From 2807d1625f7036cb4b57f631195fafd29cf4201f Mon Sep 17 00:00:00 2001 From: Andy Maloney Date: Wed, 21 Sep 2022 17:40:37 -0500 Subject: [PATCH 1/7] Marginal diagnostic tool This commit includes the marginal 1D diagnostic tool with JavaScript callbacks. --- src/beanmachine/ppl/__init__.py | 2 + .../ppl/diagnostics/tools/__init__.py | 18 + .../ppl/diagnostics/tools/js/.eslintrc.js | 73 ++ .../ppl/diagnostics/tools/js/package.json | 54 ++ .../tools/js/src/marginal1d/callbacks.ts | 112 +++ .../tools/js/src/marginal1d/index.ts | 8 + .../tools/js/src/marginal1d/interfaces.ts | 69 ++ .../diagnostics/tools/js/src/stats/array.ts | 27 + .../tools/js/src/stats/dataTransformation.ts | 16 + .../js/src/stats/highestDensityInterval.ts | 66 ++ .../tools/js/src/stats/marginal.ts | 33 + .../tools/js/src/stats/pointStatistic.ts | 15 + .../diagnostics/tools/js/src/stats/utils.ts | 82 ++ .../tools/js/src/types/fast-kde.d.ts | 6 + .../ppl/diagnostics/tools/js/tsconfig.json | 48 + .../diagnostics/tools/js/webpack.config.js | 29 + .../diagnostics/tools/marginal1d/__init__.py | 4 + .../ppl/diagnostics/tools/marginal1d/tool.py | 161 ++++ .../diagnostics/tools/marginal1d/typing.py | 166 ++++ .../ppl/diagnostics/tools/marginal1d/utils.py | 642 +++++++++++++ .../ppl/diagnostics/tools/utils/__init__.py | 4 + .../ppl/diagnostics/tools/utils/accessor.py | 79 ++ .../ppl/diagnostics/tools/utils/base.py | 46 + .../tools/utils/model_serializers.py | 32 + .../diagnostics/tools/utils/plotting_utils.py | 123 +++ src/beanmachine/ppl/diagnostics/tools/viz.py | 29 + tutorials/Coin_flipping.ipynb | 906 ++++++++++++++++-- 27 files changed, 2790 insertions(+), 60 deletions(-) create mode 100644 src/beanmachine/ppl/diagnostics/tools/__init__.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/package.json create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/tsconfig.json create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js create mode 100644 src/beanmachine/ppl/diagnostics/tools/marginal1d/__init__.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/__init__.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/accessor.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/base.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/viz.py diff --git a/src/beanmachine/ppl/__init__.py b/src/beanmachine/ppl/__init__.py index 739973024c..98270d71f3 100644 --- a/src/beanmachine/ppl/__init__.py +++ b/src/beanmachine/ppl/__init__.py @@ -8,6 +8,7 @@ from . import experimental from .diagnostics import Diagnostics from .diagnostics.common_statistics import effective_sample_size, r_hat, split_r_hat +from .diagnostics.tools import viz from .inference import ( CompositionalInference, empirical, @@ -60,4 +61,5 @@ "random_variable", "simulate", "split_r_hat", + "viz", ] diff --git a/src/beanmachine/ppl/diagnostics/tools/__init__.py b/src/beanmachine/ppl/diagnostics/tools/__init__.py new file mode 100644 index 0000000000..9b47b4890b --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/__init__.py @@ -0,0 +1,18 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import sys +from pathlib import Path + + +if sys.version_info >= (3, 8): + from typing import TypedDict +else: + from typing_extensions import TypedDict + + +TOOLS_DIR = Path(__file__).parent.resolve() +JS_DIR = TOOLS_DIR.joinpath("js") +JS_DIST_DIR = JS_DIR.joinpath("dist") diff --git a/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js b/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js new file mode 100644 index 0000000000..6dd122cd6a --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js @@ -0,0 +1,73 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +const OFF = 0; +const WARNING = 1; +const ERROR = 2; + +module.exports = { + root: true, + env: { + browser: true, + commonjs: true, + jest: true, + node: true, + }, + parser: '@typescript-eslint/parser', + parserOptions: { + allowImportExportEverywhere: true, + }, + extends: ['airbnb', 'prettier', 'plugin:import/typescript'], + plugins: ['prefer-arrow'], + rules: { + // Allow more than 1 class per file. + 'max-classes-per-file': ['error', {ignoreExpressions: true, max: 2}], + // Allow snake_case. + camelcase: [ + OFF, + { + properties: 'never', + ignoreDestructuring: true, + ignoreImports: true, + ignoreGlobals: true, + }, + ], + 'no-underscore-dangle': OFF, + // Arrow function rules. + 'prefer-arrow/prefer-arrow-functions': [ + ERROR, + { + disallowPrototype: true, + singleReturnOnly: false, + classPropertiesAllowed: false, + }, + ], + 'prefer-arrow-callback': [ERROR, {allowNamedFunctions: true}], + 'arrow-parens': [ERROR, 'always'], + 'arrow-body-style': [ERROR, 'always'], + 'func-style': [ERROR, 'declaration', {allowArrowFunctions: true}], + 'react/function-component-definition': [ + ERROR, + { + namedComponents: 'arrow-function', + unnamedComponents: 'arrow-function', + }, + ], + // Ignore the global require, since some required packages are BrowserOnly. + 'global-require': 0, + // We reassign several parameter objects since Bokeh is just updating values in the + // them. + 'no-param-reassign': 0, + // Ignore certain webpack alias because it can't be resolved + 'import/no-unresolved': [ + ERROR, + {ignore: ['^@theme', '^@docusaurus', '^@generated', '^@bokeh']}, + ], + 'import/extensions': OFF, + 'object-shorthand': [ERROR, 'never'], + 'prefer-destructuring': [WARNING, {object: true, array: true}], + 'no-nested-ternary': 0, + }, +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/package.json b/src/beanmachine/ppl/diagnostics/tools/js/package.json new file mode 100644 index 0000000000..63d75c3184 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/package.json @@ -0,0 +1,54 @@ +{ + "name": "visual-diagnostic-tools", + "version": "0.1.0", + "description": "", + "license": "MIT", + "keywords": [], + "repository": {}, + "scripts": { + "build": "webpack" + }, + "dependencies": { + "@bokeh/bokehjs": "^2.4.3", + "fast-kde": "^0.2.1" + }, + "devDependencies": { + "@types/node": "^18.0.4", + "@typescript-eslint/eslint-plugin": "^5.30.5", + "@typescript-eslint/parser": "^5.30.5", + "eslint": "^8.19.0", + "eslint-config-airbnb": "^19.0.4", + "eslint-config-prettier": "^8.5.0", + "eslint-plugin-import": "^2.26.0", + "eslint-plugin-prefer-arrow": "^1.2.3", + "ts-loader": "^9.3.1", + "ts-node": "^10.9.1", + "typescript": "^4.7.4", + "webpack": "^5.74.0", + "webpack-cli": "^4.10.0" + }, + "overrides": { + "cwise": "$cwise", + "minimist": "$minimist", + "quote-stream": "$quote-stream", + "static-eval": "$static-eval", + "static-module": "$static-module", + "typedarray-pool": "$typedarray-pool" + }, + "peerDependencies": { + "@types/cwise": "^1.0.4", + "@types/minimist": "^1.2.2", + "@types/static-eval": "^0.2.31", + "@types/typedarray-pool": "^1.1.2", + "buffer": "^6.0.3", + "cwise": "^1.0.10", + "eslint-plugin-jsx-a11y": "^6.5.1", + "eslint-plugin-react": "^7.28.0", + "eslint-plugin-react-hooks": "^4.3.0", + "minimist": "^1.2.6", + "quote-stream": "^1.0.2", + "static-eval": "2.1.0", + "static-module": "^3.0.4", + "typedarray-pool": "^1.2.0" + } +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts new file mode 100644 index 0000000000..150722efe2 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts @@ -0,0 +1,112 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +import {Axis} from '@bokehjs/models/axes/axis'; +import {cumulativeSum} from '../stats/array'; +import {scaleToOne} from '../stats/dataTransformation'; +import { + interval as hdiInterval, + data as hdiData, +} from '../stats/highestDensityInterval'; +import {oneD} from '../stats/marginal'; +import {mean as computeMean} from '../stats/pointStatistic'; +import {interpolatePoints} from '../stats/utils'; +import * as interfaces from './interfaces'; + +const FIGURENAMES = {marginal: 'marginal', cumulative: 'cumulative'}; +const figureNames = Object.keys(FIGURENAMES); + +export const updateAxisLabel = (axis: Axis, label: string | null): void => { + if ('axis_label' in axis) { + axis.axis_label = label; + } +}; + +export const computeStats = ( + rawData: number[], + computedX: number[], + computedY: number[], + hdiProbability: number, + text_align: string[] = ['right', 'center', 'left'], + x_offset: number[] = [-5, 0, 5], + y_offset: number[] = [0, 10, 0], +): interfaces.Marginal1dStats => { + const mean = computeMean(computedX); + const hdiBounds = hdiInterval(rawData, hdiProbability); + const x = [hdiBounds.lowerBound, mean, hdiBounds.upperBound]; + const y = interpolatePoints({x: computedX, y: computedY, points: x}); + const text = [ + `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`, + `Mean: ${mean.toFixed(3)}`, + `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`, + ]; + return { + x: x, + y: y, + text: text, + text_align: text_align, + x_offset: x_offset, + y_offset: y_offset, + }; +}; + +export const computeData = ( + data: number[], + bwFactor: number, + hdiProbability: number, +): interfaces.Marginal1dData => { + const output = {} as interfaces.Marginal1dData; + for (let i = 0; i < figureNames.length; i += 1) { + const figureName = figureNames[i]; + output[figureName] = {} as interfaces.Marginal1dDatum; + const distribution = oneD(data, bwFactor); + switch (figureName) { + case 'cumulative': + distribution.y = scaleToOne(cumulativeSum(distribution.y)); + break; + default: + break; + } + const stats = computeStats(data, distribution.x, distribution.y, hdiProbability); + output[figureName] = { + distribution: distribution, + hdi: hdiData(data, distribution.x, distribution.y, hdiProbability), + stats: {x: stats.x, y: stats.y}, + labels: stats, + }; + } + return output; +}; + +export const update = ( + data: number[], + rvName: string, + bwFactor: number, + hdiProbability: number, + sources: interfaces.Marginal1dSources, + figures: interfaces.Marginal1dFigures, +): number => { + const computedData = computeData(data, bwFactor, hdiProbability); + for (let i = 0; i < figureNames.length; i += 1) { + const figureName = figureNames[i]; + sources[figureName].distribution.data = { + x: computedData[figureName].distribution.x, + y: computedData[figureName].distribution.y, + }; + sources[figureName].hdi.data = { + base: computedData[figureName].hdi.base, + lower: computedData[figureName].hdi.lower, + upper: computedData[figureName].hdi.upper, + }; + sources[figureName].stats.data = { + x: computedData[figureName].stats.x, + y: computedData[figureName].stats.y, + label: computedData[figureName].labels.text, + }; + sources[figureName].labels.data = computedData[figureName].labels; + updateAxisLabel(figures[figureName].below[0], rvName); + } + return computedData.marginal.distribution.bandwidth; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts new file mode 100644 index 0000000000..ea73a8a978 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts @@ -0,0 +1,8 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +import * as marginal1d from './callbacks'; + +(window as any).marginal1d = marginal1d; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts new file mode 100644 index 0000000000..380a0726f3 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts @@ -0,0 +1,69 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +import {Plot} from '@bokehjs/models/plots/plot'; +import {ColumnDataSource} from '@bokehjs/models/sources/column_data_source'; + +export interface Marginal1dStats { + [key: string]: any; + x: number[]; + y: number[]; + text: string[]; + text_align: string[]; + x_offset: number[]; + y_offset: number[]; +} + +export interface Marginal1dDatum { + distribution: {[key: string]: any; x: number[]; y: number[]; bandwidth: number}; + hdi: { + [key: string]: any; + base: number[]; + lower: number[]; + upper: number[]; + lowerBound: number; + upperBound: number; + }; + stats: {[key: string]: any; x: number[]; y: number[]}; + labels: { + [key: string]: any; + x: number[]; + y: number[]; + text: string[]; + text_align: string[]; + x_offset: number[]; + y_offset: number[]; + }; +} + +export interface Marginal1dData { + [key: string]: any; + marginal: Marginal1dDatum; + cumulative: Marginal1dDatum; +} + +export interface Marginal1dSources { + [key: string]: any; + marginal: { + [key: string]: any; + distribution: ColumnDataSource; + hdi: ColumnDataSource; + stats: ColumnDataSource; + labels: ColumnDataSource; + }; + cumulative: { + [key: string]: any; + distribution: ColumnDataSource; + hdi: ColumnDataSource; + stats: ColumnDataSource; + labels: ColumnDataSource; + }; +} + +export interface Marginal1dFigures { + [key: string]: any; + marginal: Plot; + cumulative: Plot; +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts new file mode 100644 index 0000000000..79a3e46d28 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts @@ -0,0 +1,27 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +export const cumulativeSum = (data: number[]): number[] => { + // eslint-disable-next-line arrow-body-style + const cumulativeSumMap = ((sum: number) => (value: number) => { + // eslint-disable-next-line no-return-assign + return (sum += value); + })(0); + + const cSum = data.map(cumulativeSumMap); + const normalizationFactor = Math.max(...cSum); + const cumSum = []; + for (let i = 0; i < cSum.length; i += 1) { + cumSum.push(cSum[i] / normalizationFactor); + } + return cumSum; +}; + +export const numericalSort = (data: number[]): number[] => { + const dataCopy = data.slice(0); + return dataCopy.sort((a, b) => { + return a < b ? -1 : a > b ? 1 : 0; + }); +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts new file mode 100644 index 0000000000..3dc64a89e6 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts @@ -0,0 +1,16 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +export const scaleBy = (data: number[], scaleFactor: number): number[] => { + const scaledData = data.map((datum) => { + return datum / scaleFactor; + }); + return scaledData; +}; + +export const scaleToOne = (data: number[]): number[] => { + const scaleFactor = Math.max(...data); + return scaleBy(data, scaleFactor); +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts new file mode 100644 index 0000000000..d42247e653 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts @@ -0,0 +1,66 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +import {numericalSort} from './array'; + +export interface HdiInterval { + lowerBound: number; + upperBound: number; + lowerBoundIndex: number; + upperBoundIndex: number; +} + +export interface HdiData { + base: number[]; + lower: number[]; + upper: number[]; + lowerBound: number; + upperBound: number; +} + +export const interval = (data: number[], hdiProbability: number): HdiInterval => { + const N = data.length; + const sortedData = numericalSort(data); + const stopIndex = Math.floor(hdiProbability * N); + const startIndex = N - stopIndex; + const leftData = sortedData.slice(stopIndex); + const rightData = sortedData.slice(0, startIndex); + const hdi = []; + for (let i = 0; i < leftData.length; i += 1) { + hdi.push(leftData[i] - rightData[i]); + } + const lowerIndex = hdi.indexOf(Math.min(...hdi)); + const upperIndex = lowerIndex + stopIndex; + return { + lowerBound: sortedData[lowerIndex], + upperBound: sortedData[upperIndex], + lowerBoundIndex: lowerIndex, + upperBoundIndex: upperIndex, + }; +}; + +export const data = ( + rvData: number[], + marginalX: number[], + marginalY: number[], + hdiProbability: number, +): HdiData => { + const hdi = interval(rvData, hdiProbability); + const base = []; + const upper = []; + for (let i = 0; i < marginalX.length; i += 1) { + if (marginalX[i] <= hdi.upperBound && marginalX[i] >= hdi.lowerBound) { + base.push(marginalX[i]); + upper.push(marginalY[i]); + } + } + return { + base: base, + lower: new Array(base.length).fill(0.0), + upper: upper, + lowerBound: hdi.lowerBound, + upperBound: hdi.upperBound, + }; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts new file mode 100644 index 0000000000..898b5d7452 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts @@ -0,0 +1,33 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +import {density1d} from 'fast-kde/src/density1d'; +import {scaleToOne} from './dataTransformation'; + +// eslint-disable-next-line import/prefer-default-export +export const oneD = ( + data: number[], + bwFactor: number, +): {x: number[]; y: number[]; bandwidth: number} => { + // Calculate the 1D KDE. + const kde1d = density1d(data, { + adjust: bwFactor, + pad: 3, + bins: 512, + }); + // Extract the data points from the 1D KDE calculation. + const points: {x: number; y: number}[] = Array.from(kde1d); + const x = []; + const y = []; + for (let i = 0; i < points.length; i += 1) { + x.push(points[i].x); + y.push(points[i].y); + } + return { + x: x, + y: scaleToOne(y), + bandwidth: kde1d.bandwidth(), + }; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts new file mode 100644 index 0000000000..c1f1855f46 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts @@ -0,0 +1,15 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +export const sum = (data: number[]): number => { + return data.reduce((previousValue, currentValue) => { + return previousValue + currentValue; + }); +}; + +export const mean = (data: number[]): number => { + const dataSum = sum(data); + return dataSum / data.length; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts new file mode 100644 index 0000000000..4001255540 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts @@ -0,0 +1,82 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +const left_edge_index = (point: number, intervals: number[]) => { + const len = intervals.length; + if (point < intervals[0]) { + return -1; + } + if (point > intervals[len - 1]) { + return intervals.length; + } + let leftEdgeIndex = 0; + let rightEdgeIndex = len - 1; + while (rightEdgeIndex - leftEdgeIndex !== 1) { + const indexOfNumberToCompare = + leftEdgeIndex + Math.floor((rightEdgeIndex - leftEdgeIndex) / 2); + if (point >= intervals[indexOfNumberToCompare]) + leftEdgeIndex = indexOfNumberToCompare; + else rightEdgeIndex = indexOfNumberToCompare; + } + return leftEdgeIndex; +}; + +const lerp = (x: number, x0: number, y0: number, x1: number, y1: number): number => { + const slope = (y1 - y0) / (x1 - x0); + let res = slope * (x - x0) + y0; + if (!Number.isFinite(res)) { + res = slope * (x - x1) + y1; + if (!Number.isFinite(res) && y0 === y1) res = y0; + } + return res; +}; + +const interpolate = (points: number[], x: number[], y: number[]): number[] => { + // Implementation ported from np.interp + const n = points.length; + const results = new Array(n); + for (let i = 0; i < n; i += 1) { + const point = points[i]; + if (Number.isNaN(point)) { + results[i] = point; + // eslint-disable-next-line no-continue + continue; + } + const index = left_edge_index(point, x); + if (index === -1) { + // eslint-disable-next-line prefer-destructuring + results[i] = y[0]; + } else if (index === x.length) { + results[i] = y[y.length - 1]; + } else if (index === x.length - 1 || x[index] === point) { + results[i] = y[index]; + } else { + const x0 = x[index]; + const y0 = y[index]; + const x1 = x[index + 1]; + const y1 = y[index + 1]; + results[i] = lerp(point, x0, y0, x1, y1); + } + } + return results; +}; + +// eslint-disable-next-line import/prefer-default-export +export const interpolatePoints = ({ + x, + y, + points, +}: { + x: number[]; + y: number[]; + points: number[]; +}): number[] => { + const output = []; + for (let i = 0; i < points.length; i += 1) { + const interpolatedPoint = Array.from(interpolate([points[i]], x, y)); + output.push(interpolatedPoint[0]); + } + return output; +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts new file mode 100644 index 0000000000..f6e716e9db --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts @@ -0,0 +1,6 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + +declare module 'fast-kde/*'; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/tsconfig.json b/src/beanmachine/ppl/diagnostics/tools/js/tsconfig.json new file mode 100644 index 0000000000..37de8788f8 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/tsconfig.json @@ -0,0 +1,48 @@ +{ + "compilerOptions": { + "allowJs": true, + "alwaysStrict": true, + "baseUrl": ".", + "declaration": true, + "esModuleInterop": true, + "experimentalDecorators": true, + "importHelpers": false, + "lib": ["dom", "dom.iterable", "es2020"], + "module": "ES2020", + "moduleResolution": "node", + "noEmitOnError": false, + "noErrorTruncation": true, + "noImplicitAny": true, + "noImplicitReturns": true, + "noImplicitThis": true, + "noUnusedLocals": true, + "noUnusedParameters": true, + "outDir": "./dist/lib", + "paths": { + "@bokehjs/*": [ + "node_modules/@bokeh/bokehjs/build/js/lib/*", + "node_modules/@bokeh/bokehjs/build/js/types/*" + ], + "compute-histogram/*": ["node_modules/compute-histogram/*"], + "fast-kde/*": ["node_modules/fast-kde/*"], + "ndarray/*": ["node_modules/ndarray/*"], + "ndarray-fft/*": [ + "node_modules/ndarray-fft/*", + "bokeh_extensions/types/ndarray-fft.d.ts" + ] + }, + "resolveJsonModule": true, + "skipLibCheck": true, + "sourceMap": true, + "strictBindCallApply": false, + "strictFunctionTypes": false, + "strictNullChecks": true, + "strictPropertyInitialization": false, + "target": "es2020" + }, + "include": [ + "./**/*.ts", + "./**/*.d.ts", + "./node_modules/@bokeh/bokehjs/build/js/types/api/main.d.ts" + ] +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js b/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js new file mode 100644 index 0000000000..b60211087a --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js @@ -0,0 +1,29 @@ +const path = require('path'); + +module.exports = { + entry: { + marginal1d: './src/marginal1d/index.ts', + }, + output: { + filename: '[name].js', + path: path.resolve(__dirname, 'dist'), + }, + module: { + rules: [{test: /\.ts$/, use: 'ts-loader', exclude: /node_modules/}], + }, + target: 'web', + mode: 'production', + resolve: { + extensions: ['.ts'], + modules: ['./stats', './interfaces', './types', './node_modules'], + alias: { + 'fast-kde/src/density1d': path.resolve( + __dirname, + 'node_modules/fast-kde/src/density1d.js', + ), + }, + }, + optimization: { + minimize: false, + }, +}; diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/__init__.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/__init__.py new file mode 100644 index 0000000000..7bec24cb17 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py new file mode 100644 index 0000000000..3cf5ad1584 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py @@ -0,0 +1,161 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Marginal 1D diagnostic tool for a Bean Machine model.""" + +from typing import TypeVar + +from beanmachine.ppl.diagnostics.tools.marginal1d import utils +from beanmachine.ppl.diagnostics.tools.utils.base import Base +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples +from bokeh.embed import file_html +from bokeh.models.callbacks import CustomJS +from bokeh.resources import INLINE + + +T = TypeVar("T", bound="Marginal1d") + + +class Marginal1d(Base): + """Marginal (1D) tool. + + Parameters + ---------- + mcs : MonteCarloSamples + Bean Machine model object. + + Attributes + ---------- + data : Dict[str, List[List[float]]] + JSON serialized version of the Bean Machine model. + rv_names : List[str] + The list of random variables string names for the given model. + num_chains : int + The number of chains of the model. + palette : List[str] + A list of color values used for the glyphs in the figures. The colors are + specifically chosen from the Colorblind palette defined in Bokeh. + js : str + The JavaScript callbacks needed to render the Bokeh tool independently from + a Python server. + """ + + def __init__(self: T, mcs: MonteCarloSamples) -> None: + super(Marginal1d, self).__init__(mcs) + + def create_document(self: T) -> str: + """Create the Bokeh document for display in Jupyter.""" + # Initialize widget values using Python. + rv_name = self.rv_names[0] + bw_factor = 1.0 + hdi_probability = 0.89 + + # Compute the initial data displayed in the tool using Python. It is not quite + # clear how to initialize the tool without requiring Python to first render it + # using data it calculates. + rv_data = self.data[rv_name] + computed_data = utils.compute_data( + data=rv_data, + bw_factor=bw_factor, + hdi_probability=hdi_probability, + ) + bandwidth = computed_data["marginal"]["distribution"]["bandwidth"] + + # Create the Bokeh sources using Python with data calculated in Python. + sources = utils.create_sources(data=computed_data) + + # Create the figures for the tool using Python. + figures = utils.create_figures(rv_name=rv_name) + + # Create the glyphs and attach them to the figures using Python. + glyphs = utils.create_glyphs(data=computed_data) + utils.add_glyphs(sources=sources, figures=figures, glyphs=glyphs) + + # Create the annotations and attach them to the figures using Python. + annotations = utils.create_annotations(sources=sources) + utils.add_annotations(figures=figures, annotations=annotations) + + # Create the tool tips and attach them to the figures using Python. + tooltips = utils.create_tooltips(figures=figures, rv_name=rv_name) + utils.add_tooltips(figures=figures, tooltips=tooltips) + + # Create the widgets for the tool using Python. + widgets = utils.create_widgets( + rv_names=self.rv_names, + rv_name=rv_name, + bandwidth=bandwidth, + bw_factor=bw_factor, + ) + + # Below we create callbacks for the widgets using JavaScript. The below JS uses + # a try/catch block to ensure the proper methods are in the notebook output + # cell no matter which widget a user interacts with first. + # NOTE: When this callback is invoked, data is no longer being calculated with + # Python, and is instead being calculated by the browser. + callback_js = f""" + const rvName = widgets.rv_select.value; + const rvData = data[rvName].flat(); + let bw = 0.0; + try {{ + bw = marginal1d.update( + rvData, + rvName, + bwFactor, + hdiProbability, + sources, + figures, + ); + }} catch (error) {{ + {self.js} + bw = marginal1d.update( + rvData, + rvName, + bwFactor, + hdiProbability, + sources, + figures, + ); + }} + """ + + # Each widget requires the following dictionary for the CustomJS method. Notice + # that the callback_js object above uses the names defined as keys in the below + # object with values defined by the Python objects. + callback_arguments = { + "data": self.data, + "widgets": widgets, + "sources": sources, + "figures": figures, + } + + # Each widget requires slightly different JS, except for the sliders. + rv_select_js = f""" + const bwFactor = 1.0; + const hdiProbability = 0.89; + widgets.bw_factor_slider.value = bwFactor; + widgets.hdi_slider.value = 100 * hdiProbability; + {callback_js}; + widgets.bw_div.text = `Bandwidth: ${{bwFactor * bw}}`; + figures.marginal.reset.emit(); + """ + slider_js = f""" + const bwFactor = widgets.bw_factor_slider.value; + const hdiProbability = widgets.hdi_slider.value / 100; + {callback_js}; + widgets.bw_div.text = `Bandwidth: ${{bwFactor * bw}}`; + """ + rv_select_callback = CustomJS(args=callback_arguments, code=rv_select_js) + slider_callback = CustomJS(args=callback_arguments, code=slider_js) + + # Below is where we tell Python to use the JS callbacks based on user + # interaction. + widgets["rv_select"].js_on_change("value", rv_select_callback) + widgets["bw_factor_slider"].js_on_change("value", slider_callback) + widgets["hdi_slider"].js_on_change("value", slider_callback) + + # Create the tool view using Python. Note that all subsequent callbacks will be + # performed by JavaScript. + tool_view = utils.create_view(figures=figures, widgets=widgets) + return file_html(tool_view, resources=INLINE) diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py new file mode 100644 index 0000000000..f885a680ea --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py @@ -0,0 +1,166 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Marginal 1D diagnostic tool types for a Bean Machine model.""" + +from typing import Any, Dict, List, Union + +from beanmachine.ppl.diagnostics.tools import TypedDict + +from bokeh.models.annotations import Band, LabelSet +from bokeh.models.glyphs import Circle, Line +from bokeh.models.sources import ColumnDataSource +from bokeh.models.tools import HoverTool +from bokeh.models.widgets.inputs import Select +from bokeh.models.widgets.markups import Div +from bokeh.models.widgets.sliders import Slider +from bokeh.plotting.figure import Figure + + +# NOTE: These are the types pyre gives us when using `reveal_type(...)