarP1TYvQAN4CyQoPi9AaCJseHpFL0DNzMzMzEwvQA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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xQF8D+h/Tb10EXwDQzMzMzMxfAat5xio4kF8CgibDh6RUXwNg07zhFBxfADuAtkKD4FsBEi2zn++kWwHo2qz5X2xbAsuHplbLMFsDojCjtDb4WwB44Z0RprxbAVOOlm8SgFsCMjuTyH5IWwMI5I0p7gxbA+ORhodZ0FsAukKD4MWYWwGQ730+NVxbAnOYdp+hIFsDSkVz+QzoWwAg9m1WfKxbAPujZrPocFsB2kxgEVg4WwKw+V1ux/xXA4umVsgzxFcAYldQJaOIVwFBAE2HD0xXAhutRuB7FFcC8lpAPerYVwPJBz2bVpxXAKu0NvjCZFcBgmEwVjIoVwJZDi2znexXAzO7Jw0JtFcAEmggbnl4VwDpFR3L5TxXAcPCFyVRBFcCmm8QgsDIVwNxGA3gLJBXAFPJBz2YVFcBKnYAmwgYVwIBIv30d+BTAtvP91HjpFMDunjws1NoUwCRKe4MvzBTAWvW52oq9FMCQoPgx5q4UwMhLN4lBoBTA/vZ14JyRFMA0orQ3+IIUwGpN845TdBTAovgx5q5lFMDYo3A9ClcUwA5Pr5RlSBTARPrt68A5FMB8pSxDHCsUwLJQa5p3HBTA6Pup8dINFMAep+hILv8TwFRSJ6CJ8BPAjP1l9+ThE8DCqKROQNMTwPhT46WbxBPAMP8h/fa1E8BmqmBUUqcTwJxVn6utmBPA0gDeAgmKE8AKrBxaZHsTwEBXW7G/bBPAdgKaCBteE8Csrdhfdk8TwORYF7fRQBPAGgRWDi0yE8BQr5RliCMTwIZa07zjFBPAvgUSFD8GE8D0sFBrmvcSwCpcj8L16BLAYAfOGVHaEsCWsgxxrMsSwM5dS8gHvRLABAmKH2OuEsA6tMh2vp8SwHBfB84ZkRLAqApGJXWCEsDetYR80HMSwBRhw9MrZRLASgwCK4dWEsCCt0CC4kcSwLhif9k9ORLA7g2+MJkqEsAkufyH9BsSwFxkO99PDRLAkg96Nqv+EcDIuriNBvARwP5l9+Rh4RHANhE2PL3SEcBsvHSTGMQRwKJns+pztRHA2BLyQc+mEcAOvjCZKpgRwEZpb/CFiRHAfBSuR+F6EcCyv+yePGwRwOhqK/aXXRHAIBZqTfNOEcBWwaikTkARwIxs5/upMRHAwhcmUwUjEcD6wmSqYBQRwDBuowG8BRHAZhniWBf3EMCcxCCwcugQwNRvXwfO2RDAChueXinLEMBAxty1hLwQwHZxGw3grRDArhxaZDufEMDkx5i7lpAQwBpz1xLygRDAUB4Wak1zEMCGyVTBqGQQwL50kxgEVhDA9B/Sb19HEMAqyxDHujgQwGB2Tx4WKhDAmCGOdXEbEMDOzMzMzAwQwAjwFkhQ/A/AdEaU9gbfD8DknBGlvcEPwFDzjlN0pA/AvEkMAiuHD8AooImw4WkPwJj2Bl+YTA/ABE2EDU8vD8BwowG8BRIPwNz5fmq89A7ATFD8GHPXDsC4pnnHKboOwCT99nXgnA7AkFN0JJd/DsD8qfHSTWIOwGwAb4EERQ7A2FbsL7snDsBErWnecQoOwLAD54wo7Q3AIFpkO9/PDcCMsOHplbINwPgGX5hMlQ3AZF3cRgN4DcDUs1n1uVoNwEAK16NwPQ3ArGBUUicgDcAYt9EA3gINwIgNT6+U5QzA9GPMXUvIDMBgukkMAqsMwMwQx7q4jQzAPGdEaW9wDMCovcEXJlMMwBQUP8bcNQzAgGq8dJMYDMDwwDkjSvsLwFwXt9EA3gvAyG00gLfAC8A0xLEubqMLwKAaL90khgvAEHGsi9toC8B8xyk6kksLwOgdp+hILgvAVHQkl/8QC8DEyqFFtvMKwDAhH/Rs1grAnHecoiO5CsAIzhlR2psKwHgkl/+QfgrA5HoUrkdhCsBQ0ZFc/kMKwLwnDwu1JgrALH6MuWsJCsCY1AloIuwJwAQrhxbZzgnAcIEExY+xCcDg14FzRpQJwEwu/yH9dgnAuIR80LNZCcAk2/l+ajwJwJAxdy0hHwnAAIj029cBCcBs3nGKjuQIwNg07zhFxwjARIts5/upCMC04emVsowIwCA4Z0RpbwjAjI7k8h9SCMD45GGh1jQIwGg730+NFwjA1JFc/kP6B8BA6Nms+twHwKw+V1uxvwfAHJXUCWiiB8CI61G4HoUHwPRBz2bVZwfAYJhMFYxKB8DQ7snDQi0HwDxFR3L5DwfAqJvEILDyBsAU8kHPZtUGwIBIv30duAbA8J48LNSaBsBc9bnain0GwMhLN4lBYAbANKK0N/hCBsCk+DHmriUGwBBPr5RlCAbAfKUsQxzrBcDo+6nx0s0FwFhSJ6CJsAXAxKikTkCTBcAw/yH99nUFwJxVn6utWAXADKwcWmQ7BcB4ApoIGx4FwORYF7fRAAXAUK+UZYjjBMDABRIUP8YEwCxcj8L1qATAmLIMcayLBMAECYofY24EwHBfB84ZUQTA4LWEfNAzBMBMDAIrhxYEwLhif9k9+QPAJLn8h/TbA8CUD3o2q74DwABm9+RhoQPAbLx0kxiEA8DYEvJBz2YDwEhpb/CFSQPAtL/snjwsA8AgFmpN8w4DwIxs5/up8QLA/MJkqmDUAsBoGeJYF7cCwNRvXwfOmQLAQMbctYR8AsCwHFpkO18CwBxz1xLyQQLAiMlUwagkAsD0H9JvXwcCwGB2Tx4W6gHA0MzMzMzMAcA8I0p7g68BwKh5xyk6kgHAFNBE2PB0AcCEJsKGp1cBwPB8PzVeOgHAXNO84xQdAcDIKTqSy/8AwDiAt0CC4gDApNY07zjFAMAQLbKd76cAwHyDL0ymigDA7Nms+lxtAMBYMCqpE1AAwMSGp1fKMgDAMN0kBoEVAMBAZ0Rpb/D/vxgUP8bctf+/8MA5I0p7/7/IbTSAt0D/v6AaL90kBv+/gMcpOpLL/r9YdCSX/5D+vzAhH/RsVv6/CM4ZUdob/r/oehSuR+H9v8AnDwu1pv2/mNQJaCJs/b9wgQTFjzH9v1Au/yH99vy/KNv5fmq8/L8AiPTb14H8v9g07zhFR/y/uOHplbIM/L+QjuTyH9L7v2g730+Nl/u/QOjZrPpc+78gldQJaCL7v/hBz2bV5/q/0O7Jw0Kt+r+om8QgsHL6v4BIv30dOPq/YPW52or9+b84orQ3+ML5vxBPr5RliPm/6Pup8dJN+b/IqKROQBP5v6BVn6ut2Pi/eAKaCBue+L9Qr5RliGP4vzBcj8L1KPi/CAmKH2Pu97/gtYR80LP3v7hif9k9efe/mA96Nqs+979wvHSTGAT3v0hpb/CFyfa/IBZqTfOO9r8Aw2SqYFT2v9hvXwfOGfa/sBxaZDvf9b+IyVTBqKT1v2B2Tx4WavW/QCNKe4Mv9b8Y0ETY8PT0v/B8PzVeuvS/yCk6kst/9L+o1jTvOEX0v4CDL0ymCvS/WDAqqRPQ878w3SQGgZXzvxCKH2PuWvO/6DYawFsg87/A4xQdyeXyv5iQD3o2q/K/eD0K16Nw8r9Q6gQ0ETbyvyiX/5B++/G/AET67evA8b/g8PRKWYbxv7id76fGS/G/kErqBDQR8b9o9+Rhodbwv0Ck374OnPC/IFHaG3xh8L/4/dR46Sbwv6BVn6ut2O+/UK+UZYhj778QCYofY+7uv8Bif9k9ee6/cLx0kxgE7r8gFmpN847tv+BvXwfOGe2/kMlUwaik7L9AI0p7gy/sv/B8PzVeuuu/sNY07zhF679gMCqpE9DqvxCKH2PuWuq/wOMUHcnl6b+APQrXo3DpvzCX/5B+++i/4PD0SlmG6L+QSuoENBHov0Ck374OnOe/AP7UeOkm57+wV8oyxLHmv2Cxv+yePOa/EAu1pnnH5b/QZKpgVFLlv4C+nxov3eS/MBiV1Alo5L/gcYqO5PLjv6DLf0i/feO/UCV1ApoI478Af2q8dJPiv7DYX3ZPHuK/cDJVMCqp4b8gjErqBDThv9DlP6TfvuC/gD81XrpJ4L+AMlUwKqnfv+DlP6Tfvt6/QJkqGJXU3b+gTBWMSurcvwAAAAAAANy/gLPqc7UV27/gZtXnaivav0AawFsgQdm/oM2qz9VW2L8ggZVDi2zXv4A0gLdAgta/4OdqK/aX1b9Am1Wfq63Uv8BOQBNhw9O/IAIrhxbZ0r+AtRX7y+7Rv+BoAG+BBNG/YBzr4jYa0L+An6ut2F/Ov0AGgZVDi8y/AG1Wfa62yr8A1CtlGeLIv8A6AU2EDce/gKHWNO84xb9ACKwcWmTDv0BvgQTFj8G/AKyt2F92v7+AeVioNc27vwBHA3gLJLi/gBSuR+F6tL+A4lgXt9GwvwBgB84ZUaq/APtcbcX+or8ALGUZ4liXvwDIILByaIG/AJgRpb3Bdz8AMJkqGJWUPwD99nXgnKE/AGGh1jTvqD8A46WbxCCwP4AV+8vuybM/AEhQ/Bhztz8AeqUsQxy7P4Cs+lxtxb4/gO+nxks3wT/AiNLe4AvDP8Ah/fZ14MQ/ALsnDwu1xj9AVFInoInIP4DtfD81Xso/wIanV8oyzD/AH9JvXwfOPwC5/If0288/IKkT0ETY0D/A9Shcj8LRP0BCPujZrNI/4I5TdCSX0z+A22gAb4HUPyAofoy5a9U/oHSTGARW1j9AwaikTkDXP+ANvjCZKtg/gFrTvOMU2T8Ap+hILv/ZP6Dz/dR46do/QEATYcPT2z/gjCjtDb7cP2DZPXlYqN0/ACZTBaOS3j+gcmiR7XzfP6Dfvg6cM+A/8IXJVMGo4D8wLNSa5h3hP4DS3uALk+E/0HjpJjEI4j8gH/RsVn3iP2DF/rJ78uI/sGsJ+aBn4z8AEhQ/xtzjP1C4HoXrUeQ/kF4pyxDH5D/gBDQRNjzlPzCrPldbseU/gFFJnYAm5j/A91PjpZvmPxCeXinLEOc/YERpb/CF5z+w6nO1FfvnP/CQfvs6cOg/QDeJQWDl6D+Q3ZOHhVrpP+CDns2qz+k/MCqpE9BE6j9w0LNZ9bnqP8B2vp8aL+s/EB3J5T+k6z9gw9MrZRnsP6Bp3nGKjuw/8A/pt68D7T9AtvP91HjtP5Bc/kP67e0/0AIJih9j7j8gqRPQRNjuP3BPHhZqTe8/wPUoXI/C7z8AzhlR2hvwPyghH/RsVvA/UHQkl/+Q8D94xyk6ksvwP5gaL90kBvE/wG00gLdA8T/owDkjSnvxPxAUP8bctfE/OGdEaW/w8T9YukkMAivyP4ANT6+UZfI/qGBUUieg8j/Qs1n1udryP/AGX5hMFfM/GFpkO99P8z9ArWnecYrzP2gAb4EExfM/iFN0JJf/8z+wpnnHKTr0P9j5fmq8dPQ/AE2EDU+v9D8goImw4en0P0jzjlN0JPU/cEaU9gZf9T+YmZmZmZn1Pw==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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genusrecordspairscoveragepvaluecorrelationR^2s.e.(larvae)equationonset(KitMg)height(larvae)power
0All1122510.100.7564-0.0296-0.30464.37y = 7.04 * (x - c)^0.977.04440.9710-0.0000
1Aedes3100.000.56910.62640.39215.85y = 3.24 * (x - c)^1.613.24251.61321.3645
2Anopheles000.001.00000.00000.00000.00no fit achieved0.00000.00000.0000
3Culex5150.000.94060.0467-3.30512.72y = 7.36 * (x - c)^2.057.35662.0519-0.0000
4Other23510.020.06700.38850.15064.43y = 1.97 * (x - c)^3.551.97023.55480.5290
5Unknown811750.070.00160.34460.11811.72y = 10.91 * (x - c)^0.1210.91450.1167-1.5706
\n", "
" ], "text/plain": [ " genus records pairs coverage pvalue correlation R^2 \\\n", "0 All 112 251 0.10 0.7564 -0.0296 -0.3046 \n", "1 Aedes 3 10 0.00 0.5691 0.6264 0.3921 \n", "2 Anopheles 0 0 0.00 1.0000 0.0000 0.0000 \n", "3 Culex 5 15 0.00 0.9406 0.0467 -3.3051 \n", "4 Other 23 51 0.02 0.0670 0.3885 0.1506 \n", "5 Unknown 81 175 0.07 0.0016 0.3446 0.1181 \n", "\n", " s.e.(larvae) equation onset(KitMg) height(larvae) \\\n", "0 4.37 y = 7.04 * (x - c)^0.97 7.0444 0.9710 \n", "1 5.85 y = 3.24 * (x - c)^1.61 3.2425 1.6132 \n", "2 0.00 no fit achieved 0.0000 0.0000 \n", "3 2.72 y = 7.36 * (x - c)^2.05 7.3566 2.0519 \n", "4 4.43 y = 1.97 * (x - c)^3.55 1.9702 3.5548 \n", "5 1.72 y = 10.91 * (x - c)^0.12 10.9145 0.1167 \n", "\n", " power \n", "0 -0.0000 \n", "1 1.3645 \n", "2 0.0000 \n", "3 -0.0000 \n", "4 0.5290 \n", "5 -1.5706 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('performing power law curve fit')\n", "\n", "# perform\n", "perform('power', associations)" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ab8265cfd28c4701b8d04215a6267508", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "12e0e202b1314c25b46faa77c1b8e9b3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gaussian Curve Fit" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: performing gaussian curve fit)\n" ] }, { "data": { "text/html": [ "\n", "
\n", " \n", " Loading BokehJS ...\n", "
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", " root._bokeh_onload_callbacks = [];\n", " root._bokeh_is_loading = undefined;\n", " }\n", "\n", " var JS_MIME_TYPE = 'application/javascript';\n", " var HTML_MIME_TYPE = 'text/html';\n", " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " var CLASS_NAME = 'output_bokeh rendered_html';\n", "\n", " /**\n", " * Render data to the DOM node\n", " */\n", " function render(props, node) {\n", " var script = document.createElement(\"script\");\n", " node.appendChild(script);\n", " }\n", "\n", " /**\n", " * Handle when an output is cleared or removed\n", " */\n", " function handleClearOutput(event, handle) {\n", " var cell = handle.cell;\n", "\n", " var id = cell.output_area._bokeh_element_id;\n", " var server_id = cell.output_area._bokeh_server_id;\n", " // Clean up Bokeh references\n", " if (id != null && id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", "\n", " if (server_id !== undefined) {\n", " // Clean up Bokeh references\n", " var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", " cell.notebook.kernel.execute(cmd, {\n", " iopub: {\n", " output: function(msg) {\n", " var id = msg.content.text.trim();\n", " if (id in Bokeh.index) {\n", " Bokeh.index[id].model.document.clear();\n", " delete Bokeh.index[id];\n", " }\n", " }\n", " }\n", " });\n", " // Destroy server and session\n", " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", " cell.notebook.kernel.execute(cmd);\n", " }\n", " }\n", "\n", " /**\n", " * Handle when a new output is added\n", " */\n", " function handleAddOutput(event, handle) {\n", " var output_area = handle.output_area;\n", " var output = handle.output;\n", "\n", " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", " return\n", " }\n", "\n", " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", "\n", " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", " // store reference to embed id on output_area\n", " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", " }\n", " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", " var bk_div = document.createElement(\"div\");\n", " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", " var script_attrs = bk_div.children[0].attributes;\n", " for (var i = 0; i < script_attrs.length; i++) {\n", " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", " }\n", " // store reference to server id on output_area\n", " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", " }\n", " }\n", "\n", " function register_renderer(events, OutputArea) {\n", "\n", " function append_mime(data, metadata, element) {\n", " // create a DOM node to render to\n", " var toinsert = this.create_output_subarea(\n", " metadata,\n", " CLASS_NAME,\n", " EXEC_MIME_TYPE\n", " );\n", " this.keyboard_manager.register_events(toinsert);\n", " // Render to node\n", " var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", " render(props, toinsert[toinsert.length - 1]);\n", " element.append(toinsert);\n", " return toinsert\n", " }\n", "\n", " /* Handle when an output is cleared or removed */\n", " events.on('clear_output.CodeCell', handleClearOutput);\n", " events.on('delete.Cell', handleClearOutput);\n", "\n", " /* Handle when a new output is added */\n", " events.on('output_added.OutputArea', handleAddOutput);\n", "\n", " /**\n", " * Register the mime type and append_mime function with output_area\n", " */\n", " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", " /* Is output safe? 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\\n\"+\n", " \"