` on the outputs +# of the methods. +StatsAndLabelsData = Dict[str, Dict[str, Any]] +HDIData = Dict[str, Any] +Data = Dict[Any, Any] +Sources = Dict[Any, Any] +Figures = Dict[Any, Any] +Glyphs = Dict[Any, Any] +Annotations = Dict[Any, Any] +Tooltips = Dict[Any, Any] +Widgets = Dict[str, Union[Div, Select, Slider]] + + +# NOTE: TypedDict objects are for reference only. Due to the way pyre accesses keys in +# dictionaries, and how NumPy casts arrays when using tolist(), we are unable to +# use them, but they provide semantic information for the different types. We must +# ignore a lot of lines due to the issue discussed here +# https://pyre-check.org/docs/errors/#13-uninitialized-attribute. + + +class _DistributionData(TypedDict): # pyre-ignore + x: List[float] + y: List[float] + bandwidth: float + + +class _HDIData(TypedDict): # pyre-ignore + base: List[float] + lower: List[float] + upper: List[float] + + +class _StatsData(TypedDict): # pyre-ignore + x: List[float] + y: List[float] + text: List[str] + + +class _LabelsData(TypedDict): # pyre-ignore + x: List[float] + y: List[float] + text: List[str] + text_align: List[str] + x_offset: List[int] + y_offset: List[int] + + +class _StatsAndLabelsData(TypedDict): # pyre-ignore + x: List[float] + y: List[float] + text: List[str] + text_align: List[str] + x_offset: List[int] + y_offset: List[int] + + +class _GlyphData(TypedDict): # pyre-ignore + distribtution: _DistributionData + hdi: _HDIData + stats: _StatsData + labels: _LabelsData + + +class _Data(TypedDict): # pyre-ignore + marginal: _GlyphData + cumulative: _GlyphData + + +class _GlyphSources(TypedDict): # pyre-ignore + distribution: ColumnDataSource + hdi: ColumnDataSource + stats: ColumnDataSource + labels: ColumnDataSource + + +class _Sources(TypedDict): # pyre-ignore + marginal: _GlyphSources + cumulative: _GlyphSources + + +class _Figures(TypedDict): # pyre-ignore + marginal: Figure + cumulative: Figure + + +class _DistributionGlyph(TypedDict): # pyre-ignore + glyph: Line + hover_glyph: Line + + +class _StatsGlyph(TypedDict): # pyre-ignore + glyph: Circle + hover_glyph: Circle + + +class _MarginalFigureGlyphs(TypedDict): # pyre-ignore + distribution: _DistributionGlyph + stats: _StatsGlyph + + +class _CumulativeFigureGlyphs(TypedDict): # pyre-ignore + distribution: _DistributionGlyph + stats: _StatsGlyph + + +class _Glyphs(TypedDict): # pyre-ignore + marginal: _MarginalFigureGlyphs + cumulative: _CumulativeFigureGlyphs + + +class _MarginalFigureAnnotations(TypedDict): # pyre-ignore + hdi: Band + labels: LabelSet + + +class _CumulativeFigureAnnotations(TypedDict): # pyre-ignore + hdi: Band + labels: LabelSet + + +class _Annotations(TypedDict): # pyre-ignore + marginal: _MarginalFigureAnnotations + cumulative: _CumulativeFigureAnnotations + + +class _MarginalFigureTooltips(TypedDict): # pyre-ignore + distribution: HoverTool + stats: HoverTool + + +class _CumulativeFigureTooltips(TypedDict): # pyre-ignore + distribution: HoverTool + stats: HoverTool + + +class _Tooltips(TypedDict): # pyre-ignore + marginal: _MarginalFigureTooltips + cumulative: _CumulativeFigureTooltips + + +class _Widgets(TypedDict): # pyre-ignore + rv_select: Select + bw_factor_slider: Slider + bw_div: Div + hdi_slider: Slider diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py new file mode 100644 index 0000000000..d1e5a4e6f3 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py @@ -0,0 +1,642 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Methods used to generate the diagnostic tool.""" + +from itertools import chain +from typing import List, Optional + +import arviz as az +import numpy as np +from beanmachine.ppl.diagnostics.tools.marginal1d import typing +from beanmachine.ppl.diagnostics.tools.utils import plotting_utils +from bokeh.models.annotations import Band, LabelSet +from bokeh.models.glyphs import Circle, Line +from bokeh.models.layouts import Column, Row +from bokeh.models.sources import ColumnDataSource +from bokeh.models.tools import HoverTool +from bokeh.models.widgets.inputs import Select +from bokeh.models.widgets.markups import Div +from bokeh.models.widgets.panels import Panel, Tabs +from bokeh.models.widgets.sliders import Slider +from bokeh.plotting.figure import figure + + +PLOT_WIDTH = 500 +PLOT_HEIGHT = 500 +FIGURE_NAMES = ["marginal", "cumulative"] + + +def compute_stats( + data: List[List[float]], + kde_x: List[float], + kde_y: List[float], + hdi_probability: float, + text_align: Optional[List[str]] = None, + x_offset: Optional[List[int]] = None, + y_offset: Optional[List[int]] = None, + return_labels: bool = False, +) -> typing.StatsAndLabelsData: + """Compute statistics for the given data, and its KDE. + + Parameters + ---------- + data : List[List[float]] + JSON serializable representation of the model data for all random variables. + kde_x : List[float] + The x-axis KDE estimate of the random variable data. + kde_y : List[float] + The y-axis KDE estimate of the random variable data. + hdi_probability : float + The HDI probability to use when calculating the HDI bounds. + text_align : Optional[List[str]], optional default is ``None`` + How to display label justifications for the statistics in Bokeh. + x_offset : Optional[List[int]], optional default is ``None`` + x-axis offsets for the labels. + y_offset : Optional[List[int]], optional default is ``None`` + y-axis offsets for the labels. + return_labels : bool, optional default is ``False`` + ``True`` returns labels to be used in the Bokeh figure. + + Returns + ------- + typing.StatsAndLabelsData + A dictionary for statistics and labels for the Bokeh figures. + """ + if text_align is None: + text_align = ["right", "center", "left"] + if x_offset is None: + x_offset = [-5, 0, 5] + if y_offset is None: + y_offset = [0, 10, 0] + + flattened_data = np.array(list(chain(*data))) + kdex = np.array(kde_x) + kdey = np.array(kde_y) + mean = np.mean(kdex) + hdi = az.stats.hdi(ary=flattened_data, hdi_prob=hdi_probability) + x = [hdi[0], mean, hdi[1]] + y = np.interp(x=np.array(x).astype(float), xp=kdex, fp=kdey) + text = [ + f"Lower HDI: {hdi[0]:.4}", + f"Mean: {mean:.4}", + f"Upper HDI: {hdi[1]:.4}", + ] + stats = {"x": x, "y": y.tolist(), "text": text} + labels = {} + if return_labels: + labels = { + "x": x, + "y": y.tolist(), + "text": text, + "text_align": text_align, + "x_offset": x_offset, + "y_offset": y_offset, + } + return {"stats": stats, "labels": labels} + + +def hdi_data( + data: List[List[float]], + kde_x: List[float], + kde_y: List[float], + hdi_probability: float, +) -> typing.HDIData: + """Calculate the HDI arrays needed for the Bokeh annotation. + + Parameters + ---------- + data : List[List[float]] + JSON serializable representation of the model data for one random variable. + kde_x : npt.NDArray + The x-axis KDE estimate of the random variable data. + kde_y : npt.NDArray + The y-axis KDE estimate of the random variable data. + hdi_probability : float + The HDI probability to use when calculating the HDI bounds. + + Returns + ------- + typing.HDIData + Arrays used to create the Bokeh annotation for the HDI. + """ + flattened_data = np.array(list(chain(*data))) + kdex = np.array(kde_x) + kdey = np.array(kde_y) + hdi = az.stats.hdi(ary=flattened_data, hdi_prob=hdi_probability) + mask = np.ix_(np.logical_and(kdex >= hdi[0], kdex <= hdi[1]))[0] + base = kdex[mask] + lower = np.zeros((len(mask))) + upper = kdey[mask] + return {"base": base.tolist(), "lower": lower.tolist(), "upper": upper.tolist()} + + +def compute_data( + data: List[List[float]], + bw_factor: float, + hdi_probability: float, +) -> typing.Data: + """Compute effective sample size estimates using the given data. + + Parameters + ---------- + data : List[List[float]] + JSON serializable representation of the model data for one random variables. + bw_factor : float + Multiplicative factor used when calculating the kernel density estimate. + hdi_probability : float + The HDI probability to use when calculating the HDI bounds. + + Returns + ------- + typing.Data + A dictionary of data used for the tool. + """ + flattened_data = np.array(list(chain(*data))) + output = {} + for figure_name in FIGURE_NAMES: + output[figure_name] = {} + x, y, bandwidth = az.stats.density_utils._kde_linear( + x=flattened_data, + bw="scott", # Scott is chosen to mirror fast-kde in the JavaScript. + bw_return=True, + bw_fct=bw_factor, + ) + if figure_name == "cumulative": + y = np.cumsum(y) + y /= y.max() + stats_and_labels = compute_stats( + data=data, + kde_x=x, + kde_y=y, + hdi_probability=hdi_probability, + return_labels=True, + ) + output[figure_name] = { + "distribution": { + "x": x.tolist(), + "y": y.tolist(), + "bandwidth": bandwidth, + }, + "hdi": hdi_data( + data=data, + kde_x=x, + kde_y=y, + hdi_probability=hdi_probability, + ), + "stats": stats_and_labels["stats"], + "labels": stats_and_labels["labels"], + } + return output + + +def create_sources(data: typing.Data) -> typing.Sources: + """Create Bokeh sources from the given data that will be bound to glyphs. + + Parameters + ---------- + data : typing.Data + A dictionary of data used for the tool. + + Returns + ------- + typing.Sources + A dictionary of Bokeh ColumnDataSource objects. + """ + output = {} + for figure_name, figure_data in data.items(): + output[figure_name] = {} + for glyph_name, glyph_data in figure_data.items(): + if "bandwidth" in list(glyph_data.keys()): + glyph_data.pop("bandwidth") + output[figure_name][glyph_name] = ColumnDataSource(data=glyph_data) + return output + + +def create_figures(rv_name: str) -> typing.Figures: + """Create the Bokeh figures used for the tool. + + Parameters + ---------- + rv_name : str + The string representation of the random variable data. + + Returns + ------- + typing.Figures + A dictionary of Bokeh Figure objects. + """ + output = {} + for figure_name in FIGURE_NAMES: + fig = figure( + width=PLOT_WIDTH, + height=PLOT_HEIGHT, + outline_line_color="black", + title=f"{figure_name} distribution", + x_axis_label=rv_name, + y_axis_label=None, + ) + fig.yaxis.visible = False + plotting_utils.style_figure(fig) + output[figure_name] = fig + output[FIGURE_NAMES[0]].x_range = output[FIGURE_NAMES[1]].x_range + output[FIGURE_NAMES[0]].y_range = output[FIGURE_NAMES[1]].y_range + return output + + +def create_glyphs(data: typing.Data) -> typing.Glyphs: + """Create the glyphs used for the figures of the tool. + + Parameters + ---------- + data : typing.Data + A dictionary of data used for the tool. + + Returns + ------- + typing.Glyphs + A dictionary of Bokeh Glyphs objects. + """ + palette = plotting_utils.choose_palette(num_colors=2) + output = {} + for figure_name, figure_data in data.items(): + output[figure_name] = {} + for glyph_name, _ in figure_data.items(): + if glyph_name in ["distribution", "stats"]: + if glyph_name == "distribution": + output[figure_name][glyph_name] = { + "glyph": Line( + x="x", + y="y", + line_color=palette[0], + line_alpha=0.7, + line_width=2.0, + name=f"{figure_name}DistributionGlyph", + ), + "hover_glyph": Line( + x="x", + y="y", + line_color=palette[1], + line_alpha=1.0, + line_width=2.0, + name=f"{figure_name}DistributionHoverGlyph", + ), + } + if glyph_name == "stats": + output[figure_name][glyph_name] = { + "glyph": Circle( + x="x", + y="y", + size=10, + fill_color=palette[0], + line_color="white", + fill_alpha=1.0, + name=f"{figure_name}StatsGlyph", + ), + "hover_glyph": Circle( + x="x", + y="y", + size=10, + fill_color=palette[1], + line_color="black", + fill_alpha=1.0, + name=f"{figure_name}StatsHoverGlyph", + ), + } + return output + + +def add_glyphs( + figures: typing.Figures, + glyphs: typing.Glyphs, + sources: typing.Sources, +) -> None: + """Bind source data to glyphs and add the glyphs to the given figures. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + glyphs : typing.Glyphs + A dictionary of Bokeh Glyphs objects. + sources : typing.Sources + A dictionary of Bokeh ColumnDataSource objects. + + Returns + ------- + None + Adds data bound glyphs to the given figures directly. + """ + for figure_name, figure_glyphs in glyphs.items(): + fig = figures[figure_name] + figure_sources = sources[figure_name] + for glyph_name, glyphs in figure_glyphs.items(): + glyph_source = figure_sources[glyph_name] + fig.add_glyph( + source_or_glyph=glyph_source, + glyph=glyphs["glyph"], + hover_glyph=glyphs["hover_glyph"], + name=glyphs["glyph"].name, + ) + + +def create_annotations(sources: typing.Sources) -> typing.Annotations: + """Create any annotations for the figures of the tool. + + Parameters + ---------- + source : typing.Sources + A dictionary of Bokeh ColumnDataSource objects. + + Returns + ------- + typing.Annotations + A dictionary of Bokeh Annotation objects. + """ + palette = plotting_utils.choose_palette(num_colors=1) + output = {} + for figure_name, figure_sources in sources.items(): + output[figure_name] = {} + for glyph_name, glyph_source in figure_sources.items(): + if glyph_name == "hdi": + output[figure_name][glyph_name] = Band( + base="base", + lower="lower", + upper="upper", + source=glyph_source, + level="underlay", + fill_color=palette[0], + fill_alpha=0.2, + line_width=1.0, + line_color="white", + name=f"{figure_name}HdiAnnotation", + ) + elif glyph_name == "labels": + output[figure_name][glyph_name] = LabelSet( + x="x", + y="y", + text="text", + x_offset="x_offset", + y_offset="y_offset", + text_align="text_align", + source=glyph_source, + background_fill_color="white", + background_fill_alpha=0.8, + name=f"{figure_name}LabelAnnotation", + ) + return output + + +def add_annotations(figures: typing.Figures, annotations: typing.Annotations) -> None: + """Add the given annotations to the given figures of the tool. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + annotations : typing.Annotations + A dictionary of Bokeh Annotation objects. + + Returns + ------- + None + Adds annotations directly to the given figures. + """ + for figure_name, annotation_sources in annotations.items(): + fig = figures[figure_name] + for _, annotation in annotation_sources.items(): + fig.add_layout(annotation) + + +def create_tooltips(rv_name: str, figures: typing.Figures) -> typing.Tooltips: + """Create hover tools for the glyphs used in the figures of the tool. + + Parameters + ---------- + rv_name : str + The string representation of the random variable data. + figures : typing.Figures + A dictionary of Bokeh Figure objects. + + Returns + ------- + typing.Tooltips + A dictionary of Bokeh HoverTools objects. + """ + output = {} + for figure_name, fig in figures.items(): + output[figure_name] = { + "distribution": HoverTool( + renderers=plotting_utils.filter_renderers( + figure=fig, + search="DistributionGlyph", + glyph_type="GlyphRenderer", + substring=True, + ), + tooltips=[(rv_name, "@x")], + ), + "stats": HoverTool( + renderers=plotting_utils.filter_renderers( + figure=fig, + search="StatsGlyph", + glyph_type="GlyphRenderer", + substring=True, + ), + tooltips=[("", "@text")], + ), + } + return output + + +def add_tooltips(figures: typing.Figures, tooltips: typing.Tooltips) -> None: + """Add the given tools to the figures. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + tooltips : typing.Tooltips + A dictionary of Bokeh HoverTools objects. + + Returns + ------- + None + Adds the tooltips directly to the given figures. + """ + for figure_name, figure_tooltips in tooltips.items(): + fig = figures[figure_name] + for _, tooltip in figure_tooltips.items(): + fig.add_tools(tooltip) + + +def create_widgets( + rv_name: str, + rv_names: List[str], + bw_factor: float, + bandwidth: float, +) -> typing.Widgets: + """Create the widgets used in the tool. + + Parameters + ---------- + rv_name : str + The string representation of the random variable data. + rv_names : List[str] + A list of all available random variable names. + bw_factor : float + Multiplicative factor used when calculating the kernel density estimate. + bandwidth : float + The bandwidth used to calculate the KDE. + + Returns + ------- + typing.Widgets + A dictionary of Bokeh widget objects. + """ + return { + "rv_select": Select(value=rv_name, options=rv_names, title="Query"), + "bw_factor_slider": Slider( + title="Bandwidth factor", + start=0.01, + end=2.00, + value=1.00, + step=0.01, + ), + "bw_div": Div(text=f"Bandwidth: {bw_factor * bandwidth}"), + "hdi_slider": Slider(start=1, end=99, step=1, value=89, title="HDI"), + } + + +def help_page() -> Div: + """Help tab for the tool. + + Returns + ------- + Div + Bokeh Div widget containing the help tab information. + """ + text = """ +

+ Highest density interval +

+

+ The highest density interval region is not equal tailed like a typical + equal tailed interval of 2.5%. Thus it will include the mode(s) of the + posterior distribution. +

+

+ There is nothing particularly specific about having a default HDI of 89%. + If fact, the only remarkable thing about defaulting to 89% is that it is + the highest prime number that does not exceed the unstable 95% threshold. + See the link to McElreath's book below for further discussion. +

+ + """ + return Div(text=text, disable_math=False, min_width=PLOT_WIDTH) + + +def create_figure_grid(figures: typing.Figures) -> Row: + """Layout the given figures in a grid, and make one toolbar. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + + Returns + ------- + Row + A Bokeh layout object. + """ + toolbar = plotting_utils.create_toolbar(figures=list(figures.values())) + return Row(children=[*list(figures.values()), toolbar]) + + +def create_view(widgets: typing.Widgets, figures: typing.Figures) -> Tabs: + """Create the tool view. + + Parameters + ---------- + widgets : typing.Widgets + A dictionary of Bokeh widget objects. + figures : typing.Figures + A dictionary of Bokeh Figure objects. + + Returns + ------- + Tabs + Bokeh Tabs objects. + """ + help_panel = Panel(child=help_page(), title="Help", name="helpPanel") + tool_panel = Panel( + child=Column( + children=[ + widgets["rv_select"], + create_figure_grid(figures), + widgets["bw_factor_slider"], + widgets["bw_div"], + widgets["hdi_slider"], + ], + ), + title="Marginal 1D", + name="toolPanel", + ) + return Tabs(tabs=[tool_panel, help_panel]) + + +def update( + data: List[List[float]], + sources: typing.Sources, + figures: typing.Figures, + rv_name: str, + bw_factor: float, + hdi_probability: float, +) -> float: + """Update the tool based on user interaction. + + Parameters + ---------- + data : List[List[float]] + JSON serializable representation of the model data for one random variables. + sources : typing.Sources + A dictionary of Bokeh ColumnDataSource objects. + figures : typing.Figures + A dictionary of Bokeh Figure objects. + rv_name : str + The string representation of the random variable data. + rv_names : List[str] + A list of all available random variable names. + hdi_probability : float + The HDI probability to use when calculating the HDI bounds. + + Returns + ------- + float + Returns the bandwidth used to calculate the KDE. + """ + computed_data = compute_data(data, bw_factor, hdi_probability) + bw = computed_data["marginal"]["distribution"]["bandwidth"][0] + for figure_name, figure_sources in sources.items(): + fig = figures[figure_name] + fig.xaxis.axis_label = rv_name + figure_data = computed_data[figure_name] + for glyph_name, glyph_source in figure_sources.items(): + glyph_data = figure_data[glyph_name] + if "bandwidth" in list(glyph_data.keys()): + glyph_data.pop("bandwidth") + glyph_source.data = dict(**glyph_data) + return float(bw) diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/__init__.py b/src/beanmachine/ppl/diagnostics/tools/utils/__init__.py new file mode 100644 index 0000000000..7bec24cb17 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/__init__.py @@ -0,0 +1,4 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py b/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py new file mode 100644 index 0000000000..e866f23f77 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py @@ -0,0 +1,79 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Accessor definition for extending Bean Machine `MonteCarloSamples` objects.""" + +import contextlib +import warnings +from typing import Callable, TypeVar + +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples + + +T = TypeVar("T", bound="CachedAccessor") + + +class CachedAccessor: + """A descriptor for caching accessors. + + Parameters + ---------- + name : str + Namespace that will be accessed under, e.g. ``samples.accessor_name``. + accessor : cls + Class with the extension methods. + """ + + def __init__(self: T, name: str, accessor: object) -> None: + """Initialize.""" + self._name = name + self._accessor = accessor + + def __get__(self: T, obj: object, cls: object) -> object: + """Access the accessor object.""" + if obj is None: + return self._accessor + + try: + cache = obj._cache # type: ignore + except AttributeError: + cache = obj._cache = {} + + try: + return cache[self._name] + except KeyError: + contextlib.suppress(KeyError) + + try: + accessor_obj = self._accessor(obj) # type: ignore + except Exception as error: + msg = f"error initializing {self._name!r} accessor." + raise RuntimeError(msg) from error + + cache[self._name] = accessor_obj + return accessor_obj # noqa: R504 + + +def _register_accessor(name: str, cls: object) -> Callable: + """Register the accessor to the object.""" + + def decorator(accessor: object) -> object: + if hasattr(cls, name): + warnings.warn( + f"registration of accessor {repr(accessor)} under name " + f"{repr(name)} for type {repr(cls)} is overriding a preexisting " + f"attribute with the same name.", + UserWarning, + stacklevel=2, + ) + setattr(cls, name, CachedAccessor(name, accessor)) + return accessor + + return decorator + + +def register_mcs_accessor(name: str) -> Callable: + """Register the accessor to object.""" + return _register_accessor(name, MonteCarloSamples) diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/base.py b/src/beanmachine/ppl/diagnostics/tools/utils/base.py new file mode 100644 index 0000000000..8f290ec068 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/base.py @@ -0,0 +1,46 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Base class for diagnostic tools of a Bean Machine model.""" + +import re +from abc import ABC, abstractmethod +from typing import TypeVar + +from beanmachine.ppl.diagnostics.tools import JS_DIST_DIR +from beanmachine.ppl.diagnostics.tools.utils import plotting_utils +from beanmachine.ppl.diagnostics.tools.utils.model_serializers import serialize_bm +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples +from IPython.display import display, HTML + + +T = TypeVar("T", bound="Base") + + +class Base(ABC): + @abstractmethod + def __init__(self: T, mcs: MonteCarloSamples) -> None: + self.data = serialize_bm(mcs) + self.rv_names = list(self.data.keys()) + self.num_chains = mcs.num_chains + self.num_draws = mcs.get_num_samples() + self.palette = plotting_utils.choose_palette(self.num_chains) + self.js = self.load_js() + + def load_js(self: T) -> str: + name = self.__class__.__name__ + name_tokens = re.findall(r"[A-Z][^A-Z]*", name) + name = "_".join(name_tokens) + path = JS_DIST_DIR.joinpath(f"{name.lower()}.js") + with path.open() as f: + js = f.read() + return js + + def show(self: T): + display(HTML(self.create_document())) + + @abstractmethod + def create_document(self: T) -> str: + ... diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py b/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py new file mode 100644 index 0000000000..0c680e0959 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py @@ -0,0 +1,32 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Collection of serializers for the diagnostics tool use.""" + +from typing import Dict, List + +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples + + +def serialize_bm(samples: MonteCarloSamples) -> Dict[str, List[List[float]]]: + """Convert Bean Machine models to a JSON serializable object. + + Parameters + ---------- + samples : MonteCarloSamples + Output of a model from Bean Machine. + + Returns + ------- + model : Dict[str, List[List[float]]] + The JSON serializable object for use in the diagnostics tools. + """ + model = dict( + sorted( + {str(key): value.tolist() for key, value in samples.items()}.items(), + key=lambda item: item[0], + ), + ) + return model diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py b/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py new file mode 100644 index 0000000000..8cf19c5ca8 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py @@ -0,0 +1,123 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Plotting utilities for the diagnostic tools.""" + +from typing import List + +from bokeh.core.property.nullable import Nullable +from bokeh.core.property.primitive import Null +from bokeh.core.property.wrappers import PropertyValueList +from bokeh.models.layouts import Column, Row +from bokeh.models.renderers import GlyphRenderer +from bokeh.models.tools import ProxyToolbar, ResetTool, SaveTool, ToolbarBox +from bokeh.palettes import Colorblind +from bokeh.plotting.figure import Figure + + +def style_figure(figure: Figure) -> None: + figure.grid.grid_line_alpha = 0.3 + figure.grid.grid_line_color = "grey" + figure.grid.grid_line_width = 0.3 + figure.xaxis.minor_tick_line_color = "grey" + figure.yaxis.minor_tick_line_color = "grey" + + +def choose_palette(num_colors: int) -> List[str]: + palette_indices = [key for key in Colorblind.keys() if num_colors <= key] + if not palette_indices: + palette_index = max(Colorblind.keys()) + else: + palette_index = min(palette_indices) + return Colorblind[palette_index] + + +def create_toolbar(figures: List[Figure]) -> ToolbarBox: + toolbars = [] + for figure in figures: + toolbars.append(figure.toolbar) + figure.toolbar_location = Nullable(Null)._default + tools = [] + for toolbar in toolbars: + tools.extend(toolbar.tools) + tools = [tool for tool in tools if type(tool).__name__ != "HoverTool"] + if len(tools) == 0: + tools = [SaveTool(), ResetTool()] + return ToolbarBox( + toolbar=ProxyToolbar(toolbars=toolbars, tools=tools), + toolbar_location="right", + ) + + +def create_figure_grid(figures: List[Figure], num_figure_columns: int) -> Row: + """Layout the given figures in a grid, and make one toolbar. + + Parameters + ---------- + figures : typing.Figures + A dictionary of Bokeh Figure objects. + num_figure_columns : int + The number of columns to display the figures with. + + Returns + ------- + Row + A Bokeh layout object. + """ + toolbar = create_toolbar(figures) + figure_rows = [] + while len(figures): + figs = figures[:num_figure_columns] + for i, fig in enumerate(figs): + if i != 0: + fig.yaxis.axis_label = None + figure_rows.append(figs) + for fig in figs: + figures.pop(figures.index(fig)) + for i, figure_row in enumerate(figure_rows): + if i != len(figure_rows) - 1: + for fig in figure_row: + fig.xaxis.axis_label = None + figure_layout = [] + for i in range(len(figure_rows)): + figure_layout.append(Row(children=figure_rows[i])) + return Row(children=[Column(children=figure_layout), toolbar]) + + +def filter_renderers( + figure: Figure, + search: str, + glyph_type: str = "GlyphRenderer", + substring: bool = False, +) -> List[GlyphRenderer]: + """Filter Bokeh figure renderers given the search string. + + Parameters + ---------- + figure : Figure + A Bokeh figure to filter renderers from. + search : str + The search string to filter for. + glyph_type : str, optional default is "GlyphRenderer" + The glyph type. + substring : bool, optional default is ``False`` + Flag to indicate if we should use the search term as a substring search. + - ``False`` indicates the name of the glyph == search. + - ``True`` indicates the search is a substring of the glyph name. + + Returns + ------- + List[GlyphRenderer] + A list of Bokeh glyph renderer objects. + """ + output = [] + renderers = PropertyValueList(figure.renderers) + for renderer in renderers: + if renderer.name is not None and type(renderer).__name__ == glyph_type: + if substring and search in renderer.name: + output.append(renderer) + if not substring and renderer.name == search: + output.append(renderer) + return output diff --git a/src/beanmachine/ppl/diagnostics/tools/viz.py b/src/beanmachine/ppl/diagnostics/tools/viz.py new file mode 100644 index 0000000000..da41bd4421 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/viz.py @@ -0,0 +1,29 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Visual diagnostics tools for Bean Machine models.""" + +from typing import TypeVar + +from beanmachine.ppl.diagnostics.tools.marginal1d.tool import Marginal1d +from beanmachine.ppl.diagnostics.tools.utils import accessor +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples + + +T = TypeVar("T", bound="DiagnosticsTools") + + +@accessor.register_mcs_accessor("diagnostics") +class DiagnosticsTools: + """Accessor object for the visual diagnostics tools.""" + + def __init__(self: T, mcs: MonteCarloSamples) -> None: + """Initialize.""" + self.mcs = mcs + self.idata = self.mcs.to_inference_data() + + def marginal1d(self: T) -> None: + """Marginal 1D tool.""" + Marginal1d(self.mcs).show() diff --git a/tutorials/Coin_flipping.ipynb b/tutorials/Coin_flipping.ipynb index 04a303a3ca..504a7b5afb 100644 --- a/tutorials/Coin_flipping.ipynb +++ b/tutorials/Coin_flipping.ipynb @@ -193,14 +193,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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5E/3h5eYkUWVEtqUlec2sh0kQETWmqlaD59edaBCEpw8NwconBzAIk2RkMhleGdcd7z8UBUd73fCco2lFeODTP5FZxJUmiMwNwzARWRRVZR2e/PIIdl8oEI/JZMDCSb2w+L7eXD+YzMKD/YPwzTODoXBxFI+lKivw4Kd/IjmPO5kSmRP+1iAii5FfWo2HvziEY2nF4jEXR3usfGIAnhreWcLKiBoaEuaDn14YhhAf3fKdBWU1mPrZIRxLK5KwMiLSxzBMRBZB16tWJh7zcnXE+mcH485eARJWRnRzYX7tsHn2MER00I1ZLK1W4/FVR7D7fL6ElRFRPYZhIjJ7Z7NVmPLpn8gqrhKPdVA4Y9PzQxHdyUvCyohuzbedHBtmDcHQMB/xWI1ai+fWncAPJ7IkrIyIAIZhIjJzh69ewyNfHMa1ilrxWFf/dvhh9jB09XeXsDKi5nN3dsSapwbiroj24jGNVsBrm05h1YGrElZGRAzDRGS2ElKUmLHmKMpr1OKxvsGe2PTcUK4hTBbH2dEeH0/rh0cHdTI4vnTrBXz8x+WbXEVEpsYwTERm6Y+LBXj6q2OortOKx+4I98P6ZwdzrVayWPZ2Mrxzf2/MGd3V4Ph/dl7kbnVEEmEYJiKz8/v5fDz39QnUqnVBeHxEAFY9OQCuTlxDmCybTCbDq+O64//u7mFw/MPdKfjPzosMxERtjGGYiMzKtjO5mL3uBGo1uiA8MTIQcY/1g5MD/8ki6/H8yC5YMLGXwbFP4q/g7a0XGIiJ2hB/sxCR2fglKRtzvjsJtVYXBB6I7ogPH+4LR26mQVbomZjOWDK5t8GxVQmpWLTlHLRaBmKitsDfLkRkFn5Jysbc75Og0QsAUwcE4T8PRXFXObJqTwwJwb8f7AOZbvdmfHUoHYt+PcceYqI2wN8wRCS5HWdz8crGU9DvCHt8SCcsfyAS9naym19IZCUeHtgJ7z8UBf2/7l8fSsc72zhkgsjUGIaJSFJ7k/Mx57uTBj3CM4aFYsl9vWHHIEw25IF+QfjwkWiDQLzyQCo++P2SdEUR2QCGYSKSzIGUQjy/LhF1Gl0QfmxwJyyc1AsyGYMw2Z57ozrgvYeiDIZMfLT3Mj7akyJdUURWjmGYiCRx+Oo1PPv1cYPl06b0D8LS+3ozCJNNe6BfEN65v4/Bsfd/v4SV+7lTHZEpMAwTUZs7kV6MZ9YabqgxKaoD/v1gJIdGEAF4dFAnLL43wuDY29su4Ks/06QpiMiKMQwTUZu6kFuKGWuOoqJWIx67K6I9PpgaxclyRHqmDwvF/Ak9DY4t3HIOPyZmSVQRkXViGCaiNpNZVInpXx5FWbVaPDa6hz9WPBrNdYSJGvHsHWF4bVy4wbF//HAae5PzJaqIyPrwtw8RtYnCsho8sfoICspqxGNDw3zwyTTuLEfUlJdGd8MLo7qIbY1WwAvfJuJ4WpGEVRFZD/4GIiKTK62uw4w1R5F2rVI81rujB754sj+cHe0lrIzIMvxjfHc8OihYbFfXafH02mNIziuVsCoi68AwTEQmVV2nwbNfHce5HN0v7c6+blj71CC4OztKWBmR5ZDJZFg6uQ/uimgvHiutVuPJ1UeRWVTZxJVEdCsMw0RkMmqNFn/77iSOpOo+zg3wkOPrpwfBt51cwsqILI+9nQwfPtIXQ8K8xWMFfw0/UpbXNHElETWFYZiITEIQBLy15Rx2nddN9PFwdsDXTw9GsLerhJURWS5nR3usfHIAIjp4iMfSrlXiqTXHUFGjbuJKIroZhmEiMonP9l3F+iMZYtvZ0Q5rnhqI7u3dJayKyPK5Ozti7VODEOqje1N5JluFv313EmqNtokriagxDMNEZHS/nsrBv3cki217Oxk+mdYP/UO8m7iKiJrLz12Ob54ZbDDcaE9yARb/eh6CIDRxJRHdiGGYiIzqWFoRXt10yuDYkvt6Y3SPAIkqIrJOwd6u+HLGALjorcjyzeF0rE5IlbAqIsvDMExERnO1sBzPfn0ctWrdR7XPj+yCxwZ3krAqIusVGeSJjx6Nhv7mjW9vu4DtZ3KlK4rIwjAME5FRXCuvwVNrj6Gksk48NjEyEK+P7y5hVUTW785eAVh0b4TYFgTg5e+TcCK9WMKqiCwHwzAR3bbqOg2e/fo40vU21RgY6oX3HoqCnX6XFRGZxJNDQzEzprPYrlFr8ezXx5GmrJCwKiLLwDBMRLdFEAS8/sNpJGaUiMc6+7rhiycGcHc5ojb0zwk9cXdv3aYcRRW1eOarYyitrmviKiJiGCai2/JJ/BVsOZUjtr3dnLD2qYHwcnOSsCoi22NnJ8N/H+6L6E6e4rErhRX423cnodFyhQmim2EYJqJW23UuD//ZeVFsO9rL8MUT/RHi4yZhVUS2q35Tjo6eLuKx+IuFWL79goRVEZk3hmEiapULuaV4+fskg2Nv398HA0K5ljCRlHzbybHyyQFwddINU1p5IBWbjmdKWBWR+WIYJqIWu1Zeg5lfHUdlrUY8NjOmM6YOCJawKiKq16uDBz6Y2tfg2PyfzuJ4WpE0BRGZMYZhImqRWrUWs9clIrukSjw2MtwP8yb0lLAqIrrRXb3b47Vx4WK7VqPF8+tOGPzsEhHDMBG1gCAIeOuXsziq17sU5ueGFY9Gw55LqBGZnRdju2JSVAexrSyv/etTHbWEVRGZF4ZhImq29UczsOGYbtyhwsURq6cPhMLFUcKqiOhmZDIZ/jMlEpFBCvHYhdxS/N/mMxAErjBBBDAME1EzJWYUY9GWc2Lb3k6Gjx/rh86+XDmCyJw5O9rjiycGwN9dLh7bcioHXx5Mk64oIjPCMExEt1RYVoPZ606gTqPrSZp3dw/EdPOVsCoiaq72Cmd8+nh/ONrrhjO9s+0CDl+9JmFVROaBYZiImlSn0eLF9YnIL60Rj02MDMQzelu/EpH56x/ihbcmRYhtjVbAS+sTkavihDqybWYdhtetW4fnnnsOAwYMgFwuh0wmw9q1a1t0j4SEBLz66qvo378/fHx84OzsjB49euCNN95ASUmJSeomsibLtiXjaKpuwlz3AHe8OyUSMhknzBFZmscHd8KU/kFiW1lei9nrElGj1jRxFZF1kwlmPII+NDQU6enp8PX1hZubG9LT07FmzRrMmDGj2fdo3749lEolYmJiEB0dDZlMhvj4eJw8eRJdunTBn3/+CX9//2bfr7S0FAqFAiqVCh4eHq34rogsxy9J2fj7hiSx7e7sgC0vxXCcMJEFq67TYMpnf+Jsdql4bNrgTnj7/j4SVkVkXC3Ja2bdM7xq1SqkpaWhsLAQzz//fKvuMXfuXGRmZiI+Ph7//e9/8cEHH+DEiROYPXs2rly5gsWLFxu5aiLrkJx3fca5vg8f7ssgTGThnB3t8em0/vB01a0C8+2RDGw8xh3qyDaZdRi+8847ERISclv3eOONNxAYGGhwTCaTYcGCBQCAffv23db9iaxRWXUdnv/mBKrqdB+d/n1MN4zpGSBhVURkLMHervjo0WjoLw/+5i9ncS5HJV1RRBIx6zBsSo6O198ROzg4SFwJkXkRBAHzfjyDtGuV4rHY7n74+5huElZFRMY2opsfXhvfXWzXqrV4af1JlFXXSVgVUduz2TD85ZdfAgDGjRsncSVE5mXdkQz8djpXbHf0dMGHD0fDjjvMEVmd2SO74M6eunkzqcoKzPuRG3KQbbHJMJyUlITFixfD398fr7/+epPPrampQWlpqcGDyFqdzVZhya/nxbajvQwfT+sHhSt3mCOyRjKZDO89FIWOni7isd9O52LdkQwJqyJqWzYXhlNTUzFx4kRoNBps2LABvr5NbxqwbNkyKBQK8REcHNxGlRK1rdLqOrzwbSJqNVrx2Ly7e6JvsKd0RRGRyXm6OiHusWiDDTmW/HoeZ7M5fphsg02F4fT0dMTGxqKwsBA//PADYmNjb3nNvHnzoFKpxEdmJmfbkvURBAH/t/k0Mop044THRwTgqeGh0hVFRG0mupMX/u/unmK79q/Ndko5fphsgM2E4bS0NIwaNQo5OTnYuHEjJk6c2Kzr5HI5PDw8DB5E1ubrQ+nYdiZPbAd7u+DdKVHcWIPIhjw9PBTjeulWjEm/Von/23ya44fJ6tlEGK4PwtnZ2fj+++9x3333SV0Skdk4k6XC21sviG1Hexk+fqwfFC4cJ0xkS2QyGf4zJQpBXrrxw9vO5OGbw+kSVkVkelYThpVKJZKTk6FUKg2O6wfhDRs24P7775eoQiLzU1Gjxt82nDQYJzx/Qk9EBnlKVxQRSUbh6oiPH+tnMH546dYLuJDLyeNkvcx6kd1Vq1YhISEBAHDmzBnxWHx8PABg8uTJmDx5MgAgLi4OixcvxsKFC7Fo0SLxHqNGjUJ6ejqGDBmC06dP4/Tp0w2+jv7ziWzJoi3nkKqsENt3926P6cNCpSuIiCQXFeyJ+RN6YtFfK8vUqrX423cn8eucGDg72ktcHZHxmXUYTkhIwFdffWVw7ODBgzh48CAAIDQ0VAzDN5Oefv3jncOHD+Pw4cONPodhmGzRr6dysOlEltjuoHDG8gciOU6YiDB9WCgOpCixJ7kAAJBSUI6lW89j6eQ+EldGZHwygSPjW6S0tBQKhQIqlYqT6chiZRZVYsL/DqCsRg0AsJMBG2YNxaDO3hJXRkTm4lp5De7+3wEUlNWIxz5/oj/GR7SXsCqi5mlJXrOaMcNE1DxqjRYvf58kBmEAeGl0NwZhIjLg006OD6b2NTj2xubTyFNVS1MQkYkwDBPZmBV7L+NEerHYHhDihb+N7iphRURkrmK6+eK5O8LEdkllHeZ+nwSNlh8qk/VgGCayIUdTixC3N0Vsuzs74MNH+sLBnv8UEFHjXh3XHZFBCrF96Oo1fL7/ioQVERkXfwMS2QhV1fUeHf0OnXfu74MgL1fpiiIis+fkYIf/PRINVyfdShIf7LqEpMwS6YoiMiKGYSIbsWjLOWSXVInth/oHYVJUBwkrIiJL0dnXDYvvjRDbaq2AV75PQlWtRsKqiIyDYZjIBmw7k4ufTmaL7VAfVyzS+8VGRHQrU/oHYWJkoNi+qqzA8u0XmriCyDIwDBNZuYLSavzzpzNi204GfPBwX7jJzXqZcSIyMzKZDEsn90aAh1w89tWhdBxIKZSwKqLbxzBMZMUEQcDrm0+jpLJOPPbCqK7o18lLwqqIyFJ5ujrhP1OiDI79Y9NpqPT+jSGyNAzDRFZs/dEMxF/U9dpEdPDA38Z0k7AiIrJ0d4T74cmhIWI7r7QaC345K2FFRLeHYZjISqUpK7D0N914PicHO3z4cF84OfDHnohuz7y7eyLM101sbzmVg19P5UhYEVHr8bcikRVSa7R4ZWMSqup0M71fH98d3QLcJayKiKyFi5M9Pni4L+ztZOKxN38+y93pyCIxDBNZoc/3X0ViRonYHhrmg6eHd5auICKyOn2DPfFirG73SlVVHf7xwykIAnenI8vCMExkZZLzSvHh7kti213ugPemRsFOrweHiMgY5ozuarA73YEUJTYcy5SwIqKWYxgmsiJ1Gi1e23QKdRpdz8yieyPQ0dNFwqqIyFo52tvhg6l9Idebi/D21gsGG/wQmTuGYSIr8sX+qzibXSq27+zpjwf6dZSwIiKydl392+G1cd3FdnmNGv+3+TSHS5DFYBgmshIX88oMhkd4ODvg7fv7QCbj8AgiMq2nYzojupOn2D6QosTG4xwuQZaBYZjICqg1WvzjB8PhEQsnRSDAw1nCqojIVtjbyfCfKVEGSzcu/e0CcjhcgiwAwzCRFfjiwFWczlKJ7TE9ODyCiNrW9eES4WK7rEaNeT+e4XAJMnsMw0QWLiW/DB/+niK2PZwd8M4DHB5BRG3vmZgwg+ES+y4VYtOJLOkKImoGhmEiC6bWaPHaD6dRq9GKx97i8Agiksj14RKRBsMllvx2HrkqDpcg88UwTGTBViek4lRmidiO7e6HBzk8gogk1NXfHa+M1RsuUa3G/J/OcrgEmS2GYSILlaaswAe/622u4eyAZQ9EcngEEUluZkxnRAV7iu29yQX49XSudAURNYFhmMgCCYKAf/50BjVq3fCI+RN6or2CwyOISHoO9nb4z5RIONrr3pwv3nIOxRW1ElZF1DiGYSILtOlEFv68ck1sDw3zwcMDgyWsiIjIUHiAO14Y1VVsX6uoxdvbLkhYEVHjGIaJLExhWQ3e3qr7hSJ3sOPqEURkll6I7YKu/u3E9g8nspCQopSwIqKGGIaJLMziX89BVVUntl++Mxydfd0krIiIqHFyB3ssf6CPwbF//nQGVbUaiSoiaohhmMiC7D6fj9/0JqH0DPTAzBGdJayIiKhpA0K98cSQELGdUVRpsHU8kdQYhoksRFl1HRb8clZs28mAfz/YB472/DEmIvP2+l3d0V5v/fOVB67ibLaqiSuI2g5/ixJZiP/svIhcVbXYfiamMyKDPKUriIiomdydHbFkcm+xrRWANzafhlpvwyAiqTAME1mAxIxifHM4XWwHe7tgrt6i9kRE5m5srwDc0ydQbJ/LKcWXB1MlrIjoOoZhIjOn1mj/2r1Jd+yd+/vA1clBuqKIiFph4b294OGs+7frv7+nILuEWzWTtBiGiczc2j/TcCG3VGxP7tsBI7r5SVgREVHr+Ls7Y96EnmK7qk6DRVvOSVgREcMwkVnLKaky2HLZw9kB8+/pJWFFRES35+EBwejXyVNs/34+H7+fz5euILJ5DMNEZuxfv55Hpd56nK/f1QN+7nIJKyIiuj12djK8fX8f2NvpNgpatOUcKmvVElZFtoxhmMhM7U3Ox45zeWK7b7AnHhvUScKKiIiMo2egB54eHiq2s0uq8L89KdIVRDaNYZjIDFXVavDWL7pxdHYyYOnk3rCz45bLRGQdXr4zHIEK3drDqw+k4mJemYQVka1iGCYyQx/tTUFWsW6G9YxhndG7o0LCioiIjMtN7oBF90aIbbVWwJs/n4FWKzRxFZHxMQwTmZmU/DKsPHBVbLf3cMYr47imMBFZn3G9AnBnT3+xfSytGD+cyJKwIrJFDMNEZkQQBLz581nUaXQ9I29N6oV2cq4pTETWRyaTYeGkCDg76uLIO9svoKiiVsKqyNYwDBOZkS2ncnAktUhsj+ruh7t7t5ewIiIi0wr2dsXfx+g+/SqprMN7uy5KWBHZGoZhIjNRXqPGO9suiG0nBzssvjcCMhknzRGRdZs5ojO6+rcT298dzcDprBLpCiKbYtZheN26dXjuuecwYMAAyOVyyGQyrF27tsX30Wq1iIuLQ2RkJFxcXODn54epU6ciJYXLuJD5+GhPCvJLa8T28yO7IMTHTcKKiIjahqP99Tf/9QQBeOuXc5xMR23CrMPwm2++iS+++ALp6ekIDAxs9X2ef/55zJkzBxqNBnPmzMGECROwZcsWDBw4EOfPnzdixUStc7mgHKsTUsV2R08XzB7ZRcKKiIja1vCuvrinj+53fVJmCX5I5GQ6Mj2zDsOrVq1CWloaCgsL8fzzz7fqHn/88QdWrlyJESNGIDExEe+++y6++uorbN26FaWlpZg9e7aRqyZqGUEQsGjLOai1hpPmXJzsJayKiKjtzb+nJ1wcdf/2/Xt7MlRVdRJWRLbArMPwnXfeiZCQkNu6x8qVKwEAS5cuhVyu28Z2zJgxGD9+PPbv349Lly7d1tcguh07zuYh4bJSbN8R7odxvQIkrIiISBodPF3w0uiuYvtaRS3++zt/R5NpmXUYNob4+Hi4ublh+PDhDc6NHz8eALBv3762LosIwPWd5pb8phuq42gvw8JJvThpjohs1swRnRHq4yq2vz6Uhgu5pRJWRNbOqsNwRUUFcnNz0blzZ9jbN/zIuVu3bgDAiXQkmU/iLyNHVS22n4kJQxe/dk1cQURk3eQO9lioN5lOKwALfzkHQeBkOjINqw7DKpUKAKBQNL6NrYeHh8HzGlNTU4PS0lKDB5ExpCkr8Pk+3U5zAR5yzNH7eJCIyFbFdvfHnT11w8WOphVhy6kcCSsia2bVYdgYli1bBoVCIT6Cg4OlLomsxNKt51Gr0Yrt+ff0ght3miMiAgC8NbEXnBx0MeXtrRdQUaOWsCKyVlYdhut7hG/W81vfy3uznmMAmDdvHlQqlfjIzMw0fqFkcw6kFGL3hQKxPSTMG5MiW798IBGRtenk42qwxGRBWQ0+jb8iYUVkraw6DLu5uSEwMBCpqanQaDQNztePFa4fO9wYuVwODw8PgwfR7VBrtAaT5uxkwMJJ3GmOiOhGz4/sgg4KZ7H9xYGryCyqlLAiskZWHYYBYOTIkaioqMDBgwcbnNu5c6f4HKK2sv5oBi7ll4vtRwZ1Qs9AvskiIrqRi5M93ri7h9iuVWuxfHuyhBWRNbKaMKxUKpGcnAylUmlwfNasWQCu72ZXW1srHt+zZw927tyJO+64A+Hh4W1aK9mukspafKC3Zqa7swNeHcu/f0REN3NvVAf0D/ES21vP5OLI1WsSVkTWxqzD8KpVqzBjxgzMmDEDmzZtanDs559/Fp8bFxeHnj17Ii4uzuAesbGxmDlzJg4cOIDo6Gi8/vrrmD59Ou655x54eHjg008/bctviWzch7tTUFKp203p72O6waedvIkriIhsm0wmw1sTexkc+9dv56HRcqk1Mg6znrqekJCAr776yuDYwYMHxSEPoaGhmDx58i3v8/nnnyMyMhKff/45VqxYgXbt2mHSpEl4++232StMbSYlvwzfHE4X22G+bnhyaKh0BRERWYioYE882C8ImxOzAADnckrxw4lMPDywk8SVkTWQCVzFukVKS0uhUCigUqk4mY6aTRAETF9zDPsvFYrHvpwxAKN7cNtlIqLmyC+tRux78aisvT4h3redE/54bRTcnR0lrozMUUvymlkPkyCyFn9cLDAIwneE+yG2u7+EFRERWZYAD2e8GKvbmEhZXou4Py5LWBFZC4ZhIhOrVWux9LcLYtveToYF9/TkUmpERC30TExnBHm5iO01CWlIv1YhYUVkDRiGiUzs2yPpuKrU/WP9xJAQdAtwl7AiIiLL5Oxoj39O6Cm2azVaLNvGpdbo9jAME5mQqqoO/9uTIrYVLo54+c6bb/JCRERNu7t3ewzu7C22d5zLw7G0IgkrIkvHMExkQh//cbnBUmqerk4SVkREZNlkMhnevMdwqbWlWy+A6wFQazEME5lIZlEl1h5ME9uhPq54fEiIdAUREVmJPkEK3B/dUWyfyizBb6dzJayILBnDMJGJvLvzImo1WrH9f3f3gJMDf+SIiIzhtfHdIdf7N/XfO5JRo9ZIWBFZKv5mJjKBkxnF+PVUjtgeEOKF8RHtJayIiMi6dPR0wTMxncV2VnEVvvozTbqCyGIxDBMZmSAIeGfbBYNj87mUGhGR0c0e1QU+brp5GB/tvYziiloJKyJLxDBMZGQ7z+XjWFqx2J4YGYjoTl4SVkREZJ3cnR3x8thwsV1WrcaKvSlNXEHUEMMwkRHVqrVYvl3XK+xkb4c37uohYUVERNbtkYHB6OLnJra/OZSOVCU34qDmYxgmMqJvj6Qj7Vql2J4+LATB3q4SVkREZN0c7e0w727dRhxqrYB/b+dGHNR8DMNERlJaXYcVehtseLo64qVYbrBBRGRqY3r6Y0iY4UYcx7kRBzUTwzCRkXy+7wqK9TbY+NvoblC4OkpYERGRbWhsI47l25O5EQc1C8MwkRHkl1ZjdUKq2A72duEGG0REbah3RwUm9+0gto+nF2P3hQIJKyJLwTBMZAT/25OC6jrdBhuvjevODTaIiNrYq+O6w9Fet4zluzuSodGyd5iaxt/WRLfpSmE5vj+WKbZ7BXpgUmSHJq4gIiJTCPZ2xbTBuk/lUgrKsTkxS8KKyBIwDBPdpvd2XjToefi/u3vAzo4bbBARSWHO6K5oJ3cQ2//9/RKq67hNM90cwzDRbTiZUYztZ/PE9vCuPhjRzVfCioiIbJtPOzlm3REmtnNV1fj6UJp0BZHZYxgmaiVBELD8hrUs37irB7ddJiKS2DMxneHbTi62P/7jClR6q/0Q6WMYJmql+EuFOJKqW8fynshARAZ5SlcQEREBANzkDvj7mK5iW1VVh0/3XZGwIjJnDMNEraC9YYcjBzsZXhvXXcKKiIhI3yODOiHUR7cD6JqDqchTVUtYEZkrhmGiVvjlVDaS88rE9qODOqGzr5uEFRERkT5Hezu8Nl7XSVGj1uK/v1+SsCIyVwzDRC1Uq9biA71/UF0c7TFH7+M4IiIyDxN6B6JPR4XY/iExC1cKyyWsiMwRwzBRC31/PBOZRVVie+aIzvB3d5awIiIiaoydnQxv3NVDbGu0AnuHqQGGYaIWqK7T4KM9KWJb4eKIZ/WW8CEiIvMS080Xw7r4iO3fTufifE6phBWRuWEYJmqBrw+loaCsRmw/P7ILPJwdJayIiIhuRX/sMAC8v+uiRJWQOWIYJmqmsuo6fBqvW5rHz12O6cNCmriCiIjMQb9OXhjTw19s70kuwIn0YgkrInPCMEzUTKsTUlGst2j7S7Fd4erk0MQVRERkLl69YfnL93ayd5iuYxgmaobiilqsOpAqtjt6uuCRQcESVkRERC3Rq4MHJkYGiu1DV6/h4GWlhBWRuWAYJmqGz/ZfQXmNWmz//c5ukDvYS1gRERG11Ctjw2FvJxPb/9l5EYIgSFgRmQOGYaJbKCitxld/pontMD83PBDdUbqCiIioVcL82uHBfrp/v5MyS7D7QoGEFZE5YBgmuoW4Py6juk4rtl8ZGw4He/7oEBFZor+N6QZHe13v8Pu7LkKrZe+wLeNvdKImZBZV4rujGWK7Z6AHJvQObOIKIiIyZ0Ferpg2WLcSUHJeGX49nSNhRSQ1hmGiJny0NwV1Gl2PwT/Gh8NOb7wZERFZnhdiu8DZUReB/rc7BWqNtokryJoxDBPdRPq1CmxOzBbb/Tp5Ira7fxNXEBGRJfB3d8aMYZ3F9lVlBbacYu+wrWIYJrqJFXsuQ6M3juyVsd0hk7FXmIjIGsy6IwxuTrpVgVbsYe+wrWIYJmpEqrICP53MEtsDQ70wvKtPE1cQEZEl8XZzwlPDdb3Dadcq8dPJ7CauIGvFMEzUiBV7UqA/uXju2HD2ChMRWZmZIzrDXa7bSXTF3hTUsXfY5jAME93gckE5fknS9Q4M7uyNYV18JayIiIhMwdPVCU/F6HqHM4uq8GNiVhNXkDViGCa6QWO9wkREZJ2eiekMd2e93uE9l1GrZu+wLTH7MHzs2DFMmDABXl5ecHNzw6BBg7B+/foW3aOkpARvvfUWIiMj4e7uDl9fXwwcOBBxcXGorq42UeVkiVLyDdebHNbFB0PCOFaYiMhaKVwcMTMmTGxnl1ThhxPsHbYlZh2G4+PjERMTgwMHDmDKlCmYPXs2lEolpk2bhnfeeadZ9ygpKUH//v2xZMkSKBQKPPfcc3j00UdRXFyMOXPm4J577oFWy3eAdN2He1IgsFeYiMimPBUTCoWLo9iO25uCGrVGwoqoLZltGFar1Zg5cyZkMhn279+PlStX4r333sOpU6cQERGBhQsXIiUl5Zb3+eKLL3D16lXMnTsXBw4cwHvvvYePPvoI58+fx8CBA7F3714kJCS0wXdE5i45rxTbzuSK7RHdfDEw1FvCioiIqC14ODvi2RG6scM5qmpsPM7eYVtxW2F4z549mD9/PmJjY9GtWzd4eXmhY8eO6Nu3L5544gmsWrUKeXl5rbr33r17ceXKFTz22GOIjo4Wj7u7u2PBggVQq9VYs2bNLe9z9epVAMCECRMMjjs5OWHs2LEAgIKCglbVSNblf7sNe4VfvpO9wkREtmLG8M7wdNX1Dn+89zKq69g7bAtaHIbLy8vxzjvvoHPnzhg3bhyWLVuGffv2ITs7G25ubqiursbZs2fx7bffYtasWejUqROmTJmCgwcPtujrxMfHAwDGjRvX4Fz9sX379t3yPhEREQCAHTt2GByvq6vD7t274eLigqFDh7aoNrI+F3JLsf2s7o3byHA/9A/xkrAiIiJqS+3kDph1h27scF5pNb4/lilhRdRWWhSGP/vsM3Tt2hVvvvkmPD09sXTpUuzduxelpaWorKxEVlYWrl27hrq6OiQnJ+Orr77Cww8/jF27duGOO+7AAw88gNTU1GZ9rfohEN26dWtwzsvLC76+vs0aJjFz5kwMHDgQ77//PkaNGoV//OMf+Nvf/oaIiAhcvXoV69evR8eOHVvyv4GsUNzeywZtjhUmIrI904eGwtvNSWx/tu8Kxw7bAIdbP0Vnzpw5mDZtGv7xj3+IPa6NkclkCA8PR3h4OJ544glUVVXhu+++w7Jly/DNN9/grbfeuuXXUqlUAACFQtHoeQ8PD2Rl3Xo8j4uLC+Lj4/Hcc89h3bp1Ym+ynZ0dXnrpJcTExDR5fU1NDWpqasR2aWnpLb8mWZaU/DJsO6sbKzyqux/6BntKVxAREUnCTe6AZ0eE4d87kgEAuapqbD6RjccGd5K4MjKlFvUMJycnY+3atU0G4ca4uLjg6aefRnJyMqZPn96ia2+XUqnE2LFjcfjwYWzduhUlJSXIy8vDZ599hjVr1mDw4MEoLi6+6fXLli2DQqEQH8HBwW1YPbWFuD8uG4wVnjO64acRRERkG54YGmI4dviPy9yVzsq1KAx36dLltr6Yvb09QkJCmvXc+h7h+h7iG5WWlt6011jfK6+8gj///BObN2/GhAkToFAoEBAQgGeffRbvvvsurl69ig8//PCm18+bNw8qlUp8ZGZy/JA1uVJYjl9P6dYVjunqy7HCREQ2rJ3cAc8M160skV1ShZ8Ss5u4gixdq1eTUKvVyM3NxbVr14xZj6h+rHBj44KLi4uhVCobHU98o61bt8Lb2xuRkZENzo0ePRoAcOLEiZteL5fL4eHhYfAg6/HxH5cNdpv72xj2ChMR2brpw0MNdqWL++My1OwdtlotDsPbtm1DTEwMnJ2dERQUBH9/f7i5uaFfv36YM2cOtm3bBo3m9gebjxw5EgCwa9euBufqj9U/pym1tbUoLS1FbW1tg3OFhYUArgdesj3p1yrwS5KuV3hwZ28M6sx1hYmIbJ2HsyOe0usdziiqxBa9TxHJurQoDG/YsAGTJk3CoUOH0LNnT4wYMQIDBw6Em5sbkpKS8PHHH2PSpEkIDg7GihUrbmtntzFjxiAsLAzr169HUlKSeLysrAxLliyBg4MDZsyYIR5XKpVITk6GUqk0uM/w4cOhVquxZMkSg+M1NTXisdjY2FbXSZbrkz+uQKPXLfx39goTEdFfnh4einZyvd7hvZcNfmeQ9WhRGH777bfh6+uLxMREnDlzBvHx8Th8+DBefPFFyGQybN26FS+++CIEQcDLL7+MmJiYVm9o4eDggFWrVkGr1WLEiBGYNWsWXnvtNURFReHcuXNYtGgRwsN1y1/FxcWhZ8+eiIuLM7jP8uXL4e7ujqVLl2Lw4MF45ZVX8MILL6BXr17YuXMn+vfvj5kzZ7aqRrJcmUWV2JyoW41kQIgXhnbxkbAiIiIyJ56uTpg+TDfP6aqyAr+dZu+wNWpRGL58+TIef/xxREVFNXr+7rvvxooVK5CRkYEVK1bgzJkzGDNmDKqqqlpVXGxsLBISEhATE4ONGzfik08+gY+PD9atW4f58+c36x59+/bFiRMn8NRTTyEvLw9xcXFYu3Yt3NzcsHjxYuzfvx/Ozs6tqo8s16f7rkCt9w7/b2O6QSaTSVgRERGZm2diwuDqZC+24/Zehpa9w1anResMBwQENGudXUdHR7z00ksYMGAARo0aheXLl2Px4sWtKnDQoEHYvn37LZ+3aNEiLFq0qNFz3bp1w5dfftmqr0/WJ6ekCpuO61YFiQr2xIhuvhJWRERE5sjbzQlPDAnB5/uvAgBSCsqx41weJvQJlLgyMqYW9QxPnjwZGzduxNWrV5v1/CFDhmDq1KlYv359q4ojMoXP911BnUZ/rHBX9goTEVGjZo4Ig7OjLi6t2JPC3mEr06IwvHDhQri7u2PEiBHYtm1bs65p3759s3aKI2oLBWXV+E5vr/neHT0Q291fwoqIiMic+bnLMW2wbuxwcl4Z9ia3bj4UmacWhWEvLy/s3bsXrq6umDRpEvr164cPP/wQ2dmNL0adnZ2NDRs2oFMnbmNI5mF1Qipq1bpVTl6K5VhhIiJq2nN3hMHJXheZru9cyt5ha9GiMcMAEB4ejqSkJCxevBgff/wxXnnlFTFMjB49GoGBgZDL5cjPz8e+fftQWVmJTz/91OiFE7WUqrIO6w6li+1u/u0wrleAhBUREZEl8PdwxkMDgvDtkQwAQFJmCQ5duYZhXTnfxBrIhNt4a6NSqbBx40bs2LEDBw8ebLCMWpcuXTBv3jw8/fTTt12ouajfBlqlUnE3OguzYk8KPvj9ktj+78NRuD86SMKKiIjIUmRcq0Ts+/HiWsPDu/rg25lDJK6KbqYlee22wvCNiouLkZ2djbq6OgQEBKBDhw7GurXZYBi2TBU1agz/916UVNYBAIK8XBD/2ig42Ld6R3IiIrIxc79Pwk8ndUNDf3phGKI7eUlYEd1MS/KaUZOAl5cXevfujejoaKsMwmS5vjuaIQZhAHh+ZBcGYSIiapHZo7oYtD+JvyJRJWRMLUoDEydOxIkTJ1r1haqqqvDee+9x/DC1uRq1BisP6JYD9HOXY0p/Do8gIqKWCQ9wN5hr8vv5fFzMK5OwIjKGFoXhzMxMDBo0CGPGjMHatWubtQHH8ePH8fLLLyMkJARvvfUWfH052Jza1uYT2cgvrRHbz47oDGdH+yauICIiatyLsV0N2p/GX5aoEjKWFo0ZFgQBa9aswb/+9S9kZGTAzs4OPXr0QL9+/RAQEAAvLy9UVVWhqKgIKSkpOH78OFQqFezs7DB16lS8/fbbCA0NNeG3Y3ocM2xZ1BotRr+/DxlFlQAAhYsj/vy/0XCTt3ghFSIiIgDAE6uP4ECKEgBgJwPiX4tFJx9XiasifSafQKfVarF161asXbsW+/btQ1FRUYPn2NnZITIyEpMnT8bMmTOtZgwxw7Bl+SUpG3/fkCS2X76zG16+M1y6goiIyOIdunINj648LLYfHdQJyx7oI2FFdKOW5LVWdY/Z2dlh0qRJmDRpEgDgwoULyMrKwrVr1+Di4gI/Pz9ERERAoVC05vZERqHVCvj4D93HV65O9pgxLFS6goiIyCoMCfNGv06eSMwoAQBsPpGFv4/phvYKZ2kLo1a5rc+KExMTkZWVhZCQEIwdO9ZYNREZxe4L+biUXy62Hx8SAk9XJwkrIiIiayCTyfBibFc889VxAECtRotVB67izYm9JK6MWqNVa0tVVFRg6tSp+OWXX5CZmYm4uDjExMTgwoULxq6PqFUEQcCn+3RL3jjZ22FmTGcJKyIiImsyuoc/erR3F9vrj2agpLJWwoqotVoVht99910UFxejY8eOuOeee7By5Up89913mDNnDvLz841dI1GLHUsrxsm/Pr4CgCkDguDvwY+viIjIOGQymcG6w5W1GnxzKF3Ciqi1WhWGjxw5gjVr1qBr1674+OOPcf/992P58uUYMGAA3nvvPWPXSNRin+n1CtvJgFkjwiSshoiIrNE9fQIR7O0ittf+mYbqOo2EFVFrtGrMcGlpKYKCghAUFITRo0cDALKzs7F9+3asXLnSqAUStdTFvDLsTS4Q23f3DkSor5uEFRERkTVysLfDsyPC8NYv5wAA1ypqselEFp4YEiJxZdQSRtuPtmPHjpg5cyYcHR2NdUuiVvl8n+H2mM+NZK8wERGZxkP9g+HtppucvXL/Vag1WgkropYyWhiup9XyLwBJJ7ukCltO5YjtYV18EBnkKV1BRERk1Vyc7DF9aKjYziiqxPazedIVRC3WqjAsCAJWrlyJjIwMY9dDdFtWH0iFWqvbR+b5kV2aeDYREdHte3JoCFwc7cX2Z/uuoBV7mpFEWt0zrFAosHTpUtx1112YO3cuduzYgaqqKmPWRtQiJZW12HBM9wYtooMHRnTzlbAiIiKyBV5uTnhkULDYPpdTioTLSgkropZodRju378/vvjiC+zYsQMvvvgiUlNT8cwzzyA9PR3Lly/HkSNHOGSC2tTXh9JRWaubxfvcyC6QyWQSVkRERLZi5ogwONjpfud8dsP8FTJfMqEV/fjbt2/HTz/9hCtXrqBr164YO3YsRo8eDW9vb2i1Whw7dgy///47Dh8+DCcnJ8TExOCVV14xRf1triV7XVPbqarVYPi/96Ko4vqC58HeLvjj1VFwsDf6sHgiIqJGvfJ9En48mS22f30pBn2CFBJWZLtaktdaFYb1Xb16Fbt378bevXuhUqkQHR2NsWPHYvjw4XByckJFRQWGDx+OpKSk2/kyZoNh2Dx9fShNXNoGAJbcF4En9CY0EBERmdrFvDKM/3C/2L6nTyA+ntZPwopsV0vyWqvWGdYXFhaGWbNmYdasWRAEASdPnsTu3bvxv//9D3Z2dhg+fDhqa7k9IZmOWqPFygNXxbaPmxMeGhDcxBVERETG1729O0b38BfXut9+NhdpygqudW/mbjsM65PJZOjXrx/69bv+Lqi2thYJCQm4//77jflliAzsOJeHzCLd5M0Zw0LhrDerl4iIqK08P7KLGIa1ArA6IRVLJveWuCpqikkHVDo5OWH06NF4++23TfllyIYJgoCV+3W9wq5O9nhiKHf+ISIiaQwM9UJ0J0+xvelEpjifhcwTZxeRRTuaWoRTWSqxPXVAMDxdnZq4goiIyHRkMhlmjdDtfFpdp8W6w+kSVkS3wjBMFm3lgVTxz3Yy4OnhnSWshoiICBgX0R6dvF3F9teH0lBdp2niCpISwzBZrCuF5dh9IV9s3907EJ18XJu4goiIyPTs7WR4JkbXOaMsr8XPekuukXlhGCaLtUqvVxgAZo5grzAREZmHhwYEQeHiKLZXHrgKrZZbNJsjhmGySMryGvyYmCW2r09Y8JKwIiIiIh1XJwc8MUQ3oftKYQXiLxVIWBHdDMMwWaRvDqWjRq3b7vtZvckKRERE5uDJYSFw0tsJ9Qu91Y/IfDAMk8WprtPgG72ZuZ193XBnzwAJKyIiImrI390Zk6M7iO3DV4twRm8FJDIPDMNkcTYnZhms2fhMTGfY2ckkrIiIiKhxM2/45FJ/x1QyDwzDZFG0WsFg4py3mxMe7BckYUVEREQ3Fx7gjlHd/cT21jO5yCqulLAiuhHDMFmU3RfykaqsENtPDAmBixO3XiYiIvOlvwmHRitgzcE06YqhBhiGyaLo9wrLHey49TIREZm9oV18ENHBQ2xvOJoBVVWdhBWRPoZhshins0pwNK1IbD/QLwi+7eQSVkRERHRrMpnMYNWjiloNNh7LlLAi0scwTBbjywTDTTb0d/chIiIyZ/dEBqK9h7PYXvtnGtQabRNXUFthGCaLkKeqxm+nc8X2qO5+6OrfTsKKiIiIms/R3g5PDtMN7csuqcKu8/kSVkT1zD4MHzt2DBMmTICXlxfc3NwwaNAgrF+/vsX3KSsrw8KFC9G7d2+4urrC09MT/fr1w+LFi01QNRnb14fSoNbbxpK9wkREZGkeG9QJzo666HXjJ54kDbMOw/Hx8YiJicGBAwcwZcoUzJ49G0qlEtOmTcM777zT7PtkZGQgOjoaS5YsQYcOHTBnzhzMmDEDHTp0wObNm034HZAxVNVqsP5ohtgOD2iHmK6+ElZERETUcp6uhsuBHk8vxqnMEukKIgCATBAE4dZPa3tqtRo9evRAVlYWDh06hOjoaADXe3iHDh2Kixcv4vz58+jWrVuT99FoNBg6dCjOnj2LrVu3IjY2tsHXcXBwaHZdpaWlUCgUUKlU8PDwuPUFdNu+PZKO+T+dFdvLH+iDRwZ1krAiIiKi1rlSWI4x7+8T2/dGdcCKR6MlrMg6tSSvmW3P8N69e3HlyhU89thjYhAGAHd3dyxYsABqtRpr1qy55X1++OEHHDt2DK+99lqDIAygRUGY2p5WKxh8jOTt5oTJ0R0lrIiIiKj1uvi1Q6zeJhzbzuQiV1UlYUVktmE4Pj4eADBu3LgG5+qP7du3r8G5G33//fcAgIceegiZmZn47LPPsHz5cmzatAnl5eXGK5hMYl9KIa4U6jbZmDa4E5wduckGERFZrqf15r2otQK+PpQuYTVktt2iKSkpANDoMAgvLy/4+vqKz2nK8ePHAQAJCQmYO3cuampqxHN+fn7YuHEjRo0aZZyiyej0e4Ud7WV4Ygg32SAiIssW09UX3QPccTG/DACw/kgG5ozuClcns41lVs1se4ZVKhUAQKFQNHrew8NDfE5TCgoKAABz5szByy+/jMzMTBQWFmLFihVQqVSYPHkycnNzb3p9TU0NSktLDR7UNi7ll+FAilJsT4rsAH+9NRqJiIgskUwmw9MxoWJbVVWHHxOzpSvIxpltGDYWrfb6gtYTJ07E8uXLERQUBF9fX8yZMwdz586FSqXC6tWrb3r9smXLoFAoxEdwcHBblW7zblxy5mkup0ZERFbivr4d4e3mJLa/PJgKrdYs1zSwemYbhut7hG/W+1s/S7C597n33nsbnJs0aRIA3VCKxsybNw8qlUp8ZGZy+8S2cK28Bj+e1L1LHtTZG7073vr1JiIisgTOjvZ4fLBuZaSrhRXYd6lQwopsl9mG4fqxwo2NCy4uLoZSqbzlsmoA0L17dwCAp6dng3P1x6qqbj6LUy6Xw8PDw+BBprf+SAZq1bptKrnJBhERWZvHh4bA0V4mtr88yE04pGC2YXjkyJEAgF27djU4V3+s/jlNGT16NADg/PnzDc7VHwsNDW1tmWQCdRotvjmsm1kb7O2CO3sGSFgRERGR8fm7O2NSVAexfSBFiZS/JtVR2zHbMDxmzBiEhYVh/fr1SEpKEo+XlZVhyZIlcHBwwIwZM8TjSqUSycnJUCqVBvd56qmnIJfL8dFHHyE7O9vgPvW72E2dOtWk3wu1zPazeSgo0636MWNYZ9jbyZq4goiIyDI9Pdzwk8+vDqVJU4gNM9sw7ODggFWrVkGr1WLEiBGYNWsWXnvtNURFReHcuXNYtGgRwsPDxefHxcWhZ8+eiIuLM7hP586d8Z///AcFBQWIiorCs88+i5deegmRkZFISkrCrFmzMGbMmLb+9qgJa/U+JnJ1ssdDA4KaeDYREZHl6t1RgYGhXmJ784lsqKrqJKzI9phtGAaA2NhYJCQkICYmBhs3bsQnn3wCHx8frFu3DvPnz2/2febMmYMtW7agV69e2LBhA1avXg0fHx988cUX+Pzzz034HVBLnc4qQWJGidh+sF8QPJwdpSuIiIjIxKYPCxX/XFWnwabjnKzflmSCIHAdjxZoyV7X1HKvbEwyWGtx9yt3oKu/u4QVERERmVadRosR//4DeaXVAIBO3q7447VRHCJ4G1qS18y6Z5hsS2FZDX47pdsAZUQ3XwZhIiKyeo72dnhiqG6H1YyiSvyRXCBhRbaFYZjMxndHM1Cr0S2n9tTwUOmKISIiakOPDAyGk4Mulq39M026YmwMwzCZhVq1Fuv0llML8XHFqHB/CSsiIiJqOz7t5LhPb5m1hMtcZq2tMAyTWdh+NtdgObXpQ0Nhx7FSRERkQ/Qn0gHsHW4rDMNkFr7S+4F3dbLHFC6nRkRENubGZdZ+TOQya22BYZgkdyrTcDm1Kf25nBoREdmmGcN0m3BwmbW2wTBMkvvqho+BnhwaKkkdREREUhsXEYBAhbPY/upQGjRaroJrSgzDJKnCshr8ejpHbN8R7oeu/u0krIiIiEg6jvZ2eHyIbpm1zKIq7LmQL2FF1o9hmCS14WgG6jS6d7xP3TB5gIiIyNY8OqiTwTJr3+ittkTGxzBMklFrtFh/NENsh/i4YmS4n4QVERERSc/bzQn36i2zdiBFiauF5RJWZN0Yhkkyuy/kI1dVLbYfHxzC5dSIiIgAPKm3Ix3A3mFTYhgmyXx9SPeDLXeww0NcTo2IiAgAEBnkiahgT7H9w4ksVNSopSvIijEMkyRS8svw55VrYnty347wdHWSsCIiIiLzMl2vd7isWo2fk7IlrMZ6MQyTJG78uOeJGz4OIiIisnUT+gTC203XUfTNoXQIApdZMzaGYWpz5TVq/Jioe3fbr5MnendUSFgRERGR+XF2tMfDA4PFdnJeGY6lFUtYkXViGKY291NiFsr1xj1xkw0iIqLGTRvcCfpzy78+lCZZLdaKYZjalCAIBhPnfNyccHef9hJWREREZL6CvFwxukeA2N5xNg8FpdVNXEEtxTBMberw1SKkFOjWSnxkUDDkDvYSVkRERGTe9JdZU2sFfHc0U8JqrA/DMLWpbw6niX+2kwGPDebEOSIioqbEdPVFZ183sb3+aDrqNFoJK7IuDMPUZvJU1dh5Tre/+p09A9DR00XCioiIiMyfnZ0Mjw/RdR7ll9bg9/P5TVxBLcEwTG1m/dEMaLS6JWE4cY6IiKh5pvQPgoujblghJ9IZD8MwtYk6jRbfHc0Q22F+bhje1UfCioiIiCyHwsURk6M7iu3DV4twKb9MwoqsB8MwtYld5/JRWFYjtp8YEgKZTNbEFURERKTvyRs2qPr2hg2sqHUYhqlNfHtE9wPr4miPB/sHSVgNERGR5ekZ6IH+IV5i+8fEbFTWqpu4gpqDYZhM7kphOf68ck1s39e3AzycHSWsiIiIyDI9PqST+OeyGjW2JOVIWI11YBgmk1t/JMOgPY3LqREREbXK3b0D4eWq61D69obfsdRyDMNkUtV1GvxwIktsRwUp0CdIIWFFRERElsvZ0R4PDQgW22eyVTiVWSJdQVaAYZhM6rfTuVBV1Ylt9goTERHdnkcHdTJo68/LoZZjGCaTWqc309Xd2QGTojpIWA0REZHl6+zrhhHdfMX2llM5UFXWNXEFNYVhmEzmbLYKSXof3TzYLwguTvY3v4CIiIiaZdpgXe9wdZ0WP57MauLZ1BSGYTKZGwf168+AJSIiotYb0zMAAR5ysf3tkQwIgtDEFXQzDMNkEmXVdfglKVtsD+7sja7+7hJWREREZD0c7e3w8EBdJ9PlgnIcSS2SsCLLxTBMJvHzyWxU1mrE9uNDOHGOiIjImB4dFAw7vc1c13FHulZhGCajEwTBYIiEbzsnjI9oL2FFRERE1idQ4YIxPQPE9s5zeSgsq5GwIsvEMExGdyK9GMl5ZWJ76oBgODnwrxoREZGx6X/yWqcRsPF4poTVWCYmFDI6/V5hmazheohERERkHCO6+qKTt6vYXn8kA1otJ9K1BMMwGVVJZS22nskV2yPD/RCs90NKRERExmNnJ8NjesusZZdU4cBlpYQVWR6GYTKqHxOzUavWim3uOEdERGRaU/oHwdFeN5PuuxuWNqWmMQyT0QiCgO+O6n4AAzzkiO3uJ2FFRERE1s+3nRzj9Caq776Qj4LSagkrsiwMw2Q0J9KLkVJQLrYfHhAMB3v+FSMiIjK1x/Tm56i1Ajad4I50zcWkQkaz/qjhxLmpA4MlrIaIiMh2DA3zQYiPbo7OhmOcSNdcZh+Gjx07hgkTJsDLywtubm4YNGgQ1q9f3+r71dXVoW/fvpDJZOjRo4cRK7Vtqso6bD1tOHEuyIsT54iIiNqCnZ0Mj+jtSJdZVIUETqRrFrMOw/Hx8YiJicGBAwcwZcoUzJ49G0qlEtOmTcM777zTqnsuWbIEly9fNnKl9NPJLNToTZzjcmpERERta0r/IDjobUmnP4+Hbs5sw7BarcbMmTMhk8mwf/9+rFy5Eu+99x5OnTqFiIgILFy4ECkpKS26Z2JiIpYtW4Zly5aZqGrbdH3inG6Rb393OUb38JewIiIiItvj5y7HuAjdjnS/n89HQRkn0t2K2YbhvXv34sqVK3jssccQHR0tHnd3d8eCBQugVquxZs2aZt+vtrYWM2bMwJAhQ/DSSy+ZomSblZhRgov5hjvOOXLiHBERUZt79IaJdD9wIt0tmW1iiY+PBwCMGzeuwbn6Y/v27Wv2/RYtWoSUlBSsXr0aMpns1hdQs313w8S5hzlxjoiISBLDu/gi2NtFbG84msmJdLdgtmG4fghEt27dGpzz8vKCr69vs4dJHDt2DO+++y4WL16M8PBwo9Zp61RVdfjtdI7YHtGNO84RERFJ5caJdBlFlfjzyjUJKzJ/ZhuGVSoVAEChUDR63sPDQ3xOU2pqajBjxgxER0fj1VdfbXEdNTU1KC0tNXiQzi9J2aiu002ce2wQe4WJiIik9NAATqRrCbMNw8ayYMECpKSk4Msvv4S9vX2Lr1+2bBkUCoX4CA5m2KsnCALW62356NtOjjE9A5q4goiIiEzN390Zd+r9Pt55Lg+FZTUSVmTezDYM1/cI36z3t7S09Ka9xvUSExPxwQcfYP78+ejTp0+r6pg3bx5UKpX4yMzMvPVFNiIpswTJefoT54I4cY6IiMgMPDqYE+may2yTS/1Y4cbGBRcXF0OpVDY6nljf6dOnodFosGjRIshkMoMHAFy8eBEymQyenp43vYdcLoeHh4fBg67bcNTwjYH+GCUiIiKSzoiuvgjy0k2k23g8E4LAiXSNcZC6gJsZOXIkli1bhl27duGRRx4xOLdr1y7xOU0JDw/HM8880+i51atXQ6FQYMqUKXB15YSvliqvUeNXvYlzMV190cmH/x+JiIjMgZ2dDA8PCMb7v18CAKQqK3A0tQiDw3wkrsz8yAQzfZugVqvRvXt3ZGdn4/Dhw+jbty8AoKysDEOHDsXFixdx7tw5cXUIpVIJpVIJX19f+Pr63vL+MpkM3bt3R3Jycovqqh+eoVKpbLqX+PtjGXhj8xmx/dGj0ZgU1UHCioiIiEhfrqoKw5fvRf3Kag9Ed8QHD/eVtKa20pK8ZrbDJBwcHLBq1SpotVqMGDECs2bNwmuvvYaoqCicO3cOixYtMlgmLS4uDj179kRcXJyEVduODcd0QyQ8XR0NdrwhIiIi6QUqXDAy3E9sbz2TC1VVnYQVmSezDcMAEBsbi4SEBMTExGDjxo345JNP4OPjg3Xr1mH+/PlSl2ezLuWX4WRGidi+P7oj5A4tX6mDiIiITOthvfk8NWottpzKaeLZtslsh0mYKw6TAJb8dh6rE1LF9o6XR6BHe9v8f0FERGTO6jRaDF22B8ryWgBA744e+G3OCImrMj2rGCZB5qlGrcGPibrlWfoGezIIExERmSlHezs82D9IbJ/NLsXZ7FtvWmZLGIapRX4/n4/iSt14o0cGchMSIiIiczZ1gOHv6u+Pcc8EfQzD1CL6P0CuTvaYyBUkiIiIzFoXv3YYFOottn9OykZ1nUbCiswLwzA1W2ZRJRIuK8X2xMhAtJOb7VLVRERE9JeH9T7JLatWY/vZXAmrMS8Mw9Rsm05kQX+65cPccY6IiMgiTOgTCHe9Dqwbd5G1ZQzD1CwarYBNx3U/ON3826FfJ0/pCiIiIqJmc3Gyx719dUMbj6QWIVVZIWFF5oNhmJrlQEohclXVYvvhgcGQyWQSVkREREQt8cgNn+huPM7eYYBhmJpJf+Kco70MD/QLauLZREREZG56d/RAr0Ddcqg/nMhCnUYrYUXmgWGYbulaeQ1+P58vtsf1ag9vNycJKyIiIqKWkslkBhPpCstq8EdygYQVmQeGYbqln05mQ63VzZybyrWFiYiILNLkvh3h5KCLf5tOZDXxbNvAMExNEgQBm47rflA6KJwR09VXwoqIiIiotRSujrgror3Y3ptcgMKyGgkrkh7DMDXpTLYKF/PLxPaD/YNgb8eJc0RERJbqoQG6eT8arYCfT2ZLWI30GIapSTfONJ3SnxPniIiILNmwLr7o6Okitjcez4Sgv5GAjWEYppuqrtNgS1KO2B7c2RshPm4SVkRERES3y95Ohgf7dRTbKQXlOJWlkrAiaTEM003tPJeH0mq12H5oACfOERERWYMp/Q1/p9vymsMMw3RTP+jNMHVzsseEPu2beDYRERFZik4+rhgS5i22f03KQVWtRsKKpMMwTI3KLqlCwmWl2J4Y2QGuTg5NXEFERESWZKreJ75lNWrsPJcnYTXSYRimRm0+kQX9sfRTB3LiHBERkTW5u3cg2sl1HV2bTtjmUAmGYWpAqxUMfiDC/NzQr5OXhBURERGRsbk42WNSVKDYPnj5GjKLKiWsSBoMw9TAkdQiZBZVie2H+gdDJuPawkRERNbmxol0mxNtb0c6hmFqYJPejFI7GfCA3vIrREREZD36dfJEFz/dsqmbjmdBq7WtNYcZhslAWXUdtp3NFdsjw/0Q4OEsYUVERERkKjKZzGDp1OySKhy+ek3CitoewzAZ+O10LqrrtGJ7KtcWJiIismoPRHeEvZ1uOKStrTnMMEwG9IdIeLk6YkzPAAmrISIiIlPz93DGqHA/sb39bB5Kq+skrKhtMQyT6GphORIzSsT2fX07wsmBf0WIiIisnf5QiRq1FttO5zbxbOvCpEOiG2eQTunPtYWJiIhswege/vBydRTb+rvQWjuGYQJwfW3hnxKzxXaP9u6I6OAhYUVERETUVpwc7HBvVAexfTy9GGnKCgkrajsMwwQAOHT1GnJU1WJ7Sv8gri1MRERkQ25cc/hHG1lzmGGYABh+HGJvJ8N9fbm2MBERkS3p3dED4QHtxPbmxGybWHOYYZhQXqPGjrN5YntkuB/83OUSVkRERERtTSaT4cF+uvlC2SVVOJxq/WsOMwwTtp3JRVWdRmxz4hwREZFtuj+6I/SWHMbmE9k3f7KVYBgmbNYbIqFwccSYnv4SVkNERERS8fdwxh0Gaw7noqJGLWFFpscwbOMyiypxJLVIbE+KCoTcwV7CioiIiEhK+kMlKms12K43lNIaMQzbuIZrC3P7ZSIiIls2tlcA3J0dxPZmK19zmGHYhmm1gkEY7uLnhqgghYQVERERkdScHe0xSW/N4UNXryGruFLCikyLYdiGHUsrQmZRldh+kGsLExEREQyHSgDAj4nWO5GOYdiG6fcK28mAB6K5igQREREB/Tp5IszXTWz/mJgFQbDONYcZhm1UVa0G287oBsQP7+qL9gpnCSsiIiIicyGTyfCg3lKradcqcSK9WMKKTIdh2EbtPJeHcr2lUri2MBEREem7P7oj9EdP/mClE+kYhm2U/hCJdnIHjOvVXsJqiIiIyNx08HTB8C6+YnvrmVxU623SZS0Yhm1Qfmk1Dl5Wiu17+gTCxYlrCxMREZGhB/p1FP9cVq3GngsFElZjGmYfho8dO4YJEybAy8sLbm5uGDRoENavX9/s6xMSEvDqq6+if//+8PHxgbOzM3r06IE33ngDJSUlpivcjP2SlA2t3hj4+/X+ohMRERHVGx/RHi6Oug6zHxOtb6iEWYfh+Ph4xMTE4MCBA5gyZQpmz54NpVKJadOm4Z133mnWPaZMmYL//e9/cHd3x5NPPokXXngBrq6uePfddzFgwAAUFFjfO5xb0V8epaOnCwaFektYDREREZkrN7kD7uqtG0q571IhrpXXSFiR8ZltGFar1Zg5cyZkMhn279+PlStX4r333sOpU6cQERGBhQsXIiUl5Zb3mTt3LjIzMxEfH4///ve/+OCDD3DixAnMnj0bV65cweLFi9vguzEfF3JLkZxXJrbvj+4IOzuuLUxERESNuz9a9wmyWivg11M5ElZjfGYbhvfu3YsrV67gscceQ3R0tHjc3d0dCxYsgFqtxpo1a255nzfeeAOBgYEGx2QyGRYsWAAA2Ldvn3ELN3M/nTRcNJtDJIiIiKgpw7v6wt9dLrZvzBKWzmzDcHx8PABg3LhxDc7VH7udIOvo6AgAcHBwuMUzrYdGK+Bnvb/AUUEKdPFrJ2FFREREZO7s7WSYrNc7fCpLhcsF5RJWZFxmG4brh0B069atwTkvLy/4+vo2a5jEzXz55ZcAGg/b1urgZSUKynTjfB7ox7WFiYiI6Nb0h0oAwE8nrWcindmGYZVKBQBQKBSNnvfw8BCf01JJSUlYvHgx/P398frrrzf53JqaGpSWlho8LJX+xxoOdjJMiuogYTVERERkKXoGeqBHe3ex/fPJHGi11rE9s9mGYVNJTU3FxIkTodFosGHDBvj6+jb5/GXLlkGhUIiP4ODgNqrUuCpq1NhxVrf98qjufvB2c5KwIiIiIrIkD+p9opxdUoUjqUUSVmM8ZhuG63uEb9b7W1paetNe45tJT09HbGwsCgsL8cMPPyA2NvaW18ybNw8qlUp8ZGZmtuhrmosdZ/NQpbdrDIdIEBERUUvc17cD9BegspahEmYbhuvHCjc2Lri4uBhKpbLR8cQ3k5aWhlGjRiEnJwcbN27ExIkTm3WdXC6Hh4eHwcMS6Q+RcHd2wOge/hJWQ0RERJbG38MZw7vqPlHfdibPKrZnNtswPHLkSADArl27GpyrP1b/nFupD8LZ2dn4/vvvcd999xmvUAuQq6rCwSu67ZcnRgbC2ZHbLxMREVHL6G/PXF6jxq7z+RJWYxxmG4bHjBmDsLAwrF+/HklJSeLxsrIyLFmyBA4ODpgxY4Z4XKlUIjk5GUql0uA++kF4w4YNuP/++9voOzAfvyTlQNDffjmaQySIiIio5cZHtIerk65D7Scr2J7ZbBfZdXBwwKpVqzB+/HiMGDECjz76KDw8PPDjjz8iNTUVS5cuRXh4uPj8uLg4LF68GAsXLsSiRYvE46NGjUJ6ejqGDBmC06dP4/Tp0w2+lv7zrY0gCAb7iAd7u2BAiJeEFREREZGlcnW6vj3zj4nXh1/uT1GisKwGfnqbclgasw3DABAbG4uEhAQsXLgQGzduRG1tLSIiIrBkyRJMmzatWfdIT08HABw+fBiHDx9u9DnWHIbP55biUr5uYez7+3L7ZSIiImq9B6KDxDCs+Wt75qdjOktcVevJBEGwjkXi2kj9KhYqlcoiJtO9s+0Cvth/VWzvfXUkwrjrHBEREbWSRitg2PI9yC+9vpFXVJACv7wUI3FVhlqS18x2zDDdPo1WwC9JetsvB3syCBMREdFtsbeT4b6+htszXy203O2ZGYat2JGr18R3bQAwuS93nCMiIqLbd98NmeLnpByJKrl9DMNW7Ge9XmF7OxkmRjIMExER0e3rFeiBbv66T5t/ScqGpY68ZRi2UtV1Gmw/o9t+eXhXX4ue6UlERETmQyaTYXK0bqhE+rVKnMwska6g28AwbKX2JhegrEYttu+PZq8wERERGc+NQyV+0dvt1pIwDFupn/X+Qro42mNcr/YSVkNERETWJsjLFQNDdXsX/HY6F3UarYQVtQ7DsBUqqaxF/MVCsT22VwDc5Ga9pDQRERFZIP2hEtcqapFwWdnEs80Tw7AV2nYmD7V678zu1/uLSkRERGQs9/QJhKO9bjOvny1wqATDsBXSX0XC280JMd18JayGiIiIrJWnqxNGhvuL7V3n8lGhN2fJEjAMW5nskiocTS0S2xMjA+Foz5eZiIiITEP/E+iqOg1+P58vYTUtx5RkZfR3nANgsEMMERERkbGN6emPdnpzk36ysKESDMNW5peTuh1gOnm7ol8nT+mKISIiIqvn7GiPu3vrVq1KuKyEsrymiSvMC8OwFbmQW4qL+WVie3LfDpDJZE1cQURERHT79FeV0GgF/HbKcrZnZhi2IjfO4LyPq0gQERFRGxgS5gN/vZ1uf0piGKY2ptUK2KL3LiwySIEufu2auIKIiIjIOOztZAY70p3KLEGaskLCipqPYdhKHEsrQq6qWmzfG8Xtl4mIiKjt3Dhpf4uFDJVgGLYS+n/hZDJgEsMwERERtaGIDh4I83MT278kZUMQBAkrah6GYStQp9Fi25lcsT00zAcBHs4SVkRERES2RiaT4b4oXe/wlcIKnM8tlbCi5mEYtgIJKUoUV9aJbQ6RICIiIinc29cwg1jCUAmGYSugv9GGo70Md/cOlLAaIiIislWdfd0QGaQQ278m5UCrNe+hEgzDFq6qVoNdetsejgz3h8LVUcKKiIiIyJbpf0Kdo6rGiYxiCau5NYZhC7f7Qj4qazVi+76+HCJBRERE0pkY2QH6e35tMfM1hxmGLZz+WBxXJ3vc2TNAwmqIiIjI1rVXOGNwZ2+xvfVMLuo0WgkrahrDsAVTVdZh38VCsT2uVwBcnOwlrIiIiIgIuFdvVYmiilocvKyUsJqmMQxbsB3nclGr907rxhmcRERERFK4u3d7ONrrxkqY81AJhmELpj9EwtPVETFd/SSshoiIiOg6Lzcn3NFNl0t2nstDdZ2miSukwzBsoQpKq/HnlWtie0KfQDg58OUkIiIi86D/iXVFrQZ7LhRIWM3NMT1ZqN9O50J/h0NutEFERETmZGyvALg46uYybTmV3cSzpcMwbKF+0Rsi0d7DGYNCvZt4NhEREVHbcnVywNheulWu/kguhKqqrokrpMEwbIHSr1XgVGaJ2J4UFQg7O9nNLyAiIiKSgP4n17UaLXaey5OwmsYxDFugX2/Y5/u+vh1v8kwiIiIi6dwR7geFi25nXHNcVYJh2AL9eipX/HOYrxsiOnhIWA0RERFR45wc7DChT3ux/ecVJQrKqiWsqCGGYQtzMa8MF/PLxPbEqA6QyThEgoiIiMxT/QYcUcGemH9PL8gdzGuDMAepC6CW+e204ccLkyIDJaqEiIiI6NYGdfZG/GujEOrrJnUpjWLPsAURBAG/ndYNkejR3h3dAtwlrIiIiIioafZ2MrMNwgDDsEU5l1OKVGWF2J7EtYWJiIiIbgvDsAW5cRWJiRwiQURERHRbGIYtxI1DJCKDFAjxMd+PHIiIiIgsAcOwhUjMKEF2SZXYnhTJIRJEREREt4th2ELcuIrEPRwiQURERHTbGIYtgEYrYKveEIkBIV7o4OkiYUVERERE1oFh2AIcTS1CQVmN2OYqEkRERETGYfZh+NixY5gwYQK8vLzg5uaGQYMGYf369S26h1arRVxcHCIjI+Hi4gI/Pz9MnToVKSkpJqrauPSHSNjJgLv1tjUkIiIiotYz6zAcHx+PmJgYHDhwAFOmTMHs2bOhVCoxbdo0vPPOO82+z/PPP485c+ZAo9Fgzpw5mDBhArZs2YKBAwfi/PnzJvwObl+dRovtZ/PE9pAwH/i7O0tYEREREZH1kAmCIEhdRGPUajV69OiBrKwsHDp0CNHR0QCAsrIyDB06FBcvXsT58+fRrVu3Ju/zxx9/YPTo0RgxYgR+//13yOVyAMCePXswduxYjBgxAvv27Wt2XaWlpVAoFFCpVPDw8Gj9N9hM+y4VYvqXR8X2sgf64NFBnUz+dYmIiIgsVUvymtn2DO/duxdXrlzBY489JgZhAHB3d8eCBQugVquxZs2aW95n5cqVAIClS5eKQRgAxowZg/Hjx2P//v24dOmS8b8BI/lNb6MNBzsZ7orgEAkiIiIiYzHbMBwfHw8AGDduXINz9cea06MbHx8PNzc3DB8+vMG58ePHN/s+UqhRa7DjnG6IREw3X3i5OUlYEREREZF1cZC6gJupn9zW2DAILy8v+Pr63nICXEVFBXJzc9G7d2/Y29s3OF9/76buU1NTg5oa3UoOpaWlzarfGPZfUqKsWi22udEGERERkXGZbc+wSqUCACgUikbPe3h4iM+5nXvoP68xy5Ytg0KhEB/BwcG3rN1Y9l8qFP/sZG+HsREBbfa1iYiIiGyB2fYMm4t58+bhlVdeEdulpaVtFogX3xuBydEd8OupXNSotfBwdmyTr0tERERkK8w2DNf35t6s17Z+luDt3kP/eY2Ry+UGE+/akp2dDP1DvNE/xFuSr09ERERk7cx2mERT43mLi4uhVCpvuayam5sbAgMDkZqaCo1G0+B8U+OSiYiIiMj6mW0YHjlyJABg165dDc7VH6t/zq3uU1FRgYMHDzY4t3Pnzmbfh4iIiIisj9mG4TFjxiAsLAzr169HUlKSeLysrAxLliyBg4MDZsyYIR5XKpVITk6GUqk0uM+sWbMAAG+++SZqa2vF43v27MHOnTtxxx13IDw83KTfCxERERGZJ7MNww4ODli1ahW0Wi1GjBiBWbNm4bXXXkNUVBTOnTuHRYsWGYTYuLg49OzZE3FxcQb3iY2NxcyZM3HgwAFER0fj9ddfx/Tp03HPPffAw8MDn376aVt/a0RERERkJsw2DAPXg2xCQgJiYmKwceNGfPLJJ/Dx8cG6deswf/78Zt/n888/x4oVKyCTybBixQps3boVkyZNwtGjR9GrVy8TfgdEREREZM5kgiAIUhdhSVqy1zURERERtb2W5DWz7hkmIiIiIjIlhmEiIiIislkMw0RERERksxiGiYiIiMhmMQwTERERkc1iGCYiIiIim8UwTEREREQ2i2GYiIiIiGwWwzARERER2SyGYSIiIiKyWQzDRERERGSzHKQuwNIIggDg+p7XRERERGR+6nNafW5rCsNwC5WVlQEAgoODJa6EiIiIiJpSVlYGhULR5HNkQnMiM4m0Wi1ycnLg7u4OmUxm8q9XWlqK4OBgZGZmwsPDw+Rfj0yDr6N14OtoHfg6Wge+jtbBVK+jIAgoKytDhw4dYGfX9Khg9gy3kJ2dHYKCgtr863p4ePCH3QrwdbQOfB2tA19H68DX0TqY4nW8VY9wPU6gIyIiIiKbxTBMRERERDaLYdjMyeVyLFy4EHK5XOpS6DbwdbQOfB2tA19H68DX0TqYw+vICXREREREZLPYM0xERERENothmIiIiIhsFsMwEREREdkshmEiIiIislkMwxI4duwYJkyYAC8vL7i5uWHQoEFYv359i+6h1WoRFxeHyMhIuLi4wM/PD1OnTkVKSoqJqqYb3e7rmJCQgFdffRX9+/eHj48PnJ2d0aNHD7zxxhsoKSkxXeFkwBg/j/rq6urQt29fyGQy9OjRw4iVUlOM9TqWlZVh4cKF6N27N1xdXeHp6Yl+/fph8eLFJqiabmSM17GkpARvvfUWIiMj4e7uDl9fXwwcOBBxcXGorq42UeUEAOvWrcNzzz2HAQMGQC6XQyaTYe3atS2+T5tnHIHa1B9//CE4OTkJ7dq1E2bOnCm8+uqrQufOnQUAwttvv93s+zz77LMCAKFXr17CP/7xD+HJJ58U5HK5oFAohHPnzpnwOyBBMM7rGBAQINjb2wsjR44UXn75ZWHu3LlCdHS0AEDo0qWLkJ+fb+Lvgoz186hvwYIFgpubmwBA6N69u5ErpsYY63VMT08XunTpIshkMmHs2LHC66+/Lvz9738X7rnnHqFPnz4m/A5IEIzzOhYXFwthYWECACEmJkZ49dVXhZdeekno0qWLAEAYPXq0oNFoTPyd2K6QkBABgODr6yv+ec2aNS2+T1tnHIbhNlRXVyd06dJFkMvlQmJioni8tLRUiIiIEBwcHIRLly7d8j579+4VAAgjRowQqqurxeO7d+8WZDKZcMcdd5ikfrrOWK/j8uXLhZycHINjWq1WmD17tgBAeOGFF4xeO+kY63XUd+LECcHBwUFYsWIFw3AbMdbrqFarhYEDBwouLi7C3r17G/06ZDrGeh3//e9/CwCEuXPnGhyvqakRBg4cKAAQ9u3bZ/T66brff/9dSEtLEwRBEJYtW9aqMCxFxmEYbkM7d+4UAAhPPfVUg3MbNmwQAAjz5s275X0effTRm/5A33XXXQIA4eLFi0apmRoy1ut4Mzk5OQIAISIi4nbKpFsw9utYU1Mj9OnTR4iJiRG0Wi3DcBsx1utY/9wFCxaYoky6BWO9js8995wAQPj9998bnPvnP/8pABA2bdpklJqpaa0Nw1JkHI4ZbkPx8fEAgHHjxjU4V39s3759zbqPm5sbhg8f3uDc+PHjm30fah1jvY434+joCABwcHBo9T3o1oz9Oi5atAgpKSlYvXo1ZDKZUWqkWzPW6/j9998DAB566CFkZmbis88+w/Lly7Fp0yaUl5cbr2BqlLFex4iICADAjh07DI7X1dVh9+7dcHFxwdChQ2+zWjIlKTIOf9u2ofqB3926dWtwzsvLC76+vrccHF5RUYHc3Fz07t0b9vb2Dc7X35sT6UzHGK9jU7788ksAjf9SIOMx5ut47NgxvPvuu3jnnXcQHh5u1DqpacZ6HY8fPw7g+sTWuXPnoqamRjzn5+eHjRs3YtSoUcYpmhow1us4c+ZMfPPNN3j//fdx/PhxDBw4EDU1NdixYweKi4uxfv16dOzY0ej1k3FIlXHYM9yGVCoVAEChUDR63sPDQ3zO7dxD/3lkfMZ4HW8mKSkJixcvhr+/P15//fVW10i3ZqzXsaamBjNmzEB0dDReffVVo9ZIt2as17GgoAAAMGfOHLz88svIzMxEYWEhVqxYAZVKhcmTJyM3N9d4hZMBY72OLi4uiI+Px+OPP459+/bhvffew0cffYQrV67gscceQ0xMjFHrJuOSKuMwDBOZidTUVEycOBEajQYbNmyAr6+v1CVRMyxYsAApKSn48ssvG+3JIMug1WoBABMnTsTy5csRFBQEX19fzJkzB3PnzoVKpcLq1aslrpJuRalUYuzYsTh8+DC2bt2KkpIS5OXl4bPPPsOaNWswePBgFBcXS10mmRmG4TZU/07nZu9oSktLb/puqCX30H8eGZ8xXscbpaenIzY2FoWFhfjhhx8QGxt723VS04zxOiYmJuKDDz7A/Pnz0adPH6PXSLdmrJ/H+ufce++9Dc5NmjQJgG4oBRmfsV7HV155BX/++Sc2b96MCRMmQKFQICAgAM8++yzeffddXL16FR9++KExSycjkirjMAy3oabGuhQXF0OpVDY6Xkqfm5sbAgMDkZqaCo1G0+B8U+OuyDiM8TrqS0tLw6hRo5CTk4ONGzdi4sSJRquVbs4Yr+Pp06eh0WiwaNEiyGQygwcAXLx4ETKZDJ6enkavn64z1s9j9+7dAaDR16r+WFVVVesLpSYZ63XcunUrvL29ERkZ2eDc6NGjAQAnTpy4zWrJVKTKOAzDbWjkyJEAgF27djU4V3+s/jm3uk9FRQUOHjzY4NzOnTubfR9qHWO9joAuCGdnZ+P777/HfffdZ7xCqUnGeB3Dw8PxzDPPNPoArvdePPPMM3jyySeNXD3VM9bPY31QOn/+fINz9cdCQ0NbWybdgrFex9raWpSWlqK2trbBucLCQgCAXC6/nVLJxCTJOEZdqI2aVFdXJ4SFhQlyuVw4efKkeFx/UXH9tfMKCwuFCxcuCIWFhQb30V+QuqamRjzOTTfahrFex9TUVCEkJERwcHAQNm/e3Fbl01+M9TreDLjOcJsw1ut49epVQS6XC/7+/kJWVpbBffr27SsAEHbv3m3y78dWGet1HD9+vABAePPNNw2OV1dXi+c++ugjk34vdN2t1hk2p4zDMNzG9u7dKzg6Ogrt2rUTnn32WYPtJpcuXWrw3IULFwoAhIULFza4z8yZM7kds4SM8TrWb1U5ZMgQYeHChY0+yLSM9fPYGIbhtmOs17F+50AfHx9h5syZwosvviiEhoYKAIRZs2a10Xdju4zxOp48eVJwd3cXAAiDBg0S5s6dK8yePVvcorl///5CVVVVG35XtmXlypXC9OnThenTpwv9+vUTAAjDhw8Xj/3000/ic80p4zAMS+DIkSPCXXfdJSgUCsHFxUUYMGCAsG7dugbPa+ovikajEVasWCFEREQIcrlc8PHxEaZMmcKd59rQ7b6OAG75INMzxs9jYxiG25axXsctW7YII0aMENq1ayc4OzsL/fv3F7744gsTV0/1jPE6Xrp0SXjqqaeETp06CY6OjoKLi4vQp08fYfHixUJFRUUbfBe2a/r06U3+TtN/vcwp48gEQRCMO/CCiIiIiMgycAIdEREREdkshmEiIiIislkMw0RERERksxiGiYiIiMhmMQwTERERkc1iGCYiIiIim8UwTEREREQ2i2GYiIiIiGwWwzARkY0rKirCyy+/jJCQEDg7O6N379747rvvpC6LiKhNcAc6IiIbdunSJYwePRoqlQpTp06Fh4cH1q1bB6VSiV9//RUTJ06UukQiIpNiGCYislHl5eWIjo5GeXk59u/fj27dugEAkpKS0K9fPwwZMgR//vmnxFUSEZmWg9QFEBGRNP71r3/h8uXL2LJlixiEAaBv377o1asXDh8+jNraWjg5OUlYJRGRaXHMMBGRDSopKUFcXBwiIyMxadKkBud9fHwgCAIKCwslqI6IqO0wDBMR2aBNmzahqqoK06dPb/R8dXU1ALBXmIisHodJEBHZoO3btwMALly4gEWLFjU4f/XqVTg7O8PHx6eNKyMialucQEdEZIMCAgJQUFDQ5HOioqKQlJTUNgUREUmEwySIiGxMUVERCgoKMHLkSAiC0OCxdetWAMDw4cMlrpSIyPQYhomIbEx2djYAIDAwsNHzO3bsAADcfffdbVYTEZFUGIaJiGxMXV0dAEAulzc4V1tbi40bNyIgIADjx49v69KIiNocJ9AREdmYgIAAAEB+fn6Dc1988QXy8/PxwQcfwNHRsa1LIyJqc5xAR0Rkg7p27YqcnBxcvHgRwcHBAIDDhw/jzjvvREREBA4ePAgHB/aXEJH1YxgmIrJBq1evxsyZMxEUFIRHHnkEeXl52LhxI7p06YK9e/eiffv2UpdIRNQmGIaJiGzUp59+ig8++AAZGRkICgrCo48+innz5sHNzU3q0oiI2gzDMBERERHZLK4mQUREREQ2i2GYiIiIiGwWwzARERER2SyGYSIiIiKyWQzDRERERGSzGIaJiIiIyGYxDBMRERGRzWIYJiIiIiKbxTBMRERERDaLYZiIiIiIbBbDMBERERHZLIZhIiIiIrJZDMNEREREZLP+H0RoNKhx1z5SAAAAAElFTkSuQmCC\n", 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\n", 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\n", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -538,7 +534,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "705db67137ab444eb81bbc377b28695f", + "model_id": "e2629984497c40ad8b53832d64ad04d2", "version_major": 2, "version_minor": 0 }, @@ -552,7 +548,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6dd72777e3204d32b6549543bc382451", + "model_id": "18b0d88c4687424e9c13775deca8b55f", "version_major": 2, "version_minor": 0 }, @@ -566,7 +562,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f96b7d6222f944099e2fc7b5a1757940", + "model_id": "0e311e9743004e70a2ce4f8d0caa1c0c", "version_major": 2, "version_minor": 0 }, @@ -580,7 +576,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9c6d1fa0090d4dcd91c1b3ca7f51bae7", + "model_id": "184334942970409f8a28816635b12bd3", "version_major": 2, "version_minor": 0 }, @@ -666,14 +662,12 @@ "outputs": [ { "data": { - "image/png": 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37i/idrgWiAO2AAaIBb42xrSyLOuos80E4GHgLmA78BTwlTGmhWVZOR//lwCRwPXO398A3gH6lL6Mv2xhC0c5ev6GHhZMMB3o4NE+Z86cSWxsLLGxsUyfPp27776b1q1bM3LkSCZPnswzzzzDmDFj+Pbbb13LpKen8/zzzzNnzhwCAwOJiYlh2LBhrFu3zvXd53klJyezdOlS3nrrLfz8/AgICMjX5plnnmHhwoU8++yzdOnShcOHD/Pjjz+65gcGBvLKK68QHh7O9u3bGT9+PFWrVuXxxx8vVs0vvPACTz/9NE899RTvvvsuY8eOpUuXLkRERJCWlsaAAQO48sor+fzzz/Hz8+PVV1/llltuYePGjQQGBpKamsrf/vY3Hn/8cQICAvj4448ZNmwY69evp3nz5oAjWPfs2cPy5csJDAzk8ccfZ8+ePa4xbNu2jUcffZS4uDiuvPJKjh8/zjfffHPesee8Vq+88gq+vr6cOXOGyy+/nAceeIAaNWqwdu1axo8fT0REBN26dWPatGn88ccfNGvWjCeffBKAunXrcuDAAXr37s0dd9xBbGwsZ8+e5dlnn2XkyJF8/fXXVKlS8Ofyzp07u20V5xUREZFv13iO3bt3k5KSQvfu3V3TqlWrRufOndm8eTN33XVXgct16tSJDz74gC1bttChQwf27t3LqlWrXIebMjIySExM5P7773dbrnv37mzevBmA77//nszMTLd1R0RE0Lx5czZv3kyPHj0KrelC0OuNbSVaruc/ThESBj/sP8XMlSXro6hK+o13UrGdN+Aty3L7nkJjzFDgOHAV8JlxpMaDwHTLspY529wJHAQGA/ONMZfiCParLcuKd7b5J7De+SFgu6cKOspRUkjxVHdlZs2aNflOOssJ7hwDBgygX79+ADz00EMsW7aM6667jhtvvJHU1FTGjRvHTTfdxJEjR6hTpw7g2MU+depU1/eXz5s3jzZt2rBu3TquvfbaAseSkZHBvHnzqF+/foHzT506xdy5c5k6dSp33HEHAE2aNKFjx46uNo8++qjr35GRkYwfP55XX3212AE/YMAAbr/9dsCx23b+/Pl899133H777Sxfvty1JyHnw8rs2bNp3rw5q1ev5pZbbqF169a0bv3XDqaHH36YVatWsWLFCh555BH++OMPvv76a7744gvXcxQXF0ebNn/9gdu7dy+BgYFcf/311KhRg4YNG7r1WZhRo0bRt29ft2njxo1z/fuuu+5i/fr1LFu2jG7dulGzZk2qVq1KYGAgISEhrnYLFy6kdevWbu+FuXPn0qRJE7Zt20a7du0KXP+HH354zkMDfn5+hc5LSXH8n8n7HqhXrx779+8vdLl+/fpx7NgxevfujWVZnD17lgEDBrjGfuTIEbKysgrsd926dQAcPHgQHx8f13s4R/369ct0r4WI3RVlCz6vGjh27R9z/n4xEAp8mdPAsqzTxphvgC7AfKAzcAr4Llc/G4BUZxuPBXwwwZ7qqkzX26VLF2bPnu02Le8x76iov45y5PyBbNWqlWtazrH0Q4cOuf44VqlSxS0AGjZsSGhoKNu3by804MPDwwsNd4Dt27eTnp5O165dC23z6aefMm/ePJKSkkhNTSUrK6tEx09z1+zr60udOnU4dOgQAImJiezZs4fIyEi3ZdLS0lyHIVJTU3n++edZvXo1KSkpnD17ljNnzrj6/f333wt9jnJce+21NGzYkDZt2nDdddfRvXt3evfuTY0aNc459twfEgCysrJ46aWX+Pjjj9m/fz8ZGRlkZGRw1VVXnbOfxMREvvvuuwKvOti1a1ehAV+UqyM8bcOGDbz44ou88MILtG/fnp07dxITE8O0adOYNGlSuY9HRP5SkoB/GfgeyNnXl/OXMe9mcwrQIFebQ5ZlWTkzLcuyjDEHcy3vxhhzD3APwLJly0hLS8vXJi0tjSNHjuDj4+Oa1opW+dqVl1RSi9QuMzOTqlWrum21ufpI/auPrKws1++nT592m5adne06xp2amkpqairp6emA43nx9f3rpbUsi4yMDFcby7Jc/WZmZhIQEOC23pzpOevKWffp06fztQNISEjg7rvv5sEHH+SJJ56gVq1afPnllzz77LOu9nnXW5jcNec4c+aMa+ytWrXitddey7fcRRddRGpqKjExMaxbt44nnniCiy++mICAAB566CHS0tJITU11PWcFPUeZmZmkpqZSpUoVVq5cyaZNm1i/fj2zZs0iNjaWzz//3O2DQI6c96Yxxm3scXFxxMXFMWXKFFq2bElgYCDPP/88hw8fdrXLyspyrTf3c3/dddfxxBNPuK3Hsizq169f6HPYo0eP8+6iX7NmTYHzcj687N692+1ckAMHDlCnTp1C1xkbG0ufPn1ce5oaNWrEhAkTmDBhAmPGjCEgIAAfHx/27Nnj1seff/7p6rdmzZpkZWWxZ88et634lJQU2rVr53q/FzSGS4LOMiLSMb12QDYAYQHZrmmlFeqf5frpqT6LKtM/C6uc1l2UK2vKW1paWoUcV3lIS0sjMDCw1P0UK+CNMbOAq3Hsai/T01sty3odeB0gPj7eatmyZb42CQkJ1KlTh6CgoLIcisf5+fnh4+Nz3nH7+/u72uQEU0BAAEFBQaSmplKtWjXAcaw0KCgIf39/srOz+e233+jUqRPg2N2ckpJC69atXW2MMa5+/fz8qFKlSr6x5B5jdHQ0/v7+bNmypcBd1YmJiYSFhbntjs85cS2n37zrLUrN4AjNqlWrEhQURLt27VixYgUNGzYs9Az/rVu3MnDgQPr37+963pKTk2nevDlBQUFcdtllhT5Hfn5+buvu1asXvXr14sknn6R58+asX7++wGPROf8Rc16HHAkJCdxwww0MGzYMcAT0rl27qFWrlqtdQEBAvue/bdu2fPLJJ7Ro0cJtt3pqauo5n7+PPvrovLvoC1v+0ksvJSQkhE2bNrn2MJw5c4bNmzcTGxtb6HIZGRmu92Tu58OyLIKCgvD19SU6Opr4+HgGDBjgarNhwwb69OlDUFAQV155JX5+fmzevJnbbrsNgH379rFjxw6uuuoq1/u9oDH8N9WXhcmO6YMjqtCoOuw/U4UlyZ75m9Ax3YcGwIF0H9d6ykvPy3wIca77qzJe95e9Kt4x+ISEBNq2bevtYXiFpz7YFDngjTGzgYFAd8uyduaadcD5MwRIzjU9JNe8A0A9Y4zJ2Yp3Hruvn6tNpZKenu467pnDx8eHunXrlqpfX19fHn/8caZOnUq1atV4/PHHadmyZaG754uiRo0a/POf/yQ2NpaqVavSpUsXjh49SmJiIiNGjKBp06bs37+fjz76iA4dOvDvf//7nGdel1T//v2ZM2cOQ4YMISYmhoiICPbt28cXX3zB8OHDueSSS7jkkktYuXIlN954I35+fsyYMcPtbP5mzZrRo0cPxo8fz+zZs6lWrRpPPPGE68MSwOrVq0lKSqJLly7Url2b9evXc+rUKVq0aFGs8V5yySV8/PHHbNy4keDgYBYsWMDu3bu5/PLLXW0iIyNJSEggOTmZoKAgateuzciRI1m8eDEjR45k3Lhx1K1bl127drF06VKmTZtW6KGC0uyiN8Zw7733MmvWLJo1a8Yll1zCzJkzqV69umvrHODmm2+mbdu2PPXUUwD8/e9/Jy4ujiuuuMK1i37q1Kn06tXLtYdk9OjR3HfffbRt25ZOnTrx1ltvceDAAYYPHw5AzZo1ueOOO3j66aepW7cuwcHBPPHEE0RFRZXqfStS2RUp4I0xLwMDcIT7b3lmJ+EI6Z44zrTHGBMAXAPknHkVD1THcSw+5zh8ZyAI9+Pylca6deu49NJL3aaFhYXx888/l6pff39/xo8fz+jRo9m7dy/t27dn8eLFhZ5BX1RPPfUUF110ES+++CJ//vkn9erVc11Gd/311zN27FgmTZrEmTNn6N69OzExMTzyyCOlWmdegYGBLF26lBdeeIHhw4dz4sQJQkNDufrqq7nooosAeO655xg3bhz/+Mc/qFWrFvfee6/r0EWO1157jQcffJCbb76Z4OBgJkyY4DrOD45zIb744gteeOEFTp8+TePGjXn55Zfp3Llzscb7yCOPkJyczO23305AQACDBg2if//+bN/+1ykn999/P6NHj6Zz586cPn2a77//nsjISP71r38RGxtL//79SU9PJyIigquvvhp/f/+SP4HnMW7cOE6fPs2ECRP43//+R7t27Vi6dKnbB4qkpCQaNGjg+v2RRx7BGMO0adP4888/CQ4O5vrrr3c7vHDrrbdy7NgxZs6cSUpKCpdeeikffPCB2weSqVOn4uvry8iRIzlz5gxdu3YlLi7O7fCbiBSPyXVYvOAGxrwGDAVuBn7JNeuUZVmnnG0mApOA4cDvwBNAV8B1mZwx5l9ABM7j6jh2v++yLOu8l8mdaxd906ZNqVmz5vm6sJ2CdlkuWbKEiRMnul3yZTfn201tZ6o9f+0fb93F3G2O830Hd4mkUd0gdh9OZcl3yfnalsS8eeNptzORrU2iuffeWR7ps6h6/mMnIWGppOwP4quVTcp0XRXxMjntomdrjx492pemn6JswY92/sx7ds4UYLLz388D1YDXgNrAJqBXrmvgwXHJ3Bwg564jKwD3i2NFRETEI4pyHfx59+06j6tP5q/AL6jNMeCOYoxNRERESkj3oreRwYMH23r3vIiIFJ0CXkRExIYU8CIiIjakgBcREbEhBbyIiIgNKeBFRERsSAEvIiJiQwp4Oa/o6GjmzJnj7WGIiEgxKOC9YMyYMQQHBxMcHEz9+vVp06YNTz755Hm/SrWoPB3Ia9asYeTIkR7rr7J78803ueKKKwgLC6N79+7Ex8efs33O+6Vhw4au901wcDARERGuNgcOHGDUqFF06tSJunXrMmbMmHz99OnTx235nEdx77EvIheGknwfvHhAt27dmDdvHpmZmWzcuJEHHniAtLQ0Zs6c6e2huWRkZFC1atVSf8Pd2bNn8fHxKfUX3tjB8uXLiYmJ4YUXXuDKK69k4cKF3H777cTHx7sFdm7Tpk3jqaeecvuO6BtuuMEtmDMyMqhTpw4PPPAAixYtKrCfxYsXk5GR4bbMVVddxc033+y5AkWkwtAWvJf4+/sTEhJCREQEt912G7fddhtffPEF4Pgq2ZiYGFq0aEFYWBg9e/Zk48aNrmUzMzN56qmnaNWqFaGhobRu3ZopU6YAjq20PXv28PTTT7u20HJs2rSJ3r1706BBA6Kionj44Yc5ceKEa36fPn14+OGHefLJJ2nWrBk33HADkH+PwN69exk6dCiRkZFERkYybNgw9u3b55o/ffp0unTpwpIlS2jbti2hoaEF7p349ttvCQ4O5quvvqJ79+6Eh4dz4403sm/fPjZs2MA111xDw4YNGThwIEePHnVb9r333uPKK68kLCyMDh06EBcXR3Z2tmv+a6+9xtVXX01ERARRUVGMGzeO48ePu+YvWbKEhg0bsm7dOrp06UJERAQ33XQTu3fvLt4LWUxxcXEMGjSIO++8kxYtWjBjxgxCQkJYuHBhocvUrFmTkJAQ6tevT0hICElJSezatcv1PfPg+NrZ6dOnM3jwYGrXrl1gP7Vr1yYkJMT12LhxI6dPn2bIkCEer1NEvM92W/A7TmZy6mz2+Rt6WHXfKjSr4Vfi5atVq0ZmZiYATz/9NJ9++ilz5syhUaNGxMXF0b9/f7Zs2UJoaCjz589n1apVvPHGG0RGRrJv3z7++OMPwLGVds011zBkyBBGjBjh6v+XX37htttuY+LEibz88sscO3aMSZMmMXbsWLctvo8++ohhw4axcuVKCvqmwezsbIYMGUJAQACffvopABMmTGDo0KGsWbPGtZWenJzM0qVLeeutt/Dz8yMgIKDQ2mfMmMHUqVOpWbMmo0aNYuTIkQQEBDB79mx8fHwYPnw4M2bMYMaMGQAsWrSI6dOnM336dK644gp+/fVXHnzwQfz8/Bg1ahQAVapUYerUqTRu3Jg9e/YwceJEJk6cyLx581zrTU9P56WXXmLOnDkEBAQwevRoxo8ff87vsu/fv7/bh62CFHa74IyMDBITE7n/fvfvWOrevTubN28+Z5+5vfPOO7Rs2ZJOnToVeZmCLF68mB49ehS650BELmy2C/hTZ7P5X+a5vwK3bJT8Q8XWrVtZunQpXbt2JTU1lbfeeouXX36ZXr16ATBr1izWr1/Pm2++yeOPP87evXtp0qQJnTt3xhhDRESE64997dq18fHxoXr16oSEhLjWMWfOHG6++Wa3cJk5cybdunXj0KFD1KtXD3BsCT777LOFjnXdunX8/PPPJCQkEBkZCcCCBQto164d69at49prrwUcYTZv3jzq169/3vpjYmJcu5uHDx/OxIkT+b//+z+io6MBGDhwICtWrHC1f/HFF5k8eTJ9+/YFoFGjRjzwwAO8+eabroC/7777XO0jIyOZPHkyd9xxB3FxcVSp4thxdfbsWZ5//nmaNWsGOL6bfezYsViWVejhhJdffpkzZ86ct6aCHDlyhKysrHzPSb169Vi3bl2R+jhx4gSffPIJTz75ZInGkOOPP/5gw4YNvPvuu6XqR0QqLtsFfHXfKpQmbEu33qJbs2YNDRs25OzZs2RmZnLjjTcyY8YMdu3aRWZmptvWmY+PDx06dGD79u0ADBo0iFtuuYUOHTrQvXt3evbsyd/+9jdXcBXk+++/JykpiU8++cQ1LWcLPSkpyRXwOaFamN9//53Q0FBXuAM0btyY0NBQtm/f7gr48PDwIoU7QFRUlOvfOeNo1aqVa1r9+vU5fPgw4AjJffv2MX78eB555BFXm7Nnz7rtcfjmm2+YPXs2v//+OydPniQrK4uMjAxSUlIICwsDHIdJcsIdIDQ0lIyMDP73v/8Vups7PDy8SDWVlQ8//JDs7GwGDBhQqn4WL15MaGio60OkiNiP7QK+NLvJy1OXLl2YPXs2fn5+hIaG4ufnGPfBgwfPu2x0dDTx8fFs3LiRb775htGjR9O6dWuWL19eaMhnZ2czdOhQty3bHDmBBxAUFFTCinDb6s05GawocmrP3UfeaTnH13N+zpw5k44dOxbY3549exg4cCDDhg0jJiaG4OBgEhMTGTVqlOswCICvr/vbP2fduY/l51WaXfR16tTBx8cn32t86NChIn8YWrx4MX369Cn0A0hRZGRk8P777zNs2LB8z4GI2If+d3tJtWrVaNKkSb7pjRs3pmrVqmzatImLL74YgKysLLZs2UK/fv1c7apXr07fvn3p27cvgwYNolevXuzcuZOmTZtStWrVfCEVHR3Nb7/9VuA6i6N58+YcOHCA5ORk11b8rl27OHDgAC1atChV30VRr149wsLCSEpKYuDAgQW22bZtGxkZGTz33HP4+PgAsHr1ao+svzS76KtWrUp0dDRr1651O3N97dq19OnT57zLb9u2jZ9++ompU6eWaP05Vq5cyZEjR7jjjjtK1Y+IVGwK+AomKCiI4cOHM2XKFOrUqUNkZCRz587l0KFDrmvRX3vtNS666CLat2+Pn58fS5cupUaNGq7dxw0bNiQ+Pp7+/fvj7+/vunyqV69ejB8/nrvuuovq1auzY8cOVq1axezZs4s8vmuvvZaoqCjuuecepk2bBsDEiROJjo6ma9eunn9CCjBx4kQee+wxatWqRc+ePcnMzOSHH35g//79PPTQQ1xyySVkZ2czd+5c+vTpw3/+8x+3k+tKo7S76EePHs19991H27Zt6dSpE2+99RYHDhxg+PDhrjY5e1nmzp3rtuySJUu45JJLuPrqqwvs+8cffwTg5MmTVKlShR9//BE/Pz9atmzp1m7x4sV07dqVxo0bl6oWEanYFPAV0OTJkwHHSV/Hjx/nsssu46OPPiI0NBRwbL3Pnz+fmJgYAC6//HI+/PBD127xmJgYxo8fT7t27UhPT+fo0aNERUXx+eef89xzz9G7d2+ys7Np1KgR//jHP4o1NmMM7733Ho899pjrJLdu3boxffr0crvOfdiwYQQFBTFnzhyeeeYZAgICaNmyJXfffTfgOKY/bdo0XnnlFaZOnUrHjh2JjY2tEDfrufXWWzl27BgzZ84kJSWFSy+9lA8++ICGDRu62uzduzffcidPnmTFihVMmDCh0L67devm9vuqVato2LAhiYmJrmm7du3im2++4Y033vBANSJSkZmCLoWqaOLj4628WyEACQkJNG3alJo1a3phVN6VmppaquPlF6rKWjeo9oJq/3jrLuZuOwbA4C6RNKobxO7DqSz5Ltkj6503bzztdiaytUk09947yyN9FlXPf+wkJCyVlP1BfLWydIfWzufLu9uUaf8lkZCQQNu2bb09DK9ISEgA2NqjR4/2pelHN7oRERGxIQW8iIiIDSngRUREbEgBLyIiYkMKeBG5YGVT8U8SFvEWWwT8hXAlgIh4lmVZpGZkeXsYIhXWBR/wgYGBHD9+XCEvUolYlsXx48f5bMcpbw9FpMK64G904+vrS/Xq1Tlx4kS53WilIjhy5Ah16tTx9jDKXWWtG1R77toty6J69eocz9AHe5HCXPABD46Qr1WrlreHUa7++OMP173qK5PKWjeo9spau0hJXfC76EVERCQ/BbyIiIgNKeBFRERsSAEvIiJiQwp4ERERG1LAi4iI2JACXkRExIYU8CIiIjZkixvdiEjZ6PXGNm8PAYARkWk8VkHGInKh0Ba8iIiIDSngRUREbEgBLyIiYkMKeBERERtSwIuIiNiQAl5ERMSGFPAiIiI2pIAXERGxIQW8iIiIDSngRUREbEgBLyIiYkMKeBERERtSwIuIiNiQAl5ERMSGFPAiIiI2pIAXERGxIQW8iIiIDSngRUREbEgBLyIiYkMKeBERERtSwIuIiNiQr7cHIFJZ9XpjW7Haj4hM47FiLiMilZe24EVERGyoSAFvjOlqjFlhjNlnjLGMMXflmf+2c3rux8Y8bfyNMXOMMYeNManO/iI8WIuIiIg4FXULvjrwE/AAcLqQNl8DYbkeN+aZ/xLQDxgEXAPUBD43xvgUb8giIiJyPkU6Bm9Z1hfAF+DYWi+kWbplWQcKmmGMqQWMBIZblvWVc9pQYDfwN2B18YYtIiIi5+LJY/BXG2MOGmN+N8YsMMbUzzWvHeAHfJkzwbKsPcCvQBcPjkFERETw3Fn0q4DlQBLQGHgW+Lcxpp1lWelAKJAFHM6zXIpzXj7GmHuAewCWLVtGWlpavjZpaWkkJCR4qIQLS2Wt3U51j4jM/54+l7pVsxkRmVpGo6nYilJ77YBsAMICPPc8hfpnuX6W93Of6Z+FVU7rroj/p+z0f7240tLSCAwMLHU/Hgl4y7Lez/Xrj8aYrTh2v/8DR/CXpM/XgdcB4uPjrZYtW+Zrk5CQQNu2bUvS/QWvstZup7qLe8nbiMhUFiYHldFoKrai1D44ogqNqsP+M1VY4qHnqWO6Dw2AA+k+5f7c97zMhxDnur8q43V/2atNmfZfEnb6v15cnvpgUyaXyVmW9SewF2jmnHQA8AHq5mka4pwnIiIiHlQmAW+MqQs0APY7J20FMoGeudpEAJcC35XFGERERCqzIu2iN8ZUB5o6f60CRBpjrgCOOh+TgWU4Ar0xMA04CHwMYFnWcWPMm8DzxpiDwBFgFvADjsvrRERExIOKugXfHtjmfFQDpjj/HYvj5LnLgE+B34FFwHags2VZJ3P18SCOwP8A2ACcAvpYlpVV6ipERETETVGvg18LmHM0+XsR+kgHxjofIiIiUoZ0L3oREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNlSkgDfGdDXGrDDG7DPGWMaYu/LMN8aYycaYP40xp40xa40xUXna1DbGvGOMOe58vGOMuchzpYiIiEiOom7BVwd+Ah4AThcwfwLwMDAW6AAcBL4yxtTI1WYJ0Ba43vloC7xTsmGLiIjIufgWpZFlWV8AXwAYY97OPc8YY4AHgemWZS1zTrsTR8gPBuYbYy7FEepXW5YV72zzT2C9MaaFZVnbPVKNiIiIAJ45Bn8xEAp8mTPBsqzTwDdAF+ekzsAp4Ltcy20AUnO1EREREQ8p0hb8eYQ6f6bkmZ4CNMjV5pBlWVbOTMuyLGPMwVzLuzHG3APcA7Bs2TLS0tLytUlLSyMhIaF0o79AVdba7VT3iMj87+lzqVs1mxGRqWU0moqtKLXXDsgGICzAc89TqH+W62d5P/eZ/llY5bTu97/8tkz7L4m6VbNd42peN9DLoylfaWlpBAaWvmZPBHyZsCzrdeB1gPj4eKtly5b52iQkJNC2bdvyHlqFUFlrt1Pdj72xrVjtR0SmsjA5qIxGU7EVpfbBEVVoVB32n6nCEg89Tx3TfWgAHEj3KffnvudlPoQ41/1VJXzdc7/mX/Zq4+XRlC9PbcR4Yhf9AefPkDzTQ3LNOwDUcx6vB1zH7uvnaiMiIiIe4omAT8IR0j1zJhhjAoBr+OuYezyOM/E751quMxCE+3F5ERER8YAi7aI3xlQHmjp/rQJEGmOuAI5alpVsjHkJmGSM+Q34HXgCx0l1SwAsy/rVGLMKxxn19zj7mQ98rjPoRUREPK+oW/DtgW3ORzVgivPfsc75zwOzgdeA/wBhQC/Lsk7m6mMwkAisdj4SgaGlHL+IiIgUoKjXwa8FzDnmW8Bk56OwNseAO4o1OhERESkR3YteRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGzI19sDECkrvd7Y5u0hiIh4jbbgRUREbEgBLyIiYkMKeBERERtSwIuIiNiQAl5ERMSGFPAiIiI2pIAXERGxIQW8iIiIDSngRUREbEgBLyIiYkMKeBERERtSwIuIiNiQAl5ERMSGFPAiIiI2pIAXERGxIQW8iIiIDSngRUREbEgBLyIiYkMKeBERERtSwIuIiNiQAl5ERMSGFPAiIiI2pIAXERGxIY8EvDFmsjHGyvM4kGu+cbb50xhz2hiz1hgT5Yl1i4iISH6e3ILfDoTlelyWa94E4GFgLNABOAh8ZYyp4cH1i4iIiJOvB/s6a1nWgbwTjTEGeBCYblnWMue0O3GE/GBgvgfHICIiInh2C76Jcxd8kjHmfWNME+f0i4FQ4MuchpZlnQa+Abp4cP0iIiLi5Kkt+E3AXcBvQH3gCeA753H2UGeblDzLpAANCuvQGHMPcA/AsmXLSEtLy9cmLS2NhISE0o79glRZay9O3SMi879nLmR1q2YzIjLV28PwiqLUXjsgG4CwAM89T6H+Wa6f5f3cZ/pnYXlp3RVB7te8sv2tS0tLIzAwsNT9eCTgLcv6V+7fjTEbgZ3AncDGEvb5OvA6QHx8vNWyZct8bRISEmjbtm1Jur/gVdbai1P3Y29sK+PRlK8RkaksTA7y9jC8oii1D46oQqPqsP9MFZZ46HnqmO5DA+BAuk+5P/c9L/MhxLnuryrh6577Nf+yVxsvj6Z8eeoDTZlcJmdZ1ingZ6AZkHNcPiRPs5Bc80RERMSDyiTgjTEBQEtgP5CEI8h75pl/DfBdWaxfRESksvPILnpjzIvAZ0AyjmPwTwJBwCLLsixjzEvAJGPMb8DvOI7RnwKWeGL9IiIi4s5TJ9lFAP8PqAscwnHc/UrLsnY75z8PVANeA2rjOCmvl2VZJz20fhEREcnFUyfZDTzPfAuY7HyIiIhIGdO96EVERGxIAS8iImJDCngREREbUsCLiIjYkCe/bEYqkV5eukvciMg0292hTkSkLGgLXkRExIYU8CIiIjakgBcREbEhBbyIiIgNKeBFRERsSAEvIiJiQwp4ERERG1LAi4iI2JACXkRExIZ0JzsREanQvHXnzOL48u423h5CPtqCFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREbUsCLiIjYkAJeRETEhhTwIiIiNqSAFxERsSEFvIiIiA0p4EVERGxIAS8iImJDCngREREb8vX2AKRgvd7Yds75IyLTeOw8bUREpPLSFryIiIgNKeBFRERsSAEvIiJiQwp4ERERG1LAi4iI2JACXkRExIYU8CIiIjZU7gFvjBltjEkyxpwxxmw1xlxT3mMQERGxu3K90Y0xZgDwMjAa+Nb581/GmFaWZSWX1zjOdxMZERGRC115b8GPB962LGuBZVm/WpY1FtgP3FfO4xAREbG1cgt4Y0xVoB3wZZ5ZXwJdymscIiIilYGxLKt8VmRMOLAP6GZZ1je5pj8FDLEsq0We9vcA9wCsXLmyhb+///a8fR49erRucHDw4bIdecVUWWuvrHWDaq+MtVfWukG1BwcHB/Xo0aNeafqpsF82Y1nW68Dr52pjjPmPZVnty2lIFUplrb2y1g2qvTLWXlnrBtVuWVbj0vZTnsfgDwNZQEie6SHAgXIch4iIiO2VW8BblpUBbAV65pnVE/iuvMYhIiJSGZT3LvpZwDvGmM3ABuBeIByYV8L+zrkL3+Yqa+2VtW5Q7ZVRZa0bVHupldtJdq4VGjMamACEAT8BD+U+6U5ERERKr9wDXkRERMqe7kUvIiJiQxU64Itz33pjTDdjzHfGmCPGmNPGmN+MMY+U53g9qaT37DfGXG2MOWuM+amsx1gWivmaX2uMsQp4tCzPMXtKcV9zY0xVY0ysc5l0Y0yyMWZceY3Xk4r5ur9dyOueWp5j9oQSvOaDjTHfG2PSjDEHjDHvGmNCy2u8nlSC2scYY351/n3fbowZVl5j9RRjTFdjzApjzD7ne/auIixzmTFmnbPufcaYp4wxpkgrtCyrQj6AAUAmMAq4FJgDnAIiC2nfDhgIRAEXA3cAqcBob9dS1rXnWq42sBNYDfzk7TrK4TW/FrCAVkBoroePt2spj9ccWA5sxnElSmOgE3Ctt2sph9e9Vp7XOxT4L/CWt2sp47qvwnGp8UPOv3FXAgnAGm/XUg613+ecPwho4vxbfxLo4+1ailn3jcBU4DYgDbjrPO1r4riM/EOgtXO5k8DDRVqftws+R2GbgAV5pu0AphWjj+XA//N2LeVVu7Pep4HJF2jAF6vuXAFf19tj90LtvYDjlbH2Apa/yvk+6OLtWsr4NX8E2J1n2nDglLdrKYfavwNm55k2E/jW27WU4jk4VYSAvw84AVTLNe0JHHeFNedbR4XcRe+J+9YbY9o4267z7OjKVklrd16dEAI8W3ajKzulfM3/Y4zZb4xZY4zpXiYDLEMlrP1mYAsw3hiz1xizwxjzijGmetmN1PM89B0Vo4CfLcu6YO6nUcK6NwBhxpg+xqEuji3ZL8pupJ5Xwtr9gTN5pp0GOhpj/Dw7wgqlM7DesqzTuaatxnF5eePzLVwhAx6oC/gAKXmmp+DYHVco5x+7dOA/QJxlWSW9xt5bil27MeYyHFvud1iWlVW2wyszJXnNc76JsB9wK7AdWFPU8xUqkJLU3gS4GojGUf/9wPXA22UzxDJT4v/rAMaYWsDtwALPD61MFbtuy7LicQT6e0AGcAgwwJ1lN8wyUZLXfDUwwhjTwfnhpj1wN+Dn7M+uQin4ecqZd04V9l70pXANUB3H8akZxpgky7Le8fKYyowxxh/4AHjEsqwkb4+nPFmWtR1HqOeIN8Y0Bh4F1ntlUOWnCo7d0oMtyzoOYIy5H1htjAmxLCvvHwW7ugPHc2Hb/+M5jDGtcByrfgZH4IUBLwDzgQvuhLNiegZHoH2H40NNCrAIxz1Vsr04rgqtom7Bl/i+9ZZlJVmW9aNlWQtw3DlvcpmMsOwUt/YwHCepvOU8e/4s8BQQ5fy9V5mO1nM89V0Fm4BmnhpUOSlJ7fuBfTnh7vSr82ekZ4dXpkr7uo8CllmWddTTAytjJak7BthsWdYLlmX9YFnWamA0MNQYE1F2Q/W4YtduWdZpy7JGAIE4dk1HArtwnHB2qKwGWgEcoODnKWfeOVXIgLc8d9/6KjiO3VwwSlD7PuAy4Ipcj3nAH85/XxDHJT34ml+BI/wuGCWsfQMQnueYe3Pnz92eHWHZKc3rbozpiOMQxYW2e76kdQfiCMbccn6vkH/LC1Ka19yyrEzLsvY6D0UOBD63LMvOW/DxwDXGmIBc03oCf+L4gHNu3j6T8BxnDw7AcZzpbhxbqC/jOOuwkXP+YmBxrvZjgd44tt6aASNxnH043du1lHXtBSw/mQvzLPrivuYP4jjZrBmOyyOn4dhtfau3aymH2qsDe4CPnLVfhePWzx95u5ayrj3Xcm8Av3t7/OX4mt+F49Ky+3Ccg3EVjhMtt3q7lnKovTkw1Pl/vSPwPnAEaOztWopZd3X+2hBLw7G39Qqclwc6/4atydW+Fo4t9fdxXCZ3K45cu7Avk3MWNxrHp5R0HJ/4uuaatxZYm+v3B4GfcVz7fhzH9aGjgSrerqOsay9g2clcgAFfgtd8Ao5La04DR3Ecd7/R2zWU12sOtMBx5nEajj05rwE1vF1HOdVewxkIE7w99nKue6zz71wajj1V7wER3q6jrGvH8SFgm7Pu48AnQAtv11CCmq/FsRGS9/G2c/7bwK48y1wGfIPjKoL9OE6oPu8lcpZl6V70IiIidnTBHLcRERGRolPAi4iI2JACXkRExIYU8CIiIjakgBcREbEhBbyIiIgNKeBFRERsSAEvIiJiQwp4ERERG/r/XJhkBPmhyToAAAAASUVORK5CYII=\n", + "image/png": 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AAJgagRe5Ur9+/WSxWHTs2LEsvc5isahFixZuqSk9gYGBCgwMzFTbmTNnymKxaObMmW6tyRWOHTsmi8Wifv365XQpAADcFoEXkv4XYG71qF27dk6XCWRJdv8wupekpaVp6tSpqlmzpvLnz69ixYqpS5cuiomJyfK2du7cqc6dO6tChQrKnz+/ypcvryeffFIbN2502b63b9+uxx9/XIULF5a3t7eCgoI0d+7cLNcKAK7kmdMF4N5SsWJF9erVK911JUuWvMvVZGzs2LF64403VLp06Sy97rffflOBAgXcVBXgegMHDtT06dP14IMPatCgQfrzzz+1YMECrV69Wlu2bNGDDz6Yqe0sWbJEzzzzjKxWq5566imVLVtWJ06c0LfffqulS5cqMjLSacQ+q/uOiopSSEiIvLy81K1bN/n5+Wnx4sXq2bOnjh07pjfffNNV3QIAWULghYNKlSopPDw8p8u4rVKlSqlUqVJZfl21atXcUA3gHuvXr9f06dPVrFkzrVmzRlarVZLUp08ftWnTRi+++KI2bNiQqW29+eabMgxDW7Zscfi2ZufOnapfv77GjBnjEHizuu/U1FSFhobKYrFo48aNqlOnjiQpLCxMDz/8sMLCwtS5c2dVrlz5DnsFALKOKQ3INtt82NjYWPXo0UP+/v4qWLCg2rVrpyNHjkiSDh48qKeeekpFihRRwYIF1blzZ509e9ZhOzfOB923b58ee+wx+fn5ydfXVx06dNCvv/7qtO/0vqqOioqSxWJReHi4tm7dqpCQEBUqVEgWi8Wp5ptdvXpVkydPVlBQkAoWLCgfHx89+OCDGjp0qOLi4uzt1q9fr2effVZVq1aVj4+PfHx8VL9+ff3nP/+5w9509OOPP6pp06by9vZW0aJF1bdvX124cCHdtnv37lW3bt1UqlQpeXl5qXz58ho0aFC67b/44gs9+eSTCgwMVL58+VSkSBGFhIRo/fr16W772rVrGj9+vCpVqqR8+fKpUqVKGjt2rNLS0tJtHxMTo/79+6tChQrKly+f/P39VbduXf3zn//M1HHbfq5HjhzRxIkTVb16dVmtVnsQO3XqlMLCwtSoUSMVL15cVqtVgYGBeumll5zeV4GBgZo1a5YkqUKFCvapOTf//I8eParQ0FCVK1dOVqtVpUqVUr9+/XT8+PFM1exO06dPlyS9++679sApSa1bt1ZISIg2btyo33//PVPbOnLkiAICApymJtWtW1elSpVy6r+s7nvdunU6fPiwevToYQ+7klSwYEG99dZbSk1NVWRkZOYOHABcjBFe3JG4uDg1bdpUJUuWVN++ffX7779r2bJlOnDggJYuXapmzZqpbt26evbZZ/Xzzz9r4cKFunjxotasWeO0rSNHjqhJkyYKCgrSSy+9pJiYGH377bfavHmztmzZon/84x+ZqmnLli16//331bJlS73wwgv6448/btk+KSnJ/gu8cuXK6t+/v6xWq2JiYjRt2jT16dNHhQsXliSNHz9ehw4dUqNGjfTUU0/p4sWLWrlypQYMGKCDBw/qo48+ynon3uT777/XsmXL1KFDB7344ovauHGjvvzySx0+fFibN292aLt06VJ16dJFHh4eeuKJJ1S2bFn9+uuvmjp1qlatWqX//ve/9tol6eWXX1atWrX0yCOPqFixYoqNjdWSJUv0yCOPaPHixXryyScdtv/CCy/oiy++UIUKFfTyyy8rKSlJEyZM0JYtW5zqPnXqlIKCgpSYmKh27dqpa9euunz5smJiYjRlypQs9c2gQYO0bds2tWvXTu3bt1eJEiUkSRs3btRHH32k1q1bq2HDhsqbN6927dqlTz/9VKtWrdLOnTvl5+cnSRo8eLBmzpypPXv26LXXXlOhQoUkyeEkwv/+978KCQlRYmKiOnTooEqVKunYsWOaM2eOVqxYoa1bt+qBBx7IdN2uFhUVJW9vbzVp0sRpXUhIiFauXKkNGzaoSpUqt91W9erVtXv3bu3du1c1a9a0L9+9e7dOnz6t9u3b39G+o6KiJEmPPvqoU3vbssyORgOAq2Up8MbGxuqbb77RDz/8oAMHDujMmTMqUqSImjRpouHDh6thw4ZOr0lISFB4eLgWLVqkM2fOqGTJknrmmWcUHh4uX1/fdPczd+5cTZo0Sfv375eXl5cefvhhjRkzRvXr18/eUd6BZCNZ56+dv+v7zQ5/D39ZLdbbN7yFQ4cOZTiloVGjRmrbtq3Dsr1792rIkCGaMGGCfdmLL76oadOmqWnTpgoPD9drr70mSTIMQ+3bt9cPP/ygXbt2OYwCSdKmTZs0atQovfPOO/ZlX375pfr27atXXnlFP/74Y6aOYc2aNfr888/17LPPZqr922+/rY0bN6p3796KjIyUh4eHfV18fLzD808//VQVKlRweH1qaqoef/xxTZ48Wa+99prKlSuXqf1mZOnSpYqKirIHjWvXrumRRx5RVFSUtm3bpkaNGkmSLly4oN69e6tYsWKKjo522O+8efPUo0cPvf3225oyZYp9+a+//upU/+nTp1W/fn0NGzbMIfBGRUXpiy++UK1atRQdHS1vb29J178aT+8ExkWLFunixYuaPHmyXn31VYd1589n7d/Q3r17tWvXLqe+bNWqlc6cOSMfHx+H5bb3ydSpUzVy5EhJ1wPv7t27tWfPHg0ePNjpahkpKSnq1q2b0tLStGPHDtWqVcu+bvPmzWrRooVee+01ff/997etd8mSJdq9e3emj69Fixa3vVpIYmKiTp8+rYceesjhPWhjmxqQ2ZPXJkyYoPbt26tx48Z66qmnVKZMGZ08eVKLFy9Ws2bN9Nlnn93Rvm3/n96UhcKFC8vf3z9bJ9oBgCtkKfBOmTJF48ePV8WKFdWmTRsVL15cMTExWrJkiZYsWaJ58+apS5cu9vaJiYkKDg7W7t271aZNG3Xv3l179uzRxIkTtX79em3evNn+S9Tm/fff18iRI1WuXDkNHDhQly9f1vz589WkSROtWrXqrl5SSpLOXzuvhZcW3tV9Zlengp1U2jNrJ3Hd7PDhwxo9enS661577TWnwOvj4+MQUCWpR48emjZtmooWLeoQfCwWi7p166YffvhBe/bscQq8hQsX1htvvOGwrHfv3vrggw+0bt06nThxQmXLlr3tMdSpUyfTYffatWv67LPP5Ofnp8mTJzv9creNFtrcHBYlydPTUwMHDtSaNWu0fv169e3bN1P7zkiPHj0cRtU8PDzUt29fRUVFafv27fbA++WXXyohIUH//ve/nYJh9+7d9eGHH2r+/PkOgTe9+kuVKqVnnnlGU6ZM0fHjx1W+fHn79qXrfxDc+O+0dOnSeu211/TWW2+lW3/+/Pmdlvn7+2f28CVJw4YNS/cPh+LFi6fbvnfv3ho0aJDWrl1rD7y3s2zZMh07dkzvvPOOQ9iVpKZNm+rJJ5/UkiVLlJCQkOEf5zZLliyxT5/IrNt9lsXHx0tyfg/a2Gqytbud4OBgbdy4UZ07d9bs2bPty8uWLav+/fs7zInPzr4z85qTJ09mqlYAcLUsBd6goCBt3LhRzZo1c1i+adMmtW7dWi+++KKefPJJ+3yviIgI7d69W8OHD9f48ePt7cPCwjRmzBhFREQ4hKuYmBiFhYWpSpUq+umnn+wfnK+++qqCgoIUGhqqAwcOyNOTmRjuYvuqMrMqV67s9EeL7RdnzZo1HebP3rguNjbWaVt16tRx2pbFYlHTpk21b98+7dmzJ1OBNygoKNP1HzhwQAkJCXrkkUccvvrPyKVLl/Thhx9qyZIlOnz4sBITEx3Wnzp1KtP7zkjdunWdlpUpU0aSdPHiRfuybdu22f976NAhp9ckJSXp/PnzOn/+vD1wHjlyRGPHjtW6desUGxur5ORkp/ptgXfPnj2S5PTvPaNl7du31xtvvKGXX35Za9asUdu2bdW0adNMfd1+s1v9DBcvXqzPPvtMO3fuVFxcnK5du+ZQf2bZ+u/AgQPpfqtx5swZpaWl6ffff7/tt0szZ86856+fvGLFCnXr1k3t27fX999/r8DAQB0/flzvv/+++vfvr7179zp8UwP3C3xjucu2FVD6irrecIGdzp9u1alY5z8+76Zj49rl6P6BG2UpOT799NPpLm/WrJlatmyp1atX65dfflH9+vVlGIZmzJghHx8fvf322w7tR4wYoSlTpujzzz9XeHi4PRRFRkYqNTVVI0eOdBglqF69uvr06aNp06Zp3bp16c4RQ85Ib+TL9gfJrdalpKQ4rcto9M42fzOzI1m29plhC5CZubzZ1atX1aJFC+3cuVN16tRR7969VbRoUXl6eurYsWOaNWuWU4DMjvRGyGz9dmO4++uvvyRJ//73v2+5vcTERPn7++vQoUMKCgpSQkKCWrZsqQ4dOsjX11d58uRRVFSUNmzY4FB/fHy88uTJk+7obHp9XKFCBW3dulWjR4/WihUr9M0330iSqlatqnfeeUedO3fOxNFnvH1J+uijj/Svf/1LxYoV06OPPqoyZcrYR5QnTZqUpf639d+cOXNu2e7mP2ruFtv7IKP3fUJCgkO7W/nrr7/Uo0cPVa5cWV999ZXy5Ll+vnK1atU0a9YsxcTEaPLkyXr55ZdVsWLFbO07M6/JTK0A4A4uGyrNmzfv9Q3+/y/mmJgYnTp1SiEhIU6jdvny5VPz5s313Xff6dChQ/Y5X7c66SEkJETTpk3Thg0b7mrg9ffwV6eCne7a/u6Ev0fWvja+19x8lrjNn3/+KSlzv9glOY0q34rtRKb0Rpxv9t1332nnzp0KDQ21n8FuM3/+/Cx/pX2nbH9Q/PLLL3rooYdu237ixImKi4vT7Nmz1bNnT4d1AwcOdDqhyM/PT2lpaTp//ryKFSvmsM72M7lZzZo1tWjRIqWkpOjnn3/WihUr9PHHH6tr164KCAhI9wSo9KT3M0xNTdU777yjgIAA7d6926EmwzAUERGRqW3b2Prv+++/dzphK6vcMYfX29tbpUqV0tGjR3Xt2jWn6Ta3mjN7s+joaF28eFHBwcH2sGtjsVjUsmVLbdu2Tbt27VLFihWzte8b5/XWq1fPoX1cXJzOnz+vxo0b37ZWAHAHlwTeP/74Q2vXrlXJkiVVo0YNSbf/ML7xw/HG//fx8Un3BgeZPUEjOTnZYZTHNhKRXVaL9Y7nxSJzdu3apcTERKc/kKKjoyXJaZ6lK1StWlW+vr7avn274uLibjmt4fDhw5KkJ554wmndpk2bXF7b7TRs2FCLFy/W1q1bMxV4M6o/LS3N3sc3qlWrlnbu3KlNmzY5fbtzu+PNmzevGjVqpEaNGqlSpUrq06ePli1blunAm57z588rPj5erVu3dgrgO3bs0JUrV5xeYwtqN46M29hOst26datLAq+r5/BK1+fdzp8/X9HR0WrevLnDulWrVtnb3M7Vq1clSefOnUt3vW35jZcfy+q+g4ODNXbsWK1evVrdunVzaL969epM1woA7nDH1+FNSUlR7969lZycrIiICPsvmOye9HCnJ2iMHTtWfn5+9kdm5nzi3hAXF6dx48Y5LPvyyy/1yy+/qFWrVm75WXp6emrAgAGKj4/Xa6+95hSM4uPjdfnyZUmyz229+dJgGzZscBrxvRv69++vggULauTIkdq/f7/T+r///ts+T1XKuP7x48dr3759Tq/v06ePJGnMmDEOX+vHxsZq8uTJTu23b9+e7ii9bTQ4vZPZsqJ48eLKnz+/du7cqb///tu+PC4uToMGDUr3NUWKFJGkdE+WevLJJ1WuXDlNmDAh3VvrpqSkOPVVRmbOnCnDMDL9yOzNXV544QVJ0qhRo+yhVbp+neZVq1apefPmTnOkDx8+rAMHDjhMG2rUqJE8PDy0cOFC7d2716H9/v37NW/ePFmtVj388MPZ3nfr1q31wAMPaO7cuQ6j3ZcuXdI777wjT09Ppzu5AcDdckcjvGlpaXr22We1ceNGPf/88+rdu7er6sq2ESNGaOjQofbnCQkJhN4suNVlySS59S5szZo108cff6xt27apQYMG+v333/Xtt9/Kz89PU6dOddt+x4wZo23btumrr77Stm3b9Nhjj8lqterIkSNauXKlNm/erNq1a6tDhw4KDAxURESE9u3bp4ceekgHDx7UsmXL1LFjRy1atMhtNaanWLFimjdvnjp37qxatWqpbdu2qlatmpKSknT8+HFt2LBBjRs3tp+EOHDgQEVGRurpp59W165dVbRoUW3btk07d+5Uu3bttHy54wk0LVq0UP/+/RUZGakaNWroqaeeUnJyshYsWKBGjRpp2bJlDu3nzJmjTz75RC1atFClSpXk6+urX3/9VT/88IP8/f0zfeWMjOTJk0cvvfSSPvroI9WqVUsdOnRQQkKCVqxYofLlyysgIMDpNa1atdKHH36oAQMGqHPnzvL29la5cuXUo0cPWa1WLVy4UI899piCg4PVunVr+0j5H3/8oU2bNqlo0aI6cODAHdV9J1q2bKnQ0FDNmDFDderUUbt27ey39/X19dWnn37q9JrWrVvr+PHjOnr0qP1SbKVLl9aIESP07rvvqkGDBurYsaMCAwP1xx9/6Ntvv1VycrI++OADh/naWd23p6enZsyYoZCQEDVr1kzdu3eXr6+vFi9erKNHj+rdd9/N1gmMAOAK2Q68hmHo+eef1+zZs9WrVy9NmzbNYX12T3q40xM0rFarw9dyyJpbXZZMcm/gfeCBB/Tvf/9bw4cP19SpU2UYhh577DGNHz8+0zedyI58+fJpzZo1mjp1qmbPnq3p06fLw8PDfmk8W2jw8fHRunXrNGzYMG3cuFFRUVGqXr265syZoxIlStz1wCtJ7dq1065du/TBBx9o7dq1WrNmjby9vVWmTBn1799fvXr977TtOnXqaPXq1Ro1apQWL14sDw8PNW7cWNHR0Vq6dKlT4JWu322rSpUqmj59uqZOnaoyZcpo6NCh6tKli1Pg7d69u5KSkhQdHa3t27crOTlZZcqU0csvv6x//etf9itN3ImxY8eqSJEimjlzpj755BOVKFFC3bp10+jRo9Od1vHYY48pIiJC06dP1/jx45WSkqLg4GD16NFDktSgQQPt2bNHH3zwgX744Qdt3rxZVqtVpUuXVseOHdW9e/c7rvlOffbZZ6pZs6Y+++wzffzxx/Lx8VGHDh303nvvZSlAvvPOO/btrFmzxn4SWXBwsAYNGpTutI6s7rtly5bavHmzwsLC9PXXX+vq1auqXr263nnnHad54wBwN1kMwzCy+qK0tDSFhoYqMjJS3bt311dffeV0UsPvv/+uqlWrZniZq44dO+q7777T77//bp+f27hxY23dulWnT592mse7ZMkSPfXUU3rzzTf13nvvZbpW24d6fHz8La+lmZSUpKNHj9pviYq759ixY6pQoYL69u17z1/aCYB72T6LQxcfV+yl/00xqlDcW4PaVbU/n7L8oI6edd8VNOqf3K+Fc163P+/Uc7x2lKnutv3dqeuXJfvfybcLZpfmsmTIVTKb17Iry3N4bwy7Xbt2TTfsStdPMgsICFB0dLTTZX2SkpK0ceNGBQQEqFKlSvblthMabCc43CgrJ2gAAAAANlkKvGlpaXruuecUGRlpv1tPemFXun6pm9DQUF2+fFljxoxxWDd27FjFxcUpNDTU4fJD/fv3l6enp9577z2HqQ379+/Xl19+qYoVK6pVq1ZZKRkAAAD3uSzN4R0zZoxmzpwpHx8fValSRe+++65Tm44dO6p27dqSpOHDh2vp0qWKiIjQrl27VK9ePe3Zs0crVqxQ7dq1NXz4cIfXVqlSReHh4Ro1apRq1qypTp06KTExUfPmzVNKSoqmT5/OXdYAAACQJVlKj8eOHZMkXb58OcN5tIGBgfbA6+3traioKI0ePVoLFy5UVFSUSpYsqSFDhigsLMzpequSNHLkSAUGBmrSpEn69NNP5eXlpcaNG2vMmDFq0KBB1o4OuUJgYKCyMZUcAAAgU7IUeLNzv3g/Pz9NmDAhS/do79mzJ2f0AgAAwCXu+MYTAAAAwL2MwAsAAABTI/ACAADA1Ai8AAAAMDUCLwAAAEyNwAsAAABTI/ACAADA1Ai8AAAAMDUCL+5bM2fOlMViyfLNVAAAQO5C4IWk67eNtlgsDg8vLy+VLVtWPXr00N69e91eQ1RUlCwWi8LDw92+L5jTqlWr1KJFC/n6+qpgwYJq0aKFVq1alaVtBAYGOv1buPmxadMmh9dMmTJF/fv3V82aNeXp6SmLxaKoqKh0t5/ev7WbHx4eHtntAgBAOrJ0a2GYX8WKFdWrVy9J0uXLl7Vt2zbNmzdPixcv1rp169S4ceMcrtB1nnrqKTVq1EilSpXK6VLgAnPmzFGvXr3k7++vvn37ymKx6Ouvv1bbtm01e/bsTN+ufPDgwbp48aLT8vPnz+vf//63ChcurAYNGjise/XVVyVJpUqVUrFixXTmzJkMt1+oUCGFhYWlu27Hjh1avny5QkJCMlUrACBzCLxwUKlSJacR1lGjRum9997TyJEjtX79+pwpzA38/Pzk5+eX02XABeLi4vTKK6/I399fO3fuVNmyZSVJI0aMUN26dfXKK6/o8ccfV+HChW+7rcGDB6e7/KOPPpIk9erVS/ny5XNYt2zZMtWrV08lS5bUwIED9dlnn2W4/UKFCmX4LUaHDh0kSaGhobetEwCQeUxpwG0NGjRIkrR9+3b7stTUVE2cOFG1atVS/vz55efnp5YtW2r58uVOr09LS9OMGTMUFBSkIkWKqECBAgoMDFTHjh21ceNGSVJ4eLhatmwpSRo9erTD17vHjh2zb+vq1auaMGGC6tatK29vbxUsWFDNmjXT0qVLnfbbr18/WSwWHTlyRBMnTlT16tVltVrVr18/Sbeew7tlyxa1a9dORYoUUb58+VStWjWFh4fr77//dmprsVjUokULxcbGql+/fipZsqTy5MmT4VfaNi1atJDFYlFycrLefPNNlStXTvnz51e9evW0du1aSdKlS5f06quvqnTp0sqXL58efvhh7dixI93tnT17VkOGDFGlSpVktVrl7++vZ555Rvv27XNqu379ej377LOqWrWqfHx85OPjo/r16+s///lPutu2HeO5c+f07LPPqnjx4sqfP78aNWp02+O8G7755htdvHhRgwYNsodd6fqIq23E9ptvvrmjfXz++eeSpOeee85pXbt27VSyZMk72v6pU6e0YsUKFS9e3B58AQCuwQjvbaRcS1N8cmpOl5EpflZP5fVw/d8wFovF4blhGOratasWL16sKlWq6OWXX1ZiYqK+/vprtW/fXpMnT7Z/xStdH2WLiIhQxYoV1aNHDxUsWFCxsbHatGmT1q1bp+bNm6tFixY6duyYZs2apeDgYLVo0cL++kKFCkmSkpOT1bZtW0VFRalOnTp67rnnlJKSouXLl+vJJ5/UlClT9MorrzjVP2jQIG3btk3t2rVT+/btVaJEiVse76JFi9StWzd5eXmpa9euKl68uNauXavRo0dr9erVWr9+vaxWq8NrLly4oIcfflhFihRR165ddfXqVfn6+maqf7t27apffvlFTzzxhK5cuaI5c+aoffv22rJliwYMGKCkpCR16tRJ586d04IFCxQSEqKjR486bP/w4cP20P3oo4+qY8eOOnv2rBYtWqRVq1bpxx9/VMOGDe3tx48fr0OHDqlRo0Z66qmndPHiRa1cuVIDBgzQwYMH7aOZN7p48aKaNGkiX19f9ezZU2fPnrXX8/PPP+uhhx7K1PG6gy10P/roo07rQkJC9MYbb2jDhg164YUXsrX9LVu26LffflP9+vVVq1atOyk1QzNnztS1a9fUp08f5c2b1y37AID7FYH3NuKTU7XxxIWcLiNTmpctKv8CXi7f7scffyxJ9nmLs2fP1uLFixUcHKzVq1fLy+v6PkeOHKl69erpX//6lzp06KAKFSpIkmbMmKHSpUtr7969KlCggH27hmEoLi5OkuwBd9asWWrRokW6X/mOGTNGUVFRCg8P19tvv20P4pcuXVKrVq30z3/+U08//bQCAgIcXrd3717t2rVL5cqVu+2xXrp0SaGhofLw8NDWrVtVs2ZNe629evXS3Llz9cEHH2jUqFEOr9u3b5/69++v6dOnZ/mEo/Pnz2vv3r3y9vaWdD20devWTa1bt1abNm00d+5ceXpe/6dau3Ztvf766/r88881ZMgQ+zb69OmjM2fOaNWqVWrTpo19+ahRo1S/fn09//zzDicefvrpp/afj01qaqoef/xxTZ48Wa+99ppTf+3Zs0cvvfSSpkyZojx5rv9h1apVK4WGhmrq1KmaNm3abY919+7dWrJkSab7JjAw0D4ifysxMTGSpMqVKzutsy2ztckO2+iuu6YaGIahL774QlL6I8gAgDtD4IWDQ4cO2cOm7aS16Oho5cuXT++//74k2acARERE2MOuJJUpU0ZDhgzRiBEjNGfOHIdQ6OXlZQ9tNhaLRUWKFMlUXWlpafr0009VqVIlh7ArSQULFtTbb7+tJ554QosXL3Ya5R02bFimwq4kLVmyRBcvXtSLL75oD7u2WseNG6evv/5aM2fOdAq8Xl5eioiIyNbZ9e+995497EpSp06dlDdvXl28eFEffvihQ791795dr7/+uvbs2WNftmvXLm3ZskXPPfecQ9iVpCpVquj555/XhAkTtG/fPvso7M1hV5I8PT01cOBArVmzRuvXr1ffvn0d1nt7e2v8+PH2sCtJffv21cCBAx2mu9zK7t27NXr06Ey1laTg4OBMBd74+HhJSndOtre3tzw8POxtsury5cv6+uuvVaBAAXXv3j1b27idDRs26PDhw2ratKmqVavmln0AwP2MwAsHhw8ftgeSvHnzqkSJEurRo4feeOMN1ahRQ9L1gJU/f34FBQU5vd42Urt79277si5dumjatGl66KGH1LVrVwUHB+vhhx92CHm3c/DgQcXFxSkgICDdwHTu3DlJ0oEDB5zWpVdnRnbt2uVwHDcqW7asKlasqIMHD+rSpUsqWLCgfV2FChXk7++f6f3cqE6dOg7PPTw8VLx4cSUmJjoFddsVJWJjY+3Ltm3bJkk6c+ZMuiPjtj45cOCAPfBeunRJH374oZYsWaLDhw8rMTHR4TWnTp1y2k7lypXl4+PjsMzT01MlSpRI96oG6enXr1+mAuy9ZMGCBbp8+bL69u2b6WkqWXWr+cEAgDtH4L0NP6unmpctmtNlZIqf9c5/nCEhIVq5cuUt2yQkJDicGHQj24k7N46mffzxx3rggQc0c+ZMvfvuu3r33XeVL18+denSRR999FGmguJff/0lSdq/f7/279+fYbubg5uk287ZvVFCQsItX1OyZEkdPHhQCQkJDoE3K/u4WXohytPTM93RSttob0pKin2ZrW+WL1+e7kmDNra+uXr1qlq0aKGdO3eqTp066t27t4oWLSpPT0/7POrk5GSn12d0RQtPT09du3btFkfofrba4uPjVbSo47/XxMREXbt2LdtX5JgxY4Yk901nuHjxohYtWiRfX1916dLFLfsAgPsdgfc28nrkccu82NzM19dXf/75Z7rrbMtvDHF58+bVsGHDNGzYMJ06dUobNmxQZGSkvvzyS/u808zsU5KeeeYZLVy4MEv13nzSXWb2k5Xjy+o+XM1WS0Yn7d3su+++086dOxUaGqrp06c7rJs/f75mzZrlljol983hrVy5snbs2KGYmBinwHur+b238+uvv2rbtm2qVq2amjZtmuXXZ8bcuXN15coV9enTx2GOOwDAdQi8yLI6depo3bp1+umnn5ymC2zYsEHS9ZOr0hMQEKDu3bura9euqlatmtauXasrV64of/789vmv6Y0W/uMf/5Cvr6927NihlJQUt53FbpteEBUV5TTaFhsbq8OHD+uBBx5wGN3NabarL2zdujVTgffw4cOSpCeeeMJp3c13EHM1d83hDQ4O1rx587R69Wo1atTIYZ3tD6rg4OAs1SrdnakG7j4hDgDAdXiRDbaTmUaMGOHw1XpsbKwmTJggT09P+12tkpOTtW7dOhmG4bCNxMREXbp0SXnz5rUHXdsJbCdPnnTap6enp1588UUdP35c//rXvxz2a7Nv3z6dPXv2jo7tySeflJ+fnyIjIx2mThiGYT/ee20OalBQkBo2bKh58+ZpwYIFTuvT0tLsf4hIUvny5SVJmzdvdmi3YcMGpxFfV+vXr58Mw8j0I7PX+O3SpYv8/Pw0ZcoUnThxwr789OnTmjRpkgoVKqTOnTs7vOb06dM6cOBAhiezpaSk6KuvvlLevHnVp0+fbB/zrezevVs7d+5UzZo1Vb9+fbfsAwDACC+yoXfv3lq8eLG+++471axZU+3bt7dfh/fChQv66KOP9MADD0iSrly5otatW+uBBx5Qw4YNVa5cOV2+fFnLli3TmTNn9Prrr9uv9FCtWjUFBARo/vz5KlCggMqUKSOLxaIXX3xRfn5+Gj16tHbu3KmPP/5Yy5cvV3BwsIoVK6bY2Fj98ssv2rNnj7Zu3arixYtn+9h8fX01ffp0de/eXQ0bNlTXrl1VrFgx/fjjj9qxY4eCgoI0bNgwl/SjK82bN08tW7ZUt27dNGnSJNWrV0/58uXTH3/8oa1bt+rcuXNKSkqSdP1uXoGBgYqIiLBfueHgwYNatmyZOnbsqEWLFuXw0WRd4cKFNXXqVPXu3Vt169ZVt27dlCdPHi1YsEB//vmnvvrqK6e7rI0YMUKzZs1SZGRkun/ELF26VOfOndPTTz992/fUuHHj7CcHbt261b7MdkWT0NDQdKdEMLoLAHcHgRdZZrFYtHDhQk2ePFmzZs3SlClT5OXlpbp162ro0KEOX5XbLmX1448/atOmTTp79qwKFy6satWqafz48eratau9rYeHhxYvXqzXX39dX331lS5duiRJ6tatm/z8/GS1WrVixQp9/vnn+vLLL7Vw4UIlJyerRIkSevDBBzVw4ED7lSTuROfOnVWyZEmNHTtWixcv1t9//63AwEC99dZbev31151uK3svqFChgnbt2qUJEyZoyZIl+uKLL+Th4aFSpUqpefPm6tSpk72tj4+P1q1bp2HDhmnjxo2KiopS9erVNWfOHJUoUSJXBl7p+i1//f39NXbsWHvQrFu3rmbNmqWQkJAsby8rYXTlypUOo+iSHOamt2jRwinwJiUlac6cObJarerVq1eW6wMAZJ7FuPm7ZpNJSEiQn5+f4uPjb3lJoaSkJB09elQVKlS4JwMNANwPbJ/FoYuPK/bS/+bzVyjurUHtqtqfT1l+UEfPOl+VxVXqn9yvhXNetz/v1HO8dpSp7rb93amA0lfUtdf/Lle4YHZpnYrNn4MVScfGtcvR/SN3yWxeyy7m8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQLvTUx+lTYAuKfZPoPT+CgG4EIE3v/n6Xn9Hhypqak5XAkA3L9stw1PTCHxAnAdAu//8/DwkIeHhxISEnK6FAC4LxmGofj4eFmtViUkp+V0OQBMhFsL/z+LxaLixYvr9OnTslqt8vb2lsViyemyAMD0DMNQSkqK4uPjdfnyZZUuXTqnSwJgMgTeG/j5+enKlSs6f/68zp07l9PlAMB9xWq1qnTp0m65rSiA+xuB9wYWi0WlSpVS8eLF7fPIAADu5+Hhobx58+Z0GQBMisCbDtt8XgAAAOR+nLQGAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQ8c7oAAEDWBb6xPKdLAIBcgxFeAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpeeZ0AQDgaoFvLM/pEgAA9xBGeAEAAGBqBF4AAACYWpYD7+zZszVgwADVr19fVqtVFotFM2fOTLdteHi4LBZLuo98+fJluI+5c+cqKChI3t7eKly4sB5//HHt2LEjq6UCAAAAWZ/DO2rUKB0/flz+/v4qVaqUjh8/ftvX9O3bV4GBgY479kx/1++//75GjhypcuXKaeDAgbp8+bLmz5+vJk2aaNWqVWrRokVWSwYAAMB9LMuBd8aMGapcubLKly+vcePGacSIEbd9Tb9+/TIVVGNiYhQWFqYqVarop59+kp+fnyTp1VdfVVBQkEJDQ3XgwIEMwzIAAABwsyxPaXjkkUdUvnx5d9SiyMhIpaamauTIkfawK0nVq1dXnz59dPjwYa1bt84t+wYAAIA53ZWT1jZt2qSIiAh99NFHWr58uZKTk9NtFxUVJUl69NFHndaFhIRIkjZs2OC2OgEAAGA+d2VuwNtvv+3wvFSpUpo1a5batGnjsDwmJkY+Pj4qWbKk0zYqV65sb3MrycnJDoE6ISEhu2UDAADABNw6wlu7dm3NmjVLx44d05UrVxQTE6N33nlHFy9e1BNPPKE9e/Y4tI+Pj3eYynAjX19fe5tbGTt2rPz8/OyPsmXLuuZgAAAAkCu5NfB27NhRffr0Ufny5ZUvXz5VqlRJo0aN0uTJk5WUlKR3333X5fscMWKE4uPj7Y8TJ064fB8AAADIPXLkxhN9+/aVp6enoqOjHZb7+fllOIJrm5qQ0QiwjdVqla+vr8MDAAAA968cCbxeXl4qWLCg/v77b4fllStX1uXLl3XmzBmn19jm7trm8gIAAACZkSOBNyYmRnFxcU43owgODpYkrV692uk1q1atcmgDAAAAZIbbAu+lS5e0d+9ep+VxcXF67rnnJEndu3d3WNe/f395enrqvffec5jasH//fn355ZeqWLGiWrVq5a6SAQAAYELZutPa5s2bJUm//PKLfZntGrodO3ZUx44ddeHCBdWqVUv169dXjRo1VLx4ccXGxmrFihW6cOGC2rRpoyFDhjhsu0qVKgoPD9eoUaNUs2ZNderUSYmJiZo3b55SUlI0ffp07rIGAACALMlyety8ebNmzZrlsCw6Otp+AlpgYKA6duyoIkWK6OWXX9a2bdv0/fff6+LFi/L29laNGjXUq1cvhYaGysPDw2n7I0eOVGBgoCZNmqRPP/1UXl5eaty4scaMGaMGDRpk8zABAABwv8py4J05c6Zmzpx523a+vr6aOnVqdmpSz5491bNnz2y9FgAAALhRjpy0BgAAANwtBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYWpYD7+zZszVgwADVr19fVqtVFotFM2fOzLB9QkKChg4dqvLly8tqtap8+fIaOnSoEhISMnzN3LlzFRQUJG9vbxUuXFiPP/64duzYkdVSAQAAgKwH3lGjRuk///mPjh8/rlKlSt2ybWJiooKDgzVx4kRVrVpVQ4YM0YMPPqiJEycqODhYiYmJTq95//331bNnT/35558aOHCgunTpoujoaDVp0kRRUVFZLRcAAAD3uSwH3hkzZujYsWM6d+6cBg4ceMu2ERER2r17t4YPH67Vq1dr3LhxWrFihd5++23t3r1bERERDu1jYmIUFhamKlWqaO/evfroo4/02WefacuWLfL09FRoaKhSU1OzWjIAAADuY1kOvI888ojKly9/23aGYWjGjBny8fHR22+/7bBuxIgRKly4sD7//HMZhmFfHhkZqdTUVI0cOVJ+fn725dWrV1efPn10+PBhrVu3LqslAwAA4D7mtpPWYmJidOrUKTVp0kTe3t4O6/Lly6fmzZsrNjZWhw4dsi+3TVl49NFHnbYXEhIiSdqwYcMt95ucnKyEhASHBwAAAO5fnu7acExMjCSpcuXK6a63LY+JiXH4fx8fH5UsWfKW7W9l7NixGj16dLbrBgAAdy7wjeU5XYJbHRvXLqdLQBa4bYQ3Pj5ekhymJtzI19fXoZ3t/7PSPj0jRoxQfHy8/XHixIks1w4AAADzcNsIb06xWq2yWq05XQYAAADuEW4b4bWN1GY0ImubW3vjiK6fn1+W2gMAAAC347bAe7s5t+nN8a1cubIuX76sM2fOZKo9AAAAcDtuDbwBAQGKjo52usFEUlKSNm7cqICAAFWqVMm+PDg4WJK0evVqp+2tWrXKoQ0AAACQGW4LvBaLRaGhobp8+bLGjBnjsG7s2LGKi4tTaGioLBaLfXn//v3l6emp9957z2Fqw/79+/Xll1+qYsWKatWqlbtKBgAAgAll+aS1GTNmaPPmzZKkX375xb7Mdg3djh07qmPHjpKk4cOHa+nSpYqIiNCuXbtUr1497dmzRytWrFDt2rU1fPhwh21XqVJF4eHhGjVqlGrWrKlOnTopMTFR8+bNU0pKiqZPny5PT9OdZwcAAAA3ynJ63Lx5s2bNmuWwLDo6WtHR0ZKkwMBAe+D19vZWVFSURo8erYULFyoqKkolS5bUkCFDFBYW5nRDCkkaOXKkAgMDNWnSJH366afy8vJS48aNNWbMGDVo0CAbhwgAAID7WZYD78yZMzVz5sxMt/fz89OECRM0YcKETL+mZ8+e6tmzZ1ZLAwAAAJy4bQ4vAAAAcC8g8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFMj8AIAAMDUCLwAAAAwNQIvAAAATI3ACwAAAFPzzOkCANx9gW8sz+kSAAC4axjhBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApnZXAm9gYKAsFku6j4EDBzq1T0hI0NChQ1W+fHlZrVaVL19eQ4cOVUJCwt0oFwAAACbiebd25Ofnp8GDBzstr1+/vsPzxMREBQcHa/fu3WrTpo26d++uPXv2aOLEiVq/fr02b94sb2/vu1Q1AAAAcru7FngLFSqk8PDw27aLiIjQ7t27NXz4cI0fP96+PCwsTGPGjFFERIRGjx7txkoBAABgJvfUHF7DMDRjxgz5+Pjo7bffdlg3YsQIFS5cWJ9//rkMw8ihCgEAAJDb3LUR3uTkZM2aNUuxsbEqXLiwGjdurFq1ajm0iYmJ0alTpxQSEuI0bSFfvnxq3ry5vvvuOx06dEiVK1fOcD/Jycn258z7BQAAuL/dtcB75swZ9evXz2FZ27Zt9dVXX8nf31/S9cArKcMwa1seExOTYZuxY8cy5QEAAAB2d2VKw7PPPquoqCidO3dOCQkJ2rZtmx577DGtXLlSTzzxhH2KQnx8vKTrJ7ilx9fX16FdekaMGKH4+Hj748SJEy4+GgAAAOQmd2WE9+b5uA0bNtSyZcsUHByszZs364cfflC7du1csi+r1Sqr1eqSbQEAACD3y7GT1vLkyaP+/ftLkqKjoyX9b2Q3oxFc23zcjEaAAQAAgJvl6FUabHN3//77b0mOc3TTc7s5vgAAAMDNcjTw/ve//5V0/U5s0vUgGxAQoOjoaCUmJjq0TUpK0saNGxUQEKBKlSrd7VIBAACQS7k98P7666+6ePGi0/LNmzdrwoQJslqtevrppyVJFotFoaGhunz5ssaMGePQfuzYsYqLi1NoaKgsFou7ywYAAIBJuP2kta+//loRERFq3bq1AgMDZbVatW/fPq1evVp58uTRtGnTVK5cOXv74cOHa+nSpYqIiNCuXbtUr1497dmzRytWrFDt2rU1fPhwd5cMAAAAE3F74G3ZsqV+++037dy5Uxs2bFBSUpJKlCihrl27asiQIQoKCnJo7+3traioKI0ePVoLFy5UVFSUSpYsqSFDhigsLMzphhQAAADArbg98AYHBys4ODhLr/Hz89OECRM0YcIEN1UFAACA+0WOnrQGAAAAuBuBFwAAAKZG4AUAAICpEXgBAABgagReAAAAmBqBFwAAAKZG4AUAAICpuf06vEBuFPjG8pwuAQAAuAgjvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1Ai8AAABMjcALAAAAUyPwAgAAwNQIvAAAADA1z5wuAAAAILcJfGN5TpfgdsfGtcvpElyGEV4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYGoEXAAAApkbgBQAAgKkReAEAAGBqBF4AAACYmmdOF4DcJ/CN5TldAgAAQKYxwgsAAABTI/ACAADA1Ai8AAAAMDUCLwAAAEyNwAsAAABTI/ACAADA1Ai8AAAAMDUCLwAAAEyNwAsAAABTI/ACAADA1O7ZwLt9+3Y9/vjjKly4sLy9vRUUFKS5c+fmdFkAAADIZTxzuoD0REVFKSQkRF5eXurWrZv8/Py0ePFi9ezZU8eOHdObb76Z0yXeUuAby3O6BAAAAPy/e26ENzU1VaGhobJYLNq4caOmT5+uDz/8UHv27FH16tUVFhammJiYnC4TAAAAucQ9F3jXrVunw4cPq0ePHqpTp459ecGCBfXWW28pNTVVkZGROVghAAAAcpN7bkpDVFSUJOnRRx91WmdbtmHDhgxfn5ycrOTkZPvz+Ph4SVJCQoILq7y1tOS/79q+AOB+kJok/X350g3PE936WZuSkqSEm57fy5/tqVeuKCkh6Ybnfyst2cjBimAGdzM72fZlGO5531oMd205mzp37qyFCxdqx44dqlevntP6YsWKyWKx6OzZs+m+Pjw8XKNHj3Z3mQAAAHCxEydOqEyZMi7f7j0XeB999FGtWbNGMTExqlSpktP6ihUr6uTJkw6juDe6eYQ3LS1Nf/31l4oWLSqLxZLuaxISElS2bFmdOHFCvr6+rjkQSKJv3Ym+dR/61n3oW/ehb92HvnUfW9/+8ccfslgsCggIUJ48rp9xe89NabhTVqtVVqvVYVmhQoUy9VpfX1/eyG5C37oPfes+9K370LfuQ9+6D33rPn5+fm7t23vupDU/Pz9J/5t7e7OEhAR7GwAAAOB27rnAW7lyZUlK99JjcXFxOn/+vL0NAAAAcDv3XOANDg6WJK1evdppnW2ZrY2rWK1WhYWFOU2FwJ2jb92HvnUf+tZ96Fv3oW/dh751n7vVt/fcSWupqamqWrWqYmNjtW3bNtWuXVuSdOnSJT388MM6ePCg9u/frypVquRsoQAAAMgV7rnAK0nr169XSEiIrFarunfvLl9fXy1evFhHjx7Vu+++q5EjR+Z0iQAAAMgl7snAK0k//fSTwsLCtHXrVl29elXVq1fX4MGD1bNnz5wuDQAAALnIPRt4AQAAAFe4505aAwAAAFyJwAsAAABTM2Xg3b59ux5//HEVLlxY3t7eCgoK0ty5czP9+qioKPXo0UP/+Mc/VKhQIRUoUEBVq1bVs88+q4MHD7qx8nvfnfbtzVJSUlS7dm1ZLBZVq1bNhZXmTq5471oslgwf27Ztc2P19zZXvXcvXbqksLAwPfTQQypQoIAKFSqkunXravTo0W6oOne4075t0aLFLd+3FotFX331lRuP4N7livftxYsX9fbbb6tmzZoqWLCg/P391aBBA02dOlVJSUluqvze54q+PXnypAYMGKBy5crJy8tLAQEB6t+/v06cOOGmqu99s2fP1oABA1S/fn1ZrVZZLBbNnDkzy9tJS0vT1KlTVbNmTeXPn1/FihVTly5d0r1PQ2aY7tbCUVFRCgkJkZeXl7p16yY/Pz8tXrxYPXv21LFjx/Tmm2/edhtr167V5s2b1bBhQ/u2fvvtN3355ZeaO3euVqxYoZYtW96Fo7m3uKJvb/bOO+/o0KFDbqg293Fl/wYHB6tFixZOy8uUKePCinMPV/XtH3/8oVatWunIkSN65JFH1K5dOyUnJ+vQoUNatGiRwsLC3Hwk9x5X9G2/fv3Sfb+mpKRo7NixypMnj1q3bu2G6u9trujbixcvql69ejpy5IiaNm2qAQMGKDk5WStWrNCgQYP07bffas2aNcqTx5TjXxlyRd8ePnxYjRs31tmzZ9WmTRt17dpVMTExmjVrln744Qdt2bJFFStWvAtHc28ZNWqUjh8/Ln9/f5UqVUrHjx/P1nYGDhyo6dOn68EHH9SgQYP0559/asGCBVq9erW2bNmiBx98MGsbNEwkJSXFqFixomG1Wo2dO3falyckJBjVq1c3PD09jd9///2227ly5Uq6y9euXWtIMurXr++ymnMLV/XtjX7++WfD09PT+Pjjjw1JRtWqVV1ddq7hqv5dv369IckICwtzY7W5i6v6NjU11WjQoIGRP39+Y926denu537jjs+FGy1cuNCQZHTo0MEV5eYqrurb8ePHG5KMIUOGOCxPTk42GjRoYEgyNmzY4PL672Wu6tt27doZkozJkyc7LP/6668NSUZISIjLa88N1qxZYxw7dswwDMMYO3asIcmIjIzM0jbWrVtnSDKaNWtmJCUl2ZevXbvWsFgsRvPmzbNcl6kC76pVqwxJRv/+/Z3WzZ8/35BkjBgx4o72UbhwYaNQoUJ3tI3cyNV9m5ycbNSoUcNo2rSpkZaWdt8HXlf1L4HXmav61tb2rbfeckeZuZK7P3Pbtm1rSDKWLFlyJ2XmSq7q2wEDBhiSjDVr1jite/PNNw1JxjfffOOSmnMLV/TtlStXDE9PT6NEiRJGWlqa0/ratWsbkozDhw+7rO7cKLuBt3v37hn+MWb7XDh48GCWtmmq7zCioqIkSY8++qjTOtuyDRs2ZHv7W7duVVxcnB566KFsbyO3cnXfhoeHKyYmRp9//rksFotLaszNXN2/MTEx+vjjjzVu3DjNmzdP58+fd0mduZGr+nbBggWSpM6dO+vEiROaNm2axo0bp2+++UaXL192XcG5iDs/c0+ePKnVq1erZMmSateuXbZrzK1c1bfVq1eXJK1cudJheUpKitauXav8+fPr4YcfvsNqcxdX9O2FCxeUmpqq8uXLp/s7rEKFCpKu30gLWRcVFSVvb281adLEaV1ISIikrH+2mGoOr20ic+XKlZ3WFS5cWP7+/lma7BwVFaWoqCglJycrJiZGy5Ytk7+/vyZOnOiymnMLV/bt9u3bFRERoffff59bRP8/V793586d63DyRf78+TV69GgNGzbszovNZVzVtzt27JAkbd68WUOGDFFycrJ9XbFixfT111+nOw/VzFz9vr1RZGSk0tLS1K9fP3l6mupXVaa4qm9DQ0P11Vdf6aOPPtKOHTvUoEEDJScna+XKlYqLi9PcuXNVunRpl9d/L3NF3xYuXFgeHh46fvy4DMNwCr1Hjx6VJP3+++8uqvr+kZiYqNOnT+uhhx6Sh4eH03rbzy2rny2mGuGNj4+XJPn5+aW73tfX194mM6KiojR69GiNGzdOixYtUtmyZbVy5UrVr1/fJfXmJq7q2+TkZPXr10916tTRP//5T5fWmJu5qn+LFSumDz74QL/99psSExMVGxur2bNnq0iRIho+fLg+++wzl9adG7iqb8+ePStJGjRokAYPHqwTJ07o3Llz+vjjjxUfH6+OHTvq9OnTris8F3D1Z66NYRiKjIyUJD333HPZLzAXc1Xf5s+fX1FRUerVq5c2bNigDz/8UFOmTNHhw4fVo0cPNW3a1KV15wau6NsCBQooODhYf/75pz755BOHdYsXL9bu3bslXT9pEFmTmZ/Pje0yy1SB19XCw8NlGIYuX76sn376SdWqVVOTJk3u6DJc97u33npLMTEx+uKLL9L9yw13pnr16vrXv/6latWqqUCBAgoICFDPnj21cuVKeXl5KSwsTGlpaTldZq5k67f27dtr3LhxKlOmjPz9/TVo0CANGTJE8fHx+vzzz3O4SnNYt26djh49quDgYFWqVCmny8nVzp8/rzZt2mjbtm1avny5Ll68qDNnzmjatGmKjIxUw4YNFRcXl9Nl5koTJkyQj4+PXnnlFbVt21bDhw/X008/rc6dO6tmzZqSxO+5e4ipAq/tr4GMUn9CQkKGfzHcire3txo0aKBvv/1W1apV0wsvvKBz587dUa25jSv6dufOnZowYYJGjhypGjVquLzG3Mxd712bhx56SA0bNtSff/55310GzlV9a2vzxBNPOK3r0KGDpP9Ne7hfuOt9O2PGDEnXv46/X7mqb4cOHaotW7Zo0aJFevzxx+Xn56cSJUro+eefV0REhI4cOaJJkya5svR7nqv6tlatWtq+fbu6dOminTt3avLkyTp48KA+++wz9e7dW9L1b92QNZn5+dzYLrNMFXhvNa8jLi5O58+fT3fOTmZ5enqqZcuWSkxMvO9+sbmib/fu3atr164pPDzc6aLyknTw4EFZLBYVKlTI5fXf69z93pUkf39/SdLff/99R9vJbVzVt1WrVpWkdN+ftmVXrlzJfqG5kDvet3Fxcfr2229VqFAhPfPMMy6pMzdyVd8uX75cRYoUsY843qhVq1aSpJ9//vkOq81dXPm+rVatmhYsWKCzZ88qOTlZ+/fvV2hoqPbt2ydJ9+UUyDvl7e2tUqVK6ejRo7p27ZrT+lvNwb4VUwXe4OBgSdLq1aud1tmW2dpk16lTpyTpvjuJwhV9W6VKFT333HPpPqTrf60999xz6tOnj4urv/e5+72bmpqqnTt3ymKxqFy5ctneTm7kqr61hYNff/3VaZ1tWWBgYHbLzJXc8b6dPXu2kpOT1bNnT+XPn//Oi8ylXNW3V69eVUJCgq5eveq0zvZNpdVqvZNScx13f95eunRJ33//vYoUKaI2bdpkezv3s+DgYCUmJio6Otpp3apVq+xtsiRLFzG7x6WkpBgPPPCAYbVajV27dtmX33gx6Ruv23bu3Dnjt99+M86dO+ewnQ0bNqR7Xb1Vq1YZefPmNfz8/IzLly+77TjuRa7q24zoPr8Or6v6d8uWLU7v3ZSUFGPw4MGGJKNt27ZuPY57kav69siRI4bVajWKFy9unDx50mE7tmturl271u3Hcy9xx+dCrVq1DEkONwS4H7mqb0NCQgxJxqhRoxyWJyUl2ddNmTLFrcdyr3FV3/79999ON5xJSkoyOnfunO4NKe5Ht7sOb0Z9e+ONJ5KTk+3LufHEDdatW2fkzZvX8PHxMZ5//nnjn//8p1GhQgVDkvHuu+86tA0LC0v3Iv1+fn5GxYoVjW7duhnDhg0zXnnlFaN58+aGJCNv3rz33UW6bVzRtxm53wOvYbimf8uXL28EBgYaPXr0MIYNG2Y8//zzRtWqVQ1JRrly5ex3v7nfuOq9a7srYNGiRY3Q0FDj5ZdfNgIDAw1JxgsvvHCXjube4srPhR07dhiSjLp1696Fyu99rujbXbt2GQULFjQkGUFBQcaQIUOMF1980XjggQcMSUa9evUyvLuombmibzdt2mQUK1bM6NGjh/H6668bL774olGuXDlDkvH888+nO3B2P5g+fbrRt29fo2/fvkbdunUNSUaTJk3sy7799lt721t9JoSGhhqSjAcffNAYNmyY0adPH8NqtRp+fn7G/v37s1yX6QKvYRjGf//7X6Nt27aGn5+fkT9/fqN+/frG7Nmzndpl1NGTJk0y2rZta5QpU8awWq1Gvnz5jMqVKxuhoaHGvn377tJR3JvutG8zQuC97k77d9y4cUaLFi2MgIAAw8vLyyhQoIBRs2ZNY+TIkcZff/11l47i3uSq9+7SpUuNZs2aGT4+Pka+fPmMevXqGf/5z3/cXP29zVV9++KLLxqSjE8++cTNFecerujb33//3ejfv79Rrlw5I2/evEb+/PmNGjVqGKNHjzYSExPvwlHcm+60b48fP2507tzZKFu2rOHl5WUUKlTIaNWqlbFw4cK7dAT3pr59+xqSMnzc2I+3et9eu3bN+Pjjj43q1asbVqvVKFq0qNGpU6cs32HNxmIYhpG1SRAAAABA7mGqk9YAAACAmxF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACmRuAFAACAqRF4AQAAYGoEXgAAAJgagRcAAACm9n/4mWvKwlcIRwAAAABJRU5ErkJggg==\n", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -794,7 +788,6 @@ { "data": { "application/javascript": [ - "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", @@ -807,7 +800,7 @@ " root._bokeh_is_loading = undefined;\n", " }\n", "\n", - " const JS_MIME_TYPE = 'application/javascript';\n", + "const JS_MIME_TYPE = 'application/javascript';\n", " const HTML_MIME_TYPE = 'text/html';\n", " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " const CLASS_NAME = 'output_bokeh rendered_html';\n", @@ -930,8 +923,6 @@ " 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", @@ -967,7 +958,6 @@ " }\n", " }\n", "\n", - "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", @@ -1038,29 +1028,22 @@ " document.body.appendChild(element);\n", " }\n", "\n", - " \n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n", " const css_urls = [];\n", - " \n", "\n", - " const inline_js = [\n", - " function(Bokeh) {\n", + " const inline_js = [ function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", - " function(Bokeh) {\n", - " \n", - " \n", + "function(Bokeh) {\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", - " \n", " if (root.Bokeh !== undefined || force === true) {\n", - " \n", - " for (let i = 0; i < inline_js.length; i++) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\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", @@ -1069,7 +1052,6 @@ " const cell = $(document.getElementById(null)).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", @@ -1083,7 +1065,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n const 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 const 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 \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\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 const el = document.getElementById(null);\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(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 };\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 const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n const css_urls = [];\n \n\n const 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 (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\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 const cell = $(document.getElementById(null)).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": "(function(root) {\n function now() {\n return new Date();\n }\n\n const 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 if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const 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 \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\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 const el = document.getElementById(null);\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 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(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 };\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 const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\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 const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\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" @@ -1092,12 +1074,7 @@ "data": { "text/html": [ "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -1108,11 +1085,9 @@ "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", - " \n", - " const docs_json = 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Application\",\"version\":\"2.4.2\"}};\n", - " const render_items = [{\"docid\":\"4b864502-40f5-4e59-8146-2f4039ed6673\",\"root_ids\":[\"1401\"],\"roots\":{\"1401\":\"a6007911-5845-4220-9921-286d50db33c2\"}}];\n", + " const docs_json = 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Application\",\"version\":\"2.4.3\"}};\n", + " const render_items = [{\"docid\":\"c52c3d26-cdae-400b-872c-1db23545de89\",\"root_ids\":[\"1401\"],\"roots\":{\"1401\":\"7420dbc4-43f2-4edd-9de7-b4e0947c9235\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", - "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", @@ -1181,6 +1156,803 @@ "indicating a healthy exploration of the space." ] }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " Bokeh Application\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " \n", + " \n", + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "samples.diagnostics.marginal1d()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1203,13 +1975,13 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "078b5060c21c4eb7808209807a13fabc", + "model_id": "14f23bbba1cf4aaca7ff4d90810fab4a", "version_major": 2, "version_minor": 0 }, @@ -1223,7 +1995,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "976fe613fb7f46c89d99d5297c35c4a4", + "model_id": "cd2cb03fe69b4cb68632006180cf460c", "version_major": 2, "version_minor": 0 }, @@ -1237,7 +2009,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dcbba1b1101c4434911ef4663e28fb91", + "model_id": "981f6cb34790475ba5fcfbd98ad8c059", "version_major": 2, "version_minor": 0 }, @@ -1251,7 +2023,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7bd306afe700402da02dae20a3485983", + "model_id": "f2262fa045584852a4d06e3a0d3973d7", "version_major": 2, "version_minor": 0 }, @@ -1266,8 +2038,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 10.7 s, sys: 68 ms, total: 10.8 s\n", - "Wall time: 10.8 s\n" + "CPU times: user 11 s, sys: 86.6 ms, total: 11.1 s\n", + "Wall time: 5.43 s\n" ] } ], @@ -1290,7 +2062,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1300,9 +2072,9 @@ "\n", "0% 10 20 30 40 50 60 70 80 90 100%\n", "|----|----|----|----|----|----|----|----|----|----|\n", - "CPU times: user 1.5 s, sys: 11.8 ms, total: 1.51 s\n", - "Wall time: 1.51 s\n", - "***************************************************\n" + "*CPU times: user 504 ms, sys: 3.12 ms, total: 508 ms\n", + "Wall time: 499 ms\n", + "**************************************************\n" ] } ], @@ -1331,8 +2103,22 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" } }, "nbformat": 4, From b2c474edef29fa76cffcf35422d1ad158f7578ba Mon Sep 17 00:00:00 2001 From: Andy Maloney Date: Thu, 22 Sep 2022 16:35:11 -0500 Subject: [PATCH 2/7] Documentation & remove arviz - Removes ArviZ as a dependency from the diagnostic tool. - Adds jsdoc Documentation for the TypeScript methods. - Fixes flake8 linting error. - Adds clarifying comments to the tool. - Adds missing copyright notice. --- .../ppl/diagnostics/tools/__init__.py | 4 + .../tools/js/src/marginal1d/callbacks.ts | 89 ++++++++++++++++-- .../tools/js/src/marginal1d/index.ts | 2 + .../tools/js/src/marginal1d/interfaces.ts | 36 +++++--- .../diagnostics/tools/js/src/stats/array.ts | 12 +++ .../tools/js/src/stats/dataTransformation.ts | 16 ++++ .../js/src/stats/highestDensityInterval.ts | 28 ++++++ .../tools/js/src/stats/marginal.ts | 14 +++ .../tools/js/src/stats/pointStatistic.ts | 12 +++ .../diagnostics/tools/js/webpack.config.js | 5 ++ .../diagnostics/tools/marginal1d/typing.py | 2 +- .../ppl/diagnostics/tools/marginal1d/utils.py | 29 +++--- .../ppl/diagnostics/tools/utils/hdi.py | 90 +++++++++++++++++++ 13 files changed, 303 insertions(+), 36 deletions(-) create mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/hdi.py diff --git a/src/beanmachine/ppl/diagnostics/tools/__init__.py b/src/beanmachine/ppl/diagnostics/tools/__init__.py index 9b47b4890b..3e58d695f5 100644 --- a/src/beanmachine/ppl/diagnostics/tools/__init__.py +++ b/src/beanmachine/ppl/diagnostics/tools/__init__.py @@ -3,6 +3,10 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. +# flake8: noqa + +"""Visual diagnostic tools for Bean Machine models.""" + import sys from pathlib import Path diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts index 150722efe2..05439000c8 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts @@ -15,33 +15,71 @@ import {mean as computeMean} from '../stats/pointStatistic'; import {interpolatePoints} from '../stats/utils'; import * as interfaces from './interfaces'; -const FIGURENAMES = {marginal: 'marginal', cumulative: 'cumulative'}; -const figureNames = Object.keys(FIGURENAMES); +// Define the names of the figures used for this Bokeh application. +const figureNames = ['marginal', 'cumulative']; +/** + * Update the given Bokeh Axis object with the new label string. You must use this + * method to update axis strings using TypeScript, otherwise the ts compiler will throw + * a type check error. + * + * @param {Axis} axis - The Bokeh Axis object needing a new label. + * @param {string | null} label - The new label for the Bokeh Axis object. + */ export const updateAxisLabel = (axis: Axis, label: string | null): void => { + // Type check requirement. if ('axis_label' in axis) { axis.axis_label = label; } }; +/** + * Compute the following statistics for the given random variable data + * + * - lower bound for the highest density interval calculated from the marginalX; + * - mean of the rawData; + * - upper bound for the highest density interval calculated from the marginalY. + * + * @param {number[]} rawData - Raw random variable data from the model. + * @param {number[]} marginalX - The support of the Kernel Density Estimate of the + * random variable. + * @param {number[]} marginalY - The Kernel Density Estimate of the random variable. + * @param {number | null} [hdiProb=null] - The highest density interval probability + * value. If the default value is not overwritten, then the default HDI probability + * is 0.89. See Statistical Rethinking by McElreath for a description as to why this + * value is the default. + * @param {string[]} [text_align=['right', 'center', 'left']] - How to align the text + * shown in the figure for the point statistics. + * @param {number[]} [x_offset=[-5, 0, 5]] - Offset values for the text along the + * x-axis. + * @param {number[]} [y_offset=[0, 10, 0]] - Offset values for the text along the + * y-axis + * @returns {interfaces.Marginal1dStats} Object containing the computed stats. + */ export const computeStats = ( rawData: number[], - computedX: number[], - computedY: number[], - hdiProbability: number, + marginalX: number[], + marginalY: number[], + hdiProb: number | null = null, text_align: string[] = ['right', 'center', 'left'], x_offset: number[] = [-5, 0, 5], y_offset: number[] = [0, 10, 0], ): interfaces.Marginal1dStats => { - const mean = computeMean(computedX); + // Set the default value to 0.89 if no default value has been given. + const hdiProbability = hdiProb ?? 0.89; + + // Compute the point statistics for the KDE, and create labels to display them in the + // figures. + const mean = computeMean(marginalX); const hdiBounds = hdiInterval(rawData, hdiProbability); const x = [hdiBounds.lowerBound, mean, hdiBounds.upperBound]; - const y = interpolatePoints({x: computedX, y: computedY, points: x}); + const y = interpolatePoints({x: marginalX, y: marginalY, points: x}); const text = [ `Lower HDI: ${hdiBounds.lowerBound.toFixed(3)}`, `Mean: ${mean.toFixed(3)}`, `Upper HDI: ${hdiBounds.upperBound.toFixed(3)}`, ]; + return { x: x, y: y, @@ -52,6 +90,18 @@ export const computeStats = ( }; }; +/** + * Compute data for the one-dimensional marginal diagnostic tool. + * + * @param {number[]} data - Raw random variable data from the model. + * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when + * calculating the Kernel Density Estimate (KDE). + * @param {number} hdiProbability - The highest density interval probability to use when + * calculating the HDI. + * @returns {interfaces.Marginal1dData} The marginal distribution and cumulative + * distribution calculated from the given random variable data. Point statistics are + * also calculated. + */ export const computeData = ( data: number[], bwFactor: number, @@ -61,6 +111,8 @@ export const computeData = ( for (let i = 0; i < figureNames.length; i += 1) { const figureName = figureNames[i]; output[figureName] = {} as interfaces.Marginal1dDatum; + + // Compute the one-dimensional KDE and its cumulative distribution. const distribution = oneD(data, bwFactor); switch (figureName) { case 'cumulative': @@ -69,7 +121,10 @@ export const computeData = ( default: break; } + + // Compute the point statistics for the given data. const stats = computeStats(data, distribution.x, distribution.y, hdiProbability); + output[figureName] = { distribution: distribution, hdi: hdiData(data, distribution.x, distribution.y, hdiProbability), @@ -80,6 +135,23 @@ export const computeData = ( return output; }; +/** + * Callback used to update the Bokeh application in the notebook. + * + * @param {number[]} data - Raw random variable data from the model. + * @param {string} rvName - The name of the random variable from the model. + * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when + * calculating the kernel density estimate. + * @param {number} hdiProbability - The highest density interval probability to use when + * calculating the HDI. + * @param {interfaces.Marginal1dSources} sources - Bokeh sources used to render glyphs + * in the application. + * @param {interfaces.Marginal1dFigures} figures - Bokeh figures shown in the + * application. + * @returns {number} We display the value of the bandwidth used for computing the Kernel + * Density Estimate in a div, and must return that value here in order to update the + * value displayed to the user. + */ export const update = ( data: number[], rvName: string, @@ -90,6 +162,7 @@ export const update = ( ): number => { const computedData = computeData(data, bwFactor, hdiProbability); for (let i = 0; i < figureNames.length; i += 1) { + // Update all sources with new data calculated above. const figureName = figureNames[i]; sources[figureName].distribution.data = { x: computedData[figureName].distribution.x, @@ -106,6 +179,8 @@ export const update = ( label: computedData[figureName].labels.text, }; sources[figureName].labels.data = computedData[figureName].labels; + + // Update the axes labels. updateAxisLabel(figures[figureName].below[0], rvName); } return computedData.marginal.distribution.bandwidth; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts index ea73a8a978..3db9709cae 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts @@ -5,4 +5,6 @@ import * as marginal1d from './callbacks'; +// The CustomJS methods used by Bokeh require us to make the JavaScript available in the +// browser, which is done by defining it below. (window as any).marginal1d = marginal1d; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts index 380a0726f3..8c9570c378 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts @@ -6,6 +6,16 @@ import {Plot} from '@bokehjs/models/plots/plot'; import {ColumnDataSource} from '@bokehjs/models/sources/column_data_source'; +// NOTE: In the corresponding Python typing files for the diagnostic tool, we define +// similar types using a TypedDict object. TypeScript allows us to maintain +// semantic information about the key names and their types in the same object and +// still have the ability to loop over objects as long as we have the +// [key: string]: any; indicator in the interface definition. This boils down to a +// Python type of Dict[Any, Any], which again loses all type information about the +// object we are defining. We are mirroring what is done in Python here, so we +// keep the semantic information here at the expense of losing type information +// similarly to what is done in Python. + export interface Marginal1dStats { [key: string]: any; x: number[]; @@ -16,6 +26,7 @@ export interface Marginal1dStats { y_offset: number[]; } +// Used for both the marginal and cumulative distribution objects. export interface Marginal1dDatum { distribution: {[key: string]: any; x: number[]; y: number[]; bandwidth: number}; hdi: { @@ -44,22 +55,19 @@ export interface Marginal1dData { cumulative: Marginal1dDatum; } +// Used for both the marginal and cumulative distribution objects. +export interface Marginal1dSource { + [key: string]: any; + distribution: ColumnDataSource; + hdi: ColumnDataSource; + stats: ColumnDataSource; + labels: ColumnDataSource; +} + export interface Marginal1dSources { [key: string]: any; - marginal: { - [key: string]: any; - distribution: ColumnDataSource; - hdi: ColumnDataSource; - stats: ColumnDataSource; - labels: ColumnDataSource; - }; - cumulative: { - [key: string]: any; - distribution: ColumnDataSource; - hdi: ColumnDataSource; - stats: ColumnDataSource; - labels: ColumnDataSource; - }; + marginal: Marginal1dSource; + cumulative: Marginal1dSource; } export interface Marginal1dFigures { diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts index 79a3e46d28..2d6983b10c 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts @@ -3,6 +3,12 @@ // This source code is licensed under the MIT license found in the // LICENSE file in the root directory of this source tree. +/** + * Cumulative sum of the given data. + * + * @param {number[]} data - Any array of data. + * @returns {number[]} The cumulative sum of the given data. + */ export const cumulativeSum = (data: number[]): number[] => { // eslint-disable-next-line arrow-body-style const cumulativeSumMap = ((sum: number) => (value: number) => { @@ -19,6 +25,12 @@ export const cumulativeSum = (data: number[]): number[] => { return cumSum; }; +/** + * Numerically sort the given array of numbers. + * + * @param {number[]} data - The array of numbers to sort. + * @returns {number[]} The sorted array of numbers. + */ export const numericalSort = (data: number[]): number[] => { const dataCopy = data.slice(0); return dataCopy.sort((a, b) => { diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts index 3dc64a89e6..8048d0bc65 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts @@ -3,6 +3,14 @@ // This source code is licensed under the MIT license found in the // LICENSE file in the root directory of this source tree. +/** + * Scale the given array of numbers by the given scaleFactor. Note that this method + * divides values in the given array by the scaleFactor. + * + * @param {number[]} data - An array of numbers that needs to be scaled. + * @param {number} scaleFactor - The value to divide all array values by. + * @returns {number[]} The scaled array. + */ export const scaleBy = (data: number[], scaleFactor: number): number[] => { const scaledData = data.map((datum) => { return datum / scaleFactor; @@ -10,6 +18,14 @@ export const scaleBy = (data: number[], scaleFactor: number): number[] => { return scaledData; }; +/** + * Scale the given array to a maximum value of 1. This method uses the scaleBy method, + * and finds the maximum value of the given array to use as the scaleFactor of the + * scaleBy method. + * + * @param {number[]} data - Array of data to scale to one. + * @returns {number[]} The data scaled to one. + */ export const scaleToOne = (data: number[]): number[] => { const scaleFactor = Math.max(...data); return scaleBy(data, scaleFactor); diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts index d42247e653..cee265fa8b 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts @@ -20,6 +20,17 @@ export interface HdiData { upperBound: number; } +/** + * Find the lower and upper bound of the Highest Density Interval (HDI) for the given + * data. + * + * @param {number[]} data - Raw random variable data from the model. + * @param {number} hdiProbability - The highest density interval probability to use when + * calculating the HDI. + * @returns {HdiInterval} An object defining the lower and upper bound for the HDI of + * the given data, as well as the indices of the lower and upper bound for the + * sorted raw data. + */ export const interval = (data: number[], hdiProbability: number): HdiInterval => { const N = data.length; const sortedData = numericalSort(data); @@ -41,6 +52,21 @@ export const interval = (data: number[], hdiProbability: number): HdiInterval => }; }; +/** + * Constructs x and y arrays from the HDI bounds using the raw random variable data. It + * also returns the actual HDI bounds of the random variable data. + * + * @param {number[]} rvData - Raw random variable data from the model. + * @param {number[]} marginalX - The support of the Kernel Density Estimate of the + * random variable. + * @param {number[]} marginalY - The Kernel Density Estimate of the random variable. + * @param {number} hdiProbability - The highest density interval probability to use when + * calculating the HDI. + * @returns {HdiData} The x array for the HDI data is the "lower" key, while the y array + * for the HDI data is the "upper" key and the "base" key is the support along the + * axis the HDI is calculated. This nomenclature mirrors the Bokeh Band object, + * which is the annotation type we will use when rendering the HDI intervals. + */ export const data = ( rvData: number[], marginalX: number[], @@ -56,6 +82,8 @@ export const data = ( upper.push(marginalY[i]); } } + // NOTE: We mirror the Bokeh Band annotation object key names below. The lower key is + // an array of zeros since we will fill the Band all the way to the lower axis. return { base: base, lower: new Array(base.length).fill(0.0), diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts index 898b5d7452..cc44c8d4c8 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts @@ -6,6 +6,20 @@ import {density1d} from 'fast-kde/src/density1d'; import {scaleToOne} from './dataTransformation'; +/** + * Calculate the one-dimensional Kernel Density Estimate. This method uses the "scott" + * estimate for the bandwidth used when calculating the marginal. This bandwidth + * estimate is the same as is used in the corresponding Python KDE estimate from ArviZ. + * + * Scott, D (1979). On optimal and data-based histograms. Biometrika. 