\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
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i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };var element = document.getElementById(\"13192\");\n", " if (element == null) {\n", " console.error(\"Bokeh: ERROR: autoload.js configured with elementid '13192' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " \n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.4.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.4.0.min.js\"];\n", " var css_urls = [];\n", " \n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " function(Bokeh) {\n", " \n", " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if (root.Bokeh !== undefined || force === true) {\n", " \n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", " if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < root._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!root._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"13192\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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xdy0hXxfAxty1hHxQF8D+h/Tb10EXwDQzMzMzMxfAat5xio4kF8CgibDh6RUXwNg07zhFBxfADuAtkKD4FsBEi2zn++kWwHo2qz5X2xbAsuHplbLMFsDojCjtDb4WwB44Z0RprxbAVOOlm8SgFsCMjuTyH5IWwMI5I0p7gxbA+ORhodZ0FsAukKD4MWYWwGQ730+NVxbAnOYdp+hIFsDSkVz+QzoWwAg9m1WfKxbAPujZrPocFsB2kxgEVg4WwKw+V1ux/xXA4umVsgzxFcAYldQJaOIVwFBAE2HD0xXAhutRuB7FFcC8lpAPerYVwPJBz2bVpxXAKu0NvjCZFcBgmEwVjIoVwJZDi2znexXAzO7Jw0JtFcAEmggbnl4VwDpFR3L5TxXAcPCFyVRBFcCmm8QgsDIVwNxGA3gLJBXAFPJBz2YVFcBKnYAmwgYVwIBIv30d+BTAtvP91HjpFMDunjws1NoUwCRKe4MvzBTAWvW52oq9FMCQoPgx5q4UwMhLN4lBoBTA/vZ14JyRFMA0orQ3+IIUwGpN845TdBTAovgx5q5lFMDYo3A9ClcUwA5Pr5RlSBTARPrt68A5FMB8pSxDHCsUwLJQa5p3HBTA6Pup8dINFMAep+hILv8TwFRSJ6CJ8BPAjP1l9+ThE8DCqKROQNMTwPhT46WbxBPAMP8h/fa1E8BmqmBUUqcTwJxVn6utmBPA0gDeAgmKE8AKrBxaZHsTwEBXW7G/bBPAdgKaCBteE8Csrdhfdk8TwORYF7fRQBPAGgRWDi0yE8BQr5RliCMTwIZa07zjFBPAvgUSFD8GE8D0sFBrmvcSwCpcj8L16BLAYAfOGVHaEsCWsgxxrMsSwM5dS8gHvRLABAmKH2OuEsA6tMh2vp8SwHBfB84ZkRLAqApGJXWCEsDetYR80HMSwBRhw9MrZRLASgwCK4dWEsCCt0CC4kcSwLhif9k9ORLA7g2+MJkqEsAkufyH9BsSwFxkO99PDRLAkg96Nqv+EcDIuriNBvARwP5l9+Rh4RHANhE2PL3SEcBsvHSTGMQRwKJns+pztRHA2BLyQc+mEcAOvjCZKpgRwEZpb/CFiRHAfBSuR+F6EcCyv+yePGwRwOhqK/aXXRHAIBZqTfNOEcBWwaikTkARwIxs5/upMRHAwhcmUwUjEcD6wmSqYBQRwDBuowG8BRHAZhniWBf3EMCcxCCwcugQwNRvXwfO2RDAChueXinLEMBAxty1hLwQwHZxGw3grRDArhxaZDufEMDkx5i7lpAQwBpz1xLygRDAUB4Wak1zEMCGyVTBqGQQwL50kxgEVhDA9B/Sb19HEMAqyxDHujgQwGB2Tx4WKhDAmCGOdXEbEMDOzMzMzAwQwAjwFkhQ/A/AdEaU9gbfD8DknBGlvcEPwFDzjlN0pA/AvEkMAiuHD8AooImw4WkPwJj2Bl+YTA/ABE2EDU8vD8BwowG8BRIPwNz5fmq89A7ATFD8GHPXDsC4pnnHKboOwCT99nXgnA7AkFN0JJd/DsD8qfHSTWIOwGwAb4EERQ7A2FbsL7snDsBErWnecQoOwLAD54wo7Q3AIFpkO9/PDcCMsOHplbINwPgGX5hMlQ3AZF3cRgN4DcDUs1n1uVoNwEAK16NwPQ3ArGBUUicgDcAYt9EA3gINwIgNT6+U5QzA9GPMXUvIDMBgukkMAqsMwMwQx7q4jQzAPGdEaW9wDMCovcEXJlMMwBQUP8bcNQzAgGq8dJMYDMDwwDkjSvsLwFwXt9EA3gvAyG00gLfAC8A0xLEubqMLwKAaL90khgvAEHGsi9toC8B8xyk6kksLwOgdp+hILgvAVHQkl/8QC8DEyqFFtvMKwDAhH/Rs1grAnHecoiO5CsAIzhlR2psKwHgkl/+QfgrA5HoUrkdhCsBQ0ZFc/kMKwLwnDwu1JgrALH6MuWsJCsCY1AloIuwJwAQrhxbZzgnAcIEExY+xCcDg14FzRpQJwEwu/yH9dgnAuIR80LNZCcAk2/l+ajwJwJAxdy0hHwnAAIj029cBCcBs3nGKjuQIwNg07zhFxwjARIts5/upCMC04emVsowIwCA4Z0RpbwjAjI7k8h9SCMD45GGh1jQIwGg730+NFwjA1JFc/kP6B8BA6Nms+twHwKw+V1uxvwfAHJXUCWiiB8CI61G4HoUHwPRBz2bVZwfAYJhMFYxKB8DQ7snDQi0HwDxFR3L5DwfAqJvEILDyBsAU8kHPZtUGwIBIv30duAbA8J48LNSaBsBc9bnain0GwMhLN4lBYAbANKK0N/hCBsCk+DHmriUGwBBPr5RlCAbAfKUsQxzrBcDo+6nx0s0FwFhSJ6CJsAXAxKikTkCTBcAw/yH99nUFwJxVn6utWAXADKwcWmQ7BcB4ApoIGx4FwORYF7fRAAXAUK+UZYjjBMDABRIUP8YEwCxcj8L1qATAmLIMcayLBMAECYofY24EwHBfB84ZUQTA4LWEfNAzBMBMDAIrhxYEwLhif9k9+QPAJLn8h/TbA8CUD3o2q74DwABm9+RhoQPAbLx0kxiEA8DYEvJBz2YDwEhpb/CFSQPAtL/snjwsA8AgFmpN8w4DwIxs5/up8QLA/MJkqmDUAsBoGeJYF7cCwNRvXwfOmQLAQMbctYR8AsCwHFpkO18CwBxz1xLyQQLAiMlUwagkAsD0H9JvXwcCwGB2Tx4W6gHA0MzMzMzMAcA8I0p7g68BwKh5xyk6kgHAFNBE2PB0AcCEJsKGp1cBwPB8PzVeOgHAXNO84xQdAcDIKTqSy/8AwDiAt0CC4gDApNY07zjFAMAQLbKd76cAwHyDL0ymigDA7Nms+lxtAMBYMCqpE1AAwMSGp1fKMgDAMN0kBoEVAMBAZ0Rpb/D/vxgUP8bctf+/8MA5I0p7/7/IbTSAt0D/v6AaL90kBv+/gMcpOpLL/r9YdCSX/5D+vzAhH/RsVv6/CM4ZUdob/r/oehSuR+H9v8AnDwu1pv2/mNQJaCJs/b9wgQTFjzH9v1Au/yH99vy/KNv5fmq8/L8AiPTb14H8v9g07zhFR/y/uOHplbIM/L+QjuTyH9L7v2g730+Nl/u/QOjZrPpc+78gldQJaCL7v/hBz2bV5/q/0O7Jw0Kt+r+om8QgsHL6v4BIv30dOPq/YPW52or9+b84orQ3+ML5vxBPr5RliPm/6Pup8dJN+b/IqKROQBP5v6BVn6ut2Pi/eAKaCBue+L9Qr5RliGP4vzBcj8L1KPi/CAmKH2Pu97/gtYR80LP3v7hif9k9efe/mA96Nqs+979wvHSTGAT3v0hpb/CFyfa/IBZqTfOO9r8Aw2SqYFT2v9hvXwfOGfa/sBxaZDvf9b+IyVTBqKT1v2B2Tx4WavW/QCNKe4Mv9b8Y0ETY8PT0v/B8PzVeuvS/yCk6kst/9L+o1jTvOEX0v4CDL0ymCvS/WDAqqRPQ878w3SQGgZXzvxCKH2PuWvO/6DYawFsg87/A4xQdyeXyv5iQD3o2q/K/eD0K16Nw8r9Q6gQ0ETbyvyiX/5B++/G/AET67evA8b/g8PRKWYbxv7id76fGS/G/kErqBDQR8b9o9+Rhodbwv0Ck374OnPC/IFHaG3xh8L/4/dR46Sbwv6BVn6ut2O+/UK+UZYhj778QCYofY+7uv8Bif9k9ee6/cLx0kxgE7r8gFmpN847tv+BvXwfOGe2/kMlUwaik7L9AI0p7gy/sv/B8PzVeuuu/sNY07zhF679gMCqpE9DqvxCKH2PuWuq/wOMUHcnl6b+APQrXo3DpvzCX/5B+++i/4PD0SlmG6L+QSuoENBHov0Ck374OnOe/AP7UeOkm57+wV8oyxLHmv2Cxv+yePOa/EAu1pnnH5b/QZKpgVFLlv4C+nxov3eS/MBiV1Alo5L/gcYqO5PLjv6DLf0i/feO/UCV1ApoI478Af2q8dJPiv7DYX3ZPHuK/cDJVMCqp4b8gjErqBDThv9DlP6TfvuC/gD81XrpJ4L+AMlUwKqnfv+DlP6Tfvt6/QJkqGJXU3b+gTBWMSurcvwAAAAAAANy/gLPqc7UV27/gZtXnaivav0AawFsgQdm/oM2qz9VW2L8ggZVDi2zXv4A0gLdAgta/4OdqK/aX1b9Am1Wfq63Uv8BOQBNhw9O/IAIrhxbZ0r+AtRX7y+7Rv+BoAG+BBNG/YBzr4jYa0L+An6ut2F/Ov0AGgZVDi8y/AG1Wfa62yr8A1CtlGeLIv8A6AU2EDce/gKHWNO84xb9ACKwcWmTDv0BvgQTFj8G/AKyt2F92v7+AeVioNc27vwBHA3gLJLi/gBSuR+F6tL+A4lgXt9GwvwBgB84ZUaq/APtcbcX+or8ALGUZ4liXvwDIILByaIG/AJgRpb3Bdz8AMJkqGJWUPwD99nXgnKE/AGGh1jTvqD8A46WbxCCwP4AV+8vuybM/AEhQ/Bhztz8AeqUsQxy7P4Cs+lxtxb4/gO+nxks3wT/AiNLe4AvDP8Ah/fZ14MQ/ALsnDwu1xj9AVFInoInIP4DtfD81Xso/wIanV8oyzD/AH9JvXwfOPwC5/If0288/IKkT0ETY0D/A9Shcj8LRP0BCPujZrNI/4I5TdCSX0z+A22gAb4HUPyAofoy5a9U/oHSTGARW1j9AwaikTkDXP+ANvjCZKtg/gFrTvOMU2T8Ap+hILv/ZP6Dz/dR46do/QEATYcPT2z/gjCjtDb7cP2DZPXlYqN0/ACZTBaOS3j+gcmiR7XzfP6Dfvg6cM+A/8IXJVMGo4D8wLNSa5h3hP4DS3uALk+E/0HjpJjEI4j8gH/RsVn3iP2DF/rJ78uI/sGsJ+aBn4z8AEhQ/xtzjP1C4HoXrUeQ/kF4pyxDH5D/gBDQRNjzlPzCrPldbseU/gFFJnYAm5j/A91PjpZvmPxCeXinLEOc/YERpb/CF5z+w6nO1FfvnP/CQfvs6cOg/QDeJQWDl6D+Q3ZOHhVrpP+CDns2qz+k/MCqpE9BE6j9w0LNZ9bnqP8B2vp8aL+s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EYDOBhALrKd76dGGED2Bl+YTFUYQMBbIEHxYxhAirDh6ZVyGEBUBaOSOoEYQBxaZDvfjxhA5q4l5IOeGECwA+eMKK0YQHpYqDXNuxhAQq1p3nHKGEAMAiuHFtkYQNZW7C+75xhAoKut2F/2GEBqAG+BBAUZQDJVMCqpExlA/Knx0k0iGUDG/rJ78jAZQJBTdCSXPxlAWKg1zTtOGUAi/fZ14FwZQOxRuB6FaxlAtqZ5xyl6GUB++zpwzogZQEhQ/BhzlxlAEqW9wRemGUDc+X5qvLQZQKROQBNhwxlAbqMBvAXSGUA4+MJkquAZQAJNhA1P7xlAyqFFtvP9GUCU9gZfmAwaQF5LyAc9GxpAKKCJsOEpGkDy9EpZhjgaQLpJDAIrRxpAhJ7Nqs9VGkBO845TdGQaQBhIUPwYcxpA4JwRpb2BGkCq8dJNYpAaQHRGlPYGnxpAPptVn6utGkAG8BZIULwaQNBE2PD0yhpAmpmZmZnZGkBk7lpCPugaQCxDHOvi9hpA9pfdk4cFG0DA7J48LBQbQIpBYOXQIhtAUpYhjnUxG0Ac6+I2GkAbQOY/pN++ThtAsJRliGNdG0B66SYxCGwbQEI+6NmsehtADJOpglGJG0DW52or9pcbQKA8LNSaphtAaJHtfD+1G0Ay5q4l5MMbQPw6cM6I0htAxo8xdy3hG0CO5PIf0u8bQFg5tMh2/htAIo51cRsNHEDs4jYawBscQLQ3+MJkKhxAfoy5awk5HEBI4XoUrkccQBI2PL1SVhxA2or9ZfdkHECk374OnHMcQG40gLdAghxAOIlBYOWQHEAC3gIJip8cQMoyxLEurhxAlIeFWtO8HEBe3EYDeMscQCgxCKwc2hxA8IXJVMHoHEC62or9ZfccQIQvTKYKBh1AToQNT68UHUAW2c73UyMdQOAtkKD4MR1AqoJRSZ1AHUB01xLyQU8dQDws1JrmXR1ABoGVQ4tsHUDQ1VbsL3sdQJoqGJXUiR1AYn/ZPXmYHUAs1JrmHacdQPYoXI/CtR1AwH0dOGfEHUCK0t7gC9MdQFInoImw4R1AHHxhMlXwHUDm0CLb+f4dQLAl5IOeDR5AeHqlLEMcHkBCz2bV5yoeQAwkKH6MOR5A1njpJjFIHkCezarP1VYeQGgibHh6ZR5AMnctIR90HkD8y+7Jw4IeQMQgsHJokR5AjnVxGw2gHkBYyjLEsa4eQCIf9GxWvR5A6nO1FfvLHkC0yHa+n9oeQH4dOGdE6R5ASHL5D+n3HkAQx7q4jQYfQNobfGEyFR9ApHA9CtcjH0Buxf6yezIfQDgawFsgQR9AAG+BBMVPH0DKw0KtaV4fQJQYBFYObR9AXm3F/rJ7H0AmwoanV4ofQPAWSFD8mB9AumsJ+aCnH0CEwMqhRbYfQEwVjErqxB9AFmpN847TH0Dgvg6cM+IfQKoT0ETY8B9AcmiR7Xz/H0CeXinLEAcgQAMJih9jDiBAaLPqc7UVIEDMXUvIBx0gQDEIrBxaJCBAlrIMcawrIED7XG3F/jIgQGAHzhlROiBAxLEubqNBIEApXI/C9UggQI4G8BZIUCBA87BQa5pXIEBXW7G/7F4gQLwFEhQ/ZiBAIbByaJFtIECGWtO843QgQOoENBE2fCBAT6+UZYiDIEC0WfW52oogQBkEVg4tkiBAfa62Yn+ZIEDiWBe30aAgQEcDeAskqCBArK3YX3avIEAQWDm0yLYgQHUCmggbviBA2qz6XG3FIEA/V1uxv8wgQKQBvAUS1CBACKwcWmTbIEBtVn2utuIgQNIA3gIJ6iBAN6s+V1vxIECbVZ+rrfggQAAAAAAAACFAZapgVFIHIUDKVMGopA4hQC7/If32FSFAk6mCUUkdIUD4U+OlmyQhQF3+Q/rtKyFAwaikTkAzIUAmUwWjkjohQIv9ZffkQSFA8KfGSzdJIUBUUiegiVAhQLn8h/TbVyFAHqfoSC5fIUCDUUmdgGYhQOj7qfHSbSFATKYKRiV1IUCxUGuad3whQBb7y+7JgyFAe6UsQxyLIUDfT42XbpIhQET67evAmSFAqaROQBOhIUAOT6+UZaghQHL5D+m3ryFA16NwPQq3IUA8TtGRXL4hQKH4MeauxSFABaOSOgHNIUBqTfOOU9QhQM/3U+Ol2yFANKK0N/jiIUCYTBWMSuohQP32deCc8SFAYqHWNO/4IUDHSzeJQQAiQCz2l92TByJAkKD4MeYOIkD1SlmGOBYiQFr1udqKHSJAv58aL90kIkAjSnuDLywiQIj029eBMyJA7Z48LNQ6IkBSSZ2AJkIiQLbz/dR4SSJAG55eKctQIkCASL99HVgiQOXyH9JvXyJASZ2AJsJmIkCuR+F6FG4iQBPyQc9mdSJAeJyiI7l8IkDcRgN4C4QiQEHxY8xdiyJAppvEILCSIkALRiV1ApoiQHDwhclUoSJA1JrmHaeoIkA5RUdy+a8iQJ7vp8ZLtyJAApoIG56+IkBnRGlv8MUiQMzuycNCzSJAMZkqGJXUIkCVQ4ts59siQPrt68A54yJAX5hMFYzqIkDEQq1p3vEiQCjtDb4w+SJAjZduEoMAI0DyQc9m1QcjQFfsL7snDyNAu5aQD3oWI0AgQfFjzB0jQIXrUbgeJSNA6pWyDHEsI0BPQBNhwzMjQLPqc7UVOyNAGJXUCWhCI0B9PzVeukkjQOLplbIMUSNARpT2Bl9YI0CrPldbsV8jQBDpt68DZyNAdZMYBFZuI0DZPXlYqHUjQD7o2az6fCNAo5I6AU2EI0AIPZtVn4sjQGzn+6nxkiNA0ZFc/kOaI0A2PL1SlqEjQJvmHafoqCNA/5B++zqwI0BkO99PjbcjQMnlP6TfviNALpCg+DHGI0CTOgFNhM0jQPfkYaHW1CNAXI/C9SjcI0DBOSNKe+MjQCbkg57N6iNAio7k8h/yI0DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJwrQGuad5yioytA0ETY8PSqK0A17zhFR7IrQJqZmZmZuStA/kP67evAK0Bj7lpCPsgrQMiYu5aQzytALUMc6+LWK0CR7Xw/Nd4rQPaX3ZOH5StAW0I+6NnsK0DA7J48LPQrQCSX/5B++ytAiUFg5dACLEDu68A5IwosQFOWIY51ESxAt0CC4scYLEAc6+I2GiAsQIGVQ4tsJyxA5j+k374uLEBK6gQ0ETYsQK+UZYhjPSxAFD/G3LVELEB56SYxCEwsQN2Th4VaUyxAQj7o2axaLECn6Egu/2EsQAyTqYJRaSxAcT0K16NwLEDV52or9ncsQDqSy39IfyxAnzws1JqGLEAE54wo7Y0sQGiR7Xw/lSxAzTtO0ZGcLEAy5q4l5KMsQJeQD3o2qyxA+zpwzoiyLEBg5dAi27ksQMWPMXctwSxAKjqSy3/ILECO5PIf0