66 (3): 605–610. + * doi:10.1093/biomet/66.3.605. + * + * @param {number[]} data - The raw random variable data of the model. + * @param {number} bwFactor - Multiplicative factor to be applied to the bandwidth when + * calculating the Kernel Density Estimate (KDE). + * @returns {{x: number[]; y: number[]; bandwidth: number}} The KDE of the given data, + * along with the numerical value of the bandwidth used to calculate the KDE. + */ // eslint-disable-next-line import/prefer-default-export export const oneD = ( data: number[], diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts index c1f1855f46..91355c033b 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts @@ -3,12 +3,24 @@ // This source code is licensed under the MIT license found in the // LICENSE file in the root directory of this source tree. +/** + * Syntactic sugar for summing an array of numbers. + * + * @param {number[]} data - The array of data. + * @returns {number} The sum of the array of data. + */ export const sum = (data: number[]): number => { return data.reduce((previousValue, currentValue) => { return previousValue + currentValue; }); }; +/** + * Calculate the mean of the given array of data. + * + * @param {number[]} data - The array of data. + * @returns {number} The mean of the given data. + */ export const mean = (data: number[]): number => { const dataSum = sum(data); return dataSum / data.length; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js b/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js index b60211087a..559e53c58b 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js +++ b/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js @@ -1,3 +1,8 @@ +// Copyright (c) Meta Platforms, Inc. and affiliates. +// +// This source code is licensed under the MIT license found in the +// LICENSE file in the root directory of this source tree. + const path = require('path'); module.exports = { diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py index f885a680ea..ff918d941d 100644 --- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py @@ -20,7 +20,7 @@ # NOTE: These are the types pyre gives us when using `reveal_type(...)` on the outputs -# of the methods. +# of the methods. StatsAndLabelsData = Dict[str, Dict[str, Any]] HDIData = Dict[str, Any] Data = Dict[Any, Any] diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py index d1e5a4e6f3..f5486f7060 100644 --- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py @@ -8,10 +8,10 @@ from itertools import chain from typing import List, Optional -import arviz as az import numpy as np +import statsmodels.api as sm from beanmachine.ppl.diagnostics.tools.marginal1d import typing -from beanmachine.ppl.diagnostics.tools.utils import plotting_utils +from beanmachine.ppl.diagnostics.tools.utils import hdi, plotting_utils from bokeh.models.annotations import Band, LabelSet from bokeh.models.glyphs import Circle, Line from bokeh.models.layouts import Column, Row @@ -76,13 +76,13 @@ def compute_stats( kdex = np.array(kde_x) kdey = np.array(kde_y) mean = np.mean(kdex) - hdi = az.stats.hdi(ary=flattened_data, hdi_prob=hdi_probability) - x = [hdi[0], mean, hdi[1]] + hdi_interval = hdi.interval(flattened_data, hdi_probability) + x = [hdi_interval["lower_bound"], mean, hdi_interval["upper_bound"]] y = np.interp(x=np.array(x).astype(float), xp=kdex, fp=kdey) text = [ - f"Lower HDI: {hdi[0]:.4}", + f"Lower HDI: {hdi_interval['lower_bound']:.4}", f"Mean: {mean:.4}", - f"Upper HDI: {hdi[1]:.4}", + f"Upper HDI: {hdi_interval['upper_bound']:.4}", ] stats = {"x": x, "y": y.tolist(), "text": text} labels = {} @@ -125,8 +125,10 @@ def hdi_data( flattened_data = np.array(list(chain(*data))) kdex = np.array(kde_x) kdey = np.array(kde_y) - hdi = az.stats.hdi(ary=flattened_data, hdi_prob=hdi_probability) - mask = np.ix_(np.logical_and(kdex >= hdi[0], kdex <= hdi[1]))[0] + hdi_interval = hdi.interval(flattened_data, hdi_probability) + hdi_lb = hdi_interval["lower_bound"] + hdi_ub = hdi_interval["upper_bound"] + mask = np.ix_(np.logical_and(kdex >= hdi_lb, kdex <= hdi_ub))[0] base = kdex[mask] lower = np.zeros((len(mask))) upper = kdey[mask] @@ -158,12 +160,11 @@ def compute_data( output = {} for figure_name in FIGURE_NAMES: output[figure_name] = {} - x, y, bandwidth = az.stats.density_utils._kde_linear( - x=flattened_data, - bw="scott", # Scott is chosen to mirror fast-kde in the JavaScript. - bw_return=True, - bw_fct=bw_factor, - ) + kde = sm.nonparametric.KDEUnivariate(flattened_data) + kde.fit(bw="scott", adjust=bw_factor, gridsize=512) + x = kde.support + y = kde.density + bandwidth = kde.bw if figure_name == "cumulative": y = np.cumsum(y) y /= y.max() diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/hdi.py b/src/beanmachine/ppl/diagnostics/tools/utils/hdi.py new file mode 100644 index 0000000000..d14594beff --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/hdi.py @@ -0,0 +1,90 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +import math +from typing import Dict, List, Union + + +def interval( + data: List[float], + hdi_probability: float, +) -> Dict[str, Union[List[float], float]]: + """Find the lower and upper bound of the Highest Density Interval (HDI). + + Parameters + ---------- + data : List[float] + Raw random variable data from the model. + hdi_probability : float + The highest density interval probability to use when calculating the HDI. + + Returns + ------- + Dict[str, Union[List[float], float]] + A dictionary defining the lower and upper bound for the HDI of the given data, + as well as the indices of the lower and upper bound for the sorted raw data. + """ + N = len(data) + sorted_data = sorted(data) + stop_index = math.floor(hdi_probability * N) + start_index = N - stop_index + left_data = sorted_data[stop_index:] + right_data = sorted_data[:start_index] + hdi = [] + for i in range(len(left_data)): + hdi.append(left_data[i] - right_data[i]) + lower_index = hdi.index(min(hdi)) + upper_index = lower_index + stop_index + return { + "lower_bound": sorted_data[lower_index], + "upper_bound": sorted_data[upper_index], + "lower_bound_index": lower_index, + "upper_bound_index": upper_index, + } + + +def data( + rv_data: List[float], + marginal_x: List[float], + marginal_y: List[float], + hdi_probability: float, +) -> Dict[str, Union[List[float], float]]: + """Construct x and y arrays from the HDI bounds using the raw random variable dataself. + + It also returns the actual HDI bounds of the random variable data. + + Parameters + ---------- + rv_data : List[float] + Raw random variable data from the model. + marginalX : List[float] + The support of the Kernel Density Estimate of the random variable. + marginalY : List[float] + The Kernel Density Estimate of the random variable. + hdiProbability : float + The highest density interval probability to use when alculating the HDI. + + Returns + ------- + Dict[str, Union[List[float], float]] + The x array for the HDI data is the "lower" key, while the y array for the HDI + data is the "upper" key and the "base" key is the support along the axis the HDI + is calculated. This nomenclature mirrors the Bokeh Band object, which is the + annotation type we will use when rendering the HDI intervals. + """ + hdi = interval(rv_data, hdi_probability) + base = [] + upper = [] + for i in range(len(marginal_x)): + if marginal_x[i] <= hdi["upper_bound"] and marginal_x[i] >= hdi["lower_bound"]: + base.append(marginal_x[i]) + upper.append(marginal_y[i]) + return { + "base": base, + "lower": [0] * len(base), + "upper": upper, + "lower_bound": hdi["lower_bound"], + "upper_bound": hdi["upper_bound"], + } From 08cb37e2f694cfd2ef775f3788f832abfd47b89c Mon Sep 17 00:00:00 2001 From: Andy Maloney Date: Thu, 22 Sep 2022 21:54:33 -0500 Subject: [PATCH 3/7] Add lock file, update copyright, and update .gitignore --- .gitignore | 3 +- .../ppl/diagnostics/tools/js/.eslintrc.js | 10 +- .../tools/js/src/marginal1d/callbacks.ts | 10 +- .../tools/js/src/marginal1d/index.ts | 10 +- .../tools/js/src/marginal1d/interfaces.ts | 10 +- .../diagnostics/tools/js/src/stats/array.ts | 10 +- .../tools/js/src/stats/dataTransformation.ts | 10 +- .../js/src/stats/highestDensityInterval.ts | 10 +- .../tools/js/src/stats/marginal.ts | 10 +- .../tools/js/src/stats/pointStatistic.ts | 10 +- .../diagnostics/tools/js/src/stats/utils.ts | 10 +- .../tools/js/src/types/fast-kde.d.ts | 10 +- .../diagnostics/tools/js/webpack.config.js | 10 +- .../ppl/diagnostics/tools/js/yarn.lock | 2462 +++++++++++++++++ 14 files changed, 2536 insertions(+), 49 deletions(-) create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/yarn.lock diff --git a/.gitignore b/.gitignore index 290f701c2d..d3e3fb84af 100644 --- a/.gitignore +++ b/.gitignore @@ -100,10 +100,11 @@ botorch/qmc/sobol.c* # Sphinx documentation sphinx/build/ -# Docusaurus +# Docusaurus and diagnostic tools website/build/ website/i18n/ website/node_modules/ +node_modules # Tutorials docs/overview/tutorials/*/*.mdx diff --git a/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js b/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js index 6dd122cd6a..351d8b36f8 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js +++ b/src/beanmachine/ppl/diagnostics/tools/js/.eslintrc.js @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ const OFF = 0; const WARNING = 1; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts index 05439000c8..1086ed8c24 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ import {Axis} from '@bokehjs/models/axes/axis'; import {cumulativeSum} from '../stats/array'; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts index 3db9709cae..2669193a60 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/index.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ import * as marginal1d from './callbacks'; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts index 8c9570c378..c4ff066e52 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ import {Plot} from '@bokehjs/models/plots/plot'; import {ColumnDataSource} from '@bokehjs/models/sources/column_data_source'; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts index 2d6983b10c..d67b2e7dbf 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/array.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ /** * Cumulative sum of the given data. diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts index 8048d0bc65..a8ecb46596 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/dataTransformation.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ /** * Scale the given array of numbers by the given scaleFactor. Note that this method diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts index cee265fa8b..e0356eb0b2 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/highestDensityInterval.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ import {numericalSort} from './array'; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts index cc44c8d4c8..fed70ee34c 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/marginal.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ import {density1d} from 'fast-kde/src/density1d'; import {scaleToOne} from './dataTransformation'; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts index 91355c033b..3ee47b9876 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/pointStatistic.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ /** * Syntactic sugar for summing an array of numbers. diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts index 4001255540..fd8049989f 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/stats/utils.ts @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ const left_edge_index = (point: number, intervals: number[]) => { const len = intervals.length; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts index f6e716e9db..fb89503344 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/types/fast-kde.d.ts @@ -1,6 +1,8 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ declare module 'fast-kde/*'; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js b/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js index 559e53c58b..cbe79da94d 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js +++ b/src/beanmachine/ppl/diagnostics/tools/js/webpack.config.js @@ -1,7 +1,9 @@ -// Copyright (c) Meta Platforms, Inc. and affiliates. -// -// This source code is licensed under the MIT license found in the -// LICENSE file in the root directory of this source tree. +/** + * Copyright (c) Meta Platforms, Inc. and affiliates. + * + * This source code is licensed under the MIT license found in the + * LICENSE file in the root directory of this source tree. + */ const path = require('path'); 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There is now a one-to-one correspondence between Python TypedDict objects and the TypeScript interface objects. - Redundancy in the Python TypedDict objects has been removed. - Fixes tooltips not rendering correctly. - Updates the link in the Help Tab of the tool such that when clicked it opens a new tab in the browser. - Moves a few peerDependencies to the devDependencies section in the package.json file. This was done because yarn issued a warning indicating there were missing peer dependencies. - Refactors the tool such that no calculations for the data displayed is done in Python. All updates and all calculations for the tool data is done exclusively in the browser. The rational for this change was to prevent users from having initial data calculated by Python, and subsequent updates calculated by JavaScript in the browser when interacting with the tool. There are notes in the code indicating why this change was made. --- .../ppl/diagnostics/tools/js/package.json | 6 +- .../tools/js/src/marginal1d/callbacks.ts | 35 +-- .../tools/js/src/marginal1d/interfaces.ts | 80 ++++-- .../ppl/diagnostics/tools/js/yarn.lock | 213 +++++++++++++- .../ppl/diagnostics/tools/marginal1d/tool.py | 66 +++-- .../diagnostics/tools/marginal1d/typing.py | 48 +--- .../ppl/diagnostics/tools/marginal1d/utils.py | 272 +++--------------- .../ppl/diagnostics/tools/utils/base.py | 2 +- .../ppl/diagnostics/tools/utils/hdi.py | 90 ------ 9 files changed, 363 insertions(+), 449 deletions(-) delete mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/hdi.py diff --git a/src/beanmachine/ppl/diagnostics/tools/js/package.json b/src/beanmachine/ppl/diagnostics/tools/js/package.json index 63d75c3184..bfa790284f 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/package.json +++ b/src/beanmachine/ppl/diagnostics/tools/js/package.json @@ -20,7 +20,10 @@ "eslint-config-airbnb": "^19.0.4", "eslint-config-prettier": "^8.5.0", "eslint-plugin-import": "^2.26.0", + "eslint-plugin-jsx-a11y": "^6.5.1", "eslint-plugin-prefer-arrow": "^1.2.3", + "eslint-plugin-react": "^7.28.0", + "eslint-plugin-react-hooks": "^4.3.0", "ts-loader": "^9.3.1", "ts-node": "^10.9.1", "typescript": "^4.7.4", @@ -42,9 +45,6 @@ "@types/typedarray-pool": "^1.1.2", "buffer": "^6.0.3", "cwise": "^1.0.10", - "eslint-plugin-jsx-a11y": "^6.5.1", - "eslint-plugin-react": "^7.28.0", - "eslint-plugin-react-hooks": "^4.3.0", "minimist": "^1.2.6", "quote-stream": "^1.0.2", "static-eval": "2.1.0", diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts index 1086ed8c24..e6c002fbd6 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/callbacks.ts @@ -56,7 +56,7 @@ export const updateAxisLabel = (axis: Axis, label: string | null): void => { * x-axis. * @param {number[]} [y_offset=[0, 10, 0]] - Offset values for the text along the * y-axis - * @returns {interfaces.Marginal1dStats} Object containing the computed stats. + * @returns {interfaces.LabelsData} Object containing the computed stats. */ export const computeStats = ( rawData: number[], @@ -66,7 +66,7 @@ export const computeStats = ( text_align: string[] = ['right', 'center', 'left'], x_offset: number[] = [-5, 0, 5], y_offset: number[] = [0, 10, 0], -): interfaces.Marginal1dStats => { +): interfaces.LabelsData => { // Set the default value to 0.89 if no default value has been given. const hdiProbability = hdiProb ?? 0.89; @@ -100,7 +100,7 @@ export const computeStats = ( * calculating the Kernel Density Estimate (KDE). * @param {number} hdiProbability - The highest density interval probability to use when * calculating the HDI. - * @returns {interfaces.Marginal1dData} The marginal distribution and cumulative + * @returns {interfaces.Data} The marginal distribution and cumulative * distribution calculated from the given random variable data. Point statistics are * also calculated. */ @@ -108,11 +108,11 @@ export const computeData = ( data: number[], bwFactor: number, hdiProbability: number, -): interfaces.Marginal1dData => { - const output = {} as interfaces.Marginal1dData; +): interfaces.Data => { + const output = {} as interfaces.Data; for (let i = 0; i < figureNames.length; i += 1) { const figureName = figureNames[i]; - output[figureName] = {} as interfaces.Marginal1dDatum; + output[figureName] = {} as interfaces.GlyphData; // Compute the one-dimensional KDE and its cumulative distribution. const distribution = oneD(data, bwFactor); @@ -130,7 +130,7 @@ export const computeData = ( output[figureName] = { distribution: distribution, hdi: hdiData(data, distribution.x, distribution.y, hdiProbability), - stats: {x: stats.x, y: stats.y}, + stats: {x: stats.x, y: stats.y, text: stats.text}, labels: stats, }; } @@ -146,10 +146,10 @@ export const computeData = ( * calculating the kernel density estimate. * @param {number} hdiProbability - The highest density interval probability to use when * calculating the HDI. - * @param {interfaces.Marginal1dSources} sources - Bokeh sources used to render glyphs - * in the application. - * @param {interfaces.Marginal1dFigures} figures - Bokeh figures shown in the + * @param {interfaces.Sources} sources - Bokeh sources used to render glyphs in the * application. + * @param {interfaces.Figures} figures - Bokeh figures shown in the application. + * @param {interfaces.Tooltips} tooltips - Bokeh tooltips shown on the glyphs. * @returns {number} We display the value of the bandwidth used for computing the Kernel * Density Estimate in a div, and must return that value here in order to update the * value displayed to the user. @@ -159,8 +159,9 @@ export const update = ( rvName: string, bwFactor: number, hdiProbability: number, - sources: interfaces.Marginal1dSources, - figures: interfaces.Marginal1dFigures, + sources: interfaces.Sources, + figures: interfaces.Figures, + tooltips: interfaces.Tooltips, ): number => { const computedData = computeData(data, bwFactor, hdiProbability); for (let i = 0; i < figureNames.length; i += 1) { @@ -175,15 +176,15 @@ export const update = ( lower: computedData[figureName].hdi.lower, upper: computedData[figureName].hdi.upper, }; - sources[figureName].stats.data = { - x: computedData[figureName].stats.x, - y: computedData[figureName].stats.y, - label: computedData[figureName].labels.text, - }; + sources[figureName].stats.data = computedData[figureName].stats; sources[figureName].labels.data = computedData[figureName].labels; // Update the axes labels. updateAxisLabel(figures[figureName].below[0], rvName); + + // Update the tooltips. + tooltips[figureName].stats.tooltips = [['', '@text']]; + tooltips[figureName].distribution.tooltips = [[rvName, '@x']]; } return computedData.marginal.distribution.bandwidth; }; diff --git a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts index c4ff066e52..fe8aede9e5 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts +++ b/src/beanmachine/ppl/diagnostics/tools/js/src/marginal1d/interfaces.ts @@ -7,6 +7,7 @@ import {Plot} from '@bokehjs/models/plots/plot'; import {ColumnDataSource} from '@bokehjs/models/sources/column_data_source'; +import {HoverTool} from '@bokehjs/models/tools/inspectors/hover_tool'; // NOTE: In the corresponding Python typing files for the diagnostic tool, we define // similar types using a TypedDict object. TypeScript allows us to maintain @@ -18,7 +19,28 @@ import {ColumnDataSource} from '@bokehjs/models/sources/column_data_source'; // keep the semantic information here at the expense of losing type information // similarly to what is done in Python. -export interface Marginal1dStats { +export interface DistributionData { + [key: string]: any; + x: number[]; + y: number[]; + bandwidth: number; +} + +export interface HDIData { + [key: string]: any; + base: number[]; + lower: number[]; + upper: number[]; +} + +export interface StatsData { + [key: string]: any; + x: number[]; + y: number[]; + text: string[]; +} + +export interface LabelsData { [key: string]: any; x: number[]; y: number[]; @@ -28,37 +50,21 @@ export interface Marginal1dStats { y_offset: number[]; } -// Used for both the marginal and cumulative distribution objects. -export interface Marginal1dDatum { - distribution: {[key: string]: any; x: number[]; y: number[]; bandwidth: number}; - hdi: { - [key: string]: any; - base: number[]; - lower: number[]; - upper: number[]; - lowerBound: number; - upperBound: number; - }; - stats: {[key: string]: any; x: number[]; y: number[]}; - labels: { - [key: string]: any; - x: number[]; - y: number[]; - text: string[]; - text_align: string[]; - x_offset: number[]; - y_offset: number[]; - }; +export interface GlyphData { + [key: string]: any; + distribution: DistributionData; + hdi: HDIData; + stats: StatsData; + labels: LabelsData; } -export interface Marginal1dData { +export interface Data { [key: string]: any; - marginal: Marginal1dDatum; - cumulative: Marginal1dDatum; + marginal: GlyphData; + cumulative: GlyphData; } -// Used for both the marginal and cumulative distribution objects. -export interface Marginal1dSource { +export interface Source { [key: string]: any; distribution: ColumnDataSource; hdi: ColumnDataSource; @@ -66,14 +72,26 @@ export interface Marginal1dSource { labels: ColumnDataSource; } -export interface Marginal1dSources { +export interface Sources { [key: string]: any; - marginal: Marginal1dSource; - cumulative: Marginal1dSource; + marginal: Source; + cumulative: Source; } -export interface Marginal1dFigures { +export interface Figures { [key: string]: any; marginal: Plot; cumulative: Plot; } + +export interface Tooltip { + [key: string]: any; + distribution: HoverTool; + stats: HoverTool; +} + +export interface Tooltips { + [key: string]: any; + marginal: Tooltip; + cumulative: Tooltip; +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock b/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock index d6d1d01214..b3329256d2 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock +++ b/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock @@ -2,7 +2,15 @@ # yarn lockfile v1 -"@babel/runtime@^7.9.2": +"@babel/runtime-corejs3@^7.10.2": + version "7.19.1" + resolved "https://registry.yarnpkg.com/@babel/runtime-corejs3/-/runtime-corejs3-7.19.1.tgz#f0cbbe7edda7c4109cd253bb1dee99aba4594ad9" + integrity 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resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-4.3.0.tgz#398ad3f3c5a24948be7725e83d11a7de28cdbd1d" integrity sha512-39nnKffWz8xN1BU/2c79n9nB9HDzo0niYUqx6xyqUnyoAnQyyWpOTdZEeiCch8BBu515t4wp9ZmgVfVhn9EBpw== -estraverse@^5.1.0, estraverse@^5.2.0: +estraverse@^5.1.0, estraverse@^5.2.0, estraverse@^5.3.0: version "5.3.0" resolved "https://registry.yarnpkg.com/estraverse/-/estraverse-5.3.0.tgz#2eea5290702f26ab8fe5370370ff86c965d21123" integrity sha512-MMdARuVEQziNTeJD8DgMqmhwR11BRQ/cBP+pLtYdSTnf3MIO8fFeiINEbX36ZdNlfU/7A9f3gUw49B3oQsvwBA== @@ -1575,6 +1675,11 @@ js-sdsl@^4.1.4: resolved "https://registry.yarnpkg.com/js-sdsl/-/js-sdsl-4.1.4.tgz#78793c90f80e8430b7d8dc94515b6c77d98a26a6" integrity sha512-Y2/yD55y5jteOAmY50JbUZYwk3CP3wnLPEZnlR1w9oKhITrBEtAxwuWKebFf8hMrPMgbYwFoWK/lH2sBkErELw== +"js-tokens@^3.0.0 || ^4.0.0": + version "4.0.0" + resolved "https://registry.yarnpkg.com/js-tokens/-/js-tokens-4.0.0.tgz#19203fb59991df98e3a287050d4647cdeaf32499" + integrity sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ== + js-yaml@^4.1.0: version "4.1.0" resolved "https://registry.yarnpkg.com/js-yaml/-/js-yaml-4.1.0.tgz#c1fb65f8f5017901cdd2c951864ba18458a10602" @@ -1604,11 +1709,31 @@ json5@^1.0.1: dependencies: minimist "^1.2.0" +"jsx-ast-utils@^2.4.1 || ^3.0.0", jsx-ast-utils@^3.3.2: + version "3.3.3" + resolved "https://registry.yarnpkg.com/jsx-ast-utils/-/jsx-ast-utils-3.3.3.tgz#76b3e6e6cece5c69d49a5792c3d01bd1a0cdc7ea" + integrity sha512-fYQHZTZ8jSfmWZ0iyzfwiU4WDX4HpHbMCZ3gPlWYiCl3BoeOTsqKBqnTVfH2rYT7eP5c3sVbeSPHnnJOaTrWiw== + dependencies: + array-includes "^3.1.5" + object.assign "^4.1.3" + kind-of@^6.0.2: version "6.0.3" resolved "https://registry.yarnpkg.com/kind-of/-/kind-of-6.0.3.tgz#07c05034a6c349fa06e24fa35aa76db4580ce4dd" integrity sha512-dcS1ul+9tmeD95T+x28/ehLgd9mENa3LsvDTtzm3vyBEO7RPptvAD+t44WVXaUjTBRcrpFeFlC8WCruUR456hw== +language-subtag-registry@~0.3.2: + version "0.3.22" + resolved "https://registry.yarnpkg.com/language-subtag-registry/-/language-subtag-registry-0.3.22.tgz#2e1500861b2e457eba7e7ae86877cbd08fa1fd1d" + integrity sha512-tN0MCzyWnoz/4nHS6uxdlFWoUZT7ABptwKPQ52Ea7URk6vll88bWBVhodtnlfEuCcKWNGoc+uGbw1cwa9IKh/w== + +language-tags@^1.0.5: + version "1.0.5" + resolved "https://registry.yarnpkg.com/language-tags/-/language-tags-1.0.5.tgz#d321dbc4da30ba8bf3024e040fa5c14661f9193a" + integrity sha512-qJhlO9cGXi6hBGKoxEG/sKZDAHD5Hnu9Hs4WbOY3pCWXDhw0N8x1NenNzm2EnNLkLkk7J2SdxAkDSbb6ftT+UQ== + dependencies: + language-subtag-registry "~0.3.2" + levn@^0.4.1: version "0.4.1" resolved "https://registry.yarnpkg.com/levn/-/levn-0.4.1.tgz#ae4562c007473b932a6200d403268dd2fffc6ade" @@ -1641,6 +1766,13 @@ lodash.merge@^4.6.2: resolved "https://registry.yarnpkg.com/lodash.merge/-/lodash.merge-4.6.2.tgz#558aa53b43b661e1925a0afdfa36a9a1085fe57a" integrity sha512-0KpjqXRVvrYyCsX1swR/XTK0va6VQkQM6MNo7PqW77ByjAhoARA8EfrP1N4+KlKj8YS0ZUCtRT/YUuhyYDujIQ== +loose-envify@^1.4.0: + version "1.4.0" + resolved "https://registry.yarnpkg.com/loose-envify/-/loose-envify-1.4.0.tgz#71ee51fa7be4caec1a63839f7e682d8132d30caf" + integrity sha512-lyuxPGr/Wfhrlem2CL/UcnUc1zcqKAImBDzukY7Y5F/yQiNdko6+fRLevlw1HgMySw7f611UIY408EtxRSoK3Q== + dependencies: + js-tokens "^3.0.0 || ^4.0.0" + lru-cache@^6.0.0: version "6.0.0" resolved "https://registry.yarnpkg.com/lru-cache/-/lru-cache-6.0.0.tgz#6d6fe6570ebd96aaf90fcad1dafa3b2566db3a94" @@ -1760,6 +1892,11 @@ nouislider@^15.4.0: resolved "https://registry.yarnpkg.com/nouislider/-/nouislider-15.6.1.tgz#957aa24a583aafbef9a4ac20098ceefa99245660" integrity sha512-1T5AfeEMGrGM87UJ+qAHvauPfCe/woOjYV/o29fp21+XgGuGpkM1Udo7mPHnidu4+cxlj35rDBWKiA6Mefemrg== +object-assign@^4.1.1: + version "4.1.1" + resolved "https://registry.yarnpkg.com/object-assign/-/object-assign-4.1.1.tgz#2109adc7965887cfc05cbbd442cac8bfbb360863" + integrity sha512-rJgTQnkUnH1sFw8yT6VSU3zD3sWmu6sZhIseY8VX+GRu3P6F7Fu+JNDoXfklElbLJSnc3FUQHVe4cU5hj+BcUg== + object-inspect@^1.12.2, object-inspect@^1.9.0: version "1.12.2" resolved "https://registry.yarnpkg.com/object-inspect/-/object-inspect-1.12.2.tgz#c0641f26394532f28ab8d796ab954e43c009a8ea" @@ -1770,7 +1907,7 @@ object-keys@^1.1.1: resolved "https://registry.yarnpkg.com/object-keys/-/object-keys-1.1.1.tgz#1c47f272df277f3b1daf061677d9c82e2322c60e" integrity sha512-NuAESUOUMrlIXOfHKzD6bpPu3tYt3xvjNdRIQ+FeT0lNb4K8WR70CaDxhuNguS2XG+GjkyMwOzsN5ZktImfhLA== -object.assign@^4.1.2, object.assign@^4.1.4: +object.assign@^4.1.2, object.assign@^4.1.3, object.assign@^4.1.4: version "4.1.4" resolved "https://registry.yarnpkg.com/object.assign/-/object.assign-4.1.4.tgz#9673c7c7c351ab8c4d0b516f4343ebf4dfb7799f" integrity sha512-1mxKf0e58bvyjSCtKYY4sRe9itRk3PJpquJOjeIkz885CczcI4IvJJDLPS72oowuSh+pBxUFROpX+TU++hxhZQ== @@ -1789,6 +1926,23 @@ object.entries@^1.1.5: define-properties "^1.1.3" es-abstract "^1.19.1" +object.fromentries@^2.0.5: + version "2.0.5" + resolved "https://registry.yarnpkg.com/object.fromentries/-/object.fromentries-2.0.5.tgz#7b37b205109c21e741e605727fe8b0ad5fa08251" + integrity sha512-CAyG5mWQRRiBU57Re4FKoTBjXfDoNwdFVH2Y1tS9PqCsfUTymAohOkEMSG3aRNKmv4lV3O7p1et7c187q6bynw== + dependencies: + call-bind "^1.0.2" + define-properties "^1.1.3" + es-abstract "^1.19.1" + +object.hasown@^1.1.1: + version "1.1.1" + resolved "https://registry.yarnpkg.com/object.hasown/-/object.hasown-1.1.1.tgz#ad1eecc60d03f49460600430d97f23882cf592a3" + integrity sha512-LYLe4tivNQzq4JdaWW6WO3HMZZJWzkkH8fnI6EebWl0VZth2wL2Lovm74ep2/gZzlaTdV62JZHEqHQ2yVn8Q/A== + dependencies: + define-properties "^1.1.4" + es-abstract "^1.19.5" + object.values@^1.1.5: version "1.1.5" resolved "https://registry.yarnpkg.com/object.values/-/object.values-1.1.5.tgz#959f63e3ce9ef108720333082131e4a459b716ac" @@ -1912,6 +2066,15 @@ proj4@^2.7.5: mgrs "1.0.0" wkt-parser "^1.3.1" +prop-types@^15.8.1: + version "15.8.1" + resolved "https://registry.yarnpkg.com/prop-types/-/prop-types-15.8.1.tgz#67d87bf1a694f48435cf332c24af10214a3140b5" + integrity sha512-oj87CgZICdulUohogVAR7AjlC0327U4el4L6eAvOqCeudMDVU0NThNaV+b9Df4dXgSP1gXMTnPdhfe/2qDH5cg== + dependencies: + loose-envify "^1.4.0" + object-assign "^4.1.1" + react-is "^16.13.1" + punycode@^2.1.0: version "2.1.1" resolved "https://registry.yarnpkg.com/punycode/-/punycode-2.1.1.tgz#b58b010ac40c22c5657616c8d2c2c02c7bf479ec" @@ -1929,6 +2092,11 @@ randombytes@^2.1.0: dependencies: safe-buffer "^5.1.0" +react-is@^16.13.1: + version "16.13.1" + resolved "https://registry.yarnpkg.com/react-is/-/react-is-16.13.1.tgz#789729a4dc36de2999dc156dd6c1d9c18cea56a4" + integrity sha512-24e6ynE2H+OKt4kqsOvNd8kBpV65zoxbA4BVsEOB3ARVWQki/DHzaUoC5KuON/BiccDaCCTZBuOcfZs70kR8bQ== + rechoir@^0.7.0: version "0.7.1" resolved "https://registry.yarnpkg.com/rechoir/-/rechoir-0.7.1.tgz#9478a96a1ca135b5e88fc027f03ee92d6c645686" @@ -1948,7 +2116,7 @@ regenerator-runtime@^0.13.4: resolved "https://registry.yarnpkg.com/regenerator-runtime/-/regenerator-runtime-0.13.9.tgz#8925742a98ffd90814988d7566ad30ca3b263b52" integrity sha512-p3VT+cOEgxFsRRA9X4lkI1E+k2/CtnKtU4gcxyaCUreilL/vqI6CdZ3wxVUx3UOUg+gnUOQQcRI7BmSI656MYA== -regexp.prototype.flags@^1.4.3: +regexp.prototype.flags@^1.4.1, regexp.prototype.flags@^1.4.3: version "1.4.3" resolved "https://registry.yarnpkg.com/regexp.prototype.flags/-/regexp.prototype.flags-1.4.3.tgz#87cab30f80f66660181a3bb7bf5981a872b367ac" integrity sha512-fjggEOO3slI6Wvgjwflkc4NFRCTZAu5CnNfBd5qOMYhWdn67nJBBu34/TkD++eeFmd8C9r9jfXJ27+nSiRkSUA== @@ -1993,6 +2161,15 @@ resolve@^1.20.0, resolve@^1.22.0, resolve@^1.9.0: path-parse "^1.0.7" supports-preserve-symlinks-flag "^1.0.0" +resolve@^2.0.0-next.3: + version "2.0.0-next.4" + resolved "https://registry.yarnpkg.com/resolve/-/resolve-2.0.0-next.4.tgz#3d37a113d6429f496ec4752d2a2e58efb1fd4660" + integrity sha512-iMDbmAWtfU+MHpxt/I5iWI7cY6YVEZUQ3MBgPQ++XD1PELuJHIl82xBmObyP2KyQmkNB2dsqF7seoQQiAn5yDQ== + dependencies: + is-core-module "^2.9.0" + path-parse "^1.0.7" + supports-preserve-symlinks-flag "^1.0.0" + reusify@^1.0.4: version "1.0.4" resolved "https://registry.yarnpkg.com/reusify/-/reusify-1.0.4.tgz#90da382b1e126efc02146e90845a88db12925d76" @@ -2114,6 +2291,20 @@ sprintf-js@^1.1.2: resolved "https://registry.yarnpkg.com/sprintf-js/-/sprintf-js-1.1.2.tgz#da1765262bf8c0f571749f2ad6c26300207ae673" integrity sha512-VE0SOVEHCk7Qc8ulkWw3ntAzXuqf7S2lvwQaDLRnUeIEaKNQJzV6BwmLKhOqT61aGhfUMrXeaBk+oDGCzvhcug== +string.prototype.matchall@^4.0.7: + version "4.0.7" + resolved "https://registry.yarnpkg.com/string.prototype.matchall/-/string.prototype.matchall-4.0.7.tgz#8e6ecb0d8a1fb1fda470d81acecb2dba057a481d" + integrity sha512-f48okCX7JiwVi1NXCVWcFnZgADDC/n2vePlQ/KUCNqCikLLilQvwjMO8+BHVKvgzH0JB0J9LEPgxOGT02RoETg== + dependencies: + call-bind "^1.0.2" + define-properties "^1.1.3" + es-abstract "^1.19.1" + get-intrinsic "^1.1.1" + has-symbols "^1.0.3" + internal-slot "^1.0.3" + regexp.prototype.flags "^1.4.1" + side-channel "^1.0.4" + string.prototype.trimend@^1.0.5: version "1.0.5" resolved "https://registry.yarnpkg.com/string.prototype.trimend/-/string.prototype.trimend-1.0.5.tgz#914a65baaab25fbdd4ee291ca7dde57e869cb8d0" diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py index 3cf5ad1584..2d9a998c1d 100644 --- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py @@ -50,34 +50,37 @@ def create_document(self: T) -> str: # Initialize widget values using Python. rv_name = self.rv_names[0] bw_factor = 1.0 - hdi_probability = 0.89 - - # Compute the initial data displayed in the tool using Python. It is not quite - # clear how to initialize the tool without requiring Python to first render it - # using data it calculates. - rv_data = self.data[rv_name] - computed_data = utils.compute_data( - data=rv_data, - bw_factor=bw_factor, - hdi_probability=hdi_probability, - ) - bandwidth = computed_data["marginal"]["distribution"]["bandwidth"] - - # Create the Bokeh sources using Python with data calculated in Python. - sources = utils.create_sources(data=computed_data) - - # Create the figures for the tool using Python. + bandwidth = 1.0 + + # NOTE: We are going to use Python and Bokeh to render the tool in the notebook + # output cell, however, we WILL NOT use Python to calculate any of the + # statistics displayed in the tool. We do this so we can make the BROWSER + # run all the calculations based on user interactions. If we did not + # employ this strategy, then the initial display a user would receive + # would be calculated by Python, and any subsequent updates would be + # calculated by JavaScript. The side-effect of having two backends + # calculate data could cause the figures to flicker, which would not be a + # good end user experience. + # + # Bokeh 3.0 is implementing an "on load" feature, which would nullify this + # requirement, and until that version is released, we have to employ this + # work-around. + + # Create empty Bokeh sources using Python. + sources = utils.create_sources() + + # Create empty figures for the tool using Python. figures = utils.create_figures(rv_name=rv_name) - # Create the glyphs and attach them to the figures using Python. - glyphs = utils.create_glyphs(data=computed_data) + # Create empty glyphs and attach them to the figures using Python. + glyphs = utils.create_glyphs() utils.add_glyphs(sources=sources, figures=figures, glyphs=glyphs) - # Create the annotations and attach them to the figures using Python. + # Create empty annotations and attach them to the figures using Python. annotations = utils.create_annotations(sources=sources) utils.add_annotations(figures=figures, annotations=annotations) - # Create the tool tips and attach them to the figures using Python. + # Create empty tool tips and attach them to the figures using Python. tooltips = utils.create_tooltips(figures=figures, rv_name=rv_name) utils.add_tooltips(figures=figures, tooltips=tooltips) @@ -89,11 +92,7 @@ def create_document(self: T) -> str: bw_factor=bw_factor, ) - # Below we create callbacks for the widgets using JavaScript. The below JS uses - # a try/catch block to ensure the proper methods are in the notebook output - # cell no matter which widget a user interacts with first. - # NOTE: When this callback is invoked, data is no longer being calculated with - # Python, and is instead being calculated by the browser. + # Create callbacks for the tool using JavaScript. callback_js = f""" const rvName = widgets.rv_select.value; const rvData = data[rvName].flat(); @@ -106,6 +105,7 @@ def create_document(self: T) -> str: hdiProbability, sources, figures, + tooltips, ); }} catch (error) {{ {self.js} @@ -116,6 +116,7 @@ def create_document(self: T) -> str: hdiProbability, sources, figures, + tooltips, ); }} """ @@ -128,6 +129,7 @@ def create_document(self: T) -> str: "widgets": widgets, "sources": sources, "figures": figures, + "tooltips": tooltips, } # Each widget requires slightly different JS, except for the sliders. @@ -149,13 +151,15 @@ def create_document(self: T) -> str: rv_select_callback = CustomJS(args=callback_arguments, code=rv_select_js) slider_callback = CustomJS(args=callback_arguments, code=slider_js) - # Below is where we tell Python to use the JS callbacks based on user - # interaction. + # Tell Python to use the JavaScript. widgets["rv_select"].js_on_change("value", rv_select_callback) widgets["bw_factor_slider"].js_on_change("value", slider_callback) widgets["hdi_slider"].js_on_change("value", slider_callback) - # Create the tool view using Python. Note that all subsequent callbacks will be - # performed by JavaScript. + # Create the view of the tool and serialize it into HTML using static resources + # from Bokeh. Embedding the tool in this manner prevents external CDN calls for + # JavaScript resources, and prevents the user from having to know where the + # Bokeh server is. tool_view = utils.create_view(figures=figures, widgets=widgets) - return file_html(tool_view, resources=INLINE) + output = file_html(tool_view, resources=INLINE) + return output diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py index ff918d941d..881cd308e4 100644 --- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/typing.py @@ -66,15 +66,6 @@ class _LabelsData(TypedDict): # pyre-ignore y_offset: List[int] -class _StatsAndLabelsData(TypedDict): # pyre-ignore - x: List[float] - y: List[float] - text: List[str] - text_align: List[str] - x_offset: List[int] - y_offset: List[int] - - class _GlyphData(TypedDict): # pyre-ignore distribtution: _DistributionData hdi: _HDIData @@ -87,7 +78,7 @@ class _Data(TypedDict): # pyre-ignore cumulative: _GlyphData -class _GlyphSources(TypedDict): # pyre-ignore +class _Source(TypedDict): # pyre-ignore distribution: ColumnDataSource hdi: ColumnDataSource stats: ColumnDataSource @@ -95,8 +86,8 @@ class _GlyphSources(TypedDict): # pyre-ignore class _Sources(TypedDict): # pyre-ignore - marginal: _GlyphSources - cumulative: _GlyphSources + marginal: _Source + cumulative: _Source class _Figures(TypedDict): # pyre-ignore @@ -114,49 +105,34 @@ class _StatsGlyph(TypedDict): # pyre-ignore hover_glyph: Circle -class _MarginalFigureGlyphs(TypedDict): # pyre-ignore - distribution: _DistributionGlyph - stats: _StatsGlyph - - -class _CumulativeFigureGlyphs(TypedDict): # pyre-ignore +class _FigureGlyphs(TypedDict): # pyre-ignore distribution: _DistributionGlyph stats: _StatsGlyph class _Glyphs(TypedDict): # pyre-ignore - marginal: _MarginalFigureGlyphs - cumulative: _CumulativeFigureGlyphs - - -class _MarginalFigureAnnotations(TypedDict): # pyre-ignore - hdi: Band - labels: LabelSet + marginal: _FigureGlyphs + cumulative: _FigureGlyphs -class _CumulativeFigureAnnotations(TypedDict): # pyre-ignore +class _FigureAnnotations(TypedDict): # pyre-ignore hdi: Band labels: LabelSet class _Annotations(TypedDict): # pyre-ignore - marginal: _MarginalFigureAnnotations - cumulative: _CumulativeFigureAnnotations - - -class _MarginalFigureTooltips(TypedDict): # pyre-ignore - distribution: HoverTool - stats: HoverTool + marginal: _FigureAnnotations + cumulative: _FigureAnnotations -class _CumulativeFigureTooltips(TypedDict): # pyre-ignore +class _Tooltip(TypedDict): # pyre-ignore distribution: HoverTool stats: HoverTool class _Tooltips(TypedDict): # pyre-ignore - marginal: _MarginalFigureTooltips - cumulative: _CumulativeFigureTooltips + marginal: _Tooltip + cumulative: _Tooltip class _Widgets(TypedDict): # pyre-ignore diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py index f5486f7060..5891759c14 100644 --- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py @@ -5,13 +5,11 @@ """Methods used to generate the diagnostic tool.""" -from itertools import chain -from typing import List, Optional +from typing import List import numpy as np -import statsmodels.api as sm from beanmachine.ppl.diagnostics.tools.marginal1d import typing -from beanmachine.ppl.diagnostics.tools.utils import hdi, plotting_utils +from beanmachine.ppl.diagnostics.tools.utils import plotting_utils from bokeh.models.annotations import Band, LabelSet from bokeh.models.glyphs import Circle, Line from bokeh.models.layouts import Column, Row @@ -27,187 +25,48 @@ PLOT_WIDTH = 500 PLOT_HEIGHT = 500 FIGURE_NAMES = ["marginal", "cumulative"] - - -def compute_stats( - data: List[List[float]], - kde_x: List[float], - kde_y: List[float], - hdi_probability: float, - text_align: Optional[List[str]] = None, - x_offset: Optional[List[int]] = None, - y_offset: Optional[List[int]] = None, - return_labels: bool = False, -) -> typing.StatsAndLabelsData: - """Compute statistics for the given data, and its KDE. - - Parameters - ---------- - data : List[List[float]] - JSON serializable representation of the model data for all random variables. - kde_x : List[float] - The x-axis KDE estimate of the random variable data. - kde_y : List[float] - The y-axis KDE estimate of the random variable data. - hdi_probability : float - The HDI probability to use when calculating the HDI bounds. - text_align : Optional[List[str]], optional default is ``None`` - How to display label justifications for the statistics in Bokeh. - x_offset : Optional[List[int]], optional default is ``None`` - x-axis offsets for the labels. - y_offset : Optional[List[int]], optional default is ``None`` - y-axis offsets for the labels. - return_labels : bool, optional default is ``False`` - ``True`` returns labels to be used in the Bokeh figure. - - Returns - ------- - typing.StatsAndLabelsData - A dictionary for statistics and labels for the Bokeh figures. - """ - if text_align is None: - text_align = ["right", "center", "left"] - if x_offset is None: - x_offset = [-5, 0, 5] - if y_offset is None: - y_offset = [0, 10, 0] - - flattened_data = np.array(list(chain(*data))) - kdex = np.array(kde_x) - kdey = np.array(kde_y) - mean = np.mean(kdex) - hdi_interval = hdi.interval(flattened_data, hdi_probability) - x = [hdi_interval["lower_bound"], mean, hdi_interval["upper_bound"]] - y = np.interp(x=np.array(x).astype(float), xp=kdex, fp=kdey) - text = [ - f"Lower HDI: {hdi_interval['lower_bound']:.4}", - f"Mean: {mean:.4}", - f"Upper HDI: {hdi_interval['upper_bound']:.4}", - ] - stats = {"x": x, "y": y.tolist(), "text": text} - labels = {} - if return_labels: - labels = { - "x": x, - "y": y.tolist(), - "text": text, - "text_align": text_align, - "x_offset": x_offset, - "y_offset": y_offset, - } - return {"stats": stats, "labels": labels} - - -def hdi_data( - data: List[List[float]], - kde_x: List[float], - kde_y: List[float], - hdi_probability: float, -) -> typing.HDIData: - """Calculate the HDI arrays needed for the Bokeh annotation. - - Parameters - ---------- - data : List[List[float]] - JSON serializable representation of the model data for one random variable. - kde_x : npt.NDArray - The x-axis KDE estimate of the random variable data. - kde_y : npt.NDArray - The y-axis KDE estimate of the random variable data. - hdi_probability : float - The HDI probability to use when calculating the HDI bounds. - - Returns - ------- - typing.HDIData - Arrays used to create the Bokeh annotation for the HDI. - """ - flattened_data = np.array(list(chain(*data))) - kdex = np.array(kde_x) - kdey = np.array(kde_y) - hdi_interval = hdi.interval(flattened_data, hdi_probability) - hdi_lb = hdi_interval["lower_bound"] - hdi_ub = hdi_interval["upper_bound"] - mask = np.ix_(np.logical_and(kdex >= hdi_lb, kdex <= hdi_ub))[0] - base = kdex[mask] - lower = np.zeros((len(mask))) - upper = kdey[mask] - return {"base": base.tolist(), "lower": lower.tolist(), "upper": upper.tolist()} - - -def compute_data( - data: List[List[float]], - bw_factor: float, - hdi_probability: float, -) -> typing.Data: - """Compute effective sample size estimates using the given data. - - Parameters - ---------- - data : List[List[float]] - JSON serializable representation of the model data for one random variables. - bw_factor : float - Multiplicative factor used when calculating the kernel density estimate. - hdi_probability : float - The HDI probability to use when calculating the HDI bounds. - - Returns - ------- - typing.Data - A dictionary of data used for the tool. - """ - flattened_data = np.array(list(chain(*data))) - output = {} - for figure_name in FIGURE_NAMES: - output[figure_name] = {} - kde = sm.nonparametric.KDEUnivariate(flattened_data) - kde.fit(bw="scott", adjust=bw_factor, gridsize=512) - x = kde.support - y = kde.density - bandwidth = kde.bw - if figure_name == "cumulative": - y = np.cumsum(y) - y /= y.max() - stats_and_labels = compute_stats( - data=data, - kde_x=x, - kde_y=y, - hdi_probability=hdi_probability, - return_labels=True, - ) - output[figure_name] = { - "distribution": { - "x": x.tolist(), - "y": y.tolist(), - "bandwidth": bandwidth, - }, - "hdi": hdi_data( - data=data, - kde_x=x, - kde_y=y, - hdi_probability=hdi_probability, - ), - "stats": stats_and_labels["stats"], - "labels": stats_and_labels["labels"], - } - return output - - -def create_sources(data: typing.Data) -> typing.Sources: +# Define what the empty data object looks like in order to make the browser handle all +# computations. +EMPTY_DATA = { + "marginal": { + "distribution": {"x": [], "y": [], "bandwidth": np.NaN}, + "hdi": {"base": [], "lower": [], "upper": []}, + "stats": {"x": [], "y": [], "text": []}, + "labels": { + "x": [], + "y": [], + "text": [], + "text_align": [], + "x_offset": [], + "y_offset": [], + }, + }, + "cumulative": { + "distribution": {"x": [], "y": [], "bandwidth": np.NaN}, + "hdi": {"base": [], "lower": [], "upper": []}, + "stats": {"x": [], "y": [], "text": []}, + "labels": { + "x": [], + "y": [], + "text": [], + "text_align": [], + "x_offset": [], + "y_offset": [], + }, + }, +} + + +def create_sources() -> typing.Sources: """Create Bokeh sources from the given data that will be bound to glyphs. - Parameters - ---------- - data : typing.Data - A dictionary of data used for the tool. - Returns ------- typing.Sources A dictionary of Bokeh ColumnDataSource objects. """ output = {} - for figure_name, figure_data in data.items(): + for figure_name, figure_data in EMPTY_DATA.items(): output[figure_name] = {} for glyph_name, glyph_data in figure_data.items(): if "bandwidth" in list(glyph_data.keys()): @@ -247,14 +106,9 @@ def create_figures(rv_name: str) -> typing.Figures: return output -def create_glyphs(data: typing.Data) -> typing.Glyphs: +def create_glyphs() -> typing.Glyphs: """Create the glyphs used for the figures of the tool. - Parameters - ---------- - data : typing.Data - A dictionary of data used for the tool. - Returns ------- typing.Glyphs @@ -262,7 +116,7 @@ def create_glyphs(data: typing.Data) -> typing.Glyphs: """ palette = plotting_utils.choose_palette(num_colors=2) output = {} - for figure_name, figure_data in data.items(): + for figure_name, figure_data in EMPTY_DATA.items(): output[figure_name] = {} for glyph_name, _ in figure_data.items(): if glyph_name in ["distribution", "stats"]: @@ -541,7 +395,11 @@ def help_page() -> Div: 2nd edition. Chapman and Hall/CRC - + doi: 10.1201/9780429029608 . @@ -597,47 +455,3 @@ def create_view(widgets: typing.Widgets, figures: typing.Figures) -> Tabs: name="toolPanel", ) return Tabs(tabs=[tool_panel, help_panel]) - - -def update( - data: List[List[float]], - sources: typing.Sources, - figures: typing.Figures, - rv_name: str, - bw_factor: float, - hdi_probability: float, -) -> float: - """Update the tool based on user interaction. - - Parameters - ---------- - data : List[List[float]] - JSON serializable representation of the model data for one random variables. - sources : typing.Sources - A dictionary of Bokeh ColumnDataSource objects. - figures : typing.Figures - A dictionary of Bokeh Figure objects. - rv_name : str - The string representation of the random variable data. - rv_names : List[str] - A list of all available random variable names. - hdi_probability : float - The HDI probability to use when calculating the HDI bounds. - - Returns - ------- - float - Returns the bandwidth used to calculate the KDE. - """ - computed_data = compute_data(data, bw_factor, hdi_probability) - bw = computed_data["marginal"]["distribution"]["bandwidth"][0] - for figure_name, figure_sources in sources.items(): - fig = figures[figure_name] - fig.xaxis.axis_label = rv_name - figure_data = computed_data[figure_name] - for glyph_name, glyph_source in figure_sources.items(): - glyph_data = figure_data[glyph_name] - if "bandwidth" in list(glyph_data.keys()): - glyph_data.pop("bandwidth") - glyph_source.data = dict(**glyph_data) - return float(bw) diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/base.py b/src/beanmachine/ppl/diagnostics/tools/utils/base.py index 8f290ec068..3a624c219b 100644 --- a/src/beanmachine/ppl/diagnostics/tools/utils/base.py +++ b/src/beanmachine/ppl/diagnostics/tools/utils/base.py @@ -23,7 +23,7 @@ class Base(ABC): @abstractmethod def __init__(self: T, mcs: MonteCarloSamples) -> None: self.data = serialize_bm(mcs) - self.rv_names = list(self.data.keys()) + self.rv_names = ["Select a random variable..."] + list(self.data.keys()) self.num_chains = mcs.num_chains self.num_draws = mcs.get_num_samples() self.palette = plotting_utils.choose_palette(self.num_chains) diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/hdi.py b/src/beanmachine/ppl/diagnostics/tools/utils/hdi.py deleted file mode 100644 index d14594beff..0000000000 --- a/src/beanmachine/ppl/diagnostics/tools/utils/hdi.py +++ /dev/null @@ -1,90 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -import math -from typing import Dict, List, Union - - -def interval( - data: List[float], - hdi_probability: float, -) -> Dict[str, Union[List[float], float]]: - """Find the lower and upper bound of the Highest Density Interval (HDI). - - Parameters - ---------- - data : List[float] - Raw random variable data from the model. - hdi_probability : float - The highest density interval probability to use when calculating the HDI. - - Returns - ------- - Dict[str, Union[List[float], float]] - A dictionary defining the lower and upper bound for the HDI of the given data, - as well as the indices of the lower and upper bound for the sorted raw data. - """ - N = len(data) - sorted_data = sorted(data) - stop_index = math.floor(hdi_probability * N) - start_index = N - stop_index - left_data = sorted_data[stop_index:] - right_data = sorted_data[:start_index] - hdi = [] - for i in range(len(left_data)): - hdi.append(left_data[i] - right_data[i]) - lower_index = hdi.index(min(hdi)) - upper_index = lower_index + stop_index - return { - "lower_bound": sorted_data[lower_index], - "upper_bound": sorted_data[upper_index], - "lower_bound_index": lower_index, - "upper_bound_index": upper_index, - } - - -def data( - rv_data: List[float], - marginal_x: List[float], - marginal_y: List[float], - hdi_probability: float, -) -> Dict[str, Union[List[float], float]]: - """Construct x and y arrays from the HDI bounds using the raw random variable dataself. - - It also returns the actual HDI bounds of the random variable data. - - Parameters - ---------- - rv_data : List[float] - Raw random variable data from the model. - marginalX : List[float] - The support of the Kernel Density Estimate of the random variable. - marginalY : List[float] - The Kernel Density Estimate of the random variable. - hdiProbability : float - The highest density interval probability to use when alculating the HDI. - - Returns - ------- - Dict[str, Union[List[float], float]] - The x array for the HDI data is the "lower" key, while the y array for the HDI - data is the "upper" key and the "base" key is the support along the axis the HDI - is calculated. This nomenclature mirrors the Bokeh Band object, which is the - annotation type we will use when rendering the HDI intervals. - """ - hdi = interval(rv_data, hdi_probability) - base = [] - upper = [] - for i in range(len(marginal_x)): - if marginal_x[i] <= hdi["upper_bound"] and marginal_x[i] >= hdi["lower_bound"]: - base.append(marginal_x[i]) - upper.append(marginal_y[i]) - return { - "base": base, - "lower": [0] * len(base), - "upper": upper, - "lower_bound": hdi["lower_bound"], - "upper_bound": hdi["upper_bound"], - } From 911e0d7d3685b436436a2540672dc0c3a17eccc4 Mon Sep 17 00:00:00 2001 From: Andy Maloney Date: Fri, 7 Oct 2022 12:02:11 -0500 Subject: [PATCH 5/7] Better UX for required user interaction - Moved all Bokeh embed from the tool class to the base class. - Updated tool class docs. - New method on the base class that contains an HTML template for more intuitive required UI for better UX. - Reordered how the tool view is created in order to attach the new CSS class for the UI/UX updates. --- .../ppl/diagnostics/tools/marginal1d/tool.py | 29 +- .../ppl/diagnostics/tools/marginal1d/utils.py | 15 +- .../ppl/diagnostics/tools/utils/base.py | 53 +- tutorials/Coin_flipping.ipynb | 906 ++---------------- 4 files changed, 134 insertions(+), 869 deletions(-) diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py index 2d9a998c1d..3b19c3613f 100644 --- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/tool.py @@ -10,9 +10,7 @@ from beanmachine.ppl.diagnostics.tools.marginal1d import utils from beanmachine.ppl.diagnostics.tools.utils.base import Base from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples -from bokeh.embed import file_html from bokeh.models.callbacks import CustomJS -from bokeh.resources import INLINE T = TypeVar("T", bound="Marginal1d") @@ -34,10 +32,12 @@ class Marginal1d(Base): The list of random variables string names for the given model. num_chains : int The number of chains of the model. + num_draws : int + The number of draws for each chain of the model. palette : List[str] A list of color values used for the glyphs in the figures. The colors are specifically chosen from the Colorblind palette defined in Bokeh. - js : str + tool_js : str The JavaScript callbacks needed to render the Bokeh tool independently from a Python server. """ @@ -92,11 +92,22 @@ def create_document(self: T) -> str: bw_factor=bw_factor, ) + # Create the view of the tool and serialize it into HTML using static resources + # from Bokeh. Embedding the tool in this manner prevents external CDN calls for + # JavaScript resources, and prevents the user from having to know where the + # Bokeh server is. + tool_view = utils.create_view(figures=figures, widgets=widgets) + # Create callbacks for the tool using JavaScript. callback_js = f""" const rvName = widgets.rv_select.value; const rvData = data[rvName].flat(); let bw = 0.0; + // Remove the CSS classes that dim the tool output on initial load. + const toolTab = toolView.tabs[0]; + const toolChildren = toolTab.child.children; + const dimmedComponent = toolChildren[1]; + dimmedComponent.css_classes = []; try {{ bw = marginal1d.update( rvData, @@ -108,7 +119,7 @@ def create_document(self: T) -> str: tooltips, ); }} catch (error) {{ - {self.js} + {self.tool_js} bw = marginal1d.update( rvData, rvName, @@ -130,10 +141,12 @@ def create_document(self: T) -> str: "sources": sources, "figures": figures, "tooltips": tooltips, + "toolView": tool_view, } # Each widget requires slightly different JS, except for the sliders. rv_select_js = f""" + console.log(toolView); const bwFactor = 1.0; const hdiProbability = 0.89; widgets.bw_factor_slider.value = bwFactor; @@ -156,10 +169,4 @@ def create_document(self: T) -> str: widgets["bw_factor_slider"].js_on_change("value", slider_callback) widgets["hdi_slider"].js_on_change("value", slider_callback) - # Create the view of the tool and serialize it into HTML using static resources - # from Bokeh. Embedding the tool in this manner prevents external CDN calls for - # JavaScript resources, and prevents the user from having to know where the - # Bokeh server is. - tool_view = utils.create_view(figures=figures, widgets=widgets) - output = file_html(tool_view, resources=INLINE) - return output + return tool_view diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py index 5891759c14..959fdfe23a 100644 --- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py @@ -422,7 +422,7 @@ def create_figure_grid(figures: typing.Figures) -> Row: A Bokeh layout object. """ toolbar = plotting_utils.create_toolbar(figures=list(figures.values())) - return Row(children=[*list(figures.values()), toolbar]) + return Row(children=[*list(figures.values()), toolbar], css_classes=["bk-loading"]) def create_view(widgets: typing.Widgets, figures: typing.Figures) -> Tabs: @@ -445,10 +445,15 @@ def create_view(widgets: typing.Widgets, figures: typing.Figures) -> Tabs: child=Column( children=[ widgets["rv_select"], - create_figure_grid(figures), - widgets["bw_factor_slider"], - widgets["bw_div"], - widgets["hdi_slider"], + Column( + children=[ + create_figure_grid(figures), + widgets["bw_factor_slider"], + widgets["bw_div"], + widgets["hdi_slider"], + ], + css_classes=["bm-tool-loading", "arcs"], + ), ], ), title="Marginal 1D", diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/base.py b/src/beanmachine/ppl/diagnostics/tools/utils/base.py index 3a624c219b..b3c35c1ec3 100644 --- a/src/beanmachine/ppl/diagnostics/tools/utils/base.py +++ b/src/beanmachine/ppl/diagnostics/tools/utils/base.py @@ -13,6 +13,9 @@ from beanmachine.ppl.diagnostics.tools.utils import plotting_utils from beanmachine.ppl.diagnostics.tools.utils.model_serializers import serialize_bm from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples +from bokeh.embed import file_html +from bokeh.models import Model +from bokeh.resources import INLINE from IPython.display import display, HTML @@ -27,20 +30,56 @@ def __init__(self: T, mcs: MonteCarloSamples) -> None: self.num_chains = mcs.num_chains self.num_draws = mcs.get_num_samples() self.palette = plotting_utils.choose_palette(self.num_chains) - self.js = self.load_js() + self.tool_js = self.load_tool_js() - def load_js(self: T) -> str: + def load_tool_js(self: T) -> str: name = self.__class__.__name__ name_tokens = re.findall(r"[A-Z][^A-Z]*", name) name = "_".join(name_tokens) path = JS_DIST_DIR.joinpath(f"{name.lower()}.js") with path.open() as f: - js = f.read() - return js + tool_js = f.read() + return tool_js - def show(self: T): - display(HTML(self.create_document())) + def show(self: T) -> None: + doc = self.create_document() + html = file_html(doc, resources=INLINE, template=self.html_template()) + display(HTML(html)) + + def html_template(self: T) -> str: + return """ + {% block postamble %} + + {% endblock %} + """ @abstractmethod - def create_document(self: T) -> str: + def create_document(self: T) -> Model: ... diff --git a/tutorials/Coin_flipping.ipynb b/tutorials/Coin_flipping.ipynb index 504a7b5afb..04a303a3ca 100644 --- a/tutorials/Coin_flipping.ipynb +++ b/tutorials/Coin_flipping.ipynb @@ -193,12 +193,14 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -534,7 +538,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "e2629984497c40ad8b53832d64ad04d2", + "model_id": "705db67137ab444eb81bbc377b28695f", "version_major": 2, "version_minor": 0 }, @@ -548,7 +552,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "18b0d88c4687424e9c13775deca8b55f", + "model_id": "6dd72777e3204d32b6549543bc382451", "version_major": 2, "version_minor": 0 }, @@ -562,7 +566,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0e311e9743004e70a2ce4f8d0caa1c0c", + "model_id": "f96b7d6222f944099e2fc7b5a1757940", "version_major": 2, "version_minor": 0 }, @@ -576,7 +580,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "184334942970409f8a28816635b12bd3", + "model_id": "9c6d1fa0090d4dcd91c1b3ca7f51bae7", "version_major": 2, "version_minor": 0 }, @@ -662,12 +666,14 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "
" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -788,6 +794,7 @@ { "data": { "application/javascript": [ + "\n", "(function(root) {\n", " function now() {\n", " return new Date();\n", @@ -800,7 +807,7 @@ " root._bokeh_is_loading = undefined;\n", " }\n", "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", + " const JS_MIME_TYPE = 'application/javascript';\n", " const HTML_MIME_TYPE = 'text/html';\n", " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", " const CLASS_NAME = 'output_bokeh rendered_html';\n", @@ -923,6 +930,8 @@ " 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", @@ -958,6 +967,7 @@ " }\n", " }\n", "\n", + "\n", " function run_callbacks() {\n", " try {\n", " root._bokeh_onload_callbacks.forEach(function(callback) {\n", @@ -1028,22 +1038,29 @@ " document.body.appendChild(element);\n", " }\n", "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n", + " \n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n", " const css_urls = [];\n", + " \n", "\n", - " const inline_js = [ function(Bokeh) {\n", + " const inline_js = [\n", + " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", - "function(Bokeh) {\n", + " function(Bokeh) {\n", + " \n", + " \n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", + " \n", " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", + " \n", + " for (let i = 0; i < inline_js.length; i++) {\n", " inline_js[i].call(root, root.Bokeh);\n", " }\n", - "} else if (Date.now() < root._bokeh_timeout) {\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", @@ -1052,6 +1069,7 @@ " const cell = $(document.getElementById(null)).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", @@ -1065,7 +1083,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const 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 if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const 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 \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\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 const el = document.getElementById(null);\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 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(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 };\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 const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\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 const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\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 const 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 const 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 \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\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 const el = document.getElementById(null);\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(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\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 };\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 const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.2.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.2.min.js\"];\n const css_urls = [];\n \n\n const 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 (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\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 const cell = $(document.getElementById(null)).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" @@ -1074,7 +1092,12 @@ "data": { "text/html": [ "\n", - "