s8sQPOOU3Qk1yxAWDm0yHbeLEC94xQdyeUsQCGOdXEb7SxAhjjWxW30LEDr4jYawPssQFCNl24SAy1AtTf4wmQKLUAZ4lgXtxEtQH6MuWsJGS1A4zYawFsgLUBI4XoUrictQKyL22gALy1AETY8vVI2LUB24JwRpT0tQNuK/WX3RC1APzVeuklMLUCk374OnFMtQAmKH2PuWi1AbjSAt0BiLUDS3uALk2ktQDeJQWDlcC1AnDOitDd4LUAB3gIJin8tQGWIY13chi1AyjLEsS6OLUAv3SQGgZUtQJSHhVrTnC1A+THmriWkLUBd3EYDeKstQMKGp1fKsi1AJzEIrBy6LUCM22gAb8EtQPCFyVTByC1AVTAqqRPQLUC62or9ZdctQB+F61G43i1Agy9MpgrmLUDo2az6XO0tQE2EDU+v9C1Asi5uowH8LUAW2c73UwMuQHuDL0ymCi5A4C2QoPgRLkBF2PD0ShkuQKmCUUmdIC5ADi2yne8nLkBz1xLyQS8uQNiBc0aUNi5APSzUmuY9LkCh1jTvOEUuQAaBlUOLTC5Aayv2l91TLkDQ1VbsL1suQDSAt0CCYi5AmSoYldRpLkD+1HjpJnEuQGN/2T15eC5Axyk6kst/LkAs1JrmHYcuQJF++zpwji5A9ihcj8KVLkBa07zjFJ0uQL99HThnpC5AJCh+jLmrLkCJ0t7gC7MuQO18PzVeui5AUiegibDBLkC30QDeAskuQBx8YTJV0C5AgSbChqfXLkDl0CLb+d4uQEp7gy9M5i5AryXkg57tLkAU0ETY8PQuQHh6pSxD/C5A3SQGgZUDL0BCz2bV5wovQKd5xyk6Ei9ACyQofowZL0BwzojS3iAvQNV46SYxKC9AOiNKe4MvL0CezarP1TYvQAN4CyQoPi9AaCJseHpFL0DNzMzMzEwvQA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]}},\"selected\":{\"id\":\"12842\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"12843\",\"type\":\"UnionRenderers\"}},\"id\":\"12779\",\"type\":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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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LfRAN7CF0DiC5OpgtEXQKpgVFIn4BdAdLUV+8vuF0A+CtejcP0XQAhfmEwVDBhA0LNZ9bkaGECaCBueXikYQGRd3EYDOBhALrKd76dGGED2Bl+YTFUYQMBbIEHxYxhAirDh6ZVyGEBUBaOSOoEYQBxaZDvfjxhA5q4l5IOeGECwA+eMKK0YQHpYqDXNuxhAQq1p3nHKGEAMAiuHFtkYQNZW7C+75xhAoKut2F/2GEBqAG+BBAUZQDJVMCqpExlA/Knx0k0iGUDG/rJ78jAZQJBTdCSXPxlAWKg1zTtOGUAi/fZ14FwZQOxRuB6FaxlAtqZ5xyl6GUB++zpwzogZQEhQ/BhzlxlAEqW9wRemGUDc+X5qvLQZQKROQBNhwxlAbqMBvAXSGUA4+MJkquAZQAJNhA1P7xlAyqFFtvP9GUCU9gZfmAwaQF5LyAc9GxpAKKCJsOEpGkDy9EpZhjgaQLpJDAIrRxpAhJ7Nqs9VGkBO845TdGQaQBhIUPwYcxpA4JwRpb2BGkCq8dJNYpAaQHRGlPYGnxpAPptVn6utGkAG8BZIULwaQNBE2PD0yhpAmpmZmZnZGkBk7lpCPugaQCxDHOvi9hpA9pfdk4cFG0DA7J48LBQbQIpBYOXQIhtAUpYhjnUxG0Ac6+I2GkAbQOY/pN++ThtAsJRliGNdG0B66SYxCGwbQEI+6NmsehtADJOpglGJG0DW52or9pcbQKA8LNSaphtAaJHtfD+1G0Ay5q4l5MMbQPw6cM6I0htAxo8xdy3hG0CO5PIf0u8bQFg5tMh2/htAIo51cRsNHEDs4jYawBscQLQ3+MJkKhxAfoy5awk5HEBI4XoUrkccQBI2PL1SVhxA2or9ZfdkHECk374OnHMcQG40gLdAghxAOIlBYOWQHEAC3gIJip8cQMoyxLEurhxAlIeFWtO8HEBe3EYDeMscQCgxCKwc2hxA8IXJVMHoHEC62or9ZfccQIQvTKYKBh1AToQNT68UHUAW2c73UyMdQOAtkKD4MR1AqoJRSZ1AHUB01xLyQU8dQDws1JrmXR1ABoGVQ4tsHUDQ1VbsL3sdQJoqGJXUiR1AYn/ZPXmYHUAs1JrmHacdQPYoXI/CtR1AwH0dOGfEHUCK0t7gC9MdQFInoImw4R1AHHxhMlXwHUDm0CLb+f4dQLAl5IOeDR5AeHqlLEMcHkBCz2bV5yoeQAwkKH6MOR5A1njpJjFIHkCezarP1VYeQGgibHh6ZR5AMnctIR90HkD8y+7Jw4IeQMQgsHJokR5AjnVxGw2gHkBYyjLEsa4eQCIf9GxWvR5A6nO1FfvLHkC0yHa+n9oeQH4dOGdE6R5ASHL5D+n3HkAQx7q4jQYfQNobfGEyFR9ApHA9CtcjH0Buxf6yezIfQDgawFsgQR9AAG+BBMVPH0DKw0KtaV4fQJQYBFYObR9AXm3F/rJ7H0AmwoanV4ofQPAWSFD8mB9AumsJ+aCnH0CEwMqhRbYfQEwVjErqxB9AFmpN847TH0Dgvg6cM+IfQKoT0ETY8B9AcmiR7Xz/H0CeXinLEAcgQAMJih9jDiBAaLPqc7UVIEDMXUvIBx0gQDEIrBxaJCBAlrIMcawrIED7XG3F/jIgQGAHzhlROiBAxLEubqNBIEApXI/C9UggQI4G8BZIUCBA87BQa5pXIEBXW7G/7F4gQLwFEhQ/ZiBAIbByaJFtIECGWtO843QgQOoENBE2fCBAT6+UZYiDIEC0WfW52oogQBkEVg4tkiBAfa62Yn+ZIEDiWBe30aAgQEcDeAskqCBArK3YX3avIEAQWDm0yLYgQHUCmggbviBA2qz6XG3FIEA/V1uxv8wgQKQBvAUS1CBACKwcWmTbIEBtVn2utuIgQNIA3gIJ6iBAN6s+V1vxIECbVZ+rrfggQAAAAAAAACFAZapgVFIHIUDKVMGopA4hQC7/If32FSFAk6mCUUkdIUD4U+OlmyQhQF3+Q/rtKyFAwaikTkAzIUAmUwWjkjohQIv9ZffkQSFA8KfGSzdJIUBUUiegiVAhQLn8h/TbVyFAHqfoSC5fIUCDUUmdgGYhQOj7qfHSbSFATKYKRiV1IUCxUGuad3whQBb7y+7JgyFAe6UsQxyLIUDfT42XbpIhQET67evAmSFAqaROQBOhIUAOT6+UZaghQHL5D+m3ryFA16NwPQq3IUA8TtGRXL4hQKH4MeauxSFABaOSOgHNIUBqTfOOU9QhQM/3U+Ol2yFANKK0N/jiIUCYTBWMSuohQP32deCc8SFAYqHWNO/4IUDHSzeJQQAiQCz2l92TByJAkKD4MeYOIkD1SlmGOBYiQFr1udqKHSJAv58aL90kIkAjSnuDLywiQIj029eBMyJA7Z48LNQ6IkBSSZ2AJkIiQLbz/dR4SSJAG55eKctQIkCASL99HVgiQOXyH9JvXyJASZ2AJsJmIkCuR+F6FG4iQBPyQc9mdSJAeJyiI7l8IkDcRgN4C4QiQEHxY8xdiyJAppvEILCSIkALRiV1ApoiQHDwhclUoSJA1JrmHaeoIkA5RUdy+a8iQJ7vp8ZLtyJAApoIG56+IkBnRGlv8MUiQMzuycNCzSJAMZkqGJXUIkCVQ4ts59siQPrt68A54yJAX5hMFYzqIkDEQq1p3vEiQCjtDb4w+SJAjZduEoMAI0DyQc9m1QcjQFfsL7snDyNAu5aQD3oWI0AgQfFjzB0jQIXrUbgeJSNA6pWyDHEsI0BPQBNhwzMjQLPqc7UVOyNAGJXUCWhCI0B9PzVeukkjQOLplbIMUSNARpT2Bl9YI0CrPldbsV8jQBDpt68DZyNAdZMYBFZuI0DZPXlYqHUjQD7o2az6fCNAo5I6AU2EI0AIPZtVn4sjQGzn+6nxkiNA0ZFc/kOaI0A2PL1SlqEjQJvmHafoqCNA/5B++zqwI0BkO99PjbcjQMnlP6TfviNALpCg+DHGI0CTOgFNhM0jQPfkYaHW1CNAXI/C9SjcI0DBOSNKe+MjQCbkg57N6iNAio7k8h/yI0DvOEVHcvkjQFTjpZvEACRAuY0G8BYIJEAdOGdEaQ8kQILix5i7FiRA54wo7Q0eJEBMN4lBYCUkQLDh6ZWyLCRAFYxK6gQ0JEB6Nqs+VzskQN/gC5OpQiRAQ4ts5/tJJECoNc07TlEkQA3gLZCgWCRAcoqO5PJfJEDXNO84RWckQDvfT42XbiRAoImw4el1JEAFNBE2PH0kQGrecYqOhCRAzojS3uCLJEAzMzMzM5MkQJjdk4eFmiRA/Yf029ehJEBhMlUwKqkkQMbctYR8sCRAK4cW2c63JECQMXctIb8kQPTb14FzxiRAWYY41sXNJEC+MJkqGNUkQCPb+X5q3CRAh4Va07zjJEDsL7snD+skQFHaG3xh8iRAtoR80LP5JEAbL90kBgElQH/ZPXlYCCVA5IOezaoPJUBJLv8h/RYlQK7YX3ZPHiVAEoPAyqElJUB3LSEf9CwlQNzXgXNGNCVAQYLix5g7JUClLEMc60IlQArXo3A9SiVAb4EExY9RJUDUK2UZ4lglQDjWxW00YCVAnYAmwoZnJUACK4cW2W4lQGfV52ordiVAy39Iv319JUAwKqkT0IQlQJXUCWgijCVA+n5qvHSTJUBeKcsQx5olQMPTK2UZoiVAKH6MuWupJUCNKO0NvrAlQPLSTWIQuCVAVn2utmK/JUC7Jw8LtcYlQCDSb18HziVAhXzQs1nVJUDpJjEIrNwlQE7RkVz+4yVAs3vysFDrJUAYJlMFo/IlQHzQs1n1+SVA4XoUrkcBJkBGJXUCmggmQKvP1VbsDyZAD3o2qz4XJkB0JJf/kB4mQNnO91PjJSZAPnlYqDUtJkCiI7n8hzQmQAfOGVHaOyZAbHh6pSxDJkDRItv5fkomQDbNO07RUSZAmnecoiNZJkD/If32dWAmQGTMXUvIZyZAyXa+nxpvJkAtIR/0bHYmQJLLf0i/fSZA93XgnBGFJkBcIEHxY4wmQMDKoUW2kyZAJXUCmgibJkCKH2PuWqImQO/Jw0KtqSZAU3Qkl/+wJkC4HoXrUbgmQB3J5T+kvyZAgnNGlPbGJkDmHafoSM4mQEvIBz2b1SZAsHJoke3cJkAVHcnlP+QmQHrHKTqS6yZA3nGKjuTyJkBDHOviNvomQKjGSzeJASdADXGsi9sIJ0BxGw3gLRAnQNbFbTSAFydAO3DOiNIeJ0CgGi/dJCYnQATFjzF3LSdAaW/whck0J0DOGVHaGzwnQDPEsS5uQydAl24Sg8BKJ0D8GHPXElInQGHD0ytlWSdAxm00gLdgJ0AqGJXUCWgnQI/C9ShcbydA9GxWfa52J0BZF7fRAH4nQL7BFyZThSdAImx4eqWMJ0CHFtnO95MnQOzAOSNKmydAUWuad5yiJ0C1FfvL7qknQBrAWyBBsSdAf2q8dJO4J0DkFB3J5b8nQEi/fR04xydArWnecYrOJ0ASFD/G3NUnQHe+nxov3SdA22gAb4HkJ0BAE2HD0+snQKW9wRcm8ydACmgibHj6J0BuEoPAygEoQNO84xQdCShAOGdEaW8QKECdEaW9wRcoQAK8BRIUHyhAZmZmZmYmKEDLEMe6uC0oQDC7Jw8LNShAlWWIY108KED5D+m3r0MoQF66SQwCSyhAw2SqYFRSKEAoDwu1plkoQIy5awn5YChA8WPMXUtoKEBWDi2ynW8oQLu4jQbwdihAH2PuWkJ+KECEDU+vlIUoQOm3rwPnjChATmIQWDmUKECyDHGsi5soQBe30QDeoihAfGEyVTCqKEDhC5OpgrEoQEa28/3UuChAqmBUUifAKEAPC7WmeccoQHS1FfvLzihA2V92Tx7WKEA9CtejcN0oQKK0N/jC5ChAB1+YTBXsKEBsCfmgZ/MoQNCzWfW5+ihANV66SQwCKUCaCBueXgkpQP+ye/KwEClAY13cRgMYKUDIBz2bVR8pQC2yne+nJilAklz+Q/otKUD2Bl+YTDUpQFuxv+yePClAwFsgQfFDKUAlBoGVQ0spQIqw4emVUilA7lpCPuhZKUBTBaOSOmEpQLivA+eMaClAHVpkO99vKUCBBMWPMXcpQOauJeSDfilAS1mGONaFKUCwA+eMKI0pQBSuR+F6lClAeVioNc2bKUDeAgmKH6MpQEOtad5xqilAp1fKMsSxKUAMAiuHFrkpQHGsi9towClA1lbsL7vHKUA6AU2EDc8pQJ+rrdhf1ilABFYOLbLdKUBpAG+BBOUpQM6qz9VW7ClAMlUwKqnzKUCX/5B++/opQPyp8dJNAipAYVRSJ6AJKkDF/rJ78hAqQCqpE9BEGCpAj1N0JJcfKkD0/dR46SYqQFioNc07LipAvVKWIY41KkAi/fZ14DwqQIenV8oyRCpA61G4HoVLKkBQ/Bhz11IqQLWmeccpWipAGlHaG3xhKkB++zpwzmgqQOOlm8QgcCpASFD8GHN3KkCt+lxtxX4qQBKlvcEXhipAdk8eFmqNKkDb+X5qvJQqQECk374OnCpApU5AE2GjKkAJ+aBns6oqQG6jAbwFsipA001iEFi5KkA4+MJkqsAqQJyiI7n8xypAAU2EDU/PKkBm9+RhodYqQMuhRbbz3SpAL0ymCkblKkCU9gZfmOwqQPmgZ7Pq8ypAXkvIBz37KkDC9ShcjwIrQCegibDhCStAjErqBDQRK0Dx9EpZhhgrQFafq63YHytAukkMAisnK0Af9GxWfS4rQISezarPNStA6Ugu/yE9K0BN845TdEQrQLKd76fGSytAF0hQ/BhTK0B88rBQa1orQOCcEaW9YStARUdy+Q9pK0Cq8dJNYnArQA+cM6K0dytAc0aU9gZ/K0DY8PRKWYYrQD2bVZ+rjStAokW28/2UK0AG8BZIUJwrQGuad5yioytA0ETY8PSqK0A17zhFR7IrQJqZmZmZuStA/kP67evAK0Bj7lpCPsgrQMiYu5aQzytALUMc6+LWK0CR7Xw/Nd4rQPaX3ZOH5StAW0I+6NnsK0DA7J48LPQrQCSX/5B++ytAiUFg5dACLEDu68A5IwosQFOWIY51ESxAt0CC4scYLEAc6+I2GiAsQIGVQ4tsJyxA5j+k374uLEBK6gQ0ETYsQK+UZYhjPSxAFD/G3LVELEB56SYxCEwsQN2Th4VaUyxAQj7o2axaLECn6Egu/2EsQAyTqYJRaSxAcT0K16NwLEDV52or9ncsQDqSy39IfyxAnzws1JqGLEAE54wo7Y0sQGiR7Xw/lSxAzTtO0ZGcLEAy5q4l5KMsQJeQD3o2qyxA+zpwzoiyLEBg5dAi27ksQMWPMXctwSxAKjqSy3/ILECO5PIf0s8sQPOOU3Qk1yxAWDm0yHbeLEC94xQdyeUsQCGOdXEb7SxAhjjWxW30LEDr4jYawPssQFCNl24SAy1AtTf4wmQKLUAZ4lgXtxEtQH6MuWsJGS1A4zYawFsgLUBI4XoUrictQKyL22gALy1AETY8vVI2LUB24JwRpT0tQNuK/WX3RC1APzVeuklMLUCk374OnFMtQAmKH2PuWi1AbjSAt0BiLUDS3uALk2ktQDeJQWDlcC1AnDOitDd4LUAB3gIJin8tQGWIY13chi1AyjLEsS6OLUAv3SQGgZUtQJSHhVrTnC1A+THmriWkLUBd3EYDeKstQMKGp1fKsi1AJzEIrBy6LUCM22gAb8EtQPCFyVTByC1AVTAqqRPQLUC62or9ZdctQB+F61G43i1Agy9MpgrmLUDo2az6XO0tQE2EDU+v9C1Asi5uowH8LUAW2c73UwMuQHuDL0ymCi5A4C2QoPgRLkBF2PD0ShkuQKmCUUmdIC5ADi2yne8nLkBz1xLyQS8uQNiBc0aUNi5APSzUmuY9LkCh1jTvOEUuQAaBlUOLTC5Aayv2l91TLkDQ1VbsL1suQDSAt0CCYi5AmSoYldRpLkD+1HjpJnEuQGN/2T15eC5Axyk6kst/LkAs1JrmHYcuQJF++zpwji5A9ihcj8KVLkBa07zjFJ0uQL99HThnpC5AJCh+jLmrLkCJ0t7gC7MuQO18PzVeui5AUiegibDBLkC30QDeAskuQBx8YTJV0C5AgSbChqfXLkDl0CLb+d4uQEp7gy9M5i5AryXkg57tLkAU0ETY8PQuQHh6pSxD/C5A3SQGgZUDL0BCz2bV5wovQKd5xyk6Ei9ACyQofowZL0BwzojS3iAvQNV46SYxKC9AOiNKe4MvL0CezarP1TYvQAN4CyQoPi9AaCJseHpFL0DNzMzMzEwvQA==\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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D8w/TPzYDflPyhIs6vVtuU/YCkV6nY25j+43FJ8RLbmP3DBEjA+Nuc/UFyo0mO25z/gWhQxtTboP1iXBBgyt+g/OBzUU9o36T94KYuwrbjpP4g53/mrOeo/OAcz+9S66j8olJZ/KDzrP4gvx1Gmves/uH0vPE4/7D8ogOcIIMHsPzietIEbQ+0/IK4JcEDF7T+w/gadjkfuP4hhetEFyu4/WDbf1aVM7z/AdV5ybs/vP7heZ7cvKfA/fC5aSbxq8D88saDSXKzwP3TUqjYR7vA/yHK/WNkv8T+4W/wbtXHxP1RbVmOks/E/AEKZEaf18T/w7GcJvTfyP7hOPC3mefI/6HdnXyK88j+goBGCcf7yP5AxOnfTQPM/8M23IEiD8z+AXThgz8XzP8AWQRdpCPQ/tIkuJxVL9D+cqjRx0430PzjdXtaj0PQ/XACQN4YT9T+QeYJ1elb1PzhByHCAmfU//O7KCZjc9T9UxssgwR/2P8DD45X7YvY/sKkDSUem9j9UDvQZpOn2P2xpVegRLfc/eCKgk5Bw9z8EnyT7H7T3P5BRC/6/9/c/nMhUe3A7+D/0vdlRMX/4P2AmS2ACw/g/iEEyheMG+T9cqvCe1Er5P3xnwIvVjvk/MPyzKebS+T90ebZWBhf6P4iPi/A1W/o/sJ/P1HSf+j9EzvfgwuP6PwAVUvIfKPs/1FUF5ots+z+wbRGZBrH7PwxIT+iP9fs/NPJwsCc6/D9wrwHOzX78P/wMZh2Cw/w/zPbbekQI/T8czHrCFE39P6R0M9Dykf0/GHbQf97W/T/ACfas1xv+P6AyIjPeYP4/qNOs7fGl/j90xse3Euv+P1TyfmxAMP8/TGO45np1/z/cYTQBwrr/P2LFRssKAABAsHOcwLoiAEDiAsPNcEUAQO6Iz98saABANHfE4+6KAEAwp5HGtq0AQFBnFHWE0ABABogX3FfzAEDeaFPoMBYBQNgFboYPOQFA6AT7ovNbAUCKw3sq3X4BQIZkXwnMoQFA4t0CLMDEAUAEB7F+uecBQNqmou23CgJARIL+ZLstAkCgatnQw1ACQIRMNh3RcwJAjj4GNuOWAkBikCgH+rkCQM7ZanwV3QJAOgqJgTUAA0Dody0CWiMDQMDv8OmCRgNA7sRaJLBpA0D+4OCc4YwDQLDT5z4XsANAbOPC9VDTA0B0HbSsjvYDQIJm7E7QGQRAiIuLxxU9BEBUUqABX2AEQLaKKOirgwRAjB8RZvymBEAiKDZmUMoEQIj5YtOn7QRAMjhSmAIRBUDE6a2fYDQFQN6GD9TBVwVAPg0AICZ7BUDaEfhtjZ4FQErTX6j3wQVASkyPuWTlBUBCRs6L1AgGQC5sVAlHLAZAel1JHLxPBkA6wcSuM3MGQEBZzqqtlgZAkBVe+im6BkDsJ1yHqN0GQIoXoTspAQdA3tT1AKwkB0CazRPBMEgHQMYApWW3awdAGBNE2D+PB0BaY3wCyrIHQPgeys1V1gdAtFaaI+P5B0CME0vtcR0IQMBrKxQCQQhA8Jd7gZNkCEBsCG0eJogIQKB6ItS5qwhAwA6wi07PCEBwXRsu5PIIQKaNW6R6FglAumpZ1xE6CUCKeu+vqV0JQNYT6hZCgQlAnHQH9dqkCUC82PcydMgJQMCQXbkN7AlAshjNcKcPCkAcL81BQTMKQDrs1hTbVgpAOtlV0nR6CkDWB6hiDp4KQKQpHq6nwQpAEqj7nEDlCkAIvHYX2QgLQCSGuAVxLAtAribdTwhQC0Dk1fPdnnMLQG78/pc0lwtAAEz0Zcm6C0DY17wvXd4LQLAtNd3vAQxArm4tVoElDEB6aGmCEUkMQHquoEmgbAxAIrN+ky2QDECC4aJHubMMQNa2oE1D1wxAYtz/jMv6DEA+QTztUR4NQFY0xlXWQQ1Aqn4CrlhlDUCWfUrd2IgNQBo97MpWrA1AkpIqXtLPDUAyNz1+S/MNQA7jUBLCFg5AzmeHATY6DkDiy/cyp10OQJplro0VgQ5Alvas+ICkDkAKx+pa6ccOQGbBVJtO6w5ADo7NoLAOD0AWry1SDzIPQDKcQ5ZqVQ9A2t7TU8J4D0BeLplxFpwPQDaMRNZmvw9AkmB9aLPiD0BRy3AH/gIQQL3cAligFBBAX4g6mUAmEEDJ3li+3jcQQMlrmrp6SRBABUU3gRRbEECHGGMFrGwQQI07TTpBfhBAI7kgE9SPEEADYQSDZKEQQGvWGn3yshBADZ+C9H3EEED9MVbcBtYQQLsGrCeN5xBAR6SWyRD5EEBLsCS1kQoRQD3+YN0PHBFAq55SNYstEUB/7vyvAz8RQFumX0B5UBFADep22ethEUDrVztuW3MRQHMYovHHhBFAv+2cVjGWEUBFQxqQl6cRQFs9BZH6uBFAD8lFTFrKEUDbq8C0ttsRQH+TV70P7RFA2yXpWGX+EUDfEFF6tw8SQIEaaBQGIRJAxTAEGlEyEkDHefh9mEMSQN1jFTPcVBJAx7UoLBxmEkDhnv1bWHcSQFvHXLWQiBJAlWAMK8WZEkB1NdCv9aoSQM26aTYivBJAwx+YsUrNEkBdXhgUb94SQP9LpVCP7xJA/an3WasAE0BHNsYiwxETQAG8xZ3WIhNAQSSpveUzE0DPhiF18EQTQPM63rb2VRNAOeiMdfhmE0Bll9mj9XcTQE/DbjTuiBNA42n1GeKZE0ARHRVH0aoTQN0TdK67uxNAdzu3QqHME0BNSIL2gd0TQDXHd7xd7hNAny45hzT/E0DF72ZJBhAUQAOIoPXSIBRABZKEfpoxFEAz17DWXEIUQANhwvAZUxRAX4pVv9FjFEAnEQY1hHQUQI8nb0QxhRRArYUr4NiVFEADe9X6eqYUQBEAB4cXtxRA68dZd67HFEDTUWe+P9gUQPn6yE7L6BRAFxAYG1H5FEAz3+0V0QkVQF/J4zFLGhVAgVSTYb8qFUAjPZaXLTsVQEeIhsaVSxVAS5X+4PdbFUC/L5nZU2wVQGeh8aKpfBVAGcSjL/mMFUDDE0xyQp0VQFnAh12FrRVA8b/048G9FUDB4DH4980VQC3b3own3hVA62OclFDuFUATPgwCc/4VQFFN0ceODhZA96eP2KMeFkBFqewmsi4WQIMDj6W5PhZAU9IeR7pOFkDXrEX+s14WQPu3rr2mbhZAu7gGeJJ+FkB7Jvwfd44WQD89P6hUnhZAERCCAyuuFkBPm3gk+r0WQA3X2P3BzRZAd8lagoLdFkApmbikO+0WQKGfrlft/BZAp3v7jZcMF0C/I2A6OhwXQI/4n0/VKxdAX9eAwGg7F0CHLMt/9EoXQPcFSoB4WhdAoSXLtPRpF0ALFB8QaXkXQL0yGYXViBdA2c6PBjqYF0CXM1yHlqcXQMe8WvrqthdAYelqUjfGF0Adbm+Ce9UXQOdHTn235BdAfc7wNevzF0D7xkOfFgMYQG92N6w5EhhAZ7S/T1QhGEB//dN8ZjAYQAGGbyZwPxhAZUyRP3FOGEABLDy7aV0YQH3vdoxZbBhAgWNMpkB7GEA9acv7HooYQP8IB4D0mBhAx4QWJsGnGEDdahXhhLYYQFuoI6Q/xRhAz5tlYvHTGEC9JwQPmuIYQDHFLJ058RhAVZYRAND/GED9eOkqXQ4ZQCcZ8BDhHBlAlQNmpVsrGUBLuJDbzDkZQB+9uqY0SBlAMbAz+pJWGUB3WlDJ52QZQDnCagczcxlAiz3ip3SBGUDVhBuerI8ZQDfFgN3anRlAC7OBWf+rGUBZnJMFGroZQDd7MdUqyBlARwjcuzHWGUAJzRmtLuQZQFs2d5wh8hlAt6aGfQoAGkCtiOBD6Q0aQClhI+O9GxpAzeHzTogpGkA/+/x6SDcaQHPv71r+RBpA8WOE4qlSGkAbdHgFS2AaQGHDkLfhbRpAdY+Y7G17GkCLwmGY74gaQHkFxa5mlhpA49GhI9OjGkBVhN7qNLEaQGVuaPiLvhpAv+gzQNjLGkBDZTy2GdkaQPuAhE5Q5hpAJxYW/XvzGkAzTgK2nAAbQLOzYW2yDRtAQ0RUF70aG0B7ggGovCcbQMOHmBOxNBtANRZQTppBG0BdqmZMeE4bQBeNIgJLWxtAN+XRYxJoG0BZycplznQbQIFRa/x+gRtAyagZHCSOG0D/HkS5vZobQD86YchLpxtAf8jvPc6zG0AX8XYORcAbQDlGhi6wzBtAZda1kg/ZG0DdPaYvY+UbQPe3APqq8RtAgTB35ub9G0ABVcTpFgocQAumq/g6FhxAZ4j5B1MiHEBLVoMMXy4cQINwJ/teOhxAh0/NyFJGHECBlGVqOlIcQG0a6tQVXhxA8wZe/eRpHEB5283Yp3UcQOOFT1xegRxAjXECfQiNHEABmA8wppgcQMORqWo3pBxAD6cMIryvHEBx4H5LNLscQHMXUNyfxhxAJwfayf7RHEC3XIAJUd0cQM/HsJCW6BxAFQvjVM/zHECPDJlL+/4cQOXlXmoaCh1AtfTKpiwVHUDP6n32MSAdQFHeIk8qKx1A2VlvphU2HUCJbCPy80AdQBG6CSjFSx1An4r3PYlWHUDR2swpQGEdQIdrdOHpax1ArdHjWoZ2HUD/hRuMFYEdQLP0JmuXix1AH40c7guWHUBB0R0Lc6AdQEdlV7jMqh1ABR8B7Bi1HUBVFV6cV78dQHGvvL+IyR1ANbR2TKzTHUBNWfE4wt0dQHFSnXvK5x1Aa+D2CsXxHUAb4IXdsfsdQH/Z3emQBR5AhQ6eJmIPHkDziXGKJRkeQCMuDwzbIh5AtcM5ooIsHkA/CMBDHDYeQNu8fOenPx5AobRWhCVJHkAn40ARlVIeQNlqOoX2Wx5ASatO10llHkBxT5X+jm4eQNNbMvLFdx5ApzxWqe6AHkDP0z0bCYoeQN2GMj8Vkx5A7UyKDBOcHkB7vKd6AqUeQCkZ+oDjrR5AX2H9Fra2HkD1Wzo0er8eQK2lRtAvyB5Atb7E4tbQHkAFGGRjb9keQKkg4Un54R5AA1MFjnTqHkD1Qacn4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\",\"dtype\":\"float64\",\"shape\":[1001]},\"y\":{\"__ndarray__\":\"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genusrecordspairscoveragepvaluecorrelationR^2s.e.(larvae)equationcenter(KitMg)height(larvae)spread(KitMg^2)
0All1122510.101.00000.00000.00000.00no fit achieved0.00000.00000.0000
1Aedes3100.000.50910.69690.48265.40y = 10.63 * e^(-(x - 28.32)^2 / 2 * c)10.625628.31524.1804
2Anopheles000.001.00000.00000.00000.00no fit achieved0.00000.00000.0000
3Culex5150.001.00000.00000.00000.00no fit achieved0.00000.00000.0000
4Other23510.020.07820.37470.13864.46y = 12.96 * e^(-(x - 12.76)^2 / 2 * c)12.963612.755440.5044
5Unknown811750.070.00150.34790.12091.72y = 2.05 * e^(-(x - 3.09)^2 / 2 * c)2.04873.086510.5399
\n", "
" ], "text/plain": [ " genus records pairs coverage pvalue correlation R^2 \\\n", "0 All 112 251 0.10 1.0000 0.0000 0.0000 \n", "1 Aedes 3 10 0.00 0.5091 0.6969 0.4826 \n", "2 Anopheles 0 0 0.00 1.0000 0.0000 0.0000 \n", "3 Culex 5 15 0.00 1.0000 0.0000 0.0000 \n", "4 Other 23 51 0.02 0.0782 0.3747 0.1386 \n", "5 Unknown 81 175 0.07 0.0015 0.3479 0.1209 \n", "\n", " s.e.(larvae) equation center(KitMg) \\\n", "0 0.00 no fit achieved 0.0000 \n", "1 5.40 y = 10.63 * e^(-(x - 28.32)^2 / 2 * c) 10.6256 \n", "2 0.00 no fit achieved 0.0000 \n", "3 0.00 no fit achieved 0.0000 \n", "4 4.46 y = 12.96 * e^(-(x - 12.76)^2 / 2 * c) 12.9636 \n", "5 1.72 y = 2.05 * e^(-(x - 3.09)^2 / 2 * c) 2.0487 \n", "\n", " height(larvae) spread(KitMg^2) \n", "0 0.0000 0.0000 \n", "1 28.3152 4.1804 \n", "2 0.0000 0.0000 \n", "3 0.0000 0.0000 \n", "4 12.7554 40.5044 \n", "5 3.0865 10.5399 " ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('performing gaussian curve fit')\n", "\n", "# perform\n", "perform('gaussian', associations)" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "e00b6dae3fe14c65beb8b559994cf83b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ad13ec79ac6c488aaa48fe57a2a25478", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "hide_input": false }, "source": [ "### Viewing the Data Table" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "The data table of associations may be viewed below by clicking the View button. Each row in the table begins with a pair of indices, with the first representing the primary mosquito record and the second representing the seconday record. Each primary record may have multiple secondary records. The strength of the association is given in the weights column, followed by the distance and time interval between them. Thereafter are all the fields of both the primary and secondary records." ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "code_folding": [], "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: viewing data table button)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "29c1d1450830405588ccbe3ef7bbc994", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='View', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('viewing data table button')\n", "\n", "# function to examine record\n", "def view(_):\n", " \"\"\"Examine secondary protocol record.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", "\n", " # display in output\n", " print('displaying...')\n", " display(summary)\n", " \n", " return None\n", "\n", "# create button\n", "button = Button(description=\"View\")\n", "button.on_click(view)\n", "display(button)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "afb61bd3da7b4f07861bd05870e92661", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "af8e3d032255403ebe0eadf57299b003", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Exporting Data to CSV" ] }, { "cell_type": "markdown", "metadata": { "hide_input": false }, "source": [ "It may be desirable to export the data to a csv file. Click the Export button to export the data. You will get a link to download the csv file." ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "code_folding": [], "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: exporting data to csv button)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3ee3095683fa4b4db8add227c014c046", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='Export', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "33e4e78faa4e400092bd42435d6f746d", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Output()" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('exporting data to csv button')\n", "\n", "# create push button linked to output\n", "exporter = Output()\n", "button = Button(description='Export')\n", "display(button, exporter)\n", "\n", "# function to export to zip file\n", "def export(_):\n", " \"\"\"Export the data as a csv file.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " #write dataframe to file\n", " name = 'mosquitoes_' + secondary + '_' + str(int(datetime.now().timestamp())) + '.csv'\n", " summary.to_csv(name) \n", " \n", " # make link\n", " link = FileLink(name)\n", " \n", " # add to output\n", " exporter.append_display_data(link)\n", "\n", " return None\n", "\n", "# add button click command\n", "button.on_click(export)" ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d56109ff322b4308ad315ea5de8b37ce", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "7fc7652b3d9c43d78d321dddb94ab0b3", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing on a Map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Click the map button to generate a map of all sampling locations." ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "code_folding": [ 3 ], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: viewing the map button)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cb854ef4c7a741de8d42855aa465983b", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='Map', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('viewing the map button')\n", "\n", "# reset parameters function\n", "def illustrate(_):\n", " \"\"\"Illustrate sample locations in a map.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # clear output\n", " execute_cell_range('### Visualizing on a Map', '### Optimizing Parameters')\n", " \n", " # construct the map\n", " chart = construct_map_at_central_geo_loc()\n", " \n", " # add the markers\n", " chart = populate_map(chart)\n", " \n", " # add the controls\n", " chart = add_map_controls(chart)\n", " \n", " # display the chart\n", " display(chart)\n", " \n", " return None\n", "\n", "# create button\n", "button = Button(description=\"Map\")\n", "display(button)\n", "button.on_click(illustrate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The map is broken into 4 layers which may be selected from the Layers box underneath the full screen control on the left side of the map. The layers may be toggled on and off here. They are described as follows:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) associated primaries: this layer contains markers for all mosquito habitat observations that have been associated with secondary protocol measurements. The markers are colored according to genus as indicated by the map legend in the upper right. The size of the marker reflects the size of the larvae count. The opacity of the marker reflects the highest weight amongst secondary associations. Clicking the marker will bring up a summary table and highlight the associated secondary measurements" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) associated secondaries: this layer contains markers for all secondary protocol observations that have been associated with mosquito measurements. The markers are colored according the scale displayed in the lower right corner of the map. The opacity of the marker reflects the highest weight amongst mosquito associations. Clicking the marker will bring up a summary table and highlight the associated mosquito measurements. Where multiple measurements have occured at the same longitude, latitude coordinates, the marker with the highest weight is displayed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) unassociated primaries: this layer contains markers for all mosquito habitat observations that have not been associated with secondary protocol measurements. The markers are colored tan. The size of the marker reflects the size of the larvae count. Clicking the marker will bring up a summary table." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4) unassociated secondaries: this layer contains markers for all secondary protocol observations that have not been associated with mosquito measurements. The markers are colored according the scale displayed in the lower right corner of the map. Clicking the marker will bring up a summary table. Where multiple measurements have occured at the same longitude, latitude coordinates, the marker with the highest secondary measurement is displayed." ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b1b676247494467bbf2aae3e415f4190", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c69c5f5266484dd28b1b4e2f7f43f277", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "code_folding": [ 3 ], "hide_input": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: chiseling a range into even numbers)\n" ] } ], "source": [ "designate('chiseling a range into even numbers', 'map')\n", "\n", "# chisel subroutine to round numbers of a range\n", "def chisel(low, high):\n", " \"\"\"Chisel a rounded range from a precise range.\n", " \n", " Arguments:\n", " low: float, low point in range\n", " high: float, high point in range\n", " \n", " Returns:\n", " (float, float) tuple, the rounded range\n", " \"\"\"\n", " \n", " # find the mean of the two values\n", " mean = (low + high) / 2\n", " \n", " # find the order of magnitude of the mean\n", " magnitude = int(log10(mean))\n", " \n", " # get first and second \n", " first = int(low / 10 ** magnitude)\n", " second = int(high / 10 ** magnitude) + 1\n", " \n", " # get all divisions\n", " divisions = [(10 ** magnitude) * number for number in range(first, second + 1)]\n", " \n", " # make half division if length is low\n", " if len(divisions) < 5:\n", " \n", " # create halves\n", " halves = [(entry + entryii) /2 for entry, entryii in zip(divisions[:-1], divisions[1:])]\n", " divisions += halves\n", " \n", " # sort\n", " divisions.sort()\n", " \n", " return divisions" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "code_folding": [ 3 ], "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: mixing colors)\n" ] } ], "source": [ "designate('mixing colors', 'map')\n", "\n", "# mix colors according to a gradient\n", "def mix_colors(measurement, bracket, spectrum):\n", " \"\"\"Mix the color for the measurment according to measurment bracket and color spectrum.\n", " \n", " Arguments:\n", " measurement: float, the measurement\n", " bracket: tuple of floats, low and high measurement range\n", " specturm: tuple of int tuples, the low and high rgb color endpoints\n", " \n", " Returns:\n", " str, hexadecimal color\n", " \"\"\"\n", " \n", " # truncate measurement to lowest of highest of range\n", " low, high = bracket\n", " measurement = max([measurement, low])\n", " measurement = min([measurement, high])\n", " \n", " # determine fraction of range\n", " fraction = (measurement - low) / (high - low)\n", " \n", " # define mixing function\n", " mixing = lambda index: int(spectrum[0][index] + fraction * (spectrum[1][index] - spectrum[0][index]))\n", " \n", " # mix colors\n", " red, green, blue = [mixing(index) for index in range(3)]\n", " \n", " # define encoding function to get last two digits of hexadecimal encoding\n", " encoding = lambda intensity: hex(intensity).replace('x', '0')[-2:]\n", " \n", " # make the specific hue\n", " color = '#' + encoding(red) + encoding(green) + encoding(blue)\n", "\n", " return color" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "code_folding": [ 3 ], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: shrinking records to unique locations)\n" ] } ], "source": [ "designate('shrinking records to unique locations', 'map')\n", "\n", "# shrink the secondary dataset to unique members per location\n", "def get_unique_record_per_loc_secondary_data(records, criterion):\n", " \"\"\"Shrink the records set to the unique record at each latitude longitude, based on a criterion field.\n", " \n", " Arguments:\n", " records: list of dicts\n", " criterion: str, field to sort by\n", " \n", " Returns:\n", " list of dicts\n", " \"\"\"\n", " \n", " # add each record to a geocoordinates dictionary\n", " locations = {}\n", " for record in records:\n", " \n", " # each record is a contender for the maximum criterion\n", " contender = record\n", " location = (record['latitude'], record['longitude'])\n", " highest = locations.setdefault(location, None)\n", " if highest:\n", " \n", " # replace with record higher in the criterion\n", " contenders = [highest, contender]\n", " contenders.sort(key=lambda entry: entry[criterion], reverse=True)\n", " contender = contenders[0]\n", " \n", " # replace with condender\n", " locations[location] = contender\n", " \n", " # get the shrunken list\n", " uniques = [member for member in locations.values()]\n", " \n", " return uniques" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "code_folding": [ 3 ], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: code for stipling map with samples)\n" ] } ], "source": [ "designate('code for stipling map with samples', 'map')\n", "\n", "# stiple function to add a circle marker layer to the map\n", "def add_points_to_map(records, weights, field, fill, line, thickness, radius, opacity, click, name=''):\n", " \"\"\"Stipple the map with a layer of markers.\n", " \n", " Arguments:\n", " records: list of dicts, the records\n", " weights: list of floats, the weights\n", " field: str, the name of the appropriate field\n", " fill: function object, to determine fill color\n", " line: function object, to determine line color\n", " thickness: int, circle outline thickness\n", " radius: function object, to determine radius\n", " opacity: function object, to determine opacity as a function of weight\n", " click: function object, to act upon a click\n", " name: str, name of layer\n", " \n", " Returns:\n", " pyleaflet layer object\n", " \"\"\"\n", "\n", " # create marker layer\n", " markers = []\n", " for record, weight in zip(records, weights):\n", "\n", " # unpack record\n", " latitude = record['latitude']\n", " longitude = record['longitude']\n", " date = record['date']\n", " measurement = record[field]\n", " \n", " # make circle marker\n", " circle = CircleMarker()\n", " circle.location = (latitude, longitude)\n", " \n", " # marker outline attributes\n", " circle.weight = thickness\n", " circle.color = line(record)\n", " circle.radius = radius(record)\n", "\n", " # set fill color attributes\n", " circle.fill = True\n", " circle.fill_opacity = opacity(weight)\n", " circle.fill_color = fill(record)\n", "\n", " # add click function\n", " circle.on_click(click)\n", " \n", " # annotate marker with popup label\n", " formats = (round(weight, 3), date, round(latitude, 3), round(longitude, 3), field, int(measurement))\n", " message = 'Weight: {}, Date: {}, Latitude: {}, Longitude: {}, {}: {}'.format(*formats)\n", " circle.popup = HTML(message)\n", "\n", " # add to markers layer\n", " markers.append(circle)\n", "\n", " # make marker layer\n", " layer = LayerGroup(layers=markers, name=name)\n", " \n", " return layer" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "code_folding": [ 2 ], "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: fetching closest record subroutine)\n" ] } ], "source": [ "designate('fetching closest record subroutine', 'map')\n", "\n", "def get_closest_record(associations, latitude, longitude):\n", " \"\"\"Fetch the closest record from the list of records based on latitude and longitude\n", " \n", " Arguments:\n", " associations: list of dicts\n", " latitude: float, latitude coordinate\n", " longitude: float, longitude coordinate\n", " \n", " Returns:\n", " dict, the closest record\n", " \"\"\"\n", " \n", " # calculate the distances to all records\n", " distances = []\n", " for association in associations:\n", " \n", " # calculate squared distance\n", " geocoordinates = association['location']\n", " distance = (latitude - geocoordinates[0]) ** 2 + (longitude - geocoordinates[1]) ** 2\n", " distances.append((association, distance))\n", " \n", " # sort by distance and choose closest\n", " distances.sort(key=lambda pair: pair[1])\n", " closest = distances[0][0]\n", " \n", " return closest" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "code_folding": [ 2 ], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: exhibiting secondary markers on map subroutine)\n" ] } ], "source": [ "designate('exhibiting secondary markers on map subroutine', 'map')\n", "\n", "def plot_secondary_data_on_map(coordinates, chart, associations, field, fill, line, thickness, radius, opacity, click):\n", " \"\"\"Exhibit a record's associated records.