\n" + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Application\",\"version\":\"2.4.2\"}};\n", + " const render_items = [{\"docid\":\"4b864502-40f5-4e59-8146-2f4039ed6673\",\"root_ids\":[\"1401\"],\"roots\":{\"1401\":\"a6007911-5845-4220-9921-286d50db33c2\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", @@ -1156,803 +1181,6 @@ "indicating a healthy exploration of the space." ] }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - " \n", - " \n", - " Bokeh Application\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
\n", - " \n", - " \n", - " \n", - " \n", - "" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "samples.diagnostics.marginal1d()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1975,13 +1203,13 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "14f23bbba1cf4aaca7ff4d90810fab4a", + "model_id": "078b5060c21c4eb7808209807a13fabc", "version_major": 2, "version_minor": 0 }, @@ -1995,7 +1223,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cd2cb03fe69b4cb68632006180cf460c", + "model_id": "976fe613fb7f46c89d99d5297c35c4a4", "version_major": 2, "version_minor": 0 }, @@ -2009,7 +1237,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "981f6cb34790475ba5fcfbd98ad8c059", + "model_id": "dcbba1b1101c4434911ef4663e28fb91", "version_major": 2, "version_minor": 0 }, @@ -2023,7 +1251,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "f2262fa045584852a4d06e3a0d3973d7", + "model_id": "7bd306afe700402da02dae20a3485983", "version_major": 2, "version_minor": 0 }, @@ -2038,8 +1266,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 11 s, sys: 86.6 ms, total: 11.1 s\n", - "Wall time: 5.43 s\n" + "CPU times: user 10.7 s, sys: 68 ms, total: 10.8 s\n", + "Wall time: 10.8 s\n" ] } ], @@ -2062,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -2072,9 +1300,9 @@ "\n", "0% 10 20 30 40 50 60 70 80 90 100%\n", "|----|----|----|----|----|----|----|----|----|----|\n", - "*CPU times: user 504 ms, sys: 3.12 ms, total: 508 ms\n", - "Wall time: 499 ms\n", - "**************************************************\n" + "CPU times: user 1.5 s, sys: 11.8 ms, total: 1.51 s\n", + "Wall time: 1.51 s\n", + "***************************************************\n" ] } ], @@ -2103,22 +1331,8 @@ } ], "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.6" + "name": "python" } }, "nbformat": 4, From 3010f00f7835bae37d456da2cda56fcd473ebbfd Mon Sep 17 00:00:00 2001 From: Andy Maloney Date: Wed, 12 Oct 2022 17:27:10 -0500 Subject: [PATCH 6/7] Refactor - Refactors dosctrings from NumPy to Google style. - Added a prettierrc for dev and updated the package.json, yarn.lock files - Refactored the marginal1d tool for responsive size changes and updated to the format that allows Docusaurus integration. - Added more documentation around the accessor objects with an explicit example in the docstring. --- .../ppl/diagnostics/tools/js/.prettierrc | 8 + .../ppl/diagnostics/tools/js/package.json | 1 + .../ppl/diagnostics/tools/js/yarn.lock | 238 +++++++++--------- .../ppl/diagnostics/tools/marginal1d/tool.py | 51 ++-- .../ppl/diagnostics/tools/marginal1d/utils.py | 41 +-- .../ppl/diagnostics/tools/utils/accessor.py | 118 +++++++-- .../ppl/diagnostics/tools/utils/base.py | 85 ------- .../tools/utils/diagnostic_tool_base.py | 159 ++++++++++++ .../tools/utils/model_serializers.py | 14 +- .../diagnostics/tools/utils/plotting_utils.py | 89 ++++--- 10 files changed, 502 insertions(+), 302 deletions(-) create mode 100644 src/beanmachine/ppl/diagnostics/tools/js/.prettierrc delete mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/base.py create mode 100644 src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py diff --git a/src/beanmachine/ppl/diagnostics/tools/js/.prettierrc b/src/beanmachine/ppl/diagnostics/tools/js/.prettierrc new file mode 100644 index 0000000000..a9334cae01 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/js/.prettierrc @@ -0,0 +1,8 @@ +{ + "arrowParens": "always", + "bracketSpacing": false, + "printWidth": 88, + "proseWrap": "never", + "singleQuote": true, + "trailingComma": "all" +} diff --git a/src/beanmachine/ppl/diagnostics/tools/js/package.json b/src/beanmachine/ppl/diagnostics/tools/js/package.json index bfa790284f..38e7770cb5 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/package.json +++ b/src/beanmachine/ppl/diagnostics/tools/js/package.json @@ -24,6 +24,7 @@ "eslint-plugin-prefer-arrow": "^1.2.3", "eslint-plugin-react": "^7.28.0", "eslint-plugin-react-hooks": "^4.3.0", + "prettier": "^2.7.1", "ts-loader": "^9.3.1", "ts-node": "^10.9.1", "typescript": "^4.7.4", diff --git a/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock b/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock index b3329256d2..5ece43a2c8 100644 --- a/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock +++ b/src/beanmachine/ppl/diagnostics/tools/js/yarn.lock @@ -3,17 +3,17 @@ "@babel/runtime-corejs3@^7.10.2": - version "7.19.1" - resolved "https://registry.yarnpkg.com/@babel/runtime-corejs3/-/runtime-corejs3-7.19.1.tgz#f0cbbe7edda7c4109cd253bb1dee99aba4594ad9" - integrity sha512-j2vJGnkopRzH+ykJ8h68wrHnEUmtK//E723jjixiAl/PPf6FhqY/vYRcMVlNydRKQjQsTsYEjpx+DZMIvnGk/g== + version "7.19.4" + resolved "https://registry.yarnpkg.com/@babel/runtime-corejs3/-/runtime-corejs3-7.19.4.tgz#870dbfd9685b3dad5aeb2d00841bb8b6192e3095" + integrity sha512-HzjQ8+dzdx7dmZy4DQ8KV8aHi/74AjEbBGTFutBmg/pd3dY5/q1sfuOGPTFGEytlQhWoeVXqcK5BwMgIkRkNDQ== dependencies: core-js-pure "^3.25.1" regenerator-runtime "^0.13.4" "@babel/runtime@^7.10.2", "@babel/runtime@^7.18.9", "@babel/runtime@^7.9.2": - version "7.19.0" - resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.19.0.tgz#22b11c037b094d27a8a2504ea4dcff00f50e2259" - integrity sha512-eR8Lo9hnDS7tqkO7NsV+mKvCmv5boaXFSZ70DnfhcgiEne8hv9oCEd36Klw74EtizEqLsy4YnW8UWwpBVolHZA== + version "7.19.4" + resolved "https://registry.yarnpkg.com/@babel/runtime/-/runtime-7.19.4.tgz#a42f814502ee467d55b38dd1c256f53a7b885c78" + integrity sha512-EXpLCrk55f+cYqmHsSR+yD/0gAIMxxA9QK9lnQWzhMCvt+YmoBN7Zx94s++Kv0+unHk39vxNO8t+CMA2WSS3wA== dependencies: regenerator-runtime "^0.13.4" @@ -70,10 +70,10 @@ resolved "https://registry.yarnpkg.com/@discoveryjs/json-ext/-/json-ext-0.5.7.tgz#1d572bfbbe14b7704e0ba0f39b74815b84870d70" integrity sha512-dBVuXR082gk3jsFp7Rd/JI4kytwGHecnCoTtXFb7DB6CNHp4rg5k1bhg0nWdLGLnOV71lmDzGQaLMy8iPLY0pw== -"@eslint/eslintrc@^1.3.2": - 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version "1.0.2" - resolved "https://registry.yarnpkg.com/@humanwhocodes/gitignore-to-minimatch/-/gitignore-to-minimatch-1.0.2.tgz#316b0a63b91c10e53f242efb4ace5c3b34e8728d" - integrity sha512-rSqmMJDdLFUsyxR6FMtD00nfQKKLFb1kv+qBbOVKqErvloEIJLo5bDTJTQNTYgeyp78JsA7u/NPi5jT1GR/MuA== - "@humanwhocodes/module-importer@^1.0.1": version "1.0.1" resolved "https://registry.yarnpkg.com/@humanwhocodes/module-importer/-/module-importer-1.0.1.tgz#af5b2691a22b44be847b0ca81641c5fb6ad0172c" @@ -118,7 +113,7 @@ "@jridgewell/sourcemap-codec" "^1.4.10" "@jridgewell/trace-mapping" "^0.3.9" -"@jridgewell/resolve-uri@^3.0.3": +"@jridgewell/resolve-uri@3.1.0", "@jridgewell/resolve-uri@^3.0.3": version "3.1.0" resolved "https://registry.yarnpkg.com/@jridgewell/resolve-uri/-/resolve-uri-3.1.0.tgz#2203b118c157721addfe69d47b70465463066d78" integrity sha512-F2msla3tad+Mfht5cJq7LSXcdudKTWCVYUgw6pLFOOHSTtZlj6SWNYAp+AhuqLmWdBO2X5hPrLcu8cVP8fy28w== @@ -136,7 +131,7 @@ "@jridgewell/gen-mapping" "^0.3.0" "@jridgewell/trace-mapping" "^0.3.9" -"@jridgewell/sourcemap-codec@^1.4.10": +"@jridgewell/sourcemap-codec@1.4.14", "@jridgewell/sourcemap-codec@^1.4.10": version "1.4.14" resolved "https://registry.yarnpkg.com/@jridgewell/sourcemap-codec/-/sourcemap-codec-1.4.14.tgz#add4c98d341472a289190b424efbdb096991bb24" integrity sha512-XPSJHWmi394fuUuzDnGz1wiKqWfo1yXecHQMRf2l6hztTO+nPru658AyDngaBe7isIxEkRsPR3FZh+s7iVa4Uw== @@ -150,12 +145,12 @@ "@jridgewell/sourcemap-codec" "^1.4.10" "@jridgewell/trace-mapping@^0.3.14", "@jridgewell/trace-mapping@^0.3.9": - 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is-callable "^1.2.6" + is-callable "^1.2.7" is-negative-zero "^2.0.2" is-regex "^1.1.4" is-shared-array-buffer "^1.0.2" @@ -1067,9 +1063,9 @@ eslint-plugin-react-hooks@^4.3.0: integrity sha512-oFc7Itz9Qxh2x4gNHStv3BqJq54ExXmfC+a1NjAta66IAN87Wu0R/QArgIS9qKzX3dXKPI9H5crl9QchNMY9+g== eslint-plugin-react@^7.28.0: - version "7.31.8" - resolved "https://registry.yarnpkg.com/eslint-plugin-react/-/eslint-plugin-react-7.31.8.tgz#3a4f80c10be1bcbc8197be9e8b641b2a3ef219bf" - integrity sha512-5lBTZmgQmARLLSYiwI71tiGVTLUuqXantZM6vlSY39OaDSV0M7+32K5DnLkmFrwTe+Ksz0ffuLUC91RUviVZfw== + version "7.31.10" + resolved "https://registry.yarnpkg.com/eslint-plugin-react/-/eslint-plugin-react-7.31.10.tgz#6782c2c7fe91c09e715d536067644bbb9491419a" + integrity sha512-e4N/nc6AAlg4UKW/mXeYWd3R++qUano5/o+t+wnWxIf+bLsOaH3a4q74kX3nDjYym3VBN4HyO9nEn1GcAqgQOA== dependencies: array-includes "^3.1.5" array.prototype.flatmap "^1.3.0" @@ -1120,13 +1116,12 @@ eslint-visitor-keys@^3.3.0: integrity sha512-mQ+suqKJVyeuwGYHAdjMFqjCyfl8+Ldnxuyp3ldiMBFKkvytrXUZWaiPCEav8qDHKty44bD+qV1IP4T+w+xXRA== eslint@^8.19.0: - 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"""Marginal (1D) tool. - - Parameters - ---------- - mcs : MonteCarloSamples - Bean Machine model object. - - Attributes - ---------- - data : Dict[str, List[List[float]]] - JSON serialized version of the Bean Machine model. - rv_names : List[str] - The list of random variables string names for the given model. - num_chains : int - The number of chains of the model. - num_draws : int - The number of draws for each chain of the model. - palette : List[str] - A list of color values used for the glyphs in the figures. The colors are - specifically chosen from the Colorblind palette defined in Bokeh. - tool_js : str - The JavaScript callbacks needed to render the Bokeh tool independently from - a Python server. +class Marginal1d(DiagnosticToolBaseClass): + """ + Marginal 1D diagnostic tool. + + Args: + mcs (MonteCarloSamples): The return object from running a Bean Machine model. + + Attributes: + data (Dict[str, List[List[float]]]): JSON serializable representation of the + given `mcs` object. + rv_names (List[str]): The list of random variables string names for the given + model. + num_chains (int): The number of chains of the model. + num_draws (int): The number of draws of the model for each chain. + palette (List[str]): A list of color values used for the glyphs in the figures. + The colors are specifically chosen from the Colorblind palette defined in + Bokeh. + tool_js (str):The JavaScript callbacks needed to render the Bokeh tool + independently from a Python server. """ def __init__(self: T, mcs: MonteCarloSamples) -> None: super(Marginal1d, self).__init__(mcs) - def create_document(self: T) -> str: - """Create the Bokeh document for display in Jupyter.""" + def create_document(self: T) -> Model: # Initialize widget values using Python. rv_name = self.rv_names[0] bw_factor = 1.0 diff --git a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py index 959fdfe23a..6ef4dfebf7 100644 --- a/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py +++ b/src/beanmachine/ppl/diagnostics/tools/marginal1d/utils.py @@ -55,6 +55,11 @@ }, }, } +SIZING = { + "sizing_mode": "scale_both", + "max_height": PLOT_HEIGHT + 250, # drop down menus and tabs + "max_width": 2 * PLOT_WIDTH + 30, # tool bars +} def create_sources() -> typing.Sources: @@ -91,12 +96,13 @@ def create_figures(rv_name: str) -> typing.Figures: output = {} for figure_name in FIGURE_NAMES: fig = figure( - width=PLOT_WIDTH, - height=PLOT_HEIGHT, + max_width=PLOT_WIDTH, + max_height=PLOT_HEIGHT, outline_line_color="black", title=f"{figure_name} distribution", x_axis_label=rv_name, y_axis_label=None, + sizing_mode="scale_both", ) fig.yaxis.visible = False plotting_utils.style_figure(fig) @@ -441,22 +447,23 @@ def create_view(widgets: typing.Widgets, figures: typing.Figures) -> Tabs: Bokeh Tabs objects. """ help_panel = Panel(child=help_page(), title="Help", name="helpPanel") + fig_child = Column( + children=[ + create_figure_grid(figures), + widgets["bw_factor_slider"], + widgets["bw_div"], + widgets["hdi_slider"], + ], + css_classes=["bm-tool-loading", "arcs"], + ) + fig_child.update_from_json(SIZING) + tool_child = Column(children=[widgets["rv_select"], fig_child]) + tool_child.update_from_json(SIZING) tool_panel = Panel( - child=Column( - children=[ - widgets["rv_select"], - Column( - children=[ - create_figure_grid(figures), - widgets["bw_factor_slider"], - widgets["bw_div"], - widgets["hdi_slider"], - ], - css_classes=["bm-tool-loading", "arcs"], - ), - ], - ), + child=tool_child, title="Marginal 1D", name="toolPanel", ) - return Tabs(tabs=[tool_panel, help_panel]) + tabs = Tabs(tabs=[tool_panel, help_panel]) + tabs.update_from_json(SIZING) + return tabs diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py b/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py index e866f23f77..e4e23f6bc6 100644 --- a/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py +++ b/src/beanmachine/ppl/diagnostics/tools/utils/accessor.py @@ -3,7 +3,13 @@ # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. -"""Accessor definition for extending Bean Machine `MonteCarloSamples` objects.""" +"""Accessor definition for extending Bean Machine `MonteCarloSamples` objects. + +These methods are heavily influenced by the implementations by pandas and xarray. + +- `pandas`: https://github.com/pandas-dev/pandas/blob/main/pandas/core/accessor.py +- `xarray`: https://github.com/pydata/xarray/blob/main/xarray/core/extensions.py +""" import contextlib import warnings @@ -16,23 +22,38 @@ class CachedAccessor: - """A descriptor for caching accessors. - - Parameters - ---------- - name : str - Namespace that will be accessed under, e.g. ``samples.accessor_name``. - accessor : cls - Class with the extension methods. + """ + A descriptor for caching accessors. + + Args: + name (str): Namespace for the accessor. + accessor (object): Class that defines the extension methods. + + Attributes: + _name (str): Namespace for the accessor. + _accessor (object): Class that defines the extension methods. + + Raises: + RuntimeError: Returned if attempting to overwrite an existing accessor on the + object. """ def __init__(self: T, name: str, accessor: object) -> None: - """Initialize.""" self._name = name self._accessor = accessor def __get__(self: T, obj: object, cls: object) -> object: - """Access the accessor object.""" + """ + Method to retrieve the accessor namespace. + + Args: + obj (object): Object that the accessor is attached to. + cls (object): Needed for registering the accessor. + + Returns: + object: The accessor object. + """ + # Accessing an attribute of the class. if obj is None: return self._accessor @@ -53,11 +74,24 @@ def __get__(self: T, obj: object, cls: object) -> object: raise RuntimeError(msg) from error cache[self._name] = accessor_obj - return accessor_obj # noqa: R504 + return accessor_obj # noqa: R504 (unnecessary variable assignment) def _register_accessor(name: str, cls: object) -> Callable: - """Register the accessor to the object.""" + """ + Method used for registering an accessor to a given object. + + Args: + name (str): The name for the accessor. + cls (object): The object the accessor should be attached to. + + Returns: + Callable: A decorator for creating accessors. + + Raises: + RuntimeError: Returned if attempting to overwrite an existing accessor on the + object. + """ def decorator(accessor: object) -> object: if hasattr(cls, name): @@ -75,5 +109,61 @@ def decorator(accessor: object) -> object: def register_mcs_accessor(name: str) -> Callable: - """Register the accessor to object.""" + """ + Register an accessor object for `MonteCarloSamples` objects. + + Args: + name (str): The name for the accessor. + + Returns: + Callable: A decorator for creating the `MonteCarloSamples` accessor. + + Raises: + RuntimeError: Returned if attempting to overwrite an existing accessor on the + object. + + Example: + >>> from typing import Dict, List, TypeVar + >>> + >>> import beanmachine.ppl as bm + >>> import numpy as np + >>> import torch.distributions as dist + >>> from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples + >>> from beanmachine.ppl.diagnostics.tools.utils import accessor + >>> from torch import tensor + >>> + >>> T = TypeVar("T", bound="MagicAccessor") + >>> + >>> @bm.random_variable + >>> def alpha(): + >>> return dist.Normal(0, 1) + >>> + >>> @bm.random_variable + >>> def beta(): + >>> return dist.Normal(0, 1) + >>> + >>> @accessor.register_mcs_accessor("magic") + >>> class MagicAccessor: + >>> def __init__(self: T, mcs: MonteCarloSamples) -> None: + >>> self.mcs = mcs + >>> def show_me(self: T) -> Dict[str, List[List[float]]]: + >>> # Return a JSON serializable object from a MonteCarloSamples object. + >>> return dict( + >>> sorted( + >>> { + >>> str(key): value.tolist() + >>> for key, value in self.mcs.items() + >>> }.items(), + >>> key=lambda item: item[0], + >>> ), + >>> ) + >>> + >>> chain_results = { + >>> beta(): tensor([4, 3], [2, 1]), + >>> alpha(): tensor([[1, 2], [3, 4]]), + >>> } + >>> samples = MonteCarloSamples(chain_results=chain_results) + >>> samples.magic.show_me() + {'alpha()': [[1, 2], [3, 4]], 'beta()': [[4, 3], [2, 1]]} + """ return _register_accessor(name, MonteCarloSamples) diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/base.py b/src/beanmachine/ppl/diagnostics/tools/utils/base.py deleted file mode 100644 index b3c35c1ec3..0000000000 --- a/src/beanmachine/ppl/diagnostics/tools/utils/base.py +++ /dev/null @@ -1,85 +0,0 @@ -# Copyright (c) Meta Platforms, Inc. and affiliates. -# -# This source code is licensed under the MIT license found in the -# LICENSE file in the root directory of this source tree. - -"""Base class for diagnostic tools of a Bean Machine model.""" - -import re -from abc import ABC, abstractmethod -from typing import TypeVar - -from beanmachine.ppl.diagnostics.tools import JS_DIST_DIR -from beanmachine.ppl.diagnostics.tools.utils import plotting_utils -from beanmachine.ppl.diagnostics.tools.utils.model_serializers import serialize_bm -from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples -from bokeh.embed import file_html -from bokeh.models import Model -from bokeh.resources import INLINE -from IPython.display import display, HTML - - -T = TypeVar("T", bound="Base") - - -class Base(ABC): - @abstractmethod - def __init__(self: T, mcs: MonteCarloSamples) -> None: - self.data = serialize_bm(mcs) - self.rv_names = ["Select a random variable..."] + list(self.data.keys()) - self.num_chains = mcs.num_chains - self.num_draws = mcs.get_num_samples() - self.palette = plotting_utils.choose_palette(self.num_chains) - self.tool_js = self.load_tool_js() - - def load_tool_js(self: T) -> str: - name = self.__class__.__name__ - name_tokens = re.findall(r"[A-Z][^A-Z]*", name) - name = "_".join(name_tokens) - path = JS_DIST_DIR.joinpath(f"{name.lower()}.js") - with path.open() as f: - tool_js = f.read() - return tool_js - - def show(self: T) -> None: - doc = self.create_document() - html = file_html(doc, resources=INLINE, template=self.html_template()) - display(HTML(html)) - - def html_template(self: T) -> str: - return """ - {% block postamble %} - - {% endblock %} - """ - - @abstractmethod - def create_document(self: T) -> Model: - ... diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py b/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py new file mode 100644 index 0000000000..c08683d293 --- /dev/null +++ b/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py @@ -0,0 +1,159 @@ +# Copyright (c) Meta Platforms, Inc. and affiliates. +# +# This source code is licensed under the MIT license found in the +# LICENSE file in the root directory of this source tree. + +"""Base class for diagnostic tools of a Bean Machine model.""" + +import re +from abc import ABC, abstractmethod +from typing import Any, Dict, TypeVar + +from beanmachine.ppl.diagnostics.tools import JS_DIST_DIR +from beanmachine.ppl.diagnostics.tools.utils import plotting_utils +from beanmachine.ppl.diagnostics.tools.utils.model_serializers import serialize_bm +from beanmachine.ppl.inference.monte_carlo_samples import MonteCarloSamples +from bokeh.embed import file_html, json_item +from bokeh.models import Model +from bokeh.resources import INLINE +from IPython.display import display, HTML + + +T = TypeVar("T", bound="Base") + + +class DiagnosticToolBaseClass(ABC): + """ + Base class for visual diagnostic tools. + + Args: + mcs (MonteCarloSamples): The return object from running a Bean Machine model. + + Attributes: + data (Dict[str, List[List[float]]]): JSON serializable representation of the + given `mcs` object. + rv_names (List[str]): The list of random variables string names for the given + model. + num_chains (int): The number of chains of the model. + num_draws (int): The number of draws of the model for each chain. + palette (List[str]): A list of color values used for the glyphs in the figures. + The colors are specifically chosen from the Colorblind palette defined in + Bokeh. + tool_js (str):The JavaScript callbacks needed to render the Bokeh tool + independently from a Python server. + """ + + @abstractmethod + def __init__(self: T, mcs: MonteCarloSamples) -> None: + self.data = serialize_bm(mcs) + self.rv_names = ["Select a random variable..."] + list(self.data.keys()) + self.num_chains = mcs.num_chains + self.num_draws = mcs.get_num_samples() + self.palette = plotting_utils.choose_palette(self.num_chains) + self.tool_js = self.load_tool_js() + + def load_tool_js(self: T) -> str: + """ + Load the JavaScript for the diagnostic tool. + + Tools must be built by `yarn` in order for this method to find the appropriate + file. If no file is found, then the tools will not function, and an error will + be shown to the user. + + Returns: + str: A string containing all the JavaScript necessary to run the tool in a + notebook. + + Raises: + FileNotFoundError: Raised if the diagnostic tool has not been built by + `yarn` prior to its use. + """ + name = self.__class__.__name__ + name_tokens = re.findall(r"[A-Z][^A-Z]*", name) + name = "_".join(name_tokens) + path = JS_DIST_DIR.joinpath(f"{name.lower()}.js") + with path.open() as f: + tool_js = f.read() + return tool_js + + def _tool_json(self: T) -> Dict[Any, Any]: + """ + Debugging method used primarily when creating a new diagnostic tool. + + Returns: + Dict[Any, Any]: Creates a JSON serializable object using Bokeh's `json_item` + method and the output from `create_document`. + """ + doc = self.create_document() + json_data = json_item(doc) + return json_data + + def show(self: T) -> None: + """ + Show the diagnostic tool in the notebook. + + This method uses IPython's `display` and `HTML` methods in order to display the + diagnostic tool in a notebook. The Bokeh `Model` object returned by the + `create_document` method is converted to HTML using Bokeh's `file_html` method. + The advantage of encapsulating the tool in this manner is that it allows all the + JavaScript needed to render the tool to exist in the output cell of the + notebook. Doing so will allow the Bokeh Application to render in Google's Colab + or Meta's Bento notebooks, which do not allow calls to third party JavaScript to + be loaded and executed. The disadvantage is that it embeds duplicate JavaScript + if more than one tool is used in a notebook. + """ + doc = self.create_document() + html = file_html(doc, resources=INLINE, template=self.html_template()) + display(HTML(html)) + + def html_template(self: T) -> str: + """ + HTML template object used to inject CSS styles for Bokeh Applications. + + We inject CSS into the output for the diagnostic tools because we need users to + interact with the tool before it renders any statistics. The reason for this is + due to the lack of a callback between Bokeh and BokehJS for an "on load" event. + The necessary callback for an "on load" event is being worked on for the Bokeh + 3.0 release. Until Bokeh 3.0 is released, this is a work-around that makes the + user interact with the tool before any rendering occurs. + + Returns: + str: Template for injecting CSS in the HTML returned by `create_document`. + """ + return """ + {% block postamble %} + + {% endblock %} + """ + + @abstractmethod + def create_document(self: T) -> Model: + """To be implemented by the inheriting class.""" + ... diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py b/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py index 0c680e0959..109b588ea0 100644 --- a/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py +++ b/src/beanmachine/ppl/diagnostics/tools/utils/model_serializers.py @@ -11,17 +11,15 @@ def serialize_bm(samples: MonteCarloSamples) -> Dict[str, List[List[float]]]: - """Convert Bean Machine models to a JSON serializable object. + """ + Convert Bean Machine models to a JSON serializable object. - Parameters - ---------- - samples : MonteCarloSamples - Output of a model from Bean Machine. + Args: + samples (MonteCarloSamples): Output of a model from Bean Machine. Returns - ------- - model : Dict[str, List[List[float]]] - The JSON serializable object for use in the diagnostics tools. + Dict[str, List[List[float]]]: The JSON serializable object for use in the + diagnostics tools. """ model = dict( sorted( diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py b/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py index 8cf19c5ca8..82b48f2a1f 100644 --- a/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py +++ b/src/beanmachine/ppl/diagnostics/tools/utils/plotting_utils.py @@ -18,6 +18,15 @@ def style_figure(figure: Figure) -> None: + """ + Style the given Bokeh `figure`. + + Args: + figure (Figure): A Bokeh `Figure` object. + + Returns: + None: Styles the given figure without copying. + """ figure.grid.grid_line_alpha = 0.3 figure.grid.grid_line_color = "grey" figure.grid.grid_line_width = 0.3 @@ -26,6 +35,15 @@ def style_figure(figure: Figure) -> None: def choose_palette(num_colors: int) -> List[str]: + """ + Determine which palette from Bokeh's Colorblind to use. + + Args: + num_colors (int): The number of colors to use for the palette. + + Returns: + List[str]: A list of colors to be used as the palette for a figure. + """ palette_indices = [key for key in Colorblind.keys() if num_colors <= key] if not palette_indices: palette_index = max(Colorblind.keys()) @@ -35,6 +53,20 @@ def choose_palette(num_colors: int) -> List[str]: def create_toolbar(figures: List[Figure]) -> ToolbarBox: + """ + Create a single toolbar for the given list of figures. + + This method ignores all `HoverTool` entries in the final toolbar object. The + rational for ignoring them is to prevent the final toolbar from having too much + visual clutter. + + Args: + figures (List[Figure]): A list of Bokeh `Figure` objects that all have their own + toolbars that will be merged into one. + + Returns: + ToolbarBox: The merged toolbar. + """ toolbars = [] for figure in figures: toolbars.append(figure.toolbar) @@ -52,19 +84,15 @@ def create_toolbar(figures: List[Figure]) -> ToolbarBox: def create_figure_grid(figures: List[Figure], num_figure_columns: int) -> Row: - """Layout the given figures in a grid, and make one toolbar. - - Parameters - ---------- - figures : typing.Figures - A dictionary of Bokeh Figure objects. - num_figure_columns : int - The number of columns to display the figures with. - - Returns - ------- - Row - A Bokeh layout object. + """ + Similar to Bokeh's `grid_plot` method, except we merge toolbars in this method. + + Args: + figures (List[Figure]): A list of Bokeh `Figure` objects. + num_figure_columns (int): The number of columns for the grid. + + Returns: + Row: Returns a single Bokeh `Row` object that contains all the given figures. """ toolbar = create_toolbar(figures) figure_rows = [] @@ -92,25 +120,22 @@ def filter_renderers( glyph_type: str = "GlyphRenderer", substring: bool = False, ) -> List[GlyphRenderer]: - """Filter Bokeh figure renderers given the search string. - - Parameters - ---------- - figure : Figure - A Bokeh figure to filter renderers from. - search : str - The search string to filter for. - glyph_type : str, optional default is "GlyphRenderer" - The glyph type. - substring : bool, optional default is ``False`` - Flag to indicate if we should use the search term as a substring search. - - ``False`` indicates the name of the glyph == search. - - ``True`` indicates the search is a substring of the glyph name. - - Returns - ------- - List[GlyphRenderer] - A list of Bokeh glyph renderer objects. + """ + Find renderers in the given figure using the `search` string. + + Filters renderers from the given figure based on renderer names that match the given + search parameters. + + Args: + figure (Figure): A Bokeh `Figure` object. + search (str): A string to filter renderer names with. + glyph_type (:obj:`str`, optional): The type of renderer to search for in the + figure. Default is `GlyphRenderer`. + substring (:obj:`bool`, optional): Flag to indicate if the given `search` string + should be used as a substring search. Default is `False`. + + Returns: + List[GlyphRenderer]: A list of renderers that match the search parameters. """ output = [] renderers = PropertyValueList(figure.renderers) From f4942fad057fbc679ba05ddd0dc67c3038a366fe Mon Sep 17 00:00:00 2001 From: Andy Maloney Date: Wed, 12 Oct 2022 17:47:11 -0500 Subject: [PATCH 7/7] fix linting error --- .../tools/utils/diagnostic_tool_base.py | 26 +++++++++---------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py b/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py index c08683d293..8fd9d8c43d 100644 --- a/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py +++ b/src/beanmachine/ppl/diagnostics/tools/utils/diagnostic_tool_base.py @@ -19,7 +19,7 @@ from IPython.display import display, HTML -T = TypeVar("T", bound="Base") +T = TypeVar("T", bound="DiagnosticToolBaseClass") class DiagnosticToolBaseClass(ABC): @@ -76,18 +76,6 @@ def load_tool_js(self: T) -> str: tool_js = f.read() return tool_js - def _tool_json(self: T) -> Dict[Any, Any]: - """ - Debugging method used primarily when creating a new diagnostic tool. - - Returns: - Dict[Any, Any]: Creates a JSON serializable object using Bokeh's `json_item` - method and the output from `create_document`. - """ - doc = self.create_document() - json_data = json_item(doc) - return json_data - def show(self: T) -> None: """ Show the diagnostic tool in the notebook. @@ -157,3 +145,15 @@ def html_template(self: T) -> str: def create_document(self: T) -> Model: """To be implemented by the inheriting class.""" ... + + def _tool_json(self: T) -> Dict[Any, Any]: + """ + Debugging method used primarily when creating a new diagnostic tool. + + Returns: + Dict[Any, Any]: Creates a JSON serializable object using Bokeh's `json_item` + method and the output from `create_document`. + """ + doc = self.create_document() + json_data = json_item(doc) + return json_data