\n", " \n", " Arguments:\n", " coordinates: (float, float) tuple, the latitude and longitude\n", " chart: ipyleaflets Map object\n", " associations: list of dicts\n", " field: str, the name of the appropriate field\n", " fill: function object, to determine fill color\n", " line: function object, to determine line color\n", " thickness: int, circle outline thickness\n", " radius: function object, to determine radius\n", " opacity: function object, to determine opacity as a function of weight\n", " click: function object, to act upon a click\n", "\n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # remove last set of associates\n", " chart.layers = chart.layers[:5]\n", " \n", " # fetch the index of the closest association\n", " association = get_closest_record(associations, *coordinates)\n", " \n", " # create marker layer\n", " records = [associate['record'] for associate in association['associates']]\n", " weights = [associate['weight'] for associate in association['associates']]\n", " layer = add_points_to_map(records, weights, field, fill, line, thickness, radius, opacity, click)\n", " \n", " # add layer\n", " chart.add_layer(layer)\n", " \n", " return None" ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "code_folding": [ 3 ], "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: constructing map)\n" ] } ], "source": [ "designate('constructing map', 'map')\n", "\n", "# begin the map at a central latitude and longitude\n", "def construct_map_at_central_geo_loc():\n", " \"\"\"Construct the map at a central geolocation.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " ipyleaflet map object\n", " \"\"\"\n", "\n", " # print status\n", " print('constructing map...')\n", "\n", " # get central latitude\n", " latitudes = [record['latitude'] for record in data]\n", " latitude = (max(latitudes) + min(latitudes)) / 2\n", "\n", " # get central longitude\n", " longitudes = [record['longitude'] for record in data]\n", " longitude = (max(longitudes) + min(longitudes)) / 2\n", "\n", " # set up map with topographical basemap zoomed in on center\n", " chart = Map(basemap=basemaps.Esri.WorldTopoMap, center=(latitude, longitude), zoom=5)\n", " \n", " return chart" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "code_folding": [ 3 ], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: populating map with markers)\n" ] } ], "source": [ "designate('populating map with markers', 'map')\n", "\n", "# populate map with markers\n", "def populate_map(chart):\n", " \"\"\"Populate the chart with markers.\n", " \n", " Arguments:\n", " chart: ipyleaflet map\n", " \n", " Returns:\n", " ipyleaflet map\n", " \"\"\"\n", "\n", " # create unassociated secondary marker layer\n", " print('marking unassociated secondaries...')\n", " indices = [association['record']['index'] for association in mirror]\n", " records = [record for record in uniques if record['index'] not in indices]\n", " weights = [0.0 for record in records]\n", " parameters = [records, weights, fieldii, fillii, lineii, thicknessii, radiusii]\n", " parameters += [empty, nothing, 'unassociated secondaries']\n", " layer = add_points_to_map(*parameters)\n", " chart.add_layer(layer)\n", "\n", " # create unassociated primaries layer\n", " print('marking unassociated primaries...')\n", " indices = [association['record']['index'] for association in associations]\n", " records = [record for record in data if record['index'] not in indices]\n", " weights = [0.0 for record in records]\n", " parameters = [records, weights, field, tan, tan, thickness, radius]\n", " parameters += [empty, nothing, 'unassociated primaries']\n", " layer = add_points_to_map(*parameters)\n", " chart.add_layer(layer)\n", "\n", " # create associated secondary marker layer\n", " print('marking associated secondaries...')\n", " records = [association['record'] for association in heavies]\n", " weights = [association['associates'][0]['weight'] for association in heavies]\n", " parametersii = [chart, mirror, 'larvae', fill, black, thickness, radius, highlight, nothing]\n", " clickingii = lambda **event: plot_secondary_data_on_map(event['coordinates'], *parametersii)\n", " parameters = [records, weights, fieldii, fillii, lineii, thicknessii, radiusii, opacityii]\n", " parameters += [clickingii, 'associated secondaries']\n", " layer = add_points_to_map(*parameters)\n", " chart.add_layer(layer)\n", "\n", " # create primary marker layer\n", " print('marking associated primaries...')\n", " records = [association['record'] for association in associations]\n", " weights = [association['associates'][0]['weight'] for association in associations]\n", " parametersii = [chart, associations, feature, fillii, black, thicknessii, radiusii, highlight, nothing]\n", " clicking = lambda **event: plot_secondary_data_on_map(event['coordinates'], *parametersii)\n", " parameters = [records, weights, field, fill, line, thickness, radius, opacity]\n", " parameters += [clicking, 'associated primaries']\n", " layer = add_points_to_map(*parameters)\n", " chart.add_layer(layer)\n", " \n", " return chart" ] }, { "cell_type": "code", "execution_count": 140, "metadata": { "code_folding": [ 3 ], "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for map: enhancing map with controls)\n" ] } ], "source": [ "designate('enhancing map with controls', 'map')\n", "\n", "# enhance the map with control\n", "def add_map_controls(chart):\n", " \"\"\"Enhance the map with controls and legends.\n", " \n", " Arguments:\n", " chart: ipyleaflet map object\n", " \n", " Returns:\n", " ipyleaflet map object\n", " \"\"\"\n", "\n", " # add full screen button and map scale\n", " chart.add_control(FullScreenControl())\n", " chart.add_control(ScaleControl(position='topright'))\n", "\n", " # add genus legend\n", " labels = [Label(value = r'\\(\\color{' + 'black' +'} {' + 'Genera:' + '}\\)')]\n", " labels += [Label(value = r'\\(\\color{' + indicators[genus] +'} {' + genus + '}\\)') for genus in classification]\n", " legend = VBox(labels)\n", "\n", " # send to output\n", " out = Output(layout={'border': '1px solid blue', 'transparency': '50%'})\n", " with out:\n", "\n", " # display\n", " display(legend)\n", "\n", " # add to map\n", " control = WidgetControl(widget=out, position='topright')\n", " chart.add_control(control)\n", "\n", " # add colormap legend\n", " colors = [mix_colors(division, bracket, spectrum) for division in divisions]\n", " colormap = StepColormap(colors, divisions, vmin=bracket[0], vmax=bracket[1], caption=feature)\n", " out = Output(layout={'border': '1px solid blue', 'transparency': '80%', 'height': '50px', 'overflow': 'scroll'})\n", " text = Label(value = r'\\(\\color{' + 'blue' +'} {' + feature + '}\\)')\n", " with out:\n", "\n", " # display\n", " display(text, colormap)\n", "\n", " # add to map\n", " control = WidgetControl(widget=out, position='bottomright')\n", " chart.add_control(control)\n", "\n", " # add layers control\n", " control = LayersControl(position='topleft')\n", " chart.add_control(control)\n", " \n", " return chart" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "code_folding": [], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: making mirror of associations)\n" ] } ], "source": [ "designate('making mirror of associations')\n", "\n", "# go through primary record\n", "mirror = {}\n", "for association in associations:\n", " \n", " # and each set of associated secondary records\n", " record = association['record']\n", " for associate in association['associates']:\n", " \n", " # skip if weight is zero \n", " weight = associate['weight']\n", " if associate['weight'] > 0.0:\n", " \n", " # add default entry\n", " index = record['index']\n", " if index not in mirror.keys():\n", " \n", " # begin an entry\n", " location = (associate['record']['latitude'], associate['record']['longitude'])\n", " entry = {'record': associate['record'], 'associates': [], 'location': location}\n", " mirror[index] = entry\n", " \n", " # populate entry and sort\n", " mirror[index]['associates'].append({'record': record, 'weight': weight})\n", " mirror[index]['associates'].sort(key=lambda associate: associate['weight'], reverse=True)\n", " \n", "# make into list and sort\n", "mirror = [value for value in mirror.values()]\n", "mirror.sort(key=lambda association: association['associates'][0]['weight'], reverse=True)\n", "\n", "# shrink to uniques\n", "uniques = get_unique_record_per_loc_secondary_data(dataii, feature)" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "code_folding": [], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: shrinking mirror to highest weight per location)\n" ] } ], "source": [ "designate('shrinking mirror to highest weight per location')\n", "\n", "# construct records\n", "unshrunk = []\n", "for index, association in enumerate(mirror):\n", " \n", " # construct faux records\n", " record = association['record']\n", " faux = {'index': index, 'latitude': record['latitude'], 'longitude': record['longitude']}\n", " faux['weight'] = association['associates'][0]['weight']\n", " unshrunk.append(faux)\n", " \n", "# shrink records\n", "shrunken = get_unique_record_per_loc_secondary_data(unshrunk, 'weight')\n", "indices = [record['index'] for record in shrunken]\n", "heavies = [mirror[index] for index in indices]" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: describing markers)\n" ] } ], "source": [ "designate('describing markers')\n", "\n", "# get 5th and 95th percentile bracket\n", "measurements = [association['record'][feature] for association in mirror]\n", "low = percentile(measurements, 5)\n", "high = percentile(measurements, 95)\n", "divisions = chisel(low, high)\n", "bracket = (divisions[0], divisions[-1])\n", "\n", "# define color spectrum as dark and light rgb tuples\n", "dark = (100, 50, 150)\n", "light = (0, 255, 255)\n", "spectrum = (dark, light)\n", "\n", "# define minimum and maximum of larvae counts\n", "measurements = [association['record']['larvae'] for association in associations]\n", "minimal = min(measurements)\n", "maximal = max(measurements)\n", "\n", "# create standin functions\n", "black = lambda record: 'black'\n", "tan = lambda record: 'tan'\n", "yellow = lambda record: 'yellow'\n", "nothing = lambda **event: None\n", "empty = lambda weight: 0.0\n", "highlight = lambda weight: 1.0\n", "\n", "# create functions for primary markers\n", "field = 'larvae'\n", "fill = lambda record: indicators[record['genus']]\n", "line = fill\n", "thickness = 2\n", "radius = lambda record: min([25, 5 + int(0.1 * record['larvae'])])\n", "opacity = lambda weight: 0.1 + max([weight - 0.1, 0])\n", "\n", "# create functions for secondary markers\n", "fieldii = feature\n", "fillii = lambda record: mix_colors(record[feature], bracket, spectrum)\n", "lineii = fillii\n", "thicknessii = 2\n", "radiusii = lambda record: 20\n", "opacityii = lambda weight: 0.1 + max([weight - 0.1, 0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optimizing Parameters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Because there are many parameters involved in the association scheme, it would be nice to be able to test multiple combinations quickly. In the following table you may input lists of parameters to try. The optimization function will attempt to find the combination with highest correlation coefficient or lowest bias depending on the criteria set. To save time at the expense of thoroughness, the optimizer begins at several random combinations and climbs towards the top based on the correlations of similar parameter combinations. \n", "\n", "In the graph that follows, the size indicates the correlation, and the color indicate the bias. The two axes are chosen that seem to most affect the criterion. " ] }, { "cell_type": "code", "execution_count": 144, "metadata": { "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "*** settings -----> ^ [code] (for settings: setting optimizer ranges)\n" ] } ], "source": [ "designate('setting optimizer ranges', 'settings')\n", "\n", "# set distances to try in kilometers\n", "distances = [10, 25, 50]\n", "\n", "# set time intervals to try in days\n", "intervals = [0.5, 1, 5]\n", "\n", "# set lag times to try in days\n", "lags = [0, 2]\n", "\n", "# set confidences in percents\n", "confidences = [0.2, 0.8]\n", "\n", "# set cutoffs in percents\n", "cutoffs = [0.1, 0.5]\n", "\n", "# set inclusions\n", "inclusions = [5, 10]\n", "\n", "# regression mode ('linear', 'quadratic', 'exponential', 'power', 'gaussian')\n", "mode = 'linear'\n", "\n", "# larvae genus ('All', 'Aedes', 'Anopheles', 'Culex', 'Other', 'Unknown')\n", "genus = 'All'\n", "\n", "# set criterion ('correlation', 'pvalue')\n", "criterion = 'pvalue'" ] }, { "cell_type": "code", "execution_count": 145, "metadata": { "code_folding": [], "hide_input": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: applying the parameters button)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "085b14def5084e338cf01964468c8391", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='Apply', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "parameters selected:\n", "\n", "mode: linear\n", "genus: All\n", "criterion: pvalue\n", "distance: [10, 25, 50]\n", "interval: [0.5, 1, 5]\n", "lag: [0, 2]\n", "confidence: [0.2, 0.8]\n", "cutoff: [0.1, 0.5]\n", "inclusion: [5, 10]\n", "\n" ] } ], "source": [ "designate('applying the parameters button')\n", "\n", "# reset parameters function\n", "def update_params_with_new_settings(_):\n", " \"\"\"Update the parameters according to new settings.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # refresh cells \n", " execute_cell_range('### Optimizing Parameters', '### Thank You!')\n", " \n", " return None\n", "\n", "# create button\n", "button = Button(description=\"Apply\")\n", "display(button)\n", "button.on_click(update_params_with_new_settings)\n", "\n", "# infer mode and genus\n", "mode = fuzzy_match(mode, regressions.keys())\n", "genus = fuzzy_match(genus, indicators.keys())\n", "criterion = fuzzy_match(criterion, ('correlation', 'pvalue'))\n", "\n", "# update optimizers\n", "optimizers = [distances, intervals, lags, confidences, cutoffs, inclusions]\n", "[optimizer.sort() for optimizer in optimizers]\n", "knobs = ['distance', 'interval', 'lag', 'confidence', 'cutoff', 'inclusion']\n", "\n", "# sorting by pearson means taking the biggest, but sorting by pvalue means taking the biggest complement\n", "conditionals = {'correlation': lambda pair: pair[1]['correlation'], 'pvalue': lambda pair: 1 - pair[1]['pvalue']}\n", "condition = conditionals[criterion]\n", "\n", "# generate parameters list\n", "styles = ['mode', 'genus', 'criterion']\n", "choices = [mode, genus, criterion]\n", "selections = ['{}: {}\\n'.format(knob, optimizer) for knob, optimizer in zip(styles + knobs, choices + optimizers)]\n", "\n", "# print status\n", "print('parameters selected:\\n')\n", "print(''.join(selections))" ] }, { "cell_type": "code", "execution_count": 146, "metadata": { "code_folding": [], "hide_input": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: optimize button)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "2a9f997aa9564d8b8fd0481503918a73", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='Optimize', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('optimize button')\n", "\n", "# function to optimize\n", "def optimize(_):\n", " \"\"\"Run the optimization sequence.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # run optimization\n", " scores, panda = find_highest_corr_coef()\n", " \n", " # make graph\n", " graph = visualize_corr_scores(scores)\n", " \n", " # display graph\n", " output_notebook()\n", " show(graph)\n", " \n", " # display panda\n", " display(panda.head(10))\n", " \n", " return None\n", "\n", "# create button\n", "button = Button(description=\"Optimize\")\n", "button.on_click(optimize)\n", "display(button)" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "c5ac7d8ca99042fbab36f6387caadef1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ba1b8b59556b44f48fb13e91d49b129a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "code_folding": [], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for optimizer: seeding random starts)\n" ] } ], "source": [ "designate('seeding random starts', 'optimizer')\n", "\n", "# making random seeds\n", "def seed(number=5, size=50):\n", " \"\"\"Create random parameter seeds that are far from each other\n", " \n", " Arguments:\n", " number=5: int, number of seeds\n", " size=20: int, size of random pool\n", " \n", " Returns:\n", " list of (float) tuples\n", " \"\"\"\n", " \n", " # get all lengths of optimizers\n", " optimizers = [distances, intervals, lags, confidences, cutoffs, inclusions]\n", " lengths = [len(optimizer) for optimizer in optimizers]\n", " \n", " # make pool\n", " pool = []\n", " for _ in range(size):\n", " \n", " # make a random parameter grab\n", " trial = tuple([choice([entry for entry in range(length)]) for length in lengths])\n", " pool.append(trial)\n", " \n", " # calculate average euclidean distances and pick furthest\n", " choices = [pool[choice(len(pool))]]\n", " pool = [member for member in pool if member not in choices]\n", " for _ in range(number - 1):\n", " \n", " # calculate distances\n", " averages = []\n", " for trial in pool:\n", " \n", " # calculate average squared distance from all seeds\n", " euclidizing = lambda a, b: sum([(c - d) ** 2 for c, d in zip(a, b)])\n", " mean = sum([euclidizing(trial, trialii) for trialii in choices]) / len(choices)\n", " averages.append((trial, mean))\n", " \n", " # sort and add to choices\n", " averages.sort(key=lambda pair: pair[1], reverse=True)\n", " choices.append(averages[0][0])\n", " pool = [member for member in pool if member not in choices]\n", " \n", " # get seeds\n", " seeds = [tuple(optimizer[entry] for optimizer, entry in zip(optimizers, trial)) for trial in choices]\n", " \n", " return seeds" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "code_folding": [], "hide_input": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for optimizer: analyzing a parameter set)\n" ] } ], "source": [ "designate('analyzing a parameter set', 'optimizer')\n", "\n", "# analyze regression for one parameter set\n", "def analyze_regression_one_param_set(ticket):\n", " \"\"\"Analyze one round of regression after defining the grid.\n", " \n", " Arguments:\n", " ticket: dict\n", " \n", " Returns:\n", " regression scores\n", " \"\"\"\n", "\n", " # assemble associations and samples\n", " associations = merge_datasets(ticket)\n", " subset = get_subset_of_data(associations, ticket['genus'])\n", " samples = assemble_samples(subset)\n", " \n", " # add lengths to ticket\n", " ticket['records'] = len(subset)\n", " ticket['pairs'] = len(samples)\n", " ticket['coverage'] = round(len(subset) / len(data), 2)\n", " \n", " # perform regression\n", " score = regress(samples, ticket, spy=True)\n", " \n", " return score" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "code_folding": [], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for optimizer: cranking through regressions)\n" ] } ], "source": [ "designate('cranking through regressions', 'optimizer')\n", "\n", "# function to crank through regressions\n", "def crank_through_regressions(seed, scores):\n", " \"\"\"Crank through one set of regressions, given a seed\n", " \n", " Arguments:\n", " seed: list of floats, the settings\n", " scores: dict\n", " \n", " Returns\n", " (dict, int) tuple (scores, count)\n", " \"\"\"\n", " \n", " # start from each startpoint\n", " current = tuple(seed)\n", " delta = 1.0\n", " optimum = 0.0\n", " count = 0\n", " while delta > 0.0:\n", "\n", " # go through each super optimizer\n", " for index, optimizer in enumerate(optimizers):\n", "\n", " # go through each member\n", " trials = []\n", " for member in optimizer:\n", "\n", " # replace in current\n", " trial = [entry for entry in current]\n", " trial[index] = member\n", " trial = tuple(trial)\n", "\n", " # optimize if not already done \n", " if trial not in scores.keys():\n", "\n", " # print status\n", " count += 1\n", " print('.', end=\"\")\n", " if count % 10 == 0:\n", "\n", " # print\n", " print('({})'.format(count), end='')\n", "\n", " # create ticket\n", " ticket = {knob: setting for knob, setting in zip(knobs, trial)}\n", " ticket = issue_ticket(ticket, genus, mode)\n", "\n", " # analyze\n", " scores[trial] = analyze_regression_one_param_set(ticket)\n", "\n", " # append\n", " trials.append(trial)\n", "\n", " # check all scores and sort\n", " correlations = [(trial, scores[trial]) for trial in trials]\n", " correlations.sort(key=condition, reverse=True)\n", " current = correlations[0][0]\n", "\n", " # calculate detla and reset optimum\n", " delta = condition(correlations[0]) - optimum\n", " optimum = max([condition(correlations[0]), optimum])\n", " \n", " # exit count\n", " print('')\n", " \n", " return scores" ] }, { "cell_type": "code", "execution_count": 151, "metadata": { "code_folding": [], "hide_input": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for optimizer: climbing parameters)\n" ] } ], "source": [ "designate('climbing parameters', 'optimizer')\n", "\n", "# function to find maximum\n", "def find_highest_corr_coef():\n", " \"\"\"Climb to the highest correlation coefficient.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " (list of lists, list of lists, dataframe) tuple, (optimizers, scores, panda)\n", " \"\"\"\n", "\n", " # start at midpoints and try neighboring combinations\n", " seeds = 5\n", " scores = {}\n", " print('optimizing {} mode for genus {} by {} (using {} seeds)'.format(mode, genus, criterion, seeds))\n", " for start in seed(seeds):\n", " \n", " # crank through optimization\n", " scores = crank_through_regressions(start, scores)\n", " \n", " # sort scores\n", " scores = [item for item in scores.items()]\n", " scores.sort(key=condition, reverse=True)\n", " reports = [report for _, report in scores]\n", " \n", " # make panda\n", " panda = pandas.DataFrame(reports)\n", " columns = knobs\n", " columns += ['correlation', 'pvalue', 'records']\n", " panda = panda[columns]\n", " panda.columns = [column + units[column](feature) for column in columns]\n", " \n", " # add to optimizations\n", " optimizations.append(panda)\n", " \n", " return reports, panda" ] }, { "cell_type": "code", "execution_count": 152, "metadata": { "code_folding": [], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for optimizer: orienting plot axes)\n" ] } ], "source": [ "designate('orienting plot axes', 'optimizer')\n", "\n", "# function to orient the scores along most variant axes\n", "def orient_scores_and_find_highest_scoring_on_each_axis(scores):\n", " \"\"\"Orient the scores along most variant axis, and prune to the highest scoring along each.\n", " \n", " Arguments:\n", " scores: list of dicts\n", " \n", " Returns:\n", " (list of dicts, str, str) tuple, the pruned scores and axes names.\n", " \"\"\"\n", " \n", " # rewrite condition to get triggered only by score\n", " evaluating = lambda score: condition((None, score))\n", " \n", " # collect all maximum correlations by optimizer setting\n", " maxes = [{} for optimizer in optimizers]\n", " for score in scores:\n", " \n", " # go through each optimizer\n", " for index, optimizer in enumerate(optimizers):\n", " \n", " # add max score\n", " setting = score[knobs[index]]\n", " maxes[index][setting] = max([maxes[index].setdefault(setting, 0.0), evaluating(score)])\n", " \n", " # get axes with highest difference between max and min\n", " differences = [(index, (max(members.values()) - min(members.values()))) for index, members in enumerate(maxes)]\n", " differences.sort(key=lambda pair: pair[1], reverse=True)\n", " horizontal = knobs[differences[0][0]]\n", " vertical = knobs[differences[1][0]]\n", " \n", " # only plot the highest pearson or lowest bias at any point\n", " filterer = {}\n", " for score in scores:\n", " \n", " # get pair of coordinates\n", " faux = {'correlation': 0.0, 'pvalue': 1.0}\n", " coordinates = (score[horizontal], score[vertical])\n", " if evaluating(score) >= evaluating(filterer.setdefault(coordinates, faux)):\n", " \n", " # replace score\n", " filterer[coordinates] = score\n", " \n", " # create sample attributes\n", " scores = [score for score in filterer.values()]\n", " scores.sort(key=evaluating)\n", " \n", " return scores, horizontal, vertical" ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "code_folding": [], "hide_input": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for optimizer: visualizing correlations)\n" ] } ], "source": [ "designate('visualizing correlations', 'optimizer')\n", "\n", "# function to scatter graph correlations\n", "def visualize_corr_scores(scores):\n", " \"\"\"Visualize the different correlation scores in parameter space.\n", " \n", " Arguments:\n", " optimizers: list of lists of parameters\n", " scores: list of dicts, regression reports\n", " \n", " Returns:\n", " bokeh graph object\n", " \"\"\"\n", " \n", " # get the top scores and the axes \n", " scores, horizontal, vertical = orient_scores_and_find_highest_scoring_on_each_axis(scores)\n", "\n", " # begin the plot with labels and size\n", " parameters = {}\n", " parameters['title'] = 'Predicting larvae counts by {} using {} regression'.format(feature, mode)\n", " parameters['x_axis_label'] = horizontal + ' {}'.format(units[horizontal](feature))\n", " parameters['y_axis_label'] = vertical + ' {}'.format(units[vertical](feature))\n", " parameters['plot_width'] = 800\n", " parameters['plot_height'] = 400\n", " graph = figure(**parameters)\n", "\n", " # make annotation labels\n", " labels = knobs + ['correlation', 'pvalue', 'records']\n", " annotations = [(label, '@' + label) for label in labels]\n", " graph = annotate_plot(graph, annotations)\n", " \n", " # create ponts, relating size to pearson and color to bias\n", " blue = (0, 0, 255)\n", " cyan = (0, 255, 255)\n", " bracket = (0.5, 1.0)\n", " xs = [score[horizontal] for score in scores]\n", " ys = [score[vertical] for score in scores]\n", " sizes = [10 + int(100 * score['correlation']) for score in scores]\n", " colors = [mix_colors(1 - score['pvalue'], bracket, (blue, cyan)) for score in scores]\n", " \n", " # make columns from scores and labels, suplement with graph attributes\n", " columns = {label: [round(score[label], 2) for score in scores] for label in labels}\n", " columns.update({'xs': xs, 'ys': ys, 'sizes': sizes, 'colors': colors})\n", " source = ColumnDataSource(columns)\n", " \n", " # add sample markers\n", " parameters = {'source': source, 'x': 'xs', 'y': 'ys', 'size': 'sizes', 'fill_color': 'colors'}\n", " parameters.update({'line_width': 1, 'line_color': 'black', 'fill_alpha': 1.0})\n", " graph.circle(**parameters)\n", " \n", " return graph" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Scrutinizing All Combinations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively you may use brute force to try all parameter combinations. This will take a while, but skips shortcuts." ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "code_folding": [], "hide_input": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: scrutinize button)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "495afe2055f24a7081c888eb1225224c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='Scrutinize', style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('scrutinize button')\n", "\n", "# function to scrutinize\n", "def scrutinize_all_combinations(_):\n", " \"\"\"Run the optimization sequence.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " None\n", " \"\"\"\n", " \n", " # run optimization\n", " scores, panda = optimize()\n", " \n", " # make graph\n", " graph = visualize_corr_scores(scores)\n", " \n", " # display graph\n", " output_notebook()\n", " show(graph)\n", " \n", " # display panda\n", " display(panda.head(10))\n", " \n", " return None\n", "\n", "# create button\n", "button = Button(description=\"Scrutinize\")\n", "button.on_click(scrutinize_all_combinations)\n", "display(button)" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "code_folding": [], "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for scrutinizer: combing through all combinations)\n" ] } ], "source": [ "designate('combing through all combinations', 'scrutinizer')\n", "\n", "# function to find maximum\n", "def optimize():\n", " \"\"\"Scrutinize all combinations.\n", " \n", " Arguments:\n", " None\n", " \n", " Returns:\n", " (list of lists, list of lists, dataframe) tuple, (optimizers, scores, panda)\n", " \"\"\"\n", "\n", " # make all combinations\n", " combinations = [[]]\n", " for optimizer in optimizers:\n", "\n", " # add to combinations\n", " combinations = [combination + [member] for combination in combinations for member in optimizer]\n", "\n", " # go through all combinations\n", " combinations = [tuple(combination) for combination in combinations]\n", " print('{} combinations'.format(len(combinations)))\n", " print('optimizing by {} '.format(criterion), end=\"\")\n", " scores = {}\n", " count=0\n", " for combination in combinations:\n", " \n", " # print status\n", " count += 1\n", " print('.', end=\"\")\n", " if count % 10 == 0:\n", "\n", " # print\n", " print('({})'.format(count), end='')\n", " \n", " # analyze\n", " ticket = {knob: setting for knob, setting in zip(knobs, combination)}\n", " ticket = issue_ticket(ticket, genus, mode)\n", " scores[combination] = analyze_regression_one_param_set(ticket)\n", " \n", " # collect scores\n", " scores = [item for item in scores.items()]\n", " scores.sort(key=condition, reverse=True)\n", " scores = [value for _, value in scores]\n", "\n", " # make panda\n", " panda = pandas.DataFrame(scores)\n", " columns = knobs\n", " columns += ['correlation', 'pvalue', 'records']\n", " panda = panda[columns]\n", " panda.columns = [column + units[column](feature) for column in columns]\n", " \n", " # add to optimizations\n", " optimizations.append(panda)\n", " \n", " return scores, panda" ] }, { "cell_type": "code", "execution_count": 156, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "f6ea3ee81add424d8fb933be764ae0b8", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "13f0b04190704264b6e15cf0044e8703", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "designate('navigation buttons')\n", "\n", "# set two navigation buttons\n", "navigate_notebook()\n", "jump_to_regression()" ] }, { "cell_type": "markdown", "metadata": { "hide_input": true }, "source": [ "### Thank You!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Thanks for taking this notebook for a spin. Please feal free to direct questions, issues, or other feedback to Matthew Bandel at matthew.bandel@ssaihq.com" ] }, { "cell_type": "code", "execution_count": 157, "metadata": { "hide_input": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " ^ [code] (for main: navigation buttons)\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b7734f3b53b343478e1f73d0c2785327", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Top', style=ButtonStyle()), Button(description='Settings', style=ButtonStyl…" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "d5904843546f4cb3a44758982d61f01c", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(Button(description='Linear', style=ButtonStyle()), Button(description='Quadratic', style=Button…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ 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